Business Case Studies & Business Publications

Participatory Budgeting in Richmond

Freeman, Rupert, S...

Case

Participatory Budgeting in Richmond

Freeman, Rupert; Shaloudegi, Koushyar; Klopfenstein, Amy

QA-0973 | Published February 11, 2026 | 7 Pages Case

Collection: Darden School of Business

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Learning Objectives

Set in November 2023, this case asks students to take the perspective of Matthew Slaats, a consultant for the city of Richmond, Virginia, as he selects a voting rule for the city’s new participatory budgeting (PB) program. PB allows voters to determine how to allocate a portion of the city’s budget to a select list of projects. Most PB programs use approval voting, a rule that allows participants to vote for an unlimited number of projects. After voting concludes, the projects are ranked based on the number of votes they receive, and they are funded based on those rankings until the PB budget is exhausted. As he helps Richmond create its PB program, Slaats must decide whether to recommend approval voting or a different voting rule. To inform his decision, he plans to evaluate data from Ursynów, Poland, which used approval voting for its PB process. If his analysis reveals that approval voting disproportionately favors certain project categories, neighborhoods, or demographic groups, he may wish to suggest a different voting rule. Students receive a supplementary Excel file containing the Ursynów data, and they must analyze the data to complete the assignment. Notably, the case can facilitate a robust discussion even if students use generative AI tools, as the core aspects of the case decision all require human judgment. At the Darden School of Business, this case has been taught in the first-year core MBA “Decision Analysis” course, in an elective course on how to prepare students for data-intensive roles, and in an executive education program on data analytics. It can be used in MBA-level or executive education courses on quantitative analysis, data visualization, or data-driven decision-making.

(1) To practice data analysis and visualization techniques such as feature engineering, pivot tables, and scatterplots. (2) To use data to inform a decision, and communicate the insights that led to that decision, in the face of multiple competing and ambiguous priorities. (3) To use data to explore different ways to think about fairness.