ERS Studies Using USDA Food Consumption Survey

Data

Biing-Hwan Lin, Lisa Mancino, Francis Tuan, and Travis SmithEconomic Research Service, USDA

May 2009

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 characteristicsHealth indicators

Knowledge and attitudes about diet and health

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

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?

Who eats what and where?

0

20

40

60

80

100

Per

cent

of t

otal

Home Fast food Other

Source: USDA’s Continuing Survey of Food Intakes by Individuals, 1994-96.

Dry bean consumption by food source

Who eats what and where?

0

2

4

6

8

10

12

14

16

Ground Stew Steak Beef dish Other cut Processed

At home Away from home Restaurant Other

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).

Pou

nd

s

Additional ERS research on who eats what, when and where

Vegetables dry beans, spinach, tomatoes, frozen potatoes, onion, mushroom, garlic, cucumbers, celery, cabbage, sweet pepper, sweet potatoes, snap beans, sweet corn, carrot

Fruits oranges, apples, watermelon

Nuts tree nuts, peanuts

Animal products

beef, pork

Others sweeteners

Determinants of food choice—income

0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

5

$- $10 $20 $30 $40 $50 $60 $70 $80 $90 $100

Income (in Thousands)

Daily

Veg

etab

le S

ervi

ngs

Dark Green/ Deep Yellow Tomatoes Fried Potatoes Other

Determinants of food choice—dietary knowledge

0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

5

0 2 4 6 8 10 12

Dietary Knowledge Score

Daily

Veg

etable

Ser

vin

gs

Dark Green/ Deep Yellow Tomatoes Fried Potatoes Other

How might choices change in the future?

0

5

10

15

20

25

30

35

40

45

1977-78 1987-88 1989-91 1994-96 2003-06

Per

cent

of

Tot

al C

alor

ies/

Foo

d E

xpen

ditu

res

Food expenditures on food away from homeCalories from food away from home

Calories from fast food

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

Analysis of potato consumption indicates lower intake per person

Index (2000=100)

Potato product 2000 2005 2010 2015 2020

French fries 100 100 100 99 98

Potato chips 100 99 97 96 94

Baked potatoes 100 101 101 104 106

Other potatoes 100 99 98 96 95

Projections of per capita potato consumption, 2000-2020

Lin and Yen, “U.S. Potato Consumption: Looking Ahead to 2020.” Journal of FoodProducts Marketing, 2004, 10(2).

But total US consumption will rise

Index (2000=100)

Potato product 2000 2005 2010 2015 2020

French fries 100 104 108 112 115

Potato chips 100 103 106 109 111

Baked potatoes 100 105 110 117 125

Other potatoes 100 103 107 109 112

Projections of total US potato consumption, 2000-2020

Lin and Yen, “U.S. Potato Consumption: Looking Ahead to 2020.” Journal of FoodProducts Marketing, 2004, 10(2).

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

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

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

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

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

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.

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

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

Our approach to this issue—fixed effects analysis

Requires two or more days of dietary intake

DQit=Diet Quality on day t for individual i

FAFHit=Number of FAFH meals for i on day t

Xi=Additional explanatory variables for i that affect DQ

μi=Unobservables for i that also affect DQ

εit=Stochastic error term

itiitiit FAFHXDQ

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,

)()(

)()()(

1212

12

iiii

iiiiii

FAFHFAFH

XXDQDQ

iii FAFHDQ )(

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

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

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

謝謝 Our contact information

Biing-Hwan Lin (blin@ers.usda.gov)Lisa Mancino (lmancino@ers.usda.gov)

Francis Tuan (ftuan@ers.usda.gov)Travis Smith (tsmith@ers.usda.gov)

Economic Research Service, USDA1800 M St NW

Washington DC 20036-5831www.ers.usda.gov