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Effects of wealth inequality on entrepreneurship

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Abstract

This study examines whether personal net worth affects new venture creation and performance. Prior research on wealth and entrepreneurial entry has relied on data providing only a snapshot of the transition into self-employment. The present study draws on a sample of US nascent entrepreneurs actively attempting to start new ventures. Controlling for a number of covariates and the endogenous accumulation of wealth, we find strong evidence of higher dropout rates among low-wealth and moderately wealthy nascent entrepreneurs. However, wealth appears to have no effect on venture creation for those managing to remain in gestation. This suggests liquidity constraints theory applies more toward entrepreneurial disengagement than entrepreneurial entry. Furthermore, the suggestion that the wealthy are able to aggressively grow their ventures is only partially supported by the data when we include a set of covariates correlated with wealth. Integrating these findings, we conclude that entrepreneurial success is concentrated at the top of the wealth distribution, despite notable evidence of capability for those at the lower end of the wealth distribution.

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Notes

  1. As of the first interview in the PSED II, each venture had been at risk of termination for a period of time, resulting in left truncation—that is, the sample contains only firms that survived the period between first activity and the first interview, and strong emerging organizations may be overrepresented (Yang and Aldrich 2012).

  2. Two features of the graph are worth noting. First, the horizontal scale has been limited to −$100,000 and $1,000,000 so that we can focus on the data’s centrality—the top 8 % and lower 1 % both extend far beyond those limits and dwarf the remainder of the graph. Second, six steep vertical climbs in the (blue) empirical distribution function for the PSED II occur due to those few observations where a unique value for net worth was withheld by the respondent so that we had to impute net worth as the midpoint of the range provided by the respondent.

  3. If one envisions that starting any business requires a minimum amount of investment that is beyond the means of low-wealth individuals, then this can be recast as a −100 % rate of return on an investment beneath that threshold.

  4. The Variance Inflation Factor for each variable is less than 10, with a mean VIF of around 1.14 for each model. We are therefore confident our regression models do not suffer from multicollinearity.

  5. This should reveal any hidden underlying nonlinear relationship, but nothing noteworthy emerges (and hence the results have been suppressed from our plots for clarity).

  6. The loess method uses weighted least squares to fit the regression for each wealth percentile “section” overlaying the regression, weighting data points with a decreasing function of their distance from the wealth level being plotted (Garson 2012). Nonparametric estimation of a nonlinear function is appropriate here given our preliminary observations of the nonlinear effects of wealth on nascent entrepreneur outcomes, at the individual and firm level. These effects mirror those found in structural inequality at the societal level (Piketty 2014).

  7. However, because this approach makes the fewest assumptions on functional form, it is most susceptible to noise in the available sample of modest size.

  8. We also conducted semi-parametric and nonparametric tests to gauge the robustness of our results, given the amount of structure that we impose on the data. To facilitate interpretation of the Figures, the cubic splines, knots, and loess smoothers from these tests were removed. Additionally, the horizontal scales on each Figure are limited to range from −$100,000 to $1,000,000 so that we can focus on the data’s centrality—the top 8 % and bottom 2 % of nascent entrepreneurs in the wealth distribution extend far beyond those limits and dwarf the remainder of the Figure. The actual data points have been suppressed from the plots because they all fall along the horizontal lines at 0 and 1.

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Authors and Affiliations

  1. Pace University, 1 Pace Plaza, 4th Floor, New York, NY, 10038, USA

    Casey J. Frid

  2. Center for Entrepreneurship, College of Charleston, 5 Liberty Street, Beatty Center, Room 411, Charleston, SC, 29424, USA

    David M. Wyman

  3. Duke University, 230 Rubenstein Hall, Box 90312, Durham, NC, 27708-0312, USA

    Bentley Coffey

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  1. Casey J. Frid
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  2. David M. Wyman
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  3. Bentley Coffey
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Frid, C.J., Wyman, D.M. & Coffey, B. Effects of wealth inequality on entrepreneurship. Small Bus Econ 47, 895–920 (2016). https://doi.org/10.1007/s11187-016-9742-9

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