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.

Locations

Country

India

Region

Primary Investigator

Name

Rohini Pande

Affiliation

Yale University

Contact Primary Investigator

Other Primary Investigator(s)

PI Name

Maulik Jagnani

PI Affiliation

Yale University

Contact Investigator

Additional Trial Information

Status

On going

Start date

2019-08-01

End date

2020-07-31

Keywords

Environment & Energy

Additional Keywords

JEL code(s)

Q50, Q54, Q56, Q58, O13, O10

Secondary IDs

Abstract

Floods caused over 50,000 deaths in the last decade—including immediate fatalities and longer-run fatalities due to disease outbreak. 97.5% of this mortality occurred in lower-income nations. Early flood warning systems can play an important role in lowering immediate fatalities, waterborne disease spread, and mental illness in lower-income, flood-prone countries like India and Bangladesh and facilitate climate change adaptation. Currently, we lack causal assessments of early warning systems because such evaluations require pairing high-frequency geolocation data to measure evacuation behavior or movement in response to disaster alerts with extensive household surveys in disrupted communities to capture short- and long-run effects on socioeconomic indicators. In collaboration with the engineering team at Google’s flood forecasting initiative, we are conducting a multi-pronged pilot evaluation of their flood alert system in the Ganges-Brahmaputra river basin with field work in Winter 2019. The pilot study has two objectives: (a) to examine the causal effect of flood alerts on evacuation behavior, and consequently, physical and mental morbidity, and (b) to evaluate if existing local government structures can be leveraged to increase the reach and impact of disaster alerts.

External Link(s)

Registration Citation

Citation

Jagnani, Maulik and Rohini Pande. 2019. "Experimental Evaluation of a Flood Forecasting Tool." AEA RCT Registry. October 29. https://doi.org/10.1257/rct.4947-1.0

Sponsors & Partners

Experimental Details

Interventions

Intervention(s)

A randomly assigned group of panchayat mukhiyas (or community leaders) across six districts in the Bihar were informed about flood alerts, once an alert was triggered by Google’s flood forecasting system, via WhatsApp message and SMS. The treatment group includes panchayats (or local governing bodies) where panchayat mukhiyas were informed about the alert, via WhatsApp message and SMS, and android smartphone users with location services enabled received an alert if and when an alert is triggered. The control group includes panchayats where android smartphone users with location services enabled received an alert when an alert was triggered but panchayat mukhiyas were not informed about the alert, via WhatsApp message or SMS.

Intervention Start Date

2019-08-01

Intervention End Date

2019-10-31

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 panchayat mukhiyas (or community leaders) across six districts in the Bihar were informed about flood alerts, once an alert was triggered by Google’s flood forecasting system, via WhatsApp message and SMS. The treatment group includes panchayats (or local governing bodies) where panchayat mukhiyas were informed about the alert, via WhatsApp message and SMS, and android smartphone users with location services enabled received an alert if and when an alert is triggered. The control group includes panchayats where android smartphone users with location services enabled received an alert when an alert was triggered but panchayat mukhiyas were not informed about the alert, via WhatsApp message or SMS.

Our sample includes 181 panchayats/476 villages: 91 panchayats/232 villages in the control group, 90 panchayats/244 villages in the treatment group. According to satellite images, of these, 139 villages were flooded. Google’s flood forecasting system sent flood alerts to location-services-enabled android smartphones in 129 of these villages with a recall rate of over 90%.

In December’19-January’20, we will administer a comprehensive survey in Bihar. We will survey 1500 households in flooded treatment (65) and control (74) villages in Bihar. The survey instrument will collect information on ex-ante and ex-post adaptive behavior, ex-post recovery, post-flood socioeconomic indicators, and diffusion and source of flood alerts. In our analysis, we will compare diffusion of flood alerts as well as the networks through which said alerts were diffused between treatment and control villages. In addition, we will compare ex-ante and ex-post adaptive responses, ex-post recovery, and post-flood physical and mental morbidity between treatment and control villages.

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

181 Panchayats/476 villages

Sample size: planned number of observations

1500 households

Sample size (or number of clusters) by treatment arms

91 panchayats/232 villages in the control group, 90 panchayats/244 villages 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

IRB Approval Date

IRB Approval Number

Analysis Plan