🔎 What This Paper Tackles
Local governments dominate U.S. public administration: of the country’s 90,106 governments, 99.9% are local. These jurisdictions vary widely in institutional features, descriptive representation, and policy-making power, but political scientists have been slow to exploit that variation because comprehensive local data are often hard to obtain—unavailable or costly, difficult to replicate, and rarely updated.
🧭 How Data Were Collected and Compared
- Two independent crowdsourced data-collection projects were carried out to assemble local political information.
- Multiple measures of coder agreement (consensus measures) were evaluated to assess reliability across nonexpert coders.
- The crowdsourced outputs were validated against elite and professional datasets to benchmark accuracy.
📊 Key Findings
- Crowdsourced local data show high levels of accuracy when compared with elite and professional sources.
- Different consensus measures across coders can be used to gauge and improve data quality.
- Crowdsourcing produces data that are easier to update and replicate than many existing local datasets.
- Nonexperts can effectively collect, validate, and update detailed local political data.
💡 Why It Matters
Reliable, inexpensive, and scalable crowdsourced data remove a major barrier to studying the large and diverse universe of U.S. local governments. That opens new possibilities for research on local institutions, representation, and policy variation, and offers a practical route for maintaining up-to-date, replicable data on subnational politics.
