American Journal of Agricultural Economics最新文献

Brian E. Roe, PhD, is the Van Buren Professor in the Department of Agricultural, Environmental and Development Economics at the Ohio State University. Dr. Roe has worked broadly in the areas of agricultural and environmental economics, focusing on issues including food waste, agricultural marketing, information policy, farm nutrient management, behavioral economics, and product quality.

Roe grew up on a dairy farm near Monticello, Wisconsin, where family members continue to farm. He received his bachelor's degree at the University of Wisconsin—Madison, where he majored in in Agricultural Economics and Agricultural Journalism and was selected as part of the 1990 Truman Scholars cohort. He received his PhD from the University of Maryland—College Park, where he was advised by Bruce Gardner. His interest in behavioral economics was sparked by his post-doctoral service as a Staff Fellow at the US Food and Drug Administration where he worked with an interdisciplinary group of colleagues to assess the impacts of government product labeling and information policies.

Roe has served on the faculty at Ohio State University since 1998, where he served a decade as the Undergraduate Program Leader, formed and leads the Ohio State Food Waste Collaborative, and co-directs an National Science Foundation funded Research Network focused on systems approaches to addressing wasted food. Roe has served in several roles for the Agricultural and Applied Economics Association, including as an Editor for the American Journal of Agricultural Economics and on numerous committees.

Hurricanes are considered among the most destructive natural disasters in the United States. The exposure of agricultural production systems to hurricanes varies between regions in contrast to global risks like commodity price volatility and international trade policies. The regional differences in hurricane exposure may lead to heterogeneity in crop insurance premium rates. This work aims to measure the impact of hurricane incidence on production losses for crops grown in the Mississippi Delta. We leverage a county-month panel of insurance losses spanning 2002–2021 from the US Department of Agriculture Risk Management Agency, and daily data from the National Oceanic and Atmospheric Administration National Hurricane Center, to construct novel measures for hurricane treatment assignment under a difference-in-differences identification strategy. We find hurricane incidence results in economically significant losses that are strikingly heterogeneous across crops. We also demonstrate that measuring hurricane treatment is a critical concern for credible identification as simplistic approaches are associated with attenuation bias relative to our approach which accounts for the dynamic changes in the scope and intensity of a hurricane. We discuss implications of these results for two important policy instruments, traditional crop insurance premium rating and the more recently released Hurricane Insurance Protection—Wind Index product.

This study examines the effects of new food store openings on customer traffic to incumbent stores using a modified difference-in-differences approach and cellphone location data from the contiguous United States in 2019. Analysis of both the intensive margin (total visit counts) and the extensive margin (unique visitors) reveals substantial spatial, temporal, and demographic heterogeneity, highlighting that ignoring these variations may lead to misleading conclusions about customer behavior. Grocery store openings, for example, reduce visits to food-at-home incumbents in close proximity but affect food-away-from-home incumbents over broader distances. The magnitude of these effects varies with demographics. Entrant stores offering fresh food options, such as grocery stores, reduce traffic to outlets that sell more processed foods—such as fast-food and convenience stores. However, the reductions remain modest—generally below 5% of monthly visits—and are statistically significant for White and high-income visitors when disaggregated by demographics. Complementarity effects emerge when entrants and incumbents are not direct competitors, particularly within a one-mile range. For instance, grocery entry does not reduce traffic to fast-food stores nearby but does so at greater distances. Conversely, competition within similar store formats intensifies business-stealing effects, such as dollar store openings reducing traffic to other smaller store formats while leaving grocery stores unaffected. These findings underscore the competitive dynamics shaping the U.S. food environment.

The dominance of the US innovation and academic system relies heavily on foreign-born labor for its success. Recent literature has shown evidence of wage gaps in academia based on gender and race; however, little is known about whether a wage gap might exist for foreign-born faculty. This paper studies the compensation gap between US- and foreign-born agricultural and life science faculty at 52 US land-grant universities (LGU) using a survey of over 1400 scientists conducted in 2005 and 2015. We develop a framework to categorize the sources of a potential compensation gap into testable categories that capture direct discrimination as well as indirect (systemic) discrimination. We find wage differences in total annual compensation among the foreign-born and the US-born, tenure-track faculty, however, the gap in the base annual salary is insignificant. This suggests that additional salary components like grants and summer teachings may not be equally available to foreign-born faculty even though, on average, foreign-born scientists work are more productive than US-born scientists on most common output metrics. The decomposition analysis suggests that about one-half of the gap (at 10% level) in the base salary and 60% of the differences in total salary (at 5% level)is due to various types of direct or systemic discrimination. Using our framework, we then rule in and rule out some important types of systemic discrimination.

Recently proposed legislation regarding farm labor would impact the minimum wage for workers with H-2A visas. Adverse Effect Wage Rates (AEWRs) are regional minimum wages paid to foreign farmworkers working in the United States under the H-2A temporary agricultural guest worker program. Recent increases in AEWRs have prompted discussions over the methodology and data sources used to compute them, including debates about whether AEWRs should be frozen or capped in the future. Employer and farmworker groups hold opposing views on the desirability and implications of changes to AEWRs. In this paper, we develop a simple theoretical framework that suggests higher AEWRs may induce spillover effects that lead to higher wages for non-H-2A farmworkers. Using confidential wage data from the National Agricultural Workers Survey, we test the labor market spillover hypothesis by comparing changes in non-H-2A farmworker wages and AEWRs across U.S. regions between 1996 and 2022. Our estimates suggest that a 10% increase in the AEWR causes, at most, a 2.8% increase in the wages of non-H-2A farmworkers across the United States. We find that freezing the AEWR for 1 year would reduce the growth of wages paid to non-H-2A farmworkers by as much as $475 million. Our analysis suggests that the recent policy proposals could lower labor costs for U.S. farm employers but limit wage growth for non-H-2A farmworkers.

Access to reliable and affordable high-speed internet is critical for economic growth, and broadband internet access is of particular concern in rural America. As the United States continues to invest in this vital infrastructure, we provide the first empirical examination of the impact of broadband internet speed upgrades on farm-level sale prices and county-level cash rents in 12 Midwestern US states from 2016 to 2021. We employ a Bartik shift-share instrument with broadband technology shares to address the potential endogeneity in the deployment and upgrades of broadband internet. Our results indicate that a one megabit per second (Mbps) increase in download speed raises farmland sale prices by 0.268% and county-level cash rents by 0.070%. Additionally, a one-percentage-point increase in the download speed growth rate leads to a 0.041 percentage point increase in the growth rate of county-level cash rents. By decomposing the Bartik shift-share instrument, we demonstrate that the exogeneity of the initial market share of satellite technology contributes most to identification. Our research provides valuable insights for policymaking by quantifying the return on broadband investment and identifying areas and types of farms that benefit more from higher internet speed.

Retail food prices rose dramatically in late 2021. Some argue that this food price inflation was due to “greedflation,” or firms increasing downstream prices simply because they can. In this study, we investigate the sources of “overshifting” store-level cost shocks into downstream prices, or the apparent ability of retailers to pass along price increases that are proportionately larger than increases in cost. We use exogenous changes in minimum wages as our setting and study how food retailers pass increases in labor costs along to consumers in the form of higher food prices. We derive a new theoretical model of retail price pass-through and show that demand curvature, market power, and consumer search behavior each likely affect observed rates of retail price pass-through. Our structural analysis shows that, after controlling for the primary determinants of wage pass-through, market power and demand curvature explain much of the variation in cost pass-through, although general price inflation has an important role in accentuating the rate of minimum wage pass-through. Our findings have important implications for minimum wage policy, and for understanding the role of cost shocks in food price inflation.

In 2023, the United States produced 28% of the world's soybeans, making understanding yield trends in the United States important for understanding global price trends. We estimate yield growth due to genetic change in soybeans using Illinois variety trial data with a modified approach. Specifically, our approach uses the yield from “check varieties” that are planted consistently from 1997 to 2020 as a control variable to identify the gain in yield across years using only yield variation within the plot and year. Ours are the first estimates of genetic gain in commercial soybean varieties after 2010 and we find that soybean yields increased between 25 and 26 kg/ha (0.37–0.38 bu/acre), or 0.7%, per year at a roughly linear rate in the past two decades. We find mixed evidence that the yield of genetically engineered soybeans grew faster than conventional varieties: In many years, conventional varieties did just as well as genetically engineered ones. We also find that adding the check variety's yield as a control variable significantly impacts how models in the literature estimate the average annual yield gain from genetics in these data. Our findings suggest that genetic change continued to be a consistent driver of yield change in soybeans over the past two decades.

We study racial differences in internal migration responses to one of the most severe climatic shocks in US history—the drought of the 1930s. Using data from the 1940 census on 70 million adults, we find that individuals exposed to more severe drought between 1935 and 1940 were more likely to make an inter-county move and that this responsiveness was greater for Black individuals than White individuals. This racial difference was particularly pronounced among the rural population. Black individuals' migration premium came despite their systematic disadvantage in the economy of the 1930s and evidence along dimensions other than race that disadvantage limited individuals' ability to adapt to the drought through migration. Federal relief spending under the Agricultural Adjustment Act (AAA) magnified this racial difference, reducing the migration response to drought for White individuals and increasing it for Black individuals. These results help to better understand how the reactions of different groups aggregate to determine the magnitude and composition of migration responses to natural disasters, as well as the roles of migration and government policy in disadvantaged groups' responses to natural disasters.