The Weight of the Rich: Improving Surveys Using Tax Data

Thomas Blanchet, Ignacio Flores and Marc Morgan
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Marc Morgan: PSE - Paris School of Economics - UP1 - Université Paris 1 Panthéon-Sorbonne - ENS-PSL - École normale supérieure - Paris - PSL - Université Paris Sciences et Lettres - EHESS - École des hautes études en sciences sociales - ENPC - École nationale des ponts et chaussées - CNRS - Centre National de la Recherche Scientifique - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement, WIL - World Inequality Lab

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Abstract: It is generally accepted that household surveys fail to accurately portray the top tail of the income distribution. Indeed, studies based on tax data challenge the credibility of surveys as a source to study inequality. To date, there is no broad consensus on how to best reconcile these two datasets. This paper presents a novel method that enables a consistent combination of these two sources of data, under the assumption that tax data sets a credible lower bound on the amount of people with given levels of income. The resulting micro-dataset preserves the consistency of other socio-demographic variables at both the individual and aggregate levels. It thus allows researchers to analyze dimensions of social inequality under a more representative distributive framework. Our procedure is illustrated by empirical applications to five countries, covering both developed and less-developed contexts over numerous years.

Keywords: Income inequality; Top incomes; Inequality; Survey Representativeness; Tax Data; Reweighting; Calibration (search for similar items in EconPapers)
Date: 2018
Note: View the original document on HAL open archive server: https://pse.hal.science/hal-02878315v1
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Citations: View citations in EconPapers (64)

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Related works:
Journal Article: The weight of the rich: improving surveys using tax data (2022)
Working Paper: The Weight of the Rich: Improving Surveys Using Tax Data (2018)
Working Paper: The Weight of the Rich: Improving Surveys Using Tax Data (2018)
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