American Journal of Agricultural Economics最新文献

How do social networks impact technology adoption? Exploiting a natural experiment in the mid-20th century U.S. Upper Midwest, we find that social network expansions, in the form of mergers between congregations of the American Lutheran Church, led to increased rates of agricultural technology adoption among farmers. In counties that experienced a merger, the number of farms using chemical fertilizer increased by over 5%, and the total fertilized acreage increased by over 10% relative to counties without a merger. These effects are consistent with increased information sharing between farmers due to congregational mergers.

Food away from home (FAFH) is an integral part of U.S. consumer diets, with food delivery orders becoming more popular in recent years. However, little research has been done on whether choice patterns vary across dining settings and how this might affect the impact food policies such as a red meat tax would have on consumer welfare. We target this gap by implementing a food menu basket-based experiment (FM-BBCE) to determine consumer preferences and demand for FAFH in two dining settings: in-restaurant dining and food delivery. The FM-BBCE approach enables us to (a) identify the substitution and complementarity patterns between various food types (meat vs. plant-based food) and courses (appetizers, main courses, and side dishes), and (b) determine the demand and welfare impact of a red meat tax across the two settings. We find that respondent's orders in the delivery setting are typically higher in calories, and most items act as complements for one another, whereas menu items are substitutes in the dine-in setting. Consumers were generally more price elastic in dine-in versus delivery settings. Sociodemographics influence choice; for example, urban consumers have a higher preference for plant-based meat alternatives than rural or suburban respondents. These sociodemographic differences extend to the welfare effects of a red meat tax that we simulate, which is regressive toward low-income individuals in the delivery setting but not in the dine-in setting. Findings from this study provide new insights on FAFH consumption, which can be used by producers, policymakers, and academics.

This paper assesses the extent to which Supplemental Nutrition Assistance Program (SNAP)—one of America's largest antipoverty programs—has reached the poorest. We compute a novel measure of the floor of income, an estimated income level of the poorest individuals in society. We measure the floor with and without SNAP benefits included in family income using 29 years of Current Population Survey (CPS) data. We correct for underreporting of SNAP participation and benefits for a subset of years and examine alternative data to assess the robustness of our findings. The analysis reveals a long-term decline in the income floor, whereas adding SNAP to income and correcting for underreporting has reversed this decline and lifted the income floor by more than 75% on average between 2011 and 2016. The declining floor and the remarkable increase in income for the poorest Americans from SNAP are not revealed by poverty headcounts, which focus on changes at the poverty threshold.

In the context of rural Bangladesh, we assess whether agriculture training alone, nutrition behavior communication change (BCC) alone, combined agriculture training and nutrition BCC, or agriculture training and nutrition BCC combined with gender sensitization improve: (a) production diversity, either on household fields or through crop, livestock, or aquaculture activities carried out near the family homestead; and (b) diet diversity and the quality of household diets. All treatment arms were implemented by government employees. Implementation quality was high. No treatment increased production diversification of crops grown on fields. Treatment arms with agricultural training did increase the number of different crops grown in homestead gardens and the likelihood of any egg, dairy, or fish production but the magnitudes of these effect sizes were small. All agricultural treatment arms had, in percentage terms, large effects on measures of levels of homestead production. However, because baseline levels of production were low, the magnitude of these changes in absolute terms was modest. Nearly all treatment arms improved measures of food consumption and diet with the largest effects found when nutrition and agriculture training were combined. Relative to treatments combining agriculture and nutrition training, we find no significant impact of adding the gender sensitization on our measures of production diversity or diet quality. Interventions that combine agricultural training and nutrition BCC can improve both production diversity and diet quality, but they are not a panacea. They can, however, contribute toward better diets of rural households.

Conducting experiments can be time consuming and expensive, and may not always be reasonable. Therefore, empirical research often derives structural parameters based on observational data and reduced-form econometric models. The state-contingent approach presents a consistent conceptual framework for analyzing producer decisions under uncertainty. However, application of this structural modeling approach has been hampered by data constraints, particularly the lack of information for mapping producers' stochastic outputs onto a set of the states of nature representing different uncertain events. Consistent mapping of uncertainty is particularly critical in the context of multiple output production where weather shocks often have different effects across crops and in microeconometric analyses when unobserved farm heterogeneity may confound the effect of uncertainty. Our study demonstrates how the application of reduced-form approaches can overcome constraints of structural econometric modeling associated with the lack of relevant data and presents an approach for identifying states of nature in the context of multiple output production using reduced-form econometric models of crop yield. In an empirical application based on Hungarian farm accountancy data, we demonstrate that the proposed approach allows a consistent mapping of production uncertainty in crop farming, utilizes panel data structure, and controls for potential endogeneity due to unobserved farm heterogeneity. We anticipate the presented approach to be useful for developing further the state-contingent approach and to stimulate further studies combining the strengths of structural approaches and reduced-form models.

Due to the nature of nitrogen cycling, policies designed to address water quality concerns have the potential to provide benefits beyond the targeted water quality improvements. For example, actions to protect water quality by reducing nitrate leaching from agriculture also reduce emissions of nitrous oxide, a potent greenhouse gas. These positive effects, which are incidental to the regulation's intended target, are termed “co-benefits.” To quantify the co-benefits associated with reduced nitrate leaching, we integrate an economic model of farmer decision making with a model of terrestrial nitrogen cycling for the watershed surrounding Lake Mendota, Wisconsin, USA. Our modeling approach provides a framework that links air and water pollutants in an agri-environmental system and offers a direction for future studies. Our model results highlight the finding that the co-benefits from nitrous oxide abatement are substantial, and their inclusion increases the benefit–cost ratio of water quality policies. Consideration of these co-benefits has the potential to reverse the conclusions of benefit–cost analysis in the assessment of current water quality policies.

Past experiences influence choices, and people's preferences for more similar (habit-forming) or different (variety-seeking) experiences are reflected in these choices. We develop a structural estimation framework to capture whether people are habit forming or variety seeking and apply it to the choice of recreation site. This research contributes to the revealed preference literature by demonstrating how to account for habit or variety-seeking behavior in recreation site choice models in a two-stage framework. Using this framework, we estimate similarity weights that reflect the birders habit formation and variety seeking preferences. Predicted probabilities from the first stage model are then incorporated into the second stage, a mixed logit recreation site choice model of bird watching trips from eBird, by their members. We find that including the dynamic elements of choice, specifically variety-seeking behavior, can double the estimated willingness to pay (WTP) for individual sites relative to the static model. Although our sample of bird watching trips taken by eBird members is a sample of convenience, these results suggest that static models of recreation site choice are a lower bound on our recreation demand WTP estimates. We find variety-seeking preferences are related to land cover and the site's fixed attributes, whereas habit formation appears for seasonality in the bird watching context.

The Conservation Reserve Program is the largest agricultural land retirement program in the United States, with more enrolled acreage entering the program through a competitive auction called the General Signup than any other component. In this study, we assess the land use impacts of the Conservation Reserve Program by observing the land use decisions of parcels following the 2016 General Signup. We estimate land use impacts using a regression discontinuity design based on the Environmental Benefits Index, the program's selection and ranking mechanism. Our estimates largely rely on the auction design of the General Signup, such that we observe the land use decisions of rejected offers. We also use information on the rental rates of these offers to understand what the program pays to retire land in different uses. We estimate that a marginal acre of land enrolled in the Conservation Reserve Program replaces 0.30 acres in cropland, 0.25 acres in mixed forage, 0.32 acres in grassland, 0.12 acres in idle or fallow land, and 0.01 acres in timberland. We also find that enrollments from newly offered fields are more likely to displace cropland and less likely to displace grassland than returning fields. Consequently, we estimate that new enrollments lead to 47% greater reductions in water-driven erosion and 12% greater reductions in wind-driven erosion, compared to fields with prior enrollment.

Assessing the effects of weather and climate on agricultural production is crucial for designing policies related to climate change adaptation and mitigation. A large body of literature has identified the detrimental effects of climate change on crop yields worldwide, and farm-level adaptation has been shown to mitigate the adverse effects on agricultural production. In this study, we employ a structural model to examine farm production responses to ongoing weather trends. We investigate how farmers adjust output and input decisions by estimating a system of output supply and input demand functions, controlling for nonrandom crop selection. Using panel data with 14,796 observations reflecting 1638 German crop farms (1996–2019), we find that both the expected and realized weather determine farmers' production decisions. In the event of a drought, the supply of most considered crops and the demand for fertilizer decrease. The drought shock has also lasting effects on farmers' production decisions, with a reduced supply of protein crops and an increased level of root crops production in subsequent years. These findings highlight the need to account for farm-level production responses when assessing weather and climate impacts.

Monitoring smallholder agricultural productivity growth, one of the targets of the Sustainable Development Goals, rests on accurate measures of crop production and land area. Existing methods and protocols for measuring smallholder production and plot size are prone to various sources and forms of mismeasurement. Inaccuracies in production and land area measurement are likely to distort descriptive and predictive inferences. We examine the sensitivity of empirical assessments of the relationship between agricultural productivity and land area to alternative measurement protocols. We implement six production and six land area measurement protocols, and show that most of these protocols differ systematically in their accuracy. We find that an apparent inverse size–productivity relationship in our data is fully explained by measurement error in both production and plot size. Moreover, we show that some of the previously used “gold standard” measures are themselves prone to nonclassical measurement error, and hence can generate spurious inverse size–productivity findings. Our results also show that slight improvements in the precision of objective measures significantly reduce the inferential bias associated with the size–productivity relationship.