http://hdl.handle.net/10150/678404 2026-06-09T05:46:44Z 2026-06-09T05:46:44Z Scheitrum, Daniel http://hdl.handle.net/10150/678458 2025-09-10T08:47:11Z 2022-01-10T00:00:00Z

U.S. Cucumber Supply Scheitrum, Daniel Working paper. Prepared for: Fresh Produce Association of the Americas

2022-01-10T00:00:00Z Michler, Jeffrey D. Josephson, Anna http://hdl.handle.net/10150/678456 2025-09-10T08:49:58Z 2021-07-01T00:00:00Z

Recent Developments in Inference: Practicalities for Applied Economics Michler, Jeffrey D.; Josephson, Anna We provide a review of recent developments in the calculation of standard errors and test statistics for statistical inference. While much of the focus of the last two decades in economics has been on generating unbiased coefficients, recent years has seen a variety of advancements in correcting for non-standard standard errors. We synthesize these recent advances in addressing challenges to conventional inference, like heteroskedasticity, clustering, serial correlation, and testing multiple hypotheses. We also discuss recent advancements in numerical methods, such as the bootstrap, wild bootstrap, and randomization inference. We make three specific recommendations. First, applied economists need to clearly articulate the challenges to statistical inference that are present in data as well as the source of those challenges. Second, modern computing power and statistical software means that applied economists have no excuse for not correctly calculating their standard errors and test statistics. Third, because complicated sampling strategies and research designs make it difficult to work out the correct formula for standard errors and test statistics, we believe that in the applied economics profession it should become standard practice to rely on asymptotic refinements to the distribution of an estimator or test statistic via bootstrapping. Throughout, we reference built-in and user-written Stata commands that allow one to quickly calculate accurate standard errors and relevant test statistics. Working paper. This paper is forthcoming as a chapter of the same title in Hobbs, J., and Roosen, J. (Eds.), A Modern Guide to Food Economics. Cheltenham: Edward Elgar Publishing.

2021-07-01T00:00:00Z Blumberg, Joey Thompson, Gary http://hdl.handle.net/10150/678457 2025-09-10T08:50:04Z 2021-01-01T00:00:00Z

Nonparametric Segmentation Methods: Applications of Unsupervised Machine Learning and Revealed Preference Blumberg, Joey; Thompson, Gary Many recent efforts by econometricians have focused on supervised machine learning techniques to aid in empirical studies using experimental data. By contrast, this article explores the merits of unsupervised machine learning algorithms for informing ex ante policy design using observational data. We examine the extent to which groups of consumers with differing responses to economic incentives can be identified in a context of fruit and vegetable demand. Two classes of nonparametric algorithms—revealed preference and unsupervised machine learning—are compared for segmenting households in the National Consumer Panel. Nonlinear almost-ideal demand models are estimated for all segments to determine which methods group households into segments with different expenditure and price elasticities. In-sample comparisons and out-of-sample prediction results indicate methods using price-quantity data alone—without demographic, geographic or other variables—perform better at segmenting households into groups with sizeable differences in price and expenditure responsiveness. These segmentation results suggest considerable heterogeneity in household purchasing behavior of fruits and vegetables. Working paper. This paper is accepted for publication in the American Journal of Agricultural Economics.

2021-01-01T00:00:00Z Aradhyula, Satheesh Chin, Elena Duval, Dari Scheitrum, Daniel Thompson, Gary Tronstad, Russell http://hdl.handle.net/10150/678455 2025-09-10T08:49:52Z 2021-01-01T00:00:00Z

Impact of COVID-19 Pandemic on Fresh Tomato Shipments and Prices Aradhyula, Satheesh; Chin, Elena; Duval, Dari; Scheitrum, Daniel; Thompson, Gary; Tronstad, Russell The implementation of stay-at-home orders in March 2020 to prevent the spread of COVID-19 and the rapid, dramatic downscaling of foodservice operations across the country represents an unprecedented shock to U.S. food supply chains. Consumer spending on grocery retail saw a dramatic surge as households stocked up on supplies and demand for food retail has remained elevated since. Meanwhile, demand for food away from home collapsed as restaurants and bars were ordered to cease all dine-in service. While this market shock is interesting per se, it can also offer more detailed insight into the supply, demand, and market structure for individual commodities. This study examines the impacts of the COVID-19 pandemic on the U.S. market for fresh tomatoes. Market data quantifying the volume, price, and origin of tomatoes sold via foodservice supply chains do not exist at the industry level, nor do data on the volume sold at retail. The COVID-19 pandemic offers an opportunity to examine responses in volume and price by origin to the demand shocks caused by the pandemic, and information can be gleaned indirectly regarding the market for foodservice tomatoes and the role of tomatoes of different origins within the foodservice industry. Working paper. Prepared for: Fresh Produce Association of the Americas

2021-01-01T00:00:00Z