Reducing distortions in electricity demand

Last registered on October 23, 2019

View Trial History

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.

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

On going

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. 2019. "Reducing distortions in electricity demand." AEA RCT Registry. October 23. https://doi.org/10.1257/rct.4887-1.0

Sponsors & Partners

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

Request Information

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

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?

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