 Hi everyone, this work is about university enrollment, spatial inequality, and local labor markets, the role of public policy, and is joined with Tatiana Rosa and Martina Querejeta. First, what motivates this work? We can see in the literature that individuals' success correlate with their family economic output and the place they were born. And these two factors are related to the so-called birth lottery, and it implies low intergenerational mobility and spatial inequalities. Also, there is vast evidence of a strong transmission of human capital between parents and of springs. But we know little about how public policies can able to affect university enrollment and the interaction with parental background, but we know about some evidence that investment infrastructure can increase human capital. So we have two different research questions. The first one is if can local investment in infrastructure increase human capital in the region and can reduce spatial inequalities? And the second one is if can such a policy by increasing local opportunities, increasing intergenerational mobility in education? This paper exploits an university reform in Uruguay implement in 2008 that increased the number of campuses around the country. Historically, the public university campus were located at the main capital, and students from outside the main capital had to face extra cost in order to study. So they have, for example, face cost for moving or traveling, et cetera. This policy implemented gradually in space and time, given some budget constraints. And so we exploit this variation and evaluate a staggered difference in difference that we evaluate some outcomes such as enrollment, first-generation students, completion rates, completion for first-generation students and some local labor market outcomes. And for that, we use novel administrative data of students in Uruguay, my public university, that covers the period 2002 to 2020. And the total number of university students represents around 86% of total tertiary education students. Regarding the literature of our paper, this paper contributes to two strands of the literature. First of all, on papers that analyze the role of public policies in educational attainments, there are vast literature that analyze, for example, compulsory schooling laws or the presence of college in different localities and some effects in the short and long run. For example, years of education, income, labor income, teen pregnancy, crime, et cetera. And the evidence that analyzed the effect of public policies in inter-generational mobility is scarcer. This paper also contributes in inter-generational mobility in developing countries specifically, the one which focuses on education. Regarding the institutional context, Uruguay is a small Latin American country with 3.5 million inhabitants with high income per capita and is the one with low income inequality. Geographically, Uruguay is divided in 19 departments and each department also is divided in some localities. Also, Uruguay is well-known by providing free access to public education at all levels and without a charge. And the university has no admission exams or tuition fees. Yet completion rates of secondary school and the enrollment and tertiary education is slow. And as I mentioned, historically, the university was located at the main capital so the students that living outside, the main capital had to face extra cost in order to study. This is the timeline of the reform. Before 2008, only three departments had university campus but with high limited of educational offer. And the asterisks in the timeline indicates that the university center was open but we had limited offer and we removed the asterisks indicates that a bigger campus was open. Sorry. Regarding the data, we use different sources of information. First of all, administrative records of students from public university from 2002 to 2020. And this data set is combined with self-reported census data that students had to complete in the enrollment year on a mandatory basis. This information includes individual characteristics, the center in which they finish high school and the degree they choose and the maximum educational level attained by the parents. And we also use household survey to evaluate some local labor market outcomes as well as some educational externalities for this population. And regarding the estimation sample, we use students under 30 years old and we consider students that complete the administrative data and the census data. And we also consider those students that we can recover the information of the locality that they were studied in high school. So we have students from 140 localities and we have around 170,000 observations in our sample. These different graphs in the panel A, we can see the percentage of first-generation students before the policy implementation and the lighter colors are the departments with the low percentage of first-generation students and the darkest one, the high percentage of first-generation students. And the panel B shows the percentage of first-generation students after the policy implementation where the dots, sorry, where the orange dots reflects the departments where the campuses were open and there is a positive correlation between this reform and the percentage of first-generation students in the different departments. Here, we have the model that we estimate we estimate a two-way fixed effect difference in different model at the locality year following this model where the outcome variable stands for total enrollment, share of first-generation students, completion rates, first-generation completion and some local labor market outcomes. The parameter gamma reflects the differences between treated and not treated localities before and after policy implementation. So, it's total enrollment of students that were born in that region or? No, our students that are studied high school in that region before entry to the university. We don't have information regarding, okay. So, here, we consider three different treatment or scenarios. The first one is the one specified in the timeline. The second one used showed the distance between the locality and the campus as a continuous measure of treatment. So, we define here three buffers centered at the locality where the new campus open. So, we have a radio of 20, 30 and 50 kilometers respectively and the treatment variable value one if a new campus open at 20, 30 or 50 kilometers. So, here, we have the results. In the first graph, we can observe an increase in total enrollment after the policy implementation in those localities where campuses were open. In the second one, we can observe an increase in the share of first generation enrollment after the policy implementation. These results remind for the specification of the 20 kilometers away from the new campuses and also for the specification of 30 kilometers and the effect vanish for this specification for those localities 50 kilometers away from new campuses. When we observe the effects on completion rates, we observe a decrease in the probability of completion in those localities where campus open after three years of policy implementation and we observe an increase in the probability of completion for first generation students after three years of completion, sorry, after three years of policy implementation. And regarding the labor market outcomes for the population between 21 and 40 years tall, we observe an increase in formality rates after four years, the policy implementation and some increase after seven years on employment rates. These labor outcomes are analyzed with the National Household Survey. We also observe some heterogeneities regarding the students' characteristics. The first one, in the first one, we observe a greater and significant effect in those students with less educated parents. On the second one, we observe greater effects for those students that come from public high school. We run also some falsification checks and we provide evidence that our results are robust for different specification. And finally, we analyze some externalities on other educational outcomes where we find effects on high school attendance for population between 15 and 19 years tall and an increase in the number of people that complete high school in the localities that were treated by the policy. To conclude, we find that this policy has significant impact in educational and labor market outcomes. So we find an increase in the number of students that enroll at the university and the share of first generation students for those localities where campus open and do 20 and 30 kilometers far away. And we also find negative effect on completion rates at the locality level, but positive effects for completion rates for first generation students. And finally, we find some positive effects on some labor market outcomes such as formality and employment rates. So we can conclude that there is an important role of public policies in the reduction of inequality of opportunities. That's it.