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What Drives Citizen Demand for Pollution Enforcement?

Last registered on July 13, 2026

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Pre-Trial

Trial Information

General Information

Title

What Drives Citizen Demand for Pollution Enforcement?

RCT ID

AEARCTR-0019092

Initial registration date

July 10, 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

July 13, 2026, 8:28 AM EDT

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

Locations

Country

Region

Primary Investigator

Name

Shotaro Nakamura

Affiliation

Pennsylvania State University

Contact Primary Investigator

Other Primary Investigator(s)

PI Name

PI Affiliation

Colby College

Contact Investigator

Additional Trial Information

Status

In development

Start date

2026-07-10

End date

2026-09-30

Keywords

Behavior, Environment & Energy, Governance

Additional Keywords

air pollution, information provision, belief updating, source attribution, environmental enforcement, willingness to pay

JEL code(s)

Secondary IDs

Prior work

This trial does not extend or rely on any prior RCTs.

Abstract

This pre-analysis plan describes the design and planned analysis of an experimental module embedded in a pilot survey in Lahore, Pakistan. The module investigates what drives citizen demand for pollution enforcement and how information about pollution sources shapes that demand. The design leverages legitimate scientific disagreement about pollution source attribution in Lahore: respondents are randomized into one of three arms---two information treatment arms that present findings from competing scientific studies emphasizing different pollution sources, and a pure control arm that receives no information. Pre-registered primary outcomes are (i) an incentivized donation to a public good (air purifiers for schools), (ii) signing a customizable enforcement letter to the Urban Unit, (iii) belief updating about pollution sources (the information mechanism), and (iv) stated enforcement preferences across pollution sources. A contingent-valuation willingness-to-pay module and additional mechanism items are analyzed as secondary outcomes. The study is a pilot conducted in a single union council of Lahore (300), intended to assess comprehension, field protocols, and effect-size magnitudes ahead of a separately funded scale-up built around high-frequency, ground-monitor-based source apportionment.

External Link(s)

Registration Citation

Citation

Nakamura, Shotaro and Sanval Nasim. 2026. "What Drives Citizen Demand for Pollution Enforcement?." AEA RCT Registry. July 13. https://doi.org/10.1257/rct.19092-1.0

Sponsors & Partners

Sponsors

Experimental Details

Interventions

Intervention(s)

Respondents (urban working-class adults in Lahore) are individually randomized into one of three arms. In the two information-treatment arms, an enumerator reads a short, factually accurate statement summarizing a scientific study of air pollution in areas including Lahore and then shows the respondent, on the enumerator's tablet, a bar chart giving the estimated contribution ("parts out of 100") of four pollution sources - vehicle emissions, factories and industry, crop burning, and other sources. Arm 1 presents a vehicle-emphasizing study (vehicles approx. 83%); Arm 2 presents a multi-source study (vehicles approx. 43%, industry approx. 37%, crop burning approx. 20%). Arm 3 is a pure control that receives no information about pollution sources. The two statements exploit genuine scientific disagreement in published source-apportionment studies; no deception is used.

Intervention Start Date

2026-07-10

Intervention End Date

2026-07-31

Primary Outcomes

Primary Outcomes (end points)

(1) Incentivized donation to a public good - the amount (PKR 0-500) donated to air purifiers for schools; (2) signing a customizable enforcement letter to the Urban Unit (binary); (3) belief updating about pollution sources - the change in the stated share attributed to each source from pre- to post-treatment; (4) stated enforcement preferences across pollution sources (the top-priority source and the full ranking of the four sources).

Primary Outcomes (explanation)

Pre- and post-treatment beliefs are elicited in the same format: stated shares out of 100 across the four sources, constrained to sum to 100. Per-source updating is the post-treatment share minus the pre-treatment share; the pre-registered belief endpoints are updating for vehicles and for crop burning, together with a summary index of total absolute movement. The enforcement-preference endpoint is coded primarily as an indicator for prioritizing vehicle emissions (the source on which the two studies' figures diverge most), with the crop-burning indicator and mean ranks reported alongside. Within each confirmatory family, multiple-hypothesis adjustment uses Anderson's sharpened q-values, with Benjamini-Hochberg FDR reported as a robustness check.

Secondary Outcomes

Secondary Outcomes (end points)

Contingent-valuation willingness to pay for source-specific enforcement and monitoring (four double-bounded vignettes); the enforcement priority written into the letter and whether it was personalized; belief confidence; perceived state responsiveness; trust and information channels; enforcement awareness; and exploratory fairness and green-sticker items.

Secondary Outcomes (explanation)

Willingness to pay is a stated (contingent-valuation), not incentivized, measure; the endpoint is the interval-censored WTP estimate from the double-bounded protocol for each vignette, analyzed with interval regression (respondent random effects) and, within-subject, with respondent fixed effects to identify source- and policy-type effects. Protest responses are excluded from the main analysis and examined in robustness checks. Secondary outcomes are exploratory and are not adjusted for multiple hypotheses.

Experimental Design

Experimental Design

A three-arm, individually randomized, in-person survey experiment among urban working-class households in Lahore, conducted as a pilot in a single union council ahead of a planned scale-up. After eliciting prior beliefs about pollution sources, respondents are randomized to a vehicle-emphasizing information arm, a multi-source information arm, or a pure control, and then complete incentivized and stated outcome modules. The primary comparison is any information vs. no information; the secondary comparison is vehicle-emphasizing vs. multi-source information.

Experimental Design Details

Not available

Randomization Method

Randomization by computer (Stata, with a preset seed), at the individual respondent level, stratified by GPS coordinate pin; assignments are preloaded into SurveyCTO.

Randomization Unit

Individual respondent (household). There is a single level of randomization.

Was the treatment clustered?

No

Experiment Characteristics

Sample size: planned number of clusters

not clustered

Sample size: planned number of observations

300 respondents (target), approx. 100 per arm.

Sample size (or number of clusters) by treatment arms

Approx. 100 Arm 1 (vehicle-emphasizing), approx. 100 Arm 2 (multi-source), approx. 100 Arm 3 (pure control). Primary estimand pools Arms 1-2 (n approx. 200) vs. Arm 3 (n approx. 100).

Minimum detectable effect size for main outcomes (accounting for sample design and clustering)

At alpha = 0.05, power = 0.80, unadjusted for covariates: any-treatment-vs.-control (n approx. 200 vs. 100) gives an MDE of approx. 0.34 SD for continuous outcomes and approx. 17 percentage points for binary outcomes (e.g., letter signing, 50% base rate); Arm 1-vs.-Arm 2 (100 vs. 100) gives approx. 0.40 SD and approx. 19-20 percentage points. Within-subject WTP comparisons use up to 4 x 300 vignette observations.

Supporting Documents and Materials

Institutional Review Boards (IRBs)

IRB Name

Lahore University of Management Sciences

IRB Approval Date

2026-06-04

IRB Approval Number

LUMS-IRB-0467/06042026/FS-FWA-00030383

IRB Name

Colby College

IRB Approval Date

2026-05-29

IRB Approval Number

2026-53

IRB Name

Pennsylvania State University

IRB Approval Date

2026-07-08

IRB Approval Number

STUDY00029447

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