This chapter focusses on the question of how formal institutions, like those governing the level of freedom and the generosity of the welfare state, affect self-reported well-being. The evidence suggests, for example, that more freedom, as well as government structures which encourage civic engagement, participation and trust, have positive effects. Many studies, however, use cross-sectional data with small sample sizes, often due to institutions being measured at the country level with limited variation over time. As a consequence, further work is needed to test robustness. Stronger results hold with respect to particular types of welfare state institutions, like unemployment benefits, which are subject to quite frequent changes within nations. Increases in unemployment benefits are associated with higher levels of well-being for all workers, probably due to greater income security. However, doubt still persists as to their overall impact, due to the extent to which well-being is adversely affected by the higher taxes needed to support a more generous welfare state.
Keywords: Well-being, Freedom, Partisan Happiness, Welfare Benefits, Taxes.
MacCulloch, R. (2018). How political systems and social welfare policies affect well-being. In E. Diener, S. Oishi, & L. Tay (Eds.), Handbook of well-being. Salt Lake City, UT: DEF Publishers. DOI:nobascholar.com
This chapter is about how formal institutions in a nation affect the well-being of its people. Douglass North (1990) defines institutions as “the humanly devised constraints that structure human interactions. They are made up of formal constraints (rules, laws, constitutions), informal constraints (norms of behaviour, conventions, and self-imposed codes of conduct), and their enforcement characteristics”.
In North’s theory, formal rules are created by the polity, whereas informal norms maybe part of the heritage that is often referred to as “culture” and includes characteristics like “social trust”. This chapter focuses mostly on formal institutions, and we leave it to other authors in this book to discuss how informal norms affect well-being.
The chapter is structured as follows. In section 2 we study how the nature of the political system in a country affects well-being. Do the citizens of a country gain pleasure from having more political freedoms and civil liberties? In addition to the degree of freedom of its citizenry, we ask whether the quality of other government structures affect happiness. Does corruption of public officials lead people to experience more negative feelings? Is there a relationship between the size and quality of the regulatory state and well-being?
This section also emphasizes the importance of formal institutions that facilitate greater engagement with the political process and reinforce social networks. Is it the economic outcomes that systems yield, or the degree to which their decision-making processes are inclusive, which matter the most? The section concludes with a discussion of the role of partisan politics. Is, for example, political competition more like a team sport whereby one gets pleasure from being a supporter of the winning side, aside from the policies which actually end up being implemented?
Section 3 focusses mostly on government spending and taxes. It seeks to answer questions like whether or not countries with more generous welfare states and greater job security have higher well-being. Do the ‘happiness studies’ on this topic take into account the fact that countries with high public spending tend to tax their citizens more? On the other hand, can “sin taxes” like on cigarettes, actually increase well-being? The final section concludes.
First, we ask the question of how formal institutions governing the level of civil, political and economic freedoms affect well-being in nations. Most of the institutions governing these kinds of freedoms change only slowly over time which is probably why much of the work in this field has relied on exploiting cross-sectional variation.
Many authors have sought to test the hypothesis that freer countries have higher levels of self-reported well-being (SWB).1 Greater choice is typically regarded as a good thing. In particular, democratic freedoms are often viewed as being vital to monitor and control politicians, and to bring the decisions of government closer to the wishes of the citizenry.
Of course, not all theories highlight the positive effects of freedom. For those who have self-control problems, like eating or smoking too much, constraints on behaviour may be helpful. It is often argued that the ‘right to bear arms’ should be restricted. There are also many rules imposed on children, which include not having the right to vote, even in western democracies. Some people may voluntarily place restrictions on themselves by joining a strict religion, due a belief in the supernatural or desire to be a member of a close-knit group.
It should also be noted that economic freedoms are nearly always regulated, usually in the name of increasing social welfare. Free markets are not to be mistaken with unregulated markets. Even “unfettered competitive markets are based on a set of laws and institutions that secure property rights, ensure enforcement of contracts, and regulate firm behaviour and product and service quality” (see Acemoglu, 2009).
The “happiness studies” on this topic tend to use data at the national level and their findings are still in need of greater validation. As an early example, Veenhoven (1993) uses a cross-section of 23 countries from the World Values Survey (WVS) in the 1980s. He argues that there “is a clear correspondence between average happiness in nations and the degree to which these nations provide material comfort, social equality, freedom and access to knowledge” (pg 32). Freedom is measured, in this instance, by the Estes Index of Political Participation and also the degree to which the country has a free press.
In a later study, Veenhoven (2000a) used a cross-section of 38 countries and a single index combining political, civil and economic freedoms to again argue that freedom and well-being are positively correlated.
However, Inglehart and Klingemann (2000) found that their Freedom House democracy score was not significant in a SWB regression, after controlling for other variables, when using a cross-section of 105 countries. This result led these authors to conclude that “the interpretation that democracy causes well-being does not stand up: other factors – particularly the number of years of communist rule and the society’s level of economic development – seem to play much more powerful roles” (pg 181).
The uncertainty underlying this debate is heightened by the controversy over whether even the level of development itself influences well-being. The time-series of SWB data for many nations do not appear to support this proposition (see Easterlin, 1974) and the growth rate of GDP has often been argued to matter more (see Di Tella, MacCulloch & Oswald, 2003).
One approach to better identify the relationship between freedom and well-being has been to use within-country data. For example, Frey and Stutzer (2000) study the role of direct democracy (via initiatives and referenda) using a cross-section of 26 Swiss cantons between 1992 and 1994. A shift from the canton with the lowest direct participation rights (i.e., Geneva) to the one with the highest (i.e., Basel land) is associated with an 11 percentage point higher probability of declaring oneself completely satisfied with life (on an eight point scale ranging from “completely dissatisfied” to “completely satisfied”).
These authors argue that the institutions of direct democracy have two effects on well-being. One maybe to generate better policy outcomes. The other is to expand the possibilities of citizens to participate in political processes and procedures.
The relative sizes of these two effects can be identified since their data set includes foreigners living in Switzerland who can share in the outcomes of direct democracy, but who are not able to gain utility from participation in the process, which is only available to citizens. Consequently Swiss nationals are hypothesised to gain more from direct democracy, compared to foreigners. It turns out that about two thirds of the total gain in SWB is due to a more favourable process in political decision-making whereas one third is due to more favourable outcomes.
The robustness of the Frey and Stutzer (2000) results has, however, been questioned. For example, Dorn, Fischer, Kirchgässner and Sousa-Poza (2008) use surveys conducted between 2000 and 2002 by the Swiss Household Panel to re-evaluate the relationship between cantonal direct democracy and well-being. Their study introduces new controls for cultural determinants of life satisfaction such as languages and religion. Once these controls are included, these authors are unable to find a robust relationship.
Furthermore, Blume, Muller and Viogt (2009) are also unable to detect a correlation between direct democracy and happiness scores, although they revert to using cross-country data with a sample of 54 observations taken between 1996 and 2005.
Another approach to help better identify the relation between freedom and SWB has been to bring a time series dimension to the question. For example, Inglehart, Foa, Peterson and Welzel (2008) use different waves of the World Values Surveys, taken between 1981 and 2007. They correlate changes in SWB (measured by both self-reported happiness and life satisfaction) from the first to the last available survey for each country, with changes in two proxies for freedom, as well as changes in GDP per capita.
One of their freedom proxies is the level of democracy, measured by the Polity IV project. The other is a survey question taken from the WVS which asks, “Some people feel they have completely free choice and control over their lives, while other people feel that what they do has no real effect on what happens to them. Please use this scale where 1 means “no choice at all” and 10 means “a great deal of choice” to indicate how much freedom of choice and control you feel you have over the way your life turns out”.
Of these two proxies, the Polity IV measure of democracy is not significant. Inglehart et al (2008) instead argue that their results do “show that a growing feeling that one has free choice was by far the most important influence on whether SWB rose or fell”. The positive correlation which these authors report between changes in SWB and changes in feelings of free choice is an important one, although its implications are somewhat unclear.
First, there is a degree of ambiguity as to what the above survey question is capturing. For example, people in many countries, including Qatar, reported higher average levels of “free choice” and “control over their lives” than the United States (see WVS, 2010-2014). Yet Qatar is ranked as being “not free” by Freedom House.
Second, the WVS survey period from 1981 to 2007 covers the end of the Cold War in 1989-90 when, for example, the threat of East-West conflict was believed to be falling. The positive emotions and reduced fear around this time may have led people to both tick up their happiness scores and report feeling freer.
Third, Stevenson and Wolfers (2009) report that the subjective well-being of women in the United States fell between 1972 and 2006, both absolutely and relatively to that of men, notwithstanding a great expansion of women’s opportunities over this time.
In summary, the heavy use of cross-sectional data sets, limited time-series evidence, small number of observations and widely differing proxies for freedom used across different studies, taken together, point to a need for more work to help better identify the relationship between freedom and self-reported well-being.
In additional to the freedom of its citizenry, the quality of other government structures may affect the well-being of a nation. For example, Italy achieved the top score of 1 (out of 7) for its Political Rights and Civil Liberties in 2017 from Freedom House, yet many of its institutions are perceived to be mired in corruption.
Helliwell and Huang (2008) exploit the cross-sectional variation in life satisfaction data from 75 countries taken between 1981 and 2000 by the World Values Survey to test the relationship between the quality of government and well-being. The quality variable is measured by an average of four indices that measure governmental effectiveness, regulatory quality, rule of law, and control of corruption.
Their results show that increasing the overall quality of government by about one standard deviation would have a similar effect on life satisfaction, for a typical respondent, as moving at least halfway up the income distribution within one’s own country (measured in deciles).
The World Happiness Report (Helliwell, Layard, & Sachs, 2016) uses a larger sample of 156 countries, surveyed between 2005 and 2015, from the Gallup World Poll. The results focus mostly on cross-sectional variation. This report finds evidence of a significant and large negative correlation between SWB and an index of corruption. Well-being is measured by the Cantril “ladder of life” survey question which asks respondents to rank their lives on a 0 to 10 scale, where the worst possible life is a 0 and the best possible life is a 10.
The argument found in Frey and Stutzer (2000) that formal institutions, like referenda, may increase well-being due to the satisfaction that comes from the process of participating, aside from the actual outcomes that are produced, has important implications.
For example, Helliwell (2011) uses a case study of Singapore’s Prison Service reforms which he believes exemplifies lessons from well-being research that emphasizes the positive role of processes (such as engagement in a shared purpose). The reforms which he studies were begun in 1998 and emphasized the building of connections and trust so as to combine prisoners, staff and the public in collaborative commitments to improve the lives of all.
The outcome of these reforms appear to have been impressive, having helped contribute to a one-third drop in recidivism, as well as improved staff morale and better social connections between prisons and the rest of society.
More generally, Helliwell and Putnam (2004) use the World Values, US Social Capital Benchmark and Canadian “Equality, Security and Community” surveys to study the relationship between social networks and well-being. Marriage and family, closeness to friends, workplace ties, civic engagement, trust and trustworthiness are independently and robustly positively correlated with an individual’s happiness and life satisfaction.
At the macroeconomic level, Aghion, Algan, Cahuc and Shleifer (2010) find that measures of trust are negatively correlated with the level of government regulation whereas Djankov et al. (2002) find a positive relationship between corruption and regulation.2 Hence, the above types of findings, taken together, suggest that countries with a smaller regulatory state and higher levels of citizen participation in decision-making, as well as a greater degree of social connectedness and trust, may experience higher levels of well-being.
However, work on the topic of how specific regulations affect well-being is still quite scarce, probably due to the difficulty of obtaining comparable measures of rules, both across nations, and even within the same country over time.
Another question relates to how an individual’s political affiliation, whether it be left-wing or right-wing, as well as the partisan stance of the government, affects well-being. Di Tella and MacCulloch (2005) find that the same outcomes affect people’s life satisfaction differently, depending on which party they support, and that these differences cannot be traced back to income differences. A panel of 10 countries is used from 1975 to 1992 to help identify the results by exploiting the time series variation in the data.
This kind of evidence favours the partisan approach to modelling business cycles: right-wingers tend to care more about inflation and left-wingers seem to be more concerned with unemployment. In other words, the same economic outcomes that are associated with a particular set of institutions may be associated with different levels of well-being across individuals, depending purely on their ideological inclination.
Respondents also declare themselves to be happier when the party in power has a similar partisan inclination to themselves, even after controlling for key economic performance indicators such as unemployment, inflation and income.
The effect is quite large: a right-wing person living under a Socialist Party leader, like President Hollande of France, would have been willing to put up with an increase of 11 percentage points in the inflation rate in order to have a Conservative Party leader, like Prime Minister Theresa May, in the United Kingdom. This trade-off is derived by regressing a measure of well-being on a set of explanatory variables that include how far into the ideological right is the government and also the inflation rate, where the data are divided into two sub-samples made up of those who identify themselves as being either right-wing or left-wing.
One interpretation is that some non-economic variables may affect the two constituencies differently. Examples in America may be a party’s position on gun control or abortion. Another interpretation is that voters experience happiness when the party they support is in power, regardless of its policies, due to the personal charisma of the leader (which maybe attractive only to the party’s constituency). Alternatively, there could be a pure “victory effect”, whereby individuals care that the party which they support is in power (regardless of the leader or the policies which he or she enacts).
Furthermore, Pierce, Rogers and Snyder (2016) use daily data on the SWB of over 300,000 people before and after the 2012 US election which was won by (former Democratic) President Obama. The happiness of those individuals who identified themselves as partisan losers (i.e., Republican supporters) dropped significantly in the days immediately after the election. The drop in their in their SWB was twice as large as respondents living in Boston experienced in the aftermath of the Boston Marathon bombings. On the other hand, the SWB of the partisan winners (i.e., Democrat supporters) was not affected by the election.
Not only do economic and political outcomes affect SWB, but causality may also work in the opposite direction. Using an unbalanced panel of 15 European Union members from 1973 to 2012, Ward (2015) finds a positive correlation between voters’ SWB and their propensity to re-elect incumbent governments. He includes a range of macroeconomic control variables, like GDP growth, inflation and unemployment, as well as country and year fixed effects. A one standard deviation increase in SWB is associated with an 8.5 percentage point (86% of a standard deviation) swing in the vote share enjoyed by the governing coalition.
Liberini, Redoano and Proto (2013) also find that a drop in a person’s SWB makes them less likely to vote in favour of the ruling party, even if the reason is an event for which the government has no influence. They use British Household Panel Survey Data on just over 4,200 individuals who were interviewed between 1996 and 2008.
To summarize, these findings appear to lend more support to the idea that well-being is not driven solely by the outcomes which formal institutions generate. The success of one’s own “team”, as well as participation in the electoral process, increase well-being, independently of the policies actually implemented. In addition, although there are still few studies on the topic, lower SWB may lead to a desire to change the government, regardless of its political colour.
In this section we study how another set of formal institutions, namely those relating to the government expenditure and taxation system, affect well-being in nations. We focus in particular on those programmes that enhance income security.
An increasing body of work has explored whether certain forms of welfare state institutions, like unemployment benefits, affect well-being. For example, Richard Easterlin (2013) states that “If society’s goal is to increase people’s feelings of well-being, economic growth in itself will not do the job. Full employment and a generous and comprehensive social safety net do increase happiness”.
Some people argue that the unemployed voluntarily choose not to find a job since they don’t find work attractive. If this is the case then unemployment may not be associated with much mental distress, leading policy-makers to opt for a less generous benefit system to reduce the attractiveness of being out-of-work.
However, a large body of evidence from ‘happiness’ studies has now shown that the unemployed are far less happy than those that have work. Becoming unemployed has a larger negative effect than divorce, according to many data sets. This result has led some economists to argue that governments should bias their policies toward providing a comprehensive social safety net.
Di Tella, MacCulloch and Oswald (2003) study the effect of unemployment benefits on national well-being, as well as on different sub-groups, like the unemployed and employed. They use a sample of 271,224 people living in 12 nations between 1975 and 1992 who were surveyed by Euro-barometer. Their “happiness regressions” exploit the time-series variation in these data by controlling for country and year fixed effects, as well as country specific time trends.
People are willing to forgo GDP growth rates of 2.5% to see an increase in unemployment benefits, as a proportion of wages, equal to 3 percentage points, ceteris paribus. This trade-off is derived by regressing a measure of well-being on a set of explanatory variables that include GDP and unemployment benefits. The above change in benefits is equivalent to a shift from the Irish level to the French level (i.e., from 0.28 to 0.31). The employed and unemployed experience an increase in SWB of similar magnitude. However, doubt still persists as to the overall impact of more generous benefits, due to the potential adverse effects on well-being of the higher taxes and unemployment which they may cause.
More generally, a number of political scientists have focussed on the question of whether the welfare state facilitates higher levels of well-being due to its capacity to allow people to uphold a “socially acceptable” standard of living, independent of market participation. In this spirit, Esping-Andersen (1990) creates a “decommodification” index, which measures the extent of “emancipation” of labour from market dependency summarized across three domains: pensions, income maintenance for the ill or disabled, and unemployment benefits.
Pacek and Radcliff (2008) use Euro-barometer data from 1975 to 2002 for 11 nations to test whether the “decommodification” index is correlated with SWB. In regressions controlling for country fixed effects, they find that decommodification is positively correlated with life satisfaction, significant at the 5 percent level. A shift from the minimum to maximum value of the former variable is associated with three-quarters of a standard deviation increase in SWB. These authors conclude that the “welfare state contributes to human well-being”.
The above kinds of studies appear to build a strong case supporting a positive association between a social safety net and SWB. However, Veenhoven (2000b) argues that public welfare may crowd out private provision by friends, families and private organizations, as well as lead to a loss in individual freedoms. Due to the positive correlations noted in the previous section of this chapter between SWB and participation in private networks, the possibility of adverse effects due to crowding out do need to be investigated further.
Related to these concerns, there is still much debate regarding the question of the effect of overall government size on well-being. For example, Bjørnskov, Dreher, and Fischer (2007) correlate general government consumption spending, as a fraction of GDP, with life satisfaction using the third and fourth waves of the WVS, taken between 1997 and 2001, which cover 120,000 individuals in 74 countries. These authors find that SWB is negatively related to government consumption, although they mainly exploit cross-sectional variation by only controlling for a set of region dummies (i.e., Sub-Saharan Africa, North Africa, Middle East, Asia, Pacific and Latin America) rather than country dummies.
In summary, whereas the evidence is supportive of a positive relationship between particular dimensions of the welfare state, such as unemployment benefits, and well-being, the effect of aggregate government size is subject to much disagreement. One reason for the difficulty in assessing the overall impact of government spending on SWB is discussed in the next section.
The papers discussed above seek to explain well-being using measures of the generosity of government programmes. However they do not directly consider the impact of the higher taxes needed to fund them. Akay et al. (2012) use 26 waves of the German Socio-Economic Panel, which has surveyed about 25,000 individuals between 1985 and 2010, to correlate life satisfaction with each person’s net income, as well as the level of taxes they pay. Individual and year fixed effects are included in their regressions.
Net income is significantly positively linked with SWB whereas, conditional on net income, taxes have a positive effect. The authors interpret these findings as meaning that taxes have separate two effects on SWB: a negative effect to the extent they subtract from gross income; and a positive effect most likely due to the benefits that tax revenues provide by helping to fund public goods and social insurance programmes.
One of the few papers which correlates SWB with direct measures of both government spending and taxes at the national level is Grimes, Ormsby, Robinson and Wong (2016). They use data on 171,804 people living in 35 nations between 1981 and 2012, collected in six waves of the WVS. Taxes are divided into two categories: “non-distortionary”, defined as indirect taxes on goods and services; and “distortionary”, which include income taxes, social security contributions, and property taxes. They control for country and year fixed effects, GDP per capita, inflation, unemployment, and personal characteristics.
Higher taxes are found to be associated with lower SWB, whereas the reverse is true for public expenditures. Grimes et al (2016) main result is that non-distortionary taxes lower well-being more than distortionary taxes. Increasing the latter by 10 percent of GDP, funded by a same sized cut in the former, leads to a rise in SWB of 25% of a standard deviation, similar to the effect of getting married in many ‘happiness regressions’. They also find that devolving central government expenditure to a more local level leads to higher SWB.
Another paper which attempts to distinguish between different types of tax structures is Oishi, Schimmack and Diener (2012). Their sample is a cross-section of 54 countries from the 2007 Gallup World Poll in which the Cantril “ladder of life” question was asked to 59,634 people. These authors find that countries with more progressive income tax systems (i.e., where the tax rate increases more steeply as taxable income rises) tend to have higher levels of SWB. They argue that the mechanism occurs by virtue of a “fairer distribution of wealth” and the provision of more “quality public goods”.
Aside from taxes which are used purely to help raise revenues for the government, some taxes may instead be used to discourage addictive forms of behaviour. These are sometimes referred to as “sin taxes”. Gruber and Mullainathan (2005) find that higher cigarette taxes increase the SWB of smokers, on average, compared to non-smokers, presumably by helping them give up.
In summary, research determining the impact of taxes on SWB is still in its infancy. There is already some evidence suggesting that different kinds of taxes affect well-being differently. Few, if any papers, have been able to successfully identify the net effect of government programmes on SWB, taking into account the full impact of the taxes needed to finance them.
The aim of this chapter is to describe how formal institutions, like the political system in a country, as well as the rules surrounding the welfare state and taxes, affect well-being. There are many studies suggesting that more freedom, as well as government structures which encourage civic engagement, participation and trust, have positive effects. However since freedom-related variables have typically been measured at the national level and often vary only slowly over time, further work is needed to test robustness.
One theme of this research agenda is that processes may matter at least as much as outcomes. For example, people appear to experience higher levels of well-being from the act of participation in a referendum, regardless of the policies that actually get implemented. Similarly, the evidence points to political competition being somewhat like a team sport in which one gets pleasure from supporting the winning side. In other words, there appears to be a pure “victory effect”, whereby individuals care that “their” party is the one holding power, regardless of the policies which are enacted.
A growing body of research using country level panel data also supports the view that a comprehensive social safety net is associated with higher levels of well-being for all workers, both employed and unemployed. However, doubt still persists as to the overall impact of a generous welfare state, due to the extent to which well-being may be adversely affected by the higher taxes needed to support it.
Topics regarding the impact of (slow-moving) formal institutions on well-being opens up the question of the advantages, but also the limitations, of using happiness data to help evaluate public policies. First, there is uncertainty about the time horizon used by people in framing their survey responses. Second, since people may adapt over time, there is doubt as to the short-run versus the long-run effect of institutions on well-being. Third, there is still much disagreement as to whether a person’s well-being can even be summarized by a single number or whether there are different dimensions of well-being that cannot be aggregated.
1Examples of survey questions that have been used to measure self-reported well-being and which will be referred to in this chapter include the “happiness” question, which asks, “Taking all things together, would you say you are: very happy; quite happy; not very happy; not at all happy?”, and the “life satisfaction” question, which asks, “How satisfied are you with your life as a whole these days?”, with responses ranging from “completely dissatisfied” to “completely satisfied”. Another example is the “ladder of life” question which asks “Please imagine a ladder with steps numbered from zero at the bottom to 10 at the top. The top of the ladder represents the best possible life for you and the bottom of the ladder represents the worst possible life for you. On which step of the ladder would you say you personally feel you stand at this time?”
2Aghion et al (2010) use a measure of trust from the World Values Surveys, collected between 1981 and 2003, which they correlate with the level of regulation (measured by the number of steps that an entrepreneur must complete to open a business legally) across 57 countries. Respondents in the WVS were asked, “Generally speaking, would you say that most people can be trusted, or that you can’t be too careful in dealing with people? What would it be today?” and also “To what extent do you trust the following institutions: government, banks, foreign companies?”
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