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It is important for firms to undertake product innovation since this will enable them to incorporate additional value to its market offer and, consequently, will increase their international competitiveness. Thus, the aim of this article is to develop a new index, the Export Product Innovation Index (EPII), a metric that associates product innovation to export activity according to the value of new export products / goods traded abroad at a single company level. The proposed index is built on official export data and based on the Harmonized System Codes. Therefore, the EPII may be widely generalized and calculated for every export firm all over the world for which export shipping data is available, enabling benchmarks for companies, international comparative studies and policy making. This article uses data on Chilean fruit exporters to illustrate EPII calculation and use. Through the comparison of this new index with some previous ones measuring product innovation overall population of Chilean fruit exporters, it is demonstrated that the EPII provides more accurate information to appraise a firm´s export product innovation performance.

PurposeInternational trade transactions, extracted from customs declarations, include several fields, among which the product description and the product category are the most important. The product category, also referred to as the Harmonised System Code (HS code), serves as a pivotal component for determining tax rates and administrative purposes. A predictive tool designed for product categories or HS codes becomes an important resource aiding traders in their decision to choose a suitable code. This tool is instrumental in preventing misclassification arising from the ambiguities present in product nomenclature, thus mitigating the challenges associated with code interpretation. Moreover, deploying this tool would streamline the validation process for government officers dealing with extensive transactions, optimising their workload and enhancing tax revenue collection within this domain.Design/methodology/approachThis study introduces a methodology focused on the generation of sentence embeddings for trade transactions, employing Sentence BERT (SBERT) framework in conjunction with the Multiple Negative Ranking (MNR) Loss function following a contrastive learning paradigm. The procedure involves the construction of pairwise samples, including anchors and positive transactions. The proposed method is evaluated using two publicly available real-world datasets, specifically the India Import 2016 and United States Import 2018 datasets, to fine-tune the SBERT model. Several configurations involving pooling strategies, loss functions, and training parameters are explored within the experimental setup. The acquired representations serve as inputs for traditional machine learning algorithms employed in predicting the product categories within trade transactions.FindingsEncoding trade transactions utilising SBERT with MNR loss facilitates the creation of enhanced embeddings that exhibit improved representational capacity. These fixed-length embeddings serve as adaptable inputs for training machine learning models, including support vector machine (SVM) and random forest, intended for downstream tasks of HS code classification. Empirical evidence supports the superior performance of our proposed approach compared to fine-tuning transformer-based models in the domain of trade transaction classification.Originality/valueOur approach generates more representative sentence embeddings by creating the network architectures from scratch with the SBERT framework. Instead of exploiting a data augmentation method generally used in contrastive learning for measuring the similarity between the samples, we arranged positive samples following a supervised paradigm and determined loss through distance learning metrics. This process involves continuous updating of the Siamese or bi-encoder network to produce embeddings derived from commodity transactions. This strategy aims to ensure that similar concepts of transactions within the same class converge closer within the feature embedding space, thereby improving the performance of downstream tasks.

This chapter contains sections titled: Introduction Structural Reforms and Trade Liberalisation The 2008 Trade Policy Review Policy Challenges and Concluding Remarks References

Revision in Harmonized System (HS) and its impact of rules of origin (RoO) are matters of immense importance in international trade policy discourse due to its far reaching economic and trade implications. In this article, we analyse the impact of HS amendments on RoO, based on the change in tariff classification (CTC) by examining India’s trade agreements with South Korea and Japan. Findings of the article show that the revision in HS impacts 42% of subheadings with CTC rules in case of India- South Korea Comprehensive Economic Partnership Agreement (CEPA) and 28% in case of India- Japan CEPA, thereby potentially affecting up to 48% and 16% share of their total trade, respectively. Harmonized System, Rules of Origin, Trade Policy, Free Trade Agreements, WCO

Manual assessment of the correctness of Harmonized System codes of goods is very error-prone and time demanding task taking into account the dramatically growing amounts of cross-border trade. The paper provides an automated solution to this problem by applying machine learning methods to assess the correctness of Harmonized System codes. We use machine learning for providing predictions and recommendations of Harmonized System codes on the basis of a model learned from the textual descriptions of the products. In order to assess the correctness Harmonized System codes of goods we introduce a novel combined similarity measure based on cosine similarity of texts and semantic similarity of Harmonized System codes calculated according to their taxonomy. We also present and prove the properties of this new similarity measure. We test our method on the real open source data set of Bill of Lading Summary 2017.