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The impact of prepaid electricity meters on low income households in Cape Town South Africa

Last registered on December 16, 2014
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Pre-Trial

Trial Information

General Information

Title
The impact of prepaid electricity meters on low income households in Cape Town South Africa
RCT ID
AEARCTR-0000582
Initial registration date
December 16, 2014

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
December 16, 2014, 8:40 AM EST

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

Locations

Country
South Africa
Region
Cape Town

Primary Investigator

Name
Kelsey Jack
Affiliation
UC Santa Barbara
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Other Primary Investigator(s)

PI Name
Grant Smith
PI Affiliation
University of Cape Town
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Additional Trial Information

Status
In development
Start date
2015-01-01
End date
2015-12-31
Keywords
Environment & Energy
Additional Keywords
JEL code(s)
Secondary IDs
Abstract
Prepaid water and electricity meters offer a promising solution to lumpy and unpredictable bills by allowing customers to choose the timing and quantity of purchases. At the same time, prepayment circumvents debt accumulation, allowing utility companies to serve otherwise high-cost customers. Researchers will collaborate with the municipal utility in Cape Town, South Africa to evaluate customer responses and revenue recovery for prepaid versus credit electricity metering. The study will generate new evidence on the impacts of prepaid meters on energy use and revenue recovery using both a randomized phase-in of new prepaid customers and a retrospective evaluation of historic transitions to prepaid meters in a ten year, household database of all municipal utility customers. Researchers will explore how prepaid meters change the customer experience and the municipality’s revenue recovery.
External Link(s)

Registration Citation

Citation
Jack, Kelsey and Grant Smith. 2014. "The impact of prepaid electricity meters on low income households in Cape Town South Africa." AEA RCT Registry. December 16. https://doi.org/10.1257/rct.582-1.0
Former Citation
Jack, Kelsey and Grant Smith. 2014. "The impact of prepaid electricity meters on low income households in Cape Town South Africa." AEA RCT Registry. December 16. https://www.socialscienceregistry.org/trials/582/history/3335
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Experimental Details

Interventions

Intervention(s)
The City of Cape Town is switching approximately 2800 households in the suburb of Mitchells Plain from credit (post paid) meters to prepaid meters. These households have been identified based on property value. All properties below 300,000 ZAR (around 30,000 USD) must be on a prepaid meter according to new legislation in the City. Household meter conversion is involuntary: households that do not cooperate will have their electricity switched off until the household agrees to have a prepaid meter installed. The meter installation is not accompanied by any additional information about electricity tariffs or consumption.
Intervention Start Date
2015-01-01
Intervention End Date
2015-04-30

Primary Outcomes

Primary Outcomes (end points)
We will use two data sources to construct outcomes. First, we will use administrative data on electricity consumption (credit households) and electricity expenditures (prepaid households). These are official records from the City of Cape Town’s Electricity Department and provide outcomes at the customer level. We will also construct revenue and revenue recovery measures to analyze the impact of the meter on the City’s budget.

Second, we will gather survey data from a sub-sample of households that are part of the City’s prepaid installation project. The survey will be conducted in two waves, one after roughly half of the households have been targeted for conversion and a second after the installation project is complete. This will generate three groups of households for comparison: credit households (wave 1 only), households recently switched to a prepaid meter (waves 1 and 2) and households less recently switched to a prepaid meter (wave 2 only). The survey data will be used to analyze outcomes associated with four groups of hypotheses for why and how households are affected by prepaid electricity meters: (1) the implicit cost of electricity, (2) greater flexibility in expenditures, (3) salience of consumption, and (4) other psychological channels.
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
In collaboration with the researchers, the City will phase in the prepaid meters according to a randomized phase in design. The randomization unit is a block of approximately 200 geographically contiguous households. The 13 resulting blocks are assigned a random order and meter installation proceeds accordingly. Not all meters are installed according to the schedule. “Hold out” households receive meters late, and other households request installation before their randomization block is reached. Thus, compliance with treatment order is imperfect in both directions.

The study timeline is compressed by the demands of the City, which requires all prepaid conversions in Mitchells Plain to be complete by February. Consequently, the lag between the initial phase in block and the final phase in block will be short (~3 months), allowing for only short run impacts to be analyzed with the phase in design.

Analysis of outcomes from the administrative data will be conducted both with treatment assignment on the righthand side (ITT) and with the assigned treatment order as a predictor of actual treatment date (TOT). Using the panel nature of the data to introduce household fixed effects, a difference in difference regression will compare electricity usage outcomes (consumption or expenditure) between credit, newly prepaid and less newly prepaid households before and after the treatment date. Analysis of the survey outcomes will follow a similar design but rather than a continuous treatment timing variable, the survey will assign households to treatment (prepaid) and control (credit): their status at the time of the first survey wave.
Experimental Design Details
Randomization Method
The 13 randomization blocks are assigned a random order using a random number generator in Excel.
Randomization Unit
The randomization unit is a group of approximately 200 geographically contiguous households identified by the City for prepaid meter installation. Blocks were identified by the researchers using GIS to map the project households and identifying groups of approximately 200 households based on spatial coordinates. Other features of the map, such as roads, were not taken into consideration, making block boundaries largely arbitrary.
Was the treatment clustered?
Yes

Experiment Characteristics

Sample size: planned number of clusters
13
Sample size: planned number of observations
2800
Sample size (or number of clusters) by treatment arms
Phase in design: all clusters have both treated and control status at some point in time.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
Supporting Documents and Materials
IRB

Institutional Review Boards (IRBs)

IRB Name
IRB Approval Date
IRB Approval Number
Analysis Plan

Post-Trial

Post Trial Information

Study Withdrawal

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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