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What Explains Lifetime Earnings Differences Across Individuals?


  • Jose Mustre-del-Rio
  • Emily Pollard


Expected lifetime earnings are a key factor in many individual decisions, such as whether or not to go to college and what kind of occupation to pursue. However, lifetime earnings differ widely across individuals, and uncovering the factors that explain these differences can be challenging. Some characteristics, such as race and sex, are observable. But other intangible characteristics, such as work performance, are more difficult to quantify. To what degree observable characteristics explain lifetime earnings is an empirical question. {{p}} Jos Mustre-del-Ro and Emily Pollard use a unique data set combining administrative and survey data to assess how much variation in lifetime earnings across individuals can be explained by observable characteristics such as sex, race, education, and labor market experience. They find that labor market experience?that is, the fact that some individuals work more years than others?accounts for roughly 40 percent of the difference in earnings. Standard demographic characteristics such as sex, race, or education alone explain about 15 percent of these differences. In total, observable characteristics account for a little more than half of lifetime earnings differences.

Suggested Citation

  • Jose Mustre-del-Rio & Emily Pollard, 2019. "What Explains Lifetime Earnings Differences Across Individuals?," Economic Review, Federal Reserve Bank of Kansas City, issue Q I, pages 35-56.
  • Handle: RePEc:fip:fedker:00074
    DOI: 10.18651/ER/1q19Mustre-del-Rio-Pollard

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    JEL classification:

    • E24 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Employment; Unemployment; Wages; Intergenerational Income Distribution; Aggregate Human Capital; Aggregate Labor Productivity
    • E20 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - General (includes Measurement and Data)


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