Fostering Peer Learning Among Local Representatives

Last registered on February 13, 2023


Trial Information

General Information

Fostering Peer Learning Among Local Representatives
Initial registration date
February 12, 2023

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
February 13, 2023, 11:33 AM EST

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


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

Request Information

Primary Investigator

University of Maryland

Other Primary Investigator(s)

PI Affiliation
University of Pennsylvania
PI Affiliation
PI Affiliation
World Bank

Additional Trial Information

On going
Start date
End date
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
In most developing countries, local government officials are ultimately responsible for implementing development programs and delivering public services (Mookherjee 2015). It is therefore critical to understand how to improve the capacity and efficiency of local government. Decentralisation and other policies to broaden political representation (eg. reservations for marginalised groups) have brought new cohorts of leaders into the political system, shaping policy choices (Chattopadhyay and Duflo 2004). However, these new leaders, especially those from disadvantaged groups, may be unfamiliar with formal government processes and policies and may lack the informal networks required to navigate these processes.

Peer networks can play an important role in helping new leaders learn how to govern better. Indeed, in other contexts, informal networks have proved to be a key source of learning. Farmers’ decisions to adopt new agricultural technologies are heavily influenced by their peers’ choices (Foster and Rosenzweig 1996, Ben Yishay et al 2021). SME owners appear to learn effective management practices from peer firms (Cai and Szeidl, 2018). Peer effects are also important sources of information diffusion in education (Duflo et al 2011) and the workplace (Sandvik et al 2020).

In our setting, peer networks may be a natural mechanism through which information about good governance practices spread. This may enable successful institutional experiments to diffuse to other local governments, enhancing the benefits of decentralisation. Yet, despite their importance, we have almost no empirical evidence on politicians’ networks and how they affect governance and economic development.

In this project, we study how peer networks among local politicians affect the quality of governance and local economic development. Partnering with the Govt of Bihar, specifically the Departments of Panchayati Raj/Rural Development, we evaluate the impacts of peer groups for local politicians. Working with over 4000 local government officials – village-level elected representatives called “ward members” – our project aims to answer four questions:
1. What is the existing nature of peer networks among local politicians? Who are local politicians connected to and what information is exchanged through these networks?
2. How do peer networks among ward members affect public service delivery, implementation of government schemes and the overall quality of local governance?
3. Do peer networks facilitate the diffusion of “best practices” and increase the returns to policy experimentation?
4. What barriers prevent peer networks from forming organically?

We will also attempt to understand the mechanisms through which peer networks affect governance, and embed treatment variants to understand the role of two specific mechanisms – learning and coordination. We will also assess whether peer networks have different effects from a cheaper and easier-to-scale intervention.
External Link(s)

Registration Citation

Bamezai, Apurva et al. 2023. "Fostering Peer Learning Among Local Representatives." AEA RCT Registry. February 13.
Sponsors & Partners

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

Request Information
Experimental Details



We intend to evaluate how peer networks between local elected representatives affects development outcomes. The elected representatives we focus on are "ward members", extremely local officials who represent between 200 and 250 households. To this end, groups of ward members drawn from different Panchayats across the same district are being formed across the 8 districts of Bihar. Ward members are/will be put in groups of 10-12 and will have face-to-face interactions at regular interventions, alongside a phone component:
(i) An introductory meeting for group members in the district HQ
(ii) Follow-on in-person meetings at 8-monthly intervals
(iii) Monthly conference calls
(iv) Presence in WhatsApp groups
Intervention Start Date
Intervention End Date

Primary Outcomes

Primary Outcomes (end points)
(a) Peer networks and interactions
(b) Public services
(c) implementation of social protection programs
(d) Corruption
(e) Citizen assessment of governance quality
(f) management practices of ward members
(g) Knowledge/awareness of ward members

(Details to be provided in the PAP)
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
The sample will consist of over 4000 newly-elected ward members who took office in early 2022.

A two-tier randomisation was implemented where GPs were first assigned to treatment (approx. 600 GPs) and control (approx. 800 GPs). Then, within treated villages, 2 ward members were randomly sampled to participate in the peer networks.

Our endline evaluation survey will also focus on 2 other untreated ward members in treated GPs to understand spillovers of intervention.

This design and the associated measurement strategy will allow us to estimate spillovers and overall treatment effects that account for spillovers.
Experimental Design Details
Not available
Randomization Method
Randomization done in office by a computer.
Randomization Unit
Gram Panchayat (GP)
Was the treatment clustered?

Experiment Characteristics

Sample size: planned number of clusters
Sample size: planned number of observations
Sample size (or number of clusters) by treatment arms
600 Treated GPs and 1200 Treated wards & 1200 Spillover wards
800 Control GPs and 1600 Control Wards
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
0.1-0.2 SD

Institutional Review Boards (IRBs)

IRB Name
University of Maryland IRB
IRB Approval Date
IRB Approval Number