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Field
Trial Start Date
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Before
September 07, 2025
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After
July 21, 2026
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Field
Last Published
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Before
October 13, 2025 11:14 AM
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After
August 15, 2026 11:03 AM
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Field
Intervention (Public)
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Before
In Bangladesh, the DPHE (Department of Public Health Engineering) is the governmental body in charge of allocating and installing arsenic-safe public wells. Each fiscal year, villagers submit applications to the local DPHE office asking for the installation of DPHE wells in their neighborhood.
Within each upazila (an administrative unit, of which there about 500, comprising on average 70,000 households), one upazila-level DPHE engineer advises the upazila's chief bureaucrat (called "UNO") on which of these applications should be approved. Historically, the upazila-wide budget for DPHE wells has been evenly distributed across all of its unions (a sub-upazila administrative unit of 5,000 HHs on average). A union on average consists of 13 villages.
On top of such advisory roles, the upazila-level engineer presides over the installation and maintenance of DPHE wells. The engineer is aided by local subordinates, called "mechanics".
Despite their important role in providing arsenic-safe water, DPHE wells have been subject to suboptimal allocation–due to the lack of local knowledge on arsenic contamination status and political pressure. The intervention aims to overcome these challenges by providing the upazila-level engineers access to a website ("Arsenic Information Dashboard") that visualizes and summarizes the arsenic contamination status within their upazila. The information is provided at the sub-union level. The website also allows the users to digitize and store the list of applications that they received for this fiscal year, and to simulate the public health benefits of approving a set of applications over another. Information on the website is delivered via official mail from the central DPHE office, and field enumerators provide training sessions to both the DPHE engineer and the UNO’s subordinates.
The intervention and the baseline survey will be followed by midline and endline surveys; in the former, we will collect information on the applications received for the DPHE wells from the villagers. At the endline phase, enumerators will visit a randomly selected union within an upazila to ensure the veracity of the administrative data and to conduct simple household surveys for households surrounding the wells.
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After
In Bangladesh, the DPHE (Department of Public Health Engineering) is the governmental body in charge of allocating and installing arsenic-safe public wells. Each fiscal year, villagers submit applications to the local DPHE office asking for the installation of DPHE wells in their neighborhood. The DPHE budget for public wells is shared across unions (administrative unit of 13 villages on average). Since applications often exceed the available slots, the Union Parishad chairperson (henceforth "union leader") has to make decisions on allocating wells. The decision making process directly involves the DPHE upazila-level DPHE engineer, who advises the union leaders and oversees the logistics of installing and maintaining the DPHE wells.
Despite their important role in providing arsenic-safe water, DPHE wells have been subject to suboptimal allocation–due to the lack of local knowledge on arsenic contamination status and political pressure. Further, while a large-scale, DPHE-led field testing campaign involving testing of 6.3 million wells allows a detailed understanding of within-union arsenic contamination levels, an evaluation of upazila-level engineers’ understanding of village-level contamination revealed large gaps between the DPHE engineers’ understanding of village-level arsenic contamination and the actual arsenic contamination in these villages.
This strongly suggests that two factors pose important barriers to optimal DPHE well installation that would maximize its public health benefits: first, the information gap between expected arsenic severity and the actual arsenic severity among the responsible agents (union leaders and engineers), and second, whether availability of correct information to responsible agents will lead to actual application in DPHE well allocation.
The intervention aims to study above barriers using a field experiment where we cross-randomize an “information” and an “incentives” treatment. The information intervention provides Union Parishad leaders with a trained-access dashboard displaying sub-union arsenic data, digitizing well applications, and simulating the health benefits of alternative allocations. In the incentive intervention, leaders rank four preferred wells and are randomized to either a bonus-well tournament rewarding arsenic-focused allocations or a truth-telling condition in which one preferred well is installed regardless of its potential impact.
Overall, the two interventions–incentives and information–will be cross-randomized, hence forming four treatment arms. This design will allow us to separately identify both informational and preference-based barriers to arsenic-reducing well allocations. A total of 250 unions will be randomized into the four treatment arms, plus one more arm for “backup” unions, in case of attrition from union leaders. A total of 200 unions will thus be surveyed.
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Field
Intervention Start Date
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Before
September 07, 2025
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After
July 21, 2026
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Field
Intervention End Date
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Before
December 31, 2026
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After
September 30, 2026
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Field
Primary Outcomes (End Points)
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Before
Arsenic exposure reduction of well allocation.
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After
Arsenic exposure reduction of well allocation, from elicited allocations
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Field
Primary Outcomes (Explanation)
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Before
To compute the Arsenic Exposure Reduction of a well allocation we look at how wells are allocated across villages within a union. We will use information on the list of applications for wells in each union to calculate the minimum and maximum number of arsenic-drinking families that could be affected by an allocation. The maximum and minimum impacts are B_{max} and B_{min}, and then an allocation that reaches B_k households is scored as $(B_k - B_{min})/(B_{max} - B_{min})$.
Note that the calculation of arsenic exposure at the union level presumes that the number of wells allocated to each union will remain fixed. I.e. that the UNOs and DPHE engineers do not move wells across unions: their allocation decisions are all taken within unions. In all past years, each union has been allocated the same number of wells, and this is a strong equity norm that we do not expect will be violated during our study. In case we do observe deviations from such rules, we will assess the robustness of our results to dropping such upazilas.
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After
To compute the Arsenic Exposure Reduction, from elicited allocations, we first elicit the preferred allocation from the union leaders. We then will use information on the list of applications for wells in each union to calculate the minimum and maximum number of arsenic-drinking families that could be affected by an allocation. The maximum and minimum impacts are B_{max} and B_{min}, and then an allocation that reaches B_k households is scored as $(B_k - B_{min})/(B_{max} - B_{min})$.
Note that the calculation of arsenic exposure at the union level presumes that the number of wells allocated to each union will remain fixed. I.e. that the union parishad chairmen UNOs and DPHE engineers do not move wells across unions: their allocation decisions are all taken within unions. In all past years, each union has been allocated the same number of wells, and this is a strong equity norm that we do not expect will be violated during our study. In the very rare cases we do observe deviations from such rules, we will assess the robustness of our results to dropping such unions.
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Field
Experimental Design (Public)
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Before
We randomly assign 142 upazilas to either treatment or control, where the randomization is stratified by the arsenic exposure reduction of past public well allocations, by the severity of arsenic contamination, and by division (details below).
The 142 upazilas belong to 5 divisions. Except for division (which is predetermined for all upazilas), all data that are required for stratification come from ARRP (Arsenic Risk Reduction Program), which tested 6.3 million drinking-water wells across the country for arsenic. The data included the type of the well (public vs. private), along with the arsenic testing results from field test kits. A random subset of the well water samples were analyzed in DPHE laboratories, the results of which were used to recalibrate the test kit results.
Using the ARRP test results and their arsenic test results, we compute for each upazila the % of wells unsafe in arsenic, from Bangladesh national standard levels (50 ppb).
Finally, we compute the arsenic exposure reduction of past well allocation in each union using the same formula we describe in the explanation for its use as a primary outcome. We then average this union-level measure across unions in an upazila to obtain the upazila’s arsenic exposure reduction of past well allocations.
We split the sample of upazilas above vs below the median of the arsenic exposure reduction of their past well allocations, and above vs below the median of their % arsenic contamination. Combined with the 5 divisions, this leads to 2x2x5 = 20 strata. Removing 2 empty strata and pooling a singleton stratum with other upazilas in the same division results in 16 strata. Our randomization is conducted within these strata.
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After
We randomly assign a primary sample of 200 unions to four treatment groups. This randomization is stratified by the arsenic exposure reduction of past public well allocations and by upazila (details below). We also randomly assign an additional 50 unions to a “backup” pool to serve as replacements for either treatment or control groups if needed.
The 250 total unions belong to 23 upazilas. All data required for stratification come from the ARRP (Arsenic Risk Reduction Program), which tested 6.3 million drinking-water wells across the country for arsenic. The data includes the type of the well (public vs. private), along with the arsenic testing results from field test kits. A random subset of the well water samples was analyzed in DPHE laboratories, the results of which were used to recalibrate the test kit results.
For each union, we compute the arsenic exposure reduction of past well allocations using the same formula we describe in the explanation for its use as a primary outcome.
For upazilas with a sufficient number of unions (i.e., at least 10 unions), we split the upazila’s unions into two strata based on their past well allocation score. For upazilas with fewer than 10 unions, we use the upazila itself as the stratum. This leads to a total of 38 strata, which we use to conduct the stratified randomization into the four primary groups. The 50 backup unions will be kept in a randomly generated order; upon not being able to work with a given primary union, we will replace it with a backup union using this pre-determined sequence.
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Field
Randomization Method
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Before
Randomization done in office by a computer using R, using set.seed for reproducibility
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After
Randomization done in office by a computer using R, using set.seed for reproducibility
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Field
Randomization Unit
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Before
Upazila
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After
Union
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Field
Was the treatment clustered?
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Before
Yes
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After
No
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Field
Planned Number of Clusters
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Before
142 upazilas
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After
200 unions
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Field
Planned Number of Observations
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Before
2,288 unions / 2,500 DPHE wells / 5,000 households
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After
200 unions / 800 chosen wells
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Field
Sample size (or number of clusters) by treatment arms
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Before
70 treated upazilas, which contain 1,184 unions, 1,232 wells surveyed / 2,464 households surveyed
72 control upazilas, which contain 1,104 unions, 1,268 wells surveyed / 2,536 households surveyed
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After
Control - 50 unions / 200 chosen wells
T1 (incentive, no information) - 50 unions / 200 chosen wells
T2 (no incentive, information) - 50 unions / 200 chosen wells
T3 (incentive, information) - 50 unions / 200 chosen wells
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Field
Power calculation: Minimum Detectable Effect Size for Main Outcomes
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Before
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After
Effect of incentive: 12.63 (unit: arsenic expsoure reduction score, see above for calculation method)
Effect of information: 12.61 (unit: arsenic expsoure reduction score, see above for calculation method)
Baseline standard deviation of the outcome: 23.61 (unit: arsenic expsoure reduction score, see above for calculation method)
The values are obtained using baseline distribution of within-union allocation of DPHE wells in the tested unions.
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Field
Secondary Outcomes (End Points)
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Before
DPHE Engineers
-DPHE engineers’ understanding of local arsenic contamination
-Time spent on activities unrelated to well installation
DPHE engineers’ self-reported importance assigned to arsenic
UNOs
-UNOs’ self-reported importance assigned to DPHE engineers’ advice
-Shift of union-level well budgets
Well/villager experience characteristics
-Perceptions of the DPHE well installation process
-Accessibility of DPHE wells
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After
Union Parishad Chairman
-Arsenic understanding (after using the application–before the app is shared will be used for heterogeneity)
-Self-described motivations for final allocation
-Average levels of household wealth, education, and other observable characteristics in the mauzas chosen by the union leader
-Average level of arsenic in the mauzas chosen by the union leader
-Arsenic level of the mauzas in the wells chosen by the union leader
-Average level of population density in the mauzas chosen by the union leader
-Proxies for political gains from, and political pressure for, well allocations
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Field
Secondary Outcomes (Explanation)
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Before
DPHE engineers’ understanding of local arsenic contamination will be measured by asking engineers to score the mauzas in their upazilas from 0 to 100, with 0 meaning “all wells are safe in arsenic” and 100 “all wells are unsafe in arsenic.” This will then be compared with the true % of arsenic contamination.
Time spent on activities unrelated to well installation will be measured by asking the engineers, in the baseline and endline surveys, how much time is spent per week on the maintenance of DPHE wells–which is another major activity that the DPHE engineer office attends to.
DPHE Engineers’ self-reported importance assigned to arsenic and UNOs’ self-reported importance assigned to DPHE engineers’ advice are respectively measured by asking the engineers and UNOs to score the importance of the individual categories/criteria (e.g., “opinions of local politicians,” “number of families drinking from the well”) in their decisions of installing the wells. Their scores are subject to a budget constraint of 100, to assure comparability across scores.
Shift of union-level budgets is measured by observing whether all unions within the upazilas received identical numbers of well installations or not.
Perceptions of the DPHE well installation process and Accessibility of DPHE wells will be evaluated using household surveys, where we will solicit households’ satisfaction with the well installation process and will evaluate the public’s capacity to access the DPHE wells–and check for evidence of illicit privatization (e.g., linkage of the well to a submersible pump for private shower facilities).
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After
Union leaders’ understanding of local arsenic contamination will be measured by asking engineers to rank three mauzas based on their well-water arsenic contamination levels, and then computing the Kendall’s tau metric using true ranking. The understanding will be measured once for unions that do not receive the website, and twice for union leaders that do receive the website intervention (once before, once after, for the same pool of mauzas).
Self-described motivation for preferred allocation will be collected immediately following the solicitation of preferred allocation, where the leaders will be asked an open-ended question on what motivated their choices.
Average level of arsenic in the mauzas chosen by the union leader is the (potentially population-weighted) average of well-water arsenic contamination in the mauzas selected by the union leader for the DPHE well allocation. The individual well-level arsenic contamination % will be also obtained using the mauza-level arsenic contamination rate.
Average levels of household wealth, education, and other observable characteristics will include (population-weighted) average of (proxies) of household wealth, education, sectoral employment (agricultural/industry/service), religion, and other observable characteristics. The data for this will be obtained from the most recent census of Bangladesh, conducted in 2022 by the Bangladesh Bureau of Statistics (BBS).
Proxies for political gains and political pressure from, political pressure for, well allocations will refer to polling station-level vote margins (in favor of the incumbent BNP (Bangladesh Nationalist Party) and voter turnout in the most recent national election, the first election after the July Student Uprising of 2024. The polling station’s location will be matched with mauzas to obtain mauza-level estimates of vote returns and voter turnout. The polling station-level returns may be difficult to obtain in practice, in which case we will use upazila-level election results as a potential driver of heterogeneous treatment effects.
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