THE INFLUENCE OF INSTITUTIONS ON INDIVIDUAL WELL-BEING

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THE INFLUENCE OF INSTITUTIONS ON

INDIVIDUAL WELL-BEING

By

YOERI DIJKHOF

S2695200

University of Groningen

Faculty of Economics and Business

Msc International Economics & Business

Supervisor: dr. M.V. Nikolova

Co-assessor: prof. dr. R.C. Inklaar

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1 Abstract

This paper examines the relationship between economic freedom and subjective well-being. Using a sample of individuals from the World Value Survey and country level data from the Economic Freedom of the World index of 100 countries, the results show a positive and significant relationship of economic freedom on well-being above and beyond income. This result is robust to different measures of evaluative well-being and across countries based on their income level. Decomposing the EFW index shows that the size of the government, the quality of the legal and justice systems, and sound money policies matter specifically to measures of SWB.

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2

1. INTRODUCTION

In the economics literature, there is a growing consensus that traditional objective well-being measures such as income, economic growth and consumption are insufficient to capture human well-being in all its dimensions. For example, Brexit or the Arab Spring cannot be explained solely due to shortfalls in income. Several commissions worldwide have already been established to explore the use of alternative measures of welfare (Stiglitz, Sen, & Fitoussi, 2009), one of which being survey data on subjective well-being. The consensus among most academics is that policies should be aimed at increasing welfare in society, not increasing GDP alone (De Neve et al., 2015; Gehring, 2013; Nikolaev & Bennett, 2016). Policymakers are often willing to increase social welfare and well-being, but lack information on measures and their interpretation. This paper gives priority to the measure of well-being that asks individuals to rank their life satisfaction on a scale of 1 to 10. These responses are increasingly used to assess the effect of public policy and the valuations that individuals show on the quality of institutions (Helliwell, 2006).

Why should policy-makers care about subjective well-being? Common critique involves the heavy influence of personal traits and hedonic adaptation to circumstances (Nikolova, 2016). This instantly shows the complication with measuring and interpreting well-being, because individuals can report to be happy on a daily basis, while showing low values of life satisfaction, and vice versa. People show surprisingly high levels of satisfaction with public services, but at the same time reality demonstrates high levels of income inequality, volatile macroeconomic performance and low institutional quality (Graham & Lora, 2010).

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4 research question central to this paper is: Does the quality of institutions and public policy influence individuals' subjective well-being?

Measuring subjective well-being is difficult, especially concerning the influence of public policies and economic performance, because these only affect individual's well-being indirectly and one rarely directly influences such policies concerning their own quality of life. Objective measures that are often associated with economic performance involve for example suicide rates (Helliwell, 2007), voting patterns (Dolan et al., 2008) or even migration numbers, but it is questionable whether these show the actual perceived well-being. Taking measures of subjective well-being next to those objective measures can ensure that economic progress leads to broad improvements in the quality of life across society, not only in terms of economic capacity (De Neve, Diener, Tay, & Xuereb, 2013). A certain level of economic well-being is a necessary condition for happiness, but far from sufficient on its own. Subjective well-being measures offer important complementary information to objective indicators, because they indicate both material as well as non-material aspects of life (Nikolova, 2016b).

This paper contributes to this discussion by explaining the effect of the quality of institutions on individual subjective well-being. Using individual level data from the World Value Survey (WVS), and country level data based on the Economic Freedom of the World index (EFW) and Penn World Tables, I arrive at a significant relationship between economic freedom and subjective well-being. This paper tests the validity of several models originating from the literature and aims at explaining which dimensions of economic freedom matter the most to individuals across countries. My analysis contributes to the existing literature as it combines different measures and uses a larger dataset compared to previous studies. Furthermore, I check for robustness of the measure of the well-being variable, as well as the measure of institutional quality. Finally, the sample of countries is split into four income groups to compare the effect of economic freedom on the life satisfaction of individuals.

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5 influences and how to tackle them, because uncertainty directly hampers economic growth and development and influences subjective well-being indirectly.

The rest of the paper is organised as follows. First, a theoretical overview presents a review of the literature on subjective well-being, the income and happiness relationship, the effect of economic indicators on well-being and finally the relationship of the quality of institutions on well-being. Second, I present the data and methodology used for this study. Third, the results are presented and discussed. The final section includes the limitations of this study and a discussion about the measurement of well-being, the conclusion and policy implications.

2. THEORETICAL BACKGROUND

2.1 Subjective well-being

First, it is important to outline subjective well-being and distinguish between two1_{separate but}

related dimensions. Life satisfaction, or evaluative well-being, is a reflection of an individual's life and requires remembering and evaluating past experiences. Life satisfaction is usually measured through survey questions that involve evaluation of life as a whole, or the Cantril ladder, which asks respondents to rank their current life relative to the best life imaginable (Graham & Nikolova, 2015). On the other hand, happiness, or hedonic well-being, refers to everyday emotional states, both positive and negative and involves the intensity of day-to-day experiences (Nikolaev, 2014).

This paper studies the evaluative dimension of subjective well-being as it is more related to income and opportunities than hedonic well-being (Nikolova, 2016a). Kahneman and Deaton (2010) argue that income and education are more related to life satisfaction than happiness. High income is associated with high life satisfaction, but not necessarily with higher happiness, while low income is associated with low life satisfaction as well as low happiness. Research shows that respondents clearly distinguish between the two dimensions and answer questions regarding to both differently (Graham & Nikolova, 2015). Individuals might report to be happy one day but indicate low life satisfaction when taking all things together, or the opposite. The distinction between the two matters to public policy, as it would enable policymakers to encounter human well-being and improve overall quality of life (Graham & Nikolova, 2015).

1_{A third dimension, eudaimonic well-being, is about living well in terms of realizing one’s human potential}

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6 2.2 The happiness Paradox

In 1974, Richard Easterlin introduced data about subjective well-being (SWB) in economics and showed that higher levels of income lead to higher utility. In particular, Easterlin (1974) concluded that happiness responses are positively correlated with income at any point in time, though the responses remain rather flat with increasing average income over time. Numerous scholars have criticized this empirical finding, however two explanations offer stronger empirical base for the paradox: that happiness is based on relative rather than absolute income and that happiness adapts to changes in the level of income (Di Tella & MacCulloch, 2006).

Hedonic adaptation is one of the strongest critiques against SWB as people living in impoverished circumstances may alter their expectations to what they consider possible (Nikolova, 2016a). On the other hand, richer people never seem satisfied and have increasing expectations (Graham, 2009). As Frey and Stutzer (2002b) conclude from literature, one of the most important processes people go through is the adjustment of past experiences. People are unable to make absolute rational decisions since they always compare the present situation with experiences from the past or expectations for the future.

Clark, Frijters and Shields (2008) argue that adaptation is also evident with the consumption of material goods. Happiness increases temporarily after the consumption of goods but wears off after individuals continuously purchase goods. This adaptation effect is even visible at the country level, as the marginal utility from extra consumption approaches zero as countries become richer. Individuals in richer countries report little additional well-being over time compared to those in poorer countries after consuming (luxurious) goods. At any point in time, those with higher incomes consider themselves happier as they enjoy higher consumption and status. However, as everyone in a country gets richer over time, the only benefit to the country is from higher consumption, as the amount of status is fixed (Clark et al., 2008). Policymakers should thus be aware that an increase in consumption does only influence happiness on a temporary basis. This indicates that traditional economic measures as GDP may not be sufficient in indicating the quality of life, and measures of evaluative well-being should correct for human well-being.

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7 to its neighbours, it will report a higher average national well-being. However, this will be subject to decreasing marginal returns, unless the country can increasingly outrun its neighbours. This might offer one explanation why countries are bound in a race over growth (Clark et al., 2008). This could potentially damage the subjective well-being of a country, as it is more than twice as sensitive to negative growth as compared to equivalent positive economic growth. Therefore, De Neve et al. (2015) argue that governments should aim at nuanced growth policies and careful use of economic growth data when considering welfare effects.

In contrast, Stevenson and Wolfers (2008) find evidence that an absolute level of income raises subjective well-being and find no ‘satiation point’ at which higher income countries have no further increase in well-being. Their finding is significant and robust across countries, within countries and over time and falsifies adaptation. They claim absolute income matters more than relative income, because those enjoying materially better circumstances may also enjoy greater subjective well-being and that ongoing rises in living standards have delivered higher subjective well-being.

However, Easterlin (2016) again confirms the paradox between countries as the results show that countries with a higher rate of GDP growth have no significant improvement in well-being in comparison with countries with a lower rate of economic growth. Furthermore, Easterlin argues that when testing the paradox, it should be taken into account that the trend between happiness and income should be measured in long time series. As a result, this would show cyclical expansion of happiness and income which would overestimate the relationship.

2.3 Economic growth and well-being

Next, it is important to find out whether traditional measures of economic performance are influencing the well-being of individuals. Using panel data on Germany, Winkelmann and Winkelmann (1998) found that the indirect costs of unemployment lead to larger reduced well-being than compared to the direct costs. For men aged between 30 and 49 they found that unemployment also has large negative side effects. Apart from the direct effect of losing income, becoming unemployed has large negative psychological effects on individuals, therefore influencing both the life satisfaction as well as the reported well-being.

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8 negatively correlated to well-being, although the effect is smaller than the one for unemployment. Estimates suggest that people would trade off a 1 percent increase in the unemployment rate for a 1.7 percent increase in the inflation rate (Di Tella, MacCulloch, & Oswald, 2001). This indicates the importance of measures of subjective well-being in combination with traditional measures to improve human well-being.

Furthermore, Wolfers (2003) argues that economic volatility has a negative impact on the reported well-being. This is illustrated by the example of an economy that has altering unemployment rates between 5 percent and 15 percent. Such an economy has, on average, the same level of well-being as an economy with a constant rate of 11 percent. This indicates that not only does the unemployment level have a direct impact on affected individuals, but also that the uncertainty about the economic performance affects society as a whole.

Di Tella and MacCulloch (2008) study the paradox of flat happiness with rising income, taking into account other relevant variables that could have accompanied income growth. Studying twelve OECD countries, the paper concludes there is a positive correlation between well-being and the income level, the welfare state and life expectancy. However, it is negatively correlated with the average working hours, environmental issues, crime, openness to trade, inflation and unemployment. As a result, these variables increase the unexplained trend, which makes the unexplained paradox even bigger.

2.4 The relationship between institutions and well-being

This paper shows the relationship between the quality of institutions and well-being, and in particular the aspects that benefit the life satisfaction of individuals. Furthermore, it focuses on the individual aspects of institutions that are valued by citizens. Veenhoven (2000) argues that institutions have an influence on the individual well-being through three types of freedom. Political freedom measures the possibility for citizens to engage in the democratic process or, conversely, the restrictions on political participation. Economic freedom measures the opportunity for individuals to engage in the free exchange of goods, services, and labour. Personal freedom measures how free one is in one's private life, for example, to practice one's religion, to travel, or get married.

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9 (Veenhoven, 2009). Frey and Stutzer (2000) show that individuals are happier the better developed the institutions of direct democracy are in their area of residence. This also applies to the degree of government decentralisation, which is captured as federalism. There are two arguments why countries with more strongly developed institutions have higher subjective well-being. First, due to the more active role of citizens, government activity is closer to their expectation, as it is better monitored and controlled. Second, the institutions of direct democracy extend the possibilities to get involved in the political process.

The procedural utility indicates why individuals value democracy, they do not only care about the outcome, but value the process they can participate in as well (Frey, Benz, & Stutzer, 2004). Veenhoven (2000) finds a significant positive relationship between political freedom and happiness, however this disappears once controlled for differences in per capita income. It is argued that income captures multiple effects that influence subjective well-being, thus the first hypothesis of this paper is as follows:

Hypothesis 1: The quality of institutions has a positive effect on subjective well-being, independently from the income distribution.

2.5 Economic freedom and well-being

Hall and Lawson (2014) summarize the literature regarding the Economic Freedom of the World (EFW) index and conclude that over two-thirds of articles using the EFW index as an independent variable show a positive outcome, such as more happiness. It is designed to measure the consistency of a nation’s policies and quality of institutions with economic freedom. Although no measure is able to capture the perfect influence of institutions, the index is strengthened with the use of objective, external and transparent data. Although the majority of scholars finds a positive correlation between economic freedom and well-being, the results seem inconclusive and not robust (Nikolaev, 2014). Gehring (2013) finds that economic freedom has a positive effect on SWB, and finds that EFW dimensions like legal security and property rights, sound money and regulation are strong predictors of SWB.

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10 in good governed nations. The technical quality of the government correlates positively in both poor and rich countries, while democratic quality only significantly correlates in rich countries.

In contrast, Graafland and Compen (2012) conclude that economic freedom affects life satisfaction only through the income channel, because it only shows a significant and positive relationship if not controlled for the influence of income per capita. Only the quality of the legal system shows a robust positive relationship, but this almost disappears after including GDP per capita. The relationship becomes negative when controlling for the influence of GDP per capita, unemployment, social trust, life expectancy and aging. Therefore, the second hypothesis is as follows:

Hypothesis 2: Economic freedom influences well-being positively, but this effect decreases as countries become richer.

Concerning informal institutions, trust and religiosity appear to be important determinants for individuals on their perception of life satisfaction. Trust involves a measure of honesty and trustworthiness (Bjørnskov et al., 2010). Clarck and Lelkes (2009) find that religious people have higher life satisfaction than non-religious people. Specifically, attending church and praying significantly contribute to one's well-being. In contrast, Dolan, Peasgood and White (2008) find no direct significant relationship between religion and life satisfaction. Ovaska and Takashima (2006) find a strong positive and highly significant relationship between Christianity and life satisfaction, while Graafland and Compen (2012) show a negative significant relationship with Muslim religiosity.

Hudson (2006) analysed the impact of institutional trust on well-being in the EU. In particular trust in the national government, the law, the ECB and the UN impacts positively on individual’s well-being. Trust in institutions is endogenous to the performance of those institutions, but is affected by experiences that involve change in personal circumstances as well as endogenous influences related to the institution. This supports Frey and Stutzer’s (2002a) conclusion that happiness is not only determined by personal traits, but is also influenced by institutional performance. This indicates that policymakers can effectively measure and influence human well-being if given the right tools, therefore the results of this paper should be considered with care.

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11 education, healthcare, justice and transportation is likely to be higher when the overall quality of these institutions is higher. Perhaps more important, the future prospects of individuals might be influenced by the confidence with which they can rely on governmental services.

Nonetheless, Michalos and Zumbo (2000) conclude that variables related to crime, safety in the neighbourhood and police performance only have little impact on people's satisfaction with the quality of their lives. Criminal victimization, worries about safety and special defensive behaviour could explain only 5 percent of the variation in happiness scores, 7 percent of the variation in life satisfaction and 9 percent of the variation in quality of life.

3. METHODOLOGY

3.1 Data and variables

The main data source on subjective well-being is the integrated World Value Survey (WVS) and European Value Survey (EVS) which nationally represents individual-level data. This dataset results in a sample of 100 countries covering roughly 90 percent of the world's adult population. The surveys are conducted in six waves between 1981 and 2014, where each2

country is represented in a wave. As applied by the majority of scholars, this paper uses the life satisfaction question as the main indicator of SWB. Life satisfaction is an evaluative dimension of SWB and is much more determined by income and opportunities than hedonic well-being (Nikolova, 2016a). It involves respondents’ answers on a 1-10 scale to the question “All things considered, how satisfied are you with your life as a whole?”

Table A.1 in the appendix shows the variables from the data sources and their definitions used for this paper. Despite being widely used, the WVS has its limitations. First, neither constructing a country-level panel nor an individual effect can be accounted for as not all countries are continuously polled in every wave. Second, the data does not include actual information on income, only the respondent's valuation of household income on a national scale ranging from 1 (lowest) to 10 (highest) level in the corresponding society (Nikolova, 2016b). Therefore, country level data from the Penn World Tables (PWT) by Feenstra, Inklaar and Timmer (2015) are included to account for PPP-adjusted per capita income.

The measure of economic freedom is from the 2017 Economic Freedom of the World (EFW) Index by Gwartney, Hall, and Lawson (2017). The EFW index measures the degree of economic freedom on a country level in five major areas: size of the government, legal structure

2_{Not all countries are subsequently represented in every wave, see Table A.2 for a detailed overview of}

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12 and security of property rights, sound money, freedom to trade internationally, and regulation of credit, labour and business. In total, the index comprises 42 distinct variables each being scaled from 0 – 10. The computed averages of the index show a rating for each variable, for every country and every year3_{. The ideal situation in this rating is a society with a limited impact}

of the government which focuses solely on property right protection, the provision of public goods and a sound money system. According to De Haan, Lundstörm and Sturm (2006) the elements of the EFW index clearly prescribe an important function for government policy and the data used in the index is reliable.

Table A.2 in the appendix shows all the countries used in the sample with their corresponding average reported life satisfaction and the number of respondents over the different waves4_.

Countries that have better infrastructure or capacity of conducting large-scale surveys like the WVS might be more likely to collect responses to such surveys and are likely more inclusive, because it is easier for the rural population to engage is such surveys. It is crucial to check for such overrepresentation, otherwise the results of this paper could be biased upwards. I believe that the responses are divided on an equal basis throughout the countries that were involved in a certain wave, although the first wave has considerably less respondents in total. Table A.3 in the appendix shows the countries that are excluded5_{from the dataset after merging the WVS,}

EFW index and Penn World Tables.

3.2 Control variables

No single measure can summarize the idea of economic freedom in its full aspect, which results in scholars using diverging measures. Moreover, critics argue that these measures can be biased, as they are based on experts' perceptions and macro-economic variables. I follow Nikolaev (2014), measuring the economic and political institutions using the EFW index combined with subjective measures such as the sense of control and freedom individuals report in the WVS. This creates a new dimension of measuring actual perceived economic freedom using both country and individual level data.

3_{Data for years before 2000 and missing values are estimated by autoregressively ‘backcasting’ the data based on}

actual values in later years.

4_{The first wave differs from the other five as the EFW index only has (estimated) data for 1980 and 1985 and the}

surveys are conducted between 1981 and 1984. The scores for the EFW index therefore are either based on the estimated values of 1980 or 1985.

5_{WVS involves Great Britain and Northern Ireland while the EFW index and PWT have data for the United}

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13 I include social trust as a control variable, functioning as an indicator of informal societal institutions indicated by a measure of honesty and trustworthiness (Bjørnskov et al., 2010). Trust involves respondent’s answers to the question “Generally speaking, would you say that most people can be trusted or that you need to be very careful in dealing with people?” I also control for the marital and employment status, which both have been shown to affect well-being. Education is controlled for because people with higher educational attainment experience more life satisfaction, although the effect disappears if income is added (Graafland & Compen, 2012). This will be discussed more intensively in the discussion section.

Other controls include the respondents’ age, gender, and subjective health. Respondent’s age has shown to have a U-shaped relationship with SWB, with the turning point appearing around at the age 40 (Frey & Stutzer, 2002b), so age squared is added as a control variable. An analysis of the impact of the control variables on life satisfaction in this study is provided in the next section. Accounting for all these variables generates a total sample of 311 country-year observations from 100 countries observed in one of the survey waves. All countries that are included in the analysis are listed in Table A.2 in the appendix, descriptive statistics of the main variables are presented in Table A.4 in the appendix.

3.3 Empirical model

Economic papers generally assume ordinally comparable results on life satisfaction answers: it is unknown what the relative difference between answers to the life satisfaction question is, but individuals do share the same interpretation of happiness. This assumption is based on two psychological findings: first, individuals are somewhat able to recognize and predict the satisfaction level of others. Second, individuals in the same community have a common understanding on how to translate feelings on a numerical scale (Ferrer‐i‐Carbonell & Frijters, 2004).

Ferrer-i-Carbonnel and Frijters (2004) estimate a latent-variable model with individual fixed-effects which allows for individual-specific interpretation of the satisfaction question. Their main conclusion is that assuming interpersonal ordinality of the life satisfaction answers makes little differences to the results, as these are surprisingly close to a simple OLS regression. Therefore, this paper uses a simple OLS regression based on countries to test the effect of institutions on the happiness income relationship.

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14 are increased sharply when clustering, which leads to over-estimating precision by ignoring intra-class correlation. The Moulton Factor shows that clustering has a larger impact on standard errors when the group sizes are varying and when the correlation of regressors within groups is large. One way to overcome this is to generate a robust covariance matrix to allow for clustering and heteroscedasticity. It should be noted though that clustered standard errors are unlikely to be reliable with only a few clusters in the sample. Therefore, I cluster standard errors based on country which allows for intragroup correlation, relaxing the requirement that observations need to be independent. This affects the standard errors and variance-covariance matrix of the estimators, but not the estimated coefficients. Since the sample in this paper consists of 100 countries, I expect the clustered standard errors to be reliable.

The main analysis consists of five models aimed at explaining the relationship between well-being and institutions. Model (1) demonstrates the OLS regression of income and life satisfaction, where the observations are clustered at the country level which allows for relaxation of the assumption that individual observations within countries are independent. Model (2) demonstrates the cluster-robust results including a variable representing the average weighted scale of the five key indicators of the EFW index. This variable corresponds to the summary index of the EFW index, and indicates the degree of economic freedom in all five areas, measured in forty-two data points. Model (3) demonstrates the cluster-robust results of the regression of both the income variable and the variable representing the EFW index. This regression is displayed in equation (1), which represents the main model of this paper:

(1) 𝑆𝑊𝐵 = α + β𝑖𝑛𝑐 + βEFW+ β𝑋 + 𝑁 + 𝑇 + ε

Where SWB is the life satisfaction of individual i in country c polled in survey wave w (and measured on a scale of 1 – 10). Inc is the logarithm GDP per capita at constant 2011 national prices (in million 2011 US$). EFW is the Economic Freedom of the World index. X is a matrix of personal characteristics (comprised of age, gender, education, employment status, marital status, subjective health, perceived free choice and control, and trust in others). N is a vector of country dummies. T is a vector of wave dummies and ε is the error term. The data is cross-sectional and includes a pool of individuals from 100 countries that represents 90 percent of the world population. I use an OLS estimator with robust standard errors clustered on countries as the WVS does not consist of panel data, which will be discussed further in the analysis.

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15 economic freedom: size of the government, legal system and property rights, sound money, freedom to trade internationally, and regulation (Gwartney et al., 2017). Model (5) demonstrates the result of a robustness test for the main model of this paper. Instead of using the EFW index, following Nikolaev (2014), four questions of the WVS, indicating the self-reported perceptions on institutions, are used to assess the robustness of Model (3). These variables indicate individual perceptions of respondents, rather than the country-level base of the EFW index, which will be discussed in detail in the next section.

Model (6) demonstrates the result of a robustness test for the main model of the paper, where an alternative measure of evaluative happiness is used as the dependent variable. The happiness question polled in the WVS involves respondents’ answers to the question “Taking all things together, would you say you are…” with answers varying from “not at all happy” to “very happy”. Model (7) extends the main model by splitting the sample of countries in four income quantiles, which indicates whether the average life satisfaction changes in countries with higher GDP per capita. Finally, model (8) demonstrates the relationship between economic freedom and life satisfaction in all four income quantiles. I expect the magnitude to increase in countries with higher GDP per capita, as richer countries are more likely to have better developed institutions that affect the subjective well-being of individuals. As Carlsson and Lundstrom (2002) argue, the ability to provide qualitative public services and the role of the government changes as GDP increases. Wealthier countries are not only able to sustain a larger public sector, but also dedicate more resources to the redistribution of wealth, benefitting the vulnerable in society.

4. RESULTS

4.1 The quality of institutions

First, I start the analysis by estimating model (3) in equation (1). Table 1 shows the main results. The signs and magnitudes of the coefficient estimates of the control variables are consistent with previous findings in the happiness literature and show a significant relationship with life satisfaction. This gives some first indications for a strong reliability in the basic model used in this paper. The individual control variables are reported under ‘personal characteristics’, the country and wave dummies are not presented in the main table in order to give a more legible overview of the general findings.

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16 satisfaction. As noted, five countries (Bosnia, Montenegro, Saudi Arabia, and Serbia) are withdrawn from the sample in models 2 to 4, which can be attributed to the lack of variability in the data of the EFW index, due to the estimated results for missing years. Next, four countries (Guatemala, Saudi Arabia, Uganda, and Tanzania) are withdrawn from the sample in model (5). This can be related to the lack of responses in the WVS. The second wave (1990-1994) was withdrawn from all models due to insufficient variability within the data.

Model (1) and (2) demonstrate the effect of the logarithm of GDP per capita and the EFW variable respectively, while controlling for individual effects and clustering on a county level, with the coefficient estimates showing the expected sign. It should be noted however, that there is a minor difference in average life satisfaction and number of observations between the two models. Model (3) then demonstrates the combined effect of both (ln) GDP per capita and economic freedom on life satisfaction and provides an answer to the first hypothesis, being: the quality of institutions has a positive effect on subjective well-being, independently of the income distribution. As noted in section 2, it is expected that both income and economic freedom influence life satisfaction positively. It is argued that the income distribution captures multiple factors influencing well-being, including the quality of institutions measured as economic freedom.

This can be confirmed by the results, because model (3) demonstrates that the magnitudes of both coefficient estimates decrease and remain significant. The coefficient estimate of (ln) GDP per capita drops in magnitude in comparison with model (1) because the effect of economic freedom on life satisfaction is partly captured by the income distribution. The coefficient estimate of the EFW index drops in magnitude as well, however this seems to be just a slight alteration of the effect. This indicates that economic freedom influences life satisfaction independent of the income distribution, and therefore the results show that policymakers can influence the well-being of individuals. This will be discussed further in the next section.

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17 Table 1: Main results

Variable Model (1) Model (2) Model (3) Model (4) Model (5)

loggdp 1.104*** 0.749* 0.821** 0.644* (0.281) (0.385) (0.332) (0.347) Economic Freedom econfree 0.396*** 0.302*** (0.0876) (0.0942) sizegov 0.0732* (0.0392) legal 0.0793* (0.0412) money 0.110*** (0.0268) trade -0.107** (0.0481) regulation -0.0329 (0.0737) Confidence in… government 0.115*** (0.0156) parliament 0.0135 (0.0122) church 0.0728*** (0.0112) justice 0.0739*** (0.0118) Personal characteristics age -0.0624*** -0.0603*** -0.0606*** -0.0602*** -0.0591*** (0.00355) (0.00366) (0.00369) (0.00374) (0.00362) age2 0.000594*** 0.000579*** 0.000581*** 0.000579*** 0.000541***

(3.47e-05) (3.61e-05) (3.61e-05) (3.69e-05) (3.44e-05)

male -0.149*** -0.154*** -0.152*** -0.154*** -0.108*** (0.0200) (0.0208) (0.0210) (0.0209) (0.0167) employ 0.148*** 0.154*** 0.153*** 0.154*** 0.149*** (0.0230) (0.0252) (0.0247) (0.0244) (0.0203) college 0.249*** 0.263*** 0.261*** 0.255*** 0.254*** (0.0262) (0.0277) (0.0277) (0.0257) (0.0305) mar 0.469*** 0.470*** 0.473*** 0.476*** 0.461*** (0.0264) (0.0265) (0.0273) (0.0273) (0.0279) freedom 0.327*** 0.319*** 0.319*** 0.317*** 0.317*** (0.0181) (0.0186) (0.0187) (0.0194) (0.0151) trust 0.273*** 0.272*** 0.273*** 0.269*** 0.247*** (0.0284) (0.0295) (0.0299) (0.0302) (0.0320)

Average life satisfaction 6.70 6.75 6.75 6.77 6.80

Country dummies Yes Yes Yes Yes Yes

Wave dummies Yes Yes Yes Yes Yes

Countries dropped 0/100 5/100 5/100 5/100 4/100

Waves dropped 1/6 1/6 1/6 1/6 1/6

Observations 349,660 330,121 330,121 326,830 227,665

R-squared 0.258 0.251 0.252 0.246 0.259

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18 Furthermore, the results in model (4) demonstrate that the size of the government, legal protection of property rights and sound monetary policies have a positive and significant effect on life satisfaction. On the other hand, the variables that represent freedom to trade and regulations have negative effect on the life satisfaction, though only the coefficient estimate of the freedom to trade variable is significant. This finding stands in contrast to expectation, although scholars are not consistently pointing at a specific sign of the coefficient estimate. Nikolaev (2014) also finds a negative effect on life satisfaction, however Gehring (2013) finds a positive and significant correlation in one specification of SWB and positive but insignificant in two others. The negative and insignificant effect of regulation is against previous findings, as Gehring (2013) and Nikolaev (2014) find a positive and significant influence on life satisfaction. Finally, Nikolaev and Bennett (2016) find only a positive and significant effect for sound money policies.

It is important to note that the results from the EFW index should be interpreted with caution, because the areas are highly correlated with each other (Nikolaev, 2014). Table 2 shows a pair-wise correlation of the five areas of economic freedom. The results indicate that the five key indicators are highly correlated with each other. Government size is negatively correlated with all variables, except for regulation. All correlations are significant at a 1% level. It has been argued that the components of the EFW index result in a (positive) effect only if all key parts are taken into account. Therefore, non-significance could signify that the specific item is not important, or only important in combination with others, or that it only affects one domain of overall satisfaction (Gehring, 2013).

Table 2: Pair-wise correlation of model (4) sizegov legal money trade regulation

sizegov 1

legal -0.3227* 1

money -0.0427* 0.4902* 1

trade -0.1843* 0.6500* 0.4635* 1

regulation 0.1644* 0.5102* 0.6257* 0.4827* 1 Note: * indicates significance at p<0.01

4.2 Robustness tests

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19 the parliament is insignificant, which can be attributed to the high correlation (0.6414) with the variable that involves confidence in the government. In particular, the coefficient estimate of the variable representing confidence in the government shows a high correlation with life satisfaction. However, it should be noted that the average life satisfaction increases, while the amount of observations decreases in this model. The number of observations decreases due to the lack of responses in the WVS, as the question regarding the confidence in different institutions is not consecutively surveyed in all countries and by all respondents.

The average life satisfaction increases in model (5), which might indicate that the withdrawal of countries and individual responses mainly occurred in countries with poor average life satisfaction, potentially leading to upward-biased results. The model demonstrates that institutions correlate with life satisfaction positively, showing a significant effect above and beyond the income level. Overall, this suggests that the quality of institutions benefits life satisfaction as individuals report higher scores when confidence in institutions is high, which is in line with previous findings (Nikolaev, 2014).

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20 Table 3: Robustness with evaluative happiness

Variable Model (6) loggdp 0.0453 (0.0670) econfree 0.0877*** (0.0292) Personal characteristics age -0.0214*** (0.00109) age2 0.000179*** (1.11e-05) male -0.0381*** (0.00747) employ 0.0331*** (0.00839) college 0.0675*** (0.00784) mar 0.215*** (0.0112) freedom 0.0553*** (0.00454) trust 0.0842*** (0.00861) Average life satisfaction 6.76

Country dummies Yes

Wave dummies Yes

Countries dropped 5/100

Waves dropped 1/6

Observations 327,861

R-squared 0.166

Note: ***, **, * indicate significance at p<0.01, p<0.05, p<0.1. The model involves an OLS regression that includes clustered standard errors at the country level, which are presented in brackets. 4.3 The quality of institutions in poor and rich countries

Table 4 extends the main model by separating the sample of countries into four income quantiles. These results provide an answer to the second hypothesis, being: economic freedom influences well-being positively, but this effect decreases as countries become richer. The results in model (7) suggest that individuals report higher life satisfaction when living in countries with higher GDP per capita. The economic freedom coefficient estimate shows a positive and significant correlation with life satisfaction, which indicates that institutions do have a positive effect on life satisfaction regardless of the income differences.

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21 explained more due to the differences in income, as the coefficient estimate of the EFW index only has a minor increase in effect. This is in line with the results of Nikolaev (2014), who

Table 4: Life satisfaction per income quantile

Variable Model (7) Model (8)

econfree 0.389*** (0.0745) Income quantiles Second quantile 0.230*** (0.0808) Third quantile 0.421*** (0.105) Fourth quantile 0.549*** (0.136) EFW per income quantile

First quantile 0.387*** (0.0795) Second quantile 0.422*** (0.0785) Third quantile 0.449*** (0.0791) Fourth quantile 0.466*** (0.0765) Personal characteristics age -0.0602*** -0.0602*** (0.00393) (0.00391) age2 0.000577*** 0.000577*** (3.89e-05) (3.88e-05) male -0.144*** -0.143*** (0.0200) (0.0199) employ 0.135*** 0.134*** (0.0263) (0.0264) college 0.262*** 0.261*** (0.0273) (0.0274) mar 0.475*** 0.475*** (0.0269) (0.0267) freedom 0.318*** 0.318*** (0.0189) (0.0188) trust 0.273*** 0.272*** (0.0302) (0.0301) Average life satisfaction 6.75 6.75

Country dummies Yes Yes

Wave dummies Yes Yes

Countries dropped 5/100 5/100

Waves droppes 1/6 1/6

Observations 330,121 330,121

R-squared 0.253 0.253

Note: ***, **, * indicate significance at p<0.01, p<0.05, p<0.1. The models involve OLS regressions that include clustered standard errors at the country level, which are presented in brackets. The first income quantile in model (7) was omitted because it was used as a base category.

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22 and richest group. The results indicate that the difference in effect of economic freedom becomes less apparent when accounting for GDP per capita and log of income. Model (8) demonstrates a similar pattern. Here, while the interaction coefficient estimates remain positive and highly significant, the impact of economic freedom in rich countries can be questioned compared to that in poor countries. This might indicate that political freedom becomes more influential to well-being in rich countries, which has recently been argued by several academics (Bjørnskov et al., 2010; Gropper et al., 2011; Ott, 2010). Research on the influence of political freedom in relation with differences between national GDP per capita goes beyond the scope of this paper, but this result confirms a valuable base for future research.

5. DISCUSSION

Before reaching the conclusions of this paper, it is important to highlight some of the critiques on SWB measures and indicators of economic freedom. Critics of SWB argue that measures cannot be trusted as the perception differs from person to person or across countries and cultures. Moreover, the perception of an individual can change over time, which makes the measure non-comparable over individuals, countries or time (Di Tella & MacCulloch, 2006). Nonetheless, in the last decades scholars have sufficiently proven SWB measures as being reliable and comparable as they reflect meaningful information about the perceived quality of life. Many cross-sectional and panel data show that these measures are statistically related to important life events (Nikolova, 2016a).

As previously mentioned, the World Values Survey only involves cross-sectional data, as not all countries are consecutively involved in all polled waves. It should be noted that individual perceptions and personality traits can influence the responses. However, controlling for these effects is limited, as individuals are not continuously surveyed in succeeding waves. These perceptions can be of specific influence in the first robustness test involving individual perceptions on institutions. Happier people may report higher confidence in institutions because they are more satisfied with the overall situation which might create a measurement error. Future research should offer insight on the influence of one’s personality traits on the perceived confidence in institutions, which is related to one’s future prospects (Helliwell, 2003).

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23 influences. Economic institutions directly cause happiness, but higher levels of happiness might also indirectly enhance these institutions through the building of social capital. The quality of institutions might be altered through the feedback loop of social capital, an effect that is being magnified with higher levels of well-being. His study cannot successfully rule out the reverse causality problem between institutions and well-being, but the results show that the relationship from well-being to institutions is pressingly weak.

This paper controls for social capital through a variable of trust in other people, which can be seen as an informal institution. The coefficient estimate is positive and highly significant in all estimations, which is in line with previous findings. Estimating model (3) without controlling for trust reduces explanatory power slightly for nearly all variables, which indicates trust demonstrates the expected relationship, but no clear conclusions can be drawn. In addition, as Gehring (2013) points out, there is no psychological theory suggesting that happier people have the natural tendency to prefer economic or political freedom. Moreover, there is no evidence that migration, or target voting support the reverse causality claim. Even though evidence shows that happier people are more likely to vote, as argued by Frey and Stutzer (2000), evidence does not show that happier people directly alter institutions by voting on specific parties. Therefore, the reverse direction of causality seems to be of minor importance in this paper.

Additionally, measures of economic freedom are also criticized for only reflecting a limited perception of institutions. De Haan and Sturm (2000) have argued against the incorporation of government size as a key indicator of the EFW index, specifically the way taxation is included in the index. The rating in the EFW index only allows for two broad government functions that are consistent with economic freedom: protection of individuals and their property rights, and the provision of a few select public goods. The problem that arises here is that the index mainly focuses on the level and spending of taxes. However, I would argue that this potentially creates a measurement error, based on a country’s taxation system.

For example, in the EFW index, the Netherlands shows a score of 3.89 in 2015 on the indicator ‘size of the government’.6_{As argued by Gwartney, Lawson and Hall (2017), government}

involvement hampers individual choice, therefore reducing economic freedom, resulting in a lower score. However, government interference can result in a better social security system, less economic volatility and more social protection for those who are in need. These aspects of

6_{Available at:}

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24 a country’s economic and political system offer security and guidance to individuals, affecting their life satisfaction. Gehring (2013) argues that the measure ‘Government Share as a percentage of GDP’ which is included in the Penn World Tables is much simpler, as well as robustly significant and in line with previous research. Future research should test the validity of this measure in comparison with the EFW index.

Finally, in a recent study, Blanchflower and Oswald (2011) question the validity of the relation between higher educational attainment and reported life satisfaction. The results show that education only effects life satisfaction if there is no control for the level of income. Hence, the extra life satisfaction that is associated with better education seems to come solely from the extra income higher education brings. However, this only holds for the United States, as European data shows a positive and significant relationship with life satisfaction. Nevertheless, a growing number of academics questions the valid influence of education on happiness.

6. CONCLUSION AND POLICY IMPLICATIONS

This paper examines the link between economic freedom and subjective well-being. I find that economic freedom has a positive effect on well-being regardless of the level of income, which confirms both hypotheses argued in this paper. This result stays robust when applying different measures of evaluative well-being and when differentiating between countries based on their income level. Decomposing the EFW index shows that the size of the government, the quality of the legal and justice systems, and sound money are important determinants of SWB. This indicates that individuals value the security and protection of the legal and justice system, and economic development. Volatile economic growth and inflation thus not only seem to influence economic prospects directly, but also influence a country’s national average life satisfaction, as it becomes increasingly difficult for individuals to plan for the future and utilize economic freedom effectively for their own well-being.

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25 that uncertainty hinders economic growth and development, and individuals’ concerns should be constantly accounted for.

Furthermore, individuals’ perceptions on the trustworthiness of certain institutions prove to be important for their subjective well-being. In particular, confidence in the government and in the legal system show positive and significant correlations, which indicates that the perceived quality of these institutions are of importance for well-being measures. This indicates that policy-makers do influence the perceived well-being of individuals, and shows that they can influence the perception of future prospects and opportunities with the quality of their decisions.

Finally, when controlling for different levels of GDP per capita, there seems to be no considerable additional effect of economic freedom. Economic freedom remains positive and highly significant in all four income groups, but that impact does not seem to be significantly higher in richer countries. This might indicate that once a country reaches a certain level of economic freedom, additional well-being comes from other sources than economic freedom. In addition, hedonic adaptation would invoke individuals to adjust to their situation and have growing expectations about the functioning of their country’s economy and government. Future research should aim to provide answers to this question.

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26

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31

8. APPENDIX

Table A.1: Description of variables Main

variables Definition Values/Scale

Dependent variable

lifesat All things considered, how satisfied are you with your

life as a whole these days? 1 = completely dissatisfied 10 = completely satisfied happy Taken all things together, would you say you are… 1 = not at all happy

2 = not very happy 3 = rather happy 4 = very happy Independent variables (country level)

loggdp (ln) real GDP per capita at constant 2011 national prices (in 2011 US$)

incq Income quantiles based on the (ln) real GDP per capita.

1 = lowest quantile 4 = highest quantile econfree Summary index of measures of economic freedom,

consists of all five areas and 42 data points. Ten point scale value, where 0 = lowest value and 10 = highest value. sizegov Spending and taxation by the government, and the

size of government-controlled enterprises.

Ten point scale value, where 0 = lowest value and 10 = highest value. legal Protection of persons and their rightfully acquired

property. Ten point scale value, where 0 = lowest value and 10 = highest value. money Inflation eroding the rightfully earned wages and

savings.

Ten point scale value, where 0 = lowest value and 10 = highest value. trade Freedom to exchange in person and in business

internationally. Ten point scale value, where 0 = lowest value and 10 = highest value. regulation Regulations that limit the right to exchange, gain

credit, hire or work for whom an individual wishes or the ability to freely operate a business.

Ten point scale value, where 0 = lowest value and 10 = highest value. Independent variables (individual level)

confgov Respondent's reported confidence in the government. 1 = none at all 2 = not very much 3 = quite a lot 4 = a great deal confpar Respondent's reported confidence in the parliament. 1 = none at all

2 = not very much 3 = quite a lot 4 = a great deal confchurch Respondent's reported confidence in the church. 1 = none at all

2 = not very much 3 = quite a lot 4 = a great deal confjustice Respondent's reported confidence in the justice

system/courts. 1 = none at all 2 = not very much 3 = quite a lot 4 = a great deal Control variables (individual level)

age Respondent's age

male Respondent's gender 1 = male

0 = female

employ Respondent's employment status 1 = full-time, part-time or self-employed

0 = retired, housewife, student, unemployed or other

college Some college degree or a college diploma. 1 = some university-level education, without degree or university-level education, with degree

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32 Table A1 (continued)

mar Respondent’s marital status. 1 = married or living together

0 = divorced, separated, widowed, or single

health Respondent's subjective health. 1 = very poor

2 = poor 3 = fair 4 = (very) good freedom Respondent's perceived free choice and control over

their lives 1 = no choice at all 10 = a great deal of choice trust Respondent's opinion whether people can be trusted. 1 = most people can be trusted

0 = need to be very careful Table A.2: Descriptive values countries

Country

/ region satisfaction Avg. life Number of observations in every wave

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34 Table A2 (continued) Russia 5.47 0 1,961 4,540 1,504 2,033 2,500 12,538 Rwanda 5.48 0 0 0 0 1,507 1,527 3,034 Saudi Arabia 7.18 0 0 0 1,502 0 0 1,502 Serbia 6.11 0 0 1,280 2,712 0 0 3,992 Singapore 6.86 0 0 0 1,512 0 1,972 3,484 Slovakia 6.55 0 1,602 2,426 1,509 0 0 5,537 Slovenia 7.01 0 1,035 2,013 1,366 1,037 1,069 6,520 South Africa 6.54 1,596 2,736 2,935 3,000 2,988 3,531 16,786 South Korea 6.15 970 1,251 1,249 1,200 1,200 1,200 7,070 Spain 6.95 2,303 4,147 2,411 2,709 1,200 1,189 13,959 Sweden 7.74 954 1,047 2,024 1,187 1,003 1,206 7,421 Switzerland 8.06 0 1,400 1,212 1,272 1,241 0 5,125 Taiwan 6.60 0 0 780 0 1,227 1,238 3,245 Tanzania 3.76 0 0 0 1,171 0 0 1,171 Thailand 7.14 0 0 0 0 1,534 1,200 2,734 Trinidad and Tobago 7.12 0 0 0 0 1,002 999 2,001 Tunisia 5.27 0 0 0 0 0 1,205 1,205 Turkey 6.28 0 1,030 3,113 5,785 1,346 1,605 12,879 Uganda 5.55 0 0 0 1,002 0 0 1,002 Ukraine 5.07 0 0 4,006 1,507 1,000 1,500 8,013 United States 7.50 2,325 1,839 1,542 1,200 1,249 2,232 10,387 Uruguay 7.32 0 0 1,000 0 1,000 1,000 3,000 Venezuela 7.20 0 0 1,200 1,200 0 0 2,400 Viet Nam 6.72 0 0 0 1,000 1,495 0 2,495 Yemen 5.57 0 0 0 0 0 1,000 1,000 Zambia 5.90 0 0 0 0 1,500 0 1,500 Zimbabwe 4.97 0 0 0 1,002 0 1,500 2,502 Total 29,373 61,452 112,998 119,670 79,051 78,906 481,450

Table A.3: Overview missing countries

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35 Table A.4: Descriptive statistics variables

Variable Obs Mean Std. Dev. Min Max

THE INFLUENCE OF INSTITUTIONS ON INDIVIDUAL WELL-BEING