title: The socio-economic impact of projects financed by the Cohesion Fund : a modelling approach. Volume I, Introduction and summary : literature review : Vector auto-regression models.

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Cohesion Fund
EU financing
EU regional policy
economic and social cohesion
economic consequence

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text: A modelling approach Volume 1 Introduction and summary Literature review Vector auto-regression models European Commission The socio-economic impact of projects financed by the Cohesion Fund A modelling approach Volume 1 Introduction and summary Literature review Vector auto-regression models European Commission A great deal of additional information on the European Union is available on the Internet, ¡t can be accessed through the Europa server (http://europa. eu. int). Cataloguing data can be found at the end of this publication. Luxembourg: Office for Official Publications of the European Communities, 1999 ISBN 92-828-3064-0 © European Communities, 1999 Reproduction is authorised provided the source is acknowledged. Printed in Italy Preface The Commission entrusted the present study to the London School of Economics and Political Science in July 1995 in response to the provi sions of the Cohesion Fund Regulation. 1 These specify not only that individual projects should be thoroughly appraised before approval for as sistance by the Cohesion Fund, but also that the Commission should report annually on the "eco nomic and social impact of the Fund in the Member States and on economic and social co hesion in the Union". The study thus aims to fulfil the requirement for regular assessments to be made of the socio-economic impact of assisted investments on the beneficiary Member States and on the wider economy of the European Union. In the period 1993 to 1999 the Cohesion Fund will have contributed over Ecu16 billion towards the financing of transport infrastructure and envi ronment projects in Spain, Portugal, Greece and Ireland. A rigorous evaluation of this important commitment is an indispensable part of the Commission's responsibility to ensure that Com munity resources are allocated effectively, to pro vide evidence of the positive effect of economic and social cohesion policies and to improve fu ture projects. At the same time, it is hoped that the study will encourage the development of evaluation tech niques in the fields of infrastructure investment impact and regional economic modelling. The re- Council Regulation (EC) N° 1164/94 of 16 May 1994. suits are expected to be of interest and practical value to many working in these fields, as well as those responsible for financing transport and en vironmental infrastructure projects in the EU. The study of the London School of Economics was based on an innovative approach to the subject, with teams of economists competing to produce the most appropriate models. The out come has been the development of three distinct modelling approaches to tackle the problem. The study also involved the setting up of a detailed regional database for the four Cohesion countries and their EU neighbours. The models are thus capable of tracing the impact and spillover ef fects of investments at the provincial, regional or national economy levels. The study shows, among many important results, that: there is a strong, positive relationship be tween public infrastructure investment and private investment in the Cohesion countries over the longer-term: infrastructure projects give rise to significant employment impacts both in the short-term and long-term; estimated regional income effects in some cases exceed those resulting from more tradi tional approaches to project appraisal such as cost-benefit analvsis: Preface important spillover effects are evident be tween the Cohesion countries and their neigh bours. of such applications will be published in ad hoc reports as well as in the annual reports of the Co hesion Fund. The models have been produced and tested within a relatively short period and will require ad ditional development and refinement. They wil be further tested and applied to a range of projects in the transport and environment fields financed by the Cohesion Fund and the ERDF The results The study is intended to complement other methods of analysing the economic impact of as sisted investments. It is presented by the Com mission as a contribution to the effort to identify and quantify the lasting benefits generated by the European Union's cohesion policies. Section I — Introduction and summary Acknowledgements This study was commissioned by the Cohesion Fund directorate of DGXVI — Regional Policy and Cohesion — of the European Commission, and was carried out by the London School of Econom ics and Political Science under the direction of: Data collection and analysis within the countries covered by the study were undertaken by teams of researchers and statisticians as follows: Spain Dr Robert Leonardi, Director, the Economic and Social Cohesion Laboratory, the European Insti tute, LSE. Instituto Valenciano de Investigaciones Económi cas, Valencia: The economic modelling was undertaken by: Professor Matilde Mas Department of Economics, London School of Economics & Political Science: Professor Francisco Perez-Garcia Professor Charles Bean Professor Christopher Pissarides Professor Danny T. Quah Professor Anthony Venables Mr Henry Overman Mr Etienne Wasmer Mr Michael Gasiorek, School of European Stud ies, University of Sussex Data co-ordination and database construction were the responsibility of a team within the Eco nomic and Social Cohesion Laboratory of the Eu ropean Institute, LSE, including: Mr Richard Walker Ms Ana Maresso Acknowledgements Professor Ezequiel Uriel Mr Lorenzo Serrano Portugal Centro de Investigação de Desenvolvimento e Economia Regional, Universidade do Algarve: Professor Adriano Pimpão Ms Antonia Correia Greece Regional Development Institute, Pantheon Uni versity, Athens: Professor Nikolas Konsolas Mr Costas Rontos Ireland Italy CEEPA, University College, Dublin: Professor Raffaella Nanetti, CUPA, University of Illinois, Chicago Dr Richard Sinnott Ms Ricky Schoen UK France Professor Nigel Spence, Queen Mary and West- field College, University of London Dr Robert Leonardi, Economic and Social Cone- sion Laboratory, the European institute, LSE Section I — Introduction and summary ontents SECTION I: INTRODUCTION AND SUMMARY 1. Terms of Reference 2. introduction to the Modelling 3. VARs — Regional Labour Market Issues 4. VARs — Regional output and Employment in the Presence of Interregional Spillovers 5. Computable General Equilibrium Models S. Models of Explicit Distribution Dynamics 7. General Comments 8. Conclusions SECTION II : LITERATURE REVIEW 1. General Introduction 2. Regional Labour Markets 3. Supply Effects 4. Vars and Stochastic Kernels 5. Computable General Equilibrium Models 6. Current Commission Modelling Work 7. Where Next? Glossary List of References SECTION III : VECTOR AUTO-REGRESSION MODELS A. Regional labour market issues introduction 1. Modelling results 1. 1. Methodology 1. 2. Spain Contents Page 9 11 13 15 17 21 23 27 29 31 33 37 43 47 51 57 61 63 67 75 77 77 79 79 82 1. 3. Portugal 1. 4. Greece 1. 5. Ireland 2. Simulation results 2. 1. Introduction 2. 2. Evaluation strategy 2. 3. The impact of projects financed by the Cohesion Fund 2. 4. Spain 2. 5. Greece 2. 6. Portugal 2. 7. Ireland 2. 8. Additional Remarks on Labour Market Variables Conclusions — Summary of results Appendix 1 Appendix 2 (Spain, Correction of Wage Series) Appendix 3 (ADF Tests for the Stationarity of the Series) Appendix 4 (GDP per Capita in the Regions) Appendix 5 (Some Economectrics) Bibliography B. Regional output and employment in the presence of interregional spillovers 1. Introduction and summary 2. Theory and comparisons with other work 3. Methodology 4. Data and basic results for Spain 5. Differential effects from different sorts of public capital 6. Interregional spillovers 7. Cohesion spending simulations 7. 1. Madrid Ring Road 7. 2. Rias-Bajas Motorway 7. 3. Simulation of environmental projects 7. 4. Simulations of a « standard » project 8. Concluding remarks Bibliography Tables Figures 95 101 107 113 113 113 114 120 124 128 130 133 135 139 141 143 149 151 153 155 155 157 159 163 167 169 171 172 175 175 176 177 179 180 190 Section I — Introduction and summary SECTION I INTRODUCTION AND SUMMARY 1. Terms of Reference On 9 June 1995 the Economic and Social Cohe sion Laboratory of the London School of Eco nomics was informed that it had won the tender (6/SG. H/1994-Study of the socio-economic im pact of Cohesion Fund projects). Initially a "cohe sion financial instrument" was introduced on 30 March 1993 by Council Regulation 792/93 to im plement Article 130d of the Maastricht Treaty. Given the delays in the ratification of the new Treaty, the Cohesion Fund was finally set-up on 16 May 1994 by Council Regulation 1164/94 to provide financial support for environmental and transport infrastructure projects. The Cohesion Fund was allocated 15 150 million ECU (1992 prices) to cover the period between 1993 and 1999. One of the key elements in the two Regu lations concerning the Cohesion Fund was the need to ensure that projects financed by the new fund contributed to economic development in the countries where the investment was taking place but also to economic and social cohesion in the European Union. detailed description of the work programme fore seen by the contract. The study was broken down into four elements: (1) literature review, (2) recommended methodology, (3) data collection and coding and (4) construction of economic model or models. All of these elements are con tained in the three sections of the Final Report presented here. Section II of Volume 1 covers the literature re view and reflects very closely the requirements stipulated by the research contract: "A thorough and objective review and evaluation of the latest academic and other work in the field, including work undertaken to quantify the impact of the Structural Funds in the Cohesion countries, with conclusions on the relative strengths and weak nesses of the different approaches used". In com pleting this section, we reviewed all of the relevant academic literature in addition to looking at the re ports produced by the Evaluation Unit of DGXVI and other branches of the European Commission. Work on the study began on 2 July 1995. Article 1 of the contract stipulated that the objectives of the contract were: "to undertake a study into the socio-economic impact of projects and aggre gate interventions financed by the Cohesion Fund as they affect the regional and national economies of Spain, Portugal, Greece and Ire land and, if appropriate, those of neighbouring countries; where such a study is to include the recommendation, development and testing of an economic model or set of models for quantifying such effects and the necessary data collection and construction of a regional database of key economic indicators," and in Annex II there was a Section III of Volume 1 as well as Volume 2 bring together the results of the work undertak en to meet the requirements spelled out in the (a) recommended methodology and (b) con struction of the appropriate economic mod els. We have fully met the objectives outlined in the contract of : ". the econometric model, or models, are to be constructed by the various teams involved in the study making fully explicit the assumptions underlying the models, the model variables and specifications, strong and weak points and the probable robustness of re sults. " The four models developed (two VAR models, one regional Computable General Equi- Terms of Reference 11 librium model and one Stochastic Kernel model) fully meet these stated objectives and, in a number of instances, go well beyond these ob jectives. All four models have been fully tested on a sample of Cohesion Fund projects, and as the Final Report illustrates the results are robust. Volume 3 brings together the data collected and coded for the study. The contract stipulated that the data collection had: "to include the iden tification of key economic indicators at the appro priate geographical level, the collection and cod ing of such data, and the identification of any se rious gaps and recommendations for dealing with these. Data collection should include national and regional data in the economies concerned as well as the data required on Cohesion Fund projects and aggregate interventions. In addition to the Cohesion countries, data on neighbouring coun tries (the UK, France and Italy) will also be col lected. Full use of existing data sources will be made and close collaboration with Eurostat maintained. " These requirements on the stipula tion of appropriate units of analysis, key socio economic variables and input from Cohesion Fund projects were fully implemented. In the process we assembled a detailed data set for all four Cohesion countries plus the three "spillover" countries of the UK, France and Italy. Using data from these three additional countries we were able to trace the spill over impact of Cohesion Fund projects in the four more peripheral coun tries to their more immediate non-Cohesion neighbours. With data from the other eight coun tries not included in this study, the analysis could be extended to all of the European Union. The innovations produced by the study are nu merous. We have not only produced new models for measuring in detail the overall socio-economic impact of Cohesion Fund projects. Rather, these models can also be used (with slight modifica tions) to analyse the socio-economic impact of investments undertaken by other Funds and Agencies of the European Commission. Second, the models are operative at a level of analysis — regional and provincial — which has not been possible through the use of previous techniques or models in a rigorous scientific manner. Third, our data set is unique not only for the four Cohe sion countries but also for the other three non- Cohesion countries included in the study. Finally, combining our seven country data set and our models we are able to trace the ripple effect of Cohesion Fund projects across regional and na tional borders into neighbouring areas. 12 Section I — Introduction and summary 2, Introduction to the Modelling This report outlines the results of modelling work undertaken by the Economic and Social Cohe sion Laboratory at the London School of Eco nomics on behalf of the European Commission DGXVI (Cohesion Fund). In addition to outlining the models, it gives results from the analysis of specific Cohesion Fund investment expenditure undertaken using these models. The various modelling approaches aim to provide a coherent framework for analysing the impact of Cohesion Fund spending in the four Cohesion countries — Greece, Ireland, Portugal and Spain. The different approaches incorporate exciting new theoretical and empirical concepts. We believe that these models are well suited to addressing the issues that are relevant here, and provide the best avail able way to analyse the impact of Cohesion Fund spending. In developing the models we have sought an swers to three fundamental questions. How do regionally focused changes — investments, infra structure development and redistributions — af fect regional and aggregate economies? How might these, in turn, impact neighbouring economies and the union as a whole? What im plications do such local disturbances have for convergence, the tendency or ability of the poor er regions in the Union to catch up with those al ready richer? A number of different modelling is sues must be tackled to answer these questions. How have local disturbances propagated them selves through the nation's economic system in the past? What transmission mechanism — eco nomic structure — underlies these dynamic ad justments? How do different market competitive ness, industrial organizations and transport cost structures affect these predictions? The models outlined here, represent our attempts to resolve these issues and provide answers to these fun damental questions. The main modelling reports are in three sections of this report. Section III, Volume 1 outlines vector autoregression specifications (VARs) and sug gests how they could be used to consider the dynamic impact of Cohesion Fund spending. Section I, Volume 2 describes the specification of regional Computable General Equilibrium models (CGE models), and shows how they can be used to simulate the short and long run effects of Co hesion fund infrastructure projects. The final sec tion (Section II, Volume 2) reports results from models of explicit distribution dynamics (MEDDs). The first paper of this section characterizes the historical evolution of the distribution of economic indicators within and across Cohesion Fund countries. The second paper outlines a method ology for explaining these evolving distributions and considering the impact of whole distributions of Cohesion Fund expenditure. The papers are ordered according to the regional "specificity" of each model's outputs. The VAR models analyse the impact of an investment in a region on the economic performance of that re gion. They provide information on output, busi ness investment, employment and other labour market indicators. In addition they provide useful information on spillovers between regions, which give an indication of the aggregate and distribu tive impact of expenditure. The regional CGE models are less region specific. They are most useful for analysing the impact of investments Introduction to the Modelling 13 which change the transport costs between re gions. The model output provides information on the impact across all affected regions. Finally, the MEDDs provide the broadest possible frame work. They can be used to analyse the impact of investments on the distribution of economic vari ables across all regions of a country, or even across all regions of all Cohesion and non-Cohe sion countries in the European Union. Why have we adopted three different approach es? Firstly, because different models do not need to rely on one set of theoretical assump tions. This is important, as our current knowl edge about economic growth processes is in complete. Secondly, different models can tackle different questions. Finally, because there is no all encompassing model which can begin to an swer the important questions. We have chosen to develop new approaches because traditional approaches sometimes use methodologies that we think are invalid. More importantly, traditional approaches do not account for things that we think are crucial; namely spillovers and dynamic effects. The rest of this introduction provides a brief sum mary of each paper, considers the relationships between models and discusses their application. Readers interested in getting an initial overall pic ture of the current and historical development of each economy may like to refer to the statistical and graphical analysis provided in the historical outcomes paper of Section II, Volume 2 first. 14 Section I — Introduction and summary 3, VARs — Regional Labour Market Issues This paper estimates the relationship between public investment, business investment and labour market variables through econometric analysis of a vector autoregressive model. The VAR methodology captures the dynamic relation ship between these variables. In the model, public investment spending Influ ences aggregate economic activity as follows: 1. Public spending has a direct positive impact on private investment decisions. 2. Tnis increase in business investment further enhances the positive effect that public spending has on the employment variables in the region. 3. In turn, the movement in labour demand influ ences other labour market variables, such as employment, regional activity rates and migra tion between regions. Hence, with this model, we can evaluate the im pact of exogenous changes in public investment spending on the regional economies of the four Cohesion countries. The analysis is carried out at the regional ¡evel, but some spillovers at the na tional level are also highlighted. We investigate the impact of public spending on private invest ment decisions using a bivariate VAR. The esti mated parameters are elasticities, reflecting the percentage increase in the dependent variable (private investment, in this case) due to a 1% in crease in the exogenous variable (public spend ing). The same methodology is applied to the bi variate system involving private business invest ment and employment. We also estimate a trivariate system in employment, population and activity rate, to examine the effect of employment shocks on other labour market variables. The parameters of the relationships are estimated utilising panel data for each of the Cohesion countries. Coefficients can, therefore, be calcu lated by exploiting the information contained both in the cross-sectional and time-series dimen sions. In this way, we model the impact of public spending on the economy of a "representative" region for each country. For some countries we carry out the analysis for groups of regions of dif ferent economic well-being. In the case of Spain, the very comprehensive data set allows for fur ther disaggregation for different subsectors of in vestment spending, and for distinctions to be made in the workforce by level of education. The main findings, by country, can be sum marised as follows: The empirical results for Spain, for which the most extensive data set is available, support a number of conclusions. • A one per cent increase in public investment spending eventually generates an increase in private business capital in the local economy of 0. 16%. The whole of this increase is in re sponse to productive investment expenditure (total investment expenditure less education and health expenditures). Spending on edu cation and health does not stimulate private investments (at the regional level). VARs — Regional Labour Market Issues 15 • The long-run effect of productive investment spending is approximately the same in differ ent types of regions. There is, however, some evidence that the positive spillovers for private investment are slightly stronger in poorer re gions and, to a lesser degree, richer ones (and weaker in middle-income ones). • Public investment increases local employment but has a very small effect on wages. • An increase in local skilled employment is as sociated with a fall in local unemployment and immigration (conversely for a decrease). In contrast, a rise in unskilled employment is as sociated almost exclusively with an increase in the local activity rate. • The effects of shocks on regional employ ment, unemployment and inactivity persist in definitely. This contrasts with US regional evo lutions, where only employment shocks are persistent. For Portugal, the data indicate that: • Private investment has a positive and signifi cant correlation with total and productive pub lic spending. This correlation exists both in the short-run and in the long-run, but the latter ef fect is only slightly significant. • • In contrast with the results for Spain, public investment generates more private investment in the richest regions, but in the other regions no significant estimates were obtained. In response to labour demand shocks, re gional population is unaffected, and increased activity rates absorb most of the new jobs. For Greece, the data indicate that: • Private investment is positively and significantly correlated with total and productive public spending, both in the short-run and in the long- run. In contrast, non-productive investment has no significant impact on private investment. • Public investment generates more private in vestment in the poorest regions. • Employment shocks have little effect on re gional activity, most of the shock is absorbed by population and unemployment changes. For Ireland, the data set is still incomplete, but initial results suggest that: • Private manufacturing investment is positively and significantly correlated with total and pro ductive spending both in the short-run and the long run. • Employment shocks are much less persistent, and activity increases with a positive employ ment shock. • Population fails to adjust to the shock, given the lack of positive correlation with employ ment. • We have not been able to find a positive and significant impact of public investment spending on employment. The employment data are insufficient to estimate this model. We have not been able to use longer time-series for national employment data to impute regional employment over a longer period as we would not be able to identify the impact of regional shocks on the regional economies. These problems may be specific to this model which does not include spillovers across regions. The small size of Irish regions suggest that these spillovers will be far more important than for the other countries. The paper also gives examples of how the mod el can be used to analyse the impact of particu lar Cohesion Fund projects. The projects that are analysed are sections of the Madrid Ring Road and Rias Bajas Motorway (Spain), Spata Airport and the Evinos Dam (Greece) and the Tagus Crossing (Portugal). For the Spanish projects, re sults are presented under two scenarios. The first uses the estimates from total public invest ment, the second uses estimates from total pro ductive public investment. For the moment, pro ject evaluation taking this form is only possible for Spain for which homogenous and disaggre gated data are available. For Portugal and Greece the data are not as disaggregated. For these countries we report results using data on total public investment. The robustness of these labour market results is then tested by pooling data across all Cohesion countries. The tests suggest that these labour market results are sat isfactory. 16 Section I — Introduction and summary 4. VARs — Regional Output and Employment in the Presence of Interregional Spillovers The paper outlined in the previous section uses VAR techniques to study the impact of local pub lic investment spending on regional economic decisions by the private sector. This section of the report develops that work by extending it in a number of directions. Using a structural VAR ap proach the impact on public investment and labour market outcomes are integrated into the same system. Thus the model allows a richer range of interactions between these variables. In addition regional output is added into the picture, permitting the evaluation of the consequences of increased public investment for local output and productivity. The effect on output is, of course, a natural welfare measure to use in evaluating the effectiveness of increased Cohesion spending. The price of greater complexity is greater data re quirements. For this reason we concentrate our analysis on Spain, for which homogenous data are available for a somewhat longer period. How ever, the lessons learned from this exercise should also be applicable to the other three Co hesion countries in due course. The main theoretical ingredient required to ex tend the earlier work is an augmented local pro duction function relating regional output to local private and public inputs. The existing theoretical and empirical literature on local production func tions is considered and we take issue with sever al assumptions: • that technical progress is the same across dif ferent regions; • that measured factor inputs are effectively employed in the production process; • that adjustment costs are not important. We argue that, in fact: • technical progress may differ across regions because of differences in industrial structure; • measured factor inputs may differ from those effectively employed in the production process because of, for example, labour hoarding and capacity utilization; • adjustment cost may be important and that we should allow for a more sophisticated dy namic structure. We extend the existing work by introducing more complex dynamic behaviour and allowing for the simultaneous determination of employment and output. To do this, we specify and estimate a Structural Vector Autoregression (VAR) for private capital, public capital, employment, unemploy ment and output. The standard VAR formulation specifies each variable as a function of lagged values of both itself and all the other economic variables in the system. Nearly any dynamic macroeconomic model can be recast as a VAR. Because the VAR formulation involves only lagged values of variables it is easily estimated using standard techniques, without falling foul of the simultaneity problems that bedevil estimation of standard dynamic macroeconomic models. VARs — Regional Output and Employment in the Presence of Interregional Spillovers 17 Identification is the process of recovering the form of the underlying macroeconomic model (that is, how all the economic variables interact) from the estimated VAR. Our Structural VAR uses economic theory to identify our macroeconomic model. Our economic model is essentially of the stan dard neo-classical synthesis variety. Its main fea tures are that: • There is an equilibrium, or natural, level of out put which occurs when capital is fully em ployed and the unemployment rate is at its equilibrium levei. • The equilibrium unemployment rate is deter mined within a "battle-of-the-mark-ups" framework. One relationship describes the way wages are set (by firms to motivate work ers or as a bargaining outcome). Another rela tionship describes firms pricing and employ ment decisions. This framework is like stan dard competitive equilibrium but without the usual efficiency results — there can be equi librium unemployment. • This period's natural unemployment rate may depend on previous period's natural unem ployment rates (a hysteresis effect) but is in dependent of the size of the labour force. • Deviations around these natural levels of out put and unemployment occur when there are unexpected disturbances to aggregate de mand or when there are shocks to private or public investment. • During recessions firms may be off their pro duction functions because of hoarding of labour and other factors of production. This model allows us to specify interrelations be tween the fundamental shocks of our macroeco nomic model and the observed disturbances oí the Structural VAR. Using these interrelations, we show how the underlying macroeconomic model can be identified and outline a simple, but con sistent, estimation procedure. Results from these models are presented both statistically (reporting coefficients and significance tests) and grapnical- ly (using impulse response functions). from the definition of public capitai. We show that motorway expenditure has no explanatory power for the five other variables in the system. Further more, the (impulse) response of other variables to motorway investment is perverse (output is un changed, but unemployment increases). Plots of motorway investment show that large jumps in investment do not affect regional output and em ployment, which suggests that benefits may be accruing outside the region. We exclude motor way investment from subsequent analysis. Sec ond, the "base" model is extended by disaggre gating public capital into transport (roads, ports and airports) and other infrastructure capital (wa ter and urban structures). The impulse response functions are almost identical for the two forms of capital. Statistical tests suggest that the disag gregated model is preferred, but only because it improves the forecast of public investment. Thus, we continue to use a single aggregate public capital variable for subsequent analysis. Finally, the importance of economy-wide spillovers and local spillovers from one region into neighbouring regions is explored. These interre gional spillovers could be a result of positive ag glomeration effects and negative congestion ef fects. The average GDP of physically contiguous neighbours is used to capture locai spillovers. However, large asymmetries in regional wealth at the NUTS 11 level makes it sensible to carry out this analysis at the NUTS HI level. A lack of data on NUTS III capital stocks means that we have to assume that all NUTS HI stocks move equipro- portionately with their NUTS II counterparts. We add total current and lagged Spanish GDP as an exogenous variable to capture economy-wide spillovers. Unsurprisingly, economy-wide spillovers are large and positive. More interesting ly, local spillovers are positive, but temporary, suggesting that such spillovers are demand-side driven. Re-estimation of the "base" model sug gest that differences in impulse responses are from spillovers and not from disaggregation. The main findings are summarized as follows: • Allowing for the associated implications for employment and private investment, the elas ticity of regional output with respect to net productive public investment is around a third, that is, a 3% increase in the local public capi tal stock raises output by around 1 %. We consider two refinements to this "base" mod el. First, the "base" model excludes motorways • There ¡s no major difference in the impact of Section I — Introduction and summary transport capital and other infrastructure capi tal, with the major exception of motorways where much of the benefit appears to accrue to regions away from where the motorways are located. There is evidence of important interregional spillovers, not only from aggregate economic activity, but also from activity in geographically adjacent provinces. The estimated marginal return from public in vestment varies widely across Spanish regions, being apparently five times greater in Madrid (the highest) than in Extremadura (the lowest). The paper also gives examples of how the model can be applied to analyse the impact of specific Cohesion Fund expenditures. We use the model to analyse the impact of sections of the Madrid Ring Road and the Rias Bajas Motorway (Spain) and a sample of environmental projects. We use the Madrid Ring Road results to discuss the dif ferences between this VAR specification and the previous model. We return to this issue in Section 7 below. VARs — Regional Output and Employment in the Presence of Interregional Spillovers 19 5. Computable General Equilibrium Models This paper outlines the structures of three com putable general equilibrium (CGE) models devel oped to analyse the impact of infrastructure in vestment by the Cohesion fund. The methodolo gy employed in these models is very different from that used in the rest of the study. Rather than undertaking a rich empirical analysis using time-series data, and then using simple reduced forms, this approach builds a model with a fully articulated micro-economic structure. Incorporat ing this micro-economic structure allows the models to discriminate between different projects in a way that empirical macroeconomic models cannot. For example, the CGE models can distin guish between two similarly sized projects in the same region which affect two separate transport corridors and can trace their overall effects. Thus, these models can provide distinctions more diffi cult to incorporate into the macroeconomic mod els. Since the models are built or "calibrated" on existing information, however, the microeconomic structure is imposed and consequently not sub ject to econometric testing. The models are structured as follows: • Each model incorporates a set of regions in which economic activity takes place, linked by a transport network through which goods and services can be traded. NUTS II regions were used for Spain/Portugal and Greece, while NUTS III regions were used for Ireland. • Along with interregional trading partners within a country area, the models allow for two ex ternal trading partners: the EU and the rest of the world. These two external trading partners introduce import supplies and export de mands to the regions analysed. Each region has an endowment of three fac tors: skilled labour, unskilled labour and capital. For each of the three country areas, we divide the industrial sectors in the economy into per fectly competitive and imperfectly competitive industries; for example, Spain/Portugal has 16 sectors. We construct price indices for each industry at each location. These price indices form the basis for consumer choices and for firms' in put decisions. All sectors use primary factors and intermedi ate goods as inputs. Similarly, all sectors' outputs are used as final products and as intermediate goods. Both consumption goods and intermediate goods are traded between regions, and the volume of this interregional trade is regulated by the interregional transport costs. Thus, a high level of transport costs between two re gions will drive up the price of goods traded between those regions. This will inhibit the level of trade passing through the segment of the transport network linking these two re gions. Regions in the models also serve as ports through which exports and imports with the two external trading partners flow. Computable General Equilibrium Models 21 Using this structure, we can capture the effects of infrastructure investment on all regions in a country area in the following way: 1. The resulting reduction in transport costs from an infrastructure project directly affects prices of products in different regions, which, in turn, changes firm sales and profits. 2. In response to these changes in profits, entry and exit of firms may occur, causing a further round of effects. 3. Entry of a firm increases competition, which reduces profits, and bids up factor prices, fur ther reducing profits. 4. Entry of a firm also increases demands for in termediates, which increases suppliers' profits. 5. This, in turn, may cause supplier firms to enter. Thus, reduction in transport costs changes the structure of industry in different regions, creating forward and backward linkages and agglomera tion effects. These models capture these phe nomena and translate them into resulting impacts on each region within a country area. After explaining the structure and underlying the ory of the models, we report results for six simu lation experiments: the Madrid Ring Road and Rias Bajas Motorways (Spain), the Pathe and Eg- natia motorways (Greece), The North-South Road corridor (Ireland) and the Tagus Crossing (Portugal). We run the model for each simulation, incorporating the changed transport costs. We then compare the new equilibrium with the old. The simulations proceed in four stages: 1. Compute direct effects. We find the value of the transport costs savings with all endoge nous variables held constant. To do this, we compute the effective distance saved by the infrastructure project between and within re gions. We then compute trade costs as a function of distance, translating the effective distance saved into transport costs savings. 2. Compute the effects holding location of firms and workers fixed, but allowing output and sales to change. This approximates the in duced effects of the project in so far as it per mits changes in some endogenous variables, but ignores the possibility that the project may cause industrial relocation. This gives short run effects. 3. Compute the effects allowing the number of firms to change but holding the number of workers constant. The changes in the number of firms occur in response to short run changes in the level of profits and, as they oc cur, they further change the profits through the linkage and factor market mechanisms. This gives medium run effects. 4. Compute the effects allowing workers to move in response to induced changes in wages between regions. This stage of the ex periment allows 'perfect' factor mobility, in so far as both labour and capital move to ensure equalisation of returns across all regions. This gives long run effects. Using these four stages, we can compute the welfare and labour income changes by region re sulting from specific Cohesion infrastructure pro jects in the short run, medium run, and long run. The CGE approach can offer more specific, de tailed insights into the overall effects of infrastruc ture investment, building on the basic information of standard cost-benefit analysis. This approach highlights the spillover effects of changes in transport networks which often ripple through numerous regions in a country area, giving a rich er understanding of the results of Cohesion spending. 22 Section I — Introduction and summary 6, Models of Explicit Distribution Dynamics The first section of this paper — historical out comes — documents the dynamics of growth and convergence across the NUTS III economies in the four Cohesion countries. The techniques consider changes over time in the distribution of income over the whole cross-section of regions. in contrast, more standard techniques, such as regression analysis, will only track movements in the average. The second section of this paper extends these techniques. This extension is flexi ble enough to allow economies to become 'clos er' over time, for example, through the Improve ment of connecting transport infrastructure, and enables the importance of spillover effects to be assessed. Section A describes the dynamics of income growth and convergence patterns at the NUTS 111 level for Cohesion Fund countries. We consider a number of graphical and statistical representa tions of these dynamics: • Plots of per capita GDP on a "iongitude and latitude" base, for each of the countries, re vea! information on the dynamic spatial pat terns of regional growth in each economy. • Density estimates of relative per capita GDP across NUTS 111 regions give an idea of the nature oí movements within each economy's regional Income distribution. • Tukey Box plots indicate whether observa tions at the extremes of the distribution are large relative to the median and dispersion of the distribution. • Cross profile plots highlight the degree of churning within the income distribution as it moves over time (ie the degree of intra-distrib- ution dynamics). Ali these methods allow us to develop a more in tuitive feel for the dynamics of the cross section income distribution. Having considered these point in time snap shots we develop a more forma! methodology to investi gate intra-distribution dynamics and long run ten dencies. Initially, we calculate transition probability matrices for each income distribution. To construct these matrices, we first discretize the income space by dividing the range of incomes into differ ent states (for example, between 0 and VA of the distributions weighted average, between % and Vá etc). Each cell of the transition probability matrix then gives the probability of moving from one in come state to another (the probability of moving from rich to poor, the probability of staying poor, etc). The diagonal of each matrix describes the de gree of persistence: the probability of remaining in the current income state. We then conduct a hy pothetical experiment. Assuming that the system described by the transition probability matrix con tinues on its evolution, with no change in structure, the system will eventually reach a long-run steady state. We use the transition probability matrix to calculate the "ergodic" distribution which describes this long-run situation. These ergodic distributions suggest what may happen to these income distrib utions without Cohesion Fund intervention. Unfortunately, we show that the ergodic distribu tion results are not always robust to changes in Models of Explicit Distribution Dynamics 23 the (arbitrary) discritization of states. For example, assigning regions to classes between 0 to Vi? of the population weighted average and between Vé and 1, may change the ergodic distribution. Our solution is to let the set of states be all possible income intervals, including those infinitesimally small (technically, we consider the natural Borei sigma-algebra). The transition probability matrix with its finite number of rows and columns is now replaced by a stochastic kernel (a "transition probability matrix" with a continuum of rows and columns). These kernels can capture persistence (the poor staying poor, the rich staying rich) and the long run tendencies of the income distribution. These stochastic kernels form the basis of the conditioning work in the next section of the paper. The main empirical results from this first section are that: • The collection of all Cohesion NUTS III regions from Spain, Portugal and Greece has grown more unequal. That increase has occurred at a rate faster than that seen across countries. • While Greece has grown slowest in aggre gate, its NUTS III have shown the greatest static and dynamic cohesion. • Aggregate growth has been relatively high in both Portugal and Spain. Spain's regional inequalities have kept pace with its aggregate growth; Portugal's have In creased more than proportionately. Greek average per capita income, initially higher than that of Portugal's, fell rapidly be low the latter. If the historical pattern of development were to continue, the cross-section NUTS III in come distribution will progressively be skewed towards the relatively poor. An emerging twin-peaks property — polariza tion of the income distribution into multi-tier groupings — is observed across the world's cross-section of countries. However, i: does not occur across the Cohesion economies. Europe-wide regions do not display this twin- peaks property either. However, most of the interesting changes are hidden at gross levels of disaggregation. When we consider the Co hesion "neighbours" (UK, France and Italy) separately a more interesting picture emerges. • The UK shows the greatest increase in region al inequality, while that in France and Italy grows only roughly in step with overall growth. UK and French regional inequality has grown because of a few leading regions pulling ahead, in contrast Italy shows emerging po larization between rich and poor. Because of data problems, results for Ireland are not yet available. In section 8, we develop the basic framework to allow us to evaluate alternative explanations for the phenomena described in Section A. We be gin by constructing conditional income distribu tions. The process that tells us how to manipu late the unconditional data to get the conditional data is called a conditioning operation. We give some simple examples of conditioning operations based on spatial location and trading-bloc (or convergence club) membership and suggest why these factors might be important. To generalize these ideas, we develop the formal concept of conditioning schemes. Each conditioning scheme tells us how to construct a conditional distribution by providing information for each economy on: ti) the economies associated with that specific economy in each time period; (2) the importance of each of those associated economies to the specific economy; (3) the time lag with which those associated economies affect the specific economy. Once a conditional distribution is calculated we estimate stochastic kernels, which capture the relationship between the unconditional distribu tion and this conditional distribution. These sto chastic kernels allow us to evaluate how much of the unconditional distribution is explained by our conditioning. Conditioning schemes can take into account the fact that the 'closeness' of regions may depend on whether they are in the same country or ad ministrative region, and that Cohesion Fund pro jects affect this 'closeness' by improving the quality of transportation and communications be- 24 Section I — Introduction and summary tween regions. The technique is flexible enough to allow different economies to have different spillover influences, and for that influence to change over time. Spatial conditioning for the EU-12 NUTS II re gions show that a large part of income in equalities across the whole of Europe can be explained by spatial spillovers. For the Cohesion NUTS III regions we construct a spatial conditioning scheme as follows: (1) each economy is associated with its physical contiguous neighbours; (2) each associated economy is weighted ac cording to its population size; (3) it is assumed that associated economies have immediate impact (the time lag is zero). When the relevant stochastic kernel is calculated, most of the persistence in the unconditional distri bution is explained. For the NUTS III regions of Portugal and the NUTS II regions of Spain and Greece, we also construct a number of kernels to consider conditioning by private and public invest ment. Finally, we investigate the nature of spillovers from Cohesion to non-Cohesion countries using as specific examples the three "Cohesion neigh bours": France, Italy and the UK. We construct the distribution dynamics among these non-Cohesion economies, conditioning on hypothetical changes in the Cohesion ones. This gives us important in sights into how Cohesion Fund expenditure might help economies outside of the Cohesion Four. Fur ther analysis is possible, but extension to other non-Cohesion countries would benefit from com plete and exhaustive data for all European regions. In Greece, private investment in individual re gions helps explain distribution dynamics. Public investment and investment (public and private) in neighbouring regions have little ef fect. • In Spain, private investment matters, but so too does public productive investment in neighbouring regions — spillovers matter a great deal here. • Portugal is intermediate between Greece and Spain. Here the dynamic effects of investment alter at different levels of development: the co hesion impact of investment is strongest at low levels of income; the effect is muted as in comes rise, and disappears after a threshold income level is reached. • If the Cohesion economies see their incomes increase and experience higher tendencies to wards greater equality, then the non-Cohesion economies of France, Italy and the UK will ex perience similar outcomes. • It is the relatively poorer non-Cohesion coun tries that will benefit most from Improving conditions in the Cohesion economies. The main results are as follows: • Spillovers from physically adjacent regions are very important in explaining income distribu tion dynamics in the Cohesion countries. These results provide key insights into distribu tion dynamics across and within Cohesion coun tries which allow for analysis of whole distribu tions of Cohesion Fund expenditure. Models of Explicit Distribution Dynamics 25 7, General Comments We have chosen to use a number of different modelling approaches to assess the impact of Cohesion Fund expenditure. A combined model capable of assessing all the dimensions of the problem does not exist. Nor do we think that such a model could be developed within a sensi ble time frame. Thus the key advantage of using four different modelling approaches is that each provides immediate insights into the different as pects of the impact of Cohesion Fund expendi ture. There are however some disadvantages to the different approaches which we address here. Model Outputs The VARs are macro based empirical models whose coefficients have been estimated using time series data. Thus the CGE model's output is in terms of indicators we usually associate with micro-economic evaluation such as Cost Benefit Analysis. Results from the different approaches are not directly comparable. For example changes in "labour income" from the CGE are not capturing the same effect as the VAR estimates of changes in employment. The output from the models cannot be easily "harmonised". While this is a long term objective, at present comparisons across approaches should be made with care. Instead, the models provide different but comple mentary pictures of the impact of Cohesion Fund expenditure. The different models do not always have the same variables as model output. The VAR re gional labour markets model estimates (differen tial) employment and investment impacts. The second VAR model also considers employment effects, but also analyses the impact on GDP, capital stocks and unemployment. In contrast, the CGE models provide information on welfare change and "shadow prices". Finally the MEDDs consider solely GDP impacts. In some cases these differences are intentional. The Bean VAR model was developed to extend some aspects of the Pissarides/Wasmer VAR and thus considers additional economic indicators. Similarly the MEDDs model concentrates solely on GDP im pacts to allow in-depth consideration of spillover and distribution effects. Other differences are methodological. The CGE model is a micro based theoretical model which has been calibrat ed to actual economic data from a given year. Unresolved differences and Employment VARS The difficulty of using more than one modelling approach is that differences between model con clusions are bound to arise. Our study is no ex ception to this general rule. In particular there are a number of reported differences between the Regional Labour Market VARs and the Regional Output (the Pissarides/Wasmer VAR and the Bean VAR). When we can, we explain why these differences arise. Thus we are able to provide technical ex planations as to why the Bean VAR predicts a much larger immediate employment effect of Co hesion Fund expenditure, and why it predicts a different impact on local private investment. Sta tistically we are unable to test for the significance of these differences. More importantly, economi- General Comments 27 cally the differences are small relative to the over all estimated impact of the projects. The models also differ in the results for the impact of motor way expenditures. Again we believe the differ ences are technical and arise from the difference in initial treatment of the raw economic data. The technical problems are explained in the second paper of the report. In one important case we cannot explain completely why differences arise, although we put forward some possible explana tions. Specifically the models differ in their as sessment of the impact of environmental prob lems. We return to the issue of environmental projects below. At the present time these differ ences between the two models could not be completely resolved. We hope that as we contin ue to analyse Cohesion Fund expenditure using the models that we will be able to better under stand and reconcile these differences. Environmental Projects Environmental expenditure is a significant propor tion of Cohesion Fund expenditure and we have tried to address the analysis of the impact of such projects. Unfortunately the problems in as sessing the impact of environmental projects are numerous. There are a number of general prob lems that would hamper any attempt to system atically analyse the impact of environmental ex penditure. Environmental expenditure is very het erogeneous, but data on different categories of expenditure is scarce. We would expect these different categories of expenditure to have very different economic effects. For example environ mental expenditure that has very long term health benefits and high standard of living value will have little effect on current economic indicators. In contrast, a targeted environmental project which improves the water supply to key regional industries could be expected to have significant economic as well as social impact. The problem is compounded by the lack of a generally accept able theoretical model of the impact of environ mental expenditures on economic indicators. Our specific problems in estimating the impact of en vironmental expenditures partly stems from this general problem. What can our models tell us about the Impact of environmental expenditures? The CGE models tell us little. These models assess the Impact of transport projects by considering the implied changes ¡n transportation costs for industrial products. Unless a specific environmental project had very significant cost implications for a large number of industrial sectors in specific regions this approach could not be used to isolate the af fects of the project. Even then, analysis using a model specifically developed to analyse infra structure investments would not really be appro priate. The MEDDs approach does not distin guish between environmental and infrastructure investment, and would be inappropriate for the analysis of individual projects even if it did so. We have attempted to analyse the impact of environ mental projects within the two VAR models, but as indicated above this is an area where some differences remain unresolved. Whereas Bean finds that environmental projects have an impact on employment that ¡s not statistically different from infrastructure projects, Pissarides/Wasmer find that environmental projects have an impact that is no different from infrastructure projects in the short run, but not significantly different from zero in the long run. Thus our models should al low us to predict the short run employment im pacts of environmental expenditure. Predicting long run impacts will require additional informa tion if we are to discriminate between the two al ternatives suggested by our VAR formulations. Additional information about the characteristics of an individual project, for example from Cost Ben efit Analysis, may allow us to assess whether long run benefits are likely to be large or small. Using this additional knowledge to help us dis criminate between the two different scenarios would then be appropriate (after all we are trying to capture the macroeconomic impacts). The model results would then provide an upper and lower bound on the possible employment im pacts of environmental projects. 28 Section I — Introduction and summary 8. Conclusion The models we have developed have been used to simulate the impact of a number of individual projects. In addition, the models of explicit distri bution dynamics have been used to provide poli cy implications for whole patterns of Cohesion Fund expenditure. We should be explicit that all four different models cannot be used to simulate the effects of all types of projects. This reflects our choice to trade complete coverage by every model, for in-depth and hopefully accurate analy sis of the dynamic impact of a narrower range of projects. In addition the models do not always agree. We have tried to explain these differences when they occur, and to assess the importance of them. Unfortunately, economics is not an exact science and sometimes we have had to leave dif ferences unresolved. When this is the case we try to draw the reader's attention to any remaining differences and allow them to draw their own conclusions. Despite these difficulties we believe that the models outlined here can be used to help formu late Cohesion Fund policy. Both as tools for analysing specific project impacts and as guides for policy priorities they provide information which has hitherto been unavailable when making these important investment decisions. Conclusion 29 SECTION II LITERATURE REVIEW 1. General introduction In looking at the current modelling literature, as it relates to the analysis of Cohesion Fund spending on infrastructure and environmental projects, we need to identify a number of basic questions: How do regionally focused changes — invest ments, infrastructure development, and redistribu tions affect an aggregate economy? How might these, in turn, impact on neighbouring economies and the Union as a whole? What implications do such local disturbances have for convergence, the tendency or ability of the poorer regions in the Union to catch-up with those already richer? There are a number of economic dimensions which we consider central to answering these questions: Outline of the literature review Because of the nature of the existing literature, and the differences between our proposed mod elling approaches, the review that follows is divid ed into six sections: — Regional Labour Markets (Section 2) — Supply-side Effects (Section 3) — VARs and Stochastic Kernels (Section 4) — Computable General Equilibrium Models (Section 5). — Current Commission Modelling Work (Section 6) — regional analysis, economic geography, ag — Where Next? (Section 7) glomeration; — trade theory, economic exchange across regions; — labour market flexibility, mobility, location deci sions of firms; — aggregate business cycles, macroeconomic fluctuations; — local public finance, welfare of people in differ ent regions; — economic growth, convergence. As yet, no one research stream analyses all the interactions between these different fields. Our work should establish new linkages: linkages that will be useful both empirically and theoretically. In addition, a Glossary of technical terms and a full list of references is provided. In Section 2 we consider the impact of proposed regional projects on regional labour markets. When a project ¡s allocated to a region we need to be able to predict the short and long run ef fects on labour supply and demand in the re gion. By doing so, we hope to be able to char acterize the dynamic employment effects of spe cific projects. The existing literature has generally adopted a partial approach to regional labour markets, and we have to allow for this in our re view. Thus, we consider, in turn, the large litera tures on regional wage determination and supply responses (both migration and participation), be fore considering the much smaller literature on regional employment adjustment. Only by con sidering the demand and supply responses that General introduction 33 determine regional employment adjustment can we hope to accurately predict the effect of re gional shocks. In Section 3, we outline some of the more recent literature on the supply side effects of public in vestments. In the medium to long term these ef fects will be a key factor determining the re sponse to Cohesion Fund spending. We consider the impact of public infrastructure on the produc tivity of regional economies and also analyze some work relating to the locational decisions of firms. We suggest how our models will capture different elements of this supply side adjustment, an issue which we return to below. In Section 4, we consider new econometric ap proaches to analysing the dynamic response of economic variables to regional shocks. These new approaches use historical time series data to specify dynamic relationships between variables. We find applications of these techniques in three different strands of the literature: in the regional forecasting literature; in the growth empirics litera ture; and more recently in studies that explicitly consider regional evolutions. In our review we consider each of these strands. In addition we provide a brief theoretical justification, and com ment on the suitability of these approaches for the sort of problems we are considering. In Section 5, we outline the applied (computable) general equilibrium approach which is used ex tensively in the international trade and taxation lit eratures. We consider the basic constant returns to scale, perfect competition models, and exten sions to these models that allow for increasing returns and imperfect competition. We also out line a small number of models which explicitly model regional economies and some models that allow for transportation costs. These models form the building blocks of the model that we in tend to construct. We believe that combining fea tures of industrial organization and regional mod els will give us a model that can help predict the welfare impacts of a whole range of regional pro jects. Section 6 provides an overview of the current modelling work undertaken on behalf of the Commission. We have concentrated on the de tailed reports of ex-ante modelling which outline work on: large scale macro models; computable general equilibrium models; endogenous growth theory models; and input-output models. We have tried to highlight how our modelling work re lates to these current models, and why we be lieve that our modelling strategies represent an improvement on these existing approaches. In the final section we provide a brief overview of the relationship between the existing literature and our new modelling techniques. Supply side issues Our models will have to capture the impact of Cohesion Fund spending on the production side of the economy if they are to effectively predict the long term impacts. A lot of the earlier litera ture has tended to concentrate on demand-side effects while examination of supply side effects is a relatively recent concern1. The nature of our projects is such that we do not explicitly esti mate 'behavioural equations' that claim to ex plain the supply side of the economy. Instead our modelling approaches seek to capture sup ply side issues in ways that make use of, and contribute to, our current understanding without making our impact predictions reliant on com plete and accurate modelling of these issues2. The regional labour market models will consider supply side effects when estimating employment adjustment (firm location and hiring decisions determine the demand side of the regional labour markets). The VARs and stochastic ker nels will capture supply side effects naturally, be cause information on the regional supply reac tions to previous changes are encoded by these techniques and used when making predictions. Finally, the regional nature of the CGE model and the incorporation of increasing returns to scale, imperfect competition and differing degrees of factor mobility are a result of our desire to explic itly model the long run supply side effects of Co hesion Fund spending. Impact of environmental projects Unfortunately, the existing literature deals exclu sively with the environmental impact of economic To a certain extent we have had to reflect this bias in our review of the existing literature. See also our comments on the Commission's current modelling work. 34 Section II — Literature Review projects (ie environmental impact assessment) and we are not aware of any work concerning the macro-economic impacts of environmental projects. Obviously, it is possible within a cost benefit framework to forecast the potential im pact of environmental projects at the micro-level (eg improved tourism numbers, reduced costs to local industry). But these analyses are partial in nature and would be of little use in predicting the macro-economic impact of a wide variety of pro jects implemented over a given time period. There are two possible courses for capturing the effects of these projects within our modelling frameworks. The first would be to incorporate in formation from cost benefit analysis of individual projects as an input into the models. The second would involve considering the macro-economic impact of whole groups of projects. After examin ing a number of environmental project proposals, we would tend to favour the second approach. Such an approach would still allow us to consider the impacts of larger projects1 individually, and would be a necessary pre-requisite for predicting the macro-impacts of large numbers of small scale projects. Given the lack of relevant theoreti cal or empirical literature, we feel that it would not be prudent, at this point, to commit ourselves to a specific solution to this modelling problem. However, we are aware of the importance the Commission places on differentiating between environmental and transport projects and will 1 For example, the water distribution projects for Dublin and Athens. seek to reflect these differences within the mod elling frameworks. Three different modelling approaches Although we intend to adopt three rather different modelling approaches, there is a unifying theme which we will emphasise throughout the literature review: we believe that dynamic interactions be tween a whole host of regional variables will de termine the impact of regional spending. Our modelling approaches reflect this belief, and we think that they will prove both theoretically and empirically useful in the analysis of regional issues. Only once the three different modelling processes are complete will we be able to assess to what extent the different approaches can be integrat ed. However we have never intend to provide a single "magic bullet" model that will provide all the answers. Given the current state of economic modelling we believe that attempts to construct such a model would involve strategies that will not give accurate answers to any of the impor tant questions. By concentrating our attentions on modelling the areas of regional economies that we best understand, we hope to provide ac curate predictions for key economic variables The diversity of our different approaches is pre cisely one of the strengths that we offer. General introduction 35 2, Regional Labour Markets Introduction It would be fair to say that the dynamics of wage and employment determination ¡n local labour markets are little understood. While there is a vast literature concentrating on partial analysis of the Individual components of regional labour market adjustments, the interaction of these ef fects within a dynamic context have only been formally considered very recently. This new em phasis on dynamic interactions has provided several interesting insights, but in what follows we are, for the main part, Interested in the larger body of literature covering partial approaches to our problem. When a region is allocated a project, the immedi ate effect is to increase the demand for labour in that region. Higher demand in the labour market puts upward pressure on wages, and induces a number of supply responses from workers. Eberts and Stone (1992) identify four possible ef fects: workers increase the number of hours they work; the unemployed receive more job offers and unemployment falls; individuals who are cur rently outside the labour force look for work and labour force participation rises; workers who live outside the region enter the local labour force (i. e. , workers migrate into the region). In the short run, we would expect that the supply response may not be complete and that both wages and employment will rise. In the long run the equilibri um is reestablished in the local labour market. What does this new regional equilibrium look like? Has there been a permanent effect on em ployment? Has unemployment returned to its pre-project levels? Are regional wages higher? How have demand spillovers affected neighbour ing regions and the rest of the economy? In line with the existing literature we will subdivide this adjustment process into several separate but interrelated problems. We will consider: I) Regional wage determination — what factors determine how regional wages will adjust to the initial demand shock? il) Supply responses — what factors determine labour force participation changes and migra tion between regions? iv) Employment adjustment — how do these fac tors combine to determine regional dynamic adjustment? Regional wage determination When a project is allocated to a region, we have suggested that the initial effect is to decrease un employment and raise wages. To the extent that the induced supply response is a result of changes in these variables, we are interested in the determination of these variables and the rela tionship between them. There is a well established theoretical literature that hypothesises a long run positive correlation between regional unemployment rates and re gional wages. If we view the unemployment rate as a proxy for the probability of employment, Regional Labour Markets 37 then an individual who stays in a high unemploy ment region faces a higher probability of being out of work. Workers must be compensated for this higher probability of zero income by a higher wage when in employment. While the classical tradition predicts that, in the short run, lower wages should be associated with higher unem ployment, in the long run expected incomes will be equated across regions and we see a positive relationship between wages and unemployment. Detailed outlines and extensions of this hypothe sis, together with empirical tests using long run data, can be found in Harris and Todaro (1970), Hall (1970, 1972), Reza (1978), Behman (1978) and Adams (1985). The specifications and empir ical procedures in these articles are essentially static (pooling across states and time periods) and so we concentrate on papers which highlight issues important to our dynamic considerations. The existing studies tend to consider either mi- croeconometric or macroeconometric wage equations. The former group highlight the impor tance of individual characteristics in wage deter mination and attempt to estimate the effect of re gional variables on individual wages after control ling for these characteristics. They tend to use a mixture of panel and regional data. On the other hand macroeconometric wage equations consid er the determinants of average regional wages. From the micro studies we know that the charac teristics of individuals are important. However, in order to concentrate on the aggregate relation ships, macro studies allow for regional character istics by using compositional variables and/or re gional dummies. They tend to use aggregate time series and cross sectional data. More re cently there have been attempts to combine the two traditions. Blackaby and Manning (1987) is one of the earlier studies to include local unemployment in different specifications of a microeconometric wage equa tion. The study uses data from the 1975 General Household Survey on 7288 white male employ ees who had worked during the previous year. For each individual, data on years of schooling, work experience, work experience squared, weeks worked in previous year, marital status and industry of work, are combined with a mea sure of regional unemployment. From pooled es timation of Mincer-type earnings functions the authors conclude that "individual attributes and the regional industry mix are significant factors in [the] explanation of regional earnings differentials" (p. 161). The unemployment rate seems to have a negative affect on earnings both over time and between regions. In addition, the paper presents estimates of regional Phillips curves which sug gest that both the level and rate of change of re gional unemployment can affect regional wage inflation. The authors suggest that this evidence is consistent with determination of regional wages through a bargaining process where the regional unemployment rate in an important fac tor in the negotiations. Reported results from causality tests, which consider the explanatory power of regional wage spillover variables on residuals from the Phillips curve estimation, lend some support to the hypothesis of a "leading re gion" in the wage determination process (the South East). In Blackaby and Manning (1990), the Mincer type equations are reestimated using General Household Survey data from 1975 and 1982 with similar results. Jackman and Savouri (1991) consider the sensi tivity of wages to regional, as opposed to nation al, labour market conditions. They specify real wages as a function of regional unemployment, leading sector unemployment, national unem ployment, lagged regional wages and a time trend. Real wages are defined as average hourly wages deflated by the GDP deflator. They esti mate fourteen separate equations (classified by sex and the level of sectoral aggregation) using aggregate data from the New Earnings Survey. The more aggregated equations (eg male earn ings) allow for regional composition, less aggre gated (eg male manual workers earnings) allow for sectoral differences, while the most disaggre gated equations (eg male manual manufacturing earnings) do not account for compositional ef fects. The authors suggest that omitted variable bias explains the positive coefficient on national unemployment (it picks up the effect of supply shocks). The coefficient on leading sector unem ployment is always positive and insignificant while that on the regional unemployment rate is nega tive and significant. These absolute wage equations suffer from a number of problems. In particular, there are a large number of coefficients given the available data and considerable scope for misspecification given current understanding of national wage de termination. Empirical evidence points to consis tency of the absolute wage equation coefficients across regions, which allows the specification of a relative wage equation that reduces these 38 Section I Literature Review problems: there are fewer coefficients and all variables enter as differences from national aver ages. The estimation results imply that regional labour market conditions only matter for the wage determination of manual male workers. Possible explanations are that the aggregate un employment measure better reflects changes in the manual market, or that, because of mobility differences, the labour market for non-manuals is national in character. Although these papers do suggest possible theo retical models which are consistent with the em pirical findings (bargaining wages or efficiency hy potheses), they are not complete models of re gional wage determination. Similarly, Pissarides and McMaster (1990) are interested in the long run relationship between regional wages and un employment and the nature of adjustment to this long run relationship, rather than the exact under lying wage determination process. They consider adjustment equations for the region's relative wage in terms of the unemployment differential of the region. Estimation results from an Error Cor rection Mechanism suggest that short run relative wages are correlated with unemployment move ments — a rise in relative unemployment induces a fall in pay (a regional Phillips curve). In the long run, however, the relationship between regional wages and unemployment is a compensating one1, but adjustment to this long run equilibrium is slow. Blackaby and Manning (1992) also estimate Error Correction Mechanisms to consider regional wage and unemployment determination. Inde pendent variables are measured relative to the national average. Dependent variables are differ ences between the actual and expected values (the wage which regions should pay given nation al wage levels and the regions industrial compo sition). The results suggest a complex relation ship between unemployment and wages where unemployment (in particular long term unemploy ment) reduces earnings in the short term, but is associated with higher wages in the long run. Bartik (1991) is more concerned with the effect of regional employment on regional wages. He esti mates wage equations using U. S. Current Popu lation Survey data (1980-1987). The explanatory 1 That is, higher regional wages compensate workers for a higher probability of unemployment thus equating expected lifetime earn ings across regions. variables are demographic factors and a time trend. Unchanging area characteristics are con trolled for by differencing. A number of specifica tions (including several that use shift share vari ables for potentially endogenous employment) find a positive relationship between wages and employment. The estimated equations are actu ally the quasi reduced forms of a structural model of regional wage determination in which an effi ciency wage hypothesis holds and there is Inter action between land and labour markets. Bartik suggests that this long run relationship supports a hypothesis of regional hysteresis. An increase in local job growth leads in the short run (at least) to unemployed residents getting jobs that they would not have got otherwise. These residents who obtain jobs in the short run acquire skills that increase their long run employability and wage even after migration has responded. The relation ship between this hysteresis effect and the struc tural model of regional wage determination is not made explicit. Eberts and Stone (1992) are explicitly concerned with regional dynamics. They identify dynamic demand and supply curves through postulating a recursive dynamic structure. Employers respond to a shock by changing employment not wages — employment is a function of exogenous shocks and lagged wages. Workers make partic ipation decisions within a reservation wage framework — wages are a function of exogenous shocks and lagged employment. Skill adjusted wage differentials are estimated as the coeffi cients on dummy variables for regions in wage equations which control for personal characteris tics and are estimated using panel data. Regional equations for these skill adjusted differentials suggest a cumulative positive response of region al wages to regional employment shocks. Im pulse response functions calculated from these equations show that in response to demand or supply shocks, wages and employment over shoot before settling down to a long run equilibri um Blanchard and Katz (1992) are also concerned with regional dynamics. They construct two mod els to explain regional evolutions (with and without full employment); however, the fundamental em pirical work involves VAR techniques and will be considered in more detail later. In common with a number of papers above, results suggest a short run negative relationship and long run positive re lationship between wages and unemployment. Regional Labour Markets 39 More recently, Blanchflower and Oswald (1994) have estimated wage curves for a large number of countries using individual data supplemented by macroeconomic data on regional unemploy ment levels. They consistently find a negative re lationship between unemployment and wages af ter compensating for individual and regional char acteristics. The authors offer several possible theoretical explanations. "These include the Idea that it [the negative relationship] is an efficiency wage no-shirking condition, a contract curve, a bargaining power locus, or a scatter of disequilib rium points" (p. 13). Although these papers differ in their theoretical and empirical approaches they do highlight sev eral important issues: a) the regional wage determination process may differ in the short and long run. Specifically, in the short run unemployment may have a neg ative effect on wages, while in the long run it may have a positive effect; b) the personal characteristics of a region's labour force and the industrial composnon of the region can explain some regional wage differentials; c) there is a complex relationship between re gional unemployment and regional wages. Supply responses: migration The distribution of wages across regions implies a distribution of employment across regions as firms decide where to hire as well as how much to hire. Given the underlying distribution of popu lation across regions, these spatial hiring deci sions determine the distribution of unemployment across regions. To understand how the regional labour market develops, we need to know how labour supply responds to these distributions of unemployment and wages. The most extensively studied labour supply response is the migration response to which we now turn. In his seminal article, Greenwood (1975) points out that the vast majority of economic literature which considers migration is concerned with the determinants, rather than the effect, of migration. Here, we need to consider both these issues. Greenwood identifies several factors that can af fect gross migration rates. Firstly, distance is a serious deterrent, usually treated as a proxy for transport costs, psychic costs and information availability. Secondly, possible income streams affect the migration decision. Migrants select destinations for which the present discounted value of their expected future stream of returns is greatest. Thirdly, greater information availability will increase migration: migrants are more likely to move to areas about which they know more. This effect is captured in the "well-trodden path" or "chain-migration" theories in which migrant flows into a region are self-perpetuating. Finally, per sonal characteristics are key in explaining individ ual migration decisions. Migration decreases with age and increases with education. A large number of papers analyze the determinants of the probability of migration of a single house hold: see, for example, Davanzo (1978), Hughes and McCormick (1981, 1985, 1987), Topel (1986), Molho (1987), Pissarides and Wadsworth (1989) and Jackman and Savouri (1992). In a recent paper, Hughes and McCormick (1994), consider an integrated approach to both the decision to migrate from a region and the choice of destination region. They use a hierar chical decision tree, with the first tier containing the migration decision and the second tier the destination decision. There is feedback between the second and first tier as the possible destina tions will influence the initial migration decision. This feedback is incorporated by the use of an "inclusive variable" whose coefficient captures dissimilarity of the regions, in addition to the vari ables describing the attractiveness of the origin region relative to the mean. They use a variant of McFadden's conditional logit model to estimate the two equations. From the migration decision equation they find that the unemployed are signif icantly more likely to migrate. They find that high er regional unemployment for a socioeconomic group is associated with a lower probability of out-migration in that group. 1 In addition, high lev els of real wage discourage out-migration. Re sults from the destination equation suggest a sig nificant positive effect of relative wages on desti nation choice. Both relative turnover rates and These first two findings appear contradictory, however one refers to the individual's unemployment history, the other to the effect of ag gregate regional unemployment. 40 Section I Literature Review relative unemployment rates enter with positive coefficients that, although insignificant, hint at a possible perverse effect on destination choice1. Pissarides and Wadsworth (1989) use data from the Labour Force Survey (1984) to estimate the probability of migration but without destination choice feedback. Estimates from a migration de cision equation suggest that individual unemploy ment experience increases the likelihood of mi gration. In addition, the effect of high regional un employment has an indeterminate effect on the probability of migration. However, given the sig nificant positive influence of regional vacancy rates and relative regional wages, the authors conclude that there is evidence of statistically sig nificant migration flows in the "right" direction (i. e. higher gross outflows from high unemployment, low vacancy, low wage regions). Finally, they find that at higher overall unemployment rates the mi gration probabilities of all groups are reduced. The relationship between migration flows and na tional employment conditions is considered in more depth by Gordon (1985). From an "at risk" model, migration is found to depend on the rate of creation of employment opportunities at desti nation, differences in the expected value of those opportunities, distance, and a cyclical mobility factor. He suggests that this cyclical phenomena could be a result of changes in liquidity con straints, employment opportunities and the level of uncertainty about the future which occur over the business cycle. Results from migration equa tions provide evidence that national unemploy ment and regional differentials are important. Gordon does not include regional earnings differ entials in the migration equation specification be cause he is primarily interested in cyclical interac tions. In addition, results from Molho's (1984) pooled cross section time series estimations sug gest that earnings differences are not significant influences on the migration decision once em ployment growth rates are included. The impor tance of future expectations of employment lev- els (and thus of employment growth rates) is con sidered further by Topel (1986). These papers consider factors that are funda mentally important in determining gross flows be tween regions. Plane and Rogerson (1990) stress the importance of feedback, linkage structures and interregional Interdependence in migration flows. The flow of persons from origin region i to destination region j depends not only on condi tions ¡n i and j but also on those in other regions. In addition, they suggest that one cannot under stand these net flows purely from the analysis of individual migration behaviour (we often see net flow to low wage areas). Studies that model aggregate net flows use vari ables identified by these micro data papers. How ever, they tend to concentrate on relative regional unemployment and wage rates as explanatory factors capturing compositional factors using re gional fixed effects. For example, Pissarides and McMaster (1990) derive net migration rates by considering the determinants of the probability of migration of a single household. The gain from mi gration depends on relative wages and relative un employment rates. Regional fixed effects capture the differences in composition of households across regions (which change only very slowly over time). The authors find that while unemploy ment ratios influence migration decisions perma nently only changes in regional wage growth are significant. That is, high unemployment regions will loose population forever, but it appears that peo ple have preferences across regions that are partly independent of relative wage: only when relative wages change are they induced to migrate. Eichengreen (1993) estimates a similar equation for Italy but finds that migration flows only respond to regional wage differentials entered in levels. For similar empirical approaches see Barro and Sala-i-Martin (1991) and Blanchard and Katz (1992). Earlier studies of the migration phenome na using aggregate data can be found in Hart (1970), Creedy (1974), and Elias and Molho (1982). For a justification of this approach see Greenwood (1975). care is needed here. Firstly, there is considerable interaction be tween these variables and, one would suspect, a considerable de gree of collinearity. Secondly, it is difficult to separate two opposing effects. Higher unemployment may reflect higher long term unem ployment and thus a lower expected lifetime income stream which would reduce in-migration. On the other hand, high turnover may in crease the probability of getting a job and increase in-migration. From the reported results it is not possible to distinguish these two opposing effects and one should be careful when interpreting the re gression results. Supply responses: participation Explanations of interregional differences in labour force participation are less contentious. Gallaway, Regional Labour Markets 41 Vadder and Lawson (1991), using U. S. data, find that participation is lower in states with lower rel ative wages, lower transfer payments and higher unemployment rates. Results from Eberts and Stone (1992) and Houseman and Abraham (1990) show that changes in labour force partici pation account for a significant proportion of overall changes in employment. Employment adjustment We have outlined some of the vast literature on wage determination and migration patterns. In contrast, there are very few papers which "close" the regional labour market system by considering the interaction of these forces. Only by consider ing this interaction, can we map out the changes in employment patterns as a result of a particular project. Pissarides and McMaster (1990) do close their regional labour market model. Given estimates of migration and wage determination equations they postulate a relationship between unemployment differentials and the level of migration. Because the net migration data ¡s for all age groups, not all migration into a region will change the unemploy ment rate. The actual change will depend on the level of labour force participation. However, mi gration will create additional demand in a region, which must also be considered when specifying the relationship. Once the system is closed it can be used to trace through the effects of shocks on regions. Although the paper postulates an in traregional multiplier effect, it does not consider interregional multiplier effects. The recursive dynamic structure used by Eberts and Stone (1992), to identify the demand and supply for labour, determines employment and wages simultaneously. As with all regional struc tural models the equations are reduced forms of the underlying model and subject to the usual cri tique. 1 When the model is estimated for two sep arate but interacting sectors (manufacturing and non-manufacturing) tracing out the responses to a shock highlights the possible importance of multiplier effects within regions. Ignoring regional interactions between model sectors predicts a speed of adjustment that is far too high and in addition fails to predict significant overshooting and spillover effects. We would expect that ignor ing interactions between regions could have an even greater effect. There are a large number of regional structural models which claim to trace out some of these dy namics. We do not consider these models here. In addition to problems of estimation and identifica tion2, there are considerable doubts regarding the constancy of parameter values under changed states of nature. This problem is well recognized and generally known as the 'Lucas critique'3. We return to this problem in our evaluation of the Commission's current modelling work. See Below. See the VARs and stochastic kernels section. See Lucas (1976). The debate is far older than the current label would suggest, for an early exposition see, for example, Robbins (1932) 42 Section I Literature Review 3. Supply effects In the short run, the impact of Cohesion Fund spending will be determined through Keynesian demand effects and the relevant short run supply responses. In the medium and long term, supply side effects will play a major role. A number of re cent studies analyze the supply side effects of public investment at the regional level. This litera ture generally applies standard econometric tech niques to evaluate (a) the impact of public invest ment on the productivity of regional economies, and (b) the determinants of the location of firms and economic activity across states and regions. In our view, standard time series, cross-section, and panel data regression analyses provide an unsatisfactory approach to the investigation of dynamic comovements of regional disaggre gates. We nevertheless review briefly some of the main findings of this literature, as they represent a benchmark with which our methodology and findings will be confronted. Regional productivity and public investment In the first group of studies the empirical strategy is to evaluate the role of public infrastructure in the determination of output, productivity, and growth of regional economies. These works generally suggest a quantitatively important relationship between pub lic sector capital accumulation and private sector productivity, thus supporting the policy conclusion that publicly provided infrastructure is an important element of regional economic performance. Some authors, however, have recently questioned the methodology by which these results are obtained. Most of this literature can be traced back to the work by Aschauer (1989, 1990) on the relation ship between aggregate productivity and stock and flow government spending variables. The key message of Aschauer's analysis is that the stock of public infrastructure, as well as the stock of pri vate capital, may be a key to explaining changes in output and productivity in the private sector. Munnell (1990a) builds upon Aschauer's insight and explores whether changes in the amount of public capital, combined with the growth of pri vate capital and labour, can explain the slowdown in United States productivity. Munnell (1990b) al so considers the impact of public capital on eco nomic activity at the state and regional level, find ing evidence that those states that have invested in infrastructure tend to have greater output, more private investment, and more employment growth. Garcia-Mila and McGuire (1992) specify a regional production function that, in addition to labour and private capital, includes two publicly provided inputs (highways and education). They employ a panel data set of annual observations on the 48 contiguous U. S. states from 1969 to 1983 to estimate input elasticity coefficients un der a specification that allows for differences over time and across states. Both of the publicly pro vided inputs are shown to have a significant and positive effect on output. These studies generally find significant supply side effects of public infrastructure. However, these re sults have recently been criticized on econometric grounds. Holtz-Eakin (1994) presents estimates of production functions that use standard tech niques to control for unobserved, state-specific characteristics: these reveal essentially no role for Supply effects 43 public-sector capital in affecting private sector productivity. It is thus claimed that only estimates of state production functions that do not include such controls find substantial productivity Impacts (with the exception of Garcia-Mila and McGuire). Region-level estimates are essentially identical to those from state data, suggesting no quantitative ly important spillover effects. Hulten and Schwab (1991) had earlier provided estimates of the link between public infrastructure and economic per formance, based on accounting techniques which effectively control for state-specific effects. Their findings also suggested that this link is weak. Sturm and de-Haan (1995) review empirical evi dence for the U. S. on the impact of the oublie capital stock on productivity. They show that if the model of Aschauer is estimated in first differences — which is a minimum requirement as the vari ables used are neither stationary nor cointegrated — ambiguous results are obtained. Firm locational decisions The second group of empirical studies concen trates on the determinants of the location of eco nomic activity across states and regions. The fo cus is on the interregional analysis of the birth, death, and mobility of firms, ¡n order to evaluate how different types of incentives can influence economic activity location at the regional level. Papke (1991) examines the impact of state and local tax differentials on the location of industry using a panel data set of manufacturing firm start-ups. The number of firm births ¡s modeled as a Poisson count process and the estimation technique explicitly accounts for unobserved lo cation or state heterogeneity in the estimation. After controlling for state and industry effects, the estimates Indicate that a high state margiral ef fective tax rate reduces the number of firm births for half of the industries examined. Audretsch (1994) analyses the linkages between increasing return to production within a spatial unit of obser vation and the tendency towards increased con centration of economic activity, measured in terms of the birth of new firms. Based on 75 re gions in West Germany, evidence ¡s found sug gesting that the location of new economic activity tends to occur in regions exhibiting characteris tics reflecting convexities in production. Audretsch and Zoltan (1988, 1989) conduct a cross-section empirical analysis to examine the determinants of mobility and entry behaviour of small firms. Small-firm entry is at least partially determined by entry barriers, industry-specific characteristics facilitating retaliatory conduct by Incumbent firms, and the reliance upon innova tive strategy by small firms. Small-firm mobility is promoted in growing markets where skilled labour and technological change play an impor tant role, and hindered in high capital and adver tising intensive industries and ¡n concentrated markets. In Audretsch (1991) the survival rates of over 11,000 firms established in 1976 are com pared across manufacturing industries. The exis tence of substantial scale economies and a high capital-labour ratio appears to lower the likeli hood of firm survival, although these results tend to vary considerably with the time interval consid ered. Holtz-Eakin (1992) examines the extent to which liquidity constraints increase the likelihood of entrepreneurial failure. The results suggest that liquidity constraints exert a noticeable influence on the viability of entrepreneurial enterprises. Recent empirical investigations of the dynamics of new firm formation at the regional level ¡n a number of EU countries are provided by Hart and Gudgin (1994), Davidsson, Lindmark, and Olofs son (1994), van-der-Laan (1991), and Keeble and Walker (1994). Reynolds (1994) explores the im pact of various characteristics of 382 United States labour market areas on subsequent new firm birth rates for 1986-88 using linear regres sion modelling techniques. Growth in local de mand has a major impact in all situations. Statis tically significant evidence of an impact of urban ization/agglomeration, unemployment, personal/ household wealth, and small firm presence/eco nomic specialization is present, but tends to vary for different types of labour market areas and dif ferent economic sectors. Capturing supply side effects We have identified two ways in which Cohe sion fund spending may affect the supply side of the regional economy. All our modelling strategies will seek to capture the productivity effects of Cohesion fund projects1. Firstly, the As explained in the introduction, the nature of our projects is such that we will not explicitly estimate 'behavioural equations' that claim to explain the supply side of the economy. Rather, we will try to cap ture supply side effects using a number of different approaches that are best suited to each specific modelling strategy. 44 Section II — Literature Review regional labour market model will consider pro ductivity effects when analysing employment adjustment. Secondly, the stochastic kernel methodology is based on historical data in which both demand and supply effects are present. Finally, one of the distinguishing fea tures of our Computable General Equilibrium modelling strategy is the explicit consideration of agglomeration and industrial organizational Issues. In addition, we have suggested that Cohesion fund spending will affect firm locational decisions through two channels: Firstly, they directly affect the relative cost attractiveness of different loca tions. Secondly, they affect the pattern of local demand as the regional population distribution adjusts. Both these effects are captured by the Stochastic kernels and the CGE modelling strate gies. The regional labour market model will better capture the effects of the second channel. Supply effects 45 4, VARs and stochastic kernels Introduction We will consider econometric methods of pre dicting future patterns of economic variables from the past history of those variables, and attempt to extend this to predict the response of those variables to the shocks generated by regional fund injections. Specifically we will consider less structured models, in the form of vector autore gressions (VARs) and stochastic kernels. Previ ous applications of the VAR technique are out lined below. Existing applications of stochastic kernels involve applications of Markov chain the ory and these too are considered. These applications fall into three broad cate gories. Firstly, VARs that have been specified to improve regional forecasting performance, given the poor results from existing structural models. Secondly, studies that consider VARs and sto chastic kernel methods because of dissatisfac tion with cross-sectional approaches to the analysis of convergence. Finally, VAR specifica tions that have been used to calculate impulse response functions, and trace through the dy namics of regional specific shocks In Section 6 we outline the 'Lucas critique' of si multaneous equation models1. We have also suggested that these models can suffer from identification problems2. These problems have meant that researchers have often had to resort to large numbers of exclusion restrictions3 to achieve identification of particular models. Sims (1980) describes many of these restrictions as 'incredible'. He suggests that, even restrictions which are useful for partial equilibrium analysis, may not work well as a system of restrictions. These identification problems are further com pounded in dynamic models, as many previously 'exogenous' variables become 'endogenous' when we allow for effects to be distributed over time. As a result of these, and other problems of large scale models, Sims advocates adopting modelling strategies "without restrictions based on supposed a priori knowledge" (p. 15). The dominant modelling strategy to emerge from this 'atheoretical'4 approach is the VAR methodology. Using historical data patterns to estimate VAR specifications may allow us to test important economic hypotheses, without the burden of a whole host of maintained hypotheses, that would be needed to specify a comparable structural model. Given the current state of knowledge re garding the workings of the regional economy, reducing the importance of a-priori knowledge may pay considerable dividends in improved forecasting performance. Forecasting and regional VARs Before turning to existing regional VAR applica tions, we will briefly outline some of the reasons 1 See the comments on large scale macro-models in Section 6. 2 That is, the problem of whether there is one unique underlying para meterization of a model that is consistent with the observed data (considered in the regional labour markets section). An exclusion restriction specifies that a particular dependent variable does not appear as an explanatory variable In the equation for an other dependent variable. Used here in a loose sense — all models include some theoretical content: even if only in the choice of what variables to model. VARs and stochastic kernels 47 for considering VARs for regional forecasting and policy analysis. Kinal and Ratner (1986) suggest that: i) VARs have lower data requirements be cause fewer variables are needed to forecast than to trace out structural linkages; ii) Accuracy of forecasts should be enhanced because we can allow for national-regional and intra-regional interaction and we do not require adhoc fore casts of exogoneous variables; iii) VARs have lower model construction and updating costs. There are a number of VAR applications in the re gional economic literature. Anderson (1979), builds a VAR model of the Ninth Federal Reserve District (Minnesota, Montana, North and South Dakota, northwestern Wisconsin, Upper Penin sula of Michigan), using quarterly data. Knal and Ratner (1986), consider similar monthly data, but for a single state (New York). Gruben and others, in two papers (1984 and 1988), compare the forecasting performance of a number of different time series models of the Texan economy. Sher wood-Call (1988) uses variance decompositions to analyze the differences between comovements of state and national variables. Lesage and Reed (1989a) examine the hypothesis of downward transmission of wage effects through tiers of an urban hierarchy, by considering the (Granger) ca sual relationships among variables. Using similar methodology, Lesage and Reed (1989b) test some dynamic hypotheses suggested by the Ex port Base Model. These applications highlight a number of impor tant specification issues, but in themselves suffer from serious technical errors in application and estimation. Firstly, the treatment of stationarity is sues is generally arbitrary. Many of the papers recognize the potential for nonstationarity prob lems, but the subsequent use of differencing or power transformations is essentially adhoc, and there is little formal testing for stationarity of the transformed data. For forecasting purposes this may not be too bad, but the time path of Impulse responses will be significantly distorted as a re sult of the failure to tackle the stationarity issue consistently. Secondly, the papers tend to specify arbitrary model orders to "fully capture the dy namic properties of the series", or avoid degrees of freedom problems. Some of the forecasting papers use various forecasting performance measures when specifying lag length. Remark ably few papers use any of the order determina tion statistics that are common in the macroeco nomic literature. Finally, regional economists have concentrated on Bayesian VAR (BVAR) ap proaches. A number of comparative studies (see, for example, Shoesmith (1990)), suggest that the BVAR approach provides better forecasts for re gional economies. However the BVAR approach usually fits models which are closer to Indepen dent univariate processes for each variable than a true multivariate specification. Given the sta tionarity problems and the multicollinearlty prob lems (which are a result of arbitrary lag length specifications), it is perhaps unsurprising that the "near" univariate processes produce more favourable results. Cromwell and Hannan (1993), provides a useful critical summary of regional VAR literature. In addition it reports results and impulse response functions from a number of al ternative bivariate VAR specifications for a region al economy, which highlight the Importance of stationarity and order determination. Endogenous growth theory and time series approaches to the empirics of growth There is a large and growing literature which con siders the cross sectional growth performance of nation states and regions within states. This re newed interest in the empirics of economic growth reflects, in part, the challenge to the clas sical growth model from the new endogenous growth literature. While the classical model pre dicts convergence of output across economies (be they national or regional), the new endoge nous growth models allow for the continuation, or even the widening, of existing output differentials. Initial studies of this issue (see for example, Barro and Sala-i-Martin (1991)), used cross-section da ta to test for the convergence of various per capita income measures across different groups. Initial dissatisfaction with this empirical approach, has lead to an emphasis on time series methods by a number of researchers (see, for example, Bernard and Durlauf (1993), and Quah (1993a, 1993b)). These methods use historical time se ries data to predict the future patterns of regional / country income distributions. The early applications of stochastic kernels in the growth empirics literature sought to emphasise the importance of considering intra- distribution movements, as well as the movement of income distributions through time. Markov chains are 48 Section II — Literature Review used to approximate and estimate laws of motion for the evolving distributions. The information about intradistribution dynamics is encoded in a transition probability matrix. Calculating the er godic distribution associated with this matrix pre dicts the long term behaviour of the underlying Income distributions. Using this approach Quah (1993b), predicts a movement of the distribution of relative world Incomes towards a bimodal dis tribution, with a small rich group, a much larger poor group and little movement between the poor and rich groups. For applications of the same methodology to European data, see Neven and Gouyette (1994) and Larch (1994). An outgrowth of this empirical literature is the Quah (1994) paper which explicitly considers the relationship between regional disaggregates and national aggregates. The methodology is similar to the growth orientated papers, but the issue here is whether regional fluctuations that leave aggregate variables unchanged can affect the dynamic performance of an economic system. U. S. data are used to construct a time series of quantiles of the distribution of relative regional in comes. Alternative realizations of this distribution reflect different patterns of regional income, which leave aggregate activity levels unchanged. After conducting (Granger) causality tests, Quah concludes that, contrary to standard macroeco nomic theory, the distribution of regional incomes can interact with the time path of national aggre gates. It is precisely this sort of interaction that is important in determining the short and long run effects of a proposed project. These papers do not explicitly address the im pact of different policies, concentrating instead on future economic performance under constant policy regimes. The suitability of these models for the analysis of policy changes is an, as yet, unresolved issue. We follow Sims (1982), and model regional performance under different pat terns of regional spending, as draws from some underlying complex 'distribution'. By analyzing how this 'distribution' has behaved when subject to shocks in the past, we can map out how cur rent shocks1 may effect that 'distribution' in the future. The regional economies that we consider are continuously subject to outside shocks. We can predict the future response to shocks gener ' The VAR literature deals in 'shocks' and 'impulse response functions' because policy changes are usually modelled as effecting the error terms in the system of equations, i. e. policy changes are 'unexpect ed' given the history of the system. ated by regional projects because there is no reason to believe that these shocks will affect the underlying distribution differently to past shocks. Regional dynamics We will briefly outline the Blanchard and Katz (1992) contribution to the regional evolution litera ture. There are a number of key characteristics that separates the Blanchard and Katz paper from other regional VAR applications. Firstly, their VARs are properly specified after testing for sta tionarity of included variables. Secondly, they specifically consider the identification of labour market shocks and test the sensitivity of their re sults to this assumption. Finally they are interest ed in studying the dynamic propagation of shocks through the system, not in forecasting. 2 A number of papers outlined in the regional labour markets section estimate VAR specifications de rived from some underlying theoretical model. We consider the Blanchard and Katz paper separate ly because, although the VAR specifications could be viewed as reduced forms of two simple models, data limitations and extensions mean that they are often estimating "atheoretical" VARs. Estimates of univariate processes for a number of regional variables highlights the importance of stationarity issues. After testing the authors im pose a unit root ¡n the relative employment process — shocks have largely permanent ef fects on employment levels. In contrast, similar tests for relative unemployment rates find no unit root — states return to their mean relative unem ployment rate fairly quickly after a shock. Allow ing for these test results, the authors estimate a number of different VAR specifications. A trivariate VAR for an employment variable, a participation variable and an unemployment vari able3 is estimated. The implied impulse response functions for the employment rate, participation rate and unemployment rate can then be plotted. Although some of the other regional VARs calculate impulse re sponses we do not consider them here because of the technical problems already outlined above. The employment variable is the first difference of the log of employ ment in a state minus the first difference of the log of U. S. aggregate employment; the participation variable is the log of the ratio of the labour force to the working age population minus the same variable for the entire U. S. ; the unemployment ratio is the ratio of employ ment to the labour force minus the same variable for the entire U. S. VARs and stochastic kernels 49 The difference between the employment change and the corresponding changes in participation rates and unemployment rates must be account ed for by net-migration. The results suggest that after an adverse shock, wages initially decrease, and unemployment initially increases. These changes induce out-migration of labour and in- migration of firms. The resulting changes in labour demand and supply increase wages and decrease unemployment, and there is no long run effect on either variable. Most of the adjust ment to an adverse shock to labour demand is through out- migration of labour, not changes in participation rates, or in-migration of firms. Thus in contrast to the effect on unemployment and wages, shocks will have a long term impact on the level of employment in the state. A second trivariate VAR for the employment vari able, a wage variable and a house prices variable1 is estimated. The impulse response functions show that house prices exhibit a large negative re sponse to an adverse labour demand shock, which suggests that real wages don't change very much, and that it is unemployment which must ex plain the adjustment of labour force (ie migration) 1 The employment variable is as above; the wage variable is the differ ence between the log of hourly earnings in manufacturing in the state and its national counterpart; the housing price variable is the log of the median sales price of existing houses minus the log of the national median price. A comparison of these findings with those of the regional labour market literature, based on more theoretical models, suggests the scope of the VAR methodology in considering a number of im portant regional issues. For example, the over shooting of wages and employment in response to demand shocks is also found by Eberts and Stone (1992), and the importance of unemploy ment differentials, rather than wage differentials, in explaining migration is a feature of the Hughes and McCormick (1994) model. Conclusion We have outlined the key features of current VAR and stochastic kernel applications. The strength of these methods is that they can trace out the complex dynamic reactions of economic vari ables when shocks hit the system, allowing for interactions between those variables. By apply ing, and extending, existing VAR and stochastic kernel techniques, we will be able to consider in teractions between regional and national vari ables. It is these interactions which will be a key factor in determining, and thus predicting, the economic effects of proposed regional projects. 50 Section I Literature Review 5. Computable general equilibrium models Introduction Static Computable / Applied General Equilibrium models (AGE) have been developed over the past twenty years to analyze a whole range of policy is sues in both the developed and the developing world. At a basic level, these models are comput er representations of the real world. The equa tions that define the models are derived from con sideration of the behaviour of economic agents: consumers, producers and governments. Con sumers purchase products from producers and supply inputs to the production process. If the model includes a government, consumers may pay taxes, and in return, may receive income transfers and public goods and services. They make decisions to maximize utility subject to their budget constraints. Producers meet the demands of these consumers by combining primary factors and intermediate goods. Their outputs are deter mined by specific production technologies, and they choose output and input levels to maximize profits. The government may also be modelled as an utility maximizing economic agent subject to Its own particular budget constraint. For our purposes, we distinguish "early" AGE mod els as those which are characterised by constant returns to scale and perfect competition. We will briefly consider some representative examples of these models below. They allow us to outline the approach and highlight some of the important methodological issues. Next, we will consider vari ous extensions to these models. The most impor tant of these have emerged from the efforts of vari ous writers to analyze the importance of industrial organizational issues for trade models. These stud ies attempt to incorporate increasing returns to scale, imperfect competition, and differentiated products. Again we will outline some representative models. Finally, we will consider the much smaller literature which concerns the explicit modelling of regional and transport issues within AGE models. The early age models We will briefly outline the issues in the construc tion of constant return to scale, perfect competi tion models, using examples from the models found in Whalley (1985) and other models, where necessary. We will consider the issues of specifi cation and model predictions but do not consider solution methods — for a discussion of these see, for example, Shoven and Whalley (1992). Multicountry or single country models? Multicountry models disaggregate the world into a number of different regions. The regions in these models are countries or blocks of coun tries, the exact specification depends on the poli cy issues under consideration. There are two models discussed in Whalley (1985a), a four re gion AGE (model 1) and a seven region AGE (model 2). Model 1 was developed to analyze the impact of the 1973 GATT round on the devel oped countries involved: the four regions are the EEC, the U. S. , Japan and the"Rest of the World". Model 2 developed from model 1, but was used to analyze policy issues regarding North-South trade: the seven regions are the EEC, the U. S. , Computable general equilibrium models 51 Japan, Other Developed Countries, Organization of Petroleum Exporting Countries, Newly Industri alised Countries, and Less-Developed Countries. Single country models split the world into the country of interest and the rest of the world. For example, the well known ORANI model outlined in Dixon et al (1982), was developed to analyze the Australian economy. Single country models allow more detailed modelling of the target country (more industrial sectors, more consumers etc), but are less suitable for analysing the interactions be tween countries. Our regional model will incorpo rate elements of both models. Considering regions within a country will allow a rich specification of sectors and consumers (a feature of single country models), but will also allow Interaction between the regions (a feature of multicountry models). Dimensionality A related issue is the degree of disaggregation within regions, both in the demand and production specifications. Smaller multicountry models will usually have finer disaggregation. Model 1 allows for 33 commodities in each of the four regions and a total of 41 consuming groups1. Model 2 allows for six commodities in each of the seven trade blocs, and only seven demand side agents (one for each bloc). Single country models can cope with far greater production disaggregation. While ORANI has only one representative household, it allows for 114 different commodities. The multicountry models highlight an important feature of many AGE models: goods are often not modelled as homogeneous across countries. A Japanese car is modelled as a qualitatively dif ferent good from an American car. This Arming- ton assumption2 is contrary to theoretical reason ing but in applied models it is needed for a num ber of reasons. Firstly, it helps explain cross-haul ing of products (i. e. the simultaneous import and export of essentially the same good). Secondly, models without the Armington assumption pre dict unrealistically strong specialization effects. Fi nally, model calibration uses the available data on specific Import-export elasticities. Models which explicitly consider differentiated products do not have to rely on this arbitrary assumption. 1 A government and a corporate sector for each of the US. EEC and Japan; 10 household sectors for the US stratified by income; 1 rep resentative agent for each of the nine EEC countries, 16 household sectors for Japan stratified by income. 2 Armington (1969). Functional forms The choice of functional forms is governed by two considerations: the model needs to be theo retically consistent and analytically tractable. To gether with the assumptions of constant returns to scale and perfect competition, these consider ations have lead to a reliance on the Cobb Dou glas, Constant Elasticity of Substitution (CES) and Linear Expenditure System (LES) functional forms. The use of CES functions gets round the unitary-elasticity problems of the Cobb Douglas function. In turn, the use of nested CES functions gets round the constant elasticities of substitution across different types of good, that are a feature of the standard CES. The level of nesting will de pend both on data availability and the perceived use of the model. LES functions allow non-uni tary income elasticities. They will often be incor porated within a nested utility function. Model 1 uses a 4 level CES/LES functional form for the utility function of each household sector. At the fourth level, consumers substitute be tween import types under a CES specification to give import composites for each product. At the third level, there is (CES) substitution between this import composite and the domestic product, to give a composite good for each product type. An LES functional form ¡s used at this level which specifies minimum requirements for each import composite, and thus allows the income elastici ties of demand to be different from unity. At the second level, there is (CES) substitution within the different categories of composite goods, to give a composite non-energy good. Finally, at the first level there is substitution between this non- energy composite and the energy good. Produc tion occurs under a CES value added function. The nested hierarchy for intermediate inputs is similar to that for consumption goods. There are two primary inputs: capital and labour services. Parameter values The models are calibrated using benchmark data (actual data adjusted to reflect the specified equilib rium requirements). Because the system is not fully identified by the benchmark equilibrium (i. e. many parameter values are consistent with the actual da ta) a literature review is used to provide additional elasticities. No statistical tests of the model specifi cation are used, because parameters are calculat ed determlnistically under the assumption that the benchmark data represent an equilibrium. 52 Section I Literature Review Counterfactual equilibria Once the model is specified and the parameter values calibrated, ¡t can be used to calculate counterfactual equilibria, i. e. new equilibria under proposed policy changes. New values of policy variables are specified, for example tariff values are lowered, and the model ¡s run again. The model gives a new equilibrium which is compared to the benchmark data or to the equilibria from other pol icy simulations. There is no theoretical argument that guarantees the uniqueness of these new equilibria. However, Shoven and Whalley (1984), report that adhoc methods, such as different start ing points for solution algorithms, have not re vealed nonuniqueness ¡n previous applications. Shoven and Whalley (1984) identify the major themes from a whole host of policy simulations us ing different CRTS, perfect competition models. Firstly, the welfare effects of changes ¡n trade policy are usually relatively small compared to the effects of other kinds of policy change (generally changes in tax policy). Secondly, the terms of trade effects associated with changes in trade policy can be sig nificant. Finally, there is a considerable difference between national and global interests. Because of a number of problems with AGE models — data limitations, sensitivity to specifica tion of functional forms — a number of propo nents of AGE modelling do not advocate the use of such models in a strict forecasting sense: see for example, Whalley (1985b). However, evalua tions of forecasting performance are reported by a number of modellers. For example, in a recent paper, Kehoe, Polo and Sancho (1995), report statistics for the forecasting performance of an AGE model for the Spanish economy. They also assess the sensitivity of the model to various dif ferent closure rules and labour market specifica tions. The policy change considered1, which has mainly distributional effects, ¡s well suited to their static model and the results suggest that it per forms well. The model is relatively sensitive to dif ferent labour market specifications. For other as sessments of model performance see, for exam ple, Polo and Sancho (1993) or Lolos et al. (1995). There are a number of books and survey articles which provide detail on specifications, calibration, solution algorithms and existing models: see, for ' The change in the Spanish taxation system upon entry to the EEC. example, Shoven and Whalley (1984), Scarf and Shoven (1984), Whalley (1985a), and Shoven and Whalley (1992). Industrial organization and age models Towards the end of the 1970s, economists be gan to reconsider the Importance of increasing returns to scale and imperfect competition, as factors in explaining the observed patterns of In ternational trade: see for example, Krugman (1979, 1980), Corden (1980) and Either (1982). Harris (1984) was the first paper to incorporate these features into an AGE model. This AGE model, produced significantly different results from comparable "early" AGE models. These em pirical results, together with new approaches de veloped ¡n the theoretical literature, have encour aged a number of researchers to allow for differ ent forms of industrial organization in AGE model specifications. We will provide a brief summary of the key ΙΟAGE models, before considering the important specification issues using examples from the recent literature. The model outlined in Harris (1984) and Cox and Harris (1985), was developed to analyze the Im pact of the proposed free trade agreement be tween the United States and Canada, on the Canadian economy. Norman (1990), considers the response of a number of two region (EC and EFTA), three sector models built under different assumptions, to the removal of trade barriers. He considers a pure comparative advantage model with CRTS and perfect competition, a second similar model utilizing the Armington assumption, and a third which models one industry as an oli gopoly with and without a free entry/exit assump tion. Gasiorek, Smith and Venables (1991) build a six region model (France, Germany, Italy, UK, Rest Of EC, Rest of World (ROW)) with fourteen sectors, to analyze the Impact of completing the internal market. Burniaux and Waelbroeck (1992), extend this regional structure (ROW is divided in to Japan, North America, LDC and a residual ROW) but at the cost of less sectorial differentia tion. The model is used to analyze the same poli cy change, as is Mercenier (1995), which is a five region, nine sector model mainly concerned with the unemployment Impact of 1992. Kehoe and Kehoe (1994b) summarize four recent models which have been built to analyze the Impact of Computable general equilibrium models 53 NAFTA on the US, Canada and Mexico at the countrywide, regional and sectoral levels. the estimated model prices are formed using a weighted combination of the two prices derived under the alternative behavioural assumptions. Not all sectors will be modelled as producing un der increasing returns. For example, Harris (1984) has 20 scale economy manufacturing sectors and 9 competitive sectors. Burniaux and Wael- broeck (1992) have four scale economy sectors (household appliances, steel, shipbuilding and automobiles) and a residual perfect competition, constant returns to scale sector. Increasing returns may be incorporated through specification of fixed inputs in production. Vari able input requirements are then modelled using a standard constant returns functional form. For example, Harris (1984) includes fixed require ments for primary factors, and models variable input requirements using a constant returns to scale Cobb Douglas specification. Alternatively, a scaling function can be introduced explicitly in the cost function. Gasiorek, Smith and Venables (1991) multiply a standard cost function by a scaling function, which is decreasing in total firm output. Once increasing returns have been incorporated, models can specify imperfect competition and product differentiation in several ways. Theoreti cal considerations cannot help us choose be tween different imperfect competition specifica tions: there is no dominant account of how firms maximize profits in imperfect markets. Similarly, there is little literature on the gain from allowing for product differentiation, rather than assuming symmetric firms within the industry. The Harris (1984) and Cox and Harris (1985) pa pers consider two different behavioural specifica tions. Both specifications have homogeneous products within country industries, but allow for product differentiation between countries (the Armington assumption). The first, assumes Cournot competition with symmetric oligopolistic firms competing in quantities rather than prices. Prices are calculated as a mark-up over marginal costs, using the standard (perceived) elasticity for mula. The second, uses the "Eastman-Stykolt as sumption"1: domestic firms effectively collude, by setting domestic prices equal to the foreign price plus an adjustment to cover protective tariffs. In From the Eastman and Stykolt (1966) findings that Canadian manu facturing prices tended to track U. S. prices for similar goods with an adjustment that reflects tariff levels. Allowing products to be differentiated within in dustries has two major effects: it removes the need for the arbitrary Armington assumption, and it allows Bertrand type equilibria i. e. oligopolistic firms compete in prices instead of quantities. Bertrand markets are more competitive than comparable Cournot markets. Mercenier (1995), considers equilibrium generated under assump tions of Bertrand markets to analyze the impact of "1992" on European welfare. Model results suggest that the welfare effects under Bertrand competition are almost non-existent (a result much closer to those of the CRTS, perfect com petition models). The preferred assumption in the current literature is that firms play a Cournot game: see, for example, Brown and Stern (1989), Norman (1990), Gasiorek, Smith and Venables (1991), Kehoe and Kehoe (1994a). Once differentiated products are specified there are several ways to construct the demand side of the model. For any perfectly competitive markets the demand side specification possibilities are identical to the "early" AGE model options (ie composite goods are constructed allowing for a number of nested levels of substitution). The use of differentiated products allows us to construct composite goods and price indices by consider ing substitution, at one level, between all avail able products using, for example, a CES aggre gator. Calibration of such aggregators is difficult: models that assume constant rates of substitu tion between all products from all countries, can not account for the higher shares of domestic products that we observe in the data. There are several solutions considered in the literature. Gasiorek, Smith and Venables (1991) calculate industry wide substitution elasticities: EC wide averages of price-cost margins imply a measure of product differentiation (under the assumption of a long run, zero profit equilibrium), from which industry specific substitution elasticities are ob tained. The persistence of differences in market shares between foreign and domestic producers is accounted for by demand parameters, which describe the preferences for a consumer in coun try a, for product produced in country b. These parameters effectively give a higher weight to do mestically produced products. In contrast, Burni aux and Waelbroeck (1992) account for different market shares by estimating separate substitu- 54 Section I Literature Review tion elasticities within countries reflecting, they suggest, the influence of nonborder trade barri ers. The procedure used by Mercenier (1995) al so produces estimates of scale elasticities that differ across countries and are jointly determined with the monopoly power of domestic producers. There are two related issues on the production side: the determination of perceived elasticities of demand, and the calculation of the elasticity of substitution between differentiated intermediate inputs. All models assume that firms calculate perceived elasticities correctly1. Some models al low no conjectural variation2 and no intermediate inputs, for example, Norman (1990), Mercenier (1995). Others allow for conjectural variations, for example, Burniaux and Waelbroeck (1992). Gasiorek, Smith and Venables (1991) allow for in termediate inputs, but are forced to assume val ues for substitution elasticities due to lack of da ta. In addition pricing behaviour is complicated in the market for intermediate goods, where all de mands are derived demands, and buyers may have some monopsony power. Gasiorek et al. assume that intermediate goods are priced at av erage cost. There are a number of other specification issues, some as yet unresolved. Firstly, the models can allow for different entry and exit assumptions, for example, reflecting the short (no entry/exit) and the long (entry/exit) run. Gasiorek, Smith and Venables report results under both assumptions. Secondly, Ginsburgh (1990) shows that ambigui ties can arise from the choice of numeraire. It has been suggested, see for example, Burniaux and Waelbroeck (1992), that possible problems are reduced by a choice of numeraire from a com petitive sector. Thirdly, non-uniqueness problems Increase: Mercenier (1995) reports non-unique ness for a model specification that assumes rigid wages with free entry and exit of firms. Finally, a number of papers report increased sensitivity of results to parameter specifications and closure assumptions. That is, they calculate the percentage change in demand resulting from a change in their own good price taking into account that this change in price will affect the composite industry price index directly, and indirectly via induced changes in the equilibrium prices of other firms. ' Allowing for conjectural variation means that firms will take into ac count the effect of changes in their price levels on their competitors price levels when deciding optimal output (and not just when calcu lating elasticities to determine price given output). Regional models There are two approaches to the modelling of re gional phenomena. Firstly, top down: economic behaviour is modelled at the economy wide level and projections are disaggregated to a regional basis, without feedback from the regions. Sec ondly, bottom up: behavioural theory is applied at the regional level, with economy wide projections derived as aggregates of results. The usual AGE approach is top-down. The ORANI model outlined in Dixon et al. (1982) is ca pable of producing regional predictions. These predictions are simply decompositions of the models aggregate results using various (fixed) measures of regional sectorial composition. Hig- gs, Parmenter and Rimmer (1988) suggest a hy brid top down, bottom up modification of the ORANI model. This hybrid model partially region alises ORANI, by defining Tasmanian sub-indus tries for a number of nationally trade goods. The main changes come because some of these Tas manian disaggregated sectors then meet the models 20% export rule, and thus their export demand is modelled as endogenous. However as Higgs et al. point out: "The hybrid system is suited primarily to the analysis of the regional Im pact of economywide shocks , not the economy- wide impact of regional shocks. It can handle some regional shocks, in particular, ones that di rectly affect the subsectors of the regionalised in dustries [but] analysis of regionwide shocks re quires the more demanding bottom up ap proach" (p. 326). There are a limited number of existing bottom-up regional models. Kimbell and Harrison (1984), specify a "single country" model to analyze the impact of tax changes on the Californian econo my. Liew (1984), specifies a theoretical "multi country" regional Johansen model. Morgan (1989), builds a six region seven good model us ing US data to analyze the effects of the tax structure on factor movements and regional pro duction. Jones and Whalley (1989) outline a more sophisticated model for six Canadian regions al lowing for thirteen industrial sectors. A recent model by Buckley (1992), explicitly incorporates transportation costs in the formulation of a three regional, five sector model of the US economy. Although these models are formally similar to the "early" AGE models there are a couple of specifi- Computable general equilibrium models 55 cation issues which are not as important in the multicountry models. Firstly, there are a greater range of possible assumptions regarding mobility. Multicountry models may allow for capital mobility, but other factors are generally immobile. Regional models need to allow for some labour mobility. The three primary factors of the Jones and Whal ley (1989) model can have varying degrees of mo bility. Capital may be interregionally, intersectorally or internationally mobile. Labour is internationally immobile, sectorially mobile and interregionally partially mobile. Resource inputs are immobile. The model allows for partial labour mobility by specifying a distribution of individuals within each region, who differ in their intensity of locational preference, and who migrate when they will get greater utility from consuming their current con sumption bundle elsewhere. Different factor mo bility assumptions are capable of producing differ ent simulation results. Secondly, these models need to allow for multiple levels of regional gov ernment, and possibly transfers of income be tween regions. Thus the Jones and Whalley mod el includes a federal government which is mod elled as an eighth trading region, although it only imports goods from other regions (which ignores public goods provision). Regional governments are modelled simpllstically with all revenues raised redistributed in lump sums. Finally, regional mod els are spatial models: there is a cost of transport ing goods between regions which depends on the distance between those regions. Modelling the demand for transporting and warehousing services as a derived demand, allows Buckley (1992) to explicitly account for these costs when considering trade flows between regions. Conclusion In outlining the existing literature we have con centrated on the issues that will be involved in the specification of a regional computable gener al equilibrium model. Many of the papers consid er some of the issues we will be concerned with, none of them consider all the issues. Thus we see CRTS, perfect competition models with a specific regional or transportation component, or multicountry models which allow for increasing returns to scale and industrial organizational components. It is the combination of these two modelling streams that will allow us to specify ap propriate models for the analysis of the impact of proposed projects. 56 Section I Literature Review 6. Current Commission modelling work Introduction In our evaluation of previous modelling work, we intend to concentrate on a very limited number of reports that deal explicitly with modelling the macroeconomic effects of investments by the Structural Funds and investments that are a part of the Community Support Frameworks. Before providing a more in-depth analysis we would make the following general points: — Much of the current work deals with general 'philosophical', allocational, efficiency and imple mentation issues: that work is not covered here. — There are a number of thematic evaluations which deal with spending priorities within one particular area (for example telecommunications and transportation). We see these reports as complementary to our current modelling work. In the future such work could provide initial rankings of projects and provide some input in to the new modelling strategies. However, such thematic evaluations cannot capture the com plex interactions between projects and are not adequate for prioritising a long list of competing projects that relate to different areas. 1 — The current work that looks at the macroeco nomic effects of different Commission Pro grammes is aimed at predicting the overall im- For example, "The Evaluation of Actions in the Fields of Telecommu nications and Transportation in Greece, Ireland and Portugal" (Booz- Allen and Hamilton) provides separate rankings for different road, rail and port projects. While useful, the rankings do not allow us to choose between competing projects and, more importantly, cannot allow for the dynamic effects of these infrastructure changes. pact of entire programmes. The Commission has identified the need to look at the impact of small scale programmes. The current large scale modelling work has little chance of effec tively assessing such impacts. The methods we intend to use are the best (currently avail able or being developed) that have any chance of evaluating the dynamic, spillover and ag gregative impacts of the small-scale projects targeted by the Cohesion Fund. Large scale macro-models Large scale macro-models or structural models consist of large numbers of related equations each of which explains the formulation of one key economic variable (the dependent variable). The dependent variable from one equation may be the explanatory variable in other equations, and thus structural models explicitly capture the com plex interaction between economic variables. Various estimation techniques are used to calcu late specific parameter values for each of the equations. Once the model has been estimated, it can be used to predict the effect of a policy change, by changing the values of policy vari ables. The problem is that the 'structural' equa tions that have been estimated reflected the be haviour of economic agents under the old policy regime. If economic agents are rational, then they will, in general, change their behaviour when con fronted with a different policy. Unless the equa tions that have been estimated are true behav ioural equations (and they are generally not), the parameter values of these equations will Current Commission modelling work 57 change. 1 Variables that are determined by equa tions whose parameters have changed are pre dicted incorrectly. Worse still, the allowance for the interaction amongst variables then propa gates these errors through the rest of the system. The poor forecasting performance of these mod els is well documented and we have little faith in their results. These criticisms are particularly appropriate given the nature of the projects that we wish to ana lyze. Cohesion Fund spending is intended to have significant effects on the supply side of the economy. Advocates of large scale structural models are well aware of the weaknesses of these models when it comes to capturing the supply side effects of spending proposals. In deed much of the current work on the HER- MES/HERMIN models has sought to specify and estimate behavioural equations for the supply side2. As those involved in this sort of analysis would admit, our understanding of the supply side of the economy is at best limited. The eco nomics profession continues to seek a better un derstanding of these issue and we realise that the Commission may want to contribute to this process. However, given the current level of knowledge existing large scale models will be particularly vulnerable to the Lucas critique. We believe that in the medium term3, the only hope of predicting the impact of Cohesion fund spend ing, comes from the modelling strategies that we propose: strategies which will Incorporate and expand our understanding of supply side issues, but that are not extremely susceptible to failures to model these issues completely. Computable general equilibrium models A number of Computable General Equilibrium (CGE) models have been used to analyze the im This is the so-called Lucas critique'. See, for example, "The role of the Structural Funds: Analysis of Con sequences for Ireland in the Context of 1992" Bradley ef al (ESRI), "The Effects of the CSF 1994-99 on the Irish and Portuguese Economies" Bradley ef al (ESRI/UCP), "The Effects of the CSF 1994-99 on the Spanish Economy: An Analysis based on the HER- MIN Model" Herce/Sosvilla-Rivero (FEDEA) and "The HERMIN-S4 - A Four Sector Structural Model of the Spanish Economy for the Analysis of CSF" Herce/Sosvilla-Rivero (FEDEA). This medium term could be very long: the idea that we will have ac curate behavioural equations for all the relevant variables (or even a few of them) In the near future, would be incongruous to the vast majority of the economics profession. pact of the CSF4. These models are, in some senses, the precursor of the CGE models that we will consider. Our model differs in emphasis and theoretical background as follows: — the model will be regionally based, with a comparable sectoral decomposition. — transport costs will be modelled explicitly. — the new model will incorporate increasing re turns to scale and imperfectly competitive markets. — activities are potentially mobile, with their equi librium allocation across locations determined by: sectoral and locational specific factors, and by demand and cost linkages for firms ac tive in imperfectly competitive markets. It is this new emphasis on dynamics, imperfect competition and increasing returns, previously absent in static CGEs, that will make our model relevant to the development questions posed by the Cohesion Fund projects. We have also considered the dynamic general equilibrium analysis of the CSF and Structural Funds outlined in a number of papers by A. Pereira5. The models outlined in these reports share an obvious theoretical heritage with the modelling strategies that we intend to pursue. They provide a useful analysis of the ways in which Community transfers may affect economic perfor mance within an endogenous growth framework. In addition the models give estimates of the overall macroeconomic effect of entire spending pro grammes. While useful as a first step in analysing the growth impact of transfers, this model is un able to capture the dynamic interactions between different firms / sectors / regions of the economy which is emphasised in the endogenous growth literature. When it comes to analysing the impact of individual projects these interactions and other spillover effects need to be considered. See, for example, "Evaluation of the Economic Impact of the Com munity Support Framework on the Greek Economy using an Ex tended Computable General Equilibrium Model" Lolos/Zonzilos (DMP Ltd). "A Dynamic General Equilibrium Analysis of EC Structural Funds (with an application to Portugal)" (1991), "Structural Policies in the European Community: An International Comparison" (1994) and "Evaluation of the Community Support Framework for 1994-1999". 58 Section I Literature Review Input-output analysis The quantitative analysis of the impact of public investment programs is often carried out within an input-output (i-o) framework1 The i-o ap proach is generally advocated on the basis of lev el of sectoral disaggregation it allows, and of the relative simplicity of its implementation. Nevertheless, a number of factors make the in put-output framework unsuitable for the purpos es of our empirical analysis. Firstly, and most im portantly, i-o models are intended to perform comparative static analysis. They can thus pro vide little information on the dynamic behaviour of the variables of interest over different time hori zons. In addition, these models are explicitly de signed to quantify demand effects, while they are not meant to capture any supply-side effects. Secondly, the i-o framework suffers from the re- strictiveness of the assumptions it is built on: the demand for inputs is independent of the level of output, i. e. the percentage of total inputs required from each industry does not vary with the scale of production; technology is such that, for a given level of output, no substitutions may be made between inputs. Finally, significant problems of data availability are generally encountered in the practical implementation of i-o models, particu larly when the objective is to evaluate the impact of projects at the regional level. The latter issue of data availability and practical im plementation of impact analysis requires some ad ditional comments, as it has been the focus of the efforts in much of the recent literature on i-o mod els. The regional i-o framework requires a survey of an appropriately stratified sample (by size and industrial composition) of all the firms within a re gion. As a result of the high costs of constructing survey-based regional tables, the development of ' See, for example, "The Economic Impacts of the Community Sup port Frameworks for the Objective 1 Regions 1989-93" and "The Economic Impacts of the Community Support Frameworks for the Objective 1 Regions 1994-99" by J. Beutel. input-output tables at the sub-national level has generally relied on two alternative options: non- survey and partial survey techniques. Non-survey techniques consist essentially of ei ther modifying tables assembled from survey da ta for other regions or (in most cases) comple menting the existing national tables with very lim ited information at the regional level (Brucker, Hastings, and Latham (1990) provide an empiri cal comparison of five such regionally customized models). Partial survey techniques (see e. g. De- whurst (1992)) require some data collection but not on the scale envisaged for the complete re gional tables, and are based on a number of dif ferent techniques to make optimal use of the lim ited information available. A vast literature has developed recently in the at tempt to improve on the techniques of construc tion of i-o models and estimation of regional co efficients. Martins (1993) presents a new method for use in the construction of hybrid regional in put-output tables, based primarily on individual returns from the Census of Manufactures. Jensen (1990) provides a general overview of the progress of regional i-o models, and argues that hybrid models provide a preferable alternative for future direction in regional tables. Jackson and Comer (1993) present a table disaggregation and adjustment (TDA) method for combining the in formation from the detailed benchmark tables with the more current but aggregated data. Despite the efforts of this empirical literature, there seems to remain substantial room for im provement in the existing methods of construc tion and updating of i-o tables. Together with the more substantial objections expressed above towards the comparative static nature and the restrictive assumptions of the i-o frame work, these limitations point at the need for al ternative empirical approaches. These should be explicitly intended to describe the dynamic response of economic variables to regional shocks, while not restricting the attention to Keynesian demand effects. Current Commission modelling work 59 7. Where next? Regional labour markets We have outlined the existing literature on: — regional wage determination the impact of investment spending. Only by ex tracting from minutiae and incorporating these key factors can we hope to adequately predict the impact of Cohesion Fund spending. — migration — participation — firm locational decisions — employment adjustment We have suggested that when projects are allo cated across regions all of these factors will be important in determining their impact. Different al locations of projects lead to different distributions of labour demands across regions. Given the population distribution, a pattern of regional wages ¡s determined (regional wage determina tion). Workers make locational and participation decisions dependent on the resulting distribution of unemployment and wages (migration and par ticipation). The regional labour supplies adjust over time. As projects continue the flow of funds and the locational decisions of firms determine a pattern of labour demand which adjusts over time. The interaction of labour market supply and demand determine the dynamic pattern for em ployment (employment adjustment). Our regional labour markets modelling strategy takes into account all of these factors. It cannot deal with the gritty detail of each highway, railway, bridge and port facility. However, it does deal ex plicitly with the regional spillover effects of pro posed Cohesion Fund spending. We believe that it is these effects which will be key to determining VARs and stochastic kernels We have outlined the existing literature on: — forecasting and regional VARs — endogenous growth theory and time series approaches to the empirics of growth — regional dynamics Our modelling strategy builds from these existing regional applications, using techniques that rec ognize the nature of the dynamically evolving cross-sectional distribution of disaggregates. By working directly off the entire distribution we hope to analyze characteristics of resource mo bility and flow across regions, increasing inequali ty across rich and poor regions and the interac tion of all regions with aggregate fluctuations. Be cause these techniques work with the entire dis tribution we can use past data on movements of the key variables to predict the future response of the distribution to Cohesion Fund spending Computable general equilibrium models We have outlined the existing literature on: — the early AGE models Where next? 61 — industrial organization and AGE models — regional models Our modelling strategy is a natural progression from this existing work. The model we develop will be a regional model with explicit treatment of transport costs and factor mobility with imper fect competition / increasing returns to scale for- m at v\/e believe that the new emphasis on dy namics, imperfect competition and increasing re- turns will be vital if a CGE model is to capture the long run growth impact of Cohesion fund spending. 62 Section II — Literature Review Glossary Bayesian Vector Autoregression: Specifies prior distributions for the parameter values on certain vari ables which are then updated during the estimation procedure. If the modeller has a strong feeling that χ is not important in explaining y then they will specify a prior with a mean close to zero and a small vari ance. If the modeller has a strong feeling that χ is very important in explaining y then they may specify no prior distribution and let the estimation technique give the parameter value. Bimodal: The mode is the most frequently occurring observation and is represented by a 'hump' ¡n the probability distribution. A bimodal probability distribution has two 'humps'. Compositional Variable: A variable that reflects certain characteristics of a region; eg the sectorial com position or the sex and age composition. Differencing: The first difference of a variable is the change in that variable from one period to the next. The second difference of a variable is the change in the change in that variable from one period to the next. Economists often difference nonstationary variables in an attempt to get a stationary variable. Dummy Variables: Typically a binary variable (taking the value 0 or 1) that allows us to control for differ ences across units that are not explained by a model, but that are not a result of random errors; eg in a migration equation we may use a dummy variable for regions that have a better climate and are thus favoured for retirement — migration flow to such a region will be larger than their relative unemployment and wage rates would predict, and this effect is captured by the dummy variable for 'hot regions'. Ergodic Distribution: The 'long run' equilibrium distribution which is unchanged by the application of the relevant stochastic kernel (see below) Forecasting Performance Measures: A measure that is constructed by comparing the predicted val ues from a model with the actual values that are observed. Granger Causality: A variable χ 'Granger Causes' a variable y if knowledge of current and past values of χ can improve forecasts of future values of y, even when we know the entire history of the variable y, ie knowledge of the χ process gives us information about future y that we do not get from knowledge of the y process alone. Impulse Response Functions: When a model is specified to explain the determination of a variable (the dependent variable) there will be movements in that variable that cannot be totally explained by that model. Given values for the explanatory variables the model will predict a value for the dependent vari able. If the model is correctly specified (ie is an accurate representation of the real economy) then the Glossary 63 model errors (the difference between the predicted and actual values) should be random. When a model is constructed these random errors will be included as an additional 'explanatory' variable. The effect of an event that changes the value of the dependent variable but not the explanatory variables ¡s not pre dictable by the model. Such events are labelled shocks, and their effect must be incorporated in the er ror term. Many dynamic models include lagged (past) values of the dependent variable as explanatory variables. Thus shocks in one period can effect the value of the dependent variable for many periods to come. The dynamic effect of a shock is given by the impulse response function which shows how the dependent variable is changed over time by changing the value of the error term in a certain period (or periods). In multivariate models every equation may have an error term and the effect of each of these error terms on each of the dependent variables can be represented by an impulse response function. Markov Chain: A process which generates a random variable is a Markov process if all the usable infor mation about future values of the variable is given by knowledge of the current value of the variable, ie the best possible prediction of future values can be made using only the current value; Information about past values would not improve this prediction. A Markov Chain is a Markov Process that takes only dis crete values Nonstationarity: A variable that ¡s not stationary (see below) ¡s nonstationary. The level of GDP and the level of prices tend to drift up over time which suggests that they are nonstationary. Nonstationary vari ables are often classified by their order of integration. A variable is integrated of order d if it needs to be differenced d times before it is stationary. An equation that uses variables of different orders will generally be misspecified unless special (cointegrating ) relationships exist between the variables of higher order. Panel Data: Data collected for a cross section (of individuals, regions, firms etc) at different points in time. The combination of cross section and time series data allows us to control for individual effects (ef fects that vary over individuals but not over time, eg sex) or time effects (effects that vary over time but are constant over individuals, eg the national inflation rate). Phillips Curve: Strictly, a negative relationship between unemployment and the rate of wage inflation first suggested by Phillips (1958). Now used to describe a broad range of (negative) relationships be tween unemployment and prices or wages. Power Transformations: In a similar way to differencing, economists use power transformations (eg taking logs, or square roots) to obtain a variable that is 'better behave' less variable and thus easier to model. Some apparently nonstationary variables may become stationary under a power transformation. Quantiles: The values of the variable for which there is a certain probability of making a draw from the distribution which is less than that value. For empirical distributions it is the value for which there is a cer tain percentage of the population with outcome lower than that value. For example the lowest Income decile is the income value at which 10% of the population earn less than that value. Stationarity: A weakly stationary variable has mean, variance and covariances that are independent of time. A strongly stationary variable has a distribution that is independent of time. Stationary variables fluctuate around a constant mean value without ever moving permanently away from that mean value. Unemployment and GDP growth rates are often thought to be stationary values. Various statistical tests exist which help determine whether a variable is stationary. Stochastic Kernels: A mathematical operator which encodes the information about movements within a distribution and movements of the entire distribution over time. When this mathematical operator is applied to the 'description' of a given distribution, the outcome is a 'description' of a new distribution. Transition Probability Matrix: If the 'world' of interest is divided into different discrete states and ¡s ob served over time, then the transition probabilities are the probabilities that an 'individual' moves from one state in one period to another state in a different period. For example given an income distribution (the world), we consider people's income levels and define them as poor or rich (the states). The transition 64 Section II — Literature Review probabilities then give the probability of becoming rich, given that one is poor; staying poor given that one ¡s poor etc. A transition probability matrix records this information in matrix form. In our example we would have a two by two transition probability matrix. Information about Markov Chains ¡s conveniently stored ¡n transition probability matrices. A transition probability matrix is the concrete manifestation of a general stochastic kernel when the distributions are over only discrete states. Unit Root: A process with a unit root is a nonstationary variable with certain (well Investigated) properties. Univariate process: A model that attempts to explain the behaviour of a variable using only lagged (past) values of the variable (an autoregression) or only current and lagged values of a random variable (a moving average process) or only a mixture of both (an autoregressive moving average model). Variance Decomposition: Because the equations of models usually Include an 'explanatory' term which is a random error, the dependent variables themselves are random variables (the observed time series represent draws from the random variables joint distribution). The variances of the dependent vari ables will depend on the variances of the random errors. A variance decomposition gives the percentage of the variance of one of the variables that can be explained by the error term from one of the equations for different points in time. Vector Autoregression: A model which explains a vector of (n) variables in terms of lagged (past) values of those (n) variables. It is the multivariate equivalent of a univariate autoregression. Glossary 65 List of references Official Publications Barret, G. (1995) Meta-evaluation of the CSF and Operational Programmes for the Objective 1 Regions 1994-1999 Ireland. Beutel, J. (1995) The Economic Impacts of the Community Support Frameworks for the Objective 1 Re gions, 1994-99, Report to the Directorate- General for Regional Policies, Commission of the European Communities. Booz-Allen & Hamilton (1993) Evaluation of Actions in the Fields of Telecommunication and Transporta tion in Greece, Ireland and Portugal. 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Herce, J. and Sosvilla-Rlvero, S. , (1994) The Effects of the Community Support Framework 1994-99 on the Spanish Economy; An Analysis based on the HERMIN Model, FEDEA. Herce, J. and Sosvilla-Rlvero, S. , (1994) HERMIN-S4, A Four Sector Structural Model of the Spanish Economy for the Analysis of Community Support Framework, FEDEA. List of references 67 Honohan, P. (1994) Ex Ante Appraisal of the Irish National Development Plan 1994-99. ESRI. Karantinos, D. , Konsolas N. , Mitropoulos, C, Prodromidis, and K, Vakakis, F, (1994) Greek Regional Development Plan 1994-1999 Appraisal, KANTOR. Kennedy, K. (1993) Evaluation of Industriai Policy Issues: Ireland, ESRI. Lolos, S. and Zonzilos, N. , (1992) Evaluation of the Economic Impact of the Community Support Frame work on the Greek Economy using an Extended Computable General Equilibrium Model, DM Ρ Ltd. O'Connell, P. J. (1993) Evaluation of Structural Funds Expenditures in Favour of Human Resourses in Ire land, ESRI. 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(1987) Unemployment, the regions and labour markets: reactions to recession Green, A. and Owen, D. (1991) "Local labour supply and demand interactions in Britain during the 1980s " Regionalstudies 25(4) 295-314 Green, A. et al (1986) "What contribution can labour migration make to reducing unemployment" in Hart ed. Unemployment and labour market policies Greenwood, M. (1985) "Human migration: Theory, models and empirical studies" Journal of Regional Science 25: 521-44 Gruben, W. and Long, W. (1988) "Forecasting the Texas economy: applications and evaluation of a sys tematic multivariate time series model" Federal Reserve Bank of Dallas Quarterly Review Gruben, W. , Hoehn, J. and Fomby, Τ (1984) "Time series models of the Texas economy: a comparison" Federal Reserve Bank of Dallas Economic Review 70 Section II — Literature Review Hall, R. (1970) "Why is the unemployment rate so high at full employment?" Brookings Papers on Eco nomic Activity 1 369-402 Hall, R. (1972) "Turnover in the labour force" Brookings Papers on Economic Activity 3 709-764 Harris, J. and Todaro, M. (1970) "Migration, Unemployment and development, a two sector analysis" American Economic Review 60 139-49 Harris, R. (1984) "Applied general equilibrium analysis of small open economies with scale economies and imperfect competition" American Economic Review 75 (5) 1016-32 Hart, R. (1970) "A model of interregional migration in England and Wales" Regional Studies 4 279-296 Hart. M. , Gudgin, G. (1994) "Spatial Variations in New Firm Formation in the Republic of Ireland, 1980- 1990", Regionalstudies, 28(4), pp. 367-80. Higgs, P. , Parmenter, Β. and Rimmer, R. (1988) "A hybrid top down, bottom up regional computable general equilibrium model" International Regional Science Review 11 (3) 317-328 Holtz-Eakin, D. (1993) "Sticking it Out: Entrepreneurial Survival and Liquidity Constraints", Princeton In dustrial Relations Section Working Paper n. 319. Holtz-Eakin, D. (1994) "Public-Sector Capital and the Productivity Puzzle", Review-of-Economics-and- Statlstics; 76(1), February 1994. Houseman, S. and Abraham, K. (1990) "Regional labour market responses to demand shocks: A com parison of the United States and West Germany" Paper presented at the Association for Public Policy Analysis and Management, San Francisco. Hughes, G. and McCormick, B. (1981) "Do council housing policies reduce migration between regions" Economic Journal 91 919-37 Hughes, G. and McCormick, B. (1985) "Migration intentions in the UK: which households want to mi grate and which succeed?" Economic Journal 95 Conference Supplement 76-95 Hughes, G. and McCormick, B. (1987) "Housing markets, Unemployment and labour market flexibility in the UK" European Economic Review 615-45 Hughes, G. and McCormick, B. (1994) "Did migration in the 1980's narrow the North-South divide?" Economica 61 509-27 Hulten, C. and Schwab, R. (1991) "Public Capital Formation and the Growth of Regional Manufacturing Industries", National-Tax-Journal; 44(4), Part 1, December 1991, 121-34. Jackman, R. and Savouri, S. (1991) "Regional wage determination in Great Britain" LSE CEP Discussion paper: 47 Jackson, R. , Comer, J. (1993) "An Alternative to Aggregated Base Tables in Input-Output Table Reglon- alization", Growth and change, 24(2), pp. 191- 205. Jensen, R. (1990) "Construction and Use of Regional Input-Output Models: Progress and Prospects", International Regional Science Review, 13(1-2), pp. 9-25. Jones, R. and Whalley, J. (1989) "A Canadian Regional general equilibrium model and some applica tions" Journal of Urban Economics 25 368- 404 List of references 71 Keeble, D. , Walker, S. (1994) "New Firms, Small Firms and Dead Firms: Spatial Patterns and Determi nants in the United Kingdom", Regional Studies, 28(4), pp. 411-27. Kehoe, T. and Kehoe, P. 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(1978) "Geographical differences in earnings and unemployment rates" Review of Economics and Statistics 60 201 -8 Robbins, L. (1932) An Essay on the Nature and Significance of Economic Science Scarf, H. and Shoven, J. (1984) Applied General Equilibrium Analysis Sherwood-Call, C. (1988) "Exploring the relationships between national and regional economic fluctua tions" Federal Reserve Bank of San Francisco Economic Review Shoesmith, G. (1990) "The forecasting accuracy of regional bayesian VAR models with alternative na tional variable choices" International Regional Science Review 13 (3) 257-296 List of references 73 Shoven, J. and Whalley, J. (1992) Applying general equilibrium Shoven, J. and Whalley, J. 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(1985b) "Hidden challenges in recent applied general equilibrium exercises" in Piggot, J. and Whalley, J. eds. New developments in applied general equilibrium analysis 74 Section II — Literature Review SECTION VECTOR AUTO-REGRESSION MODELS Α. REGIONAL LABOUR MARKET ISSUES1 Chris Pissarides and Etienne Wasmer Department of Economics, London School of Economics Introduction A key question for analysing the impact of Cohe sion Fund expenditure is how this public invest ment spending influences private investment ac tivity and employment at the local level? In this paper we investigate this question for the four cohesion countries at the regional level (NUTS2 or NUTS3 depending on data availability). Unfor tunately, the labour market data for Ireland does not allow a complete analysis for that country. We carry out the analysis for an average ("repre sentative") region and also for poorer and richer regions separately. Our approach is to ask first, how do private and public Investments interact at the regional level and what does that Imply for the overall effect of public investment spending on regional investment? Then we ask, how does public investment influence local employment and wages? The question of whether an increase ¡n local employment is realised through an in crease in the region's population (inmigration), or through a fall ¡n unemployment or discourage ment of workers, is taken up next. This latter question is investigated separately for skilled and unskilled workers ¡n the case of Spain. We carry out the analysis for overall Investment spending and also for some of the different components of spending. This paper comprises three parts. In the first part we describe the modelling methodology and the econometric analysis of the data. In the second part, we report results from the simulation of ac tual Cohesion Fund expenditure on the regional economies in the four Cohesion countries. The third part summarizes our results and concludes. We want to thank Charlie Bean, Henry Overman and Danny Quah for numerous comments on the previous versions of this report. We also thank Monika Reti and especially Katharina Gassner, who made a very important contribution to the simulation report. Introduction 77 1, Modelling Results We begin with an outline of the methodology and the results of econometric analysis. In the first section we describe the methodology that we apply to the analysis of the impact of cohesion fund spending on the functioning of local labour markets. In the next four sections we describe the results of the econometric analysis for each of the four cohesion countries, Spain, Portugal, Greece and Ireland. 1. 1. Methodology Our goal is to measure the impact of structural public investment spending on the regional and, to a lesser extent, national economies. We first evaluate the effect of spending on business in vestment, employment and labour supply by av eraging across regions and computing the effect on the economy of the "representative" region. We also study some of the spillovers at the na tional level within the same framework. Subse quently, we disaggregate and estimate the effects of public investment spending in regions of differ ent economic well-being and for different kinds of investment spending. In order to estimate the impact of investment spending on the economy of the representative region, we express all local variables relative to the national average. We then take the logarithm of each variable, so what we shall call for brevity "regional investment", "regional employment", etc. , should be understood as the percentage re gional share or, ¡f the variable is an average, such as the employment rate, wages etc. , as the per centage deviation of the regional variable from the national average. Public investments have a variety of characteris tics but our main interest ¡s on the impact of the overall spending. So we conduct most of our analysis for total investment spending measured as a percentage of GDP. However, where possi ble, we also disaggregate investment spending into "productive" and "non-productive". "Non productive" investments include spending on ed ucation and health. These "non-productive" in vestments must surely have beneficial economic effects. However, these effects are likely to be long term, and not confined to the region where the expenditure take place. Public investment spending influences aggregate economic activity through direct job creation and also indirectly, by increasing the productivity of private capital. The latter channel stimulates pri vate Investment which leads to more job cre ation. At the aggregate level, such job creation is likely to lead to a fall in unemployment, since the labour force ¡s not likely to respond one for one to the new job creation. But at the regional level, the supply of labour is more responsive to eco nomic incentives, because of inter-regional mi gration. Despite the frequent assertion that mi gration rates in Europe are low, inter-regional mi gration is still a much larger cause of regional populations than natural growth is. The response of both private investment and inter regional migrations to public investment spending is likely to be slow and spread over time. We pick up the response functions through vector auto-re- Modelling Results 79 gressions (VAR) models. This amounts to estimat ing the effect of the lagged values of all variables on the present value of each. By making use of the estimates, we can then simulate the dynamic response of each variable to a unit change in a given exogenous variable. In terms of equations, we estimate: These restrictions could be criticised because the impulse response we derive is dependent on them in the shortrun. Moreover, there is a prob lem of selfcorrelation of the residuals: it can be shown that cov{ef, ef. ,) = 0 if and only if «5 = 0 or if a. '  β, which need not to be true. The non zero covariance of the residuals will induce a bias on the least square estimators of the coefficients. X; A M X, + ε; d) where Xit is a vector of variables at time t in re gion /, A,(L) is a polynomial matrix of the lag oper ator /_, defined by LX, = X,_7, and (e ) is a vector of disturbances. The matrix A may or may not be regionspecific, according to the restrictions im posed on the estimate. The restrictions imposed on the shocks e are im portant. A simple example with public investment and private investment with one lag is helpful. Suppose we are interested in the coefficients a, α', β and ß' in the equations: Plt = C + a ■ P/(_7 + β · Bl,_, + ef (2) and S/f = C' + a'P/i_7+ß'e//_7+ef (3) where PI is public investment, Bl is private (busi ness) investment, and the e are such that E(e) = 0, and V(e) = σ. Now, there might be an immediate positive impact of public Investment on private capital productivity and therefore on business investment. In the model above, the im mediate effect of PI on Bl can only arise from the shock ef. Therefore, we need to consider the correlation between ef and ef, where we expect Cov{ef, ef) > 0 if public investment has an im mediate impact on private investment. In con trast, as public investment is sometimes counter cyclical ¡n order to decrease the impact of busi ness cycles fluctuations, the above covariance may be negative. Given the complexity of the problem, we may impose identification restric tions on the shocks, i. e. define a matrix linking the structural variables (the pure exogenous shocks) to the innovations (estimated as the residuals of the regressions above). In all the cas es, we have to rewrite the model by decompos ing ef into the sum of two terms: <5ef character ising the immediate effects, and a ζ, such that cov(sf, ζ,) = 0. A convenient way of dealing with these two prob lems, which relies only on one restriction on the data, is the following. If we assume that at time t an exogenous shock in business investment (im plied for instance by a regional business shock) has no contemporaneous effect on public invest ment, then a natural model to estimate is: Ρ /(=0 + α·Ρ/,_7+β·Β/(_7+μί (4) and Bl, = C' + a' ■ P/(_7 + β' ■ Blt_, + γ ■ PI, + μΕ, (5) where γ represents the immediate effect of public investment on private investment. In this model, we assume that a shock in private investment is uncorrelated with public investment at time t. Also it can be shown that now, cov (ßf, ¿¿f_,) = 0. This way of modelling the dynamic Interactions be tween the regional series was used by Blanchard and Katz (1992) in their study if the US regional evolutions. They assumed that a shock at time t on activity, or population, or wage rates, had no impact on the level of employment in the same year, but only ¡n the next year. This assumptions is obviously restrictive but the payoff is that it re duces the arbitrariness of the assumptions on the identification matrix. The parameter γ giving the shortrun effect of one variable on another ¡s esti mated (consistently) from the data, and not pos tulated. A discussion of another source of bias, a correlation between PI, and μ,, which would affect the estimate of the coefficient γ, is reported in the Appendix, where we show that this leads to sys tematic errors which will be small in our case. Once the parameters have been estimated, we can simulate the response of a one percent in crease in public investment on the other vari ables. Given the logarithmic transformations that we have already described, the estimates are elasticities, that is, they show the percentage in crease in the dependent variables due to a 1 % increase in one of the exogenous variables. If our main interest, however, is in the effect of one ad 80 Section I ■ Vector Autoregression Models ditional unit of public investment on spending in the business sector, employment, e t c. , we can easily convert from elastlcises to real numbers by multiplying the impulse response calculated above by an appropriately defined factor. For in stance in the short run, by definition: ABI _ API Bl ~T PI which implies that: ABI = γ ■ API ÊL PI (6) (7) In others words, if the elasticity estimated for a group of regions is lower than for another group of regions, ¡t does not necessarily imply that the impact in terms of units of spending is lower on the first group. It will depend on the average ratio of the variables, BI/PI like above. We will con stantly refer to both the elasticities and the gross numbers in the sections detailing the econometric analysis. In the second part of our report, where we simulate the impact of Cohesion Fund expen diture, we will emphasize the gross numbers per unit of spending or per project, since this is obvi ously the primary interest of the Cohesion Fund. We can also estimate the effect of this shock on cumulated private investment, which can be in terpreted as the longrun effect on the stock of private capital, ¡f depreciation of capital is zero. An issue that was raised during discussions with the Commission was the relevance of the endo genelty or exogeneity of public Investment spending with respect to the private sector (that is, whether public investment responds to private sector conditions). In the VAR model defined by equations (3) and (4), the first equation describes the dynamic evolution of public investment, both as a function of its past values and of past busi ness investment. In this specification, public in vestment ¡s endogenous (it depends on past business investment). It may be thought that, in absence of any consensual theory of public in vestment, it is better to consider the model with restrictions on the coefficients of the first equa tion. If the model was estimated while imposing say β = 0, it would mean that public investment depends only on its past values (public invest ment is exogenous). The econometric innovation (the public Investment shock) would be followed by successive innovations with decreasing size. Alternative estimations could impose both α and β to be equal to 0. In the case of these partial (or restricted VAR), we have not found very strong significant differences in the results. Cohesion Fund spending could represent additive invest ments (case of exogenelty, restricted VARs) or a substitution for other investment (in which case the unrestricted VARs are more appropriate). Which scenario actually holds depends on the re sponse of member states to Cohesion Fund in tervention. Unless specified, we have chosen the first option. It should be emphasised that a change in this assumption leads to little addition al computational efforts, and that we are ready to consider both scenarios in future simulations. This methodology can be extended to many other blvariate systems: some of them Involving disag gregated series of public investment and private investment (for instance, productive public invest ment and nonproductive investment, investment in roads, water infrastructures, etc. ), and others looking for the effect of public Investment and em ployment on other relevant labour market vari ables. Of course, it would be interesting to esti mate a system with more than two variables. The problem here is that, in the case where two lags are imposed in the equations, the number of co efficients to estimate in each equation is equal to 5 or 6 with two variables, 7 or 8 with three vari ables, and more generally 2xk+1 (+1) when k is the number of variables. Thus, there ¡s a tradeoff between the improvement from taking into ac count more of the interactions between the vari ables and the cost of reducing the number of de grees of freedom of the estimates. There are two different estimators of the coeffi cients. Which of them ¡s optimal ¡s conditional on whether data are timeseries or crosssection. The first estimator consists of estimating the co efficients state by state, and then averaging them by taking either a weighted or an unweighted av erage. This method is appropriate when the data are available for a long period of time, as for in stance in Spain. The second method consists of estimating the VAR as a panel, restricting the slope coefficients to be equal. The intercept can be either common, regionspecific or a random effect. In our discussion below we always use the panel method with common slope coefficients. For Spain we estimated the parameters in both ways and a comparison of the results led to very similar conclusions. Modelling Results 81 The VAR methodology can also be applied to study what happens to labour market variables when regional employment is shocked by, say, one percentage point. We again estimate Having outlined our methodology, we now pre sent results from the application of this methodol ogy to each Cohesion country. X/ | f=A(/)X,,,+ß^+E;, (8) 1. 2. Spain where A(L) is a polynomial of matrices, L the lag operator, and S is a matrix of maximum rank (/c1) where k has the dimension of X, and can be written as, β: 0 α ß 0 0 0 0 0 0 (9) The term BX represents the impact of labour de mand shocks on the other variables within the same period. This avoids defining the identifying restrictions by the assumption that the second, third etc. variables have no immediate effect on the first variable in the vector X. As in Blanchard and Katz, we will assume that the first variable is the level of employment, and the other variables will be chosen among the following set: unem ployment rate, activity rate (participation), 1564 population, total population and wages. Thus, our analysis will tell us whether a one percent employment shock is translated into more inmi gration, a fall in unemployment, an increase in participation, an increase in wages, or a combi nation of these (refer to Appendix 1 for details). We test for the stationarity of the time series be fore obtaining the estimates. The level of disag gregation of the analysis is sometimes NUTS2 and sometimes NUTS3, depending on data avail ability. The estimated elasticities are generally lower when the degree of disaggregation is high er (see Appendix 3). This is probably due to the fact that there are measurement errors which are inversely related to the size of the regional units, which biases downwards the estimates of the parameters. One solution is to weight the esti mates with the size of the region, or to use a two step OLS/GLS procedure to weight the estimates with the crosssection variance. There is a nice economic interpretation to this estimator. In the first step, we have information on which regions behave differently from the others (this is given by the variance of the residuals region by region). Then, in the second step, we estimate a model of a representative region with the biggest weight given to the core regions, where core means the regions behaving similarly. We begin our analysis with Spain. Good quality data are available at the NUTS2 level (17 Re gions) for public investment and the capital stock, disaggregated into eight categories from 1964 to 1991. Private sector investment and capital stock are available for the same period for 23 sectors of the economy. For the same period 196491, data for population, population aged 1564, labour force, employment and unemploy ment are available at the NUTS3 level (50 provinces) and disaggregated into five levels of skill (except for total population). Wages are avail able every two or three years between 1955 and 1993 for NUTS3, and every year between 1980 and 1991 from another source for NUTS2. For the six years where the data overlap, the differ ence between the two series is less than 3%, and we can predict 97% of the variance of one variable with a constant and the other variable. This close correspondence allows us to define a third regional wage series for NUTS2 with six more points (this is explained in Appendix 2). 1. 2. 1. Dynamic relations between public investment, private investment and private capital stock at the regional level. Public investment spending, the sum of which represents on average 2. 3% of GDP, is disaggre gated into two main categories: productive and "nonproductive". Productive investment is mainly on infrastructures (roads, ports, airports, motor ways and urban structures). "Nonproductive" in vestment is in the education and health sectors. Table 2. 1 displays some summary statistics, and Figures 2. 1 and 2. 2 (at the end of this section) provide additional illustration. The second column of the table reports the annual growth rate for each form of investment. In comparison, real GDP growth in Spain over this period was 3. 4% p. a. As described in Section 1, we estimate a bivari ate VAR between relative public investment and private sector investment. Augmented Dickey Fuller tests run region by region reveal that both series are stationary. Thus in the following, the variables are entered in their levels. 82 Section III — Vector Autoregression Models TABLE 2. 1. Public and Private Sector Investment. Summary Total Public Investment Productive Roads Water Facilities Urban Structures Ports (Pub. Adm. ) Ports (Non Pub. ) Motorways Airports Non Productive Health Education Total Business Sector Investment 64-91 Av 64-91 growth (%) 100 (ref) 77 25. 6 25. 1 10. 4 0. 6 4. 4 8. 0 3. 1 23 8 15 748 3. 9 3. 7 4. 3 1. 7 10. 9 18. 5 -0. 01 -5. 7 2. 1 4. 8 4. 3 5. 2 2. 0 We estimate, with two lags, the equations: PI, =C + a, -P/f_7 +α2·Ρ/ί_2+β7 -ΒΙ,_, + +β2-β/ί-2+μΓ and Bl, = C + δ7 -PI, _7 + δ2 -PI,_2 + λ7 -Bl,_7 + +X2-BI,_2+yPI, +μ,β (10) (11) where γ is the important coefficient representing the immediate effect of public investment on business Investment. The results are summarised in Table 2. 2 and illustrated in Figure 2. 3. The numbers reported in the table are the im pulse response to a unit shock in public invest ment, in the short and the long-run. Private in vestment has a positive and significant correla tion with total and productive public spending, both in the short-run and the long-run. (Appendix 5 explains how the standard-error of the estimat ed effect on the cumulated private investment was calculated. ) Looking at the different compo nents of productive investments, the main effect on the business sector is from investments in roads and motorways, while other types of in vestments are too small to produce an observ able effect on the regions. In contrast, non-pro ductive investment has no significant impact on private investment. The insignificant impact of education investment on regional private investment ¡s a result that de serves further comments. We consider all our re sults in terms of shares in the national aggregate. The most plausible interpretation of this result is that increasing the education spending in a re gion improves the outcomes of all regions, and that the relative regional Impact is not significantly stronger than this aggregate Impact. The em ployment results suggest one possible mecha nism: the regional mobility of unskilled workers is low, and the mobility of the skilled workers is much higher. Improving the education level in a region may lead to more mobility, which is cer tainly in aggregate more efficient, but does not only benefit the region where the investment takes place. We can also predict the long-run impact of public investment on employment. Because Augmented Dickey-Fuller tests tend to indicate that employ ment is non-stationary (see Appendix 3 for the tests), the VAR is estimated with the first differ ence of employment. The results are summarised in Table 2. 3 and for aggregate public investment and business investment only, in Figure 2. 4. The results are similar: we have a positive impact of productive investment on employment, account ed for mainly by investment in roads and motor ways. We finally estimate the impact of public invest ment on wages. For that, we need to calculate the wage rate for some years between 1964 and 1979. We describe our calculations in Appendix 2. The results are reported in Table 2. 4 and illus trated in Figure 2. 5. Modelling Results 83 TABLE 2. 2. Shortrun and GLS 2 step estimators longrun estimated impact of public spending, in panel, weights=crosssectional variance Shock on Total Public Investment (442 obs. ) Productive + NonProductive Productive Public Investment (442 obs. ) Total (442 obs. ) Water Facilities (442 obs. ) Urban Infrastructures (442 obs. ) Roads (191 obs. ) Motorways (11 Reg. ) (213 obs. ) Ports (10 Reg. ) (260 obs. ) Ports Non Admin. (10 reg) (329 obs. ) Airports (16 reg. ) NonProductive Public Investment (442 obs. ) Total (442 obs. ) Education (442 obs. ) Health Effect on Private Investment Effect on Private Capital Stock Effect on Private Investment Effect on Private Capital Stock in the shortrun (γ) in the longrun in the shortrun in the longrun Elasticities' Elasticities Gross Number Gross Number 0. 0 3 7 *" [0. 016] 0. 0 3 3 *" [0. 013]  0. 0 03 [0. 013]  0. 0 0 04 [0. 01] 0. 0 3 5 "* [0. 01] 0. 0033 [0. 004]  0. 0 02 [0. 006] 0. 0015 [0. 012] 0. 0045 [0. 005]  0. 0 01 [0. 0016]  0. 09 [0. 14]  0. 0 04 [0. 015] 0. 1 6 0" [0. 05] 0. 1 3 7 *" [0. 04]  0. 0 35 [0. 04] 0. 0718* [0. 05] 0. 1 8 8 *" [0. 04] 0. 028** [0. 013]  0. 0 28 [0. 031] 0. 032 [0. 039] 0. 002 [0. 02] 0. 001 [0. 11]  0. 0 27 [0. 08] 0. 049 [0. 08] 0. 28 1. 20 0. 32 1. 55 NS NS 1. 02 NS NS NS NS NS NS NS 1. 04 2. 14 5. 49 1. 70 (+) NS 4. 40 (+) NS NS NS NS 1 The columns headed "Elasticities" report the elasticity estimates from the logarithmic regression in the text. The columns headed "Gross Numbers" report the elasticity times the ratio of Business to Public Investment, which is the number of units of private in vestment or capital stock that follow an increase of one unit of public spending. (shortrun: weighted standard errors ; longrun: standard errors computed from the covariance matrix on the estimators; (+) the average of Business over Public Investment was calculated in the subsample of regions and years where Public Investment was different from zero, ' , " , "" significantly different from 0 at the 10, 5 and 1 % level, NS means Not Significant) The results in the last three tables suggest that public investments have a positive impact on capital productivity, positive effects on employ ment, but not much effect on wages. Our estimates are for a representative region, es timated with a twostage procedure to correct the crosssectional heteroscedasticity. We can go a step further and examine the relevance of this representative region. First, we look at the size of the residuals for each region. This identi fies regions that behave differently from the repre sentative one, as ones with large residuals. The following Table 2. 5 (column 2) gives the ratio of the standard deviation of the residuals for each region over the standard deviation for all the residuals. A value below one implies that the re gion is close to the representative region. A value above one implies that the region is different from the representative one. We can see that for three regions, there is a value significantly above one: Baleares, Extremadura and Aragon. 84 Section I ■ Vector Autoregression Models TABLE 2. 3. Shortrun and longrun estimated impact of public spending GLS 2 steps estimators in panel, weights=crosssectional variance Shock on Total Public Investment (425 obs. ) Productive + NonProductive Productive Public Investment (425 obs. ) Total (425 obs. ) Water Facilities (425 obs. ) Urban Infrastructures (425 obs. ) Roads (191 obs. ) Motorways (203 obs. ) Ports (10 Reg. ) (250 obs. ) Ports Non Admin. (10 reg) (319 obs. ) Airports NonProductive Public Investment (425 obs. ) Total (425 obs. ) Education (425 obs. ) Health Business Investment (425 obs. ) Employment Employment Employment Employment in the shortrun (γ) in the longrun in the shortrun in the longrun Elasticities' Elasticities Gross Number Gross Number 0. 014— [0. 004] 0. 012"* [0. 003] 0. 004* [0. 0026] 0. 0006 [0. 002] 0. 004* [0. 0028] 0. 0015* [0. 0011] 0. 0010 [0. 0012] 0. 006" [0. 003] 0. 001 [0. 0026] 0. 0040 [0. 0044] 0. 0030 [0. 0033] 0. 002 [0. 004] 0. 03** [0. 01] 0. 0084" [0. 004] 0. 0076— [0. 003] 0. 0033 [0. 003] 0. 0016 [0. 004] 0. 0076— [0. 003] 0. 0035*" [0. 001] 0. 0048" [0. 002] 0. 0024 [0. 003] 0. 0004 [0. 002] 0. 0024 [0. 007] 0. 0040 [0. 006] 0. 0003 [0. 005] 0. 032*** [0. 006] 73 000 44 000 81 300 51 500 81 520 15 550                   21 000 22 000 ' The columns headed "Elasticities" report the elasticity estimates from the logarithmic regression in the text. The columns headed "Gross Numbers" report the number of jobs created per 12 000 000 workers (Spanish employment) in response to an increase of public investment spending of 1 % of GDP. (shortrun: weighted standard errors in parentheses; longrun: standard errors computed from the covariance matrix on the esti mators. *. '". '" significantly different from 0 at the 10, 5 and 1% level) Second, we can estimate the VAR for each re gion separately. Because we lose the cross sectional variance of the variables, the confi dence interval on the regressors is much larger. Moreover, we have calculated the standard de viation of the long run value of cumulated pri vate investment on the assumption that the co efficients are random normal. This assumption is asymptotically valid, so acceptable when the number of observations is large. Our panel has 442 observations but our time series only 26, so the value of the standard deviation in the brackets ¡n the third column may not be accu rate. Modelling Results 85 TABLE 2. 4. Shortrun and longrun estimated impact of public spending, GLS 2 step estimators in panel, weights=crosssectional variance Shock on Total Public Investment Productive Public Investment NonProductive Public Investment Business Investment Wages in the shortrun (γ) Elasticities 0. 0048 [0. 0038] 0. 0033 [0. 0030]  0. 0 0 06 [0. 004] 0. 270* [0. 20] (weighted standard errors in parentheses, *, ", "* significantly different from 0 at the 10, 5 and 1% level; longrun elasticities are not reported because they are all insignificantly different from zero) TABLE 2. 5. Longrun impact, individual regional Total Public and Private Investment estimates, Com. Valenciana Andalucía Navarra La Rioja Pais Vasco Madrid Catalunya Asturias Murcia Galicia Castilla Y Leon Cantabria Canarias Castilla la Mancha Baleares Extremadura Aragon VAR Total Public Investment and Private Investment LongRun Effect Estimated Individually 0. 39 0. 58 0. 61 0. 76 0. 81 0. 84 0. 85 0. 89 0. 98 1. 00 1. 04 1. 05 1. 08 1. 15 1. 40 1. 41 1. 47 0. 380 [0. 29] 0. 540 [0. 46] 0. 081 [0. 04] 0. 122 [0. 09] 0. 231 [0. 97] 0. 23 [0. 45] 0. 461 [0. 17] 0. 223 [0. 18] 0. 216 [0. 06] 0. 140 [0. 17] 0. 570 [0. 99] 0. 368 [0. 47] 1. 03 [0. 36] 1. 87 [1. 23] 0. 849 [0. 64] 0. 01 [0. 74] 0. 099 [0. 15] (standard errors computed from the covariance matrix on the estimators) We can repeat the same exercise with the bivari ate system that has productive public investment and private investment, since the diffe'ence ob served across regions may be due to heterogene ity in the nature of investment. The dispersion of the longrun value estimated for each region (col umn 3) is smaller here, because the estimated se ries of investment are more homogenous. 86 Section III — Vector Autoregression Models TABLE 2. 6. Longrun impact, individual regional estimates Productive Public and Private Investment VAR Productive Public Investment and Pri /ate Investment Com. Valenciana Andalucía Navarra La Rioja Pais Vasco Madrid Catalunya Asturias Murcia Galicia Castilla Y Leon Cantabria Canarias Castilla la Mancha Baleares Extremadura Aragon oi / σ 0. 40 0. 57 0. 61 0. 76 0. 81 0. 83 0. 34 0. 89 0. 97 1. 00 1. 03 1. 06 1. 06 1. 15 1. 39 1. 41 1. 47 LongRun Effect Estimated Region by Region 0. 380 [0. 26] 0. 340 [0. 36] 0. 065 [0. 03] 0. 107 [0. 07] 0. 221 [0. 82] 0. 288 [0. 24] 0. 359 [0. 13] 0. 228 [0. 15] 0. 170 [0. 041 0. 111 [0. 12] 0. 578 [1. 16] 0. 268 [0. 35] 0. 657 [0. 26] 1. 336 [0. 97] 0. 789 [0. 54] 0. 002 [0. 71] 0. 054 [0. 14] (standard errors computed from the covariance matrix on the estimators) Given the problems of estimating region by re gion, we turn to groups of regions which can form natural smaller panels. We group regions according to their GDP per capita and ask: Do the poor regions benefit more from public invest ments than the rich ones? We divide the panel of 17 Spanish regions into three groups: the six poorest, the five richest and the remaining six in a middleincome group (see Appendix 4 for de tails), and then run the previous regressions for each of the subgroups. Having less crosssec tional variance, the confidence intervals are now larger. The results indicate that productive investments in the middleincome regions have stimulated less private investments than in the richest and poorest regions. Thus, substitutability between business and public investment appears to be stronger at the extremes of the distribution. This result, however, ¡s not likely to be robust, give the large standard errors of the estimate. The regressions with nonproductive Investment do not give significant results. This is summarised in the Tables 2. 7 and 2. 8. The longrun elasticity of cumulated private investment with respect to public Investment is 35% higher in the bottom third of the distribution than ¡n the top third. The dynamic path of the response of private invest ment ¡s the same as ¡n the representative re gion. Modelling Results 87 TABLE 2. 7. Long-run estimated impact of public spending on private investment subgroups of regions, GLS 2 steps estimators in panel, weights=cross-sectional variance Shock on different groups of regions Elasticities in the long-run: cumulated private investment Poor, 156 obs. Intermediate, 156 obs. Rich, 130 obs. Total Public Investment Productive Public Investment Non-Productive Public Investment Shock on different regions Total Public Investment Productive Public Investment Non-Productive Public Investment 0. 199 [0. 17] 0. 178* [0. 12] - 0. 31 [0. 27] 0. 104 [0. 10] 0. 093* [0. 08] 0. 037 [0. 18] 0. 1 3 4" [0. 06] 0. 1 1 3" [0. 05] 0. 112 [0. 14] Elasticities in the long-run: cumulated private investment Catalunya, 26 obs. Andalusia, 26 obs. Galicia, 26 obs. 0. 4 6 1 "* [0. 17] 0. 3 5 9 "* [0. 13] 0. 632 [0. 92] 0. 540* [0. 46] 0. 340 [0. 36] - 0. 3 77 [0. 74] 0. 140 [0. 17] 0. 111 [0. 12] - 0. 8 4" [0. 39] (long-run: standard errors computed from the covariance matrix on the estimators, *, ", '** significantly 5 and 1 % level) different from 0 at the 10. TABLE 2. 8. Long-run estimated impact subgroups of regions, GLS 2 steps estimators on employment of public spending, in panel, weights=cross-sectional variance Shock on different groups of regions Elasticities in the long-run: employment Poor, 150 obs. Intermediate, 150 obs. Rich. 125 obs. Total Public Investment Productive Public Investment Non-Productive Public Investment Shock on different regions Total Public Investment Productive Public Investment Non-Productive Public Investment 0. 0065 [0. 012] 0. 0076 [0. 009] - 0. 0 0 36 [0. 017] 0. 0 1 3" [0. 007] 0. 0 1 0" [0. 006] 0. 0044 [0. 014] 0. 0 0 8" [0. 004] 0. 0 0 6 9" [0. 0038] 0. 0076 [0. 0095] Elasticities in the long-run: employment Catalunya, 25 obs. Andalusia. 25 obs. Galicia, 25 obs. 0. 0 4 0 *" [0. 015] 0. 0 3 1 "* [0. 012] 0. 1 2 6" [0. 07] 0. 0067 [0. 04] 0. 0 5 0 *" [0. 02] - 0. 0 68 [0. 078] - 0. 0 04 [0. 03] -0. 0007 [0. 02] - 0. 1 3 3— [0. 055] (long-run: standard errors computed from the covariance matrix on 5 and 1 % level) the estimators, ', '*, '" significantly different from 0 at the 10. Section III — Vector Auto-regression Models 1. 2. 2. The Impact of labour demand shocks Public investment spending affects private capital accumulation and so also affects labour produc tivity, wages and employment. Here we evaluate the impact of the public spending shocks on the regional labour markets. We consider the dynam ic effect of labour demand shocks on activity rates and regional population. All the relative variables "Regional Employment", "Regional Activity Rate" and "Regional 15-64 Population" (in logarithm) are non-stationary, ac cording to appropriate Augmented Dickey-Fuller tests reported in the Appendix 3. This implies that the new equilibrium reached after a shock is different from the previous one for the employ ment level, the activity rate and the population. As discussed in Blanchard and Katz (1992), the migration of workers explains the non-stationarity of employment and population. But a new result here is that the activity rate and unemployment are also affected by the past shocks in the long- run. During our sample period, some regions did consistently better than others in terms of em ployment, activity rates and in-migration. We estimate the VAR model in (8) for the three variables Employment, Activity Rate and 15-64 Population. The VAR has two lags and is estimat ed as a panel with fixed regional effects. The re gressions where activity and population are the exogenous variables allow for an immediate ef fect of employment. The estimator is discussed in the technical Appendix 5. We introduce here a new degree of disaggrega tion in the workforce: the skill level, which is avail able in the 50 provinces since 1964. We then es timate a VAR for the regional series, and for each skill level (00, 01, 02, 03, 04, described in the Appendix 2). It appears that the response to a 1 % employment shock was similar for the cate gories 00 and 01 and for 02, 03 and 04, but dif ferent between these two groups. We therefore aggregated categories 00 to 01 (referred to as the unskilled), and categories 02, 03 and 04 ( re ferred to as the skilled). The results of the impulse response of employ ment on the activity rate and population aged 15- 64 can be seen in Figures 2. 6-2. 8. The elastici ties are summarized in Table 2. 9. TABLE 2. 9. Impul se Response of Labour Market Variables Summary. 50 Provinces, GLS 2 steps panel estimator, weights to Employment Shocks, =cross-sectional variance VAR Spain Spain Spain Spain Spain Number of Observations Number of Regional Units Shock: 1300 50 +1 % Total Employment 1300 50 +1% Skilled Employment 1300 50 +1% Skilled Employment 1300 50 +1% Unskilled Employment 1300 50 +1% Unskilled Employment Short-run elasticity of Total Activity Rates Total 15-64 Population Skilled Activity Rates Skilled 15-64 Pop. Unskilled Activity Rates Unskilled 15-64 Pop. Long-run Elasticity of Total Employment Total Activity Rate Total 15-64 Population Skilled Employment Skilled Activity Rate Skilled 15-64 Pop. Unskilled Employment Unskilled Activity Rate Unskilled 15-64 Pop. 0. 675 [0. 01]*" 0. 026 [0. 005]"* - - - - - - 0. 240 [0. 01 ]*** 0. 282 [0. 01]*" - - - - 0. 239 [0. 01]*" 0. 266 [0. 01]*" - -0. 05 [0. 007]*" - - - - 0. 797 [0. 01]*" 0. 072 [0. 008]"" - - - -0. 04[0. 01]"* 0. 807 [0. 01]*** 0. 066[0. 007]"* 1. 05 0. 62 0. 12 - - - - - - - - - 1. 28 0. 33 0. 44 - - - - - - 1. 29 0. 32 0. 47 - - 0. 01 - - - - - - 0. 95 0. 68 0. 12 - - - - - 0. 09 0. 98 0. 68 0. 15 (short-run: weighted t-stat in parentheses : long- '" significantly different from 0 at the 10, 5 and + un: standard errors 1 % level) computed from the covariance matrix on the estimators, ", *', Modelling Results 89 The important result here is that the skilled and the unskilled responses to the employment shock are very different from each other, both in the shortrun and in the longrun. In response to a positive employment shock, the skilled move from unemployment to employment and from outside the region Into it. In contrast, the un skilled move from inactivity to employment and not at all from unemployment to employment. In migration for the unskilled is also much weaker than for the skilled. The Implication of this finding is that the main effect of negative shocks on em ployment for the unskilled is discouragement (workers leaving the labour force because of lack of employment opportunities). They do not leave the region, even if labour demand is high else where. In contrast, skilled workers migrate if they cannot find jobs in their region, and they respond quickly to the differential in demand for labour elsewhere. 1. 2. 3. Conclusions for Spain. economy an increase in private business capi tal of 0. 16%. The whole of this increase is ac counted for by public Investment spending ¡n Infrastructure. Spending on education and health do not stimulate private Investments. 2. The longrun effect of productive Investment spending is approximately the same in differ ent types of regions. There is, however, some evidence that the positive spillovers for private Investment are slightly stronger in poorer re gions and, to a lesser degree, richer ones (and weaker in middleincome ones). 3. Public investment Increases local employment but has very small effects on wages. 4. An increase in local skilled employment ¡s as sociated with a fall in local unemployment and ¡ηmigration (conversely for a fall). In contrast, a rise in unskilled employment is associated almost exclusively with an Increase in the local activity rate. Our empirical results for Spain support the fol lowing five conclusions: 1. A 1 per cent increase in public investment spending eventually generates in the local The effects of the shocks on regional employ ment, unemployment and inactivity persist in definitely. This contrasts with US regional evo lutions where only employment shocks are persistent. 90 Section III — Vector Autoregression Models Figure 2. 1: Components of Total Public Investment (Percentages, 1964-1991 Averages) Figure 2. 2: Public Expenditure as a Percentage of GDP, 1964-1991 Spain 0,04 0,035 0,03 0,025 0,02 0,015 0,01 0,005 0 CD Water Facitilites 25% Ports, Public Admin. 1% Urban Structure 11% Ports, Non-Public Admin. 4% Motorways 8% y ■'■I** -; -= I , U——, ■ Ports, Public Admin. Π Water Facitilites Θ Urban Structure Β Ports, Non-Public Admin. ■ Roads O Motorways □ Airports Ξ Health Π Education o Q. = 5' CQ 33 Φ CO CD ro en Φ o o o > c o à o α Φ Spain Figure 2. 3: Cummulative Response of Private Business Investment to a 1% Shock in (a) Total Public Investment and (b) Productive Public Investment Years | f / Total Public Investment Shock Productive Public Investment Shock Spain Figure 2. 4: Response of Local Employment to a 1% Shock to (a) Private Business Investment and (b) Public Investment / Shock to Private Investment / Shock to Public Investment Figure 2. 5: Response of Wages to a 1% Shock to (a) Private Business Investment and (b) Public Investment Years / Shock to Private Investment / Shock to Public Investment - -/ Modelling Results 93 co ω Φ ο ο' 3 Ι Î > c δ* ο =3 ο α Φ Spain Figure 2. 6: Response of Employment to a 1% Shock in Employment Figure 2. 7: Response of Activity Rate to a 1% Shock in Emploment 1,6 τ Figure 2. 8: Resonse of Population to a 1% Shock in Employment Figure 2. 9: Population of Skilled and Unskilled Workers, 19641992 35000 30000 - 25000 20000 15000 10000 5000 ■ * j - r " - o c 5 c o o ) C \ j i r > c o i- c o < o h - h » r ^. r - ~ c o c o c o o> c n o i o i o i c n c n c n o i c ñ o) LEGEND Total Population Skilled Workers Unskilled workers Years Years Years 1. 3. Portugal We now turn to Portugal. Public investment by municipalities ¡s available at the district level (18 regions, and for most years, the islands of Acores and Madères as well) between 1979 and 1986 and from 1990 to 1993. Central Govern ment Investment Spending is also available, but only since 1990. Because the latter sample ¡s rather small, we focus only on local public spend ing by the municipal authorities. This causes two problems for a crosscountry comparison: first, the nature of these investments is different from the investment by central government. Second, municipal investments in Portugal are strongly concentrated in the richest districts (Lisboa and Porto), so it is difficult to detect any strong effect of public spending in the regions where the shocks on municipal investments are small, since their Impact on the business sector may be mar ginal. Private investment is available at the NUTS3 level between 1980 and 1993, without disaggregation by type of spending. Wages are available be tween 1971 and 1993 (except for 1978, 1980 and 1990) but before 1980 the series is very volatile and probably not reliable. We have em ployment, population and population aged 1564 since 1970 at the district level and unemploy ment since 1975. 1. 3. 1. Dynamic relations between public investment, private investment and private capital stock at the regional level. The comparison of Portuguese investment data with Spanish data ¡s difficult: in Portugal, the ratio of business to local public investment ¡s 15. 2, in stead of 7. 4 in Spain. The interpretation of the re sults in Portugal must be done carefully. We again estimate a bivarlate VAR between relative public investment and relative private sector In vestment. The missing data between 1987 and 1989 are a problem, and result in the loss of 5 data points for each regional unit. We estimate with two lags the equations: PI, =C + a, -PI,_, + a2 -PI,_2 + ßj -Bl,_, + +β2·β/ί-2+μΓ Bl, =C + δ, -PI,_, + δ2 -PI,_2 + λ, -Bl,_, + +l2-BI,_2+yPI, +μ,β (11) (12) where γ represents the Immediate effect of public investment on business investment. The results are summarized in Table 3. 1 and also in Figure 3. 1. The figure shows cumulative investment de clining after the first 2-3 years. The reason is that some of the estimated coefficients are negative, implying that some of the initial positive effect on private Investment is offset by crowding out at a later stage. Not much should be read into this fluctuation, however, given the data limitations. TABLE 3. 1. Short-run and long-run estimated impact of public spending, GLS 2 step estimators in panel, weights=cross-sectional variance Shock on Private Investment Private Capital Stock Private Investment Private Capital Stock in the short-run (ï) in the long-run in the short-run in the long-run Elasticities' Elasticities Gross Number Gross Number Local Public Investment (1 lag, 126 obs. ) Local Public Investment (2 lags, 90 obs. ) 0. 1 7 0 *" [0. 062] 0. 1 5 3" [0. 078] 0. 046 [0. 05] 0. 050 [0. 06] 2. 58 2. 33 0. 70 0. 76 The columns headed "Elasticities" report the elasticity estimates from the logarithmic regression In the text. The columns headed "Gross Numbers" report the elasticity times the ratio of Business to Public Investment, which is the number of units of spending of private investment or capital stock following an increase of one unit of public spending. (short-run: weighted standard errors in parentheses : long-run: standard errors computed from the covariance matrix on the esti mators. *, ". '" significantly different from 0 at the 10, 5 and 1% level) Modelling Results 95 The numbers reported in the table are the im pulse responses to a one unit shock on public In vestment, in the short and the longrun. Private investment has a positive and significant correla tion with total and productive public spending, both in the shortrun and in the longrun, the lat ter effect being slightly significant. See Appendix 5 for a description of the calculation of the stan darderror on the cumulated private investment. We can also predict the long run impact of public Investment on employment. Because Augment edDickey Fuller tests tend to indicate that em ployment is nonstationary, the VAR is estimated with the first difference of employment. The re sults are summarised in Table 3. 2 and in Figure 3. 2. TABLE 3. 2. Shortrun and longrun estimated impact of public spending, GLS 2 step estimators in panel, weights=crosssectional variance Shock on Local Public Investment (2 lags, fixed effects, 108 obs. ) Local Public Investment (2 lags, random effects, 108 obs. ) Business Investment (2 lags, fixed effects, 162 obs. ) Business Investment 2 lags, random effects, 162 obs. ) Employment in the shortrun (γ) Employment in the longrun Elasticities' Elasticities 0. 001 [0. 05] 0. 054 [0. 068] 0. 029 [0. 038] 0. 010 [0. 037] 0. 015* [0. 01] 0. 025 [0. 03] 0. 054* [0. 035] 0. 007 [0. 006] ' The columns headed "Elasticities" report the elasticity estimates from the logarithmic regression in the text. (shortrun: weighted standard errors in parentheses; longrun: standard errors mators, ", *", *"* significantly different from 0 at the 10, 5 and 1 % level) computed from the covariance matrix on the esti In order to test for the relevance of the concept of the representative region, we first look at the size of the residuals for each region. This pro vides information on which regions behave like the representative one, and which are different. Table 3. 3 (column 2) gives the ratio of the stan dard deviation of the residuals for the region over the standard deviation for all the residuals. A val ue below one implies that the region is close to the representative region. A value above one im plies that the region is different from the repre sentative one. There is a value significantly above one for two regions only: Bragança and Castelo Branco. In the Portuguese case we do not try to estimate the equations district by district, be cause the number of observations across time is too small. As in the Spanish case, we address the question of the distributive effect of public spending. Do poor regions benefit more from public investment than rich ones? We divide the panel of 18 Por tuguese districts into three groups, the 6 poorest, the 6 next and the 6 richest, and run the previous regressions for each of the subgroups. Having less crosssectional variance, the confidence intervals will be larger. The results are different from the Spanish ones: for Portugal it is found that public investment generates more private investment in the richest regions, but in the other regions no sig nificant estimates were obtained. This last result is probably due to the fact that Portuguese regions differ a lot in terms of GDP per capita and in terms of the amount of local public spending. When the public investment shocks are small, it is difficult to detect any significant impact on the private sector and our estimator is not efficient. 96 Section I Vector Autoregression Models TABLE 3. 3. Relative standard deviation of residuals by region /AR Local Public Investment and Private Investment Long Run Effect Estimated Region by Region NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA σι /σ 0. 41 0. 56 0. 19 2. 57 2. 35 0. 60 0. 30 0. 43 0. 94 0. 16 0. 10 0. 74 0. 37 0. 21 0. 75 1,15 0. 32 1. 04 AVEIRO BEJA BRAGA BRAGANÇA CASTELO BRANCO COIMBRA ÉVORA FARO GUARDA LEIRIA LISBOA PORTALEGRE PORTO SANTARÉM SETÚBAL VIANA DO CASTELO VILA REAL VISEU (standard errors computed from the covariance matrix on the estimators) TABLE 3. 4. Shortrun and Longrun estimated impact on private investment of public spending, subgroups of regions, GLS 2 steps estimators in panel, weights=crosssectional variance Shock on different groups of regions Elasticities in the long run: cumulated private investment Poor, 42 obs. Intermediate, 42 obs. Rich, 42 obs. Productive Public Investment (1 lag) LR -0. 065 [0. 20] 0. 040 [0. 11] 0. 1 7 0" [0. 10] (short run: weighted standard errors ; longrun: standard errors computed from the covariance matrix on the estimators, significantly different from 0 at the 10, 5 and 1% level) 1. 3. 2. Impact of labour demand shocks We consider the dynamic effect of labour de mand shocks on activity and population. Again, we estimate the VAR model described in (8), for the three variables Employment, Activity Rate and 1564 Population, and then replacing the 1564 Population by the Total Population. The VAR has two lags, and is estimated in a panel without fixed regional effects, given the length of longitudinal (time) dimension. The regressions where activity and population are the exogenous variables allow for an immediate effect of employ ment. The elasticities are summarized in the fol lowing table: Modelling Results 97 TABLE 3. 5. Impulse Response of Labour Market Variables to Employment Shocks, Summary. 13 Regions, GLS 2 steps estimator in panel, weights=cross-sectional variance VAR With 15-64 Population With Total Population Number of Observations 234 234 Short-run elasticity of Activity Population Long-run Elasticity of Employment Activity Population 0. 974 [0. 02] *" - 0. 0 41 [ 0. 0 1 ] "* 0. 949 [ 0. 0 1 ] *" 0. 015(0. 015] 0. 852 0. 829 - 0. 0 27 0. 846 0. 782 0. 004 | (short-run: weighted t-stat in parentheses : long-run: standard errors computed from the covariance matrix on the estimators. '. ", '** significantly different from 0 at the 10, 5 and 1 % level) As it is also Illustrated by Figure 3. 2, what is strik ing here is the lack of mobility appearing from the figures: regional population Is hardly influenced by the employment change, and as for the un skilled in Spain, the activity rates absorb most employment shocks. 1. 3. 3. Conclusions for Portugal For Portugal, the public investment data relate to local investment, not government investment. Therefore, an important issue is the degree of comparability between the results for Portugal and the other countries. Differences could result from the nature of investments covered by the data. Local administrative units (Municipalities) have different objectives and spending from cen tral governments. The average spending of local units is a mixture of different projects (roads, wa ter facilities. ) with possibly different proportions from the proportions of central government in vestment spending. The extent of the differences in the proportions is rather difficult to evaluate, however. The major difference is likely to be the importance of the location of the returns to public investment. In the case of local administrative units, it is likely that the objective function is to in crease private investment/employment locally, whereas the objective function of the central gov ernment is certainly less geographically specific. Since our method does not account for spillovers across regions, considering only local investment is likely to lead to an upwards bias of the impact of public investment spending in the region where investment is spent. The degree of the bias is, however, impossible to evaluate. With this caution in mind, our results for Portugal indicate that: 1. Private investment has a positive and signifi cant correlation with total and productive pub lic spending. This correlation exists both in the short-run and in the long-run, but the latter ef fect is only slightly significant. In contrast with the results for Spain, public investment generates more private investment in the richest regions, but in the other regions no significant estimates were obtained. In response to labour demand shocks, re gional population is unaffected, and increased activity rates absorb most of the new jobs. 98 Section III — Vector Auto-regression Models Figure 3. 1 : Response of Cummulative Private Business Investment to a 1% Shock to Local Public Investment Portugal Local Public Investment Shock ι o Q_ CD CQ DD CD CO o o ω CD CQ CD O Q. CD Portugal Figure 3. 2: Response of (a) Employment (b) Activity Rate and (c) Population to a 1% Shock in Employment Years • Total Employment ■ - Total Activity Rate ■A —Total Population 1. 4. Greece At the NUTS 2 regional level (13 regions), public investment and private investment are available from 1980 to 1993, without disaggregation by type of spending. At the NUTS3 level (51 nomos), data on public and private investment are avail able for 4 years only (1980 to 1983). From Eurostat, we have data at NUTS2 level for 1988-94 on Population, 15-64 Population, Labour Force, and Employment. Unemployment is available at the NUTS3 level. There is no disag gregation by skill level. These data can be partial ly matched with the Labour Force Survey since 1981, despite problems of changing definitions of the regions. Hourly and weekly wages of salary and wage earners are available between 1990 and 1993 at NUTS2, with a disaggregation for 28 sectors. We have also gross production in manu facturing at current prices at NUTS3 level, be tween 1980 and 1993. Regional GDP (NUTS3) is available from 1970 onwards for 12 sectors. 1. 4. 1. Dynamic relations between public investment, private investment and private capital stock at the regional level In Spain, public investment is 7. 4 times smaller than private investment. In Greece, public invest ment is only 1. 44 times smaller than private in vestment during our sample, so the definition of public investment spending in Greece is broader. Again, we estimate a bivariate VAR between rela tive public investment and private sector invest ment. We estimate, with two lags, the equations: PI, =C + a, -ΡΙ,_Ί +α2-ΡΙ,_2 +β7 -S/(_, + + β2- β /ί_2+ μί Ρ (13) and Bl, =C + b1 -PI,. , +δ2-ΡΙ,_2+λ1 o +K2-Blt_2+yPlt +μ,β -Bl,. , + (14) where γ represents the immediate effect of public investment on business investment. The results are summarized in Table 4. 1 and shown in Figure 4. 1. TABLE 4. 1. Short-run and long-run estimated impact of public spending, GLS 2 steps estimators in panel, weights=cross-sectional variance Shock on Private Investment Private Capital Stock Private Investment Private Capital Stock in the short-run M in the long-run in the short-run in the long-run Elasticities' Elasticities Gross Number Gross Number Total Public Investment (Sample 1,13 regions, with fixed effects, 1 9 8 0 - 9 3, 156 obs. ) Total Public Investment (Sample 2, 51 regions, without fixed effects, 1980-83, 102 obs. )2 0. 195*** [0. 03] - 0. 01 [0. 03] 0. 5 3 8 *" [0. 07] 0. 6 0 3 *" [0. 20] 0. 28 0. 77 NS 0. 87 ' The columns headed "Elasticities" report the elasticity estimates from the logarithmic regression in the text. The columns headed "Gross Numbers" report the elasticity times the ratio of Business to Public Investment, which is the number of units of spending of private investment or capital stock following an increase of one unit of public spending. 2 As an alternative specification, we have estimated for the 50 regions between 1980-83 a random effect model with two lags, useful when the time series dimension is small. The short run elasticity was found to be 0. 036 [0. 07] and the long-run elasticity 0. 661 [0. 271. so not strongly different to the results of the table. (short-run: weighted standard errors in parentheses ; long-run: standard errors computed from the covariance matrix on the esti mators, ", ", '" significantly different from 0 at the 10, 5 and 1% level) Modelling Results 101 The numbers reported in the table are the impulse response to a one unit shock on public invest ment, in the short and the long-run. Private in vestment has a positive and significant correlation with total and productive public spending, both in the short-run and in the long-run. On the contrary, non-productive investment has no impact signifi cantly different from zero on private investment. We can also predict the long run impact of public investment on employment. Because Augment- ed-Dickey Fuller tests tend to indicate that em ployment is non-stationary, the VAR is estimated with the first difference of employment. The re sults are summarized in Table 4. 2 and in Figure 4. 2. We also show the results of the estimate of the impact of a shock on private capital stock to employment. The elasticity of the impact of pri vate investment on employment is smaller than the corresponding elasticity for public investment. To get a consistent comparison, we have to mul tiply the elasticities by the average size of each kind of investment. In this case, it appears that public investment still has a larger impact, since the ratio of private to public investment is 1. 44 in average over the period. TABLE 4. 2. Short-run and long-run estimated impact of pubi GLS 2 steps estimators in panel, weights=cross-sectional c spending, variance Shock on Total Public Investment (without fixed effects, 1988-93, 49 obs. ) Business Investment (without fixed effects, 1988-93, 49 obs. ) Employment in the short-run Employment in the long-run Elasticities1 Elasticities 0. 1 7 0" [0. 09] - 0. 32 [0. 52] 0. 1 2 6" [0. 06] 0. 017 [0. 03] ' The columns headed "Elasticities" report the elasticity estimates from the logarithmic regression in the text. (short-run: weighted standard errors in parentheses ; long-run: standard errors computed from the covariance matrix on the esti mators, *, **, "** significantly different from 0 at the 10, 5 and 1 % level) Do poor regions benefit more from public invest ment than rich ones? As with Spain and Portu gal, we check if our representative region con cept is useful by looking at the size of the resid uals for each region. Table 4. 3 (column 2) gives the ratio of the standard deviation of the residu als for the region over the standard deviation for all the residuals. A value below one implies that the region is close to the representative region. A value above one implies that the region is dif ferent from the representative one. Again, for three regions only, there is a value significantly above one: Anatoli Makedonia & Thrakl, Sterea Ellada and Borio Aigaio. Second, we estimate region by region, as for Spain, the long-run elas ticity of public and cumulated private invest ment, with the restriction that the standard error provided in the cells is only valid asymptotically1. Again, we learn less from the region by region estimations, than from the aggregated system. We can divide the panel of 13 Greek regions in to two groups, the 7 poorest, and the 6 richest, and run the previous regressions for each these subgroups. The results are different from the Spanish ones: for Greece it is found that public investment generates slightly more private in vestment in the bottom half of the regions. How ever, when we multiply the elasticities by the av- As explained in Appendix 5 and in Section 2, when the sample size is small, we cannot make the assumption of normality of the coeffi cients and the formula for the variance of the long run limit is itself bi ased. 102 Section III — Vector Auto-regression Models erage ratio of business over public investment (1. 56 in the richest regions, 1. 35 in the poorest), one third of the differences in the elasticities dis appear. Given the standard error (for the gross numbers, 0. 18 for the richest, and 0. 11 for the poorest), the impact of public investment is still stronger in the poorest regions. See table 6. 4 for a summary. TABLE 4. 3. Relative standard deviation of residuals, by region VAR Total Public Investment and Private Investment Anatoli Makedonia and Thraki Kentriki Makedonia Dytiki Makedonia Thessalia Ipeiros Ionia Islands Dytiki Ellada Sterea Ellada Attiki Peloponnisos Borio Aigaio Notio Aigaio Kriti (standard errors computed from the covariance matrix on the estimators) σ ί /σ 1. 95 0. 19 0. 98 0. 21 0. 69 0. 91 0. 72 1. 65 0. 20 1. 03 1. 27 0. 78 0. 53 Long-Run Effect Estimated Individually 1. 09 [0. 62] 0. 477 [0. 40] - 1. 18 [0. 46] 1. 11 [0. 39] - 0. 1 91 [0. 05] 0. 680(0. 16] 0. 220 [0. 03] - 0. 3 07 [0. 12] 0. 505 [0. 10] 0. 183 [0. 05] 0. 748(0. 13] 0. 626 [0. 08] 0. 678 [0. 04] TABLE 4. 4. Long-run estimated impact on private investment of public spending, subgroups of regions, GLS 2 step estimators in panel, weights=cross-sectional variance Shock on different groups of regions Elasticities on private investment (SR) and cumulated private investment (LR) Poor (84 obs. ) Rich (72 obs. ) Total Public Investment (1lag, 1980-93, with fixed effects) Short Run Total Public Investment (1 lag, 1980-93, with fixed effects) Long Run 0. 3 4 5 "* [0. 05] 0. 765*** [0. 08] 0. 039 [0. 033] 0. 329*** [0. 12] (short run: weighted standard errors ; long-run: standard significantly different from 0 at the 10, 5 and 1 % level) error: > computed from the covariance matrix on the estimators, *, **, ""* Modelling Results 103 1. 4. 2. Impact of labour demand shocks We consider the dynamic effect of labour de mand shocks on activity and population. Again, we estimate the VAR model described in 1. 7, for the three variables Employment, Activity Rate and 15-64 Population. The VAR has two lags, and ¡s estimated as a panel without fixed regional effects, given the length of longitudinal (time) dimension. The re gressions when activity and population are the exogenous variables allow for an immediate ef fect of employment. From these impulse re sponses, it is possible to estimate the effects of the employment shocks on the gross numbers (variation in active population, unemployment, 15-64 population). The results are displayed in Figure 4. 3. The elasticities are summarised in Table 4. 5: TABLE 4. 5. Impulse Response of Labour Market Variables to Summary. 13 Regions, GLS 2 step estimator in Employment Shocks, panel, weights=cross- sectional variance VAR Number of Observations Short-run elasticity of Activity 15-64 Population Long-run Elasticity of Employment Activity 15-64 Population Total 62 - 0. 1 93 [ 0. 0 5 8 ] "* 0. 470 [0. 073] "* 1. 03 -0. 081 0. 504 (short-run: weighted t-stat in parentheses ; long-run *" significantly different from 0 at the 10, 5 and 1 % : standard evel) errors computed from the covariance matrix on the estimators. ', ". We see here that activity slightly decreases with a positive employment shock, which is consistent with the existence of an added-worker effect: in downturns, households reduce the impact of a loss of income by supplying more hours, with ad ditional workers. This is a characteristic feature of poorer countries or poorer fractions of population (see Mincer (1962) for instance). In the long run however, activity rates do not exhibit the effect of the initial shock, contrary to Spain. Mobility is on average more important in Greece than in Spain as a supply of labour response. In conclusion, population changes are significantly correlated to employment changes, while activity seems to play a minor role in Greece. 1. 4. Conclusions for Greece Our empirical results for Greece support the fol lowing conclusions 1. Private investment is positively and significant ly correlated with total and productive public spending, both in the short-run and in the long-run. In contrast, non-productive invest ment has no significant Impact on private in vestment. Public Investment generates more private in vestment in the poorest regions. Employment shocks have little affect on re gional activity, most of the shock is absorbed by population and unemployment changes. 104 Section I Vector Auto-regression Models Figure 4. 1: Response of Cummulative Private Business Investment to a 1% Shock to Total Public Investment Greece Years / Total Public Investment Shock ;0,60% 0,50% 0,10% 0,00% o D. ω CD DD o αϊ o CD CO CD I o o > Greece Figure 4. 2: Response of (a) Employment (b) Activity Rate and (c) Population to a 1% Shock in Employment Years • Total Employment - β - Total Activity Rate ——Α —Total Population 1. 5. Ireland The country was divided into 8 units: East, South-East, South-West, Mid-West, West, Mid lands, and Donegal&Northwest and North-East, the last two being sometimes aggregated into a region called Border to fit with Eurostat data. Regional Public Investment data are available for each year between 1976 and 1995. Public In vestment is disaggregated into 8 subdivisions: 1. Housing and Building 2. Roads, Transport and Safety 3. Water Supply and Sewerage 4. Development Incentives and Control 5. Environmental Protection 6. Recreation and Amenity 7. Education, Health and Welfare 8. Miscellaneous Services Disaggregated estimations were not successful, so we aggregated 1. , 2. , 3. and 4. to get a vari able that we called Productive Public Investment (68% of Total Public Investment). This variable should be consistent with the Spanish definitions. Regional Private Capital Stocks data are available between 1979 and 1990, for the Manufacturing Sector. We have used two alternative series as a proxy for Business Investment, along the lines of the previous estimations for other countries. First, we have used the Acquisitions of Capital Assets (BIT2), and second the Net Additions to Capital Assets (BIT3)1. From Eurostat, we have Population data for the period 1977-93, and data from Labour Force Surveys for Employment, Unemployment (84-95) and Wages (81-91). "Stocks" are equivalent to the American "Inventories". Stock changes are calculated by subtracting the value of stocks at the be ginning of the year from the value at the end of the year. In general the value of stocks at the end of one year does not equal the value at the beginning of the next. This is due to reclassification of estab lishments from one NACE category to another, changes in reporting year, commencements or cessations of business, etc; small differ ences may also arise because of the method of estimation for small establishments and non-respondents. Capital assets (land, buildings, plant and equipment) are defined as goods with an expected useful life of more than one year, intended for use by the establishment it self. Acquisitions include purchases from other establishments and production by the establishment itself of capital for its own use. Ma jor alterations, improvements and repairs that extend the useful life of an asset or increase its productivity are included. The value of work put in place during the year is included whether or not completed. Acquisitions are valued at total cost Including installation charges and fees or duties but excluding deductible VAT and financial costs. Sales are valued at the price actually received excluding VAT. 1. 5. 1. Dynamic relations between public investment, private investment and private capital stock at the regional level. We faced a few difficulties in applying the methodology that we use for the other countries. First, the ratio of Manufacturing Private Invest ment over Total Public Investment is 0. 68. In Spain, this ratio was 7. 4. The difference arises from two factors. First, Manufacturing Investment is smaller than Total Private Sector Investment because Agriculture and Services are not includ ed. Second, the definition of Public Investment may be broader in Ireland than in Spain. For in stance, in the category 4, Development Incen tives and Controls, we find Representational Functions, Promotion of the Interest of the Com munity, Twinning of Local Authorities Areas, and Administration & Miscellaneous, that account for one third to one half of the total. With the de scriptions of the data available to us, we were not able to decide which category to use as Public Investment, so we used the aggregate spending with the restriction that the definition covers more than in the case of Spain. Another problem encountered was the high cyclicity of manufacturing investment, as shown in the Figures 5. 1 and 5. 2, which show the loga rithm of the share of the regions in manufacturing investment. This high cyclical component was re moved in a standard way, by filtering the series of business investment with a Hodricks-Prescott fil ter2. The variables HPBIT2 and HPBIT3 are the series minus their cyclical component, taken in logarithms of their share in the national aggre gate. We then estimate the bivariate VAR between rela tive public investment and private sector invest ment. We estimate the equations: PI, = C + a-PI,_1 +β·Β/,_7 +mp and Bl, =C + 8-PI,_1+X-BI,_1+y-PI, +μ,β where γ represents the immediate effect of public investment on business investment. The results are summarized in Table 5. 1. The parameter λ was chosen equal to 100, which is the standard yearly value to cut the business cycle frequencies. Modelling Results 107 TABLE 5. 1. Short-run and long-run estimated impact of public spending, GLS 2 steps estimators in panel, weights = cross-sectional variance Shock on HPBIT2 (Gross Capital Changes) in the short-run Ol in the long-run in the short-run in the long-run Private Investment Private Capital Stock Private Investment Private Capital Stock Elasticities' Elasticities Gross Number Gross Number Total Investment (1 lag, 88 obs. ) Productive Public Investment (1 lag, 88 obs. ) 0. 026 [0. 021] 0. 011 [0. 007] 1. 2 1 *" [0. 45] 0. 5 1 5 "" [0. 20] 0. 017 0. 011 0. 76 0. 47 1 The columns headed "Elasticities" report the elasticity estimates from the logarithmic regression in the text. The columns headed "Gross Numbers" report the elasticity times the ratio of Business to Public Investment, which is thought as the number of units of spending of private investment or capital stock following an increase in one unit of public spending. (short-run: weighted standard errors in parentheses : long-run: standard errors computed from the covariance matrix on the esti mators. ', ", '" significantly different from 0 at the 10, 5 and 1 % level) The numbers reported in the table are the im pulse response to a one unit shock on public in vestment, in the short and the long-run. Private investment has a positive and significant correla tion with total and productive public spending, both in the short-run and in the long-run. The disaggregation of public investment into produc tive investment Including the first four categories of public investment leads to smaller impact on private investment. The alternative definition of business investment HPBIT3 yields similar results reported ¡n Table 5. 2: TABLE 5. 2. Short-run and long-run estimated impact of public spending, GLS 2 steps estimators in panel, weights=cross-sectional variance Shock on HPBIT3 (Gross Capital Changes) in the short-run (H in the long-run in the short-run in the long-run Private Investment Private Capital Stock Private Investment Private Capital Stock Elasticities' Elasticities Gross Number Gross Number Total Investment (1 lag, 88 obs. ) Productive Public Investment (1 lag, 88 obs. ) 0. 038 [0. 021] 0. 011 [0. 008] 1. 44"* [0. 45] 0. 5 7 2 *" [0. 22] 0. 026 0. 010 0. 99 0. 57 ' The columns headed "Elasticities" report the elasticity estimates from the logarithmic regression in the text. The columns headed "Gross Numbers" report the elasticity times the ratio of Business to Public Investment, which is thought as the number of units of spending of private investment or capital steck following an increase in one unit of public spending. (short-run: weighted standard errors in parentheses: long-run: standard errors computed from the covariance matrix on the esti mators, ', '", '" significantly different from 0 at the 10, 5 and 1% level) 108 Section I Vector Auto-regression Models We have not been able to find a positive and sig nificant impact of public investment spending on employment in the case of Ireland. There are two main reasons. The first one, ¡s that the sample of employment data was Insufficient to allow accu rate estimation of the model. We have not been able to use longer time-series of national employ ment data to impute regional employment over a longer period. This method would not have al lowed us to identify the Impact of regional shocks on the regional economies. The second reason is more specific to the nature of our model which does not include spillovers across regions. It ¡s likely that part of the employment effect generat ed by public investment spending is located in other regions, which prevents us from finding any significant effect. This problem was unimportant in the case of Spain and Greece, since the aver age size of the regions considered was sufficient ly large. This problem is certainly very significant for Ireland since the country is divided into much smaller units. Bean's methodology (inclusive of spillovers) should lead to better estimates. 1. 5. 2. Impact of labour demand shocks Again, we estimate the VAR model described in (8) for the three variables Employment, Activity Rate and Population. The VAR has two lags, and is a panel with fixed regional effects. The regres sions where activity and population are the ex ogenous variables allow for an immediate effect of employment. From these Impulse responses, it is possible to estimate the effects of the employ ment shocks on the gross numbers (variation in active population, unemployment, 15-64 popula tion). The elasticities and gross numbers effects are summarized in Table 5. 1 : TABLE 5. 3. Impulse Response of Labour Market Variables to Employment Shocks, Summary. 13 Regions, GLS 2 steps estimator in panel, weights=cross- sectional variance VAR Number of Observations Short-run elasticity of Activity Population Long-run Elasticity of Employment Activity Population Total 52 0. 569 [0. 047]*" -0. 036 [0. 029]* 0. 501 0. 269 -0. 050 (short-run: weighted t-stat in parentheses ; long-run: standard errors computed from the covariance matrix on the estimators, ', '" significantly different from 0 at the 10, 5 and 1 % level) We see here that first employment shocks are much less persistent, second that activity in creases with these positive employment shock. Finally, migration seems to fail to adjust to the new shock, given the lack of a positive correla tion between employment and population. With respect to the other Cohesion countries, this denotes a lack of mobility, maybe due to the small size of the regions in Ireland: workers in regions facing decreasing employment are still close from more economically active re gions. Modelling Results 109 Figure 5. 1. Manufacturing: Acquisitions of Capital Asset by Regions ^ — —- V N- ^ " \ / / \ / ν \ ^^ v. —- x- ^ I I Ι ι ι ι ι ι ι ι ι ι I I I I I I 1. 0- 1. 5- 2. 0- 2. 5- 3. 0- 3. 5- 40 76 78 80 82 84 86 88 90 92 94 BIT2E BIT2MID BIT2MW BIT2NE BIT2NW BIT2SE — BIT2SW — BIT2W 110 Section III — Vector Auto-regression Models Figure 5. 2. Manufacturing: Net Additions To Capital Assets by Regions ■0. 5 ■1. 0-1 ■1. 5 -2. 0 -2. 5 -3. 0 -3. 5 -4. 0 τ 1 1 1 Γ 76 78 80 82 84 86 88 90 92 94 BIT3E BIT3MID BIT3MW BIT3NE BIT3NW BIT3SE BIT3SW BIT3W Modelling Results 111 2, Simulation Results 2. 1. Introduction In this second part, the results from the econo metric analysis outlined in the previous sections are used to simulate the impact of selected pro jects financed by the Cohesion Fund on the re gional economic environment. We briefly recall the results from the previous part. Despite differences ¡n the coverage of public Investment data in the four Cohesion countries, we showed a robust, positive and significant impact of public invest ment on private investment and employment, both in the short and the long run. For Spain, where the most comprehensive data set was available, our results indicated that infrastructure investment (roads, motorways and urban struc tures) had the strongest impact, while other types of Investment, most noticeably non-productive public Investment (that is, health and education) had less significant impact in the long run. This re sult was also supported in the other cases where disaggregation, according to productive and non productive public investment was possible. We then investigated how the regional labour mar kets were affected by employment shocks result ing from the Increase in public Investment and the induced increase ¡n private investment. In most of the cases, employment rose, fuelled by higher labour force participation and immigration Into the region. Again, this increase occurred in the short and the long run. Given data limitations, weaker correlations were sometimes found, as ¡n Greece between employment and activity rates and ¡n Portugal between employment and population. This part consists of 7 additional sections. In Sec tion 2, we describe our approach to the evalua tion of the projects. Section 3 details the evalua tion process and summarizes the main results. In Sections 4-7 we then report the results of the ac tual evaluations for the projects in Spain, Greece, Portugal, and Ireland respectively. Section 8 makes some further comments with respect to the simulation of labour market variables. 2. 2. Evaluation Strategy We have estimated the coefficients of reduced form economic relations in the previous sections, using aggregated data at the NUTS2 or NUTS3 level according to availability. We now use these results to forecast the Impact of specific projects financed by the Cohesion Fund, which represent an increase in regional public spending. The im pact on regional business Investment and labour supply is evaluated. As the impact of the whole project on the region has to be evaluated, we consider total spending on the project, rather than the 85% of total expenditure which is fi nanced by the Cohesion Fund. Guided by the availability of the data, we have proceeded in the following way for the Spanish, Portuguese, Greek and Irish project evaluations: For all the projects, we provide the forecasts on the basis of results obtained from the national models that were estimated across regions of a country, to capture specific national structural ef fects. The relationship between total public in vestment, business investment and employment Simulation Results 113 is estimated for each country, with the exception of Ireland where the relationship between public investment and employment cannot be estimat ed because of the lack of sufficient employment data. Initially, the projects in each country are evaluated using the national coefficients coming from these sets of estimations. given the differences in the definition and cover age of public investment across countries, such a panel would be meaningless. However, pooling across countries has been employed to estimate the coefficients for the trivariate system Employ ment, Activity and Population, given the greater similarities across countries. Since the Spanish data set is the most complete of the four Cohesion countries, we are able to ex tend the model to include the following additional version for more detailed analysis of the Spanish projects. For the first scenario, a project is evalu ated using the elasticities provided by estimations based on data for total public investment in Spain. Disaggregated data then allow the follow ing variation: Scenario Two estimates the elastici ties again, now using data sets for total produc tive public investment. For the two scenarios, re lationships between these public investment ag gregates, business investment and employment are estimated. The resulting elasticities thus re flect the nature of the project in greater magni tude. However, this improvement is also accom panied by a loss in precision of the coefficients, as the basis upon which they are estimated be comes more narrow. This fact must be kept in mind when interpreting the results. For the mo ment, project evaluation taking this form is only possible for Spain, for which homogeneous and disaggregated data is available. The approach adopted for the evaluation of the Greek and Portuguese projects is a result of the more limited data availability (notably no disag gregation of public spending data) and the re stricted time frame of this report. For Greece and Portugal, we use the aggregate public investment model (Scenario 1) to evaluate the impact of both road and environmental pro jects. Results obtained are only approximate, since the Spanish results based on disaggregat ed data show that there are strong differences in the intensity of impacts of projects once they can be differentiated by investment type. An addition al scenario for these countries is created by pool ing the data for all four countries in order to de crease the variance on the estimated coefficients. Here, a further set of elasticities is added to the ones estimated on a solely national basis. These new coefficients then provide the input for an ad ditional model variation. This pooling method has not been used in the case of the bivariate sys tems including public investment spending since, The Irish project simulations are still largely con strained by the incomplete data set available for this country, notably by the lack of regional data for GDP. Only one scenario based on estimates of the relationship between public investment and private investment in manufacturing was simulated. 2. 3. The Impact of the Projects Financed by the Cohesion Fund 2. 3. 1. Methodology Having specified models of the relationship be tween public investment, business investment and labour force variables using coefficients esti mated as described in the first part, we proceed in the following manner: Spending on each project is considered to be a shock to regional public investment as compared to average regional public spending. In its first ap plication, the model evaluates the impact of these shocks on business investment in that region. The VAR approach allows for estimation not only of the immediate effect that occurs during the years of actual project spending, but also of the long term effect on private capital stock. In its second stage of manipulation, the model is used to evalu ate the impact of the public investment shocks on regional employment. Movements caused in the regional employment variable are then used as the initial shock occurring in the trivariate system evolving around the response of employment, population and activity rates to this shock. For reasons of econometric robustness, models for each country are estimated using panel data specific to that country. The elasticities thus refer to a "representative region" in each country. Hence, impacts of total project spending are evaluated on the basis of these representative elasticities combined with the region specific data for GDP, employment, population and activity rates. In this way, the project investment can be evaluated in the context of the economic charac- 114 Section III — Vector Auto-regression Models teristics of the region in which it is located. How ever, we stress once again that our evaluations are restricted by the data we have available. This is particularly relevant for Portugal and Ireland and to a lesser extent, Greece. 2. 3. 2. Evaluation of the Percentage Increase in Public Investment Caused by the Projects The projects evaluated in these sections are - Spain: Madrid Ring Road (93/65/010+modif, 94/65/011, 93/64/017) Rias Bajas Motorway (93/65/002, 94/65/002, 95/65/004) - Greece: Spata Airport Evinos Dam (93/09/61/011, 93/09/61/074 + modif, 94/09/61/011-1) - Portugal: Tagus Crossing (94/10/65/005) - Ireland: North-South Road Link (93/07/65/007-010,029,032,036; 94/07/65/007) North-South Rail Link (93/07/65/018,019-23- 24-25+modif. ) Evaluation on the Odeleite-Beliche Water Supply project in the Portuguese Algrave region has been undertaken. However, as some pivotal data are still missing results for this project have not been included. The evaluation of the two Irish projects has to be interpreted with caution be cause of limitations in the regional data available. Notably the lack of regional GDP data prevents the evaluation of the Irish projects within the same formal framework. The summarizing results reported below refer therefore only to the Span ish, Portuguese and Greek projects. The projects have been evaluated with respect to their total eligible costs, 85% of which are fi nanced by the Cohesion Fund. Project size varies between 0. 43% and 1. 88% of regional GDP, the smallest project in terms of this measure being the Madrid Ring Road, the largest, the Tagus Crossing in Lisbon. 10. 8% in Greece (See Figures 3. 1 to 3. 3 for these cross-country comparisons of project spending). Therefore, the size of the projects should be compared on the basis of their relative magnitudes with respect to GDP and public spending. The magnitude of a project's impact is seen as a shock to regional public spending that also depends on the size of the country's public sector. For example, for similar project sizes (Madrid Ring Road: 303. 87 MECUs; Spata Air port: 292. 12 MECUs), total spending on the Madrid Ring Road represents 0. 43% of Madrid's GDP, and 0. 83% of Athen's GDP. The difference obviously reflects the higher GDP of Madrid. Nev ertheless, as a shock to average annual regional public spending, the total spending on the Madrid Ring Road represents a 18. 7% increase, yet the shock to spending due to the Spata Air port is only 7. 5%. The contrast in relative magni tude of these two shocks hence reflects the dif ference in size between the Spanish and the Greek public sector (see also Figures 3. 1 to 3. 3). 2. 3. 3. Evaluation of the Impact on Business Investment The impact of the public spending shocks on the private capital stock (the long run effect) varied be tween 3% (Madrid Ring Road) and 10% (Rias Ba jas Motorway). This last project was also among the largest in terms of percentage expenditure of GDP, with total spending representing 1. 58% of the regional GDP of Galicia and Castilla y Leon. 2. 3. 4. Evaluation of Project Impact on Labour Markets The number of jobs created varies between 3,000 and 20,000, the extreme values occurring in the Greek and notably the Portuguese case. However, the flawed and incomplete data for these countries recommend caution in the inter pretation of these results. The results of the simulations show that this job creation will reduce unemployment, lead to inter regional immigration and cause an increase in the activity rate. This result has been obtained and discussed in the first part of our contribution. The magnitude of the projects with respect to public investment must be interpreted accounting for the different sizes of the public sector in the three countries. Public spending in Spain is, on average, 2. 3% of GDP, 3. 1% in Portugal, and No analysis in terms of skilled and unskilled workers has been done for this report. Such an investigation could be undertaken for future eval uations as far as Spain is concerned; the possi bilities for other countries are less certain. Simulation Results 115 05 o > c Õ o Q. CD Spain: Average PI/GDP Average PIPT/GDP Average pipm/GDP Madrid Ring Road Project Spending (Millions of ECUs) 1993 1994 1995 1996 Total Spending Regional GDP Total Spending as % of Regional GDP Rias Baias Motorway Project Spending (Millions of ECUs) 1993 1995 1996 1997 1998 Total Spending Regional GDP Total Spending as % of Regional GDP 0,023 0,01771 0,00184 167,96 71,88 51,65 12,38 303,87 70 512,20 0,43% 23,44 156,75 326,48 217,87 61,93 786,47 49 758,70 1,58% Total Project Spending in Millions of ECUs Figure 3. 1 Rias Bajas Motorway Madrid Ring Road 100 200 300 400 500 600 700 ~y 800 Total Project Spending as a Percentage of Regional GDP Rias Bajas Motorway Madrid Ring Road w 3 c_ S. õ' =3 φ Greece: Average PI/GDP Figure 3. 2 0,108 Total Project Spending in Millions of ECUs Evinos Dam Project Spending (Millions of ECUs) 1993 1994 1995 105,35 107,58 4,51 Total Spending Regional GDP Total Spending as % of Regional GDP 217,44 35 796,46 0,61% Spata Airport Project Spending (Millions of ECUs) 1996 1997 1998 1999 16,41 56,85 101,39 117,47 Total Spending Regional GDP Total Spending as % of Regional GDP 292,12 35 796,46 0,82% Λ Spata Airport Zl Evinos Dam 100 200 300 400 500 600 700 800 Total Project Spending as a Percentage of Regional GDP 2,00% ' 1,60% 1,20% 0,80% 0,40% 0,00% Evinos Dam Spata Airport Portugal: Average PI/GDP 0,03089006 Total Project Spending in Millions of ECUs Figure 3. 3 Taqus Crossinq Project Spending (Millions of ECUs) 1995 1996 1997 209,52 122,15 34,51 Total Spending on Project Regional GDP Total Spending as % of GDP 366,18 19 426,63 1,88% Tagus Crossine V 100 200 300 400 500 600 700 y 800 Total Project Spending as a Percentage of Regional GDP Tagus Crossing CO c? o > c o α CD y? 3 ç_ S õ' 13 1} CD ω c_ ω CD Figure 3. 4 Spending on Madrid Ring Road as a Percentage of Regional GDP Spending on Evinos Dam as Percentage of Regional G DP Spending on Tagus Crossing as a Percentage of Total Regional GDP 0,7% ·' 1,2% ' 1993 1994 1995 1996 1993 1995 1996 1995 Spending on Rias Bajas Motorway as Percentage of Regional GDP Spending on Spata Airport as a Percentage of Regional GDP 0,7% 0,6% 0,5% 0,4% 0,3% 0,2% 0,1% 0,0% 0,7% 0,0% 1993 1995 1996 1997 1998 1996 1997 1998 1999 L 2. 4. Spain 2. 4. 1. The Madrid Ring Road The project is located in the outskirts of Madrid. Spending on the project takes place between 1993 and 1996 and represents a total amount of 303. 87 MECU. This amounts to 0. 43% of regional GDP in 1992. (Refer to Figure 3. 4 for an illustration of the spending pattern over the four year period. ) 1 st Scenario: Spending as a Percentage of Total Regional Public Investment Given that total average public spending in Spain represents approximately 2. 3% of GDP, total spending on the project amounts to 18. 7% of av erage annual public spending in the reg on. Fig ure 4. 1 A1 illustrates the spending pattern as a percentage of annual regional public spending over the four year period. The first scenario considers annual project spending compared to the average total public spending as the regional shocks. The shocks vary between 0. 76 and 10. 4% (Figure 4. 1 A1) and cause an immediate response in regional business investment of up to 0. 5%. The cumulat ed long term effect on business investment is 3% (Figure 4. 1 B1). The additional public and private investment has according to our simulation the following positive impact on employment ¡n the region: in the long term, approximately 2,900 jobs were created by the project (Figure 4. 1 C1). This increase in employment can partly be ac counted for by an increase in the activity rate and employment, and partly by immigration into the region. If requested, a trivariate model involving the variables Employment, Unemployment and Activity could be estimated for future reports. This would then permit a more accurate evalua tion of the impact of public investment shocks on unemployment of the region. 2nd Scenario: Spending as a Percentage of Regional Productive Public Investment Knowing that total public productive spending in Spain represents approximately 1. 7% of GDP, the project amounts to 24. 3% of average annual public productive spending in the region. Figure 4. 1 A2 illustrates the spending pattern over the four year period. This second scenario considers annual project spending compared to the average spending on productive public investment as the regional shocks. The shocks vary between 0. 99 and 13. 45% and cause an immediate response in re gional business investment of up to 0. 56%. The cumulated long term effect on business invest ment ¡s 3. 3% (Figure 4. 1 B2). The impact on em ployment in the region in the long term is positive — approximately 3,400 jobs are created by the project (Figure 4. 1 C2). We see that for the sec ond scenario, the impact on business investment and employment is larger. This is due to the fact that the shocks on the system are naturally larg er, total public productive Investment being a subset of total public investment. 2. 4. 2. Rias Bajas Motorway The project is located in the regions of Galicia and Castilla y Leon. Spending on the project takes place between 1993 and 19981 and repre sents a total amount of 786. 47 MECU. This amounts to 1. 58% of bi-regional GDP ¡n 1992. (Refer back to Figure 3. 4 for an illustration of the spending pattern over the five year period. ) 1 st Scenario: Spending as a Percentage of Total Regional Public Investment Given that total public spending in Spain repre sents approximately 2. 3% of GDP, total project spending amounts to 68. 7% of average annual public spending in the region. Figure 4. 2 A1 illus trates the spending pattern as a percentage of annual regional public spending over the five year period. The first scenario considers annual project spending compared to the average total public spending as the regional shocks. The shocks vary between 2. 05% and 28. 53% (Figure 4. 2 A1) and cause an Immediate response in regional business investment of up to 1. 17%. The cumu lated long term effect on business investment is 11. 0% (Figure 4. 2 B1). The additional public and private investment has a positive Impact on em ployment ¡n the region: ¡n the long run , approxi mately 3,300 jobs are created by the project (Fig ure 4. 2 C1). The initial phase of the project started in 1993. a break occurs in 1994, and the project financing begins in 1995. We assess the shocks were over a 5 year period, the break in 1994 being omitted. 120 Section III — Vector Auto-regression Models 2nd Scenario: Spending as a Percentage of Regional Productive Public Investment Knowing that total public productive spending in Spain represents approximately 1. 7% of GDP, to tal project spending amounts to 89. 25% of aver age annual public productive spending in the re gion. Figure 4. 2 A2 illustrates the spending pat tern over the five year period. This second scenario considers annual project spending compared to the average spending on productive public investment as the regional shocks. The shocks vary between 7. 03 and 37. 05% (Figure 4. 2 A2) and cause an Immediate response in regional business investment of up to 2. 0%. The cumulated long term effect on business investment is 12. 28% (Figure 4. 2 B2). The impact on employment in the region in the long term is positive — approximately 3,900 jobs are created by the project (Figure 4. 2 C2). We see that for the second scenario, the impact on business investment and employment is larg er. This is due to the fact that the shocks to the system are naturally larger, total public produc tive investment being a subset of total public In vestment. Simulation Results 121 Figure 4. 1: Madrid Ring Road Response of (a) Business Investment and (b) Cumulated Business Investment to Project Employment Response to Project Investment (Number of Jobs Created) Spending on Project as a Percentage of : Scenario T. Total Regional Public Investment Scenario 2: Regional Productive Public Investment Scenario 3: Regional Investments in Motorways A1 Business Investment LZ. J Cumulated Business Investment B1 8,0% 6,0% 4,0% 2,0% 0,0% 1993 1994 1995 1996 Years Scenario 1: Total Public Investment 10% 5% 0% Scenario 2: Productive Public Investment C1 Years C2 Years ro IV) ω Φ o c? o o O o. Φ Figure 4. 2: Rias Bajas Motorway Spending on Project as a Percentage of : Scenario 1: Total Regional Public Investment Scenario 2: Regional Productive Public Investment Response of (a) Business Investment and (b) Cumulated Business Investment to Project Employment Response to Project Investment (Number of Jobs Created) A1 J Business Investment [ZU Cumulated Business Investment B1 Scenario 1: Total Public Investment 40% 30% 20% 10% 0% Scenario 2: Productive Public Investment 1993 1995 1996 1997 1998 A2 Years B2 1993 1995 1996 1997 1998 Years C1 Years C2 Years w 3 ç_ ω õ' IO œ ro ω 2. 5. Greece Consistency with the general results as outlined in the previous report can also be reported in the Greek case. Both projects are situated in the rich region of Athens, where we have reported a stronger impact of public investment on ousiness Investment than in the previous report. The im pact of Cohesion Fund spending on the regional economy are found to be positive and persistent. The simulation results concerning job creation are more pronounced than in the Spanish case, but because the data sets used were inferior, these results should be interpreted with caution. 2. 5. 1. Evinos Dam The project is located in the region of Athens. Spending on the project takes place between 1993 and 1995 and represents a total amount of 217. 44 MECU. This amounts to 0. 607% of GDP in 1992. (Refer back to Figure 3. 4 for an illustration of the spending pattern over the three year period. ) vestment shocks have on labour market vari ables. Second scenario evaluations done for the Greek projects base the simulation of the effect of project spending on the coefficients concern ing the trivariate system of employment, popula tion and activity rate obtained through pooled da ta from all four Cohesion countries (refer to the Initial sections of this report. ) Figure 5. 1 C2 Illustrates that this additional simu lation supports the original results obtained in Scenario 1, although job creation ¡s slightly higher in this simulation. 2. 5. 2. Spata Airport The second Greek project to be evaluated is also located in the region of Athens. Spending on the project takes place between 1996 and 1999 and represents a total amount of 292. 12 MECU. This amounts to 0. 816% of GDP in 1992. (Refer back to Figure 3. 4 for an illustration of the spending pattern over the three year period. ) 1 st Scenario: Spending as a Percentage of Total Regional Public Investment 1st Scenario: Spending as a Percentage of Total Regional Public Investmen: Given that total public spending in Greece repre sents approximately 10. 8% of GDP, the project amounts to 5. 6% of average annual public spending in the region. Figure 5. 1 A1 illustrates the spending pattern as a percentage of total re gional public spending over the three year period. The first scenario considers annual project spending compared to the average annual public spending as the regional shocks. The shocks vary between 0. 117% and 2. 78% (Figure 5. 1 A1) and cause an immediate response in regional business investment of up to 0. 67%. The cumu lated long term effect on business investment is 3% (Figure 5. 1 B1). The additional public and pri vate investment has a positive impact on employ ment in the region — in the long run, approxi mately 13,000 jobs are created (Figure 5. 1 C1). Given that total public spending in Greece repre sents approximately 10. 8% of GDP, the project amounts to 7. 6% of average annual public spending in the region. Figure 5. 2 A1 illustrates the spending pattern as a percentage of total re gional public spending over the three year period. The first scenario considers annual project spending compared to the average annual pub lic spending as the regional shocks. The shocks vary between 0. 42 and 3. 03% (Figure 5. 2 A1) and cause an immediate response in regional business investment of up to 0. 82%. The cumu lated long term effect on business investment is 4. 03% (Figure 5. 2 B1). The additional public and private investment has a positive impact on em ployment in the region — in the long run, ap proximately 8,800 jobs are created (Figure 5. 2 C1). 2nd Scenario: Spending as a Percentage of Total 2nd Scenario: Spending as a Percentage of Total Regional Public Investment — Estimation of the Coefficients with Pooled Data from all Cohesion Countries Regional Public Investment — Estimation of the Coefficients with Pooled Data from all Cohesion Countries The difference in this second evaluation of the project concerns only the effects that public in- The difference in this second evaluation of the project concerns only the effects that public in- 124 Section III — Vector Auto-regression Models vestment shocks have on labour market vari- ables. Second scenario evaluations done for the Greek projects base the simulation of the effect of project spending on the coefficients concerning the trivariate system of employ- ment, population and activity rate obtained through pooled data from all four Cohesion countries (refer also to the initial sections of this report. ) Figure 5. 2 C2 illustrates that this additional simu- lation supports of the original results obtained in Scenario 1, although job creation is slightly higher in this simulation. Simulation Results 1 25 Spending on Project as a Percentage of Total Regional Public Investment Response of (a) Business Investment and (b) Cumulated Business Investment to Project Employment Response to Project Investment (Number of Jobs Created) Figure 5. 1: Evinos Dam A1 Business Investment Γ~Ί Cumulated Business Investment B1 C1 Æ*5 ¿3t 16000 12000 8000 ¿7 4000 Years Years ro co o 3 cí > c Õ o Q. φ Scenario 1: Total Public Investment Scenario 2: Panel Data: Random Effects Q? 3 o 3 IO CD vl Scenario 1: Total Public Investment Scenario 2: Panel Data: Random Effects Figure 5. 2: Spata Airport Spending on Project as a Percentage of Total Regional Public Investment Response of (a) Business Investment and (b) Cumulated Business Investment to Project Employment Response to Project Investment (Number of Jobs Created) I Business Investment I Cumulated Business Investment Bl C1 Years 16000 14000 12000 10000 8000 6000 4000 2000 0 16000 14000 12000 10000 8000 6000 4000 2000 0 Years C2 y*7 ¿m ¿3\ ¿3\ ¿ζ? Years 2. 6. Portugal As far as Portugal is concerned, data shortages still limit the evaluation work. The positive and significant correlation between public spending and business investment is confirmed. However, we could only proceed with the evaluation of the Tagus Crossing project located in the Lisbon area. Regarding the water sewage project Odeleite-Beliche, there have been difficulties with the relevant regional data that could not be re solved within the time frame of this report. 2. 6. 1. Tagus Crossing The project is located in the region of Lisbon. Spending on the project takes place between 1995 and 1997 and represents a total amount of 366. 18 MECU. This amounts to 1. 88% of GDP in 1992. (Refer back to Figure 3. 4 for an illustration of the spending pattern over the two year period. ) 1 st Scenario: Spending as a Percentage of Total Regional Public Investment Given that total public spending in Portugal repre sents approximately 3. 09% of GDP, total project spending amounts to 5. 75% of total public spending in the region. Figure 6. 1 A1 Illustrates the spending pattern as a percentage of average regional public spending over the two year period. The first scenario considers annual project spending compared to the average annual public spending as the regional shocks. The shocks vary between 5. 75% and 34. 91% (Figure 6. 1 A1) and cause an immediate response ¡n regional business investment of up to 5. 3%. The cumulat ed long term effect on business investment is 3. 1% (Figure 6. 1 B1). The additional public and private investment has a positive impact on em ployment in the region — in the long run, approx imately 16,500 jobs are created (Figure 6. 1 C1). 2nd Scenario: Spending as a Percentage of Total Regional Public Investment — Estimation of the Coefficients with Pooled Data from all Cohesion Countries The difference in this second evaluation of the project concerns only the effects the public in vestment shocks have on the labour market vari ables. The second scenarios done for the Por tuguese project evaluations base the simulation of the effect of the project on the coefficients concerning the trivariate system of employment, population and activity rate obtained through pooled data from all four Cohesion countries (re fer to the initial sections of this report. ) Figure 6. 1 C2 illustrates that this additional simu lation supports the original results obtained in Scenario 1. 128 Section I Vector Auto-regression Models ΰ? 3' m õ' Z3 DO Φ CO Scenario 1: Total Public Investment Scenario 2: Panel Data: Random Effects Spending on Project as a Percentage of Total Regional Public Investment Response of (a) Business Investment and (b) Cumulated Business Investment to Project Employment Response to Project Investment (Number of Jobs Created) Figure 6. 1: Tagus Crossing B9 Business Investment LJCumulated Business Investment B1 Years C1 s~7 ¿5s! 22000 20000 18000 16000 14000 12000 10000 8000 6000 4000 2000 0 A  Years C2 ¿"3; ¿H* μ Years Æ i 1 θ! Ί A- A 3 22000 20000 18000 16000 14000 12000 10000 8000 6000 4000 2000 0 2. 7. Ireland 2. 7. 2. North - South Rail Link Data shortages limit the evaluation work in the Irish case: the relationship between public invest ment and private investment in manufacturing can be estimated, not, however, the relationship between public investment and employment da ta. The lack of regional data concerning GDP and employment make it difficult to evaluate the Irish projects and the results should be interpreted with caution. 2. 7. 1. North - South Road Link The North — South Road Link comprises financ ing of a road extension (Dunleer — Dundalk) as well as two by-passes (Drogheda by-pass, and Balbriggan By-pass). The project is located at the North-Eastern border of Ireland in the two re gions East and North-East. Total spending on the project up to the year 1999 will be 198. 36 MECU. This represents about 32% of public in vestment for the two regions combined (see Fig ure 7. 1 A for the repartition of the investment across the years). Our analysis shows that work on the project should lead to a direct response of private invest ment close to 2% in the first ten years of the pro ject (see Figure 7. 1 Β for illustration). The North-South Rail Link comprises financing of the rail links between Dublin and Cork as well as Dublin and Belfast. We evaluated the project in the region East (Dublin). Total spending on the project between the years 1993 and 1995 in 70. 53 MECU. This represents about 13% of pub lic Investment in manufacturing for the region (see Figure 3. 2 A for the repartition for spending across the years). Our analysis shows that work on the project should lead to a direct response of private invest ment around 1 % in the first five years of the pro ject (see Figure 3. 2 Β for illustration). 2. 7. 3. Labour Market Variables The link between investment shocks and their ef fect on employment could not be estimated be cause of data shortage. We have however simu lated the response of the labour market variables Employment, Activity Rate, and Immigration for a hypothetic shock of 1% on labour demand. The response in terms of job creation are illustrated in Figures 7. 1 C and 7. 2C for the regions where the projects are located. No significant impact on population can be reported. 130 Section III — Vector Auto-regression Models 3 o Ζ) Φ Ireland (MECU) Average PI/BI 0,6 Northsouth Road Link Project Spending 1993 1994 1995 1996 1997 1997 1997 Total 6,87 32,46 35,96 37,14 40,44 28,25 17,24 198,36 Regional PI (East & NorthEast) Total Spending as %age of Reg. PI 614,36 32,29% Figure 7. 1: NorthSouth Road Link Figure 7. 1 Β Response of (a) Business Investment and (b) Cumulated Business Investment to Project 18,0% 16,0% 14,0% 12,0% 10,0% 8,0% 6,0% 4,0% 2,0% 0,0% Years Figure 7. 1 A Figure 7. 1 C Spending on Project as Percentage of Total Regional Public Investment Response of the Labour Market to a 1% Shock to Demand for Labour CO 1993 1994 1995 1996 1997 1998 1999 ¿71 t<7\ ¿7\ ff 4^Λ ¿em áS& U L L Ä J iÄ Years IRELAND (MECU) Average PI/BI North  South Rail Link Project Spending 0,6 1993 1994 1995 Total 21 24,25 25,28 70,53 Regional PI (East) Total Spending as % age of Reg. PI 536,26 13,15% Figure 7. 2: NorthSouth Rail Link Figure 7. 2 B Response of (a) Business Investment and (b) Cumulated Business Investment To Project 9,00% 8,00% 7,00% ¡ j 6,00% 5,00% 4,00% 3,00% -1 ét η n m Λ ι ; ca m 2,00% 1,00% 0,00% ^mññññññmüeeúlaai 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 Figure 7. 2 A Figure 7. 2 C Spending on Project as a Percentage of Total Regional Public Investment Job Creation as a response to a 1% shock to Demand for Labour Λ Æ Ζ M y 5,oo% r 4,00% 3,00% 2,00% 1,00% 0,00% y y 1993 1994 1995 % fåk f% Years ω ro o 3 > C o o Q. CD 2. 8. Additional Remarks on Labour Market Variables 0. 17% increase is estimated, representing a change in activity rate from 50. 4% to 50. 61 %. For the Spanish projects, the simulation results for the trivariate system including employment, population and activity rates support the findings in the previous report. Namely, an increase in employment caused by project spending can be fulfilled by two effects: (1) an increase in the ac tive population (including those workers who move from inactivity to the labour force and those who immigrate to the region) and (2) through a decrease in unemployment. Insofar as the impacts of the two projects analysed for Spain, an increase in employment of approxi mately 3,000 in the case of both projects ¡s ac companied by a population increase of 900. This can be interpreted as partly being the result of in terregional migration into the areas in which the projects are located. As far as the activity rates are concerned, the Madrid Ring Road invest ment leads to a long-run increase of 0. 05%. That is, the activity rate increases from 49. 02% to 49. 07%. In the case of Rias Bajas Motorway, a A more precise interpretation of these results would separate the contributing factors that lead to increased employment. Namely, by developing a different version of the model, we could differ entiate between decreases in unemployment and increases in the active population. The trivariate system would now also include unemployment, allowing for more detailed results on the impacts of public investment expenditures. In the Greek and the Portuguese case, the re sults concerning population and activity rate are subject to considerably less detailed data. We feel that these estimates have to be redefined in order to undergo a more extensive analysis for these two countries. In the Irish case, the link between public invest ment and employment can not at the present stage be estimated because the employment se ries stated only in the mid-80s. For this reason, the impact of public spending on the labour mar ket cannot so far be simulated. Simulation Results 133 3. Conclusions Summary of Results S. 1. Impact of Public Investment on the Business Sector and Implications of Inter-regional Transfers of Public Investment Spending This table is a summary of our empirical findings on the responses of private business investment spending: TABLE S. 1. Impact of Positive Aggregate Public Spending Shocks Short Run Long Run Rich Mid Poor Spain Portugal Greece Ireland ++ ++ ++ 0 ++ ? TABLE S. 2. Long Run Impact of disaggregated Public Spending Shocks in Spa n Infrastructure Spending Roads Ports Non Adm. Motorways Urban Infrastr. Non-Infrastructures ++ ++ + + 0 Conclusions 135 This last table has to be interpreted cautiously. We showed, in the case of Spain, that non-pro ductive investment had no significant relative ef fect. It does not mean that this effect does not exist. It could be delayed, so that our VAR mod els with two lags in the equations will not capture them. Another possibility is that there may be an aggregate effect, but no relative effect: improving education in a region will improve human capital, but may not have a relative effect locally. This could receive some support from our table 2. 9: if upgrading the skill level of a poor region is a way to increase the mobility of the workers, then this may also have adverse effects on this region. The next tables offer a summary of the impact of public investment spending. The first row shows the differences across groups of regions of the ratio of business over public investment. The second and third rows give the shares of the re gions in these two investments. The fourth gives the long-run elasticities of cumulated business in vestment ¡n response to public investment changes, and the fifth gives the same effect, but converted into units of spending. Finally, the last row is the net effect on business investment of a net transfer from the other groups of regions to the group of regions of the column. For instance, in Spain, given that business investment reacts more strongly in the richest and the poorest re gions, increasing their share in public investment has a net positive effect, while increasing the share of the middle-class regions has a net nega tive effect. It should be recalled that the elastici ties estimated in Spain are not statistically differ ent. However, this is not the case ¡n Portugal and Greece, where the differences appear to be more significant. TABLE S. 3. Summary of the impact of public spending, Spain Spain Poor Intermediate Rich Ratio of Business Over Public Investment % Share of National Business Investment % Share of National Public Investment Effect of Public Investment on Business Investment : Elasticity Effect of Public Investment on Business Investment : Units of Spending Effect of Public Investment on Business Investment : Increase in National Share in % Inter-regional transfers of 1 % share of Public Investment on Business Investment 7. 0 34. 6% 43. 2% 0. 199 1. 393 10. 0 41. 9% 37. 0% 0. 104 1. 036 10. 8 23. 4% 19. 8% 0. 130 1. 420 + 0. 159% + 0. 128% + 0. 154% + 0. 022% - 0. 029% + 0. 009% TABLE S. 4. Summary of the impact of public spending, Portugal Portugal Poor Intermediate Rich Ratio of Business Over Public Investment % Share of National Business Investment % Share of National Public Investment Effect of Public Investment on Business Investment : Elasticity Effect of Public Investment on Business Investment : Units of Spending Effect of Public Investment on Business Investment : Increase in National Share in % Inter-regional transfers of 1 % share of Public Investment on Business Investment 4. 37 8. 1% 7. 9% - 0. 0 65 -(0. 28) 4. 39 9. 1% 8. 9% 0. 040 (0. 18) 25. 2 82. 7% 80. 5% 0. 170 4. 28 (-0. 22)% (0. 12)% +0. 099% - 0. 0 8 9% - 0. 0 8 9% +0. 099% (the parentheses mean that the elasticity is not significant given the small size of local investment in these regions) 136 Section III — Vector Auto-regression Models TABLE S. 5. Summary of the impact of public spending, Greece Greece Ratio of Business Over Public Investment % Share of National Business Investment % Share of National Public Investment Effect of Public Investment on Business Investment : Elasticity Effect of Public Investment on Business Investment: Units of Spending Effect of Public Investment on Business Investment: Increase in National Share in % Inter-regional transfers of 1 % share of Public Investment on Business Investment Poor 1. 35 38. 3% 44. 1% 0. 765 1. 032 0. 66% +0. 3% Rich 1. 56 61. 7% 55. 9% 0. 329 0. 513 0. 36% -0. 3% S. 2. Labour Mobility This final table gives the sign of the changes at the regional level of employment in the long-run, activity and changes in population (proxying mi gration flows), in response to short-run employ ment changes. TABLE S. 6. Long Run Effects of Positive Employment Shocks Employment Activity Rate Mobility Total Skilled Unskilled Spain Portugal Greece Ireland + ++ ++ + + 0+ + 0 + ++ - + + ++ + 0 + 0 Conclusions We have looked for a link between cohesion fund spending and local economic activity and found it. The key link is provided by private investment activity. Cohesion fund spending, like other types of public investment, create incentives for private investment. Private investment then in turn en courages more investment spending and this vir tuous cycle of activity leads to more employment at the local level. There are also spillovers and national effects from this activity but we were not concerned with those effects in this part of the study — see Bean's chapter for the spillover ef fects. Data for Spain were richer than for the other countries and let us go deeper into the effects of cohesion fund spending. We found that the inter action between public and private investment spending was strong both in the short term and the long term. It was especially strong in richer Conclusions 137 regions, though also in some of the poorer re gions. Mid-income regions did not fair as well. Results with rich regions were similar in Portugal, though in Greece poorer regions seemed to ben efit more from cohesion fund spending than rich er regions did. Of the different types of spending, spending on roads and ports had the greatest impact on eco nomic activity. Spending on 'environmental' pro jects and other non-infrastructure projects had less impact, though still positive. Unfortunately, data for the other countries were not rich enough to enable a breakdown of spending by project type, but results for Spain should be indicative of results elsewhere. The investment activity that is generated by co hesion fund spending feeds mainly on to employ ment, with little, ¡f any, wage effects. If anything, we found that the employment effects of cohe sion fund spending were 'too high', given our ex pectations. This was especially true ¡n Spain but it was also true of the other countries, with the possible exception of Ireland, where results were weaker. Higher employment, however, did not always lead to a reduction in local unemployment. In Spain, increased demand for skilled labour led mainly to more in-migration, but also to some in crease in the region's activity rate. Unemploy ment benefited only slightly, though more for un skilled labour. We found similar results for the other countries. In Portugal and Ireland the main effect of the increase in employment was on the region's activity rate whereas in Greece it was on labour mobility. 138 Section I Vector Auto-regression Models Appendix 1 In the VAR system, if A, = AE/E, l2 = AL/L - AP/P and /3 = AP/P, then AL/L = AP/P + l2 = l3 +12- We also have AUE = (l2 +13) I {1 -u) and AP/E = P/E. /3. Then, taking the average employment and un- employment rates over the period considered, we can have a approximation of the number of persons who will enter the labour force, or who will leave the region in response to a unit labour for demand shock, Appendix 1 139 Appendix 2: Spain, Correction of the wage series Having two series of wages for the wage and salary earners available, we first try to see how consistent they are. It can be seen that the av erage deviation of the first wage definition to the other is extemely weak. Region by region, the same picture is true. The table A provides some regressions in panel data. In the fourth column, we predict 97% of the variance of W2TO. The fourth regression is used to define W3TO, which is equal to W2TO if available, and the esti mator of W2TO for the other years, so for 6 years between 80 and 91. TABLE A-1. Consistency of the two regional wage series W2TO W2TO W2TO W2TO Constant W1TO R square Total Panel Obs Time series length -0. 019"* [0. 007] 1. 205*** [0. 08] 0. 696 102 6 Region Specific -0. 024"* [0. 009] Region Specific -0. 055 [0. 11] 0. 959 102 6 Region Specific Region Specific 0. 908 102 6 0. 968 102 6 (weighted standard errors in parentheses, ', " *" significantly different from 0 at the 10, 5 and 1 % level) Appendix 2: Spain. Correction of the wage series 141 Appendix 3: ADF tests for the stationarity of the series The tables report the tstatistic of the coefficient b in the regressions: ΔΧ, = C + b ■ X, „j + a, · ΔΧ, _, + a2 ■ AX, _2 + ε, where Xt are the regional relative variables (log of the regional over the national value). Appendix 3: ADF tests for the stationarity of the series 143 PO ω co o õ' r3 o q > c o Q. CD N obs Crit. val. ( 1 %, 5%, 10%) AND ARA AST BAL CAL CAN CAT CAY CNR COM EXT GAL MAD MUR NAV PAI LAR 1 st order Self Correlation 1/2period persistence of shocks BITT 25 -3. 72 -2. 98 -2. 63 -1. 71 -2. 30 -1. 95 -2. 54 -2. 19 -0. 80 -1. 45 -1. 71 -1. 65 -1. 11 -2. 10 -1. 70 -1. 59 -0. 09 -2. 17 -1. 50 -2. 02 0. 821 3. 5 TABLE A2. ADF tests, Spain EMTO UNMP ACTO PATO POPU W1T0 25 -3. 72 -2. 98 -2. 63 -2. 61 -3. 44 -3. 00 -3. 05 -2. 06 -1. 57 -1. 92 -2. 61 -2. 29 -2. 85 -1. 79 -2. 65 -2. 37 -0. 83 -2. 77 -2. 79 -2. 50 26 -3. 71 -2. 98 -2. 63 -1. 97 -2. 48 0. 38 -1. 44 -2. 77 -0. 43 -1. 89 -1. 85 -1. 30 -1. 37 -2. 16 0. 69 -2. 11 -0. 54 -1. 74 -1. 85 -1. 92 26 -3. 71 -2. 98 -2. 63 -1. 94 -3. 09 -1. 80 -2. 41 -0. 93 -2. 00 -1. 52 -0. 72 -2. 05 -0. 96 -1. 84 -1. 40 -2. 51 -1. 41 -2. 70 -2. 46 -0. 65 26 -3. 71 -2. 98 -2. 63 -1. 33 -2. 87 26 -3. 71 -2. 98 -2. 63 -2. 23 -0. 98 27 -3. 70 -2. 98 -2. 63 -1. 77 -1. 60 10 -4. 32 -3. 22 -2. 76 -1. 38 -2. 35 0. 23 0. 41 1. 19 0. 29 -3. 16 -1. 61 -0. 82 -1. 56 -2. 20 -1. 71 -0. 32 -2. 45 -0. 57 -2. 91 -1. 18 -2. 12 -2. 05 -1. 88 -1. 72 -2. 99 -0. 29 -2. 77 -2. 48 -0. 77 -2. 59 -2. 71 -1. 82 -3. 70 -1. 31 -2. 52 -3. 43 -1. 68 -0. 64 -2. 80 -1. 90 -2. 80 -2. 33 -1. 30 -2. 64 -2. 58 -1. 69 -3. 31 0. 03 -1. 41 -2. 01 -2. 63 -1. 58 -1. 55 -1. 25 -1. 08 -0. 93 -1. 92 -1. 25 -1. 58 -1. 49 -1. 54 -2. 61 -1. 49 -1. 38 -1. 27 0. 695 1. 9 0. 672 0. 927 0. 871 0. 862 0. 924 0. 937 1. 7 9. 1 5. 0 4. 7 8. 8 10. 7 > n "O ω > O Ν obs Crit. Val. (1%, 5%, 10%) AVEI BEJA BRGA BRGN CAST COIM EVOR FARO GUAR LEIR LISB PRTL PRTO SANT SETU VIAN VILA VISE 1st order Self-Correlation 1/2-period persistence of shocks 4^ en TABLE A-3. ADF tests, Portugal PILT EMP1 UNMP ACTO POPA POPU 9 -4. 46 -3. 27 -2. 78 -1. 95 -1. 06 -1. 68 -1. 72 -1. 90 -2. 13 -1. 13 -0. 61 -1. 88 -0. 12 -0. 37 -1. 91 -2. 00 0. 12 -1. 04 -1. 90 0. 07 -0. 85 0. 176 0. 4 6 -5. 25 -3. 55 -2. 93 -0. 58 0. 83 0. 32 -0. 53 -1. 95 -0. 64 -1. 40 -2. 64 -0. 81 -14. 2 -0. 74 -0. 61 -1. 47 -1. 17 0. 20 -0. 51 -0. 04 0. 24 0. 715 2. 1 18 -3. 86 -3. 04 -2. 66 -0. 76 -0. 48 -0. 31 -2. 80 -0. 16 -0. 38 -0. 88 -3. 05 -0. 14 -0. 61 -1. 39 -0. 80 -0. 29 -1. 76 -1. 07 -2. 43 -3. 37 -0. 68 0. 916 7. 9 18 -3. 86 -3. 04 -2. 66 -0. 72 -1. 80 -2. 54 -2. 48 -1. 50 -0. 39 -2. 03 -1. 88 -1. 79 -2. 25 -1. 79 -1. 17 -0. 71 -2. 00 -1. 94 -1. 99 -1. 26 -1. 45 0. 653 1. 6 13 -4. 07 -3. 12 -2. 70 -0. 34 -2. 38 -1. 06 -4. 01 -0. 96 -0. 47 -2. 30 -2. 38 -0. 65 -0. 56 0. 16 -2. 31 -1. 32 -0. 87 -1. 00 -1. 59 -3. 59 -0. 49 0. 762 2. 6 21 -3. 79 -3. 01 -2. 65 0. 081 -1. 30 0. 39 0. 89 -2. 21 -1. 41 -1. 13 -1. 86 -1. 62 -1. 98 -1. 83 -1. 26 0. 72 -1. 13 -2. 76 -3. 39 -1. 40 -2. 18 0. 774 2. 7 21 -3. 79 -3. 01 -2. 65 0. 00 -2. 72 0. 21 0. 07 -2. 65 -2. 18 -2. 68 -0. 70 -1. 67 -0. 81 -3. 21 -2. 59 0. 18 -2. 33 -2. 63 -2. 72 -1. 43 -2. 22 0. 774 2. 7 wn 10 -4. 32 -3. 22 -2. 76 -1. 91 -0. 95 -0. 74 -1. 13 -1. 19 -0. 82 -0. 89 -1. 31 -0. 30 -1. 24 -5. 05 -4. 13 -3. 62 -1. 20 -2. 21 -0. 22 -1. 52 0. 32 0. 092 0. 3 TABLE A4. ADF tests, Greece UNMP ACTO POPA POPU Ν obs Crit. val. (1%, 5%, 10%) R1 R2 R3 R4 R5 R6 R7 R8 R9 R10 R11 R12 R13 ω CD o > 1 st order SelfCorrelation 1/2period persistence of shocks ': insufficient length of time series PITT 11 -4. 22 -3. 18 -2. 73 -1. 63 -5. 40 -2. 80 -3. 62 -3. 56 -4. 17 -6. 35 -3. 10 -2. 03 -1. 92 -2. 40 -2. 56 -3. 08 0. 650 1. 6 11 -4. 22 -3. 18 -2. 73 -2. 40 -2. 35 -I2. 0 -1. 80 -6. 95 -2. 03 -0. 45 -2. 27 -1. 36 -9. 60 -1. 23 -1. 31 -4. 47 0. 921 8. 4 EMP2 9 -4. 46 -3. 27 -2. 78 ■ * • 9 -4. 46 -3. 27 -2. 78 • « • 9 -4. 46 -3. 27 -2. 78 * • -1. 66 -1. 22 -2. 96 • • • • • • • • * * • • • • • • • • • ♦ • • • -1. 84 0. 552 0. 6 -3. 18 0. 278 0. 5 -1. 89 0. 140 0. 4 20 -3. 80 -3. 01 -2. 65 -2. 49 -2. 52 -2. 58 -2. 54 -2. 38 -2. 52 -2. 63 -2. 61 -1. 34 -2. 51 -2. 59 -2. 54 -2. 56 0. 956 15. 4 9 -4. 46 -3. 27 -2. 78  « • 0. 24 • • • • ■ • -1. 96 0. 760 2. 5 > "O "O CD > D TABLE A-5. ADF tests, Ireland HPBIT2 EMPI UNMP ACTO N obs 12 20 POPU 14 wn Crit. Val. (1%, 5%, 10%) -4. 32 -3. 86 -4. 46 -4. 46 -4. 46 -4. 01 -4. 64 East SouthEast MidWest West Midlands SouthWest NorthEast Northwest -3. 22 -3. 04 -3. 27 -3. 27 -3. 27 -3. 10 -3. 3 -2. 75 -1. 86 -11. 2 -2. 66 -2. 78 -2. 78 -2. 78 -2. 69 -2. 82 -0. 86 1. 19 -3. 19 0. 79 -1. 86 -2. 15 -1. 32 -1. 65 0. 62 0. 10 0. 24 -1. 22 -3. 01 -2. 67 -1. 11 -2. 60 -1. 17 - 0. Í -2. 35 -9. 71 -1. 14 -0. 81 -1. 51 -4. 06 -1. 36 -0. 60 -1. 54 -1. 48 -0. 37 -1. 61 -3. 30 1. 16 -1. 36 -4. 24 -3. 06 -2. 63 -1. 87 -1. 86 -0. 29 -0. 06 -3. 54 -2. 06 -4. 21 -0. 89 -0. 83 -1. 70 -1. 36 -1. 71 -1. 36 -2. 92 -0. 94 -1. 16 -1. 10 1 st order Self-Correlation 0. 961 0. 422 0. 258 0. 238 0. 274 0. 922 0. 480 -Ë. 1/2-period persistence of shocks 17. 4 0. 80 0. 5 0. 5 0. 5 8. 5 0. 9 Appendix 4: GDP per capita in the regions. TABLE A-6. Spain Panels Regions GDP per head (1 = Spain, average 80-92) Poor Regions Intermediate Regions Rich Regions EXTREMADURA ANDALUCÍA GALICIA CASTILLA-la-MANCHA MURCIA CASTILLA-y-LEON CANARIAS COMM. VALENCIANA ASTURIAS CANTABRIA ARAGON CATALUNA LA RIOJA MADRID PAIS VASCO NAVARRA BALEARES 0. 65 0. 77 0. 81 0. 82 0. 92 0. 93 0. 94 1. 01 1. 01 1. 03 1. 10 1. 19 1. 19 1. 21 1. 23 1. 25 1. 32 Appendix 4: GDP per capita in the regions. 149 TABLE A-7. Portugal Panels Regions GDP per head (1 = Portugal, 1992) Poor Districts Intermediate Districts Rich Districts VISEU GUARDA BEJA CASTELO BRANCO LEIRIA COIMBRA VILA REAL VIANA DO CASTELO FARO AVEIRO BRAGANÇA ÉVORA BRAGA PORTALEGRE SANTARÉM PORTO SETÚBAL LISBOA 0. 36 0. 40 0. 50 0. 52 0. 54 0. 54 0. 55 0. 57 0. 58 0. 58 0. 68 0. 84 0. 88 0. 95 0. 97 1. 04 1. 31 1. 72 Panels Poor Ypa Rich Ypa TABLE A-8. Greece Borio Aigaio Ipeiros Dytiki Ellada Ionia Islands Anatoli Makedonia and Thraki Thessalia Kriti Kentriki Makedonia Dytiki Makedonia Peloponnisos Notio Aigaio Attiki Sterea Ellada GDP per head (1 = Greece, average79-92) 0. 72 0. 76 0. 87 0. 89 0. 91 0. 92 0. 92 0. 98 0. 99 1. 00 1. 03 1. 09 1. 24 150 Section III — Vector Auto-regression Models Appendix 5: Some Econometrics Estimation of the variance of the limit of the cumulated private investment. public investment shock is given by a nonlinear combination of the coefficients estimated in the VAR. This limit is equal to: C3 +C4 i C5 1C,C2 where C^ and C2 are the autoregressive coeffi cients of Business Investment and C3 to C5 are the lagged effect of Public Investment. A linear Taylor approximation of that function around the true value of the parameters gives (see Greene 1993 for details): EstVarl ■ d/(ß) ,. dl (β) y ' -Var (β - b)· y ' dß dß where β is the vector of the estimated parame ters and b is their true value. The variance of the parameters is estimated as usually, s2(X'X')~\ where s2 is the sum of squared residuals. We then refer to the normal distribution to provide an interval confidence around the estimated value. The choice of the estimators. We have developed the concept of a representa tive region, which is a useful benchmark to con sider. This consideration lead us to use the fol lowing econometric approach. In a first step, we estimate the coefficient from a standard OLS regression, assuming that the co efficient are the same, except the intercepts which are allowed to be regionspecific. In a sec ond "step, we use the residuals estimated from the first step to build a estimated matrix of vari ancecovariance. Then, we assume that the true matrix of variancecovariance of the residuals is a block matrix of the matrices of variancecovari ance of the residuals is the identity matrix times a regionspecific variance. The economic interpre tation ¡s straightforward: the closer the region is to the representative region, the smaller this re gional coefficient. Then, we estimate the coeffi cients a GLS estimator from the matrix of vari ancecovariance estimated above. In the text, we show the regional variance of the residuals from this second stage, and show that they are not strongly different. The choice of this GLS versus more sophisticat ed estimators like GMM to correct for a correla tion between the fixed effects and the regressors can be explained as follows. When we estimate a model like: ν;,(=ς. +βγ;ι(_;+ε, I,t we can interpret the C¡ as an intercept and an OLS estimator is consistent under the assump tion of no autoregressive errors. See Nickell (1981). If we interpret C, as a fixed effect such that ECj = 0 across regions, then we have a bias ¡n the estimation of β coming from the correlation between the residuals and y,,_,. Then, the bias is proportional to the variance of the C, across re gions. Macroeconomists, considering small Appendix 5: Some Econometrics 151 crosssections and long time series, usually do not take this into account, assuming that the bias is small. Moreover, the usual corrections imply a first order differentiation plus instrumentation with the lagged regressors, meaning a loss in the number of observations. Given our data limita tions, we have preferred to use the GSL estima tor, to offer an overview of what sort of estima tions of the impact of the Cohesion Fund we can provide. In a later stage, we think that in the case of Spain only we are able to provide other esti mates based on other estimators, as a check of the robustness of the results. Identification of the shocks. Let us come back on the following equation: Bl, = C' + a'PI,_,+$'BI,_,+yPlt +μ,β The residuals μ, are shocks on regional business investment, linked for instance to regional busi ness cycles. A recurrent problem is the possible correlation between these residuals and the re gressors, here the public investment spending in the same year Plt. On the one hand, we expect a positive correlation because in downturns, bud get are smaller. On the other hand, investment spending could be used to absorb the shocks, and the correlation could be negative. We have assumed implicitly that, in absence of good theory to explain how governments spend their budget, the most reasonable assumption was to say the public investment spending is de cided one year before, which means that PI, is not correlated with μ,. Moreover, we consider our relative variables, and regional business cycles have much smaller sizes, from the data have considered. But as an alternative test of robustness of the re sults, we have explicitly considered these possi ble correlation. Assuming a range of [a, a] for the correlation coefficient corrjPI,, μ^, we are able to estimate the size of the bias on the coeffi cients. Indeed, if we estimate: Y = bX + z with a correlation coefficient c between one of the regressors X and e, then we can show that the OLS estimator β is biased, such that: E[ß-ö] = C se ,1/2 χΙχ1/2 where of course the first term of the right hand side goes to infinity when c goes to 1, and the second term can be rewritten as: \1/2 Izßl R2 where R2 is the fraction of the variance explained. In the estimation of the dynamic link between business investment and public investment, the Rsq are all close to 0. 98 for the Cohesion coun tries, such that even for large correlations, c=+/0. 6, the bias is small, about 8% for Greece and Portugal, and 1 1% for Spain. Of course, when the size of residuals increases, the bias be comes larger. This could be an alternative expla nation for the negative and non significant effects of public investment spending that we found in Spain (especially Water Facilities and NonPro ductive Investment). 152 Section III — Vector Autoregression Models Bibliography Blanchard O. J. and L. Katz, 1992, Regional Evolutions, Brooking Papers on Economic Activity (1), pp1- 61 Blanchard O. J. and D. T Quah, 1989, The Dynamic Effect of Aggregate Demand and Supply Distur bances, American Economic Review 79(4), September, pp655-73 Greene W. , 1993, Econometric Analysis, second ed. , Prentice-Hall International, Inc Mincer J. , 1962, "Labour Force Participation of Married Women: A study of Labour Supply", in Gregg Lewis, ed, Aspects of Labour Economics, Princeton University Press Nlckell S. , 1981, Biases in Dynamic Models with Fixed Effects, Econometrica, 49(6), Nov, pp1417-26 Bibliography 153 Β. REGIONAL OUTPUT AND EMPLOYMENT IN THE PRESENCE OF INTERREGIONAL SPILLOVERS Charles Bean 1, Introduction and summary The previous chapter of this report used vector autoregressive (VAR) techniques to study the impact of local public investment spending on regional economic decisions by the private sec tor. First, it explored the impact of public spending on private investment decisions. Sec ond, it explored the effect of regional move ments in labour demand (which might, for in stance, be the consequence of movements in public investment) on other labour market vari ables such as local wages and migratory popu lation movements. This section of the report develops that work by extending it in a number of directions: (1) The impact on private investment and labour market outcomes are integrated into the same system, allowing a richer range of inter actions; (2) Regional output is also added into the picture, permitting also the evaluation of the conse quences for local output and productivity of increased public investment. The effect on output is, of course, the natural welfare mea sure to use in evaluating the effectiveness of increased Cohesion spending: (3) The importance of local spillovers in economic activity from one region Into neighbouring re gions is explored. An understanding of the strength and direction of these is important in moving from the impact on local economic variables to the aggregate Impact on the na tional economy. The price of this greater complexity is greater da ta requirements. For this reason we have presently concentrated on analysing Spain as ho mogenous data is available for a somewhat longer time period than for the other three Cohe sion countries. However, the lessons learned from the exercise should also be applicable to them and in due course the analysis should be capable of being extended to them. The main findings are as follows: (1) Allowing for the associated implications for employment and private investment, the elas ticity of regional output to net productive pub lic investment is around a third, that is a 3% increase in the local public capital stock raises output by around 1%; (2) There is evidence of important interregional spillovers, not only from aggregate economic activity, but also from activity ¡n geographically adjacent provinces. Consequently a localised public sector investment project has signifi cant economic effects on surrounding regions and, to a lesser extent, on remote regions. The aggregate output multiplier can be as large as seven, although for most regions this multiplier will be rather small; (3) The estimated marginal return from public In vestment varies widely across Spanish regions, being roughly five times greater in Madrid (the highest) than in Extremadura (the lowest). Introduction and summary 155 2. Theory and comparisons with other work The main theoretical ingredient required to ex tend the earlier work is simply an augmented lo cal production function relating regional output to local private and public inputs: Yit = F(Ait, Kit, Git, Nit) (1) where Yit is output in region I at date, A¡, is an in dex of the state of technical knowledge, Kit is the private capital stock, Git is public capital stock, and Nit is employment. Although technical knowledge may be common across an economy, since regions differ in their industrial mix and the rate of technical progress differs across indus tries, it makes sense to allow the Ait to differ across regions, as well as evolving over time as the state of technical knowledge advances. A number of authors have estimated production functions, augmented to include public capital in puts. These include Aschauer (1989), Holtz-Eakin (1992), and Hulten and Schwab (1993) for the United States, and Mas ef al. (1995, 1996) for Spain. These last two works are especially rele vant in this case as they use similar data to that used here. In Mas et al. (1996) a Cobb-Douglas production function is assumed for F(. ) ínY¡t = a¡ + bt + cínK¡, + dínG¡, + eínN¡„ (2) which is then estimated for 17 Spanish (NUTS II) regions over the period 1964-1991 (1964 and then alternate years from 1967). Country-specific constants are allowed (the a¡), but the slope para meters are all assumed to be the same across re gions. In particular the rate of technical progress is assumed to be the same across all regions. It seemed natural to begin our investigation of the output effects of public investment by replicating their results, and exploring their robustness. The results of re-estimating their basic equation is giv en in the second column of Table 1. Regional output is real gross value added, deflated by the economy-wide GDP deflator since region-specific deflators are unavailable; the private capital mea sure is the net non-residential private capital stock; and the public capital measure is the total productive net public capital stock (roads + water + urban structures + ports + airports, but exclud ing motorways; the reason for excluding motor ways is discussed further below). Similar results (not reported) obtain if gross capital stock mea sures are used. The results essentially reproduce the findings of Mas et al} The estimated elastici ties of output with respect to the inputs are 0. 11 with respect to public capital, 0. 48 with respect to private capital, and 0. 6 with respect to em ployment. While these results seem intuitively plausible, they are not without their problems. First, as noted, Mas et al. assume that the rate of technical progress is common across regions, despite the fact that is likely to differ because of differences in industrial structure. When we re-ran their model allowing for interregional differences in the rate of technical progress (i. e. b¡ rather than b) as well as The minor differences that remain appear to be due to small differ ences in the data set. In particular Mas er al. use observations for 1964, 1967 and biennially thereafter corresponding to the availability of the output series. In order to ensure consistency of the VARs esti mated below, we prefer to work with regularly spaced data and have therefore chosen to construct an estimated output series for 1965 by linear interpolation of the 1964 and 1967 observations, and then use data for every other year, beginning in 1965. Theory and comparisons with other work 157 the constants, we obtain the results detailed in the third column of Table 1. The additional re gion-specific time trends are statistically highly significant and the input elasticities become 0. 16 with respect to public capital, 0. 49 with respect to private capital, and 0. 41 with respect to em ployment. While the results do not change that much, the low value of the employment elasticity and the high value of the private capital share is somewhat troubling since under competitive as sumptions the labour elasticity should be equal to labour's income share. Second, the results assume that measured factor inputs are the same as those effectively em ployed in the production process. In practice firms hoard some labour, particularly skilled labour that might be difficult to replace, during re cessions rather than lay it off. Similarly capacity utilisation varies over the business cycle. The consequence is that the estimated parameters in Table 1 partly reflect short-run business-cycle ef fects and not just the underlying production para meters. This suggests the need to introduce sep arate variables to control for such business cycle effects. Finally, and partly related to this, the model ig nores the complications introduced by adjust ment costs. For instance newly hired labour may be less productive than experienced labour be cause it needs first to acquire firm-specific skills before it can become fully productive and this takes time. Similarly the installation of new capital may temporarily interfere with current production activities. This limitation is revealed in the Durbin- Watson tests for serial correlation in the estimat ed errors; these are highly significant and point to the need to allow for a more sophisticated dy namic structure. Mas et al. (1995) bears many similarities to Mas et al. (1996), except that rather than estimating the elasticities of output with respect to private capital and labour directly, as in Table 1, it relies on the fact that in competitive markets where fac tors receive their marginal product by way of re muneration, the elasticity of output with respect to the labour (capital) input will be equal to labour's (capital's) income share. This can be used to construct a "modified total factor produc tivity residual" which in essence is simply an esti mate of the joint contribution of technical progress (Ait) and changes in public capital to output growth. This has the advantage of not re quiring that the production function be con strained to take a Cobb-Douglas form, but the disadvantage of requiring that private factors re ceive their marginal products by way of remuner ation. The cumulated estimated productivity residual is then regressed on the public capital stock and a time trend (to capture technical progress) in order to estimate the former's contri bution to output. Now they find that the public capital variable does not seem to be particularly important in explaining movements in output, over and above that due to changes ¡n private factor inputs. The apparent inconsistency with the results of Mas et al. (1996) is left unexplained, but the analysis is open to all the same objections as apply to Mas et al. (1996), namely the apparently invalid assumption that the rate of technical progress is the same in all regions, and the ne glect of business cycle and dynamic factors. 158 Regional Output and Employment in the Presence of Interregional Spillovers 3. Methodology In this chapter of the report we shall extend this analysis by introducing more complex dynamic behaviour, as well as allowing for the simultane ous determination of employment and output. Vector autoregressive methods are ideally suited for situations where there may be complicated dynamic interactions of unknown form between variables. Suppose we have a set of n economic variables, Xt, whose evolution is described by the economic model BXt = d, + Α,ΧΜ + A2Xt_2 +. + AX, ; + et (3) where the A¡ and Β are nxn matrices, d, is an nx1 vector describing the net effect of any deter ministic or exogenous variables that are deter mined outside the system, and e, is an nx1 vec tor of serially and mutually uncorrelated shocks with E[et] = 0 and Var[et] = D is a diagonal matrix. The presence of the Β matrix allows for arbitrary simultaneous interactions between the elements of Xt. Virtually any macroeconomic system one cares to write down can be written in essentially this form. While (3) may be the representation of interest, however, it cannot be estimated directly by Ordinary Least Squares in general if there are arbitrary nonzero elements off the diagonal of B, i. e. there are simultaneous interactions between the variables, because the righthand side en dogenous regressors in any regression will be correlated with the equation error. We can, how ever, always estimate the reduced form of (3) X, = f, + Ο Λ ι + C2X,2 +·■+ QXH + u, (4) where C¡ = Β"1Α, f, = B"1d„ u, = ß1et. Equation (4) is the vector autoregression. The question is then under what circumstances one can recover the coefficients of interest, A¡ and B, from estimates of this reduced form? The classic way to identify models of this kind, following Sims (1981), has been to assume the economy has a recursive structure so that Β has a triangular form with ones along the diagonal and zeroes above the diagonal. This means that variables at the top of Xt are regarded as the "most" exogenous, whilst those at the bottom are the "least" exogenous, being heavily influ enced by contemporaneous developments in the other endogenous variables of the system. Mak ing this assumption leaves n(n1)/2 coefficients to be identified in B, together with the η variances along the diagonal of D, I. e. a total of n(n+1)/2 coefficients. This can be exactly accomplished by noting that ΒΩΒ' = D, where Ω = Var[uJ contains exactly n(n+1)/2 independent parameters. Since Ω can be estimated from the covariance matrix of the residuals in (4), it is straightforward to ob tain the implied estimate of Β from this residual covariance matrix. Such a triangular ordering is for instance invoked in the model of the previous chapter where it is assumed that investment in public capital is "more" exogenous than private capital. However, in many cases there is no compelling reason for assuming that Β is triangular, and in stead a number of authors have suggested in voking restrictions from economic theory con cerning either the shortrun (e. g. Bernanke, 1986) or longrun (e. g. Blanchard and Quah, 1987) in teractions between variables; this is the socalled "structural VAR" approach. In order to address Methodology 159 the limitations of the results of Table 1, we shall be studying a sequence of VAR models describ ing the joint evolution of: the private (net) capital stock; the public (net) capital stock(s); employ ment; output; and unemployment, the last of these being included in the system so as to cap ture business cycle influences. While it may make sense to assume that, because of gestation lags, both private and public current investment are largely predetermined, this is not the case for output, employment and unemployment, which are likely to be simultaneously determined. We therefore choose the avenue of identifying the system by means of theorybased assumptions. In essence these assumptions will turn out to be similar to those employed in building traditional aggregate macroeconometric models. However, the VAR structure forces one to be quite clear ex actly what assumptions are being made, some thing that is not always the case with convention al macroeconometric models. The assumptions that we invoke here are of the following form. Let ux be the residual in the VAR corresponding to variable x, where χ is variously the logarithm of the private capital stock (k); the logarithm of the public capital stock (g), the loga rithm of employment (n); the logarithm of output (y), and the unemployment rate (u). The driving shocks, corresponding to the components of the vector e, in (3), are identified with shocks to: pri vate capital formation (e^, e. g. due to changes in interest rates or the animal spirits of investors; public capital formation (eg); the labour force (ef), e. g due to demographic developments; the nat ural rate of unemployment in the region (eu); and to aggregate demand (ed). Since investment in ei ther public or private capital is likely to have an effect on aggregate demand through the usual Keynesian multiplier channels, the last of these should be thought of as due to nonInvestment related movements in aggregate demand, e. g. changes in savings behaviour. Our economic model is then essentially of the standard neoclassical synthesis variety. The equilibrium, or natural, level of output occurs when capital is fully employed and the unem ployment rate is at its equilibrium, or natural, lev el. This equilibrium unemployment rate is thought of as being determined within a "battle ofthemarkups" framework in the manner of Layard, Nickell and Jackman (1991) or Phelps (1994) and summarised in Bean (1994). In the models of those authors there is one relationship describing the way in which wages are set, ei ther by firms who need to motivate their workers or as the outcome of bargaining, which makes the real "supply" wage a decreasing function of the unemployment rate, and another pricing (or labour demand) relationship which describes firms' pricing and employment decisions and makes the real "demand" wage (usually) a de creasing function of the level of employment. Equilibrium employment, and unemployment, then corresponds to the level of activity at which the supply and demand wages are equalised. This is really just like a standard competitive equilibrium, except that it does not have the usual efficiency properties of a perfectly compet itive market, because of the presence of imper fections ¡n both goods and labour markets. Movements ¡n the natural rate can occur be cause of changes in e. g. unemployment bene fits, taxes, import prices, or the bargaining strength of workers. Today's natural rate may al so be a function of previous levels of the actual unemployment rate because of socalled "hys teresis" effects (see e. g. Blanchard and Sum mers, 1986; Layard and Bean, 1989). However, we will make the assumption that the natural rate of unemployment is independent of the size of the labour force. This is consistent with the observation that over long periods of time, stretching back to the last century, the labour force has steadily risen whilst the unemployment shows no persistent trend in either direction. Thus it must be the case that increases in the labour force ultimately show up entirely in em ployment. Deviations around these natural levels of output and unemployment will, however, occur when there are unexpected disturbances to aggregate demand if wages and/or prices are slow to ad just. Because changes in the capital stock will af fect the marginal product of labour, and therefore the demand for labour at any given real wage, it is also reasonable to believe that the effect of in vestment shocks on unemployment may be rather different from the effect of changes in de mand due to e. g. shifts in savings behaviour. This generates a conventional business cycle. In our model such business cycles can be driven either by shocks to private or public investment (through ek and eg respectively) or by other things (represented by ed). When a recession happens, firms may be "off" their production function be cause of the hoarding of labour and other factors of production. This reasoning leads us to the fol 160 Regional Output and Employment in the Presence of Interregional Spillovers lowing specification for the interrelations between the fundamental shocks, et, and the observed disturbances, ut: uy = auk + ßug + yun + ed (Production) uk = ek (Private investment) ug = eg (Public Investment) uu = 7iuk + pug + φβα + eu (Unemployment) un = §uu + e, (Employment) The following comments are in order. First, the parameters α and β on the two investment dis turbances in the production relationship reflect both their direct influence on supply (through the usual production function relation) and their Indi rect effect on the extent of labour hoarding via their impact on aggregate demand. Their sepa rate influences are not identifiable. Second, both private and public Investment are assumed to be exogenous within the current pe riod, and are only affected by other variables with a lag. This is justified by the presence of the con siderable planning and timetobuild lags involved in capital expenditures. Third, the unemployment rate is allowed to devi ate from its natural rate in the short run to the extent that there are cyclical disturbances pre sent (due to either investment, or other de mand, shocks) or because of changes in private or public capital which as noted above may change the marginal product of labour and hence shift labour demand independently of any effects they have through aggregate demand. The parameters π and ρ thus capture the influ ence on unemployment of shocks to private or public capital through both channels; again the size of the separate effects is not identifiable. The presence of "hysteresis" effects, etc. , will of course be picked up by the lagged terms in un employment that appear as regressors in the VAR. Fourth, the term cpuu in the employment equation allows discouraged worker effects on labour supply. This occurs when unemployed workers stop looking for work because unemployment is high and the probability of finding work low. Consequently measured unemployment under states the true amount of slack in the labour market, and the measured labour force (identi cally equal to the sum of employment and un employment) understates the available labour supply. If such an effect were not present then one would have φ =1 and employment would In crease oneforone with the labour force, and decrease oneforone with increases ¡n unem ployment. However, with such an effect present an increase in unemployment will also generate a simultaneous decline in the measured labour force and therefore φ >1. Remembering that But = e,, this set of assump tions gives the following specification for the Β matrix, where X' = {k, g, u, n, y}, u' {uk, Ug, uu un, uy} and e' = (ek, eg, eu, e,, edj: 1 0 0 0 0 0 10 0 0 B = α φ -π βφ-ρ 1 γφ -φ (5) 0 0 -φ 1 0 -α -β 0 -γ 1 This has seven free parameters, whereas there is enough information to identify ten. Hence there are three overldentlfying restrictions which can be used to test the model. Since the parameters {α, β, γ, π, ρ, φ, φ} enter In to Β nonlinearly, estimation of the model is at first sight not entirely straightforward. However, consideration of the structure suggests an easy way to proceed. First notice that our assumption that movements in the labour force are neutral with respect to the natural rate of unemployment means that uu is uncorrelated with e, and hence a consistent estimate of φ can be obtained by re gressing the residual from the employment equa tion in the VAR (an estimate of un) on the residual from the unemployment equation (an estimate of un), with the residual in this equation being an es timate of er. Next we can estimate α, β, and γ by regressing the residual from the output equation in the VAR (an estimate of uy) on the residuals from the private and public capital equations (es timates of uk, and ug> respectively) and the resid ual from the employment equation (an estimate of un). Estimation by Ordinary Least Squares will not give consistent estimates because un ¡s cor related with ed. However, we know that un is cor related with e, and that ed and e, are uncorrelat ed. Hence we can estimate α, β, and γ consis tently ¡f we estimate it by Instrumental Variables, using as an instrument for un the estimate of e( Methodology 161 just obtained. The residual in this regression is ing restrictions can be based on the quantity (see then an estimate of ed. Finally we can obtain con Bernanke, 1986): sistent estimates of π, ρ, and φ by regressing the residual from the unemployment equation (an es 2/:n|B| + ÌT ίηίΒΩΒ'ί ~ y2ím) tímate of uu) on the residuals from the private and public capital equations (uk, and ug) and :he esti mate of ed just obtained. Ordinary Least Squares ts sufficient here. Finally a test of the overidentify- where m is the number of overidentifying restric tions. ~^ ® 162 Regional Output and Employment in the Presence of Interregional Spillovers 4, Data,and basic results for Spain The basic data used in the analysis is given in Figures 15. The data is biennial 196591 for 17 Spanish NUTS II regions. Because of the large number of variables in the model it is not possible to estimate VARs separately for all the regions as there are only 14 time series observations for each region and after introducing lags we will have almost that many parameters to estimate. We therefore pool the data across regions, im plicitly assuming common dynamic responses across regions, although we do allow for region specific intercepts and time trends. However, be fore reporting results for our baseline model we first need to look at the time series properties of the various series to see whether they are sta tionary or not and, if the latter is true, whether there are any cointegrating relationships present1. To do this we again pool the data and assume that each region has its own intercept and re gionspecific time trend, but that otherwise the dynamic properties are the same across regions, and then carry out standard unit root tests. That is we estimate the model Axit = a¡ + b¡t + c|Axit_| + c2Ax¡,_2 + dxiM (7) for χ equal to k, g, u, n, and y in turn where Δχ( (= Xt  xt_i) ¡s the difference operatör. If d is signifi cantly below zero then the variables are best re garded as stationary around their regionspecific trend. However, if d is close to, or above, zero then the variable in question is nonstationary and potentially needs to enter the VAR in first dif ferences. Table 2 gives the parameter estimates A cointegrating relationship is a linear relationship between a set of nonstationary variables, which is itself stationary. for d for each variable. Unfortunately the statisti cal properties of this test for a unit root in pooled data is presently unknown. However, in each case the estimate of d is so far below zero that it seems natural to assume that all the variables in the model can be treated as trend stationary. Table 3 reports the results of estimating our baseline VAR model allowing for 2 lags on each variable. For brevity the coefficient estimates for the intercepts and time trends are not reported. Table 4 reports Ftests for the joint significance of each variable in each equation. Finally Table 5 re ports the results of estimating the B matrix de scribing the simultaneous interactions between the variables, along the lines laid out in the previ ous section. In general the coefficients in this es timated B matrix are significant, have the right sign and are of plausible magnitude. Finally the test of overidentifying restrictions does not reject the model. The easiest way to evaluate the model, however, is not by inspection of the individual coefficients, but rather by examining how the model responds to exogenous shocks (the e, in equation (3)). These "impulse responses" are given in Figure 6. They are constructed by inputting a "typical" (one standard error) shock to ek, eg, eu, ef, and ed in turn and then tracing through their effects on the variables in the model. Reading down each col umn of the array gives the response of each vari able in turn to the particular shock noted at the head of the column, while reading across each row gives the response of the variable designated in the lefthand margin to each shock in turn. Thus the first column gives the response of each Data and basic results for Spain 163 of the variables to a private investment shock (ej, the second column gives the response of each of the variables to a public investment shock (eg), and so on. In reading the graphs it may be helpful to remember that the variables are all in loga rithms (other than the unemployment rate which is already in the correct units), so that in each case a. 01 movement corresponds to a 1% point change. In addition, reading across any row, the scale on the vertical axis is the same for all of the shocks. Finally remember that the data ¡s biennial, so that the length of a "period" is here two years; the horizontal axis in each of the plots thus corre sponds to a 24-year horizon. Since the Impact of public spending is the main focus of interest in this project we begin by dis cussing the impact of a shock to public invest ment (the second column). The size of the shock is such that it adds 3% to the public capital stock, which then decays away over the next 14 years. Since this might be thought of as the life of a "typ ical" public sector investment project, this "shock" can be thought of as representing investment in a project which is not subsequently maintained as it depreciates1. We see that this has a complemen tary effect on private investment, boosting the pri vate capital stock by a maximum of 0. 75% after about 10 years. The multiplier effects on demand, as well as any beneficial effects on the productivi ty of labour, also lead to a small rise in employ ment (peaking at 0. 85% after 6 years) and a cor responding decline in the unemployment rate (of 0. 44% points after 6 years). The increase in all three factor inputs implies that output must also rise, the maximum effect on output coming after 8 years and registering at 0. 96%. Is such an effect on output reasonable? Suppose that the production function can indeed be well represented by a Cobb-Douglas production func tion as in equation (2), and that the supply of labour is fixed, as is the cost of capital. Then it is not difficult to show that the elasticity of employ ment with respect to public capital, allowing for the induced effect on private capital formation will be d/(1 -c)2. If we plug in the estimates for c and d from equation 1 of Table 1 then we get a value Analysing a permanent change in the level of the capital stock can be achieved by inputting an appropriate sequence ot eg shocks, rather than a single one. However It is usually easier to assess the reasonableness of a model by looking at one-off shocks. If fr\Y = a + cfnK + ófnG + e/'nN then d(fnY)/d(fnG) = d + c. d(fnK)/d(rnG) if N is fixed. The profit-maximising condition for pri vate capital is aY/K = r, where r is the user cost of capital. Hence if r is fixed d(fnY)/d(/nK) = 1 and hence d(rnY)/d(cnG) = d(fnK)/d(mG). Hence the result in the text. for this elasticity of 0. 31, i. e. a 3% Increase in pri vate capital should generate a 1% increase in both output and private capital. This is very much of the same order of magnitude obtained from the Impulse responses. While it is the effect of changes in the stock of public capital that ¡s of most interest, it is also in structive to look at the impulse responses to oth er types of shock to see if these too are reason able. As far as exogenous shifts ¡n private invest ment go (column one), we see that in the short run this boosts employment and output. Howev er some years down the road the effect is to re duce employment raise and raise unemployment, perhaps because the extra capital is predomi nantly directed to substituting for labour. Interest ingly public capital also declines after a number of years. This may have something to do with the political economy of how public investment has been directed regionally in the past, e. g. ¡t has typically been directed to depressed rather than booming areas. A reduction in the natural rate of unemployment, e. g. as a result of the improved matching of un employed workers to vacant jobs, is associated with an immediate rise in employment. There is also an induced expansionary effect on private capital. Together this boosts output. In this case the numerical magnitudes are not quite so plau sible, however. With a Cobb-Douglas production function as in equation (2), the elasticity of both output and private capital with respect to an ex ogenous increase in employment, holding the cost of capital fixed, would be e/(1-c); using the estimates in Table 1 again this comes out at 1. 23. However, the percentage increase in both private capital and output is less than a half of the percentage increase in employment. One ex planation for this may be that in reality the extra workers that find jobs as a result of a decrease in the natural rate are typically of lower than av erage skill and quality. As far as an increase in labour supply goes, this has immediate beneficial effects on employment and output (this is an automatic consequence of our assumption that the natural rate of unem ployment is Independent of the labour force. There is a small induced positive effect on public capital formation, but ultimately a slightly de pressing effect on private capital formation. Again this is a slightly odd result. 164 Regional Output and Employment in the Presence of Interregional Spillovers Finally a demand shock has entirely conventional effects, boosting employment and, later, both pri vate and public capital formation, with the conse quence that output expands while unemploy ment falls. There are therefore some peculiarities in the model, in particular in relation to the impact of labour market disturbances, that merit some fur ther experimentation with alternative identification schemes, i. e. alternative assumptions for the Β matrix. However, overall the results seem suffi ciently sensible — particularly in respect of the Impact of public investment shocks — to make the use of the model to evaluate the regional im pact of public spending decisions worthwhile. In the rest of the chapter, we therefore explore a va riety of relevant extensions to the model, whilst retaining the basic identifying and overidentifying assumptions as embodied in B. We begin with a consideration of the implications of heterogeneity in public capital goods. Data and basic results for Spain 165 5. Differential effects from different sorts of public capital As noted earlier the definition of public capital (G) used in both the simple production function estimates of Table 1, and our basic VAR model excludes motorways. Is there any statistical or economic justification for this? A simple way to evaluate the impact of motorway expenditure is to note that in(1 +x) is approximately equal to χ ¡f χ is small. If we let Gmot be the motorway capi tal stock, then in(G+Gmot) is approximately equal to (nG + Gmot/G. Consequently to evalu ate the Incremental impact of motorway expen diture all we need to do is add (Gmot/G), the ratio of motorway capital to other public capital, into the VAR and evaluate Its significance. When we do this, we find that the (Gmot/G) have no ex planatory power for the five other variables in our system (a formal test gives a test statistic of χ2(10)=12. 81 which is barely significant at the 75% level). Furthermore the impact of motor ways investment, such as it is, is very different from that obtained with the aggregate of the other sorts of public capital. This is illustrated in Figure 7 which gives the impulse responses from this augmented model1. Whereas an in crease in other public capital boosts output and employment by a significant, albeit modest, amount, increased motorway investment seems to have an essentially negligible effect on out put, and actually raises, rather than reduces, unemployment. The statistical reason why motorways seem to have such a nugatory effect is clear from a com parison of the plots for public capital (Figure 2) and output (Figure 5). If capital is measured in cluding motorway capital (dashed line) then there are very large upward movements in the mid 1970s in a number of regions, particularly Navar ra and La Rioja, yet there is no corresponding in crease in output in these regions, or in any of the other variables. Consequently one would not ex pect to find a significant role for an aggregate capital measure which Included motorways2. All this begs the question as to why motorways should be excluded from the measure of local public capital, while other roads are included. While the motorways ¡n question are apparently actually operated by the private sector under franchise, it is not clear why this should affect their impact on local economic activity. One ex planation may be that the construction of a mo torway through a region has the effect of reduc ing transport costs and so encouraging the ag glomeration of economic activity at the ends of the motorway rather than being distributed more evenly as in the status quo ante. This sort of ef in both this, and the next, model the assumptions underlying the ma trix summarising the simultaneous interactions between the variables, Β (now 6x6). are generalised in the obvious manner and are therefore assumed to take the form, where the first three variables in the sys tem are private capital and then the two sorts of public capital: 1 0 ao - π 0 -α 0 1 β ο -ρ 0 -β 0 0 δφ - σ 0 -σ 0 0 1 0 0 0 γφ 1 -γ This does invite the question as to why Pissarides and Wasmer ap parently find an important role for motorway investment. We believe the answer lies in the fact that their approach, as emphasised in Section 7, models the behaviour of public and private investment relative to the economywide average. Now if motorway investment rises in all regions simultaneously, as is partly the case here, relative public investment will hardly change. Consequently they are not faced with having to deal with the troubling observation that the large increase in motorway expenditure did not apparently produce a large contemporaneous increase in output or employment. Differential effects from different sorts of public capital 167 feet comes out of the computable general equi librium simulations carried out in this project, for instance. In that case the economic effects on the region through which the motorway passes may be nugatory, or even perverse as here, al though it may well have beneficial effects on sur rounding regions. Identifying exactly which sur rounding regions will benefit probably requires being precise about exactly how the motorway system functions, however. By contrast general road networks whose impact on a region is more diffuse need not have the same effect of encour aging agglomeration. The next step is to see whether (nonmotorway) transport capital (I. e. roads + ports + airports) have a different effect from other infrastructure capital (i. e. water + urban structures). Figure 8 gives the impulse responses for a VAR model that includes the two sorts of capital separately (for brevity the full statistical results are not re ported). Looking down the second column (cor responding to an increase in water and urban structure investment) and the third column (corre sponding to an Increase in transport investment) we see that the impulse responses of output, employment, unemployment, and private capital are remarkably similar. The only minor asymmetry appears to be in the interaction between the two sorts of public capital, with increases in transport investment producing a small subsequent decline in water and urban capital, but the opposite be ing true, mutatis mutandis for an increase in wa ter and urban investment. However the differ ences appear to be slight, and one is naturally lead to ask whether they are statistically different. This is not an entirely straightforward question as the capital stock variables are in logarithms, so that the base model VAR ¡s not nested within the VAR with disaggregated public capital. However, we can construct a suitable nonnested test by taking the fitted values from the equations for k, u, n and y ¡n the VAR with disaggregated public capital together with the fitted values of a regres sion of the aggregate public capital stock, g, on the same set of regressors, and then including them in the respective equations of our base model VAR. If they are significant then it indicates that there is some explanatory power in the VAR with disaggregated public capital that is not pre sent in the base VAR model1. Somewhat surpris ingly given the similarity of the impulse responses in columns two and three of Figure 8, the model with disaggregated capital is preferred (the test statistic is χ2(5) = 21. 70 which is significant at the 99% level). However, this is entirely because it improves the predictability of aggregate public capital to model the two components separately! The improvement in the fit of the other equations of the system — which after all are the ones we are most interested in — is by contrast slight. Hence in what follows we have continued to use a single aggregate public capital variable2. This is just a multivariate generalisation of the DaviqsonMcKinnon (1981) Jtest. Again one may be tempted to compare this with some of the results that Pissarides and Wasmer obtain when they disaggregate across capital types. Two observations are worth recording. First, their dis aggregated estimates are often rather imprecise, so that while the point estimates for different categories may be different, those differ ences are often statistically not all that significant. Second, we have lumped together water and urban public capital on the one hand, and all transport capital on the other hand, a rather different disag gregation from them. Consequently one should exercise caution in comparing the results of the two studies. 168 Regional Output and Employment in the Presence of Interregional Spillovers 6. Interregional spillovers The analysis so far has treated each region as though it were a separate, isolated, economy. In practice this is not a particularly plausible as sumption as one would expect what is happening in one region to affect what is happening in anoth er. Such interregional spillovers can have the ef fect of amplifying, or attenuating, the effect of in creased public investment and are thus potentially important for evaluating the economic effects of Cohesion Fund spending. One obvious source of Interregional spillovers would be through demand ¡n the goods market. Although the suppliers of services, such as haircuts, may be affected only by changes in the demand conditions in their local market, manufacturers are obviously affected by demand developments throughout the economy, and indeed in other economies too. This is not the only possible source of interre gional spillovers, which may occur on the supply side as a result of externalities from producers' location and production decisions. These exter nalities may be either positive or negative. First, it may be sensible to locate near suppliers or cus tomers in order to reduce transport and informa tion costs. Second, the concentration of particu lar sorts of production in a given locale will en courage specialised labour to locate there too. Both of these considerations will encourage new producers' to site their activities in the same lo cale since they reap extra benefits from being close to existing producers. The result is a ten dency to the agglomeration of economic activity. The chapter by Venables in this report exploits computable general equilibrium techniques to study these forces ¡n a variety of settings. Working against these factors are so-called con gestion externalities which arise when the entry of another producer into a locale puts pressure on existing infrastructure facilities and reduces the attractiveness of the area to other firms. The obvious example is the effects on traffic, and the consequent delays, but to the extent that the general environment deteriorates such conges tion may mean that workers have to be paid more to live and work there, etc. These agglomeration and congestion effects are essentially local effects. Accordingly, in evaluating the importance of interregional spillovers, it makes sense to consider local, as well as econo my-wide, spillovers. While the latter might rea sonably be captured by the level of economic ac tivity in the economy as a whole, the choice of variable to capture local effects is less obvious. As a first stab at the question, however, ¡t seems reasonable to use the average level of economic activity in the immediately surrounding regions as suitable proxy variable. However, some of the 17 NUTS II regions we have been working with so far are rather large, and once one starts aggre gating activity in surrounding regions it begins to look quite similar to aggregate Spanish GDP. In order to examine the Influence of local spillovers it would therefore be highly desirable to work at a finer, NUTS III, level of disaggregation. Unfortu nately, while there is output and labour market data available at NUTS III level for Spain, the cap ital stock data ¡s only available at the NUTS II lev el. We therefore are forced to compromise by as suming that the private and public capital stock in each NUTS III province moves equiproportion- ally with their regional NUTS II counterparts. Interregional spillovers 169 Before presenting results including regional spillover effects we first check that the results for our base model using the provincial data, is ap proximately the same as we obtained earlier us ing the regional data. Again for brevity the full sta tistical results are not reported, but Figure 9 gives the impulse responses from the model estimated on the new data set. Comparing these with Fig ure 6 we see that they are broadly similar, partic ularly in regard to the impact of public invest ment. This suggests that any subsequent changes to the results will be the consequence of introducing spillover effects rather than the move to a finer level of disaggregation. We then add into our VAR, as exogenous influ ences1, the current and two lagged values of (the logarithm of) Spanish GDP, and the current and two lagged values of the average level of (the log arithm of) output in those other regions that di rectly border any given province2. Tables 6 and 7 give the associated statistical results for this case, while the impulse responses for provincial shocks are given in Figure 10 and for the spillover effects in Figure 11 The spillovers from both local output in the surrounding provinces and total output are statistically highly significant (the test statistics for their joint significance are χ2 (15) = 70. 45 for local output and χ2 (15) = 219. 9 for total output re spectively). This is still the case even if only lagged values of total and local output are includ ed (the test statistics in this case are χ2 (10) = 22. 54 for local output and χ2 (10) = 191. 0 for total In treating them as exogenous influences, we are neglecting the fact that activity in the surrounding provinces is potentially affected by activity in the home province which will lead the current average level of activity in the surrounding provinces to be correlated with the er ror in the equation, which may lead to some bias in the coefficient estimates. In principle one could address this difficulty by using In strumental Variables, but unfortunately It is not obvious what could be used as an Instrument in this instance. For this purpose we assume that Las Palmas adjoins Tenerife, and vice versa, and that Baleares adjoins none. output). That total output should be significant is perhaps not too surprising, but the fact that out put in adjoining provinces also seems to have an independent influence is a quite striking result. Turning now to the impulse responses, there are some notable differences between the model with interregional spillovers (Figure 10) and the model without (Figure 9). In particular, and most relevant for this exercise, the effect on private in vestment is almost eliminated, while the effect on output and employment is almost halved ¡n mag nitude. Indeed the effect of most shocks is signif icantly attenuated (note that for ease of compari son the scales in the two figures are identical). However, it should be emphasised that this does not imply that the total outputenhancing effect of public investment is any lower, because one needs to take into account the spillover onto the rest of the economy, which are not included in the experiments of Figure 10. Some clue about the effects of these spillover ef fects is given by Figure 11. Here the experiment that is conducted is a onceoff (i. e. temporary) in crease of 5% points in aggregate Spanish GDP (first column) and a onceoff increase of 5% points in average output in the adjoining provinces (second column). Notable in each case is the strong positive spillover onto within province output, as well as the strong positive spillover of total output onto private capital for mation. The fact that the spillovers onto output are so shortlived suggests the primary channel at work here is through the demand side. It is possible that some of the supplyside effects dis cussed earlier might be better captured by in cluding average capital in the surrounding provinces, rather than average output, but unfor tunately in the absence of capital stock data at a finer level of disaggregation it is difficult to investi gate this hypothesis. 170 Regional Output and Employment in the Presence of Interregional Spillovers 7. Cohesion spending simulations To conclude the analysis of the Spanish data we report the results of some simulations of the ef fects of increased public capital spending. There are at least two types of question that one might ask of the model(s). The first ¡s the positive ques tion of how large is the impact of an actual Co hesion Fund project on the Spanish regions. Here we simulate the impact of the Madrid Ring Road, the Rias-Bajas motorway, and a set of en vironmental projects covering all but two of the NUTS II regions. The second type of simulation that the Commission might be interested in in volves calculating how the impact of a given "standard size" project varies across regions ac cording to its location. Knowing the answer to this should help the Commission address the normative question as to how it should direct Its spending in the most cost-efficient fashion. Be fore presenting the results, however, we discuss a few general issues. Conceptually there are at least two ways one might wish to use the models for simulation pur poses. The first is to assume that Cohesion Fund spending represents a genuine addition to other public capital spending, and leaves the latter unaffected. In general this is the objective of the Fund, namely that funds are tied to par ticular projects and are not supposed to be an excuse for local or national governments to si multaneously reduce their spending on other projects. In order to do this we therefore need to cut off the endogenous response of public capital to the other variables of the model, i. e. treat public capital as an exogenous process, and simply input the new public capital se quence. This is effectively how the simulations in the chapter by Pissarides and Wasmer are conducted. While this is easily accomplished in a formal sense, it can be argued that ¡t is not really a valid procedure, since any change in the time series behaviour of a policy variable like public capital spending may induce a corresponding change in private sector rules of behaviour. In other words this procedure is open to the so- called "Lucas Critique" which requires that mod els for policy evaluation be specified at the "deep" level of tastes and technology, rather than being in terms of estimated parameters which are most likely complicated convolutions of these "deep" parameters and the rules gov erning the evolution of policy and exogenous variables. Whether this is a serious practical ob jection in the current context is, however, debat able, and almost certainly the procedure of treating the public sector capital stock as ex- ogenously determined represents the way mem bers of the Commission usually think about and evaluate the impact of projects. An alternative approach to simulating the effects of changes in public capital spending is to input them as innovations in spending, i. e. as an incre ment to eg. Other public capital spending is then allowed to respond. As an example, the conse quence of building the Madrid Ring Road may be to lead the Spanish government to do less (or more) spending on other roads in the Madrid re gion, or else on the local rail network, than it was originally planning to do; it might then use the funds so released to increase public consump tion or else to lower taxes. While this may not be Cohesion spending simulations 171 what the Commission intends to happen as a re sult of Cohesion Fund spending, it is clearly ap propriate at least to consider the possibility. We have therefore explored the consequences of simulating the model(s) under both assumptions, namely with exogenous public capital and en dogenous public capital. The actual outcome for any particular project might then lie somewhere between these two scenarios, depending how ef fective are the Commssion's procedure for ensur ing that Cohesion projects represent additional spending and do not end up merely substituting for other public sector projects already scheduled to be undertaken. As noted the simulations by Pissarides and Wasmer in the previous chapter of this report assume that public sector capital is exogenous and therefore do not allow for the "crowding-out", or "crowding-in", of non-Cohe sion public investment, although it turns out that it that it makes relatively little difference to the re sults if their model is simulated under the alterna tive assumption that public sector capital is en dogenous. A secondary issue relates to the treatment of de preciation. Since we have used net capital stock measures in the model, the value of the original investment will be declining as time passes un less there is adequate replacement investment to cover depreciation. The capital stocks are in fact calculated assuming 4% depreciation. Therefore in conducting the simulations with an exogenous public capital stock we have as sumed sufficient continuing replacement invest ment to maintain the public capital stock at its new higher level indefinitely. Similarly for the en dogenous public capital stock assumption we have input as the sequence for eg not merely the immediate costs of the project, but also a sub sequent replacement investment allowance of 4% per annum of the expended initial project costs. 7. 1. Simulations of selected road transport projects: Madrid Ring Road mer. The ECU value of spending for the Madrid Ring Road as supplied by the Commission is giv en in Table 8, together with the current and con stant-price peseta equivalents. Because of our difficulties in identifying the regional Impact of motorway spending, pending resolution of that puzzle1 we have presently treated both the Madrid Ring Road and the Rias-Bajas Motorway as though they were general spending on roads. We begin by reporting the results obtained from simulating the base model, under both endoge nous and exogenous public capital assumptions. The simulation output itself ¡s given in Table 9, while Figures 12 (endogenous public capital) and 13 (endogenous public capital) give the same in formation in a slightly more digestible graphical form2. The most notable feature is the contrast between the results under the two different assumptions. The impact effects on output, employment, etc. , are identical. However, in the medium term (by the end of the millenlum) the effects under the exogenous public capital assumption are about twice as large as the effects under the endoge nous public capital assumption. Thus employ ment is 56 thousand higher under the former, and 21 thousand higher under the latter. By the end of the simulation period (the year 2015) the public capital stock under the endogenous public capital assumption is just 20% of its value under the exogenous capital assumption. The effects on the other variables are similarly attenuated with output only 42 billion 1990 pesetas higher rather than 325 billion higher as under the exoge nous capital assumption. Similarly there is virtual ly no effect on employment and unemployment by the end of the period under the endogenous capital assumption, while employment is some 12-13 thousand higher and unemployment some 7 thousand lower with exogenous public capital. Overall the estimated impact on employment is much higher, even under the endogenous capital assumption, than found by Pissarides and Was mer who obtain an effect on employment of 3 to 4 thousand (there is little difference between their medium and long-run estimates). In this section we explore in depth the impact of the Madrid Ring Road, using both the base mod el which treats regions as separate entities, and the full model with interregional spillovers, as well as attempting to reconcile the results for the same project obtained by Pissarides and Was- 1 It is perhaps worth recording that the particular features of the capi tal stock data for motorways that give rise to these difficulties is not due to any statistical oddity, such as a change in definition, and evi dently does represent a genuine feature of the data. 2 The slight discrepancy between the numbers in Table 9 and those reported in preliminary drafts of the report is the result of uncovering a minor programming error in the earlier simulations. 172 Regional Output and Employment in the Presence of Interregional Spillovers We next turn to simulations with the full model in corporating interregional spillovers. As noted in our earlier discussion of this model, the introduction of the interregional spillovers has the effect of attenu ating the impulse response functions and therefore might be expected to reduce the impact on output and employment within the Madrid region, as well as leading to a leakage into the rest of the econo my. While the model is perfectly well-behaved un der the endogenous public capital assumption, we encountered problems in simulating the model with exogenous public capital. Shutting off the public capital equation has the effect of removing an important stabilising influence within the model and leads it to become (slightly) unstable, with em ployment and output continuing to grow beyond the horizon of the simulation period, despite the fact that public capital is held constant. This is less troubling than it seems, because as already noted the validity of conducting simulations in this way ¡s open to question. We therefore only report results with endogenous public capital. As the full model contains 250 separate endoge nous variables (five variables each for the 50 NUTS III regions) we do not report the full set of estimates in detail. Instead Table 10 reports nu merical results for just the Madrid region and the whole economy, as does Figure 14. However, Fig ures 15a to 15e do plot the simulated effects on each of the five variables for all of the 50 NUTS III regions and give a clear picture of the important role of the Interregional spillovers. (Note in reading these, and similar later, figures that for any vari able the scales are the same for all regions, ex cept the region(s) in which the shock occurs. ) Comparing these results with the relevant portion of Table 9, we see that the effects in Madrid is now estimated to be somewhat smaller. For in stance in 1997 regional employment ¡s 17 thou sand higher as against 25 thousand before, while the corresponding numbers for 2001 are 7 thou sand as against 21 thousand. As a counterpoint to this, however, we see considerable spillover effects into the rest of the economy, with the ag gregate effects on output and employment some three to four times greater than the effect on Madrid alone. Roughly speaking the percentage increase in each of these variables in the rest of the economy is half the percentage increase in Madrid. However, for private capital the percent age increase in Madrid and elsewhere is similar (indeed if anything it is slightly lower ¡n Madrid), reflecting the absence of any marked direct effect of local public investment spending onto local private investment, other than via its effect onto aggregate output. Are these results more consistent with Pis- sarides-Wasmer? There are two dimensions to consider: the absolute levels of investment and employment; and the shape of the responses over time. As far as the first of these goes, at first glance the results of Table 10 still seem much larger than those obtained by Pissarides and Wasmer, ¡n at least the first decade or so of the simulation period. However it needs to be re membered that they model the behaviour of pri vate investment and employment relative to the average across all regions (a sort of "representa tive" region). Thus their results pertain only to dif ferential movements between a region and the rest of the economy. A shock that raises employ ment equiproportlonally ¡n all regions would then register as a nil response in their simulations. Consequently their estimates for the number of jobs created, and for the increase ¡n private in vestment, in Madrid as a result of the Ring Road are strictly only valid under the assumption that employment and investment elsewhere in the economy does not change. But the simulations of the model with interregional spillovers shows that there is a significant element of the response which is common to all regions. In that case the Pissarides-Wasmer results should be Interpreted as telling us about the excess response of these variables ¡n the Madrid region, where by "excess response" is meant the additional employment and private investment over and above what would have happened if Madrid had behaved like the "representative" region. A graphical description of the differences in the estimates of these differential responses is given in Figure 15f. The variables in the Pissarides- Wasmer model1 are (the logarithm of) the ratio of employment in a region to total economy em ployment and (the logarithm of) the ratio of pri vate investment in a region to total economy pri vate investment. The output from the simulation is thus an estimate of the excess of the percent age change ¡n employment in Madrid over the percentage change in employment in the whole economy as a result of the Ring Road, and simi larly for private investment, mutatis mutandis. We can easily calculate the same statistics for the '' The simulations of the Pissarides-Wasmer model here treat public investment as endogenous, rather than exogenous as in their paper. However, the differences are minor. Cohesion spending simulations 173 output from the model with spillovers1. Figure 15f graphs the output from the two models, the up per panel being for the differential employment response and the lower panel for the Investment response. It is clear that the responses from the two models are somewhat different, particularly in respect of their time profile. Thus as far as employment goes, there is significant front loading of the ex cess response in the model with spillovers as compared to the Pissarides-Wasmer model where the excess response is rather flat. And for investment, the share of the Madrid region in total private investment rises initially in Pissarides- Wasmer, but falls in the model with spillovers. We need to ask, first, how the differences come about and, second, whether the differences are either statistically or economically significant. As far as the first question goes this is relatively easy to answer, at least at a technical level. The key feature that determines how big the estimated excess responses are in the spillover model is the size of the coefficients on the spillover terms in aggregate and local output in the five equations of the model. The bigger these are, then the big ger is the réponse of the regions outside Madrid to the Ring Road project, and the smaller is the estimated excess response of Madrid. In a sense, therefore, one can say that the spillover effects lead to "too little" response in employment outside Madrid, and to "too much" response in private investment outside Madrid, relative to the Pissarides-Wasmer benchmark. This suggests that further work to narrow the differences be tween the two models might in the first instance concentrate on refining the way the spillovers are modelled. As far as the second question goes, clearly it is desirable that there be no difference between the answers given by the two models. However, the differences could be either statistically (insignifi cant or economically (in)significant or both. As far as the statistical significance of the differences go, this is hard to give an answer to. In principle one can calculate confidence bands for the im pulse responses for both models by stochastic simulation, and then see whether they overlap. However, this is impractical for the model with spillovers, as it would require re-estimating the To calculate Investment we use our knowledge of the path of the capital stock together with the assumption of an annual depreciation rate of 4%. model on artificial data and simulating it at least a thousand times. The best we can do is simply note that models of this kind usually do have quite large confidence bands associated with the impulse responses, and thus it is entirely possi ble that the differences are not that statistically significant. More important for the Commission's purposes is the question of the economic importance of the differences. Figure 15f makes the differences look large. However they need to be looked at in comparison to the overall estimated impact of the project, and here they seem rather less large. The maximum excess response of the spillover model's estimate of the movements in relative employment over the Pissarides-Wasmer model's estmate of the same variable is 0. 43 percentage points (in 1997). Since a 0. 1% change in employ ment in Madrid equates to about 1. 6 thousand jobs, this translates into a maximum excess re sponse of the spillover model over Pissarides- Wasmer of just under 7 thousand jobs in 1997, while for most of the time the difference is very much less. This is in comparison to an estimated total employment response in Madrid in 1997 of nearly 18 thousand, and 60 thousand in Spain as a whole. As far as private investment is concerned, the dif ferences turn out to be somewhat more signifi cant. The maximum difference between the esti mates from the two models is 0. 68 percentage points (again in 1997). This is equivalent to a dif ference in the annual rate of private investment in Madrid of 5bn 1990 ptas, whereas the estimated change in the annual rate of investment in Madrid as a result of the project is also in the region of 5bn 1990 ptas in 1997. However the annual rate of investment in Spain as a whole is higher by 63bn 1990 ptas in 1997 as a result of the pro ject, so when compared to the estimated overall impact of the project, the difference between the two models is again relatively small. What should be of more importance to the Commssion than any differences between the estimates from the two models of the relative employment and private investment move ments, is whether the overall economy-wide re sponse is reasonable, and about which the Pis sarides-Wasmer model says nothing. In fact the multipliers seem somewhat on the large side. For instance by 1997 public capital in the whole of Spain has risen by 56bn 1990 ptas, while 174 Regional Output and Employment in the Presence of Interregional Spillovers Spanish output is estimated to be 379bn ptas higher, a ratio of 1:7 which seems intuitively somewhat high. In part this high "bang per ECU" is due to the project being in Madrid where the present ratio of public capital to out put is rather low, and the marginal effectiveness of extra public spending therefore high (see also Section 7. 4 below). For the simulations below of the Rlas-Bajas Motorway, where the marginal effectiveness of public infrastructure spending is lower, the ratio is a more reasonable 1:3. 5. Even so it is probably a bit bigger than one might have expected. Further work on this model would best be directed at exploring the robust ness of these sort of numbers. 7. 2. Simulations of selected road transport projects: Rias-Bajas Motorway Henceforth we just report simulations using the full model with interregional spillovers, as the re sults are more plausible. The ECU value of spending for the Rias-Bajas Motorway as sup plied by the Commission is also given in Table 8, together with the current and constant price peseta equivalents. In evaluating this project we have had to make assumptions both about the initial allocation of public and private capital across NUTS III regions (recall that we only have data for these variables at the NUTS II level), and as to how the spending on the Rias-Bajas Motorway is broken down across those same regions. As far as the first question goes, we have simply assumed that the public and private capital stocks are distriubuted equally across the constituent NUTS III regions within any NUTS II region. As to the breakdown of the spending on the motorway itself, we have as sumed that the spending on the Orense-Porrino leg ¡s allocated equally between Orense and Pontevedra, and that spending on the Ca- marzana-Orense leg is allocated equally be tween Orense and Zamora. Once again we do not give the full detailed simu lation results. However, Table 11 reports the re sults for the three regions in which the project takes place, i. e. Zamora, Orense and Ponteve dra, together with the impact on the whole econ omy, while Figure 16 reports the impact on these three regions as a whole, together with the whole economy impact. Figures 17a to 17e then graph the impact on each of the five variables in all the 50 NUTS III regions. Once again the important role of interregional spillovers is illustrated, with a total economy out put effect some four times greater than the local effect. The peak effect on aggregate output oc curs in 1999 and corresponds to a %% increase in Spanish GDP. Employment in the whole econ omy rises by a maximum of 60 thousand, corre sponding to 1/2% of the employed labour force. The regional employment effect represents only a quarter of this. Some features of the interre gional spillovers are worth remarking upon. Most notable is the large local spillover effect onto output in the adjoining regions of Lugo and La Coruna. These powerful local effects reflect the "peninsula" character of Galicia, so that spillover effects get "bottled up". This is in contrast to the results for the Madrid Ring Road, where the large number of adjoining regions means that the effect is diffused over a wider area. A second feature worth noting is that private investment actually falls in the three target regions (remem ber that for the Madrid Ring Road, private in vestment in Madrid rose, albeit somewhat less than in the rest of the country). Again this reflects the low direct response of local investment to lo cal public spending, coupled with idiosyncrasies in the way that the spillovers work themselves through. 7. 3 Simulation of environmental projects We have also used the full model with interre gional spillovers to simulate a large number of environmental projects (water, urban, etc. ) These cover 15 of the 17 NUTS II regions, the exceptions being Cantabria and La Rioja where no Cohesion projects were specified1. Because we were unable to find any statistically robust differences between the impact of different types of public sector projects (other than mo torways) we have not tried to draw any distinc tion between the different sorts of projects, and simply reported the estimated impact of aggre gate spending in the region. The ECU value of spending on these projects is given in Table 12. In some cases one might have knowledge The Information supplied to us also listed a number of projects that were not explicitly tied to a specific NUTS II region. These were omit ted from the simulation. Cohesion spending simulations 175 about the characteristics of a particular project, for instance from microeconomic cost-benefit analysis, that leads one to expect the effects will be larger or smaller than that for the "typical" public sector project to which the model simula tions refer. In that case it would be perfectly ap propriate to magnify or attentuate the simulated effects appropriately, using this ancillary knowl edge. However, since we do not presently have information about the precise nature of the pro jects, we did not feel it was appropriate to make such ad hoc adjustments here. As with the sim ulation of the Rias-Bajas Motorway, we have as sumed that private and public capital is distrib uted evenly throughout any given NUTS II re gion, and that the urban and environmental pro jects are likewise distributed evenly within the NUTS II region, so that the size of the public spending shock is the same ¡n all NUTS III re gions within a given NUTS II region. More detail on the individual projects would enable this as sumption to be refined. Again given the large amount of information, we do not give details for every region. However Table 13 gives the effect on the aggregate economy, while Figure 18 gives a graphical pre sentation of the same information. The detailed responses by NUTS III region, for each of the variables, is given in Figures 19a to 19e. As there is rather a lot of output, we shall not dis cuss the results for each region in detail. How ever, generically the time profile of the inputs is rather similar across regions, being concentrat ed over the three-year period 1993-5. Conse quently the general characteristics of the dy namic responses are also extremely similar across regions, and replicate the gérerai fea tures discussed above in connection with the transport projects. Where they do differ across regions is in the absolute magnitude of the re sponses. In part this is due to the different sizes of the projects in the different regions, as evi denced by Table 12. However, they also differ because of the initial positions of regions differ, with some regions having lower levels of public capital relative to output, and therefore a higher marginal product of public capital, than others. This leads us naturally to the question of where the Commission should direct its funds if it wishes to get the maximum "bang-per-buck" in terms of output per ECU expended. This is best done by looking at how the effects of a "stan dard" project of fixed size vary according to which region it is located in. 7. 4 Simulations of a "standard" project What we have therefore done is use the model to estimate the impact on the level of regional gross value added of a public capital project of specified size for each region in turn. Because of lack of time, we have not been able to do this for the full model (which requires running 50 separate simulations). However, the results on the base model on the 17 NUTS II regions are indicative of the sort of results one might expect. The project size is set at 7480 million ECU, which is roughly equivalent to one billion 1990 pesetas. The impact after 12 years, under both the endogenous and exogenous public capital assumptions is given in Table 14 (the rank order ing after other simulation periods ¡s Identical). There is a wide range of effects. For instance, under the endogenous public capital assump tion, the smallest effect on output, ¡n Ex tremadura, is only 330 million pesetas. By con trast the largest effect, in Madrid, comes in at 1. 67 billion pesetas. In case this seems like rather a wide spread, it should be pointed out that the differences in the figures are a direct consequence of the differences in output-public capital ratios across regions, since with the par ticular production technology assumed here the marginal return to public investment will be di rectly proportional to the output-public capital ratio. The return to a public project is so much higher in Madrid than in Extremadura simply be cause the output-capital ratio is five times higher in Madrid than ¡n Extremadura. Clearly it would be desirable to work with more flexible produc tion functions that did not necessarily have quite such a close link between the output-public capital ratio and the marginal return to public sector investment. However, it is unlikely that there is enough information in the available data to pin down the relationship between the out put-public capital ratio and the marginal return to public sector investment much more precisely than here, and using the existing public capital to output ratio as a guide to the location of Co hesion projects seems as good a rule of thumb to use as any. 176 Regional Output and Employment in the Presence of Interregional Spillovers 8. Concluding Remarks This chapter has extended the work of Pissarides and Wasmer in developing a more complex VAR model of the Spanish regions. The key features ar eas follows. First, regional output is incorporated into the model, as well as public and private invest ment, and employment and unemployment. Sec ond, the structure is generalised to allow feedback from output and the labour market to public and private investment decisions, with economic theory being used to identify the model structure. Third, by modelling the levels of each of the variables, rather than defining them relative to the economy- wide average or aggregate as in Pissarides-Was mer, we can say something about absolute, as well as relative movements in the variables of interest. Fourth, we extend our Base Model to allow for economy-wide and local Interregional spillovers, which are found to be both statistically and eco nomically important. These interregional spillovers are also an important feature of the general equilib rium modelling of transport networks undertaken by Venables elsewhere in this study. From an economic perspective, the main findings are as follows: (1) Allowing for the associated implications for employment and private investment, the elas ticity of regional output to net productive pub lic investment is around a third to a half, that is a 3% increase in the local public capital stock raises output by around 1-1. 5% (this is true in both the Base Model and the model with interregional spillovers, although in the latter case there are also significant positive effects on activity outside the region in which the investment takes place); (2) There is evidence of important interregional spillovers, not only from aggregate economic activity, but also from activity in geographically adjacent provinces. Simulations of specimen Cohesion Fund projects Indicate that these greatly magnify the impact of a typical project. For instance more than two-thirds of the in creased output due to the Madrid Ring Road takes place outside the Madrid region, while only a quarter of the Increase in output due to the Rias-Bajas Motorway extension occurs in Zamora-Orense- Pontevedra. The presence of these interregional spillovers is also impor tant in reconciling the results obtained here with those obtained by Pissarides and Was mer for the same projects; (3) The estimated marginal return from public in vestment varies widely across Spanish re gions. We estimate it to be five times greater in Madrid (where the return is the highest) than in Extremadura (where the return is the lowest), although at the present time this re sult is provisional being based only on simula tions with the base model, rather than with the full model with interregional spillovers. In conclusion, it should be noted that this model is still somewhat in the developmental stage, having grown out of the earlier work of Pissarides and Wasmer. We have seen that their work, while highly suggestive, misses the economy-wide conse quences of public sector investment projects by fo cussing on movements relative to the representative region; these appear to be quantitatively significant. The first objective in extending this work should be exploring the robustness of the estimated magni tude of these spillover effects onto the rest of the economy. Subsidiary issues needing further investi gation include the best way to incorporate motor ways and further exploration of the differences with Pissarides-Wasmer. Finally the model needs to be extended to the other Cohesion countries. Concluding Remarks 177 Bibliography Aschauer, David (1989), "Is public expenditure productive?", Journal of Monetary Economics, 23 (March), 177-200. Bean, Charles R. (1994), "European unemployments survey", Journal of Economic Literature, 32 (June), 573-619. Bernanke, Ben (1986) "Alternative explanations of the money-income correlation", Carnegie-Rochester Conference Series on Public Policy, 25, 49-100. Blanchard, Olivier, and Danny Tyson Quah (1989), "The dynamic effects of aggregate demand and sup ply disturbances", American Economic Review, 79 (September), 655-673. Blanchard, Olivier, and Lawrence Summers (1986), "Hysteresis and the European unemployment prob lem", NBER Macroeconomics Annual, 1, 15-77. Davidson, Russell, and James McKinnon (1981), "Several tests for model specification in the presence of alternative hypotheses", Econometrica, 49, 781-793. Holtz-Eakin, Douglas (1992), "Public sector capital and the productivity puzzle", Working paper 4144, National Bureau of Economic Research. Hulten, Charles R, and Robert M. Schwab (1993), "Endogenous growth, public capital, and the conver gence of regional manufacturing industries", Working paper 4538, National Bureau of Economic Research. Layard, P. Richard G. , and Charles R. Bean (1989), "Why does unemployment persist?", Scandinavian Journal of Economics, 91(2), 371-396. Layard, P. Richard G. ; Stephen Nickell, and Richard A. Jackman (1991), Unemployment: Macroeconom ic Performance and the Labour Market. Oxford University Press. Mas, Matilde; Joaquin Maudos; Francisco Perez and Ezequiel Uriel (1995), "Public capital and productiv ity in the Spanish regions (1964-1991)", mimeo, Instituto Valenciano de Investigaciones Económicas. Mas, Matilde; Joaquin Maudos; Francisco Perez and Ezequiel Uriel (1996), "Infrastructuresand produc tivity in the Spanish regions", Regional Studies, 30 (forthcoming). Phelps, Edmund S. (1994) Structural Slumps, Harvard University Press. Sims, Christopher, (1980), "Macroeconomics and Reality", Econometrica, 48, 1-48. Bibliography 179 TABLE 1 : Simple Production Functions (Dependent variable is f. nY) Independent Variable Equation 1 Equation 2 Trend /πΚ iriG rnN Standard error Durbin-Watson Note: t-statistics in parentheses. 0315 (7. 21). 477 (15. 25). 105 (2. 84). 598 (11. 19). 055 1. 15 TABLE 2: Unit Root Tests Variable k 9 u n y d -. 279 -. 3 77 -. 801 -. 8 74 -. 70 Region- specific. 497 (14. 55). 156 (3. 14). 405 (6. 44). 046 1. 57 t-statistic 9. 02 6. 52 11. 56 10. 76 11. 03 TABLE 3 : Estimate of Base VAR Model Dependent Variable: Private Capital (k) Public Capital (g) Unemployment (u) Employment (n) Output (y) k,-, kt_2 9t-i 9. -2 Ul-1 U,-2 n,-i Π|-2 Yt-1 y. -2 Standard Error 1. 09 (14. 85) - 0. 44 (6. 82) - 0. 04 (0. 65) 0. 06 (1. 26) - 0. 14 (0. 95) - 0. 48 (2. 89) 0. 05 (0. 70) -0. 28) (4. 15 0. 17 (4. 11) - 0. 08 (1. 73) 0. 022 - 0. 03 (0. 29) - 0. 23 (2. 34) 0. 91 (11. 17) - 0. 22 (2. 97) 0. 04 (0. 19) 0. 06 (0. 22) 0. 10 (1. 03) - 0. 05 (0. 53) 0. 18 (2. 77) - 0. 04 (0. 58) 0. 033 - 0. 01 (0. 18) 0. 16 (3. 19) - 0. 06 (1. 50) 0. 03) (0. 68 0. 79 (6. 66) - 0. 26 (1. 99) - 0. 01 (0. 14) 0. 05 (0. 87) -. 09 (2. 61) - 0. 02 (0. 44) 0. 017 - 0. 24 (1. 78) - 0. 11 (0. 91) 0. 11 (1. 12) - 0. 02 (0. 27) 0. 36 (1. 29) 0. 10 (0. 34) 0. 61 (5. 03) - 0. 23 (1. 85) 0. 11 (1. 37) 0. 10 (1. 18) 0. 040 - 0. 03 (0. 17) 0. 02 (0. 13) 0. 08 (0. 69) 0. 14 (1. 31) 0. 55 (1. 67) - 0. 31 (0. 84) 0. 38 (2. 64) - 0. 23 (1. 56) 0. 54 (5. 82) - 0. 16 (1. 67) 0. 048 Note: Region specific constants and trends omi ted: t-statistics in parentheses. 180 Regional Output and Employment in the Presence of Interregional Spillovers TABLE 4: F-Tests of Joint Significance of Regressors in Table 5 Dependent Variable: Private Capital (k) Public Capital (g) Unemployment (u) Employment (n) Output (y) k 9 u n y 138. 85 0. 87 8. 02 10. 13 8. 97 8. 42 92. 73 0. 08 0. 53 3. 85 11. 77 1. 33 23. 41 0. 44 3. 84 8. 34 0. 97 0. 89 13. 15 2. 00 0. 01 3. 88 1. 40 3. 51 17. 16 Note: All statistics are distributed as F(2,160). TABLE 5: Estimates of Β Matrix Uy = 0. 67uk + 0. 17ug + 0. 33un + ed (4. 39) (2. 76) (1. 88) St. error: 0. 042 Uu = - 0. 32uk - 0. 09ug - 0. 05ed + eu St. error: 0. 014 (6. 33) (2. 60) (1. 73) Un = - 1. 78uu + (17. 18) e, St. error: 0. 023 Test of overidentifying restrictions: χ2(3) = 5. 73 Tables 181 TABLE 6: Estimate of VAR Model with Spil overs Dependent Variable: Private Capital (k Public Capital (g) Unemployment (u) Employment (n) Output (y) k,-, ki-2 9 t -i 9t-2 U|-1 U|-2 nt-i n,_2 yt-i yt-2 yt yt-i yt-2 y*t yVi y*t-2 Standard Error 1. 05 (25. 67) -0. 47 (13. 48) 0. 03 (1. 14) 0. 01 (0. 46) -0. 11) (2. 21 -0. 14 (2. 71) -0. 01 (0. 31) -0. 07 (3. 12) 0. 00 (0. 06) -0. 05 (3. 01) 0. 37 (9. 34) -0. 10 (1. 65) 0. 22 (4. 60) -0. 04 (1. 53) 0. 03 (1. 12) 0. 08 (2. 93) 0. 016 0. 12 (1. 68) -0. 32 (5. 43) 0. 92 (22. 16) -0. 31 (7. 76) -0. 01 (0. 17) 0. 04 (0. 45) 0. 02 (0. 56) 0. 10 (2. 52) 0. 01 (0. 27) 0. 00 (0. 16) 0. 11 (1. 61) 0. 20 (1. 99) -0. 23 (2. 76) 0. 01 (0. 22) -0. 12 (2. 44) 0. 06 (1. 32) 0. 028 0. 02 (0. 48) 0. 24 (6. 18) -0. 06 (2. 21) 0. 02 (0. 95) 0. 57 (10. 37) -0. 28 (4. 79) 0. 00 (0. 16) -0. 03 (1. 20) -0. 01 (0. 40) -0. 01 (0. 41) -0. 15 (3. 48) 0. 07 (1. 05) -0. 16 (3. 05) -0. 08 (2. 49) 0. 02 (0. 68) -0. 02 (0. 64) 0. 018 -0. 01 (0. 11) 0. 32 (3. 61) 0. 13 (2. 14) -0. 12 (1. 97) -0. 12 (0. 96) 0. 34 (2. 48) 0. 56 (10. 35) 0. 04 (0. 68) -0. 08 (1. 82) 0. 12 (2. 85) 0. 43 (4. 24) -0. 33 (2. 23) 0. 12 (0. 96) 0. 21 (2. 89) -0. 08 (1. 10) 0. 05 (0. 69) 0. 042 0. 04 (0. 33) 0. 00 (0. 00) 0. 15 (2. 39) -0. 02 (0. 40) 0. 03 (0. 25) -0. 07 (0. 50) 0. 09 (1. 56) 0. 11 (1. 84) 0. 40 (9. 35) -0. 18 (4. 22) 0. 56 (5. 43) -0. 39 (2. 62) 0. 14 (1. 17) 0. 51 (6. 84) -0. 33 (4. 37) 0. 08 (1. 13) 0. 042 Note: y, Is economy-wide output; y", is average of output (y,) In adjoining provinces; region-specific constants and trends omitted for brevity; t-statistics in parentheses. TABLE 7: Estimates of Β Matrix for Model with Spillovers uy= 0. 21 uk + 0. 06Ug + 0. 29un + ed (0. 97) (2. 03) (0. 61) uu = - 0. 3 9 uk- 0. 07ug - 0. 04ed + eu (1. 55) (2. 21) (1. 46) St. error: 0. 037 St. error: 0. 016 un = - 1. 26uu + e, St. error: 0. 031 (15. 76) Test of overidentifying restrictions: χ2(3) = 5. 40 182 Regional Output and Employment in the Presence of Interregional Spillovers TABLE 8: Values of Road Transport Projects 1993 1994 1995 1996 1997 1998 168 26874 22432 72 11501 9215 Madrid Ring Road Eligible costs (M. ECU) Current prices (M. pesetas) 1990 prices (M. pesetas) Rias-Bajas (Galicia) Eligible costs (M. ECU) Current prices (M. pesetas) 1990 prices (M. pesetas) Rias-Bajas (Cas. &Leon) Eligible costs (M. ECU) Current prices (M. pesetas) 1990 prices (M. pesetas) Source: Data provided by Cohesion Fund 52 8264 6318 133 25080 19174 12 1981 1450 65 12318 9017 212 40239 29458 15 2741 1919 170 32119 22492 53 9909 6641 Tables 183 co 4^ IQ O' n tu m 3 CD "0 GO Table 9: Simulations of Madrid Ring Road with Base Model 1995 1999 1997 1993 2003 2001 2005 2011 These simulations treat the public capital stock as exogenous and assume 4% per annum replacement for depreciation. Private Capital (%) 2. 10 Public Capital (%) 5. 25 Unemployment (%) -1. 07 Employment (%) 2. 19 Output (%) 3. 79 Private Capital (B. 1990 ptas) 143. 43 Public Capital (B. 1990 ptas) 39. 44 Unemployment (Thousands) -19. 90 Employment (Thousands) 35. 89 Output (B. 1990 ptas) 319. 97 2. 69 5. 25 -1. 76 3. 20 3. 87 183. 88 39. 44 -32. 70 52. 40 326. 98 2. 18 5. 25 -1. 91 3. 47 3. 92 149. 29 39. 44 -35. 53 56. 75 330. 88 1. 36 5. 25 -1. 83 3. 33 3. 69 93. 02 39. 44 -34. 09 54. 47 311. 45 0. 52 5. 25 -1. 49 2. 67 2. 92 35. 83 39. 44 -27. 82 43. 67 246. 75 2. 82 5. 25 -1. 48 2. 75 3. 79 192. 69 39. 44 -27. 52 45. 01 319. 56 2. 38 5. 25 -1. 07 2. 16 3. 76 162. 74 39. 44 -20. 01 35. 35 317. 13 0. 07 5. 06 -0. 89 1. 55 1. 77 4. 62 38. 01 -16. 62 25. 41 149. 64 2. 67 5. 25 -1. 22 2. 35 3. 75 182. 26 39. 44 -22. 67 38. 51 316. 28 0. 00 2. 99 -0. 26 0. 46 0. 67 0. 00 22. 46 -4. 79 7. 50 56. 46 2009 2007 2013 2015 1. 91 5. 25 -1. 16 2. 37 3. 83 130. 32 39. 44 -21. 54 38. 84 322. 97 1. 83 5. 25 -1. 28 2. 59 3. 85 125. 35 39. 44 -23. 75 42. 43 325. 09 These simulations treat the public capital stock as 0. 07 Private Capital (%) Public Capital (%) 5. 20 Unemployment (%) -0. 66 Employment (%) 1. 13 1. 16 Output (%) Private Capital (B. 1990 ptas) 4. 62 Public Capital (B. 1990 ptas) 39. 05 Unemployment (Thousands) -12. 21 Employment (Thousands) 18. 51 Output (B. 1990 ptas) 97. 71 0. 00 2. 99 -0. 26 0. 46 0. 67 0. 00 22. 46 -4. 79 7. 50 56. 46 endogeno 0. 36 4. 93 -0. 86 1. 56 1. 53 24. 53 37. 00 -15. 99 25. 56 129. 11 us and assume 4% per annum replacement for depreciation. 0. 87 4. 03 -0. 83 1. 60 1. 91 59. 36 30. 29 -15. 45 26. 18 160. 96 1. 32 3. 21 -0. 68 1. 32 1. 92 90. 46 24. 13 -12. 67 21. 61 162. 25 1. 49 2. 49 -0. 47 0. 89 1. 63 101. 62 18. 70 -8. 66 14. 62 137. 82 1. 35 1. 83 -0. 22 0. 46 1. 28 92. 03 13. 76 -4. 12 7. 47 108. 02 1. 02 1. 30 -0. 01 0. 12 0. 99 69. 97 9. 78 -0. 26 1. 94 83. 55 0. 65 0. 98 0. 10 -0. 05 0. 77 44. 38 7. 33 1. 80 -0. 83 65. 34 0. 32 0. 87 0. 10 -0. 04 0. 62 21. 79 6. 51 1. 82 -0. 71 52. 39 0. 09 0. 93 0. 02 0. 09 0. 53 6. 21 6. 99 0. 37 1. 48 44. 75 -0. 01 1. 10 -0. 09 0. 27 0. 50 -0. 74 8. 24 -1. 69 4. 50 42. 48 ÇJ CD CO en Table 10: Simulations of Madrid Ring Road with Full Model with Interegional Spillovers 2009 2011 2013 2015 1999 1993 2003 1995 2005 2007 2001 0. 07 3. 02 -0. 16 0. 28 0. 41 4. 81 21. 15 -3. 04 4. 58 34. 19 0. 32 4. 57 -0. 62 1. 07 1. 63 21. 83 32. 05 -11. 56 17. 52 137. 36 0. 19 5. 18 -0. 48 0. 96 1. 25 13. 25 36. 37 -8. 90 15. 63 105. 58 1997 These simulations treat the public capital stock as endogenous and assume 4% per annum replacement for depreciation. Madrid Private Capital (%) Public Capital (%) Unemployment (%) Employment (%) Output (%) Private Capital (B. 1990 ptas) Public Capital (B. 1990 ptas) Unemployment (Thousands) Employment (Thousands) Output (B. 1990 ptas) Whole Economy Private Capital (%) Public Capital (%) Unemployment (%) Employment (%) Output (%) Private Capital (B. 1990 ptas) Public Capital (B. 1990 ptas) Unemployment (Thousands) Employment (Thousands) Output (B. 1990 ptas) 0. 56 1. 98 -0. 42 0. 42 1. 28 38. 56 13. 90 -7. 82 6. 95 107. 98 0. 46 3. 15 -0. 58 0. 78 1. 60 31. 43 22. 07 -10. 75 12. 76 135. 07 0. 26 0. 47 -0. 22 0. 43 0. 66 147. 28 48. 60 -27. 47 53. 63 336. 46 0. 46 0. 54 -0. 30 0. 48 0. 74 260. 74 55. 82 -37. 52 59. 91 378. 78 0. 15 -0. 41 0. 26 -0. 48 -0. 25 83. 60 -42. 39 32. 41 -60. 76 -124. 97 0. 71 0. 27 -0. 25 0. 30 0. 70 402. 25 27. 87 -31. 33 37. 37 357. 52 0. 65 0. 05 -0. 09 0. 04 0. 45 370. 96 4. 94 -11. 32 4. 71 231. 30 0. 45 -0. 20 0. 09 -0. 24 0. 11 256. 46 -20. 20 11. 87 -30. 75 55. 65 0. 63 0. 45 -0. 32 0. 45 0. 77 354. 93 46. 33 -40. 73 56. 13 393. 05 0. 07 0. 23 -0. 07 0. 15 0. 24 40. 43 23. 56 -9. 21 18. 89 123. 21 0. 56 1. 25 -0. 22 0. 09 0. 79 38. 21 8. 75 -4. 15 1. 53 66. 82 0. 41 0. 81 -0. 04 -0. 19 0. 32 28. 13 5. 71 -0. 67 -3. 06 26. 86 0. 15 0. 57 0. 12 -0. 41 -0. 07 9. 95 3. 97 2. 19 -6. 76 -5. 81 -0. 16 0. 46 0. 21 -0. 54 -0. 33 -11. 26 3. 20 3. 93 -8. 76 -27. 64 -0. 18 -0. 54 0. 35 -0. 61 -0. 51 -100. 50 -55. 22 44. 46 -76. 36 -261. 67 -0. 43 0. 48 0. 22 -0. 52 -0. 44 -29. 22 3. 37 4. 14 -8. 45 -37. 08 -0. 43 -0. 53 0. 35 -0. 58 -0. 64 -245. 53 -54. 13 44. 58 -73. 09 -325. 35 -0. 57 0. 63 0. 15 -0. 35 -0. 39 -38. 71 4. 44 2. 77 -5. 73 -33. 02 -0. 55 -0. 38 0. 26 -0. 41 -0. 59 -314. 11 -38. 78 32. 79 -51. 63 -302. 84 -0. 54 0. 88 0. 01 -0. 08 -0. 19 -37. 15 6. 20 0. 24 -1. 31 -16. 16 -0. 51 -0. 13 0. 10 -0. 14 -0. 39 -291. 20 -13. 09 12. 67 -17. 91 -199. 88 co CD u CD CD O' "O O 3 CD "Ό 3 cg o' CO "D -0. 64 23. 16 -2. 76 4. 62 7. 40 -5. 85 34. 92 -4. 96 7. 59 26. 11 -0. 28 28. 78 -2. 24 4. 25 5. 41 -2. 54 43. 38 -4. 01 6. 98 19. 10 -0. 77 15. 02 -2. 30 2. 83 6. 49 -7. 09 22. 64 -4. 13 4. 65 22. 92 0. 04 18. 21 -0. 79 1. 13 1. 61 0. 39 27. 46 -1. 41 1. 85 5. 68 2005 -0. 31 6. 40 -0. 97 0. 59 2. 46 -1. 39 7. 02 -0. 74 0. 37 5. 00 2003 -0. 49 9. 33 -1. 48 1. 23 4. 19 -2. 21 10. 25 -1. 12 0. 77 8. 51 2001 -0. 56 14. 98 -2. 12 2. 64 5. 94 -2. 52 16. 45 -1. 61 1. 65 12. 08 1995 0. 05 12. 94 -0. 56 0. 82 1. 16 0. 24 14. 21 -0. 43 0. 51 2. 35 1999 -0. 45 22. 63 -2. 49 4. 30 6. 36 -2. 03 24. 85 -1. 88 2. 69 12. 93 Table 11: Simulations of Rias-Bajas Motorway with Full Model with Interegional Spillovers Zamora 1997 Private Capital (%) -0. 11 Public Capital (%) 26. 58 Unemployment (%) -1. 84 Employment (%) 3. 35 Output (%) 4. 23 Private Capital (B. 1990 ptas) -0. 50 Public Capital (B. 1990 ptas) 29. 18 Unemployment (Thousands) -1. 39 Employment (Thousands) 2. 09 Output (B. 1990 ptas) 8. 60 Oense Private Capital (%) Public Capital (%) Unemployment (%) Employment (%) Output (%) Private Capital (B. 1990 ptas) Public Capital (B. 1990 ptas) Unemployment (Thousands) Employment (Thousands) Output (B. 1990 ptas) Pontevedra Private Capital (%) Public Capital (%) Unemployment (%) Employment (%) Output (%) Private Capital (B. 1990 ptas) Public Capital (B. 1990 ptas) Unemployment (Thousands) Employment (Thousands) Output (B. 1990 ptas) Whole Economy Private Capital (%) Public Capital (%) Unemployment (%) Employment (%) Output (%) Private Capital (B. 1990 ptas) Public Capital (B. 1990 ptas) Unemployment (Thousands) Employment (Thousands) Output (B. 1990 ptas) 0. 07 0. 56 -0. 08 0. 16 0. 24 37. 33 57. 32 -10. 12 20. 14 121. 18 0. 23 0. 96 -0. 23 0. 46 0. 64 130. 74 98. 55 -29. 45 57. 53 323. 98 0. 39 0. 89 -0. 30 0. 48 0. 66 220. 05 91. 40 -38. 16 60. 48 333. 90 0. 49 0. 63 -0. 30 0. 40 0. 58 275. 79 64. 35 -37. 62 50. 45 296. 68 0. 50 0. 34 -0. 20 0. 24 0. 48 285. 36 35. 06 -25. 74 30. 62 246. 77 0. 43 0. 11 -0. 06 0. 04 0. 32 243. 74 11. 14 -7. 79 4. 95 161. 87 -0. 14 4. 12 -1. 04 1. 50 2. 86 -1. 31 6. 21 -3. 85 4. 74 28. 59 -0. 06 9. 49 -1. 09 2. 27 3. 19 -0. 56 14. 30 -4. 01 7. 20 31. 84 0. 04 8. 81 -0. 43 0. 69 1. 07 0. 35 13. 28 -1. 60 2. 18 10. 70 -0. 09 6. 75 -1. 22 2. 06 3. 68 -0. 84 10. 17 -4. 51 6. 53 36. 70 -0. 20 2. 58 -0. 76 1. 07 1. 81 -1. 82 3. 89 -2. 83 3. 40 18. 05 -0. 52 6. 69 -1. 11 0. 90 2. 56 -4. 77 10. 08 -2. 00 1. 47 9. 04 -0. 72 9. 41 -1. 62 1. 48 4. 37 -6. 61 14. 19 -2. 90 2. 42 15. 43 -0. 21 1. 98 -0. 54 0. 81 1. 11 -1. 92 2. 98 -2. 00 2. 57 11. 09 2007 -0. 13 5. 30 -0. 69 0. 41 1. 44 -0. 57 5. 82 -0. 52 0. 26 2. 93 -0. 29 5. 79 -0. 84 0. 75 1. 63 -2. 64 8. 73 -1. 51 1. 24 5. 77 -0. 19 1. 87 -0. 42 0. 66 0. 81 -1. 72 2. 82 -1. 54 2. 09 8. 04 2009 -0. 04 5. 08 -0. 53 0. 37 1. 07 -0. 18 5. 58 -0. 40 0. 23 2. 18 -0. 14 5. 70 -0. 70 0. 72 1. 37 -1. 32 8. 59 -1. 26 1. 18 4. 83 -0. 18 1. 92 -0. 35 0. 57 0. 70 -1. 69 2. 89 -1. 31 1. 79 7. 02 2011 -0. 08 5. 16 -0. 44 0. 34 1. 04 -0. 35 5. 66 -0. 33 0. 22 2. 12 -0. 15 5. 81 -0. 60 0. 67 1. 39 -1. 34 8. 75 -1. 08 1. 10 4. 90 -0. 23 1. 96 -0. 31 0. 50 0. 68 -2. 12 2. 96 -1. 13 1. 59 6. 77 2013 -0. 20 5. 27 -0. 39 0. 34 1. 11 -0. 90 5. 79 -0. 29 0. 21 2. 26 -0. 26 5. 90 -0. 53 0. 63 1. 46 -2. 41 8. 90 -0. 96 1. 04 5. 15 -0. 31 1. 98 -0. 27 0. 47 0. 63 -2. 86 2. 99 -1. 00 1. 50 6. 80 2015 -0. 33 5. 38 -0. 40 0. 40 1. 19 -1. 47 5. 90 -0. 30 0. 25 2. 42 -0. 40 5. 96 -0. 52 0. 65 1. 51 -3. 71 8. 98 -0. 93 1. 07 5. 33 -0. 38 2. 01 -0. 27 0. 50 0. 70 -3. 49 3. 03 -0. 99 1. 57 7. 02 2017 -0. 39 5. 48 -0. 47 0. 52 1. 27 -1. 75 6. 02 -0. 35 0. 33 2. 58 -0. 49 6. 03 -0. 58 0. 75 1. 56 -4. 46 9. 09 -1. 03 1. 24 5. 50 -0. 39 2. 07 -0. 31 0. 58 0. 75 -3. 60 3. 13 -1. 14 1. 82 7. 50 0. 28 -0. 07 0. 07 -0. 14 0. 10 158. 19 -6. 68 8. 38 -18. 21 50. 40 0. 09 -0. 18 0. 15 -0. 26 -0. 11 50. 09 -18. 41 18. 68 -33. 14 -56. 16 -0. 09 -0. 22 0. 17 -0. 29 -0. 24 -49. 83 -22. 59 21. 83 -36. 71 -122. 93 -0. 20 -0. 18 0. 14 -0. 23 -0. 27 -114. 50 -18. 01 18. 13 -29. 31 -137. 58 -0. 23 -0. 06 0. 07 -0. 11 -0. 21 -130. 29 -5. 86 9. 10 -13. 91 -105. 75 -0. 18 0. 10 -0. 02 0. 04 -0. 08 -99. 45 10. 18 -2. 37 4. 63 -42. 13 TABLE 12: Values of the Environmental Projects (M. ECU) Andalucía Aragon Asturias Baleares Canarias Cantabria Castilla y Leon Castilla-La Mancha Cataluna Com. Valenciana Extremadura Galicia Madrid Murcia Navarra Pais Vasco La Rioja 1993 18. 16 0 7. 89 0 0 0 0 0 0 0 0 0 61. 32 0 0 2. 46 0 1994 41. 00 0 15. 32 0 16. 21 0 0 0 24. 23 7. 12 0 0 22. 52 21. 42 0 0 0 1995 86. 82 19. 14 41. 60 20. 55 0 0 1. 78 18. 51 140. 15 38. 52 9. 61 85. 46 22. 44 33. 60 23. 64 6. 14 0 Tables 187 CO co CQ O' Table 13: Simulation of Environmental Projects with Full Model with Interregional Spillovers 1993 1995 1997 1999 2001 2003 2005 2007 2009 2011 2013 2015 Whole Economy Private Capital (%) 0. 05 0. 27 0. 58 0. 81 0. 92 0. 86 0. 64 0. 31 -0 03 -0 29 -0 40 -0 35 Public Capital (%) 0. 36 1. 07 1. 15 0. 95 0. 59 0. 21 -0. 13 -0. 38 -0. 51 -0. 49 -0 33 -006 Unemployment (%) -0. 06 -0. 27 -0. 46 -0. 49 -0. 40 -0. 18 0. 07 0. 26 0 34 0 32 0 21 0 05 Employment (%) 0. 12 0. 52 0. 81 0. 67 0. 48 0. 13 -0. 21 -0. 47 -0. 58 -0 53 -0 34 -0 08 Output (%) 0. 19 0. 80 1. 22 1. 09 0. 94 0. 67 0. 32 -0. 07 -0 36 -0 49 -0 44 -0 25 Private Capital (B. 1990 ptas) 29. 81 150. 97 327. 58 462. 20 523. 99 488. 84 362. 21 175. 61 -17 97 -163 59 -226 18 -200 15 Public Capital (B. 1990 ptas) 37. 38 109. 61 117. 91 97. 46 60. 28 21. 06 -13. 36 -39. 15 -52. 68 -50. 75 -33 66 -6 61 P. Unemployment (Thousands) -7. 78 -33. 87 -58. 03 -61. 45 -50. 65 -22. 45 8. 53 32. 11 42. 81 40 01 26 20 6 28 Employment (Thousands) 14. 94 65. 70 101. 45 84. 40 59. 78 16. 79 -26. 41 -59. 46 -73. 53 -66 67 -42 91 -10 39 Otìtput (B. 1990 ptas) 95. 39 406. 17 622. 96 553. 09 481. 06 341. 96 161. 62 -33. 83 -183. 94 -248. 25 -222. 66 -128^32 CD T) CD CO CD 13 O Φ O CO η Table 14: Effect on Regional Output (Millions of 1990 Pesetas) of a One Billion Peseta Project (1990 Prices) after 12 Years Endogenous Public Capital Exogenous Public Capital Andalucía Aragon Asturias Baleares Canarias Cantabria Castilla y Leon Castilla-La Mancha Cataluna Com. Valenciana Extremadura Galicia Madrid Murcia Navarra Pais Vasco La Rioja 465 456 512 989 546 484 438 356 1148 796 329 650 1669 530 599 610 633 696 683 767 1482 818 725 656 533 1720 1192 493 974 2500 794 897 914 949 Tables 189 Fig. 1 : Private Capital Stock Andalucía Castilla y Leon Madrid Aragon Castilla-La Mancha Murcia Asturias Cataluna Navarra Baleares Com. Valenciana Pais Vasco Canarias Extremadura La Rioja Cantabria Galicia 190 Regional Output and Employment in the Presence of Interregional Spillovers Fig. 2: Public Capital Stock (dashed is with motorways) Andalucía Castilla ν Leon Madrid Aragon Castilla-La Mancha Murcia Asturias Cataluna Navarra *>* // -. /, i/ Baleares Com. Valenciana Pais Vasco / , - / Λ/ / Canarias Extremadura La Rioja Cantabria Galicia / κ y •- / / ~ i,n , HS <M Figures 191 Fig. 3: Unemployment Rate Andalucía Castilla y Leon Madrid Aragon Castilla-La Mancha Murcia Asturias Cataluna Navarra Baleares Com. Valenciana Pais Vasco Canarias Extremadura La Rioja Cantabria Galicia 192 Regional Output and Employment in the Presence of Interregional Spillovers Fig. 4: Labour Force (Dashed) and Employment (Solid) Andalucía Castilla y Leon Madrid s k / " χ^^-/ V A r a g on Castilla-La Mancha Murcia y / / / y ^ -" Γ~ Ij \ / yl \ / Asturias Cataluna Nas-arra Baleares Com. Valenciana Pais Vasco Canarias Extremadura La Rjoja Cantabria Galicia Λ '/ — *ly \ / Figures 193 Fig. 5: Output Andalucía Castilla y Leon Madrid Arapon Castilla-La Mancha Murcia Asturias Cataluna Navarra Baleares Com. Valenciana Pais Vasco Canarias Extremadura La Rioja Cantabria Galicia 194 Regional Output and Employment in the Presence of Interregional Spillovers Fig 6: impulse Responses for Base Model Shock to: Private Investment Public Investment Natural Rate Labour Supply Demand Private Capital Public Capital Unemployment Employment " ' ' ' ' ' ' ' χ ' ' ' ' ' ' ' ' * " " " ' ' ' ' * ' ' ' " 1 I 1 l 1 1 t 1 I 11. J Output — _ _^ CO cn CD CD o' O "O o Τ) CD D Fig 7: Impulse Responses for Model including Motorways Shock to: Private Investment Pu )lic Investment Motorway Inv. Natural Rate Labour Supply Demand Private Capital A. t. , Public Capital \^yZ Motorway Capital ^ ~~. - I ^ " " "* \ ^ ' ' ' ■ · · · ' ■ · * "" ' ' ' * ' ' ' * ■" " I Jncmploymcnt ^ "\ y "" ^ ' ' ' ' ■ " " ' ' >. , I employment x _ ^-^Z * ■■ " W-r- ; : ■ ; -— ( I m p i li \ CD CD Fig 8: Impulse Responses for Model with Disaggregated Public Capital Shock to: Private Investment Water&Urban Investment Transport Investment Natural Rate Labour Supply Demand Private Capital Water&Urban Capital Transport Capital Unemployment Employment Output \ , CD CD co co o' ΓΠ "O o ■< 3 CD CD CD O' Fig 9: Impulse Responses for Base Model (NUTS III) Shock to: Private Investment Public Investment Natural Rate Labour Supply Demand on · 0 01 Private Capital ββ0 4 01 ^\ v_^ 1 Î 1 * i 4 7 1 I |0 11 13 1 J * ) i τ I 1 1 ι τ I » Public Capital ββ0 Unemployment Employment Output 11 1 « > * 7 t t IB tl IS : ι * i > ( 7 I t ID II 11 I I 10 11 II ] I « 1 < 1 7 ) i 7 I » |0 n υ Fig 10: Impulse Responses for Model with Spillovers (NUTS III) Shock to: Privale Investment Public Investment Natural Rate Labour Supply Demand 1 » t I I I» II I] ι ι ι ' ι ι J ι ι ie ■1 11 1 1. 1. 71 13 1. 1* » I« 11 II O il ODI 3 0! ■ ao? o oi ■ 301 001 ■ s o i: 30«) O t ti 4 MB 0 C I5 J O ÏÎ ; 0 01 ' va ■ « 01  OBI 0 01 ■ 301. I 1 1 4 1 i 1 I I 10 II 11 < 1 1 « 1 1 11 io n il ^r- ———. _ 1 1 1 * 1 ( 3 11 10 II 11 L i 1 1 1 t 7 1 t 10 11 |J 1 1 1 " S ( 1 1 t ie n υ I 1 « J t 1 I io a n 1 1 1 4 1 1 7 t! IO M 11 ι : ι « J ι ι ι t io u fi ^~^_ \ \ ^ w* Private Capital Public Capital Unemployment Employment Output co CD 1 1 1 ( 1. 71 1 1. 1. 71 ι « i i ι ι k io n ¡t 1 ì I * 1 t 1 t ι IO 11 11 1 I. 1 t I I I Fig 11: Impulse Responses to Output Spillovers Shock to: Total Output Local Output Private Capital Public Capital Unemployment Employment \ 0 01 - 0. 01 0. 00 ■ -0 01 - o o: 0 01 - -0 Ol W -002 " i 4 ¿ á IO n -oca 2 i i t 10 ή Output \ \ 0. 01 - C. Ol -0 01 i ' i -0 07 : ■. i 1 10 ii 200 Regional Output and Employment ¡η the Presence of Interregional Spillovers Fig 12: Madrid Ring Road,Base Model,Endogenous Public Capital Public Capital (Billions of 1990 Ptas) Private Capital (Billions of 1990 Ptas) 1992 2000 2008 2016 1992 2000 2008 2016 Employment (Thousands) Unemployment (Thousands) 28. 0 1992 2000 2008 2016 1992 2000 2008 2016 Output (Billions of 1990 Ptas) 1992 2000 2008 2016 Figures 201 Fig 13: Madrid Ring Road,Base Model,Exogenous Public Capital Public Capital (Billions of 1990 Ptas) Private Capital (Billions of 1990 Ptas) 1992 2000 2008 2016 1992 2000 2008 2016 Employment (Thousands) Unemployment (Thousands) 1992 2000 2008 2016 1992 2000 2008 2016 Output (Billions of 1990 Ptas) 350- 300 - 250 - 150- / 100 - sn - 1992 f 1 2000 2008 2016 202 Regional Output and Employment in the Presence of Interregional Spillovers Fig 14: Madrid Ring Road,Full Model,Endogenous Public Capital Public Capital (Billions of 1990 Ptas) Private Capital (Billions of 1990 Ptas) 480 1990 2000 2010 1990 2000 2010 -320 Employment (Thousands) Unemployment (Thousands) -80 1990 2000 2010 1990 2000 2010 Output (Billions of 1990 Ptas) 400 -400 1990 2000 2010 Figures 203 Fig. 15a: Madrid Ring Road,Full Model. Private Capital (%) Zirigia β. 4 / \ 0. 4 ■0 991 20CI A5U1TÍ13 / ~\ □ I 0. 4 0. 0 0. 4 973 2001 ' B i i i r as 04 /\ 0 4 m looi 1 V V /Λ, 0 4 0. 4 Lis Piimii / "\ 0. 4 0. 0 «4 V V w: 200* 1 19 92 200* M u c hi Tenerife / ~\ D 4 00 «4 / ~\ 04 0. 0 0. 4 V V 1 »92 2001 1992 2001 J i ai C i n t i b r ii ru / "\ 4 / ~\ V V 0. 4 19 91 2001 S t i l i ci / ^\ 4 0 4 19 » 200« ■' Sevill» X~\ V. 4 ■ 1991 IODI ,. Α ν θι. 4 / ~\ B u r p» 4 ■ 1992 JOO* A /\ 4 V V V ι »! mot 1992 200* C i t t h na Leoa / "\ 4 4 A ' V 1991 lOOt 1992 lOOt. H u e sa Pal e n dt 4 /\ ' ' ν 204 Regional Output and Employment in the Presence of Interregional Spillovers Fig. 15b: Madrid Ring Road,Full Model Public Capital (%) rh Zaragoza ■013 1992 OJO OD 0. 00  0 13 /\ O JO 0 13 0 00  0 13 Bal arcas \ / J J Barcelona 1"«° YJ 2001 Gorma 0 13 0. 00 ■0. 23 1992 2 0 0* Segovñ 1 OU y OJO 0 13  0Ü 1992 OJO 0 13 ooo  ou V/ 2 0 01 1 »2 2 0 01 y Sora Lenaa 1 OJO 0 13  0 13 1992 y \y 2 0 0* 1972 2 0 01 OJO 0 13 0. 00  0 13 19 92 2 0 01 OJO OU SOO  OU 1992 y \j am Z a m o ra A l i a r ti Grana di V d l a d o l id Tampona ± JO 0 13 0 00  0 13 " OJO 0 13 °  0 13 \ y 2 0 01 Albacete y 1? 2 0 0* OJO ou 0 00  OU 19 91 2 0 01 y OJO ou ■ou IV n Castellan \ J 2 0 01 M a j a» C k i d a d R e i] V il m ai / 0 13  0 13 19 η ^Λ \ \ y 2 0 0* OJO 0 13 0~00  O lí 195 η 2004 y 1 OJO ou ou 195 2 2 0 01 y Sevilla Biagoi Cuenca Badajoz Guipúzcoa \ y aoot CaiaJum \ y 2 0 0* O lí  O lí UK ΓΪ tu ■ t lí OU /Λ  ou ■ 1 9! 1 \ y 2 0« \y 2 0 0* Guadal afara C a c e ra Vizcaya OJO •u ■ ΟΛΟ  OU / 199 2 2 0 0* 199 1 2 0 0* y Huesca Pal encía Toledo La C o r u na La Rioja 0 13 -Olí \ y O lí ΧΛ ■025 OU ou \ y \ / \ J \ J 2 0 01 Cadiz \ y 2 0 0* Cordoba \ J ZOO* \ J 2 0 01 H u e l va | \ y 2 0 0* J i cn \ y 2 0 0* OD 0. 00  OD 1992 OJO OU 0 00  OD 1992 OJO OU om  OD 1992 OJO OD 0 00 02J 1992 OJO QU o« ■ 0 1) 1992 OJO 0 1} ' ■0. 23 1992 0 13 '  0 13 1992 OJO 0 1 3 ΟΛΟ  0 13 1992 O JO 0 13 0 , 00  O lí Figures 205 Fig. 15c: Madrid Ring Road,Full Model,Unemployment (%) 206 Regional Output and Employment in the Presence of Interregional Spillovers Fig. 15d: Madrid Ring Road,Full Model,Employment (%) Figures 207 Fig. 15e: Madrid Ring Road,Full Model,Output (%) Gerona Γ\ O. TO OD -OU \y ατο ou Alicante ^^—- / \ ■013 \ v^ Badajoz Guipúzcoa Vizcaya La Rioja 208 Regional Output and Employment in the Presence of Interregional Spillovers Fig 15f: Comparison of Models for Madrid Ring Road Relative Employment (Per Cent) 1990 1995 2000 2005 2010 2015 Relative Private Investment (Per Cent) r. - il » » J 1 Piss. -Wasmer y \ ' \ ' \ 1 \ > \ / \ / N ' N / ^\ \ / \ / ^v Λ ^\ / ^ -v / ^ ~~ Χ 1. 00 - 0. 75 - 0. 50 - 0. 25 -ï -015 - -0. 50 - -0. 75 - 1990 1 1995 1 2000 )1 2005 2010 2015 Figures 209 ' Fig 16: Rias-Bajas Motorway, Full Model Public Capital (Billions of 1990 Ptas) Private Capital (Billions of 1990 Ptas) Whole Economy Z-O-P 300 100 75 - 50 25 - 1990 2000 2010 -150 4 1990 2000 2010 Employment (Thousands) Unemployment (Thousands) 1990 2000 2010 1990 2000 2010 Output (Billions of 1990 Ptas) r\ \ ƒ / \ V/ 350- 280 - 210- 140 - 7 0- -70 - 140 - 1990 2000 2010 210 Regional Output and Employment in the Presence of Interregional Spillovers Fig. 17a: Rias-Bajas Motorway, Private Capital (%) I77Î ÌOOC TOM MU 17*1 TOCO TO« MU 1777 TOC« ï f t ft TOH 1777 70CC » et 7711 1777 TOOO ìrat 7011 Figures 211 Fig. 17b: Rias-Bajas Motorway, Public Capital (%) ι»« »oo ro« ï'il xa* ici« 212 Regional Output and Employment in the Presence of Interregional Spillovers Fig. 17c: Rias-Bajas Motorway, Unemployment (%) Figures 213 Fig. 17d: Rias-Bajas Motorway, Employment (%) •*12 iOOO » ei Mit im »oo » ci »Ol X01' C»oV I »H ÎOQO ÎOOt xv m »oc »ot » i« tn »00 »Ol Ml« Cordoba 1M2 »00 »01 » li m »oc m » ce not »κ m »oo Le Ρ* INI 2000 2001 »κ Huchn »οι »κ :*n »oo » ot » ii im ΐοοα »OO »Ot MU » ot au tn »oo » oi ¡m » oc » et »u JMS CM «ka im 2000 »Ol 201« im »oo » οι » it im »oo loot nu Avil CMadIUd im »oo. m »oo : im » oe » οι »u tn »oo »o< »u »oo se« Burgo« MJ 3000 »01 MU im »oo 3 Vtatyi U R np im »oo » οι x i« »oo » oi »u im » or »α »u im » oc ΜΗ WH im »oo roca » ι« I M; »OO ;ca 214 Regional Output and Employment in the Presence of Interregional Spillovers Fig. 17e: Rias-Bajas Motorway, Output (% i»i »MO »et »ot »u Figures 215 Fig 18: Environmental Projects Full Model Public Capital (Billions of 1990 Ptas) Private Capital (Billions of 1990 Ptas) Whole Economy ¡Λ \ / / \ / \ \ \ \ / 100 - 8 0- 60 - 40 - 20 - -20 -40 - -60 - /\ f \ / \ / \ 4 8 0- 3 6 0- 240 - 120 -120- -240- 1990 1995 2000 2005 2010 2015 1990 1995 2000 2005 2010 2015 \ \ ν Employment (Thousands) Unemployment (Thousands) 1990 1995 2000 2005 2010 2015 1990 1995 2000 2005 2010 2015 Output (Billions of 1990 Ptas) 750 500 250 -250 1990 1995 2000 2005 2010 2015 216 Regional Output and Employment in the Presence of Interregional Spillovers Fig. 19a: Environmental Projects, Private Capital (%) »ot »u i»n »αϊ »κ m? »oo »οι »κ im »oo im ¡αχ »οι »;* Figures 217 Fig. 19b: Environmental Projects, Public Capital (%) 218 Regional Output and Employment in the Presence of Interregional Spillovers Fig. 19c: Environmental Projects, Unemployment (%) Π 'Τ /^ 1 °™r ' "Ν \ \ Figures 219 Fig. 19d: Environmental Projects, Employment (%) 220 Regional Output and Employment in the Presence of Interregional Spillovers ÎCOC > Mt »u Fig. 19e: Environmental Project, Output (%) »Ol » II »ο« » ι* Figures 221 »oo xx» » II European Commission The socio-economic impact of projects financed by the Cohesion Fund — A modelling approach Volume 1: Introduction and summary — Literature review —Vector auto-regression models Luxembourg: Office for Official Publications of the European Communities 1999 — 221 pp. — 21 χ 29. 7 cm ISBN 92-828-3064-0 (Volume 1) ISBN 92-828-3065-9 (Volume 2) ISBN 92-828-3066-7 (Volume 3) ISBN 92-828-7238-6 (Volumes 1, 2 and 3) Price (excluding VAT) in Luxembourg: EUR 86 (Volumes 1, 2 and 3) Venta · Salg · Verkauf · Πωλήσεις · Sales · Vente · Vendita · Verkoop · Venda · Myynti · Försäljning BELGIQUE/BELGIE ÖSTERREICH Jean De Lannoy Avenue du Roi 202/Koningslaan 202 B1190 Bnjxelles/Brussel Tél. 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