happinessbeauty[1]

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BEAUTY IS THE PROMISE OF HAPPINESS*?

Daniel S. Hamermesh and Jason Abrevaya**

*Stendhal (Marie-Henri Beyle), La beaut n'est que la promesse du bonheur , De lAmour , Ch. 17, 1822.

**Sue Killam Professor in the Foundations of Economics, University of Texas at Austin, and professor ineconomics, Royal Holloway University of London, IZA and NBER; Fred Hofheinz Professor of Economics,University of Texas at Austin. The authors are listed in ascending order of their looks. They thank Joel Waldfogelfor inspiring this project and Katrin Auspurg, George Borjas, Chris Bollinger, Gbor Kzdi, Markus Klemm,Andrew Oswald, Karl Scholz, two anonymous referees, and participants at several universities and institutes for helpful comments. They are also grateful to Sean Banks, Steven Boren, and David Northrup for help in obtaining theolder data sets.
http://www.citations.com/litterature-et-beaute/citations-et-phrases-celebres-pour-beaute-612-.htmhttp://www.citations.com/litterature-et-promesse/citations-et-phrases-celebres-pour-promesse-438-.htmhttp://www.citations.com/litterature-et-promesse/citations-et-phrases-celebres-pour-promesse-438-.htmhttp://www.citations.com/litterature-et-beaute/citations-et-phrases-celebres-pour-beaute-612-.htm


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ABSTRACT

We measure the impact of individuals loo ks on life satisfaction and happiness. Using six data sets, fromCanada, Germany, the United Kingdom, and the United States, we construct beauty measures in a number of different ways. Beauty raises happiness: A one standard-deviation change in beauty generates about0.08 standard deviations of additional satisfaction/happiness among men, 0.07 among women. Thefinding is robust to a rare opportunity to measure it using an instrumental variables approach. Accountingfor a wide variety of covariates, particularly educational, marital, and labor-market outcomes that might

be affected by beauty, the gross effects are roughly halved, with small reductions arising from the impactof beauty on monetary outcomes.


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I. Introduction

While economists have studied happiness for several generations (Scitovsky, 1976; Easterlin, 2010),

interest in it has burgeoned in the last 15 years. The Frey and Stutzer (2002) survey captured part of the

literature, but there has been a continuing outpouring of research on happiness from an economic

viewpoint (e.g., Clark et al , 2008; Stevenson and Wolfers, 2008; Kahneman and Deaton, 2010; Oswald

and Wu, 2011). Much of the analysis focuses on measuring the short- and long-run effects of changes in

income on happiness, but the mutual relationship between happiness and other outcomes that are at least

partly economically determined (divorce, fertility and others) has also been discussed.

At the same time a smaller, but also burgeoning literature on the effects of beauty on various

outcomes has emerged (e.g., Hamermesh and Biddle, 1994; Mbius and Rosenblat, 2006; Benjamin and

Shapiro, 2009; Mocan and Tekin, 2010; Hitsch et al , 2010). The general issue is how human beauty

determines outcomes in various markets and shifts the distribution of surpluses among participants in

those markets. For example, studies have looked at how beauty affects earnings, alters occupational

choice, and influences marital bargaining.

Here we put the two literatures together, examining how happiness is affected by beauty. Some

psychologists h ave correlated subjects happiness and their self -assessed beauty, but that approach seems

flawed. Others have compared the happiness of college students to observers ratings of their looks

(Mathes and Kahn, 1975; Diener et al , 1995), or examined simple averages of several measures of

happiness among a random sample of people whose beauty was rated by interviewers (using one of the

data sets we use, Umberson and Hughes, 1987). Our central question is how beauty affects happiness and

how much of the effect, if any, works through monetary measures.

The analysis should not merely reflect the idiosyncrasies of measuring the subjective concepts of

happiness and human beauty. These differences in how satisfaction is measured may give rise to different

biases in the estimated relationships of satisfaction to other variables, such as appearance. For that reason

we use six sets of surveys from four different countries, which we discuss in Section II. The measures of

satisfaction/happiness differ across surveys, and even within a given survey different measures are


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generated. Given evidence of the sensitivity of responses to questions about happiness to the framing and

scaling of the questions (Conti and Pudney, 2011), replicating the results using variously constructed

measures should minimize concerns over survey-based idiosyncrasies in eliciting responses about

happiness.

Were it possible to find a convincing instrument for beauty or to undertake some experiment that

altered beauty, we would take one of those analytical approaches to studying happiness. The former is

not generally possible, although one of our data sets does allow the creation of an instrument; and the

latter would not seem ethical. Instead, we take advantage of the fact that the surveys use four different

approaches to measuring beauty and in Section III delineate the types of measurement errors implicit in

each approach and the directions of the biases that each generates on the measured impact of beauty on

happiness. This discussion is applicable to a number of other examples where various measures of the

crucial independent variable contain different sorts of measurement error. In the end, the validity of our

results, which we present in Sections IV and V, depends on their robustness to differing approaches to

measuring beauty and to eliciting peoples expressions of satisfaction/happiness.

The raw effect of peoples looks on their satisfaction/happiness could operate through some of the

many channels that have been shown in the beauty literature to offer routes by which beauty affects

economic outcomes. We decompose these indirect effects into those that work through non-monetary

mechanisms, including the ability to obtain more education, and monetary mechanisms, specifically

impacts on own and spous es earnings. These may be at least as important as the direct effects of beauty

on satisfaction/happiness the halo that good looks might impart to a person independent of the effects of

beauty on any market-related outcomes. In the main economic exercise in this study, presented in Section

VI, we decompose the measured impact of beauty on satisfaction/happiness into its indirect component,

both non-monetary and monetary parts, and its direct component and thus examine the extent to which

any beauty-happiness relation works through markets.

II. Data Sources and Descriptive Statistics


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The six sets of data that we use are especially diverse in their methods of assessing beauty. The

first two consist of the two cross sections of the Quality of American Life (QAL) surveys, undertaken in

1971 and 1978 as random samples of the U.S. population age 18 and over. At the end of the interview in

each of these surveys the interviewer assessed the interviewees looks on a five -to-one scale, with 5 being

strikingly handsome or beautiful and 1 being homely . The complete list of descriptions associated

with each rating of beauty is shown in the first column of the first panel of Table 1, which also includes

codes describing the type of error in measuring beauty in the survey that we discuss at length in Section

III. There, and in our estimates, we merge the top two categories into one, good-looking , and do the

same for the bottom two into a category, bad-looking , since only minute fractions of the samples are

rated in the extreme categories (strikingly handsome or beautiful or homely). Economic and other

outcomes for good- and bad-looking individuals have been compared to those for people with average

looks (the excluded category) in a variety of studies (e.g., Hamermesh and Biddle, 1994; Leigh and

Susilo, 2009).

Both QAL surveys provide the same measures of happiness, each on a three-to-one scale, as the

description in column (3) of Table 1 shows. The surveys also provide direct measures of life satisfaction,

focused on the current moment (1971 survey) and on the persons total experience (1978 survey),

measures that are standard in the literature. Henceforth we distinguish between the determinants of life

satisfaction and those of happiness. The analysis of these surveys is thus data-driven, so that we are not

inquiring into the various aspects of satisfaction/happiness that have been identified by psychologists

(e.g., Seligman, 2004), but merely using general expressions of happiness, as has become standard in

economics.

The Quality of Life (QOL) survey was a longitudinal study conducted in Canada biennially from

1977 through 1981, beginning by randomly sampling Canadians ages 18 plus in 1977. In each of its three

waves a wide array of subjective information was obtained. As in the QAL, at the end of each interview

the interviewer rated the subject s look s using the same five-point rating system; and as there, and for the

same reason, we combined the ratings to construct three categories. Interviewers differed across the


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years, so that for those participants who remained in the study for all three years we have three

independent measures of their beauty. The satisfaction measures use different wording and a different

scale from those in the QAL, while the happiness measure is similar; both are obtained at the end of

survey in each wave of the study.

The German contribution to the General Social Survey program in 2008, the ALLBUS

( Allgemeine Bevlkerungsumfrage der Sozialwissenschaften ), included measures of beauty and happiness.

As in the QAL and QOL the interviewer rated the subjects looks at t he end of the interview (in this case,

on an eleven-point scale); but the interviewer also provided a rating (on the same scale) at the very start of

the interview upon first contact with the subject. The survey also obtained a four-point rating of

happiness, more backward-looking than the happiness measures in the QAL and QOL. We use data on all

respondents ages 18 plus for whom information is available on the crucial variables.

The Wisconsin Longitudinal Survey (WLS), a study of a cohort of Wisconsin high-school

graduates from 1957, also contains measures of beauty and happiness. Unlike the previous three sets of

studies, beauty in the WLS was based on assessments of high-school graduation photographs of the

participants. In 2004 e ach respondents pic ture was rated by 12 individuals (6 men and 6 women)

aged 63 to 91. In the available data, the beauty measure has already been normalized to be mean zero with

standard deviation one. Each respondent was interviewed in 1992 and 2004 (at ages 53 and 65) and asked

how many days last week s/he was happy, how many days s/he enjoyed life, and how many days s/he was

sad. The happiness measures were thus obtained 35 and 47 years after the photographs from which the

responden ts beauty was rated were taken. 1

The fifth data set is the British National Child Development Study (NCDS), a longitudinal

examination of all Britons born March 3- 9, 1958. At age 7, and again at age 11, each students teacher

assessed his/her attractiveness during the school year, along a scale shown in column (1) of Table 1. We

defined attractive as good-looking, the excluded category as average-looking and both unattractive

1Other studies have assessed beauty from school pictures taken nearly two decades before the outcome linked to theassessments (Biddle and Hamermesh, 1998), and one study even showed a high correlation between the assessmentsof pictures of 10-year-olds and those of the same individuals at age 50 (Hatfield and Sprecher, 1986, p. 283).


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and abnormal feature as bad -looking (since only a tiny fraction of respondents were classified as having

an abnormal feature). This aggregation is similar to previous work relating these ratings to subsequent

earnings (Harper, 2000).

In various later waves of the survey, including 1991, 1999, 2004 and 2009 (at ages 33, 41, 46 and

51), the remaining respondents were asked questions designed to elicit their happiness/life satisfaction,

some which have been studied before using these data (e.g., Blanchflower and Oswald, 2008). In the

three most recent waves life satisfaction was elicited in a question (column (2) of Table 1) focusing on the

respondents entire life experience. Happiness at age 51 was also measured in a backward -looking

manner, while happine ss at age 33 was measured with reference to the respondents current situation

only.

In Appendix Tables 1a-1e we present descriptive statistics for the six sets of surveys. For each of

the interview studies (the QAL, QOL and ALLBUS), here and in subsequent sections, all the results are

calculated using sample weights. Consider first the QAL. As is usual in assessing beauty, more people

are rated in the top two categories than in the bottom two and a majority is rated as average-looking. Also

as is usual, more women are rated as good-looking or bad-looking than men (Hamermesh, 2011, Chapter

2). Consistent with the previous satisfaction/happiness literature, most people are fairly happy and

satisfied.

The beauty measures in the QOL, shown in Appendix Table 1b, look remarkably similar

qualitatively to those in the QAL, except for a lesser Canadian willingness to classify subjects as below-

average looking. Also, the gender differences are reversed from those in the QAL, perhaps due to

differences in measurement and context of the question, perhaps to true underlying differences in how

beauty is perceived. For the ALLBUS, shown in Appendix Table 1c, the crucial thing to note is that the

ratings of both mens and womens beauty are higher at the end of the interview than at the start, although

men are on average rated lower at both times.

Because the beauty measures in the WLS were normalized, we do not list them in Appendix

Table 1d. In these data, people report being happy on most days (on average, between 5 and 6 days) in


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the week before the survey. The number of days reported as being sad is typically 20 percent or fewer

than the number of happy days. With one exception number of days reported happy in the 1992 wave

of the survey male respondents are happier than females.

Appendix Table 1e shows that in the NCDS females looks (in this case at age 11) were rated

more extremely than males . Perhaps, however, because of their close acquaintance with their charges,

the teachers who rated the students attractiveness included more students in the attractive (good-looking)

category than in the excluded category (children viewed as neither attractive nor unattractive). Most of

the respondents were fairly happy or satisfied at ages 33-51.

There is no consistent gender difference in average satisfaction/happiness across the sets of

surveys, possibly due to differences in the nature of the questions eliciting subjective well-being (Nolen-

Hoeksema and Rusting, 1999). In the QAL the comparisons are mixed; in the QOL and the ALLBUS

women are more satisfied/happier, although the differences are minuscule, while the opposite is true in

the WLS and in most of the NCDS waves. The differences in the nature of the measures across the

surveys make them non-comparable along this dimension, but considering them together underscores the

benefits of using various different measures of satisfaction/happiness to prevent incorrect generalizations

based on only one sample or one type of question.

III. Measuring Beauty in Relation to Happiness

To understand the nature of the measurement difficulties in these data sets, Figure 1 presents the

timing of the assessment of the respondents beauty in relation to the el icitation of his/her

satisfaction/happiness. A negative denotes that beauty is assessed after the respondent answers

question(s) about his/her satisfaction/happiness; and the widths of the bars are in proportion to the square

root of the number of people rating the subjects beauty. Obviously there is no universal measure of

human beauty it is in the eye of the beholder. But a huge literature (summarized in Hamermesh, 2011,

Chapter 2) shows that there is substantial agreement by people about each others looks. The best

possible measure would average ratings by large numbers of individuals who have no physical contact

with a set of subjects who are dressed the same way and have the same standard facial expression. Since


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that kind of measure has not ever been obtained, we are thrown back on thinking about how the measures

in these data sets generate errors in inferring the impacts of beauty. Due to a lack of cross-validation data,

we cannot estimate the sizes of the various measurement errors involved; but we can infer the directions

of bias to the estimated impacts of beauty on satisfaction/happiness generated by the various sources of

measurement error.

To focus only on the beauty rating, consider the following simple linear regression model, with

(satisfaction/happiness) as the dependent variable and true (latent) beauty as the sole explanatory

variable: 2

(1)

The subscript t indicates the time at which the happiness measure is observed.

We consider three possible types of difficulty in measuring beauty in relation to happiness:

(1) Classical measurement error in the beauty rating : The beauty rating used in the actual regression

is an imperfect measure of , because of the small number of raters.

(2) Attenuation in the accuracy of the beauty rating : Since beauty changes, albeit slowly, over time,

the inherent noise in the beauty rating will be larger the more that the rating pre-dates the

satisfaction/happiness measure. The variance of the reported B will be an increasing function of

the time interval between observation of the beauty rating and observation of the happiness

measure.

(3) Bias in the beauty rating : If the beauty rating is elicited after the rater has spent time interacting

with the subject, we would expect a positive correlation between the beauty rating and the

unobservable component of the happiness outcome. For instance, an interviewer might have a

better opinion of a subjects beauty if the subject projects self - confidence in the interview, which

might occur if the subject is happier.

2To simplify notation we assume homoskedasticity throughout this section and therefore omit conditioning on .


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The following stylized model for the observed beauty rating incorporates each of these three

possible sources of difficulty:

(2)

where is the time at which the beauty rating is obtained. The attenuation component of the

measurement error is - , which has a variance assumed to be linear in the time interval - :

(3)

The other component of the error, denoted , is similar to a classical measurement error, except that we

allow it to be correlated with the happiness residual :

(4)

For this general model, the inconsistency of the least-squares estimator is given by the probability limit of

the slope estimate:

(5)

where denotes the variance of . (Note that the textbook case of classical-measurement error is a

special case of this formula corresponding to (no rating bias) and (no depreciation effect),

for which .)For the QAL, QOL and the ALLBUS(end) data, the beauty rating is provided by the interviewer

slightly after the satisfaction/happiness measures are elicited. (Below the listing for each data set in Table

1 we present the type of measurement error contained in the beauty rating.) There is no depreciation

effect, since , but the interview format leads to the possibility of a bias in the beauty rating (

). In this case the probability limit of the slope estimate simplifies to

(6)


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If is positive, the usual inconsistency associated with classical measurement error is opposite the

inconsistency associated with the beauty-rating bias. The overall direction of the inconsistency depends

on whether (upward inconsistency) or (downward inconsistency). In regressions

based on these data sets the estimated contains measurement errors of Types 1 and 3. 3

Estimates based on the ALLBUS (start), in which the interviewers rating of the respondents

attractiveness is obtained at the very start of the interview, avoid Type 3 measurement error. In this case

the classical errors-in-variables result that is all that remains (because only one person (the interviewer) assesses the subjects looks ). Estimates of the impact of beauty on

satisfaction/happiness that are based on this measure are therefore likely to be lower-bound estimates.

In the WLS data the beauty rating is based upon a subjects high -school picture, and the

happiness measure is elicited during late adulthood. There will be an attenuation effect, since , but

the lack of interaction between the rater and the subject eliminates concerns about beauty-rating bias.

Entering into the general formula yields:

(7)

Inconsistency is expected here due to mis-measurement and attenuation, although the Type 1

measurement error is minimized by the large numbers of raters of the photographs. The estimated beauty

slope from the WLS regressions should therefore be considered too low , since it contains no Type 3

measurement error.

Finally, for the NCDS dataset, all three difficulties could arise, since the beauty ratings were

assessed in childhood ( ) by only two teachers, both of whom were very familiar with the subject

( ). As a result, the general probability-limit formula in (4) would apply. As with the QAL, QOL

and ALLBUS(end) data, the beauty-rating bias acts in the opposite direction from the other sources of

3In all of these studies the interviewer assessed th e subjects looks very near the end of the interview, andsubstantially after the subjects satisfaction/happiness was elicited. It is very unlikely that the subjects specificresponse to happiness questions directly affected the beauty rating.


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measurement error. The impacts of the other errors here, however, would be expected to be much larger

than in those data sets, since beauty is assessed in the NCDS decades before the expression of happiness

is elicited (as captured by the attenuation bias (- ) ). Although it is difficult to say how the sizes of compare between the NCDS and those interview-based data sets, the large difference in the variance

of measurement error between the NCDS and them suggests that the probability limits for the NCDS

estimates would be lower than the true effects.

IV. Basic Results

Here we estimate linear models relating life satisfaction/happiness to beauty in each of the six

data sets. In each case the variable is treated exactly as it is coded in the raw data (e.g., in the QAL the

variable takes on seven integer values). We present linear regression estimates even though most of the

satisfaction/happiness measures are defined as ordinal rankings, as this facilitates comparisons across the

data sets. In each case (except the WLS, where we estimated Poisson models), we also estimated ordered

probits describing these measures, with results on beauty and other covariates that are qualitatively (in

terms of sign and statistical significance) identical.

For each data set we initially estimate Specification 1, measuring the total effect of beauty on

satisfaction/happiness, including as regressors only the beauty measure(s) and, in the QAL, QOL and

ALLBUS, a quadratic in age and a measure of race in the U.S., or ethnicity (French-speaker in Canada,

German-origin in Germany), which might affect happiness but which cannot be caused by differences in

beauty. Specification 2 adds a number of covariates that have been shown to affect happiness and that

may be affected by beauty. These include educational attainment, marital status and self-assessed health,

and other variables, including location, as they are available in the various surveys. 4 It also includes a

4The subjective nature of self-assessed health may create an artificial positive relationship to satisfaction/happiness.We do not believe this biases our results on beauty, since the correlations of health and beauty are nearly zero in allof our samples.


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measure of the respondents own earnings (or income) and, where it is available, his/her spouses

earnings or income. 5

The upper part of Table 2a presents the estimates based on the two QAL surveys. Among both

men and women all the coefficients have the expected signs positive on the indicator for good looks

(above-average or beautiful the upper third of looks), negative on the indicator for bad looks (below-

average or homely the bottom eighth of looks). Moreover, many of the parameter estimates are

significantly non-zero.

The effects of differences in beauty on life satisfaction or happiness are not small in either set of

data. In the 1971 QAL going from the bottom eighth of womens (mens) looks (those rated below -

average) to the top third (those rated above-average) raises life satisfaction and happiness by 0.24 and

0.33 (0.17 and 0.25) standard deviations, respectively. In the 1978 data, similar calculations show effects

of 0.30 and 0.39 for women (0.30 and 0.31 for men), respectively. These gross effects are substantial,

implying movements up the distribution of happiness of at least five percentiles.

The bottom half of Table 2a presents the results of adding all the covariates, both non-monetary

and monetary, that might be affected by differences in beauty and that in turn might affect

satisfaction/happiness. One of the 16 coefficients changes sign when we move to Specification 2, and

three of those that were statistically significant in Specification 1 no longer are. In the 1971 data the

estimated effects among men drop nearly to zero, while among women they drop by half; in the 1978 data

they decrease by about 25 percent. Clearly, in these data at least some of the impact of beauty on

happiness works indirectly.

The QOL results, shown in in Table 2b, are qualitatively similar to those of the QAL. All but one

of the estimated effects in each Specification is in the expected direction, and the negative impact on

satisfaction/happiness of being among the small fraction of Canadians classified as being below-average

5There is substantial evidence that married people are happier (e.g., Blanchflower and Oswald, 2004; Oswald andWu, 2011); but ones gains from marriage are affected by ones looks (Hamermesh and Biddle, 1994). Since beautymatters in the marriage market, we include marital status as one of the channels through which beauty can indirectlyaffect happiness.


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in looks is substantial. There is no obvious gender difference in the impacts of beauty. Moving from

Specification 1 to Specification 2 reduces the estimated impact of beauty on happiness among both men

and women, but not by very much. The estimated effects in Specification 2 are larger than those in the

QAL, perhaps because fewer Canadians are classified as below-average: Going from the bottom twelfth

of womens (mens) looks to the top third raises life satisfaction by 0. 21 (0.32) standard deviations and

happiness by 0.52 (0.73) standard deviations.

Table 2c presents the estimates of the impacts of beauty, using the ALLBUS(start) and

ALLBUS(end) measures, on happiness in Germany. Specification 2 expands Specification 1 by adding

the basic measures of education, marital status, health and own income. 6 Increases in the eleven-point

beauty rating have significant positive effects on happiness in all cases, although adding the covariates

typically reduces the impacts by about one-third. The effects are slightly smaller among men than among

women, but the gender differences are not significant statistically. Most interestingly, and as predicted in

the discussion of measurement error, the effects are somewhat smaller when we use the ALLBUS(start)

ratings (with the differences being greater among men), suggesting but not very large. Using

the estimated effects in Specification 2, picking the same percentile points as in the distribution of looks

in the QAL and moving from the equivalent of the median below-average looking woman (man) to the

median above-average looking woman (man) produces an increase in happiness of 0.24 (0.17) standard

deviations based on ALLBUS(start), and 0.29 (0.26) standard deviations based on ALLBUS(end). The

former is smaller than in the QOL, perhaps because using ALLBUS(start) vitiates Type 3 measurement

error. 7

6The education categories are other, mittlere Reife , and Hochschul, each accounting for about one -third of eachsample. We include a separate indicator of life partner here but nowhere else, because nearly 10 percent of thesample reported being unmarried but having a life partner.

7The eleven-point scale in these beauty ratings allows us to compare how well different pairs of indicators of goodlooks and bad looks describe happiness. While no pair performs as well as the eleven-point measure, defining goodlooks as the top third of respondents on the eleven-point scale, and bad looks as the bottom sixth, roughly thedivisions shown in the other interview data, describes happiness in these samples better (in terms of adjusted R-squared) than any other pair of indicators.


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The results from the WLS, with number of days happy, enjoyed and/or sad, are presented in

Table 2d. The upper part of the table contains results from equations including only the unit-normal

measure of beauty, while Specification 2 in the bottom part adds the usual measures of years of education,

marital status, and own and household income, but also the number of children, BMI observed at high-

school graduation, and current BMI. As with the results for the QOL and the ALLBUS data sets, adding

this vector of covariates reduces the estimated impacts of attractiveness on the measures of

satisfaction/happiness, but not greatly. There is no significant impact of attractiveness on happiness

among men at either of the two ages at which these adults are observed. Among women, however, in all

the equations the more attractive respondents are significantly happier at age 53 than less attractive

respondents. The impacts are smaller relative to the standard deviations of satisfaction/happiness than in

the interview data sets, as suggested by the discussion of measurement error arising from attenuation.

We can explain the disappearance of the results for women as they age by the possibility that the

correlation of attractiveness at age 18 with attractiveness at age 53 may be greater than that with

attractiveness at age 65. The absence of any relation between attractiveness and happiness among men is

harder to explain, especially in light of the fact that the labor-market effects of beauty are at least as large

among men as among women. One possibility is that there is inherently more measurement error in the

ratings (assigned over 40 years after the pictures were taken) of mens high -school graduation pictures

than of womens.

Table 2e shows the results of relating measures of happiness and satisfaction in adulthood to

attractiveness assessed by a childs teacher at age 11 in the United Kingdom using the NCDS sample.

Specification 1 includes indicators for being rated as attractive or as unattractive (with the middle

category excluded). Although two of the parameter estimates have unexpected signs, all of the

statistically significant estimates have the expected signs, and there is no obvious difference in the sizes

of the effects between men and women. Specification 2 contains the common expansion of variables plus


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measures of the number of children, BMI at age 11 and current BMI. 8 The estimated effects of

attractiveness are typically somewhat attenuated when the control variables are added, although the

overall conclusions remain the same: Where significantly nonzero, the beauty measures have the

expected effects; and, as in the upper part of the table, the impacts of beauty are roughly the same by

gender. 9 Unlike in the WLS, there is no clear variation with age in the impacts of looks on either

happiness or life satisfaction.

In Tables 2a-2e the adjusted R 2 describing the equations fits are not high by general standards.

By comparison to those found in the happiness literature (e.g., Blanchflower and Oswald, 2008),

however, they are fairly high. Most important, nearly all of the estimated effects of beauty are of the

expected sign, and many are statistically significantly non-zero. We have produced many estimates of the

impact of looks 30 coefficients for each gender for each of Specifications 1 and 2: 8 from the QAL

surveys, 4 from the QOL, 2 from the ALLBUS, 6 from the WLS and 10 from the NCDS. Among men, in

Specification 1(2) 28(24) of the 30 estimated coefficients have the expected signs, of which 12(7) are

significantly nonzero. Among women, in Specification 1(2) the comparable summaries are 27(25) and

15(11). Only one of the few incorrectly signed parameter estimates is statistically different from zero.

The data strongly support the notion that better looks produce a gross positive effect on life

satisfaction/happiness, even after we adjust for a large number of monetary and non-monetary factors that

might affect happiness and that might themselves be correlated with or caused by differences in beauty.

These comparisons clearly suggest a positive answer to the titular question of this study. The

results for Specification 2 even suggest that some of the total effect of beauty on happiness is direct and

does not work through monetary and non-monetary economic channels. The difficulty is how to compare

8The categories represented by the vector of education indicators are: cse or equivalent, O-level or equivalent, A-level or equivalent, higher qualification, or university degree or higher, with no qualification as the excludedcategory.

9Current BMI and BMI at age 11 have mixed effects on happiness/satisfaction, but their inclusion hardly alters theimpacts of beauty on happiness. This absence of any effect is not surprising, in light of the demonstratedinsignificant correlations between beauty and BMI over all but the very extreme upper tail of BMI (e.g., Thornhilland Grammer, 1999).


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the estimates across what are six independent samples for each gender (two QAL samples and the other

four data sets). To do so we calculate the effect of being at different percentiles of the distribution of

beauty on the level of satisfaction/happiness measured in standard deviations. Thus, for example, we

assume that the average male among the 12.5 percent rated as below-average in the QAL 1971 is at the 6 th

percentile of the distribution of looks. That percentile in the unit normal distribution is 1.53 standard

deviations below the mean, and we impute that as the average beauty among those men rated as bad-

looking, implicitly assuming that the distribution of beauty in the lower tail is symmetric around its value

for the median person in that tail. 10 We use this type of approximation for the QAL, QOL and NCDS.

For the ALLBUS we find the percentile points of the distributions of the eleven-point scale corresponding

to percentiles in the averages of the QAL, QOL and NCDS, and for the WLS we do the same thing at the

percentiles of the unit normal deviates. We use these approximations to convert the parameter estimates

to what are essentially beta-coefficients, thus placing all the estimates in standard-deviation units.

The results of these calculations for Specification 2 are shown in Figures 2a and 2b, with each of

the points in a Figure representing the fractional change in standard deviations of satisfaction/happiness

generated by a movement from the mean beauty to the beauty (in standard deviation units) of the average

person rated as bad-looking (to the left of the vertical line at zero) or good-looking (to its right). Among

women (men) the average good-looking respondent is 0.79 (0.89) standard deviations above the mean of

beauty, while the average bad-looking respondent is 1.62 (1.67) standard deviations below the mean. On

average, among women (men) the gain from being this good-looking is 0.061 (0.046) standard deviations

along the satisfaction/happiness index compared to the average male (female), while the loss from being

this bad-looking is 0.134 (0.125) standard deviations of satisfaction/happiness. 11

Assuming, as these calculations must, that the effects are linear within the categories above-

average and below-average, or attractive and unattractive, the results in the expanded specifications imply

10One could argue that the distribution becomes thinner further out in each tail, although we have no evidence onthat. If so, the difference in average beauty between the upper and lower tails would be less, and our calculationswould be understating the impacts of differences in beauty.

11These are averages weighted by the numbers of observations in each of the six samples.


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that a one standard-deviation increase in beauty raises satisfaction/happiness by 0.069 (0.081) among

women (men). These are far smaller than the impact of income on happiness in a cross-section

(computed from Frey and Stutzer, 2002, Table 1), although that calculation is based on decile averages

rather than individual observations. In comparison to standard-deviation impacts of the crucial

experimental variables that are reported in related literatures, however, including those on education

and health, they are not small.

The relative sizes of the estimates shown in Figures 2a and 2b generally accord with the

discussion of measurement error. They are largest in the QAL, QOL and ALLBUS(end), where the

assessment of beauty late in a long interview might have created upward biases due to Type 3

measurement error. They are somewhat smaller in the ALLBUS(start); and they are smallest, and

certainly negatively biased, in the WLS, where changes in beauty will have led to Type 2 measurement

error that has grown over time. The direction of the bias in the NCDS is unclear, since the errors induce

opposite-signed biases, but the estimates are generally below those from the interview studies.

V. Using Alternative Measures of Beauty

Concerns about measurement issues in beauty underlie the comparisons across the data sets used

in Section IV. We address those concerns further by using alternative estimates that might remove some

remaining difficulties with these measures. 12 It is difficult to impossible to construct instruments for

beauty that would allow one to claim convincingly to have eliminated concerns about causality. The

QOL does, however, provide such an opportunity, as lagged beauty measurements (two years prior) can

be used as instruments for beauty measurements in the year that satisfaction and happiness are assessed.

We therefore re-estimate the models in Table 2b for the 1979 and 1981 waves using the 1977 and 1979

12In addition to these experiments and adjustments with alternative measures of beauty, we also experimented withadding controls that were uniquely available in the various data sets. Thus, in the QOL and the NCDS, we added ameasure of whether the respondent had gotten divorced or had gotten married since the previous interview. In the

NCDS we added a vector of indicators of region of residence at age 11, and in the WLS an indicator of whether therespondent resided in Wisconsin at age 53 (65). None of these additions altered the estimates of the impact of

beauty on satisfaction/happiness. In the NCDS we added variables indicating the respondents parents social classduring the respondents childhood, and whether a parent had died since the previous interview. While thesevariables had significant effects on satisfaction/happiness, they were not correlated with the beauty measure and thusdid not qualitatively alter the estimated impacts of beauty.


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measures of beauty, respectively, as instruments. Table 3 presents the results of re-estimating

Specifications 1 and 2 using this approach. The changes in the parameter estimates are small: In some

cases the effects are larger with the IV approach, in others smaller. Implicitly, these results suggest that

concerns about reverse causality in the context of beauty and happiness what we have identified as Type

3 measurement error may be misplaced, at least in this data set.

In the WLS we re-estimated both specifications using first the normalized beauty ratings given by

female raters to pictures of female respondents, and by male raters to male subjects, then switched and re-

estimated the equations using opposite-sex ratings. Most of the estimates are attenuated slightly, just as

expected assuming that there is more Type 1 measurement error when fewer raters are used; but all of

those that were statistically significant (women in 1992) remain so.

The NCDS respondents appearance was also assessed by their teachers at age 7. To the extent

that measurement error in the variable we used arose from random errors in individual teachers

assessments of the childs appearance, averaging the teachers ratings at ages 7 and 11 will reduce Type 1

error. These average measures replace the age-11 measures in the estimating equations, and the age-11

BMI is replaced with the average of BMI at ages 7 and 11. 13 In a few cases some previously insignificant

parameter estimates in Table 2e become marginally significant, but otherwise there is no change.

Implicitly, whatever measurement error exists in the age-11 proxies is highly positively correlated with

that in the age-7 data and is not much reduced by averaging. 14

Another concern is that different assessors rate beauty differently and that their idiosyncrasies

may be correlated with the subjects happines s. With most teachers in the NCDS assessing only one

subjects appearance , this issue cannot be examined in those data; and we cannot identify the raters in the

13As Lubotsky and Wittenberg (2006) show, the appropriate method in this case and in the re-estimation for theQOL is to introduce the information from the other two years separately rather than as averages. We discuss onlythe estim ates based on averages to save space, as the sums of the coefficients, and the pairs statistical significance,are always almost identical to those of the averages for both data sets.

14We also used the age-7 measures alone looks and BMI in place of the age-11 measures. Perhapsunsurprisingly, the results were slightly weaker than with the age-11 measures, and thus somewhat weaker still thanthe specifications based on the age-7 and age-11 averages.


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WLS. In the interview surveys, however, we know which raters assessed each subjects beauty.

Accordingly, we re-estimate the equations in Tables 2a-2c adding interviewer fixed effects. With one

exception (the impact of bad looks on women s life satisfaction in the QAL 1978 data) none of the

significant impacts shown in Tables 2a-2c became statistically insignificant, nor did any of the estimated

effects of looks on satisfaction/happiness reverse sign. The general conclusion from this and the other re-

estimates is that the results are robust to all the possible alternative measures of beauty that the data sets

allow.

As noted in Section III, the ALLBUS(start) assessment of beauty is uniquely free of all but Type

1 measurement error, so that regressions based on it provide a lower-bound estimate of the impact of

beauty on satisfaction but only for this one data set. If, however, we assume that the upward biases in

the estimates based on the QAL and the QOL due to the Type 3 measurement error in the beauty ratings

of beauty are proportionate to those that distinguish the ALLBUS(end) from the ALLBUS(start)

estimates, we can expand the set of lower-bound estimates by prorating the QAL and QOL estimates by

the ratios of the estimates based on the ALLBUS(start) to those based on the ALLBUS(end). In

Specification 2 in the ALLBUS these ratios are 0.919 (0.760) among women (men), implying that Type 3

measurement errors are not greatly inflating the estimated impacts of beauty on happiness. The prorated

estimates of Specification 2 in Tables 2a and 2b still suggest substantial impacts of beauty on

satisfaction/happiness, suggesting that the lower-bound effect of beauty on these outcomes is not small.

VI. Inferring and Decomposing the Direct and Indirect Effects of Beauty

The main economic question in this study is whether the effect of beauty on

satisfaction/happiness works through markets: How much of the effect is direct with people who are

otherwise identical in every respect being happier/more satisfied than their less good-looking peers? How

much is indirect due to beauty enhancing ones outcomes in various markets, including the labor and

marriage markets? 15 Of the indirect effect, how much results from monetary variables differences in

15The effect of a different personal endowment, height, on happiness was decomposed into these components byDeaton and Arora (2009), with the adjustment limited to accounting for the impact of height on earnings.


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own and spouses incomes, how much from non-monetary variables? Obviously the answers depend on

the sets of variables available to represent the different components of these indirect effects, just as in any

decomposition of a gross effect (e.g., the attribution of wage differences to those generated by differences

in employer and employee characteristics Abowd et al , 1999).

The estimate of in equation (1) is the gross effect of beauty on satisfaction/happiness. Adding

the covariates X t that are included in Specification 2, both those representing monetary measures, the

vector , and those representing non-monetary measures, the vector , we obtain: (8)

The direct effect of beauty on satisfaction/happiness is , and the indirect effect is the difference .

If we exclude the vector and re-estimate (8), we obtain a third estimate of the impact of beauty onsatisfaction/happiness , . The indirect effect can then in turn be decomposed into its non-monetary and

monetary components as:

[ . (9a)Alternatively, if we exclude the vector and again re-estimate (8), we obtain a fourth estimate of theimpact of beauty on satisfaction/happiness, . This will differ from the estimate and will yield a

different decomposition of the indirect effect into its non-monetary and monetary components:

+ [ . (9b)

As we noted in Section IV, a lower-bound estimate of the effect of beauty on happiness is based

on using the German ALLBUS(start) measure. Accordingly, we concentrate initially on the results from

this data set using this measure of beauty. We first re-estimate (7) sequentially without the vectors and respectively to obtain the estimates of and . We calculate the direct effect of beauty, basedon the estimated ; the indirect effect, based on the estimated ; and the alternative

decompositions of the indirect effect into nonmonetary and monetary components, based on (9a) and (9b).

We combine these with the standard deviations of the many-valued measure of beauty and the ordinal


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happiness measure to infer the impacts of a one standard-deviation increase in beauty on the change in

happiness measured in standard-deviation units.

Table 4 presents these decompositions of the impact of beauty on happiness using the

ALLBUS(start) measure of beauty. The indirect effects of beauty through demographic and monetary

variables are slightly below half of the gross effects for men and are relatively smaller for women.

Depending on the decomposition used, perhaps nearly half of the indirect effect is accounted for by

monetary variables among men. Among women in both decompositions essentially none of the indirect

effect is due to its effects on womens earnings. The calculations based on these data, probably the best

available, indicate both substantial direct and indirect effects of beauty, and that some, but not very much

of the latter among men results from beauty affecting happiness through its impacts on monetary

outcomes. 16 That the direct effects are relatively more important among women is consistent with the

general result in the literature that earnings effects of beauty are smaller among women than among men.

While Table 4 lists the best estimates of the decompositions in light of the discussion of the

various types of measurement error, it ignores the evidence from all the other samples on which

Specifications 1 and 2 were estimated. To combine all of the results in constructing these

decompositions, we re-estimated Specification 2 over each of the data sets, in each case dropping first the

monetary and then the non-monetary measures. We then take these re-specifications and Specification 1,

combine results in the same way that we used the estimates of Specification 2 to construct Figures 2 and

obtain decompositions in terms of standard deviations of the impacts of good- or bad looks on happiness.

Rather than presenting the estimates of these expanded specifications or more charts like Figure

2, we summarize the results of the decompositions in Table 5 by averaging the impacts over all the sets of

estimates. Using all the results in this manner yields inferences that are somewhat like those in Table 4

based on the ALLBUS(start) measures of beauty, but there are some differences. The direct effect of

16One might be concerned that, even though, as noted earlier, estimates of (7) based on ordered probits gaveidentical inferences to the least-squares estimates, perhaps the decomposition proposed here would generatedifferent inferences depending on the method of estimation. The inferences are identical if we use ordered probits:The parameter estimates on beauty in Specifications 1, 2 and Specifications 2 without X Mt (X Nt) also suggest similar conclusions to those implied in Tables 4 and 5.


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beauty on happiness in this average accounts for a much larger fraction of the gross effect among women

than men; and the monetary component of the indirect effect among men is also larger than that among

women.

Our proxies for both monetary and non-monetary outcomes in the labor and marriage markets

that are affected by beauty are far from perfect. It thus seems fair to conclude that the estimates in Tables

4 and 5 suggest that the direct effect of beauty is about one-half of the total effect, but it may be less.

Moreover, measurement errors in the monetary variables are almost certainly greater than in such non-

monetary indicators as educational attainment, marital status and numbers of children. Thus to an

unknowable extent we have probably underestimated the sizes of the indirect impacts of beauty on

satisfaction/happiness and have underestimated the shares of those impacts that are attributable to

monetary outcomes. 17

VII. Conclusions and Extensions

We have examined the relationship between peoples life satisfaction/happiness and their beauty.

While the beauty measures introduce difficulties into inferring the true effect of beauty on happiness,

those difficulties, which differ across our data sets, do not result because we make the simple mistake of

essentially relating happiness to a proxy for happiness. The difficulties with the beauty measures are

more subtle in our context, but the use of a large number of different types of assessments of beauty and

the one data set that allows IV estimation enable us to circumvent them.

The results suggest that a persons beauty does increase his/her satisfaction/happiness, with

effects that are not small. Among both men and women at much as half of the increase in

satisfaction/happiness generated by beauty is indirect, resulting because better-looking people achieve

more desirable educational, health and labor-market outcomes (higher earnings) and do better in the

17One might object that the surveys key the respondents into thinking about economic issues when they respond toquestions about their life satisfaction/happiness, so that the relative importance of the indirect effects is overstated.We do not believe that this is a problem in these data sets. In the ALLBUS and the NCDS questions about pay andincome long precede those on satisfaction/happiness, in the QOL the opposite is the case, while in the QAL own payis elicited long before satisfaction/happiness, while family income is elicited long after. In sum, survey-induced

biases seem to be minimal.


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marriage market (including obtaining higher-income spouses). While the majority of the indirect impact

arises from non-monetary outcomes that are correlated with beauty, we show that monetary measures do

have some indirect impacts on satisfaction/happiness, especially for men.

Given the importance of happiness in affecting other outcomes, our results set the stage for work

relating beauty to a variety of objective behaviors, especially since some effort has begun by economists

to relate happiness to these outcomes (e.g., Daly and Wilson, 2009). These include workplace

productivity, clinical depression, suicide and no doubt others too. While none of the many data sets used

here allow these additional steps, they offer a chance to expand the literatures on beauty and happiness to

additional, objective outcomes.

In order to draw the specific inferences about these impacts of beauty we have had to circumvent

problems that arise in the collection of data measuring what is obviously a subjective characteristic. This

has led us to identify three types of measurement error and to infer the directions of the biases that they

induce in our estimates (not their magnitudes, since none of the data sets allowed that). While the

exercise was specific to our particular problem, the notion of these various types of measurement error

applies mutatis mutandis to any problem where measures of a subjective characteristic are obtained and

used in describing some objective or subjective outcome. For examples, studies of the impacts of non-

cognitive characteristics on childrens development , educational outcomes and adult wages would be

informed by this discussion, as would studies examining interviewers assessments of workers job -

readiness and fit for different types of work. The central point is that considering the nature of

measurement error, as we have done here, is important in a variety of contexts in the social sciences.


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Table 1. Descriptions of Beauty, Happiness and Satisfaction Measures, Five Data Sets

Beauty Satisfaction Happiness (Measurement error type(s) )

QAL1971,1978

US

5-point rating by interviewer at end of interview: Strikingly handsome or beautiful Good-looking (above average for age and sex)

Average looks for age and sex Quite plain (below average for age and sex) Homely (1,3)

1971: How satisfied are you withyour life as a whole these days?(7 to 1 scale)

1978: How satisfied are you withyour life as a whole (100 pointscale)?

Taking all things together, howwould you say things are these days--- would you say youre very

happy, pretty happy or not toohappy these days? (3 to 1 scale)

QOL1977,

Same as QAL 1971 and 1978 (1,3)

All things considered, howsatisfied would you say you are?

Generally speaking, how happy areyou with your life as a whole?

1979, (11 to 1 scale) (Very, fairly, not too).1981 CDN

ALLBUS 2008 DE

11-point scale, attractive to unattractive (1); (1,3)

If you look at your entire life,would you say you are: veryhappy, rather happy, not veryhappy, not happy at all?


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Table 1, cont.

WLS Wisc.

Constructed from ratings on an 11-point scale, withendpoints labeled as "not at all attractive" (1) and"extremely attractive" (11), based upon an individual'shigh-school yearbook photo (in 1957); each photo wasrated by six men and six women, and the constructedmeasure is an average of the z-scores across raters (1,2)

Age 53: On how many days duringthe past week did you feel happy?(sad?) (values 0 through 7)

Age 65: Same

NCDSUK

Teachers' ratings of the student's appearance at age 7 andat age 11. Which best describes the student? Attractive;unattractive; looks underfed; abnormal feature; scruffyand dirty. "Looks underfed and "scruffy and dirty" werecoded as missing, attractive as good -looking,unattractive and abnormal feature as bad -looking,others as neither. (1,2,3).

Age 41, 46, 51: How satisfied areyou with the way your life hasturned out so far? (10 to 0 scale,from completely satisfied tocompletely unsatisfied)

Age 33: All things considered,how happy are you? (4 to 1scale)

Age 51: On balance I look back on life with a sense of happiness.Often; sometimes; not.

Note: The codes for the types of measurement error, as described in Section III, are: Type 1 (classical measurement error in the beauty rating),Type 2 (attenuation in the accuracy of the beauty rating), and Type 3 (bias in the beauty rating).


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Table 2a. Results from Regressions of Life Satisfaction and Happiness on BeautyRatings, QAL 1971 and 1978*

Men Women Good looks Bad looks Good looks Bad looks

Specification 1: LS, beauty, age quadratic, race, 1971

Life Satisfaction 0.007 -0.199 0.114 -0.191 (0.099) (0.130) (0.087) (0.103)

Adj. R 2 0.013 0.023

Happiness 0.058 -0.096 0.103 -0.099 (0.048) (0.063) (0.041) (0.048)

Adj. R 2 0.014 0.036 N = 831 1138

Specification 1: LS, beauty, age quadratic, race, 1978


Adj. R 2 0.022 0.008

Happiness 0.127 -0.087 0.127 -0.058 (0.032) (0.050) (0.030) (0.044)

Adj. R 2 0.020 0.024 N = 1371 1852

Specification 2: Add, education indicators, number of children, married, health, income, 1971

Life Satisfaction -0.101 -0.056 0.083 -0.062 (0.098) (0.129) (0.086) (0.102)

Adj. R 2 0.090 0.087

Happiness 0.010 -0.029 0.075 -0.030 (0.048) (0.064) (0.040) (0.048)

Adj. R 2 0.064 0.100

Specification 2: Add education indicators, number of children, married, health, income, 1978


Adj. R 2 0.049 0.047

Happiness 0.103 -0.062 0.101 -0.028 (0.032) (0.050) (0.030) (0.043)

Adj. R 2 0.044 0.069 *Estimates that are significantly non-zero, one-sided 5-percent level, in bold . The observations used in Specification2 are identical to those used in Specification 1.


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Table 2b. Results from Regressions of Life Satisfaction and Happiness on BeautyRatings, QOL, 1977-81*

Men(N = 492)

Women(N = 622)

Good looks Bad looks Good looks Bad looks

Specification 1: LS, beauty, age quadratic, language, yearindicators.


Adj. R 2 0.027 0.029

Happiness 0.030 -0.165 0.073 -0.283 (0.054) (0.082) (0.049) (0.093)

Adj. R 2

0.033 0.030

Specification 2: Add, education indicators, number of children, married, health, province, family income,personal income.


Adj. R 2 0.080 0.090

Happiness 0.008 -0.155 0.040 -0.196 (0.052) (0.084) (0.045) (0.081)

Adj. R 2 0.063 0.120

*Estimates that are significantly non-zero, one-sided 5-percent level, in bold . Standard errors are clustered onindividuals.


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Table 2c. Regressions of Happiness on Beauty Ratings, ALLBUS Germany, 2008*

Men (N = 1554) Women (N = 1623) Beauty rated at: Start End Start End

Specification 1: LS, beauty, age quadratic, German

Beauty rating 0.047 0.056 0.062 0.066 (0.008) (0.008) (0.008) (0.008)

Adj. R 2 0.022 0.030 0.040 0.044

Specification 2: Add education indicators, married, partnered, health, own income

Beauty rating 0.025 0.033 0.040 0.044 (0.008) (0.008) (0.008) (0.008)

Adj. R 2 0.117 0.120 0.123 0.125

*Estimates that are significantly non-zero, one-sided 5-percent level, in bold .


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Table 2d. Regressions of Days Happy, Enjoyed or Sad on Beauty Rating,

WLS Ages 53 and 65 Men Women

1992 2004 1992 2004

N = 801 788 993 952

Specification 1: LS, beauty only

# days happy -0.012 0.016 0.128 -0.026 (0.056) (0.044) (0.048) (0.044)

Adj. R 2 -0.001 -0.001 0.006 -0.001

# days enjoyed 0.069 0.008 0.142 0.006 (0.056) (0.045) (0.050) (0.049)

Adj. R 2 0.001 -0.001 0.007 -0.001

# days sad -0.030 -0.014 -0.100 -0.040 (0.033) (0.028) (0.042) (0.039)

Adj. R 2 -0.000 -0.001 0.005 0.000

Specification 2: Add completed education, married, number of children, HS BMI,current BMI, health, own income, household income

# days happy -0.035 0.008 0.108 -0.037 (0.056) (0.045) (0.049) (0.044)

Adj. R 2 0.037 0.064 0.056 0.085

# days enjoyed 0.043 -0.004 0.108 -0.011 (0.056) (0.046) (0.050) (0.046)

Adj. R 2 0.030 0.054 0.077 0.070

# days sad -0.029 -0.008 -0.092 -0.046 (0.031) (0.030) (0.043) (0.041)

Adj. R 2 0.054 0.047 0.037 0.080 *Estimates that are significantly non-zero, one-sided 5-percent level, in bold .


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Table 2e. Results from Regressions of Life Satisfaction and Happiness on BeautyRatings, NCDS Ages 33, 41, 46 and 51

Men Women

Attractive Unattractive Attractive Unattractive Age 11 Age 11 Age 11 Age 11

Specification 1: LS, beauty only

Age 33 N = 3455 3712

Happiness 0.037 -0.031 0.035 -0.027 (0.020) (0.035) (0.022) (0.037)

Adj. R 2 0.001 0.001

Age 41 N = 3271 3656


Adj. R 2 0.005 0.001

Age 46 N = 2886 3231

Life Satisfaction 0.112 -0.220 -0.021 -0.219 (0.055) (0.104) (0.061) (0.104)

Adj. R 2 0.003 0.001

Age 51 N = 2622 2928


Adj. R 2 0.001 0.007

Happiness 0.106 -0.045 0.047 0.021 (0.046) (0.084) (0.049) (0.084)

Adj. R 2 0.002 -0.000


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Table 2e, cont.

Men Women

Attractive Unattractive Attractive Unattractive

Age 11 Age 11 Age 11 Age 11

Specification 2: Add education indicators, number of children, BMI 11, BMI current, married/partnered, health,own earnings, family income (or partners earnings)

Age 33

Happiness 0.017 -0.019 0.017 -0.001 (0.019) (0.034) (0.022) (0.036)

Adj. R 2 0.071 0.075

Age 41

Life Satisfaction 0.151 0.046 0.003 -0.030 (0.061) (0.108) (0.072) (0.121)

Adj. R 2 0.113 0.101

Age 46

Life Satisfaction 0.013 -0.094 -0.109 -0.147 (0.051) (0.089) (0.056) (0.094)

Adj. R 2 0.172 0.181

Age 51


Adj. R 2 0.168 0.174

Happiness 0.045 -0.033 -0.028 0.001 (0.043) (0.078) (0.045) (0.077)

Adj. R 2 0.154 0.169 *Estimates that are significantly non-zero, one-sided 5-percent level, in bold . The observations used in Specification2 are identical to those used in Specification 1.


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Table 3. IV Estimates of the Impact of Beauty on Life Satisfaction and Happiness,QOL, 1979-81*

Men(N = 492)

Women(N = 622)

Good looks Bad looks Good looks Bad looks

Specification 1: LS, beauty, age quadratic, language, yearindicators.


Adj. R 2 0.030 0.031

Happiness 0.061 -0.096 0.073 -0.158 (0.048) (0.114) (0.048) (0.082)

Adj. R 2

0.029 0.019

Specification 2: Add, education indicators, number of children, married, health, province, family income,personal income.


Adj. R 2 0.080 0.093

Happiness 0.048 -0.253 0.051 -0.137 (0.049) (0.113) (0.043) (0.080)

Adj. R 2 0.059 0.117

*Estimates that are significantly non-zero, one-sided 5-percent levels, in bold . Standard errors are clustered onindividuals.


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Table 4. Decomposition of Lower-Bound Estimates of Beauty on Happiness, inSDHappiness/SDLooks Units, ALLBUS Germany, 2008*

Men Women

Specification No.:

1. (Gross effect) 0.149 0.196

2. (Direct effect) 0.081 0.127

Indirect effect: 0.068 0.069

Based on (9a):Monetary 0.011 0.003

Non-monetary 0.057 0.066

Based on (9b):Monetary 0.032 0.000

Non-monetary 0.036 0.069

*Based on the impacts of beauty from Specifications 1 and 2, using ALLBUS(start), with its decompositions inEquation (9a) (9b) based on estimates of a third (fourth) specification that excludes monetary (non-monetary)variables.


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2

Table 5. Average Effects of Beauty on Satisfaction/Happiness, in SDOutcome/SDLooks Units, SixData Sets, Four Countries*

Good Looks Bad LooksMen

Specification No.:1. (Gross effect) 0.081 -0.186

2. (Direct effect) 0.046 -0.125

Indirect effect: 0.035 -0.061

Based on (9a):Monetary 0.006 -0.002

Non-monetary 0.029 -0.059

Based on (9b):Monetary 0.012 -0.039


Women

Specification No.:1. (Gross effect) 0.072 -0.150

2. (Direct effect) 0.061 -0.134

Indirect effect: 0.011 -0.016Based on (9a):Monetary 0.008 -0.009


Based on (9b):Monetary 0

Non-monetary >0.011


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tH tB

Figure 1. Relative Timing of Beauty and Happiness Measures*

*The widths of the bars are proportional to the square root of the number of people evaluating eachsubjects looks. Their heights indicate the length of time between the evaluation of looks and the elicitingof the subjects satisfaction/happiness , with the scale on the vertical axis necessarily non-linear.

QAL QOLALLBUS

(END)ALLBUS(START) NCDS WLS

-1 hr

-0.1 hrs0

0.75 hrs

26 yrs

35 yrs

44 yrs47 yrs


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Figure 2a. Effects of Beauty on Satisfaction/Happiness, Men, AllData Sets*

*Each point represents one beta-coefficient of the impact of beauty on satisfaction/happiness.

-0.6

-0.5

-0.4

-0.3

-0.2

-0.1

0.0

0.1

0.2

-2.0 -1.5 -1.0 -0.5 0.0 0.5 1.0 1.5 2.0

S t d e v

( H a p p i n e s s

/ S a t i s

f a c t i o n

)

Stdev(Beauty)

QAL

NCDS

WLS

QOL

ALLBUS


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Figure 2b. Effects of Beauty on Satisfaction/Happiness, Women, All

Data Sets**Each point represents one beta-coefficient of the impact of beauty on satisfaction/happiness.

-0.6

-0.5

-0.4

-0.3

-0.2

-0.1

0.0

0.1

0.2

-2.0 -1.5 -1.0 -0.5 0.0 0.5 1.0 1.5 2.0

S t d e v

( H a p p i n e s s

/ S a t i s

f a c t i o n

)

Stdev(Beauty)

QAL

NCDS

WLS

QOL

ALLBUS


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Appendix Table 1a. Descriptive Statistics, QAL71 and QAL78, All Observations with BeautyRating, Satisfaction and Happiness Responses*

1971 1978Men Women Men Women

Good looking 0.265 0.307 0.341 0.354

Bad looking 0.130 0.174 0.097 0.120

Life satisfaction 5.550 5.523 82.305 81.975 (0.042) (0.037) (0.353) (0.339)

Happiness 2.185 2.194 2.231 2.204 (0.021) (0.018) (0.015) (0.014)

N 831 1138 1371 1852

*Sample averages with their standard errors for non-binary variables in parentheses here and in Appendix Tables 1b-1e..


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2

Appendix Table 1b. Descriptive Statistics, QOL, 1977-81

Men Women

Good-looking 0.301 0.313

Bad-looking 0.073 0.060

Life Satisfaction 8.661 8.820 (0.042) (0.036)

Happiness 2.382 2.454 (0.014) (0.013)

N individuals 492 622


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3

Appendix Table 1c. Descriptive Statistics, ALLBUS Germany, 2008, All UsableObservations*

Men Women

Start End Start End

Beauty rating 7.324 7.462 7.491 7.612 (0.050) (0.048) (0.050) (0.049)

Happiness 3.047 3.053 (0.016) (0.016)

N 1554 1623


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4

Appendix Table 1d. Descriptive Statistics, WLS, All Observations with Beauty Rating,Satisfaction and Happiness Responses

Men Women 1992 2004 1992 2004

# days happy 5.321 5.673 5.466 5.654 (0.027) (0.058) (0.054) (0.053)

# days enjoyed 5.763 6.040 5.701 5.907 (0.065) (0.057) (0.057) (0.05b)

# days sad 0.668 0.465 1.115 0.841 (0.042) (0.037) (0.049) (0.044)

N 801 788 993 952


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Appendix Table 1e. Descriptive Statistics, NCDS, All Observations with Beauty Rating

Men Women

Attractive age 11 0.501 0.621

Unattractive age 11 0.089 0.095

Men Women Men Women (Age 33) (Age 41)

Happiness 3.324 3.389 (0.010) (0.010)

Life Satisfaction 7.282 7.360 (0.031) (0.033)

N 3455 3712 3271 3656

Men Women Men Women (Age 46) (Age 51)

Happiness 4.320 4.213 (0.022) (0.021)

Life Satisfaction 7.530 7.628 7.364 7.348 (0.026) (0.027) (0.033) (0.034)

N 2886 3231 2622 2928

happinessbeauty[1]

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