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When Are Federations More Unequal? The Political Economy of Interregional Redistribution in Developing Federations

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Abstract

Why do some countries redistribute more to poorer regions than others? This paper explores which factors explain variation in the degree of interregional redistribution among countries between 1983 and 2010. The main argument is that interregional redistribution increases with the need and capacity of strong presidents to build territorial coalitions with governors from poorer regions and, by implication, decreases with the ability of strong governors in rich districts to resist pressures to extract resources from their units. Using a multilevel structural equation model (the Generalized Linear Latent Multilevel Model, GLLAMM), the study analyzes these and competing claims on the determinants of interregional redistribution using original data from the subnational units of four types of cases, ranging from decentralized and federal to centralized, unitary countries. Empirical results indicate that the type of redistributive coalition between presidents and governors complement purely structural and institutional models to explain fiscal redistributive outcomes in the context of sharp regional inequality.

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Notes

  1. Most of these studies analyze the redistributive power of the central government, which expresses the central government’s capacity to reduce regional disparities in terms of income. The redistributive power in a given country is estimated out of the elasticity coefficients of regional revenue and expenditure in relation to their initial revenue, which is the revenue existing prior to public sector action.

  2. This is a key reason for studying interregional redistribution in the five selected cases. The lack of comparative data and time series for analyzing interpersonal redistribution in these countries is another important motive.

  3. Some of the most relevant works in this literature explore whether democratic institutions (since the classic work of Meltzer and Richard 1981), electoral rules (Austen-Smith 2000; Persson and Tabellini 2000; Iversen and Soskice 2006), constitutional veto points (Bradley et al. 2003), the strength of the working class and leftist political parties (Hicks and Swank 1984; Boix 1998; Huber and Stephens 2001; Pontusson et al. 2002; Bradley et al. 2003; Kwon and Pontusson 2003, unpublished; Iversen and Soskice 2006), unionization and wage-bargaining centralization (Wallerstein 1999; Pontusson et al. 2002; Bradley et al. 2003), welfare spending (Bradley et al. 2003), and previous levels of inequality (Meltzer and Richard 1981; Kenworthy and Pontusson 2005; Iversen and Soskice 2006) have a systematic effect on interpersonal redistribution. Other works find that capital mobility and immigration (Alderson and Nielsen 2002); development (gross domestic product (GDP) per capita); education (Nielsen and Alderson 1995); or demographic variables, such as female participation in the labor force and the proportion of female-headed households (Bradley et al. 2003), have an impact on interpersonal redistribution.

  4. Other studies report large variation in how grants are distributed depending on the type of grant under analysis. Instead of focusing in a particular type of grant, this paper includes all transfers (legally mandated and discretionary) from the central government to subnational units in the operationalization of interregional redistribution (see below).

  5. On the effect of overrepresentation on the distribution of federal grants, see also, among others, Atlas et al. (1995), Lee (2000) as well as Rodden (2002), Arretche and Rodden (2004) unpublished, for Brazil, Gibson and Calvo (2000), Gibson, Calvo, and Falleti (2004), (Lodola 2005, 2010, unpublished), and Gordin (2006) for Argentina.

  6. I include tax autonomy (or authority) into this discussion to stress why less developed provinces do not have it as a first-order preference as the more developed do. This discussion has implications for provincial preferences over redistribution.

  7. A large literature measures the redistributive capacity of the central government, since the early MacDougall Report 1977 to more recent works (e.g., Sala-i-Martin and Sachs 1992; Bayoumi and Masson 1995; Mélitz and Zumer 1998; Barberán et al. 2000). There are also works measuring interpersonal redistribution that use a similar estimation strategy but using a different unit of analysis (individuals’ final income instead of governments’ transfers) (e.g., Bradley et al. 2003: 196; Iversen and Soskice 2006: 172).

  8. Federal transfers account for 75 % average of the total revenue, or more (reaching in some cases over 90 %), for the entire series (1983–2011) in almost half of the Argentine provinces (10 out of 24: Catamarca, Chaco, Corrientes, Formosa, Jujuy, La Rioja, Misiones, San Juan, San Luis, Santiago del Estero, and Tucumán), three Brazilian states (Acre, Amapá, and Roraima), all regions in Chile, four Colombian departments (Amazonas, Guainía, Vaupés, and Vichada), and six Mexican states (Campeche, Chiapas, Hidalgo, Tabasco, Tlaxcala, and Yucatán).

  9. Regulated by the revenue-sharing laws in the different countries (see Table 2, online Appendix).

  10. Bagchi 2003; Bosch et al. 2010; Cappelen et al. 2003; Castells and Solé Ollé 2005; Coulombe and Lee 1995; de Oliveira 2008; Garrido and Sotelsek 2002; Maciel et al. 2008; Martinez-Vazquez and Timofeev 2010; Porto 1994; Ramakrishnan and Cerisola 2004; Rangarajan and Srivastava 2004.

  11. These variables do not take into account the fact that party control in subnational legislatures can change over time and during the governor’s term in office, especially in contexts with high levels of party factionalization.

  12. The index of gubernatorial power is a composite measure of all the aforementioned shares and dummies. Dummies contribute 0.5 points to the index in case they are coded as 1, to balance the effect of each measure. I am assuming that a 50 % share of votes received by the governor, a 50 % share of the seats in the state legislative controlled by the governor’s party, whether the main party in the legislature is the party of the governor and whether the president and the governor are in the same governing coalition all weight equally in the index. The maximum possible theoretical value is 4, but since the dummies are coded 0.5 instead of 1, the maximum possible value is 3 and the minimum is 0. I calculated the average value for each year and for all governors and classified the average partisan power of governors (a single measure for each year and each country). The gubernatorial partisan power index is “very high” when values range between 3 and 2, “high” for values between 2 and 1.6, “medium” for values between 1.6 and 1.4, “low” for values between 1.4 and 1, and “very low” for values less than 1.

  13. In Argentina, this region includes the provinces of Buenos Aires, Córdoba, Santa Fe, and the Federal Capital. In Brazil, it encompasses all the Southern region states and the Federal District. In Colombia, the departments of Antioquia, Atlántico, Bogota, Santander, and Valle del Cauca. In Mexico, the DF, Campeche, Chihuahua, Nuevo León, and Quintana Roo.

  14. This region includes all the other provinces, states, or departments.

  15. For Colombia, I use data from Battle and Puyana (2011), who calculated the index based on Mainwaring and Jones’s (2003) formula.

  16. For Argentina, I include years before and after the 1988 fiscal decentralization reforms and the 1992–1993 legal changes that decentralized health and education policies. In Brazil, I include years before and after the 1988 fiscal decentralization reforms and the changes in the Unified Health System (SUS) and in the education fund system (FUNDEF) after 1994. For Colombia, I include observations for the period before and after political decentralization reforms that led to the election of mayors and local authorities (1988), as well as the fiscal and administrative decentralization policies implemented after the 1991 constitutional reform. The same is done with Mexico (after the presidencies of Miguel de la Madrid, 1982–1988, and Carlos Salinas de Gortari, 1989–1994) and Chile (after the 1992 mayoral election).

  17. Only to test the consistency of results correcting for heteroskedasticity, I run a generalized least squares regression (GLS). These results are almost identical to those in PCSE. They are available upon request.

  18. There is debate on how many level 2 units should be included in a multilevel model. Gelman (2006: 524) models a multilevel regression with as little as 3 level 2 units. Besides taking care of the normality assumption, another important requirement for him is getting a non-zero variance. Gelman recommends running a multilevel model because alternative non-multilevel models are similar to it with a group-level variance set to 0 or infinity.

  19. Argentina stands out as the country that redistributes the most (it has the largest average IIR) and where interregional inequality has experienced the sharpest decline (according to a Gini index that measures income differences between the average income of each province and the national average) but still remains as the most interregionally unequal of the selected nations. Brazil closely follows Argentina in terms of interregional inequality.

  20. I do not include party system fragmentation and nationalization in the same model due to the high correlation between the two variables (−0.8 and p = 0.00001).

  21. This relationship is statistically significant in PCSE, but it does not reach the usual standards in GLS.

  22. These are the results of the summing up the coefficients for (the log of) gubernatorial power and the interaction term between this variable and the dummies for developed and less developed districts.

  23. These are the results of the summing up the coefficients for (the log of) presidential electoral power and the interaction term between this variable and the dummies for developed and less developed districts.

  24. The coefficients for the control variables are omitted in Table 5b, online Appendix, to save space.

  25. These are the results of the summing up the coefficients for number of presidential allies and the interaction term between this variable and the dummies for developed and less developed districts. The share of allies (instead of the number of allies) is not a statistically significant variable explaining changes in the outcome. This may mean that specific allies are more important than the total share of them, but more research is needed to code different types of allies.

  26. This is the case for most models in the different tables. The coefficients for industrial GDP do not allow us to reach any clear conclusion.

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Acknowledgments

Support for this research has been provided by CONICET and the Carolina Foundation-CEALCI Research Grant. An earlier version of the paper was delivered at the Conference on “Federalism and Inequality in the Global South,” Brown University, May 22-23, 2013; the XXXII International Congress of the Latin American Studies Association (LASA), May 21-24, 2014; the Universidad de Salamanca, February 24, 2014; the Jornadas de Investigación at the Universidad de San Martín, March 27-28, 2014; and the University of Limpopo, South Africa, November 13, 2014. I want to thank Pablo Beramendi, German Lodola, David Samuels, Richard Snyder, Hillel Soifer, Jonathan Rodden, and the three anonymous SCID reviewers for their insightful comments and suggestions. The usual caveat applies. Ana Bovino and Julia Rubio provided crucial research assistantship for this paper.

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  1. National Scientific and Technical Research Council (CONICET), Buenos Aires, Argentina

    Lucas I González

  2. Universidad Católica Argentina (UCA), Av. Alicia Moreau de Justo 1300, C1107AAZ, Buenos Aires, Argentina

    Lucas I González

  3. Universidad Nacional de San Martín (UNSAM), Av. 25 de Mayo y Francia, 1650, Buenos Aires, Argentina

    Lucas I González

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I González, L. When Are Federations More Unequal? The Political Economy of Interregional Redistribution in Developing Federations. St Comp Int Dev 51, 209–234 (2016). https://doi.org/10.1007/s12116-015-9205-3

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