Arun Chandrasekhar

Arun Chandrasekhar is an associate professor in the economics department at Stanford. He works on development economics and studies the role that social networks play in developing countries. He is particularly interested in how the economics of networks can help us understand information aggregation failures and the breakdown of cooperation in the developing world. His research approach is methodologically diverse, and includes novel data collection, field experiments, and observational data analysis. 

Professor Chandrasekhar holds a BA from Columbia University and a PhD in economics from MIT.

King Center Supported Research

2023 - 2024 Academic Year | Global Development Research Funding

Enhancing ASHAS' Role in Improving Community Healthcare Outcomes in India 

India has over one million community health workers (CHWs) known as Accredited Social Health Activists (ASHAs) and Primary Health Centre Officers (PHCOs) who operate in villages across the country and provide the main point of contact that rural Indians have with the healthcare system. However, CHWs spend 2-3 hours every day completing data entry tasks that reduce the time they spend with patients and cause months-long delays in receiving payment for their work. In this project we experimentally provide technological and data entry assistance to CHWs to study the effects of reducing administrative burdens in rural healthcare settings. We focus on three main outcomes: amount of work completed across different healthcare tasks; provision of care across different socioeconomic groups; and worker job satisfaction. We will estimate which tasks CHWs are forgoing on the margin due to constraints on their time, and whether those tasks have disparate benefits across villagers. Our results will inform policymakers of the potential benefits of reducing administrative frictions in healthcare systems in developing countries.

2021 - 2022 Academic Year | Global Development Research Funding

Identifying Effective Strategies to Increase COVID Vaccine Takeup in India

Chandrasekhar will study how to effectively increase Covid-19 vaccination in India using insights from social learning, network science, and reinforcement learning. To do so, Chandrasekhar will address the following questions. First, to what extent do frictions hinder future take-up? Second, can hand-holding help to increase take-up? Third, how do we locate people who need healthcare services and would be open to taking up those services? Can we use community information and insights about sampling on a network to find persuadable individuals and use techniques from artificial intelligence (reinforcement learning) to refine policy selection?

2018 - 2019 Academic Year | Junior Faculty Research Grant

Affirmative Action, Attitudes and Social Networks: Evidence from Caste-Based Reservation in India

In many settings, affirmative action policies have been adopted as a way to compensate for unequal access to resources between historically disenfranchised and privileged groups. However, as individuals do not exist in vacuums, changes in the distribution of resources towards members of different groups can have far-reaching network effects. This research examines the role of affirmative action-type policies in shaping social structure in the context of Indian political reservations for historically disadvantaged caste groups in the state of Bihar, India. The project involves many components, including a complete census, a beliefs and resources survey, and two experiments. The interpretation of these results builds upon existing observations of social attitudes and relationships between scheduled castes in India.