Estimating<i>Ex Ante</i>Cost Functions for Stochastic Technologies

Chambers, Robert G.; Serra, Teresa

doi:10.1093/ajae/aay068

Cited by 6 publications

(2 citation statements)

References 31 publications

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“…Moreover, I only observe ex‐post outputs while the farmer minimizes cost based on expected output which can lead to biased estimates of the cost function (e.g., Chambers & Serra, 2019; Chavas, 2008; Moschini, 2001). In agriculture, deviations of realized from expected output typically result from weather conditions that differ from the farmers’ expectations (e.g., Finger et al., 2018; Key & Sneeringer, 2014; Schlenker & Roberts, 2009).…”

Section: Datamentioning

confidence: 99%

Markups, organic agriculture and downstream concentration at the example of European dairy farmers

Koppenberg

2023

Agricultural Economics

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A competitive environment, highly concentrated processing and retailing sectors as well as increasing decoupling of direct payments from production volumes and the area under cultivation incentivizes farmers to find alternative ways to improve their bargaining position towards downstream companies. This article explores the possibilities of organic agriculture to enhance the bargaining power of farmers along with the role of concentration in downstream industries. Using a dataset with more than 200,000 observations from approximately 40,000 dairy farms, I estimate markups of price over marginal cost in dairy farming as a measure of market power in the EU. The results show that organic farmers achieve a significant markup premium over conventional farmers. With increasing market shares of organic milk in total milk production markups of conventional farmers diminish whereas those of organic farmers are unaffected. Farm‐level markups decrease with increasing market shares of medium‐sized dairy processors and increase with increasing market shares of large processors. The presence of large multinational retail chains shows an adverse impact on farmers’ markups.

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Section: Datamentioning

confidence: 99%

Markups, organic agriculture and downstream concentration at the example of European dairy farmers

Koppenberg

2023

Agricultural Economics

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“…The advantage of using data on producers' ex‐ante input allocations has already been recognized for some time. Considering that information on farmers' expected output levels are unknown and unobservable to researchers, a number of insightful contributions (Chambers & Serra, 2018; Chavas, 2008; Moschini, 1988; Pope & Just, 1996, 1998; Pope & Chavas, 1994) have explored the options for estimating ex‐ante cost functions. Further, LaFrance and Pope (2010) have examined the necessary and sufficient conditions for variable input demands using observational data on input prices, quasifixed inputs, and total variable cost.…”

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confidence: 99%

State‐contingent production technology formulation: Identifying states of nature using reduced‐form econometric models of crop yield

Bokusheva,

Baráth

2023

American J Agri Economics

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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.

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The Sources of Measured US Agricultural Productivity Growth: Weather, Technological Change, and Adaptation

Chambers¹,

Pieralli²

2020

American J Agri Economics

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The interaction between US state‐level TFP growth and weather is investigated using growth‐accounting techniques. The focus is on examining how that interaction changed between the 1960s and the end of the twentieth century. An empirical approximation to the production frontier constructed using state‐level data and mathematical programming techniques is used to decompose observed state‐level agricultural TFP growth into four components: technical change, weather‐related shifts in the frontier, input/scale effects, and adaptation to the frontier. Technical change and adaptation to the frontier play a significant role in determining average state total factor productivity. Weather‐related effects differ across Climate‐Hub Regions but are of particular importance in the Midwest.

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EstimatingEx AnteCost Functions for Stochastic Technologies

Cited by 6 publications

References 31 publications

Markups, organic agriculture and downstream concentration at the example of European dairy farmers

Markups, organic agriculture and downstream concentration at the example of European dairy farmers

State‐contingent production technology formulation: Identifying states of nature using reduced‐form econometric models of crop yield

The Sources of Measured US Agricultural Productivity Growth: Weather, Technological Change, and Adaptation

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