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Promoting Healthy Choices: Information versus Convenience


Abstract and Figures

Success in slowing obesity trends would benefit from policies aimed at reducing calorie consumption. In a field experiment at a fast-food sandwich chain, we address the effects of providing calorie information, mimicking recent legislation, and test an alternative approach that makes ordering healthier slightly more convenient. We find that calorie information reduces calorie intake. Providing a daily calorie target does as well, but only for non-overweight individuals. Making healthy choices convenient reduces intake when the intervention is strong. However, a milder implementation reduces sandwich calories, but does not reduce total calories due to compensatory effects on side orders and drinks. (JEL I12, I18, L81)
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American Economic Journal: Applied Economics 2 (April 2010): 164–178
During the past 20 years, the United States has seen a dramatic increase in obe-
sity. In 1991, only four states had obesity prevalence rates as high as 15 percent,
and not a single state had a rate above 20 percent. By 2005, only ve states reported
rates below 20 percent, with 17 states registering rates equal to or above 25 per-
cent (H. M. Blanck et al. 2006). Economic analyses of this trend have implicated
a variety of potential causal factors (e.g., Shin-Yi Chou, Michael Grossman, and
Henry Saffer 2004; Eric A. Finkelstein, Christopher J. Ruhm, and Katherine M.
Kosa 2005), but much of the rise in obesity can be attributed to an increase in caloric
intake as opposed to a change in energy expenditure (David M. Cutler, Edward L.
Glaeser, and Jesse M. Shapiro 2003).
In this paper, we compare the efcacy of two different types of interventions
intended to change the food intake of fast food restaurant patrons. The rst inter-
vention provides calorie information to consumers and is intended to mimic recent
legislation requiring chain restaurants to display calorie information prominently on
their menus, as recommended by the Center for Science in the Public Interest (2003).
The other intervention is based on insights from the eld of behavioral economics
* Wisdom: Depar tment of Social and Decision Sciences, Carnegie Mellon University, 208 Porter Hall,
Pittsburgh, PA 15213 (email:; Downs: Depa rtment of Social a nd Decision Sciences,
Carnegie Mellon University, 208 Porter Hall, Pittsburgh, PA 15213 (email:; Loewenstein:
Department of Social and Decision Sciences, Carnegie Mellon University, 208 Porter Hall, Pittsburgh, PA 15213
(email: We thank the United States Food and Drug Administration (USDA) Economic
Research Service (grant numbers 58400060114 and 59400080077) and the Center for Behavioral Decision
Research at Carnegie Mellon University for nancial support, and Howard Seltman, Jay Variyam, and Roberto
Weber for numerous helpful suggestions on the design and analysis of our results. We also thank Michael Benisch,
Lauren Burakowski, Aya Chaoka, Charlotte Fitzgerald, Nathaniel Gales, Lizzie Haldane, Min Young Park, Eric
Tang, and Victoria Vargo for help with data collection.
To comment on this article in the online discussion forum, or to view additional materials, visit the articles
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Promoting Healthy Choices:
Information versus Convenience
By J W, J S. D,  G L*
Success in slowing obesity trends would benet from policies aimed
at reducing calorie consumption. In a eld experiment at a fast-food
sandwich chain, we address the effects of providing calorie informa-
tion, mimicking recent legislation, and test an alternative approach
that makes ordering healthier slightly more convenient. We nd that
calorie information reduces calorie intake. Providing a daily calorie
target does as well, but only for non-overweight individuals. Making
healthy choices convenient reduces intake when the intervention is
strong. However, a milder implementation reduces sandwich calo-
ries, but does not reduce total calories due to compensatory effects
on side orders and drinks. (JEL I12, I18, L81)
Promoti ng Healthy Choic es:
Information versus Convenience 164
I. Prior Attempts to Encourage Healthy Behavior 165
A. Information Provision 165
B. Asymmetr ic Paternalism 165
II. The Cu rrent Studies 166
A. Methods 167
B. Participants 168
III. Results 168
A. Informational Effects on Total Meal Calor ies 168
B. Asymmetr ically Paternalistic Effects on Total Meal Calories 168
C. Mechanisms 169
IV. Discussion 175
References 177
VOL. 2 NO. 2 165
and strives to make healthier meal choices marginally more convenient. We nd that
providing either calorie information for menu items or a recommendation for daily
caloric intake decreases total calories ordered. We also nd suggestive evidence
that daily calorie recommendations may not be effective for overweight people who,
arguably, could benet most from reduced calorie intake. Results for convenience
are mixed. A relatively heavy-handed intervention that makes unhealthy sandwiches
harder to choose reduces total caloric intake, but a milder version of the intervention
does not. Additional analyses reveal the importance of addressing compensatory
behavior, as the milder version of the convenience intervention did lead to increased
choice of lower calorie sandwiches, but the effect was undermined by an increase in
the caloric content of side dishes and drinks. Overall, neither approach (providing
calorie information or making lower calorie sandwiches more convenient) appeared
to interfere with the other, so an additive approach of both classes of intervention
may be promising for future policy consideration.
I. Prior Attempts to Encourage Healthy Behavior
A. Information Provision
The Nutrition Labeling and Education Act (NLEA), requiring consistent nutritional
information for packaged foods, was implemented in 1994 (USDA 1994). Research
on the impact of the NLEA suggests that it had some benecial effects, including
directing consumers’ attention to negative nutrition attributes such as sodium levels
(Siva K. Balasubramanian and Catherine Cole 2002); reducing fat intake (Marian
L. Neuhouser, Alan R. Kristal, and Ruth E. Patterson 1999; Alan D. Mathios 2000);
reducing fat calories, cholesterol, and sodium intake (Sung-Yong Kim, Rodolfo M.
Nayga, Jr., and Oral Capps, Jr. 2000); and even decreasing body weight—albeit only
among some groups (Jayachandran N. Variyam and John Cawley 2006). However,
these benecial effects vary according to individual characteristics, such as age and
cognitive ability (Cole and Gary J. Gaeth 1990), motivation (Christine Moorman
1996), and self-control (Finkelstein, Ruhm, and Kosa 2005).
One reason for the limited effects of labeling may be that information alone can-
not overcome other forces, such as the high cost of healthy foods (Kelli K. Garcia
2007) or the delayed and intangible nature of benets from dieting (John G. Lynch,
Jr. and Gal Zauberman 2006; Scott Rick and Loewenstein 2008). Furthermore,
choices made in restaurants may be even less amenable to informational interven-
tions. Restaurant consumers tend to be hungry and in a hurry, and thus may be
more short-sighted and less motivated to process nutritional information. Our stud-
ies explore whether a similar pattern of partially benecial effects, as found for the
NLEA, results from providing calorie information on a fast-food menu, and examine
how these effects interact with demographic characteristics.
B. Asymmetric Paternalism
The second class of approach we test is what behavioral economists refer to
as an “asymmetrically paternalistic” intervention (Colin Camerer et al. 2003;
Richard H. Thaler and Cass R. Sunstein 2003), which seeks to steer consumers
toward better” behaviors without limiting their freedom of choice. Many such
interventions exploit biases that usually detract from the quality of decision mak-
ing to, instead, change behavior in benecial ways (Peter Kooreman and Henriëtte
Prast 2007; Loewenstein, Troyen Brennen, and Kevin G. Volpp 2007; Rebecca
K. Ratner et al. 2008). The intervention in our studies plays on two such biases.
The rst is present-biased preferences, whereby individuals place disproportion-
ate weight on immediate costs and benets at the expense of delayed outcomes
(David Laibson 1997; Ted O’Donoghue and Matthew Rabin 1999). To offset the
immediate, calorie-promoting allure of a large and tasty meal, our intervention
aimed to make healthier options slightly more convenient, thereby introducing
an immediate cost—the cost of the extra effort required to order a less healthy
meal—to choosing the unhealthy option. Avoidance of this small immediate cost,
accentuated by present-biased preferences, weighed in favor of healthy selections.
The second bias is the tendency for people to stick with the default option, even
if superior options are available (William Samuelson and Richard Zeckhauser
1988). Policies that set the desired behavior as the default have been shown to
increase retirement put-asides (Brigitte C. Madrian and Dennis F. Shea 2001) and
organ donation (Eric J. Johnson and Daniel Goldstein 2003). The default bias was
invoked in our experiment by making healthy options the implicit default.
Biases such as these have been explored in interventions aimed at diet. For exam-
ple, changes in convenience have been shown to reduce snacking (James E. Painter,
Brian Wansink, and Julie B. Hieggelke 2002) and alter decisions about peripheral
meal items such as chips and candy (Herbert L. Meiselman et al. 1994), typically
without consumer awareness (Brian Wansink 2006). The intervention in this paper
examines the effectiveness of a similar strategy exploiting such biases applied to the
main entrée of a fast-food meal.
II. The Current Studies
This paper reports ndings from two studies designed to assess the effects of
informational and asymmetrically paternalistic approaches to encouraging low-cal-
orie meal choices. The two studies included identical informational manipulations,
but differed in the implementation of the asymmetrically paternalistic manipulation,
with the second study employing a somewhat weaker intervention than the rst.
The informational manipulations were: (1) providing a daily calorie recommenda-
tion, and (2) providing specic information about the caloric content of menu options
(so as to mimic much recent legislation). The asymmetrically paternalistic interven-
tion in both studies made healthy sandwich options slightly more convenient. Based
on the mixed effects of information provision observed in prior research, coupled with
the great success of default manipulations applied to savings behavior, we anticipated
that the convenience manipulation would have more robust effects than the informa-
tional manipulations. Although the main dependent measure in the two studies is the
caloric content of diners’ selections, we also had subjects complete a short survey that
elicited items that we thought might interact with the experimental interventions or
help to explain variance in food choice that was unrelated to the interventions.
VOL. 2 NO. 2 167
A. Methods
During lunch hours, we approached customers entering a fast-food sandwich res-
taurant and offered them a free meal of their choice in exchange for completing a
survey. Patrons who agreed to participate were instructed to pick their meal from
the provided menu, rst selecting a sandwich, then a side dish and drink. Next,
participants completed the survey, after which they were handed a gift card and a
coupon with their order to give to the restaurant. To minimize subjects’ concern that
they would be judged on the basis of their food choice, the setup was designed to
give the impression that the meal choice was incidental—merely compensation for
completing the survey.
The menus varied in a 2 (daily calorie recommendation offered or not) × 2 (calo-
rie information for menu items shown or not) × 3 (convenience of healthy options)
design. Daily calorie recommendations, when offered, were presented for men and
women with sedentary versus active lifestyles. Calorie information, when provided,
was listed prominently next to each menu item, including sandwiches, side dishes,
and drinks. The two informational interventions were identical in both studies,
which permits us to aggregate the data from both studies in analyzing their impact,
maximizing statistical power.
The convenience intervention was implemented only for choice of sandwiches,
not for side dishes or drinks, with two slightly different manipulations in each of
the studies. In both studies, the rst page after the instructions listed ve of the
ten sandwich options as “featured” sandwiches. This page contained either the ve
most caloric, least caloric, or a mix of high- and low-calorie sandwich options, as
presumably would be seen in a real-world eating environment. Study 1 implemented
a stronger version of the manipulation. Participants were informed that they could
choose from the featured menu page or, as noted in large print at the bottom of the
page, that they could choose from the complete menu by opening a packet with addi-
tional options. That packet listed the ve remaining, non-featured, sandwiches and
was sealed with a small round paper sticker. Participants were asked to indicate their
sandwich choice, whether from the featured or non-featured list. Using the paper
seal in Study 1 enabled us to record whether the second menu had been opened, pro-
viding insight into the mechanism driving any effect of the manipulation but added
a small extra component of difculty to choosing a sandwich off the featured menu
page, thus making the intervention slightly more heavy-handed.
Study 2 implemented the convenience manipulation in a subtler manner by listing
the second set of sandwiches on the next page. To request a sandwich from the rst
page, the participants merely checked their choice on a form. To request a sandwich
from the second page, the participant was required to write it out, similar to some
sushi restaurants, where ordering something other than a standard combination plat-
ter requires writing down one’s specic selection of sushi.
In both studies, after indicating their sandwich choice, participants chose their
drink (e.g., diet or non-diet soda, juice, or water) and side dish (e.g., potato chips
or fruit), with calorie information listed for each option in the conditions in which
calorie information was provided. The presentation of drinks and side dishes was
identical across convenience manipulation treatments.
The survey, which was completed following meal selection, asked subjects to
estimate the caloric content of their chosen meal as well as their recommended daily
calorie intake. In addition, the survey asked subjects to rate, on a seven-point scale,
their hunger, anticipated enjoyment of the meal, the extent to which they carefully
considered what to order, whether they ordered less than usual, ordered healthier
than usual, were usually careful about what they ate, considered calories when
ordering, how often they ate at that fast-food chain, whether they were currently
dieting, their height and weight, and other demographic information.
B. Participants
A total of 638 diners participated (292 in Study 1 and 346 in Study 2). Across both
studies more than half of the customers who were approached agreed to participate.
The sample was 61 percent male, 54 percent white, 11 percent African American, 30
percent Indian/Asian, 2 percent Hispanic, and 3 percent other. Participants were 29
years old on average (range 18 to 86). The average body mass index (BMI, calculated
as the ratio of self-reported weight in kilograms to squared height in meters) was
25 (range 16 to 44). Forty-one percent of participants were overweight by conven-
tional standards (BMI 25). Twenty-one percent of participants reported that they
were currently dieting. Participants reported a mean hunger level of 5.1 and a mean
anticipated meal enjoyment of 5.7 (both on 1-to-7 scales). On average, participants
reported that they visited the restaurant chain where the study was conducted about
twice a month.
III. Results
A. Informational Effects on Total Meal Calories
The impact of the manipulations on total calorie intake was estimated with OLS
regression, with controls for demographic characteristics (Table 1). Aggregating the
data from the two studies (column 3), providing specic calorie information led
participants to order signicantly fewer calories (B = 60.7, t(621) = 3.20, p <
0.001), as did the daily calorie recommendation (B = 37.8, t(621) = 2.01, p <
0.05). Although we had anticipated that the recommendation might help people to
use the specic information more effectively, the interaction between these variables
did not approach signicance. Rather, the effects of the informational interventions
appear to be additive, with the combination of the two reducing meals by almost 100
calories (Figure 1).
B. Asymmetrically Paternalistic Effects on Total Meal Calories
Two dummy variables were included in the regression (Table 1) to test for the
effects of the convenient menu comprising healthy or unhealthy sandwiches, both
compared to a convenient menu with a mix of both types of sandwiches. In Study 1
(column 1), the healthy featured menu had a large and signicant negative impact on
total meal calories (B = 76.65, t(282) = 2.25, p < 0.03), despite being applied
VOL. 2 NO. 2 169
only to the sandwich menu and not to the side dishes or drinks. In Study 2 (column 2),
however, the subtler convenience manipulation did not reduce total meal calories (B
= 22.23, t(334) = 0.72, p = 0.47). Column 3 of Table 1 allows for an explicit com-
parison of the impact of the two interventions. The signicant positive interaction
between the dummy for Study 2 and convenience indicates that Study 1’s stronger
manipulation had a signicantly larger impact on total calories than did Study 2’s
weaker manipulation.
C. Mechanisms
Sandwich versus Non-Sandwich Calories and Compensatory Behavior.— Clues
about how and why the different interventions did or did not reduce total meal calo-
ries are provided in separate OLS regressions examining the determinants of sand-
wich choice (Table 2) and non-sandwich (i.e., side dish and drink) calories (Table 3).
The informational manipulations achieved their effects purely through lowering
non-sandwich calories. Table 2 shows that there was no signicant impact of the
informational interventions on sandwich choice, and Table 3 shows that a signicant
decrease in non-sandwich calories resulted from both the specic calorie informa-
tion (B = 48.57, t(621) = 3.25, p < 0.001) and the daily calorie recommenda-
tion (B = 35.91, t(621) = 2.42, p < 0.02). Although participants randomized to
receive information had seen it at the time when they made their sandwich decision,
their choice of sandwich does not seem to have been affected. This suggests that par-
ticipants may have found it easier to cut calories by changing side dishes or drinks,
or that doing so detracted less from their anticipated enjoyment of the meal than
changing the sandwich would have.
The convenience manipulation, on the other hand, had its strongest effect on
sandwich choice (Figure 2), which is not surprising since this manipulation changed
F 1. E M M  T M C C,  B S,  
F  P S C I   D C R
Note: Bars indicate two standard errors.
Study 1
Study 2
Total meal calories
Calorie information
Percentage of participants
ordering a low-calorie
Featured menu
Healthy Mixed Unhealtthy
Not provided Provided
Study 1
Study 2
Calorie recommendation
Not provided
only the convenience of ordering different types of sandwiches, but not drinks or
side orders. The effect of the convenience manipulation was stronger in Study 1,
where participants were 44 percent more likely (an increase of 23 percentage points)
to choose a low-calorie sandwich when the healthy menu was made convenient (B
= 0.23, t(282) = 3.47, p < 0.001), and 44 percent less likely to do so when the
unhealthy menu was convenient (B = 0.23, t(282) = 3.35, p < 0.001) (Table 2).
The weaker manipulation of Study 2 resulted in a smaller but still signicant impact
of the healthy menu on sandwich choice. When healthy sandwiches were made more
convenient, subjects were 35 percent more likely to order a low-calorie sandwich (B
= 0.15, t(335) = 2.22, p < 0.03), but making unhealthy sandwiches more conve-
nient had no effect. However, in Study 2, the convenience manipulation appeared to
produce a compensatory effect on non-sandwich calories (Table 3), which increased
sufciently in the healthy sandwich condition (B = 57.81, t (334) = 2.40, p < 0.02)
T 1—T C C
Study 1 Study 2 Combined stud ies
Constant 843.62**
Calorie in formation provided 48.05
Daily calorie recommendation provided 37.46
Healthy featured menua76.65*
Unhealthy featured menu 16.00
Female 69.47*
Age (in years) −0.75
Africa n American 130.47**
Study 2 46.00
Study 2 × healthy menu 98.65*
Study 2 × unhealthy menu 10.98
N = 290 N = 342 N = 632
F(7, 282) = 3.12,
p = 0.003
F(7, 334) = 3.64,
p = 0.001
F(10, 621) = 6.42,
p < 0.001
R2 = 0.07 R2 = 0.07 R2 = 0.09
Note: Standard errors in parentheses.
Combined effect of the healthy menu across studies without interaction effects: B = 24.63, t(623) = 1.08,
p = 0.28.
** Signicant at the 1 percent level.
* Signicant at the 5 percent level.
VOL. 2 NO. 2 171
to completely offset the impact of the manipulation on sandwich calories, leaving
no effect of the convenience manipulation on total calorie consumption (Table 1,
column 2).
This compensatory effect may result from the attention drawn to the forgone items,
which was heightened in the second study. In Study 1, only 38 percent of people
(similar in all three conditions, χ2 = 0.08, p = 0.96) opened the packet to see alter-
native options. In the second study, however, all subjects are likely to have seen the
additional options when turning the menu page, before choosing their side dish and
drink. Choosing from the healthy menu may have led to a sense of deservingness
upon seeing the unhealthy sandwiches that were passed up, leading people to reward
themselves with higher-calorie side dishes and drinks. Regardless of the mechanism
driving these results, they point to the fact that compensatory behavior can be critical
in determining the overall impact of interventions aimed at behavior change.
Perceptions of Caloric Content.—A likely mechanism for the effect of informa-
tion on eating behavior is improved knowledge about, and consideration of, caloric
content and guidelines. To explore these effects, we performed a series of regressions
T 2—C   L-C S
Study 1 Study 2 Combined stud ies
Constant 0.52**
Calorie in formation provided (versus not)0.08
Daily calorie recommendation provided
(versus not)
Healthy featured menua0.23**
Unhealthy featured menu 0.23**
Female 0.01
Age (in years) −0.001
Africa n American 0.17
Study 2 0.09
Study 2 × healthy menu 0.08
Study 2 × unhealthy menu 0.23*
N = 290 N = 343 N = 633
F(7, 282) = 7.12,
p < 0.001
F(7, 335) = 1.99,
p = 0.06
F(10, 622) = 6.25,
p < 0.001
R2 = 0.15 R2 = 0.04 R2 = 0.09
Notes: Standa rd errors in parentheses. OLS regressions; logistic regression produced virtually identical results.
Combined effect of the healthy menu across studies without interaction effects: B = 0.19, t(624) = 4.00, p
< 0.001.
** Signicant at the 1 percent level.
* Signicant at the 5 percent level.
predicting the difference between participants’ estimates of recommended daily cal-
ories and actual recommended values (Table 4, column 1), the absolute value of this
difference (column 2), the difference between participants’ estimates of the calories
in their chosen meal and their meal’s actual calories (column 3), the absolute value
of this estimation error (column 4), and the extent to which participants reported
considering calories when deciding on their meal (column 5).
Participants’ knowledge of recommended daily caloric intake and of the caloric
content of their meal choices was relatively poor. Overall, participants greatly under-
estimated daily recommended calorie intake (mean difference between estimated
and actual recommendation = 547.2, p < 0.001) and the calories in their meal
(mean difference between estimate and actual meal calories = −119.2, p < 0.001).
Receiving calorie information signicantly reduced the magnitude of the absolute
error in estimating meal calories (Table 4, column 4; B = 67.70, t(595) = 2.82,
p < 0.01). The daily calorie recommendation marginally increased estimates of
T 3—N-S (Side Dish and Drink) C C 
Study 1 Study 2 Combined studies
Constant 445.59**
Calorie in formation provided (versus not) −34.13
Daily calorie recommendation provided (versus not) −40.52
Healthy featured menua22.58
Unhealthy featured menu 37.13
Female 68.97**
Age (in years) −1.19
Africa n American 90.06*
Study 2 23.72
Study 2 × healthy menu 80.54*
Study 2 × unhealthy menu 42.55
N = 290 N = 342 N = 632
F(7, 282) = 2.73,
p < 0.01
F(7, 334) = 4.48,
p < 0.001
F(10, 621) = 6.79,
p < 0.001
R2 = 0.06 R2 = 0.09 R2 = 0.10
Note: Standa rd errors in parentheses.
a Combined effect of the healthy menu across studies without interaction effects: B = 20.13, t(623) = 1.12, p
= 0.26
** Signicant at the 1 percent level.
* Signicant at the 5 percent level.
VOL. 2 NO. 2 173
recommended daily allowance of calories, moving them closer to the recommenda-
tions that we provided (from 1,680 to 1,838 for women, and from 2,072 to 2,090 for
men; Table 4, column 1; B = 127.45, p = 0.056), and signicantly increased esti-
mates of meal calories to a corresponding degree (Table 4, column 3), but did not
reduce error.
It may seem contradictory that the daily calorie recommendation would increase
estimates of how much one should eat but reduce the actual amount that one does
eat. These results suggest that the daily recommendation worked not by improv-
ing knowledge, but, perhaps, by raising the salience of calorie considerations.
This account is supported by the fact that those who received the recommendation
were more likely to report that they considered calories when ordering (Table 4,
column 5), even though they were no more accurate in reporting the calories in their
meal (Table 4, column 4).
Effects among Overweight Participants.—The analyses above assess the degree
to which these three interventions reduce calorie intake among the population as a
whole. However, people who are not overweight are not the intended audience of
such campaigns because they have no special reason to reduce their calories. Thus,
it is of particular importance to measure the impact of these interventions on the
people who most need to change behavior—those who are overweight or obese.
Table 5 presents regressions including only the subsample of overweight (BMI 25)
participants (n = 262; 41 percent of sample), predicting total meal calories (column
1), choice of a low-calorie sandwich (column 2), and non-sandwich calories (column
3) using the same independent variables as the main analyses.
F 2. T P  P W C  L-C S  E S  
F   “F” M
Note: Bars indicate two standard errors.
Study 1
Study 2
Total meal calories
Calorie information
Percentage of participants
ordering a low-calorie
Featured menu
Healthy Mixed Unhealtthy
Not provided Provided
Study 1
Study 2
Calorie recommendation
Not provided
Although the smaller sample size diminishes statistical power and reduces most
effects to being non-signicant, comparison of effect sizes reveals no evidence that
provision of specic calorie information, or the convenience manipulation, has a dif-
ferent effect on total calories consumed among overweight individuals compared to
the broader sample. The calorie recommendation, on the other hand, seems to have
no benet for this population (indeed, the direction of the effect for total calories is
reversed from that found in the full sample, although it is not signicantly different
from zero). If the effect in the overall population is due to increasing salience of
calorie information rather than providing usable information, as proposed above,
perhaps this nding speaks to the greater attention already paid to calorie consider-
ations by overweight people. Although this analysis is merely exploratory, it speaks
T 4—E  I   C M  K
 P
Dependent var iable
actual meal
aAbsolute meal
calorie erroraConsidered
Constant –280.38**
featured menu
featured menu
Female –288.57**
Age –8.53**
Africa n American –362.60**
Study 2 –180.18
Study 2 × healthy
featured menu
Study 2 × unhealthy
featured menu
N = 622 N = 622 N = 606 N = 606 N = 621
F(10, 611) = 6.75,
p < 0.001
F(10, 611) = 4.37,
p < 0.001
F(10, 595) = 4.13,
p < 0.001
F(10, 595) = 3.66,
p < 0.001
F(10, 610) = 5.48
p < 0.001
R2 = 0.10 R2 = 0.07 R2 = 0.07 R2 = 0.06 R2 = 0.08
Note: Standa rd errors in parentheses.
a Eight extreme outliers (with estimate errors > 1700) were removed from these analyses.
b Endorsement of “I considered calor ies when ordering”, on a 1-to-7 scale.
** Signicant at the 1 percent level.
* Signicant at the 5 percent level.
Sign icant at the 10 percent level.
VOL. 2 NO. 2 175
to the need to examine target populations more closely, so as to ensure that the
intended effects are affecting the behavior of those most in need of behavior change.
IV. Discussion
The results of this study indicate that both information and convenience can
affect the food choices of fast-food restaurant patrons. Averaging across all par-
ticipants, both calorie information and a calorie recommendation decreased total
calories ordered. The more heavy-handed convenience manipulation also reduced
total calories signicantly, whereas the lighter intervention of Study 2 inuenced
sandwich choice but not total calories.
Although the daily calorie recommendation appears, on average, to decrease cal-
orie intake, there is a disturbing suggestion in the data that it may not be helpful for
those who need to cut back on calories—those who are overweight. The potentially
different, and less impressive, results for people who are overweight have important
implications for policies targeted at restaurant, and specically fast food, dining.
Before expanding the implementation of policies that involve provision of informa-
tion, it would be very helpful to understand more about when information helps and
T 5—E  I  C M  O
(BMI 25) P
Dependent var iable Total calories
Choice of a low-
calorie sandwich
Constant 779.31**
Calorie in formation provided (versus not)–41.27
Daily calorie recommendation provided
(versus not)
Healthy featured menu –93.37
Unhealthy featured menu –16.94
Female –43.37
Age (in years)–0.26
Africa n American 126.48**
Study 2 73.12
Study 2 × healthy menu 80.56
Study 2 × unhealthy menu 2.92
N = 259 N = 260 N = 259
F(10, 248) = 2.61,
p < 0.01
F(10, 249) = 3.27,
p = 0.001
F(10, 248) = 2.80
p < 0.01
R2 = 0.10 R2 = 0.12 R2 = 0.10
Note: Standa rd errors are in pa rentheses.
** Signicant at the 1 percent level.
* Signicant at the 5 percent level.
when it does not, and why these patterns emerge. In addition, although the provision
of calorie information reduced non-sandwich calories, it appeared to have no effect
on the choice between high- and low-calorie sandwiches, which was the rst choice
made by participants, and is arguably the main course of the meal. Further research
is needed to understand this pattern, and specically to determine whether calorie
information might have differential effects on central versus peripheral components
of a meal, or whether its effects might be greater in a situation in which sandwich
calories were not bimodally distributed, as they were constructed to be for this study.
Because informational strategies do not appear to be a panacea for the obesity
epidemic, future interventions should consider additional methods for changing eat-
ing behavior. In these studies, simply making it easier for consumers to choose a
low-calorie option signicantly reduced sandwich calories. However, the impact of
the weaker convenience manipulation was undermined by compensatory choices of
drinks and side orders. This raises the question of whether a similarly weak conve-
nience manipulation would have been more effective if it had encompassed drinks
and side orders (e.g., made bottled water and fruit part of the featured menu).
The compensatory effect on non-sandwich items highlights the importance of
including and analyzing choices that are not directly targeted by interventions. The
convenience manipulation was applied only to sandwich choice, allowing us the
opportunity to measure compensatory effects on nontargeted choices within the
entire meal, and these proved to be important. However, all participants had the
option of purchasing additional items (e.g., cookies) when they collected their meal,
or purchasing other items at nearby establishments selling food, and were also likely
to make further food choices later in the day. Those who choose lower calorie meals,
whether due to information or convenience, may feel hungrier later in the day, or
more entitled to indulge, and may end up consuming more calories later. We were
unable to measure such purchases, so we cannot rule out that they occurred or, if so,
assess what impact they had on total calories consumed in the different conditions.
Note that a similar criticism applies to the studies examining the impact of changing
defaults on retirement contributions. Although these studies have revealed uniformly
positive effects of high defaults, without information about other nancial activities
there is no way of knowing whether the net impact of such changes increases sav-
ing. It is possible, for example, that those contributing more to retirement plans may
incur credit card debt, or even take out payday loans to compensate for their lower
It is, moreover, unclear whether either type of intervention could produce sus-
tained changes in behavior. People might learn to work around the interventions, for
example, if they discover that their preferred options were always reserved for later
in the menu, or they might come to ignore calorie information. Conversely, and more
optimistically, if either of these kinds of manipulations work for some period, they
might prove to be habit-forming, thereby creating long-term changes in diet even
if the interventions were removed. In addition, restaurants may change their menu
options and nutritional content, for better or worse, as a result of the legislation, thus
either aiding or undermining individual behavior.
In this study, we aimed to test for effects of information and convenience inde-
pendently. However, it is possible that our subjects interpreted the composition of the
VOL. 2 NO. 2 177
featured menu as conveying information, and even more likely that this would be true
in real-world settings. People could assume that items made more convenient were
selected based on health concerns, as would likely be the case if such a presentation
were mandated by legislation. Alternatively, selection for menu prominence might
be interpreted as conveying other information about the selected options, such as
that they are more popular, fresher, or tastier. Thus, the context in which consumers
understand the menu may lead them to glean different kinds of information from
the fact that some options are made especially prominent, potentially affecting their
behavioral response.
The current study explores the promise of different kinds of interventions, includ-
ing both the standard approach of providing more information, as well as subtler
attempts to nudge people in a healthier direction. Both kinds of intervention showed
some promise, and they did not interact with one another, suggesting that there
would be no downside to applying them simultaneously. However, before imple-
menting either type of interventions on a broader scale, more research is needed to
understand and guard against both the potential for perverse effects on subsets of the
population and the possibility of compensatory behaviors.
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... While some studies suggest that covert influences may have a greater effect than overt influences on choice outcome (e.g., Duffy & Verges, 2008;Goldstein et al., 2008;Nolan et al., 2008;Wisdom et al., 2010), to our knowledge the question of which is more acceptable has not been addressed empirically. If covert influences are found to be more acceptable, then the most effective decisional enhancement programs would also be the most acceptable, providing a clear path for implementation of such programs. ...
... The scenarios are in the Appendix. The effectiveness at reducing caloric intake of manipulations along each of these lines has been studied (Chapman & Ogden, 2012;Downs et al., 2009;Harnack & French, 2008;Pulos & Leng, 2010;Roberto et al., 2010;Tandon et al., 2011;Wisdom et al., 2010), although to our knowledge their relative acceptability has not been examined. ...
Full-text available
Ubiquitous cognitive biases hinder optimal decision making. Recent calls to assist decision makers in mitigating these biases—via interventions commonly called “nudges”—have been criticized as infringing upon individual autonomy. We tested the hypothesis that such “decisional enhancement” programs that target overt decision making—i.e., conscious, higher-order cognitive processes—would be more acceptable than similar programs that affect covert decision making—i.e., subconscious, lower-order processes. We presented respondents with vignettes in which they chose between an option that included a decisional enhancement program and a neutral option. In order to assess preferences for overt or covert decisional enhancement, we used the contrastive vignette technique in which different groups of respondents were presented with one of a pair of vignettes that targeted either conscious or subconscious processes. Other than the nature of the decisional enhancement, the vignettes were identical, allowing us to isolate the influence of the type of decisional enhancement on preferences. Overall, we found support for the hypothesis that people prefer conscious decisional enhancement. Further, respondents who perceived the influence of the program as more conscious than subconscious reported that their decisions under the program would be more “authentic”. However, this relative favorability was somewhat contingent upon context. We discuss our results with respect to the implementation and ethics of decisional enhancement.
... Calorie information plays a prominent role in calibrating food choices-field experiments demonstrate that informing consumers about the calorie content of the foods they order changes their preferences and often facilitates the choice of less energy-dense options (Wisdom et al., 2010). Results on consumers' caloric estimates of food options are inconclusive. ...
... Additionally, when consumers watch videos depicting environments in which food resources are becoming increasingly scarce, their preferences increase for dishes deemed to be higher in calories compared to consumers exposed to control videos (Folwarczny et al., 2021). It remains unknown why food preferences change under the influence of environmental cues, but calorie ratings do not, despite their essential role as a criterion for food choice (Mathiesen et al., 2022;Otterbring & Shams, 2019;Wisdom et al., 2010). Here, we propose that the lack of association between eating environments and calorie perceptions, as previously demonstrated in the literature, may stem from the failure to consider an important moderator: the inferred healthiness of the available food alternatives. ...
Extant research has found that the addition of vegetables to a meal induces a “health halo,” thereby lowering the perceived calorie content of the entire dish. We investigated whether environmental stimuli that convey naturalness could trigger such a halo effect. Specifically, we tested whether meals accompanied by a natural, as opposed to an urban, background scenery were estimated to be lower in their calorie content and whether this effect was moderated by the perceived healthiness of the food alternatives. In a mixed (between-within-subjects) design experiment, 200 participants estimated the caloric content and rated the healthiness of 18 complex meals presented against either a natural or an urban background. Our results showed no main effect of the food rating background. However, there was a negative relationship between inferred food healthiness and the estimated calorie content of the meals. In addition, we found a significant interaction between food rating background and inferred healthiness of the evaluated food alternatives. Specifically, when participants evaluated meals against a natural background, they rated relatively unhealthy food alternatives as lower in calories than when they evaluated such alternatives against an urban background. Overall, our results highlight the moderating role of perceived food healthiness in studying the effects of environmental cues on consumers’ calorie judgments.
... Alongside supermarkets, quick-service restaurants have an important role within the food environment (22,163,164). Our results showed that ESM member states on average have more quick-service restaurant outlets than supermarkets (3.7 and 2.4 per 1000 inhabitants, respectively). ...
... As such, similar to supermarkets, regulations affecting quick-service restaurants could potentially benefit from being implemented at national level. Potential policies could be the implementation of nudging techniques and menu-labelling which have shown to be effective in schools and among non-overweight individuals, respectively (163)(164)(165)(166). However, first more research is required to identify the unique national companies, understand the national food environment and summarize the commitments already made by quick-service restaurants. ...
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The main objective of this thesis was to analyse from an economic and a public health perspective how the food industry (packaged food and beverage manufacturers, quick-service restaurants and supermarkets) influences what foods we acquire, prepare and consume. This influence takes place each time that we interact with the physical, economic, political and socio-cultural aspects of the environment surrounding us, collectively called the food environments. Examples of such interactions are the advertisements on TV, the sweets at supermarket checkouts and the higher prices of healthier products compared to less healthy alternatives. Other activities take place behind closed doors. Think of lobbying, campaigns to influence the public opinion, mergers, acquisitions and how companies position themselves on the market. These are all activities which can provide companies with the ability to structure food environments to suit their own private interests.Within the economic perspective, it was assessed how market concentration and diversity across Europe affects food environments. It was found that the major food and beverage manufacturers were similar across European countries with moderately to highly concentrated product markets. The latter may be of concern as this thesis found that market concentration significantly predicted an increase in the proportion of sales derived from (less healthy) ultra-processed foods. Based on the similarity of food and beverage manufacturers across Europe this thesis suggested that food and beverage markets may benefit from being approached as homogenous across Europe from a public health point of view. This would facilitate the implementation of nutrition policies for the industry and reduce the administrative burden accompanying policy development, implementation and monitoring. The picture observed for quick-service restaurants and supermarkets was different from the one for food and beverage manufacturers. Comparable to the latter, the biggest quick-service restaurants were similar across Europe, but there was a remarkably higher number of small companies at national level. In contrast, several different supermarkets were observed across Europe, but at country level only a limited number of different supermarkets could be identified. As such it is suggested to approach these industries as heterogeneous across Europe from a public health perspective. Consequently this thesis recommended to maintain the development and implementation of nutrition policies affecting quick-service restaurants and supermarkets at country level.Within the public heath perspective, this thesis assessed the transparency, comprehensiveness and specificity of the nutrition-related commitments made by the food industry as well as some company practices and the relationship between commitments and practices. At European level it was found that publicly available nutrition-related commitments of major food companies fell short of recommended best practices. The lion’s share of sales was for ultra-processed product categories and product categories not-permitted to be marketed to children. A similar pattern was observed within the case studies in Belgium and France where companies had the opportunity to verify and complete the publicly available commitments. The additionally collected performance data across domains such as ‘Product formulation’ and ‘Product and brand promotion’ documented largely unhealthy company practices. There was no indication of a relationship between the strength of companies’ commitments and the healthiness of company practices according to the performance indicators.All results indicated a need for European and national government regulation to guide, monitor and support food industry efforts to improve food environments.
... Prior studies that estimate the effects of salient nutrition information on food and beverage choice have not separated the nutrition information from choice decisions. Previous studies have placed calorie labels (Jue et al., 2012;Wisdom et al., 2010), traffic light labels , descriptive labels (Grabenhorst et al., 2013;Olstad et al., 2014), and the NFL (Fang et al., 2019;Kim et al., 2021;Neuhofer et al., 2020) either on the item's package, in conjunction with the choices, or visibly displayed alongside the choice options. Our contribution is that we separate consumers' initial exposure to the nutrition information from their subsequent choice, which will nudge consumers toward healthier beverage options, as seen in the preordering literature. ...
... In the case of sandwiches, a positive effect of calorie reduction is observed for normal-weight individuals. However, in the case of overweight individuals, their caloric intake does not decrease (Wisdom et al., 2010). Conversely, other research shows the opposite effect on beverages, where the introduction of calorie messaging increased the selection of sugar-sweetened beverages (Jue et al., 2012). ...
Full-text available
This study used eye-tracking data to determine if attention to nutrition information before making purchasing decisions between beverages was associated with selecting a sugar-sweetened beverage and whether the updated Nutrition Facts Label (NFL) garnered more attention than the previous NFL. Participants were randomly assigned to view either the previous or updated NFL, and time to first fixation (TFF) and total visit duration (TVD) were collected while participants viewed NFLs for beverages without additional product details. TFF and TVD were collected for three areas of interest – sugar content, calorie content, and the entire NFL. After viewing nutrition information without product details, participants made non-hypothetical choices between beverages that varied by sugar and calorie content. Results show that without additional product information, the sugar content presented by the updated NFL was more salient and garnered more attention. Although, results about the influence of information salience and the attention given to information on subsequent beverage choices were mixed. Attention to nutrition information may outweigh salience when forming decisions. The updated NFL was more effective at priming individuals with a higher body mass index by influencing subsequent beverage choices.
... At least two studies have found evidence that when individuals do make lower calorie choices as a result of calorie labels, some but not all of this reduction in energy is compensated for through consumption of additional energy later in the day. 79,80 However, other studies have not found evidence that later energy intake is increased after exposure to energy or nutrition labels on foods. [81][82][83] It is also plausible that among a minority of people, energy information about alcoholic drinks could result in compensatory behaviours that result in an increased likelihood of experiencing alcohol related harm (e.g. ...
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Alcohol is calorie dense, but unlike food products, alcoholic drinks tend to be exempt from nutritional labelling laws that require energy content information to be displayed on packaging or at point of purchase. This review provides a perspective on the likely efficacy of alcoholic drink energy labelling as a public health policy to reduce obesity and discusses key questions to be addressed by future research. First, the contribution that alcohol makes to population level daily energy intake and obesity is outlined. Next, consumer need for alcohol energy labelling and the potential impacts on both consumer and industry behaviour are discussed. Pathways and mechanisms by which energy labelling of alcoholic drinks could reduce obesity are considered, as well as possible unintended consequences of alcoholic drink energy labelling. Would widespread energy labelling of alcoholic drinks reduce obesity? The unclear effect that alcohol has on population level obesity, the modest contribution calories from alcohol make to daily energy intake and limited impact nutritional labelling policies tend to have on behaviour, suggest alcohol energy labelling alone may have limited impact on population obesity prevalence as a standalone policy. However, there are a number of questions that will need to be answered by future research to make definitive conclusions on the potential for alcohol energy labelling policies to reduce obesity. This article is protected by copyright. All rights reserved. Would widespread energy labelling of alcoholic drinks reduce obesity? We provide a perspective on the potential of this obesity policy" cd_value_code="text
... 14 In support to this finding, previous studies have reported that using calorie information on menus helped consumers to choose foods with fewer calories at fast-food restaurants. 15,16 In contrast, in another study, no difference was observedin terms ofaverage calories purchased before and after calorie-label regulations. 17 A prospective study examined the implementation of calorie labeling in a large fast-food restaurant franchise, and found a small reduction in mean calories purchased per transaction (60 calories/ transaction); however, this reduction diminished over one year of follow-up. ...
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A policy that mandates posting calorie information on monitors and printed menus in restaurants was implemented in Saudi Arabia in 2018. This may help consumers make healthier decisions. This study assessed the level of awareness regarding calorie labeling on menus and its association with restaurant food choices among Saudi university students. A cross-sectional study was conducted using 400 female Saudi students at Princess Nourah Bint Abdulrahman University (PNU), Riyadh. Pregnant or lactating students or those who had any dietary restrictions were excluded. An online self-administered questionnaire regarding demographic characteristics, awareness of calorie labeling, and restaurant food choices were used. Pearson’s and MaNemar’s chi-square tests, the odds ratio, and multivariate binary logistic regression were used in the analysis. While 73.5% of the students noticed the calorie labeling on the restaurant’s menus, only 24% of students read it carefully. No statistically significant association was observed between the awareness of calorie labeling and food selection. Comparing good versus poor awareness about labels, 67.7% versus 49% of participants change their food selections based on the calorie information on the menus (p < 0.0007), and 77.8% versus 22.2% select items with fewer calories (p < 0.001). Calorie labeling may be an effective method for improving food choices among those who have an awareness of such and use calorie information. Nevertheless, public health education campaigns are needed to increase awareness of calorie requirements and the value of calorie labeling on restaurant menus.
... We used prominent placement and a font that was large relative to the administrative and clinical information to maximize the saliency of the treatment, given the space constraint. Note that similar constraints will likely exist for other vaccination or testing clinics due to the amount of information that UHS needs to communicate [65,66]. ...
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Background Influenza seasons can be unpredictable and have the potential to rapidly affect populations, especially in crowded areas. Prior research suggests that normative messaging can be used to increase voluntary provision of public goods, such as the influenza vaccine. We extend the literature by examining the influence of normative messaging on the decision to get vaccinated against influenza. Methods We conduct a field experiment in conjunction with University Health Services, targeting undergraduate students living on campus. We use four posters, randomized by living area clusters to advertise flu vaccination clinics during the Fall. The wording on the posters is varied to emphasize the individual benefits of the vaccine, the social benefits of the vaccine or both benefits together. We collect survey data for those vaccinated at the vaccination clinics, and for those not vaccinated via an online survey. Results We find that any normative message increases the percentage of students getting the flu vaccine compared with no message. In terms of the likelihood of getting the flu vaccine, emphasizing both the individual and social benefits of vaccination has the largest increase in the vaccination rate (19–20 percentage point increase). However, flu vaccinations did not reach the herd immunity threshold (70% of students vaccinated). Conclusions This study provides evidence that there is a pro-social component that is relevant in individual vaccination decisions which should be accounted for when designing vaccination campaigns. The results of this normative, pro-social messaging experiment could be extended to other at-risk communities where the number of background risks is much larger. This is especially relevant nowadays, as other seasonal vaccines are being rolled out and younger adults are the ones with the lowest uptake.
... Several studies have been done to investigate the effects of implementing a menu calorie labeling policy on energy consumption among consumers (16)(17)(18). Most of the previous studies, reported partially positive effects of calorie labeling implementation in the cafeterias, while others showed little or no effects, particularly in fast food restaurants (19)(20)(21)(22). ...
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Background Menu calorie labeling is a useful means to encourage consumers to be informed about healthy eating and food choices. It is projected as an innovative method that will change the food environment and increases consumers' awareness of calories. Objective This study aims to determine the consumer's knowledge, attitudes, and practices toward menu calorie labeling in Saudi Arabia. Methods This is a descriptive cross-sectional study involving 435 consumers in Saudi Arabia. The participants filled out an online electronic survey questionnaire that assesses the demographic factors, knowledge, attitudes, practices, and barriers toward menu calorie labeling. Logistic regression was performed to determine the predictor of attitudes of consumers toward menu calorie labeling. Results Of those 435 consumers, 50.1% were men, 33% were in the age group of 30–39, and 49.4% had a bachelor's degree. The majority of the participants reported that they can understand the calorie labels that were presented on the menus of the restaurants ( N = 365, 83.9%). A high percentage of participants reported that calorie labeling encourages them to choose foods with a smaller number of calories ( N = 387, 89%) and supported the posting of calorie content next to the price of the food items on the menus ( N = 405, 93.1%). Barriers to using calorie labels were time-consuming and low-calorie food items are usually costly. Gender and educational attainment were found significantly associated with consumers' knowledge while marital status and BMI level were found significantly associated with attitudes and practices to using calorie labels ( p < 0.05). Conclusion Overall, the participants had adequate knowledge and positive attitudes about menu calorie labeling in Saudi Arabia. Menu calorie labeling may be a useful policy tool for promoting healthy eating habits and appropriate caloric consumption.
... In our studies, a descriptive social norm nudge, and a saliency nudge, did not significantly change people's intentions to vaccinate against COVID-19. Despite being effective in other contexts (Wisdom et al., 2010;Allcott, 2011;Hallsworth et al., 2017), and despite the potential for constituting a 'soft' public approach to promote vaccination, it does not seem to change people's mind. Being transparent with respect to the psychological channel through which the nudges are potentially effective, and their goal, also seems not to affect people's reported intentions. ...
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Once vaccines against COVID-19 became available in many countries, a new challenge has emerged – how to increase the number of people who vaccinate? Different policies are being considered and implemented, including behaviourally informed interventions (i.e., nudges). In this study, we have experimentally examined two types of nudges on representative samples of two countries – descriptive social norms (Israel) and saliency of either the death experience from COVID-19 or its symptoms (UK). To increase the legitimacy of nudges, we have also examined the effectiveness of transparent nudges, where the goal of the nudge and the reasons of its implementation (expected effectiveness) were disclosed. We did not find evidence that informing people that the vast majority of their country-people intend to vaccinate enhanced vaccination intentions in Israel. We also did not find evidence that making the death experience from COVID-19, or its hard symptoms, salient enhanced vaccination intentions in the UK. Finally, transparent nudges as well did not change the results. We further provide evidence for the reasons why people choose not to vaccinate, and whether different factors such as gender, belief in conspiracy theories, political ideology, and risk perception, play a role in people's intentions to vaccinate or susceptibility to nudges.
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Four studies investigate the Nutrition Labeling and Education Act's (NLEA's) impact on how consumers use nutrition information. Field and laboratory studies compare, but do not detect any changes in, consumers' search for nutrition information or their recall of this information in the pre- and post-NLEA periods. However, the search activities of a select group (highly motivated and less knowledgeable consumers) benefited more from the NLEA than did other groups. Additional results from the field and lab studies indicate that the NLEA changed attention to negative nutrition attributes (such as fat and sodium, of which less is better) more than it changed attention to positive attributes such as calcium and vitamins. Analyses of scanner databases confirm this trend (with the exception of calories). Focus group results also reflect these findings. The authors discuss implications for public policy, management, academic research, and consumer welfare.
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Most consumer decisions involve trade-offs of costs and benefits over time. The research literature on "intertemporal choice" examines behavioral regularities in how people think about such decisions, drawing from marketing, psychology, and behavioral economics. This diverse literature is relevant to the analysis of public policy issues related to consumers' discounting of future outcomes "too much" compared with sooner outcomes. A stream of outcomes can be viewed as occurring in three temporal regions: the present, the near future, and the more distant future. Somewhat different research streams have developed around the topic of underweighting outcomes in the distant (compared with the near) future and of overweighting outcomes in the present compared with any point in the future. The authors review key concepts from the literature on underweighting the distant future versus the near-term future to analyze policy issues related to consumers' saving for retirement and their response to rebates. The authors review key concepts from the literature on impulsive behavior and present-biased preferences to analyze the problems of self-control that people have in their consumption of "sin" products that are proximate and that affect rewards in the present. The authors critique current information and incentive remedies that ignore behavioral principles from the literature, focusing their recommendations on policy interventions designed to influence eating habits and obesity and on cooling-off laws that govern return policies for consumers' big-ticket purchases.
Conference Paper
Regulation by the state can take a variety of forms. Some regulations are aimed entirely at redistribution, such as when we tax the rich and give to the poor. Other regulations seek to counteract externalities by restricting behavior in a way that imposes harm on an individual basis but yields net societal benefits. A good example is taxation to fund public goods such as roads. In such situations, an individual would be better off if she alone were exempt from the tax; she benefits when everyone (including herself) must pay the tax.
Obesity, one of the 10 leading U.S. health indicators,¹ is associated with increased risk for hypertension, dyslipidemia, type 2 diabetes, coronary heart disease, stroke, and certain cancers.² A Healthy People 2010 objective is to reduce to 15% the prevalence of obesity among adults in the United States (objective 19-2).¹ Both national-level data from the National Health and Nutrition Examination Survey (NHANES)³ and state-level data from the Behavioral Risk Factor Surveillance System (BRFSS)⁴ indicate that the prevalence of obesity among adults continued to increase during the past decade. In 2003, one study estimated that state-specific, obesity-attributable medical expenditures ranged from $87 million in Wyoming to $7.7 billion in California.⁵ To assess the prevalence of obesity among adults by state and demographic characteristics since 1995, data were analyzed from the 1995, 2000, and 2005 BRFSS surveys. The results of these analyses indicated that 23.9% of U.S. adults were obese in 2005, and the prevalence of obesity increased during 1995-2005 in all states. To reverse this trend, a sustained and effective public health response is needed, including surveillance, research, policies, and programs directed at improving environmental factors, increasing awareness, and changing behaviors to increase physical activity and decrease calorie intake. The findings in this report indicate that state-level prevalences of obesity in adults, based on self-reported weight and height, increased significantly during 1995-2005, moving states farther away from the Healthy People 2010 target of 15% prevalence of obesity. According to the surgeon general's Call to Action to Prevent and Decrease Overweight and Obesity,⁶ for obesity prevention and control to be successful, changes that promote recognition of obesity as a public health threat and assist persons in balancing healthful eating with regular physical activity must be made at multiple levels (i.e., individual, family, community, state, and nation) and across multiple sectors (i.e., education, government, and business). The Task Force on Community Preventive Services has identified evidence-based strategies to reduce weight and increase physical activity. For example, seven worksite interventions with both nutrition and physical activity components (e.g., nutrition education, physical activity “prescription,” and behavioral skills development and training) were effective, resulting in average weight losses of 4.4-26.4 lbs during a minimum 6-month period.⁷ In addition, the Guide to Preventive Community Services‡ recommends informational, behavioral, social, environmental, and policy approaches to increase physical activity, including school-based physical education and creation of, or enhanced access to, locales for physical activity in the community.
The author reports a longitudinal quasi experiment that uses the implementation of the Nutrition Labeling and Education Act (NLEA) to examine the consumer and information determinants of nutrition information processing activities. Over 1000 consumers from balanced demographic, geographic, and site categories and across 20 different product categories were observed and surveyed within a supermarket setting. Findings suggest that consumers acquired and comprehended more nutrition information following the introduction of the new labels. The NLEA did not, however, always influence these outcomes irrespective of individual consumer differences. Specifically, the new nutrition labels were comprehensible to consumers with varying levels of motivation and most types of nutrition knowledge. However, the new labels appeared to widen consumer differences in terms of how much nutrition information was actually acquired - more motivated consumers and less skeptical consumers acquired more information after the NLEA was passed. Finally, consistent with the NLEA's apparent ability to reduce comprehension differences, the new labels narrowed comprehension differences across healthy and unhealthy products. In contrast, the NLEA widened differences in nutrition information acquisition in favor of unhealthy product categories. These results have implications for public health gains, as well as for the degree to which nutrition may become the basis for competition in unhealthy product categories.
Results from three experiments indicate how age, cognitive style, and perceptual aid affect consumers' use of nutritional information. In experiments 1 and 2, age and perceptual aid influence accuracy in a cereal choice task. In experiment 3, perceptual aid interacts with cognitive style, influencing accuracy and decision time. The authors discuss implications for aging theory, consumer education, and public policy.
The federal government has declared obesity and overweight to be one of the most pressing public health threats facing the United States today and is engaged in a “war on obesity”. Unfortunately, the main arsenal employed in this war has been ineffective media based health information campaigns that encourage people to diet and exercise. This article critically evaluates the federal government’s efforts in the “war on obesity” that emphasize individual based change and do not address the structural barriers, such as inability to afford healthy foods and a lack of safe, accessible places to exercise that keep people from following the government’s dietary and exercise guidelines. By failing to address the barriers people face in maintaining a healthy lifestyle, while promoting the message that weight loss is simply a matter of self-control, the federal government is promoting the stigma associated with being overweight and obese, while failing to meet its public health duties. This article addresses these shortcomings and argues that the federal government should shift its focus away from individual level information based campaigns and should instead collaborate with state and local governments and private actors to create environments that make the healthiest choice be the most attractive and easiest option.