Agribusiness最新文献

As demand for food away from home increases, outbreaks at restaurants become an important source of food-related illness. In the United States, over 60% of foodborne illness outbreaks occur at restaurants and 97% of outbreaks are limited to a single state. Despite this, we currently know little about restaurant outbreaks and in particular, single-state outbreaks are not well understood. We use Chipotle Mexican Grill's eight outbreaks (2015–2018) to evaluate the media and stock market responses to both single and multistate outbreaks. Using news and stock market data, we provide evidence that multistate outbreaks brought swift stock price declines and single-state outbreaks' impact depended on their timing, rather than their severity. Before Chipotle's more well-known, multistate outbreaks, the firm's single-state outbreaks brought little reporting and no financial losses, whereas after the multistate food safety events, single-state events resulted in national media coverage and large financial impacts. Our findings are consistent with the literature on food scares that can result in chronic low-level anxiety, which can bring about a large resurgence of concern for smaller outbreaks. The lessons learned from Chipotle's case underscore the importance of investment in outbreak prevention. [G14 (Information and Market Efficiency, Event Studies, Insider Trading)].

In recent years concerns have been raised regarding the environmental consequences of over-use of nitrogen fertilizers on a global level. However, the balance between sustainability and agricultural productivity, a central concern for policy makers in developing countries, has not been sufficiently addressed. In this paper, I evaluate farmers’ fertilization practices and their effect on yield using unique plot level data from India. I estimate quadratic crop response functions for different crops and cropping systems. To address endogenous input choices, I use input prices and cost shifters from the fertilizer industry as instrumental variables for the fertilization practice. I find that a large share of Indian cultivators overuse nitrogen relative to the other two nutrients, and could benefit from simply reducing the amount of nitrogen used while keeping the other nutrients fixed. This suggests a potential win-win situation where both productivity and sustainability can be improved by changing fertilizer application. The widespread “nitrogen-only” fertilization pattern is rejected as optimal in most cases. [EconLit Citations: Q12, Q15, Q16, E23, C26, C14].

Transformation of the previous centrally growth-oriented economic systems to a sustainable bio-economy is a global political trend, where public policy is a key factor in making this successful. Designing effective and efficient policies requires understanding the linkages between policy choices and outcomes. Most existing studies are missing a direct link to policy choices and ignore fundamental model uncertainty present in policy analysis. We empirically estimate a sector-specific, nested two-stage policy impact function to address these shortcomings. We apply a Bayesian estimation approach that combines existing statistical data with a priori information from political experts, thus reducing data and estimation problems. This is linked with a Computable General Equilibrium to model the entire link from policies to outcomes. We derive a theoretical framework that allows the definition of indicators for key sectors of an efficient Pro-Poor-Growth strategy. In our generalized framework, we show that indicators based only on growth-poverty linkages might be misleading. To deal with model uncertainty inherent in the application, we derive a set of metamodels via simulations conducted under different model parameter settings and apply Markov Chain Monte Carlo sampling. Applying Bayesian model selection allows drawing statistical inferences on competing models to generate relatively robust policy-relevant messages even under model uncertainty. The approach is empirically applied to Ghana, Senegal, and Uganda, analyzing the allocation of public spending on agriculture under the Comprehensive Africa Agriculture Development Programme. [EconLit Citations: C11—Bayesian Analysis: General; C63—Computational Techniques, Simulation Modeling; D58—Computable and Other Applied General Equilibrium Models; O55—Africa; Q01—Sustainable Development; Q18—Agricultural Policy].

This paper examines how India's national lockdown (March 25–May 31, 2020), in response to the spread of Covid-19, affected the on-farm family labor supply of 351 farm households in the rural–urban interface of Bangalore. We combine face-to-face survey data collected just before the start of the lockdown with phone survey data collected during the last 2 weeks of the lockdown. We find that 66% of farm households reduced their daily on-farm family labor supply during the lockdown, by on average almost 40% compared with prelockdown levels. Changes in on-farm family labor supply differed by key pre-Covid-19 household characteristics. Farm households that were engaged in crop marketing decreased their on-farm family labor supply by an average of 3–4 h/day. In turn, farm households that relied on off-farm income increased their on-farm family labor supply by on average 3–4 h/day [EconLit Citations: J22, J43, Q12, Q13, Q54].

The agricultural economics profession plays an essential role in the transition towards more sustainable and resilient food systems. The interdisciplinary perspective of the profession, the rapidly evolving methods and new data, as well as the diverse agricultural, climatic, and political and cultural landscapes of the European Union member states and associated countries pose specific challenges. In this study, we summarize what this implies for Early-Career researchers and how the European Network of Early Career Agricultural Economists (AgEconMeet) tries to support these youngsters in their career development. Finally, we introduce the articles of this special issue that represent the diverse landscape of agricultural economics in Europe. [EconLit Citations: A11, Q1, Q18].

The food industry is one of the most important industries in developed economies. The progressive professionalization of its workforce and the persistent demand for low-skilled employment in this sector may be leading to educational mismatch with its concomitant effects on wages and productivity. Yet the economic literature has not addressed wage structures, gender differences, and the composition of the labor force together in this food industry. We analyzed the effects of educational mismatch on wages in the Spanish food industry from a gender perspective. To this end, we used data from the 2018 Wage Structure Survey collected by the Spanish National Institute of Statistics. We applied the overeducated, required educated, undereducated specification to these data. The main results show that educational mismatch leads to gender wage differentials in this sector. The analysis also shows that the Spanish food industry places more value on men with adequate education and women with some level of overeducation. The results on occupational segregation show that the food industry penalizes access to female-dominated occupations, and that this wage disadvantage is greater for female workers. We also observed wage differences in the remuneration of certain personal and professional factors that could be worsening the gender differentials in the Spanish food industry [EconLit Citations: I26, J16, J21, J24, J31].

Climate-smart agricultural (CSA) practices are increasingly being promoted as nature-based solutions to improve the livelihoods of smallholder farm households amid a sharp increase in climate-change anomalies. However, the extent to which CSA practices contribute to smallholder food security and dietary diversity remains unclear. In this study, we use panel and nationally representative data from Tanzania to examine the association between two climate-smart agricultural practices, namely, improved maize varieties and maize-legume intercropping, and food security in smallholder farm households. We use maize yield per acre, adult-equivalent food expenditure, and household dietary diversity scores to measure household food security, representing three of the four food security pillars: availability, access, and utilization. We also examine the complementarity and potential advantages of combining improved maize seeds with fertilizers. Using standard panel data estimation approaches, we find a positive association between the adoption of improved maize varieties and maize-legume intercropping and an increase in food production measured through higher crop productivity. However, we do not find a corresponding improvement in household dietary diversity or increased food expenditure, despite the higher crop production. Several factors might explain this outcome, including the challenges faced by farmers in accessing markets to sell surplus produce, the influence of established dietary habits, gender issues, and other local factors that promote the consumption of cereal-based foods such as maize. Our findings suggest that CSA practices may help improve food production and availability, but more effort is needed to translate increased food production into improved dietary diversity and better food security among smallholder farmers in sub-Saharan Africa. [EconLit Citations: C23, D12, D13, D24, Q12, Q16, Q18, Q54].

For a significant global segment, the volatility in grain prices presents a substantial menace to food accessibility and security. In the global pandemic and the Russia–Ukraine conflict (RUW), numerous nations were caught off guard, exacerbating this predicament and leading to instances where citizens faced purchasing restrictions on sunflower oil. This study employs the VAR (1)-Asymmetric BEKK-Generalized Autoregressive Conditional Heteroscedasticity (GARCH) (1,1) model to assess the extent of risks stemming from the pandemic and RUW in critical Turkish agricultural commodity markets: namely, wheat, barley, corn, and sunflower oil. The analysis reveals that the conditional variances of their return series are positively influenced by short- and long-term uncertainties. With the escalations in the global oil market, the enduring hazards within these selected markets in Türkiye have intensified concurrently. The COVID-19 pandemic has induced a decrease in long-term uncertainty within wheat and barley markets, wherein noteworthy spillover risks in the barley, corn, and sunflower oil markets have exacerbated risks in the corn market. Empirical findings imply that COVID-19 and RUW disrupt the agricultural supply chain, leading to impediments in food provisioning and security. The outcomes provide valuable insights to fortify policies, guarantee consistent access to plant-based protein, and address nutritional insecurity within the nation. These policy measures align with the initiatives undertaken by the United Nations and Türkiye, which actively engage in establishing a grain corridor to facilitate Ukraine's grain exports, thereby ensuring food security and safeguarding agricultural lands.  [EconLit Citations: A1, E3, G1, Q0, Q1, Q2, Q4].

This paper presents liquidity measures in high resolution and investigates the impact of trading volume on market liquidity and prices in China's soybean complex markets. We document a U-shaped distribution of volume and spreads over the course of a trading day. Quantile regression results show that trading volume tends to tighten bid-ask spreads but widen spreads in the lower tail. We further find that the impact of trading volume on prices is significantly more pronounced during the opening hours, possibly indicating a higher prevalence of informed trading. Additionally, smaller-sized transactions have a disproportionately larger impact on prices compared to large-sized orders, a possible indication that stealth trading, that is, shaving large orders into smaller slices to conceal private information, presents in China's soybean complex.

Formal credit plays an important role for the development of the agriculture sector in developing countries because many farmers are characterized as liquidity constrained. Access to credit can increase farmers' purchasing power for inputs and agricultural technology, thus raising the overall productivity. Farmers in Mali are particularly vulnerable to shocks, such as heavy precipitation events. Access to liquidity to increase the resilience of the agricultural sector is essential. Therefore, higher financing volumes are required, which make the analysis of loan demand in agriculture of interest. The purpose of this paper is to empirically investigate the role of the interest rate, the macroeconomic environment, the agricultural cycle and the gender of the farmer on the loan demand in the agricultural sector from a country in the Sahel. Unique and comprehensive loan data at the farm level, provided by a commercial Malian bank, is used for this analysis. The analysis covers the period from 2010 to 2020. Two different estimation strategies are combined. First, an ordinary least square regression is applied with the granted loan amount as the dependent variable. Second, the machine learning technique, least absolute shrinkage and selection operator, is applied to select the most relevant features to be used as explanatory variables in the estimation. The results reveal that the interest rate, the gross value added, the farmer's gender as well as the agricultural cycle have statistically significant effects on the granted loan demand in agriculture. These results are of interest to policymakers, who deal with financial inclusion as well as market failures, and agricultural financial institutions who could incorporate such information in the design of future loan products to stimulate farmers' loan demand, especially for female farmers. [EconLit Citations: G20, G21, O13, O16, Q14, Q18].