A Field Experiment on Tax Avoidance

Last registered on May 16, 2022


Trial Information

General Information

A Field Experiment on Tax Avoidance
Initial registration date
May 16, 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
May 16, 2022, 5:28 PM EDT

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


Primary Investigator

UC Berkeley

Other Primary Investigator(s)

PI Affiliation
University of Chicago
PI Affiliation
UT Dallas

Additional Trial Information

On going
Start date
End date
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
We designed a field experiment to study tax avoidance among households. The subjects in our field experiment are households who pay property taxes and face the opportunity to legally reduce their property taxes via filing a tax appeal. We invite a sample of households' to complete a survey. Within a survey, we conduct an information-provision experiment. We then measure how the information affects their posterior beliefs and the subsequent decision to appeal property taxes (based on administrative records).
External Link(s)

Registration Citation

Holz, Justin, Ricardo Perez-Truglia and Alejandro Zentner. 2022. "A Field Experiment on Tax Avoidance ." AEA RCT Registry. May 16. https://doi.org/10.1257/rct.9298-1.0
Experimental Details


We study households who were about to have the opportunity to protest the tax assessments of their homes. We sent letters to 100,432 of those households to invite them to participate in a survey. Within the survey, we conduct an information-provision experiment.
Intervention Start Date
Intervention End Date

Primary Outcomes

Primary Outcomes (end points)
The main outcome is a dummy variable indicating whether the household protested or not by the deadline (May 16th, 2022).
Primary Outcomes (explanation)
Due the nature of the setting and the timing of the protest process, we expect our information to affect primarily whether the households protested directly. For that reason, we will focus on direct protests as the primary outcome. We plan on using direct protests in previous years as falsification/placebo outcomes, in the spirit of event-study analysis.

Secondary Outcomes

Secondary Outcomes (end points)
Right after the information-provision, the survey includes a few questions that could be used as outcomes. A sample of the full survey instrument is attached to this application. The most important survey outcome is the intention to protest. This outcome can potentially pick up transitory effects of the information and also allows us to compare the stated intentions to protest in the survey versus the revealed actions in the administrative data. We have another way of measuring the intention to protest indirectly, through the usage of aid provided at the end of our survey (more details below).

There is another secondary outcome that is quite important. At the end of the survey, we use a willingness-to-pay (WTP) exercise to measure whether the household wants to help other households file a tax protest. We elicit this WTP twice (in random order): the WTP to help a household in the richest 1% of households, and the WTP to help a household in the poorest 1% of households. These two outcomes allows us to test whether the beliefs affect not only the household's intention to file a protest, but also the household's views as to weather rich and poor households should protest.
Secondary Outcomes (explanation)
Our intervention provides all respondents with help for filing a protest, in the form of a website with step-by-step instructions. This data allows for a secondary outcome that indicates whether the household had the intention to protest: a dummy variable that takes the value 1 if the household accessed the website with the information on how to protest. We are able to collect this data because the access to the help website requires to introduce the validation code included in the letter. We are able to compare the protest probability for households who visit the webpage with instructions and those who do not.

Experimental Design

Experimental Design
We sent letters to a sample of 100,432 households from Dallas County (Texas) who were about to have the opportunity to protest the tax assessments of their homes. A sample of the letter is attached to this pre-registration. Our letters included an invitation to participate in an online survey. A sample of the survey instrument is attached to this pre-registration as well.
Experimental Design Details
Since each subject must enter a survey code, we can link their survey responses back to administrative records (including, but not limited to, whether they filed a protest in 2022 or in any of the previous years).

We want to measure how the information provided inside the survey affected the subsequent decisions to file a protest. The main focus of our information-provision experiment is not to investigate if providing information (relative to not providing information) has an effect on the average probability of protesting. When provided with the information, some individuals may update their beliefs down and others may update the beliefs up, so those effects may cancel each other out. Instead, our main focus is to measure the causal effects on beliefs, by exploiting how individuals update relative to their prior beliefs. With that goal in mind, we will use econometric models and robustness checks used in some of our previous work such as:

Cullen, Z. and Perez-Truglia, R. (2018). How Much Does Your Boss Make? The Effects of Salary Comparisons. Journal of Political Economy, Vol. 130 (3), pp. 766-822
Nathan, B.; Perez-Truglia, R. and Zentner, A. (2020). My Taxes are Too Darn High: Why Do Households Protest their Taxes? NBER Working Paper No. 27816.
Giaccobasso, M.; Nathan, B.; Perez-Truglia, R. and Zentner, A. (2022). Where Do My Tax Dollars Go? Tax Morale Effects of Perceived Government Spending. NBER Working Paper No. 29789.
Bottan, N. and Perez-Truglia, R. (2020). Betting on the House: Subjective Expectations and Market Choices. NBER Working Paper No. 27412.

We elicit beliefs on, and provide information about, three topics:
1) The share of households who filed a protest among the richest-1% of households.
2) The expected savings from filing a protest.
3) The share of households who filed a protest among households that are similar to the respondent's household (within $50K of the respondent's own home value).

The main hypothesis is the "trickle-down tax avoidance": the higher the perception of the share of the richest-1% households who file tax appeals, the higher the likelihood that the respondent files a appeal. This is precisely why we measure belief 1) above.

The other two beliefs are intended to disentangle causal mechanisms.

One potential mechanism is that when finding out that the richest-1% of households are likely to protest, respondents infer from that information that they could be saving more money if they protested themselves. In other words, households may think that if the richest-1% protest, it must be because filing a protest is the financially smart thing to do. The belief in 2) above is intended to disentangle this mechanism.

Another potential mechanism is that households do not care about the share of households protesting among the richest-1% specifically, but more generally about the behavior of households that are similar to them. That is, households could be reacting to the information about the richest-1% simply because they are extrapolating from that information. The belief in 3) above is intended to disentangle this second mechanism.

We have a lot of additional survey data and administrative data for heterogeneity analysis that may help to disentangle causal mechanisms. For example, we can reproduce the analysis separately for Democrat vs. Republican households or by age and race.
Randomization Method
Randomization done in office by a computer.
Randomization Unit
Was the treatment clustered?

Experiment Characteristics

Sample size: planned number of clusters
100,432 households will be invited to the survey. 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 originally selected 100,616, but 198 were dropped by the mailing company by using the NCOA (Change of Address) system. We would like to clarify that some additional households will need to be dropped from the subject pool due to reasons that we cannot observe ex-ante.

- Most importantly, some households may respond to the survey after they protested or after the window to submit a protest had been closed (in which cases, by construction, the information provided in the survey could not affect their choices).
- Some households may sign with a tax agent before they respond to our survey, in which case they will be excluded from the sample.
- Following prior information-provision studies, we'll exclude subjects who are outliers in terms of their prior misperceptions. If a respondent is extremely off in his or her prior belief, that is a sign that the household is either not paying attention or misunderstood the question.
Sample size: planned number of observations
Since we do not know the response rate in advance, the number of subjects participating in the survey can be anywhere from 0 to 100,432. Based on the response rate in a previous experiment (3.7%, from Nathan et al., 2020 and 3.6% from Giaccobasso et al., 2022), our best guess is that we'll have around 3,600 responses. References: Giaccobasso, M.; Nathan, B.; Perez-Truglia, R. and Zentner, A. (2022). Where Do My Tax Dollars Go? Tax Morale Effects of Perceived Government Spending. NBER Working Paper No. 29789. Nathan, B.; Perez-Truglia, R. and Zentner, A. (2020). My Taxes are Too Darn High: Why Do Households Protest their Taxes? NBER Working Paper No. 27816.
Sample size (or number of clusters) by treatment arms
We cross-randomized subjects to receive three pieces of information, with 50% probability each. So 1/8 of the sample will receive all three pieces of information, 1/8 of the sample will receive the first piece of information only, 1/8 will receive the second piece of information only, ..., and 1/8 of the sample will receive no information.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
Since we do not know the response rate yet, we cannot estimate the minimum detectable effect size in advance.
Supporting Documents and Materials


Document Name
Sample Letter
Document Type
Document Description
This is a sample letter used to invite subjects to participate in the survey.
Sample Letter

MD5: 4af58799c1779f7391db8ebb34246e2d

SHA1: 30da75026aa94544587c9d77bd22b5466444dbf1

Uploaded At: April 25, 2022

Document Name
Sample Survey Instrument
Document Type
Document Description
This is a sample of the survey instrument. Each survey is tailored to the specific respondent, and this is just a sample.
Sample Survey Instrument

MD5: c626dcbfb1914ccfb6087ea9eeb7a463

SHA1: 892354cfb5fd1f43578a8f0c27028a78c459cadc

Uploaded At: April 25, 2022


Institutional Review Boards (IRBs)

IRB Name
University of Chicago Social and Behavioral Sciences IRB
IRB Approval Date
IRB Approval Number
IRB Name
The University of Texas at Dallas IRB
IRB Approval Date
IRB Approval Number


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