Making the Most of Limited Government Capacity

Last registered on January 02, 2022


Trial Information

General Information

Making the Most of Limited Government Capacity
Initial registration date
October 06, 2019

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
October 07, 2019, 9:33 AM EDT

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

Last updated
January 02, 2022, 1:31 PM EST

Last updated is the most recent time when changes to the trial's registration were published.


Primary Investigator

New York University

Other Primary Investigator(s)

PI Affiliation
PI Affiliation

Additional Trial Information

Start date
End date
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
We study how mechanisms that seek to make an optimal use of limited government capacity perform in realistic settings, in which participants need not play according to equilibrium. Our motivating example is that of a tax authority with only limited capacity to perform audits ahead of collection.

The experiment will offer players the opportunity to settle a debt that they owe to a collection authority. The treatments of the experiment will vary the method of collection, settlement offers and information provided to participants about the settlement decisions of others.
External Link(s)

Registration Citation

Chassang, Sylvain, Lucia Del Carpio and Samuel Kapon. 2022. "Making the Most of Limited Government Capacity." AEA RCT Registry. January 02.
Experimental Details


We vary the method of collection, the information that players receive about others, and discounts for settling early.
Intervention Start Date
Intervention End Date

Primary Outcomes

Primary Outcomes (end points)
The outcomes we are interested in are total amount collected, total number of settlers and the speed of settlement.
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
-Sessions run in NYU CESS, with each subject playing all treatments randomly ordered.
-Sessions run on Amazon Mechanical Turk with groups of 10 workers, each only playing one session.
Experimental Design Details
Randomization Method
Randomization done by computer.
Randomization Unit
Session level - each group will be randomly assigned one treatment.
Was the treatment clustered?

Experiment Characteristics

Sample size: planned number of clusters
12 sessions in NYU CESS (each session corresponding to three treatments)
~40 Sessions on MTurk (each session corresponding to 2, 3 or 4 treatments)

Sample size: planned number of observations
~300 individuals in NYU CESS ~1250 individuals on Amazon Mechanical Turk
Sample size (or number of clusters) by treatment arms
Each player in the NYU session plays all 3 treatments, randomly ordered.
On MTurk, each player plays just 1 treatment
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
Supporting Documents and Materials


Document Name
Theory, Design and Instructions
Document Type
Document Description
This document includes the main theoretical predictions, experimental design and a sample of instructions.
Theory, Design and Instructions

MD5: 2cd3f8d36264c0bfde774b50fbe2da45

SHA1: 63dae05a36041774025dfd230950dfe7eeb6ffef

Uploaded At: September 30, 2019

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Institutional Review Boards (IRBs)

IRB Name
University Committee on Activities Involving Human Subjects
IRB Approval Date
IRB Approval Number
FY-IRB# 2016-1401
IRB Name
Princeton University Institutional Review Board
IRB Approval Date
IRB Approval Number
IRB 13998


Post Trial Information

Study Withdrawal

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Is the intervention completed?
Data Collection Complete
Data Publication

Data Publication

Is public data available?

Program Files

Program Files
Reports, Papers & Other Materials

Relevant Paper(s)

Reports & Other Materials