Applied Economic Perspectives and Policy最新文献

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.

Agricultural extension programs must demonstrate their value to compete for limited government funding. As extension professionals measure the value of their programs, the risk exists that the information they report will provide a biased or an inaccurate measure of value. We examine the evaluation process for extension programs and extension personnel to identify potential sources of bias or inaccuracies. We find that bias and inaccuracy in program evaluation often stems from a focus on short-term outcomes, rather than long-term impacts, while bias in personnel evaluation can result from information asymmetries that exist between extension personnel and their evaluators.

Prior research on mechanization outsource services has not explored seasonal variations in drivers and outcomes of adoption by smallholders. These omissions are important because seasonality of crop cultivation may influence intensity of demand for machines, while seasonality of demand for non-farm labor may influence the availability and cost of agricultural workers and the opportunity costs of on-farm self-employment. We analyze seasonal correlates of combine harvester outsource service use in Myanmar. Surprisingly, adoption is not associated with lower labor costs or higher net margins from paddy cultivation. Rather, using combines releases family labor into more remunerative dry season non-farm employment.

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.

Food assistance, in its various forms, has played a significant role in tackling food insecurity and improving nutrition in many countries. Through an examination of the historical development and a comparison with other countries, the paper reviews the experiences and lessons of the evolution of Chinese food assistance programs and proposes pathways forward to further strengthen food assistance in China. Future efforts of food assistance should focus more on improving the effectiveness of programs through enhancing governance and public-private partnerships, conducting evidence-based evaluations of existing programs using data-driven methods, adopting innovative technologies and digital tools, and employing a targeted approach to address the issue of double burden of malnutrition.

This study examines the spillover effect of California's 2016 single-use bag ban (SB 270) on unregulated single-use plastics (SUPs) like cutlery, cups, and plates. We observed an insignificant 4.8% decrease in SUP sales over the entire post-policy period, but a larger 8.1% drop in the second post-year, which was only marginally significant. The findings suggest weak spillover effects from such environmental policies, indicating that direct regulations on certain environmentally harmful products might be more effective if jurisdictions aim to prohibit citizens from excessive use, rather than relying on the positive spillover effect from existing policies.

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.