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A Comprehensive Empirical Model of Welfare State Retrenchment
Unformatted Document Text:  two sets variables. One is the “volume-effect” (Amable, Gatti and Schumacher 2006) variable (PBU_GDP): total social expenditures as a percentage of GDP, which comes from Huber, Ragin, and Stephens’ Comparative Welfare States Dataset (Huber, Ragin, Stephens, Brady, Beckfield, and Stephens 2004); the others are the “structure-effect” variables: the net replacement rates of sickness and unemployment insurance programs adopted by Allan and Scruggs (2004). Both data are from Comparative Welfare States Dataset. SICK represents sickness insurance single person replacement rates. It is the ratio of net insurance benefit for general short-term illness to net income for single person earning the APW wage. UE refers to unemployment insurance single person replacement rates. It is the ratio of net unemployment insurance benefit to net income for an unmarried single person earning the average production worker (APW) wage (Huber, Ragin, Stephens, Brady, Beckfield, and Stephens 2004). These two net replacement rats are more structural measures of social entitlements and enable me to capture the structural development of social protection in those developed countries. Independent variables Institutional variables 1. Political partisanship: variable LEFTCAB is included to measure the effects of political partisanship. The data is from comparative welfare states dataset (Huber et al. 2004). It represents left seats as a percentage of seats held by all government parties. 2. Veto points: I will follow Huber and Stephens’ (2001) measure of institutional veto points to assess the extent to which governments may find their “room to maneuver” restricted by general constitutional structures. Huber and Stephens adopted an additive index of federalism, presidentialism, bicameralism, and the use of popular referenda as a normal element of the political process. In my analysis, I also include the value of judicial review. The five variables come from Huber et al.’ (2004) dataset and they are measured as federalism (0=no, 1=weak, and 2=strong); presidentialsim (0=parliamentary, 1=president or collegial executive); bicameralism (0=no second chamber or second chamber with very weak powers, 1=weak bicameralism); referendum (0=none or infrequent, 1= frequent); and judicial review (0=no, 1=yes). A high score means high dispersion of political power and the presence of multiple constrains in the policy 10

Authors: Sun, Feng.
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two sets variables. One is the “volume-effect” (Amable, Gatti and Schumacher 2006)
variable (PBU_GDP): total social expenditures as a percentage of GDP, which comes
from Huber, Ragin, and Stephens’ Comparative Welfare States Dataset (Huber, Ragin,
Stephens, Brady, Beckfield, and Stephens 2004); the others are the “structure-effect”
variables: the net replacement rates of sickness and unemployment insurance programs
adopted by Allan and Scruggs (2004). Both data are from Comparative Welfare States
Dataset. SICK represents sickness insurance single person replacement rates. It is the
ratio of net insurance benefit for general short-term illness to net income for single person
earning the APW wage. UE refers to unemployment insurance single person replacement
rates. It is the ratio of net unemployment insurance benefit to net income for an
unmarried single person earning the average production worker (APW) wage (Huber,
Ragin, Stephens, Brady, Beckfield, and Stephens 2004). These two net replacement rats
are more structural measures of social entitlements and enable me to capture the
structural development of social protection in those developed countries.
Independent variables
Institutional variables
1. Political partisanship: variable LEFTCAB is included to measure the effects of
political partisanship. The data is from comparative welfare states dataset (Huber et al.
2004). It represents left seats as a percentage of seats held by all government parties.
2. Veto points: I will follow Huber and Stephens’ (2001) measure of institutional
veto points to assess the extent to which governments may find their “room to maneuver”
restricted by general constitutional structures. Huber and Stephens adopted an additive
index of federalism, presidentialism, bicameralism, and the use of popular referenda as a
normal element of the political process. In my analysis, I also include the value of
judicial review. The five variables come from Huber et al.’ (2004) dataset and they are
measured as federalism (0=no, 1=weak, and 2=strong); presidentialsim (0=parliamentary,
1=president or collegial executive); bicameralism (0=no second chamber or second
chamber with very weak powers, 1=weak bicameralism); referendum (0=none or
infrequent, 1= frequent); and judicial review (0=no, 1=yes). A high score means high
dispersion of political power and the presence of multiple constrains in the policy
10


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