Using Contests to Deliver Cost-Effective Energy Conservation in Vietnam

Last registered on December 13, 2022


Trial Information

General Information

Using Contests to Deliver Cost-Effective Energy Conservation in Vietnam
Initial registration date
December 05, 2022

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
December 13, 2022, 10:47 PM EST

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



Primary Investigator

Duke University

Other Primary Investigator(s)

PI Affiliation
University of California - San Diego
PI Affiliation
University of Illinois at Urbana-Champaign
PI Affiliation
The University of British Columbia
PI Affiliation
Virginia Tech

Additional Trial Information

In development
Start date
End date
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
The energy sector in low- and middle-income countries is characterized by two stylized facts: (a) higher rates of particulate and carbon emissions per unit electricity generated and (b) low aggregate energy production resulting in lack of access and reliability of electricity. These concerns have led policy makers to encourage energy conservation in urban households through a variety of programs including tiered pricing, behavioral nudges, and direct “bonus” payments to keep energy use below a target maximum.

Urban energy conservation could help address the problem of grid balancing, that is reducing peak demand, especially as LMICs (including Vietnam where we propose our study) transition to a larger share of their electricity being generated from renewable resources. This is crucial to achieving the emissions reduction promise of renewable energy.

However, the issue of incentivizing agents (households) to exert costly, unobservable effort (energy abatement) is a long-standing and open question in economics. In many settings, including ours, the principal (utility) observes a performance measure (energy use) correlated with agent’s effort but not the effort directly because the principal is unable to observe shocks beyond the control of the agent (e.g. weather, household demand shock). One possible solution is to use rank-ordered tournaments that incentivize relative performance, thereby obviating the need for the principal to observe common shocks. In this project, we draw on a rich theoretical and nascent empirical literature on contracts and rank-ordered tournaments to test what features of tournaments are best suited to encourage greater conservation.
External Link(s)

Registration Citation

Garg, Teevrat et al. 2022. "Using Contests to Deliver Cost-Effective Energy Conservation in Vietnam." AEA RCT Registry. December 13.
Experimental Details


Intervention Start Date
Intervention End Date

Primary Outcomes

Primary Outcomes (end points)
The cost effectiveness of performance feedback and energy conservation reminders in reducing carbon and particulate matter emissions.
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
Through our partnership with the utility, we will send emails and text messages to all households in Hanoi (~2 million), encouraging them to sign up for an energy game with the potential to win meaningful sums of money (as determined by local context). Households will be given a web link to sign up for the utility’s mobile app and will be required to answer a short survey, which will serve as our baseline survey. Once we have the pool of participants, we will randomly assign them to different treatments. The first treatment we will test is the impact of providing feedback to households about their performance in reducing energy use relative to other households. In addition to this treatment, we will also test the effect of sending reminders to households to check their relative performance on the utility’s mobile app.
Experimental Design Details
Randomization Method
Randomization done in Python.
Randomization Unit
Individual randomization.
Was the treatment clustered?

Experiment Characteristics

Sample size: planned number of clusters
Sample size: planned number of observations
6,000 people
Sample size (or number of clusters) by treatment arms
1500 control group, 1500 for each treatment group
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)

Institutional Review Boards (IRBs)

IRB Name
Using Contests to Deliver Cost-Effective Energy Conservation in Vietnam
IRB Approval Date
IRB Approval Number
UCSD IRB#802882
Analysis Plan

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Post Trial Information

Study Withdrawal

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Is the intervention completed?
Data Collection Complete
Data Publication

Data Publication

Is public data available?

Program Files

Program Files
Reports, Papers & Other Materials

Relevant Paper(s)

Reports & Other Materials