Applied Economic Perspectives and Policy最新文献

Since the COVID-19 pandemic and the Russia–Ukraine War, global food markets have been in turmoil. Agricultural input and energy prices doubled between 2020 and 2022, with immediate consequences on food accessibility. We examine the drivers of the EU food inflation patterns, and how trade integration shapes these dynamics. We find that food price inflation has been mainly driven by surges in agricultural production costs and, to a lesser extent, by global food price increases. Trade openness has not exacerbated the inflating dynamics during this period. We conclude with reflections on inflationary, production, and trade policies.

This article analyzes the geostrategic dimensions in food policy responses to two recent global shocks: the Corona-SARS-CoV-2 pandemic and the Russian war against Ukraine. The analysis adds to the general assessments of economic security by providing a case study of food policy and food security as an element of economic security. Strong national path dependent trajectories in food policies can be observed. However, geostrategic dimensions related to interstate interaction and political considerations in policymaking that go beyond food security are also observed.

Long-run projections are crucial for climate change policies, food security, and agricultural productivity, yet these projections often overlook recently identified patterns in how trade policies and domestic support evolve as countries develop. The grain market projections over 50 or 100 years presented here take these policy patterns into account, as well as the risks of more significant trade policy disruptions. The results demonstrate that different assumptions about economic development can have far-reaching effects on the market context, greenhouse gas emissions, and grain prices.

This article uses the time-varying parameter vector autoregressive model to assess the impact of geopolitical risk and shocks on agricultural commodity markets, accounting for demand—exports; supply—input prices; inventory; speculation; and economic fluctuations. The results show that geopolitical risks significantly impact corn and soybean futures prices and market behaviors with context-specific implications in the short to medium term. In addition, heightened geopolitical risk during the Russia–Ukraine war increased oil prices and indirectly elevated agricultural commodity prices. These insights are crucial for agricultural risk management, informing federal policies and forecasting future price trends in an increasingly uncertain global market environment.

Leveraging advancements in remote data collection and using the Food Insecurity Experience Scale (FIES) as a proxy measure of resilience, we show that machine learning models (such as Gradient Boosting Classifier, eXtreme Gradient Boosting, and Artificial Neural Networks), can predict resilience with relatively high accuracy (up to 81%). Key household-level predictors include access to financial institutions, asset ownership, the adoption of agricultural mechanization as evidenced by the use of tractors, the number of crops cultivated, and ownership of nonfarm enterprises. Our analysis offers insights to researchers and policymakers interested in the development of targeted interventions to bolster household resilience.

We employ a neural network (NN) approach—Long Short-Term Memory (LSTM)—to study agricultural commodity prices during the 2008 Great Recession and the COVID-19 recession. Our analysis reveals more structural breaks and higher volatility in plant-based commodities like corn and soybeans during recessions compared with animal-based commodities. The price reactions varied among commodities, with corn responding first to both recessions, while milk price, which was found independent of other prices, recovered last from the financial recession and first from the disease-induced recession. This insight into commodity behavior during recessions can aid in trend prediction and recession preparation for investors and researchers.