Food Labels and Weight Loss: Evidence from the National Longitudinal Survey of Youth

1. Food Labels and Weight Loss:Evidence from the National Longitudinal Survey of Youth Bidisha Mandal Washington State University AAEA ‘08, Orlando

2. Motivation Who reads nutrition labels? Any link with body weight? • Policy Implications • NLEA enacted in 1994 • Estimated health benefits over 20 years estimated to be $4.4 – 26.5b (FDA, 1993) • NLEA’s impact on body weight over 20 years estimated to be $63 – 166b (Variyam & Cawley, NBER WP 2006) • Nutrition labeling meets consumers need for accurate, standardized and comprehensible information (WHO)

3. Use of nutrition information • FDA Consumer, 1995 • Survey of 1,000 individuals very soon after the 1994 enactment • ~50% of those who saw the label changed decisions • Of those, 70% cited fat content • Neuhouser et al., JADA 1999 • Label use higher among women, more than HS educated • Label use significantly associated with lower fat intake • Strong predictors of label use – belief in importance of eating low-fat diet, belief in association between diet and cancer • Kristal et al., JADA 2001 • Use of food labels strongly associated with lower fat intake, weaker association with increase in fruits and vegetables consumption

4. Welfare effects of nutrition information • Zarkin et al., AJPH 1993 • Potential health benefits from expected change in food consumption in terms of life years gained • Life expectancy increase between 0.12-2.06 years • Teisl and Levy, JFDR 1997 • Income and substitution effects of nutrition labels • Kim et al., JARE 2000 • Endogenous switching regression techniques to control for heterogeneity in the label use decision • Teisl et al., AJAE 2001 • Nutritional labeling affects purchasing behavior • May not necessarily increase consumption of ‘healthy’ foods, but may cause substitution within ‘unhealthy’ foods

5. Model Label preference parameter ith individual’s utility from reading label (j) at time t Proxied by time spent on buying groceries by i at t (TGit)

6. Label Preference Aggregate effects Trying to lose weight or not Habit capital Temporal and permanent personal random effects Demographics, income Binary variable for label use

7. Implications of the Model • Probability of reading nutrition labels • Who reads nutrition labels? • Purely out of habit? • Effect of individual’s actions regarding weight • Transition probability • Probability of transition between label use • What happens when individual starts trying to lose weight • Propensity to lose weight • Are individuals more successful if they read nutrition labels?

8. NLSY Panel Data • Survey years - 2002, 2004, 2006; Number of observations – 6,895 • Age – 37 to 50 years • Education • Less than HS – 10.17% • HS – 43.21% • Some college – 24.29% • College and above – 22.33% • Gender • Male – 47.6% • Female – 52.4% • Race • White – 50.43% • Black – 30.73% • Hispanic – 18.42% • Other – 1.02% • Income (in $10,000) – Mean = 6.83, Within SD = 6.63

9. Survey Questions • When you buy a food item for the first time, how often would you say you read the nutritional information about calories, fat and cholesterol listed on the label? • Don’t buy food (~ 0.01% - dropped) • Always • Often • Sometimes • Rarely • Never • Are you now trying to lose weight, gain weight, stay about the same, or are you not trying to do anything about your weight? • 47.14% trying to lose weight (within SD is 24.38%) Users – 66.58% (within SD is 23.32%) Non-users

10. Who tries to lose weight? Percentages – average across survey years

11. Time Cost • American Time Use Survey • Time spent on various activities – includes buying grocery • Eating and Health Module (2006) – also collects weight and height data (BMI) • Hot-deck Imputation • Impute data from ATUS-EH module and match by gender, education and BMI within age range of 37-50 years • Hot-deck imputation works better than Mean imputation • Mean (SD) of ATUS-EH only – 42.3 (29.9) minutes • Mean (SD) using mean imputation – 42.1 (4) minutes • Mean (SD) using hot-deck imputation – 40.8 (29.6) minutes • Minimum and maximum are also matched better with hot-deck

12. Empirical Estimation • Random effects model • Data from survey years 2004 and 2006 analyzed using ‘habit’ from 2002 and 2004 waves respectively • ‘Habit’ • Calculation • Lagged label use • Two-stage probit • Alternative hypotheses: Persistence in unobserved personal effect • Serial correlation • Unobserved heterogeneity

13. Probit Model of Reading Labels

14. Serial Correlation • Let • Serial correlation hypothesis implies improvement in likelihood value if add in addition to (Shachar, 1994)

15. Transition Overall, individuals are ~23%more likely to start reading food labels when they decide to try to lose weight.

16. Transition Probability of Reading Labels 95% Confidence Intervals Dependent Variables: Starts reading food labels

17. Predictions and Fit • Probability of reading labels • 82% accurate predictions • Predictions suffer among • African-Americans • HS or less educated • Male • Transition probability • Actual average proportion of those not reading labels in sequential wave and previously reading labels is 9% - predictions using lagged ‘habit’ is 11.5% • Actual average proportion of those reading labels in sequential wave and not reading labels previously is 14.5% - predictions using lagged ‘habit’ is 11.6%

18. Propensity to lose weight Marginal effects (z-statistic) Dependent Variable: Weight loss in sequential wave or not * Covariates are income, exercise duration, and demographics

19. Conclusions • Those who try to lose weight are more likely to read nutrition labels – certain demographic groups more so • Habit is still the best predictor of label use • Yet, likelihood ratio tests show weight loss preference is an important factor • Transition probabilities also support the notion that individuals who decide to lose weight are overall more likely to start reading food labels • Propensity to lose weight and label use • Those who read labels are more likely to be successful in losing weight