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Globalization significantly transforms labor markets. Advances in production technologies, transportation, and political integration reshape how and where goods and services are produced. Local economic conditions and diverse policy responses create varying speeds of change, affecting regions' attractiveness for living and working -- and promoting mobility.
Competition for talent necessitates a deep understanding of why individuals choose specific destinations, how to ensure their effective labor market integration, and what workplace factors affect workers' well-being.
This thesis focuses on two crucial aspects of labor market change -- Migration and workplace technological change. It contributes to our understanding of the determinants of labor mobility, the factors facilitating migrant integration, and the role of workplace automation for worker well-being.
Chapter 2 investigates the relationship between minimum wages (MWs) and regional worker mobility in the EU. EU citizens are free to work anywhere in the common market, which allows them to take advantage of the significant variation in MWs across the EU. However, although MWs are set at the national level, it is also their local relevance that varies substantially -- depending on factors such as the share of affected workers or the extent to which they shift local compensation levels. These variations may attract workers from elsewhere, from within a country or from abroad.
Analyzing regional variations in the Kaitz index, a measure of local MW impact, reveals that higher MWs can significantly increase inflows of low-skilled EU workers, particularly in central Europe.
Chapter 3 examines the inequality in returns to skills experienced by immigrants, focusing on the role of linguistic proximity between migrants' origin and destination countries. Harmonized individual-level data from nine linguistically diverse migrant-hosting economies allows for an analysis of the wage gaps faced by immigrants from various origins, implicitly indicating how well they and their skills are integrated into the local labor markets. The analysis reveals that greater linguistic distance is associated with a higher wage penalty for highly skilled immigrants and a lower position in the wage distribution for those without tertiary education.
Chapter 4 investigates an institutional factor potentially relevant for the integration of immigrants -- the labor market impact of Confucius Institutes (CIs), Chinese government-sponsored institutions that promote Chinese language and culture abroad. CIs have been found to foster trade and cultural exchange, indicating their potential relevance in shaping attitudes and trust of natives towards China and Chinese individuals. Examining the relationship between local CI presence and the wages of Chinese immigrants in local labor markets of the United States, the analysis reveals that CIs associate with significantly reduced wages for nearby residing Chinese immigrants. An event study demonstrates that the mere announcement of a new CI negatively impacts local wages for Chinese immigrants, independent of the CI's actual opening.
Chapter 5 explores how working in automatable jobs affects life satisfaction in Germany. Following earlier literature, we classify occupations by potential for automation, and define the top third of occupations in this metric as \textit{automatable jobs}. We find workers in highly automatable jobs reporting a lower life satisfaction. Moreover, we detect a non-linearity, where workers in moderately automatable jobs (the second third of the distribution) experience a positive association with life satisfaction. Overall, the negative relationship of automation is most pronounced among younger and blue-collar workers, irrespective of the non-linearity.
Income is one of the key indicators to measure regional differences, individual opportunities, and inequalities in society. In Germany, the regional distribution of income is a central concern, especially regarding persistent East-West, North-South, or urban-rural inequalities.
Effective local policies and institutions require reliable data and indicators on
regional inequality. However, its measurement faces severe data limitations: Inconsistencies
in the existing microdata sources yield an inconclusive picture of regional inequality.
While survey data provide a wide range of individual and household information but lack top incomes, tax data contain the most reliable income records but offer a limited range of socio-demographic variables essential for income analysis. In addition, information on the
long-term evolution of the income distribution at the small-scale level is scarce.
In this context, this thesis evaluates regional income inequality in Germany from various perspectives and embeds three self-contained studies in Chapters 3, 4, and 5, which present different data integration approaches. The first chapter motivates this thesis, while the second chapter provides a brief overview of the theoretical and empirical concepts as well
as the datasets, highlighting the need to combine data from different sources.
Chapter 3 tackles the issue of poor coverage of top incomes in surveys, also referred to as the ’missing rich’ problem, which leads to severe underestimation of income inequality. At the regional level this shortcoming is even more eminent due to small regional sample sizes. Based on reconciled tax and survey data, this chapter therefore proposes a new multiple
imputation top income correction approach that, unlike previous research, focuses on the regional rather than the national level. The findings indicate that inequality between and within the regions is much larger than previously understood with the magnitude of the adjustment depending on the federal states’ level of inequality in the tail. To increase the potential of the tax data for income analysis and to overcome the lack
of socio-demographic characteristics, Chapter 4 enriches the tax data with information on education and working time from survey data. For that purpose, a simulation study evaluates missing data methods and performant prediction models, finding that Multinomial
Regression and Random Forest are the most suitable methods for the specific data fusion scenario. The results indicate that data fusion approaches broaden the scope for regional inequality analysis from cross-sectional enhanced tax data.
Shifting from a cross-sectional to a longitudinal perspective on regional income inequality, Chapter 5 contributes to the currently relatively small body of literature dealing with the potential development of regional income disparities over time. Regionalized dynamic microsimulations provide a powerful tool for the study of long-term income developments. Therefore, this chapter extends the microsimulation model MikroSim with an income module
that accounts for the individual, household, and regional context. On this basis, the potential dynamics in gender and migrant income gaps across the districts in Germany are simulated under scenarios of increased full-time employment rates and higher levels
of tertiary education. The results show that the scenarios have regionally differing effects on inequality dynamics, highlighting the considerable potential of dynamic microsimulations for regional evidence-based policies. For the German case, the MikroSim model is well suited to analyze future regional developments and can be flexibly adapted for further specific research questions.