BLS WORKING PAPERS 

 

U.S. Department of Labor 

U.S. Bureau of Labor Statistics 

Boston/New York Regional Office 

Women’s Increasing Wage Penalties from Being Overweight and Obese 

David Lempert, U.S. Bureau of Labor Statistics 

Working Paper 414

December 2007

All views expressed in this paper are those of the author and do not necessarily reflect the views or policies of the 

U.S. Bureau of Labor Statistics. 
 

Women’s Increasing Wage Penalties from Being Overweight and Obese* 

by 

David Lempert 

U.S. Bureau of Labor Statistics 

Abstract 

This paper first utilizes annual surveys between the 1981 and 2000 waves of the National 

Longitudinal Survey of Youth to estimate the effect of being overweight on hourly 

wages. Previous studies have shown that white women are the only race-gender group 

for which weight has a statistically significant effect on wages. This paper finds a 

statistically significant continual increase in the wage penalty for overweight and obese 

white women followed throughout two decades. A supporting analysis from a crosssectional dataset, comprised of the 1987 National Medical Expenditure Survey and the 

2000 and 2004 waves of the Medical Expenditure Panel Survey, also shows an increasing 

wage penalty. The bias against weight has increased, despite drastic increases in the rate 

of obesity in the United States. Alternatively, the increasing rarity of thinness has led to 

its rising premium. 

* Submitted in partial fulfillment of the requirements for the degree of Master of Arts 

(Economics), Hunter College, The City University of New York. 
1

I. Introduction 

 Obesity has become a serious public health concern in the United States. Besides the 

obvious personal health consequences associated with being overweight, there are costs 

to society in terms of increased medical expenditures and loss of productivity. Like race 

and gender, weight is another factor that leads to discrimination. Rebecca Puhl of the 

Rudd Center for Food Policy and Obesity at Yale University states, “The stigma of 

obesity has actually worsened in the past 40 years with the increase in prevalence of 

obesity. The bias [has become] more socially acceptable.1” Although almost everyone 

has more contact with the stigmatized group, the stigma is growing along with the 

national waistline. 

 Obesity is a relevant issue in labor economics. A large body of economic literature 

explores how race and gender have impacted wages of women and minorities throughout 

the history of the United States. In a similar vein, our culture promotes and rewards 

thinness and beauty, providing consequences for being overweight. Since the rate of 

obesity has increased dramatically in the United States, researchers have studied the 

economic consequences of weight for at least 15 years. Studies have shown statistically 

significant penalties from weight on wages. This paper explores how the wage penalty 

from weight has changed between 1981 and 2004. We might expect a declining penalty 

due to the increase in the percentage of overweight individuals. On the other hand, 

increasing awareness of and bias against weight may have increased the penalty over 

time. 

 

1 Personal Interview. Tatiana Andreyeva, Rudd Center for Food Policy and Obesity, 

 Yale University, November 2006.
2

 Body Mass Index (BMI) is a measure of a person’s body weight, scaled according to 

height. Defined as weight (kg) divided by the square of height (m)2, it has been 

criticized for its inability to distinguish between muscle and fat. Regardless, researchers 

use this measure of body mass to determine weight status. A BMI reading over 25 

classifies the person as overweight, and a reading over 30 signifies obesity. A reading 

over 40 classifies the person as morbidly obese. More than 64 percent of American 

adults were overweight or obese in the 1999-2000 National Health and Nutrition Survey 

(NHANES) (Flegal, 2002). This represents a 36 percent increase from NHANES II 

(1976-1980), when the rate was 47 percent. Approximately 59 million adults, or 31 

percent, were considered obese in 2000, having increased from 15 percent in the earlier 

NHANES sample. 

 The effect of obesity on wages is of specific interest to economists. Obesity is a 

discriminatory factor in hiring and promotions. The growing literature already shows a 

statistically significant penalty from higher weight on wages. Since the rate of obesity 

has increased in the United States, it is important to understand not only the level of the 

wage penalty, but also how the penalty has changed. While the population of “normal” 

weight people has declined, the awareness and stigma of weight seem to have increased. 

This paper utilizes econometric models to show how the wage penalty has changed 

throughout twenty years of data. Section II includes a review of economic literature. 

Section III discusses datasets. Section IV explains econometric models, analysis, and 

results. Section V concludes, with suggestions for further research on the changing 

mechanisms through which weight has affected wage levels.

 

2 Equivalently, it is calculated as 703*weight (lb) / height2

 (in). 
3

II. Literature Review 

 The economics literature studying the economic effects of being overweight or obese 

has grown over the past 15 years. One primary issue is whether or not, and the extent to 

which, increasing weight affects wages. Register and Williams (1990) study the effect of 

obesity on wage rates with a sample of roughly 8000 men and women between the ages 

of 18 and 25, from the 1982 round of the National Longitudinal Survey of Youth 

(NLSY). Controlling for the link between physical appearance and occupational choice, 

their results show a significant 12 percent penalty for obese women, but no significant 

penalty for obese men. The authors note the need to repeat the analysis for an older 

sample, since many 18- to 25- year old individuals are in school, and therefore have 

highly variable wages. 

 Loh (1993) continues this research with analyses of wage levels of full-time workers 

in the 1982 NLSY and wage changes between 1982 and 1985. He finds no significant 

effects in 1982 for either males or females. However, wages grew roughly 5 percent less 

between the two rounds for obese men and women. Concerns remained regarding the 

endogeneity of weight. Weight may very well be correlated with unobserved factors in 

the error term of the wage equation, causing bias. For instance, wage model covariates 

do not include a measure of the rate of time discount. If obese individuals discount future 

outcomes more steeply, they are less likely to invest in health and human capital since the 

current value of future outcomes is smaller. If there is less investment in human capital, 

wages are lower (Burkhauser, 2004). 

 Averett and Korenman (1996) use a sample of respondents ages 23 to 31 (5090 

women, 4951 men) from the 1988 wave of the NLSY to study the effects of BMI on 
4

income, marital status, and hourly pay differentials. To control for the endogeneity of 

weight, Averett and Korenman utilize 1981 BMI in their first model. They show a 

statistically significant 15 percent penalty on hourly wages for women with a BMI 

greater than or equal to 30. Using 1988 BMI, they find a 10 percent penalty on wages. 

For men, there is an 8 percent penalty using 1981 BMI, and a 3 percent penalty using 

1988 BMI. The primary discrimination, however, results from the marriage market, as 

both the probability of being married and spouse’s earnings account for a large portion of 

the difference in economic status. Further, the authors estimate models including an 

interaction between obesity in 1981 and 1988. Women who became obese between 1981 

and 1988 seem to be no worse off than women of recommended weight, while those who 

were obese at younger ages face significant wage penalties. 

 Pagan and Davila (1997) estimate cross-sectional wage models similar to those of 

Averett and Korenman, using 1989 NLSY data. First, the authors run a multinominal 

logit model to compare the occupational distribution of the obese and the non-obese, 

separated by gender. They find that obese men choose jobs where they have a 

productivity advantage over the non-obese, or where they receive a premium for 

undertaking risks. Men face low barriers when moving across occupations, and are more 

likely to work in repair, transportation, and manufacturing industries. The authors then 

utilize an instrumental variables (IV) approach, first regressing BMI on exogenous 

variables (health limitations, “self esteem” dummy variables, family poverty level in 

1988, education, experience, race, ethnic group, marital status, school enrollment, region, 

and occupation). They find that the net effect of obesity on the earnings of men is 

slightly positive. Larger women, confined to low-level service sector and clerical 
5

occupations, face wage penalties of at least 10 percent. The set of instruments are, 

however, directly related to the dependent variable. Furthermore, health limitations and 

“self esteem” variables could be related to the rate of time discount, found in the error 

term of the wage equation. 

 Since the literature up to this point shows significant wage penalties for larger women, 

questions appeared regarding the mechanisms through which weight affects wages. 

Discrimination could be based upon stereotypes of larger people, specifically that they 

lack self-discipline, and are lazy, less conscientious, and slower. Further, employers 

might be paying lower wages due to the higher costs of insuring obese and overweight 

individuals. A few papers, with conflicting results, address this possibility. Baum and 

Ford (2004) explore the mechanisms by which obese workers earn lower wages, 

proposing several possibilities, using data from the 12 years between 1981 through 1998 

NLSY rounds in which weight and height were requested. 

 Individual models estimate a statistically significant 6 percent wage penalty from 

obesity for women, and a 3 percent penalty for men. Individual difference models 

estimate a 2.3 percent penalty for women, and a 0.7 percent penalty for men. By 

including a term interacting obesity and health insurance dummy variables, they show 

that there is actually less of a wage penalty when employers provide health insurance. In 

the individual difference models, obese men obtain a 4.7 percent increase in their pay, 

while women obtain a 2.7 percent increase. Finally, they show that workers in customeroriented occupations earn less, but do not face a larger wage penalty for being obese. 

 Bhattacharya and Bundorf (2005) estimate a model in which they compare the wage 

penalty for obesity using two separate cohorts, those with health insurance and those 
6

without health insurance. Using 1989-1999 NLSY data, they provide evidence that the 

obese pay for higher expected medical expenditures through lower wages, contrary to the 

findings of Baum and Ford (2004). The authors show that obese people with health 

coverage were paid $1.70 per hour less than the insured non-obese throughout the entire 

period. The difference in wages between non-insured obese and non-obese people was 

only $0.40. Therefore, the insured obese indirectly pay for higher expected medical 

expenditures through lower wages. While the results contrast with those of Baum and 

Ford (2004), note that these authors did not take unobserved heterogeneity and 

endogeneity of weight into account when specifying their models. Further, neither Baum 

and Ford nor Bhattacharya and Bundorf explore the effect of being overweight, but not 

obese, on wages. 

 Cawley (2004) utilizes 1981-2000 NLSY data from the years in which weight and 

height are included. He obtains estimates of true weight and height in the NLSY data by 

utilizing coefficients reported in NHANES III, which includes self-reported numbers and 

actual values taken from physical examinations. Separately by race-gender groups, 

actual weight is regressed on reported weight and its square. Judging by the high Rsquared of .995, reported weight and its square are strong predictors of actual weight. 

The same process was repeated for height, leading to similarly significant results. In 

NHANES III, self reported height and weight of NLSY-aged white females result in 

underestimated BMI by an average of 1.58 percent, while male calculated BMI is 

underestimated by an average of 1.0 percent. Finally, self-reports of both weight and 

height in the NLSY are multiplied by the coefficients reported in NHANES III according 

to race-gender group. In his wage models, Cawley finds statistically significant results 
7

for women, though substantially less for black and Hispanic women. For white women, 

the coefficient on BMI is -0.008, and the coefficient on weight in pounds is -0.0014. For 

a two standard deviation increase in weight (64 lbs.), white women are paid 9 percent 

less. Black men seem to receive higher wages with higher weight, and Hispanic men 

incur a wage penalty. 

 Due to the endogeneity of weight, Cawley turns to IV, attempting to find a set of 

instruments that are correlated with BMI, but uncorrelated with the error term in the wage 

equation. The first instrument discussed is sibling BMI, which is highly correlated with 

respondent BMI. Further, he assumes that sibling BMI is uncorrelated with the 

respondent’s wage residual. To control for the age and gender of the sibling, the author 

also includes these variables in the set of instruments. The instruments are good 

predictors of a woman’s weight. For white females, the first stage regression shows a tstatistic of 23.47. However, it is impossible to prove that sibling BMI is uncorrelated 

with the residual in the wage equation. The respondent’s rate of time discount is included 

in the residual. If this rate of discount is influenced by the family, then the sibling’s BMI 

is also correlated with it. This paper will focus on associations between BMI and wages, 

rather than causal effects, due to the difficulty involved in determining an appropriate 

instrument. 

 Results show that BMI and weight in pounds have statistically significant effects on 

wages at the 5 percent level for white females only. The coefficient on BMI is -0.0017, 

and the coefficient on weight in pounds is -0.0028. These coefficients are, in absolute 

value, twice as large as those found in the earlier OLS models. Here, a two standard 

deviation increase in weight is associated with an 18 percent decline in wages. White and 
8

Hispanic males’ BMI has an effect on wages significant at the 9 percent level. Results 

for all other race-gender groups are insignificant, including black and Hispanic females. 

Averett and Korenman (1999) explain that increased weight has a stronger negative 

impact on the self-esteem of white females. 

 Although researchers have reported the negative effect of weight on wages, few have 

examined how the effect changes over time. As a continually increasing proportion of 

the population becomes classified as overweight, the question arises. Since significant 

effects are found mostly for white females, this paper’s focus is on the changing penalty 

from weight on wages for that group. Surveys do not have sufficient observations on 

non-whites to find such differences. Our conclusions will be specific to white female 

observations in our datasets.

III. Data 

 This paper utilizes two datasets, one panel and one cross-sectional, to explore the 

changing penalty from weight on wages. The panel dataset allows year-by-year 

comparisons among a cohort of women who are in their youth at the beginning of the 

sample. The cross-sectional dataset captures a broader representation of society, since 

each year of data contains a much wider age range than the panel. Further, this dataset is 

uncomplicated by group composition effects. In the panel, the cohort of women who 

grow older could have characteristics specific to their group that affect wages. 

III.I National Longitudinal Survey of Youth 

 The first dataset utilized in this paper is taken from the National Longitudinal Survey 

of Youth (NLSY), as cleaned and specified by Cawley (2004). This panel survey, 

conducted by the U.S. Department of Labor, Bureau of Labor Statistics, began with 
9

annual interviews of 12,686 young males and females in 1979. Since 1994, interviews 

have been conducted every other year. The NLSY simplifies race categories into three 

groups, black, Hispanic, and non-black/non-Hispanic (referred to as white in the 

literature). The NLSY includes self-reported weight in 13 years included in our sample, 

1981, 1982, 1985, 1986, 1988, 1989, 1990, 1992, 1993, 1994, 1996, 1998, and 2000. 

Height was reported in 1981, 1982, and 1985; height in 1985 is used in our models. The 

dataset includes pooled data from these 13 years, spanning two decades. 

 As described above in Literature Review, self-reports of weight and height are 

adjusted according to race-gender specific coefficients determined by regression in the 

NHANES III. Females who are pregnant when they report weight are dropped from the 

sample. Wages for all years are adjusted to 1990 dollars according to the CPI – All 

Urban Consumers series. Outliers in the wage variable are recoded such that the wage 

range is $1 to $500, since the dependent variable is the natural log wage. 

 OLS models with white females rely on 25,843 observations ranging from ages 16 to 

43. Models are estimated on the sample of working women. Figure 1 shows the 

dramatic increases in rates of being overweight and obese in white females from 1981 to 

2000. See Table 1 for summary statistics.

III.II National Medical Expenditure Survey / Medical Expenditure Panel Survey 

 The second dataset utilized in this paper is a pooled cross-sectional dataset made of 

the 1987 National Medical Expenditure Survey (NMES) and the 2000 and 2004 waves of 

the Medical Expenditure Panel Survey (MEPS). The NMES, conducted by the United 

States Department of Health and Human Services (Agency for Health Care Policy and 

Research), is a three-part national survey of the U.S. population. It includes questions on 
10

health care services utilized, demographic variables, and personal health characteristics. 

Data is made available for member institutions of the Interuniversity Consortium for 

Political and Social Research (ICPSR) at the University of Michigan. The Household 

Survey includes 38,846 individuals; 30,038 of these individuals are also included in the 

Health Status Questionnaire and Access to Care Supplement. The Household Survey 

contains all variables utilized in analysis here, with the exception of body height and 

weight, found in the health supplement. 

 Efforts to publish data on medical expenditures were continued with the Medical 

Expenditure Panel Survey (MEPS), which began in 1996. The MEPS, conducted by the 

United States Department of Health & Human Services (Agency for Healthcare Research 

and Quality), is a set of large-scale surveys of families and individuals, their medical 

providers, and employers. There are two major components, the Household Component 

and the Insurance Component. The MEPS has demographic and body characteristics 

data similar to the NMES. The survey is called a panel because questions are asked of 

respondents in numerous rounds. However, there is a new set of respondents every two 

years. This paper utilizes data from the 2000 and 2004 MEPS since the previous MEPS 

samples do not include measures of weight and height. 

 Again, self-reports of weight and height are adjusted according to race-gender 

coefficients determined by regression in the NHANES III, as described above in 

Literature Review. Wages for all years are adjusted to 2004 dollars according to the CPI 

– All Urban Consumers series. Outliers in the wage variable are recoded such that the 

wage range is $1 to $65.633

. OLS models with employed white females rely on 11,899 

 

3 The maximum wage reported in NLSY is $500, whereas the maximum wage reported in MEPS is $65.63. 
11

observations, ranging from ages 16 to 64. See Table 2 for summary statistics of white 

females in the OLS dataset. Average BMI increases from 24.33 in 1987 to 26.97 in 2000. 

It further increases to 27.33 in 2004. Similarly, average BMI increases 23.53 in 1998 to 

26.80 in 2000 in the NLSY sample. 

IV. Econometric Methods and Analysis 

IV.I Econometric Model 

 Based on previous studies of the effects of obesity on wages, this paper focuses on 

white women. Cawley’s results present a convincing case that BMI has statistically 

significant effects on wages, but for white women only. Studies propose that larger white 

women have self-esteem problems not experienced by other race-gender groups, which 

could influence their ability to advocate for higher wages. As seen in Figure 1, the cohort 

becomes increasingly overweight and obese throughout two decades. Studies have 

described the effects of race and gender on wages, and how the effects have changed over 

time. This paper introduces analysis exploring how the effect of weight on wages has 

changed for white women. 

 All models utilizing NLSY data have robust standard errors, clustered to account for 

the fact that the individuals are in the sample more than once. Wages are bottom coded at 

$1.00 since our dependent variable is the natural log. Further, all models are limited to 

the sample of working white women who report positive wages. Previous literature has 

provided evidence that BMI has a negative effect on wages. The probability of being 

employed could decline with increased BMI because of the disincentive to work that is 

associated with the lower wages. If this is the case, then we will underestimate the effect 

of weight on wages; overweight women who are discouraged to work are excluded from 
12

our models. The effective wage of these discouraged workers is zero. The goal is to 

explore how the weight penalty has changed over time. If the effect of BMI on 

participation has increased or decreased, the disincentive to work could also affect the 

time trend we seek to capture. 

 Starting with the baseline model, variations are added to explore the year-by-year 

changing effect of BMI on the log wage. 

(1) ln Wit = BMIit β + Xit γ + εit 

The dependent variable is the natural log of the hourly wage. BMI is the body weight 

status measurement. The set of control variables includes linear measures of age and 

time. The NLSY includes the age of the woman’s youngest child, and the total number 

of children to whom she has given birth. Additional control variables include general 

intelligence (derived from ten Armed Services Vocational Aptitude Battery tests), highest 

grade completed, mother’s highest grade completed, father’s highest grade completed, 

years of actual work experience, job tenure, an indicator variable for white collar or blue 

collar work, current school enrollment, county unemployment rate, a part-time work 

dummy variable, marital status indicators, region of residence, and finally, dummy 

variables for missing data associated with each regressor (except weight). The 

NMES/MEPS includes the total number of children living in the household. Additional 

control variables include highest grade completed, marital status indicators, and 

geographic region of residence. 

 The model described in equation (1) assumes a constant BMI effect over time. In 

order to capture potential changes in the BMI effect, the following model is estimated. 

(2) ln Wit = BMIitβ + YRNtδ + YRNt*BMIitλ + Xit γ + εit 
13

The variable YRN is defined as Year less 1981 (NLSY) or 1987 (NMES/MEPS). The 

variable YRN*BMI captures the changing effect of BMI on wages. 

 Prior to estimating the restrictive model for the NLSY, a more general dummy 

variable model was attempted. Instead of having the YRNt*BMIit variable, there was a 

set of dummy variables for the each of the years (excluding 1981), and a set of terms 

interacting the year dummy variables with BMI. This model is more general than the 

restrictive model, since it allows the effect of BMI on the wage to vary completely over 

time. None of the Year*BMI interaction variables had a statistically significant effect on 

log wage (results not shown). Additionally, the coefficient on BMI was not significant. 

Therefore, we return to the results from the more parsimonious model. 

IV.II Results from the National Longitudinal Survey of Youth 

 The coefficient on YRNt*BMIit is an estimate of the linear trend of the effect of BMI 

on wages since 1981. Results are in Table 3. The coefficient on BMI is -0.00577, and is 

significant at the 1 percent level. The coefficient on YRNt*BMIit is -0.00023, and it is 

significant at the 10 percent level. This suggests that when holding all other factors 

constant, the penalty from higher BMI grows throughout two decades of data. First by 

multiplying the coefficient on YRNt*Bit by the values of YRNt, we obtain the year-byyear differing penalty from BMI on wage. We add these values to the initial effect, the 

coefficient on BMI, to obtain the year-by-year penalties from a one-point increase in BMI 

on log wage. 

 Comparisons in this paper will be made between the 25th and 75th percentiles for BMI 

of white women, or roughly one standard deviation. Keeping all other variables constant 

at their mean values, we will simulate the percentage wage penalty incurred by going 
14

from the lower quartile to the upper quartile. Shen (2006) lists the 25th and 75th

percentiles for BMI at 21.0 and 28.4, a difference of 7.4 points. For a 5’4” woman (64 

in.), these BMI values correspond to weights of 122.36 lbs. and 165.47 lbs. The penalty 

for being at the 75th percentile, rather than the 25th percentile, grows from 4.29 percent in 

1981 to 7.47 percent in 2000 (Figure 2). This analysis shows that the weight penalty 

almost doubles over a period during which weight stigma increased. 

IV.III Results from the National Medical Expenditure Survey / Medical Expenditure 

Panel Survey 

IV.III.I Baseline Model 

 Section IV.II includes models run on a panel dataset. Results would lead us to think 

that the wage penalty for being overweight has increased over time due to increased 

weight stigmatization. However, the first set of analyses involved the same women being 

followed for twenty years. While the coefficient under examination is that on Year*BMI, 

and we find significant results, it could be that these effects result from a group 

composition effect. That is, the panel of women could show an increasing wage penalty 

due at least partially to the effects of increasing age within the group, and cumulative 

discrimination over many years. Supporting analyses from a cross-sectional dataset will 

help determine whether the negative, significant coefficient on BMI*Year is strictly from 

a group composition effect, or if it is indicative of what has happened in society at large. 

Therefore, it provides a clearer indication of the average penalty from weight on wage. 

While 2000 data from the NLSY includes women roughly 40 years of age, the 2000 

MEPS data captures the entire age spectrum. Our next step is to utilize the NMES/MEPS 

data, and see how results compare to the NLSY results. As stated above, this second 

analysis uses data from 1987, 2000 and 2004. 
15

 The coefficient on BMI is -0.00489, and is significant at the 1 percent level (Table 4). 

The coefficient on YRNt*BMIit is not significant, suggesting that the effect of BMI on the 

wage has not changed over time. Averett and Korenman (1996) found that women who 

became obese between 1981 and 1988 were no worse off than women of recommended 

weight, while those who were obese at younger ages face significant wage penalties. 

Unlike that study, we find in the cross-sectional data that all white women face some 

wage penalty from weight. 

IV.III.II Synthetic Cohort Models 

 The NLSY dataset specified by Cawley begins with respondents between the ages of 

16 and 24 in 1981. The NLSY models in this paper study white women’s changing wage 

penalties from being overweight between 1981 and 2000. It is found that penalties 

increase over time, presumably due to an increase in weight stigmatization. The initial 

NMES/MEPS results do not show the same significant time trend. However, this dataset, 

unlike the NLSY, is a cross-section, so it better represents the average population. For 

instance, the 2000 data includes the entire age spectrum, whereas in NLSY, the youngest 

respondents in 2000 are 35 (since they were 16 in 1981). 

 For a further comparison, we can construct a “synthetic cohort,” pretending that the 

NMES/MEPS dataset is a panel. For instance, since 1981 data in NLSY includes ages 

16-24, 2000 data includes ages 35-43. In NMES/MEPS, we can use ages 22-30 in 1987 

(since this pretend cohort was 16-24 in 1981), and therefore, ages 35-43 in 2000, and 39-

47 in 2004. This synthetic cohort will allow us to make comparisons to the results from 

the NLSY panel data; we can follow the “same” women throughout a seventeen-year 

period. Besides using the same ages as those in NLSY data, we can also utilize a broader 
16

age range. For instance, we can study women under the age of 60 in 2004. Therefore, 

we use ages 16-42 in 1987, ages 29-55 in 2000, and then ages 33-59 in 2004. By using 

these synthetic cohorts, we can study the changing effects of being overweight on wages. 

Further, we can compare our results with NLSY models, and see if they support the idea 

that the wage penalty has increased for women, or declined as the rate of obesity has 

increased. 

 Using the aforementioned equation (1), the first synthetic cohort model follows the 

NLSY ages. Therefore, 1987 data is restricted to ages 22-30, 2000 data is restricted to 

ages 35-43, and 2004 data is restricted to ages 39-47. The coefficient on YRNt*BMIit is 

insignificant (Table 5). An alternative is to restrict 1987 data to ages 16-24, and 

therefore, 2000 data is restricted to ages 29-37, and 2004 data is restricted to ages 33-41 

(Table 6). The coefficient on BMI is insignificant. The coefficient on YRNt*BMIit is 

-0.00055, and it is significant at the 5 percent level. Holding all other factors constant, 

the penalty from BMI grows throughout two decades of data. By multiplying the 

coefficient on YRNt*BMIit by the values of YRNt, we obtain the year-by-year increase in 

wage penalty from a one-point increase in BMI. Using the one standard deviation 

increase in BMI of 7.4 points, the interpretation shows that by 2004, compared to 1987, 

there is a 7.25 percent larger penalty (Figure 3). 

 The last synthetic cohort model uses a much wider age range. Although 65 is the 

typical retirement age in the United States, 2004 observations are restricted to ages less 

than 60, since ages 60-65 can be considered a pre-retirement period. Therefore, 1987 

data contains ages 16-42, 2000 data contains ages 29-55, and 2004 data contains ages 33-

59. The coefficient on BMI is -0.00376, and is significant at the 5 percent level (Table 
17

7). The coefficient on YRNt*Bit is -0.00028, and is also significant at the 5 percent level. 

Holding all other factors constant, the penalty from BMI grows throughout two decades 

of data. First by multiplying the coefficient on YRNt*BMIit by the values of YRNt, we 

obtain the year-by-year differing penalty from BMI on wage. We add these values to the 

initial effect, the coefficient on BMI. Our final results are the year-by-year wage 

penalties from a one standard deviation increase in BMI of 7.4 points (Figure 4). The 

penalty grows from 2.81 percent in 1987 to 6.29 percent in 2004. This analysis shows 

that the weight penalty more than doubles as weight stigma increases. 

 Thus far, the NLSY and NMES/MEPS synthetic cohort models generally show an 

increasing penalty from BMI on the wage. In the last synthetic cohort model, 

observations were restricted such that the maximum age in 2004 is 59. As such, we do 

not obtain the explanatory benefits of a non-restricted dataset. The final model uses an 

unrestricted dataset, however, dummy variables are created to represent those individuals 

not in the synthetic cohort. Therefore, the baseline group in this model is the synthetic 

cohort. By having an unrestricted dataset, but still delineating the differential effects of 

weight on wages between those in the cohort and those not in the cohort, there is an 

acknowledgement that the cohort could have unique group composition effects. While 

the cohort shows an increasing penalty, it could be that those outside of this group have 

either a declining penalty or no clear penalty at all. 

(1) ln Wit = BMIitβ + YRNtδ + YRNt*BMIitλ + YNGχ + OLDφ + YNG*BMIitφ + 

OLD*BMIitη +Xit γ + εit 

 The dummy variables YNG and OLD specify whether the individual is younger or 

older than the synthetic cohort in the year of the observation. The variables YNG*BMIit

and OLD*BMIit are interactions between the YNG and OLD dummy variables and BMI. 
18

The coefficient on BMI is -0.00439, and is significant at the 1 percent level (Table 8). 

The coefficient on YRNt*Bit is -0.00021, and is significant at the 10 percent level. 

Holding all other factors constant, the penalty from BMI grows throughout two decades 

of data. Our final results are the year-by-year penalties from a one standard deviation 

increase in BMI on log wage for those in the synthetic cohort (Figure 5). The penalty 

grows from 3.26 percent in 1987 to 5.99 percent in 2004. However, careful interpretation 

also shows an increasing penalty for those individuals who are older and younger than the 

cohort. This analysis shows an increasing wage penalty beyond a composition effect. 

This seems to suggest that the average wage penalty in society has increased. Put another 

way, thinness has an increasing premium. 

 Simulated hourly wages for the nine series can be found in Figure 6. Average values 

are used for the control variables. There are three groups, young, cohort, and thin, 

combined with two body types, a BMI of 21, and a BMI of 28.4. For a height of 5’4”, 

these BMI values correspond to body weights of 122.36 and 165.47, respectively. There 

are no values for the young group in 1987, since it does not exist in that year (the 

synthetic cohort includes the youngest age, 16). For each group, there are increasing 

wage penalties. For the younger group in 2000, a BMI of 28.4 is associated with a 

slightly higher wage than a BMI of 21. However, in 2004, there is a $0.04 penalty 

associated with the BMI of 28.4. For the synthetic cohort, the penalty grows from $0.36 

in 1987 to $0.64 in 2000, and then to $0.75 in 2004. For the older group, the penalty 

grows from $0.08 in 1987 to $0.31 in 2000, and then to $0.40 in 2004. In line with 

NLSY results, the NMES/MEPS models show that weight is associated with an 
19

increasing wage penalty over time. Again, this paper does not explore the underlying 

reasons for the increasing penalty, but rather, proves and acknowledges its significance. 

V. Conclusion 

 The purpose of this paper is to estimate the changing wage penalties for being 

overweight and obese in the United States since the early 1980s, a period which has seen 

a dramatic increase in the rate of obesity. Coincidentally, the stigma placed on larger 

people may have increased. Previous literature has studied the effects of weight on the 

wage level for either single-year datasets, or data pooled from many years. It is generally 

found that white women face significant wage penalties from weight, while other racegender groups face smaller or no effects. Therefore, this paper focuses on white women. 

After verifying the negative impact of weight on wages, this paper steps forward by 

studying how the wage penalty from weight has changed. 

 We might expect that the penalty has declined, because as the population of “normal” 

weight individuals has declined, there would be less room for discrimination. However, 

the bias against weight appears to have increased. By utilizing two datasets, with 

multiple econometric specifications, this paper helps us understand that weight has had 

consistently increasing penalties on wages. The samples are limited to employed women 

who report positive wages. Therefore, the estimates do not include the effects of larger 

women who might have been discouraged from working by the lower wages they would 

have received. 

 First, thirteen years of data from the National Longitudinal Survey of Youth are 

pooled together for an analysis. This dataset follows the same panel of respondents. An 

OLS model shows that the total penalty from a one standard deviation increase in BMI 
20

(7.4 points) increases from 4.29 percent in 1981 to 7.47 percent in 2000. The wage 

penalty almost doubles as weight stigma increases. 

 It is important to remember that the panel data follows the same women for many 

years. A cross-sectional dataset better captures the societal averages, since each survey 

year includes a new group of people. The second dataset utilized in this paper is culled 

from the 1987 National Medical Expenditure Survey, and the 2000 and 2004 waves of 

the Medical Expenditure Panel Survey. The cross-sectional data captures the average 

effect over a wider age range. Initial attempts suggest that the weight penalty on wages 

has not changed over time. 

 A synthetic cohort model is attempted with data restricted to ages 16-24 in 1987, ages 

29-37 in 2000, and ages 33-41 in 2004. This model shows that by 2004, compared to 

1987, each one standard deviation increase in BMI is associated with a 7.25 percent 

larger penalty on the wage. In the last synthetic cohort model, we utilize a wider age 

range such that the maximum age in 2004 is the pre-early retirement age of 59. Data 

from 1987 includes ages 16-42, 2000 includes 29-55, and 2004 includes 33-59. Using 

the OLS model, we find an increasing penalty as we found in NLSY. The wage penalty 

from a one standard deviation in BMI increases from 2.81 percent in 1987 to 6.29 percent 

in 2004. 

 Finally, we utilize a model that has no restrictions on observations, but rather, uses 

dummy variables to signify observations that are younger and older than the synthetic 

cohort in the given year. These dummy variables are interacted with BMI. Both the 

dummy variables and interaction variables are included in the baseline OLS model. 

Therefore, the synthetic cohort becomes the baseline part of the model. The penalty for a 
21

one standard deviation in BMI grows from 3.26 percent in 1987 to 5.99 percent in 2004. 

Alternatively, the dollar value wage penalty for an observation with a BMI of 28.4, 

compared to the same observation with a BMI of 21, grows from $0.36 in 1987 to $0.64 

in 2000, and then to $0.75 in 2004. Further, the wage penalty grows for individuals who 

are older and younger than those in the synthetic cohort. We might have explained the 

NLSY results by saying that they could be partially affected by the composition effect of 

the group. However, increasing penalties for younger and older observations suggest that 

the increasing penalty is not simply a composition effect. 

 The increasing wage penalty corresponds to current psychological research that 

demonstrates increased weight stigmatization in the United States. Further, as larger 

women age, their wages incur the effects of years of cumulative discrimination. With 

other factors controlled, their starting wages are lower. Throughout their working 

careers, these women receive less frequent raises and promotions. Therefore, we see 

increasing penalties in both NLSY data and the synthetic cohort constructed from 

NMES/MEPS data. This paper has shown that an obese 43 year-old woman received a 

larger wage penalty in 2004 than she received at 20 in 1981. This paper also provides 

some evidence that an obese 20 year-old woman receives a larger wage penalty today 

than she would have in 1981 at age 20. Future literature should further explore this 

aspect of the story, as well as the mechanisms by which the wage disparities occur. It can 

be concluded that increased body weight has drastic economic consequences that have 

grown over time. Alternatively, the increasing rarity of thinness has created its growing 

wage premium. 
22

References 

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Averett, Susan, and Sanders Korenman. 1999. “Black-White Differences in Social and 

Economic Consequences of Obesity.” International Journal of Obesity 

23:166-73. 

Baum, Charles L. II and William F. Ford. 2004. “The wage effects of obesity: a 

 Longitudinal study.” Health Economics 13: 885-89. 

Behrman, Jere R., and Mark R. Rosenzweig. 2001. “The Returns to Increasing Body 

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Bhattacharya, Jay and M. Kate Bundorf. April 2005. “The Incidence of the Healthcare 

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Burkhauser, Richard and John Cawley. 2002. “Obesity, Disability, and Movement onto

 The Disability Insurance Rolls.” University of Michigan Retirement Research 

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Cawley, John. 2000. “Body Weight and Women’s Labor Market Outcomes.” NBER 

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Cawley, John. 2004. “The Impact of Obesity of Wages.” Journal of Human Resources, 

 39(2):451-74. 

Flegal, KM, et. al. 2002. “Prevalence and trends in obesity among US adults, 

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288(14) (October):1772-3. 

Loh, Eng Seng. 1993. “The Economic Effects of Physical Appearance.” 

Social Science Quarterly 74(2):420-38. 

Pagan, Jose, and Alberto Davila. 1997. “Obesity, Occupational Attainment, and 

 Earnings.” Social Science Quarterly 78:3 (September), 756-70. 

Register, Charles A. and Donald R. Williams. 1990. “Wage Effects of Obesity Among 

 Young Workers.” Social Science Quarterly 71(1):130-41. 

Shen, Wei, et. al. 2006. “Waist Circumference Correlates with Metabolic Syndrome

 Indicators Better Than Percentage Fat.” Obesity 14(4):727-36. 
23

Table 1: Descriptive Statistics of Variables Used in NLSY Analysis (Pooled Sample)

Variable N Mean Std. Dev. Minimum Maximum 

Log Wage 25845 1.98 0.60 0 6.16 

Corrected BMI 25845 24.08 5.37 6.73 88.07 

Year*BMI 25845 214.39 162.67 0 1673.28 

Year 25845 8.54 5.63 0 19 

Corrected Weight 25845 143.32 33.61 42.60 572.13 

Year*Weight 25845 1276.57 975.86 0 10870.44 

Corrected Height 25845 64.65 2.27 51.03 80.74 

Year*Height 25845 552.28 365.03 0 1370.54 

Ever born 25845 0.99 1.16 0 10 

Young kid 25845 2.58 4.12 0 27 

No. Children in Household 25845 0.50 0.50 0 1 

White Collar 25845 0.59 0.49 0 1 

Missing White Collar 25845 0.09 0.29 0 1 

Mother's HGC 25845 11.37 3.31 0 20 

Missing Mother's HGC 25845 0.04 0.19 0 1 

Father's HGC 25845 11.19 4.47 0 20 

Missing Father's HGC 25845 0.07 0.26 0 1 

Married, Spouse Present 25845 0.53 0.50 0 1 

Married, Other 25845 0.15 0.36 0 1 

GCPS1 (Intelligence) 25845 0.09 0.93 -3.88 2.40 

Missing GCPS1 25845 0.03 0.17 0 1 

HGC 25845 13.14 2.40 0 20 

Missing HGC 25845 0.00 0.06 0 1 

Enrolled in School 25845 0.13 0.34 0 1 

Years of Work Experience 25845 7.24 5.33 0 23.7 

Missing Years of Work 25845 0.07 0.25 0 1 

Years at Current Job 25845 3.05 3.66 0 23.12 

Missing Years at Current Job 25845 0.01 0.10 0 1 

Part-time Work Indicator 25845 0.85 0.36 0 1 

Missing Part-Time Work 25845 0.04 0.20 0 1 

Age 25845 28.54 6.09 16 43 

Low Unemployment in County 25845 0.43 0.49 0 1 

High Unemployment in County 25845 0.20 0.40 0 1 

Missing Unemployment 25845 0.02 0.15 0 1 

Northeast 25845 0.19 0.39 0 1 

North-Central 25845 0.30 0.46 0 1 

South 25845 0.33 0.47 0 1 

Definitions: Log Wage: natural log of wage, recoded to $1 to $500. Corrected BMI: BMI adjusted with 

NHANES III coefficient. Year*BMI: (year-1981)*Corrected BMI interaction variable. Year: (year-1981). 

Corrected Weight: weight adjusted with NHANES III coefficient. Year*Weight: (year-1981)*Corrected 

Weight interaction variable. Corrected Height: height adjusted with NHANES III coefficients. Year*Height: 

(year-1981)*Corrected Height interaction variable. Ever born: number of children ever born. Young kid: 

age of youngest child. Children Indicator: Dummy variable indicating whether or not there are children. 

White Collar: Dummy variable indicating job status (white collar, blue collar). Missing White Collar: 

Dummy variable for observations with missing White Collar value. Mother’s HGC: Mother’s highest grade 

completed. Missing Mother’s HGC: Dummy variable for observations with missing Mother’s HGC value. 

Father’s HGC: Father’s highest grade completed. Missing Father’s HGC: Dummy variable for observations 

with missing Father’s HGC value. Married, Spouse Present: Indicator – married, spouse present. Married, 

Other: Indicator for been married, but not with spouse. GPCS1 (Intelligence): General Intelligence. 

Missing GCPS1: Dummy variable for observations with missing GPCS1. HGC: highest grade completed. 

Missing HGC: Dummy variable for observations with missing HGC value. Enrolled in School: Dummy 

variable for school enrollment value. Years of Work Experience: Years of actual work experience. Missing 
24

Years of Work Experience: Dummy variable for observations with missing Years of Work Experience value. 

Tenure: Years at current job. Missing Tenure: Dummy variable for observations with missing tenure value. 

Part-Time Work Indicator: Indicator for part-time work (continued on next page). 

Missing Part-Time Work Indicator: Dummy variable for observations with missing Part-Time Work 

Indicator value. Age: Age in years (16-43). Low unemployment in county: Indicator for unemployment 

under 6% in county. High unemployment in county: Indicator for unemployment greater than or equal to 

9% in county. Missing Unemployment: Dummy variable for observations with missing unemployment 

data. Northeast: Indicator for Northeast region. North-Central: Indicator for North Central region. 

South: Indicator for South Region. Year Dummy: dummy variables for years 1982-2000. Year Dummy * 

BMI: Interaction variables for Year Dummy * Corrected BMI. 
25

Table 2: Descriptive Statistics of Variables Used in NMES/MEPS Analysis (Pooled Sample)

Variable N Mean Std. Dev. Minimum Maximum 

Log Wage 11899 2.45 0.60 0 4.18 

Corrected BMI 11899 26.21 6.38 12.17 195.95 

Year*Corrected BMI 11899 270.01 220.60 0 2547.39 

Year 11899 9.92 7.51 0 17 

Test 11899 0.21 0.41 0 1 

Test 2 11899 0.25 0.44 0 1 

Test 5 11899 0.66 0.47 0 1 

Test 5 Young 11899 0.19 0.39 0 1 

Test 5 Old 11899 0.15 0.36 0 1 

Test5Young*Corrected BMI 11899 4.89 10.57 0 195.95 

Test 5 Old * Corrected BMI 11899 3.99 9.79 0 150.79 

Age 11899 38.22 12.26 16 64 

HGC 11899 12.96 2.70 0 17 

Married, Spouse Present 11899 0.56 0.50 0 1 

Married, Other 11899 0.19 0.39 0 1 

Midwest 11899 0.24 0.43 0 1 

South 11899 0.35 0.48 0 1 

West 11899 0.23 0.42 0 1 

No. of Children in HH 11899 0.72 1.10 0 11 

Definitions: Log Wage: natural log of wage, recoded to $1 to $65.63. Corrected BMI: BMI adjusted with 

NHANES III coefficient. Year: (Year-1987)*Corrected BMI interaction variable. Year: (Year-1987). 

Test=dummy for those in cohort ages 16-24 in 1987. Test2: dummy for those in cohort ages 22-30 in 

1987. Test5: dummy for those in cohort ages 16-42 in 1987. Test 5 Young: dummy for those younger 

than those in test cohort. Test 5 Old: dummy for those older than those in test cohort. 

Test5Young*Corrected BMI: interaction between test5yng and Corrected BMI. Test 5 Old * Corrected 

BMI: interaction between Test 5 Old and BMI. Age: age from 5/3 rounds. HGC=highest grade completed 

(0-17). Married, Spouse Present.. Married, Other: been married, spouse no longer present. Midwest: 

Dummy for those in Midwest. South: Dummy for those in South. West: dummy for those in West. No. of 

Children in HH: number of children in household. 
26

Table 3: NLSY Results 

Variable Coefficient Std. Err. 

Corrected BMI -0.00577** 0.00190 

Year 0.00940* 0.00446 

Year*Corrected BMI -0.00023**** 0.00014 

Ever born -0.0357*** 0.00924 

Young Kid -0.00557** 0.00176 

Children Indicator -0.00298 0.01926 

White Collar 0.17435*** 0.01200 

Missing White Collar 0.11728*** 0.01470 

Mother's HGC -0.00066 0.00338 

Missing Mother's HGC 0.00829 0.05043 

Father's HGC 0.00579* 0.00248 

Missing Father's HGC 0.08362* 0.03976 

Married, Spouse Present 0.03345* 0.01440 

Married, Other 0.06894*** 0.01824 

GCPS1 (Intelligence) 0.05670*** 0.00871 

Missing GCPS1 -0.07590* 0.03576 

HGC 0.05920*** 0.00399 

Missing HGC 0.81294*** 0.10592 

Enrolled in School -0.12323*** 0.01332 

Years of Work Experience 0.02527*** 0.00255 

Missing Years of Work 0.29621*** 0.03532 

Tenure 0.02608*** 0.00182 

Missing Tenure 0.07869 0.06505 

Part-Time Work Indicator 0.02653 0.01805 

Missing Part-Time Work -0.12348*** 0.02394 

Age -0.00154 0.00371 

Low Unemployment 0.06828*** 0.00976 

High Unemployment -0.00726 0.01122 

Missing Unemployment -0.00129 0.03107 

Northeast -0.00507 0.02157 

North Central -0.13047*** 0.01914 

South -0.11282*** 0.01906 

Constant 0.96621*** 0.09783 

 Legend: * p<0.05; ** p<0.01; *** p<0.001;   p<0.10 
27

Table 4: NMES/MEPS Results, Baseline Model

 Variable Coefficient Std. Err. 

 Corrected BMI -0.00489**  0.00150 

 Year * Corrected BMI 0.00005 0.00011 

 Year 0.00425 0.00292 

 Age 0.00811*** 0.00047 

 HGC 0.09324*** 0.00179 

 Married, Spouse Present 0.14882*** 0.01315 

 Married, Other 0.10046*** 0.01638 

 Midwest -0.10175*** 0.01476 

 South -0.12187*** 0.01386 

 West -0.04664** 0.01500 

 No. of Children in HH -0.01528*** 0.00458 

 Constant 0.98478*** 0.04753 

 Legend: * p<0.05; ** p<0.01; *** p<0.001 
28

Table 5: NMES/MEPS Results, Synthetic Cohort, Ages 22-30 in 1987

 Variable Coefficient Std. Err. 

 Corrected BMI -0.00516 0.00307 

 Year * Corrected BMI -0.00023 0.00023 

 Year 0.00568 0.00683 

 Age 0.01822*** 0.00368 

 HGC 0.09084*** 0.00363 

 Married, Spouse Present 0.05534* 0.02536 

 Married, Other -0.01813 0.03215 

 Midwest -0.09373** 0.02905 

 South -0.10691*** 0.02739 

 West -0.06401* 0.02935 

 No. of Children in HH -0.00602 0.00901 

 Constant 0.84782*** 0.13075 

 Legend: * p<0.05; ** p<0.01; *** p<0.001 
29

Table 6: NMES/MEPS Results, Synthetic Cohort, Ages 16-24 in 1987

 Variable Coefficient Std. Err. 

 Corrected BMI -0.00178 0.00401 

 Year * Corrected BMI -0.00057* 0.00028 

 Year 0.03129*** 0.00777 

 Age 0.00637 0.00412 

 HGC 0.10105*** 0.00404 

 Married, Spouse Present 0.11838*** 0.02527 

 Married, Other 0.09030* 0.03517 

 Midwest -.015436*** 0.03077 

 South -0.17810*** 0.02893 

 West -0.06592* 0.03071 

 No. of Children in HH -0.02806** 0.00862 

 Constant 0.84596*** 0.13707 

 Legend: * p<0.05; ** p<0.01; *** p<0.001 
30

Table 7: NMES/MEPS Results, Synthetic Cohort, Ages 16-42 in 1987

 Variable Coefficient Std. Err. 

 Corrected BMI -0.00376* 0.00188 

 Year * Corrected BMI -0.00028* 0.00014 

 Year 0.01606*** 0.00376 

 Age 0.00708*** 0.00087 

 HGC 0.09388*** 0.00220 

 Married, Spouse Present 0.08514*** 0.01682 

 Married, Other 0.05006* 0.02039 

 Midwest -0.09647*** 0.01794 

 South -0.12574*** 0.01691 

 West -0.05954** 0.01825 

 No. of Children in HH -0.01735** 0.00549 

 Constant 1.04471*** 0.05923 

 Legend: * p<0.05; ** p<0.01; *** p<0.001 
31

Table 8: NMES/MEPS Results, Synthetic Cohort Base, Ages 16-42 in 1987

 Variable Coefficient Std. Err. 

 Corrected BMI -0.00439** 0.00169 

 Year * Corrected BMI -0.00021**** 0.00012 

 Year 0.012887*** 0.00331 

 Test 5 Young -0.31515*** 0.05335 

 Test 5 Old -0.15621* 0.06350 

 Test 5 Young * BMI 0.00740*** 0.00188 

 Test 5 Old * BMI 0.00347 0.00225 

 Age 0.00735*** 0.00077 

 HGC 0.09170*** 0.00179 

 Married, Spouse Present 0.12641*** 0.01331 

 Married, Other 0.08121*** 0.01644 

 Midwest -0.09841*** 0.01471 

 South -0.11657*** 0.01382 

 West -0.04384** 0.01495 

 No. of Children in HH -0.01774*** 0.00456 

 Constant 1.05418*** 0.05242 

 Legend: * p<0.05; ** p<0.01; *** p<0.001; **** p<0.10 
32

Figure 1: White Females, Overweight/Obese 1981-2000 

Year Overweight Obese Overweight + Obese 

1981 8.6% 4.1% 12.6% 

1982 10.5% 4.7% 15.2% 

1985 14.3% 6.8% 21.1% 

1986 16.5% 8.0% 24.5% 

1988 18.8% 9.3% 28.1% 

1989 19.0% 10.8% 29.7% 

1990 19.5% 11.7% 31.3% 

1992 22.4% 13.7% 36.1% 

1993 22.9% 14.4% 37.2% 

1994 22.2% 17.3% 39.5% 

1996 24.2% 18.6% 42.7% 

1998 24.2% 22.5% 46.8% 

2000 24.8% 25.6% 50.4% 
33

Figure 2: NLSY Results 

Wage Penalty from a 7.4 Point Increase in BMI

4.00%

4.50%

5.00%

5.50%

6.00%

6.50%

7.00%

7.50%

1980 1984 1988 1992 1996 2000

Year

Penalty

Penalty
34

Figure 3: NMES/MEPS Results, Synthetic Cohort, Ages 16-24 in 1987 

Increase in Wage Penalty from a 7.4 Point Increase in BMI

0.00%

1.00%

2.00%

3.00%

4.00%

5.00%

6.00%

7.00%

8.00%

1986 1989 1992 1995 1998 2001 2004

Year

Penalty

Penalty
35

Figure 4: NMES/MEPS Results, Synthetic Cohort, Ages 16-42 in 1987

Wage Penalty from a 7.4 Point Increase in BMI

2.50%

3.00%

3.50%

4.00%

4.50%

5.00%

5.50%

6.00%

6.50%

1986 1989 1992 1995 1998 2001 2004

Year

Penalty

Penalty
36

Figure 5: NMES/MEPS Results, Synthetic Cohort Base, Ages 16-42 in 1987 

Wage Penalty from a 7.4 Point Increase in BMI

3.00%

3.50%

4.00%

4.50%

5.00%

5.50%

6.00%

6.50%

1986 1989 1992 1995 1998 2001 2004

Year

Penalty

Penalty
37

Figure 6: NMES/MEPS Results, Synthetic Cohort Base, Ages 16-42 in 1987 – 

Simulated Wages 

 1987 2000 2004 

Young, 

Thin (n/a) $10.78 $11.15 

Young, 

Avg. (n/a) $10.80 $11.11 

Cohort, 

Thin $11.33 $12.64 $13.09 

Cohort, 

Avg. $10.97 $12.00 $12.34 

Older, Thin $10.43 $11.64 $12.04 

Older, Avg. $10.35 $11.33 $11.64 

Simulated Wages

$8.00

$9.00

$10.00

$11.00

$12.00

$13.00

$14.00

1985 1990 1995 2000 2005

Year

Wage

Young, Thin

.Young, Obese

Cohort, Thin

.Cohort, Obese

Older, Thin

Older, Obese
38

Younger than Cohort

$10.75

$10.80

$10.85

$10.90

$10.95

$11.00

$11.05

$11.10

$11.15

$11.20

1999 2000 2001 2002 2003 2004 2005

Year

Wage

Thin

Obese

Cohort

$10.00

$10.50

$11.00

$11.50

$12.00

$12.50

$13.00

$13.50

1985 1990 1995 2000 2005

Year

Wage

Thin

Obese
39

Older than Cohort

$10.20

$10.40

$10.60

$10.80

$11.00

$11.20

$11.40

$11.60

$11.80

$12.00

$12.20

1985 1990 1995 2000 2005

Year

Wage

Thin

Obese