1
Large Women Small Pay: An Empirical Study on
the Impact of Obesity on White Women
Mario Halasa
May 8th, 2013
2
I.
………………………………….
II.
…………………………………. Methods and Previous Studies
III.
…………………………………. A Model of Weight and Wages
IV.
………………………………….
Data
V.
………………………………….
Empirical Results
VI.
………………………………….
Conclusion
………………………………….
References
Appendix:
- Figure (1) OLS Estimates
-Figures (2 & 3) The Reg Procedure
(4 & 5) The Means Procedure
-Figure (6) Oaxaca Decomposition
-SAS Code
Introduction
3
ABSTRACT
This paper tests the hypothesis that obese white women will experience a lower wage
then non-obese white women. I first observe the difference using an OLS regression
followed by an Oaxaca Decomposition Model. I failed to reject the hypothesis that
obese white women will experience a lower wage then non-obese white woman. I
found that obese women on average earn a wage of 8.67% lower an hour, or roughly
$1.46. I also found in my Oaxaca Decomposition that there is a 16.81% gap in the
wage between obese and non-obese white women, of which 8.29% is unexplained. 1
I.
Introduction
In 20011 more than one-third of U.S. adults (35.7%) and approximately 17%
of children and adolescents are obese. Of the 75 million obese people in the US 40
million are women (CDC 2013). Not only does being overweight or obese affect
your health, but recent studies have also shown that being obese may have a
negative impact on wage (Cawely 2004).
This paper will test the hypothesis that obese white women will experience a
lower wage then non-obese white woman. Cawely 2004 has concluded,
discrimination aside, there are three main explanations of why obesity and lower
A special thanks to Dr. Renna and Dr. Fang for helping me construct a well written
paper.
1
4
wages are correlated. The first explanation is that simply being obese will lower
your wages; for example obese people might be less productive then non-obese
people, resulting in a lower wage. For example if you are working in retail maybe
the more attractive a worker is correlates to how productive they can be. The
second is that lower wages causes obesity, assuming that because you are poor; the
quality of food you consume is less then that of a non-poor person. The third
category is that unobserved variables cause both obesity and low wages.
There also could be a scenario that white women are simply discriminated
against because of their weight. Employers and co-workers may have certain
stereotypes in which working with more slender women fits their preferences, in
which case a lower wage might ensue.
My paper uses economic methods such as an OLS Regression followed by an
Oaxaca Decomposition in an attempt to show a wage penalty for obese white
women. The reason I chose to run an Oaxaca/Binder Decomposition is because
according to Oaxaca (1973), the decomposition method is the best was to look at
inner-group differences (such as obese/non-obese) in the means levels of those due
to the differences in the characteristics of variables compared to those due to the
differences in the coefficients.
Recent literature such as Cawley (2004) and Han (2011) uses data from the
National Longitudinal Survey of Youth to show that obesity has a negative impact on
wages. This is an appropriate dataset for the proposed study because it gathers
information, on the same respondents about labor market information such as
5
occupation, schooling, and wages for different groups of women starting from 1979
on.
II.
Methods and Previous Studies
Several studies have linked obesity to labor market outcomes, mostly wages.
Averett & Korenman (1996) looks at the economic differences by body mass index
for a sample of men and women age 23-31. To test the hypothesis that obesity has a
negative impact on wages, the author uses cross sectional data from the NLSY 1979
cohort, they found that obese women have lower family incomes and lower hourly
wages than those women who fell in the recommended weight-for–height range.
Averett and & Korenman suggest that women who were obese or overweight at
ages 16-24 have at ages 23 to 31 lower spousal income and are less likely to be
married. When looking at men age 16 to 24, they found a negative correlation
between weigh and wages, however there results were not statistically significant
unlike the results for women.
One problem with Averett & Korenman (1996) is that the weight variable
could be endogenous. The OLS estimates would produce biased estimates of the
relationship between obesity and wage. Pagan & Alberto (1997) used OLS to find
that obese females make less then their more slender coworkers. Using a Hausman
specification test to determine whether their OLS estimate is biased, they fail to
reject the hypothesis that weight is uncorrelated with the error term of the wage
equation. However, Pagan & Alberto call their test into question because their
“instruments” (family poverty level, health limitations, and indicator variables about
self esteem) are likely correlated with the error term in the wage equation. They
6
further state that given their IV test is probably hindered by the same kind of bias as
their OLS, it is not surprising that they fail to reject the hypothesis that the weight
variable is exogenous (Pagan 1997).
Cawley (2000) found that weight lowers wages of white women, and a
difference in weight of standard deviation (which they equate to 65 pounds) is
associated with a wage penalty of 7%. Cawely (2004) extends the Mincer equation
be including a measurement of body weight. Using data collected from the NLSY
Cawely 2004 finds that weight lowers wages for white females; OLS estimates
conclude that a difference of 2 standard deviations in weight, (65 pounds) for white
women, show a 9% decrease in wages. This is equivalent to the wage effect of 1.5
years of education or what Cawely attributes to three years of work experience.
When looking at the relationship between weight and wages of other gender-ethnic
groups, he finds a negative correlation, which he attributes to unobserved
heterogeneity. One thing the paper lacks is the explanation of why there seems to be
a dramatic difference across gender and race of the negative effect obesity has on
wage rates (Cawley 2004).
Han et al. (2008) explores the extent to which the effects of obesity on labor
market outcomes varies with age, skills, and occupation. Using data from the NLSY
along with 1970- 1990 census information they found that women who were obese,
relative to underweight or normal weight, had a decrease in the likelihood of
employment, except in the case of blacks. Han et al. (2008) ends their paper by
stating that their results may be subjective because endogeniety may exist in the
marital status variable, and the number of children a woman has.
7
Han et. al (2011) examines the direct and indirect effects of body weight in
the late- teenage years on wages, taking into account education and occupation
choices. Using the Mincer equation, accompanied by an IV equation they conclude
that a one unit increase of BMI is associated with a decrease in hourly wage of
1.83% for women, however, after controlling for education followed by occupation
the decrease was .67 and .53% respectively (Han 2011). Han et. al (2011) also
concludes that limited evidence supports the fact weight lowers wages for Hispanic
women and no evidence weight lowers wages for black women.
III.
A Model of Weight and Wages
Developed in 1974 the Mincerian wage Regression looks at the statistical
relationship between market wages, education and experience. For my model
assume the wages W and Body Mass Index B have the following relationship for
individual i at time t you observe the following:
LnW it  Bit  X it  it
In the above equation X is a vector of variables that effect wage, such as education

and experience, and  is the error term. Furthermore  represents the effect of BMI
on log wages. As long as Body Mass Index is exogenous then running an OLS
regression will work.
Estimating the model first through OLS, I have chosen to set up my equation using a
dummy variable to represent obesity. Then to further my model, and to introduce
something new to the field of economics and to try something that hasn’t been done
before, I will evaluate the results with an Oaxaca Decomposition Model.
8
Introduced in 1973 this model explains the gap in the means of an outcome
variable between two groups (obese/non-obese) (Oaxaca 1973). As previously
stated this gap is then broken down into the part that is due to group differences in
the variables, as well as group differences in the coefficients.
ˆ NO  NO
ˆ NO  
LogWage NO  
ˆOO
ˆ O  
LogWage O  
I ran the log wage equation separately for non-obese then obese females. It can be

shown that the % gap is equal to the difference between the log wage of obese and
non-obese females. By doing this you are able to see what the percentage difference
in wage is from obese to non-obese white females.
WageNO  WageO
 gap%
WageNO
Once here I was able to decompose my % gap into two parts, explained and

unexplained. The unexplained part is explaining the discrimination percentage. So
of my % gap so much will be explained and what’s left over is discrimination.
Decomposition:
(LogW
NO
O
 LogW )  (X
NO
O
 X ) NO  ( NO   O )  ( NO   O )X O

Part of the equation that explains
Part of the equation that explains the
the difference in variables.
difference in coefficients.
9
IV.
Data: National Longitudinal Survey of Youth
The data used in this study are from the National Longitudinal Survey of
Youth (NLSY 79). All respondents were between the ages of 14 and 21 years old as
of December 1979, when the first interviews where starting to be conducted.
At the start of this survey, participants were to distinguish between gender,
age, and ethnicity. For this paper weight from the year 2000 was gathered along
with height, which was recorded in 1985; the respondents were between the ages of
20 and 27 when their height was recorded. The measurement of weight for this
paper is BMI. The medical definition to be considered obese is having a BMI above
30, which is the cut off line I used for this paper (CDC 2013). Also any outliers in
wage that were recorded were modified so that any hourly wage under $1 is deleted
and any wage above $500 an hour is deleted. The variables used in this model are as
follows:
Figure 1- OLS Procedure, Parameter Estimates Included
Variable
Definition
Estimates
HGCM
HGCF
Managerial
AFQT
Attending
Technical
Full time
Sales
Tenure
Administrative
Service
HGC
Weeks worked
Highest grade completed by the mother
Highest grade completed by father
Occupation
Armed Forces Qualifier (intelligence)
Attending school
Occupation
Works more then 20 hours/ week
Occupation
Job Tenure
Occupation
Occupation
Highest grade completed
Number of weeks worked since past
interview
Occupation
.00644
.01081*
.22720*
.00176**
-.02226
.22081
-.06573
.04042
.00434**
.00270
-.21050
.06093**
.00633**
Farming
-.00123
10
Repair
Assemblers
Transportation
NE
NC
S
NM
M
Obese
Occupation
Occupation
Occupation
North East Region
North Central Region
Southern Region
Never Married
Married
Dummy Variable
.17878
.04288
.14016
-.04945
-.15807**
-.11965**
-.09487
-.04071
-.08677**
Significant at 95% * at 99%**
V.
Empirical Results
When evaluating my results I found that I failed to reject my hypothesis that
obese white women experience a lower wage then non-obese white woman. When
looking at Figure 1, you can see that obese white women on average earn 8.67% less
an hour then their non-obese counterpart, and the variable was statistically
significant at the 99% Confidence Level. This 8.67% converted to a dollar figure is
about $1.46 less an hour. Other variables that are statistically significant include the
coefficient for Armed Forces Qualifying Test (AFQT), which is .00716, meaning that
for every additional point on your AFQT score you make .17% more an hour. The
coefficient for highest-grade completed (HGC) is .06093, which implies that for
every additional grade completed you earn 6.1% more an hour. If you live in the
North Central region, you make on average 15.8% less an hour compared to if you
lived in the Western Region. The coefficient for the Managerial variable is .22720,
which implies that you make on average 23% more an hour then if you worked as a
laborer. The last variable I would like to point out is highest grade completed by
father (HGCF). If your father increased his education by one year, you make 1.2%
more an hour, than if he didn’t, when you are looking at the results in comparison to
11
the mean. What is also interesting is that there is no statistical significance when
looking at the variable highest grade completed by mother (HGCM).
When evaluating my results ran through an Oaxaca Decomposition model I
found that 8.58% of the 16.81% gap in wages is explained and 8.29% is unexplained
or deemed the discriminatory percent. When referring back to my results ran
through an OLS regression I found a 8.67% difference in the wage of an obese white
woman compared to a non-obese white women.
VI.
Conclusion
This paper looks at and measures the correlation between weight and wages
of obese white females in the United Stated. My hypothesis that obese white females
are discriminated against by making a lower wage than their non-obese counterpart
is not rejected, with statistical significance when ran through and OLS regression
followed by an Oaxaca Decomposition. The reason I chose to specifically look at
obese white women is because past research has found no statistical significance
when looking as wage differences of obese black or Hispanic women.
As previously stated if BMI is strictly exogenous then OLS estimates of
 can be looked at as the unvarying estimate of the true effect of BMI on wages.
Cawley (2004) suggests otherwise, as variation in BMI may be lined to nongenetic
factors such as individual choices and environment. Further instead of obesity
effecting wages, it could be that wages effects obesity, so you could have an issue of
simultaneity. Their-for a Two Staged Least Squared or an IV test can be run to have
more exact results. But because of time and preference it was not.
12
When looking at my variable selection I would next time include an
experience variable to represent the work experience women had prior to their
wage that was recorded in the NLSY 79. I also need to examine whether the
reproductive history has an effect on women’s wages. To further control for
discrimination by obesity I could separate occupational fields by categories which
involve physical labor, like farming, and jobs that do not, like sales to see if there is a
pay difference between those jobs. If there is maybe we can better relate obesity
more convincingly to productivity and bring more concrete answers to this field of
study in economics.
13
References
Averett, Susan; Korenman, Sanders. The Economic Reality of the Beauty Myth.
The Journal of Human Resources. Vol. 30 No 2. p 304-330 1996.
Cawley, John. The Impact of Obesity on Wages. Journal of Human Resources. Vol. 32
No 2. p. 451-474. 2004.
Center for Disease Control and Prevention (CDC). http://www.cdc.gov/. 2013.
Comuzzie, A. G., and D. B. Allison. 1998. "The Search for Human Obesity Genes." Science 280:1374-77.
Han, Euna; Norton, Edward C. Stearns, Sally C Weight and Wages: Fat versus Lean
Paychecks. Health Economics. Vol 18. No 5. p 535-548. 2009.
Han, Euna; Norton, Edward C; Powell, Lisa M. Direct and Indirect Effects of Body
Weight on Adult Wages. Economics and Human Biology. Vol 9 No 4. p 391-392.
2011.
Oaxaca, R. 1973. “Male-Female Wage Differentials in Urban Labor Markets.”
International Economic Review 14: 693–709.
Pagan, Jose A., and Alberto Davlia. 1997. “Obesity, Occupation Attainment, and
earnings.” Social Science Quarterly 8(3): 756-70.
Journal
Wada, Roy; Tekin, Erdal. Body Composition and Wages. NBER Working Paper Series.
2007.
14
Appendix:
Figure 2 - The Reg Procedure: Obese= 0
Variable
DF
Intercept
HGCM
HGCF
AFQT
Attending
Managerial
Technical
Sales
Administrative
Service
Farming
Repair
Assemblers
Transportation
NE
NC
S
NM
M
Full_time
Tenure
HGC
Weeks_worked
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
Parameter
Estimates
1.18166
0.01259
0.00710
0.00210
-0.00130
0.21428
0.25493
0.05313
-0.00847
-0.25331
-0.14482
0.20608
0.05031
0.06386
-0.06519
-0.19212
-0.16101
-0.10932
-0.04989
-0.06718
0.00032997
0.05843
0.00660
Standard Error
t Value
Pr>ItI
0.19111
0.00852
0.00623
0.00079453
0.07922
0.14067
0.15967
0.14568
0.14072
0.14326
0.24062
0.16575
0.15599
0.18239
0.05384
0.04700
0.04676
0.06724
0.03906
0.05242
0.00005749
0.00924
0.00118
6.18
1.48
1.14
2.64
-0.02
1.52
1.60
0.36
-0.06
-1.77
-0.60
1.24
0.32
0.35
-1.21
-4.09
-3.44
-1.63
-1.28
-1.28
5.74
6.33
5.59
<.0001
.1398
0.2545
0.0084
0.9869
0.1280
0.1106
0.7154
0.9520
0.0773
0.5474
0.2140
0.7471
0.7263
0.2262
<.0001
0.0006
0.1043
0.2017
0.2002
<.0001
<.0001
<.0001
Standard Error
t Value
Pr>ItI
0.29175
0.01259
0.00989
0.00130
0.17201
0.17903
0.19303
3.40
-0.76
2.09
0.63
-0.83
1.30
0.71
0.0008
0.4480
0.0374
0.5313
0.4069
0.1945
0.4802
Number of Observations -1131
Figure 3 The Reg Procedure - Obese= 1
Variable
DF
Intercept
HGCM
HGCF
AFQT
Attending
Managerial
Technical
1
1
1
1
1
1
1
Parameter
Estimates
0.99329
-0.00957
0.02067
0.00081590
-0.14287
0.23282
0.13645
15
Sales
Administrative
Service
Farming
Repair
Assemblers
Transportation
NE
NC
S
NM
M
Full_time
Tenure
HGC
Weeks_worked
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
-0.02189
0.02802
-0.08047
0.06193
0.04211
-0.02943
0.41005
-0.02062
-0.04284
0.01800
-0.04266
0.00229
-0.02894
0.00039658
0.06671
0.00475
0.18406
0.17668
0.18007
0.23079
0.22484
0.21079
0.28190
0.09784
0.07743
0.09285
0.09285
0.06857
0.09335
0.00009148
0.01474
0.00198
-0.12
0.16
-0.45
0.27
0.19
-0.14
1.45
-0.21
-0.55
0.23
-0.46
0.03
-0.31
4.34
4.53
2.40
0.9054
0.8741
0.6553
0.7886
0.8516
0.8890
0.1469
0.8332
0.5805
0.8167
0.6463
0.9733
0.7568
<.0001
<.0001
0.0172
Number of Observations- 315
Figure 4 The Means Procedure - Obese = 0
Variable
N
Mean
Std Dev
Minimum
Maximum
ID
HGCM
HGCF
Race
Gender
AFQT
Height
Attending
Occupation
Full_time
Weight
Tenure
Wage
Region
Marital
HGC
Weeks_worked
Lnheight
Lnweight
Female
Lnwage
Bmi
1131
1131
1131
1131
1131
1131
1131
1131
1131
1131
1131
1131
1131
1131
1131
1131
1131
1131
1131
1131
1131
1131
3484.29
12.0433245
12.2166225
3.00
2.00
56.6523979
64.8328912
0.0415561
271.5402299
0.8938992
141.8479222
306.1679929
16.8985500
2.5075155
1.3492485
13.9071618
45.9460654
4.1710698
4.9436129
1.00
2.6065148
23.6945400
2673.26
2.2766372
3.1373621
0
0
25.9574777
2.5015776
0.1996609
216.3108320
0.3081028
21.2444650
290.6697303
14.7889623
0.9724816
0.9724916
2.3685349
14.0473822
0.0385712
0.1494941
0
0.6397782
3.0640512
14.00
0
0
3.00
2.00
1.0330
58.00
0
5.00
0
84.00
1.00
1.00
1.00
1.00
6.00
0
4.0604430
4.4308168
1.00
0.0099503
14.4169922
12140.00
20.00
20.00
3.00
2.00
100.00
72.00
1.00
889.00
1.00
215.00
1202.00
192.30
4.00
4.00
20.00
52.00
4.2766661
5.3706380
1.00
5.2590567
29.9831383
16
White
NE
1131
1131
1.00
0.1724138
0
0.3779068
1.00
0
1.00
1.00
NC
1131
0.3227233
0.467727
0
1.00
S
1131
0.3297966
0.4703471
0
1.00
W
1131
0.1750663
0.3801919
0
1.00
NM
M
Managerial
1131
1131
1131
0.0751547
0.7038019
0.4235190
0.2637575
0.4567814
0.4943346
0
0
0
1.00
1.00
1.00
Technical
Sales
1131
1131
0.0380195
0.0946065
0.1913278
0.2928002
0
0
1.00
1.00
Administrative
Service
Farming
Repair
Assemblers
Transportation
1131
1131
1131
1131
1131
1131
0.2166225
0.1220159
0.0061892
0.0274094
0.0415561
0.0167993
0.4121254
0.3274490
0.0784624
0.1633453
0.1996609
0.1285756
0
0
0
0
0
0
1.00
1.00
1.00
1.00
1.00
1.00
Laborers
1131
0.0132626
0.1144477
0
1.00
Figure 5 The Means Procedure - Obese = 1
Variable
N
Mean
Std Dev
Minimum
Maximum
ID
HGCM
HGCF
Race
Gender
AFQT
Height
Attending
Occupation
Full_time
Weight
Tenure
Wage
Region
Marital
HGC
Weeks_worked
Lnheight
315
315
315
315
315
315
315
315
315
315
315
315
315
315
315
315
315
315
3467.26
11.4603175
11.5142857
3.00
2.00
52.8785524
64.3238095
0.0222
305.1460317
0.9111
210.1492063
321.2317460
13.3238703
2.55
1.222
13.4190
46.133
4.1629693
2716.60
2.5565290
3.1026420
0
0
25.9718977
2.8165635
0.1476401
207.0388074
0.2850361
43.0173268
304.0358682
9.2683291
0.9060
0.9975199
2.3162990
2.31639
0.0429705
3.00
0
1.00
3.00
2.00
0.2490
53.00
0
8.00
0
150.00
1.00
1.790
1.00
0
7.00
0
3.9702919
12135.00
18.00
20.00
3.00
2.00
99.8190
73.00
1.00
888.00
1.00
600.00
1181.0
102.560
4.00
6.00
20.00
52.00
4.2904594
17
Lnweight
Female
Lnwage
Bmi
White
NE
315
315
315
315
315
315
5.3311550
1.00
2.4384262
35.6074312
1.00
0.1238095
0.1752794
0
0.5242298
6.2682797
0
0.3298882
5.0106353
1.00
0.5822156
30.0354004
1.00
0
6.3969297
1.00
4.6304480
91.2197232
1.00
1.00
NC
315
0.3587302
0.4803909
0
1.00
S
315
0.3587302
0.4803909
0
1.00
W
315
0.1619048
0.3689495
0
1.00
NM
M
Managerial
315
315
315
0.1492063
0.6634921
0.2984127
0.3568586
0.4732667
0.4582896
0
0
0
1.00
1.00
1.00
Technical
Sales
315
315
0.0730159
0.1142857
0.2605765
0.3186642
0
0
1.00
1.00
Administrative
Service
Farming
Repair
Assemblers
Transportation
315
315
315
315
315
315
0.2317460
0.1555556
0.0253960
0.0285714
0.0380952
0.0126984
0.4226190
0.3630101
0.1575775
0.1668637
0.1917308
0.1121476
0
0
0
0
0
0
1.00
1.00
1.00
1.00
1.00
1.00
Laborers
315
0.01904776
0.1369099
0
1.00
Figure 6 Oaxaca Decomposition
Variable
ID
Mean
obese=
0
beta for
obese=0
obese=1
Beta for
obese=1
explained
unexplained
0
0
0
0
HGCM
12.0433
11.4603
0.01259
-0.00957
0.00733997
0.253960248
HGCF
12.2166
11.5142
0.0071
0.02067
0.00498704
-0.156247694
race
0
0
0
0
gender
0
0
0
0
AFQT
56.6523
52.8785
0.00792498
0.067901282
height
attendin
g
occupati
on
64.8328
64.3238
0
0
0.0416
271.540
2
0.0222
0.003142854
full time
weight
0.8939
141.847
9
210.1492
tenure
306.167
321.2317
wage
16.8986
2.5075
region
0.0021
0.0008159
-0.0013
-0.14287
-0.00002522
0
0
-0.06718
-0.02894
0.001155496
-0.034840464
0
0
0.0003299
0.0003965
-0.004970602
-0.021397244
13.3239
0
0
2.5555
0
0
305.146
0.9111
18
marital
1.3492
1.2222
0
0
HGC
weeks
worked
13.9072
13.419
0.05843
0.06671
0.028525526
-0.11110932
45.9461
46.1333
0.0066
0.00475
-0.00123552
0.085346605
0
0
0
0
male
female
0
0
0
0
lnwage
2.6065
2.4384
0
0
bmi
23.6945
35.0674
0
0
hsp
0
0
0
0
blk
0
0
0
0
white
0
0
0
0
NE
0.1724
0.1238
-0.06519
-0.2062
-0.003168234
0.017457038
NC
0.3227
0.3587
-0.19212
-0.04284
0.00691632
-0.053546736
S
0.3298
0.3555
-0.16101
0.018
0.004137957
-0.063638055
W
0.1751
0.1619
0
0
NM
0.0752
0.1492
-0.10932
-0.04266
0.00808968
-0.009945672
M
0.7038
0.6634
-0.04989
0.00229
-0.002015556
-0.034616212
0
0
0
0
0
0
0
0
0.4235
0.2984
0.21428
0.23282
0.026806428
-0.005532336
married
nottoget
her
manage
rial
technica
l
sales
administ
rative
0.038
0.0731
0.25493
0.13645
-0.008948043
0.008660888
0.0946
0.1143
0.05313
-0.02189
-0.001046661
0.008574786
0.2166
0.2371
-0.00847
0.02802
0.000173635
-0.008651779
service
0.122
0.1555
-0.25331
-0.08047
0.008485885
-0.02687662
farming
0.0062
0.0234
-0.14482
0.06193
0.002490904
-0.00483795
repair
assembl
ers
transpor
tation
0.0274
0.0286
0.20608
0.04211
-0.000247296
0.004689542
0.0416
0.0381
0.05031
-0.02943
0.000176085
0.003038094
0.0168
0.0127
0.06386
0.41005
0.000261826
-0.004396613
laborers
0.0132
0.019
0.0858146
-0.082865358
0.0858146
-0.0828654
SAS Coding
data one;
set mario;
if weight <= 0 then delete;
if height <=0 then delete;
lnheight=log(height);
lnweight=log(weight);
male = 0;
19
female = 0;
if wage < 0 then delete;
wage=wage/100;
AFQT=AFQT/1000;
if wage <1 then delete;
if wage > 500 then delete;
lnwage=log(wage);
if gender = 1 then male = 1;
if male = 1 then delete;
if gender = 2 then female = 1;
if gender < 0 then delete;
if HGCM < 0 then delete;
if HGCF < 0 then delete;
if AFQT < 0 then delete;
if attending = -4 then attending = 0;
if attending = -5 then delete;
if occupation < 0 then delete;
if full_time < 0 then delete;
if tenure < 0 then delete;
if region < 0 then delete;
if marital < 0 then delete;
if HGC < 0 then delete;
if weeks_worked < 0 then delete;
obese=0;
bmi= ((weight*703)/(height*height));
if bmi >30 then obese=1;
else obese=0;
if race = 1 then hsp = 1;
else hsp=0;
if race = 2 then blk = 1;
else blk=0;
if race = 3 then white = 1;
else white=0;
if region = 1 then NE =1;
else NE = 0;
if region= 2 then NC=1;
else NC=0;
if region=3 then S=1;
else S=0;
if region=4 then W=1;
else W=0;
if marital = 0 then NM=1;
else NM=0;
if marital = 1 then M=1;
else M=0;
if married = 2 then nottogether=1;
if married = 3 then nottogether=1;
if married = 6 then nottogether=1;
else nottogether=0;
if 3<=occupation<=199 then managerial=1;
else managerial= 0;
if 203<=occupation<=235 then technical=1;
else technical=0;
if 243<=occupation<=285 then sales=1;
else sales=0;
if 303<=occupation<=389 then administrative=1;
else administrative=0;
20
if 403<=occupation<=469 then service=1;
else service=0;
if 473<=occupation<=499 then farming=1;
else farming=0;
if 503<=occupation<=699 then repair=1;
else repair=0;
if 703<=occupation<=799 then assemblers=1;
else assemblers=0;
if 803<=occupation<=859 then transportation=1;
else transportation=0;
if 863<=occupation<=889 then laborers=1;
else laborers=0;
run;
data white;
set one;
if white=0 then delete;
run;
proc reg data=white;
model lnwage= HGCM HGCF AFQT attending managerial technical sales
administrative service farming repair assemblers transportation NE NC S
NM M full_time tenure HGC weeks_worked obese;
run;
proc sort;
by obese;
run;
proc means;
by obese;
run;