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09-08-2019

Regional income inequality: a link to women-owned businesses

Authors: Tessa Conroy, Steven Deller, Philip Watson

Published in: Small Business Economics | Issue 1/2021

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Abstract

We assess how women-owned and operated businesses relate to income inequality at the community level. Using U.S. county-level data within the framework of modeling uncertainty, we employ a spatial Bayesian model averaging approach to identify which specific control variables are most consistent with the underlying data generating process for inequality. We find that higher income inequality is linked to larger shares of women-owned and managed businesses. These results are consistent with women-owned businesses being more prevalent at the extremes of the household income distribution where some women are pulled into business ownership at the lower end of the income distribution spectrum and others are driven by opportunities at the higher end of the distribution. We also found meaningful differences in the underlying control variable across our three measures of income inequality. Only a handful of control variables, such as the unemployment rate, rates of college education, and housing costs, are consistent predictors of income inequality.

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Examples of specification criteria include changes in the equation F statistic, \( {\overline{R}}^2 \), Mallows’ C_p statistic, Amemiya criteria (PC), Akaike Information Criteria (AIC), Sawa Bayesian Information Criterion and/or the Schwarz Bayesian Information Criterion (BIC) as well as the Jeffreys-Bayes posterior odds ratio, among others (see Burnham and Anderson 2004; Judge et al. 1985; Kuha 2004; Posada and Buckley 2004 for formal discussions)

Previous literature on income inequality can be grouped into three broad categories: (1) exploring alternative measures of income and inequality (e.g., Katz 1999; Frank 2014); (2) exploring explanations for increasing inequality (e.g., Partridge et al. 1996; Moller et al. 2009; Florida and Mellander 2016); and (3) seeking to understand the potential outcomes of rising inequality (e.g., Partridge 1997, Partridge 2005; Aghion et al. 1999; Fielding and Torres 2006; Oishi et al. 2018).

In addition, previous studies have found that results vary across different measures of income inequality (e.g., Cancian and Reed 1998). For example, should income be defined to include only earnings or all sources of income; or pre- or post-tax income? Should inequality be measured by a traditional Gini coefficient, some entropy-based measure such as a Theil or Shannon Index, a ratio of high to low income, or as Piketty (2014) suggests, the share of total income going to the top 1% of households? Despite the volume of research on the topic of income inequality measures (e.g., Allison 1978), there are no definitive answers to these questions and we are left with the alternative of testing the sensitivity of our results across different measures.

We explored several variations of income inequality measures ranging from several entropy measures across earnings, family and household income with income measured as both earnings and total income. The three measures selected for analysis tended to be the most consistently correlated with the block of potential measures.

It is important to keep in mind that the SBMA approach provides no insight in the direction of the relationship between the independent variables and income inequality but only the consistency of the underlying data generating processes.

We also tested for multicollinearity in the final specifications of the three models (three measures of income inequality) and while the condition index tends to be modestly high (around 160), the individual variance inflation factors were all below four.

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Title: Regional income inequality: a link to women-owned businesses
Authors: Tessa Conroy
Steven Deller
Philip Watson
Publication date: 09-08-2019
Publisher: Springer US
Published in: Small Business Economics / Issue 1/2021
Print ISSN: 0921-898X
Electronic ISSN: 1573-0913
DOI: https://doi.org/10.1007/s11187-019-00224-y

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