About

Jennifer Bussell is an associate professor of Public Policy and Political Science at the Goldman School of Public Policy, University of California, Berkeley. She is a political scientist with a focus on comparative politics and the political economy of development and governance, primarily in South Asia and Africa. Her research explores the effects of formal and informal institutions—such as corruption, coalition politics, and federalism—on policy outcomes. Her scholarly work includes the authoring of the book 'Clients and Constituents: Political Responsiveness in Patronage Democracies,' which investigates constituency service by high-level elected officials in India and other countries, utilizing elite and citizen surveys, interviews, qualitative shadowing, and experiments to analyze citizen-state relations and public service delivery. Her first book, 'Corruption and Reform in India: Public Services in the Digital Age,' examines how corrupt practices influence government adoption of information technology across Indian states, based on fieldwork in India, South Africa, and Brazil. Bussell's research also covers the politics of disaster preparedness policies in developing countries. Her work has been published in various academic journals including Political Analysis, Governance, Comparative Political Studies, and Perspectives on Politics. Prior to her current position, she taught at the LBJ School of Public Affairs at the University of Texas, Austin. She holds a Ph.D. in political science from the University of California, Berkeley.

Research topics

• Sociology
• Political Science
• Computer Science
• Artificial Intelligence
• Social Science
• Positive economics
• Epistemology
• Data science
• Geography
• Mathematics
• Law
• Development economics
• Political economy
• Statistics
• Cartography
• Economics

Selected publications

• Shocks and Politics

  Cambridge University Press eBooks · 2025-02-06 · 4 citations

  book1st authorCorresponding

  When will government elites prepare for natural hazards? Existing research posits that governments will respond to disasters, but rarely have incentives to prepare for them. This Element argues that disaster preparedness can, and does, occur in the context of both motivated ruling elites and a capable state. Ruling elites can be mobilized to lead preparedness efforts when there is a risk that past exposure to hazards will lead to political instability in the face of a future hazard. Where elites anticipate a threat to their rule in the face of a future hazard, due to substantial past exposure and significant opposition strength, they will be motivated to engage in disaster preparedness. The quality and character of these efforts subsequently depend on the government's capacity to coordinate the design and implementation of preparedness plans. The Element tests this argument using a medium-N, country case study approach, drawing on evidence from ten countries in Africa and three in South Asia, as well as subnational analysis in India.

  PublisherDOI

• Rethinking the Study of Electoral Politics in the Developing World: Reflections on the Indian Case

  Perspectives on Politics · 2021 · 29 citations

  • Political Science
  • Sociology
  • Political Science

  In the study of electoral politics and political behavior in the developing world, India is often considered to be an exemplar of the centrality of contingency in distributive politics, the role of ethnicity in shaping political behavior, and the organizational weakness of political parties. Whereas these axioms have some empirical basis, the massive changes in political practices, the vast variation in political patterns, and the burgeoning literature on subnational dynamics in India mean that such generalizations are not tenable. In this article, we consider research on India that compels us to rethink the contention that India neatly fits the prevailing wisdom in the comparative politics literature. Our objective is to elucidate how the many nuanced insights about Indian politics can improve our understanding of electoral behavior both across and within other countries, allowing us to question core assumptions in theories of comparative politics.

  PublisherOA PDFDOI

• Data for Haqdarshak: Leveraging Technology and Entrepreneurship to Increase Access to Welfare Programs

  Harvard Dataverse · 2021-08-10

  datasetOpen access

  This package contains replication data for the project "Haqdarshak: Leveraging Technology and Entrepreneurship to Increase Access to Welfare Programs.” The package contains two main sets of data: i) de-identified raw data files for baseline, intervention and endline for the pilot sample (646 households), conducted in 10 Indian villages in Dausa district, Rajasthan, from December 2017 to April 2019; ii) the baseline data collected for the main experimental sample (4808 households), which was launched in July 2019 across 94 villages in the district of Sikar, Jhunjhunu and Bhilwara, and completed in February 2020. The main intervention and endline were put on pause because of COVID-19, so there is only baseline data for the main experiment. The code, produced in Stata, contains both cleaning and analysis code, which generate intermediate data and latex tables, respectively. For further details on the data or code, please see the readme file. Finally, the package also contains the survey instruments used in data collection as well as a narrative report from the project that provides analysis on the pilot and baseline data.

  PublisherDOI

• Shadowing as a Tool for Studying Political Elites

  Political Analysis · 2020 · 18 citations

  1st authorCorresponding
  • Computer Science
  • Sociology
  • Computer Science

  This article offers a description and discussion of “shadowing” as a data collection and analytic tool, highlighting potential research opportunities related to the direct observation of individuals—principally political elites—in their normal daily routine for an extended period of time, often between one day and one week. In contrast with large-scale data collection methods, including surveys, shadowing enables researchers to develop detailed observations of political behavior that are not limited by the availability of administrative data or the constraints of a questionnaire or an interview guide. Unlike more in-depth qualitative methods, such as ethnography, shadowing is scalable in a manner that allows for larger sample sizes and the potential for medium-N inference. I provide a detailed account of how to design and conduct a shadowing study, including sampling strategies, techniques for coding shadowing data, and processes for drawing inferences about the behavior of shadowed subjects, drawing on examples from a completed shadowing-based study. I also discuss ways to mitigate selection and observer biases, presenting results that suggest these can be no more pronounced when shadowing political elites than in other forms of observational research.

  PublisherOA PDFDOI

• AppendixTable17.txt

  Harvard Dataverse · 2020-01-01

  datasetOpen access1st authorCorresponding

  :unav

  PublisherDOI

• AppendixTable9.txt

  Harvard Dataverse · 2020-01-01

  datasetOpen access1st authorCorresponding

  :unav

  PublisherDOI

• AppendixTable7.txt

  Harvard Dataverse · 2020-01-01

  datasetOpen access1st authorCorresponding

  :unav

  PublisherDOI

• AppendixTable6.txt

  Harvard Dataverse · 2020-01-01

  datasetOpen access1st authorCorresponding

  :unav

  PublisherDOI

• Read_Me_for_Shadowing_Political_Elites.Rmd

  Harvard Dataverse · 2020-01-01

  datasetOpen access1st authorCorresponding

  Readme for Shadowing Political Elites replication materials

  PublisherDOI

• AppendixTable19.txt

  Harvard Dataverse · 2020-01-01

  datasetOpen access1st authorCorresponding

  :unav

  PublisherDOI

Frequent coauthors

• Neelanjan Sircar

  Centre for Policy Research

  2 shared

• Simon Chauchard

  2 shared

• Adam Colligan

  1 shared

• Rahul Verma

  University of Delhi

  1 shared

• Lisa Björkman

  University of Louisville

  1 shared

• Francesca R. Jensenius

  1 shared

• Sarika Gupta

  1 shared

• Gareth Nellis

  1 shared