Um, good morning, everyone, and welcome to our, um, World Bank Development Economics Research Groupe seminar series on COVID COVID-19. Um, it's a great pleasure to, uh, be, uh, chairing, uh, today's session. My name is Art Gray and I'm the, uh, um, director of the, the research group. Um, today's, um, subject is development policy and COVID, Different approaches for developing countries. Um, uh, our two speakers today are, um, first of all, we have our very own Norman Loaeza who is joining us from, uh, from the, our DCRG presence in KL, so it's very late for you. Uh, Norman, thank you for joining us. Um, Norman Loeza is a, um, Normalize is a, is a lead economist in the development research group, um, and needs a little introduction to World Bank and many other colleagues who are on this call. He's, uh, worked extensively across a wide range of, uh, topics in economic development and he currently leads the, um, uh, the, the DCRG hub in, uh, Kuala Lumpur. I'd also like to, um, welcome, uh, Titan Hamon, who is, um, uh, Uh, who's going to be our second speaker today. Um, Titan is an assistant professor at UC San Diego. Um, his work focuses on macroeconomics, labor, and growth. Um, and he is joined on the call or hopefully will soon be joined by his co-authors on this project, Minkki Kim and Mitchell Van Buren, who are PhD students at UCSD and also David Lagacos, who is, um, I'm sure well known to many of you, um, and is, uh, is also on faculty at UCSD. So we will, um, run this, uh, with, um, sort of two presentations of about 25 to 30 minutes each, and then we'll open up for Q&A. Um, the best way to send in your questions is to do so through the, um, through the chat function on Webex, please. Um, and if you're watching on YouTube, you'll also be able to send in, um, questions, uh, through the chat function. So without further ado, we'll turn over to, uh, Norman to begin. Many thanks, Art for the nice uh introduction. Let me share my, my presentation. Today I will talk about the costs and trade-offs in the fight against the COVID-19 pandemic. From the perspective of developing countries. Well, we know that the world is going through the worst pandemic in 100 years and the worst recession since World War II. In 2020, it is expected, it is projected that the world economy will grow 7.7% points lower than was expected, with 90% of countries experiencing negative growth. Well, what a difference a few months make. In this graph on the right on the horizontal axis, we see the projections made for growth in 2010 2020 in January of this year, and on the vertical axis we see also the projections for 2020 but made in June. As you can see Countries are very far from the 45 degree line. Most countries are well below some countries even by more than 10% points of a contraction in GDP growth. For developing countries there has been a combination of two large shocks. One is external. With export demand, commodity prices, tourism remittances, and external finance going to record lows and a negative domestic shock which in some cases is even larger. Partly because of the pandemic itself, illness, uncertainty, but mostly because of the measures that governments have undertaken to prevent contagion, and I'm referring to lockdowns. According to the IMF and the World Bank, There is a severe risk of an even worse outcome. World GDP growth in 2020 could drop an additional 3% points. And this could happen if the lockdowns are extended to the second part of 2020. Now with this drop in GDP growth, there will be a weak recovery, a volatile recovery for years to come. And of course this will have an impact on poverty. According to some conservative estimates, using centered growth projections and assuming no change in inequality, the economic crisis will push 135 million people to poverty worldwide in 2020. This will make this year the first since 1998, the year of the Asian financial crisis, that the global poverty rate. Increases This calculation of 135 million new poor is based on having different poverty lines for different income groups. Now if inequality increases in every country, and I will argue in this presentation that this is likely to be the case, and that is if the Gini increases by about 22%. The number of new poor will rise to 200 million people. And what we see is that in fact it is the poorer countries where there will be a larger increase in the poverty headcount ratio. The regions that will be most affected are in South Asia and in Latin America and the Caribbean. But other regions will be affected as well, of course. Now if there is a more severe growth contraction. Materializes, that is GDP dropping by 3% points more. Now the number of new poor will rise to 422 million. This is more or less equivalent to the population of the whole of South America. Beyond just monetary poverty, there is a loss that is associated with this increase in poverty. This is a loss in nutrition. In human capital, health and education, and in living conditions in general. In fact, according to some calculations, the annual income of the bottom 40% in developing countries could drop by 3013.4% points. This is what has led organizations such as the International Labor Organization to say that about half of the working force in the world, 1.6 billion. Workers who are informal, they could see their livelihoods destroyed due to the continuous decline in working hours brought on by lockdowns. Why would these developing countries suffer more from this economic aspect of the pandemic crisis? The reason is that most developing countries lack the resources, the capacity to deal with large systemic shocks. They are likely to suffer more in terms of worsening poverty, human capital losses, economic disruption, and uncertainty, and this could have consequences that can last for years or even decades. There are 3 structural characteristics that make developing countries more vulnerable to the pandemic crisis. The first is that they have large informal sectors. For the medium developing country, the informal sector is about 70% of employment. These workers have no social protection, no insurance, and Almost none of them have the ability to work from home. And it is certainly the case that Poorer countries have much larger informal sectors, with some countries, the poorest of them, having an informal labor share of total employment even above 90%. The ability to work from home is really hard in lower income countries. This graph is taken from a paper by Dingel and Neyman 2020. According to them, the share of jobs that can be done at home for advanced countries goes from about 35% to 45%. For developing countries, this share is from 5% to 25%, so much lower for lower income countries, and this is clearly related to the fact that they are informal. The second structural characteristic that makes these countries informal, vulnerable to the pandemic crisis is the limited fiscal space. They have risky debt in terms of exchange rate risk and also in terms of maturity risk. Their tax base is small and to worsen the situation, most countries would have run a deficit this year that will be double in 2020. For developing countries, on average, this deficit will be 9% of GDP. For advanced countries, even more, around 12%. This graph shows the fiscal deficit projections for 2020 on the horizontal axis, the projections made in October of last year, and on the vertical axis, the projections made in April. As you can see, most countries will run these very large deficits, so they will be competing for funds and for finance, and that will make it harder and more expensive for these countries, especially developing countries. To be able to cover their financing needs. The third characteristic is poor governance. Meaning corruption. Inefficiency and in some cases even fragility and conflict, and it is the case that Government effectiveness is lower in poorer countries. This limits their ability to counteract these large systemic shocks. So in this context, how to deal with a pandemic crisis, I'm going to argue throughout this presentation that there should be different strategies for different contexts. Dealing with the pandemic crisis should take into account both public health and economic considerations jointly. We should recognize the institutional constraints of governments and the structural challenges. That economies and societies face, particularly in developing countries. And We should consider people's needs and their incentives to comply with health regulations. Well, governments have had really difficult choices to make at the beginning of this pandemic 3 or 4 months ago. In the face of uncertainty, some governments chose indiscriminate, strict lockdowns. With varying degrees of success. Yeah. I think we, without a vaccine and without proper treatment, we can expect that this low, we can expect that this pandemic will be here in the world for maybe 1 year, maybe a year and a half, or even more. So can lockdowns be the foundation of a sustainable strategy? I will argue that no, they cannot. And with the benefit of time and evidence, can governments make better choices? I will argue that yes. But before I tell you about these alternatives, I would like to reflect a bit about why I believe that lockdowns are this strict. Type of lockdowns are problematic. They are ineffective when imposing cities with pervasive, overcrowded dwellings and neighborhoods. In those cases, instead of social distancing, what happens is social compression. Leading to more infections. They are ineffective when they produce massive displacements of people. As we have seen in India, in Kenya, and in Peru, displacements of people, mostly from. Urban centers to rural areas. Taking the virus. Spreading the virus rather than containing it. They're also ineffective when compliance is low, not because the governments. Don't use ways of Enforcing compliance. In fact, many countries, many governments have done it in oppressive ways. No, compliance is low, mostly because there is a dire need of poor people. To come out and work and make a living. These lockdowns are too costly, as I said before, because they carry these massive contractions in economic activity and it's not just about profits in this case, really it's about Families not being able to meet basic needs. It's about putting families at risk of starvation, disease, and crime. And they are costly or too costly when the loss of resources, particularly public resources, affects the ability to provide vital services for education and healthcare that can also have long run consequences. There is evidence from India, Pakistan, and some sub-Saharan African countries that During these lockdowns there have been fewer prenatal visits, more unattended home births. Fewer child immunizations. Lower adherence to cancer and tuberculosis treatments. In fact, according to a recent paper from Johns Hopkins University, for every life that is saved from the pandemic in sub-Saharan Africa, 140 children could die because they have not received their vaccinations. So how do we understand these alternatives to lockdowns, and here I'm going to use insights from epidemiology and economics to Disciplines that have communicated quite a lot in recent months. The first insight from epidemiology. is that there are different vulnerabilities for different age groups. This pandemic is usually related. To the Spanish flu of Of 20 of 1918. Now the Spanish flu killed disproportionately children and young people. The same happens actually with malaria and with many other infectious diseases. COVID-19 is different. It's older adults, 70 plus especially, who are much more vulnerable to to severe cases of the disease. The fatality rate, infection fatality rate, as you can see in the graph, changes quite significantly according to age group, increasing for people who are 60 or plus. And being really high for people who are 80 years of age and above. There are two implications from this insight. The first is that people who are most economically active. Who can actually work are at considerably lower risk. So if they take certain precautions, they could work and be at a lower risk of contracting a severe case of the disease. At the macroeconomic level, the second implication is that Having younger populations, developing countries face lower mortality risk. Now this mortality risk depends not only on age but also on certain comorbidities like cardiovascular disease, chronic respiratory disease, and diabetes. But actually these comorbidities are less prevalent in lower income countries. than they are in more advanced ones. This graph shows us the demographic profile by different regions. On the left column, the most left column you have sub-Saharan Africa. The most right column you have OECD countries. So in sub-Saharan Africa, about 1 in 20 people are over the age of 60 in OECD countries. It's about 1 in 5 people and in the rest of the regions it's something in between for a typical developing country. It's about 1 in 10 people are over the age of 60, so clearly according to age, mortality rates or the risk of mortality is lower for lower income countries. The second insight from epidemiology is that There are in fact different mortality risks for various mitigation and suppression scenarios. For this, I need to introduce very briefly the canonical epidemiological model that is used. One of these interesting important applications is by a team at Imperial College led by Patrick Walker. Well, in this canonical SIR or SIR model, there are 3 groups of people in the population susceptible, infected, and recovered. Susceptible are those who have not been yet infected with the disease and actually could contract. Infected are those who are going through the disease and who are also infectious, that is, they could spread the disease to others. And the last group are those who actually have recovered from the disease or who have unfortunately died. Now in this in the projections that Walker Eal produced. Uh, they Adjust the mortality risk by age considering what I already explained before. The contagion risk is also adjusted by age because people at different ages interact with each other in different frequencies, and that leads to contagion. And Contagion risk also is driven by these mitigation and suppression strategies. In a paper with one of my colleagues here in Kuala Lumpur. We, in addition, adjust for the mortality risk of critical patients according to different qualities of healthcare across countries. There are 5 mitigation and suppression scenarios considered from inaction to lockdowns. Let me show you, summarize the result through this this graph. On the vertical axis we have the mortality rate, and on the horizontal axis we have these different. scenarios from no intervention to suppression. With mitigation in the middle. So the difference between these scenarios is that there are more and more strict social distancing measures. So we can draw 3 conclusions from this. The first is that there is a lower mortality risk across all the scenarios. For lower income countries. The second conclusion is that in fact, These mitigation and suppression measures are able to drive mortality rates down. The 3rd The third, maybe more subtle conclusion is that the gains of going to more strict measures are lower than the gains that are obtained for. From less strict measures and this is particularly true for lower income countries and you can see that by the slope of the curves. The slope for the curves for low income countries are less steep than for higher income countries. Now there's an important caveat that this study does not take into account issues of compliance, so It is assumed that people comply with the different measures and that this compliance does not change across countries. Let's suppose that then that if the compliance is lower in lower income countries, then these curves will have an even less steep slope. Meaning that the gains from more strict suppression suppression measures would be lower. For lower income countries. I already told you what the main results are. OK, so I think that's what we can learn, of course there's much more, but I'm summarizing what we can learn from epidemiology. So number 1, that there are different vulnerabilities for different age groups, and number 2, that there are different gains that come from various measures to contain the contagion of the disease. Now from economics what we learn is that it is necessary to assess public health and economic considerations jointly. It is really remarkable how our profession, the economics profession, has reacted to this crisis, that there is a wealth of papers about this, and I will just try to summarize some of what we have learned, what I have learned from reading all these papers. The basic approach that is taken in our economic models of the pandemic is to embed this canonical SIR model in a macroeconomic setup. In this way, the relationship between the pandemic dynamics and economic activity can be studied. Now in epidemiological models. Social interactions that lead to contagion. Are exogenous. In economic models, these social interactions are endogenous. They are driven by economic activity, meaning consumption and work, mainly. So because of this endogeneity of interactions, this relationship can be studied, and we can derive joint results on public health and economic outcomes. In these economic studies, there is usually a social welfare function. And there is room for governments to intervene and to provide an optimal intervention that can improve social welfare. This is because there are two basic externalities. Infection and congestion externalities. Infection because an infected person may not internalize the bad effects of infecting others. And congestion externalities because a person who needs care in a hospital does not realize that. This person is congesting the Hospital provisions that may be given to other people as well. OK, in these social welfare functions there are two potential losses. One is from fatalities and the other is from economic contraction itself. These models have uh have a. An increasing complexity and it is a bit interesting and maybe even funny to be making economic history out of models that have been present only for 3 or 4 months. Well, the early models. Consider only homogeneous agents. The most recent models like the one we are here next. By Titan and coauthors consider heterogeneous agents, heterogeneous in the sense that they could belong to different sectors, for instance, essential and nonessential sectors. Differences in productivity across agents, for instance, young people could be more productive than old people, and vulnerability, for instance, old people may be more vulnerable to the disease than young people. The complexity has also been in regards to the types of types of measures that governments can undertake, from indiscriminate lockdowns, just blanket lockdowns that prohibit everyone from leaving home, for instance, to what we could call smart measures based on isolating the infected and shielding the vulnerable. Now economic models have also introduced political economic considerations that are particularly relevant for developing countries. For instance, they have helped us realize that there are distributional consequences that the old will benefit more than the young from extended lockdowns. Or more strict lockdowns that the rich may also be more benefited by that kind of lockdown than the poor because the rich maybe can work from home or they can have savings that will help them survive through this extended quarantines. There is also the issue of imperfect compliance that I alluded to before, that is, if people don't have enough, enough support during the quarantine period, they may not be willing or even able to comply with the with the public health measures. And an important political economic consideration is that there is this very clear interaction between mitigation measures, that is the public health measures and economic policy, and it is this interaction that can make both of them either successful or a failure. The second insight from economics is that smart mitigations can ease the trade-off between lives and livelihoods, and I will refer to only one paper, and I will briefly, briefly introduce the paper that we will hear next. As amoglu and co-authors have produced a very comprehensive model. You will see why I'm calling it comprehensive now, but this is mostly for advanced economies. It has been thought for advanced economies. A benchmark in the benchmark scenario in Asimoglus model, the only available measure is a uniform lockdown, a blanket lockdown. In this case, the optimal policy prescribes strict and extended quarantine. And the fatality rate is around 1.83% of the adult population and an output loss of almost one quarter of GDP. Now if we if they then consider departures from this benchmark scenario, one of them is a targeting scenario where there are differential lockdowns across groups of people. Then the optimal policy prescribes targeted shielding of the old and vulnerable and light social distancing for the rest so that they can go out and work. In this case, the fatality rate and the economic loss dropped to 150%. They also consider a scenario where apart from this targeted isolation or shielding, there is testing, tracing, and isolating of the infected. In this case, social losses can be reduced even more. For example, if all symptomatic people are tested and quarantined, the fatalities will reduce to 1/3 of the benchmark scenario, and economic loss to 1/4. Well, in the paper that we'll hear next by Titan and co-authors. This is a model that has been conceived particularly for developing countries, and this is why it is so useful for us and for our audience. It has been conceived for developing countries in the sense that it takes into account the structures of developing countries and it has also been calibrated to developing countries. This is a graphic representation of what I have been saying and I'm coming to the conclusion to this part of the talk. On the left hand side we see the trade-offs between economic loss and debts. On the right hand side we see the effect of this. More smart measures. So on the left hand side we have two curves, one representing in red lower income countries. In in orange we have high income countries. And as you can see, the most important difference is the steepness of the curve. This downward slope represents the trade-off that countries face. In lower income countries, there is a greater loss for every life that is saved or For every death that is prevented. On the right hand side we see how this trade-off can actually be eased if instead of a uniform lockdown what is implemented is a targeted lockdown. According to this graph, what we want to do in order to increase welfare is to move gradually towards zero, where 0, the origin, means zero economic loss and zero deaths. Clearly optimal targeted lockdowns are better than uniform lockdowns, and What is even better is if we add group distancing and also testing, tracing, and isolating of the infected. In conclusion, The trade-off between saving lives and saving livelihoods is difficult. It is excruciating, but it is also real and unavoidable. Developing countries. Have limited ability to cope with the pandemic crisis. Therefore, a single-minded goal of saving lives just from the pandemic is unrealistic and can lead to considerable human losses. Developing countries Face different tradeoffs and because of these different trade-offs they should adopt different strategies. For instance, for poorer and younger countries, more moderate measures are best. And developing countries can ease the trade-offs with coordinated. And pragmatic economic and public health policies. Economic policies directed at relief and recovery. And public health policies being smart and sustainable. They can actually ease a very difficult trade-off. Let me tell you that I believe that if we don't have, if developing countries and even advanced countries, if they don't have a smart and sustainable public health policy, economic policy is going to be very difficult to implement and the economy will probably not revive quickly or as we would expect. Without producing massive loss of lives. Drawing a bit from the WDR, the World Development Report 2014 on managing risk, I think this is smart and sustainable public health policy should be based on these three pillars of knowledge, prevention and treatment. Knowledge, for instance, that can be acquired through testing. For antibodies and testing for antigens and also research and treatment for the vaccines prevention that is about shielding the elderly and comorbid. And using other measures that can actually prevent the contagion of the disease, and I have given you some examples here and treatment that has to do basically with two components. One is tracing and isolating the infected, and the other is improving the capacity to treat critically ill patients. I think the goal in the coming months and probably years is going to be reviving the economy while mitigating health and mortality risks. And what I'm advocating here is that we change from a containment mentality to a risk management perspective. The challenge for developing countries is implementation. This is where the hard part comes, and this is going to require ingenuity for adaptation, a renewed effort by national authorities who may have made some mistakes at the beginning, but who can change, change course now, and support of the international community in terms of better technologies, better treatments, and in the end, hopefully a vaccine that will come soon. Thank you very much. Thanks very much, Norman. Um, let's, uh, turn directly over to, uh, Titan for your presentation. Um, go ahead, Titan. Great. Yes, we see the screen well. Go ahead. Excellent. So, uh, thank you, Norman, for that great presentation. Uh, thank you, obviously to the organizers for inviting us to let us share our work. Uh, today, I'll be presenting, uh, a brief, uh, summary of my paper with, uh, Minky Kim, David Lagagos, and Mitchell Van Buren. The name of our talk will be How Should Policy Responses to the COVID. COVID-19 pandemic differ in the developing world. And the motivation, uh, is very much the same as what Norman kind of laid out. We all know that COVID-19 is accelerating in the developing world right now. And the question, uh, that we're posing is sort of to what extent can policymakers in the developing world learn lessons from what has occurred in the West? And to what extent, uh, will the spread of COVID-19 pose a unique set of challenges that requires a unique set of, uh, policy solutions? And so, our approach, our general approach in a sentence would be that we are going to build a quantitative general equilibrium model with heterogeneous agents, incomplete markets, and uninsurable income and health risk. And so functionally, what this allows us to do is focus on three things relative to these uh homogeneous agent models that Norman referred to earlier. In particular, we're able to study distributional policies and distributional consequences of different classes of policies. We're able to study targeted policies that are focused on protecting or or uh uh shielding certain members of the population, while letting others to go about their business. And the third is, uh, to make sure that policies that are passed are consistent with individual economic incentives, and therefore can be sustained over longer periods of time. Uh, the, in, in terms of, uh, trying to shape the model to focus on the developing world in particular, we focus on five key reasons that we think policies in the developing world, uh, to combat COVID will differ from what we've seen in the West. Uh, I will not provide a lot of evidence justifying these channels because Norman, I think, has done a fantastic job of that already. So we'll just list them. The kind of 5 key things that we want to focus on is that in the developing world, Generally, populations are substantially younger. And as uh risks related to COVID health complications sort of drastically change with age, this is something that we really have to take into account in terms of susceptibility of the overall population. The second channel is the lower fiscal capacity. In particular, we're focused on developing countries' inability to manage large tax and transfer programs efficiently, which is often necessary, uh, to go kind of hand in hand with lockdown policies, which often take away a lot of income, uh, from members of society. The third one, which will turn out to be very important, is the fact that in developing countries, there are large, uh, large segments of the population that are engaged in informal labor. And so we conceptualize informal labor, uh, kind of as unskilled labor, but more importantly, as a, a kind of labor market activity that takes place beyond the purview of the state. So, in a sector where the government has a hard time taxing, uh, regulating and imposing kind of lockdown measures. The 4th, uh, is maybe self-explanatory. It's the fact that developing countries typically have weaker public health infrastructure. And the fifth, of course, is that there are more hand to mouth households in the developing world, that is more people with low assets that are kind of living paycheck to paycheck, who would suffer a lot under policies that take them away from their income source for prolonged periods of time. And so, why do we consider these things in sort of a general equilibrium model? Because looking at this list, and there are other factors we consider as well, several of these forces kind of go in opposite directions. So, for instance, having a younger population would make you less susceptible to disease, would perhaps make you think that in the developing world, COVID-19 isn't as much of a concern as it was in the developed world with older populations. But then on the other hand, of course, we have this weaker public health infrastructure, which pushes pushes in the other direction. Some of the interactions of these channels are even more sort of a high order or nuanced. So, for instance, uh, the presence of the large informal sector will make compliance with lockdowns that aren't aligned with economic incentives of individual households difficult to sustain. However, the more people who kind of uh shirk lockdowns and go into the informal sector, the more that will erode fiscal capacity and the government's ability to uh sustain transfers to keep people kind of at home and complying with these policies. And this sort of uh negative cycle between weak fiscal capacity and large informality is exacerbated when there are large numbers of hand to mouth households, uh, that really have to worry about where their next paycheck is going to come from. And so just a quick recap of what our main results are going to be, uh, is that uh our main finding is that blanket lockdowns are actually much less effective in developing countries than they are in, in developed countries. Uh, and the metric we sort of cite for this is the amount of lives saved per unit of GDP lost under various blanket lockdown scenarios. And we find that in the developing world, uh, blanket lockdowns save about half as many lives for every unit of GDP lost. Uh, and so while, while blanket lockdowns are not that effective, of course, they're still better than doing nothing. But both of these, uh, situations pale in comparison to the effectiveness that we find for age-dependent or targeted shielding policies, uh, in the developing world. Even compared to, uh, initial results about the effectiveness of these policies in, uh, advanced economies, we find that they're even more effective in the developing world. They save way more lives for every. Unit of GDP lost, even compared to benchmark scenarios in the advanced economies. And the real way they do this is they allow developing countries to sort of leverage one of their innate strengths, that is this younger population and very small old population to combat some of their weaker structural problems, that is, uh, inability to sustain large fiscal, uh, tax and transfer programs. And so we'll get into more details, uh, as we go on through the model. So, before we kind of uh give you all the details of how we get our quantitative results, I just want to take a minute quickly to introduce the model, it's kind of main ingredients, why we've included them. I'm not going to have time here to talk about everything. So for those who are interested, I would encourage you to go look at the details in the paper. But I hope you can get a flavor, at least for the main ingredients that we're, we're considering here. So, uh, broadly speaking, the model has kind of 4 primary components. The first is uh households that face uninsurable income and health risk, health risks obviously being those that stem from the COVID-19 pandemic. And households in our model are heterogeneous, and accumulate assets endogenously facing credit constraints on their abilities to smooth consumption. The, the kind of key thing here is that in the presence of these credit constraints, uh, income and health risk can pose especially large welfare problems for households, uh, particularly those that have low income and low savings. To this, we're going to add uh epidemiology dynamics, uh, which have become popular in this literature, in particular, the epidemiology in our model will follow the SICR framework with age heterogeneity that, uh, I think the first paper to, to kind of use this was this Glover et al. paper in 2020. This is the SIR model. That Norman, uh, discussed briefly in his, uh, in his, uh, presentation as well, with one new state, that is the critical state C, which is when people become hospitalized. And this is going to allow us to talk about uh the effects of hospital, hospital capacity on the risks the public perceives related to the health epidemic, and To the welfare losses and the fatalities that might emerge from it. And of course, as again, in Norman's presentation, the disease epidemiology is going to feature externalities, both in the treatment and in the spread of disease. Uh, infected people are going to experience productivity losses and some increased risk of mortality, which is obviously bad and should be avoided. There will be two sectors of production. There will be a formal skilled sector that is taxed and regulated by the government, but there's also going to be widespread informality, which we think of broadly as unskilled production. But the key idea here is that there's limited state purview. The government doesn't have full capacity to uh enforce lockdowns, enforce policies, and tax activity in this sector. And finally, there will be a government, uh, which is going to be subject to limited fiscal capacity and has the ability to, uh, issue taxes and transfers. In the interest of time, I'm not going to have a lot of, uh, uh, time to actually go over the government problem formally. So all we really need to think about here is that we model limited fiscal capacity as sort of resources lost in managing tax and transfer programs. So if a if a country with weak fiscal capacity wants to manage a $100 transfer program, we're going to think of this government as needing to raise more than $100 in revenue from the population to kind of account for these lost resources. And so now, uh, jumping into the details, let's start with the households. So on the household side, we have a continuum of heterogeneous agents that are differentiated uh in terms of their asset holdings. But in addition, in terms, in addition to this heterogeneity and asset holdings and income risk, we also have two age groups, the young and the old. And in the current specification of the model, we're going to think about these groups as fixed, mainly because the timeline of pandemics is sufficiently short relative to lifespans that we don't really need to think about aging, uh, when taking into account the welfare consequences of this. The actual preferences of each of these households is going to be given by the following formula, which is basically saying that households are interested in streams of consumption, they discount their lifetime uh utility derived from streams of consumption. And the new thing that we add here is sort of this U bar, which is capturing the value of Being alive, or in some ways, you can think of it as the analog of, of being dead. And the, the purpose of having this here is to explicitly uh model the fact that households uh perceive some uh utility value to being alive. And therefore, when the pandemic arrives, we'll take sort of uh precautionary actions and preventative action. Actions to avoid dying, even in the absence of government uh policies. Uh, and at the same time, they may also take actions that are, um, you know, against sort of government policies, if it means reducing their own mortality risk or, or reducing their own income risk. And this is, again, goes to the issue of compliance that Norman talked about that we want to make sure we're capturing. So how does the production sector work? Uh, the production sector work in the economy? There's basically two sectors. There's a skilled sector and an unskilled sector. Individuals select into the sector of work, uh, they like, uh, freely. And we have kind of, uh, the skill, these two sectors are differentiated in terms of skill. So we have a, uh, skilled sector where the individual has productivity Z. This is essentially their relative productivity in the formal sector. It's distributed randomly in the population. And in addition to this permanent Component individuals face the sort of normal idiosyncratic productivity shock, set another way. This is sort of idiosyncratic income risk, as in the Ayagari model, following this first order relationship here, and then in each period, individuals choose what sector to work in. So, that's sort of the uh production, this is sort of the production side at the individual level. We can also think about the general equilibrium uh on the production side. And here, we're going to model a final output as a Cobb Douglas uh aggregate of both labor and capital. Capital itself is going to come from both domestic and foreign sources. So we're going to think about developing countries as operating in a small open economy environment. And labor itself is going to be a CES aggregate of labor in the formal and informal sector. And here, we're going to allow cross-country productivity differences to appear as skill biased. So you see here the a term in the labor aggregate, the idea is uh similar to what we see in Caselli and Coleman, that most exogenous productivity differences across countries are in fact skill biased, and we're going to capture that through this a term, which will vary uh from country to country. So, pulling this together now, we've introduced the kind of a household problem, we've introduced uh the sectors of production. Let's just summarize this all together, uh, in terms of what it means for the household borrowing constraints. So, the household is maximizing lifetime utility, making decisions for consumption and savings, uh, uh, to maximize their lifetime utility subject to this budget constraint. Their sources of income are basically their savings, A, a set of transfers they receive from the government, T, which might be augmented during lockdown policies. And then some income source that they get, uh, either depending on if they are in the informal sector, in which case, it's just a function of wage times their idiosyncratic productivity, or if they're in the formal sector, it's the permanent productivity, idiosyncratic productivity, the wage prevailing in the formal sector, and then, of course, government taxes. Note, again, here, there are no government taxes on the informal sector. This again, is reflecting this idea of limited state purview over informal activity. OK, so now, this is sort of where the economic model meets the uh epidemiology model, is we want to ask, what does the pandemic look like in the context of these heterogeneous agent models. And so, uh, we're really going to be using this SICR framework, S. Being uh uh reflecting states of the world where individuals are susceptible, I being the state where they are infected, R being the state where they have recovered from the pandemic and are now immune, C being a state where they enter a critical state and need to be hospitalized, and D, of course, representing death. And what this flow chart kind of shows you is that everyone starts out uh susceptible, and then with some probability, depending on your age, and uh what sector of the economy you're in, whether you're in a sector of the economy that is complying with lockdown or not complying with lockdown, susceptible people will become infected. And then with some probability, again, depending on age, infected people will either recover, or they will enter a critical state where they need to be hospitalized. And once they're hospitalized, whether or not they recover is now going to depend on things like hospital capacity, which we reflect, which we uh uh model with this uh capital theta, and also uh congestion effects. So the number of other people that are in the hospital also seeking treatment at the same time as you. So, basically, once you're critical, you can either become recovered, or you can, uh, sadly, if possible capacity isn't sufficiently large to, to, uh, uh, to treat the disease, you, you may die. And so, uh, one thing I want to emphasize is that this is sort of the transition. This is kind of the evolution of the disease in the context of our model, but each of these states is also associated with some productivity loss. So this again is a link between the health dynamics, epidemiological dynamics, and the kind of real economy in our model. So, when someone is susceptible, they know that they're susceptible, they just go around uh producing as normal, but they may change their behavior, taking into account the fact that things, for instance, like the sector of uh production, can, can influence the probability that they move to an infected state or a critical state or eventually to death. When individuals enter the infected state, we're going to assume that they lose some fraction of their labor, uh income because they experience a productivity shock. And, uh, we're going to represent that just by a fixed loss in income represented by Ada. And when they enter a critical state, we're going to assume that they just can't work, that they're laying in a hospital bed, uh, and that they basically have zero labor income. And of course, uh, when you die, you have no, no labor income. So, as I mentioned here in this figure, one unique state here that we want to talk about is this critical state, see what happens when individuals enter this critical state. To us, when an individual enters a critical state, they have to be hospitalized. And so when you're hospitalized, uh, now, uh, the probability that you recover, or that you die once you've entered a critical state is going to be determined by the quality of care that you're able to receive, uh, in the hospital. And in particular, what we're focused on is the, uh, extent to which uh a country or a or or a public health infrastructure may be overwhelmed by large numbers of infections, which is certainly a concern that many policymakers in the West had in designing these uh lockdown policies. So how do we capture that here? We're going to index by theta, again, the maximum ICU capacity a country has per capita. And so per capita, it's between 0 and 1. You can really think about this is how many ICU beds are available per member of the population. And so we're going to assume when someone arrives in a hospital in a critical state to be treated for COVID-19, if there is an available ICU bed, they will be assigned to it. But if there's not an uh uh available ICU bed, if the number of critical people, for instance, is greater than the number of ICU beds available, that essentially these beds will be rationed in a random fashion. And so the probability that someone receives a bed will be equal to one, if hospital capacities are not uh uh strained, and then equal to uh a random draw, essentially from the available beds relative to the number of people in the population who need them. And what is the advantage of getting a uh ICU bed? It's basically that, uh, in the presence of an ICU bed, you have some probability of recovery. Uh, that is of avoiding death, indexed by uh an individual's age J. And when you're not assigned an ICU bed, your probability of death from being in a critical state actually jumps, and it jumps by a factor of Kappa, where Kappa is really meant to govern how bad is hospital over, is, is, is hospital overuse for exacerbating fatality rates for critically ill patients. And this is something that we're going to try to measure from the data, uh, and that we're still working on going forward. Um. OK. So now the final kind of uh element that we have to include here is sort of the, uh, the lockdown policies. We're going to think about a lockdown policy as a twofold, lambda W lambda Z. When the lockdown goes into effect, individuals are going to lose some fraction of their uh skilled income, the income in the formal sector, which is going to be represented by lambda W. But they're also going to be Less exposed to the disease. And we represent this drop in the exposure to the COVID-19 disease by lambda H. And so lambda W and lambda H together will index the type of policies, uh, countries are pursuing. Lower lambda W, lower lambda H, essentially are stricter lockdown policies that lead to more income loss, but also more mitigation, uh, of the spread of the disease. OK, so that's, uh, basically the setup of the model. I'm now going to kind of jump into showing you some of the initial, uh, results that we had. So first, let me tell you, how do we quantitatively, uh, make this model representative of what's going on in a developing country. We're going to take the kind of standard approach, we're going to solve this heterogeneous agent model for the stationary distribution and calibrate two different versions of it. One meant to represent the uh advanced economies like a a country like the United States, and the other, uh, meant to represent the developing economies. We're then going to allow the pandemic to arrive as an MIT shock. So for those who are unfamiliar with this terminology, it basically means we're going to assume that before the pandemic arrived, nobody actually saw it coming, which in this case is actually a realistic assumption. But then, of course, once the pandemic arrives, we're going to assume that people then have perfect information about the way the, the pandemic unfolds, uh, going forward. And then once we've kind of calibrated the model, we're then going to solve for the transition paths for both, both economies, and compare how each economy fares under different classes of lockdown policies, uh, in a sort of quantitative way. So again, in the interest of time, I don't have, uh, uh, enough bandwidth here to go through all of the details of the calibration. I would encourage you to read the paper if you're kind of interested in the details. I just want to highlight here, uh, the kind of margins that we allow to differ between advanced and developing countries. Uh, the key things that we want to point out here is sort of these differences in fiscal capacity. Uh, developing countries are about half as good at raising taxes. as, uh, advanced ones, they have much lower hospital capacity, much greater share of a young population, and obviously much higher levels of uh labor market informality. Uh, these are kind of, uh, the themes we discussed at the beginning and things that Norman kind of showed us a lot of evidence for as well. So I'll just go on, but I want to say, you know, we kind of have, uh, we've done a very detailed job here in trying to make sure we've captured these markets, uh, these margins, uh, accurately. OK, so the first question you might want to ask then, is, uh, you know, the point of the lockdown is to essentially flatten the curve on the spread of disease. And the first most obvious question is, how good are these policies at doing that in the developing and developed countries. And so what we have here is just the dynamics of the infection rate, starting at the point where 1% of the population is infected in advanced economies. And then the, the red here is the uh trajectory of infections under no lockdown policy in the advanced economies. See, it reaches a peak of around 20%, but under these lockdown policies, uh, they're really effective at actually flattening the curve. And actually, the, the longer and more severe the lockdown, the kind of more the curve is flattened on infection rates. In contrast, in developing economies, we see that these policies, uh, do flatten the curve to some extent, but are much, much less effective. And, uh, you know, just kind of anticipating, uh, the reasons that we'll give you a little bit later. This is really stemming from the widespread labor market informality and the fact that compliance with very strict lockdown policies that are going to impose large economic costs on people that are already Struggling, uh, living hand to mouth, uh, are just not going to be complied with. And when there's no compliance, the sort of health externalities continue to allow the disease to spread, and you don't get nearly the same mitigation that you would get, uh, in an advanced economy from these kind of blanket lockdown policies. Of course, infections are not uh necessarily the key outcome that we're focused on. They are in some sense, an intermediate outcome. Really, what policymakers and members of these uh economies are interested in, is sort of welfare, the amount of GDP loss, And uh the amount of people who die from the pandemic. So we have several different versions of these, which we discussed in the paper. I just want to highlight what we think of as kind of the benchmark lockdown policy, this 28 week lockdown, versus no lockdown scenarios in uh advanced and developing economies, which are, which are here on the bottom. And basically, things I want to highlight, first of all, is that in the, uh, in under a benchmark lockdown policy, you know, GDP losses are much greater in the advanced economy than in the uh developing economies, and fatalities are also much greater uh in advanced economies than in developing countries. So, you know, I kind of first glance, you would look at this and think that blanket lockdowns actually perform better in developing economies. But if you actually take into account, You know, relative to the relative to the no lockdown scenario, how many lives were saved relative to each unit of GDP lost, you can actually quickly see with the mental math, that actually about half as many lives are saved in developing economies for every unit of GDP lost. And this, of course, translates into welfare, but in a way that's not immediately uh algebraically clear that you really need the model, uh, to sort of get at. And so, this is kind of the first indication that we have that, you know, blanket, you know, blanket lockdowns obviously do, do a good job uh at increasing the welfare and developing economies, but they're far less efficient in terms of the trade-off they face between lives and livelihoods than those in advanced economies. And we'll talk in a moment a bit uh about why. So, really, what we'd like to do now is to use the mechanic the mechanisms of the model to try to understand what exactly is driving these sort of differential outcomes in advanced and uh developing economies. And the way we're going to do this is we're going to start with the calibration of the model, uh, that reflects the uh economy and epidemiology and demographics of an advanced economy, and then sequentially add the sort of uh unique characteristics that we've identified. that Norman has talked about in developing economies to kind of see in terms of fatalities, in terms of GDP in terms of welfare, what's really driving uh the outcomes we observe in the aggregate. And so here to begin, we've, we've plotted the number of fatalities, uh, per, I think they should say 100,000, but it says 1000 on the, on the, uh, the, oh, actually, no, it is 1000. I apologize. The number of fatalities per, per 1000 people. And what you really see here is in an advanced. Economy under lockdown, you'd have about 7.8 fatalities per 1000. As we begin to add these, uh, different characteristics of, uh, developing countries, say, the larger informality, the lower fiscal capacity, the fact that they're more hand to mouth consumers, we see just a steady rise in the amount of, uh, fatalities that these economies would experience even under a 28 week lockdown. So, looking at the first four columns here in the middle, The suggestion is that the kind of channels that define uh developing economies as being different from advanced ones, and almost uniformly tend to exacerbate the fatalities uh that would emerge under a blanket lockdown in advanced economies. The one sort of silver lining is something that we have here in the last column, and that's the advantages of having a younger population. And really the kind of key point here is that the advantages of having a younger population, just above and beyond trump any of the negative consequences that come from these other channels. And this is really coming from the fact that, uh, as Norman pointed out, COVID-19 is uh especially heterogen. in terms of the health risks it poses by age. And so when we look at developing economies that typically have average ages, you know, that are in the twenties or in the high teens, uh, there's really just very small segments of the population, uh, that really have a high susceptibility and high infection rates from these, from these diseases. Now, of course, fatalities is only part of the picture. Another thing, particularly in the developing world that we're interested in is, uh, what is the GDP lost from these lockdown policies, and how does it compare to the amount of lives saved? So that's what these orange lines are meant to represent here. It's the percent loss in GDP over the duration of the 28 week lockdown. And so again, we see in the advanced economies, there's a tremendous amount of GDP lost. But once we include the large informal sector, the amount of GDP loss is halved. And the key idea here, again, is something that Norman talked about. It's the fact that in the presence of a large scale, large scale labor market informality, those in the economy who are most in need of incomes, or who have the lowest assets, who are struggling most, uh, in terms of supporting their livelihoods are able to select out of strict lockdown policies to basically avoid lockdown policies by entering the informal sector. And this mechanism alone is able to cut the GDP losses associated with uh strict lockdowns into about half. And so, in terms of the GDP loss, actually, this widespread informality is quite good in some sense. The negative consequence of that, of course, is that when these people violate the lockdown policies and enter the informal sector, you know, they're going to start uh getting infected and through the externality spreading the disease to the rest of society. So as, as we see, there's sort of a great savings on GDP, but it does, of course, cause, uh, fatalities and the spread of disease to tick up in the advanced economies. In, in the developing economies. This is somewhat exacerbated with the poor fiscal capacity of developing economies, which are then because many people are moving into informality, lacking the capacity to then uh issue transfers to the rest of society to keep them also out of informality. The rest of the, uh, once we account for informality, the rest of these channels really, uh, kind of offer just marginal, marginal effects. So, of course, finally, the, the headline number we're most interested in is how do these things translate into uh consumer welfare. And so here in these heterogeneous agent models, welfare is a sort of complicated function of distributional considerations, and these average fatality and average GDP losses. And really, what we see here again, is that, uh, in general, advanced economies on all developing economies and all of these channels tend to experience biggest Bigger welfare losses. Uh, however, the one saving grace again, is this younger population that just at the end of the day, makes them much less susceptible to this disease, allows large portions of the population to just go about, uh, business as usual, uh, and not worry too much about the health risks, uh, posed by COVID-19. And so, one last statement, one last thing I want to talk about is we break down using again, the heterogeneous structure of our model, we break down, uh, the sort of welfare fatality, uh, and income loss results by age. And it's really quite stark when you look at it, that a lot of these considerations are being driven, uh, by, uh, what happens to old people, basically, the most vulnerable people. Uh, in society. And given that this is such a small population in the developing, uh, world, and that they are the ones who are really driving a lot of the, uh, welfare, welfare losses, uh, this sort of environment just begs for the kind of age dependent, uh, policies or advanced shielding policies, um, that Norman alluded to that have been covered in sort of work by Asamoglu, uh, and others. And so, just as a final exercise, we ask what happens if developing economies instead of instituting blanket lockdowns, decided to pursue age dependent policies. And so what we've plotted here is under the same 28 week policy, the amount of lives saved per 100,000 people for every unit of GDP lost under blanket lockdowns and under age-dependent lockdowns. And the key here is, as we already said, in developing economies, blanket lockdowns are about half as effective. They save about half as many lives for every unit of GDP lost. Age-dependent policies are more effective than blanket lockdowns in both advanced and developing economies. So we see here, in advanced economies, they increase the amount of lives saved by about 2.5 times. But in developing economies, it's almost, it's increased by almost a factor of 10. And the key idea here, again, is really, this is these age dependent policies are a way for developing countries to leverage a unique strength that they have, that is a young working age population that is not very exposed to mortality risk related to COVID-19. In order to overcome some of their structural weaknesses, for instance, a weak fiscal capacity and widespread labor market informality. And while developing countries might lack the institutional, uh, uh, uh, power to institute blanket lockdowns, uh, with full compliance for long periods of time, they do seem to have sufficient capacity to do this for a smaller isolated part of the population. And if they really focus that on those who, uh, uh, face the greatest risk, namely, The, the old, and those who have all sorts of comorbidities, they can actually craft very effective policies, uniquely effective policies, I should say, uh, for the developing world. And so as I'm out of time, I'm just going to wrap up briefly. Uh, the kind of main conclusions that we have in this preliminary, uh, uh, past with our model is that blanket lockdowns are, of course, better than nothing in developing economies, but they're not very effective, particularly not in mitigating the spread of the disease, because of sort of a widespread noncompliance. Now, of course, while that's sort of a negative thing, the silver line is that the case for shielding the old, and these targeted uh shielding policies are much stronger in developing economies. Again, because they allow them to leverage sort of their unique strengths to to combat some of these structural weaknesses that they might face. There's, of course, lots of caveats, and we continue to update, uh, our, our conclusions in sort of real time as better estimates of both the epidemiology and economic consequences of lockdowns become available. And In the moment, we're actually, uh, going through a big, uh, uh, uh, extension of the model, where we are taking into account a richer, uh, kind of intergenerational household structure, thinking more about children, and how lockdown policies also interact with things like school closures, uh, and sort of, uh, congestion in the household sector and things that Norman kind of talked about. So this is stuff that's kind of underway. And hopefully, uh, we'll be able to share that with you all, uh, you know, through our working papers in, in the coming months. OK, so that's it for me. Um, and yeah, thank you very much for coming and thank you very much for listening. Thanks very much, Titan. Um, uh, uh, and, uh, thank you also, Norman, for your presentation. So we're gonna, the, the, um, sort of the virtual floor is open for, um, comments and questions from the audience. And while people are, uh, sort of formulating their, uh, questions and typing them in, and, and I'll, uh, I'll I'll direct them to the, to the, to the speakers. Let me, um, ask a couple of, uh, questions to kick things off, um, both to, uh, uh, to, to, to Norman's, uh, uh, and, and also to Titan and his co-authors. So just starting with, um, uh, Titan and his co-authors, uh, on the second presentation, the, um, You, you get a huge amount of mileage out of one key difference between rich and poor countries, which is that, uh, poor countries have younger age structures in the population. But, um, I wanted to ask you to speak a little bit more about sort of the, the, the, the science of the benefits of that, um, because, you know, we know that in rich countries, older people are more likely to die than younger people. Um, but we don't, I don't think, at least to my knowledge from the little that I've read about it, we don't know whether it's because they're young per se or because the young are healthier. And to the extent that in, you know, poor countries, uh, yes, the age structure of the population is younger, but if people have sort of low, uh poorer baseline health, um, combined with other risk factors like say, um, high exposure to air pollution in urban slums and so on, then, you know, it may be the case that you don't have the benefits of that sort of much lower mortality among the young that, um, that is sort of very important for the differences you're finding between rich and poor countries. Um, And I'm just gonna fold into that one question which came from the audience, um, already, which is that when thinking of, you know, even if the young do have a lower mortality risk, um, you know, we know, or we, we're, we're starting to learn that COVID can have sort of permanent health, um, adverse health, uh, consequences for the survivors. And then if you have sort of young survivors who have to live with the sort of COVID-induced disability for more life years, um, then that might, um, also affect some of the, the trade-offs, um, that you're finding. Um. The, uh, just a small kind of nerdy point as well, which is that, um, so you point out that because there's a large informal economy where people can avoid lockdowns, that, um, sort of lockdowns are less costly in terms of GDP, of course, we won't see that in the data though because it's informal sector GDP. So that, that will, um, this is not to take away from the value of the point, but it's, uh, uh, it's an important one when sort of, you know, tracing out the economic impacts, uh, with data. Um, and then one last, um, question for Titan's, uh, uh, uh, paper is that I was actually quite intrigued with what you were saying about the intergenerational aspect of, um, of the, the model that, uh, that we're going to develop because here there's this interesting question that sort of shielding the elderly is a good idea. But sort of the incentives to shield the elderly are naturally going to be different if it's your parents or your grandparents versus somebody else's grandparents. And so thinking about, you know, a differential, you know, degree of altruism, altruism that is going to exist sort of within households versus between households might give some, some mileage. And, um, uh, for Norman's presentation, I wanted to, you know, so you and you both presentations emphasize the importance of smart containment policies of various sorts and particularly age-dependent shielding as sort of the key difference in poor countries versus rich countries. Um, And, you know, and subject to some of the caveats that I've mentioned before, that that those seem like um uh good observations. But it would be interesting to hear both of you say a little bit more about the challenges of implementing those in poor countries, which are greater, right? So, you know, crowded living quarters, it's harder to shelter the elderly in poor countries than in rich countries because you've got, you know, 10 people living in a single room, um, or even, you know, the aspects. Of smart containment that are based on testing. I mean, you know, rich countries can barely muster adequate rates of testing with all the resources that they have available. Um, you know, it's hard to imagine, you know, the, some of the poorest countries where this is most relevant having either the, you know, financial or sort of human infrastructure to be able to actually implement these smart policies. So let, let me, um, just quickly look at the two other questions that have come in. Um, Uh, so one question that's coming from the audience is saying that the selective, uh, from Mark Thomas, the, uh, selective lockdown result is very strong and actually attracts attention, but it doesn't seem to have had any policy traction yet. Two questions. Do you think that the differential impacts in developing economies is a robust prediction and what What policies would need to be in place to make it more acceptable to the public? Um, and actually, now somebody else is coming in, uh, so, uh, uh, and sorry if I'm mispronouncing your name, Nusrat Babi, um, who's raising the question, based on the number of deaths in Bangladesh, young people are dying more than older populations. Uh, this is mostly attributable to malnutrition, pollution, poor health facilities. Uh, so I think that's, uh, echoing a little bit the question that I had asked, um, earlier. Um, so let me, uh, um, stop there, uh, and turn over to, um, you know, maybe Norman, do you want to go first with a couple of, uh, reactions? And then, uh, I see David, uh, Lagaos is joining us on video as well. So if David and Titan want to respond, let's maybe try sort of quick responses to this so that we have time for a second round if there's more questions. Um, so go ahead, Norman. Thanks, thanks. Well, I think that these smart policies are good under almost all the scenarios for advanced and for developing countries, and I agree with Titan and David and the co-authors that they are more useful for. for developing countries. But I can see the point that the implementation challenge is also, is also, is also steeper. It's also harder precisely because they are smart policies. These are not these as opposed to just uh simple policies. So the challenge is implementation and I think that that's something that we can contribute as economies. But I see mostly this as a challenge for people in engineering, in logistics, and for, for, for ministries of health. I think once they have decided that they will pursue this combination of smart policies, then they can devise the right logistic measures to implement them. What I see, what I have seen up to now is that countries are just obsessed with implementing this. A uniform strict lockdown so they haven't seen beyond what all the possibilities can actually bring about once they change their mind to the right sort of smart combination of policies, they will, they will, I believe, start thinking about the implementation challenges. I see that they are difficult but not insurmountable surmountable. On the question of whether people are more younger people are more susceptible to the disease, I think that the science is clear that it's age-related and there are certain comorbidities that are driving, and these comorbidities are more prevalent in high income countries. Now. Why do we see that in Bangladesh, for instance, more Younger people who are dying because there are more younger. There are more younger people in a country like this, but we were, I think, in a Uh, Titan's presentation as in mind we were referring to the differential rates of infection. I'll, I'll stop at that. Hey, just before I turn over to Titan and David, I'll just toss in a couple of other questions that came in through the chat that are all kind of in the same vein. So don't worry, we're not expanding your to do list. Um, Roberto Cheng, uh, says thank you for the excellent presentations about wondering if recommending smart policies to less developed countries would work given the difficulties they have to implement even simple policies. So that's a little bit the, the, the, the point you were speaking to, Norman. Um, uh, from, uh, Han Yi Lee, we have the question about shielding the elderly. Um, and, uh, sorry, I'm just paraphrasing here, but the question simply is, uh, about the feasibility given that, uh, there's so much more, you know, co-residents of young and old, um, in developing countries, um, how to do that effectively. And um there's also a question from uh Maria Gabanel who was asking in terms of implementation, um any thoughts on spatial heterogeneities and particularly urban versus rural areas in developing countries. So now I guess you do have a pretty long to do list, but over to you, David and I. OK, thanks. Um, so Titan, can I, I get, we agreed that I would take the lead on the questions, at least at first. So thanks for this, um, great list of questions. I took frantic notes, Art, you gave me a whole lot, uh, gave us a whole lot at once. Let me just make some comments about, um, yeah, Norman answered the question, I think, pretty well about age versus comorbidities. Let me add a couple of thoughts on Kind of where I think the age-dependent policies are the least likely to work, you know, the key, the key, it's about age, but, you know, if you want to think more broadly, it's about age or people with compromised immune systems or just compromised ability to fight. Disease and once you take a country with high prevalence of HIV, suppose you have a country that's got 25% infected with HIV. And suppose another 2 or 3% are old. This, I don't think the blanket lockdown would work in such a country because now you're shielding, you know, maybe 30% of the population. Uh, I don't think the fiscal space is there. So, one of the things we're working on now is doing some kind of simulations on what type of country would actually be able to shield. The shielding works the best in the model. Putting aside implementation stuff when you have a small number of people. Once you expand it to, you know, a huge fraction of the population, I think it, I think it wouldn't work. Um, two places that I'm the least worried about are hypertension and diabetes. I think kind of I read in the literature, um, is that that's clearly um linked to age and in the case of diabetes, uh, much more prevalent in richer countries than poor. So I, I think that's, um, I think that's not gonna be the main implementation challenge. Malaria, um, I do worry a lot about. Um, I don't think we kinda know enough yet about interactions between malaria and um And um And COVID, I, um, I've had malaria. Malaria is miserable. I wouldn't wanna have malaria and, um, And COVID, and the, and the joke that we keep making amongst the co-authors is that the only good news is Trump, Trump has given us the solution. You just take hydroxychloroquine and then you knock out both the malaria and the COVID. That's a joke, of course. Um, I, in all seriousness, yeah, these comorbidities are super important, and they're super important for thinking about implementation. So let me say a little bit more about um implementation. Somebody said, um, But this hasn't gotten a lot of policy traction. Yeah, I'm not completely sure that's true. Um, as we've been looking. You know, kind of looking through all, you know, all, all possible policy discussions we've been able to come across. It seems like this has been, has been taken up in a bunch of different countries. Turkey is a famous, famous example. They, they have mandatory lockdowns for people over 65 and for kids, interestingly enough, Colombia has done the same. Serbia has done the same. So it's not that there's no countries that have done this. A number of them have. have, uh, have gone to some policies like this. Still, I think the reason why it's not more widely spread is the various challenges of implementation, which, um, require a lot more thought. It's, it's easier said than done. And, um, so let me talk a little bit about implementation. Um, So, um, I think the kind of the key challenge that, that came up in, in, um, Titan mentioned it, but several audience members mentioned it is the, is the cohabitation. So one of the things I think that as a practical matter has to be considered very, very seriously for this to work is uh whether schools are going to be able to be open. In most advanced economies, if you look at the matrices of who interacts with who, there's not that much interaction between the people over 65 and the people who are like under 15, let's say. And the reason is they live, um, they live separately for the most part. Of course, there's people who live with their grandparents in Germany, and the United States, and other rich countries. In low-income countries, almost every older person lives in a household with younger people. And so, even though young kids themselves may not um have a high chance of succumbing to COVID. Um, if they're bringing the disease back and they're infecting their grandparents, any, any issue about age-dependent policies is really, um, any attempts rather to impose age-dependent policies could completely backfire. So we're trying to analyze that formally now, but I suspect what we're going to find is that you're going to get a, a serious, um, You know, seriously less effective age-dependent policies if the kids are in school and they're coming back to infecting the grandparents. So that's a, that's a very important complementary policy issue, and I'd love to hear thoughts from people who've, who've studied education more. Of course, that's not costly because you have a whole generation of kids who are gonna have less education. Um, you know, how big, how big of a deal that is, is something that needs to be calculated. Um. The, um, Roberto asked, you know, how, how hard is it to implement given that they can't even implement the, the blanket ones. I mean, that's a very legitimate point. You know, one of the things I was kind of been thinking about is how the transfers actually get made in practice. So you have to think about how you're going to make the transfers in practice and how you're gonna impose, impose things in practice. It, um, So I guess I'll just, the, the first broad reaction to that is, is in our paper, we've assumed that um That there's absolutely no compliance whatsoever in an informal sector, and our informal sector and our model looks, looks like what Norman mentioned earlier, the typical developing country is about 70% informality. So just to be clear, our, our model prediction is about a scenario where 70% of the people don't comply at all. Um, in fact, I think it's even higher in our model. I think our calibrated model is an informal sector that's, it's in the 80s. So, so it's not that you need perfect compliance for this to be effective. If you can even get partial compliance, this can still be much more effective than the blanket ones. In terms of how to get the transfers only to old people, I mean, I, in a colleague of mine in Ghana was saying Ghana's doing the transfers through the electric company. Why would they do that? Well, that's a place where they have a database of, of, uh, taxpayers, or at least people who are paying for their electricity. And you could have, you have to show an ID to get your, um, to get your To get your payment, and they have to check that it's you, they can also check your age. The challenge there, of course, is that there's not that many IDs. I was talking with some of your colleagues um in the ID, um, identification group here. I mean, that's, it's all the more reason to, to build identification systems that allow this thing to work. But I agree with Roberto that this Um, this is gonna be hard to implement and that, and that, um, I don't, I don't know if it's insurmountable. I think even with, even with some, some effectiveness, we can still get, you can still get a lot better than a blanket lockdown. Um, spatial heterogeneity, it's very important. I mean, I, again, like I say, Ghana is the developing country I know the most about. I'm in contact with colleagues there every week. There's, there's hardly been any spread so far to, to rural areas. I know that's not true in every developing country by any stretch of the imagination, but, um, one component of the lockdown can be restricting movement, to the extent possible of older people to the cities. There's lots of migrant labor who that typical times comes to cities to look for work, to the extent possible to restrict, restrict people's movement out of their rural villages. That may be uh something that, that is, um. is that, you know, as a compliment to trying to keep people in their homes, just keep them in, in rural areas, keep them away from big cities where they're likely to be um infected. Um, so I could say more about a number of these things. Let me just add one response to what Art said. I'm surprised you said the formal is not in the data. I guess I, it's certainly true that not all formal is in the data, but one of the, one of the, excuse me, informals in the data. Uh, you know, one of the things we have in mind by the informal sector is, you know, rural agriculture. I mean, that's hard to measure. A lot of that certainly is going to be in, in, um, You know, in the data, at least crudely, and then, you know, like retail trade in the cities, I, the several times I've talked to national income accountants about agriculture, they say, well, that's not even the biggest problem. We don't know what to do with retail trades, we estimate employment, we have some crude estimate of their margin, that's how we get value at. So I think there are crude estimates to keep, keep a lot of this stuff in the data. Certainly not all of it's in the, in the data. Um. Yeah, so I, I could say more, but maybe I can stop there and let others, uh, yeah, I'd like to just add one thing. I think David's answers were fantastic. It's pretty much said everything that I was thinking as well. I just really want to parrot one point. Uh, and this is the, the point raised about, um, you know, our age dependent or sophisticated policies, uh, able to be implemented when even simple ones can't be. There's sort of the implementation. On, on behalf of the government. But I, I think I want to re-emphasize, as David did, and Norman did, there, the compliance issue is almost way greater than the implementation issue. And one of the reasons the age dependent policies work better is that they are more aligned with economic incentives of individual households, and hence endogenously lead to more compliance. So there, there is sort of this implementation channel on behalf of the governments that we have to think about, but Really, I want to just emphasize that the model allows for the fact that people may not follow the rules, especially if they're not in their interest. And one reason that we're finding such strong effects of these policies is that they are more consistent with individual incentives and hence, in sense, you know, have higher levels of compliance. So I just want to parrot that point. It's not something we're ignoring, it's something we're very explicitly dealing with. Yeah. Thanks very much, Norman and Titan and David. And, uh, that, that's actually a good note to wrap up on because I think, uh, Norman started out by, by, um, reflecting on what economists bring to the table and, you know, incentives, incentives, incentives is often what we're, uh, we're bringing to the discussion and, and here, uh, you know, it's a very good point you're making at the end. We did, so we have 1 minute, and there are a few other questions that have come in from the audience. So, why don't to wrap up, I'll just quickly summarize what those comments. are, but we won't have time to go back for, uh, for responses, but, um, hopefully they'll be helpful to, uh, to, you know, both, uh, both teams, uh, as you carry on this work. So from Josh Lake, we have a question about long-term, what, what do you see as long-term structural changes in the labor market due to the coronavirus pandemic? Um, on that one, I'll actually advertise that we do actually have an upcoming, um, uh, e-seminar on labor market implications of, uh, of COVID. So, um, I hope that Josh will join us for that. Um, And then we had one or two other questions coming in that we're still emphasizing the point about the difficulty of, um, implementing age-targeted policies and particularly sheltering the um elderly in developing countries. Um, uh, Luke Kong came in with a question, um, uh, about sort of the external factors. How do we think about age-dependent Policies in countries that say rely on international tourism. Um, I'm, uh, I'm, I'm reading into the question a little bit here, but I, I would imagine that many tourists tend to be elderly and so that would be, uh, particularly harmful to economic recovery. Um, and then from, uh, Ikaari, um, Uh, we have a question about how it is, um, that countries can avoid this sort of cycle of lockdowns and relaxations followed by lockdowns as COVID comes back and as we've already seen in a few countries and we're anticipating in, uh, uh, unfortunately in, in other countries. So, um, again, I'm sorry, we don't really have time to go back, uh, to solicit responses to those very good additional questions, but I wanted to, um, thank, uh, uh, thank our speakers, uh, Norman and Titan and David for, uh, for their excellent presentations and responses. The comments, um, and, um, thank, uh, our audience members for joining us and again a plug for the upcoming seminars that are listed at the bottom of the announcement of this one. So hope to, uh, see you on Webex in the near future again soon. Thank you very much, everyone. Thank you, everyone. Thank you.

showAllTranscripts

duration

PT1H30M1S

scene7File

worldbank/DifferentStrategies

scene7Domain

https://worldbank.scene7.com/

scene7FileAvs

worldbank/DifferentStrategies-AVS

title

DifferentStrategies

description

DifferentStrategies

showTimestampAndTranscript

yes

sec-spacing

section-none

sec-spacing-bottom

section-none

sec-swoosh

sec-swoosh-type16

style

default, transparent-white-components, heading-center, add-style, bg-primary-blue-70-gradient, bg-dynamic-height

col-xs-12

col-sm-12

col-md-3

col-lg-3

col-xs-12

col-sm-12

col-md-7

col-lg-7

add-style
lp-body-content

June 23, 2020 -- This seminar explored the economic and social costs of the pandemic and measures to contain it. Panelists discussed why alternatives to indiscriminate lockdowns are needed in developing countries and what these “smart” alternatives may consist of. The lockdowns may be easing, but the fight against the pandemic is likely to last for months, if not years. Coordinated and sustainable public health and economic policies are needed before the economy can begin to recover.

lp-heading-top-medium

lp-heading-bottom-medium

col-xs-12

col-sm-12

col-md-2

col-lg-2

sec-spacing

section-none

sec-spacing-bottom

section-padding-between

style

default, video-transcript

everything

https://webapi.worldbank.org/aemsite/everything/search

true

{"facets":[],"orderby":"contentDate desc","select":"*","searchFields":"*","search":"*","filter":"contentType eq 'Video'","top":20,"skip":0}

See Rest of Series

yes

sec-spacing

section-none

sec-spacing-bottom

section-padding-between

style

default, bg-neutrals-15-gradient, remove-border, lp-mini-card-primary

Different Strategies for Different Contexts: Fighting the Pandemic in Developing Countries

RELATED

See Rest of Series