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Okay. Good morning. So we should probably start. My name is Thomas Zion and I'm in chair of this session and I would like to begin by acknowledging the traditional owners of the country that we of the lands that we meet on today and pay my respects to the ages past present and emerging.

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This session is a little bit different from other sessions like they usually have a common theme in terms of a problem.

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Here is more about the method and type of data that we use. So it's about bringing together data on people and and places and you leveraging this data for for research on social certification and mobility.

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We've got 4 presentations today. That will be around up to 20 min. Just 4 so we can.

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For spending a little more time on each of them and the first presentation is by Inaka.

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Hmm.

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Thank you. Okay, good morning. So I would like to present to you my research that I'm doing together with Markoven able on Career mobility in the past.

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In the nineteenth century and how it changed. Due to our during modernization

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Yes, yes. Okay, so there is there is increasing evidence that modernization open up society in the sense that the occupations of the children are less.

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Define this coerced or that's less strongly connected to the occupations of the parents and especially the father.

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But screen mobility has been studied much less and also the hypothesis from modernization thesis about what would would happen with grim mobility are less clear.

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So what do we what do we mean this career mobility? What do I mean with creating mobility in today?

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There is this triangle. So what we want to explain is status later in life, occupational status later in life.

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And there are 2 determinants. First is early status. And that's this connection I would say is career mobility, so the smaller this effect.

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There's the more the more mobility and but we also look at what father state how how much is determined by father status of the later stages so that is controlled for early status.

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So how much is the influence of the parents on status later in life

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So we, the hypothesis are from modernization theory. And the first suppose this is that this effect of early on late occupational stages would be stronger with higher levels of modernization.

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And it is because this modernization the labor market becomes more regulated. There's a, diploma certificates become more important to obtain a certain occupation.

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And that means that it is more difficult to during your life to just hop from one type of occupation to the other one.

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So when you're when you start your career in a certain occupational fields and you are much more likely to stay there because you have this certificates and you would have to go back to school to move to another occupation.

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And there is the establishment of internal labor market. So companies are growing in size and internal labor markets are established and they in this labor market is more likely that I was more profitable to stay in this labor market.

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Because in the beginning you earn relatively less but you're rewarded for that with higher income later on and then you stay also in the same kind of field of occupation in your career becomes more predictable.

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We also think that modernization led to better Let to less career mobility because more than during modernization the occupational there was an upgrading of the occupational structure so the occupational opportunities became better.

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And if you And so more people start already at a higher level. They're their occupational career.

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So there is less upward mobility possible and there's also because during people's life the orchestra structure further upgrades.

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It is less likely that you will be forced to be downwardly mobile. So also for this reason, mobility will be less in a modernized modernizing a society.

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But you can also argue for the opposite from the same theory. So then the opposite would be that the effect of early on late occupational state is weaker with higher levels of early on late occupational state is weaker with higher levels of modernization.

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And that is because modernization, during modernization, a lot happens in the labor market. Some sectors a decline, others become bigger, think of the farming sector that is disappearing.

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So people are forced sometimes to to go to another 2. To continue their career in a completely different sector.

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So there, and for this, it doesn't matter so much whether the later occupation would be better.

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So there are some people say, and we argued like that just a minute ago that there is in general uptraining of the occupational structure but it could also be temporarily downgrading so for company in the beginning of the industrialization process.

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Factories opened with with many very very bad unskilled working position so there was also some downgrading of the occupational structure but it doesn't matter actually which of the 2 the result is the same so early status would be not such a good predictor of your later status if there is a lot of turbulence in the labor

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market.

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We also have a hypothesis on the direct effect of the father on later occupation status so controlled for early occupational status.

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From modernization theory and that is the standard hypothesis of modernization theory and that is that ascribed characteristics become less important indicators of future success in the labor market.

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With modernization. And that is because of 3 theoretical mechanisms. First is structural changes. So if your father is a farmer but you are going to work as an office clerk then your father cannot really help you in your career.

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No other is that modernization goes together with value change. So it becomes also less accepted. To, to discriminate people on basis of, for example, their class of origin.

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And in society, during modernization, in society, cultural differences usually become smaller. So there's also less reason to discriminate people from other classes.

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And the third, the third argument is that with with modernization there's also anonymization. Which means that, the employer, doesn't know so much anymore about the people who are working for him or her because people travel further there are trains there are people are more likely to work in a place that they don't live

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in. So there is also less possibility to discriminate on characteristics if you don't know them.

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So we expect this effect of the father to become smaller.

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So we have this is the reason that I'm in this session that we have different types of data to test this hypothesis.

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On the individual level and on the context level. On the individual level, we use marriage records. The records of the, so we studies for the Netherlands and we have the Mets records.

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Of everyone who married between 1,813 and 1,922. They've been digitized, but only for 5 of the 11 Dutch provinces.

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So it's about half of the country. There are one there are over 1 million of these marriages.

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And we do all kind of selections for example. We have we have to link the MERRIS records of 2 generations.

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And we only take So the merit record of the first child of the band that we link to each other.

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As a result we have 120,000 Cases at the individual level. I'll tell you more about it.

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Yeah, so here. This is the linking that we do. So to the left you see the Mayor Certificate of the parents and of course these those of the children are the same they have the same content.

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For the parent, that is the respondent is the bright groom. On that, the respondent is the bridegroom.

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On that, the certificate is, is his occupation, but also the occupation of His father and the occupation of also of his mother but we don't use that in this time for the brighter is the same information.

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We link that to the oldest child of the of the bridegroom that can be a girl or A border doesn't matter.

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What we use there is the occupation of the father because that is They say the bright gloom on the left certificates is the father on the right certificate.

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So the bright, occupation on the left. S is the early status. Any occupation of the right side is the late status when this first child is marrying, which is about 30 years later.

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And we link those to contextual information on the places where these people marry. And these contexts are municipalities.

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Yes. Oh, yes. I'm fine. Municipalities and we measure municipalities in certain years.

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So this is the location times time thing. We measure modernization in 2 ways. We measure it with a number of indicators, the number of inhabitants of the place where these people live.

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The number of steam engines, which is industrialization, students in secondary education. Education expansion presence of a post office so the communication that is possible and the present of a train station, so how how far can they travel?

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We combine that into a one bit factor analysis into one variable for level of modernization, but we also look at the speed of modernization because The structural changes have more to do with how fast these things are changing then with the level at 1 point of time.

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So we also look at how fast does the change in 10 years after the merits of the of the men that we're looking at.

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To have 23,000 contexts that is a municipality in a certain year.

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Well, here you see how modernization changed in the Netherlands between 1,810 and 1,890 and where you see is not much as going on in the first half of the century then it's slowly some modernization takes place but from 1,865 onwards it really goes

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fast. the demonization in the Netherlands.

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Okay, so we do multi-level models with individuals, nested. In municipality times years nested in municipalities.

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And we first of the base model on the individual level without variables of the context and here you see the 2 most important are at top.

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So early status as an as an effective point. 29 on later status. And father status has a similar effect.

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So the father states in early status have similar effects on on later states. And these are unstandardized effects.

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So this is about 1 one third of a point status. Do they get from one stage point of their father?

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We also look quickly, although we don't have an hypothesis about that on how this effect of early status and of father state has changed over time.

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And there she did, they are rather constant, but that at the end, early status becomes somewhat more important at the end of the century.

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And father status becomes less somewhat less important at the end. Of the century.

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But here are our main results. So to left you see the effects of the level of modernization and speed of modernization.

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On the effect of early states on later status. So this is the interaction. You see that the higher modernization, the stronger the effect of early status on later status.

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So that was our hypothesis one. I'll come back to that later, of course.

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And speed of modernization does not have an effect here. And to the right, you see that the effects on farther status as predicted are negative.

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So with level of level and with speed of modernization the effect of the father decreases yeah decreases

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We do 2 further analysis to understand this better. So some people say you read this better. So some people say you read it often in the literature that okay that is often in the literature that just things are happening with the farmers in the literature that, just things are happening with the farmers that, okay, that is just things are happening with the farmers.

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Of course, things are happening with the farmers. Of course, with the industrialization, these farmers disappear and all these what you see in all these effect is driven Now that is not the case here.

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So as you can see, it was actually driven by the non-farmers. So the for the non-farmers that is this.

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Positive effect of modernization. Only affects ongoing mobility, so on the effect of early stages on later status becomes stronger.

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And this is also the non-farmer, the non-farmers where the effect of father status declines.

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Well here you could say it is also true for the farmers. It's not significant, but it is of equal size.

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It is just because there are fewer farmers than non-fivers in the Netherlands already at this time.

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So, so, but it's definitely not driven by the farmers. Our results. And the second analysis that we do is we look at we first standardize our variables and then we do the same analysis.

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And the idea behind that is that it is all driven by changes in the occupational structure. So this is the old idea that the old distinction between total mobility and relative mobility so here we look as good as we can without doing log clearing models too.

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Relative mobility so taking away the effect of changes in the occupation structure. And there you see that the same pattern actually remains.

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So it's not only driven by change in the occupational structure, there's more going on.

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The becomes really more strongly connected the the early and later status so the the careers become more predictable with modernization and father status effect is still negative also if you take into account the changes in the in the occupational structure.

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So conclusions, there is support for monetization theory. We derived all hypotheses from modernization theory.

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So. But we were opposing hypothesis and so there is support. That for the hypothesis that says okay there are stronger boundaries in the labor market.

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Predictable overtime. And the effect of the father decreases so just like we found for a regular intergenerational mobility analysis it is also the case that for Later, later in life, if you take early states into account, the effect of the father becomes smaller.

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But did not disappear. It was still there at the end of the century. And there is no support for this structural hypothesis that says that there would be an increase in career mobility due to all kind of changes in the occupation structure.

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Thank you. Thank you.

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I think the staff who do something.

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Oh

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Oh, I think it's.

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Otherwise it would have been more difficult to tell my story so I'm happy that it works now. Good morning everyone.

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My name is Sophie Bears. I'm a PhD student from the University of Froinga and together with my co-authors listed here I will tell you of our project which is still a work in progress titled Female CEOs as agents of change the lasting impact of early career

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imprints. So to start, men are still in a fast majority in higher occupational positions, but women are finally increasing to occupy those higher organizational positions as well.

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And researchers have asked the questions whether this trickles down into the organizations and that they help other women in the organization as well.

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And some studies find yesterday they do, so women in leadership positions have been found to increase employee female employees wages so they reduce the gender wage gap and they improve other women's chances of reaching high organizational positions as well.

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So in this literature these women are referred to as being agents of change. So that all sounds very optimistic. It's closed. Well, unfortunately not.

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Because some studies find mixed or no effects and here these women are referred to as cogs in the machine who don't really change anything or they even find negative effects and that is often referred to as the Queen B theory.

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And that is when women end up distancing themselves from others.

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So with this paper we hope to contribute to a better understanding of the mechanisms behind female CEOs becoming agents of change.

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And there are 2 major limitations in literature that we hope to interest with this paper. So the first is that most of these studies focus on women in middle management or supervisory positions, which is very interesting because they are the ones who are more in contact with employees, they are able to maybe put someone forward for a promotion or a pay raise but they

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don't have the organizational power to set those wider organizational policies. So that is why we will focus on female CEOs.

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Secondly, is that research does not take the career experiences into account. And we think this is really important to do because career experiences of leaders are gendered.

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So the vast majority are still men in leadership positions. So the way up to the top in an organization, the experiences and the social environment will be very different for female leaders than for male leaders.

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And research shows this because women in leadership positions report more experiences of sexual harassment, bullying, intimidation in the workplace and job burnout symptoms and it really affects them once they are in that position of power.

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And these career experiences can lead to social identity threats and women distancing themselves from others. And this is often due to the internalization of negative stereotypes because if you're constantly surrounded by male leaders you start to believe like oh that is the typical leader and I am the minority here.

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Yeah, so that's leads to social identity threats. So we do not only look at gender, we also take career experience into account.

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So I'm quickly going through the theoretical framework so one of the most important theoretical frameworks that we use is imprinting theory and imprinting theory explains us that during certain career phases and mainly the start of a new organizational role such as your first leadership role because you don't get promoted from being an

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employee to a CEO immediately. You probably have some directful experience before that. During that time, it's very uncertain, you still need to learn, so you look a lot at your social environment and you look at cues from others how to behave you learn how work is supposed to be performed here you learn the norms.

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And especially for leaders and especially for female leaders, the social context is really important. To form their authentic leadership styles.

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So that is why we will look at the first directorial experience. So from that female CEOs in our data set, I will explain this later.

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We will track down their first editorial experience and see what the social context was during that point. And to further emphasize why the social context is so important, we also have some insights from relational inequality theory, which is a framework which helps us describe that these social interactions and the social processes are the place where these ideas and social

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inequality are produced and sustained in organizations.

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So based on these insights from the literature, we have several predictions. So we think that female CEOs will become agents of change if their first executive experience was in a gender equal board environment.

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So we call this an equality imprint. Whether they started in a board where the majority were women, so we called this a majority imprint or whether they started under a female CEO.

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We call this the role model imprint. Because for a female employees it has been found to have If you have a veteran or senior colleague who is similar to you, so who is a woman, they really take on a role model role and you tend to.

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Yeah, it helps to legitimize you being in a higher organizational position.

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So the outcomes, we will of course look at look at the gender wage gap because it's still a very persistent phenomenon.

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Women earning less than men for the same work is still the case also in the Netherlands. Don't think that I've mentioned that yet but we focus on the Netherlands with our data and we will look at hourly wage as the outcome and we will take the log hourly wage in our data.

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So let's look at the data. We use Dutch register micro data from the Central Bureau of Statistics.

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So this is population data which is amazing to work with honestly really large data sets and one of the most important ones is the on the top.

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Left the Chamber of Commerce MUSTUREs really starting so that allows us to identify who the executives are in our data sets.

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So this is an individual level data set. It has information on all directors and the start and end dates of their job.

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So we are able to reconstruct our whole directorate careers. Unfortunately, we cannot identify who the CEOs are just in this data set, but we can connect it to information from the text records to obtain their wages.

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And this is a proxy, but we assume that the board member, because they're usually part of a board of directors that the board member with the highest wage is the CEO.

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So from the text records we also have information on all employees in the companies that they manage. So we have their wages, also their starts and end dates and everything.

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We connected with information from the municipality register to get information on their age and gender. And also information from the company register.

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So here we have all. Organizational level information such as the size, the sector and the location. So a lot of merging steps later and we ended up with a panel data set between 2,010 and 2,019 and this data set is on the individual employee level so for each individual employee we have information on what type of

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CEO they have, what kind of imprint that CEO have. Which organisation they work for, etc.

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And we in a sample we took or we selected economically active organizations and we ended up with over 10 million observations.

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So for this type of data, the data set is unbalanced and we do have serial correlation presence, so we employ fixed effects linear regression models and we cluster our standard errors.

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And this way we can make the most robust estimations dealing with panel data of this nature. And we include job fixed effects.

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So the panel idea is the combination between the individual identifier and the organization identifier and together they identify a job spell of an individual.

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So please know that within our regression analysis or the results, it shows the within job variation. So the variation in predictor and outcomes is something that is specific to this job and is not explained by things that happen to all jobs simultaneously.

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And we also include, year fixed effects to account for temporal shots that affect all organizations such as changing economic conditions.

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And of changing political cycles for example. So what is important to note that when I talk about the variables that we include, you won't see variables that are constant over time because it's not possible to include them in fixed effects analysis.

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So we are not able to control for sector, for example, education level or gender of the employee. Yeah, because that is automatically control for, in, in our design.

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So our main predictors are of course the imprint so for each director in the data set we tracked back their whole careers and we saw what the board composition looked like when they started.

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We have the female CEO so we identified what the CEO was female or not and the female employee because it is possible to interact those time.

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Constant variables in an interaction effect. And we build our way up to a 3 way interaction. And here I want to emphasize that the D minus one means that these variables are lacked by one year.

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Because a CEO entering an organization does not automatically change everything up. It takes some time to happen so that lacks by one here.

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So the control variables, I'm just going to explain the ones on the board level. So these represent averages across the board.

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So we control for average age of the board, the tenure, the size. And the proportion of women.

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And I want to emphasize that these are led by 2 years because board composition can affect whether a female CEO is hired.

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So these are selection effects and that is something that we want to account for. So it is lacked by one year extra.

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So first some preliminary results. There is still a gender wage gap in our data set as you can see here.

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I still need, I need to take a small simple drink.

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Okay, so as you can see there's still a gender wage gap. Recent publication by Penner at all already established that for the Netherlands and in our data set of course we find the same.

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And also I ranked the imprints which are the most common and I actually created more in print than just the ones that I talked about earlier and it probably comes as no surprise that most of the female CEOs started in a token position followed by equality imprints and then actually the role model imprints which I forgot to put

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on the slides and then the majority in print. So I'm just I'm not showing all the results because it's quite a lot and I figured we had 50 min but I think I could have done a bit little bit more.

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So in the first model, so this is when we only look at the gender of the CEO.

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So in the first model you see that having a female CEO is actually associated with a lower hourly wage for employees, which is interesting.

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And in the second model, we interact this with the employee being female and we actually find a positive interaction effect.

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So to kind of understand what might be going on here. So yes, they do end up making things more equal, but it does come with a wage penalty and this might in indicate wage distributions in the organizations in which men are, yeah, they have a little bit lower wages than the women, but it does end up producing the gap between

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the 2. So this is only when we look at gender. So now let's take the career experiences into account.

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So this is the result for the majority imprint. And so on the left you see the the base model that I just showed you and on the right we include the three-way interactions.

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So does the reduction of wage gap by female CEO become stronger when she has a majority imprint?

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The results show that no this is not the case it's not significant so we cannot conclude that the majority imprint matters here.

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It is positive but this ultimately goes against our expectations.

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And here you see the result for the equality imprint. So on the right you see that this coefficient is actually negative and statistically significant.

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So actually a female CEO with an equality imprint has a negative effect on the reduction of wage gap by a female CEO.

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So this is actually in the opposite direction of our expectation, which is really interesting. And here we have the role model imprint and here you can see that having a role model imprint for a female CEO.

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This actually aligns with our expectation because the three-way interaction is positive and significant. So for a female CEO with a role model imprint this strengthens the reduction in wage gap by a female CEO.

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As you can see, the observations are quite lower because I talked about that our data set has 10 million observations, but that's because of the lagging of the variables you of course lose a lot of the cases because you go before our observation period.

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Yeah, so these are our main results we have up until now. So my plans are to also do this analysis for meal CEOs because I'm interested to see whether mill CEOs with certain imprints also help to reduce the gender wage gap.

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And I also want to model this for the token position in parentheses what happens there because We see that things go in opposite directions than we expected.

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So some concluding remarks, yes, career history matters, but not always how we expect it to. And I might hear you think, OK, it's nice that you look at the first experience, but what about later career experiences?

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Oh good point. So we actually try to control for the proportion of time spent in majority or minority positions for example only this variable correlated very high with the first experience which makes sense because it contains it.

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So what we are planning to do is to control for a last experience. And we will plot all the marginal effects to be the disentangle all these results from the interaction effects.

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So I think in the end we will find that career experience matters. It's now time to disentangle whether it's really the first or maybe also the later career experiences.

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We will also look at additional outcomes. So these were the results on ourly wage, but I also want to look at weight progression and additionally it has been documented that female employees often have more chance of being stuck on temporary employment contracts compared to their mill colleagues.

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So their chances of a permanent contract are lower. So I want to see if female CEOs can maybe be an agent of change if we look at a distribution of types of contracts.

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So I hope in that way we can also broaden the concepts of gender inequality and organizations a bit more.

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Then something that came up in thinking about my research is, okay, the exposure to a female CEO and the imprint that comes from that.

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What if that female CEO is a Queen B. What does that mean? Then it isn't such a positive experience anymore.

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So I am still thinking about how to control for that so if you have any ideas I would be really happy to hear them.

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One of the things that we are thinking about is to control for the layover rates of female board members under that female CO because maybe that can indicate that the female CEO in that case was a Queen Bee.

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And lastly, because we cannot control for things like education level or sector, we are planning to maybe redo analysis on sub samples of the of the population so we can still Yeah, we really want to delve into what are the boundary conditions of these imprints and the agents of change mechanisms.

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To work. And thank you for your attention. And this is a statue in the Melbourne trades hall of a woman who changed herself for the gender weight gap.

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I walked past it and I thought this is really interesting, relates to my research, also a bit sad that I'm still here to present the gender wage gap.

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Thank you.

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Okay.

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Shoot, thank you very much. And so the next next presentation is Just see your mother. My explicit question.

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25%.

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Okay, hello everyone. My name is Marek Boschakovsky and today together with my colleagues Agnes Gaquin Domincha and We would like to tell you a little bit about our research regarding gender labor market inequalities among high education graduates in Poland in the context of kovat.

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19 pandemics. So the main idea behind our research is to check how the social characteristics including demographical, economic and epidemic epidemiological affect the labor market transition from high education to to the labor market.

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And in order to do that we have to combine a data registered data from different sources. And on different aggregation levels.

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The main sources that we used were the data from the seventh edition of Polish Graduate Tracking System, known also as Ella system.

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Those data are on individual level and I'll tell you a little bit about the system itself a little bit more in just a minute.

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And we also used some aggregated level data. First of all, we use the data from statistics Poland.

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The data on demographic and economic characteristic of whole yet so more or less counties in Poland. We also used data from the Polish Ministry of Health from the Health Center.

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Those are aggregated data regarding kovat 19 epidemiological features. On the level of povits as well.

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And the last source that we use is the Covid-nineteen stringency index, which is a part of the Oxford covet 19 government response trucker a research project

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Now, when it comes to Ella system, it is a system that has been developed by the National Information Processing Institute for the Ministry of Education and Science in Poland.

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It is fully register based. It covers over 6.5 million people. And it has 2 main register sources.

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First of all be in Poland, the higher education register in Poland that's where we get the information.

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On their educational paths on the high education level and the second source is Zeus the social insurance institution which provides us with information on the labor market status of the graduates including childcare for instance.

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And also provides us information on the earnings of the graduates.

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So our population that we gonna, show you today and then we conducted research on are the the 2,018 master's degree graduates from all polo HGI so it's an entire population of almost 150,000 people.

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And we observed them for 3 years each and every month as we get their monthly in the monthly rhythm we get information on their labor market activities and we observe them for 3 years, 2,019 to 2,021.

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So this period includes the kovat 19 pandemics era so it is covered by the research that we want to see what happened to them at that time.

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So we wanted to see what are the typical. Paths to enter the labour market when graduating from the from the higher education so for each and every graduate we have checked their labor market status for each month.

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Of those 36 month long period. And we calculated the individual paths for for each and every one of them and some of the paths are more similar to others.

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Than to some others. And so we calculated the differences between those paths. And on that basis we have gathered them in clusters.

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So within a single cluster we have people who have a similar entry path to the labor market. Now, the idea is to find out what features sense people on this path and what on the other path on the labor market entry.

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So we made a multinomial logistic regression model in order to predict in which cluster they're gonna end up on the basis of the of their features and the features of their places.

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Now these are the results for the entire population of 150,000 people. So you can see the chart.

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It has 36 columns and within a single column each color indicates the percentage of graduates who had a given labor market status in this month.

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As you can see the most common color on the chart is red which means employment contract. Which is the most short after form of employment stable one with 4 or social benefits.

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Below the chart you can see the line here. This is the kovat 19 stringency index which informs on how strict the kovat.

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19 lockdown regulations were at a given time point so we can see the whether there are any changes in the labour market status in the time of more strict regulations.

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And there are also little shields here. A little bit up from there and this indicates the period in which the government was introducing their anti-COVID shields to protect the economy.

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So this indicates that period. Now as I mentioned before we have separated the graduates into 10 clusters regarding their labor market entry paths.

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So for instance the first cluster here it has a very characteristic group Here the yellow one. Those are people, this is childcare.

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And it has quite interesting dynamics as it goes up in time and then it the percentage goes down. It starts going down in October, 2,020.

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Now if we go back 9 months from there We will see we will end up in January, 2,020.

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That was the time when the people started to get worried about. That something is going on and the situation is getting dangerous in Europe.

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So you can see that it had influence on the decision. Of this group. The second cluster is very similar to the first one.

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This darker yellow here is childcare but during the childcare the people were still working and the dynamics here are.

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Are the same as in the previous one. Cluster 3, it's a self-employment cluster.

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It's a dark blue here. As you can see it's the dominant here and the percentage grows in time.

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And the percentage of people with employment contract is being reduced. And the most interesting part here is the dark gray line on the very top.

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As you can see those 2 bumps here which happens at the time of kovat. 19 lockdown restrictions getting more harsh.

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So at this time the unemployment in this a particular cluster increases. The fourth one is the cluster with multiple forms of contracts so at the same time they have the employment contract and some civil law contract as well.

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And this is the group with the highest earnings.

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And the fifth cluster is mostly people who are inactive or with unknown status so no mark left in the registers as you can see it the inactivity the light gray area here is increasing during the COVID pandemics but the dynamics changes after introduction of the governed shields and and then the inactivity percentage gets a little bit

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lowered. A cluster 6 that's further education. Either while working dark green or with no work just studying the light green part.

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As you can see there are some sharp changes here. So those happens in October. That's that's when the academic year in Poland starts so that's when we can expect the percentage of people studying going up.

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As you can see it happens in 2,019 and in 2,020 but not in in 2,021 as we can see that people just stopped starting new study programs.

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In in that in that cohort who graduated in 2,018. The seventh cluster is the delayed employment contract.

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So people in this cluster starts with I have an activity or unemployment or a civil law contract but in time they managed to find a stable job.

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Cluster 8 The biggest one with over half of the graduates been in this particular cluster is the employment contract with just a tiny bit addition of some others activity forms it has the second highest earnings from all the clusters.

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A cluster 9 civil law contract. This is the light blue here. As you can see the percentage of people with this civil law contracts.

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Also referred to as junk contracts, the not stable form of employment. It increases over time but it gets replaced.

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In part with employment contracts after the introduction of the government shoots. When you take a look on the earnings in this group, they're they're quite low in comparison to the others.

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And the last group is the unemployment cluster. And And you can see the dynamics, the unemployment rate in this cluster goes up during the COVID era, but getting reduced after the introduction of the government shields.

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So the question is what characteristics, what features tend to send the graduates in a different clusters.

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So we made a multinomial logistic regression model in order to find out. So our dependent variable is the cluster that the graduates end up in and our reference category is cluster number 8 the employment contract the one that has that over half of graduates are in.

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And when it comes to independent variables, we can we have 3 groups first regards the individual characteristics of graduate.

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The second group, the studies that that they graduated from, then the third one the features of the places of residence.

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So let's take a look on the coefficients. Of our model. Let's start with the individual features of graduates.

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Now those numbers here indicates the probability of being in a given cluster in comparison to being in the reference cluster number 8 the employment contract the biggest one so the higher the number the higher are the chances of a person with that feature to end up in that particular cluster rather than in employment contract.

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We negative numbers of means that the chances of getting in that cluster are lower. And so. Because the employment cluster is the one that you want to be in, that's why the positive numbers we have called it red, the negative ones we call it green because they mean that you have high chances in ending up in the employment contract

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cluster. Now when we compare women to men, we will see that the women are way more likely to end up in class in their childcare clusters, either with work or without it.

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And they are less likely to be in the self-employment cluster. When we take a look on the foreigners, let's start at the at the bottom here as we didn't call it this particular bracket and the reason is the foreigners are less likely to be unemployed because You have to work for quite some time in order

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to have the right to go and register yourself as an unemployed person so it's a more of a mechanical thing that we see here as a result of the of the regulations.

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Not about their their decisions or job market position whatsoever. We can see that if foreigners do have children they are more likely to work at that time probably being more worried about their economic status and their needs.

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And also we can see that the foreigners are more likely to be in the inactive cluster which is very understandable because if they leave Poland they do not leave any traces in the Polish registers and therefore we We see no activity of them.

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When it comes to job experience, our reference category here are those who had job experience. During enrollment.

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So if we compare those who have no job experience before graduation to that particular group, we can see that those with no job experience are more likely to end up in an employment cluster or the one with unstable contracts or to be delayed in the labor market entry.

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On the other hand, they are less likely to have children. When we take a look on those who had job experience during studies we see the same pattern here but the but but not as she strong differences when it comes to no job experience.

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Now when it comes to the studies that they graduated from let's start with comparing full-time studies to part-time studies.

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Those who graduates from full-time studies were less likely to end up in unemployment cluster as It seems that they received a better quality education.

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They are less likely to have children which is in part because of the age differences between those groups. And they are more likely to to educate further and well just for the record in Poland full-time studies in public universities are free of charge.

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But part time studies you have to pay for. So it is understandable that those who graduated from from full time studies might be more likely to conduct further education.

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When it comes to the area of study, we have picked humanities as our reference category. We can see that the medical graduates from medical studies are way more likely to have multiple contracts, multiple types of contracts at the same time or being self-employed.

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Or of having children than the than our humanities who are our reference category here. When we take a look on the technical sciences we can see that most of the numbers here are negative so it means that are more likely to end up in cluster 8 our reference cluster, the employment contract, which is the most natural form of employment for

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them. So most of most of them here are negative numbers. When we take a look on the social sciences we see similar patterns but with not such strong differences.

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On the other hand, when it comes to the arts, the situation is very different. All the numbers here are positive.

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So it means that the least likely to end up in the in the employment cluster in comparison to the others.

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And so the more way more likely to end up in in self-employment or in unemployment when we compare them to other graduates.

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And now when it comes to the features of their places of residence When we compare big cities and medium cities to our reference category, which is small cities and towns, we will see that the bigger the city, the lower are your is your risk of being unemployed and then end up in the last cluster.

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Also the bigger the city, the lower the chances of having a child because we observe different patterns. When it comes to parental decisions.

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In in bigger cities so there are more more children being born in rural areas and at the at earliest stage of their parents life.

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Next, let's take a look on the unemployment rate in the period of residence. So the coefficients here are per one percentage point.

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Of unemployment rate, we will see that the higher unemployment rate in the place of residents, the higher the risk of ending up in the unemployment cluster, which might seem obvious, but we see that it's that people who had who had higher education also are not immune to that effect.

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On the other hand, they are less likely to. To go to further education or to be self-employed.

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Now we've also checked the features of the poviets when it comes to COVID-19 pandemics descriptive.

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So here we have the kovat 19 death rate per 1,000 people And we see that the whole idea behind it was that we wanted to check if if we have a high death rate in a given period that means that the situation there were more harsh during pandemic and therefore they might have more troubles with entering the labor market with with bad things

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happening around in the economy as well. So we can see that the higher the death rate the higher is the risk of ending up in the unemployment cluster.

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We have also checked if the vaccinations per 1,000 people would affect the labor market entrance and as you can see we didn't even color those they're virtually 0 and it has no effect on the labour market entry of graduates.

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To sum it up we have presented the model of analyzing the labor market entry of graduates that there are different atoms of entering this labor market and that they are features.

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That can affect the chances of taking this or that path to the labour market and the likelihood of getting on these paths can be affected both on the features of the individuals as well as the features.

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Of the places of residence. Thank you for your attention.

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Present the changes.

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Good morning everyone. I'm Chi. I'm a postdoc from the Hong Kong University of Posts of Science and Technology Guangzhou.

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They've gone through campus of HK USD is a newly established university located in the great Wood of Bay Area in China.

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It is in the Guangdong. And it has a unique academic structure that aims to promote interdisciplinary research.

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I have to promote interdisciplinary research. I have an education background indisciplinary research.

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I have an education background in economic and disciplinary research. I have an education background in economic and urban environmental engineering and I am now working with.

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I have a education background in economic and urban environmental engineering and I have a education background in economic and urban environmental engineering and I'm now working with Professor Joanne.

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Economic and Urban Environmental Engineering and I'm now working with Professor Joanne.

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And I'm now working with Professor. And I'm now working with Professor Droni Jung and she's online now.

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She is a sociologist with research experience in social stratification and population migration and spatial factors into traditional social science research and the present and my presentation today is just one of our endeavors today. It's just one of our endeavors.

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And as you may have noticed that the title of my presentation is different from the program. The result of that one is not that interesting so I we have changed our research. The result of that one is not that interesting.

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So the result of that one is not that interesting. So I we have changed our research direction and I am connected to expecting to present our latest finding with you.

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I hope you enjoy the presentation. Okay, our research it's about neighborhood satisfaction and we are investigating the native migrant difference and the effects of urban service mix and accessibility.

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Okay, neighborhood selection relates to individual well-being, place attachment and the social cohesion.

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It is a central topic. In the research field of urban planning and sociology and also environmental psychology.

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And the neighborhood that section research. Focus on the determinants of neighborhood selection and Neo has reviewed existing research and categorized the determinants into 3 groups.

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The first one is the objective features of the neighborhood. And in previous characteristics, and also the perceptions of the neighborhoods.

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And among these 3 groups of determinants, the objective features of the neighborhood is of great importance. Because it will provide guidance for the urban planning.

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That can promote the building of that fracturing neighborhood.

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In literature that are focused on the objective features of neighborhoods and neighborhood satisfaction. The social economy environment and the field environments of neighborhood were investigated.

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The social economic environments referred to racial composition, elderly concentration, the income level, and also education and etc.

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The built environments of neighborhood refers to facility proximity, connectivity of neighborhood refers to facility proximity, connectivity of roads to facility proximity, connectivity of roads and walkability proximity, connectivity of roads and walkability of roads and walkability and also the proximity, connectivity of roads and

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walkability of roads and walkability and also the environment slides noise pollution and granary and land use mixed.

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So, although there are some general consensus that such as more granary and more proximity to parks or health care and some other facilities are positively relates to the neighborhood satisfaction.

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Findings concerning other build environment features remain mixed. And also limited studies focus on the facility proximity have considered both the reviewed and the pretentious measure accessibility.

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This extinction is very important because not all individuals are fully used, now fully aware of the presence of the facilities in their neighborhood.

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And finally, studies on the land use mix usually consider the evenness of land use types. Between commercial, industrial, residential and open space.

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A few of them have to investigated. They makes distribution of various types of urban services. And this one is important because especially in highly urbanized context.

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So with the attempts to fill the gaps in the literature, we developed this. A conceptual framework.

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So we consider both reviewed accessibility and the potential accessibility to urban services. Review the accessibility is assessed.

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By a survey. By asking the respondents about whether they use If facilities or whether there is a certain kind of facilities in their neighborhood.

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And potential accessibility refers to the actual presence of certain type of facility. It usually measured by the number of the the type of hostility such as restroom, entertainment facilities, public transports, medical and health facilities.

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And the potential accessibility to facilities. Also reflects the urban service mix. Which is another feature of the environment of neighborhood.

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And so our interest is to investigate the relationship during the spatial accessibility to open services and the urban service mix.

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To neighborhood satisfaction. And since different group of people may attach barriers importance to different of environmental feature features.

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So and we are particularly particularly interested in whether there is a native movement difference in these effects.

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So we choose Shanghai as the study area of this research and this is because Shanghai has undergone rapid urbanization and modernization and it has developed a diverse urban services and a melodies with great quantity and Shanghai is also the most developed city in China in a checked in internal migrants from other

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provinces in China. And also the findings of Shanghai can provide implications for planning practice. To other places in China and beyond.

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For the analysis, we, we combine survey data with point of interest data. Your survey data is come from the Shanghai Urban Neighborhood Survey.

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It's, it is conducted by Shanghai University. The sons data is a neighborhood based spatially structured social survey and it has constructed a city wide representative sample of the population in Shanghai.

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Here is the map, the map shows the service sites of the survey.

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And there survey has 2 waves. And since wave 2 include more survey items about neighborhood. So this study choose wave 2 as the analysis sample and web 2 was completed in 2,019 it has surveyed over 7,000 individuals from 4 over 4,000 households.

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The point of interest data was extracted from the Tajong Dimin, which is a cataloging website in China which is similar to the yep.

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And here is the snapshot of the website and it also has a app. At, you know, it has apps, smartphone.

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And by 2,000 writing and after the data cleaning we obtained over 800,000 PO eyes and the pure ice can be categorized into 8 types of open services which is restaurants, entertainment, culture services and public transportation, sports and 15 medical and health shopping and living services.

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And here is the figure that shows how they survey data and the POI data can be linked.

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Since the survey data was. A drill reference so we know the location of the household and so we can calculate the number of each type of their facilities within a certain distance.

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Of their household. So we can see from this figure that for some survey sites, so for some. For some households from some certain service like the there are more restaurants around there around their neighborhood.

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But for some service sites they The restaurants are NASA many.

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01:08:40.000 --> 01:09:00.000
And the a dependent variable of our analysis is the neighborhood satisfaction. It is it is assessed by asking the respondents yeah, how satisfies are you with your neighborhood?

330
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It is from running from one to 5. From extremely dissatisfied to extremely satisfied. And they revealed the Bayesian accessibility to urban services is is by is use is by asking the respondents whether there is.

331
01:09:19.000 --> 01:09:32.000
Certain type of the facilities. Within the 50 min walk from their home and their facilities include culture, sports, house.

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Out of care parts and or the administrative supermarket and wet markets. And these potential spatial accessibility to urban services is measured.

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01:09:43.000 --> 01:10:04.000
The number of POIs of each types of the facilities within the 500 meter radius from the home location within the 500 meter radius from the home a 500 meter radius from the home location and the POI type considered our best runs Entertainment, hardware, sports, health, shopping and living

334
01:10:04.000 --> 01:10:24.000
services. And then we use the entropy index to measure the level of urban service mix. And The entropy index is a index that commonly used in biology and and the land land use studies.

335
01:10:24.000 --> 01:10:33.000
And with a higher value in it, it indicates a higher level of mix of urban services.

336
01:10:33.000 --> 01:10:55.000
Okay, after dropping the observations that I have missing values in terms of our variables. We obtained over 5,000 individuals in over 3,000 households from 52 neighborhoods in Shanghai.

337
01:10:55.000 --> 01:11:07.000
Then we can see that, yeah, the summer statistics shows the difference between shahanated and migrants.

338
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We see Chiang Native's halves. Have are less satisfied with their neighborhood than my parents and there are and also significant difference in terms of their individual characteristics.

339
01:11:24.000 --> 01:11:54.000
And for the reviewed spatial accessibility and potential spatial accessibility we can see that Shaha Natives are has more more reviews visual accessibility than migrants but in terms of the spacious the potential spatial accessibility their own only the medical and health facilities has a significant difference.

340
01:11:55.000 --> 01:12:16.000
Then we conduct generalized linear regression analysis and we can see that the root spatial accessibility to culture facilities and the sports facilities has a positive relationship to neighborhood satisfaction and other facilities.

341
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That's not has a significant correlation so we omitted the coefficients here and the positive relationship persists when we control for individual characteristics and the neighborhood fix the effect.

342
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And for the pretentious spasier accessibilities. We find that the number of restaurants around has a negative relationship to neighborhood satisfaction.

343
01:12:45.000 --> 01:12:56.000
And these negative relationships persist. When we control for the individual character characteristics and labor who speak the effect.

344
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And the urban service mix has a similar similar relationship of to the restaurant. It's also negative. So when we put all the speech reviewed and potential and the accessibility.

345
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To into the model. Wait. The relationship we find remained the same.

346
01:13:26.000 --> 01:13:52.000
And when we control for participants in activities and neighborhood culture facilities and the occurrence of burglary around neighborhood and walk around neighborhood alone and night and the frequency of communication with neighbors and these 4 variables are more connected with We are never neighborhood selection.

347
01:13:52.000 --> 01:13:59.000
We can see the coefficients are large and significant. But the coefficients are large and significant.

348
01:13:59.000 --> 01:14:09.000
But the reviewed spatial accessibility and the reviewed spatial accessibility, potential spatial accessibility and neighborhood and spatial accessibility, potential spatial accessibility and potential spatial accessibility and neighborhood and urban service mix are still significant.

349
01:14:09.000 --> 01:14:22.000
And also we find that Shanghai natives are less satisfied compared to the migrants. And when we, and then next week we consider whether there is.

350
01:14:22.000 --> 01:14:28.000
Native migrant difference in the relationship. We find that no native migrant difference in the positive effect of reviewed accessibility.

351
01:14:28.000 --> 01:14:49.000
To culture and sports facilities. And the negative effect of potential accessibility to restaurants and urban service mix are stronger for Shahai natives than migrants.

352
01:14:49.000 --> 01:15:02.000
Okay, here is just a side analysis. And which also can be served as a robustness. We used a sorted partial effect method to.

353
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To estimate the native migrant difference in. Neighborhood satisfaction. We can see that the average part effect of Shanghai native is negative which is consistent with our GentM analysis and the story partial effect shows that there are certain a group of Shahi Natives that are the least satisfied.

354
01:15:29.000 --> 01:15:54.000
And this part and then we characterize this. Least satisfied Shahi native we find that this least satisfied Shahinatis has a relatively shorter duration of residency and has a relatively higher household income level.

355
01:15:54.000 --> 01:16:08.000
And there, and this least satisfied Shahaini Dips are living in a neighborhood that with more numbers of restaurants around and with more with higher level of urban service mist.

356
01:16:08.000 --> 01:16:19.000
And even though the convincing tools are large, but these 2 groups are are separated so they are a difference.

357
01:16:19.000 --> 01:16:42.000
Oh, just a quick summary of all we find by so far. We find that Shanghai natives are less satisfied with their best engine neighborhood than migrants do and the least satisfied Shanghai neighbors tend to be richer and have a longer duration of residency in the current neighborhood and there tend to live in neighborhoods with

358
01:16:42.000 --> 01:17:00.000
more restaurants around and have a higher level of urban service mix. And the revealed accessibility to culture and sports facilities positively relates to a neighborhood satisfaction and there is no native native migrant difference in the positive relationship.

359
01:17:00.000 --> 01:17:15.000
But in terms of the potential accessibility to more restaurants and are higher levels of urban service mix. Within the 500 meter ratings from home locations.

360
01:17:15.000 --> 01:17:30.000
These, these factories, relates to a lower level of neighborhood satisfaction. And we find that the negative relationships are stronger for Shahheinetics than the migrants.

361
01:17:30.000 --> 01:17:48.000
Okay, the study is still ongoing and I am happy to have your comments. Thank you very much.

362
01:17:48.000 --> 01:18:02.000
Thank you, the presenters for your great presentations. It's time for questions and I suggest that we have a question to a specific presenter and the presenter responds and that we move on to another question.

363
01:18:02.000 --> 01:18:04.000
Okay.

364
01:18:04.000 --> 01:18:17.000
We should be so kind and walk to the microphone so people on Zoom can know the question as well.

365
01:18:17.000 --> 01:18:19.000
Yeah, I have a question to Nike. Such a fantastic paper. I was delighted to see it.

366
01:18:19.000 --> 01:18:31.000
I wondered 2 things. First, whether you had any problems of attrition because of spatial mobility.

367
01:18:31.000 --> 01:18:43.000
And migration. I wonder whether these may affect the results. Knowing that migrants are a select population and that they change things.

368
01:18:43.000 --> 01:18:58.000
And the second short question is do you have access to death certificates because they would complete the trajectory of social mobility of a life course.

369
01:18:58.000 --> 01:19:03.000
Can you talk about how to use this option? I think I guess it's right. So the, yeah, of course you're right.

370
01:19:03.000 --> 01:19:25.000
Migration is often a problem. It's a historical data. But in this case it's less because we have half of the Netherlands and So all the internal migration between this area is covered because people can be.

371
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Can be linked and recognized between the provinces. For an earlier paper we investigated how much that was maybe under Magnika's work he investigated how much

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Problems that were caused by the fact that people migrate over longer distances and that was actually quite rare.

373
01:19:50.000 --> 01:20:07.000
In that period. So that is not such a big issue nothing And your the second part, are the test registers that in principle that's possible And there is also a data set in which these are please.

374
01:20:07.000 --> 01:20:13.000
So you could, but the information on the death certificates is of course is this more specific?

375
01:20:13.000 --> 01:20:36.000
Because not always has a overcome an occupation of the person who is deceased because if this person was already olds then and this person of course does not come to the office himself himself to say I'm dead so someone else is is going to tell that this person else is is going to tell that this person may not know what this occupation was.

376
01:20:36.000 --> 01:20:55.000
So this information is of a different type. But this is interesting for to go converse to this. Okay.

377
01:20:55.000 --> 01:21:12.000
That's the yeah, so I have, couple questions for Sophie. So first one, I'd like to hear a bit more about this population analyses that you were talking about because I was going to ask you if you could.

378
01:21:12.000 --> 01:21:30.000
Control for industry or sector and it sounds like you can't. The second question is really clarification about I guess the way you've identified CEOs because you said that what you did was you looked at boards and you looked at the highest pay person on the board.

379
01:21:30.000 --> 01:21:45.000
And I just know in in in corporate context that I know so in Australia and other countries the board might be separate from the executive and the CEO might not necessarily be on the board and

380
01:21:45.000 --> 01:21:53.000
Would have a different set of responsibilities as well. So I just wondered if you could comment on that.

381
01:21:53.000 --> 01:21:56.000
Yes, thank you. So, so for the soft populations, if I understand your question correctly, you must know which subpopulations I might be interested in.

382
01:21:56.000 --> 01:22:07.000
Yes, so one of them is the public sector because they might be interested in. Yes, so one of them is the public sector because they of course have more of a role model.

383
01:22:07.000 --> 01:22:18.000
Role actually so I would like to see if maybe the effects there are stronger because they're more likely or there's more needs and external pressure for them to increase gender equality and organizations.

384
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And we also maybe want to look at different educational levels of the employees, but I'm not sure yet what the theoretical background for that would be.

385
01:22:26.000 --> 01:22:35.000
So that would also be interesting. So those are the 2 that we are focusing on right now. And for the boards, yeah, so in the Netherlands.

386
01:22:35.000 --> 01:22:51.000
I hope I explains correctly. So. How are you? There's also an advisory board in Netherlands and that one is separate.

387
01:22:51.000 --> 01:23:02.000
That's not what I have data on. Which actually would be interesting because that advisory board is actually where the gender quotas are implemented but it's not a case for the executives that I look at.

388
01:23:02.000 --> 01:23:13.000
Yeah. So, hope that answered 2.

389
01:23:13.000 --> 01:23:25.000
Thank you very much. And so my questions go to Innakar and be getting your hypothesis number 3, and forgetting your hypothesis number 3, your set about there, and be getting your hypothesis number 3, you're set about, a hypothesis number 3, you're set about there is

390
01:23:25.000 --> 01:23:31.000
a failure chance and there's a less discrimination. My question is how do you analyze about the discrimination?

391
01:23:31.000 --> 01:23:51.000
Is it based on the regulation of the employment or the practice that happened. Among certain group of people. Yeah, so, we would very much like to measure the discrimination and the value change.

392
01:23:51.000 --> 01:24:07.000
So this this was just theoretical reasoning of behind the hypothesis. So we tried in some of our analysis but not yet in this paper to get in indications of how traditional the values are in a certain region.

393
01:24:07.000 --> 01:24:31.000
And one of the indicators that we used is for example the age difference between spouses so the idea is that if Older man, marry more from younger women, so be the big age difference that that would be an indication of more traditional values in that is of course not not I mean it's very far away from the direct

394
01:24:31.000 --> 01:24:37.000
measurement of of discrimination. So we would love to, but it's not up for historical data.

395
01:24:37.000 --> 01:24:48.000
You cannot ask these people anymore, so it is very difficult. Okay.

396
01:24:48.000 --> 01:24:57.000
Thanks. I to start with questions. So yeah, just, oh, thanks. Just to, really, Hopefully simple questions.

397
01:24:57.000 --> 01:25:11.000
One of our technical nature so one is about matching so how do you march the you know matching your certificates how do you make sure that the bride's groom is the father and the price mate is the mother?

398
01:25:11.000 --> 01:25:17.000
I don't know how common can manage the solution. Well, since that's a period, but you know.

399
01:25:17.000 --> 01:25:34.000
Just interest how you do that. And the other one may be kind of a bit more conceptual. So just wondering about the have you considered and looked at the relevance of the items that that you're using to capture modernization and how they might change over time.

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Because having done own research in deprivation space for example you know we're using European household plan to do a set of items around color TV for example, possession of call TV and some punk, you know, it stopped being kind of a useful market of deprivation.

401
01:25:50.000 --> 01:25:59.000
So I'm just wondering about things like steam engines and so on, bit less relevant all the time.

402
01:25:59.000 --> 01:26:22.000
So first, the linking. So in the Netherlands that is, that is with these data that is very well doable and that's just because women keep their maiden name and they they often have more They're also used to Catholic party in their bettors than they have then, the top

403
01:26:22.000 --> 01:26:33.000
3. Complete an nature like me for example and my official name is that will be in on the certificate so that has very detailed information.

404
01:26:33.000 --> 01:26:42.000
And there is of the the 2 parents and there is their birthplace, their birth. So there is a lot of detailed information.

405
01:26:42.000 --> 01:26:52.000
So with about so over 95% can be linked with this rather large like to who did this actually them.

406
01:26:52.000 --> 01:26:59.000
There is of course some spelling variation about but since there's so a lot of information that works very well.

407
01:26:59.000 --> 01:27:22.000
We did not do it ourselves. And so it is the International Institute of Social History. They do that with a large computer program to The other the measures yeah so the point the period is nineteenth century so we stop in So, well, not actually the last marriage are in 9 22, but it's for late

408
01:27:22.000 --> 01:27:23.000
status. And so the early state is in the measurement of modernization is 30 years earlier.

409
01:27:23.000 --> 01:27:34.000
So it's the end of the nineteenth century and that's the time that that steam engines are still very important.

410
01:27:34.000 --> 01:27:54.000
But we, you're absolutely right that this, that if so sometimes we would like to do an analysis that are more into the beginning of the twentieth century And, and then, you have to turn towards more importance of electricity, etc.

411
01:27:54.000 --> 01:28:10.000
And, you do have some information on that as well. But it is difficult to connect these, these different indicators of, Whatizing the production process over time.

412
01:28:10.000 --> 01:28:20.000
So this is this is definitely an issue and the same is of course with So the trains cover the transport possibilities rather well in this period.

413
01:28:20.000 --> 01:28:25.000
But then later of course you get cars and at that time and then because there's all that stuff.

414
01:28:25.000 --> 01:28:34.000
It is an important issue but I think for this period we are Roger Shakes.

415
01:28:34.000 --> 01:28:41.000
Yeah, thank you everyone. Very interesting set of presentations. I have kind of questions for 3 presenters.

416
01:28:41.000 --> 01:28:49.000
Can I take them like in one soup? Yeah, and so I won't since I didn't get the questions yet.

417
01:28:49.000 --> 01:28:56.000
I'll start with Marek. And I didn't get questions yet. I'll start with Marek.

418
01:28:56.000 --> 01:29:09.000
I was, I didn't get the questions yet. I'll start with Marek. It was just a curious question because you had the the medical fields graduates that were more likely to be in childcare and more likely to the temporary contracts.

419
01:29:09.000 --> 01:29:28.000
And I was, first of all, I was wondering whether that's driven by gender and I was also wondering whether the temporary contracts has to do with part-time employment and that people are sort of patching together part-time employment contracts in for example, nursing or other medical related fields.

420
01:29:28.000 --> 01:29:31.000
So that was

421
01:29:31.000 --> 01:29:53.000
Yeah, just curious about that. And then for Sophie, I was wondering So, am actually not sure if I understood your analysis correctly because you were starting out with the CEOs and tracking back their histories and then looking at the

422
01:29:53.000 --> 01:30:09.000
Effect of the employees in general. The on their salaries. Anyways I was wondering whether you have any information about the type of management management.

423
01:30:09.000 --> 01:30:29.000
Joe. They started in if it was sort of operational line management or support management because that typically has consequences for career trajectories and they are also sort of gendered And,

424
01:30:29.000 --> 01:30:38.000
Yeah, I guess that's the and yeah, I wanted to sort of. And tip you off for some literature that might be relevant.

425
01:30:38.000 --> 01:30:44.000
There's an article. Not so long ago bicycle now how with a new one Mary Blair Lloyd.

426
01:30:44.000 --> 01:30:49.000
That might be useful.

427
01:30:49.000 --> 01:30:55.000
It has to do with them. Effect of.

428
01:30:55.000 --> 01:31:18.000
Gender balance in corporate boards on sort of not living in management. Anyways, and then the last question for you is about I just I'm I'm not sure I understood you correctly and I came a little bit late to your presentation unfortunately but why would father status.

429
01:31:18.000 --> 01:31:23.000
Influence the sun's later state is controlling for the early status. Why would that sort of change be influenced by father's status?

430
01:31:23.000 --> 01:31:37.000
I'm curious if you have any. Thoughts on that.

431
01:31:37.000 --> 01:31:52.000
Thank you. So, when it comes to a medical sciences graduates, Yes, this includes medicine and first of all yet there are more way more women than men among those graduates.

432
01:31:52.000 --> 01:31:58.000
So when it comes to, the child care clusters, that might be the factor.

433
01:31:58.000 --> 01:32:14.000
On the other hand, you should add also that in Poland those are the longest possible study course as it lasts 6 years in comparison but in general you could get a master degree in comparison but in general you could get a master degree in 5 years, but in general you could get a master degree in 5 years.

434
01:32:14.000 --> 01:32:19.000
So they are a little bit longer you could get a master degree in 5 years so they are a little bit longer so the graduates are little bit older so there might be more likely to decide to have children.

435
01:32:19.000 --> 01:32:35.000
Now when it comes to as you say that those kinds of contracts I I'll start with that it's not the case that I'm more likely to end up in civil law contract cluster.

436
01:32:35.000 --> 01:32:47.000
They are way more likely to end up. Into employment plus a civil law contract cluster so that they have at the same time stable employment plus some other job.

437
01:32:47.000 --> 01:32:58.000
On the civil law contract. And, and the thing is that the doctors in public, the young doctors are the most hardworking group.

438
01:32:58.000 --> 01:33:19.000
They tend to have they tend to have multiple contracts at the same time. And around 10% of them at some point they tend to have 3 forms of employment at the same time having an employment contract plus civil law contract plus being self-employed all in one month.

439
01:33:19.000 --> 01:33:31.000
So those are the so this is a lot of jobs at the same time. On the other hand, it is worth mentioning that it is a group that achieves the greatest success financially.

440
01:33:31.000 --> 01:33:44.000
It is the group that has the best dynamics of of the earnings and they in time they exceed even the IT graduates so they become the best earning group.

441
01:33:44.000 --> 01:33:55.000
Okay, so that's that kind that should Bring this, do this whole picture together. Thank you.

442
01:33:55.000 --> 01:34:00.000
Yes, thank you for your questions for the literature suggestion. I will definitely look into it. So the question was whether we have information on the type of management jobs.

443
01:34:00.000 --> 01:34:15.000
Unfortunately we don't so the data set the studios lately starts is really about the highest level of management so it would translate to executives.

444
01:34:15.000 --> 01:34:29.000
And I think this is a shame because I would be interested to see How career experiences before reaching an executive position would influence our results because yeah, it's very gendered and I think also the middle management, the supervisory positions are really important.

445
01:34:29.000 --> 01:34:34.000
But that's just not a story that we can tell with our data, but it's definitely a suggestion for future research.

446
01:34:34.000 --> 01:34:43.000
Yeah, so I hope that's a huge endeavors.

447
01:34:43.000 --> 01:34:54.000
Okay, so why would the father still have an effect on native status? well, I think, the most The office examples are farmers.

448
01:34:54.000 --> 01:35:08.000
So what often happens with farmers is they're the farming sons first are going to work as a farm laborer for another farm and they're not a farm and then their father retires and then they become the farmer on the farm of the father.

449
01:35:08.000 --> 01:35:35.000
So this is of course a very clear influence of the father on native status but also in many other occupations so fathers who have More resources can help the children if they for some reason their career gets into becomes problematic they can give them a new start or they can help them opening their own company or So there are, possibilities to

450
01:35:35.000 --> 01:35:51.000
rethink and also Also, nowadays you can still see that there is an effect. Of forest resources, obviously social maybe as well on their children's career development after the first job.

451
01:35:51.000 --> 01:36:01.000
What's that? Answer. Our parents or during your whole life.

452
01:36:01.000 --> 01:36:10.000
Just wanted to make sure that and if anyone on Zoom wants to ask a question please raise your hand

453
01:36:10.000 --> 01:36:14.000
Don't see any hands.

454
01:36:14.000 --> 01:36:18.000
Definitely.

455
01:36:18.000 --> 01:36:30.000
I have a question for Qi and I'm just wondering when you when you control, you have potential and revealed accessibility.

456
01:36:30.000 --> 01:36:38.000
I'm wondering what potential accessibility means after you've controlled for revealed accessibility. I mean, I'm wondering how it makes sense.

457
01:36:38.000 --> 01:36:51.000
I'm trying to understand that negative coefficient. And in a sense, what you've done is you've taken out and so what you've got left is whatever in potential accessibility is sort of orthogonal and so what you've got left is whatever in potential accessibility is sort of orthogonal and I just

458
01:36:51.000 --> 01:36:58.000
don't understand what ever in potential accessibility is sort of orthogonal. And I just don't understand quite what that is.

459
01:36:58.000 --> 01:37:02.000
So I wonder if you could talk a little bit about that. And then the other thing I'm also wondering about just in terms of the measurement of potential accessibilities.

460
01:37:02.000 --> 01:37:24.000
If you do a radius, I guess you're really talking about straight line distance and and so the my question is is a straight line distance what you actually want to know about potential accessibility in the sense that if it's not actually kind of you know, accessible except by a straight Thank you.

461
01:37:24.000 --> 01:37:32.000
Just just because I have a comment that's very closely related to Mark's first question. So, I have a comment that's very closely related to Mark's first question.

462
01:37:32.000 --> 01:37:44.000
So, yeah, you kind of mentioned this very closely related to Mark's first question. So yeah, you kind of mentioned this a little bit, but I was wondering whether you could have mentioned this a little bit, but I was wondering whether you could elaborate a bit about the conceptual importance of including both the reviewed and potential importance of including both the reviewed and potential

463
01:37:44.000 --> 01:37:50.000
to elaborate a bit about the kind of conceptual, importance of including both the reviewed and potential, activity and simultaneously a bit bit more on that.

464
01:37:50.000 --> 01:38:07.000
Thank you for the question. So. And you thought you are asking. What does the potential accessibility means when after controlling for the revealed or accessibility?

465
01:38:07.000 --> 01:38:20.000
I think one important feature that the potential accessibility measure is the Foot environment of the residential place.

466
01:38:20.000 --> 01:38:31.000
So. For example, I live in this neighborhood and I usually go to that supermarket. Actually there are several around here.

467
01:38:31.000 --> 01:38:44.000
I think this is the difference between the reviewed and the potential. Yeah, I have I have more choice but I choose one but for another person.

468
01:38:44.000 --> 01:38:56.000
Shi may have made SS the supermarket. Because only one exist in nearby. So this are the difference between these 2.

469
01:38:56.000 --> 01:39:06.000
People is to neighborhood. So, and so this is the reason that the 2 kind of Space Accessibility should be.

470
01:39:06.000 --> 01:39:22.000
Controlled at the same time. Yes, so another dimension that the potential accessibility. Reflects is the mix of the urban services.

471
01:39:22.000 --> 01:39:36.000
So in the. Can you pick in a radiance? Of a certain distance. From the home location there are there may be various kind of urban services and third distribution, the composition of them are different.

472
01:39:36.000 --> 01:40:06.000
So this reflects the diversity of the environment of different neighborhoods. And as you ask whether they are 500 meter this You see, audience distance is is a good choice to reflect the exposure of the in individual exposure so first of all this is a important topic in urban planner, how do you choose the parameter?

473
01:40:09.000 --> 01:40:24.000
One that 1,000 meter or 1,500 meter or the network distance there are various kind of select selections.

474
01:40:24.000 --> 01:40:53.000
So for this study and since this is the very preliminary analysis. So we use the most simple one. And also I try the 1,000 meter and 1,500 meter they give very similar results so I think that can be used as a robustness check and for more advanced analysis we can incorporate the network, the road networks of

475
01:40:53.000 --> 01:41:08.000
Shanghai to to see whether that make a significant change in the results. But according to the literature in both urban planning and also some social science.

476
01:41:08.000 --> 01:41:16.000
According to the literature in both urban planning and also some social science research that incorporate this kind of network based systems.

477
01:41:16.000 --> 01:41:28.000
They have Actually, they did not make very large difference in the results by including the network.

478
01:41:28.000 --> 01:41:50.000
Because the analysis is in covered inhibitor later with the data so the difference may not be that large but if we For example, in urban planning or we just use the Space your data or at a certain level of degree ought to be used to other units.

479
01:41:50.000 --> 01:42:01.000
This the pyramid or whether we whether we consider the road networks can make great difference. So thank you very much for your question.

480
01:42:01.000 --> 01:42:17.000
I think we can incorporate this. Details and their variation of the parameters in our further analysis. Thank you very much.

481
01:42:17.000 --> 01:42:42.000
3 people. So we'll have some diversity in those. To back as a final speaker so I'm already curious about the role of So I think that the for example you What's your found in your analysis is that the primary impact of the accessibility of facilities and enabled satisfactions.

482
01:42:42.000 --> 01:42:49.000
But on the other hand, there might be the haterogeneity dialogue of the accessibility. On the It might depend on the statistics of people.

483
01:42:49.000 --> 01:43:13.000
One reason for that is the, you know, each people can't consider that is the, you know, each people can't consume their own thing, can consume their own thing, can purchase everything so they can actually buy the from the facility but only have 4 people cannot buy those things even though there are 20 large numbers of sorts of facilities in the

484
01:43:13.000 --> 01:43:25.000
neighborhood. So also the another question is about the same radiation by associating stages. So I think that the source, situations have a big impact.

485
01:43:25.000 --> 01:43:36.000
On the association between the you know accessibility and liberal satisfactions. Hi.

486
01:43:36.000 --> 01:43:39.000
Do you want to ask a question to choose well?

487
01:43:39.000 --> 01:43:51.000
Would it be a question to see as well? Oh, okay. Yeah, it was just making sure we have.

488
01:43:51.000 --> 01:44:05.000
Well, thank you for your question. So you asked about. Whether there's other socioeconomic status can be a moderator of a relationship I exam.

489
01:44:05.000 --> 01:44:23.000
Yes, so because this section is books on social certification. First off, my attempt is to check whether people from different level of household income deferred in this relationship.

490
01:44:23.000 --> 01:44:47.000
But actually when I do the regression I see. Actually when I do the regression I see the household income it's not a significant predictor of the neighborhood test section so I thought I thought I didn't try that moderating effect so and and our top interest is the native migrant difference.

491
01:44:47.000 --> 01:44:59.000
And then I also see the House ownership, house ownership and

492
01:44:59.000 --> 01:45:12.000
Maybe the education education is also an important predictor of the neighborhood section. So I think next I would like to check this.

493
01:45:12.000 --> 01:45:24.000
These characteristics whether they are they make a difference in the relationship. Yeah, and in terms of the second question, I am sorry.

494
01:45:24.000 --> 01:45:43.000
Give you additional microphone. Yeah. I think Yes, there may be segregation but And in terms of the survey data we use, the survey data do the, left, the sample.

495
01:45:43.000 --> 01:45:48.000
Through a stage of

496
01:45:48.000 --> 01:46:00.000
Sweet selection of the subject surface. Sub-districts the name it was at the household and

497
01:46:00.000 --> 01:46:18.000
Yeah. If you look at the summaristic statistics statistics again, the table shows that the potential accessibility to facilities now there is no significant difference between the

498
01:46:18.000 --> 01:46:28.000
So I think they say it's an interesting. Finding yeah the potentially actor presents of the facilities There is no difference.

499
01:46:28.000 --> 01:46:38.000
At the reviewed accessibility, there is a difference. So maybe Shahhan's tips, they are more or less familiar with their neighbourhood.

500
01:46:38.000 --> 01:46:56.000
So they they they know what kind of, what kind of facility presents in here and they use them. But for migrants they even though there is such kind of facility there, but they are since they are not familiar with this place.

501
01:46:56.000 --> 01:47:10.000
They in the survey they answered no yeah this may be one reason that yeah we need more investigation. Thank you very much.

502
01:47:10.000 --> 01:47:18.000
Trade we are out of time Sorry. I didn't expect that we would be out of time to be honest.

503
01:47:18.000 --> 01:47:27.000
Just 4 presentations. So again, thank you for the presenters for your great presentations. Thank you all for coming and for the great discussion.

504
01:47:27.000 --> 01:47:37.000
So to continue asking those questions. You to come here. Thank you.