MODELING THE LIKELIHOOD OF SUSTAINABLE DEVELOPMENT ACHIEVEMENT THROUGH TRADE OPENNESS: A BAYESIAN LOGISTIC REGRESSION APPROACH
DOI:
https://doi.org/10.18623/rvd.v22.n7.4042Keywords:
Sustainable Development, Trade Openness, Bayesian Logistic RegressionAbstract
This study investigates how trade openness affects the probability of achieving sustainable development across countries. Using panel data from 73 countries during 2002–2020, we employ a Bayesian logistic regression to model the likelihood that a country attains a relatively high level of sustainable development, measured by a composite index aligned with the Sustainable Development Goals. The results indicate that trade openness has a positive and robust effect on the probability of achieving sustainable development. Importantly, the findings also reveal nonlinear effects, suggesting that both excessive trade expansion and abnormally low trade openness may weaken this positive impact. This highlights that the trade–sustainable development relationship is heterogeneous and context dependent. By adopting a Bayesian logistic framework, the study explicitly captures parameter uncertainty and cross country heterogeneity, offering a more refined assessment of sustainability outcomes. The findings provide policy relevant insights by emphasizing the need for balanced trade strategies and supportive institutional conditions to ensure that trade contributes effectively to long term sustainable development.
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