Modeling Production Risk with a Two-Step Procedure

Modeling Production Risk with a Two-Step Procedure

16 Pages · 2003 · 1.12 MB · English

This study deals with modeling of production risk by means of a two-step procedure. In contrast to earlier studies of production risk, we do not immediately adopt restrictive functional forms for the risky production technology. We first test for the presence of production risk. If production risk

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Journal ofAgricultural and Resource Economics 24(2):424439 Copyright 1999 Western Agricultural Economics Association Modeling Production Risk with a TwoStep Procedure Frank Asche and Ragnar Tveteras This study deals with modeling of production risk by means of a twostep procedure In contrast to earlier studies of production risk, we do not immediately adopt restrictive functional forms for the risky production technology We first test for the presence of production risk If production risk is found to be present, the mean and risk functions are estimated separately This allows the use of more flexible func tional forms for both the mean and the risk functions than commonly found in the literature An empirical application to Norwegian salmon farming, where restrictive specifications of the technology are rejected, demonstrates the validity of our approach Presence of production risk in many primary production sectors implies that this approach should be considered in productivity studies Key words: flexible functional forms, production risk, salmon Introduction Output risk is an inherent part of the production process in most primary industries, eg, agriculture, aquaculture, fisheries, mining, and oil extraction In those developing countries where subsistence agriculture predominates, production risk is an issue of great concern For producers in industrialized countries, output risk may not have the same grave consequences, but is nevertheless an important economic issue since the level of risk will, for example, determine insurance costs and interest costs on loans In the worstcase scenario, an adverse production shock can lead to bankruptcy for the producer An important characteristic of risky production processes is that random production shocks can be observed only after input decisions have been made This is in contrast to the standard certainty case, where the only determinants of optimal input demands are the structure of the production technology and input and output prices facing the producer In the presence of risk, the producer's risk preference structure and expecta tion formations also will be important in determining optimal behavior In particular, when it comes to relative input uses, a source of deviation from competitive levels is the input's marginal contribution to the level of output risk (Ramaswami) Some inputs may reduce the level of output risk (eg, pesticides), while others may increase risk (eg, fertilizers) Asche is associate professor, Centre for Fisheries Economics, Norwegian School of Economics and Business Administration, BergenSandviken, Norway; Tveteras is associate professor, Department of Business Administration, Stavanger College, Stavanger, Norway, and Centre for Fisheries Economics, Norwegian School of Economics and Business Administration, BergenSandviken, Norway The authors are grateful to Almas Heshmati, Subal Kumbhakar, and two anonymous referees for helpful comments Financial support from the European Commission (FAIR Contract No 961814) is also acknowledged The views expressed herein are the authors', and do not necessarily reflect the opinions of those individuals and organizations acknowledged above Modeling Production Risk with a TwoStep Procedure 425 Most studies dealing with production risk are based on the JustPope (JP) postulates (Just and Pope 1978) However, specifications of risky production models which are in accordance with the JP postulates are often difficult to work with empirically, since the optimization problem becomes nonlinear when popular functional forms such as the translog are used Because of this, the functional forms used in empirical work are often highly restrictive The most commonly used functional form in the JP model framework is the CobbDouglas The usual econometric translog specification has multiplicative interaction between the translog function and the error term This specification violates the JP postulates, but may be extended to allow for greater flexibility with respect to production risk (Kumbhakar) Here, we exploit the fact that production uncertainty can be regarded as hetero skedasticity when the JP postulates hold In the JP model, y = f(x; a) + h(z; p)e; inputs x influence both mean output and output risk through a mean production function E[y] = f(x) and a variance (risk) production function var(y) = h(z)2a2, where z may contain some or all of the elements in x and/or

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