For as long as economists have tried to measure economic output, the informal sector has remained a persistent blind spot. It is—depending on one’s point of view—a source of resilience, a policy headache, or simply an empirical challenge. What is clear is that informal businesses, by definition, operate outside the purview of formal registration systems. They rarely file taxes, seldom interact with banks, and, crucially, do not appear in national business registries under any International Standard Industrial Classification (ISIC) code. Yet the informal economy is anything but trivial; in some countries, it accounts for over half of total employment and a significant share of value added. The problem is: how do we measure it, given these constraints?
Traditional approaches, such as enterprise surveys or administrative data, are obviously ill-suited. If a street vendor, an unregistered construction crew, or a family-run tailoring shop never files paperwork, then by ISIC’s logic, they do not exist. And yet, markets teem with such activity. The reality is that, while ISIC codes are indispensable for the formal economy, some ingenuity is required to apply their logic to the informal sector.
This is where hybrid estimation methods have gained ground. Over the last two decades, researchers have blended household surveys, observational studies, and even remote sensing technologies to approximate the structure and dynamics of informal economies. The principle is deceptively simple: if we cannot measure informal activity directly via business registers, perhaps we can triangulate it indirectly—using data that can be mapped, even if loosely, onto ISIC frameworks.
Household surveys remain a workhorse for informal sector research. Typically, respondents are asked about their primary and secondary income-generating activities, regardless of registration status. Careful questionnaire design is essential: instead of referencing ISIC codes, surveys use plain language—“Do you repair cars for income?” or “Do you sell goods at a market?” Responses are then mapped back to ISIC-like categories by trained coders. This mapping is never perfect—there is often ambiguity, and respondents may underreport—but it does provide a rough sketch of the sectoral composition of informal activity.
Satellite imagery has emerged as an unexpected ally in recent years. Researchers, unable to visit every street market or building site, use high-resolution images to identify areas dense with economic activity. For example, a cluster of informal construction sites can be mapped to ISIC division 4390 (“Other specialized construction activities”), while vibrant open-air markets align with wholesale and retail codes. Analysts validate these observations with ground surveys, creating what is, effectively, an ISIC-based proxy for informal activity. This hybrid approach can be especially useful in urban settings, where informality often clusters in visible patterns—urban peripheries, transport hubs, or rapidly growing neighborhoods.
There are, of course, limits. Satellite imagery cannot distinguish between legal and illegal activity, nor can it parse out nuances such as the gender composition of vendors or the size distribution of informal firms. Still, when combined with household survey data and administrative records—tax receipts, utility usage, sometimes even mobile phone data—researchers can produce multi-layered estimates that are both more granular and more reliable than any single source alone.
A recurring challenge is how to ensure that these hybrid models remain anchored in a common classification. ISIC provides the vocabulary for formal activity, but for informality, translation is required. One approach is to develop “ISIC proxies”—essentially, categories based on observable characteristics rather than registration status. For example, the presence of street kitchens, roadside mechanics, or itinerant vendors can be mapped to their nearest ISIC equivalent. Researchers in Latin America have refined this methodology, producing detailed sectoral breakdowns of informality that inform both academic work and policy debates.
Comparing methodologies across regions is instructive. In Southeast Asia, household survey methods tend to dominate, reflecting the tradition of labor force surveys and national statistical capacity. In sub-Saharan Africa, a blend of ground surveys and spatial mapping is more common, given challenges of rapid urbanization and data scarcity. In parts of Eastern Europe, tax and utility data are sometimes used as a proxy—if an area shows high electricity consumption but few registered firms, informal production is suspected.
Unified guidelines are still evolving, but there are a few emerging best practices. First, transparency in assumptions and mapping logic is vital. Researchers should document how household responses or satellite images are translated into ISIC-like categories and acknowledge sources of ambiguity. Second, triangulation—combining as many independent data sources as possible—improves reliability. Third, validation, ideally through targeted ground-truthing, helps ensure that estimates are not simply artifacts of modeling choices.
The implications of better informal sector measurement are significant. Policymakers can design more effective interventions, targeting support where informal activity is most concentrated, or anticipating the impact of regulatory changes. Accurate measurement also improves headline economic indicators—GDP, employment rates, productivity—by accounting for activity that is otherwise invisible. This, in turn, enhances the credibility of national statistics and supports better international comparisons.
Still, there is a note of caution. The informal sector is, by its nature, fluid and adaptive. As measurement improves, so too do the strategies for remaining invisible. Tax changes, regulatory shifts, or even the threat of enforcement can reshape informal patterns almost overnight. Hybrid estimation models must, therefore, be continuously updated and refined. There is no final answer, only a moving target.
In the end, measuring the informal economy is a pragmatic exercise—one of approximations, proxies, and perennial revision. By blending the logic of ISIC classification with innovative data sources, economists can shed light on the shadowed corners of economic life. The process is never perfect, but it is essential, and it brings us one step closer to understanding the true shape of our economies.