Electronic copy available at: http://ssrn.com/abstract=2079840

From Coke to Coors: A Field Study of a Fat Tax and its Unintended Consequences

Brian Wansink, PhD Andrew S. Hanks, PhD

David R. Just, PhD

Cornell University

Author Information: Brian Wansink is Corresponding Author. John S. Dyson Professor of Marketing in the Charles H. Dyson School of Applied Economics and Management at Cornell University. Address: 15 Warren Hall, Ithaca, NY 14853. Phone: 607-254-6302. Fax: 607-255-9984. E-mail: foodandbrandlab@cornell.edu. Andrew S. Hanks ah748@cornell.edu. David R. Just drj3@cornell.edu. John Cawley jhc38@cornell.edu. Harry M. Kaiser hmk2@cornell.edu. Jeffery Sobal js57@cornell.edu. Elaine Wethington ew20@cornell.edu. William D. Schulze wds3@cornell.edu. Funding and Acknowledgments: We gratefully acknowledge financial support from the National Institutes of Health, grant 1RC1HD063370-01; the NIH played no other role in the conduct of the study. No person on the research team has received any support from the soft drink industry or the beer industry. The authors have no conflicts of interest. The Institutional Review Board approved the design of this study (Protocol ID#1110002491).

Electronic copy available at: http://ssrn.com/abstract=2079840

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From Coke to Coors: A Field Study of a Fat Tax and its Unintended Consequences

ABSTRACT

Could taxation of calorie-dense foods such as soft drinks be used to reduce obesity? This policy-

level debate curiously neglects how an understanding of consumer behavior and marketing could

offer insight to this question. To address this, a six-month field experiment was conducted in an

American city of 62,000 where half of the 113 households recruited into the study faced a 10%

tax on calorie-dense foods and beverages and half did not. The tax resulted in a short-term (1-

month) decrease in soft drink purchases, but no decrease over a 3-month or 6-month period.

Moreover, in beer-purchasing households, this tax led to increased purchases of beer. To

marketing scholars, this underscores the importance of investigating unexpected substitutions.

To public health officials and policy makers, this presents an important empirical result and more

generally points toward wide ranging contributions that marketing scholarship can make in their

decisions.

Keywords: fat tax, soda tax, behavioral economics, public policy, public health, soft drinks,

substitution, compensation, unintended consequences, calorie-dense foods, obesity

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From Coke to Coors:

A Field Study of a Fat Tax and its Unintended Consequences

Taxes on energy-dense foods have been proposed to address the growing obesity problem

(e.g. IOM, 2009; Nederkoorn et al. 2011; Brownell and Frieden, 2009; Jacobson, 2004). In the

United States, the tax that has received the most attention is a tax on sugar-sweetened beverages,

often referred to as a “soda tax” or “soft drink tax,” which has been proposed by the Institute of

Medicine (IOM), the Centers For Disease Control and Prevention (CDC) and several state and

local governments (Paterson 2008; IOM Report 2009; Roehr 2009; Rudd Report 2009; Smith,

Lin, and Lee 2010). The aim of such a tax would be to reduce calorie intake, improve diet and

health, and generate revenue that governments could use to further address obesity-related health

problems (Brownell and Frieden 2009; Duffey et al. 2010; Jacobson and Brownell 2000; Powell

and Chaloupka 2009, Smith, Lin, and Lee 2010).

These reports and the subsequent policy debates have had two curious omissions. First,

they have omitted any discussion of consumer behavior and marketing responses other that

simply assuming that if the price increases people will pay less. Indeed, no marketing or

consumer behavior research from the Journal of Marketing – or any leading marketing journals –

was cited in the reports by the IOM or by the CDC. Second, they lacked empirical evidence as to

how people would respond to a tax on food – instead relying on epidemiological models of

tobacco taxes. This tobacco-food parallel may not be accurate. In 2011, Denmark imposed a tax

on foods with 2.3% or more saturated fat (Zafar, 2011), increasing the cost of foods, such as

butter, meats, and desserts, by as much as 30%. After one year, they repealed it, claiming it did

not improve health and it hurt many small businesses because it merely led people to buy lower-

  4  

priced food or to make a stockpiling drive to Germany – which was foreshadowed in Grether and

Holloway’s (1967) Journal of Marketing article nearly half of a century ago. The purpose of this

research is to empirically investigate the impact of a soft drink tax in a way that can introduce

both the consumer and marketing into important policy debates in this area and in other areas

such as portion sizes (Mohr, Lichtenstein, and Janiszewski 2012), advertising regulation (Parsons

and Schumacher 2012; Kolsarici and Vakratsas 2010), deceptive marketing (Tipton, Bharadwaj,

and Robertson 2009), and fast food restrictions (Dhar and Baylis 2011).

Up to this point, the two principle techniques have been used to assess the effectiveness

of a tax on sugar-sweetened beverages (SSBs). The first relies on the natural variation in current

soft drink taxes across states to identify responses in demand (Besley and Rosen,1999; Zheng

and Kaiser 2008, Fletcher, Frisvold, and Tefft 2010a). The second estimates price elasticities for

beverages and uses these elasticities to estimate responses to increases in prices of SSBs. A

complement to these two methods is a controlled field experiment. In a controlled field

experiment could more cleanly provide within- and between- subject variation, household

specific demographic information, and a semi-controlled environment where the salience of the

tax is not a concern (List 2011; List 2009; Levitt and List 2009; Harrison and List 2004).

Furthermore, if conducted over a period of time it would also provide household-level insights

related to effectiveness, substitution, and decaying impacts of a tax.

After reviewing the literature on how taxation influences demand and substitution, we

describe a controlled field experiment we conducted in three major grocery stores in a small city

(pop. 62,000) the eastern United States. In this study, 113 households in their shopper rewards

program were randomly assigned to either face a 10% tax on SSBs or to be in the control group

and their individual household purchases were recorded over a seven-month period. The results

  5  

suggest that a tax may have a much different potential outcome than what is currently being

assumed. We hope this research will make three key contributions. First, it offers important

empirical results for the policy debate. Second, it underscores to marketing researchers the key

importance of potentially overlooked product substitutions in the field. Third, it could help

introduce public health officials and policy makers to wide ranging contributions that marketing

scholarship can make in their decisions.

Background

The most widely considered policy tool to address obesity in the United States has been

to impose taxes on energy-dense (i.e. sugary, fatty, and high-calorie) foods and beverages (see

Jacobson, 2004). A tax on soft drinks has been implemented in many states. As of 2009, 33

U.S. states had implemented such a tax (Smith, Lin, and Lee, 2010). A tax proposed by Brownell

and colleagues (2009) that is commonly considered by legislators is “an excise tax of 1 cent per

ounce for beverages that have any added caloric sweetener.” Depending on whether a person

purchased a $6 twelve-pack of cans or a $1 two-liter bottle, the tax would range from 24%-68%

(Smith, Lin, and Lee 2010).

Taxation and Demand

The argument for a tax on soft drinks – or on SSBs in general – is three-fold. First, a tax

decreases quantity demanded for these beverages by raising the effective price, particularly

among those individuals who have the least disposable income (and often the highest level of

obesity). Second, revenue from a tax could be used for anti-obesity campaigns or health

education initiatives. Third, a tax would be administratively feasible to impose because nearly

  6  

two-thirds of all states currently have some tax related to soft drinks or on their recyclable

containers (Fletcher et al., 2010a).

Taxing food to reduce consumption assumes that consumers respond to a higher price by

purchasing less. As a result, the success of a soft drink tax partly depends on how much less a

person consumes given the higher price – one’s own-price elasticity (Smith et al 2010). One

review of food demand research by Andreyeva, et al. (2009) suggested the own-price elasticity

for soft drinks to be between -0.8 and -1, and in recent studies the Rudd Center has used an

elasticity of -1.2 to predict consumers’ responses to taxing soft drinks (Brownell et al., 2009).

However, a tax on SSBs may lead to substitution. That is, taxes change relative prices,

leading consumers to not only decreased consumption of the taxed item but also, increased

consumption of other items, specifically, those with positive cross-price elasticities.

A second approach to estimating the health impact of a tax on SSBs is to examine how

the body mass indices (BMIs) of children (Powell et al. 2009; Sturm et al. 2010) and adults

(Fletcher et al. 2011a differ across states with different levels of soft drink taxes. Fletcher et al.

(2010a) calculate that a 1% increase in a state’s soft drink taxes leads to an estimated BMI

decrease of 0.003. The estimated impact is greater among low-income adults (0.015 point

decrease in BMI for those who earn less than $10,000) and Hispanics (0.016 point decrease in

BMI). Given the BMIs associated with normal weight (under 25), overweight (25-30), obese (30

and over), income levels, and race, the effect of a 1% increase in soft drink taxes on body weight

is estimated to be small.

Existing research tends to find little or no association between sales taxes on soft drinks

and effects on weight or BMI. To some extent this is not surprising, because existing soft drink

taxes are small (Smith, Lin, and Lee 2010). Moreover, a sales tax is not reflected in the shelf

  7  

price; it is added at the checkout counter and may not have been salient to the purchaser (Chetty,

Looney, and Kroft 2009). As a result, consumers may be unaware of a new sales tax or

unresponsive to a tax increase when making retail purchases (Zheng, McLaughlin, and Kaiser,

2012).

Taxation and Substitution

Fewer studies examine how such taxes influence substitution to other high-calorie

beverages. For example, Fletcher et al. (2010b) find that while soft drink taxes reduce

consumption of soft drinks among children and adolescents, they substitute towards other high

calorie drinks (see also Fletcher et al 2011b). Chouinard et al. (2007) estimate that a 10% tax on

fat content would have little impact on demand for dairy products and would result in negligible

substitution between products. Kuchler, et al. (2004) argued that while a tax on salty snacks

might decrease demand for salty snacks, consumers will likely substitute toward similar, non-

taxed foods. Mytton et al. (2007) predicted that a tax on saturated fat would lead consumers to

substitute to other goods high in salt, which may cancel out the health benefits from a decrease in

fat intake. This previous research suggests that, while in some cases, taxes might be effective in

reducing demand for the targeted food or beverage, these effects will likely be offset by

substitution towards other foods or beverages (see Smith, Lin, and Lee 2010; Fletcher, Frisvold,

and Tefft 2010a,b; 2011b).

In the context of SSBs, Smith, Lin, and Lee (2010) addressed some of the limitations of

studies that estimated only own-price elasticities by estimating a beverage demand system based

on the beverage purchases from a panel of American households over a 10-year period (1998-

2007). These elasticities were then applied to beverage intake data from a nationally

  8  

representative survey of individuals, which enabled them to estimate changes in calorie

consumption due to a hypothetical tax on SSBs.1 Their analysis led them to conclude that a 20%

tax on SSBs could reduce net calorie intake from all beverages by an average of 37 calories per

day for adults and 43 calories per day for children. They concluded that these daily calorie

reductions, all else held constant, would translate into an average reduction of 3.8 pounds per

year for adults and 4.5 pounds per year for children. This study, however, assumed perfect

saliency of the tax, which is questionable (Zheng, McLaughlin, and Kaiser, 2012).

Another ambiguity is how long any effect might persist. With retail price promotions

(i.e. discounts), there is often an immediate increase in demand that quickly decays as consumers

acclimate to the new price (e.g., Gallet and List 2003). In the case of a sugar-sweetened

beverage tax, consumers may immediately respond by decreasing use. Similar to reactions to

price discounts, however, use will likely return to normal levels after an adjustment period.

Method: A Field Study of Taxation

Field studies are valuable methods for studying economic phenomena. Controlled field

studies allow researchers to use randomization to the treatment group as an instrumental variable

in order to identify the impact of the treatment (List, 2011; List, 2009; Levitt and List, 2009;

Harrison and List, 2004). Field studies can also be constructed with control groups and control

periods, allowing researchers to address concerns about selection and unobserved heterogeneity

(List, 2011). This also allows for the construction of the counterfactual so that changes can be

measured against baseline measurements that were not affected by the treatment.

                                                                                                               1  In fast food and full-service restaurants, consumers often pay for a meal “combo” that includes beverages and often free refills. This creates a disconnect between quantity purchased and price. Because of these marketing conditions, consumers are likely to react differently to a price increase at home than away from home. While it would be difficult to estimate the away-from- home demand for beverages due to data deficiencies, most studies apply at-home elasticities to total at-home and away-from-home consumption.  

  9  

For this study, we conducted what has been termed a “framed field experiment” (List,

2011) to study the impact that a tax on SSBs has on purchases of these beverages, with specific

emphasis on soft drinks. Since the tax was on all foods and beverages deemed “less healthy,” we

understand that there are other substitutions and complementarities that we do not consider. Yet,

beverages are unique enough that we consider their closest substitutes as other items within the

beverage category (for example, see Smith, Lin, and Lee 2010 or Fletcher, Frisvold, and Tefft

2011). The next section discusses our experimental methods in depth.

Procedure

To study the impact of a tax on soft drinks and other SSBs, we recruited a sample of

representatives from 113 households in a grocery chain in the eastern United States and observed

their purchases for one month (August). Then, we randomly assigned each participating

household to one of two groups: a tax group–56 households–that faced a 10% tax on all foods

with few nutrients per calorie (including soft drinks), and a control group–57 households–who

did not face such a tax. We then observed their purchases for an additional six months. This

random assignment into groups provides exogenous variation in price. In this study we focus on

the impact that this tax had on purchases of SSBs, particularly soft drinks.

The grocery chain where we conducted our study uses a proprietary algorithm based on

nutrients per calorie that assigns items one of four health grades. The grade is then affixed to a

label on the grocery store shelf to guide shoppers to healthier choices.2 In our experiment, foods

and beverages that received the lowest health rating were taxed while foods and beverages that

received one of the three higher or better health ratings were not taxed. All soft drinks, sugar

                                                                                                               2  Foods with no calories received no rating.

  10  

sweetened fruit juices, and whole milk received the lowest health grade while beverages such as

no sugar added fruit juices, 1% milk, and all natural fruit juices generally received the higher

three rankings, depending on the amount of nutrients per calorie. We also add that alcohol was

not rated so purchases of alcoholic beverages were not discounted and/or taxed.

Before entry in the experiment, each recruited shopper was required to complete a survey

of marital status, family size, household income range, how much of the shopping the recruited

shopper does for the household, and how many times per week the respondent drinks full calorie

soda, diet soda and fruit juice. Once the shopper had completed and submitted the survey,

he/she received an ID card that was to be scanned before each purchase at an outlet of the

participating grocer. Every time the ID card was scanned, the items purchased would be recorded

for the study and the shopper would receive a discount on purchases, the incentive for

participating in the experiment. This discount was loaded onto a bank debit card on a periodic

basis.

Once all the ID cards had been distributed and participants were registered in a database,

shopping data for all households were collected from July 17 to September 9, 2010, yet we only

use a full four weeks (August 13-September 9 prior to the intervention period). We do this

because not until August 3 had all participants received their ID card and to keep track of four

week intervals in the study, which we refer to as months. We refer to the first set of four weeks

as August (the control period) and refer to the subsequent sets of four weeks as October,

November, etc (the intervention period). On the dates of September 7-9, we contacted each

participating household and notified them of their respective treatment–control or tax–so that by

September 10, each household was aware of its respective treatment, so we refer to this as the

beginning of the intervention period. We then proceeded to collect data through March 12, 2011,

  11  

but we only use data up through February 24 since this is the last day of a full four weeks of data

collection.

As incentive for participating, all participants received a 10% discount on all rated foods

and beverages purchased during the control period. During the treatment period, participants in

the control group continued to receive the 10% discount on all their purchases of rated foods and

beverages. Participants in the tax group received a 15% discount on all rated items but were

taxed 10% on rated items that received the lowest health grade. The remaining 5% discount on

less healthy foods encouraged participants to conduct all of their shopping at the grocery stores

in question (so we can observe how their total purchases changed), while the additional 10%

discount on more healthy foods resulted in a tax on the less healthy foods. Table 1 summarizes

the pricing structure in the experiment.

In Table 2 we report descriptive measures of the sample based on the demographic

information we collected. We show that for most of the demographic variables, there is no

statistical difference between participants in the tax and control group. We do find a statistical

difference between the percentage of non-married participants in the tax group and control

group, 5.4% and 19.3%, respectively. This relates to the difference we find in the number of

children in each household. We find that participants in the tax group generally have more

children, most likely because more participants in the tax group are married. This might suggest

differences in purchasing behavior between the two groups due to marital status, but we found

that nearly 2% of the participants in each group reported drinking fruit juice every day of the

week. Furthermore, 10% of the participants in the tax treatment and 15% of the participants in

the control treatment reported drinking soft drinks (full calorie and diet) every day of the week, a

statistically insignificant difference.

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Data

On a weekly basis, purchase records for each participant were collected from store

purchase records. Information available in the store data includes expenditures, price, the health

rating an item received, specific product descriptions–trade mark names–that assisted us in

separating beverages into various categories, specific item weights (fluid ounces, liters, or

gallons), and product classifications such as soft drinks, milk, or alcohol. With this information,

we were able to generate fluid ounces purchased of any beverage for each participant on any

shopping trip. Instead of expenditures, we study changes in fluid ounces purchased because this

is a more accurate measure of the quantity of beverages purchased. This method has been used

in other studies related to beverage consumption (see French, Lin, and Guthrie 2003; Duffey and

Popkin 2007)

We aggregated, by month, the data over specific beverage types, such as soft drinks and

fruit juice and rely mostly on data from the United States Department of Agriculture Nutrient

Database website for calorie estimates. We use these values to approximate the calorie counts

in our beverage groups. In this study we focused our attention on four categories of beverages:

soft drinks, sugar-sweetened fruit juice (fruit juices without added sugar received a higher rating

so were not taxed), whole and flavored milk, and beer. We used the USDA Nutrient Database to

find calories per fluid ounce for soft drinks (11 kcal/fluid ounce), full-calorie beer (12 kcal/fluid

ounce), sugar-sweetened fruit juices (10 kcal/fluid ounce), and chocolate milk (19kcal/fluid

ounce).3 With this information we can estimate the differences between the tax and control

group by month.

                                                                                                               3  The  website  for  soft  drinks,  full  calorie  beer,  and  sugar-­‐sweetened  fruit  juice  is:  http://www.nal.usda.gov/fnic/foodcomp/cgi-­‐bin/list_nut_edit.pl  and  was  accessed  on  December  7,  2011.  

  13  

Before we estimated the impact of the tax on fluid ounces of beverages purchased, we

took several measures to prepare the data. To minimize household specific idiosyncratic noise

we aggregated individual household data by month and beverage category. Many households

purchased some beverages in some months and not in others. When a household did not

purchase a certain type of beverage in one month but did so in another, there would be no

observation in the non-purchase month. In this case, the non-observation is entered as zero

expenditure for the particular item. In order to handle outlying observations, we also identified

observations for specific beverages that were greater than three standard deviations from the

beverage’s average monthly fluid ounces purchased. We then excluded these observations from

the analysis. We drop these observations because in almost every situation where a household

spent more than this designated amount, it did so only once in the study or during holiday

months (November and December). When a household made one large purchase, it generally

made other small food and beverage purchases so it is apparent that a large purchase is a one-

time event, such as a party. While purchasing behavior related to parties is interesting, the

purpose of this study is to examine household level purchases for household consumption.

Purchases for parties would bias this analysis. It is possible, however, that households make

large, one-time purchases for storage purposes. Since we do not have this information, we

assume these large purchases are outside the scope of this study. We leave out the large

purchases in the holiday months because we are interested in behavior unassociated with these

external factors. After removing these data for soft drinks, there were 39 households in the tax

treatment and 50 households in the control group, for a total of 89 households whose purchases

                                                                                                                                                                                                                                                                                                                                                                     The  website  for  chocolate  milk  is:  http://ndb.nal.usda.gov/ndb/foods/show/92?fg=&man=&lfacet=&count=&max=&sort=&qlookup=&offset=&format=Abridged&new=  and  was  accessed  on  January  27,  2012.  

  14  

were recorded over the course of a 7-month period, which brings the total number of

observations to 623.

In Table 3 we report average fluid ounces of soft drinks purchased by the participants in

each treatment group, during each month of the study, and by marital status, income range, and

family size. On average, participants in the treatment group purchased 11.71 fewer fluid ounces

of soft drinks per month, which is 12.6% of the control group mean, or about one can of soda.

Also, there is some evidence that all households in the study purchase a greater volume of soft

drinks during the holiday months of November and December. We find an increase of

approximately 15 fluid ounces per household per month between October and November, a

similar increase between November and December, and then a decrease of more than 20 fluid

ounces per month from December to January. There also seems to be differences in

consumption patterns between those who are married and those who are single. Our descriptive

measures do not reveal much of a difference in behavior among those in different income

categories, but there is some evidence that larger families purchase a greater volume per month

of soft drinks, which is not surprising. These are purely descriptive measures for understanding

patterns in the data. We will rely on our empirical method to determine the statistically

significant impact that the treatment groups, months, and household characteristics have on the

volume of soft drinks and other beverages purchased.

Analysis Approach

To analyze the data collected from participants, we rely on a random effects panel

regression technique with robust standard errors. Robust standard errors allow us to improve the

efficiency of our results by accounting for heteroskedasticity in the random errors. Since this is a

  15  

controlled field experiment, the random assignment to a treatment group is used as the

instrumental variable that identifies the changes we observe in the data (see List, 2011).

We rely on two similar regression techniques to estimate the impact of the tax on the

volume of soft drinks purchased. In the first regression, we simply estimate the impact that the

tax and other variables have on fluid ounces of soft drinks purchased. Our estimation equation is

!!"# = !! + !!!"# + !!!"#$%&!'" +!"#!! + !!!! + !!" + !!"# ,

(1)

where !!"# is fluid ounces of a specific beverage, b, purchased by household i during month t,

and !! is a constant. The independent variable labeled TAX is a dummy variable for those whose

purchases of SSBs were taxed, REIMBURSE is the amount the household received that month

for participating in the study, Mth is a matrix of dummy coded variables that correspond to the

months of September, October, November, etc., respectively, and Xi is a matrix of household

specific characteristics–marital status, income category, and number of children. The term !!" is

a group specific random error component, and !!"# is a random error term with mean zero and

variance equal to !!"! . Since the form of heteroskedasticity is unknown, we relied on White’s

heteroskedastic robust estimator to compute the standard errors. We use the random effects

estimation procedure because this way we are able to account for heteroskedasticity in the

within-group error component and at the same time estimate the effects of household

characteristics on fluid ounces purchased.

The second model for analysis is

!!"#$ = !!!! + !!!!!"# + !!!!!"#$%&!'"  +  !!!!!"#$!+  !!!!!"#$! ∗ !"# +!"#!!" +

!!!!" + !!"# + !!"#$ , (2)

  16  

where !!"#$ is the difference of fluid ounces or calories purchased, represented by the subscript

c, of specific beverages, where the difference of interest is purchases of brand b, purchased by

household i during month t. The subscript b represents the difference between beer and soft

drinks, soft drinks and sugar-sweetened fruit juice, and soft drinks and whole/flavored milk. The

elements of the model are the same as those in equation (1) except this time we include two new

variables: FREQr and FREQr*TAX–which we use to measure preferences for soft drinks and to

identify potential substitutions. FREQr represents how many months–or frequency of purchase–

during the study a specific household purchased the rrh beverage, where r is either beer or soft

drinks. FREQr*TAX is the interaction between the tax variable and the frequency variable. In

the model, we defined frequency as a continuous variable that equals 0 if the household did not

purchase any of the rth beverage during the study (equivalent to a purchasing frequency of 0), 1 if

the household purchased the rth beverage at least once (equivalent to a purchasing frequency of

1), and so forth.

Random error components !!"# and !!"#$ are also the same as in equation (1) but differ

based on which difference in beverages is used as the dependent variable. Once again, we use

the random effects estimation procedure because this way we are able to account for

heteroskedasticity in the within-group error component and at the same time estimate the effects

of household characteristics on fluid ounces purchased. In Table 4, we report average purchase

frequencies for beer and soft drinks and find that overall, participants purchased soft drinks

approximately three or four months out of the study and they purchased beer two out of three

months of the study.

There are several important reasons why we use the frequency variable to measure

beverage use. First of all, participants in the study received an e-mail each time their rebate was

  17  

added to their bank card. This rebate would remind them of the potential gains they could earn

by purchasing less of the less healthy items and more of the healthier items. This repeated

reminder could potentially affect the frequency at which they purchase soft drinks, yet simple

descriptive statistics do not reveal any obvious impact (Table 3). Purchase frequency is also less

ad hoc than determining a measure with which to identify potential heavy or light users of soft

drinks. Third, purchase frequency could be considered a consistent measure because regardless

of dollar amounts purchased, it only counts whether a household actually purchased soft drinks

or not. Thus, large purchases are not given greater weight. Finally, purchase frequency could be

considered a good proxy for soft drink preference since regular purchases, by month, suggest that

a household habitually consumes the beverage as opposed to a household that purchases soft

drinks only once or twice during the study. This could be for a party or another type of one-time

event at which non-household members would consume the soft drinks.

The frequency variable may, however, appear to be a dependent variable, since the

decision to purchase a specific amount of a soft drink is highly correlated with how often it is

purchased, and can be affected by the tax. In this study, though, the frequency variable simply

identifies whether a household purchased a type of beverage at least once in a four-week period.

Given this construct, it is not unrealistic to assume that frequency defined in this manner is

independent of the tax treatment. If it were dependent on the tax, purchase frequency would

change from one or more purchases in August to no purchases thereafter, which is not the case in

the data. Furthermore, monthly purchases are a function of daily purchases and the current

construct of the frequency variable makes it independent of this variation.

  18  

Results

When experimentally examining the impact of a tax on purchase patterns, it is not only

important to examine influences on demand and demand over time, but it is also important to

understand potential substitution that may occur with other products. A tax, such as the one in

our field experiment, generates own-price and cross-price effects that feed into purchasing

patterns. While we were not able to calculate these effects, we did find that total fluid ounces

purchased for all beverages remained relatively flat over the course of the study. Since there is a

stable upper limit on fluid ounces of beverages purchased during the study, decreases in fluid

ounces purchased of one beverage that apparently trigger increases in fluid ounces purchased of

another suggest substitution effects. We began our analysis by first estimating how the tax

affected soft drink purchases over the course of the study. We then study patterns of substitution

between soft drinks and other beverages, specifically diet soft drinks, sugar-sweetened fruit

juice, whole/flavored milk, water, and beer.

In Figure 1, we plot the average monthly fluid ounces purchased over the course of the

study, where fluid ounces in each month are differenced from fluid ounces purchased in August–

the control month. Even though the tax on SSBs did result in a decrease in fluid ounces of soft

drinks purchased between August and September, this behavior differed very little from soft

drink purchases by participants in the control group. Through November, we find insufficient

evidence to claim that the tax had an impact on fluid ounces of soft drinks purchased. Decreases

in the volume of soft drinks purchased in December and January appear to trigger purchases of

other beverages (see Figure 2), such as diet soft drinks (December) and water (January).

This similarity in tax and control group behavior is illustrated in Figure 3. In the figure,

it is clear that both the tax and control groups decrease fluid ounces purchased between August

  19  

and September (the first and second bars in the figure for the tax and control treatments), so it is

not clear whether or not the tax had an effect (note that this decrease was not statistically

significant). If purchases for September through November are averaged, as they are in the third

bar for both treatments in Figure 3, it appears that any effect that may have occurred had

diminished. Finally, the fourth bar for both treatments in Figure 3 shows that from September to

February, participants in the tax treatment purchased more fluid ounces of soft drinks than they

did in August. Those in the control group purchased roughly the same amount.

We then estimated the parameters in equation (1), using fluid ounces of soft drinks and

diet soft drinks purchased as the dependent variables, and find that, over the course of the study,

the tax had no statistically significant impact on fluid ounces purchased of either beverage (Table

5), which is consistent with the unconditional data presented in Figures 1 and 2. Households did

take advantage of the rebate they received from participating by purchasing 19.21 more fluid

ounces (p < 0.01) of soft drinks each month. There is also suggestive evidence (p < 0.1) that

larger families purchase 42.78 more fluid ounces of soft drinks, which is expected.

We also find a marginally significant result in purchases of soft drinks by households in

both groups in December, an increase of 39.2 fluid ounces for the month, so we estimate a

separate regression (not reported) that interacts the tax variable with each month variable in order

to capture any holiday effects that may occur. We find no statistically significant result, but we

do find a drop of approximately 46 fluid ounces purchased between November and December.

Thus, the point estimates suggest an impact in December but it is imprecisely estimated.

Even though the tax had no significant impact on the fluid ounces of soft drinks

purchased, it is important to study how fluid ounces of diet soft drinks, a close substitute of soft

drinks, were affected. It is not surprising, however, that we find that the tax had no significant

  20  

impact on the purchases of diet soft drinks (Table 5). Insignificance in the results is not a matter

of salience since shoppers received an e-mail soon after each shopping trip that notified them of

their rebate. This idea that shoppers received a rebate notice after each shopping trip suggests

that there might be a relationship between frequency of purchases of soft drinks and other

beverages. In other words, frequent buyers of soft drinks might respond more to the tax

compared to those who purchase it with a lesser frequency. We explore this further in the

paragraphs that follow.

In the previous section we introduced our construction of purchase frequency as the

number of months in which a shopper did not purchase a specific beverage at least once. If a

shopper never purchased water then the value would equal 0; a purchase of at least once during

one month only would result in a value of 1, and so forth. To analyze the way in which

fluctuations in purchases of soft drinks trigger purchases of other beverages we estimate equation

(2) using four different dependent variables–difference between fluid ounces of soft drinks and

diet soft drinks, sugar-sweetened fruit juice, whole/flavored milk, and water. It is important to

note that sugar-sweetened fruit juice and whole/flavored milk were also taxed in the experiment.

Thus shoppers have the same incentive to reduce purchases of those beverages as they do soft

drinks. Yet, we don’t find this is completely supported in the data for sugar-sweetened fruit

juice. Results in Panel A of Table 6 show that the tax led consumers to purchase 63.42 fewer

fluid ounces of full calorie soft drinks than diet soft drinks (p < 0.05). Since households use at

least a portion of their rebate to purchase more soft drinks, it appears that their purchases of full

calorie soft drinks have diminished relative to purchases of diet soft drinks. The tax had no

impact on the difference of fluid ounces purchased between full calorie soft drinks and the other

beverages.

  21  

Purchase frequency does have a statistically significant impact for sugar-sweetened fruit

juice (32.41 fl oz; p < 0.01), whole/flavored milk (35.21 fl oz; p < 0.01), and water (33.00; p <

0.05). In other words, consumers who typically purchased soft drinks with greater frequency

ended up purchasing more fluid ounces of soft drinks relative to sugar-sweetened fruit juice,

whole and flavored milk, and water. Even though soft drinks, as well as sugar sweetened fruit

juice and whole and flavored milk, is taxed, all households still demonstrate a strong preference

for the beverage. Note, however, that the frequency variable includes households from both the

control and treatment groups, but the interaction term separates out the households in the

treatment group.

The interaction terms in Panel A of Table 6 suggest that households in the tax group that

purchase soft drinks more frequently end up buying 15.51 fewer fluid ounces of whole and

flavored milk (p < 0.05). Even though both soft drinks and whole and flavored milk are all

taxed, the policy ineffectively steers consumers from the higher calorie beverages, especially

those who are accustomed to frequently purchasing sugar-sweetened beverages. Households in

the tax group also make use of the reimbursement to purchase 9.06 more fluid ounces (p < 0.05)

of diet soft drinks, relative to full calorie soft drinks. Results in Table 6 also show a holiday

effect such that consumers purchase 55.27 more fluid ounces of sugar-sweetened fruit juice (p <

0.05) and 50.03 more fluid ounces of whole and flavored milk (p < 0.05) relative to purchases of

full calorie soft drinks.

To isolate households with strong preferences for soft drinks, we estimated equation (2)

but conditioned the estimation procedure on households that purchased soft drinks in three or

more months. In doing this, we find that the tax actually leads consumers to purchase 425.4 (p <

0.01) more fluid ounces of soft drinks, relative to water. Frequent soft drink buyers purchase

  22  

41.17 fluid ounces more of soft drinks relative to sugar-sweetened fruit juice (p < 0.01) and an

additional 49.26 fluid ounces (p < 0.05) relative to whole and flavored milk, again demonstrating

strong preferences for soft drinks. We also find that the tax is effective in decreasing the fluid

ounces of soft drinks purchased relative to sugar-sweetened fruit juice, a 43.50 fluid ounce

decrease (p < 0.05), and water, a 94.89 fluid ounce decrease (p < 0.01). Even though initial

results suggested that the tax had no impact on purchasing behavior, it did have an impact on

those who purchase soft drinks with greater frequency. Indeed, a tax triggered these shoppers to

purchase not only more water, but also more sugar-sweetened fruit juice. Without calorie

counts, though, it would be difficult to determine whether the tax had a net positive or negative

effect on potential health outcomes.

In Panel A of Table 7, we report results for estimation of equation (2) when the

dependent variable is the difference between calories of soft drinks purchased and sugar-

sweetened fruit juice and whole/flavored milk. We do not include purchases of diet soft drinks

or water because these beverages have no calories. Taxing sugar-sweetened beverages does not

affect the difference in calories purchased. Once again, frequent soda buyers seem to prefer

calories from soft drinks, relative to those from sugar-sweetened fruit juice and whole and

flavored milk. When all three are taxed, though, there is no difference in calories purchased.

Results in Panel B of Table 7 echo this same result. When the sample is conditioned on

households that purchase soft drinks during at least three months, they purchase 459 (p < 0.01)

and 530 (p < 0.01) more calories of soft drinks relative to calories of sugar sweetened fruit juice

and whole and flavored milk, respectively. Households who exhibit a strong preference for soft

drinks do demonstrate a preference for calories from soft drinks over calories from sugar-

  23  

sweetened fruit juice, such that they purchase 468 (p < 0.05) more. There is no difference,

though, in calories purchased of whole and flavored milk.

Our initial results indicate that the tax had no significant impact on fluid ounces

purchased of soft drinks. Among frequent buyers of soft drinks, we find evidence of a strong

preference for soft drinks, such that households prefer calories from this beverage relative to

other full calorie beverages that may have more nutrients (sugar-sweetened fruit juice and whole

and flavored milk). Yet, in a rather startling set of results, we also find that the tax drives

frequent buyers of beer to purchase more beer than they would have without the tax. Even

though there are other substitutes available, frequent beer buyers seem to prefer the trade-off of

soft drinks for beer over trade-offs for other beverages.

For more statistical evidence related to this interesting interaction, we turn to the results

found in Table 8. These are results from estimating the parameters in equation (2) in which the

dependent variable is the difference between fluid ounces (or calories) of beer and soft drinks

purchased. In this equation we also include a variable that accounts for frequency of beer

purchases and interact this variable with the tax dummy variable. We find that the tax decreases

the amount of beer purchased, relative to soft drinks, by 41.1 fluid ounces per month, though this

result is statistically insignificant.

We also find that the interaction between purchase frequency–which we use to proxy for

preferences for soft drinks and beer–and the tax treatment suggests a significant correlation

between frequent beer buyers in the tax treatment and fluid ounces of beer purchased over fluid

ounces of soft drinks. Specifically, the data suggest that the more frequent buyers of beer

respond to the tax by purchasing 31.5 more fluid ounces more beer each month, translating into

an additional 352 calories (p < 0.01 for both). Not only did the tax increase the amount of

  24  

alcohol purchased by beer-drinking households, it also increased the amount of calories

purchased as well. We note that the reimbursement for participation in the study was not used

for purchasing beer, thus strengthening the claim that households substituted away from soft

drinks and purchased more beer.

Discussion

Responses in demand to taxes on energy-dense food and beverages are not well

understood. While econometric analyses of BMI in states that currently have different types of

soft drink taxes shows little to no effect (Fletcher, Frisvold, and Tefft 2010), demand estimation

models predict a sizable (34 calorie a day) influence of a 20% tax on all SSBs (Smith, Lin, and

Lee 2010). Some studies suggest that substitutions and compensation might occur that moderate

any effect on consumption (Fletcher et al., 2010b; 2011b; Chouinard et al., 2007), but most

evidence is correlational.

We report the results of a field experiment that implemented a 10% tax on less healthy

foods. The results suggest that such a tax results in small reductions in sales of soft drinks in the

first month, but there is no detectable change in such sales after 3 months or 6 months. One

explanation for this finding is a reduced salience of the tax (Chetty, Looney, Kroft 2009).

However, the treatment group was explicitly and repeatedly told of the 10% tax. Furthermore,

each participating household had their participation award (which in the treatment group equaled

15% of healthy foods and 5% for less healthy foods) loaded onto a bank card that was mailed to

them once they submitted the initial survey, reminding participants of the financial consequences

of their shopping decisions. As a result, the lack of impact of the tax on soft drink purchases

after the first month may reflect acclimation more than reduced salience, especially when

  25  

compared to soft drink taxes levied by states. These taxes are well hidden in the grocery bill.

Also, a tax would likely need to be much greater in order to generate a substantial decrease in

purchases of soft drinks.

Consistent with the previous literature, we find suggestive evidence that consumers

engage in substitution as a result of specific food taxes. Specifically, we found that households

that frequently buy beer bought even more beer and that households that frequently bought soft

drinks purchased even more. Even though we find evidence that the tax triggered sales of water,

any health benefit was completely overridden by the additional calories purchased through soft

drinks.

Taxes on less healthy foods may not succeed in reducing soft drink consumption or in

reducing calorie consumption. Of greater concern is that such a tax may encourage an increased

consumption of alcohol among some households.

Limitations and Future Research

This study has several limitations. First, we have data for only 113 households for less

than a year, which limits our statistical power and sample generalizability. Second, we observe

purchases only through one supermarket chain, and households may have bought some foods in

other stores. However, both the treatment and control group received participation incentives

(10% off all food for the control group, 5-15% off foods for the treatment group) that encouraged

participating households to do all of their shopping at the participating grocery stores. Third, we

observe only purchases, not consumption. Adda and Cornaglia (2006) found that smokers

extracted more nicotine per cigarette when cigarette taxes rose. Analogously, our shoppers may

have become more efficient consumers, throwing away less of each unit, in response to the tax.

  26  

Fourth, our control period lasted only one month but the treatment period lasted six. It would be

useful to run a control period for at least two months to average out idiosyncratic noise specific

to the control month. Fifth, we do not estimate substitutes as defined in economics. This would

require price indices, which we do not have. Furthermore, we are unable to identify direct and

indirect tax effects on purchases of substitutes and/or complements. Yet, we identify

correlational movements between beverage purchases that, along with our other data, point

towards substitutions and complementarities among beverages. Sixth, we focused only on SSBs,

which limits our discussion to trade-offs between beverages only. Yet, there are other

interactions that exist between foods and beverages that would be worthwhile to examine in

other research.

Conclusion

Despite the health-based intentions of proponents of a tax on soft drinks, we find that a

10% tax on less healthy foods leads to no significant change in soft drink purchases at either 3

months or 6 months. This suggests that taxes on energy-dense foods may not be as effective an

anti-obesity strategy as some have projected (IOM, 2009; Brownell and Frieden, 2009).

Moreover, we find evidence of unintended consequences in the form of increased purchases of

beer by certain households. The results of this field experiment complement earlier estimates of

the impact of food taxes by Fletcher et al. (2010a) and Chouinard et al. (2007). Future research

should further investigate unintended substitutions that may result from such taxes.

  27  

References

Adda, J., and F. Cornaglia. 2006. “Taxes, Cigarette Consumption, and Smoking Intensity.” Am.

Econ. Rev. 96 (4): 1013-28.

Andreyeva, T., M. Long, and K. Brownell. 2010. “The impact of food prices on consumption: a

systematic review of research on price elasticity of demand for food.” Am. J. Public Health.

100 (2): 216-222.

Beasley, T., and H. Rosen. 1999. “Sales Taxes and Prices: An Empirical Analysis.” Natl. Tax J.

52 (2): 157-78.

Brownell, K., and T. Frieden. 2009. “Ounces of Prevention—the Public Policy Case for Taxes

on Sugared Beverages.” N. Engl. J. Med. 360: 1805-1808.

Brownell, K., T. Farley, W. Willett, B. Popkin, F. Chaloupka, F. Thompson, J. Thompson, and

D. Ludwig. 2009. "The Public Health and Economic Benefits of Taxing Sugar-Sweetened

Beverages". New. Engl. J. Med. 361 (16): 1599–1605.

Campbell, D. 2011. “Experts Call for 10% ‘Fat Tax’ on Soft Drinks to Prevent Obesity.” The

Guardian, December 12, 2011. url: http://www.guardian.co.uk/society/2011/dec/21/sugary-

soft-drinks-obesity-tax?INTCMP=SRCH. Accessed on July 18, 2012.

Chetty, R., A. Looney, and K. Kroft. 2009. “Salience and taxation: theory and evidence.” Am.

Econ. Rev. 99 (4): 1145–1177.

Chouinard H., D. Davis, J. Lafrance, and J. Perloff. 2007. "Fat taxes: Big money for small

change." Forum Health Econ. Policy. 10 (2): Article 2.

Dhar, Tirtha and Kathy Baylis (2011), “Fast-Food Consumption and the Ban on advertising

Targeting Children: The Quebec Experience,” Journal of Marketing Research Vol. 48 Issue

5, 799-813

  28  

Duffey, K., P. Gordon-Larsen, J. Shikany, D. Guilkey, D. Jacobs Jr., and B. Popkin. 2010.

“Food Price and Diet and Health Outcomes: 20 Years of the CARDIA Study.” Archives

Intern. Med. 170 (5):420-426.

Duffey, K., and B. Popkin. 2007. “Shifts in Patterns and Consumption of Beverages Between

1965 and 2002.” Obesity 15: 2739-2747.

Fletcher, J., D. Frisvold, and N. Tefft. 2010a. “Can soft drink taxes reduce population weight?”

Contemp. Econ. Policy. 28 (1): 23-35.

Fletcher, J., D. Frisvold, and N. Tefft. 2010b. “The Effects of Soft Drink Taxes on Child and

Adolescent Consumption and Weight Outcomes.” J. Pub. Econ. 94 (11-12): 967-974.

Fletcher, J., D. Frisvold, and N. Tefft. 2011a. “Are Soft Drink Taxes an Effective Mechanism

for Reducing Obesity?” J. Policy Anal. Manage. 30(3): 655-662.

Fletcher, J., D. Frisvold, and N. Tefft. 2011b. “Soda Taxes and Substitution Effects: Will

Obesity Be Affected?” Choices 26(3): 1-4.

French, S., B-H. Lin, and J. Guthrie. 2003. “National Trends in Soft Drink Consumption

Among Children and Adolescents Age 6 to 17 Years: Prevalence, Amounts, and Sources,

1977/1978 to 1994/1998.” J. Amer. Diet. Assoc. 103(10): 1326-1331.

Gallet, C., and J. List. 2003. “Cigarette demand: a meta-analysis of elasticities.” Health Econ.

12 (10):821-835.

Grether, E. T and Robert J. Holloway (1967) “Impact of Government upon the Market System,”

Journal of Marketing, 31:2, 1-5.

Harrison, G., and J. List. 2004. “Field Experiments.” J. Econ. Lit. 42 (4): 1009-1055.

Institute of Medicine. 2009. “Local Government Actions to Prevent Childhood Obesity.”

Institute of Medicine Report. September 1.

  29  

Jacobson, M. 2004. “Steps to End the Obesity Epidemic.” Science. 305 (5684):611.

Jacobson, M., and K. Brownell. 2000. “Small Taxes on Soft Drinks and Snack Foods to

Promote Health.” Am. J. Public Health. 90 (6): 854-7.

Kolsarici, Ceren and Demetrios Vakratsas, (2010), “Category- Versus Brand-Level Advertising

Messages in a Highly Regulated Environment,” Journal of Marketing Research, Vol. 47

Issue 6, 1078-1089

Kuchler F., A. Tegene, and J. Harris. 2004. "Taxing snack foods: Manipulating diet quality or

financing information programs?" Rev. Agri. Econ. 27: 4-20.

Levitt, S., and J. List. 2009. “Experiments in Economics: The Past, the Present, and the Future.”

Eur. Econ. Review. 53: 1-18.

List, J. 2009. “An Introduction to Field Experiments in Economics.” J. Econ. Behav. Organ. 70:

439-442.

List, J. 2011. “Why Economists Should Conduct Field Experiments and 14 Tips for Pulling One

Off.” J. Econ. Perspect. 25 (3): 3-15.

Mohr, Gina S, Donald R. Lichtenstein, Chris Janiszewski (2012), “The Effect of Marketer-

Suggested Serving Size on Consumer Responses: The Unintended Consequences of

Consumer Attention to Calorie Information,” Journal of Marketing, 76:1, 59-75.

Mytton, O., A. Gray, M. Rayner, H. Rutter. 2007. "Could targeted food taxes improve health?"

J. Epidemiol. Community Health. 61 (8): 689–694.

Mytton, O., D. Clarke, and Mike Rayner. 2012. “Taxing Unhealthy Food and Soft Drinks to

Improve Health.” Br. Med. J. 344:e2931.

  30  

Nederkoorn, C., R. C. Havermans, J. C. Giesen, A. Hansen. 2011. “High Tax on High Energy

Dense Foods and its Effects on the Purchase of Calories in a Supermarket. An Experiment.”

Appetite. 56(3): 760-765.

Parsons, Andrew G. and Christoph Schumacher (2012) “Advertising regulation and market

drivers,” European Journal of Marketing, Vol. 46 Issue 11/12, p1539-1558.

Paterson, D. 2008. “Commentary: Why We Need an Obesity Tax.” December 18, 2008.

http://www.cnn.com/2008/US/12/18/paterson.obesity/index.html?iref=allsearch. Accessed

January 19, 2012.

Powell, L., J. Chriqui, and F. Chaloupka. 2009. “Associations between state-level soft drink

taxes and adolescent body mass index.” J. Adolesc. Health. 45(3): S57-S63.

Powell, L., and F. Chaloupka. 2009. “Food prices and obesity: Evidence and policy implications

for taxes and subsidies.” Milbank Q. 87 (1): 229-257.

Press Association. 2011. “UK Could Introduce ‘Fat Tax’, Says David Cameron.” The Guardian,

October 4, 2011. url: http://www.guardian.co.uk/politics/2011/oct/04/uk-obesity-tax-david-

cameron?INTCMP=SRCH. Accessed on July 18, 2012.

Roehr, B. 2009. “US “soda tax” could help tackle obesity, says new director of public health.”

B.M.J. (Int. Ed.) 339 (7715):316.

Rudd Report, 2009. “Soft Drink Taxes: A Policy Brief.” Rudd Center for Food Policy and

Obesity. Yale University. www.yaleruddcenter.org

Smith, T., B-H. Lin, and J-Y. Lee. 2010. “Taxing Caloric Sweetened Beverages: Potential

Effects on Beverage Consumption, Calorie Intake, and Obesity.” ERR-100, U.S. Department

of Agriculture, Economic Research Service, July 2010.

  31  

Sturm, R., L. Powell, J. Chriqui, and F. Chaloupka. 2010. “Soft drink taxes, soft drink

consumption, and children’s body mass index.” Health Affairs. 29 (5): 1052-1058.

Tipton, Martha Myslinski, Sundar G. Bharadwaj, Diana C. Robertson (2009) “Regulatory

Exposure of Deceptive Marketing and Its Impact on Firm Value,” Journal of Marketing.

73:6, 227-243.

Zafar, A. 2011. “Denmark Institutes First-Ever ‘Fat Tax’.” Time NewsFeed. September 30. url:

http://newsfeed.time.com/2011/09/30/denmark-institutes-first-ever-fat-tax/ Accessed 1-5-12.

Zheng, Y., and H. Kaiser. 2008. “Advertising and U.S. nonalcoholic beverage demand,”

Agricultural and Resource Economics Review. 31 (2):147-159.

Zheng, Y., E. McLaughlin, and H. Kaiser. 2012. “Consumer Income and Knowledge on Tax

Status on Food and Beverages.” Poster Presentation. AgEconSearch: Research in

Agricultural and Applied Economics.

  32  

Table 1: Price Discounts for Households in the Control and Treatment Groups

    August September-February

Control Group 10% discount on all rated items 10% discount on all rated items

Tax Treatment 10% discount on all rated items

5% discount on items with lowest health rating 15% discount on rated items with higher health ratings

Table 2: Frequencies of Demographic Characteristics are Similar between the Tax and Control Treatments

Variable Tax Control t-statistic of Difference

Married 0.875 0.772 1.436 (0.334) (0.423) (0.154) Not Married 0.054 0.193 -2.281 (0.227) (0.398) (0.024) Marital Status–Unknown* 0.071 0.035 0.857 (0.260) (0.186) (0.394) $10K-$40K 0.268 0.281 -0.152 (0.447) (0.453) (0.880) $40K-$70K 0.125 0.070 0.978 (0.334) (0.258) (0.330) > $70K 0.250 0.281 -0.366 (0.437) (0.453) (0.715) Income-Unknown 0.357 0.368 -0.124 (0.483) (0.487) (0.902) ≤ 1 Child 0.661 0.825 -2.012 (0.478) (0.384) (0.047) ≥ 2 Children 0.339 0.175 2.012 (0.478) (0.384) (0.047)

a. The participant either did not respond or preferred not to answer. *** p < 0.01. ** p < 0.05. * p < 0.1.

  33  

Table 3: Fluid Ounces of Sugar-Sweetened Soft Drinks Purchased by Demographics

Full calorie soft drinks Fruit Juice

Variable Number of Households

Average (fl oz)

Std. Dev.

Average (fl oz)

Std. Dev.

Taxation Condition 39 80.95 124.12 84.30 139.15 Control 50 92.66 143.81 111.22 194.40 August 89 83.25 140.69 87.08 154.99 September 89 57.55 107.14 84.89 158.91 October 89 86.27 128.91 91.09 158.79 November 89 101.44 152.67 101.74 170.87 December 89 119.17 164.92 105.43 160.25 January 89 84.51 124.66 109.02 171.13 February 89 80.50 117.26 117.79 184.92 Marital Status–Unknown* 6 64.61 98.05 89.39 128.17 Married 73 88.48 136.25 100.74 169.79 Not Married 10 94.34 149.45 97.35 160.15 $10K-$40K 25 85.38 142.08 94.29 163.24 $40K-$70K 25 87.86 136.61 95.75 154.03 Income–Unknown* 10 94.23 136.95 110.64 173.02 > $70K 29 86.79 129.19 102.68 174.91 ≤ 1 Child 69 76.51 120.43 97.69 163.71 ≥ 2 Children 20 125.53 173.37 106.60 175.08

* The participant either did not respond or preferred not to answer.

  34  

Table 4: Average Purchase Frequency by Treatment Group

Number of Households

Average Frequency* Std. Dev.

Full calorie soft drinks Taxation Condition 39 3.87 1.69 Control Group 50 3.90 1.70

Beer Taxation Condition 30 4.27 2.00 Control Group 32 4.19 1.69

* Measured as the number of months during the study when a household purchased a particular beverage.

  35  

Table 5: A Soft Drink Tax had No Significant Impact on Fluid Ounces Purchased of Soft Drinks (standard errors in parentheses)

Dependent Variables Independent Variables Full calorie soft drinks Diet soft drinks Tax -4.13 -2.69 (17.17) (28.56) Reimbursement 19.21*** 7.24* (4.44) (3.99) September -20.62 -13.29 (18.00) (24.65) October 5.19 -26.29 (22.07) (27.40) November 22.09 -4.56 (23.58) (23.62) December 36.19 9.46 (22.10) (24.65) January 0.38 2.59 (21.44) (28.34) February -13.54 3.46 (21.07) (31.14) Married -13.42 -17.27 (20.10) (54.18) No Response (marital) -32.89 17.58 (29.71) (84.87) $10K-40$K 7.77 -53.63 (21.46) (48.06) > $70K -7.29 -12.60 (22.08) (60.75) No Response (income) 7.19 -23.48 (32.62) (82.40) > 1 Child 42.78* -3.14 (22.11) (33.76) Constant 41.06* 128.32** (23.78) (57.65)

Results are coefficients from two separate panel regressions of fluid ounces of soft drinks purchased on dummy variables for the tax treatment, months in the experiment, and other household characteristics.

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

  36  

Table 6: For Frequent Purchasers of Soft Drinks, Taxes Triggered Additional Purchases of Sugar-Sweetened Fruit Juice and Water (standard errors in parentheses)

Dependent Variable

Fl oz of full calorie soft drinks minus fl oz of: Independent Variables Diet Soft Drinks Fruit Juice Whole/Flavored Milk Water

Panel A: Regression results from estimation of equation 2 Tax -63.42** 33.28 -31.54 112.90 (29.28) (27.41) (29.48) (63.37) Frequency 6.04 32.41*** 35.21*** 33.00**

(8.81) (7.36) (7.59) (14.52) Tax*Frequency 17.13* -12.52 -15.51** -29.17*

(10.31) (9.74) (9.79) (15.51) Reimbursement 9.06** 0.31 5.29 -1.54

(4.10) (3.07) (2.58) (5.16) September -4.19 -18.42 -10.20 -3.05

(22.75) (20.99) (21.57) (43.32) October 12.54 16.71 6.23 13.12

(29.94) (23.40) (24.46) (38.81) November 38.97 29.46 52.49* 60.00

(30.80) (26.83) (28.78) (44.15) December 31.09 55.27** 50.03** 78.30*

(26.11) (26.26) (23.27) (41.46) January 4.83 -11.81 14.04 10.88

(24.63) (23.67) (26.82) (45.97) February 7.67 -15.06 -17.50 33.51

(31.22) (21.41) (23.13) (41.97) Married -13.10 -12.05 4.44 -47.43

(48.92) (21.94) (26.45) (42.35) No Response (marital) -29.40 -32.29 16.79 -151.07

(58.00) (26.99) (40.94) (95.61) $10K-$40K 8.08 7.40 -1.86 30.79

(29.82) (22.97) (25.98) (41.90) >$70K -30.74 -5.48 15.01 -37.95

(32.30) (22.89) (24.79) (45.98) No Response (income) 24.57 -7.19 -36.99 -64.20

(45.08) (22.08) (42.87) (64.11) > 1 Child 5.41 8.87 35.06 -3.97

(32.72) (21.58) (24.33) (44.13) Constant -17.14 -53.24* -104.47*** -88.28

(52.63) (32.21) (37.72) (59.20) R 0.06 0.15 0.21 0.10 N 455 483 413 420

Panel B: Estimation of equation 2 conditioned on participants who purchased full calorie soft drinks in at

  37  

least three months

Tax -102.23 179.46* 177.66 425.48*** (153.99) (66.19) (114.75) (145.59) Frequency 1.04 41.17*** 49.26** 42.69*

(16.28) (13.11) (15.54) (25.47) Tax*Frequency 22.89 -43.50** -44.97 -94.89***

(31.61) (21.82) (26.75) (31.31) Reimbursement 15.92** 1.71 4.96 0.83

(6.99) (4.82) (3.96) (7.47)

R 0.04 0.13 0.17 0.16

N 259 287 238 266 *** p ≤ 0.01. ** p ≤ 0.05. * p ≤ 0.1.

  38  

Table 7: For Frequent Buyers of Soft Drinks, a Tax on Sugar-Sweetened Beverages Triggered 470 Additional Calories from Sugar-Sweetened Fruit Juice (standard errors on

parentheses) Dependent Variable

Fl oz of full calories soft drinks minus fl oz of: Independent Variables Fruit Juice Whole/Flavored Milk

Panel A: Regression results from estimation of equation 2

Tax -575 -805 (520) (664) Frequency 359*** 377*** (80) (103) Tax*Frequency 134 178 (106) (140) Reimbursement 12 25 (33) (30) September -206 -224 (228) (264) October 176 -69 (256) (290) November 311 490 (292) (351) December 602** 471 (285) (313) January -127 42 (259) (349) February -165 -506 (233) (323) Married -129 -238 (241) (339) No Response (marital) -377 351 (284) (655) $10K-$40K 79 82 (248) (399) >$70K -65 300 (245) (373) No Response (income) -62 -642 (227) (725) > 1 Child 114 402 (233) (365) Constant -574 -1147** (354) (504) R 0.16 0.15

  39  

N 483 413 Panel B: Estimation of equation 2 conditioned on participants who purchased full calorie soft drinks in at

least three months Tax -1338* -1324 (724) (1111) Frequency 459*** 530*** (143) (204) Tax*Frequency 468** 475 (237) (380) Reimbursement 30 21 (52) (54)

R 0.14 0.12

N 287 238

*** p ≤ 0.01. ** p ≤ 0.05. * p ≤ 0.1.

  40  

Table 8: Frequent Purchasers of Beer Bought More Beer When Soft Drinks Were Taxed

(standard errors in parentheses) Dependent Variable

Variable Difference between fluid ounces of beer

and soft drink purchases Difference between calories from beer

and soft drink purchases Tax -41.1 464 (26.8) (307) Frequency 10.8 144 (8.1) (89) Tax*Frequency 31.5*** 352*** (8.4) (98) Reimbursement -9.1** -97** (3.8) (42) September -14.0 -161 (24.6) (282) October -30.0 -320 (25.9) (294) November -49.4* -516 (29.6) (341) December -62.8** -685** (29.0) (337) January -81.5*** -927*** (25.5) (291) February -53.7* -598* (29.0) (334) Married 38.5 441 (41.0) (465) No Response (marital) 46.4 525 (47.1) (536) $10K-$40K 43.6* 493* (26.2) (290) >$70K 27.7 304 (29.8) (330) No Response (income) 22.0 265 (25.4) (278) > 1 Child -28.3 -307 (22.3) (248) Constant -48.5 -564 (41.5) (473) R 0.13 0 N 385 385

  41  

Results are coefficients from two separate panel regressions. The dependent variables are the difference in fluid ounces, and then calories, of beer and full calorie soft drinks purchased. The independent variables

capture the impact of the tax, purchase frequency, months, and other demographic variables on the dependent variables.

*** p ≤ 0.01. ** p ≤ 0.05. * p ≤ 0.1.

  42  

Figure 1: A Tax on Full Calorie Soft Drinks has No Impact on Fluid Ounces Purchased

-­‐30  

-­‐20  

-­‐10  

0  

10  

20  

30  

40  

50  

September   October   November   December   January   February  

Average  Fluid  Ounces  Purchased  

(Difference  from

 August)   Tax  

Control  

Figure 2: A Tax on Full-Calorie Soft Drinks did not Trigger Sales of Diet Soft Drinks or Water

-­‐100  

-­‐80  

-­‐60  

-­‐40  

-­‐20  

0  

20  

40  

60  

September   October   November   December   January   February  

Average  Fluid  Ounces  Purchased  

(Difference  from

 August)  

Sugar-­‐Sweetened  Soft  Drinks  

Diet  Soft  Drinks  

Water  

  44  

Figure 3: Fluid Ounces of Soft Drinks Were Unaffected by Tax Over Time

 

 

 

 

0  

10  

20  

30  

40  

50  

60  

70  

80  

90  

100  

Tax   Control  

Average  Fluid  Ounces  Purchased  

August   Sep   Sep-­‐Nov   Sep-­‐Feb