Experimental Evaluation of the Use of Artificial Intelligence in Building Permit Review

Last registered on January 06, 2026
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Pre-Trial

Trial Information

General Information

Title
Experimental Evaluation of the Use of Artificial Intelligence in Building Permit Review
RCT ID
AEARCTR-0017575
Initial registration date
December 30, 2025

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
January 06, 2026, 7:13 AM EST

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

Locations

Country
Chile
Region

Primary Investigator

Name
Juan Díaz
Affiliation
University of Chile
Contact Primary Investigator

Other Primary Investigator(s)

PI Name
Gabriel Weintraub
PI Affiliation
Stanford University
Contact Investigator

Additional Trial Information

Status
In development
Start date
2026-01-02
End date
2026-12-31
Keywords
Other
Additional Keywords
Administrative Process Efficiency, Artificial Intelligence, Digital Government, Human–AI Collaboration.
JEL code(s)
H11, O33, D73, C93
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
This paper examines how artificial intelligence can enhance administrative efficiency in public service delivery, focusing on the processing of building permit applications. We evaluate an AI-assisted system specifically designed and implemented to experimentally assess its impact on bureaucratic performance. Using a field experiment in Chilean municipalities, the platform integrates AI tools into the review process to support technical assessment while preserving regulatory discretion. The analysis focuses on how AI adoption affects processing times, workflow efficiency, and administrative congestion—key sources of delay that generate economic costs for the construction sector and broader welfare losses. By reducing waiting times within a highly regulated approval process, the intervention illustrates how purpose-built AI systems can improve productivity in highly congested and bureaucratic institutional environments. Finally, the study explores heterogeneous effects using reviewers’ pre-intervention administrative histories to assess how baseline performance conditions shape the impact of AI assistance.
External Link(s)

Registration Citation

Citation
Díaz, Juan and Gabriel Weintraub. 2026. "Experimental Evaluation of the Use of Artificial Intelligence in Building Permit Review." AEA RCT Registry. January 06. https://doi.org/10.1257/rct.17575-1.0
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Experimental Details

Interventions

Intervention(s)
The intervention introduces an AI-assisted decision-support system into the administrative review of building permit applications. The system is designed to support public officials by structuring regulatory checks, organizing documentation, and facilitating the technical assessment of compliance with existing building codes. Applications are processed either with or without AI assistance, allowing for a comparison of review times and workflow efficiency. The intervention aims to reduce administrative delays and improve productivity in a highly regulated and congestion-prone public service environment.
Intervention Start Date
2026-01-15
Intervention End Date
2026-04-01

Primary Outcomes

Primary Outcomes (end points)
The primary outcome of interest is the time required to complete the technical review of building permit applications, measured from the date of formal submission to the completion of the initial administrative assessment. This outcome captures the core dimension of administrative efficiency and directly reflects delays experienced by applicants and the broader construction sector. Processing time is measured using administrative system records and allows for precise comparison across treatment and control conditions. The primary analysis focuses on whether access to AI-assisted decision support reduces review duration relative to standard manual procedures. By centering on processing time, the study evaluates whether AI tools can meaningfully improve the speed of service delivery in a highly regulated and capacity-constrained public sector environment, while maintaining existing institutional rules and professional discretion.
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
Secondary outcomes examine how AI assistance affects patterns of congestion, workload distribution, and heterogeneity in performance across reviewers and permit types. These include measures of queue length and backlog accumulation, the number of applications processed per reviewer over time, and variation in processing times across case complexity categories. Additional outcomes assess whether AI support differentially benefits certain types of permits or reviewers, shedding light on where productivity gains are most pronounced. Together, these measures allow the analysis to move beyond average effects and explore how AI reshapes administrative dynamics, workflow organization, and bottlenecks within the permitting system. All secondary outcomes are constructed from administrative records generated during routine operations, ensuring consistency and comparability across treatment and control groups.
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
The study implements a randomized controlled trial (RCT) to evaluate the causal impact of AI-assisted decision support on the processing of building permit applications in two Chilean municipalities. The intervention is embedded within routine administrative operations and compares standard manual review procedures with an AI-assisted workflow. Assignment to treatment and control is conducted using a completely randomized design within predefined strata defined by reviewer and permit type. This ensures balance between treatment and control groups across key dimensions of administrative heterogeneity. The design allows for credible causal inference on the effects of AI assistance on processing times and administrative efficiency, while preserving existing institutional rules and decision authority.
Experimental Design Details
Not available
Randomization Method
Randomization is implemented through a computerized procedure designed to ensure a Completely Randomized Design (CRD) within predefined strata. Specifically, building permit applications are randomly assigned to treatment or control groups within strata defined by reviewer and permit type. Within each stratum, assignment is fully random and independent, ensuring balanced allocation between treatment and control conditions while preserving the simplicity and transparency of a CRD framework. The randomization procedure is programmed to follow established best practices in experimental design, ensuring reproducibility, balance, and protection against discretionary assignment or manipulation. This approach supports unbiased estimation of treatment effects under standard assumptions of randomized experiments.
Randomization Unit
The unit of randomization is the individual building permit application. Each application is independently assigned to treatment or control within its corresponding stratum. Reviewers may therefore evaluate both treated and control applications, enabling efficient estimation of treatment effects while accounting for reviewer-specific heterogeneity.
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
N/A
Sample size: planned number of observations
The planned sample consists of approximately 240–360 building permit applications. Each municipality processes on average 60 eligible applications per month, and data collection is expected to span 2 to 3 months. The unit of observation is the individual permit application. Applications are distributed across reviewers and permit types, generating variation both within and across strata defined by reviewer × permit type.
Sample size (or number of clusters) by treatment arms
Applications are randomly assigned to treatment and control groups using a completely randomized design within each reviewer × permit type stratum. Within each stratum, approximately 50% of applications are assigned to the AI-assisted treatment and 50% to the control condition. This implies an expected allocation of roughly 120–180 treated and 120–180 control applications overall, depending on realized inflow. This stratified CRD ensures balance across both reviewer-level and permit-type characteristics.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
Supporting Documents and Materials
IRB

Institutional Review Boards (IRBs)

IRB Name
Comité de Ética para la Investigación con Seres Humanos de la Facultad de Economía y Negocios de la Universidad de Chile
IRB Approval Date
2025-12-29
IRB Approval Number
22/2025
Analysis Plan