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A Comprehensive Empirical Model of Welfare State Retrenchment
Unformatted Document Text:  decision process. According to the literature, I will expect the effect of the level of veto points (VETO) on retrenchment is negative. Socio-economic variables 3. Economic shock (structural change): Following what Amable, Gatti and Schumacher did in their paper, I will use industry value added shares (IND_GDP) and labor force shares (LABOR_POP) in OECD STAN industrial database to measure economic shock or consequential structure changes (2006). 4. Unemployment rate: this variable (UNEM_RATE) is used to account for the size of population that is likely to use the social protection. The data is from the OECD unemployment rates dataset. 5. Economic growth: variable GDPGROW is used to account for the “Wagner’s law” (Amable, Gatti and Schumacher 2006), that is the social expenditures depend on the prosperity of economy. I use the GDP growth rate from WDI dataset (library database). 6. Budget deficit: I will use the budget balance variable (DEFI_GDP) measured as general government deficit as percentage of GDP suggested by Allan and Scruggs (2004) to account for the effect of deficit on the welfare retrenchment. All the data are from Huber et al.’s (2004) dataset. 7. Military expenditure: MIL_EXP represents the military expenditure as percentage of GDP. Obviously, since the total expenditure is limited, the more the military expenditure, the less the social spending. Globalization variables 8. Economic openness: this is a variable (TRADE_GDP) measured as sum of exports and imports as a percentage of GDP. I use this variable to explain the role of economic openness, which is accepted as a widespread measure of the globalization influences. 9. FDI outflow as the percentage of GDP: this variable (FDIOUT_GDP) indicates the percentage of the foreign direct investment from those advanced industrial countries to less developed countries. I use this variable to account for the capital mobility due to globalization. Demographic variables 11

Authors: Sun, Feng.
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decision process. According to the literature, I will expect the effect of the level of veto
points (VETO) on retrenchment is negative.
Socio-economic variables
3. Economic shock (structural change): Following what Amable, Gatti and
Schumacher did in their paper, I will use industry value added shares (IND_GDP) and
labor force shares (LABOR_POP) in OECD STAN industrial database to measure
economic shock or consequential structure changes (2006).
4. Unemployment rate: this variable (UNEM_RATE) is used to account for the
size of population that is likely to use the social protection. The data is from the OECD
unemployment rates dataset.
5. Economic growth: variable GDPGROW is used to account for the “Wagner’s
law” (Amable, Gatti and Schumacher 2006), that is the social expenditures depend on the
prosperity of economy. I use the GDP growth rate from WDI dataset (library database).
6. Budget deficit: I will use the budget balance variable (DEFI_GDP) measured as
general government deficit as percentage of GDP suggested by Allan and Scruggs (2004)
to account for the effect of deficit on the welfare retrenchment. All the data are from
Huber et al.’s (2004) dataset.
7. Military expenditure: MIL_EXP represents the military expenditure as
percentage of GDP. Obviously, since the total expenditure is limited, the more the
military expenditure, the less the social spending.
Globalization variables
8. Economic openness: this is a variable (TRADE_GDP) measured as sum of
exports and imports as a percentage of GDP. I use this variable to explain the role of
economic openness, which is accepted as a widespread measure of the globalization
influences.
9. FDI outflow as the percentage of GDP: this variable (FDIOUT_GDP) indicates
the percentage of the foreign direct investment from those advanced industrial countries
to less developed countries. I use this variable to account for the capital mobility due to
globalization.
Demographic variables
11


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