Natural disasters strike with force, but their economic effects unfold in ways that are anything but random. For those tasked with disaster-risk financing—whether in government, insurance, or international agencies—the challenge is to anticipate where losses will fall, how fiscal liabilities will accumulate, and what kind of targeted support will be needed when crisis hits. This is, fundamentally, a question of sectoral exposure. Here, the International Standard Industrial Classification (ISIC) codes provide a framework for tracing these exposures with greater precision and for designing smarter, more responsive financial instruments.
The process begins with data collection. When disasters occur, insurance companies record claims, and governments disburse aid—each usually tagged to the affected business or economic activity. However, these records are often organized by company name, location, or rough sector description rather than a standardized classification. The first methodological step is to link these records to the appropriate ISIC codes. For example, claims from damaged farm operations would be mapped to ISIC 0111 (Crop and animal production), while payouts to building contractors would fall under ISIC 4120 (Construction of buildings).
This mapping is more than clerical detail. It allows analysts to aggregate exposures and losses by sector, both within and across disaster events. In practice, this means compiling a database of historical insurance claims and government aid disbursements, each entry tagged with the correct ISIC code. Some national insurance regulators and aid agencies are beginning to require such tagging at the point of reporting, which accelerates the process. Where this is not yet standard, analysts may need to match records manually using business registries or cross-reference with other government data sources.
With these data in hand, the next step is estimation. The aim is to assess potential fiscal liabilities—how much aid might be required, sector by sector, after a major disaster. This involves building a probabilistic model that draws on historical loss ratios, sectoral asset values, and exposure patterns. For example, regions with a large share of economic activity in ISIC 0111 are likely to see concentrated losses after floods or droughts, whereas cyclone-prone urban areas may show spikes in ISIC 4120 exposures. The more granular the data, the more accurately one can model risk.
A key point is that not all sectors face the same level of risk or require the same type of financial response. Agriculture, for instance, may need rapid liquidity injections to enable replanting, while construction might require targeted loans or subsidies for rebuilding. Manufacturing sectors could experience disruptions in supply chains that have long-tail effects on employment and tax revenue. By quantifying exposures at the ISIC code level, policymakers and insurers can design contingency funds that are sector-specific, reflecting both typical loss magnitudes and recovery timelines.
The methodology for designing these funds can be outlined in a few steps. First, use ISIC-coded claims and aid data to estimate average and extreme loss scenarios for each major sector. Second, apply these loss estimates to current sectoral output, asset base, or employment data—again, aligned by ISIC code—to translate percentages into absolute figures. Third, consult with sector stakeholders to understand unique recovery needs or bottlenecks, adjusting fund parameters accordingly. Finally, formalize the design: contingency funds might take the form of pre-funded reserves, contingent credit lines, or parametric insurance products, each sized and triggered based on sectoral exposure estimates.
The advantage of this approach is twofold. On one hand, it enables more accurate budgeting and risk pooling at the national level, reducing the likelihood of fiscal surprises or underfunded responses. On the other, it supports more equitable and effective distribution of aid, ensuring that sectors hit hardest by a particular disaster receive adequate and timely support, rather than relying on ad hoc or politically driven allocations.
There are, of course, limitations. The accuracy of the approach depends on the quality and completeness of data—many disasters still produce significant uninsured losses, or losses that are not systematically reported. Some economic activities, especially in the informal sector, may escape ISIC coding altogether. For this reason, the methodology should be seen as iterative, improving over time as data and reporting standards advance.
Still, the benefits are substantial. By bringing ISIC codes into disaster-risk financing, analysts and policymakers gain a more detailed, actionable map of vulnerability and potential fiscal pressure. This, in turn, allows for smarter, more resilient economic planning—turning what is often a reactive process into one grounded in evidence, preparedness, and sectoral nuance. As the frequency and intensity of natural disasters grow, the need for such rigor will only become more pressing.