 So I'm going to present quite similar work, but for a different continent we are now traveling to sub-Saharan Africa and I'm presenting joint work with Eva Tasseva at the London School of Economics and my colleague at Susbury, Gemma Wright. So what's the starting point of our paper? Oh, sorry. Well, if a shock happens to household income, there are many ways how households can cope with this, right? And especially in the absence of social protections. So individuals can either self-insure, they can borrow from their friends, from their neighbors, they can use up the savings, they can rely on support from family members, or they can insure privately on the market. However, not everyone can save or borrow and the ability to rely on others may be quite constrained at times of crisis. So at times like COVID, for example, where many people suddenly find themselves unemployed or with earning shock, the ability to borrow from your friend or your neighbor is quite reduced. And on top of that, private insurance against a job loss usually does not exist in many, yeah, in general. So in these cases, government policy response via social protection benefits is key for redistributing resources and providing social assistance and insurance. So this, how is the situation of social protection in sub-Saharan Africa? Well, we actually know from the literature that a large share of benefits do go to the poor, but we know that there is limited benefit coverage of the poor and limited effectiveness. So even if people are covered, the amount they receive is often not sufficient to move them above the poverty line. We also know that poverty has actually increased in sub-Saharan Africa following the COVID-19 pandemic, but also the surging in prices due to the Russian war in the Ukraine. While at the same time, government spending often has not increased in the region. And there is very little evidence on how responsive social protection systems are to the negative shock in lower and in lower and lower middle income countries. So we think it's important to understand how social protection systems react to such shocks in order to improve the design of benefits. And that's where we're coming from with this paper. We examine the performance of social protection systems in five African countries. We are focusing on two lower middle income countries, Ghana and Tanzania, and on three low income countries which are Mozambique, Uganda, and Zambia. And we study the population coverage of social protection benefits and their impact on consumption poverty in normal times and times of crisis. So even though this session is called something about COVID, we are actually not focusing on COVID as such, but we are simulating an artificial shock to all these countries. We are focusing on two scenarios. One is which we call normal times. So that's a pre-pandemic 2019 situation. And then we induce a crisis situation where we simulate a hypothetical reduction to household earnings or employment. And similar to David, we use micro simulation tax benefit models. We are using models for Sub-Saharan Africa, and all of them have been developed in the Southmore Project, which is quite an important project of UNU wider. We use nationally representative household service to calculate benefit entitlements, tax liabilities, and household net income in normal times, and how it changes during our crisis situation. And we study the effects of benefits on household consumption. So we are trying to answer two questions. First of all, we assess the extent to which social protection benefits provide support to household in normal times, which is quite important because the better households can cope in normal times, the better they are prepared with an income shock. And secondly, we examine how effective benefits are in protecting income and consumption during crisis, which is called the automatic stabilization characteristic of existing social protection system. Because the more responsive policies are to changes in people's circumstances, the more insurance and income consumption smoothing they provide when a shock happens. So why are automatic stabilizers important? We have already seen that they played quite little role in protecting poorer households during COVID-19 in Latin America. So we want to know how well they provide support in Sub-Saharan Africa. And automatic stabilizers are the in-built flexibility of existing benefits to respond automatically to a change in the household's income situation. We know from the literature that better automatic stabilizers decrease the variation in household incomes and consumption and provide social insurance against risk. And they also decrease poverty volatility over the business cycle and redistribute resources. However, a lot of this literature is actually focused on high income countries. So what we want to add to the literature here is the focus on Sub-Saharan Africa. To continue, why are automatic stabilizers important? Well, there are many advantages of automatic stabilizers over discretionary government responses because if you already have benefits in place that take changes to the household's income situation into account, there is no extra government needed when a crisis actually happens. So there is no time delay between government decision and new policy. And having good automatic stabilizers in place also ensures that support is provided as long as needed and targeted to those in need. And policy provision is already there via existing administrative and infrastructure. So you don't need to create new policies and new administrative channels when a crisis or a shock happens, which also means the policy makers are freed up to focus on the idiosyncratic and anticipated aspects of a crisis. So the additional risks when a crisis happens. However, there are also constraints to automatic stabilizers. So by design, policies may not respond automatically to fluctuations in household incomes or only respond with a delay. And we see this in many Sub-Saharan African countries because the design of the policies is not that they have a direct income means test, but they use proxy means test. So they use information in the household that is more stable and also information from the past to kind of proxy means test whether a household is eligible for a benefit or not. There is also limited effectiveness due to limitations of existing policies like, for example, gaps in coverage or low value of benefit payments. So if the benefits are not adequate to improve the household's income situation in normal times, they are most likely also not adequate to provide support when a shock happens. And of course, there is a very important constraint in Sub-Saharan Africa, which is the limited fiscal space to expand spending in crisis. So for example, the inability to borrow, which limits the impact of policies. However, what we've seen during the COVID-19 pandemic is that many countries did have some room for implementing emergency response to the income drops. So these are the benefits that we are focusing on. You can see that there are non-means test benefits in Ghana and Zambia. There is an old age benefit that is quite important in Uganda. And it's also the only national policy that is included in Uganda. There are also some farmers, agricultural related benefits in Zambia. And then there are some means test benefits like social assistance related benefits in Ghana, Mozambique, Tanzania and Zambia. And social insurance pension in Ghana, Mozambique and Zambia. So these are the benefits that are included in the South Mood models. And these are also the most important programs that provide cash support or quasi cash support to households in these five Sub-Saharan countries. So what are the characteristics of these benefits? Well, we know that there is an eligibility test for means test benefits, which includes an income test in Mozambique and Tanzania. However, most of the support that is provided actually relies on proxy means test, such as food insecurity, vulnerability. Also, there are categorical definitions like households with children, female headed households or households with a disabled member or chronically ill member. Unemployment insurance programs often don't exist or they are just in development right now and they often only protect a very small share of the population. And there is overall little spending on social protection as percentage of GDP. So for example, it's 1.7% in Tanzania compared to 3.8% on average in Africa or 12.9% in the world globally. So as I said, we're using SouthMod models and household budget service for our analysis. And we simulate two types of shocks. One is an earning shock where we reduce 10% of individuals' earnings and the other one is an employment shock where we randomly move people out of employment and until we reach an aggregate earnings fall by 10%. Why are we not using the COVID shock? Well, we want to use the stress testing methodology developed by Atkinson and we want to keep as many factors constant across the countries as possible. So we want to introduce the same shock to all the countries to see how they react to this drop in incomes. So let's turn to the results in normal times. This is the benefit coverage of individuals living in households which is at 54% in Ghana and also quite high in Sambia and quite low in all the other countries. If you look at whether the coverage is due to non-mince-tested benefits or means-tested benefits or social insurance public pension, you see that a lot of these benefits in Ghana are actually coming through non-mince-tested programs while in Sambia, 40% receive non-mince-tested programs and 23% means-tested benefits. The situation is quite different in the other three countries. Now here we show the benefit coverage by income, green tiles or consumption green tiles and we see there is some progressivity in the coverage in Ghana, Sambia and to some extent in Tanzania. We usually see that it's more progressive when we focus on consumption groups rather than income groups which shows that most of these countries actually focus on consumption poor households. Now if we look at poverty, what we show here is the total poverty rate based on consumption and the extreme poverty line as defined by the World Bank and we also see how the poverty situation would look like if there weren't any benefits in the countries. To better understand where the programs that are currently in place reduce the situation and we only see small effects in Ghana and Sambia and very negligible effects in the other three countries. Okay, so I'm skipping the summary for normal times and I'm now moving to the crisis situation. So now we have again the benefit coverage in normal times which I've just presented to you and then how the coverage changes once we introduce an employment shock and an earning shock and what we see is that actually benefit coverage does not change. So this means that there are hardly any automatic stabilizers in terms of benefit coverage built into the current social protection systems in sub-Saharan African countries. If we look at the same impact but now again for consumption poverty rate, again the first three columns is what I've just showed you and then the last three columns is whether the situation changes once we introduce the shock and if you just look at the final column you see that actually there are hardly any poverty cushioning effects across all the countries. So even in countries where there are more advanced social protection systems like in Ghana or Sambia they are not able to cushion income or consumption shocks during times of crisis. This shows how mean net incomes changes across the income distribution and again we see that there are drops across the distribution and what we were interested to see here is actually the blue bar where the means test the benefits now chip in and cushion the income shock and you can see that there are hardly any effects or no effects at all. You can't see in any of the countries these bright blue bars popping up. So in conclusion we assess the effectiveness of benefit systems to respond to negative shocks in five low and lower middle income countries in sub-Saharan Africa. The benefit system in all countries is ineffective in stabilizing income and consumption during crisis. Even though benefit coverage is higher in Ghana and Sambia there is almost no cushioning effects for the poverty situation and there is almost no benefit coverage in the other three countries. The simulated shocks to earnings and employment of course lead to a reduction in net income and consumption and to an increase in poverty and the benefits are not responsive to changes in persons earnings or employment because they are universal within a certain group and they are linked to proxies of income and not income itself. So they are not designed to be automatic stabilizers across the five countries. In any case, designing strong benefit stabilizers is important to prepare for future crisis and that's one of the main takeaways of our paper. Thank you very much.