How Does Participatory Budgeting Affect Council Member Priorities?
Bibliographic record
Abstract
There is a growing literature concerning participatory budgeting (PB), which transfers some element of budgetary decision making from the executive or legislature to the citizens. It is widely held that this practice originated in Porto Alegre, Brazil in 1989, although there is evidence of antecedents from the 1970s and 1980s and co-developments elsewhere in Brazil (Goldfrank, 2007; Souza, 2001). During the earlier years of development, this practice was found primarily in less developed countries. Early PB reoriented government expenditures to better focus on the needs of the populace. Substantial shares of the budget (9.8-21%) were allocated through participatory process (Souza, 2001). The first documented instance of PB in North America is in Guelph, Canada beginning in 1999 (Pinnington, Lerner, & Schugurensky, 2009). The first United States municipality to adopt PB was Chicago in 2009 (Stewart, Miller, Hildreth, & Wright-Phillips, 2014). New York City adopted a version of participatory budgeting with a process occurring in 2011-2012 with budgetary decisions for Fiscal Year 2013 (Lerner & Secondo, 2012; New York City Council, [2015]; Stewart et al., 2014). Initially four city council members committed a portion of their member selected discretionary capital projects to participatory budgeting, for a total of $6 million. Over the years the number of council members has increased; in the 2016-17 cycle, 31 council members contributed $40 million of their discretionary capital spending. As of September 2017, the City Council website shows 31 council members participating in the 2017-18 process (New York City Council, 2017c, 2017d). Discretionary spending refers to what is more commonly known as earmarks. In New York City, earmarks are in two large groups, one for the expenditure budget and the other for capital budget. The New York City variant of PB is only associated with the capital budget. The $40 million is less than 1% of the capital budget and, in fact, is a relatively small share of the member directed capital spending. Data on member item capital spending is available from 2002 through the most recent budget decision period. For this study, data has been collected through FY 2017. The data are aggregated into eight categories: education, parks and recreation, arts, culture and communities, transit, housing, public safety, seniors, and all other. The data availability across these years suggests a natural experiment: PB allows citizens to select projects. However, council members contribute only part of their capital discretionary funds to PB. While citizens may select specific projects, council members may balance their overall allocations by adjusting priorities within the discretion they retain. If there is actual impact on priorities, there should be some shift in the funding among the eight categories. If there is no substantial shift, then that suggests that the council members are using their remaining discretion to maintain their aggregate priority preferences. This study uses time series of allocations of participating and nonparticipating members to determine whether allocation changes differ between the two groups.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.050 | 0.202 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.014 | 0.009 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.017 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".