Does Banning Carbonated Beverages in Schools Decrease ... · which could change their carbonated beverage consumption but are not related to carbonated beverage bans in schools. The

This work is distributed as a Discussion Paper by the

STANFORD INSTITUTE FOR ECONOMIC POLICY RESEARCH

SIEPR Discussion Paper No. 12-023

Does Banning Carbonated Beverages in Schools Decrease Student

Consumption? By

Shirlee Lichtman

Stanford Institute for Economic Policy Research

Stanford University

Stanford, CA 94305

(650) 725-1874

The Stanford Institute for Economic Policy Research at Stanford University supports

research bearing on economic and public policy issues. The SIEPR Discussion Paper

Series reports on research and policy analysis conducted by researchers affiliated

with the Institute. Working papers in this series reflect the views of the authors and

not necessarily those of the Stanford Institute for Economic Policy Research or

Stanford University

Does Banning Carbonated Beverages in Schools

Decrease Student Consumption?

Shirlee Lichtman∗

December 2012†

JOB MARKET PAPER - PRELIMINARY - PLEASE DO NOT CIRCULATE

In an effort to combat childhood obesity, many schools have banned the sale of carbonated

beverages on school grounds. I evaluate the effectiveness of these measures by investigating

their impact on household carbonated beverage consumption. I match households in Nielsen

Homescan Data to their school district’s carbonated beverage policies over the last 10 years. I

use variation across school districts in whether the policy was implemented and the timing

of the policy, as well as whether the household has children in the age group included in

the policy. I find that when high schools ban the sale of carbonated beverages to students,

households with a high school student experiencing the ban increase their consumption of

non-diet carbonated beverages by roughly the equivalent of 3.5 cans per month. Increased

consumption is greater in the quarter following the policy implementation and appears to

persist even a year after the introduction of the ban. I present evidence that the average

high school student consumes roughly 4.5 cans of non-diet soda per month in school, when

carbonated beverages are available. Thus, the results suggest that the drop in student school

consumption is substantially offset by increased household consumption. JEL Classifications: I15, I18

I am grateful to my advisors, Caroline Hoxby, Ran Abramitzky and Matthew Harding for their advice and comments. I thank

Roy Mill, Jenny Ying, and attendees at the Stanford Applied Economics Lunch and at the U.S. Department of Agriculture "Using

Scanner Data to Answer Food Policy Questions" Conference for useful comments and conversations. I am grateful to the U.S.

Department of Agriculture, Economic Research Services for providing the Nielsen Homescan Data. ∗Dept. of Economics, Stanford University; Stanford, CA 94305. E-mail [email protected]. † This version updated December 20, 2012.

mailto:[email protected]

1 Introduction

1

1 Introduction

Two thirds of adults and one third of school-aged children in the United States are either overweight or

obese. Consumption of calorie-dense and sugar-sweetened foods and beverages has been shown to be

a leading factor contributing to obesity and overweight rates.1 As such, combatting obesity effectively

requires a change in individuals’ eating habits. This raises the question: Can limiting access to unhealthy

foods be effective in the efforts to reduce obesity? Proponents of limiting access to unhealthy foods

inherently assume that these limitations promote healthier eating habits. However, given that it is

infeasible to block all sources of unhealthy foods, it may be that individuals will respond to the limitation

by finding an alternative source for what is being limited, thus decreasing effectiveness. This paper

investigates this debate by evaluating bans on carbonated beverages in schools and their impact on

students’ household consumption. Specifically, I ask: Does compensation at the household level take

place when a student experiencing a ban on carbonated beverages in school is present?

I match households in Nielsen Homescan data to their school districts and these school districts’

corresponding carbonated beverage availability policies. The introduction of carbonated beverage

bans in school districts generates variation over time and across school districts and allows me to

estimate the effect of the bans on household carbonated beverage consumption through a difference-in-

differences (DID) analysis. My preferred estimates for the effect of carbonated beverage bans in schools

on household carbonated beverage consumption come from a difference-in-differences-in-differences

(triple differences) model. The triple differences model exploits a third source of variation - the presence

of school-aged children affected by the policy within the household. The triple differences framework

allows me to evaluate the plausibility of the identification restrictions in the DID analysis, namely that

households in school districts introducing carbonated beverage bans were not affected by other factors

which could change their carbonated beverage consumption but are not related to carbonated beverage

bans in schools.

The results show that households with children in high school compensate for the lack of availability of

carbonated beverages when high schools ban the sale of carbonated beverages. The compensation takes

1 There is also significant literature on the economic factors contributing to the rise in obesity rates: Cutler, Glaser and Shapiro

(2003) attribute the increase in unhealthy eating habits to technological innovations which placed more weight on food originating

from mass production in the household’s food consumption bundle. Chou, Grossman and Saffer (2004) attribute increased

obesity to the following: increased value of time, particularly for women who have increased their labor force participation

rates; rise in the real cost of cigarette smoking; and increased availability of fast food. Ladkawalla, Philipson and Bhattacharya

(2005) examine how welfare-enhancing technological changes that simultaneously lowered food prices and increased the cost

of physical activity could have lead to the unintended consequence of higher obesity prevalence. Bertrand and Schanzenbach

(2009) attribute higher caloric intakes to “secondary eating”, i.e. eating or snacking while engaged in an alternative activity, which

decreases the attention placed on the content and amount of food being consumed while eating.

1 Introduction

2

place for non-diet carbonated beverages and no compensation is observed for diet carbonated beverages.

In addition, no household compensation is observed for bans occurring in elementary or middle schools.

Quantitatively, the average household increases consumption of non-diet carbonated beverages by

roughly 43 fluid ounces per month for each treated high school household member, equivalent to an

increase of over 3.5 cans of soda per month. An analysis with the compensation effect varying by quarter,

following the policy implementation, suggests that treated households exhibit positive compensation

levels even one year after the ban was introduced. I present evidence that the average consumption of

non-diet carbonated beverages in high schools, when these are available, is roughly 4.5 cans per month.

Thus, the estimated 3.5 can increase in non-diet carbonated beverage consumption in response to bans

in high schools suggests that compensation at home offsets approximately 80% of the average high school

student’s consumption levels of carbonated beverages when these are available in schools.

The paper’s main contribution is its ability to evaluate a policy which aims to promote a healthier

habit beyond the premises in which the policy takes place. The results show that policy evaluations

cannot be limited to the local level of policy implementation (i.e. in our case, the school) and that a

broader approach, which accounts for compensation or substitution of the unhealthy habit, has to be

considered.2 The results emphasize the importance of individual preferences and their persistence, even

when altering potential access to the desired consumption bundle. The results support previous findings

regarding compensatory behavior concerning unhealthy eating habits (Fletcher, Frisvold and Tefft 2010;

Wisdom, Downs and Loewenstein 2010) as well as other unhealthy habits, such as cigarette smoking

(Adda and Cornaglia 2006; Adda and Cornaglia 2010). Fletcher, Frisvold and Tefft (2010) also look into

household responses to policy measures aimed at curbing obesity, namely taxes on soda at the state

level. Using data on child and adolescent body mass index (BMI) and food and beverage consumption

recall from the National Health and Nutrition Examination Survey (NHANES), the authors find that

taxes on soft drinks lead to a decrease in child and adolescent soft drink consumption. However, this

reduction in soda consumption is completely offset by increases in consumption of other high-calorie

drinks. Both the results of this paper and the results presented in Fletcher et al. (2010) complement

each other by highlighting the persistence of eating habits within the household, even following various

policy interventions intended to change these - while in Fletcher et al. (2010) households find substitute

products, in this paper households find alternative sources for obtaining their carbonated beverages.

The paper contributes to existing literature on school food environments, which mostly examines

2 Just and Price (2010) and French et al. (1997) are examples of studies which evaluate an intervention to promote healthier eating

habits only in the setting where the intervention took place.

3

1 Introduction

how specific school food environments affect children’s obesity or BMI outcomes.3 However, very few

papers in the literature address the underlying consumption mechanisms amongst children when school

food environments change - i.e how households or individuals respond to changes in policies related to

the school food environment. While it is important to assess the final outcome of childhood obesity in

response to variations in school food environments, it is also important to understand the effectiveness

of various policy measures in terms of limiting child consumption or access to unhealthy foods. In this

aspect, this paper is different from most of the past literature on childhood obesity and school food

environments.

A closely-related paper focusing on households’ responses to policy measures is by Huang and Kiesel

(2012). Huang and Kiesel ask the same question this paper is addressing regarding carbonated beverages:

whether soft drink bans in schools result in compensation at home. Similar to this paper, Huang and

Kiesel apply a triple differences framework using Nielsen Homescan data, but beyond these similarities,

there are significant differences between the two papers’ empirical approaches. In Huang and Kiesel, the

variation in legislation concerning carbonated beverage availability at schools is at the state level, rather

than the school district level.4 Policy variation at the school district level has several empirical benefits,

in comparison to variation at the state level. First, significant differences may exist between states

with respect to characteristics potentially correlated with household carbonated beverage consumption,

thus jeopardizing the comparability of the control group to the treatment group when variation is at

the state level. In contrast, with school district level variation, the control group frequently includes

school districts within the same state as a treated school district, thus creating more plausible controls.

Second, using district-level policy variation decreases sensitivity to the inclusion or exclusion of any

particular ban, as there are far more districts than states which generate the policy variation.5 Huang

and Kiesel identify households who are treated by the policy using an identifier provided in the Nielsen

data as to whether any children aged 6-17 are present in the household. In contrast, this paper uses

3 Anderson and Butcher (2006) evaluate the impact of “junk food” availability in schools and “pouring rights” contracts with

beverage companies on students’ body mass index (BMI) outcomes. Currie et al. (2010) use data from California high schools

to measure whether high schools’ geographic proximity to fast food restaurants increases students’ probability of being obese.

Anderson, Butcher and Schanzenbach (2011) assess the effects of pressures on school accountability from the No Child Left

Behind Act on student obesity, under the assumption that higher pressures for student achievement reduce school investment

in other areas, such as recess or physical education. Figlio and Winicki (2005) find that Virginia schools threatened with

accountability sanctions increase the caloric content of their lunches on testing days in an apparent attempt to boost short-term

student cognitive performance. Schanzenbach (2009) asks whether school lunches contribute to childhood obesity. Her positive

findings differ from those of Gleason et al. (2009), who find no evidence that school lunch participation is related to students’

body mass index or risk of obesity. 4 This paper’s investigation into school district nutrition policies has revealed that using state-level legislation is not always precise,

as school district policies frequently precede state legislation, or alternatively, school districts implement a policy in states which

have no restrictions in place. 5 In Huang and Kiesel, the policy variation is generated by one state ban in Connecticut.

4

2 School Food Environment - Carbonated Beverages

household members’ birth year and month and the cutoff birth month for entering kindergarten in the

state of residence to identify more precisely whether children affected by the school district policy are

present in the household. Calculating the exact age of household members allows me to differentiate

between the effect of a ban on carbonated beverages for the various school levels - elementary, middle

and high school - which will prove important in the paper’s results. Huang and Kiesel’s strategy results

in the authors finding no effect of carbonated beverage restrictions at the state level on household soft

drink consumption, while this paper finds a significant increase in households’ carbonated beverage

consumption in response to having a high school student affected by the policy in the household. The

contrasting results are most likely due to the differences between the two papers’ empirical strategies

and identification.

The paper starts by discussing the school food environment, highlighting important landmarks in

public awareness and its regulations over the last 25 years, with a focus on carbonated beverage policies

at schools. Section 3 discusses the main data sources used for this paper: the Nielsen Homescan Data

and an independently constructed data set on school districts’ carbonated beverage policies over the

last 10 years. Section 4 discusses the empirical strategy used for the analysis, as well as the identifying

assumptions. Section 5 presents the results and discusses their interpretation, followed by Section 6,

which presents some of the results’ robustness to various alternative specifications. Section 7 provides

some concluding remarks.


The U.S. Department of Agriculture (USDA) sets federal standards for the provision of foods and beverages

on school grounds to students. Nutritional standards have always been in place (and are continuously

improving) for the provision of lunch and breakfast, as part of the National School Lunch Program (NSLP)

and School Breakfast Program (SBP), respectively. These include restrictions on the fat and sugar content

of the foods provided, calories per serving, and nutritional composition (protein, fruits, vegetables, etc.).

Food items sold on school grounds outside the NSLP and SBP are categorized as competitive foods.

Competitive foods can be sold in vending machines, school stores, cafeteria a la carte lines, or at fund

raisers. The USDA has no regulations in place for the sale of competitive foods, with the exception of

forbidding the sale of foods of minimal nutritional value (FMNV ) in meal service areas during meal

time - i.e. when and where the NSLP and SBP are served. FMNV include seltzer, chewing gum, lollipops,

5


jelly beans, and carbonated sodas.6 , 7 With the exception of this restriction concerning FMNV, USDA

regulations permit the sale of FMNV anywhere else on campus, including just outside the cafeteria, at

any time of day.

Competitive foods have increasingly become a source of revenue for schools. The proceeds from sale

of competitive foods go directly to the school and generally fund extra-curricular activities, such as clubs

or athletic activities, which are not allocated funds through more conventional revenue resources. To

effectively maximize the revenue stream, many large school districts have increasingly signed exclusivity

agreements, entitling the provision of competitive beverages to one exclusive beverage provider for the

entire school district, in exchange for higher commission rates to the schools and school district. Of the

three school levels - high, middle and elementary schools - high schools benefit most from competitive

foods sales, in terms of revenues, and a large high school’s stream of annual commissions from a beverage

provider can range from several thousand dollars to over $20,000.8 High schools have the greatest revenue

streams from competitive foods sales due to penetration and availability of competitive foods increasing

as grade-levels increase.9 , 10

Around the early 2000’s there was a rise in public awareness concerning the link between soda drinking

and obesity.11 In response to this, many states and school districts began to question the provision

of carbonated beverages on school grounds and to contemplate whether they should enact stricter

regulations on the sale of competitive foods, and in particular FMNV, than those mandated by the USDA.

As it is generally thought that revenues from competitive foods are higher when unhealthy snacks and

beverages are available to students, the debate concerning these restrictions generated some controversy

and talks of jeopardizing schools’ revenue streams from competitive foods sales were prevalent. When

restrictions were put in place, they frequently varied between elementary, middle and high schools, with

6 Source: http://www.fns.usda.gov/cnd/menu/fmnv.htm 7 Many low-nutrition foods are not considered FMNV, such as chocolate, candy bars, chips, cookies, snack cakes, and fruitades

(containing little fruit juice), and therefore may be sold in the school cafeteria during meal times. 8 A 2005 report from the U.S. Government Accountability Office estimated that during the 2003-2004 school year, exclusive beverage

contracts generated more than $15,000 per school in nearly one-quarter of high schools with exclusive beverage contracts. 9 According to Fox et al. (2009), during the 2004-2005 school year, vending machines accessible to students were found in 98.4,

87.1 and 26.5 percent of high schools, middle schools and elementary schools, respectively. It should be noted that vending

machines do not necessarily carry carbonated beverages - Fox et al. (2009) also calculate that carbonated soda was the most

commonly consumed competitive food for 19, 13.7, and 2.3 percent of children who consumed competitive foods at high, middle

and elementary school, respectively. 10 The average consumption of a middle school student of beverages at school outside the NSLP and BSP during 2004 was 10.2

fluid ounces, while for the average high school student, this figure was 28.3 fluid ounces over the same period. For the average

elementary school student this figure was 1.3 fluid ounces. (Source: American Beverage Association Report 2007, Tables 1-3). 11 This can be particularly seen through media attention to the link between soda drinking and obesity. Searches on Lexis-Nexis

show that only around the early 2000’s does media coverage on this subject start to expand and become prevalent. Moreover, bids

for exclusivity agreements for the provision of carbonated beverages in high schools in the late 1990’s were covered by the media

without much criticism, whereas in the early 2000’s these bids and requests for proposals were covered in a much more critical

tone.

http://www.fns.usda.gov/cnd/menu/fmnv.htm

6


the strictest regulations generally applying to elementary schools, so that high schools’ commissions

will not be as severely hurt. The restrictions’ severity depended on whether the restriction applied to

the entire school day or just during school meals12 and the types of beverages restricted - only non-diet

carbonated beverages versus all carbonated beverages.

In June 2004, the U.S. Congress passed the Child Nutrition and WIC Reauthorization Act of 2004. This

law conditioned federal funding of the NSLP and SBP on each school district having a written wellness

policy, which includes nutritional guidelines “for all foods available on each school campus under the

local educational agency during the school day with the objectives of promoting student health and

reducing childhood obesity”13 , by the first day of the 2006-2007 school year. Beyond the existent USDA

regulations concerning foods available at schools, this law set no additional criteria on the nutritional

guidelines which should be implemented at schools. However, the opportunity to formalize nutritional

guidelines for foods available on school grounds prompted some school districts, which were already

actively debating which foods should be available to students, to implement restrictions on the availability

of carbonated beverages.

In May 2006, following increasing pressures from the media and public advocacy groups, the American

Beverage Association, representing most beverage providers in the U.S., along with The Coca-Cola

Company, Dr Pepper Snapple Group, and PepsiCo, voluntarily signed a memorandum of understanding

jointly with the Clinton Foundation, the American Heart Association, and the Alliance for a Healthier

Generation, setting new guidelines on competitive beverage availability in schools during the school

day. The guidelines do not allow any carbonated beverages in elementary or middle schools during the

extended school day.14 In high schools, only diet or no-calorie carbonated beverages are to be available

during the extended school day.15 These regulations were to be gradually phased into schools over the

next four years. In 2010, Pepsi reported 99% compliance with the new regulations. Amongst the school

districts covered in this study, all Alliance for a Healthier Generation guidelines implementations were

during 2008 or 2009.

12 Restrictions on the timing of vending machine availability are implemented through setting timers on school vending machines. 13 Source: Section 204 of Public Law 108-265—June 30, 2004 - http://www.fns.usda.gov/tn/healthy/108-265.pdf 14 Extended school day includes the entire school day and hours during which after-school activities operate on school grounds. 15 The exact guidelines for high schools are regarding the calories per serving of beverages. Carbonated beverages in high schools

are limited to those containing up to 10 calories per 8 oz.

http://www.fns.usda.gov/tn/healthy/108-265.pdf

7

3 Data

3 Data

3.1 Nielsen Homescan Data

The main data set used in this study is the Nielsen Homescan data for the years 2004-2009.16 The Nielsen

Homescan data makes use of a nationally representative sample of volunteer households across the

United States. These households document their food item purchases at the barcode level using a scanner.

The purchases are from a wide variety of store types, including traditional food stores, supercenters and

warehouse clubs, as well as online merchants. The households record the date purchased, the price paid

and any coupons or promotions used in the purchase.17 It is important to note that food purchases

from restaurants or vending machines are not documented. In addition, while small purchases from

convenience stores should be documented by the household, these purchases are generally omitted from

most households’ scanning activity. Thus, for the most part, the data does not document any beverage

purchases students make in school and it very likely does not capture any beverage purchases students

make on their way to/from school or while on lunch breaks off campus in various convenience stores,

grocery stores, or fast food restaurants.

In addition to nearly full disclosure of the households’ food purchasing patterns, Nielsen also annually

documents various demographic characteristics for these households: race; income; household heads’

age, education and working status; household size; household composition; and for years beyond 2006,

the birth month and year of every household member. Household geographic information includes the

household’s state, county and census tract. The number of households covered varies from year to year.

For 2004-2006 the number of households was slightly under 40,000, while for 2007-2009 the number of

households ranged from 60,000 to 63,000.

The unit of observation in the Nielsen data is at the barcode level - every single product the household

purchases is documented. For each barcode, the date of purchase is provided, as well as the price paid,

the quantity and the amount in ounces, fluid ounces, or units when ounces cannot be measured. To

capture household purchasing trends, the data for this study was aggregated to monthly purchases for

each household for the food items of interest. The food items of interest are split into three categories:

non-diet soda, non-diet carbonated beverages, and diet soda. Non-diet soda is a narrower category than

16 This data was purchased from Nielsen by the U.S. Department of Agriculture, Economic Research Services and was generously

provided to me for the purpose of this research. 17 Relying on individuals’ reporting of transactions raises the potential for caveats in the Nielsen Homescan data. Einav, Leibtag

and Nevo (2010) compare the Nielsen Homescan data to transactions reported from a large supermarket retailer and find

discrepancies in reported shopping trips, products, prices and quantities. However, to the extent that these discrepancies should

not apply differentially to households in treated or control groups, the results from the difference-in-differences strategy applied

in this paper should still remain valid.

8

3 Data

non-diet carbonated beverages and all beverages in the non-diet soda category are also found in the

non-diet carbonated beverages category. The difference between the two categories is that non-diet

soda is limited to only beverages categorized as carbonated soft drinks, while the non-diet carbonated

category includes any sparkling drinks, such as sparkling water or juices.

3.2 School District Carbonated Beverage Policies

In addition to the Nielsen Homescan data, I use a dataset, which I independently constructed, document-

ing the presence, timing and extent of restrictions concerning the availability of carbonated beverages on

school grounds for 39 large school districts throughout the United States over the last 10 years. As part of

this data construction effort, I was in direct contact with school district representatives from Nutrition

Services, Purchasing, Communications Offices, School Board, and the Superintendent’s Office, as well as

officials from state-level departments of education, who assisted in better understanding the state-level

regulations and the timing these went into effect. Initially, I conducted an online survey for school district

nutrition services representatives. Following this, personal contact was made to verify the information

provided and ask follow-up questions. Documentation was frequently requested, such as: school districts’

past and present wellness policies; contracts school districts have or had in place over the last 10 years

with beverage providers; board minutes documenting any board decisions or discussions regarding

the district’s policy on carbonated beverage sales to students; and requests for proposals the school

district issued for the provision of carbonated beverages in schools. Frequently, the documentation had

to be requested through the state’s public records act. Lastly, I verified the information provided with

online and media sources containing information on the school district’s policies concerning carbonated

beverages.

Households are matched to their respective school districts by selecting school districts which cover

entire counties. The county identifier for each household in the Nielsen Homescan data allowed me to

assess with a fairly high degree of certainty that a household with school-aged children in this county has

children attending the schools within this school district.18 The study’s targeted counties have a single

18 There is the possibility that children in the household are attending private schools, rather than the public school system in

that county. According to a U.S. Census Bureau School Enrollment Report, the national private school enrollment rate in 2000

was 10.4 percent. Based on enrollment figures at the state and school district levels from the U.S. Census Bureau, the National

Center for Educational Statistics (NCES), and State Departments of Education, it was estimated that no more than half of the

covered counties in the sample have private school enrollment rates exceeding 10 percent. The highest private school enrollment

rates are for Washington, D.C. and San Francisco, CA, at approximately 30 percent and 27 percent, respectively, but all other

covered school districts have private school enrollment rates below 20%. Private school enrollment rates are primarily driven by

higher-income households in the population. To the extent that the Nielsen Homescan data is less representative of households

in the highest income brackets, the private school enrollment rates amongst households in the sample will be lower.

9

3 Data

school district and at least 60 households in the 2007 Nielsen Homescan data.19 Using these criteria, 51

school districts in 15 states were initially targeted. If details of carbonated beverage restrictions could not

be verified sufficiently or produced conflicting information, school districts were eliminated from the

sample, as including them would likely measure the timing and nature of the treatment with substantial

error. This resulted in a sample of 39 school districts in 14 states. Table 9 in the Appendix details all target

school districts excluded from the analysis and the reasons for their exclusion. Table 1 presents separate

summary statistics from the Nielsen Homescan data for 2004-2009 households in the covered counties,

the excluded target counties, and the entire Nielsen data set. As can be seen, the counties covered in

the analysis encompass nearly 73% of all households in the target counties. With the exception of race

and the number of months appearing in the data, the households in covered counties and excluded

target counties are very similar in terms of their demographic characteristics and beverage consumption

patterns. This is reassuring and alleviates potential concerns for selection of school districts into the

covered sample, based on characteristics potentially correlated with carbonated beverage consumption

or households’ response to carbonated beverage bans at schools. Table 1 also shows that households

in the covered or target school districts are different from households in the entire Nielsen Homescan

sample. This is expected as school districts covering entire counties are more frequently found in the

Southeastern states, and the limitation to counties consisting of at least 60 households in the Nielsen

Homescan 2007 data resulted in relatively more urbanized counties.20

The following information on carbonated beverage restrictions was required from school districts:

the month and year the restriction went into effect; which school levels the restriction effected (elemen-

tary/middle/high); and which carbonated beverages the restriction covered (diet or non-diet). Many

schools over the last 10 years implemented restrictions on the availability of carbonated beverages during

part of the school day (generally during lunch period or up to 1 hour after the last lunch period). These

restrictions were not considered carbonated beverage restrictions for the construction of the policy

data set, and only restrictions on the sale of carbonated beverages during the entire school day were

considered carbonated beverage restrictions. The logic behind this decision was the desire to capture the

timing when carbonated beverages were no longer available in schools at all (at least for all the hours

19 The threshold of 60 households was determined based on the fact that approximately 2/3 of households in the Nielsen Homescan

data do not have any children under the age of 18 present. Those that do have children present, do not necessarily have school-

aged children. It was therefore determined that 60 households in a given school district would justify the efforts of obtaining that

school district’s carbonated beverage policies over the last 10 years. 20 It should be noted that the summary statistics presented in Table 1 for the entire Nielsen Homescan data set are not weighted by

the recommended weights in order to be representative of the entire U.S. population. No analysis in this paper uses the Nielsen

Homescan weights, as the covered counties only represent a fraction of the entire Nielsen Homescan data.

10

3 Data

Table 1: Nielsen Homescan Data Household-Level Summary Statistics, 2004-2009: Covered School Dis-

tricts, Excluded Target School Districts and Entire Nielsen Sample

Notes: For all continuous variables, numbers in parenthesis are standard errors. Column (4) represents

the P-value for a T-test for the difference of each row’s values between households in covered counties

and households in excluded target counties. For a list of covered and excluded target counties, see the

appendix.

students spend in school). The restrictions were most frequently implemented in the summer months,

when it was easiest to transition the content of vending machines, as the school year was not in progress.

However, some school districts also chose to implement restrictions in the course of the school year.

Figure 1 presents the variation in the timing of the restrictions. Table 10 in the Appendix provides a

detailed list of all covered school districts in the sample and the timing of restrictions on carbonated

beverages.

Two sample periods are covered in the analysis: 2004-2007 and 2004-2009. For the 2004-2007 data, the

analysis has a single dummy variable post-treatment. The extended period in the 2004-2009 analysis

enabled me to assign separate post-treatment dummy variables, for each quarter following a carbonated

beverage ban in school. The main reason for the two separate sample periods is that beverage providers

frequently implemented the Alliance for a Healthier Generation Guidelines without the school district

documenting or recording when this occurred.21 Thus, the only information school district represen-

tatives were able to provide in many of these cases was that it occurred sometime after January 2008,

21 Beverage companies were contacted in an attempt to uncover the timing of the implementation. However, their response was

that they are restricted from providing any client-specific information.

11

3 Data

Figure 1: Treatments by Month and Year

Notes: This figure shows the number of treatments for each month and year during the full sample period

- Jan. 2004 through Dec. 2009. Treatment refers to a school district experiencing a restriction on the sale

of carbonated beverages during the entire school day. School districts which experienced more than

one treatment month – whether treatments were for different school levels or for different beverages (or

both) – have all treatment periods represented in the figure. Treatments during July and August shift to

September of that year, to represent the school-year months.

12

3 Data

Table 2: Covered School Districts by State and Treatment

Notes: Treatment refers to any restriction on the availability of carbonated beverages during the entire

school day at any school level in the school district implemented during the sample period.

when beverage providers began implementing the guidelines voluntarily. In addition, inclusion of school

districts in the 2004-2007 sample, which includes only a single dummy variable post-treatment, was

conditional on the school district having no restrictions on the availability of carbonated beverages

implemented between January 2002 and January 2004. This restriction provides sufficient time for all

households in the sample to adapt to any past restrictions implemented in their child’s school, if this

occurred prior to the start of the sample period. This condition resulted in the exclusion of one additional

school district from the 2004-2007 sample (San Francisco Unified School District). Table 2 summarizes

the covered school districts by state for each of the two sample periods and how many school districts

received any treatment (i.e. implemented bans on carbonated beverage sales at any grade level) during

the relevant sample period.

Recognizing whether a household has a child in the school level experiencing the change in the

availability of carbonated beverages (i.e. whether the child is in elementary, middle or high school)22

is most precise using the birth year and month of all household members, as well as the age threshold

22 In general, elementary school is K-5t h grade, middle school is 6t h -8t h grade, and high school is 9t h -12t h grade. This is verified

for every school district using school information provided on school district web sites. Occasionally, school districts had schools

that did not match this division, but this was always a small minority out of all the schools in the school district.

13

4 Empirical Strategy

set for that school district for entering kindergarten.23 As noted above, individual household members’

birth year and month are provided in the Nielsen data only beginning January 2006. Fortunately, many

households remain in the sample for several years, making it possible to extrapolate members’ birth year

and month for households in 2004 and 2005, which appear in either 2006 or 2007. Accordingly, over 64%

and 53% of households with 3 or more household members in 2005 and 2004, respectively, reappear in

either 2006 or 2007 and have their members’ birth year and month provided.24


The empirical analysis aims to identify the effect of carbonated beverage restrictions in schools on

children’s household carbonated beverage consumption. An increase in household consumption in

response to a carbonated beverage ban in a child’s school is evidence that households offset for the

lack of availability of carbonated beverages in their child’s school via home consumption. I employ a

difference-in-differences (DID) approach followed by a difference-in-differences-in-differences (triple

differences) approach. The term treatment refers to the introduction of a ban on carbonated beverages

in a school district for the entire school day during the sample period.

4.1 Difference-in-Differences (DID) Analysis

The DID analysis estimates changes in households’ carbonated beverage consumption in school districts

which introduced carbonated beverage restrictions relative to school districts which had not. This exploits

variation over time, in terms of changes in school district policies occurring at different time-periods for

the different school districts, and geography, in terms of counties (i.e. school districts) which are either

treated or not treated. Because the timing of treatments often differs between school-levels (elementary,

middle and high) within the same school district, the DID approach estimates the effect of the restriction

for each school level in a separate regression, with each regression run only for households with children

in that particular school-level.

The validity of the design depends on the exogeneity of the introduction of the carbonated beverage

23 The threshold for entering kindergarten at a given state was determined for each birth year using primarily a data set already

constructed in Elder and Lubotsky (2006). For Maryland, the information was modified due to online sources providing slightly

different information, and for Pennsylvania, the kindergarten cutoff is determined by each school district individually, so it had to

be verified separately for the School District of Philadelphia using primarily Lexis-Nexiss. 24 To the extent that household composition may change over the years, it can be safely assumed that those departing the household

will rarely be the school-aged children. However, to address this concern, the analysis presented in this paper was also conducted

using for 2004-2005 only households which reported having the same household size (or less to account for child births) as in

2006-2007 and the results remained very similar.

14


restriction in the school district. It can be argued that these restrictions are put in place in response to

certain trends in the school district’s community. If there were pressures from within the community to

impose these restrictions, then it may be that any change within the household in carbonated beverage

consumption following a restriction will be biased downwards, as these restrictions are also accompanied

by increased awareness concerning the potential harmful effects of carbonated beverages. Therefore, we

should see households decreasing their consumption of carbonated beverages with the policy imple-

mentation (or slightly before). If a trend of increased soda consumption within households is causing

local policy makers to revise the school district policy regarding carbonated beverage availability, then

the estimates of the effect of carbonated beverage restrictions on household consumption will be biased

upwards. While these are potentially valid arguments, the former possibility is not of as much concern,

due to the positive effect found. Therefore, if the results are biased downwards, then this implies that

household compensation following carbonated beverage restrictions is even greater than that estimated.

However, to address any potential endogeneity argument regarding the restriction of carbonated bev-

erages within the school district, I argue that at least the exact timing of the implementation of these

restrictions is unrelated to household preferences in their respective school district. In particular, these

restrictions were often enacted many months before their implementation. This was either because the

decision was made during the school year and it was decided to change the content of vending machines

only between school years, when the change is easier to implement, or because the school district made

a decision to restrict carbonated beverages while still signed on a contract with a beverage provider

and it was decided to implement the new poilcy only after the contract expires. 25 In addition, due to

the significant expected loss in revenues from the sale of competitive foods, the actual decisions were

frequently followed by several years of an ongoing debate among board members, and the carbonated

beverage restrictions passing (or not passing) only marginally.26 Lastly, some school district changes

in carbonated beverage policies were due to state laws, which are not necessarily determined by local

trends.27 Moreover, the state law could have passed many months prior to the implementation of the

25 For example, Miami-Dade County Public Schools (Florida) ruled on a soda ban in all its schools in November 2005, but the policy

only came into effect in July 2006. Baltimore County Public Schools (MD) implemented its ban on non-diet carbonated beverages

in high schools and all carbonated beverages in middle schools in August 2008, when its exclusivity agreement for 2002-2008

expired. 26 In Jefferson County Public Schools (Kentucky), the board ruled to eliminate all FMNV from school vending machines and promote

healthier snacks, with the exception of soda, already in 2004. Only in 2006, after two years of debates, did a decision pass to

eliminate all carbonated beverages from school vending machines as well (and not just restrict the availability to certain hours

during the school day). Hillsborough County Schools (Florida) had an item in its July 2008 board meeting agenda to approve

implementation of the Alliance for a Healthier Generation Guidelines in all its schools. However, this item was withdrawn at the

last minute (appears crossed out in archived board meeting agendas), due to high school principals’ objections. All carbonated

beverages remained available in Hillsborough County Schools as of the end of 2011. 27 For example, school districts in Louisiana and New Mexico.

15


i c m y

policy.28

For the DID analysis, I estimate the following regression for the sample of households with at least one

child in school-level s (s representing elementary, middle or high school).

T

yi c m y = β0 + β1

) T r t C n t y ∗ T r t T i me

t ∗ Sc l C hl d

s

t =1

+ β2 T r t C n t y ∗ Sc l C hl d s

T

+ )

βt ∗ T r t T i me t ∗ Sc l C hl d s

+ β4 Sc l C hl d s

i c m y 3 t =1

i m y i m y

+ γXi y + hi + bim y + mm + εi c m y (1)

The dependent variable in equation (1) is a measure of monthly household carbonated beverage

consumption - either non-diet or diet. i indexes household, c county of residence, m month of purchase,

and y year of purchase. T r t C n t y is a dummy for whether a county’s school district imposed a restriction

on the availability of carbonated beverages during the entire school day at any time during the sample

period for grade-level s.29 T r t T i me t is a series of dummy variables indicating the beginning of each

treatment period (differentiated by superscript t ) for the specific carbonated beverage in the dependent

variable. Sc l C hl d s measures the number of children in the household in school-level s. This variable

is not a dummy, but rather ranges from zero to four in the sample of households, and therefore can be

thought of as capturing the intensity of treatment, in terms of how many treated children in school-level s

are actually in the household when looking at a household in a treated county. In a standard DID

framework, the coefficient of interest is the coefficient on the interaction between being treated and being

observed post-treatment. In this DID framework, this is represented by the interaction between T r t C n t y

and T r t T i me t . However, equation (1) multiplies the interaction between T r t C n t y and T r t T i me t by

Sc l C hl d s . This results in β1 , our coefficient of interest, estimating the intensity of treatment effect - i.e.

for each treated child in school-level s within the household, by how much does monthly carbonated

beverage consumption change following the introduction of a ban on carbonated beverages in the child’s

school?

28 In Albuquerque Public Schools (New Mexico) the state law forbidding carbonated beverage sales during the school day (but

allowing diet carbonated beverages in high schools) passed in February 2006. However, the school district only implemented the

new ruling in Summer 2006, as this was a more convenient period for changing the content of vending machines. 29 Note that some counties may have a value of zero for T r t C n t y , although they actually do restrict carbonated beverage sales

during the entire school day in school-level s. These are school districts which introduced the restriction at least 2 years prior

to the start of the sample period (i.e. prior to 2002). The assumption is that households in these counties with children in the

school-level with the restriction had sufficient time to adjust their purchasing patterns by the start of the sample period, and

therefore we do not gain any information on households’ purchasing patterns in response to carbonated beverage restrictions in

schools by classifying these counties as treated for that specific school-level.

16


β2 in equation (1) controls for consumption trends in treated households irrespective of when they are

observed. β3 is a vector with t components for every post-treatment period, and it controls for purchase

trends in all households in the various post-treatment periods, irrespective of whether the households

are in treated counties or not. β4 controls for trends amongst households with a certain number of

children in school-level s, irrespective of whether the household is treated and when it is observed.

Household fixed effects, month fixed effects and bi-monthly-year fixed effects are included to control for

unobserved household characteristics, seasonality in food purchasing patterns, and general trends in

purchasing patterns over time, respectively. Inclusion of a series of dummies for post-treatment periods

is captured by the bi-monthly-year dummies, due to the fact that carbonated beverage restriction policies

documented for the covered school districts were always implemented at an uneven month.30 X controls

for time-varying household characteristics, such as household size, the presence of children under six

years old in the household, whether either household head is 55 or over, whether annual household

income is less than $10,000 per household member, and full-time employment of either household head.

These variables vary annually, when Nielsen updates its surveys of household members’ demographic

characteristics.31 Robust standard errors that account for the correlation of unobservables within a

school district are reported for all coefficients.32 In order to capture the true effect of the availability of

carbonated beverages in school on household purchasing patterns, the regression is run only for the

months of September-June, when the school year is in place and aggregated monthly purchases for July

and August are dropped.

4.2 Difference-in-Differences-in-Differences (Triple Differences) Analysis

Carbonated beverage bans in schools only affect households with school-aged children and the policy

implementations are not uniform across school-levels. Due to this, a third source of variation, besides

the time and geographical variation discussed in the DID analysis, arises: the presence of school-aged

children within the household. The presence of treated school-aged children in the household provides

variation across households within each treated county, which also changes over time, due to children

entering and exiting the various school levels, which may or may not be treated. The DID regressions

are run for households which have at least one child in a specific school-level, and therefore the sample

30 The exception to this is policies taking effect beginning August, but this is not of concern due to limiting the analysis to purchases

during the school year, i.e. September-June, and dropping all monthly aggregated purchases for July and August. 31 Non-time-varying household characteristics, such as race or household heads’ education levels, cannot be controlled for due to

the household fixed effects in the regression. 32 Robust standard errors in all regression Tables are clustered at the school district (county) level to correct for general autocorrela-

tions among the errors across households in the same county (Bertrand, Duflo, and Mullainathan 2004).

17


of households within each treated county only includes treated households. In contrast to this, the

triple differences analysis includes all households in the covered counties, irrespective of whether they

have school-aged children or their children’s school-level. Thus, the triple differences analysis allows

me to control for trends within the treated counties, by simultaneously observing purchases of both

treated and untreated households in treated counties. Moreover, the time-varying aspect of having a

child in a specific school-level makes some households serve as both control and treatment households

during the treatment period within their county of residence, due to changes over time in their children’s

school-levels. While the DID analysis limits the set of observations to households with at least one child

in the designated school-level, some households may have children in other school-levels present. This

raises concern that the measurement of differences in household carbonated beverage consumption is

imprecise as policies for other school-levels are implemented during the sample period and any response

the household has to these can bias the estimated effect of the households’ response to carbonated

beverage bans in the designated school-level. This concern is eliminated in the triple differences analysis,

by differentiating the effect of carbonated beverage bans across school-levels within the same regression.

A triple differences framework can be viewed as breaking down the households from the Nielsen

Homescan data into eight groups: households in treated counties with school-aged children before

or after the treatment; households in treated counties without school-aged children before or after

the treatment; households in control counties with school-aged children before or after a designated

treatment period in a treated county; and households in the control counties without school-aged

children before or after a designated treatment in a treated county. This is in contrast to the DID analysis,

which only includes households with children in a specific school-level, and therefore breaks down the

Nielsen Homescan data into four groups: households in treated counties before or after the treatment

and households in control counties before or after a designated treatment period in a treated county.

Figure 2 describes the household breakdown in both the DID and triple differences frameworks. 33

To illustrate the design of the triple differences analysis, consider a simplification which compares

between two counties - one treated county and one control county.34 Let C on s(C hi l d s , C oun t y s , Y e ar )

represent carbonated beverage consumption for a household with or without a child in school-level

33 There are multiple treatment periods, as each treated county has its own start date for a carbonated beverage ban. Thus, the

time dimension splits the control counties into more than just these two groups and Figure 2 is a simplification of the actual

breakdown. In addition, note that in the triple differences framework the actual treatment is not occurring in all households in

treated counties with school-aged children, but rather just in those households in treated counties with school-aged children in

the treated school level in that county. 34 This simplification only looks at one treated county and one control county because of the multiple treatment periods when

looking at multiple treated counties, which introduced their restrictions on carbonated beverages at various times.

18


Figure 2: Household Breakdown - DID vs. Triple Differences

DID Triple Differences

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9(%7,-:)1# !7(# +3<(#

9(%7,-:)1# !7(#

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Notes: Red cells represent control households included in the regression analysis listed above, and green

cells represent treated households.

s {Sc l C hl d s , N oSc l C hl d s }, in a treated or control county for school-level s {T r t C n t y s , N oT r t C n t y s },

and either pre or post treatment for the specific treated county {P r e, P o s t }. Thus, the triple differences

identification assumption is the following:

{E fC on s(Sc l C hl d

s , T r t C n t y

s , P o s t )/N oT r t

l − E

fC on s(Sc l C hl d

s , T r t C n t y

s , P r e )

l} −

{E fC on s(N oSc l C hl d

s , T r t C n t y

s , P r e )/N oT r t

l − E

fC on s(N oSc l C hl d

s , T r t C n t y

s , P r e )

l} =

{E fC on s(Sc l C hl d

s , N oT r t C n t y

s , P o s t )

l − E

fC on s(Sc l C hl d ,

s N oT r t C n t y

s , P r e )

l} −

{E fC on s(N oSc l C hl d


s , P o s t )

l − E

fC on s(N oSc l C hl d


s , P r e )

l} (2)

Equation (2) presents the following assumption: If there were differences in consumption between the

pre- and post- periods for households with treated school children, which were unrelated to restrictions

in the treated school-level, it would have affected in the same way either households with children in the

treated school level in control counties (if the cause of the difference was an event mutual to all children

in that school-level), or households within the treated county without treated school-aged children (if the

cause of the difference was an event mutual to all households in that county).

For the triple differences analysis, my estimating equation is:

19


3 T

yi c m y = β0 + )

βs )

T r t C n t y s ∗ T r t T i me t ∗ Sc l C hl d s

s=1

3 T

1 t =1

i c m y

3

+ )

βs )

T r t C n t y s ∗ T r t T i me t

+ )

βs T r t C n t y s ∗ Sc l C hl d s

s=1

T

2 t =1

3

c m y 3 s=1

3

i c m y

+ ) )

βt s

t s

) s s

t =1 s=1 4 ∗ T r t T i me ∗ Sc l C hl di m y +

s=1 β5 Sc l C hl di m y + γX i y + hi + bim y + mm + εi c m y (3)

The dependent variable, T r t T i me t , Sc l C hl d s , X , hi , bim y , and mm are as defined above for equation

(1). The superscript s, which indexes one of three school-levels (elementary, middle or high school) is

now added to our main coefficient of interest, β1 and to the dummy variable T r t C n t y . T r t C n t y s is a

dummy for whether a county’s school district imposed a restriction on the availability of carbonated

beverages during the entire school day in school-level s at any time during the sample period. There are

three coefficients of interest in equation (3), one for each of the three school levels: βe l m , βmi d , and βh s 1 1 1

for elementary, middle and high school, respectively. They ask the following: for every additional child in

the household in treated school-level s, what is the change in household monthly carbonated beverage

consumption after implementation of the carbonated beverage restriction at school-level s?

β2 in equation ((3)) is controlling for trends in purchasing patterns for the entire county population,

following the implementation of the policy, irrespective of whether a treated child is present in the

household, and β3 controls for trends in households with children at the treated school level in their

respective counties, irrespective of the timing of the policy implementation. β4 is a vector with t ∗ s

components for every post-treatment period and treated school level combination, and it controls for

purchase trends in all households with children in the various post-treatment periods. Each of the t ∗ s

combinations is controlled for, irrespective of whether the households are in treated counties or not.

Two sample periods are analyzed: 2004-2007 and 2004-2009. The main reason for limiting the analysis

through 2007 is limitations regarding the availability of the exact timing of policy implementations for

several school districts after 2007, which required changing the sample of covered school districts for any

analysis that would include household consumption data past 2007. The 2004-2007 analysis presents

results with a single dummy for the post-treatment period, as specified in equations (1) and (3). This

sample includes all control county observations (that didn’t experience a change in their carbonated

beverage policies during 2004-2007) and observations from treated counties (that did experience a

change in their carbonated beverage consumption) from 12 months before and 12 months after the

20


change in the school district’s carbonated beverage policy.35 The 2004-2009 analysis expands on the

triple differences framework by asking whether the impact of the carbonated beverage restriction on

household consumption varies differentially over time, using separate dummies for each quarter after the

restriction implementation. The number of quarters post-implementation is limited to up to 8. Therefore,

this analysis limits the observations from treated counties to those that are up to 24 months before and

after implementing the carbonated beverage ban.

The specification for the 2004-2009 analysis takes the post-treatment dummy in equation (3), T r t T i me t ,

and splits it into multiple dummies, with each dummy representing the number of quarters post-

treatment. In order for the time-effects to be as precise as possible and not having a single dummy

bundle all time-effects past a certain number of quarters post-implementation, treated counties’ obser-

vations were limited to 24 months before and after implementation of carbonated beverage restriction.36

This results in the following estimating equation:

8 3

yi c m y = β0 + ) )

βs,q

T r t Q u ar t e r s,q ∗ Sc l C hl d s (4) 1

q =0 s=1

8 3

i c m y

3

+ ) )

βs,q s,q ) s s s

q =0 s=1 2 T r t Q u ar t e rc m y +

s=1 β3 T r t C n t y ∗ Sc l C hl di c

T 3 3

+ ) )

βt s

t s

) s s

t =1 s=1 4 ∗ T r t T i me ∗ Sc l C hl di m y +

s=1 β5 Sc l C hl di m y + γX i y + hi + bim y + mm + εi c m y

The subscripts and superscripts remain the same as those in Equation (3), with the added superscript

q, representing the number of quarters since the policy was introduced - q = 0 represents the quarter the

policy was implemented.37 Equation (4) has 27 coefficients of interest (9 quarter-dummies * 3 school-

levels), represented by the vector β1 , which asks: For each additional treated household member in

school-level s, what is the change in household carbonated beverage consumption q quarters after the

change in the school district’s carbonated beverage policy, in comparison to the period prior to the policy

implementation?

35 When different policies (for different school levels) were implemented at separate times in a school district, the 12 month windows

were assigned for 12 months before the earliest implementation and 12 months after the latest implementation. Regressions

were also run for a fixed calendar period (2004-2007) for all observations and the results were very similar. 36 When different policies (for different school levels) were implemented at separate times in a school district, the 24 month windows

were assigned for 24 months before the earliest implementation and 24 months after the latest implementation. Regressions

were also run for a fixed calendar period (2004-2009) for all observations and the results were very similar. 37 Note that Equation (4) controls for trends in the post-treatment period amongst all counties in the sample with the dummy

T r t T i me t from Equation (3), rather than the quarterly dummies, because the quarterly dummies are individual for each county,

and therefore cannot be applied to all counties in the sample, as is required when controlling for trends post-treatment amongst

all counties.

21

5 Results

The analysis with a differential effect over time can be extended to include dummies for before

the implementation of the policy and through that check for pre-existing time-trends in household

carbonated beverage purchases, prior to the implementation of the policy. This results in adding dummies

for up to 8 quarters prior to the policy implementation. One of the pre or post dummies has to be excluded,

in order for the coefficients to give estimates relative to some reference point in time. The quarter of the

policy implementation was taken out in the results presented, to insure that there is an omitted dummy

for almost all treated counties, including those treated relatively early in the sample period. 38

4.3 Additional Specifications

Another interesting question is whether any of the changes in household carbonated beverage consump-

tion following a school district’s restriction depend on household characteristics, such as education,

income level, or placement in the distribution of the overall carbonated beverage consumption amongst

households in the Nielsen Homescan data. While this question is very important for evaluating the policy

of implementing carbonated beverage restrictions in schools, this analysis did not produce any significant

differential effects based on household characteristics. The results and a discussion concerning this

analysis can be found in the Appendix.

The Appendix also shows results for regressions which estimated the effect of carbonated beverage

bans in schools on the consumption of other beverages. Because carbonated beverage restriction often

coincided with other beverage restrictions (such as juices not containing a certain percentage of natural

juice or other sugary drinks), the beverages chosen were flavored milk, milk and water. School districts

did not restrict these beverages during the sample period.

5 Results

The results distinguish the effect of a carbonated beverage restriction in a school district among the three

school levels: elementary, middle and high school. The differential effects among school levels stem

from the institutional background of these restrictions, as detailed in Section 2. Carbonated beverage

availability during the school day has been very rare in elementary schools for the last 15 years, even if

not officially restricted by the state or school district. For middle schools, the prevalence of carbonated

beverages over the past 15 years has also been relatively limited. However, in high schools, carbonated

38 Results were very similar to those presented when omitting the quarter prior to the policy implementation from the regression

specification.

5 Results

22

i c m y

beverages have generally been widely available for student consumption during the school day until

the state or school district introduced restrictions. For this reason, we expect the restriction to be most

binding and have the greatest effect for high schools. For middle or elementary schools, it is not clear

whether there will be any effect. In addition, specifying a separate effect for elementary, middle or high

schools is required as restrictions are not implemented uniformly across all school levels.

As already mentioned, the analysis examines the impact of school district restrictions on the availability

of carbonated beverages separately for non-diet and diet carbonated beverages. This is due to the treat-

ment across school districts varying as to whether the restriction is limited to just non-diet carbonated

beverages or also includes diet carbonated beverages. For non-diet carbonated beverages, both non-diet

soda and non-diet carbonated beverages are examined as dependent variables, with non-diet carbonated

beverages being a broader beverage category than non-diet soda. For diet carbonated beverages, results

will be presented for diet soda.39 The dependent variables examined are total ounces consumed per

month by the household and the share of the specific beverage (diet or non-diet carbonated beverage)

out of total monthly household beverage consumption.40

5.1 Difference-in-Differences Analysis

The results from the DID specification (equation (1)) for each of the three school-levels are presented

in Table 3. Each cell in columns (1), (2), (6), and (7) presents the coefficient estimate on T r t C n t y ∗

T r t T i me t ∗ Sc l C hl d s

for the school-level s (elementary, middle or high school) indicated at the start

of the row. Columns (1) and (2) have as the dependent variable total monthly fluid ounce consumption for

the relevant beverage (Non-Diet Soda, Non-Diet Carbonated and Diet Soda for the first, second and third

panel, respectively), while Columns (6) and (7) examine changes in the fraction of beverages attributed to

the specific relevant beverage out of total monthly household beverage consumption. Columns (1) and

(6) show results without time-varying household characteristics, and Columns (2) and (7) show results

which include in the regression specification time-varying household characteristics.

Statistically significant increases in carbonated beverage consumption in response to bans in schools

can be found for the fraction of non-diet carbonated or soda beverages consumed for all school-levels.

However, statistically significant increases in total fluid ounce non-diet soda and carbonated beverage

consumption are only found in response to bans occurring in high schools. For non-diet soda, no

statistically significant response is observed, with the exception of a decrease in the fraction of beverages

39 The same analysis was also conducted for diet carbonated beverages and the results were very similar to those of diet soda. 40 Total household beverage consumption excludes milk.

5 Results

23

Table 3: The effect of carbonated beverage bans in schools on households carbonated beverage con-

sumption - Difference-in-differences Analysis

Notes: Each cell in columns (1), (2), (6) and (7) presents coefficient estimates (and standard errors in

parenthesis) from separate regressions. Each regression includes only households which have at least

one child in the school level analyzed (indicated at the start of each row). See Appendix for the list of

covered counties. Additional controls are time-varying household characteristics: household size, child

less than 6 present, annual income less than $10K per household member, at least one head of household

employed in full-time work, at least one head of household over 55. Treated counties’ observations

are only in the time frame between 12 months prior and 12 months after the restriction on carbonated

beverages was introduced. All regressions include household, month and bi-monthly-year fixed effects.

Standard errors are clustered at the county level. *** p<0.01, ** p<0.05, * p<0.1

attributed to diet soda following a restriction in middle school.

The point estimates for high school carbonated beverage bans suggest that for each high school

student in the household attending a school which introduced a carbonated beverage ban, the household

increased its monthly non-diet carbonated beverage consumption by 32-36 fluid ounces per month,

which is equivalent to a little less than three cans of soda per month. The fraction of beverage increase is

2.3-2.7 percentage points. Both the total fluid ounce and the fraction of beverages increases estimated

represent roughly a ten percent increase from the mean of the dependent variable.

5.2 Triple Differences - Single Dummy Post-Treatment

Table 4 presents the triple differences results, as specified in equation (3), showing the estimated coeffi-

cient (and standard errors in parenthesis) on T r t C n t y s ∗T r t T i me ∗Sc l C hl d s for s = {E l e me n t ar y, M i d d l e, H i g h

}.

5 Results

24

Columns (1) and (2) have as the dependent variable total monthly fluid ounce consumption for the rele-

vant beverage (Non-Diet Soda, Non-Diet Carbonated and Diet Soda for the first, second and third panel,

respectively), while Columns (3) and (4) examine changes in the fraction of beverages attributed to the

specific relevant beverage out of total monthly household beverage consumption. Columns (1) and (3)

show results without time-varying household characteristics, and Columns (2) and (4) show results which

include in the regression specification time-varying household characteristics.

The results show that households with treated high school children are positively responding to

carbonated beverage bans in terms of non-diet carboanted beverage consumption, but no such response

is evident for households with treated elementary or middle school students. Households with treated

high school students increased their non-diet carbonated beverage consumption by roughly 43 fluid

ounces per month for each treated high school household member (equivalent to an increase of roughly

3.5 cans of soda per month). In terms of the fraction of beverage consumption attributed to non-diet

carbonated beverages, this went up by approximately 1.9 percentage points per month for each treated

high school child within the household.41 Table 4 does not present evidence of an increase in diet soda

consumption following a high school ban on. The coefficient estimates are not necessarily positive

and a negative and statistically significant coefficient appears for middle school bans on the fraction of

total beverage consumption attributed to diet soda, although this statistical significance is not apparent

when estimating the specification with total fluid ounce consumption as the dependent variable. In all

regressions, the coefficient estimates are very similar, whether controlling for time-varying household-

level characteristics or not. This is evidence that the positive and statistically significant coefficients

cannot be attributed to relative changes in households’ observable characteristics.

5.3 Triple Differences - Post-Treatment Effect Differs Over Time

The triple differences analysis, as specified in equation (4), allows me to vary the effect of carbonated

beverage restrictions in schools over time. With this analysis, it is possible to examine whether the

compensation observed in Section 5.2 applies in the long-run, short-run, or both, as well as whether a

compensation at the household level is the only significant change in household carbonated beverage

41 These numbers represent an increase of roughly 20 percent in the household total fluid ounce consumption, compared to the

mean monthly fluid ounce consumption per household presented, but in terms of the fraction of beverages, the percent increase

is only around 10 percent. The disparity between the two percentage figures is due to the mean of the depenent variable in Table

4 representing all households in the sample, while the treatment is only for households with school-aged children. Thus, the

average consumption levels amongst all households in the sample will be lower than the average consumption levels amongst

treated households, which have more members due to the presence of children. See Table 3 in the DID Analysis (Section 5.1) for

the mean dependent variables amongst households with school-aged children.

5 Results

25

Table 4: Triple Differences: The effect of a carbonated beverage ban in schools on household carbonated beverage consumption - Estimated coefficient for T r t C n t y ∗ T r t T i me ∗ Sc l C hl d

Notes: Robust standard errors in parentheses clustered at the county level. See Appendix for the list of

covered counties. Additional controls are time-varying household characteristics: household size, child

less than 6 present, annual income less than $10K per household member, at least one head of household



beverages was introduced. *** p<0.01, ** p<0.05, * p<0.1

5 Results

26

consumption after implementing a carbonated beverage restriction at the child’s school (i.e. whether any

negative effects on household consumption are also found, possibly in the longer run, due to the policy

generating increased awareness amongst households concerning the negative impact of carbonated

beverages).

The results are presented in Figures 3 - 5. Figures 3 and 4 show the coefficient estimates on each of

the 27 interaction terms T r t Q u ar t e r s,q ∗ Sc l C hl d s . Each smaller figure shows the coefficient estimates

for 9 interaction terms at a specific school level, along with their 95% confidence intervals. They also

show how these coefficient estimates vary as the quarters after the policy implementation progress,

from the actual quarter of the policy implementation (0 on the horizontal axis) to 8 quarters past the

policy implementation. The left-hand-side figures have total monthly fluid ounce consumption at the

household level as the dependent variable, and the right-hand-side figures have the fraction of beverages

attributed to the beverage examined as the dependent variable.

The results in Figure 3 show that for non-diet soda, the positive effect observed in Section 5.2 for house-

holds with treated high school children is primarily driven by an increase in non-diet soda consumption

in the quarter immediately following the policy implementation (i.e. for q = 1 in the Figure).42 The point

estimate for the average increase in monthly non-diet soda household consumption is approximately

64 fluid ounces (the equivalent of almost 5.5 cans of soda) for each treated high school student within

the household.43 Following the first quarter post-policy implementation, the coefficient estimates on

the change in household monthly non-diet soda consumption are still positive, although of a lesser

magnitude (mostly in the 20-40 fluid ounce range), and if they are statistically significant, it is only at

the 10% level. Consistent with the results in Section 5.2 for middle and elementary school restrictions,

Figure 3 does not find statistically significant effects in any of the individual quarters after a restriction

is implemented in elementary and middle schools. In addition, as in Section 5.2, Figure 4 shows that

the positive effect does not seem to be occurring for diet soda, when it is restricted at the various school

levels.

Figure 5 includes coefficient estimates for quarters prior to the policy implementation and examines

non-diet soda consumption.44 The results verify that the positive effect we are observing for non-

diet soda in response to high school carbonated beverage restrictions is not due to trends prior to the

42 While this result is very clear in the figure for total fluid ounce consumption, it is a little more subtle for the fraction of beverages

attributed to non-diet soda. This is because the statistical significance is only at the 10% level. 43 These results are very similar to those obtained when the dependent variable is non-diet carbonated beverages rather than

non-diet soda. 44 The excluded quarter, which is the reference point, is the quarter of occurrence. A similar analysis was also conducted excluding

the quarter prior to the policy implementation, and the results were very similar.

5 Results

27

Figure 3: Time-Varying Changes in Monthly Non-Diet Soda Consumption

Notes: This figure shows the change in monthly household non-diet soda consumption following the

restriction of non-diet carbonated beverages in high school, elementary, or middle school for each

treated child in the household. The horizontal axis represents the number of quarters after the policy

implementation. The dotted lines are 95% confidence intervals. Treated counties’ observations are only

24 months pre and post-bans.

5 Results

28

Figure 4: Time-Varying Changes in Monthly Diet Soda Consumption

Notes: This figure shows the change in monthly household diet soda consumption following the restriction

of diet carbonated beverages in high school, elementary, or middle school for each treated child in the

household. The horizontal axis represents the number of quarters after the policy implementation. The

dotted lines are 95% confidence intervals. Treated counties’ observations are only 24 months pre and

post-bans.

5 Results

29

Figure 5: Time-Varying Changes in Monthly Non-Diet Soda Consumption - Including Period

Policy Implementation

s Prior to


restriction of non-diet carbonated beverages in high school, elementary, or middle school for each

treated child in the household. Treated counties’ observations are only 24 months pre and post-bans.

The horizontal axis represents the number of quarters before or after the policy implementation. The

dotted lines are 95% confidence intervals.

5 Results

30

policy implementation. For high school treatment, the coefficient estimates for quarters prior to policy

implementation are relatively close to zero and not statistically significant. They are not necessarily

positive, as is almost exclusively in the post-policy implementation coefficient estimates.

5.4 Interpretation

The evidence presented shows an increase in household non-diet carbonated beverage consumption

following a high school ban on carbonated beverages. This brings up three main questions:

1. How does this increase compare with what high school students were actually consuming in school

prior to the restriction? Are households fully compensating for the lack of availability of carbonated

beverages in school, or do these restrictions still contribute to a decrease in overall carbonated

beverage consumption amongst students?

2. Why is the compensation at the household level observed only for non-diet carbonated beverages

and not for diet carbonated beverages?

3. Why is no household compensation observed for restrictions in middle and elementary schools?

These questions can be answered using data from two different sources: Reports on beverage con-

sumption in U.S. schools issued by the American Beverage Association (ABA), and details of beverage

consumption at the school level during 2000 for Sarasota County Schools in Florida.

The first data source is a series of reports commissioned by the ABA, following the memorandum

of understanding signed between them and the Alliance for a Healthier Generation (jointly with the

American Heart Association and Clinton Foundation), outlining new guidelines for the availability of

beverages to students during the school day (see Section 2 for a discussion of these guidelines and how

they came about). These reports, released between 2005 and 2010, use data on all beverage shipments to

U.S. schools for the purpose of calculating student consumption of various types of beverages and how it

changed between 2004 and 2009.45 The data analysis in these reports was performed by an independent

research policy firm.

The second data source was obtained as part of the data collection efforts to better understand

carbonated beverage restrictions in the covered school districts for this study. The data collection

45 The first report, dated November 2005, actually preceded the agreement signed in May 2006 between the ABA and Alliance for a

Healthier Generation, but it may have been part of the efforts, prior to signing the agreement, to evaluate the nature of beverage

consumption in schools. In any case, reports from 2006 onwards explicitly mention that they are part of the memorandum of

understanding signed in May 2006, with the purpose of tracking the ABA’s progress in implementing the Alliance for a Healthier

Generation Guidelines.

5 Results

31

frequently required requesting documentation from school districts, such as requests for proposals

(RFP) from beverage companies for the provision of beverages in schools. In early 2001, Sarasota County

Schools in Florida issued an RFP for an exclusivity agreement with a beverage provider for the entire

school district. In response to questions raised by potential bidders, the school district released records

of total sales of specific beverages in various schools during 2000. These records allowed me to calculate

the average consumption levels of various types of beverages in schools. In 2000, Sarasota County high

schools had no limitations on sale of carbonated beverages in schools. Middle schools could only sell

carbonated beverages (or any competitive foods, for that matter) one hour after the last lunch period. In

elementary schools, carbonated beverages were restricted for the entire school day.

Table 5 summarizes the average student weekly consumption levels by carbonated beverage type

(non-diet vs. diet) and school level provided in the ABA reports. These estimates are reached after

obtaining proxies of carbonated beverage shipments to each of the school levels intended for student

consumption during the 2004 calendar year, and dividing these figures by the total number of students

enrolled in the U.S. for each of the school levels in 2004. The Table shows that high school students

consume significantly more non-diet carbonated beverages than diet carbonated beverages (over 84% of

high school carbonated beverage consumption is attributed to non-diet) and that high school students

consumed in 2004 significantly more carbonated beverages than middle and elementary school students.

As these estimates are based on total school enrollment in the U.S., they include schools which banned

carbonated beverages by 2004. Thus, they are probably a lower bound of what average consumption

actually is when carbonated beverages are available in school. However, in 2004, relatively few high

schools had restrictions in place on the availability of carbonated beverages to students,46 and therefore

this estimate for the average high school student probably is not far from the actual average. According to

Table 5, the average high school student consumes 12.5 ounces of non-diet carbonated beverages per

week. This roughly translates into 4.5 cans of non-diet carbonated beverages per month.

Sarasota County Schools’ RFP data on carbonated beverage consumption documented carbonated

beverage sales for four high schools. The documentation does not distinguish between diet and non-diet

sales. I used this data to calculate the average student consumption levels in school, under the same

assumptions made in the ABA reports.47 I find that high school students’ average consumption of diet

46 Out of 39 school districts in our sample, three school districts restricted the availability of carbonated beverages in high schools

during the entire school day prior to Jan. 2004. 47 The ABA reports’ main assumption is that only 75% of beverages shipped to high schools are accessible by students. The

remainder go to vending machines in faculty lounges, administration buildings, or are sales to persons outside of the student

body within the school premises (adult evening classes, sporting events attended by the entire community, parent-teacher

association/band/club fundraising events, etc.). The ABA reports also assume that the school year comprises of 36 weeks. Thus,

5 Results

32

Table 5: Average Weekly Student Carbonated Beverage Consumption in School - 2004

and non-diet carbonated beverages in Sarasota County in 2000 ranged from 3.7 to 6.9 cans per month.

Averaging this out among the four high schools resulted in an estimate of 5.4 cans per month of diet and

non-diet carbonated beverages for each high school student.48 According to the ABA reports, non-diet

carbonated beverages accounted for 84.8% of total carbonated beverage consumption by high schools in

2004. I thus multiply this percent value by the 5.4 cans per month estimate from Sarasota County high

schools to obtain an average consumption level of 4.6 cans per month for the average high school student

in Sarasota County high schools during 2000. This estimate is almost exactly the same as the 4.5 cans per

month estimate presented in the ABA 2004 report, thus lending further credibility to the ABA high school

student consumption estimate.

Given the 4.5 can estimate of monthly non-diet carbonated beverage consumption for the average

high school student, we can compare this figure to the increase in household fluid ounce consumption

observed in the results in Sections 5.2 and 5.3. Table 4’s results show that the average monthly increase in

household non-diet carbonated beverage consumption for up to a year after introducing carbonated

beverage restrictions in high schools is roughly 3.5 cans per month for each treated high school student

in the household. This suggests an offsetting of roughly 80% from what is being consumed in school by

high school students when carbonated beverages are available. Given that the Nielsen Homescan data

does not capture all potential consumption channels through which offsetting the lack of availability

of carbonated beverages may take place (see Section 3 for a discussion on the omission of restaurant,

vending machines and small purchases from the Nielsen Homescan data), I conclude that households’

offsetting of non-diet carbonated beverages in response to high school bans is substantial.

in order to maintain consistency with the ABA reported weekly estimate, monthly consumption levels were calculated by dividing

annual sales by 9. 48 The same number is obtained using a simple average or a weighted average with the weights being school enrollment as of 2001

(2001 is the earliest year Florida school enrollment figures were found for).

5 Results

33

The information provided in the ABA reports can also help explain why households are not found to

compensate when diet soda is restricted in schools. According to the report, in 2004, the average high

school student consumed only 1.9 ounces of diet carbonated beverages per week. Compared with 12.5

ounces per week of non-diet carbonated beverages, it seems that high school students, to begin with, do

not consume diet carbonated beverages in large quantities. In addition, in the 2009-2010 school year,

when diet carbonated beverages were the only carbonated beverages available in many high schools (due

to the implementation of Alliance for a Healthier Generation Guidelines in many schools by then), the

average diet carbonated beverage consumption per high school student remained extremely low at 1.2

ounces per week. However, this estimate is difficult to compare to past consumption of both diet and

non-diet carbonated beverages in high schools because by the 2009-2010 school year, many high schools

had no carbonated beverages available at all (including diet).49

As mentioned already, the lack of household compensation in middle and elementary schools in

response to restrictions on carbonated beverage availability can be attributed to the restriction not

being binding, given limited availability in elementary and middle schools, even without an official

restriction in place. Both the data in the ABA report and the Sarasota County School RFP confirm that

the average middle and elementary school student consumed significantly less non-diet carbonated

beverages than the average high school student. According to the ABA report, while the average high

school student’s weekly consumption of non-diet carbonated beverages in school was 12.5 ounces in 2004,

for the average middle and elementary school student this figure drops to 3 and 0.3 ounces, respectively.

Total carbonated beverage consumption (both non-diet and diet) in middle and elementary schools was

4.1 and 1.6 fluid ounces per week, respectively. In Sarasota County middle schools in 2000, carbonated

beverages were restricted until one hour after the last lunch period. In the Sarasota County RFP data from

only one middle school in 2000 is provided. According to this, average consumption levels of both diet

and non-diet carbonated beverages for middle school were 3.6 ounces per month. This is significantly

less than the ABA estimate for the average middle school student’s consumption - the average Sarasota

middle school student was consuming in one month less than what the average middle school student in

the ABA report consumed in one week. These estimates suggest that although no restriction is in place

for the entire school day, restrictions on carbonated beverage availability for part of the school day likely

limited consumption substantially. As these partial restrictions were more commonly seen in elementary

and middle schools prior to a full ban, it is likely that the full ban had a smalller effect on elementary and

49 Of the 35 covered school districts in the 2004-2009 sample, 16 had no diet carbonated beverages available in high schools by Sept

2009.

34

6 Robustness Checks and Additional Specifications

middle school students’ access to carbonated beverages, and thus we expect the household’s response to

the ban to be marginal, if any.

To summarize the results: households are compensating in response to non-diet carbonated beverage

restrictions in high schools. These compensation levels are a substantial fraction of what was actually

consumed by children in high school when carbonated beverages were available. I presented evidence

that carbonated beverage consumption in middle and elementary schools is relatively limited even when

an official restriction on sale of carbonated beverages is not in place. Therefore, it is only the restriction

at the high school level which is actually binding and having a significant effect on students’ sources

for carbonated beverages, and this is why household compensation is only observed in response to

restrictions at the high school level. I also presented evidence that diet soda consumption is limited in

schools, even when diet soda is the only carbonated beverage available in the school. Thus, students’

tastes don’t appear to tend towards diet soda, which could provide an explanation to why no household

compensation is observed in response to diet soda restrictions in schools.


6.1 Are the Results Driven by Seasonality Trends?

One possible critique of the results is that they are driven by seasonality of carbonated beverage consump-

tion, due to the fact that most restrictions are implemented at the start of the school year - see Figure 1

for a distribution of the treatments by month and year. While the regression specifications in equations

(3) and (4) control for monthly fixed effects, these monthly fixed effects are still mutual for both treated

and untreated households, and if treated households have their own seasonality patterns, then this may

be driving the results. Thus, an alternative specification was also run. This specification initially regresses

monthly household carbonated beverage consumption measures on monthly fixed effects separately for

treated and untreated counties. Then each set of estimated coefficients on the monthly fixed effects is

subtracted from the treated/untreated respective monthly household carbonated beverage consumption

levels. This is the new deseasonalized dependent variable, which goes into equations (3) and (4), but only

now the monthly fixed effects are excluded from the regression.

The results following this procedure hardly change, in comparison to the results in Sections 5.2 and 5.3

and reassure that the compensation levels observed are not being driven by separate seasonality trends

in carbonated beverage consumption in treated versus untreated counties. As Table 6 shows, households

35


-·" .... ... __ -- ...

I

Figure 6:Time-Varying Changes in Seasonally-Adjusted Monthly Non-Diet Soda Consumption

Non-Diet So<b,lotalfluidOuooes·HiCh Sdlooltreatl114!nt

•·D'"So""·'"ct" "'"' '"'·... h""""'' "m'

,'... , ' '

-.·.·.:

/ '·...... ',-

..,.. ,-' - .'- -

·"

,'i, . lbn ul(J-J<·O .:oliru. . I

, _ ,_ _ ,.

Non-Diet Soda, Total fluid 0..-.us ·Mkk!t. Trutmtnt

Non·Dict Sod;, Fr.: ttion of Bever.:

---- ,_- '------ - -- .... ,

r,.._ Non-Ol et Soda,Tout fluldOunus· El tmtntlrylrt l'!mef\1

.. ,-

I:

-------:-'--- - ------'--c-----

1 ·

N on-Oiet Soda,rractionol8rwtraJes -Eiementary Trntment


restriction of non-diet carbonated beverages in high school, elementary, or middle school for each treated

child in the household. Monthly household non-diet soda consumption has been de-seasonalized on a

monthly basis. The horizontal axis represents the number of quarters after the policy implementation.

The dotted lines are 95% confidence intervals. Treated counties' observations are only 24 months pre and

post-bans.

with treated high school students appear to be increasing their monthly non-diet carbonated beverage

consumption also when this consumption is detrended for any seasonal effects. Figure 6 shows that

this effect is primarily driven by an increase in non-diet carbonated beverage consumption during the

quarter immediately following the implementation of the high school restriction. Quantitatively, the

coefficient estimates are also very similar to those presented in the results in Sections 5.2 and 5.3.

36


Table 6: Triple Differences: The effect of a carbonated beverage ban in schools on seasonally-adjusted

household carbonated beverage consumption - Estimated coefficient for T r t C n t y S ∗T r t T i me ∗

Sc l C hl d s

Notes: Robust standard errors in parentheses clustered at the county level. For a list of covered counties,

see Appendix. Additional controls are time-varying household characteristics: household size, child less

than 6 present, annual income less than $10K per household member, at least one head of household



beverages was implemented.

37


c m y

4

6.2 Placebo Tests

In order to evaluate whether changes in school districts’ carbonated beverage policies are correlated with

household characteristics, I run a few placebo tests, using a triple differences framework.

Table 7 presents descriptive statistics for households in treated versus untreated counties for the period

2004-2007. The term “treatment” refers to households in counties which regulated a ban on carbonated

beverages in schools sometime during 2004-2007. Overall, the 2004-2007 sample period covers over

5,800 households in 38 counties in 13 states. The treated counties’ observations are limited to 12 months

before and 12 months after the policy implementation. The crude breakdown of treated and untreated

counties does not provide a sufficiently precise comparison between the control households and treated

households in the sample, as households in treated counties are not actually treated if they do not have

children in the treated school-level within the household. Thus, in order to assess whether treated

households are significantly different from non-treated households, a triple differences framework is

applied, testing whether households with children in treated school levels post treatment are significantly

different from all other households in the sample.50 The coefficient for having a treated child (at the

treated school level) in the household during the period following a carbonated beverage ban is estimated

with the following specification:

T

H H C h ari c m y = β0 + β1

) T r t C n t y ∗ T r t T i me t ∗ Sc l C hl di c m y

t =1

T

+ β2

) T r t C n t y ∗ T r t T i me

t + β3 T r t C n t y ∗ Sc l C hl di c m y

t =1

T

+ )

βt ∗ T r t T i me t ∗ Sc l C hl di m y + β5 Sc l C hl di m y + bim y + mm + εi c m y (5) t =1

Equation (5) is similar to the triple differences specification in equation (3), except that the dependent

variable in equation (5) is one of the household characteristics examined in Table 7 and Sc l C hl d is a

dummy variable indicating that the household has at least one treated child. The results, presented in

column (3) of Table 7 are the coefficient estimates for β1 . Table 7 shows that with the exception of the

probability of being black and household size, treated households are not significantly different from

non-treated households in terms of their characteristics.51

50 For the triple differences estimation discussed below, the 12 month windows before and after implementation were not applied,

in order to not bias the results with differences between treated and untreated households in the time-span observed. 51 Note that the statistically significant difference between treated and non-treated households in Table 7 with respect to the number

of months in the sample is mechanical, as treated households are always observed post-treatment and are therefore more likely

38


Table 7: Placebo Tests - 38 Covered Counties, 2004-2007

Notes: Numbers in parenthesis are standard errors. For a list of the covered counties, see the Appendix.

Treated counties refers to counties introducing a restriction on carbonated beverages at any school level

for the entire school day for at least non-diet soda during the sample period (2004-2007). Treated counties’

observations are only in the time frame between 12 months prior and 12 months after the restriction on

non-diet carbonated beverages was implemented. Columns (3) (Triple Differences Estimation) presents

the estimated coefficient for a household being in a treated county with a child in the treated school-level

post-treatment in a regression where the dependent variable is the demographic variable of interest.

When the dependent variable is a dummy variable, the triple differences specification was a probit

regression and the estimates presented are changes in the probability of having the specific household

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

39


Table 8 presents descriptive statistics for households in treated versus untreated counties for 2004-2009.

The expansion of the sample time-span results in an increase in the number of treated counties. For

the 2004-2009 sample, 26 out of 35 covered counties implemented a restriction in carbonated beverage

availability during the entire school day for at least one school-level. Overall, the 2004-2009 sample

period covers over 7,400 households in 35 counties in 14 states. Treated counties’ observations are limited

to 24 months before and after the implementation of the school district’s carbonated beverage restriction.

The triple differences regression results, for which the specification is provided in equation (5), show that

differences are present with regards to income levels and household size.

6.3 Additional Robustness Checks

In further robustness testing (not shown), the baseline regressions were run with a placebo timing of

+/- 18 months for a carbonated beverage restriction in each school district experiencing a ban during

the sample period. These regressions showed no effect of the placebo carbonated beverage bans on

carbonated beverage household consumption. To ensure the results were not driven by a single school

district in the sample, the baseline regressions were run excluding a single school district and the results

remained unchanged (not shown).

to have been observed for a longer period in the data.

40


Table 8: Placebo Tests - 35 Covered Counties, 2004-2009

Notes: Numbers in parenthesis are standard errors. For a list of covered counties, see the Appendix.

Treated counties refers to counties introducing a restriction on carbonated beverages at any school level

for the entire school day for at least non-diet soda during the sample period (2004-2009). Treated counties’

observations are only in the time frame between 24 months prior and 24 months after the restriction on

non-diet carbonated beverages was implemented. Columns (3) (Triple Differences Estimation) presents

the estimated coefficient for a household being in a treated county with a child in the treated school-level

post-treatment in a regression where the dependent variable is the demographic variable of interest.

When the dependent variable is a dummy variable, the triple differences specification was a probit

regression and the estimates presented are changes in the probability of having the specific household

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

41

7 Conclusions

7 Conclusions

Childhood obesity is a major health concern. In an effort to decrease this phenomenon, many policy

measures have been introduced. This paper evaluates one such policy measure: banning carbonated

beverages in schools. I show that banning carbonated beverages in high schools, where the restriction is

generally most binding, does not necessarily reduce carbonated beverage consumption for the average

high school student, due to compensation occurring at the household level. The evidence suggests that

the compensation is greatest during the quarter immediately following the ban and that the household

compensation persists further, even a year post policy implementation. If restricting carbonated bev-

erages in schools is intended to reduce student school consumption levels prior to the restriction, this

paper shows that this policy measure is less effective than intended. The results are robust to several

alternative specifications, including specifications which take into account the seasonality of the bans,

mostly occurring between school years.

The results are consistent with some past empirical findings in the literature. Anderson and Butcher

(2006) find that access to junk food in schools increases student body mass index (BMI). However, their

results are driven entirely by adolescents who have an overweight (and not obese) parent, so for nearly

70% of the student population in their sample, the provision of junk food in school has no effect on

BMI. This is consistent with most students’ steady consumption of junk food, irrespective of whether it

is available in school or not, and possible compensation and/or substitution at the household level in

response to lack of availability, as this paper’s results suggest. Dubois, Griffith and Nevo (2012) compare

household food purchases between the US, UK and France and find that while the economic environment,

as reflected in food prices and attributes, has a large impact on food purchase differences across countries,

it does not provide a full explanation of this, and differences across countries in consumer preferences

also have to be accounted for. Their results emphasize the importance of predetermined preferences,

which is likely the reason behind the compensatory behavior observed in this paper. This paper’s results

are also complementary to papers regarding habit persistence, in particular when evaluating a policy

intended to limit access to resources associated with unhealthy habits (Adda and Cornaglia 2006; Adda

and Cornaglia 2010; Fletcher et al. 2010; Wisdom et al. 2010).

If policy makers wish to battle obesity, this paper shows that restricting access to unhealthy foods is

not likely to suffice. Alternative policy measures to consider include changes in the relative prices of

unhealthy foods or nutritional education. With regards to prices, Fletcher et al. (2010) show that taxing

carbonated beverages is not effective in limiting children’s caloric consumption if calorie-dense drinks

42

7 Conclusions

are available from other beverage sources. Similarly, in Adda and Cornaglia (2006), taxing cigarettes also

proves ineffective due to the possibility of increasing nicotine intake through greater extraction from

each cigarette. In Harding, Leibtag and Lovenheim (2011) smokers evade state taxes on cigarettes by

crossing the border to lower tax jurisdictions. Thus, pricing strategies have also proven ineffective when

consumers can readily find suitable substitutes. Further research is required to investigate the impact

of educational policy measures on obesity. However, it should be noted that this paper shows that even

by restricting carbonated beverages in an educational setting, and thus invalidating its consumption at

least to some extent, compensation is still observed. Thus, this specific educational measure may not be

as effective as policy makers may wish. If educational policy measures are considered, it appears they

should either be more aggressive or, alternatively, target the non-adolescent population in the household.

If preferences persist so adamantly, and policy makers wish to change these preferences, it may be

required to tackle directly channels through which preferences are shaped and/or formed. In the United

States, 98% of the television food ads seen by children are for products high in fat, sugar or sodium (Powell

et al. 2007). Children and the general population are exposed to additional food marketing efforts through

product placements in the entertainment content of movies, television shows, music, and video games;

the internet; and sponsorships of sports and entertainment events.52 If these marketing campaigns are

effective in shaping preferences and persist while other policy measures are introduced, then they may

be partially responsible for reducing the effectiveness of various policy measures.

The last two decades have witnessed a change in understanding what drives obesity and overweight

among individuals. The initial emphasis was on individuals changing their own behavior, but more

recently, there is increased recognition of environmental factors related to food, which may have played

a role in the increasing rates of obesity and overweight. While this is an important development for

understanding the factors attributed to obesity, this paper shows that individual preferences should not

be entirely neglected when evaluating the obesity epidemic. Although these preferences may have been

formed by changes in individuals’ food environments over the last few decades, it appears that changing

them requires more than simple modifications in food environments.

52 See Harris et al. (2009) for a summay of how food marketing contributes to childhood obesity.

43

References

References

[1] Adda, Jerome and Francesca Cornaglia. 2006. “Taxes, Cigarette Consumption, and Smoking Intensity.”

American Economic Review, 96 (4): 1013-1028.

[2] Adda Jerome and Francesca Cornaglia. 2010. “The Effect of Bans and Taxes on Passive Smoking.”

American Economic Journal: Applied Economics, 2 (1): 1-32.

[3] American Beverage Association. 2005. “Measuring the Purchases of Soft Drinks by Students in U.S.

Schools.” Available at: http://www.ameribev.org/files/240_soft_drink_purchases.pdf

[4] American Beverage Association. 2010. “Alliance School Beverage Guidelines Final Progress Re-

port.” Available at: http://www.foodpolitics.com/wp-content/uploads/School-Beverage-Guidelines-

Final-Progress-Report-Executive-Summary.pdf

[5] Anderson, Patricia M. and Kristin F. Butcher. 2006. “Reading, Writing and Refreshments: Do School

Finances Contribute to Childhood Obesity?” Journal of Human Resources, 41 (3): 467-494.

[6] Anderson, Patricia M., Kristin F. Butcher and Dianne W. Schanzenbach. 2011. “Adequate (or Adipose?)

Yearly Progress: Assessing the Effect of ’No Child Left Behind’ on Children’s Obesity”. NBER Working

Paper No. 16873.

[7] Bertrand, Marianne, Esther Duflo, and Sendhil Mullainathan. 2004. “How Much Should We Trust

Differences-in-Differences Estimates?” Quarterly Journal of Economics, 119 (1): 249-275.

[8] Bertrand, Marianne and Dianne W. Schanzenbach. 2009. “Time Use and Food Consumption.”

American Economic Review Papers and Proceedings, 99 (2): 170-176.

[9] Chou, Shin-Yi, Michael Grossman, and Henry Saffer. 2004. “An economic analysis of adult obesity:

results from the Behavioral Risk Factor Surveillance System.” Journal of Health Economics, 23:

565-587.

[10] Currie, Janet, Stefano Dell Vigna, Enrico Moretti, and Vikram Pahania. 2010. “The Effect of Fast Food

Restaurants on Obesity and Weight Gain.” American Economic Journal: Economic Policy, 2: 32-63.

[11] Cutler, David M., Edward L. Glaeser, and Jesse M. Shapiro. 2003. “Why Have Americans Become

More Obese?” Journal of Economic Perspectives, 17 (3): 93-118.

http://www.ameribev.org/files/240_soft_drink_purchases.pdf

http://www.foodpolitics.com/wp-content/uploads/School-Beverage-Guidelines-

44

References

[12] Einav, Liran, Ephraim Leibtag, and Aviv Nevo. 2010. “Recording Discrepancies in Nielsen Homescan

Data: Are They Present and Do They Matter?” Quantitative Marketing and Economics, 8(2).

[13] Elder Todd E. and Darren Lubotsky. 2006. “Kindergarten Entrance Age and Children’s

Achievement: Impacts of State Policies, Family Background, and Peers.” Available at SSRN:

http://ssrn.com/abstract=916533 or doi:10.2139/ssrn.916533

[14] Figlio, David N. and Joshua Winicki. 2005. “Food for thought: the effects of school accountability

plans on school nutrition.” Journal of Public Economics, 89: 381-394.

[15] Fletcher, Jason M., David E. Frisvold and Nathan Tefft. 2010. “The effects of soft drink taxes on child

and adolescent consumption and weight outcomes.” Journal of Public Economics, 94: 967–974.

[16] Fox, M. K., Gordon, A., Nogales, R., and Wilson, A. 2009. “Availability and consumption of competitive

foods in US public schools.” Journal of the American Dietetic Association, 109: Supplement, S57-S66.

[17] French, S. A., R. W. Jeffery, M. Story, P. Hannan, and M. P. Snyder. 1997. “A pricing strategy to promote

low-fat snack choices through vending machines.” American Journal of Public Health, 87 (5): 849-

851.

[18] Gleason, Philip, Ronette Briefel, Ander Wilson, and Allison Hedley Dodd. 2009. “School Meal Pro-

gram Participation and its Association with Dietary Patterns and Childhood Obesity.” New Jersey:

Mathematica Policy Research, Inc.

[19] Government Accountability Office. 2005. “School Meal Programs: Competitive Foods Are Widely

Available and Generate Substantial Revenues for Schools.” Report No. GAO-05-563. Washington, DC:

Government Accountability Office.

[20] Griffith, Rachel, Pierre Dubois and Aviv Nevo. 2012. “Do Prices and Attributes Explain International

Differences in Food Purchases?” Working Paper.

[21] Harding, Matthew, Ephraim Leibtag and Michael Lovenheim. 2012. “The Heterogeneous Geographic

and Socioeconomic Incidence of Cigarette Taxes: Evidence from Nielsen Homescan Data,” American

Economic Journal: Economic Policy, 4 (4): 169-198.

[22] Harris, Jennifer L., Jennifer L. Pomeranz, Tim Lobstein and Kelly D. Brownell. 2009. Annual Review

of Public Health, 30: 211-225.

http://ssrn.com/abstract%3D916533

45

References

[23] Huang, Rui and Kristin Kiesel. 2012. “Does limited access at school result in compensation at home?

The effect of soft drink bans in schools on purchase patterns outside of schools.” European Review

of Agricultural Economics, 39 (5): 797-820.

[24] Just, David and Joseph Price. 2011. “Using Incentives to Encourage Healthy Eating in Children.”

Working paper, Brigham Young University, Provo, UT.

[25] Lakdawalla, Darius, Thomas Philipson, and Jay Bhattacharya. 2005. “Welfare-Enhancing Techno-

logical Change and the Growth of Obesity.” American Economic Review, Papers and Proceedings, 95

(2): 253-257

[26] Powell, Lisa M., Glen Szczypka, Frank J. Chaloupka and Carol L. Braunschweig. 2007. “Nutritional

Content of Television Food Advertisements Seen by Children and Adolsecents in the United States.”

Pediatrics, 120: 576-583.

[27] Schanzenbach, Diane W. 2009. “Do School Lunches Contribute to Child Obesity?” Journal of Human

Resources, 44 (3): 684-709.

[28] Wisdom, Jessica, Julie S. Downs and George Loewenstein. 2010. “Promoting Healthy Choices: Infor-

mation vs. Convenience.” American Economic Journal: Applied Economics, 2 (2): 164-178.

School District State County 2004-2007 Sample 2004-2009 Sample

San Francisco Unified School

District CA

San Francisco

No - Carbonated beverages restricted in all schools beginning

August 2003 Yes

Broward County Public Schools

FL

Broward

August 2004, but school district representatives claim all carbonated beverages are still available in middle and high school. Former

communications director, who worked in school district through Nov. 2005 claims carbonated beverages were taken out of schools

before he left. Also, FL Dept of Education documentation state that Alliance for a Healthier Generation Guidelines went into effect

prior to Dec. 2009.

Duval County Public Schools

FL

Duval

Yes

No - no one was able to confirm when Alliance for a Healthier

Generation Guideliens went into effect.

Lake County Schools

FL

Lake

No- Conflicting information- Findings from Lexus-Nexus reveal that the district had an exclusivity contract with Coke that

excluded carbonated beverages already in 2004. District representative claimed carbonated beverages banned only in 2006 when

wellness policy came out. However, the wellness policy from 2006 does not explicitly mention any restriction on the availability of

carbonated beverages.

School District of Lee County

FL

Lee

Yes



Pasco County Schools

FL

Pasco

Yes



Polk County Public Schools

FL

Polk

No - Conflicting information - Exclusive contract with Pepsi from 2007 explicitly indicates that low-calorie carbonated beverages

will be available in high schools. However, wellness policy from 2006 states that within 6 months all carbonated beverages will be

eliminated from all schools.

St. Lucie County Public Schools

FL

St. Lucie

Yes



Sarasota County Schools

FL

Sarasota

No - Conflicting information - Exclusive 5-year contract with Coke expired in Summer 2006. Nutrition services claimed AHG

Guidelines went into effect May 2006, prior to contract expiration. Purchasing claimed new standards went into effect during

Summer 2006. However, the 2006 contract was extended through the end of December 2006 with the same product composition as

the prior contract to allow time to renegotiate the conditions of the new contract under theAHG Guidelines. Only in Oct. 2006 did the

Board approve implementing the AHG Guidelines in the new contract with Coke. In the January 2007 contract, the new guidelines

are explicit.

Caddo Parish Public Schols LA Caddo No- No response to any requests for information (telephone, e-mail, FOIA)

Anne Arundel County Public

Schools

MD

Anne Arundel

No - Conflicting information -According to contracts received, there are no restrictions on sugar drinks in Anne Arundel schools.

Machines have to be turned off between the start of the school day and the last lunch period. However, in the media, there is plenty

of evidence that the board voted to ban soda during the school day with the implementation of the wellness policy in July 2006. No

one was able to confirm or negate this.

New York City Department of

Education

NY

Bronx,

Brooklyn,

Queens, Staten

No- Inconsistent policy- The Chancellor ruled in 2002 to restrict all carbonated beverages in all schools by the start of the 2003-

2004 school year. However, an audit by the State Comptroller in New York City schools in 2009 revealed that 20 out of 30 schools

visited sold soda during the school day, in direct violation of the Chancellor's policies.

Gaston County Schools

NC

Gaston

No- Inconsistent policy- Abrupt changes in policy implementation in 2004-2005 due to state auditing and lack of enforcement prior

to this.

Lincoln County Schools NC Lincoln No- No response to any requests for information (telephone, e-mail, FOIA)

Rowan-Salisbury School System

NC

Rowan-Salisbury

No- Conflicting Information- Evidence from school district policy that already in 1998 carbonated beverages were not allowed in

schools, but no school district representative was able to confirmor negate that.

Virginia Beach City Public

Schools VA

Virginia Beach

No- No response to any requests for information (telephone, e-mail, FOIA)- Viriginia public records law only requires a response to

Virignia residents

- 2"

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47

Table 10: Covered School Districts and the Timing of Treatments

48

A Differential Effects Based on Household Characteristics

A Differential Effects Based on Household Characteristics

Differential treatment effects based on household characteristics can be obtained through a permutation

of equation (3), by adding an additional variable interacting T r t C n t y s ∗ T r t T i me ∗ T r t C hl d s with

the household characteristic dummy variable of interest (and controlling for separate trends at the

demographic level for each county, post-treatment period, treated school level, and the combination

between these). The household characteristics defined for this analysis were: black, hispanic, low-

income, having at least one household head with a college degree, and heavy soda consumption, defined

as approximately the top 15% of households in terms of non-diet soda consumption on an annual basis.

Low income households were defined using the variable for household income defined in the Nielsen

data, provides annual household income in income brackets.53 The top 15% of households in terms

of non-diet soda consumption were households with average monthly non-diet soda consumption

exceeding 250 fluid ounces per household member over the course of an entire calendar year.

The dependent variables in Table 11 are for monthly non-diet soda consumption. For each of the

five household characteristics examined, Table 11 provides the differential effect for households with a

treated child in school-level s and the relevant household characteristic (the estimated coefficient on

T r t C n t y s ∗ T r t T i me ∗ Sc l C hl d s ∗ H H C h ar ), as well as the effect in overall population for households

with a treated child in school-level s (and without the relevant household characteristic) (the estimated

coefficient on T r t C n t y s ∗T r t T i me ∗Sc l C hl d s . The sample period is 2004-2007, the sample period used

for the short-term analysis in Section ??. All regressions control for time-varying household characteristics

and include household fixed effects, month fixed effects, and bi-monthly-year fixed effects. The results

with dependent variable being monthly non-diet carbonated beverage consumption were very similar.

The results in Table 11 do not present evidence that the compensation effect found for household

non-diet carbonated beverage consumption with treated children is differential across household charac-

teristics. Thus, it appears that the compensation effect found for households with treated high school

children is taking place across the board and not just in certain population segments.

53 The upper bar of the income bracket was defined as the actual annual household income and then this figure was divided by the

number of household members. Households with an annual income less than $10,000 per household member were defined as

low-income. This roughly corresponds to federal measures of household eligibility for free or reduced lunches. For example,

for the 2006-2007 school year, 4-member households with an annual income up to $37,000 were eligible for free or reduced

price meals in school lunches. This annual income for this household size was defined as 185% of 2006 Federal income poverty

guidelines. Source: http://www.fns.usda.gov/cnd/governance/notices/iegs/IEGs06-07.pdf

http://www.fns.usda.gov/cnd/governance/notices/iegs/IEGs06-07.pdf

Household Characteristic

(1) (2)

Black

(3) (4)

Hispanic

(5) (6)

Low Income

(7) (8)

College Graduate

(9) (10)

Heavy Soda Consumption

DEPENDENT

VARIABLE

Fraction of

Total Fl Oz Bev

Fraction of

Total Fl Oz Bev

Fraction of

Total Fl Oz Bev

Fraction of

Total Fl Oz Bev

Fraction of

Total Fl Oz Bev

SCHOOL LEVEL

Elementary

Elementary - Differential

Middle

Middle - Differential

High

High - Differential

With Household Char

Mean Dep Variable

16.943 0.023

(36.197) (0.015)

26.359 0.027

(40.908) (0.040)

11.613 0.015

(20.554) (0.013)

-6.835 -0.010

(36.505) (0.033)

51.911*** 0.030**

(14.594) (0.012)

-45.585 -0.060**

(40.546) (0.029)

0.129 0.129

193.44 0.19

27.920 0.019

(30.419) (0.019)

-27.774 -0.015

(134.624) (0.064)

-14.674 0.010

(19.272) (0.012)

-78.490 -0.027

(82.175) (0.033)

46.146** 0.021**

(17.922) (0.010)

-27.849 0.021

(81.966) (0.028)

0.066 0.066

193.44 0.19

63.659* 0.038**

(31.689) (0.014)

-112.828 -0.052

(71.953) (0.036)

-5.639 0.013

(18.561) (0.010)

-42.494 -0.005

(54.431) (0.028)

43.695** 0.011

(16.133) (0.010)

1.229 0.034

(64.849) (0.037)

0.105 0.105

193.44 0.19

45.226 0.010

(50.803) (0.028)

-46.772 0.006

(67.060) (0.040)

-19.228 0.016

(44.329) (0.020)

6.401 -0.007

(50.811) (0.024)

26.153 0.011

(21.716) (0.015)

27.082 0.018

(30.727) (0.019)

0.505 0.505

193.44 0.19

21.090 0.016

(28.310) (0.012)

67.729 -0.026

(143.382) (0.047)

8.823 0.015

(16.359) (0.013)

-426.782** -0.042

(202.484) (0.084)

34.790** 0.017

(16.820) (0.011)

101.685 0.038

(91.808) (0.037)

0.109 0.109

193.44 0.19

Observations

Numb of Households

111,456 103,476

5,804 5,789

111,456 103,476

5,804 5,789

111,456 103,476

5,804 5,789

111,456 103,476

5,804 5,789

111,456 103,476

5,804 5,789

The dependent variables are for consumption of non-diet soda beverages. All regressions control for household fixed effects, bimonthly-year fixed effects, and monthly

fixed effects. Time-varying household characteristics are included. Robust standard errors in parentheses clustered at the county level. Counties: CO -Denver, Douglas,

Jefferson; DC; FL- Brevard, Duval, Hillsborough, Lee, Manatee, Marion, Miami-Dade, Orange, Palm Beach, Pasco, Pinellas, St. Lucie, Volusia; KY- Fayette, Jefferson;

LA- East Baton Rouge, Jefferson; MD - Baltimore County, Harford, Montgomery, Baltimore City; NM - Albuqurque; NV- Clark; NC - Forsyth, Guilford, Iredell-

Statesville, Mecklenberg, Wake; PA- Philadelphia; TN- Nashville-Davidson, Hamilton, Knox; VA- Fairfax; WV- Kanawha. Treated counties' observations are only in

the time frame between 12 months prior and 12 months after the restriction on carbonated beverages was implemented.

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

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B What happens to consumption of other goods?

50


An interesting question arises from the analysis: Do households change purchasing patterns for other

foods in response to carbonated beverage bans at their children’s schools? The lack of availability of

carbonated beverages through one specific source can have implications on consumption of other goods

through interactions between carbonated beverages and these other goods, whether they complement

carbonated beverages or substitute for carbonated beverages. Thus, it is not clear-cut what results to

expect when evaluating the impact of carbonated beverage bans at schools on household consumption

of other goods.

When choosing which goods to analyze, it is important to bear in mind that carbonated beverage bans

at schools almost always coincided with a much broader wellness policy, which may have restricted many

other food and/or beverage items based on calories per serving, high sugar, sodium, caffeine, or fat levels,

or serving sizes. In terms of beverages, this frequently included any juices which contained less than

a certain percentage of real fruit juice or drinks exceeding a certain number of calories per serving at

the high school level.54 Thus, in order to assess the true impact of carbonated beverage bans on other

beverage items, it is necessary to select beverage items which were not restricted simultaneously with

carbonated beverages.

If we are interested in beverage items with a high level of sugar, one such item which continued to be

served and available at schools during our sample period, even amidst carbonated beverage restrictions,

is flavored milk. An 8-ounce serving of low-fat flavored milk contains more sugar than a 12-ounce can of

Coca Cola.55 However, due to calcium and other essential nutrients found in milk, flavored milk remained

a beverage option as part of the NSLP in almost all schools. Many public health officials and school food

authorities argue that without flavored milk many children will not consume milk at all, which is worse

than the added sugar content in flavored milk. However, these arguments have become heavily debated

in recent years and beginning around 2010, school districts have increasingly been limiting or restricting

the availability of flavored milk at schools. What is important, though, is that these restrictions only

started gaining momentum in 2010 and not during our sample period (2004-2009). Accordingly, there is

no evidence of a ban on flavored milk in any of the covered school districts during the sample period.56 It

54 Examples of school districts for whom the carbonated beverage ban includes bans on other beverages (such as fruit juices

containing less than 100% real fruit juice) are: Washington DC, Brevard (FL), Miami-Dade (FL), Harford (MD), and Montgomery

(MD). 55 Source: Jamie Oliver’s Food Revolution, the Hard Facts about Flavored Milk. Accessible at:

http://www.jamieoliver.com/us/foundation/jamies-food-revolution/ cms/uploads/JOFR_milkfactsheet_6.3.pdf 56 A 2012 report by MilkPEP (Milk Processors Education Programs), an organization of U.S. milk processors, lists all school districts

in the U.S. which banned flavored milk as of 2012. The only school districts in our sample which have banned flavored milk as

http://www.jamieoliver.com/us/foundation/jamies-food-revolution/


51

should be noted that the collection of carbonated beverage policy information from school districts did

include wellness policies and a review of these did result in finding two school districts which restricted

strawberry-flavored milk during the sample period:57 Miami-Dade County Public Schools and School

District of Palm Beach County (both in FL). As flavored milk is identified in the Nielsen data based on an

identifier which does not distinguish between the various flavorings of flavored milk, these two school

districts were omitted from the flavored milk analysis.

Two additional beverages were also included in the analysis to test whether the carbonated beverage

bans had any effect on other beverage items: regular milk and bottled water. These are the two healthy

beverage options promoted amongst public health and nutrition advocates, and it was decided to explore

whether any interaction exists between these and carbonated beverage consumption.

The regressions run are the exact regressions presented in equation (3) for non-diet soda restrictions,

only the dependent variables are no longer measures of household carbonated beverage consumption but

rather measures of flavored milk/milk/bottled water consumption. For flavored milk, the analysis looks

at both total monthly fluid ounce consumption at the household level, as well as the fraction attributed

to flavored milk out of total household monthly milk consumption. As flavored milk is purchased by

less than 50% of the households in the sample, separate regression results are presented for the entire

sample and for just households who reported positive consumption levels. Milk and bottled water

regression results are reported only with total fluid ounce consumption as the dependent variable and

for all households in the sample.58

Table 12 presents the results when estimating the effect of non-diet carbonated beverage bans on

other beverages consumed within the household. What is most striking is that it appears that for every

elementary school student experiencing a non-diet carbonated beverage ban within the household,

household flavored milk consumption decreases substantially. Quantitatively, the decrease is over half

of the average household’s monthly consumption level of flavored milk and equivalent to roughly eight

servings of flavored milk when conditioning on households that had at least on month of positive flavored

milk consumption during the sample period (columns (3)-(4)).59

of 2012 are: Denver Public Schools (CO), DC Public Schools (Washington, DC), School District of Palm Beach County (FL) and

Baltimore City Public Schools (MD). According to the report, and as verified through online searches, all these school districts

implemented their bans on flavored milk post-2009. The report can be accessed at: www.milkpep.org/files/.../flav-milk-tracking-

report-2-10-12.xlsx (last accessed October 2012). 57 Strawberry-flavored milk has an even higher sugar content than chocolate-flavored milk, which may explain why some school

districts chose to restrict only this type of flavored milk. 58 The regressions for milk and bottled water were also run for only households reporting positive consumption levels of these

dependent variables, and the results (not shown) were similar to those presented below for regular milk and bottled water

consumption. 59 One serving of milk/flavored milk is 8 fluid ounces.

http://www.milkpep.org/files/.../flav-milk-tracking-

http://www.milkpep.org/files/.../flav-milk-tracking-


52

Table 12: Triple Differences: The effect of a non-diet carbonated beverages ban in schools on Flavored

Milk, Regular Milk and Bottled Water household consumption - Estimated Coefficient of TrtC-

nty*TrtTime*SclChld by School Level

When interpreting this result, it is important to bear in mind that non-diet carbonated beverage

restrictions in elementary schools generally did not result in any actual change in the content of beverages

available to elementary school students. However, as already mentioned, these carbonated beverage

restrictions were frequently simultaneously implemented with restrictions on certain sugar-dense juices,

sports drinks, or juices which were not 100% natural. These restrictions on juices and sports drinks may

have actually had an effect on the availability of beverages to elementary school students. One plausible

explanation for the results presented in Table 12 is that when certain juices were no longer available for

purchase in elementary schools (whether in a la carte lines or outside of the cafeteria), elementary school

students replaced their consumption of these with flavored milk. As a result, in order to maintain past

consumption levels of flavored milk, prior to the new restrictions, households cut back on flavored milk

consumption, which the students were now drinking more of at school.

Documents

Does Banning Carbonated Beverages in Schools Decrease ... · which could change their carbonated beverage consumption but are not related to carbonated beverage bans in schools. The