2.3.1 Data description
In order to test the hypotheses stated in the previous section, the empirical analysis relies on a dataset covering 153,268 respondents from twelve EU countries over the time period between 1990 and 20008. The data for the individual-level variables are derived from the Eurobarometer Survey Series. Next to the dependent variable (life satisfaction) this includes a number of control variables: gender, age, ideological preferences, relative income, marital status, education level, employment status and the number of children.
The sample of respondents for each Eurobarometer Survey is drawn based on a multi-stage, random probability procedure and is hence designed to convey a representative picture of the population aged fifteen years and over in the EU member states. The interviews were organized by research firms under the direction of the European Commission and were conducted in a face-to-face setting in people’s homes and in the appropriate national language.
The data for the life satisfaction variable is based on the question ’On the whole, are you very satisfied, fairly satisfied, not very satisfied or not at all satisfied with the life you lead?’ (the small number of respondents answering ’Don’t know’ and ’No answer’ is ignored) measured on a scale that runs from 1 to 4, where a higher value indicates a higher level of satisfaction.
Several findings in the economic and psychological literature justify using this data. First, there is mounting evidence that self-reported well-being is correlated with physical reactions such as the frequency of smiling (Ekman et al., 1990; Pavot et al., 1991) or heart rate and blood pressure reactions to stress (Shedler et al., 1993). Second, people’s perceptions of their own well-being coincide with recall of positive events in life (Seidlitz et al., 1997) and reports of relatives and friends (Diener, 1984; Sandvik et al., 1993). Third, experimental studies reject the hypothesis that subjects bias their response upwards due to social desirability (Konow and Earley, 2008). Finally, data on subjective well-being has been shown to be negatively correlated with suicide in individual-level multivariate regressions (Daly and Wilson, 2009).
8The analysis is limited to this time period for several reasons. First, OECD data on government spending is not available before 1990. Second, some variables in the Eurobarometer Survey Series are not available after 2000: The number of children is not recorded from 2001 to 2003, while the same applies to relative income from 2004 to 2007.
Figure 2.1 displays values for life satisfaction and government size for twelve EU coun-tries averaged across the time period from 1990 to 2000 (and the individuals in a particular country). Denmark is clearly identified as the country where people are on average most satisfied with their lives with an average value of 3.6 on a scale that runs from 1 to 4. At the lower end of the distribution are Germany, Italy, and France with averages of at most 2.9.
The order in which the countries appear in the bar-chart is quite stable over time and across other surveys such as the World Values Survey or the European Social Survey.
Figure 2.1: Averages of life satisfaction and government size, 1990 - 2000
LUXEMBOURG
IRELAND UK
NETHERLANDS
GERMANY
BELGIUM AUSTRIA
ITALY FRANCE
FINLAND DENMARK
SWEDEN
2.833.23.43.6
Average life satisfaction
40 45 50 55 60
Government expenditures as % of GDP
Sources: Eurobarometer, OECD National Accounts
In addition, figure 2.1 illustrates the large cross-country variation in terms of government size across the twelve EU countries in the sample. More specifically, it becomes evident that there are two extreme types of government in the EU: Scandinavian welfare states and Anglo-Saxon governments with an average of about 57 and 42 percent of GDP, respectively. Luxembourg as a particularly small country represents an exception to this classification. Figure 2.2 provides an overview with respect to the size and functional composition of public expenditures for the twelve countries. The time series plots on the left reveal that there is also some variation over time in the degree of government involvement. For Finland, Sweden and Ireland this variation amounts to up to 10 percentage points in the time period considered here.
The pie chart on the right of figure 2.2 disaggregates public expenditures according to the purposes on which they are spent and displays unweighted averages for the twelve countries across the relevant time period. Social protection expenditures represent the highest share of public spending (37.8%), followed by expenditures on general public services, health and education. Smaller categories with a share of less than 10% include economic affairs, public order and safety, and defense. The residual category sums up spending on recreation, culture
Figure 2.2: Size and composition of government expenditures, 1990 - 2000
1990 1995 2000 1990 1995 2000 1990 199 5 2000 19 90 1995 2000
AUSTRIA BELGIUM DENMARK FINLAND
FRANCE GERMANY IRELAND ITALY
LUXEMBOURG NETHERLANDS SWEDEN UK
%
Government expenditures as % of GDP
3.3%9.7%
Public order and safety Social protection
Expenditure types as % of total expenditures
[1] The time-series plots on the left-hand side illustrate the evolution of government size over the period from 1990 to 2000 for each of the twelve EU countries. The pie chart on the right-hand side depicts shares of the respective expenditure categories averaged over the twelve EU countries and the period from 1990 to 2000.
Source: OECD National Accounts
and religion (2.2%), environmental protection (1.3%) and housing and community amenities (2.1%). Tables 2.7 to 2.10 in the appendix provide a more detailed overview of the data and its sources as well as definitions of the expenditure categories. In the estimations in section 2.4, the focus is on education, health and social protection expenditures which on average sum up to more than 60% of the total budget.
The set of controls at the country-level includes three macroeconomic variables that are taken from the OECD databases. First, all estimations include the log of GDP per capita owing to the long tradition of investigations regarding the effect of a nation’s prosperity on well-being (Easterlin, 1974; Oswald, 1997). Second, unemployment rates are incorporated into the regression analysis given that Lucas et al. (2004) find a large and persistent effect of unemployment on life satisfaction. It appears that even people, who find a job after being unemployed for a while, do not return to their initial level of life satisfaction. In this context, one has to keep in mind that the unemployment rate also captures negative effects on well-being through social problems such as crime (Edmark, 2005)9 and social exclusion. The third macroeconomic variable to be found in all estimations is inflation as a result of Di Tella et al.’s (2001) evidence that high inflation depresses well-being in the United States and Europe, even if the effect is smaller than for unemployment.
The political and institutional environment related to the efficient satisfaction of voters’
preferences is also likely to affect well-being as suggested by Hudson (2006) and Wagner et al. (2009).10 The first variable of interest is corruption which is measured by means of the
9Using a panel of Swedish counties ranging from 1988 to 1999, she finds that unemployment has a signifi-cantly positive effect on property crimes such as burglary, car and bike theft.
10Hudson (2006) provides evidence that institutional performance and the resulting level of trust in insti-tutions has a direct impact on subjective well-being in EU countries, while Wagner et al. (2009) find that
Corruption Perceptions Index (CPI)11 and for which data is available on an annual basis. In order to facilitate the interpretation of the slope coefficients in the estimations, this measure is rescaled as Corruption = 10 - CPI score. The second institutional factor that is considered is the extent of decentralization measured as the share of sub-national expenditures in total public expenditures. Data on expenditure decentralization is provided by the World Bank as part of the Fiscal Decentralization Indicators.
2.3.2 Empirical strategy
The regression model that is best suited to this analysis is an ordered response model, where the dependent variable - people’s observable satisfaction with life - is discrete and defined on a finite ordinal scale, i. e. Lifesatitc∈ {1,2,3,4}. The first part of the ordered response model consists of a structural equation with respect to the latent, continuous dependent variable:
Lifesat∗itc =α+βIndividualitc+γExpenditurestc+δMacrotc+ωt+µc+itc, (2.1) where the subscripts represent individuals, time periods and countries. Expenditurestc repre-sents both total government expenditures as a share of GDP as well as expenditure subcate-gories as a share of total expenditures, whileitc represents the error term which we assume to be i.i.d. and normally distributed. Therefore, we are estimating an ordered probit model.
Individualitc includes a number of characteristics of the respondents such as gender, age, rel-ative income, ideological preferences, marital status, education level, employment status and the number of children.
On a country level, Macrotcincludes the log of GDP per capita, unemployment rates and inflation rates.12 In addition, all regressions include time fixed effects ωt in order to control for common exogenous shocks, an intercept α, and country fixed effects µc. Country fixed effects are included due to the available evidence that measures of subjective well-being are not internationally comparable (Diener and Oishi, 2006). In some of the regressions nonlinear relationships between government expenditures and life satisfaction are tested by means of interactions with institutional factors and a quadratic government expenditures term. These are not explicitly specified in equation 2.1 to save space.
The second part of the ordered response model (equation 2.2) is an observation rule for the ordinal dependent variable, which relates the observable dependent variable to the latent
institutional quality measured by the rule of law, well-functioning regulation and low corruption has a positive effect on people’s satisfaction with democracy. This may lead to higher subjective well-being in general.
11The CPI is a ’poll of polls’ using information from up to 12 individual surveys. Country scores correlate strongly with other available indexes. For further details on its construction see Treisman (2007). Data reaching back to 1995 for a large number of countries are available athttp://www.transparency.org/policy_
research/surveys_indices/cpi.
12Note that even though the dataset does not include respondents’ absolute income, the simultaneous inclu-sion of individual income quartiles and GDP per capita allows us to approximate individual income levels.
variable. It simply spells out how Lifesatitc changes its value if Lifesat∗itc crosses a fixed given
The estimation of these models in section 2.4.1 is followed by three robustness checks that involve the exclusion of outliers, the inclusion of economic openness13 and OLS estimations (section 2.4.2). The least-squares estimations have the advantage that the interpretation of the coefficients is more straightforward than for ordered probit estimations.