Back to History Current Version

Can Smart Technology Systems Improve Direct Benefit Transfer Performance and Increase Participation? Evidence from MGNREGA in India

Last registered on November 07, 2016
View Trial History

Pre-Trial

Trial Information

General Information

Title
Can Smart Technology Systems Improve Direct Benefit Transfer Performance and Increase Participation? Evidence from MGNREGA in India
RCT ID
AEARCTR-0001292
Initial registration date
November 07, 2016

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
November 07, 2016, 3:43 PM EST

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

Locations

Country
India
Region
North-central

Primary Investigator

Name
Rohini Pande
Affiliation
Yale University
Contact Primary Investigator

Other Primary Investigator(s)

PI Name
Yusuf Neggers
PI Affiliation
Brown University
Contact Investigator
PI Name
Eric Dodge
PI Affiliation
Harvard University
Contact Investigator
PI Name
Charity Troyer Moore
PI Affiliation
Harvard University - Evidence for Policy Design
Contact Investigator

Additional Trial Information

Status
In development
Start date
2016-11-15
End date
2018-05-31
Keywords
Finance & Microfinance, Governance
Additional Keywords
Technology for development, administrative capacity building, social programs
JEL code(s)
O39
Secondary IDs
Abstract
The implementation of social protection programs remains a challenge in developing countries, often to the particular detriment of the most vulnerable intended beneficiaries. We will investigate the potential of a new internet- and mobile-based management and monitoring platform, developed in direct collaboration with the Indian Ministry of Rural Development, to improve the administration of a large government welfare scheme. A randomized control trial across multiple states will be conducted in which we provide the platform to different levels of the bureaucratic hierarchy responsible for program administration. The study will determine the extent to which heightening officials' performance incentives versus lowering their costs of information acquisition is effective, and whether complementarities between the two exist, in reducing payment delays and subsequently improving program uptake. We will additionally examine how impacts are mediated by bureaucrats’ own personality traits.
External Link(s)

Registration Citation

Citation
Dodge, Eric et al. 2016. "Can Smart Technology Systems Improve Direct Benefit Transfer Performance and Increase Participation? Evidence from MGNREGA in India." AEA RCT Registry. November 07. https://doi.org/10.1257/rct.1292-1.0
Former Citation
Dodge, Eric et al. 2016. "Can Smart Technology Systems Improve Direct Benefit Transfer Performance and Increase Participation? Evidence from MGNREGA in India." AEA RCT Registry. November 07. https://www.socialscienceregistry.org/trials/1292/history/11676
Sponsors & Partners

There is information in this trial unavailable to the public. Use the button below to request access.

Request Information
Experimental Details

Interventions

Intervention(s)
Intervention Start Date
2016-11-15
Intervention End Date
2018-05-31

Primary Outcomes

Primary Outcomes (end points)
Our main outcomes of interest are average days taken to pay wages, both overall and by payment sub-step, and MGNREGA activity (expenditure, participants, days worked).
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
We will evaluate PayDash using an RCT in the states of Chhattisgarh, Jharkhand, and Madhya Pradesh. The three study states together are composed of 98 districts and 704 blocks. We conducted a pilot roll-out in one district in each of our study states in summer 2016. The full-scale intervention will be implemented across the set of 95 non-pilot districts beginning in fall 2016.Our evaluation design has four district-level treatment arms (block PayDash; district PayDash; district+block PayDash; and control). Approximately 23-24 districts, covering roughly 171 blocks, will be randomly assigned to each treatment arm. Within each state, random assignment of districts into arms is stratified on above/below median: average days to payment across transactions and total transactions over the previous year. We will conduct heterogeneity analysis of treatment impacts by officer-specific traits including intrinsic motivation.
Experimental Design Details
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
98 districts
Sample size: planned number of observations
Approximately 8,400 block-months.
Sample size (or number of clusters) by treatment arms
704 blocks: approximately 176 blocks for each of 4 treatment arms (Block PayDash, District PayDash, District+Block Paydash, and Control)
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
Supporting Documents and Materials
IRB

Institutional Review Boards (IRBs)

IRB Name
Harvard University Committee on the Use of Human Subjects
IRB Approval Date
2016-06-10
IRB Approval Number
IRB16-0798
Analysis Plan

Post-Trial

Post Trial Information

Study Withdrawal

There is information in this trial unavailable to the public. Use the button below to request access.

Request Information

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