While the crisis was not a result of a lack of proper data and the quality of statistics in the pre-crisis period was deemed satisfactory, still the crisis brought to the surface some areas that were rather poorly covered by the statistics. Highly globalized world marked by high and rising trade and especially financial interconnectedness among economies, as well as strong interlinkages between different sectors within the economies, in particular financial sector (including non-regulated segment) and non-financial corporations due to strong macrofinancial linkages ask for enhanced data sets. Broader data sets will help in better understanding the developments in the economic and financial sector, cross-sectoral and cross-border linkages and subsequently, in adopting timely measures to prevent build-up of imbalances, as well as in dealing more effectively with the unfolding effects of the crisis. This time, international efforts in reexamining the statistical frameworks and data gaps are reflected in G20 Data Gaps initiative. It contains 20 recommendations in the four key segments: build-up of risk in the financial sector, cross-border financial linkages, vulnerability of domestic economies to shocks, and improving communication of official statistics. The initiative has been serving as a guideline for most of the progress in the area of statistics, ever since. Filling in the statistical gaps that has been set as a medium to long-term objective is concentrated along a couple of dimensions: availability of data – especially availability of granular data and data related to unconventional instruments, international comparability, further quality improvements and timeliness of the data.
Credit registry established in 1998 that provides a loan–by-loan data appears to be very useful tool for financial stability and monetary policy purposes. The recent crisis has emphasized that despite the fact that there is a vast data on credit available, still more granular, frequent and flexible credit data is needed. Though this is mainly for the purpose of credit risk assessment and financial stability issues, to which I will refer later, the availability of loan-by-loan data and their characteristics allows for more profound monetary policy research. In particular, the individual loan-by-loan data can be utilized for instance, for exploring the risk-taking monetary policy channel, which has been on the margin of the research. Further, data from the credit registry allowed us to investigate where are the credit flows channeled – to what extent it supports the SME segment, which is a key segment in reviving the growth and boosting employment, to what extent it is channeled to non-tradable sector creating possible bubbles down the road. Of course, the banking system is only one sequence of the monetary policy transmission mechanism. The corporates and households are the other very important part, whose behavior affects the outcome of the undertaken monetary policy measures. As a first line of policymaking, we surely still rely on stylized macro models, where we strive to estimate or calibrate the average behavior of the economy, or the behavior of representative agents. Of course, the reality can be much more different and heterogeneity much more pronounced than envisaged.