The weight of the rich: improving surveys using tax data

Abstract

Household surveys often fail to capture the top tail of income and wealth distributions, as evidenced by studies based on tax data. Yet to date there is no consensus on how to best reconcile both sources of information, given the multiple biases at play. This paper contributes a novel method, rooted in standard calibration theory, to directly confront the problem of survey non-response between survey micro-data and anonymous tax data under reasonable assumptions. Our key innovation is to endogenously determine a "merging point" between the datasets, above which we start to incorporate information from tax data into the survey, under the assumption that the rate of representativeness is constant, then decreasing with income. This is followed by a "reweighting" and a "replacing" step, which preserves the microdata structure of the original survey, assuming no re-ranking of observations. We illustrate our approach with simulations, which show that our method is robust to the existence of income misreporting, and performs better than alternative methods. We also apply it to real data from five countries, both developed and less developed, finding changes to the levels and trends in income inequality. We discuss several limits to our approach and suggest some guidelines for future research.

Global Carbon Inequality, 1990-2019: The Impact of Wealth Concentration on the Distribution of World Emissions

“…The results presented in this paper reveal the very large level of concentration of individual carbon emissions that characterizes contemporary global economy: while a tenth of the global population is responsible for nearly half of all emissions, a half of the population emits no more than 12% of it. 11 Global carbon inequalities have been rising at the top of the distribution (Figure V) since 1990. How to explain this fast increase at the top of the distribution of world emitters?…”

“…10 In 1990, global emissions from the investment sector of the economy represented 25.6% of the total, vs. 32.3% in 2019, see SI Table 1. 11 Replaced in perspective, carbon inequalities are lower than income or wealth inequalities (the global top 10% of earners captures 52% of total income and the global top 10% of wealth owners owns 76% of total wealth, see [22]). 12 See SI Section 7.…”

“…This explains why Bruckner et al [12] solely focus on year 2014. 15 In addition, it is now well-known that household consumption surveys tend to underestimate consumption levels of richest groups, due to misreporting and sampling errors (see [11]). Using household surveys without additional datasets on inequality therefore tends to underestimate carbon emissions associated to rich individuals' consumption by construction.…”

Drawing a Line: Comparing the Estimation of Top Incomes between Tax Data and Household Survey Data

“…In this paper, we assess where in the income distribution the differences between these two types of data start and what might be driving these differences. This is important because recent attempts (Bourguignon 2018;Blanchet, Flores, and Morgan 2018;Lustig 2020) to combine the two data sources in order to get a better grasp on income distributions, require a cutpoint, i.e., a point in the distribution above which the comparison is focused. Should comparisons (and possible replacement of survey data by tax data) address just the top 1 percent or should they reach "deeper", including, say, the top five percent?…”

Mind the wealth gap: a new allocation method to match micro and macro statistics for household wealth

“…The ideal solution for overcoming these problems would be to link survey data with administrative records (such as tax records or credit registers, as in Blanchet et al, 2018;Garbinti et al, 2018Garbinti et al, , 2020. Alternative approaches to data linkage are directly based on the use of wealth (tax) records (Alvaredo and Saez, 2009) or on the use of capital income information from tax records to construct wealth estimates assuming certain rates of return on wealth (Saez and Zucman, 2016).…”