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Endogeneity Corrected Stochastic Production Frontier and Technical Efficiency


  • Apurba Shee
  • Spiro E. Stefanou


A major econometric issue in estimating production parameters and technical efficiency is the possibility that some forces influencing production are only observed by the firm and not by the econometrician. Not only can this misspecification lead to a biased inference on the output elasticity of inputs, but it also provides a faulty measure of technical efficiency. We extend the Levinsohn and Petrin (2003) approach and provide an estimation algorithm to overcome the problem of endogenous input choice in stochastic production frontier estimation by generating consistent estimates of production parameters and technical efficiency. We apply the proposed method to a plant-level panel dataset from the Colombian food manufacturing sector for the period 1982-1998. This dataset provides the value of output and prices charged for each product, expenditures, and prices paid for each material used, energy consumption in kilowatt per hour and energy prices, number of workers and payroll, and book values of capital stock. Empirical results find that the traditional stochastic production frontier tends to underestimate the output elasticity of capital and firm-level technical efficiency. The evidence in this research suggests that addressing the endogeneity issue matters in stochastic production frontier analysis.

Suggested Citation

  • Apurba Shee & Spiro E. Stefanou, 2015. "Endogeneity Corrected Stochastic Production Frontier and Technical Efficiency," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 97(3), pages 939-952.
  • Handle: RePEc:oup:ajagec:v:97:y:2015:i:3:p:939-952.

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    Cited by:

    1. Frick, Fabian & Sauer, Johannes, 2016. "Deregulation and Productivity – Empirical Evidence on Dairy Production," 2016 Annual Meeting, July 31-August 2, Boston, Massachusetts 236032, Agricultural and Applied Economics Association.
    2. Kapelko, Magdalena & Oude Lansink, Alfons, 2017. "Dynamic multi-directional inefficiency analysis of European dairy manufacturing firms," European Journal of Operational Research, Elsevier, vol. 257(1), pages 338-344.
    3. Njuki, E. & Bravo-Ureta, B., 2018. "Accounting for the Impacts of Changing Configurations in Temperature and Precipitation on U.S. Agricultural Productivity," 2018 Conference, July 28-August 2, 2018, Vancouver, British Columbia 277140, International Association of Agricultural Economists.
    4. repec:eee:proeco:v:201:y:2018:i:c:p:53-61 is not listed on IDEAS
    5. repec:wly:agribz:v:33:y:2017:i:4:p:505-521 is not listed on IDEAS
    6. repec:ebl:ecbull:eb-16-00551 is not listed on IDEAS
    7. repec:kap:jproda:v:49:y:2018:i:1:d:10.1007_s11123-017-0519-1 is not listed on IDEAS
    8. McFadden, Jonathan R., 2017. "Yield Maps, Soil Maps, and Technical Efficiency: Evidence from U.S. Corn Fields," 2017 Annual Meeting, July 30-August 1, Chicago, Illinois 258120, Agricultural and Applied Economics Association.
    9. repec:oup:ajagec:v:99:y:2017:i:3:p:783-799. is not listed on IDEAS
    10. Njuki, Eric & Bravo-Ureta, Boris E., 2016. "Measuring agricultural water productivity using a partial factor productivity approach," 2016 Fifth International Conference, September 23-26, 2016, Addis Ababa, Ethiopia 246948, African Association of Agricultural Economists (AAAE).
    11. Apurba Shee & Spiro E. Stefanou, 2016. "Bounded learning-by-doing and sources of firm level productivity growth in colombian food manufacturing industry," Journal of Productivity Analysis, Springer, vol. 46(2), pages 185-197, December.
    12. Magdalena Kapelko, 2017. "Dynamic versus static inefficiency assessment of the Polish meat‐processing industry in the aftermath of the European Union integration and financial crisis," Agribusiness, John Wiley & Sons, Ltd., vol. 33(4), pages 505-521, September.

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