Research

We examine how diversification in import sourcing moderates resilience to upstream shocks, focusing on two specific but recurrent risks: pandemics and geopolitical conflicts. Using annual data on global trade and weekly data from Swiss importing firms, we assess how diversification before an upstream shock affects the growth rate of imports at the product level.

Guided by a theoretical framework that introduces risk aversion into global sourcing, we estimate a reduced-form gravity model and report two sets of empirical findings. First, we establish that both shocks significantly reduce the growth rate of imports. Second, using various diversification measures defined at the importer-product level, we show that more diversified firms exhibit greater resilience to these disruptions — importers with higher pre-shock source diversity show lower declines in import growth during a shock. That the results align across macro and micro levels enhances the external validity of our findings.

Recent shortages and price volatility have exposed vulnerabilities in international agri-food value chains. This paper develops a new framework for measuring trade diversification based on Hill numbers, which capture both the extensive and intensive margin of trade connections. Using UN COMTRADE data for 188 countries from 2000 to 2022, I first document global patterns of diversification and concentration across agri-food sectors.

The descriptive evidence shows that most countries trade with many partners in principle, yet effectively rely on only a few — especially for grains and oilseeds. I then estimate the effect of import and export diversification on within-year food price volatility using a shift-share instrumental-variable design that exploits exogenous foreign sector-level shocks weighted by lagged bilateral trade exposure. The empirical results show that more balanced and diversified import portfolios are strongly associated with lower food price volatility, whereas export diversification plays a more limited role.

Risks are inherent and increasingly recognized in global agricultural value chains; however, their effects and firm-level responses are poorly understood. Using novel global firm-to-firm trade data, we show that agricultural markets are highly concentrated and have sparse network structures, with buyers typically relying on a limited set of suppliers, and prices are shaped by the two-sided market power of both importers and exporters.

We find that U.S. import prices are lower when importers account for a larger share of purchases, and that price volatility is reduced when firms diversify their foreign suppliers, for example in response to the Ukraine-Russia war. Motivated by these findings, we develop the first structural model of international sourcing under matching frictions and risk aversion applied to agricultural markets. Firms adjust along three margins: absorbing shocks, renegotiating with existing suppliers, or diversifying by switching to new suppliers. Our counterfactual exercises show that policies promoting import-source diversification and reducing matching frictions enable importers to access new suppliers and help stabilize agricultural prices.

Food has long been a policy instrument in times of conflict and political crisis. As a necessity, food can be used to advance geopolitical goals, including projecting state power and ensuring national security. We review the literature at the intersection of agricultural economics and political science, examining how food policy can further geopolitical interests.

We bridge the gap between distinct literatures in agricultural economics and political science by synthesizing extant research on how state power and national security concerns shape global food policy, and identify directions for future research. By linking fragmented analyses of contemporary events with historical and theoretical perspectives, we highlight how geopolitical uncertainty continues to influence food policy.

We investigate whether EU border rejections of food and agricultural products are driven primarily by food safety concerns or by political and trade-policy considerations. Using machine learning methods, we build a predictive model of rejection risk and decompose the relative importance of safety, regulatory, and geopolitical factors across product-origin pairs.

We study how geopolitical risk — measured through both index-based approaches and event studies — transmits into agricultural commodity prices. The project combines structural time-series methods with high-frequency market data to identify the channels through which political uncertainty reaches food markets.

We estimate the effect of minimum wage regulations on agricultural export competitiveness and trade flows, exploiting cross-country and cross-sector variation in minimum wage levels and changes.