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.