Abstract
Poverty and inequality refer to the economic order of society, in which an individual level welfare remains at the core. Because the private and especially labor market serves as the primary source of income for most individuals, these economic outcomes are linked with how individuals engage with the market and how they derive the incomes needed to operate, economically speaking. But the roles of welfare state policies are also important as they provide the supports needed to ensure minimally adequate living standard for everyone. The economically dependent population including the old, sick, unemployed, disabled, and otherwise vulnerable in particular may need help from the state as they cannot harness the opportunities available in the market to the fullest extent possible. The role of welfare state policies does not stop at ensuring the minimum living standard, however, since high disparities in income distribution help create uneven playing fields, preventing societies from achieving the goal of equality of opportunity. The concepts of poverty and inequality in this sense are similar as the degree of poverty is likely to be higher in a society with greater inequality than in another with lower inequality.
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Notes
- 1.
The individual level encompasses households, families, or individuals as the unit of economic analysis. Even though households or families represent the most widely used social and economic units where economic resources are pooled in order to meet the appropriate consumption needs, the term “individual level” is used as a broader concept to distinguish from the “aggregate level,” which refers to the levels of a community, society, state, or economy.
- 2.
To recall, these are computed as the product of higher poverty headcount ratios (%) estimated from application of the poverty lines at 60 % of the national median equivalized incomes and Gini indices (ranging from zero to one) to obtain the inequality-adjusted poverty headcount ratios in percentage terms.
- 3.
This exercise can also be extended to all three measures of immigration minorities as well as lower poverty headcount ratios, 90/10 income ratios, and inequality-adjusted poverty headcount ratios focusing on any combination of these variables. But the profiles presented here are expected to provide a cursory view of how the relationships hold across the indicators of ethno-racial, religious, and immigration heterogeneity as well as the indicators of poverty and inequality.
- 4.
These can include numerous micro- and macro-level variables affecting poverty or inequality as well as welfare state policies. Depending on data availability, this analysis incorporates a number of theoretically relevant exogenous, explanatory variables including union density, left party activism, wage setting coordination, democratic institutions, GDP per capita and its growth, foreign direct investment, international trade, size of the manufacturing sector, inflation, unemployment, labor force participation, population over 65, life expectancy, and population size.
- 5.
The indicators of poverty include the higher and lower poverty headcount ratios whereas the indicators of inequality include Gini indices and 90/10 income ratios used separately. These indicators also include inequality-adjusted poverty headcount ratios.
- 6.
The factor loading for poverty or inequality would be one and that for welfare state policies would be 0 with the associated measurement error also being 0.
- 7.
These latent variables appear on both sides of equation 6.4 in order to incorporate the roles of welfare state policies in determining poverty.
- 8.
While the model does not identify the specific exogenous variables, x’s, that can differ across poverty, inequality, and welfare state policies, the underlying equations to be estimated will include different x’s helping with the model identification.
- 9.
As discussed in Chap. 5, this aggregate immigration variable is created by transforming the three variables into scales and taking their simple, unweighted average.
- 10.
The use of these imputed data on welfare state decommodification becomes more important due to the nature of analytical technique used here. Unlike in classical regression models, for example, structural equations models involve multiple indicators, with missing data on any single indicator causing non-use of all affected observations. The positive side of this is that the use of imputed data on one of the three indicators of welfare state policies may not significantly alter the estimates. Nevertheless, one must be mindful that the impact of imputed data, however small the affected number of observations, can still cause biased results, a caveat that requires careful comparison with results from the unimputed data.
- 11.
An exclusion of this variable is needed to maximize the effective sample size as its data include missing values for years beyond 2002 entirely.
- 12.
Given that fit indices can be swayed by a number of factors including the sample size, number of parameters to be estimated, and number of outcome variables and covariates used, the suggestion is to use multiple fit indices simultaneously (Bollen 1989; Byrne 2012; Hu and Bentler 1995). The Chi square value relative to the degrees of freedom measures between 1.6 and 2.3, something that is comfortably satisfactory. Of the other indices, the cumulative fit indices (CFI) of over 0.93 are highly acceptable as are the root mean square errors approximation (RMSEA) of less than 0.06 and standardized root mean square residuals (RMR) of less than 0.11, indicating a good fit to minimize both Type I and II errors (Bentler 1990; Bollen 1989; Hu and Bentler 1999).
- 13.
The models, for example, explain between 70 and 74 % of the variations in these outcome and latent variables respectively. It must be noted, however, that the relative inability to capture cross-national variations in human capital, discrimination, demographic traits, and other important variables may have resulted in the R-squares that are less than optimal.
- 14.
The Chi square relative to the degrees of freedom, for example, hovers around four. The RMSEA’s of around 0.08, standardized RMR’s of around 0.07, and especially the CFI’s of around 0.8, albeit still acceptable as fair modelfits, are considered less than ideal.
- 15.
The difference in fit indices across these model variations is interesting. The models using extended data, for example, have better Chi square statistic relative to the degrees of freedom, CFI, and RMSEA even though they have weaker standardized RMR indices. The sizes of R-square in particular switch between welfare state policies and 90/10 income ratios in case of the models with immigration index when it comes to using the limited and extended data.
- 16.
These marginal differences are reasonable since the fit indices reflect on the data on both poverty and inequality where the latter have produced slightly weaker fit indices in the first place. The first of such differences involve the weaker size of Chi square relative to the degrees of freedom especially in case of extended data. Other fit indices producing weaker estimates include CFI, RMSEA, and standardized RMR.
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Waglé, U.R. (2013). Ethnic Heterogeneity and Poverty and Inequality. In: The Heterogeneity Link of the Welfare State and Redistribution. Springer, Cham. https://doi.org/10.1007/978-3-319-02815-6_6
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