The persistent gap between men’s and women’s earnings is a subject of ongoing concern, debate, and policy experimentation in economies around the world. Yet meaningful action requires a clarity of diagnosis that often eludes high-level statistics. For economists and policymakers, understanding not only the existence but the distribution of gender pay gaps—across sectors, firm sizes, and regions—is critical. The International Standard Industrial Classification (ISIC) system offers a valuable lens for this analysis, providing the granularity needed to move from headline averages to sector-specific insight.
The first step is assembling wage survey data that is disaggregated both by gender and by ISIC code. Most national statistical agencies and many large-scale labor force surveys now collect wage and employment data at this level of detail. The analyst’s task is to extract, for each sector—manufacturing (ISIC 20), finance (ISIC 64), and beyond—the average or median wage for men and for women. This allows for the calculation of raw pay gaps by sector, which can then be compared or ranked to identify where disparities are largest.
However, simple averages rarely tell the whole story. Differences in firm size, occupation, and regional cost of living can distort comparisons and, if left unadjusted, may obscure where genuine inequalities persist. The next step is to control for these factors. For each ISIC-coded sector, break wage data down further by firm size (micro, small, medium, large), by occupation or job level, and by region. Some surveys include explicit cost-of-living adjustment indices, while others may require constructing proxies from external sources.
Once this information is in hand, a multivariate analysis can be conducted. Regression techniques, for example, allow for the isolation of the “unexplained” portion of the pay gap—that is, the component not accounted for by occupation, firm size, or regional price levels. This is often where the most persistent and policy-relevant disparities are found. For instance, a finance sector (ISIC 64) may show a high raw pay gap, but after adjustment, much of it may be explained by differences in occupation or concentration in high-cost urban centers. In contrast, a sector like manufacturing (ISIC 20) might reveal a stubborn gap even after accounting for observable characteristics.
The value of using ISIC codes is that it brings transparency and comparability to this exercise. Policymakers can see, sector by sector, where to target interventions—be it through equal pay audits, stronger anti-discrimination enforcement, support for women in managerial pipelines, or tailored negotiation training. Sector-specific diagnostics also help avoid the pitfall of “one-size-fits-all” solutions, which may be ineffective or even counterproductive if applied indiscriminately.
There are, as always, limitations. Not all data sources allow for such fine disaggregation, and informal employment, common in some sectors, may be underrepresented in survey-based analyses. Classification systems may lag behind new or hybrid sectors, and reporting biases can creep in—especially in self-reported data. Nonetheless, the discipline of ISIC-based pay gap analysis provides a more nuanced, actionable foundation than the use of aggregate statistics alone.
Over time, regular monitoring of sector-specific pay gaps, with continual refinement of adjustment techniques and data sources, will yield both a more accurate picture and a more effective policy toolkit. The enduring challenge is to move beyond description to real change—ensuring that efforts to close gender pay gaps are focused where they are needed most, and that progress is tracked in a way that is transparent, credible, and, above all, fair.