How Does Participatory Budgeting Affect Council Member Priorities?
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,050 | 0,202 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,003 | 0,005 |
| Études des sciences et des technologies | 0,007 | 0,004 |
| Communication savante | 0,014 | 0,009 |
| Science ouverte | 0,002 | 0,007 |
| Intégrité de la recherche | 0,003 | 0,004 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,017 | 0,002 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».