Using Distributional Random Forests for the Analysis of the Income Distribution

Martin Biewen () and Stefan Glaisner
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Martin Biewen: University of Tuebingen
Stefan Glaisner: University of Tübingen

No 17774, IZA Discussion Papers from Institute of Labor Economics (IZA)

Abstract: This paper utilises distributional random forests as a flexible machine learning method for analysing income distributions. Distributional random forests avoid parametric assumptions, capture complex interactions among covariates, and, once trained, provide full estimates of conditional income distributions. From these, any type of distributional index such as measures of location, inequality and poverty risk can be readily computed. They can also efficiently process grouped income data and be used as inputs for distributional decomposition methods. We consider four types of applications: (i) estimating income distributions for granular population subgroups, (ii) analysing distributional change over time, (iii) spatial smoothing of income distributions, and (iv) purging spatial income distributions of differences in spatial characteristics. Our application based on the German Microcensus provides new results on the socio-economic and spatial structure of the German income distribution.

Keywords: small area estimation; poverty; inequality; grouped income data (search for similar items in EconPapers)
JEL-codes: C55 D31 I3 (search for similar items in EconPapers)
Date: 2025-03
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