Export-Structure Similarity and Higher-Unit-Value Positioning in Korea’s Automotive-Parts Trade

Published: 27 July 2026| Version 1 | DOI: 10.17632/4gw2xh3mcr.1
Contributor:
myungho an

Description

This dataset provides the processed data and workbook-based replication materials for the study “Export-Structure Similarity and Higher-Unit-Value Positioning in Korea’s Automotive-Parts Trade.” The study examines Korea’s bilateral automotive-parts trade with China, the United States, and Japan during 2000–2019, treated as a historical pre-pandemic benchmark. It uses a fixed classification of 213 HS6 products and six partner–product-group panels covering electrical and electronics parts and general parts. The 21-worksheet repository workbook documents the HS6 product classification, product-group IPC coverage lists, annual trade indicators, available patent counts, two-year-lagged patenting gaps, and core variable definitions. The IPC lists contain 195 electrical and electronics classes and 285 non-overlapping general-parts classes, totalling 480 classes after 16 cross-list duplicates were removed. These are product-group coverage lists, not pairwise HS6–IPC links. Additional worksheets document formula-based annual ESI×TSI states, full-sample and sub-period transition counts and proportions, RCA×IIT classifications, annual structural position index (SPI) series, SPI leave-one-component-out checks, state summaries, sensitivity checks, and reproducibility audits. The workbook supports verification and reproduction of the reported results from the processed data. The repository does not include the original UN Comtrade or USPTO data, raw-data extraction scripts, external analysis scripts, or exact figure-generation code. Alternative unit-value bands cannot be recomputed because the deposited annual aggregates do not contain HS6-level quantity, net-weight, or unit-value inputs. The underlying raw data are available from UN Comtrade and the USPTO, subject to their access conditions. The analyses are descriptive and do not identify causal effects.

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Trade, Automotive Industry

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