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The role of entrepreneurial risk in financial portfolio allocation

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Abstract

Entrepreneurs or small business owners are confronted by a number of risks in their business activities. Previous literature suggests that entrepreneurs balance business-related financial risk by changing their personal investment strategy to include fewer investments in risky stocks. However, existing studies cannot separate the effects of business-related financial risk from other household and individual factors that influence investment choices. A causal identification strategy is needed to provide actionable evidence regarding whether efforts to ease business-related financing limitations such as low-interest loan or venture capital programs are likely to influence entrepreneurial activity. This analysis uses instrumental variables (IV) estimation to isolate the effects of business ownership on personal finances. Results from the Survey of Consumer Finances (SCF) suggest that entrepreneurs do mitigate business-related financial risk by reducing their personal portfolio share of risky financial assets, but at only half the magnitude estimated by previous studies.

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Notes

  1. See Kanbur (1979), Kihlstrom and Laffont (1979), Bruce (2000, 2002), and Cullen and Gordon (2007) for examples of theoretical models. Vereshchagina and Hopenhayn (2009) develop a model of endogenous risk taking to explain why entrepreneurial returns, assumed to be inherently risky, do not receive a large risk premium. Their model suggests that entrepreneurs are relatively risk tolerant at the time of entrepreneurial entry.

  2. Heaton and Lucas (2000b) reach analogous conclusions about background risks in general. Although not focused on differences between entrepreneurial and non-entrepreneurial households, research suggests that labor income risks (Guiso et al. 1996; Davis and Willen 2000), borrowing constraints (Guiso et al. 1996), and medical expenditures (Goldman and Maestas 2013) are important in determining portfolio allocations. Other factors affecting portfolio allocations include wealth and education (Bertaut 1998; Calvet et al. 2009).

  3. Population weights were used to generate all statistics. Slight differences are likely between the statistics presented in this analysis and those presented in Bucks et al. (2009) because this analysis is limited to the public-use version of the data.

  4. Data from 1989 are included for comparisons to earlier work but are excluded from the regression analyses due to the lack of availability of the IV variables for earlier time periods.

  5. Note that while the patterns are generally consistent with Gentry and Hubbard (2004), the numbers differ because assets are defined differently. Assets are defined as in Bucks et al. (2009). Financial assets include transaction accounts, certificates of deposit, savings bonds, stocks, pooled investment funds, retirement accounts, cash value of life insurance, and other managed assets.

  6. We note that some caution in interpreting the data is needed as there are key differences in how asset values are reported. Survey respondents are encouraged to use documents, including statements, when answering questions about financial assets. For business assets, respondents are asked how much they could sell their share of the business for, and for house value, respondents are asked what the property would be worth if sold today. These differences might affect the calculated portfolio shares across categories (e.g., higher than market estimates of business value will lead to lower calculated financial asset shares).

  7. See Angrist and Pischke (2009) for more information on the omitted variables bias formula.

  8. We use the STATA cmp command.

  9. One way to address the limitation of having cross-sectional data that is not a panel is to treat the surveys as repeated cross-sections following recent SCF research (Sabelhaus and Pence 1999; Moore 2004). However, repeated cross-section estimation is a version of IV estimation (Moffitt 1993) and requires that all of the standard conditions are met (Verbeek and Vella 2005). Common groupings, such as age cohorts, are not an option for this analysis as the entrepreneurial decision is commonly associated with age and other potential grouping variables.

  10. Results are comparable when age 35 is used to establish the IV values and when an average of IV values from 25 to 35 is used. Results are listed in Table 4.

  11. Real interest rates are calculated using inflation rates from the Bureau of Labor Statistics Consumer Price Index.

  12. Nominal interest rates are used in the regression analysis because a consistent CPI measure (CPI-RS) is only available back to 1978.

  13. Note that the number of spousal siblings is truncated at 6 in the SCF data.

  14. Number of siblings has been used in previous analyses of self-employment or entrepreneurship (Hout and Rosen 2000; Dunn and Holtz-Eakin 2000) but not in conjunction with controls for inheritance.

  15. See Bracke et al. (2014), Fairlie (2013) and Wang (2012) for empirical evidence on home ownership and entrepreneurship. Papers examining housing and portfolio choice include Fratantoni (1998), Heaton and Lucas (2000b), Campbell and Cocco (2003), Cocco (2005), Chetty et al. (2017), and Yao and Zhang (2005). We expect the measure of home ownership to be subject to less reporting error and variation across respondents than alternative measures such as home equity or mortgage liability.

  16. A limitation of our approach is the use of a linear model for a fractional dependent variable, which generates concerns about out-of-range predicted values or bias when means are near the extremes (Elsas and Florysiak 2015; Ramalho and Ramalho 2017; Murteira and Ramalho 2016; Ramalho et al. 2011). This concern around extreme values (i.e., means near zero) is mitigated because we are not using the model to predict outcomes and by our use of a selection equation. We rely on the best linear approximation of our estimator to generate similar results, akin to using a linear probability model for binary outcomes (Angrist and Pischke 2009).

  17. Other candidates for control variables, such as income, health insurance status, and perceived labor income risk are excluded from the baseline specification as they are likely to be jointly determined with the covariate of interest, entrepreneurial status. Results remain the same when these variables are included.

  18. Please see footnote 15. Mortgage debt is included as the natural log of mortgage debt (in $10,000). Home equity is included as the inverse hyperbolic sine (to allow for negative and zero values in $10,000).

  19. The selection model addresses the concern that households likely have a reference point (e.g. $500, $1000, etc.) and do not invest in stocks when their risky asset allocation is below this threshold. Tobit models produce comparable results.

  20. Specifically, the results are obtained from the Stata cmp command, which allows for the maximum likelihood estimation of a broad range of mixed process Seemingly Unrelated Regressions (SUR) models (Roodman 2011).

  21. The average age of respondents in the labor force is 46. Results approximate those without the labor force restriction.

  22. Use of the fixed effects model is a key component of our identification strategy, although we note that the same limitations as using a linear model described in footnote 15 also apply.

  23. Results are presented for weighted data. Adjusting estimates for the presence of multiple implicates as in Kennickell (2017) does not meaningfully alter the results for unweighted data.

  24. The IV variables are jointly significant at the 1% level (Hansen J = 0.740, P value = 0.390).

  25. Similar to Chetty et al. (2017), the coefficients are of opposing signs in which mortgage debt has a negative coefficient while home equity has a positive coefficient.

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Acknowledgments

This research was supported by a grant from the Ewing Marion Kauffman Foundation for which the author is grateful. The author thanks seminar participants at the University of Indiana School of Public and Environmental Affairs for helpful comments.

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Authors and Affiliations

  1. Department of Health Policy and Management, School of Medicine, University of Kansas Medical Center, 3901 Rainbow Blvd, Kansas City, KS, 66160, USA

    Tami Gurley-Calvez

  2. Labor Force Statistics Branch, Social, Economic, and Housing Statistics Division, U.S. Census Bureau, 4600 Silver Hill Rd, Suitland, MD, 20746, USA

    Josephine Lugovskyy

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Correspondence to Josephine Lugovskyy.

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This research was funded in part by the Ewing Marion Kauffman Foundation. The contents of this publication are solely the responsibility of the authors. Any views expressed are those of the authors and not necessarily those of the U.S. Census Bureau.

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Gurley-Calvez, T., Lugovskyy, J. The role of entrepreneurial risk in financial portfolio allocation. Small Bus Econ 53, 839–858 (2019). https://doi.org/10.1007/s11187-018-0104-7

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