Data-Driven Court Monitoring to Strengthen Judicial Efficiency

Last registered on May 18, 2026
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Pre-Trial

Trial Information

General Information

Title
Data-Driven Court Monitoring to Strengthen Judicial Efficiency
RCT ID
AEARCTR-0018240
Initial registration date
May 13, 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
May 18, 2026, 7:02 AM EDT

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

Locations

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Primary Investigator

Name
Jose Joaquin Silva
Affiliation
Innovations for Poverty Action
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Other Primary Investigator(s)

PI Name
Rene Marlon Panti
PI Affiliation
Innovations for Poverty Action
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PI Name
Danielle Kent
PI Affiliation
University of Sydney
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PI Name
Cesi Cruz
PI Affiliation
University of Michigan
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Additional Trial Information

Status
In development
Start date
2026-05-01
End date
2026-11-30
Keywords
Behavior, Governance
Additional Keywords
Justice, Case Flow Management
JEL code(s)
Structure, Scope, and Performance of Government; Public Administration; Civil Law• Common Law
Secondary IDs
Prior work
This trial is based on or builds upon one or more prior RCTs.
Abstract
This study will evaluate whether regular feedback and monitoring reports can motivate court staff to improve efficiency and court-level outcomes. Using data already collected by the Supreme Court of the Philippines (SC), the feedback tool will track key indicators relating to criminal case management (e.g., clearance and disposition rates).

The study will employ a randomized controlled trial (RCT), assigning courts to either a control group or treatment groups receiving the feedback on their key indicators with region and cross-region comparisons. By leveraging social comparisons as a nonfinancial incentive, the intervention aims to encourage staff to enhance case management through collaboration. The study will assess whether regular feedback fosters accountability, improves case management, and ultimately reduces case backlogs. If effective, this low-cost, scalable approach could inform broader judicial reforms and improve access to justice in the Philippines.

To complement the RCT, the study will also include interviews with court staff and surveys with both litigation lawyers and court personnel to capture perceptions of court performance and the scorecards’ perceived impact. These qualitative components will help contextualize the results and provide insight into behavioral responses to the intervention.

External Link(s)

Registration Citation

Citation
Cruz, Cesi et al. 2026. "Data-Driven Court Monitoring to Strengthen Judicial Efficiency." AEA RCT Registry. May 18. https://doi.org/10.1257/rct.18240-1.0
Sponsors & Partners

Partner

Name
Supreme Court of the Philippines
Type
government
URL
https://sc.judiciary.gov.ph

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Experimental Details

Interventions

Intervention(s)
Treatment 1 (T1) - Courts receive the case management feedback with their own case management efficiency data alongside the regional average from the previous month, testing whether locally grounded feedback motivates improvement.

Treatment 2 (T2) - Courts receive the feedback tool as T1, plus case management efficiency data from a paired court region matched on key attributes such as court size, hearing type, and location. This tests whether broader peer visibility further amplifies behavior change.
Intervention Start Date
2026-06-30
Intervention End Date
2026-11-30

Primary Outcomes

Primary Outcomes (end points)
Court efficiency indicators (e.g. clearance rate, disposition rate, growth rate, number of pending cases, administrative compliance rates)
Primary Outcomes (explanation)
Case Clearance Rate: This rate shows the court’s ability to manage incoming cases, calculated as the percentage of cases disposed relative to new cases.
Case Disposition Rate: This indicator measures the proportion of decided and resolved cases within the total active caseload, reflecting the court’s efficiency in handling its workload.
Case Growth Rate: This rate tracks monthly changes in pending cases, indicating whether the court’s backlog is increasing or decreasing.
Case Aging: Case aging represents the length of time cases remain unresolved, displayed as the number of cases across specific time intervals (in years).
Report Compliance Rate: This indicator is an index measuring the timeliness and accuracy of courts’ Monthly Reports of Cases (MRC).

Secondary Outcomes

Secondary Outcomes (end points)
Profile of detainees and persons deprived of liberty (PDLs)
Secondary Outcomes (explanation)
The profiling aims to summarize the number of detainees and PDLs for every second-level court in the sample.

Experimental Design

Experimental Design
The Case Management Efficiency Feedback Tool will be evaluated using a randomized controlled trial (RCT), with around 600 second-level courts randomly assigned across three conditions and outcomes tracked over six (6) months.

1. Treatment 1 (T1): Own-region comparison. Courts receive the case management feedback with their own case management efficiency data alongside the regional average from the previous month, testing whether locally grounded feedback motivates improvement.

2. Treatment 2 (T2): Cross-region comparison. Courts receive the feedback tool as T1, plus case management efficiency data from a paired court region matched on key attributes such as court size, hearing type, and location. This tests whether broader peer visibility further amplifies behavior change.

3. Control (C): Business as usual. Courts operate without a case management feedback tool, providing the baseline against which treatment effects are measured.

This three-arm design isolates the effect of self-referential feedback (T1) from the added effect of social comparison (T2), generating clear evidence on which approach most effectively drives improvements in court efficiency.
Experimental Design Details
Not available
Randomization Method
Randomization done in office by a computer software application
Randomization Unit
Cluster randomization with possible match-pairing
Was the treatment clustered?
Yes

Experiment Characteristics

Sample size: planned number of clusters
Estimated 50-100 clusters (court-station level) for a three-treatment arm intervention
Sample size: planned number of observations
600 second-level courts across the Philippines
Sample size (or number of clusters) by treatment arms
200 second-level courts per treatment arm
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
We reviewed relevant literature to determine the expected effect size for our study. A meta-analysis by McCarthy and Morina (2020) reviewed the impact of social comparisons on mental health, specifically in relation to symptoms of depression and anxiety. This study found that the average effect size to be 0.44. However, effect sizes varied depending on the specific type of comparison and the targeted mental health outcome. Another systematic review on social interventions focused on the effectiveness of social incentives by Nguyen-Van et al (2021), including social comparison, for promoting pro-environmental behaviors. The average effect size for internal social influences that motivate pro-environmental behaviors was 0.8454 while that of external influence was 0.468. Given the evidence provided by these two systematic reviews, we chose to use a conservative lower bound effect size of 0.44 in our power calculations. We calculate the estimated sample sizes below to detect a significant difference using the following values: 1. effect_size = 0.44 2. alpha = 0.05 # significance level 3. power = 0.80 # typical power value Based on our conservatively assumed values, we estimate a minimum sample size of 82 observations is required to detect an effect size of 0.44 in our randomized controlled trial. These power calculations suggest that we have sufficient observations to observe an effect if our intervention is successful. Our design allows for significant attrition over time without compromising the integrity of our study. From our experience, we might expect an attrition rate of between 10 - 30%. However, given this intervention is under a directive from the SC executive, we may observe less attrition over time. In any case, we have overpowered our design to mitigate such risks.
Supporting Documents and Materials
IRB

Institutional Review Boards (IRBs)

IRB Name
INNOVATIONS FOR POVERTY ACTION IRB – USA
IRB Approval Date
2025-06-18
IRB Approval Number
N/A
Analysis Plan