The world economy is a consequence of a complex amalgam of economic policies that together operationalize globalization. A distinctive attribute of its pervasiveness lies in the participation of industries in global value chains (GVCs) by adding value through the performance of a specific slice of activity for which they have an absolute or comparative advantage. Participation of industries in GVCs accounts for a significant and increasing share of international trade, with the OECD estimating that around 70% of global trade is associated with GVCs1. Driven by advances in technology and transportation, industries can strategically position themselves within these complex networks to create and deliver value. This provides a viable strategy for job creation and healthy economic growth.
However, due to its inherently complex nature, understanding the networks of GVCs presents a significant challenge. Amidst the fog of uncertainty stemming from shifting geopolitics, the COVID-19 pandemic and climate change, vulnerabilities in the supply of strategic goods have become more so exposed. This has led to considerable scrutiny of the structure and resilience of supply chains, with dependencies on certain industries and countries being called into question. The implementation of policies aimed at controlling certain aspects of GVCs are becoming increasingly commonplace, risking the production of unintended consequences that reverberate globally. This necessitates a comprehensive understanding of supply chains, particularly focusing on the integration and interdependencies they create between industries and regions.
Traditional measures of economics fail to accurately gauge the contribution made by an industry by its participation in a GVC. Instead, using of input-output tables (IOTs) effectively reveals inter-industry dependencies in the context of international trade. IOTs break down the total output produced by an industry into intermediate and final consumption, providing valuable insight into the dependencies required to produce its output. Understanding the output of an economy through this lens presents an accurate picture of the structure and composition of GVCs. To study the level of economic integration and the networks of interdependencies within the St. Lawrence – Great Lakes (SLGL) region, we employ the use of OECD’s Inter-Country Input-Output (ICIO) tables.
OECD’s ICIO represents the most comprehensive and current sets of IOTs constructed to date, utilizing a slew of datasets. To contextualize the ICIO dataset to the bi-national SLGL region, we rely on sub-national IOTs, as the ICIO only measures trade dependencies at the national level. We use IOTs published by Statistics Canada and the U.S. Census Bureau to track the flow of trade among the two Canadian provinces and the eight US states. With the help of concordance, we harmonize the classification of industries with the ICIO which groups industries based on the 4th revision of the International Standard Industrial Classification of All Economic Activities (ISIC). However, these sub-national IOTs lack the requisite level of granularity on intermediate consumption at the industry level, necessitating computations on our part to make the data more meaningful. Crucially, stateior has been used to perform these computations for the eight US states. The matrix table below lists the source of data for each value within the IOT constructed for the SLGL dataHub.
In essence, our IOT meticulously maps production and trade flows for forty-five industries producing goods and services on an annual basis from 2012 to 2020. These tables facilitate the study of trade within the bi-national SLGL region at the sub-national level. Beyond the SLGL region, our IOT covers forty three countries, including twenty-seven members of the EU and sixteen other major economies (Australia, Brazil, Canada, China, India, Indonesia, Japan, Mexico, Norway, Russia, South Korea, Switzerland, Taiwan, Türkiye, the United Kingdom and the United States) covering about 85 per cent of world GDP in 2022 (at current prices)2. Additionally, the table provides a categorization for the rest of the world to accommodate trade with countries beyond the IOT’s coverage.
These IOTs serve as the foundational dataset of the SLGL dataHub, enabling the analysis of economic dynamics both within and beyond the SLGL region. The SLGL dataHub will continually be updated to incorporate new datasets, which, when combined with the IOTs, will be instrumental in designing new indicators aimed at revealing fresh insights.
References
1. OECD (n.d.)