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Ethnic Heterogeneity Politics of Welfare State in the United States: A Time Series Analysis, 1940–2016

Abstract

Welfare state policies in the United States have expanded significantly, with some short-term fluctuations, since the 1940s. This paper examines the politics of ethnic heterogeneity in order to explain these welfare state changes. Results from various time-series regressions of public social transfer expenditures with or without health suggest welfare state-limiting roles of race and immigration, with the growing religious heterogeneity consistently helping to bolster welfare state policies. Albeit more nuanced than groundbreaking, findings help understand the nature of the American welfare state politics driven in part by the way ethnic heterogeneity shapes the evolving public opinion and policy preferences.

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Notes

  1. To cite an example, recent immigrants do not qualify for Food Stamps or TANF supports even when they are granted permission to live and work as permanent residents (Wagle, 2012).

  2. While social transfers through such mandatory programs as retirement accounts and health insurance make the size of US welfare state highly comparable to those of many West European countries (Hacker 2002; Hacker and Pierson 2002), they also do not typically enter political discussions.

  3. Operationally, an alternative to using the relative size of ethno-racial and religious minorities would be to use “fractionalization measures” which include all possible ethnic groups in calculation (Alesina and La Ferrara 2005). While this approach is more widely used in the literature, the reason for not adopting it has to do with the requirement of detailed data on specific ethno-racial and religious groups. More important than this aggregation procedure, however, is the accuracy of data since there are many such groups on which historical data are unavailable.

  4. An important indicator of this demand would be poverty for which consistent historical data are not unavailable. Also, descriptive statistics of the power resources and demographic and economic variables including GDP growth, unemployment, population 65 and over, population size, union density, and Democratic control of government are presented in Table 3. A note is also in order on the way the Democratic control of government is measured: by aggregating data on the Democratic control of presidency (1/2), Senate (1/4), and House (1/4) in any given year. While this measure fails to account for other nuances of party control, it provides a reasonable basis to incorporate partisan politics as relevant to policymaking in the United States.

  5. While this adjustment could be made by focusing on all of the economically inactive population, such adjustment would not be useful. Despite qualification for some unemployment insurance and child tax credits, social transfer benefits available to children, the unemployed, and those out of the labor force do not compare well with those of the elderly who qualify for Social Security, Medicare, and a host of other programs.

  6. This method is chosen for its ability to handle models with explanatory variables that are not strictly exogenous, making alternative methods such as Prais–Winston or Cochrane–Orcutt not fully consistent (Wooldridge 2009). But the procedure of lagging this way creates a two-way lag, in which the Newey–West lags apply to the already lagged dependent variables, something methodologically plausible for models with potential serial correlation. Although this goes further than typically required, the goal is to rule out any of such methodological concerns. Results also show that lagging is in fact beneficial empirically.

  7. The archives of Social Security Bulletins serve as the unified source of data on public expenditures on welfare state transfers. Data on population including ethno-racial heterogeneity and foreign-born population are derived from the Census Bureau. Data on naturalized citizens are derived from the Department of Homeland Security and those on net migration are derived from the Organization for Economic Cooperation and Development. Because the Census Bureau lacks historical data on religious breakdowns, aggregate data from the Gallop Poll monthly random samples provide reasonable basis to incorporate trends on religious minorities over time.

  8. This can give the impression of the presence of significant missing data which, as can be gleaned from Table 1, is limited to religious and immigration variables especially at the beginning of the time series.

  9. This aggregation involves taking simple average of “pre-transformed” scales of the three immigration variables. This pre-transformation follows the equation, \(X_{\text{Index}} = \frac{{X_{i} - X_{\text{Min}} }}{{X_{\text{Max}} - X_{\text{Min}} }}\), where Xi is the given value for a specific year, and XMin and XMax are the minimum and maximum values, respectively. The resulting aggregate immigration index can range between zero (none) and 100 (the highest possible).

  10. While the dependent variable, welfare state expenditures relative to GDP (with and without health), remains unchanged, models are estimated with alternative specifications allowing sensitivity analysis. To avoid potential multicollinearity, specifications vary by introducing one ethnic heterogeneity variable at a time, except for a model variation in which all three measures of immigration are used simultaneously given their unitary focus. A model variation incorporating a unified immigration index also helps examine further sensitivity due to potential multicollinearity of the three immigration measures. Model estimates are mostly consistent across the different specifications, indicating that the observed results do not emanate from random chance alone. Some of the differences in results emanate from sample sizes varying across specifications.

  11. While this could be carried out for the adjusted measures as well, results would not be significantly different.

  12. This exercise can also be extended to expenditures excluding health and/or unadjusted expenditures.

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Correspondence to Udaya R. Wagle.

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Wagle, U.R. Ethnic Heterogeneity Politics of Welfare State in the United States: A Time Series Analysis, 1940–2016. Race Soc Probl 11, 185–204 (2019). https://doi.org/10.1007/s12552-019-09261-4

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Keywords

  • Welfare state
  • Ethnic heterogeneity
  • Politics
  • United States
  • Time-series data