An important topic in the study of segregation are comparisons across space and time. This article extends current approaches in segregation measurement by presenting a five-term decomposition procedure that can be used to understand more clearly why segregation has changed or differs between two comparison points. Two of the five terms account for differences in segregation that are due to the differing marginal distributions (e.g., the gender and occupational distributions), while one term accounts for differences in segregation due the different structure of segregation (what might be termed “pure” segregation). The decomposition thus presents a solution to the problem of margin dependency, frequently discussed in the segregation literature. Finally, two terms account for the appearance or disappearance of units when analyzing change over time. The method can be further extended to attribute structural changes to individual units, which makes it possible, for instance, to quantify the effect of each occupation on changing gender* segregation. The practical advantages of the decomposition are illustrated by two examples: a study of changing occupational gender segregation in the United States and a study of changing residential segregation in Brooklyn, New York.
Solga (2020) Cross-national differences in social background effects on educational attainment and achievement: absolute vs. relative inequalities and the role of education systems, Compare:
How do educational systems prepare workers for the labor market? Stratification research has often made a distinction between two ideal-types: “qualificational spaces,” exemplified by Germany with a focus on vocational education, and “organizational spaces,” exemplified by France with a focus on general education. However, most studies that investigated this distinction did so by focusing only on the size of the vocational sector, not on whether graduates with a vocational degree actually link strongly to the labor market. Moreover, they often studied male workers only, ignoring potential gender differences in how school-to-work linkages are established. In this paper, we map the change in education–occupation linkage in France and Germany between 1970 and 2010 using an approach that can distinguish between changes in rates and changes in the structure of school-to-work linkages. Surprisingly, we find that the German vocational system in 1970 was not, on average, substantially more efficient in allocating graduates to specific occupations than the French system. This finding is a major departure from earlier results, and it shows that the differences between 1970’s France and Germany, on which the qualificational-organizational distinction is based, are smaller than previously assumed. Partly, this is due to the fact that the female labor force was omitted from earlier analyses. We thus show that ignoring the female workforce has consequences for today’s conception of skill formation systems, particularly because a large share of educational expansion is caused by an increase in female enrollment in (higher) education.
In 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 6m refugees flowing across the Nowcasting population displacement in Ukraine using social media border in just a few weeks, but information was scarce regarding displaced and vulnerable populations who remained inside Ukraine. We leveraged near real-time social media marketing data to estimate subnational population sizes every day disaggregated by age and sex. Our metric of internal displacement estimated that 5.3m people had been internally displaced away from their baseline administrative region by March 14. Results revealed four distinct displacement patterns: large scale evacuations, refugee staging areas, internal areas of refuge, and irregular dynamics. While this innovative approach provided one of the only quantitative estimates of internal displacement in virtual real-time, we conclude by acknowledging risks and challenges for the future.
In times of volatility and crisis, it is essential to have real-time data mapping population movements to facilitate a rapid and effective humanitarian response. Considerable attention has been placed on the 5.8 million Ukrainian refugees crossing the border as of early May 2022, but information is scarce to quantify and locate over 38 million people who remain in the country, many of whom have been displaced from their homes or remain vulnerable to conflict. This report and its supplementary data leverage near real-time digital trace data from social media users, along with demographic and geo-spatial methods to produce daily estimates of current population sizes and changes sub-nationally disaggregated by age and sex. Using our estimation methods, we quantify large reductions in populations from conflict areas (e.g. Kyiv city), particularly women and children, and population increases in western Ukraine (e.g. Lviv Oblast). Examining additional Oblasts (administrative regions) such as Cherkasy, we show evolving and mixed demographic changes as populations transit through different areas. Mapping population dynamics through time, we illustrate the net changes in population sizes in Oblasts from the start of the war until present, providing a daily metric of total negative population drops. As of May 06, this metric suggested that 6,529,949 people were displaced away from their baseline Oblast. While this data and approach is innovative and is one of the few estimates available to quantify and map internal displacement in virtual real-time during a crisis, we conclude by acknowledging deficiencies and future extensions.
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