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Tax Protesters: Evidence from a Large-Scale Field Experiment
Last registered on June 16, 2020

Pre-Trial

Trial Information
General Information
Title
Tax Protesters: Evidence from a Large-Scale Field Experiment
RCT ID
AEARCTR-0005992
Initial registration date
June 15, 2020
Last updated
June 16, 2020 11:42 AM EDT
Location(s)

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Primary Investigator
Affiliation
UCLA
Other Primary Investigator(s)
PI Affiliation
University of Texas at Dallas
PI Affiliation
University of Texas at Dallas
Additional Trial Information
Status
On going
Start date
2020-05-20
End date
2020-12-31
Secondary IDs
Abstract
We designed and conducted a field experiment to study people's decision to protest their
property taxes. We examine the role of information frictions and fairness concerns.
External Link(s)
Registration Citation
Citation
Nathan, Bradley, Ricardo Perez-Truglia and Alejandro Zentner. 2020. "Tax Protesters: Evidence from a Large-Scale Field Experiment." AEA RCT Registry. June 16. https://doi.org/10.1257/rct.5992-1.0.
Experimental Details
Interventions
Intervention(s)
Our subject pool is comprised of around 80,000 households who were about to face the opportunity to protest the tax assessments of their homes. We sent letters to around 51,000 of those households, and randomized the information included in the letters. We want to measure how the information included in the letters affected the subsequent decision to protest their taxes
Intervention Start Date
2020-05-20
Intervention End Date
2020-06-15
Primary Outcomes
Primary Outcomes (end points)
The main outcome is a dummy variable indicating whether the household protested or not by the deadline (June 15th, 2020).
Primary Outcomes (explanation)
We will also be able to study this outcome separately by type of protest: i.e., whether the owner protested directly or whether the owner used a tax agent to protest on their behalf. We expect that some of our information treatments may have a bigger effect on direct protests, specially among less affluent households.
Secondary Outcomes
Secondary Outcomes (end points)
The secondary outcome is whether they protest was successful or not. We can construct this outcome as a dummy variable or as an intensity variable (e.g., the %-reduction in the appraised market value as a result of the protest). We may also be able to observe other secondary outcomes described below, depending on data availability. We will also be conducting a supplemental survey with a separate sample (Amazon Mechanical Turk) to provide some evidence about the underlying causal mechanisms.
Secondary Outcomes (explanation)
We may also be able to measure other secondary outcomes. It is possible we access data on the opinion of value included in the protest form, which would give us a measure of the intensity of the protest (e.g., whether the taxpayer is requesting a 1% or a 20% reduction in property taxes). We could also use data on longer-term outcomes observed one year after our intervention: i. whether the subject pays the 2020 property taxes on time; ii. whether the subject protests their 2021 property taxes.
Last, we included a link to a survey in the letter (full survey instrument attached to this registration). Based on evidence from other studies (e.g., Bottan and Perez-Truglia, 2020) we expect not to be able to use the survey outcomes due to a very low and selective response rates (e.g., people who plan to protest and found our letter useful may be substantially more likely to participate in the survey). Due to those limitations, we will conduct a supplemental survey experiment with an auxiliary online sample (Amazon Mechanical Turk) to complement the evidence from the field experiment. The survey instrument for the supplemental survey is attached to this registration too. The supplemental survey tries to replicate the information-provision experiment conducted in the field experiment. The ideal is to measure how the beliefs about the tax rate paid by neighbors affect the perceived fairness and the willingness to protest property taxes. We are planning to collect around 2,000 survey responses from this platform. We will collect this data at around the same dates as the intervention from the field experiment.
Experimental Design
Experimental Design
Our subject pool is comprised of around 80,000 households who were about to face the opportunity to protest the tax assessments of their homes. We sent letters to 50,983 of those households, and randomized the information included in the letters. A sample of the envelope and the letter are attached to this pre-registration. We want to measure how the information included in the letters affected the subsequent decision to protest their taxes.
Experimental Design Details
Not available
Randomization Method
Randomization done in office by a computer.
Randomization Unit
Household
Was the treatment clustered?
No
Experiment Characteristics
Sample size: planned number of clusters
79,322 households
These households are a subsample of the universe of all homeowners in Dallas County, Texas. We arrived to this subsample by applying a number of filters (e.g., focusing on single-family homes, excluding business properties). We would like to clarify that some households will need to be dropped from the subject pool due to reasons that we do not observe ex-ante (because the data is not posted in real-time) but we can measure ex-post such as:
- Some letters will not be deliverable (e.g., vacant addresses, letters returned to sender).
- Some households may have sold their properties before we shipped the letters. Since only the current owner can protest and pay property taxes, those households will have to be dropped from the sample.
- Some households may have already protested their taxes before we shipped the letters
Sample size: planned number of observations
79,322 households
Sample size (or number of clusters) by treatment arms
- We randomized 50,983 to receive a letter and 28,322 not to receive a letter.
- We cross-randomized 2/3's to see the information about averages.
- We cross-randomized 1/2 to see the additional row with tax rates.
- We cross-randomized 1/2 to see the additional message with aid for filing the protest
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
Since there are multiple treatment arms, there are multiple Minimum Detectable Effect Sizes. We provide the effect of the aid message as illustration. Power calculations based on protest data from the previous year (i.e. "placebo" regressions using the previous year's protest outcome as dependent variable) suggest a minimum detectable effect size of 0.52 percentage points. We have more power for the letter vs. no-letter comparison. And we have a bit less power for the effects of the information on county averages included in the letter.
Supporting Documents and Materials
Documents
Document Name
Sample Envelope and Sample Letter
Document Type
other
Document Description
File
Sample Envelope and Sample Letter

MD5: 2e4e5aa5b5c1c8de0412bd11ca9ba5b2

SHA1: bdb5ed1cfbb53d8daf781be1b0e827029ecf3c4d

Uploaded At: June 15, 2020

Document Name
Survey Instrument (Supplemental Survey)
Document Type
survey_instrument
Document Description
This is the supplemental survey to be conducted on Amazon Mechanical Turk.
File
Survey Instrument (Supplemental Survey)

MD5: f3708235e3971e37b0d8238fcc95f09c

SHA1: e406052379a6af8eb660425286f553b2754de8a0

Uploaded At: June 10, 2020

Document Name
Survey Instrument (Letter Survey)
Document Type
survey_instrument
Document Description
This is what respondents saw when they went to the URL included in the letter.
File
Survey Instrument (Letter Survey)

MD5: 70bdcbcfb51733cfde6f4f6a4ffabe2b

SHA1: c60da4f603d549c5a7d45056d8fab339ffa80bc4

Uploaded At: June 10, 2020

IRB
INSTITUTIONAL REVIEW BOARDS (IRBs)
IRB Name
Office of Research Integrity and Outreach, University of Texas at Dallas
IRB Approval Date
2020-05-04
IRB Approval Number
20MR0126