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The shift towards an eco-welfare state: growing stronger together

Published online by Cambridge University Press:  27 February 2023

Valon Hasanaj*
Affiliation:
Institute of Political Science, University of Bern, Bern, Switzerland
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Abstract

Poverty, inequality and climate change are profoundly interconnected issues and represent grave threats to the future of our planet and civilization. Failure in one will result in failure in the other; thus, government responses to such threats must be meticulously coordinated, especially across environmental and welfare state programs. In recent years, a growing body of research has examined the links between these two domains, lauding the eco-welfare state as a viable path forward. As the literature on the eco-welfare state is at an early stage, this study proposes two essential theoretical and empirical contributions. First, it examines the most prominent theoretical interpretations of the concept of eco-welfare state and proposes a refined understanding. Second, using model-based cluster analysis for 42 countries, this study empirically unveils a global shift towards and the existence of an eco-welfare state.

Information

Type
Original Article
Copyright
© The Author(s), 2023. Published by Cambridge University Press on behalf of Social Policy Association
Figure 0

Table 1. Operationalization: eco-welfare state.

Figure 1

Table 2. List of indicators.

Figure 2

Figure 1. Cluster plots for 2001. The figure shows three cluster plots for each model. Each value represents one country.

Figure 3

Figure 2. Cluster plots for 2015. The figure shows three cluster plots for each model. Each value represents one country.

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Table 3. Cluster Analysis Results for 2015

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Table 4. Cluster Analysis Results for the Difference in 2015-2001

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Table A1. Country sample.

Figure 7

Figure B1. Model selections: 2001 and 2015. The figure to the left shows the model selection for 2001 data, whereas the one to the right shows the model selection for the 2015 data. Bayesian Information Criterion (BIC) selects the best models or an optimal number of clusters. Hence, the model with the largest BIC score is chosen as the one with the strongest evidence.

Figure 8

Figure C1. Robustness checks: Trimmed Clusters for 2001 and 2015. Using tclust in R Studio, these two pictures show the cluster plots of the robustness check for 2001 and 2015, respectively. Outlier countries are shown in empty bullets “○.”