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This paper aims to investigate the impact of selected macro-economic variables like real effective exchange rate (REER), GDP, inflation (INF), the volume of trade (TR) and…
This paper aims to investigate the impact of selected macro-economic variables like real effective exchange rate (REER), GDP, inflation (INF), the volume of trade (TR) and money supply (M2) on-budget deficit (BD) in Bangladesh over the period of 1980–2018.
By using secondary data, the paper uses the Vector Error Correction Model (VECM) and Granger Causality test. Johansen’s cointegration test is used to examine the long-run relationship among the variables under study.
Johansen’s cointegration test result shows that there exists a positive long-run relationship of selected macroeconomic variables (real effective exchange rate, inflation, the volume of trade and money supply) with the budget deficit, whereas GDP has a negative one. The short-run results from the VECM show that GDP, inflation and money supply have a negative relationship with the budget deficit. The Granger Causality test results reveal unidirectional causal relationships running from BD to REER; TR to BD; M2 to BD; GDP to REER; M2 to REER; INF to GDP; GDP to TR; M2 to GDP and bidirectional causal relationship between GDP and BD; TR and REER; M2 and TR.
Bangladesh has been experiencing a budget deficit since 1972 due to a decline in sources of revenue. This study contributes to the empirical debate on the causal nexus between macroeconomic variables and budget deficits by employing VECM and Granger Causality approaches.
The recent coronavirus disease 2019 (COVID-19) pandemic poses numerous challenges to supply chains. This pandemic is quite unique when compared to previous epidemic…
The recent coronavirus disease 2019 (COVID-19) pandemic poses numerous challenges to supply chains. This pandemic is quite unique when compared to previous epidemic disruptions and has had a severe impact on supply chains. As a result, the operational challenges (OCs) caused by COVID-19 are still unknown among practitioners and academics. It is critical to comprehensively document current OCs so that firms can plan and implement strategies to overcome them. Consequently, this study systematically identifies and ranks COVID-19-related OCs.
This study uses an integrated methodology combining expert interviews and the best-worst method (BWM) to analyze the results. The data have been collected from the electronics industry of Bangladesh, an emerging economy. This study also conducts a sensitivity analysis to check the robustness of the results.
The results reveal 23 COVID-19-related OCs under five categories: sourcing, production and inventory management, demand management and distribution, return management and after-sales service, and supply chain-wide challenges. The quantitative investigation reveals that overstock in finished goods inventory, low end-customer demands, order cancellations from dealers and retailers, high inventory holding costs and lack of transportation are the top five OCs.
The findings will help practitioners to understand the OCs and allow them to prepare for future major disruptions and formulate long-term strategies for operations during and after the COVID-19 pandemic.
This study contributes to the literature on supply chain complexity and challenges by considering a major pandemic outbreak. Moreover, the study also contributes to the knowledge on emerging economies, which have been largely neglected in the current literature.