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The Generational Welfare Contract

Justice, Institutions and Outcomes

Simon Birnbaum, Tommy Ferrarini, Kenneth Nelson and Joakim Palme

This groundbreaking book brings together perspectives from political philosophy and comparative social policy to discuss generational justice. Contributing new insights about the preconditions for designing sustainable, inclusive policies for all of society, the authors expose the possibilities of supporting egalitarian principles in an aging society through balanced generational welfare contracts.
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Appendix

Simon Birnbaum, Tommy Ferrarini, Kenneth Nelson and Joakim Palme

All empirical analyses in this book follow the same basic approach where descriptive analyses are combined with statistical regression. Although statistical analyses are based on state-of-the-art regression techniques, the reader need not have special statistical skills to understand the results. For the less experienced reader only some general understanding is required on how associations between variables are displayed in regressions. A positive coefficient simply shows that there is a positive association between two variables, where a high value for one of the variables tends to go hand in hand with a high value for the other variable. Conversely, a negative coefficient indicates that a high value for one of the variables is related to a low value for the other variable.

For each regression coefficient we also report the standard error, which is used to inform about the precision of our estimate (i.e. whether we are on target or not). The smaller the standard error (relative to its regression coefficient), the more precise the estimate. Based on the size of the regression coefficient and its standard error, we can decide whether our estimate is significantly different from zero. In the empirical analyses of this book, we follow common standards of statistical analyses in the social sciences and use two levels of statistical significance testing, noted with asterisks in the regression tables. Coefficients with one asterisk show that there is the likelihood of observing the correct estimate in at least 95 analyses out of 100, while two asterisks denote cases where there is the likelihood of having the correct estimate in at least 99 out of 100 analyses. With this brief note on inferences in statistical analysis, we believe that the reader has the necessary knowledge to understand the core results from the regression analyses in this book.

Table A.1  Poverty rates disaggregated by age-related risk category in 17 OECD countries 1980–2010

Note: Ch 5 childhood risk category, Wa 5 working-age risk category, Oa 5 old-age risk category.

Source: The Cross-National Data Center in Luxembourg (LIS), own calculations.

Table A.2  Balance and overall level of income replacement in age-related social insurance (and social assistance) and poverty at various income thresholds. Country-fixed effects structural equation models of 17 OECD countries 1980–2010

Notes: * p < 0.05, ** p < 0.01. Cluster robust standard errors in parentheses. Constants are not shown. New Zealand is excluded from analysis. 1 Italy is also excluded.

Table A.3  Balance and overall level of income replacement in age-related social insurance and poverty using the square root equivalence scale at various income thresholds. Country-fixed effects structural equation models of 17 OECD countries

Note: * p < 0.05, ** p < 0.01. Cluster robust standard errors in parentheses. Constants are not shown. New Zealand is excluded from analysis.

Table A.4  Balance and overall level of income replacement in age-related social insurance and poverty in different age-related risk groups. Country-fixed effects structural equation models of 17 OECD countries

Note: * p < 0.05, ** p < 0.01. Cluster robust standard errors in parentheses. Constants are not shown. The childhood risk category includes families with dependent children. The working-age risk category includes childless households in working age. The old-age risk group includes people 65 years and older. New Zealand is excluded from analysis due to missing data.

Table A.5  Country-fixed effects structural equations of various employment outcomes on balance and overall level of income replacement in age-related social insurance in 18 OECD countries 1985–2010

Note: * p < 0.05, ** p < 0.01. Cluster robust standard errors in parentheses. Constants are not shown.

Table A.6  Country-fixed effects structural equation models of female labor force participation on balance in age-related social insurance and levels of income replacement for separate age-related social risks in 18 OECD countries 1985–2010

Note: * p < 0.05, ** p < 0.01. Cluster robust standard errors in parentheses. Constants are not shown.

Table A.7  Country-fixed effects structural equations of income replacement in age-related social insurance and cumulative partisan incumbency in 18 OECD countries

Overall level of income replacement
1960–20101980–2010
Balance of income replacement0.231**0.181**
(0.067)(0.071)
Left cabinet shares0.078–0.289
(0.384)(0.433)
Confessional cabinet shares–0.2330.096
(0.321)(0.302)
Unemployment0.2750.003
(0.440)(0.461)
GDP per capita0.2990.212
(0.482)(0.497)
Old-age dependency ratio0.1670.304
(0.204)(0.194)
Civilian labor force0.055–0.127
(0.373)(0.239)
Service sector employment0.0900.082
(0.498)(0.495)
Balance of income replacement
Left cabinet shares0.785**0.666*
(0.261)(0.326)
Confessional cabinet shares0.503*0.467
(0.247)(0.304)

Note: * p < 0.05, ** p < 0.01. Cluster robust standard errors in parentheses. Constants are not shown.

Table A.8  Country-fixed effects structural equations of levels of income replacement for separate age-related risks on balance in age-related social insurance and cumulative partisan incumbency in 18 OECD countries 1980–2010

Note: * p < 0.05, ** p < 0.01. Cluster robust standard errors in parentheses. Constants are not shown.

a 1980–2005.