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Field
Abstract
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Before
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.
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After
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.
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Field
Trial Start Date
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Before
August 01, 2019
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After
June 01, 2021
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Field
Trial End Date
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Before
July 31, 2021
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After
July 31, 2022
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Field
Last Published
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Before
July 20, 2020 10:24 AM
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After
June 30, 2021 02:05 PM
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Field
Intervention (Public)
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Before
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.
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After
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.
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Field
Intervention Start Date
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Before
August 01, 2019
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After
July 15, 2021
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Field
Intervention End Date
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Before
October 31, 2019
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After
November 30, 2021
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Field
Experimental Design (Public)
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Before
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.
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After
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’21-January’22, 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.
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Field
Planned Number of Clusters
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Before
181 Panchayats/476 villages
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After
319 panchayats
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Field
Planned Number of Observations
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Before
1500 households
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After
5000 households
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Field
Sample size (or number of clusters) by treatment arms
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Before
91 panchayats/232 villages in the control group, 90 panchayats/244 villages in the treatment group
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After
159 panchayats in the control group, 160 panchayats in the treatment group
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Field
Keyword(s)
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Before
Environment And Energy
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After
Environment And Energy
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