United States 

Department of 

Agriculture

Economic

Research

Service

Economic

Research 

Report

Number 118

June 2011

The Effect of Food and Beverage 

Prices on Children’s Weights

Minh Wendt

Jessica E. Todd
www.ers.usda.gov

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Wendt, Minh and Jessica E. Todd. The Effect of Food and Beverage Prices 

on Children’s Weights. ERR-118, U.S. Department of Agriculture, 

Economic Research Service. June 2011.
United States

Department

of Agriculture

www.ers.usda.gov

A Report from the Economic Research Service

Abstract

One factor that may be important in explaining rising childhood obesity is food prices. 

This report explores the effect of food prices on children’s Body Mass Index (BMI) 

using data from the Early Childhood Longitudinal Study, Kindergarten Class of 1998-99 

(ECLS-K) and the Quarterly Food-at-Home Price Database. On average, higher prices 

for soda, 100 percent juices, starchy vegetables, and sweet snacks are associated with 

lower BMIs among children. In addition, lower prices for dark green vegetables and 

lowfat milk are associated with reduced BMI. The effect of subsidizing healthy food may 

be just as large as raising prices of less healthy foods.

Keywords: Food prices, BMI, ECLS-K, Quarterly Food-at-Home Price Database 

(QFAHPD).

Acknowledgments

Many thanks to Corttney Penberthy, who outlined the methodology for calculating the 

Thrifty Food Plan weekly cost. Thanks to Lisa Powell, Institute for Health Research 

and Policy, University of Illinois-Chicago; Ashlesha Datar, RAND Corporation, Santa 

Monica, CA; Shin-Yi Chou, Department of Economics, Lehigh University, and Ilya 

Rahkovsky, Economic Research Service, USDA for their comments and suggestions. 

The authors also thank Dale B. Simms for editorial assistance and Cynthia A. Ray for 

layout and design.

Minh Wendt 

Jessica E. Todd

The Effect of Food and Beverage 

Prices on Children’s Weights Economic

Research

Report

Number 118 

June 2011
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Contents

Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iii

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

Food and Beverage Prices and Consumption Among 

U.S. Children and Adolescents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

Applying the Household Economic Framework . . . . . . . . . . . . . . . . . . . 5

Data and Variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

Empirical Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

Implications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17

References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18

Appendix A: Quarterly Food-at-Home Price 

Database Market Groups, 2002-2006 . . . . . . . . . . . . . . . . . . . . . . . . . . . 21

Appendix B: Construction of the 

Thrifty Food Plan Weekly Cost . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
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Summary

The rate of overweight among children has tripled over the past 30 years. 

First Lady Michelle Obama’s Let’s Move campaign highlights the growing 

public interest in finding ways to reverse this trend. One factor that may be 

important in shaping children’s dietary intake and weight is food prices. 

Previous research has shown that there is substantial geographic variation the 

relative price of healthy foods (Todd et al., 2011). This report estimates the 

effect of food prices on children’s Body Mass Index (BMI) using variation in 

food prices across time and geographic areas. 

What Did the Study Find?

Food prices have small but statistically significant effects on children’s BMI, 

but not all food prices have the same effect. While the magnitude of the price 

effects is similar for healthier and less healthy foods, the direction differs. 

Lower prices for some healthier foods, such as lowfat milk and dark green 

vegetables, are associated with decreases in children’s BMI. In contrast, 

lower prices for soda, 100 percent juices, starchy vegetables, and sweet 

snacks are associated with increases in children’s BMI. These results show 

that the effect of subsidizing healthy food may be just as large as raising 

prices of less healthy foods. Specifically:

•	A 10-percent price decrease for lowfat milk in the previous quarter is

associated with a decrease in BMI of approximately 0.35 percent, or 

about 0.07 BMI units average for an 8- to 9-year-old.

•	A 10-percent drop in the price of dark green vegetables (e.g., spinach and

broccoli) in the previous quarter is associated with a reduction in BMI of

0.28 percent.

•	A decrease in the price of sweet snacks during the previous quarter is

associated with an increase in BMI of 0.27 percent.

Not surprisingly, there is sometimes a delay between when prices change and 

when measurable changes occur in children’s BMI.

•	A 10-percent price increase for carbonated beverages 1 year prior is associated with a decrease of 0.42 percent in the average child’s BMI. The 

same price increase for 100 percent juices or starchy vegetables (e.g., 

potatoes and corn) is associated with a decrease in BMI of 0.3 percent 1 

year later.

In addition to the effects varying over time, the effects of prices vary by other 

characteristics.

•	Soda prices have a greater effect on children in households with income

below 200 percent of the Federal poverty line, as compared with children 

in households with higher income.

•	Prices for healthy foods such as lowfat milk and green vegetables have

larger effects on higher BMI children than on children of average weight.
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• Prices for less healthy food groups such as carbonated beverages, fruit 

drinks, and starchy vegetables have larger effects on BMI for children of 

average weight. 

How Was the Study Conducted? 

Panel data on children’s BMI, demographic, and household characteristics from the Early Childhood Longitudinal Study, Kindergarten Class of 

1998-99 were linked to average retail food prices from the Quarterly Food-atHome Price Database. BMI was regressed on lagged prices (one-quarter and 

1-year lags) using fi xed-effects regressions to control for unobserved factors 

that are likely correlated with BMI. Alternative specifi cations included price 

changes over the previous quarter and previous year. Regressions were 

conducted on the full sample and also separately for boys and girls. Quantile 

regressions were used to explore whether heavier children have different 

responses to food prices than thinner children. 
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Introduction

The prevalence of childhood overweight has risen dramatically in the last 

several decades in the United States, and is currently considered to be 

epidemic (U.S. Department of Health and Human Services, 2007; Institute 

of Medicine, 2008). According to the Centers of Disease Control and 

Prevention (CDC), overweight rates of U.S. children and adolescents age 

6-11 have more than tripled in the last 3 decades, from 6.5 percent in the 

1970s to 19.6 percent in 2007-08 (Ogden and Carroll, 2010).1 Moreover, the 

extent to which children’s body mass index (BMI) exceeds the overweight 

threshold is also increasing (Flegal and Troiano, 2000; Jolliffe, 2004). 

Childhood overweight is linked to a number of medical problems such as 

type II diabetes, high blood pressure, sleep apnea, and breathing problems; 

obese children are also more likely to become obese adults than are children 

of normal weight (U.S. DHHS, 2007; Steinberger et al., 2001; Must and 

Strauss, 1999; Whitaker et al., 1997). For children and adolescents age 6 to 

17, overweight-related hospital costs increased more than threefold from $35 

million per year during 1979-81 to $127 million during 1997-99 (Wang and 

Dietz, 2002).2 As overweight and obese children become adults, their weightrelated morbidities will lead to even greater economic costs. Medical costs 

of obesity in the United States were estimated to be as high as $147 billion in 

2008, up from $78.5 billion in 1998 (Finkelstein et al., 2009).

First Lady Michelle Obama’s Let’s Move campaign highlights the growing 

national interest in identifying ways to reverse this trend. Recognizing that 

obesity is the result of many interrelated factors, the campaign encourages 

families, schools, and communities to improve dietary intake and increase 

energy expenditure among children. 

One factor that may be important in shaping children’s dietary intake is food 

prices. Previous research has show that there is substantial geographic variation in both the absolute price of foods (Todd and Leibtag, 2010) as well as 

the relative price of healthy foods (Todd et al., 2011). Economic literature 

on consumer behavior has shown that consumers change their purchases 

in response to prices changes. Previous research has shown that own-price 

elasticities (the percentage change in purchases of a good from a 1-percent 

change in its price) of foods and beverages are relatively large, ranging from 

0.27 to 0.81, with food away from home, soft drinks, juice, and meat being 

most responsive to price changes (Andreyeva et al., 2010). 

Recent studies have investigated the relationship between prices of certain 

food groups—such as meat, fruits/vegetables, and fast food—and childhood 

obesity. The consensus thus far is that higher prices for fast food and lower 

prices for fruits and vegetables are associated with lower children’s weights 

(Auld and Powell, 2009; Powell and Bao, 2009; Sturm and Datar, 2008, 

2005). However, while these studies examined the effect of market-level 

food prices, they did not study the effect of beverage prices. 

This study’s main innovation is to estimate the impact of food prices on 

childhood obesity by directly linking a unique database of food prices,

the Quarterly Food-at-Home-Price Database (QFAHPD), with clinically 

measured body mass of children. The average retail prices for five beverage 

1According to the CDC definitions, 

overweight children age 2 to 19 have 

BMI-for-age between the 85th and 

95th percentiles in the BMI-for-age and 

gender growth charts, obese children 

have BMI-for-age at or above the 95th 

percentile in the BMI-for-age and 

gender growth charts. BMI is defined 

as the ratio of weight (in kilograms) 

over height (in meters) squared. It can

also be expressed as weight (in pounds) 

divided by height (in inches) squared

and multiplied by 703. 

2In constant (2001) dollar value.
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types, two types of vegetables, and sweet snack foods are linked to a longitudinal database tracking children’s height and weight from kindergarten 

through eighth grade. The QFAHPD allows the comparison of food and 

beverage prices over time within and across geographic areas, enabling us to 

identify the effect of food prices on children’s weight status.

We estimate models that test whether prices of carbonated beverages, fruit 

drinks, 100 percent juices, lowfat milk, whole and 2% milk, starchy vegetables (e.g., corn and potatoes), dark green vegetables (e.g., spinach and 

broccoli), and sweet snacks affect BMI among a cohort of U.S. children as 

they age from 5 to 14 years old. We selected these food groups because, with 

the exception of dark green vegetables, they represent a substantial portion 

of daily calorie intake among children and adolescents. Nielsen and Popkin 

(2004) show that soft drinks, fruit drinks, milk, fruit juice, and other beverages comprised 22.4 percent of daily calorie intake for children 2-18 years 

old in 1999-2001. Almost half of these beverage calories (10.3 percent) 

were from soft drinks and fruit drinks. Reedy and Krebs-Smith (2010) show 

that grain-based desserts (e.g., cakes and cookies) comprised 7.2 percent of 

average daily caloric intake among children age 2-18 in 2005-06. We include 

prices for dark green vegetables because they are nutrient-dense and lowcalorie alternatives to starchy vegetables. 
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Food and Beverage Prices and Consumption 

Among U.S. Children and Adolescents 

The price index for carbonated drinks has been below both the consumer 

price index (CPI) and the indexes for all non-alcoholic beverages and whole 

milk over the last 25 years or so (fig. 1). That is, the real prices for carbonated drinks are actually declining over time. In contrast, the price index for 

all fruits and vegetables,3 particularly fresh, is increasing faster than the 

CPI.4

At the same time, consumption of carbonated sweetened beverages (CSBs) 

and fruit drinks has increased among U.S. children and adolescents, while 

consumption of milk has declined. Mean intake of CSBs more than doubled, 

from 5 fluid ounces per day in 1977-78 to 12 fluid ounces in 1994-98 (fig. 2). 

Per capita daily caloric contribution from CSBs and 100 percent fruit juices 

increased from 242 kcal per day in 1988-94 to 270 kcal per day in 1999-

2004. The largest increase—of about 20 percent—occurred among children 

age 6 to 11 years (Wang et al., 2008). 

The combination of lower real prices and increased consumption lead many 

to argue that prices have a strong influence on consumption. However, this 

is ultimately an empirical question, as the full price effect depends on how

much intake responds to price and how much weight changes in response to 

changes in caloric intake (Chow and Hall, 2008). We estimate the (reduced 

form) relationship between price and weight outcomes based on a traditional 

household economic framework. 

3Includes fresh, canned, and frozen 

categories.

4This price structure does not take into 

account either the quality or variety of

fresh fruits and vegetables, which have 

improved over the last 30 years. See 

Kuchler and Stewart (2008) for more 

details. 

Figure 1

Price indexes for selected foods and beverages, 1980-2010

Notes: Prices for each group are annual average prices for all urban consumers. All fruits and 

vegetables include fresh, canned, and frozen. Base period 1982-84=100.

Source: Bureau of Labor Statistics, http://data.bls.gov/pdq/querytool.jsp?survey=cu

Price Index (1982-1984=100)

1980 83 86 89 92 95 98 01 04 07 10

0

50

100

150

200

250

300

350

CPI

Carbonated drinks

Non−alcoholic beverages

Whole milk

Fresh fruits and vegetables

All fruits and vegetables
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Figure 2

Soda, fruit drink, and milk consumption trend for children age 2-19, 

1977-78 to 2003-06

Source: Smith et al., 2010. Data are from 1977-78, NFCS (Nationwide Food Consumption 

Survey), USDA; 1989-91 and 1994-98 CSFII (Continuing Survey of Food Intakes by Individuals), USDA; 1999-2006 NHANES (National Health and Nutrition Examination Survey).

Ounces per day

B

B

B

B

B

J

J

J

J

J

H H

H H H

1977-78 1989-91 1994-98 1999-02 2003-06

0

2

4

6

8

10

12

14

16

Regular soda

Milk

Fruit drink
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Applying the Household Economic Framework

The household production function (Becker, 1965) has been widely used in 

economics to study determinants of children’s health in the United States 

(Variyam et al., 1999; Senauer and Garcia, 1991). In this framework, households combine time, human capital (knowledge and skills), and purchased 

goods to produce outcomes—such as health of a child—to maximize the 

overall household’s utility. The market goods purchased by households 

(e.g., foods) derive their values by supplying characteristics (e.g., nutrients) 

necessary for the production of the outcome (e.g., body weight), in addition 

to other benefits such as taste and socialization while eating. In this model, 

when the price of a particular type of food increases, households reduce their 

consumption of that food in order to equate price with the benefit enjoyed

from the last unit purchased (marginal utility).5 Since weight is determined 

by net energy intake, we assume that children’s body weight is determined 

by food intake as well as other factors (X) that would affect activity, such as 

household income and parents’ education. 

BMI = f(food, X) (1)

Food intake is, in turn, determined by food prices, income, and demographic 

factors that affect preferences (Z).

Food = g(food prices, income, Z) (2)

Since we do not directly observe the amount of food consumed by individuals, we can substitute equation 2 into equation 1 and obtain a (reducedform) equation for children’s BMI.

BMI = k(food prices, income, Z, X) (3)

Thus, we can think of a child’s weight or BMI as determined by food prices, 

income, and other factors, such as personal characteristics. Equation 3 allows

us to estimate the effect of food prices on BMI, recognizing that the effect is 

transmitted through the effect that prices have on food intake. 

5Although substitution both within 

and across food groups could influence 

how these price effects translate into 

weight changes.
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Data and Variables

Individual and Household Data

Individual and household data are from the Early Childhood Longitudinal 

Study, Kindergarten Class of 1998-99 (ECLS-K). The ECLS-K is a nationally representative sample of kindergarten students who were in kindergarten 

during the 1998-99 school year. Children are observed in kindergarten as 

well as during 1st, 3rd, 5th, and 8th grade. The five rounds of data used in 

this study correspond to the 1998-99, 1999-2000, 2001-02, 2003-04, and 

2006-07 school years.6

The ECLS-K includes detailed household information, students’ demographics, parents’ background and characteristics, as well as classroom 

and school environment. An advantage of this survey is that, unlike other 

child-level data that rely on self-reported measurements, children’s height 

and weight were measured by survey staff and collected during all survey 

rounds. Some children are lost from the sample mainly because they changed 

schools or their families moved outside of the survey’s primary sampling 

units. Approximately 50 percent of “movers” were randomly selected to be 

followed by ECLS-K. Therefore, most of the children lost for followup were 

those randomly selected and would be unlikely to bias the results. For more 

details on sample attrition, including nonresponse and change in eligibility 

status over time, see Tourangeau et al. (2009).

Food Price Data

Food prices are from the ERS Quarterly Food–at–Home Price Database 

(QFAHPD).7 The QFAHPD was constructed from Nielsen Homescan data, 

in which households report their food-at-home purchases from all store 

types, including grocery stores, convenience stores, mass merchandisers, 

club stores, and supercenters. Average quarterly prices are provided for 52

narrowly defined food groups, such as carbonated soda, fruit drinks, and 

vegetables grouped by type (dark green, starchy, orange) and processing 

method (fresh, frozen, or canned). These prices were estimated as the 

weighted average of household-level quarterly prices for each food group,

where the household-level prices are the mean price paid by each household for foods within each food group weighted by purchase frequency, not

expenditure share within the food group (see Todd et al., 2010, for more 

details on the construction of the QFAHPD).

The QFAHPD includes prices for market areas covering the contiguous 

United States. There are 26 metropolitan markets, which are either single 

metro areas—such as Philadelphia, Baltimore, and Los Angeles—or a group 

of metro areas, such as Metro Ohio, which includes Cincinnati, Cleveland, 

and Columbus. Between 1998 and 2001, areas in the lower 48 States not 

included in these 26 metro markets are grouped into 4 nonmetro regions; 

between 2002 and 2006, they are grouped into 9 census divisions (see fig. 

A1). Although these prices are constructed from household-level purchase 

data, and therefore are affected by market-level demand, they allow for 

identification of price effects because they are averages for large geographic 

areas. This means that they are not sensitive to any one household’s demand 

and are thus not influenced by individual household preferences. 

6We merged two waves of data that 

were collected in the fall and spring of 

the kindergarten year to form the first 

round of data in our analysis.

7The QFAHPD can be downloaded at 

<http://www.ers.usda.gov/Data/qfahpd/

index.htm>
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The QFAHPD provides a list of all of the county Federal information 

processing standards (FIPS) codes covered by each market. We first assign a 

QFAHPD market to each child based on the child’s county of residence and 

then merge the QFAHPD prices to each child-year observation. Since Alaska 

and Hawaii are not included in the QFAHPD (or in the Homescan data), 190 

children living in these States in the ECLS-K sample are excluded from the 

analysis.8 An additional 60 children from the lower 48 States are excluded 

because their FIPS county code lacks a corresponding code in the QFAHPD 

data (perhaps due to coding errors in ECLS-K or unidentified changes in 

FIPS coding over time). QFAHPD prices are nominal, so we convert them to 

real prices (in 2000 dollars) using the average monthly CPI for each quarter,

calculated from monthly data from the Bureau of Labor Statistics. 

Variables

Our main outcome variable is children’s BMI as calculated from each child’s 

survey-collected height and weight. Although each student’s height and 

weight were measured by trained field workers, recording errors might occur. 

We check for consistency across survey waves to correct recording errors, 

and limit the range of extreme BMI measures using CDC growth charts. The 

3rd percentile of BMI for children age 4 to 16 is 13.6, and the 97th percentile 

is 29.3 (CDC, 2000), so we limit the lowest BMI measure to 10 (affecting 50 

children’s measures) and the highest to 42 (affecting 90 children). We delete 

from the sample 10 children with obvious recording errors, such as having 

BMI greater than or equal to 98. We also determine whether each child is

above the 85th (overweight) or 95th percentile (obese) of the growth charts 

in each wave using the assessment date information and children’s birthdays 

(available in the restricted-use data) to calculate each child’s age in months at 

the time height and weight were measured.9

To control for students’ demographics and characteristics, we include their 

age, race, gender, and birth weight. Students’ family structure is captured by 

their living arrangements categorized as living with two biological parents, 

one biological and one other parent, single mother, single father, or with 

adoptive parents or a guardian. We also control for household income relative to the poverty threshold (four categories) and parent’s education level 

(five categories). 

To control for market demand conditions, we include the median household 

income in the county for the year (obtained from the U.S. Census Bureau). 

To control for the overall price level of food, we include the average weekly 

price of a market basket (the Thrifty Food Plan, TFP) that provides a healthy 

diet to a family of four, constructed from the QFAHPD data, in real 2000 

dollars. The TFP outlines the quantities (in pounds) of various food groups

for individuals by age and gender that will provide a diet that meets the 

2005 Dietary Guidelines at a low cost (Carlson et al., 2007). We use average 

market prices, not lowest cost, to construct the TFP basket cost for a family 

of four, so the measure reflects the average cost of a healthy diet in the 

child’s market area (see Appendix B for more details on the construction of 

the TFP basket price). 

8Due to rules regarding access and 

use of restricted data, all numbers of 

observations in this report are rounded 

to the nearest 10.

9We also estimated our model using 

standardized continuous BMI (z-scores) 

and percentiles in the BMI-for-age 

distributions as the dependent variable. The continuous BMI scores are 

provided by ECLS-K. BMI z-scores 

are calculated based on the 2000 CDC 

Growth Reference. BMI percentiles are 

calculated by normalizing the z-scores. 

These are standard measures of children’s BMI that are used in previous 

research. Each measure has its advantages and disadvantages. For detailed 

discussion, see Cole et al. (2005). The 

results are consistent and comparable to 

our main measure.
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Sample

Our analysis sample is limited to those students with nonmissing explanatory 

variables, for a total of 15,090 children, with 51,160 child-by-year observations. All children that live in Hawaii are excluded due to lack of food price 

information.10

The average BMI in the sample is 18.46, and obesity (BMI>95th percentile) is estimated at 16.2 percent (table 1). The obesity rate in our sample is 

comparable to national estimates for children age 6-11 (16.3 percent) and 

adolescents age 12-19 (16.7 percent) in 2001-02 from the National Health 

and Nutrition Examination Survey (Ogden and Carroll, 2010). The average 

age over all child-by-year observations is 106 months (a few months shy of 9 

years old); 51 percent are girls, 64 percent are White, 5 percent are Asian, 11 

percent are Black, 16 percent are Hispanic, and 4 percent are some other race 

or ethnicity.

Thirty-eight percent of the child-by-years have at least one parent who 

completed a college degree or more education, 34 percent have a parent 

whose highest education is some college, 21 percent have parents whose 

highest education is a high school degree, and 7 percent have parents with 

less than a high school education. About 68 percent of the sample lives with 

two biological parents. The other children in the sample live with two adults 

where one is a biological parent (9 percent), their single mother (19 percent), 

their single father (2 percent), or adoptive or foster parents (3 percent). 

Mean QFAHPD prices across the sample (table 1) indicate that sweet snacks 

are the most expensive items per 100 grams (at $0.74), while carbonated 

beverages are the least expensive ($0.07). Lowfat milk is less expensive 

than whole milk ($0.09 vs. $0.11 per 100 grams); 100% juice ($0.16 per 100 

grams) is more expensive than fruit drinks ($0.11) and carbonated beverages. Dark green vegetables are more expensive per 100 grams ($0.26) than 

starchy vegetables ($0.17). The average weekly TFP cost for a family of four 

was $166 over 1998-2007.

10The ECLS-K did not sample in 

Alaska.
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Table 1

Descriptive statistics (N=51,160 observations on 15,090 children

Variable Mean SD Min Max

Body Mass Index (BMI) - mean 18.46 4.18 10 42

Percent obese (BMI>95th percentile) 16.16

Child characteristics

Age (months) 105.64 32.85 52.50 190.50

Female 0.51 0.50 0 1

Birth weight (ounces) 118.57 21.10 16 219

White 0.64 0.48

Asian 0.05 0.22

Black 0.11 0.32

Hispanic 0.16 0.36

Other race/ethnicity 0.04 0.21

Household/community characteristics

Parents have less than high school education 0.07 0.26

Parents completed high school 0.21 0.41

Parents completed some college 0.34 0.47

Parents completed college or more 0.38 0.48

Household income >200% poverty threshold 0.64 0.48

Household income between 130 and 200% poverty threshold 0.13 0.34

Household income between 100 and 130% poverty threshold 0.07 0.26

Household income below poverty threshold 0.16 0.36

Child lives with 2 biological parents 0.68 0.47

Child lives with 2 other parents 0.09 0.29

Child lives with single mother 0.18 0.39

Child lives with single father 0.02 0.13

Child lives with other guardians 0.03 0.16

County median annual household income ($) 44,870 11,758 17,344 104,984

(Prices, 1 quarter lag, $ per 100 grams)

Carbonated beverages 0.07 0.01 0.06 0.09

Fruit drinks 0.11 0.02 0.07 0.19

100 percent juices 0.16 0.03 0.11 0.25

Lowfat milk (skim, and 1%) 0.09 0.01 0.05 0.12

Whole milk 0.11 0.01 0.08 0.14

Fresh and frozen dark green vegetables 0.26 0.03 0.19 0.41

Fresh and frozen starchy vegetables 0.17 0.02 0.11 0.24

Sweet snacks 0.74 0.07 0.60 1.07

Thrifty food plan basket weekly cost ($2000) 166.31 14.75 137.13 211.67

Note: Number of observation is rounded to the nearest 10. 

Source: ERS calculations based on data from the Early Childhood Longitudinal Study, Kindergarten Class of 1998-99, rounds 

covering 1998-2007.
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Empirical Analysis

One of the most common concerns when estimating the effect of various 

factors on BMI is omitted variable bias, or failing to control for factors that 

might affect both the explanatory variable of interest (i.e. food prices) and the 

outcome (i.e., children’s body weight). Examples of possible omitted variables in this study include availability of food retailers in an area and other 

neighborhood or locality characteristics. Since these factors are either unobservable or unavailable, econometric techniques should be used to reduce

possible bias in the results. We employ the fixed-effects regression method, 

which controls for unobserved characteristics of each child—gender, race, 

and preferences for health and nutrition in the household—that do not change 

over time. We compare results from the fixed-effects model to a model that 

does not control for individual-level unobserved characteristics (ordinary 

least squares, OLS) to explore the extent to which ignoring these characteristics would change our estimated effects.11 We also employ another model 

(quantile regression) to investigate whether the effects of prices differ for

heavier versus thinner children (at different levels of the conditional BMI 

distribution).

More technically, since we are using a fixed-effects model (a within estimator), only the variation over time in BMI for each child is used to identify 

the effect of price. This gives us short-term estimates of the effects of price 

changes. In contrast, a cross-sectional model that uses only variation across 

geographic areas (across individuals) provides a longrun estimate of the 

effect of price. Thus, our contribution complements previous research by 

providing shortrun estimates of price effects. Given the frequency of food

price spikes in recent years, the significance of shortrun price changes is 

heightened. 

In our fixed-effects model, we compare results using different measures of 

price: the previous quarter’s price and the price four quarters prior. Recent

research indicates that changes in caloric intake take time to lead to changes 

in weight and vary according to the type of macronutrients consumed (Chow 

and Hall, 2008).12 Comparing the results using the previous quarter’s price

to price from four quarters prior tests whether there are differences in when

price changes result in weight changes. Given that the market definitions in 

QFAHPD change for “nonmetro” counties over the time period of the study, 

price changes in these areas may be due more to the changing definition of 

markets than actual price changes. Thus, we also estimate a model where 

children in these “nonmetro” areas are excluded to test the robustness of our 

full-sample results. 

In our fixed-effects model, we include all child and household-level variables 

that vary over time, and in our OLS and quantile models, we include all child

and household-level characteristics listed in table 1. In all models, we correct 

for clustering at the school level and for heteroscedasticity using the HuberWhite covariance matrix.13

11A Hausman test rejected a randomeffects model with a chi-square value of

146.55 and 24 degrees of freedom

12In addition, Chow and Hall (2008) 

find that changes in weight are not necessarily constant over time (linear).

13We also test the sensitivity of our 

specification by clustering at the county 

level, and the results are not affected.
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Results

Average Effects of Food Prices

Table 2 reports the results from the fixed-effects (FE) and OLS models, 

providing estimates of the average associations between prices of selected 

foods/beverages and children’s BMI. Generally, higher prices for lowfat 

milk and dark green vegetables are associated with higher body weight while 

higher prices for sweet snacks are correlated with lower weight 3 months 

later. On the other hand, when 1-year lagged prices are used, higher prices 

for carbonated beverages, 100 percent fruit juice, and starchy vegetables are 

associated with lower weight. This means that the effects of prices on body 

Table 2

Estimation results, fixed-effects (FE) and OLS models, lagged prices

BMI (Body Mass Index)

FE OLS

1st quarter 1-year 1-year1 1st quarter 1-year

(1) (2) (3) (4) (5)

Carbonated beverages -0.003 -0.042*** -0.030** -0.028 -0.030

(0.014) (0.013) (0.015) (0.019) (0.019)

Fruit drinks 0.004 -0.007 -0.013 -0.007 0.002

(0.007) (0.006) (0.008) (0.011) (0.009)

100 percent juices -0.005 -0.030*** -0.039*** -0.012 -0.030***

(0.007) (0.009) (0.013) (0.009) (0.011)

Lowfat milk (skim, and 1%) 0.035*** 0.012 0.011 0.019 0.036**

(0.010) (0.010) (0.012) (0.014) (0.016)

Whole milk 0.001 0.008 0.006 -0.008 -0.022

(0.007) (0.010) (0.011) (0.012) (0.017)

Fresh and frozen dark green vegetables 0.028** 0.012 0.024** 0.037* 0.047***

(0.012) (0.010) (0.012) (0.020) (0.015)

Fresh and frozen starchy vegetables -0.006 -0.030*** -0.013 -0.029** -0.025*

(0.008) (0.007) (0.008) (0.014) (0.013)

Sweet snacks -0.027*** 0.003 0.000 -0.008 0.000

(0.008) (0.010) (0.011) (0.015) (0.016)

Thrifty Food Plan basket -0.055** -0.010 -0.058* 0.032 -0.025

(0.027) (0.026) (0.032) (0.045) (0.034)

Number of observations 51,160 51,160 36,770 51,160 51,160

Number of student clusters 15,090 15,090 11,150

R-squared (within) 0.710 0.710 0.699

R-squared (between) 0.131 0.131 0.130

R-squared (overall) 0.290 0.291 0.284 0.327 0.327

*** p < 0.01, ** p < 0.05, * p < 0.1. 

Notes: Number of observations is rounded to the nearest 10. Standard errors in parentheses are adjusted using Huber-White covariance 

matrix estimate. FE control variables include the child’s age (in months), household income, parent types, and survey round of data. The 

OLS control variables also include birth weight, indicators for whether the child is female, Asian, Black, Hispanic, or other race/ethnicity, and 

parent’s education. BMI and all prices are in log scale. 

1Children in the “nonmetro” areas in QFAHPD are excluded.

Source: ERS estimates using data from the Early Childhood Longitudinal Study, Kindergarten Class of 1998-99 and the Quarterly Food-atHome Price Database.
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weight vary not only by type of food/drink, but that the same food or drink 

can have price effects that become more pronounced over time. The FE and 

OLS estimates differ, indicating that not accounting for unobserved characteristics would bias our estimates. Thus, our preferred model is FE, so we 

focus on interpreting the FE results going forward.

Since BMI and all prices are in log scale, the estimated coefficients tell us 

the percentage change in BMI associated with a 1-percent change in price. 

More technically, the estimated coefficients are the price elasticities of 

BMI. Unlike adults, children growing normally should see increases in BMI 

as they age. To provide context for the magnitude of the changes in BMI 

that are associated with price, we convert the percent changes to BMI unit 

changes using the average BMI in the sample. The BMI unit changes are 

actually quite small, but by comparing them to the expected change (growth)

over 1 year at a certain point in the BMI distribution (such as the overweight 

threshold for a given age), we highlight the extent to which price influences 

short-term changes in BMI among children. 

All else equal, a 10-percent price increase for lowfat milk in the previous

quarter is associated with a 0.35-percent increase in children’s BMI. For the

average BMI measure in our sample, 18.5, at the average age of 8 years old 

(see table 1), such a price increase in lowfat milk equals an average increase

of 0.07 unit of BMI. This is equivalent to about 13 percent of annual BMI

growth for an 85th percentile boy, and 11 percent for an 85th percentile girl. 

This estimate assumes that a boy at the 85th percentile for BMI will gain 

about 0.5 BMI units between age 8 and 9 (2.8 percent, from 18.06 to 18.57), 

while a girl will gain 0.6 units (3.3 percent, from 18.44 to 19.06). 

We also find that higher prices for dark green vegetables in the previous 

quarter are associated with greater BMI; a 10-percent price increase leads to

an increase in BMI of 0.28 percent (or 0.05 unit, on average), equivalent to

10 percent and 8 percent of annual growth for a boy and girl, respectively, at 

the 85th percentile of BMI. The previous quarter’s price of starchy vegetables is not significantly related to BMI. On the other hand, a price increase 

for sweet snacks has an effect in magnitude similar to dark green vegetables, 

but with the opposite effect: a 10-percent increase is associated with a 0.27-

percent decrease in BMI. Higher overall food prices, as measured by weekly 

cost of the TFP, reduce BMI, but including this variable does not affect our 

estimates for specific foods.

Results are different when we model 1-year lagged prices instead of the 

previous quarter’s prices (column 2). A 10-percent price increase for carbonated beverages is associated with a decrease in BMI of 0.42 percent (0.08 

unit) 1 year later, while a 10-percent price increase for 100 percent juices 

reduces BMI 0.3 percent (0.06 unit). A 10-percent price increase for fresh 

and frozen starchy vegetables is associated with a 0.3-percent decrease in 

BMI 1 year later. 

The 1-year lagged prices for carbonated beverages, 100 percent juices, 

and starchy vegetables are larger and statistically stronger than their onequarter lagged prices, while the opposite is true for lowfat milk, dark green

vegetables, and sweet snacks. This means that the prices of lowfat milk, dark 

green vegetables, and sweet snacks might have stronger immediate effects on 
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children’s BMI, while carbonated beverages, 100 percent juices, and starchy 

vegetables might either take some time to appear or have longlasting effects 

that get stronger over time. 

Approximately 25 percent of our sample resides in “nonmetro” areas as 

defined in the QFAHPD, and the definition of these areas changes slightly 

over the study’s time period. Thus, the QFAHPD prices may not be as reliable for children in these areas as in the metro market areas. As a robustness check, we estimate the model using 4th quarter lag prices when these

children are excluded. The results are fairly similar to those from the full 

sample, except for vegetables, in which the effect of the price of dark green 

vegetables is now positive and significant, while that for starchy vegetables is 

no longer significant (table 2, column 3). 

Robustness Checks

Our results do not change when we use other measures of BMI and weight 

status, lending confidence to our main findings. Specifically, we test the 

sensitivity of our regression results in several ways. First, we use standardized z-scores for BMI according to the U.S. 2000 reference growth charts as 

our dependent variable. Standardized z-scores compare the difference in an 

individual’s BMI and the mean BMI in units of standard deviation. Although 

we do control for age and gender in all of our models, standardized z-scores 

might be more sensitive to subtle changes in BMI for children across time. 

Second, we also use percentiles that are normalized from the BMI z-scores as 

a dependent variable. While the percentiles are easier to match up with standard growth charts, they are bounded between 0 and 100, which poses some 

econometric challenges in selecting appropriate models for analyzing the 

data. Therefore, we use these results for confirming and validating purposes 

only. The results of these two alternative measures for BMI, although 

different in magnitude, are consistent in terms of signs and significance levels 

with our main model’s results.14

Differences in Effects by BMI

It is possible that individuals of different BMIs might respond differently to 

changes in food prices. More precisely, do food prices affect children with 

higher BMI differently than children with lower BMI? To test for this, we 

use quantile regression, an econometric method that allows us to compare the

effects of prices across different levels of BMI. 

Prices of healthier foods (e.g., lowfat milk, 100 percent juice, and dark green 

vegetables) have larger effects on children with higher BMI, while prices of 

less healthy foods (carbonated beverages, fruit drinks, whole milk, starchy 

vegetables) have greater impacts on children with lower BMI (table 3). One 

possible explanation for this is that while carbonated beverages are widely 

regarded as unhealthy drinks, less attention is devoted to other calorie-dense 

drinks such as fruit juice. Therefore, parents might restrict overweight children from consuming soda but not other calorie-dense beverages. Another 

possibility is that preferences for carbonated beverages are more entrenched 

among heavier children, such that their consumption responds less to price as 

compared to children with lower BMI. 

14As a further robustness check, we 

also estimated a model where the 4th 

quarter lag price is replaced with the

average price of the 4th-7th previous 

quarters, and found qualitatively and

quantitatively similar results (results

available upon request).
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More technically, we use quantile regressions to test for heterogeneity in

price responses examining the 25th, 50th, and 85th quantiles, estimating the

effect of one-quarter and 1-year lagged prices separately.15 It is difficult to 

incorporate individual fixed-effects in a quantile regression, but we are more

interested in the pattern of response across the distribution of BMI and so we 

focus on comparing the results and not on individual coefficients per se. 

Some interesting patterns emerge across the conditional distribution of BMI. 

First, for carbonated beverages, the effects of price are largest at the low end 

of the distribution and insignificant at the 85th quantile for both 1 quarter and

1-year lagged prices; the result is similar for starchy vegetables. In contrast, 

the effect of the price of 100 percent juices is largest at the 85th quantile

15Because the quantile regression

examines the relationship between the 

independent variables and conditional 

quantiles of the independent variable,

not percentiles in the BMI growth 

charts, the 85th quantile translates to

approximately the 95th percentile in the 

BMI growth charts. Note that about 16 

percent of the sample can be classified 

as overweight (over the 85th percentile 

in BMI-for-age).

Table 3

Estimation results, quantile regression, lagged prices

BMI (Body Mass Index)

Lagged prices Lagged prices

1 quarter 1 year

25th 

quantile

50th 

quantile

85th 

quantile

25th 

quantile

50th 

quantile

85th 

quantile

(1) (2) (3) (4) (5) (6)

Carbonated beverages -0.044*** -0.036** -0.010 -0.032*** -0.026** -0.000

(0.013) (0.014) (0.024) (0.011) (0.012) (0.026)

Fruit drinks 0.005 0.007 -0.019 0.017*** 0.012* -0.015

(0.006) (0.008) (0.017) (0.006) (0.007) (0.014)

100 percent juices 0.001 -0.012 -0.033** -0.015** -0.036*** -0.068***

(0.007) (0.007) (0.015) (0.006) (0.007) (0.018)

Lowfat milk (skim, and 1%) 0.003 0.014 0.048*** 0.022** 0.026** 0.048**

(0.008) (0.009) (0.018) (0.009) (0.011) (0.023)

Whole milk 0.017** -0.009 -0.034** -0.012 -0.024** -0.019

(0.007) (0.009) (0.016) (0.009) (0.012) (0.023)

Fresh and frozen dark green vegetables 0.006 0.041*** 0.075*** 0.019* 0.037*** 0.082***

(0.011) (0.014) (0.026) (0.011) (0.013) (0.025)

Fresh and frozen starchy vegetables -0.019* -0.019 -0.025 -0.041*** -0.021** 0.007

(0.010) (0.013) (0.018) (0.009) (0.010) (0.023)

Sweet snacks -0.012 -0.005 -0.021 -0.008 0.002 0.003

(0.009) (0.012) (0.024) (0.010) (0.014) (0.025)

Thrifty food plan basket 0.016 0.016 0.052 0.003 -0.004 -0.041

(0.027) (0.037) (0.063) (0.023) (0.027) (0.054)

Number of observations 51,380 51,380 51,380 51,380 51,380 51,380

Number of student clusters 15,090 15,090 15,090 15,090 15,090 15,090

Pseudo R-squared 0.151 0.192 0.228 0.151 0.193 0.228

*** p < 0.01, ** p < 0.05, * p < 0.1

Notes: Number of observations is rounded to the nearest 10. Tests of equality of the coefficients across the two quantiles for drinks 

(carbonated beverages, fruit drink, 100 percent juice, both types of milk) and foods (dark green vegetables, starchy vegetables, and sweet 

snacks) are statistically significant at 95%. Standard errors in parentheses are adjusted using Huber-White covariance matrix estimate. 

Fixed-effects control variables include the child’s age (in months), household income, parent types, and survey round of data. The ordinary 

least squares control variables also include birth weight; indicators for whether the child is female, Asian, Black, Hispanic, or other race/

ethnicity; and parent’s education. Body Mass Index and all prices are in log scale.

Source: ERS estimates using data from the Early Childhood Longitudinal Study, Kindergarten Class of 1998-99 and the Quarterly Food-atHome Price Database.
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(BMI), with a similar result for lowfat milk and dark green vegetables. The 

effects of prices for fruit drinks, 100 percent fruit juice, both types of milk, 

and dark green vegetables on the BMIs of children at the 85th quantile are

statistically different from those for children at the median. 

Our quantile regression results are similar to those of Auld and Powell

(2009), although different specifications make it difficult to compare. First, 

the price index used in Auld and Powell’s study is for a group of seven fruits 

and vegetables, while ours has specific categories. Second, they analyze a 

sample of adolescents while our sample is younger. Third, they include a 

price index for fast food but not for drinks, while we have specific categories 

for different drinks but not for fast food. However, the overall conclusions 

are similar in that the price effects of certain foods are more significant for 

children at the 85th percentile compared to children at the median of the 

weight distribution.

Subgroup Analyses

In addition to comparing effects by BMI, we estimate models for various 

population subgroups: gender, three income groups, and race (table 4). 

Overall, the effects of prices on children’s body weight vary somewhat 

between boys and girls, across income levels, and among ethnic groups. 

Generally, girls’ and boys’ BMI responds similarly to food prices. The two 

exceptions are that a higher price for carbonated soda reduces boys’ BMI but 

does not affect girls’, while higher prices of dark green vegetables increases 

girls’ BMI but not boys’. 

We also estimate separate models for three income groups: household 

income over 185 percent of the poverty line, between 130 and 185 percent of 

the poverty line, and at or below 130 percent of the poverty line. Consistent 

with expectations that price elasticities are smaller among higher income 

households, we find that higher prices of carbonated soda reduce BMI of 

children in households with income below 200 percent of the Federal poverty 

line, but not of children in higher income households. There is also variation 

across income groups in the significance of the prices of other foods (juice, 

dark green vegetables, and starchy vegetables). 

Across racial groups, higher prices of carbonated beverages reduce BMI for 

White and Hispanic children, but not Blacks. Higher prices for juice reduce 

BMI of White and Black children, but not Hispanics. The price of starchy 

vegetables is significant for White children only.
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Table 4

Estimation results, fixed effects, 1-year lagged prices, by gender, income group and race

BMI (Body Mass Index) Boys Girls

Household 

income 

> 185% 

pov.

Household

income 

130 

-185% 

pov.

Household 

income 

<130% 

pov. White Black Hispanic

Carbonated beverages -0.058*** -0.023 -0.021 -0.085** -0.051* -0.031** -0.032 -0.071**

(0.016) (0.016) (0.014) (0.037) (0.026) (0.015) (0.032) (0.030)

Fruit drinks -0.007 -0.005 -0.011 0.008 -0.003 -0.001 0.023 -0.009

(0.008) (0.008) (0.007) (0.019) (0.011) (0.007) (0.019) (0.011)

100 percent juices -0.027** -0.034*** -0.022** -0.040 -0.033** -0.030*** -0.071*** -0.024

(0.012) (0.012) (0.011) (0.026) (0.016) (0.011) (0.027) (0.018)

Lowfat milk (skim, and 1%) 0.003 0.022* 0.019 0.022 0.010 0.012 -0.004 0.016

(0.013) (0.012) (0.011) (0.028) (0.019) (0.012) (0.023) (0.023)

Whole milk 0.010 0.008 0.007 0.017 -0.004 0.014 0.040 -0.021

(0.013) (0.012) (0.012) (0.030) (0.019) (0.011) (0.030) (0.023)

Fresh and frozen dark 

green vegetables -0.006 0.029** 0.009 -0.065** 0.021 -0.011 0.014 0.004

(0.013) (0.012) (0.011) (0.029) (0.018) (0.011) (0.030) (0.024)

Fresh and frozen starchy 

vegetables -0.035*** -0.024** -0.030*** -0.022 -0.014 -0.042*** -0.008 -0.025

(0.009) (0.010) (0.008) (0.023) (0.015) (0.008) (0.021) (0.018)

Sweet snacks 0.015 -0.009 -0.009 -0.012 0.015 -0.001 0.016 0.007

(0.012) (0.013) (0.012) (0.029) (0.017) (0.013) (0.027) (0.020)

Thrifty Food Plan basket 0.018 -0.040 -0.009 0.216*** -0.018 0.060** 0.016 0.013

(0.033) (0.033) (0.028) (0.071) (0.050) (0.028) (0.079) (0.061)

Number of observations 25,920 25,250 32,850 6,660 15,330 32,620 5,810 7,940

Number of children 7,700 7,390 10,520 4,120 6,900 9,040 2,040 2,510

R-squared (within) 0.694 0.727 0.701 0.709 0.723 0.708 0.734 0.731

R-squared (between) 0.136 0.125 0.177 0.251 0.209 0.126 0.151 0.142

R-squared (overall) 0.286 0.296 0.289 0.292 0.297 0.301 0.301 0.295

*** p < 0.01, ** p < 0.05, * p < 0.1

Notes: Number of observations is rounded to the nearest 10. Standard errors in parentheses are adjusted using Huber-White covariance matrix 

estimate. Fixed-effects control variables include the child’s age (in months), household income (not for income subgroups), parent types, and 

survey round of data. Body Mass Index and all prices are in log scale.

Source: ERS estimates using data from the Early Childhood Longitudinal Study, Kindergarten Class of 1998-99 and the Quarterly Food-atHome Price Database.
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Implications

There are three main implications of our findings. First, they support the idea 

that food prices have small, but statistically significant effects on children’s 

BMI. Lower prices for soda, starchy vegetables, and sweet snacks have likely 

led to increases in children’s BMI. The reverse is true for some healthier 

foods such as lowfat milk and dark green vegetables. Others have found 

that lower real prices for fruits and vegetables predict lower weight (Powell 

and Bao, 2009; Auld and Powell, 2009) or a smaller gain in BMI for young 

school-age children (Sturm and Datar, 2005, 2008). By separating the price 

of dark green vegetables from higher calorie starchy vegetables, we find that 

the price effect is not the same for all vegetables. 

A second implication of our analysis is that there may be a considerable 

delay between when prices change and measurable changes in children’s 

BMI. That is, although changes in food prices might affect purchasing 

behavior immediately, effects on BMI are likely to take some time to appear, 

depending on the type of food. This finding highlights the need to have longitudinal data, allowing for individuals to be tracked over time and a rigorous 

examination of the longrun effects of changes in food prices on children’s 

BMI and overweight status. 

Third, these results highlight the fact that there are heterogeneous responses 

to changes in price, particularly across household income and the distribution of BMI. Larger effects among children in lower income households 

are consistent with economic theory that higher income households are less 

responsive to price. The finding of differences across the BMI distribution 

is consistent with Sturm et al. (2010), who found no average effect of Statelevel soda sales taxes on BMI, but a negative and statistically significant 

effect on BMI among children at or above the 85th percentile. 

While lower food-at-home prices for some foods likely contribute to rising 

obesity rates, we cannot comment on the effects in comparison with other 

factors, such as prices of food-away-from-home, access to specific foods 

in schools, or availability of calorie content labels in restaurants and other 

eating places. Cross-sectional studies find that higher fast food prices are 

associated with lower adolescent BMI (Chou et al., 2008; Auld and Powell, 

2009) and a lower probability of overweight (Powell et al., 2009). 
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References

Andreyeva, T., M. Long, and K. Brownell. 2010. “The Impact of Food 

Prices on Consumption: A Systematic Review of Research on the Price 

Elasticity of Demand for Food.” American Journal of Public Health 100: 

216-222.

Auld, M.C., and L. Powell. 2009. “Economics of Food Energy Density and 

Adolescent Body Weight,” Economica 72: 719-40.

Becker, G.S. 1965. “A Theory of the Allocation of Time,” Economic Journal

75: 493-517. 

Carlson, A., M. Lino, W.-Y. Juan, K. Hanson, and P.P. Basiotis. 2007. 

Thrifty Food Plan, 2006. CNPP-19. U.S. Department of Agriculture, 

Center for Nutrition Policy and Promotion.

Centers for Disease Control and Prevention (CDC). 2000. “Percentile Data 

Files with LMS Values.” http://www.cdc.gov/growthcharts/percentile_

data_files.htm.

Chou, S.Y., I. Rashad, and M. Grossman. 2008. “Fast-food restaurant advertising on television and its influence on childhood obesity,” Journal of 

Law and Economics 51: 599–618.

Chow, Carson C., and Kevin D. Hall. 2008. “The Dynamics of Human 

Body Weight Change,” PLoS Computational Biology 4(3): e1000045. 

doi:10.1371/journal.pcbi.1000045.

Cole, T.J., M.S. Faith, A. Pietrobelli, and M. Heo. 2005. “What is the best 

measure of adiposity change in growing children: BMI, BMI %, BMI 

z-score or BMI centile?” European Journal of Clinical Nutrition 59: 

419-425.

Finkelstein, E.A., J.G. Trogdon, J.W. Cohen, and W. Dietz. 2009. “Annual 

Medical Spending Attributable To Obesity: Payer-And Service-Specific 

Estimates,” Health Affairs 28: 822-831.

Flegal, K., and R. Troiano. 2000. “Changes in the distribution of body mass 

index of adults and children in the US population,” International Journal 

of Obesity-Related Metabolic Disorders 24: 807-18.

Kuchler, F., and H. Stewart. 2008. Price Trends Are Similar for Fruits, 

Vegetables, and Snack Foods. Economic Research Report Number 55, 

U.S. Department of Agriculture, Economic Research Service, March.

Institute of Medicine. 2008. “Nutrition Standards and Meal Requirements

for National School Lunch and Breakfast Programs: Phase I. Proposed 

Approach for Recommending Revisions.” V. Stallings and C. Taylor, eds. 

Washington, DC: National Academies Press.
19

The Effect of Food and Beverage Prices on Children’s Weights / ERR-118

Economic Research Service/USDA

Jolliffe, D. 2004. “Extent of overweight among U.S. children and adolescents 

from 1971 to 2000.” Journal of the International Association for the Study 

of Obesity 28:4-9. 

Must, A., and R. Strauss. 1999. “Risks and consequences of childhood and

adolescent obesity,” International Journal of Obesity-Related Metabolic 

Disorders 23: S2, 2-11. 

Nielsen, S.J., and B. Popkin. 2004. “Changes in beverage intake between 

1977 and 2001.” American Journal of Preventive Medicine 27: 205-210.

Ogden, C., and M. Carroll. 2010. “Prevalence of Obesity Among Children 

and Adolescents: United States, Trends 1963–1965 Through 2007–2008.” 

Health E-Stat. http://www.cdc.gov/nchs/fastats/overwt.htm

Powell, L., and Y. Bao. 2009. “Food prices, access to food outlets and child 

weight,” Economics and Human Biology 7: 64-72.

Reedy, Jill, and Susan M. Krebs-Smith. 2010. “Dietary Sources of Energy, 

Solid Fats, and Added Sugars among Children and Adolescents in 

the United States,” Journal of the American Dietetic Association 110: 

1477-1484.

Senauer, B.. and M. Garcia. 1991. “Determinants of the Nutrition and Health 

Status of Preschool Children: An Analysis with Longitudinal Data,” 

Economic Development and Cultural Change 39: 371-90.

Steinberger, J., A. Moran, C. Hong, D. Jacobs, and A. Sinaiko. 2001. 

“Adiposity in childhood predicts obesity and insulin resistance in young 

adulthood,” Journal of Pediatrics 138: 469-73.

Sturm, R., and A. Datar. 2005. “Body mass index in elementary school 

children, metropolitan area food prices and food outlet density,” Public 

Health 119:1059–68.

Sturm, R., and A. Datar. 2008. “Food prices and weight gain during elementary school: 5 year update,” Public Health 122: 1140–43.

Sturm, R., L. Powell, J. Chriqui, and F. Chaloupka. 2010. “Soda Taxes, Soft

Drink Consumption, and Children’s Body Mass Index,” Health Affairs

29: Web First.

Todd, Jessica E., Lisa Mancino, Ephraim Leibtag, and Christina Tripodo. 

2010. Methodology Behind the Quarterly Food-at-Home Price Database, 

Technical Bulletin No. 1926, U.S. Dept. of Agriculture, Economic 

Research Service.

Todd, Jessica E., and Ephraim S. Leibtag. 2010. “New Database Shows 

Substantial Geographic Food Price Variation,” Amber Waves (Data 

Feature), Vol. 8, Issue 3, Economic Research Service, U.S. Department of 

Agriculture, September 2010, p. 52-53.
20

The Effect of Food and Beverage Prices on Children’s Weights / ERR-118

Economic Research Service/USDA

Todd, Jessica E., Ephraim Leibtag and Corttney Penberthy. 2011. 

Geographic Differences in the Relative Price of Healthy Foods. EIB-78, 

U.S. Department of Agriculture, Economic Research Service, June.

Tourangeau, K, C. Nord, T. Le, S. Wan, and J. West. 2009. “Early Childhood 

Longitudinal Study, Kindergarten Class of 1998-99: User’s Guide to the 

Longitudinal Kindergarten-Eighth Grade Public-Use Data File,” National 

Center for Education Statistics.

U.S. Department of Health and Human Services (U.S. DHHS). 2007. “The 

Surgeon General’s Call to Action to Prevent and Decrease Overweight 

and Obesity.” http://www.surgeongeneral.gov/topics/obesity/calltoaction/

fact_adolescents.htm 

Variyam, J., J. Blaylock, B. Lin, K. Ralston, and D. Smallwood. 1999. 

“Mother’s nutrition knowledge and children’s dietary intakes,” American 

Journal of Agricultural Economics 81: 373–384.

Wang, G. and W. Dietz. 2002. “Economic Burden of Obesity in Youths 

Aged 6 to 17 Years: 1979-1999,” Pediatrics 109: 1-6.

Wang, Y., S. Bleich, and S. Gortmaker. 2008. “Increasing Caloric 

Contribution From Sugar-Sweetened Beverages and 100% Fruit Juices 

Among US Children and Adolescents, 1988-2004,” Pediatrics 121: 

e1604-e1614.

Whitaker, R.C., J.A. Wright, M.S. Pepe, K.D. Seidel, and W.H. Dietz. 1997. 

“Predicting Obesity in Young Adulthood from Childhood and Parental 

Obesity,” New England Journal of Medicine 337: 869-73. 
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Appendix A—Quarterly Food-at-Home Price 

Database Market Groups, 2002-06

1 Hartford 

2 Urban NY

3 Western NY/PA 

4 Philadelphia

5 Metro Midwest1

6 Metro Midwest2

7 North Florida 

8 Metro South 1

9 Baltimore

10 Metro South 2

11 Metro South 3

12 Metro Mountain 

13 Salt Lake City

14 Metro California

15 Los Angeles

16 Chicago

17 South Florida 

18 San Antonio

19 Boston

20 Other NY 

21 Metro Ohio

22 North Pacific

23 San Francisco

24 Atlanta

25 Metro South 4

26 Washington, DC

91 Nonmetro New England

92 Nonmetro Middle Atlantic 

93 Nonmetro East North Central 

94 Nonmetro West North Central 

95 Nonmetro South Atlantic 

96 Nonmetro East South Central 

97 Nonmetro West South Central 

98 Nonmetro Mountain

99 Nonmetro Pacific

Notes: For 1999-2001, market 81 is composed of markets 91 and 92; market 82 is composed of markets 93 and 94; market 83 is composed of 

markets 95, 96, and 97; and market 84 is composed of markets 98 and 99.

Source: Todd et al. (2010).
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Appendix B – Construction of the Thrifty Food Plan 

Weekly Cost

The Thrifty Food Plan (TFP) identifies quantities (in pounds) of foods as

purchased that will allow an individual or family to consume a healthy diet 

(one that meets the dietary guidelines) at a low cost. The cost of the TFP is 

used to determine the maximum Supplemental Nutrition Assistance Program 

(SNAP) benefit, but is estimated at the national level only. Here, we document how we calculated the average cost of the TFP basket for a family of 

four (two adults, age 19-50, one child age 6-8 and one child age 9-11) for 

each quarter and an average for the year in each Quarterly Food-at-Home

Price Database (QFAHPD) market group.

There is not a perfect correspondence between the TFP food categories 

and the QFAHPD categories. For example, the TFP includes fresh, frozen, 

and canned forms in its total pounds of dark green vegetables, while the 

QFAHPD includes market prices for fresh and frozen dark green vegetables 

and another set of prices for canned dark green vegetables. In such cases, 

we compute the national expenditure share on each form within a category. 

The price for the TFP category is then the weighted average of the QFAHPD 

prices, weighted by that national expenditure shares. 

In other cases, the TFP identifies quantities for groups that are not identified

in the QFAHPD (such as popcorn and other whole-grain snacks). In these 

cases, we use the QFAHPD price that is most similar (in this case, salty 

snacks) to serve as the price for the TFP category. 

A few of the TFP categories that are not priced in the QFAHPD are excluded 

from our calculations of the TFP. These include coffee and tea, dry soups, 

and gravies, sauces, and condiments. A list of the comparisons is at the end 

of this appendix. 

The TFP lists the pounds of each food category for various age/gender 

groups, from which we determine the total for our family of four. The total 

TFP weekly cost is simply the sum of the costs of each food category, calculated by multiplying the total pounds needed times the price per pound. 

Since the QFAHPD provides average market group prices and not necessarily the lowest available prices, our estimates of the cost of the TFP may 

be higher than the minimum required to purchase a healthy diet. However,

because the basket identifies a nutritionally adequate diet that meets the

Dietary Guidelines, we feel that this metric is useful to compare costs of a 

fixed basket of foods over time. 
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Appendix table B1

Thrifty Food Plan (TFP) categories and corresponding Quarterly Food-at-Home-Price Database

(QFAHPD) food groups 

TFP category QFAHPD food groups

Whole fruits Fresh and frozen whole fruit; canned fruit

Fruit juices Fruit juice

Dark green vegetables Fresh and frozen dark green vegetables; canned dark green vegetables

Orange vegetables Fresh and frozen orange vegetables; canned orange vegetables

All potato products Fresh and frozen starchy vegetables; canned starchy vegetables

Other vegetables Fresh and frozen other vegetables with select nutrients; canned vegetables 

with select nutrients; fresh and frozen other vegetables; canned other vegetables

Canned and dry beans/legumes Fresh and frozen legumes; canned legumes

Whole grain breads, pasta, flours, cereals, 

and snacks

Whole grain packaged products; whole grain flours and mixes; frozen readyto-cook whole grains

Refined grain breads, pasta, cereals, flours, 

pies, pastries, and snacks

Refined-grain packaged products; refined-grain flours and mixes; frozen 

ready-to-cook refined grains, commercially prepared baking mixes, ready-toeat bakery items, commercially prepared packaged snacks

Lower fat and skim milk and yogurt Lowfat and skim milk; low fat yogurt

Whole fat milk, yogurt, and cream Whole and 2% milk, whole-milk yogurt; other whole-fat dairy products

Milk drinks and milk desserts Frozen ice cream and other frozen desserts

All cheese Lowfat cheese; regular fat cheese

Beef, pork, veal, lamb, bacon, sausages, and 

lunch meats

Fresh and frozen low-fat meat; fresh and frozen regular fat meat; canned 

meat

Chicken, turkey, and game birds Fresh and frozen poultry; canned poultry

Fish and fish products Fresh and frozen fish; canned fish

Nuts, nut butters, and seeds Raw nuts and seeds; processed nuts and nut butters

Eggs Eggs

Table fats and oils Oils; solid fats

Soft drinks, sodas, fruit drinks, and ades Carbonated soft drinks; noncarbonated fruit drinks and ades

Sugars, sweets, and candies Raw sugars and sweeteners; packaged sweet snacks

Frozen or refrigerated entrees Frozen entrees

Soups (ready-to-serve and condensed) Canned soups and sauces

TFP categories not included in QFAHPD price estimate:

Gravies, sauces, and condiments

Coffee and tea

Soups (dry)