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Can Smart Technology Systems Improve Direct Benefit Transfer Performance and Increase Participation? Evidence from MGNREGA in India - Bihar extension
Last registered on October 22, 2020

Pre-Trial

Trial Information
General Information
Title
Can Smart Technology Systems Improve Direct Benefit Transfer Performance and Increase Participation? Evidence from MGNREGA in India - Bihar extension
RCT ID
AEARCTR-0005837
Initial registration date
May 12, 2020
Last updated
October 22, 2020 12:46 PM EDT
Location(s)
Region
Primary Investigator
Affiliation
University of Michigan
Other Primary Investigator(s)
PI Affiliation
Yale University
PI Affiliation
Yale University
Additional Trial Information
Status
On going
Start date
2019-07-01
End date
2021-06-01
Secondary IDs
Abstract
In this follow-up study to our initial experiment in the states of Jharkhand and Madyha Pradesh,
we investigate how a new internet- and mobile-based management and monitoring platform
aimed at improving the administration of a large government workfare scheme in India can facilitate
effective benefits delivery in the state of Bihar, an environment viewed as having relatively
low capacity even within India. Our immediate measure of interest is payment processing time
for program participants. We will also consider how the impacts of the platform are mediated
by previous locality-level performance.
External Link(s)
Registration Citation
Citation
Moore, Charity, Yusuf Neggers and Rohini Pande. 2020. "Can Smart Technology Systems Improve Direct Benefit Transfer Performance and Increase Participation? Evidence from MGNREGA in India - Bihar extension." AEA RCT Registry. October 22. https://doi.org/10.1257/rct.5837-1.1.
Experimental Details
Interventions
Intervention(s)
This randomized control trial (RCT), which takes place at scale in the state of Bihar, investigates how wage payment delays to workers in the government's large workfare program are affected by decreasing the costs of acquiring management-relevant information and monitoring for mid- and lower-level bureaucrats.
Intervention Start Date
2019-07-01
Intervention End Date
2020-08-31
Primary Outcomes
Primary Outcomes (end points)
Our primary outcome of interest is average time to payment–both overall across substeps under any officer purview and for the payment substeps under each officer type. We will also consider effects on variability of time to payment delivery, relying primarily on average absolute deviations.
Primary Outcomes (explanation)
Secondary Outcomes
Secondary Outcomes (end points)
We will additionally examine impacts in future periods on the total number of person-days requested and worked, the number of individuals worked, total expenditure, and - if we are able to obtain the necessary administrative data - household-level work patterns. To better understand the means by which PayDash affects timely payments, we will also examine measures of information and management practices, conditional on being able to field follow-up surveys with officials. We will additionally consider block and district officer transfers as a downstream outcome, and other staff management outcomes, such as issuance of show cause notices (used to reprimand officers for poor performance) if we can obtain the underlying data necessary to measure these.
Secondary Outcomes (explanation)
Experimental Design
Experimental Design
Access to PayDash is randomized at the district level; log-ins are user-specific so officers can only
log into the platform using their own credentials, and they view summary information on payment
delays for areas under their jurisdiction. Treatment arms are designed as follows:
1. Control: District and block-level MGNREGA administrators do not have access to PayDash
2. TD: PayDash provided to district-level MGNREGA administrators only
3. TB: PayDash provided to block-level MGNREGA administrators only
4. TDB: PayDash provided to both district and block-level MGNREGA administrators
Experimental Design Details
Not available
Randomization Method
The randomization of districts was completed in an office by a computer using STATA.
Randomization Unit
We randomly assign two cross-cutting treatments at the district level: provision of PayDash to (1) district officials and (2) block officials.
Was the treatment clustered?
Yes
Experiment Characteristics
Sample size: planned number of clusters
38 districts. The randomization strategy includes providing access to the platform to all lower block-level officials in randomly selected districts. In addition to the control arm, our study includes districts where only district-level officials have access to the platform (TD), districts where only lower block-level officials have access (TB), and districts where both district and block-level officials have access (TDB).
Sample size: planned number of observations
The 38 districts contain 534 blocks (subdistricts) and 8,406 gram panchayats (village clusters) - we plan to focus our analysis at the block- and panchayat-month levels over a 12+ month period.
Sample size (or number of clusters) by treatment arms
In Bihar, the four treatment categories (Control, TD, TB, TDB) were randomly assigned across
38 districts in approximately equal proportions (9 control, 9 TD, 10 TB, 10 TDB) at the district
level, stratifying by above/below the state-level median values for average monthly person days
worked and average monthly days to payment over the period April 2017 to March 2018.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
IRB
INSTITUTIONAL REVIEW BOARDS (IRBs)
IRB Name
Harvard University Committee on the Use of Human Subjects
IRB Approval Date
2018-06-22
IRB Approval Number
IRB16-0798
Analysis Plan

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