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It should load your points here. Yeah, yeah, yeah.

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Okay, so that's kick off. Good morning. Thank you so much for the opportunity to talk about this project.

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This is a very, I think, specific project because it is data collected from science who participated in a career sort of enhancing workshop.

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So. The workshop was called Magic and it stands for mentoring and guidance in careers.

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And I have to tell you that methodologically this is at this point a very inductive study so there'll be no explicit theoretical framework although I think we'll be using an emotional labor concept later on and there will be no formal hypotheses.

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Before I go any further, I need to explain a couple things. Firstly, what the workshop was, then why the data was collected and we basically have here a retrospective semi structured interviews.

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And also that I will just focus on one issue which has to do with the fact that scientists in this project but also in Australia and probably elsewhere in the world.

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Are very often people who are migrants and migrants in this case it is relevant from non-English speaking background and I just want to kind of focus on that in this part of the project.

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And probably, you know, in your countries as in Australia the last 10 to 5 years. Were very much about at least in this space about kind of enhancing gender equity in science.

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So Australia as many other countries has government-funded schemes. One of them is Sage, which stands for Science in Australia, Gender Equity.

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And there are basically very ambitious goals that claim that by x point in time we will have in all areas of science in all science disciplines 50% of employed women.

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But when university leaders are kind of told that hang on a moment only 30% of all science doctorates are actually awarded to women in Australia and in some science fields it's a lot less than that.

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The usual answer, the usual strategy is we're going to hire from overseas. So that's why the order I think what I'm about to tell you about is quite relevant.

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And of course, even though we looked in this particular project at the evaluation of the impact of this career orientated workshop, we really asked because of the timing of it also about the impact of COVID so this will be another focus in what I'm trying to say.

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So what is magic? Magic were week-long workshops. There were residential and they happened in 2,017.

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So it's one week in the year, 2,018 and 2,019 and in 2,020 there was a halt due to Covid and they will probably resume next year.

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The leader of the workshop in nuclear physics in Mathematics and in chemistry. And this Women are the recipients of the Australian Research Council.

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Laureate Georgina Sweet Fellowships. Now what are these? These are fellowships or grants which are awarded in Australia to the absolute top senior researchers in their field.

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And we first you have to get one and doesn't matter whether you are male or female, but then you can sign up for the genus with variation of the fellowship and this gives you extra money to advance your fellow female colleagues in the field.

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So there's only one every year and they started in 2,011 I think so we've got all together maybe 13 Georgians with Laureate Fellows in Australia.

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So this is important to to kind of to recognize. And in terms of what we've got in our data is altogether there was enough money to fund about 106 participants over the span of 3 years and we managed to interview 49 of them and this created 50 h over 50 h of recording and half a million of words in

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transcript. So it's quite a lot of narrative. It turns out that 55% of our participants had English as their second language.

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30% only grew up in Australia and 30% mentioned parenting children because a lot of what they said in the context was not asked about directly, just kind of.

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Came up in the interviews. And these characteristics are not just specific to the small group of people. If you look at the Australian Bureau of Statistics data, you'll find out that across Australia in the relevant fields of science and here we are talking about mathematics, physics, astronomy and statistics and the related branches of engineering.

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you have 57% of women working in these areas in Australia having born overseas. In the public sector, 64% of women and if you look at academia you're really just looking at maybe three-quarters of women born overseas and half of them have been come from countries where languages other than English are used.

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But the important thing about the scientists in this study is that much like the leaders of the workshops, they are successful.

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So access to the workshops was not kind of voluntary. It was competitive and the success rate was about 50% and people were evaluated and admitted to the workshops not on the basis of the very good CVs and you know excellent potential in their field but also excellent potential in their field.

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But also they had to submit a narrative that explained excellent potential in their field. But also, they had to submit a narrative that explained how they would give back to the community of their fellow scientists.

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Because they are so successful, many of them feature on their university websites in the university websites in the media and and it just is basically known in the country because this is not a very large population.

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To say anything about them and not to identify them because as soon as I tell you what particular project and they're doing some mind-blowing research, some unbelievable stuff.

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So as soon as I tell you what they're doing there's only just one or 2 people in the country or maybe one team who does that.

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The other thing that turned out to be interesting about this project and I have to make this admission now is that all the money could be raised to fund the projects.

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It was extremely difficult to raise any money for this evaluation and collection of data. So we managed to get a little bit to do the actual data collection but for the analysis we are actually short of money so this is why I won't be showing you any fancy graphs.

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With the results of a latent Dirichlet allocation topic analysis because we simply are behind on schedule with this.

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So what is it that I want to talk about in substantive terms. I want to talk about certification by visa stages.

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And very, very soon in those interviews, it became apparent that it is not easy for people from known English speaking background and this is what I have on the slide here but I also have PGR with which stands for a patriarchal gender regime.

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So there's a group of countries from which, there's a group of countries from which, so there's a group of countries from which, when we talk to scientists, their experiences were distinctly, you know, when we talked to scientists, when we talked to scientists, their experiences were distinctly, I would say, marked by

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extra-emotional labor and extra-emotional labor and extra struggles. So the funny thing about this is that these are very marked by extra emotional labor and extra struggles.

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So firstly, it is very difficult to get visa sometimes to guess out to study science overseas, particularly in Australia, but specifically under COVID.

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We heard about stories where long-term residency strategies of these scientists because they came to Australia and for a number of reasons they couldn't, get or wouldn't take out citizenship.

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So they were here on working visas. But what happened under Covid in Australia was that the working visa program was brought to a sudden halt.

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And what that meant for these people was that practically you know within a week they were facing the prospect of basically leaving the country and not they were actually given 60 days to leave the country and that was unexpected and that was very troubling for them but also traveling for others.

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Who are in a similar situation. In terms of postdocs and junior people who are on grants of big professors, you know, we have a story here at the top of the slide where somebody came out here and they were hoping to extend their postdocs.

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But what happened in Australia was that the government didn't support financial universities. So all of a sudden we had the financial austerity crisis which meant that a lot of people were laid off across universities and also we were told to freeze our grants in that unless there was exceptional circumstances that mandated that money be spent now, we were told not to spend money until the next

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year. So the implications this had for those false dogs were that all of the sudden they couldn't for instance get their passports renew their passport because due to lockdown they could travel to their embassy.

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They couldn't get an extension on their job and the only place they could go to from Australia they had to leave was back to their home country.

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And he would also have a quote from somebody who avoided with a young family that fight and they were very about how lucky they were because otherwise they would have to leave on a very short notice.

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Those of young scientists who were in teaching related positions found as we won't be surprised to hear that they're teaching loads doubled basically.

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And There is a precarious employment relations with supervisors are also illustrated by this quality towards the bottom of the slide where sometimes upon completion of their PhD they were not given a postdoc in their home university but a research assistant job because the supervisor knew they needed residency for visa purposes and they couldn't afford not to take

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this offer. Language and cultural capital issues came up repeatedly in different settings. And we have somebody here at the top of the slide who was although young, already a leader of a very large grant and this person felt that there was not enough trust put in international or non-import speaking background leaders of these grants because although within her institution she had a

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lot of support, she ran those multi 1 million dollar grants. Which we funded by external bodies and when lockdowns happened and labs were closed the project slowed down and but the funders were not in the same area of the countries.

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They didn't understand the lived experience of the lockdowns and she felt that because she was from a different cultural background, they suspected her and her staff of not basically working hard enough on the project.

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And we have also stories about sort of mediating really difficult relationships. I don't know if you realize that but in science, postdocs very often but in science postdocs very often supervise PhD students of their supervisors so often supervise PhD students of their supervisors, so the professors.

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So what happened was that these people found it very difficult, so the professors. So what happened was that these people found it very difficult to receive the amount of respect and deference. So they found it very difficult to receive the amount of respect and deference.

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So basically they found it very difficult to receive the amount of it very difficult to receive the amount of expect respect and deference so basically they found it difficult to be respected on the daily job and of course, so basically they found it difficult to be respected on the daily job.

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So basically they found it difficult to be respected on the daily job. And of course, under COVID, the numbers of microaggressions that occur in face-to-face interactions are reduced because you are at home.

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But nevertheless, the amount of emotional labor that these scientists described that they had to invest daily to kind of keep that necessary level of respect was really quite enormous So one of the strategies that they engaged in, even very senior, and again, I can't tell you who this person is, but they had a very senior position in one of

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the tech companies that if I told you what it is you would know, but it would also identify her and also at the university.

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So they engage in strategies of withdrawal. So for instance, electing not to present their work, not to take credit for it.

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And the only way they could get out of these strategies and be credited with recognition for their work was if they came across at voluntary kind of mentor and here we've got a story about female mentor but they were also male mentors who repeatedly took it upon themselves.

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To work against silencing these scientists against ignoring them or even against others taking credit for their work. So I think I'm kind of, I don't have that much time.

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I just wanted to acknowledge that this is not just the case that no English speaking scientists had a very unique COVID histories.

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There was a lot that was shared in common. So there were caring issues, there were health issues, there were deaths that they had to deal with.

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And here I've got a quote from somebody who's not from the English speaking, who's basically amber or scientist and the person who was early career researcher but a mature person so it illustrates the kind of and I don't have the details here the kind of problems that were also in the background.

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But the interesting thing is to kind of wrap up about those Can't really diverse scientists is that although all scientists experience or many of the ones in the fields that we looked at, heightened work in intensity because their fields were related to COVID.

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In some cases, those culturally diverse scientists, their success was turned into some sort of difficult situation. We've got an example here of a young scientist who secured in the first year of COVID, 2 half 1 million dollars grants.

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But one was prestigious and one less so. And she was forced to step down from the prestigious grounds on the basis that the institute needed to hold on to both of them.

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The work had to be done in a timely fashion. So a senior male colleague was given the more prestigious drum which actually she secured and she had no support from within the institution because as far as the institution was concerned as long as both Browns stayed within the institute or was good.

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There were a lot of work family balanced stories and not all of them were negative. Some scientists who normally worked in labs that were detrimental for pairing pregnancy.

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We're actually happy because there could carry pregnancy to term in a healthier environment. But there were also some heartbreaking stories about people who left jobs and went somewhere else in the hope of permanency and then as a result of Kobe the positions were re-considered or they were made red redundant after having just

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traveled to the other end of the world. So I suppose the conclusions are here and what we try to emphasize here is that there's really relentless demand for mobility in science and Kovat made this really very difficult for all scientists but particularly those in fixed term positions and in junior positions.

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And this kind of cultural, capital issue is a huge problem which is completely unaddressed. So just to wrap up, our leaders when they say we're going to hire overseas should look a little bit more at the experiences of these culturally diverse scientists because they are brought into the country and they experience what they experience it's actually sometimes quite

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shocking and nothing is done about that. I just wanted to finish up. I don't have time for this by saying that it is not just them who are affected by this strategy but also the locally trained scientists who feel they are refused jobs because they are told they are not nationally internationally competitive.

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It means they are not prepared to be so mobile. So I'll just finish that and thank you for your time and attention.

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So we were just to speak with the time. Here listed in the program we first finished the presentations and then discussed rather than

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So first. Return to the displayer.

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Hello.

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And our second presentation is remote. If you online, are you online? Yeah.

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Yes. Yes, can you hear me?

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Yeah, I can hear you. Let me And then stop sharing some sharing option. So I think she could share your screen.

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Can you see my screen?

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Can you see my slides?

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Please share your screen. Okay. Okay, I think it's working out. Yep,

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Hello.

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The housekeepers of my life. Yeah, it's working now and you can get Yep.

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Okay. Thank you. Hey, good morning everyone. Thank you so much to Dr.

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Claudia Bachman organized this session. And to Dr. Sikihan, who chair was during the session today.

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Are you there? Wait a second. Can you hear me?

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I also went to thank, and my collaborator. This is collaborative work and he works at the Ponteficiary Ocean at

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Okay, so I will present preliminary results for a study overall this study is part of a project called Mothers pay labor and household dynamics in the context of COVID.

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19 pandemic evidence from a high mortality context. NISA study is focused on Peru.

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I am presenting results exploring potential impacts of household composition and specifically with an emphasis on age of the youngest child.

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To understand mothers employment patterns during the pandemic in 2,020. So these are some of the topics that I will touch upon very quickly.

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A sicker I forgot to put my Okay, my timer. Okay, so this is for. We are addressing a mother's employment during the COVID emergency.

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At this point in history, Peru is a middle income country. And it has the world's, the worst global death rate.

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You to COVID-19. Above 221,000 deaths in a country of 33 million of population which means that a above 0 point 5% of the Peruvian population died due to COVID.

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Oh.

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Okay, just to let you know you are sharing your presenter view throughout. Can you change it to like normal view?

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Because we are seeing your presenter view here. Sorry for the interaction.

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Okay, now it's working great. Sorry for the

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No, please. Is it better now? Thank you so much. Thank you for letting me know.

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Okay, so and as I said about a 0 point 5% of the Peruvians that due to copying in the first year of the pandemic.

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And these, numbers I mentioned them to give, you a sense of the sense of emergency.

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Due to copied 19 that the whole country experienced. The first batch of vaccines arrived to the country in 2,021 which means that through 2,020 the whole country we set was in a state of acute distress.

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What do we know about female labor and COVID? We know quite a bit at this point as we know the sociologically territory we have now a robust.

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Body of the territory that has a commented gender impacts of the COVID-19 pandemic in work trajectories.

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And mothers have been pointed out as a particularly vulnerable group, right? And we have 2 main explanations for this both of them particular to the COVID.

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19 emergency. The first one is a demand issue with a pandemic. But pre-pandemic patterns of gendered, occupational segregation and, the pandemic affecting sectors and industries.

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That it were more female dominated no in particular in particular services and sales no On the other hand, the supply side of the story due to increases in the need for unpaid family labor.

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No, especially schools and childcare closures. Given especially the second point matters with young children have been pointed out identified as particularly affected in their employment due to the pandemic.

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Well, we know that these research is largely focused on wealthy nations. These 2 factors plausibly apply to Peru as well.

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These 2 factors plausibly apply to Peru as well. And as an example in terms of to Peru as well.

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And as an example, in terms of occupational segregation, and as an example in terms of occupational segregation, this is a graph with the data that we use for occupational segregation, this is a graph with the data that we use for this study.

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This is Peru, Urban areas in 2,019 and we see a graph with the data that we use for this study.

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This is Peru, Urban Area in 2,019. This is Peru, Urban Area in 2,019.

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And we see with the arrows in 2,019 and we see with the arrows the service and sales sector being way more female than a male right above 30% of workers women workers occupied in service and sales versus less than 10% among men.

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Currently, the literature and the media tend to over a present with many advantage at contexts. And this is something that happens, I would say, in the holy chapter of family in sociology.

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And we do have this sort of representation of women in advantage positions, nations but also subgroups, no nations in general and subgroups in particular.

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I relieve lead to an important extent we have kept the analytical framework used for studying previous financial shocks or recessions.

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This includes looking at employment as an always positive outcome which of course makes sense thinking on previous recessions. And also less attention putting to the impact of health risk and health risk assessments in analysis of employment during the COVID pandemic.

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We would argue that when analyzing context of social economic disadvantage with treatment to the workforce could be conceptualized as a sellable outcome.

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So we think that possibly the potential impacts of household composition and matters employment outcomes are different in programming, are different in non wealthy countries.

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And this is study in a specific investigates the impacts of household composition on women's employment. And we have this overall, guiding, question.

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We were exploring whether Peruvian mothers with young children. Retreat from employment during the emergency tour in a larger proportion.

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I, are not as straightforward. On the one hand, there was a widespread scope.

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Actually, to a larger period than in many other countries. But on the other hand, Peru has a weak social safety net with little financial protection against health or unemployment shows.

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So we may expect that women and mothers may have remained in the labor force due to financial need despite increases in fire in family labor.

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In a context of what we're well mean, Precarious labor overwhelmingly and overwhelming progress labor more prevalent among women than men.

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Work was attached to health risk during the COVID emergency. And we may not have that, much time, to discuss this, but this is a graph with the point here that female labor force participation in Peru is high.

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That's the red line as you can see with this in this graph includes the 5 largest countries in Latin America.

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By population as well as Australia and the US as points of reference. So this is the the female labor market in Peru.

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And these are women highly attached to the labor force.

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Data myths. Are working with the Peruvian National Houseful Survey, a which is nationally representative.

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And he has a special, would say, no. New survey. It has a new survey the US, the Peruvian Census Bureau.

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That has this a longitudinal design with households of served annually between 2,016 and 2,020.

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They sample design was suggested to people presentitivity during the pandemic emergency. Importantly, data is collected around the same time each year that Almost all observations are within the same month.

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Data source allows us to for constructing two-year panels. That is in individuals observe it twice once a year in the same period each year.

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I contact analysis predicting employment in 2,020 during the pandemic among women of served working for pay in the previous year to 2,019.

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Which allows us to, roll out a set of selection issues that are often important for understanding. Women's labor outcomes.

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The analytic example is women in prime working age, 25 to 54, household header partners in urban areas observed working for pay in 2,018 and we excluded women with family workers with 0 income.

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The analytical sample is comprised by a 1,384 women. The outcome of interest is employed during emergency in 2,020.

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And the main exploratory variable in this case has been H of the youngest child. With 5 categories ranging from no children in the household.

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Youngest and being 6 or younger than that. 2, the youngest is 18 or older. So the category 5 implies that the youngest child is already an adult.

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All their explanatory variables are whether the woman has a partner or not. And Did you? Presence of household members who need family labour.

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These are defined as a members who are 65 or older or any member with a disability in the household.

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Also, we include a set of variables measured in 2,019. A small film, whether the woman was working in a firm that has 20 workers or less.

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Salaried worker with a salarate versus self-employed, both self-employed workers and workers on small firms tend to be disadvantaged in Latin American Labour markets, also household income after deducting women's earnings.

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An occupation in 2,018 by 6 categories as well as educational attainment. We conceptualize vocational attainment, household income after deducting mother's earnings and job related characteristics in the baseline year as variables are tapping into overall resources.

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That mothers could have drum upon in 2,020 during the pandemic.

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So the analysis is predicting employment in 2,020 is done as I mentioned among mothers observed working for pay in the previous or based year, 2,019.

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We conduct linear probability models. And you too to their ease of interpretation and they recommend that where the outcome of interest is between these kind of wide range.

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And among in our example, the outcome of interest is 66%. So, it fits our purposes.

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However, we conducted Robinson's checks using a logistic approach.

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And here we have the script for statistics, for our sample. And all women working for pay in 2,019.

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65% were working for pay as well in 2,020. And, 65 is actually low compared to previous years due to the obvious reason of COVID in other years is close to 90%.

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A for our main Explanatory variable of feature is that we are exploring at these in this study.

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We see. That women working for pay in 2,020 by age of the youngest child.

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Those who, obviously those who had no children at home were the more likely to remain employed. It's 75% of the the the bar at the left.

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We have here that the ones that were less likely to remain employed in 2,020 during the pandemic where mothers were whose youngest child was already an adult, 18 or older.

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And as you may remember, our analytical sample comprises only women in prime working age, meaning 25 to 54 years old.

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So this pattern is a to what we have seen in several other countries, wealthy nations where this matter has been analyzed and these studies tend to show that the group more affected is that of mothers with young kids in school age no due to obvious reasons.

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The the

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So, yeah, these are selected coefficients for in a probability model predicting employment in 2,020 as I mentioned.

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I will show other coefficients, but, several things to, mention here.

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Having my youngest child who is 18 and older, we find that it's associated here with, 11 percentage point decrease in the probability of being employment during the pandemic.

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Compared to women with not children and hot at home and controlling for several other variables in the model. At this point we have tried with several other specifications and the pattern remains consistent, no constant.

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In contrast, our coefficient for the present of a household member who is 65 for older or who has a disability it does not reach a statistical significance.

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We also see that a women with college completed were more likely to remain employed, which actually feeds a pattern of a remote boat work being mostly available for a women with white color positions.

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And among other coefficients of interest, we find that household income in 2,018, so in the base 10 year after deducting women's earnings is negatively associated to employment.

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As well as working a small firm.

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Thank you as a whole, results from our analysis aligned with descriptive results. I'm on programming women having a youngest child who is already 18 or older is associated to a lower probability of employment during the pandemic.

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All models controlled for age and pattern is a stable and process specifications. And we feel that whatever is happening here, we suspect that youngest child is already an adult, works as a proxy for a potential additional source of income during the pandemic.

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All their children could be seen as resources in these contexts for mothers in a period of acute financial and health districts.

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Hey, aligned with this notion is the fact that our coefficient for householding coming in 2,019 after deducting women's earnings also points to a protective role of household resources.

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We hope this this work that these are all still preliminary results, er, of pointing to our consistent, er, pattern.

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Oh, it's gonna help to thinking to a consider previous assumption. Now, coffee is different than previous recessions and it may.

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Us to think about what factors protected vulnerable women, vulnerable working women from working. From employment in a context of appealed health risk associated to employment.

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Well, thank you.

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Thank you. Very long time. So we will have our last presentation. Oh, here in prison.

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Okay. Can you share the screen?

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Oh yes, thank you. Thank you. Aida for making the past for me just to step in and I think your results are really interesting because they contradict in a way a lot of research we know from other countries where mother have been particularly suffering from kovat and did not work and that's something I'm going to look in my my analysis

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that is how far in what respect school and daycare reopenings, respective closures had affected parents employment and life satisfaction.

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How does it? No. So in most industrialized countries or western countries or wealthy countries as you phrase it in Minneapolis.

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There has been a debate on the effect of school and data daycare closures on parents work. So women put careers on hold, women face mental exhaustion, gender inequality during paid and unpaid work.

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There was a huge debate on how the pandemic affected in particular parents work. And

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The interesting thing is there is this debate that school closures matters but we don't know exactly to what extent school closures actually mattered and this is because several other containment measures such as closures and retail in services, bars, restaurant, hospitality and a decline in overall economic activity had occurred parallel to school closure.

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So there were the school closure and at the same time a lot of other containment measures happen. So we don't really know how the closures made a difference in contrast to all these others containment measures.

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And that's what we are going to look at here. We want to know exactly how school and day care closures had an impact on parents working hours and life satisfaction.

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And that's not easy to analyze because there was this parallel occurrence of closures and containment measures.

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But we have a solution. Perhaps one note in the beginning, I'm not going to talk about unemployment of parents because in Germany unemployment during the first lockdown was particularly low and this is due to a government scheme that is called short time allowance that let people keep in their employment with 0 working hours.

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It's a measure to prevent unemployment but keep people in their jobs. So I'm going to talk about working hours.

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So what we think or what what we will argue is Assume that closures did affect parents. That seems to be quite.

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Sensible or quite reasonable to do so but if we assume that then the opening of school in daycare facilities should lead immediately to a resumption of working hours and to an increase in life satisfaction again.

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So we are not going to look at the closures because they had this parallel occurrence of containment measures, but we're going to look at the openings and then conclude from if there is an opening effect, then we see that schools really matter.

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So what what do we have in Germany as in many other countries? So in Germany, nationwide school closures occurred from mid-March on so I remember being a mother exactly the day it was the seventeenth of March school.

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School closed from Monday on. Friday it was announced. Monday was in place. So on that occurrence, this Friday was occurring in all German federal state and school had been closed until the end of May, 2,020.

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But then from June on, the federal state started to reopen schools again. And here the interesting thing comes because the opening was dependent on federal state government decisions.

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So there is some variation. Some states opened earlier, other states opened less early and they were also free to define the degree.

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So how much on-site, which school grades, which age of children, though there is some variation going on in the reopening.

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Where as closures was immediately similar everywhere, the closure had some variation. And what we do now is we use this veryation in reopening between the federal states and the different time to analyse the changes that occur in the week after.

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In a federal state, when schools have been opened again and we look at the changes in work hours and life satisfaction.

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And we add to this that we not only look at parents, how parents reacted to the reopening of schools.

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The parents increase the working hours, but we compare these parents with non-parents in the same federal state and why do we do so because we think that non-parents should not be affected by the reopening because they don't have children that's staying at home any longer.

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So we see if really school make a difference to parents. And then by comparing these parents that are assumed to be affected by the openings and non-parents that are assumed not to be affected by the opening.

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We see how much difference the openings make. And you will see in a minute what I mean with that.

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So the data we use is a high-frequency online personal panel conducted by the Federal Employment Research Institute and we can combine that with we just registered data of employment and this survey was set up in May, 2,020.

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So it was a covid survey and the interesting thing is that is was conducted monthly from May on.

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And it included questions on how had your working hours been before COVID. Yes, and that's what we're going to use here.

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So what we're going to use here is the actual weekly working hours before Covid-nineteen.

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And in June, 2,020, June, 2,020, because there was this reopening gradually coming into place.

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And we also look at life satisfaction and here we compare the measurement in May, the twentieth 20 again with a measurement in June 2020 to see if life satisfaction had changed in the course of the opening of schools.

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For working hours we focus on individuals who had been employed before the pandemics or we looked at employed individuals only and for life satisfaction we look at individuals who had been observed both in May and in June.

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Okay, we also look at parents and we decided similar to Ida's on, yeah, 2 ideas that we look at those who have one child under 15 in the household.

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And we compare that with persons without children of this age. So if we talk about non-parents, it could be that these parents do have children, but they are older than 15.

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We have a lot of associate demographic control variables and characteristics of the employment. That we control in the regression models.

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Then for what does opening mean, we decided to define opening by every child in the federal state where the person lives experienced at least some on-site schooling or daycare.

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Closure means on the other hand that some children are still fully at home so we are rather conservative it's not that children are back into school completely or back into daycare completely but at least every child had the opportunity to be at least sometime in school or in daycare.

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So what we use as method is a difference in different method comparing before Copy perspective May, 2,020 to June, 2,020 to compare the parents in states where opening had been taking place to those who had not been in these states and comparing those to non-parents.

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And. So, what do we see? If we look at working hours before the pandemic, that's what we report, how the working hours have been before COVID, we see that parents work significantly fewer hours on average in June, 2,020.

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So we have the before COVID situation and the June situation on the right. And this differs by whether or not school and day care are open or whether school and day care are still closed in the federal state, the where the parents live.

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So where the schools and daycare remain closed which is the

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Circle, parents work a lot less hours compared to those in states in which schools have already been opened again.

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So schools do make a difference. If we compare that now to nonparents who should not be affected by openings, we see, yes, employed person without young children also works significantly fewer hours in June, 2,020 because of short-term allowances because of economic decline and so on.

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But they are not affected by the closures. Though they do work less, but compared to the parents where we see this huge difference, closed states, open states.

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That doesn't make much of a difference for the non parents. So the slope is the same in the 2 situations.

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So, and if we run regression models, we see yes that parents and non-parents did reduce the working hours in the observe time spin as they had been doing in many countries.

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So there is this kovat shock going on for parents and nonparents. We see that openings have a positive effect on working hours for parents only.

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So parents profit from school openings. And this effect is significant. It's different for parents and nonparents.

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If we compare men and women, interestingly we see that there is a positive effect of opening for both for fathers and mothers.

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Both Work more hours after the opening has occurred in the federal state. But the effects are not significant if we Look at men and women separately.

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So let's have a look at life satisfaction. So here we compare May, 2,020, so in the lockdown and June, 2,020 when the openings have already been in place or started to open some federal states.

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What we see first is that the life satisfaction of mothers is considerably lower than that of non-Mazas.

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So it's a left-hand side compared to the right hand side. Mothers have been suffering.

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In the lockdown situation in May. But if we look at how school closures affect the life satisfaction of mothers, we see that mothers in states were school and daycare had opened again do much better than those who are still in the lockdown situation where still the kids are at home.

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Compared to the non-mothers on the right-hand side, we say, well, it doesn't much make a difference whether or not school or daycare is open again.

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So we see again school and take care closures mattered for women or respective. The openings lift the burden from others.

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For farmers, we see much less going on, though there is not much of a difference if we compare the right hand side and the left-hand side in May so Ferns her father seemed not to be bothered by the children at home in May when it What we see is a small increase of life satisfaction in June for fathers if

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school and day care are closed, which we cannot explain why fathers do better, schools are still closed.

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On the right hand side we see that life satisfaction of non-fathers is not a factor by opening.

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So for non-fathers, nothing changes whether or not schools are opened or not.

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We also ran regression analyses. We see that there is a positive and significant effect of reopenings on life satisfaction for mothers as we've seen in the descriptive analyses, but that's not occurring for fathers.

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And we see that there is a significant difference between mothers and non mothers. That does not show up for father and non-father.

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So life satisfaction and school closures seem to be seem to matter for schools seem to matter for the life satisfaction of mothers but not for fathers.

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Okay, so what have we seen? We have advanced previous research by trying to isolate the effect of school and daycare closure openings.

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So this is everyone thinks to know yes school mirrors and we have been able to really put it down how much school methods over and above other containment measures.

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What we see is that openings had an effect on parents working hours and on life satisfaction beyond these other pandemic related changes.

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Parents working hours increased by about 1.5 to 2 h per week. And mother's life satisfaction increased by about 0 point 4 points on this life satisfaction scale.

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Father's life satisfaction interestingly was not affected by the openings. It was neither affected by the closures nor affected by the openings.

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Conclusions? What we see is really childcare is important for parents for parents. Working hours and for parents life satisfaction.

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The parents struggled a lot once the children had been at home and the school openings, daycare openings lifted some of the burdens that parents faced.

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And what we see interestingly is that we see no gender difference in the resuming of work hours.

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But we see this gender difference for life satisfaction where mothers have been struggled more during the closures than brothers.

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Thank you. Oh, perhaps I should add. The working hour results have in the meantime been published in just a few weeks ago, 4 weeks ago they have been plagued.

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We are still working on the life satisfaction paper. So if you're interested, here's a QR code.

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Yep.

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You Because this we Only have 2 papers And it's still quite early. I think we could just.

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Let's see first do we have

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Oops. Stop sharing, right? Return and How can I go back to the 2?

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Share at making support. Yeah, I thought I should stop sharing that.

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Oh, okay. So,

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Great. Let's see if we have.

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For for a few of you who are online if you have questions please do type in the chat box and so that or participants can.

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See it and respond to you so

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There's no formal discussion law here so we can just do the Q&A directly. Worst in Georgia.

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I think Aida can you hear me now?

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Now I can hear you. Yes.

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Okay, so let's look what maybe the system. You know, I just like to sit here because I have to sit here.

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Okay, so you can't see us but you can hopefully hear us. Is that right? Okay.

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Yeah, yes. Thank you.

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So I have a question for

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Is how much did your participant report that the program helped them in dealing with the struggles they had.

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So look, we had some, I think the leaders would be very happy. Like we had some really enthusiastic and reports on the program.

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To the point that some of them even reported that they had a child which they were afraid to have before the workshop as a result of the workshop.

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But that actually had nothing to do with the content of the workshop. It was more about getting together with other women in their situation and some of these women were pregnant or illegal children.

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And so the main thing that worked for them because going into COVID we were thinking are we moving these workshops online you know and they said well it would be good for access so that people who can't be admitted to funding restrictions could benefit.

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So it's a bit shocking in a sense that I also discovered it by being an observer in the workshop.

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How long you can be in academia? And never have exposure to this crucial information how to help your career.

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Do you know what I mean? Because we kind of think we all know it, but then we don't.

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So these women were very enthusiastic. But it's not surprising that the ones who participated in in our interviews were the ones who are particularly interested in that. You know what I mean?

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So there were 106 of them and the 49 that agreed to talk to us and remember this was the end of 2,021 that agreed to talk to us and remember this was the end of 2,020 and remember this was the end of 2,021 and maybe you don't know but that was very

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tough time and maybe you don't know but that was very tough time in Australia because we had a long time in Australia because we had a long time of lockdowns and they're just absolutely exhausted and they're just absolutely exhausted and they're just absolutely exhausted and they even sometimes traveled to find that time to talk to us

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because they even sometimes traveled to find that time to talk to us because they were honestly, although they had different science specializations, they were honestly, although they had different science specializations, a lot of work of them, they were honestly, although they had different science specializations, a lot of work of them worked in science specializations, a lot of work of them worked

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in COVID related activities. So there was one woman there who was a kind of mathematician, but what they did was that the health system in Australia suspended all all non-copic treatment and surgeries.

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So she was doing these models and planning. Because you know the hundreds and thousands of surgeries that were not happening and the health system had to even plan for later.

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So they were very enthusiastic most of them and as I said some of them even said you know changed my life I still have you know the piece of paper that was given to me it's on my desk So in a very hard warming but also it makes you wonder you know that they have been in the field for so many for quite

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a few years prior and in with all of those initiatives going on there was so little to give them this feeling of you know community and that's why I have also put questions for both of you.

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Either for you, I have more of a comment because you were showing something about Australia in one of your slides.

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At the beginning, you know, when you were looking at labor force participation rights and you were showing Australia and you were also saying that families are larger in Peru so there might be a different relationship between family size and workforce participation.

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Is that what you said?

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In that sense, yes. So. In particular what we have is a higher prevalence of extended households.

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So family size, the number of children is actually at replacement rate has been for a while now. But there is a higher prevalence of extended households.

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Which is likely to positively affect women's labour force participation. Mixes?

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Yeah, yeah, but I also wondered that, you know, this is important to say, but for those, US and particularly for Australian sort of data, I think you need to bear in mind what Marita was talking about, namely that undercobied there were government sort of in Australia it was called the jobkeeper program

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that meant that you didn't really go to work but you still received your full pay but that also depended on your industry.

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So for instance, the government in Australia at the time of Copy didn't like universities because they thought we we you know we are trouble makers so they excluded universities from this provision but you know I think if you do any element of comparison between your Peruvian situation and for instance you introduce Australia as a reference.

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You know, on the one hand side, you've got those extended households which function maybe as a kind of support for the family, but on the other hand side you need to somehow factor in those welfare state provisions.

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You know, government payments because and this is why I think Marita's strategy of looking at working hours rather than whether people are in their labor force.

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If you can follow it would be better because you're not running into the problem where you know some people are nominally working but not actually working.

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That's a very good point. Actually, that graph was showing patterns of female labor force participation from 2,010 to the 1,022.

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Just showing rates of familiar for participation. So let's actually not focusing in COVID. For that graph just showing the fact that this is a population with a very high rate.

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Okay.

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Of female labor force participation. Even for Latin American standard 6. It's high. So yes, that was kind of part of the I should have.

303
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And talk to more about it. It was part about the context. A showing that this is a contribute high rates of female workforce participation that no I wasn't focusing on COVID ban is actually the whole decade to show an overall pattern.

304
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Okay. I also have a question for you, The first thing is in the panel survey do you know when the months of the interview have been so that the month had not been in February because then everyone was working and it was Colby just somewhere so that could be interesting to know when the time of the interview was for your interpretation but I think it's it's

305
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less it's later than February but just just for clarification. And the second question that we or the signing you presented were really puzzling that it was the mothers with the older children that it worked less.

306
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So what did the mothers of the young children do had there been school closures who was taking care of the kids what happened in Peru though that's really really interesting.

307
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Thank you. About the first question, actually, it fortunately yes, no, the This data I am very excited because as you may know for a for Many countries, longitudinal rate data is quite rare.

308
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So thankfully we do have the specific month in which the interview was conducted. So for all analysis, I'm restricting to only the COVID period.

309
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And thankfully interviews are conducted in the same month in the same month across years mixes. So I'm able to restrict the sample to only the copy time and also.

310
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All interviews in 2,019 if they were in March they were also in March well much wouldn't be there no but if they were in June in 2,018 they were also conducted in June in 2,020.

311
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So thankfully that that is, that is there. That is, the sample, the survey design.

312
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About what we were doing. It's a it's a very interesting question in my view. What this webinar we're doing, And something I am trying to emphasize in the in the paper here is that Oh, this is a completely different context from what we have seen in,

313
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Most of the return. Let's say no, we still need to know a lot about, to learn a lot about COVID.

314
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But there is less about a context with. High mortality rates due to COVID and with very little. Social protection nets.

315
00:59:17.000 --> 00:59:24.000
Oh. According to all work done in the subject, it was very little social protection next to these women in general.

316
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So what is pushing people is financial meat. That's why. Believe that's going on. So with women and with the smaller children more than what they were doing it's indeed a very important question but I think what was driving this agreement was the financial need that there was There was a need for income.

317
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And. Very little social life to safety needs providing with, income to these households. The very important question of how they did it.

318
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I think that one potential explanation, although I cannot explore it because like I don't have comparative data.

319
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Gross national on this pattern on this matter but I do believe that the prevalence of extended households explains a little bit.

320
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So I have work with a comparative studies comparing Peru with Brazil for instance. In other Latin American countries, extended households are less prevalent.

321
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They are very prevalent in Peru and it is connected very likely to the fact that women are so much inserted in the labor force.

322
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So I think that in this context of acute need other family members in these households provided for childcare.

323
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Yes, if that, if that makes sense. It's a 40% of mothers in Peru who live with extended family.

324
01:00:50.000 --> 01:00:57.000
So, he is a a strategy to for women to keep working.

325
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Okay, thank you.

326
01:00:58.000 --> 01:01:01.000
Of course, thank you.

327
01:01:01.000 --> 01:01:12.000
So I have a comment maybe a question for you Marisa about your study and I'm guessing that in Germany there was just one lockdown so the style of etc.?

328
01:01:12.000 --> 01:01:29.000
Yeah. Okay, so maybe this will be more relevant. So my comment has something to do with my own analysis, which I haven't shown in this session here but I presented it elsewhere and I've got some data from young people in Australia.

329
01:01:29.000 --> 01:01:49.000
And also kind of trying to, what I tried to do in the first season is I got data for 2,019 one of the outcome variables is life satisfaction but it's not about mothers it's about young people and you know and then the other variables have to do with the kind of hard material hardships because there was a

330
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lot of talk about hospitality closing down and these are the kind of sectors of industries where young people tend to work when they also study in Australia.

331
01:01:59.000 --> 01:02:16.000
So my problem is my struggle with my favorite paper it is not published at all is that I've got data for 4 points of time and if I had data I think for just 2 points of time, which is, you know, you can do difference in differences.

332
01:02:16.000 --> 01:02:23.000
It's, it's a kind of classical setup. Then I think some of my results will be easier to interpret.

333
01:02:23.000 --> 01:02:31.000
But what I'm trying to say is it would be really interesting to see, but I'm being a bit of a Debbie's alphabet.

334
01:02:31.000 --> 01:02:51.000
Whether when you have the same thing happen later you have the same sort of pattern because that's not what I'm getting and I don't know at this point whether it's me not doing the analysis correctly or whether actually the situation but you know I have different issues so we have to remember that the situation is that for instance following COVID

335
01:02:51.000 --> 01:03:06.000
in Australia I rents and the prices of property went up so incredibly that young people are really in you know and this was kind of put on hold under COVID because there was a memo random and you couldn't for instance evict tenants.

336
01:03:06.000 --> 01:03:22.000
So if somebody was not paying you know you couldn't get rid of them and now in 2,022 you know so we have those kind of negative effects you know kind of skyrocketing so to say, so have you thought about this?

337
01:03:22.000 --> 01:03:31.000
Perhaps 2 2 answers at least to the first part of your question. So the first part is have there been other lockdowns of school closures?

338
01:03:31.000 --> 01:03:54.000
Yes, in November, December, January and February, 2,021. So in winter. European winter there were again school work close but at that time people were better prepared schools were better prepared though they have this online teaching it was different compared to the first lockdown which was a surprise for everyone and everyone was overwhelmed so that the person the second

339
01:03:54.000 --> 01:03:57.000
thing is.

340
01:03:57.000 --> 01:04:16.000
The research design we applied for this opening of schools and this gradual opening by federal state was fitting perfectly to the situation in June and we have this strong difference of the surprised and overwhelmed parents that are had a relief once schools have been opened again.

341
01:04:16.000 --> 01:04:24.000
So we don't know if this is the same in winter. First, so this is had there been a further lockdowns.

342
01:04:24.000 --> 01:04:31.000
Yes. But they differed from the first one. And, So, so we didn't stretch.

343
01:04:31.000 --> 01:04:37.000
The analysis to win the end of the final thing is the data for winter so the monthly survey get worse.

344
01:04:37.000 --> 01:04:53.000
Wave of the wave because people drop out. So we couldn't. Even if we wanted to, we wouldn't have been able to conduct an analysis with looking at March, February, March, 2022, 21.

345
01:04:53.000 --> 01:05:03.000
To see if they have the same pattern again. So, but what we did is we were looking at the UK using the understanding society.

346
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And Survey which is host also and there we could really look at the first lockdown in the UK and the winter lockdown in the UK and there we could follow parents over several months and see how their life satisfaction develop.

347
01:05:20.000 --> 01:05:42.000
But we did not look at the school closures here just described how the life satisfaction changed in the first lockdown and how it recovered and then sell back again and the question we were asking is whether or not there had been accumulation to parents get exhausted in any longer or if there had been a adaptation that parents know new kids are at home they had

348
01:05:42.000 --> 01:05:49.000
a new routine so they were not shocked and went and that's something we were looking at with the pay data.

349
01:05:49.000 --> 01:05:58.000
Yes, we did. Just also recently. I can send you. Yeah, because my question is really about having Walter.

350
01:05:58.000 --> 01:06:12.000
And it is not picking your thinking as It's it's a description. It's not again this Thank you.

351
01:06:12.000 --> 01:06:30.000
If I may ask a few questions. So, for the first paper my question is about is there any information in the interviews about Sorry, should speak to this about the extension of grants that funders offer because this is a situation that myself experienced.

352
01:06:30.000 --> 01:06:43.000
So I do my post up during COVID. In New York, which is really, really bad COVID situation there.

353
01:06:43.000 --> 01:06:59.000
And then there is this ongoing threat. That we face is that, okay, we have to. Oh, work schedule is disrupted and we have to expect that the project takes longer and we don't know it takes how long to really finish it.

354
01:06:59.000 --> 01:07:15.000
So then we're starting to apply for extension and that I mean that depends on who the founders are do they think the project is priority rather so I feel like that is a Pretty influential situation here.

355
01:07:15.000 --> 01:07:36.000
And the second question I have is about this LDA. The topic model. I know that you haven't finished that, but I was curious about how many documents or interviews are there and how large is the document size because it's obviously gonna affect how effective the LDA would be.

356
01:07:36.000 --> 01:07:48.000
And for our session paper I think MERITA already my question is about this older children a little bit anti-intuitive finding where you have already answered.

357
01:07:48.000 --> 01:08:09.000
For the third paper I think my question is just a 2 clarification questions. The first is that this working hour variable Does it only include formal working outside the home or does it include those unpaid?

358
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Labor or working like formally. How's that all working out? Actually, how many hours did you work last week?

359
01:08:19.000 --> 01:08:29.000
In. So I felt like there might be an increase in unpaid working out for mothers that can explain why.

360
01:08:29.000 --> 01:08:47.000
Mothers and fathers. Would be different. Is there a direct measure in Okay, and the second clarification question is, so at the beginning you told us a questionnaire about why studying the time of reopening rather than closure.

361
01:08:47.000 --> 01:08:49.000
Right. Because when school closes a lot of other institutions also close and they are correlated.

362
01:08:49.000 --> 01:09:05.000
But isn't it also true that when school reopens other institutions also reopen? So how would studying the reopening directly rule that out?

363
01:09:05.000 --> 01:09:13.000
So just A quick question. Yes, that's all. So who's that in a room?

364
01:09:13.000 --> 01:09:25.000
Right, we stop we answer in a room Yeah, I can go. So what about the reopening of bars restaurant and so on? Yes, that's true.

365
01:09:25.000 --> 01:09:28.000
They, what about the reopening of bars restaurant and so on? Yes, that's true. They also happened.

366
01:09:28.000 --> 01:09:33.000
We're in the school where we opened in June. And we cannot rule out that something else was going on.

367
01:09:33.000 --> 01:09:44.000
That's why we also looked at the non-parents. Because they profited from opening up a hospitality of recipients of whatever, but they should not be affected by the opening of schools.

368
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That's why we need the comparison of parents and non-parents. Not parents profited from whatever other containments had containment murder had been relieved but they should not be affected by the openings and that's why we need this non-parents situation in the same week of the reopenings of schools.

369
01:09:59.000 --> 01:10:14.000
So that's how we try to get that. Analyze how much we opening up schools mattered for parents.

370
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It should not matter for the non parents. That's why we need them. Do. And the second question was unpaid labor, well there is a question on unpaid label but it's much less precise than the one we can use for working hours.

371
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It's just more or less more than average, something like that. And that's why we decided to go for the working hours.

372
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I think you asked me whether we have information. About but you mean like whether they got more money into the grant because of COVID?

373
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If they do get longer time, Ask about this information if it's affected personal situation of the scientists.

374
01:10:58.000 --> 01:11:05.000
So you know we have quite a few false dogs. So if they told us that the primary purpose of research or data collection is to ask about the impact of that workshop.

375
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That they participated in 4 years or in a few years back. And then everything else is whatever they call them here.

376
01:11:17.000 --> 01:11:26.000
So we did ask them general questions and you know how how has quality affected your work and your personal situation but we didn't push if they didn't want to talk.

377
01:11:26.000 --> 01:11:40.000
Okay, so we've got their CVs so we know how they went so if they told us I lost my job or I didn't get an extension because the ground situation was as it was then we found out about it.

378
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Some of them had continuing positions either at the time that they were in the work club or later. So it wasn't an issue.

379
01:11:49.000 --> 01:12:01.000
So from these kind of dye data, it is difficult to get an overall picture of what happened to Trump recipients, you know, and all stocks in particular.

380
01:12:01.000 --> 01:12:09.000
So that's, you know, so that's not somewhere that we are going with in terms of, of, you know, writing up the results.

381
01:12:09.000 --> 01:12:16.000
The second thing that you asked was the IVA. Definitely we're not going to put in this these are semi-structured interviews so they had several sections.

382
01:12:16.000 --> 01:12:27.000
So the sections that are going in for LDA once we finish cleaning the data which is huge. This is what's kind of keeping us from doing the analysis because the data is very difficult to clean.

383
01:12:27.000 --> 01:12:45.000
It will be just a section that we identify as being relevant to this. That means there'll be some you know decision making and probably will not have all of the texts that should be there because you cannot keep a semi structured interviewed.

384
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Although I have to tell you that if you ever do this kind of the research, don't get sociologists to do your interviews, get science communicators, people who are trained in journalists and journalistic interviews because they keep the order of interviews much better.

385
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Where it's sort of sociologists that go all around and then when you look at the transcript It's you know, a little bit of coffee here and hidden there.

386
01:13:08.000 --> 01:13:29.000
So the goal would be to just take the chunks that are related to Kobe and put them into an LVA so that you have those Because how it works is it looks at the context of each word placement or trace placement, but it also counts, you know, and the relative frequency of, yeah.

387
01:13:29.000 --> 01:13:39.000
So basically if you put everything together even if your software can process it. Then what you get out is still a lot of garbage.

388
01:13:39.000 --> 01:13:43.000
Yeah, so that's what we plan to do. How well it would work you don't know.

389
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You just need to

390
01:13:48.000 --> 01:13:55.000
But I said, well, it's good to know, you know, if we get stuck, we'll send you a desperate email.

391
01:13:55.000 --> 01:14:08.000
Yes, yes, but you know I think the only thing you can do is just to be honest with what you have done And we, there's no intention on our part to just treat it as a kind of.

392
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You know, magic for the key to the data. That's why we're spending time just basically listening to the data and going through the transcripts.

393
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And just relying on our interpretation because at the end of the day when it spits out some topics you know, quotation marks, you still have to interpret these topics.

394
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And if they make no sense. You know, unfortunately you have to close down the show and forget your IDA.

395
01:14:31.000 --> 01:14:43.000
I think that's what it is. Is that what you felt might happen? I'm just trying to figure out what is Is it like a whole evening or

396
01:14:43.000 --> 01:14:56.000
Not just the section. No, I think the call if you put in the whole interview It's like trying, I think it's like trying to put all your variables in the survey into a regression model.

397
01:14:56.000 --> 01:15:20.000
Oh please. The body of how Yeah, so I agree with you and I but I don't know how exactly I think you very right if you meant to know how exactly I think you very right if you meant to signal that how we're very right if you meant to signal that how we selected chunks of text to go

398
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into signal that how we select the chunks of text to go into the LDA will the chunks of text to go into the LDA will matter greatly for the outcomes.

399
01:15:23.000 --> 01:15:26.000
I think you are very right for the outcomes. I think you're very right. And I don't know exactly how we'll do that.

400
01:15:26.000 --> 01:15:41.000
I think you're very right and I don't know exactly how we'll do that but this is not my most pressing headache at the moment at the moment it's just getting through guitar cleaning because that turns out to be a huge issue also because you know we have a large number of people from non-English speaking background and the

401
01:15:41.000 --> 01:15:52.000
transcription was semi-autonated. And that means we've got half a million words and we have to now slowly go through the half a million so it's taking a very long time.

402
01:15:52.000 --> 01:15:55.000
Please.

403
01:15:55.000 --> 01:16:11.000
And to do what? To clean the Sorry. No, no, no, no, they are all in English, but the transcription, not translations, transcripts were generated by other other software.

404
01:16:11.000 --> 01:16:18.000
I don't know whether you've used it. It was like it doesn't recognize the words.

405
01:16:18.000 --> 01:16:26.000
So basically so what it does and with because this is you know a lot of things that they talk about this is you know a lot of things that they talk about the subject matter of their activities is very technical.

406
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So even if they tell you what they do, you know the terminology that they did every time I did one of these interviews myself I learned 5 to 10 new words and they learned something some of the stuff that they do I don't know I can tell you for instance they work on hydro for hydro-fuel you know hydro cells or

407
01:16:44.000 --> 01:17:01.000
something like this. So there's like one place in Australia that does research on this and you know there's others in the in the world but if you are in this field you know exactly who these people are and if they tell you what they work on in terms of hydro-fuel you will know exactly.

408
01:17:01.000 --> 01:17:14.000
So, so it's very specific and they use you know what they tell you about their career trajectory they tell you about the prizes they tell you about the coincidence and funding agencies so there's a lot of proper nouns in the text.

409
01:17:14.000 --> 01:17:30.000
So it's, you know, and I don't know that we really need to get them right, but, up front again when you're going to something like ADA, you don't want to feed too much junk into it because it will return junk.

410
01:17:30.000 --> 01:17:36.000
Would you agree? Yeah.

411
01:17:36.000 --> 01:17:41.000
Oh. Fancy if we have

412
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See if we have any other questions or online questions.

413
01:17:48.000 --> 01:17:56.000
So I don't see any chat. In the chat box.

414
01:17:56.000 --> 01:18:13.000
Okay, also if there is No other Questions then I think we will conclude our session earlier today and Thank you for our 3 presenters today and your interesting research projects.

415
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I hope what we have discussed today would be beneficial for moving forward. And thanks for attending this really early session on the last day.

416
01:18:25.000 --> 01:18:33.000
And. I think that's the conclusion of our session today. Thank you.

417
01:18:33.000 --> 01:18:34.000
Thank you so much to you all. Thank you.

418
01:18:34.001 --> 01:18:52.000
So this is important to to kind of to recognize. And in terms of what we've got in our data is altogether there was enough money to fund about 106 participants over the span of 3 years and we managed to interview 49 of them and this created 50 h over 50 h of recording and half a million of words in

419
01:18:52.000 --> 01:19:01.000
transcript. So it's quite a lot of narrative. It turns out that 55% of our participants had English as their second language.

420
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30% only grew up in Australia and 30% mentioned parenting children because a lot of what they said in the context was not asked about directly, just kind of.

421
01:19:11.000 --> 01:19:35.000
Came up in the interviews. And these characteristics are not just specific to the small group of people. If you look at the Australian Bureau of Statistics data, you'll find out that across Australia in the relevant fields of science and here we are talking about mathematics, physics, astronomy and statistics and the related branches of engineering.

422
01:19:35.000 --> 01:19:59.000
you have 57% of women working in these areas in Australia having born overseas. In the public sector, 64% of women and if you look at academia you're really just looking at maybe three-quarters of women born overseas and half of them have been come from countries where languages other than English are used.

423
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But the important thing about the scientists in this study is that much like the leaders of the workshops, they are successful.

424
01:20:11.000 --> 01:20:31.000
So access to the workshops was not kind of voluntary. It was competitive and the success rate was about 50% and people were evaluated and admitted to the workshops not on the basis of the very good CVs and you know excellent potential in their field but also excellent potential in their field.

425
01:20:31.000 --> 01:20:37.000
But also they had to submit a narrative that explained excellent potential in their field. But also, they had to submit a narrative that explained how they would give back to the community of their fellow scientists.

426
01:20:37.000 --> 01:20:53.000
Because they are so successful, many of them feature on their university websites in the university websites in the media and and it just is basically known in the country because this is not a very large population.

427
01:20:53.000 --> 01:21:03.000
To say anything about them and not to identify them because as soon as I tell you what particular project and they're doing some mind-blowing research, some unbelievable stuff.

428
01:21:03.000 --> 01:21:08.000
So as soon as I tell you what they're doing there's only just one or 2 people in the country or maybe one team who does that.

429
01:21:08.000 --> 01:21:19.000
The other thing that turned out to be interesting about this project and I have to make this admission now is that all the money could be raised to fund the projects.

430
01:21:19.000 --> 01:21:32.000
It was extremely difficult to raise any money for this evaluation and collection of data. So we managed to get a little bit to do the actual data collection but for the analysis we are actually short of money so this is why I won't be showing you any fancy graphs.

431
01:21:32.000 --> 01:21:44.000
With the results of a latent Dirichlet allocation topic analysis because we simply are behind on schedule with this.

432
01:21:44.000 --> 01:21:53.000
So what is it that I want to talk about in substantive terms. I want to talk about certification by visa stages.

433
01:21:53.000 --> 01:22:04.000
And very, very soon in those interviews, it became apparent that it is not easy for people from known English speaking background and this is what I have on the slide here but I also have PGR with which stands for a patriarchal gender regime.

434
01:22:04.000 --> 01:22:21.000
So there's a group of countries from which, there's a group of countries from which, so there's a group of countries from which, when we talk to scientists, their experiences were distinctly, you know, when we talked to scientists, when we talked to scientists, their experiences were distinctly, I would say, marked by

435
01:22:21.000 --> 01:22:37.000
extra-emotional labor and extra-emotional labor and extra struggles. So the funny thing about this is that these are very marked by extra emotional labor and extra struggles.

436
01:22:37.000 --> 01:22:47.000
So firstly, it is very difficult to get visa sometimes to guess out to study science overseas, particularly in Australia, but specifically under COVID.

437
01:22:47.000 --> 01:22:57.000
We heard about stories where long-term residency strategies of these scientists because they came to Australia and for a number of reasons they couldn't, get or wouldn't take out citizenship.

438
01:22:57.000 --> 01:23:06.000
So they were here on working visas. But what happened under Covid in Australia was that the working visa program was brought to a sudden halt.

439
01:23:06.000 --> 01:23:23.000
And what that meant for these people was that practically you know within a week they were facing the prospect of basically leaving the country and not they were actually given 60 days to leave the country and that was unexpected and that was very troubling for them but also traveling for others.

440
01:23:23.000 --> 01:23:38.000
Who are in a similar situation. In terms of postdocs and junior people who are on grants of big professors, you know, we have a story here at the top of the slide where somebody came out here and they were hoping to extend their postdocs.

441
01:23:38.000 --> 01:24:02.000
But what happened in Australia was that the government didn't support financial universities. So all of a sudden we had the financial austerity crisis which meant that a lot of people were laid off across universities and also we were told to freeze our grants in that unless there was exceptional circumstances that mandated that money be spent now, we were told not to spend money until the next

442
01:24:02.000 --> 01:24:14.000
year. So the implications this had for those false dogs were that all of the sudden they couldn't for instance get their passports renew their passport because due to lockdown they could travel to their embassy.

443
01:24:14.000 --> 01:24:23.000
They couldn't get an extension on their job and the only place they could go to from Australia they had to leave was back to their home country.

444
01:24:23.000 --> 01:24:36.000
And he would also have a quote from somebody who avoided with a young family that fight and they were very about how lucky they were because otherwise they would have to leave on a very short notice.

445
01:24:36.000 --> 01:24:42.000
Those of young scientists who were in teaching related positions found as we won't be surprised to hear that they're teaching loads doubled basically.

446
01:24:42.000 --> 01:25:09.000
And There is a precarious employment relations with supervisors are also illustrated by this quality towards the bottom of the slide where sometimes upon completion of their PhD they were not given a postdoc in their home university but a research assistant job because the supervisor knew they needed residency for visa purposes and they couldn't afford not to take

447
01:25:09.000 --> 01:25:34.000
this offer. Language and cultural capital issues came up repeatedly in different settings. And we have somebody here at the top of the slide who was although young, already a leader of a very large grant and this person felt that there was not enough trust put in international or non-import speaking background leaders of these grants because although within her institution she had a

448
01:25:34.000 --> 01:25:50.000
lot of support, she ran those multi 1 million dollar grants. Which we funded by external bodies and when lockdowns happened and labs were closed the project slowed down and but the funders were not in the same area of the countries.

449
01:25:50.000 --> 01:26:04.000
They didn't understand the lived experience of the lockdowns and she felt that because she was from a different cultural background, they suspected her and her staff of not basically working hard enough on the project.

450
01:26:04.000 --> 01:26:21.000
And we have also stories about sort of mediating really difficult relationships. I don't know if you realize that but in science, postdocs very often but in science postdocs very often supervise PhD students of their supervisors so often supervise PhD students of their supervisors, so the professors.

451
01:26:21.000 --> 01:26:30.000
So what happened was that these people found it very difficult, so the professors. So what happened was that these people found it very difficult to receive the amount of respect and deference. So they found it very difficult to receive the amount of respect and deference.

452
01:26:30.000 --> 01:26:38.000
So basically they found it very difficult to receive the amount of it very difficult to receive the amount of expect respect and deference so basically they found it difficult to be respected on the daily job and of course, so basically they found it difficult to be respected on the daily job.

453
01:26:38.000 --> 01:26:48.000
So basically they found it difficult to be respected on the daily job. And of course, under COVID, the numbers of microaggressions that occur in face-to-face interactions are reduced because you are at home.

454
01:26:48.000 --> 01:27:03.000
But nevertheless, the amount of emotional labor that these scientists described that they had to invest daily to kind of keep that necessary level of respect was really quite enormous So one of the strategies that they engaged in, even very senior, and again, I can't tell you who this person is, but they had a very senior position in one of

455
01:27:03.000 --> 01:27:10.000
the tech companies that if I told you what it is you would know, but it would also identify her and also at the university.

456
01:27:10.000 --> 01:27:17.000
So they engage in strategies of withdrawal. So for instance, electing not to present their work, not to take credit for it.

457
01:27:17.000 --> 01:27:35.000
And the only way they could get out of these strategies and be credited with recognition for their work was if they came across at voluntary kind of mentor and here we've got a story about female mentor but they were also male mentors who repeatedly took it upon themselves.

458
01:27:35.000 --> 01:27:48.000
To work against silencing these scientists against ignoring them or even against others taking credit for their work. So I think I'm kind of, I don't have that much time.

459
01:27:48.000 --> 01:27:55.000
I just wanted to acknowledge that this is not just the case that no English speaking scientists had a very unique COVID histories.

460
01:27:55.000 --> 01:28:03.000
There was a lot that was shared in common. So there were caring issues, there were health issues, there were deaths that they had to deal with.

461
01:28:03.000 --> 01:28:17.000
And here I've got a quote from somebody who's not from the English speaking, who's basically amber or scientist and the person who was early career researcher but a mature person so it illustrates the kind of and I don't have the details here the kind of problems that were also in the background.

462
01:28:17.000 --> 01:28:29.000
But the interesting thing is to kind of wrap up about those Can't really diverse scientists is that although all scientists experience or many of the ones in the fields that we looked at, heightened work in intensity because their fields were related to COVID.

463
01:28:29.000 --> 01:28:46.000
In some cases, those culturally diverse scientists, their success was turned into some sort of difficult situation. We've got an example here of a young scientist who secured in the first year of COVID, 2 half 1 million dollars grants.

464
01:28:46.000 --> 01:29:00.000
But one was prestigious and one less so. And she was forced to step down from the prestigious grounds on the basis that the institute needed to hold on to both of them.

465
01:29:00.000 --> 01:29:18.000
The work had to be done in a timely fashion. So a senior male colleague was given the more prestigious drum which actually she secured and she had no support from within the institution because as far as the institution was concerned as long as both Browns stayed within the institute or was good.

466
01:29:18.000 --> 01:29:27.000
There were a lot of work family balanced stories and not all of them were negative. Some scientists who normally worked in labs that were detrimental for pairing pregnancy.

467
01:29:27.000 --> 01:29:45.000
We're actually happy because there could carry pregnancy to term in a healthier environment. But there were also some heartbreaking stories about people who left jobs and went somewhere else in the hope of permanency and then as a result of Kobe the positions were re-considered or they were made red redundant after having just

468
01:29:45.000 --> 01:30:06.000
traveled to the other end of the world. So I suppose the conclusions are here and what we try to emphasize here is that there's really relentless demand for mobility in science and Kovat made this really very difficult for all scientists but particularly those in fixed term positions and in junior positions.

469
01:30:06.000 --> 01:30:28.000
And this kind of cultural, capital issue is a huge problem which is completely unaddressed. So just to wrap up, our leaders when they say we're going to hire overseas should look a little bit more at the experiences of these culturally diverse scientists because they are brought into the country and they experience what they experience it's actually sometimes quite

470
01:30:28.000 --> 01:30:46.000
shocking and nothing is done about that. I just wanted to finish up. I don't have time for this by saying that it is not just them who are affected by this strategy but also the locally trained scientists who feel they are refused jobs because they are told they are not nationally internationally competitive.

471
01:30:46.000 --> 01:30:56.000
It means they are not prepared to be so mobile. So I'll just finish that and thank you for your time and attention.

472
01:30:56.000 --> 01:31:09.000
So we were just to speak with the time. Here listed in the program we first finished the presentations and then discussed rather than

473
01:31:09.000 --> 01:31:20.000
So first. Return to the displayer.

474
01:31:20.000 --> 01:31:21.000
Hello.

475
01:31:21.000 --> 01:31:23.000
And our second presentation is remote. If you online, are you online? Yeah.

476
01:31:23.000 --> 01:31:28.000
Yes. Yes, can you hear me?

477
01:31:28.000 --> 01:31:43.000
Yeah, I can hear you. Let me And then stop sharing some sharing option. So I think she could share your screen.

478
01:31:43.000 --> 01:31:48.000
Can you see my screen?

479
01:31:48.000 --> 01:31:49.000
Can you see my slides?

480
01:31:49.000 --> 01:31:52.000
Please share your screen. Okay. Okay, I think it's working out. Yep,

481
01:31:52.000 --> 01:31:54.000
Hello.

482
01:31:54.000 --> 01:31:59.000
The housekeepers of my life. Yeah, it's working now and you can get Yep.

483
01:31:59.000 --> 01:32:06.000
Okay. Thank you. Hey, good morning everyone. Thank you so much to Dr.

484
01:32:06.000 --> 01:32:11.000
Claudia Bachman organized this session. And to Dr. Sikihan, who chair was during the session today.

485
01:32:11.000 --> 01:32:14.000
Are you there? Wait a second. Can you hear me?

486
01:32:14.000 --> 01:32:27.000
I also went to thank, and my collaborator. This is collaborative work and he works at the Ponteficiary Ocean at

487
01:32:27.000 --> 01:32:38.000
Okay, so I will present preliminary results for a study overall this study is part of a project called Mothers pay labor and household dynamics in the context of COVID.

488
01:32:38.000 --> 01:32:45.000
19 pandemic evidence from a high mortality context. NISA study is focused on Peru.

489
01:32:45.000 --> 01:32:54.000
I am presenting results exploring potential impacts of household composition and specifically with an emphasis on age of the youngest child.

490
01:32:54.000 --> 01:33:05.000
To understand mothers employment patterns during the pandemic in 2,020. So these are some of the topics that I will touch upon very quickly.

491
01:33:05.000 --> 01:33:17.000
A sicker I forgot to put my Okay, my timer. Okay, so this is for. We are addressing a mother's employment during the COVID emergency.

492
01:33:17.000 --> 01:33:26.000
At this point in history, Peru is a middle income country. And it has the world's, the worst global death rate.

493
01:33:26.000 --> 01:33:47.000
You to COVID-19. Above 221,000 deaths in a country of 33 million of population which means that a above 0 point 5% of the Peruvian population died due to COVID.

494
01:33:47.000 --> 01:33:48.000
Oh.

495
01:33:48.000 --> 01:33:53.000
Okay, just to let you know you are sharing your presenter view throughout. Can you change it to like normal view?

496
01:33:53.000 --> 01:34:03.000
Because we are seeing your presenter view here. Sorry for the interaction.

497
01:34:03.000 --> 01:34:04.000
Okay, now it's working great. Sorry for the

498
01:34:04.000 --> 01:34:09.000
No, please. Is it better now? Thank you so much. Thank you for letting me know.

499
01:34:09.000 --> 01:34:18.000
Okay, so and as I said about a 0 point 5% of the Peruvians that due to copying in the first year of the pandemic.

500
01:34:18.000 --> 01:34:23.000
And these, numbers I mentioned them to give, you a sense of the sense of emergency.

501
01:34:23.000 --> 01:34:44.000
Due to copied 19 that the whole country experienced. The first batch of vaccines arrived to the country in 2,021 which means that through 2,020 the whole country we set was in a state of acute distress.

502
01:34:44.000 --> 01:34:52.000
What do we know about female labor and COVID? We know quite a bit at this point as we know the sociologically territory we have now a robust.

503
01:34:52.000 --> 01:34:59.000
Body of the territory that has a commented gender impacts of the COVID-19 pandemic in work trajectories.

504
01:34:59.000 --> 01:35:09.000
And mothers have been pointed out as a particularly vulnerable group, right? And we have 2 main explanations for this both of them particular to the COVID.

505
01:35:09.000 --> 01:35:23.000
19 emergency. The first one is a demand issue with a pandemic. But pre-pandemic patterns of gendered, occupational segregation and, the pandemic affecting sectors and industries.

506
01:35:23.000 --> 01:35:30.000
That it were more female dominated no in particular in particular services and sales no On the other hand, the supply side of the story due to increases in the need for unpaid family labor.

507
01:35:30.000 --> 01:35:53.000
No, especially schools and childcare closures. Given especially the second point matters with young children have been pointed out identified as particularly affected in their employment due to the pandemic.

508
01:35:53.000 --> 01:36:01.000
Well, we know that these research is largely focused on wealthy nations. These 2 factors plausibly apply to Peru as well.

509
01:36:01.000 --> 01:36:03.000
These 2 factors plausibly apply to Peru as well. And as an example in terms of to Peru as well.

510
01:36:03.000 --> 01:36:09.000
And as an example, in terms of occupational segregation, and as an example in terms of occupational segregation, this is a graph with the data that we use for occupational segregation, this is a graph with the data that we use for this study.

511
01:36:09.000 --> 01:36:14.000
This is Peru, Urban areas in 2,019 and we see a graph with the data that we use for this study.

512
01:36:14.000 --> 01:36:19.000
This is Peru, Urban Area in 2,019. This is Peru, Urban Area in 2,019.

513
01:36:19.000 --> 01:36:32.000
And we see with the arrows in 2,019 and we see with the arrows the service and sales sector being way more female than a male right above 30% of workers women workers occupied in service and sales versus less than 10% among men.

514
01:36:32.000 --> 01:36:39.000
Currently, the literature and the media tend to over a present with many advantage at contexts. And this is something that happens, I would say, in the holy chapter of family in sociology.

515
01:36:39.000 --> 01:36:55.000
And we do have this sort of representation of women in advantage positions, nations but also subgroups, no nations in general and subgroups in particular.

516
01:36:55.000 --> 01:37:03.000
I relieve lead to an important extent we have kept the analytical framework used for studying previous financial shocks or recessions.

517
01:37:03.000 --> 01:37:21.000
This includes looking at employment as an always positive outcome which of course makes sense thinking on previous recessions. And also less attention putting to the impact of health risk and health risk assessments in analysis of employment during the COVID pandemic.

518
01:37:21.000 --> 01:37:32.000
We would argue that when analyzing context of social economic disadvantage with treatment to the workforce could be conceptualized as a sellable outcome.

519
01:37:32.000 --> 01:37:42.000
So we think that possibly the potential impacts of household composition and matters employment outcomes are different in programming, are different in non wealthy countries.

520
01:37:42.000 --> 01:37:52.000
And this is study in a specific investigates the impacts of household composition on women's employment. And we have this overall, guiding, question.

521
01:37:52.000 --> 01:38:01.000
We were exploring whether Peruvian mothers with young children. Retreat from employment during the emergency tour in a larger proportion.

522
01:38:01.000 --> 01:38:09.000
I, are not as straightforward. On the one hand, there was a widespread scope.

523
01:38:09.000 --> 01:38:22.000
Actually, to a larger period than in many other countries. But on the other hand, Peru has a weak social safety net with little financial protection against health or unemployment shows.

524
01:38:22.000 --> 01:38:32.000
So we may expect that women and mothers may have remained in the labor force due to financial need despite increases in fire in family labor.

525
01:38:32.000 --> 01:38:41.000
In a context of what we're well mean, Precarious labor overwhelmingly and overwhelming progress labor more prevalent among women than men.

526
01:38:41.000 --> 01:38:57.000
Work was attached to health risk during the COVID emergency. And we may not have that, much time, to discuss this, but this is a graph with the point here that female labor force participation in Peru is high.

527
01:38:57.000 --> 01:39:03.000
That's the red line as you can see with this in this graph includes the 5 largest countries in Latin America.

528
01:39:03.000 --> 01:39:10.000
By population as well as Australia and the US as points of reference. So this is the the female labor market in Peru.

529
01:39:10.000 --> 01:39:20.000
And these are women highly attached to the labor force.

530
01:39:20.000 --> 01:39:29.000
Data myths. Are working with the Peruvian National Houseful Survey, a which is nationally representative.

531
01:39:29.000 --> 01:39:37.000
And he has a special, would say, no. New survey. It has a new survey the US, the Peruvian Census Bureau.

532
01:39:37.000 --> 01:39:45.000
That has this a longitudinal design with households of served annually between 2,016 and 2,020.

533
01:39:45.000 --> 01:40:00.000
They sample design was suggested to people presentitivity during the pandemic emergency. Importantly, data is collected around the same time each year that Almost all observations are within the same month.

534
01:40:00.000 --> 01:40:11.000
Data source allows us to for constructing two-year panels. That is in individuals observe it twice once a year in the same period each year.

535
01:40:11.000 --> 01:40:21.000
I contact analysis predicting employment in 2,020 during the pandemic among women of served working for pay in the previous year to 2,019.

536
01:40:21.000 --> 01:40:31.000
Which allows us to, roll out a set of selection issues that are often important for understanding. Women's labor outcomes.

537
01:40:31.000 --> 01:40:46.000
The analytic example is women in prime working age, 25 to 54, household header partners in urban areas observed working for pay in 2,018 and we excluded women with family workers with 0 income.

538
01:40:46.000 --> 01:40:59.000
The analytical sample is comprised by a 1,384 women. The outcome of interest is employed during emergency in 2,020.

539
01:40:59.000 --> 01:41:08.000
And the main exploratory variable in this case has been H of the youngest child. With 5 categories ranging from no children in the household.

540
01:41:08.000 --> 01:41:20.000
Youngest and being 6 or younger than that. 2, the youngest is 18 or older. So the category 5 implies that the youngest child is already an adult.

541
01:41:20.000 --> 01:41:29.000
All their explanatory variables are whether the woman has a partner or not. And Did you? Presence of household members who need family labour.

542
01:41:29.000 --> 01:41:40.000
These are defined as a members who are 65 or older or any member with a disability in the household.

543
01:41:40.000 --> 01:41:52.000
Also, we include a set of variables measured in 2,019. A small film, whether the woman was working in a firm that has 20 workers or less.

544
01:41:52.000 --> 01:42:09.000
Salaried worker with a salarate versus self-employed, both self-employed workers and workers on small firms tend to be disadvantaged in Latin American Labour markets, also household income after deducting women's earnings.

545
01:42:09.000 --> 01:42:29.000
An occupation in 2,018 by 6 categories as well as educational attainment. We conceptualize vocational attainment, household income after deducting mother's earnings and job related characteristics in the baseline year as variables are tapping into overall resources.

546
01:42:29.000 --> 01:42:39.000
That mothers could have drum upon in 2,020 during the pandemic.

547
01:42:39.000 --> 01:42:50.000
So the analysis is predicting employment in 2,020 is done as I mentioned among mothers observed working for pay in the previous or based year, 2,019.

548
01:42:50.000 --> 01:43:01.000
We conduct linear probability models. And you too to their ease of interpretation and they recommend that where the outcome of interest is between these kind of wide range.

549
01:43:01.000 --> 01:43:07.000
And among in our example, the outcome of interest is 66%. So, it fits our purposes.

550
01:43:07.000 --> 01:43:14.000
However, we conducted Robinson's checks using a logistic approach.

551
01:43:14.000 --> 01:43:23.000
And here we have the script for statistics, for our sample. And all women working for pay in 2,019.

552
01:43:23.000 --> 01:43:41.000
65% were working for pay as well in 2,020. And, 65 is actually low compared to previous years due to the obvious reason of COVID in other years is close to 90%.

553
01:43:41.000 --> 01:43:50.000
A for our main Explanatory variable of feature is that we are exploring at these in this study.

554
01:43:50.000 --> 01:43:57.000
We see. That women working for pay in 2,020 by age of the youngest child.

555
01:43:57.000 --> 01:44:08.000
Those who, obviously those who had no children at home were the more likely to remain employed. It's 75% of the the the bar at the left.

556
01:44:08.000 --> 01:44:20.000
We have here that the ones that were less likely to remain employed in 2,020 during the pandemic where mothers were whose youngest child was already an adult, 18 or older.

557
01:44:20.000 --> 01:44:29.000
And as you may remember, our analytical sample comprises only women in prime working age, meaning 25 to 54 years old.

558
01:44:29.000 --> 01:44:48.000
So this pattern is a to what we have seen in several other countries, wealthy nations where this matter has been analyzed and these studies tend to show that the group more affected is that of mothers with young kids in school age no due to obvious reasons.

559
01:44:48.000 --> 01:44:52.000
The the

560
01:44:52.000 --> 01:44:59.000
So, yeah, these are selected coefficients for in a probability model predicting employment in 2,020 as I mentioned.

561
01:44:59.000 --> 01:45:07.000
I will show other coefficients, but, several things to, mention here.

562
01:45:07.000 --> 01:45:19.000
Having my youngest child who is 18 and older, we find that it's associated here with, 11 percentage point decrease in the probability of being employment during the pandemic.

563
01:45:19.000 --> 01:45:34.000
Compared to women with not children and hot at home and controlling for several other variables in the model. At this point we have tried with several other specifications and the pattern remains consistent, no constant.

564
01:45:34.000 --> 01:45:45.000
In contrast, our coefficient for the present of a household member who is 65 for older or who has a disability it does not reach a statistical significance.

565
01:45:45.000 --> 01:46:03.000
We also see that a women with college completed were more likely to remain employed, which actually feeds a pattern of a remote boat work being mostly available for a women with white color positions.

566
01:46:03.000 --> 01:46:15.000
And among other coefficients of interest, we find that household income in 2,018, so in the base 10 year after deducting women's earnings is negatively associated to employment.

567
01:46:15.000 --> 01:46:21.000
As well as working a small firm.

568
01:46:21.000 --> 01:46:34.000
Thank you as a whole, results from our analysis aligned with descriptive results. I'm on programming women having a youngest child who is already 18 or older is associated to a lower probability of employment during the pandemic.

569
01:46:34.000 --> 01:46:50.000
All models controlled for age and pattern is a stable and process specifications. And we feel that whatever is happening here, we suspect that youngest child is already an adult, works as a proxy for a potential additional source of income during the pandemic.

570
01:46:50.000 --> 01:46:59.000
All their children could be seen as resources in these contexts for mothers in a period of acute financial and health districts.

571
01:46:59.000 --> 01:47:14.000
Hey, aligned with this notion is the fact that our coefficient for householding coming in 2,019 after deducting women's earnings also points to a protective role of household resources.

572
01:47:14.000 --> 01:47:22.000
We hope this this work that these are all still preliminary results, er, of pointing to our consistent, er, pattern.

573
01:47:22.000 --> 01:47:31.000
Oh, it's gonna help to thinking to a consider previous assumption. Now, coffee is different than previous recessions and it may.

574
01:47:31.000 --> 01:47:46.000
Us to think about what factors protected vulnerable women, vulnerable working women from working. From employment in a context of appealed health risk associated to employment.

575
01:47:46.000 --> 01:47:54.000
Well, thank you.

576
01:47:54.000 --> 01:48:08.000
Thank you. Very long time. So we will have our last presentation. Oh, here in prison.

577
01:48:08.000 --> 01:48:15.000
Okay. Can you share the screen?

578
01:48:15.000 --> 01:48:37.000
Oh yes, thank you. Thank you. Aida for making the past for me just to step in and I think your results are really interesting because they contradict in a way a lot of research we know from other countries where mother have been particularly suffering from kovat and did not work and that's something I'm going to look in my my analysis

579
01:48:37.000 --> 01:48:54.000
that is how far in what respect school and daycare reopenings, respective closures had affected parents employment and life satisfaction.

580
01:48:54.000 --> 01:49:05.000
How does it? No. So in most industrialized countries or western countries or wealthy countries as you phrase it in Minneapolis.

581
01:49:05.000 --> 01:49:19.000
There has been a debate on the effect of school and data daycare closures on parents work. So women put careers on hold, women face mental exhaustion, gender inequality during paid and unpaid work.

582
01:49:19.000 --> 01:49:30.000
There was a huge debate on how the pandemic affected in particular parents work. And

583
01:49:30.000 --> 01:49:56.000
The interesting thing is there is this debate that school closures matters but we don't know exactly to what extent school closures actually mattered and this is because several other containment measures such as closures and retail in services, bars, restaurant, hospitality and a decline in overall economic activity had occurred parallel to school closure.

584
01:49:56.000 --> 01:50:10.000
So there were the school closure and at the same time a lot of other containment measures happen. So we don't really know how the closures made a difference in contrast to all these others containment measures.

585
01:50:10.000 --> 01:50:19.000
And that's what we are going to look at here. We want to know exactly how school and day care closures had an impact on parents working hours and life satisfaction.

586
01:50:19.000 --> 01:50:28.000
And that's not easy to analyze because there was this parallel occurrence of closures and containment measures.

587
01:50:28.000 --> 01:50:47.000
But we have a solution. Perhaps one note in the beginning, I'm not going to talk about unemployment of parents because in Germany unemployment during the first lockdown was particularly low and this is due to a government scheme that is called short time allowance that let people keep in their employment with 0 working hours.

588
01:50:47.000 --> 01:50:58.000
It's a measure to prevent unemployment but keep people in their jobs. So I'm going to talk about working hours.

589
01:50:58.000 --> 01:51:08.000
So what we think or what what we will argue is Assume that closures did affect parents. That seems to be quite.

590
01:51:08.000 --> 01:51:24.000
Sensible or quite reasonable to do so but if we assume that then the opening of school in daycare facilities should lead immediately to a resumption of working hours and to an increase in life satisfaction again.

591
01:51:24.000 --> 01:51:40.000
So we are not going to look at the closures because they had this parallel occurrence of containment measures, but we're going to look at the openings and then conclude from if there is an opening effect, then we see that schools really matter.

592
01:51:40.000 --> 01:51:51.000
So what what do we have in Germany as in many other countries? So in Germany, nationwide school closures occurred from mid-March on so I remember being a mother exactly the day it was the seventeenth of March school.

593
01:51:51.000 --> 01:52:07.000
School closed from Monday on. Friday it was announced. Monday was in place. So on that occurrence, this Friday was occurring in all German federal state and school had been closed until the end of May, 2,020.

594
01:52:07.000 --> 01:52:17.000
But then from June on, the federal state started to reopen schools again. And here the interesting thing comes because the opening was dependent on federal state government decisions.

595
01:52:17.000 --> 01:52:30.000
So there is some variation. Some states opened earlier, other states opened less early and they were also free to define the degree.

596
01:52:30.000 --> 01:52:36.000
So how much on-site, which school grades, which age of children, though there is some variation going on in the reopening.

597
01:52:36.000 --> 01:52:57.000
Where as closures was immediately similar everywhere, the closure had some variation. And what we do now is we use this veryation in reopening between the federal states and the different time to analyse the changes that occur in the week after.

598
01:52:57.000 --> 01:53:07.000
In a federal state, when schools have been opened again and we look at the changes in work hours and life satisfaction.

599
01:53:07.000 --> 01:53:12.000
And we add to this that we not only look at parents, how parents reacted to the reopening of schools.

600
01:53:12.000 --> 01:53:29.000
The parents increase the working hours, but we compare these parents with non-parents in the same federal state and why do we do so because we think that non-parents should not be affected by the reopening because they don't have children that's staying at home any longer.

601
01:53:29.000 --> 01:53:45.000
So we see if really school make a difference to parents. And then by comparing these parents that are assumed to be affected by the openings and non-parents that are assumed not to be affected by the opening.

602
01:53:45.000 --> 01:53:53.000
We see how much difference the openings make. And you will see in a minute what I mean with that.

603
01:53:53.000 --> 01:54:08.000
So the data we use is a high-frequency online personal panel conducted by the Federal Employment Research Institute and we can combine that with we just registered data of employment and this survey was set up in May, 2,020.

604
01:54:08.000 --> 01:54:15.000
So it was a covid survey and the interesting thing is that is was conducted monthly from May on.

605
01:54:15.000 --> 01:54:25.000
And it included questions on how had your working hours been before COVID. Yes, and that's what we're going to use here.

606
01:54:25.000 --> 01:54:31.000
So what we're going to use here is the actual weekly working hours before Covid-nineteen.

607
01:54:31.000 --> 01:54:37.000
And in June, 2,020, June, 2,020, because there was this reopening gradually coming into place.

608
01:54:37.000 --> 01:54:52.000
And we also look at life satisfaction and here we compare the measurement in May, the twentieth 20 again with a measurement in June 2020 to see if life satisfaction had changed in the course of the opening of schools.

609
01:54:52.000 --> 01:55:05.000
For working hours we focus on individuals who had been employed before the pandemics or we looked at employed individuals only and for life satisfaction we look at individuals who had been observed both in May and in June.

610
01:55:05.000 --> 01:55:15.000
Okay, we also look at parents and we decided similar to Ida's on, yeah, 2 ideas that we look at those who have one child under 15 in the household.

611
01:55:15.000 --> 01:55:26.000
And we compare that with persons without children of this age. So if we talk about non-parents, it could be that these parents do have children, but they are older than 15.

612
01:55:26.000 --> 01:55:35.000
We have a lot of associate demographic control variables and characteristics of the employment. That we control in the regression models.

613
01:55:35.000 --> 01:55:51.000
Then for what does opening mean, we decided to define opening by every child in the federal state where the person lives experienced at least some on-site schooling or daycare.

614
01:55:51.000 --> 01:56:11.000
Closure means on the other hand that some children are still fully at home so we are rather conservative it's not that children are back into school completely or back into daycare completely but at least every child had the opportunity to be at least sometime in school or in daycare.

615
01:56:11.000 --> 01:56:28.000
So what we use as method is a difference in different method comparing before Copy perspective May, 2,020 to June, 2,020 to compare the parents in states where opening had been taking place to those who had not been in these states and comparing those to non-parents.

616
01:56:28.000 --> 01:56:48.000
And. So, what do we see? If we look at working hours before the pandemic, that's what we report, how the working hours have been before COVID, we see that parents work significantly fewer hours on average in June, 2,020.

617
01:56:48.000 --> 01:57:02.000
So we have the before COVID situation and the June situation on the right. And this differs by whether or not school and day care are open or whether school and day care are still closed in the federal state, the where the parents live.

618
01:57:02.000 --> 01:57:11.000
So where the schools and daycare remain closed which is the

619
01:57:11.000 --> 01:57:17.000
Circle, parents work a lot less hours compared to those in states in which schools have already been opened again.

620
01:57:17.000 --> 01:57:38.000
So schools do make a difference. If we compare that now to nonparents who should not be affected by openings, we see, yes, employed person without young children also works significantly fewer hours in June, 2,020 because of short-term allowances because of economic decline and so on.

621
01:57:38.000 --> 01:57:51.000
But they are not affected by the closures. Though they do work less, but compared to the parents where we see this huge difference, closed states, open states.

622
01:57:51.000 --> 01:58:01.000
That doesn't make much of a difference for the non parents. So the slope is the same in the 2 situations.

623
01:58:01.000 --> 01:58:10.000
So, and if we run regression models, we see yes that parents and non-parents did reduce the working hours in the observe time spin as they had been doing in many countries.

624
01:58:10.000 --> 01:58:15.000
So there is this kovat shock going on for parents and nonparents. We see that openings have a positive effect on working hours for parents only.

625
01:58:15.000 --> 01:58:31.000
So parents profit from school openings. And this effect is significant. It's different for parents and nonparents.

626
01:58:31.000 --> 01:58:37.000
If we compare men and women, interestingly we see that there is a positive effect of opening for both for fathers and mothers.

627
01:58:37.000 --> 01:58:53.000
Both Work more hours after the opening has occurred in the federal state. But the effects are not significant if we Look at men and women separately.

628
01:58:53.000 --> 01:59:06.000
So let's have a look at life satisfaction. So here we compare May, 2,020, so in the lockdown and June, 2,020 when the openings have already been in place or started to open some federal states.

629
01:59:06.000 --> 01:59:14.000
What we see first is that the life satisfaction of mothers is considerably lower than that of non-Mazas.

630
01:59:14.000 --> 01:59:19.000
So it's a left-hand side compared to the right hand side. Mothers have been suffering.

631
01:59:19.000 --> 01:59:40.000
In the lockdown situation in May. But if we look at how school closures affect the life satisfaction of mothers, we see that mothers in states were school and daycare had opened again do much better than those who are still in the lockdown situation where still the kids are at home.

632
01:59:40.000 --> 01:59:50.000
Compared to the non-mothers on the right-hand side, we say, well, it doesn't much make a difference whether or not school or daycare is open again.

633
01:59:50.000 --> 01:59:59.000
So we see again school and take care closures mattered for women or respective. The openings lift the burden from others.

634
01:59:59.000 --> 02:00:17.000
For farmers, we see much less going on, though there is not much of a difference if we compare the right hand side and the left-hand side in May so Ferns her father seemed not to be bothered by the children at home in May when it What we see is a small increase of life satisfaction in June for fathers if

635
02:00:17.000 --> 02:00:22.000
school and day care are closed, which we cannot explain why fathers do better, schools are still closed.

636
02:00:22.000 --> 02:00:30.000
On the right hand side we see that life satisfaction of non-fathers is not a factor by opening.

637
02:00:30.000 --> 02:00:36.000
So for non-fathers, nothing changes whether or not schools are opened or not.

638
02:00:36.000 --> 02:00:48.000
We also ran regression analyses. We see that there is a positive and significant effect of reopenings on life satisfaction for mothers as we've seen in the descriptive analyses, but that's not occurring for fathers.

639
02:00:48.000 --> 02:00:56.000
And we see that there is a significant difference between mothers and non mothers. That does not show up for father and non-father.

640
02:00:56.000 --> 02:01:08.000
So life satisfaction and school closures seem to be seem to matter for schools seem to matter for the life satisfaction of mothers but not for fathers.

641
02:01:08.000 --> 02:01:17.000
Okay, so what have we seen? We have advanced previous research by trying to isolate the effect of school and daycare closure openings.

642
02:01:17.000 --> 02:01:27.000
So this is everyone thinks to know yes school mirrors and we have been able to really put it down how much school methods over and above other containment measures.

643
02:01:27.000 --> 02:01:34.000
What we see is that openings had an effect on parents working hours and on life satisfaction beyond these other pandemic related changes.

644
02:01:34.000 --> 02:01:46.000
Parents working hours increased by about 1.5 to 2 h per week. And mother's life satisfaction increased by about 0 point 4 points on this life satisfaction scale.

645
02:01:46.000 --> 02:01:55.000
Father's life satisfaction interestingly was not affected by the openings. It was neither affected by the closures nor affected by the openings.

646
02:01:55.000 --> 02:02:03.000
Conclusions? What we see is really childcare is important for parents for parents. Working hours and for parents life satisfaction.

647
02:02:03.000 --> 02:02:12.000
The parents struggled a lot once the children had been at home and the school openings, daycare openings lifted some of the burdens that parents faced.

648
02:02:12.000 --> 02:02:18.000
And what we see interestingly is that we see no gender difference in the resuming of work hours.

649
02:02:18.000 --> 02:02:25.000
But we see this gender difference for life satisfaction where mothers have been struggled more during the closures than brothers.

650
02:02:25.000 --> 02:02:35.000
Thank you. Oh, perhaps I should add. The working hour results have in the meantime been published in just a few weeks ago, 4 weeks ago they have been plagued.

651
02:02:35.000 --> 02:02:41.000
We are still working on the life satisfaction paper. So if you're interested, here's a QR code.

652
02:02:41.000 --> 02:02:47.000
Yep.

653
02:02:47.000 --> 02:02:59.000
You Because this we Only have 2 papers And it's still quite early. I think we could just.

654
02:02:59.000 --> 02:03:04.000
Let's see first do we have

655
02:03:04.000 --> 02:03:18.000
Oops. Stop sharing, right? Return and How can I go back to the 2?

656
02:03:18.000 --> 02:03:45.000
Share at making support. Yeah, I thought I should stop sharing that.

657
02:03:45.000 --> 02:03:56.000
Oh, okay. So,

658
02:03:56.000 --> 02:04:02.000
Great. Let's see if we have.

659
02:04:02.000 --> 02:04:14.000
For for a few of you who are online if you have questions please do type in the chat box and so that or participants can.

660
02:04:14.000 --> 02:04:20.000
See it and respond to you so

661
02:04:20.000 --> 02:04:30.000
There's no formal discussion law here so we can just do the Q&A directly. Worst in Georgia.

662
02:04:30.000 --> 02:04:40.000
I think Aida can you hear me now?

663
02:04:40.000 --> 02:04:43.000
Now I can hear you. Yes.

664
02:04:43.000 --> 02:04:59.000
Okay, so let's look what maybe the system. You know, I just like to sit here because I have to sit here.

665
02:04:59.000 --> 02:05:03.000
Okay, so you can't see us but you can hopefully hear us. Is that right? Okay.

666
02:05:03.000 --> 02:05:07.000
Yeah, yes. Thank you.

667
02:05:07.000 --> 02:05:10.000
So I have a question for

668
02:05:10.000 --> 02:05:21.000
Is how much did your participant report that the program helped them in dealing with the struggles they had.

669
02:05:21.000 --> 02:05:32.000
So look, we had some, I think the leaders would be very happy. Like we had some really enthusiastic and reports on the program.

670
02:05:32.000 --> 02:05:37.000
To the point that some of them even reported that they had a child which they were afraid to have before the workshop as a result of the workshop.

671
02:05:37.000 --> 02:05:50.000
But that actually had nothing to do with the content of the workshop. It was more about getting together with other women in their situation and some of these women were pregnant or illegal children.

672
02:05:50.000 --> 02:06:05.000
And so the main thing that worked for them because going into COVID we were thinking are we moving these workshops online you know and they said well it would be good for access so that people who can't be admitted to funding restrictions could benefit.

673
02:06:05.000 --> 02:06:11.000
So it's a bit shocking in a sense that I also discovered it by being an observer in the workshop.

674
02:06:11.000 --> 02:06:18.000
How long you can be in academia? And never have exposure to this crucial information how to help your career.

675
02:06:18.000 --> 02:06:23.000
Do you know what I mean? Because we kind of think we all know it, but then we don't.

676
02:06:23.000 --> 02:06:34.000
So these women were very enthusiastic. But it's not surprising that the ones who participated in in our interviews were the ones who are particularly interested in that. You know what I mean?

677
02:06:34.000 --> 02:06:44.000
So there were 106 of them and the 49 that agreed to talk to us and remember this was the end of 2,021 that agreed to talk to us and remember this was the end of 2,020 and remember this was the end of 2,021 and maybe you don't know but that was very

678
02:06:44.000 --> 02:06:55.000
tough time and maybe you don't know but that was very tough time in Australia because we had a long time in Australia because we had a long time of lockdowns and they're just absolutely exhausted and they're just absolutely exhausted and they're just absolutely exhausted and they even sometimes traveled to find that time to talk to us

679
02:06:55.000 --> 02:07:02.000
because they even sometimes traveled to find that time to talk to us because they were honestly, although they had different science specializations, they were honestly, although they had different science specializations, a lot of work of them, they were honestly, although they had different science specializations, a lot of work of them worked in science specializations, a lot of work of them worked

680
02:07:02.000 --> 02:07:13.000
in COVID related activities. So there was one woman there who was a kind of mathematician, but what they did was that the health system in Australia suspended all all non-copic treatment and surgeries.

681
02:07:13.000 --> 02:07:23.000
So she was doing these models and planning. Because you know the hundreds and thousands of surgeries that were not happening and the health system had to even plan for later.

682
02:07:23.000 --> 02:07:39.000
So they were very enthusiastic most of them and as I said some of them even said you know changed my life I still have you know the piece of paper that was given to me it's on my desk So in a very hard warming but also it makes you wonder you know that they have been in the field for so many for quite

683
02:07:39.000 --> 02:07:53.000
a few years prior and in with all of those initiatives going on there was so little to give them this feeling of you know community and that's why I have also put questions for both of you.

684
02:07:53.000 --> 02:08:01.000
Either for you, I have more of a comment because you were showing something about Australia in one of your slides.

685
02:08:01.000 --> 02:08:19.000
At the beginning, you know, when you were looking at labor force participation rights and you were showing Australia and you were also saying that families are larger in Peru so there might be a different relationship between family size and workforce participation.

686
02:08:19.000 --> 02:08:21.000
Is that what you said?

687
02:08:21.000 --> 02:08:32.000
In that sense, yes. So. In particular what we have is a higher prevalence of extended households.

688
02:08:32.000 --> 02:08:42.000
So family size, the number of children is actually at replacement rate has been for a while now. But there is a higher prevalence of extended households.

689
02:08:42.000 --> 02:08:48.000
Which is likely to positively affect women's labour force participation. Mixes?

690
02:08:48.000 --> 02:09:13.000
Yeah, yeah, but I also wondered that, you know, this is important to say, but for those, US and particularly for Australian sort of data, I think you need to bear in mind what Marita was talking about, namely that undercobied there were government sort of in Australia it was called the jobkeeper program

691
02:09:13.000 --> 02:09:20.000
that meant that you didn't really go to work but you still received your full pay but that also depended on your industry.

692
02:09:20.000 --> 02:09:43.000
So for instance, the government in Australia at the time of Copy didn't like universities because they thought we we you know we are trouble makers so they excluded universities from this provision but you know I think if you do any element of comparison between your Peruvian situation and for instance you introduce Australia as a reference.

693
02:09:43.000 --> 02:09:55.000
You know, on the one hand side, you've got those extended households which function maybe as a kind of support for the family, but on the other hand side you need to somehow factor in those welfare state provisions.

694
02:09:55.000 --> 02:10:05.000
You know, government payments because and this is why I think Marita's strategy of looking at working hours rather than whether people are in their labor force.

695
02:10:05.000 --> 02:10:17.000
If you can follow it would be better because you're not running into the problem where you know some people are nominally working but not actually working.

696
02:10:17.000 --> 02:10:29.000
That's a very good point. Actually, that graph was showing patterns of female labor force participation from 2,010 to the 1,022.

697
02:10:29.000 --> 02:10:47.000
Just showing rates of familiar for participation. So let's actually not focusing in COVID. For that graph just showing the fact that this is a population with a very high rate.

698
02:10:47.000 --> 02:10:48.000
Okay.

699
02:10:48.000 --> 02:10:53.000
Of female labor force participation. Even for Latin American standard 6. It's high. So yes, that was kind of part of the I should have.

700
02:10:53.000 --> 02:11:08.000
And talk to more about it. It was part about the context. A showing that this is a contribute high rates of female workforce participation that no I wasn't focusing on COVID ban is actually the whole decade to show an overall pattern.

701
02:11:08.000 --> 02:11:36.000
Okay. I also have a question for you, The first thing is in the panel survey do you know when the months of the interview have been so that the month had not been in February because then everyone was working and it was Colby just somewhere so that could be interesting to know when the time of the interview was for your interpretation but I think it's it's

702
02:11:36.000 --> 02:11:51.000
less it's later than February but just just for clarification. And the second question that we or the signing you presented were really puzzling that it was the mothers with the older children that it worked less.

703
02:11:51.000 --> 02:12:03.000
So what did the mothers of the young children do had there been school closures who was taking care of the kids what happened in Peru though that's really really interesting.

704
02:12:03.000 --> 02:12:20.000
Thank you. About the first question, actually, it fortunately yes, no, the This data I am very excited because as you may know for a for Many countries, longitudinal rate data is quite rare.

705
02:12:20.000 --> 02:12:30.000
So thankfully we do have the specific month in which the interview was conducted. So for all analysis, I'm restricting to only the COVID period.

706
02:12:30.000 --> 02:12:42.000
And thankfully interviews are conducted in the same month in the same month across years mixes. So I'm able to restrict the sample to only the copy time and also.

707
02:12:42.000 --> 02:12:55.000
All interviews in 2,019 if they were in March they were also in March well much wouldn't be there no but if they were in June in 2,018 they were also conducted in June in 2,020.

708
02:12:55.000 --> 02:13:02.000
So thankfully that that is, that is there. That is, the sample, the survey design.

709
02:13:02.000 --> 02:13:24.000
About what we were doing. It's a it's a very interesting question in my view. What this webinar we're doing, And something I am trying to emphasize in the in the paper here is that Oh, this is a completely different context from what we have seen in,

710
02:13:24.000 --> 02:13:29.000
Most of the return. Let's say no, we still need to know a lot about, to learn a lot about COVID.

711
02:13:29.000 --> 02:13:38.000
But there is less about a context with. High mortality rates due to COVID and with very little. Social protection nets.

712
02:13:38.000 --> 02:13:45.000
Oh. According to all work done in the subject, it was very little social protection next to these women in general.

713
02:13:45.000 --> 02:14:04.000
So what is pushing people is financial meat. That's why. Believe that's going on. So with women and with the smaller children more than what they were doing it's indeed a very important question but I think what was driving this agreement was the financial need that there was There was a need for income.

714
02:14:04.000 --> 02:14:18.000
And. Very little social life to safety needs providing with, income to these households. The very important question of how they did it.

715
02:14:18.000 --> 02:14:24.000
I think that one potential explanation, although I cannot explore it because like I don't have comparative data.

716
02:14:24.000 --> 02:14:32.000
Gross national on this pattern on this matter but I do believe that the prevalence of extended households explains a little bit.

717
02:14:32.000 --> 02:14:39.000
So I have work with a comparative studies comparing Peru with Brazil for instance. In other Latin American countries, extended households are less prevalent.

718
02:14:39.000 --> 02:14:52.000
They are very prevalent in Peru and it is connected very likely to the fact that women are so much inserted in the labor force.

719
02:14:52.000 --> 02:15:02.000
So I think that in this context of acute need other family members in these households provided for childcare.

720
02:15:02.000 --> 02:15:11.000
Yes, if that, if that makes sense. It's a 40% of mothers in Peru who live with extended family.

721
02:15:11.000 --> 02:15:18.000
So, he is a a strategy to for women to keep working.

722
02:15:18.000 --> 02:15:19.000
Okay, thank you.

723
02:15:19.000 --> 02:15:22.000
Of course, thank you.

724
02:15:22.000 --> 02:15:33.000
So I have a comment maybe a question for you Marisa about your study and I'm guessing that in Germany there was just one lockdown so the style of etc.?

725
02:15:33.000 --> 02:15:50.000
Yeah. Okay, so maybe this will be more relevant. So my comment has something to do with my own analysis, which I haven't shown in this session here but I presented it elsewhere and I've got some data from young people in Australia.

726
02:15:50.000 --> 02:16:10.000
And also kind of trying to, what I tried to do in the first season is I got data for 2,019 one of the outcome variables is life satisfaction but it's not about mothers it's about young people and you know and then the other variables have to do with the kind of hard material hardships because there was a

727
02:16:10.000 --> 02:16:20.000
lot of talk about hospitality closing down and these are the kind of sectors of industries where young people tend to work when they also study in Australia.

728
02:16:20.000 --> 02:16:37.000
So my problem is my struggle with my favorite paper it is not published at all is that I've got data for 4 points of time and if I had data I think for just 2 points of time, which is, you know, you can do difference in differences.

729
02:16:37.000 --> 02:16:44.000
It's, it's a kind of classical setup. Then I think some of my results will be easier to interpret.

730
02:16:44.000 --> 02:16:52.000
But what I'm trying to say is it would be really interesting to see, but I'm being a bit of a Debbie's alphabet.

731
02:16:52.000 --> 02:17:12.000
Whether when you have the same thing happen later you have the same sort of pattern because that's not what I'm getting and I don't know at this point whether it's me not doing the analysis correctly or whether actually the situation but you know I have different issues so we have to remember that the situation is that for instance following COVID

732
02:17:12.000 --> 02:17:27.000
in Australia I rents and the prices of property went up so incredibly that young people are really in you know and this was kind of put on hold under COVID because there was a memo random and you couldn't for instance evict tenants.

733
02:17:27.000 --> 02:17:43.000
So if somebody was not paying you know you couldn't get rid of them and now in 2,022 you know so we have those kind of negative effects you know kind of skyrocketing so to say, so have you thought about this?

734
02:17:43.000 --> 02:17:52.000
Perhaps 2 2 answers at least to the first part of your question. So the first part is have there been other lockdowns of school closures?

735
02:17:52.000 --> 02:18:15.000
Yes, in November, December, January and February, 2,021. So in winter. European winter there were again school work close but at that time people were better prepared schools were better prepared though they have this online teaching it was different compared to the first lockdown which was a surprise for everyone and everyone was overwhelmed so that the person the second

736
02:18:15.000 --> 02:18:18.000
thing is.

737
02:18:18.000 --> 02:18:37.000
The research design we applied for this opening of schools and this gradual opening by federal state was fitting perfectly to the situation in June and we have this strong difference of the surprised and overwhelmed parents that are had a relief once schools have been opened again.

738
02:18:37.000 --> 02:18:45.000
So we don't know if this is the same in winter. First, so this is had there been a further lockdowns.

739
02:18:45.000 --> 02:18:52.000
Yes. But they differed from the first one. And, So, so we didn't stretch.

740
02:18:52.000 --> 02:18:58.000
The analysis to win the end of the final thing is the data for winter so the monthly survey get worse.

741
02:18:58.000 --> 02:19:14.000
Wave of the wave because people drop out. So we couldn't. Even if we wanted to, we wouldn't have been able to conduct an analysis with looking at March, February, March, 2022, 21.

742
02:19:14.000 --> 02:19:24.000
To see if they have the same pattern again. So, but what we did is we were looking at the UK using the understanding society.

743
02:19:24.000 --> 02:19:41.000
And Survey which is host also and there we could really look at the first lockdown in the UK and the winter lockdown in the UK and there we could follow parents over several months and see how their life satisfaction develop.

744
02:19:41.000 --> 02:20:03.000
But we did not look at the school closures here just described how the life satisfaction changed in the first lockdown and how it recovered and then sell back again and the question we were asking is whether or not there had been accumulation to parents get exhausted in any longer or if there had been a adaptation that parents know new kids are at home they had

745
02:20:03.000 --> 02:20:10.000
a new routine so they were not shocked and went and that's something we were looking at with the pay data.

746
02:20:10.000 --> 02:20:19.000
Yes, we did. Just also recently. I can send you. Yeah, because my question is really about having Walter.

747
02:20:19.000 --> 02:20:33.000
And it is not picking your thinking as It's it's a description. It's not again this Thank you.

748
02:20:33.000 --> 02:20:51.000
If I may ask a few questions. So, for the first paper my question is about is there any information in the interviews about Sorry, should speak to this about the extension of grants that funders offer because this is a situation that myself experienced.

749
02:20:51.000 --> 02:21:04.000
So I do my post up during COVID. In New York, which is really, really bad COVID situation there.

750
02:21:04.000 --> 02:21:20.000
And then there is this ongoing threat. That we face is that, okay, we have to. Oh, work schedule is disrupted and we have to expect that the project takes longer and we don't know it takes how long to really finish it.

751
02:21:20.000 --> 02:21:36.000
So then we're starting to apply for extension and that I mean that depends on who the founders are do they think the project is priority rather so I feel like that is a Pretty influential situation here.

752
02:21:36.000 --> 02:21:57.000
And the second question I have is about this LDA. The topic model. I know that you haven't finished that, but I was curious about how many documents or interviews are there and how large is the document size because it's obviously gonna affect how effective the LDA would be.

753
02:21:57.000 --> 02:22:09.000
And for our session paper I think MERITA already my question is about this older children a little bit anti-intuitive finding where you have already answered.

754
02:22:09.000 --> 02:22:30.000
For the third paper I think my question is just a 2 clarification questions. The first is that this working hour variable Does it only include formal working outside the home or does it include those unpaid?

755
02:22:30.000 --> 02:22:40.000
Labor or working like formally. How's that all working out? Actually, how many hours did you work last week?

756
02:22:40.000 --> 02:22:50.000
In. So I felt like there might be an increase in unpaid working out for mothers that can explain why.

757
02:22:50.000 --> 02:23:08.000
Mothers and fathers. Would be different. Is there a direct measure in Okay, and the second clarification question is, so at the beginning you told us a questionnaire about why studying the time of reopening rather than closure.

758
02:23:08.000 --> 02:23:10.000
Right. Because when school closes a lot of other institutions also close and they are correlated.

759
02:23:10.000 --> 02:23:26.000
But isn't it also true that when school reopens other institutions also reopen? So how would studying the reopening directly rule that out?

760
02:23:26.000 --> 02:23:34.000
So just A quick question. Yes, that's all. So who's that in a room?

761
02:23:34.000 --> 02:23:46.000
Right, we stop we answer in a room Yeah, I can go. So what about the reopening of bars restaurant and so on? Yes, that's true.

762
02:23:46.000 --> 02:23:49.000
They, what about the reopening of bars restaurant and so on? Yes, that's true. They also happened.

763
02:23:49.000 --> 02:23:54.000
We're in the school where we opened in June. And we cannot rule out that something else was going on.

764
02:23:54.000 --> 02:24:05.000
That's why we also looked at the non-parents. Because they profited from opening up a hospitality of recipients of whatever, but they should not be affected by the opening of schools.

765
02:24:05.000 --> 02:24:20.000
That's why we need the comparison of parents and non-parents. Not parents profited from whatever other containments had containment murder had been relieved but they should not be affected by the openings and that's why we need this non-parents situation in the same week of the reopenings of schools.

766
02:24:20.000 --> 02:24:35.000
So that's how we try to get that. Analyze how much we opening up schools mattered for parents.

767
02:24:35.000 --> 02:24:48.000
It should not matter for the non parents. That's why we need them. Do. And the second question was unpaid labor, well there is a question on unpaid label but it's much less precise than the one we can use for working hours.

768
02:24:48.000 --> 02:24:57.000
It's just more or less more than average, something like that. And that's why we decided to go for the working hours.

769
02:24:57.000 --> 02:25:09.000
I think you asked me whether we have information. About but you mean like whether they got more money into the grant because of COVID?

770
02:25:09.000 --> 02:25:19.000
If they do get longer time, Ask about this information if it's affected personal situation of the scientists.

771
02:25:19.000 --> 02:25:26.000
So you know we have quite a few false dogs. So if they told us that the primary purpose of research or data collection is to ask about the impact of that workshop.

772
02:25:26.000 --> 02:25:38.000
That they participated in 4 years or in a few years back. And then everything else is whatever they call them here.

773
02:25:38.000 --> 02:25:47.000
So we did ask them general questions and you know how how has quality affected your work and your personal situation but we didn't push if they didn't want to talk.

774
02:25:47.000 --> 02:26:01.000
Okay, so we've got their CVs so we know how they went so if they told us I lost my job or I didn't get an extension because the ground situation was as it was then we found out about it.

775
02:26:01.000 --> 02:26:10.000
Some of them had continuing positions either at the time that they were in the work club or later. So it wasn't an issue.

776
02:26:10.000 --> 02:26:22.000
So from these kind of dye data, it is difficult to get an overall picture of what happened to Trump recipients, you know, and all stocks in particular.

777
02:26:22.000 --> 02:26:30.000
So that's, you know, so that's not somewhere that we are going with in terms of, of, you know, writing up the results.

778
02:26:30.000 --> 02:26:37.000
The second thing that you asked was the IVA. Definitely we're not going to put in this these are semi-structured interviews so they had several sections.

779
02:26:37.000 --> 02:26:48.000
So the sections that are going in for LDA once we finish cleaning the data which is huge. This is what's kind of keeping us from doing the analysis because the data is very difficult to clean.

780
02:26:48.000 --> 02:27:06.000
It will be just a section that we identify as being relevant to this. That means there'll be some you know decision making and probably will not have all of the texts that should be there because you cannot keep a semi structured interviewed.

781
02:27:06.000 --> 02:27:21.000
Although I have to tell you that if you ever do this kind of the research, don't get sociologists to do your interviews, get science communicators, people who are trained in journalists and journalistic interviews because they keep the order of interviews much better.

782
02:27:21.000 --> 02:27:29.000
Where it's sort of sociologists that go all around and then when you look at the transcript It's you know, a little bit of coffee here and hidden there.

783
02:27:29.000 --> 02:27:50.000
So the goal would be to just take the chunks that are related to Kobe and put them into an LVA so that you have those Because how it works is it looks at the context of each word placement or trace placement, but it also counts, you know, and the relative frequency of, yeah.

784
02:27:50.000 --> 02:28:00.000
So basically if you put everything together even if your software can process it. Then what you get out is still a lot of garbage.

785
02:28:00.000 --> 02:28:04.000
Yeah, so that's what we plan to do. How well it would work you don't know.

786
02:28:04.000 --> 02:28:09.000
You just need to

787
02:28:09.000 --> 02:28:16.000
But I said, well, it's good to know, you know, if we get stuck, we'll send you a desperate email.

788
02:28:16.000 --> 02:28:29.000
Yes, yes, but you know I think the only thing you can do is just to be honest with what you have done And we, there's no intention on our part to just treat it as a kind of.

789
02:28:29.000 --> 02:28:38.000
You know, magic for the key to the data. That's why we're spending time just basically listening to the data and going through the transcripts.

790
02:28:38.000 --> 02:28:47.000
And just relying on our interpretation because at the end of the day when it spits out some topics you know, quotation marks, you still have to interpret these topics.

791
02:28:47.000 --> 02:28:52.000
And if they make no sense. You know, unfortunately you have to close down the show and forget your IDA.

792
02:28:52.000 --> 02:29:04.000
I think that's what it is. Is that what you felt might happen? I'm just trying to figure out what is Is it like a whole evening or

793
02:29:04.000 --> 02:29:17.000
Not just the section. No, I think the call if you put in the whole interview It's like trying, I think it's like trying to put all your variables in the survey into a regression model.

794
02:29:17.000 --> 02:29:41.000
Oh please. The body of how Yeah, so I agree with you and I but I don't know how exactly I think you very right if you meant to know how exactly I think you very right if you meant to signal that how we're very right if you meant to signal that how we selected chunks of text to go

795
02:29:41.000 --> 02:29:44.000
into signal that how we select the chunks of text to go into the LDA will the chunks of text to go into the LDA will matter greatly for the outcomes.

796
02:29:44.000 --> 02:29:47.000
I think you are very right for the outcomes. I think you're very right. And I don't know exactly how we'll do that.

797
02:29:47.000 --> 02:30:02.000
I think you're very right and I don't know exactly how we'll do that but this is not my most pressing headache at the moment at the moment it's just getting through guitar cleaning because that turns out to be a huge issue also because you know we have a large number of people from non-English speaking background and the

798
02:30:02.000 --> 02:30:13.000
transcription was semi-autonated. And that means we've got half a million words and we have to now slowly go through the half a million so it's taking a very long time.

799
02:30:13.000 --> 02:30:16.000
Please.

800
02:30:16.000 --> 02:30:32.000
And to do what? To clean the Sorry. No, no, no, no, they are all in English, but the transcription, not translations, transcripts were generated by other other software.

801
02:30:32.000 --> 02:30:39.000
I don't know whether you've used it. It was like it doesn't recognize the words.

802
02:30:39.000 --> 02:30:47.000
So basically so what it does and with because this is you know a lot of things that they talk about this is you know a lot of things that they talk about the subject matter of their activities is very technical.

803
02:30:47.000 --> 02:31:05.000
So even if they tell you what they do, you know the terminology that they did every time I did one of these interviews myself I learned 5 to 10 new words and they learned something some of the stuff that they do I don't know I can tell you for instance they work on hydro for hydro-fuel you know hydro cells or

804
02:31:05.000 --> 02:31:22.000
something like this. So there's like one place in Australia that does research on this and you know there's others in the in the world but if you are in this field you know exactly who these people are and if they tell you what they work on in terms of hydro-fuel you will know exactly.

805
02:31:22.000 --> 02:31:35.000
So, so it's very specific and they use you know what they tell you about their career trajectory they tell you about the prizes they tell you about the coincidence and funding agencies so there's a lot of proper nouns in the text.

806
02:31:35.000 --> 02:31:51.000
So it's, you know, and I don't know that we really need to get them right, but, up front again when you're going to something like ADA, you don't want to feed too much junk into it because it will return junk.

807
02:31:51.000 --> 02:31:57.000
Would you agree? Yeah.

808
02:31:57.000 --> 02:32:02.000
Oh. Fancy if we have

809
02:32:02.000 --> 02:32:09.000
See if we have any other questions or online questions.

810
02:32:09.000 --> 02:32:17.000
So I don't see any chat. In the chat box.

811
02:32:17.000 --> 02:32:34.000
Okay, also if there is No other Questions then I think we will conclude our session earlier today and Thank you for our 3 presenters today and your interesting research projects.

812
02:32:34.000 --> 02:32:46.000
I hope what we have discussed today would be beneficial for moving forward. And thanks for attending this really early session on the last day.

813
02:32:46.000 --> 02:32:54.000
And. I think that's the conclusion of our session today. Thank you.

814
02:32:54.000 --> 02:33:06.000
Thank you so much to you all. Thank you.

815
02:33:06.000 --> 02:33:07.000
Goodbye.

816
02:33:07.000 --> 03:07:43.000
Thank you. Okay, so now I am gonna And goodbye. 4. Okay, so I'm gonna leave meeting.