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Identifying the effect of college education on business and employment survival


We use a multipronged identification strategy to estimate the effect of college education on business and employment survival. We account for the endogeneity of both education and business ownership with a competing risks duration model augmented with a college selection equation. We estimate the model jointly on the self-employed and salaried employees in the National Longitudinal Survey of Youth 1979. Unlike most previous studies, we find that college does not increase business survival. By contrast, a college degree significantly increases employment survival. Cognitive skills have a positive impact on survival for both the self-employed and employees. These findings suggest that college benefits the self-employed less than salaried, perhaps by generating skills more useful in employment than self-employment, or because of differences in the value of signaling.

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  1. 1.

    The Appendix contains a detailed description of how we constructed the employment status.

  2. 2.

    Keane and Wolpin (1997) do a similar exercise, but construct their employment variables looking at only 9 weeks during the year. They do so for computational reasons. We do not have the same limitations so working status uses all the information/weeks available. Keane and Wolpin also do not consider summer quarters to avoid picking up students’ summer jobs. We calculate the working status with and without summer weeks. The correlation across individuals between the two definitions ranges between .9 in 1979 and .97 in 2003.


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We thank Luigi Zingales, Erik G. Hurst, Robert J. LaLonde, Susanne M. Schennach, Steven N. Kaplan, Adair Morse, Luigi Guiso, Margarita Tsoutsoura, James J. Heckman, the participants to many seminars at the University of Chicago and the participants to the Brownbag Seminar at Boston Fed for useful comments. Jacob Bergmann Larsen provided excellent research assistance. All remaining errors are ours. This research was conducted with restricted access to Bureau of Labor Statistics (BLS) data. The views expressed here do not necessarily reflect the views of the BLS. Financial support from the Jan Wallander and Tom Hedelius Foundation is gratefully acknowledged.

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Correspondence to Andrea Asoni.

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A. Asoni: The views presented here are my own and do not necessarily reflect those of CRA or any CRA employee.


Appendix 1

See Tables 1, 2, 3, 4 and 5.

Table 1 Comparison between men and women
Table 2 Human capital and business survival
Table 3 Human capital and employment survival
Table 4 Instrumental variables in the college selection equation
Table 5 Validity of instrumental variables

Appendix 2: Construction of yearly employment status

The discrete observation period is assumed to be a calendar year. Any construction of yearly employment and schooling status starting from weekly or monthly self-reported data is somewhat arbitrary since an individual can be in several alternatives in a given year. There is no unequivocal solution to this problem. We followed the classification method proposed by Keane and Wolpin (1997) who used the same dataset to estimate a life-cycle model.

Every individual is assigned to one of four mutually exclusive states (employment, self-employment, non-employment or school) in the following hierarchical way. First, we establish whether someone can be classified as employed, non-employed, self-employed or his/her status is missing for the year. (a) Missing Values, Employed or Non-employed: If the weekly working status is missing for more than 2/3 of the weeks in 1 year, then the yearly status is missing. When weekly status is available for more than two-thirds of the weeks, then an individual is considered working if he/she reports doing so for more than two-thirds of the non-missing weeks and averages at least 20 h of work per week. Otherwise the yearly status is coded as “non-employment.”Footnote 2 (b) Self-Employed: If an individual reports working as self-employed for more than half of the working weeks, then he/she is considered self-employed for the year.

Second, we establish whether someone classified as “non-employed” is, in fact, in school. An individual is classified in school during the current calendar year if he/she is not already classified as employed or self-employed and one of the two following statements is true: (a) He/she reports one more year of education the following calendar year and reports attending school at least during 1 month in the current calendar year; or (b) he/she reports attending school for at least 4 months during current calendar year. The second part of this definition is meant to capture those individuals who spent most of their time in school but for whatever reason did not complete the grade. We decided to give priority to the employment information rather than the schooling attendance variable because the former seems to be more accurate. First, it is collected on a weekly basis rather than a monthly basis. Second, in order to be employed, someone needs to work for more than 20 h a week. Third, according to the rules of the NLSY79, it is enough to have attended school for just 1 day in order to be classified as in school for the entire month.

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Asoni, A., Sanandaji, T. Identifying the effect of college education on business and employment survival. Small Bus Econ 46, 311–324 (2016).

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  • Business survival
  • Employment survival
  • College education
  • Cognitive skills
  • Locus of control

JEL Classifications

  • C41
  • J24
  • L26