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2013 | OriginalPaper | Buchkapitel

5. Ethnic Heterogeneity and the Welfare State

verfasst von : Udaya R. Waglé

Erschienen in: The Heterogeneity Link of the Welfare State and Redistribution

Verlag: Springer International Publishing

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Abstract

Countries cumulate very different experiences on welfare state development and change and the process dominant in one context may not be consistent over time. How welfare states develop and change has partly to do with changes in the need and demand for social protections as well as those in political attitudes and preferences that dictate policy actions and decisions. An integral component of how welfare states change can also be ethnic heterogeneity as it can affect both the economic contexts of needs and demands and the political contexts of attitudes and preferences. Yet, while the existing theories of the welfare state underscore these political and economic contexts of social protection, they fail to adequately link welfare state developments and changes with the changes in ethnic heterogeneity. Given the major changes in welfare state policy provisions and practices, coupled by a landscape of growing ethnic heterogeneity in high-income countries, it is important to examine if this form of social heterogeneity can be a significant factor in thinking about the needs and demands as well as the political attitudes and preferences, which help shape and reshape welfare state policies.

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Vorheriges Kapitel Poverty and Inequality

Nächstes Kapitel Ethnic Heterogeneity and Poverty and Inequality

Nur mit Berechtigung zugänglich

Immigration data specifically focus on the relative size of foreign-born population. While this exercise can be extended to cover other forms of immigration heterogeneity, these data reported for illustrative purposes capture the degree of immigrant mixes in the national population.

Data are unavailable for Austria, Belgium, Denmark, and Ireland.

This part of the analysis used for illustrative purposes also leaves out other forms of immigration heterogeneity, something that can be incorporated for further analysis.

These control variables can be numerous since almost any trait is associated with changes in policy provisions and practices. Other relevant political institutions or practices include, for example, federalism, cumulative control of social and Christian democratic parties, and electoral systems and veto points. Many variables relating to economic policy and performance including the composition of economy, capital openness and investment, and labor market structure would also be likely candidates as would be other demographic characteristics. The idea, however, is to capture what is both relevant and available, with the note that inclusion of a large number of covariates causes compromises on the degrees of freedom.

Debates continue over the appropriate analytical techniques for cross-sectional time series data in using countries as the population units rather than as sample units. Whereas the most widely used econometric approach involves panel data techniques to efficiently deal with the time series as well as cross-sectional variations (Wooldridge 2002), political scientists conducting comparative research even advocate the use of ordinary least squares regressions with proper adjustments for the time series component (Beck 2001; Beck and Katz 2001). No doubt, techniques with first differencing or lagged variables, which are used in welfare state research (Iversen 2001; Iversen and Cusack 2000; Iversen and Soskice 2006; Swank 2001, 2005), could have been used if the dataset was composed of unimputed data. But, concerned more with expanding the broader time coverage rather than the use of specific techniques to efficiently handle the nuanced methodological issues, using panel data techniques has been a general practice in examining variations in welfare state policies across countries (Brady 2003, 2009).

While the aggregation involves taking simple average of the values of three immigration variables, the sets of these initial values with different units of measurement need pretransformation for consistent aggregation. The following formula transforms the initial values into indices ranging between a low of zero and a high of 100:

\( Index=\frac{ Value- Min}{ Max- Min} \)where Value is the given value for a country in a specific year and Min and Max are the minimum and maximum values in that variable, respectively.

In fact, the numbers of years for which data on poverty and inequality are available are even more limited as they have to come from nationally representative surveys. All of the data used in this chapter will be used again in the next chapter while analyzing poverty and inequality.

This software is designed to handle missingness, especially involving cross-sectional time series data. The software and related documentation and analyses are found in Honaker and King (2010), Honaker et al. (2009), and King et al. (2001).

Results from the associated likelihood ratio tests suggest, for example, that each version of the mixed model regression estimated here demonstrates a relative good fit with the null hypothesis of no value-added over the linear model (variance of the random intercept associated with countries being zero) rejected with a very high confidence.

These variations occur due to the differences in assumptions about the structure of country-level intercepts, which are fixed in case of fixed effects regressions and variable in case of mixed model regressions.

Spain, for example, does not even have data on wage setting coordination. Data are also unavailable on the parliamentary seats held by left parties after 2006 for all countries. These, together with missing data on other fragmented cases, have reduced the number of cases with data to 496 in case of wage setting coordination and to 459 in case of the parliamentary seats held by left parties.

It must be noted that this exercise is not beneficial for the models of welfare state decommodification since data on decommodification are lacking in its source, Scruggs (2005), entirely for Spain as well as for beyond 2002 for all countries.

These are based on the fixed effects and mixed model regressions estimated with the largest numbers of observations possible. In case of public social transfer expenditures, these estimates are based on the models presented in Table 5.10, which include 504 observations. In case of decommodification, however, the estimates are based on the models presented in Tables 5.8 and 5.9 that include 365 observations, as the exclusion of wage setting coordination and parliamentary seats held by left parties does not allow larger effective sample sizes.

It is for this reason that the fixed effects regressions predict country-specific intercepts that are fixed and do not change over time. The mixed model regressions, on the other hand, predict random intercepts that vary both across countries and over time, which are estimated at the country level to determine the country-level effects.

Ireland can as well be considered a leader in the country-level effects on public social transfer expenditures due to its largest positive effect size.

The degree of correlation among these variables is still relatively low at below 0.36, however, as indicated in Table 5.23 in the Appendix.

After all, the strength of left politics may have waned over time as the number of countries where left political parties were in a majority or close to a majority in the parliament declined from five in the 1990s to three in the 2000s.

Because these results are reported for illustrative purposes, the underlying models (as well as the models reported in Table 5.22) are not estimated with cluster-robust standard errors or the generalized least square procedures that would further inflate the standard errors minimizing the ability to detect significant relationship.

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Titel: Ethnic Heterogeneity and the Welfare State
verfasst von: Udaya R. Waglé
Verlag: Springer International Publishing
Buch: The Heterogeneity Link of the Welfare State and Redistribution
Print ISBN: 978-3-319-02814-9

Electronic ISBN: 978-3-319-02815-6

Copyright-Jahr: 2013
DOI: https://doi.org/10.1007/978-3-319-02815-6_5