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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
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
Nutrition Reviews®4
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).
Nutrition Reviews® 5
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
Nutrition Reviews®6
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%
Nutrition Reviews® 7
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%
Nutrition Reviews®8
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
Nutrition Reviews® 9
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
igni
fican
t;N
R,no
trep
orte
d;.R
CT,r
ando
miz
edco
ntro
lled
tria
l;SE
S,so
cioe
cono
mic
stat
us;V
AT,v
alue
-add
edta
x;W
HO
,Wor
ldH
ealth
Org
aniz
atio
n;↑,
incr
ease
;↓,d
ecre
ase.
aO
utco
me
mea
sure
sre
fert
oth
efo
ods
repo
rted
in“I
nter
vent
ion.
”b
Kant
ar(p
revi
ousl
yTN
S)W
orld
pane
ldat
afo
rare
pres
enta
tive
sam
ple
ofho
useh
olds
who
reco
rdqu
antit
y,pr
ice,
bran
d,ch
arac
teris
tics
ofgo
ods
purc
hase
d,an
dth
est
ore
whe
reth
epu
rcha
ses
wer
em
ade;
excl
udes
purc
hase
sco
nsum
edaw
ayfr
omho
me.
cN
iels
enH
omes
can
data
:are
pres
enta
tive
sam
ple
ofho
useh
olds
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
erro
rsw
ere
adju
sted
forr
epea
ted
obse
rvat
ions
onin
divi
dual
s,th
ees
timat
ion
met
hod
does
nott
ake
adva
ntag
eof
the
long
itudi
nalo
bser
vatio
nsto
acco
untf
orun
obse
rved
indi
vidu
al-le
velh
eter
ogen
eity
(i.e.
,the
poin
test
imat
esar
eba
sed
oncr
oss-
sect
iona
lpro
bita
ndO
rdin
ary
Leas
tSqu
ares
estim
atio
ns).
Nutrition Reviews®10
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.
Nutrition Reviews® 11
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
Nutrition Reviews®12
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
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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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