What is the effect of the business cycles on an individual’s happiness?

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What is the effect of the business cycles on an individual’s happiness?

what is the research question?

why it is important?

why we care?


Rafael Di Tella, Robert J. MacCulloch, and Andrew J. Oswald*

Abstract—We show that macroeconomic movements have strong effects on the happiness of nations. First, we find that there are clear microeco- nomic patterns in the psychological well-being levels of a quarter of a million randomly sampled Europeans and Americans from the 1970s to the 1990s. Happiness equations are monotonically increasing in income, and have similar structure in different countries. Second, movements in reported well-being are correlated with changes in macroeconomic vari- ables such as gross domestic product. This holds true after controlling for the personal characteristics of respondents, country fixed effects, year dummies, and country-specific time trends. Third, the paper establishes that recessions create psychic losses that extend beyond the fall in GDP and rise in the number of people unemployed. These losses are large. Fourth, the welfare state appears to be a compensating force: higher unemployment benefits are associated with higher national well-being.

I. Introduction

NEWSPAPERS regularly report changes in macroeco-nomic variables. It is also known that economic vari- ables predict voters’ actions and political outcomes (Frey and Schneider, 1978). These facts suggest that aggregate economic forces matter to people. Yet comparatively little is known empirically about how human well-being is influ- enced by macroeconomic fluctuations.1 When asked to eval- uate the cost of a business cycle downturn, most economists measure the small drop in gross domestic product.

This paper adopts a different approach. It begins with international data on the reported well-being levels of hun- dreds of thousands of individuals. The paper’s first finding is that there are strong microeconomic patterns in the data, and that these patterns are similar in a number of countries. Happiness data behave in a predictable way. We then show that, after controlling for the characteristics of people and countries, macroeconomic forces have marked and statisti- cally robust effects on reported well-being. GDP affects a country’s happiness. Furthermore, pure psychic costs from recessions appear to be large. As well as the losses from a fall in GDP, and the direct costs of recession to those falling unemployed, a typical business cycle downturn of one year’s length would have to be compensated by giving each citizen—not just unemployed citizens—approximately $200 per year.2 This loss is over and above the GDP cost of a year of recession. It is an indirect, or “fear,” effect that is

omitted from economists’ standard calculations of the cost of cyclical downturns.

In spite of a long tradition studying aggregate economic fluctuations, there is disagreement among economists about the seriousness of their effects. One view, associated with Keynes, argues that recessions are expensive disruptions to the economic organization of society. Recessions involve considerable losses—underutilization of invested capacity, emotional costs to those who lose their jobs, and distribu- tionalunfairness.Adifferentviewisadoptedby real-business- cycle theorists. They argue that Keynesians overestimate the costs of business cycles: downturns follow booms, and business cycles do not affect the average level of economic activity. Given that individuals are optimizing, recessions are desirable adjustments to productivity shocks. This means that the costs of business cycles are small—perhaps only 0.1% of total consumption in the United States (Lucas, 1987).3 Consequently, these economists have turned their attention to economic growth and away from fluctuations.

Our paper derives a measure of the costs of an economic downturn that can be used in such debates. In doing so, the paper employs data of a kind more commonly found in the psychology literature. Collected in standard economic and social surveys, the data provide self-reported measures of well-being, such as responses to questions about how happy and satisfied individual respondents are with their lives. We begin by showing that life-satisfaction regression equa- tions—where individuals’ subjective well-being levels are regressed on the personal characteristics of the respon- dents—have a broadly common structure across countries. A large set of personal characteristics has approximately the same influence on reported happiness, regardless of where well-being questions are being asked. This regularity sug- gests that happiness data contain potentially interesting information.

II. Conceptual Issues

From the outset, the paper has to face up to two concep- tual concerns. The first is caused by the approximately untrended nature of reported happiness [as noted by Richard Easterlin (1974)]. For the usual unit-root reasons, we cannot then regress happiness on trended variables such as GDP. This paper experiments with equations in which there are (i) year dummies, (ii) country-specific time trends, and (iii) change-in-GDP variables. The second conceptual problem is that variables such as GDP per capita, unemployment, and

Received for publication July 12, 2000. Revision accepted for publica- tion September 9, 2002.

* Harvard Business School, Princeton University, and University of Warwick, respectively.

For helpful comments, we thank James Stock (editor) as well as George Akerlof, Danny Blanchflower, Andrew Clark, Ben Friedman, Duncan Gallie, Sebastian Galiani, Julio Rotemberg, Hyun Shin, John Whalley, and seminar participants at Oxford, Harvard, and the 1997 NBER Behavioral Macro Conference. The third author is grateful to the Economic and Social Research Council (macroeconomics program) for research support. The working paper version of this paper is “The Macroeconomics of Happi- ness,” Center for Economic Performance 19, (July 1997).

1 It is known that suicide rose markedly in the Great Depression, but that was probably too extreme an episode to allow any easy judgement.

2 In 1985 U.S. dollars, which is the middle of our sample.

3 Even when market imperfections are introduced, the costs rise by only a factor of 5, and they are significantly lower if borrowing is allowed: see Atkeson and Phelan (1994). A different approach to measuring the costs of business cycles, using asset prices, is developed in Alvarez and Jermann (1999).

The Review of Economics and Statistics, November 2003, 85(4): 809–827 © 2003 by the President and Fellows of Harvard College and the Massachusetts Institute of Technology

inflation are not exogenous. These variables are influenced by politicians’ choices; their choices are shaped by reelec- tion probabilities; those probabilities in turn can depend on the feeling of contentment among a country’s citizens. A further possible source of simultaneity is that happier people may work harder and thus produce more output. It is not straightforward to find believable macroeconomic instru- ments that can identify the well-being equation. Instead, the paper experiments with different forms of lag structures, to attempt to see if movements in macroeconomic forces lead, later on, to movements in well-being.

Traditionally, economists assume that it is sufficient to pay attention to decisions. This is because people’s choices should reveal their preferences. More recently, however, it has been suggested that an alternative is to focus on expe- rienced utility, a concept that emphasises the pleasures derived from consumption (for example, Kahneman and Thaler, 1991). Kahneman, Wakker, and Sarin (1997) pro- vide an axiomatic defense of experienced utility with appli- cations to economics. We make the assumption that survey measures of happiness are closer to experienced utility than to the decision utility of standard economic theory. Al- though a number of conceptual questions remain unan- swered (for example, with respect to how people are af- fected by comparisons and reference points), it has been argued by some that self-reports of satisfaction may help deal with the challenges posed by the need to understand experienced utility (see Rabin, 1998, for instance).

There has been comparatively little research by econo- mists on the data on reported well-being. Richard Easterlin (1974) began what remains a small literature, and recently updated his work in Easterlin (1995). Other contributions include Gruber and Mullainathan (2002), Von Praag and Frijters (1999), Ng (1996, 1997), Blanchflower and Oswald (1999), Frank (1985), Inglehart (1990), Fox and Kahneman (1992), Frey and Stutzer (2000), Konow and Earley (1999), Oswald (1997), Winkelmann and Winkelmann (1998), Gardner and Oswald (2001), and Alesina, Di Tella, and MacCulloch (2001). Di Tella, MacCulloch, and Oswald (2001) study people’s preferences over inflation and unem- ployment. Di Tella and MacCulloch (1999) use happiness data to examine the properties of partisan versus opportu- nistic voting models. See Frey and Stutzer (2002) for a review.

The paper’s main data source is the Euro-Barometer Survey Series. Partly the creation of Ronald Inglehart at the University of Michigan, the surveys record happiness and life-satisfaction scores of approximately 300,000 people living in twelve European countries over the period 1975– 1992. We also use the United States General Social Survey. It records similar kinds of information on approximately 30,000 individuals over the period 1972–1994. Section III introduces our happiness data and studies how they are affected by personal characteristics.

It is well known that individuals’ answers to well-being questions can be influenced by order and framing effects within a survey, and by the number of available answer categories (in our main data set, there are only four). Apart from the pragmatic defense that we are constrained by the data as collected, some of these problems can be reduced by averaging across large numbers of observations, and by the inclusion of country fixed effects in the macroeconomic regressions. Section IV describes our empirical strategy.

Section V studies the relationship between well-being data and national income per capita. The survey questions do not ask people whether they like economic booms. Instead, respondents are asked how happy they feel with their lives, and their collective answers can be shown— unknown to the respondents themselves—to move system- atically with their nation’s GDP.4 In section VI we calculate the effect of other macroeconomic variables, such as the unemployment rate, on happiness. We then use these results to calculate the costs of recessions.

Section VII studies what happens to reported happiness when governments try to reduce the impact of economic fluctuations. The focus here is on the welfare state, and especially on the impact upon well-being of an unemploy- ment benefit system. We show that countries with more generous benefit systems are happier (or, more strictly speaking, say that they are happier). Some economists who study European unemployment have claimed a causal link between the region’s relatively generous welfare provision and its unemployment problems. By making life too easy for the unemployed, the argument goes, the welfare states of Europe have taken away the incentive to work and so fostered voluntary joblessness. We test, and fail to find evidence for, this common supposition. Contrary to conven- tional wisdom, the gap in happiness between the employed and the unemployed has stayed the same since the 1970s. It has apparently not become easier, over the decades, to be out of work in Europe.

Section VIII summarizes.

III. Happiness Data and Microeconometric Patterns

A random sample of Europeans is interviewed each year and asked two questions, among others, that are of interest here. The first is “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?” (small “Don’t know” and “No answer” categories are not studied here). The surveys also report the answers of 271,224 individuals across 18 years to a “life satisfaction” question. This ques- tion is included in part because the word happy translates imprecisely across languages. It asks, “On the whole, are

4 Thus, our approach differs from that of Shiller (1996), Di Tella and MacCulloch (1996b), Boeri, Borsch-Supan, and Tabellini (2001), Luttmer (2001), and MacCulloch (2001), who use survey data directly related to the issue being studied (inflation, unemployment benefits, welfare state reform, redistribution, and revolutions, respectively).


you very satisfied, fairly satisfied, not very satisfied, or not at all satisfied with the life you lead?” (Once again, the small “Don’t know” and “No answer” categories are again not studied.)

Raw well-being data are presented in Tables 1 and 2. We focus principally on life satisfaction data because they are available for a longer period of time—from 1975 to 1992 instead of just to 1986. Happiness and life satisfaction are correlated (the correlation coefficient is 0.56 for the period 1975–1986). Blanchflower and Oswald (1999) have shown that where British data on both are available, the micro- econometric equations have almost identical forms. Our paper finds, in a later table, the same for Europe. The Appendix presents summary statistics, describes the data sets, gives equations individually for nations, and explains how our later macroeconomic variables are measured. Table 1 provides a cross-tabulation of life satisfaction for Europe.

The analysis also examines well-being data from the United States General Social Survey (1972–1994). There is a similar happiness question that reads “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?” (Small “Don’t know” and “No answer” categories are not studied in this paper.) This was asked in each of 23 years

and covers 26,668 individuals. There was no life satisfac- tion question for the United States. Table 2 summarizes the happiness responses for the United States. With only three response categories, this question may be less revealing than the life-satisfaction question, which offers four. An odd number of categories may allow less introspection, since people can choose the middle category when unsure of their choice.

Taking at face value the numbers in tables 1 and 2, well-being scores appear to be skewed towards the top of the possible answer distribution. In other words, individuals seem to answer optimistically. On average they say that they are fairly happy and very satisfied. Whatever the appropriate interpretation of this pattern, it is clear that in both Europe and the United States the unemployed and divorced are much less content. These events are two of the largest negatives in life. Marriage and high income, by contrast, are associated with high well-being scores. These are two of the largest positives.

To consider the case for happiness regression equations, are there good reasons why economists should use subjec- tive well-being data in formal analysis?

One is a market-based argument: people who study men- tal health and happiness for a living (psychologists) use


Reported Life Satisfaction

All (%)

Unemployed (%)

Marital Status

Married (%)

Divorced (%)

Very satisfied 27.29 16.19 28.90 19.18 Fairly satisfied 53.72 44.70 53.85 51.80 Not very satisfied 14.19 25.52 12.98 20.90 Not at all satisfied 4.80 13.59 4.27 8.11

Reported Life Satisfaction

Sex Income Quartiles

Male (%)

Female (%)

1st (Lowest)

2nd 3rd 4th (Highest)

Very satisfied 26.81 27.75 22.80 24.98 28.07 33.07 Fairly satisfied 54.45 53.01 50.43 54.25 55.66 54.38 Not very satisfied 13.90 14.47 18.86 15.65 12.66 9.82 Not at all satisfied 4.84 4.77 7.92 5.11 3.61 2.73

Based on 271,224 observations. All numbers are expressed as percentages.


Reported Happiness

All (%)

Unemployed (%)

Marital Status

Married (%)

Divorced (%)

Very happy 32.66 17.75 39.54 19.70 Pretty happy 55.79 52.66 52.51 61.75 Not too happy 11.55 29.59 7.95 18.55

Reported Happiness

Sex Income Quartiles

Male (%)

Female (%)

1st (Lowest)

2nd 3rd 4th (Highest)

Very happy 31.95 33.29 24.07 29.46 34.80 40.78 Pretty happy 56.33 55.31 56.04 58.02 56.22 53.14 Not too happy 11.72 11.39 19.88 12.52 8.98 6.08

Based on 26,668 observations. All numbers are expressed as percentages.


such data. There are thousands of papers that do so in psychology and other social-science journals. Unless econ- omists believe they know more about human psychology than psychologists, there is a case for considering how such survey information can inform the discipline of economics. A second argument is that the data pass so-called validation exercises. For example, Pavot et al. (1991) establish exper- imentally that people who report themselves as happy tend to smile more.5 Diener (1984) shows that people who say they are happy are independently rated by those around them as happy. Konow and Earley (1999) describe other ways in which subjective well-being data have been vali- dated. Self-reported measures of well-being are also corre- lated with physiological responses and electrical readings in the brain (for example, Sutton and Davidson, 1997). An- other of the checks is that, as explained, different measures of self-reported well-being seem to exhibit high correlations with one another. Third, we regressed suicide rates on country-by-year average reported happiness, using the same panel of countries used later in the paper. We controlled for year dummies and country fixed effects, and corrected for heteroskedasticity using White’s method. Consistent with the hypothesis that well-being data contain useful informa- tion, the regression evidence revealed that higher levels of national reported well-being are associated with lower na- tional suicide rates (statistically significant at the 6% level). Last, we obtained an approximate measure of consistency by comparing the structure of happiness responses across countries.

A single individual’s answers on a well-being question- naire are unlikely to be reliable: there is no natural scaling to allow cross-person comparison of terms like “happy” or “satisfied.” However, in a well-being regression equation that uses large samples, this difficulty is less acute. In some settings, measurement error does little harm in a dependent variable (though well-being variables would be less easy to use as independent variables).

Tables 3, 4, and 5 present microeconometric well-being equations for Europe and the United States. Because of data limitations, Table 4 cannot be estimated over the full set of years.

The equations of tables 3–5 include a dummy for the year when the survey was carried out (and, in the case of the Europe-wide data, for the country where the respondent lives). Two features stand out. One is that—comparing for example table 4 with table 5—approximately the same personal characteristics are statistically associated with hap- piness in Europe and in the United States. Another, on closer examination, is that the relative sizes of the effects do not vary dramatically between the two sides of the Atlantic. For example, the consequences of employment status, of being a widow, and of income appear to be similar in the United States and Europe. The effect of unemployment is always

large: it is equivalent to dropping from the top to the bottom income quartile. Similar results obtain if we examine the individual nations within Europe (in the appendix). The regression evidence here is consistent with the idea that unemployment is a major economic source of human dis- tress [as in the psychiatric stress data of Clark and Oswald (1994)]. More generally, independent of the country where the respondent lives, the same personal characteristics ap- pear to be correlates with reported happiness. Having family income classified within a higher income quartile increases the likelihood that a respondent says he or she is satisfied with life. This effect is monotonic. To an economist, it is reminiscent of the utility function of standard economics. A strong life cycle pattern in well-being also emerges. In every country in our sample, happiness is U-shaped in age.

IV. Empirical Strategy

In order to estimate the costs of aggregate economic fluctuations, we start by evaluating the role of national5 See also Myers (1993).


Independent Variable Coefficient Standard


Unemployed �0.505 0.020 Self-employed 0.060 0.012 Retired 0.068 0.014 Home 0.036 0.009 School 0.012 0.020 Male �0.066 0.007 Age �0.028 0.001 Age squared 3.2e�4 1.3e�5 Income quartile:

Second 0.143 0.011 Third 0.259 0.013 Fourth (highest) 0.397 0.017

Education to age: 15–18 years old 0.060 0.009 �19 years old 0.134 0.013 Still studying 0.159 0.022

Marital status: Married 0.156 0.010 Divorced �0.269 0.017 Separated �0.328 0.025 Widowed �0.145 0.013

Number of children: 1 �0.032 0.008 2 �0.042 0.010 �3 �0.094 0.016

Country: Belgium 0.498 0.051 Netherlands 0.887 0.022 Germany 0.363 0.023 Italy �0.110 0.034 Luxembourg 0.756 0.026 Denmark 1.206 0.032 Ireland 0.590 0.043 Britain 0.533 0.019 Greece �0.187 0.043 Spain 0.205 0.020 Portugal �0.234 0.037

Number of observations: 271,224. Log likelihood � �276,101. �2(50) � 10,431. Cut1 � �1.67, Cut2 � �0.80, Cut3 � 0.87. The regression includes year dummies from 1975 to 1992. The base country is France. The exact question for the dependent variable is: “On the whole, are you very satisfied, fairly satisfied, not very satisfied or not at all satisfied with the life you lead?”

Dependent variable: reported life satisfaction.


income per capita (GDP) in affecting individuals’ reported happiness. A fundamental issue is the potential role of reference groups, that is, the possibility that individuals care about their position relative to others in society and not just about the absolute level of income (see, for example, East- erlin, 1974; Diener, 1984; Frank, 1985; Fox & Kahneman, 1992). Hence we estimate a regression that controls for, first, the income quartile to which the respondent’s family belongs and, second, also the average income per capita in the country. A key parameter of interest is the coefficient on GDP in a happiness regression equation of the form

HAPPYjit � � GDPit � � Personaljit � εi � �t � �jit, (1)

where HAPPYjit is the well-being level reported by individ- ual j in country i in year t, and GDPit is the gross domestic product per capita in that country (measured in constant 1985 dollars). Personaljit is a vector of personal character- istics of the respondents, which include income quartile, gender, marital status, education, whether employed or

unemployed, age, and number of children.6 In some speci- fications, country-specific time trends are also added. Be- cause many of the personal variables are potentially endog- enous, a later section of the paper checks alternative econometric specifications in which only exogenous vari- ables, such as age and gender, are used as microeconomic controls. The data set does not contain the person’s income, only the quartile of the income distribution within which it lies.

We also include a country fixed effect εi and a year fixed effect �t. The first captures unchanging cultural and insti- tutional influences on reported happiness within nations, and the second any global shocks that are common to all countries in each year. The data are made up of a series of cross sections, so no individual person-specific effects can be included. The categorical nature of the data is dealt with by the use of an ordered probit model. To obtain the correct standard errors, an adjustment is made for the fact that the level of aggregation of the left-hand variable, happiness, is different than those of the right-hand macroeconomic vari- ables.7

6 An alternative two-step procedure that allows the coefficients on personal characteristics to vary across countries is explained in our working paper. Results are available upon request.

7 See Moulton (1986) for a discussion of the necessary correction to the standard errors. Although the present study uses repeated cross-sectional data on large numbers of individuals living in each country and year, for


Independent Variable Coefficient Standard


Unemployed �0.390 0.023 Self-employed 0.038 0.016 Retired 0.060 0.020 Home 0.060 0.015 School �0.015 0.031 Male �0.067 0.013 Age �0.035 0.002 Age squared 3.6e�4 1.9e�5 Income quartile:

Second 0.131 0.014 Third 0.259 0.017 Fourth (highest) 0.378 0.019

Education to age: 15–18 years old 0.025 0.012 �19 years old 0.076 0.019

Marital status: Married 0.249 0.017 Divorced �0.291 0.027 Separated �0.398 0.040 Widowed �0.197 0.021

Number of children: 1 �0.033 0.012 2 �0.041 0.016 �3 �0.111 0.027

Country: Belgium 0.559 0.054 Netherlands 0.850 0.023 Germany 0.146 0.017 Italy �0.366 0.048 Luxembourg 0.389 0.037 Denmark 0.656 0.052 Ireland 0.548 0.053 Britain 0.360 0.027 Greece �0.467 0.058 Spain 0.132 0.028 Portugal �0.179 0.040

Number of observations � 103,990. Log likelihood � �92,127. �2(42) � 4,575. Cut1 � �1.21, Cut2 � �0.59. The regression includes year dummies from 1975 to 1992. The base country is France. The exact question for the dependent variable is: “Taking all things together, how would you say you are these days—would you say you’re very happy, fairly happy, or not too happy these days?”

Dependent variable: reported happiness.


Independent Variable Coefficient Standard


Unemployed �0.379 0.041 Self-employed 0.074 0.023 Retired 0.036 0.031 Home 0.005 0.023 School 0.176 0.055 Other �0.227 0.067 Male �0.125 0.016 Age �0.021 0.003 Age squared 2.8e�4 3.0e�5 Income quartile:

Second 0.161 0.022 Third 0.279 0.023 Fourth (highest) 0.398 0.025

Education: High school 0.091 0.019 Associate/junior college 0.123 0.040 Bachelor’s 0.172 0.027 Graduate 0.188 0.035

Marital status: Married 0.380 0.026 Divorced �0.085 0.032 Separated �0.241 0.046 Widowed �0.191 0.037

Number of children: 1 �0.112 0.025 2 �0.074 0.024 �3 �0.119 0.024

Number of observations � 26,668. Log likelihood � �23941.869. �2(50) � 2269.64. Cut1 � �1.217, Cut2 � �0.528. The regression includes year dummies from 1972 to 1994. The exact question for the dependent variable is: “Taken all together, how would you say things are these days? Would you say you are very happy, pretty happy, or not too happy?”

Dependent variable: reported happiness.


Easterlin (1974) points out that happiness data appear to be untrended over time. By contrast, nations grow richer over the years, so income per capita is trended. Hence, if happiness is a stationary variable and the equation is wrongly specified, then � in a simple regression equation is likely, for standard reasons, to be biased towards zero.8 In that case, a potential solution is to focus on the growth rate of GDP or to study macroeconomic variables measured relative to trend.

We explore this issue. The paper includes time dummies for the panel of countries, studies different lengths of lag, and experiments with a simple distributed lag structure. We also include country-specific time trends (along with the year and country fixed effects) and change-in-GDP vari- ables. These issues are not simply technical ones. The economics of the problem suggests that we should allow for the presence of adaptation effects, whereby, other things equal, high levels of income in the past might fail to produce large effects on happiness because they lead to higher aspirations and altered comparisons. This is related to a particularly important question. Does higher GDP have permanent effects on a nation’s well-being? Conventional economics assumes that it does. The inherited wisdom in this field, due to Richard Easterlin and others, is that it may not and that a concern for relative income is what could explain the untrended nature of happiness survey responses (see for example Easterlin, 1974; Blanchflower & Oswald, 1999). Another possibility is that GDP does buy extra happiness, but that other factors have gradually been wors- ening in industrial societies through the decades, and these declines have offset the benefits from extra real income. If so, it might be possible to make the idea that GDP buys happiness compatible with the fact that well-being survey data do not trend upwards. A panel approach, with country and year dummies and country-specific time trends, would then provide an appropriate testing ground. Furthermore, controlling for the income quartiles to which individuals belong to in our regressions provides some reassurance that the results on aggregate income do not just reflect concerns for relative income (with the reference group based on the whole economy).

If income per capita can be shown to affect happiness, a regression designed to value other macroeconomic influ- ences can be estimated. This has the following form:

HAPPYjit � � GDPit � � Unempit � Macroit � Personaljit � εi � �t � �jit, (2)

where Unempit is the unemployment rate in country i in year t, and Macroit is a vector of other macroeconomic variables that may influence well-being. Macroit includes Inflationit, the rate of change of consumer prices in country i and year t, and Benefitit, the generosity of the unemployment benefit system, which is here defined as the income replacement rate. To explore possible problems of simultaneity, in some equations we use only personal controls that are exogenous (such as gender and age) and study macroeconomic vari- ables measured with a time lag.

In most regression equations, this paper’s specifications include as a regressor a personal variable for whether the individual is unemployed. That enables us, because we are then controlling for the personal cost of joblessness, to test for any extra losses from recessions—including economy- wide indirect psychic losses of a kind normally ignored by economists. As the effect of the business cycle on personal unemployment is thus controlled for within the microeco- nomic regressors, a correction has to be done later, when the whole cost of a recession is being calculated, to add those personal costs back into the calculation. In other words, an increase in joblessness can affect well-being through at least two channels. One is the direct effect: some people become unhappy because they lose their jobs. The second is that, perhaps because of fear, a rise in the unemployment rate may reduce well-being even among those who are in work or looking after the home. To calculate the full losses from a recession, these two effects have to be added together.

The paper also examines the way that governments have tried to alleviate the costs of business-cycle downturns. It has often been argued that the European welfare state has allowed life to become too easy for the jobless—and thus made recessions more lasting. Structural unemployment in Europe is routinely blamed on the continent’s welfare sys- tem. To test this hypothesis in a new way, we use well-being data. The paper restricts the sample to those individuals who are either employed or unemployed (thus excluding the retired, those keeping house, and those attending school). A regression of the following form is then estimated:

HAPPYjit � � Benefitit � � MacroBit � � Personaljit

εi � �t � � Benefitit � � MacroBit

� Personaljit � �i � �t) � Dunemjit


a review of the issues surrounding estimation using individual-level panel data with fixed effects and discrete dependent variables, see Arellano and Honore (2001).

8 Easterlin (1974) made this observation looking at U.S. data. It is not the norm, however, in our sample of 12 European countries. Life- satisfaction data exhibit an upward trend in Italy and Germany, while in Belgium they seem to have a downward trend. If anything, other European countries present a drift towards more happiness, although the effect in general is not statistically significant. For more on the specific country trends, the reader is referred to our working paper.


Statistic Obs. Mean Std. Dev. Min Max

Reported life satisfaction 271,224 2.035 0.778 0 3 GDP per capita (1985

U.S.$) 190 7,809 2,560 2,145 12,415 �GDP per capita 190 244 234 �968 902 Benefit replacement rate 190 0.302 0.167 0.003 0.631 Inflation rate 190 0.079 0.056 �0.007 0.245 Unemployment rate 190 0.086 0.037 0.006 0.211


where Dunemjit is a dummy taking the value 1 if respondent j is unemployed and 0 otherwise. Personaljit is the same vector of personal characteristics defined above (which includes Dunemjit), and MacroBit is a vector of macroeco- nomic variables (GDP per capita, inflation rate, and unem- ployment rate). Our interest is the value of �, which is the interaction effect of benefits on the happiness gap, that is, on the difference in well-being between employed people and unemployed people.

The size of different variables’ effects on well-being is of interest. Unfortunately, there is no straightforward, intuitive way to think of what the coefficients mean in an ordered probit. However, the formula for a calculation is as follows. In our main regression equations there are three cutpoints: call them a, b, and c. If a person’s happiness score (mea- sured in utils) is equal to H, then the chance that she will declare herself “very happy” (the top category) is Prob- (“very happy”) � F(H � c), where F� is the standard cumulative normal distribution.9 If for example, H � c, then F(0) � 0.5 (or, in other words, a 50% chance). To interpret the coefficients, therefore, if a change in an ex- planatory variable leads to a change �H in one’s happiness score, the change in the probability of calling oneself “very happy” will go up by �Prob(“very happy”) � F(H �H � c) � F(H � c).

As background, table 6 sets out the means and standard deviations for the macroeconomic variables, and table 7 contains correlation coefficients.

V. The Effect of GDP on Happiness

The first hypothesis to be tested is whether macroeco- nomic movements feed through into people’s feelings of well-being. A second task is to calculate the size of any effects. In order to put a value on recessions and booms, the paper compares the marginal effect of income on happiness with the marginal effect of an unemployment upturn on happiness. In other words, it calculates the marginal rate of substitution between GDP and unemployment.

Recessions mean there are losses in real output, and higher levels of joblessness. By exploiting well-being data, it is possible to test for additional costs. We find that there

is evidence for what appear to be important psychic losses that are usually ignored in economic models.

Table 8 presents simple specifications for happiness equa- tions in which macroeconomic influences are allowed to enter. It focuses on GDP, and, for transparency, examines a variety of lag lengths. Column (1) of table 8 regresses reported well-being on the set of personal characteristics of the respondent and on the country’s current GDP per capita. The GDP variable enters with a coefficient of 1.1 and a standard error of 0.34 (where GDP here has been scaled in the regressions by a factor of 10,000). The data cover a dozen nations from 1975 to 1992. To control for country and year effects, dummies for these are included. Since we are controlling in column 1 of table 8 for the quartile to which the respondent’s family income belongs, the coefficient on GDP reflects the effect of an absolute increase in national income on individual happiness while keeping constant the relative position of the respondent. There is evidence of a positive and well-determined effect of GDP per capita on individuals’ perceived well-being. An extra $1,000 in GDP per capita (in 1985 dollars) has systematic and nonnegli- gible consequences.10 It can be shown that it raises the proportion of people in the top happiness category (“very satisfied” with their lives) by approximately 3.6 percentage points, which takes this category from 27.3% to 30.9%.11 It lowers the proportion in the bottom category (“not at all satisfied” with life) by 0.7 percentage points, from 4.8% to 4.1%.12 In these data, contemporaneous happiness and GDP are strongly correlated.

To help understand the dynamics, and to check robust- ness, columns (2) and (3) of table 8 give corresponding results when lagged levels of GDP are used. Going back one

9 More formally, a person’s happiness score is the predicted value of the underlying continuous variable from the ordered probit regression given their observed personal characteristics.

10 Value in 2001 dollars equals value in 1985 dollars multiplied by approximately 1.6. Hence we are considering a rise of $1,600 when expressed in 2001 values.

11 This is calculated as follows: the average predicted happiness score, H, for the column 1 regression equals 1.16. A $1000 rise in GDP per capita increases the predicted happiness score by �H � 0.00011 � 1000 � 0.11. The top cutpoint c � 1.84. Hence �Prob(“very satis- fied”) � F(1.16 0.11 � 1.84) � F(1.16 � 1.84) � 0.284 � 0.248 � 0.036. Similar calculations can be done to find a confidence interval for this point estimate (where one standard error below and above the GDP coefficient equals 0.8 and 1.4, respectively). The interval is (0.025, 0.048).

12 Since �Prob(“Not at all satisfied”) � F(�0.70 � (1.16 0.11)) � F(�0.70 � 1.16) � 0.024 � 0.031 � �0.007, where the bottom cutpoint a � �0.70.


Reported Life Satisfaction

GDP per Capita (1985 U.S.$)

�GDP per Capita

Benefit Replacement

Rate Inflation


Reported life satisfaction 1 GDP per capita (1985 U.S.$) 0.209 1 �GDP per capita 0.056 0.278 1 Benefit replacement rate 0.281 0.471 0.111 1 Inflation rate �0.161 �0.659 �0.379 �0.521 1 Unemployment rate �0.023 �0.151 0.062 �0.016 �0.230



Independent Variable (1) (2) (3) (4) (5) (6)

GDP per capita 1.094 1.220 (0.335) (0.763)

GDP per capita (�1) 0.927 0.575 (0.357) (1.283)

GDP per capita (�2) 0.640* �0.875 (0.389) (0.870)

�GDP per capita 0.953 (0.719)

�GDP per capita (�1) 1.761 (0.780)

Personal Characteristics

Unemployed �0.502 �0.503 �0.504 �0.502 �0.505 �0.504 (0.020) (0.019) (0.019) (0.020) (0.020) (0.020)

Self-employed 0.062 0.061 0.061 0.061 0.060 0.060 (0.011) (0.011) (0.012) (0.012) (0.012) (0.012)

Retired 0.068 0.068 0.068 0.068 0.067 0.068 (0.014) (0.014) (0.014) (0.014) (0.014) (0.014)

Home 0.036 0.036 0.036 0.036 0.036 0.036 (0.009) (0.009) (0.009) (0.009) (0.009) (0.009)

School 0.014 0.015 0.014 0.014 0.011 0.012 (0.020) (0.020) (0.020) (0.020) (0.020) (0.020)

Male �0.067 �0.067 �0.066 �0.067 �0.066 �0.066 (0.007) (0.007) (0.007) (0.007) (0.007) (0.007)

Age �0.028 �0.028 �0.028 �0.028 �0.028 �0.028 (0.001) (0.001) (0.001) (0.001) (0.001) (0.001)

Age squared 3.1e�4 3.1e�4 3.2e�4 3.1e�4 3.2e�4 3.1e�4 (1.3e�5) (1.3e�5) (1.3e�5) (1.3e�5) (1.3e�5) (1.3e�5)

Income quartile:

Second 0.144 0.144 0.144 0.144 0.143 0.143 (0.011) (0.011) (0.011) (0.011) (0.011) (0.011)

Third 0.261 0.260 0.260 0.261 0.259 0.260 (0.013) (0.013) (0.013) (0.013) (0.013) (0.014)

Fourth (highest) 0.398 0.398 0.398 0.397 0.397 0.397 (0.017) (0.017) (0.017) (0.017) (0.017) (0.017)

Education to age:

15–18 years old 0.061 0.061 0.061 0.061 0.061 0.061 (0.009) (0.009) (0.009) (0.009) (0.009) (0.009)

�19 years old 0.134 0.134 0.133 0.135 0.135 0.136 (0.013) (0.013) (0.013) (0.013) (0.013) (0.013)

Marital status:

Married 0.156 0.156 0.156 0.156 0.156 0.156 (0.010) (0.010) (0.010) (0.010) (0.010) (0.010)

Divorced �0.269 �0.269 �0.269 �0.269 �0.269 �0.269 (0.017) (0.017) (0.017) (0.017) (0.017) (0.017)

Separated �0.328 �0.328 �0.327 �0.329 �0.328 �0.329 (0.025) (0.025) (0.025) (0.024) (0.025) (0.024)

Widowed �0.144 �0.144 �0.144 �0.144 �0.145 �0.145 (0.013) (0.013) (0.013) (0.013) (0.013) (0.013)


year makes little difference: the coefficient on lagged na- tional income per capita in a well-being equation is only slightly reduced. Column (2) of table 8 thus continues to display a well-determined GDP effect. Things weaken in column (3), which goes back to a 2-year lag of GDP; but the coefficient remains positive, with a t-statistic of approxi- mately 1.7. Year dummies (not reported) enter significantly. They are trended down over the period, so some general force, common to these European nations, is acting to reduce people’s feelings of happiness. Our paper will not attempt to uncover what it might be, but this remains a potentially important topic for future research.

It might be argued that, despite the inclusion of the year dummies, the mix of an I(0) happiness variable with an I(1) GDP regressor still provides an unpersuasive estimator for the effect of national income on well-being. There seem to be two potential solutions. The first is to shift focus entirely to the growth rate in income. As an intermediate step that helps assess how restrictive this shift might be, we include in column 4 of table 8 a set of variables for GDP per capita current, lagged once and lagged twice (this is, of course, an unrestricted version of entering the level of GDP and its change). As might be expected, the GDP terms in column (4) of table 8 are then individually insignificantly different from 0. Nevertheless, solving out for the implied long-run equation, the steady-state coefficient on GDP per capita is positive and similar in absolute value (equality cannot be rejected) to the coefficient on GDP per capita in columns (1) and (2) of table 8. This point estimate is inconsistent with the idea of complete adaptation—the idea that individuals entirely adjust to their income levels after a while and only derive happiness from increases in income—although the standard errors themselves in column (4) are large.

Columns (5) and (6) turn attention to growth in national income, �GDP per capita and �GDP per capita (�1). These are defined, respectively, for one lag and two lags

[where the former measures GDP minus GDP (�1), and the latter measures GDP (�1) minus GDP (�2)]. The latter, �GDP per capita (�1), in column (6) of table 8, is positive, well defined, and economically important in size. Hence there is evidence in our data that bursts of GDP produce temporarily higher happiness. Those sympathetic to the Easterlin hypoth- esis can find support in column (6) of table 8.

Another check is to include country-specific time trends. We do this—repeating the earlier analysis of table 8 to allow an exact comparison—in table 9. Here the set of personal characteristics has been estimated in the same (one-step) way as in table 8, with extremely similar coefficients, so those personal coefficients are not reported individually in the tables. Other specification changes, such as using log GDP, do not change the main results of our paper.

The results are again supportive of the idea that increases in national income are associated with higher reported happiness. Column (1) of table 9 shows that the current GDP per capita enters with a similar coefficient into the specification without country-specific trends. However, in columns (2) and (3), lagged GDP levels are now weaker than before, with one sign reversing itself. In column (4) of table 9, all three of the GDP terms are again entered together. In this case the steady-state coefficient is poorly determined and now numerically close to 0. By contrast, in columns (5) and (6), the change-in-GDP variables work even more strongly than in table 8.

We draw the conclusion that there is evidence in these data for the existence of both level and change effects on nations’ happiness. First, consistent with standard economic theory, it appears that well-being is robustly correlated, in a variety of settings, with the current GDP. As far as we know, this is the first empirical finding of its kind. Second, re- ported well-being is also correlated with growth in GDP, and this result is consistent with adaptation theories in which the benefits of real income wear off over time.


Independent Variable (1) (2) (3) (4) (5) (6)

Number of children:

1 �0.032 �0.032 �0.032 �0.032 �0.032 �0.032 (0.008) (0.008) (0.008) (0.008) (0.008) (0.008)

2 �0.043 �0.042 �0.042 �0.042 �0.043 �0.042 (0.010) (0.010) (0.010) (0.010) (0.010) (0.010)

�3 �0.095 �0.094 �0.094 �0.095 �0.094 �0.094 (0.016) (0.016) (0.016) (0.016) (0.016) (0.016)

Country fixed effects Yes Yes Yes Yes Yes Yes Year fixed effects Yes Yes Yes Yes Yes Yes Country-specific time trends No No No No No No

Pseudo-R2 0.08 0.08 0.08 0.08 0.08 0.08 Number of observations 271,224 271,224 271,224 271,224 271,224 271,224

Standard errors in parentheses. Bold-face: significant at 5% level; *: at 10% level. Cutpoints (standard errors) are �0.70 (0.30), 0.18 (0.31), 1.84 (0.31) for reg. (1); �0.86 (0.32), 0.01 (0.32), 1.68 (0.32) for reg. (2); �1.13 (0.34), �0.26 (0.34), 1.41 (0.34) for reg. (3); �0.84 (0.34), 0.04 (0.34),

1.70 (0.34) for reg. (4); �1.65 (0.07), �0.77 (0.07), 0.89 (0.07) for reg. (5); �1.63 (0.07), �0.76 (0.07), 0.91 (0.07) for reg. (6). GDP is scaled by a factor of 10,000. Dependent variable: reported life satisfaction.


Finally, lagged levels of GDP are statistically significant in certain specifications.

To go decisively beyond these conclusions, and to try to say whether it is level effects or change effects that domi- nate the data, will probably require longer runs of data than are available to us.13 Our conjecture is that there is strong adaptation, so that human beings get used to a rise in national income, but that not all of the benefits of riches dissipate over time. Hence GDP matters, even in the long run, but there are strong �GDP effects in the short run. Whether that conjecture will survive future research remains to be seen.

VI. Costs of Recessions

Having established that income is correlated with happiness, we turn to other macroeconomic variables to see if their inclusion removes the correlation between happiness and GDP. It does not. Table 10, for example, repeats the previous anal- ysis, and incorporates also the rate of unemployment, the inflation rate, and an indicator of the generosity of the welfare state. Column (1) in table 10 demonstrates that the macro variables enter with the signs that might be expected. All are statistically significant at normal confidence levels.

How costly are recessions? It can be shown that there are large losses over and above the GDP decline and rise in personal unemployment. To demonstrate this, we use a slightly unusual welfare measure.

To explore economic significance, we take as a yardstick a downturn that is equal to an increase in the unemployment

rate of 1.5 percentage points. The number 1.5 was chosen by taking the average of the eleven full business cycles in the United States since the Second World War, and dividing by 2 to get the average unemployment deviation. It is then possible to calculate, from the coefficients in column 1 of table 10, the marginal rate of substitution between GDP per capita and unemployment. Pure psychic losses can then be estimated. The ratio of the two coefficients implies that, to keep their life satisfaction constant, individuals in these economies would have to be given, on top of compensation for the direct GDP decline, extra compensation per year of approximately 200 dollars each (0.015 � 1.91/0.00014).14

Measured in 2001 dollars, that is 330. This would have to be paid to the average citizen, not just to those losing their jobs. Such a calculation makes the implicit assumption that, over the relevant range, utility is linear, so that the margin is equal to the average. This seems justifiable for normal recessions, where national income changes by only a few percent, but it might not for a major slump in which national income fell dramatically.

Column (6) in table 10 allows us to make these calcula- tions using the growth rate in GDP per capita. The estimated coefficients indicate that the average person (employed or unemployed) would experience no change in well-being if, in the event of a business downturn which increased the rate

13 As a start in this direction we included a level term in regression (5) in table 8. The coefficient on GDP per capita is 1.057 (standard error � 0.356), while that on �GDP per capita equals 0.429 (s.e. � 0.757), so in this specification the level effect dominates. Including country-specific time trends brings the coefficients closer in size and significance.

14 This number, of course, has a standard error attached. The factor 0.015 comes from the assumption that a typical economic downturn adds 1.5 percentage points to unemployment. The factor 1.91 is the coefficient on unemployment rate in table 10, column (1). The divisor 0.00014 comes from the coefficient of 1.4 on GDP in column (1) of Table 10, after rescaling back by a factor of 10,000.


Independent Variable (1) (2) (3) (4) (5) (6)

GDP per capita 1.031 1.133* (0.455) (0.626)

GDP per capita (�1) 0.301 0.654 (0.500) (0.888)

GDP per capita (�2) �0.801 �1.652 (0.492) (0.716)

�GDP per capita 1.390 (0.552)

�GDP per capita (�1) 1.920 (0.620)

Personal characteristics Yes Yes Yes Yes Yes Yes Country fixed effects Yes Yes Yes Yes Yes Yes Year fixed effects Yes Yes Yes Yes Yes Yes Country-specific time trends Yes Yes Yes Yes Yes Yes

Pseudo-R2 0.09 0.09 0.09 0.09 0.08 0.08 Number of observations 271,224 271,224 271,224 271,224 271,224 271,224

Standard errors in parentheses. Boldface means significant at the 5% level; *, at 10% level. Cutpoints (standard errors) are �1.37 (0.43), �0.49 (0.43), 1.18 (0.43) for reg. (1); �1.01 (0.42), �0.13 (0.42), 1.54 (0.42) for reg. (2); �0.51 (0.42), 0.37 (0.42), 2.04 (0.42) for reg. (3); �0.69 (0.40), 0.19

(0.40), 1.86 (0.41) for reg. (4); �0.96 (0.37), �0.08 (0.37), 1.59 (0.37) for reg. (5); �0.82 (0.30), 0.06 (0.30), 1.73 (0.30) for reg. (6). GDP is scaled by a factor of 10,000. Dependent variable: reported life satisfaction.


of unemployment by 1.5 percentage points, his/her income were to be increased by approximately 3%.15

Such calculations underestimate the full cost to society of a rise in joblessness. The reason for the underestimation is that these regressions hold constant the personal cost of being unemployed (as a microeconomic regressor). It can be calculated from column (1) in table 10 that an increase in the unemployment rate from 0% to 1.5% would have a cost in utils—for want of a better term—equal to approximately 0.029 (1.91 times 0.015). This is for the average citizen, whether employed or unemployed. On the other hand, a person who becomes unemployed experiences an actual loss (in utils) equal to 0.5. This number comes from the coeffi- cient on being unemployed in column (1) in table 10 (which is unreported but is similar to those given in table 8). The full social cost of an increase of 1.5 percentage points in the unemployment rate in well-being units is therefore the sum of two components: it is (0.5 � 0.015) (1.91 � 0.015) � 0.0075 0.029 � 0.036.16 Measured in dollars this is equal

to approximately $260 (�0.036/0.00014). For an individual who loses her job during the recession the actual loss is approximately $3,800, that is, (0.5 0.029)/0.00014.

The regressions in table 10 establish that high unemploy- ment in the economy is unpleasant even for people who are employed. One possibility is that this is some form of fear-of-unemployment effect (see for instance Blanch- flower, 1991). There may also be a—presumably fairly small—taxation effect, because if unemployment goes up, the people at large have to pay more tax to fund the increased bill for unemployment benefits. The indirect ef- fects, when added to the direct ones on those who actually lose their jobs, amount to a substantial well-being cost. This stands in contrast to the view that unemployment involves layoffs with short and relatively painless jobless spells. The ex post effect on someone who actually loses his or her job is 20 times larger than the effect on those who still have a job. The indirect fear losses are even larger, in aggregate, because they affect more people.

The large well-being cost of losing a job shows why a rise in a nation’s unemployment might frighten workers. Be- coming unemployed is much worse than is implied by the drop in income alone. The economist’s standard method of judging the disutility from being laid off focuses on pecu- niary losses. According to our calculations, that is a mistake,

15 Since 0.015 � 1.95/0.000118 � 248 dollars, which represents 3.2% of the average GDP per capita across the nations and years in the sample (�248/7809).

16 The following calculations may help clarify this. Call the total welfare in society W � (1 � u) E uV, where u is the unemployment rate and E and V are the utilities of being employed and unemployed respectively. The function E is defined over net income (because it includes taxes), inflation, and unemployment, and the function V is defined over benefits, unemployment, and inflation. Then dW/du � (1 � u) dE/du u dV/du � (E � U). The expressions dE/du and dV/du can be thought of fear-of-unemployment effects for the employed and the unemployed

respectively. The third term is the personal cost of falling unemployed. The first two terms sum to 1.91, whereas the third term equals 0.50.


Independent Variable (1) (2) (3) (4) (5) (6)

GDP per capita 1.408 1.305* 1.132 1.020 (0.361) (0.784) (0.552) (0.668)

GDP per capita (�1) 0.576 0.628 (1.305) (0.890)

GDP per capita (�2) �0.561 �1.455 (0.842) (0.698)

�GDP per capita 0.775 1.184 (0.725) (0.583)

Benefit replacement rate 1.027 1.026 0.665 0.883 0.854 0.769 (0.219) (0.223) (0.213) (0.363) (0.359) (0.372)

Unemployment rate �1.909 �1.845 �2.703 �1.291 �1.481 �1.954 (0.664) (0.675) (0.694) (0.823) (0.722) (0.673)

Inflation rate �0.994 �0.963 �0.780 �1.042* �0.804 �0.845 (0.464) (0.480) (0.470) (0.585) (0.601) (0.600)

Personal characteristics Yes Yes Yes Yes Yes Yes Country fixed effects Yes Yes Yes Yes Yes Yes Year fixed effects Yes Yes Yes Yes Yes Yes Country-specific time trends No No No Yes Yes Yes

Pseudo-R2 0.08 0.08 0.08 0.09 0.09 0.09 Number of observations 271,224 271,224 271,224 271,224 271,224 271,224

Standard errors in parentheses. Boldface means significant at the 5% level; *, at the 10% level. Cutpoints (standard errors) are �0.31 (0.34), 0.57 (0.35), 2.24 (0.35) for reg. (1); �0.41 (0.37), 0.47 (0.38), 2.14 (0.38) for reg. (2); �1.67 (0.12), �0.80 (0.12), 0.87 (0.12) for reg. (3); �2.39 (0.62), �1.51

(0.62), 0.16 (0.62) for reg. (4); �1.40 (0.61), �0.52 (0.61), 1.15 (0.61) for reg. (5); �1.54 (0.46), �0.66 (0.46), 1.01 (0.46) for reg. (6). GDP is scaled by a factor of 10,000. Dependent variable: reported life satisfaction.


because it understates the full well-being costs, which, according to the data, appear to be predominantly nonpe- cuniary.

The coefficients in table 10 also allow us to put a value on the cost of inflation by comparing the marginal effect of income on happiness with the marginal effect of an inflation upturn on happiness. In other words, we can also calculate the marginal rate of substitution between GDP and inflation. Using the ratio of the two coefficients on GDP per capita and the inflation rate in column (1) implies that, to keep his/her life satisfaction constant, an individual would have to be given compensation of approximately 70 dollars (0.01 � 0.99/0.00014) for each 1-percentage-point rise in inflation.

A. Simultaneity and Other Tests

Happiness, personal characteristics, and macroeconomic variables might be simultaneously determined. It is hard to think of a convincing instrument in such a setting. A full treatment of these issues will have to be left for future research and different data sets. Some reassurance in this respect can be obtained by running regressions where only

truly exogenous personal characteristics are included, such as age and gender, and where all macroeconomic variables are entered with a lag. Table 11 checks the outcome. The substantive conclusions remain the same as in earlier ta- bles.17

Another interesting issue is how well-being in a country is affected by the amount of inequality. Assume utility functions are concave. Then it might be thought that in- equality must automatically reduce the average level of happiness. We hope to tackle this issue properly in future work, but one test was done on these data. Provided that income inequality depends negatively on welfare generosity (and we would expect that government help for the poorest would reduce inequality), higher unemployment benefits in a society should raise the happiness of lower-income people relative to higher-income people. Given concavity, the poor dislike their relative position more than rich people like

17 We also experimented with regressions that included several lagged changes in GDP per capita. In a specification that adds the second lagged change in GDP to the specification in column (6) in Table 11, the coefficient on �GDP per capita (�1) equals 1.734 (s.e. � 0.575), and the coefficient on �GDP per capita (�2) equals 0.238 (s.e. � 0.574).


Independent Variable (1) (2) (3) (4) (5) (6)

GDP per capita (�1) 1.275 2.315 0.521 1.518 (0.361) (0.826) (0.503) (0.680)

GDP per capita (�2) �2.025 �1.471 (1.357) (0.957)

GDP per capita (�3) 0.987 �0.421 (0.805) (0.606)

�GDP per capita (�1) 1.608 1.771 (0.713) (0.549)

Benefit replacement rate (�1) 0.907 0.911 0.592 1.238 1.249 1.254 (0.235) (0.235) (0.217) (0.375) (0.384) (0.389)

Unemployment rate (�1) �1.659 �1.765 �2.426 �0.929 �1.314 �1.188* (0.726) (0.688) (0.709) (0.746) (0.703) (0.637)

Inflation rate (�1) �0.718 �0.712 �0.550* �0.633* �0.417 �0.464 (0.313) (0.333) (0.322) (0.375) (0.372) (0.360)

Personal Characteristics

Male �0.019 �0.019 �0.019 �0.018 �0.019 �0.019 (0.007) (0.007) (0.007) (0.007) (0.007) (0.007)

Age �0.014 �0.014 �0.014 �0.014 �0.014 �0.014 (0.001) (0.001) (0.001) (0.001) (0.001) (0.001)

Age squared 1.4e�4 1.4e�4 1.4e�4 1.4e�4 1.4e�4 1.4e�4 (1.2e�5) (1.2e�5) (1.2e�5) (1.2e�5) (1.1e�5) (1.2e�5)

Country fixed effects Yes Yes Yes Yes Yes Yes Year fixed effects Yes Yes Yes Yes Yes Yes Country-specific time trends No No No Yes Yes Yes

Pseudo-R2 0.06 0.06 0.06 0.06 0.07 0.07 Number of observations 271,224 271,224 271,224 271,224 271,224 271,224

Standard errors in parentheses. Boldface means significant at the 5% level; *, at the 10% level. Cutpoints (standard errors) are �0.48 (0.36), 0.36 (0.36), 1.98 (0.37) for reg. (1); �0.48 (0.38), 0.36 (0.39), 1.99 (0.39) for reg. (2); �1.69 (0.10), �0.85 (0.10), 0.77 (0.10) for reg. (3); �2.41 (0.53), �1.56

(0.53), 0.06 (0.53) for reg. (4); �1.70 (0.55), �0.85 (0.55), 0.77 (0.55) for reg. (5); �2.19 (0.36), �1.34 (0.37), 0.28 (0.37) for reg. (6). GDP is scaled by a factor of 10,000. Dependent variable: reported life satisfaction.


their own. As a test, therefore, we repeated all the regression specifications reported in the earlier table 4 but also in- cluded interactions of our measure of benefit generosity with each of the income quartiles. As expected, the results show a significantly positive differential effect (at the 5% level) of benefits on the happiness of the poor relative to the rich.

VII. Happiness Evidence on the Role of the Welfare State

Tables 10 and 11 show that the coefficient on benefits, our indicator of the generosity of publicly provided unemploy- ment insurance, is positively correlated with happiness levels and is well defined statistically. Column (1) in table 10 implies that individuals who live in a country such as Ireland, where the replacement rate averaged 0.28 over the sample period, would be willing to pay 214 dollars (U.S. 1985) to live in a country with a more generous welfare state such as France, where the replacement rate averaged 0.31.18 In terms of table 10’s column (6), which includes country-specific time trends and has a well-defined coefficient on �GDP per capita, people seem to be willing to forgo growth rates of 2.5% in order to see an improvement in the summary measure of the parameters of the unemployment benefit system from the Irish level to the French level. Such numbers should, however, probably be thought of as upper bounds on the correct esti- mates, because the regressions cannot adjust for the need in an improved welfare state for higher taxes. It is worth recalling,

however, that there are potential identification problems in all macroeconomic analysis of this kind. We require the social safety net here to be uncorrelated with omitted variables in the happiness equation.19

Besides providing a way to assess the returns from a welfare state, this paper’s approach can be used to shed light on the validity of one criticism of European-style welfare states. A number of economists have argued that generous welfare provision has made life too easy for the unemployed, leading to poor labor market performance in a number of European countries. The average OECD-calculated benefit replacement rate across the sample of countries rose from 0.31 to 0.35 over the period of our data. The strictness with which benefit rules were enforced, moreover, is believed by some observers to have diminished.

We first approach this problem by partitioning the sample into employed and unemployed workers, and estimating a similar set of regressions to those presented in table 10. Columns (1) and (2) in table 12 show that happiness and benefits are positively correlated for both the unemployed and the employed subsample. Moreover, the two coeffi- cients on the benefits variable, 1.25 and 1.44, are similar. Hence an increase in the generosity of unemployment ben- efits helps the well-being of the unemployed and employed by a similar amount (perhaps because the employed know they may in the future lose their jobs, and the jobless know

18 Since (0.31 � 0.28) � 1.0/0.00014 � 214 dollars.

19 The literature that can be used as a guide in the search for instruments in this context is small. Di Tella and MacCulloch (1996a) presents some theory and evidence behind the determination of unemployment benefits. See also the voting model of Wright (1986).


Independent Variable Employed Unemployed Gap Employed Unemployed Gap

(1) (2) (3) (4) (5) (6)

GDP per capita 1.418 1.053* 0.208 (0.439) (0.614) (0.714)

�GDP per capita 1.028 0.991 0.084 (0.853) (1.110) (1.249)

Benefit replacement rate 1.248 1.438 �0.385 0.910 1.227 �0.480 (0.268) (0.408) (0.510) (0.247) (0.395) (0.497)

Unemployment rate �1.660 �3.046 1.788 �2.486 �3.573 1.573 (0.747) (1.096) (1.256) (0.778) (1.033) (1.177)

Inflation rate �1.388 �1.602 0.422 �1.117 �1.551* 0.634 (0.508) (0.809) (0.836) (0.506) (0.857) (0.871)

Personal characteristics Yes Yes Yes Yes Yes Yes Country fixed effects Yes Yes Yes Yes Yes Yes Year fixed effects Yes Yes Yes Yes Yes Yes Country-specific time trends No No No No No No

Pseudo-R2 0.09 0.06 0.10 0.09 0.06 0.09 Number of observations 136,570 12,493 149,063 136,570 12,493 149,063

Standard errors in parentheses. Boldface means significant at the 5% level; *, at the 10% level. Cutpoints (standard errors) are �0.27 (0.42), 0.63 (0.43), 2.38 (0.43) for reg. (1); �0.58 (0.65), 0.31 (0.65), 1.70 (0.65) for reg. (2); �0.33 (0.42), 0.56 (0.42), 2.28 (0.43) for reg. (3); �1.71 (0.13), �0.81 (0.13),

0.94 (0.13) for reg. (4); �1.58 (0.23), �0.69 (0.23), 0.70 (0.23) for reg. (5); �1.69 (0.13), �0.80 (0.13), 0.92 (0.13) for reg. (6). GDP is scaled by a factor of 10,000. The gap equations are derived by combining the samples of employed and unemployed people, and then estimating a life satisfaction equation in which, as well as the usual microeconomic regressors, a set of

interaction terms are included. These interact a dummy for being unemployed with each of the independent variables. The reported coefficients, in columns (3) and (6), are the coefficients on those interaction terms. Dependent variable: reported life satisfaction.


they may find a job). More formally, column (3) of table 12, which estimates the difference in the corresponding coeffi- cient estimate across the two subsamples, is a test of the hypothesis that the welfare state made life too easy for the unemployed (at least relative to the employed). That hypothe- sis is not supported by the data. The reason is that the benefits variable enters the gap equation—where the gap can be thought of as the difference in well-being between those with jobs and those looking for a job—with a coefficient that is

insignificantly different from zero. Table 13 redoes the equa- tions to check for robustness to country-specific time trends.

Further evidence comes from direct examination of the data on the life satisfaction of employed and unemployed Europeans. Figures 1 and 2 plot the raw numbers. As figure 1 shows, there is no marked rise over time in the happiness of the jobless compared to those in jobs. The two series run roughly together over the years. Figure 2, which is a plot of the gap itself, in fact reveals a slight widening of the difference


Independent Variable Employed Unemployed Gap Employed Unemployed Gap

(1) (2) (3) (4) (5) (6)

GDP per capita 1.394 2.473 �0.133 (0.642) (0.911) (0.999)

�GDP per capita 1.463 1.592 �0.294 (0.708) (1.061) (1.213)

Benefit replacement rate 1.068 1.403 �0.477 0.915 1.061 �0.253 (0.443) (0.536) (0.728) (0.442) (0.539) (0.719)

Unemployment rate �0.858 �2.233* 1.683 �1.709 �4.093 2.880 (0.969) (1.248) (1.415) (0.785) (1.058) (1.210)

Inflation rate �1.540 �1.498* 0.162 �1.295 �1.096 �0.035 (0.642) (0.845) (0.718) (0.658) (0.880) (0.746)

Personal characteristics Yes Yes Yes Yes Yes Yes Country fixed effects Yes Yes Yes Yes Yes Yes Year fixed effects Yes Yes Yes Yes Yes Yes Country-specific time Yes Yes Yes Yes Yes Yes

Pseudo-R2 0.09 0.06 0.10 0.09 0.06 0.10 Number of observations 136,570 12,493 149,063 136,570 12,493 149,063

Standard errors in parentheses. Boldface means significant at the 5% level; *, at 10% level. Cutpoints (standard errors) are �2.76 (0.69), �1.86 (0.69), �0.11 (0.69) for reg. (1); �3.53 (1.15), �2.63 (1.15), �1.24 (1.15) for reg. (2); �2.73 (0.68), �1.84 (0.68), �0.12 (0.68) for reg. (3); �1.70 (0.48),

�0.80 (0.48), 0.95 (0.48) for reg. (4); �1.61 (1.06), �0.72 (1.07), 0.67 (1.07) for reg. (5); �1.68 (0.48), �0.79 (0.48), 0.93 (0.48) for reg. (6). GDP is scaled by a factor of 10,000. Dependent variable: reported life satisfaction.


Based on a random sample of 271,224 individuals. The numbers are on a scale where the lowest level of satisfaction is 1 and the highest 4.


in well-being levels (though it is not statistically significant) between the two groups. These life-satisfaction data seem to paint a clear picture. It has not become easier and less unpleas- ant, over this period, to be out of work in Europe.

VIII. Conclusions

This paper shows that macroeconomic movements have strong effects on the happiness of nations. It also suggests a new way to measure the costs of business cycle downturns.

We use psychological well-being data on a quarter of a million people across twelve European countries and the United States. The data come in the form of answers to questions such as “How happy are you?” or “How satisfied are you with life as a whole?” Ordered probit equations are estimated. Differences in people’s use of language are viewed as a component of the error term. Using normal regression techniques, the paper starts by showing that happiness data have a stable structure. Microeconometric well-being equations take the same general form in 12 European countries and the United States. An estimated happiness equation is increasing in income—like the econ- omist’s traditional utility function.

Macroeconomics matters. People’s happiness answers en masse are strongly correlated with movements in current and lagged GDP per capita. This is the main finding of the paper.

An important conceptual issue is whether improvements in national income lead to permanent or only temporary gains in national happiness. In other words, is it the level or change in GDP that influences well-being? After an exam- ination of a range of specifications, we conclude that there is statistical support for both kinds of channel. The persua- sive evidence for a change-in-GDP effect upon a country’s happiness is consistent with theories of adaptation. It seems likely, therefore, that some of the well-being gains from extra national income wear off over time. Our conjecture is that there

are strong habituation effects, so that human beings get used to a rise in national income, but that not all of the benefits of riches dissipate over time. Future research, with longer runs of data, will have to revisit that conjecture.20

Losses from recessions are large. It is not just that GDP drops and that some citizens lose their jobs. On top of those costs to society, and after controlling for personal charac- teristics of the respondents, year dummies, and country fixed effects, we estimate that individuals would need 200 extra dollars of annual income to compensate for a typical U.S.-size recession. In our sample, $200 is approximately 3% of per capita GDP. This loss is over and above the actual fall in income in a recession. One potential interpretation is that, in an economic downturn, people suffer a fear-of- unemployment effect.21 For those actually becoming unem- ployed, moreover, we conclude that falling unemployed is as bad as losing approximately 3,800 dollars of income a year. Standard economics tends to ignore what appear to be important psychic costs of recessions.

The methods developed in the paper have other applica- tions. Economists who analyze high European unemploy- ment, for example, often claim that the problem lies with a growing generosity of the welfare state in these countries: benefits have made life too easy for the unemployed. Using well-being data, the paper tests this hypothesis. It does not find evidence to support it.

There are likely to be other ways in which macroeconomics can harness the kind of subjective well-being data studied here.

20 This means that some explanation will have to be found for the negative trend in year dummies in the happiness equations estimated here.

21 Strictly speaking, our specifications imply that even unemployed people suffer a psychic or fear cost as the unemployment rate rises. One possible interpretation is that a higher unemployment rate makes a jobless person feel he or she is less likely to find work quickly.


Based on a random sample of 271,224 individuals.



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1. Tables


Independent Variable U.K. France Germany Italy

Unemployed �0.591 �0.258 �0.421 �0.538 (0.035) (0.028) (0.036) (0.033)

Self-employed 0.034 0.122 0.023 0.065 (0.029) (0.026) (0.029) (0.021)

Retired 0.113 0.351 0.079 0.057 (0.027) (0.030) (0.027) (0.027)

Home �3.5e�4 0.149 0.024 0.010 (0.022) (0.022) (0.022) (0.022)

School 0.051 0.245 0.027 0.031 (0.046) (0.034) (0.033) (0.031)

Male �0.104 �0.060 �0.029 0.012 (0.017) (0.015) (0.016) (0.016)

Age �0.027 �0.026 �0.008 �0.032 (0.003) (0.003) (0.003) (0.003)

Age squared 3.3e�4 3.0e�4 1.2e�4 3.2e�4 (2.9e�5) (3.0e�5) (2.9e�5) (2.9e�5)

Income quartiles: Second 0.225 0.213 0.186 0.184

(0.023) (0.020) (0.020) (0.019)

Third 0.368 0.371 0.319 0.297 (0.024) (0.021) (0.021) (0.020)

Fourth (highest) 0.561 0.580 0.452 0.392 (0.026) (0.023) (0.022) (0.021)

Education to age: 15–18 years old 0.035 0.117 0.001 0.044

(0.021) (0.018) (0.018) (0.019)

�19 years old 0.116 0.243 0.110 0.055 (0.028) (0.021) (0.023) (0.020)



Independent Variable U.K. France Germany Italy

Marital status: Married 0.153 0.043 0.154 0.210

(0.023) (0.022) (0.023) (0.021)

Divorced �0.281 �0.179 �0.330 �0.235 (0.042) (0.043) (0.037) (0.086)

Separated �0.347 �0.241 �0.408 �0.250 (0.063) (0.069) (0.076) (0.065)

Widowed �0.114 �0.175 �0.078 �0.069 (0.034) (0.036) (0.033) (0.033)

Number of children: 1 �0.101 �0.079 �0.014 �4.27e�4

(0.022) (0.019) (0.021) (0.018)

2 �0.128 �0.075 �0.027 �0.004 (0.024) (0.023) (0.028) (0.025)

�3 �0.199 �0.169 �0.046 �0.071 (0.037) (0.033) (0.049) (0.048)

Observations 25,565 28,841 28,151 29,263

Cut1 �1.853 �1.636 �1.944 �1.493 (0.071) (0.069) (0.071) (0.066)

Cut2 �1.087 �0.715 �0.850 �0.511 (0.070) (0.069) (0.069) (0.066)

Cut3 0.556 1.136 1.086 1.206 (0.070) (0.069) (0.070) (0.066)

Log likelihood �25968 �29619 �25881 �31872

Belgium Netherlands Denmark Luxembourg

Unemployed �0.354 �0.532 �0.444 �0.915 (0.030) (0.032) (0.035) (0.135)

Self-employed �4.1e�4 0.052 0.012 0.015 (0.028) (0.033) (0.030) (0.052)

Retired 0.051 0.101 �0.084 7.84e5 (0.030) (0.032) (0.032) (0.053)

Home 0.073 0.015 0.009 0.071 (0.024) (0.023) (0.034) (0.044)

School 0.003 �0.011 0.039 0.034 (0.037) (0.035) (0.033) (0.068)

Male �0.045 �0.187 �0.133 �0.083 (0.017) (0.019) (0.016) (0.034)

Age �0.023 �0.041 �0.029 �0.028 (0.003) (0.003) (0.003) (0.005)

Age squared 2.4e�4 4.5e�4 3.5e�4 3.6e�4 (2.9e�5) (3.2e�5) (3.1e�5) (5.9e�5)

Income quartiles: Second 0.131 0.124 0.097 0.236

(0.022) (0.021) (0.024) (0.038)

Third 0.262 0.281 0.260 0.395 (0.024) (0.022) (0.027) (0.040)

Fourth (highest) 0.370 0.459 0.433 0.452 (0.026) (0.023) (0.028) (0.041)

Education to age: 15–18 years old 0.045 0.071 0.059 0.016

(0.019) (0.020) (0.021) (0.039)

�19 years old 0.092 0.064 0.091 0.050 (0.023) (0.023) (0.023) (0.047)


Independent Variable Belgium Netherlands Denmark Luxembourg

Marital status: Married 0.085 0.169 0.147 0.161

(0.024) (0.024) (0.023) (0.042)

Divorced �0.340 �0.404 �0.186 �0.190 (0.047) (0.044) (0.040) (0.086)

Separated �0.286 �0.670 �0.249 �0.312 (0.053) (0.113) (0.079) (0.125)

Widowed �0.233 �0.266 �0.120 �0.188 (0.036) (0.039) (0.036) (0.066)

Number of children: 1 �0.043 �0.026 �0.042 0.040

(0.021) (0.022) (0.022) (0.038)

2 �0.020 �0.041 �0.034 �0.058 (0.027) (0.023) (0.027) (0.051)

�3 0.004 �0.080 �0.123 0.036 (0.041) (0.038) (0.050) (0.087)

Observations 25,304 28,118 26,738 8,051

Cut1 �2.350 �2.802 �2.686 �2.073 (0.084) (0.080) (0.078) (0.135)

Cut2 �1.511 �1.972 �1.870 �1.227 (0.083) (0.078) (0.074) (0.131)

Cut3 0.190 �0.199 �0.259 0.504 (0.082) (0.077) (0.073) (0.131)

Log likelihood �25233 �24879 �22179 �7460

Ireland Spain Portugal Greece

Unemployed �0.607 �0.406 �0.502 �0.280 (0.032) (0.047) (0.062) (0.049)

Self-employed 0.094 0.081 0.128 0.027 (0.026) (0.039) (0.034) (0.023)

Retired 0.089 0.153 0.007 0.092 (0.039) (0.043) (0.043) (0.033)

Home �0.045 0.082 �0.021 0.130 (0.028) (0.037) (0.035) (0.027)

School 0.012 0.022 0.116 0.089 (0.050) (0.049) (0.051) (0.039)

Male �0.164 0.012 �0.040 �0.007 (0.023) (0.028) (0.024) (0.020)

Age �0.024 �0.037 �0.034 �0.026 (0.003) (0.004) (0.004) (0.003)

Age squared 3.4e�4 3.8e�4 3.5e�4 2.8e�4 (3.5e�5) (4.0e�5) (4.2e�4) (3.2e�5)

Income quartiles: Second 0.129 0.132 0.126 0.197

(0.024) (0.032) (0.033) (0.022)

Third 0.248 0.244 0.213 0.318 (0.025) (0.033) (0.034) (0.024)

Fourth (highest) 0.485 0.355 0.414 0.490 (0.027) (0.036) (0.036) (0.025)

Education to age: 15–18 years old 0.126 �0.024 0.055 0.105

(0.020) (0.031) (0.032) (0.021)

�19 years old 0.204 0.021 �0.002 0.155 (0.030) (0.032) (0.032) (0.024)


2. Data Sources

2.a The United States General Social Survey (1972–1994)

The General Social Surveys have been conducted by the National Research Center at the University of Chicago since 1972. Interviews have been undertaken during February, March, and April of 1972, 1973, 1974, 1975, 1976, 1977, 1978, 1980, 1982, 1983, 1984, 1985, 1986, 1987, 1988, 1989, 1990, 1991, 1993, and 1994. There were no surveys in 1979, 1981, and 1992. There were a total of 32,380 completed interviews (1,613 in 1972, 1,504 in 1973, 1,484 in 1974, 1,490 in 1975, 1,499 in 1976, 1,530 in 1977, 1,532 in 1978, 1,468 in 1980, 1,506 in 1982, 354 in 1982 black oversample, 1,599 in 1983, 1,473 in 1984, 1,534 in 1985, 1,470 in 1986, 1,466 in 1987, 353 in 1987 black oversample, 1,481 in 1988, 1,537 in 1989, 1,372 in 1990, 1,517 in 1991, 1,606 in 1993, and 2,992 in 1994).

2.b The Euro-Barometer Survey Series (1975–1992)

The Euro-Barometer Surveys were conducted by various research firms operated within the European Community (E.C.) countries under the direction of the European Commission. Either a nationwide multistage probability sample or a nationwide stratified quota sample of persons aged 15 and over was selected in each of the E.C. countries. The cumulative data file used contains 36 attitudinal, 21 demographic, and 10 analysis variables selected from the Euro-Barometers, 3–38. Data for Belgium, France, Germany, Ire- land, Italy, Luxembourg, Netherlands, and the United Kingdom were avail- able for the full sample period (1975–1992), whereas data were only available from 1981 to 1992 for Greece and from 1985 to 1992 for both Spain and Portugal.

3. Data Definitions

● Reported life satisfaction: The answer to the Euro-Barometer Sur- vey question that asks, “On the whole, are you very satisfied, fairly


Variable Mean Standard Deviation

Dependent variable: Reported life satisfaction 2.035 0.778

Independent variables: Unemployed 0.046 0.210 Self-employed 0.098 0.298 Retired 0.167 0.373 Home 0.211 0.408 School 0.072 0.258 Male 0.471 0.499 Age 43.4 17.6 Age squared 2192 1662 Income quartiles:

Second 0.248 0.432 Third 0.256 0.436 Fourth (highest) 0.253 0.435

Education to age: 15–18 years old 0.390 0.488 �19 years old 0.203 0.402

Marital status: Married 0.630 0.483 Divorced 0.026 0.159 Separated 0.010 0.100 Widowed 0.082 0.274

Number of children: 1 0.156 0.362 2 0.099 0.299 �3 0.039 0.193

Based on 271,224 observations.


Variable Mean Standard Deviation

Dependent variable: Reported happiness 2.211 0.631

Independent variables: Unemployed 0.032 0.175 Self-employed 0.112 0.316 Retired 0.119 0.323 Home 0.164 0.370 School 0.018 0.132 Other 0.011 0.106 Male 0.471 0.499 Age 44.7 16.9 Age squared 2280 1674 Income quartiles:

Second 0.240 0.427 Third 0.266 0.442 Fourth (highest) 0.266 0.442

Education: High school 0.523 0.500 Associate/junior college 0.040 0.196 Bachelor’s 0.129 0.335 Graduate 0.058 0.233

Marital status: Married 0.612 0.487 Divorced 0.104 0.305 Separated 0.033 0.178 Widowed 0.090 0.286

Number of children: 1 0.158 0.365 2 0.244 0.430 �3 0.329 0.470

Based on 26,668 observations.


Independent Variable Ireland Spain Portugal Greece

Marital status: Married 0.114 0.114 �0.008 0.169

(0.023) (0.034) (0.034) (0.027)

Divorced �0.072 �0.055 �0.246 �0.183 (0.257) (0.150) (0.092) (0.073)

Separated �0.535 �0.075 �0.334 �0.374 (0.079) (0.100) (0.116) (0.147)

Widowed �0.142 �0.157 �0.222 �0.126 (0.038) (0.051) (0.052) (0.043)

Number of children: 1 �0.051 0.003 �0.037 �2.63e�4

(0.025) (0.030) (0.027) (0.022)

2 �0.070 �0.014 �0.052 �0.001 (0.026) (0.036) (0.036) (0.026)

�3 �0.104 �0.053 �0.157 0.080 (0.025) (0.055) (0.059) (0.053)

Observations 20,075 10,973 12,497 20,003

Cut1 �2.103 �2.012 �1.803 �1.108 (0.080) (0.103) (0.096) (0.084)

Cut2 �1.423 �0.963 �0.819 �0.314 (0.079) (0.102) (0.096) (0.084)

Cut3 0.102 0.479 1.316 1.004 (0.078) (0.102) (0.096) (0.084)

Log likelihood �21029 �12324 �12082 �24879

The regressions include country dummies and year dummies from 1975 to 1992. Dependent variable: reported life satisfaction.


satisfied, not very satisfied or not at all satisfied with the life you lead?” (The small “Don’t know” and “No answer” categories are not studied here.)

● Reported happiness: The answer to the U.S. General Social Survey and Euro-Barometer questions that ask, “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?” (The small “Don’t know” and “No answer” categories are not studied here.)

● Benefit replacement rate: The OECD index of (pretax) replacement rates (unemployment benefit entitlements divided by the corresponding wage). It attempts to capture the situation of a representative or average individual. Consequently, the unweighted mean of 18 numbers based on the following scenarios is determined: (1) three unemployment durations (for persons with a long record of previous employment); the first year, the second and third years, and the fourth and fifth years of

employment; (2) three family and income situations: a single person, a married person with a dependent spouse, and a married person with a spouse in work; and (3) two different levels of previous earnings: average and two-thirds of average earnings [for further details see the OECD Jobs Study (OECD, 1994)]. Since this index was calculated only for odd-numbered years, for even-numbered years we made a linear interpolation.

● Unemployment rate: The standardized unemployment rate from the CEP OECD data set.

● Inflation rate: The inflation rate as measured by the rate of change in consumer prices, from CEP OECD Data Set.

● GDP per capita: Real GDP per capita at the price levels and exchange rates of 1985 (in U.S. dollars) from OECD National Accounts (OECD, 1997).

● �GDP per capita: GDP per capita minus GDP per capita (�1).


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