Kerstin Ostermann

@kostermann.bsky.social

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What has place to do with social inequality? PostDoc in Sociology @University Bielefeld & @IAB | neighborhoods | local labor markets | quantitative methods | spatial norms https://sites.google.com/view/kerstin-ostermann/start

Me presenting the project at the INAS
Predicted child age at which the respondent rates maternal full-time employment as appropriate for four subgroups (childless men, childless women, fathers and mothers). Fathers in neighborhoods with dominant traditional employment among women report the highest child age, whereas fathers and childless women in neighborhoods in which traditional employment patterns are not dominant report the lowest child ages.
Screenshot of the articles title "Stratified Scars: social inequality in the labour market consequences of apprenticeship dropout" and the abstract: While the association between apprenticeship dropout and negative labour market consequences is well documented, the causal link and social stratification in this effect are less clear. Using georeferenced German administrative data and a conditional instrumental variable approach that exploits distance between place of residence and large firms, we find negative financial consequences but show that the dropout penalty is entirely concentrated among individuals from disadvantaged backgrounds. We further show that these stratified scars partly reflect unequal educational reenrolment rates and unequal employment outcomes among dropouts who do not reenrol. Our results highlight the potential of policies targeting higher graduation rates to reduce social inequality and suggest social advantage buffers the negative financial consequences of apprenticeship dropout, even in institutional settings with strong links between credentials and labour market outcomes.
Example for the two staged unsupervised machine learning algorithm using point data as input. Backlayer maps depict Hamburg. The map shows neighborhoods in different sizes and forms, sometimes following administrative borders (black lines) sometimes not. Three differently colored neighborhood types are displayed, each representing a different social group of residents.
Results for CEM and neighborhood fixed effects regressions. We find robust and significant positive effects (blue coefficients) of childhood exposure to different ethnicities on the likelihood of interethnic marriage.
The picture shows the train station city sign of Nuremberg
Presentation with the title Childhood Exposure to Ethnic Outgroup Predicts Inter-Ethnic Marriage
Confernce-Me explaining how we identify the effect of a neighbor's ethnicity on later interethnic marriage outcomes.
View on Zürich City from Zürichberg
The figure shows 4 maps of Germany that depict the regional distribution of training dropouts at the district level for the years 2005, 2010, 2015, and 2020. In addition to a general trend towards more dropouts, the maps show increasing regional differences, particularly between 2010 and 2020. While the difference between the lowest and highest dropout rates was still 13.2 percentage points, this difference rose to 31.2 percentage points by 2020. In general, the trend shows that dropouts are becoming more frequent in the north-east and west of Germany, whereas the south and southeast still have comparatively low dropouts rates.