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Paying for urban services: utility bills, prepaid metering and spending patterns of the poor

Last registered on January 20, 2017

Pre-Trial

Trial Information

General Information

Title
Paying for urban services: utility bills, prepaid metering and spending patterns of the poor
RCT ID
AEARCTR-0001886
Initial registration date
January 20, 2017

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
January 20, 2017, 8:36 AM EST

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

Locations

Region

Primary Investigator

Affiliation
UC Santa Barbara

Other Primary Investigator(s)

PI Affiliation
Brown University
PI Affiliation
University of Cape Town and J-PAL

Additional Trial Information

Status
In development
Start date
2017-08-01
End date
2018-12-31
Secondary IDs
Abstract
Revenue recovery is a challenge for urban service providers in developing countries. Poor customers often struggle to pay monthly bills and providers face both high costs and political economy barriers to enforcing payment. Prepayment is an increasingly popular solution to this problem in the electricity and water sectors. In our study setting, low income households purchase prepaid electricity every 3 days, on average, and use 12 percent less electricity than when they are billed monthly. We will investigate the preferences and constraints that give rise to these spending and consumption patterns on prepaid metering and is organized around two core questions:
a) Is the reduction in consumption associated with prepaid metering a choice by the household, or is it driven by constraints such as liquidity constraints and higher transaction costs?
b) Do high frequency purchasing patterns indicate demand for self-control, cash-on-hand liquidity constraints, or demand for savings?
Our findings will both shed light on factors that contribute to current high rates of payment delinquency on monthly billing and will inform the design of interventions to improve revenue recovery for urban services while simultaneously meeting the needs of low income households.
External Link(s)

Registration Citation

Citation
Jack, Kelsey, Kathryn McDermott and Anja Sautmann. 2017. "Paying for urban services: utility bills, prepaid metering and spending patterns of the poor." AEA RCT Registry. January 20. https://doi.org/10.1257/rct.1886-1.0
Former Citation
Jack, Kelsey, Kathryn McDermott and Anja Sautmann. 2017. "Paying for urban services: utility bills, prepaid metering and spending patterns of the poor." AEA RCT Registry. January 20. https://www.socialscienceregistry.org/trials/1886/history/13268
Sponsors & Partners

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Experimental Details

Interventions

Intervention(s)
We will offer transfers of electricity credit to prepaid electricity customers. Specifically, we will offer households choices among bundles of free electricity transfers that vary the size and timing of the transfers. Piloting will determine the exact tradeoffs that we will vary across the choice sets, which will be designed to identify preferences over frequency, transfer size, and commitment, independent from potential liquidity constraints or differential (unobserved) transaction costs.

Intervention Start Date
2017-08-01
Intervention End Date
2018-07-31

Primary Outcomes

Primary Outcomes (end points)
Our analysis will include three main outcomes: transfer choices, electricity purchases and electricity consumption. Analyzing impacts on the latter two (purchases and consumption) relies on the first as an instrument.
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
Our variation will come from sets of choices over free electricity transfers, which will vary across customers. Variations in the choice sets, combined with the choices customers make, will reveal customer preferences. The randomized choice sets will also instrument for variation in the actual transfers received. Piloting is underway to finalize the design of the choice sets.
Experimental Design Details
Randomization Method
Randomization done by computer
Randomization Unit
Households
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
N/A
Sample size: planned number of observations
Up to 1250 households
Sample size (or number of clusters) by treatment arms
250 households control , up to 1000 treatment households
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
IRB

Institutional Review Boards (IRBs)

IRB Name
IRB Approval Date
IRB Approval Number
Analysis Plan

Analysis Plan Documents

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