Life Satisfaction and Preferences over Economic Growth and Institutional Quality

Abstract

This paper demonstrates that institutional factors have differential impacts on subjective well-being of individuals in rich versus poor countries. A lower level of corruption, a more democratic government and better civil rights increase the well-being of individuals in rich countries, whereas an increase in per capita income has no impact. On the contrary, in poor countries the extent of corruption, democracy or civil rights has no influence on happiness, but an increase in per capita income impacts happiness positively. We provide evidence that this stark contrast may be due to the difference of preferences over economic growth and institutional factors.

Introduction

The effect of economic growth on subjective well-being has been investigated extensively. Much of this research has focused on the Easterlin Paradox – a finding that suggests economic growth in a country does not improve the life satisfaction of that country’s residents, especially in high income countries. Although this finding, demonstrated by Easterlin (1973 and 1995), has been challenged recently,¹ several authors attempted to explain the paradox. A number of hypotheses are proposed as to why the effect of economic growth on average life satisfaction could be diminished or eliminated as countries develop economically. One argument is centered around the relative income hypothesis which suggests that individuals evaluate their incomes relative to others. Since a rise in national income generally induces an increase in everyone’s income, under this hypothesis economic growth does not lead to improvements in life satisfaction, (Clark et al. 2008). An alternative explanation is the possibility that individuals may adapt to changes in their income (Di Tella et al. 2010). That is, increases in average income in a country may increase the residents’ happiness only temporarily.

A third possible explanation as to why the effect of economic growth on life satisfaction disappears in high income countries involves individuals’ basic needs. For example, Di Tella and MacCulloch (2010) suggest that economic growth does not improve average life satisfaction once a threshold standard of living is reached. The “hierarchy of needs” hypothesis, originally proposed by Maslow (1943), suggests that once their basic needs (for example, physiological needs such as food and shelter) are satisfied, individuals change their focus towards their “higher order needs” that are not materialistic. These higher order needs may include such items as a functioning democracy, lack of corruption, the extent of civil liberties. An individual who lives in a poor country is less likely to have satisfied their basic needs compared to their counterparts in a high income country. Increases in per capita GDP help satisfy basic needs more strongly compared to “higher order needs” in low income countries. Consequently, economic growth may improve life satisfaction of a poor country’s resident more than that of an individual in a rich country. Once a certain living standard is achieved, individuals in poor countries may start deriving utility from non-materialistic aspects of life.

Although this explanation has been proposed by previous researchers, it has not been tested at the individual level. Under this hypothesis, individuals’ preferences over economic growth and favorable institutional characteristics differ according to the level of per capita income in their country. In a high income country, individuals are more likely to prefer favorable institutional characteristics over economic growth compared to the residents of low income countries. Using data obtained from 200,000 individuals from 74 countries, we investigate whether differences in individuals’ preferences in rich versus poor countries could explain the effect of institutional quality and economic growth on life satisfaction.

We use two approaches to test this hypothesis. First, at the individual level, we test whether the effect of per capita GDP on life satisfaction is confounded by the relationship between GDP and institutional quality.² We find that favorable institutional characteristics (as measured by a lower level of corruption, a more democratic government and better civil rights) increase individuals’ life satisfaction in high income countries but not in the low income countries. In high income countries, per capita GDP is positively associated with greater life satisfaction, but this effect disappears when institutional characteristics are controlled for. The positive influence of per capita income on individual subjective well-being in low income countries persists even after controlling for institutional characteristics. Our results are consistent with the studies that investigate the same question at the country level. For example, Bjørnskov et al (2010) and Helliwell and Huang (2008) report that favorable institutional characteristics are positively correlated with average life satisfaction only in high income countries.

Second, we test whether there is a systematic difference in preferences over favorable institutional characteristics and economic growth between residents of low versus high income countries. We find that residents of high income countries are more likely to prefer institutional characteristics that are associated with a democratic regime. In addition, they are less likely to value economic growth. Taken together, our results provide support for the possibility that the decrease in the influence of per capita GDP on life satisfaction could be observed because of a change in individuals’ preferences as their countries experience grow economically.

Data

The data set is obtained from the first four waves of World Values Survey, and it includes more than 200,000 individuals living in 74 different countries between years 1981 and 2002.,^{3 4} In some countries, surveys are conducted multiple times. For the purposes of our study, we divided our sample into two sub-samples: the rich and the poor countries. We employ the definition of World Bank which uses $11,500 GDP per capita as the threshold to separate the rich countries from the poor ones. Republic of Korea belongs to different categories in different years according to World Bank’s definition. All of the remaining countries belong to either rich or poor group throughout all the survey years.⁵ The measure of individuals’ life satisfaction is based on the question “All things considered, how satisfied are you with your life as a whole these days?” Possible answers range from “Most dissatisfied” (1) and “Most satisfied” (10). This measure of subjective well-being is similar to those used by previous research that evaluates the effect of individual characteristics and macroeconomic factors on satisfaction with life (Di Tella and MacCulloch 2010; Di Tella et al. 2003; Oswald 1997).

We constructed the measures of preferences over favorable institutional characteristics based on how individuals rate several descriptions of governance in their country or how much they agree on statements about governance. For example, the indicator variables, Rogue Leader takes the value of one if the individual believes that having a strong leader who does not have to bother with parliament and elections. Similarly, variables Army Rule and Democratic System indicates whether the individual believes that an army rule or a democratic political system is a very good or fairly good way of governing the country. Democracy is Better denotes whether the individual agrees or strongly agrees with the statement “Democracy may have problems but it is better than any other form of government.” In the surveys, individuals also reported how much “Someone accepting a bribe in the course of their duties” was justifiable. The answer options ranged from 1 (Never justifiable) to 10 (Always justifiable). Based on their answers, we constructed the variable Bribe is not justifiable which takes the value of one if the individual chose options 1-5, and zero otherwise. To build variables that measure individuals’ valuations of economic growth versus democratic rights, we utilized their opinions about what the most and second most important national goals of their country should be. Specifically, individuals were posed the following question: “There is a lot of talk these days about what the aims of this country should be for the next ten years. On this card are listed some of the goals which different people would give top priority. If you had to choose, which of the things on this card would you say is the most important and the next most important?” The options presented on the card were: 1. A high level of economic growth, 2. Strong defense forces, 3. People have more say about how things are done, and 4. Trying to make our cities and countryside more beautiful. We interpret that choosing option 3 reveals individual’s preference for more democratic rights. The indicator variable 1 ^st Goal: Economic Growth is equal to one if the individual stated that a high level of economic growth should be the top priority goal of the country. Similarly, 1 ^st Goal: Promoting People’s Involvement takes the value of one, if the individual thought that giving people more say about how things are done is the most important national goal. We also constructed an indicator variable, Economic Growth More Important, for whether an individual viewed economic growth as a more important goal than promoting people’s involvement in governance.

Both individual attributes as well as country characteristics are employed as control variables in the regressions. Individual-level control variables include gender, age (and its square), income, education level, employment and marital status and the number of children. The source of all individual-level variables is the World Values Survey. In some cases, the information about individual characteristics that are used as control variables is missing at the data source. These individual characteristics are not the main focus of the paper. To avoid small sample sizes due to missing data in controls, we replaced the missing variable that measures the personal characteristic with a constant (zero in the case of a dummy variable, and the sample average for a continuous variable), and included it in the regressions together with a dummy variable that takes the value of one for missing information for that personal characteristic. For example, if an individual did not respond to the question about their income, then the dummy variables that measure their income status (Medium Income and High Income) took on the value of zero, and the dummy variable that indicates whether income information is missing (Income Missing) took the value of one. This method was used by other researchers in the past (Mocan and Rees 2005). Our findings are not sensitive to including or excluding these observations.

The country-level control variables are per capita GDP, inflation rate and unemployment rate, carbon dioxide emission per capita and the birth rate of the country. These controls are used to capture various aspects of the country, such as development level, pollution, and health condition of the overall population. They are obtained from various sources, such as World Bank’s World Development Indicators, Penn World Tables and International Labour Organization’s KILM Database.

Among the key explanatory variables are Low Corruption, Civil Rights and Democracy. The corruption level in the country is measured by a variable constructed using the Transparency International’s Corruption Perceptions Index. The constructed variable Low Corruption ranges between 0 (most corrupt) and 10 (least corrupt).⁶ The variable Civil Rights is created based on Freedom House’s Civil Liberties Index. Civil Liberties Index measures freedom of expression, assembly, association, and religion. The created variable Civil Rights takes values between 1 (least civil rights) to 7 (most civil rights).⁷ From Polity IV, we obtained Democracy variable, which ranges between -10 and 10. While a -10 indicates the regime is an autocracy, a 10 means a democratic government is in the office.⁸ The summary statistics of the key variables, their definitions and sources are presented in Table 1.

Table 1 Descriptions and Summary Statistics of Variables of Interest

Influence of GDP per Capita and Institutional Factors on Life Satisfaction

In this section, we estimate following equation using ordered probit over the whole sample and over the samples of low and high income countries:

$$ Satisfactio{n_i}_{,c,t}=f\left\{{Z}_{i,c,t},\ {K}_{c,t},\ GD{P}_{c,t},\ {S}_{c,t}\right\} $$

(1)

where Satisfaction _i,c,t stands for the level of subjective well-being reported by the individual i, in country c in year t.⁹ It is constructed based on the answers of the individuals to the question “All things considered, how satisfied are you with your life as a whole these days?” The per capita real income in country c in year t is denoted by GDP _c,t . Per capita income enters into the regressions in natural logs. Institutional variables, such as Low Corruption, Civil Rights and Democracy make up the vector S _c,t . The vectors Z _i,c,t and K _c,t include individual-level characteristics and country-level controls, respectively.¹⁰ The choice of control variables follows the previous work (Di Tella et al. 2003, Alesina et al. 2004, Blanchflower and Oswald 2008).

The results obtained from estimation of Eq. (1) with ordered probit are presented in Table 2 where marginal effects for the highest life satisfaction category are reported. Standard errors which are clustered at the country-year level are in parentheses. The sample over which the Eq. (1) is estimated is listed at the top of each column. For example, results presented in columns 1, 4 and 7 are obtained from estimating Eq. (1) over the whole sample. The results in 2, 5 and 8 (3, 6 and 9) are obtained from low income countries whose per capita GDP is less than $11,500 (high income countries whose GDP per capita is greater than $11,500), respectively. For brevity, Table 2 lists only the marginal effects of the variables of interest: GDP per capita, Democracy, Civil Rights, and Low Corruption.

Table 2 Effect of National Income and Institutions on Life Satisfaction in Poor vs. Rich Countries

In the first three columns of Table 2, the institutional factors are excluded from the regressions. In all samples, per capita GDP is positively associated with probability of being in the highest life satisfaction category.¹¹ In columns 4 to 6, we present the results of the regressions that include both per capita GDP and the institutional factors as explanatory variables. Per capita GDP is positively correlated with individuals’ life satisfaction in the whole sample (column 4). Columns 5 and 6 depict the stark contrast about the effect of economic growth and institutional factors on satisfaction with life. Specifically, the influence of per capita GDP on satisfaction with life remains statistically significant when institutional factors (democracy, civil rights, and level of corruption) are controlled for in the sample of low income countries. However, the coefficient of per capita GDP becomes statistically insignificant in the sample of high income countries. In addition, favorable institutional characteristics are positively associated with life satisfaction only in the sample of high income countries’ residents. For completeness, we estimate Eq. (1) excluding per capita GDP and including the institutional factors. The results that are presented in columns 7 to 9 of Table 2 indicate that the extent of civil rights and the level of democracy are significant determinants of satisfaction with life for residents of high income countries, but not for their counterparts living in low income countries.

In the regressions presented in Table 2, we employed per capita income level of $11,500 as the threshold for low versus high income countries. We check the sensitivity of our results to the choice of threshold by using different cutoffs of per capita GDP for the high income countries. Specifically, we estimated Eq. (1) with cut-offs of $9,500, $10,500, and $12,500. The results presented in Appendix Table 7 suggest that our results are not sensitive to the threshold choice. That is, regardless of the threshold employed, in both high and low income samples, if institutional characteristics are excluded from the regressions, per capita GDP has a statistically significant and positive influence on the probability of being in the highest life satisfaction category. Conditioning on institutional characteristics eliminates the statistical significance of per capita GDP in high income countries, but not in the low income countries.

Deaton (2008) argues that the World Values Survey, the data set we use, suffers from sampling errors. Specifically, he suggests that several poor countries in the World Values Survey are in Eastern Europe or were once part of the Soviet Union, and that people in those countries are exceptionally dissatisfied with their lives.¹² In addition, Deaton (2008) argues that World Values Survey samples in countries such as India, China, Ghana, and Nigeria consist of mostly the elite (who are highly satisfied with their lives) and they are not representative of the whole population. To check whether our results are sensitive to inclusion of these countries, we estimate Eq. 1 without using individuals from these countries. The results in Appendix Table 8 show that our findings are not sensitive to the inclusion of these countries mentioned in Deaton (2008).

We conducted additional robustness checks, results of which are not reported, but they are available if requested. Specifically, we estimated Eq. 1 using OLS, instead of ordered probit. Separately, we included the level of GDP per capita in the regressions instead of its natural logarithm. Results did not change. We also replicated Table 2 by omitting observations with missing personal characteristics info (instead including them with an indicator for missing data). Despite much smaller sample sizes, the results remained the same.

As an extension, in the regressions, we included per capita GDP 20 years ago and the growth rate in per capita GDP in the 20 years prior to the survey date instead of the current per capita GDP.¹³ Results are presented in Table 3. For both poor and rich country residents, when institutional characteristics of a country are not controlled for, past per capita GDP is positively associated with greater probability of being in the highest life satisfaction category (columns 1-3). Economic growth in the past decades has an additional effect of life satisfaction in poor countries. This pattern continues in poor countries when institutional characteristics are controlled for in the regression (column 5). However, for residents of high income countries, the positive relationship between past national income and economic growth is eliminated once institutional measures are included in the regression (column 6).

Table 3 Effects of Past GDP, Economic Growth and Institutions on Satisfaction with Life

Individual Preferences Over Institutions and Economic Growth

In this section, we investigate whether individual preferences over favorable institutional characteristics systematically differ in high vs low income countries by estimating the following:

$$ Preferenc{e_i}_{,c,t}=f\left\{ High\ IncomeCountr{y_c}_{,t},\ {Z}_{i,c,t},\ {K}_{c,t}\right\} $$

(2)

where Preference _i,c,t stands for individual i’s preference over economic growth or institutional characteristics of country c in year t. High Income Country is an indicator for whether the individual lives in a country where per capita GDP is above a certain threshold. We use the threshold $11,500 consistent with the analysis in the previous section. Using different thresholds ($9,500, $10,500, $12,500) does not change our findings. In other regressions, instead of an indicator variable, we include the natural logarithm of per capita GDP of the country in the Eq. (2). Z and K denote the individual and country level controls, and they include the same variables used in the previous section. Outcome variables are indicator variables, and Eq. (2) is estimated with probit. The marginal effects are reported in Panels of Table 4.

Table 4 Preferences over Institutional Quality and Economic Growth in High versus Low-Income Countries

In Panel A of Table 4, we report the estimates obtained from the regressions where the outcome variables are individual’s preferences about government forms and bribe. The outcome variables are listed at the top of each column. Every cell presents the marginal effect of the variable of interest from a separate regression. The estimates of the full set of variables are available if requested. Results suggest that residents of high income countries are less likely to believe that having a strong leader who does not have to bother with parliament and elections (Prefers Rogue Leader), or an army rule (Prefers Army Rule) is a good way of governing the country (columns 1-2). Instead, they are more likely to prefer a democratic political system (columns 3 and 4). In addition, individuals who live in high income countries are more likely to agree that accepting bribe is not justifiable (column 5). These findings are robust to different thresholds for high income countries. Including the natural logarithm of the per capita GDP in the regressions instead of High Income Country indicator does not change the findings (row 2).

In Panel B of Table 4, the outcomes are measures of individual’s preference about whether economic growth or improving people’s involvement in country’s governance should have greater priority among national goals in the next ten years. For example, the outcome variables in columns 1 and 2 take the value of one if an individual believes that the highest priority national goal should be promoting economic growth and enhancing people’s involvement in running the country, respectively.¹⁴ Results in columns 1-2 show that individuals who live in high income countries are less likely to prefer economic growth as the top priority national goal. At the same time, they are more likely to choose promoting people’s involvement in country’s governance as the top national goal.¹⁵

29,071 individuals did not place the highest importance to either economic growth or promoting people’s involvement in governance. Column 4 of Panel B in Table 4 shows that within this sample, residents of high income countries have a greater tendency to rank promoting people’s involvement in governance as the second most important national goal. However, they are not different from individuals living in low income countries in probability of choosing economic growth as the second highest priority national goal (column 3). The dependent variable in column 5 of Panel B is equal to 1 if the individual places greater importance to economic growth as a national goal than promoting people’s involvement in governance, and zero otherwise. The results in column 5 suggest that high income country residents are less likely to rate economic growth as more important compared to promoting people’s involvement. Similar results are obtained when the natural logarithm of per capita GDP is included in regressions instead of indicator for living in a high income country.

Summary and Conclusion

A number of previous papers that investigate the relationship between economic growth and subjective well-being focus on the Easterlin Paradox – a finding that suggests economic growth in a country does not improve the life satisfaction of that country’s residents over time, even though in a cross section high income individuals or high income countries are happier in comparison to their low income counterparts (Easterlin 1973 and 1995). While some papers provide counter evidence to this argument (Stevenson and Wolfers 2013; Deaton 2008), others attempt to provide an explanation to the paradox. Examples of these explanations include the relative income hypothesis (Clark et al. 2008) and individuals’ adaptation to income (Di Tella et al. 2010). An additional explanation proposed is the basic needs hypothesis (Di Tella and R. MacCulloch 2010). Specifically, income may not have a significant influence on individuals’ life satisfaction once their basic needs are satisfied. Individuals start deriving utility from non-materialistic aspects of life once their basic needs are satisfied, an idea initially put forward by Maslow (1943).

Under this hypothesis, individuals may have different preferences with respect to institutional quality and economic growth in low versus high income countries. In this paper, we test this hypothesis at the individual level using two approaches. First we estimate the influence of per capita income and institutional factors on life satisfaction. Using data on 200,000 individuals from 74 countries, we find that institutional factors such as the extent of democracy, civil rights, and corruption have an influence on reported well-being of individuals who live in high income countries. Per capita income has no effect on subjective well being in these high income countries. On the other hand, life satisfaction of individuals who live in low income countries is not impacted by the quality of institutional factors. Instead, an increase in income per capita improves happiness.

Second, we test whether preferences over institutions and economic growth are systematically different for individuals who live in high versus low income countries. We find that compared to their counterparts in low income countries, residents of high income countries are more likely to prefer democratic political regimes over an autocratic or militaristic government. In addition, individuals in high income countries are more likely to rank promoting people’s involvement in governance above economic growth as their preference of national goals of their countries in the next decade.

Taken together our results provide evidence for a change in preferences over improvements in living standards (GDP per capita) and favorable institutional characteristics as a country experiences economic growth. Our results are along the same lines with Frey and Stutzer (2000), who report that direct democratic institutions in Switzerland contribute positively to the happiness of the Swiss, with Bjornskov, Dreher and Fischer (2010) and Helliwell and Huang (2008) who show at the country level that institutional quality increases the average happiness in rich countries but not in poor countries, with Di Tella and MacCullogh (2010) who suggest that economic growth does not improve individual’s life satisfaction beyond a threshold. Our findings may help explain Easterlin’s (1995) observation that in economically developed countries average happiness does not rise with increases in per capita GDP over time. Specifically, the developed world generally has not experienced sensational improvement in institutional quality in the last decades. However, it has experienced economic growth continuously. If residents of the developed world value improvements in institutions more than increases in per capita GDP (a possibility supported by our paper) then it is not surprising to observe that the average happiness in these countries has not changed significantly over time.

It has been shown that modern and democratic institutions promote economic growth (Papaioannou and Siourounis 2008; Acemoglu, Johnson, and Robinson 2001; Minier 1998). From a policy perspective, this finding implies that improvements in institutional quality of a country, such as a shift towards a democracy, will lead to better living standards and greater life satisfaction. Our results suggest that the increase in life satisfaction due to improvements in institutions in low income countries may not be immediate. In low income countries, policies that directly target to promote economic growth may improve subjective well-being of the residents more quickly than those that aim at developing institutional quality. This is because, low income countries’ residents value economic growth more than they do favorable institutions.

Appendix

Table 5 Descriptions and Summary Statistics of All Variables

Table 6 Marginal Effects of All Variables from Ordered Probit Estimation Dependent Variable: Satisfaction with Life

Table 7 Effect of National Income and Institutions on Life Satisfaction in Poor vs. Rich Countries (Sensitivity to the cutoffs)

Table 8 Effect of National Income and Institutions on Life Satisfaction in Poor vs. Rich Countries (Sensitivity to the countries in the sample)