Support for Benefit-Based Taxation and Redistribution: Evidence from a Field Experiment

Last registered on May 21, 2021

Pre-Trial

Trial Information

General Information

Title
Support for Benefit-Based Taxation and Redistribution: Evidence from a Field Experiment
RCT ID
AEARCTR-0007483
Initial registration date
May 19, 2021

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 21, 2021, 9:34 AM EDT

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

Locations

Primary Investigator

Affiliation
UT Dallas

Other Primary Investigator(s)

PI Affiliation
UC Berkeley
PI Affiliation
The University of Texas at Dallas
PI Affiliation
UCLA

Additional Trial Information

Status
In development
Start date
2021-04-15
End date
2021-12-31
Secondary IDs
Abstract
We designed a field experiment to explore households' support for taxation and redistribution. We designed an information-provision experiment with households who pay property taxes. We measure the households' support for taxation via survey data and by revealed-preference via the households' decisions to appeal their property taxes.

We examine two potential drivers of the support for taxation and the decision to protest property taxes:

1) The extent to which households benefit from the services that are funded through property taxes (more precisely, public schools).
2) The extent to which the property taxes are not spent in the same community but shared with more disadvantaged communities (more precisely, recapture of property taxes).
External Link(s)

Registration Citation

Citation
Giaccobasso, Matias et al. 2021. "Support for Benefit-Based Taxation and Redistribution: Evidence from a Field Experiment." AEA RCT Registry. May 21. https://doi.org/10.1257/rct.7483
Experimental Details

Interventions

Intervention(s)
We study households who were about to have the opportunity to protest the tax assessments of their homes. We send letters to 78,128 of those households to invite them to participate in a survey. Within the survey, we conduct an information-provision experiment. We elicit beliefs on, and provide information about, two topics:

1) The share of property taxes that correspond to school taxes.
2) The share of school taxes that are redistributed to, or from, other school districts.

The goal is to measure how the information included in the letters affected the attitudes about taxation and redistribution, and the households' subsequent decision to protest property taxes.
Intervention Start Date
2021-04-15
Intervention End Date
2021-05-17

Primary Outcomes

Primary Outcomes (end points)
The main outcome is a dummy variable indicating whether the household protested or not by the deadline (May 17th, 2021).

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, and study the protests through tax agents as a secondary outcome.
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
Right after the information-provision, the survey includes a number of questions designed to be used as outcomes: e.g., the respondent's posterior beliefs, and a series of subjective questions such as whether the household's own property taxes are fair or whether recapture of property taxes is fair. The full survey instrument is attached to this application.

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.
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
We sent letters to a sample of 78,128 households from Dallas County (Texas) who were about to have the opportunity to protest the tax assessments of their homes. A sample of the envelope and the letter are attached to this pre-registration.

Most importantly, our letters included an invitation to participate in an online survey. 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 2021 or in any of the previous years).

We want to measure how the information provided inside the survey affected the subsequent attitudes about property taxes and households' 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 used in some of our previous work such as:

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.
Bottan, N. and Perez-Truglia, R. (2020). Betting on the House: Subjective Expectations and Market Choices. NBER Working Paper No. 27412.
Cullen, Z. and Perez-Truglia, R. (2018). How Much Does Your Boss Make? The Effects of Salary Comparisons. NBER Working Paper No. 24841.

In the survey, we elicit beliefs on, and provide information on, two topics:

1) The share of property taxes that correspond to school taxes.
2) The share of school taxes that are redistributed to, or from, other school districts.

The hypotheses are the following:

1) Benefit-based taxation hypothesis: households are more tolerant of taxes (and less likely to protest) when they benefit from the government services that are funded by those taxes. Households who send their children to local public schools should be more tolerant of taxation when they find out that school taxes comprise a larger share of the total property taxes. This effect may be weaker, or go in the opposite direction, for households without children (i.e., because they do not benefit from public schools).

2) Redistribution hypothesis: households may be more (less) tolerant of taxation when they find out that their school district is a net beneficiary (contributor) in the tax recapture system. This effect may be weaker, or even have the opposite sign, for households who send their kids to local public schools relative to households that do not.

As described above, whether the household has any kids enrolled in the public school district constitutes a key form of heterogeneity for the data analysis. There is a second form of heterogeneity we are interested in: fairness norms. Some households may think that it is fair that households without kids should pay school taxes, while other individuals may see that as unfair. We included a question in the survey to elicit these fairness norms directly. Likewise, there may be different fairness norms about the recapture system: some households may think that it is fair that the richest districts help the more disadvantaged districts, while other households may see that as unfair. We included another questions to measure this fairness norm and thus analyze this additional form of heterogeneity. Relatedly, to the extent that there fairness views may be different across political parties, we plan on studying the heterogeneity by political party (which we elicit in the survey and which we can also infer from the voter files).

While these are not the key sources of heterogeneity that we plan on studying, we have a lot of additional questions in the survey that may help to disentangle causal mechanisms. Moreover, we have sources of administrative data on other household characteristics that we could use to investigate causal mechanism: e.g., a proxy for whether the property was over-valued or under-valued based on Redfin estimates, gender, race, age, Internet connectedness.

Last, there is a minor detail of the letter that was randomized: half of the subjects were notified in the letter that in exchange for responding to the survey, they would be entered into a raffle of 20 prizes worth 100 USD each. Half of the sample was selected to receive a message informing about these prizes in the letter. This randomization was meant to assess if raffle prizes provide a significant bump to the response rates, and whether it changes the composition of the survey respondents, which would be useful to inform researchers wanting to conduct similar field experiments in the future.
Experimental Design Details
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
78,128 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 would like to clarify that a minority of households will need to be dropped from the subject pool due to reasons that we cannot observe ex-ante: e.g., 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).
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 78,128. Based on the response rate in a previous experiment (3.7%, from Nathan et al., 2020), our best guess is that we'll have around 3,000 responses. Reference: 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 two pieces of information, with 50% probability each. So 25% of the sample will receive both pieces of information, 25% of the sample will receive the first piece of information only, 25% will receive the second piece of information only, and 25% 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

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IRB

Institutional Review Boards (IRBs)

IRB Name
THE UNIVERSITY OF TEXAS AT DALLAS
IRB Approval Date
2021-04-06
IRB Approval Number
IRB-21-414

Post-Trial

Post Trial Information

Study Withdrawal

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Intervention

Is the intervention completed?
No
Data Collection Complete
Data Publication

Data Publication

Is public data available?
No

Program Files

Program Files
Reports, Papers & Other Materials

Relevant Paper(s)

Reports & Other Materials