Insuring Against Extreme Heat: Experiential Learning and the Demand for Heat Insurance

Last registered on February 04, 2026

Pre-Trial

Trial Information

General Information

Title
Insuring Against Extreme Heat: Experiential Learning and the Demand for Heat Insurance
RCT ID
AEARCTR-0017810
Initial registration date
February 01, 2026

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
February 04, 2026, 10:07 AM EST

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

Locations

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Primary Investigator

Affiliation
London School of Economics

Other Primary Investigator(s)

PI Affiliation
London School of Economics
PI Affiliation
London School of Economics
PI Affiliation
World Bank

Additional Trial Information

Status
In development
Start date
2026-02-02
End date
2026-10-31
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
This randomised study evaluates demand for parametric heat insurance among informal women workers in India. The insurance product, designed by the Self Employed Women's Association (SEWA), provides automatic payments to informal workers when district-level temperatures exceed 43°C for two consecutive days.
We test whether experiential learning (via an "insurance game") increases willingness to pay for this novel product. Participants are randomly assigned at the individual level to either a control group, who receive standardised verbal information about the product, or a treatment group that additionally plays an interactive game simulating two years of weather during the month of May (typically the hottest month of the year). In the game, participants draw random weather cards and work with the enumerator to calculate payouts under these randomly drawn scenarios. We elicit willingness to pay using an open-ended question followed by a bidding ladder. We consider the measure incentive compatible because respondents are told they will be contacted by SEWA administration to collect the amount they are willing to pay. In addition to collecting willingness-to-pay in the lab during February 2026, we will also observe real sign-ups in SEWA's administrative data during the 2026 hot season.
We also seek to understand which factors most influence demand. To test the role of product characteristics, we implement a discrete choice module where participants choose between alternative insurance designs varying in price and product attributes. To examine other demand factors - risk tolerance, climate beliefs, financial experience and heat exposure - and estimate heterogeneous treatment effects, we collect extensive baseline data.
External Link(s)

Registration Citation

Citation
Jalal, Amen et al. 2026. "Insuring Against Extreme Heat: Experiential Learning and the Demand for Heat Insurance." AEA RCT Registry. February 04. https://doi.org/10.1257/rct.17810-1.0
Experimental Details

Interventions

Intervention(s)
Individual-level randomised experiment with approximately 1,000 SEWA members in Anand district, Gujarat, stratified by prior heat insurance purchase.
Participants are randomly assigned to either: (1) Control group: receives standardised verbal information about SEWA's heat insurance product; or (2) Treatment group: receives the same information plus plays an interactive insurance game simulating two years of weather outcomes.
All participants complete willingness-to-pay elicitation and a discrete choice module. We also observe real-world sign-ups in SEWA's administrative data.
Intervention Start Date
2026-02-02
Intervention End Date
2026-03-02

Primary Outcomes

Primary Outcomes (end points)
Our primary outcomes are:
Real-world sign-up: Binary indicator for whether the SEWA member signed up for heat insurance in 2026.
Stated maximum willingness to pay, elicited via open-ended question and bidding ladder, with participants given the opportunity to revise after being reminded of the stakes, i.e., that their response will be passed to SEWA's grassroots leader who will contact them to collect payment.
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
1. Family 1:
a. Initial stated willingness to pay (INR), before opportunity to revise
b. Payout beliefs
(i). Expected total payout if purchases insurance
(ii). Subjective probability of receiving a payout
c. Heat insurance product comprehension (number of correct responses to comprehension check questions)
d. Concern about heat impacts on earnings this year (0-10)
2. Marginal willingness to pay for product attributes (from discrete choice experiment)

We will construct an Anderson (2008) index of outcomes in Family 1. Additionally, following Benjamini, Krieger and Yekutieli (2006), we will use false discovery rate corrections to account for multiple hypothesis testing within Family 1.
Secondary Outcomes (explanation)
Beyond overall demand, we examine whether experiential learning affects participants' understanding of and preferences over insurance.
We measure initial WTP (before the revision opportunity) to check whether the game anchors participants' valuations despite not mentioning the true premium.
We measure expected payout and subjective probability of receiving a payout to test whether the game makes participants more/less optimistic about how much they would receive.
We measure comprehension to test whether experiential learning helps participants better understand the insurance product.
We measure concern about heat impacts on earnings to test whether the game increases salience of heat risk.
Finally, we estimate implicit valuations of product attributes using conditional logit and linear probability models on the discrete choice data, to test whether the intervention shifts how participants think about the decision - for example, by changing which product features they prioritise. We will cluster errors at the respondent level for these estimates.

Experimental Design

Experimental Design
Individual-level randomised experiment with approximately 1,000 SEWA members in Anand district, Gujarat, stratified by prior heat insurance purchase.
Participants are randomly assigned to either: (1) Control group: receives standardised verbal information about SEWA's heat insurance product; or (2) Treatment group: receives the same information plus plays an interactive insurance game simulating two years of weather outcomes.
All participants complete willingness-to-pay elicitation and a discrete choice module. We also observe real-world sign-ups in SEWA's administrative data.
Experimental Design Details
Not available
Randomization Method
Randomisation of individuals to treatment and control group done on Survey CTO
Randomization Unit
Individual
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
N/A
Sample size: planned number of observations
1,000 SEWA members (survey sample) + all available administrative records.
Sample size (or number of clusters) by treatment arms
500 individuals control, 500 individuals treatment
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
IRB

Institutional Review Boards (IRBs)

IRB Name
London School of Economics and Political Science
IRB Approval Date
2026-01-12
IRB Approval Number
530185