Targeting Incentive Contracts in Heterogeneous Populations

Last registered on September 30, 2020


Trial Information

General Information

Targeting Incentive Contracts in Heterogeneous Populations
Initial registration date
September 19, 2019

Initial registration date is when the trial was registered.

It corresponds to when the registration was submitted to the Registry to be reviewed for publication.

First published
September 20, 2019, 9:46 AM EDT

First published corresponds to when the trial was first made public on the Registry after being reviewed.

Last updated
September 30, 2020, 6:12 PM EDT

Last updated is the most recent time when changes to the trial's registration were published.



Primary Investigator

UC Santa Cruz

Other Primary Investigator(s)

PI Affiliation
University of Chicago Booth

Additional Trial Information

On going
Start date
End date
Secondary IDs
Providing financial incentives to encourage behavioral change is increasingly common: policymakers pay people to exercise, to study more, and maintain tree cover. In designing incentives for behavior change, there is frequently a tradeoff between motivating those with high and low costs of engaging in the behavior: whereas the optimal contract for high-cost individuals will have a large incentive or a low behavior target, the optimal contract for low-cost individuals will have a small incentive or a large behavior target. This project will investigate methods for targeting incentive contracts for behavioral change to individuals with heterogeneous behavior cost. In a randomized controlled trial among individuals with diabetes and prediabetes, we will experimentally evaluate two methods for targeting incentive contracts for walking: individual contract choice, and targeting on observable characteristics. We will first show that in our setting, contracts with a higher step target are more effective for individuals with low walking costs at baseline. Second, we will assess the relative performance of our two targeting methods in terms of targeting precision and walking encouragement. Finally, we will explore the role of key challenges to implementing each method, including the endogeneity and limited information content of observable characteristics, and the principal and agent’s imperfect knowledge of preferences.
External Link(s)

Registration Citation

Dizon-Ross, Rebecca and Ariel Zucker. 2020. "Targeting Incentive Contracts in Heterogeneous Populations." AEA RCT Registry. September 30.
Experimental Details


Intervention Start Date
Intervention End Date

Primary Outcomes

Primary Outcomes (end points)
Daily steps taken during intervention.
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
Indicators for daily steps above 10,000, 12,000, and 14,000; Distance from assigned step target to “optimal” step target; step target assigned; steps taken during “phase-in period”.
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
We randomly assign individuals to one of three targeting regimes: choice, targeting on observables, and random assignment. In addition, a small monitoring group is included for benchmarking purposes; this group will not receive any incentive contract, but we will monitor their steps. We will cross-randomize information on which step target we believe is most likely to be most effective at encouraging walking.
Experimental Design Details
Experiment 1: The design will entail three incentive-targeting treatment groups: choice, targeting on observables, random assignment. A fourth treatment group – the monitoring group – will not receive incentives; this treatment is not of primary interest but included so that we can benchmark the size of the other treatment effects against the size of the gap between the groups receiving incentives (pooled) and the monitoring group. An “information” intervention will be cross-randomized among a subset of these treatment groups. Individuals in all treatment groups will be enrolled in a one-week phase-in period followed by a four-week intervention period.
During the phase-in period, all individuals will receive a fitbit, and will be encouraged to report their walking to the investigators through an automated calling system. Some individuals will have a 6-day Phase In, some will have a 13-day Phase in.
During the intervention period, individuals in the monitoring group will receive fitbits, and will be encouraged to continue to report their walking through the calling system. All other individuals will receive a fitbit and an incentive contract that pays them a fixed rate each day they report reaching a fixed step target through the calling system (e.g. 20 rupees per day 10,000 steps are reached). The rate and step target will vary across individuals, and will be assigned differently across each of the treatment groups. The assignment mechanism in each group is explained below.
Choice 1: The main choice group will be given their preferred contract among a menu with three options: a low-target low-payment option, a middle-target middle-payment option, and a high-target high-payment option. The three contracts are:
• Low: 10,000 step target for 16 INR
• Middle: 12,000 step target for 18 INR
• High: 14,000 step target for 20 INR
Choice 2: A small second choice group will be given their preferred contract among a second menu of three-options with the same three targets of 10, 12, and 14 thousand steps but payments of 10, 15, and 20 INR, respectively.
Targeting: The targeting on observables group will receive a contract where the step target is assigned according to average observed walking in a one-week “pre-period”. The target is either 10,000, 12,000, or 14,000 steps. We assign the target among these that is closest to average steps in the first 6 days of Phase in + 5,500. The assignment formula is based on walking patterns in an earlier experiment incentivizing individuals to walk 10,000 daily steps in exchange for 20 rupees (Aggarwal, et al). The payment level will be 20 rupees in all contracts.
Random: The random assignment group will receive a contract with a randomly assigned daily target of either 10,000, 12,000, or 14,000 steps. The payment level will be 20 rupees in all contracts.
All individuals not in the Targeting group make all menu choices, but not all individuals are assigned an intervention based on these choices. To assess how the lack of information on future walking performance in each contract influences contract choice, we implement an “Information” intervention. In this intervention, we inform individuals of the contract in the first menu that we believe will encourage them to walk the most.

Experiment 2:
In order to better understand the role of private information and learning in choice, we will layer a second experiment on top of Experiment 1 beginning on December 9, 2019. This experiment introduces a new treatment group, the "Baseline Choice" group. The Baseline Choice group is identical to Choice 1 except that they make their choice at Baseline, prior to the Phase-in period.

Experiment 3:
Beginning on January 29, 2020, we estimate that we will have enrolled our target sample for Experiment 1. However, we would like to continue to assess the role of private information as in Experiment 2. Finally, we would like to better understand how whether individuals demand weakly "dominated" contracts, which pay the same for higher step targets. At that point, we will re-shuffle our experimental design in the following ways:
1) We will eliminate the information treatment
2) We will introduce a new "Choice" group. The three contracts on the menu will the same step targets as the other menus (10K, 12K, and 14K) but all contracts will pay 20 INR.
3) We will change our treatment group assignment probabilities.
4) We will layer a new experiment on the existing, and cross-randomize the duration of phase-in between 6 and 13 days (implement starting 2/17)
Randomization Method
Randomization is stratified based on our forecasted median age (49) and gender. Ordered treatment assignment lists for each stratum cell are randomly generated in the office by a computer using Stata 15, and treatments are assigned to participants on rolling basis.
Randomization Unit
Was the treatment clustered?

Experiment Characteristics

Sample size: planned number of clusters
Sample size: planned number of observations
We aim to enroll approximately 4,000 diabetics, pre-diabetics, hypertensives, and prehypertensives in Experiment 1. We aim to enroll approximately 750 diabetics, pre-diabetics, hypertensives, and prehypertensives in the "Baseline Choice" group added in Experiment 2 and continuing into Experiment 3. We aim to enroll approximately 3-4,000 diabetics, pre-diabetics, hypertensives, and prehypertensives in Experiment 3. We will continue enrollment in Experiment 3 as our funding allows.
Sample size (or number of clusters) by treatment arms
The following treatment group distribution is approximate, since enrollees will be assigned to treatments according to baseline strata in a pre-determined order, and some may withdraw following randomization but prior to the intervention.

Experiment 1:
Incentive Groups:
T1 – Random, 10,000 step target: 15%
T2 – Random, 12,000 step target: 25%
T3 – Random, 14,000 step target: 15%
T4 – Choice 1: 25%
T5 – Choice 2: 0.5%
T6 – Targeting: 17.5%

Non-incentive Group:
Monitoring Only: 2%

Information Treatment:
60% of the experimental sample excluding the Targeting group will be cross-randomized into the Information treatment prior to deciding which contract they prefer in Choice Menu 1.

Experiment 2:
20% Baseline Choice
80% Allocated to Experiment 1 (no change to those within Experiment 1)

Experiment 3:
Incentive Groups:
T1 – Random, 10,000 step target: 9.3%
T2 – Random, 12,000 step target: 9.3%
T3 – Random, 14,000 step target: 9.3%
T4 – Choice 1: 19%
T5 – Choice 2: 0.5%
T8 – Choice 3: 19%
T6 – Targeting: 10%
T7 – Baseline Choice: 19%
Non-incentive Group:
Monitoring Only: 4.25%

Phase-in period duration cross-randomization:
60% of the experimental sample will be cross-randomized into a 13-day (rather than 6-day) phase-in period.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)

Institutional Review Boards (IRBs)

IRB Name
AURA IRB at University of Chicago
IRB Approval Date
IRB Approval Number


Post Trial Information

Study Withdrawal

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Data Publication

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Program Files

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Reports, Papers & Other Materials

Relevant Paper(s)

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