Abstract
A key concern in high-inequality countries is whether greater tax revenue and progressivity could help to decrease inequality. Studies have shown that while people care about inequality, providing them with information about current levels of inequality or about specific policies has little impact on their preferences for redistribution. These preferences have usually been proxied by their preferences for government spending without any reference to taxation. In this study in Mexico, we take a novel approach by carrying out a randomized intervention in which we provide information related to (i) corruption, (ii) public health, or (iii) public safety. We then ask individuals about their willingness to pay higher taxes, and we ascertain their support for tax progressivity by asking about their preferred tax rates for the rich, the middle-income, and the poor. We find that willingness to pay higher taxes is affected only by the information on corruption and public health (2.56 and 2.89% points in additional taxes, respectively). We find that this information does not increase their preference for tax progressivity.
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Data is available upon request.
Notes
We built and embedded our experiment on an app and then ran it on Prolific. For a more detailed discussion of this platform see Peer et al. (2017). Previous studies using this platform include: Wiwad et al. (2021), which is especially relevant to our study; Khenfer et al. (2020); MacGregor et al. (2020); Moynihan et al. (2018); Schild et al. (2019); Shepherd and Fitzsimons (2020); and Stanovich and Toplak (2019).
This is a step before consideration of tax compliance, and it is more aligned with the nature of our study. It remains an open empirical question whether the mechanisms affecting the willingness to pay taxes are the same as those explaining tax compliance.
Prolific is a crowdsourcing platform for recruiting human subjects for behavioral research that enables large-scale data collection by connecting researchers and participants worldwide. It is similar to Amazon’s Mechanical Turk: both platforms allow requesters or researchers to offer monetary incentives to workers to perform cognitive jobs. However, Prolific offers fewer restrictions on the types of tasks that researchers can request from participants, more transparent information regarding its pool of workers, and it promotes an environment of ethical payments. For more details, see Peer et al. (2017).
We have data for 1244 individuals. However, some of them did not answer the questions about sex (n = 6), educational level (n = 1), or household income in the previous quarter (n = 3). We exclude these individuals from our analysis.
To further enhance the validity of our results, we implemented additional measures beyond those provided by the Prolific platform. To encourage participants to engage attentively, we incorporated brief messages prompting them to answer all questions and backtrack if necessary. Additionally, we minimized potential psychological biases by designing sliders that mitigated anchoring effects. To gauge the consistency of responses, we included questions aimed at this purpose. Moreover, our experiment was designed for seamless accessibility across devices (e.g., computers, cellphones, tablets), with mechanisms in place to prevent duplicate participation. Once a participant completed the experiment on a device, they were unable to access it again from the same device, ensuring the exclusion of potential robot participation or multiple entries from the same individual using different accounts on Prolific.
We divide the sample into the following regions: South: Campeche, Chiapas, Guerrero, Oaxaca, Quintana Roo, Tabasco, Veracruz, and Yucatán. Center: Aguascalientes, Colima, Estado de México, Guanajuato, Hidalgo, Jalisco, Michoacán, Morelos, Puebla, Querétaro, San Luis Potosí, and Tlaxcala. North: Baja California, Baja California Sur, Chihuahua, Coahuila, Durango, Nayarit, Nuevo León, Sinaloa, Sonora, Tamaulipas, and Zacatecas. We consider Mexico City as an independent region.
The “high-income household” variable is constructed by asking people about their household income in the previous quarter according to specific ranges. If the person answered that their household had received more than MXN $30,000, they are included in this category.
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RC-V and SR-O designed the study, SR-O implemented the randomized intervention, RC-V and SR-O wrote and reviewed the manuscript. Both the authors read and approved the final manuscript.
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Campos-Vazquez, R.M., Restrepo-Oyola, S.D. A randomized intervention to gauge preferred tax rates and progressivity. Int Tax Public Finance 32, 782–804 (2025). https://doi.org/10.1007/s10797-024-09852-x
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DOI: https://doi.org/10.1007/s10797-024-09852-x