ERS Studies Using USDA Food Consumption Survey Data

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?
Dry bean consumption by food source
Source USDAs Continuing Survey of Food Intakes
by Individuals, 1994-96.
6
Who eats what and where?
Ground beef is consumed more in outlets away from
home that at home
Pounds
Source USDA, ERS, Agriculture Research Service,
2000 1994-96 and 1998 Continuing Survey of Food
Intakes by Individuals (CSFII).
7
Additional ERS research on who eats what, when
and where
8
Determinants of food choiceincome
9
Determinants of food choicedietary 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
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
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 changesbackground 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 databasesPyramid
  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 womens 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 mothers
  BMI were significantly correlated with child BMI
• Lin, Huang and French, International Journal of
  Obesity (2004), 28

19
Food choices and healthfew 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 causalityexample 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 FAFHs
  impact on diet quality

22
Our approach to this issuefixed effects
analysis

• Requires two or more days of dietary intake
• DQitDiet Quality on day t for individual i
• FAFHitNumber of FAFH meals for i on day t
• XiAdditional explanatory variables for i that
  affect DQ
• µiUnobservables for i that also affect DQ
• eitStochastic error term

23
Our approach to this issuefixed 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
??

• Our contact information
• Biing-Hwan Lin (blin_at_ers.usda.gov)
• Lisa Mancino (lmancino_at_ers.usda.gov)
• Francis Tuan (ftuan_at_ers.usda.gov)
• Travis Smith (tsmith_at_ers.usda.gov)
• Economic Research Service, USDA
• 1800 M St NW
• Washington DC 20036-5831
• www.ers.usda.gov