a systematic review of the effectiveness of food taxes and ... · the present systematic review,...

A systematic review of the effectiveness of food taxes andsubsidies to improve diets: Understanding the recent evidence

Anne Marie Thow, Shauna Downs, and Stephen Jan

There has been significant growth in political, public, media, and academic interestin taxes and subsidies to encourage healthy food consumption over the past 3 years.The present systematic review, including an assessment of study quality, wasconducted on new evidence published between January 2009 and March 2012 forthe effect of food taxes and subsidies on consumption. Forty-three reportsrepresenting 38 studies met the inclusion criteria. Two of these were prospectiverandomized controlled trials that showed price changes were effective in bothgrocery store purchasing (subsidy) and away-from-home food purchasing (tax)contexts. The most robust modeled studies (considering substitution) showed largereffects for taxes on noncore foods or beverages for which there are close untaxedsubstitutes (such as soft drinks or “unhealthy” foods, based on nutrient profiling).Taxes and subsidies are likely to be an effective intervention to improve consumptionpatterns associated with obesity and chronic disease, with evidence showing aconsistent effect on consumption across a range of tax rates emerging. Futureresearch should use prospective study methods to determine the effect of taxes ondiets and focus on the effect of taxation in conjunction with other interventions aspart of a multisectoral strategy to improve diets and health.© 2014 International Life Sciences Institute

INTRODUCTION

Political interest in taxes and subsidies to improve dietsand prevent chronic disease remains high, with risinghealthcare costs prompting governments to investigatemultisectoral strategies for preventive health.1 In 2011,the United Nations General Assembly High-LevelMeeting on Non-Communicable Diseases recommendedimplementation of “fiscal measures” to improve diets andhealth.2 Later that year, Denmark implemented the firstnational “fat tax,” followed closely by Hungary.3 Public,media, and academic interest has kept pace: the Factivadatabase reports over 8,000 news articles on fat or softdrink taxes published in the past 2 years (Figure 1).

The premise for taxation of unhealthy foods (orsubsidy of healthy foods) is the well-established role ofprice as a driver of food choice.Advocates argue that such

fiscal policies would correct for the tendency of marketforces to encourage the consumption of ever-cheaperfatty, sugary, and salty foods.3 Critics counter by pointingout that such taxes could have a very small effect and thattaxes on goods are regressive and would thus be bornedisproportionately by the poor.4

In theory, taxes and subsidies would create fiscalincentives for consumers to consume less (or more) oftargeted foods, thus improving overall diets. Althoughfood in general is a necessity and as a product categoryhas a price elasticity of demand between zero and one,specific foods may have higher price elasticities ofdemand.5,6 The high price elasticity of demand for specificfood types is due largely to the ability of consumers tosubstitute between such foods, and it is this substitutionthat provides the mechanism by which fiscal measures(taxes and subsidies) can be employed to encourage

Affiliations: AM Thow and S Downs are with the Menzies Centre for Health Policy, University of Sydney, Sydney, New South Wales,Australia. S Jan is with the The George Institute for Global Health, Sydney, New South Wales, Australia.

Correspondence: AM Thow, Menzies Centre for Health Policy, Victor Coppleson Building (D02), University of Sydney, Sydney, NSW, 2006,Australia. E-mail: [email protected]. Phone: +61-2-9036-7003. Fax: +61-2-9351-5204.

Key words: diet, food tax, obesity, public policy, subsidies

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Lead Article

doi:10.1111/nure.12123Nutrition Reviews® 1

mailto:[email protected]

healthy diets. Examples of such substitution might bewhole-grain bread for low-fiber breads,7 or unsweetenedbeverages for sugar-sweetened beverages.8

Moreover, consumers at different levels of incomewill respond differently to taxes and subsidies, dependingon the nature of the targeted good. In general, as incomesincrease, the demand for most food types will tend toincrease, and vice versa (known as “income elasticity ofdemand,” which measures a percentage change in quan-tity demanded given a 1% change in income). There are,however, certain classes of foods, i.e., “inferior goods,” inwhich the demand-income relationship works in oppo-site directions. An example may be lower-quality meatproducts in which demand may increase as incomesdecrease.6 The significance of this is that fiscal measurestend to have income as well as substitution effects. Forexample, a food tax may effectively lower the income of ahousehold as purchases at the taxed price further depletethe household budget.

The diversity of studies, in terms of research method,subjects, tax, target food, and recommendations, createsconfusion and renders shaky the ground on which policymight be built.9,10 In particular, collecting complete dataon changes in consumption (and other measures) inresponse to tax or subsidy-induced price changes remainschallenging for several reasons: dietary surveys do notcollect pricing data; location-specific interventions andeconomic data give a limited perspective on consump-tion; and sales data tell little about individual behavior.

Recent reviews have examined the findings of spe-cific types of studies, including experimental studies11 andmodeling studies,12,13 or the findings on specific out-comes, such as chronic disease.3 This review adds to the

literature by assessing international evidence from a widerange of study types and developing a classificationchecklist to guide assessment of study quality in this field,based on the Cochrane system used most in public health.Systematic reviews are gold standard methods for assess-ing effectiveness in public health and clinical/medical set-tings; in the present review, this method is applied to anarea of broader public policy in which there is an inter-section between public health, policy, and economics. Inassessing effectiveness, the effect of tax and subsidy poli-cies on consumption, which is the basis for the effect oftaxes and subsidies on body weight and chronic diseasewas emphasized. The aim was to reduce confusion andinform policymaking by consolidating the recent evi-dence and explaining the differences in methodologiesused.

METHODS

Inclusion criteria

The criteria for inclusion of a study in this review were asfollows: 1) study was based on empirical data, excludingreviews, commentaries, and editorials; 2) study examineda tax or subsidy targeted to influence the price of a spe-cific food product or nutrient (i.e., general agriculturalsubsidies and general food taxes were excluded); and 3)study assessed the effect of the tax on food and/or nutri-ent consumption. Modeling and stated preference studieswere included because of their high prevalence in thisfield and the likelihood that such evidence heavily influ-ences policymaking in this area.

Figure 1 Media references to taxes on fat or soft drink (search results from Factiva database).Note: Search terms were “(fat and tax and health) or (soda and tax and health).”

Nutrition Reviews®2

Search strategy

The MEDLINE, Web of Knowledge, EconoLit, and Busi-ness Source Premier academic databases and GoogleScholar (the first 15 pages of each search using GoogleScholar were examined) were searched using the term(“tax” or “subsidy”) with the terms (“food” and “con-sumption”), “soft drink,” “obesity,” “diet,” “nutrition,” and“fat” or their equivalent Medical Subject Heading terms,as appropriate, for the time period January 2009 to March2012. Only English-language literature was included.Grey literature, where it met the inclusion criteria, alsowas included because it comprises an easily accessiblesource of evidence for policymaking and needs to becritiqued along with formally published studies.

A total of 191 unique citations were identified fromthe databases on the basis of title (37 from BusinessSource Premier, 21 from Google Scholar, 43 fromEconoLit, 71 from Web of Knowledge, and 19 fromMEDLINE). When the titles from each database werecombined, 32 citations were excluded as duplicates,

leaving 159 abstracts for review. Of these, 54 met thecriteria for full-text review, and 43 papers representing 38studies met the inclusion criteria (Figure 2). Thirty-twostudies were reported in the peer-reviewed literature andeight in the grey literature (two were reported in both).

Study quality was assessed using the criteria describedin Box 1. The assessment tool was based on the Cochranehierarchy of evidence,14 using as the reference pointwhether the study directly and prospectively observedconsumer responses to a fiscal policy intervention(revealed preferences) when compared with studies that 1)did not observe behavior in response to an interventionbut rather extrapolated from data in which there was nodirect assessment of effect of a tax (e.g., routinely collectedexpenditure data and/or dietary data), 2) estimated theeffect of state level taxes at a population level, or 3) col-lected data on stated preference in response to a hypotheti-cal scenario. This consideration was augmented withspecific nutritional and economic considerations, detailedbelow, to highlight the strengths of other methods fromnonepidemiological fields for understanding the effect offiscal policy interventions. The feasibility of the fiscalmeasure, which is an essential precursor to effectiveness,was also considered in order to acknowledge the impor-tant contribution by those studies that undertake the dif-ficult task of assessing the impact of a “real world” policyintervention. The assessment checklist was thus based onthe following: 1) the strength of the methodology used tocollect data, with respect to whether behavior wasobserved prospectively rather than being observed retro-spectively, self-reported, or imputed; 2) the strength ofanalysis,with respect to whether key variables are linked inthe dataset – namely, purchasing behavior or consump-tion, and price; 3) the completeness of the dataset, withrespect to the inclusion of foods other than the target food(allowing measurement of substitution responses) and ahigh degree of specificity of foods and nutrients; and 4) thefeasibility of implementation, with respect to whether thefiscal intervention was an actual tax or subsidy (i.e., imple-mented by a governmental body).

159 poten ally relevanton the basis of the tle(excluding duplicates)

105 excluded on the basis ofabstract (most because theydid not assess a specific tax)

54 retrieved for full textreview

9 excluded on the basis offull text review

43 papers (represen ng 38studies) included in review

Figure 2 Selection of manuscripts for systematic reviewof studies on the effect of fiscal policies on foodconsumption.

Box 1 Assessment checklist for study quality.Methodological assessment:– Does the study rely on prospective evaluation of observed behavior at point of purchase?– Is the study based on data about all food consumed (i.e., not just a subset, such as home consumption)?– Is the study based on price data linked to food purchase/consumption data for the same population?Nutritional assessment:– Does the study report the effect on diet or calorie intake more broadly than on just the target food?– Does the study apply the tax or subsidy on the basis of individual food composition (i.e., not on the basis of broad

food groups)?Administrative assessment:– Does the study assess an actual tax or subsidy rather than hypothetical measures?

Nutrition Reviews® 3

RESULTS

Four types of fiscal policies to encourage healthy dietswere assessed in the literature: sugar-sweetened beveragetaxes, fat- and calorie-based taxes, nutrient profiling-based taxes, and healthy food subsidies.

Types of studies

The range of studies reviewed drew on a wide variety ofdata and methods, summarized in Table 1.15–55 Each ofthese study types has strengths and limitations, andassessments of study quality need to consider a widerange of factors.

Prospective, directly observed evidence for the effec-tiveness of fiscal policies comes from the two randomizedcontrolled trials (RCTs).These studies observed consumerbehavior in response to price changes induced by hypo-thetical taxes on both the target foods and on other foodspurchased from the same venue, enabling the assessmentof substitution to a certain extent.However, the RCTs werelocation specific, which prevented the inclusion of data onall food purchases or consumption. Thus, substitutionoutside of the study location could not be accounted for indetermining effect size in these studies.

Thirty studies modeled estimates of effect on thebasis of a wide variety of sources of data on previouslymeasured consumer behavior. These studies used house-hold expenditure surveys, dietary survey data, longitudi-nal data, state-level obesity prevalence data, and/or salesdata. There were 13 studies based on purchase/expenditure data, in which price and purchasing behaviorwere linked. These modeled studies thus used data inwhich observed patterns of consumption could be seen tobe sensitive to variation in actual prices paid. However,such designs may also be subject to endogeneity, as pricepaid may reflect price-searching behavior on the part ofconsumers (e.g., individuals who have a high preferencefor a given food may seek out a low price). In addition,none of the modeled studies that link price and purchaseprovided information on whole consumption, just foodpurchased for “at home” consumption. Two of thesestudies assessed an actual tax or subsidy. In othermodeled studies, such as those based on dietary intakesurveys, prices were aggregated at the population leveland designed to assess the population-level effect of taxa-tion. These studies tend to utilize high-quality nutritiondata matched to aggregate price data, although sometimesfor different populations. Fourteen of the modeled studiesconsidered substitution between foods as a result of thetax, which provides a broader perspective on the effect ofthe tax on the diet as a whole.

Seven studies examined stated preference usingsurveys and online and laboratory shopping experiences.

The data generated in these studies links price directly topurchasing and also enables consideration of substitutionto other products within the hypothetical shoppingvenue.While the studies of stated preference have data onprice and purchase of individual foods, they rely on self-reported data about hypothetical purchasing decisions. Itis unclear to what extent these self-reported data reflectreal-world decisions.

Findings of studies

Details of the design and findings of the studies includedin the review are presented in Table 2.15–55 Thirty studies(79%) reported the percent change in consumption of thetarget food or nutrient, or the percentage of total calories,and also presented the tax (or hypothetical tax) as a per-centage of price. Data from these studies are presentedgraphically (Figure 3); however, the findings presented intext include all studies reviewed.

Subsidies on healthy food

The reviewed studies reported subsidies on healthy foodsthat ranged from 1.8% to 50%, and all found an increasein consumption of targeted foods (which were classifiedwithin healthy food categories or were fruit and veg-etables) of at least half the magnitude of the tax applied.One RCT conducted in supermarkets in New Zealandfound that a subsidy of 12.5% increased healthy foodpurchases by around 10%, with little to no effect onunhealthy nutrient consumption15 (Figure 3A). Similarly,a study of stated preferences from the United States founda subsidy of 50% on fruit and vegetables would increaseconsumption by 25%.54 Four studies that modeled fruitand vegetable subsidies of around 10% showed increasesin consumption of around 5%,22,30,33,36 with one study esti-mating a 1.5% increase in consumption in response to a1.8% price decrease.32

The effect of subsidies on total calorie intake isunclear. Three studies based on models found that subsi-dies paired with taxes in the range of 10–20% (sugar taxwith fruit, vegetable and fish subsidy; fat tax with fruit andvegetable subsidy; unhealthy food tax with fruit and veg-etable subsidy) could reduce total calories by a smallamount (approx. 1%).25,30,36 However, three additionalstudies based on models and three studies of stated pref-erences found an increase in overall food consumptionand total calorie intake by 1–17%, as well as in increasetarget (healthy) food consumption, with subsidies of10–30%.7,25,37,49,50,52

Taxes on sugar-sweetened beverages

Sixteen studies modeled the effect on consumption ofsugar-sweetened beverage taxes that ranged from 5% to


Table 1 Study quality in relation to checklist (Box 1).Reference Methodology Nutrition Administration

Prospectivestudy ofobservedbehavior

Data on all foodconsumption

Prices linkedto purchase

Considerssubstitution

Based onindividual foodcomposition

Actual tax/subsidy

Randomized controlled trialsNi Mhurchu et al. (2010)15 ✓ ✓ ✓ ✓Temple et al. (2011)16 ✓ ✓ ✓ ✓

Modeling studies (simulated or predicted effect based on previously reported expenditure and consumption in household purchase/expenditure surveys)

Allais et al. (2010)17 ✓ ✓Bonnet & Réquillart (2012)18 ✓ ✓ ✓Bonnet & Réquillart (2011)19 ✓ ✓ ✓Claro et al. (2012)20 ✓ ✓Dharmasena & Capps (2011),8

Dharmasena et al. (2011)21✓ ✓ ✓

Dong & Lin (2009)22 ✓ ✓ ✓Finkelstein et al. (2010)23 ✓ ✓Gustavsen et al. (2011)24 ✓ ✓Kotakorpi et al. (2011)25 ✓ ✓ ✓ ✓Lin et al. (2011)26

Smith et al. (2010)27✓ ✓ ✓ ✓

Nordström & Thunström (2011),28

builds on findings reported inNordström & Thunström (2009),7

further details in Nordström &Thunström (2011)29

✓ ✓ ✓

Tiffin & Arnoult (2011)30 ✓ ✓ ✓Zhen et al. (2011)31 ✓ ✓ ✓

Modeling studies (simulated or predicted effect based on previously reported behavior in population dietary intakesurveys)

Dallongeville et al. (2011)32 ✓ ✓Lin et al. (2010)33 ✓ ✓ ✓Miao et al. (2011)34 ✓ ✓ ✓Miao et al. (2012)35 ✓ ✓ ✓Nnoaham et al. (2009)36 ✓ ✓ ✓Okrent & Alston (2011)37 ✓ ✓ ✓Sacks et al. (2011)38 ✓ ✓Wang et al. (2012)39 ✓ ✓ ✓

Modeling studies (simulated or predicted effect based on sales data)Andreyeva et al. (2011)40 ✓Khan et al. (2012)41 ✓ ✓ ✓Lopez & Fantuzzi (2012)42 ✓ ✓

Modeling studies (simulated or predicted effect based on previously reported behavior in longitudinal studies)Duffey et al. (2010)43a ✓Khan et al. (2012)44

Smith-Spangler et al. (2010)45 ✓Modeling studies (simulated or predicted effect based on existing state-level taxes and population-level consumption/obesity

prevalence)Fletcher et al. (2010),46 also

reported in brief in Fletcheret al. (2010)47

✓ ✓

Sturm et al. (2010)48 ✓ ✓Survey-based studies (stated preferences rather than revealed preferences)

Epstein et al. (2010)49 ✓ ✓ ✓Lacroix et al. (2010)50 ✓ ✓ ✓ ✓Giesen et al. (2011)51 ✓ ✓ ✓Giesen et al. (2012)52 ✓ ✓ ✓Nederkoorn et al. (2011)53 ✓ ✓ ✓Waterlander et al. (2012)54 ✓ ✓ ✓Waterlander et al. (2012)55 ✓ ✓ ✓

a Although this study utilized data from a longitudinal study, and the standard errors were adjusted for repeated observations on individuals, theestimation method does not take advantage of the longitudinal observations to account for unobserved individual-level heterogeneity (i.e., the pointestimates are based on cross-sectional probit and Ordinary Least Squares estimations).


Tabl

e2

Des

ign

and

findi

ngs

ofst

udie

sin

clud

edin

the

revi

ew.

Refe

renc

eSt

udy

desi

gn(d

ata,

outc

ome

mea

sure

a )Po

pula

tion

(no.

,loc

atio

n,ag

e)In

terv

entio

nFi

ndin

gsPe

rcen

tcha

nge

inta

rget

Inte

rven

tion

stud

ies

(RCT

sof

resp

onse

tosp

ecifi

cta

xes)

NiM

hurc

huet

al.

(201

0)15

;pee

rre

view

ed

Stud

yty

pe:1

5-m

opa

ralle

lRCT

Dur

atio

nof

inte

rven

tion:

6m

oD

ata:

Sale

svi

ael

ectr

onic

scan

ner

Out

com

em

easu

res:

Purc

hase

volu

me

(food

&nu

trie

nts)

n=

1,10

4;N

ewZe

alan

d;8

supe

rmar

kets

(age

≥18

y,m

ain

hous

ehol

dsh

oppe

r)

Pric

edi

scou

nts

thro

ugh

Shop

’NG

osy

stem

onco

re(h

ealth

ier)

food

sth

atm

etTi

ckpr

ogra

mcr

iteria

(4gr

oups

:pric

edi

scou

nts

onhe

alth

ierf

oods

,ta

ilore

dnu

triti

oned

ucat

ion,

disc

ount

spl

used

ucat

ion,

cont

rol).

Subs

idy:

12.5

%pr

ice

redu

ctio

n(G

STre

mov

al)

Effec

tive

(hea

lthie

rfoo

dsat

6m

o↑1

1%;a

t12

mo

↑5%

);in

effec

tive

(no

effec

ton

satu

rate

dfa

tpur

chas

ed)

No

sign

ifica

nteff

ecto

nsa

tura

ted

fat

purc

hase

d;he

alth

ier

food

sat

6m

o↑1

1%

Tem

ple

etal

.(20

11)16

;pe

erre

view

edSt

udy

type

:RCT

,par

ticip

ants

blin

ded

tost

udy

purp

ose

Dur

atio

nof

inte

rven

tion:

1m

eal

Dat

a:M

easu

red

lunc

hco

nsum

ptio

n(w

eigh

t&en

ergy

dens

ityof

food

reco

rded

prio

rto

lunc

h,th

enre

peat

edfo

rlef

tove

rs)

Out

com

esm

easu

res:

Cons

umpt

ion

(food

s&

calo

ries)

n=

41;U

SA;n

onob

ese

(n=

21)&

obes

e(n

=20

);20

mal

es;

18–5

0y

Traffi

clig

htla

bels

base

don

nutr

ition

alva

lue

(not

ener

gyde

nsity

).O

non

evi

sit,

allf

oods

wer

em

arke

tpric

e,on

anot

herv

isit,

the

pric

eof

“red

”fo

ods

was

incr

ease

dby

25%

(ord

erof

visi

tsco

unte

rbal

ance

d).

Tax:

25%

Effec

tive

(con

sum

ptio

nof

“red

food

s”↓1

0%(n

onob

ese)

&↓4

0%(o

bese

),le

ssen

ergy

cons

umed

fora

ll,no

data

give

n)

Cons

umpt

ion

of“r

edfo

ods”

↓10%

inno

nobe

se(b

ack-

calc

ulat

edfr

omde

rived

elas

ticiti

es)

Mod

elin

gst

udie

s(s

imul

ated

orpr

edic

ted

effec

tba

sed

onpr

evio

usly

repo

rted

expe

ndit

ure

and

cons

umpt

ion

inho

useh

old

purc

hase

/exp

endi

ture

surv

eys)

Alla

iset

al.(

2010

)17;

peer

revi

ewed

Dat

a:Ka

ntar

Wor

dpan

elb

hous

ehol

dpu

rcha

seda

ta,1

996–

2001

(mod

esti

ncom

e)O

utco

me

mea

sure

s:Co

nsum

ptio

n

n=

30,0

00;F

ranc

e;ho

useh

olds

Food

s:Ch

eese

-but

terc

ateg

ory,

suga

r-fa

tpro

duct

s,&

/orr

eady

mea

lsTa

x:10

%

Very

smal

leffe

ct(t

otal

ener

gypu

rcha

sed

↓0.7

9%(w

ell-o

ff)&

↓1.2

0%(m

odes

tinc

ome)

Tota

lene

rgy

purc

hase

d↓0

.79%

(wel

l-off)

&↓1

.20%

(mod

est

inco

me)

Bonn

et&

Réqu

illar

t(2

012)

18;g

rey

liter

atur

e

Dat

a:Ka

ntar

Wor

dpan

elb

hous

ehol

dpu

rcha

seda

ta,2

003–

2005

Out

com

em

easu

res:

cons

umpt

ion

n=

19,0

00;F

ranc

e;ho

useh

olds

Food

s:Ca

rbon

ated

soft

drin

ks(a

lso

cons

ider

edsu

bset

ofsu

gar-

swee

tene

dbe

vera

ges)

Tax:

0.07

16eu

ros

perl

iterf

orso

ftdr

inks

≈10%

Effec

tive

(sof

tdrin

kco

nsum

ptio

n↓3

L/p

erso

n/y.

Suga

r-sw

eete

ned

beve

rage

cons

umpt

ion

decr

ease

sm

ore

with

suga

r-sw

eete

ned

beve

rage

s-on

lyta

x)

Soft

drin

kco

nsum

ptio

n↓1

5%

Bonn

et&

Réqu

illar

t(2

011)

19;p

eer

revi

ewed

Dat

a:Ka

ntar

Wor

dpan

elb

hous

ehol

dpu

rcha

seda

ta,2

003–

2005

Out

com

em

easu

res:

Cons

umpt

ion

n=

19,0

00;F

ranc

e;ho

useh

olds

Food

s:Su

gar&

suga

r-sw

eete

ned

beve

rage

s.Eff

ectiv

esu

bsid

y:re

mov

alof

pric

eflo

or,i

mpo

rtdu

ties,

expo

rtsu

bsid

ies,

&qu

otas

fors

ugar

(pric

ede

crea

seby

36%

in20

06–2

009

led

to3%

decr

ease

inso

ftdr

ink

pric

e)

Effec

tive

(con

sum

ptio

nof

regu

lars

oftd

rinks

↑1L

/per

son/

y,ad

ded

suga

r↑12

4g/

pers

on/y

)

No

%ch

ange

fors

ugar

;so

ftdr

ink

cons

umpt

ion

↑7.5

%

Clar

oet

al.(

2012

)20;

peer

revi

ewed

Dat

a:H

ouse

hold

food

cons

umpt

ion

data

,20

02–2

003

(7-d

ayfo

odpu

rcha

sere

cord

)O

utco

me

mea

sure

s:To

talc

alor

ies

from

suga

r-sw

eete

ned

beve

rage

sco

nsum

edan

dpe

rcen

tage

ofto

talc

alor

ies

purc

hase

d

n=

48,4

70;B

razi

l(re

pres

enta

tive)

;ho

useh

olds

Food

s:Su

gar-

swee

tene

dbe

vera

ges

Tax:

Exci

seta

x30

%(a

pplie

dpe

rlite

r)1.

00%

incr

ease

inpr

ice

led

to0.

85de

crea

sein

SSB

calo

ries;

30%

tax

decr

ease

dco

nsum

ptio

n25

%

Cons

umpt

ion

ofsu

gar-

swee

tene

dbe

vera

ges

↓25.

0%

Dha

rmas

ena

&Ca

pps

(201

1)8 ;p

eer

revi

ewed

Dha

rmas

ena

etal

.(2

011)

21;g

rey

liter

atur

e

Dat

a:N

iels

enH

omes

can

Cons

umer

Pane

lcda

ta,

1998

–200

3O

utco

me

mea

sure

s:Vo

lum

eof

beve

rage

spu

rcha

sed;

calo

ries

n=

NR;

USA

;hou

seho

lds

Food

s:Su

gar-

swee

tene

dbe

vera

ges

(isot

onic

s,re

gula

rsof

tdrin

ks,&

frui

tdrin

ks)

Tax:

20%

Effec

tive

(cal

orie

sfr

omis

oton

ics,

regu

lars

oft

drin

ks,d

iets

oftd

rinks

,hig

h-fa

tmilk

,&fr

uit

drin

ks↓2

6.04

,552

.01,

2.81

,15.

94,&

112.

69ca

lorie

s/pe

rson

/mo,

resp

ectiv

ely.

Calo

ries

from

frui

tju

ices

&lo

w-f

atm

ilk↑4

5.02

&20

7.67

calo

ries/

pers

on/m

o,re

spec

tivel

y)

Suga

r-sw

eete

ned

soft

drin

ks↓4

9%(t

akes

subs

titut

ion

into

acco

unt)

Don

g&

Lin

(200

9)22

;U

SDA

repo

rt(g

rey

liter

atur

e)

Dat

a:N

iels

enH

omes

can

Cons

umer

Pane

lcda

ta,

2004

(by

inco

me

grou

ps)

NH

ANES

,199

9–20

02(fo

odco

nsum

ptio

n)O

utco

me

mea

sure

s:co

nsum

ptio

n

Nie

lsen

Hom

esca

n:n

=N

R;U

SA;

hous

ehol

dsN

HAN

ES:n

=17

,074

;USA

(rep

rese

ntat

ive,

24-h

reca

ll)

Food

s:Fr

uit&

vege

tabl

esSu

bsid

y:10

%Eff

ectiv

ebu

tver

ysm

all(

frui

tcon

sum

ptio

n↑2

.1–5

.2%

;veg

etab

les

↑2.1

–4.9

%)

Frui

tcon

sum

ptio

n↑2

%;

vege

tabl

esco

nsum

ptio

n↑2

%

Fink

elst

ein

etal

.(2

010)

23;p

eer

revi

ewed

Dat

a:N

iels

enH

omes

can

Cons

umer

Pane

lcda

ta,

2006

Out

com

em

easu

res:

Volu

me

ofbe

vera

ges

purc

hase

d;ca

lorie

s

n=

NR;

USA

;hou

seho

lds

Food

s:Ca

rbon

ated

suga

r-sw

eete

ned

beve

rage

sor

all

suga

r-sw

eete

ned

beve

rage

sTa

x:20

%&

40%

Effec

tive

(car

bona

ted

suga

r-sw

eete

ned

beve

rage

s↓4

.2(2

0%ta

x)&

↓7.8

(40%

)kca

l/d/

pers

on).

Alls

ugar

-sw

eete

ned

beve

rage

s↓7

.0(2

0%ta

x)&

↓12.

4(4

0%)k

cal/d

/per

son

Calo

ries

from

suga

r-sw

eete

ned

beve

rage

s↓1

5.4%

(20%

tax)

,↓26

.3%

(40%

tax)

;cal

cula

ted

base

don

daily

ener

gyfr

omsu

gar-

swee

tene

dbe

vera

ges

&ch

ange


Gus

tavs

en&

Rick

erts

en(2

011)

24;

peer

revi

ewed

Dat

a:H

ouse

hold

expe

nditu

resu

rvey

,19

89–2

001,

diffe

rent

iate

dby

light

,mod

erat

e,an

dhe

avy

drin

kers

Out

com

em

easu

res:

Purc

hase

;cal

orie

s

n=

16,0

00;N

orw

ay;h

ouse

hold

s(e

xclu

des

purc

hase

saw

ayfr

omho

me)

Food

s:Su

gar-

swee

tene

dca

rbon

ated

soft

drin

ksTa

x:In

crea

seVA

Tfr

om13

%to

25%

(cur

rent

nonf

ood

tax)

Effec

tive

(low

-pur

chas

ing

hous

ehol

dsre

duce

purc

hase

sby

≈5L,

high

-pur

chas

ing

hous

ehol

dsby

≈20

L(N

S).

Pric

ein

crea

seof

10.6

%=

↓10.

8%re

duct

ion

inpu

rcha

se

Kota

korp

ieta

l.(2

011)

25;g

rey

liter

atur

e

Dat

a:H

ouse

hold

Budg

etSu

rvey

1995

–199

6,19

98,2

001,

&20

06,m

atch

edby

mon

thto

Cons

umer

Pric

eIn

dex

data

toes

timat

epr

ice

elas

ticity

Hea

lth20

00Su

rvey

(indi

vidu

alco

nsum

ptio

n)O

utco

me

mea

sure

s:Co

nsum

ptio

n

Budg

etsu

rvey

:n=

17,0

00;F

inla

nd;

hous

ehol

dsH

ealth

2000

Surv

ey:n

=10

,000

(rep

rese

ntat

ive,

age

NR)

Food

s:Su

gar,

frui

t,ve

geta

bles

,fish

Tax

&su

bsid

y:Ta

xof

1eu

rope

rkilo

gram

ofad

ded

suga

r(pr

ice

↑9.2

%fo

rsug

ar/s

wee

t);a

bolit

ion

ofth

ecu

rren

tVAT

onfr

esh

frui

t,ve

geta

bles

,&fis

h(p

rice

↓11.

5%).

Com

bine

dre

form

:bot

hof

the

refo

rms

abov

e

Effec

tive

(sug

arta

x:su

gar&

swee

t↓23

%.V

ATcu

t:fr

uit&

vege

tabl

ede

man

d↑5

%;fi

sh↑1

1%;c

ombi

ned

tax

&su

bsid

yha

dsl

ight

lyla

rger

effec

t)

Suga

r&sw

eetd

eman

d↓2

3%;f

ruit

&ve

geta

ble

dem

and

↑5%

;fish

dem

and

↑11%

Lin

etal

.(20

11)26

;pe

erre

view

edSm

ithet

al.(

2010

)27;

USD

Are

port

(gre

ylit

erat

ure)

Dat

a:N

iels

enH

omes

can

Cons

umer

Pane

lcda

ta,

1998

–200

7(p

rice

elas

ticiti

es)

Appl

ied

toin

divi

dual

food

inta

keda

tafr

omN

HAN

ES,2

003–

2006

Out

com

em

easu

res:

Beve

rage

cons

umpt

ion;

calo

riein

take

Nie

lsen

Hom

esca

n:n

=N

R;U

SAN

HAN

ES:n

=15

,613

;USA

(2y+

,24

-hre

call)

Food

s:Su

gar-

swee

tene

dbe

vera

ges

Tax:

20%

Effec

tive

(cal

orie

inta

kefr

omal

lbev

erag

es:

low

-inco

me

adul

ts↓1

1%;h

igh-

inco

me

adul

ts↓1

2%;l

ow-in

com

ech

ildre

n↓8

%;

high

-inco

me

child

ren

↓11%

)

Calo

riein

take

from

all

beve

rage

s:ad

ults

↓11%

;chi

ldre

n↓8

%;

take

ssu

bstit

utio

nin

toac

coun

t

Nor

dstr

öm&

Thun

strö

m(2

011)

,28bu

ilds

onfin

ding

sre

port

edin

Nor

dstr

öm&

Thun

strö

m(2

009)

,7

furt

herd

etai

lin

Nor

dstr

öm&

Thun

strö

m(2

011)

29;a

llpe

erre

view

ed

Dat

a:M

arke

tres

earc

hda

ta,2

003

Hou

seho

ldex

pend

iture

data

,199

6O

utco

me

mea

sure

s:Co

nsum

ptio

n

Mar

ketr

esea

rch:

n=

1,33

6;Sw

eden

(dai

lyre

cord

ing

ofho

useh

old

purc

hase

s)H

ouse

hold

expe

nditu

re:n

=1,

104

Food

s:Ke

yhol

e-la

bele

dgr

ain

prod

ucts

(hea

lthy

labe

ling

stra

tegy

bySw

edis

hN

atio

nalF

ood

Adm

inis

trat

ion)

Subs

idy

&ta

x:1)

0%VA

T(1

0.7%

subs

idy)

onKe

yhol

e-la

bele

dbr

ead

&br

eakf

astc

erea

ls;3

4.2%

VAT

onba

kery

good

s&

read

ym

eals

2)50

%su

bsid

yon

Keyh

ole-

labe

led

brea

d&

brea

kfas

tcer

eals

;113

.8%

VAT

onba

kery

good

s&

read

ym

eals

3)Su

bsid

yof

SEK

0.04

6pe

rgra

mof

fiber

per

kilo

gram

ofgr

ain

prod

uct;

exci

sedu

tyof

SEK

0.18

2pe

rgra

mof

adde

dsu

gar.

4)Su

bsid

yof

SEK

0.04

6pe

rgra

mof

fiber

per

kilo

gram

ofgr

ain

prod

uct;

exci

sedu

tyof

SEK

0.32

5pe

rgra

mof

satu

rate

dfa

t

Litt

leeff

ect:

1)Fi

ber↑

3%Eff

ectiv

e:2)

Fibe

r↑35

%;f

ood

grou

pco

nsum

ptio

n↑3

8%;

bake

ry−1

0%;k

J↑1

7%3)

Fibe

r↑15

%;f

ood

grou

pco

nsum

ptio

n↑3

%;

bake

ry↓1

0%;k

J↑1

0%4)

Fibe

r↑11

%;f

ood

grou

pco

nsum

ptio

n↑4

%;

bake

ry↓6

%;k

J↑5

%

Com

plex

tax

&ou

tcom

em

easu

res

Tiffi

n&

Arno

ult

(201

1)30

;pee

rre

view

ed

Dat

a:Ex

pend

iture

and

Food

Surv

ey,2

005–

2006

Out

com

em

easu

res:

Cons

umpt

ion

n=

6,76

0;U

K(2

-wk

food

diar

y,7

y+)

Food

s:Sa

tura

ted

fats

;fru

it&

vege

tabl

esTa

x&

subs

idy:

Incr

ease

the

pric

eof

fatt

yfo

ods

by1%

fore

very

perc

ento

fsat

urat

edfa

tsth

eyco

ntai

n(c

eilin

g15

%);

subs

idy

onfr

uit&

vege

tabl

esto

offse

ttax

burd

en

Smal

leffe

ct(s

hift

inco

nsum

ptio

nto

war

dre

com

men

datio

ns,≈

15%

frui

t&ve

geta

ble

subs

idy

effec

tive

inin

crea

sing

aver

age

inta

kes

tore

com

men

ded

inta

kes)

Ineff

ectiv

e(n

eglig

ible

effec

ton

dise

ase)

Tax

onw

hole

milk

(=2.

6%):

↓2.2

%co

nsum

ptio

nTa

xon

cris

ps(=

13.7

7%):

↓14.

24%

cons

umpt

ion

Zhen

etal

.(20

11)31

;pe

erre

view

edD

ata:

Nie

lsen

Nat

iona

lCon

sum

erPa

nel,

1998

–200

7,sy

nthe

ticlo

w-a

ndhi

gh-in

com

eho

useh

olds

wer

ecr

eate

dO

utco

me

mea

sure

s:Co

nsum

ptio

n

n=

6,16

1lo

w-in

com

e&

27,0

45hi

gh-in

com

eH

omes

can

hous

ehol

dsb ;U

SA

Food

s:Re

gula

rcar

bona

ted

soft

drin

k,di

etca

rbon

ated

soft

drin

k,w

hole

milk

,low

-fat

milk

,bo

ttle

dw

ater

,spo

rts

&en

ergy

drin

ks,f

ruit

juic

e,co

ffee

&te

a,su

gar-

swee

tene

dfr

uitd

rinks

Tax:

0.5

cent

spe

roun

ce

Effec

tive

[long

-run

hous

ehol

dsu

gar-

swee

tene

dbe

vera

ges

↓118

–135

12-o

zca

ns/y

(low

-inco

me)

;↓11

0–12

812

-oz

cans

/y(h

igh-

inco

me)

]

No

data

give

n

Mod

elin

gst

udie

s(s

imul

ated

orpr

edic

ted

effec

tba

sed

onpr

evio

usly

repo

rted

beha

vior

inpo

pula

tion

diet

ary

inta

kesu

rvey

s)D

allo

ngev

ille

etal

.(2

011)

32;p

eer

revi

ewed

Dat

a:D

ieta

ryin

take

sfr

omIn

divi

dual

and

Nat

iona

lStu

dyon

Food

Cons

umpt

ion

(INCA

2),2

006–

2007

Publ

ishe

del

astic

ityda

ta,i

nter

natio

nalb

utco

mpa

red

with

Fren

ches

timat

esO

utco

me

mea

sure

s:Co

nsum

ptio

n

n=

2,62

4(1

8–79

y)&

1,45

5(3

–17

y);F

ranc

eFo

ods:

Frui

t&ve

geta

bles

Subs

idy:

3.4%

redu

ctio

nin

VAT

(pric

e−1

.8%

);fo

odst

amp

(sub

sidy

)for

frui

t&ve

geta

bles

VAT

redu

ctio

neff

ectiv

e(m

ean

frui

t&ve

geta

ble

cons

umpt

ion

↑4.8

g/da

y;↑5

,024

LYS)

Food

stam

psu

bsid

yre

duce

sdi

spar

ity(m

ean

frui

t&ve

geta

ble

cons

umpt

ion

↑0.4

g/da

y;m

ean

cons

umpt

ion

bylo

w-in

com

ein

divi

dual

s↑7

.0g/

day)

Cons

umpt

ion

↑≈1.

5%

Lin

etal

.(20

10)33

;pe

erre

view

edD

ata:

Nat

iona

lFoo

dSt

amp

Prog

ram

Surv

ey,

1996

–199

7N

HAN

ES,1

999–

2002

Out

com

em

easu

res:

Cons

umpt

ion

Food

stam

psu

rvey

:n=

900;

USA

(hou

seho

lds,

food

forh

ome

cons

umpt

ion

only

;foo

dco

st)

NH

ANES

24-h

diet

ary

reca

ll:n

=7,

291

(2–1

9y)

&8,

322

(20+

y)

Food

s:H

ealth

yfo

od:f

ruits

(juic

e&

nonj

uice

),ve

geta

bles

,&flu

idm

ilkSu

bsid

y:10

%

Effec

tive

buts

mal

l(co

nsum

ptio

n:ve

geta

bles

↑4.7

%,f

ruits

↑7.0

%,d

airy

prod

ucts

↑4.2

2%)

Vege

tabl

es↑4

.7%

,fru

its↑7

.0%

,dai

rypr

oduc

ts↑4

.22%


Tabl

e2

Cont

inue

dRe

fere

nce

Stud

yde

sign

(dat

a,ou

tcom

em

easu

rea )

Popu

latio

n(n

o.,l

ocat

ion,

age)

Inte

rven

tion

Find

ings

Perc

entc

hang

ein

targ

et

Mia

oet

al.(

2011

)34;

grey

liter

atur

eD

ata:

NH

ANES

,200

3–20

04(c

onsu

mpt

ion)

Food

Pric

esD

atab

ase

2003

–200

4,m

atch

edby

food

code

toN

HAN

ESda

taO

utco

me

mea

sure

s:Co

nsum

ptio

n

NH

ANES

:n=

3,01

5;U

SA(2

4-h

reca

ll,ag

e20

+y)

Food

s:Ad

ded

suga

r&so

lidfa

tFo

odite

ms

with

high

erva

lues

than

the

refe

renc

e(a

vera

ge)v

alue

info

odgr

oup

are

clas

sifie

das

high

fat/

high

suga

r,w

hile

food

sw

itheq

ualo

rlo

wer

valu

esth

anth

ere

fere

nce

are

clas

sifie

das

low

fat/

low

suga

r.Ta

x:M

odel

scen

ario

sth

atre

duce

calo

ries

bysa

me

amou

ntas

soda

tax

of1

cent

perl

iqui

dou

nce

Effec

tive

(cal

orie

inta

ke↓2

.19%

,by

desi

gn;i

nter

nalc

ompo

sitio

nof

food

grou

psch

ange

sto

war

dle

aner

&lig

hter

choi

ces

toab

ate

the

taxe

s)Su

gart

ax:“

Soft

drin

ks,c

arbo

nate

d,”“

Suga

rs&

swee

ts,”“

Coffe

e&

tea”

↓16%

orm

ore;

“Fru

itju

ices

”↓11

%

No

data

(tax

notg

iven

aspe

rcen

tage

)

Mia

oet

al.(

2011

)35;

peer

revi

ewed

Dat

a:In

dust

ryco

nsum

ptio

n:20

02Ec

onom

icCe

nsus

Indu

stry

Serie

sRe

port

s;pu

blis

hed

pric

eel

astic

ityda

taIn

divi

dual

cons

umpt

ion:

2002

Cons

umer

Expe

nditu

reSu

rvey

Out

com

em

easu

res:

Pric

e,co

nsum

ptio

n

USA

Food

s:Ad

ded

suga

rTa

x:Re

duce

sca

loric

swee

tene

rcon

sum

ptio

nby

10%

Fina

lpro

duct

tax

optio

n:39

.30%

on“s

wee

tene

rpr

oduc

ts”

Inpu

ttax

optio

n:27

.47%

onsu

gars

,42.

95%

onco

rnsw

eete

ners

,&a

very

smal

lrat

eon

othe

rsw

eete

ners

Effec

tive

(10%

redu

ctio

nin

calo

ricsw

eete

ner

cons

umpt

ion,

byde

sign

;tax

onin

puts

min

imiz

eslo

ssof

cons

umer

wel

fare

)

10%

redu

ctio

n,by

desi

gn

Nno

aham

etal

.(2

009)

36;p

eer

revi

ewed

Dat

a:20

03–2

006

Expe

nditu

rean

dFo

odSu

rvey

;pr

ice

elas

ticiti

esfr

omN

atio

nalF

ood

Surv

ey,

1988

–200

0;eff

ecto

nCV

Dan

dca

ncer

mor

talit

yfr

ompr

evio

usm

eta-

anal

yses

Out

com

em

easu

res:

Food

cons

umpt

ion

Expe

nditu

resu

rvey

:n=

16,0

85pe

ople

with

in6,

785

hous

ehol

ds;

UK

(hou

seho

lds,

age

7+y,

2-w

kfo

oddi

ary)

Food

s:Sa

tura

ted

fat;

unhe

alth

yfo

ods;

frui

t&ve

geta

bles

Tax:

1)17

.5%

VAT

onm

ajor

sour

ces

ofsa

tura

ted

fat

2)17

.5%

VAT

onun

heal

thy

food

s3)

17.5

%VA

Ton

unhe

alth

yfo

ods

&17

.5%

subs

idy

onfr

uit&

vege

tabl

es4)

17.5

%VA

Ton

unhe

alth

yfo

ods

&32

.5%

subs

idy

(rev

enue

neut

ral)

onfr

uit&

vege

tabl

es

1)In

effec

tive:

Calo

ries

↓0.5

%,s

atur

ated

fat

↓2.4

%,s

alt↑

0.2%

,fru

it&

vege

tabl

es↓2

.7%

2)In

effec

tive:

Calo

ries

↓2.4

%,s

atur

ated

fat

↓3.1

%,s

alt↓

1.9%

,fru

it&

vege

tabl

es↓1

.5%

3)Eff

ectiv

e:Ca

lorie

s↓0

.9%

,sat

urat

edfa

t↓1%

,sa

lt↓1

.1%

,fru

it&

vege

tabl

es↑4

.8%

4)Eff

ectiv

e:ca

lorie

s↑0

.4%

,sat

urat

edfa

t↓0

.8%

,sal

t↓0.

5%,f

ruit

&ve

geta

bles

↑11%

Com

plex

tax

&ou

tcom

em

easu

res

Okr

ent&

Alst

on(2

011)

37;p

eer

revi

ewed

Dat

a:20

02be

nchm

ark

inpu

t-ou

tput

com

mod

ityus

est

atis

tics;

NH

ANES

2003

–200

4;pu

blis

hed

pric

eel

astic

ities

(farm

com

mod

ities

)O

utco

me

mea

sure

s:Ca

lorie

s

NH

ANES

n=

NR;

USA

(age

18+

y,24

-hdi

etar

yre

call)

Food

s:Ag

ricul

tura

lpro

duce

;unh

ealth

yfo

ods

Tax

&su

bsid

y:Re

mov

alof

allg

rain

subs

idie

s;re

mov

alof

alla

gric

ultu

rals

ubsi

dies

,inc

ludi

ngbo

rder

mea

sure

s;im

plem

enta

tion

of10

%fr

uit&

vege

tabl

epr

oduc

t&co

mm

odity

subs

idie

s;$0

.005

tax

perg

ram

offa

t,$0

.002

688

tax

perg

ram

ofsu

gar,

&$0

.000

165

tax

perc

alor

ie(d

esig

ned

toca

use

equi

vale

ntca

lorie

redu

ctio

n)

Farm

subs

idy

rem

oval

ineff

ectiv

e(n

eglig

ible

effec

t)Ta

xes

effec

tive

[cal

orie

cons

umpt

ion

↓19,

642

kcal

/adu

lt/y

(mor

eth

anha

lffr

omfo

odaw

ayfr

omho

me)

:cal

orie

tax

leas

tdi

stor

tiona

ry]

Subs

idy

ineff

ectiv

e(in

crea

sein

frui

t&ve

geta

ble

cons

umpt

ion,

incr

ease

inca

lorie

sby

≈1%

;co

mm

odity

subs

idy

less

dist

ortio

nary

than

prod

ucts

ubsi

dy)

No

data

for%

chan

ge

Sack

set

al.(

2011

)38;

peer

revi

ewed

Dat

a:19

95N

atio

nalN

utrit

ion

Surv

ey,f

ood

cons

umpt

ion

data

;UK

pric

eel

astic

ityes

timat

esO

utco

me

mea

sure

s:Co

nsum

ptio

n

n=

≈13,

800;

Aust

ralia

(age

20+

y)Fo

ods:

Swee

tbak

ery

prod

ucts

,sna

ckfo

ods,

conf

ectio

nary

,sof

tdrin

ksTa

x:10

%pr

ice

incr

ease

Effec

tive

(ene

rgy

inta

ke↓1

74(m

ales

)kJ/

d&

↓121

(fem

ales

)kJ/

dN

oda

tafo

r%ch

ange

Wan

get

al.(

2012

)39;

peer

revi

ewed

Dat

a:N

HAN

ES20

03–2

006

(con

sum

ptio

n);

publ

ishe

dpr

ice

elas

ticiti

es[A

ndre

yeva

etal

.(201

0)5 ]&

aver

age

pric

eus

edto

estim

ate

effec

toft

axO

utco

me

mea

sure

s:Co

nsum

ptio

n

NH

ANES

:n=

NR;

USA

(food

freq

uenc

yqu

estio

nnai

reda

ta,

25–6

4y)

Food

s:Su

gar-

swee

tene

dbe

vera

ges

Tax:

Exci

seta

xof

1ce

ntpe

roun

ce(≈

15–2

5%)

Effec

tive

(sug

ar-s

wee

tene

dbe

vera

geco

nsum

ptio

n↓1

5%)

Cons

umpt

ion

↓15%

Mod

elin

gst

udie

s(s

imul

ated

orpr

edic

ted

effec

tba

sed

onsa

les

data

)An

drey

eva

etal

.(2

011)

40;p

eer

revi

ewed

Dat

a:In

dust

ryco

nsum

ptio

n/sa

les,

2008

(Bev

erag

eM

arke

ting

Corp

orat

ion)

;cen

sus

popu

latio

npr

ojec

tions

;pub

lishe

del

astic

ityda

ta[A

ndre

yeva

etal

.(201

0)5 ]

Out

com

em

easu

res:

Cons

umpt

ion

USA

(vol

ume

indu

stry

data

onre

gion

alco

nsum

ptio

n;to

tal

sale

sof

suga

r-sw

eete

ned

beve

rage

s.Co

nsum

ptio

nac

ross

stat

esde

term

ined

bysh

are

inU

Spo

pula

tion)

Food

s:Su

gar-

swee

tene

dbe

vera

ges

(car

bona

ted

soft

drin

ks,f

ruit

beve

rage

s,re

ady-

to-d

rink

teas

,spo

rts

drin

ks,fl

avor

ed/e

nhan

ced

wat

ers,

ener

gydr

inks

,&re

ady-

to-d

rink

coffe

es)

Tax:

1ce

ntpe

roun

ce(≈

20%

)

Effec

tive

(sug

ar-s

wee

tene

dbe

vera

geco

nsum

ptio

nfr

om19

0–20

0ca

lorie

s/ca

pita

/dto

145–

150

calo

ries/

capi

ta/d

,ifn

osu

bstit

utio

nto

othe

rcal

oric

beve

rage

sor

food

)

Cons

umpt

ion

ofsu

gar-

swee

tene

dbe

vera

ges

↓24%

Khan

etal

.(20

12)41

;gr

eylit

erat

ure

Dat

a:St

ore-

leve

lsca

nner

data

onpl

ain

milk

sale

s,20

01–2

006

Out

com

em

easu

res:

Mar

kets

hare

,pur

chas

ere

spon

seto

pric

e

n=

1,50

0st

ores

;USA

(sal

es,p

rice,

prom

otio

nin

form

atio

n)Fo

ods:

Milk

Tax:

5–10

%pr

ice

incr

ease

Effec

tive

(1%

incr

ease

inpr

ice

ofw

hole

milk

decr

ease

dco

nsum

ptio

nby

2.73

%(s

hift

tolo

wer

-fat

milk

).

For1

%in

crea

sein

pric

e,co

nsum

ptio

n↓2

.73%


Lope

z&

Fant

uzzi

(201

2)42

;pee

rre

view

ed

Dat

a:Sa

les

from

Info

scan

data

base

cons

umer

char

acte

ristic

sfr

omBe

havi

oral

Risk

Fact

orSu

rvei

llanc

eSu

rvey

(BRF

SS)

Out

com

em

easu

res:

Sale

s,ca

lorie

s

Sale

s:n

=10

400;

USA

(26

bran

ds×

20ci

ties

×20

quar

ters

;do

llars

ales

,vol

ume

sold

,&%

volu

me

w/p

rom

otio

n).

BRFS

S:n

=40

,000

(100

rand

omdr

aws

perm

arke

t[ea

chci

ty&

quar

terc

ombi

natio

n])

Food

s:Ca

rbon

ated

soft

drin

ksTa

x:10

%Eff

ectiv

e(c

alor

icca

rbon

ated

soft

drin

ks↓5

.8%

)Cr

oss-

pric

e(b

rand

)ela

stic

ities

low

com

pare

dw

ithow

n-pr

ice

elas

ticiti

es,i

.e.,

will

subs

titut

ew

ithou

tsid

ego

ods

rath

erth

anw

ithot

her

soft

drin

kbr

and

Calo

ricca

rbon

ated

soft

drin

ks↓5

.8%

Mod

elin

gst

udie

s(s

imul

ated

orpr

edic

ted

effec

tba

sed

onpr

evio

usly

repo

rted

beha

vior

inlo

ngit

udin

alst

udie

s)D

uffey

etal

.(20

10)43

;pe

erre

view

edD

ata:

Long

itudi

nals

tudy

:qua

ntita

tive

food

freq

uenc

yqu

estio

nnai

re,1

985–

2006

d ;na

tiona

lfoo

dpr

ice

data

Out

com

em

easu

res:

Die

tary

inta

ke,o

vera

llen

ergy

inta

ke

n=

5,11

5;U

SA(1

8–30

y;ba

lanc

edre

pres

enta

tion

ofag

e,se

x,et

hnic

ity,&

educ

atio

ngr

oup

in4

citie

s)

Calc

ulat

edre

spon

seto

pric

ech

ange

sin

suga

r-sw

eete

ned

beve

rage

s&

pizz

aTa

x:10

%pr

ice

incr

ease

Effec

tive

fora

llou

tcom

es:1

0%in

crea

sein

pric

eof

soda

=↓7

.12%

calo

ries

from

soda

;10%

incr

ease

inpr

ice

ofpi

zza

=↓1

1.5%

calo

ries

from

pizz

a$1

.00

incr

ease

inso

dapr

ice

=lo

wer

daily

ener

gyin

take

(↓12

4kc

al)

$1.0

0in

crea

sein

the

pric

eof

both

soda

&pi

zza

=to

tale

nerg

yin

take

↓181

.49

kcal

Suga

r-sw

eete

ned

beve

rage

s:↓7

.12%

ener

gyin

take

Pizz

a:↓1

1.5%

ener

gyin

take

Khan

etal

.(20

12)44

;pe

erre

view

edD

ata:

Early

Child

hood

Long

itudi

nalS

tudy

,200

4&

2007

(freq

uenc

yof

fast

food

cons

umpt

ion)

;fo

odpr

ice

data

mat

ched

byye

arba

sed

oncl

oses

tcity

avai

labl

e;co

ntex

tual

outle

tde

nsity

data

forf

astf

ood

rest

aura

nts

Out

com

em

easu

res:

Freq

uenc

yof

fast

food

cons

umpt

ion

(wee

kly)

n=

11,7

00;U

SA(c

hild

ren

in5th

grad

e&

8thgr

ade)

Food

s:Fa

stfo

odTa

x:10

%pr

ice

incr

ease

Effec

tive

(freq

uenc

yof

fast

food

cons

umpt

ion

↓5.7

%)

Freq

uenc

yof

wee

kly

fast

food

cons

umpt

ion

↓5.7

%

Smith

-Spa

ngle

reta

l.(2

010)

45;p

eer

revi

ewed

Dat

a:Es

timat

edch

ange

inco

nsum

ptio

nus

ing

publ

ishe

des

timat

esof

elas

ticity

[Myt

ton

etal

.(2

007)

59,a

UK

stud

y]O

utco

me

mea

sure

s:Sa

ltin

take

USA

;40–

85y

Food

s:So

dium

Tax:

Exci

seta

xon

sodi

umus

edin

com

mer

cial

food

prod

uctio

nth

atw

ould

incr

ease

the

pric

eof

salty

food

sby

40%

Effec

tive

(pop

ulat

ion

sodi

umin

take

−6%

)So

dium

inta

ke−6

.0%

Mod

elin

gst

udie

s(s

imul

ated

orpr

edic

ted

effec

tba

sed

onex

isti

ngst

ate-

leve

ltax

esan

dpo

pula

tion

-leve

lcon

sum

ptio

n/ob

esit

ypr

eval

ence

)Fl

etch

eret

al.

(201

0),46

also

repo

rted

inbr

iefi

nFl

etch

eret

al.

(201

0)47

;bot

hpe

erre

view

ed

Dat

a:N

HAN

ES19

89–2

006

24-h

reca

ll;st

ate

soft

drin

kta

xra

tes

Out

com

em

easu

res:

Cons

umpt

ion

NH

ANES

:n=

21,0

4046

;n=

20,9

6847

USA

(rep

rese

ntat

ive,

age

3–18

y)Fo

ods:

Soft

drin

ksTa

x:Ex

istin

gst

ate-

leve

ltax

es(s

ales

,exc

ise,

etc.

),m

ost<

5%

No

effec

tove

rall

[1%

poin

tinc

reas

eta

x=

↓6ca

lorie

sfr

omso

da(5

%);

none

teff

ecto

nca

lorie

s(o

ffset

bysu

bstit

utio

nw

ithm

ilk)]

Soda

calo

ries:

↓5%

Tota

lcal

orie

s:no

effec

t

Stur

met

al.(

2010

)48;

peer

revi

ewed

Dat

a:Ea

rlyCh

ildho

odLo

ngitu

dina

lStu

dy,2

004

(sof

tdrin

kco

nsum

ptio

n);s

tate

-leve

ltax

data

;co

ntro

lled

forl

ocal

area

food

stor

e&

rest

aura

ntav

aila

bilit

yan

dlo

cala

rea

SES

Out

com

em

easu

res:

Cons

umpt

ion

(freq

uenc

y)

Early

Child

hood

Long

itudi

nalS

tudy

–Ki

nder

gart

enCo

hort

:n

=7,

300;

USA

(dat

afo

r5th

grad

e)

Food

s:So

ftdr

inks

Tax:

Stat

e-le

velg

roce

ryst

ore

soda

taxe

s,av

erag

eta

x4.

2%,r

ange

0–7%

No

effec

ton

freq

uenc

yof

cons

umpt

ion

(sm

all

sign

ifica

ntne

gativ

eeff

ectf

orso

me

popu

latio

ngr

oups

)

No

data

onco

nsum

ptio

nvo

lum

e

Surv

ey-b

ased

stud

ies

(sta

ted

pref

eren

ces

rath

erth

anre

veal

edpr

efer

ence

s)Ep

stei

net

al.(

2010

)49;

peer

revi

ewed

Surv

ey:S

imul

ated

groc

ery-

purc

hasi

ngta

skus

ing

pict

ures

offo

odin

labo

rato

ry.5

shop

ping

task

sD

ata:

Part

icip

ants

told

toim

agin

eno

food

inho

use,

mon

ey($

22.5

0ba

sed

onpr

evio

usre

sear

ch)t

obe

used

topu

rcha

segr

ocer

ies

for

fam

ilyfo

rthe

wee

k,re

quire

dto

spen

dal

lm

oney

allo

cate

d.“P

rodu

ct”s

elec

tion

and

purc

hase

mea

sure

dun

derl

abco

nditi

ons

Out

com

em

easu

res:

Ener

gy&

mac

ronu

trie

nts

purc

hase

d

n=

42;U

SA(h

adat

leas

t1ch

ildbe

twee

n6

y&

18y

ofag

ere

sidi

ngin

the

hous

ehol

dan

dw

asre

spon

sibl

efo

rthe

prim

ary

groc

ery

shop

ping

fort

hefa

mily

)

Pric

esba

sed

onth

ecu

rren

tpric

esat

loca

lgro

cery

stor

es,o

rlow

ered

forl

ow-c

alor

ie-fo

r-nu

trie

ntfo

ods,

orra

ised

forh

igh-

calo

rie-fo

r-nu

trie

ntfo

ods.

Ord

erof

cond

ition

sw

asco

unte

rbal

ance

d&

rand

omiz

ed.

Tax

&su

bsid

y:25

%&

12.5

%ta

xes

onhi

gh-c

alor

ie-fo

r-nu

trie

ntfo

odpr

oduc

ts;2

5%&

12.5

%su

bsid

ies

onlo

w-c

alor

ie-fo

r-nu

trie

ntfo

ods

Tax

effec

tive

(bas

edon

elas

ticiti

es:1

0%ta

xde

crea

sed

calo

ries

purc

hase

dby

6.5%

&im

prov

ednu

triti

onal

qual

ity(fa

tcal

orie

s↓1

2.8%

,car

bohy

drat

es↓6

.2%

))Su

bsid

yin

effec

tive

(10%

subs

idy

incr

ease

dca

lorie

spu

rcha

sed

↑9.8

%,n

osh

iftin

diet

qual

ity)

10%

tax:

calo

ries

purc

hase

d↓6

.5%

Lacr

oix

etal

.(20

10)50

;re

sear

chre

port

(gre

ylit

erat

ure)

Surv

ey:C

ompu

ter-

base

dsu

rvey

of“f

ood

days

.”Fo

urse

lect

ions

mad

e(2

4-h

reca

ll,th

enfo

ods

atm

arke

tpric

es;t

hen

inte

rven

tions

with

all

pric

ech

ange

svi

sibl

e)D

ata:

Allf

ood

purc

hase

rint

ende

dto

cons

ume

over

the

next

24h;

180

prod

ucts

,ret

ailp

rices

liste

dO

utco

me

mea

sure

s:Vo

lum

epu

rcha

sed

n=

107;

Fran

ce(w

omen

aged

20–5

2y;

74in

low

esti

ncom

ede

cile

;ref

eren

cesa

mpl

eof

33w

omen

inin

com

eca

tego

rygr

eate

rtha

nor

equa

lto

the

third

deci

le)

Subs

idy

onfr

uit&

vege

tabl

es;s

ubsi

dyon

frui

t,ve

geta

bles

,&“o

ther

heal

thy

prod

ucts

”;ta

xon

unhe

alth

ypr

oduc

ts.

Choi

ces

gene

rate

dre

alsa

les

aten

dof

expe

rimen

t(1

food

day

rand

omly

sele

cted

).Ta

x&

subs

idy:

30%

Tax

effec

tive

(unh

ealth

yfo

od−6

9g

inbo

thgr

oups

)Su

bsid

yon

frui

t&ve

geta

bles

effec

tive

(↑19

7g

inre

fere

nce

grou

p&

↑102

glo

w-in

com

e),b

utw

ider

subs

idy

ineff

ectiv

e(o

ther

heal

thy

prod

ucts

↑133

gin

refe

renc

egr

oup

&↓1

0g

low

-inco

me;

frui

t&ve

geta

bles

↑128

gin

refe

renc

egr

oup

&↑1

22g

inlo

w-in

com

e)

Subs

idy:

frui

t&ve

geta

bles

↑25%

Tax:

unhe

alth

yfo

od↓3

0%W

ider

subs

idy:

noda

tagi

ven


Tabl

e2

Cont

inue

dRe

fere

nce

Stud

yde

sign

(dat

a,ou

tcom

em

easu

rea )

Popu

latio

n(n

o.,l

ocat

ion,

age)

Inte

rven

tion

Find

ings

Perc

entc

hang

ein

targ

et

Gie

sen

etal

.(20

11)51

;pe

erre

view

edSu

rvey

:Lun

chm

enus

onco

mpu

ters

cree

n.Ei

ght

men

uch

oice

sfo

reac

hco

urse

.Par

ticip

ants

wer

ele

dto

belie

veth

eym

ight

rece

ive

1of

thei

rcho

sen

lunc

hes.

Thre

ese

lect

ions

mad

eD

ata:

Lunc

hse

lect

ion

Out

com

em

easu

res:

Calo

ries

purc

hase

d

n=

178;

The

Net

herla

nds

(95

men

,un

iver

sity

stud

ents

)1)

Loca

lpric

es2)

Pric

esfo

rhig

h-ca

lorie

prod

ucts

(rel

ativ

eto

food

grou

p,ba

sed

onca

lorie

spe

rpor

tion)

wer

e12

5%of

loca

lpric

es3)

150%

oflo

calp

rices

.Par

ticip

ants

rand

omly

assi

gned

toth

efo

llow

ing:

high

budg

et/c

alor

iein

form

atio

n;hi

ghbu

dget

/no

calo

riein

form

atio

n;lo

wbu

dget

/cal

orie

info

rmat

ion;

orlo

wbu

dget

/no

calo

riein

form

atio

n.Ta

x:25

%&

50%

pric

ein

crea

se

Effec

tive

(red

uced

calo

ries,

buto

nly

inab

senc

eof

calo

riein

form

atio

nbe

caus

eof

effec

tof

calo

riein

form

atio

non

“hig

h-re

stra

ined

eate

rs”)

[sig

nific

antm

ain

effec

tfor

tax

(est

imat

e,↓0

.435

;sig

nific

anti

nter

actio

nof

tax

byca

lorie

info

rmat

ion

(est

imat

e,=

0.34

5)]

No

data

give

n

Gie

sen

etal

.(20

12)52

;pe

erre

view

edSu

rvey

:Onl

ine

groc

ery

stor

e(>

700

prod

ucts

with

pict

ure

and

desc

riptio

n,€1

0fo

r1da

y’s

food

),bl

inde

dto

stud

yai

m.T

wo

purc

hasi

ngta

sks

Dat

a:G

roce

ryse

lect

ion;

base

don

split

onst

opsi

gnal

reac

tion

time,

part

icip

ants

wer

ede

sign

ated

mor

e/le

ssim

puls

ive.

Out

com

em

easu

res:

Calo

ries

purc

hase

d

n=

70;T

heN

ethe

rland

s(6

1fe

mal

e,un

derg

radu

ate

stud

ents

,re

ceiv

edco

urse

cred

its)

Firs

ttas

kpr

ices

base

don

loca

l;fo

rsec

ond

task

,pa

rtic

ipan

tsw

ere

rand

omly

assi

gned

tota

xsc

enar

io(h

igh-

ener

gy-d

ense

prod

ucts

≥30

0kc

al/

100

g)or

subs

idy

(low

-ene

rgy-

dens

epr

oduc

ts≤

150

kcal

/100

g)co

nditi

on.

Tax

&su

bsid

y:50

%

Subs

idy

effec

tive

for“

less

impu

lsiv

e”pe

ople

(mea

nca

lorie

sno

tsig

nific

antly

diffe

rent

)but

ineff

ectiv

efo

r“m

ore

impu

lsiv

e”pe

ople

[mea

nca

lorie

s↑1

,022

(cal

cula

ted)

]Ta

xin

effec

tive

for“

less

impu

lsiv

e”pe

ople

(una

ffect

ed)b

uteff

ectiv

efo

r“m

ore

impu

lsiv

e”pe

ople

(mea

nca

lorie

s↓4

98)

No

data

give

n

Ned

erko

orn

etal

.(2

011)

53;p

eer

revi

ewed

Surv

ey:I

nter

nets

uper

mar

ket(

>700

prod

ucts

with

pict

ure

and

desc

riptio

n),w

ithus

ual

budg

etof

part

icip

antf

or1

day’

sfo

od.S

ingl

epu

rcha

sing

task

Dat

a:G

roce

ryse

lect

ion

Out

com

em

easu

re:C

alor

ies

purc

hase

d

n=

306;

The

Net

herla

nds

(rec

ruite

dvi

ain

tern

etad

vert

isem

ents

,≥1

8y)

Part

icip

ants

rand

omly

assi

gned

toco

ntro

l(no

rmal

pric

es)o

rtax

(HED

≥30

0kc

al/1

00g)

food

s;33

%of

alla

vaila

ble

prod

ucts

wer

eta

xed)

Tax:

50%

Effec

tive

(HED

↓16%

;with

outt

ax,p

urch

ase

was

1,19

9H

EDkc

al/e

uro;

with

tax

(cal

cula

ted)

,pu

rcha

sew

as80

0H

EDkc

al/e

uro.

Infa

ct,

purc

hase

was

992

kcal

/eur

o,in

dica

ting

part

ialc

ompe

nsat

ion

fort

ax).

Tota

lcal

orie

s↓8

%

Hig

h-en

ergy

-den

sefo

ods

purc

hase

↓16%

Tota

lcal

orie

spu

rcha

se↓8

%

Wat

erla

nder

etal

.(2

012)

54;p

eer

revi

ewed

Surv

ey:T

hree

-dim

ensi

onal

web

-bas

edsu

perm

arke

t(>5

00pr

oduc

tsw

ithph

otos

&la

bels

),bl

inde

dto

stud

yai

ms.

Sing

lepu

rcha

sing

task

Dat

a:G

roce

ryse

lect

ion

Out

com

em

easu

res:

Frui

t&ve

geta

ble

purc

hase

s,ot

herf

ood

expe

nditu

res;

calo

ries

n=

115;

The

Net

herla

nds

(une

mpl

oyed

and/

orha

dco

mpl

eted

am

ediu

mse

cond

ary

voca

tiona

ledu

catio

nor

low

er;

≥18

y;D

utch

lang

uage

spea

ker;

ran

his/

hero

wn

hous

ehol

d)

Part

icip

ants

rece

ived

afix

edbu

dget

and

wer

eas

ked

tobu

yw

eekl

yho

useh

old

groc

erie

sat

the

web

-bas

edsu

perm

arke

t.Pa

rtic

ipan

tsw

ere

rand

omly

assi

gned

toco

ntro

l(lo

calp

rices

)or

inte

rven

tion

(25%

disc

ount

onfr

uits

&ve

geta

bles

)Su

bsid

y:25

%

Effec

tive

(frui

t&ve

geta

ble

purc

hase

s↑2

5%,

sam

eca

lorie

sas

cont

rol)

Frui

t&ve

geta

ble

purc

hase

↑25%

Wat

erla

nder

etal

.(2

012)

55;p

eer

revi

ewed

Surv

ey:T

hree

-dim

ensi

onal

web

-bas

edsu

perm

arke

t,bl

inde

dto

stud

yai

m,

rand

omiz

ed.S

ingl

epu

rcha

sing

task

Dat

a:G

roce

ryse

lect

ion

Out

com

em

easu

res:

Volu

me

purc

hase

d,bu

dget

spen

ding

,&ca

lorie

s

n=

117;

The

Net

herla

nds

(une

mpl

oyed

and/

orha

dco

mpl

eted

am

ediu

mse

cond

ary

voca

tiona

ledu

catio

nor

low

er;

≥18

y;D

utch

lang

uage

spea

ker;

ran

his/

hero

wn

hous

ehol

d)

Pric

ere

duct

ion

onhe

alth

yfo

ods

(non

e,25

%,o

r50

%)×

pric

ein

crea

seon

unhe

alth

yfo

ods

(5%

,10

%,o

f25%

).H

ealth

yve

rsus

unhe

alth

yfo

odde

fined

usin

g“C

hoic

es”f

ront

-of-

pack

nutr

ition

logo

(crit

eria

base

don

WH

Ore

com

men

datio

nsab

outs

atur

ated

fat,

tran

sfa

t,so

dium

,&ad

ded

suga

r).

Tax

(5%

,10%

,or2

5%)&

subs

idy

(25%

or50

%)

Subs

idy

effec

tive

(mea

nhe

alth

yfo

odpu

rcha

ses

↑6.6

2fo

ods;

prop

ortio

nof

heal

thy

food

sun

affec

ted;

calo

ries

↑10,

505

kcal

).Ta

x:N

oeff

ect

Tax:

0%ch

ange

Abbr

evia

tions

:BRF

SS,B

ehav

iora

lRis

kFa

ctor

Surv

eilla

nce

Surv

ey;C

SD,c

arbo

nate

dso

ftD

rinks

;CVD

,car

diov

ascu

lard

isea

se;G

ST,G

oods

and

Serv

ices

Tax;

HED

,hig

hen

ergy

-den

se;L

YS,l

ife-y

ears

save

d;N

HAN

ES,N

atio

nalH

ealth

and

Nut

ritio

nEx

amin

atio

nSu

rvey

;NS,

nons

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t;N

R,no

trep

orte

d;.R

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ando

miz

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stat

us;V

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rted

in“I

nter

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Kant

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orld

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ldat

afo

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pres

enta

tive

sam

ple

ofho

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who

reco

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antit

y,pr

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d,ch

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teris

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ofgo

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em

ade;

excl

udes

purc

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nsum

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omho

me.

cN

iels

enH

omes

can

data

:are

pres

enta

tive

sam

ple

ofho

useh

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who

scan

and

reco

rdal

lite

ms

purc

hase

din

diffe

rent

reta

iltr

ade

loca

tions

,exc

lude

spu

rcha

ses

cons

umed

away

from

hom

e.d

Alth

ough

this

stud

yut

ilize

dda

tafr

oma

long

itudi

nals

tudy

,and

the

stan

dard

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adju

sted

forr

epea

ted

obse

rvat

ions

onin

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dual

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met

hod

does

nott

ake

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ntag

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itudi

nalo

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vatio

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obse

rved

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velh

eter

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(i.e.

,the

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rdin

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Leas

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ares

estim

atio

ns).


30%. All showed a reduction in consumption of thesebeverages, ranging from 5% to 48%, demonstratingoverall a response in consumption that was proportionalto the taxes applied (Figure 3B). Of these, four studies thatmodeled substitution between beverages in response totaxes of 5–20% suggested that consumers would reduceconsumption of sugar-sweetened beverages, reducingcaloric intake from these beverages by 10–48% in adultsand by 5–8% in children, and increase consumptionof a variety of other beverages, such as milk, low-caloriebeverages, tea, and coffee.8,26,31,46 Three of these studiesshowed an overall reduction in calorie consumptionfrom all beverages due to these taxes, while one studyestimated that children will substitute whole milk forsoft drinks and thus show no reduction in overall calorie

consumption.46 Six studies that did not consider sub-stitution with other beverages also found significantreductions in consumption of sugar-sweetened bever-ages or soft drinks of 10–25% in response to taxes of10–30%.18,20,24,39,40,42

Three studies of existing state-based soft drink taxesin the United States showed little difference in consump-tion between states with small taxes (around 5%) andstates without such taxes.46,46,48 One study based on datafrom the USA Coronary Artery Risk Development inYoung Adults found that a tax that increased the price ofsugar-sweetened beverages by 10% could reduce con-sumption by 7%.43 Similarly, a study that used longitudi-nal data from the Nurses’ Health Study to estimate theeffect of modeled reductions in soft drink consumption

Figure 3 Effect of taxes and subsidies (%) on consumption of the target food/nutrient (%). Numbers in figurescorrespond to reference numbers. Data are presented only for studies that presented the following: 1) subsidies and taxes asa percentage, and 2) findings of effect as percent change in consumption of target food, nutrient, or calories. Details on all foodsand study populations are found in Appendix 1.A: Subsidies for healthy foods.15,22,25,32,33,50,54 B: Taxes and subsidies on sugar-sweetened beverages. Subsidies appear as negativetaxes, i.e., a subsidy of 10% appears here as a tax of −10%.8,19,20,21,26,40,46,42,24,18,43,23 C: Taxes on individual nutrients (fat, salt,sugar).25,30,35,41,45 D: Taxes based on nutrient profiling.16,17,43,44,49,50,53,55

*Nonsignificant.


found that a penny-per-ounce tax could reduce soft drinkconsumption by 15%.39

Taxes on individual nutrients

Six studies reviewed here assessed taxes on fat, sugar, andsalt19,25,34,36,41,45 (Figure 3C). These taxes ranged fromaround 5–40% and reduced consumption of the targetednutrient by 0–8%. However, only one study consideredthe effect on other intake of nutrients: this study sug-gested that a focus on a single nutrient may increaseintakes of other unhealthy nutrients.36

Four studies used models to show that relativelysmall taxes on fat (5–17.5%; $0.005/gram) can reduce fatand/or saturated fat consumption by 0–3%, substantiallyreduce consumption of certain high-fat foods (e.g., crisps[potato chips], by 14%), and induce substitution withlower-fat options, particularly where there are close sub-stitutes such as full-fat and reduced-fat milk17,30,34,37

(Figure 3D). One study performed in the UnitedKingdom used models to show that a 17.5% tax onsources of saturated fat could reduce consumption ofsaturated fat by 0–3%.36 However, this study indicatedthat this targeted fat tax could have unintended conse-quences by possibly increasing salt intake and decreasingfruit and vegetable consumption. Two modeled studies inthe United States suggested that consumers would substi-tute between full-fat and low-fat options within foodgroups (e.g., dairy) as the result of taxes on fat.34,41

Two modeling studies found that sugar taxes wouldreduce consumption of the “sugar and sweets” food cat-egory by 23% in Finland (tax of 1€/kg) and aggregateadded sugar intakes by 8% in the United States (tax of$0.003/gram), partly mediated through reductions in softdrink consumption.25,34 Conversely, Bonnet andRequillart19 found that the European Union’s sugar policyreform (an implicit subsidy) would result in a pricedecrease for sugar of 36%, which, based on householdexpenditure data, would decrease sugar-sweetened softdrink prices by 3% and increase consumption by 7.5%.

Smith-Spangler et al.,45 using a modeled analysis ofsuch taxes in the United States (although limited by use ofprice elasticity data from the UK), found that a sodiumtax that increased the price of salty foods by 40% wouldreduce sodium consumption by 6%.

Taxes based on nutrient profiling

Taxes on foods deemed “unhealthy” on the basis of nutri-ent profiling ranged from 10 to 50%, and all but one studyfound reductions in purchase and consumption of targetfoods that ranged from 6.5% (total calories) to 30%(target food purchase) (Figure 3d). The prospective inter-vention by Temple et al.16 showed that a 25% tax on “red”

labeled foods (using traffic light nutrient profiling) in theUnited States significantly reduced consumption ofunhealthy foods among obese participants (by 40%) andreduced consumption among nonobese participants by10%. Five survey-based studies of the effect of taxes of25–50% on “high calorie for nutrient” foods showed thepurchase of target foods was reduced by up to 30% andthe overall calorie consumption by 6.5–8%,49–53 althoughone survey showed taxes of up to 25% (applied to foodsnot meeting the “Choices” front-of-pack label criteria)had no effect on purchases.55

Similarly, an Australian model-based study foundthat a tax that raised the price of “junk foods” by 10% wasa cost-effective measure to reduce body weight, based onthe likely reduction in energy consumed.38 A model-based study in Sweden found that tax and subsidy com-binations based on saturated fat and fiber content(respectively) could encourage substitution towardshealthy grain products.7

Of the two model-based studies that used longitudi-nal data to assess the effect of 10% taxes on fast food, onestudy found a reduction in the frequency of fast foodconsumption in children44 and the other found an 11.5%decrease in energy intake from pizza.43

Distributional effects

One model-based study in the United Kingdom and twoFrench studies (one model-based and one survey-based)found the poor would spend a greater proportion of theirincome on unhealthy food or beverage taxes than thewealthy.17,36,50 However, one modeled study from theUnited States found a sugar-sweetened beverage tax tohave negligible differential effects by income group.26

Four other modeled studies from Brazil, Finland, and theUnited States found that the higher price sensitivity oflow-income households meant that they were more likelythan high-income households to reduce their consump-tion in response to a tax,20,25,41,48 and two modeled studiesfrom the United States and Sweden reported that thelargest share of revenue would come from high-incomehouseholds because these households were less likely tochange their behavior in response to the tax.23,29 Onestudy from the United States identified the application ofsugar taxes on inputs rather than final products as a strat-egy for promoting progressivity, finding that – for thesame reduction in consumption,– a tax applied to sugarproducers would result in a loss in consumer surplus thatis only one-fifth of that caused by a tax on final productsthat contain sugar.35

Two studies based on modeled estimates of effect andone study of stated preference found that subsidiesranging from 3 to 30% may disproportionately benefitwell-off household rather than assisting low-income


households.29,32,50 One of these, a French study, found thattargeting fruit and vegetable subsidies to food stamprecipients reduced health inequalities between low- andhigh-income consumers when compared with a generalsubsidy that widened the gap in consumption.32

DISCUSSION

The studies reviewed indicate that fiscal measures can beeffective in promoting desired dietary changes. Based onthe evidence reviewed, soft drink taxes and subsidiesappear most effective in inducing consumption change,with strong evidence from robust modeling studies andone RCT, although there is some evidence that subsidiescan increase overall calorie consumption.

In contrast, taxes on fat, sugar, and salt are likely toapply to “core” foods (i.e., those recommended by dietaryguidelines) as well as unhealthier foods; one of the chal-lenges of influencing consumption through this mecha-nism is that people eat foods, not nutrients. These taxesmay thus have unintended effects on consumption ofother nutrients. Nevertheless, the modeling studies basedon large-scale panel and nutrition survey data reviewedhere show a relatively small but positive effect on con-sumption of target nutrients.

All but one of the studies that examined nutrientprofile taxes found substantial reductions in target foodconsumption.55 These taxes appear less likely than tar-geted nutrient taxes to have unintended consequencesand are also less likely to apply to “core” foods, since theselection of target foods is based on the entire nutrientcomposition of the foods. These studies were largelysurvey based, which means that the evidence base may belimited by the use of hypothetical purchasing scenarios.

This review adds to the literature by considering awide range of study types. By providing a framework forassessing the different types of evidence available, policyadvisers and decision-makers will be better equipped tointerpret the evidence available. This review confirms thatincreased interest in fiscal measures has been reflected ina large increase in the number of studies in recent years.The first review of such studies found only 24 studiespublished prior to 2009, with 13 of these published in thegrey literature.10 These earlier studies lacked evidencelinking taxes and subsidies directly to dietary outcomesand relied wholly on modeled studies that estimatedeffect based on previous behavior. The evidence base hasimproved somewhat in terms of quantity and quality, andeach of the wide range of studies reviewed here – fromprospective, to laboratory, to large panel-data basedmodeling studies – adds valuable perspectives in under-standing the potential impact of fiscal interventions. Nev-ertheless, the evidence base is still far from conclusive and

remains heavily dependent on modeling studies andextrapolated or surveyed – rather than observed –outcomes.

Is there a threshold?

Other reviews have proposed that 20% is the threshold atwhich taxes have a meaningful effect on consumptionand disease.3 However, the relatively robust studiesreviewed here, including the prospective observationalstudies and the modeled studies based on data thatincludes purchase price and considers substitution, showconsistent effects on target food consumption for taxesand subsidies ranging from 10 to 20%, with proportion-ately larger effects for larger taxes as well as for taxes andtax/subsidy combinations on noncore foods or beverages(such as unhealthy grain products or soft drinks) forwhich there are close untaxed substitutes. These findingssupport the findings of reviews targeted at specific studytypes.12 However, it is important to note that the effect canvary considerably, depending on the type of food taxed orsubsidized. The effects of fat- and calorie-based taxeswere the most varied, which may be due to challenges indifferentiating between nutrient-dense and non-nutrient-dense fatty foods.

Contextual considerations

One study reviewed here suggested that price elasticitiesalone do not account for consumer reactions to largetaxes that in practice may be fortified by complementaryconsumer education policies.27 Similarly, the applicationof taxes may reinforce efforts to educate consumers andpublic awareness that a product has been taxed because itis unhealthy may discourage purchases. Lacanilao et al.56

observed this effect in Canada when warning labels wereplaced on products that were taxed (up to 50%) becauseof their high fat content. However, two studies simulatingtaxes (25% and 50%) on unhealthy foods in the UnitedStates found no interaction between taxation and label-ing in reducing unhealthy food purchases by universitystudents.16,51

The tax policy and administrative context is anotherimportant consideration. Soft drink taxes and healthyfood subsidies, besides appearing to be highly effective,are also likely to be the least burdensome administra-tively, with generally simple definitions of the target foods(e.g., where subsidies are applied to fruit and vegetables).In contrast, targeted nutrient taxes are more likely torequire burdensome administrative requirements, as theyapply to a wide range of different foods at a number ofdifferent tax rates. For sugar taxes, it might be possible toreduce this burden through the application of sugar taxesto sugar producers, which would have fewer distortionary


effects.35 Although nutrient profile taxes are also admin-istratively complex, tax administration could be stream-lined if the taxes were applied using a systematic(compulsory) nutrient profiling system.

Further research into the response of industry tohealth-related taxes and subsidies would provideincreased understanding about the effects of these mea-sures on food prices and purchases. All but one of thestudies reviewed here assume that the tax is passed fullyto the consumer.18 This study suggests that strategicpricing of firms is very important in determining theeffect of taxes or subsidies on price. Only two studies haveinvestigated industry response to an actual tax, both ofwhich were case studies of the effect of removing a tax(implicit subsidies) on soft drinks.18,57 One study foundovershifting, i.e., the discount passed to consumers, wasgreater than the tax removed,18 and the other (earlier)study found undershifting, i.e., the discount passed toconsumers, was less than the tax removed.57

Differential effects

Regressivity (a greater tax burden for low-incomeearners) is an expected effect of taxes on goods, particu-larly foods, and as such the effect on poorer consumers isan important consideration. Similar to earlier literature,10

the modeled studies reviewed here report varied esti-mates of regressivity. The three studies that reported highlevels of regressivity examined taxes or tax/subsidy com-binations that target entire food groups (based largely onfat content), rather than specific food items.17,36,50 Thetarget foods included core foods such as dairy products.In contrast, greater positive dietary effects on low-incomeconsumers were seen in three studies of taxes targeted tononcore foods (e.g., on sugar or sugar-sweetened bever-ages) for which untaxed close substitutes were avail-able.20,23,34 This reflects the findings of an Organization forEconomic Cooperation and Development review ofobesity prevention interventions, which found that fiscalmeasures were “the only intervention producing consis-tently larger health gains in the less well-off” across thecountries studied.58 In practice, while such taxes are argu-ably inequitable from the point of view of fiscal financing,they can also be considered as equitable as public healthmeasures, since a regressive tax represents a strongerdeterrent in lower income groups.

Limitations

The present study is limited by its restriction to Englishlanguage literature and by the lack of studies from low-and middle-income countries. The wide variety of targetsof taxation that have been proposed and modeled adduncertainty to the conclusions that can be drawn regard-

ing public health and policy measures. This study is alsolimited by its focus on assessments of fiscal policy inter-ventions, which means that other, possibly relevantstudies that focused only on price would have beenexcluded.

CONCLUSION

This review suggests that fiscal measures, particularly softdrink taxes and healthy food subsidies, can be effective inpromoting desired dietary changes. The new and detailedtaxonomy of study quality, specific to this field and pre-sented here, highlights the strengths and weaknesses ofdifferent methodologies and can assist policymakers inunderstanding the contribution of different types ofstudies. While prospective observational studies providevaluable information about consumer behavior inresponse to price, robust modeling studies also provideimportant insights into the potential for taxes and subsi-dies to affect consumption by utilizing data about all foodconsumption and by furnishing opportunities to assessactual taxes and subsidies. Experimental survey-basedstudies can also provide valuable data about consumerchoice and detailed consumption data in controlled set-tings. To extend the current evidence base, more inter-vention studies as well as studies of implementation ofactual (implemented) taxes and subsidies will be neededto give a better understanding of the effect of fiscal inter-ventions on consumer behavior, including potential dif-ferential effects. Future research could also consider theeffect of taxation in conjunction with other interventions(as part of a multisectoral strategy to improve diets andhealth), the effect of brand variation (i.e., consumers sub-stituting with cheaper brands or varieties of a product inresponse to a tax), and industry responses to taxation.

Acknowledgments

The authors acknowledge Professor Stephen Leeder forhis oversight during preparation of the manuscript andthe anonymous reviewers for their thoughtful and con-structive comments.

Declaration of interest. The authors have no relevantinterests to declare.

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