Lelo Nxumalo

In the world of risk modeling, natural disasters are notoriously difficult to quantify. While frequency is relatively predictable, economic impact is chaotic. A single "Black Swan" event—like the 2011 Tohoku Earthquake or the 2004 Indian Ocean Tsunami—can cause more economic damage in an afternoon than thousands of smaller events combined over a decade. I analyzed global disaster data from EM-DAT (2000-2025) to understand these patterns. Below, I look at the geography of these events and then at how I estimate losses for the many events that report none. Update: an earlier version of this post described a hand-calibrated formula. I have since replaced it with the standard damage regression from the disaster-economics literature and, more importantly, tested it properly: every score below comes from years the model never saw. The Geography of Risk To understand the scope, I first look at where these events occur. As the data s...

In the fast-moving world of international trade, data is often the bottleneck. Detailed Inter-Country Input-Output (ICIO) tables—the maps that tell us exactly how many semiconductors Japan sold to Germany—often lag by years. Yet, policymakers and analysts need to know what the world looks like today . How do we reconcile the detailed structural data of the past with the aggregate economic realities of the present? In this analysis, we deployed the RAS Method (Bi-Proportional Adjustment) to "Nowcast" the 2022 global trade structure using only historical patterns and current headlines. The Methodology: A Blind Simulation To rigorously test the RAS capability, we set up a "Blind Simulation" using Python. Although we possessed the full 2022 Input-Output tables, we deliberately ignored the interior data —the specific supply chain connections between countries. Instead, we fed the algorithm only two specific ...

A general-equilibrium model of Turkey's dual labour market says the cost of taxing formal work doesn't show up where we usually look for it. The bottomline: Making Turkey's transfer to the unemployed a third more generous raises unemployment from 8.6% to 9.2% . That is the honest cost, and it is not large. How you pay for it matters more than whether you pay for it. Funded by payroll taxes, the poor end up 0.6% worse off than before the transfer was raised — the policy defeats itself. Funded by VAT, they are 0.5% better off . The reason is not unemployment, which is nearly identical under both. It is informality . A higher payroll tax pushes formal jobs into the unregistered sector, where the wage is 43% lower and nothing is taxed. Turkey is not on the wrong side of the payroll-tax Laffer curve — revenue peaks around 53%, well above today's ~37.5% wedge. But the marginal cost...