Incorporating risk in a positive mathematical programming framework: a dual approach

Arata, Linda; Donati, Michèle; Sckokai, Paolo; Arfini, Filippo

doi:10.1111/1467-8489.12199

Cited by 23 publications

(23 citation statements)

References 32 publications

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“…The main principle underlying these methods is to estimate risk preferences by comparing the observed behaviour of agricultural producers with respect to input and output choices to behaviour predicted by theoretical models incorporating risk and risk preferences. Econometric applications dominate this group, but recently there have been interesting developments aimed at estimating risk preferences through farm-level mathematical programming models (Gόmez-Limόn et al, 2003 andArata et al, 2017). The seminal work within this group of articles are Keeney and Raiffa, 1976;Antle 1983 and1987;Chavas and Holt, 1996and Bar-Shira et al, 1997and Heckelei and Wolff, 2003.…”

Section: Cumulative Prospect Theorymentioning

confidence: 99%

Measuring Farmer Risk Preferences in Europe: A Systematic Review

Iyer¹,

Bozzola²,

Hirsch³

et al. 2019

J Agricultural Economics

126

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We present a systematic review of the extensive body of research on farmer risk preference measurement across Europe. We capture the methodological developments over time and discuss remaining challenges and potential areas for further research. Given the constantly evolving policy environment in Europe, and increasing climate-change related risks and uncertainties, there is large value to be gained from enhancing our understanding of this fundamental aspect of farmers' decision making processes and consequent actions.

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Section: Cumulative Prospect Theorymentioning

confidence: 99%

Measuring Farmer Risk Preferences in Europe: A Systematic Review

Iyer¹,

Bozzola²,

Hirsch³

et al. 2019

J Agricultural Economics

126

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show abstract

“…When applied to farmers in southern Alberta, Canada, the results from the three models led to several observations. First, Rozakis (2011, 2015) used a logarithmic utility function (and thus DARA) and linear cost functionmodel PRD, while that of Arata et al (2017) employed an exponential utility function (CARA), quadratic cost function and primal/dual approachmodel AC. The calibrated PRD model provided a better fit to the available regional-level time-series data than the AC model, which performed better when farm-level, cross-sectional data were available.…”

Section: Discussionmentioning

confidence: 99%

“…To calibrate the model parameters, we implement the GME method to derive the values of the cost function parameters and CARA coefficient φ. Similar to Arata et al (2017), we merge the first-order conditions from the 1 st and 2 nd steps of the standard PMP. The ME problem for estimation is:…”

Section: Subject Tomentioning

confidence: 99%

Calibration of agricultural risk programming models using positive mathematical programming

Kooten

Duan

2020

Aus J Agri & Res Econ

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Beginning in the 1960s, agricultural economists used mathematical programming methods to examine producers' responses to policy changes. Today, positive mathematical programming (PMP) employs observed average costs and crop allocations to calibrate a nonlinear cost function, thereby modifying a linear objective function to a nonlinear one to replicate observed allocations. The standard PMP approach takes into account producers' risk aversion, which is not a very satisfying outcome because it intricately entangles the cost parameters and the producer's attitudes -biophysical aspects of production and human behavior are intertwined so that one cannot study the impact of policy on one in the absence of the other. Several approaches that calibrate both the risk coefficient and cost function parameters have been proposed. In this paper, we discuss two methods mentioned in literature -one based on constant absolute risk aversion (exponential utility function) and the other on decreasing absolute risk aversion (logarithmic utility function). We compare these methods to an approach that employs maximum entropy method. Then we use historical data from a region in Alberta's southern grain belt to compare the different outcomes to which the three approaches lead. We find that the latter approach is robust and easier to employ.Abstract: Beginning in the 1960s, agricultural economists used mathematical programming (MP) methods to examine producer responses to policy changes. Today, positive mathematical programming (PMP) employs observed average costs and crop allocations to calibrate the parameters of an assumed nonlinear cost function, thereby modifying a linear objective function to a nonlinear one to replicate observed crop allocations exactly. The standard PMP approach takes into account producers" risk aversion, which is not a very satisfying outcome because it intricately entangles the cost parameters and the decision maker"s attitudesbiophysical aspects of agricultural production and human behavior are intertwined so that one cannot study the impact of policy on one in the absence of the other. Several approaches that calibrate both the risk coefficient and cost function parameters have been proposed by different researchers. In this paper, we discuss two methods mentioned in literatureone based on assumed constant absolute risk aversion (and exponential utility function) and the other on decreasing absolute risk aversion (logarithmic utility function). We compare these methods to a more standard approach that employs maximum entropy (ME) method. Then we use crop insurance and historical data from a region in Alberta"s southern grain belt to compare the different outcomes to which the three approaches lead. We find that the latter approach is robust and easier to employ.

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“…AESs are supposed to enhance the environment and, in some cases, they include specific rotation prescriptions for committed farms; hence, they are expected to increase the biodiversity. However, a negative effect on crop diversity could be due to the AESs income stabilization role that has been largely analysed and modelled in relation to the decoupled payments (see, for example, [60] and references therein) while for the AESs it has been only suggested [61]. Moreover, crop diversity has been extensively assessed as a risk management tool, both theoretically ( [62,63]) and empirically (e.g., [64]).…”

Section: Psm Applicationmentioning

confidence: 99%

AES Impact Evaluation With Integrated Farm Data: Combining Statistical Matching and Propensity Score Matching

et al. 2018

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A large share of the Common Agricultural Policy (CAP) is allocated to agri-environmental schemes (AESs), whose goal is to foster the provision of a wide range of environmental public goods. Despite this effort, little is known on the actual environmental and economic impact of the AESs, due to the non-experimental conditions of the assessment exercise and several data availability issues. The main objective of the paper is to explore the feasibility of combining the non-parametric statistical matching (SM) method and propensity score matching (PSM) counterfactual approach analysis and to test its usefulness and practicability on a case study represented by selected impacts of the AESs in Emilia-Romagna. The work hints at the potentialities of the combined use of SM and PSM as well as of the systematic collection of additional information to be included in EU-financed project surveys in order to enrich and complete data collected in the official statistics. The results show that the combination of the two methods enables us to enlarge and deepen the scope of counterfactual analysis applied to AESs. In a specific case study, AESs seem to reduce the amount of rent-in land and decrease the crop mix diversity.

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Incorporating risk in a positive mathematical programming framework: a dual approach

Cited by 23 publications

References 32 publications

Measuring Farmer Risk Preferences in Europe: A Systematic Review

Measuring Farmer Risk Preferences in Europe: A Systematic Review

Calibration of agricultural risk programming models using positive mathematical programming

AES Impact Evaluation With Integrated Farm Data: Combining Statistical Matching and Propensity Score Matching

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