American Journal of Agricultural Economics最新文献

Forest fires are among the main drivers of deforestation and forest degradation in the drylands of Sub-Saharan Africa. We use remote sensing data on forest fires and remaining tree cover to estimate the effectiveness of a project targeted at reducing fire incidences in 12 protected forests in arid Burkina Faso. The project consisted of two components that were implemented in the villages surrounding the target forests: a campaign aimed at raising community awareness about the detrimental effects of forest fires, and a program to support establishing and maintaining forest fire prevention infrastructures. Using the synthetic control method, we find that the project resulted in an overall reduction of $��35�\%�$ in the number of days on which an average forest grid cell was detected to be on fire in the month of the year when fires tend to be most prevalent—November, at the very end of the agricultural season. This impact is, however, short lived (as the reduction only occurred in the November months in the first 4 years of the program), and the overall reduction in forest fire occurrences was not sufficiently large to result in a detectable increase in vegetation cover. We then try to uncover the underlying mechanisms to shed light on which of the project's components were effective to also learn how the program can be improved.

The Conservation Reserve Program (CRP) allows agricultural producers to temporarily remove environmentally sensitive farmland from production in exchange for a yearly rental payment. While enrolled in the CRP, land typically complies with standards for organic certification, which prohibit the use of synthetic pesticides and fertilizers for three years prior to harvest. In this paper, we study the extent to which recent participation in the CRP promotes organic certification. We do so by estimating the relationship between exiting CRP contracts and new organic certifications at the county level over the years 2011–2020. Our primary results are based on an instrumental variables estimator, where we use the number of expiring CRP contracts as an instrument for endogenous net-exiting CRP contracts. We find that the exit of land from the CRP leads to increases in organic adoption, and estimate a 0.029% increase in new organic operations in response to a 1% increase in net-exiting CRP contracts. By highlighting this important co-effect of the CRP, our analysis contributes to contemporary discussions exploring the long-term linkages between land conservation policies, organic agriculture, and other conservation practices.

Since joining the North American Free Trade Agreement, Mexico has increased its meat production and exports and become more dependent on imported feedstuffs such as genetically engineered (GE) corn. Mexico recently banned the use of GE corn in corn-based foods and called for a gradual substitution away from the use of GE corn for other uses (e.g., feed). This paper considers how a complete ban on GE corn might affect Mexican households using a computable general equilibrium (CGE) model to simulate the impact over the medium run (5 years). Results indicate that Mexico decreases corn imports by 76.9% and increases corn production by 65.6%—an increase that would require 3.3 million hectares more land for corn. The policy leads to a 24.8% increase in Mexico's corn price and up to a 6% increase in the prices of other agricultural products. But Mexico might have difficulty shifting land to corn; as such, we consider an alternative scenario that restricts land movements. We find that impacts are further exacerbated in this scenario—for example, corn prices triple. Our final contribution is to pair these results with a compensating variation calculation based on the almost ideal demand system. We find that Mexican households would need to spend, on average, between 6.7 and 13.9% more on food, depending on the scenario, to compensate for the resulting price escalations. Ultimately, our results show that a move toward greater food sovereignty in Mexico is ultimately borne by consumers via higher food prices.

We examine how estimates of household food demand elasticities and store profit margins vary with alternative geographic market extents using structural models of household store choice and retailer competition. Our consumer store choice model is novel, simultaneously accounting for the heterogeneity of store choice sets, households' travel distance to stores, and their store-specific shopping basket prices. We estimate the models using a unique combination of datasets on grocery purchases. We find that the geographic market extent is positively associated with household demand elasticity and negatively associated with store profit margins. The maximum market extent at which changes in demand elasticities become statistically insignificant varies by retailers, ranging between 10 and 16 km. These findings are robust to alternative assumptions of store competition. Our results imply that overlooking the locality of retail competition can result in overestimating the magnitudes of household demand elasticities while underestimating store profit margins, characterizing a relatively more competitive market.

This article investigates the effectiveness of lottery incentive schemes for eliciting consumer valuations in large-scale online experiments. We implement a fully incentivized condition within a geographically dispersed sample of consumers in which bids for a Criollo steak elicited by a Becker-DeGroot-Marschak mechanism are realized with certainty and the products are priority shipped in dry-ice coolers. The fully incentivized condition is compared to between-subject random incentivized schemes, in which only a fraction of subjects realize their choices. We tested two treatments with a 10% probability framed as a percentage or an absolute number of subjects, one treatment with a 1% probability, and a purely hypothetical reference condition. The results reveal that between-subject random incentivized schemes with 10% and 1% payment probabilities are effective in eliciting valuations that are statistically indistinguishable from the fully incentivized scheme. In addition to finding insignificant statistical differences between 10% and 1% and the fully incentivized scheme, all incentivized conditions mitigate hypothetical bias, resulting in lower product valuations than the purely hypothetical condition. We contribute a novel methodological framework for conducting large-scale experiments with geographically diverse and representative subjects, increasing the external validity and producing reliable valuations while significantly reducing financial and logistic constraints.

Index insurance programs in developing countries have focused almost entirely on agricultural production risk (i.e., yield) while largely avoiding output marketing risk (i.e., price). This omission may miss an important constraint on smallholder investment and may partially explain underwhelming demand for yield-based insurance policies. Here, we explore the viability of an area-revenue index insurance policy and how its performance may compare to that of an area-yield index insurance policy. Using data from Ghana, we estimate reduced-form regression analysis and calibrate a simulation model, generating several important results. We show that there is a negative correlation between farm investment and covariate price risk. Moreover, our simulation predicts that in many market contexts, area-revenue index insurance suffers from less basis risk, exhibits higher demand, and is more effective at crowding in advanced input adoption compared to area-yield index insurance. Our results also demonstrate important contexts in which area-yield index insurance outperforms area-revenue index insurance. We therefore find that revenue insurance may be a valuable and impactful product in Ghana but would not outperform area-yield index insurance in all contexts.

Climate change continues to fuel concern about the future cost of publicly subsidized crop insurance programs in developed nations. These changes in climate are expected to alter the upper and lower tails of crop yield distributions differently. This may best be captured by modeling the climate–yield relationship heterogeneously across different parts of the yield distribution. To this end, we consider a mixture model with the parameters expressed as nonparametric functions (to capture any nonlinearities) of weather variables estimated by machine learning methods (neural net). By doing so, we are able to identify possibly heterogeneous effects of climate change on each component, the mixing probabilities, and thus all moments of the yield distribution. We find changing climate alters, quite significantly, the entire shape of the yield distribution. The overall probability of the lower tail tends to increase as temperatures rise, to the point where some yield distributions become positively skewed. Across a range of climate change scenarios, premium rates for fixed guarantees are expected to rise 20–66% relative to no climate change by 2040. However, if we allow the yield guarantees to also fall because of additional losses from climate change, premium rates (albeit not comparable given yield guarantees are different) increase notably less (6–14%), suggesting less solvency issues than first thought.