 All right, so yeah, thanks for everyone for joining the presentation session on social protection. My name is Yeselastonen, I'll be working as the chair of this session. I'm a research associate at UNU wider, working mainly on tax benefit micro simulation modelling in our SouthMod project. I'd also like to welcome our presenters. We have Omalola Adeola from the University of Cape Town in South Africa, Valentina Martinez-Pabon from Tulen University in the U.S. We have Jara Gomez from the Federal University of Pernambuco in Brazil, Amakametu from the Namdi-Atsikiva University in Nigeria, and finally Christof Troupat from the German Development Institute. The five presentations are based on very interesting papers covering research on the impact of COVID-19 and related social protection measures on poverty and inequality in both South America and also Africa. So in terms of practicalities, we will have these five presentations back to back and we'll open it up for questions and answers at the end of the session. Presentations will be around six, seven minutes each. And as I already mentioned to the presenters, please try to keep with the time and have your presentations kind of ready to go so that we have around 15 minutes at the end for questions from the audience, which can be asked in the chat or in person if you let me know in the chat that you'd like to ask a question. And I think we'll start with Omalola, who actually has a pre-recorded presentation, but it is also here in person if the audience wants to ask questions. So I would ask Anna from wider communications to share that pre-recorded session and we'll start with that. So thanks. Good morning everyone and welcome to our presentation on COVID-19 social relief of district grants in South Africa. I am Omalola Adiola and this is a joint work with Rejoice Mabena. We are both post-doctoral research fellows with the Southern Africa Liberal and Development Research Unit, Soudru and African Center of Excellence for Inequality Research Asia at the University of Cape Town in South Africa. At the start of the pandemic, we saw various countries implement lockdown measures to cut the spread of the virus. South Africa was one of the countries that implemented the most stringent measures around the world and some of these measures worsened already existing imbalances such as inequality in various dimensions. What we know is that some of these sustainable development goals such as ending poverty and hunger might have been compromised and some of the measures implemented by South African governments to help citizens cope with the economic effects of the pandemic was to increase the value of existing grants and introduce a temporary social relief of district grants of 350 rand. Therefore, the objective of our study is to determine if the SRD grants significantly improved the living conditions of households as there was a large take-up of the grants between May and September 2020 where 9.15 million applications received the SRD grants. Social grants in South Africa is a comprehensive social protection measure which has reached over 18 million grants paid monthly as of March 2021. The data for our study is the needs crime data set. Needs crime is a national income dynamic study coronavirus rapid mobile survey and this is a data set drawn from the needs with five samples. The needs is a nationally representative panel study of over 28,000 South Africans followed every two to three years which started in 2008. The needs crime was carried out technically which started in the early days of the lockdown in 2020 to track people's income and employment status over the period of the pandemic. To date there are five waves of the needs crime available but we make use of the first four waves in our study. We estimate a conditional fixed effects logistic regression where households experiencing hunger take the value of one and zero otherwise and this first figure will see that household and child hunger were highest in the first wave with at least 22% and 15% respectively but by the second wave with the introduction of the SRD grants and top up to the existing grants we notice a reduction in household and child hunger and the second figure here we see that households that lost their main source of income during the lockdown experienced more hunger than households that did not lose their main source of income in all the waves. Hunger experiences however reduced drastically in the second wave which coincided with the introduction of the SRD grants. As we see it fell from 31.67% in the first wave to 22% in the second wave. In this third figure we see that households with at least one employed member had less chance of experiencing hunger compared to households with all members unemployed or not economically active. The results of our conditional fixed effect logistic regression shows that the recipients of social grants lowers the probability of experiencing hunger whereas the SRD grants did not have the same effect. We also notice that in our study that the existence of significant association between hunger outcomes and labour market status. Households with economically inactive individuals have a higher chance of experiencing hunger relative to households with employed members. When employment increases the odds of experiencing hunger. We see that people living in urban areas are the higher chance of experiencing hunger and non-monetary supports from the government NGO and community were significant in reducing hunger experiences for households. We also notice that for school for children in school feeding schemes there was less chance for likelihood of experiencing hunger which is really significant in our study. Therefore we see that the SRD grants lessened the chances of child hunger suggesting that the SRD grants had a positive impact on children's access to food more than it did for adults as you can see and this fourth and fifth color. So in conclusion in our study we see that social grants reduce hunger for households however the impact of the SRD grants was less obvious. This we see is probably due to the minimal value of the grants. During the lockdown non-monetary support mechanisms such as the community support research of food parcels and NGO support helped households to be more food secure. South Africa's high unemployment rate needs a more sustainable solution beyond the current social protection and therefore we recommend the introduction of a basic income grant with value over the upper bound poverty line in South Africa. Thank you for listening. Thank you. I then give it again to Anna who will share Valentina's presentation which is also prerecorded. Good morning my name is Valentina Martinez and I'm here to present my paper the impact of COVID-19 and expanded social assistance on inequality and poverty in Argentina, Brazil, Colombia and Mexico. This is joint work with Norelustik, Federico Sanz and Stephen Junger. This paper addressed two main research questions and the first one is what is the potential impact of COVID-19 on inequality and poverty in 2020. The second is who are the biggest losers across the precrisis income distribution and the third one is to what extent does the expanded social assistance mitigate the negative impacts. We obtained our estimates by simulating potential income losses at the household level using micro data from household surveys and the first step of our micro simulation is to identify a risk and not a risk income. So we defined not a risk income as the income coming from cash transfers, pensions, public employment remittances, labor income from essential sectors and labor income from white colors workers with internet access at home. We defined at risk income and the income coming from a not essential sectors, labor income from street pending and household income from rents. Once we identified the components of the household income, one of those is the income address. We simulate potential losses using two key parameters. The first one is the share of households with at risk income that lose income and the second is the share of at risk income loss for those losing income. In this matrix, which is an example, is the one for Argentina, we have in the rows the person that the share of households losing income and on the columns the share of income loss, so each parameter here, so each cell in this matrix represents a different scenario. The number in each cell is a total income that will contract based in that specific scenario. So we focus out of the 100 scenarios, the possible scenarios, we focus only on those for which the contraction comes closest to that predicted by the IMF for each country in 2020 and of those scenarios, in this case, the ones that are highlighted there, we focus only on the scenario for which the share of households that lose income as a role goes closest to that suggested by the high-frequency service. The third step in our simulation is to construct an income distribution that incorporates the losses and compare it with the extended income distribution and the fourth step is to simulate an income distribution that incorporates the effect of the pandemic, but also the new compensatory social assistance measures. Let me go directly to the results. Here we have the impact on inequality may measure by the unique coefficient and for each country we have the extent of the exposed and the exposed plus social assistance distributions. The first thing to highlight here is that the potential impact on inequality could be quite significant, but also that the social assistance significantly offsets the effect on inequality, so as you can see here based on the inequality but also here in poverty measured by the 5.5 ppp poverty line in Brazil for example, we have that the poverty is even lower than in the pre-exante situation. We have here ex ante exposed without and exposed with expanded social assistance, so on our results suggest that the government that have introduced new and substantial expansions on the existing social assistance offset a significantly share of the poverty and inequality caused by the crisis. So finally, let me present the Non-Anonymous Grat Incidents Curves. These figures presents the change in income at each percentage of the ex ante income distribution, so the bold line suggests that on average all households are worse off than in the situation before the crisis and the dash line suggests that once you account for the expanded social assistance, new and expanded social assistance on average the households are in a very situation, in particular the households and the poorer households and the negative slope in the bottom indicates that mitigation has been purported but also that the poorer households are end up with an income that is even larger than in the pre-crisis income in the pre-crisis. That is all, thank you very much. Thank you. Next we will have Jara Komes, who will actually present in person or well virtually in person, so you can start Jara. So good morning, sorry for the technical issues. So this paper was made with cahos and the name of the paper is liquided constraints, cash transfers and the demand to have care in the COVID-19 pandemic. So cash transfers during the pandemic have been justified on the basis of allow adherence to lockdown measures and also mitigate effects on the labor market and aggregate demand, but we think that cash transfers might have a third effect, that is cash transfers might directly affect demand for medical care. So why this might happening? So people might be suffering from liquided constraints, so the cost of opportunity of the daily earnings may be too high. So we predicted that with these cash transfers we will have diminished the lag between symptoms and medical care, but we will also increase hospitalizations just after people receive cash transfers because now they will be able to seek medical care. So Brazil implemented an emergency cash transfer program from April to December of last year. It was an conditional cash transfer, it was a very large program and it was relatively generous to for you to all have an idea. It was bigger than our currently bigger social program that was most of a million. So the government implemented a calendar that was made for other banks. So it was, the money was available by the month of the birth. So people who was born in January and February received the money earlier and people who was born in November and December received the money for the last. So we have this data for the municipalities and for each cohort and we have a daily data. So with this data we were able to use that opportunity in time. And the intuition behind this method is for each cohort to compare hospitalizations and the like to medical care just before and just after the transfer is made. So we are interested in the fact just after the cash transfer and the data was from the minister of health. So we have a large drop in the days to hospital. Can we? I don't know if it's. We can, I think. I think I have some problem here. Okay, so our results are that just after the cash transfer a drop in the like between people have the first symptom and seeking medical care, we measure this like between, they reported the symptom and here. Okay, so I think it's back. So we measure this, the first symptoms and when they get their COVID test. So after the, after they receive the cash transfer we have, so people get to the COVID test much earlier than it was getting before the cash transfer. And the fact of this was about 13% in the diminution of this like. And we also have increasing hospitalizations. So just after the cash transfer was made we have an increase in hospitalizations. So our explanation for this was so severe critical strain and they couldn't go to the hospital. And now we've received this cash transfer, they were able to demand medical care, they couldn't demand earlier. So I think this is the results. I'm having trouble, but it's, I'm done. Thank you. Thank you so much. Very interesting work. Then we have two more presentations. The next we will have Amaka Mehtu from the Nambi Azigiva University in Nigeria. Amaka, you want to share your screen? And just to say you have minutes, we don't have much time. Okay, sorry for that. No worries at all. Go ahead please. Okay, the topic is post COVID-19 pandemic. Does foreign aid mutine achieving inclusive growth in sub-Saharan Africa? The way the effect of COVID-19 in Africa may not be that much in terms of health status, in terms of debt effect, but it has caused a chance to measure flows in terms of trade, foreign direct investment and remittances. And for the fact that this flow of income has been affected, there is the possibility that sub-Saharan African countries, we find it very difficult to finance their economic activities in terms of reduction in poverty, inequality and unemployment. And we know that sub-Saharan Africa is characterized already by these ills of poverty, inequality and unemployment, showing that sub-Saharan African country economic growth is not inclusive, it does not encourage economic opportunities in terms of job employment and others. And despite these COVID-19 activities, the foreign aid flows into African countries from donor countries in 2020 alone is about $162 billion and bilateral aid to Africa at least developed countries rose by almost 1.1 and 1.8 respectively. And hence we assume that sub-Saharan African countries can leverage on these foreign aid in order to improve their economic activities in the pandemic. And our objective is to examine how sub-Saharan African countries can leverage this foreign aid in improving inclusive growth post COVID-19 pandemic and to examine the role of institutional environment. We were looking at the role of institutional environment because we found that despite it flows to African countries that they still find it difficult to achieve, to reduce poverty and unemployment. And studies by Goara has said that the environmental effect, the environment and institutions are the causes of Africa not achieving inclusive growth using their foreign aid. And we, in terms of methodology, we use for eight sub-Saharan African countries using data from 2000 to 2019 and we looked at foreign aid in terms of inclusive growth and we use inequality adjusted growth instead of the GDP which others have been using. And we also looked at other variables which literature has found to affect growth and our findings shows that institutional policy has a significant minimal impact on the level of inclusive growth in African countries. And then foreign aid, when we interacted foreign aid institution, it shows that foreign aid could actually improve inclusive growth. Based on these findings, our study suggests, is recommending for Africa to leverage on foreign growth and to leverage on these foreign aid coming into the country that there is need to strengthen commitment to development by improving government effectiveness. Also the donor countries can channel these aid to agriculture and education because of their link to human capital development. Thank you very much. Thank you so much Amaka and thanks for being quick. I was really impressed with that speed. So finally we have Christoph Struppat from the German Development Institute. Christoph, feel free to share your screen. Yeah, thank you for giving me the opportunity to present. This is joint work with Sam Sam, Arjun and Matthias. They are all at the Rasmus University in Rotterdam and the topic is, it's not fully related to social protection. We are focusing mostly on the COVID-19 vaccines in low income countries in particular from, we are focusing on Ethiopia. What is important to understand, of course, we all know in order to successfully control the pandemic, we really have to rely on the COVID-19, on the available COVID-19 vaccines. But what we can see and what we know also from the news and so on is that many low income countries in particular from Sub-Saharan Africa are lagging behind in their vaccination campaigns, which is mostly, of course, due to the limited vaccine supply. And it is important, of course, given these kind of limited vaccine supply, to plan the, hopefully, upcoming vaccine campaigns in low income countries very well in order to use these scarce resources most effectively. And therefore the vaccine acceptance and also maybe the willingness to pay for COVID-19 vaccines are important in months and factors in our view that needs to be considered. And when you look at the literature, you see that there are many studies. So the field of the literature is growing very fast. Many studies are focusing on the willingness to take COVID-19 vaccines in particular focused on high and middle income countries. But we are also seeing now many studies coming from low income countries, for example, also from many countries from Sub-Saharan Africa. And so the evidence base is growing. Only few studies have explored the willingness to pay for COVID-19 vaccines in low income countries. And what is also important to know that many of these studies are using phone or online surveys, which have some kind of limitations. What we have done, we have done an in-person survey of 2,332 randomly selected households in Ethiopia in October and November 2020, right before the conflict also started in Ethiopia. So we managed to do a national representative survey, which is only representative for the informal sector. And it was a joint project with the ILO and the Friedrich Ibert Foundation. And the overall aim of the project was really to understand the impacts of the COVID-19 pandemic on the informal sector. But here in this presentation, we will focus on the vaccines. And first, of course, we would like to know what is the willingness to take and pay for COVID-19 vaccines in Ethiopia. And then, of course, second, is like to understand the socio-demographic colorates of the willingness to take and pay for COVID-19 vaccines. Yeah, in order to dive into the results, so the descriptive results, so we see that there's a high willingness to take COVID-19 vaccines in Ethiopia. We only see the first slide. Have you been switching slides in between? Okay, can you see it now? Yes, yes. And can you go to full-screen mode as well? Yeah, maybe this is troubling. It's a cause to trouble. But can you see now the slide switch? Yes, yes. Okay, I will leave it like this because the full-screen mode brings the trouble. You can see here now the willingness to take COVID-19 vaccine when it's available at the local market. So we have put these two questions. So here's again the slide with the research objectives, which you could not see probably, but here we have the descriptive results. So we see that almost 90% of our respondents, so as I said, representative for the informal sector in Ethiopia are willing to take COVID-19 vaccines. And conditional on those that are willing to take the COVID-19 vaccines, we see that 33% of them are also willing to pay for the COVID-19 vaccine. So we see this kind of gap between, of course, willingness to take the COVID-19 vaccine and willingness to pay for it, which is interesting. And in order to do a kind of analysis, our second research objective was to understand what are these sociodemographic correlates. So we have run a logistic regression model, very simple, including different predictors like age, gender education, and so on, but also health insurance coverage, for example. You know in Ethiopia we have a successful community-based health insurance scheme, and so on. And here the main results. So you can see what the turn of mind is willing is to take the COVID-19 vaccine. So this just correlates, but you can see that those families or those respondents that have experience with COVID-19 had an infection, of course, are more willing to take the COVID-19 vaccine. So we see a very strong positive correlation here. And what is also interesting, you have included question on trust in government and institutions, which also seems to be a kind of, seems to be strongly correlated, associated with the willingness to take the COVID-19 vaccine. So the higher the trust in the government institutions, the higher the willingness to take the COVID-19 vaccine seems to be a bit like this. We see nothing on education here, nothing on income. We see a bit something on age, that some of the age groups are less likely, or this seems to be a negatively correlated with the willingness to take COVID-19 vaccines. And then the second one is like, what the turn of mind is the willingness to pay for COVID-19 vaccines. So as I said, this is conditional on those that are willing to take as a COVID-19 vaccines. And here what we see here is a kind of income gradient. So those that are more wealthy are also more willing to pay for the COVID-19 vaccines. So 60% of those that are in the highest income groups are also willing to pay for COVID-19 vaccines. What is also interesting when you are covered by health insurance, those are also more willing to pay for it, like 40% of them. And you also see something a bit on trust and so on. So interesting correlations. Just to sum it up, what we find, what is very interesting is that there's in general a high willingness to take the vaccines in Ethiopia, which would also lead to the achievement of herd immunity. Potential reasons are we have very successful health insurance campaigns in the past and so on, substantial investments in health infrastructure. But of course, as I said, we have done the survey before the conflict and now this is maybe the main challenge given the conflict, how probably the vaccine, the willingness to take the vaccine has decreased in Ethiopia, which is very serious. We see these kind of large gaps between willingness to take and willingness to pay. But we see that many of our respondents are in the lowest income categories where the majority is not willing to pay. And this is really a strong call for free or subsidized COVID-19 vaccination programs and campaigns. It's very important in Ethiopia. And yeah, I will conclude by this statement and yeah, thank you for giving me the opportunity to do that, this work. Thanks a lot, Christof. So we were almost in time, so I'm really happy with this. We have 10 minutes for questions. If anyone from the audience wants to ask questions, you can write that in the chat. And while waiting, I have a few questions of my own. So maybe just to follow up on Christof's presentation, you showed that two thirds of those who wanted the vaccine would not like to have paid for one in the first place. And actually, as you also mentioned, those that would like to pay for a vaccine were mostly in the top of the income distribution. So kind of a practical question, how expensive are vaccines in Ethiopia and do people actually have to pay for them if they want one in certain regions, for instance? Should I answer directly? Yes, you can answer. Yeah, sure. Thanks. Yeah, currently the vaccines are, as I understood, are for free. So the vaccine campaigns are also very limited. I don't know the exact share of the population that is covered by the vaccines, but it's not so it's very low as compared to a high and middle income countries, unfortunately. And yeah, the prices are also changing. It's also difficult to say. So for example, the mRNA vaccines are more expensive than the vector-based vaccines and so on. And it depends. So, but yeah, in general, we think it's very important to mention that many are not willing to pay for it or maybe cannot pay for it. And therefore, yeah, the government of Ethiopia has to care about it, but also the external donors that these COVID-19 doses and so on should be for free and so on in order to achieve a high share of people that can get the vaccine. Thanks. That makes it clearer. Then I maybe ask Jora about your findings. So you basically find that cash transfer may increase demand for medical care. And something I get wanting to know more about is what's the mechanism behind this? Why do people actually, why are people more likely to seek medical care after such a grant or after they receive some support? So what do you think is, what do we think is happening? It's because people, okay, so some contacts for Brazil, healthcare is free. So people don't need to pay for healthcare. So people who use the public health system here are very poor families. So the data we have so what do you think is happening is that people can't stop working. So now that they receive this cash transfer, they can't stop working to go to the doctor. So they have opportunity for losing a day of work, a day of job is too high for them because they are very poor. So now that they have this cash transfer for the government, now they can, okay, I'm sick. Now I can seek medical, we think that's the mechanism. Right. Yeah, thanks. That makes it easier to understand. Then we have one question from the audience. That's also for Christof. Basically, Raj Kattula is asking about, is the unwillingness to take and pay for vaccine, does that have any relation to social stigma, do you think? Yeah, this is a very good question. I think this could be the case. So what we see is that most of the people in our survey are willing to take COVID-19 vaccine is just like 10%. So said that they are not willing and it could be that these 10% this is due to stigma. It also could be that these numbers that we have in our survey, they are from October, November. So it might be that the willingness to take the COVID-19 vaccine has decreased over time due to the conflict, but also due to the rumors of safety of some of the COVID-19 vaccines. So it might be that this kind of stigma issue has come up until now and it explains some of the reasons why or explains why some of the respondents or some of the people don't want to take the COVID-19 vaccine. Yeah, but it's a good point. Thanks. Then maybe I'll ask one question from Omalola. So basically I was interested you mentioned that SRD grant didn't really reduce hunger at least effectively. I'm simply wondering what's the main explanation behind this. Is it simply the small value of the grants and how could that be made better by for instance a basic income transfer that you recommend? Okay, thank you very much for the question. So for the SRD grants what we saw was that they didn't have much impact in reducing hunger and where we think probably because of the low value of the amount, but at the same time the people that received these SRD grants, the requirements for the receipt of the SRD grants are people that are unemployed. So those people were already worse off at the lower end of the income distribution and so they had higher chances of experiencing hunger compared to the other people in the population and so we see that the SRD grants the value was quite low at 350. So we saw that it did not make much impact but for the other grants we saw that people could receive multiple grants when it came to that like the child support grant and the old expansion. So in households where you have multiple grants, other grants you could have the hunger experiences being reduced compared to also to its just SRD grants. Thank you. Then I actually noticed that there are questions from the audience in the Q&A box. So let's take the questions from Kwabina from you and you wider. He asks for instance, as safety nets are usually targeted on the poorest of the poor, did you examine effects by quintiles or by bars of the income distribution? Okay, so yes. So it's from Valentina. Yeah, that one's for me. Yes, we indeed analyzed the effect of the shock and also the expanded by centiles of the distribution. So that's why we find out our estimate suggests that in average all households across the income distribution are affected, mostly those in the middle of the distribution and that the expanded social assistance was like indeed targeted to the bottom of the distribution and those are the households that benefited the most from the expanded social assistance. Thanks. And then let's go for the question from Nandiki Najak for Omo Lola. So she asks, can Omo Lola tell us more about the profile of the beneficiaries, both SRD and non-SRD and also what's the proportion of population that's getting these transfers? Okay, so for the SRD grants, as I explained earlier, most of it for you to be qualified, the requirement is to be unemployed during the period. So the government wanted to support those that were unemployed and those that lost employment during the ad lockdown, which started in March 2020. So you see that households that applied for this sort of grant had members, most of their members unemployed and they were mostly people at the lower end of the income distribution and so compared to the other grants we saw, the non-SRD grants, we observed that women were more represented in the non-SRD grants than the SRD grants and the SRD grants targeted those that lost employment, which most of them were women, but because they were already getting some other grants, they could not qualify for these SRD grants, which reduced the population of the gender, there was imbalance in the gender receipt of the SRD grants compared to the other grants we had and then we saw that most of the people that received these SRD grants as well were young adults, most of them between the age of 20 and 24, which has the highest unemployment rate in South Africa. As well, we saw people in urban areas, areas like the KwaZulu Natal and Outen, receiving more of these grants than the other provinces in the country. Thanks, Mullala. I think I need to stop you right here because we are actually, it's 11 o'clock finish time exactly now, so I want to thank everybody for the nice presentations, I really enjoyed learning about these different topics and great to see all of you here. I guess good luck with the rest of the conference and I hope you enjoy the rest of the session you decide to go see and if there are any questions or you want to talk more via email, let me know and I guess that's it, so thanks a lot. Thank you.