¹ See, for example Dawson, Richard E. and Robinson, James A., “Interparty Competition, Economic Variables, and Welfare Policies in the American States,” Journal of Politics, 25 (05, 1963), 265–289 CrossRefGoogle Scholar; Hofferbert, Richard I., “The Relationship Between Public Policy and Some Structural and Environmental Variables in the American States,” this Review, 60 (03, 1966), 73–82 Google Scholar; Sharkansky, Ira, “Economic and Political Correlates of State Government Expenditures,” Midwest Journal of Political Science, 11 (05, 1967) 173–192 CrossRefGoogle Scholar; Crittenden, John, “Dimensions of Modernization in the American States,” this Review, 61 (12, 1967), 989–1001 Google Scholar; Dye, Thomas R., Politics, Economics and the Public: Policy Outcomes in the American Stales (Chicago: Rand McNally, 1966)Google Scholar.

² See Footnote 1.

³ Lorenz, M. C., “Methods of Measuring the Concentration of Wealth,” Publications of the American Statistical Association, 9 (1905), 209–219 CrossRefGoogle Scholar; see also Bowman, Mary J., “A Graphical Analysis of Personal Income Distributions in the United States,” American Economic Review, 35 (09, 1945), 618–628 Google Scholar.

⁴ Gini, Corrado, “On the Measure of Concentration with Especial Reference to Income and Wealth,” Paper Delivered before the Cowles Commission, 1936 Google Scholar; see also Morgan, James “The Anatomy of Income Distributions,” The Review of Economics and Statistics, 44 (08, 1962), 270–280 CrossRefGoogle Scholar.

⁵ These Gini indices were computed by Thomas D. Hopkins from income distributional data for total families and unrelated individuals in 1959 from U. S. Bureau of the Census, U. S. Censics of Population: 1960 serves PC (1) 1C to 53C, “General Social and Economic Characteristics,” Table 65. See Hopkins, Thomas D., “Income Distribution in Grants-in-Aid Equity Analysis,” National Tax Journal, 18 (06, 1965), 209–213 CrossRefGoogle Scholar. Slightly different Gini indices were computed from the same source by Al-Samarrie and Miller which correlate .89 (product moment) with the indices above and produce substantially the same results. See Al-Samarrie, Ahmad and Miller, Herman P., “State Differentials in Income Concentration,” American Economic Review, 57 (03, 1967), 59–72 Google Scholar.

⁶ These findings parallel those of Al-Samarrie and Miller who undertook a comprehensive explanation of income inequalities in the states. They regressed Gini coefficients on ten independent variables, including property income as a percent of total personal income, agricultural earnings as a percent of total labor earnings, median school years completed by persons 25 years and over, percent non-white population, and employment as a percent of population. Percent non-white turned out to be their single most explanatory variable; they did not employ a measure of family income. See Al-Samarrie, Ahmad and Miller, Herman P., “State Differentials in Income Concentration,” American Economic Review, 57, (03, 1967), 59–72 Google Scholar.

⁷ Hofferbert, Richard I., “Socio-economic Dimensions of the American States: 1890–1960”, Midwest Journal of Political Science, 12 (08, 1968), 401–418 CrossRefGoogle Scholar.

⁸ The composition of Hofferbert's factors can be observed in the following factor loadings: “Industrialization” — value mfg. .91, pop. in mfk. .88, farm value .83, density .78, foreign born .70, population .67, urban .66, telephones .65, number of employees .64, income .57, business failures .42, property .13, negro .07, illiteracy .04, pop. increase – .00, school years –.03, tenancy –.27, owner-occupied –.32, divorce –.33, acreage –.50, motor vehicles –.57; “cultural enrichment”—school years .91, property .79, income .73, motor vehicles .70, telephones .68, pop. increase .55, urban .52, acreage .49, divorce .43, business failures .29, owner-occupied 24, foreign-born 23, population .05, farm value .02, value mfg. .01, density .01, pop. in mfg. –.13, number of employees –.35, tenancy –.47, illiteracy –.74, negro –.75: Ibid.

⁹ These political system variables are: Competition: one minus the percentage of votes cast in Gubernatorial elections 1954 to 1964 for majority party, one minus the proportion of seats held by the majority party in the upper and lower chambers of the state legislature 1954 to 1964; voter participation: average voter turnout in gubernatorial elections 1954 to 1964, average voter turnout in congressional elections 1962 and 1964; Democratic success: the Democratic percentage of the two-party vote for Governor 1954 to 1964, and the Democratic proportion of seats in the upper and lower chambers of state legislatures 1954 to 1964; malapportionment: the index of representatives suggested by Dauer, Manning J. and Kelsay, Robert G. “Unrepresentative States,” National Municipal Review, 44 (12, 1955), 571–575 CrossRefGoogle Scholar, updated to 1960; the index of urban underrepresentation suggested by David, Paul T. and Eisenberg, Ralph, Devaluation of the Urban and Suburban Vote (Bureau of Public Administration, University of Virginia, 1961)Google Scholar; and the apportionment score suggested by Schubert, Glendon and Press, Charles. “Measuring Malapportionment,” this Review, 58 (06, 1964), 302–327 Google Scholar, with corrections published December, 1964, pp. 966–970; interest group strength: judgements of respondents reported by Zeller, Belle, American State Legislatures (New York: Crowell, 1964), pp. 190–191 Google Scholar; length of constitution, number of constitutional amendments and number of elected state agency heads: see Froman, Lewis A., “Some Effects of Interest Group Strength in State Politics,” this Review, 60 (12, 1966), 952–962 Google Scholar; governors formal power index: see Schlesinger, Joseph M., “The Politics of the Executive” in Jacob, Herbert and Vines, Kenneth (eds.), Politics in the American States (Boston: Little, Brown, 1965), p. 229 Google Scholar.

¹⁰ Unfortunately, the degree of inter-relatedness between income inequality (Gini) and income levels as well as other socio-economic variables render partial correlations and beta values unreliable as a means of testing the independent effect of income inequality on political system variables or public policy. A simple comparison of the size of the correlation coefficients is probably the most efficient method of comparing income inequality with levels of socioeconomic resources.

¹¹ The variables in Table 4 were selected from over 100 policy measures for which similar results were obtained. The specific measures in Table 4 are: Education: per pupil expenditures in ADA 1961–62, educational expenditures as a percent of total personal income 1961, state percentage of total state-local expenditures for education 1961, average teachers salaries 1961–62, average size of school district 1961–62, teacher pupil ratio, high school graduates in 1963 as a percent of 9th graders in 1959, percent of selective service examinees disqualified for failing mental test 1962; Health and Welfare: unemployment compensation average weekly payment 1961, ADC average monthly payment 1961, general assistance average monthly payment 1961, ADC recipients per 10,000 population 1961, per capita state-local welfare expenditures 1961, per capita state-local health expenditures 1961, state percentage of total state-local expenditures for welfare, state percentage of total state-local expenditures for health; taxation: total state-local revenue per capita 1961, taxes as a percent of personal income 1961, state percentage of total state-local tax revenue 1961, sales taxes as a percent of state tax revenue 1961, income taxes as a percent of state tax revenue 1961; Highways: per capita state-local expenditures for highways 1961, state percentage of total state-local expenditures for highways 1961, percentage of highway revenues diverted to non-highway purposes 1961, percent of state highway grants going to counties rather than municipalities 1961, highway user revenues as a percent of total state revenue 1961.