Gender Bias and Property Taxes
Gordon Burtch, Alejandro Zentner
TL;DR
This study investigates gender bias in property tax protest hearings using Harris County, TX data, exploiting randomized ARB panel assignments to identify how appellant and panelist gender influence outcomes. Combining 114,515 structured hearings with 80,197 audio recordings processed by a multimodal LLM, the authors show that female appellants are systematically less likely to receive reductions, especially when evaluated by female-dominated panels, with effects persisting even after conditioning on appellant behavior. The analysis uses econometric models with extensive fixed effects and two rounds of audio analysis—one with the Gemini LLM and one with human checks—demonstrating that unvoiced perceptions likely drive much of the bias, beyond observable behavior. The work highlights both the potential and limits of AI in extracting insights from unstructured administrative data and has policy implications for reducing gender bias in administrative decision-making.
Abstract
Gender bias distorts the economic behavior and outcomes of women and households. We investigate gender biases in property taxes. We analyze records of more than 100,000 property tax appeal hearings and more than 2.7 years of associated audio recordings, considering how panelist and appellant genders associate with hearing outcomes. We first observe that female appellants fare systematically worse than male appellants in their hearings. Second, we show that, whereas male appellants' hearing outcomes do not vary meaningfully with the gender composition of the panel they face, those of female appellants' do, such that female appellants obtain systematically lesser (greater) reductions to their home values when facing female (male) panelists. Employing a multi-modal large language model (M-LLM), we next construct measures of participant behavior and tone from hearing audio recordings. We observe markedly different behaviors between male and female appellants and, in the case of male appellants, we find that these differences also depend on the gender of the panelists they face (e.g., male appellants appear to behave systematically more aggressively towards female panelists). In contrast, the behavior of female appellants remains relatively constant, regardless of their panel's gender. Finally, we show that female appellants continue to fare worse in front of female panels, even when we condition upon the appelant's in-hearing behavior and tone. Our results are thus consistent with the idea that gender biases are driven, at least in part, by unvoiced beliefs and perceptions on the part of ARB panelists. Our study documents the presence of gender biases in property appraisal appeal hearings and highlights the potential value of generative AI for analyzing large-scale, unstructured administrative data.