Date of Award


Degree Type


Degree Name

Doctor of Philosophy in Environmental and Natural Resources Economics


Environmental & Natural Resource Economics

First Advisor

Thomas W. Sproul


The focus of this dissertation is on discrete classification problems in agricultural and behavioral economics. In my first two manuscripts, I take up the issue of producer misperceptions of yield risk relative to their objective, a well-established phenomenon in which farmers tend to be overly optimistic in their perceptions of yield risk, forecasting yields with higher mean and lower variance than historical outcomes would suggest. Manuscript 1 focuses on estimating both how such misperceptions are distributed across individual forecasts, as well as how such misperceptions might arise. Manuscript 2 goes on to look at how these misperceptions of yield risk affect farm-level crop insurance coverage level choices, simulating cross-coverage crop insurance demand across a broad set of scenarios. In my third chapter, I present a hierarchical Bayesian methodology for disaggregating, or downscaling, aggregated count data using an outside statistical sample. As an application, this chapter demonstrates how stakeholders can use readily available yet incomplete land use (e.g. agricultural) count data in combination with censored/aggregated census data provided at the county level to recover/estimate land use count data at the municipality (town, city, or any other sub-county region) level.

In Manuscript 1, we estimate the distribution of miscalibrated perceptions of yield risk, using the expectation maximization algorithm to perform a latent class analysis to uncover potential heterogeneity (clustering) in the parameters of our yield miscalibration model. Using self-reported yield forecasts and yield history from rural Chinese farmers, we estimate miscalibration parameters for each of our 879 forecast/history observations, using expectation maximization to fit these parameters to a Gaussian mixture model. We find that forecasts can best be described as coming from three distinct distributions or clusters that can best described as ‘optimistic’, ‘unbiased’, and ‘pessimistic’. We find that roughly 67% of forecasts can be defined as optimistic with producers perceiving that, on average, yields face only half (55%) of their true risk. 12% of forecasts can be defined as pessimistic with producers perceiving that, on average, yields face 50% more risk than that of their objective risk. The remaining 21% of forecasts can be classified as unbiased, with perceived yield risk being largely in line with objective yield risk. In addition, we find that our optimistic group separates cleanly into two distinct clusters of roughly equal size – one comprised of ‘mild optimists’, and another comprised of ‘extreme’ optimists.

We go on to examine the possible causes of these misperceptions of risk, finding that such misperceptions are not inherent to the producer, but rather result from crop-specific yield experience. Using regression methods, we find statistically significant evidence that recent historic losses increase the amount of producer’s level of perceived risk, while increases in the length of time since experiencing a historic loss decrease the level of perceived risk.

These results have important implications for crop insurance demand modeling. These findings also suggest that a targeted subsidy approach based on outcome history may be more cost-effective at inducing insurance participation than subsidies that are fixed across locations. Namely, Not only is it important to incorporate miscalibrated perceptions of risk in crop insurance demand models, it is also important to include heterogeneity with regard to those misperceptions.

Manuscript 2 takes up the question of how misperceptions of yield risk effect producers’ decisions regarding which crop insurance coverage level to participate in. We simulate cross-coverage level crop insurance demand for both yield and revenue insurance across four potential models of risk misperception and three potential models of decision-making - one based on expected utility and two based on cumulative prospect theory yet differentiated by whether decisions are framed within the broader context of farm risk-management, or whether decisions/outcomes are more narrowly framed - for a total of twelve choice models. Optimal coverage level choices are simulated for both corn (based on data from York Count, NE and considered to be ‘low risk’) and wheat (based on data from Sumner County, KS and considered ‘high risk’). We find that increases in optimism bias drive down the optimal choice of coverage level, eventually inducing producers not to participate in crop insurance at all. Conversely, pessimism causes producers to increase their coverage level to the point of maximum coverage. We also find that this effect is strongest in the case of yield insurance, although the effect is still significant for revenue insurance. Further sensitivity analyses suggest that these results are not highly sensitive to correlations between prices and yields.

The aim of Manuscript 3 is to help stakeholders obtain policy-critical micro-level statistical data in cases where such data may only exist at a higher level of aggregation than is desired (e.g. aggregated census data). In this manuscript, published in the December 2017 edition of Agricultural and Resource Economics Review, we develop a hierarchical Bayesian methodology for downscaling regional count data to the sub-region level through the incorporating of an outside statistical sample in the form of sub-regional lower bounds (e.g. sub-regioni has at least xi farms, sub-regioni has at least xi farms, etc…). Our methodology combines numerical simulations with exact calculations of combinatorial probabilities in order to determine which values of sub-regional counts are most likely to have resulted in the available statistical sample given the information contained in our two data sources. Although our method is designed to provide municipality count data based on county level data, as a proof of concept, we demonstrate our approach by estimating Rhode Island county level farm counts (which are known, but are not used in the estimation procedure) using state level farm count data provided by the Ag Census, along with a sample of Rhode Island farm locations collected by the University of Rhode Island. By estimating values that are known we are able to measure the accuracy of our estimates. We are able to show that not only do our estimates outperform those obtained via maximum likelihood, but that they are robust to sampling variability across heterogeneous population sizes. We go on to expand our model to incorporate spatial considerations and demonstrate how the use of an informative prior based on relevant sub-region characteristics (land area, in our application) can further improve the estimates.



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