1. ERS Studies Using USDA Food Consumption Survey Data Biing-Hwan Lin, Lisa Mancino, Francis Tuan, and Travis Smith Economic Research Service, USDA May 2009

2. What We Eat in America (WWEIA)  Part of the National Health and Nutrition Examination Survey (NHANES)  Includes one or two days of dietary recall— what was eaten, how much, where, and when  Can be linked to: Socio-demographic characteristics Health indicators Knowledge and attitudes about diet and health

3. Food and Commodity Economic Database (FCED)  Created by USDA to use with food survey data  Used to translate foods all the 7,000+ foods reported consumed into a limited number of commodities  Needed to bridge food consumption data with commodity consumption analysis

4. Four main areas of ERS research with these data  Who eats what, when and where?  What are the economic and behavioral determinants theses choices?  How might these choices change in the future?  How do these choices affect health?

5. Who eats what and where? Source: USDA’s Continuing Survey of Food Intakes by Individuals, 1994-96. Dry bean consumption by food source

6. Who eats what and where? Ground beef is consumed more in outlets away from home that at home Source: USDA, ERS, Agriculture Research Service, 2000: 1994-96 and 1998 Continuing Survey of Food Intakes by Individuals (CSFII). Pounds

7. Additional ERS research on who eats what, when and where Vegetablesdry beans, spinach, tomatoes, frozen potatoes, onion, mushroom, garlic, cucumbers, celery, cabbage, sweet pepper, sweet potatoes, snap beans, sweet corn, carrot Fruitsoranges, apples, watermelon Nutstree nuts, peanuts Animal productsbeef, pork Otherssweeteners

8. Determinants of food choice— income

9. Determinants of food choice— dietary knowledge

10. How might choices change in the future?

11. Consumption projections  Regression analyses are conducted to examine the effects of income, social, and demographic factors on commodity consumption  Regression results are used to project commodity consumption

12. Analysis of potato consumption indicates lower intake per person Index (2000=100) Potato product20002005201020152020 French fries100 9998 Potato chips10099979694 Baked potatoes100101 104106 Other potatoes10099989695 Projections of per capita potato consumption, 2000-2020 Lin and Yen, “U.S. Potato Consumption: Looking Ahead to 2020.” Journal of Food Products Marketing, 2004, 10(2).

13. But total US consumption will rise Index (2000=100) Potato product20002005201020152020 French fries100104108112115 Potato chips100103106109111 Baked potatoes100105110117125 Other potatoes100103107109112 Projections of total US potato consumption, 2000-2020 Lin and Yen, “U.S. Potato Consumption: Looking Ahead to 2020.” Journal of Food Products Marketing, 2004, 10(2).

14. A comprehensive projection Economic and demographic factors Lin, Variyam, Allshouse & Cromartie. “Food and Agricultural Commodity Consumption in the United States: Looking Ahead to 2020.” ERS 2003

15. Possible changes— background on our analysis  Foods are separated into 25 groups, consumed at home and away from home  Food consumption is affected by social, demographic, and economic characteristics  Forecast future food consumption by using forecasted social, demographic, and economic conditions  Food consumption is converted to commodity (22 groups) using two technical databases—Pyramid Servings Database and Food and Commodity Intake Database

16. Changes in demographic makeup indicates more fruit Lin, Variyam, Allshouse & Cromartie. “Food and Agricultural Commodity Consumption in the United States: Looking Ahead to 2020.” ERS 2003

17. Changes in dietary patterns and awareness have additional impact Lin, Variyam, Allshouse & Cromartie. “Food and Agricultural Commodity Consumption in the United States: Looking Ahead to 2020.” ERS 2003

18. Associations between diet and health  Correlations between women’s BMI and age, race, dietary patterns, TV watching, and smoking for both low- and high-income  Beverage consumption, eating out, importance of maintaining healthy weight, and exercise correlated with BMI only among women from high-income household  Among children, age, race, income, and mother’s BMI were significantly correlated with child BMI Lin, Huang and French, International Journal of Obesity (2004), 28

19. Food choices and health— few Americans eat a healthy diet Percent change from 2001-2002 consumption needed to meet 2005 Guidelines Source: National Health and Nutrition Examination Survey 2001-2002.

20. Why might that be a problem?  Majority of American adults are either overweight or obese  Rates are increasing among children as well  Obesity is believed to cause a number of health problems  Certain dietary patterns are associated increased risk of obesity  But do these dietary patterns cause poor diets

21. Why it can be hard to show causality— example of food away from home  What to eat is jointly determined with where to eat  Not accounting for relevant unobservables will bias estimates  If choosing FAFH is driven by fondness for certain (less nutritious) foods → ↓bias FAFH’s impact on diet quality

22. Our approach to this issue— fixed effects analysis Requires two or more days of dietary intake DQ it =Diet Quality on day t for individual i FAFH it =Number of FAFH meals for i on day t X i =Additional explanatory variables for i that affect DQ μ i =Unobservables for i that also affect DQ ε it =Stochastic error term

23. Our approach to this issue— fixed effects analysis With two days of dietary intake, we find within individual differences over both days Or more simply,

24. Our data  Two days of dietary recall data  As dependent variables, we focus on calories and specific components of diet quality  Control for meal patterns and whether intake day was a weekend

25. Our findings  After controlling for self-selection issues, each additional meal away from home adds about 130 daily calories significantly lowers intake of fruit, whole-grains and dairy and increases intake of certain fats and added sugars  Eating one meal away from home each week translates to almost one extra kilogram a year

26. Other applications  This could be easily extended to specific commodities or food groups  It would be simple to use this sort of fixed effects estimator with more days of intake data

27. 謝謝 Our contact information Biing-Hwan Lin (blin@ers.usda.gov)blin@ers.usda.gov Lisa Mancino (lmancino@ers.usda.gov)lmancino@ers.usda.gov Francis Tuan (ftuan@ers.usda.gov)ftuan@ers.usda.gov Travis Smith (tsmith@ers.usda.gov)tsmith@ers.usda.gov Economic Research Service, USDA 1800 M St NW Washington DC 20036-5831 www.ers.usda.gov