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 i th individual’s utility from reading label (j) at time t Label preference parameter Proxied by time spent on buying groceries by i at t (TG it )

6. Label Preference Trying to lose weight or not Aggregate effects Demographics, income Habit capital Temporal and permanent personal random effects 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? CategoryBMITrying to lose weight Stay about the same Trying to gain weight Not trying anything Obese30.0 and +68.6513.070.3417.94 Overweight25.0 – 29.945.0132.451.8820.66 Normal weight18.5 – 24.920.0645.366.5728.01 UnderweightBelow 18.511.2636.6229.5822.54 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 VariablesMarginal effectz-statistic95% CI Trying to lose weight0.2006.38( 0.138, 0.261) Not trying anything-0.396-11.17(-0.466, -0.327) Age0.0142.52( 0.003, 0.025) Male-0.255-9.34(-0.308, -0.201) White0.1013.69( 0.047, 0.155) Less than HS education-0.431-7.99(-0.537, -0.325) Income ($ 10,000)0.0652.91( 0.021, 0.108) Time buying grocery (hour)0.0843.16( 0.032, 0.136) ‘Habit’1.28347.49( 1.230, 1.335) ≈ 0

14. Serial Correlation Let Serial correlation hypothesis implies improvement in likelihood value if add in addition to (Shachar, 1994) Likelihood function 11760.212.74 11772.9 Number of individuals6895

15. Transition 2004 Label Use YesNo 2002 Label Use Yes83.98%16.02% No36.27%63.73% 2006 Label Use YesNo 2004 Label Use Yes84.92%15.08% No34.49%65.51% 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 CohortIndependent Variables Trying to lose weightNot trying to lose weight Female, non-white, ≥ HS(-0.079, 0.250)(-0.291, 0.081) Female, non-white, < HS( 0.132, 0.837)(-0.314, 0.558) Female, white, ≥ HS( 0.192, 0.493)(-0.273, 0.108) Female, white, < HS(-0.248, 0.859)(-1.688, 0.108) Male, non-white, ≥ HS( 0.001, 0.354)(-0.366, 0.053) Male, non-white, < HS(-0.343, 0.499)(-0.887, 0.220) Male, white, ≥ HS( 0.123, 0.444)(-0.244, 0.145) Male, white, < HS(-0.081, 1.033)(-0.623, 0.799) 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 Label Use*Trying to lose weight Not trying to lose weight Started to read food labels0.141 (1.96)0.109 (1.63) Continue to read food labels0.096 (1.87)0.097 (2.08) 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