A growing body of research examines the political sources of economic inequality in the United States. A second literature examines the political consequences of who votes. The current study contributes to both literatures by examining the influence of income bias in voter turnout on income inequality in the American states from 1980 to 2010. I use power resources theory and research demonstrating growing partisan polarization across income levels as theoretical foundations. Using time-series and cross-sectional analysis, I find that states with greater income bias in turnout have higher levels of income inequality than states with greater parity in voter turnout across income levels, findings that are robust across various model specifications. The implications of these findings for our understanding of economic inequality, low-income voter turnout, and state electoral laws are discussed.
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While some research finds little differences across income levels on many policy issues (especially when comparing high- and middle-income citizens), important differences are found in welfare and tax policy preferences between high- and middle-income citizens on one hand and low-income citizens on the other (Enns and Wlezien 2011; Soroka and Wlezien 2008).
As Langer (1999) notes, the Gini estimates based on Census survey data like the American Community Survey Reports produces estimates of income inequality that are less reliable than estimates based on decennial Census data, especially for states with smaller populations and, consequently, smaller sample sizes. Likewise, as both Langer (1999) and Kelly and Witko (2012) point out, these state-level Gini estimates are based on pre-tax and post-transfer income, while we would ultimately prefer post-tax and post-transfer estimates. However, overall, Langer (1999) concludes that “the battery of validity and reliability tests demonstrate that we can have confidence in using these measures” (62).
The Voter Supplement File asks respondents to identify their income level using a 14-point ordinal scale. Using the national sample, I identify the cut points on this 14-point scale that correspond to roughly the top 5th and the bottom 5th of the distribution for the national sample. For all years examined the smallest sample size is for the bottom 5th of the distribution for the 1990 Connecticut sample, with a sample of 99. Several other states tend to have smaller samples including Maine, Delaware, Nevada, and Hawaii. However, for most state/years samples for the bottom and top quintiles are well over 100.
The current paper considers income bias in turnout that compares turnout rates of the top of the income distribution with turnout rates of the bottom of the distribution. It is possible, however, that turnout bias between (1) the middle and the bottom of the income distribution and (2) the top and the middle of the income distribution also influence state income inequality. These possibilities are examined and reported in the online Appendix B.
Statistical Abstracts of the United States can be found here: www.census.gov/compendia/statab/.
Following Erikson et al. (2002), I use David Mayhew’s list of important laws updated through 2012 (http://davidmayhew.commons.yale.edu/datasets-divided-we-govern/). Laws are coded 0 if they are ideologically ambiguous, +1 if they are liberal, −1 if they are conservative, +2 if they are exceptionally important and liberal, and −2 if they are exceptionally important and conservative.
While taking the first difference of a non-stationary variable does not guarantee that it will be stationary (Banerjee et al. 1994), tests reveal that the first differenced Gini indexes are stationary.
Analysis of median lag length shows that the long-term effect is largely limited to two lags. Income bias in turnout in the November 2000 election may influence income inequality in 2005 and 2006 but does not have a sizeable influence beyond then.
Analysis examining the influence of high-income turnout with middle-income turnout, also reported in the online Appendix B, produces findings inconsistent across fixed and random effects models.
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An earlier version of this paper was presented at the 2014 meeting of the Midwest Political Science Association. I am grateful to Skylar Davidson for research assistance and Jeff Fine, Twyla Blackmond Larnel, Joal Lieske, Mark Peffley, Ramya Vijaya, the editors, and five reviewers for helpful comments on earlier drafts. All remaining errors are the author’s alone.
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Avery, J.M. Does Who Votes Matter? Income Bias in Voter Turnout and Economic Inequality in the American States from 1980 to 2010. Polit Behav 37, 955–976 (2015). https://doi.org/10.1007/s11109-015-9302-z
- Economic inequality
- Income bias
- Voter turnout
- Power resource theory