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Determinants of growth in non-farm proprietor densities in the US, 1990–2000

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Abstract

The number of non-farm proprietorships in the US has expanded significantly in past decades, but this expansion has not occurred evenly over space. Regression analysis correcting for spatial autocorrelation reveals that proprietors respond rationally to economic incentives. Parameter estimates for variables measuring collateral, age, ethnic mix, government policy, female labor force participation, and natural amenities, each have the expected signs. A few options are available to policymakers for influencing growth in self-employment densities over time.

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Notes

  1. The data are from the Regional Economic Information System, Bureau of Economic Analysis, US Department of Commerce, Washington, DC; ordering information for the compact disc is available at: http://www.bea.gov/bea/regional/docs/cd.asp. Nation-wide (rural and urban areas), the number of non-farm proprietorships increased from 12.3 to 27.8 million, with the share of proprietors in all jobs nationally rising by more than 50%, from 10.5% to 15.4%. In this study, “rural” and “non-metro” are used interchangeably, as are the terms proprietorship and self-employed. According to the Current Population Survey (2004 March Supplement), the proprietors work in these industries (ranked by order of importance): services, construction, retail, FIRE, manufacturing, transportation and public utilities, wholesale, and information (Low et al. 2005). Given the data limitations, we are largely unable to examine differences across scales of proprietorship operations, and this is one shortcoming of the analysis; in terms of sector detail, we control for construction, services, retail trade, and high-tech employment, and assume that these operate as intercept rather than slope shifters.

  2. In particular, the degree of risk acceptance and creativeness or innovativeness likely distinguishes true entrepreneurs from the self-employed, and so the analogy between the two types of individuals is not without problems. We draw on the entrepreneurship literature to generate maintained hypotheses for variables to include in the regression.

  3. This assumption may not fully explain the decisions of rural people. They may be willing to give up some level of income (wealth) to live in rural areas. In a spatial world, income maximizing individuals would simply move out of rural areas, but many choose not to. Researchers have suggested that because of lower opportunity costs, rural business owners are willing to accept lower rates of return. This possibility is embedded in our model as shift factor τ.

  4. We are grateful to an anonymous reviewer for this point.

  5. To compare our results with those of previous studies, we also included shares of different ethnic groups in a separate regression. This is discussed in the results section.

  6. We thank a reviewer for this point.

  7. Again, we thank a reviewer for pointing out the significance of government policies in our study and directing us on where to find measures to incorporate in the empirical analysis.

  8. Although most data are from publicly accessible sources, we will make the data set used in this study available for anyone who wishes to replicate or extend our results.

  9. We use GeoDa software (available at www.geoda.uiuc.edu) for LISA and diagnostic (robust LM) tests and Lesage’s (1999) Spatial Econometrics Toolbox for Matlab (available at http://www.business.txstate.edu/users/jl47) spatial model estimation.

  10. We are grateful to an anonymous reviewer for this point.

  11. In a separate regression (not reported here), we also included the percent of the population that is Asian, Hispanic and African-American. Coefficient estimates were positive in each case, and statistically significant at the 5% level for Hispanics and at the 10% level for the other two groups (in a one-tailed test only); the other coefficient estimates were robust to this specification change.

  12. In a separate regression, we re-estimated the model for rural areas (rural–urban continuum codes 4–9) only. The results are remarkably robust in this case; the only exceptions are the following variables, which no longer differ statistically from zero when only the 2,339 rural counties are used: female labor force participation and the ARC dummy variable.

  13. More research on the effectiveness of alternative entrepreneurship or proprietorship promotion programs delivered through universities as a substitute for time-consuming experience of the potentially self-employed, is warranted.

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Acknowledgements

Senior authorship is shared equally. The authors gratefully acknowledge the valuable comments of the editor and anonymous reviewers.

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Authors and Affiliations

  1. The Northeast Regional Center for Rural Development, Department of Agricultural and Regional Economics, The Pennsylvania State University, 7E Armsby Building, University Park, PA, 16802-5602, USA

    Stephan J. Goetz

  2. Department of Agricultural Economics and Agribusiness, New Mexico State University, Las Cruces, NM, 88003-8003, USA

    Anil Rupasingha

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  1. Stephan J. Goetz
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Correspondence to Anil Rupasingha.

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Goetz, S.J., Rupasingha, A. Determinants of growth in non-farm proprietor densities in the US, 1990–2000. Small Bus Econ 32, 425–438 (2009). https://doi.org/10.1007/s11187-007-9079-5

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