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Income, Occupation, and Preferences for Redistribution in the Developing World

Abstract

Much of the theoretical work on preferences for redistribution begins with the influential Melzer–Richard model, which makes predictions derived both from position in the income distribution and the overall level of inequality. Our evidence, however, points to limitations on such models of distributive politics. Drawing on World Values Survey evidence on preferences for redistribution in 41 developing countries, we find that the preferences of low-income groups vary significantly depending on occupation and place of residence, union members do not hold progressive views, and inequality has limited effects on demands for redistribution and may even dampen them.

Marginal Effect of Manual Workers on Preferences for Redistribution as Capshare Increases (Model 5, Table 5)

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Notes

  1. 1.

    In earlier versions, we also controlled for GDP. This, however, had no effect and for clarity of presentation, we do not show results here.

  2. 2.

    For contrasting perspectives on these questions, however, see models of relative deprivation by Gurr (1970) and Hirschman and Rothchild (1973) that suggest resentment can arise even when material conditions are improving. More recently, Reenock et al. (2007) find empirical support that “regressive socioeconomic development”—increasing wealth combined with continuing poverty—is positively linked to political conflict and the breakdown of democracies.

  3. 3.

    The countries in the sample are Argentina, Brazil, Bulgaria, Burkina Faso, Chile, China, Colombia, Cyprus, Egypt, Ethiopia, Georgia, Ghana, Guatemala, Hong Kong, India, Indonesia, Iran, Iraq, Jordan, Malaysia, Mali, Mexico, Moldova, Morocco, Peru, Poland, Romania, Russia, Rwanda, Serbia, Slovenia, South Africa, South Korea, Taiwan, Thailand, Trinidad and Tobago, Turkey, Ukraine, Uruguay, Vietnam, and Zambia.

  4. 4.

    The single exception is with respect to our findings on manual and agricultural labor reported in Table 4, which fail to reach standard levels of significance. Results from the alternative specifications are available on request from the authors.

  5. 5.

    Put differently, multilevel modeling provides a way for researchers to address the problem of “clustering” that arises because individual responses are not independent of each other within certain clusters (Primo, Jacobsmeier, and Milyo 2007). Without correcting for intracluster correlation, results underestimate the size of standard errors and therefore overpredict p values and significance even though the true number of observations is closer to the number of clusters (countries) rather than the number of individual survey responses. Simply clustering standard errors by country, however, does not allow us to incorporate level 2 variables or to perform cross-level interactions.

  6. 6.

    In addition to the findings on manual workers discussed here, Table 7 shows that the coefficient on the interaction between capshare and “big-city” manual workers is negative, although not statistically significant.

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Correspondence to Stephan Haggard.

Appendix

Appendix

Table 8 Response to dependent variable V118 by country
Table 9 Income Distribution by Country (V253, on ten point scale)
Table 10 Level 2 variables by country

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Haggard, S., Kaufman, R.R. & Long, J.D. Income, Occupation, and Preferences for Redistribution in the Developing World. St Comp Int Dev 48, 113–140 (2013). https://doi.org/10.1007/s12116-013-9129-8

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Keywords

  • Inequality
  • Redistribution
  • Developing countries
  • Social class