Measuring Subjective Wellbeing: A Summary Review ...

Measuring Subjective Wellbeing: A Summary Review of the Literature By Pedro Conceição and Romina Bandura Office of Development Studies, United Nations Development Programme, New York 18 April 2008

Measuring Subjective Wellbeing: A Summary Review of the Literature

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Contents INTRODUCTION ............................................................................................................... 3 I.

MEASURING WELLBEING ..................................................................................... 4 A.

OBJECTIVE MEASURES: ONE-DIMENSIONAL WELLBEING ................................... 4 GDP as a measure of utility ........................................................................... 4 GDP measurement flaws ................................................................................ 5 B. OBJECTIVE MEASURES: MULTIDIMENSIONAL WELLBEING ................................. 6 II.

SUBJECTIVE WELLBEING MEASURES (SWB) ....................................................... 7 A. B. C.

III.

THE MEANING OF SUBJECTIVE WELLBEING ....................................................... 8 WHY IS HAPPINESS RELEVANT FOR ECONOMIC RESEARCH AND POLICYMAKING?9 DETERMINANTS OF HAPPINESS ........................................................................ 9

ECONOMIC DETERMINANTS OF HAPPINESS ........................................................ 10 A.

HAPPINESS AND INCOME ............................................................................... 10 Happiness and income across time: The Easterlin Paradox.......................... 10 Happiness and income at one point in time .................................................. 12 Responses to the Easterlin paradox .............................................................. 16 B. HAPPINESS, UNEMPLOYMENT AND INFLATION ................................................ 19 C. HAPPINESS AND INEQUALITY ......................................................................... 20 IV.

NON-ECONOMIC DETERMINANTS OF HAPPINESS................................................ 21 A.

SOCIO-DEMOGRAPHIC VARIABLES ................................................................. 21 Health .......................................................................................................... 21 Family Life................................................................................................... 23 Age .............................................................................................................. 24 B. INSTITUTIONAL VARIABLES ........................................................................... 24 V.

PERSPECTIVES FROM DEVELOPING COUNTRIES................................................. 25 A. B.

VI.

HAPPINESS AND INCOME ............................................................................... 25 PERSONAL ASSESSMENTS .............................................................................. 27

CONCLUSION: SOME LIMITATIONS OF SWB ...................................................... 29 A. B. C.

IS HAPPINESS THE ULTIMATE GOAL IN LIFE? ................................................... 29 HAPPINESS AS A WELLBEING INDICATOR ........................................................ 30 THE QUALITY OF HAPPINESS INDICATORS ....................................................... 30

REFERENCES................................................................................................................. 31


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Introduction

“We must acquire a life style which has as its goal maximum freedom and happiness for the individual, not a maximum Gross National Product.” (Nordhaus and Tobin 1973 )

The above quote could have been written today—it was written, instead, about 35 years ago in a pioneering study of what became known as estimates of “green GDP.” It shows that our concern about the limitations of economic growth as a benchmark of wellbeing is not new. But the fact that it could have written today also shows how important the topic is: to think through the meaning and measurement of wellbeing. Wellbeing is a notion that people and policymakers generally aspire to improve.1 However, it is an ambiguous concept, lacking a universally acceptable definition and facing often with competing interpretations. Wellbeing is generally viewed as a description of the state of people’s life situation (McGillivray 2007, p. 3). Often, wellbeing has been equated with the material position of a country, measured by its Gross Domestic Product (GDP). However, GDP does not capture all the aspects of human life and it was increasingly recognized that new measures were needed. New indicators and datasets were created to capture social and environmental aspects that GDP failed to incorporate. This included indicators measuring education achievements, health outcomes and environmental degradation. More recently, economists have transcended the boundaries of their field incorporating findings from psychology and behavioral sciences into wellbeing research. This has led to an exploding literature on subjective wellbeing, or more commonly referred to as “happiness”. The following paper explores the growing literature on subjective wellbeing. Part I briefly reviews the main clusters of wellbeing measures, “objective” and “subjective” indicators. It analyses GDP as a wellbeing measure and then moves on to multidimensional measures that rely on composite indices. Part II analyzes the meaning, importance and determinants of subjective wellbeing. Parts III and IV discuss the main findings from the literature on the economic and non-economic determinants of happiness. Part V offers some perspectives from developing countries. As a conclusion, part VI offers some limitations on the use of happiness as a measure of wellbeing.

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McGillivray and Clarke (2006) point out that concepts such as quality of life, welfare, well living, living standards, utility, life satisfaction, prosperity, progress, needs fulfillment, human poverty, development, empowerment, capability expansion, human development, poverty, human poverty, happiness have been used interchangeability with wellbeing without clear distinction.


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I.

Measuring wellbeing

Wellbeing is difficult to define but it is even harder to measure. In general, wellbeing measures can be classified into two broad categories: objective and subjective measures. The first category measures wellbeing through certain observable facts such as economic, social and environmental statistics. People’s wellbeing is assessed indirectly using cardinal measures. On the other hand, subjective measures of wellbeing capture people’s feelings or real experience in a direct way (van Hoorn 2007). They assess wellbeing through ordinal measures. Traditionally, wellbeing has been identified with a single objective dimension: material wellbeing measured by income or GDP. It then expanded to such measures as income per capita and poverty. The link between income and wellbeing rests on the assumption that income allows increases in consumption and consumption increases utility. Yet there is disagreement on how increases in consumption represent improvements in wellbeing. Moreover, GDP has its measurement flaws and does not capture all the aspects of human life. Thus, instead of relying on a single dimension, wellbeing measurements have progressed to encompass broader dimensions such as social aspects, environmental and human rights (Table 1). It is now widely accepted that the concept of wellbeing is multidimensional: encompassing all aspects of human life. Table 1: Evolution of the dominant meaning and measurement of wellbeing Period Meaning of Wellbeing Measurement of Wellbeing 1950s Economic wellbeing GDP growth 1960s Economic wellbeing GDP per capita growth 1970s Basic needs GDP per capita growth + basic goods 1980s Economic wellbeing GDP per capita growth and rise of non-monetary factors 1990s Human development/capabilities Human development and sustainability 2000s Universal rights, livelihood, freedom MDGs and new areas such as risk and empowerment Source: Sumner 2006 (p. 56)

a.

Objective measures: One-dimensional wellbeing

GDP as a measure of utility While it is often asserted that economists are primarily concerned with GDP levels and growth, it is important to step back a little and remember that what matters most as an “objective function” is people’s wellbeing. A fundamental assumption of standard economic analysis is that people’s well-being increases with consumption (of food, clothing, housing, entertainment, and many other goods and services). It is primarily due to this assumption that GDP—all that is produced, and therefore either consumed or


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invested, by a country in a year—is so often taken as the yardstick of progress. The fact that GDP is the sum of consumption and investment should, by itself, give an indication that GDP may not be the ideal yardstick of wellbeing. If large increases in GDP take the form of growth in investment rather than consumption, then GDP itself does not necessarily mean improved well being. In more technical language, consumption is the most important (often, the only…) argument in the utility function used by economists in order to capture the extent to which consumption translates into the well-being of an individual. The distinction between consumption and utility may seem like a technicality, but it is important for reasons that will become apparent later. Is it valid to assume that more consumption leads to more utility? More systematic evidence on the limitations of using GDP as a yardstick for wellbeing come from more direct indicators of quality of life (Easterlin and Angelescu 2007). Some of these indicators are objective (increases in nutrition or life expectancy increase quality of life, while increases in crime rates or congestion decrease it), others subjective (self-reported status of well-being by people in surveys of happiness, life satisfaction, or prevalence of positive moods). As far as objective indicators go, growth does usually and eventually translate into higher consumption of goods and services. But most studies suggest that the general conclusion is that while cross-country data shows a correlation between GDP per capita and objective indicators of quality of life (for example, richer countries tend to have life expectancy), time series analysis provides very little support for GDP per capita causation of improvements in the objective indicators (for example, in a large number of countries the turning point towards sustained economic growth anteceded by many decades sustained improvements in life expectancy). A very comprehensive and systematic cross-country and time-series study by William Easterly of the relationship between GDP growth and improvements in objective indicators of well-being found that there was only robust indication that GDP growth was the prime cause for the improvement in three out of possible 81 indicators (calorie intake, protein intake, and telephones) (Easterly 1999). In addition, the evidence shows that growth comes accompanied with objective indicators of “bads” that lower quality of life, such as higher levels of pollution and, beyond a certain income threshold, dietary habits that increase obesity. Thus, sometimes growth brings with it the “consumption” of aspects that tend to lower wellbeing. GDP measurement flaws GDP has some measurement flaws. For example, some activities that are included in the GDP estimates are difficult to calculate, for example government services. As these services are given to consumers at a subsidized price, their output cannot be valued at market prices. Moreover, GDP does not take into account changes in asset values which


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influence a person’s consumption patterns. Externalities such as pollution or the depletion of natural resources are not counted. Finally, GDP does not take into account non-market activates, such as housework or illegal activities and the value of leisure (Giovannini, Hall and d’Ercole 2007). GDP does not capture all the aspects of human life, thus other objective or subjective measures have been used to paint a more realistic picture of human wellbeing. b.

Objective measures: multidimensional wellbeing

Despite its flaws, given that GDP data is readily available and reliable, it is still widely used as a proxy for wellbeing (McGillivray and Clarke 2006). However, there is widespread agreement that wellbeing is multidimensional, that it encompasses all aspects of human life. Thus, different approaches have been taken to go beyond the GDP measure, conceptualizing wellbeing in a more holistic way. One approach has been to construct objective measures to complement GDP, offering social and environmental information beyond the economic stance. Since the 1970s many non-economic indicators have been created to complement GDP. Indicators in areas such as education, health and nutrition, environment and empowerment and participation have been elaborated to complement GDP. These non-economic indicators tend to capture more medium and long term assessment of wellbeing, as they asses the outcomes of policy (such as being healthy or educated) rater than the inputs (such as having more income). However, the quality and availability of this data makes intercountry comparisons difficult (Sumner 2006; McGillivray 2007). A second approach is to adjust GDP by monetizing different aspects that are not counted in GDP measurement, for example, social and environmental factors. The problem with these adjustments is that it is difficult to quantify and monetize some of these additional factors. The first adjustment to GDP is to allow purchasing power party among countries. Another further adjustment to GDP includes differences in income distribution, for example, by providing weighted shares of growth by population groups. As income per capita is a national average it does not provide the real picture of income from different population subgroups or regions. A third more complex adjustment is for example, to take into account social and environmental factors such as the value of leisure or the damage of pollution. Nordhaus and Tobin’s 1972 Measure of Economic Welfare (MEW) deducted from GNP an allowance for defense expenditure, pollution, congestion and crime and added an estimate on the value of leisure and services of consumer durables (McGillivray 2007, p. 6). A recent adjustment has been made by the World Bank through green accounting, with its estimates of wealth and adjusted net saving (or genuine savings). Adjusted net savings measure the savings rate in an economy after taking into account investments in human capital, depletion of natural resources and damage caused by pollution. 2 2

Adjusted net savings are derived from standard national accounting measures of gross national savings by making four types of adjustments. First, estimates of capital consumption of produced assets are deducted


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A third approach is to replace GDP by constructing composite measures that would capture the multidimensional aspect of wellbeing. These measures are usually constructed using different components, weighted in some form to form a single index. The first attempt to construct a composite index of wellbeing was the Overseas Development Council’s and Morris’s 1979 Physical Quality of Life Index (PQLI). This index combined infant morality, life expectancy and adult literacy. Another example is the well-known and debated Human Development Index created in 1990, combining income per capita (in PPP terms), life expectancy at birth, adult literacy and education enrollment ratios. Although indices are useful information tools, there are many criticisms and caveats to be taken into account when confronting such measures (McGillivray 2007). Indices tend to oversimplify a very complex reality that cannot be captured in a single index. Moreover, the methodologies used to construct these indices and assessments are not always transparent. For example, the choice of indicators used, the issue of collinearity amongst indicators, the weights assigned to different categories and the quality of data used to construct the index. Moreover, criticism is also directed towards using the same methodology for industrial and developing countries alike. Indices can be subject to manipulation by politicians and thus they are more “creative accounting” exercises than objective measures. Finally, they tend to “glorify” the same countries year after year and then are used to “name and shame” others without adding any value (Bandura 2005). II.

Subjective wellbeing measures (SWB)

Another approach to measuring multidimensional wellbeing is through self reported happiness and life satisfaction. For many centuries the subject of happiness was the realm of theologians and philosophers but recently it transcended into social sciences, first in psychiatry and since 1950 into mainstream social sciences and economics (Easterly 2004).

to obtain net national savings. Then current expenditures on education are added to net domestic savings as an appropriate value of investments in human capital (in standard national accounting these expenditures are treated as consumption). Next, estimates of the depletion of a variety of natural resources are deducted to reflect the decline in asset values associated with their extraction and harvest. Estimates of resource depletion are based on the calculation of resource rents. An economic rent represents the excess return to a given factor of production. Rents are derived by taking the difference between world prices and the average unit extraction or harvest costs (including a 'normal' return on capital). Finally, pollution damages are deducted. Many pollution damages are local in their effects, and therefore difficult to estimate without location-specific data. Here we estimate health damages due to urban air pollution. As for global pollution damages, the estimates include damages from carbon dioxide emissions. http://web.worldbank.org/WBSITE/EXTERNAL/TOPICS/ENVIRONMENT/EXTEEI/0,,contentMDK:205 02388~menuPK:1187778~pagePK:148956~piPK:216618~theSitePK:408050,00.html


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a.

The meaning of subjective wellbeing

Subjective wellbeing involves a multidimensional evaluation of life, including cognitive judgments of life satisfaction and affective evaluations of emotions and moods (McGillivray and Clarke 2006, p. 4). Some economists use the phrase “subjective wellbeing” as a synonym for “happiness” but in psychology, happiness is a narrower concept than SWB. Psychologist distinguish among 1) life satisfaction which is a cognitive element 2) affection, the affective element and 3) SWB, as a state of wellbeing, synthetic of long duration which includes both the affective and cognitive component (Bruni and Porta 2007, p. xvii). SWB is comprised by i) pleasant emotions ii) unpleasant emotions iii) global life judgment (life evaluation) and iv) domain satisfaction (marriage, health, leisure etc). Thus, SWB comprises all these 4 components. Happiness is a narrower concept than SWB and different from life satisfaction: although both happiness and life satisfaction are components of SWB, life satisfaction reflects individuals’ perceived distance from their aspirations while happiness results from a balance between positive and negative affect. In general SWB emphasizes an individual’s own assessment of his or her own life—not the judgment of others—and includes satisfaction (in general and satisfaction with specific domains), pleasant affect, and low negative effect. Thus, there is no single index that captures SWB or what it means to be happy. In this approach, SWB is a synonym of “being happy” (the Aristotelian approach of happiness as eudaimonia) whereas concepts such as “satisfaction” and “happiness” are considered “feeling happy” (a hedonic approach) (Bruni and Porta 2007, p. xviii). Despite these differences, economists have used the terms “happiness” and “life satisfaction” interchangeably as measures of subjective wellbeing (Easterlin 2004). There is no clear consensus on what “happiness” means thus, instead of trying to define happiness from an outside perspective, economists try to capture it though other means. The three different ways of capturing happiness are: 1) subjective happiness 2) objective happiness and 3) experience sampling measures (Frey and Stutzer 2002b, pp. 4-6). Subjective happiness asks people how happy they feel themselves to be. They result from surveys where people are asked to self report about how happy they feel, all things considered. Richard Easterlin, Bruno Frey, and others pioneered the economic analysis of happiness data. Today there are several surveys that evaluate happiness. One type of question asks “Taken all together, how would you say things are these days: would you say that you are very happy, pretty happy or not too happy?” (For example, the General Social Surveys). The second type of question asks people to rate their life satisfaction, on a scale from 0 to 10 (for example, the World Values Survey -WVS).3 3

The WVS also asks the question: “Taking all things together, would you say you are: Very happy, Rather happy, Not very happy and Not at all happy.” [www.worldvaluessurvey.org] The Eurobarometer Survey has questions on life satisfaction and happiness. It asks “On the whole, are you very satisfied, fairly satisfied, not very satisfied or not at all satisfied with the life you lead? And “Taking all things together, how would you say things are these days - would you say you're very happy, fairly happy, or not too happy these days?” [ www.gesis.org/en/data_service/eurobarometer/standard_eb_trend/indexframe_trend.htm]


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Objective happiness is a physiological approach which aims to capture happiness through the measurement of brain waves. Yet a third way to capture happiness is through sampling people’s moods and emotions several times a day for a prolonged time (Frey and Stutzer 2002b, pp. 4-6). b.

Why is happiness relevant for economic research and policymaking?

Happiness can guide policymaking by studying its determinants. For example, certain policies that affect employment and inflation can be evaluated with respects to how they change happiness levels. One can analyze the trade-off in terms of happiness between inflation and unemployment and thus opt for a policy that minimizes the loss of happiness. Institutional conditions can have an impact on happiness so increasing transparency, accountability and social cohesion maybe desirable from the point of view of increasing subjective wellbeing. It is also important to study happiness from the point of view of its consequences on behavior, although causality is an issue. For example, happier people are more successful in the labor market and happier people are more cooperative thus more likely to help others. Happiness research can illuminate economic theory, adding new knowledge. It can advance on the theory of how people make choices and what drives the utility function. Happiness research also is useful to challenge existing views, such as that noneconomic variables have no impact on self reported satisfaction or that work is a burden (Frey and Stutzer 2002b, pp.7-16; 2007; Layard 2007). c.

Determinants of happiness

What makes people happy? Although people may define happiness in their own terms, in general people mention similar things that make them happy (Easterlin 2002). Surveys conducted in groups of countries have found that the factors that people mostly mention are everyday life issues, in an extent, that they control (Easterlin 2004; Frey and Stutzer 2002b, p. 29).4 Material conditions and consumption are most prominently mentioned in these surveys. A fulfilling family life, such as being married, having children and getting along with relatives is also an important part. Personal and family health is another determinant. Job satisfaction and personal character are also mentioned. Although The Gallup World Poll conducted in 130 countries has elaborated an index of wellbeing based on the following questions: “Please imagine a ladder with steps numbered from 0 at the bottom to 10 at the top. Suppose we say that 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. If the top step is 10 and the bottom step is 0, on which step of the ladder do you feel you personally stand at the present time? Just your best guess, on which step do you think you will stand on in the future, say 5 years from now? Were you treated with respect all day yesterday? Did you smile or laugh a lot yesterday? Did you learn or do something interesting yesterday? Did you experience the following feelings during a lot of the day yesterday?” [www.gallup.com/poll/102259/Denmark-New-Zealand-Canada-Rank-Highest-WellBeing.aspx] 4 See Cantril (1965).


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international and domestic issues (politics, war, etc) are rarely mentioned as determinants for wellbeing, studies have found that political institutions have an influence on people’s happiness (Frey and Stutzer 2002b). To simplify the analysis, we divide the determinants of happiness into economic (i.e. income, unemployment, inflation and inequality) and non-economic determinants (i.e. personality, socio-demographic and institutional factors). The next sections explore the main findings from the happiness literature. III.

Economic determinants of happiness

a.

Happiness and income

Happiness and income across time: The Easterlin Paradox Does higher income lead to more happiness? In 1974 Easterlin showed that, for the United States, individually self-reported happiness increased with individual income (Figure 1), although there were rapidly “decreasing happiness returns” to increases in income. The cross-individual relationship between income and happiness was found to be far from linear, and essentially flat for high levels of income. Although this is consistent with the diminishing returns to increases in consumption that are typically assumed for theoretical utility functions there is debate on this topic. Thus, Easterlin found clear evidence of a positive effect of income on happiness at the individual level, in-line with the assumptions of standard economic theory – but in contrast with the findings of objective measures of quality of life. Figure 1- Happiness and Real Income in the United States

Note: Average reply to the following question: ‘Taken all together, how would you say things are these days? Would you say that you are…?’ Responses are coded as (3) Very Happy, (2) Pretty Happy, and (1) Not too Happy. Happiness data are drawn from the General Social Survey. Source: Frey and Stutzer 2002a.


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However, Easterlin (1974) also found in the same study that aggregate national happiness over time was essentially flat, seemingly irresponsive to sustained increases in GDP per capita (Error! Not a valid bookmark self-reference.). This finding is often known as the “Easterlin paradox,” in that growth in per capita income is not reflected in increasing happiness. Figure 2- Happiness and Real Income Per Capita in the US, 1973-2004

Note: Average reply to the following question: ‘Taken all together, how would you say things are these days? Would you say that you are…?’ Responses are coded as (3) Very Happy, (2) Pretty Happy, and (1) Not too Happy. Happiness data from the General Social Survey. Source: Clark, Frijters, and Shields 2008, (p. 96).

Is the Easterlin paradox exclusively a U.S. phenomenon? Easterlin’s own update (Easterlin 1995) of his original study as well as numerous studies since then have shown that happiness is essentially flat in most developed countries (Figure 3).5 Figure 3- Life Satisfaction in Five European Countries, 1973-2004

Note: Average responses to the following question: ‘On the whole how satisfied are you with the life you lead’. Responses are coded as (4) Very Satisfied, (3) Fairly Satisfied, (2) Not Very Satisfied, and (1) Not at all Satisfied. Life satisfaction data from the Eurobarometer Survey. 5

See also, for example, Blanchflower and Oswald (2000); Frey and Stutzer (2002).


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Source: Clark, Frijters, and Shields 2008 (p. 97).

The case of Japan is often used in the literature to counter the hypothesis that a country’s happiness increases as a country makes progress from being a developing country towards becoming a high income country. Japan is a rare case of a country for which long-term happiness data is available, starting from the late 1950s when it was a relatively poor country with income per capita below $3,000. Japan’s GDP per capita rose more than five-fold from 1958 to 1991, without any change in reported happiness (Figure 4). Figure 4- Satisfaction with Life and Income Per Capita in Japan, 1958-1991

Source: Frey and Stutzer 2002a (p. 413).

Happiness and income at one point in time What happens when we compare happiness indicators with different income levels across countries at one point in time? Cross-country evidence suggests that the relationship between economic development and happiness is curvilinear: as income per capita increases, happiness increases steeply up to a threshold of $10,000 (Inglehart 2000). From then onwards, levels of happiness are very weakly correlated with further increases in income per capita (


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Figure 5).


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Figure 5- Subjective Well-Being and GNP per Capita in 60 Countries, 1990-1996

Note: The subjective well-being index reflects the average of the percentage in each country who describe themselves as “very happy” or “happy” minus the percentage who describe themselves as “not very happy” or “unhappy”; and the percentage placing themselves in the 7-10 range, minus the percentage placing themselves in the 1-4 range, on a 10-point scale on which 1 indicates that one is strongly dissatisfied with one’s life as a whole, and 10 indicates that one is highly satisfied with one’s life as a whole. Subjective well-being data from the 1990 and 1996 World Values Surveys. GNP per capita for 1993 data from World Bank, World Development Report, 1995 Source: Inglehart 2000 (p.217)

This “threshold” hypothesis states that above some critical level of GDP, income has no effect on happiness. Some supporters of this view explain that income improves happiness only when basic needs are met. But beyond a certain level, income does not matter for happiness (Veenhoven 1991). Inglehart (2000) points out that societies at early stages of development put a very high price on economic growth. However once they move beyond a certain threshold, they demand better quality of life with such concerns as environmental protection, friends and a good family life (Clark, Frijters and Shields 2008). Thus, under this theory the relationship between income and happiness is curvilinear. Prolonged time series are unavailable for developing countries but looking again at the case of Japan (Figure 4) raises doubts about the curvilinear relationship (Esterlin 1995).6 Japan was a very poor country in 1958, although it was past subsistence levels. It advanced from income levels that were lower than or equal to those prevailing in many of today’s developing countries, without impacting subjective wellbeing. In the words of Easterlin, “the magnitude of Japan’s subsequent advance in living levels does encompass a transformation from a ‘subsistence level’ of consumer durables to plenitude, with no impact on subjective well-being” (1995, pp. 40-41). 7 6

See also Stevenson and Wolfers (2008) who also do not find a curvilinear relationship. Oswald (2008) contests the existence of diminishing marginal utility of income. 7 See also Easterlin (2005b) for a critique of generalizing diminishing marginal utility of income for the cross sectional studies. He uses an international cross section of happiness and income and a within country for the United States and tests whether as income increases within the range covered in the cross-


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Moreover, an important aspect of these cross plots is that the countries in the Former Soviet Union (FSU) are outliers: they report much lower levels of subjective wellbeing than poorer countries (such as India and Nigeria) (Error! Not a valid bookmark selfreference.). Even as economic conditions have improved in FSU countries (12 of the world's 20 fastest-growing economies between 2000 and 2003 are in this group) life satisfaction scores are very low. Deaton (2008) finds that “low satisfaction ratings from high-growth countries in these regions largely account for the seemingly paradoxical finding that overall across the 132 nations studied, income growth is negatively related to life satisfaction”. Figure 6- Life satisfaction and GDP per capita in 121 countries, 2003

Note: Data from Gallup Word Poll 2006. The graph shows the relationship between a country's average income (per-capita GDP in 2003, plotted on a logarithmic scale) and its residents' average life satisfaction rating (on a zero-to-ten scale). The circles represent countries, with diameters proportional to population. The regression line through the middle is the best fit between the two variables among all the countries studied; the closer a country is to the line, the better it fits the overall pattern. Source: Deaton 2008 (p.41)

In another study Deaton (2007) finds that the threshold effect explanation of income and happiness is not accurate: the correlation between income and life satisfaction is very high but remains positive and substantial, even among rich countries. Deaton first regresses average happiness against the logarithm of income, which makes the relationship close to linear (Figure 7; Table 1). The correlation of life-satisfaction and per capita PPP GDP yields a coefficient of 0.85 with an estimated standard error of 0.051 (Column 1 of Table 1). He then uses different specifications, the quadratic of log of income and dividing the sample among groups of income (Columns 2-7 of Table 1). He arrives to results that support the visual impressions of Figure 7 that the logarithmic fit with a constant slope is sectional analyses, happiness over time reproduces the point in time relationships to income. He finds zero marginal utility of income; not diminishing marginal utility of income.


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adequate for all countries, rich or poor, and if there is any evidence for deviation, it is small and in the direction of the slope being higher among the richer countries.

Figure 7- Life satisfaction and the logarithm of GDP per capita

Source: Deaton 2007 (p. 41)

Table 1- Cross-country regressions of average life-satisfaction on the logarithm of per capita GDP

Income cutoff

(1)

(2)

(3)

(4)

(5)

(6)

(7)

None

None

y< 12,000

y >= 12,000

y>= 20.000

None

None

–0.859 (0.778) 0.101 (0.046)

0.708 (0.085) --

1.743 (0.287) --

0.617 (0.649) --

0.751 (0.081) -0.032 (0.021) --

0.677 (0.064) --0.073 (0.018)

0.728 114

0.480 78

0.521 36

0.040 24

0.722 114

0.752 114

lny 0.850 s.e. (0.051) (lny)2 -s.e. lny*I(y>12,000) s.e. lny*I(y>20,000) s.e. R2 0.716 # of countries 114 Source: Deaton 2007 (p. 37)

Responses to the Easterlin paradox As mentioned earlier, the Easterlin paradox refers to the fact that despite increase of income over time, people have not reported increasing levels of happiness. What are possible explanations for Easterlin’s findings?


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Happiness is based on relative rather than absolute income: One of the key arguments for the Easterlin paradox is that happiness depends on relative consumption, in addition to absolute consumption. That is, how much one consumes relative to what the others consume may influence happiness. This was suggested by Easterlin in his seminal 1974 paper as a possible solution to his own paradox, and has become the subject of a large literature that broadly suggests that happiness does depend in part on relative consumption. Raising everyone income does not raise everyone’s happiness because when compared to others, incomes have not improved (Easterlin 1995; Box 1).8 Box 1- Point-of-time and life-cycle results Imagine your income increases substantially while everyone else’s stays the same – would you feel better off? The answer most people give is yes. But now, let’s turn the example around. Think about a situation in which your real income stays the same, but everyone else’s increases substantially – then how would you feel? Most people say that they would feel less well off, even though their real level of living hasn’t, in fact, changed at all. Now what this thought experiment is demonstrating is that as far as material things are concerned one’s satisfaction with life depends not simply on one’s own objective condition, but on a comparison of one’s objective situation with a subjective (or internalized) living level norm, and this internal norm is significantly affected by the average level of living of the people around us. At any given time, the living conditions, or real incomes, of others are fixed, and happiness differences depend, therefore, only on differences in people’s own, actual, income. This is the point-of-time relationship. Over time, however, as everyone’s income increases, so too do the internal norms by which we are making our judgments of happiness. The increase in internal norms is greater for those with higher income, because as we go through the life cycle, we increasingly compare ourselves with those with whom we come in closest contact, and contacts are more and more limited to those of similar income. The increase over time in one’s internal living level norm undercuts the effect on well-being of the growth of one’s own, actual, income, and, as a result, happiness remains unchanged. The subversive effect of rising internal norms also explains why people think that over the life course more money will make them happier, when, in fact, it doesn’t. When people think about the effect of having more money, they implicitly assume that their own income increases while everyone else’s stays the same, and hence conclude that they’ll be happier. What actually happens, of course, is that when their own income increases, so too does that of everyone else. This means the internal living level norms used to evaluate happiness also increase. In thinking about the effect of future higher incomes on well-being, people fail to factor this prospective increase in their internal norms into their judgments of how well-being will be affected, and hence mistakenly conclude that more money will make them happier. But it does not – happiness stays the same as their income, and everyone else’s, goes up. Here, at last, we seemingly have a validation of the psychologists’ model – in the material goods domain there does appear to be complete hedonic adaptation. Source: Easterlin 2004 (p. 32)

One recent study for the U.S. finds that “[…] results are consistent with the view that happiness is higher the greater one’s income is relative to one’s neighbors, with the effect concentrated among those with above-average incomes,” that is, relative consumption matters more at higher levels of income (Dynan and Ravina 2007). Another recent and innovative paper chooses to measure happiness, or lack thereof, using suicide

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See also Luttmer (2004).


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deaths in the U.S. It shows that interpersonal income differences appears to be a strong driver of suicides in the US (Daly, Wilson and Johnson 2007). Happiness adapts to changes in income: A second type of argument is that people are very poor at predicting ex-ante what will make them happy ex-post (Gilbert 2006). People tend to exaggerate the positive impact of events they think will make them happy as well as the negative effect on happiness of tragic events. Evidence shows that lottery winners are made momentarily happier, but then adjust over time to close to the level of happiness prevailing before the windfall. On the other hand, people that became paraplegic in the short run become unhappy but over time revert to levels of happiness close—yet lower, to those prior to the event that led them to become paraplegic . In psychology, “set-point theory” suggests that individuals have a fixed setpoint of happiness in their life which is determined by personality and genetics. Life events have an initial impact of people’s happiness, for example they are happier when they get married or saddened when widowed, but on the lifespan the individual adjusts to these events and returns to his or her given setpoint. This means that people “adapt” to a large extent to changes in their environment and that they are not very good at affective forecasting – the technical term for the Gilbert effect described above (Easterlin 2003). There is some controversy in terms of how much people adapt. Evidence suggests that people completely adapt to income—to pecuniary factors—but there is incomplete adaption to other life events (marriage, unemployment, widowing etc)—non pecuniary factors. In the case of income, this means that at the beginning people might increase their happiness due to the increase in income but then get accustomed to the increase so happiness remains invariant. An explanation for income adaptation is that people have changing aspiration levels. The pleasure provided by additional material goods and services (that extra income buys) wears off and this makes people strive for higher aspirations. In consequence a) the upward adjustment of exceptions makes people want to accomplish more and more and they are never satisfied b) wants are insatiable c) greater opportunities provided by higher income may generate higher aspirations and lower subjective wellbeing d) people tend to think they were unhappy in the past and expect to be happier in the future (Frey and Stutzer 2002b, pp. 78-79; Easterlin 2005a). Norms and values: Another argument is perhaps more fundamental, but still less studied in terms of how it relates to happiness. It relates to norms, values, and other determinants of happiness that are not captured exclusively by consumption as the sole argument of a utility function. People are happy when they feel they are doing “the right thing,” whether the right thing is determined by ethics, principles, religion, custom, or social context. George Akerlof (2007) has shown how the incorporation of norms in standard utility analysis—by adding arguments to the utility function—dramatically affects the predictions of standard macroeconomic results. Just to provide one concrete illustration, one of the important behavorial determinants is related to altruism, the “warm glow” we feel from giving – or the utility we derive by giving some of our consumption away, a field of study that James Andreoni, among others, has been exploring.


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Omitted variables: There are studies that investigate the Easterlin paradox by incorporating omitted variables that could have accompanied income growth (such as hours worked pollution and crime) and that a standard model predicts may reduce utility. Di Tella and MacCulloch (2008) take this approach using data for 350,000 people in 12 OECD countries during 1975-1997. They find that this strategy deepens the Easterlin puzzle, it does not resolve it. Given the evolution of these variables over time, happiness should have risen even more. This means that adding the actual impact of other variables besides income leads one to expect happiness levels that are even higher, making the unexplained trend in happiness data larger, than when just changes in income are considered. In other words, introducing omitted variables only worsens the incomewithout happiness paradox. Rebuttals to the Easterlin paradox: A very recent paper by Stevenson and Wolfers (2008) contests the Easterlin paradox altogether. They analyze multiple datasets spanning recent decades and a broader array of countries. Consistent with earlier findings, they conclude that wealthier societies are happier than poorer societies and wealthier members of a given country are happier than the poorer members. But they also find that societies get happier over time as they become richer. They base their analysis on three observations about the inferences that can be supported by existing datasets: 1) Absence of evidence (between happiness and income over time) should not be confused with evidence of absence. This is particularly important given both the variability of happiness aggregates between surveys, and the limited range of variation in GDP within, rather than between countries. 2) They re-analyze the data, finding that happiness has in fact risen in Europe, and for Japan the data also invites a re-examination of earlier conclusions. In the case of the U.S. the failure of happiness to rise remains puzzling outlier 3) as more data have become available, both extended national time series, and adding observations from new countries, evidence that happiness rises with GDP has started to accumulate. b.

Happiness, unemployment and inflation

The literature on happiness has expanded to explore how other economic variables such as unemployment, inflation and business cycles relate to happiness. These studies can be an important guide for policymakers. When considering pursuing monetary and fiscal policies that will impact inflation and unemployment, they can analyze the magnitude of their effects in terms of reduced happiness. Standard macroeconomic theory assumes that social welfare is reduced both by a higher rate of inflation and by a higher rate of unemployment. This literature has been subject to both a fundamental critique and a question about magnitudes. The fundamental critique is that nominal aspects of an economy like inflation should be of no consequence to rational people (Di Tella and MacCulloch 2006).9

9

See Shafir, Diamond and Tversky (1997), for a discussion on “money illusion”, the tendency of people to think in terms of nominal rather than real monetary values.


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Despite the critique, there is evidence that unemployment and inflation reduce people’s happiness, consistent with welfare theory (Clark and Oswald 1994; Oswald 1997). Inflation―apart from corroding purchasing power―creates feelings of reduced morale and national prestige and exploitation (Di Tella and MacCulloch 2008). Unemployment, aside from the pecuniary loss, is associated with costs such as loss of self esteem, depression, anxiety, and social stigma. These studies have also compared the trade-off between unemployment and inflation in terms of happiness, that is, how much unemployment is equal to a percentage point of higher inflation, or vice versa. It is found that unemployment affects happiness more than inflation, thus the “misery index”, which adds up the rate of unemployment and inflation, distorts the real picture. Life satisfaction is not captured exactly by a simple linear misery function, W= W (π + U). Unemployment has a larger weight (Di Tella, MacCulloch and Oswald 2001). Di Tella, MacCulloch and Oswald (2001) analyze data from Eurobarometer which surveys 264,710 people living in 12 European countries during 1975-1991 and from the General Social Survey for 26,668 people in the United States in 1972-1994. They find that reported well-being is strongly correlated with inflation and unemployment. People are not asked whether they dislike inflation and unemployment. Instead, individuals are asked in surveys how happy they are with life, and the paper demonstrates that their answers move systematically with their nation’s level of joblessness and rate of price change. Moreover, the authors find that there unemployment create more unhappiness than inflation. They find that the tradeoff between unemployment and inflation is 1.66: a percentage point of unemployment creates 1.66 times more unhappiness than a percentage point of inflation. Wolfers (2003) arrives to similar results: he finds robust evidence that high inflation and unemployment lowers perceived well-being. Moreover, greater macroeconomic volatility also undermines well-being. Using data from 500,000 people in 16 European countries for the period 1973-1998 he shows that—after controlling for other variables—both inflation and unemployment reduce happiness. However, his estimates show that the public seems to be more averse to unemployment than in Di Tella, MacCulloch and Oswald’s (2001) calculation. Wolfers arrives to an unemployment-inflation trade-off close to 5, that is, a percentage point of unemployment causes almost 5 times more unhappiness than a percentage point of inflation. 10 c.

Happiness and inequality

Moreover, there is evidence that inequality is negatively related with happiness. Alesina, Di Tella and MacCulloch (2001) find that there is a large, negative and significant effect of inequality on happiness in Europe but not in the U.S. They also find that the distaste for inequality is concentrated in some groups in Europe, mainly the left and poor. The breakdown of Europeans and American into rich and poor shows that in Europe, the poors’ happiness is strongly affected by inequality but not for the rich. In the United States inequality generated' unhappiness is only for a sub-group of rich leftists. Based on 10

See also, Di Tella, MacCulloch and Oswald (2003).


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these results, the authors favor the hypothesis that inequality affects European happiness because of their lower social mobility since no preference for equality exists amongst the rich or the right. Graham and Pettinato (2001) analyze subjective well being in 17 Latin American countries and Russia and they find that relative income differences have important effects on how individuals assess their well being. Those in the middle or lower middle of the income distribution are more likely to be dissatisfied than are the very poorest groups. Also, volatility in income flows can have negative effects on perceived well being, even among upwardly mobile individuals. IV.

Non-economic determinants of happiness

Although socio-demographic variables might not be as relevant from an economic standpoint as they cannot be easily controlled for (e.g. age, gender, and marriage) they have an effect on happiness and thus should be included as controls in regression analysis, so as not generate biases in the estimations (Frey and Stutzer 2002b). 11 More fundamentally, these socio-demographic factors take us back to the discussion of set point theory and adaptation. Many studies have found that people completely adapt to changes in income yet they have an incomplete adaptation to other life events; that is their level of happiness is permanently affected by such events as a severe injury, widow and divorce, amongst others (Easterlin 2004). Moreover, recent research on institutional variables—such as social capital and democracy—are also found to be associated with happiness. In this section, we briefly review some of these factors and the studies’ findings. 12 a.

Socio-demographic variables

Health Contrary to setpoint theory, adverse health changes have a lasting negative effect on happiness and adaptation is incomplete to deteriorating health (Easterlin 2004). In the case of severe changes in health, although humans have very strong resilience and can cope (Gilbert 2006, pp. 166-68), people that have suffered a terrible or accident or illness report lower happiness levels than their comparison group (Easterlin 2003; 2004; Brickman, Coates and Jannoff-Bulman 1978; Mehnert and others 1990). 13 11

Personality factors—such as genetic predisposition to be happy or people with optimistic outlook—are also important determinants but are not covered in this note. See Frey and Stutzer (2002b, pp. 49-52). 12 See also, see also Heliwell (2003); Frey and Stutzer (2002b) and van Praag, Frijters and Ferrer-iCarbonell (2003). 13 Brickman, Coates and Jannoff-Bulman (1978, p. 924) state “The results for accident victims appear to be less supportive of adaptation level theory. The accident victims did not tend to take more pleasure in ordinary events and rated themselves significantly less happy in general than controls.”


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What about health in general over the life cycle? Among adults, real health problems increase as people age but what do persons say about their health? Easterlin (2003) contends that if adaptation were complete to adverse changes in health, then the life course trend in self-reported health should be flat and would also be flat if persons implicitly evaluate their health only by comparison with others of their age. But what do the numbers show? Self-reported health declines throughout the life course. Moreover, he finds that throughout the life cycle those who report they are less healthy also say they are less happy. In cohorts spanning ages from the twenties through the seventies, happiness is systematically less, on average, the poorer the state of selfreported health. The conclusion is that adverse health changes have a lasting and negative effect on happiness, and that there is less than complete adaptation to deteriorating health (Easterlin 2003). 14 Table 2- Mean happiness by self-reported health status, birth cohorts over indicated age spans, 1972–2000

Source: Easterlin 2003 (p. 11178)

However, Deaton (2007) warns us about using health satisfaction measures as indicators of wellbeing in international comparisons. He examines the interactions of life satisfaction, health satisfaction and objective health measures from the World Poll for developing and rich countries concluding that the lack of correlations between life and health satisfaction and health measures shows that happiness (or self-reported health) measures cannot be regarded as useful summary indicators of human welfare in international comparisons. He contends that “All of the health measures, in levels and in rates of decline with age, are particularly unfavorable in Eastern Europe and the countries of the former Soviet Union. Some of the dissatisfaction with health in these countries can certainly be linked to their recent decline in health and life-expectancy. But there have been much larger declines in lifeexpectancy elsewhere associated with the HIV/AIDS epidemic, and people in countries with high prevalence do not express anything like the same levels of dissatisfaction with their health.”

14

Other studies such as Blanchflower and Oswald (2007b), relate blood pressure problems with decreased happiness.


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Family Life Studies have found that marriage raises happiness, across countries. Married people have a higher subjective wellbeing than singles, divorced, separated or widowed. Why is this so? Some explanations are that marriage provides additional sources of self esteem, support and companionship (Frey and Stutzer 2002b). Blanchflower and Oswald (2000) examine well-being data on 100,000 randomly sampled Americans and Britons from the early 1970s to the late 1990s and find that a lasting marriage is worth $100,000 per annum when compared to being widowed or separated. They also find that reported well-being is greatest among women, married people, the highly educated, and those whose parents did not divorce. Also, second marriages are less happy. Diener and others (2000) using a sample of 59,169 persons in 42 nations, found that relations between marital status and subjective well-being to be very similar across the world, taking cultural aspects into account. Specifically, in terms of life satisfaction, the benefit of marriage over cohabitation was greater in collectivist than in individualist nations. Heliwell (2003) also finds that married people have higher levels of subjective wellbeing. All these studies show that people do not fully adapt to their circumstances (as singles, widowed, divorced) as their level of happiness is lower than married people. If they adapted to their circumstances fully, they would not aspire to a happy marriage. Easterlin (2005a) further corroborates this point in a new study. He does a cohort analyses of 1978 and 1994 survey responses of the good life and finds that adaption is incomplete regarding marriage aspirations. “If aspirations adjusted fully to actual circumstances, then one would expect that those who are not in a happy marriage would, in time, abandon their aspirations for such a marriage, but desires for a happy marriage exist among more than half of those who do not, in fact, have one. Especially noteworthy is the fact that among persons who have been single their entire lives and are ages 45 and over, more than four in ten cite a happy marriage as part of the good life as far as they specifically are concerned. If these respondents had typically adjusted their marital aspirations to accord with their actual status, one would hardly expect such a sizeable proportion still to consider a happy marriage to be desirable for them personally. Similarly, among women for whom the prospect of remarriage is quite low (those widowed, divorced, and separated and 45 years and older) almost 6 in 10 say that they view a happy marriage as part of the good life. In marked contrast to the economic domain, adaptation seems to occur only to a limited extent with regard to marriage aspirations.” Easterlin (2005a) also analyses aspirations in terms of quantity of children, as economists compare the demand for children with the demand for consumer durables. He asks whether when people have more children, do their family size desires increase commensurately—similarly when buying more big-ticket consumer goods, the desires for such goods tend to grow to about the same extent. He does not find the same parallel: ”while income growth over the life cycle is accompanied by persistent growth in aspirations for big ticket consumer goods, income growth is seemingly not associated with growth in desires for either the number or quality of children.” Measuring Subjective Wellbeing: A Summary Review of the Literature

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Age When it comes to age the general belief is that, older people are unhappier that younger people. However, recent studies have found that happiness is U-shaped through the life cycle: high amongst the young, reaches its minimum at around 30 or mid 40s (depending on the study) and then lifts back up again (Blanchflower 2008; Blanchflower and Oswald 2007a; Helliwell 2003; Oswald 2007). The problem with the U-shaped argument is that there are likely to be omitted cohort effects (earlier generations may have been born in, say, particularly good or bad times). Thus, Blanchflower and Oswald (2007a) design a test that makes it possible to allow for different birth-cohorts. Using data on approximately 500,000 Americans and Europeans, they find a robust U-shape of happiness in age. Ceteris paribus, well-being reaches a minimum, on both sides of the Atlantic, in people's mid to late 40s. They provide some potential answers to this U-shaped relathioship. One possibility is that individuals learn to adapt to their strengths and weaknesses, and in mid-life quell the infeasible aspirations of their youth. Another is that cheerful people live systematically longer than the miserable, and that the U-shape in a way traces out in part a selection effect. A third is a comparison effect: people see their friends die so they value what they have during the remaining years. b.

Institutional variables

There is evidence that shows that institutions are strongly correlated, across countries, with happiness. The deepness of democracy and the extent of political, economic, and personal freedom correlate fairly strongly with reported happiness across countries (Frey and Stutzer 2002b). However, it is important to note that these cross-country correlations can only be taken as indicative of the influence of non-economic factors on happiness, and not as having a causal impact. A study of 6,000 residents of Switzerland shows that taking other things constant, individuals are happier the better developed the institutions of direct democracy and government decentralization in their area of residence (Frey and Stutzer 2000). Veenhoven (2000) studies how freedom in nations can affect the happiness of citizens. He finds that freedom does not always breed happiness. He shows that freedom is positively related to happiness among rich nations, but not among poor nations. Opportunity for free trade is positively related to happiness in poor nations, but not in rich nations. Similarly, the relation between economic freedom and happiness is strongest in nations where capability to choose is lowest. Other studies take into account variables such as social capital and quality of institutions. For example, Helliwell (2003) analyses measures of subjective well-being drawn from three successive waves of the World Values Survey (the first wave in 1980–1982, the second in 1990–1991, and the third in 1995–1997) totaling 87, 806 observations and 46 different countries. He finds a positive association between the degree of connectedness measured by the participation to voluntary organizations and subjective wellbeing. He also finds a positive association between responsibility—measured as the rejection to Measuring Subjective Wellbeing: A Summary Review of the Literature

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cheat on taxes—and subjective wellbeing. In a later paper, Helliwell (2004) also finds that more social capital and higher levels of trust are also associated with lower national suicide rates. He finds a strong negative correlation between national average suicide rates and measures of life satisfaction. He concludes that social capital does appear to improve well-being, whether measured by higher average values of life satisfaction or by lower average suicide rates. V.

Perspectives from developing countries

What do the people living in developing countries self-report in terms of happiness and wellbeing? To this important question, there is still very little that we know systematically to provide a definitive answer. But recent studies provide bits of somewhat “kaleidoscopic” information. A pioneering study was launched by the World Bank about 10 years ago to hear the voices of the poor, providing narratives of how people living in poverty perceived their own conditions and the deprivations they faced. 15 a.

Happiness and income

For developing countries, long-run series comparable to those available for developed countries are inexistent. But some insights can be drawn from looking at cross-country evidence and at recent results for some important developing countries. Fore example, results are available for China for the period between 1994 and 2005. During this time, real income per capita in China increased by more than 2.5 times. Increase in consumption of goods was substantial: ownership of color TVs jumped from 40% to more than 80% of all households, and of a telephone from 10% to 63% (Kahneman and Krueger 2006). But reported happiness did not increase, and the percentage of people who reported satisfaction decreased almost 20 percentage points to above 60% in 2005 from close to 80% in 1994. At the same time, the share of dissatisfied people almost doubled, from slightly more than 20% in 1994 to close to 40% in 2005 (

15

See Narayan and others (2000a;b;c)


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Figure 8). Thus, it seems that the “income without happiness paradox” is also present in China.


26

Figure 8- Life Satisfaction in China, 1994-2005

Note: The question was: Overall, how satisfied or dissatisfied are you with the way things are going in your life today? Would you say you are very satisfied, somewhat satisfied, somewhat dissatisfied, or very dissatisfied? (In 1997, 1999 and 2005, respondents were given four response categories -- In 1994, respondents were given a fifth response category: “neither satisfied nor dissatisfied.”) Thirty-eight percent of respondents chose the neutral category in 1994; those respondents were allocated in proportion to the number who responded that they were satisfied or dissatisfied in that year. Source: Kahneman and Krueger 2006.

b.

Personal Assessments

Moreover, recent time-series evidence has recently become available from public opinion surveys in Africa, the poorest region of the world, undertaken by Afrobarometer. The series are short (start only in 2000) so it is difficult to speak about trends, but the results are particularly important because since 2000 economic performance in the continent has actually been quite good–in absolute terms, but especially when compared with the preceding two decades or so. Assessments of personal living conditions are low to begin with, but views of “present living conditions” follow a gradual but steady downward curve, falling monotonously from 31% circa 2000, to 30% circa 2002, to 27% circa 2005 (

Figure 9). The fall of 4 percentage points is within the sampling error, and cannot therefore be read as a trend. But at least it is valid to assume that perceptions of living conditions have not improved, despite strong economic growth. Does this imply that life is becoming more difficult for Africans? The Afrobarometer “indexes of lived poverty” records the frequency with which survey respondents report shortages of basic human needs like food, water, medical care, and cash income at least once during the previous year. The most commonly reported shortage is cash income, which always affected three-quarters or more of all Africans interviewed (Figure 10). This aspect of poverty was followed by shortages of medical care, food, and clean water for household use, consistently in that order. In most cases, shortages of basic human needs were somewhat more frequent circa 2005 than circa 2000. Medical care shortages showed no change during this period. But food and income shortages rose by 3 percentage points and water shortages by 6 points. It is therefore safe to project a continental trend of increasing poverty in relation to access to clean water supplies, but


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for all other aspects of poverty, it can only be confirmed that average levels are probably holding steady. Figure 9: Personal Living Conditions in Africa

Note: Mean of percentage indicating approval/better in the responses. Source: Bratton and Cho 2006.

Figure 10: Experience of Poverty in Africa

Note: Mean of percentage going without (food, water, medical care, or income) at least once. Source: Bratton and Cho 2006.

Insights also come from household surveys and micro studies, the analysis of which has been pioneered by Angus Deaton and his colleagues at Princeton University, and by Esther Duflo and Abhijit Banerjee, at MIT.16 They find that the poor generally do not complain about either their health or about life in general either, even though they do feel poor. Their levels of self-reported happiness and self-reported health levels are not very 16

Including Banerjee, Deaton and Duflo (2004); Banerjee and Duflo (2007).


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low. However, and consistently with the message that emerges from the Afrobarometer data on “lived poverty,” the poor do say they suffer from stress, both financial and psychological. In Udaipur, about 12 percent say that there has been a period of one month or more in the last year in which they were so “worried, tense, or anxious” that it interfered with sleeping, working, and eating. Levels of stress reported by the poor in South Africa and Udaipur (India) are close to each other, but much lower than those reported in the US. The most frequently cited reason for stress is health problems (29% of responses), followed by lack of food and death (13% each). Over the last year, in 45% of the extremely poor households in Udiapur (and 35% of those living under $2 a day) adults had to cut the size of their meal at some point during the year and in 12 percent of them, children had to cut the size of their meals. In the extremely poor households under $1 per day, 37% report that, at some point in the past year, the adults in the household went without a meal for an entire day. Cutting meals is strongly correlated with unhappiness, again consistent with Afrobarometer data that suggest that poverty is often perceived most intensively when there are shortages of food. In conclusion, the poor of the world do not see themselves as being extremely unhappy. This can be explained, in part, by the “adaptation” effect that emerges from the insights that psychology is bringing to the analysis of the determinants of happiness. They do indicate, however, that they are under severe stress, much more so than what is reported by the U.S., where stress levels are often assumed to be high due to the demands of a competitive market economy. Further, despite strong economic growth over the last few years in the world’s poorest region, Africans do not feel better off—and may even be worse off than before the outset of growth. Certainly there hasn’t been much progress from their point of view. This again reinforces my message that growth is important, but far from the only requirement for progress.

VI.

Conclusion: some limitations of SWB

Although happiness indicators can shed new light for economic research and policy, as a wellbeing measure it has also received a number of criticisms. We can conclude that both objective and subjective measures are important for measuring wellbeing; none should be used exclusively to make a complete assessment of human welfare. a.

Is happiness the ultimate goal in life?

Some people believe happiness should be the ultimate goal of life and should be the explicit aim of government intervention.17 Other people argue that happiness is only one ingredient of the good life which should be accompanied by human development and justice. Others include companionship and freedom in the aims. Yet others consider 17

See for example Layard (2005; 2007).


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other set of factors to be as important as happiness, such as trust, self esteem, satisfaction with work and family life etc (Frey and Stutzer 2002b, p. 3) Amartya Sen has supported happiness as an important aspect of human life yet he contends that there are things in life that are more important and should come first such as freedom, justice or rights (Bruni and Porta 2007, p. xxxiii). Thus, happiness is useful as a wellbeing measure but should be combined with other objective indicators and values. “It is quite easy to be persuaded that being happy is an achievement that is valuable, and that is evaluating the standard of living, happiness is an object of value (or a collection of object of value, if happiness is seen in a plural form). The interesting question regarding this approach is not the legitimacy of taking happiness to be valuable, which is convincing enough, but its exclusive legitimacy. Consider a very deprived person who is poor, exploited, overworked and ill, but who has been made satisfied with his lot by social conditioning (through, say, religion, political propaganda, or cultural pressure). Can we possibly believe that he is doing well just because he is happy and satisfied? Can the living standard of a person be high if the life that he or she leads is full of deprivation? The standard of life cannot be so detached from the nature of the life the person leads” (Sen 1991, pp. 7-8). b.

Happiness as a wellbeing indicator

Related to the previous point, if happiness is taken as the ultimate goal in life and the sole wellbeing measure, this can be dangerous from a policy standpoint. Consider Deaton’s findings (2007) on life satisfaction and health outcomes. He finds that HIV prevalence appears to have little or no effect on the fraction of the population reporting dissatisfaction with their health. Thus, if policymaker would be guided by life or health satisfaction reports, then they might be compelled not to act on this urgent issue. And, if observe the time series of happiness (available for some countries), they might conclude that people in general are satisfied with life, so be complacent to change policies. Moreover, as Giovannini, Hall and d’Ercole (2007) point out the indicator used to measure happiness is a bounded variable—people are asked to rank their satisfaction on a scale from 0 to 10 or 1 to 10—thus if a society scores very high up, let us say 10, then, will the policymaker have any incentives to enact policy? c.

The quality of happiness indicators

Just at single objective measures such as GDP or composite indices such as the HDI have received numerous criticisms on their measurement and methodology, happiness indicators have some problems in terms of their reliability, validity and comparability across nations (van Hoorn 2007; Frey and Stutzer 2002b). However, these problems can be mitigated by careful survey design or appropriate measurement methods.


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Reliability: van Hoorn (2007) defines the reliability of an indicator as its overall quality: its consistency and its ability to give the same results in repeated measurement. Minor differences in circumstances and technical features of the specific questionnaire used affect the reported level of SWB so he points out that reliability can be observed with the test-retest correlation of the specific measure under scrutiny. In general, as evidenced by test-retest correlation the reliability of SWB measures is substantially lower than that found for common microeconomic variables (such as personal income). The test-retest correlation is not only substantially influenced by the lag between times of asking (e.g. one hour, two weeks), but also by the specific measurement scale used. Studies show that the more advanced measures, such as multi-item questionnaires, produce more reliable SWB scores (Krueger and Schkade 2008). Validity: van Hoorn (2007) points out that a measure has validity if it captures the construct it intends to capture, such as SWB. The challenge of SWB measures in terms of their validity is their demonstrated sensitivity to minor life events. However, the literature has found that the errors generally seem to be of a random rather than a structural nature, and consequently do not imply that the measure is systematically biased. Using large enough samples can address possible problems introduced by contextual factors influencing the reported level of SWB. Comparability across Nations: SWB face a more substantial challenge to their validity, specifically with respect to cross-national comparisons. This concerns the possible impact of culture and language on SWB ratings which will introduce biases in country-level SWB scores. Although culture plays a role, there are various measurement methods to deal with these distortions, for example the social desirability scale. Overall, cultural differences have been found to be not as strong to make cross-country comparisons of happiness insignificant (Frey and Stutzer 2002b ; Diener, Diener and Diener 1995). References Akerlof, George A. 2007. “The Missing Motivation in Macroeconomics.” American Economic Review. 97(1): 5-36. Alesina, Alberto, Rafael Di Tella and Robert MAcCulloch. 2001. Inequality and Happiness: Are Europeans and Americans Different? NBER Working Paper No. 8198. Cambridge, MA: National Bureau of Economic Research. Bandura, Romina. 2005. “Measuring Country Performance and State Behavior: A Survey of Composite Indices”. UNDP/ODS Background Paper. New York, Office of Development Studies. [www.thenewpublicfinance.org/background/measuring.pdf] Banerjee, Abhijit V., Angus Deaton, and Esther Duflo. 2004. "Wealth, Health and Health Services in Rural Rajasthan." American Economic Review. 94 (2): 326-330. Banerjee, Abhijit V. and Esther Duflo. 2007. “The Economic Lives of the Poor.” Journal of Economic Perspectives. 21(1): 141-167. Blanchflower, David G. 2008. “International Evidence on Well-being”. IZA DP No. 3354. Institute for the Study of Labor, Bonn.


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