Polarization and Social Cohesion in Latin America

1. Polarization and Social Cohesion in Latin America Luis F. Lopez-Calva Chief Economist, RBLAC UNDP

2. Main Idea • Beyond inequality and poverty, the concept of social cohesion addresses the issue of whether a social group is able to define a common objective and whether the institutional context is conducive to the collective pursuit of such objective • Polarization is proposed as a measure that recovers such feature in society

3. Conceptual Foundations • Social cohesion has been discussed in several academic disciplines, from economics and political science to anthropology and sociology. • However, a specific definition of social cohesion is often lacking

4. Example • Social capital versus social cohesion • Bonding vs. Bridging social capital

5. Literature • In a recent study, ECLAC defines social cohesion as the dialectical relationship between mechanisms of social inclusion/exclusion and people's responses, perceptions and attitudes to the ways these operate in producing a sense of belonging to society. • Proposal of more than thirty indicators to measure the distinct dimensions of social cohesion.

6. A Simple Definition of Social Cohesion Social cohesion could be simply defined by: • a group of individuals that potentially make up a single body, a politically constituted community • and the force which drives them together, which unifies a group towards a common goal (taking the definition of cohesion and adding the “social” part to it)

7. Normative vs Functional Notions

8. The polarization concept Unimodal dist Poverty F Inequality

9. The polarization concept Bimodal F’ Middle class

10. Polarization measures • Esteban, Gradín and Ray (2006) introduced an extension • of the Esteban and Ray [1994] (ER) measure of polarization • that can be applied to density Functions. This derivation has • the virtue of casting both measures in the context of a unified fremework. • The ER formulation relies on what they called the identity- • alienation framework. This means that individuals are ‘identified’ with others who are ‘close’ to them, while they are ‘alienated’ from other who are ‘far away’.

11. Polarization measures • The ER measure assume that the data arrives pre-grouped into appropiate population clusters, within which there are bonds of identification and across which are the tensions of alienation. But, the statistical classes into which the distributional data may be grouped may have nothing to do with the former conceptual grouping. • ER suggest an extension of the measure to deal with them as follows:

12. Polarization measures, example • Suppose that a fixed number of income cutoffs are given to the researcher (income groups). • These define the number of groupings but not their locations • They propose to pin down group locations by minimizing the dispersions within the clusters created by any number of income cutoffs. • Then, they apply the ER measure to the discrete groupings with a correction for intra-group dispersions.

13. Polarization measures • This yields an extended measure of polarization which can be applied to all sorts of income distributions, especially when they are in the form of densities. • As byproduct they derive the W measure as a particular special case of this formulation to cast both the ER and W measures in the context of a (statically) unified framework.

14. The identity-alianation framework: Conceptual issues Suppose that an individual with income x feels group identification I(x, F) under the distribution F, and alienation r(x, y) with respect to some individual with income y. As in ER, they take the effective antagonism that individual x feels towards y as some function T(I, r) strictly increasing in r. Effective antagonism increases with alienation, but this alienation is taken to be fueled by some sense of identification as well. Polarization is the ‘sum’ of all effective antagonisms:

15. The identity-alienation framework: Conceptual issues • The approach taken in ER is to combine this relatively broad starting point with a set of intuitive axioms that might compare polarization across distributions. • The ER characterization is restricted, however, to distributions that are pre-arranged in groups so that for an individual with income x belonging to some group i, I(x, F) simply equals pi, the proportion of individuals in that group (under the distribution F). • But there is no reason to believe that the grouping of income distribution data will conveniently conform to the psychological demands of group identification.

16. The identity-alianation framework: Conceptual issues • Take alienation to be simply the linear distance between x and y, with the identification zone netted out: • r(x,y) = max {|x - y| - D, 0}. (i) • Then, a natural generalization of the ER measure is (ii) where αis some positive constant capturing the importance of group Identification in the determination of interpersonal effective antagonism.

17. The identity-alianation framework: Conceptual issues • Observe that if group identification is unimportant, then we can take D = 0 and α= 0 as well, in which case the measure in (i) reduces to a measure of inequality, the Gini coefficient. Thus it is the presence of identification that makes a measure of polarization fundamentally different from one of inequality (for more on this, see ER). • There are some features of (ii). First, if the distribution is clustered entirely within a support of D, then polarization is zero. Second, and more problematic, is the fact that the measure is still not operational.

18. A ‘statistical’ approach • The extension of the ER measure of polarization can be summarized as follows: • The ER polarization measure for discrete groups should be used only after the population has been regrouped in a way that captures the group identification structure of society. • This regrouping or clustering will lose some of the initial information that concerns the dispersion of the population around the clusters that we are treating as single groups.

19. Polarization in Latin America • Main Findings from Gasparini, et al. studies • RBLAC commissioned two papers on polarization in • Latin America: one on the dynamics and characteristics of • polarization, the other on polarization, inequality and • social conflict. • 2) As a region, Latin America is highly polarized – 44% • more so than Europe.

20. Polarization in Latin America

21. Polarization in Latin America • 3) Most of the indicators find for income polarization: • A slight increase in polarization between circa 1990 and circa 2004 • A convergence across countries – high polarization countries saw reductions in polarization (Chile, Brazil); Low polarization countries saw increases …

22. …(CR, Uruguay, Venezuela); with no exceptions: Bolivia and Colombia, which started high and saw increases. • 4) For polarization by characteristics, the findings are: Groups delineated by educational attainment show the highest income polarization, followed by groups denominated by: • Labor status (formal/informal) • Region (urban, rural) – or race in Paraguay, Bolivia and Brazil Polarization in Latin America

23. Polarization in Latin America • 5) Changes in the size of the middle-income groups (in terms of population and income) have been similar to those reported for polarization and inequality: • The “middle-class” seems to have been shrinking in most of South America, with the exceptions of Brazil and Chile. • Changes in Central America and Mexico have been milder, without clear signs of a significant reduction in the middle class.

24. Polarization in Latin America • 6) On the relationship between income distribution, institutions and conflict, the findings are: • Negative correlation between indicators of income distribution and indicators of the quality of broad- based institutions • The most polarized countries are on average the ones that have higher levels of conflict • (See Fajnzylber, et al. )

25. Discussion • Is polarization a good “sufficient statistic” for social cohesion? • It does have interesting properties • When is polarization different from Poverty and Inequality • This is what the work in progress is moving towards