A Methodology for Identifying Parcel Trade in Global Statistical Classifications

Abstract

International parcel trade constitutes an indispensable and strategically significant segment of the contemporary global economy, yet it remains only partially captured in existing statistical classifications. The aim of this study is to develop a methodological approach that isolates cross-border B2C parcels within core international trade datasets and establishes a parsimonious set of indicators for their economic assessment. The paper reviews the limitations of three key reference frameworks: the Harmonized System (HS), which lacks any identifier of the delivery channel; the Central Product Classification (CPC), which records only service flows; and the Extended Balance of Payments Services classification (EBOPS-2010), which aggregates monetary flows without linking them to merchandise values. The feasibility of integrating these three data blocks is explored through primary “tagging” of individual records. A minimum set of compulsory criteria is proposed-gross weight approximately 2 kg, individual (non-resident) consignee and transport code 50 “postal/courier”-supplemented by reinforcing attributes (Incoterms DDP/DAP, popular HS items, delivery lead time approximately 15 days) derived from CN 22/23 customs forms, UPU S10 tracking codes and banking payment data. On this basis, three integrated indicators are constructed: the number of B2C parcels (N), the average customs value per parcel (Vсер) and the cost-per-parcel (CPP), the latter calculated with the aid of EBOPS sub-code 3.4.2 “Courier services”. The resulting triad enables cross-country comparison of parcel flows, measurement of import elasticity with respect to the de minimis threshold, and rapid appraisal of the tariff burden borne by consumers. The proposed approach offers low implementation costs, high international comparability and flexible scalability (e.g., the future inclusion of fiscal or environmental metrics such as CO₂-per-parcel). Further research directions include modelling the fiscal effects of alternative de minimis scenarios, incorporating a carbon indicator and developing a “demand–tariff” simulation model to forecast micro-export dynamics over a medium-term horizon.