Income differences across US cities are well documented, but little is known about the level of standard of living in each city—defined as the amount of market-based consumption that residents are able to afford. In this paper we provide estimates of the standard of living by commuting zone for households in a given income or education group, and we study how they relate to local cost of living. Using a novel dataset, we observe debit and credit card transactions, check and ACH payments, and cash withdrawals of 5% of US households in 2014 and use it to measure mean consumption expenditures by commuting zone and income group. To measure local prices, we build income-specific consumer price indices by commuting zone. We uncover vast geographical differences in material standard of living for a given income level. Low-income residents in the most affordable commuting zone enjoy a level of consumption that is 74% higher than that of low-income residents in the most expensive commuting zone.
We then endogenize income and estimate the standard of living that low-skill and high-skill households can expect in each US commuting zone, accounting for geographical variation in both costs of living and expected income. We find that for college graduates, there is essentially no relationship between consumption and cost of living, suggesting that college graduates living in cities with high costs of living—including the most expensive coastal cities—enjoy a standard of living on average similar to college graduates with the same observable characteristics living in cities with low cost of living—including the least expensive Rust Belt cities. By contrast, we find a significant negative relationship between consumption and cost of living for high school graduates and high school drop-outs, indicating that expensive cities offer a lower standard of living than more affordable cities. The differences are quantitatively large: High school drop-outs moving from the most to the least affordable commuting zone would experience a 26.9% decline in consumption.
We thank Matthias Hoelzlein, Erik Hurst, Matt Turner, and seminar participants at Berkeley, Boston University, Cornell, Dallas Fed, Harris, Irvine, IZA, London Business School, Mannheim, MIT Sloan, NBER Summer Institute, Ohio State, OSUS, Penn, Stanford, Toronto, UCLA Anderson, UPF, Urbana, Wisconsin and Wisconsin Business School for useful comments. Abhisit Jiranaphawiboon, Anais Galdin, and Raymond Lee provided outstanding research assistance. Diamond acknowledges support from the Stanford Graduate School of Business and the National Science Foundation (CAREER Grant 1848036). Results using NielsenIQ data is based on researchers' own analyses calculated (or derived) based in part on data from Nielsen Consumer LLC and marketing databases provided through the NielsenIQ Datasets at the Kilts Center for Marketing Data Center at The University of Chicago Booth School of Business. The conclusions drawn from the NielsenIQ data are those of the researchers and do not reflect the views of NielsenIQ. NielsenIQ is not responsible for, had no role in, and was not involved in analyzing and preparing the results reported herein. The views expressed herein are those of the authors and do not necessarily reflect the views of the National Bureau of Economic Research.