Improving state effectiveness in environmental risk mitigation: Experimental evidence from Bangladesh

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.

2026-07-21

2026-09-30

Arsenic exposure reduction of well allocation, from elicited allocations

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.

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
-Choice of the union leader's own mauza

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.
Choice of the union leader's own mauza is the indicator variable of whether the any of the wells (or individually chosen wells) is placed in the mauza inhabited by the union leader himself. The union leader's residence mauza will be elicited in the survey.

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.

Not available

Randomization done in office by a computer using R, using set.seed for reproducibility

Union

No

200 unions

200 unions / 800 chosen wells

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

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.