Gendered cities: Studying urban gender bias through street names

Daniel Oto-Peralías and Dolores Gutiérrez Mora
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Daniel Oto-Peralías: Universidad Pablo de Olavide

No b9n4k, OSF Preprints from Center for Open Science

Abstract: This paper uses text analysis to measure gender bias in cities through the use of street names. Focusing on the case of Spain, we collect data on 15 million street names to analyze gender inequality in urban toponyms. We calculate for each Spanish municipality and each year from 2001 to 2020 a variable measuring the percentage of streets with female names over the total number of streets with male and female names. Our results reveal a strong gender imbalance in Spanish cities: the percentage of streets named after women over the total named after men and women is only 12% in 2020. We also observe that there are substantial differences across the Spanish regions, and concerning new streets, gender bias is lower but still far from parity. The second part of the paper analyzes the correlation of our indicator of gender bias in street names with the cultural factor it is supposed to capture, with the results suggesting that it constitutes a useful cultural measure of gender inequality at the city level. This research has policy implications since it helps to measure a relevant phenomenon, given the strong symbolic power attributed to street names, which has been elusive to quantify so far.

Date: 2021-03-05
New Economics Papers: this item is included in nep-big, nep-eur, nep-gen and nep-ure
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Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:osf:osfxxx:b9n4k

DOI: 10.31219/osf.io/b9n4k

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