Graduate Studies

 

First Advisor

Cory Walters

Second Advisor

Amalia Yiannaka

Degree Name

Doctor of Philosophy (Ph.D.)

Committee Members

Eric Thompson, Simanti Banerjee

Department

Agricultural Economics

Date of this Version

5-2025

Document Type

Dissertation

Citation

A dissertation presented to the faculty of the Graduate College at the University of Nebraska in partial fulfillment of requirements for the degree of Doctor of Philosophy

Major: Agricultural Economics

Under the supervision of Professors Cory Walters and Amalia Yiannaka

Lincoln, Nebraska, May 2025

Comments

Copyright 2025, Iyore Eronmwon. Used by permission

Abstract

This dissertation examines decision-making under risk in grain marketing, focusing on how information influences behavior, attitudes toward accessible information, and methods for forecasting commodity basis. Through three essays, it explores the impact of information on hedging decisions, the determinants of information-seeking behavior, the extent to which students can serve as proxies for farmers in risk experiments, and the effectiveness of different forecasting approaches for commodity basis.

The first essay examines the impact of information on pre-harvest grain marketing hedging decisions and grain marketing information-seeking behavior. Specifically, it evaluates two types of information hypothesized to influence decision-making: expected cost of production and price information, which includes historical and current price evolution. Using a between-subject experimental design, participants were randomly assigned to different groups to assess the effects of these treatments. The findings reveal that providing participants with both expected cost of production and price information significantly increased the quantity of grain hedged pre-harvest compared to those who received no information. Participants who had access to both types of information also earned higher profits. The study highlights a complementary relationship between hedging and crop insurance, showing that participants hedged more grain in years when crop insurance was available. Furthermore, the results identify key factors influencing information-seeking behavior, such as participants' need for cognition, knowledge of grain marketing concepts, and affective responses to risk. These insights emphasize the importance of factors beyond pecuniary ones, such as targeted information, in improving grain marketing decisions and promoting more effective risk management strategies.

To address concerns about external validity in economics experiments that often rely on student samples, the second essay compares the behavior of students and farmers in a controlled setting simulating grain marketing decisions. The experiment uses a between-subject design to examine pre-harvest hedging decisions under varying information treatments, including expected production cost and grain price evolution information. The findings reveal critical behavioral differences between the two groups: while farmers significantly increased their hedging in response to expected cost information, students did not. However, both groups responded similarly when provided with cost and price evolution information. Additionally, while both groups viewed crop insurance as a complementary risk management tool to pre-harvest hedging, farmers exhibited significantly higher levels of hedging when crop insurance was present than when it was not. The study also finds that farmers are more risk averse than students and that risk aversion significantly influences hedging behavior for farmers but not for students.

The third essay compares simple historical average models and complex time-series methods for forecasting corn nearby and new crop basis, aiming to identify the optimal location-specific forecasting method. The study evaluates model performance under different elevator attributes and changing climate conditions. The results indicate superior performance from time-series models compared to simple historical average models, with the ARIMA model emerging as the most optimal method for forecasting basis. However, the findings demonstrate that forecast accuracy for historical averages significantly improves when supplemented with current market information. The results further indicate that forecasting nearby basis is more challenging than new crop basis, with greater difficulty observed in ethanol plants compared to non-ethanol plants. Additionally, the results show that drought affects forecast accuracy, with the magnitude of the impact varying by model and basis type.

Advisors: Cory Walters and Amalia Yiannaka

Download

Share

COinS