Research

SUBMITTED MANUSCRIPTS

• Mitchell Roznik, Ashok K. Mishra, Hongqiang Yan. Field Rotation Related Soil Productivity Changes, Yield Risk, and Efficiency in Crop Insurance Rating.
• Hongqiang Yan, Serkan Aglasan, Le Chen, Roderick Rejesus. The Dual Damage from Soil Erosion: Lower Yields and Higher Risk in US Agriculture. Latest version
• Jiatong Li, Hongqiang Yan. Uniform Inference in High-Dimensional Threshold Regression Models. Latest version Supplementary Material

  WORKING PAPER

• Hongqiang Yan, Barry Goodwin, Mehmet Caner. Global Maize Market Integration: A High-Dimensional Local Projection Approach with Mixed-Frequency Data and Regime Switching.

  Abstract: This paper investigates the degree of market integration, exchange rate pass-through, and the market factors that contribute to deviations from perfect integration. To analyze the price linkage dynamics, we apply the novel debiased LASSO for Local Projection Approach, including linearity testing within high-dimensional Regime Switching regression models. Our findings reveal significant global maize market integration, particularly when incorporating threshold effects and key market factors. Notably, consumer prices and unemployment emerge as important determinants of price linkages, underscoring their relevance in the global commodity market.

• Hongqiang Yan, Mark Manfredo, Ashok K. Mishra. Machine Learning Forecasts for Food Price Inflation: Expanding FRED-MD.
  Abstract: In recent years, U.S. food prices have surged amid supply chain disruptions, labor shortages, rising input costs, and global shocks such as COVID 19 and the war in Ukraine. Because of this, there is renewed interest in forecasting food price inflation by economists, policymakers, and agribusiness firms alike. This study leverages machine learning methods and the availability of large economic databases to improve forecasts of U.S. food price inflation. We show that forecasts generated from machine learning models incorporating a large set of covariates—particularly using a modified version of the FRED-MD dataset—are more accurate than univariate benchmark forecasts over several alternative horizons. Notably, forecast combination strategies that combine forecasts generated from different machine learning methods as well simplistic univariate models often outperform forecasts generated from individual models and therefore warrant greater attention.

• Hongqiang Yan, Barry Goodwin, Ashok K. Mishra. Detecting Structural Change in the Demand for Multiple Peril Crop Insurance.

  Abstract: Understanding what shapes producers’ adoption of crop insurance is essential for evaluating the U.S. Federal Crop Insurance Program. This study analyzes county-level corn and soybean data from 1992 to 2023 using a high-dimensional panel regression with an unknown structural break to account for major policy changes following the 1994 Reform Act. The model selects key weather, soil, and market variables, allowing their effects to vary across policy regimes. Results show inelastic demand—average price elasticities of –0.031 for corn and –0.113 for soybeans—with significant heterogeneity across regions. High-return counties exhibit lower price responsiveness, while responses to moderate and extreme heat vary by crop type and policy period. The findings emphasize that farmers’ production environment and policy context shape farmers’ insurance decisions. While crop insurance enhances risk management, it may also affect regional crop diversity and the long-term sustainability of farming systems due to uneven responsiveness across producer types and locations.

• Hongqiang Yan, Ashok K. Mishra, Jaweriah Hazrana. Spatial Analysis of Modern Rice Varieties Technology on Production: Evidence from Panel Data in Bangladesh.
  Abstract: Although modern rice varieties have the potential to enhance yields, their adoption among smallholder farmers in Bangladesh remains limited. Many farmers continue to favor inbred high-yielding or traditional local rice varieties, despite their suboptimal productivity. However, there is a lack of research examining how the adoption of modern rice varieties influences productivity in developing nations. This study explores this relationship by incorporating village- and location-specific heterogeneity in production technology within an advanced econometric framework. Using data from the Bangladesh Integrated Household Survey, we analyze spatial heterogeneity by integrating geographical coordinate information with a household-level panel dataset on rice production and technology adoption. Furthermore, we develop and estimate a location-dependent production function incorporating Hicks-neutral productivity. The findings reveal that the productivity impact of improved seed varieties is highly variable, primarily influenced by spatial characteristics rather than the specific rice variety type. These results underscore the crucial role of spatial factors in the adoption of modern rice varieties in Bangladesh and highlight important policy implications for optimizing resource and financial allocations to promote the adoption of improved agricultural technologies and support sustainable agricultural development in emerging economies.

• Mitchell Roznik, Ashok K. Mishra, Hongqiang Yan. Extreme Climate, Financial Health, and Credit Default Risk: An American Landscape.
  Abstract: Climate change is expected to increase the frequency and severity of drought in the United States. This study investigates the effect of drought conditions on farm credit default risk and examines the vulnerability of farms to enhanced drought risk. We use individual-level data from Farm Service Agency loan data covering seven-year operating loans. The study applies Cox proportional hazard and Generalized Gamma parametric survival models with various established financial health variables and drought. Findings suggest that lagged drought conditions, occurring in the growing season before loan origination, significantly increase the probability of default. A drought-affected borrower is about 11% more likely to default than an equivalent borrower who has not experienced drought. Non-white and Hispanic farmers affected by drought have higher default risks than their counterparts. Similarly, small and medium-sized farms, compared to large farms affected by drought, exhibit higher loan risk. These findings have important implications for lenders and policymakers, emphasizing the need for comprehensive risk assessment strategies that account for financial, demographic, and environmental factors in the agricultural sector.

Work in Progress

• Cost Functions Under Uncertainty: A Conditional Value-at-Risk Approach (with Ashok K. Mishra)

• Uniform Inference and Variable Selection in High-Dimensional Cross-Section-Varying Panel Data Models with Structural Breaks (with Jiatong Li)

PEER-REVIEWED JOURNAL ARTICLES

• Yan, H., Mishra, A. K., & Zhou, X. (2025). Do all food and beverage firms benefit from voluntary ESG reporting? Evidence from China’s listed companies. Agribusiness: An International Journal, forthcoming. Online version

• Khanal, A. K., Mishra, A. K., Hazrana, J., & Yan, H. (2025). Risk attitude, perception, management experience, and productivity: Evidence from a semiparametric approach and a less-developed economy. European Review of Agricultural Economics, forthcoming.