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.

This paper presents new findings about inequality dynamics in Brazil, India, the Middle East, and South Africa from the World Inequality Database (WID.world). We combine tax data, household surveys, and national accounts in a systematic manner to produce estimates of the distribution of income, using concepts coherent with macroeconomic national accounts. We document an extreme level of inequality in these regions, with top 10 percent income shares above 50 percent of national income. These societies are characterized by a dual social structure, with an extremely rich group at the top, whose income levels are broadly comparable to their counterparts in high-income countries, and a much poorer mass of the population below top groups. We discuss the diversity of regional contexts and highlight two explanations for the levels observed: the historical legacy of social segregation and modern economic institutions and policies.

In this paper we bridge the gap between two different approaches to measure inequality: one based on household surveys and summary measures such as the Gini, and the other focused on taxable income and top income shares. We explore how these approaches adjust the Gini for equivalised household income in 26 European countries over . On average, the Gini increases by around 2.4 points as a result of the WID adjustment, for both gross and disposable income, with notable differences across countries, affecting rankings, despite limited impact on trends. We find that differences in inequality depend less on the adjustment method and more on whether it relies on external data sources such as tax data. In fact, SILC countries that rely on administrative register data experience relatively small changes in inequality after the WID adjustment. For recent years, we find that the Gini for 'non-register' countries increases by 2.8 points on average while in 'register' countries it does so by 0.9 points. We conclude by proposing ways in which household surveys can improve their representativeness of income and living conditions. 2003-2017 using the EU-SILC, focusing on the World Inequality Database (WID) adjustment as proposed in

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