Hi everyone, we're running a little bit over so in case everyone isn't here in the next hour 45 and maybe it's a great chat and everyone wants to say longer. I'm just going to give you a little bit of our notes for tonight, if that's fine. We decided to do it before rather than at the end. So the dinner is at the two oceans aquarium tonight.  And we're going to be there from 7pm. So we're going to meet. If you're staying at the breakwater large hotel in B block foyer, we're going to meet the attend to seven and we're all going to walk across together. Alternatively, all our DPR you people will be stationed along the walk. It's very simple. It's across the road in this direction. It's a couple of minutes away. Anyone at the hotel can help you. We can also direct you, but we'll be in B block foyer attend to seven. If you want to walk together. That's what I have to say. This is, this is really difficult. I think I've given talks or hosted sessions before lunch or before dinner, but never before penguins. So it's, this is, I'm not sure we can, we can, we can compete with this. Thank you so much, Casey. Thanks so much. Haroon and the organizers. My name is Christian Meyer. I'm at the University of Oxford. I'm not going to take much more time from the session because I want to get right to our speakers, but I did want to say two words about, you know, what the heck are these Oxford people doing here why is this houses all fit together. And the point is that I am the director at Oxford of a research program called the Oxford Martin program in the future of development. And at this research program, we're quite interested to think about livelihoods in low and middle income countries and how these livelihoods are changing and we're trying to get a better sense from various angles and through various lenses on on what that means for sort of development trajectories and for people and for firms in low and middle income countries. The way in which livelihoods will change certainly is through decarbonization for sure decarbonization efforts to decarbonize or economies will impact livelihoods around the world in rich countries and poor countries. And there's lots of policy discussion around this concept of a just transition a just green energy transition. South Africa is one of the key countries where this is a this is an important policy issue. I'm just taking this here from the website. What does the just transition mean well, it could be called, it could be described as a green energy transition that maximizes the social and economic opportunities of climate action. Very vague. Very broad intentionally very broad. This session here aims to do is to try and be a conversation starter for academic economists like us labor economists development economists working in this space to think about what this means for an academic economist. You know how will this green energy transition these decarbonization efforts impact employment skills demand and earnings earnings in equality. You know there's obviously a large literature going all the way back to Tim Bergen on, you know, wage inequality polarization there's more recent work David Otter there not some more blue polarization you know you know the vast literature you need to talk to you about this you know the vast literature around skill bias technological change here. Are we are we thinking about the same types of issues here with decarbonization some of these might be the same some issues might be the same some might be different. What are the implications for the creation of new jobs. Where will these need new jobs be created when will they be created there's going to be presumably a bunch of mismatches in that where and when between the destruction and the creation of new jobs. What is the role of labor market intermediaries and overcoming frictions in these transitions we've we all know about the frictions that abound, especially in low and middle income country labor markets. It makes it even more important for a labor market intermediary to step up. We also know that the green energy transition in many ways different from the sort of, you know, automation and digitalization because we're going to make conscious decisions to turn off parts of the economy where we as a you know society social contract will come to a decision to say turn off power power plants in South Africa. And so that is somewhat different from this more diffuse, you know, sense of automation and machines and it's going to happen anyway. What are then the implications for social protection systems how to best design them how to deliver human capital for this. And importantly how do we tackle these types of questions and more data constrained environments where we don't have you know burning us online vacancy data to understand what the labor market is doing. So this session here we as a as this Oxford program we're bringing together we hope to bring together a couple of different economists that look at these types of issues from different angles. I think we have a really, really cool lineup will have Francesco who's going to start with some empirical work mostly focused on high income countries. And we'll have Stefanos with some really interesting, I think theoretical conceptual framework for us to think about this, and then Margaret wrapping this up with sort of a bit more of a policy policy insights, putting on some of her work or drawing in some of her work that she's been doing in the policy space around this, this issue. So these three different speakers are meant to sort of, you know, be brain teasers in a way and and sort of conversation starters. I'll also say as a last thing. We work with her runes group at DPR you our broke program and Oxford works with her runes group to try and tackle some of these issues in the context of South Africa. So we have a whole power coal value chain specifically and the types of jobs and the numbers of jobs that will be impacted, but more generally over the next few years, our program at Oxford we're hoping to be a bit of a convening place for more sort of academic work in this space. So if you're interested in this space. If you think about other contexts will be interesting to do research on this, you know, the experiences of decarbonization will vary across the world. Come and see us come and see me. We can chat find the speakers here. I'm hoping that this can really be the starting point for a little bit of a community of practice of academics working on these types of issues. So I'll stop there and hand it over to Francesco who has done some really, really, really cool work on on these types of questions, mostly in high income countries to start us off with some really interesting work. Thank you so much. Thanks a lot for the nice introduction Christian and so today I'm going to I'm going to give you a broad overview I would say of the measurement issue to capture the impact of decarbonization on the labor market. I'm Francesco and Professor of the University of Milan and I coordinate a group on these topics are the foundation and where we have, you know, quite, quite a big group working exactly on these issues. So today, my starting point is to try to convince you of which are the right measures to to speak like this. On which are the right, which is the right measurement framework to capture the labor market implication of the of the green transition. And I want I want to be clear on one point here that as Christian said that what what we are we're meaning by looking at the decarbonization effort is something that has to do with the mitigation policies. Okay, so actually mitigation policies are something that is the mainstream in a way in developed countries but it's not the mainstream in developing countries. Okay, so actually all what I'm saying here perhaps will be a talk of the past very soon, if soon we will need to accept that all the adaptation is the solution to climate change. I hope no, but it can be or perhaps nuclear fusion that will solve all the problems. So actually, I'm going, I'm going to start with the very, very quick introduction, where I give you the main message of my talk in the case I'm not going to in 100% probability I'm not going to finish. Then I will discuss, let's say, new way to measure green green employment and the notion of green employment and then I will move to some application of this measure of green employment to measures of green skills and estimation of the green wage premium, and then I will go going to talk about a measure of instead of brown employment that is the carbon. As you know the debate in developed countries on the green transition is very polarized from the job killing argument to the fact that, as Biden says, when I hear climate I think about jobs, high paying union jobs. So this polarization, as especially in the US often obscure the key challenges for policy makers and obviously key challenges are already has been already mentioned by Christians who are the winner losers. So this is just transition for worker and regional left behind by policies here my notion of just transition is a bit different from the one of the ILO and how to favor a smooth labor allocation toward green jobs. And especially which skills and retaining policies are important to this. Well, the one first starting point to identify the winner and losers is to avoid all the confusion that usually surround the concept of green jobs. And the confusion emerged, especially because there is no one definition of green jobs that we should use we should use two definitions of the first one is the intuitive one. I mean, what is green or inverse green is what is brown in a way is the direct pollution content of an occupation. The definition is more involved in carbon intensive or highly polluted production processes is more relatively more carbon intensive or more brown than another occupation. So that that's the first definition then there is a second definition that instead is the, what we call green, and actually this is more related to the technological side so these are these are the occupations and tasks that are involved in in the trade use environmental. So for instance, a wind turbine technician is an occupation that have a high carbon content. But when you produce a wind turbine, you more than offset the high carbon content of the wind turbine because you're going to produce clean energy. Okay, so a green turbine technician is a is a is a brown occupation and a green occupation. So, and then there is a local what I call low carbon, sometimes during the presentation is a subset of technologies that are for the carbonization I mean the carbonization is not all the only environmental goal of our society obviously. Well, the main point of my talk and I hope I will be convincing on this is that you need two notions of green jobs to cut to roll the aspect of the transition. You need an ocean of brown, you need an ocean of green. Why because for instance, the policy effect on will be mostly on the job destruction side on brown jobs, of course, and this will be mostly the effect of carbon pricing policies, or other environmental taxes, whereas on the green side you'll have the job creation effect that is mostly related to green subsidies and stuff like that. So these two set of occupational except for the wind turbine technician which is a distant exception, they are very different. Okay, the sectors where they are involved is very different that mining metals cement for brown utility construction machinery waste management for green, and the types of occupation are very different mining engineers, moulders for brown roofers fuel cell technicians for green. So actually, let me start with the with my talk with the measuring of green jobs. Obviously, this is more problematic. Why is more problematic because there are no data on on green jobs. I mean standard occupation classification were simply not designed to capture green jobs or better standard occupation classification adapt the job titles without substantial delay. So and actually these are more new occupations and so it's normal that we cannot, we cannot see them in the data. And obviously not having good data on green jobs may twist the argument the political debate toward the job killing argument because we don't have good data on green jobs we can only observe the job destruction effect. And so actually, how we solve this problem, we take advantage of the task based model, develop your nearby auto revenue more than, and then others, and we apply this to the transition. Okay, so actually let me be quick that we use the on that database and in the database there are two types of green jobs existing jobs that will change because of the green transition and new jobs, okay, like the wind jobs. And in on that that is this occupation information network that is for the United States. There is there are around 100 over 1000 occupations that are considered green, according to this classification. And on that contains also another piece of information that is much more useful than the simple binary type of information which is the task content of the occupation so you can imagine each occupation as a vector of task okay is defined as a vector of task descriptors that for these two particular types of occupation is partitioning to sub vector. So the sub vector of green task and the sub vector of non green task. Let me give you some example in the sub vector of green task for instance is crime wind turbine or remove a bestos kind of clearly green green green task in the sub vector of non green task there are stuff like participating audit expect roofs to determine determine repairing procedures. So, using this information you can build another measure of green jobs that is not any more a binary measure so you don't classify occupation as green or not in a 01 binary way, but you build a continuous measure of goodness, which is the share of green task. And this is the continuous indicator of of the average time spent by this occupation on green task. Okay, obviously this is an average for the entire US economy is not allowing to capture heterogeneous pattern across region. There is a lot of limitation, but as I will show you in one second is much better than the other binary measure. So just to give you an example here, I'm just considering green and answered occupation and green new and emerging occupation, and you see that occupation with goodness equal one are clearly green like environmental engineer or recycling coordinator or wind stuff. Occupation with greenness between like say 30% and 50% are clearly not completely green like roofers or atmospheric scientist, okay, or construction worker in the binary definition they will be considered fully green. Obviously these are very, very big mistake because construction worker are also a big occupation in in in the greenness classification, they have a greenness of around 0.09 so nine percent. So actually, I'm not saying that these are very accurate and precise measure but it's much better than the binary measure. So let me show you this by calculating two indexes of the green employment share one using the binary measure, the other reweighting the share of employment of occupation that are green by the greenness that is a proxy of the share of time spending to be location. Okay, so actually using these two measures, I can plot the evolution of green employment shirts in the United States here you don't see trends but there are trends because the scale is very different. Okay, there are trends and actually green employment is more prosycical than non green employment but this is not my my the goal is not this one here. But my goal is to start looking at the differences in level the difference in level is that the share of green employment using the binary definition is above 10%. Whereas the share of green employment using the task based approach is around 3% or even less 2% if we only retain the core task because on that also distinguish between core and non core task into the net classification. Okay, just to which which one is right which one is wrong well, there are data on green production in the US for 2010 2011. They share using this data of green production is around two, two, three percent. So either you are convinced that green production are five times more labor intensive than non green production, which is not realistic, or this definition is much more. Okay, so then we can compute also the share of the greenness by occupation, and we end up with four occupations that are the most green one engineers and architects, life science, life and physical scientists construction and extraction worker installation maintenance and repairing work. These are the four main green jobs. Well, the question is, obviously, what to do without the net so net is only available for the US, how we can solve the problem of not having a net. So the first way of solving the problem is using data direct data on green technologies. Okay, so we know from the task based approach that the task composition depend on the on the technology you use. If you don't have data on task, you can use data on technology. So actually this is what we are trying to do with Europe building a database of green production at a very, very disaggregated level. Okay, so then a very common practice has been the one of imputing on that task measure to other occupational classification using crosswalks. Okay, this has been quite successful for using the for the routine task intensity index. This is not about the work of goose on the Salomon set up, but in this case is much more problematic. There are two problems here. One problem that you usually obviously know is the problem of showing the same task content for different countries this problem is is is minor for Europe and the US that have the similar technological level but it's very big as shown in several work of people, I think also present here of the World Bank. That is very problematic for developing countries. Okay, and the second problem is the main problem here is the problem of aggregation. Okay, so usually labor force surveys are data that are much more aggregated than the US one. So when you are going to aggregate the data without having employment weights, you are going to overestimate by a very, very large amount the share of the employment. Okay, so you end up with share of the employment that are completely unrealistic and and this is problematic why because I want to recall you the share of the employment is 2% of total employment. Okay, if the employment was bigger, the measurement error will be much smaller. Okay. So and then there is a new frontier of job vacancy data, especially using Gordon does data, and I will, I will, I will show that is the most according to me the third and the first for me are the most promising way of proceeding. But the second is not particularly fruitful in this case, and I will show you some data now on job vacancy data of course there is a usual problem that low skill worker and around the represented that is only capturing the demands. So we developed with other quarters, the this this kind of natural language processing algorithm to identify using burning us data, low carbon job ads. In the interest of time I don't want to, to speak too much about this but we also validate this classification using expert elicitation, and we end up with 445 low carbon job identifier, and then we really did what I did already using this data, also for the US most more or less the same stuff so the share of look here is only low carbon remember before it was all green, what does it mean all green include waste management which is a big sector okay here is low carbon so if the share is smaller is around 1.5% Okay, and here we can also look at the share different by occupation, but and now they evolve over time. And also, if we look at which are the main green occupation we found the same occupation so architect and engineer, life and physical scientist construction and extraction and installation and maintenance. Plus, we found transportation material moving why because we include we assume that bus drivers are basically green. Okay, and all public transport is green. We can question this. There is no problem. Okay, so I hope I convinced you that I mean these job vacancy approach can be a good way of proceeding I mean at least for the US we find a good matching. I mean the data tell you more or less the same stuff, but I think that even more important identifying green jobs is more important to identify greens kids so if you take seriously the task based model. The distinction between task as kids. So the task is the demand side of the reflect the technological level. The skills are the capability to perform certain tasks. Okay. And as you know the skills are very important for several reasons that the drivers of innovation that the meeting factor of inequality, and even more important, the, the, from the skill distance is the key variable that determines the success of a job to job transition. So if you want to take a worker that is a coal miner to employ him or her in the wind turbine sector. Well, you have to know which skills, the distance of the skill set is the main variable to determine how much is going to be productive in the new job. And so it's very important to understand the skill bias of green technologies in general, and I want to stress one thing, you don't, you don't have necessarily to have to, you don't need necessarily to have data on task for this is just enough to have good data on technology. Okay, so if you have good data on green patterns of green production is enough to do what I'm going to present now. Okay. So actually, I already told you we take very seriously the distinction between tasks and skills and just very small theoretical notation in the task model the assignment of skills to task depend on a review comparative advantage, and this review comparative advantage can be revealed by the data, sorry, a comparative advantage and this comparative answer can be revealed by the data. Okay, using a standard kind of a kind of a donic approach. On it provides a detailed catalog of general skill so besides this vector of task that I told you, there is a huge vector of skills that is defined for all the occupation so for instance, there is a problem solving that is defined both for construction worker and for engineers engineers are very high share very high score of of problem solving and construction worker a low one. Okay, and this vector is very long it's like a 400, 400 steps. Okay, so what we did is to search the skill that are relevant, the skill L that is relevant for occupation, okay, and regressing the skill score on the greenness of occupation. Okay, controlling for stock three digit dummy, why we want to control for stock three digit dummy, we don't want to compare the skill set of a construction worker with a skill set of an engineer. We want to compare the skill set of a win engineers with a skill set of a traditional engineer. Okay, otherwise, the comparison is not Apple with Apple. Okay. So actually, we're doing this we can see the green skills skills green if this data is statistically significant 1% and positive, and we found 16 green general skills. Then 16 we found 16 too many. And so what we did is to use principal component analysis to rank and group them. Okay, and these come out very nicely because the ranking and the grouping was very, very consistent with what we would expect. So we found these four green skills that are engineering and technical these six are inside this group. This is the most important one that explain up to 40% of the variation in the data. Then we have operation management, the second one that are you know managerial skill, more practical monitoring, I mean like evaluating information to determine contents with standards and science. These are the four green skills. So now I just talk about these four green skills rather than talking about each single item. So actually, the interesting thing is to the first question is to say well, which type of information additional information these green skills gives to us, compared to standard human capital measure. Okay, and actually, this is very interesting because if you consider the second operation management, the third monitoring and the fourth green skill science. They are not very nicely correlated for instance with the routine task intensity index what does it mean that they don't have a lot in terms of information compared to the routine task intensity. However, the most important green skill which is engineering and technical is not at all correlated with the routine task intensity index. Okay, and moreover, if we do the same with the, with the, with the years of schooling, we found exactly the same. So engineering and technical skills are not only skills that you acquire in engineering school are also a lot of skills that you acquire invocational and secondary second after secondary school so it's not that's why it's not correlated with years of schooling. So then we put this green skill measure at work and we found a very, very strong and robust across various application evidence that these skills are the most important one for the green transition. So here, I'm talking about the results of causal rigorous policy evaluation so for instance if we use the standard quasi experiment of the cleaner up we found that the, the, the, the, the, the non attainment US regions of the region with more stringent environmental policies. And there are also the one where there is an increase in the demand of green skills, especially technical engineering skills, using energy price shock with the shift share shift share IV, we found that the long term increase in the demand of energy price increase the demand of technical skills in Europe and and more over we found that the effectiveness of the US green fiscal push of Obama is crucially mediated on the availability of the local availability of green skill so the regions that have more green skills are the one that have much larger job multiplier effect. Okay, so actually that's super important. And actually we found also that the technical skill biasness of green job is confirming job vacancy data so we did all the analysis with job vacancy data and we found the same. However, in this case we observed that the skill gap are more spread and they pertain also it and social skills, and he is not surprising because as you know, the decarbonization of the economy require also the so called twin transition so require also the use of digital meters and stuff like that. Okay, so it's not surprising that it is kids are more important for the subset of low carbon jobs. Okay, and finally we found also a green jobs require more on the job training, which is very often technical is used to teach technical skills and stem ground. Okay, so how much time do we have. So let me skip here I mean here is I mean job vacancy data. I mean let me go directly here I'm using job vacancy data to show you a result that is also found in in not in normal on that data. Here we measure for the two occupations the nice stuff of job vacancy data is that you can measure what happened within not only the final occupation. We have enough density of job us to see what happening, for instance within engineers and within construction and extraction workers. Okay, so these are the two occupation that are more interesting for the transition because they contain both green jobs. So low carbon jobs, high carbon jobs and neither I carbon and no low carbon jobs. So what what we found here is that there is a skill gap in favor of low carbon jobs for technical social manager IT and cognitive skills. I carbon jobs are more similar to low carbon jobs than two other jobs. This is the bottom line of these these figure and the same is for construction and also the one you use on it. Okay, and this you can also see computing confidence interval and so computing statistical significance exploiting the variation of the skill gap across commuting zone in the US and I highlighted in green. The gap that has to say significant and larger than 3.5%. And you see that the biggest gap for technical, but they're only the biggest gap when we compare low carbon and generic ads. But when we compare low carbon and high carbon ads, the gaps are much smaller. Okay. So then let me talk about wages. This is a very important point because I mean, then we have that green jobs low carbon jobs are more complex in terms of skill so we should expect that they pay higher wages. Okay, actually, unfortunately, this is not the case. Actually, it was the case. Just after the Obama green stimulus push. I'm not taking cause idea just, you know, speaking about some correlation after the Obama green stimulus push the the premium for green jobs within occupations and controlling for education, and etc, etc. Experiencing all that all the information that you have in job vacancy data was was larger was was positive and statistically significant, but it declined dramatically in the second in the second period of our analysis of 2017 2019. So after in the second period, the green jobs, which premium, it almost disappeared for all all occupational except installation and maintenance and engineering technicians. Okay, if we compare this green wage premium with a brown wage premium again controlling for a lot of intervening factors. Here we see that the brown wage premium is very, very large in the first period is above 20% controlling for several characteristics. It declines but it remains much larger than the green wage premium. Okay, so you may wonder this just the demand side what happened with the real US data so we use the American Community survey data to see if this is the same. The same picture emerged with American Community survey data and yes, is the same picture. So actually, you see these are for high skilled worker, the fossil fuel worker pay 46.2 hourly wage, compared to 38.5. These are low carbon high skilled worker, and these are the matched occupations similar occupation at two digits of level, or a three digits of level that they pay slightly less than than low carbon jobs, but very similar to low carbon jobs. Another interesting feature is that both low carbon fossil fuel worker are much, much more intensive of stem degrees. Okay, so they require much more stem degrees than senior occupation stem science technology engineering and math. The green jobs I already told you this but I want to remind you this require much more on the job training, then both brown jobs and matched two digit or two digit jobs. The same picture is for low skilled jobs so I skip this slide. Okay, so then I want to finish the part on wages by showing you this graph that is a bit for me is a bit. Why is a bit worrisome? Why is a bit worrisome? Here we measure the returns of a stem graduate in several positions, finance, stem jobs, so like Google, green jobs and other jobs. Okay, you see that there is a huge gap between what what stem graduates can get if he goes to work to Goldman Sachs in red without bonus. So this can be three times more. And if he goes to work for for Google or Amazon, compare how much he gets if he goes to work in a green job. This is very worrisome because this means that the talents that we need to do more innovation in the green economy still we need a lot of innovation in the green economy. Well, they're not going to probably they're not going to work in jobs but they're going to work in in in these positions. So let me finish with a new measure. I promise you that I will speak also about brown jobs only give me two minutes more because I present you a new measure that can be useful also for for other countries of the terminal content of jobs so actually a similar problem of of measuring of measuring green jobs we have also for brown jobs why we have a similar problem. We know that coal miners will be affected by the green transition, but we also know that there has been a huge already a lot of decrease in the carbon intensity of several production so this can be captured cannot be captured by standard occupation classification that are intrinsically static. Okay, so how we can capture this, we can capture this using a measure of the carbon content of job we do it for France for almost two decades, because we have good data but this can be done also in other countries, where we have this is the CO2 emission of the at the plant level for a time t over the total employment at the plant at time t, and we wait all these, we have data on the universe of the employees in the front the front says the best data probably in the world on the use of employees in implants I in occupation or over the total number of employees occupation. We can compute this carbon content of occupation it is time barring so it captures also to what external occupation the carbonize. If you don't have that level data you can do the same stuff using sector level data. Okay, so it will work perfectly fine. Okay, I will be very quick here and I want to show some feature of the high carbon jobs. So the high carbon jobs have particular features that are more exposed to all the other shops that you can they are more exposed to the China shock. Okay, that is a positive correct because I can compute the same stuff for replacing CO2 for CO2 with import for instance, or I can replace CO2 for with capital, whatever. Okay, and so I did this and we compute that this is positive correlated between the carbon content of the occupation and the import from China. Okay, there is a positive correlation between the capital intensive and the carbon content. And this is the first period and this is the last period just to, to see if there's been an evolution on some type. There's a positive correlation between the carbon content of jobs and the, the concentration of the job in a particular region. So, actually, this is important to understand to what extent there will be negative job multiplier effect, and they will be so actually this is a caveat very important seems to be found that they are so much correlated with other shock that negatively affect the labor market condition of worker. You have to be very careful to this entanglement, which is the real contribution of, for instance, a carbon tax with respect to the China shock with respect to automation and other structural factor because that's extremely correlated. So you may end up capturing an effect that you attribute to the carbon pricing or to energy pricing. But this is actually an effect that is related to other factors. Then let me skip this and let me go to my final slide. This is really the final slide. Here we estimate the correlation between the change in the log of why can be wages of or employment of occupation or time T on the initial carbon intensity of job, the initial of job we we did what I suggest you not to do so we impute the greenness of net to France but since we have 411 occupation in France, this is quite accurate because we move from 1000 occupation to 411 is quite accurate the way of measuring the for France. So actually, we regressed these the controlling for the China shock, the share of manufacturing employment capital intensity location of this is just correlation is of course not causal. But we found what we call the, what we are calling now is in progress work, what we're calling now the green wage premium puzzle we are pushing toward this direction. You see that the log, the, the, the, the growth rate of wages is significantly higher in occupation at a higher carbon intensity. The opposite is for the greenness. So the occupation at a higher greenness they have a lower growth rate, growth, growth, wage growth, and for employment is the other way around. We have a negative employment growth for occupation that have a higher low, low carbon content, and a positive employment growth for occupation that have a higher greenness. Okay. And when we quantify this effect we want to find this effect of an interquantile change in the low carbon content, compared to the historical changing wages of the occupation we found that this effect around 10% of the historical changes positive for 11% for, for a carbon content and around nine to 6% for them. Okay. So actually let me conclude the transition is like a double as worth we need two metrics to understand it one is not enough engineering and technical skills not necessarily associated with high qualification are the key feature of green and low carbon occupation. The risk yielding and targeted on the job training are essential to enhance the effectiveness and the equity of green recovery package. I didn't show you this a lot about this is our key result for the evaluation of the Obama green stimulus package. Here I want to give you a kind of pessimistic view because there is a database of the OECD about green recovery package. The only 2% of the measure in this in this database is that the least measure for the for the green economy are for retraining workers. Okay. Then we found that there is this green wage premium puzzle and this is something where more research is needed using longitudinal level data and we are moving in this direction. I want to thank all my co-authors these are all these papers are several papers with a lot of quotas starting from my main one Joanni marine pop. So these are all the groups. Thanks a lot. And then at the end the idea was to have a more general discussion for a few more minutes. But if there are immediate clarifications right now for Francesco. No, okay. And with that to our next speaker. I'll wait for the slides for a moment. I don't know why this is zooming towards me. Okay, here we are. So, thanks a lot everyone for still being here for the last session of today. I would like to recognize and Christian for making these special sessions where I can present our work with my amazing course for Saturday nights on the green transition and skill reallocation. So what we do. So what we do in this work, we take Francesco's work as a starting point, and then trying to find out what are the more general outcomes of it for the decarbonization of the economy as a whole. And that moving towards a green transition is essentially a large technological transformation where as Francesco already mentioned, you have on the one hand the brown sector contracting and the green one expanding, but you also have sectors that need to go through a transformation in order to meet green demand. The first question is do firms actually adopt the technologies when they become available. And if you look at the US and the energy intensive sector, you can see that the average age of technologies in use is around seven eight years. This means that even if fully carbon neutral technologies were to be made available today, it would still take another seven eight years for the economy as a whole to decarbonize. And that's a long period given that we have about 2025 maximum 30 years to decarbonize. So what we do is that we want to study this pace of green technology adoption, and more specifically to see how it interacts with labor markets. This is a graph from a recent question of Dutch firms where employers were asked, do your employees need to have the following skills to work with energy efficient energy technologies. And now you can only look at the top answer, which is no other skills than before, only half of them say that their employees do not need new skills to work with energy efficient technologies. Okay, so seeing it the other way around half of the employers say that if they were to adopt green technologies, their workers would need different sets of skills. So now the question is, are these skills out there, or is there a shortage and Francesco talked lots about it. I'm not going to go through it. But his work shows that actually the skills to some extent there are out there. The people that work in brown job do have similar skills to some extent to the green to what the green jobs required, which is why in our work with focus on skills sorting, namely the reallocation of workers with green skills from brown to green occupations and not for aggregate skills sorting percent. What we do is that we study the green technology updating and it's to the two way interaction that it gives rise to between labor markets and the carbonization. On the one hand, the presence of labor market frictions makes it harder for firms to find the right workers when they update, which increases the cost of new technology adoption and slows down the carbonization. On the other hand, if we push through a quick decarbonization, which I also hope we do. This is going to affect labor markets as it might give rise to higher labor market transitions as firms try to find the right workers for their new green technologies. So we study this interaction, and we find a couple of things. The first thing is that frictions result into workers with green skills being locked in brown jobs. And this happens because this brown occupation extend the use of their technology by around 35% is what we find and half of it is directly attributed into imperfect skill Now the effect of this can become even stronger if the new green technologies that arrive are even more specialized. And then we look at the carbon tax as a potential policy to affect this and we find that of course a carbon tax does lead to a higher pace of the carbonization, but does not act on the skills themselves of course because a carbon tax only acts on the production. So this is the only equation that I have in my slides, I promise. What we do is that we set up a model of a label market with frictions, which means it takes time for firms to find people to interview and find people to hire. And this label market has skill heterogeneity bones on the worker side so workers have different types of skills and also the technologies require different skills to operate. Now, if a firm hires a worker, a match is formed between a worker and a certain technology, we use this equation as a proxy of the productivity of this match, which has two parts. So the part on the left hand side depends on the age of the technology. We see that as the technology age increases, this reduces exponentially, this is relatively to the most new technology available. So this is essentially to say that we assume that technologies become exponentially more green with time, where the pace of this creation of new technologies is given by it. So you can think about this either as new technologies become more energy efficient with time, or you can also think about it as the demand for green products increasing with time. So this is the first part. The second part has to do with something that Francesco also noted a few times, it has to do with the skills gap between the worker and the firm. So if a worker are not the same as the skills required by the technology to operate, then the matching will will be less productive, and this is given by this x squared term or x is the skills gap. Gamma here gives the importance of the skills gap in production, which we interpret as the specialization of the job. Let's see how this model looks. Time wise, it has three stages firms enter the market. When they buy a new technology, the technology then starts aging as the markets produce and then at some point they scrap the technology and they can update to a new one. So in the first stage firms paying investments cost I to purchase the newest available technology. And then they look for a worker. If they don't find a worker in the first period, because they didn't find someone to interview. Of course, there's no production. If they do find someone to interview, then the skill match between their technology and the worker is realized if the skill match is small, they decide to match and they start working together. If the skill match is large, then they don't. And the firm moves on to the next period. The technology ages. Now a firm that didn't manage to get a worker in the previous period goes through the same process they look for someone to interview, and they look whether the skill match mismatch is small or large. But a firm that already hired a worker in the previous period has three possible outcomes. The first one is that matches are exogenous this lot for whatever reason these are just random shocks to jobs. And then two possible things can happen as the technology ages, if the skill mismatch is large, then it might be that relatively to a new technology that matches not productive enough and the worker decides to leave and look for a better job. If, though, the technology is not old enough, then they just keep on producing. Now at some point, the technology becomes so old that it cannot be productive with any worker enough to pay their wages, which means that the firm describes the technology. This is at age a star which I will call for now on the scrapping age exits the market and breaks the matching and then they can reenter by buying the new technology. So what we do is that we solve this model and we calibrate using US data. And now I want to show you what is the outcome of this. So this first graph gives the age distribution of worker firm matches. Now the blue line is the case where there's no labor market frictions in that case firms by a technology and straight away they find the perfect worker. What happens is they keep the worker until at some point at the scrapping age of the competitive equilibrium a star C at that point. The newest available technology is so much more productive that it works for them to just scrap their old technology and update to the new. Now the black lines represent cases where there is labor market frictions. The first thing that you see, as there's labor market frictions it takes some time in the beginning for firms to find their workers they don't find them straight away. Then they start producing, and then they reach the age at which in the competitive case they would have updated. Now though they take into account that if they update to the newest available technology they might also have to look for a new worker. This adds an extra cost to updating, which makes it less desirable, hence firms will keep the technology for longer and go on working. In our calibration this increases the age in use of technology from around 12 years to around 16 so that's an increase of about 35% due to frictions. This is the first part of our two way interaction right the presence of labor market frictions slows down the decarbonization as firms with all technologies keep them for a longer period of time. Now, importantly, as Francesco discussed plenty of workers that are here in brown jobs do have the skills that would be needed to operate also newer green technologies. As a result, if a new firm wants to enter and be a green firm, it will not do because the worker is still hired by the old brown firm, hence it will be more costly for them to look for a new worker. This is what we this is what I mean when I say that workers with green skills are locked into brown jobs, these old firms lock in the workers, hence new green firms cannot enter because they will not have the right workers to operate. Now what I do is that I focus only on the scrapping age so you can forget the rest of the distribution. We have already seen that the first part is due to some cost, namely that firms keep on their technology for a while and they not update straight away because they need to pay upfront costs. And then the second part is due to frictions, but we want to disentangle this part. So here you can focus for just on the left panel. So that we plot the scrapping age that we discussed before on the x-axis we have eta, which is the pace of the creation of new technologies. Okay, how quickly greener technologies become available in the market. Now the first thing that you see is that the scrapping age goes down with increased pace of technology. This makes sense, right. If the available technologies become greener faster firms will also update quick. And so that the first part, the larger part of the scrapping age is due to the fact that investment costs are some they need to be paid upfront when you buy the technology. The second part that increases by 35% we actually can split it into parts. The first one is due to the search friction themselves, namely the cost of having to look for a worker to interview. The second part is the mismatch effect, which is due to the fact that even if you find someone to interview, they might not have the right skills to operate the technology. On the right panel, you look at exactly the same case but now with a higher specialization, a higher specialization of the green technology would mean that it's even harder to find someone with the line with the correct skills, which means that the mismatch effect would become stronger in that case. So now I want to move to the other side of the two way interaction and look at labor market transitions. So on the y axis we have labor market transition namely how many people move in and out of employment. And we see this again on the x axis we have the pace of technological creation, and we look at this for different specializations. Now the first thing you see is that higher specialization leads to labor market transitions, namely because mismatch there is more important workers become redundant more often and firms need to look for new workers. The more interesting part though is what happens along the x axis. We currently stand or at least in the US around here. To reach our goals of a carbon neutral economy by roughly 2014-2050 predictions are that we need this to rise to roughly 0.04. As a result, you see that if we were to push with a green transition in the pace that we have to in order to decarbonize our economies, labor market transitions would increase by about 10%. This is because firms will have to update more quickly into new green technologies, which means that they will more often have to look for new workers which is going to lead to more fires and hirings as these firms look for people with the right skills. This is the second part of our two way effect, a higher pace of decarbonization leads to more labor market transitions, which is important for the acceptance for the essentially political economy side of the green transition as Francesco has also talked to one of his small but very nice papers. And now what we can do about it. Now the first question always with decarbonization politics is a carbon tax. This is easily modeled. Oh, sorry, I promise that we'll only have one equation. This is the same one just with an extra term. This extra term says that if a technology is old, you tax it. So this is essentially what the carbon tax is. And we look again at the scrapping age along different pace of decarbonization. Okay, the blue line is without a carbon tax. The thick line is if we didn't have frictions and then the other one is if we or if we had frictions what we've already seen. What you see is that when a carbon tax is added. So the black lines. Indeed, this the scrapping age reduces so decarbonization goes quicker. We don't really see an effect in the gap between those two lines. Okay, so the carbon tax does not act directly into the part that has to do with frictions, which makes perfect sense because the carbon tax has nothing to say about skills. What have we said so far. So skill sorting extends the life of older technologies because it makes it more costly to update as a result workers with green skills are locked in brown jobs. This effect is fast is stronger if the green technology are highly specialized. And also as a result, if we push for a quick decarbonization, we're going to have effects in the label market. What can we do beyond the carbon tax, namely, what can we do about retrain. Now I'm going to look at a few ingredients that I haven't formally added in the model, but this is work for the future. And how would this affect this picture that we see. Now for once, when firms update their technology they will not always have to look for a new worker. Sometimes the worker that they have can stay. They can use the light blue, given that they do not have to always look to interview a new work. Nicely enough, they can also retrain the worker. If you can retrain the worker then the mismatch becomes less important because you can reduce it, which means that this, the mismatch effect. The other blue line would also become small. On the other hand, we had said that skill sorting is the important factor that we're looking at here, because people in brown jobs do have the right skills. But this doesn't mean that there is not at all any skill sorting shortage. We found also in the US by paper of pop it out and also at an upcoming OECD paper that we are having that different countries in different areas do have a different supply of green skills which means that some areas will have aggregate skill shortages. If the skill shortages are there, then the dark blue line will be higher because there would be higher mismatch in the economy. The effect of subsidies would be to reduce this gray part, the one that has to do with some costs. Now I was told that I would have to train also, and this is my last slide so no worries, of how so we think about developing countries within this context. Now nicely enough, this context does not talk about the development level. It's a general framework that can be applied in principle in many different areas. Of course though it can result to different quantitative effects. The first one is if the investment to productivity ratio is different. If productivity is lower in a developing country, the relative cost of investing for new technologies is higher, which means that this gray part is going to be higher. Okay, we're going to have a slower adoption of technologies simply because they're relatively less expensive. It's okay just for a moment I thought it was mine and that would have been funny. The second part is that label markets can be more inefficient. Okay, if there's no, if there's not much online vacancies and so on. This can mean that there's higher frictions in the label market, meaning that the blue parts of our scrapping age can be high. So both those two factors can lead to a slower decarbonization pace, not mine again. But these are two things that we can relatively easy calibrate using exactly the same things that we used in the US scenario that is the unemployment level unemployment duration vacancy duration and so on. So these things could lead to different quantitative results, but it's something we can relatively easy calibrate. The last one part is a bit more tricky and has to do with the green skill supply. Namely, how important is the part that has to do with aggregate skill shortage, which we showed in the previous slide that would increase the dark blue part. How important is this for countries and different development. If the aggregate skills green skill supply is lower. This will also slow down the decarbonization. So what can we do about it. So, in a recent in a not recent upcoming OCD paper with a colleague. We do. We do look at different countries but only at OCD countries. So basically, the US Europe and Australia, and there it's, as Francesco has only said, it's relatively okay to assume that occupations have relatively the same skill content. So we can just use the owner data to calculate how the green skill supply varies across these countries, making the same assumption about the task content of occupation across countries of different development level is probably not a very good idea. So the question is how would one do this. Now we also had, I can find her Hannah I think earlier had a presentation of looking at the skill context in Uruguay which I found very interesting. Another work that is ongoing and I expected to come out as a working paper in a bit by Marcel Timmer in the Netherlands. So we will look at countries along different development levels but for different reasons, and what they use as a skills proxies not occupation but occupation per sector. And they claim that this can be a better proxy, because occupations can have different skill intensity across different development levels, but probably not as much if you look at occupation per sector. So this means that we could create skill measures of occupation per sector using a baseline in the US or wherever else, and then use this to estimate the green skill supply of countries of different development levels. In this way, we could use this framework also for developing countries, and see one how fast can the decarbonization occur. And secondly, also, what will be the effect of decarbonization on the label market specifically on the affected workers in brown occupations. That's it. And thanks a lot and open to any questions. Same, same as before, question of clarifications. Now, otherwise, moving for our third and last speaker, Margaret. Anyone. No. Thank you so much. Thank you. The courtesy here. So it should be there. Okay. Thank you. Good afternoon, everyone. Thank you, Christian for for this great opportunity and to to meet this wonderful group. Thank you, of course, Aaron as well for this conference. So I'm I'm Margaret, I'm a boogo I'm working at the University of Pretoria here in South Africa, whether not from here in beautiful Cape Town. Firstly, before I start, let me have two disclaimers.  The fear is simply that I won't probably not be able to go through everything I want to go through today. So what did I think about, I thought, I will skip over some slides. So if I go too fast is simply to overcome this fear that I have, we can always come back to this if you if you'll find it interesting. And the second disclaimer is simply that while I was part of the team that developed the tool that I want to talk about now. I was not always part of the teams that did the practical work in the different countries. So I'm presenting this essentially on behalf of the ILO the ILO is really responsible and as well as some UN organizations is a page member partnership for action for green jobs, as well as a climate action for jobs member. And as controversial, it has nothing to do with the ILO or the modulus to disclaimers done. Let's go. Okay, so so in terms of outline, I will really just talk about this network, the gain network, which is responsible for the tool that that I would like to, to say a little bit about but more enthusiastic to show how it is applied in the different countries and what types of information and data that you get out of that. So then I talk about the green jobs training guidebook. This is the ILO book that I wanted to talk about. I will talk as well about the green jobs, G jams as we call them modeling. So conceptualization of this model, everyone here was trained as an economist to understand immediately when I start talking about this because we are taught about this in the first year of economics, believe it or not is still relevant today, at least we think so. The green economy modeling in different countries as I said is what I want to focus on in different applications. I will emphasize perhaps to specific countries just to show some of the data that one gets. And perhaps to also go to some of the questions about skills in a different context. I will end with just very quickly talking about data issues in these types of modeling, and these types of countries that we live in. So again is an international network of individual researchers, research institutions, international organizations, particularly the ILO and UN agencies. And the idea really is to try and develop tools that would assist policy makers in making more informed decisions on justice transitions, particularly in terms of climate change. So this particular tool we talk about is really grounded in national accounting systems as I said this is what we learn in the very first years of economics in university. And it's allowing us to say something about quantities in terms of greening. So if you invest in green sectors or green activities, what is likely to be the impact on policy. And this is framed within the just transition because essentially what had happened is that when we had intended a nationally determined contributions, some of you might know this or seen this, particularly in our developing countries. They were relatively dry in the sense that they would talk about emission. They talked about ambitions. They talked about the timeframes for emissions. They talked about the sectors from which governments were projecting these emissions to come out of. And finally, they talked a little bit about the investments that were required. So, so to some extent, there was nothing really in terms of the socio economy. How does the economy react? What do we say about jobs? What is it about the just transition that we can say about this NDCs. As a result, we tended to see that in many countries there was a task team that was set up to deal with the Paris Agreement and climate change, and the rest of the economy continued with business as usual. So, so obviously this doesn't work. Questions started to be asked by I law and many of you yourselves talking about, well, but what is the issue in terms of just transition and particularly in terms of jobs gained or lost during this transition. So this is the tool. This is the book rather the guidebook, which is owned by the I law as I mentioned. And the tool is aiming at laying down the main foundation in terms of knowledge and practice in order to allow country ownership of green jobs assessment model. So it's very much related to policy and works hand in hand with policymakers, as I said, particularly in terms of drafting the NDCs. So, so moving from the INDCs to the new NDCs the revised ones for 2021 you will see for the countries at least that I mentioned here that there is already recognition of the importance of some of the work that is coming out of the modeling. This model of course can be used by anybody's freely available to anyone. So I will not belay by the point of what green jobs I think Francisco did a great job there. What we simply look for is in the different sectors or different activities or different industries, can we distinguish between activities that reduce consumption of energy and raw materials that limit greenhouse gas emissions that minimize waste and pollution that protect and restore ecosystems or contribute to adapting to climate change. So are these processes or these goods and services and then we make that distinction in measuring the green job. So the book goes in detail in terms of how we do this using of course the I law definitions and as well as UNEP. So just in terms of the modeling the conceptualization and I do not have any equations. Simply, the idea is really to understand the inter industry transactions that take place. Okay, so, so when you have an industry, it produces goods and services that are sold, either to other industries or to final demand, the usual of circular flow of income is what we use. This is of course captured in the in the supply and use tables that the majority of our countries here developed and developing to produce so so so using that kind of analysis and setting up a nice data set that shows the shape and form of an structure of an economy would then use that data set to try and understand a little bit. What happens when you invest in a sector, how many jobs directly produced how many jobs indirectly are produced so so again to look at the forward stages. Over time, how does the economy or the structure of the economy change as you green your economy more and more and therefore what happens to jobs, what happens to those brown jobs, and what happens then to the overall economy in terms of net job creation. That's essentially what we do. So, so this data set in the ways organized allows us to capture the circulation of products within the economy within a given period. So this data set allows us to handle all these complexities that we would otherwise at least certainly policymakers would not have been able to to handle in terms of the effects of activities, the counter effects actions the reactions that would take place within an economy, when there is an investment. So, so we're moving away so so while we're using the micro approach. We also understanding that this has a macro implication on the economy so it's a combination of these two. So this this tool would allow us in the end to distinguish between direct and indirect relations and interactions between economic agents. Essentially we ask the following questions. So, so if you invested in green technology, if you promise in your indices to reduce emissions by so much by 2030, for example, and you say to your colleagues in other countries I'm going to invest in this different economies, what happens to jobs, of course what happens to output of course we want to know this as well but you generally say what happens to to emissions. So so the tool allows us to be able to understand investing in sustainable practices. What is the impact on it on our economies and this particularly these three variables output emissions and jobs, or you could ask for a given level of investment. That means you do not have money which we don't have in developed developing countries. We tend to think well with the limited amount of money that I have in which sectors must I invest. I think these were questions also that it mentioned been mentioned earlier. So, where would I get the best back for a bang for my back so to speak, where is the greatest number of jobs going to come from, if I have to choose a certain sectors. So this is, as I say traditionally, you would have used the input output modeling. Of course, we sophisticated much more now because we have learned a lot more since Leon Jeff and his Nobel Prize. We make these models more dynamic more price we adjust to prices so this limited limiting assumptions are we able to get out of them. And as I said we connect this to the macro model. Okay. So I want to skip over a few things. Important is that we need supply and use tables and supply and use data from different countries. I will say at the end, something about the limitations in different countries on this, the quality and the quantity and the frequency of supply and use data. And I want to just also mention that I want to talk to you about some of the countries. Yeah, I want to spend a bit of time talking about a country like Zimbabwe, which many of us think is really redundant but you will see the type of work that had been done there is quite interesting. I want to say a little bit of work on Mauritius particularly to talk about skills. I want to say a bit about South Africa because I was part of this modeling as well but then I will not really have enough time to talk about this but these are reports I love reports that are available in case you are interested in there. The methodology itself, as I said, is really using that inter-industry interactions here and here, and then also of course taking into account that when you produce you also sell to final use. As I say this is, I'm preaching to people who already know many of you are trained in this way, but we then also sophisticated in some senses by making it more dynamic so we want to understand the impact over time, and not only the static impacts. So by taking into account population growth, GDP growth, as well as interactions in terms of elasticity for consumption, we're able to say a little bit more about what happens, the complexities that happen within any column. I want to say here, so this is not forecasting, we're not forecasting, we're projecting. We're saying if you invested in this sector with so much funding, this is what would happen compared to the baseline. So we will say something about the direction and possible size of effects, oh sorry, but not necessarily be worried about the exact amounts of jobs that are created, we get an indication. Which indication is absolutely required by policymakers when they make promises about NDCs and as they prepare the economies to meet the nationally determined contributions. Okay, I will go over this, we'll talk about scenarios, therefore, this is not forecasting, we're simply comparing a new way or an imagined future, which is what we do in NDCs compared to business as usual, so to speak. Okay. So let's go to some applications, start with this application in Zimbabwe. The application is interesting and what's important up front is that the typical tool should really be used per country because the country's structure matters a great deal. As you will see when I present the results of this country, the size of the economy matters, the size of the investment matters and as well the business as usual the current state of the economy matters. So this tool is really very good because it's the same tool that you can use for many different countries, but taking into account the complexities of the different countries. In the case of this country, the NDC sectors that are promised except for waste are taken into account into the modeling energy sectors, the IPPU sectors as well as the agriculture and forestry sectors. In the end, 11 sectors are modeled or we ask questions about how investment in each of these sectors or processes would impact jobs in this country. And also, so for instance, I can already tell you that the biggest investment promised in the NDC for Zimbabwe is building a dam and generating energy out of this, but also if you add this biogas commercial solar of grid solar, etc. And then some smart agriculture in addition to deforestation, reducing deforestation by coming up with efficient cookstalls. So the main finding, not surprisingly, is that the biggest investment promise produces as well the biggest jobs. This is not in terms of direct jobs. This is not surprising. And the reason is really that the commitment for investment is 5.4 billion US dollars to build this dam. This is to, for example, investment promised for biomass, which is 100 million. So the modeling is a 10 year modeling to try and understand over the 10 years, what would happen in our economy in this country. Therefore, the dam building itself, in terms of direct jobs because of construction, produces the most in terms of GDP, the most in terms of jobs and of course the most in terms of emissions during the building process. So the question then becomes well, this is all good, but if we were to compare, not run for run but dollar for dollar in this particular economy what would happen in the different sectors. So very interestingly, you will see that in fact, comparing a for rent and the returns to this investment in the dam building, hydro building after the dam is built and electricity is being produced. While it is greener, it does not produce any more jobs than the normal electricity. Okay, so the impact actually have simply because of course you have more electricity in the economy so the modeling itself is not replacing a brown sectors it's simply building up to on top of what we have in the brown sectors. We think this is more realistic in African countries, not to phase out but rather to phase down a green brown sectors. So on the contrary, you will see that in other sectors, the biogas sector where the investment was much smaller, the multiplier is much higher. So, so, for instance, you see that in the biogas plant building in terms of multipliers if you were to invest a hundred one million dollars in this sector you create about 130 economy wide jobs, compared to 100 if it was in the dam building, which has the highest investment, and much smaller if you were talking about commercial solar. This is more the technology here is many of us know tends to be more capital intensive. The dam I already explained is really because after you build the dam there's not much extra activities going on, but this particular biogas plant continues over time in terms of producing jobs. So, so what this. Okay, before I go to that implication I just wanted to show you that some numbers here. So what what this tool can do it allows us to understand the total numbers of jobs created over a 10 year period, given the investment. And we saw very clearly that the hydro produces the majority of jobs while the biomass doesn't produce that much, whereas it also allows us to to judge pay 100,000 or a million or one US dollar, how many jobs are created in this case you can see, as I said biogas is much more than the hydro. But these are anyway all the other sectors I don't have time to really talk about. What what also you can do with this type of tool and if you have good data and that's very important, so you can disaggregate this by gender as well and by skill. So you will be able to know in 2030, what is the likely impact of this investment in order for us to meet the commitment that we made in the nationally determined contributions. And what we saw that one result that is important is that the size of investment matters. So the question then is also what about the type of investment and we see clearly that the sector in which you invest matters. This is information that policy makers would not necessarily have had on their fingertips in terms of selecting which sectors they would prioritize.  It might be that if you have limited resources or if you use your own resources because remember for indices you have committed investment versus the conditional conditional So for for for the unconditional you're using your own funding as a country and for conditional you're looking to get from elsewhere so you might in this case leave out the big investments for external funding while you look at smaller investment that do create more more multipliers or higher multipliers than the big investments. That makes sense. I will not say too much here just simply to say that after they intended in DC for this country you will see now the revised in DC for 2021 is totally different. It's no longer as dry as the first ones is now talking about the potential for job creation is certainly more ambitious. It also recognizes that they are core benefits that come out of a mission reductions. Once again giving policy makers much much more better voice in when they negotiate in terms of the indices and when they make commitments for national national determined contributions. Few other examples and I think I'm probably going to run out of time very soon, but just for Mauritius five more minutes good. Mauritius did the same. So I will not repeat that but what I want to show is that you what you can do with these two is go a little bit further and ask questions about skills. While we say that countries would gain so many jobs in the future. The question is, are countries prepared for this and therefore using this tool you can be able to already project what types of skills and we're using the I score in this particular case. So, not necessarily the most perfect but the sector we're asking for different skills, what are you likely to need, how can you prepare. So once again, countries or policy makers start to work together. Okay, so it's not only the climate ministry or department, but also the education department is important right for, for preparing for training for the future so in the agricultural sector how many managers do you need how many of these sectors we used only the nine sectors there in high school. And then you'll see the same is true for different sectors if you're talking manufacturing and industry and textile the number of managers that you require for the future is different. Of course the type of management is also very different. You do the same for accommodation lodging sector, and again, prepare for the future in terms of the skills that are required. So that by the time this comes if you do indeed a transition, the transition is just people are ready for the jobs for the green jobs. The same is true for renewables. And so this is an additional modeling that you can do once you know how sectors would react to a given investment. In South Africa, we did a modeling that was very much related to what Trudy was talking about earlier, or what we called the Ramaphosa plan or the economic reconstruction and recovery plan. And we, we model to understand the conventional policies impacts on jobs, the public works impacts, accumulatively that is, and then green elements. And also as well, couldn't we be more ambitious in South Africa and see what happens with the green push, and without too much wasting too much time we see very clearly that conventional policies, while they help you to grow your economy as we would expect in any case, the investment in conventional policies is much higher, as I've shown now earlier for the case of Zimbabwe but if you were to be to be daring and green, you know, be more ambitious in in terms of greening your economy. You see that this not only allows you to do better in terms of output production, but also in terms of employment. And indeed, this greening is the only way that would then of course allow you to reduce CO2 emissions. If you continued with business as usual you would soon come back to the pre COVID emissions. So so clearly showing the benefits of being ambitious in terms of green modeling. Yes, so so I will not talk about the rest of these other countries as I had, you get this information will be there in case you're interested the reports are also available in ILO. Finally, I want to end with a song. Are you ready. Don't worry Tishnash, I'm not actually going to sing. So the song is really about data. So data is a problem. We need to be to bring this up every time we're in a room where there is potentially a policymaker here or somebody who is interested in data collection, collecting this type of data or using these types of tools needs a lot of data micro data. So for South Africa and a few other countries we have very good supply and use tables very fortunately, they are not collected as frequently as we would like, but for many countries, even though they might have supply and use tables, they do not have enough information. So we don't know much about gender we don't know about the agricultural sectors we don't know enough about the informal sectors. We don't know enough about the ages of the people who are working in order for us to be more precise in what we inform a policy in particularly in preparation for the transition that we said, and someone mentioned earlier this is inevitable it has to happen. So, so just again, the song is the more data we have the better the better quality data we have the better we can inform and assist with policymaking. Thank you very much. And then, as a final final round, we can do one quick round of clarifications of any or otherwise more general reflections on any three speakers. We can briefly acknowledge the fact that there are penguins later. Francesca. Thank you very much for the presentation for the last talk and for my also for my book. I mean, we always have to consider that we have also to look at the effect on the environment. When you consider biogas as green. Very, very, very questionable in biogas is going to lead to a lot of different deforestation on the one hand, and on the other hand, anyway, if biogas is not generated by biomass that is from recycled, you know, this is going to be a bit a bit tricky. And the second, the second point is related to the, because we focus a lot myself also is also a comment comment about myself. I mean, we focus a lot about the issue of decarbonization, but I think especially for developing countries, there is a general issue of core co benefit and co damages because you are going to create perhaps another environmental problem by resolving the first one. I mean, I think also mining lithium mining cobalt is going to create other social environment perhaps I mean social environment. I mean that is true. So, so if you if you look at the NDCs, I think the ambitions for most of the developing countries is to go gradual. Okay, so, so where you can start to go green, not fully 100% green if you can go 50% green. That's already a way towards green. I think that is the attitude that they are taking. Yeah, yeah, yeah, I know. Yeah. So, so this is a warning to policymakers right so what we're modeling is what they have in their NDCs. Yeah, yeah, yeah. Thank you very much for three excellent presentations one common theme was labor market transition costs and to be honest with you I hadn't really thought that much about that as an extra cost for greening. So my question is, do you have a sense or approximation of how large those labor market adjustment costs are the transition costs. And is it much higher in developing countries because I think of like them are to to McLaren study and they are where they estimated that transition costs for workers to switch occupations is something like five or six times annual income. And the estimates that the World Bank sticky wages sticky feet report said the, they're even larger potentially in developing countries. So there's these incredibly large estimates of these adjustment costs I'm wondering, are we are using those or did you find others or do you really. I mean, how are you thinking about these transition labor market transition costs. I could say that we really don't take enough. The tool does not give us enough information about transition costs. What we can certainly look at is the investments and the cost there, but we're not tracking the whole transition and all those costs that you're talking about the nuances and so forth because the costs also involve how does the labor market itself react, the dynamics within the labor market, the tool is not allowing us to do that. So we do make quite a number of assumptions it's a module after all, but but certainly they would be costs that that we haven't taken into consideration. Okay, so, so in our work essentially the only part that touches upon it is the second part of the two effect where it shows that if we are to go quicker, then this will increase labor market transitions right and you have two ways this could go so one is due to a quicker decarbonization the other one is due to potentially this new technology being more specialized which it could be if we have to push for this kind of new technologies. Now, if you have data on the cost of this for particular workers, I would love to try and do a welfare analysis on this because essentially what I would like to take this is to try and do a bit of different cost analysis because on the one hand you have a carbon tax that kind of makes a transition faster, but this does not account for the fact on the workers that are affected. So if I could do that, then I would really like that, but I don't. So it's more like me asking you for my own I guess. Can I also say something about this. Well, these I thought a lot with my colleagues doing a computational general equilibrium model how to build this, this, this transition cost and actually is quite complicated because of course there are several when I show the results in terms of you remember the stem worker how much they get in different position, I showed that they get less in green jobs compared to other jobs so there is a misallocation so you have to consider that in this transition costs you have to consider also the lost wages. But I think that the most straightforward way that I mean is like a lower bound can be just to quantify the retraining required and this you can do it on it actually because in on it you have the required on the job training. So the simplest way is to say how much is the gap to move worker you know using this indicator, and I think this is a pretty accurate component, there are several components but this is the component that should be, should be the one that the public sector should, you know, take into consideration should, should, should, should. Sorry. Of course but these I also think that these the cost that the state should bear the other cost are private cost at the end you know, so of course. Yeah, if I may jump in here, but but the, but the cost of the transition is endogenous better policy so I'm kind of reluctant to think that we estimate was the what is the cost of the transition for workers in Kentucky or Welsh valleys, and then we take that as a way that we should plug into the other models, because if you have countries are past sort of like transitions where there was like no effort to mitigate that cost, you end up with a high number. You can just say you we impose that on the entire world is very high it doesn't really make sense, but it's totally dependent on the past policy. So, so I would be reluctant to say that, yeah, that's the number like estimated for the US or the UK just extrapolate for the entire. I don't know OECD or or or even other countries, even more so it could be kind of sort of like space specific, like location specific. I think it's very different when you have like let's say mining hub in Welsh valleys or Rumpa Malanga here, which is sort of like has very few other economic opportunities going to be very different in let's say in Poland where the mining hub is in the most industrialized region. So, I would, I would be kind of reluctant to say that it has to be high, because it was high in some of the past transitions to someone. I'm a little bit scared at looking at the list of jobs that are involved in green or brown, because to me and correct me if I'm a bit biased here they look all quite male dominated jobs. And so I wanted to know what is the future for women in the green economy. I don't have data, but I mean the, the, the basis is a bit discouraging. You're opening a very, very, very, very big issues and I didn't have a we never put some data on this because actually would be a separate paper there is a nice paper of this young researcher from Birmingham University she did something like this is exactly what you would say they are all male dominated both green and brown jobs. So actually, and, and they're because mostly, they're also construction jobs, you know, and also you have to think and these are very nice point of the last presentation. What happened over time is all this construction job will appear at the beginning, and then they will disappear, you know, and so actually, this will be, you know, the gender and bias will be, you know, there will be very big also over the transition so of course this is something that, but we have to promote STEM education with the, with the female, I think that's for sure very important. So, so maybe I could also say what one thing that this type of modeling can allow us is to prepare for the future right. So we could bias the future training towards women. So just more women managers produce more etc etc how easy that is is of course a question that we know how difficult that can be but it's an opportunity to start to ask and answer some of these questions. So, I think that the first clue is indeed about STEM jobs because STEM jobs women are highly underrepresented, but I would also like to say that this might be even more boosted that the fact that the second big transition that you have at the same time can also ask acts in a similar way across across gender so it's not only the green transition but is the green transition and automation and the creation of new jobs at the same time that acts differentially across genders. So, yeah, just any longer between us and the aquarium and thank our speakers again, Francesco Stefano and Margaret thanks so much for the three different perspectives on this important issue. Thanks all for speaking with us and have a wonderful evening looking forward to seeing you over dinner.