 The project consists of a set of five papers anchored by a field experiment in Bangalore, India. Three of the papers represent collaborations with Isha Ray (Associate Professor, Energy and Resources Group, U.C. Berkeley). This research has been funded by Berkeley's Development Impact Laboratory under USAID Cooperative Agreement AID-OAA-A-13-00002. Please do not hesitate to contact me if you would like to read one of these papers. PAPERS
“Flows, Leaks, and Blockages in Informational Interventions: Field Experimental Evidence from Bangalore's Water Sector” 2018. World Development.
106: 149-160.
(with Tanu Kumar and Isha Ray)
Many policies and programs based on informational interventions hinge upon the assumption that providing citizens with information can help improve the quality of public services, or help citizens cope with poor services. We present a causal framework that can be used to identify leaks and blockages in the information production and dissemination process in such programs. We conceptualize the ‘‘information pipeline” as a series of connected nodes, each of which constitutes a possible point of blockage. We apply the framework to a field-experimental evaluation of a program that provided households in Bangalore, India, with advance notification of intermittently provided piped water. Our study detected no impacts on household wait times for water or on how citizens viewed the state, but found that notifications reduced stress. Our framework reveals that, in our case, noncompliance among human intermediaries and asymmetric gender relations contributed in large part to these null-to-modest results. Diagnostic frameworks like this should be used more extensively in development research to better understand the mechanisms responsible for program success and failure, to identify subgroups that actu- ally received the intended treatment, and to identify potential leaks and blockages when replicating existing programs in new settings.
“Frontline Worker Compliance with Transparency Reforms: Barriers Posed by Family and Financial Responsibilities” (with Christopher Hyun and Isha
Ray) 2018. Governance. 31: 65-83. (Formerly "Why Street Level Bureaucrats Don't Comply: Insights from the Valvemen of Bangalore") Significant
development funding flows to informational interventions intended to improve
public services. Such “transparency fixes” often depend on the cooperation of
frontline workers who produce or disseminate information for citizens. This
article examines frontline worker compliance with a transparency intervention
in Bangalore’s water sector. Why did
compliance vary across neighborhoods, and why did workers exhibit modest rates
of compliance overall? Drawing on ethnographic observation and an original
dataset, this article finds that variation in workers’ family responsibilities
and financial circumstances largely explains variation in compliance with the
intervention. Furthermore, workers often prioritize longstanding
responsibilities over new tasks seen as add-ons, leading to modest rates of
compliance overall. Perceptions of
“core” jobs can be sticky—especially when reaffirmed through interactions with
citizens. This study represents one of the first multi-method companions to a
field experiment, and illustrates how the analysis of qualitative and
observational data can contribute to impact evaluation.
“Crowd-Sourced Data for Comparative Politics: Insights from Urban India.” Forthcoming. Studies in Comparative International Development. (with Anustubh Agnihotri and
Christopher Hyun) In much of the developing world, states fail to collect accurate information on social and
political phenomena such as protest activity, the extent and reach of public services, and
corruption. Crowd-sourced data—data sourced electronically from disparate individuals
or groups—now offers researchers new information that can potentially correct for biases
in existing data about such processes. In this paper, we highlight ways in which crowd-
sourced data can be used to cast new light on major problems in comparative politics and
show how important analytic pitfalls can be avoided. Descriptive inference using crowd-
sourced data can be improved by explicitly examining how data is contributed and
conducting ground truthing exercises. Assessments of the causal impact of crowd-
sourced data on political processes can be strengthened by randomizing exposure at the
cluster rather than individual level, or by measuring information spillovers. We illustrate
strategies for addressing inferential difficulties in urban India, through a study of the
collection of crowd-sourced information on water delivery. In this study, crowd-sourced
data on water allocation is used to revisit long-standing debates on distributive politics
and newer debates regarding the potential effects of informational interventions on
citizen political participation. “Distributive Politics in
Networked Systems: The Political Geography of Water Intermittency” (with Tanu
Kumar, Megan Otsuka, Francesc Pardo-Bosch, and Isha Ray) (in preparation)
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