IDEAS home Printed from https://ideas.repec.org/p/osf/osfxxx/b9n4k.html
   My bibliography  Save this paper

Gendered cities: Studying urban gender bias through street names

Author

Listed:
  • Oto-Peralías, Daniel

    (Universidad Pablo de Olavide)

  • Gutiérrez Mora, Dolores

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.

Suggested Citation

  • Oto-Peralías, Daniel & Gutiérrez Mora, Dolores, 2021. "Gendered cities: Studying urban gender bias through street names," OSF Preprints b9n4k, Center for Open Science.
  • Handle: RePEc:osf:osfxxx:b9n4k
    DOI: 10.31219/osf.io/b9n4k
    as

    Download full text from publisher

    File URL: https://osf.io/download/60431e17fbab57002d75b780/
    Download Restriction: no

    File URL: https://libkey.io/10.31219/osf.io/b9n4k?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Grimmer, Justin & Stewart, Brandon M., 2013. "Text as Data: The Promise and Pitfalls of Automatic Content Analysis Methods for Political Texts," Political Analysis, Cambridge University Press, vol. 21(3), pages 267-297, July.
    2. Oded Galor & Ömer Özak & Assaf Sarid, 2020. "Linguistic Traits and Human Capital Formation," AEA Papers and Proceedings, American Economic Association, vol. 110, pages 309-313, May.
    3. Russell Weaver & Chris Holtkamp, 2016. "Determinants of Appalachian Identity: Using Vernacular Traces to Study Cultural Geographies of an American Region," Annals of the American Association of Geographers, Taylor & Francis Journals, vol. 106(1), pages 203-221, January.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Dolores Gutiérrez-Mora & Daniel Oto-Peralías, 2022. "Gendered cities: Studying urban gender bias through street names," Environment and Planning B, , vol. 49(6), pages 1792-1809, July.
    2. repec:osf:osfxxx:b9n4k_v1 is not listed on IDEAS

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. repec:osf:osfxxx:b9n4k_v1 is not listed on IDEAS
    2. Bernhardt, Lea & Dewenter, Ralf & Thomas, Tobias, 2023. "Measuring partisan media bias in US newscasts from 2001 to 2012," European Journal of Political Economy, Elsevier, vol. 78(C).
    3. Ntentas, Raphael, 2021. "Quantifying political populism and examining the link with economic insecurity: evidence from Greece," LSE Research Online Documents on Economics 112579, London School of Economics and Political Science, LSE Library.
    4. Victoria Baranov & Ralph Haas & Pauline Grosjean, 2023. "Men. Male-biased sex ratios and masculinity norms: evidence from Australia’s colonial past," Journal of Economic Growth, Springer, vol. 28(3), pages 339-396, September.
    5. Helena Seibicke & Asimina Michailidou, 2022. "The Challenges of Reconstructing Citizen-Driven EU Contestation in the Digital Media Sphere," Politics and Governance, Cogitatio Press, vol. 10(1), pages 97-107.
    6. Lin, Annie E. & Young, Jimmy A. & Guarino, Jeannine E., 2022. "Mother-Daughter sexual abuse: An exploratory study of the experiences of survivors of MDSA using Reddit," Children and Youth Services Review, Elsevier, vol. 138(C).
    7. Yasuhiro Hara, 2024. "Dynamic Relationship between Information Dissemination by Local Governors and Mobility during the COVID-19 Pandemic," Discussion papers ron373, Policy Research Institute, Ministry of Finance Japan.
    8. Bastiaan Bruinsma & Moa Johansson, 2024. "Finding the structure of parliamentary motions in the Swedish Riksdag 1971–2015," Quality & Quantity: International Journal of Methodology, Springer, vol. 58(4), pages 3275-3301, August.
    9. Anselm Küsters, 2022. "Applying Lessons from the Past? Exploring Historical Analogies in ECB Speeches through Text Mining, 1997–2019," International Journal of Central Banking, International Journal of Central Banking, vol. 18(1), pages 277-329, March.
    10. Rybinski, Krzysztof, 2020. "The forecasting power of the multi-language narrative of sell-side research: A machine learning evaluation," Finance Research Letters, Elsevier, vol. 34(C).
    11. Keith Carlson & Michael A. Livermore & Daniel N. Rockmore, 2020. "The Problem of Data Bias in the Pool of Published U.S. Appellate Court Opinions," Journal of Empirical Legal Studies, John Wiley & Sons, vol. 17(2), pages 224-261, June.
    12. Rauh, Christian, 2015. "Communicating supranational governance? The salience of EU affairs in the German Bundestag, 1991–2013," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 16(1), pages 116-138.
    13. Grajzl, Peter & Murrell, Peter, 2021. "A machine-learning history of English caselaw and legal ideas prior to the Industrial Revolution I: generating and interpreting the estimates," Journal of Institutional Economics, Cambridge University Press, vol. 17(1), pages 1-19, February.
    14. Pratima (Tima) Bansal & Jury Gualandris & Nahyun Kim, 2020. "Theorizing Supply Chains with Qualitative Big Data and Topic Modeling," Journal of Supply Chain Management, Institute for Supply Management, vol. 56(2), pages 7-18, April.
    15. Heinemann, Friedrich & Kemper, Jan, 2022. "Inflation of objectives instead of focus on inflation? Evidence on the ECB objective function from a textual analysis," ZEW Expert Briefs 22-07, ZEW - Leibniz Centre for European Economic Research.
    16. Grajzl, Peter & Murrell, Peter, 2025. "From status to contract? A macrohistory from early-modern English caselaw and print culture," Explorations in Economic History, Elsevier, vol. 97(C).
    17. David Bholat & Stephen Hans & Pedro Santos & Cheryl Schonhardt-Bailey, 2015. "Text mining for central banks," Handbooks, Centre for Central Banking Studies, Bank of England, number 33, April.
    18. Julia Seiermann, 2018. "Only Words? How Power in Trade Agreement Texts Affects International Trade Flows," UNCTAD Blue Series Papers 80, United Nations Conference on Trade and Development.
    19. Sami Diaf & Jörg Döpke & Ulrich Fritsche & Ida Rockenbach, 2020. "Sharks and minnows in a shoal of words: Measuring latent ideological positions of German economic research institutes based on text mining techniques," Macroeconomics and Finance Series 202001, University of Hamburg, Department of Socioeconomics.
    20. Latifi, Albina & Naboka-Krell, Viktoriia & Tillmann, Peter & Winker, Peter, 2024. "Fiscal policy in the Bundestag: Textual analysis and macroeconomic effects," European Economic Review, Elsevier, vol. 168(C).
    21. Sara Kahn-Nisser, 2019. "When the targets are members and donors: Analyzing inter-governmental organizations’ human rights shaming," The Review of International Organizations, Springer, vol. 14(3), pages 431-451, September.

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:osf:osfxxx:b9n4k. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: OSF (email available below). General contact details of provider: https://osf.io/preprints/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.