How to measure and proxy permanent income: evidence from Germany and the U.S.
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Permanent income (PI) is an enduring concept in the social sciences and is highly relevant to the study of inequality. Nevertheless, there has been insufficient progress in measuring PI. We calculate a novel measure of PI with the German Socio-Economic Panel (SOEP) and U.S. Panel Study of Income Dynamics (PSID). Advancing beyond prior approaches, we define PI as the logged average of 20+ years of post-tax and post-transfer (“post-fisc”) real equivalized household income. We then assess how well various household- and individual-based measures of economic resources proxy PI. In both datasets, post-fisc household income is the best proxy. One random year of post-fisc household income explains about half of the variation in PI, and 2–5 years explain the vast majority of the variation. One year of post-fisc HH income even predicts PI better than 20+ years of individual labor market earnings or long-term net worth. By contrast, earnings, wealth, occupation, and class are weaker and less cross-nationally reliable proxies for PI. We also present strategies for proxying PI when HH post-fisc income data are unavailable, and show how post-fisc HH income proxies PI over the life cycle. In sum, we develop a novel approach to PI, systematically assess proxies for PI, and inform the measurement of economic resources more generally.
KeywordsIncome Permanent income Lifetime income Measurement Longitudinal and panel data Social class
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We appreciate suggestions from JOEI editor Markus Jantti and reviewers, Kenneth Couch, Rob Freeland, David Grusky, Brad Heim, Jan Paul Heisig, Peter Krause, Bruce Meyer, Jürgen Schupp, and John Robert Warren. We also thank Daniel Schnitzlein for assistance with the literature review, Chuck Huber for helpful comments on the estimation of confidence intervals of effect sizes in extreme areas, and Dean Lillard for help with the CNEF. The collection of the PSID data was partly supported by the National Institutes of Health under grant number R01 HD069609 and the National Science Foundation under award number 1157698.
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