 This is organized by the African Economic Secretary of the UN, we thank the UN Wider for giving us the opportunity. I just introduced presenters here. All of us are. The first presentation will be by Professor Eric Torbek. I just read out the title. It's on the interrelationships between, let me just, growth in equality and poverty. Some implications for fighting the COVID epidemic in Sub-Saharan Africa. It's pre-recorded. Lina, you can help us upload it so that it runs. I want to welcome you from the Redwoods of California. It's very early in the morning here. And I'm very happy to be part of this very important conference. So the topic, the theme of my paper, my presentation is, as you can see from the PowerPoint, the interrelationship between growth inequality and poverty. And some implication for fighting the COVID epidemic in Sub-Saharan Africa. Let me give you some background into the genesis of my presentation. It builds upon a number of earlier projects undertaken under the auspices of the African Economic Research Consortium. And more specifically, the most recent one, which is on reexamining the growth, poverty, inequality, and redistribution relationships in Africa. About 14 individual research projects have been completed on a competitive basis under this project. The novelty of the GIP program is to explore the reverse causality between poverty reduction and subsequent higher growth. We all know from the literature that economic growth is very good for poverty reduction. In this necessary condition, it has been confirmed by a number of studies, but the reverse link from poverty reduction to subsequent growth is still somewhat of a conjecture. If you look at the slide, the link from growth to poverty reduction, as I mentioned, is well known. And the kind of strategy that economists have promoted is what we call a pro-poor growth strategy. The link between poverty reduction and growth, on the other hand, is still to be confirmed, but I think we have plenty of evidence based on the studies in this project that a direct intervention on trying to reduce poverty is a good thing for economic growth. So now let me try to bring out some of the implications of the findings of this project, which may be relevant to the fight against COVID-19 in Africa. The first point I would like to make is that the negative impact has not been as bad in Africa as in other parts of the world. The latest statistics showed that the incidence of COVID was 10 times lower in Africa than in the USA and five times lower than in Europe. Well, undoubtedly, there is a very large undercounting, but even taking that into account means that Africa has not been as badly affected by the crisis. Among the speculative mitigating factors are Africa's previous experience with outbreaks, masks have not been politicized, early shutdown, and demographics, which is very important. I think the average age in Africa is something like 16 or 18, so we're dealing with very young people who are less susceptible. In the short run, there's of course a trade-off between prioritizing health and reducing transmission and the consequent loss of economic welfare, which can be very hard on the poor. So what have we learned? I would submit that in the medium to a long run, the successful containment has a positive effect on health and the economy, and the trade-off tends to vanish. So those, and again, the evidence suggests that the African countries that prioritized the health of the population and the containment were also countries that accepted a short-term negative economic trade-off, which is going to pay off in the long run. So what are the lessons that some of the lessons that we have learned from the projects? In order to answer this, I think we have to ask which households have been most negatively affected by COVID-19 in sub-Saharan Africa? And the answer here is very straightforward. The unscaled, the less educated, the poor segment of the population who have very few assets except their labor were disproportionately hit by the crisis, both health-wise and economically. Since most of the unscaled workers are employed in the informal sectors, it makes sense to try gradually to nurture this sector so that the people in the informal sector are not really chased out of the sector. The second implication is that while the COVID-19 is a real challenge, it could provide the impetus and the opportunity for a much more ambitious than African program of massive infrastructure construction. The advantage of this is such a project would require a lot of unscaled worker to reduce transportation costs and promote in tri-African trade. Another implication is that fiscal impoverishment should be avoided. Fiscal reforms are needed to make revenues and the expenditure pattern more productive. And then also, there's plenty of evidence that subsidies to the poor in the form of nutrition, health, and education are crucial in reducing the inequality of opportunity and leveling the playing field. So these are my main comments and I thank you for having listened to me and I'm now going to have breakfast, have a wonderful conference. Thank you so much, Eric. I think this was a very crisp and highly relevant message that you have delivered, which actually follows nicely with the next paper that we all co-authored, myself, Juguna and Eric, who pushed us to really investigate exactly what he has said at the end of his presentation, which is for African countries, of course, the management of the COVID-19 has been painful in terms of economic and social disruptions. But then it would be also important to see if those policy responses have worked for Africa, if the empirical evidence supports that some countries who have applied the guidelines by the WHO fare better than those who do not in terms of preserving lives. So, apologies for this. We are learning Hopin. It's a nice application. So in this, I think, as I said, it's a bit of a continuation of what Eric has said. This is a paper by Juguna Dungu, myself and Eric, preserving lives or livelihoods. I mean, we are asking this question because of the trade-off he just mentioned earlier so that to assess the policy responses in Africa. So I will just go through the motivation, objectives and data results. It's going to be very quick. I think as we all know, at the onset last year of the COVID-19 pandemic, all countries have been advised by the WHO to follow some guidelines in order to contain the spread of the pandemic, at the same time also preserve lives. We have all gone through that. And I don't, I mean, no need to indicate what has been done. A lot of measures have been done by governments. Now the question, some researchers are asking, for example, Asimogulu et al. just published in the American Economic Review Insights and Alvarez and also their co-authors about whether there could have been a possibility of minimizing the loss function in both aspects. That means minimizing the impact of the pandemic, at the same time also minimizing the spread of the virus. So they have shown through various models it is possible for governments to design and devise a much more nimble policy response than what has been suggested by WHO. When it comes to African countries, even the situation is dire because a recent paper also Brown, Ravalian and others published in the NBR suggested that for African countries, the WHO guidelines tend to be less transferable. Because most of those guidelines, their application requires a setting that is more or less that exists in developed countries. Social distancing, people don't have enough rooms to share. So if somebody is sick, you cannot isolate. Many people don't have access to information, TV, tele, etc. So with this in mind, we have asked ourselves in this paper how effective anyway the policy response have been particularly lockdowns and other preventing measures. And also we present in the paper the economic and social disruptions and then we want to draw lessons on minimizing potential trade-offs. I mean why Africa? Two things. One is shock tend to persist. So economic and social shocks, their economies are not as resilient as foreign developed countries. And the other one is the issues that Eric mentioned in his presentation, whether African governments have the fiscal capacity and also the reform and the institutional ability to turn the COVID-19 from a challenge into opportunity. To assess and answer those research questions, we have used extensively novel data that's available in the open sources on infection, daily infection rates, daily fatality rates, daily confirmance, COVID-19 cases. And then lockdown measures also we used as those reported by Google for mobility of people in all of the 54 African countries. A stringency index from Oxford University. But also community understanding of the COVID-19. A lot of the indices have been developed by WHO and other testing and tracing index. COVID-19 related daily violence. So we started to see the extent to which all of these come together. So we have applied a simple empirical model that links the rate at which the pandemic spread or the fatality rates or even the decline in economic activity can be linked to the treatment era. We call them those policy or treatment variables, such as for instance stringency indices, changing mobility of people, testing and contact tracing, etc. So that we can pick the impact easily using internal instruments available because this is a daily data set. So you can imagine from about January 2020 up to November 2020, daily data for all of these variables. And basically we found, at least at a descriptive level, stringency indices seem to be correlated with actual compliance in terms of mobility of people. From their usual routine. So, however, at a higher level of stringency index, you see high variability, which means compliance has not been perfect. But also you could see the pattern of the lockdown between it started around March and got really the median one hit the bottom around May. And then it started climbing up as countries eases the mobility and people started to come back to normal life. But then the stringency index also shows similar patterns. But generally this helps this heterogeneity and variation helps us to capture the impact at a descriptive level. You can see the average rate of growth in infection of the COVID-19 causing virus, the fatality rates, etc. All of this just give us how huge variation it exists between countries. And the average more or less also complies with what we hear in the news every day. The result, I'm just showing the main result based on IV estimation of GMM for a large number of African countries over this period. You can see, I think, clearly our instrument, the first stage regression I did not report, but it's quite significant showing the validity of the instruments. But also, at least in terms of over identification, it meets the criterion that the excluded variables we have done it correctly. And the results, let me read it out here. The result shows us reductions in mobility. That means actual mobility of people around the mean, which is minus 18% from the usual one, could lead to daily reductions in infection rates by one person and fatality rates by about 0.6%. Similarly, a stringency index also led to significant reduction in infection rates. So basically lockdowns have worked in Africa even on the average. Countries that have implemented the lockdown strategies following the guidelines, whether it is partial or complete, they have done quite well in terms of reducing the infection rates and also reducing fatality rates. However, there has been also limits to the extent to which this has been effective. And we were able to identify even in this limited analysis that other factors than simply lockdowns could be beneficial in terms of managing the pandemic. One of it is the capacity of countries for testing and tracing. This you can see from the regression, it has had a very large and significant impact on fatality rates particularly. The community understanding. I think this is a very interesting also finding we have. Community understanding basically means how much percentage of the population is aware of the COVID-19 pandemic, but also how it spreads and what are the protective measures and whether they believe in those protective measures. So there is an index capturing this and we were able to show that it's an unlinear one. That means if the population that believes and commits to protect themselves and their families is within less than 20 percent, then it has no effect on infection, actual infection rates rise. But as the population becomes more and more aware, which is above 20 percent, we see a reduction in dead infection rates by 1.6 percent. However, all of these efforts have come at a cost for countries. We have also managed to look at the real GDP growth effects using night light data. Thanks to TO for accessing this data for us. It's a monthly data. And we have tried to regress against the same on mobility because that is a good proxy for compliance. And after controlling for country fixed effects and also time during the period, we've been able to capture that there is a significant impact of lockdowns on the economy. So using some conversions from the literature, we can see that the one standard deviation decline in mobility could lead to 2 percent decline in real GDP growth. And lockdowns also affected social cohesion. We were able to document that the daily average number of COVID-19 related violence generally tend to be very high in countries that implemented significant lockdowns. Now what's the policy implication? The policy implication is we are not denying lockdowns have significant social and economic impact, but we succeeded in containing the spread of the pandemic. Now in terms of managing the balance, the trade-off between the two, some of the lessons for the policymakers is that they could manage the spread of the virus through a wide range of tools available to them while they continue also the economy to run. So there is a possibility for smart policies. One of the examples even discussed today in another conference was the role played by digital finance, where for instance people would not have access to those facilities where the first to suffer from loss of income, jobs, and also livelihoods, basically going hungry. We have not shown here, but we have it in the paper, all of those impacts on vulnerable households from high-frequency phone service in 10 African countries. So basically the message we have here is the lockdowns have worked, but there was a limit to those effectiveness because they have come also at a higher cost, but there are also many other issues that countries can address in order to make the management of the pandemic easier while also they support vulnerable groups. So this is my colleagues, Juguna and Eric will come back to, yeah. So I'm done. So the next presentation, we go to Theo. I hope we... Thank you for organizing this session and for the opportunity to present this study. It is still a work in progress, so your comments and suggestions are very welcome. And the paper is about monetary policy responses to the COVID-19 and the central bank independence in Africa. So the outline is that you can see here, I go through the motivation, then I will present very briefly what I call here the conventional and non-professional monetary policies, I suppose. And then the data, the empirical strategy and preliminary results and how we think we can conduct and dig further the study. And so what we know so far about the economic crisis consecutive to the pandemic is that governments around the world are responding from all angles to the crisis. For example, the economic and social consequences of the lockdown, the sharp decrease in economic activity. This is just what Abebe mentioned. And developed countries are relatively well equipped to address the issue compared to developing countries. And in fact, many countries have limited resources. And as you know, the technology to develop a vaccine even to the financial resources to buy a vaccine are not available. Or even keeping some of this vaccine at the required temperature is a huge challenge for some developing countries. So there is a limited fiscal space for developing countries, particularly in Africa. And even before the crisis, what we know is that the pre-pandemic, there is a pre-pandemic macroeconomic instability. This figure that you can see shows some responses to the COVID-19 in Africa compared to other countries. And we can clearly see here that the rate cut, which is the central bank policy rate cut after the crisis in percentage to the initial value before the crisis. And we also use the reserve requirements cuts, which is the change in reserve requirements in percentage of the initial value. And also the macro financial, which correspond to measures undertaken by the humanitarian authorities from all aspects. The response in Africa is lower compared to the rest of the world. And we know that monetary policy plays a key role in such a context of crisis. And we lack a clear picture on how central bank across Africa has been handling the crisis. What determines the response and how central bank independence plays out. And although it is one of the government's most important economic tools, most economists think that monetary policy is best conducted by a central bank that is independent of the elected governments. So this belief stems from research, as you know, some years back and that emphasized the problem of time inconsistency. So in this paper, we conduct a systematic review on all central bank policies in Africa after the COVID-19 outbreak, all the central bank policies in Africa. And then we investigate the size and the determinant of the response in Africa and the potential role of central bank independence in that context. And one of the novelty of this study is that we consider the timing in central bank reaction to the crisis and the potential coordination issue across central bank. So to give you a flavor of preliminary results, what we find is that central bank in Africa have taken multiple measures to tackle the pandemic and more than two-thirds of central bank have taken at least five measures. And the other thing is that these measures are similar across countries and also both conventional and non-conventional policy tools have been used. Also, African Central Bank responds less compared to the rest of the world in terms of monetary policy rate cuts and also in terms of celerity in the response. However, in terms of reserve requirements, caught African Central Bank react large. Let me go quickly through this monetary policy responses in Africa. Just to give you a very brief description of these policies, this figure shows the number of monetary policy measures by country. And the key takeaway here is that countries undertake at least five measures, most of countries at least. And also regional central bank and North African Central Bank undertake more measures compared to others. For example, we see Morocco, Mauritius, Egypt, and they took 12 measures compared to Eritrea, Ziro and Ghana, very few. And most of the BCAO zone, they took, let's say, yes, seven measures and so on. So this is the key takeaway from this monetary policy responses. This figure also shows that two countries, Eritrea and Libya, for example, did not take any monetary policy measures. And again, most countries took five or more measures. In terms of celerity, this figure shows the average number of days between the first case in the country and the Central Bank reaction. What we can see here is that the average is higher for Africa. So African countries, they took more than a week to react compared to advanced economies and other developing countries. So having in mind this, let me skip this. We have also classified, we make a kind of typology of the monetary policies and we classify them in terms of conventional and non-conventional. Although the definition of conventional and non-conventional is something I would say very subtle. So we have this classification, which is one of the contribution of this distribution. So let me skip that one and move to this. So having in mind that Central Bank have been doing, I mean, the question we asked is, okay, what determines these policies? And we capture this using a data. Right now, we only have two waves of this data set and with various data sources. And the first wave is from February 2021. And the second is from May 2021. And we hope that we will complete the sample with at least two or three other rounds of data set. And we have 134 countries and we capture two dimensions of monetary policy. The first is the size of the monetary stimulus and the second is the celebrity in the response. So the size of the monetary stimulus is measured here by the Central Bank policy rate cuts and the reserve requirements rate cuts. Can you try to go a bit faster past your nine minutes? Okay, so we also use the celebrity as a measure of monetary policy and also an index of this policy. So the value of interest that we use the Central Bank independence taken from this guy, a dummy of Africa to capture what is specific to Africa and the interaction between Africa and the dummy of Africa and the Central Bank independence. We also control for some of the variable inflation rates, the depth and the COVID-19 instigancy index and the exchange rates and the GDP growth forecast. So this is what we did. So we run this estimation, which is a preliminary approach where the monetary policy is the dependent variable and as I described and we run this regression. So the results that we get is that when you look at these estimations, the Africa Central Bank experienced less policy rate cuts compared to other countries. And the independence, independent Central Bank also know less rate cut compared to less independent ones. And the interaction terms here, as you can see, yes, it is positive and significant, meaning that the more independent the Central Bank is, the more it reacts. But when we introduce later on, you know, we increase the controls in the regression and we add, for example, you know, the exchange rate regimes will lose the significance, which means that we suspect a kind of strong correlation between fixed exchange rates and the Central Bank independence. Let me summarize now. The other result that we get, and then I move to the conclusion, when we use the policy reserve current cuts, we observe that reserve current cut is higher in Africa compared to other countries. However, the Central Bank independence is associated with less reserve current cuts. Also that case, a fatality rate affects significantly the policy reserve current cuts. Now, what about the celebrity? The celebrity shows that Africa Central Bank were low, slow in responding to the crisis compared to others, and that an increase in Central Bank independence tends to slow the responses. And lastly, when we run the regression on Africa's sample, what we get is that Central Bank monetary policy response in Africa is mostly driven by the growth of costs and GDP per capita and to some extent the Central Bank independence. So as conclusion, first, Central Bank react similarly in many ways to the pandemic. Second, this preliminary analysis shows that compared to the rest of the world, Africa Central Bank react less to the crisis. And finally, that Central Bank independence matter in addressing the crisis. So the way forward is what we study in progress. And we will continue updating the data set to include new versions, new waves, about two to three, even four if you're a bit lucky. We would love to have previous waves, but unfortunately, the author of the data, they just remove from the website the previous waves of the data set. So we also intend to analyze and put the coordination in Central Bank policy across Africa. So the question is, okay, monetary policy to do what? Okay. And yes, we may think that no, the role of this policy as any other policy is to question the shock of the economic consequences of the pandemic. And then the question is, can you just finish? Thank you. Yeah, Tio has taken too much of your time, Chuku and Alex. I don't know, Lina, how is it your timing? I mean, are we going to be banned? No, we're in to, we're past the time, but we can still continue. But the main stage presentation has already begun. So some people may be moving over to that. Okay, so the younger ones, Chuku and Alexander, can you do it in six minutes? Maybe, yes, yes, I can do it in six minutes. Okay. Do you see my slides? Yeah, just click on your slide, I think. Yeah, it's coming, we are seeing it. I said full screen. Yes, yes. No, Aveve, thank you. Thank you, ARC and Juguna, thanks for this opportunity. Let me, because of the time we already have, we have a short period of time. This is a joint paper with Alex. You know, these are just thoughts, we have some working papers. Let me concentrate on showing a couple of what we'll consider some of the fascinating chats that might interest you. And this is in relation to, sorry, this is in relation to, do my slides still appear? Yeah, we see it, but you need to, yeah. The problem is when I make this full screen, I don't see you anymore. Okay, let me concentrate on showing just a few slides. And here we are concentrating on the macro environment. The question we are asking is, what is the difference between a country that is more resistant to exogenous shocks and what helps countries to recover quickly from shocks? And if you look in the last 20 years, you see that African countries have been buffeted by several shocks. And here we just list six of them. The most recent one, the COVID-19 2020 local innovation in eastern Africa, the 2014 commodity price long, the Great Recession, and there are several others, Ebola. And the consequence of these shocks have been very severe social and economic damage. And that's what you show Eric and Abebe in your papers. And in the African economic outlook, we estimate that Africa suffered the worst economic recession in half a century. Lost GDP, what of 2.1 percentage points? The consequence is that these kind of shocks, exogenous shocks, leave deep scars in the continent. And this affects Africa's ability to achieve some of the ambitious development projects. The AU's Agenda 2063, the Sustainable Development Goals, and several others. So policy makers need to learn how to build resistance and how to recover quickly from shocks. And that's what this paper is really all about. There are three main questions we ask. The first one, you can think of it from a normative perspective. What makes a country more resilient to shocks than others? Although, you know, this is more or less a normative question. We use data-driven models to try to answer these questions. The next exercise we do is we try to rank countries according to their capacity to absorb shocks and according to their speed to recover from shocks. Now, I think the interesting part is the question three, where we say, okay, after answering these two questions, it helps us to determine the exact entry points for resilience building and intervention in African countries, you know, in a country by country sense. Let me leave this part and just go to show you some of the interesting charts I talked about. So the three things we'll talk about in the stylized facts here. Hysteresis, absorption and rebound. Rebound here, you can think of rebound as recovery. Absorption is the ability of a country. It's like the shock absorber you have in a car. The ability of an economy to absorb exogenous shocks. And we'll look at the stylized facts. There's another point that I think is interesting you might want to know. We also search for the so-called Singaporean paradox. And the idea is think about Singapore, you know, a small island country. In terms of the structural characteristics, it is vulnerable. It's far away from global markets. It's small, there are no natural resources. But in spite of this vulnerability, Singapore has managed to use homegrown economic policies to build resilience. So we'll look in the data and see if there are any African countries that are also experiencing this Singaporean paradox syndrome. So here we just show first, now these are stylized characterization of what the impact of a shock could be on an economy. And the possible paths for recovery for different economies. I want to point you to panel B. If you look in panel B, here we call this negative hysteresis. This is a situation where a country is affected by shock. And then even after it has recovered after the shock, because policy makers are not able to respond in the appropriate way, the future path of growth is lower than the previous path of growth. And this is what we are really looking for in panel D. What we call the bouncing forward. A situation where the country is affected by shock. And then policy makers can reorganize the economy and use structural change and structural transformation to cause the economy to bounce forward to even faster growth trajectory. So let's look at the data and see if we find evidence of this thing. Here we plot the chronology of Africa's business cycle for the past 22 years. If you look at this chart, what you observe is that Africa has experienced three main recession recovery episodes, cycles. The first one in the last 20 years is the global financial crisis. That was this 2014 commodity price lump. And there's a COVID-19 pandemic. And if you look at these three episodes, you'll see they have different characteristics. The debt, the duration of the recession and recovery are all different. And we discuss, we try and characterize these different episodes in the paper. But here, I think this one is also an interesting chart because what we do is plot all African countries and see if there's any synchronous movement in the way they respond to shocks, in the way they recover from shocks in the future. And we see that, yeah, there's a high degree of synchronous movement. But there are a couple of outliers, exceptions. Look at Ethiopia, for example. The global financial crisis, Ethiopia was even growing as the yellow line. It was even growing faster. I mean, they had slower growth just immediately after. Look at Rwanda. Rwanda has maintained a steady growth. Even during 2020, growth rates in Uganda were still positive. Zimbabwe is down here. It's an entirely different pattern we see for Zimbabwe. And we understand why there are a couple of other issues. So what we did is to look at the two most significant, you know, recession, recovery cycles for Africa and try to characterize both of them. This was the global financial crisis. Here, what we do is to compute an absorption index and a recovery index. And we show, because of time, I won't discuss how we compute those. But we see that for the global financial crisis, the countries that had the strongest absorption capacity were the same countries that had the fastest speed of recovery. That's why you see this positive relationship between absorption and recovery. Now, fast forward to 12 years now. Look at the 2020 COVID-19 crisis. And we see that that positive relationship has disappeared. It has flattened. So here we don't see the countries that are the most absorptive, that have the strongest absorption capacity are not necessarily the ones that have recovered so far from the quarterly data we have seen in the last few months. So the summary to take away from here is that country resilience capacity is not a fixed attribute. Policy makers should not think that, because maybe Itopia Rwanda was able to weather the global financial crisis in 2008. Therefore, they still have that strength and ability to weather the COVID-19 shocks, because shocks come in different character, with different characterizations and affect economies differently. That's one message we see from here. So resilience capacity is not static. It's not a static concept. It evolves with an economy's characteristics. And here what I showed in this chat, what we show is the so-called Singaporean progress. I want to concentrate on two panels, two quadrants. Look at this quadrant, this top right quadrant. This quadrant, we call it the homemade quadrant. This is the quadrant where you have countries with strong resilience and high vulnerability. In other words, these are countries that, by their structural characteristics, they should be very vulnerable. And yet they've used economic policies to strengthen their resilience. And we have just a few countries in this category. We have Botswana, Rwanda, and Namibia. But in this subsequent slide, excuse me, quadrant, you have so many other countries. We call these the self-inflicted countries, where they have access to global markets. They have access to natural resources. Yet because of the kind of policies they run, they've become very vulnerable and less resilient to shocks. Let me spare you because of time some of the empirical, the details of the empirical strategy. But let me go straight to the result, to talk a little about that. And let me show you the results from what we do. But the objective of this empirical strategy was to estimate a dynamic business cycle model. And the idea is to be able to identify the most important factors that help a country to recover from its shock and the most important factors that help a country to absorb exogenous shocks. And here we have five different variables, which I'll talk about straight in the results. Now here is an important chart that I'd like to call your attention to. What we do here is to plot the contribution of factors to shock absorption. And what we find, so shock absorption capacity, what we find here is that the business environment is the most significant factor that determines a country's ability to absorb shocks. And credit market regulation, in other words, the freedom of credit market is the biggest shock amplifier for most African countries. That's what we find. Other factors that help a country to absorb shocks include the stability of the government because then they're able to make good policies that are implementable. And for recovery, our results for recovery reinforce the Keynesian prescription, that it is stimulative aggregate demand policies that help to accelerate recovery. So policy makers should concentrate on... Tuku, try to write it up, papa. Okay. Okay baby, I think that I just have one more slide after this one. So policy makers should focus on private consumption, you know, boosting private consumption to help to drive this recovery. Other things that can focus on include, you know, boosting exports and government consumption, but things that slow down recovery include this one you see on this slide, You know, weak reserves, high debt levels, and increased imports. And so let's go to takeaway before Alice tells us one more, maybe if you allow a baby on debt. The main takeaway we have here is that there's significant heterogeneity in Africa's capacity to absorb shops and their resilience. But what we found from this study is that there are at least two important entry points for resilience-enhancing interventions. So policy makers should focus on improving the business environment, because this will help to facilitate agile reallocation of resources. And policy makers should also focus on stimulative aggregate demand measures, you know, boosting private consumption, spending more on firms and households, and this will help to accelerate the recovery. And Alex, you want to say one word about, if a baby will let, about the, about debts, how debts can be used for it? Yeah, Chuku, I think, you know, I don't think that we have enough time to do that, so you can take time to complete it or to do that. Yeah, Chuku, we get you, I know you have good negotiation skills, but, but you know, I think this is about it, thanks, thank you, I wish we had more time, because I have two questions for you, a baby, and I have questions for you too. Yeah, but this is a very exciting paper, all of them, Tio, Chuku, and Eric, and of course, I need also to say our paper is a good one. So, Lina, how far can we go? We have some participants here, I see some of them have dropped, but we still have some people watching you, you're welcome to continue. We can open for discussion, how do, how do they make comments or questions? The Q&A has been open, but I don't see any questions in there, so we can ask if anyone has a question, they can write it in the Q&A now, and… Yeah, okay, so maybe Chuku, you say you have some questions. Oh yes, thank you, I'm a bit, no, I was, you know, I was trying to tie the paper that moves from Eric's first paper to Tio's paper with Andrew Guna and Eric together. The big question I have is, now you try to measure, you try to say something about the effectiveness of COVID-19 restrictions, based on distrigencies, and interestingly, we do something similar for the African economy, and your conclusion is that no lockdowns have been affected, although you try to nuance that. And my sense is that we should, I think it deserves more nuance, and because when we compare, we did a similar thing, but with a slightly different approach, we used linear projection methods that the African will combine with the last year. When we compare the effectiveness of lockdowns in Africa versus what we see in Europe, we saw, for example, that the lockdowns were effective in reducing the spread of COVID-19 by around 0.4 percentage points in Europe. In Africa, we got something around 0.1 percentage points. And we also found that it's not all kinds of lockdowns that are effective. There were some, you know, we tried to decide again. So in that sense, I would say that our conclusion maybe should be targeted, you know, you're referring to the Asimogloup paper, because I think what they were saying today is, OK, if you can target this instead of wholesome stringency in position, you know, lockdowns, that would help, you know, target the areas, target the groups to better achieve, to minimize the loss function from both livelihoods and lives. That was just the question I had that. Do we need to nuance that conclusion more about the effectiveness of lockdowns in Africa? Do we need to say more about that? Yeah, I think so. I mean, let's be honest. We have gone all of us through it. And the lockdown has had significant disruptions in livelihoods. So the Asimogloup and others are saying young people, foreign citizens who are in the labor market, but less also vulnerable to the disease, at least in terms of fatality, you know, they can recover quickly. It's it's much better to let them work while you protect those who are vulnerable, because you see the vulnerability to the virus and to the disease is not the same across age groups and sex and other conditions. So taking those factors into account and having a very resilient health care system and social protection programs and also information and the testing, tracing and what you call also abolition, I think countries can manage things better. I mean, the examples they give in in the health community is that of Singapore and New Zealand. And if you look at their experience, they have not locked down that much, at least, but they have really used and tied on this way from the source, the earliest source infection. They have learned how to manage infectious disease. So they have a very total system of testing, tracing and isolating. And when when they see a flare up, they know where to target the lockdown. So so those are the lessons we're highlighting. But we are also providing the significant disruption that has come to the economy. Those countries, for instance, the GDP loss is five percent. Actually, the night light data gives you a much more precise measure of the loss because it's at least for African countries, it serves us well. Maybe, you want to say something? Yeah, you're right. Oh, sorry, Eric, you want to say? I have a word. Yes, the word that I would want to add is that lockdowns are that much more effective when they are complimented by a number of policies that help the the most vulnerable segments of society. So it can take a number of different forms. It could be food subsidies. It could be clinics which are available for the for the poor. But the important point is that lockdowns just by themselves are not sufficient. They can be greatly the effectiveness can be greatly reinforced by appropriate complimentary measures. And I think that's one of the findings of the various projects that have been undertaken. Thank you, Eric. And I just wanted to ask you that even even, you know, lockdown, I think, as Eric said, if it's supplemented by targeted social protection problem, that would help. But of course, that was slow in terms of being implemented because most of the countries were suffering from, you know, the erosion of the fiscal space. But we have seen that. So lockdowns and even the severity is sort of very, very location specific. And I was actually telling about that. If you look at our urban slums, you know, who can say this? And if you just had data just focus on them, you can get actually more more disastrous results than what we're getting at the national level. That is because we are not, for example, you can talk about, Eric has talked about food subsidies. Well, that depends very much in terms of, you know, who is how many people are in those in those surrounding areas. And so it means that for me, what actually worked in some countries is a targeted social protection problem. But then when you talk about lockdown, you know, it was a combination of two things. One in a particular area where you cannot leave a particular radius of praise. And the other one was hours of business. And so these are quite pervasive in some of the areas, especially in Islam areas. I don't know how Theo has left. And I wanted to tell him that maybe we should also have looked at it in terms of some central parts used because they could not actually lower interest rates. What they did was also to use for it, defend the nominal exchange rate. Some of them actually tried to approach, use their reserves to protect the exchange rate in terms of movements. And we can look at that in terms of two aspects. One of them is what was actually ejecting liquidity and what was the effect. The other one was in the other years, in the other months of the pandemic, some central banks used their synergy revenue to pass on to the government. Maybe that had an effect of trying to observe or maybe try to cushion the liquidity shortfall, but something that we can do some testing on. Thank you. Oh, no, no, no. OK, sorry, I think a few of may have been disconnected somehow. I don't know. I saw he dropped off, but we are going to discuss this. But these are very exciting papers. I like your presentations in your papers without it. This is quite exciting. And because we're going, we actually revisit and see what you they said earlier when you talked about coordination, but also we may also look at somehow the initial conditions that is very good in terms of the coordination failure was quite it's very important at that level. It may be something that we can revisit once we come with the tangible results. That is very good. That's very innovative presentation. And it has nice things to do. It shows that we can run some lessons from previous shocks, for example, the group of financial crisis and the group of financial crisis are targeted financial sector. And then to have a pillow, but that this one is actually targeting from the economy from the surprise side, but also the demand side. And we can actually see what exactly happened. It's just like I was telling about that, even when they are all down, there was some a degenerative problem when you have about a crash on the suppose your neighbor died of COVID. We don't react the same in terms of opposing the lockdown or even adhering to business tracing. You actually become more active in terms of precautions. So there are some endogenous measures that we may look for. Thank you very much. Sorry, you were talking about you may look at the exchange channel one to provide liquidity because in some countries and I saw that in Kenya. But on the other hand, they were trying also to protect the the nominal change rate. And you can see that that that that works in a busway because it's actually the destroys the basis of one proposal because if you get the nominal change rate to become the automatic stabilizer or automatic stabilizer, but the moment you defend it, then obviously everything else doesn't go. But it's something that we can try to see how it works through the question. Thank you very much. OK, thank you. Yeah, thank you. Yeah, so so I think we have had a very productive discussion. Lina, do we have any questions from the audience? I see that there is one question in the Q&A. That says, did the hot weather in Africa contribute to the results? I'm not sure which presentation they are referencing. Yeah, it could be about the infection or about Chupu. I think if it's on the path of infection, probably, I mean, a lot of scientists, I think there are a lot of people who are interested in it. I mean, I think there are a lot of people who are interested in it. I think there are a lot of people who are interested in it. Scientists say the temperature in Africa may have been somehow a blessing. But I don't know, there is so much controversy about it. But I believe there is some evidence to suggest that when it is warmer, the virus doesn't like it. So it can be, yeah, I think so. But if it comes to the point of Chupu, I think, I like the typology you have done in terms of the shocks and the recovery paths that have been followed by different countries. It's a very interesting finding. And the factors also that seem to stem the shock and the factors that seem to accelerate recovery are the usual suspects. That means business environment, etc., and all of those institutions, which means this is a time for African countries also to think very hard that if they want to survive another wave of shocks, they have to pick up reforms that they have neglected for a long time. So I think it's a very strong message. Otherwise, you know, the shock could be permanent as you have shown in the simulation. But Abebe, I think I can add to that. And you see that you look at the most of the four paths on the waves. Yes, we have the severity of the waves. And like in Kenya, we were going to the fourth wave. It was actually consistent with the weather changes when it was getting stronger in Nairobi. It was very consistent with the weather changes. What we don't know in the Chukubeteras is that, for example, in the warm up coastal area where the changing temperatures are not very significant, did that have any effect? Because here we are looking at the average results. But you can see that the focus that was being done by the medical practitioners was actually consistent with when it turns cold, not when it turns warm. I don't know whether that is a correlation, but that is the way it appeared. How was it in Abidjan? I think Abidjan was the most successful. I mean, the country was free most of the time. There was never a lockdown. And yet the rates were very low. But you know, Abidjan has a very humid weather. Abebe didn't really like the weather. Any time I watch his up, he's like, Oh, Abebe, so humid. So I think I agree with you that the weather, I think the question is more related to your work. You know, Abebe in Lebanon, Eric, because the science shows that you know, weather affects the spread of the virus. And what I've seen in many papers, some of the ones you referred to, is that they control for the weather. You know, they have this daily weather because the stranger I see is a very high frequency. I think it's a daily frequency. They control for the weather at different times. And they show that, although now, can we really claim that? Because I mean, not Africa, not America, during the summer. And yet, you know, you have increased rates of the spread of the Delta variant. So that's science about the strong correlation between weather and maybe breaking. But I think it's definitely an important point to consider. Thank you so much. Lina, maybe we have really done quite well. Maybe we just tank wider for the opportunity. And, yeah, let's give a round of applause. I think we have had quite a good audience, about 40 at the beginning of the meeting. That is wonderful for the parallel session. Really, Teo, thank you for the excellent papers. And you have made ARC proud. I'm a son of ARC. Thank you, I will be. Thank you very much. And thank you, Eric, for this good presentation. Yeah, we hope to see you in ARC sessions too. Yes. Okay, thank you so much. Thank you. And thanks, Lina, for all your help. Thanks, Eric. I hope it was worth it. It was a very good session. Thank you. Thank you so much and have a good day.