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Wait. Yeah, okay. Okay. I hope.

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Yeah, it's really nice to be here. I'm a PhD student at the university, which is in Germany.

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And I just published my first study together with my supervisor and it's on the relevance of spatial context for refugees transition to that in Germany.

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So, with a large in terms of refugees to Germany around 2,01516. Their integration into education and employment now poses a substantial challenge for the host country.

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In Germany, former qualification and labor market outcomes are closely linked. So the completion of either the general academic.

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Or the vocational track of education is crucial for professional and social integration. And this is especially relevant for the refugee cohort from around 2,01516.

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Is nearly half of the refugees are aged between 16 and 30. So they are of appropriate age to enter that.

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Or university. While entering that naturally requires a higher school living certificate. Which refugees usually do not possess.

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There's no such requirement for entering the dual that sector. For clarification, the German debt system consists of 3 sectors.

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The transitional systems, school-based vocational education programs. And the dual system. So the transitional system includes only a preparatory programs for potentially later entry to fully qualifying that program.

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So it does not lead to nationally recognized that certificate. Which is why we don't consider it in our study.

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The school base vocational education programs which comprise only classroom training. They do lead to such a certificate.

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The admission to the sector generally requires the acquisition of intermediate school leaving certificate. And this is a major obstacle for refugees who either didn't attend school long enough in the country of origin or their certificates.

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So we decided to only focus on a dual that sector in our study. And as you can see, it also plays the most dominant role within the gym that system with about half of only the entrance transitioning to it.

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Hey, it combines both classroom and company based training programs. And because companies are free to choose their own apprentices, there are no formal entry requirements.

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For example, in terms of educational certificates, which is obviously quite beneficial for refugees. So transitioning to especially the dual that sector can generally be considered the most promising option for refugees.

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Hi there. Yeah, access to training places in a dual that sector is mainly determined by the regional interplay of supply and demand.

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And the fact there's a constitute this interplay such as the legal market structure, economic productivity, demographic development, they vary to a considerable extent between the German districts.

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And this leads to different fit ratios between available training places and applicants depending on the region. And for refugees this might be especially relevant.

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Because when they arrive in Germany, they are distributed proportionally. Choice on the federal and then on the district level.

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But this quota system is mainly based on local population numbers. So the socioeconomic contextual factors of the respective regions.

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They remain largely disregarded. So we assume that there is an association between the socioeconomic conditions of the German districts.

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And the chances of young refugees finding a training place. With ever ask, which spatial characteristics of the place of residence are associated with increased chances of refugees to transition to that.

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Now, what other pursuit associations between these spatial characteristics and the transition strategies to that? So the supply side basically means how many training places are available in specific region.

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The economic productivity as measured by GDP. It indicates how high the retail orders situation is. If it was, you know, order situation is high, there's also higher demand for labor force and therefore also for premise.

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So we can assume that the higher the GDP in the region is, the more training places are provided and transit like transition chances to that to increase the cooling.

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Yeah, So training you apprentices is quite custom tensor. But hiring already qualified work is not so much.

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And with a higher unemployment rate and there's also larger pool. Like already qualified workers to hire. So that means a few apprentices will be trained.

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So with a higher unemployment rate and the chances of finding the training place should be lower. I mean the next 2 factors.

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They refer to as a segmentation approach. According to this other neighbor and therefore the debt market is heterogeneous.

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And generally consist of different segments. There's a bit of a scientific debate about the exact number of segments.

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But the widely accepted view is that there are 2 segments like a higher and a lower one. And the higher segment recruiting and training apprentice that quite cost enhancement.

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So companies only train few apprentices. In the lower segment like both recruiting and training apprentices.

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Is like rather inexpensive. As they can be used almost directly as fully fledged workers. They are hired in large numbers in order to secure current production.

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And both micro and small enterprises and skillcraft enterprises belong to the lower segment. So you can assume that the more of these enterprises there are in the region, then more training places are provided.

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And again, transition chances are likely to be higher. Yes, and then with the demand side, it basically refers to the competition for training.

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So first we can assume that refugees represent competition for each other when applying for training places. So with the higher number of people with a refugee backgrounds in the region.

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To like the chances of finding a training place again should decrease. And second, a school leader is almost likely to be competition for refugees as well.

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As they represent the main group of applicants for many places. So the total number of school leaders in the region is high, the transition of like transition chances of refugees are likely to be though.

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However, as we know now, the bad market is divided into different segments. So we can also assume that not all, also represent like really serious competition for refugees.

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Refugees are most likely to enter the lower that second just like people with a lower secondary on those meeting certificate.

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And therefore the higher the number of these 2 types of school levers the lower the chance for finding a training place I'd like you to be.

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So much for the theory. So far there hasn't been much research on the transition chances or determinants of refugees to that.

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So cannot includes, they have been able to show that the refugee cohort of 2,01516 does indeed mostly transition to the tool that sector.

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And this could be assumed that transition chances are a lot lower than those of persons without a migration background.

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You have to see where I was who dream this finding. I think they also found that refugees have a similar transition rate persons with a migration but without a refugee background.

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The actual determinants for a successful transition of refugees to have not yet been examined. It does however seem likely that the transition patterns of refugees I still want to those of persons with a rather low endowment in the capital.

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And there's a big evidence that spatial contextual factors affect persons with varying levels of school leaving, So for example, transition probabilities of, schooling is decreased.

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When the regional unemployment rate is high. And this is that day for those with the lower secondary schools. And furthermore, specifically the chances of low performing school leaders finding a training place increase significantly with a higher proportion of micro and small enterprises in the region.

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Yes, then for our empirical analysis, we combine the data via, the EAP Bump SIP service refugees and administrative data at the district level, which corresponds to the master level.

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We have used only 5 that are currently available so that our observation period ranges from 2,016 to And in our sample we have focused only on refugees who are aged between 18 and 30.

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As they are the ones who are most likely to transition to that. Independent variables to be able to transition to location education training.

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However, we were not able to distinguish like, whether a transition had occurred to the dual or the school is that sector.

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We were only able to exclude transitions to the transitional system. And this is the relationship between your 4 mentioned socioeconomic contextual factors.

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And the transition chances of refugees to that we estimated a multi-level, discrete time survival model, which is technically a random intercept, and the decision for the screen timeline was made against the background in Germany the vast majority of transitions to that occur in full.

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Oh, the process itself is Yes, to control for a possible selection bias. We included the multitude of compounds.

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We could tell I don't have to interpret the results, but focus exclusively on the relationship between the regional socioeconomic conditions.

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And transition chances to that. Yes, descriptively we found that only 14% of a sample transition to that during the observation period.

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Almost allowing you know. It must be taken into account that this is an unconditional transition rate.

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So I've monitored transitions for the entire age group. Not just for those who are eligible to really make a transition to that.

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So it is naturally lower than the potential conditional transition rate. But at this point in time we simply did not have enough information on who among the refugees is actually eligible for transitioning that.

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Here on the right you can see the couple of miles. Which indicates how many people are still in the risk.

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So if not, you've experienced a transition. And one somewhat incidental finding was that the grown up dynamic Which is led to a nation like L done in 2,020 did not substantially worth.

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So the transition would have occurred at 10.5. Yeah, but for the main analysis, the multivariate models revealed that some of the selected socio economic characteristics of the residential district are indeed associated with refugees chances of transitioning to that.

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And this includes about higher economic productivity as measured by GDP and low unemployment rate. And a higher number of small enterprises in the district.

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So all the regional factors, especially on the demand side, they show significance at the 10% level. But in view of a rather large sample size.

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I wouldn't want to highlight them too much now, but if anyone's questions So yeah, then we have also estimated some alternative models to check whether our results are robust.

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First we wanted to test whether our sample restrictions are significantly affected the results. So for one thing, we have expanded the sample to include all refugees age 50 to 30.

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And find out though we have to sample up 80 to 30 year olds. Excluded those persons who transition into higher education.

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Yeah. That to specifically test whether the relationship between refugees transition chances to bad. And the GDP in the residential district is robot.

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We ran an additional model that controls for the share of employees. Involved in the manufacturing and the service sector.

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So this way we wanted to ensure that the results were not simply due to different distribution of economic sectors in the industry.

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And lastly, we just do for nonlinearity by adding the square terms of the region of variables each morning.

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And in conclusion, our results remain robust in every single office. Just lucky. Yes, last slide. So based on our results, we conclude that refugees place of residence is indeed associated with their chances of entering that.

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So the special placement of refugees in Germany. Is likely to be related to their educational outlooks and subsequently their integration into the labor market.

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And therefore with the current distribution policy. Which is primarily based on local population numbers and largely this regards essential socioeconomic like conveyance in the region.

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They can like, yeah, it can at least be a quick question. Yeah, also the limitations to our study.

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For example, a period of observation only comes 5 years, while it can be assumed that refugees transition chances to that or look sorry like the transition to that does not take place until several years after the And since the server refugees only begin in 2,016.

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You're not able to, to use refugees here of arrival as a starting point, but we had to use this over here at the timeline.

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Not ideal, obviously. But nevertheless we believe that we can make a contribution to the debate on the spatial distribution of refugees.

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Which is all the more urgent now against the backdrop of increasing numbers of refugees in Germany. Yeah, thank you for your questions.

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So.

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Well, thank you, Francisca. I guess you didn't hear that the room was clapping and thank you for keeping the time are quite quite well.

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I would first take questions from the audience and then move to questions from the virtual audience.

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Right.

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Come to the microphone and maybe. Yourself before asking the question.

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Did you?

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Okay.

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Into classes.

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I am sorry I couldn't hear anything. As as it probably through repeated like into my microphone.

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Coming to the bottom because it seems like that So again

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Okay, oh yeah, Oh.

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Oh cool.

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Thank you very much for your for your talk. To Germany as well. And there also asked for terrification.

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You tested.

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Skewed Croft Enterprises is a factor for successful or long success. I didn't quite understand what you meant by that, skilled craft enterprises.

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What kind of companies are those?

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Yeah, this is like a huge sector in Germany. The in Germany would simply be like comes back, but like many, many types of professions belong to it.

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So it can either be like people who actually, no, I don't want to come here.

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I have people who actually like do like reparations in your house but it can also be like people who bake bread in your house, but it can also be like people who bake bread, you know, or like people who are like reparations in your house, but it can also be like people who bake bread, you know, or like people who

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cut your hair. So everything, I mean, like, sort of like manual labor.

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Okay, further questions?

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I'd like the theory behind it. Yeah, like the theory behind it was clear, right? Like why, like, refugees are support or are likely to transition mostly to that sort of sector because they hire like the most apprentices and also like looks like yeah like a lot like a school meeting certificate is not as much of importance

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as like in other sectors.

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Question from the audience.

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Miyagiako from University of Cologne and

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Questions.

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Oh wow.

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Interesting and relevant because it's actively interesting in particular for policymakers and that has been said in your conclusion.

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And so I think it's very valuable to research. Though what I was asking myself is how do the processes that you describe that you observe for the refugees differ from Other.

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Groups that try to enter the vocational education training that are also low qualified so the low qualified school diverse those with migration background and so on so I know that you cannot observe this because your data does not allow so, but I think it would be worse to think if there is anything that is different for these refugees and something that might be different is

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whether or not in the region they are living there are particular sentiments or or particular anti refugee attitudes that so just to make sure that you don't tell a story that we already know for other migrant groups or for low qualified school levers but that you point out that there is something going on that is specific for refugees.

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So this is the first thing do you, is there anything that's different? The second question is I would suggest not only to focus on age 18 to 30 because there might be the entry into the labor market directly more attractive for those who are almost 30 or 8 older than 25 and perhaps have something for your robustness checks just to focus on the

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15 to 25 that might be the more relevant group for vocational education and training and what you might also think about is how do the refugee Adolescence know about vocational education and training.

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Do not do you know anything about advice of the employment agency? What does the employment agency device to 24 or 25 year olds?

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Do they advise them to go to the labour market or do they advise them to do dual training? So that's also something I would like to know your opinion at and then a marginal question is I think you're Hi hypotheses about if there are other refugees in the area in the region was negative so the more they are

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the worse but it could be the other way round in in a sense of the social contact hypotheses, the more they are, the more our people are acquainted to refugees and the better the chances for young refugees.

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Okay, 1 s. This is time to make this done.

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Of course. So many interesting questions. I, yeah, everything I wanted to talk about here.

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It was a pity that, yeah, that the presentation was only 15 min. So I'll start with like the easiest question.

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Yes, why did we look at, like refugees. Yes, why did we look at, like refugees, 18 to 30?

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Maybe I can start like with a lower age, 18 to 30. Maybe I can start like with a lower age 5 that we started 18 and not 15.

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So it's the daytime. So, yeah, the Zap survey, it does have like, data on, refugees like age 15 to 80, but it's the parents who give the information and they get a different questionnaire.

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So for many of our key, if the key variables such as like language competencies for example, which is like really, really valid and relevant like to you or like, yeah, for them to control for it wasn't available for this yeah age group so this is why we need the robustness check but only with a truncated like

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yeah like set of confounders. And like I said, the results remain robust, but it's yeah, it wouldn't have been like enough to like like run like a proper model with this age group simply because yeah there's so many missing variables.

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Yeah, meaningful. Then for the older ones, like why did we look at refugees, 30.

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Yes, I mean it is true that like in Germany, like most people who start at an education education training program, they are up to 25 years.

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But I would say that like and we also argued about this. Like what I say in the paper.

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Yeah, then it might be like a different situation for refugees because like when they arrive in Germany, they like, this is basically like a different situation for refugees because like when they arrive in Germany, they like, this is basically like the only way of like getting a perspective, you know, of having this professional and social innovation even if

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they're like 26. We have to admit that like their human capital isn't valid as much.

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So like all the professions they could have in Germany, they are going to be like on a very like low like like yeah, lower.

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Segment and it's mostly gonna be like they're not gonna get like much yeah income and like social integration is gonna suffer from that too.

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So it is like the best of beginning for them to really like And yeah, I hope that people, like this was like another question.

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I hope that people like, yeah, get informed about location education training because it is a system that is very.

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I mean like other countries have it as well, but I mean like not that many countries. So it's not like entirely specific to Germany but it's like a very important part in the German educational system.

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So yes, there are. A mentoring programs for example by the federal agency of employment there are also active labor market programs that for example, the ASF program, and like certain, yeah, courses provided by the federal agency. Yeah.

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So, we hope that they also fund them about that. I am currently working on a study actually on like the effect of these ALPs on the transition of refugees to that.

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I am currently working on a study actually on like the effect of these ALPs on the transition of refugees to like different And yeah, yeah, I don't wanna like spoil too much or any, but you can actually see that.

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I mean, there's like a dependency that like the, federal agency of employment seems to be, like they seem to want.

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And like the refugees out of their statistics. So they kind of push them into the labor market, like, what this other, ESF pump program, which is like funded by the, by the European Union.

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They are like the numbers that I really like from location education training. So they do get like a really like professional like base of like professional German and been for my key competencies and then they managed to actually make the transition to that.

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Yeah, I would have to look at the range again but I wouldn't say that it's like a like just like narrow down to like 25 year olds also I guess like for, I can like move it back to the first question.

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Let us still think about it. Like, for a German, yeah, like a dollar sons, like when they leave school, they are like 15 and 16 and then it makes sense to almost like start immediately like a vocation education training program because otherwise that what do you want to do but for refugees who have just arrived I think

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even if they are 2627 still it's obviously next to the best opportunity but I think it's also they don't have like this like this yeah it would be like so much at the gap as like for 15 year olds you know so it does make sense to also transition there.

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So then how do these processes that I have described different from, the processes of other groups? Yeah, that's, yeah, this is also what I, would have ruled from the like state of research that the transition patterns of refugees are really, really likely like to be as like the same or similar to

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those of persons with rather like low endowment capital. So for example, like there are similar as the process of processes of persons without the school meeting certificate.

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And you can see that the processes they are very similar. They would say that the starting situation, you know, like the starting point is a different one.

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Because, like refugees, they cannot be like for perfectly distributed around Germany. So, yeah, like I said, like the quota system is based mainly on local population numbers.

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So their chances of like ending up somewhere like in the middle of nowhere, but like in a region where like GDPs are low where unemployment rate is high.

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It's just like comparatively really high. And then you have at the beginning as settlement restriction in general.

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So, this means that for the first 3 years after their, like after their asylum procedure has already been finished and obviously also during the asylum procedure they have to reside in the same district.

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So this restriction is lifted once they find either a training place or a job. But we know from various studies that, refugees are oftentimes unaware of the legal framework that applies to them.

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So it is pretty likely that. Yeah, but it's still like an issue for them.

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That's like this, restriction. It still hinders them to actually make a transition like into like another like you know another district.

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And then again like also mobility patterns. We don't really know that much about like the mobility behavior of refugees.

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But sorry, my roommate. Yeah, we don't know much that, yeah, but the mobility behavior, we can assume that they're not as mobile as, yeah, but they can assume that they're not as mobile as like auto phones are because they obviously don't have

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like a driving license. A lot of phones are because they obviously don't have like a driving license.

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And so they really rely on how a driving license. And so they really rely on how to driving license. And so they really rely on the transport.

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So we would say that it is like for them like being in a place where like for them like being in a place where I would say that it is like like for them like being in a place where like for them like being in a in a place where like for example public transport isn't that good for them like being in a place where like for example public transport isn't that good but I mean this is not variable but

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it's highly that connected like with the GDP unemployment rate stuff like that, which is like in a deprived region.

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I think it's gonna bet. It's a lot worse for refugees than for a person with a, yeah, without a schooling certificate.

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But yeah, the processes are likely to be similar. And then the, oh sorry, yeah.

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Oh, I just wanted to say thank you for your answers but maybe there's 1 point still missing and I just take the chance to second what include a measure of Anti-fugee anti immigrant sentiment.

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We turn out of the alternative for Germany so the ride will

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To have like

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Specific mechanism that prevent refugees get getting into training places, but maybe you wanted to comment.

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Yeah. Yes, I mean, yeah, definitely. Like it is definitely a factor, I think, because we do know that, discrimination varies like among like both the federal states, but also on the district level.

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So the focus of this study was to look only at factors that really influence like the regional so like interplay or supply and demand.

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And discrimination simply doesn't. There's another really interesting study though. It's by, I guess, they, get, they, look, they were also working with the same data.

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And they managed to find out that the perceived level of discrimination is highly correlated with like different like integration variables.

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So for example, like the higher the perceived level of discrimination for refugees is the lower the chances of finding a job or finding a flat.

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They didn't look at location education training, but it can be assumed that this is also, yeah, that definitely like plays a major factor.

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But like I said, yeah, we just really wanted to look at this like into play of supply and demand and discrimination simply doesn't cut into it.

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Yeah.

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Questions to Francesca.

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Zoom are there questions?

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Thank you again for the presentation and let's move. Presentation which is in person. By Linda Hoffman and it's the title has changed slightly.

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Now she is talking about spatial mobility, occupational orientation and regional opportunity structures during the transition to vets in Germany.

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If you could tell me the screen.

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Yes, and hopefully I need Yeah, Yes, Mr. Berger, just let me. Looking like this.

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Oh great, yeah, thank you very much. I'm Linda Hoffman and I am really happy to present to you my work actually on 2 research questions I have been or am working on together with Aleksandar Vis as part of my dissertation that focuses In general, on the interplay of spatial

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mobility, occupational orientations and regional opportunity structures for successful transitions to vet in Germany. I want to start with a short general introduction into into the motivation to study this topic and then present you some key findings of our first paper that however actually already got published recently and therefore in the second part I also want to present you a short

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preview on the second paper I just started working on. So first of all, why is it important to look at the spatial mobility of young people starting that in Germany?

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And when we look at the Q and situation on the labor or training market in Germany, we see prevalent regional disparities.

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On the right, for example, we see the extent of so-called metering problems, so the fit between offered and demand and training positions for different regions in Germany and quite clearly there are regional differences.

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In light of these regional disparities, spatial mobility is often coined as an important means to mitigate these regional disparities and hence also facilitate successful transitions into it, especially for the students coming from unfavorable regions.

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Research on the spatial mobility in the context of school to veteranizations, however, is still relatively scarce and precisely this is what we want to study a bit deeper.

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So this leads us to our first paper on the prevalence and the predictors of spatial mobility among that students.

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So do they become mobile? Where to and why? I did originally submit as mentioned the abstract on this, on this research for the conferences.

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But of course it is already published so I will only give a short overview of the study. Theoretically we drew on 2 frameworks.

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Firstly, subjective expected utility theory and the mortality framework both these theories emphasize that individuals decisions to become spatially mobile are dependent on their individual preferences and goals or their genetic motivations, including of course their occupational orientations, their social ties and the structural opportunities and constraints they face.

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Here we further drew on specific theories that highlight the importance of the regional opportunities structure for spatial mobility.

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Firstly, the concept of place utility which plays utility which states in short that different regional characteristics denoting the general utility or attractiveness of attractiveness of a region are decisive and secondly person environment fit theory that more specifically states that the fit or match between individuals preferences or aspirations and the available opportunities in their environment or

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home region as decisive. So based on this here you can see the graphical representation of our research model so we examined the joint relevance of individual occupational orientations as a genetic push factors, regional opportunity structure as structural push factors and social ties as possible binding factors on the spatial mobility of you starting bed.

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So how did we do this? We used panel data from the German National Educational Panel Study and to map the regional opportunity structures we merged additional regional data at the small scale district level.

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Our dependent variable is of course spatial mobility. However, we did not apply common distance approaches, but rather distinguished spatial mobility within or across borders are functional labor market regions.

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I do not want to get too much into detail into the delineation of the regional levels but on the right you can see in green the German district so the nuts 3 level and on top in blue the regional labor markets further grouping the districts into functional labour market regions.

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And these regional labor markets represent economically meaningful regions and therefore also more accurately the actual scope of action of the young people searching for that.

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The table already shows how the spatial mobility is distributed in our sample and in total nearly 40% of students starting that do become spatially mobile and more precisely 60% do so within their regional labor market and nearly 22% even across them.

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So what leads to this mobility? And therefore here an overview of our predictor variables in short for the individual occupational orientations as a genetic push factors we look at the status aspirations of the students and the search duration.

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Regarding the structural push factors, 2 indicators were used to map the regional opportunity structures in the home district of the students.

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The inverted regional commuter balance as an indicator for the general unattractiveness of the region and the inverse share of employees in the vocational segment in which the students aspire to work as an individualized indicator for the regional person.

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And lastly, 3 indicators were used to measure possible binding effects of social ties. The participation in any group organisations at home and the satisfaction with family and with friends.

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So what did our results show? Here you can see that average marginal effects of our predictor variables on the respective mobility category.

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So normalability gray within mobility green and mobility between regional labor markets in blue. I do not want to get into too much detail with the interpretation but rather highlight our main results.

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So looking first at the individual occupational orientations. We see that high status aspirations show significant association for mobility within regional labor markets in green and a higher search duration as relevant for mobility between regional labor markets in blue.

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So students with higher aspirations and higher search durations are more likely to become spatially mobile when they start that.

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Then the regional opportunity structures turned out to be crucial past factors as well. And for higher general unattractiveness of the home region we see significant association formability within as well as between regional labor markets and a higher person environment.

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Mismatch is also relevant for mobility within regional labour markets in green. So students from unfavorable regions are more likely to become spatially mobile for their vet as well.

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And lastly for the social binding factors we did not find any significant effects for participation in groups and satisfaction with family but a high satisfaction with friends on the law.

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The last, thing turned out to be significant binding factor regarding mobility between regional labor markets in blue.

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I'm so to sum up our results of our first study showed that the actual spatial mobility of students starting that is dependent on both the occupational orientation so higher status aspirations and longer search durations and unfavorable regional orcop opportunity structures which may which increased the likelihood of the students to become spatially

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mobile when starting with. However, this indicates also that it is important to further investigate possible changes in the decision-making process of need students.

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So students that are not in education, employment or training while they are searching for it. So do they adapt or increase their willingness for spatial mobility and or their occupational radius over time.

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And this is what we are looking at in our second paper. I'm sorry. Can you hear me?

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Okay, I'm sorry. This is what we're looking at on our second paper. Short disclaimer, this is really, really early work in progress.

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I just started looking at the data. I still have a lot of work to do but I am happy to be able to present you some simple first results as any feedback at this stage would be greatly appreciated.

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So as mentioned we now want to look at possible changes during the search for bet. Precisely we want to look at if young people seeking that adapt their mobility radio so where are they searching and or their occupational radio so what occupations are they searching for over time?

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Theoretically, indeed both the formation of occupational orientations and mobility decisions can be seen as a process that is dependent of course on time but also individuals initial preferences or aspirations and regional opportunities structures.

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We again use the naps panel data however we now focus on the application behavior of youth not in education employment or training during their search for that.

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Our working sample at the moment is nearly 1,500 applicants that have at least 2 observable measures at the same time on where and which that position they applied for.

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So yeah, our 2 dependent variables are of course the change in mobility radios and the change in occupational radios.

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And as explanatory variables we want to focus on of course the search duration so how do these changes unfold over time, assuming of course an increase in mobility radius or occupational radios if the students are unable to find a suitable bed for longer durations and secondly their initial status aspiration so to students initial aspirations

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when starting to search for that, influence whether they change their mobility or occupational radios. And the regional opportunity structures, of course, again, assuming that unfavorable regions, students from unfavorable regions should be more likely to increase their mobility or occupational radius.

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So some first results after quickly looking at the data. Firstly, regarding any changes in the mobility radios to operationalize this we look at the information of respondents when they were asked about the training positions they applied for, precisely how many kilometers that are out of the furthest training place.

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Right now we simply use the first and last observable measurement and the answer categories were not a continuous measurement so we differentiated 3 groups according to whether they're mobility radios increased, decreased or stayed the same from the first to the last measurement.

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And when we look at the distribution in our sample right now, we see that most applicants mobility radios is relatively stable, but one in 3 actually do increase their mobility radios over time.

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Then regarding any changes in the occupational radios, we use information on the number of different occupations they applied for.

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The questions were slightly different depending on different questionnaires but the main phrasing was something aligned the lines of for how many different professions or occupations did you apply.

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We used the difference again of the first and last measurement as a metric measure indicating changes in the occupational radios.

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And looking at the distribution we see that On the right side of the graph, over half of the applicants do increase their occupational radios over time.

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So they apply for a higher number of different professions or occupations. So good thing is we have changes and more precisely as anticipated we have increases in both Larry levels.

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In the next step we wanted to see how these changes relate to each other. Here you can see the changes in occupational radios dependent on whether they also change their mobility radios.

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We see them for applicants that did not or decrease their mobility radios, there also seems to be no changes in the occupational radios.

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But for applicants that increase them mobility radios, we also see a positive change so an increase in the occupational radios.

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This indicates that these changes are not one or the other decision but whether go hand in hand. And further we had a first look if any differences regarding the initial state aspirations of the applicants can be observed.

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It has to be pointed out that right now we used information after leaving school. So regarding changes in the mobility radios, firstly and differences in the status aspirations we see that the groups of the applicants that in increase the mobility radios has similar status aspirations than the ones that did not change their mobility radios.

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But the group that decreased their mobility radios has slightly higher status aspirations. Then looking at changes on the occupational radios dependent on initial status aspirations, we see in the scatter plot that there seems to be a slight U-shaped relationship with slightly higher increases for applicants with high and low initial status

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aspirations. Therefore, we further generated a categorial variable looking at students with high medium and low initial status aspirations and here the differences become more visible.

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So the group of applicants with high and with low status aspirations show higher averages increases in their occupational radios.

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And lastly, we looked at any possible influence of the regional opportunity structure. I was not able to include any comprehensive regional measures for now.

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The only measure available in the data was whether they live in East or West Germany. So I had a look at this.

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And regarding changes in the mobility radios we see that for applicants in the West Germany and 50% do not change their mobility radios but one in 3 show an increase in their mobility radios this is similar to the general distribution we saw earlier.

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In East Germany on the other hand the groups are more equally distributed and more people increase but also decrease their mobility radios.

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Then lastly looking at any changes in the occupational radios between East and West Germany, we see that in the West we have a slightly higher mean and in the east more decrease.

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This is quite surprising at first glance however studies on unemployed and unfavorable regions in the east also revealed that they surprisingly seemed to stay stuck in their bad situation.

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So yeah, to sum up what what we found after first looking at the data. The good thing is we see changes and more importantly increases in both the mobility radius and the occupational radios of you seeking vet over time and it also seems to be a joint decision so they seem to increase both things at the same

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time. Regarding any influence of those status aspirations we see that applicants with higher initial status aspirations show no increase in their mobility radios but applicants with high and with low status aspirations show higher increase in their occupational radios.

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And regarding regional conditions, applicants in West Germany show stable mobility radios but higher increase in their occupational radios.

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On the other hand, applicants in East Germany show more pronounced changes in the mobility radios and more decrease in their occupational radios.

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So finally, what is next? Still a lot. So firstly, of course, I have to include refined measures of regional matters.

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And maybe, yeah, then you can see the next steps, any feedback regarding these open questions might greatly be appreciated.

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Thank you very much.

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Well, thank you so much for your presentation. Thank you. Lots of information questions from the floor.

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Okay, I don't know who was first. Do you remember who was first? Look down in the rest of their hands work.

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We just need to stop sharing.

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Let's do this one screen. Sorry guys, 1 s.

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Okay, I can see it. Okay, so they were hands in the virtual room

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Or they were just clapping hands for. Take them as with. Okay, is that a question?

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Okay, please come ahead.

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I also have to apologize to the ones in Zoom that the camera to the room shut down because the battery ran out.

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So unfortunately, you can't see the audience any more or the person asking the question.

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Trying to do that. I'm honored it's still expanding with talks that

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Strength. Yes, yeah.

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Us a big doing some research on

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As well as

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On my So.

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For those online, my name is Louisa Bambry. I'm from Charles Sturt University here in Australia.

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And I've been doing some research though on Tasmanian youth labor markets and I guess I'm interested in what you are thinking about as the components of disadvantage that you might look at.

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I mean when I looked at the Tasmanian ones I looked at sort of the issues around the housing market.

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And you talked a little bit about the embeddedness within the social relations. So I wondered if you could expand on that a little bit more and tell me yeah what sort of disadvantages are you looking at and how are you going to try and measure social embeddedness?

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Yeah. Okay, I have to go here. Yeah, thank you very much. Yeah, actually in our first research we looked at the social embeddedness and we were quite surprised because we thought especially students starting that are relatively young they Don't get much.

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Money for their vet position so we thought okay there might be dependent more on their family for help quite surprisingly our results show that the family doesn't play such a big role.

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Further to clarify we looked We regarded spatial mobility either if they commute or if they move.

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We did a robustness check and also Yeah, analyze an additional model, controlling whether they moved actually to the training place.

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So the results are controlled for this, but it holds in general whether they commute or they move to another place.

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So there might be differences regarding the actually how tough is the housing market in any relocations yet.

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Okay, further questions from the floor?

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Or from Zoom.

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Okay, please come up.

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Yeah, hi, I'm Margaret Siegers. I'm your colleague. It's just a short question.

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So you say that the parental background doesn't have any influence on the Yeah, okay, no, I meant the social embeddedness with the family.

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It is a different. I mean the social embeddedness with the family. It is it is a different we look at the social embedded as our maybe I should go.

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We look at the social embedded as our, maybe I should go here again. As our primary indicator looking at social ties or how close are they with the family.

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As our primary indicator looking at social ties or how close are they with a family. So how close are they with their family?

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We also controlled, for, the parent indicator looking at social ties or how close are they with their family.

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We also controlled them for the parental socioeconomic background. And this also had no big differences.

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And do you have do you have any information about the qualification or something like this? From the parents.

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Yeah, we we looked at the SES will use the Mother and father and looked at the highest one so yeah

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Okay, last chance.

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Otherwise I also would like to second the point here that was just raised about The differences are like heterogeneous effects by family background when it comes to adapting the choice of Training options and adapting the search radios.

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Of course we know that bet students or highly qualified parents, their children mostly go into like the academic track but there still might be some heterogeneous effects.

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Where you can differentiate that maybe. Higher status children do not adapt to the pressure of time and they don't adapt to the their choices and they're more likely to move.

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So maybe you have some first. Assumptions about that or. Otherwise, I'm happy to hear more results on that once you have them.

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Yeah, thank you very much. I don't have any first results on this as I just looked at the data and of course I didn't include any possible confounders, control variables that might influence the relationship.

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This is definitely one key variable that is on a list. We have to control for the background, the family background, but also Quite a hint.

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At our, analysts the, educational qualification of the students. We didn't account for this either.

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We just look at their aspirations, but of course we have now different students that have maybe gymnasium, so higher educational background or left school after grade 9.

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So we have to account for these. Marvels as well. This is also on our list for the next steps, yeah.

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Okay. Well, we will have more room for discussing all 4 papers than at the end. So thank you, Linda.

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Again, and we will move on to our third presentation by Yunas. The gendered role of spatial mobility for graduate earnings.

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And I hope I do this right now here. You can share probably the screen. Yes, great. Thank you.

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Okay.

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Screen muted now you can hopefully hear me So hello everyone, my name is. I'm glad to speak to you today.

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About my research project, which is also part of. My dissertation feeling with the tenant role of spatial mobility for graded earnings.

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And first of all, it's great to be, connected. So, around the whole, to you and Yeah, it's really cool.

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To speak to you today.

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Yeah, why is my research, important? First of all, I would say that the transition from higher education to the labor market.

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So formative life event both for young people themselves, but also for the future careers.

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And that is, it's often also accompanied by mobility decisions. And, what's even more that, those, transitions.

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Are, yeah. Often gender specific resulting and differences we see and Yeah, lower incomes among female graduates than mayor graduates.

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What from what we know about labor market opportunities and how they are distributed across regions. We further can assume that there's a potential role of space or mobility.

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And this is why I want to ask how and if male and female graduates use mobility. To achieve higher earnings in their first job after gradation.

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Yeah, I want to argue that mobility readiness such as the willingness to relocate to commute or even to do telecommuting.

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Can be seen as an investment in the access to a broader range of jobs. And also to better jobs and to better paid jobs.

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Furthermore, there's a common assumption that man use mobility more career strategically than women.

317
00:54:50.000 --> 00:55:06.000
However, it's not that clear if those, Oh, sorry. It's not that clear if, those gender differences and mobility benefits exist already among graduates.

318
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There's not much research with tests specifically focused on that aspect. What previously search, however, has shown is that 10 differences in mobility benefits are very pronounced.

319
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Among people working people and also among couples and married people which is of course a very different life situation to Graduates, normally.

320
00:55:33.000 --> 00:55:44.000
And this can be also backed up by, documentation based on the theory of household economics.

321
00:55:44.000 --> 00:55:58.000
Which says that households become mobile, the sum of all individual benefits is greater than some of costs. Meaning that it's the sum and not the individual benefits so that, Awesome.

322
00:55:58.000 --> 00:56:04.000
It's men who have higher, income prospects and therefore it would be rational that they.

323
00:56:04.000 --> 00:56:10.000
Yeah, also dominate the mobility decisions of the households. There's also another, argumentation based on general theory.

324
00:56:10.000 --> 00:56:27.000
Which basically says that there are gender roads and partnerships which play role such as the stereotypical image of the May bread winner.

325
00:56:27.000 --> 00:56:38.000
Which make it then more likely that mail also mail, man man dominate. Work related mobility decisions over women.

326
00:56:38.000 --> 00:56:46.000
Yeah, following these theoretical ideas I, came up with, few hypotheses.

327
00:56:46.000 --> 00:56:51.000
First of all, I want to see that all graduates who've been their first job in a new region.

328
00:56:51.000 --> 00:57:00.000
Receive higher earnings than graduates who stay in their study region. Secondly, II would.

329
00:57:00.000 --> 00:57:09.000
Assume that the benefits of mobility after graduation in the overall picture are very similar for male and grand and female graduates.

330
00:57:09.000 --> 00:57:24.000
Thirdly, however, I would assume that partnership plays the role. Bye, being by by assuming that mobility benefits are higher for female graduates if they do live together with a partner.

331
00:57:24.000 --> 00:57:32.000
Whether they I would assume that those mobility benefits are higher for May graduates if they

332
00:57:32.000 --> 00:57:39.000
Sorry, for female credits, I would assume them to be hired. They don't. Live together with a partner.

333
00:57:39.000 --> 00:57:48.000
Of course, it's the other way for main part, for males. They live together with a partner, I would assume them to be higher.

334
00:57:48.000 --> 00:58:00.000
As these assumptions, I am using data from the National Educational Panel Study. And yeah together more homogeneous sample of higher education graduates.

335
00:58:00.000 --> 00:58:12.000
Have some sample restrictions First of all, with regards to the I only look at graduates who started their studies at the age of 25 or below.

336
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They had to start a dependent, regular employment. With a certain amount of minimum hours per week and and so on and the employment also had to start.

337
00:58:22.000 --> 00:58:37.000
Within the first 2 years after graduation and had also to last for more than 3 months. To ensure it's a regular employment.

338
00:58:37.000 --> 00:58:46.000
Yeah, and after applying multiple invitation methods, I end up with a sample of more than 4,000 graduates.

339
00:58:46.000 --> 00:59:00.000
And those in the video data I combine with regional data at the district level. Which is equivalent to the next 3 just shortly I will mention the measurements.

340
00:59:00.000 --> 00:59:15.000
I use our way just in the first job after gradation as my outcome variable. And here I take the monthly gross income divided by the contract working hours both self reported in the data.

341
00:59:15.000 --> 00:59:31.000
And yeah. For the treatment variable I'm using mobility and here I use distant information on the place of study, the living place during higher education to be more specifically Then I use the place of employment.

342
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And the place where secondary schooling. Has been taking place. And by combining this information, I can differentiate.

343
00:59:41.000 --> 01:00:05.000
Yeah, for categories of mobility. First, I, differentiate the graduates who stay at the place of Hi education for the first job as the reference category and then I, I, There are back movers, graduates who move back to the place of school.

344
01:00:05.000 --> 01:00:16.000
And thirdly, I have those graduates who move to a new place and because this is a more heterogeneous group and because I also could observe that.

345
01:00:16.000 --> 01:00:29.000
There are some nonlinear effects. I divided this group. By, having short distance movers removed to new place within a 90 min driving radius.

346
01:00:29.000 --> 01:00:44.000
And those us who move beyond the 90 min driving radius. As long distance moves. And as you can see on the right hand side, the distribution is quite generally equal.

347
01:00:44.000 --> 01:00:57.000
We have over 30% of graduates who stay at the place of study. And we have rather relatively low shelf, whether to return back home.

348
01:00:57.000 --> 01:01:07.000
Okay, so, my analytic strategy. Is that I'm applying entropy balancing weights.

349
01:01:07.000 --> 01:01:28.000
With mobility, as my multi-dimensional treatment valuable. Main problem is that, we, know that there's positive self selection of mobile graduates and to account for this amount of self-selectivity we use this entropy balancing technique to rewrite the observations.

350
01:01:28.000 --> 01:01:41.000
So that, the means, yeah, this is of observed. Yes, practically the same in the control group and all treatment groups.

351
01:01:41.000 --> 01:01:50.000
And therefore I can use a large set of relatively large that I would say of individual selection variables, including personal.

352
01:01:50.000 --> 01:02:05.000
Yeah, characteristics such as status expectations and personality. Field of study working hours and so on but also social background indicators and also an indicator on the sampling.

353
01:02:05.000 --> 01:02:13.000
Because, in the sample there, were teachers that students were. Somewhat oversampled.

354
01:02:13.000 --> 01:02:29.000
This can be all balanced by this technique. Furthermore, I include regional district information on the place of study and I decided to include them as control areas in the model not to overload the balancing model.

355
01:02:29.000 --> 01:02:40.000
And yeah. By applying these techniques, I estimate predicted How are you just based on general linear models?

356
01:02:40.000 --> 01:02:52.000
Which I will show now. To the resides for women. We see first of all that there's not much evidence for mobility benefits.

357
01:02:52.000 --> 01:03:02.000
Overall. That 2 is the slides, like there's a positive association here. For short distance movers.

358
01:03:02.000 --> 01:03:13.000
So that it's the lockout, so they have a 6% higher. Body weights if they, move.

359
01:03:13.000 --> 01:03:26.000
Within a shorter distance. Then if they stay at the place of study. However, this association disappears when we include the entropy balancing rates and the regional controls and model 2.

360
01:03:26.000 --> 01:03:33.000
And in one of the, the picture gets even more interesting. Yeah, we have the in with partnerships.

361
01:03:33.000 --> 01:03:44.000
And as you can see, we have first of all a positive main effect of I'm short distance mobility in the female graduates living alone.

362
01:03:44.000 --> 01:03:54.000
Profit from shortlist and small ability Secondly, we have a positive association of living with a partner for those.

363
01:03:54.000 --> 01:04:06.000
That's the main effect of those. FEMA credits who stay at the place of study they profit from from having a partner from living together with them

364
01:04:06.000 --> 01:04:19.000
But, even more interesting, do you see that, those positive, benefits are not valid for, those female credits who live together with a partner and become mobile.

365
01:04:19.000 --> 01:04:30.000
Especially for those who, move back and move within a shorter distance we see, that there's a negative interaction.

366
01:04:30.000 --> 01:04:38.000
Turning to the resides for man. The picture first in the first 2 models looks a bit similar.

367
01:04:38.000 --> 01:04:49.000
There's not much evidence for high mobility benefits. And here, interestingly, in model 2 we see that short, sorry, long distance movers.

368
01:04:49.000 --> 01:04:56.000
Have have higher mobility benefits. So here, Mayer graduates are moving. More than 90 min.

369
01:04:56.000 --> 01:05:15.000
To new place. Have around 6% higher hourly wages overall but then in Modi we can, see very interestingly that this, positive long distance.

370
01:05:15.000 --> 01:05:24.000
Effects, is actually only holds true for male graduates who live together with their partner.

371
01:05:24.000 --> 01:05:40.000
So here we have a positive association and we have a similar positive interaction term for male graduates who live together with a partner and move back to the home place.

372
01:05:40.000 --> 01:05:53.000
So maybe these numbers look a little bit. Yeah. It's bad. II plotted them as predicted hourly way just by partnership to get a better understanding just of similar models.

373
01:05:53.000 --> 01:06:00.000
So here we can see that, On the left hand side, we see graduates living alone.

374
01:06:00.000 --> 01:06:09.000
And on the right hand side we see credits living together with a partner and we can see over that it's the May graduates who receive higher early wages.

375
01:06:09.000 --> 01:06:32.000
However, we see that the difference between men and women is. Differently small or big. First of all on the left hand side we see that the differences the gender differences gets smaller if graduates become mobile, especially this especially true for the short distance mobility.

376
01:06:32.000 --> 01:06:43.000
Where we could see before that women living alone have higher mobility benefits. So, here living alone.

377
01:06:43.000 --> 01:06:56.000
Women can not close but decrease the gap to the earnings of the men. Whereas on the right hand side you can see for graduates living together with our partner.

378
01:06:56.000 --> 01:07:09.000
That mobility actually increases the gender gaps in credit earnings. And that's especially true for, graduates moving back home and graduates moving.

379
01:07:09.000 --> 01:07:23.000
Beyond so for the long distance mobility. Yeah, that's actually the main Besides, which I wanted to show here you now.

380
01:07:23.000 --> 01:07:26.000
In the presentation to conclude. Once more I would say that there are surprisingly maybe surprising maybe not.

381
01:07:26.000 --> 01:07:40.000
Weak evidence for general mobility benefits that can be several reasons for that. I mean. We know that we have also and Germany.

382
01:07:40.000 --> 01:07:48.000
Higher education. Locations that, often have a Very good labor market, very good labor market opportunity.

383
01:07:48.000 --> 01:07:58.000
So it's not always a bad decision to. To change. The location maybe.

384
01:07:58.000 --> 01:08:10.000
Secondly, we could see that overall graduates mobility decisions. Cannot really explain, income inequalities between man and women in the overall picture importantly.

385
01:08:10.000 --> 01:08:19.000
Because, if we take into account, partnerships. We can see that gender differences are much more pronounced now.

386
01:08:19.000 --> 01:08:30.000
And specifically, we could see that female graduates who live alone receive high mobility benefits. So, this is very much in favor of.

387
01:08:30.000 --> 01:08:41.000
Yeah females that are But they. Can ban it much more of their mobility if they are not.

388
01:08:41.000 --> 01:08:50.000
So that may have to make a compromise with that partner or with the household. Whereas for May credits it's the opposite.

389
01:08:50.000 --> 01:09:00.000
If they live together with our partner, we could see that they have higher mobility benefits. And this was especially true for a long distance moves and moves back.

390
01:09:00.000 --> 01:09:15.000
To the home place. Yeah, that's it for now. Thank you very much for my 10 for your attention and looking forward to your comments and questions.

391
01:09:15.000 --> 01:09:22.000
Okay, well thank you. You may UN share your screen. Thank you. Questions?

392
01:09:22.000 --> 01:09:40.000
Yes, please come again to the podium.

393
01:09:40.000 --> 01:09:49.000
So hi, thank you Yunas for your presentation, which is a great contribution to a lot of research considering the gender gap among graduates.

394
01:09:49.000 --> 01:10:03.000
I have a question regarding your results or maybe a theoretical explanation for your results and this depends on when living together with a partner was measured, was it before they entered the labor market or was it simultaneously at the moment when you measure their income?

395
01:10:03.000 --> 01:10:16.000
Because if it was measured simultaneously, then the explanation would be rather easy that women lose because of their mobility may be due to them following a partner who has already moved.

396
01:10:16.000 --> 01:10:39.000
And they accept a job just to live together with a partner. And on the other hand, for males, that also makes sense because they move only to their partner if they do have a benefit and that's something that has been shown by research on adults or in the general population who move.

397
01:10:39.000 --> 01:10:48.000
So men move only to the partner if they gain something. Women move to the partner at any cost.

398
01:10:48.000 --> 01:11:10.000
Yes, thank you very much for this. Very good, remark. So the information on living together with, with our partner was actually measure it annually and what I did was that I Yeah, just took the information which was before the time point of graduation.

399
01:11:10.000 --> 01:11:20.000
So At least I could ensure that it was measured before, but what I cannot really ensure is what their partner did and if.

400
01:11:20.000 --> 01:11:31.000
The partner moved before they moved or if the partner made a which was And the time, before or afterwards.

401
01:11:31.000 --> 01:11:39.000
That's a data constraint also because I'm using the in real-life data and not How household level data.

402
01:11:39.000 --> 01:11:49.000
So. Yeah, but that's a really good and important to keep that in mind. That's thank you.

403
01:11:49.000 --> 01:12:05.000
Further questions, comments? In the room on and zoom okay Ladies first, please.

404
01:12:05.000 --> 01:12:10.000
Hi, Joanna. Amy Pushmith. I'm also working at the Federal Institute.

405
01:12:10.000 --> 01:12:24.000
I only have a short question and I was really wondering the whole time why you only when you're looking at gender differences why you don't why you wouldn't take into account if they have shared an or not.

406
01:12:24.000 --> 01:12:32.000
You caught you're only looking up if they are in a partnership. So that's really something that I would look personally.

407
01:12:32.000 --> 01:12:40.000
But maybe. I thought maybe because you're only looking at young adults. That don't have children yet that might be even but.

408
01:12:40.000 --> 01:12:42.000
Yeah.

409
01:12:42.000 --> 01:12:49.000
Maybe you can do a robustness checks or something, so I would be really interested in the revive if that make the difference.

410
01:12:49.000 --> 01:12:50.000
Thank you.

411
01:12:50.000 --> 01:13:00.000
Yeah, thank you. Yeah, I already looked at that and as you, gave the answer actually yourself.

412
01:13:00.000 --> 01:13:05.000
It's, we see that it's only around 3% of higher education credits who have.

413
01:13:05.000 --> 01:13:14.000
Phone children in my data. So, I, did do some robust checks with that, but, Yeah, it's not really.

414
01:13:14.000 --> 01:13:24.000
I cannot really explain something with that information of those 3%.

415
01:13:24.000 --> 01:13:29.000
Okay, then there was another question from the audience.

416
01:13:29.000 --> 01:13:39.000
Okay, it turns out it was the same question about having children. Okay, I look towards the chat if there is a question in there.

417
01:13:39.000 --> 01:13:49.000
I can't see anything. I have some questions and comments. So you look at the earnings of the first.

418
01:13:49.000 --> 01:14:00.000
Let's say significant job after regulation. Have you considered looking at other outcomes, especially when thinking of like the paper that Linda presented.

419
01:14:00.000 --> 01:14:14.000
In terms of duration, search duration. Our mobile graduates. Quicker when entering the labor market, are they more satisfied with their job?

420
01:14:14.000 --> 01:14:23.000
Or do they have a more status adequate job when they move so not just the earnings but other labor market outcomes.

421
01:14:23.000 --> 01:14:30.000
That that's the first question. Are you considering that? And or more like a reflection on that.

422
01:14:30.000 --> 01:14:49.000
You're looking at a relatively recent cohort of graduates. So maybe they already belong to the Gen Z where like leisure and work-life balance counts more than for Let's say older generations.

423
01:14:49.000 --> 01:15:04.000
So maybe looking at something like moving to a region with a high leisure impact. Like in high leisure value and how humability turns out that way.

424
01:15:04.000 --> 01:15:12.000
And then it more something for clarification. I might have just missed it. You said it's a positive self selection of those that move.

425
01:15:12.000 --> 01:15:26.000
Could you explain that to me again? Because I also could imagine it's like a negative self selection that they They don't, those who do not find a job in the region have to move.

426
01:15:26.000 --> 01:15:31.000
So yeah, thanks.

427
01:15:31.000 --> 01:15:38.000
Yes, thank you for your good questions. So let me answer it them shortly.

428
01:15:38.000 --> 01:15:48.000
Yeah, I didn't really look at many other outcome variables. I did look at something I had a quick look at.

429
01:15:48.000 --> 01:16:09.000
If the occupation is in the field of the aspired occupation. So do we. But, for me, earnings are when more straightforward to look at because I mean it's still a very important outcome.

430
01:16:09.000 --> 01:16:17.000
It's the also associate with, yeah. Long term inequalities between. Which we see between men and women.

431
01:16:17.000 --> 01:16:39.000
So that was my major, reason to look at that. Also I would admit that there are many other interesting outcomes such as also the or subjective like, how satisfied you are and how, Maybe how important.

432
01:16:39.000 --> 01:16:49.000
The free time later aspect is in your life. But, I mean, that's something, for future research maybe.

433
01:16:49.000 --> 01:16:57.000
Just to have a right on the positive or negative selection. Yeah, that's, you could argue both.

434
01:16:57.000 --> 01:17:15.000
So first of all, I would argue that, graduates who become mobile. Normally should be the ones who are more ambitious who who have maybe also more opportunities.

435
01:17:15.000 --> 01:17:30.000
Who can afford becoming mobile. Maybe have also, Better qualifications. Because they want to invest in their carriers whereas those who stay in the region maybe they just Yeah, they just accept the conditions.

436
01:17:30.000 --> 01:17:34.000
In their region. However, you could also argue the other way around. That's right.

437
01:17:34.000 --> 01:17:45.000
That would be a more demand side approach, saying that, those who do not find job in the region.

438
01:17:45.000 --> 01:17:54.000
That they need, so it would be a more forced migration. That they need to become mobile to find a job somewhere else.

439
01:17:54.000 --> 01:18:01.000
However we do account for both if we if we balance the samples.

440
01:18:01.000 --> 01:18:08.000
If you balance the groups. So yeah, that was the main reason.

441
01:18:08.000 --> 01:18:16.000
Okay, that means the The theoretical assumption doesn't impact the balancing.

442
01:18:16.000 --> 01:18:19.000
Mechanism or method. It's the same. Okay.

443
01:18:19.000 --> 01:18:34.000
No, it shouldn't. I mean. Through, technically it's really just, ensuring that the distribution of all observed variables is the same in the different mobility groups.

444
01:18:34.000 --> 01:18:45.000
And how they observable. Affects your mobility benefits is. And the next question.

445
01:18:45.000 --> 01:18:54.000
Okay, thank you. Last chance or Pre last chance for questions for your nurse.

446
01:18:54.000 --> 01:19:02.000
Thank you. Otherwise we move on to our last presentation and we leave the German context and move to France.

447
01:19:02.000 --> 01:19:11.000
And serial is presenting something on territorial inequalities, technological transformations and social mobility in France.

448
01:19:11.000 --> 01:19:14.000
The screen is yours and you already took it.

449
01:19:14.000 --> 01:19:23.000
Okay, thank you very much. So thank you for giving me the opportunity to present my work. So that's a really, walk in progress.

450
01:19:23.000 --> 01:19:35.000
So there are still some issues that I'm You could help me to solve them and some results are quite exploratory but I hope also to to improve them in the future.

451
01:19:35.000 --> 01:19:45.000
So. Now, 3, objectives to this work. The 2 first one are quite distinct.

452
01:19:45.000 --> 01:19:51.000
So the first one is to study to what extends the geographic variation of social mobility in France.

453
01:19:51.000 --> 01:20:01.000
Depends on gender The second one is to study the role of technological change in explaining the geographic variation of social mobility.

454
01:20:01.000 --> 01:20:15.000
And so the third one is to try to reconcile these 2, objective. So to examine, to examine whether the change in the distribution of job that may be due to technology can change.

455
01:20:15.000 --> 01:20:23.000
Could explain the different geographies of social mobility between men and women.

456
01:20:23.000 --> 01:20:36.000
As you know, as you might know, there is now a growing number of expiration of the infrared national, growing number of expiration of the infrared number of expiration of the infrared national variation of the infrared national variation of social mobility in different countries

457
01:20:36.000 --> 01:20:46.000
that in the US in Germany. So it's a really, growing film. But many of the studies they do not distinguish between men and women social mobility.

458
01:20:46.000 --> 01:20:55.000
They also never distinguish between what I would call matronon and patron normal mobility so the fact reproduce a mother's position or the fastest position.

459
01:20:55.000 --> 01:21:06.000
And we could expect that because of the genders segregation of the labor market and also because labor market are also very different in different areas.

460
01:21:06.000 --> 01:21:28.000
The geography of social mobility could be very different from men and for women. So, the first, I tried to, test was, Weather the there is not just one geography of social media But whether there are areas in which the men are more mobile are not the same than those in which women are

461
01:21:28.000 --> 01:21:37.000
more mobile. And, I would, I wanted also to test where the, the disappearance between matron and.

462
01:21:37.000 --> 01:21:53.000
And patronal, social mobility. So to test whether areas in which the men and the women have the same position and their father are not the same than those in which they have the same position and them.

463
01:21:53.000 --> 01:22:04.000
Okay. Oh, I'm just like on points. So, my work was influenced by the work of Berger and Excel.

464
01:22:04.000 --> 01:22:19.000
That have shown how technological change can affect income and explain some important special variations. Z, they show, notably that the automation eroded the upward income meeting.

465
01:22:19.000 --> 01:22:29.000
But it was especially for for men and for the bottom of the distribution because it was for each advisor.

466
01:22:29.000 --> 01:22:40.000
So the decrease of jobs in of qualified job in industry. So, my objective was more to understand the effect of technological change.

467
01:22:40.000 --> 01:22:51.000
4 class per meeting. With the idea that, technology can change the distribution of JEFF or jobs, which should affect social mobility.

468
01:22:51.000 --> 01:23:01.000
And, because the distribution of job is different for men and women, it could have different effects on men and women.

469
01:23:01.000 --> 01:23:12.000
So my apologies. Towards the effects of technological change. Was more than it should be associated with more service class position.

470
01:23:12.000 --> 01:23:20.000
Or professional position. And therefore it should, foster mobility among the children of the working class.

471
01:23:20.000 --> 01:23:30.000
And, because it gives them, so it gives them more opportunity to, to move, to the professional class or the service class.

472
01:23:30.000 --> 01:23:43.000
And on the contrary, it gives more opportunity for children of the service class. To stay in their class and so it should foster immobility among the children of the service class.

473
01:23:43.000 --> 01:23:54.000
And, So to. Test, this, this, and how is they, Depend on the gender.

474
01:23:54.000 --> 01:24:02.000
I, I use data from the French laborer for service that was, connected between 2,003 and 2,020.

475
01:24:02.000 --> 01:24:11.000
And so I have They are more than 2 million individuals with a father for which for whom I have the farther social class.

476
01:24:11.000 --> 01:24:16.000
And little less than 1 million, phone. I have the Mosul social chess. I can, have the workplace of the individuals at the department.

477
01:24:16.000 --> 01:24:37.000
So that's correspond to the next 3, and I have some viable at the department level so it's still a little food but I hope to, improve, that and to include more viables in the future.

478
01:24:37.000 --> 01:24:44.000
So I have the number of patents that I will use as a proxy to measure. Technological change and innovation.

479
01:24:44.000 --> 01:24:53.000
I have the GDP, at the department, that I will use also to, test whether, the effect of patterns is, wouldn't be only.

480
01:24:53.000 --> 01:25:08.000
An effect of wealth. And then, to measure the change in the distribution of jobs or the effect of the distribution of a job, I will use a share of service classworker and the share.

481
01:25:08.000 --> 01:25:14.000
Of Manuel Walker as a broker at the department level.

482
01:25:14.000 --> 01:25:23.000
And I use a 6 class version of the European socioeconomic classification. I so I collapsed it.

483
01:25:23.000 --> 01:25:27.000
In the sixth class.

484
01:25:27.000 --> 01:25:42.000
So, now I will essentially, present. Talked about resource sharing mobility at the department even and so this thing we should, patron or social abilities.

485
01:25:42.000 --> 01:25:54.000
So the share of individuals that have the same social class and their father and mature known social mobility is a show of individual that have the same sort Some, some results.

486
01:25:54.000 --> 01:26:00.000
So here you can see, maps of, of, Metropolitan France.

487
01:26:00.000 --> 01:26:13.000
So the more red, the more immobility. So at in, left, this is, Oman and, meant for at the writer.

488
01:26:13.000 --> 01:26:28.000
So the picture is a little clearer for for men as you see that there are more there is more inability in the north of France which was quite expected and in the central and east.

489
01:26:28.000 --> 01:26:34.000
For women it's less, clear if you look at the agenda. You can see that the number are quite, different.

490
01:26:34.000 --> 01:26:52.000
So I divided in, portile. So because you can see that for example for women,ability can go from 90 as from 19 in a department to a 34% of peer of women were were the same social care as their father.

491
01:26:52.000 --> 01:27:02.000
And for men, it varies from 27 to 48%. But this is, due to the use of the further social class.

492
01:27:02.000 --> 01:27:27.000
If now I use the, the situation. Is reverse and the picture change changes a little or because if you look at the legend you can see that matronon immobility is stronger for women and is weaker for men but but also So to test whether there's a map looks like I have computed

493
01:27:27.000 --> 01:27:36.000
some quite simple correlations coefficients So on the Heft, you have, the correlation at the department.

494
01:27:36.000 --> 01:27:55.000
So the between, the, So that format it's a black triangle and for woman it's a blank spangle so it's no Sorry.

495
01:27:55.000 --> 01:28:11.000
Oh, I don't know. Bring triangle. You see the next decoration, for turn known in MBT, is weak in India and it's almost close to 0.

496
01:28:11.000 --> 01:28:33.000
So what does it mean? It means that here, so that's the correlation between the share of women that reproduce their father's position and the share of of men who is our position so it means that the the department in which men have the same position and their father are not the same that the department

497
01:28:33.000 --> 01:28:52.000
in which women have the same position than their mother. And so the graphics, the figure on the right, give a similar result, but with a different point of view because now I, I look at, the correlation between the the faster and the pattern is the patronon in the

498
01:28:52.000 --> 01:29:10.000
Matronon. So here again we have at least for the last generation quite weak coefficients that show that the partner in which people will produce their Moses position are not the same than the department in which they were published their father's positions.

499
01:29:10.000 --> 01:29:19.000
Oh, just a result here seems to show that there is not one geography of social mobility. And that's a geography of Mobility is not the same for men and women.

500
01:29:19.000 --> 01:29:27.000
And it depends also on whether we use the Mosul social class or the father. After I have tried to.

501
01:29:27.000 --> 01:29:39.000
To look at which role plays a distribution of social class in the area and which role for innovation and technological So I have used, Mitchell webinar there.

502
01:29:39.000 --> 01:29:47.000
So random in terms of models. One is a generation, born in the, between the seventies and the, late of the eighties.

503
01:29:47.000 --> 01:29:58.000
People who have at least 35 years. Are you probability model? To predict social inability among different social groups.

504
01:29:58.000 --> 01:30:06.000
So those are quite simple model for now. I I use individual level, which are the age education or evil and contextual levels of the age education arrival and contextual levels of department level.

505
01:30:06.000 --> 01:30:19.000
And contextual levels of department level, I use the number of patterns, the GDP, so the department level, I use the number of patterns, the GDP, the share of service class workers and the share of service class workers and share of manual worker.

506
01:30:19.000 --> 01:30:27.000
So here are the results. So on the probability to, to stay in for, for.

507
01:30:27.000 --> 01:30:32.000
For people from manual worker origin.

508
01:30:32.000 --> 01:30:48.000
So, and so I introduced progressively the different, contextual viables to a test, what else they are affecting, what happened when I put them together to know, how much, They measure the same, kind of things.

509
01:30:48.000 --> 01:31:04.000
So here I have some negative result but which are not significant for for a woman for men. Which seems to show That or at least for women is that the more innovation or.

510
01:31:04.000 --> 01:31:16.000
As a Mobility because that's an effect on an immobility. But this, effect, quickly become non significant for women then it change.

511
01:31:16.000 --> 01:31:24.000
Sign for for men when I introduce other viable so it doesn't seem very robust

512
01:31:24.000 --> 01:31:30.000
It is only the same for the effect of GDP. So that seems to go in the same direction.

513
01:31:30.000 --> 01:31:43.000
The Some more wealth. But then, Well, when I enter the number of patterns, it's it's remained in the same direction.

514
01:31:43.000 --> 01:31:50.000
So it seemed to indicate that. It's still, valid.

515
01:31:50.000 --> 01:31:57.000
But after when I enter the share of the share in the Department of Manual Worker or of service class.

516
01:31:57.000 --> 01:32:14.000
Effect become non significant Are we home? So here it means that the more manual worker in the department, the more people from manual worker origin.

517
01:32:14.000 --> 01:32:28.000
Reproduce their social class so And on the contrary, the more service class position in the department, the more people are, inclined to move.

518
01:32:28.000 --> 01:32:39.000
So it shows that social mobility here really depend on the opportunity structure of the department.

519
01:32:39.000 --> 01:32:49.000
If now, I look at your service class. I have a result that seems to be a little more reversed for, the number of patents.

520
01:32:49.000 --> 01:32:51.000
So I have a positive effect. Contro it before it was a negative effect here for when the origin was manual worker.

521
01:32:51.000 --> 01:33:07.000
He also was at the original service class. So it means that when there are more, patents, so we seem to measure either technological innovation or maybe wealth.

522
01:33:07.000 --> 01:33:14.000
There is more immobility so it's consistent with the idea that it's more associated with service cars that offer more.

523
01:33:14.000 --> 01:33:26.000
Oh, you are staying in the service class for people from the service class. And if I enter the GDP instead of the patents, I have a result in the same direction.

524
01:33:26.000 --> 01:33:37.000
And if I enter both Of course they, they are both we just, but they remain in the same direction and, positive.

525
01:33:37.000 --> 01:33:47.000
But, they are most only with us if I enter. The, I tried to, to enter the distribution of social class in the department.

526
01:33:47.000 --> 01:33:55.000
So if I enter the share of manual workers. They become much less stronger

527
01:33:55.000 --> 01:34:05.000
And with the distribution of social class, I obtain results that are reversed, which is a lot of obvious.

528
01:34:05.000 --> 01:34:17.000
Than before it means that here, if there are more manual workers in the department, people from service class origin have less probability to reproduce their social class of their parents.

529
01:34:17.000 --> 01:34:33.000
And it's true for men and women. And on the contrary, I went more service class position in the department people are more inclined to reproduce That's so, origin.

530
01:34:33.000 --> 01:34:42.000
So finish. So I'm sure that Geography of Social, Mobility Defer by gender.

531
01:34:42.000 --> 01:34:59.000
And, maybe yeah. Go with a sub, sub point before. So the department, characteristic matter to explain social mobility as we seen especially with the share of the different social groups.

532
01:34:59.000 --> 01:35:11.000
But here, I found that the mechanisms I studied have similar effect for both gender. So I did not really, find good explanation for the difference of geography of social mobility.

533
01:35:11.000 --> 01:35:30.000
IPhone more mechanism that for both gender as I mentioned, but it seems to me difficult to separate innovation that is linked to GDP, GDP and the share of and the distribution of social groups of that's a an issue I have to an extent I can wait.

534
01:35:30.000 --> 01:35:38.000
Separate them or do they merge the same thing? Those are are quite well correlated

535
01:35:38.000 --> 01:35:57.000
So finally, my objective is to try to better understand the gender, Is that I obtain a when Some, avenue I have explored and that, I don't have the result, sufficiently robust to present them, but.

536
01:35:57.000 --> 01:36:03.000
I use the Wheeling Grischi Micro class approach to better describe the evolution of the type of job at the department level.

537
01:36:03.000 --> 01:36:18.000
So not only the social class but with the micro class I can have a more more precise description of the jobs and also taking into account the access to just your education at the department level and the kind of education.

538
01:36:18.000 --> 01:36:35.000
The type of education that is provided at the department level because, also in the data, the, Mobility is very well correlated to the possibility to access to a trustworthy education.

539
01:36:35.000 --> 01:36:42.000
Thank you.

540
01:36:42.000 --> 01:36:54.000
Well, thank you for some insights into the French case and we move on to the floor for. Questions?

541
01:36:54.000 --> 01:36:57.000
And they will come up to the podium again.

542
01:36:57.000 --> 01:37:04.000
Thank you.

543
01:37:04.000 --> 01:37:19.000
Hi, Cyril. Thank you very much for your for your talk which I enjoyed. I've got some questions and some of them are simply due to the fact that I don't know enough about France so I hope you can help me with that.

544
01:37:19.000 --> 01:37:24.000
The first question hasn't got to do with France. But I wonder whether you looked at social mobility in both directions.

545
01:37:24.000 --> 01:37:35.000
Upwards and downwards because I guess the assumption is more and more that a people struggle to reach the social position that their parents had.

546
01:37:35.000 --> 01:38:05.000
Nowadays. The second question is about about potential reasons for the mobility patterns that you found. I mean you alluded briefly at 1 point to the north of France with sort of a a dying industrial landscape maybe.

547
01:38:05.000 --> 01:38:20.000
Or there are there and and there I don't know the geography of of France well enough the economic geography Are there other patterns like that that can explain some of the findings that you developed?

548
01:38:20.000 --> 01:38:50.000
And finally, you distinguished at a few points between The service class and working class, I think those were the terms you used and I wonder whether that is not a little bit crude because we know that the service class for example doesn't mean many different things and many of the probably least attractive, least well-paid

549
01:38:56.000 --> 01:39:08.000
jobs are service jobs. But I don't know whether that fits with your notion of service class or whether you you mean something else.

550
01:39:08.000 --> 01:39:18.000
So those are the questions from my side.

551
01:39:18.000 --> 01:39:26.000
So, thanks a lot for, Very interesting question. So on upward or down one mobility.

552
01:39:26.000 --> 01:39:34.000
So, for now I haven't really looked at don't want so much. But I think that's something I have to do.

553
01:39:34.000 --> 01:39:50.000
For upwormability, yes, that's a result of phone that the, the effect where Johnny in favor of current with result and the service class.

554
01:39:50.000 --> 01:39:57.000
In the sense that, innovation or GDP was, associated with much more, move.

555
01:39:57.000 --> 01:40:03.000
From the manual walking class to the what I call the service class and through and towards the third question.

556
01:40:03.000 --> 01:40:16.000
Yeah, by service class, I meant, more professionals. So that's a up for while the a part of the distribution.

557
01:40:16.000 --> 01:40:26.000
So more in the sense that the highest categories in in EGP, or in the European history economic classification.

558
01:40:26.000 --> 01:40:33.000
A schema And so, yeah, to the, explain the pattern, the geographical part.

559
01:40:33.000 --> 01:40:40.000
That's something I'm walking on. So as you. As you mentioned, so the NORTH.

560
01:40:40.000 --> 01:40:53.000
Is among the poorest poorest part of France sorry because it's why it used to be a very industrial part in France and it's a part of this industry has declined.

561
01:40:53.000 --> 01:41:10.000
And so for other Other part there are clearly there is currently the part around Paris. Part of Paris, especially the west of Paris, the western part of Paris is much richer.

562
01:41:10.000 --> 01:41:21.000
And for example, if, for the patent. Hi and I used to use the logarithm because like the patents were so numerous in the western part of France.

563
01:41:21.000 --> 01:41:27.000
That's it was a way to. To account. For that.

564
01:41:27.000 --> 01:41:38.000
And then it's less clear because so I think big region which are specialized of course are so source regime.

565
01:41:38.000 --> 01:41:43.000
Is that are obviously more specialized in tourism but not only

566
01:41:43.000 --> 01:41:51.000
Britanni for example so in the west are regions that are becoming very attractive and you people are moving 0.

567
01:41:51.000 --> 01:42:02.000
So I don't have we freestyle I'm sorry but that's what I'm working on on better understanding the difference of industry in the difference of industry in a different region of

568
01:42:02.000 --> 01:42:04.000
That's it. Thank you very much.

569
01:42:04.000 --> 01:42:18.000
Okay, we had one more question from the floor and then I will have to join us into Zoom.

570
01:42:18.000 --> 01:42:19.000
Yeah, I see.

571
01:42:19.000 --> 01:42:23.000
Hi, Cereal. You can't see me. I'm Patrick from EDH, Zurich in Switzerland.

572
01:42:23.000 --> 01:42:30.000
I'm glad I'm the second person to ask a question because it's going to pick up on the last comment that was made by the previous person.

573
01:42:30.000 --> 01:42:37.000
I not actually completely convinced that this service class versus manual class is the right way to look at technological change.

574
01:42:37.000 --> 01:42:47.000
And I know that David Orter and Levi and Manane in their 2,003 paper they make this distinction between their 2,003 paper they make this distinction between analytical.

575
01:42:47.000 --> 01:42:53.000
They make this distinction between analytical versus manual about analytical versus manual about also routine versus non-routine between analytical versus manual about also routine versus non-routine.

576
01:42:53.000 --> 01:43:00.000
And I think that the routine non-routine distinction is really important, but also routine versus non routine.

577
01:43:00.000 --> 01:43:17.000
And I think that the routine non-routine distinction is really important here because perhaps a lot of the non-routine distinction is really important here because perhaps a lot of the occupations that are being carried out by women in lower social classes are

578
01:43:17.000 --> 01:43:22.000
Okay, well thanks for your comment. I don't think I have much Wednesday except that II will take it in the sense that, yeah, you're right.

579
01:43:22.000 --> 01:43:33.000
There's something I should definitely. use so I try to do it Thanks a lot.

580
01:43:33.000 --> 01:43:40.000
Okay, Jonas, a virtual question to the virtual presenter.

581
01:43:40.000 --> 01:43:41.000
Okay.

582
01:43:41.000 --> 01:43:55.000
Yes, thank you very much for sharing your interesting research first of all. I was wondering because I mean, Probably you also have a high amount of horizontal gender segregation in France.

583
01:43:55.000 --> 01:44:09.000
So I was wondering whether you might need to account for that a little bit. By, because I mean, occupational structure might be differently also on a horizontal level on between regions.

584
01:44:09.000 --> 01:44:21.000
So for example, if you have. Hi, Shares of, STEM occupations in the region or highest shares of female dominated occupations.

585
01:44:21.000 --> 01:44:30.000
Let's say health and character patients. Then, I would assume that, mobility might be, Hi for those.

586
01:44:30.000 --> 01:44:41.000
Because then they have more occupations to choose from maybe. But it's just an idea.

587
01:44:41.000 --> 01:44:46.000
Okay, yeah thanks but you're right. I mean, that's the difficulty with class probability is that you have.

588
01:44:46.000 --> 01:44:54.000
So much difference kinds of mobility is not just more or less mobility. So here I have.

589
01:44:54.000 --> 01:45:02.000
I was more concentrated already, classy mobility as stay in the same class and monitor on up one mobility.

590
01:45:02.000 --> 01:45:16.000
But that's a definitely something I will, try to do and that also why I'm interested in that because of those different kind of probability and I think because the sector of as a kind of industry or people are working in.

591
01:45:16.000 --> 01:45:39.000
Are very important and that will also My, I my purchase is that you know. In region where there are more for example care activity or older people, it may have different impact for women because that's care activities are more often for women and on the contrary as in the burger and exit paper, or that region with more industry

592
01:45:39.000 --> 01:45:51.000
should have more, different, it should. It's more important to have more change in industry should have more impact on men than women.

593
01:45:51.000 --> 01:45:58.000
So, yeah, that's what that, but that's this kind of thing that I will try to do in the future.

594
01:45:58.000 --> 01:46:01.000
Thank you.

595
01:46:01.000 --> 01:46:07.000
Okay, last chance for a question.

596
01:46:07.000 --> 01:46:14.000
I have more, yeah, it's. It's maybe an idea and maybe I can connect.

597
01:46:14.000 --> 01:46:23.000
2 of you on the screen for some future research at our institute we do a lot on like technological change.

598
01:46:23.000 --> 01:46:32.000
Transformation and so I was a little surprised by the indicator you used like this patent number of patents.

599
01:46:32.000 --> 01:46:51.000
And I know that Jonas and colleagues are or have been developing an index of like digitalization. So maybe you can take a step back and not go fully digital but take some of the items that go into that index.

600
01:46:51.000 --> 01:47:09.000
I think it's like a degree of automation. Like the availability of broadband internet so that you have a larger variation in something that reflects technological change in the industry.

601
01:47:09.000 --> 01:47:23.000
And if France has similar rich data than Germany on the Nuts-three level maybe you could even have like a little cooperation there trying to have the index.

602
01:47:23.000 --> 01:47:36.000
That was developed by you and co-authors applied to France. So maybe you don't have to like talk about that now but you should having exchanged afterwards.

603
01:47:36.000 --> 01:47:40.000
Yeah, okay. Thanks.

604
01:47:40.000 --> 01:47:49.000
Okay, I guess we are at the end of our session. It's 20 past 5 here in Melbourne.

605
01:47:49.000 --> 01:48:07.000
I would like to thank all the presenters for getting up so early in Europe and for adjusting to the time here in Melbourne and to the rich discussion from the audience and I hope and I guess we will see more of that.

606
01:48:07.000 --> 01:48:15.000
Like spatial mobility in the RC 28 which is normally like social mobility but so it's now like spatial social mobility.

607
01:48:15.000 --> 01:48:19.000
Okay, well thank you and enjoy your day.

608
01:48:19.000 --> 01:48:24.000
Thanks a lot.

609
01:48:24.000 --> 01:48:25.000
Okay.