MANDATORY CALORIE DISCLOSURE:
A COMPREHENSIVE ANALYSIS OF ITS EFFECT ON
CONSUMERS AND RETAILERS
In 2018 restaurants in the United States will need to provide calorie information on their
menus as part of the Patient Protection and Affordable Care Act. In the present research, we
examine the efficacy of this legislation in reducing restaurant based food calorie consumption.
Specifically, we explore the likely effect of the new policy on both the supply and demand
side, that is, consumer and retailer behaviors. To achieve this, two studies are included in this
research: a meta-analysis of 186 studies investigating the effect of calorie disclosure on
calories selected, and a meta-analysis of 41 studies examining the effect of calorie disclosure
on calories offered by retailers. Across these two studies we reveal a significant and
unequivocal calorie disclosure effect for menu labels; disclosure results in both fewer calories
selected (-27 Calories) and fewer calories offered by retailers (-15 Calories).
Keywords: Calorie Labels, Mandatory Disclosure, Meta-Analysis
The increasing prevalence of obesity has become a major cause for concern in the
modern world. With more than 30% of American adults aged 20 and over being classified as
obese (Clarke et al 2016), and with the World Health Organization estimating that 2.8 million
(5%) of global deaths are attributable to obesity (WHO 2011), innovative approaches in
preventing and treating obesity are urgently needed. In addition to a portfolio of interventions
focusing on individual and parental education to encourage personal responsibility for food
consumption (Dobbs et al 2014), restaurants and other retail food outlets are the latest
conscripts in the fight against obesity.
Experts estimate that Americans spend half (50.1%) of their food dollars on meals
purchased outside of the home (ERS Food Expenditure Series 2016), with restaurant food
sales valued at $799 Billion (National Restaurant Association 2017), and that food away from
home accounts for an average 33% of an individual’s total consumed calories (Powell et al.
2012). Given these figures, the retail food environment is a critical aspect of the built
environment that can contribute to the prevalence, and most importantly, the prevention of
obesity within a population (Binks 2016).
The World Health Organization has recently called for an emphasis on the provision of
supportive retail environments to encourage consumers to make healthier food choices (WHO
2016). Suggested strategies that food retailers can implement in influencing consumers into
eating better include: the provision of smaller sized servings (Holden et al 2016; Zlatevska et
al 2014); in-store signage, e.g. drawing attention to healthier choices; structure, e.g. changing
store layout and organization to prompt heathier product selections; and service e.g. making
electronic aids and apps available to consumers (Wansink 2017). Another suggested strategy
involves the provision of nutritional information at the point of purchase to enhance a
consumer’s ability to regulate their own food purchase behavior (Binks 2016).
In response to the call to provide consumers with more nutritional information at the
point of purchase in food retail establishments, legislation (part of the Patient Protection and
Affordable Care Act) was passed in 2010, requiring restaurants in the United States to include
calorie information on their menus. Prior to the legislation, some cities (e.g. New York),
counties (e.g. King County), and states (e.g. California) had passed their own laws requiring
the posting of nutritional information on menu boards in chain restaurants. According to the
legislation, menu boards are required to list the name of every menu item on offer, including
options like meal combinations, and the calorie counts for each (FDA 2014). Supporters of the
legislation argue that consumers are often unaware, or underestimate, the nutritional content
of the food they are purchasing. Hence, equipping consumers with caloric information will
encourage them to make considered and possibly healthier product selections (Burton et al.
2006; Burton and Kees 2012).
The legislation applies to quick and table service retail establishments that are part of a
chain with 20 or more locations. It also covers grocery stores that sell restaurant type food and
are part of a chain with 20 or more locations doing business under the same name (FDA
2014). Retailers have until May 2018 to comply with the imposed guidelines. Some retailers
have already, voluntarily complied with the legislation. The initial cost of implementing the
proposed menu changes is estimated to exceed $388.43 million for food retailers, with an
ongoing cost of compliance of $55.13 million (FDA 2014).
Because of the large mandatory cost imposed on retailers (VanEpps et al 2016) and the
opposition by some industries, there is strong interest in whether the benefits of the proposed
legislation will outweigh the expenses and required efforts for restaurants to implement menu
labels. Furthermore, given that few obesity-related policy changes have actually been
implemented in the United States within the last 10 years (VanEpps et al 2016), there is strong
public interest in the success of the proposed menu label policy. Academic studies
investigating the possible effect of calorie labeling initiatives have provided mixed results.
For instance, Long et al. (2015) present summary data revealing that disclosure of calories is
correlated with selecting fewer calories, whereas other studies (e.g., Schwartz et al. 2011)
suggest that calorie disclosure does not affect food choices. Thus, a critical, outstanding
question is: will mandatory calorie disclosure in food retail establishments be successful in
changing consumer behavior?
In the present research, we examine the likely efficacy of the new legislation. First, we
summarize extant research exploring the effect of calorie labeling initiatives. When reviewing
previous efforts of synthesizing the existing literature on menu labeling initiatives, we find
that existing research has significant methodological shortcomings. In particular, many
reviews are not of a meta-analytic nature (that is, they are qualitative, conceptual reviews),
and those that are quantitative, suffer from potential biases. The biases include lack of control
for moderating variables in the meta-analysis and strong limitation in synthesized studies
resulting in small sample sizes (6–38 studies). These two problems make it difficult to come
to conclusions about general effects.
To shed light on the likely overall calorie disclosure effect, we present a meta-analytic
approach using multilevel modeling techniques. This meta-analysis method accounts for the
potential sources of bias mentioned above and relies on 186 synthesized cases (representing
an analysis of 1,677,265 consumption choices). In particular, our meta-analysis accounts for
various sources of heterogeneity by including moderators into the model and by capturing
dependencies imposed by the nested structure of experiments from the same authors as well
as situations where multiple interventions are compared to the same control condition, thus
further reducing bias in estimates (Neumann and Böckenholt 2014; Janakiraman, Syrdal and
Freling, 2016). Our findings based on this robust estimation indicate a significant and
unequivocal calorie disclosure effect for menu labels on consumer behavior: consumers select
fewer calories following disclosure.
Furthermore, we also note that the majority of prior research focuses on consumers’
reaction to new labels and less on the actions of the supply side. However, retailers and their
menu adjustments play a key role in the ultimate success of any policy, independent of the
calorie information disclosure and consumer reactions (Moorman et al 2012). Obligatory
incentives often drive the behavior of information providers, sometimes for the better and
sometimes for the worse (Lowenstein et al. 2014). Without a comprehensive examination of
the effect of calorie disclosure on both the sides of the consumer and the retailer, determining
whether or not the legislation will have substantial, broad-based effects is difficult. Following
this rationale, we present a second study to investigate likely supply-side adjustments to the
new legislation. We perform a meta-analysis of 41 studies (representing an analysis of 33,029
menu items) examining the calories offered by retailers before and after menu changes began
to voluntarily be implemented in the United States. Our findings reveal that disclosure of
calorie information also significantly leads to lower calorie offerings by food retailers.
The Effect on Consumer Behavior
The implementation of mandatory calorie disclosure on menu boards at the point of
purchase is expected to have a positive effect in encouraging consumers to make healthier
food choices (Burton et al 2015). However, although momentum has continued to gather
around menu labeling policies with widespread support by consumers (national polls show
that between 67% and 83% of people support calorie disclosure (Roberto et al 2009)),
evidence supporting the efficacy of the initiative remains unclear.
Multiple studies have investigated the impact of mandatory calorie disclosure on
restaurant menus across many academic disciplines, but have reached little consensus as to
the overall effect the legislation will have on consumers. For example, Bollinger et al. (2011),
Roberto et al. (2010), and Hammond et al. (2013) conduct experiments illustrating that calorie
disclosure reduces energy consumption. In contrast, Schwartz et al. (2012) and Downs,
Wisdom, and Lowenstein (2015) perform field experiments and find no significant calorie
reduction related to changes in consumers’ food choices. The observational results of
Dumanovsky et al. (2011) and Girz et al. (2012) even suggest an increase in calories
consumed following disclosure.
However, it is not only individual studies which have come to conflicting conclusions
regarding the magnitude of the effect of calorie disclosure on a consumer’s food selections.
We identified eight review studies that synthesized experimental research on calorie
consumption measures (see Appendix A). Some of the reviews report that disclosure is
effective in reducing the number of calories selected for a meal. For example, Littlewood et
al. (2016, p. 1) conclude that the results of their review show a “statistically significant effect”
of menu labeling where overall calories ordered was reduced by 100 Calories. Long et al.
(2015) found a much smaller but also significant decrease of 18 Calories selected per meal.
Yet, Sinclair et al. (2014) found no significant reduction in their review of studies that tested
calorie content labels (without additional contextual information).
What could explain the contradictions in research findings on the influence of calorie
disclosure? After reviewing the nine summary studies, we make several key observations that
seem to provide plausible explanations about the mixed results among the existing reviews.
First, five of the eight studies (Harnack and French 2008; Swartz et al. 2011; Lazareva 2015;
Fernandes et al. 2016; Van Epps et al. 2016) represent qualitative reviews where the research
team summarized experimental data under the lens of several key criteria, often subjectively
grouped by two to three raters. In contrast to a quantitative meta-analysis, such narrative
review can be biased by the views of the raters or the selection of studies (Rosenthal and
DiMateo 2001). Moreover, qualitative groupings and analyses suffer from lack of
transparency and assessment standards, such as the use of established effect sizes that account
for study precision or statistical methods that are deployed to neutrally determine final
We also find that the three review studies that represent traditional quantitative meta-
analyses were based on low sample sizes with between 12 and 19 reported effect sizes
(Littlewood et al. 2016; Long et al. 2015; Sinclair et al. 2014). Any outcomes based on such a
small number of effect sizes could be biased because of sampling variance or a very
restrictive sampling framework (DerSimonian and Laird 1986). When reviewing the three
studies more closely, we also find that the selection criteria of these reviews have been limited
in terms of regions, publication status, years of publication, and labeling methods (see
Appendix B). These sampling limitations reduced the pool of synthesized studies and may
have created an unrepresentative subsample of all available studies.
In addition to the restricted sample sizes, the three existing meta-analyses did not
account for moderating variables when estimating the average effect size. The three reviews
present qualitative and subgroup analyses to investigate the impact of study characteristics. A
subgroup analysis only allows investigating one variable at a time, and conducting multiple
tests raises the risk of false-positive results because of chance alone (Yusuf et al. 1991).
To address issues concerning generalizability of the results from existing quantitative
reviews and to shed light on the overall effect of calorie disclosure on consumer food choices,
we remove the restrictions above and perform a comprehensive meta-analysis of 186 Calorie
label intervention versus control (no intervention) comparisons. Furthermore, to gain a better
understanding of heterogeneity across the different studies, we carry out a meta-regression on
different study characteristics as well as a multilevel modelling estimation. Meta-regression is
an extension to subgroup analysis, simultaneously allowing accounting for the effects of both
continuous and categorical moderators (Thompson 2002).
Study 1: The Effect of Calorie Disclosure on Consumer Behavior
Studies relevant for the meta-analysis were initially identified through a search of
ABI/Inform, ProQuest Digital Dissertations, Business Source Premier, Web of Science,
PsychInfo, Scopus, Google Scholar, and other databases using the following keywords: menu
labeling, restaurant labeling, calories on menu, calorie disclosure, nutritional information on
menu, Patient Protection and Affordable Care Act. References in articles found in our search
were also examined to identify further studies. The search was not restricted to particular
years of publication, country of data collection, or languages.
Intervention and Study Characteristics
A study was deemed eligible for inclusion in the meta-analysis if it involved a
disclosure of calorie information on a (real or hypothetical) restaurant menu as an
intervention. In an example of calorie disclosure on a real restaurant menu, participants in
Platkin (2014) were provided with a Burger King menu from which they were asked to
choose food items. Whereas, in an example of a hypothetical restaurant menu, participants in
Dodds et al (2014) were asked to make their selections from a menu, not specific to a branded
restaurant, but which did contain a selection of foods commonly found at quick service
Studies included in the analysis were not restricted to a particular food category, or
eating occasion. Rather, studies examined the selection of both food and beverages, and these
were across both unhealthy (e.g., Lee and Thompson 2016) and perceived healthy (e.g.
Kreiger et al. 2013) categories1. We included studies that collected data only at lunch (e.g.,
Temple et al 2011), only at dinner (e.g., Liu et al 2012), or across different meal times (e.g.,
Vanderlee and Hammond 2013). Furthermore, for the purpose of the analysis, retail
1 Given the opposition to enforcing calorie labeling by the pizza industry, we also examined the effect of labeling for pizza
restaurants were defined as either quick-service (e.g., Yamamoto et al. 2005) cafeterias (e.g.,
Holmes et al. 2013) or table service (e.g., Fotouhinia-Yepes (2013), exploring a labeling
intervention in a fine dining restaurant and Liu et al (2012) exploring a labeling intervention
in a table service restaurant chain (Chilli’s).
Both field and laboratory based studies were included in the meta-analysis. Studies
that manipulated calorie (or kilojoule which was the converted to calorie, e.g., Morley et al.
2013) disclosure along with another contextual intervention (e.g., calories plus traffic light
symbols (e.g. Hammond et al. 2013)), calories plus energy expenditure (e.g. Platkin et al
2014) and calories plus additional nutrients (Burton et al. 2006)), were also included in the
analysis. However, studies that manipulated only a symbol and not calories (e.g., heart healthy
stickers on menu items) were excluded from the analysis (Freedman and Connors 2011; Levin
1996; Sharma et al. 2011; Vyth et al 2010). Burton et al. (2015) was also excluded from the
analysis, because it explored the nutrition facts panel rather than calorie disclosure on
restaurant menus. Conditions that did not provide an intervention of calorie disclosure, but did
manipulate another contextual variable instead (e.g. traffic lights, Dodds et al. 2014) were not
included in the meta-analysis.
Studies included in the meta-analysis were a mixture of between subject, within
subject, and other designs. In the between subject designs participants were randomly
assigned to an intervention of calorie disclosure or a control group involving no calorie
disclosure (e.g. Hammond et al. 2013)2. In within subject designs, all participants in the study
made selections from a control menu containing no calorie information, and an intervention
menu that did (e.g. Reale and Flint 2016). Other designs included cross-sectional study
designs, difference in difference, pre-post, and pre-post with control. Cross sectional designs
involved studies where purchase data was obtained from restaurants located in cities that had
2 In an exception to this, Bassett al (2008) was also classified as a between subject design, where the comparison was
between people who did (intervention) and did not (control) notice the calories posted on the Subway menu.
implemented labeling (intervention) compared to comparable (based on socio-demographic
factors and the brand name) restaurants in cities that had not implemented calorie labeling on
their menus (control, e.g. Seenivasan and Thomas 2016). Difference-in-difference designs
involved first taking the difference between treatment and baseline for study participants
exposed to the calorie information. Then, this result was subtracted from the difference
between the original and matching period for study participants who were not exposed to
calorie information (e.g. Finkelstein et al 2011). Pre-post study designs involved purchase
data from restaurants both before (control) and after they implemented calorie labels on their
menus (intervention) (e.g., Pulos and Leng 2010). Pre-post with control designs involved
purchase data where before-labeling and after-labeling differences within a restaurant
(intervention) were compared to before and after differences in purchase data from a
comparable (control) location where mandatory labeling was not in effect (e.g. Elbel et al
To be eligible for inclusion, studies were required to report on the number of calories
selected or purchased (e.g., Krieger et al. 2013) following calorie disclosure on a food retail
menu3. One study was excluded because it provided information about the proportion of items
selected from the menu, rather than the amount of calories selected (Davis-Chervin et al.
1985). Likewise, both Driskell et al. (2008) and Hwang and Lorenzen (2008) were excluded
from the analysis because their outcome variables were not of interest.4 All necessary
information was extracted from the published articles, protocols, and commentaries related to
each study. In some cases, where raw data were not available, assumptions and calculations
3 In some cases, articles reported on the calories consumed as the variable of interest (Aaron, Evans, Mela 1995; Girz et al
2012; Hammond et al 2013; Harnack et al 2008; Hoefkens et al 2008; James et al 2015; Platkin et al 2014; Roberto et al
2010; Temple et al 2011; Vanderlee, Hammond 2013). We also ran an analysis comparing differences between calories
selected and consumed in our multilevel model and found no significant differential effect of this outcome variable.
4 Use or nonuse of nutrition labels and willingness to pay more for healthier food items, respectively.
were made from the figures included in the articles or the explanation of the results in text5.
Two studies (Mayer et al. 1987; Webb et al. 2011) examining the effect of calorie disclosure
on menus could not be included because of lack of data even though they fit the eligibility
A total of 186 studies reported in 54 articles representing 1,677,265 meal choices were
included in the meta-analysis (see Appendix C for a summary of effects sizes for the included
studies). Of the studies included in the meta-analysis, 68 percent reported a reduction in
calories (ranging from - 400 Calories (Temple et al 2011) to -1 Calorie (Pulos and Leng
2011). For studies reporting an increase in consumption, the range varied between 1 Calorie
(Lee and Thompson 2016) and 217 Calories (Temple et al 2011).
One of the greatest strengths of a meta-analysis is investigating not only one average
effect of interest, but also how moderating variables lead to differences in these effects
(Janakiraman, Syrdal and Freling, 2016). For each effect included in the meta-analysis, three
expert raters independently coded the potential moderators (see Table 1 for a summary). There
was 95 percent agreement among the raters on the coding of the moderator variables. In cases
of disagreement, the lead author made a judgement call.
Potential moderators accounted for in the meta-analysis were related to either (1)
study or (2) demographic characteristics. The former includes the labeling intervention
(calories only versus calories plus a contextual intervention), study design (between-versus
within-subject vs. other designs), actual versus hypothetical choice scenarios6, healthiness
(whether the food category was unhealthy or not), restaurant type (table service vs. other
restaurant services), eating occasion (lunch only versus dinner only vs. other), and food type
5 For example, in Rainville et al (2010) the raw differences, standard deviations, and the number of menu days were
provided in Table 12 as was the t value for the differences taking into account the control. These data were used to calculate
the standard error while the sample size was calculated using the number of menu days times the average number of
participants per school.
6 The category lab and field was highly correlated with the category actual and hypothetical choice scenario; therefore, we
have used only one of these categories (actual, hypothetical choice) as a moderator.
(pizza vs. beverage vs. other). Demographic characteristics include gender (females only,
males only, both males and females), BMI (whether, on average, participants in the study had
a normal BMI of under 25, or an overweight BMI of 25 or over), and whether the meal
selected was intended only for a child (<18 years) or not. In addition, we follow the
suggestion of Rosenthal and DiMatteo (2001) to include a control variable for the publication
year of a study. Specifically, we add to the model the time lag between the publication year of
each study and the first data point in our research synthesis.
INSERT TABLE 1 ABOUT HERE
For the effect-size measure of our meta-analysis, we computed the raw mean
differences, as well as the corresponding standard errors, for the calorie selection between the
intervention and the control7. Using the raw means instead of standardized measures (which
divide the raw means by a study’s standard deviation) provides two advantages: (1) we can
easily interpret the results (possible calorie reductions) and (2) our effect size is 100% scale-
free. That is, using a raw metric allows us to strictly separate the impact of calorie disclosure
on means from study-to-study differences in standard deviation (Bond Jr., Wiitala, and
To evaluate the effect magnitudes provided by the synthesized meta-analysis data, we
produced a funnel plot (Sterne and Egger 2001), which maps effect estimates from individual
studies against a measure of study precision (e.g., standard errors). A visual inspection of the
funnel plot of our data (see Figure 1) shows that the scatter resembles a symmetrical inverted
funnel with fairly evenly spread data points. This pattern suggests that the risk of publication
bias across studies is low. Moreover, we find that the center of the funnel is in the negative
7 Where studies did not report the p-value, but did report significance, effect size calculations were based on a conservative
p-value of 0.05 (or 0.01, 0.001 where stated in the original paper). For insignificant effects, a conservative p-value of 0.5 was
used. Where sample size for each cell was not provided in the original paper, an assumption of equal cell sizes was made for
between subject designs.
region of the effect scale, suggesting an average effect of a calorie reduction across the 186
INSERT FIGURE 1 ABOUT HERE
Model and Estimation
For our meta-analysis, we use the Metafor package (Viechtbauer 2010) and obtain the
estimates through maximum likelihood estimation. The mixed-effects model8 estimate
consists of three levels: the first level encompasses the effect sizes, the second incorporates
conditions where multiple interventions were compared to the same control condition in a
study9, and the third level adds the articles that provide the comparisons. All independent
variables in the model are grand-mean centered to provide an average estimate of the effect
size across all conditions (Janakiraman, Syrdal and Freling, 2016). The independent variables
are included in level 1 if they vary within studies; otherwise they are included in level 3. A
correlation matrix can be found in Appendix E. These specifications lead to the following
Levels 1 and 2 – Effect Size Model and Shared Control Comparisons
(1) ESijk = β00 + β11 PIZZAijk + β12 BEVERAGEijk + β13 LABELijk + β14 FEMALEijk + β15
MIXEDijk + β16 CHILDRENijk + β17 OVERWEIGHTijk + β18 HEALTHYijk + rijk + vij,
Level 3 –Article Specific Characteristics
(2) β00 = γ000 + γ100 WITHINk + γ200 BETWEENk + γ300 TABLESERVICEk + γ400 LUNCHk +
γ500 DINNERk + γ600 SCENARIOk + γ700 YEARSk + uk,
where subscript i is used for the effect sizes for multiple within article studies (i = 1, …, 186),
subscript j for the comparisons of multiple within article interventions that shared a control
condition (j = 1, …, 130) and subscript k is used for the individual articles used in the analysis
8 Precision weighting was used in our analysis, where the observations are weighted by the inverse variance.
9 In some cases two or more different interventions are compared to the same control condition (e.g. Harnack et al. 2008).
This creates further dependencies in the data. We follow the procedure outlined in Neumann, Böckenholt and Sinha (2016) to
add another level in the model to control for this data structure.
(k = 1, …, 54). Moreover, rijk represents the random effect (on level 1), vij the random effect on
level 2 (the effect size comparisons that shared a control), and ukj the random article effect (on
level 3). The vectors uk, vij and rijk are specified to follow a multivariate normal distribution
with the dispersion matrices Τu, Τv and Τr.
Table 2 exhibits the results from the overall multilevel model including all moderators
of the effect. The explanatory power of our model reveals that 99.9% of the variance between
articles, 13.4% of the variance among shared base conditions, and 39.2% of the variance
among effect sizes is captured by our tested independent variables.10 Our intercept shows that
on average there is a significant reduction of 27 Calories selected by consumers following
calorie disclosure (γ00 = -27.21, p < .001)11.
In addition to the average calorie reduction, our multilevel model identifies a number
of moderators for the effect of disclosure on consumer behavior. We find that the calorie
reduction is significantly stronger for overweight individuals (γ13 = -66.85, p < .001), females
(γ13 = -75.16, p < .01), table-service restaurant settings (γ13 = -29.61, p = .02) as well as
hypothetical choice scenarios (γ13 = -42.88, p = .01). We also find that the calorie reduction is
more effective for lunch meals (γ13 = -26.62, p = .03) than for other eating occasions, and
marginally more effective for samples containing a mixture of males and females (γ13 =
-45.55, p = .06), but marginally less effective for healthy meals (γ13 = 24.87, p = .07). Our
meta-analysis suggests no statistically significant differences in effect sizes based on different
labeling techniques, children versus other subject groups, or particular experimental design
comparisons (within vs. between vs. other). We also find no significant trend pattern in
10 The model does not explain much variation at the shared control condition level since we have no moderators included
on this level. The addition of the mid-level was done to account for any correlations among studies that share a comparison
with the same control condition.
11 We ran a sensitivity analysis (removing Bollinger et al 2010 from our data) and found that the findings are not affected by
this single data point, with little change to the estimated coefficients. That is, all tested moderating variables have the exact
same level of significance, while the average calorie reduction effect is about the same.
reported effect sizes over the years and no significantly different consumer behavior for
various food types across our data.
Having established a robust effect of calorie disclosure on consumer behavior, we next
turn our examination to the effect of calorie disclosure on supply side adjustments.
INSERT TABLE 2 ABOUT HERE
Study 2: The Effect of Calorie Disclosure on Retail Behavior
Although the calorie disclosure legislation has widespread consumer backing, support
from retailers is mixed. Some retail chains such as McDonald’s and Starbucks have already,
voluntarily complied with the legislation, though, many retailers are still actively fighting the
legislation, with some asking for exemptions because of fear of the increasing cost of
compliance and a possible negative effect on sales (Roberto et al. 2009). For example, the
ruling has faced strong opposition from the pizza industry (the American Pizza Community)
who have argued that they should be exempt from calorie labeling because of the complexity
and cost of implementing the scheme on their menus given the customizable nature of their
product offerings (Roberto et al. 2009). Instead, they have pushed for an alternate bill, the
Common Sense Nutrition Disclosure Act. Under this alternate bill, pizza chains would not
have to post calories in store and would not have to post total per item calories, rather calories
per single serve (as determined by the retailer).
However, advocates of the new legislation argue that in addition to positive
consumer-driven impact, the legislation could also have favorable supply-side effects. In
particular, mandatory calorie labels may encourage the restaurant industry to reformulate their
product offerings such that they also contain less calories (Van Epps et al. 2016). To uncover
this possible retailer reaction, in study 2 we use a meta analytic approach to examine how
calorie disclosure affects the number of calories offered by restaurants.
The search strategy adopted in study 2 was the same as that utilized in study 1. The
key difference in the meta-analytic methodology adopted between the two studies was the
primary outcome examined. To be eligible for inclusion in study 2, studies were required to
report on the amount of calories offered by retailers both before and after restaurants had
voluntarily started to disclose caloric information on their menus following the announcement
of the legislation in 2010. Studies included in the meta-analysis were a mixture of between
subject design (where the difference in calories offered was examined between restaurants
that did voluntarily implement calorie disclosure and restaurants that did not), within subject
(the calorie offerings were compared for the same restaurant before and after voluntary
disclosure), and pre post with control designs (where the calorie offerings were compared for
the same restaurant before and after voluntary disclosure with restaurants that that did not
disclose calories for the same time period).
A total of 41 studies reported in 7 articles representing 33,029 menu items were
included in the meta-analysis examining the effect of calorie disclosure on calories offered by
retailers (see Appendix D for a summary of included studies). Again, we computed the raw
mean differences as well as the corresponding standard errors for the calories offered between
the intervention and the control. A visual inspection of the funnel plot of our data (see Figure
2) shows that the scatter resembles a symmetrical inverted funnel with fairly evenly spread
data points. Of the studies included in the meta-analysis, 66 percent reported a reduction in
calories (ranging from -200.8 (Bleich et al 2016) to -0.5 Calorie (Bleich et al 2015)). For
studies reporting an increase in calories, the range varied between 0.5 Calories (Bleich et al
2015) and 113 Calories (Namba et al 2013).
INSERT FIGURE 2 ABOUT HERE
In study 2, we perform a random-effects meta-regression and include several potential
moderators in our model to account for potential differences based on various study and
demographic characteristics captured in the data.12 For our analysis of calorie adjustments
across retailers, we control for study design (within vs. between vs. other comparisons), the
food category (healthy or not), the food type (beverage vs. pizza vs. other food) and whether
the meal selected was intended only for a child (<18 years) or not. In addition, we account for
the time interval between the calorie adjustments, that is, how many years had passed between
menu adjustments. Table 3 provides a summary of all tested variables included in the analysis.
INSERT TABLE 3 ABOUT HERE
Model and Estimation
We again estimate the model using maximum likelihood and the Metafor package
(Viechtbauer 2010). All independent variables in the model are grand-mean centered (a
correlation matrix can be found in Appendix F), resulting in the following effect size (ES)
(3) ES= β0 + β1 BETWEEN + β2 WITHIN + β3 CHILDREN + β4 PIZZA + + β5 HEALTHY +
β6 TIMESPAN + r,
where r is the random effect across studies with a multivariate normal distribution (ΤR).
Similar to the study 1 meta-analysis examining demand-side policy reactions, our
proposed meta-analysis suggests a good fit to the data, explaining 73% of the variation in
calorie reductions across retail menu offerings. On average retailers reduce their nutritional
offerings by about 15 Calories (β1 = -15.34, p <.001) after introducing food labels (see Table
INSERT TABLE 4 ABOUT HERE
We find that this effect seems to be even stronger for between-case comparisons,
which indicate additional menu calorie reductions of around -86.20 (p < .01). Within-case
12 While we do have a nested structure for study 2 (effect sizes within studies), the limited number of level 2 data does not
allow for estimating an extra effect for studies.
designs, the time period between comparisons, age and whether the food category is
considered healthy or not does not result in significant effect differences for our data.
The central focus of the present research was to investigate the effect of mandatory
calorie disclosure on food retail menus on both consumer choices (study 1) and retail practice
(study 2). Our multilevel model, across all study characteristics and sample demographics (k
= 186), shows that when calories are disclosed on food retail menus, consumers select 27
fewer calories per meal on average. Moreover, calorie disclosure on food retail menus
encourages those who are overweight to reduce their selection by an additional 67 Calories
per meal. We also find that females are more responsive than males to adjusting their intake
(reduction of an additional 75 Calories) following calorie disclosure, and a marginal effect
was found when the sample was comprised of a mixture of males and females (reduction of
an additional 46 Calories). Given that these two results are surprising, and in particular, that
mandatory calorie disclosure has a greater effect for overweight individuals, and that our
meta-analysis is bound by the available data, we encourage future research to explore why the
effect may occur. We also highlight that even though the majority of studies where
participants were, on average, overweight, depicted an actual choice; half of these were lab
studies, and not field based experiments. The calories selected for lab studies were greater (on
average -115 Calories) than the calories selected for field based studies (on average -48
Calories). Thus, the result could be a reflection of overweight individuals underreporting their
consumption, rather than a measurement of actual consumption behavior.
Our results also positively identify that fewer calories are selected following
disclosure at table service restaurants and specifically for lunchtime meal choices (30 and 27
Calories, respectively). While our meta-analysis does not allow insights into the underlying
mechanism, it could be that individuals are more likely to notice calorie information on a
menu during a sit-down meal, and when they are more likely to be making individual choice
selections, such as at lunchtime. Future research should further explore both the proportion of
individuals who notice calories on restaurant menu boards and the factors that may encourage
consumers to better notice the caloric information included on menus.
Moreover, our meta-analysis also suggests that consumer calorie reduction is greater
for intentions (reduction of 43 Calories) compared to actual behavior. One possible
explanation could be that people have good intentions, but struggle to follow through when
faced with the actual choice. The short run versus the long run impact of calorie disclosure on
actual purchase behavior and their motivations are great avenues to expand consumption
According to our analysis and data, calorie disclosure for healthy meals also results in
a significantly smaller effect (reduction of 2 Calories per meal), everything else being equal.
The result suggests that consumers distinguish between healthy and other food categories and
may regard the former as less problematic in terms of nutrition value.
In addition to the meta-analysis on the impact of calorie disclosure on consumer
behavior, our work sheds light on supply-side adjustments as a result of mandatory calorie
disclosures. To the best of our knowledge, it is the first research to synthesize the reactions of
retailers to the new legislation. We performed a meta-analysis of 41 studies comparing the
effect of calorie content differences on menu items between restaurants that did, and did not,
voluntarily introduce menu labeling efforts once the legislation was first announced in 2010.
According to our analysis, we find that retailers also respond to mandatory disclosure of
calorie information, by reducing on average 15 Calories per menu item.
The calorie reduction effect across menu items is also stronger for between subject
designs in study 2, that is, when comparing between retail outlets that did implement calorie
disclosure on their menus and those, comparable, retail outlets that did not. In these cases,
retail outlets reduced their menu items by additional 86 Calories; however, we note that this
result is based on only a selection of six studies. We encourage future research to further
explore this effect.
Limitations and Research Directions
While our model accounting for both study and demographic characteristics presents a
very good fit, there is the possibility for future work to examine additional heterogeneity
sources, which could not be tested in our meta-analytic framework. For example, we couldn’t
find any product-type effect on consumer’s food consumption. While we were bound by the
menu items that were tested across the data pool of our studies, future experiments could
investigate which specific food items (in addition to healthy ones) significantly alter
consumer’s energy intake. Likewise, there may be very distinct labelling techniques that could
not be tested in our analysis but may further enhance consumer perceptions of nutrition value.
In particular, it would be worthwhile to conduct experimental studies that compare how each
label element (contextual information, style of presentation etc.) influences choice beyond
calorie information alone.
Furthermore, a general limitation of meta-analysis is the sample size of included
studies to test certain moderators. For instance, our non-significant effect for children could
be related to the limited number of cases which clearly refer to children. How supply and
demand side changes vary for different target groups is another fruitful area for the next wave
of primary research.
Finally, our findings on the supply-side reactions toward policy changes raise several
questions. A possible limitation of study 2 relates to a self-selection bias by the restaurants in
our intervention condition. The use of non-experimental, field data meant that we examined
supply side adjustments by restaurants that introduced menu labelling after the legislation was
announced in 2010. Were retailers’ calorie reductions driven by the legislation or other
phenomena? Hence, future research should closely monitor retailer menu adjustments over
time once the legislation is enforced by those retailers who have not already voluntarily
disclosed calorie information. Against this background, the interplay of calorie reduction and
portion sizes modifications is another crucial research stream that requires attention from
regulators and policy makers
For many consumers, making healthier food choices in-store is hard to do. Recent
research has suggested that the food retail environment may override an individual’s ability to
control their consumption behavior (Larson and Story 2009). However, retail stores are also
an opportune place to harness marketing power to prevent obesity. Providing consumers with
nutritional information on menus is believed to be an avenue by which food retailers can
positively encourage consumer to make better nutritional choices. Though, given the cost of
implementing, and to date, the reported mixed effects of calorie disclosure, there has been
considerable controversy surrounding the legislation. The legislation was announced in 2010,
and its enforcement has been delayed each year because of retailer opposition.
The findings of our research reveal a promising effect of calorie disclosure and have
relevant implications for both retailing practice and public policy. Overall, our two meta-
analyses show that when restaurants are required to release information on the calorie content
of their foods, they will reduce the offered calorie count in response, while consumers
simultaneously select less calories. Thus, the net effect on consumer’s nutrition is likely larger
than estimated in previous consumer studies which neglect the impact of retailer menu
adjustments. It is uncertain if the two effects are entirely complementing each other, but, when
combining the average effects of both the supply side and demand side identified in our work,
a minimum of 42 Calorie reduction per person and per meal could occur. Hence, even with
these conservative numbers, people are likely to decrease their calorie consumption by at least
126 units during a single day, potentially much more depending on the number of menu items
they tend to consume per meal. Hill, Wyatt, Reed, and Peters (2003) suggest that an increase
in people’s energy intake of as little as 50–100 Calories per day is enough to account for the
rise in obesity. Furthermore, with a possible effect of a total calorie reduction of 110 units per
meal for overweight individuals, the upcoming legislation seems specifically promising for
the population group that could benefit the most from healthier nutrition.
In conclusion, even though the retail food environment has often traditionally been
criticized for its potential contribution to the prevalence of obesity within a population, the
results of our study show that it is well placed to constructively assist in the fight against
obesity: restaurants canencourage consumers to make healthier choices in store when the
appropriate information is provided. The expected behavioral effects found in our study reveal
both supply-side and demand-side adjustments to calorie disclosure, providing promise for the
legislation’s potential influence on the rising rates of obesity.
*Aaron, Jacqueline I., Rhian E. Evans, and David J. Mela (1995), "Paradoxical effect of a
nutrition labelling scheme in a student cafeteria." Nutrition Research 15(9): 1251-1261.
*Auchincloss, Amy H., Candace Young, Andrea L. Davis, Sara Wasson, Mariana Chilton, and
Vanesa Karamanian (2013), "Barriers and facilitators of consumer use of nutrition labels
at sit-down restaurant chains." Public health nutrition 16(12): 2138-2145.
*Balfour, D., R. Moody, A. Wise, and K. Brown (1996), "Food choice in response to
computer‐generated nutrition information provided about meal selections in workplace
restaurants." Journal of Human Nutrition and Dietetics 9(3): 231-237.
*Bassett, Mary T., Tamara Dumanovsky, Christina Huang, Lynn D. Silver, Candace Young,
Cathy Nonas, Thomas D. Matte, Sekai Chideya, and Thomas R. Frieden (2008),
"Purchasing behavior and calorie information at fast-food chains in New York City,
2007." American Journal of Public Health 98(8): 1457-1459.
Berman, Mark, and Risa Lavizzo-Mourey (2008), "Obesity prevention in the information age:
caloric information at the point of purchase." Journal of the American Medical
Association, 300(4): 433-435.
Binks, Martin (2016) "The role of the food industry in obesity prevention." Current Obesity
Reports 5(2): 201-207.
*Bleich, Sara N., Julia A. Wolfson, and Marian P. Jarlenski (2015) "Calorie changes in chain
restaurant menu items: implications for obesity and evaluations of menu
labeling." American Journal of Preventive Medicine 48(1): 70-75.
*Bleich, Sara N., Julia A. Wolfson, Marian P. Jarlenski, and Jason P. Block (2015),
"Restaurants with calories displayed on menus had lower calorie counts compared to
restaurants without such labels." Health Affairs 34(11): 1877-1884.
*Bleich, Sara N., Julia A. Wolfson, and Marian P. Jarlenski (2016). "Calorie changes in large
chain restaurants: declines in new menu items but room for improvement." American
Journal of Preventive Medicine 50(1): e1-e8.
*Bleich, Sara N., Julia A. Wolfson, and Marian P. Jarlenski (2017) "Calorie changes in large
chain restaurants from 2008 to 2015." Preventive Medicine 100: 112-116.
*Bollinger, Bryan, Phillip Leslie, and Alan Sorensen (2011), "Calorie posting in chain
restaurants." American Economic Journal: Economic Policy 3(1), 91-128.
Bond Jr, Charles F., Wyndy L. Wiitala, and F. Dan Richard (2003), "Meta-analysis of raw
mean differences." Psychological Methods 8(4): 406.
*Brissette, Ian, Ann Lowenfels, Corina Noble, and Deborah Spicer (2013), "Predictors of total
calories purchased at fast-food restaurants: restaurant characteristics, calorie awareness,
and use of calorie information." Journal of Nutrition Education and Behavior 45(5): 404-
*Bruemmer, Barbara, Jim Krieger, Brian E. Saelens, and Nadine Chan (2012), "Energy,
saturated fat, and sodium were lower in entrées at chain restaurants at 18 months
compared with 6 months following the implementation of mandatory menu labeling
regulation in King County, Washington." Journal of the Academy of Nutrition and
Dietetics 112(82): 1169-1176.
Burton, Scot, Laurel Aynne Cook, Elizabeth Howlett, and Christopher L. Newman (2015)
"Broken halos and shattered horns: overcoming the biasing effects of prior expectations
through objective information disclosure." Journal of the Academy of Marketing
Science 43(2): 240-256.
13 *References used in the meta-analysis are marked with an asterisk
Burton, Scot, Elizabeth H. Creyer, Jeremy Kees, and Kyle Huggins (2006), "Attacking the
obesity epidemic: the potential health benefits of providing nutrition information in
restaurants." American Journal of Public Health 96(9): 1669-1675.
Burton, Scot, and Jeremy Kees (2012), "Flies in the ointment? Addressing potential
impediments to population-based health benefits of restaurant menu labeling
initiatives." Journal of Public Policy and Marketing 31(2): 232-239.
Cantor, Jonathan, Alejandro Torres, Courtney Abrams, and Brian Elbel (2015), "Five years
later: awareness of New York City’s calorie labels declined, with no changes in calories
purchased." Health Affairs 34(11): 1893-1900.
Chandon, Pierre, and Brian Wansink (2007), "The biasing health halos of fast-food restaurant
health claims: lower calorie estimates and higher side-dish consumption
intentions." Journal of Consumer Research 34(3): 301-314.
*Chu, Yong H., Edward A. Frongillo, Sonya J. Jones, and Gail L. Kaye (2009), "Improving
patrons' meal selections through the use of point-of-selection nutrition
labels." American Journal of Public Health 99(11): 2001-2005.
Tainya C. Clarke, Brian W. Ward, Gulnur Freeman, and Jeannine S. Schiller, (2016), “Early
release of selected estimates based on data from the Janualry-September 2015 National
Health Interview Survey”, National Centre for Health Statistics.
Davis-Chervin, Doryn, Todd Rogers, and Mia Clark (1985), "Influencing food selection with
point-of-choice nutrition information." Journal of Nutrition Education 17(1): 18-22.
*Deierlein, Andrea L., Kay Peat, and Luz Claudio (2015), "Comparison of the nutrient
content of children’s menu items at US restaurant chains, 2010–2014." Nutrition
Journal 14(1): 80.
*DerSimonian, Rebecca, and Nan Laird (1986), "Meta-analysis in clinical trials." Controlled
clinical trials 7(3): 177-188.
Dobbs, Richard, Corinne Sawers, Fraser Thompson, James Manyika, Jonathan Woetzel, Peter
Child, Sorcha McKenna, Angela Spatharou (2014). Overcoming obesity: An initial
economic analysis. McKinsey Global Institute, (November), 120.
* Dodds, Pennie, Luke Wolfenden, Kathy Chapman, Lyndal Wellard, Clare Hughes, and John
Wiggers. (2014), "The effect of energy and traffic light labelling on parent and child fast
food selection: a randomised controlled trial." Appetite 73: 23-30.
* Downs, Julie S., Jessica Wisdom, Brian Wansink, and George Loewenstein (2013),
"Supplementing menu labeling with calorie recommendations to test for facilitation
effects." American Journal of Public Health 103(9): 1604-1609.
Downs, Julie S., Jessica Wisdom, and George Loewenstein (2015), “Helping Consumers Use
Nutrition Information: Effects of Format and Presentation”, American Journal of Health
Economics, 1(3): 326-344.
*Dowray, Sunaina, Jonas J. Swartz, Danielle Braxton, and Anthony J. Viera (2013), "Potential
effect of physical activity based menu labels on the calorie content of selected fast food
meals." Appetite 62: 173-181.
Driskell, Mary-Margaret, Sharon Dyment, Leanne Mauriello, Patricia Castle, and Karen
Sherman (2008), "Relationships among multiple behaviors for childhood and adolescent
obesity prevention." Preventive Medicine 46(3): 209-215.
*Dumanovsky, Tamara, Christina Y. Huang, Cathy A. Nonas, Thomas D. Matte, Mary T.
Bassett, and Lynn D. Silver (2011), "Changes in energy content of lunchtime purchases
from fast food restaurants after introduction of calorie labelling: cross sectional customer
surveys." BMJ, 343: d4464.
*Elbel, Brian, Rogan Kersh, Victoria L. Brescoll, and L. Beth Dixon (2009) "Calorie labeling
and food choices: a first look at the effects on low-income people in New York
City." Health Affairs 28(6): w1110-w1121.
*Elbel, Brian, Joyce Gyamfi, and Rogan Kersh (2011). "Child and adolescent fast-food choice
and the influence of calorie labeling: a natural experiment." International Journal of
Obesity 35(4): 493-500.
*Elbel, Brian, Tod Mijanovich, L. Beth Dixon, Courtney Abrams, Beth Weitzman, Rogan
Kersh, Amy H. Auchincloss, and Gbenga Ogedegbe (2013) "Calorie labeling, fast food
purchasing and restaurant visits." Obesity 21(11): 2172-2179.
*Ellison, Brenna, Jayson L. Lusk, and David Davis (2013), "Looking at the label and beyond:
the effects of calorie labels, health consciousness, and demographics on caloric intake in
restaurants." International Journal of Behavioral Nutrition and Physical Activity 10(1):
*Ellison, Brenna, Jayson L. Lusk, and David Davis (2014), "The impact of restaurant calorie
labels on food choice: results from a field experiment." Economic Inquiry 52(2): 666-681.
*Ellison, Brenna, Jayson L. Lusk, and David Davis, (2014), "The effect of calorie labels on
caloric intake and restaurant revenue: evidence from two full-service
restaurants." Journal of Agricultural and Applied Economics 46(2): 173-191.
ERS Food Expenditure Series (2016), Economic Research Services (US)
FDA 2014 - Food Labeling: Nutrition Labeling of Standard Menu Items in Restaurants and
Similar Retail Food Establishments Final Regulatory Impact Analysis FDA–2011–F–
Fernandes, Ana C., Renata C. Oliveira, Rossana PC Proença, Cintia C. Curioni, Vanessa M.
Rodrigues, and Giovanna MR Fiates. (2016), "Influence of menu labeling on food
choices in real-life settings: a systematic review." Nutrition reviews 74(8): 534-548.
*Finkelstein, Eric A., Kiersten L. Strombotne, Nadine L. Chan, and James Krieger (2011)
"Mandatory menu labeling in one fast-food chain in King County,
Washington." American Journal of Preventive Medicine 40(2): 122-127.
Fotouhinia-Yepes, Maryam (2013) "Menu Calorie Labelling in a Fine Dining Restaurant: Will
it Make a Difference?." Journal of Quality Assurance in Hospitality and Tourism 14(3):
Freedman, Marjorie R., and Rachel Connors (2011) "Point-of-purchase nutrition information
influences food-purchasing behaviors of college students: a pilot study." Journal of the
American Dietetic Association 111(5): S42-S46.
*Gerend, Mary A. "Does calorie information promote lower calorie fast food choices among
college students?." Journal of Adolescent Health 44, no. 1 (2009): 84-86.
*Girz, Laura, Janet Polivy, C. Peter Herman, and Helen Lee (2012), "The effects of calorie
information on food selection and intake." International Journal of Obesity 36(10): 1340-
*Hammond, David, Samantha Goodman, Rhona Hanning, and Samantha Daniel (2013) "A
randomized trial of calorie labeling on menus." Preventive Medicine 57(6): 860-866.
Harnack, Lisa J., and Simone A. French (2008) "Effect of point-of-purchase calorie labeling
on restaurant and cafeteria food choices: a review of the literature." International Journal
of Behavioral Nutrition and Physical Activity 5(1): 51
*Hoefkens, Christine, Carl Lachat, Patrick Kolsteren, John Van Camp, and Wim Verbeke
(2011) "Posting point-of-purchase nutrition information in university canteens does not
influence meal choice and nutrient intake." The American Journal of Clinical
Nutrition 94(2): 562-570.
Holden, Stephen, Natalina Zlatevska and Chris Dubelaar (2016), “Whether Smaller Plates
Reduce Consumption Depends on Who's Serving and Who's Looking: A Meta-
Analysis”, The Journal of the Association of Consumer Research, 1(1), 134-146.
*Holmes, Ashley S., Elena L. Serrano, Jane E. Machin, Thomas Duetsch, and George C.
Davis (2013), "Effect of different children’s menu labeling designs on family
purchases." Appetite 62: 198-202.
*Hunsberger, Monica, Paul McGinnis, Jamie Smith, Beth Ann Beamer, and Jean O’Malley
(2015) "Calorie labeling in a rural middle school influences food selection: Findings from
community-based participatory research." Journal of Obesity 22(March).
Hwang, Johye, and Carol L. Lorenzen (2008) "Effective nutrition labeling of restaurant menu
and pricing of healthy menu." Journal of Foodservice 19(5): 270-276.
*James, Ashlei, Beverley Adams-Huet, and Meena Shah (2015) "Menu labels displaying the
kilocalorie content or the exercise equivalent: effects on energy ordered and consumed in
young adults." American Journal of Health Promotion 29(5): 294-302.
Janakiraman, Narayan, Holly A. Syrdal, and Ryan Freling (2016) "The effect of return policy
leniency on consumer purchase and return decisions: A meta-analytic review." Journal of
Retailing 92(2): 226-235.
*Krieger, James W., Nadine L. Chan, Brian E. Saelens, Myduc L. Ta, David Solet, and David
W. Fleming (2013) "Menu labeling regulations and calories purchased at chain
restaurants." American Journal of Preventive Medicine, 44(6): 595-604.
Larson, Nicole, and Mary Story (2009), "A review of environmental influences on food
choices." Annals of Behavioral Medicine 38: 56-73.
Lazareva, Yulia (2015), "Can nutrition menu labelling positively influence consumer food
choices? A review of the literature." Surrey Undergraduate Research Journal 1(1).
*Lee, Morgan S., and Joel Kevin Thompson (2016), "Exploring enhanced menu labels’
influence on fast food selections and exercise-related attitudes, perceptions, and
intentions." Appetite 105: 416-422.
Levin, Sarah (1996), "Pilot study of a cafeteria program relying primarily on symbols to
promote healthy choices." Journal of Nutrition Education 28(5): 282-285.
*Lillico, H. G., R. Hanning, S. Findlay, and D. Hammond (2015) "The effects of calorie
labels on those at high-risk of eating pathologies: a pre-post intervention study in a
University cafeteria." Public Health 129(6): 732-739.
Littlewood, Jodie Anne, Sofia Lourenço, Cecilie Lauberg Iversen, and Gitte Laub Hansen
(2016) "Menu labelling is effective in reducing energy ordered and consumed: a
systematic review and meta-analysis of recent studies." Public Health Nutrition 19(12):
*Liu, Peggy J., Christina A. Roberto, Linda J. Liu, and Kelly D. Brownell (2012) "A test of
different menu labeling presentations." Appetite 59(3): 770-777.
Long, Michael W., Deirdre K. Tobias, Angie L. Cradock, Holly Batchelder, and Steven L.
Gortmaker (2015), "Systematic review and meta-analysis of the impact of restaurant
menu calorie labeling." American Journal of Public Health (May)
Loewenstein, George, Cass R. Sunstein, and Russell Golman (2014), "Disclosure: Psychology
changes everything." Annual Review of Economics, 6:391-419.
*Lowe, Michael R., Karyn A. Tappe, Meghan L. Butryn, Rachel A. Annunziato, Maria C.
Coletta, Christopher N. Ochner, and Barbara J. Rolls (2010) "An intervention study
targeting energy and nutrient intake in worksite cafeterias." Eating behaviors 11(3): 144-
*Mayer, Joni A., Timothy P. Brown, Joan M. Heins, and Donald B. Bishop. "A multi-
component intervention for modifying food selections in a worksite cafeteria." Journal of
Nutrition Education 19, no. 6 (1987): 277-280.
*Milich, Richard, Judy Anderson, and Marcia Mills (1975), "Effects of visual presentation of
caloric values on food buying by normal and obese persons." Perceptual and Motor
Skills, 42(1): 155-162.
Moorman, Christine, Rosellina Ferraro, and Joel Huber (2012), "Unintended nutrition
consequences: firm responses to the nutrition labeling and education act." Marketing
Science 31(5): 717-737.
*Morley, Belinda, Maree Scully, Jane Martin, Philippa Niven, Helen Dixon, and Melanie
Wakefield (2013), "What types of nutrition menu labelling lead consumers to select less
energy-dense fast food? An experimental study." Appetite, 67: 8-15.
*Namba, Alexa, Amy Auchincloss, Beth L. Leonberg, and Margo G. Wootan (2013) "Peer
Reviewed: Exploratory Analysis of Fast-Food Chain Restaurant Menus Before and After
Implementation of Local Calorie-Labeling Policies, 2005–2011." Preventing Chronic
Disease 10: E101.
National Restaurant Association (2017) http://www.restaurant.org/News-
Neumann Nico and Böckenholt Ulf (2014), “A meta-analysis of loss aversion in product
choice,” Journal of Retailing 90(2): 182-97.
Neumann, Nico, Ulf Böckenholt, and Ashish Sinha (2016), "A meta-analysis of extremeness
aversion." Journal of Consumer Psychology 26(2): 193-212.
*Pang, Jocelyn, and David Hammond (2010), "Efficacy and consumer preferences for
different approaches to calorie labeling on menus." Journal of Nutrition Education and
Behavior 45(6): 669-675.
*Platkin, Charles, Ming-Chin Yeh, Kimberly Hirsch, Ellen Weiss Wiewel, Chang-Yun Lin,
Ho-Jui Tung, and Victoria H. Castellanos (2014), "The effect of menu labeling with
calories and exercise equivalents on food selection and consumption." BMC obesity 1(1):
Powell, Lisa M., Binh T. Nguyen, and Euna Han (2012) "Energy intake from restaurants:
demographics and socioeconomics, 2003–2008." American Journal of Preventive
Medicine 43(5): 498-504.
*Prins, Amber, Dana Gonzales, Tina Crook, and Reza Hakkak (2013), "Impact of menu
labeling on food choices of Southern undergraduate students." The FASEB Journal 27(1),
*Pulos, Elizabeth, and Kirsten Leng (2010) "Evaluation of a voluntary menu-labeling
program in full-service restaurants." American Journal of Public Health 100(6): 1035-
*Rainville, Alice Jo, Kyunghee Choi, D. Mark Ragg, Amber King, and Deborah H. Carr
(2010), "Nutrition information at the point of selection in high schools does not affect
purchases." Journal of Child Nutrition and Management 34(2).
*Reale, Sophie, and Stuart W. Flint (2016) "Menu labelling and food choice in obese adults: a
feasibility study." BMC Obesity 3(1): 17
Roberto, Christina A., Marlene B. Schwartz, and Kelly D. Brownell (2009), "Rationale and
evidence for menu-labeling legislation." American Journal of Preventive Medicine 37(6):
* Roberto, Christina A., Peter D. Larsen, Henry Agnew, Jenny Baik, and Kelly D. Brownell
(2010), "Evaluating the impact of menu labeling on food choices and intake." American
Journal of Public Health 100(2): 312-318.
*Roseman, Mary G., Kimberly Mathe-Soulek, and Joseph A. Higgins (2013), "Relationships
among grocery nutrition label users and consumers’ attitudes and behavior toward
restaurant menu labeling." Appetite 71: 274-278.
Rosenthal, Robert, and M. Robin DiMatteo (2001) "Meta-analysis: Recent developments in
quantitative methods for literature reviews." Annual Review of Psychology 52(1): 59-82
*Schmitz, Mary F., and Jonathan E. Fielding (1986) "Point-of-choice nutritional labeling:
evaluation in a worksite cafeteria." Journal of Nutrition Education 18(2): S65-S68.
* Schwartz, Janet, Jason Riis, Brian Elbel, and Dan Ariely (2012) "Inviting consumers to
downsize fast-food portions significantly reduces calorie consumption." Health
Affairs 31(2): 399-407.
*Seenivasan, Satheesh, and Dominic Thomas (2016), "Negative consequences of nutrition
information disclosure on consumption behavior in quick-casual restaurants." Journal of
Economic Psychology 55: 51-60.
Sharma, Shilpa, Ashwini Wagle, Kathryn Sucher, and Nancy Bugwadia (2011) "Impact of
point of selection nutrition information on meal choices at a table-service
restaurant." Journal of Foodservice Business Research 14(2): 146-161.
Sinclair, Susan E., Marcia Cooper, and Elizabeth D. Mansfield. "The influence of menu
labeling on calories selected or consumed: a systematic review and meta-
analysis." Journal of the Academy of Nutrition and Dietetics 114, no. 9 (2014): 1375-
Sterne, Jonathan AC, and Matthias Egger (2001), "Funnel plots for detecting bias in meta-
analysis: guidelines on choice of axis." Journal of Clinical Epidemiology 54(10): 1046-
*Stites, Shana D., S. Brook Singletary, Adeena Menasha, Clarissa Cooblall, Donald Hantula,
Saul Axelrod, Vincent M. Figueredo, and Etienne J. Phipps (2014), "Pre-ordering lunch at
work. Results of the what to eat for lunch study." Appetite 84: 88-97.
*Streletskaya, Nadia A., Wansopin Amatyakul, Pimbucha Rusmevichientong, Harry M.
Kaiser, and Jura Liaukonyte (2016), "Menu‐Labeling formats and their impact on dietary
quality." Agribusiness 32(2): 175-188.
*Stutts, Mary Ann, Gail M. Zank, Karen H. Smith, and Sally A. Williams (2011) "Nutrition
information and children's fast food menu choices." Journal of Consumer Affairs 45(1):
Swartz, Jonas J., Danielle Braxton, and Anthony J. Viera (2011) "Calorie menu labeling on
quick-service restaurant menus: an updated systematic review of the
literature." International Journal of Behavioral Nutrition and Physical Activity 8(1): 135.
*Tandon, Pooja S., Chuan Zhou, Nadine L. Chan, Paula Lozano, Sarah C. Couch, Karen
Glanz, James Krieger, and Brian E. Saelens (2011), "The impact of menu labeling on
fast-food purchases for children and parents." American Journal of Preventive
Medicine 41(4): 434-438.
*Temple, Jennifer L., Karena Johnson, Kelly Recupero, and Heather Suders (2011),
"Nutrition labels decrease energy intake in adults consuming lunch in the
laboratory." Journal of the American Dietetic Association 111(5): S52-S55.
*Temple, Jennifer L., Karena M. Johnson, Kelli Archer, Allison LaCarte, Christina Yi, and
Leonard H. Epstein (2011), "Influence of simplified nutrition labeling and taxation on
laboratory energy intake in adults." Appetite 57(1): 184-192.
Thompson (2002). Cochrane Handbook for Systematic Reviews of Interventions Version 5.1.0
*Vanderlee, Lana, and David Hammond (2014) "Does nutrition information on menus impact
food choice? Comparisons across two hospital cafeterias." Public Health Nutrition 17(6):
Eric M. VanEpps, Julie S. Downs, and George Loewenstein (2016) “Calorie Label Formats:
Using Numeric and Traffic Light Calorie Labels to Reduce Lunch Calories,” Journal of
Public Policy and Marketing, 35(1): 26-36.
VanEpps, Eric M., Christina A. Roberto, Sara Park, Christina D. Economos, and Sara N.
Bleich (2016) "Restaurant menu labeling policy: review of evidence and
controversies." Current Obesity Reports 5(1):72.
Viechtbauer, Wolfgang (2010), “Conducting Meta-Analyses in R with the metafor Package”
Journal of Statistical Software 36(3):1-48
*Viera, Anthony J., and Ray Antonelli (2015), "Potential effect of physical activity calorie
equivalent labeling on parent fast food decisions." Pediatrics: peds-2014.
Vyth, Ellis L., Ingrid HM Steenhuis, Annet JC Roodenburg, Johannes Brug, and Jacob C.
Seidell (2010), "Front-of-pack nutrition label stimulates healthier product development: a
quantitative analysis." International Journal of Behavioral Nutrition and Physical
Activity 7(1): 65.
Wansink, Brian (2017), "Healthy Profits: An Interdisciplinary Retail Framework that
Increases the Sales of Healthy Foods." Journal of Retailing, 93(1): 65-78.
Webb, Karen L., Loel S. Solomon, Jan Sanders, Carol Akiyama, and Patricia B. Crawford
(2011), "Menu labeling responsive to consumer concerns and shows promise for
changing patron purchases." Journal of Hunger and Environmental Nutrition 6(2): 166-
*Wei, Wei, and Li Miao (2013) "Effects of calorie information disclosure on consumers’ food
choices at restaurants." International Journal of Hospitality Management 33:106-117.
*Wisdom, Michel, Julie S. Downs, and George Loewenstein (2010), "Promoting healthy
choices: Information versus convenience." American Economic Journal: Applied
Economics 2(2): 164-178.
*Yamamoto, Julienne A., Joelle B. Yamamoto, Brennan E. Yamamoto, and Loren G.
Yamamoto. (2005), "Adolescent fast food and restaurant ordering behavior with and
without calorie and fat content menu information." Journal of Adolescent Health 37(5):
Yusuf, Salim, Janet Wittes, Jeffrey Probstfield, and Herman A. Tyroler (1991), "Analysis and
interpretation of treatment effects in subgroups of patients in randomized clinical
trials." JAMA 266(1): 93-98.
Zlatevska Natalina, Dubelaar Chris and Holden Stephen, (2014) “Sizing up the Effect of
Portion Size on Consumption: A Meta-Analytic Review,” Journal of Marketing, 78 (3),
WHO 2016, Obesity and overweight, Fact sheet N°311, available at:
Study 1: Coded moderators and prevalence
Moderators Categorical Coding Studies
Study characteristics (k =186 )
Labeling Intervention Calories & more (1)a
Calorie alone (2)
Study Design Between subject (1)
Within subject (2)
Other design (3)b
Study Scenario Hypothetical choice (1)
Actual choice (2)
Healthiness Healthy food category (1)c
Other food category (2)
Restaurant Type Table service (1)
Other type (2)d
Eating Occasion Lunch (1)
Food Type Pizza (1)f
Other Food (3)
Gender Females only (1)
Males only (2)
Mixed gender (3)
BMI Overweight (1) g
Normal weight (2)
Age Children’s meal (1) h
Adult meal (2)
a Calories and contextual elements included, for example, exercise expenditure required to burn off calories, traffic light
labeling, other nutritional information, recommended caloric intake
b Other designs included; cross sectional, difference in difference, pre-post, pre-post with control
c Salads and pastas as well as sandwich chains such as Subway (based on the findings of previous research investigating
health halos of restaurant chains (Chandon and Wansink 2007)) were classified as healthy
d Other restaurants types included quick service (QS) and cafeteria (C)
e Other eating occasions included snacks, as well as data collected across both lunch and dinner
f Given the opposition to enforcing calorie labeling by the pizza industry, we also examined the effect of labeling for pizza
g Sample on average was overweight (BMI ≥ 26)
h The meal selected was intended for a child <18 years old
Study 1 estimates for the overall model
Label Intervention Calories and Other -10.72
Study Design Between Subject
Study Type Scenario -42.88*
Healthiness Healthy 24.87†
Restaurant Type Table Service -29.61*
Eating Occasion Lunch -26.62*
Food Type Pizza
Gender Female -75.16**
Mixed Gender -45.55†
BMI Overweight -66.85***
Age Children 9.21
k (studies) 186
N (articles) 54
Variance Level1 (Tu)
Variance Level 2 (Tv)
Variance Level 3 (Tr)
†p < .10; *p < .05; **p < .01; ***p < .001
We used dummy coding (0,1) such that the coefficients are interpreted as a difference
compared to another category.
Study 2: Coded moderators and prevalence
Moderators Categorical Coding Studies
Study characteristics (k = 41)
Study Design Between subject (1)
Within subject (2)
Other design (3)a
Healthiness Healthy food category (1)b
Other food category (2)
Food Type Pizza (1)c
Other Meal Item (3)
Age Children’s meal (1)
Adult meal (2)
Time factor Treatment time window 41
a Other designs included pre-post with control
b Salads and sandwiches (based on the findings of previous research investigating health halos of restaurant chains (Chandon
and Wansink 2007)) were classified as healthy
c Given the opposition to enforcing calorie labeling by the pizza industry, we also examined the effect of labeling for pizza
Study 2 estimates for the overall model
Study Design Between Subject
Healthiness Healthy -14.63
Food Type Pizza 4.75
Age Children 7.34
k (studies) 41
N (articles) 7
†p < .10; *p < .05; **p < .01; ***p < .001
We used dummy coding (0,1) such that the coefficients are interpreted as a difference
compared to another category.
Funnel plot study 1a
aWe removed three outliers (Elbel et al 2011 (male and female), Tandon et al 2011 (children)) from the figure who had an
SE>250, however, these data points were included in the overall analysis. We also ran the multilevel model with three
outliers (SE>250) removed and found no difference in our results.
Funnel plot study 2