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What kinds of taxation are least objectionable to voters? The question is a classic preoccupation of public officials and social scientists alike. We exploit two advances that make it possible to shed new light on this question. The first is a large, new corpus of digitized documents describing municipal tax policies of heterogeneous design that have been directly subjected to popular referendum in the state of California. The second is a suite of new computational methods for the analysis of large corpora of texts and their associated quantitative metadata. By applying these new methods to those new data, we show that tax policies of different description vary systematically in their popularity at the ballot box. We find that official descriptions of tax policy differ along two interpretable dimensions that are associated with voters’ willingness to approve the tax. We interpret these dimensions as risk pooling and community orientation, and we show that measuring these dimensions can modestly improve our ability to predict the popularity of a tax, relative to a conventional regression specification that omits information about qualitative policy design. We discuss implications of our findings for the research on tax policy design.