Bayesian Versus Classical Perspectives: Does Financial Inclusion Truly Drive Economic Growth?

Objectives: This study synthesizes quantitative evidence on the relationship between financial inclusion (FI) and economic growth (EG) to estimate the overall effect size, comparing classical (frequentist) and Bayesian meta-analytic approaches to understand how methodological choices influence the interpretation of the FI-EG nexus. Methods: A meta-analysis of 27 studies was conducted. Effect sizes (Fisher’s z) were pooled using classical random-effects and Bayesian hierarchical models. Heterogeneity was assessed using Cochran’s Q, I², and τ estimators (classical), as well as posterior τ distributions with Bayes factors (Bayesian). Results: Both approaches confirm significant positive FI-EG relationships (classical: 0.682, 95% CI [0.582, 0.782]; Bayesian: 0.616, 95% CrI [0.342, 0.824]). Substantial heterogeneity was detected (classical τ = 0.076; Bayesian τ = 0.195, BF₁ > 1000), with Bayesian analysis suggesting larger variation magnitude. Conclusion: Financial inclusion has a significant impact on economic growth. However, substantial heterogeneity indicates context-dependent impacts, requiring tailored policies. The Bayesian framework provides a richer characterization of uncertainty, offering more conservative and realistic evidence assessment.

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