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Study More Tomorrow
Last registered on November 27, 2020

Pre-Trial

Trial Information
General Information
Title
Study More Tomorrow
RCT ID
AEARCTR-0005446
Initial registration date
February 28, 2020
Last updated
November 27, 2020 9:46 AM EST
Location(s)
Region
Primary Investigator
Affiliation
Reed College
Other Primary Investigator(s)
PI Affiliation
Oregon State University
PI Affiliation
Oregon State University
Additional Trial Information
Status
On going
Start date
2020-01-06
End date
2021-06-11
Secondary IDs
Abstract
More than two of every five students who enroll in college fail to graduate within 6 years (National Center for Education Statistics 2016). College students who perform poorly often exhibit ineffective study habits, such as cramming for exams (Beattie, Laliberté, and Oreopoulos 2016). Students who are likely to procrastinate, as measured by delays in registering for courses, perform worse than those less likely to procrastinate (Banerjee and Duflo 2014; De Paola and Scoppa 2015; Pugatch and Wilson 2018). Students whose randomly selected roommate brought video games to campus studied less and earned worse grades (Stinebrickner and Stinebrickner 2008). These patterns suggest that time-inconsistent preferences help to explain poor academic outcomes among college students.

Outside of education, researchers have countered the presence of time-inconsistent preferences by offering commitment contracts to encourage savings (Thaler and Benartzi 2004; Ashraf, Karlan, and Yin 2006) or promote healthy behaviors (Rogers, Milkman, and Volpp 2014). Yet despite the likely contribution of time-inconsistent preferences to poor education outcomes, research on commitment devices in schools is scant. Ariely and Wertenbroch (2002), Himmler, Jaeckle, and Weinschenk (2017) and Patterson (2018) have demonstrated that commitment devices can improve performance of university students. Yet each of these studies offered a single type of contract to all treated students, thereby identifying only one point on the demand curve. The nature of student demand for commitment contracts remains largely unknown.

In this project, we introduce “Study More Tomorrow,” a commitment device for college students. The program allows college students to pre-commit to change study habits if their midterm exam grade falls below a pre-specified threshold. We use randomized assignment to offer the service to students enrolled in introductory Economics courses at Oregon State University. Students offered the contract agree to lose entries in a lottery if they fail to attend course-specific tutoring within two weeks after midterm grades are released. The amount at stake is either half or all of their chances of winning the lottery, with the amount randomly assigned. We remind students whose midterm exam score falls below the threshold of their commitment and of the consequences if not honored.

The name and structure of the program deliberately echo the seminal “Save More Tomorrow” program studied in Thaler and Benartzi (2004). Like that program, “Study More Tomorrow” attempts to leverage several of the likely behavioral biases in our study population. First, college students tend to procrastinate, as noted above. Second, college students are often overconfident in their expectations of course performance. In pilot data for this project, two of three introductory microeconomics students at Oregon State University expected to receive an A in the course, though only 18% earned at least an A-. These findings echo others in the literature (Grimes 2002; Nowell and Alston 2007). Procrastinating or overconfident students may benefit most from commitment. Third, college students are loss averse; the original demonstration of the endowment effect involved college students trading (or failing to trade) coffee mugs (Kahneman, Knetsch, and Thaler 1990). Varying the amount at stake for failing to honor the “Study More Tomorrow” contract attempts to channel this loss aversion.

We will measure the effect of the randomly assigned offer of a commitment device on the following outcomes:

1. Take-up: the share of students opting into commitment devices measures demand. Random variation in the foregone probability of winning the lottery allows us to characterize demand as a function of the cost in expected earnings. Contract demand reflects a lower bound on the proportion of students who expect to exhibit time-inconsistent study behavior. “Sophisticated procrastinators” – individuals who know that they are likely to procrastinate in the future – should consider the benefits of the commitment device relative to the costs and some may choose to “purchase” a commitment device. In contrast, “naïve procrastinators” should never purchase this device because they do not know that they are likely to procrastinate in the future.
2. Use of academic support services (peer tutoring)
3. Course grades

A baseline survey will measure the initial time consistency of student study habits, using methods adapted from survey measures of discount rates. The baseline will also measure a student’s expected course grade. We expect that students reporting greater time inconsistency and higher expected grades at baseline will have higher demand for commitment.

The enrollment of our study population in large introductory courses serves two purposes. First, poor performance in introductory courses is associated with failure to graduate (Kovacs 2016). The particular courses under study also appear on the university’s shortlist of problematic courses due to their high DFW (grade of D, F, or Withdraw) rates and predictive power for dropout. Second, large courses provide large samples to maximize statistical power.

This project builds on previous studies of pre-commitment devices among university students in two ways. First, we experimentally vary the consequences for failing to honor the contract. This allows us to elicit a measure of willingness to pay for the commitment device that is new to the literature. In contrast, the commitment contract in Ariely and Wertenbroch (2002) carries a standard penalty, while the contracts in Himmler, Jaeckle, and Weinschenk (2017) and Patterson (2018) do not penalize students. Second, we experimentally manipulate demand for commitment contracts by providing information about the course grade distribution.
External Link(s)
Registration Citation
Citation
Pugatch, Todd, Elizabeth Schroeder and Nicholas Wilson. 2020. "Study More Tomorrow." AEA RCT Registry. November 27. https://doi.org/10.1257/rct.5446-1.1.
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Experimental Details
Interventions
Intervention(s)
In this project, we introduce “Study More Tomorrow,” a commitment device for college students. The program allows college students to pre-commit to change study habits if their midterm exam grade falls below a pre-specified threshold. We use randomized assignment to offer the service to students enrolled in introductory Economics courses at Oregon State University. Students offered the contract agree to lose entries in a lottery if they fail to attend course-specific tutoring while enrolled in the course. The amount at stake is either half or all of their chances of winning the lottery, with the amount randomly assigned. We remind students whose midterm exam score falls below the threshold of their commitment and of the consequences if not honored.
Intervention Start Date
2020-01-06
Intervention End Date
2021-06-11
Primary Outcomes
Primary Outcomes (end points)
1. Take-up: the share of students opting into commitment devices measures demand. Random variation in the foregone probability of winning the lottery allows us to characterize demand as a function of the cost in expected earnings. Contract demand reflects a lower bound on the proportion of students who expect to exhibit time-inconsistent study behavior. “Sophisticated procrastinators” – individuals who know that they are likely to procrastinate in the future – should consider the benefits of the commitment device relative to the costs and some may choose to “purchase” a commitment device. In contrast, “naïve procrastinators” should never purchase this device because they do not know that they are likely to procrastinate in the future.
2. Use of academic support services (peer tutoring)
3. Course grades
Primary Outcomes (explanation)
Secondary Outcomes
Secondary Outcomes (end points)
Secondary Outcomes (explanation)
Experimental Design
Experimental Design
We use individual-level randomization and will examine intervention take-up, use of student support services, and learning outcomes.
Experimental Design Details
Not available
Randomization Method
Randomization done in office by a computer.
Randomization Unit
The unit of randomization is the individual.
Was the treatment clustered?
No
Experiment Characteristics
Sample size: planned number of clusters
The study does not use a cluster-randomized design.
Sample size: planned number of observations
Approximately 2,000 students.
Sample size (or number of clusters) by treatment arms
Approximately 667 students in each treatment arm (prices of half or all lottery chances) and 667 students in the control arm.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
The minimum detectable effect for take-up is 1.6%. Assumes 80% power, 5% test size, N=2,000, no take-up among control group, and a comparison of a single treatment arm vs. control group.
IRB
INSTITUTIONAL REVIEW BOARDS (IRBs)
IRB Name
Oregon State University Human Research Protection Program
IRB Approval Date
2019-01-15
IRB Approval Number
8402