Flood Risk and Differential Firm Investment: Evidence from Dakar, Senegal

Last registered on November 14, 2024

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Pre-Trial

Trial Information

General Information

Title

Flood Risk and Differential Firm Investment: Evidence from Dakar, Senegal

RCT ID

AEARCTR-0013840

Initial registration date

June 18, 2024

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

June 24, 2024, 2:10 PM EDT

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

Last updated

November 14, 2024, 2:47 PM EST

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

Locations

Country

Senegal

Region

Dakar

Primary Investigator

Name

Molly Doruska

Affiliation

Cornell University

Contact Primary Investigator

Other Primary Investigator(s)

Additional Trial Information

Status

In development

Start date

2024-07-01

End date

2025-12-31

Keywords

Environment & Energy, Firms & Productivity

Additional Keywords

JEL code(s)

Secondary IDs

Prior work

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

Abstract

In many urban communities, insufficient drainage, and minimal urban planning means that seasonal rainfall brings with it the risk of regular flooding of homes and businesses. Persistent flooding poses a multitude of challenges for businesses in these areas as it can destroy inventory, limit movement of workers and customers, and overall decrease firm performance. Changing rainfall patterns due to anthropogenic climate change heightens the need to better understand the currently underexplored impacts of seasonal flooding, particularly on small firms. My work seeks to understand the impact of seasonal flooding on small firm performance and investment in urban Dakar, Senegal by collecting detailed firm-level data on investment, inventory, worker behavior, consumer behavior and flood risk perception, community data on infrastructure and drainage characteristics. I will then run a randomized experiment to understand the impacts of flood adaptation strategies and to identify local spillover effects of flood adaptation. With parameters estimated from the data and the experiment, I will develop a model of investment by firms to explore barriers to adaptation and defensive investment. I will use this model to highlight risks of anthropogenic climate change and potential policy interventions to decrease the impact of seasonal flooding.

External Link(s)

Registration Citation

Citation

Doruska, Molly. 2024. "Flood Risk and Differential Firm Investment: Evidence from Dakar, Senegal." AEA RCT Registry. November 14. https://doi.org/10.1257/rct.13840-6.0

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Experimental Details

Interventions

Intervention(s)

Following the baseline cross-sectional data collection described above, I will conduct a clustered randomized experiment designed to reduce barriers to adaptation and understand the local spillover effects of firm adaptation strategies. I will randomly assign groups of firms to the control or one of two treatment arms where firms are offered their choice of vouchers for different flood adaptation strategies. I will use this experimental design to understand the impacts of these flood adaptation strategies, to explore the cost barrier to flood adaptation measures, and to identify the local spillover effects of different flood adaptation strategies.

Intervention Start Date

2024-07-08

Intervention End Date

2024-11-30

Primary Outcomes

Primary Outcomes (end points)

Choice of treatment technology, investment in flood control technology

Primary Outcomes (explanation)

Investmetn in flood control technology will be measured by number of new or improved shelves, wooden pallets, or barriers added to the firm since July 2024

Secondary Outcomes

Secondary Outcomes (end points)

Flood experience during the 2024 rainy season, firm inventory and stockouts, firm operating hours, customer traffic, worker attendance, firm monthly revenue and profits.

Secondary Outcomes (explanation)

Experimental Design

Neighborhoods of firms will be allocated to one of two treatment arms or the control. In the first treatment arm, the individual strategy treatment arm, firm owners will be given a choice of vouchers for two bags of cement or two wooden pallets. I will finalize the list of strategies and the amounts of each item following a short piloting exercise with local firms. I selected two flood mitigation strategies where one strategy only helps an individual firm while at least one other strategy impacts firms in the local neighborhood. I give firms the choice of strategy because I anticipate sufficient heterogeneity across firms. Some firms may not have space for additional shelving units while other firms may not need or want any sort of barrier outside their store. The menu of options allows firms to choose the strategy that best fits their location and needs.

In the other treatment arms, I will gather all firms in the neighborhood cluster, and they will decide as a group which vouchers they would like to receive. Each firm will still receive the same number of vouchers; however, the choice is at the neighborhood level. While some strategies benefit the firm individually (wooden pallets) other strategies (barriers) may benefit or hurt firms across the group differently. The variation in choice at the individual or neighborhood level will allow me to identify the impact of local spillovers.

Experimental Design Details

Not available

Randomization Method

Stratified randomization done in office by a computer

Randomization Unit

Neighborhood

Was the treatment clustered?

Yes

Experiment Characteristics

Sample size: planned number of clusters

150 neighborhoods

Sample size: planned number of observations

750 firms

Sample size (or number of clusters) by treatment arms

250 control, 250 individual treatment, 250 neighborhood treatment

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

We will recruit 750 firms for the study, 250 firms for each treatment arm. Assuming a standard deviation of 0.3, 0.05 significance level, and 85% power, I estimate a detectable difference of 15% in flood investment strategies based on flood risk with a sample of 44 neighborhoods of five firms or 220 firms in each treatment arm.

Supporting Documents and Materials

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