Journal of Public Economics 94 (2010) 967–974

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Journal of Public Economics

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The effects of soft drink taxes on child and adolescent consumption andweight outcomes☆

Jason M. Fletcher a, David E. Frisvold b,⁎, Nathan Tefft c

a School of Public Health, Division of Health Policy and Administration, Yale University, 60 College Street, New Haven, CT 06510, USAb Department of Economics, Emory University, 1602 Fishburne Drive, Atlanta, GA 30322-2240, USAc Department of Economics, Bates College, 4 Andrews Road, Lewiston, ME 04240, USA

☆ This study was supported, in part, by the Robert WoEmory Global Health Institute. We thank Alexandra Ehrlsupport with the restricted NHANES data and Jeremy GreJohn Zimmerman for excellent research assistance. WeLori Kowaleski-Jones, seminar participants at BowdoinRudd Center for Food Policy and Obesity at Yale UniverUniversity, and participants at the 2009 InternationalWorld Congress, 2009 Robert Wood Johnson Foundatio2009 Association for Policy Analysis and Management faAssociation of America Annual Meeting, and 2010Economists conference for helpful comments. The finpaper are those of the authors and do not necessarilResearch Data Center, the National Center for HealthDisease Control and Prevention.⁎ Corresponding author.

E-mail addresses: jason.fletcher@yale.edu (J.M. Fletch(D.E. Frisvold), ntefft@bates.edu (N. Tefft).

0047-2727/$ – see front matter © 2010 Elsevier B.V. Aldoi:10.1016/j.jpubeco.2010.09.005

a b s t r a c t

a r t i c l e i n f o

Article history:Received 7 September 2009Received in revised form 8 September 2010Accepted 8 September 2010Available online 17 September 2010

JEL classifications:I18H75

Keywords:ObesitySoft drink taxation

Childhood and adolescent obesity is associated with serious adverse lifetime health consequences and itsprevalence has increased rapidly. Soft drink consumption has also expanded rapidly, so much so that softdrinks are currently the largest single contributors to energy intake. In this paper, we investigate the potentialfor soft drink taxes to combat rising levels of child and adolescent obesity through a reduction in consumption.Our results, based on state soft drink sales and excise tax information between 1989 and 2006 and theNational Health Examination and Nutrition Survey, suggest that soft drink taxation, as currently practiced inthe United States, leads to a moderate reduction in soft drink consumption by children and adolescents.However, we show that this reduction in soda consumption is completely offset by increases in consumptionof other high-calorie drinks.

od Johnson Foundation and theich and Stephanie Robinson foren, Alejandra Carrillo Roa, andalso thank Meena Fernandes,College, Mt. Holyoke College,sity, and Washington and LeeHealth Economics Associationn Healthy Eating Conference,ll conference, 2010 PopulationAmerican Society of Healthdings and conclusions in thisy represent the views of theStatistics, or the Centers for

er), david.frisvold@emory.edu

l rights reserved.

© 2010 Elsevier B.V. All rights reserved.

1. Introduction

While soft drink taxes have been used for decades as a way to raiserevenues, lately there have been an increasing number of proposals toconsiderably increase these taxes in order to combat the rapidlygrowing obesity epidemic in the United States. These proposals haveoften framed soda taxation as a “sin tax” and made comparisonsbetween soft drink taxation and cigarette taxation, which has bothlowered tobacco consumption and raised considerable revenues. Theprice elasticity for soda is estimated to range between−0.8 and−1.0(Andreyeva et al., 2010), which indicates that further taxation could

lead to substantial consumption reduction. Additionally, soft drinkrevenues in the United States are approximately $70.1 billion per year,suggesting a relatively large tax revenue potential, even accountingfor the drop in consumption from a soda tax (Sicher, 2007); incomparison tobacco revenues are approximately $93.1 billion per year(Tobacco-NAFTA Industry Guide, 2009). Thus soft drink taxation couldimprove health by lowering consumption, as well as generatesubstantial revenues to relax government budget constraints andcould potentially be used for further obesity prevention or reduction.However, the potential behavioral responses to increasing soda taxeshave not been fully examined. There is relatively weak information onthe tax elasticity on consumption of soft drink taxes, and, importantly,the substitution patterns between soft drinks and other (potentiallyhigh calorie) beverages has not been adequately explored in thiscontext. Thus soda taxes could have unintended consequences andmay be an ineffective “obesity tax”.

There aremultiple examples documenting howbehavioral responsesto public policies can counteract the intent of the policies or lead tounintended consequences. One of the best known examples in theeconomics literature is an evaluation of a set of automobile safetyregulations in the late 1960s, where Peltzman (1975) shows that driversresponded to increased safety regulations by driving faster, whichcompletely offset any reductions in highway fatalities. More recently,Adams and Cotti (2008) show that smoking bans in bars have leadto increases in fatal accidents due to an increase in the distance drivento bars that allow smoking. Adda and Cornaglia (2010) find thatsmoking bans increase the exposure of non-smokers to tobacco smoke

968 J.M. Fletcher et al. / Journal of Public Economics 94 (2010) 967–974

as a result of the relocation of smokers away from bars and restaurants.There is also some evidence documenting an unintended rise in obesityand weight due to smoking bans (Fletcher, 2009) and higher cigarettestaxes, although there is mixed evidence on taxes (Chou et al., 2004,2006; Gruber and Frakes, 2006; Baum, 2009). Courtemanche (2009)seeks to harmonize these disparate findings and has shown acounterintuitive reduction in obesity following cigarette taxation,where the proposed behavioral mechanisms are through changes inexercise and food consumption. Additionally, Evans and Farrelly (1998)find that smokers respond to higher cigarette taxes by smokingcigarettes with higher tar and nicotine content. Similarly, Adda andCornaglia (2006) find that smokers adjust their behavior to smokemoreintensely in response to higher taxes. Finally, recent theoretical andsimulation research by Schroeter et al. (2008) shows there are plausiblescenarios where taxes on certain types of food (e.g. food away fromhome) could lead to increases in body weight simply due to substitutionamong types of food. Likewise, soda taxes could cause substitution toother, high-calorie beverages and increase or have no effect on netcaloric intake. In the extreme, these potential substitution patterns couldresult in a case where individuals consume no soda but offset sodaconsumption with other high-calorie beverages, such as fruit juice, juicedrinks, or whole milk. The impact of these behavioral responses wouldbe no tax revenues as well as no weight reduction and the policy wouldaccomplish neither of its proposed goals.

In this paper we combine newly collected soft drink tax databetween 1989 and 2006 with the restricted-access version of thenationally representative National Health Examination and NutritionSurvey (NHANES) in order to examine the effects of soft drink taxeson child and adolescent soft drink consumption, substitution patterns,and weight outcomes. We use standard empirical specifications fromthe cigarette taxation literature (Chaloupka and Warner, 2000),including year and state fixed effects, and conduct a series ofrobustness checks to increase confidence in the results. Overall, wefind evidence of moderate reductions in soft drink consumption fromcurrent soda tax rates. However, we also show that reductions incalories from soda are completely offset by increases in calories fromother beverages. Thus, we find that, as currently practiced, soda taxesdo not reduce weight in children and adolescents and is, therefore,likely an ineffective “obesity tax”. These results suggest that publichealth policymakers should consider behavioral responses whencrafting policies to reduce obesity.

1 See Chriqui et al. (2008) for an overview of U.S. soft drink taxes in 2007.2 http://online.wsj.com/article/SB124208505896608647.html#mod=rss_Health

(last accessed May 12, 2009).3 http://www.cnn.com/2008/HEALTH/12/18/paterson.obesity/ (last accessed May

11, 2009).4 In addition to attempting to reduce obesity, the goals of these proposals were also

to raise revenue. The tax in New York was proposed to raise revenues to help balancethe state budget and the Center for Science in the Public Interest proposed to raiserevenues to pay for national health care reform.

5 Powell et al. (2009) also take a reduced form approach and focus on adolescentweight. Like Fletcher et al. (2010a), they find no evidence of reduced form effects onadolescent weight. However, the interpretation of their results is unclear because theyare unable to control for unobserved state-level characteristics that could be correlatedwith soft drink taxes. They also are only able to examine a small set of ages (8th, 10th,and 12th graders), and can provide no evidence of whether adolescents are pricesensitive in their soda consumption decisions.

2. Background literature

The rise in childhood and adolescent obesity in the US and otherdeveloped countries has been the source of considerable debate andpublic policy effort. The effects of obesity on chronic health conditionshave been compared to the effects of aging twenty years in adults, andthe health costs associated with obesity are even greater than twoother behaviors widely recognized to cause significant harm: cigarettesmoking and alcohol consumption (Sturm, 2002). Since childhoodand adolescent obesity may increase the risk of adult obesity (Naderet al., 2006), prevention, rather than treatment, of adult obesity maybe the more effective tool in limiting lifetime health care costs.Finkelstein et al. (2009) estimate that themedical costs attributable toobesity were as high as $147 billion in 2008, which is nearly 10% of allmedical spending nationally. The additional medical costs of obesityrepresent an externality because the obese do not fully pay for theirhigher costs within pooled health insurance and public healthinsurance programs and because being insured increases obesity(Bhattacharya et al., 2009). There have been many efforts at reducingthe burden of obesity, some focus on food prices and advertising(Chou, Rashad, and Grossman 2008) and others highlight the need toincrease exercise opportunities (Cawley et al., 2007). Recently, therehave been a large number of proposals that focus on reducing

consumption of products with little nutritional value, such as softdrinks.

Soft drinks were the single largest contributor to energy intakeduring the last decade (7%) (Block, 2004) and soft drink consumptionincreased by almost 500% during the past 50 years (Putnam andAllshouse, 1999). While soft drinks are a large and growing categoryof caloric intake, it may be unclear whether focusing on a single classof consumption can lead to weight change. In fact, there is emergingevidence that small net changes in caloric consumption can lead tosubstantial changes in the prevalence of obesity over time (Cutler,Glaeser, and Shapiro, 2003). For example, Hill et al. (2003) suggestthat reducing energy intake by only 100 cal per day, which is less thanone fewer can of soda per day, could prevent weight gain in over 90%of the population. Ludwig et al. (2001) demonstrate that consumingone additional sugary drink per day over a period of eighteen monthsincreased the odds of being obese in children by 60%. Harnack et al.(1999) find that total energy intake is positively associated with theconsumption of nondiet soft drinks. Thus, it may be possible toeffectively reduce weight by targeting a single food item.

A policy that has garnered recent attention as a possible methodfor reducing weight and thus improving health is a tax on soft drinks.1

The taxation of soft drinks by states, which dates back to at least 1920(New York Times, 1920), has historically been used to raise revenue(Caraher and Cowburn, 2005). Recently, however, taxes on soft drinkshave been proposed at federal, state, and local levels as an indirect taxto reduce obesity, while continuing to raise revenue (Chouinard et al.,2007; Brownell and Frieden, 2009). For example, in May 2009 theCenter for Science in the Public Interest planned to propose a federalexcise tax on sweetened soft drinks and other beverages.2 Brownelland Frieden (2009) suggest a tax equal to a penny per ounce, which islarger than all prior taxes on soft drinks, to our knowledge. GovernorDavid Paterson of New York proposed an “obesity tax” in December2008, which consisted of an 18% tax on sugared beverages.3,4

These tax proposals have been offered with little evidence toassess their potential effectiveness. In fact, most of the rigorousevidence that exists appears to suggest limited effectiveness. Forexample, in the context of adult outcomes, Fletcher et al. (2010a) userepeated cross-sections of the Behavioral Risk Factor SurveillanceSystem (BRFSS) data combined with state and year fixed effects toshow that the reduced from effect of soft drink taxes on adult weightis negligible. The reduced form approach was unable, though, to shedlight on whether there were consumption responses to taxation.5

While the current evidence suggests no effects from soft drinktaxation on adult weight, it is possible that there may be differentialeffects of soda taxation on children. For example, Chaloupka andWarner (2000) review multiple studies of cigarette price elasticity,noting that youth have often been found to be more responsive toprice than adults (although these conclusions are complicated by thefact that youth are also subject to access restrictions). One explanationfor this possibility is that children and adolescents may spend a largershare of their budget on soft drinks than adults or households and are

969J.M. Fletcher et al. / Journal of Public Economics 94 (2010) 967–974

therefore more responsive to changes in price. If youth exhibit arelatively strong price response as in the case of cigarette consump-tion, policies that moderately change soft drink prices could lead tosubstantial declines in obesity over time among children andadolescents.

3. Conceptual and empirical framework

We conceptualize the demand for soft drinks by child i in state s asa function of individual and family socio-demographic characteristics(X), household income (I), and the prices of soda (Ps) and otherbeverages (Po):

sodais = f Ps τsð Þ; Po;Xi; Iið Þ; ð1Þ

where soft drink prices are a function of soft drink taxes imposed bythe state (τs). Our focus in this paper is estimating the effects on sodaconsumption and weight from increasing soda tax rates.6 But in orderto gain a complete understanding of the effects, it is useful to recall thedecomposition of the effects:

∂sodais∂τs

=∂sodais∂Ps

∂Ps∂τs

; ð2Þ

where∂sodais∂Ps

is the price elasticity of demand of soda and∂Ps∂τs

is the

proportion of tax pass-through or shifting from suppliers toconsumers. Because we do not have adequate price data for theindividuals in this sample and because price is endogenous in demandanalysis (Gruber and Frakes, 2006), we focus on the reduced formeffects of (exogenous) taxes on consumption. Before presenting ourempirical model, we outline what is known about the twocomponents of the reduced form estimates. This discussion alsohighlights the uncertainty of calculating consumption responseswhen not using evidence from direct tax effects.

Estimates of the price elasticity of demand for soft drinks areavailable, but there does not seem to be a consensus. While a recentsurvey of the literature suggest typical price elasticities of −0.8 to−1.0 (Andreyeva et al., 2010), more recent examples of compensatedprice elasticity estimates range from −0.15 (Zheng and Kaiser, 2008)to−1.90 (Dharmasena and Capps, 2009). The differences in estimatesare, in part, due to the specificity of food and drink categories thatmight be considered candidates for complements or substitutes. Inany case, there is a relatively large range of uncertainty for policymakers to predict demand responses to price changes given theevidence accumulated thus far.

In addition to the uncertainty surrounding price elasticities, thereis scant evidence on how soft drink taxes might affect behaviorthrough prices. Since policy makers often do not set prices directly,they must rely on indirect methods such as taxation. Kotlikoff andSummers (1987) describe the theory of tax incidence and show thattax shifting, or the proportion of a tax that is reflected by a change inprice, varies by market. In fact, Besley and Rosen (1999) show that thechange in soft drink price exceeds a tax change by 29% and suggest thatthis overshifting of the tax burden is the result of imperfectcompetition in the soft drink industry.7 Overall, however, as a resultof the recent changes in states' soft drink taxes, additional work isneeded in this area.

Even after establishing the effect of taxes on consumptionbehavior through prices, the further question remains regardinghow effectively reducing soft drink consumption will improve childweight and obesity prevalence. In fact, if youths reduce theirconsumption of caloric and non-caloric soft drinks in response to a

6 We assume the income effect is unimportant in these decisions.7 Kenkel (2005) also finds evidence of overshifting between alcohol taxes and

prices.

tax but increase their consumption of non-taxed high-caloriebeverages such as juice or whole milk, then a tax on soft drinkscould actually increase weight (Schroeter et al., 2008). For example,while a 12 ounce can of soda contains 140 cal, 12 oz of whole milkcontains 219 cal. The empirical uncertainty surrounding soft drinkprice elasticity and tax shifting combined with the theoreticalambiguity of the effect of soft drink taxes on obesity warrants ananalysis of the direct tax effects on both consumption and weightoutcomes when considering the dual policy goals of revenuegeneration and child health.

3.1. Empirical framework

Our empirical strategy regarding soft drink taxes follows theliterature on cigarette taxation (Chaloupka and Warner, 2000) andwork examining adult BMI (Fletcher et al., 2010a) by estimating stateand year fixed effects models using the repeated cross-sections of theNHANES datasets. Specifically, we estimate the empirical models,

Yistq = β′1Xistq + β2Tstq + μs + δt + γq + εistq; ð1Þ

where Yistq is the consumption or weight outcome of individual i instate s in year t in quarter q, which is determined by individual andenvironmental characteristics (Xistq), the soft drink tax in state s inyear t in quarter q (Tstq), and state, year, and quarter of year fixedeffects.8 Using this strategy, the impacts of soft drink tax rates areidentified from changes in the tax rate within states over time. Toestimate the direct impact of soft drink taxes on soda consumption,we examine whether any soft drinks are consumed, the amount ofcalories from soft drinks, and the total grams of soft drinks that areconsumed. To estimate whether children respond to soft drink taxesby changing their consumption of other beverages, we examine theamount of calories consumed from juice, juice-related drinks, andwhole milk. To estimate the impact of soft drink taxes on weightoutcomes, we examine an age and sex-specific normalizedmeasure ofbody mass index and the weight classifications of obese, overweight,and underweight.

A limitation of this strategy is the potential that unobservedcharacteristics that vary within states and over time are related to softdrink taxes and BMI. To address this limitation, we estimate additionalspecifications that also include variables that measure state economicand health conditions.

4. Data

4.1. Soft drink taxes

States currently tax soft drinks through excise taxes, sales taxes,and special exceptions to food exemptions from sales taxes. For thispaper, we define the soft drink tax as the tax on soft drinks net of taxeson other food items.

To determine the soft drink tax rate, we combine sales taxinformation with details on other soft drink taxes. The “Book of theStates” (The Council of State Governments, 1990–2007) is used toidentify state sales tax information. Published annually, it lists taxinformation and whether each state has a sales tax food exemption.This resource, LexisNexis Academic, and state departments of revenueweb sites allow us to compile state sales tax rates and effective foodtax rates by quarter. To understand how states tax soft drinks, weconduct further research with the above resources. Using LexisNexisAcademic and departments of revenue web sites, we are able to

8 With our relatively short panels of states (1989–1994 and 1999–2006) andbecause not every state is included in the survey in every year, we do not includestate-specific time trends in our preferred results, but we do perform severalrobustness checks outlined below.

Table 2Descriptive statistics.Sources: NHANES 1989–1994 and 1999–2006.

Variable Mean Standarderror

Samplesize

BMI Z-score 0.418 0.011 22,132Obese 0.148 0.004 22,132Overweight 0.297 0.005 22,132Underweight 0.033 0.002 22,132Total calories 2063.071 11.293 21,040Total calories from soft drinks 115.247 2.531 21,040Consumed any soft drink 0.564 0.006 21,040Total grams of soft drink consumption 314.876 6.645 21,040Total calories from juice 51.626 1.270 21,040Consumed any juice 0.338 0.006 21,040Total grams of juice consumption 110.739 2.621 21,040Total calories from juice drinks 66.414 1.613 21,040Consumed any juice drinks 0.369 0.006 21,040Total grams of juice drinks consumption 170.059 4.713 21,040Total calories from whole milk 58.617 1.525 21,040Consumed any whole milk 0.254 0.005 21,040Total grams of whole milk consumption 94.329 2.489 21,040Dietary recall is based on a weekday 0.626 0.005 21,040Vitamin C intake (dietary recall measure) (mg) 88.438 1.045 21,040Caffeine intake (dietary recall measure) (mg) 34.499 1.042 12,707Serum intake of vitamin C (mg/dL) 1.110 0.008 9265Serum intake of vitamin D (ng/mL) 25.923 0.116 10,449Female 0.491 0.005 22,438Age 10.513 0.043 22,438Black 0.149 0.002 22,438Other race/ethnicity(including Hispanic)

0.241 0.004 22,438

White 0.610 0.005 22,438Soft drink tax rate 2.707 0.029 22,438

Notes: Descriptive statistics are weighted using the NHANES survey weights.

Table 3The impact of soft drink taxes on soft drink consumption and calories consumed fromsoft drinks.

Consumed a softdrink

Total grams of soft drinkconsumption

Calories from softdrinks

Soft drink taxrate

−0.005 −18.052** −5.920**(0.005) (7.333) (2.834)

Observations 21,040 21,040 21,040R-squared 0.089 0.169 0.161

Notes: Heteroskedasticity-robust standard errors in parentheses that allow forclustering within states. Additional variables include female, age, age squared, black,other race, weekday, state, year, and quarter. All regressions utilize NHANES surveyweights.*Significant at 10%; **significant at 5%; ***significant at 1%.

Table 1Summary statistics: soft drink tax rates, 1989–2006.

Year All states States with a positive tax rate

Mean taxrate

Standarddeviation

Count Mean taxrate

Standarddeviation

1989 1.623 2.335 20 4.139 1.8411990 1.839 2.526 21 4.465 1.9061991 1.971 2.591 22 4.569 1.8821992 2.067 2.587 23 4.583 1.7761993 2.334 2.919 24 4.960 2.2201994 2.334 2.919 24 4.960 2.2201995 2.084 2.618 23 4.621 1.8221996 2.076 2.608 23 4.604 1.8151997 2.076 2.608 23 4.604 1.8151998 1.954 2.603 21 4.745 1.7421999 1.934 2.584 21 4.698 1.7482000 1.875 2.544 20 4.783 1.5492001 1.758 2.488 19 4.718 1.5642002 1.728 2.550 18 4.897 1.6422003 1.755 2.589 18 4.974 1.6632004 1.895 2.676 19 5.087 1.6612005 1.888 2.667 19 5.067 1.6582006 1.890 2.674 19 5.074 1.677

Note: Column variables represent means or percents across all states for the given year.

970 J.M. Fletcher et al. / Journal of Public Economics 94 (2010) 967–974

determine if soft drinks fall under food exemptions, or if they aretaxed wholly or additionally by sales or excise taxes.

We convert all tax descriptions such that they may be incorporatedinto tax rates.9 For taxes that are quoted in dollars per quantity, we

9 “Soft drinks” are commonly defined similarly across states, although this is notalways true. The broadest definitions include nonalcoholic beverages with addednatural or artificial sweeteners. In most cases the definitions include both carbonatedand noncarbonated beverages as well as soda water. In some cases there is a maximumpercent content of fruit or vegetable juice, ranging anywhere from 10% to 50%, abovewhich beverages may be excluded. Milk, milk products, and milk substitutes such assoy or rice are explicitly excluded in most cases.

convert any level tax into a percent of expenditure using the Bureau ofLabor Statistics annual nationwide price index for a quantity of soda.We do not include taxes on soft drink syrups because we do not haveinformation on the expected amount of syrup per quantity of soft drink.

Using the effective dates of each tax, we are able to combineinformation on excise taxes and sales taxes to calculate quarterly softdrink tax rates for each state. We confirm this data collection effort bycomparing with soft drink tax descriptions in Jacobson and Brownell(2000) and Chriqui et al. (2008).

Table 1 shows the average annual tax rates across all states for1989 through 2006. The average soft drink tax rate varies between 1.5and 2.3% during this period. The number of states with any tax on softdrinks in each year varies between 19 and 24 and, among states with atax, the average rate varies between 4.1 and 5.1%. Also, there were 53tax rate changes within states over time.

4.2. NHANES

The NHANES surveys are administered by the National Center forHealth Statistics (NCHS) of the CDC to assess the health and nutritionalstatus of the civilian, non-institutionalized population of the UnitedStates using a complex, multistage probability sample design. NHANESIII includes nearly 34,000 respondents and was conducted between1988 and 1994. In 1999, the NHANES program changed to consist of anationally representative sample of about 5000 persons each year;however, the sampling design remained similar to NHANES III. Of theserecent surveys, we will utilize information from 1999 to 2006.

NHANES data is collected through survey questionnaires andphysical examinations that occur primarily in a mobile examinationcenter. Importantly for this analysis, the NHANES data containinformation on body mass index (BMI), youth's soft drink and otherbeverage consumption, and demographic characteristics. Additional-ly, state of residence information is available through the NCHSRestricted Data Center, which allows us to merge our state-level taxinformation with the individual level data. As a result of a possibledisclosure risk as deemed by the NCHS staff with the restricted-accessdata in our analysis, we exclude 1988 from our sample.

Height and weight were measured by trained health techniciansduring the physical examinations and BMI was calculated as weight inkilograms divided by height in meters squared. We constructdichotomous measures of obese (BMI≥95th percentile of thehistorical age of month- and sex-specific distribution), overweightand obese (which we call overweight throughout the text)(BMI≥85th percentile), and underweight (BMIb5th percentile) andthe continuous measure of BMI Z-score10 from BMI for all individuals

10 The BMI Z-score for an individual is calculated as the BMI minus the mean BMI ofthe reference population, which is then divided by the standard deviation of thereference population, where the reference population is all individuals of the same sexand month of age. Thus, the units of the BMI Z-score are standard deviations from themean.

13 Serum levels of vitamin C are only available for ages 6 and older and are notavailable between 1999 and 2002. Missing values between 1988 and 1994 occurredbecause of “inadvertent misdilution of the ascorbic acid–serum ratio” (CDC, 2006, p.123). Serum levels of vitamin D are available for ages 12 and older in 1988 through1994, ages 6 and older in 2001 through 2002, ages 3 and older in 2003 through 2006,and are not available in 1999 through 2000. The specific measure in NHANES is 25hydroxyvitamin D (25(OH)D), which is the most reliable index of vitamin Dnutritional status and has a half-life of 2 to 3 weeks (CDC, 2009, 2010). There are atleast two sources of measurement error in serum levels of vitamin D in NHANES (CDC,2010). The first source is a reformulation of the DiaSorin radioimmunoassay (RIA) kit,which lead to differences in the measurement of 25(OH)D between 1988 through

Table 4The impact of soft drink taxes on non-soft drink beverage consumption and caloriesconsumed from non-soft drink beverages.

Juice consumption Juice drink Whole milk

Panel A: Caloric intakeSoft drink tax rate −0.058 1.857 7.670***

(1.521) (2.332) (2.156)Observations 21,040 21,040 21,040R-squared 0.033 0.032 0.066

Panel B: Grams of consumptionSoft drink tax rate 1.312 5.499 11.153***

(3.099) (6.830) (3.532)Observations 21,040 21,040 21,040R-squared 0.031 0.034 0.064

Notes: Heteroskedasticity-robust standard errors in parentheses that allow forclustering within states. Additional variables include female, age, age squared, black,other race, weekday, state, year, and quarter. All regressions utilize NHANES surveyweights.*Significant at 10%; **significant at 5%; ***significant at 1%.

Table 6The impact of soft drink taxes on nutrient intake.

Serumintake ofvitamin C

Serumintake ofvitamin D

Vitamin C intake(dietary recallmeasure)

Caffeine intake(dietary recallmeasure)

Soft drinktax rate

0.008 0.072 1.634 −3.164***(0.011) (0.372) (2.019) (1.141)

Observations 9260 10,445 21,040 12,707R-squared 0.172 0.371 0.043 0.160

Notes: Heteroskedasticity-robust standard errors in parentheses that allow forclustering within states. Additional variables for the serum intake outcomes includefemale, age, age squared, black, other race, the length of the fasting period in minutes,the time of the day that blood was drawn (morning, afternoon, and evening), state,year, and quarter. For the dietary recall outcomes, additional variables include female,age, age squared, black, other race, weekday, state, year, and quarter. All regressionsutilize NHANES survey weights.*Significant at 10%; **significant at 5%; ***significant at 1%.

Table 5The impact of soft drink taxes on total caloric intake.

Total caloric intake

Soft drink tax rate −7.840(12.353)[−32.944, 17.264]

Observations 21,040R-squared 0.145

Notes: Heteroskedasticity-robust standard errors in parentheses and confidenceinterval in brackets that allow for clustering within states. Additional variablesinclude female, age, age squared, black, other race, weekday, state, year, and quarter. Allregressions utilize NHANES survey weights.*Significant at 10%; **significant at 5%; ***significant at 1%.

Table 7The impact of soft drink taxes on BMI, obese, and overweight.

BMI Z-score Obese Overweight Underweight

Soft drink tax rate 0.015 0.009 0.002 −0.002(0.016) (0.006) (0.011) (0.003)

Observations 22,132 22,132 22,132 22,132R-squared 0.028 0.022 0.027 0.008

Notes: Heteroskedasticity-robust standard errors in parentheses that allow forclustering within states. Additional variables include female, age, age squared, black,other race, state, year, and quarter. All regressions utilize NHANES survey weights.*Significant at 10%; **significant at 5%; ***significant at 1%.

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between the ages of 3 and 18 using the sex-specific BMI for agethresholds from the 2000 CDC Growth Charts.

During the physical examinations, survey respondents completed a24-hour dietary recall with a trained dietary interviewer that detailedall foods and beverages, except water, that were consumed in theprevious 24 h.11 Children aged five years and younger, and manychildren aged six to 11 years, completed the dietary recall through aproxy respondent. The dietary interviewers contacted schools andother care providers to obtain complete dietary intake information.Individual foodswere coded and classified using the U.S. Department ofAgriculture's Survey Nutrient Database System. Using the dietary intakeinformation, we construct measures of soft drink consumption,including whether the youth consumed a soft drink during the recallperiod, the total grams consumed, and the total calories consumed fromsoft drinks. To explore the possibility of substitution effects, we alsoconstruct similarmeasures for juice, juice-like drinks, andwholemilk.12

To further examine the nutritional impacts of soft drink taxation, weexamine nutrient intake derived from the dietary recall informationfrom the laboratory component of NHANES through blood collection ofa subsample of respondents. As two composite measures of nutritionrelated to soft drink and juice consumption, we examine total caffeineintake and vitamin C intake from the dietary recall module (informa-tion for many other vitamins and nutrients, such as vitamin D, is not

11 To be consistent with NHANES III and NHANES 1999–2004, we use recall data fromonly the first day of the dietary interview for the 2005–2006 survey wave.12 These categories are defined in the Appendix. Although we are unable todistinguish between diet and regular soft drinks between 1999 and 2006, we haveinformation on grams of consumption as well as calories, which allows us some abilityto distinguish diet and regular soda consumption since diet soda does not havecalories. Under most state definitions of soft drinks with respect to taxation (discussedin an earlier footnote), soft drinks defined using the appendix categories would besubject to taxation while juice and whole milk would not. Juice drinks may be subjectto taxation depending on percent juice content, but this is not measured in NHANES.

available in this module). However, we also are able to supplement thedietary recall information with data from the laboratory module,allowing blood serum measures of vitamin D and C. Although thesesingle-nutrient analyses have several limitations,13 we focus onelements that reflect our focus on soft drink, juice, milk, and otherbeverage consumption patterns.

We merge NHANES III data with the NHANES 1999–2006 data.Most relevant survey questions are asked similarly across the surveyyears, with the exception of race and ethnicity. We measure race andethnicity as black non-Hispanic, white non-Hispanic, and other race orethnicity to construct categories which are consistent throughout thesurvey. We restrict the sample to children and adolescents betweenthe ages of 3 and 18 with non-missing height and weight or soft drinkconsumption information.14

Table 2 displays the summary statistics. Fifteen percent of childrenare obese and 30% are overweight or obese. Fifty nine percent ofchildren consume any soft drink during the day with an average of

1994 and 2001 through 2006 (CDC, 2010). Following the analytical recommendationsin CDC (2010), we adjusted the values from 1988 through 1994 to be comparable tothe measurements in 2001 through 2006. The second source of measurement error isdrifts in the assay performance that occurred between 2001 and 2006 (CDC, 2010).The year dummy variables in our empirical specifications are likely to control for some,but not all, of the assay drifts during these years.14 We focus on children in this paper in part because of the availability of previousstudies showing little to no effects of soft drink taxation on adult weight. We note,though, that we find similar results to Fletcher et al. (2010a), using the NHANES dataand find no statistically significant effects of soft drink on adult weight outcomes.Additionally, our findings suggest very small effects on soft drink consumption, whichwe plan to investigate further in future research.

Table 8Alternative specifications that control for additional state characteristics.

BMIZ-score

Obese Overweight Soft drinkcalories

Juicecalories

Juice drinkcalories

Whole milkcalories

TotalCalories

Controlling for lagged state average BMI 0.021 0.009 0.004 −5.000* 0.626 2.644 7.815*** −6.006(0.016) (0.007) (0.011) (2.726) (1.370) (2.034) (2.411) (12.031)

N 21,872 21,872 21,872 20,804 20,804 20,804 20,804 20,804Controlling for lagged state unemployment rate 0.015 0.009 0.002 −6.107** −0.069 1.708 7.643*** −8.175

(0.017) (0.006) (0.010) (2.644) (1.523) (2.119) (2.201) (12.328)N 22,132 22,132 22,132 21,040 21,040 21,040 21,040 21,040Controlling for cigarette taxes 0.017 0.010 0.004 −4.193 0.113 1.413 7.280*** −9.166

(0.017) (0.007) (0.011) (2.898) (1.484) (2.238) (2.251) (12.465)N 22,132 22,132 22,132 21,040 21,040 21,040 21,040 21,040Controlling for all of the above 0.024 0.010 0.006 −4.178 0.755 1.569 7.422*** −8.728

(0.018) (0.007) (0.011) (2.778) (1.345) (1.915) (2.677) (12.523)N 21,872 21,872 21,872 20,804 20,804 20,804 20,804 20,804

Notes: Heteroskedasticity-robust standard errors in parentheses that allow for clustering within states. Each cell represents a separate regression. Additional variables includefemale, age, age squared, black, other race, state, year, and quarter. All regressions utilize NHANES survey weights.*Significant at 10%; **significant at 5%; ***significant at 1%.

972 J.M. Fletcher et al. / Journal of Public Economics 94 (2010) 967–974

332 g or 12 oz and 122 cal per day. Although the average caloric intakefrom soda represents only 6% of the average total caloric intake, softdrinks represent a significant component of children's diets comparedto other beverages. Children are approximately twice as likely to drinkany soft drink during the day as juice, juice-related drinks, or wholemilk and the calories consumed from soft drinks are also approximatelytwice the amount of calories consumed from these other drinks.

5. Results

Table 3 presents the baseline associations between soft drink taxrates and soft drink consumption. The soft drink tax coefficientsrepresent the effects of a one percentage point increase in the tax rateon the probability of consuming a soft drink, the total grams of softdrinks consumed, and the total calories from soft drinks consumed. Allregressionmodels throughout the paper include year, quarter, and statefixed effects (unreported), and standard errors are clustered at the statelevel.15 Regressions are estimated using ordinary least squares andNHANES survey weights are used throughout the paper.16,17

As shown in Table 3, we find little influence of a tax on soft drinks onthe probability that a youth consumes soda. However, a tax on softdrinks does influence the amount of soda that youths consume. A onepercentage point increase in the soft drink tax rate reduces the amountof calories consumed by soda by nearly 6 cal, which is about 5% of theaverage calories from soda. This reduction in calories is likely not causedby a switch to diet soft drinks as there is an 18 gram decrease in softdrink consumption from a one percentage point increase in the softdrink tax rate, which is also about 5% of the average grams from soda.

Thus, our initial findings suggest that increasing the taxes on softdrinks will lead to reductions in soft drink consumption by children

15 We follow results from Cameron et al. (2008) that suggest the use of “Moulton-type” standard error estimators in the case of data with 30 or more clusters. Analternative is the “pairs-cluster bootstrap” estimator, though Cameron et al. show thatit performs no better with more than 30 clusters. In addition, Rao andWu (1988) showthat bootstrap techniques produce inconsistent estimates when used with samplingweights.16 The outcome variables that measure the grams and calories consumed of differentbeverages are naturally censored at 0. An alternative to linear regression in thepresence of censored dependent variables is the Tobit model. Our results arequalitatively similar to estimates based on Tobit models. However, estimates fromTobit models are not consistent in the presence of heteroskedastic errors or fixedeffects. Following Angrist (2001), we report the linear regression estimates as ourpreferred estimates.17 For regressions with one of the consumption outcomes as the dependent variable,we use the survey weights for the dietary recall data. For regressions with one of theweight outcomes as the dependent variable, we use the survey weights for the mobileexamination center data. To construct survey weights for our combined NHANESdataset, we follow the NCHS recommended analytic guidelines and divide the NCHS-supplied weights for each cycle by the number of cycles in our dataset. These weightsincorporate the probability of being surveyed and adjustments for non-response.

and adolescents. Themagnitude of the reduction is somewhatmodest.As discussed before, there is evidence that reducing consumption by100 cal per day could prevent weight gain in 90% of the population(Ludwig et al., 2001), but typical increases in the soda tax of 1 to 2percentage points would not dramatically affect caloric intake. Thepoint estimates suggest, though, that an increase in the tax rate ofover 16 percentage points would be required to affect a reduction insoda of approximately 100 cal per day; however, an increase of thismagnitude is outside the support of our data. Indeed, several recentproposals have called for this magnitude of tax rate increase—forexample Governor Patterson of New York recently suggested an 18%“obesity tax” on soft drinks. However, while soda consumption maybe reduced with a large tax increase, it is important to understandwhether this represents a reduction in total calories or whetherindividuals may respond to the tax by increasing consumption of non-taxed items of similar calories.

We next examine potential substitution patterns between softdrinks and other high-calorie consumption items that are nottypically included in soft drink tax definitions, such as juice, juice-related drinks, and whole milk. As shown in Table 4, there is somesuggestive evidence that soft drink taxes affect the consumption ofjuice or juice-related drink. The results do show that whole milk is asubstitute for soft drinks; a one percentage point increase in the softdrink tax rate increases caloric intake from whole milk by nearly 8 calper day, which is 13% of the average calories from whole milk.Similarly, a one percentage point increase in the soft drink tax rateincreases whole milk consumption by 11 g or 12% of the averagegrams of whole milk. The decrease in calories from soft drinks inresponse to an increase in the soft drink tax rate is completely offsetby the increase in calories from whole milk. Thus, in Table 5, theresults show that there is no statistically significant impact of the softdrink tax rate on total calories.

To provide additional evidence of the substitution patterns we findin Table 4, in Table 6 we examine the impacts of the soft drink tax rateon serum levels of vitamins C and D from the laboratory module aswell as composite intakes of caffeine and vitamin D from the dietaryrecall module. Both our main results in Table 4 and those in columns 1and 3 of Table 6 show effects consistent with positive but insignificantincreases in juice and other items containing vitamin C. We also findin column 2 a positive but insignificant increase in vitamin D bloodserum levels. The unit increase (measured in nanograms/milliliter) isconsistent with a small increase in whole milk consumption.18 Finally,

18 Vitamin D supplements with a dose of 400 IU (international units) per day havebeen shown to increase blood levels of vitamin D by approximately 10 ng/dl (Vieth,1999). One serving (244 g, or 146 cal) of whole milk includes 97.6 IU of vitamin D, sothat an 8 calorie increase in whole milk consumption per day may be expected toincrease vitamin D by approximately 0.13 ng/ml.

21 A few states have introduced restrictions on the sale of soft drinks in vendingmachines in schools in recent years. Consistent with the results of Fletcher et al.(2010b), which show that states' restrictions on vending machines in schools do notreduce soft drink consumption, we find that there is no interactive influence of softdrink taxes and vending machine restrictions, either full or partial bans, on soft drinkconsumption. However, an important caveat to this conclusion is that state-widerestrictions on vending machines began in 2003, which is near the end of our sample

Table 9The impact of soft drink taxes on consumption by day of the week/school year.

Calories fromsoft drinks

Total grams of softdrink consumption

Juicecalories

Juicedrinkcalories

Wholemilkcalories

Panel A: WeekdaySoft drinktax rate

−6.889** −21.568** −1.607 2.463 7.794***(3.179) (8.678) (1.361) (3.097) (2.567)

Observations 14,308 14,308 14,308 14,308 14,308R-squared 0.154 0.163 0.050 0.035 0.071

Panel B: WeekendSoft drinktax rate

−5.439 −17.019 3.798 0.690 4.389(5.679) (13.808) (3.539) (2.232) (2.665)

Observations 6732 6732 6732 6732 6732R-squared 0.186 0.191 0.032 0.054 0.079

Panel C: School yearSoft drinktax rate

−6.514** −20.787*** 0.739 4.161 6.847***(2.408) (5.924) (2.695) (4.030) (2.084)

Observations 12,164 12,164 12,164 12,164 12,164R-squared 0.143 0.152 0.024 0.049 0.060

Panel D: On vacation from schoolSoft drinktax rate

−5.103 −9.634 −2.747 −0.725 11.046*(3.931) (11.852) (2.338) (5.666) (5.530)

Observations 2981 2981 2981 2981 2981R-squared 0.159 0.163 0.040 0.070 0.070

Notes: Heteroskedasticity-robust standard errors in parentheses that allow forclustering within states. Additional variables include female, age, age squared, black,other race, state, year, and quarter. Regressions in panels C and D also include weekdayas an additional covariate. All regressions utilize NHANES survey weights.*Significant at 10%; **significant at 5%; ***significant at 1%.

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consistent with our soft drink consumption findings from Table 4, ourresults show a 3 milligram decrease in caffeine consumption as aresult of a 1% soft drink tax increase.19

Given the results that a change in the soft drink tax rate inducesyouths to substitute wholemilk for soft drinks and that themagnitudeof these effects is similar, it is not likely that an increase in soft drinktaxes would decrease obesity. Indeed as shown in Table 7, the resultsconfirm that soft drink taxes have little influence on BMI, overweight,or obesity among children and adolescents. Similar to Fletcher et al.'s(2010a) estimates for adults, the estimates are small in magnitudeand not statistically significant.20

Overall, our estimates, which are identified using variation withinstate over time, demonstrate that soft drink taxes reduce soft drinkconsumption. However children and adolescents are found to respondto the taxes by shifting consumption to other high-calorie beverages,such as whole milk. Therefore, the net effect of soda taxes on caloricintake is minimal, and we find no effect on weight outcomes inchildren and adolescents. Soda taxes seem to be an ineffective“obesity tax” due to a standard behavioral response to the policy,where children and adolescents consume more calories of relativelycheaper beverages, which is milk in this case.

5.1. Robustness checks

In addition to our baseline results, we also perform severalrobustness checks in order to increase confidence that we areestimating the causal effects of soft drink taxes on outcomes. Oneconcern about the validity of these estimates is whether changes instate soft drink taxes are endogenous. To address this possibility, weinclude time-varying state characteristics as additional covariates. As

19 A 12 ounce Coca-Cola contains 140 cal and 35 mg of caffeine.20 As shown in Appendix Table 1, restricting the sample to states that have ever had asoft drink tax has little influence on the results shown in Tables 3, 4, and 7.

shown in Table 8, the main results are robust to the inclusion of theone-year lagged state mean of adult BMI, one-year lagged unemploy-ment rates, and cigarette tax rates. These additional results demon-strate that the main findings in this paper are unaffected by thepossibility that states raise soft drink taxes in response to changes inpopulation weight or changes in the macro economy.

Given that children spend a large portion of their time in school, anadditional concern is that the changes in soft drink tax rates coincidewith changes in school food policies.21 For example, Clark and Gleason(2006) find that participation in the National School Lunch Program isassociated with a decrease in soft drink consumption and an increasein whole milk consumption. To address this possibility, we examinethe impact of soft drink taxes on beverage consumption on weekdaysand on weekends and we compare consumption during the schoolyear with consumption during school vacations.22 As shown inTable 9, we find evidence of a decrease in calories consumed fromsoft drinks and an increase in calories consumed from whole milkboth during school and away from school, although the estimates onweekdays and during school vacations are generally measured withless precision.

6. Conclusion

In this paper, we present the first evidence of whether soft drinktaxes are linked with consumption decisions and weight outcomes ofchildren and adolescents. We use a national sample that containsweight outcomes and consumption patterns of children and adoles-cents between 1989 and 2006. We then merge newly collected state-level soft drink tax data for this time period with the survey data inorder to use a quasi-natural experimental design to estimate the shortterm effects of soft drink taxation. Our results suggest that soft drinktaxation, as currently practiced in the United States, leads to amoderate decrease in the quantity of soft drinks consumed bychildren and adolescents. As a result, soft drink taxation may yieldlower revenues for states than expected if behavioral responses to thetax are not accounted for. Additionally, soft drink taxes do not appearto have countered the rise in obesity prevalence because anyreduction in soft drink consumption has been offset by theconsumption of other calories. Cast in this light, the revenuegeneration and health benefits of soft drink taxes appear to beweaker than expected.

Despite this evidence against the effectiveness of soft drink taxesto reduce obesity, we believe that there are at least two directions forfurther inquiry in this area. First, although there is no evidence thatsoft drink taxes improve weight outcomes in children and adoles-cents, the fact that children and adolescents substitute morenutritious whole milk for soft drinks when taxed suggests that theremay be broader health benefits that are not yet understood. Second,most historical tax rates are considerably lower than those that havebeen recently proposed, so that extrapolating our results to muchlarger increases in tax rates may not be appropriate. It is possible thatthere is a tax rate threshold at which consumers' reactions are greatlymagnified, so it is unclear whether consumer substitution patternswould be sufficiently different with large tax changes to reduce total

period.22 In each NHANES wave, individuals between the ages of 6 and 19 are askedwhether they are in school or on vacation between grades. Ninety two percent ofindividuals report being on vacation between grades during May through August;defining school vacations as vacation from school during these months yields similarresults.

974 J.M. Fletcher et al. / Journal of Public Economics 94 (2010) 967–974

caloric intake. Findings from these areas of inquiry could suggestthat there are pathways by which it is possible that soft drink taxescould indirectly improve child and adolescent health. However, theevidence to date is that soft drink taxes are ineffective as an “obesitytax”.

Appendix A. Supplementary data

Supplementary data to this article can be found online at doi:10.1016/j.jpubeco.2010.09.005.

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