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can you see the presentation? Yeah, just give me a set. So each each presenter will have a 12 min and 5 min after. Immediately after that we have a Q&A people move to the next presenters. Oh, this session has a 6 paper, so we have to run fast, so that please actually be on sharp on your 70 min.

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you know maximum. If you go for 12 more minutes and we'll have to have a last minute for the Q. And A. This is a session for the use

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Young People's you know, and over Rc. 28 and Rc. 20, we'll have a 2 business meeting actually tonight at this displays at 7 30. So you guys are more than welcome. I'm the secretary of Rc. 28. And I'm this is my last meeting actually to be in charge. So I'm more than happy actually to finish my job here. But I guess in many of you, not yet. But member of Barc 20. I encourage you that you to join us, which is one of the largest actually our sees in Isa. So

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please think about the joining us, and without further ado, we'll start with the topic, please.

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Davide Bussi: Okay, thank you.

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Davide Bussi: I'm to see how it that one of the she, a paper about a transition to available to Italy in the last century

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Davide Bussi: more precisely from the beginning of the twentieth century to the beginning of the 20 first century. So I'm going to present our early results.

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Davide Bussi: Okay? First of all, why is it transition to other interesting But first, because it may show how it's and constraints, and also such a norms can affect the construction of the individuals, life courses.

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Davide Bussi: And also we are interested in the

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Davide Bussi: how the effects, and whether the effects of try such a norm in an institutional constraint

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Davide Bussi: change over the years, and if even today, the the such a norms still apply in shaping the life courses of individuals.

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Davide Bussi: and also the timing of transition and the sequence of the steps that regulate the passage to other 2

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Davide Bussi: can assume different configuration and meetings according to different social groups, different courts, and different geography of the context.

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Davide Bussi: So first of all, we have to define transition to how to do which can be defined by this looking at different aspects. So we can look at the chronological time. So people

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Davide Bussi: became a able to when they reach a particular stage of the physical maturation, or we can look at the historical time. So

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Davide Bussi: when people belongs to a given cohort, or we can look at such a time. So when people acquire a specific social role

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Davide Bussi: we're mainly interested in search of time kind of transition to altitude. In fact, we define transition to identify for significant steps.

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Davide Bussi: The first one is when people leave at school. and the second one is when they find the their first job.

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Davide Bussi: The third one is when they get married, and the fourth one is the when they at their first child. So the first 2 steps

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Davide Bussi: represent the achievement of economic deepness. Why the last 2 represent the assumption of family parenting social rule.

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Davide Bussi: So this is the typical biography. so social norms

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Davide Bussi: shape in the typical of so people, they usually finished school. Once they have finished school, they start looking for the first job.

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Davide Bussi: When they get the first job they get married or start the commutation.

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Davide Bussi: and when they get married they have their first child.

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Davide Bussi: So we are interested in whether this typical biography still applies today, or the order of these steps have changed over the years.

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Davide Bussi: So

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Davide Bussi: our aim is to test the whether transition to adding to is characterized by greater flexibility in the recent courts

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Davide Bussi: a. Or.

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Davide Bussi: if it's still regulated by so your norms that shape the typical biography, or whether the biography individually is a

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Davide Bussi: shaped by individual choice. More precisely, we want to test the post Modernist theory

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Davide Bussi: this theory, according to this theory. society nowadays is, is characterized by the weakening of such a large collective. Ca. such a. The the the progressive loss of the relevance of the subject classes, and so on. So

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Davide Bussi: According to this theory.

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Davide Bussi: people are more free to choose that they won't be all their feet, and a lot of.

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Davide Bussi: So to test the theory, we use the data from Italian Life Survey.

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Davide Bussi: which is a let me do that. So we started in June 20 19, and collecting information on on a wide range of such a teams.

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Davide Bussi: And the sample comprised almost 9,000 subjects

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Davide Bussi: we use to mainly to techniques the Mt. Story. And obviously it more precisely a couple of my, your estimates and the sequence

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Davide Bussi: a couple of my estimates that allows us to compute the meetings at which each step.

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Davide Bussi: each significant step.

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of course.

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Davide Bussi: while the sequence analysis

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Davide Bussi: allows us to have a more broad picture of the transition to other, because with the even Eastern now we can look just to one step at a time.

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Davide Bussi: With the sequence analysis, we can look at it all the possible combination between the steps, and that we can also see all the set, all the steps at the same time. So we have a more broad picture of the transition to output.

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Davide Bussi: We use the optical matching distance to compute the differences between the sequence of the individuals, and also we completed the Iterated Index or Entropy Index, which is a major.

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Davide Bussi: an index index of complexity. I'm sure that show the degree of the similarity, the similarity between the

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Davide Bussi: sequences of the individuals.

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Davide Bussi: So I'm going to show you the our results. We start with the couple of my estimates so as we can see. look at the and the the school. We can see that people

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Davide Bussi: over the year 10 to stay in the school more in the last school than the first course

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Davide Bussi: both women and men.

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Davide Bussi: interestingly, starting from the 1966, 1980, course

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Davide Bussi: men, women

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Davide Bussi: study as much as men, or even more. And this is something that is well known in the Church.

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Davide Bussi: We can explain this by the expansion of the school system. So.

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Davide Bussi: and also by the social norms that

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Davide Bussi: allow people to spend more time in the school system and study more.

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Davide Bussi: The second step is the first job

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Davide Bussi: as we can see here. people find their first job later in their life. The last call. So then, in the first cohorts

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Davide Bussi: Contrary to the end of school, we can see that

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Davide Bussi: men find their their first job earlier than we, and they get. But between you and a man

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Davide Bussi: remain almost the same over the years.

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Davide Bussi: Why, people find their first job later in life. Well.

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Davide Bussi: one explanation can be that the expansion of the is consistent.

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Davide Bussi: So people delay the they exit from the school system, and therefore people start looking for the first job later, and also we can explain it by the worsening of the economic condition, especially in Italy

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Davide Bussi: and the rising of levels of unemployment since the mid seventies.

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Davide Bussi: Okay, now we can look at the first as we can see here.

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Davide Bussi: both women and men, and started from the 56 65 cohorts.

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Davide Bussi: start to to postpone

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Davide Bussi: the time of the first Union

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Davide Bussi:  and also we can see that the gap between women and men reduced in the last court, but generally

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Davide Bussi: men get married or start the commutation later. Then we.

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Davide Bussi: And if we see the if we look at the

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Davide Bussi: first child graph, it's the same feature.

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Davide Bussi: Even here we can see that people tend to

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Davide Bussi: postponed the timing of the first child the starting from the 56 65 coord onwards.

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Davide Bussi: and also here we can see that may men tend to have their first child

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Davide Bussi: later than we met. and also they get between women and men reduced in the.

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Davide Bussi: We can explain these 2 last results by

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Davide Bussi: the fact that

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Davide Bussi: on one hand, people achieve their economic independence later than before, and so

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Davide Bussi: they postpone the the the time when they decide to build a family.

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Davide Bussi: And and also we can explain by institutional constraints like welfare state

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Davide Bussi: which doesn't allow women, especially in some women, to

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Davide Bussi: They are a working life with the parental rule.

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Davide Bussi: So before talking about the sequence analysis, we can

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Davide Bussi: show the order of each step for each cohorts.

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Davide Bussi: As we can see, this is the sequence of the man. As we can see, the the path term of the the sequence is the same in each code.

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Davide Bussi: and they this is the typical, the typical geography apply in each record. So people

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Davide Bussi: man, in this case it.

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Davide Bussi: First they any school, then they start looking for a job. They they get married or start the computation, and then they have a child.

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Davide Bussi: which is what is interesting here is that the gap between they

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Davide Bussi: the achieving achievement of economic independence and the starting of a new family. the gap has increased over the and if we look at at the we

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Davide Bussi: see the exactly same picture with the exception in the first cohort

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Davide Bussi: where we have the homemakers. The first go. It is characterized by the homemakers that.

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Davide Bussi: and and so

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Davide Bussi:  the the, the, the, the immediate age. At the first job is very high, because those are the women who never entered the labour market or entered the very late.

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Davide Bussi: and the whom and we can see the homemakers. I hope you can see the graph the the green area, which is like

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Davide Bussi: red and

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Davide Bussi: comprises the homemakers. So those are the women who

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Davide Bussi: finish school that never found a job.

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Davide Bussi: and yet they get married, and they every day, and have a child and have their first child.

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Davide Bussi: and as we can see

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Davide Bussi: over the course the green area has reduced.

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Davide Bussi: So in the 66, 80 course the the homemakers are

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Davide Bussi: fewer than in the first.

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Davide Bussi: The second important aspect is the blue area. the blue era comprise the people who never transition to others. So those are the people who

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Davide Bussi: never finish school.

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Davide Bussi: never. I mean, there's this study and never found a job.

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Davide Bussi: They never get married, never as a child. and as we can see

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Davide Bussi: over the years, the blue area increase. So

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Davide Bussi: at the beginning of the love life course.

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Davide Bussi: people tend to stay more in the school system. And this is something that we have already

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Davide Bussi: seen before. and the last

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Davide Bussi: aspect is the Entropy Index, which is the green line

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Davide Bussi: in each in each graph the Entropy Index. We can see that the

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Davide Bussi: maximum

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Davide Bussi: of the index is higher for the women than for the men.

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Davide Bussi: This means this that we may have.

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Davide Bussi: I'm more

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Davide Bussi: complex and more women are more dissimilar in their trajectories to to other, to them.

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Davide Bussi: And and the second

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Davide Bussi: thing is that the the Entropy Index reaches its maximum later in the last course. So in the in the last.

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Davide Bussi: So people tend to have that

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Davide Bussi: quite similar sentences at the beginning of their love, of course.

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Davide Bussi: and then the the

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Davide Bussi: they tend to have a more different trajectories to. So this is, can be an in that.

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Davide Bussi: people tend to choose their own geography.

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Davide Bussi: I'm not

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Davide Bussi:  to conclude with them

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Davide Bussi: 2 widely shared. Such a norms still apply the first one, the first union.

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Davide Bussi: Should the crew, when education has been completed and the secure job has been found.

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Davide Bussi: and the second, such a norm is that the first reproductive event should only happen when a reasonably stable union has been

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Davide Bussi: so. thank you for your, I think.

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Thank you.

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So we have a probably just time for 1 min. So any questions or one questions, one questions.

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yeah. Please come out to the microphone because they need to hear you.

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Hello! Thanks for your presentation. My question is what you showed actually only kind of averages, if I understood it correctly, and I wondered how how many people actually deviated from the from the normal order of the events.

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Davide Bussi: Okay, yeah. those are where Median ages. And

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Davide Bussi: if we, if we look at the

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Davide Bussi: the couple of my, all, all the on the core officer. We can see that

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Davide Bussi: the the the the results are the same so for instance, over the live course.

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Davide Bussi: may man, if we look at the first time, for example.

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Davide Bussi: over the life course may always the

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Davide Bussi: have their child later than than than the women. So also, they

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Davide Bussi: 25% of the the the sample in the 25% of the sample.

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Davide Bussi: Men have their first child later than than the the women. Also, if you look at it.

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Davide Bussi: 75% of the simple men that have their child later than the women. This is true for all the 4 steps, so

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Davide Bussi: there are division from the the Median.

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There must be individuals who have their child before they leave school. We see them in class.

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So sorry I can. I can see you very well.

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Oh, oh, sorry! I mean there must be individuals who have their first child before they leave school. We see them in university. And there must be people who.

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okay, yeah.

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Davide Bussi: okay, yeah, that we can see in the sequence analysis. But actually, we can see them because they are very, very few.

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Davide Bussi: the the the that we can see, here are the the typical one. So they, the the people who

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Davide Bussi: find that the finish school, and then they never find a job. They never start a new family, or for reasons we have the people who finish school, and then they they find the first job. But

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Davide Bussi: they don't start a new family. So the the typical we, we can see that because they are very few.

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Okay, so thank you. So we'll move to the next paper. Before we move to the next paper, I will actually make a statement. I forgot as we

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can we?

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Okay, good.

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Okay. So I will actually acknowledge the country, so on behalf of everyone gathers, I pay special. Respect! I pay respect to the spread of the country. Here in Nadam, where we are meeting. I acknowledged the past and present Colon nation.

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war, and Jay Wong as the sovereign people here the cultural knowledge holders who maintain and promote the Aboriginal law, customs and custodians ship responsibility for country and the people that country continue to sustain generation after generations into the future of for all of us. I recognize the important contribution that first and Asian thinkers have made to sociology, including challenges to our dominant way of thinking.

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We thank all the first Nation delegate in the room today for their contribution to knowledge in the display. So thank you. So we'll move to the second. Actually, papers.

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Welcome everybody. Today I want to present this study. I did, together with Louisiana. In this study we looked at the effect of parental socioeconomic status on the piece. Outcomes of students, and how this effect of parental background varies between the different immigrant groups and what we might learn from that.

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So the motivation of this study was basically other studies before have found that even when we control for parental background, there is still a big difference in the educational achievements of migrants from different immigrant minorities, especially Asian immigrants have been discussed, and why they outperform in the piece that has even been we control for their parental background, like

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the key word here is maybe this tiger mom and this basically, there is the theory that culture can explain for this that these Asian immigrant minorities. They value culture values education more than other immigrant groups, maybe, and therefore they invest more in education. And that's why they outperform.

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I think. that this over and performance of immigrant students, and the variance thereof can also, or maybe better, be explained by the different patterns of intergenerational transmission of socioeconomic status.

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 first, we need to look at the theory why socioeconomic status of parents would matter for the educational success of students. And basically, there is 2 pathways in the literature. There is the home environment that means

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parents with high socioeconomic status. They have more resources to invest in their children and provide a better learning environment at their home.

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maybe there's less stress in the family. The parents fight less often, which also makes the learning environment better. There's often. These studies that look at the number of books in a parental home, assuming that that is a good proxy for the learning environment at home, and they find a big effect on the piece of scores with and other learning outcomes.

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And then there's the other pathway. And this pathway is the home external factors. Basically, this comprises all the ways in which Their socioeconomic segregation and how that affects learning outcome status. Students from high status families. They go to schools with other students of high socio-economic status, and they live in neighborhoods with other students of high socio-economic status, and their friends have a higher socio-economic status.

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And we, a number of studies find effects

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erez.

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And so there is indirect effect, basically of the resources spent. And then there is an indirect effect over the segregation. These are the 2 ways in which parental socioeconomic status would influence the learning outcomes, and this is very becomes interesting for the specific group of immigrants, because immigrant group follow also different patterns from like

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the Host country society at large in terms of segregation. So assimilation 3 is the almost well-known in this regard. It basically predicts that when immigrant groups arrive are relatively new to the host country, they are very segregated, and with time they assimilate, and also they become less segregated.

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this assimilation theory also predicts that it's mostly the low socio-economic status immigrant groups that are segregated, and when they advance, and the socio-economic status becomes better, then they are less segregated.

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So this segregation of immigrants, we argue, would influence their learning outcomes. because segregation affects the learning, outcomes over these home external factors that I out earlier.

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and since the segregation is driven by the socioeconomic status of that immigrant group, it is not only the individually held socioeconomic status that might matter for the learning outcomes, but also the so-called economic status of the immigrant group that could matter because it drives so so academic segregation.

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The data we used in the study is the Pisa

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survey of 2,018. It covers 12,600 immigrant students from 64 different immigrant groups ranges around. So from 16 house countries and 40 or rich in countries, I only left in groups with 50 students or bigger. To make these groups a bit better comparable.

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the key variable. Here is socioeconomic status, and we used the international socioeconomic index provided by Pisa, and combined it with the international use of education. To do that we ranked both these variables, the Esa and the years of education within the host country. And then we combined them.

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yeah. And we used the Pv's data package of Mcdonald 2,008 to deal with these multiple plausible values in the Psa survey.

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The method used for this analysis is a multi, our different multi-level models with random intercept and random coefficients. That way we can capture the over and under-performance in Pisa, with the random intercept and the difference in the effect of parental so-communic status on learning outcomes over the random coefficient

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and yeah, the group level is immigrant groups, because we want to compare different immigrant groups.

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So here we go straight to the results. The Y-axis here is the random intercept of these different immigrant groups. After controlling for individual level control variables, we, the parental socioeconomic status, the migrant generation, first, the second generation, the gender and the language spoken at home after we control for all these variables.

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The random intercept still captures a huge differences in this pizza performance.

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So we see, for example, up here Chinese immigrants in New Zealand, Chinese immigrants in Australian and Chinese immigrants in Canada they perform close to 100 points better in the Pisa test than other immigrant groups. On average, even after we control for these individual level variables and on the bottom, we see, for example, Turkish immigrants in Denmark, Somalians, immigrants in Denmark, Iraqi. Immigrants in Denmark.

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oh, yeah. And they perform a lot worse than these groups in the rocks. red circle, and that is even when, after controlling for individual level variables. And we see when we plot that against the average so toconic status of that immigrant group. There is a big correlation at Our Square is 61

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meaning the average social economic status of an immigrant group can explain 60% of the variance in the Pisa outcomes.

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and

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we see the same thing when we plotted against the random coefficient, and that means that the effect of parental socioeconomic status is a lot larger for immigrant groups with high soto-echemic status.

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so can we see why the what could explain these patterns, these patterns and over and underperformance, and the variance in the effects. strength of parental socio-conic status and learning outcomes. We argued that it could be because of socioeconomic sorting.

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So First we look at the average socio-economic status, and here on the Y-axis, the different in school ses between immigrant and native students with similar ses. What we did is we match the immigrant students

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with 5 native students based on solely on socioeconomic status. So the Y-axis is basically the socio-economic status of the school of students. between immigrant and local students of the same socioeconomic background. And we see that the average social economic status of immigrant groups can explain 41% of the difference in the school ses between immigrant and natives that other than that share the same. ses!

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And here. if we

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plot. This difference in school? Ahes! That's like a measure of socio-economic segregation against the random intercept. We see again a really a large correlation of 61%. And we also see our Asian immigrant groups there again on the group, Koreans in Canada or Chinese in Australia.

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meaning that they immigrant from these schools go to schools with a very high Soiconic status higher than their individual socioeconomic status would predict, and that could explain why they overperform.

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So yes, this over and under performance can be explained to a large degree by patterns of socioeconomic segregation that varies between different immigrant groups. And can we see the same for the variance and the association between parental socio-economic status. And in effect

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the answer is, Yes.

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first on the X-axis, the every so screaming status of the immigrant group, and on the Y-axis, the correlation between the parental background and the school so toconic status. And we see that the parental socio-economic status matters more for the school quality. The students go to, or the socio-economic status, immigrant groups.

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And the this is also again, because of different patterns of socio-economic segregation. So when we look at the correlation between the parental socio-conic status and the socio-economic status of the school.

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it can explain a very large share of the random coefficients. So the variance in the parental background effect 60% again.

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So yes, the so to kind of sorting can also explain. the difference in these parental effects. So that brings me to the conclusion, and what we learn from looking at these patterns first, we see that

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it appears that the largest part of the effect of parental socio-communic status and learning outcomes actually happens outside of the home. It's not so much the home environment, but it's more desegregation. The school, the neighborhood the student goes to and lives.

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we see that socio-economic segregation doesn't only happen for so for immigrant groups with low average status. But it also happens for high status, immigrant groups, and there it benefits those immigrants.

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and we can see that these patterns of segregation can actually explain a large share of the over underperformance of students from these groups.

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Yes, so these unexplained differences between these different immigrant groups is largely driven by different patterns of socioeconomic status and culture appears to play maybe a smaller role. Maybe it drives these patterns of socioeconomic segregation.

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But in the end it's again segregation. And who these immigrant students associate themselves to that drive? The difference in the effect of parental background on learning outcomes.

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Yeah, thank you for your attention.

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Okay. any questions.

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please.

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Thank you very much. I'm wondering if you've looked at all at gender differences, because some research would suggest that

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boys are more sensitive to parent ses, and external

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 in neighborhood effects, etc., than girls.  that's a very good point. I actually haven't, because I controlled for it.

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But the the problem is, if I split the sample even more, a lot of these immigrant groups will become too small or disappear, and then the it becomes more difficult to just compare these immigrant group effects because the composition would change, and

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but it's a good point, and I should at least try to do it and see what happens to it. Yeah, thank you. You know.

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Okay.

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so did I understand it correctly that you now find that the 2 of from high school, actually, probably more from an Iss

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from the highest age groups from more from a hypothesis, I guess, as school environments.

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it is

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because it's often the

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no. But I find is that students from high ses immigrant group benefit more from their parental socioeconomic status.

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And so here we see

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the average.

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So we see the average socioeconomic status here of that immigrant group on the X-axis and the Y-axis is the socio-economic status different. the difference in socioeconomic status of the school, between that immigrant student and local students with the same parental background, so to speak. So we see, for example, the chairman immigrants in Luxembourg. They go to schools that have a higher ses.

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and Luxembourg students in Luxembourg with the same parental socioeconomic status, so they're positively segregated in that sense. So they yes.

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okay, I have a one question that.

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in fact, seem to me that you why don't you consider a country as a Thor? You know the lever, so that, for instance, the Koreans in Canada, Koreans in the Us. I'm I'm not sure. Is it because of their Korean war, because they actually went to us versus Canada. So there must be some. Actually, the destination countries has impact on this

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even same as in the you know, the the immigrant group. So I'm curious about how your models actually capture this destination effect, you know, in separate from this, you know the immigration effect.

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Yeah.

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yeah, we didn't. look directly at the destination effect. Here. We we just wanted to compare different immigrants. And the effect the destination has is basically captured indirectly, also because we ranked

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the social economy. Status is in the country. So what we only take into consideration, so to speak, from the destination country is the rank, the social position relative to the Host society that those immigrants share and compare them on that dimension. But we don't look at different school system effect. Here, for example, exactly. Okay.

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I think we have a time for one more question. Yes, please.

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Really interesting presentation. Thank you. So it sounds like you're saying that the outcomes are German, less by culture and more by socio-economic status.

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right between group socioeconomic state is different. So it's not only, but not only the privately held socio-economic status that matters, but also the sochionic status of that immigrant group at large also has an important effect.

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Suggest that scs more so than racing. Ethnicity drives achievement outcomes. So I'm wondering. So I would. I would assume then, that, regardless of the the coast country and so forth that higher seis is going to then be associated with higher performance. How do you distinguish, I guess, between the culture and the culture, not necessarily driving it, but more so. Just have more resources and more accessibility, regardless of your your immigration status.

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Well.

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but we would assume that

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well, we would basically assume that the the resources that an individual holds has the same effect on the learning outcomes of the immigrant students across all these groups. So it's not. And then, when we assume this, and we look at the variation, the the pure fact that we see still this correlation between the average certificonic status of the immigrant group, and

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The the over and under performance to me suggests

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that it is the status 60% of it and not the culture. It's difficult to capture culture directly, and we didn't look at it specifically. We just looked at an alternative explanation to culture, and found that it explains a large share of it. So maybe we don't need the construct of culture for the explanation for that. That's basically the okay. Thank you. So we'll move to the next paper.

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and

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and hell. I'm Frank Pond from the Academy as well, and I'm Frank Pond, from the Academy as well. And today I'm going to talk about the effects of parent children, disappearances in education, aspirations on those, and mental health in Taiwan.

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So this studies examined the impact of parent to children. Descriptions in education experiences on a dozen's mental health in Taiwan. So the first question is, why a dose in mental health.

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first, mental health issues among a doses not only contributed to layer illness and disabilities, but also serves as a significant indicator of future happiness, and the satisfactory satisfactions in adulthood.

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and several factors contribute to mental problems in a doses, including parental styles supporting ankle from friends. Academic performance. Educational experience is not both fulfilled and unfulfilled.

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and the inference of Cram's golden.

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Okay? Then why we focus on the mismatch between parental education expectations and their children's educational aspirations on a dozen's mental health. So why focus on this because many researchers found that positive effect of this distribution

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academic achievement, while the impact on students mental health issues is unclear and it is also unclear in its concept, the relationship with among other aspects and its estimations.

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So here we need to figure out what the concept is about the discrepancies. So there are 2 words that we need to be clear at first. The different between aspirations and the expectations. According to scholars, aspiration means that students are capable. Go.

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and the expectation is that the ghost that needs students efforts to achieve meaning it may not be hard to obtain.

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And so.

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since there is a difference is the layer of types of discrepancies. The diffusion between experiences or exploitation is crucial. Studies have used different types of discrepancies to investigate layer impact on students. Academic performance

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and this study choose the discrepancies between parental expectations and the children's aspirations, because in Taiwan parents often have higher expectations for their children's academia performance and encouragement to work hard to achieve it.

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They may hold higher educational expectations for their children, so we choose at least 2,

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and the formation of expectation or aspirations is a continuous process, and it is highly connected with other aspects. One is, the previous performance will affect both parental expectations, and the children's experiences move over it is also influenced the likelihood of parents enrolling layered children into crime schools.

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This in terms affect students, mental health conditions.

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So past researchers, exams like it's it's explicit disparities between parental and children's expectation or but aspirations, however, to obtain that a better assessment of the discrepancies least study proposed to approach. The discrepancy in 2 component.

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One complement is called the perception components focus on identifying discrepancies between parental expectations and the children's aspirations similar to previous research.

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Second one is the action component. Let's not use cram-school attendance as the action taken to adjust the perceived discrepancies between parent transportations and the children's aspirations.

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so based on the following about the discussions that we, this study applied 3 analytical strategies

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as follows, we used students, previous academia performance to predict their parents, expectations and experiences at last at a later time point. Hence the discrepancy and mental health conditions

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we excused participate who attend the private school and cram school in the first semester of the 7 grabs to ensure that lis fetters did not confound the assessment of this causal relationship. We divide the student's previous performance into 5 strata.

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This allows us to understand fewer relationships between performance, crime schooling and discrepancies, between parental exportation and the children's aspirations.

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Okay, the data we use is the Taiwan education panel studies in 2,001 and 2,003

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tabs is a national representative panel data, and considering 2 of 2 birth co-hosts of students. English studies. We use the Coh of students Bros. Around 90, 90, 87, or 90, 88.

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These students were surveyed in the first time in seventh grade and surveyed after in the nigrade.

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after eliminate, eliminating, mixing data, private school attendance, and the students who, going to crime schooling in the first semester of junior high. The sample size is around 5,000 people.

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Okay, so as to our core

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variable or the dependent variable is the mental health condition we use. The irt-based mental health

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is the variables that I compose from the don't want to socialize with people. Depression and yowling can concentrate feeling a long-term sleep and feeling nonlinear.

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The focal explanatory variables is the discrepancies in parental educational exportation and the children's aspirations. The precision component is the discrepancies between parents is but transportation and the children's experience in nice grades. The action component is a crime school link in ninth grade.

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Okay? So here is the result. And to put a rate. I just only show the significance. One. So I adjust it

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deleting some models. The first one is the 4 months that we consider performance parental descriptions, and transfer attending here, so only the parents higher. The parent's expectation hires land, children's aspirations is significant.

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and the multitude shows the interaction with the actual and the perceived component. And we found that okay.

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only feel. okay. So here the crime schooling shows significantly. Well, it's short, not significant. In a previous one, and we're looking to the Stratas, and we find that in the lowest well, only crimes going in the nice, great, significant, and in the middle one is the crime. Schooling with parental ispetration. Sorry it's expectation higher than children's experience

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is significant. So as the conclusion our primitive research findings is that the findings indicate that if parental exportation were higher than children's aspirations.

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least discrepancies will worse than the students mental health problems, the finding also indicated that the crime. Schooling has a little influence on students. Mental health problems

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is a fast manly appealed. After considering the interaction between actual and precise component.

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however, those effects

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of perceive and actual component will be conditioned by the level of academia performance. When we close the the results at each performance center. So

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as the conclusion, we find that maybe the discrepancy may not be as useful as those literature is argues about, and it needs more delicated research to tour is effect. So.

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And that is my presentation today. Thanks. Thank you.

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You have to actually 4 more minute to present. So really, I show the 5, we can say that one that you want to know is fine for

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all right. And the questions.

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comment.

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okay, so I will

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ask a question while you guys are thinking, I'm just curious about why you use only for pounds expectation rather than aspiration, and you use your children's aspiration rather than expectation, I mean.

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don't you like to be consistent. Let me see whether it's about aspiration or expectations. I'm curious about it, because you don't have measure for parental aspiration rather than expectation.

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I mean. And also, I think, if the discrepancy between expectation and aspiration must be larger for parents, I mean, parents have no idea about what

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there are kids doing like extract kids know, like what their scores like you. So I'm curious about that kind of the difference as well, too.

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Thank you, Professor Professor park that the first question, or it relies to the translation in Chinese, because in Chinese the only term we use is.

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it's the same. It. There is no difference between experiences and expertise. So that's why we need to

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focus on the theory to form those differences. Ok, so as the second correction is the same, and we found that, according to our frequency tables or the descriptive ones.

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each type of discrepancies and have, like around 30% each. So there is not much differences in their parents always have higher expectations than their children's experience. So I think there is not much differences. So yeah.

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okay, this, this.

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I'm going to sound a bit like a broken record. But I'm curious if you've looked at gender differences, because at least in Norway, where I'm from, the

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and expectations are hired girls, and also the mental health issues associated with school and school stress is much higher than her voice.

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Yeah, yes, I didn't show the gender difference. I I do put it into control and the results revealed that the female students have higher mental health problems, relevant male students. And

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a.

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Initially, it's because of the the discrepancy departments, because There are other research indicate that the males. Parliament is the sorry.

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The male. A thousand's problems may be in, you know, misbehaviors like smoking drinkings. But here we only focus on the mental issues. So maybe females, we are worse than, and and I need more time to figure that out. Thank you.

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Any other questions.

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Yes, please.

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I have one clarifying question and one more standard machine. And in the presentation of your data. You mentioned that you had excluded people. If you don't go to high school.

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or later on in your results, the house of inspection might be in basketball or not. just wanted to know how you're working.

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So I've got to know what about liquid like. And the more substantive question is is that.

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And the ability of this student seems to be one of the point of this bit unaddressed in this paper.

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and it's maybe also contributing to the measurement of the brand aspirations which are the benefit of all the students. So how is the ability of the students actually playing a role in your study.

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So About the timing, because we asked students in the layers, second, semester in seventh grade and the first semester in ninth grade.

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And so the creation. Is it kind of like, as you know, to rethink about layer condition in the first semesters? So we eliminated them because of, we think that the crimes going, taking, or just parents send their children to? private schools that may have certain cultural meanings or certain expectations that the parents hold for their children's. So we, it's good to simplify the cause of relationship

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to the second part, questions that because we ask parents and both children about

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what is the highest educational level that you want to attend. So we can do the comparison to finer discrepancies. Okay, I hope that is clear enough. Okay.

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any other questions.

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We have a few more minutes. So any questions.

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But I don't actually ask one more question. If

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I'm actually quite curious about how you conceptualize this cram-schooling as the actual component of this Percy, because it depends on the context. In some country the law tripping kids are more likely to take cram school to actually catch up. But in some other context, actually higher achieving kids actually want to take more cram school to even move forward. So I think it all depends on the context of what the crampschooling actually operates. So

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I kind of know what the

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how it works. Actually, it may works actually in Taiwan. But I'd love to hear how you actually conceptualize a cram school as a tractor component, which is, I think, it's quite interesting attempt, but also seem like a you know, it really holds depends on who actually want to take a cram schools to actually achieve their goal.

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Thank you, Professor Park, to the the reason why I conceptualize the crime school in attending as the as

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component of discrepancies, because in Taiwan the all the parents but believes that hardworking makes you great scores or make you higher performance. So ha! If they have higher or they don't, they will encourage their children to go to crime schooling.

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And The list. Phenomenon is not only in the high ses panelists, but also in low ses families, but with different considerations for a higher ses. Penalties. Lane may encourage you, or when they use the ten-color compensatory advantages that to help them choose as to

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help their low-performed children to perform better, however, in the lower ses families. The logic is that they want the best performed children to perform better.

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So it's the idea kind of like an investment so like in vast layer best performance children. So I use the term because it reflects the parents' expect expectations, and not no matter the if it's patrons, is higher or lower than their children's aspirations. So

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all right. So then, we'll move to the next paper. So please.

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thank you.

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Hi.

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so I'm going to talk about gender now. Surprised.

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and let's see how this works.

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Alright. So my title is sorry. My title is a bit different than what I set out to do when I sent my abstract because it didn't work out this. We thought it would the paper that we were working on in the beginning. So

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this is a paper that and

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 yeah, I'm not gonna say it. It's it's I set out to do a comparison between Norway and the United States. This is a paper only on Norway.

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Unfortunately, anyways, the background is that girls are outperforming boys. In most academic subjects women have surpassed men in completion of higher education. We've seen this already today.

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with regard to Italy. But it's also true. In most of the developed world.

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Girls spend more time on homework, and are more stressed about school than boys, and this gender gap in education is really not well understood in the literature and recently gender gaps in the timing of puberty, maturation, or cognitive maturation has gained increasing interest.

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But we think that in the scholarship about outperforming boys academically, little attention has been paid to horizontal gender. So segregation and education in the labour market, and we think that there are reasons to believe that this

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influences how boys and girls act in school.

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and how much they invest in their education.

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So why gender segregation? Well, at least in Norway most occupations are gender segregated in the Norwegian labour market. Only about 15% of the employees work in what we can call gender balanced occupations

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and women are overrepresented in higher education, but

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as much as one- of female students in higher education are enrolled in a limited number of large female-dominated fields of study, like nursing and early childhood education and social work where there are very few male applicants, and we calculate that that actually three-quarters of the female surplus in higher education in Norway is attributed to the female surplus in these fields of study. It's about a handful of of large fields of study.

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Also, girls have higher relative returns to grades and to higher education than boys, and in Norway this is due in part to the high returns to male-dominated vocational education.

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So our research questions are, how do educational expectations and gendered occupational aspirations contribute to explaining the gender gaps in academic achievement. We also look at whether timing of puberty and planfulness help explain the gender gaps in academic achievement, and whether educational expectations and occupational aspirations contribute to explaining the gender gap in higher education, completion over and above their influence on academic achievement

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so very briefly on the theoretical framework. So we're kind of looking at 2 different things. We're looking at motivation on the one hand, and maturation, on the other hand, and I'm not going to talk much about maturation today because of the time limits some focusing on motivation. And the idea is that educational expectation. Occupational aspirations are very gendered, and this affects how kids work.

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or how they use school

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for their

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future plants. And this is what we're trying to sort of look at, whether we can use Boudon's concepts of primary and secondary effects of class differentials

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for this analysis. And in this case, look at gender differentials instead of class differentials.

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and an important part of this idea is the concept of anticipatory decisions which Jackson Jackson and her colleagues talked about in a paper in 2,007, where they argue that

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it's not only the case that in that case social background affects academic achievement. and also affect choices.

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controlling for academic achievement. But future choices also influence performance. So that's the anticipatory decisions.

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idea.

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So the the the data we use are longitudinal data from youth in Oslo, Norway. It's a cohort born in 1992, and we have 3 waves of data collection from a survey. We have

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data from fall in ninth grade, spring in tenth grade and the winter of year 2 of upper Secondary school. So they've been surveyed 3 times. And then we have linked this data to register date on school outcomes. And we have data until the year 2020. So we follow these kids for 14 years from age 14 to age 28

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and we use a a bunch of nested regression models. And we predict Gpa in tenth grade, using ols and completion of higher education by age 28, using logistic regression with with the Carlson Home and Breen adjustment

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to be able to compare across models we control for social class and minority status, and we test the changing coefficient of interest which is the coefficient for male

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And I'm not going to show you all our findings, just the highlights. But first I'm going to just say a few words about our main explanatory variable. So gender, of course, male is one female 0

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for the motivation variables. We have the gender typing of their occupational aspirations from when they were 15 years old, and it was an open-ended question about

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what they wanted to do for occupation when they become adults, and and we hand coded that into occupational codes and matched it with labor force data for young adults to get the gender composition of the occupations they aspire to.

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And then we have a couple of questions about whether they expect a role in higher education, and if so, for shorter or longer degrees, and whether they thought they would enroll in vocational education, which was us when they were 14. Both of those

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questions, and for maturation we have a measure of relative puberty, development. Self-reported, they are asked to compare themselves to their peers, and to say if they are more or less developed

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from when they were 14? And we have made an index of planfulness based on these questions. You see on the screen 5 questions on how they plan their goals and organize their time.

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which was asked when they were 15, 5 min, all right, results. So just to show you the by their relationships between these. it

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explanatory variables and Gpa, I'm not going to say much about this. But what you can see is that the

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maturation variables, p. Virtual development and planfulness don't seem to do much, especially for boys.

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but that expectations and social class make a big difference. I'm getting back to that a little bit later, I think. All right. So this is the main findings on

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Gpa, and I'm just showing you sort of the

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the male coefficient. This is the coefficient for male. I took out all the other variables from the regression model not to confuse you too much. But what we see is that

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boys have about a third of a standard deviation lower grades than girls, and not controlling for anything in Model 2 we control for social class and minority status, and that reduces the coefficient a little bit. But And when we add

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the maturation variables. It actually increases a little bit. and

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the main drop comes when we add occupational aspirations. So here I put in a dummy, for whether they expect or aspire to a male-dominated occupation.

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and, as we can see in the

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table below, and the changing coefficient is significant, and it reduces the coefficient by 22%.

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I'm gonna just there's a a further reduction of about 10%. But it's not so statistically significant when we add in higher education, expectations.

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All right.

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predicting higher education, completion.

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And here what we see is that model. One has only the background, variables added, and then in Model 2, we add

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Gpa from tenth grade, and it's square term, because it's nonlinear and in the

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table on your right. You can see that that reduces the coefficient for male with about almost 60 so that's a major, of course, influence on the gender gap in

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higher education completion. But when we add in male-dominated occupational as aspirations, the coefficient for male is reduced further by 37%, and

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and it becomes non-significant

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and

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yeah.

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so

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preliminary conclusions. Gender gaps in grades and higher education. Completion are both partially explained by gender occupational aspirations.

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with regard to the gender gaps in Gpa. What matters most, for grades are the background variables, ses, and and minority status, but not for the gender gap. It doesn't make make so much of a difference for the gender gap, but also vocational orientation and higher education. Expectations affect grades

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with regard to maturation. We do have a weak measure of t virtual maturation, so I'm not sure how much emphasis we should put on the sort of null finding here.

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01:04:50.530 --> 01:05:16.900
but also planfulness, planfulness matters very little, and only for girls. And it. It doesn't make any difference for the gender gap. With regard to motivation, it doesn't explain all of the gender differences in grades, but 22% of the gap can be attributed to gendered occupational aspirations, and 32% of the gap can be attributed to all the 3 motivation variables when considered together

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and for completion of higher education, we find that 37% of the gender gap in higher education completion by age 28 can be attributed to gender occupational, gendered, occupational aspirations at age 15. And that's after controlling for Gpa.

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Yes, thank you.

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Questions.

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I make another question. My, I was thinking. Indeed, I have actually questioned.

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you know. Study shows that gender difference actually starts to emerge already at age 5, or even younger than your main interests about expectations seem like a too late for me, like you know the when. In other words, these things may be carried over already that exists at the age of 5, or even before. So in that sense, I wonder what kind of you know how to we actually reconcile these things? That's the number one question

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01:06:18.840 --> 01:06:30.409
number 2. Question is, you emphasize the effect of the occupational expectations, you know, to explain the gender difference. But your mother is a nested, so it may depends on the actually the

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when you, when you introduce this variable, for instance, if I introduce high higher education expected and be per occupational expectation, maybe I actually see it on much larger. You know the explanation powers of higher edge gift expectations. So in other words, I mean, you know, just find, including variables one by.

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you know. Sequence doesn't seem like it. Really explain to me that it really occupation, expectation is a key variable rather, you know, including everything scattered, and show me that that is the a larger, variable, you know something like that. So I love to hear more about. You know the sequence of the the nastyed models that you did in your analysis.

395
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Thank you very much for those questions. with regard to the first question on how gender gaps already emerge early. I think that

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I think that's a valid point, but it also I mean. So I am showing that these variables don't explain everything, but that they contribute to the gender gap and and and when including them, we reduce the gender gap in the sense that yeah. So I think that they they help explain some of the variants.

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01:07:46.010 --> 01:08:10.129
but or some of that the gender gap, but obviously not all of it. And we tried to capture. this maturation issue, which tends to be the explanation associated with the gender gap happening earlier. But but we don't have good enough data to look at at the a longer time frame. Unfortunately.

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with regard to your second question about the nested models, and when I add the variables in in the models. Of course I should have now remembered. what It looked like when we did it differently, because we've tested all sorts of ways of doing it. but I have been advised before to maybe do a hockey decomposition instead, and to look at all the variables simultaneously.

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in one model, and to see their relative importance. And so that's maybe one way forward.

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All right. So question yes, Nicole, please.

401
01:08:52.530 --> 01:08:56.869
So just for clarification. So did you.

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is you now control for previous performance or not, because there was something like and 3 bigger than. And I don't know that I just

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you didn't get it.

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01:09:10.360 --> 01:09:33.499
I'm not sure which. let's see, if was it here or before? Here? Yeah. So we only have yeah, yes.

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control for P. Just for once, because it could actually be that your previous performance steers your. Yes, yes, yes, that's a very good point. Unfortunately, we don't have that possibility. So what I'm sort of ending up doing, I think, is to say

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that I can't say

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clearly, or I can't sort of show the direction of the causal arrow. But I my. let's say my argument is that it's at least a feedback loop.

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So I I think that occupation, occupational aspirations and higher education expectations interact

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with a school performance. But I wouldn't say that it caused this one or the other.

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Okay, so I think that will be the conclusion of this presentation, and we'll move on to the next.

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Thank you.

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In the portal. Okay.

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right?

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Which one I'm supposed to do. and no person is there.

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Hello! Are you there. Chris Mill?

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Okay, let me see whether

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oh, then I should have given more time, for which I feel bad, but we'll come back then, as a kind of the overall, the Kubernetes time. All right. So then, the final presentation of today. Okay, let me back up

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this.

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we'll still have a 12 min. Now. Yeah, sure, I have, to be sure.

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All right. Good morning. Thank you all for coming.

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So this presentation is about the rule of internal migration. So that's migration within countries on the transmission

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of socioeconomic inequalities between generations. And I'll start with a disclaimer. I am not a sociologist. I'm a demographer. That's my first attempt at looking at this topic. So please be understanding today.

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All right. So there's been lots of research on the intergenerational transmission of inequalities, as you all know, and we now know that it's a multi-dimensional process. There are many mechanisms that underpin social reproduction. You can see them listed here. And this paper simply argues that internal migration.

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maybe an additional mechanism. So I'm not talking here about residential mobility. I'm talking about long-distance moves within countries so moves at break

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social networks and social dye. Now we've known for a long time that where one grows up matters, and that's because some regions offer better and more educational and employment opportunities.

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and these social special inequalities are reflected in the fact that they are geographical variations within countries in the rate of social mobility. So, for example, in England.

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People born outside London and the Southeast, who migrate out of these regions tend to have a better or higher right of social mobility than stairs.

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And so in that context, the aim of this paper is to conceptualize the rule of internal migration as a metadata between childhood circumstances on the one hand, and your socioeconomic status in

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later life, as shown on this diagram.

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And so this idea came about by bringing together 3 separate lines of inquiry. The first one is the growing budget of work. In the literature about the transmission of migration behavior from pants to children. We now know that kids who migrate internally, and so People who migrate into living in childhood are more likely to migrate

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01:14:09.360 --> 01:14:22.909
mit Ctl, and once the adult, we don't really know the mechanism. But the hypothesis is that if you learn how to leave and enter a new social context as a child you may be able to deploy these skills later in life, 150.

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And that's the how you can see in purple.

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We also know from the literature that people who migrate internally as adults, they tend to have better labour, market outcomes, higher earnings and higher rights of

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upward occupational mobility. And this is because often people move to what we call escalated regions region that offer better and more educational and employment opportunities. And that's the hour you can see here.

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01:14:50.680 --> 01:15:11.659
Now, the problem is that migration is highly selective. So we've known for a long time that the more educated you are, the more likely you are to migrate. We've done some work a few years ago for the Unesco, and we looked at 80 countries around the world, and we found that, regardless of the region, the level of development, you have this positive association.

436
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And that's the hour here. And that's me. That means that kids formula socioeconomic backgrounds. They're less likely to be exposed to migration in early life, and therefore they're less likely to migrate when they adult and be able to reap the economic benefits of migrating later in life.

437
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And that's the core idea and the proposition of the paper. Now, as I said earlier, there are other mechanisms through which transmission occurs. And so we need to control for this in the model.

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And finally, it might be the case that as a child, you migrate to a let's say, capital city region that offers great educational and employment opportunities.

439
01:15:52.100 --> 01:16:15.450
And then you may not need to migrate as an adult, so it could also be the case that childhood migration promote social mobility without the need to migrate later on, and that the the hour there now the empirical model is a bit more complex because of the data. But in a nutshell the idea of the paper is that kids from the ses they at they face a double disadvantage

440
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being from a low S. Yes, but also being less migratory, which has consequences over the entire life. Of course.

441
01:16:22.660 --> 01:16:39.119
So I test this hypothesis using retrospective survey data from share in Europe. So share collected has a retrospective module. So it was collected twice, I mean, depending on the country. But basically, each person was asked once.

442
01:16:40.280 --> 01:17:03.179
and in the in the paper, I have 16 countries because some countries didn't collect didn't collect parental education, all right. So life histories were collected on 4 domains. So here we focus on education. But we also use migration from life histories from the other modules as control variables.

443
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And so the sample here we focus on individuals born before 1,965. So the many baby boomers. And we look at the whole. My Life! History from birth to age, 50

444
01:17:16.760 --> 01:17:19.840
for a sample of over 23, 7 people.

445
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all right. So key variables of interest. Socioeconomic status is measured at 3 points in time so adverse. We have parental education at H. 10. I use a principal component analysis on these variables to create an index of socio-economic status based on the number of rooms, facilities, books.

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and occupational prestige of the main bread winner. And then, again, I do the same thing at age 50, based on highest educational attainment, occupational and prestige and home ownership. We don't have income here because income is not collected retrospectively

447
01:17:55.650 --> 01:18:11.610
in terms of migration. We look at, we define migration as a change of next 2 region. So to the course that's long distance migration. So, for example, migration between region in France. That would be, and we have rural and urban status of each place of residence.

448
01:18:12.280 --> 01:18:28.630
So here are very quickly some descriptive statistics. You can see that in our sample. migration is the rare event. And this we've known this for long time. So only 10% of European baby boomers migrated in childhood and about 23% in adulthood

449
01:18:28.980 --> 01:18:38.909
and finally control variables. We have gender bursts, cohort, and for models in adulthood. We also have whether the person ever married number of children, personality traits

450
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which are important because they can confound some of these relationships, and we also include a country and wave that fixed effect.

451
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So some descriptive statistics. So here we look at child socioeconomic status.

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One on the x-axis, one being the least advantage and and the most advantage. And on the y-axis you have the average number of migration in childhood, and you can basically see that the top 2 desiles are much more likely to migrate in childhood.

453
01:19:08.720 --> 01:19:24.180
Here we are looking at the association between the number of childhood migration on the X-axis and the number of adult migrations on the Y-axis, and you can see this idea that you know. The more you migrate you as a child, the more likely you are to migrate once you're an adult.

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and finally, the association between the number of internal migration in adulthood again, your socioeconomic status at age 50, and you can also see again he, this kind of linear association.

455
01:19:37.360 --> 01:19:49.600
So the next step was to do this in a regression framework, and because I assume that migration is a mediator. I use structural equation modeling. That's a system of 7 equations that I'm not going to show today.

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So I just represent the model visually.

457
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all right? So so we start with socioeconomic. Well, the end actually variable is the so you can mix it as at age 50.

458
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it's a product of your migration history, but also your socioeconomic status at age 10, and where you were residing at age 18, whether it was rural or urban. As I said, if you grew up in a kind of urban environment, maybe you don't need to migrate to achieve this upward mobility, and that may influence your migration history.

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01:20:24.820 --> 01:20:41.909
So the key argument of the paper is that your migration behavior, adult Tod, is determined by your childhood migration experiences. So here, because of the data we distinguish between migration from age 10 to 17, in migration from birth to 8, 9,

460
01:20:42.400 --> 01:20:45.679
which also influenced where you were at age 10.

461
01:20:46.830 --> 01:20:56.510
I argue, that's a product of where you were born, whether it's rule or urban, and the socioeconomic state is at birth, which is your parental education. And finally.

462
01:20:56.630 --> 01:21:01.859
you know your socioeconomic status at birth influence, which is your economic status at 8 10.

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So now I'll just show you very briefly some regression coefficients to look at the relationships and the odd ratios. Here I am showing you

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all right. So here you can see that we find a relationship between your migration history and adulthood and your socioeconomic status later in life.

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And a positive association between your migration experience as a child and your migration experience as an adult. What's interesting here is that migration experience in in adolescent seems to be more important, and it could be either because the latest experience is more relevant or perhaps adolescent or more aware of these migration experiences than young children.

466
01:21:49.190 --> 01:21:57.270
But what's important here is that your parental socioeconomic status really shapes whether or not you expose to migration early

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in life.

468
01:21:59.320 --> 01:22:15.100
and then finally, migration is also important, because if you migrate it as a child, you are likely to live in an urban area which has also a significant impact on your socioeconomic status. but measured at age 50.

469
01:22:15.600 --> 01:22:43.060
Now last a slide of results. I was also interested in thinking. Does the number of times you matter as a you migrate as a child matter, and you can see. So here on the X-axis you have the number of childhood migration, and then on the Y axis the odds, ratios of migrating as an adult you can see that the confidence intervals overlap, and they're very large, because very few children migrate more than 4 times.

470
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But basically here this is suggesting that what matters in childhood is being exposed to the challenges and benefits of migrating, at least once more so than the number of migration per se.

471
01:22:55.070 --> 01:22:57.440
so briefly to wrap up.

472
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So this seems to suggest that migration contributes to the transmissions of inequalities between generations and from a policy perspective. I think what this means is that we really need place-based interventions in the most disadvantaged region that focus on this low ses, because this is suggesting that these children are likely to be stuck

473
01:23:17.920 --> 01:23:44.349
in place. And if we think about, you know, industrial, restructuring and so forth. We need to make sure that these people have the ability to move if they need to like later in life. And it's really important, because actually, most of the work I do doesn't relate to this, but trends into the migration. And we actually know that the level of migration has been declining, particularly in Australia and the Us. There are few and fewer people who, migrating between states.

474
01:23:44.380 --> 01:24:00.290
But what's really noticeable is that this decline in the level of migration is particularly stronger for the, you know, low ses, particularly in the United States people, low-income part-time workers, African Americans. And that suggest that these kids.

475
01:24:00.370 --> 01:24:09.790
now of less export to migration, much much more so than admin, and children in the Us. Who are still migrating at a similar rate as they were in the 90 s. Or eighties.

476
01:24:10.320 --> 01:24:32.039
Now, from a theoretical perspective in migration, this really reinforced the need to look at migration as a trajectory that unfolds over the entire life course rather than at the last recorded migration, as we often do, for example, in the census. Did you migrate last year? Yes or no. We actually need to look at the entire life course trajectory

477
01:24:32.330 --> 01:24:46.180
and actually, the paper was published last week or the week before last in demography, so you can have a look at it if you have any more, if you want to have more details. But I'm equally happy to answer any questions. You may have. Thank you.

478
01:24:50.670 --> 01:24:52.010
Okay, question

479
01:24:56.100 --> 01:24:58.120
questions. Yeah, it's an

480
01:25:04.260 --> 01:25:21.710
So we, this is a vision of our productivity in vibration. And I've seen that you want to controls. Do not think that you have sufficiently take care of that. In those cases

481
01:25:21.820 --> 01:25:30.060
I was just that people were even more ambitious, would be more like the target.

482
01:25:30.170 --> 01:25:39.009
A mission may it may not be a much reincarnated with, as it is, both in your parents and in yourself

483
01:25:39.210 --> 01:25:42.770
family lodged by the

484
01:25:43.870 --> 01:25:46.839
from her, and not a remnation.

485
01:25:47.110 --> 01:25:50.610
you know. That's an excellent question. So a couple of points about this, so

486
01:25:51.180 --> 01:26:09.589
I couldn't. The only thing I could do which I did is this personality traits controlling for them? But I understand I agree. That's not enough. And I don't think I can do more with this particular data set. But recently I reviewed a very nice paper that dealt with this issue, and the way they deal with it was to look at force migration.

487
01:26:10.040 --> 01:26:31.870
And actually they looked at forced migration, and they found the same relationship that kids who come from backgrounds or they were involved in forced migration. They still have higher odds of migrating that people who were not, and supposedly in this forced migration. You get rid of this endogenity and selection issue. You kind of talking about. So in this paper? I I can. I can do much more than this.

488
01:26:31.870 --> 01:26:44.519
And actually, I guess my ambition was more than thinking, Ok, that's the most robust model kind of putting the idea out there that if we look at these different trends of literature, we can come up with this

489
01:26:44.810 --> 01:27:02.470
a solution that and done this solution. But this proposition that internal migration may contribute. But I agree with your point. I think we need to do you know more work on this problem of selectivity, and how we explain the transmission of migration from parents to children? Is it the migration, the lived experience itself

490
01:27:02.540 --> 01:27:10.500
or other factors that your parents transmit. So I think forced migration is one way to deal with this issue.

491
01:27:10.980 --> 01:27:13.850
but yeah.

492
01:27:13.860 --> 01:27:14.870
this

493
01:27:20.020 --> 01:27:23.170
in the

494
01:27:23.680 --> 01:27:26.619
the movie is also scattered. Because

495
01:27:26.640 --> 01:27:32.270
yes, yes.

496
01:27:32.300 --> 01:28:02.030
that's interesting. So in the in the original manuscript, I actually looked at gender, and then the reviewer said, No, there is too much to get rid of it. So I got rid of it. So I can tell you the results, because that's exactly what I was expecting that a woman particularly dislike baby boomer cohort, they were more likely to be tied migrants for their husband, and so I was expecting that it will be less beneficial for them to migrate in adulthood. And so what I found is that a male benefits the most

497
01:28:02.030 --> 01:28:28.959
female? Followed by female who's never had children, so presumably they didn't. They're not, you know, in their relationship. Don't follow their husband. But I also found that you know woman in relationship and with children who migrated, they still have a it's beneficial in a long time, and I think it's because in the long term there's some adjustment mechanism that happens as well. But I hadn't thought about flipping this around and seeing that it could be a force migration as well.

498
01:28:29.230 --> 01:28:34.519
But that doesn't solve the problem of parents to children transmission. So yes.

499
01:28:35.550 --> 01:28:43.660
okay, thank you. So we'll win the all the presenters, the the, and they will like to start getting more questions. So please. You know

500
01:28:43.710 --> 01:28:45.959
this in it in front of the

501
01:28:45.970 --> 01:29:05.259
all those speakers. So I like to take actually the question for each presenter first, rather than having all the question to one you know the presenter. So if you have actually the question for someone asked to the same presenters, hold on, after, you know to for the second round of the question.

502
01:29:05.410 --> 01:29:07.309
So any questions

503
01:29:08.500 --> 01:29:18.410
I know that you may not remember what the first paper was, but remember, you know. but still it's open to everyone.

504
01:29:19.860 --> 01:29:32.499
So you know, I have a question for the final paper, so that, you know we can actually begin the conversation. Seem to me this migration seemed like a quite heterogeneous actually banned in this case, like, you know, the

505
01:29:32.500 --> 01:29:52.060
you can move because their counts may want to find a new school, or they can move to another state because they broke up. And you know the that's what the kind of you know the importantly band for the younger. You know the special, your childhood, you know, to moving to another. You know the play. So I'm just curious about like, you know the

506
01:29:52.060 --> 01:30:00.990
I know that probably this actually has a limitation of the data, but still curious about whether you could look at, for instance, like it was related to the

507
01:30:01.600 --> 01:30:25.950
you know, the divorce of parents, so that they actually moved to another schools? Or is it because they actually move out for the better school? You know the to something like that, so that you know whether you can actually look up to some nature of the migration. Otherwise it's really seemed to me so somehow quite understand, hard to understand that only a highly educated or high as parents are more likely to move to another

508
01:30:25.950 --> 01:30:36.399
place. But it may also depends on the you know the country right in some country. Maybe you see the upper the case rather than that direction. So I love to hear more about the context of migration

509
01:30:36.710 --> 01:30:38.210
for the

510
01:30:38.970 --> 01:30:40.870
for the

511
01:30:41.370 --> 01:30:48.969
all right. There is a question I know, I know. Right. So we'll close. Then

512
01:30:53.040 --> 01:30:54.319
I'll take close.

513
01:30:56.390 --> 01:30:57.890
just close

514
01:30:59.970 --> 01:31:03.440
and to session display.

515
01:31:03.920 --> 01:31:05.010
And June.

516
01:31:07.400 --> 01:31:11.040
yeah, I will stop here. Yes, we have a

517
01:31:11.520 --> 01:31:22.690
David here is for the presentations. So any other questions. So yeah, don't answer that question immediately. I will gather a few more questions for some other pretenders. Yes, please.

518
01:31:26.460 --> 01:31:27.600
Hi.

519
01:31:27.640 --> 01:31:45.269
I have a question for Liza. I was wondering you mentioned the planfulness index that was had like a very weak effect, and mostly on birth, and I don't want to over read about on this. But I was wondering on your take, because for me it speaks a little bit about the idea of

520
01:31:45.330 --> 01:32:02.010
what privilege is, and the idea that if you don't have if you're under privilege, you need to plan. And now planning has a very detrimental effect. So I was also wondering if you look at it in interaction with ses, and maybe there you could find even more of an effect on the gender gap.

521
01:32:02.200 --> 01:32:03.040
Thank you.

522
01:32:03.170 --> 01:32:08.190
Okay, so one more question. Then we'll let the speakers actually respond to the questions.

523
01:32:10.070 --> 01:32:14.870
especially for those who have not asked the question, please, it's your time. So.

524
01:32:17.320 --> 01:32:24.230
Ok, if not, then I will let them speak while you guys actually are thinking about the next round of question. Yes, please, for the migration, for

525
01:32:24.760 --> 01:32:36.150
thanks for your question. So I think what you are talking about. You know, mobility link to a divorce, parental separation, or just changing the school is not about residential local mobility.

526
01:32:36.260 --> 01:32:41.929
That's why I default migration as a change of nuts to region which in the Us, you know.

527
01:32:42.470 --> 01:33:00.140
would be a migration between State. Probably. So I'm not looking at this residential mobility which is often actually actually said to disadvantage. So this local moved off and into, you know, a period of an employment housing instability, particularly for people who migrate a lot of times in their in their childhood. So here

528
01:33:00.400 --> 01:33:12.579
I really wanted to look at this long-distance migration, which is, I think, qualitatively and quantitatively, very, very different.

529
01:33:12.630 --> 01:33:40.259
And yes, so that's why I didn't look at it now in terms of looking at the motivation. Why, parents migrate to that's actually not possible, you know, respectively, to the like this. And I guess that's yeah. One of the limitation of the of the paper. But I think it's really important in general just to make this difference between residential mobility. That's very local. Then this kind of internal migration that's long distance, and that's really different in in its motivation and and a consequences.

530
01:33:40.980 --> 01:33:41.870
Okay.

531
01:33:42.120 --> 01:33:42.890
next one.

532
01:33:44.460 --> 01:33:45.710
Thank you.

533
01:33:46.060 --> 01:33:46.830
Okay.

534
01:33:50.680 --> 01:34:05.370
thank you for that question. you asked about planfulness and how it's related to privilege. I think. First of all, I actually wrote a paper many years ago about ses and planfulness that

535
01:34:05.740 --> 01:34:26.310
yeah, that was never published. But anyways the idea was that that there's a theory out there that there's there are gender differences in the maturation of the executive function in on average, in boys and girls, and that's why we were looking at the gender differences in planfulness.

536
01:34:26.310 --> 01:34:36.710
and as part of the idea of maturation as in fact, it affecting the gender gap. But you are right, and I think it's a very good point

537
01:34:36.710 --> 01:34:43.839
to look at the interaction between plants and this and ses, and to actually, maybe even

538
01:34:43.890 --> 01:34:47.220
make yes, yes, more

539
01:34:47.260 --> 01:34:51.480
of a focus in the paper, I think. Thank you.

540
01:34:52.500 --> 01:34:57.610
Okay, so any other questions for any of the speakers.

541
01:34:59.630 --> 01:35:00.930
Yes, please.

542
01:35:02.320 --> 01:35:06.549
Okay, David, are you there

543
01:35:07.780 --> 01:35:26.230
right right in the middle of the night there, so I'm sorry for keeping you awake. But I did have a a question about that age of having children in your last cohort, that you showed it said that women were having their first child. I think, when they were 36, or something like that.

544
01:35:26.230 --> 01:35:54.390
and I know that that the age of family formation in Italy is very old, but that seems a little bit too old, and so I wondered whether or not it was something like an artifact to the fact that that last cohort finishes with people born in 1,996. And these people probably aren't even at that age yet. And so you can't quite measure it properly. So I'm just curious to know how you found that in relation to what the actual age is in the general population, does that make sense?

545
01:35:54.490 --> 01:35:56.030
Davide Bussi: Yeah, in the

546
01:35:56.080 --> 01:36:10.080
Davide Bussi: in the couple of my estimates the last call over to, in fact, doesn't have the Median age of the first child in the middle age of the first, you know, for the last court, because people were too young to

547
01:36:10.160 --> 01:36:16.900
Davide Bussi: I have a a child, or or or or better, few people a and a child, and

548
01:36:17.020 --> 01:36:21.999
Davide Bussi: and to get married and and when we completed the sequence analysis.

549
01:36:22.230 --> 01:36:26.709
Davide Bussi: we just consider people who had at least a 40 years old.

550
01:36:26.720 --> 01:36:30.819
Davide Bussi: So we are able to look at the

551
01:36:31.220 --> 01:36:34.619
Davide Bussi:  the entire life course of people, people.

552
01:36:34.680 --> 01:36:37.369
Davide Bussi: people who were too young. We didn't consider them

553
01:36:41.290 --> 01:36:45.580
okay. Any other questions. Further comment.

554
01:36:47.710 --> 01:36:49.750
Yes, please come out.

555
01:36:54.470 --> 01:37:10.570
It's just a little to know for me. My question is for Lisa, and thank for the very interesting presentation. I'll be sounding like a program record. And instead of gender I will be saying, and if you thought about whether the effect of gender would also be different. For people with a migration background.

556
01:37:10.650 --> 01:37:17.809
for instance, women from a migrant background might be different study, Georges, than the native.

557
01:37:18.230 --> 01:37:22.090
What would you think that that interaction or that could that look like?

558
01:37:28.410 --> 01:37:38.230
Thank you. for that question. So I do have, in my models. A dummy for migration background, which is

559
01:37:38.250 --> 01:37:57.329
Whether or not they have 2 parents born abroad, so regardless of whether they themselves are born in the country or not. And we have tested whether there are differences according to migration background. And there isn't. There isn't

560
01:38:00.640 --> 01:38:03.590
okay any other further questions.

561
01:38:07.450 --> 01:38:31.260
Actually, if not, then we'll just close, so that you guys have a little more conversations after you know the presentation with the speakers and you for next stage next session. You can have one more coffee, remember, I see 20 has a business meeting this evening at 7 30 here, so I' to see you all back at that time. So thank you for the presenters and audience. Thank you.