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The Fiscal Contract up Close: Experimental Evidence from Mexico City

Last registered on September 27, 2022


Trial Information

General Information

The Fiscal Contract up Close: Experimental Evidence from Mexico City
Initial registration date
September 26, 2022

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
September 27, 2022, 11:40 AM EDT

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



Primary Investigator


Other Primary Investigator(s)

Additional Trial Information

On going
Start date
End date
Secondary IDs
Prior work
This trial is based on or builds upon one or more prior RCTs.
When governments cannot perfectly enforce taxation, they may seek to exchange services for voluntary citizen tax compliance. This fiscal contract requires that tax morale responds to public service provision. In this paper, we present experimental evidence of the impact of local public infrastructure on tax compliance, leveraging a large public investment experiment and individual property tax records from Mexico City.
External Link(s)

Registration Citation

Garfias, Francisco. 2022. "The Fiscal Contract up Close: Experimental Evidence from Mexico City." AEA RCT Registry. September 27.
Experimental Details


Intervention Start Date
Intervention End Date

Primary Outcomes

Primary Outcomes (end points)
-- We will focus on the intensive margin of compliance; that is, we will use the amount of yearly property taxes that is paid in 2012 as our main outcome.
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
-- In a secondary analysis, we will report results using the extensive margin; from recorded payments, we will construct an indicator vari- able that takes a value of one if any payment is made in a given bimester (including retroactively for yearly payments, which are an available option for taxpayers).
-- To examine the medium-term effects, we will also use the yearly property taxes paid in 2013.
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
We will reanalyze a large experiment conducted between 2009 and 2011 as part of the randomized evaluation of a major national infrastructural spending program (Ordóñez-Barba et al. 2013; McIntosh et al. 2018). The Hábitat program targeted low-income neighborhoods and invested in local amenities to improve the quality of life in benefited communities.
Experimental Design Details
Randomization Method
We rely on the prior randomization implemented by McIntosh et al. (2018).
Randomization Unit
The randomized evaluation of Hábitat consisted of the selection of eligible low-income urban “polygons,” roughly equivalent to neighborhoods, across 65 participating municipalities. The randomization proceeded in two stages. First, the probability of polygon assignment into treatment was randomly assigned, between 0.1 and 0.9, at the municipal level. This led to different polygon treatment assignment probabilities in each municipality. Second, using the realized assignment probability in each municipality, polygons were assigned into treatment or control within participating municipalities.
Was the treatment clustered?

Experiment Characteristics

Sample size: planned number of clusters
-- In Mexico City, the original evaluation included 20 polygons.
-- In a secondary analysis, we will also use additional polygons from the scale up phase of the program as additional control units to increase statistical power. Specifically, we will use polygons that received Hábitat during 2014 but were not included in the program during the program's evaluation (2009–2012) or during 2013.
-- Finally, if we are unable to reject the main null hypothesis, we will construct an even bigger control sample, using areas in Mexico City that were not covered by the program until at least 2014. We will design a data-driven algorithm to cut the spatial data into polygons that match the treated polygons on observable characteristics.
Sample size: planned number of observations
We anticipate the total number of properties included in the main analysis --- which will use the 20 polygons from the original evaluation --- to be as large as 11,000. We will know the exact number of observations when we match the randomized evaluation data to the property tax administrative records.
Sample size (or number of clusters) by treatment arms
Theres is only one treatment arm in this study.
-- In the main analysis that uses the assignment from the original evaluation, 12 polygons were assigned to treatment and 8 to control.
-- In the secondary analysis with and augmented sample of control polygons, 12 polygons were assigned to treatment and 29 to control.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
See Pre-Analysis Plan.

Institutional Review Boards (IRBs)

IRB Name
IRB Approval Date
IRB Approval Number
Analysis Plan

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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