This paper examines the impact of local participatory democracy initiatives on individual voter turnout in ordinary elections, using the example of participatory budgeting (PB). Such initiatives often aspire to create more activated citizens, but there is still limited empirical research validating these claims. We link participants in New York City’s participatory budgeting process to their state voter file records to test whether PB increases participants’ likelihood of voting in regular elections. We use coarsened exact matching to identify similar voters from council districts where PB was not implemented. Comparing PB voters to similar individuals who were not exposed to PB, we find that engaging with participatory budgeting increased individuals’ probability of voting by an average of 8.4 percentage points. In addition, we find that these effects are greater for those who often have lower probabilities of voting—young people, lower educated and lower income voters, black voters, and people who are the minority race of their neighborhood.
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These goals were explicitly stated in the official rule books for PB in each of these cities (City of Vallejo 2013 and PBNYC 2013). These goals have not been not idle words hidden in formal documents; themes of political transformation and democratic renewal have pervaded the outreach materials, framing of meetings and public events, and the explicit hopes for PB shared between observers and practitioners in the field.
See up to date info on where PB is happening in North America here: https://www.participatorybudgeting.org/case-studies/ (Accessed 12/21/2020).
With the exception of one district, only capital projects are eligible for PB funding, not operations funding.
In New York City the “delegates,” who develop and deliberate on the proposed projects, represent on average 2% of the total voting body in a District.
Replication code and instructions to access data are available at https://github.com/csjohns/pb-voter-turnout. Individual level data was provided in partnership with New York Civic Engagement Table who retain the data but will make it freely available to researchers wishing to replicate the work upon request. Please follow contact instructions in the GitHub repository.
We use the 5-year 2011–2015 American Community Survey estimates, accessed directly from R using the acs package.
Two different cycles of Freedom of Information Act requests failed to elicit machine readable election results or results at the election district (the equivalent of precinct in most other jurisdictions).
In addition, a small subset of voters whose votes were recorded in 2012 in centrally held records, but who were not definitely assigned to a PB district are also included, as the first PB votes in the city occurred in 2012 and thus we are confident of recording these voters’ initial experience with PB.
See Online Appendix for detail on demographic balance of PB and non-PB voters before and after matching.
With many more controls than PB voters, we randomly sample one matching control individual for every PB voter in each strata. An alternative would be to allow many matches from the control population and then use appropriate weights in the subsequent regressions. With our relatively large treatment case sample size, one-to-many matching with weights actually provides a larger sample than is necessary to fit reliable and informative models, while significantly increasing complexity and computation time.
See the Online Appendix for comparisons across match specifications.
See Angrist and Pischke (2008) for a introduction to difference-in difference models of increasing complexity.
8.4% (95% CI 7.8–9%) is the marginal effect of the post-treatment variable in the best-fitting difference-in-difference model, without including any interactions of the post-treatment indicator with other covariates, calculated with the R margins package.
Model selection processes used in-sample percent correctly predicted and AIC/BIC criteria to identify and exclude appropriate transformations and redundant variables, such as a census-tract flag for majority white population (this variable was not informative once race and flag for majority race membership was included).
Thanks to an anonymous reviewer for suggesting this ‘placebo’ framing of a non-PB comparison group.
Note, it is not entirely clear what the causal story behind the small district-level effect of PB may be. It could be that the effect of PB has has absorbed district characteristics correlated with the introduction of PB or it could be that the assorted effects of PB on community networks and civil society produce spillover effects that mean even non-voters are more activated in subsequent election cycles.
See the online appendix for more detail on the balance of in- and out-of-sample PB voters
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The authors wish to thank the New York Civic Engagement Table, Participatory Budgeting-New York City, Participatory Budgeting Project, Christopher King, Peter Ramand, and Loren Peabody for collaboration and sharing of PB voter data as well as Christopher Adolph, Laine Rutledge, and two anonymous reviewers for helpful advice regarding methodology and presentation.
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Appendix: Sub-group Interaction Model Estimates
Appendix: Sub-group Interaction Model Estimates
See Table 3.
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Johnson, C., Carlson, H.J. & Reynolds, S. Testing the Participation Hypothesis: Evidence from Participatory Budgeting. Polit Behav (2021). https://doi.org/10.1007/s11109-021-09679-w
- Participatory budgeting
- Participatory democracy
- Voter turnout
- Civic engagement
- Political mobilization