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Self-employment and the paradox of the contented female worker


A large literature finds that the self-employed are more satisfied in their jobs. Interestingly, like in the wage and salary sector, ceteris paribus, self-employed women are found to have more satisfaction in their jobs than self-employed men, even though the gender earnings differential is higher for the self-employed. This paper examines the so-called paradox of the contented female worker for both sectors, focusing on the importance of certain job attributes and whether workers actually experience these attributes. Properly controlling for the gap between desiring and actually obtaining these attributes ‘explains’ the gender differential in job satisfaction of the self-employed.

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  1. 1.

    The majority of the literature in this area investigates the reasonably consistent finding that individuals are happier in self-employment compared to wage and salary work (see for example, Bradley and Roberts 2004; Kawaguchi 2008, for the U.S.; Fuchs-Schundeln 2009 for Germany; Andersson 2008, for Sweden; and Blanchflower and Oswald 1998; Benz and Frey 2004, for multiple-country studies).

  2. 2.

    We note that while some of the research (Crosby 1982; Powell and Eddleston 2008) defines the “paradox of the contented female worker” as equal job satisfaction for males and females despite the female earnings penalty, this paper follows the line of literature (Clark 1997) that defines the paradox as higher job satisfaction for females, despite the female earnings penalty.

  3. 3.

    Powell and Eddleston (2008) refute this hypothesis. They find that the gender effect in job satisfaction cannot be explained by differential inputs whereby women invest less time in their businesses and as a result perceive their subpar success as equitable. Furthermore, Millán et al. (2013) only find this negative relationship between working hours and job satisfaction in the wage and salary sector and conjecture that because self-employed workers can choose their number of working hours, they are more likely to be satisfied in their work.

  4. 4.

    Using 2003 Current Population Survey (CPS) data, Roche (2014) estimates a 61 % earnings gap between male and female median annual earnings, and a 70 % earnings gap between male and female median hourly earnings among the self-employed.

  5. 5.

    This finding is only evident with women in blue collar jobs. These women have relatively flat returns to education across the earnings distribution.

  6. 6.

    Research in Europe by Congregado et al. (2016), however, suggests that the incidence of mismatch is lower for women, even among the self-employed. Why there is this difference is not quite clear though it may have something to do with differences in the nature of self-employment across countries.

  7. 7.

    The survey question is “When thinking about a job, how important is each of the following factors to you? (salary, benefits, etc.).”

  8. 8.

    The survey question is “Thinking about your principal job…, please rate your satisfaction with that job’s… (salary, benefits, etc.).”

  9. 9.

    ‘Dissatisfied’ is defined here at workers responding that they are either ‘somewhat dissatisfied’ or ‘not at all satisfied’ with the provision of the attribute. Robustness checks using just the latter answer generates qualitatively similar results in the regressions below and are available from the authors upon request. In addition, we combine into one big group all those who identify the attribute as not ‘very important’ regardless of their satisfaction with the attribute.

  10. 10.

    While focusing on a homogeneous group is useful in our analysis, we acknowledge that the findings may not extrapolate to explain the behavior of all individuals in self-employment. It is worth noting, however, that this sub-set of self-employed workers is an important group in the macroeconomy. Van Praag and van Stel (2013) find that educated entrepreneurs create more jobs and own larger firms relative to business owners without a college degree.

  11. 11.

    The regressions used here are ordered probits, given the ordinal nature of the satisfaction equations. Collapsing the job satisfaction variable into a binary variable (for very satisfied or not) and estimating the regressions using probit maximum likelihood techniques gives qualitatively similar results and are available upon request.

  12. 12.

    This variable comes from a question in the NSCG asking how closely related a worker’s job and education in their last degree is. Possible answers are: ‘closely related’, ‘somewhat related’, and ‘not at all related’ which we refer to as ‘matched’, ‘moderately mismatched’ and ‘severely mismatched’ respectively.

  13. 13.

    It should be noted, however, that this is a different educational sample than most others in the literature, as everyone in this sample has at least a bachelor’s degree and that the marginal effects are fairly small.

  14. 14.

    Having subjective well-being measures on both the right and left-hand side of the regression can generate biases since in cross sections there may be unobservable traits (e.g. overall happy nature) that may affect both sets of variables (see Hamermesh, 2004). While this would bias the estimated coefficients of the ‘Gap’ variables here, it should not bias the coefficient on the key variable of interest—the female indicator, unless there is a correlation between gender and the unobservable trait (e.g. women being ‘naturally’ happier than men) which we do not think is the case.


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Correspondence to Kristen Roche.



See Table 8.

Table 8 Descriptive statistics

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Bender, K.A., Roche, K. Self-employment and the paradox of the contented female worker. Small Bus Econ 47, 421–435 (2016).

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  • Job satisfaction
  • Self-employment
  • Gender differences
  • Job attributes
  • Paradox of the contented female worker

JEL Classifications

  • J16
  • J21
  • J28
  • J31
  • L26