Volume I, Issue 1, 2024
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This study examines the impact of health, as measured by life expectancy (LE), on labour productivity, measured by GDP per capita (GDPC), in Morocco from 1990 to 2021. Utilizing a dynamic Autoregressive Distributed Lag (DYNARDL) model, along with the Kernel-Based Regularized Least Squares (KRLS) method, we assess the counterfactual impact of life expectancy while holding other variables constant. Our findings indicate that life expectancy has a significant and positive effect on labour productivity in both the short and long term. Specifically, a 1% increase in LE leads to a 6% increase in GDPC in the long run, while in the short run, this effect is even more pronounced, with a 1% change in LE resulting in a 14% variation in GDPC. These results highlight the critical role of health improvements in enhancing economic productivity in developing economies, aligning closely with the Sustainable Development Goals (SDGs), particularly Goal 3 - Good Health and Well-being and Goal 8 - Decent Work and Economic Growth. Additionally, DYNARDL simulations suggest that a projected 10% increase in life expectancy could initially accelerate labour productivity, although this acceleration rate diminishes over time, eventually stabilising. These findings underscore the importance of sustained health investments to achieve not only long-term economic growth in Morocco but also broader SDG targets, such as reducing inequalities and fostering sustainable, inclusive economic development.
© The Author(s) 2024. Published by RITHA Publishing under the CC-BY 4.0. license, allowing unrestricted distribution in any medium, provided the original work, author attribution, title, journal citation, and DOI are properly cited.
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This study introduces a novel methodological approach to modelling volatility in currency bid - ask spreads, comparing classical and modern volatility models to assess currency resilience among emerging market economies and categorise them based on relative strength of the estimated parameters. Utilising historical price range data from currency bid–ask spreads, we analyse 27 currencies in the post-global recession period, excluding extraordinary events such as the global oil price plunge in 2014, outbreak of the COVID-19 pandemic and Russia – Ukraine War in 2023. Employing Thomson Reuters daily historical range data, we estimate classical return-based and modern range-based volatility models.
Our results indicate that the range-based volatility model outperforms the return-based standard volatility model in terms of significant estimated parameters and model selection criteria. By leveraging full price range information, the range-based volatility model yields more accurate results. We categorise currencies based on their performance, identifying distinct currency regimes across 27 emerging market economies. This study contributes to the literature by attempting volatility modelling for bid – ask spreads in the currency market. Our findings provide policymakers with a deeper understanding of currency price determination and adjustment, enabling countries to implement safeguard measures to protect their exchange rates from potential volatility spillovers.
© The Author(s) 2024. Published by RITHA Publishing under the CC-BY 4.0. license, allowing unrestricted distribution in any medium, provided the original work, author attribution, title, journal citation, and DOI are properly cited.
