Reducing distortions in electricity demand

Last registered on April 16, 2021
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Pre-Trial

Trial Information

General Information

Title
Reducing distortions in electricity demand
RCT ID
AEARCTR-0004887
Initial registration date
October 23, 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
October 23, 2019, 4:09 PM EDT

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

Last updated
April 16, 2021, 10:18 AM EDT

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

Locations

Country
Rwanda
Region
North, South

Primary Investigator

Name
Megan Lang
Affiliation
UC Berkeley Dept of Agricultural and Resource Economics
Contact Primary Investigator

Other Primary Investigator(s)

Additional Trial Information

Status
Completed
Start date
2019-10-14
End date
2020-12-31
Keywords
Environment & Energy, Finance & Microfinance, Welfare
Additional Keywords
Solar, rural electrification, prepaid electricity, economic development, liquidity constraints, credit.
JEL code(s)
O13, O22, Q41
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. 2021. "Reducing distortions in electricity demand." AEA RCT Registry. April 16. https://doi.org/10.1257/rct.4887-2.0
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
Half of the consumers who are offered a solar line of credit will be required to repay the loan within one week of the loaned system access time expiring in order to keep using the line of credit, and half will have use of the line of credit regardless of the time it takes to repay a loan. Within each of these groups, I cross-randomize the size of the credit line and the flat fee that consumers face when repaying the loan. This yields a total of eight cross-randomized cells with 250 consumers in each cell. The control group are all remaining consumers in the sample of around 11,730 consumers.

I stratify based on the utilization rate for the 90 days preceding the last week of September. The 90-day utilization rate is the proportion of days the consumer had access to their solar home system over the past 90 days. To understand how the line of credit impacts consumers in different parts of the 90-day utilization distribution, I stratify based on four bins: 0%-30%, 30%-65%, 65%-80%, and over 80%, always inclusive of the bottom of the range. Table \ref{Strat_Size} shows the relative size of the treatment and control group in each of these stratification bins.
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.
Supporting Documents and Materials
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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Post-Trial

Post Trial Information

Study Withdrawal

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Intervention

Is the intervention completed?
Yes
Intervention Completion Date
February 14, 2020, 12:00 +00:00
Data Collection Complete
Yes
Data Collection Completion Date
June 14, 2020, 12:00 +00:00
Final Sample Size: Number of Clusters (Unit of Randomization)
11,360 pay as you go solar customers (2,000 treated, 9,360 control)
Was attrition correlated with treatment status?
No
Final Sample Size: Total Number of Observations
11,360 pay as you go solar customers
Final Sample Size (or Number of Clusters) by Treatment Arms
250 high fee/high borrowing limit/time limit on repayment, 250 high fee/high borrowing limit/no time limit on repayment, 250 high fee/low borrowing limit/time limit on repayment, 250 high fee/low borrowing limit/no time limit on repayment, 250 low fee/high borrowing limit/time limit on repayment, 250 low fee/high borrowing limit/no time limit on repayment, 250 low fee/low borrowing limit/time limit on repayment, 250 low fee/low borrowing limit/no time limit on repayment, 9,360 control.
Data Publication

Data Publication

Is public data available?
No

Program Files

Program Files
No
Reports, Papers & Other Materials

Relevant Paper(s)

Abstract
I show that transaction costs for perishable goods create welfare loss. The loss comes from the trade-off between transaction costs and the waste that occurs when perishable goods expire, a trade-off that is compounded by liquidity constraints. I explore this trade-off using prepaid solar access time in rural Rwanda, a strictly non-storable good with transaction costs. I randomly offer 2,000 current solar customers a line of credit for solar access time, which alleviates liquidity constraints and lowers transaction costs. Consumers who previously bought in bulk respond by eliminating wasteful consumption, reducing demand by up to 6.4%. Those who are the most likely to be liquidity constrained increase demand by 88%. My results illustrate that transaction costs for perishable goods distort willingness to pay in opposite directions for different subsets of consumers. I estimate consumer surplus from electricity under the less distorted conditions of my experiment. My estimates indicate a stronger cost-benefit proposition for universal electrification than others in the literature, but indicate that marginal households' willingness to pay still falls below current cost-covering levels.
Citation
Lang, Megan (2021). "Consuming Perishable Goods in the Presence of Transaction Costs and Liquidity Constraints."
URL
https://www.dropbox.com/s/tnxi971dtz32474/Contracting_Electricity_LICs.pdf?dl=0

Reports & Other Materials