Field
Trial Status
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Before
in_development
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After
completed
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Field
Abstract
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Before
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.
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After
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 generates new evidence on the impacts of prepaid meters on energy use and revenue recovery using a randomized phase-in of new prepaid customers. Researchers will explore how prepaid meters change the customer experience and the municipality’s revenue recovery.
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Field
Trial End Date
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Before
December 31, 2015
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After
September 30, 2016
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Field
Last Published
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Before
December 16, 2014 08:40 AM
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After
December 11, 2016 07:55 AM
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Field
Study Withdrawn
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Before
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After
No
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Field
Intervention Completion Date
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Before
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After
April 30, 2016
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Field
Data Collection Complete
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Before
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After
Yes
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Field
Final Sample Size: Number of Clusters (Unit of Randomization)
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Before
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After
27 clusters
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Field
Was attrition correlated with treatment status?
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Before
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After
No
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Field
Final Sample Size: Total Number of Observations
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Before
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After
4246
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Field
Final Sample Size (or Number of Clusters) by Treatment Arms
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Before
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After
Phase in design over 27 clusters
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Field
Data Collection Completion Date
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Before
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After
September 30, 2016
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Field
Intervention (Public)
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Before
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.
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After
The City of Cape Town switched approximately 4000 households from credit (post paid) meters to prepaid meters. These households have been identified based on property value. Household meter conversion was involuntary. The meter installation is not accompanied by any additional information about electricity tariffs or consumption.
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Field
Intervention End Date
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Before
April 30, 2015
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After
April 30, 2016
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Field
Experimental Design (Public)
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Before
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.
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After
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 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.
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Field
Randomization Method
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Before
The 13 randomization blocks are assigned a random order using a random number generator in Excel.
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After
The randomization blocks are assigned a random order using a random number generator in Excel.
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Field
Planned Number of Clusters
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Before
13
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After
27
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Field
Planned Number of Observations
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Before
2800
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After
4500
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Field
Did you obtain IRB approval for this study?
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Before
No
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After
Yes
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