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Dynamic Inconsistency in Food Choice:
Experimental Evidence from Two Food Deserts
Sally Sadoff
University of California, San Diego
Anya Samek
University of Southern California
Charles Sprenger
University of California, San Diego
September, 2014
This Version: April 3, 2018
Abstract
We conduct field experiments to investigate dynamic inconsistency and com-
mitment demand in food choice. In two home grocery delivery programs, we
document substantial dynamic inconsistency between advance and immediate
choices. When given the option to commit to their advance choices, around half
of subjects take it up. Commitment demand is
negatively
correlated with dy-
namic inconsistency, suggesting those with larger self-control problems are less
likely to be aware thereof. We evaluate the welfare consequences of dynamic
inconsistency and commitment policies with utility measures based on advance,
immediate and unambiguous choices. Simply offering commitment has limited
welfare (and behavioral) consequences under all measures.
JEL classifications:
C91, D12, D81
Keywords
: dynamic inconsistency, commitment demand, field experiment, behavioral
welfare analysis
University of California at San Diego, Rady School of Management, 9500 Gilman Drive, La Jolla,
CA 92093; sadoff@ucsd.edu.
University of Southern California, 635 Downey Way, Los Angeles, CA 90035; samek@usc.edu.
University of California at San Diego, Rady School of Management and Department of Economics,
9500 Gilman Drive, La Jolla, CA 92093; csprenger@ucsd.edu.
1 Introduction
Models incorporating temptation impulses and self-control are among the most promi-
nent in behavioral economics (Strotz, 1955; Thaler and Shefrin, 1981; Laibson, 1997;
O’Donoghue and Rabin, 1999; Gul and Pesendorfer, 2001; Fudenberg and Levine, 2006).
The dynamic inconsistencies predicted by these models provide a reason for the ob-
served difficulty of people to save more for the future, exercise more, eat healthier and
quit smoking. Based on the insights generated by these models, prescriptions such as
offering commitment devices have grown prominent in policy circles.
In this paper, we address two open questions in the literature on self-control. The
first is the relationship between self-control problems and awareness thereof. Several
experimental studies find weak positive correlations between hallmarks of dynamic
inconsistency and take-up of products with commitment features (Ashraf, Karlan and
Yin, 2006; Augenblick, Niederle and Sprenger, 2015; Kaur, Kremer and Mullainathan,
2015). This suggests at least a weakly positive correlation between self-control problems
and awareness thereof, a finding that is confirmed by recent work eliciting both behavior
and beliefs (Augenblick and Rabin, forthcoming).
1
In contrast, outside of controlled
experimental settings, there is limited evidence for anything more than tepid demand
for commitment devices (Laibson, 2015). This suggests that perhaps those with the
worst self-control problems may not be aware of them.
2
Ultimately, relatively little
is known about the relationship between behavior and beliefs in non-experimental
settings. Given that the impact of commitment policies depends on this real-world
relationship, data from field settings has the potential to provide substantial value.
The second open question is the assessment of the welfare consequences of com-
mitment policies. This assessment depends on two critical factors. The first is the
aforementioned relationship between self-control problems and awareness thereof, and
the second is the chosen welfare criterion. Ambiguity in welfare evaluations may exist
in the context of self-control problems since there is inconsistency between ‘long-run’
preferences measured absent temptation and ‘short-run’ preferences measured under
temptation. A practice has emerged that bases welfare calculations on long-run pref-
erences under the positive justification that short-run preference deviations represent
mistakes (Herrnstein, Loewenstein, Prelec and Vaughan, 1993; Gruber and Kőszegi,
1
In experimental settings, dynamic inconsistency can explain only about 5% of the variation in
commitment demand (Augenblick et al., 2015) and individuals seem to understand less than 25% of
their self-control problems (Augenblick and Rabin, forthcoming).
2
Limited commitment demand could have other sources. Laibson (2015) demonstrates that with
an uncertain environment and costly commitment, commitment demand may be limited even among
agents who are aware of their self-control problems.
1
2001; O’Donoghue and Rabin, 2006). More recently, Bernheim and Rangel (2007,
2009) develop an alternative approach to behavioral welfare analysis based on unam-
biguous choice – i.e., using choices that are consistent across the long- and short-run
– and provide a theoretical evaluation of dynamically inconsistent preferences. Yet
to our knowledge, there exists no empirical evaluation of the welfare consequences of
dynamic inconsistency and commitment policies recognizing potential disagreement
across welfare criteria.
We combine field evidence on dynamic inconsistency and commitment demand
with a behavioral welfare exercise that evaluates commitment policies through the
lens of alternative welfare criteria. Our field experiments are conducted in a natural
setting, and individuals are not told that they are in an experiment, which mimics
naturally occurring markets. Further, our experiments test dynamic inconsistency over
consumption using longitudinal decisions with limited scope for arbitrage, which aligns
tightly with theoretical models. Finally, we collect within-subject data on dynamic
inconsistency and commitment over time, which allows us to investigate stability of
these measures.
Our setting is a food delivery service for low-income participants in two cities:
Chicago, Illinois and Los Angeles, California. Three-hundred eighty-nine subjects com-
pleted a 3-4 week food delivery program. Subjects were given a budget and asked to
construct a bundle from a list of 20 foods for home delivery one week later. On the day
of delivery, the delivery-person brought the pre-ordered bundle and also surprised sub-
jects with additional foods available for exchange. Subjects were given the opportunity
to make up to 4 exchanges. Every bundle that could be constructed with immediate
exchanges (on the day of the delivery) is one that was available at the time of advance
choice (one week earlier). As such, dynamic inconsistencies are identified as violations
of revealed preference between advance and immediate choices.
In the second and third weeks of the study, subjects again made advance choices.
However, before the delivery, they were asked if they would like the option to make
exchanges at delivery again, or whether they would like to stick to their pre-ordered
choices. Commitment demand is identified as choosing to restrict oneself to the ad-
vance bundle. The correlation between dynamic inconsistency (in the first week) and
subsequent commitment demand provides data on the relationship between self-control
problems and awareness thereof that can be used to evaluate commitment policies.
We find that when commitment is not available, 46% of subjects exhibit dynamic
inconsistencies, exchanging at least one item from their advance bundle. Regularities
exist in the nature of these inconsistencies. Immediate bundles contain significantly
2
fewer fruits and vegetables and more calories (primarily from fat) than advance bundles.
When commitment is available, 53% of subjects take it up, preferring to restrict
themselves to their advance bundle. Importantly, subjects who were previously dy-
namically inconsistent are
less
likely to demand commitment (44%) than subjects who
were previously dynamically consistent (60%). This suggests a negative correlation
between self-control problems and awareness thereof.
A structural estimation exercise that formulates utilities in terms of food charac-
teristics indicates the value of fruits and vegetables is significantly lower in immedi-
ate versus advance choice. The structural estimates are built using standard random
utility methods and allow for tests that inconsistencies would arise by chance under
dynamically-consistent preferences. Tests of consistent preferences are rejected for the
aggregate data and for inconsistent subjects at all conventional levels. Utility estimates
from when commitment is not available show that subjects who ultimately commit have
substantially smaller differences between advance and immediate preferences than those
who ultimately do not demand commitment.
To understand the welfare consequences of dynamic inconsistencies and commit-
ment policies, we evaluate welfare using three criteria: the advance utility estimated
from foods chosen before making exchanges, the immediate utility estimated from foods
chosen after making exchanges and the unambiguous utility estimated from foods that
were never exchanged. In the spirit of Bernheim and Rangel (2007, 2009), the third
welfare criterion allows for welfare evaluation based only on unambiguous choices.
Using the standard ‘long-run’ welfare criterion of advance utility, we find that ag-
gregate welfare declines by about 2% between advance and immediate choice overall,
and by 4-5% for inconsistent subjects. Interestingly, aggregate welfare costs to incon-
sistency are also found when using the unambiguous and immediate welfare criteria.
3
Despite similarity in the aggregate estimates, there is heterogeneity between and within
individual-level welfare measures. The median inconsistent subject exhibits disagree-
ment between their advance and immediate utility measures: advance preferences are
more likely to show welfare costs to inconsistency and immediate preferences are more
likely to show welfare benefits to flexibility. Where this disagreement exists, the con-
flict between advance and immediate welfare measures may be helpfully arbitrated by
the unambiguous utility measure. Fifty percent of subjects have unambiguous welfare
reductions due to inconsistency.
3
This similarity across criteria may seem surprising. It is driven by a general agreement in both
advance and immediate choice that fruits and vegetables are desirable. When inconsistencies occur,
they come in the form of deviating from this general agreement by exchanging fruits and vegetables
for less desirable items, lowering total utility.
3
We combine utility estimates and subsequent commitment demand to evaluate the
welfare consequences of three potential policies: the standard policy of offering com-
mitment to those who desire it, mandated advance choice and a tailored policy that
mandates advance choice only for people who, by our estimates, exhibit unambiguous
welfare costs to inconsistency. Given that few dynamically inconsistent subjects ulti-
mately demand commitment, simply offering commitment is predicted to have limited
welfare effects. Only 20% of subjects are predicted to be affected, roughly equally split
between those made better and those made worse off by their commitment decision.
Mandated advance choice would affect about 45% of subjects, again about evenly split
between winners and losers. The tailored mandate would affect about 20% of subjects
– those with unambiguous costs to inconsistency – with winners outnumbering losers
according to the other (advance and immediate) welfare criteria by at least two-to-one.
We also evaluate these policies on the basis of behavior change, specifically on how
they impact the nutritional value of foods chosen. Among the three policies, mandated
advance choice is predicted to have the greatest effects, increasing the number of fruits
and vegetables, and decreasing the number of calories consumed. Simply offering com-
mitment is predicted to have virtually no effect given the observed negative correlation
between commitment demand and prior inconsistency. This prediction can be tested in
our data in weeks when commitment is available. Indeed, simply offering commitment
has virtually no effect on the nutritional value of foods ultimately chosen.
Our two core findings: dynamic inconsistency reflecting changing preferences be-
tween advance and immediate choices; and a negative correlation between dynamic
inconsistency and demand for commitment are observed at both study sites. The orig-
inal version of this paper featured only data from Chicago. Los Angeles was added as a
full-scale replication and extension of the previously documented findings. Replicating
the findings – in particular, the demonstration in field data that those with the most
substantial self-control problems may be the least aware thereof – helps to assure the
results are not obtained simply by chance.
This paper provides contributions along three principal avenues. First, our data on
commitment demand provide evidence on a central assumption around which policy
prescriptions for behavioral consumers are built. We show demand for commitment,
but find that agents who demand commitment have systematically smaller self-control
problems than those who do not. Much of the previous literature on self-control has
relied on tests of diminishing patience over monetary rewards rather than consump-
tion, and has used decisions made at a single point in time rather than longitudinally
4
(Sayman and Onculer, 2009; Halevy, 2015; Sprenger, 2015, provide discussion).
4
With
the exception of Read and Van Leeuwen (1998), who studied snack choice among em-
ployees but did not study commitment, participants in these studies knew they were
part of an experiment, which could affect their decisions. We study subjects in their
natural setting, which could explain the difference in our results relative to the weakly
positive correlation between self-control and awareness implied by prior research.
Second, our experimental populations sit in the cross-hairs of the food policy debate.
Our neighborhoods are considered ‘food deserts,’ implying a high rate of poverty and
limited access to fruits and vegetables.
5
Obesity and related diseases are at an all-time
high in the United States, are largely driven by poor food choice, and disproportionately
affect low-income communities.
6
Americans consume fewer than the recommended
servings of fruits and vegetables, and too many high-calorie, low-nutrient foods. Food
assistance programs such as the Supplemental Nutrition Assistance Program (SNAP)
are one tool for improving healthfulness of food choice in low-income communities. A
policy change is now being piloted that would allow retailers to accept SNAP dollars
for pre-ordered food.
7
Our results add to an understanding of the impact of this policy
change on behavior and welfare.
Third, our exercise provides a demonstration of the value of combining structural
methods and behavioral welfare analysis. Behavioral welfare measures require that
researchers do not arbitrarily honor a given preference ranking without a clear rea-
son to do so. In dynamically inconsistent choice, this delivers a natural intuition that
virtually nothing concrete can be said with regards to welfare. We demonstrate that
this is not necessarily the case. In our structural setting, the body of food choices
are informative of how decision-makers value food characteristics. Through the lens
of the model, we construct and compare welfare measures that deliver clear welfare
implications. And we join a small list of empirical studies that investigate the welfare
consequences of behavioral phenomena (Chetty, Looney and Kroft, 2009; Allcott, Mul-
lainathan and Taubinsky, 2014; Allcott and Taubinsky, 2015; Rees-Jones and Taubin-
sky, 2016; Taubinsky and Rees-Jones, forthcoming). We join an even smaller list that
recognizes the corresponding ambiguity in welfare estimates that may arise in the Bern-
4
Related studies include Duflo, Kremer and Robinson (2011) for farmer fertilizer purchase; Au-
genblick et al. (2015) for effort choices in a laboratory experiment; and subsequent to our study,
Augenblick and Rabin (forthcoming) also for effort choices in the laboratory.
5
A food desert is defined as having a poverty rate of 20% or greater and at least 33%
of the census tract lives more than one mile from a supermarket or large grocery store
(http://apps.ams.usda.gov/fooddeserts/fooddeserts.aspx).
6
See https://www.cdc.gov/obesity/data/adult.html.
7
See https://www.fns.usda.gov/snap/online-purchasing-pilot.
5
heim and Rangel (2007, 2009) welfare framework (see Bernheim, Fradkin and Popov,
2015).
In what follows, Section 2 provides an overview of the experimental design and de-
scribes the structural analysis, Section 3 describes our results and Section 4 concludes.
2 Empirical Design
2.1 Experimental Setup
We conducted two field experiments with a total of 389 subjects at grocery stores in
Chicago, Illinois and Los Angeles, California.
8
The first experiment was implemented
with 218 subjects in 2014 at Louis’ Groceries, a small-format neighborhood grocery
store in the low-income community of Greater Grand Crossing in Chicago. The sec-
ond experiment was implemented with 171 subjects in 2016-17 at Northgate Gonzalez
Market, a large supermarket in low-income South-Central Los Angeles.
9
The grocery stores carried out a promotion inviting customers to sign up for a free
home food delivery program. Recruitment for both experiments was conducted on
a rolling basis. Two research assistants worked at each grocery store to conduct the
experiment and deliver the foods. Subjects for the study were recruited at a table set up
at the store. We assured that foods were fresh and produce was not bruised at the time
of delivery by working with the grocery stores and preparing deliveries as close to the
delivery time as possible. In keeping with the natural field experiment methodology,
subjects were not told that they were in an experiment.
10
In the Los Angeles study,
to increase naturalism, research assistants partnered with a store associate to deliver
items in the Northgate store delivery van. Thus, we were able to observe subjects in
their natural environment as they made a series of food allocation decisions.
A total of 20 different foods were used in each experiment. Figure 1 displays the
8
Four hundred and ten subjects were initially recruited into the study. Of these 410, 21 (5.12%)
are considered attrited from the study due to not completing the full set of deliveries (17), never being
offered a commitment decision due to experimenter error (3) or opting out after the study ended (1).
9
According to the 2010 U.S. Census, Greater Grand Crossing has a population of 35,217, the
majority of whom are African Americans (97.8%). South-Central Los Angeles has a population of
169,453. The majority of residents are Hispanic (74%) and African-American (24%). A larger share
of our LA study participants were Hispanic (98%), since the store caters to Hispanic customers. Both
neighborhoods have high rates of poverty (28.5%-33.6%).
10
In the Chicago experiment, The University of Wisconsin-Madison Institutional Review Board
(IRB) required us to notify subjects after the study was complete that they had participated and give
them the option to withdraw their data. One subject chose to withdraw, and this subject’s data is not
in the dataset. The Los Angeles experiment was approved by the University of Southern California’s
IRB, which did not have this requirement.
6
(a) Chicago
(b) Los Angeles
Figure 1: Study Foods
promotion sheet of foods used. Foods were selected in consultation with store managers
to determine which foods would be appealing to customers at each site. In each study,
10 of the foods were fruits or vegetables while the other 10 were sweets or salty snacks.
Foods varied substantially in their caloric and nutritional content. Appendix Table A1
provides nutritional information for the foods included in each study.
Upon signing up for the program, subjects were asked whether they had eaten each
of the 20 foods before and then rated those they had eaten on a Likert scale from
1 (least preferred) to 7 (most preferred). The use of Likert scales to rate foods has
been promoted in the nutrition literature as a means of assessing dietary preferences
(Geiselman, Anderson, Dowdy, West, Redmann and Smith, 1998).
11
Subjects were
11
In Chicago, the question was worded as,
Please tell us how much you like the following foods,
where 1 is DO NOT LIKE AT ALL and 7 is LIKE VERY MUCH.
The question was worded slightly
7
generally aware of and had eaten all 20 of the foods. On average, subjects rated 18.6
of 20 foods and the average food rating was 5.58 out of 7.
12
In return for participating in the program – including selecting foods, receiving the
weekly deliveries and completing surveys – subjects received a participation payment.
This payment was a $20 cash voucher in the Chicago study and a $25 Northgate store
gift card in the Los Angeles study.
2.2 Experimental Timeline
The experimental timeline is presented in Table 1. The Chicago study offered a
2-week food delivery program while the Los Angeles study offered a 3-week food
delivery program. In Week 1, each subject decided on foods for delivery in Week
2. Upon receiving the delivery in Week 2, each subject was surprised with the
option to make immediate exchanges. In Week 2, each subject also decided on
foods for the second delivery in Week 3. All Chicago subjects subsequently made
a commitment choice, deciding whether to have the option to make exchanges (i.e.,
not commit) or to stick to their pre-ordered choices (i.e., commit) for the second
delivery. To investigate the stability of inconsistency and commitment demand,
we randomly assigned half of the subjects in Los Angeles to receive commitment
offers for both the second and third delivery. We assigned the other half to make a
second surprise exchange and offered this group commitment only for the third delivery.
Week 1, Advance Choice:
In Week 1, subjects received an order sheet and brochure
listing available foods and decided on foods for their first delivery. All foods were also
available at the store, and the fresh foods were visible to the subjects as they made
their decisions. To simplify the selection process, each food was valued at $1, with
cheaper foods bundled into several for $1 (e.g., 2 green apples together cost $1). All
foods were priced as closely as possible to their respective market price. Subjects were
asked to create a ‘basket’ of foods valued at $10 in total, by choosing from any of the
20 foods, including selecting the same food more than once. Subjects also selected
differently in Los Angeles. It was,
For foods that you have eaten, I’d like to know how much you like
eating the food. When you answer how much you like eating the food, please think carefully about how
much you enjoy the food, including aspects such as how the food tastes to you. [point to food] How
much do you like eating the food? Do you not like it at all, do not like it, do not like it a little, have
no preference, like it a little, like it or like it very much?
12
Completing a rating for all foods was voluntary; nevertheless, most subjects rated a large number
of foods, with 357 of 389 (92%) rating 15 or more foods. In Chicago 191 of 218, or 88% rated at least
15 foods. In Los Angeles 166 of 171, or 97% rated at least 15 foods. This difference could be because
in Chicago, subjects wrote down their responses while in Los Angeles, subjects responded verbally.
8
Table 1: Summary of Experiment
Week 1
Week 2
Week 3
Week 4 (L.A. only)
Pick Delivery 1
items
Pre-Survey
Food Ratings
Get Delivery 1
Decide about changes
to Delivery 1
Pick Delivery 2
items
Commitment choice
for Delivery 2 (Chicago
& half of L.A. subjects)
Get Delivery 2
If no commitment:
decide about changes
to Delivery 2
Pick Delivery 3
items (L.A. only)
Commitment choice
for Delivery 3 (L.A.
only)
Get Delivery 3
If no commitment:
decide about changes
to Delivery 3
Post-Survey (Week
3 in Chicago)
their preferred dates and times of delivery.
Subjects were informed that they would need to be home during their delivery,
and would need to show a picture ID to receive their basket. Delivery was scheduled
as close to 7 days after sign-up as possible, taking into account the constraints faced
by the research assistants (i.e., a maximum number of deliveries can be made in any
day) and the availability of the subject. Subjects were required to give a current
phone number and address to facilitate delivery. All subjects received a phone call to
confirm enrollment upon sign-up, which also allowed us to validate their phone number.
Week 2, Immediate Choice:
A few days before scheduled delivery in Week 2, we initi-
ated a reminder call to ensure that subjects would be home at the pre-arranged time
and then proceeded with delivery. Upon delivery, subjects were surprised with the
opportunity to make up to 4 exchanges. In Chicago, we brought a customized box of
4 foods selected from the 20 that were available previously, whereby we tried to select
foods that the subject liked. This box contained their highest rated fruit or vegetable,
their highest rated fruit or vegetable not included in their original bundle, their highest
rated sweet or salty snack and their highest rated sweet or salty snack not included in
their original bundle. In Los Angeles, we brought a box with one of each of the 20 foods
that were available previously, and subjects could make exchanges with any of these
foods. As before, cheaper foods were bundled into several for $1. Subjects were not
told in advance that they would have this opportunity to exchange. The opportunity
to exchange was described by a research assistant serving as a delivery-person and was
fully scripted as:
Hello, I am here with your basket. Please take a look [Bring open basket,
9
allow person to look through]. We also have some extra items available. If
you like, you can exchange any one item in your basket for one of these
items [ show extra items on tray ]. I brought 4 additional items, so you
can make up to 4 exchanges. Do you want to make any exchange? [Great
thanks, let me note that on your order sheet.]
13
After making any exchanges, subjects used a new order sheet to make a decision
about the contents of their second delivery, scheduled for Week 3.
Weeks 2-3, Commitment Choice:
We elicited demand for commitment by asking sub-
jects whether they would like to have the option to make exchanges during the Week
3 delivery, or whether they would like to stick to their pre-ordered choices. We asked
this of all subjects in Chicago and half of subjects in Los Angeles. The question was
again fully scripted in both study locations. In Chicago, the script was:
Last time, we brought some extra items for you so you could exchange if
you changed your mind from your previous choices. This time, we can also
bring extra items, but I wanted to check if you’d like that or not. It is up
to you: would you like me to bring extra items this time, or not?
In Los Angeles, the script was:
For this week’s delivery, you had the option to change your mind by ex-
changing items in your basket. This time, you can choose whether you
want the option to make exchanges, or whether you want to stick to your
pre-ordered choices. It is no trouble for us either way, it is entirely up to
you. Do you want to have the option to make exchanges, or do you want
to stick to your pre-ordered choices?
In Chicago, the commitment question was asked via phone during the reminder call
before the next delivery. In Los Angeles, the commitment question was asked in person
immediately after the order for the next delivery was placed. If a subject answered that
they wanted to have the option to make exchanges, additional items were presented at
the next delivery as before. If a subject answered that they would like to stick to their
13
In Los Angeles, the message was slightly different,
Here is your food delivery [show box]. Please
take a look [bring open basket, allow person to look through]. We also have some extra items available.
If you like, you can exchange any one item in your basket for one of these items [show extra items in
tray]. I brought all the menu items, and you can make up to 4 exchanges. Do you want to make any
exchange? [Great thanks, let me note that on your order sheet].
10
pre-ordered choices, the box of additional items was not brought along with the delivery.
Weeks 3-4, Final Delivery and Commitment Choice:
The subjects in Los Angeles not
assigned to the commitment treatment were offered the opportunity to make exchanges
in Week 3. The subjects in Los Angeles assigned to the commitment treatment only
had the option to make exchanges if they previously chose not to commit. After
delivery in Week 3, all Los Angeles subjects used a new order sheet to make a decision
about the contents of their third delivery, scheduled for Week 4. After completing this
order sheet, all subjects were asked the commitment question applied to their Week
4 delivery. At the final delivery (Week 3 for Chicago and Week 4 for Los Angeles),
subjects completed a survey and received compensation for participating.
2.3 Design Considerations
Our Chicago and Los Angeles studies follow similar procedures. The Los Angeles
study was constructed as a replication and extension and so allowed us to address
potential concerns with respect to identifying dynamically inconsistent preferences and
commitment demand. We are indebted to thoughtful comments from colleagues that
helped guide these design alterations.
First, dynamic inconsistencies are identified from exchanges between advance and
immediate food choice. An intuitive direction of inconsistency is exchanging objects
such as fruits and vegetables for sweets and salty snacks. An interpretation that at-
tributed such inconsistencies to changing preferences could be challenged by several
concerns in the Chicago design. First, in the Chicago study, all fruit and vegetable
items were perishable while no sweets and salty snacks were perishable. If perishable
items wound up being damaged, spoiled or less attractive than expected upon deliv-
ery, exchange could be driven by such negative surprises rather than by inconsistent
preferences. Recognizing this critique, the Los Angeles study was designed with pri-
marily perishable items, only two non-perishable fruit and vegetable items (diced peach
cup and canned diced tomatoes) and 2 non-perishable snack items (Doritos and Takis
Chips). Additionally, 2 fruits and vegetables came in factory packaging (baby carrots
and salad) while most snack items came from the bakery department without factory
packaging (e.g., Salvadoran bread).
Second, in our Chicago study, we brought only 4 additional items selected based
on subjects’ rating data. Any lack of dynamic inconsistency could be driven by our
inability to match subjects with tempting items for exchange. Though this suggests
any exchanges would speak to a lower bound on inconsistent preferences, in the Los
11
Angeles study we improved on this design by making all 20 items available for exchange.
To keep the designs as similar as possible, however, we retained the design element of
allowing only up to 4 exchanges. In practice, this restriction rarely binds, with only 1
of 389 subjects making 4 exchanges at their first delivery.
Third, our Chicago subjects only made one exchange decision prior to being of-
fered commitment. It may be that any observed dynamic inconsistency is ephemeral,
a product of shocks or changing circumstances. These random shocks should not de-
liver a systematic direction for inconsistency. Nevertheless, having more data at the
subject level as we do in the Los Angeles study allows us to further rule out that the
inconsistencies are due to random shocks.
Fourth, the phrasing of our commitment offer in Chicago may have had the unin-
tended effects of making commitment appear socially desirable and/or may have failed
to emphasize that commitment induces a restriction to advance choice. Subjects who
did not want to trouble the delivery person may have opted to commit to save him or
her work. Subjects opting out of the exchange opportunity may not have realized that
this was equivalent to a choice to commit to the advance bundle. For these reasons, the
Los Angeles study script highlights that neither choice is more costly for the delivery
person, and that the decision to commit is equivalent to sticking with advance choice.
In both of our studies, we observe choices but not consumption of food items. One
may worry that subjects’ choices do not represent their true preferences, but rather
reflect their external opportunities to trade food items. For example, a subject who can
trade tomatoes for chips more advantageously outside of the experiment may choose a
bundle consisting only of tomatoes, conduct appropriate trades and generate for herself
an opportunity set which dominates that provided by the researchers. Such arbitrage
would imply that subject choices are not informative of preferences at all, but rather
only of external constraints and the researchers’ mis-pricing of items.
14
Several aspects
of the experimental environment minimize the possibility of arbitrage. The prices in
the stores are similar to those faced in the experiments. Hence, external exchanges are
unlikely to be advantageous. Additionally, our stores are in ‘food deserts,’ and many
study foods - e.g., fresh fruits and vegetables and bakery goods - are difficult to obtain
elsewhere. Conducting exchanges with others in the neighborhood is also practically
difficult given the cost of identifying interested parties and the perishability of some
foods. Importantly, even if arbitrage opportunities exist, one would not expect them
to change dramatically over a single week in our studies. Hence, if choice is driven
14
A similar arbitrage argument is used to question the use of monetary payments in studies of
intertemporal choice (Cubitt and Read, 2007; Chabris, Laibson and Schuldt, 2008; Andreoni and
Sprenger, 2012; Augenblick et al., 2015).
12
by arbitrage strategies, dynamic inconsistencies should be rare. The data themselves
can provide some indication of arbitrage strategies by examining the prevalence of
completely concentrated bundles, consisting of only a single food. Such bundle con-
centration is never observed, with the average advance first week bundle having 9.3
unique items. Further, we rarely see a more limited version of concentration: subjects
choosing exclusively fruits and vegetables or exclusively sweets and salty snacks. Only
14 of 389 advance bundles in the first week are concentrated this way.
An additional concern posed by not observing food consumption is that if foods
are not consumed immediately, temptation may be limited. In our Los Angeles study,
we measure the speed with which foods are consumed by including questions about
consumption in our post-experiment survey. Subjects were asked, for the foods they
ordered in their Week 3 delivery, how quickly they ate the foods - within 1-3 days, 4-7
days or in more than 7 days. Most foods were consumed within 1-3 days, ranging from
79% (for canned tomatoes) to 87% (for Palmiers). Importantly, the non-perishable
foods are eaten within 1-3 days as frequently as the perishable foods. This suggests
that most foods are indeed being consumed rapidly, within the time frames thought to
be relevant for temptation. That subjects do not apparently store more long-lasting
foods helps to alleviate the perishability issue discussed previously.
Finally, commitment demand may be an imperfect proxy for awareness about self-
control problems. An alternative approach is to elicit beliefs about future behavior,
as in Augenblick and Rabin (forthcoming). We did not elicit beliefs for two reasons.
First, we wanted to maintain the naturalism of the study. Second, using incentives to
elicit beliefs (to make the beliefs incentive compatible) is also a form of providing a
commitment device because deviating from predicted behavior in immediate choice is
costly (see Augenblick and Rabin, forthcoming, for discussion). Further, Augenblick
and Rabin (forthcoming) find that participants may seek to match their behavior to
earlier predictions, suggesting that predictions may affect future behavior rather than
serving purely as an exogenous measure of self-awareness.
15
2.4 Structural Analysis, Dynamic Inconsistency and Welfare
Subjects in our experiments choose a bundle of 10 foods from a set of 20 potential
options. From such data, reduced form and structural analysis of dynamic inconsis-
tency in food choice can be conducted. The structural method we propose follows
15
To address these concerns, Toussaert (2015) elicits beliefs about the behavior of similar others
rather than oneself. However, de Oliveira and Jacobson (2017) demonstrate that people may have
systematically different beliefs about their own time preferences versus those of others.
13
standard random utility techniques, establishing the value of a given item as being
derived from a set of characteristics. This allows for simple tests of dynamically incon-
sistent preference, recognizing the existence of random shocks. The estimated utilities
lend themselves naturally to evaluation of commitment policies under different welfare
criteria.
Following methodology from Beggs, Cardell and Hausman (1981), we define each
food as a bundle of underlying attributes and analyze subject choices using rank order
discrete choice methods.
16
Let the utility of each food,
j
∈ {
1
,...,J
}
, be written as a
linear combination of attributes,
V
j
=
x
j
β
+

j
j
= 1
,...,J,
where
x
j
represents a vector of food characteristics and

j
represents a random utility
shock drawn iid from a Type-1 extreme value distribution. The probability that a given
food,
j
is preferred to alternatives
1
,...,J
K
1
is
F
j
[
x
1
,...,x
J
K
1
,x
j
;
β
] =
exp
(
x
j
β
)
exp
(
x
j
β
) +
J
K
1
i
=1
exp
(
x
i
β
)
.
Consider a subject who chooses
K
unique food items. Order the foods as
r
≡{
1
,...,J
K
1
,J
K,J
K
+ 1
,...J
}
, with the final K foods being the chosen items. The
probability of observing such an ordering is thus
Prob
(
r,
x
;
β
) =
J
j
=
J
K
F
j
[
x
1
,...,x
J
K
1
,x
j
;
β
]
,
where
x
≡{
x
1
,...,
x
J
}
is the matrix of attributes corresponding to the provided order.
Indexing individuals by
i
= 1
,...,N
, one constructs the log-likelihood of seeing a given
N rankings as
L
(
β
) =
N
i
=1
log(
Prob
(
r
i
,
x
i
;
β
))
.
(1)
This structure assumes that any chosen item is preferred to
all
unchosen items.
16
An alternative structural methodology is to consider each bundle of 10 items as a potential option
and consider the discrete choice problem of picking the best bundle. With 20 foods, there are
(
20
10
)
=
184,756 possible bundles of 10 unique items, and
(
20+10
1
10
)
= 20,030,010 possible bundles of 10 items
with repetitions. For both tractability and interpretability, we opt to formulate food and bundle
utilities as being derived from a set of characteristics. Note, however, that our construction is not
able to capture, for example, a preference for diversity in the bundle or complementarities between
particular items.
14
Within the sets of chosen and unchosen items, no explicit ranking exists. In the lan-
guage of rank order logit models, the ranks within these sets are ‘tied’ as all per-
mutations of rankings within these sets would be consistent with observed behavior.
Standard methodology exists for incorporating the probability of these ties into maxi-
mum likelihood estimates of the parameters of interest,
β
. We augment the probability
of equation (1) with Efron’s (1977) method for handling ties in rank order data, im-
plemented in
Stata
.
2.4.1 Tests of Dynamic Inconsistency
Consider two rankings of foods: one from advance decisions and one from immediate
decisions. Let
r
A
and
r
I
represent the advance and immediate rankings, respectively.
Maximum likelihood estimation of attribute weights,
β
A
and
β
I
, based upon these
rankings provide a means of comparing preferences across choice environments. Fur-
ther,
β
A
and
β
I
can be estimated simultaneously and one can test the null hypothesis
of dynamically
consistent
preferences,
β
A
=
β
I
, using standard
χ
2
tests. Such tests
establish the probability that observed exchanges would occur by chance under the
extreme value error structure without dynamically inconsistent preferences.
Two points related to our structural tests of dynamic consistency are worth noting.
First, in both of our studies, subjects were only allowed to make up to 4 exchanges.
This restriction limits the inconsistencies that can be observed between
r
A
and
r
I
.
Though in practice, only 1 of 389 subjects made all 4 exchanges at their first delivery,
this design feature could in principle, work against finding differences between
β
A
and
β
I
. Second, in our Chicago study, our design called for bringing only 4 additional items
when making food deliveries. As such,
r
I
may be additionally restricted to be similar
to
r
A
by our inability to provide subjects with sufficiently tempting alternatives, again
working against finding differences between
β
A
and
β
I
. Our Los Angeles design does not
suffer from this potential issue, as all foods were available for exchange when subjects
made immediate choices. These points suggest that findings of dynamic inconsistency
and the corresponding changes in preferences estimated in our study may be lower
bounds.
2.4.2 Welfare Evaluation
Estimated utility weights,
β
A
and
β
I
, speak to two different potential welfare criteria
based on advance and immediate preferences, respectively. One can construct the
15
deterministic utility portion of any proposed bundle under advance preferences as
V
A
(
q
) =
J
j
=1
q
j
x
j
β
A
,
where
q
=
{
q
1
,...,q
j
,...q
J
}
is the proposed bundle with quantity
q
j
of food
j
.
17
Simi-
larly, one can construct the immediate utility,
V
I
(
q
) =
J
j
=1
q
j
x
j
β
I
.
These two measures can be used to evaluate the welfare consequences of dynamic
inconsistency and commitment policies. If disagreement in choice, and hence potential
differences between
β
A
and
β
I
exist, welfare statements may be ambiguous.
V
A
(
·
)
and
V
I
(
·
)
may disagree on the value of policies.
Where disagreement in choice exists across welfare relevant choice conditions, Bern-
heim and Rangel (2007, 2009) advocate for formulating welfare statements around an
unambiguous choice relation that never contradicts choice. By examining only foods
that were never exchanged, we can construct this unambiguous relation. Consider the
ordering
r
U
≡ {
1
,...,J
E
K
1
,J
E
K,J
E
K
+ 1
,...J
E
}
with the
final
K
foods being the chosen items and
E
being the number of items that were ever
exchanged from advance to immediate choice conditions. The likelihood
Prob
(
r
U
,
x
;
β
U
) =
J
E
j
=
J
K
E
F
j
[
x
1
,...,x
J
K
1
,x
j
;
β
U
]
can be used to estimate unambiguous utility values
β
U
, ignoring any exchanged items.
If no items are ever exchanged, the rankings are identical and
β
U
=
β
A
=
β
I
. If
exchanges are made,
β
U
can differ from both
β
A
and
β
I
. One can then construct the
unambiguous utility of a proposed bundle
q
,
V
U
(
q
) =
J
j
=1
q
j
x
j
β
U
.
It is important to note that though
β
U
is estimated without foods that were ever
17
Note that the intensive margin of choice represented by the quantities
q
is not a feature of the
estimated likelihood, but is present in the determination of utility values. Given that most chosen
bundles consist of only unique food items, the distinction between the extensive and intensive margin
is rarely of importance in our setting.
16
exchanged, an unambiguous utility value is generated for exchanged foods. This means
that though
r
U
does not contradict choice,
β
U
will potentially assign different utility
values to two items that were exchanged for each other.
18
As such,
β
U
, informed
by subjects’ other decisions, may arbitrate between these two foods. If a subject
unambiguously chooses fruits and vegetables over sweets and salty snacks,
β
U
will
reflect this in utility weights that are positive to fruit and vegetable characteristics.
Exchanging a bag of chips for a piece of fruit would be viewed as an improvement
under
β
U
, while the opposite would be viewed as deleterious. We view the arbitration
between conflicting advance and immediate welfare criteria as a valuable feature of our
structural exercise and evaluate the consequences of commitment policies through the
lens of all three measures,
V
A
(
·
)
, V
I
(
·
)
and
V
U
(
·
)
.
3 Results
We present the results in three sub-sections. Sub-section 3.1 discusses reduced form
evidence on dynamic inconsistency and assesses the relationship between dynamic in-
consistency and commitment. Sub-section 3.2 evaluates the welfare consequences of
dynamic inconsistency and commitment policies. Sub-section 3.3 is dedicated to ro-
bustness tests and evaluation of additional data.
3.1 Reduced Form Evidence: Dynamic Inconsistency and Com-
mitment Demand
3.1.1 Dynamic Inconsistency
Our analysis of dynamic inconsistency contrasts advance and immediate decisions when
commitment is not available. In Chicago, 82 of 218 subjects (37.6%) exhibit dynamic
inconsistency in the first week by making at least one exchange between advance and
immediate choice. Similarly, in Los Angeles, 66 of 171 subjects (38.6%) exhibit dynamic
inconsistency in the first week. Of the 256 allocations in Los Angeles where commitment
is not offered, 121 (47.3%) exhibit inconsistencies. Pooling our study sites, 203 of 474
(43%) allocations made without commitment offered exhibit dynamic inconsistency.
Of 389 total subjects, 177 (46%) ever exhibit such an inconsistent allocation.
18
This is the sense in which our analysis is in the spirit of Bernheim and Rangel (2007, 2009).
Whereas welfare statements constructed from an unambiguous choice relation will never contradict
choice, welfare statements constructed from utility estimates based upon unambiguous choices may
do so.
17
Figures 2 and 3 explore the nature of these inconsistencies at the aggregate and
individual level. Though there are many ways in which the data can be examined, we
begin by evaluating a simple observable characteristic: whether the chosen food is a
fruit or vegetable, or a sweet or salty snack. Figure 2 graphs the frequency with which
each food appears in immediate and advance bundles across study sites, where each
point represents the raw frequency with which each food is chosen over all subjects
in a location-week. Given one week of data prior to being offered commitment in
Chicago and two weeks of data prior to commitment being offered to all subjects in
Los Angeles, there are 60 total foods represented. Of the 30 fruits and vegetables, 22
are chosen less frequently in immediate choice. Of the 30 sweets and salty snacks, 23
are chosen more frequently in immediate choice. Figure 3 graphs the proportion of
fruits and vegetables contained in chosen bundles, where each point now represents a
subject-week prior to commitment being offered.
19
Among observations that change
the proportion of fruits and vegetables between advance and immediate choice, 79%-
96% show reductions in fruits and vegetables in immediate choice. A clear pattern
emerges – fruits and vegetables are chosen more often in advance choice, while sweets
and salty snacks are chosen more often in immediate choice.
The systematic patterns of inconsistencies discussed above are supported by the
statistics in Table 2, which also includes analysis along additional nutritional dimen-
sions. For each subject at each point in time, we aggregate bundle characteristics
by summing over the chosen foods along observable and nutritional characteristics.
We estimate differences between advance and immediate choice using Ordinary Least
Squares (OLS) estimation with standard errors clustered at the individual level. We
observe significant differences between advance and immediate bundles in almost every
nutritional category at both study sites. Inconsistent subjects substitute lower calorie,
lower fat and lower carbohydrate foods with higher calorie, higher fat and higher car-
bohydrate foods. These patterns largely come from exchanging fruits and vegetables
for sweets and salty snacks.
3.1.2 Commitment Demand
Our design elicits commitment demand in the form of giving up the option to exchange
foods for the next delivery date. Of 218 subjects in Chicago, 73 (33.5%) demand
commitment for their second delivery. In Los Angeles, commitment demand is more
frequent than in Chicago. Of 171 subjects in Los Angeles, 134 (78.4%) ever demand
19
Appendix Figure A1 shows similar information for calories, fat grams, carbohydrate grams and
protein grams.
18
cheetos
fudge brownies
lays potato chips
oreo cookies
cucumber
green peppers
oranges
garden salad
0
50
100
150
200
Immediate Frequency
0
50
100
150
200
Advance Frequency
Fruits and Vegetables
Snacks and Sweets
Figure 2: Frequency of Foods in Advance and Immediate Choice
Notes:
Each point represents the frequency with which each food is chosen over all subjects in a
location-week. This makes 60 points in total - 30 fruits and vegetables and 30 sweets and salty snacks.
Foods appearing more frequently in advance versus immediate bundles lie below the 45
line. Of the
30 fruits and vegetables, 22 are chosen less frequently in immediate choice. Of the 30 sweets and salty
snacks, 23 are chosen more frequently in immediate choice. While some foods are more popular than
others, all foods are chosen with some frequency.
commitment, with 69 of 86 (80.2%) doing so in Week 2 and 127 of 171 (74.3%) doing
so in Week 3. A potential reason for the difference across study sites is that we offered
commitment to Chicago subjects a few days prior to the next delivery, while we offered
commitment to Los Angeles subjects immediately after they made their advance choices
for the next delivery. However, differences in the sample population and study design
across sites make it difficult to identify the underlying reason for this difference.
Figure 4 displays the association between dynamic inconsistency and subsequent
commitment demand. In Chicago, 55 of 136 (40.4%) dynamically consistent subjects
demand commitment, while only 18 of 82 (22.0%) dynamically inconsistent subjects
do so. In Los Angeles, 95 of 105 (90.5%) of subjects who are dynamically consis-
tent in their first delivery ever demand commitment, while only 39 of 66 (59.1%)
dynamically inconsistent subjects do so. Of 256 total allocations made in Los Angeles
prior to being offered commitment, 123 of 135 (91.1%) dynamically consistent obser-
19