Self-employment of older Americans: do recessions matter?


As high unemployment rates linger following the latest recession, job opportunities can be sparse, especially for older workers. This might prompt older Americans to seek out opportunities in self-employment. Alternatively, recession-related decreases in economic activity might make self-employment less attractive. Using the Health and Retirement Study, we find that unemployed respondents are more likely to enter self-employment and that these decisions are clearly affected by recessions, although the effects differ by recession and gender. Unlike men, women’s self-employment decisions are very sensitive to other sources of household income, and women are less likely to become self-employed the deeper the recession.

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  1. 1.

    Although they constitute a larger portion of the self-employed, the percentage of older workers who are self-employed has actually fallen over this time period (from 13.3 percent to 11.9 percent).

  2. 2.

    It is also possible that the payment remains the same but that payments are reduced on a per hour basis.

  3. 3.

    We measure entrepreneurship using self-employment as it is the most reliable measure available in our survey data set and is commonly used as a measure of entrepreneurship in the economics literature; for more detail on this, see Parker (2005, 2009).

  4. 4.

    As pointed out in Hurst and Lusardi (2004), the relationship between liquidity and entrepreneurship is nonlinear. The positive relationship between capital availability and entrepreneurship is found among individuals with the highest income, while this relationship is not found for the majority of households.

  5. 5.

    See table 5 in Caliendo and Kritikos (2010) for the specific results of the survey.

  6. 6.

    The entry sample includes those who report being employed in the wage and salary sector or unemployed.

  7. 7.

    Wave 5 occurred in 2000 so the variable measures the effects of a recession occurring between waves.

  8. 8.

    A potential concern for our analysis is that own unemployment status might be endogenous. For example, an individual who is contemplating starting a business might put less work effort into their wage and salary job resulting in a greater probability of being fired or laid-off. We rate this as a fairly minor concern given the structure of the HRS—survey responses are collected every 2 years, so there is a significant lag between observed own unemployment and employment status in the next survey.

  9. 9.

    Following Hurst and Lusardi (2004), we use wealth quartiles to represent the non-linearity of the relationship between wealth and the likelihood of entering self-employment. Wealth quartiles include four dummy variables. For example, the first wealth quartile variable is equal to one if a household’s wealth is in the first quartile and zero otherwise.

  10. 10.

    We strongly prefer the fixed effects model over the random effects model, as the assumption of zero correlation between included covariates and the individual-specific effect is unlikely to be met. For this reason, we estimate linear probability models instead of a random effects probit model. Results from a probit model with errors clustered at the individual level are qualitatively consistent with our linear probability results.

  11. 11.

    A probit model with standard errors clustered at the individual level produced a larger marginal effect for men (14.9 percentage points) and a smaller effect for women (9.9 percentage points), both significant at the 1 percent level.

  12. 12.

    We also test whether our decision to limit the sample to workers aged 61 or less has significant consequences for our conclusions. Given that many policy decisions are made assuming retirement at age 65, we expand our dataset to include workers who are 65 and under. Results are similar to the baseline estimates.

  13. 13.

    Results are unchanged when we add dummy variables to control for regions.

  14. 14.

    Including other income and wealth quartile variables produces results nearly identical to our baseline specification.

  15. 15.

    Note that in a linear probability model it is possible to get negative predicted probabilities.

  16. 16.

    Question worded as follows: (1) On the same scale from 0 to 100 (where 0 means absolutely no chance and 100 means absolutely certain), what are the chances that you will lose your job during the next year? (2) Suppose you were to lose your job this month. What do you think are the chances that you could find an equally good job in the same line of work within the next few months? and (3) You told us earlier that you were looking for a new job. On this 0–100 scale, what are the chances that you will find a job like the one you're looking for within the next few months?

  17. 17.

    Weighted results are generally similar and we discuss unweighted results unless otherwise noted.

  18. 18.

    Lagged variables are used for comparability to our main analysis and also to mitigate issues of simultaneity and reverse causation.

  19. 19.

    Results are presented for the male estimates only, but female estimates are similar to those presented here.


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We thank participants at the Small Business, Entrepreneurship, and Economic Recovery: A Focus on Job Creation and Economic Stabilization Conference for helpful comments.

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Correspondence to Amelia M. Biehl.

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Biehl, A.M., Gurley-Calvez, T. & Hill, B. Self-employment of older Americans: do recessions matter?. Small Bus Econ 42, 297–309 (2014).

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  • Entrepreneurship
  • Recession
  • Older Americans
  • Economics of gender
  • Unemployment

JEL classifications

  • L26
  • J14
  • J16