Risk Management Software and Customs Enforcement in Pakistan

Last registered on February 04, 2026
View Trial History

Pre-Trial

Trial Information

General Information

Title
Risk Management Software and Customs Enforcement in Pakistan
RCT ID
AEARCTR-0017801
Initial registration date
January 30, 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
February 04, 2026, 9:58 AM EST

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

Locations

Country
Pakistan
Region

Primary Investigator

Name
Michael Best
Affiliation
Columbia University
Contact Primary Investigator

Other Primary Investigator(s)

PI Name
Faraz Hayat
PI Affiliation
IGC
Contact Investigator
PI Name
Tim Dobermann
PI Affiliation
IGC
Contact Investigator

Additional Trial Information

Status
On going
Start date
2026-01-25
End date
2026-03-31
Keywords
Governance
Additional Keywords
JEL code(s)
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
The Federal Board of Revenue (FBR) of Pakistan is evaluating a new risk management software (RMS2), as a potential enhancement to its existing Risk Management System (RMS1) for customs clearance. RMS classifies Goods Declarations (GDs) into color-coded risk channels—Red (high risk), Yellow (medium risk), and Green (low risk)—which determine the level of documentary review and physical inspection. This randomized controlled trial assesses whether RMS2 improves the identification of high-risk imports and supports better decision-making by customs officers.
The study has two primary objectives: first, to compare the accuracy of RMS2’s risk-scoring algorithm with that of RMS1 in identifying risky GDs; and second, to evaluate whether providing officers with additional risk-related information through the RMS2 user interface enhances officer performance. To achieve these aims, the study implements two interventions. In the first, GDs are independently scored by both RMS2 and RMS1, and a stratified random sample is assigned to a forced-red regime to generate ground-truth inspection outcomes while keeping assessment officers blinded to original risk signals. In the second intervention, assessment officers are randomly assigned to either receive access to the RMS2 interface or continue under standard procedures.
The study will run for approximately 60 days and cover about 9,000 GDs. Outcomes will inform FBR’s procurement decision and provide evidence on the operational value of integrating RMS2 into customs risk management processes.
External Link(s)

Registration Citation

Citation
Best, Michael, Tim Dobermann and Faraz Hayat. 2026. "Risk Management Software and Customs Enforcement in Pakistan." AEA RCT Registry. February 04. https://doi.org/10.1257/rct.17801-1.0
Sponsors & Partners

Sponsors

Partner

Name
Federal Board of Revenue
Type
government
URL
https://www.fbr.gov.pk
Experimental Details

Interventions

Intervention(s)
Intervention Start Date
2026-01-25
Intervention End Date
2026-03-31

Primary Outcomes

Primary Outcomes (end points)
1) Algorithm Performance: To determine if RMS2 is more effective at identifying high-risk Goods Declarations (GDs) for inspection than FBR's current Risk Management System (RMS1).
2) Information Effect: To measure whether providing customs officers with access to the full RMS2 User Interface (UI) leads to better decision-making and improved outcomes.
Primary Outcomes (explanation)
The primary outcomes are constructed using administrative data generated during the customs assessment and inspection process. Variables such as assessed and declared value, unit prices, taxes, hs codes, and country of origin are collected and will be used to define risk i.e. amount of discrepancy between declared and assessed values. See the analysis plan for more details.

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
The study uses two linked experimental components to evaluate the performance of the new RMS2 customs risk management system and its effect on assessment officers’ decisions. First, to create reliable “ground-truth” outcomes for comparing RMS2 with the existing FBR Risk Management System (RMS1), a randomly selected subset of Goods Declarations (GDs) is assigned to a forced-Red channel. All GDs are initially risk-scored and classified by RMS1 as Red, Yellow, or Green. Within each classification and risk score range, GDs are randomly selected for mandatory full documentary review and physical inspection, regardless of their original channel. This forced-Red process generates verified inspection results that enable a direct ex-post comparison of how accurately RMS2 and RMS1 identify high-risk filings, while all other GDs proceed under standard business-as-usual procedures.

Second, to measure the effect of providing officers with additional risk information, assessment officers are randomly assigned to either a control group or a treatment group. Control officers follow existing procedures without access to the RMS2’s interface and channel assignments, while treated officers receive full access to RMS2’s channel assignments and detailed features such as is there a predicted hs code discrepancy, is there an origin country discrepancy, and is there a valuation discrepancy, and if so, then how much. Outcomes are compared across forced-Red and business-as-usual cases, allowing the study to isolate improvements in risk targeting, the informational effect of RMS2 on officer decision-making, and the overall operational impact of integrating RMS2 into customs processes.
Experimental Design Details
Not available
Randomization Method
The UI was randomized in an office on a computer by us. The "forced-red" sample of goods declarations is randomized in the scheduler system.
Randomization Unit
officer for the UI intervention. Good Declaration for the "forced-red" sample for measurement.
Was the treatment clustered?
Yes

Experiment Characteristics

Sample size: planned number of clusters
8400 GDs for the "forced-red" sample. 96 officers for the UI intervention.
Sample size: planned number of observations
8400 GDs
Sample size (or number of clusters) by treatment arms
48 treated officers and 48 controls. approximately 4200 GDs per treatment arm.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
Supporting Documents and Materials
IRB

Institutional Review Boards (IRBs)

IRB Name
IRB Approval Date
IRB Approval Number
Analysis Plan

Analysis Plan Documents

analysis plan

MD5: b74586642adfa45bcca2849db450e58d

SHA1: d75b14a32b3173b10b8682a9e093ce02ab8283c5

Uploaded At: January 30, 2026