Greener on the Other Side? Inequity and Property Tax Collection
Last registered on April 29, 2019

Pre-Trial

Trial Information
General Information
Title
Greener on the Other Side? Inequity and Property Tax Collection
RCT ID
AEARCTR-0004055
Initial registration date
April 01, 2019
Last updated
April 29, 2019 3:10 PM EDT
Location(s)

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Primary Investigator
Affiliation
Columbia University
Other Primary Investigator(s)
PI Affiliation
Columbia University
PI Affiliation
World Bank
PI Affiliation
Oberlin College
PI Affiliation
London School of Economics
Additional Trial Information
Status
On going
Start date
2019-02-09
End date
2019-12-31
Secondary IDs
Abstract
This project works with the government of the city of Manaus, Brazil to improve compliance with the municipal property tax. Specifically, we aim to understand the role that inequity -- similar households being treated differently by tax policy -- plays in determining compliance with the tax. We do this by combining a novel survey experiment raising the salience of horizontal inequity with rich administrative data on tax liabilities and tax compliance.
External Link(s)
Registration Citation
Citation
Best, Michael et al. 2019. "Greener on the Other Side? Inequity and Property Tax Collection." AEA RCT Registry. April 29. https://www.socialscienceregistry.org/trials/4055/history/45614
Sponsors & Partners

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Experimental Details
Interventions
Intervention(s)
We will conduct a survey experiment in a sample of 9,000 households located along tax sector boundaries in February and March 2019 (first payments of tax bills are due in mid March).

The survey experiment is designed to study the impact of two treatments. Our main treatment will inform property owners of how the tax system works and how their tax liability compares to their neighbors on the other side of the tax sector boundary. The experiment informs property owners that there is inequity in the system and that they are treated better (for those on the low tax side) or worse (for those on the high tax side) than comparable peers. The second treatment highlights key tools the government has available to enforce the payment of the property tax by households.

We will then measure self-reported and actual tax compliance to test for the existence of peer-comparison effects, and separately for advantageous equity and disadvantageous equity effects, in the taxation context. We will also test for impacts on policy preferences.
Intervention Start Date
2019-02-21
Intervention End Date
2019-03-23
Primary Outcomes
Primary Outcomes (end points)
Beliefs about fairness of the property tax.
Willingness to pay the property tax.
Actual payment of the property tax.
Primary Outcomes (explanation)
Beliefs about fairness are measured using the answers to the questions "Do you consider the rules for calculating the IPTU liabilities for properties in Manaus to be fair?" and "Do you consider the amount of IPTU charged for your property to be fair compared to what other properties in Manaus are charged?"

Willingness to pay the property tax is measured in two ways.

First, using the answers to the questions "Not paying IPTU this year can be justifiable for families with a property and IPTU bill similar to mine."; "It may be justifiable for households not to pay their IPTU bill this year if their IPTU bill is higher than the IPTU bill of families with a similar property and neighborhood"; and "It may be justifiable for families like mine not to pay their IPTU bill this year if they are struggling to pay other bills".

Second, we perform two list experiments (and randomize their order).
The first list is (** denotes the response of interest)
1-I will move to a property with a lower IPTU liability
2-I will not pay my electricity bill on time
3-IPTU revenues should be used to improve the road surfaces in Manaus
4- ** I will not pay my IPTU bill on time this year
5- I have held an informal job.

The second list is
1- I am in favor of lowering the IPTU tax rates
2- I will not pay my water bill on time this year.
3- I have made a purchase without asking for a VAT invoice
4- ** I will not pay my IPTU bill on time this year
5- I will protest formally to the municipality about the IPTU liabilities in Manaus.

Finally actual payment will be analyzed by merging the survey data with the government's administrative data which contains actual payment details.
Secondary Outcomes
Secondary Outcomes (end points)
Opinions about the severity of penalties for noncompliance with the property tax.
Preferences about criteria to use to set property tax liabilities.
Beliefs about overall level of compliance with the IPTU and its responsiveness to tax rates.
Secondary Outcomes (explanation)
Opinions about the severity of penalties are measured as agreement with the statement "I find the penalties for taxpayers who do not pay the IPTU to be very severe"

Preferences about the criteria to use to set property tax liabilities are measured using the responses to the question "If you could decide the IPTU calculation criteria, which ones would be more important? Indicate "A" in the
item considered most important to you; "B" in the second most important item; "C" in the third
item, "D" in the fourth item and "E" in the fifth item. If the respondent does not know how to
respond, mark "Z" in do not know.
______ Taxpayers who receive the same services from the city hall must pay the same amount
______ Contributors who have the same income must pay the same amount
______ Contributors who are neighbors must pay the same amount
______ Taxpayers with buildings of the same size (square meter) must pay the same amount"

Beliefs about the overall level of compliance with the IPTU are measured using the responses to the questions:
In your opinion, in general, how many families will fully pay the IPTU due this year (2019)?
Note: read options and select the one closest to the person’s case
o Less than 2 families for every 10 families
o Between 2 and 4 families for every 10 families
o Between 4 and 6 families for every 10 families
o Between 6 and 8 families for every 10 families
o More than 8 families for every 10 families

and

What if the value of the IPTU was cut in half? (for example, an account of 500 reais would be
250 reais)
NOTE: Read options (if necessary repeat) to select the one closest to the person's case. This
question is the same as the previous one, only with the value of the IPTU cut in half.
o Less than 2 families for every 10 families
o Between 2 and 4 families for every 10 families
o Between 4 and 6 families for every 10 families
o Between 6 and 8 families for every 10 families
o More than 8 families for every 10 families
Experimental Design
Experimental Design
Sampling
There are approximately 600,000 taxable properties in Manaus. Our government partners shared their cadaster with us, anonymizing each property such that for each property, the finest level at which it could be identified is the lot the property is on. There are 456,000 lots in the data. We identified all lots that are on blocks that are either adjacent to the boundary of a tax sector or adjacent to a block that is adjacent to a tax boundary to form a buffer two blocks deep around the boundaries of the tax sectors.
Within these blocks, we drop non-residential properties, apartment buildings (entering apartment buildings requires the superintendent's written permission, complicating surveying significantly), and blocks where the average tax change for residential properties were they to be located on the other side of their closest tax boundary is either less than 4% or less than 15 Reais. This results in a sample of 39,306 properties on 32,908 lots.

Randomization.
We randomized the lots into 6 treatments.
1- Control survey; Short list first (in list randomization)
2- Control survey; Long list first (in list randomization)
3- Enforcement treatment survey; Short list first (in list randomization)
4- Enforcement treatment survey; Long list first (in list randomization)
5- Fairness treatment survey; Short list first (in list randomization)
6- Fairness treatment survey; Long list first (in list randomization)

We stratified the randomization into 40 strata, combining
1- 2 groups for above and below median delinquency in 2017.
2- 4 groups for quartiles of the size of the tax liability.
3- 5 groups for the size of the tax jump. For the negative jumps (people on the high tax side of the boundary) we split them into the 40% largest negative jumps (group 1), the next 40% (group 2) and the 20% smallest negative tax jumps. Similarly for the positive tax jumps (people on the low tax side of the boundary) we split them into the 40% largest positive jumps (group 3), the next 40% (group 4) and the 20% smallest positive tax jumps. The 20% smallest positive and negative tax jumps combined to create group 5.

We then randomized the lots into the 6 treatments, with probabilities that varied with the strata depending on the tax jump stratum the lot was in. In the strata with small tax jumps (group 5) we randomized 50% of lots into the enforcement treatment and 50% into the control survey. In the strata with the large (positive or negative) tax jumps we randomized 20% of lots into the enforcement treatment, 40% of lots into the fairness treatment, and 40% into the control survey. These proportions were picked to maximize overall power subject to the constraint that no lots in the small tax jump group would receive the fairness treatment.
Experimental Design Details
Not available
Randomization Method
Randomization done by a computer.
Randomization Unit
Lot
Was the treatment clustered?
Yes
Experiment Characteristics
Sample size: planned number of clusters
The randomization was performed on the full sample of 32,908 lots and this is what will be given to the survey teams. However, due to the difficulty with performing unincentivized household surveys in an urban environment, we expect response rates to be modest, at around 25-30%. and so we will run the survey until we have around 9,000 responses. Given the average number of households on a lot is 1.19 this corresponds to around 7,500 lots.
Sample size: planned number of observations
9,000 household survey responses.
Sample size (or number of clusters) by treatment arms
1,575 lots in group 1 (Control survey; Short list first)
1,575 lots in group 2 (Control survey; Long list first)
975 lots in group 3 (Enforcement treatment survey; Short list first)
975 lots in group 4 (Enforcement treatment survey; Long list first)
1,200 lots in group 5 (Fairness treatment survey; Short list first)
1,200 lots in group 6 (Fairness treatment survey; Long list first)




1,167 households in treatment group 1
1,150 households in treatment group 2
1,276 households in treatment group 3
1,284 households in treatment group 4
1,759 households in treatment group 5
1,806 households in treatment group 6
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
We use the standard expressions for the MDE (see attached document) and assume that with the baseline administrative data we will able to achieve an R^2 of 0.4 in predicting tax compliance (previous compliance is an extremely strong predictor). Doing this, we estimate we have an MDE of under 0.1 standard deviations for both continuous and binary outcomes.
Supporting Documents and Materials
Documents
Document Name
Power Calculations
Document Type
other
Document Description
File
Power Calculations

MD5: 87168f277b6ea0895ce36bda7f99bccb

SHA1: 37139a302b07131837e77c9d6fc82f123b7405f1

Uploaded At: March 31, 2019

Document Name
Survey Instrument (Portuguese)
Document Type
survey_instrument
Document Description
This is the survey actually implemented in the field
File
Survey Instrument (Portuguese)

MD5: 63520febc4850c261fdf36fdba43887d

SHA1: 9463fa1bf2d91cb829814fe94c8afc2d873c30e7

Uploaded At: March 31, 2019

Document Name
Survey instrument (English)
Document Type
survey_instrument
Document Description
This is an English translation of the survey. The survey actually implemented in the field is the one in Portuguese also attached.
File
Survey instrument (English)

MD5: 8db22966c7f6fea441d6f6979b904513

SHA1: 9af46b56a6fe801b7bf8bb40ad514626b88f91ea

Uploaded At: March 31, 2019

IRB
INSTITUTIONAL REVIEW BOARDS (IRBs)
IRB Name
Oberlin College IRB
IRB Approval Date
2019-02-08
IRB Approval Number
#S19EEK-01
IRB Name
Columbia University IRB
IRB Approval Date
2019-01-28
IRB Approval Number
IRB-AAAS2595
Analysis Plan

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