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Who Participates in Local Government? Evidence from Meeting Minutes

Published online by Cambridge University Press:  14 October 2018

Katherine Levine Einstein Open the ORCID record for Katherine Levine Einstein [Opens in a new window] ,
Maxwell Palmer  and
David M. Glick
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Abstract

Scholars and policymakers have highlighted institutions that enable community participation as a potential buffer against existing political inequalities. Yet these venues may bias policy discussions in favor of an unrepresentative group of individuals. To explore who participates, we compile a novel data set by coding thousands of instances of citizens speaking at planning and zoning board meetings concerning housing development. We match individuals to a voter file to investigate local political participation in housing and development policy. We find that individuals who are older, male, longtime residents, voters in local elections, and homeowners are significantly more likely to participate in these meetings. These individuals overwhelmingly (and to a much greater degree than the general public) oppose new housing construction. These participatory inequalities have important policy implications and may be contributing to rising housing costs.

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Perspectives on Politics , Volume 17 , Issue 1 , March 2019 , pp. 28 - 46
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Copyright © American Political Science Association 2018 

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Footnotes

*Data replication sets are available in Harvard Dataverse at https://doi.org/10.7910/DVN/RRF7EU

This research was funded by Boston University’s Initiative on Cities. Many thanks to Mirya Holman, Spencer Piston, Jessica Trounstine, and participants at the Vanderbilt Local Political Economy Conference, University of Wisconsin-Milwaukee Political Science Research Workshop, American Political Science Association “New Faces of Urban Politics” Mini-Conference, and Boston Area Research Initiative Spring 2018 Conference for their helpful comments. We gratefully acknowledge our outstanding research assistants Luisa Godinez Puig and Sarah Sklar. All errors are our own.

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Einstein et al. Dataset

Dataset

https://doi.org/10.7910/DVN/RRF7EU
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