Improving the Measure of the Distribution of Personal Income (ESCoE DP 2019-14)


Improving the Measure of the Distribution of Personal Income (ESCoE DP 2019-14)

By Dennis Fixler, Marina Gindelsky, David Johnson

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With releases of GDP in the U.S., there are typically stories about the impact on inequality and the distribution of growth. The Financial Times stated: “What’s the matter with GDP?” suggesting that GDP is missing information about who gets the increase (Smith, July 2018). Interest has grown regarding the relationship between the distribution of aggregate growth and increase in inequality. This disconnect has been amplified during the past few years, fueled by the Great Recession. The recent rise in inequality, especially at the top of the distribution, has reinvigorated the effort to produce distributional measures. Along with the creation of the World Inequality Database and Piketty, Saez and Zucman (PSZ) (2018), new consistent measures of the distribution of the national accounts have been developed (see also Auten and Splinter (2018) and Zwijneneburg (2019)). As Kuznets (1955) stressed, a distribution of the national accounts is necessary to completely examine how economic growth, whose measures rely on national account statistics, is distributed. In earlier work at the Bureau of Economic Analysis (Fixler and Johnson (2014) and Fixler et al. (2017)), tried to develop a distribution of personal income using survey data. This paper uses survey data, tax records, and administrative data for 2007 and 2012 to improve the measures of the distribution. Supplementary data sources are particularly important for measuring the top income categories and accordingly, we adjust the Current Population Survey (CPS) data to reflect higher income households and estimate alternative measures of inequality. Though reducing the 90/10 ratio, the tail adjustment and inclusion of incomes from supplementary sources significantly raises top income shares and mean income compared to measures calculated using the internal CPS data alone.

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