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(Why) Do self-employed parents have more children?

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Abstract

Using data from the U.S. Census, we find that married self-employed men have between 0.1 and 0.2 more children as compared to if they are not self-employed. This is consistent with the hypothesis that self-employed households have a preference for larger families to raise the likelihood that an inside family member will be a good match at running the business. This empirical relationship is established using a regression framework, including the use of instrumental variables estimation to allow for the possibility of endogeneity of the respondent’s self-employment status and whether the respondent’s spouse stays at home. Moreover, we find a slightly higher demand for sons among the self employed who are over 40 at the time of the 1990 U.S. Census: namely, given the exogenous variation in gender mix of prior children, self employed parents born prior to 1950 appear to have an increased demand for a third child if the first two children were not sons. This effect does disappear, however, for the later generation of respondents.

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Notes

  1. A recent paper by Gevrek and Gevrek (2010) shows that children of self-employed parents perform worst in college and are less likely to attend graduate school. These results are consistent with children of the self-employed having the option of taking over or working in the family business.

  2. Beckett and Hess (2008) investigate a similar political science question: namely, whether non-democratic leaders have more children than democratic ones. They provide a theory whereby non-benevolent, non-democratic leaders increase their expected family size to raise the likelihood that a child will be a match at continuing the regime’s survival. They find that fully non-democratic leaders have approximately 1.5–2.5 more actual children as compared to if they are fully democratic.

  3. Bertrand and Schoar (2006) address how important family values and norms are in the organization and efficiency of family businesses. Our paper is consistent with their approach as we suggest that the desire for larger families by self employed households could be due to the desire to ensure that the same values and norms remain in the family business, even generations later, by ensuring the business remains in the family.

  4. Bhattacharya and Ravikumar (2005), provide an overlapping generations framework to model the evolution of family firms. In their paper, the family faces a trade-off between hiring an insider, to manage the firm, who values the firm the same and hiring an outsider, who may be more productive but whose interest may not be aligned with the family. We argue that families can guarantee that an insider is most qualified to manage the business, while at the same time keeping interests aligned with the family, by having more children.

  5. Demsetz and Lehn (1985) define the term ‘amenity potential’ to refer to non-pecuniary benefits associated with family control. These benefits are not accrued through profits but instead provide some benefit to the owner that may not be valued by an outsider, such as the family name.

  6. In line with our approach, Burkart et al. (2003) present a model where there is no superior manager available with sufficient resources to buy the firm due to his/her inability to pay for the private benefit accruing to the founder (i.e. ‘amenity potential’). Moreover, the paper by Caselli and Gennaioli (2006) explores the inefficiency of dynastic management. The authors present the case that in countries where the contract-enforcement infrastructure is weak such that potential buyers are unable to obtain financing to purchase the family business, dynastic management will be higher. While poor contract-enforcement is not a likely argument in developed countries the key implications of both of these studies is that market frictions can lead to an equilibrium where small family firms find it more profitable to pass the family business to an heir. In our case, if the “amenity potential” is large enough, family firms will want to ensure they have a good match to takeover the business.

  7. Instructions given to the enumerators of the census data specifically state that individuals coded as self-employed are individuals ‘who work for profit or fees in OWN business, farm, shop, office…’ and does not include individuals who are hired to manage a business or who work for commission.

  8. An earlier version of the paper uses data from the General Social Survey which asked questions about the total number of children ever born and their expected number of children. Results from the earlier version were similar to the results obtained below—see Broussard et al. (2003).

  9. Accordingly, because the Census does not track children outside of the household, we checked (not shown) that our results for the IPUMS were robust by examining a sub-sample of the data where this problem was mitigated—see Angrist and Evans (1998).

  10. Though not shown, we find broadly consistent findings in both sub-groups. The sample includes men who report themselves currently working, individuals who report themselves as being unemployed or not in the labor force are excluded from our sample of self-employed and employed men. Also excluded are individuals in the armed forces and individuals who report themselves as having a job but are not currently working. Our findings are almost identical when we include these individuals.

  11. Friedlander and Silver (1967) argue that less dense populations are likely to have lower living costs, and thus would be more likely to have higher fertility rates.

  12. More specifically, the instrumental variables approach we adopt is the two-step efficient Generalized Method of Moments estimator.

  13. The decrease in the sample size from Tables 1 and 2 to Table 3 is necessitated by the construction of the instrumental variables HHEXP.

  14. These authors argue that raising children is labor intensive which is why it accounts for the negative relationship with the mother’s labor market participation, particularly in the short run.

  15. Previous research such as Borjas (1986), and Fairlie and Meyer (1996), has found that several immigrant groups have a statistically higher self-employment rate than the native born. While we do not show a statistical positive correlation between immigration status and self-employment it could be due to the fact that we did not distinguish directly between different immigrant groups, though we do control for a respondent’s ethnic origin.

  16. Examining the desire to have a second child given the gender of the first is made problematic by the strong desire for mixed gender. Furthermore, we did not find any differential desire for children by self employment status for four or more children. This could be because while parents may have a preference for mixed gender or sons, at some point the costs of additional children begins to systematically outweigh these preferences.

  17. This also allows us to investigate a gender bias among a representative sample. Recall that our sample consists of married men whose spouse reports the number of children ever born being equal to the number her husband reports as living in the household. For older respondents, it is less likely that all of their children would be currently living at home which leaves us with a selected sample. Respondents between the ages of 35–45 are a sub-sample that is most likely to have completed their lifetime fertility and still have all their children living in the household.

  18. The number of hours worked may not capture all the benefits of self-employment since the self-employed may have more flexibility about the timing of work or have the ability to have their children at the workplace.

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Acknowledgments

We thank Bryan Engelhardt for excellent research assistance, and the Reed Institute for Decision Sciences at Claremont McKenna College for Broussard’s funding. We also thank Heather Antecol, Kelly Bedard, Martin Browning, Pierre-Andre Chiappori, Gordon Dahl, Eric Helland, Joshua Rosett, Kwanho Shin and seminar participants at Claremont McKenna College, Korea University, the University of California, San Diego and the University of Copenhagen and for their comments. The opinions expressed are those of the authors and do not necessarily reflect views of the IMF.

Author information

Authors and Affiliations

  1. The Ohio State University, Columbus, OH, USA

    Nzinga H. Broussard

  2. International Monetary Fund, Washington, DC, USA

    Ralph Chami

  3. Department of Economics, Claremont McKenna College, 500 E. Ninth St., Bauer Center, Claremont, CA, 91711, USA

    Gregory D. Hess

  4. CESifo, Munich, Germany

    Gregory D. Hess

Authors
  1. Nzinga H. Broussard
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  2. Ralph Chami
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  3. Gregory D. Hess
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Corresponding author

Correspondence to Gregory D. Hess.

Appendix

Appendix

1.1 Variables used in the analysis

Age:

Age of Respondent.

Black:

Dummy variable equal to 1 if the race of the respondent is black, and 0 otherwise.

White:

It is similarly defined for if the race of the respondent is white.

City size:

Dummy variables representing the respondent’s city of residence’s population: less than 100,000, between 100,000 and 1 million, or greater than 1 million.

Educational degree:

Dummy variables representing the highest degree obtained by the respondent and the respondent’s spouse: did not complete high school, a high-school or GED certificate, a 4 year college degree, or a graduate degree.

Ethnic origin:

The Country the respondent’s ancestors came from: Africa, Austria, French Canada, Other Canada, China, Czech Republic, Denmark, England, Wales, Finland, France, Germany, Greece, Hungary, Ireland, Italy, Japan, Mexico, Dutch Holland, Norway, Philippians, Poland, Puerto Rico, Russia, Scotland, Spain, Sweden, Switzerland, West Indies, Other.

HHEXP:

Equals the minimum of the value of the respondent’s house (if he is a homeowner) and the respondent’s state of residence Homestead exemption. If respondent is a renter, HHEXP equals the respondent’s state of residence Homestead exemption.

Income:

Total Family Income in tens of thousands of dollars.

Immigration status:

Dummy variable equal to 1 if respondent was not born in the U.S., and 0 otherwise.

Industry:

The industry the respondent reports working in: Agriculture, Mining, Construction, Manufacturing, Transportation, Wholesale, Retail, Finance, Entertainment, Professional, Administration.

KIDS:

The number of Children respondent has ever had (Asked only to women respondents).

Labor market experience:

Respondent’s labor market experience. Equal to Age minus years of school minus 6.

Marital status:

Dummy variable equal to 1 if respondent is currently married, and 0 otherwise.

Mixed gender:

Dummy variable equal to 1 if respondent’s first two children were not the same gender (only for respondents with 2 or more children).

Regions:

The census regions the respondent resides in: New England, Mid-Atlantic, Central NE (North East), Central SE (South East), South Atlantic, Central SE, Central SW, and Pacific.

SELFE:

Dummy variable equal to 1 if respondent reports himself as self-employed in either an incorporated or non-incorporated firm.

Spouse’s work status:

Dummy variable equal to 1 if respondent’s spouse reports herself as being out of the labor force and also reports not being in school or suffering from a disability that prevents her from working, and 0 otherwise.

Two girls:

Dummy variable equal to 1 if respondent’s first two children were girls (only for respondents with 2 or more children).

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Broussard, N.H., Chami, R. & Hess, G.D. (Why) Do self-employed parents have more children?. Rev Econ Household 13, 297–321 (2015). https://doi.org/10.1007/s11150-013-9190-0

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