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Trial Status in_development completed
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. 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.
Trial End Date December 31, 2015 September 30, 2016
Last Published December 16, 2014 08:40 AM December 11, 2016 07:55 AM
Study Withdrawn No
Intervention Completion Date April 30, 2016
Data Collection Complete Yes
Final Sample Size: Number of Clusters (Unit of Randomization) 27 clusters
Was attrition correlated with treatment status? No
Final Sample Size: Total Number of Observations 4246
Final Sample Size (or Number of Clusters) by Treatment Arms Phase in design over 27 clusters
Data Collection Completion Date September 30, 2016
Intervention (Public) 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. 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.
Intervention End Date April 30, 2015 April 30, 2016
Experimental Design (Public) 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. 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.
Randomization Method The 13 randomization blocks are assigned a random order using a random number generator in Excel. The randomization blocks are assigned a random order using a random number generator in Excel.
Planned Number of Clusters 13 27
Planned Number of Observations 2800 4500
Did you obtain IRB approval for this study? No Yes
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Irbs

Field Before After
IRB Name Tufts University
IRB Approval Date December 18, 2014
IRB Approval Number 1408017
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Field Before After
IRB Name Massachusetts Institute of Technology
IRB Approval Date October 07, 2014
IRB Approval Number 1409006598
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Field Before After
IRB Name University of Cape Town
IRB Approval Date August 14, 2014
IRB Approval Number NA
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Papers

Field Before After
Paper Abstract The standard approach to recovering the cost of electricity provision is to bill customers monthly for past consumption. If unable to pay, customers face disconnection, the utility loses revenue, and the service provision model is undermined. A possible solution to this problem is prepaid metering, in which customers buy electricity upfront and use it until the prepaid amount is consumed. We use data from Cape Town, South Africa to examine the effects of prepaid electricity metering on residential consumption and returns to the electric utility. Over 4,000 customers on monthly billing were involuntarily assigned to receive a prepaid electricity meter, with exogenous variation in the timing of the meter replacement. Electricity use falls by about 13 percent as a result of the switch, a decrease that persists for the following year. This creates a tradeoff for the utility: revenue from consumption falls but more of it is recovered on time and at a lower cost. The benefits to the electric utility outweigh the costs, on average, though results are very heterogeneous. Poorer customers and those with a history of delinquent payment behavior show the greatest improvement in profitability when switched to a prepaid meter. These findings point to an important role for metering technologies in expanding energy access for the poor.
Paper Citation Jack, B.K. and G. Smith, (2016) "CHARGING AHEAD: PREPAID ELECTRICITY METERING IN SOUTH AFRICA," NBER Working Paper w22895.
Paper URL http://www.nber.org/papers/w22895
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