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Abstract
Research has shown that admission to prestigious higher education programs varies by students' socio-economic status (SES). Access to these programs is characterized by high competition and often rather complex admission procedures. Thus, access may depend not only on students' performance and decisions to apply but also on their application patterns: Where and how they apply, which may vary by social background due to differences in educational achievement, aspirations, and constraints. Using applications to highly prestigious medical programs in Germany, we examine whether admission chances are socially selective even among the positively selected group of applicants, and whether this is due to SES differences in application patterns or performance. Based on complete application register data, we identify application patterns through cluster analysis. We then used the resulting cluster model to predict cluster membership in the 2018 applicant cohort, for which we collected survey data with information on applicants' SES, preferences, and motivations. We find that application patterns vary primarily by applicants' performance (grades and test scores) and SES-specific geographic constraints. However, our multivariate analyses on admission chances show that application patterns do not mediate SES differences in admission chances. Instead, these differences are entirely due to SES differences in applicants' performance.

Abstract
School integration is an important indicator of equality of opportunity and racial reconciliation in contemporary South Africa. Despite its prominence in public and political discourse, however, there is no systemic evidence on the levels and patterns of school segregation. Drawing on the literature on the post-apartheid political settlement and sociological theories of opportunity hoarding, we explain how the small White minority and, to a lesser extent, the new Black middle class monopolized access to South Africa's most prestigious schools following the abolition of de jure segregation in 1994. Using the 2021 Annual School Survey—an administrative dataset covering all South African schools—and the 2019 Trends in International Mathematics and Science Study school survey, we find very high levels of school segregation along racial as well as socioeconomic lines. White students almost exclusively attend former White schools, have little exposure to the low-income Black majority, and are vastly overrepresented in elite public and private schools. We argue that in South Africa and other contexts with under-resourced education systems, elite capture of the few high-performing schools serves to reproduce race and class privilege.

We use PIAAC data to study the relationship between parental education and educational success among adults from 23 advanced economies. We consider educational success in terms of both educational attainment (formal qualifications) and educational achievement (competencies) and in both absolute and relative terms (i.e. as the individual's rank in the distribution of educational success). Parental education effects are stronger for educational attainment than for achievement in all countries. Cross-national variation in the strength of social background effects follows broadly similar patterns for the different ways of measuring success, but a few countries combine relatively strong achievement with relatively weak attainment effects and vice versa. Tracking in secondary education is associated with stronger background effects for educational attainment but not for achievement. Greater prevalence of formal (non-formal) AET is associated with stronger (weaker) background effects for both attainment and achievement, while vocational orientation of upper secondary education does not matter much.

This paper provides an in-depth analysis of the changing structure and consequences of the links between the educational system and labor markets in Brazil in the past 30 years. Using the linkages methodology proposed by DiPrete et al. (2017) and Elbers (2021), we identify changes in education-occupation linkage that were offset by Brazil's rapid educational expansion, distinguishing between changes in rates and changes in the structure of school-to-work linkages. We find that the overall compositional shift towards higher educational levels did not have substantial effects on occupational allocation, yet the trends for younger and older workers, as well as for workers with different fields of study in the tertiary system, were fundamentally different. Our findings suggest that the features of educational expansion in Brazil – i.e., privatization, field of study concentration, and quality discrepancies – contributed to a weakening of the links between education and occupational systems.

AbstractIn times of crisis, real‐time data mapping population displacements are invaluable for targeted humanitarian response. The Russian invasion of Ukraine on February 24, 2022, forcibly displaced millions of people from their homes including nearly 6 million refugees flowing across the border in just a few weeks, but information was scarce regarding displaced and vulnerable populations who remained inside Ukraine. We leveraged social media data from Facebook's advertising platform in combination with preconflict population data to build a real‐time monitoring system to estimate subnational population sizes every day disaggregated by age and sex. Using this approach, we estimated that 5.3 million people had been internally displaced away from their baseline administrative region in the first three weeks after the start of the conflict. Results revealed four distinct displacement patterns: large‐scale evacuations, refugee staging areas, internal areas of refuge, and irregular dynamics. While the use of social media provided one of the only quantitative estimates of internal displacement in the conflict setting in virtual real time, we conclude by acknowledging risks and challenges of these new data streams for the future.