 Thank you all for joining and welcome to our session on labour market and employment. My name is Simona Shatter and I'm a research associate at UNewider. And today we're going to discuss the labour market implications of the COVID-19 pandemic and the associated policy measures. We have a very interesting lineup of speakers with broad geographic coverage with country case studies from Latin America, Asia and the Middle East and North Africa. And our speakers will specifically look at the impact of the pandemic on workers depending on the type and location of the activities they engage in, the degree of formalization as well as important demographic characteristics. Please feel free to drop your questions in the Q&A section of the sessions tab. We will administer those at the end of the session so we first listen to all speakers. So without further ado I would now turn to our first speaker who is Samar Abdel-Maghid, assistant lecturer at the British University in Egypt and she's going to talk about economic support versus containment measures and empirical study of the impacts of COVID-19 on labour markets. Hello everyone, hope you're all doing well. My name is Samar Abdel-Maghid. Thank you very much for giving me the opportunity to present my paper which is entitled economic support versus containment measures and empirical study of the impacts of COVID-19 on labour markets. So my presentation will include an introduction and the main research question followed by the main methods and data employed by the study. Then I'm going to present the main results and end up with a conclusion. So the impact of the current COVID-19 crisis are undeniable and felt by all countries in the world. So since the pandemic states all over the world have first enforced a group of actions that aim to limit the spread of the disease then they move to adopt a series of economic support measures to mitigate the economic ordeal that accompanies containment measures. Therefore the main aim of this paper is to investigate the impact of the economic support measures implemented by different countries in the world on the labour markets in face of the negative implications of the containment measures. The research will focus more on the many regions which is one of the world's regions that have been already facing many challenges in its economies where one-third of the workers are in the risk of losing their jobs or witnessing reductions in their working hours and wages due to the pandemic. So to examine the impacts of the measures adopted by different countries the study relies on the data of the Oxford COVID-19 government response tracker which are available for over 180 countries and include an overall government response index along with three sub-indexes including stringency containment and health and economic support. Moreover to study the microeconomic impacts of the pandemic on people living in the minor regions the study uses the COVID-19 manna monitor survey which were available for Egypt, Jordan, Morocco and Tunisia for two rounds in November 2020 and February 2021 comprising a total of 10,478 respondents including data about their different socio-economic characteristics. The study fits a group of regression models to examine the impacts of different government response measures on the unemployment rates in 2020 and the logistic regression model is fitted to explore the probabilities and the individual socio-economic characteristics associated with experiencing income decreases during COVID-19 and receiving governmental cash support among people living in the minor regions. So the main results show that many countries have average levels above the world average for the government response index the containment and health index as well as the stringency index. However the average level for menna is lower than the world average for the economic support index. This can also be shown from the heat map of the overall government response index for individual menna countries from January 2020 to April 2021 where darker colors refer to higher levels and it can be shown that the the overall government response index had higher levels in the menna region compared to the economic support index. Moving on to the results of the regression analysis it can be shown that the government overall response index had a positive impact on unemployment rates meaning that it had higher unemployment rates in 2020 the same also applies for the stringency index however no association was found between the economic support index and the unemployment rates in 2020. For the microeconomic impact of the pandemic on people living in the menna region according to the COVID-19 menna monitor survey among their respondents 30.7 percent reported that they witnessed decreased decreases in their monthly in their household monthly income by more than 25 percent. Moreover among those reported being unemployed in the survey 53.3 percent were private sector wage workers while 17.3 percent were employers or self-employed as of the end of February 2020. As for the business difficulties 18 percent of the respondents reported loss in demand as for the changing working conditions another 18 percent or an 18 percent proportion reported changing working hours and for the governmental cash support by respondents the majority of respondents in all of the menna country surveyed reported receiving no cash support from the government. Moving on to the results of the logistical regression it has been shown that the females have higher probabilities of witnessing income decreases also workers in the informal sector in the irregular or irregular workers and wage workers in the private sector have also higher probabilities of decreases in income and moving on to the logistical regression of receiving governmental cash support it has been shown that living in Tunisia and Egypt is associated with lower probabilities of receiving governmental cash support and working as a private sector wage worker or as an irregular worker is associated with higher probabilities of receiving governmental cash support. Now to conclude it can be said that government response and distringency indexes have led to increases in the unemployment rates among worldwide countries during 2020. On the other hand the economic support measures didn't have any significant correlations neither with unemployment nor with GDP growth rates. On average the menna region had lower levels of adopted economic support measures compared to the world and in the menna region despite of having an average chance of more than 45% of experiencing at the season income the average chance of receiving a governmental cash support was only about 1.5%. The chances of having income decreases during the pandemic in the menna region were higher than 50% percent among females private sector wage workers irregular workers and workers in the informal sector being an unemployed female with less than basic education who was previously working irregularly in the private informal sector is associated with a 90% chance of income decrease during the pandemic and only a 1.8% chance of receiving a governmental cash support in the menna country. To conclude the countries in the menna region needs to work on expanding and enhancing their targeting mechanisms to reach more of the groups who are most in need of economic support during the ongoing pandemic crisis. Thank you very much for listening. Thank you very much Samar. We directly would move to our next speaker who's Francesca Pereira based at the National University of General Samiente in Argentina and she's going to talk about precarization or protection the impact of digital platform labor for domestic workers in Argentina in times of the pandemic. Many thanks for having us at the conference our presentation is about the impact of digital platform labor for domestic workers in Argentina in times of pandemic before starting a very quick note about the significant weight of this occupation in countries like ours domestic service represents 6% of the total occupation in Argentina and 21% of all salary given and even though the legislation of the activities highly protective the main problem to access labor right has to do with the fact that we still have 75% of all domestic workers in informatics in this context we decided to study the digital platform solvers which is the only digital platform in the sector in the country it was born in 2014 it reports having nearly 20 000 active workers in the country and it's important to note that the platform does not charge workers rather it charges employers for for each job placement it makes so the platform is an intermediary in the sector that contacts employers with workers also employers can choose to join what is called the solvers payment system which is not compulsory but is aggressively promoted by the platform this system implies that for a monthly fee charged to employers they receive some services from the platform basically the platform takes care of making all the payment of workers salary each month through an automatic debit from employers bank accounts and also if the employer wishes so the platform formalizes the worker and this means that the platform takes care of all the bureaucratic procedures to do so and also again takes care of all the monthly payments for social security by debiting them from the employer's bank account this has benefits for workers as well they get a free bank account open when they receive the payment through solvers system a debit card a credit card and they also access personal loans which are highly valued by them our work is based on in-depth interviews with platform workers plus a survey of 300 platform workers where we were able to collect information about nearly 1000 job positions and we asked about job positions before and during the pandemic in order to establish comparisons we also used to the our argentina's household permanent survey in order to establish comparisons between solvers and the sector as a whole this slide will consider registration rates outside and inside the platform and we restrict the analysis to job positions of up to 23 weekly hours because 95% of all jobs created by solvers do not surpass that time limit this is the platform uh over represents short hour positions is more successful in this segment of the the sector and these are particularly the job positions that have been more resilient to formalization policies in the country in solvers this kind of job positions have a registration rate of 43 percent in comparison with 16 percent of the national level and if we consider jobs that are paid via the solvers payment system that i referred to before the registration rate goes up to 66 percent as the testimonies of workers show solvers constantly sends information about labor rights and registration in particular and many times workers use this information as a backup in order to negotiate with employers to finalize what happened with job protections in the platform during the pandemic domestic service was indeed one of the most affected occupations worldwide in terms of job destruction in graph three we do observe that registrations imply high rates of job preservation within the platform and this is also the case outside the platform however we also want to share this other graph with you which might look as a paradox because even if the platform exhibits much higher registration rates in global terms job destruction was significantly higher here as we can see more than half of the job positions were lost whereas outside the platform this percentage drops to 20 percent which is still high but job destruction was much more devastating within the platform this apparent paradox has to do with what we call the vulnerability effects of short work in our positions this missile costs consist of one monthly wage per year and since salaries are very low because in the short working hours the severance base becomes very accessible plus the rotation in this type of positions is considerably higher than in full-time positions and most workers do not reach more than one year in the job thus the dismissal cost tends to consist of a very low monthly wage this trend is verified in the sector as a whole but obviously becomes more intense in solvers because this is the type of job insertion that prevails here to conclude the platform shows an interesting capacity to promote registration precisely among those work positions that have proven more resilient to formalization policies this is short work in our positions within the sector and the tools used by the platform to promote registration have to do with constant information and regulation disseminate them on workers and employers and also the solver payment system constitutes an additional incentive as it offers to take care of bureaucratic procedures and payments required by registration since there are numerous employment agencies intermediating in the sector perhaps these are tools that are worth to promote and expand in terms of the pandemic even though registration did have a protective effect against job destruction this was severely reduced within the platform because of the vulnerability effect of short work in our positions that prevail in the combat thank you very much and we look forward to your question thank you francis test and our next presenter will be subaria and live assistant professor at the department of economics federal or the university of art science and technology islamic Pakistan and she's going to talk about the impact of covet 19 on labor market outcomes in Pakistan close yet hello everyone first of all i would like to say thanks to you and wider for organizing this conference on the most important theme of our times and thank you so much for giving us this opportunity to present our work in front of this game discussant in audience i'm zubari and live and i'm working as an assistant professor at department of economics federal or university of art science and technology islamic Pakistan and today i'm going to talk about the impact of covet 19 pandemic on labor market outcomes in Pakistan and i think this studies will be relevant for the other developing economies so let me introduce the topic first the covet 19 pandemic is an example of the global economic crisis in recent times it has affected almost every sector of the economy women and young workers and those who are working in informal sector are more affected by this pandemic according to ilo monitor almost nine percent of global working powers were lost in the last year which is alternatively equivalent to 255 million full-time jobs so here comes the implication of the human capital theory as education helps individuals as to cope up with any kind of disability on this slide we have presented the snapshot of the Pakistani labor market we can see overall made and female labor force participation rates are almost stagnant are a very little variation for the last decade irrespective of the gender most of the employed persons are working in informal sector for the last 20 years we have found immense literature about the impact of covet 19 pandemic on labor market outcomes for different economies around the globe but we could not find any notable study in case of Pakistan based upon the previous discussion and facts and figures now we are able to specify the objective of the present study the present studies are first-ever attempt to capture the impact of the covet 19 pandemic on labor market outcomes in case of Pakistan we have applied linear probability model and binary logistic model um the first model dependent variable is employment disruption in the second model the dependent variable is employment loss and in the third model we have taken both employment disruption our employment costs as a dependent variable whereas xi represents different symmetry variables we have used the special rapid appraisal survey conducted by the Pakistan Bureau of Statistics during October November 2020 the sample size comprised of 6000 households across Pakistan and almost 23,000 individuals aged 10 years and above so this is a variable description table and we have included different age groups educational categories occupations and employment statuses and also region and province of residence descriptive statistics according to the descriptive statistics most of the younger workers who belong to the age group of 15 to 25 years have to suffer from employment disruption our employment costs male constitutes the highest share in the employment losses category married workers comprise a sizable proportion in the three groups that are employment loss disruption and same status workers with no formal education represents the highest proportion of the sample across all categories the highest proportion of employed in the same status groups are skilled agricultural workers followed by the workers in elementary eco-patients and service workers most urban workers have to suffer from employment losses as compared to their rural counterparts over empirical results provide two interesting policy insights as compared to the age group of 46 years are older young workers who belong to the age group of 15 to 25 years are more likely to face employment losses male workers were less likely to suffer from employment disruptions compared to female workers male workers had a lower probability of both experiencing employment disruptions as well as employment process the empirical results show a higher probability of employment disruption for almost all education levels compared to the base category of no formal education workers employed in white collar jobs having a higher probability of facing employment disruptions compared to the workers in elementary eco-patients which is letters and senior officials and professionals skilled agricultural workers and technicians had a lower probability of losing employment compared to the elementary workers in terms of employment status the result revealed that paid employees self-employed and employers all had a higher probability of both experiencing employment disruption and employment losses individuals residing in urban areas of the country have higher chances of experiencing employment disruption but have lower probability of suffering from employment losses the special survey also collected information on the different types of social protection benefits that's the selected household were receiving or had received during the pandemic period 24 percent of individuals who lost their jobs received cash transfers through the SS emergency program that was launched as part of the fiscal stimulus package announced by the government of Pakistan to provide basic income support to low-income households here are the conclusions of our discussion uh workers who belong to the younger age group are more likely to suffer from employment disruptions as compared to the reference category k pk province workers belong to the other three provinces are more likely to face employment losses the empirical estimates also revealed that there is less probability for skilled agriculture workers and technicians as compared to other occupational groups to suffer from employment disruptions workers in most occupations faced a lower probability of employment loss compared to the reference category of elementary workers however workers with degree level of education had a higher probability of facing employment disruption while workers with metric and above level of education were less likely to experience too um in employment loss over empirical analysis indicates that to improve the situation for those workers who are suffering from employment losses and disruptions the government should design special program and policies to provide employment opportunities to the low-income and vulnerable segment of the employed community the empirical analysis also highlighted that there's a need to improve the education level of the adult workforce to increase their resilience to any future employment shock in future expanding the access to secondary and higher education is particularly important in this regard it is also important to focus on digital literacy among the employed workforce there are two limitations and also we propose the future directions thank you so much and I'm looking forward for comments to our suggestion to improve our work great thanks a lot um Subirian just a reminder for our audience to also drop questions in the Q&A section I try to start off the conversation with two questions there and please feel free to add we would then move to our next speaker who's Anand Fakir PhD candidate in economics at the University of Western Australia Anand is going to do his presentation live and I'm going to announce a minute left after six minutes if that's okay with you so please suggest yes thank you thank you for having me here we're looking into essentially the role of mobility restrictions in halting the spread of COVID-19 and this study was inspired by essentially the lives versus livelihood debate that's been going around lately right so we know that due to the COVID-19 we've seen the biggest state led mobility and activity restrictions there is a debate as to whether it's it was too slow and insufficient or whether it was too extreme and also is it too much or too little and there's a wide literature out there that mentions that it really depends on the effectiveness of these measures they're not they're examples of when they're not always successful and can worsen the situation so to that effect what we're really interested in in this in the study is how effective are these measures in containing the COVID-19 contagion and what contribute to the effectiveness but of course we know that any studies like these are prone to endogeneity so we know that restrictions are in response to expected dizzy situation of the future whereas watchful populations and governments can take early action so we have to account for this endogeneity the way we address this endogeneity is we focus the studies mainly on the first phase and taking the perspective that governments in deciding the level of restrictions looked at not only the dizzy situation at their home country but also what they would expect to happen in the absence of measures of strict measures so they looked into actions taken by the surrounding countries as well assuming that your countries around you are imposing stricter restrictions there would be greater pressure on your own country to enact the same so based on that we develop a 2SLS model so it's an instrument variable approach where we look at the stringencies in the countries in the particular sub region except my own country to instrument for how much stringency I would enacting in my own country and then essentially using that to figure out its effect on growth rate of cases and other outcome measures as I will quickly show now the exclusion criteria here I will get back to that in a bit but before that the data comes from 127 countries and we focus on the first phase so 15th 5th to 30th July we take stringency measures from the Oxford Oaks GRT government response tracker the mobility data comes from the Google mobility data and the tests cases and deaths data comes essentially from the Johns Hopkins repository and also from our world's data now the exclusion restriction hinges on for example if I am responding to the restriction measures in other countries but not in my own home country the instrument becomes invalid so in that case we see that that really doesn't happen in day zero is when the country went under a national lockdown and we see that that's when stringency jumps up and that's also where mobility restrictions tend to fall down in the majority of the cases okay so the first set of results is just showing that there is a lag zero and lag 14 and 5 in fact this goes on to lag 828 but we don't show that here that as the stringence index goes up there is a reduction mobility across all these measures and increase in residential mobility okay and we also see a similar effect in terms of case to test ratio and deaths to test ratio these are the two measures we'll be focusing on and we believe these are the better approximation of this measure now we know that covid is um covid data is riddled with a lot of limitations which I'm happy to talk about later on so the next thing we do is we break the effect by certain characteristics of the country and this is by population demographics and also by other measures such as corruption, perception index, democracy score, government effectiveness and so on so um in a nutshell what's really happening is that restrictions curve mobility better in developing countries surprisingly we weren't expecting this at least using the google mobility data that we're using essentially countries that are more densely populated poor or more unequal or more polluted younger but healthy populations and more health infrastructure restrictions seem to work better in these countries however restrictions contained the contagion better in developed countries countries that are richer more equal less polluted older but healthier populations better health infrastructure countries that are more democratic with better government effectiveness now um this does not necessarily mean that less mobility translates to better contagion containment okay so um the next thing that we try to see is that um compared to changes in mobility so only due to changes in mobility due to stringent policy measures how much of the growth rate of the contagion was affected so we use a three-stage recursive cmp model to assess that and um the results are fairly in in line um so the way to interpret this statement would be that a one unit increase in transit station mobility causes cases to test test ratios to increase by this amount and once again we see that countries that manage to curb mobility better due to stringent measures um acted better in countries that have more education that have um more older age for the population that have a greater cpi score democracy score government effectiveness hospital beds and so on so what's really happening is that um the reduction in mobility is not really curbing the contagion as much in developing countries as much as it did in developed countries now there are multiple reasons that can have that led to this happen and i'll leave that for the discussion later on but in conclusion what i want to point out is that we need to complement restriction policies with awareness and other assistance schemes in developing countries mobility restrictions are not alone not sufficient in developing countries as it is in developed countries i think i'm right on time so i'll stop here thank you perfect thank you adnan and great timing actually it didn't have to jump in just on time perfect we would then before moving into the discussion move to our last speaker who's minnow higa um phd students and economics at simon fraser university and he's going to present about the persistent effect of covered 19 on labor outcomes evidence from peru thank you hi everyone i am minnow higa a phd student at simon fraser university i would like to thank the organizers for the opportunity to present this paper that is titled the persistent effects of cobit 19 labor outcomes evidence from peru this is joint work with carlos ospinno and fernando aragon so there are several studies that examine the effects of cobit 19 unemployment they have documented large negative effects especially for vulnerable workers and these studies has focused their attention mainly on short term effects during the first months of the pandemic which is useful to identify initial costs of lockdowns but it is less informative if societies adapt to the shock in these studies there is also limited evidence about latino america and in particular about peru despite being one of the hardest hit countries in the world as you can see in this graph where you can locate peru in the top left corner uh with the largest confirmed death per million people and also the largest drop gdp and this is for the second quarter of 2020 so in this paper we answer the question what are the short and medium term effects of a cobit 19 on labor outcomes um something important uh to note this here is that we uh we can only identify uh the vandal of shocks related to the pandemic but we won't be able to identify specific policies like social distancing or lockdown uh from other phenomena that is happening in the disease environment and we're going to focus our analysis for peru and in particular for lima which is the capital city that concentrates one third of the population one third of the labor market half of the gdp and also half of the cobit 19 cases and cobit 19 the related to answer this question we are going to use an event study framework and we are going to take advantage of uh the panel of individual that we have in our data set that covers the period uh from january 2019 to june this year so our results um from the event study are presented here so first we corroborate the dramatic uh dropping labor outcomes at the beginning of of the pandemic uh found by other studies but something else to notice is that this negative effect alternates over time however it is persistent and sizable uh even by the second quarter of this year so this graph is for hours work we have uh similar results for labor income uh limitation in this study is that there might be some changes in sample composition so for instance some sub-populations uh maybe were difficult to serve during the pandemic so to address this we check two things so first we check the sample size uh in the months before and after the pandemic and instead for march 2020 uh all uh months have uh similar sample sizes and second we did this exercise uh that is reported in the table so what we did here is is we restrict our sample or we restrict our analysis uh to the observations uh that are in our panel data so if you want to address changes in composition let's keep those individuals who were before and after the pandemic so in columns one and three what you have is the estimates that we use to build a graph that i showed you before but in column two and four i restrict the observations to the panel sample and we control for individual fix effects so there you can see that uh similar results are very similar uh for hours work and labor income and using also our panel sample we explore who were more negatively negatively affected so in the first lockdown informal and less educated workers were more affected and there is a sizable difference so the magnitude of the negative shop is hard if you're a formal and better educated worker in the immediate term less educated workers workers in vulnerable industries um like retail hospitality and transport and women with small children were more affected and regarding this last point we find some suggestive evidence of within household relocation of labor because in households with small children women decrease their hours of work and income and we find the opposite effect for males and once we analyze the effect of having uh small children for the entire household we don't we don't find any effect so apparently these negative effects for females and these positive effects for males are set uh when we look at the household level uh when we look the outcomes of the household level so to conclude this persistent negative effect uh highlights the limitation of policy and the capacity of the society to adapt to the shock and also suggest uh that the economic cost will be significant uh as long as there is a threat of covid and this is relevant for the less developed countries where there is a slow-producing vaccination and there is a threat of search of more aggressive variants and finally our heterogeneous effect suggests uh um some long lasting effects for instance decrease in the income gap between educated and less educated workers and the awakening of uh women's bargaining power within the household so that's it thank you so much okay great thanks so much i hope we have francesca back on stage soon and we have about eight minutes left for q and a um let me maybe go in the order of the speakers and ask the first question to samar who investigated the impact on a number of um um in our countries and i was wondering to what extent you prefer to disentangle the effect of this government's stringency measure with the effect of the pandemic overall so to say like whether you control for how large or how much a country and the economy had been affected by the pandemic shock as such and try to disentangle this further from the government's stringency level to try to give an idea of how we can attribute these employment effects and thank you muted yeah yeah thank you very much for the question so um the database which is the oxford government response tracker database has several indexes to detect the response to to the pandemic and it has the overall response index and then it have it it has three sub indexes so um i try to disentangle the effects by um running the model or using each one of these sub indexes so i examine the effect of the overall government response index which combines all of the indicators used to measure the responses and then i try to examine the the effect of the stringency index which includes some measures such as the closure restrictions of schools of public places and social gatherings and so on and i found that the the overall index and the stringency index both have uh like negative impacts just to give the association or the correlation focusing on the economic support index which is again one of the sub indexes used to measure the overall government response index but i found no correlation or no association between the the economic support index which is measured mainly by the support given by the government to workers who lost their jobs or who lost part of their income and also the that that relief that some governments have offered but i couldn't find any relationship between the economic support index and the unemployment rates so um i try to investigate the impacts of the different indexes used by the database to measure the different aspects of the responses regarding the unemployment rate in order to disentangle these effects Thank you for your clarification and if you have any questions think it's unmasked and it's hard to identify a possibility here of policies that usually often don't just choose a topic that's quite interesting to see in that sense The next question was to be consistent and moderating when it has this kind of global effect of institutional platforms like i think the first part of the presentation it was a quite positive picture but depending on the actual increasing registration and formalization in terms of this talk it seemed like the qualitative demonstrations in terms of the value they had for the workers and in terms of how they were so high and i just wondered what is your takeaway maybe in a quick response like on various platforms Okay in terms of formalization in the in terms of the particular dimension of formalization the platform effect is positive because workers access to labor rights such as pay holidays minimum wage and so on but the problem is that they have short work in our positions within the platform and short our positions are more vulnerable to job extraction this is this happens inside and outside the platform the problem is that they predominate within the platform so perhaps some particular policy in terms of these short hours positions and the severance pace they imply because it's very easy to dismiss a worker that even if she's formal if she works for very low hours she will lose the job during the pandemics for example so in this particular dimension formality the platform has very positive effects but because of the nature of the fetters of the the jobs that predominate within the platform job stability is not one of the positive outcomes not for the platform not outside the platform and there are other aspects of the platform i want to point out that are not very bright but because of time restrictions we couldn't develop them i don't want to pay the picture the platform is an excellent tool because it has many problems for example of back qualification systems and low wages lower than outside the platform and so on but in terms of formality which is a bit concerning argentina when it comes to domestic service the platform has a positive outcome but it doesn't reach protections because of the short work in our positions that dominate within the the company thank you so much for the question and yes industry industry survey we are considering the earning loss only and secondly we have labor force survey data for a longer time period and the last round off this survey was conducted this in 2017-18 and so in the next round of survey i expect to get the data about the post-covid scenario of employment and unemployment thank you thanks to all the speakers and to the audience and maybe i can ask you now if you still want to hang on or i can send you a message okay i have been wondering about your result where we see kind of a larger reduction in mobility in developing countries but then a more pronounced effect in the developed countries and i was wondering whether we have some type of selection effect there that you're maybe new mobility data and the google mobility data only capture the better part of the population more likely to be able to stay home on the one hand and then the other one i was wondering about whether we might have some type of settlement effect of people just living in very densely populated areas and some sort of township or other type of slum environment where you don't need to be far to get in touch with many people kind of and therefore it's adjusted thinking about this impossible channels and your thoughts on this yeah you're absolutely right we do control for population density at a quite a minute level so that it's that's not something we're worried about but we think that the data is definitely selected simply because google mobility data traces people who uses their google accounts and etc so there's definitely a selection bias happening there so that's why we don't like stressing on that particular set of results because it doesn't make sense and there's definitely a selection issue going there but i thought what was more important is the fact that cdblogs grt and without using the mobility data we do see a stronger falling containing the contagion in developed countries and that's what we wish to focus on a bit more yeah but you're absolutely right there is a selection going on there and there's another thing is that we can't disentangle so for example when people are self-disciplining versus actually responding to stringent measures the effect is capturing both of them we can't disentangle the two like is it due to the stringent measures or is it due to a certain subset of the population being self-disciplining themselves yeah that's another caveat that we have thank you single that's your absolutely spot on yeah great thanks so much and i can actually verbally thank all the presenters and the audience for this very nice session and i think what we saw across the presentation is that informal workers and workers in more vulnerable type of positions have been particularly affected by the pandemic and there remains much need for policy to address this in their post-pandemic context um thanks so much and hope you enjoyed the rest of the conference see you thank you thank you bye