Using distributional random forests for the analysis of the income distribution

Martin Biewen, Stefan Glaisner and Simon Zeller

No 26051, RFBerlin Discussion Paper Series from ROCKWOOL Foundation Berlin (RFBerlin)

Abstract: This paper explores 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) small-area estimation 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: inequality; poverty; small-area estimation; grouped income data (search for similar items in EconPapers)
JEL-codes: D31 I32 (search for similar items in EconPapers)
Date: 2026-02
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.rfberlin.com/wp-content/uploads/2026/02/26051.pdf (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:crm:wpaper:26051

Access Statistics for this paper

More papers in RFBerlin Discussion Paper Series from ROCKWOOL Foundation Berlin (RFBerlin) Contact information at EDIRC.
Bibliographic data for series maintained by Moritz Lubczyk () and Matthew Nibloe ().