Crowdsourcing Reliable Local Data

Jane Lawrence Sumner, Emily M. Farris, Mirya R. Holman

Research output: Contribution to journalArticlepeer-review

18 Scopus citations


The adage “All politics is local” in the United States is largely true. Of the United States’ 90,106 governments, 99.9% are local governments. Despite variations in institutional features, descriptive representation, and policy-making power, political scientists have been slow to take advantage of these variations. One obstacle is that comprehensive data on local politics is often extremely difficult to obtain; as a result, data is unavailable or costly, hard to replicate, and rarely updated. We provide an alternative: crowdsourcing this data. We demonstrate and validate crowdsourcing data on local politics using two different data collection projects. We evaluate different measures of consensus across coders and validate the crowd’s work against elite and professional datasets. In doing so, we show that crowdsourced data is both highly accurate and easy to use. In doing so, we demonstrate that nonexperts can be used to collect, validate, or update local data.
Original languageEnglish (US)
Pages (from-to)244-262
Number of pages19
JournalPolitical Analysis
Issue number2
StatePublished - Apr 1 2020

Bibliographical note

Funding Information:
With access to research funds, scholars may use professional services for local data collection, including paying others to collect local data or purchasing local data previously collected by professional organizations. For example, Smith’s (2014) research, funded by a National Science Foundation (NSF) dissertation improvement grant, used a survey research firm to call city clerks to collect data. Others use the U.S. Census on Governments (Goodman 2018) or data from the International City and County Managers Association (ICMA) (Watson and Hassett 2004). ICMA also sells access to surveys on different topics related to local government and policy, with costs increasing based on the recency of data.1 Researchers o en combine these datasets with other data; for example, Nelson and Svara’s (2010) work combines ICMA surveys with data from the National League of Cities to produce a dataset of the form of government for most U.S. cities (N = 2883) with a population over 10,000. Others use large databases maintained by external organizations like signatories to the International Council for Local Environmental Initiatives (Krause, Yi, and Feiock 2016) or the National Sheriffs’ Association (Farris and Holman 2015). Others use datasets that do not have an explicit urban focus with additional local data. For example, Einstein and Kogan (2016) combine the Harvard Election Data Archive dataset of 2008 electoral returns with census-block and municipal data to examine local voting patterns.

Publisher Copyright:
Copyright © The Author(s) 2019. Published by Cambridge University Press on behalf of the Society for Political Methodology.


  • crowdsourcing
  • data collection
  • local politics
  • replication
  • surveys


Dive into the research topics of 'Crowdsourcing Reliable Local Data'. Together they form a unique fingerprint.

Cite this