For anyone who has worked with international economic data, the peculiarities of classification systems are all too familiar. Most analysts have, at some point, encountered the tangle of industry codes that form the backbone of any rigorous study of sectoral growth, trade flows, or productivity. At the global level, the International Standard Industrial Classification (ISIC) system—maintained by the United Nations—serves as the lingua franca. Meanwhile, in North America, the North American Industry Classification System (NAICS) dominates, used by the United States, Canada, and Mexico for everything from census-taking to tax collection.
It’s tempting to treat ISIC and NAICS as more or less interchangeable, their differences smoothed away by concordance tables and translation tools. But in reality, the two systems reflect distinct institutional histories, policy priorities, and assumptions about the shape of the economy. Overlooking their divergences isn’t just an academic error; it risks introducing systematic misclassifications into cross-border analyses, potentially skewing results in subtle but important ways.
Start with the basics: ISIC is designed to be globally applicable, adaptable to a wide variety of economies. Its latest revision, ISIC Rev. 4, categorizes economic activity in a hierarchical structure—sections, divisions, groups, and classes—each with increasing specificity. NAICS, by contrast, is a cooperative venture between the U.S., Canada, and Mexico, developed explicitly to reflect the economic structures of North America. Where ISIC strives for universal relevance, NAICS is unapologetically tailored, emphasizing detail in industries that loom large regionally (think automotive assembly, petroleum extraction, or certain types of advanced services).
This distinction leads to differences in granularity. NAICS is, on the whole, more detailed in areas important to North American economies. For example, it breaks down manufacturing sectors—particularly in technology, chemicals, and food production—into finer slices than ISIC. Conversely, ISIC sometimes aggregates sectors that NAICS divides, reflecting a need for global comparability over local specificity. These differences are not trivial. An analyst mapping U.S. advanced manufacturing growth using NAICS data will find more subcategories, more nuance, than a peer using ISIC codes for international comparison.
The divergence is perhaps most evident in newer sectors. Take information and communication technology (ICT) services. NAICS, updated regularly to reflect shifts in the North American economy, offers a highly granular breakdown of ICT, distinguishing between software publishing, data processing, and web hosting, among others. ISIC, while not blind to these industries, often places them under broader umbrellas, in part to maintain comparability across countries where such activities are less developed. For the analyst, this can create headaches: mapping NAICS-based ICT trends in the U.S. onto an ISIC-based dataset from Europe or Asia means accepting a certain loss of resolution, and possibly, interpretive clarity.
Forestry offers another instructive example. Under NAICS, forestry, logging, and related support activities are carefully separated, reflecting both the economic and regulatory significance of these distinctions in North America. ISIC, aiming for a global standard, groups some of these activities together. This seems minor, but it can affect everything from trade flow analyses to the measurement of sectoral employment. Cross-border studies that gloss over such nuances risk drawing erroneous conclusions, for instance, about the apparent productivity or export share of a given industry.
How, then, do analysts bridge the gap? Concordance tables—painstakingly developed by statisticians—offer a partial solution. These tables map codes from NAICS to ISIC (and vice versa), enabling translation of sectoral data for comparative work. But these are, at best, approximations. There are cases—sometimes numerous—where a one-to-one correspondence is impossible. An NAICS sub-sector might straddle two ISIC codes, or an ISIC group may contain several NAICS codes with no perfect match. The act of translation becomes, inevitably, a matter of judgment.
Seasoned analysts learn to flag these gray zones. It is, in my experience, better to acknowledge the messiness upfront. When presenting findings, a note about concordance limitations is not mere pedantry; it’s a safeguard against false precision. Policymakers, particularly those using cross-border studies to design interventions, need to be aware of these pitfalls. It’s not just about technical accuracy. Misclassification can lead to misguided policies—say, overestimating the export potential of a nascent tech sector or underestimating the labor intensity of natural resources.
That said, there is value in the comparative process itself. Analysts forced to grapple with the differences between ISIC and NAICS often gain deeper insight into both. The process highlights not only where economies diverge but also where they converge, and, occasionally, how policy priorities shape data collection. For instance, the evolution of NAICS over time—its periodic updates to capture new industries—can reveal changing economic realities more rapidly than the slower-moving ISIC. Conversely, ISIC’s durability and wide adoption make it invaluable for long-term, global trend analysis.
Technology may eventually ease some of these challenges. Machine learning tools, fed large amounts of firm-level and sectoral data, are beginning to identify commonalities between classification systems in ways that surpass manual concordance. Even so, the analyst’s judgment remains paramount. No algorithm can yet replace the need for close reading, local context, and, occasionally, a frank admission of uncertainty.
ISIC and NAICS are both indispensable. Each brings strengths and weaknesses to the table, and neither should be treated as a mere adjunct to the other. For global analysts, the real work lies not just in translating codes but in understanding the assumptions, histories, and practicalities behind them. Only by respecting the subtleties can we avoid the trap of false comparability—and arrive, instead, at genuinely useful cross-border economic insights.