American Journal of Agricultural Economics最新文献

This paper presents a novel study examining the effect of a property rights law reform that legalized land transfers on land quality. Using unique Chinese county-level land erosion data, we show that formally legalizing land transfers significantly reduces land erosion. This is an important and surprising benefit of a secure land transfer right to the land resource itself and a positive biophysical spillover to the natural environment that is largely ignored in the existing literature and in the policy making process. We further demonstrate that the land quality improvement brought by the law reform was associated with an increase in farming investments that can improve land quality but are subject to economies of scale. Land concentration made such investments economically feasible. We also show that the land quality-improving benefits are unevenly distributed across regions with different socioeconomic backgrounds. Future land law reforms should consider both the potential efficiency and equality implications in terms of land quality.

We study household food security dynamics in the United States from 2001 to 2017 using a new measure, the probability of food security (PFS), the estimated probability that a household's food expenditures equal or exceed the minimum cost of a healthful diet. We use PFS to analyze household-level and subpopulation-scale dynamics by investigating the conditional distribution of estimated food insecurity spells and the chronic and transient components of estimated food insecurity. We find that two-thirds of households experienced no estimated food insecurity during the 2001 to 2017 period and more than half of newly food insecure households regain food security within 2 years. Households headed by female, non-White, or less educated individuals disproportionately suffer persistent, chronic, and/or severe food insecurity.

Commodity inventories are the key state variable determining the magnitude of commodity price responses to supply and demand shocks. Many firms in commodity supply chains use storage, but we know little about which firms and why. The economic theory of storage asserts that firms in a competitive market for inventories will store based on current and expected market prices and the per-unit cost of storing. We empirically test at the firm-level the importance of one major cost of storage: the opportunity cost of capital used to value foregone revenue from deferred commodity sales. To do so, we use panel data from thousands of Illinois farms who hold inventories of corn and soybeans. Although interest rates as a measure of capital costs are unlikely to vary widely across firms in this context, we exploit variation in the weighted average cost of capital due to cross-firm differences in capital structure. Using two-way fixed effects regressions, we find a statistically significant average effect of capital costs on inventory that masks notable heterogeneity across firms. Panel quantile regressions reveal two groups of firms: one whose inventory holdings are responsive to changes in the opportunity cost of storage and another whose are not. Our results suggest some farms behave like the profit-maximizing ones from theory but substantial inframarginal commodity inventories are held by farms for other reasons.

Pulse production in India has stagnated relative to staple grains and cash crops, raising concerns about rural protein consumption. We experimentally evaluate an effort to increase local pulse production in Bihar. This intervention consisted of 2 years of input subsidies and extension to facilitate learning, followed by the creation of marketing organizations and a year of output price support to raise profitability. Farmers respond to price signals by expanding inputs when subsidized and increasing pulse sales under price supports. However, we see no evidence that the program shifted equilibrium production portfolios as pulses return to pre-intervention levels after the support ends. Results indicate that short-term learning by doing cannot overcome long-run barriers to local pulse production, even when farmers have a viable outlet to sell their surplus output.

This study investigates whether cover crop adoption reduces extreme-weather-related crop insurance losses. To achieve this objective, we utilize a county-level panel data set with information on cover crop adoption acres, crop insurance losses (i.e., specifically due to drought, excess heat, or excess moisture), and a number of weather variables. The data cover the main row crop production region in the Midwestern United States (US) for the period 2005 to 2018. We utilize linear fixed effects econometric models and a number of robustness checks in the empirical analysis (i.e., a fractional regression approach, two “external-instrument-free” estimation procedures, and a variety of alternative empirical specifications). The estimation methods used take advantage of the panel nature of the data to address various specification and endogeneity issues. We find evidence that counties with higher cover crop adoption tend to have lower crop insurance losses due to drought, excess heat, or excess moisture. Our analysis also indicates that cover crops likely have stronger loss mitigation effects against excess moisture events (like floods) and somewhat weaker loss mitigation impacts against droughts and excess heat. Nonetheless, our results overall suggest that cover crops can enhance resilience to extreme weather events and have the potential to be an effective climate change adaptation strategy in US agriculture.

Evaluations of agricultural technologies rarely consider the implications of how adoption may alter the labor allocation of different individuals within a household. We examine intrahousehold decision-making dynamics that shape smallholder households' decision to use mechanical rice transplanting (MRT), a technology that disproportionately influences demand for women's labor. To study the adoption decision, we experimentally estimate the willingness to pay for MRT services both at the individual and household level. We find that women value MRT more than men, especially when they participate in transplanting on their own farms. This preference heterogeneity is evident in the unconditional differences between women's and men's valuation and differences conditional on their individual observable characteristics. Despite having stronger preferences for MRT, women have less influence on the household's technology adoption decision than men. This differential influence over the MRT adoption decision reflects the intrahousehold power structure: in households where women have less control over assets, they also have less influence over the MRT adoption decision. Our results highlight how technological changes interact with unobserved, gender-based intrahousehold power relations to influence agricultural production decisions and, by extension, the gendered allocation of labor and welfare of women.

Accurately reflecting expected prices in stated preference designs can be challenging for foods like ribeye steak, which exhibit stark fluctuations in prices across time and space. To address this issue, we introduce a novel price vector design, the reference-price-informed (RP-informed) design, which directly incorporates individual's reference prices into discrete choice experiments. By presenting consumers with posted prices that align with their expected prices, this design reflects real-world food markets. We test this design in a discrete choice experiment evaluating consumer preferences for “low carbon” beef. Our results project a very small market share of low-carbon ribeye (3%–5%) with conventional meat taking up most of the market. Our results also show that a reference-price-informed design reduces reference price uncertainty and leads to more conservative market share estimates than traditional designs, thus preventing the potential overestimation of product's market potential.

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.