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Reducing distortions in electricity demand
Initial registration date
October 23, 2019
October 23, 2019 4:09 PM EDT
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UC Berkeley Dept of Agricultural and Resource Economics
Other Primary Investigator(s)
Additional Trial Information
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.
Intervention Start Date
Intervention End Date
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 (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)
We randomly offer a solar-specific line of credit to current solar consumers in Rwanda using stratified random sampling.
Experimental Design Details
Randomization done in office by a computer.
Individual solar customer.
Was the treatment clustered?
Sample size: planned number of clusters
11,730 solar consumers.
Sample size: planned number of observations
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.
INSTITUTIONAL REVIEW BOARDS (IRBs)
University of California at Berkeley Committee for the Protection of Human Subjects
IRB Approval Date
IRB Approval Number