Published online by Cambridge University Press: 10 August 2020
Are elected officials more responsive to men than women inquiring about access to government services? Women face discrimination in many realms of politics, but evidence is limited on whether such discrimination extends to interactions between women and elected officials. In recent years, several field experiments have examined public officials’ responsiveness. The majority focused on racial bias in the USA, while the few experiments outside the USA were usually single-country studies. We explore gender bias with the first large-scale audit experiment in five countries in Europe (France, Germany, Ireland, Italy, and Netherlands) and six in Latin America (Argentina, Brazil, Chile, Colombia, Mexico, and Uruguay). A citizen alias whose gender is randomized contacts members of parliament about unemployment benefits or healthcare services. The results are surprising. Legislators respond significantly more to women (+3% points), especially in Europe (+4.3% points). In Europe, female legislators in particular reply substantially more to women (+8.4% points).
The authors would like to thank Cecilia Martinez-Garrardo and Anna Bassi for their constant support during the initial stages of this project. They are also grateful to Luke Chanarin and Bilyana Petrova for reading different versions of the manuscript, as well as the participants in the European Political Science Association Meeting in Milan, Italy, and the anonymous reviewers for their helpful comments. Finally, they would like to thank Beatriz Rey and Jelle Koedam for their help with translation. The authors declare that there is no conflict of interest. They have no affiliations with or involvement in any organization or entity with any financial or non-financial interest in the subject matter or materials discussed in this manuscript. The data, code, and any additional materials required to replicate all analyses in this article are available at the Journal of Experimental Political Science Dataverse within the Harvard Dataverse Network, at: https://doi.org/10.7910/DVN/VVXJBJ under: Magni and Ponce de Leon, 2020.