An Empirical Study on the Effectiveness of Farmland Consolidation Programs Using Matching Algorithms

Last registered on August 22, 2025
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Pre-Trial

Trial Information

General Information

Title
An Empirical Study on the Effectiveness of Farmland Consolidation Programs Using Matching Algorithms
RCT ID
AEARCTR-0016589
Initial registration date
August 20, 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
August 22, 2025, 6:00 AM EDT

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

Locations

Country
Japan
Region

Primary Investigator

Name
Kengo Kurosaka
Affiliation
Tohoku Gakuin University
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Other Primary Investigator(s)

PI Name
Naoki Onodera
PI Affiliation
Iwate Prefectural University
Contact Investigator

Additional Trial Information

Status
On going
Start date
2025-08-18
End date
2029-03-31
Keywords
Agriculture
Additional Keywords
land fragmentation, plot exchange, matching theory
JEL code(s)
Q15, D47, C93
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
This study aims to demonstrate that a farmland consolidation program utilizing a web-based application and matching algorithms can promote farmland consolidation more efficiently than conventional approaches. The program collects farmers’ preferences regarding farmland plots—specifically, the plots they wish to cultivate and those they prefer not to cultivate. Based on this information, the program applies a matching algorithm to generate a consolidation plan, which is then presented to the participants.

To empirically evaluate this proposition, we will conduct a randomized controlled trial (RCT). Each participating municipality will be asked to nominate two candidate districts for program implementation at the time of application. One of the two districts will be randomly assigned to the treatment group, where the program will be implemented, while the other will serve as the control group. Changes in farmland use before and after implementation will then be compared between the treatment and control groups to assess the program’s impact.
External Link(s)

Registration Citation

Citation
Kurosaka, Kengo and Naoki Onodera. 2025. "An Empirical Study on the Effectiveness of Farmland Consolidation Programs Using Matching Algorithms." AEA RCT Registry. August 22. https://doi.org/10.1257/rct.16589-1.0
Sponsors & Partners
Experimental Details

Interventions

Intervention(s)
Treatment group: Conduct our farmland consolidation program utilizing a web-based application and matching algorithms.

Control group: Do not conduct our farmland consolidation program. We allow farmers to conduct traditional workshop-style negotiations for the plot exchange.
Intervention Start Date
2025-08-21
Intervention End Date
2029-03-31

Primary Outcomes

Primary Outcomes (end points)
The percentage change in each farmer’s standard distance (SD) before and after the program.
Primary Outcomes (explanation)
The standard distance (SD) is defined as the value obtained by dividing the sum of the distances between the coordinates of each plot cultivated by a farmer and the mean center coordinate by the total number of plots. A smaller value indicates a higher degree of farmland consolidation, whereas a larger value indicates greater farmland dispersion. We measure the percentage change in each farmer’s standard distance before and after the program.

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
Participating municipalities are requested to present two districts as candidates for program implementation at the time of application. The research team will then randomly select one of these districts as the treatment group and the other as the control group.
Experimental Design Details
Not available
Randomization Method
Pairwise randomization of districts within each participating municipality
Randomization Unit
District
Was the treatment clustered?
Yes

Experiment Characteristics

Sample size: planned number of clusters
40 districts
Sample size: planned number of observations
200 farmers at a minimum
Sample size (or number of clusters) by treatment arms
Control: 20 districts, 100 farmers at a minimum

Treatment: 20 districts, 100 farmers at a minimum
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
Power analysis was conducted in StataNow 19.5 for a cluster-randomized two-sample z test of means (sd₁ = sd₂ = sd). We tested H₀: m₂ = m₁ versus Hₐ: m₂ < m₁ with α = 0.050 (one-sided). Based on pilot data (Δ = -0.14, SD = 0.35, ICC = 0.09), the target power was 0.80. The required minimum cluster size was estimated to be 5, with 20 clusters in the treatment group and 20 clusters in the control group.
Supporting Documents and Materials
IRB

Institutional Review Boards (IRBs)

IRB Name
Tohoku Gakuin University Human Subjects Research Ethics Committee
IRB Approval Date
2025-07-31
IRB Approval Number
2025-015
Analysis Plan