Risk-sharing to mitigate local environmental risks

Last registered on August 26, 2021


Trial Information

General Information

Risk-sharing to mitigate local environmental risks
Initial registration date
August 24, 2021

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
August 26, 2021, 12:36 PM EDT

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


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Primary Investigator

Michigan State University

Other Primary Investigator(s)

PI Affiliation
Michigan State University
PI Affiliation
Columbia University
PI Affiliation
Michigan State University

Additional Trial Information

On going
Start date
End date
Secondary IDs
NSF Proposal # 1851928, 1853289
Prior work
This trial does not extend or rely on any prior RCTs.
This project studies the role of risk-sharing and social networks in the mitigation of local environmental risks. We focus on two aspects: first, risk mitigation through informal insurance contracts between households; and, second, the importance of social monitoring for enforcing informal insurance contracts. Our objective is to understand whether informal insurance contracts, where compliance may be primarily enforced by existing social structure and reciprocity norms, can effectively reduced the impact of a local environmental risk. To test this, we have chosen as context a major disaster of natural origin: toxic levels of arsenic in groundwater pumped by millions of private wells in Bangladesh. We randomly assign interventions on (a) ex-ante risk sharing, (b) leveraging social monitoring. The proposed study will be carried out in the form of a randomized controlled trial (RCT) in 135 villages in rural Bangladesh.

External Link(s)

Registration Citation

Barnwal, Prabhat et al. 2021. "Risk-sharing to mitigate local environmental risks." AEA RCT Registry. August 26. https://doi.org/10.1257/rct.5071
Sponsors & Partners


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Experimental Details


We carry out two interventions.
1. Informal contracts between households to share water from private wells: We distribute 10 pre-printed contracts (per private well) called "water-sharing coupons" to households to exchange with other households in the same village or sub-village (called para). The text on the contract indicates the agreement to share drinking water from private wells, if either household's well turns out to be high in arsenic in a village-wide arsenic testing program. In other words, two well-owner households mutually insure each other by exchanging water-sharing coupons, meaning that after the arsenic testing of their private wells, the household with a safe well will share the well water with the household whose well turns out to be high in arsenic for daily drinking needs. This sharing of water from private wells, as mentioned on the contract, would be effectively conditional on one households well turning out to be high in arsenic. So, no action is expected if either both or none of two households have high-arsenic wells. However, each well-owner household is provided a large number of water-sharing coupons to make these informal agreements with others, so it is highly likely that, for each high-arsenic well-owners at least one of the exchanges is to a low-arsenic well owner, and vice versa.

2. Social monitoring: We will make the information about agreements public through text messages to phones. This will be done in a limited way and only after collecting consent from households. After collecting the coupon exchanging information from the villagers and once wells are tested for arsenic, we send text messages to well-owners telling each of them who else their co-insurers exchanged coupons with.
Intervention Start Date
Intervention End Date

Primary Outcomes

Primary Outcomes (end points)
1. The number of water-sharing agreements made by households ("coupon exchange"), 2. Access to low-arsenic water at household-level, 3. Whether households with unsafe wells switch to a low-arsenic well ("switching"), 4. Whether households with safe wells provide access to their well to others with high-arsenic well ("sharing"), 5. Changes in norms and preferences about sharing low-arsenic water from private wells.
Primary Outcomes (explanation)
The number of water-sharing agreements: the number of water-sharing coupons exchanged by each household.
Access to low-arsenic water: a binary variable denoting if a household's primary well used for drinking usage has low-arsenic.
Switch to a low-arsenic well: a binary outcome denoting whether an unsafe well-owner eventually switches to safe water sources.
Share safe well: a binary outcome denoting whether a safe well-owner eventually shares their water.
Norms and preferences regarding sharing low-arsenic water from wells with others are constructed using a set of survey questions.

Secondary Outcomes

Secondary Outcomes (end points)
Whether two households make water-sharing agreement (dyad level)
Secondary Outcomes (explanation)
Using the network of coupons exchanged, we will create dyad-level binary variable indicating whether two households have exchanged coupons.

Experimental Design

Experimental Design
We have total 135 villages in the sample. In case of large villages, a predefined part of the village (called para) is considered as one village unit.
There are two main treatment groups. In the first group of 33 villages, all well-owning households will be provided coupon-style contracts (more details provided below).
In the second treatment group (66 villages each), coupon-style contracts will be provided along with a notification that the information on coupon exchanges may be revealed to other villagers. Well-owners will be informed that at most 3 neighbors of them may receive text messages revealing the co-insurers of the households. For example, if household A exchanged coupons with household B, and household B exchanged coupons with household C, then A may get a text message from the study team mentioning "B made agreement to share water with C". Only in half of the second group villages (i.e., randomly selected 33 villages) , household will be actually sent this information. This design allows us to disentangle the net effect of social monitoring from the strategic selection of risk-sharing pairs at the risk-sharing formation stage under the potential threat of social monitoring.
From total 135 villages in our sample, we set 36 of them as the pure control, in which no coupon-style contracts will be facilitated.
Wells in all treatment and control villages will be tested for arsenic.

Water-sharing coupons:
In all 99 coupon treatment villages, for those wells that were claimed sole ownership, the owner receives ten coupons for him or her to share. For those wells that were claimed joint ownership, each owner receives five coupons for him or her to share. The number of coupons each well-owned household receives is based on the number of wells claimed by the household. For example, if a household claimed primary ownership of 2 wells, then the household would receive 20 coupons. The well ownership is identified from a census of households and wells conducted in January 2020.

The risk-sharing contract is designed using these water-sharing coupons in the following way. These coupons include a printed statement that upon sharing this coupon with the designated household, the well owner should agree to share water with the designated household if the water is tested to be safe. More information such as well unique well ID and name of the household head owning the well is mentioned on the coupon. Since there is no legal validity of such contract, it should be considered as an informal agreement to share risk or informal mutual insurance.

Within a couple days after the field agents deliver the coupons to each of the well-owning households, the field agents return to the village, ask the households to show all the coupons they received from other households and the remaining coupons to check if the coupons the household received and the coupons the household is remained with sum up to ten (or five for joint ownership). Households keep the coupons for their record. The study team records information from these coupons.

Experimental Design Details
Not available
Randomization Method
Randomization done in office by a computer
Randomization Unit
Village based cluster randomization.
Was the treatment clustered?

Experiment Characteristics

Sample size: planned number of clusters
135 villages. In case of large villages, a predefined part of the village (called para) is considered as one village unit.
Sample size: planned number of observations
Total about 16,000 Households from the villages. About three-fourth of all households are expected to own a private well.
Sample size (or number of clusters) by treatment arms
36 pure control villages; 99 villages with coupon-style contracts. Among 99 villages, 66 villages will be asked consent for sharing information about coupon exchanges by other villagers in a limited way through text message, but only half of these villages (i.e., 33 villages) will receive the text message.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
The impact of the treatment is expected on the switching rate (i.e., a high-arsenic well household switching to a nearby low-arsenic well). According to the well listing conducted in January 2020, on average each village has 89 private well owners. We set the cluster size to be 80 considering potential attrition. There are 36 control villages with no explicit risk-sharing agreement and 99 villages with the explicit risk-sharing agreement. The baseline switching rate is set to be 0.28 based on Barnwal et al. (2017). Alpha and Beta are set conventionally to be 0.05 and 0.8. Then: MDE = 0.182 when ICC = 0.1; MDE = 0.250 when ICC = 0.2; MDE = 0.303 when ICC = 0.3; MDE = 0.348 when ICC = 0.4; MDE = 0.388 when ICC = 0.5. For comparison, Tarozzi et al. (2021) use the ICC for switching decision of 0.09 in their power calculation. The 0.09 ICC was calculated from the data collected by Bennear et al. (2013). Note that the switching rate depends not only on the number of coupons villagers shared but also on the arsenic level of their own wells. To understand the magnitude of these detectable effects, we use a simulation exercise to calculate the combinations of the average number of agreements and the likelihood of having high arsenic well that achieves these effects. For example, if each villager makes 5 agreements with others in a village of only 30% of wells are safe, then each villager still has over 80% chance to get at least one safe well among the households they exchanged coupons with and thus achieving a switching rate of 80%. The simulation provides that to achieve an MDE of 0.388 (ICC 0.5), villagers need to make 5.4 agreements with others in a village with 20% of safe well, and about 1.7 agreements in a village with 50% of the safe well. Since the ongoing intervention shows that the current villagers are making almost 6 agreements on average and the targeted villages are predicted to have over 30% of safe wells, the calculated MDE is achievable. These numbers are calculated by assuming that villagers are perfectly committed to the agreement. The required number of coupon-exchanges will increase marginally to achieve this MDE when the commitment to the agreement is near perfect. If we consider ICC of 0.09, as in Tarozzi et al. (2021), our experiment is sufficiently powered to detect a much smaller treatment effect. References: Tarozzi, A., Maertens, R., Ahmed, K. M., & Van Geen, A. (2021). Demand for Information on Environmental Health Risk, Mode of Delivery, and Behavioral Change: Evidence from Sonargaon, Bangladesh. The World Bank Economic Review, 35(3), 764-792. Bennear, L., Tarozzi, A., Pfaff, A., Balasubramanya, S., Ahmed, K. M., & Van Geen, A. (2013). Impact of a randomized controlled trial in arsenic risk communication on household water-source choices in Bangladesh. Journal of environmental economics and management, 65(2), 225-240.
Supporting Documents and Materials

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Institutional Review Boards (IRBs)

IRB Name
Michigan State University - Science/Behavioral/Education IRB
IRB Approval Date
IRB Approval Number
IRB Name
Columbia University - Morningside IRB
IRB Approval Date
IRB Approval Number