Insights from the Crop Insurance Reform Act of 1994

Jose, H. Douglas; Valluru, Ram S. K.

doi:10.1002/(sici)1520-6297(199711/12)13:6<587::aid-agr4>3.0.co;2-#

Cited by 18 publications

(15 citation statements)

References 4 publications

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“…Using a similar framework, surveys of farmers in Nebraska (Jose & Valluru, ) and California (Blank, Carter, & McDonald, ) produced similar results a decade later with price and yield risks again being identified as the most important sources of variability. The 1996 USDA Agricultural Resource Management Survey (ARMS) performed similar analysis for a representative sample of the US farm sector (Harwood, Heifner, Coble, Perry, & Somwaru, ).…”

Section: Introductionmentioning

confidence: 89%

Farmer perceptions of risk in 2017

2018

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Agribusinesses seeking to serve farmer clientele, as well as those buying products from the farm for processing or transport, need to understand the factors impacting on-farm decision making. A bestworst choice experiment was employed to determine farmer perceptions of five general risk areas: production, marketing, financial, human, and legal risks. Results indicate, on average, farmers were more concerned with traditional farm business risks (production, financial, and marketing risks) relative to human and legal risks when managing their farm businesses. However, significant heterogeneity in perceptions of risk areas was observed. A latent class model identified three segments of producers that were characterized by preferences for traditional farm business risks, well-balanced preferences for all five risks, and a large preference share for human risks. Understanding how different farm types and managers with varying demographics prioritize risk areas is helpful for targeting specific risk management programming and support services to relevant farm audiences. [EconLit citations: Q1].

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Section: Introductionmentioning

confidence: 89%

Farmer perceptions of risk in 2017

2018

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“…These studies approach insurance decisions at the aggregate level over time [see for example the work of Goodwin and Smith (1995), Goodwin (1993), Barnett, Skees, and Hourigan (1990), Hojjatti and Bockstael (1988), Gardner and Kramer (1986), and Nieuwoudt et al (1985)] or at the micro level for a single period [see Sherrick et al (2004), Sherrick (2003), Mishra and ElOsta (2002), Jose and Valluru (1997)]. From this literature has emerged a set of factors that influence decisions to purchase crop insurance such as farmer-specific characteristics (e.g., education, risk attitude, off-farm income), economic factors (e.g., crop returns, insurance premium), and farm-/region-specific characteristics (e.g., crop mix, location, soil quality).…”

Section: Related Literaturementioning

confidence: 99%

“…Most studies at the county level have measured changes in these participation measures across decision units in space, although some have combined spatial and time series data. Micro-level studies have used surveys to ask whether farmers use crop insurance or not, and have used limited dependent variable regression techniques to model the dichotomous choice on participation and/or coverage level to farmer characteristics (Sherrick et al 2004, Sherrick 2003, Mishra and El-Osta 2002, Jose and Valluru 1997. These micro studies are at a single point in time, which constitutes a shortcoming in the study of dynamic crop insurance participation decisions.…”

Section: Measuring Participationmentioning

confidence: 99%

Modeling Exit and Entry of Farmers in a Crop Insurance Program

Cabas

Leiva

Weersink

2008

Agric. resour. econ. rev.

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This paper examines the factors influencing farmer participation in crop insurance schemes, but unlike previous studies that focus on total demand, participation is disaggregated into entrants and those exiting. Modeling entry and exit decisions separately illustrates that the effect of a given variable is often muted by aggregation. In addition, the approach in this paper distinguishes between price and yield variables rather than total returns and is consequently able to demonstrate that price variables are particularly important for farmers considering enrolling in crop insurance, while yield variables and other risk management opportunities are more important for farmers who have been in the program but are deciding to exit. The result suggests that moral hazard is reduced significantly by calculating the coverage yield level for an individual producer on the basis of a moving average of past yields for that farmer. While yield and its variance are particularly influential in the participation decision for farmers currently enrolled, its significant impact on the insurance decision for all farmers highlights the importance of crop insurance as a potential adaptation strategy to weather events. Key Words: crop insurance, entry and exit, panel dataFarmers increasingly are relying on crop insurance as a risk management tool that replaces government stabilization programs. Previous studies on the demand for crop insurance have not examined how the demand factors vary between those farmers currently enrolled in the program and those farmers without crop insurance. Past research on crop insurance participation has relied on aggregate state-or county-level data over a period of time or on a specific micro-level survey for a given period. Examples of the former include Goodwin and Smith (1995), Goodwin (1993), Barnett, Skees, and Hourigan (1990), Hojjatti and Bockstael (1988), Gardner andKramer (1986), andNieuwoudt et al. (1985). These time series studies generally estimate the proportion of eligible area within a region that is insured as a function of factors such as expected return to insurance and demographic/physical characteristics of the region that proxy alternative risk management options. Studies of the latter sample individual farmers at a given point in time and examine the decision to participate and the corresponding level of coverage as a function of socioeconomic and farm characteristics of the producer (i.e., Sherrick et al. 2004, Garrido et al. 2002, and Coble et al. 1996. None of the studies have used panel data to examine the drivers of exit and entry decisions in a crop insurance plan. This paper decomposes demand into not only total participation but also the number of farmers who enter and exit a crop insurance program, and in the process illustrates that the effect of a given variable is often muted by the aggregation. While previous studies help us understand the factors that matter for all farmers enrolled in a crop insurance program, they offer limited information to understand how the sam...

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“…Many factors that generate financial risk are not able to be controlled by the farmer [1,2]. For some farmers, risk mitigation is possible.…”

Section: Introductionmentioning

confidence: 99%

“…Two farms are risk profiled by the authors: one a sheep and beef farm which is not irrigated, whereas the dairy farm modelled is irrigated. Although specific event insurance may be purchased to reduce the risk of a large reduction of income, this is more common in horticulture and arable situations (e.g., hail cover) [1].…”

Section: Introductionmentioning

confidence: 99%

Establishing a Risk Profile for New Zealand Pastoral Farms

Grafton

Manning²

2017

Agriculture

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Abstract:In this paper, the risk profile of two pastoral production systems in New Zealand are examined. All farmers must manage and mitigate a multitude of risks. Traditionally, a farm budget is solely undertaken to satisfy a lending institution. Limited variance analysis takes place, usually for output prices and inputs such as: interest rates, energy costs, and fertiliser. The authors of this paper use "@Risk", a risk profiling plug-in tool for Microsoft Excel to demonstrate how farm budgets can be more relevant to farmers. Many risk factors that affect farm financial performance, such as climate and commodity prices, are not controlled by the farmer. Wet summers help hill country sheep and beef pastoral farmers, as more grass growth occurs, which thereby reduces the cost of production and increases revenue, as more stock is finished. Whereas in drought years income falls as stock must be sold prior to finishing, in severe droughts capital stock may also be sold. Input costs also rise as pasture weed invasion occurs; health issues such as rye grass staggers may also add cost. Monte Carlo simulations on model farm budgets for a North Island sheep and beef property and a Canterbury dairy farm help demonstrate the risk profile of each farm type.

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Insights from the Crop Insurance Reform Act of 1994

Cited by 18 publications

References 4 publications

Farmer perceptions of risk in 2017

Farmer perceptions of risk in 2017

Modeling Exit and Entry of Farmers in a Crop Insurance Program

Establishing a Risk Profile for New Zealand Pastoral Farms

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