Cooperation to reduce water pollution in the Mekong Delta

Last registered on February 13, 2023


Trial Information

General Information

Cooperation to reduce water pollution in the Mekong Delta
Initial registration date
February 10, 2023

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 13, 2023, 11:31 AM EST

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


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Primary Investigator

Newcastle University

Other Primary Investigator(s)

PI Affiliation
Newcastle University
PI Affiliation
Newcastle University
PI Affiliation
Newcastle University
PI Affiliation
Can Tho University
PI Affiliation
Can Tho University

Additional Trial Information

In development
Start date
End date
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Aquaculture is one of the fastest growing industries in Vietnam (Joffre et al. 2019), both nationally and internationally. This specific industry in the VMD is not only currently dealing with climate change, which affects economic gains and pond productivity (Ahmed 2013, Jonell and Henriksson 2015) but also production itself has led to severe environmental impacts, including pollution and mangrove cover loss (Thornton et al. 2003). One of the main sources of water for aquaculture ponds are water canals, where polluted water from the pond during a production cycle is exchanged by clean water coming from public sources such as rivers. Unfortunately, the wastewater from aquaculture is often discarded into the same water body serving as source water, leading to persistent and severe disease outbreaks and economic losses to the farmers (Ngoc et al. 2021; MARD 2017). This is particularly risky for intensive farmers (Anh et al. 2010; Pham et al. 2018). Thus, adaptation strategies to climate change that simultaneously preserve biodiversity, reduce pollution and reduce the risk of losing farmers’ sources of income are needed (Do et al. 2022).
Our research project aims to disentangle the mechanisms for local farmer cooperation to eliminate water pollution from aquaculture-prevalent communities. More specifically we aim to understand whether farmers can collaborate to build canals to separate polluted from clean water, reducing the likelihood of disease outbreaks in ponds and increasing economic gains for farmers, as well as the sustainability of the ponds and the industry in the long-term.
External Link(s)

Registration Citation

Chilton, Susan et al. 2023. "Cooperation to reduce water pollution in the Mekong Delta ." AEA RCT Registry. February 13.
Sponsors & Partners

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Experimental Details


Intervention Start Date
Intervention End Date

Primary Outcomes

Primary Outcomes (end points)
Contributions to a threshold public good, number of water units extracted
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
To analyse farmer cooperation, we will implement a pen-and-paper, incentivized experiment (where payment of participants is based on their decisions), which is the most common strategy to run experiments in economics. In addition, all participants will receive a fixed show-up or participation fee for taking out time to join the study.
The experiment will be a ‘threshold public good game’ (TPGG hereafter; Cadsby et al. 2008; Croson and Marks 1999; Suleiman and Rapoport 1992), where farmers allocate part of their experimental income to a common project that will give them benefits if a specific level of contributions is reached by the group as a whole. This game has been used in the past to explain how communities can come together to build bridges, dikes to prevent flood damage or how farmers can invest collectively in a pump to avoid waterlogging (Reinhard et al. 2022; Carlsson et al. 2015).
The experiment will be a 2 x 3 design, where the endowments of the participants vary, as well as the mode of enforcement (to reach the threshold of the public good). Thus, our experiment will be composed of six treatments, summarized as follows:

Equal endowments Unequal endowments
Baseline (No enforcement/No matching) T1 T2
Enforcement by a third party (fine) T3 T4
Matching of contributions by a third party T5 T6

There are other 5 games in addition to the TPGG: irrigation game, trust game, risk aversion, real-effort task and loss aversion. We will calibrate this to have similar average payments.


• Unequal endowments: In real life, not all farmers have the same resources or income to invest. Studies have shown that differences in these characteristics can hinder success in reaching the threshold of the public good by triggering a disagreement about how to properly take this heterogeneity into account. We will include in our experiment treatments with unequal incomes across all participants (T2, T4, T6 above) and treatments with equal incomes (T1, T3 and T5 above) with the objective of understanding how the burden of contributions is shared by participants.
• Enforcement mechanism: The enforcement mechanism will be a third-party (centralized), automatically applied fine to the lowest contributor if the threshold for implementation of the public good is not reached (Andreoni and Gee 2015; Kamijo et al. 2004). The fine will be proportional to the contribution of the participant who is the lowest contributor (that is, we will consider that the inequality of endowments impacts absolute contributions when calculating the fine; Brekke et al. 2017). These sanctions are formal, third-party sanctions (to mimic the ones given by local governments to farmers).
• Matching mechanism: We will implement a simple 1-to-1 matching of contributions if the threshold is reached (Gee and Schreck 2018; Helms McCarty et al. 2018; Knutsson et al. 2019). In other words, if the threshold is reached, a similar amount will be put forward by the third party. Offering matching funds reduces the cost of contribution (Vesterlund 2016) and increases the likelihood of giving towards a project, charity (Karlan and List 2007) or in our case, a TPGG (Rondeau and List 2008). We would be extending this line of research for the case of the Global South, where local institutions usually drive initiatives to improve farmers’ profits and for the case of TPGGs.
Experimental Design Details
Not available
Randomization Method
Between treatments: randomization done by a computer. Within treatments: Die (to select a task to be paid) and coin flip (depending on the task).
Randomization Unit
Experimental session
Was the treatment clustered?

Experiment Characteristics

Sample size: planned number of clusters
150 groups (minimum)
Sample size: planned number of observations
600 individuals (minimum)
Sample size (or number of clusters) by treatment arms
25 groups (minimum) x 6 treatments
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)

Institutional Review Boards (IRBs)

IRB Name
Newcastle University Ethics Committee
IRB Approval Date
IRB Approval Number