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The Impact of Non-Tariff Measures on Sri Lankan Tea Trade: A Bayesian Inference of the Gravity Model

Kumar Rakesh Ranjan & J. C. Edirisinghe

Sri Lankan Journal of Agricultural Economics2020https://doi.org/10.4038/sjae.v21i1.4637article
ABDC C
Weight
0.34

What the paper says

Sri Lankan tea industry is confronting the danger of losing key markets on the grounds of exceeding the Maximum Residual Levels (MRLs) in chemicals permitted by the European Union. This study assesses the impact of Non-tariff measures on Sri Lankan tea exports because of MRLs stipulated for the pesticide, Endosulfan. This study employs a Bayesian version of the Gravity equation using a Multi-level Mixed Model, because of the additional advantages that Bayesian analysis provides. Panel data from 2003 to 2017 for fourteen prime destinations of Ceylon tea were considered for this study. The estimated coefficient of MLR for Endosulfan suggests that a 1% increase in the regulatory stringency on Endosulfan (tighter restrictions on pesticide) can result in a 0.67% (approximately US$ 8,907,708.15 in 2020) decrease and a 1% increase in the tariff rate is prompt to a 0.03% (approximately US$ 398, 852.60 in 2020) decline in the value of tea exports. Compared to the tariff, the MRL is associated with a higher impact on trade. Therefore, the negative impact of MRLs is found to outweigh the impact of import tariffs, highlighting the greater role that non-tariff measures play.

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https://doi.org/https://doi.org/10.4038/sjae.v21i1.4637

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@article{kumar2020,
  title        = {{The Impact of Non-Tariff Measures on Sri Lankan Tea Trade: A Bayesian Inference of the Gravity Model}},
  author       = {Kumar Rakesh Ranjan & J. C. Edirisinghe},
  journal      = {Sri Lankan Journal of Agricultural Economics},
  year         = {2020},
  doi          = {https://doi.org/https://doi.org/10.4038/sjae.v21i1.4637},
}

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Evidence weight

0.34

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.00 × 0.4 = 0.00
M · momentum0.80 × 0.15 = 0.12
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

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