Evaluating data fusion methods to improve income modelling - Jana Emmenegger; Ralf T. Münnich; Jannik Schaller (Universität Trier, Fachbereich IV - Volkswirtschaftslehre, 2022)
Income is an important economic indicator to measure living standards and individual well-being. In Germany, there exist different data sources that yield ambiguous evidence when analysing the income distribution. The Tax Statistics (TS) - an income register recording the total population of more than 40 million taxpayers in Germany for the year 2014 − contains the most reliable income information covering the full income distribution. However, it offers only a limited range of socio-demographic variables essential for income analysis. We tackle this challenge by enriching the tax data with information on education and working time from the Microcensus. For that purpose, we examine two types of data fusion methods that seem suited for the specific data fusion scenario of the Tax Statistics and the Microcensus: Missing-data methods on the one hand and performant prediction models on the other hand. We conduct a simulation study and provide an empirical application comparing the proposed data fusion methods, and our results indicate that Multinomial Regression and Random Forest are the most suitable methods for our data fusion scenario.
Keywords: 330; Statistical Matching; Multi-source Estimation; Missing Data; Income Analysis; Statistical Learning