Steering economies toward stability in the wake of Covid-19 crisis: insights from mathematical model

Anna Tykhonenko () and Nahla Dhib ()
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Nahla Dhib: LJAD - Laboratoire Jean Alexandre Dieudonné - UNS - Université Nice Sophia Antipolis (1965 - 2019) - CNRS - Centre National de la Recherche Scientifique - UniCA - Université Côte d'Azur

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Abstract: This paper sought to examine the impact of policy-maker decisions on economic convergence in a period characterized by turbulence, uncertainty, and complexity, like the COVID-19 pandemic. Recognizing the critical need for structure in times, we used a Markov decision process. This framework has proven invaluable modeling for modeling making decision scenarios where outcomes are determined by actions of decision-makers. First, our study is deeply rooted in Bayesian approach which analyze growth dynamics. Then we used k-means clustering to categorize EU-states into three categories which allows us to analyze the performance of each group. By utilizing MDP model, we examine three political scenarios for decision-makers then basis on the assumptions of our model, we determined the optimal policy for the scenario which maximizes the reward function. We found that effective decision-making is crucial for achieving economic convergence in a post-crisis world. Furthermore, our research indicated that a global and coordinated approach might offer several benefits. The COVID-19 crisis could paradoxically provide an opportunity for poorer states to implement optimal policies they might not have been able to afford under normal circumstances.

Keywords: dynamic distribution; MDP model; dynamic panel data model; convergence rate; Bayesian shrinkage estimator; optimal policy; Covid-crisis (search for similar items in EconPapers)
Date: 2023-11-21
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