Trade policy—whether in the form of tariffs, free trade agreements, or targeted quotas—has always been a lever with far-reaching consequences. Policymakers know this, of course. The challenge isn’t so much in appreciating the complexity as in making sense of it—quantifying winners, losers, and unintended consequences. In this regard, the International Standard Industrial Classification (ISIC) system has become one of the most reliable instruments in the policy toolkit. By offering a common vocabulary and a granular view of the economy, ISIC codes underpin the sophisticated models that governments now use to assess trade policy impacts.
The process often begins with a deceptively simple question: “Who will be affected?” When a new tariff is imposed on, say, textile manufacturing (ISIC 1311–1399), the immediate thought is for the producers of fabrics and garments. But the real world is never so neatly contained. Modern economies are intricate webs; textiles flow into retail, logistics, even advertising and auxiliary services. The ISIC system helps economists map these interdependencies with much greater precision than the old, ad hoc categories that once dominated trade analysis.
A typical trade policy assessment uses computable general equilibrium (CGE) models—a class of economic models that simulate how an entire economy might adjust to a given shock, such as the introduction of a tariff or the signing of a trade agreement. ISIC codes are invaluable here. Each sector in the model is defined according to ISIC, allowing for a detailed simulation of how changes ripple through the system. When a tariff is applied to ISIC-coded textiles, the CGE model can trace not only the direct effects—higher costs for textile producers—but also the indirect effects on downstream industries: retailers (ISIC 4719), logistics firms (ISIC 4923), and even unrelated sectors affected by changing consumer prices.
The power of ISIC granularity is evident in employment analysis. When a tariff hits a particular ISIC category, it’s not just output that changes, but labor demand as well. Sometimes, the initial shock is cushioned by a shift to related sectors—laid-off workers from textiles might find jobs in logistics or retail, if those sectors are expanding. More often, though, there is a net employment loss in the affected region or skill group, and the details matter for designing effective adjustment policies.
Consider the example of agricultural tariffs. Imposing a tariff on processed food imports (ISIC 1079) may benefit local producers, but at the same time raise input costs for domestic food retailers and catering services (ISIC 5610). The net effect on GDP and employment depends not just on the size of these sectors, but on their linkages—something that ISIC mapping reveals in useful detail. Similarly, trade agreements that liberalize technology imports (ISIC 2620) can boost productivity across manufacturing sectors, with positive spillovers for logistics, professional services, and even education and training (ISIC 8549).
The modeling is, admittedly, not without its difficulties. ISIC codes, for all their structure, are only as accurate as the underlying data. Misclassification, aggregation issues, and the challenge of rapidly evolving sectors—think digital services or green technologies—require constant attention and occasional reinterpretation. And the outputs of CGE models are only as good as the assumptions built into them: elasticities, substitution rates, and behavioral responses can all vary by country and period.
Nevertheless, the marriage of CGE modeling and ISIC coding has produced results that are both credible and actionable. For policymakers, the insights are invaluable. Instead of broad generalizations—“tariffs help workers” or “trade agreements harm small businesses”—there is nuance. Which sectors win, which lose, and by how much? What are the distributional effects across regions, skills, or income groups? Which downstream sectors are most vulnerable to price shocks or supply chain disruptions?
A case in point: the debate over tariffs on steel (ISIC 2410) in North America and Europe. CGE models, grounded in ISIC data, have shown that while such tariffs can temporarily boost domestic steel production, they tend to raise input costs for auto manufacturing (ISIC 2910), construction (ISIC 4100), and a host of smaller industries. The end result is often a redistribution of jobs rather than a net gain, with pockets of concentrated harm that might have been missed in aggregate statistics.
Beyond ex-post evaluation, ISIC-based modeling also supports ex-ante policy design. Governments can simulate alternative scenarios—a higher or lower tariff, a phased agreement, sector-specific exemptions—and compare their likely impacts. This allows for more informed negotiation, better risk management, and, ultimately, a more adaptive approach to trade policy.
It is also worth emphasizing that ISIC-based assessments improve transparency and public understanding. When policy debates can be framed in terms of clearly defined sectors and their interconnections, stakeholders—businesses, unions, regional governments—can engage more meaningfully. The process is still technical, but less opaque, and more grounded in a shared empirical language.
Trade policy is, and will remain, a domain where precision and foresight matter. The ISIC system, paired with robust economic modeling, offers a way to meet that need—revealing the often unexpected pathways through which policy choices affect employment, output, and social welfare. As the global trading environment becomes more volatile, this kind of detailed, sector-based analysis will only grow in importance.