Xavier Jaravel, London School of Economics
"Distributional Consumer Price Indices"
Abstract
This paper develops a new public database providing estimates of inflation heterogeneity across sociodemographic groups in the United States in real time. These distributional CPIs (D-CPIs) are fully consistent with the methodology of the official CPI and are available from 2002 to the present day, by household income, age, race and other characteristics. Using this data set, I establish three results showing that D-CPIs have important implications for the measurement of long-run trends in inequality and poverty, as well as of real wage dynamics during crises. First, I find that “real” income inequality across households between 2002 and 2019 has increased about 45 % faster with D-CPIs, compared to the official CPI. While the income gap between the top and bottom income quintiles increased by 15.6 % during this period according to the official CPI, it increased by 22.6 % with D-CPIs. Similarly large adjustments apply to consumption inequality and inequality in pre-tax and post-tax national incomes. Second, I find that today 2.3 million people are below the “real” poverty line using D-CPIs but above the poverty threshold using the official CPI. This population should become eligible for poverty alleviation programs tied to the poverty line, such as Medicaid. Third, focusing on the inflation burst in the years following the Covid-19 pandemic, I find that inflation was higher for the middle class, compared to low-income and high-income households. This pattern is driven by gas and vehicles and implies that the compression of “real” wages was about 25 % faster with D-CPIs than with the official CPI. Similar patterns of inflation heterogeneity hold in extensions allowing for geographic heterogeneity in inflation, non-homothetic price indices, and expanding the analysis to include data from 1984 onward. Given that D-CPIs are available in real time (each month) and follow data construction steps that are identical to the official CPI, they can be readily adopted by statistical agencies for the production of statistics on inequality and poverty, for example in the context of distributional national accounts.
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