Bayesian nonparametric partial clustering: Quantifying the effectiveness of agricultural subsidies across Europe

TL;DR

This paper introduces a Bayesian nonparametric partial clustering method to evaluate the heterogeneous effectiveness of agricultural subsidies across European countries, accounting for diverse land-use impacts and policy factors.

Contribution

It develops a novel Bayesian approach combining multinomial logit models with BNP priors for regional clustering, improving policy impact assessment across heterogeneous EU regions.

Findings

CAP impact varies significantly across EU countries

Subsidies need to be tailored for optimal effectiveness

The proposed model effectively captures regional heterogeneity

Abstract

The global climate has underscored the need for effective policies to reduce greenhouse gas emissions from all sources, including those resulting from agricultural expansion, which is regulated by the Common Agricultural Policy (CAP) across the European Union (EU). To assess the effectiveness of these mitigation policies, statistical methods must account for the heterogeneous impact of policies across different countries. We propose a Bayesian approach that combines the multinomial logit model, which is suitable for compositional land-use data, with a Bayesian nonparametric (BNP) prior to cluster regions with similar policy impacts. To simultaneously control for other relevant factors, we distinguish between cluster-specific and global covariates, coining this approach the Bayesian nonparametric partial clustering model. We develop a novel and efficient Markov Chain Monte Carlo (MCMC) algorithm, leveraging recent advances in the Bayesian literature. Using economic, geographic, and subsidy-related data from 22 EU member states, we examine the effectiveness of policies influencing land-use decisions across Europe and highlight the diversity of the problem. Our results indicate that the impact of CAP varies widely across the EU, emphasizing the need for subsidies to be tailored to optimize their effectiveness.