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.