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Using New Measures of Fatness to Improve Estimates of Early Retirement and Entry onto the OASI Rolls

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?

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Using New Measures of Fatness to Improve Estimates of Early Retirement and Entry onto the OASI Rolls

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  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. 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)

  4. 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

  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. 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. 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 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 - Xitδ]

  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.

  18. Table 7: Probit Regressions Early Receipt of OA Benefits on Measures of Fatness 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. 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.

  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.

  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.

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