1. Using New Measures of Fatness to Improve Estimates of Early Retirement and Entry onto the OASI Rolls Richard V. Burkhauser John C. Cawley

2. 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.

3. Discrete when health is continuous Error-ridden since individuals’ scales are different Endogenous to retirement decision. Bond, Steinbricker and Waidmann (2006) Problems with Subjective Measures of Health

4. Body Mass Index (BMI) BMI = kg/m 2 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

5. 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)

6. BMI Poor Measure of Fatness BMI alone accounts for just 25% of between- individual 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).

7. " 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from the new [Nov. 2005] INTERHEART report is that current practice with body-mass index as the measure of obesity is obsolete, and results in considerable underestimation of the grave consequences of the overweight epidemic.”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.

8. % Obese According to Body Mass Index % Obese According to Percent Body Fat Black Women33.1174.56 White Women21.7169.33 B-W Difference11.40*5.23* Black Men19.3929.00 White Men18.8345.26 B-W Difference0.56-16.26* Percent Obese by Definition, Race, Sex 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.

9. Fat-Free Mass (FFM) (in kg) Total Body Fat (TBF) (in kg) Percent Body Fat (PBF) (%) Black Women47.5927.9535.27 White Women44.0324.7934.48 B-W Difference3.56*3.16*0.79 Black Men64.0118.2821.19 White Men62.6820.6124.04 B-W Difference1.33*-2.33*-2.85* Mean FFM, TBF, PBF by Race and Sex Note: Asterisk indicates difference by race statistically significant at the 1% level. NHANES III, Ages 18-65. Author’s calculations.

10. 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

11. Bioelectrical Impedance Analysis (BIA) PSID does not collect BIA measurements Are collected as part of recent National Health and Nutrition Examination Surveys (NHANES III, 1999- 2004) We convert BIA readings into PBF, TBF, FFM using equations in Chumlea et al. (2002) separately by gender, race

12. Estimate Body Fat for PSID Respondents In NHANES, regress TBF, PBF, FFM on self- reported 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

13. Estimating Impact of Fatness Latent Health is assumed to be a function of fatness F it and other characteristics X it Specifically: H it = F it β + X it δ + u it Health not observed, but know whether individual takes OA at 62: SS it =0 if H it ≥H* SS it =1 if H it <H* Estimate probit models of early OA benefits: Pr[SS it = 1│F it, X it ] = Pr[u it < - F it β - X it δ]

14. 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)

15. 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.”

16. 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

17. Table 6: Probit Regressions Early Receipt of OA Benefits 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. BMI0.042* Weight in kg0.014* Height in cm-0.022 TBF (kg)0.070* FFM (kg)-0.040 PBF0.050** Obese (BMI)0.661** Obese (PBF)0.297*

18. Table 7: Probit Regressions Early Receipt of OA Benefits on Measures of Fatness 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 7: Probit Regressions Early Receipt of OA Benefits 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. BMI0.050 ** Weight in kg0.016 ** Height in cm-0.028 * TBF (kg)0.085 ** FFM (kg)-0.050 PBF0.061 ** Obese (BMI)0.638 ** Obese (PBF)0.302

19. Table 8: IV Probit Regressions Early Receipt of OA Benefits 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. BMI0.012 Weight in kg0.005 Height in cm-0.018 TBF (kg) FFM (kg) PBF0.035 Obese (BMI)-0.564 Obese (PBF)0.722

20. 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.

21. 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.