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Disentangling the Contemporaneous and Life-Cycle Effects of Body Mass Dynamics
on EarningsDonna B. Gilleskie, Univ of North Carolina
Euna Han, Univ of Illinois at Chicago
Edward C. Norton, Univ of Michigan
March 2011Econ 850
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Body Mass of Females as we Age(same individuals followed over time)
Body Mass Index
Age
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Description of Average Body Mass by Age (using repeated cross sections from NHIS data)
Source: DiNardo, Garlick, Stange (2010 working paper)
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Empirical Distribution of the Body Mass Index
•The distribution of BMI (among the US adult female population) is changing over time.
•The mean and median have increased significantly.
•The right tail has thickened (larger percent obese).
Source: DiNardo, Garlick, Stange (2010 working paper)
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Trends in Body Mass over time
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1999
Obesity Trends* Among U.S. AdultsBRFSS, 1990, 1999, 2008
(*BMI 30, or about 30 lbs. overweight for 5’4” person)
2008
1990
No Data <10% 10%–14% 15%–19% 20%–24% 25%–29% ≥30%
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Body Mass Index
Calories in < Calories out
Calories in > Calories out
Caloric Intake: food and drink
Caloric Expenditure: requirement to sustain life and exercise
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Body Mass and Wages• Evidence in the economic literature that
wages of white women are negatively correlated with BMI.
• Evidence that wages of white men, white women, and black women are negatively correlated with body fat (and positively correlated with fat-free mass).
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Potential Problems…• Cross sectional data: we don’t want a snapshot of
what’s going on, but rather we want to follow the same individuals over time.
• Endogenous body mass: to the extent that individual permanent unobserved characteristics as well as time-varying ones influence both BMI and wages, we want to measure unbiased effects.
• Confounders: BMI might affect other variables that also impact wages; hence we want to model all avenues through which BMI might explain differences in wages.
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Year 1984 Year 1999
Mean BMI
75% percentile
Females
Males
Body Mass Index Distribution of same individuals in 1984 and 1999
Source: NLSY79
% obese: 6.7 (1984) ; 30.2 (1999)
% obese: 5.4 (1984) ; 25.6 (1999)
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Year 1984 Year 1999
Mean BMI
75% percentile
Whiten = 1341
Blackn = 873
Body Mass Index Distribution of same females in 1984 and 1999
% obese: 3.9 (1984) ; 21.2 (1999)
% obese: 11.2 (1984) ; 43.4 (1999)
Source: NLSY79
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The Big Picture Schooling
Observed Wagest
Body Masst
Employment
Body Masst+1
Schooling
Employment
Marriage
Children
Historyt of:Current
decisionst regarding:
t-1 t t+1
Marriage
Children
Contempor
- aneous
Life-Cycle
Intaket
Exerciset
Info known entering period t
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Individual’s optimization problem
consumption
Let indicate the schooling (s), employment (e), marriage (m), and kids (k) alternative in period t
leisure kids & marriage
preference shiftersinfo entering period t
future uncertainbody mass, wages,
and preference shocks
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Individual’s optimization problemLet indicate the schooling (s), employment (e),
marriage (m), and kids (k) alternative in period t
non-earnedincome
tuition child-rearing costs
optimalcaloric intake
optimalcaloric expenditure
time working time in school time with family
earned income
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Individual’s optimization problemLet indicate the schooling (s), employment (e),
marriage (m), and kids (k) alternative in period t
body massproduction function
wage function
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Model of behavior of individuals as they age
w*t st, et, mt, kt
Bt+1
beginning of age t
beginning of age t+1
draw from wage distribution
schooling, employment,
marriage, and child accumulation
decisions
evolution of body mass
Ωt= (Bt, St, Et, Mt, Kt,
Xt, Pt )
Ωt+1= (Bt+1,St+1, Et+1, Mt+1, Kt+1,
Xt+1, Pt+1 )
cit, co
t
caloric intake, caloric output
dt ct
dt = d(Ωt , Pdt , Pc
t ) + edt
ct = c(Ωt , dt , wt , Pct) + ec
t
Bt+1 = b(Bt , cit, co
t ) + ebt
wt | et = w(Ωt) + ewt
more frequent shocks
unobs’d biol. changes
= b’(Ωt , dt , wt , Pct) + eb
t
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Data: National Longitudinal Survey of Youth (NLSY)
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Information entering period t (endogenous state variables)
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Exogenous variables Xt : race, AFQT score, non-earnedincome, spouse income if married,ubanicity, region, and time trend
Information entering period t (endogenous state variables)
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Exogenous price and supply side variables
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Exogenous price and supply side variables
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Exogenous price and supply side variables
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Decision/Outcome Estimator Explanatory Variables
Endogenous Exogenous Unobs’d Het
Initially observed state variables
2 logit 7 ols
X1, P1, Z1 iμ
Enrolled logit Bt, St, Et, Mt, Kt Xt, Pst, Pe
t, Pmt, Pk
t, Pb
sμ, st
Employed mlogit Bt, St, Et, Mt, Kt Xt, Pst, Pe
t, Pmt, Pk
t, Pb
eμ, et
Married logit Bt, St, Et, Mt, Kt Xt, Pst, Pe
t, Pmt, Pk
t, Pb
mμ, mt
Change in kids mlogit Bt, St, Et, Mt, Kt Xt, Pst, Pe
t, Pmt, Pk
t, Pb
kμ, kt
Wage not obs’d logit Bt, St, Et, Mt, Kt Xt nμ, nt
Wage if emp’d CDE Bt, St, Et, Mt, Kt Xt, Pet wμ, wt
Body Mass CDE Bt, St+1, Et+1, Mt+1, Kt+1
Xt, Pb bμ, bt
Attrition logit Bt+1, St+1, Et+1, Mt+1, Kt+1
Xt aμ, at
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What do hourly wages look like among the employed?
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… and ln(wages)?
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Empirical Model of Wages
ln(wt ) | et ≠ 0 = η0 + η1 St
+ ρw μ + ωw νt + εwt
+ η2 Et + η3 1[et = 1 (pt) ]
+ η4 Bt + η5 Mt + η6 Kt
+ η7 Xt + η8 Pet + η9 t
Schooling (entering t)
work experienceand part time indicator
productivity
exogenous determinantsand changes in skill prices
unobserved permanent and time varying
heterogeneity
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Unobserved Heterogeneity Specification
• Permanent: rate of time preference, genetics
• Time-varying: unmodeled stressors
uet = ρe μ + ωe νt + εe
t
where uet is the unobserved component for equation e decomposed into
• permanent heterogeneity factor μ with factor loading ρe
• time-varying heterogeneity factor νt with factor loading ωe
• iid component εet
distributed N(0,σ2e) for continuous equations and
Extreme Value for dichotomous/polychotomous outcomes
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Individual’s optimal decisionsabout schooling, employment, marriage, and child accumulation
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School
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Body Mass Transition
Bt+1 = η0 + η1 Bt
+ ρb μ + ωb νt + εbt
+ η2 St+1 + η4 Et+1 + η5 Mt+1 + η6 Kt+1
+ η7 Xt + η8 Pbt
3.95 55.75 22.77 17.53 0.74 46.25 37.91 15.10
Under Normal Over Obese
FemaleMale
body massproduction function
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What does the distribution of body mass look like?
Females
Mean = 25.41St.Dev. = 5.95
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What does body mass look like as we age?
undernormal
overobese
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What does body mass look like as we age?
30 4020
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How should we estimate wages?• OLS: quantifies how variation in rhs variables
explain variation in the lhs variable, on average.
• It explains how the mean W varies with Z.
• In estimation, we also recover the variance of W.
• The mean and variance of W define the distribution of wages (under the assumption of a normal density).
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• So, using OLS, we can obtain the marginal effect of Z on W, on average.
• But what if Z has a different effect on W at different values of W?
• Might BMI have one effect on wages at low levels of the wage and a different effect on wages at higher levels of the wage?
• How can we capture that?
How should we estimate wages?
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Might there be a more flexible way of modeling the density
that allows for different effects of covariates Z at different points of support of W?
?
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Conditional Density EstimationDetermine cut points such that 1/K th of individuals are in each cell.
Let
Replicate each observation K times and create an indicator of which cell an individual’s wage falls into.Interact Z’s with α’s fully.
Estimate a logit equation (or hazard), λ(k,Z).
Then, the probability of being in the kth cell , conditional on not being in a previous cell, is
Define a cell indicator
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Variable Model 1
BMIt -0.008 (0.002)***
BMIt ≤ 18.5 -0.047 (0.024)**
25 ≤ BMIt< 30 -0.022 (0.013)*
BMIt ≥ 30 -0.049 (0.025)**
BMIt x Black 0.006 (0.002)***
BMIt x Hisp 0.002 (0.003)
BMIt x Asian 0.001 (0.005)
Model Includes:
Xt, Bt
Marginal Effect of 5%
0.1220.057
Preliminary Results: ln(wages) of females
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Variable Model 1 Model 2
BMIt -0.008 (0.002)***
-0.008 (0.002)***
BMIt ≤ 18.5 -0.047 (0.024)**
-0.029 (0.019)
25 ≤ BMIt< 30 -0.022 (0.013)*
-0.008 (0.012)
BMIt ≥ 30 -0.049 (0.025)**
-0.010 (0.021)
BMIt x Black 0.006 (0.002)***
0.004 (0.002)**
BMIt x Hisp 0.002 (0.003) 0.001 (0.003)
BMIt x Asian 0.001 (0.005) 0.001 (0.004)
Model Includes:
Xt, Bt Xt, Bt, St, Et, Mt, Kt
Marginal Effect of 5%
0.1220.057
0.1020.062
Preliminary Results: ln(wages) of females
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Variable Model 1 Model 2 Model 3
BMIt -0.008 (0.002)***
-0.008 (0.002)***
-0.008 (0.00***2)
BMIt ≤ 18.5 -0.047 (0.024)**
-0.029 (0.019) -0.028 (0.019)
25 ≤ BMIt< 30 -0.022 (0.013)*
-0.008 (0.012) -0.010 (0.012)
BMIt ≥ 30 -0.049 (0.025)**
-0.010 (0.021) -0.013 (0.021)
BMIt x Black 0.006 (0.002)***
0.004 (0.002)**
0.004 (0.002)*
BMIt x Hisp 0.002 (0.003) 0.001 (0.003) 0.000 (0.003)
BMIt x Asian 0.001 (0.005) 0.001 (0.004) 0.002 (0.004)
Model Includes:
Xt, Bt Xt, Bt, St, Et, Mt, Kt
Xt, Bt, St, Et, Mt, Kt, Pe
t
Marginal Effect of 5%
0.1220.057
0.1020.062
0.0990.063
Preliminary Results: ln(wages) of females
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Variable Model 1 Model 2 Model 3 Model 4
BMIt -0.008 (0.002)***
-0.008 (0.002)***
-0.008 (0.00***2)
-0.003 (0.002)
BMIt ≤ 18.5 -0.047 (0.024)**
-0.029 (0.019) -0.028 (0.019) -0.046 (0.014)***
25 ≤ BMIt< 30 -0.022 (0.013)*
-0.008 (0.012) -0.010 (0.012) 0.008 (0.009)
BMIt ≥ 30 -0.049 (0.025)**
-0.010 (0.021) -0.013 (0.021) -0.006 (0.015)
BMIt x Black 0.006 (0.002)***
0.004 (0.002)**
0.004 (0.002)*
0.004 (0.003)
BMIt x Hisp 0.002 (0.003) 0.001 (0.003) 0.000 (0.003) 0.004 (0.003)
BMIt x Asian 0.001 (0.005) 0.001 (0.004) 0.002 (0.004) -0.003 (0.006)
Model Includes:
Xt, Bt Xt, Bt, St, Et, Mt, Kt
Xt, Bt, St, Et, Mt, Kt, Pe
t
Xt, Bt, St, Et, Mt, Kt,Pe
t, fixed effects
Marginal Effect of 5%
0.1220.057
0.1020.062
0.0990.063
0.034-0.006
Preliminary Results: ln(wages) of females
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White Females
Black Females
Predicted change in wage by BMI
Source: NLSY79 data
$
$
* Reference BMI = 22
Predicted change in wage by BMI
BMI Specification: Single index with fixed effects
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Preliminary Results – quantile regressionVariable 25th
percentile50th
percentile75th
percentile
BMIt -0.053 (0.012)***
-0.071 (0.010)***
-0.102 (0.015)***
BMIt ≤ 18.5 -0.241 (0.083)***
-0.232 (0.082)***
-0.298 (0.124)***
25 ≤ BMIt< 30 -0.173 (0.085)**
-0.117 (0.083) -0.070 (0.109)
BMIt ≥ 30 -0.281 (0.126)***
-0.225 (0.110)**
0.004 (0.170)
BMIt x Black 0.035 (0.008)***
0.044 (0.009)***
0.050 (0.013)***
BMIt x Hisp 0.021 (0.011)**
-0.001 (0.010) -0.009 (0.018)
BMIt x Asian -0.007 (0.023) 0.006 (0.022) 0.061 (0.024)
Model Includes:
Xt, Bt, St, Et, Mt, Kt, Pe
t
Marginal Effect of 5%
0.0680.044
0.0830.050
0.1060.067
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Unobserved Heterogeneity Distribution
1.0
1
1 = 0
2
3
2 = 0.557
3
= 1
0.353
0.464
0.183
Probability1.0
1
t1 = 0
2
t2 = 0.520
t3
= 1
0.539
0.299
0.162
t
Probability
3
Permanent Time-varying
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• No updating: simply compute the effect of a change in BMI given the values of one’s observed RHS variables entering the period.
• Hence, this calculation provides only the direct effect of BMI on wages.
• We find that a 5% decrease in BMI leads to $0.26 decrease in wages.
• Also, more likely to be enrolled, full time employed; less likely to be married or have a child.
Preliminary Results: using preferred model
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• Specification of BMI (more moments, interactions)
• Selection into employment
• Endogenous BMI
• Endogenous state variables (related to history of schooling, employment, marriage, and children)
• Random effects vs fixed effects
• Permanent and time-varying heterogeneity
• Modeling of effect of BMI on density of wages
Sources of differences from preliminary models
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Preliminary Conclusions
• Unobservables that influence BMI also affect wages (and therefore must be accounted for)
• In addition to its direct effect on wages, BMI has a significant effect on wages through particular endogenous channels: schooling, work experience, marital status, and number of children.
• Conditional density estimation results suggest that it is important to model the effect of BMI across the distribution of wages, not just the mean. (And to model the different effects of past decisions across the distribution of current BMI.)
• BMI appears to have a statistically significant direct effect on women’s wages (an effect that remains unexplained).
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Additional Feedback …• The body mass index (BMI) specifies a
particular relationship b/w weight and height
• In light of the positive height effect and conjectured “beauty” effect on wages, are the appropriate measures simply weight and height?
• How might we simulate a change in BMI or weight when updating?
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To Do…• Estimate wages under the assumption that the
endogenous rhs variables are exogenous and evaluate the effect of Bt on wt
(with and without fixed effects).
• Replace all endogenous rhs variables in wage equation with their reduced form arguments and evaluate the effect of Bt on wt
(with and without fixed effects).
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To Do…• Estimate equations explaining variation in all
endogenous input variables and use their predicted values in estimation of wages, and evaluate the effect of Bt on wt
(with and without fixed effects).
We can calculate BMI's direct, indirect, and total effects here.
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To Do…• Estimate equations for all endogenous
variables jointly allowing for flexible unobserved correlation in both the permanent and time-varying individual dimension and evaluate effects of Bt on wt.
We can calculate BMI's direct, indirect, and total effect here.
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To Do…• Estimate equations for all endogenous
variables jointly using conditional density estimation (CDE) to model wages and BMI in order to determine if BMI has a different effect at different points of support of the wage distribution.
Similarly, we can evaluate whether behavioral inputs have a different effect at different points of support of the BMI distribution.
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Unobserved Heterogeneity Distribution
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Model of behavior of individuals as they age
wt st, et, mt, ktBt+1
beginning of age t
beginning of age t+1
draw from wage
distribution
schooling, employment,
marriage, and child accumulation
decisions
evolution of body mass
Ωt= (Bt, St, Et, Mt, Kt,
Xt, Pt )
Ωt+1= (Bt+1, St+1, Et+1, Mt+1, Kt+1,
Xt+1, Pt+1 )
And we jointly model the set of structural equations that describe these observed outcomes,
allowing unobserved components to be correlated
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Behavioral Equations: Employment
ln[p(et = d)/p(et = 2)] e = 0 (non-employed) e = 1 (part-time) e = 2 (full-time)
= δ0d + δ1d Bt + δ2d St + δ3d Et + δ4d Mt + δ5d Kt + δ6d Xt
+ δ7d Pst + δ8d Pe
t + δ9d Pmt + δ10d Pk
t + δ11d Pbt
+ ρed
μ + ωed
νt
58.48 23.09 18.4477.72 12.89 9.39
Full time Part time Non-employed
FemaleMale
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ln[p(st = 1)/p(st = 0)] s = 0 (not enrolled) s = 1 (enrolled)
= γ0 + γ 1 Bt + γ2 St + γ3 Et + γ4 Mt + γ5 Kt + γ6 Xt
+ γ7 Pst + γ8 Pe
t + γ9 Pmt + γ10 Pk
t + γ11 Pbt
+ ρs μ + ωs νt
Behavioral Equations: Schooling
84.12 15.8883.49 16.51
Not enrolled Enrolled
FemaleMale
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ln[p(mt = 1)/p(mt = 0)] m = 0 (not married) m = 1 (married)
= α0 + α1 Bt + α2 St + α3 Et + α4 Mt + α5 Kt + α6 Xt
+ α7 Pst + α8 Pe
t + α9 Pmt + α10 Pk
t + α11 Pbt
+ ρm μ + ωm νt
Behavioral Equations: Marriage
47.20 52.8050.72 49.28
Not married Married
FemaleMale
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ln[p(kt = j)/p(kt = 0)] k = 0 (no change in children) k = -1, 1
= β0j + β1j Bt + β2j St + β3j Et + β4j Mt + β5j Kt + β6j Xt
+ β7j Pst + β8j Pe
t + β9j Pmt + β10j Pk
t + β11j Pbt
+ ρkj μ + ωk
j νt
Behavioral Equations: Children
88.96 8.60 2.4489.01 8.38 2.61
# of kids acquired: 0 1 -1
FemaleMale