SA-TIED Seminar Series: Climate shocks, labour market dynamics, and inequality in South Africa

Thanks for the opportunity to present, um, this work. So, this is joint work, uh, with Johannes and Yvana. Um, uh, a bit of a quick overview of what we'll be talking about today. Uh, I'll I'll take us briefly through the motivation of the, uh, paper, um, discussing, uh, the the research question that we ask, why it is relevant, um, in the context of South Africa, and then we move on to the data and identification issues, uh, discuss the findings as well as, uh, potential implications for, uh, policy. All right. So, um, the the question we ask really is about the impact of temperature shocks, um, and so we know that the impact of temperature shocks and extreme weather events, uh, are quite well documented, um, in the literature. And, um, uh, a very well-established a very well-established literature, um, demonstrates negative effects on several outcomes. Um, for instance, um, evidence suggests that temperature shocks lead to poor health outcomes, um, increased, uh, mortality, um, temperature shocks, um, [snorts] have also been found to hinder entrepreneurship, labor productivity, economic performance. Uh, what we do is try to, uh, quantify the effect of temperature shocks on, um, income inequality, uh, income and inequality in South Africa, um, and we also use the latest, uh, Coupled Model Intercomparison Project, uh, which we often refer to as the, uh, CMIP6 temperature projections, uh, for South Africa to examine how global warming may influence uh income um over uh the short-term, medium-term, and long-term um horizons. Um so based on this framework or this research question, um our key outcomes of interest uh are earnings. And then we later on also look at total income, which which uh adds on other forms of income to earnings, and then as well as income inequality. Um income inequality is measured at the municipality level. Um and we take advantage of the uh tax data um that SA TIDE and the UNU-WIDER framework uh has available together with satellite weather data. Now, um why does this research question matter for for South Africa? Um the we we find South Africa to present a very um relevant policy setting and and it offers particularly unique insight. Um the first, South Africa um grapples with challenging labor market conditions, and this includes um high levels of uh unemployment, persistent inequality, uh and and this um historical legacy of segregation that has persisted um over time. Um while the country, as we know it based on recent macroeconomic statistics, has made certain significant strides in economic development, um there are still uh inequality still persistent. And so South Africa still has some of the highest um levels of inequality in the world, which remains a key structural challenge. Um Second, South Africa relies quite heavily on coal as the predominant energy source for power generation, and its reliance on coal position sits among the top 15 global contributors to greenhouse gas emissions. So, as an example, in 2017, um South Africa's net emissions reached over 15 million tons of carbon dioxide equivalent, and this was a significant increase of about 14% since 2000. Uh and so, increasingly, um the the South African economy is quite is becoming quite carbon intensive. Then, third, um according to the World Bank, the vulnerability of South Africa to climate change places it among some of the most affected countries in the world. So, um average annual temperatures in South Africa have increased at twice the global rate, and this increase has been accompanied by increased frequency of extreme heat waves and cold snaps, um which uh a lot of you in South Africa would have been experiencing. Um rainfall patterns, uh as we know it, have altered significantly, and there is this shift in seasonality and an increase in rainfall um intensity. Um Importantly, since the 19 80s, the country has experienced um almost a hundred extreme uh weather-related disasters. And this has uh adversely impacted over 22 million individuals. And in 2018, um we know Cape Town came close to becoming the first major city globally to experience complete water shortage. So, this trajectory of increased um extreme weather-related disasters continued well into um the early 2020s with flooding events across all the provinces. So, South Africa is considered one of the world's most climate-vulnerable nations. And understanding the role of um >> My lip is Sorry, sir, but if we could all please keep our microphones muted. >> Yep. So, um understanding the role of climate shocks in explaining inequality um and labor market dynamics is is therefore critical uh especially in the context of South Africa. Now, um what are the conceptual channels we we are looking at? Um there there are several channels that we could explore, but then um we we focus on uh just about four of them. And a lot of this is hindered uh really by the availability of data which I will discuss uh a bit more shortly. So, first um temperature shocks directly reduce productivity. Um and the channel is quite straightforward. Um heat uh extreme heats can cause dehydration, it can cause fatigue, um health problems, and there's evidence suggesting that it can lower cognitive function as well. Um and this tends to reduce um, hours worked as well as efficiency. And this reduction in efficiency is is particularly more pronounced in in outdoor settings as well as uh, very physically demanding sectors such as um, agriculture, transport. Um, can we please mute microphones? I seem to be getting some feedback. >> [clears throat] >> They're muted now. Please go ahead. >> Perfect. All right, thank you very much. Uh, where was I? Second, um, the second channel relates to agricultural productivity. Um, and so temperature shocks uh, tend to reduce agricultural productivity and this happens by uh, lowering crop yields. So, um, this cuts demand for farm labor and it weakens earnings. And when this happens, workers may try to move into non-farm activities, but then that relocation is often incomplete uh, because of limited jobs. Um, there are also skills mismatches. For example, trying to move from agricultural sector to non uh, agricultural sectors. Um, if your skills don't match, it becomes a problem. And um, there are other labor market frictions. And so, um, when this happens, total labor supply can fall um, and income losses can persist between uh, beyond the the initial shock. Uh, the initial temperature shock that uh, actually caused that shift. Now, the third um, has to do with uh, how temperature shocks tend to shape, uh, adaptive capacity. So, for instance, um, wealthier regions or or firms or households are generally, uh, able to better protect themselves from temperature shocks. And this can be done through, um, infrastructure, um, technology, as well as other forms of coping strategies. Um, however, poorer and more vulnerable groups, they tend to have fewer resources, uh, to respond to the same temperature shocks. And this would likely result in, um, larger income losses, uh, for such regions. Um, when this happens, this could actually, um, worsen the inequality, uh, gap that one might expect to see in certain context. Then there's also, uh, uh, um, uh, a behavioral dimension. So, um, climate change, for example, can change investment decisions. Evidence suggests that it influences mobility, and more generally, how individuals respond to risk. So, uh, these responses tend to differ across different, uh, income groups. And as a result, uh, through this channel, uh, temperature shocks can both shape earnings, as well as long run, um, inequality. Now, the data. So, um, we acquired data from, uh, two main sources. Um, the data that we used to examine, uh, inequality and labor market outcomes, we obtained that from the, um, administrative tax database uh and this database collects uh data based on anonymous individual tax records. Uh so basically the the database provides um a panel data set that merges corporate income tax records with um individual employee tax certificates at the individual level. Um the certificates uh basically payroll-related tax document which are submitted annually by employers that report the income that are earned by employees. The data links firm-level uh information with detailed employment record and by doing this it produces a data set with uh tens of millions of observations which allows us to um uh facilitate our analysis at the individual level across formal employment in South Africa. Um I I must emphasize that um because the data set is capturing formal employment, um it does not capture the informal sector. And and this is one of the the major limitations of um the the data set as well as the project and indeed uh other studies that use the the tax records. Um but uh having said that, it has its own strength and we'll talk more about uh this later and how it could potentially uh have influenced the results. Um the data set captures multiple types of uh income uh reported for tax purposes, but this predominantly uh includes um uh formal the formal sector wages and salaries. Um overall, we we have a data set that includes up to 20 million observations per year with information on individual income. Uh but then we limited the analysis to the working age population and therefore included individuals aged from 15 to 64 uh from the year 2009 to uh 2022. Um in terms of temperature data or weather-related data, we take the temperature and rainfall measures from the ERA5 um satellite reanalysis data. So, the the reanalysis data uh combines information from ground stations, satellites, weather balloons, um and other inputs uh with the climate model to estimate uh several weather variables. Uh we use the air temperature, which is measured as a daily average in degrees Celsius, and rainfall measured as the daily sum of precipitation in millimeters. Um we then merged this temperature and rainfall data with the South African tax administrative data using um identifiers for the municipalities which are available in both the the weather data as well as the tax data. Um in the end, we we have um we had a data set that basically um merged the weather variables with the tax records which includes um income and then uh other relevant variables such as age, gender, and employment duration. Um this is quite limited in the sense that compared to survey data sets, which are collected purposely and therefore capture things like race, uh education, and other demographic variables, um the tax data does not have those records. And so, um we were quite limited in terms of what we can control for or the additional dimensions of um heterogeneity analysis that we could conduct. Now, how do we define our variables? Um temperature shocks, uh we defined that for each local municipality. And so, basically, what our analysis entailed was um essentially, we're looking at the impact of um temperature shocks in the municipality in which an individual lived on that individual's income. And so, we defined temperature shock for each municipality as the the difference between the observed temperature um at a given time and the long-run average for each municipality divided by the long-run standard deviation for the same municipality. So, um our measure of temperature shock captures the deviation in actual temperature from the historical mean for a municipality at a given time. So, this captures unexpected short-term deviations from the long-term um norm in in temperature values. So, the long-term climatic norm, it captures that deviation. And And this is consistent with a standard definition of uh shock in the climate economy research. Um however, in robustness checks, we also consider an alternative way of measuring temperature shocks, and that is a temperature bin approach, which is quite common uh in the literature. And so, we split the the average temperatures into multiple bins uh below 10%, above 10%, and other in between. And then for our outcomes, uh individual level analysis, we uh measure earnings as the log of wages and salaries. So, basically, total earnings. But then in robustness check, we also consider uh total income, and that is income from all sources, which includes um wages, salaries, dividends, and interest. Um and then for inequality, we measure inequality at um local municipality municipality level using the Gini index. And then also uh considered other um income ratios in robustness checks. Now, our empirical strategy is um consistent with the literature that has examined the impact of temperature shocks on a bunch of outcomes. So, the idea generally is that um this literature tends to treat temperature shocks as exogenous in the sense that uh we do not expect um um our outcome to influence um temperature shocks. Um however, we expect that um our model will be compromised by omitted variable bias. And so, the identification strategy is meant to deal with omitted variable bias. Now, even though the expectation is that reverse causality is not a problem and therefore temperature shocks is treated exogenously. In the literature, we tend to use the lag of the temperature shocks. And in that way, it mitigates any additional concerns of potential reverse causality. But then, as I said, omitted variable bias is likely to be a problem and the way this is addressed is addressing the model is to include relevant fixed effect. And so, basically we run a fixed effect model that that controls for individual districts, year fixed effects, as well as some interactions between this in order to address the unobserved heterogeneity and common shocks in our model. We then control for the limited number of covariates that we have available in the temperature shocks literature, we tend to control for rainfall shocks. And then, we also control for age, its quadratic term giving evidence suggesting a quadratic relationship between age and income. And then, we also control for sector indicators where relevant. We we run a similar model for income inequality, but this does not include the um age and age squared at individual level covariates, but this analysis is collapsed to the uh municipality level given that uh income inequality is more of an aggregate indicator which we capture at the municipality level. Now, let's talk about some findings. Um so basically we find that uh a standard deviation increase in uh average temperature is associated with a 1% decline in earnings. And then when we add on um the other uh sources of income including dividends and all of the other income to get total income, uh we find that uh a standard deviation increase in average temperature is associated with a 1.3% decline uh in total income. Um however, we find no statistically significant uh effects on inequality. Um so basically to summarize, our findings suggest that temperature shocks reduce formal income. And the emphasis here is formal income uh because uh our data set does not capture the informal sector. Um The the the the real benefit of um tax records is that it's extensive, it's objective, it's better than survey data when it comes to reporting income. Uh people are likely to mis- misreport income. Um, there are potential issues, measurement errors with survey data and income. People perhaps don't want to report their income accurately for various reasons. But, with the tax data, we are able to get that accurately and from source. But, the disadvantage is that because, >> [snorts] >> um, the informal sector is not captured in the tax records, um, unfortunately, uh, we don't have information on that. So, the emphasis here really is that it it reduces formal income. Um, but then, we do not detect any significant effect on, uh, income inequality. And again, this is within the tax-registered population, not overall population, given that, um, the informal sector has not been captured. Now, we then move on to, uh, focus on, uh, some heterogeneity, uh, by gender and age. And so, uh, we basically examine whether the effect of temperature shocks vary by gender, and whether it varies by age. Um, as you can see, we find that across both, um, the male and female subsamples, um, the effect of temperature shocks on income are consistently negative. Um, um, however, we find that the negative effects of temperature shocks are marginally more pronounced for, uh, females than males. Then, uh, when it comes to, uh, age, we examine, um, the heterogeneous effect across different age categories and we focus on five different 10-year age cohorts starting from 15 in in in um in in 10-year intervals until our limit which is 64. Um and as you can see here we find that there's no significant effect on the 15 to 24 age group um neither do we find an effect for the 40 45 to 54 age group. The largest decline we have is for the 25 to 34 age group which arguably could be seen as perhaps the the most productive age group given you know these are not too young they've got a bit of experience coupled with um a lot of energy to to promote productivity. Next we um examine the heterogeneous effect of >> [snorts] >> temperature shocks across different sectors. Um this analysis is really important given that different economic sectors tend to exhibit um different vulnerabilities to to climate change. Um so within every sector you could expect that there's a difference in the level of exposure the productivity channels that are play and and more generally even adaptive capacity. Um for example for for example in in South Africa agriculture is a major sector that faces, you know, acute risks when it comes to temperature shocks. Um similarly, when you consider um some service sectors, um there's this reduced demand and informal worker displacement due to extreme temperatures. Um and so, the effects are likely to differ depending on the sector you find yourself, given that within each sector, people tend to um face different levels of exposure, the ability to adapt is different um among others. Um we find that exposure to temperature shocks um result in um a reduction in earning across all of the sectors that we examined. Um the trade and accommodation sector, uh as you can see from the screen, was the only sector where we did not find a significant result. Um temperature shocks were also found to uh positively affect earnings in the financial services sector, uh which is the last one there on the slide. And then the the largest negative effect or adverse consequence of temperature shocks uh were observed uh in the case of the transport, storage, and communication sector. Um This this sector experiences the most significant negative earning shocked, and then followed by um the agricultural sector. And then the least affected sector is the community, social, and personal services sector. Um The the most pronounced effects that we observe in the transport storage communication as well as agriculture sectors. That that's consistent with the nature of operations in in these sectors. So, for example, the the transport sector tends to rely heavily on outdoor operations, you know, tracking, logistics, which are vulnerable to heat-induced fatigue and and delays. And then for [snorts] the agricultural sector, we already know and we are quite familiar with the idea that when there are extreme temperature events, whether heat or excessive rainfall, there tends to be some consequences. So, for example, heat excessive heat can impair crop yields and farmer productivity. And excessive rain could also mess up with expectations, especially when you have crops in a particular season where too much rain is actually bad for productivity. The insignificant effect for the accommodation sector could be linked to mainly indoor operations. Whereas the positive effect for the financial services sector could be because individuals within this sector tend to work in office spaces, which tend to be highly regulated with temperatures for comfort. And so, the idea of adaptation might um play quite well here in the context of these results. Then the overall effect suggesting that we there's no effect on inequality. Now, this is an important finding and and perhaps as you see on the slide, I'm telling this the most policy relevant non-result. Um, and this is likely the case for a number of reasons. Um, so one potential argument is that the aggregate effect of temperature shocks may be offset by other counteracting mechanisms. So, for instance, um, while some studies find that higher temperatures reduce agricultural yields and it tends to lower incomes, um, others highlight how adaptive behaviors mitigates disproportionate impact of temperature shocks. And um, the evidence suggests that adaptations are quite widespread in South Africa and to an extent this could explain why the net effect on inequality in is insignificant. Um, however, um, we believe that the data limitations are perhaps also playing a bigger role in this finding, right? So, as I mentioned before, uh, while tax data administrative tax data um, typically presents the advantage of being very precise in measuring income levels, um, they do not cover the informal sectors. Uh, and this this sector, the informal sector in most countries, um, including South Africa, they tend to be the most vulnerable to climate shock. And so, basically, uh, by focusing on um the formal sector alone, uh, the result, uh, cutting out an important sector, uh, within this, uh, inequality argument, right? So, if temperature shocks primarily affect informal workers, or perhaps unrecorded activities due to operations within the underground or shadow economy, um their inequality effects will not be captured in the tax records. And so, this is an important demographic group when it comes to, um, understanding, um temperature shocks and its effects. And so, the findings for inequality needs to be qualified carefully and and taken cautiously, um, in the sense that they may not represent the entire distributional effects within the economy, but strictly the formal sector. And so, this finding is likely shaped by the data scope rather than definitive proof that, um, the the impact of climate shocks are distributionally neutral and therefore they have no, uh, significant effects on, um, inequality. Uh, sorry, um, jumping. Yes. All right, so, we move on to the next slide where, um, we try to, uh, adopt various climate scenarios, uh, based on simulated data, uh, to see what the, the effect of climate change would be on future outcomes. So, basically, we are simulating the effect of climate change on future income. And we do this by combining our regression estimate with projected, uh, weather data, um, from 2040 to 2099. So, um, for the rest of the decade. So, um, to do this, we, we, we, we took future climate projection data from the, uh, CMIP6 project, uh, which is overseen by the World Climate Research Program. So, this is, uh, basically, the, the forecast used as the basis of the, uh, IP, I, IPCC Assessment Reports, the, um, uh, Intergovernmental Panel on Climate Change Assessment Reports. So, the CMIP6 climate projections, um, data set, uh, for South Africa provides temperature projections for what we refer to as a short run, which is from 2040 to 2059, um, the medium term, which is from 2060 to 2079, and then the long term, which is from 2080 to 2099. Um, so, for, for this short, medium, and long term, there are multiple, uh, climate trajectories or scenarios, right? And so, for example, we've got the, um, SSP1 1.9 scenario. And this scenario aligns closely with the Paris Agreement's uh target of uh 1.5° C, right? And then it goes all the way to the SSP5.8.5, which is the uh highest emissions scenario available. So, the SSP1.9 is the uh most optimistic scenario, uh which is designed to limit um global warming uh by uh the year 2100 to below 1.5° C. And this was introduced following the Paris Agreement um where nations um committed to pursuing efforts to cap um the rise of uh temperature at 1. 5° C. Uh Then the the least optimistic scenario is the SSP5.8.5, which represent the very high emission scenario, indicating where high levels of fossil fuel and uh you know, very high levels of energy demand uh among others. Um this scenario leads to a significant increase in uh global temperatures way above the 1.5° C by uh the 2100. Now, what we see here, um the top the upper range the upper range of uh percentages you see here corresponds with the SSP1.9. And so, basically in the short term, what we are seeing here is that um climate change uh in the short term will be associated with uh about 1.2% decline in income. Uh if we are on the most optimistic trajectory. So, if South Africa is on the most optimistic scenario, um doing everything radically to limit uh climate change, then in the short term, it will be associated with a 1.2% decline. In the medium term, 1.1, and then in the long term, 1%. So, basically, um this is intuitive in the sense that the effect of climate change reduces over time if we sort of uh deal with it appropriately. So, if you are dealing with it appropriately, you uh under the SSP1-1.9 scenario, over time we we expect the effect to reduce. However, the figures at the end, the extreme end, 2.2% for the short term, that captures the SSP5-8.5 scenario, which is if we leave climate change unchecked. And so, if we leave climate change unchecked, the negative effects that we would observe on um income into the future will become incremental. And so, 2.2% decline on average income in in the short term increases to 3.5% in the long term, sorry, in the medium term, and then in the long term, increases to 5%. So, um the message really is that uh um uh climate change tends to have non-linear accumulation effects, right? So, uh income losses will will stay relatively contained under ambitious mitigation targets. But then, if we don't um then its effects become incrementally wider and wider and wider. Uh what are the policy implications? Um from the literature and from the findings, it's it's really important that we engage in adaptation, um invest in uh resilient workplaces, systems that will be less vulnerable to um temperature shocks. Um we could also, uh based on the the projections we have, if we could focus on the aggressive target of addressing um climate change, then its potential effects over time would likely reduce. Um and then, it's it's really important uh given given that uh our findings do not capture the informal economy and the likelihood that um they are one of the most affected groups, it's really important that uh there are targeted policies uh uh you know, thrown at these blind spots. Um the strengths and limitations, uh as I've been emphasizing, the the really good thing about the administrative tax data is that improved income measurement. It allows for a very large longitudinal panel, which is really appropriate for the estimation strategy. Um however, the limitations, uh Uh, one of the biggest limitation is the fact that it only applies to the formal sector and does not capture the informal economy, which, uh, as I explained earlier, could be playing a key role in influencing, uh, our result, especially for the inequality. Um, then another limitation is, uh, tax records don't tend to capture detailed demographic information, which becomes problematic. And then, um, the mechanisms, we are not able to test mechanisms appropriately, uh, because again, the tax records are limited in terms of what is available. Um, however, these uh, things are able to be captured quite appropriately, uh, within the survey data context, given that you can actually collect specific data that you are interested in, um, to look at the um, mechanisms. So, the key takeaway. Temperature shocks reduce formal earnings, and the emphasis is on formal earnings, uh, and total taxable income in South Africa. Um, we find no significant effect on inequality, but these results is probably bound by the idea that we focus on the formal sector. And, uh, mitigation and adaptation are really important because future, uh, warming path imply meaningfully to to different income losses, and, um, there are potential effects that could have long-reaching consequences. Uh, thank you very much for your audience, and I would be happy to take any questions or comments that anyone may have. >> Thank you very much, Sifiso, for that presentation and just for sharing your research with us. I think we can all agree that these are increasingly important issues for South Africa, particularly as climate-related risks begin to intersect more directly with with jobs and incomes and long-term policy planning. We now have some time for discussion, and if you'd like to comment, I I'd like to ask you to please raise your virtual hand. Alternatively, you're welcome to type your question in the chat. >> Okay, I'll stop sharing my screen now so I can see everyone's faces and uh also see any questions that pop up. >> Okay, I'll check for hands. Okay, we have a question in the chat, Sefa. >> Okay. >> Um it is from Don Steenkamp. Let me just try and open it. And Don asks, "Should these findings be viewed primarily as climate as a climate policy issue, an adaptation issue, or a labor market policy issue?" And then goes on to ask us two questions in one. And the second one is, "Do the findings suggest a need for stronger heat-related workplace protections or sector-specific adaptation policies?" >> Um thanks thanks for the question, Don. So, uh I think in terms of uh the policy angle to take this from, I I think it cuts across everywhere. Um what we have seen increasingly within climate policy is that when it comes to evidence on the impact of uh climate change and Sorry. Sorry about that. Um when it comes to the impact of climate change or temperature shocks, uh what we've seen um within the policy domain is that it's not just uh an issue focused on one sector. It's It's It's an issue that cuts across multiple disciplines. And so as part of the mechanisms, the discussions around the mechanisms that I put across earlier, I have emphasized that the effect of temperature shocks are likely to be different across different subgroups based on their ability to adapt, right? And so some groups adapt more appropriately than others, and the effect of temperature shocks are likely to be different for these groups compared to other groups that don't adapt well. So, this makes it an adaptation issue. And it also makes it an an important climate policy issue, but at the same time, it's also a labor market policy issue in the sense that when we look at the sector-by-sector results the findings that temperature shocks influence different sectors quite differently suggests that some sectors are possibly adapting better than others. And it's important that labor market, you know, intersect with climate policy to ensure that the various sectors are on top of their game when it comes to issues relating to adaptation and vulnerability to climate change or temperature shocks. And yes, I agree that the findings suggest a need for stronger heat-related policies in the workplace, as well as sector-specific adaptation policies. And then this was quite obvious from the the findings from the sectoral findings that the better adapted you are if if you have strong adaptation policies that are implemented appropriately then the likelihood that the effect of climate change will be detrimental would would reduce. Then um I see another question in there. Uh so the question says just a question about whether the findings are measuring climate impact or only hotter temperatures. Um hotter weather can also lead to more climate disasters like fire and storms. I'm wondering if this impact captured in the study. All right. So the way we measure temperature shocks is basically deviation from the norm. And the norm would be either heat extreme heat as in like taking like standard heat as the baseline and therefore deviation from it could be extreme heat as well as cold and deviation from it could be extreme cold. And yes, the study captured this impact. Uh in additional findings which I didn't present in the on the slides we actually separated the findings by looking at impact of extreme heat as well as extreme cold. So um yeah, there are there are separate findings which we captured using the bending method as well as a deviation method focused on extreme heat separately as well as extreme cold separately. >> Thank you, Sefa. We have an additional question in the chat from Timothy and he asks, does this paper capture responsiveness of observed organizations to climate change policies in Southern Africa. >> Unfortunately not Timothy. We are not able to capture that. This is not information that we were privy to and so could not incorporate directly into the analysis. Then I see another question from Dan. >> Who's Dan? Yes. >> What practical role could treasury or labor institution play in mitigating this risks? That's a tough one. I'll defer this to the audience. Are there any thoughts on this? Is there anyone from the treasury or within the labor market institution that could provide some insight on what they believe they could be doing in their capacity as labor market institutions or treasury. And any thoughts? >> Yeah, I see two treasury colleagues on the call but I'll let them come in if they wish. I won't call them out. Treasury colleagues? They seem reluctant. >> All right. >> Okay. Yeah. >> Sure, go ahead Abnah. You were saying something. >> We have a question from Nezeka. >> Uh let me let me attempt to speculate um on on Dan's second question. Now I I do not know the scope of responsibilities for the South African Treasury. Um and so and that's why this is a really difficult question for me to answer. But then for labor market institutions, And you think of labor market institutions in general in terms of what they do, one of the key things they do is they they regulate what happens in the labor market. So, it's really important that standards are set to ensure that across the different sectors within the economy um if these standards are set and we expect organizations within the economy to adhere to this standard um these could play important roles in mitigating potential risk associated with the effect of temperature shocks. Now, Treasury being a government institution might have bigger powers in terms of potential legislation or policy changes. But as I do not know the scope of what a Treasury does in South Africa, I'm reluctant to specifically say what they can be able to do. But I would imagine it would be within the idea of as a government institution with broad powers, they could potentially influence policy that would ensure that the impact of temperature shocks are mitigated as much as possible. And this does not this not limited only to the Treasury, but really across board across all government departments, right? Every government department um you know, has got a role to play in in reassessing what they do practically and ask themselves, what can we do from a policy perspective or from a practical perspective to ensure that you know, adaptation is optimal and the effect of temperature shocks are mitigated. um uh perhaps that might not have been a a very satisfactory question answer to Dan's question, but this is from a very limited perspective. Then um I move on to Nazita. I hope I pronounce that well. Uh on the inequality [snorts] side, you find significant income effect, but limited effect on measured income inequality within the tax register population. Could this reflect a composition effect? For instance, where lower income workers drop out of the tax base entirely after shocks, making the remaining distribution appear more equal. Uh yes, I agree, and and I think this ties in with the response or the explanation I had given earlier that um we are basically capturing just the formal economy using the tax data. And so, when capturing inequality, um this paper, as well as all the other papers that have used the administrative tax data in capturing inequality, are are neglecting to capture a very important component of the economy when it comes to inequality, and that is the informal sector, right? Now, uh interestingly, evidence suggests that this groups uh of low-income and and that's what you flagged uh in terms of uh lower income workers dropping out of the tax base. Um this lower income group tend to be reflected uh quite disproportionately within the informal sector, right? And as a result, um that finding of no result could just primarily be because temperature shocks are likely pushing the informal sector deeper and deeper into the lower income tax bracket. But then, this feature is not represented represented in the in the data set in the inequality data, and therefore it is limited in that context. Um then there is a second question from Naseka and apologies again if I'm not pronouncing that correctly. Do you think the absence of a significant inequality effect will change once you have factored you factor in the informal and substance economy? Uh I could only speculate because we've not run the analysis. I could only speculate, but given that the informal economy is largely affected by temperature shocks as demonstrated by previous studies, I would speculate and say yes, it's likely to be that the result will change and become significant, demonstrating greater inequality in the result. But like any other empirical exercise, um you cannot know until you actually run the analysis. >> Professor Biyase, we have about 2 minutes remaining. >> All right, I would go quickly. Um >> Two questions. >> Do you conclude that labor market majorly affected by climate change in South Africa or other factors need to be accompanied the impact of no equality effect on this result? I'm not sure I fully understand this question. >> Um Tadesse, would you like to come in? Hope I'm pronouncing that correctly. >> Yeah, I think there's probably some type of mission somewhere and so um yeah, reading it doesn't sound I'm not sure I fully understand it. But in the interest of time, I will jump to the next question and if uh that Tadesse would want to jump in later, I'm I'm more than happy to consider it. Uh I want to suggest that sequence research should focus on the sectoral basis that we can have an in-depth absolutely. I agree uh Onuk, that's certainly a great idea uh jumping into the sectoral basis and looking at it into more detail. Then I think Tadesse is back again. Empirical evidence on climate change mitigations taking as lessons globally for your study. Again, I'm not sure I understand. Um It's Tadesse, do you want to come in and elaborate on what point you're trying to put across? >> Tadesse, you're welcome to unmute yourself. >> Okay, maybe Tadesse is probably not keen on speaking up. [laughter] >> Yes. Thank you. Um thank you, Sefa. Um yeah, and thank you to everyone who joined us this afternoon and for contributing to the discussion. We will be sharing