Macroeconomic Determinants of Manufactured Jute Exports in Bangladesh: An ARDL Bounds Testing Approach
DOI:
https://doi.org/10.66348/brr.26.a86Keywords:
Manufactured jute exports, ARDL bounds testing, structural breaks, export determinants, BangladeshAbstract
Manufactured jute exports are a critical but understudied source of non-garment foreign exchange for Bangladesh. Grounded in open-economy trade theory, which links export performance to price competitiveness, domestic productive capacity, and external demand, this study examines how the exchange rate, interest rates, manufacturing production index, and world import volume jointly determine manufactured jute export value using 84 monthly observations from January 2019 to December 2025. Integration orders are confirmed through complementary unit root tests, and structural breaks from the COVID-19 pandemic, the Russia-Ukraine conflict, and a domestic interest rate regime change are controlled via step dummy variables. An autoregressive distributed lag (ARDL) bounds testing framework confirms cointegration, complemented by Toda-Yamamoto causality tests for short-run dynamics. World import volume is the sole statistically significant long-run determinant, with an elasticity of 2.903 exceeding unity, while causality runs strictly from the global trade environment to the domestic export sector without feedback. The exchange rate and domestic interest rates carry no discernible long-run influence, and manufacturing production exerts a short-run effect that fully reverses within one period. This study provides the first break-adjusted, multivariate macroeconomic examination of manufactured jute export determinants in Bangladesh, establishing the sector as externally demand-driven and redirecting policy attention from domestic price instruments toward market diversification and product upgrading.
Received: 2026-05-18 | Revised: 2026-07-19 | Accepted: 2026-08-12 | Published: 2026-08-16
Declarations
Ethics and Guidelines: Not applicable. This study used publicly available secondary data and did not involve human participants, human data, human tissue, or animals.
Consent to participate: N/A.
Consent to publish: The authors have provided consent to publish.
Competing interests: The authors declared no potential conflicts of interest with respect to the research, authorship and/or publication of this article.
Data availability statement: The data used in this study are available from publicly accessible sources.
Funding: The authors received no financial support for the research, authorship and/or publication of this article.
Clinical Trial Number: Not Applicable.
Declaration of using generative AI: During the preparation of this work the author(s) used ChatGPT in order to correct the grammatical errors. After using this tool/service, the author(s) reviewed and edited the content as needed and take full responsibility for the content of the published article.
Author Contributions: T.S.R., S.T., and H.B.K.B.: Conceptualization, methodology, writing—original draft preparation, writing—review and editing. All authors have read and agreed to the published version of the manuscript.
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Copyright (c) 2026 Tahsin Shabab Rajee, Sinthia Tasnim, Hafsa Binte Karim Benoy (Author)

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