The Effect of Smart Metering on Revenue Collection, Electricity Access, and Supply in Jammu and Kashmir

Last registered on July 29, 2022

Pre-Trial

Trial Information

General Information

Title
The Effect of Smart Metering on Revenue Collection, Electricity Access, and Supply in Jammu and Kashmir
RCT ID
AEARCTR-0009708
Initial registration date
July 28, 2022

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
July 29, 2022, 5:14 PM EDT

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

Locations

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Primary Investigator

Affiliation
University of Chicago

Other Primary Investigator(s)

PI Affiliation
Yale University
PI Affiliation
University of Chicago
PI Affiliation
London School of Economics and Political Science

Additional Trial Information

Status
In development
Start date
2022-08-01
End date
2024-07-31
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
Almost 800 million people in developing countries are not connected to the electricity grid. Those with power are often subject to unreliable supply and frequent outages. One reason for this is that utilities are frequently unable to reliably bill consumers or collect on payments, forcing them to limit power purchases and ration supply to stave off bankruptcy. This project will evaluate the use of new technology (smart meters) in India to break this cycle of low-payments and low-quality through a large-scale randomized control trial. India recently launched arguably the largest roll-out of new metering in the developing world, with a target of installing about 250 million new meters by 2025. Unfortunately, little evidence exists on the best way to use these meters and specifically on the impacts of different features on outcomes such as payment rates, supply quality, or consumer satisfaction.

In partnership with the state of Jammu and Kashmir, researchers will partner with the government utility to carry out an impact evaluation of different metering features that are currently being rolled out in India, including remote disconnections, online monitoring, and pre-paid metering. To the extent that smart meters allow utilities to reduce arrears especially those accumulated by large consumers, they are better able to improve supply to all consumers. Additionally, the use of smart meters allows for better fault identification and improved monitoring of supply quality, potentially allowing for a virtuous cycle of better quality and more reliable payments. Rigorously quantifying these impacts will provide governments with guidance on how best to deploy this new technology. The project will generate some of the first evidence of large-scale metering impacts in a high theft, low-income setting with a widespread norm of incomplete payments.
External Link(s)

Registration Citation

Citation
Burgess, Robin et al. 2022. "The Effect of Smart Metering on Revenue Collection, Electricity Access, and Supply in Jammu and Kashmir." AEA RCT Registry. July 29. https://doi.org/10.1257/rct.9708
Experimental Details

Interventions

Intervention(s)
Intervention Start Date
2022-08-01
Intervention End Date
2024-07-31

Primary Outcomes

Primary Outcomes (end points)
Consumption of a domestic consumer, payment rates, billing efficiency, energy injection, perception of theft, feeder/DT losses
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
The experiment aims to test the efficacy of specific features of smart meters in increasing billing accuracy, revenue and energy accounting for the utilities, through a staggered roll out of features by the utility like remote disconnections, remote billing and prepaid smart metering.
Experimental Design Details
Not available
Randomization Method
Randomization will be done through STATA (a statistical software) on a computer
Randomization Unit
The randomization unit will be distribution transformers (DTs).
Was the treatment clustered?
Yes

Experiment Characteristics

Sample size: planned number of clusters
360 DTs
Sample size: planned number of observations
40,000 domestic consumer households
Sample size (or number of clusters) by treatment arms
240 DTs in treatment, 120 DTs in control
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
IRB

Institutional Review Boards (IRBs)

IRB Name
Institute for Financial Management and Research
IRB Approval Date
2022-03-07
IRB Approval Number
IRB00007107