Dynamics of global economic growth: a Bayesian exploration of basic and augmented Solow models

The Solow growth model has long served as a cornerstone in economic theory, offering critical insights for formulating growth policies. Nevertheless, its principal limitation is the omitted variable bias arising from the inclusion of constant exogenous variables. Furthermore, the frequentist framework's susceptibility to multicollinearity complicates the incorporation of multiple variables. In addressing these challenges, Bayesian methods present a compelling alternative. This study rigorously examines both the basic and augmented Solow growth models using a Bayesian non-linear approach applied to a global panel dataset spanning 1970 to 2019. The results demonstrate that the augmented Solow model, which incorporates heterogeneous population growth, savings, technology, and depreciation rates, significantly outperforms the basic model in predictive accuracy. Crucially, the elasticity of output with respect to capital, as estimated through this advanced econometric approach, aligns more closely with widely accepted empirical values. These findings reaffirm the validity of the Solow growth model when evaluated with enhanced econometric techniques and high-quality data. The study's implications are particularly relevant for policymakers, who are encouraged to leverage the insights provided by the augmented model. Specifically, strategies such as increasing investment, fostering technological innovation, enhancing human capital, and optimizing resource allocation should be prioritized to drive sustainable economic growth.