Abstract
Can governance impact directly on well-being? In this paper we examine subjective well-being using data for a cross section of countries from the World Values data set. We find that regional democratic satisfaction impacts on both individual happiness and life satisfaction. However, the impact is less evident for women, rich people and for those in rich countries. The fact that the governance variable analysis is based on regional differences in democratic satisfaction reminds us that governance and indeed happiness often differs within countries as well as between them. The use of regional satisfaction in this manner helps overcome endogeneity problems which would be present if we were to use individual democratic satisfaction. Other significant determinants of happiness include settlement size, marital status, income, unemployment and being part of a linguistic minority/majority grouping.
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Notes
Using data from the World Values Survey.
At 1997 prices.
Certain key variables are not included in the fifth wave. These include democratic satisfaction.
We include country fixed effects, hence the impact of religion is related to the individual rather than on the culture of the country.
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Data Appendix: Variable Definitions
Data Appendix: Variable Definitions
1.1 Attitudinal Variables
- Happiness:
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A dependent variable; coded 1 if the individual responded that taking all things together they were not at all happy to 4 very happy
- Life satisfaction:
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A second dependent variable; scaled from dissatisfied (1) to satisfied (10)
- Democratic satisfaction:
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Coded 1 if the respondent is very dissatisfied satisfied with the way democracy is developing in their country to 4 (very satisfied)
1.2 Socio Economic Variables
- Male:
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Binary variable, coded 1 if the respondent is a man
- Age:
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Age of the respondent in years
- Married:
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Binary variable, coded 1 if the respondent is married
- Widow:
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Binary variable, coded 1 if the respondent is widowed
- Children:
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Binary variable, coded 1 if the respondent has children
- Education:
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Coded from 1 (no formal education) to 9 (university level education with degree)
- Income:
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Coded from 1 to 10 reflecting increasing levels of household income—the exact classification varies from country to country. In effect this is a relative income measure
- Savings:
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Coded 1 if the respondent’s family saved money in the previous year to 4 if they spent savings and borrowed money
- Poor health:
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The self-perceived state of the individual’s health. A Binary variable, coded 1 if in poor or very poor health
- Unemployed:
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Binary variable, coded 1 if the respondent is unemployed
- Location:
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Coded 1–8 (large city) reflecting the size of the settlement in which the individual lives
- Religious Group:
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Binary variables, coded 1 if the individual identified themselves as members of a religious group (Catholic, Protestant, Orthodox, Jew, Muslim, respectively)
- Linguistic Minority:
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The proportion of the country’s population having as their first language the same as the respondent, where first language is defined as the one normally spoken at home
- Religious Minority:
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The proportion of the country’s population having the same religion as the respondent
- GDPPC:
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The level of GDP per capita in the individual’s country in the year 2000 in US$ purchasing power parity (Source: World Bank data set)
Countries included: Albania, Argentina, Bangladesh, Bosnia, Canada, Chile, Egypt, India, Indonesia, Iran, Japan, Jordan, Kyrgyz, Macedonia, Mexico, Moldova, Montenegro, Morocco, Peru, Philippines, Puerto Rico, Serbia, South Africa, Spain, Tanzania, Uganda, USA, Vietnam,