Enhanced Salience of Nonlinear Pricing and Energy Conservation

Last registered on October 04, 2023
View Trial History

Pre-Trial

Trial Information

General Information

Title
Enhanced Salience of Nonlinear Pricing and Energy Conservation
RCT ID
AEARCTR-0012178
Initial registration date
September 28, 2023

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
October 04, 2023, 4:42 PM EDT

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

Locations

Country
Viet Nam
Region
Hanoi

Primary Investigator

Name
Minkyong Ko
Affiliation
Virginia Tech
Contact Primary Investigator

Other Primary Investigator(s)

PI Name
Chi Ta
PI Affiliation
Virginia Tech
Contact Investigator
PI Name
Teevrat Garg
PI Affiliation
UC San Diego
Contact Investigator

Additional Trial Information

Status
In development
Start date
2023-09-29
End date
2024-03-29
Keywords
Behavior, Environment & Energy
Additional Keywords
JEL code(s)
Q41, D03, D12
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
Demand management has become an important strategy for utilities to address electricity production shortfalls and intermittent issues resulting from the transition to renewable energy. Many utilities apply increasing block tariffs to prevent pricing low-income households out of basic electricity access, while simultaneously discouraging wasteful overconsumption by high-income households. However, consumers' limited understanding of or attention to such complex pricing systems can result in private and socially sub-optimal behavior, rendering nonlinear pricing ineffective for energy conservation. This project aims to determine whether enhancing the understanding of energy use and nonlinear electricity pricing can help households respond to marginal pricing, thereby increasing energy conservation. We are conducting a large-scale experiment covering 45,000 users of a recently launched mobile app from a state-owned electric utility company in Vietnam. The treatment groups receive either a real-time app display of their estimated daily marginal prices or their total estimated bills to date, for a minimum of six months. To assess and compare the persistent effects of providing high-frequency nonlinear price and total cost information on consumer behavior, we will gather electricity billing data from the participants for one year before and after the experiment.
External Link(s)

Registration Citation

Citation
Garg, Teevrat, Minkyong Ko and Chi Ta. 2023. "Enhanced Salience of Nonlinear Pricing and Energy Conservation." AEA RCT Registry. October 04. https://doi.org/10.1257/rct.12178-1.0
Sponsors & Partners
Experimental Details

Interventions

Intervention(s)
Different information is provided to the control and treatment groups via the newly launched utility's app.
Intervention Start Date
2023-09-29
Intervention End Date
2024-03-29

Primary Outcomes

Primary Outcomes (end points)
Reduction in electricity consumption at the household level, resulting from the provision of information on the nonlinear pricing system
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
We are conducting a large-scale experiment covering 45,000 users of a recently launched mobile app from a state-owned electric utility company in Vietnam. The treatment groups receive either a real-time app display of their estimated daily marginal prices or their total estimated bills to date, for a minimum of six months.
Experimental Design Details
The experiment will be conducted on a subset of EVN Hanoi's customers, who have installed the utility's app. The app displays each household’s daily consumption, and it compares customers’ current and past consumption levels. We collaborate with I-Com, EVN Hanoi’s app developer, to develop functionality on the mobile app to assign treatments to different groups and to provide households with our designed and customized messages and information. Specifically, we will take the universe of households who have installed the app as our population interest and randomly select and assign 45,000 households into a control and two treatment groups. The first and second treatment groups will receive real-time pricing and real-time billing information, respectively.
Randomization Method
Randomization done in office by a computer
Randomization Unit
individual household
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
N/A
Sample size: planned number of observations
45,000 households
Sample size (or number of clusters) by treatment arms
15,000 households in control, 15,000 households in each treatment groups
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
For the low variance σ^2 = 0.05, to detect MDE=1%, the required sample size of each treatment and control group is 6,011.
Supporting Documents and Materials
IRB

Institutional Review Boards (IRBs)

IRB Name
Virginia Tech Institutional Review Board
IRB Approval Date
2022-02-22
IRB Approval Number
22-127
Analysis Plan

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

Request Information

Post-Trial

Post Trial Information

Study Withdrawal

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

Request Information

Intervention

Is the intervention completed?
No
Data Collection Complete
Data Publication

Data Publication

Is public data available?
No

Program Files

Program Files
Reports, Papers & Other Materials

Relevant Paper(s)

Reports & Other Materials