the economic reality of the beauty myth

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AUTHOR: Susan Averett; Sanders Korenman

TITLE: The Economic Reality of The Beauty Myth

SOURCE: The Journal of Human Resources v31 p304-30 Spr '96

The magazine publisher is the copyright holder of this article and it is reproduced with permission.

Further reproduction of this article in violation of the copyright is prohibited.

PUBLISHER ABSTRACT AB We investigate income, marital status, and hourly

pay differentials by body mass (kg/m[sup2) in a sample of 23- to 31-year-olds

drawn from the 1988 NLSY. Obese women have lower family incomes than women whose

weight-for-height is in the "recommended" range. Results for men are weaker and mixed.

We find similar results when we compare same-sex siblings in order to control for family

background (for example, social class) differences. Differences in economic status by body

mass for women increase markedly when we use an earlier weight measure or restrict the

sample to persons who were single and childless when the early weight was reported.

There is some evidence of labor market discrimination against obese women. Differences in

marriage probabilities and spouse's earnings, however, account for 50 to 95 percent of

their lower economic status. There is little evidence that obese African American women

suffer an economic penalty relative to other African American women.

I. INTRODUCTION

Eating disorders (such as anorexia nervosa and bulimia) are a major social problem in the United

States. Estimates of the prevalence of anorexia nervosa range from 1 to 4 percent of the femalepopulation (Autry et al. 1986). Although estimates of the prevalence of bulimia nervosa run as high as

20 percent of college and high school females, studies based on representative samples suggest the

actual proportion is less than 5 percent of college females and less than 1.5 percent of males.(FN1)

Concern about eating disorders has led to a recent explosion of consciousness-raising efforts, perhaps

best exemplified by the best-selling book The Beauty Myth by Naomi Wolf.

There is also growing awareness of the social stigma attached to being overweight (examples from

the popular press include Kolata, Brody, and Rosenthal 1993; Coleman 1993; Lampert 1993). These

articles leave little doubt that Americans (especially women) experience great social and psychological

pressure with respect to body size, and they provide poignant accounts of ridicule and

discrimination experienced by obese persons.

Until very recently, economists have had little to say on the issue of body weight in contemporary

industrialized societies; our search of the Journal of Economic Literature index uncovered only one

study of obesity and economic outcomes, a cross-sectional analysis of wage rates (Register and

Williams 1990).(FN2) However, there are several new studies of the economic effects of obesity and

appearance more generally (Gortmaker et al. 1993; Loh 1993; Hamermesh and Biddle 1993; Sargent

and Blanchflower 1994). We summarize these below.

In this paper, we describe economic differentials by body mass for a sample of men and women

aged 23 to 31 in 1988. In so doing, we hope to contribute to a literature that describes social and

psychological pressures that may contribute to the development of eating disorders and to gender

differences in prevalence rates.(FN3) Accurate information about economic differentials by body mass

can aid in the formulation of appropriate public health and social policies. Raising awareness of the

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social and psychological pressures surrounding fear of weight gain, and of the stereotyping of obese

persons, represents an important step in addressing associated social and public health problems;

however, we hypothesize that attempts to change a variety of attitudes and behaviors surrounding

body weight, including the social stigma attached to being overweight, eating disorders such as

anorexia nervosa and bulimia, and obsession with body image, diet, and weight loss, will face greater

difficulties if economic differences reinforce social and psychological pressures.(FN4)

That there is an economic aspect to behavior surrounding body weight would hardly be surprising.A recent Business Week article (Armstrong and Mallory 1992) reported that U.S. companies had 1991

sales of $8.4 billion in products and services for serious dieters. A less restrictive definition that

includes expenditures on items such as health club fees and artificial sweeteners raises the figure to

$33 billion, roughly the GDP of Pakistan, Egypt, or Hungary (World Bank 1992, Table 3).

Naomi Wolf provides additional anecodotal evidence that U.S. college students invest heavily in

appearance (weight loss) related human capital. She finds that audiences of college women have little

trouble answering a series of specific questions about the caloric content of different foods, the

number of calories one must consume to lose weight at different rates, the number of calories

consumed by different amounts of physical exercise and so forth. An economist is naturally led to ask

if there is an economic return to such investments. A number of recent articles suggest that there is.

II. STUDIES OF OBESITY AND ECONOMIC STATUS

Register and Williams (1990) examine the effect of obesity on wage rates in a sample of 18- to

25-year-olds from the 1982 round of the National Longitudinal Survey of Youth (NLSY). In

employment-selectivity corrected wage equations, the pay differential is minus 12 percent for obese

women and minus 5 percent for obese men. They interpret their results as evidence of

discrimination against obese women, although they are aware of other interpretations. In particular,

they mention potential problems of reverse causality: "... the obese may have such status [obesity ]

because of low earnings, to the extent that income level affects food and nutritional consumption

behavior" (p. 138, emphasis in original).(FN5)

Loh (1993) followed up Register and Williams (1990) with an analysis of wage levels of full-time

workers in the 1982 NLSY and of wage changes between 1982 and 1985. He finds no effect of

obesity on male or female wage levels in 1982, although wages grew about 5 percent less between

1982 and 1985 for obese men and women.

Sargent and Blanchflower (1994) study a British cohort of 23-year-olds in the 1981 wave of the

National Child Development Study. They report that women who were obese at age 16 earn about 7

percent less per hour at age 23 than their nonobese peers; there are no significant differences for

men. In addition to standard controls, they include a set of controls for social class (occupation of the

household head when the respondent was 11) and IQ (a test of math and reading ability administered

at ages 7, 11, and 16). They also report that women who were obese at age 16 suffered a similar

wage disadvantage at age 23, whether or not they remained obese at age 23.

Hamermesh and Biddle (1993) study the effects of physical attractiveness on earnings for men and

women in several data sets. Although they focus on interviewer ratings of appearance, they include a

control for obesity status (also determined by interviewer observation) in their analysis of the 1977

quality of Employment Survey. In log wage models that also control for a subjective rating of beauty,

they find that obese women earned about 12 percent less than their counterparts of "average" weight,

although this difference was not statistically significant.

Gortmaker et al. (1993) estimate effects of obesity (FN6) on several social and economic outcomesin the NLSY. They relate an indicator of obesity --being above the 95th percentile of National Center

for Health Statistics standards of the Body Mass Index (BMI; defined as weight in kilograms divided by

the square of height in meters)--at ages 16 to 24 to ages 23 to 31 values of household income, years

of education completed, an index of self-esteem (measured in 1987), and probabilities of being

married, graduating from college, and being poor. In unadjusted comparisons, obese women exhibit

substantial disadvantages in all outcomes except self-esteem. When Gortmaker et al. use multivariate

regressions to adjust for baseline (1979) values of income, education, marital status, maternal and

paternal education, work-limiting chronic health conditions, height in 1981, self-esteem in 1980, age

in 1981, and race or ethnic group, statistically significant differentials remain in marital status,



income, proverty, and years of education; differences in later self-esteem and in the fraction

completing college are small and not significant. Differences by obesity status for men are smaller

and, except for the lower fraction married at ages 23 to 31 among men who were obese at ages 16 to

24, were not significant at the 0.01 level.

Gortmaker et al. investigate the importance of two explanations for the deficits in social and

economic status among obese women. First, they find no evidence to support the hypothesis that

obesity differentials are confounded by health status, since controlling for work-related healthlimitations does not change their results. Second, they reject the hypothesis that socioeconomic origin

or ability accounts for the obesity differentials, because significant differentials in income, marriage,

and years of education remained "after we controlled for base-line differences in potentially

confounding factors" (p. 1011). Gortmaker et al. conclude that discrimination may explain the

residual (adjusted) deficits in socioeconomic status among obese women:

In summary, overweight during adolescence has important social and economic consequences that are greater than

those associated with many other chronic physical and health conditions. Discrimination against people who are

overweight may account for these results. The recent Americans With Disabilities Act prohibits discrimination in

employment and in establishments serving the public. Our data suggest that the extension of this act to includeoverweight persons should be considered.

We build upon and extend these analyses in several respects. First, we estimate differences in

hourly pay at ages 23 to 31. This wage analysis focuses on the area of central importance to labor

market antidiscrimination policies such as the Americans with Disabilities Act. Gortmaker et al. do not

study labor market outcomes. Like Register and Williams, and Sargent and Blanchflower, we find

evidence consistent with pay discrimination against obese women. There is also evidence that obese

women are more likely than other women to report having experienced gender-based discrimination

in the labor market.

Second, in addition to studying hourly wages, we study marriage probabilities, spouse's earnings,

and family income. We are therefore able to assess the relative contributions of marriage-market

factors (probability of marriage and spouse's earnings) and labor market factors (employment and

earnings) to the overall differences in family income between obese persons and persons of

recommended weight. This accounting exercise allows us to assess the potential for labor market

antidiscrimination measures to increase the economic status of obese women.

Third, we use same-sex sibling differences as an alternative (and perhaps more complete) way tocontrol for social class or family background differences between obese persons and others. For

women, estimates based on sibling differences are broadly consistent with those based on

conventional cross-sectional estimates. These sibling differences address the concern raised by

Stunkard and Sorensen (1993) in their editorial that accompanied the Gortmaker et al. piece: "The

possibility, indeed the probability, that a common factor or factors influence both obesity and

socioeconomic status greatly complicates the attribution of causality. In addition to control for social

factors, as was carried out so effectively by Gortmaker et al., further assessment of the relation will

also require control for parental obesity " (p. 1037, italics added).

Fourth, we attempt to address the possibility of bias from reverse causality in contemporaneous

relations between economic status and body weight. We estimate contemporaneous (in other words,

at ages 23 to 31) relationships between economic or social outcomes and body weight, in addition to

relationships between outcomes at ages 23 to 31 and obesity at ages 16 to 24 (the latter are similar

to those estimated by Gortmaker et al.). Contemporaneous relationships between social and economicoutcomes and obesity (at ages 23 to 31) are weaker than lagged relationships (namely, those

between outcomes at ages 23 to 31 and obesity at ages 16 to 24). As noted, women who are obese

at ages 16 to 24 have lower economic status at ages 23 to 31. However, although most women who

are obese at ages 16 to 24 are also obese at ages 23 to 31 (as Gortmaker et al. note), only about 30

percent of women who are obese at ages 23 to 31 were obese at ages 16 to 24. Moreover, women

who become obese in their mid- to late 20s (the majority of women who are obese at ages 23 to 31)

appear to be better-off financially than those who were obese at both ages, and they do not differ

greatly from those in the recommended weight range. This finding suggests that contemporaneous



social and economic differentials are most likely not the result of adverse labor or marriage market

outcomes causing weight gain; using a lagged BMI measure (which should be less affected by reverse

causality) strengthens the adverse association between obesity and economic status.

A final extension of previous research is our estimation of separate models by black or Hispanic

identification of sample members. Unlike Gortmaker et al., who report that "The addition of interaction

terms to the models to determine whether the relation of obese to subsequent social and economic

characteristics varied according to race or ethnic group did not alter the results (p. 1011)," we findsome statistically significant and large racial differences in the obesity differentials; the social and

economic penalties attached to being overweight appear to be smaller among black women.

III. METHODS, DATA, VARIABLES

A. THE NLSY DATA

The sample is derived from the National Longitudinal Survey of Labor Market Experience of Youth

(NLSY), which has been conducted annually since 1979 (CHRR 1992). At baseline (1979), respondents

were aged 14 to 21. The NLSY oversamples black, Hispanic, and economically disadvantaged nonblack

and non-Hispanic youths. Respondents were asked to report their current weight in the 1981, 1982,

1985, 1986, 1988, 1989, and 1990 interviews. Height information was collected in 1981, 1982, and

1985. Height information was not collected after 1985, presumably because individuals are assumed

to have attained adult stature. (Sample members were aged 20 to 27 in 1985.)

We focus on labor market and marriage-market outcomes measured in the 1988 interview. Our

sample consists of 5,090 women and 4,951 men who were interviewed in 1988 and for whom we had

the requisite height, weight, and hourly wage information (if they were employed; nonemployed

persons are also included in the sample).(FN7)

B. VARIABLES

The explanatory variables of chief interest are categories of the Body Mass Index (BMI; Bray

1978), which, as noted, is defined as weight in kilograms divided by the square of height in meters.

Although there are many ways to combine height and weight to estimate amount of body fat, these

ways tend to be highly correlated and are considered reliable (Kannel 1983; Abraham 1983). In

addition, Bray reports a correlation between the BMI and various anthropometric measures (such as

skinfold thickness) of 0.7 to 0.8.We use BMI categories that correspond to weight-for-height tables formulated by Metropolitan Life

Insurance Company (1983). The recommended (based on associated mortality risks) BMI range is 20-

25 for men and 19-24 for women. We follow convention (Bray 1979) in referring to persons below the

recommended range as "underweight," men with BMIs between 25 and 29 and women with BMIs

between 24 and 29 as "overweight," and men or women with BMIs 30 and over as "obese." We must

emphasize that the recommended weight refers to a range associated with low mortality risks, and

may not correspond to social norms about what might constitute an overweight or underweight

appearance. We adopt these ranges because they are conventional, widely used, and are a convenient

way to classify the sample. The Metropolitan Life recommended weight tables are appropriate for

individuals aged 18 to 30 (Greenwood 1983); we use weight data for a sample aged 16 to 24 in 1981

and 23 to 31 in 1988.

We use BMI measures at two ages: an average of the 1981 and 1982 BMIs, and an average of the

1988 and 1989 BMIs (based upon 1985 height and 1988 and 1989 weights; height information waslast collected in 1985). For simplicity, we refer to these measures as the 1981 and 1988 BMIs. We

examined height data for inconsistencies and flagged 177 women and 240 men who appeared to

"shrink" more than two inches in height between either the 1981 and 1982 interviews, or the 1981 or

1982 interview and the 1985 interview. For these individuals, we assigned heights based on a

combination of the three reports.(FN8) In addition, if 1981 and 1982 heights were both missing, they

were set to the 1985 value.

Outcome variables are taken from the 1988 interviews when respondents are aged 23 to 31. Basic

control variables include age (or actual work experience for wage models), highest grade completed



as of 1988, region, SMSA, and black or Hispanic identification. We also conduct some analyses

separately by race/Hispanic identification. In addition to these basic control variables, we included a

set of more detailed controls in order to investigate hypotheses about the source of earnings and

income differentials by body mass. These variables include an indicator of work-related health

limitations in 1988 (equal to 1 if the respondent reported that health limited ability to work or amount

or type of work; and 0 otherwise), the respondent's score on the Armed Forces Qualifications Test (a

test of academic ability and achievement administered in 1980), and an index of low self-esteembased on responses to a battery of ten questions asked in 1980. To compute the index of low self-

esteem we summed the number of "low esteem" responses and formed a variable with mean of 0 and

standard deviation of 1; higher values indicate lower self-esteem. In some models we also enter

controls for marital status in 1988, number of children, and age of youngest child in 1988. Finally,

although we do not attempt to control for sex discrimination in any models, we present proportions

who responded yes to question last asked in 1983: "Have you ever experienced discrimination at

work? ... Was that on the basis of sex?" (No questions were asked about weight-based

discrimination.)

Most of the variables used in the analyses are standard and self-explanatory; two require additional

explanation. Actual labor market experience is based on reports of weeks worked each year after age

18. A good experience measure may be important for a study of body mass and wages because, for

example, if childbearing is associated with higher body mass it may lead higher body mass to be

associated with lower wages through the effect of childbearing on the accumulation of labor marketexperience (for example, Mincer and Polachek 1974; Filer 1993).

Our principal measure of economic well-being is the income-to-needs ratio in 1988, based on 1987

income (reported in the 1988 interview) and family composition as of the 1988 interview. It is defined

as the family income of the respondent divided by the U.S. Census poverty line for the family based

on its size and age composition (number of adults and children).(FN9)

C. SAMPLE DESCRIPTION

Table 1 presents a cross-tabulation of the distribution of the sample by BMI categories in 1981

and 1988. In 1988, at ages 23 to 31, about half the sample women are in the recommended (19 to

23) BMI range, 30 percent are between 24 and 29, 13 percent had BMIs over 30, and 7 percent were

below 19. Especially notable is the increase with age (between 1981 and 1988) in the fraction in the

top two categories: the fraction above 30 rose from five to 13 percent, and the fraction above 23 rose

from 24 to 43 percent. The substantial increase between 1981 and 1988 in the fraction of women in

the overweight and obese categories highlights the possibility that reverse causality may bias

estimates of contemporaneous relations between BMI and economic status (specifically, that adverse

economic outcomes may cause weight gain). Although relatively few women (16 percent) who were

obese in 1981 exited the obese category between 1981 and 1988, only about 31 percent (199 out of

646) of women who were in the obese category at ages 23 to 31 were also in the obese category at

ages 16 to 24.

There was a similar increase with age (between 1981 and 1988) in the fraction of men in the two

heaviest BMI categories; the fraction with BMIs above 29 rose from 4 to 11 percent, and the fraction

above 24 rose from 25 to 48 percent, a slightly greater increase than among women. The similar

pattern of weight gain for men and women suggests that the increase for women was not entirely due

to biological aspects of pregnancy and childbirth.(FN10) However, we explore this possibility below.

In Table 2 we present sample means and frequencies for women classified by BMI at ages 16 to 24(1981). According to all indicators, obese women are of lower socioeconomic status than women of

recommended weight. Family income, income/needs, spouse's earnings, hourly wages, years of

schooling, Armed Forces Qualifications Test score, the fraction in managerial/professional occupations,

and the fraction employed are lower in the higher BMI categories, while the fractions poor, minority,

and those in unskilled manufacturing occupations are higher. In addition, obese women are more

likely to report health limitations and having experienced sex discrimination. Overweight and obese

women are somewhat more likely to have children and are less likely to be married. The fact that

body weight is inversely associated with economic status in developed societies is well documented

(Kannel 1983). One goal of this study is to use sibling comparisons to investigate whether this



relationship reflects the influence of family background (for example, social class) on weight, or

whether there appears to be an association between weight and economic status in later life, net of

family background differences.

Table 3 presents means and frequencies for men by BMI category at ages 16 to 24 (1981). The

patterns for men differ from those for women. Income appears to be lowest among underweight men;

differences across the other BMI categories are modest. Men in the lowest BMI category appear less

likely to be married, more likely to be divorced or separated, and less likely to have children. A largerproportion are black. Spouse's earnings are slightly lower among obese men. Armed Forces

Qualifications Test scores are lower for underweight men; obese men are more likely to report health

problems.

IV. RESULTS

Table 4 summarizes the results of multivariate models of income, marriage, spouse's earnings,

and hourly wages for women and men. We estimated each model for the full sample (separate models

by sex) using, alternatively, BMI at ages 23 to 31 (1988) and BMI at ages 16 to 24 (1981). In

addition, each model is estimated for the subsample of persons who were childless and single (never

married) in 1982 using the 1981 BMI. The models contain a set of basic controls for age (or actual

work experience in wage models), years of schooling completed, and dummy variables for region (3),

residence in an SMSA (2), and black and Hispanic identification.

A. RESULTS FOR WOMEN

The figures in the table are coefficients of BMI categorical variables from least squares

regressions, except for those under the heading "P(married)," which are derivatives based on logit

coefficients, evaluated at the sample means. These derivatives may be interpreted as percentage

point differences in the fraction married. The reference group for all models are persons in the

"recommended" BMI range. Same-sex sibling differences are estimated by least squares for the three

continuous outcomes,(FN11) and fixed-effects logits (Chamberlain 1980) for the dichotomous outcome

(marital status in 1988). Because necessarily sample sizes are much smaller for sibling analyses,

coefficient estimates are less precise.(FN12) We therefore look to the sibling analyses for general

support for, or obvious contradiction of, evidence from cross-sectional analyses. Cross-sectional

estimates have the advantage of being based on larger samples, but may suffer from bias induced by

unmeasured family background differences.In the first panel of the table we present differentials in outcomes at ages 23 to 31 (1988)

according to BMI category at ages 16 to 24 (1981). The cross-sectional models suggest that women

who were obese or overweight at ages 16 to 24 have, at ages 23 to 31, lower family income and lower

hourly wages (30+ category only for the latter), are less likely to be married, and have lower spousal

income (if married) than women in the recommended BMI range. The pattern of coefficients from the

sibling analysis is similar, suggesting that family background heterogeneity bias is not serious in

cross-sectional estimates.(FN13)

In the second panel we present differentials in outcomes at ages 23 to 31 (1988) according to BMI

category at ages 23 to 31. Differentials by BMI category in family income, spouse's earnings, and

wages are similar to but smaller (in absolute value) than those in the first panel. There is no evidence

of lower probabilities of marriage among heavier women in the second panel. What do we learn from

the comparison between the first two panels of the table? First, differences in income, marriage,

spousal income, and wages by BMI category in the second panel (ages 23 to 31 BMI, ages 23 to 31outcomes) do not appear to be, on net, biased downward (in other words, are not made "too

negative") by (reverse) causality running from obesity to economic outcomes at ages 23 to 31. If

anything, they appear to be biased upward. Second, the fact that heavier women are no less likely to

be married according to the second panel but are less likely to be married according to the first panel

suggests that the source of upward bias in the second panel may be that marriage (or perhaps

childbearing) raises weight. As we shall see later, however, controlling for marital status, the presence

of children, and the age of the youngest child has no effect on the estimated wage differentials, and

only a modest effect on the income differentials by BMI.



We also ran models similar to those in Table 4 in which we included an interaction between

obesity in 1981 and 1988, as well as dummy variables for obese in 1981 and obese in 1988. In

models of income-to-needs, the coefficients (standard errors) of the dummy variables were as follows:

obese in 1981, -0.36 (0.15); obese in 1988, 0.05 (0.05); interaction term, 0.08 (0.17). Clearly, the

largest penalty is associated with obesity at the younger ages. Women who became obese between

1981 and 1988 appear to be no worse off than women of recommended weight. These results do not

support the hypothesis that our estimates are biased because poor economic outcomes causesubsequent weight gain.

The wage models with interactions were slightly different. The coefficients (standard errors) were:

obese in 1981, -0.08 (0.08); obese in 1988, -0.04 (0.03); interaction term, -0.05 (0.09). Thus,

women who were obese in both 1988 and 1981 had the lowest wages in 1988, roughly 17 percent

below those of women of recommended weight (p < .01). Women who became obese between 1981

and 1988 had only slightly lower wages than women of recommended weight.

Finally, in the third panel of the table we present differentials in outcomes at ages 23 to 31 (1988)

according to BMI at ages 16 to 24 (1981) for the subsample of women who were single (never

married), childless, and not pregnant in 1982. Results are similar to those in the first panel, but

differences across BMI categories are larger (in absolute value). Differences in the two marriage-

market outcomes are especially dramatic.

B. RESULTS FOR MENResults for men are presented in the bottom half of Table 4. Significant results in the first panel

include lower income and lower spouse's earnings among men who were underweight at ages 16 to

24, and lower wages among those who were obese at these ages. When we relate outcomes at ages

23 to 31 to BMI at the same age (second panel for men), we find that heavier men are more likely to

be married. Results for men tend to be weaker than those for women. For example, unlike results for

women, results from sibling differences for men are often not consistent with those based on standard

cross-sectional analyses. However, as with the results for women, a comparison of the first and

second panels suggests, if anything, that differences in outcomes at ages 23 to 31 between obese or

overweight men and men in the recommended weight range according to age 23 to 31 BMI category

are biased upward by reverse causality, possibly the result of weight gain associated with marriage.

This idea is supported by the finding (reported in the last panel) that among men who were single at

ages 16 to 24, obesity is indeed associated with lower odds of being married at ages 23 to 31.(FN14)

C. ACCOUNTING FOR INCOME DIFFERENCES

How much of the difference in income between obese women or underweight men and their

counterparts in the recommended BMI ranges is accounted for by labor market differences (wages and

employment), and how much by marriagemarket differences (probability of being married and

spouse's earnings if married)? The answer to this question depends somewhat on the sample (for

example, full sample or single/childless only) and the age at which the BMI is measured. The following

figures are based on BMIs at ages 16 to 24 (1981), the full samples of men and women, and have

been adjusted for race, education, age, region, and urban residence.(FN15)

The vast majority (80 percent) of the difference in family income between obese women and those

in the recommended weight range results from differences in the marriage market (lower fraction

married and lower spouse's earnings if married), compared to about 17 percent from the labor

market. Using the age 23 to 31 (1988) BMI measure raises the proportion of the obesity differentialin adjusted income accounted for by labor market differences to about one third, and lowers the

proportion due to marriage-market outcomes to about one half. On the other hand, restricting the

sample to women who were single and childless at ages 16 to 24 raises the proportion of the obesity

differential that is accounted for by marriage-market differences to 96 percent ($7,014 out of

$7,332); labor market differences account for 12 percent in this subsample. (The figures sum to more

than 100 percent because obese women have slightly higher unearned family income.) In short,

although obese women appear to be disadvantaged in the labor market, marriage-market differences



account for the great majority their substantial deficits in economic status. The reverse is true for

underweight men.

D. DETAILED WAGE AND INCOME MODELS

In Tables 5 and 6 we present more detailed analyses of wage and income differences for women

in order to investigate alternative hypotheses concerning the source of the obesity differences in

wages and income.(FN16) A leading hypothesis is that the lower wages of obese women reflectdifferences in health status. Both underweight and obesity are associated with health problems; in

fact, these categories are defined by their associations with health risks. Although health differences

could explain the lower earnings of underweight men and obese women, it is not obvious why a

gender difference in these relationships should exist (namely, the hypothesis that the relationship

between economic status and BMI is confounded by unmeasured health status would predict wage

penalties for underweight women and obese men).

A second hypothesis is that low self-esteem is associated both with obesity and with adverse labor

market and marriage-market outcomes.(FN17) To explore this hypothesis we include an index of low

self-esteem (measured in 1981) in the wage and income models. Finally, in the wage models, we also

control for occupational status to investigate whether residual differences in wages are explained by

occupational sorting or segregation.(FN18) In the first column of Table 5 we present (for convenience)

the coefficients reported in the corresponding column of Table 4. In the second column we add to the

variables included in the first column: dummy variables for married and divorced/separated in 1988, a

dummy variable for the presence of children in 1988, an interaction of this variable with the age of the

youngest child, and controls for Armed Forces Qualifications Test score, a dummy variable if the

respondent reported a work-related health limitation in 1988, and the index of low self-esteem

measured in 1981. In the third column we add seven dummy variables representing the occupational

categories listed in Table 2. The remaining three columns are sibling-differenced versions of the same

models. Adding controls for marital status, children, occupation, Armed Forces Qualifications test

score, health, and self-esteem has little effect on the wage differentials by body mass for women; the

coefficient of the 30+ BMI category is stable across the cross-sectional specifications. Sister-

differenced estimates are similar in magnitude to the cross-sectional estimates, although the

estimated effect of obesity is not statistically different from zero and appears more sensitive to the

addition of occupation controls.

Systematic differences in pay linked to a personal characteristic which remain after human capital

differences have been accounted for are often interpreted as evidence of pay discrimination. In this

sense, the wage equation estimates provide evidence of pay discrimination against obese women.

(FN19) Such pay differentials may also reflect unmeasured productivity differences correlated with

body mass. Although it is not possible to determine the importance of the two factors from

information in the NLSY, the hypothesis that at least part of these pay differentials results from labor

market discrimination is bolstered by reports by obese and overweight women that they are

subjected to labor market discrimination because of their weight (for example, Coleman 1993;

Kolata, Brody, and Rosenthal 1992) and, indirectly, by the responses to the sex discrimination

question summarized in Table 2.

In Table 6 we present models of the (log) income-to-needs ratio. As in Table 5, the first column

reports results from the specification used in Table 4, which we repeat here for convenience. In the

second and fifth columns of Table 6, we add the Armed Forces Qualifications Test score, the health

limitations dummy variable, and the index of low self-esteem. Adding these controls has little effect onthe obesity differential in either the cross-sectional or sibling analyses, despite their large effects on

income in many of the models.

In the third column we add dummy variables for married and divorced or separated in 1988, a

dummy variable for the presence of children in 1988, and an interaction of this variable with the age

of the youngest child, in order to assess the degree to which marriage and children appear to

"mediate" the relationships between obesity and economic outcomes. Controlling for marital status

and the presence and age of children has surprisingly little effect on the cross-sectional income gap

for the heaviest women [compare Columns (2) and (3) in Table 6]. However, adding these controls



has a larger effect on the sibling differences [Columns (5) and (6)], so it is difficult to reach a

definitive conclusion.

E. RACE DIFFERENCES

There is a literature that suggests that there are cultural differences in norms pertaining to ideal

body type (for example, Furnham and Alibhai 1983). In particular, there may be a smaller social

penalty attached to being overweight for African American than white women. Consistent with thishypothesis, black women are more likely to be above recommended body weight, but they are less

likely to perceive themselves to be overweight (Dawson 1988).

This literature suggests several testable hypotheses. The most obvious is that if social pressure is

an important determinant of weight, the prevalence of overweight should be higher among African

Americans than among whites, especially for women. The figures in Tables 2 and 3 confirm that, for

our sample, this is indeed the case. Second, since marriage markets continue to be highly segmented

by race, especially for black women (Kalmijn 1993), we would expect these social norms to be

reflected in marriage probabilities, so that a given weight differential should be associated with larger

differences in marriage probabilities for whites than blacks. Finally, one might also predict that the

difference in hourly wages between obese and recommended-weight black women should be smaller

(in absolute value) than the corresponding difference among white women, although this prediction

requires more explanation.(FN20)

In Table 7 we repeat the analyses of Table 4 for subsamples of Hispanic, non-Hispanic black, and

non-Hispanic white women. Figures in Table 7 are cross-sectional differences in income, marriage,

hourly wages, and spouse's earnings for the full sample according to categories of BMI at ages 16 to

24 (1981) and 23 to 31 (1988). Sibling sample sizes were too small to permit separate analyses of

sibling differences for black and Hispanic subsamples.

We find smaller differences by body mass for African American women than for white women. For

example, regression-adjusted differences in the log of family income/needs at ages 23 to 31 (1988)

between women who were in the 30+ BMI range and those in the 19-24 (reference) range were -0.42

for whites, -0.21 for Hispanics, and -0.08 for blacks. The lower penalty among overweight black

women is also apparent in models of marriage and hourly pay. However, differences across BMI

categories in spouse's earnings (conditional on marriage) do not appear to be smaller among African

Americans than among whites or Hispanics.

In order to determine whether the substantial race differences that appear in these tables are

statistically significant, we reran the models presented in the first panel of Table 4 for women and

introduced an interaction term between obesity in 1981 and black racial identification. The coefficient

of this interaction term in the income/needs equation was (positive) 0.30, and was significant at the

0.01 level. The coefficient of the obesity variable itself was -0.38 (p < 0.01), about the size of the

corresponding coefficients for white and Hispanic women presented in the first panel of Table 7. The

corresponding wage equation coefficients exhibited the same pattern: the coefficient of the black *

obese (in 1981) interaction was 0.13 (p < 0.10). In fact, since the coefficient of the black variable

itself was minus 0.05, obese black women earned on average about 8 percent more per hour than

obese white women (although this difference is not statistically significant; p = 0.20), controlling for

labor market experience, education, and so forth.

Results for men in the bottom panel of Table 7 suggest an obesity wage penalty for white men

(last two columns) only, according to either the contemporaneous or the early BMI measure. Family

income deficits among underweight men appear for all three groups.

V. SUMMARY AND DISCUSSION

We have presented a range of evidence about the association between obesity and economic

status for men and women. There are substantial deficits in family income at ages 23 to 31 among

obese women compared to women with BMIs in the recommended range. There is evidence that

disadvantages in both the marriage and labor markets contribute to these deficits, although marriage

markets seem to play a more important role in determining family income deficits among obese

women. We find similar results when we compare same-sex siblings as a way to control for family



background (for example, social class) differences.

For men, there is less evidence of an effect of body mass (overweight or underweight) on family

income or marriage-market outcomes. There is some evidence of wage penalties associated with both

underweight and obesity among men, although this evidence is less consistent across samples and

model specifications than the evidence for an obesity effect among women.

We took advantage of the longitudinal nature of the data to argue that social and economic

differentials are most likely not the result of adverse labor market or marriage-market outcomescausing weight gain among women. In particular, using an earlier weight measure strengthens the

adverse association between obesity and economic status.

Finally, the link between empirical analyses and recommendations for protection of obese persons

from labor market discrimination under the Americans with Disabilities Act (for example, by

Gortmaker et al. 1993) requires further clarification. Such recommendations are most properly based

on differences in labor market outcomes, not family income. But, as noted, the great majority (as

much as 96 percent) of the economic deficit associated with obesity among women in our sample

results from differences in the marriage market (especially lower probabilities of marriage), not the

labor market. Labor market remedies such as the Americans with Disabilities Act, therefore, have

limited ability to close the gap in family income between obese women and those of recommended

weight.

Added material

Susan Averett is an assistant professor of economics and business at Lafayette College (Penn.);Sanders Korenman is an associate professor of public affairs at the University of Minnesota. The

authors thank Dennis Ahlburg, Ted Joyce, Robert Kaestner, Felicia LeClere, Samuel L. Myers, Jr.,

Cordelia Reimers, and seminar participants at Lafayette College, Princeton University, and the

University of Minnesota for their comments. Deborah Campbell provided outstanding research

assistance. The authors take responsibility for all errors. The data used in this article can be obtained

beginning in August 1996 through July 1999 from Susan Averett, Department of Economics and

Business, Lafayette College, Easton, PA 18042.

[Submitted May 1994; accepted February 1995]

Table 1 Distributions of 1988 Body Mass Index, by 1981 Body Mass Index, Full Sample

1988 Body Mass Index

Women <19 19-23 24-29 30+ All N

1981 BMI<19 35.9 60.3 3.8 0.0 14.1 711

19-23 3.7 63.0 30.3 3.1 62.2 3,139

24-29 0.1 11.3 52.1 36.5 19.1 962

30+ 0.0 3.0 12.7 84.3 4.7 236

All 7.4 49.9 29.9 12.8 100.00

N 371 2,521 1,510 646 5,048

1988 Body Mass Index

Men <20 20-24 25-29 30+ All N

1981 BMI

<20 26.7 66.6 6.7 0.0 10.3 506

20-24 1.4 60.5 35.3 2.8 65.1 3,196

25-29 0.2 11.0 60.5 28.3 20.4 1,003

30+ 0.0 3.4 14.6 82.0 4.2 205All 3.7 48.7 36.6 11.0 100.0

N 183 2,389 1,798 540 4,910

Table 2 1988 Sample Means and Frequencies, by Body Mass Index in 1981, Women (figures are

percents unless indicated)

Body Mass Index in 1981



All <19 19-23 24-

29 30+

Family income ($) 26,387 29,423 27,747

21,829 16,978

Income/needs 2.9 3.2 3.0

2.2 1.8

Poor 20.5 15.2 18.6

26.4 39.3Income missing 17.0 14.9 16.9

19.2 16.9

Married 51.7 55.6 53.5

47.3 34.7

Divorced/separated 5.5 4.5 5.0

6.9 9.7

Schooling (years) 12.8 13.0 12.9

12.3 11.8

Age (years) 26.7 26.2 26.6

26.9 27.3

Any kids? 61.5 56.5 61.1

67.0 57.6

Any * age youngest 2.1 1.8 2.02.5 2.6

Black 25.9 15.2 24.6

33.2 44.9

Hispanic 15.6 12.9 15.1

20.5 11.0

Body Mass Index, 1988 24.4 19.9 23.2

28.9 36.5

Body Mass Index, 1981 22.3 18.1 21.3

26.2 34.0

Employed 79.2 79.5 80.4

76.7 72.0

Hourly wage ($)(FNa) 7.52 7.56 7.77

6.99 5.84

Actual experience (years)(FNa) 6.1 6.1 6.3

5.7 5.3

AFQT, 1980 39.8 42.6 41.8

34.0 27.8

Low self-esteem, 1980 0.0 0.1 -0.0

0.1 0.2

Low self-esteem, 1987 0.0 -0.1 -0.0

0.1 0.3

Health limits, 1988 8.4 7.5 8.1

8.8 14.1

Sex discrimination, 1983 11.0 10.2 10.3

10.8 17.1

Occupation(FNa)

Managerial/professional 21.6 25.6 22.817.3 11.0

Sales 9.4 9.1 9.7

9.0 7.2

Clerical 22.7 21.7 23.5

21.9 18.2

Services 16.5 15.0 15.9

18.4 21.2

Manufacturing, unskilled 8.2 7.9 7.6

10.0 10.2



Manufacturing, skilled 2.0 1.5 2.1

1.8 2.1

Other 19.6 19.1 18.3

21.6 30.1

Spouse's earnings(FNb) 23,482 26,314 24,171

19,331 16,776

Unmarried, no kids, 1982 52.8 57.4 54.5

45.0 47.5Sample size 5,048 711 3,139

962 236

FOOTNOTES

a. If employed.

b. Annual earnings of spouse, if sample person is married and spouse employed.

Table 3 1988 Sample Means and Frequencies, by Body Mass Index in 1981, Men (figures are

percents unless indicated)

Body Mass Index in 1981

All <20 20-24 25-

29 30+

Family income ($) 27,997 23,638 28,128

29,766 27,267

Income/needs 3.2 2.7 3.3

3.3 3.1

Poor 11.1 14.8 11.1

9.0 12.3

Income missing 21.4 25.3 21.2

19.4 24.4

Married 44.2 36.8 43.7

49.8 43.9

Divorced/separated 4.1 5.9 4.0

4.0 2.0

Schooling (years) 12.6 12.4 12.7

12.6 12.5Age (years) 26.5 25.5 26.5

27.0 27.2

Any kids? 35.1 31.0 34.0

41.1 32.7

Any * age youngest 0.9 0.7 0.8

1.2 0.9

Black 26.8 25.7 28.8

22.4 20.5

Hispanic 16.3 13.4 15.3

18.8 25.4

Body Mass Index, 1988 25.4 21.3 24.5

28.5 34.3

Body Mass Index, 1981 23.4 19.0 22.426.8 33.0

Employed 87.9 82.6 88.0

89.5 91.2

Hourly wage ($)(FNa) 9.37 8.23 9.50

9.59 8.93

Actual experience (years)(FNa) 7.0 6.2 6.9

7.5 7.8

AFQT, 1980 39.7 35.1 39.9

41.3 40.4



Low self-esteem, 1980 0.0 0.1 0.0

-0.0 0.1

Low self-esteem, 1987 0.0 0.1 0.0

0.0 0.1

Health limits, 1988 5.7 6.2 5.1

6.2 9.8

Sex discrimination, 1983 4.0 4.6 3.9

4.2 1.5Occupation(FNa)

Managerial/professional 18.3 15.8 19.2

16.8 17.6

Sales 6.5 5.5 6.7

6.6 6.8

Clerical 7.6 8.1 7.7

7.2 7.3

Services 10.6 10.1 10.6

11.3 9.8

Manufacturing, unskilled 24.4 22.3 24.3

24.8 29.3

Manufacturing, skilled 19.0 22.1 18.3

19.5 18.5Other 13.5 16.0 13.2

13.8 10.7

Spouse's earnings(FNb) 12,538 11,355 12,751

12,580 11,068

Unmarried, no kids, 1982 79.7 87.2 81.3

71.4 78.5

Sample size 4,910 506 3,196

1,003 205

FOOTNOTES

a. If employed.

b. Annual earnings of spouse, if sample person is married and spouse employed.

Table 4 Regression-Adjusted Percent or Percentage Point Differences in Marriage and Labor MarketOutcomes in 1988 According to Body Mass Index Category in 1988 or 1981

Income/Needs(FNa) Pr(Married)(FNb) Spouse's

Earnings(FNc) Hourly Wage(FNd)

SIB SIB

SIB SIB

XSEC DIFFS XSEC DIFFS XSEC

DIFFS XSEC DIFFS

Women

Full sample

1981 BMI(FNe)

30+ -0.25(FN*) -0.33(FN*) -0.19(FN*) -0.23(FN*)

-0.23(FN*) -0.25 -0.15(FN*) -0.1224-29 -0.14(FN*) -0.12 -0.05(FN*) -0.10(FN*)

-0.17(FN*) 0.03 -0.01 -0.03

<19 0.03 0.02 0.01 0.02

0.09(FN*) 0.14 -0.00 -0.07

N 4,188 609 5,048 299 2,137

198 3,996 593

Full sample

1988 BMI(FNe)



30+ -0.14(FN*) -0.07 -0.01 0.02

-0.23(FN*) 0.27 -0.10(FN*) -0.04

24-29 -0.10(FN*) -0.03 0.02 0.02

-0.09(FN*) -0.14 -0.05(FN*) -0.04

<19 -0.09(FN#) 0.17 -0.03 -0.05 -0.01

0.22 -0.04 -0.04

N 4,188 609 5,048 299 2,137

198 3,996 593Single & childless

1981 BMI(FNe)

30+ -0.33(FN*) -0.27 -0.31(FN*) -0.71(FN*)

-0.42(FN*) -1.06 -0.24(FN*) -0.15

24-29 -0.15(FN*) -0.01 -0.10(FN*) -0.17(FN#)

-0.21(FN*) 0.09 -0.03 0.04

<19 0.03 0.08 0.01 -0.19(FN#)

0.17(FN*) 0.14 0.00 -0.10

N 2,189 256 2,663 146 1,030

79 2,559 288

Men

Full sample

1981 BMI(FNe)30+ -0.04 -0.22 -0.04 -0.14 -0.09

-0.10 -0.08(FN*) -0.09

25-29 0.05 -0.03 0.02 -0.02

-0.06 0.02 -0.01 -0.04

<20 -0.14(FN*) -0.04 -0.04(FN#) 0.02

-0.20(FN*) -0.32 -0.09(FN*) -0.03

N 3,859 615 4,910 356 1,493

105 4,317 781

Full sample

1988 BMI(FNe)

30+ 0.05 -0.22(FN*) 0.08(FN*) 0.12 -0.02

-0.44 -0.03 -0.06

25-29 0.12(FN*) 0.08 0.08(FN*) 0.09(FN*)

0.15(FN*) 0.19 0.02 0.01

<20 -0.29(FN*) 0.01 -0.09(FN*) 0.05 0.03

-0.00 -0.07(FN#) 0.03

N 3,859 615 4,910 356 1,493

105 4,317 781

Single & childless

1988 BMI(FNe)

30+ -0.02 -0.31 -0.07(FN#) -0.15 0.00

-0.37 -0.06 -0.13

25-29 0.06 -0.09 -0.00 -0.02 0.06

-0.11 -0.02 -0.08(FN#)

<20 -0.17(FN*) 0.03 -0.01 -0.00

-0.19(FN#) -0.35 -0.09(FN*) -0.02

N 3,015 430 3,915 264 1,05460 3,442 570

Notes: N is the number of observations, or (approximately) the number of pairs for same-sex

sibling differences. See text for details.

FOOTNOTES

a. Dependent variable = In(income/needs) in 1987, where needs are defined as the poverty line

for the family unit. Regression controls include in addition to dummy variables for BMI category: age,



years of schooling, and dummy variables for region (3), racial/Hispanic identification (2), and

residence in an SMSA (2).

b. Dependent variable is dichotomous: "married in 1988." Controls entered in the logit models

include in addition to dummy variables for BMI category: age, years of schooling, and dummy

variables for region (3), racial/Hispanic identification (2), and residence in an SMSA (2). Numbers

shown are derivatives evaluated at the sample mean and may be interpreted as percentage point

differences in the probability of being married in 1988. Sibling differences are from fixed-effects logitmodels.

c. Dependent variable = In(spouse's annual earnings) in 1987. The sample consists of sample

members who are married and whose spouses earned at least $100 in 1987. Regression controls

include in addition to dummy variables for BMI category: age, years of schooling, and dummy

variables for region (3), racial/Hispanic identification (2), and residence in an SMSA (2).

d. Dependent variable = In(sample member's hourly wage in survey week). The sample consists of

sample members who worked for pay during the survey week. Regression controls include in addition

to dummy variables for BMI category: actual labor market experience, years of schooling, and dummy

variables for region (3), racial/Hispanic identification (2), and residence in an SMSA (2).

e. 1981 BMI is based on an average of weights reported in 1981 and 1982. 1988 BMI is based on

an average of weights reported in 1988 and 1989.

* p < 0.05;

# 0.05 < p < 0.10.

BMI at Ages 16 to 24 (1981)

Women Men

19-23 BMI 20-24 BMI

Minus 30+ BMI Minus <20 BMI

Outcomes at

Ages 23 to 31 ($) (% ) ($) (% )

Labor market 1,050 17.0 3,373 85.2

Marriage market 4,957 80.4 1,426 36.0

Other 162 2.6 -839 -21.2

Total adjusted income 6,169 100.0 3,960 100.0

difference

Table 5 OLS Regressions of 1988 Wages on 1981 Body Mass Index Categories, Women (dependent

variable = ln(hourly wage))

Cross Section Sister Differences

(1) (2) (3) (1) (2)

(3)

Body Mass Index, 1981

30+ -0.15 -0.16 -0.14 -0.12

-0.10 -0.07

(0.03) (0.03) (0.03) (0.12)

(0.12) (0.12)

24-29 -0.01 -0.01 -0.01 -0.03

-0.02 -0.02(0.02) (0.02) (0.02) (0.05)

(0.05) (0.05)

<19 0.00 0.00 -0.01 -0.07

-0.05 -0.07

(0.02) (0.02) (0.02) (0.06)

(0.06) (0.06)

Schooling (years) 0.07 0.04 0.03 0.06

0.04 0.03



(0.00) (0.00) (0.00) (0.01)

(0.01) (0.01)

Experience 0.06 0.05 0.04 0.05

0.04 0.04

(0.00) (0.00) (0.00) (0.01)

(0.01) (0.01)

Ill health -0.05 -0.05

-0.01 -0.00(0.03) (0.03)

(0.08) (0.08)

AFQT/100 0.38 0.32

0.49 0.47

(0.04) (0.04)

(0.13) (0.12)

Low self-esteem -0.02 -0.02

-0.03 -0.02

(0.01) (0.01)

(0.02) (0.02)

Married -0.00 -0.01

-0.03 -0.03

(0.02) (0.01)(0.04) (0.04)

Divorced or separated 0.05 0.05

0.04 0.05

(0.03) (0.03)

(0.09) (0.09)

Kids (any) -0.10 -0.08

-0.06 -0.06

(0.02) (0.02)

(0.05) (0.05)

Kids * age of youngest/10 0.04 0.03

0.06 0.05

(0.03) (0.03)

(0.09) (0.09)

Occupational dummies? No No Yes No No

Yes

Adjusted R[sup2 0.30 0.33 0.37 0.10

0.12 0.20

Sample size 3,996 3,996 3,996 593 593

593

Notes: Standard errors in parentheses. Cross-section models also include dummy variables for

black and Hispanic identification, urban residence (2), and region (3). Sister-differenced models also

include urban and region controls.

Table 6 OLS Regressions of 1988 Family Income/Needs on 1981 Body Mass Index Categories,

Women (dependent variable = ln(income/needs))

Cross Section Sister Differences

(1) (2) (3) (1) (2)

(3)

Body Mass Index, 1981

30+ -0.25 -0.22 -0.19 -0.33

-0.29 -0.10

(0.06) (0.06) (0.05) (0.16)

(0.16) (0.15)



24-29 -0.14 -0.13 -0.09 -0.12

-0.11 -0.06

(0.03) (0.03) (0.03) (0.08)

(0.08) (0.07)

<19 0.03 0.05 0.03 0.02

0.03 0.02

(0.04) (0.04) (0.03) (0.09)

(0.08) (0.07)Schooling (years) 0.14 0.09 0.06 0.10

0.08 0.06

(0.01) (0.01) (0.01) (0.02)

(0.02) (0.02)

Age 0.02 0.01 0.01 0.01

0.00 0.00

(0.01) (0.01) (0.01) (0.02)

(0.02) (0.02)

Ill health -0.19 -0.19

0.03 -0.08

(0.04) (0.04)

(0.11) (0.10)

AFQT/100 0.72 0.590.58 0.64

(0.06) (0.06)

(0.19) (0.17)

Low self-esteem -0.08 -0.06

-0.03 -0.03

(0.01) (0.01)

(0.03) (0.03)

Married 0.70

0.71

(0.02)

(0.02)

Divorced or separated -0.22

-0.13

(0.05)

(0.11)

Kids (any) -0.64

-0.54

(0.03)

(0.07)

Kids * age of youngest 0.02

0.02

(0.00)

(0.01)

Adjusted R[sup2 0.25 0.29 0.45 0.06

0.06 0.27

Sample size 4,188 4,188 4,188 609 609

609

Notes: Standard errors in parentheses. Cross-section models also include dummy variables for

black and Hispanic identification, urban residence (2), and region (3). Sister-differenced models also

include urban and region controls.

Table 7 Regression-Adjusted Percent or Percentage Point Differences in Marriage and Labor Market

Outcomes in 1988 According to Body Mass Index Category in 1988 or 1981, by Race

[Table Omitted]

Table A1 Regression-Adjusted Percent or Percentage Point Differences in Marriage and Labor



Market Outcomes in 1988-1990 (pooled data) According to Body Mass Index Category in 1988 or

1981

Income/Needs(FNa) Pr(Married)(FNb) Spouse's

Earnings(FNc) Hourly Wage(FNd)

SIB SIB

SIB SIBXSEC DIFFS XSEC DIFFS

XSEC DIFFS XSEC DIFFS

Women

Full sample

1981 BMI(FNe)

30 + -0.21(FN*) -0.25(FN*) -0.19(FN*) -0.50(FN*)

-0.36(FN*) 0.26 -0.14(FN*) -0.20(FN*)

24-29 -0.09(FN*) -0.11(FN#) -0.08(FN*) -0.13

-0.11(FN*) -0.01 -0.03(FN#) 0.01

<19 0.02 0.05 0.03 0.04

0.08(FN*) 0.00 -0.02

N 4,859 802 5,048 299

2,707 288 4,449 698

Full sample1988 BMI(FNe)

30 + -0.14(FN*) -0.20(FN*) -0.07(FN*) -0.04

-0.21(FN*) -0.02 -0.011(FN*) -0.10(FN#)

24-29 -0.07(FN*) 0.01 0.01 0.07

-0.03 -0.11 -0.04(FN*) -0.02

<19 -0.00 0.11 -0.02 -0.00

0.03 0.14 -0.00 0.07

N 4,859 802 5,048 299

2,707 288 4,449 698

Single & childless in 1982

1981 BMI(FNe)

30 + -0.31(FN*) -0.14 -0.28(FN*) -0.63(FN*)

-0.57(FN*) -0.30 -0.23(FN*) -0.21(FN#)24-29 -0.10(FN*) -0.01 -0.13(FN*) -0.19(FN#)

-0.18(FN*) -0.12 -0.05(FN*) 0.05

<19 0.01 0.13 0.03 0.17(FN#)

0.16(FN*) -0.01 0.01 -0.01

N 2,554 332 2,663 149

1,342 122 2,444 319

Men

Full sample

1981 BMI(FNe)

30 + -0.02 -0.30(FN*) -0.05 -0.16

-0.14 0.19 -0.10(FN*) -0.19(FN*)

25-29 0.05(FN#) 0.04 0.03 -0.04

0.02 0.14 -0.01 -0.03

<20 -0.14(FN*) -0.15(FN*) -0.03 0.06-0.18(FN*) -0.08 -0.08(FN*) -0.05

N 4,607 866 4,910 348

2,103 219 4,583 875

Full sample

1988 BMI(FNe)

30 + 0.02 -0.10 0.06(FN*) 0.13(FN#)

-0.03 0.02 -0.03(FN#) -0.04



25-29 0.07(FN*) 0.03 0.08(FN*) 0.15(FN*)

0.07(FN#) 0.18 0.02 0.01

<20 -0.30(FN*) -0.21(FN#) -0.07(FN#) -0.08

-0.04 0.12 -0.13(FN*) -0.00

N 4,607 866 4,910 348

2,103 219 4,583 875

Single & childless in 1982

1981 BMI(FNe)30 + -0.00 -0.42(FN*) -0.06 0.25(FN#)

0.01 -0.04 -0.09(FN*) -0.20(FN*)

25-29 0.05(FN*) -0.01 0.01 -0.03

0.04 -0.13 -0.02 -0.06(FN#)

<20 -0.16(FN*) -0.14 -0.03 0.01

-0.16(FN*) 0.22 -0.09(FN*) -0.04

N 3,650 631 3,915 258

1,508 129 3,671 641

Notes: N is the number of observations, or (approximately) the number of pairs for same-sex

sibling differences.

FOOTNOTES

a. Dependent variable = average of In(income/needs) in 1987, 1988, and 1989, where needs are

defined as the poverty line for the family unit in the interview year. Regression controls include in

addition to dummy variables for BMI category: age, years of schooling, and dummy variables for

region (3), racial/Hispanic identification (2), and residence in an SMSA (2), all based on averages of

values from 1988-1990 interviews.

b. Dependent variable is dichotomous: "married at the time of the 1988, 1989, or 1990 interview."

Controls entered in the logit models include in addition to dummy variables for BMI category: age,

years of schooling, and dummy variables for region (3), racial/Hispanic identification (2), and

residence in an SMSA (2), all based on averages of values from 1988-1990 interviews. Numbers

shown are derivatives evaluated at the sample mean and may be interpreted as percentage point

differences in the probability of being married in at least one of the 1988-1990 interviews. Sibling

differences are from fixed-effects logit models.c. Dependent variable = average of In(spouse's annual earnings) over the years 1987, 1988, and

1989 in which the respondent was married. The sample consists of sample members who were

married as of the 1988, 1989, or 1990 interview, and whose spouses earned at least $100 in any year

in which they were married. Regression controls include in addition to dummy variables for BMI

category: age, years of schooling, and dummy variables for region (3), racial/Hispanic identification

(2), and residence in an SMSA (2), all based on averages of values taken over the 1988-1990

interviews at which the respondent was married.

d. Dependent variable = average of In(sample member's hourly wage in survey week) 1988, 1989,

and 1990. The sample consists of sample members who worked for pay during the survey week.

Regression controls include in addition to dummy variables for BMI category: actual labor market

experience, years of schooling, and dummy variables for region (3), racial/Hispanic identification (2),

and residence in an SMSA (2), all based on averages of values taken over the 1988-1990 interviews.

e. 1981 BMI is based on an average of weights reported in 1981 and 1982. 1988 BMI is based onan average of weights reported in 1988, 1989, and 1990.

* p < 0.05;

# 0.05 < p < 0.10.

FOOTNOTES

1. In their review, Autry et al. (p. 537) report a prevalence of 5 percent of college and high

school females and 1.5 percent of males but even these rates may be too high. Drewnoski, Hopkins,

and Kessler (1988) report a prevalence of 1 percent of college females and 0.2 percent of college



males in a nationally representative sample of college students. The prevalence among female

undergraduates living in group quarters on campus, the group at highest risk, was 2.2 percent.

2. There are Development Economics and Economic History literatures on weight and stature. Short

stature and low weight-for-height are indicators of nutritional deficits; height and weight, therefore,

may be interpreted as indicators of the economic status of populations. For an excellent review, see

Steckel (1991).

3. For example, Autry et al. call for research to address "Psychological factors that influence thedevelopment and maintenance of anorexia nervosa and bulimia" and "Genetic, environmental, and

psychosocial studies that might elucidate why the phenomena are more prevalent among females" (p.

541).

4. For example, Sciacca et al. (1991) find in a survey of university students that, although 17

percent of women and 20 percent of men were above "normal" body weight, 40 percent of women and

24 percent of men considered themselves overweight. In addition, 53 percent of women and 20

percent of men reported experiencing a fair amount or great deal of discomfort from being

overweight. Sciacca et al. recommend that efforts should be made to help students who are not over

recommended weight-for-height, but who consider themselves overweight, to change perceptions or

expectations about their bodies (p. 167). While changing self-perception may be an important step in

combating eating disorders, we must recognize the possibility that such students do not have

"distorted body images," but rather, the discrepancy between recommended weight and self-perceived

overweight may reflect an accurate perception of the social norms surrounding body weight. Putdifferently, there is no reason to think that social norms should conform to recommended weights that

are, after all, based on mortality risks.

5. They also note the need to repeat the analysis for an older sample (p. 139), presumably because

the wages of young workers are highly variable (for example, 5 percent of males and 40 percent of

females in their sample were enrolled in school). Other indications of the need for reanalysis are the

small magnitude and lack of significance of several standard wage equation coefficients. For example,

in the male wage equation, coefficients of black racial identification, union status, education, age,

health status, marital status, and labor market experience are small and insignificant. Whether this

result is due to the selectivity correction cannot be determined from the information presented.

6. We use the term "obese" to refer to persons with Body Mass Indexes of 30 or more, and the

term "overweight" for persons with BMIs between 24 and 29 for women, or 25 to 29 for men.

Gortmaker et al. use the term "overweight" to refer to persons above the 95th percentile of NCHS

standards of weight for height, age, and sex. Since this group corresponds closely to the group we

refer to as "obese,"

7. Of 11,602 potential respondents, 10,465 or 90.2 percent were interviewed in 1988 (CHRR 1992).

Of these, we dropped 72 due to missing wages or hourly wages less than one dollar or greater than

100 dollars per hour, and 352 due to missing height information.

8. Among those who appeared to lose more than two inches in height, we identify two types: those

who lost more than two inches between 1982 and 1985 (Group A) and those who lost more than two

inches between 1981 and 1982 (Group B). For Group A we imputed heights as follows. For those who

lost less than two inches from 1981 to 1985, height in 1981, 1982, and 1985 equals the average of

the 1981 and 1985 heights. For those who lost less than two inches from 1981 to 1982, all three

heights were set to the average of the 1981 and 1982 heights. Although everyone in Group A lost

more than two inches in height from 1982 to 1985, if they grew at all between 1981 and 1985, then

1982 height was set to a weighted average of the 1981 and 1985 heights. For Group A individuals who

lost more than two inches from 1981 to 1985, all three heights were set equal to the mean of the

1981 and 1982 heights.

Case B individuals were handled in the following manner. First, if they lost less than two inches or

grew between 1982 and 1985, then we extrapolated linearly to impute the 1981 value. If they lost

more than two inches between 1981 and 1982, then all three heights were set to the mean of the

three of the reported height measures.

9. We get similar results when we use family income as the dependent variable rather than the

income/needs ratio.

10. The distributions (not shown) by BMI in 1982 and 1988 for the subsample of persons who were



single and childless in 1982 are remarkably similar to those for the entire sample.

11. Same-sex sibling differences are computed as differences from within-family means. This

procedure is equivalent to entering a dummy variable for each family of origin (for example, Greene

[1993], pp. 466-69). One differenced observation per family is dropped.

12. For fixed-effects logits, sister pairs contribute to the likelihood function only if sisters differ with

respect to the outcome (namely, one is married in 1988 and one is not).

13. Estimates of obesity differences from cross-sectional models for the pooled sibling subsampleare similar to (or smaller than) those for the full sample.

14. Appendix 1 presents a parallel analysis to Table 4, where we have averaged data from all

available interview years 1988-1990 for each sample member interviewed in 1988. The cross-

sectional results are similar to those in Table 4, whereas results from sibling comparisons are mostly

strengthened. For example, the difference in marriage probabilities between an obese woman and her

sister of recommended weight increases from - .23 to - .50; the difference in wages rises from - .12

to - .20. One exception to this pattern is that the sister difference of spouse's earnings conditional on

being married changes signs from - .25 to + .26; however, this coefficient is not precisely estimated

in either model. The standard error of the coefficient estimate in Appendix 1 is 0.30. For men, cross-

sectional estimates are similar to those in Table 4; however, there is more evidence of an obesity

penalty for men in sibling comparisons when the three years of data are pooled.

15. These figures were computed as follows. For each respondent, total family income is partitioned

into three components: own earnings, spouse's earnings, and other income. Unmarried persons wereassigned 0 for spouse's earnings. Persons with no annual "own" earnings were assigned 0 for own

earnings. We ran four regressions each for men and women (one of the four is redundant); dependent

variables are total family income, own earnings, spouse's earnings, and other income. Controls were

those included in the models summarized in Table 4, except we substituted age for actual experience

in the earnings models. The numbers reported in the text tables are the coefficients of the obese

dummy variables (for women) and the underweight dummy variables (for men).

16. Models for men are available from the authors.

17. One version of this hypothesis holds that obesity effects are confounded by unmeasured self-

esteem, amounting to a suggestion that heavier persons earn lower wages due to low self-esteem and

not weight per se. However, self-esteem may be affected by social treatment, income, or wages.

Therefore, a second version holds that the low economic status of obese women may be accounted for

by low self-esteem, but views low self-esteem to be primarily the result of obesity (for example, see

Gortmaker et al. [1993], who find no effects of obesity on an index of self-esteem).

18. Table 2 also suggests that obese women are slightly less likely to be employed than women of

recommended weight. However, we estimated logit models of labor force participation which included

BMI controls and demographic characteristics and found that BMI was not a significant determinant of

labor force participation.

19. There is also some evidence in Appendix Table A1 of wage discrimination against underweight

and overweight men.

20. For a more detailed (albeit speculative) discussion, see Averett and Korenman (1993).

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the economic reality of the beauty myth

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