In developing economies, rural income inequality remains a persistent and pressing challenge, this study identifies grain processing clusters as a pivotal factor for mitigating disparities. Based on the National Fixed Point Survey (NFP) from 2004 to 2017, we constructed an unbalanced panel dataset covering 2300 counties. Using the National Economic Industry Classification Code, we identified information on rough and deep grain processing enterprises in the China Academy for Rural Development-Qiyan China Agri-research Database (CCAD), thereby generating a unique dataset for grain processing clusters. Employing a two-way fixed effects model, we empirically analyze how grain processing clusters affect rural income inequality in China and explore the underlying mechanisms. The baseline regression results indicate heterogeneity in the impact of grain processing clusters on income inequality. Deep processing clusters significantly reduce both farm and non-farm income inequality, whereas rough processing clusters have a significant effect only on non-farm income inequality. Mechanism analysis reveals that grain processing clusters primarily enhance the income of low-income households through three channels: providing inclusive employment opportunities, accelerating the deepening of capital, and increasing grain revenues, thereby reducing the extent of inequality. Heterogeneity analysis further demonstrates that this effect is more pronounced in China’s major grain-producing regions and central areas. The research results of this paper comprehensively analyze the effective path to reduce income inequality in rural China while ensuring food security, providing valuable insights for policymakers.
The COVID-19 lockdown caused severe supply-chain bottlenecks across India, disrupting the transport of agricultural commodities from farm to market, especially for perishable commodities such as vegetables. India exhibits widespread variation in vegetable production. West Bengal and Uttar Pradesh are leading states in vegetable production. However, agricultural commodities like vegetables vary in their levels of perishability; this study measures it using the cross-sectional variation in maximum storage days for 10 vegetables. This paper investigates how supply chain bottlenecks caused by lockdown stringency during the COVID-19 pandemic affected product prices in vegetable-producing states, using secondary panel data on daily wholesale price for all the wholesale markets in the two states, West Bengal and Uttar Pradesh. Using an interactive fixed-effects panel methodology, the paper finds that an increase in stringency leads to a significant increase in the price of vegetables. An increase in price due to stringency implemented during the lockdown is prominent for vegetables that have fewer storage days (high perishability) compared to those that can be stored for more days (low perishability). Doubling of stringency leads to 3% increase in prices for highly perishable commodities but reduces the prices by 1% for high storage vegetables. Further, the market’s location at the epicentre of the production region reduces the impact of stringency on prices. The availability of cold storage facility at the location, and market reforms, and better logistic facilities in the state attenuate the impact of stringency on prices in the wholesale market. The study indicates that the market reforms, infrastructure development, like the creation of green corridors and multipurpose cold storage facilities, will be highly beneficial.
This paper assesses the economic consequences of the 2025 U.S. trade policy on global agricultural markets, with a focus on the "reciprocal tariffs" initiated by the United States. By using a multi-region, multi-sector computable general equilibrium model, we evaluate the macroeconomic and sectoral effects of alternative tariff scenarios, with a particular focus on the U.S. agricultural sector. Our results indicate that the imposition of uniform tariffs on U.S. imports from the rest of the world generates substantial welfare losses for the U.S. (0.63%) and China (1.28%), with corresponding GDP declines of 0.82 and 0.39%, respectively. Tariffs result in significant contractions in global agricultural trade and higher consumer prices for key agri-food products in the US, including vegetables (6.05%), crops (7.48%), and cattle (4.13%). China's oilseed imports from the U.S. decrease by 38.32%, while imports from Canada and Brazil increase by 17.48 and 3.92%, respectively, if U.S.-China tariffs are enacted. U.S. imports of high-value vegetables and fruits from Canada and Mexico decline sharply, partially offset by increased imports from Australia and Argentina if new tariffs are enacted in North America. These findings show the complex sectoral adjustments and market realignments driven by escalating trade tensions.
Although previous research on sustainable consumption has investigated consumers’ trade-offs of product sustainability against other valued product features, the differences in consumers’ perceptions of social versus environmental dimensions of sustainability tend to be overlooked. This study examines consumers’ choices that involve trade-offs between social and environmental sustainability. From a metacognitive perspective, this study investigates the roles that metacognitions (attitude magnitude and attitude uncertainty) play in consumers’ trade-offs. Based on 715 valid questionnaires from Chinese consumers, this study tests the distinct effects of social and environmental sustainability attributes of agricultural products on consumers’ choices using a discrete choice experiment. The results reveal that social sustainability attributes (traceability, safety certification, and poverty alleviation) have stronger effects on consumer choices than environmental sustainability attributes (packaging, location, and temperature). Consumers respond to social sustainability attributes more positively when their environmental attitudes have higher magnitude or lower uncertainty; interestingly, consumers are less responsive to environmental sustainability attributes when their environmental attitudes have a higher magnitude or lower uncertainty. These findings provide important implications for interventions on ethical consumption.
This paper explores coverage selection strategies for the Annual Forage insurance program using robust portfolio optimization approaches, such as shrinkage and ensemble learning methods. The objective of the proposed models is to obtain an expected return with the lowest risk possible. Compared to previous efforts, a wider range of coverage selection parameters (i.e., coverage level, productivity factor, and index intervals) is considered. The proposed methods are used to protect cool-season forage production in Texas, using historical market, production, and actuarial data. This work provides empirical evidence on the effectiveness of the Annual Forage program in managing forage production risks.
