Evidence-based policy-making in sports funding using a data-driven optimization approach

TL;DR

This paper presents a data-driven optimization model for allocating sports funding in Austria, aiming to maximize children's access to sports facilities by considering socio-economic factors and club budgets.

Contribution

It introduces a novel simulation-based approach that tailors sports funding strategies to regional socio-economic conditions, improving effectiveness over naive methods.

Findings

Optimized funding increases children's access to sports clubs by up to 117%.

A sub-linear relationship exists between club membership and budget, influenced by socio-economic factors.

Regionally tailored funding strategies outperform naive approaches significantly.

Abstract

Many European countries face rising obesity rates among children, compounded by decreased opportunities for sports activities during the COVID-19 pandemic. Access to sports facilities depends on multiple factors, such as geographic location, proximity to population centers, budgetary constraints, and other socio-economic covariates. Here we show how an optimal allocation of government funds towards sports facilitators (e.g. sports clubs) can be achieved in a data-driven simulation model that maximizes children's access to sports facilities. We compile a dataset for all 1,854 football clubs in Austria, including estimates for their budget, geolocation, tally, and the age profile of their members. We find a characteristic sub-linear relationship between the number of active club members and the budget, which depends on the socio-economic conditions of the clubs' municipality. In the model, where we assume this relationship to be causal, we evaluate different funding strategies. We show that an optimization strategy where funds are distributed based on regional socio-economic characteristics and club budgets outperforms a naive approach by up to 117\% in attracting children to sports clubs for 5 million Euros of additional funding. Our results suggest that the impact of public funding strategies can be substantially increased by tailoring them to regional socio-economic characteristics in an evidence-based and individualized way.