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Good afternoon, colleagues. Thank you for staying. Back to the until the very last day of the conference.

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I'm sure you have enjoyed. The exciting week we had so far in Melbourne even the weather is not that well coming but you know we know you are well comed.

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I before we started the session today I just want to acknowledge the country on behalf of everyone gathered I pay respect to Sorry.

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Yep. Okay, yeah, got it. Sorry. Very sensitive.

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I pay respect to the spirit of the country here in Narum where we are meeting I acknowledge the past and present a coordination or on Dangeri, boyfriend as a sovereign people here, the cultural knowledge holders who maintain and promote the NARA Aboriginal Law, custom, and custodian shape responsibilities for country

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and the people that country continues to sustain generations after generation into the future for all of us. I recognize the important contribution that First Nations thinkers have made to sociology including challenges to our dominant ways of thinking.

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We thank all First Nations that delegates in the room and in this conference today for their contribution to knowledge in this display.

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So. Nice start. Okay. I think today we are going to have 4 presentations because the session, the overall session time is relatively long.

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We planned presentations plus discussant. I think I will give our presenters more than 15 min to present the paper and then we have the shorter discussion at the end.

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Okay, so we're going to have the presentation in the order of the schedule. The first one is on race.

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And or discriminate my immigrant status as a meta-analysis I think by LinkedIn, right?

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So he, I need to click how I click, click, click. Yep. So So.

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Alright.

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So we can have a couple minutes after your presentation. If anyone have a burning questions and then we can have a longer discussion after that.

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OK? Thank you.

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Okay, yes, thank you and thank you for sticking around for this presentation. I'm a Lincoln-killian of Northwestern University in the United States and the paper I'm presenting is joint work.

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With Arnfin. Vote in the worse his name here. There it is, I guess. Didn't mean to set it up as an animation where I press click to get it to appear but who's at the University of Oslo.

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So what I'm gonna talk about is, the discrimination against immigrants. In particular and discrimination against immigrants is you know in immigration theory is largely discussed as being part of kind of the modes of reception that immigrants experience in their host society and is widely recognized as an important factor that influences immigrating corporation and of course basic kind

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of norms of fairness in society. It turns out there there aren't that many studies that specifically examine discrimination against immigrants.

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There are a few as I will discuss. And so I'm gonna try to establish kind of some new facts about discrimination against immigrants looking across a broad sample of field experiments of discrimination from 13.

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Different countries. So, so one question I'm gonna ask is just how much discrimination is there against immigrants in Western labor markets?

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Looking at all at immigration receiving countries. And the and then I'm also gonna ask the question of what drives discrimination against immigrants.

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Of kind of what's its nature to the extent that we can tell from certain field experimental results.

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And of course, that won't resolve all questions about that, but it will provide some insights.

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So most empirical work that looks at immigrants has examined things like economic outcomes using demographic kind of observational data of immigrants like group unemployment rates over immigrant generations.

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And that does provide some. Indirect evidence about discrimination but because when you observe an outcome like unemployment rate of a group There are several possible explanations of it.

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It doesn't provide kind of direct evidence about discrimination that you would ideally want if you're interested in that topic.

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Particular. So there have been a small number of studies as well as just a general movement toward studies that use field experimental methods to try to assess discrimination.

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And generally these studies have worked kind of through 2 in 2 ways. Some of them have been where people send out a resume.

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With a signal that indicates racer ethnicity most often a racially or ethnically typed name.

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And then they, so these are falsified applications made for real jobs and then they see how many positive responses they get.

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And they have both majority and minority applicants usually like I said signaled through the name and they use that as a measure of discrimination.

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A smaller number of studies have also done the same thing in general but using face-to-face procedures where they effectively hire people to portray job seekers.

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Like my colleague David Pager hired students at the University of Wisconsin-milwaukee to pursue to play job seekers and had them go out and apply for jobs, giving them kind of identical background characteristics so that race or ethnicity was the main thing that distinguished them.

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So a few these studies have actually been conducted quite a few around the world now and the a few field experiments have have considered the question of whether being far and born as opposed to native born is a separate source of discrimination.

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And the handful of studies that have looked at this have come to what I think you could call highly variable conclusions so they don't lend a highly variable conclusions so they don't lend themselves to any kind of simple summary.

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A couple of studies that were conducted in Scandinavia found no more discrimination against foreign born. Minoritized groups then native born.

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But then there are other studies which have found different conclusions in different places, a study of 5 European countries that did field experiments and included information on foreign born or native born status found that there was a disadvantage to being far and born, but it was small.

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And then there's a recent study in the US. That looked at foreign born and native-born Latinos and seemed to find that foreign born Latinos received far fewer invitations to go to the next step of the job process than native born so it made it look like there was a big effect of being far and born.

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Okay, and sort of before I get to the empirical results, I think makes sense to think a little bit about what are the potential mechanisms.

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You know, why might immigrants be discriminated against the labor market? So one kind of possibility is that there's what you might view as.

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Direct discrimination based on on foreign born status okay and I think that this type of discrimination maybe has gotten more plausible if you think about our kind of political climate that in both Europe and the United States there has been the rise of far right political parties in some ways the American Republican parties, now a far right political party.

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And they have anti-immigration is kind of a very central part of their agenda. That so there's been a growing politicization of immigration.

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And if that has the effect of making employers decide that they don't want to hire immigrants. Were employers concerned that if they hire immigrants that their customers may be mad about it then they may choose to be not higher immigrants, right?

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So, so there's reason to think that there might be direct discrimination linked to foreign foreign status, especially because of the politicization of this topic in many countries.

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And there is some evidence actually that indicates that things like political events can affect hiring decisions that employers make. A few studies that have found.

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Good evidence when study found that right after Donald Trump was elected, the proportion of job applications that were made to, this was Somali immigrants in Minneapolis.

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All of a sudden went down and it looks like that was a result of the fact that he was unexpectedly elected in 2,016 likewise i wrote a paper with john lee which found evidence that there was a significant increase in hiring discrimination against people from the Middle East.

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In North Africa in several countries in Europe, which seem to be linked to the 9 11 attacks or right after that.

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So political events, it seems like maybe can't have effects on how employers make decisions, which makes us all seem more plausible.

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A second possibility is that immigrants in a way are disadvantaged by their cultural kind of structural positions in the labor market.

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So if you have foreign educational credentials rather than domestic credentials, those may not have the same value.

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On the domestic labor market and there's a nice paper by Zang and Shia that shows for Asian-americans the United States this is an important component of inequality is lower returns to foreign degrees it's also possible there are legal issues regarding right to work.

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That are one of the reasons why employers could be reluctant to hire and also issues of concerns about language for efficiency and cultural familiarity.

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So these last 2 issues though are going to get a little bit bracketed in my empirical results.

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And the reason for that is because most field experiments have discrimination to have been set up to try to signal rather clearly to employers that the applicants have strong native language skills, strong skills and strong language skills in the country that they're living in.

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For instance, by making sure that they have very clean cover letters. In in the language of the country that the application is going on in and in many cases all by signaling that the person immigrated it in early age, so they should have a strong language skills.

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So those last 2 aren't really gonna get very much attention in my presentation because of field experiments have kind of been set up to not look at that, those specific things.

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Okay, so a third possibility of course is racial and ethnic discrimination. That a lot of immigrants to Western countries now are non-white and even when they are white there's some degree of ethnic difference.

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And if there is racial and ethnic discrimination, then that of course can be something that disadvantages immigrants for that reason.

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And there's a large body of field experimental studies across many countries in the world now. And they almost always find that there is discrimination against on the basis of race and ethnicity.

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And There is some argument that maybe some of those studies have been in part capturing immigration status.

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But that's something we'll be able to look at through this analysis. The and you know racial theories and segmented assimilation have emphasized kind of the persistent effects of race.

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Over time and that would be consistent if there with the possibility that there's strong racial or ethnic discrimination that's disadvantaging minorities, especially those who do not have European origins.

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Okay, so the the data that we're going to use is evidence on discrimination and hiring from field experiments.

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So as I described, these are experiments in which falsified applications are made for real jobs. And race and ethnicity is most often signaled by name in some face-to-face studies.

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It's signaled by personal appearance of course but those are only maybe 10% of studies or a little bit less than that are face-to-face and those are mostly older studies too because nowadays with the internet it's become quite a bit easier to do a correspondence or audit experiment online and with resumes.

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And so they're increasingly few that are done face to face. So we we made an effort as part of a some other papers I published to find code and meta-analize the results.

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Of all field of experimental studies that had been done in the world looking at race or ethnic discrimination and hiring.

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So I'm going to use a subset of that data. And so we chose out of that to look here at countries where we had enough field experiments that included signals of foreign born status to be able to make a reasonable contrast.

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With native-born people and that was 13 countries in total. Countries are they're all in North America, Western Europe, and it also includes Australia in total the data is going to be from 139 separate field experiments of discrimination.

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And those include 244 separate estimates of discrimination against racial and ethnic minority groups. So the way that these applications signal whether or not someone is foreign born or native born are typically a few different ways.

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The one way is they some studies directly put place of birth on the resume, which you know at European labor markets, some European labour markets, it's fairly common to have your place of birth on your resume, although not in all of them.

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So some studies directly just indicate place of birth that way. Some studies have like a cover letter statement of some kind that indicates things about immigrant origin.

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So it says something like. You know I immigrated with my parents to Norway at age 6 from Pakistan something like that so it signals very directly a kind of immigration background.

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And indirectly too from places of education on the resume, some of them signal. Foreign birth status.

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So, you know, in some cases they might list even early schooling, but they'll list the place of education to give a signal about where the person was.

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Okay, so here's just kind of some descriptive results about the data that we have. A 140 the number of farm born effect sizes you can see that most of the effects sizes you can see that most of the effect sizes are for native born and then effect sizes.

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You can see that most of the effect sizes are for native born and then a smaller percentage are foreign born.

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Listed there and the number of effects and studies so affects our discrimination estimates against a particular racial or ethnic group.

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And many studies include more than one racial or ethnic target group that could be discriminated against like a study in the US that includes say blacks and Latinos so then that would have 2 effect sizes which is why they're more effect sizes and studies.

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But we cluster our results at a study level to account for that in calculating things like standard errors.

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So the outcome that we're going to use this thing the discrimination ratio, which is the proportion of callbacks that are received.

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By members of the white native born majority as opposed to the minoritized groups. That are in the study.

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And so it's the proportion of callbacks. That are received by the white majority divided by the proportion received by the minoritized group that is being examined in that case.

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So if you get a discrimination ratio of one that's like no discrimination, 1.5 says that white natives are getting 50% more callbacks in that minoritized group and higher numbers indicate more discrimination.

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OK, so then we use these kind of methods in the meta-analysis literature, you know, a well-developed in the meta-analysis literature, you know, a well-developed statistical literature on how to analyze statistical literature on how to analyze groups of results, especially groups

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of experimental literature on how to analyze groups of results, especially groups of experimental results. We run a kind of meta regression where the outcome is the log of the discrimination ratio and that's a function of like a key variable here would be whether or not the the minoritized group is foreign born or not, whether that's signaled in

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the applications or not. And then we also have controls for things like country. For the minority group. And for a series of other characteristics of the jobs and the and the hypothetical applicants.

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And then there's kind of a variance structure which has both a kind of study level variance component as well as what you might think of as a individual application, a variance component part to it.

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So the additional just to mention the additional control variables, here's a list of the basic ones we include, whether the studies face-to-face, or the year it was done, the education level of the applicant in the study, the gender of the study applicants and then controls for some things about the occupations that were applied

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for. In that particular study. We coded the cut from detailed minority groups to these kind of larger groups that work have continental origins to kind of facilitate analysis so we have African black European white Middle East North African origins Latin American Hispanic or Asian And okay, so now to get into the results.

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So the. First I'm just showing you the foreign kind of effects, foreign birth, foreign education, foreign nationality.

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The first 3 models just include foreign birth. And you can see it's generally not statistically significant.

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So we actually didn't find that being of foreign birth itself. Was associated with experiencing more discrimination or statistically significantly more discrimination.

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However, we did find that if you had your highest educational credential from a foreign place, then you got fewer callbacks.

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That was a significant form of disadvantage. And you received you know what oh people who had their their domestic educational credentials got sort of around 40% more callbacks so a significant difference.

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For foreign nationality we found that was a form of disadvantage as well if you're not a citizen of the country even though you have legal right to work that was a form of disadvantage and getting called back so that it wasn't as big.

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But we did come out of this with the conclusion that it didn't look like there was a lot of negative impact of being farm born per se.

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Now there was also the race or ethnicity effects in that model and I'm showing them here.

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They were not shown on the previous slide. And this seemed to show that, you know, there was, as a lot of other studies have found, significant penalties to being a racer ethnic.

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Member of a minoritized group. So the kind of intercept part there is actually in a way comparing negative-born weights to the African black group and on average if your native born whites are getting around 40, 45% more callbacks than people who are African black and background.

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European ethnic group members, the negative there is meaning that they actually have a penalty too but the penalty is not as big as it is.

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For most of the non-European groups in terms of getting callbacks. And then, the other non-winning groups actually were not statistically significantly different than the penalty for the African.

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Black group. So this looks like there is significant discrimination based on race or ethnicity, including when we account for foreign-born versus native born status.

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So another way we can actually do the analysis too is we can do a within analysis. So what we did there is we take because we observe some studies that had both foreign-born and native-born.

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In, you know, applicants in the same study and reported outcomes separately for those 2 groups. We could put in something that's kind of like a study fixed effect, right?

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Or we could contrast in a way do within Khan study contrasts for those studies that had that particular design characteristic.

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Now the trade-off in a lot of ways that controls for a lot of stuff that you want to control for. That's great.

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The downside is there are many fewer studies that we can use because most of the studies don't have foreign born and native born with separate results in the same study.

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So our end goes down to 9 from like whatever, 130 something. But still we actually get fairly similar results to what we got before, which is that once we account for foreign education, it doesn't look like there's a foreign birth penalty per se, but there is a disadvantage of foreign education.

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So this kind of puts together this set of results to give you an idea of the size of disadvantages relative to someone who's a white native.

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Which would be where the red line is. And then further to the right is more disadvantaging callbacks, right?

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The percentage disadvantage of callbacks relative to someone who's white native in background and who's a white member.

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Of the native group in the country. So the foreign birth between estimate and within that but those are actually kind of similar.

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Foreign nationality had some disadvantage far and credentials had bigger disadvantage. And then you can see that there was definitely disadvantage to being not European and there was also disadvantage to being a white ethnic group.

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Although the disadvantage was smaller than it was to the non-European origin.

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So there was definitely a race or ethnicity effect that was pretty strong. So we also tried to look at whether these effects differed by country.

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And so here we have a little bit of an issue that you know we don't have that much power within by first single country analysis.

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So we're looking here at the 5 countries where we have the most. Kind of data and this makes it look like it's possible that for the US there's actually more of a disadvantage to being far and born than there is for other countries.

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However, you know, that's the only place where we get statistical significance. Although, you know, if you, if you think about, well, I should maybe do a multiple comparison adjustment here because I'm contrasting across 5 different groups then that marginally significant effect then becomes non-significant.

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So, we don't find a lot of evidence that there's country variability in these effects.

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However, we do not have a lot of power. So maybe it's there and we just can't find it.

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Okay, so foreign birth per se doesn't seem to be a major source of hiring discrimination. However, highest degree from a foreign institution or lacking citizenship are significant disadvantages.

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And this is consistent across between study and within study results. Minoritized status is a consistent source of disadvantage for many immigrants and their descendants, especially if they're not white.

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And so, we also find that the race ethnic discrimination estimates are not in some way inflated by a lack of control for place of birth because once we put that in it doesn't change.

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Anything in those estimates. So, you know, it is worth, I think, keeping in mind that the resumes in cover letters generally were kind of set up in a certain way on average in these studies.

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And usually they were trying to isolate race or ethnic discrimination usually. They didn't want language to be a confounding factor.

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So they did things to try to signal that. That language proficiency was high for all applicants. The and in most of the studies in our analysis the foreign-born applicants are immigrated as as children right although we find evidence that certainly if you don't have our highest educational degree from the country then there's a negative effect and those

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are from more adult immigrants. Okay, so you know it was a little bit surprising to me that despite the politicization of immigration in Western countries We couldn't find much effective foreign birth if immigrants are otherwise the same as natives.

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So it's also possible too that the politicization has impacted, you know, race ethnic discrimination more than it has.

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Far on birth discrimination per say exactly.

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But there are definitely disadvantages for this first generation if they have foreign education. And you know, we find that there is this persistent effect of race and ethnicity.

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And that's a form of disadvantage for many immigrants. It's also one that won't attenuate across generations in the country.

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Potentially and so that suggests that maybe it may make sense to think about things like segmented assimilation theory which have suggested that there may be forces that kind of push racial and ethnic minority groups.

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Into a different position structurally and that kind of persists across time. And that theory may apply across a wider range of countries than just the US where the theory was developed.

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Okay, thank you.

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Thank you so much. I can imagine the collecting the data process would be quite I extra so quite a task right it's quite fascinating so any burning questions to Professor Quentin?

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Yes, I think that's one back. Didn't say what time was

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But yeah. Yeah, so it is a range of jobs. Yeah, so the question is I didn't talk about what type of jobs they're applying for.

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So it is a range of jobs that are almost all entry-level jobs. They don't require some kind of super special skill or a ton of previous experience.

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But it is a range of different jobs that are typically things that you could apply for with either the equivalent of a US high school or college.

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Background you know we put in controls for different categories of the occupation to try to account for the fact that there's variability in the studies as to the particular type of jobs that are being applied for.

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Most of the time the jobs are being applied for like if you go to a job bank and you just pull all of the ads that you could apply for with.

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Like a secondary diploma in the US a high school degree. Then they just go out and apply for all of those jobs.

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Is the typical way that these studies work. A few of them look at more specialized sub-markets.

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Like say just waiters or something like that and you know we're hoping our occupational controls kind of somewhat account for that.

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It's it's this range of kind of entry level jobs, I guess is the answer.

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No. Thank you so much. I think we will have more time for more discussion after the presentation. I think that's very a good question.

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I think in the different type of job market, even type occupation, they might have a quite different callback pattern, especially for example in medical practitioner doctors right so race will have a quite different kind of pattern or the profile.

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Okay, we have our second paper by Tristan Ivory of his and his colleagues. Okay, let me just click.

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Oops, here we go is up.

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So, another, fascinating topic, degrees of freedom. I liked the first 2 words already.

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Or for us yours to continue the conversation on the kind of similar.

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Thank you for the introduction. So, yes, thank you for all being here today on the last day.

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Really appreciate the audience and again for those who are tending virtually as well. So this is research that is in progress.

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So we're really interested in hearing some feedback in terms of what's working, what might what we might do in order to think through some issues that will raise during the presentation.

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I'll go ahead and get started. I wanted the order of the presentation. We'll give a little bit of background.

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Then we'll introduce the puzzle in the question, talk a little bit about previous literature. Then the selection of cases that we have.

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We'll go over data methods and then steps moving forward. So the genesis behind the project is, so my colleague here, Gar May and I have known each other for quite a while and have been interested in thinking through large cross-national surveys and some of the gaps within those cross national surveys, particularly with education and

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how education is actually quantified and usually it's kind of a blunt instrument. In terms of thinking through just getting years of education.

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So, Jeremy and I actually spent quite a good deal of time thinking about how we would intercede on this.

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We decided to start like locally with things that we were a little bit more familiar with. And so the first project that we worked down was a joint project with another scholar.

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A senior scholar named Hero, he's a Takisha who is actually here but has left to go back to Japan.

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That research has just been published in International Migration Review. I think Jeremy said that they just tweeted it out this morning so that's great for us you know a little bit of a nudge and that joint project was looking at ideas of social integration, so inner marriage and how intermarriage is generally treated as a kind of

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uniform positive but not necessarily taking into context the context of reception and particularly the gendered context of reception.

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So within that paper we looked at foreign board movements labor force participation in the United States, Sweden and Japan and by looking at Japan which at the time was just recently allowing scholars to go ahead and look at some of the census information we were able to see quite convincingly that foreign-born women were in the labor force at fairly high

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numbers when single or married to non-Japanese individuals, but when married to Japanese men, they fall out of the labor market pretty convincingly.

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So this idea of this gendered context of reception being quite important for whether or not people are actually working into the labor force or not, right?

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So thinking about the differences between social integration and economic integration, which are generally in the literature a lot of times lumped together.

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So through that project we started to think more about this idea of educational credentials and how we would go ahead and intercede on that.

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We wanted to use a similar 3 country framing to address this differential value of credentials and so thank you again for Lincoln you set us up brilliantly in terms of thinking about the main finding about the educational credentials like we couldn't have asked for a better introduction because that was something that we also were thinking quite quite

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in-depth about in terms of how this signaling of educational credentials matters and whether or not individuals that are earning those credentials within country have a better outcome.

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In the labor market, at least within the short term. And so I'd be remiss without actually talking about the other individuals that have been working on this project.

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So we've had contributions from Lee Tran. Who's at Deacon University here in Melbourne.

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George Tan and then Shin-chun Lee who are at the University of Adelaide and then another contribution from a graduate student at Cornell University who's seated here with us, Haliang, who's done a tremendous work on thinking through the United States side of things.

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So we've had gearmay who's been largely in charge of thinking through the Swedish data.

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We've had contributions from How in terms of the US data and then from our Australian team in terms of thinking through the Australian debt as well.

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I didn't know if, did you have something to do? So the puzzle here is the literature on immigrant integration suggests that credentials earned in the host country help to ease some of the labor market discrimination that Lincoln was talking about in-depth from the previous presentation.

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But and here's the important caveat and something that again was queued up from Lincoln's presentation as well the literature on ethno-racial and gender discrimination demonstrate that credential downgrading happens for marginalized individuals.

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So in that sense, we wanted to know well where is the sort of gap between these 2 things right so is it that if we have individuals that are actually ethnic or racial minorities engendered?

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Does that help to compensate or are they still suffering penalties that can't be overcompensated just by having a credential that's more locally based.

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It's further complicated again by the fact that a lot of cross-national surveys generally only ask about attainment.

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So just how many years do you have? Not where you got the degree from and like what degree you even got right and so as we know from looking at the literature especially on signaling tremendously important right so that not all degrees are created equal even within a host country.

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So.

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Yeah, and, addressing these concerns in literature, we come, we try to answer to the following questions.

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First one is what role of degree type plays in the likelihood of experiencing downgrading, a evaluation of your degree.

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In for people who earn those degrees in Detroit Country Study. I was fairly in the United States.

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So we define a mismatch as a vertical mismatch, which is when the individual works in the occupation that doesn't require the level of skill that comes with their degree.

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And. If mismatch it we see what's the degree of incompetent that's experienced by the miserable videos.

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So if we think about our kind of 3 buckets of literature, previous literature that we're drawing from.

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There's one that talks about foreign born credential mismatch and generally thinking that individuals who actually show up in a country and try to get a job that's commiserate with their previous credential generally aren't not very successful doing so.

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And so are then forced to actually get jobs that don't necessarily match their training. So this can be thought of as the sort of taxi driver for phenomenon or maybe a rideshare driver phenomenon.

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So a number of individuals across multiple countries wind up driving ride sharing car vehicles because they can't necessarily get a job that's actually commensurate with their training.

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And so this process of trying to rebound from being mismatched can take several years sometimes people never fully are able to overcome sometimes people go and get a second degree in more in-country in order to deal with that sort of thing.

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We also see this idea of occupational downgrading in general, which is not exactly the same but is can be thought of as a cousin of sorts.

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So the idea being that individuals who are working within a particular segment. Sometimes are downgraded even within if they have their degree actually recognized right so within the occupations they might not necessarily be doing the sort of work that they could be doing otherwise.

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So maybe starting at very sort of junior positions, maybe being tracked on to different tracks. So in a case that the gear man I know quite well the case of Japan right sometimes there is a foreign track and there's a Japanese track right and so on the foreign track you might not necessarily be able to raise up as high in the corporation as you would on

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the Japanese track. And so this kind of occupational downgrading is something that many times foreign individuals are subject to and can become quite difficult to actually work your way out of.

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Then there was a whole basket of literature talking about labor market integration within the 3 countries and how that is something that can be difficult to traverse again for foreign-born individuals.

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So across all 3 literatures, you can see that there is, it's quite complicated and difficult for form-born individuals to actually get the same sort of recognition that native-born individuals might have in the labor market.

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And so we wanted to make sure that we did some sort of deference to. Bodies of literature that have actually been in existence for quite a while.

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I tried to put up a few citations that are mostly 20 first century citations but again this goes far back into the twentieth century as well it's been an issue that scholars have been dealing with for quite a while.

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Yeah, so I'll go briefly through the data set that we're using. For Australia have just fairly senses and temporary ancient integrated data set which includes those those individuals who obtained the following degree in in Australia and link those records to the Australian census so that we can have incomes and occupations, etc.

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Oh, yeah. So we have 2,016. As the reference here And it's the important features of this data set.

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It has this we rich data of these as held and the transitions between physical categories. It has some limits in terms of which variables we can add to it, but it allows us to single out those people who got there for degrees in in Australia who came in students and remained afterwards.

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It and then we have switch race the data well in the best state we have the entire universe of legally residing individuals.

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Everybody who's ever lived in the country for at least one year is in the that data set so we can construct any kind of set to match the other 2.

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For example, can have variables of tenure after before. Obtained high steak verification, we can link of the rest is by definition linkage of several different administrative data sources.

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The information about

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Information about university is slightly more limited in the sense that we don't know anything about degrees that were obtained abroad and this individual has reported but you know everything that has happened in Sweden so I know somebody has been to universe and obtained the grid there.

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And lastly we have the National Society of College graduates in the United States. It has a very long history of several waves from 1,013, 2,021 and it's connected to the US census.

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Because it's a collaboration 10 years since the NSF collaboration. It has a very rich date in college tenure and employment history and has but has a very restricted geographic micro data so this challenge that we faced when trying to compare these 3 countries using these datasets is to compare the 3 countries using these 3 datasets is to find a set of a

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basket of variables that could be used in no models and they could be studied that would be harmonizable across the 3 ones.

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So in some cases we're going to have data sets are being underutilized because we had to exclude some variables so they could match to the other 2.

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Listen to me. Yeah. So, and another feature of these 3 data sets that I told you, just, just told you about is that they have very restricted at access they usually hosted in their own isolated secure access servers so it's impossible to analyze them simultaneously.

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So we use simple, separate limit probability models for one model for each country predicting first of all vertical education is matched that this is the equivalent of a logistic regression, but it's we use linear probability models because you issues compared when comparing coefficients across logistic models.

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So we use linear probability once. And use ordinary list cross regression for estimating the income. And the predictors of interest here, the degree type, vision of origin, sex, vision of interest.

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In each country is made separate as I mentioned earlier because of the data as x as safe roads. Oh yeah, so I'm just going to present some of our preliminary results using the variables of interest.

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So first, first of all I want to show you this bar plot showing M match and field of study.

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So the blue color in the bars show the proportion of people who are mismatched in their jobs.

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The striking thing here is that Australia and Sweden seem to be show a higher degree of mismatch than the.

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Most of the individuals in some point the West they work in jobs that are required degree of skill that they get from the from the educations.

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And you all see that there's no clear trend when it comes to, to the type of degree.

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Although student seems to be slightly high. To have higher The voice of people working in STEM. We don't see any strong, strong differences in the way that we see from his match.

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And when we look to we look at the results from a models so here we have 2 models on the left we have the results for the linear probability models predicting mismatch and on the right we have the limit probability MoD splitting log income.

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So I start from the left. To your left if you look on the top you have the the effect of being in a stem field or not.

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And surprisingly enough in Australia, STEM gradients are more likely to be this match than the rest of the country.

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And when we saw this result we We worry that there might have been a miscoding somewhere called a mistake or error in the data construction.

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So we ask the Australian counterparts to check it out and turns out it checks out and for some reason in Australia those people who are in STEM feel they are more likely to be mismatch after the afterwards when they get a job.

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But if you look to the blue and to the green ones that represent the blue, the us in the green Sweden.

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You see that they there the effect is more like what we would expect people who are in STEM fields are less likely to be mismatched after they get in job after getting a job so the credentials are less likely to be devalued.

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Now moving on to the, to depend on your right. The one that's title log income, then see that being in the stem field.

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Makes him heavy. Or it's associated with having a higher log income, interpreting this in the log scale.

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You could say that For example, being in a for Sweden or the US have been in a stem field.

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Is about 15% higher income than not being in the stem field. But when you look at the mismatch, and here again you see some cross-sectional variation here in Also standing out here is the result for Australia where Extra being is matched.

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It is to a higher income. The higher log income in contrast to what we see for the US and for freedom were being this match again intuitively according to our expectations that it's the lower income overload income.

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That over with you, is. Yes, so the next step is we're in the process of trying to get some of the micro-data for the United States of trying to get some of the micro data for the United States of course.

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The problem here is, as you may have mentioned before, is that there is a small basket of variables that we can compare across.

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And so the big elephant in the room too is race, right? So race isn't necessarily coded the same way.

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We could go with ethnicity, but as you've probably heard at nauseam, you can't necessarily construct race all the time from ethnicity depending on what country you're coming from as well.

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So we have an imperfect tool. For measuring that across all 3 countries, but the idea being that we could actually do quite a number of novel things with some of the micro data including looking at location to try and see whether there are locational differences.

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Whether people actually have degrees in the country as well. So the next step might be instead of doing a couple of cross-natural comparisons to delve really deep into the United States and sort of some of these issues out as well.

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We wanted to run all the models with a little bit more fine-grained all the models with a little bit more fine-grained geodata so we can kind of see whether there are regional differences.

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So we can kind of see whether there are regional differences. It's one thing that we were interested in beforehand is thinking.

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It's one thing that we were interested in beforehand is thinking through does it matter if people are interested in beforehand is thinking through does it matter if people are getting a degree and then moving somewhere further from where they got the degree. Does it matter if people are getting a degree and then moving somewhere further from where they got the degree?

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As we well know for a lot of people are getting a degree and then moving somewhere further from where they got the degree as we well know for a lot of native born individuals.

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Employment prospects are usually much better, closer to where the degree was received because there's just more knowledge of what the degree means and there's more networks.

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So we wanted to put that to test as well. Here we are. We wanted to go ahead and refine our models based on some feedback so you'll provide us hopefully a little bit of good feedback and things to look for forward to as we start to.

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Refine the models themselves. Then hopefully finish a draft of the paper by the end of the year, which seems reasonable.

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So thank you. Appreciate all of your time.

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Thank you so much. To a very exciting project. Any any burning issues before we move on to next paper?

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Thank you so much. I am so pleased that Dr. Dr. Yaloo put together this very kind of sensible session that we can have this continued conversation on on the same kind of theme.

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So next paper we're going to have another dimension of integration. The returns to returning, so which is another process of migration and integration into the labor market.

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I think we're going to listen to When-engen. Representation next one. Thank you so much.

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Okay. Hi, so my name is Wechen. I'm based at Stockham University.

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In Sweden and also affiliate to Sutton University and Aubur Academy in Finland. And so this project is funded by the Baltic Sea Foundation at Selectron University if you whatever have interest to work in Sweden I can let you know it's ready easier money to get.

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And so like our our research question is how is the economic return of migration and remigration we initially we plan to do like migration.

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Linking Swedish and Finish address data but we stuck in in the application process so we So now we do this comparing like return migrants with those who have not migrated and how does the the experience of migration and coming back a federally labor market performance in the later and the literature pointed out.

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Strongly relies on the human capital model that's migrants. They move out for accumulates wealth skills.

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And which could benefit to to their career once they return. And but on the other hand, many studies also show that like experience of migration does not necessarily help labor market outcome once they return because sometimes it's difficult to readapt in in their house country.

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So obviously one crucial matter is selection because we're comparing orange and apple and we compare those who migrate and host those who do not and It's selected by like.

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Their their family background, their education, like what kind of job are they are they going into and So.

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So some of studies before try to. Try to address this issue by and using sibling fix if I approach a very famous study from Abramisky linking the Norwegian register and US sensors in early early twentieth century.

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And and comparing siblings so so basically with this we are sort of maximally leave out the influence such as family background and like parental education family wealth upbringing like what kind of community they live in in the triangle etc.

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And to some extent like genetic traits that could lead to could affect later outcomes. And so we use a similar approach.

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And we focus on the case of.

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Migration from from Finland majorly to Sweden I mean because our initial initial plan was to link the 2 country but Nevertheless, there was a large migration flow from from Finland.

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After the Second World War because the country was like distracted. And needs money to to rebuild and most of them are going to Sweden.

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Almost half of those migrants who, who move our, a destination with Sweden because it's close country and very similar culture and to some extent language.

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So you was continuing peaking to the seventies and like. Until the in the nineties when Finland's economy eventually catch up.

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But it's also the fact that every second of the migrants have returned within 3 years.

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So actually. Half of them did not really stay in Sweden which give us the opportunity to examine like how they will be doing after they return so Unfortunately we don't have data on destination country before 1989 but we can see there like from 1989 on war which is almost the end of this

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migration flow and still like 30% are going to Sweden and adding up other Nordic countries, Norway, America and Iceland is almost like 50%.

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And then it's a other Western advanced countries.

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Previous findings by my colleague Yamaha who's also co-author in this paper.

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It's quite striking that some retail migrants have quite substantial worse outcome in both employment and income. So, so these 2 figures are from his previous paper and And with the one as the baseline of employment rates or for the like those who have not migrated and it's Consistently like worse, like both for women and

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men. For the returners and his later studies suggests that it's due to a storm active selection.

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Of migration and it shows that the selection is the best of the worst which means Those who return to Filla are not already the best among those who migrate but the the rest the whole migrate like migrant group are just like so so much worse.

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Like so much negatively is still elected to work abroad yeah, but then recently there are some contour evidence, but they are not based on registered data and and they show an There's no gap existing since the nineties which is reasonable

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So now we are looking at what if we net out of those. Family and community and factors if we use a use a sibling comparison approach and we use an Yeah, registered data from from statistically Finland.

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And so it's annually updated from 87 to 2,020 and Before that, we have confined your data updated every 5 years from 70 to 85.

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We select individuals, in prime working age, 18 to 55 years and then we select those.

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In individuals who can be our sample like we The returners are like on those who migrate. Of course they have to be migrating as adults.

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And they should have stayed at least 2 years abroad so that they have enough time for experience. Abroad and then then the return and then we find their like siblings with the same gender who have never migrated.

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And we focus on cohorts of, 1,953 to 2,001.

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And for the sake of data quality and

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And they are all native one so we also leave that out and also they are censored if they die, if they retire or if they migrate again.

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And we we find that the return group is slightly older but not so much. And our outcomes to examining, we examine both income and employment status.

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Income is Mmm, because we have a relatively long time frame. So, we use the income measure that's consistent over time.

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I mean that's which is available throughout the period and the income is an earned income plus entrepreneurial income which are both like taxable income, company in Euro.

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An adjusted for consumption. Seep consumption. P what is P? CPI.

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Yeah, yeah, consumer price index. Right, thank you. Yeah, and we do lock transformation because its income is so unevenly distributed and in order to do this those who have no income we code them as €1 like which will not make very big difference and the employment measurement is a bit crude is

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the so they are employed by the end of year. So we have more accurate like how many months and even days they have been played in the year but that's That data is not a not so available in in earlier stages we use, IS regression models so So it will be a linear probability model for the

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employment. Employment one and first we estimate the the regular model and then we add a sibling fix a fan and then controls we controls an age mother tongue.

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To let you know the Finan is a bilingual country and Swedish and Finnish are both the official languages and.

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So that we have to do this control. Period education and family composition if they have like spouse or children.

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And rural urban division and we also look into like the time they spent abroad and the time Please spend in villain after return.

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And returning to first place because it's suggesting literature days. Loss of the local contacts as one main reason why the return migrants are under performing.

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And we we think that while maybe they could do better if just to go back to their hometown and where they have more social capital And the age and mother tongue are no controls when we use sibling fix the bed because there's little variation.

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Within the siblings. Almost no variation for for language

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So I have to mention the as using registered data we have very high standard of ethical integrity and we don't own the data it's analyzed by remote and access platform and ours.

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Yeah, or our results are like will be checked by by the statistic bureau if they are appropriate to to to put out for us So there should be.

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Very clear.

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So, because we have an the income is only available until 2018 we don't have the latest update yet, so it will be The sample size will be slightly different when we analyze income and employment and it's more women the men which is consistent to like the fact And we first look at some descriptive

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trajectories and very surprisingly when we plot So basically we try to look at. How the returners are doing on average by the the years of the return and And the reference is the average value of the displayers in the same in the same year and very surprisingly for men they catch up only after 2 years and surpass the

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They are why the women it's more it's more consistent to previous findings that they are under under performing like until maybe they'll catch up in in 10 years.

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But then we found that this man pattern is due to some extreme values because when we lock the income and do the same pattern it's essentially the same.

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And we even like track the distribution of income like between returner and stare and you say you can see it's actually the return man return not have a lot more extreme values to to the to the right end.

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And for employment it's even worse like they they never catch up to to the stairs. And also we show that return on subpoena lies even more if they have state.

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Abroad for for a long time.

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And then it turns out our modeling, so we have the first model without sibling fix it at and the second with.

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And with Surprisingly, it's the results are essentially very similar. Like, like we use sibling fix.

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Ned out like family background and early operating but we we thought without that we have Yeah, we had essentially the effect that's no change that's Ritten migrants are like quite significantly and substantially disadvantaged both in for employment and income.

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And the gap is smaller for women.

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Yeah, same for employment. And, Some control variables are like consistent to do our expectation and we also you some alternative outcomes and so we we check labormark inactivity so for those who are like no active in labor markets as as the outcome.

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Which is different from being employed. Unemployed and And we also like restrict the sample to those who are active in the labor market and estimate the risk of employment and we find essentially very similar things and then we break down by educational level and So here are the estimated employment rate and log income.

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After estimation So for both, returners and stairs and, and both men and women, the, the outcome obviously.

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Gets better and better with higher and higher education but the gaps do exist like Like you if you compare like tertiary educators there and tertiary educated Did the returner still do a disadvantage?

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Yeah, especially for women with low education and then we do something more like combining their experience and how like How many years?

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So the gap here means that the time they stay abroad and the After and also the years after the return and We also see that first the longer they they stay outside the worse it is And but for all those groups like for all for all those groups for all those groups with like ears.

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A range of years being outside and their situation get better with the time they come back. And adapt to to the labor market but it's mostly for women for men it's essentially like Yeah.

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Staying home for longer time does not have. Much much effect but it's starting point is like much higher for men rather than women.

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And we just try some other interactions and rural residents are worse and We also break down to like where is destination, which is available after 89 and those who've been to Central Eastern Europe are at the worst and followed by Sweden and other countries and followed by the other countries with is practically like in Western Europe

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in North America. We plan to do more on that because migrants are selected into different destinations for sure.

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And So surprisingly, we do not find like returning home is better for their their career. They get worse but it's of course It's also selection network that's the might not even be healthy like if they have to return home especially if they have to return to a rural home.

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But we, we will explore more later. And we do some robustness analysis.

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Yeah, using a population of or. Or certain alternative outcomes were income in employment. And then we also did a check.

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On the sibling groups who are like more similar to each other we track and those they have the same highest educational level.

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And also they are no very different in age. Then we restrict the we'd also try restricting the destination to Sweden and other countries where We know what's what's going on there and unless Yeah, up before you pull all the destinations we might be very confused because they are very different groups.

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And then Also try to make use the the confine your data before an 85 We are quite robust like some patterns could differ like I remember like in the cranial data it's like the the returner some disadvantage but the gap is larger for women

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So basically we conclude that

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Yeah, we find very strong disadvantage for for. We return migrants in in the Finnish context.

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And although they could catch up after the longer time they they have been back. But it's hardly really It's hardly really catched up even in the later times more very small gap existing.

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And so what we have in So compared to the previous presentations we So we have very special case that we think Well, we or like to a lesser extent those finished migrants if we think the majority are going to another Nordic countries.

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The language barrier, the institutional barrier and discrimination is quite quite minimized. So, we also kind of leave that out and and the consistency between our models with and without sibling fix the facial status.

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And selection is quite strongly on individual level so it's the difference between siblings who that might drive them into migration or not and then also contribute to the difference in later performance.

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And so I think one contribution is that we we make a case for migration among high income countries. Mean compared to the previous presentations it's not a very a vulnerable minority group, which I think the one over my other group is of course more more of social significance and but we we have showed this no very well study like

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how how migrants, the migration integration among high income countries where they they They don't experience.

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The much and discrimination and institutional barriers. Especially when we think of Finland and Sweden. It's very close.

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And my my colleague and Malian Tunisian and Andrea Monty works on the Sweden and Norway to.

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Which is quite recent now it's a joke there's every suite you ask they have some they know someone who work in Norway Yeah.

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So yeah, so be careful because they also find the same, like you get worse after.

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Yeah, but our limitation is that we have highly selective population and We have kind of good internal validity with sibling fix a f, but like we essentially throw our like everyone else.

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Who don't have a sibling as as migrants so we don't have we cannot like journalize our results to the to the public to the general population and then we we still don't know we don't know what's going on up to the migrates because possibly if we've

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If we finally access the link registered data we can explore, is for those who go to Sweden, but for now it's a huge black box.

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Yeah. And some filter work and we may see. Heterogeneity across occupation a destination of migration and maybe like the purpose of migration we don't know because I mean of course many go to study.

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And we don't know. But I think we have some solutions to kind of figure out.

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And then other outcomes such as house and

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In the intergenerational transmission of these advantages. Thank you.

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So I came to the One, we did the fun discussion. In our presentation. Okay, we have a pre-recorded presentation, the last one.

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Is more contemporary topic. Ukraine refugees topic I think we will let's hope we can

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Equality.

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By a comrade and his colleague. How do I do it?

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Both always be out of the way.

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Yeah, just pre-recorded, right?

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Good afternoon. I think he's caught up in GB app and together with, in, sense will present, results of our common research on Ukrainian.

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Refugees in Vienna and we cut it out. Last year.

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Between May. And, an August, the collection. Okay. And June.

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We are. This research which we are presenting is an is, is a group effort.

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And here you can see. The people that were involved apart from us in this research.

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So important role, play duties, and as well as, drinks. And,

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People were involved in the They are. Planning and

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Collection and data analysis. As far as the. Background information.

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Carried out research on forced migrants from Ukraine who have flat the country after the beginning of the full scale invasion in February.

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2,022. And As you know, the worst started in 2,014. And resulted in 1 million and a half internally displaced persons.

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But this huge exodus of Ukraine in refugees, started after the full screen invasion and, Poland, Germany.

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And check. Where some of the countries with the most.

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Sort of deeply affected by this of people especially at the very beginning. Hugely unexpected invasion.

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And a huge flow of people. Nice special year and of February and March and and April, but also a significant number of refugees made it the way to to Austria as of, August, 2,022 when we were writing down analyzing the data which we collected there were

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1,300,000, refugees to up to 2 million. And around 80,000 Ukraine in refugees in Austria This situation obviously has slightly changed.

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After almost a year after. Moment there's in Poland. So 1 million refugees who have so called active registration number.

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But, are also figures show estimation showing that the population may be higher.

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And a half and obviously they are. This population adds to So, 1 million Ukrainian economic migrants who are arriving to Poland especially.

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Significantly from the beginning of 2,015.

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The floor to English.

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Thank you. Conrad. So the key question which was also and a lot of discussion in the media was who are the arriving populations in the diverse most countries.

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And we already know from several very early admission studies at the beginning of the at the onset of the war.

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That there may be a high self selection among those who made it to Europe and moved even for further towards.

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Western countries. So it seems to be that self selection increases the further they migrate. This pattern has also been and also observed in atom migration contexts.

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For example, the refugees from Syria. There they could also find assortative migration patterns in forest migration settings.

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As as an example here. Or as an exam. As an example, for example, for example, lower educated refugees were more often to feed to neighboring countries.

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Than higher educators were more often to Europe. So our questionnaire aims to answer the question of who would arriving populations are.

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And it's on the next slide, please. This is the survey, which is the case for Yana for Austria and it's called Ukrainian arrivals in Vienna.

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This is a rapid response survey ended focus on 3 distinct areas. Next slide please. So the key research questions in the survey are who are the Ukrainian refugees focusing on your socio-genographic background?

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For focused on their own intention to stay or return back to their home country. So our team was in the field between April and June.

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And we use service in the form of paper and pencil and web service. And provided the survey in 3 different languages in Ukrainian, Russian and English.

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Yeah, The sample size is about 1,000 respondents. On the next slide we can see the content of this service so as you can see we try to get a very comprehensive picture of the arriving populations and it started with basic social demographics which cover it.

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Demographics, education and employment, but we also serve with further items for example on health housing and so on.

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And as some of you might have even noticed and point 7 we also collect information on partner and children so we do.

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Have this design as a household survey and collect even more information. Just correspondence. On the next slide, we can see that we also have a wonderful partner serving in Krakow, which is almost identical and this is very good for our research because it allows us to have comparative research.

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Also here we have the same field phase. We have a paper and pencil format and the sample size is a bit smaller so we have 500 correspondence here.

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Here, the sampling threaded here are the samples for both service to the left is Vienna and to the right we can see Krakow.

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And we see an age sex distribution. Which, we have already seen. More or less also from other service so it's predominantly female and also we have many Aldi and many of middle age and also lots of children and just a small group of Matt.

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Yeah.

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Male, Ukrainians. Who are at to diverse locations. And as most of us are, that sampling is a major challenge we face when we're serving refugees.

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And this is mainly due to the fact that they are a hidden group. They are highly mobile. And it's really hard.

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To make a survey that really represents the real refugee preparation. Especially in the absence of registers. So we tried our best to do so.

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And do we? Which our sampling frame is very decisive in whether we really cover the target population.

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In our service. So on the next slide we can see. A representation of our sample in Vienna again to the left.

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And on the right side we see the population pyramid of those who arrived in Vienna.

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As from the National Statistics Office. And we can see that. If we kick 2 times more that there is a minor over representation of male in middle age and a minor under reputation of male teenagers.

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So we have very good coverage of what we have sampled compared to dose what is visible in national registers.

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And the reason for that is might be that we had a unique server location so we serving refugees at the first reception.

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Center in Vienna and this was at that time the only place. Where every Ukraine had to go.

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To register and also to receive help. And all displayed Ukrainians who wanted to stay in Vienna had to go there so there was no No transit for them.

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So it's very likely that there is no major difference between the potential pool of all Ukraine and arrivals in Vienna and the target population at the reception center.

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In the case of Poland we can again see the on the left hand side and on the right hand side to national register pizza.

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And also here we can see that we just have minor deviations from and from what is visible in national statistics.

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Also here a slide. Overrepresentation of women. And to my not under a representation of teenagers.

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Here, so. Or in the Polish case it has to be noted that we also call it convenience sampling.

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There was no. Such the center but data was collected in numerous places where refugees received help. And could register each documents or assembled.

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So let's take, let's now we're going to the results. And the next slide we.

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Start before we really drive into the main results we see again. Both population pyramids above samples and here we can also see the data included with partner and children.

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Arrived with the correspondence in Austrian. And on the next slide, we can also see that we.

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Did not only include on Those 2 dimension but also we know how many of the partners and children's Live abroad.

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And this is can be seen by the dark red and the light blue colors which form. The population pyramid.

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So in total we have a large sample of a thousand 253 persons in Poland. And 2,792 Ukrainian refugees.

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In our Austrian sample.

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Yes, next. Origin of the respondents. You may see that's a significant, large number of our respondents came from the regions which were most.

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Heavily affected by the ongoing

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As you remember the at the very beginning of the full scale invasion. There was an effort.

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Of the Russian army to take over control. For the capital CTTF. So very significant number and this is also.

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A high density population place, the key of city and regions. So a very significant number of people. Our sample came from this region and other regions where the wall was the most sort of severe I would say and so there is overrepresentation of Eastern regions and the central region of Kiev.

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Which is in line with what we know from other registers and information from where people were escaping.

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So this is clear and this is both in the case of Austria and Poland this higher representation of people.

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And, what we have noticed, in line with, with this, approach of, analyzing the self selection of, migrants for migrants is that the we can see that.

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There is, They are high selective group in terms of education as you can see between 42 to 54% of the respondents hold to hide education masters or doctoral degree and as you can see And it is particularly higher, in Vienna.

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Higher in Vienna than in Krakow. In Vienna, 54% of their respondents halt a master doctor.

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We can also see, when we look into various groups, compare the all women and men that in all groups.

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Refugees, Ukrainian refugees in Austria. Have the highest sort of human capital. Then, Paul and, and we also looked into the data from Ukraine and we could see that this was significantly lower.

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In all groups, all women.

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One of the key questions we try to answer with our survey was a question why Austria Why Poland?

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Why did they choose to to go to stay in these places at least temporary. And one of the important, results we noticed was that in the case of persons that we've interviewed in Krakow.

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Pointing out that was a place which is relatively close with very good connection with your highway.

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You crying and especially at the time when we were carrying out this research or May June data collection phase, they were a lot of people.

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Believed that this was just a temporary and they would quickly return back to the country. As you can see the reasons of having family or other sort of networks played a important role in the decision making.

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Both in in Poland and in Austria. But this was something which would as you as you can see clearly.

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Sticks out I believe hugely can be explained by the temporariness of the situation but but still a lot of people decide to stay in.

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In Poland simply because it is close and they can relatively easily, especially women and children. Travel back and forth.

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For Ukraine. We asked about the return. Intentions and as you can see they are much more.

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Prevalence in the case of Poland. Lower number of people think about staying. In the case of Vienna.

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Almost 50% of the people, interviewed the wanted to stay so they came with the intention of staying in a country.

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Obviously one of the reasons there are various reasons why people want to return and one of the reasons that is in this decision making process is the fact whether they have left.

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Their partners back in the country. Obviously departments are most of the time men who are as you know. Not allowed to leave the country if they are between the age of 18.

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And 60 and there are special conditions under which man can the country but but most of them are mobilized to fight with the invasion.

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Of the Russians and and this is also a key factor that as you can see influences the decision of potential sort of returning tensions.

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As far as the reasons to stay in the country, arrival. So Poland, one of the very important factors that influences the decision is the situation that the labor market and the easiness of finding a job so the factor of finding a job was a very important in the decisions of staying and We could see that as well, especially in the case of.

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Of the Austrian sample. And, when it comes to the final slides and the the factors again influencing the decisions of return.

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The key important is where where to return. What are the regions from from where the migrant force migrants come from and whether this places are

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Are affected by the ongoing or, or they are not. As you remember at the very beginning, there was a very high number of people who left as I mentioned from a key SC and a key region and this was obviously a region which, was, liberated.

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And we could especially see in the summer.

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2,022 increasing number of people who were in thinking about the return and a lot of them actually returned.

454
01:32:29.000 --> 01:32:51.000
So the green. Color you can see here in the pie charts, especially quite visible, 42% in the key city and key of region also very high and if we go to the east, you can see a very significant number of people, 70% in the eastern Ukraine.

455
01:32:51.000 --> 01:33:05.000
Who say that they have nothing to return. These are the people who come from the places such as for example Mariopol who were totally devastated by the invasion and they are being occupied by the Russians.

456
01:33:05.000 --> 01:33:19.000
And and obviously the returning tensions among the refugees who come from these regions are much much lower than among the refugees who come from other parts of, Ukraine.

457
01:33:19.000 --> 01:33:31.000
And just to answer key takeaways from us are the following. Iranian refugees in Austria and to lesser degree in Poland are highly self selected.

458
01:33:31.000 --> 01:33:45.000
Usually urban middle class population. I think we've noticed that the further refugees move from Ukraine the higher There's socioeconomic status, educational attainment and the less pronounced the returning tensions.

459
01:33:45.000 --> 01:33:53.000
And, important, one of the important lessons from our research is that host countries need to adopt their integration support policies.

460
01:33:53.000 --> 01:34:16.000
Do the social demographic profiles of. This refugee populations. Thank you very much. For your We are happy to answer your any email, any inquiries through for email.

461
01:34:16.000 --> 01:34:31.000
And very timely. Project is still unfolding and we will watch the space with interest. I think you know we appreciate that how difficult this kind of project could be in a field.

462
01:34:31.000 --> 01:34:43.000
Ongoing, you know, it's a challenge that we appreciate their hard work. I think, we can

463
01:34:43.000 --> 01:34:51.000
Oh yeah, we have a few online participants. Sorry, I didn't. Oh yeah, sure, sure.

464
01:34:51.000 --> 01:35:02.000
I notice we have a few online participants. I wonder whether anyone, a few online participants. I wonder whether anyone, attending online have any comments and observations.

465
01:35:02.000 --> 01:35:07.000
Yeah, go ahead. Narish?

466
01:35:07.000 --> 01:35:10.000
Thank you very much. Can you hear me?

467
01:35:10.000 --> 01:35:11.000
Yes, we could.

468
01:35:11.000 --> 01:35:19.000
Yeah, thank you. All the presenters know the presenters very nicely and wonderfully distressed so many compelling sites.

469
01:35:19.000 --> 01:35:27.000
I have it some small actually clarification from the first presenters. At well in, I think. Can you hear me?

470
01:35:27.000 --> 01:35:28.000
Yes.

471
01:35:28.000 --> 01:35:48.000
Yeah, so, on my first observation in case of that's actually what kind of definition actually, under differential you have taken consideration to unifying the, concept or variable in, in the contest of the 13 different countries to actually look at into that, destination.

472
01:35:48.000 --> 01:36:13.000
First, in, racial, racial discrimination and discrimination. And second important, nature of person is that, is any kind of actually an intensity of the distribution or a in the context of racial or ethnic discrimination within the context of 13 countries, is, or among the, variation in the.

473
01:36:13.000 --> 01:36:33.000
discrimination. And third observation is that, what kind of actually policies or administrative framework you have taken considerations in the context of discrimination or administrative framework we have taken considerations in the context of discrimination or implementation of they have taken considerations in the context of discrimination or implementation of the policy and differentiation of the contest of discrimination or implementation of the policy and differentiation of the at the access of

474
01:36:33.000 --> 01:36:41.000
the or implementation of the policy and differentiation of the at the access of the or implementation of the policy and differentiation of the access of the, policy, framework or there's a legislative action in the contest of this, you know, destination in the context of the immediate end. Thank you.

475
01:36:41.000 --> 01:36:46.000
Thank you very much.