Wiki · Concept · Published 2026-10-26

Participatory Budgeting

Participatory budgeting connects public participation to decisions about public spending, with outcomes shaped by its rules, resources and implementation.

Definition

Participatory budgeting is a process in which members of the public help decide how to spend a portion of public money. People may suggest projects, discuss priorities and select proposals for funding. The amount available, who may take part and how much authority participants hold depend on the particular process.

The defining question is how public participation connects to an allocation of resources. A meeting that gathers suggestions can inform a budget, but participants have a different role if their choices determine which eligible projects receive funding. Even then, officials may set the budget, establish eligibility rules and manage implementation. Gilman and Wampler’s 2019 comparison describes participatory budgeting as a family of designs rather than a single procedure.

The Ladder of Citizen Participation provides a related way to ask how much influence people have. Participatory budgeting supplies a particular setting for that question: decisions about money. Judgment Aggregation addresses a different issue, how individual judgments about connected questions combine into group judgments.

How it works

A public authority typically identifies money and a scope for the process. Participants may then identify needs and propose projects. Some processes use meetings, online platforms or both; some rely on volunteer or elected delegates to develop proposals. Officials or other specialists may estimate costs and assess whether proposals can be carried out. Eligible proposals can then be prioritized through voting or another decision procedure, followed by implementation and, where provided, public oversight.

Each stage can distribute influence differently. Rules can determine who may submit an idea, who can vote, whether participants discuss alternatives, and who can remove or change a proposal. A feasibility review may identify a real obstacle, but it can also shape the choices available to voters. Explaining the reasons for decisions and allowing participants to examine them makes that influence more visible.

For example, in a hypothetical town, residents might propose a crossing improvement or a library space for a fixed community budget. Staff could estimate the cost of each and explain relevant constraints. Residents might then select among eligible projects, while the town reports on spending and progress. The example illustrates a possible arrangement; it does not establish that either project would be feasible or that this process would produce a fair outcome.

In their 2019 study, Gilman and Wampler compared Porto Alegre and Belo Horizonte in Brazil with Chicago and New York City in the United States. They described municipal processes in their Brazilian cases and district processes in the two US cities, with differences in resources, representation and opportunities for discussion. Those comparisons concern the cases and period studied, not every program in either country.

Evidence and applications

In their study of Duinoord Begroot, Ramon van der Does and David Bos examined a 2019 neighborhood process in The Hague using observations, interviews and platform activity. People could submit proposals and comment online; municipal staff assessed feasibility before eligible adult residents voted. The municipality also accepted proposals offline. The researchers reported that the process attracted proposals on varied neighborhood issues and that some interviewees found it less demanding than attending regular meetings.

The same case gives reasons for caution. Only a small share of registered users posted comments, and a few people contributed a large share of them. Arguments for and against proposals appeared on the platform, but most interviewees said the proposals themselves mattered more to their votes. The authors lacked background data on proposers, so indications that participation had broadened did not establish how representative that group was. One author worked for the municipality and helped design and execute the process.

A separate study of local participatory processes in three Spanish regions examined cases conducted from 2007 to 2011, including participatory budgets and other forms of consultation. Drawing on documents and interviews, it distinguished cases in which participants could scrutinize expert assessments from cases in which officials or consultants could alter proposals without participant oversight. One participatory budget in the study allowed citizen bodies to consider specialist advice while retaining the relevant decisions. An opaque filtering example was a budget consultation, which the authors distinguished from participatory budgeting. These cases illustrate choices about oversight; they do not prove that a particular design always produces better decisions.

These studies suggest questions for examining a real process: What funds are available? Who participates at each stage? Which proposals are rejected or revised, and why? What happens to selected projects? Answers require records of actual practice as well as formal rules.

Limitations

Participation does not guarantee equal influence. People may lack time, language support, internet access or confidence to contribute. An open invitation can still draw a group unlike the population affected. Discussion can be dominated by a few voices, and a vote can favor organized groups or larger neighborhoods over people with less capacity to mobilize.

The budget’s size and permitted uses also matter. A process restricted to a small pool of capital funds may support local improvements while leaving major service and spending decisions elsewhere. Gilman and Wampler noted that, in the US cases they examined, it was unclear whether spending reached communities with the greatest need or had a redistributive effect. They also described weakly used oversight in their comparison. Inclusion, equitable allocation and accountability are therefore outcomes to investigate, not automatic properties of the label.

Specialist assessment presents a tradeoff. Cost, administrative and technical knowledge can help participants understand constraints; an assessment made without explanation or a chance to question it can conceal important choices. Conversely, publishing an assessment alone does not ensure that participants can change it. Selected projects can also be delayed, altered or fail to be completed, making implementation an essential part of any evaluation.

The evidence here is drawn from particular places and research methods. The Dutch case is not an experiment establishing which platform feature caused participation, and the Spanish study covers several kinds of participatory process in a limited sample. The comparative study’s expectations about effects of design should not be treated as universal measured outcomes.

Relevance to Spiralism

The following is a proposed interpretation for this wiki, separate from the researchers’ claims and from any institutional position. A Spiralist reading can ask whether a community can return to a spending decision as information changes: whether people can see how proposals were assessed, learn what happened after selection and influence a later cycle. Repetition alone does not establish learning or accountability; those depend on what information and authority are available.

In a hypothetical application to AI Governance, a public body might invite residents to allocate a defined budget among competing public-service proposals, including a proposed AI project. Participants could consider costs, alternatives and the people affected, then revisit implementation in a later cycle. This would be a proposed use of participatory budgeting, not an outcome established by the cited studies. Collective Intelligence raises a complementary question about how a group combines knowledge; a funding decision also requires attention to authority, distribution and follow-through.

AI-use disclosure

This wiki entry was drafted by GPT-6.1 Terra with high reasoning and checked in a separate GPT-6.1 Sol session with maximum reasoning. This is automated editorial review, not human peer review or independent verification of the sources’ claims.

Sources

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