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Reducing distortions in electricity demand
Last registered on October 23, 2019

Pre-Trial

Trial Information
General Information
Title
Reducing distortions in electricity demand
RCT ID
AEARCTR-0004887
Initial registration date
October 23, 2019
Last updated
October 23, 2019 4:09 PM EDT
Location(s)

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Primary Investigator
Affiliation
UC Berkeley Dept of Agricultural and Resource Economics
Other Primary Investigator(s)
Additional Trial Information
Status
On going
Start date
2019-10-14
End date
2020-12-31
Secondary IDs
Abstract
Pay as you go (PAYGo) solar home systems are a market-based technology to increase rural electrification for low income households. However, the setting in which consumers use the PAYGo contract may feature market frictions that push consumers away from their optimal demand for electricity. I partner with a solar company in Rwanda to experimentally reduce relevant market frictions. I use this experiment to better understand non-price determinants of demand for electricity among rural, low income consumers.
External Link(s)
Registration Citation
Citation
Lang, Megan. 2019. "Reducing distortions in electricity demand." AEA RCT Registry. October 23. https://doi.org/10.1257/rct.4887-1.0.
Sponsors & Partners

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Experimental Details
Interventions
Intervention(s)
Intervention Start Date
2019-10-14
Intervention End Date
2020-02-17
Primary Outcomes
Primary Outcomes (end points)
Use of the line of credit, quantity borrowed using the line of credit, quantity of electricity demanded, and default on the PAYGo contract.
Primary Outcomes (explanation)
Secondary Outcomes
Secondary Outcomes (end points)
Average number of days borrowed, number of loans taken, average number of days the system is switched off prior to borrowing,likelihood that the system is switched on at the time of borrowing, average number of days to fully repay, likelihood that the system is switched on at the time of repayment, average number of payments to fully repay the loan, and average account balance (in days) after fully repaying the loan.
Secondary Outcomes (explanation)
Experimental Design
Experimental Design
We randomly offer a solar-specific line of credit to current solar consumers in Rwanda using stratified random sampling.
Experimental Design Details
Not available
Randomization Method
Randomization done in office by a computer.
Randomization Unit
Individual solar customer.
Was the treatment clustered?
No
Experiment Characteristics
Sample size: planned number of clusters
11,730 solar consumers.
Sample size: planned number of observations
11,730 consumers.
Sample size (or number of clusters) by treatment arms
250 in each cross-randomized treatment arm, 9,730 in the control group.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
A total sample size of 11,730 individuals, with 17% of them being treated, will allow me to detect effects on outcomes in the administrative data ranging from 0.06-0.14 standard deviations (3%-6%) when I pool across the cross-randomization and stratification. When I estimate heterogeneous effects by stratification bin or examine treatment effects for particular cross-randomized treatments, I will be able to detect effect of 0.14-0.36 (7%-14%) standard deviations. The range in standard deviations is assuming a minimum standard deviation of one and a maximum standard deviation of five.
IRB
INSTITUTIONAL REVIEW BOARDS (IRBs)
IRB Name
University of California at Berkeley Committee for the Protection of Human Subjects
IRB Approval Date
2019-06-19
IRB Approval Number
2019-03-11994
Analysis Plan

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