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The cost of gendered attitudes on a female candidate: Evidence from Google Trends

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  • Corbi, Raphael
  • Picchetti, Pedro

Abstract

How much can negative attitudes towards women affect voting for a female candidate on a major election? We measure gender animus by calculating a proxy based on Google search queries that include gender-charged language. Such approach likely elicits socially sensitive attitudes by limiting the concern of social censoring, circumventing usual difficulties associated with survey-based measurements. We compare the proxy to Hillary Clinton’s vote share in the presidential election of 2016, controlling for the vote share of the previous Democratic presidential candidate, Barack Obama. Our results indicate that a one standard deviation increase in our proxy is associated with a 2 percentage points relative loss for Hillary and suggest that online-based observable behavior can be useful for measuring different kinds of hard-to-measure social attitudes.

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  • Corbi, Raphael & Picchetti, Pedro, 2020. "The cost of gendered attitudes on a female candidate: Evidence from Google Trends," Economics Letters, Elsevier, vol. 196(C).
    • Handle: RePEc:eee:ecolet:v:196:y:2020:i:c:s0165176520303049
    DOI: 10.1016/j.econlet.2020.109495
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    1. Stephens-Davidowitz, Seth, 2014. "The cost of racial animus on a black candidate: Evidence using Google search data," Journal of Public Economics, Elsevier, vol. 118(C), pages 26-40.
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    3. Alice H. Wu, 2018. "Gendered Language on the Economics Job Market Rumors Forum," AEA Papers and Proceedings, American Economic Association, vol. 108, pages 175-179, May.
    4. Broockman, David E. & Soltas, Evan J., 2020. "A natural experiment on discrimination in elections," Journal of Public Economics, Elsevier, vol. 188(C).
    5. De Paola, Maria & Scoppa, Vincenzo & Lombardo, Rosetta, 2010. "Can gender quotas break down negative stereotypes? Evidence from changes in electoral rules," Journal of Public Economics, Elsevier, vol. 94(5-6), pages 344-353, June.
    6. Alexandre Mas & Enrico Moretti, 2009. "Racial Bias in the 2008 Presidential Election," American Economic Review, American Economic Association, vol. 99(2), pages 323-329, May.
    7. Conley, T. G., 1999. "GMM estimation with cross sectional dependence," Journal of Econometrics, Elsevier, vol. 92(1), pages 1-45, September.
    8. Colella, Fabrizio & Lalive, Rafael & Sakalli, Seyhun Orcan & Thoenig, Mathias, 2019. "Inference with Arbitrary Clustering," IZA Discussion Papers 12584, Institute of Labor Economics (IZA).
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    Cited by:

    1. Gutiérrez, Antonio, 2023. "La brecha de género en el emprendimiento y la cultura emprendedora: Evidencia con Google Trends [Entrepreneurship gender gap and entrepreneurial culture: Evidence from Google Trends]," MPRA Paper 115876, University Library of Munich, Germany.
    2. Matheus Pereira Libório & Petr Iakovlevitch Ekel & Carlos Augusto Paiva Martins, 2023. "Economic analysis through alternative data and big data techniques: what do they tell about Brazil?," SN Business & Economics, Springer, vol. 3(1), pages 1-16, January.
    3. Guzi, Martin & Mikula, Štěpán, 2021. "Careful what you say: The effect of manipulative information on the 2013 Czech presidential run-off election," Economics Letters, Elsevier, vol. 209(C).

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    More about this item

    Keywords

    Gender; Discrimination; Election; Google;
    All these keywords.

    JEL classification:

    • D72 - Microeconomics - - Analysis of Collective Decision-Making - - - Political Processes: Rent-seeking, Lobbying, Elections, Legislatures, and Voting Behavior
    • J16 - Labor and Demographic Economics - - Demographic Economics - - - Economics of Gender; Non-labor Discrimination

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