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Journal of Productivity Analysis

, Volume 43, Issue 1, pp 75–83 | Cite as

Using ex ante output elicitation to model state-contingent technologies

  • Robert G. Chambers
  • Teresa Serra
  • Spiro E. Stefanou
Article

Abstract

Survey-elicited ex ante outputs are used to develop an empirical representation of an Arrow–Debreu–Savage state-contingent technology in an activity-analysis framework. An empirical test of output-cubicality is developed for that framework. We apply those tools to assess production characteristics of a sample of Catalan farmers specialized in arable crops. Results suggest that imposing nonsubstitutability between ex ante outputs results in no significant loss of information. Even though the technology appears to be output cubical, efficiency measurements based on ex post output observations do not appear to adequately represent the stochastic production environment and apparently yield downward biased technical efficiency measures.

Keywords

State-contingent production Uncertainty Inefficiency Output cubicality 

JEL classification

D21 D81 Q12 

Notes

Acknowledgments

The authors gratefully acknowledge financial support from Instituto Nacional de Investigaciones Agrícolas (INIA) and the European Regional Development Fund (ERDF), Plan Nacional de Investigación Científica, Desarrollo e Innovación Tecnológica (I+D+i), Project Reference Number RTA2012-00002-00-00.

References

  1. Antle JM (1983) Testing the stochastic structure of production: a flexible moment-based approach. J Bus Econ Stat 1:192–201Google Scholar
  2. Beattie J, Loomes G (1997) The impact of incentives upon risky choice experiments. J Risk Uncertain 14:155–168CrossRefGoogle Scholar
  3. Camerer C (1995) Individual decision making. In: Kagel J, Roth A (eds) The handbook of experimental economics. Princeton University Press, Princeton, pp 587–703Google Scholar
  4. Chambers RG (2007) Valuing agricultural insurance. Am J Agric Econ 89:596–606CrossRefGoogle Scholar
  5. Chambers RG, Quiggin J (1998) Cost functions and duality for stochastic technologies. Am J Agric Econ 80:288–295CrossRefGoogle Scholar
  6. Chambers RG, Quiggin J (2000) Uncertainty, production, choice, and agency. Cambridge University Press, CambridgeGoogle Scholar
  7. Chavas JP (2008) A cost approach to economic analysis under state-contingent production uncertainty. Am J Agric Econ 90:435–446CrossRefGoogle Scholar
  8. Chavas JP, Chambers RG, Pope RD (2010) Production economics and farm management: a century of contributions. Am J Agric Econ 92:356–375CrossRefGoogle Scholar
  9. Cummings RG, Harrison GW, Rutström EE (1995) Homegrown values and hypothetical surveys: is the dichotomous choice approach incentive-compatible? Am Econ Rev 85:260–266Google Scholar
  10. Cummings RG, Taylor LO (1999) Unbiased value estimates for environmental goods: a cheap talk design for the contingent valuation method. Am Econ Rev 89:649–665CrossRefGoogle Scholar
  11. Davis DD, Holt CA (1993) Experimental economics. Princeton University Press, PrincetonGoogle Scholar
  12. Day R (1965) Probability distribution of field crop yields. J Farm Econ 47:713–741CrossRefGoogle Scholar
  13. Fuller W (1965) Stochastic fertilizer production functions for continuous corn. J Farm Econ 47:105–119CrossRefGoogle Scholar
  14. Harrison GW (1994) Expected utility theory and the experimentalists. Empir Econ 19:223–253CrossRefGoogle Scholar
  15. Just RE, Pope RD (1978) Stochastic specification of production functions and economic implications. J Econom 7:67–86CrossRefGoogle Scholar
  16. Just RE, Pope RD (1979) Production function estimation and related risk considerations. Am J Agric Econ 61:277–284CrossRefGoogle Scholar
  17. Koopmans TC (1951) An analysis of production as an efficient combination of activities. In: Koopmans TC (eds) Activity analysis of production and allocation, Cowles Commission for research in economics. Monograph no. 13. Wiley, New York, pp 33–97Google Scholar
  18. Kumbhakar SC (2002) Specification and estimation of production risk, risk preferences and technical efficiency. Am J Agric Econ 84:8–22CrossRefGoogle Scholar
  19. Kumbhakar SC, Lovell CAK (2000) Stochastic frontier analysis. Cambridge University Press, CambridgeCrossRefGoogle Scholar
  20. Li Q (1999) Nonparametric testing the similarity of two unknown density functions: local power and bootstrap analysis. J Nonparametr Stat 11:189–213CrossRefGoogle Scholar
  21. List JA (2001) Do explicit warnings eliminate the hypothetical bias in elicitation procedures? Evidence from field auctions for sport cards. Am Econ Rev 91:1498–1507CrossRefGoogle Scholar
  22. List JA, Shogren JF (1998) Calibration of the differences between actual and hypothetical valuations in a field experiment. J Econ Behav Organ 37:193–205CrossRefGoogle Scholar
  23. Lusk JL (2003) Effects of cheap talk on consumer willingness-to-pay for golden rice. Am J Agr Econ 85:840–856CrossRefGoogle Scholar
  24. Manski C (1999) Analysis of choice expectations in incomplete scenarios. J Risk Uncertain 19:49–66CrossRefGoogle Scholar
  25. Nauges C, O’Donnell C, Quiggin J (2011) Uncertainty and technical efficiency in Finnish agriculture: a state-contingent approach. Eur Rev Agric Econ 38:449–467CrossRefGoogle Scholar
  26. O’Donnell CJ, Griffiths WE (2006) Estimating state-contingent production frontiers. Am J Agric Econ 88:249–266CrossRefGoogle Scholar
  27. O’Donnell CJ, Chambers RG, Quiggin J (2010) Efficiency analysis in the presence of uncertainty. J Prod Anal 33:1–17CrossRefGoogle Scholar
  28. Shankar S, O’Donnell C, Quiggin J (2011) Production under uncertainty: a simulation study. CEPA working paper series no. WP05/2010Google Scholar
  29. Silverman BW (2009) Density estimation for statistics and data analysis. Chapman and Hall, LondonGoogle Scholar
  30. Simar L, Zelenyuk V (2006) On testing equality of distributions of technical efficiency scores. Econ Rev 25:497–522CrossRefGoogle Scholar
  31. Smith VL, Walker JM (1993) Monetary rewards and decision costs in experimental economics. Econ Inq 31:245–261CrossRefGoogle Scholar
  32. Wang H (2002) Heteroscedasticity and non-monotonic efficiency effects of a stochastic frontier model. J Prod Anal 18:241–253CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  • Robert G. Chambers
    • 1
    • 2
    • 3
  • Teresa Serra
    • 4
  • Spiro E. Stefanou
    • 5
    • 6
  1. 1.University of MarylandCollege ParkUSA
  2. 2.University of QueenslandBrisbaneAustralia
  3. 3.University of Western AustraliaPerthAustralia
  4. 4.CREDACastelldefelsSpain
  5. 5.Pennsylvania State UniversityUniversity ParkUSA
  6. 6.Wageningen UniversityWageningenThe Netherlands

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