Abstract
This paper aims at shedding light upon the impact of universities on graduates’ entrepreneurial choice. Previous studies analyze the relationship between the choice of university and labor market success of graduates in terms of their subsequent wages, employability or over-education, whereas the possible link between the choice of university and entrepreneurial choice is neglected. Using 1998–2008 data on graduates from Swedish higher education institutions, the paper finds significant variation in the impact of universities on the career choice of graduates. The results suggest that graduates with degrees in the social sciences, natural sciences, medicine and teacher education from more prestigious universities systematically differ from others in their entrepreneurial choice. At the same time, no statistically significant difference is found for technical science graduates.
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Notes
Though Swedish higher education institutions (HEI) comprise universities, university colleges and colleges, we use HEI and universities interchangeably.
Entrepreneurship education is measured by a dummy variable, which is 1 if the individual agrees to the statement “my school education helped me to develop my sense of initiative”.
There is an observed tendency by business owners to underestimate their business income. Using an extensive set of data from different sources, Statistics Sweden has estimated the average underreporting and decided to multiply the reported business income by 1.6.
The incomes derived from profits are not included, since they are categorized as capital income.
With or without wage income.
The fraction of mixed employees is twice higher for individuals having a PhD degree.
In line with Statistics Sweden correction for underreporting for self-owning entrepreneurs, we have multiplied the reported business income by 1.6 provided it is larger than zero. Since some employees have a negative business income we have used the absolute value of the business income. As an alternative we constructed the index by replacing negative business income by a small positive number. Though this alternative results in a slightly different distribution of career choices, our main empirical findings do not change. It is worth noting that we use the index of entrepreneurial activity for descriptive purposes as well as to check for the robustness of results when describing the career choice by a continuous variable.
The list of Swedish HEIs listed in various international rankings is rather stable and is presented in Appendix 1.
Universities also differ in students’ pre-enrollment quality. The median grade point average (GPA) score in upper secondary school is above 15 for students of internationally ranked universities and below 14 for the others.
Due to educational reforms the room for possibilities for teachers to form companies and sell their services to the public sector has increased substantially during the last decade. But it will probably take some more years before this is reflected in entrepreneurship rates.
The interest in entrepreneurial occupations might be also influenced by the entrepreneurship courses offered by universities; however, we do not have access to such data at individual levels and our major interest is in the effect of the overall university quality.
Our data do not allow directly controlling for the prior working experience.
Though we use an individual index, the variable varies by labor market and is the same for all individuals working in the same labor market.
We have also tried to control for the industrial composition of regions by including a variable indicating the share of service-oriented industries. However, the correlation coefficient between the number of regional employees and the share of service-oriented industries is 0.82, which results in collinearity problems and biased estimates.
One can also use unordered multinomial models, which do not account for the ordered structure of the categorical variable.
For more details see Hill et al. 2007.
For more details about the random effects generalized probit model see Pfarr et al. 2011.
Also called endogeneity of explanatory variable.
At least one variable not used in the main equation is required.
As mentioned in Wooldridge (2002), linear probability models can give quite accurate estimates of structural parameters. Angrist and Pischke (2008) give several examples where the marginal effects of a dummy variable estimated by LPM and probit techniques are “indistinguishable”, and conclude that, though a non-linear model provides a better fit for limited dependent variable models, this matters little when it comes to marginal effects.
As a third alternative we have also used treatment effects model with pooled data. Though this approach disregards the skewed distribution of the dependent variable, it allows estimating the treatment effect, i.e. the effect of graduating from a ranked university.
We have also tried ordered and multinomial probit models with pooled samples and the results are identical. The comparison of pooled and panel estimates provide support for the latter.
We have done this exercise for economists and sociologists. The results are available from the authors.
The regression results can be requested from authors.
The linear probability estimates for university selection equation are available from the authors.
Some universities, like for example, The Royal Institute of Technology in Sweden, have recently decided to include “social impact” as a quality component.
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We would like to thank two anonymous reviewers for providing valuable comments and suggestions.
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Daghbashyan, Z., Hårsman, B. University choice and entrepreneurship. Small Bus Econ 42, 729–746 (2014). https://doi.org/10.1007/s11187-013-9501-0
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DOI: https://doi.org/10.1007/s11187-013-9501-0