Using New Measures of Fatness to
Improve Estimates of
Early Retirement and
Entry onto the OASI Rolls
Richard V. Burkhauser
John C. Cawley
Research Question
• Our research question: Is there a causal relationship
between fatness and taking Old-Age benefits at age
62?
• Fatness is a risk factor for morbidity and mortality in
the medical literature
• Innovations:
– Utilize alternative measures of fatness to capture health.
– Test for causal link using method of instrumental variables.
Problems with Subjective
Measures of Health
• Discrete when health is continuous
• Error-ridden since individuals’ scales are
different
• Endogenous to retirement decision.
Bond, Steinbricker and Waidmann (2006)
Body Mass Index (BMI)
• BMI = kg/m2 is most common measure of fatness in
social science research
– NIH, WHO use BMI to define obesity (BMI>=30)
• Advantage: weight and height found in many social
science datasets, easy to calculate
• Disadvantage: BMI does not distinguish between fat
and muscle
– Overestimates fatness among the muscular (U.S. DHHS,
2001; Prentice and Jebb, 2001)
– Underestimates fatness among those with small frames
Accurate Measures of Obesity Must
Distinguish Body Composition
• Fatness (not muscle/bone/blood) causes
morbidity, mortality
• Previous studies that define obesity using body
mass index (BMI) likely misstate correlation
between fatness and economic outcomes
• Better measure of fatness: Percent Body Fat
(PBF)
– Obesity defined as PBF>25 for men, PBF>30 for
women (NIH, 2006)
BMI Poor Measure of Fatness
• BMI alone accounts for just 25% of betweenindividual differences in percent body fat
(Gallagher et al., 1996)
• False negatives: BMI correctly identifies only
44.3% of obese men and 55.4% of obese
women (judged by measurement of actual
body fat); Smalley et al (1990)
• False positives: 9.9% of non-obese men and
1.8% of non-obese women. Smalley et al.
(1990).
"The main message from the new [Nov. 2005]
INTERHEART report is that current practice
with body-mass index as the measure of
obesity is obsolete, and results in
considerable underestimation of the grave
consequences of the overweight epidemic.”
--Editorial titled "A Farewell to Body-Mass Index?" in The Lancet
Vol. 366, p. 1590, Nov. 5, 2005.
Percent Obese by Definition, Race, Sex
Black Women
White Women
B-W Difference
Black Men
White Men
B-W Difference
% Obese
According to
Body Mass Index
% Obese
According to
Percent Body Fat
33.11
21.71
74.56
69.33
11.40*
5.23*
19.39
18.83
29.00
45.26
0.56
-16.26*
Notes: BMI based on measured weight and height. PBF calculated using TBF and
FFM generated from BIA readings. Asterisk indicates difference in means by race
statistically significant at the 1% level. NHANES III, ages 18-65, author’s
calculation.
Mean FFM, TBF, PBF by Race and Sex
Fat-Free
Mass (FFM)
(in kg)
Total Body
Fat (TBF)
(in kg)
Percent Body
Fat (PBF)
(%)
Black Women
White Women
B-W Difference
47.59
44.03
3.56*
27.95
24.79
3.16*
35.27
34.48
0.79
Black Men
White Men
B-W Difference
64.01
62.68
1.33*
18.28
20.61
-2.33*
21.19
24.04
-2.85*
Note: Asterisk indicates difference by race statistically significant at the 1% level.
NHANES III, Ages 18-65. Author’s calculations.
Accurately Measuring Fatness
• Variety of ways to measure TBF, PBF
– Each has advantages and disadvantages
– “Gold standard” measurements are expensive,
immovable, lab-based: MRI, X-Ray Absorpiometry
– Less accurate but accepted by NIH: field-based
methods like Bioelectrical Impedance Analysis
(BIA)
• Uses electric current to estimate fatness:
– Muscle (mostly water) conducts electricity
– Fat is insulator
Bioelectrical Impedance Analysis (BIA)
• PSID does not collect BIA measurements
• Are collected as part of recent National Health and
Nutrition Examination Surveys (NHANES III, 19992004)
• We convert BIA readings into PBF, TBF, FFM using
equations in Chumlea et al. (2002) separately by
gender, race
Estimate Body Fat for PSID Respondents
• In NHANES, regress TBF, PBF, FFM on selfreported weight, self-reported height, their squares,
age, age squared, other variables
• “Transport” NHANES coefficients and multiply by
PSID values to construct estimated body fat for PSID
respondents
Estimating Impact of Fatness
• Latent Health is assumed to be a function of fatness
Fit and other characteristics Xit
• Specifically: Hit= Fit β + Xit δ + uit
• Health not observed, but know whether individual
takes OA at 62:
SSit=0 if Hit≥H*
SSit=1 if Hit<H*
• Estimate probit models of early OA benefits:
Pr[SSit = 1│Fit, Xit] = Pr[uit < - Fit β - Xit δ]
Generating Causal Estimate
• Fatness will likely be affected by retirement, both will
be affected by variables unobserved by us
• Most convincing would be Random Control
Experiment but unethical, impossible
• We use method of Instrumental Variables, with
weight of a biological relative (adult child parent) as
instrument for weight of PSID respondent
– Identify biological relatives using PSID Family
Information Mapping System (FIMS)
Relative’s Weight as Instrument for
Respondent Weight
• Powerful: 25-40% variation in body fat due to
genetics (Bouchard et al., 1998)
– Siblings, and parents and children, share half their genes
• Valid: similarity due to genes, not shared environment
(Hewitt, 1997; Grilo and Pogue-Geile, 1991)
– Adopted children as similar to biological parents as
children raised by biological parents (Vogler et al., 1995)
– Genetic, but no shared environment, impact on diet and
eating behaviors (Tholin et al., 2005; Hur et al., 1998)
– Hewett (1997): “The impotence of shared family
environment for obesity.”
Data
• Panel Survey of Income Dynamics
– Weight, height available for 1986, 1999, 2001, and 2003
– Outcomes: OA benefit receipt at age 62
– FIMS used to identify adult biological children, adult full
siblings, and biological parents whose weight/height were
collected in main PSID for 1986, 1999, 2001, 2003
– Sample male heads who reach age 62 after 1986 but before
2003
• National Health and Nutrition Examination Survey III
(1988-1994)
– Self-reported weight and height
– Measured weight and height
– BIA measurements
Table 6: Probit Regressions Early Receipt of
OA Benefits
BMI
0.042*
Weight in kg
0.014*
Height in cm
-0.022
TBF (kg)
0.070*
FFM (kg)
-0.040
PBF
Obese (BMI)
Obese (PBF)
0.050**
0.661**
0.297*
Notes: t statistics in parentheses, Marginal effects listed below t statistics, Statistical significance indicated with asterisks
*** p<0.01, ** p<0.05, * p<0.1, Other regressors include: the year respondent turned age 62, indicator for African-American.
Table 7: Probit Regressions Early Receipt of
OA Benefits
BMI
0.050**
Weight in kg
0.016**
Height in cm
-0.028*
TBF (kg)
0.085**
FFM (kg)
-0.050
PBF
Obese (BMI)
Obese (PBF)
0.061**
0.638**
0.302
Notes: t statistics in parentheses, Marginal effects listed below t statistics, Statistical significance indicated with asterisks: ***
p<0.01, ** p<0.05, * p<0.1, Other regressors include: the year respondent turned age 62, highest grade completed, age of wife
when head turned age 62, and indicator variables for African-American and marital status.
Table 8: IV Probit Regressions Early
Receipt of OA Benefits
BMI
0.012
Weight in kg
0.005
Height in cm
-0.018
TBF (kg)
FFM (kg)
PBF
Obese (BMI)
Obese (PBF)
0.035
-0.564
0.722
Notes: t statistics in parentheses, Marginal effects listed below t statistics, Statistical significance indicated with asterisks: *** p<0.01
** p<0.05, * p<0.1, Other regressors include: the year respondent turned age 62, indicator for African-American, Instrument is the same
measure of fatness for the respondent’s adult biological child, controlling for the adult child’s age and gender, The probit IV model in which
TBF and FFM were the measures of fatness failed to converge, so column 3 is left blank.
Conclusions
• Strong association between fatness and
early OA acceptance that is robust across
alternative measures of fatness and
controls.
• Some evidence that fatness is a causal
factor in OA acceptance.
Conclusions (continued)
• Public policy implications: Substantial increase
in fatness in new generation of older workers
may offset to some degree any decrease in OA
acceptance at age 62 associated with reducing
the actual value of such benefits from .8 to .7
PIA.
• Data Collection Implications: Post theoretically
better measures of fatness on social science
level data file to the PSID HRS.