Student regional origins and student entrepreneurship

ABSTRACT

Using data from a large public research university in the United States, we examine the relationship between the characteristics of undergraduate college students’ regional origins and students’ interest in entrepreneurship. We find that several entrepreneurial indicators of students’ regional origins predict students’ entrepreneurial intent and ideation. One possible explanation for our results is that the extent of entrepreneurial activity in a region may imprint attitudes towards entrepreneurial careers on people growing up in those regions. Our findings suggest the composition of admitted students’ geographical origins may impact the entrepreneurial focus of universities’ student bodies.

KEYWORDS:

• entrepreneurship
• entrepreneurial
• region
• admissions
• student entrepreneurship

JEL:

• I23
• I25
• L26
• R15

ACKNOWLEDGEMENTS

The authors appreciated feedback provided on this paper by the seminar participants at the Department of Management, University of Bologna, Bologna, Italy. Rachel Keranen and Kai Gu provided research assistance that greatly improved this research.

DISCLOSURE

Professor Eckhardt serves on the board of Extract Systems, is a co-founder of gener8tor, and occasionally consults via Peregrine Analytics, LLC.

Correction Statement

This article has been corrected with minor changes. These changes do not impact the academic content of the article.

Notes

1. We thank the reviewers and David Audretsch of Indiana University School of Public and Environmental Affairs for suggestions that improved this review.

2. Welter (2011) provides a conceptual framework for considering how context – in this case, geographical context – can influence why some people become involved in entrepreneurship. The effects of context can be top down, where attributes of context influence the propensity for individuals to engage in entrepreneurship, or it can be bottom up, where actions undertaken by entrepreneurship can change dimensions of context, such as formal or informal institutional norms, laws, and policy.

3. Specifically, we use the proportion of the residential addresses in a zip code as provided in the housing and urban development (HUD) crosswalk that are also in the other geographic (e.g., CBSA) as the probability a student in a given zip code lives in a given CBSA. We use the population-weighted average of county-level geographical indicators to construct CBSA-level geographical indicators. With these objects, we compute expected values of students’ CBSA-level geographical indicators’ conditional on zip code using the standard formula.

4. We ran F-tests testing whether 1VCInvestmentq4 – VCInvestmentq3 + 2SelfEmploymentq4 – SelfEmploymentq3 + 3Patentsq4 – Patentsq3 + 4PopDensityq4 – PopDensityq3 = 0. The resulting F-statistics were all above the critical values.

5. The reported unadjusted R² in our analysis does not exceed 0.11 in all analyses, which suggests that other factors are important in predicting the entrepreneurial proclivity of students. However, note that the unadjusted R² as a measure of model fit is limited in our analysis because the unconditional means of the dependent variables are at most is 0.23, and R² in linear probability models that are used in this analysis is depressed by the dichotomous dependent variable.

Additional information

Funding

This research team is grateful for funding that supported this work, which was provided by the Ewing Marion Kauffman Foundation, American Family Insurance, the Richard M. Schulze Family Foundation and the Wisconsin Alumni Research Foundation. Fault for any errors is with the authors, not with the funders.