AEA RCT Registry

Trial Information

General Information

Title

Experimental Evaluation of a Flood Forecasting Tool

RCT ID

AEARCTR-0004947

Initial registration date

October 28, 2019

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

October 29, 2019, 9:59 AM EDT

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

Last updated

December 24, 2023, 8:27 AM EST

Last updated is the most recent time when changes to the trial's registration were published.

Locations

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Primary Investigator

Name

Rohini Pande

Affiliation

Yale University

Contact Primary Investigator

Other Primary Investigator(s)

PI Name

Maulik Jagnani

PI Affiliation

University of Colorado Denver

Contact Investigator

Additional Trial Information

Status

On going

Start date

2021-06-01

End date

2025-07-31

Keywords

Environment & Energy

Additional Keywords

JEL code(s)

Q50, Q54, Q56, Q58, O13, O10

Secondary IDs

Prior work

This trial does not extend or rely on any prior RCTs.

Abstract

Between 2011-20, floods caused over 45,000 deaths with most occurring in lower-income countries. Early warning systems (EWS) for floods can lower human and economic losses, and improve post-flood recovery. But, underdeveloped dissemination infrastructure in lower income countries typically limits their reach and adoption by the most vulnerable: poor less educated citizens in more remote areas. Cost-effective infrastructure investments are also hindered by a lack of rigorous evidence on how best to relay flood alerts at scale. In collaboration with the engineering team at Google’s flood forecasting initiative, we will conduct the first experimental evaluation of a flood EWS in India; it will pair cutting-edge forecasting and android-based alerting system with grassroots volunteers armed with android phones and trained in community outreach activities. We anticipate research insights on how to disseminate time-sensitive forecasts that encourage high-cost, higher-return avoidance behavior in response to environmental stressors induced by climate change.

External Link(s)

Registration Citation

Citation

Jagnani, Maulik and Rohini Pande. 2023. "Experimental Evaluation of a Flood Forecasting Tool." AEA RCT Registry. December 24. https://doi.org/10.1257/rct.4947-7.0

Sponsors & Partners

Experimental Details

Interventions

Intervention(s)

A randomly assigned group of panchayats (or local governing bodies) across twelve districts in the Bihar will be informed about flood alerts, once an alert was triggered by Google’s flood forecasting system. The treatment group includes panchayats where community-based volunteers will inform households about the alert via loudspeakers, door-to-door interactions, phone calls, and text messages; and, android smartphone users with location services enabled will receive an alert when an alert is triggered. The control group includes panchayats where android smartphone users with location services enabled will receive an alert when an alert is triggered but community-based volunteers will not be active.

Intervention Start Date

2021-07-15

Intervention End Date

2024-11-30

Primary Outcomes

Primary Outcomes (end points)

Ex-ante and ex-post adaptive behavior, ex-post recovery, post-flood socioeconomic indicators, and diffusion and source of flood alerts.

Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)

Secondary Outcomes (explanation)

Experimental Design

A randomly assigned group of panchayats (or local governing bodies) across twelve districts in the Bihar will be informed about flood alerts, once an alert was triggered by Google’s flood forecasting system. The treatment group includes panchayats where community-based volunteers will inform households about the alert via loudspeakers, door-to-door interactions, phone calls, and text messages; and, android smartphone users with location services enabled will receive an alert when an alert is triggered. The control group includes panchayats where android smartphone users with location services enabled will receive an alert when an alert is triggered but community-based volunteers will not be active.

Our sample includes 319 panchayats: 159 panchayats in the control group, 160 panchayats in the treatment group.

In November’22-January’23, we will administer a comprehensive survey across our sample to collect information on avoidance behavior, compensatory behavior, post-flood socioeconomic indicators (e.g., income, physical and mental morbidity, and mortality), and the diffusion and source of flood alerts. Our analysis will simply compare outcomes of interest between treatment and control panchayats.

Experimental Design Details

Not available

Randomization Method

Randomization done in office by a computer.

Randomization Unit

Unit of randomization: panchayat; panchayats are local governing bodies that typically include 2 to 5 villages.

Was the treatment clustered?

Yes

Experiment Characteristics

Sample size: planned number of clusters

319 panchayats

Sample size: planned number of observations

5000 households

Sample size (or number of clusters) by treatment arms

159 panchayats in the control group, 160 panchayats in the treatment group

Minimum detectable effect size for main outcomes (accounting for sample design and clustering)

Supporting Documents and Materials

IRB

Institutional Review Boards (IRBs)

IRB Name

Yale University - Human Research Protection Program

IRB Approval Date

2022-04-28

IRB Approval Number

N/A

Analysis Plan