Political power in the United States and many other democracies is divided between national, state, and local governments. Despite the overwhelming attention devoted to federal politics in the US – by journalists, researchers, and the public at large – local governments also make important policy decisions that affect our daily lives in policy areas ranging from infrastructure to public health. For example, during the COVID-19 pandemic while (and in some cases even before) lockdown policies and redistribution programs were being debated in the U.S. Congress, cities like Atlanta, GA, enacted moratoriums on housing evictions. Other cities like Spokane, WA, suspended water shut-offs and late fees on utility bills. These decisions are generally taken and debated in public meetings, where local government officials like city councilmembers make key policy decisions with the public’s input.
Even with all this activity at the local level, it is generally much easier to observe the policy deliberations and decisions happening in Washington D.C. and state governments. We argue this is mainly because of a lack of standardized data on local policy-making. Policy debates and roll call votes on the floor of Congress are transcribed and made easily available in the Congressional Record, and similar resources exist for most state governments (e.g. sessions of governments like the Massachusetts State Senate and Texas House of Representatives are recorded and released as videos). However, records of local government deliberations are scattered across individual city websites with no standardized format. There is no Congressional Record for town hall. As a result, those hoping to study local government deliberations typically must undergo the costly, time-consuming process of manually collecting and transcribing their own data.
To help address this problem, we are introducing LocalView, a database of local government public meetings, to aid the study of local politics and policy-making. We collect over 100,000 videos of local government meetings and their transcripts from over 1,000 cities and towns across the United States. The data are processed, downloaded, cleaned, and publicly disseminated (at our website, localview.net) for analysis across places and over time. In our paper, we discuss how we validated this dataset using a variety of methods and demonstrate how it can be used to map local governments' attention to policy areas. This map shows the current coverage of LocalView, where each dot represents a government in our sample and cities with populations over 500,000 have their names displayed. More details on our sample selection procedures can be found in the paper.
Researchers could use LocalView in many ways, but one is to measure patterns in local-level deliberations of particular policies. For example, in a recent influential paper, Grossman et al. (2020) study how political preferences influenced the relationship between governors' communications about social distancing policies and residents' mobility patterns. Their work provides evidence that the partisan affiliations of individuals and state governors played a role in influencing individual decisions to abide by physical distancing orders. Conducting a similar analysis at the local level, where many key public health decisions are made, would require collecting information from thousands of independent local governments across the United States.
With LocalView, we can run a preliminary analysis by calculating the number of times each policy was mentioned per meeting across our sample. The plot below shows normalized word counts (per meeting) from 2019 through late 2022 for several COVID-relevant public health policies: vaccines, social distancing, quarantine / lockdown orders, and masking requirements. The line colors indicate average partisanship of a city measured by the average vote-share across two sets of recent elections.
This relatively straightforward analysis shows clear patterns — unlike the similar discussion patterns for social distancing and quarantine, both vaccines and masking have more erratic discussion patterns. For example, local vaccine discussions largely follow the general trend of US COVID-19 cases with peaks throughout 2021 and early 2022, and generally lower attention during times of fewer cases. We also see that safely Republican-voting areas consistently discuss vaccines less than others, perhaps consistent with Republican hesitancy against vaccines more generally (e.g. Pink et al. 2021).
On the other hand, attention to masking clearly does not follow these same partisan patterns during 2020 and 2021, perhaps illustrating the inconsistencies and confusion around masking requirements that characterized the US during the onset of the COVID pandemic (see, for example, Gostin et al. (2020). Republican-leaning areas begin to consistently discuss masking less often starting in mid to late 2021. Attention to masking then shrinks universally across all areas throughout mid and late 2022, coinciding with the return to the lower case rates after a spike in early 2022.
While this analysis is not conclusive in any sense, our paper provides evidence that crucial political issues like climate change, affordable housing, crime, gun control, and others are discussed extensively in our meetings. LocalView provides an unprecedented look into how these issues and others are deliberated and ultimately turned into policy across the United States.
- Grossman, Guy, Soojong Kim, Jonah M. Rexer, and Harsha Thirumurthy. "Political partisanship influences behavioral responses to governors’ recommendations for COVID-19 prevention in the United States." Proceedings of the National Academy of Sciences 117, no. 39 (2020): 24144-24153.
- Gostin, Lawrence O., I. Glenn Cohen, and Jeffrey P. Koplan. "Universal masking in the United States: the role of mandates, health education, and the CDC." Jama 324, no. 9 (2020): 837-838.
- Pink, Sophia L., James Chu, James N. Druckman, David G. Rand, and Robb Willer. "Elite party cues increase vaccination intentions among Republicans." Proceedings of the National Academy of Sciences 118, no. 32 (2021): e2106559118.
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