Policymakers can effectively incorporate the insights from survival analysis and machine learning models by first understanding the key variables and risks influencing economic growth trajectories. By leveraging advanced statistical techniques such as survival analysis, policymakers can gain a comprehensive understanding of the temporal dynamics associated with achieving specific economic targets. Machine learning models, particularly DeepSurv, can help capture non-linear interactions and provide accurate predictions for time-to-event data, such as reaching a 5% increase in GDP per capita at purchasing power parity (PIBpcPPP) within a specified timeframe.
To accelerate economic convergence, policymakers should use the findings from these models to develop tailored strategies that address vulnerabilities, risks, and capacities specific to each country. By focusing on a balanced approach to risk-taking, strategic vulnerability reduction, and investment in governmental capacities and social cohesiveness, policymakers can make informed decisions that promote sustainable economic growth. Additionally, incorporating policy guidelines that consider the complex dynamics at play will ensure that interventions are targeted and effective in driving economic convergence across the Americas.