Lelo Nxumalo

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Tensions and Global Supply Chain Restructuring

Executive Summary Between 2017 and 2025, U.S. trade policy underwent two dramatic transformations. The 2018-19 trade war elevated tariff rates from 1.5% to 6% . Then in 2025, tariffs spiked to 52% before reversing to 13% . China's import share collapsed from 21.6% to 8.7% , while Mexico and Vietnam captured market share across all categories. Part I: The Tariff Timeline 2017 Baseline: Era of Low Tariffs U.S. average tariff rate: 1.5% — lowest in modern history Reflected decades of trade liberalization through WTO framework 2018-19 Trade War: First Escalation July 2018: First Section 301 tariffs (25% on $34B Chinese imports) → rate jumps to 2.8% ...

Lost in the Warehouse: "General" vs. "Special" Trade Systems in Turkey-Germany Trade

Methodological Deep Dive Unraveling the Turkey-Germany asymmetry through the lens of trade data standards (GTS vs. STS). In bilateral trade, the math should be simple: what Country A reports as an export to Country B should theoretically match what Country B reports as an import. In practice, however, these numbers rarely align perfectly. I analyzed the trade relationship between Turkey (Reporter: Export) and Germany (Partner: Import) to quantify this "Asymmetry Gap." Using Databricks and PySpark, I processed annual trade records to distinguish between systemic reporting differences and massive, commodity-specific anomalies. Methodology Data Source UN Comtrade mont...

Mapping the Blast Radius: What Happens When the "World's Factory" Stops?

In global trade, "efficiency" often masks "fragility." We know that China is central to the global electronics supply chain, but how central? And if that node were to go dark, who would feel the shockwaves first? To answer this, I moved beyond standard trade statistics and built a network simulation using the OECD Inter-Country Input-Output (ICIO) Tables (2023 Edition) . This dataset maps the DNA of the global economy, tracking every dollar of input across 66 countries and 45 industries. 1. The Blast Radius: Tracing the Contagion I treated the global economy as a directed graph and simulated a total supply shock to Chinese Electronics (CHN_C26) . By tracing the flow of inputs across three tiers of buyers, I visualized the "Blast Radius" of this disruption. Fig 1: The Supply Chain Cascade. The shock originates in China (Red) and immediately hits "Tier 1" assembly hubs (Dark Blue) before cascading to global consumers...

Who Really Owns the Factory? Mapping the Corporate Control of Global Trade

In my previous post, we looked at where value is created in the global economy. We saw how a "German" car is actually a European collaborative effort. But that map leaves one critical question unanswered: Who owns the factory? Does a country own its own production, or is it merely a host for foreign capital? To answer this, I dove into the OECD Activity of Multinational Enterprises (AMNE) database. By distinguishing between domestic and foreign-owned firms, we can reveal the "hidden architecture" of corporate control. 1. The Sovereignty Gap: Who Relies on Foreign Capital? The first metric I calculated is the Foreign Penetration Ratio : the percentage of a nation's total Gross Value Added (GVA) that is generated by foreign-controlled affiliates. The results reveal a stark divide in the global economy between "Sovereign Giants" and "Global Hubs." Fig 1: The "Hubs" vs. The "Giants" — Sha...

A Network Analysis of Global Value Chains (GVCs)

We often speak of trade in simple, bilateral terms: "Germany exports cars to France" or "China ships phones to the US." But in a modern hyper-connected economy, these statements are deceptively simple. A "German" car might contain a Polish engine, Italian electronics, and Chinese batteries, while a "Chinese" phone may effectively be a bundle of Korean chips and American intellectual property wrapped in plastic. To understand who really creates value in the global economy, we cannot rely on standard customs data. We need to look deeper. The interactive network map below does exactly that, peeling back the layers of "Gross Trade" to reveal the Global Value Chains (GVCs) underneath. Global Value Chain Network (2021) Interactive Map: Hover over any arrow above to see the exact trade volume (USD Millions). Click an arrow to highlight the path. The Data Problem: The "$1,000 iPhone" Illusion ...

Taming the "Fat Tail": Decrypting Climate Disaster Costs

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, Wi look at the geography of these events and, crucially, how I am using a Composite Log-Normal Pareto model to estimate their economic costs when data is missing. The Geography of Risk To understand the scope, I first look at where these events occur. As the data shows, the distribution is far from uniform. Figure 1: Natural Disasters by Region. Asia is the undisputed global epicenter of natural disaster frequency, accounting for nearly double the event count o...