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Social Indicators Research
Published in: Social Indicators Research 3/2017

11-10-2016

Welfare State Development, Individual Deprivations and Income Inequality: A Cross-Country Analysis in Latin America and the Caribbean

Author: Gibrán Cruz-Martínez

Published in: Social Indicators Research | Issue 3/2017

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Abstract

Several scholars have confirmed the role that the welfare state (WS) plays in reducing poverty, promoting equality and ensuring the common wellbeing. One of the limitations of the scholarship has been the conceptualization and operationalization of the WS and poverty as one-dimensional variables. The purpose of this paper is to examine the relationship between welfare state development, single-dimensions deprivations and income inequality in Latin America and the Caribbean, before and after controlling for demographic and cyclical factors. The WS is operationalized as a one-dimensional variable, but also taking into account its multidimensional nature. Three individual deprivations suffered by people on poverty and two income inequality indicators are used as dependent variables. Three pooled time-series cross-section regression analyses with panel-corrected standard errors models were carried out on 18 countries in the region around 2000, 2005 and 2010. This paper shows that the development of social-welfare programs and institutions seems to be an effective way of tackling individual deprivations suffered by people on poverty in the region. On the other hand, the WS development didn’t appear to be effective to reduce income inequality. The outcomes of welfare institutions appear to be the pivotal dimension to reduce income inequality and income deprivations in the region.
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Appendix
Available only for authorised users
Footnotes
1
Huber and Stephens (2012) coined this term to describe the welfare state systems in Latin America and the Caribbean.
 
2
The outcomes dimension is included in the MWI because the outcomes of welfare institutions are considered to be a vital dimension of the welfare state. Even though some scholars might consider infant mortality rate and level of education as welfare outcomes, in this paper they are used to operationalize the outcomes of welfare institutions (i.e. health and education institutions). The quality of health and education institutions is considered relevant to reduce levels of individual deprivations and income inequality; therefore, they are included in the multidimensional indicator.
 
3
The main reason behind the selection of these eight indicators is data availability. However, the justification of the indicators selected to present different dimensions of the emerging welfare state in the region has been addressed previously in Segura-Ubiergo (2007) –only the first 4- and in Cruz-Martínez (2014).
 
4
Thanks to the high correlation between the indicators in each of the three dimensions it was possible to reduce the data to three individual indexes without loosing too much information. Two tests were necessary on each data set to assess the statistical relevance of the PCA and to confirm that the correlation between variables was high: the determinant of the correlation matrix and the Bartlett’s sphericity test.
 
5
Because the dimensions of individual welfare indexes were normalized (max = 1, min = 0) it was not possible to use the geometric mean as in the new HDI method. One of the limitations of using an arithmetic mean is the disadvantage of missing some information because of not being able to give more weight to outliers.
 
6
A group of scholars have developed the Individual Deprivation Measure (IDM) to operationalize a multidimensional poverty index that is able to overcome one of the main limitations of poverty household data: not measuring individual deprivations by not considering intra-household inequalities. The IDM takes into account 15 areas of life to present individual deprivations suffered by people in poverty (e.g. food, water, shelter, energy, education, health deprivations). See Wisor et al. (2015) and Bessell (2015).
 
7
According to the FAO (2013) “(…) it measures the percentage of the population that is at risk of not covering the food requirements associated with normal physical activity, and therefore including also those who, even though cannot be considered chronically undernourished, are likely being conditioned in their economic activity by insufficient food”. It is not an indicator of insufficient income to purchase food –as the ECLAC poverty indicator- rather it represents the probability of selecting an undernourished individual in a population. So it does not consider the cause (food availability or income) of the undernourishment but the fact the individual is undernourished. An individual with food inadequacy will not be able to develop its basic capabilities, in turn limiting the achievement of freedom and well-being.
 
8
It is a measure of the deviation of the distribution of income among individuals or households within a country from a perfectly equal distribution. A value of 0 represents absolute equality, a value of 1 absolute inequality (World Bank 2013).
 
9
One of the reviewers was worried about the problem of having welfare outcomes indicators as dependent and explanatory variables. However this is not the case, but is relevant to be clear about what the outcome dimension in the MWI is measuring. This third dimension is not measuring welfare outcomes, but outcomes of welfare institutions (i.e. outcomes of health and education institutions). Therefore, there are not welfare outcomes indicators in both side of the regression formula. There is a dimension in the MWI (explanatory variable) that measures outcomes of welfare institutions and there are two indicators as dependent variables (individual deprivations and income inequality) measuring two of the multiple welfare state objectives.
 
10
This analysis presents the degree of correlation and significance between variables, but do not pretend to explain poverty/inequality levels and poverty/inequality structure in the region.
 
11
Beck and Katz (1995) show in their article the usefulness of OLS for TSCS data, over the feasible generalized least square estimator suggested by Parks (1967) and popularized by Kmenta (1986).
 
12
The regression coefficient analysis is ceteris paribus, which means that we are going to examine how much a 1 unit change in X variable changes a Y variable after holding other X’s constant. In this case, the paper examines the effect of 1 unit change in the MWI over poverty and inequality, after holding constant the elderly population, the unemployment rates and the gross domestic product per capita.
 
13
How can a country increase the value of its MWI, so that it can have an effect on poverty and income inequality? As I mention before, the MWI is a composite index of three individual indexes referring to the three dimensions of the welfare state considered in this study. Each dimension arbitrarily contributes to one-third of the MWI score. Meanwhile the weights of the initial 8 indicators that form the individual indexes (SSDI, CDI, ODI) are assigned by the PCA. Looking at the loadings matrix (not shown here) we can have an idea of the ‘importance’ or ‘explanatory power’ each initial indicators have within the individual indexes. The component loadings show the correlation between the PC1 and the original indicators, so they tell us how much of the variation in the initial indicator is explained by the PC1.
 
14
Here I don’t refer to elasticity but rather a simple analysis of the slope. How does a 1-unit change in the MWI affect the poverty level (%).
 
15
In a previous paper, the strong and significant relationship between social spending per capita and individual deprivations was confirmed in a cross-country comparative analysis (21 cases) between 1990 and 2010 [See Cruz-Martínez (2015b)].
 
16
History matters as Mesa-Lago (1994) would argue, therefore the legacy of the bismarckian model in Latin American welfare programmes might shed some light over this puzzle. In the bismarckian model, the coverage and benefits of social welfare programmes are closely linked to class and status. Rather than creating unified welfare programmes, Latin American countries developed stratified programmes during the XX century, where public servants, the military and organized workers were able to enjoy better quality of coverage than the majority of the population (Barba Solano 2009, 2005). The main problem with this duality in the coverage of welfare programmes, is that the large majority of the population worked in the agriculture or the informal sector. This large majority was then relegated to stigmatized and scarce social assistance with low replacement rates. Further analysis should examine if the specific structure of coverage in each country (e.g. defined contribution or defined benefit, universal or stratified) might explain this puzzle.
 
17
Although not significant with the In2 as dependent variable.
 
18
Again, here I don’t refer to elasticity but rather a simple analysis of the slope. How does a 1-unit change in the explanatory variables affect the poverty level (%), ceteris paribus.
 
19
See Table 1 on Aston and Jones (2012: 4) to understand how different types of social protection (social transfers, social insurance, social welfare services for marginalized groups and social equity) may improve child malnutrition.
 
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Metadata
Title
Welfare State Development, Individual Deprivations and Income Inequality: A Cross-Country Analysis in Latin America and the Caribbean
Author
Gibrán Cruz-Martínez
Publication date
11-10-2016
Publisher
Springer Netherlands
Published in
Social Indicators Research / Issue 3/2017
Print ISSN: 0303-8300
Electronic ISSN: 1573-0921
DOI
https://doi.org/10.1007/s11205-016-1465-4

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