x

We are happy to announce that all trial registrations will now be issued DOIs (digital object identifiers). For more information, see here.
The Impact of Restaurant Menu Calorie Labeling on Food Choice
Last registered on August 03, 2018

Pre-Trial

Trial Information
General Information
Title
The Impact of Restaurant Menu Calorie Labeling on Food Choice
RCT ID
AEARCTR-0000940
Initial registration date
August 02, 2018
Last updated
August 03, 2018 2:40 PM EDT
Location(s)
Primary Investigator
Affiliation
Cornell University
Other Primary Investigator(s)
PI Affiliation
Louisiana State University
PI Affiliation
Cornell University
Additional Trial Information
Status
Completed
Start date
2015-11-09
End date
2017-09-30
Secondary IDs
Abstract
The prevalence of obesity in the U.S. has more than doubled since 1980. In part to address this rise, as well as to promote healthy eating more generally, the U.S. passed a nationwide menu label law as part of the Patient Protection and Affordable Care Act of 2010. Starting December 1, 2016, all chain restaurants in the U.S. will be required to list calorie counts next to each menu item. This study tests how providing such calorie count information affects restaurant patrons’ food choices. A randomized experiment will be conducted, with some restaurant patrons receiving menus with calorie counts, while others will receive the same menus but without calorie counts. Patrons’ choices of appetizers, drinks, entrees, and desserts will be recorded. The project tests whether providing calorie information leads to fewer calories ordered, and whether such decreases are particularly common in the dessert category. Differences in the effect by gender and other characteristics will be examined. The results of this study will be informative about the effects of the nationwide menu label law that takes effect in May 2018.
External Link(s)
Registration Citation
Citation
Cawley, John, Alex Susskind and Barton Willage. 2018. "The Impact of Restaurant Menu Calorie Labeling on Food Choice." AEA RCT Registry. August 03. https://doi.org/10.1257/rct.940-1.0.
Former Citation
Cawley, John et al. 2018. "The Impact of Restaurant Menu Calorie Labeling on Food Choice." AEA RCT Registry. August 03. https://www.socialscienceregistry.org/trials/940/history/32625.
Sponsors & Partners

There are documents in this trial unavailable to the public. Use the button below to request access to this information.

Request Information
Experimental Details
Interventions
Intervention(s)
The experiment will be conducted at two sit-down restaurants. Parties of patrons will be randomized into the treatment group or control group. The treatment group will receive menus with calorie labels whereas the control group will receive the same menus without such calorie counts.

Data on each patron’s order will be taken by the server and recorded in the Point of Sales (POS) system already in place. Research assistants will record whether the table in question was in the treatment group (received the menus with the calorie labels) or the control group (received the menus without calorie labels). After the meal, patrons will be surveyed in order to collect information on their age, education, interest in health, and the frequency and extent of their participation in exercise. Patrons will also be asked whether their menus contained calorie information, whether they used that information, and whether they approve or disapprove of including calorie counts on menus. Research assistants will also collect information from servers on whether the patrons asked about or discussed the calorie labels.

The patron survey will also record whether the patron has dined at the restaurant since the experiment began. Such repeat customers will be dropped from the analysis sample.
Intervention Start Date
2015-11-09
Intervention End Date
2017-09-30
Primary Outcomes
Primary Outcomes (end points)
The hypotheses we will test are as follows:
H1: Provision of calorie labels will result in patrons ordering fewer calories. We will test this hypothesis overall as well as within each course. We will also examine whether provision of information reduces the probability that patrons order any appetizer, or any dessert (i.e. the extensive margin of each course).

H2: Provision of calorie labels will result in patrons being more likely to choose the daily special; the reason is that daily specials are exempted from the calorie label requirement. The logic is that some consumers may exhibit optimism bias and hope that the daily special has fewer calories than the listed items, some consumers may be risk loving and gamble that the special has fewer calories, and some consumers may find the calorie labels disagreeable and deliberately choose not to know how many calories they are consuming. Only one of the two restaurants examined offers specials, however, and not even every day, so this will have limited power.

H3: Patrons will report seeing, using, and appreciating the presence of the calorie labels. This information will be collected in the patron survey.

H4: Provision of calorie labels will lower revenue and "profit" for the restaurant. For our measure of "profit", only food ingredient costs, not labor costs, can be considered.
Primary Outcomes (explanation)
Secondary Outcomes
Secondary Outcomes (end points)
Secondary Outcomes (explanation)
Experimental Design
Experimental Design
This experiment takes place at a public restaurant. When patrons approach the maitre d', the entire party is randomized into treatment or control using a smartphone app. The treatment group receives menus with calorie counts, and the control group receives the usual menu without calorie counts. Orders are recorded by the server, and after the meal patrons are asked to take a survey.
Experimental Design Details
Randomization Method
smartphone app (Randomizer)
Randomization Unit
The randomization unit is the entire party (group of patrons dining together).
Was the treatment clustered?
Yes
Experiment Characteristics
Sample size: planned number of clusters
Approximately 2,000 "parties" (groups of restaurant patrons).
Sample size: planned number of observations
5,000
Sample size (or number of clusters) by treatment arms
Roughly half treatment, and half control (exact proportions depend on randomization).
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
We assume that the control group orders an average of 1,400 calories at dinner, with a SD of 500. We assume that the treatment effect of providing calorie information is to reduce calories ordered by 5.8% (this is the effect found by Bollinger et al., (2011). Setting a 5% threshold for statistical significance, to have 80% power we need a sample size of 1,196.
IRB
INSTITUTIONAL REVIEW BOARDS (IRBs)
IRB Name
Cornell University Office of Research Integrity and Assurance, IRB for Human Participants
IRB Approval Date
2015-10-22
IRB Approval Number
1509005830
Post-Trial
Post Trial Information
Study Withdrawal
Intervention
Is the intervention completed?
Yes
Intervention Completion Date
September 30, 2017, 12:00 AM +00:00
Is data collection complete?
Yes
Data Collection Completion Date
September 30, 2017, 12:00 AM +00:00
Final Sample Size: Number of Clusters (Unit of Randomization)
Data were collected from two restaurants: a total of 1,487 parties from Restaurant A, and a total of 1,213 parties at Restaurant B. This is before dropping any observations because they are ineligible (e.g. a minor or didn't speak English) or have missing data.
Was attrition correlated with treatment status?
No
Final Sample Size: Total Number of Observations
Data were collected from two restaurants: a total of 4,187 patrons at Restaurant A, and a total of 4,130 patrons at Restaurant B.
This is before dropping any observations because they are ineligible (e.g. a minor or didn't speak English) or have missing data.
Final Sample Size (or Number of Clusters) by Treatment Arms
The total number of parties is 1293 in the treatment group and 1348 in the control group. The total number of individuals is 4129 in the treatment group and 4093 in the control group. This is before dropping any observations because they are ineligible (e.g. a minor or didn't speak English) or have missing data.
Data Publication
Data Publication
Is public data available?
No
Program Files
Program Files
No
Reports and Papers
Preliminary Reports
Relevant Papers