The 2018 United States Farm Bill has opened the possibility for farmers to increase their profits through hemp cultivation. The literature suggests hemp has the potential to replace soybeans in soybean–wheat double-cropping because hemp shares key attributes of soybeans as a rotation crop (profitability, potential as an energy crop, and maintenance of soil fertility). Nonetheless, due to a short history of hemp cultivation in the USA, it is difficult to predict a time series relationship between hemp, soybean, and wheat through conventional approaches. In this article, we use Bayesian time series models and data from Statistics Canada and the Alberta Agricultural and Rural Development Department to examine a time series relationship between hemp, wheat, and soybean acreage and therefore predict farmers’ decision when hemp is a legal alternative agricultural commodity. Our results show evidence of complementary and substitution relationships for hemp–wheat and hemp–soybean, respectively. In addition, the results indicate a potential of hemp monoculture as a positive response to self-positive shock on hemp acreage that lasts for years.

Objective The purpose of this study is to examine how cannabis legalization and corresponding taxation would affect the supply-side of the cannabis market. Specifically, the study considers various scenarios in which Oklahoma legalizes recreational cannabis for adult use and simulates changes in state-level market sales for other legal states and the average grower profits in Oklahoma. We assume that legalizing recreational cannabis in medical-only states would significantly increase the demand quantity in the legalized states and the local government would levy a significant level of tax on recreational cannabis. These assumptions are based on the post-legalization phenomena in other legalized US states. Method We simulate outcomes in the cannabis industry under the assumption of representative consumers with constant elasticity of substitution demand behavior and profit-maximizing firms with a Cobb-Douglas profit function. All agents are assumed to take exogenous prices as given. We calibrate the model using state-level sales data from 2020 and explore potential policies in Oklahoma and at the federal level. Results We find that, under the scenarios we consider, legalization of recreational cannabis in Oklahoma would lead to a decrease in the quantity of cannabis sold in Oklahoma’s medical cannabis market as well as decreases in the quantity of cannabis sold in other states on average. Furthermore, we find that as the excise tax rate on recreational cannabis in Oklahoma is increased, the demand quantity in recreational cannabis market would decrease while the other markets’ demand quantity would increase on average. As the elasticity of substitution between state-level products increases, the overall demand quantity would increase and the market quantity across states become more sensitive to Oklahoma’s tax policies. This pattern could become starker as the elasticity of substitution between recreational and medical cannabis increases. In terms of profit, heavy taxation and price decrease due to legalization would significantly decrease cannabis producers’ production and profit levels unless the cost reduction strategies complement legalization. Conclusion Based on our results, the legalization of recreational cannabis has the potential to generate tax revenue to fund critical government projects and services. However, such legalization would have to be done carefully because heavy excise taxes would decrease the legal cannabis market demand and growers’ profit, which would incentivize producers and consumers to move to the illicit cannabis market. Policymakers would have to compromise between the levels of interstate transportation and taxation to ensure that cannabis suppliers also realize some profit within the cannabis supply chain.

This paper examines how aging and underemployment affect household income and household income disparity between agricultural and non-agricultural sectors. Our study uses household panel data from South Korea for the period 2009–2016, which include, on average, 6721 representative households each year. A three-step regression analysis was conducted to estimate the aging and underemployment effects on household income and the income disparity between agricultural and non-agricultural households. First, we estimate aging and underemployment effects on household income from all households using a year fixed-effect longitudinal model. Second, our study investigates whether the marginal effect of aging and underemployment on household income differs between agricultural and non-agricultural sectors. Finally, we simulate the estimated model to illustrate how government policies could help reduce the income disparity. Our results show that aging and underemployment affect household income negatively overall. The negative marginal effect of the two factors was greater in the agricultural sector than in the non-agricultural sector. Results from policy simulations suggest that the implementation of proper government policies to address aging and underemployment problems in agricultural households could significantly reduce the income disparity between agricultural and non-agricultural sectors.