This study investigates consumer preferences for two emerging food waste reduction technologies – gene editing and all-natural spray coating – applied to apples. Using a discrete choice experiment with a nationally representative sample of U.S. consumers (n = 413), we estimate willingness to pay for gene-edited apples, spray-coated apples, and untreated apples. A generalized mixed logit model in willingness to pay space reveals that consumers exhibit the highest WTP for gene-edited apples ($2.45/lb), followed by spray-coated apples ($2.37/lb), with untreated apples valued least ($1.79/lb). Latent Class Analysis identifies three consumer segments: Price-Sensitive Skeptics, Sustainability-Oriented Consumers, and Selective Technology Adopters. Sustainability-Oriented Consumers showed the strongest support for both technologies, while Selective Technology Adopters displayed a clear preference for gene editing. Behavioral attitudes, rather than demographic variables, were the main drivers of segmentation. These findings suggest that tailored marketing strategies and policy interventions, including sustainability messaging, pricing incentives, and educational outreach, can support the adoption of food waste-reducing technologies. Overall, consumers are receptive to both gene-edited and spray-coated apples, though concerns about biotechnology and price sensitivity remain. Results offer insights for producers, retailers, and regulators aiming to enhance fresh produce sustainability and reduce food waste along the supply chain.

This study examines how income, location and regional disparities are associated with food expenditure and dietary diversity in Mexico. Using household expenditure data and an entropy-based approach, we confirm Engel’s Law: food budget shares decline with income but do so unevenly across urban–rural areas and regions. Consistent with the Engel curve for variety, wealthier households diversify their diets, spending more on high-value foods, while poorer rural households remain reliant on staples. Quantile regression shows that income has the strongest positive effect on diversity at lower quantiles, with diminishing returns at higher levels. Household characteristics, education, region, and food prices further influence diet. The results thereby underscore the need for income-sensitive, regionally targeted nutrition policies.

Using an administrative nationwide dataset of 1,673 tea producers, this study examines the key factors that drive tea pricing. Empirical results indicate that price levels vary across production regions, tea varieties, altitude, and certification status. On average, black tea commands higher prices, while other teas (green, Oolong, and Baozhong) are typically lower. However, as a special local tea, Oriental Beauty (and other teas) has the highest price. The cultivation altitude and organic certification are significantly associated with price premiums. In summary, this study provides strong evidence to show that regional origin, growing conditions, and certification may greatly influence tea’s market price, offering practical insights for producers and policymakers.

Traditionally, many meat demand analyses have used publicly available data amassed by the U.S. Department of Agriculture (USDA) and the Bureau of Labor Statistics (BLS). Circana retail point-of-sale scanner data offer an alternative to these publicly available data sources. Scanner data allow for quantity-weighting retail prices to account for increased purchases at lower prices due to sales and promotions. We find that quantity-weighted scanner-based beef and pork prices are lower than those reported by USDA, whereas quantity-weighted chicken prices are higher. Rotterdam demand models are estimated using both publicly available and scanner data sources. Own- and cross-price elasticities estimated using scanner data are greater in magnitude than those estimated using publicly available data, suggesting meat consumers may be more price sensitive than indicated by elasticity estimates from publicly available data sources. Scanner data insights are further explored by estimating demand for meat products with organic or natural claims. Demand elasticities for these differentiated meat products are more elastic than those for meat products without such claims, highlighting a greater amount of consumer price sensitivity when purchasing such products.

The Food and Drug Administration (FDA) recently published a final rule regarding preharvest irrigation requirements for leafy greens production. We use an equilibrium displacement model (EDM) to quantify the effects of this policy. The model is modified to allow for nonlinear equilibrium trajectories. The results show that the FDA rule will cause North American leafy greens production to decline by 0.99% and farmgate prices to increase by 0.59%. A proposal to increase land buffer areas around confined animal feeding operations (CAFOs) and large composting sites would further increase leafy greens prices and reduce consumption.

This study uses a basket-based choice experiment with 2,010 U.S. adults to analyze alcohol and cannabis preferences in social settings following cannabis legalization. Through descriptive statistics and multivariate logistic modeling, we highlight the heterogeneous preferences consumers have for alcohol and cannabis products. Specifically, we demonstrate that a substantial portion of the survey respondents prefer to consume these substances together in social settings, while others view them as independent markets. Regression analysis then reveals that males and younger consumers are most likely to bundle these substances, while personality traits also correlate with expected simultaneous substance use. These results offer valuable insights to improve public health policy and messaging on the potential short- and long-term risks associated with cosubstance use.

Geographical indications (GIs) are information signals based on a product’s geographical origin. They reduce information asymmetry for consumers and protect producers from imitation. This paper examines the local economic impact of GIs by focusing on the renowned Champagne AOC in France. Champagne is protected under the Appellation d’Origine Contrôlée (AOC), the EU’s strongest GI classification. My identification strategy leverages the fact that the municipal-level boundary of the Champagne AOC was historically determined by political decisions, rather than viticultural qualities (i.e., terroir). Using granular geographic, economic, and fiscal data, I provide causal evidence that this institutional protection improves local economic outcomes beyond the wine sector. Despite a sharp increase in vineyard prices over the past two decades, there is no evidence of crowding out of other economic activity over time. These findings suggest that the projected expansion of the Champagne AOC could stimulate further regional economic development for newly admitted municipalities.

This paper examines the impact of Sunday blue law reforms on small-scale wineries in the United States. Using establishment-level data from 2000 to 2020, we employ an event-study framework to analyze the impact of changes in Sunday alcohol sales regulations across states on the performance of small wineries. We find that deregulation is associated with substantial declines in the performance of small wineries. On average, sales fell by 25.5%, employment declined by 7.8%, and survival rates dropped by 5.2% following the repeal of Sunday sales restrictions. These adverse effects are particularly pronounced among the smallest wineries and those located in metropolitan counties. The results suggest that while deregulation increased consumer access to alcohol on Sundays, it also intensified competition from large-scale retail outlets, thereby undermining the direct-to-consumer sales channels that are critical to small wine producers.

While previous scientific literature has found evidence that warming winters result in a loss of crop yields, recent studies suggest that temperature alone may not be the sole factor in determining yields. In this study, we investigate multiple climatic factors (freezing degree days (FDD) and snow cover fraction) to evaluate the heterogeneous impact of snowpack insulation on winter wheat yields in China. We find that a unit increase in FDD causes a loss of 2.37 kg/hectare in winter wheat yields. Furthermore, our results show that snowpack insulation has a statistically significant negative effect on winter wheat yields at lower and middle quantiles, but a statistically significant positive effect at higher quantiles of the winter wheat yields distribution, suggesting that snowpack insulation is important in maintaining higher winter wheat yields.

The USDA’s resilience strategy of subsidizing small meat-packer entry has prompted studies on plant size, market structure, and resilience, each study employing a different conception of resilience. None accounts for the duration and speed of slaughter downturns and recoveries. We account for these factors by developing metrics across 35 U.S. states and estimating how the metrics vary with plant size, labor conditions, and COVID-19 policies. We find medium-sized plants enhanced resilience during COVID-19, raising questions about the USDA’s narrow focus on smaller plants. This highlights the need for more nuanced strategies to strengthen the resilience of the beef processing sector.

An increasing number of disaster relief programs rely on weather data to trigger automated payouts. However, several factors can meaningfully affect payouts, including the choice of data set, its spatial resolution, and the historical reference period used to determine abnormal conditions to be indemnified. We investigate these issues for a subsidized rainfall-based insurance program in the U.S. using data averaged over 0.25° × 0.25° grids to trigger payouts. We simulate the program using 5x finer spatial resolution precipitation estimates and evaluate differences in payouts from the current design. Our analysis across the highest enrolling state (Texas) from 2012 to 2023 reveals that payout determinations would differ in 13% of cases, with payout amounts ranging from 46 to 83% of those calculated using the original data. This potentially reduces payouts by tens of millions annually, assuming unchanged premiums. We then discuss likely factors contributing to payout differences, including intra-grid variation, reference periods used, and varying precipitation distributions. Finally, to address basis risk concerns, we propose ways to use these results to identify where mismatches may lurk, in turn informing strategic sampling campaigns or alternative designs that could enhance the value of insurance and protect producers from downside risks of poor weather conditions.

Analysis of feeder and early weaned pig markets, important segments in pork production, is nearly nonexistent. We derive and estimate a structural econometric model relating demand and supply for market hogs, feeder pigs, and early weaned pigs. Estimates from the econometric model predict how disruptions are transmitted through hog and pig markets. Results indicate that hog and pig markets are most sensitive to hog processing plant utilization relative to capacity and that this sensitivity has increased compared to prior estimates. A set of counterfactual scenarios quantify the effects of shocks to hog processing capacity, wholesale pork demand, and supply response.

While individuals are expected to perceive similarly identical quantities, regardless of the used units (e.g., 1 ton or 1000 kg), several scholars suggest that consumers over-infer quantities when they are presented in bigger and phonetically longer numbers. In two experimental studies, we examine this numerosity bias in the context of household food waste. Unlike previous scholars, manipulating numerosity revealed no effect: perceptions of food waste volume and likelihood to reduce it are not influenced by the used numeric value (2500 g vs. 2.5 kg; Study 1) nor the number of syllables (two kilos eight hundred seventy-five grams vs. three kilograms; Study 2).

We consider the effect of labor market volatility on employment and wages in the meat processing sector. The period of study includes the COVID-19 pandemic, which resulted in significant labor market shocks in the sector. We examine the relationship between historical volatility of employment and wages and current employment and wages, focusing on the animal slaughtering and processing sector (NAICS 3116). We utilize county-level data to estimate dynamic panel data models of employment and wages. We find that historical volatility in both employment and wages had a significant negative impact on employment in the sector. In the case of wage volatility, we find that wages are higher following periods of significant wage volatility, suggesting that workers demand higher wages under conditions of market volatility. During COVID, smaller meat processors had lower levels of employment, but a small number of large processors had significantly higher levels of employment. In contrast, wages were higher after COVID-19 for almost all counties included in the analysis. In an aggregate sense, COVID tended to largely reduce employment but increase wages in the meat processing sector.

The decline in fed cattle cash sales and its impact on price discovery are concerning. This study extends existing literature by utilizing machine learning to explore factors, particularly decision trees and random forests, to explore factors influencing fed cattle price ranges, complementing traditional regression analyses. These models uncover hidden patterns and provide additional insights into the cattle market. Key variables such as weight range, head count, and trade location, are found to be associated with price ranges. Notably, the weight range emerges as the primary variable influencing the price range, with smaller weight ranges linked to lower price ranges.

Historical ambiguity on how cover crop use influences future crop insurance eligibility has been proposed as one explanation for low cover crop adoption rates. However, explicit guidance on cover crop use for crop insurance participants was added in the 2018 Farm Bill. This study uses farm level data from the Agricultural Resource Management Survey to ascertain whether crop insurance participation influenced adoption of cover crops and to what degree that influence persisted after the 2018 Farm Bill. Estimation of a double hurdle model, combined with a control function approach to address endogeneity, suggests statistically and economically significant effects between crop insurance expenditures and cover crop use at the “extensive margin,” but no statistically significant effect at the “intensive margin.” Estimation on subsets of the data defined by before and after the 2018 Farm Bill suggest that the effect is primarily attributable to participation trends prior to the 2018 Farm Bill. Following the 2018 Farm Bill, no statistically significant effects are observed between cover crop use and crop insurance expenditures.

Foodborne illnesses are costly to society and have been associated with local produce. The affordable “3-step wash” cleaning procedure was designed to reduce pathogens on produce. We estimate consumer willingness to pay (WTP) for food safety (i.e. 3-step washed), prepackage, and sales location attributes in locally grown produce (e.g., lettuce). On average, consumers are willing to pay $1.46 more for 3-step washed and $0.30 more for prepackaged lettuce. Additionally, consumers are willing to pay $0.16 more for fresh produce sold in natural stores and farmers markets compared to supermarkets, but $0.22 less for produce sold in other direct-to-consumer locations such as roadside stands. Higher WTP for the food safety attribute is associated with consumers who have greater risk aversion, less knowledge of foodborne illness, and stricter food safety cleaning and handling practices. Consumers highly concerned about foodborne risks also show higher WTP for both food safety and prepackage attributes. These findings can guide local farmers in making decisions about adopting pathogen-reduction cleaning procedures, selecting sales locations, and developing effective marketing strategies.

The study examines the influence of markups on the export decisions and subsequent export intensity of firms within the Hungarian wine sector. Additionally, we evaluate the impact of entering and sustaining a presence in export markets on firms’ markups and compare the markup levels between exporting and non-exporting firms. We find that markups have a positive impact on both the probability of exporting and the export intensity, which aligns with previous findings. We demonstrate that exporting leads to an increase in markups. We find that exporters maintain higher markups even when accounting for productivity differences. Additionally, exporting can lead to higher markups because of the learning-by-exporting phenomenon. The results have significant implications. The findings imply that markups have a significant impact on the decision-making process and performance of Hungarian wine exports. Policymakers should facilitate to increase the markups of firms in order to enhance the export of wine and promote economic growth. Wine exporting firms should enhance their productivity and implement strategic pricing strategies to increase their markups and expand their exports.

Public food procurement incentives and targeted policies by state and Federal governments are one of the most frequently enacted strategies to leverage food spending to promote co-benefits related to economic, environmental, and social outcomes. Here we use an optimization model to explore potential outcomes of policy alternatives and integrate co-benefit dimensions into schools' agri-food supply chains via Farm to School procurement incentives. We find that in the absence of policy supports, school food authorities are unlikely to participate in local food procurement programs. We then place the findings in context by inferring the level of financial incentives that are needed to reduce barriers to schools' participation. Our findings have implications for community and economic development policies, particularly those seeking to support agriculturally dependent areas via elevated institutional food procurement using the case of policies framed for a school setting.

Bovine trichomoniasis is a venereal disease that causes significant losses in the US beef industry. The USDA Animal and Plant Health Inspection Service views bovine trichomoniasis as endemic and delegates control to state agencies and producers. Disease management’s positive externalities are not reflected in a producer’s profit maximization problem, leading to potentially suboptimal levels of control. Our objective was to assess the economic impacts of 50% and 100% reductions of herd-level bovine trichomoniasis prevalence. The cumulative present value of net welfare increased by $388.856 and $193.222 million under the 100% and 50% scenarios, respectively. Feeder cattle producers and retail beef consumers benefit most from enhanced control.