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We measure the impact of individuals' looks on their life satisfaction or happiness. Using five data sets from the U.S., Canada, the U.K., and Germany, we construct beauty measures in different ways that allow putting a lower bound on the true effects of beauty on happiness. Personal beauty raises happiness, with a one standard-deviation change in beauty generating about 0.10 standard deviations of additional satisfaction/happiness among men, 0.12 among women. Accounting for a wide variety of covariates, including those that might be affected by differences in beauty, and particularly effects in the labor and marriage markets, the impact among men is more than halved, among women slightly less than halved. The majority of the effect of beauty on happiness may work through its effects on economic outcomes.
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Institute for the Study
of Labor
“Beauty Is the Promise of Happiness”?
IZA DP No. 5600
March 2011
Daniel S. Hamermesh
Jason Abrevaya
“Beauty Is the Promise of Happiness”?
Daniel S. Hamermesh
University of Texas at Austin,
Maastricht University, NBER and IZA
Jason Abrevaya
University of Texas at Austin
Discussion Paper No. 5600
March 2011
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available directly from the author.
IZA Discussion Paper No. 5600
March 2011
“Beauty Is the Promise of Happiness”*?
We measure the impact of individuals’ looks on their life satisfaction or happiness. Using five
data sets from the U.S., Canada, the U.K., and Germany, we construct beauty measures in
different ways that allow putting a lower bound on the true effects of beauty on happiness.
Personal beauty raises happiness, with a one standard-deviation change in beauty
generating about 0.10 standard deviations of additional satisfaction/happiness among men,
0.12 among women. Accounting for a wide variety of covariates, including those that might
be affected by differences in beauty, and particularly effects in the labor and marriage
markets, the impact among men is more than halved, among women slightly less than
halved. The majority of the effect of beauty on happiness may work through its effects on
economic outcomes.
We measure the impact of individuals’ looks on their life satisfaction or happiness using
various sets of data from the U.S., Canada, the U.K., and Germany. The results show that:
1. Personal beauty raises happiness.
2. The majority of this positive effect comes about because personal beauty improves
economic outcomes – incomes, marriage prospects, and others – that increase happiness.
Thus much of the positive effect of beauty on happiness is indirect – through its effects on
aspects of economic life that increase happiness.
3. The total effects of beauty on happiness are about the same for men and women. But the
direct effect is larger among women – beauty affects their happiness independent of its
impact on their incomes, marriage prospects, and other outcomes.
Because the beauty measures are collected in a variety of ways, and because happiness is
also measured in various ways, we can be quite confident in the general validity of the
JEL Classification: I30, J10, C20
Keywords: life satisfaction, measurement error, looks
Corresponding author:
Daniel S. Hamermesh
Department of Economics
University of Texas
Austin, TX 78712
* Stendhal (Marie-Henri Beyle), “La beauté n’est que la promesse du bonheur,“ De l’Amour, Ch. 17, 1822.
The authors are listed in ascending order of their looks. They thank Joel Waldfogel for inspiring this project,
Katrin Auspurg, Chris Bollinger, Gábor Kézdi, Markus Klemm, Andrew Oswald, Karl Scholz, 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 the older data sets.
I. Introduction
While economists have studied happiness for several generations (Easterlin, 2010; Scitovsky, 1976),
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; Deaton and Kahneman, 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 relation of happiness to other outcomes that are at least partly economically
determined (divorce, fertility and others) has also been subject to discussion.
At the same time a smaller, but also burgeoning literature on the effects of beauty on various
outcomes has been created (e.g., Hamermesh and Biddle, 1994; Möbius and Rosenblat, 2006; Mocan and
Tekin, 2010; Hitsch et al, 2010). In these studies the economic focus is on such topics as how beauty is
traded for income, how it alters occupational choice, and how it affects marital bargaining. The general
issue is how human beauty determines outcomes in various markets and shifts the distribution of
surpluses in those markets among participants.
Here we put the two literatures together, examining how happiness, measured in various ways, is
affected by beauty. Some psychologists have correlated subjects’ happiness and their self-assessed
beauty, but that approach seems flawed. Others have compared the happiness of college students to
ratings of their looks by observers (Mathes and Kahn, 1975); related ratings of photographs of college
students to their happiness (Diener et al, 1995); 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); and offered partial correlations of happiness measures
and survey respondents’ waist-to-hip ratio, used as a proxy for beauty (Plaut et al, 2009).
We need to be as certain as possible that our analysis does not merely reflect the idiosyncrasies of
measuring the subjective concepts of happiness and human beauty. For that reason we use five different
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
generated. Given evidence of the sensitivity in responses to questions about happiness to the framing and
scaling of the questions (Conti and Pudney, 2011), using these variously constructed measures should
minimize the concerns over survey-based idiosyncrasies in eliciting responses about happiness. The
surveys use four different approaches to measuring beauty, and in Section III we delineate the types of
measurement errors implicit in each approach and their implications for inferring the impact of beauty on
happiness. These considerations enable us to place a lower bound on this impact. Finally, while
happiness is obviously self-rated by the respondents, none of the beauty measures is—we are not relating
a person’s subjective assessment of one aspect of life to his/her assessment of another (Hamermesh,
2004). 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 people’s expressions of
There are two main general mechanisms through which people’s looks could affect their
satisfaction/happiness. The first is through the many channels that have been shown in the beauty
literature to offer routes by which beauty affects economic outcomes. These indirect effects may be at
least as important as the direct effects of beauty on satisfaction/happiness—the halo that one’s 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 any measured impact of
beauty on satisfaction/happiness into its direct and indirect components and thus examine the extent to
which a beauty-happiness relation works through markets.
II. Data Sources and Descriptive Statistics
The five sets of data that we use are especially diverse in terms of their methods of assessing
beauty. The first consists 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 up. At the end of the
interview in each of these surveys, the interviewer assessed the interviewee’s looks on a five-to-one scale,
with 5 being strikingly handsome or beautiful, 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. This
measure has been used in a variety of studies linking beauty to economic outcomes (e.g., Hamermesh and
Biddle, 1994; Leigh and Susilo, 2009), although typically with the top two categories combined into a
category “good looks” and the bottom two combined into “bad looks,” because of the paucity of
respondents rated 5 or 1.
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 and on the person’s total experience, measures that are standard in the life
satisfaction 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, sampling Canadians ages 18 and over in 1977. In each of the three waves a wide
array of subjective information was obtained from a random sample of the population. As in the QAL, at
the end of each interview the interviewer rated the subject’s looks using the same five-point rating
system. Interviewers differed across the 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.
The German contribution to the General Social Survey program in 2008, the ALLBUS
(Allgemeine Bevölkerungsumfrage der Sozialwissenschaften), included measures of beauty and
happiness. As in the QAL and QOL the interviewer rated the subject’s looks at the 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—when s/he first came into contact with the subject. The survey also
obtained a measure of happiness, more backward-looking than the happiness measures in the QAL and
QOL, and measured on a four-point scale. We use data on all respondents ages 18 and up for whom
information is available on the crucial variables.
Another data set with measures of both beauty and happiness is the Wisconsin Longitudinal
Survey (WLS), a study of a cohort of high-school graduates from 1957. Unlike the previous three sets of
studies, beauty in the WLS was based on assessments of high-school graduation photographs of the
participants. These photographs were provided in 2004 to panels of raters, nearly all of whom were born
earlier than the respondents, and who thus had a feel for what was considered as good or bad looks in the
late 1950s. Each respondent’s picture was rated by 12 individuals (6 men and 6 women), with raters’
ages ranging between 63 and 91 (in 2004). The ratings were unit-normalized within a given rater and
averaged over raters. 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. These happiness measures were thus obtained 35 and 47 years after the photographs from which the
respondents’ 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 student’s teacher
assessed his/her attractiveness, along a scale shown in column (1) of Table 1. We aggregated these into
the three categories, good-looking, average-looking and unattractive, 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 or 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 respondent’s entire life experience. Happiness at
age 51 was also measured in a backward-looking manner, while happiness at age 33 was measured with
reference to the respondent’s current situation only.
1Other studies have assessed beauty from school pictures taken nearly two decades before the outcome to which the
assessments were linked (Biddle and Hamermesh, 1998), and one study even showed a high correlation between the
assessments of pictures of 10-year-olds and those of the same individuals at age 50 (Hatfield and Sprecher, 1986, p.
In Appendix Tables 1a-1e we present descriptive statistics for the five 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 the majority are rated as average-looking.
Also as is usual, women are rated more extremely 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. In the ALLBUS the crucial thing to note is that
the ratings of both men’s and women’s 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 normed, 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 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. In the QAL the comparisons are mixed; in the QOL and the ALLBUS women are more
satisfied/happier, while the opposite is true in the WLS and NCDS. The differences in the nature of the
measures across the surveys make them non-comparable along this dimension; but considering them
underscores the benefits of using various different measures of satisfaction/happiness.
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 respondent’s beauty in relation to the elicitation 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 subject’s 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 other’s looks. The best
possible measure would average the 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
that kind of measure has not been obtained in any study we know of, we are thrown back on thinking
about how the measures in these data sets generate errors in inferring the impact of beauty on
To focus only on the beauty rating, consider the following simple linear regression model, with
H (satisfaction/happiness) as the dependent variable and true (latent) beauty B
* as the explanatory
, 0, 
. (1)
The subscript t indicates the time at which the happiness measure is observed. B
* is the true measure of
beauty at the time t.
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 B
*, because of the small number of raters.
(2) Attenuation in the accuracy in 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
2To simplify notation we assume homoskedasticity throughout this section and therefore omit conditioning on B
satisfaction/happiness measure. The variance will be an increasing function of the time interval
between observation of the beauty rating and observation of the happiness measure, a problem in
both the NCDS and the WLS.
(3) Bias in the beauty rating: If the beauty rating is elicited after the rater has spent time interacting
with the subject (e.g., in an interview, as in the QAL, QOL or the end of the ALLBUS, or as a
teacher, as in the NCDS), we would expect a positive correlation between the beauty rating and
the unobservable component ε of the happiness outcome. For instance, an interviewer might
have a better opinion of a subject’s beauty if the subject projects self- confidence in the interview,
which might occur if the subject is happier.
The following stylized model for the observed beauty rating incorporates each of these three possible
sources of difficulty:
 , (2)
where st is the time at which the beauty rating B is obtained. The attenuation component of the
measurement error is νt‐s, which has a variance assumed to be linear in the time interval t‐s:
,0. (3)
The other component of the error, denoted η , is similar to a classical measurement error, except that we
allow it to be correlated with the happiness residual ε :
,0. (4)
For this general model, the inconsistency of the least-squares estimator is given by the probability limit of
the slope estimate:
 ,
 
, (5)
where σ*
denotes the variance of B
*. (Note that the textbook case of classical-measurement error is a
special case of this formula corresponding to 0 (no rating bias) and st (no depreciation
effect), for which plimβ
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 st , but the interview format leads to the possibility of a bias in the beauty rating
(σ 0). The probability limit of the slope-estimate in this case simplifies to
. (6)
If β 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 β contains measurement errors of Types 1 and 3.3
Estimates based on the ALLBUS(start), in which the interviewer’s rating of the respondent’s
attractiveness is obtained at the very start of the interview, avoids Type 3 measurement error. In this case
the classical errors-in-variables result, plimβ
, is all that remains; and it occurs because
only one person (the interviewer) is assessing the subject’s looks.
In the WLS data the beauty rating is based upon a subject’s high-school picture, and the
happiness measure is elicited during late adulthood. There will be an attenuation effect, since st , and
3In all of these studies the interviewers assessed the subject’s looks very near the end of the interview, and
substantially after the subject’s satisfaction/happiness was elicited. It is very unlikely that the subject’s specific
response to happiness questions directly affected the beauty rating.
the lack of interaction between the rater and the subject eliminates concerns about beauty-rating bias.
Entering σ 0 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 as “too low” (a lower bound to the true
Finally, for the NCDS dataset, all three difficulties could arise, since the beauty ratings were
assessed in childhood (st) by only two teachers, both of whom were very familiar with the subject
(σ 0). 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 an opposite direction from the measurement error.
The impact of the other errors here, however, would be expected to be much larger than in those data sets,
since the beauty ratings in the NCDS are assessed decades before the expression of happiness is elicited
(as captured by the attenuation bias t‐sσ
). Although it is difficult to say how the sizes of σwould
compare between the NCDS and those interview-based data sets, the large difference in the variance of
measurement error between the two studies leads us to believe that the probability limits for the NCDS
estimates would be lower than the true effects.
IV. Basic Results
In this section we estimate linear models relating life satisfaction/happiness to beauty in each of
the five data sets. For each we first include as regressors only the beauty measure(s) and, in the QAL,
QOL and ALLBUS, a quadratic in age and a measure of race/ethnicity, which might affect happiness but
which cannot be caused by differences in beauty. Then we add a number of covariates that have been
shown to affect happiness but may not mediate the effect of beauty on satisfaction/happiness. In the next
section we report on a large number of robustness checks that include varieties of additional controls,
alternative beauty measures and more complex estimation procedures.
Table 2a presents the estimates based on the two QAL surveys. Among 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). This is true whether or not we control for age, education, race, number of
children and marital status.4 Indeed, the addition of the vector of controls hardly alters the point estimates
of the coefficients among women; and nearly all the estimates are statistically significantly different from
zero. Among men almost all of the point estimates have the expected sign, and they are generally
statistically significant in the 1978 data. As with women, adding the vector of controls does not greatly
alter the point estimates. The effects of differences in beauty on life satisfaction or happiness are not
small, at least in the 1978 data. Using the estimates from Specification 2, going from the bottom eighth
of women’s (men’s) looks (those rated below-average) to the top third (those rated above-average) raises
satisfaction with life by 0.45 (0.48) standard deviations; the effects on happiness of this difference in
beauty are 0.38 (0.48) standard deviations. The impacts of differences in beauty in the 1971 data are
smaller, but still average about 0.20 standard deviations.
The QOL results, shown in in Table 2b, are qualitatively similar to those of the QAL. Almost all
the effects are in the expected directions, and the negative impact on satisfaction/happiness of being
among the small fraction of Canadians classified as being below-average in looks is substantial. There is
no obvious gender difference in the impacts of beauty. Adding indicators of education, marital status and
number of children has little effect on the estimates. The estimated effects are even larger than those in
the QAL: Going from the bottom twelfth of women’s (men’s) looks to the top third raises life satisfaction
by 0.36 (0.45) standard deviations, and raises happiness by 0.64 (0.75) standard deviations.
4Whether we should be controlling for marital status here is unclear. There is substantial evidence that married
people are happier (e.g., Blanchflower and Oswald, 2004; Oswald and Wu, 2011); but one’s gains from marriage are
affected by one’s looks (Hamermesh and Biddle, 1994). In the data sets used here, however, there is mixed
evidence on the relationship between beauty and the probability of being married, with better-looking people being
significantly more likely to be married in the NCDS, but with little evidence of any effect in the other studies.
For the ALLBUS data Table 2c presents the estimates of the impacts of beauty on happiness, in
one case using the assessment of beauty from the start of the interview, ALLBUS(start), in the other using
the assessment from near the end, ALLBUS(end). Specification 2 adds indicators of educational
attainment, of marital status and of partnered status.5 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 in line with the discussion of
measurement error, the effects are smaller when we use the ALLBUS(start) ratings, with the change being
larger among men. 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 women (man) to the median above-
average looking woman (man) produces an increase in happiness of 0.31 (0.23) standard deviations based
on ALLBUS(start), and 0.34 (0.31) standard deviations based on ALLBUS(end). The former is
somewhat smaller than in the QAL or QOL, perhaps because using ALLBUS(start) vitiates what we have
denoted as Type 3 measurement error.
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 years of education, marital status,
number of children, BMI observed at high-school graduation, and current BMI. These latter two allow
for possible correlations between ratings of attractiveness and overweight/obesity (although the evidence
for the labor market suggests that the correlations, and their impacts on wages, do not affect the estimated
effects of beauty on outcomes—Hamermesh and Biddle, 1994). As with the results for the interview data
sets, adding this vector of covariates hardly alters the estimated impacts of attractiveness on the measures
of satisfaction/happiness. 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 estimated
5The education categories are other, “mittlere Reife,” and “Hochschul,” each accounting for about one-third of each
sample. We include a separate indicator of life partner here but nowhere else, because nearly 10 percent of the
sample reported not being married but having a life partner.
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, suggesting the importance 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 men’s high-school graduation pictures
than of women’s.
Table 2e shows the results of relating measures of happiness and satisfaction in adulthood in the
NCDS sample to attractiveness as assessed by a child’s teacher at age 11. The first part of the table
includes only indicators for being rated as attractive or as unattractive (with a middle category excluded).
All of the estimated impacts that are statistically significant are of the expected sign, and there is no
obvious difference in the size or significance of the effects between men and women. The second part of
Table 2e reports the estimates of the impacts of the beauty measuress when indicators for educational
attainment, marital status, number of children, BMI at age 11 and current BMI are added to the
equations.6 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.
There are a large number of estimates of the impact of looks here—30 coefficient estimates for
each gender for each of Specifications 1 and 2. Among men in all the samples taken together, in
Specification 1(2) 27(27) of the 30 estimated coefficients have the expected signs, of which 14(9) are
6The 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 the excluded category.
significantly nonzero. Among women in Specification 1(2) the comparable summaries are 28(26) and
(17)14. None of the few “incorrectly” signed parameter estimates is statistically different from zero. The
data strongly support the notion that better looks produce a gross effect on life satisfaction/happiness.
While these comparisons clearly suggest a positive answer to the titular question of this study, we
would like to compare the estimates across the samples, given the differences in the potential biases. 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 6th percentile of the
distribution of looks and is thus 1.53 standard deviations below the mean beauty of men. We use this
type of approximation for all the QAL, QOL and NCDS results. 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 that were the constructed beauty ratings.
The results of these calculations are shown in Figures 2a and 2b, with each of the 30 points in a
Figure representing the fractional change in standard deviations of satisfaction/happiness generated by a
movement from the mean beauty to some point in the distribution below or above the mean. 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, in Specification 2 among women (men) the gain from being this good-looking is 0.053 (0.048)
standard deviations along the satisfaction/happiness index compared to the average male (female), while
the loss from being this bad-looking is 0.157 (0.176) standard deviations of satisfaction/happiness.
Assuming, as these calculations must, that the effects are linear within the categories above-average and
average, or attractive and unattractive, the results in the expanded specifications imply that a one
standard-deviation increase in beauty raises satisfaction/happiness by 0.087 (0.088). These are not large,
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 regressions based on the QAL, QOL and
ALLBUS(end), where the assessment of beauty late in a long interview might have created Type 3
measurement error. They are somewhat smaller using the ALLBUS(start); and they are smallest, and
certainly negatively biased, in the estimates based on 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 estimates based on
the NCDS is unclear, since the errors induce opposite-signed biases, but the estimates are generally below
those from the interview studies.
Overall the estimates from the five sets of data suggest:
1. There is a positive gross effect of good looks on satisfaction or happiness, and a negative gross
effect of bad looks, even accounting for a variety of demographic variables that might be
correlated with beauty and/or satisfaction/happiness, and even accounting for a variety of issues
of measurement.
2. These effects are not huge, but by the standards of the labor, education and health literatures
they are not tiny.
3. The gross impacts of beauty or its absence on satisfaction/happiness seem roughly the same
among women and men.
V. Robustness Checks and Methodological Extensions
Although we were concerned about measurement issues in discussing the results in Section IV, in
none of the estimation did we consider alternative measures and specifications, nor did we use alternative
approaches to estimation. We do that here, in each case basing the estimates on the expanded
specifications with control variables (Specification 2) in Tables 2a-2e.
A. Re-specifying Proxies for Beauty and Considering Confounding Variables
No sensible reformulations of the beauty ratings in the QAL or ALLBUS surveys can be done to
check their robustness, but we can use alternative measures from the other three data sets.7 In the QOL
we substituted measures from the other two years in which the respondent’s satisfaction/happiness was
elicited for the measure noted at the end of the particular interview. This approach will reduce Type 1
measurement error and also reduce Type 3 measurement error (but not eliminate it, assuming there is
some correlation in happiness across the biennia), but it will introduce some Type 2 (attenuation) error.
We present the results of this re-estimation in Table 3. The estimated effects are generally larger and more
significant statistically than the comparable estimates shown in the bottom panel of Table 2b. Implicitly
the reductions in Types 1 and 3 measurement error have larger effects than does the introduction of some
Type 2 measurement error.
In the WLS we re-estimated the expanded specifications using first the normalized beauty ratings
given by female raters to pictures of female respondents, and by male raters to male subjects. We 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 measurement error in these assessments of beauty
when fewer raters are used; but all of those that were statistically significant (women in 1992) remain so.
The NCDS respondents’ appearance was assessed by their teachers at age 7 as well as by their
teachers at age 11 (the measures used in Section III). To the extent that the measurement error in the
variable we used arose from random errors in an individual teacher’s assessment of the child’s
appearance, averaging the teachers’ ratings at ages 7 and 11 reduces that error. Accordingly, we average
the indicator variables for appearance at 11 with identically defined variables describing appearance at
age 7. These average measures replace the age-11 measures in the estimating equations, and the age-11
7In the QAL 1971, for example, only 59 of the respondents are rated as strikingly handsome or beautiful, and only
44 are rated as homely. When we re-estimated the models with measures encompassing each of the five beauty
ratings, unsurprisingly, given the cell sizes at the extremes, this extension hardly altered the conclusions. The
number in the lowest category in the QOL panel is even less.
BMI is replaced with the average of BMI at ages 7 and 11. 8 In a few cases some previously insignificant
parameter estimates in Table 3b become marginally significant, but otherwise there is no change.
Implicitly, whatever measurement errors exist in the age-11 proxies are highly positively correlated with
those in the age-7 data and thus cannot be eliminated by averaging.9
Another concern is that different assessors rate beauty differently, and that their idiosyncrasies
may be correlated with the subjects’ happiness. With most teachers in the NCDS assessing only one
subject’s appearance, this issue cannot be examined in those data; and we cannot identify the raters in the
WLS. In the interviewer surveys, however, we know which raters assessed each subject’s beauty.
Accordingly, we re-estimate the equations in Tables 2a-2c adding interviewer fixed effects. With one
exception (the impact of bad looks among women 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.
Another potential difficulty with the results is that there are location-specific determinants of
beauty that may also directly affect people’s perceived satisfaction/happiness. For example, perhaps
living in Los Angeles with its proximity to mountains and ocean makes people happier and also attracts
good-looking people. In the two data sets containing long-duration longitudinal data there is strong
evidence, consistent with Gautier et al (2010), that changes in location are related to beauty.10 To
8As Lubotsky and Wittenberg (2006) show, the appropriate method in this case and in the re-estimation for the QOL
is to introduce the information from the other two years separately rather than as averages. We present and discuss
only the estimates 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.
9We also used the age-7 measures alone—looks and BMI—in place of the age-11 measures. Perhaps
unsurprisingly, the results were slightly weaker than with the age-11 measures, and thus somewhat weaker still than
the specifications based on the age-7 and age-11 averages.
10For example, in the WLS we can compare the beauty of the Wisconsin high-school graduates who remained in the
state to those who were not living there at age 51. The average beauty of those still residing in Wisconsin at age 53
was 0.004 (s.e.= 0.045), while the beauty of those who had left was 0.098 (s.e.= 0.030). Those who remained are
significantly worse-looking than those who left. Because the NCDS was national, it allows us to compare the
beauty of those who entered, those who stayed and those who left an area. Because the definitions of the British
regions were not the same in all the NCDS waves, we cannot examine mobility and beauty for all areas; but
southeast England, and Scotland and Wales, are consistently identified at ages 11 and 33. (Since most geographic
mobility in the sample occurs between these ages, this is the most useful single comparison.) 0.572 (s.e. = 0.013) of
investigate this issue in the QAL and QOL surveys, we add state or province fixed effects to the basic
equations in Tables 2a and 2b.11 As in the other re-specifications, this extension hardly altered the results.
Although the vectors of state and province effects were themselves statistically significant, and their
inclusion did increase slightly the absolute values of the estimated effects of the beauty indicators, no
qualitative changes resulted from their addition.
In the WLS the only locational information is whether the high-school graduate still resides in
Wisconsin at the time of the interview. Adding this location variable to the specifications for the three
outcomes in Table 2d produces only minute changes in the estimated impacts of beauty. No signs
change, and the impacts remain statistically significant only for women observed in 1992.
In the NCDS we can account for regional effects in both the assessments of beauty during
childhood and the effect of childhood beauty on adult happiness. There may be regional differences in
beauty standards, which using region in childhood as a control would account for; and there may be
regional differences in the relationship between beauty and satisfaction/happiness, which using location
as an adult could account for. We thus re-specify the equations in Table 2e to include vectors of regional
indicators at age 11 and at the time the respondent’s happiness/satisfaction is reported. In no case did
either of these vectors of fixed effects approach statistical significance; nor did their inclusion
qualitatively alter the impacts of beauty on satisfaction/happiness. In these data, at least, regional
differences in childhood and adulthood just are not important in affecting the estimated relationships
between beauty and satisfaction/happiness.
Before examining additional covariates that we have ignored, consider a conundrum in the WLS
results: The effects of beauty on happiness are apparent (in Table 2d) in 1992 (at age 53) but not in 2004
those who moved to the Southeast were good-looking at age 11, but only 0.545 (s.e. = 0.013) of those who stayed
were, and only 0.512 (s.e.= 0.032) of those who left were good-looking. The Southeast attracted good-looking
people, while less good-looking people moved elsewhere in the U.K. While not statistically significant, the
differences in Scotland and Wales are exactly opposite those in the Southeast: 0.571 (s.e.= 0.040) of those who
entered were good-looking; 0.587 (s.e.= 0.013) of those who stayed were; and 0.598 (s.e.= 0.031) of those who left
11While we used indicators for all states in the QAL, in the QOL we divided the country into the Atlantic provinces,
Quebec, Ontario and the West.
(at age 65). One reason might be that beauty effects on happiness generally diminish as one advances
past middle age. To explore this possibility the only way that these data sets allow, we re-estimate
Specification 2 for the interview surveys, adding interactions of the quadratic in age with the variables
measuring beauty assessments. Only in one of the re-specifications for the QAL was the vector of
interactions statistically significant; and in none of the re-specifications was the average beauty effect
altered by accounting for possible nonlinear effects in age.
Might the results differ if we had good objective measures of intelligence and non-cognitive
abilities? In the QAL the interviewer is asked to assess the intelligence of the interviewee, with a result
that is essentially uncorrelated with the assessment of looks; but these subjective measures are not very
satisfying as controls in equations where the central explanatory variable is also assessed by the
interviewer. The other interview studies suffer from a similar absence of objective measures of these
characteristics. The NCDS, however, provides test scores of the respondent’s general ability while a
child, and we add these to the estimated equations. While those respondents who tested as more able as
children are typically more satisfied/happy in these re-estimates, the inclusion of this additional measure
has tiny and erratic impacts on the estimated impacts of beauty on satisfaction/happiness. Also, Scholz
and Sicinski (2011) show that in the WLS measures of IQ and a wide array of measures of non-cognitive
characteristics are not correlated with the raters’ assessments of the photographs of the subjects. The
answer to the initial question of this paragraph would seem to be negative.
Other studies (see Stevenson and Wolfers, 2008, Blanchflower and Oswald, 2008) have found
health status to be an important determinant of happiness. The difficulty here is that in all the data sets
the measures of health are subjective, self-assessed, so that they are very likely to be determined by the
same factors that determine happiness/satisfaction. Nonetheless, we add self-reported health to the
second specifications in each data set. In the QAL surveys subjective health is based on the response to a
question about whether the respondent has health problems (with about 30 percent responding yes). All
the other sets of data report self-rated health on five- or four-point scales that typically range from
“excellent” to “poor.” Indicators of these subjective responses, measured as whether the person states that
s/he is in one of the top two categories of self-reported health, are strongly positively correlated with the
happiness and satisfaction measures in all the specifications. The estimated impacts of the beauty
measures on these outcomes, however, change only slightly, with only small decreases in their statistical
significance and no changes in their signs.
An additional set of checks includes covariates related to the respondent’s parents. To the extent
that there are family background effects in happiness (Hartog and Oosterbeek, 1997), including these
measures could help to isolate the effect of beauty independent of any correlation that it might have with
unmeasured characteristics in the respondent’s family background. In the NCDS we know whether the
respondent’s parents are alive at his/her age 41, 46 and 51. Since parental death might affect well-being,
for these latter two waves we thus form an indicator equaling 1 if a respondent’s parent died in the five
years preceding the interview (which occurred for 16 percent of the respondents at age 46 and 14 percent
at age 51).12 In each of the six equations describing satisfaction/happiness at ages 46 and 51 a parental
death in the quinquennium does have a negative effect; unsurprisingly, given that there is no reason to
expect that the assessment of beauty at age 11 will be correlated with parental mortality three decades
later, this additional variable leaves the estimated beauty effects essentially unchanged.13 The NCDS also
provides data on the social status of the respondent’s parents at age 11. Adding indicators of parental
status to the equations describing adult satisfaction/happiness also produces only tiny changes in the
estimated impacts of beauty.14
A final set of possibilities relates to changes in the respondent’s marital status. In the estimates
based on the QOL and the NCDS we added indicators for whether the respondent got married since the
previous interview or whether her/his marriage had ended since then. In no case did the inclusion of these
indicators produce any but minute changes in the estimated effects of beauty.
12Five- and even two-year retrospectives may be too long to observe an effect on current happiness, but those are
what the data sets limit us to.
13The effects are not changed greatly in any of the data sets when all the additional variables are included at the
same time. This is not surprising, as within each data set these additional variables are typically nearly orthogonal.
14Roughly one-fourth of the sample was coded as top-class, and one-half as middle class.
B. Methodological Extensions
All of the specifications reported in Tables 2a-2e were estimated by least squares—although in
most cases the expressions of satisfaction/happiness take only a small number of values. To examine
whether a discrete-variable modeling approach would change the results, we re-estimate the models
(except for satisfaction in the QAL 1978, where the questionnaire allowed a large range of discrete
responses). Except for the WLS this means estimating ordered probits over these specifications; in the
WLS, since the measures are of numbers of days (ranging from 0 to 7), we re-estimated the model using a
count-data method (Poisson estimation). These various estimation methods yielded qualitatively very
similar results to those reported in Section IV, with all previously significant effects remaining.
In the WLS data the respondents’ underlying happiness is measured in three ways—by the days
s/he identifies as being happy, as sad, or as having been enjoyed in the past week. Each of these measures
can be viewed as a noisy measure of the respondent’s underlying mental state. To remove some of the
noise we first simply subtract days sad from days happy and re-estimate the specifications that were
presented in the bottom half of Table 2d. Then, since we do not know what the appropriate weights on
these particular expressions of happiness might be, we re-estimate the equations using the first
eigenvector (first principal factor) to weight the three expressions of happiness—days happy, days sad
and days enjoyed These alternatives do not add much to the basic results. Among men the effects of
beauty are positive but statistically insignificant; among women they are positive and highly significant in
1992, insignificantly negative in 2004.
VI. Inferring 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 or more satisfied than their less good-looking peers?
How much is due to the fact that beauty enhances one’s outcomes in various markets, including the labor
and marriage markets?15
We can interpret the estimate of β in equation (1) as the gross effect of beauty on
satisfaction/happiness. If we add as many covariates as we have information—both those included in the
expanded specifications in Tables 2, and also self-reported health, and measures of earnings and spouse
quality—we obtain:
. (8)
We can then define the direct effect of beauty on satisfaction/happiness as ′, and the indirect effect as
the difference′.
In the QAL the only additional covariates included in the vector X are self-reported health and the
available measures of individual income. No doubt this paucity of additional covariates will generate an
additional upward bias in the estimated direct effect beyond the possible overall bias that we already
noted. In the QOL and the ALLBUS we add self-reported health, personal income and family income
(thus presumably proxying the income of a spouse if one is present) to the covariates included in
Specification 2. In the WLS for each of the two years we add health status and variables measuring the
respondent’s own income and the household’s income. Finally, in the NCDS estimates for the age 33, 41
and 51 waves we add self-reported health and own and spouse/partner’s weekly earnings, while in the age
46 wave we replace spouse/partner’s earnings with family income (spouse/partner’s weekly earnings
being unavailable).
Rather than presenting the estimates of these expanded specifications, in Table 4 we simply list
the average effects of moving from the mean beauty to being good-looking or bad-looking, measured in
standard-deviation units of satisfaction/happiness. (The statistics listed in Table 4 for Specification 2 are
based on the averages of those shown in Figures 2.) Taking the results at face value suggests that roughly
half of the gross effect of looks works through the marriage and labor markets; but while this further
15The effect of a different personal endowment, height, on happiness was decomposed into these components by
Deaton and Arora (2009), with the adjustment limited to accounting for the impact of height on earnings.
expansion of the estimates accounts for some of the indirect effects of beauty on satisfaction/happiness,
the available data do not allow us to account for impacts in other markets. As just one example, there is
growing evidence that beauty confers benefits on good-looking borrowers in lending markets
(Hamermesh, 2011, Chapter 7). Moreover, our proxies for the outcomes in the labor and marriage
markets that are affected by beauty are far from perfect. It thus seems fair to conclude that the expanded
estimates suggest that the direct effect of beauty is at most one-half of the total effect, and perhaps much
less. The majority of the impact of beauty on satisfaction/happiness appears to be economic—through its
effects on outcomes in various markets.16
These simple averages ignore the different directions of the biases induced by the various types of
measurement error discussed in Section III. To take advantage of the exclusion of Type 3 measurement
error from the ALLBUS(start) assessment of beauty, we assume that the biases in the QAL and the QOL
ratings induced by that error component are proportionate to those in the ALLBUS. We then re-calculate
the estimates from those data for each gender by prorating them by the difference between the estimates
in the ALLBUS using the ALLBUS(start) and ALLBUS(end) measures. Thus we scale down the
estimates from the other interview studies based on the proportionalities between the estimates from the
ALLBUS in each of the three Specifications. Taking these re-estimates and those from the ALLBUS
based on beauty rated at the start of the interviews, these estimates offer a lower bound on the impact of
beauty on satisfaction/happiness in the interview-based studies.17
16One might object that the surveys key the respondents into thinking about economic issues when they respond to
questions 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 and
income long precede those on satisfaction/happiness, in the QOL the opposite is the case, while in the QAL own pay
is elicited long before satisfaction/happiness, while family income is elicited long after. Moreover, to the extent that
there are reporting errors in own and spouse’s pay (or incomes), they will lead us to underestimate the indirect
effects. In sum, survey-induced biases seem to be minimal or, indeed, will lead us to understate the relative
importance of indirect effects.
17Regressing the end-of-interview beauty rating on the rating at the start and the interview duration, the estimated
effect of start-of-interview beauty is positive but statistically less than one, suggesting regression to the mean.
Among men, conditional on the initial rating, the length of the interview (of contact between interviewer and
interviewee) had no effect on the rating at the end; among women, the effect was positive, statistically significant,
but tiny compared to the dispersion in the beauty rating.
Averaging these re-estimates together across all three interview-based studies (QAL, QOL and
the ALLBUS) yields the lower bounds shown in parentheses in Table 4. In some cases these lower
bounds exhibit larger effects than the simple averages, which is not surprising since the latter include
results from the WLS and NCDS, in which the beauty ratings suffer from Type 2 measurement error.
Overall, however, these lower bounds yield the same conclusions as do the simple averages—at least half
of the impact of beauty on satisfaction/happiness is indirect.
The results in Table 4 show only slightly greater gross effects of beauty among women than
among men. The direct effects, however, are substantially larger among women, with relatively less of the
impact of beauty on women’s satisfaction/happiness working through markets. This gender difference
may explain why, although most of the studies measuring the impact of beauty on earnings find larger
effects among men than among women, laypeople believe that looks matter more to women.18
VI. Conclusions and Extensions
We have examined the relationship between people’s life satisfaction/happiness and their beauty.
Both are subjective, although in each of our empirical examples the agent describing his/her satisfaction
differs from the agent(s) describing his/her beauty. While the beauty measures introduce difficulties into
the inference of 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 they allow us to
place a lower bound on the magnitudes of the true impacts of beauty on happiness.
The results suggest that a person’s beauty does increase his/her satisfaction/happiness, with
effects that are not tiny. Moreover, among both men and women at least half of the increase in
satisfaction/happiness generated by beauty is indirect, resulting because better-looking people achieve
more desirable outcomes in the labor market (higher earnings) and the marriage market (higher-income
spouses). That relatively more of the impact among women is direct, not mediated through the effects of
18“Women face greater discrimination when it comes to looks,” Wall Street Journal, Friday-Saturday, November 26-
27, 1993, page 1, quoting indirectly Naomi Wolf, author of The Beauty Myth.
beauty on market outcomes, may help to explain gender differences in people’s attitudes about their own
Calls from political leaders and important economic theorists for combining happiness with GDP
to obtain new measures of economic welfare may have some popular appeal.19 Substantial evidence (see
Hamermesh, 2011, Chapter 2), however, makes it clear that even radical measures to alter one’s looks
have fairly small effects. Coupling that observation with our findings implies that much of the differences
in happiness that exist in a society arise from characteristics that are beyond one’s control. While there
are many other good reasons to avoid combining GDP measures with measures of subjective well-being,
our discussion showing the importance of this one, essentially immutable determinant of happiness
suggests that focusing on creating a happier society may not be fruitful.
19This is argued by the Report by the Commission on the Measurement of Economic Performance and Social
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Table 1. Descriptions of Beauty, Happiness and Satisfaction Measures, Five Data Sets
Beauty Satisfaction Happiness
(Measurement errors)
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)
1971: How satisfied are you with
your life as a whole these days?
(7 to 1 scale)
1978: How satisfied are you with
your life as a whole (100 point
Taking all things together, how
would you say things are these days
--- would you say you’re very
happy, pretty happy or not too
happy these days? (3 to 1 scale)
1977, Same as QAL 1971 and 1978
(1,3) All things considered, how
satisfied would you say you are? Generally speaking, how happy are
you with your life as a whole?
1979, (11 to 1 scale) (Very, fairly, not too).
11-point scale, attractive to unattractive
(1); (1,3) If you look at your entire life,
would you say you are: very
happy, rather happy, not very
happy, not happy at all?
Table 1, cont.
Wisc. Constructed from ratings on an 11-point scale, with
endpoints labeled as "not at all attractive" (1) and
"extremely attractive" (11), based upon an individual's
high-school yearbook photo (in 1957); each photo was
rated by six men and six women, and the constructed
measure is an average of the z-scores across raters
Age 53: On how many days during
the past week did you feel happy?
(sad?) (values 0 through 7)
Age 65: Same
UK Teachers' ratings of the student's appearance at age 7 and
at age 11. Which best describes the student? Attractive;
unattractive; looks underfed; abnormal feature; scruffy
and dirty. "Looks underfed” and "scruffy and dirty" were
coded as missing, “attractive” as good-looking,
“unattractive” and “abnormal feature” as bad-looking,
others as neither.
Age 41: How satisfied are you
with the way your life has turned
out so far? (10 to 0 scale, from
completely satisfied to
completely unsatisfied)
Age 51: Same
Age 33: All things considered,
how happy are you? (4 to 1
Age 51: On balance I look back
on life with a sense of happiness.
Often; sometimes; not.
Table 2a. Results from Regressions of Life Satisfaction and Happiness and Beauty
Ratings, QAL 1971 and 1978*
Men Women
Good looks Bad
looks Good looks Bad looks
Specification 1: LS, beauty, age quadratic, race, 1971
Life Satisfaction -0.034 -0.255 0.116 -0.203
(0.096) (0.129) (0.084) (0.100)
Happiness 0.041 -0.093 0.097 -0.124
(0.047) (0.062) (0.039) (0.046)
Specification 1: LS, beauty, age quadratic, race, 1978
Life Satisfaction 2.673 -2.290 1.423 -3.279
(0.745) (1.155) (0.703) (1.016)
Happiness 0.108 -0.068 0.120 -0.096
(0.031) (0.048) (0.028) (0.041)
Specification 2: Add, education indicators, number of
children, married, 1971
Life Satisfaction -0.088 -0.177 0.113 -0.122
(0.096) (0.130) (0.083) (0.099)
Happiness 0.019 -0.058 0.082 -0.077
(0.047) (0.064) (0.039) (0.046)
Specification 2: Add education indicators, number of
children, married, 1978
Life Satisfaction 2.653 -2.405 1.323 -3.421
(0.075) (1.159) (0.707) (1.016)
Happiness 0.089 -0.044 0.101 -0.084
(0.031) (0.048) (0.028) (0.041)
*Estimates that are significantly non-zero, one-sided 5-percent level, in bold.
Table 2b. Results from Regressions of Life Satisfaction and Happiness and Beauty
Ratings, QOL, 1977-81*
Good looks Bad looks Good looks Bad looks
Specification 1: LS, beauty, age quadratic, language, year
Life Satisfaction -0.095 -0.451 0.160 -0.299
(0.140) (0.232) (0.134) (0.246)
Happiness 0.031 -0.225 0.057 -0.200
(0.053) (0.088) (0.040) (0.076)
Specification 2: Add, education indicators, number of
children, married.
Life Satisfaction -0.075 -0.448 0.156 -0.259
(0.146) (0.234) (0.130) (0.243)
Happiness 0.033 -0.219 0.053 -0.168
(0.053) (0.086) (0.040) (0.073)
*Estimates that are significantly non-zero, one-sided 5-percent level, in bold. Standard errors are clustered on
Table 2c. Regressions of Happiness on Beauty Ratings, ALLBUS Germany, 2008*
Men Women
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)
Specification 2: Add education indicators, married, partnered
Beauty rating 0.033 0.042 0.048 0.052
(0.008) (0.009) (0.008) (0.009)
*Estimates that are significantly non-zero, one-sided 5-percent level, in bold.
Table 2d. Regressions of Days Happy, Enjoyed or Sad on Beauty Rating,
WLS Ages 53 and 65
Men Women
1992 2004 1992 2004
Specification 1: LS, beauty only
# days happy -0.012 0.016 0.128 -0.026
(0.056) (0.044) (0.048) (0.044)
# days enjoyed 0.069 0.008 0.142 0.006
(0.056) (0.045) (0.050) (0.049)
# days sad -0.030 -0.014 -0.100 -0.040
(0.033) (0.028) (0.042) (0.039)
Specification 2: Add completed education, married, number of children, HS BMI,
current BMI
# days happy -0.028 0.025 0.118 -0.052
(0.056) (0.046) (0.049) (0.045)
# days enjoyed 0.058 0.010 0.116 -0.026
(0.056) (0.047) (0.051) (0.047)
# days sad -0.035 -0.012 -0.096 -0.032
(0.032) (0.030) (0.043) (0.041)
*Estimates that are significantly non-zero, one-sided 5-percent level, in bold.
Table 2e. Results from Regressions of Life Satisfaction and Happiness on Beauty
Ratings, 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
Happiness 0.036 -0.023 0.036 -0.034
(0.020) (0.035) (0.022) (0.036)
Age 41
Life Satisfaction 0.267 -0.053 0.146 -0.043
(0.058) (0.104) (0.068) (0.114)
Age 46
Life Satisfaction 0.118 -0.247 -0.005 -0.227
(0.049) (0.088) (0.055) (0.092)
Age 51
Life Satisfaction 0.132 -0.177 0.203 -0.367
(0.060) (0.107) (0.069) (0.118)
Happiness 0.106 -0.120 0.057 -0.060
(0.042) (0.076) (0.044) (0.077)
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
Age 33
Happiness 0.029 -0.021 0.026 -0.013
(0.020) (0.034) (0.022) (0.036)
Age 41
Life Satisfaction 0.228 -0.006 0.025 -0.073
(0.062) (0.110) (0.073) (0.123)
Age 46
Life Satisfaction 0.070 -0.120 -0.060 -0.176
(0.052) (0.093) (0.058) (0.098)
Age 51
Life Satisfaction 0.053 -0.091 0.137 -0.267
(0.064) (0.114) (0.073) (0.126)
Happiness 0.099 -0.054 0.028 0.049
(0.046) (0.082) (0.048) (0.084)
*Estimates that are significantly non-zero, one-sided 5-percent level, in bold.
Table 3. Estimates from Regressions of Life Satisfaction and Happiness Using
Average of Other Years’ Beauty Ratings, QOL, 1977-81*
Good looks Bad looks Good looks Bad looks
Specification 1: LS, beauty, age quadratic, language, year
Life Satisfaction 0.237 -0.773 0.323 -0.330
(0.184) (0.394) (0.187) (0.343)
Happiness 0.143 -0.255 0.068 -0.191
(0.070) (0.114) (0.055) (0.097)
Specification 2: Add, education indicators, number of
children, married.
Life Satisfaction 0.309 -0.811 0.335 -0.264
(0.193) (0.392) (0.181) (0.349)
Happiness 0.162 -0.253 0.069 -0.161
(0.073) (0.113) (0.054) (0.095)
*Estimates that are significantly non-zero, one-sided 5-percent levels, in bold.
Standard errors are clustered on individuals.
Table 4. Average Effects (Lower Bounds in Interview-Based Studies) of Beauty on
Satisfaction/Happiness, SDOutcome/SDLooks, Five Data Sets, Four Countries
Good Looks Bad Looks
SD Difference from Mean 0.886 -1.672
Specification No.
1. (Gross effect) 0.062 (0.049) -0.181 (-0.238)
2. 0.048 (0.033) -0.176 (-0.124)
3. (Direct effect) 0.022 (0.010) -0.087 (-0.114)
SD Difference from Mean 0.792 -1.620
Specification No.
1. (Gross effect) 0.068 (0.100) -0.217 (-0.324)
2. 0.053 (0.104) -0.157 (-0.180)
3. (Direct effect) 0.042 (0.064) -0.124 (-0.170)
tH – tB
Figure 1. Relative Timing of Beauty and Happiness Measures
-1 hr
-0.1 hrs
0.75 hrs
26 yrs
35 yrs
44 yrs
47 yrs
Figure 2a. Effects of Beauty on Happiness/Satisfaction, Men, All
Data Sets
2.0 1.5 1.0 0.5 0.0 0.5 1.0 1.5 2.0
Figure 2b. Effects of Beauty on Happiness/Satisfaction, Women, All
Data Sets
2.0 1.5 1.0 0.5 0.0 0.5 1.0 1.5 2.0
Appendix Table 1a. Descriptive Statistics, QAL71 and QAL78, All Observations with Beauty
Rating, Satisfaction and Happiness Responses*
1971 1978
Men Women Men Women
Good looking 0.271 0.308 0.334 0.348
Bad looking 0.125 0.174 0.101 0.120
Life satisfaction 5.556 5.538 82.313 82.096
(0.041) (0.036) (0.342) (0.319)
Happiness 2.187 2.195 2.232 2.217
(0.020) (0.017) (0.014) (0.013)
N 870 1217 1482 2069
*Sample averages with their standard errors for non-binary variables in parentheses.
Appendix Table 1b. Descriptive Statistics, QOL, 1977-81
Men Women
Good-looking 0.298 0.301
Bad-looking 0.081 0.066
Happiness 2.371 2.457
(0.014) (0.012)
Life Satisfaction 8.680 8.772
(0.040) (0.036)
N individuals 517 767
*Sample averages with their standard errors for non-binary variables in parentheses.
Appendix Table 1c. Descriptive Statistics, ALLBUS Germany, 2008, All Usable
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
*Sample averages with their standard errors for non-binary variables in parentheses.
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
*Sample averages with their standard errors for non-binary variables in parentheses.
Appendix Table 1e. Descriptive Statistics, NCDS, All Observations with Beauty Rating
Men Women
Attractive age 11 0.469 0.592
Unattractive age 11 0.100 0.103
N 7886 7450
Men Women Men Women
Age 33:
Age 41:
Happiness 3.322 3.388
(0.009) (0.010)
Life Satisfaction 7.277 7.359
(0.028) (0.030)
N 3514 3886 3945 4381
Men Women Men Women
Age 46:
Age 51:
Happiness 4.315 4.207
(0.020) (0.020)
Life Satisfaction 7.562 7.657 7.335 7.314
(0.024) (0.024) (0.029) (0.031)
N 3583 3920 3559 3773
*Sample averages with their standard errors for non-binary variables in parentheses.
... For each video clip, we collected sales volume, the number of audiences and any other provided directly by the live streaming platform. Studies suggested that people everywhere are all using the same, or at least similar criteria in their judgments [11] . Due to there is a considerable agreement across individuals and cultures (and sometimes species) on what is found attractive, facial attractiveness is a quantifiable trait that can be assessed by computer algorithms [21] . ...
... Wong and Penner (2016) analyze the pecuniary benefits derived from physical appearance, quantifying the monetary reward of 'attractive' people at 20%, compared to those of average attractiveness, with no gender differentials in this reward. Hamermesh andBiddle (1994, 1998) and Hamermesh and Abrevaya (2013) also document the positive effects of 'beauty' on labour market outcomes. Physical appearance has also been found to positively impact the status attained within social groups (Anderson et al., 2001). ...
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In this paper, we analyze the relationship between Body Mass Index of the members of the couple and the distribution of housework within the same couple. Prior research has documented that higher-BMI spouses compensate their partners by increasing their market work hours. The question remains, does this compensation mechanism extend to the share of the time dedicated to housework. Using the British Household Panel Surveys for the years 2004 and 2006, we analyze the relationship between spouses’ Body Mass Index and the time devoted to housework. Our results show that a relatively higher BMI of any member of the couple is related to a decrease in his/her fraction of housework. This result is maintained when we restrict our sample to two-earner couples. We find no evidence for the compensation mechanism by which higher-BMI spouses work longer hours. By analyzing the relationship between housework time and Body Mass Index within the couple, we examine an important issue, given the significant contribution that members of couples make to their households via housework time.
Using data from China Education Panel Survey (CEPS), this study aims to investigate the association between perceived appearance, body shape and adolescent academic achievement, elucidate underlying mechanisms, and examine the heterogeneous relationships across different groups. The results suggest that there is an ugliness penalty rather than a beauty premium in Chinese middle school. Overweight and obese students have lower academic achievement than normal weight students. These findings remain robust after replacing the independent variable and dealing with selection bias. Mechanism analysis reveals that teacher preference and non‐cognitive ability are important transmission mechanisms linking perceived appearance with academic achievement. Academic achievement disadvantage in obese students compared with normal weight students is partially transmitted through cognitive ability. Additionally, poor perceived appearance and obesity have a greater negative impact on the academic achievement of disadvantaged groups. These findings better our understanding of perceived appearance and body shape in the formation of educational stratification and provide a scientific basis for equalizing the educational process within schools.
Background Facial appearance has been a flagbearer of “beauty” since time immemorial. Perception of beauty is highly influenced by cultural, interpersonal, and intra-personal variations. Objectives This study aimed to assess the perception of facial beauty and appearance through multidimensional influencing indicators among the Indian population, and to determine whether the physically attractive person possesses more personal and socially desirable traits than the comparatively less attractive individual. Materials and Methods A study population of 474 with equal male and female population of Indian origin was selected. Their perception was assessed based on the prevalidated, self-administered questionnaire using a tool with five major multidimensional indicators. Six images were selected, three each of male and female subjects, and labeled as A, B, and C, in descending order of attractiveness. The multidimensional influencing indicator tool was self-administered to the participants and the responses were recorded individually. Results Photograph A scored the highest out of the three grading scales in both males and females. Conclusion The most attractive photograph, in both males and females, was deemed to be associated with higher scores of attractiveness and success.
Using the Canadian General Social Survey of 2016, a large nationally representative dataset, the present paper examines how satisfaction with one’s physical appearance correlates with the division of household chores, as well as satisfaction with this division. The results show that satisfaction with one’s physical appearance negatively associates with the level of contribution to household chores for female partners. In addition, holding the number of chores constant, satisfaction with one’s physical appearance predicts a greater satisfaction with the division of chores for both genders, with a somewhat larger marginal effect for male partners. Hence, the results provide evidence for the positive influence of physical appearance on intra-household bargaining power. The implications are discussed, and venues for future research are proposed.
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Physical attractiveness has been linked to mental health, intelligence, ability and performance. Most of the studies on attractiveness have been experimental in nature and focused on perceptions of mental health and achievement rather than actual mental health and achievement. Operating within a status characteristics framework, we analyze the impact of attractiveness on the actual achievement and mental health of individuals in a national sample. We find consistently significant and monotonic relationships of attractiveness with four measures of achievement and eight measures of psychological well-being. Based on these analyses, we conclude that survey research findings corroborate experimental findings on attractiveness; that one's attractiveness does impinge on achievement and psychological well-being; and that status characteristics theory can be used to explain the effects of attractiveness on well-being and achievement.
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Three studies were conducted to determine whether physical attractiveness (PAT) is related to subjective well-being (SWB). In the first study ( N = 221), unselected students were photographed and videotaped. In the second study ( N = 131), participants were selected on the basis of extremes in PAT, and in the third study ( N = 155), participants were preselected for extreme scores on SWB. Correlations between SWB and PAT varied from .03 to .33. In Study 1 the mean correlation between PAT and SWB was .13. When appearance enhancers (hair, clothing, and jewelry) were covered or removed in Studies 2 and 3, the correlation between PAT and SWB dropped, suggesting that part of the SWB–PAT relation might be due to happier people doing more to enhance their beauty. The impact of PAT on SWB may be mitigated by the fact that others agree on a target's PAT at only modest levels. It was found that self-perceptions of PAT were correlated with both one's objective PAT and one's SWB. (PsycINFO Database Record (c) 2012 APA, all rights reserved)
We use unique longitudinal data to document an economically and statistically significant positive correlation between the facial attractiveness of male high school graduates and their subsequent labor market earnings. There are only weak links between facial attractiveness and direct measures of cognitive skills and no link between facial attractiveness and mortality. Even after including a lengthy set of characteristics, including IQ, high school activities, proxy measures for confidence and personality, family background, and additional respondent characteristics in an empirical model of earnings, the attractiveness premium is present in the respondents' mid-30s and early 50s. Our findings are consistent with attractiveness being an enduring, positive labor market characteristic.
Abstract Previous studies document that attractiveness predicts life outcomes, including well-being and social connectedness. This study investigates whether the attractiveness–outcomes link is especially strong in settings, such as many urban areas, that promote relationship constructions as a product of personal choice. This link may weaken in settings, such as many rural areas, that promote less voluntaristic-independent relationship constructions. Analyses of survey data from a national representative (United States) sample supported these hypotheses. Attractiveness (operationalized as waist-to-hip ratio) predicted well-being and social connectedness among urban (n = 257) but not rural (n = 330) women. Social connectedness mediated the urban–rural moderation of the attractiveness/well-being link. Findings suggest that frequently observed attractiveness effects are the product of particular, modern social contexts that promote relationship choice.
Evidence has accumulated in recent years supporting the hypothesis that both facial and bodily physical attractiveness in humans are certifications of developmental and hormonal health. Such evidence indicates that physical attractiveness is an honest or Zahavian signal of phenotypic and genetic quality. The hypothesis that physical beauty connotes health was first proposed by Westermarck and was discussed later by Ellis and Symons. It has been suggested that facial attractiveness in women is a deceptive signal of youth, unrelated to phenotypic and genetic quality. This sensory-bias or super-stimulus hypothesis is not supported by this study of men’s ratings of the attractiveness of photographs of 92 nude women. Independent ratings of photographs of faces, fronts with faces covered, and backs of the same women are significantly, positively correlated. The correlation between the ratings of different photos implies that women’s faces and external bodies comprise a single ornament of honest mate value, apparently constructed during puberty by estrogen and also probably by developmental adaptations for symmetry. Thus, women’s physical attractiveness in face and body honestly signal hormonal and perhaps developmental health.
Evidence has accumulated in recent years supporting the hypothesis that both facial and bodily physical attractiveness in humans are certifications of developmental and hormonal health. Such evidence indicates that physical attractiveness is an honest or Zahavian signal of phenotypic and genetic quality. The hypothesis that physical beauty connotes health was first proposed by Westermarck and was discussed later by Ellis and Symons. It has been suggested that facial attractiveness in women is a deceptive signal of youth, unrelated to phenotypic and genetic quality. This sensory-bias or super-stimulus hypothesis is not supported by this study of men’s ratings of the attractiveness of photographs of 92 nude women. Independent ratings of photographs of faces, fronts with faces covered, and backs of the same women are significantly, positively correlated. The correlation between the ratings of different photos implies that women’s faces and external bodies comprise a single ornament of honest mate value, apparently constructed during puberty by estrogen and also probably by developmental adaptations for symmetry. Thus, women’s physical attractiveness in face and body honestly signal hormonal and perhaps developmental health.
Presents a theoretical discussion of the puzzling relation, or lack of it, between income and happiness. Where the economist sees satisfaction as the supplying of consumer needs, the psychologists recognizes a wider and more complex system of need. These theoretical considerations are discussed as they apply in detail to the American scene. (PsycINFO Database Record (c) 2012 APA, all rights reserved)