In 2023 all Supplemental Nutrition Assistance Program (SNAP) participants were allowed to start grocery shopping online. This paper provides the first answer to the question: What is online shopping worth to the SNAP participant in dollars? Using meal-kit pricing and time-use data, an implicit wage rate and dollar value distribution are estimated for time saved in home food production from online grocery shopping. We report the 95th, 75th, 50th, 25th, and 5th percentile results. We simulate saving 50%, 75%, and 90% of grocery shopping time and estimate the savings per hour per meal. For example, if online shopping saved 75% of shopping time, the median saving per hour per meal would be $2.59. If a family of four made 15 to 30 meals a month, this corresponds to an implicit 5% to 11% increase in the benefits per month due to the time saved. The implicit wage rate provides simple and elegant economic insights into many aspects of food production and consumption not obtainable by just considering the money price.

Supplemental Nutrition Assistance Program (SNAP) makes an exception to its rules, which allows elderly and/or disabled individuals, their spouses, as well as homeless beneficiaries, to buy hot prepared food from restaurants if they live in a state that participates in the Restaurant Meals Program (RMP). Using the staggered countywide adoption timeline in California, coupled with a stacked difference-in-differences empirical strategy, I examine the intent-to-treat (ITT) nutritional effects of RMP on the elderly population. Overall, I find no evidence that obesity rates for the elderly are any different in counties with RMP versus those without RMP. I can statistically rule out moderate effects. Additional evidence from some of the early-adopting counties suggests that RMP is associated with a reduction in food insecurity among the elderly.

Farmers make pest and disease management decisions without facing the social costs derived from their input choices. But given the sizable externalities involved, there is a rationale for government intervention. We model the profit-maximizing problem of a representative farmer by specifying a functional form for the damage function that incorporates the biological impact of the pathogen-vector system on yield as well as the abating impact of insecticides on the vector population. We use citrus greening disease in Florida as a case study because farmers there adopted an insecticide program that caused toxicity per acre to increase by 472%. Our simulation results show that a tax rate based on toxicity provides farmers with a strong incentive to substitute highly toxic chemicals with less toxic alternatives. Such a tax is also more efficient relative to a quantity-based tax that achieves a similar reduction in toxicity because it results in a significantly lower reduction in farmers’ yield and profit.

For almost eight decades, productivity in the United States agricultural sector has substantially increased, in large extent due to the adoption of technological innovations. Despite the increased utilization of technology, questions remain regarding which producers are more likely to adopt a greater number of technological innovations. This research seeks to understand how commodity diversification strategies, farm characteristics, producer perceptions of risk, conservation, information sources, climate adaptation, and producer demographic characteristics are associated with technology adoption among beef cattle producers in the Southeast United States. Utilizing data from an online survey and an Ordered Probit model, we show that beef cattle producers who also produce fruit have an increased probability of adopting a greater number of technologies. The opposite effect is found for other commodities such as vegetables, row crops, and other livestock. Policy recommendations are also discussed.

We estimate the value of water quality improvements for recreational activities on and near the Brandywine Creek in Delaware. We divide water-based recreational activities into two groups: water-contact and non-water-contact. We then consider the behavioral change of the recreationists in each group when faced with water quality improvements posed in a stated preference (contingent behavior) survey. We use five common physical attributes of water quality in the contingent behavior analysis: water clarity, catch rate of fish, safety of eating fish, safety for swimming, and ecological health. The survey was constructed in such a way that each of these attributes of water quality is valued. The per-trip value for water quality improvements ranged from $4 to $63 for contact use and from $4 to $29 for non-contact use depending on which and how many attributes of water quality improve. Even though we measure sizable benefits, rough cost estimates appear to make it difficult for incremental improvements to exceed costs unless attribute improvement is widespread.

Commodity index trading in futures markets is a relatively new investment strategy whose consequences are not fully understood. This paper tests the hypothesis that long-only, passive index trading in agricultural futures markets influences futures prices. Vector Autoregressive (VAR) models are a common empirical research approach for analyzing index trading. Factor-Augmented Vector Autoregression (FAVAR) models are a new approach to analyzing index trading. FAVAR models can incorporate a large data set into the traditional VAR framework. Using a FAVAR model improves the analysis by including additional market factors relevant to futures price formation. Models were estimated for 13 agricultural commodities (corn, soybean, soybean oil, soybean meal, soft red winter wheat, hard red winter wheat, cotton, cocoa, sugar, coffee, live cattle, feeder cattle, and lean hog) from January 2006 to December 2022. The results demonstrate the added value of FAVAR models in explaining the dynamics between prices and index trading. The conclusions are similar to other findings that prices lead index positions; however, adding demand-related data through a FAVAR model allows for a better understanding of market dynamics.