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The 2018 farm bill is the latest in a history that dates to 1933. Commodity assistance is the only program in all farm bills, but with evolutionary changes. Current farm commodity programs largely make payments to farms, a stark contrast to the 1930s when they limited supply, put a floor under market price, and dampened price increases via public stocks. Crop insurance, which began as an experimental pilot program in 1938, now has its own farm bill title. Almost all commodity and insurance programs have provided assistance based on a calculation specific to an individual commodity's price and/or yield. However, an evolutionary change to whole farm commodity programs may be in its infant stages. They provide assistance for variation in a farm's aggregate revenue across multiple crops. Whole farm experiments currently exist in both the commodity and crop insurance titles. Analysis of a whole farm commodity program finds that its payments differ by year from actual payments made by current commodity programs and are smaller in total.

The federal crop insurance program has been a major fixture of U.S. agricultural policy since the 1930s, and continues to grow in size and importance. Indeed, it now represents the most prominent farm policy instrument, accounting for more government spending than any other farm commodity program. The 2014 Farm Bill further expanded the crop insurance program and introduced a number of new county-level revenue insurance plans. In 2013, over $123 billion in crop value was insured under the program. Crop revenue insurance, first introduced in the 1990s, now accounts for nearly 70% of the total liability in the program. The available plans cover losses that result from a revenue shortfall that can be triggered by multiple, dependent sources of risk— either low prices, low yields, or a combination of both. The actuarial practices currently applied when rating these plans essentially involve the application of a Gaussian copula model to the pricing of dependent risks. We evaluate the suitability of this assumption by considering a number of alternative copula models. In particular, we use combinations of pair-wise copulas of conditional distributions to model multiple sources of risk. We find that this approach is generally preferred by model-fitting criteria in the applications considered here. We demonstrate that alternative approaches to modeling dependencies in a portfolio of risks may have significant implications for premium rates in crop insurance.

This article assesses the political economy of the 2014 U.S. farm bill, with a focus on the farm support safety net. The farm bill secured substantial bipartisan majorities in a politically contentious Congress. Planned outlays are predominately for nutrition assistance programs directed toward a traditional nonfarm constituency in the farm bill coalition, while annual fixed direct payments to farmers are eliminated but replaced with enhanced downside risk protection against low prices or revenue. The new support programs may prove more or less costly than the foregone fixed payments, with farmers offered a choice between a price countercyclical program with increased reference prices and a revised moving-average revenue guarantee program. The role of insurance is enhanced, notably by replacing past support programs with a new upland cotton revenue insurance program and dairy milk-to-feed margin protection program. Open policy issues that are highlighted include the costs and distortionary effects of moving-average revenue benchmarks versus fixed reference prices, the overall level of insurance premium subsidies, the potential for overlap between commodity and insurance programs, and lastly, food, environmental, and biofuels concerns that reflect the diverse portfolio of products demanded from agriculture. In an international context, we conclude that the 2014 farm safety net likely would not have been enacted had multilateral agreement been reached on the 2008 Doha Round World Trade Organization negotiating documents. Conversely, the 2014 farm bill makes achieving those limits more difficult. Research is discussed that can elucidate the ongoing political economy of U.S. farm policy and help shape future program design.

Farm programs influence the parameters of typically estimated supply functions. We develop and apply an approach that uses detailed information about farm program incentives and constraints to identify underlying structural acreage response parameters when the data reflect behavior under complex government commodity programs. We illustrate the approach with data on rice acreage response to market price in the United States. For U.S. rice, estimates that fail to appropriately incorporate the program rules under which market data were generated are three to four times smaller than the structural parameters that are useful for most policy analysis or projections under alternative policies.

We analyze the effects of Price Loss Coverage (PLC), Agriculture Risk Coverage (ARC), individual revenue protection insurance (RP), and Supplemental Coverage Option (SCO) on the RP coverage level, certainty equivalent, and program payments. The model is calibrated to a representative wheat farm in Mitchell County in Kansas to analyze the effects of various policies. The result highlights that when insurance is framed as an investment, cumulative prospect theory predicts farmers’ coverage decisions accurately at 70%. ARC or PLC program increases the RP coverage level to 75%, but PLC and SCO jointly decrease the RP coverage level to 70%.

Background Effective drug procurement guarantees the sustainable supply of products for health and eliminates excessive costs. However, there is limited information on the area of pharmaceutical procurement practice in Ethiopia. Thus, this study aimed at assessing the procurement practices of program drugs and its challenges at the Ethiopian Pharmaceuticals Supply Agency. Methods A cross-sectional study accompanied by qualitative assessment was conducted between February 21 and April 20/2020 to examine the procurement practice of the Ethiopian pharmaceutical supply agency. The quantitative data were gathered by reviewing documents and electronic records. Mean forecast error, price paid to international price reference, number of emergency orders placed, and lead time variability were the measurements used to assess the procurement practice. A statistical package for the social sciences version 23 was used to analyze the data. The results were then summarized using tables and texts. The qualitative data were collated through face-to-face in-depth interviews to explore the challenges behind the procurement practice. And the data were analyzed manually using the thematic analysis technique. Results The agency had its own procurement list which defines the items to be procured. The overall mean forecast error in the 2018/19 budget year was 27.8%. Of the 70 program commodities included in the study, 52 (74.3%) items had a mean price less than the international price reference. Three of the 14 orders (21.4%) placed in the aforementioned year were emergency purchases made through direct procurement. The mean lead time for the suppliers of the agency was 137.3 days. Poor data quality from service delivery points, staff capacity constraints, communication problems, and policy issues became the major challenges to implement an effective procurement system in the agency. Conclusion The procurement practice at the agency has strong side. However, it was not without weaknesses. Using a procurement list is a worthwhile practice. Despite this, much remains to improve lead times and forecasting accuracy.

We analyze the effects of Price Loss Coverage (PLC), Agriculture Risk Coverage (ARC), individual revenue protection insurance (RP), and Supplemental Coverage Option (SCO) on the RP coverage level, certainty equivalent, and program payments. The model is calibrated to a representative wheat farm in Mitchell County in Kansas to analyze the effects of various policies. The result highlights that when insurance is framed as an investment, cumulative prospect theory predicts farmers’ coverage decisions accurately at 70%. ARC or PLC program increases the RP coverage level to 75%, but PLC and SCO jointly decrease the RP coverage level to 70%.

Agricultural economists helped develop farm programs to respond to the dire economic situation of the 1920s and 1930s. Some early authors appreciated that such policies created problems in markets for commodities and inputs. Over time, our understanding of agricultural issues and policies has deepened. Through the application of improved models and tools of analysis to more extensive data, we have developed better answers to old questions, and have responded to changing policy instruments, market contexts, and policy concerns. This article traces the evolution of our deepening economic understanding of the causes and consequences of agricultural policy.

Research priorities for the U.S. farm economy include increasing the productivity and cost efficiency on current land resources while understanding production agriculture across the globe. Providing unbiased objective analysis to policymakers with regard to commodity programs, insurance markets, agricultural credit, and the production of bioenergy are important issues that directly affect not only the U.S. farm economy but other agricultural regions. The ability to manage risk, the increasing complexity of farm operations, the ability of the U.S. farm sector to be nimble to changes in individual and societal preferences, and the efficient discovery of information through efficient markets offer a wealth of research opportunities.

Maintaining an adequate level of inventory is critical since an enormous amount of capital tied up with it. Having excess inventory leads to wastage. On the contrary, insufficient commodity leads to stock out. Hence, this study aimed to assess inventory management practices of program commodities at Ethiopian Pharmaceutical Supply Agency. A descriptive cross-sectional study complemented with a qualitative method was conducted from February 21 to April 20/2019. Order fill rate, wastage rate, frequency of emergency order, acceptable storage condition met, and stock out were the metrics we used to measure the inventory management practices of the agency. Quantitative data were collected through physical observation of the warehouses and review of logistics management tools. Seventeen in-depth interviews were conducted to explore the challenges of inventory management. From the total 70 program commodities managed by the agency, 2.1% wasted due to expiration and damage. These resulted in a loss of over US $2 million. The highest wastage was recorded for antimalarials which accounted for 13.1% of the malaria commodities' total inventory value. Only 14.8% of the orders were fulfilled above 80%. Thirty-seven items were stock out on average for 8.5 average days. Longer duration of stock out (260 days) was recorded for TB commodities. Seventeen items from different programs were purchased through emergency orders with a higher frequency of levonorgestrel purchase. Only 6 (60%) warehouses met acceptable storage conditions. Space deficit, outdated warehouse designs, shortage of warehouse equipment, lack of precise data, and capacity building gaps were the inventory management challenges identified. Though the wastage rate was near to the acceptable range, there were lesser order fill rates, storage condition inadequacy, and significant stock-outs of program commodities. The finding implies the need for an improvement in inventory management practice of the agency.