IDEAS home Printed from https://ideas.repec.org/a/oup/ajagec/v97y2015i5p1478-1493..html
   My bibliography  Save this article

Estimation of Input Distance Functions: A System Approach

Author

Listed:
  • Efthymios G. Tsionas
  • Subal C. Kumbhakar
  • Emir Malikov

Abstract

This article offers a methodology to address the endogeneity of inputs in the input distance function (IDF) formulation of the production processes. We propose to tackle endogenous input ratios appearing in the normalized IDF by considering a flexible (simultaneous) system of the IDF and the first-order conditions from the firm's cost minimization problem. Our model can accommodate both technical and (input) allocative inefficiencies among firms. We also present the algorithm for quantifying the cost of allocative inefficiency. We showcase our cost-system-based model by applying it to study the production of Norwegian dairy farms during the 1991–2008 period. Among other things, we find both an economically and statistically significant improvement in the levels of technical efficiency among dairy farms associated with the 1997 quota scheme change, which a more conventional single-equation stochastic frontier model appears to be unable to detect.

Suggested Citation

  • Efthymios G. Tsionas & Subal C. Kumbhakar & Emir Malikov, 2015. "Estimation of Input Distance Functions: A System Approach," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 97(5), pages 1478-1493.
  • Handle: RePEc:oup:ajagec:v:97:y:2015:i:5:p:1478-1493.
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1093/ajae/aav012
    Download Restriction: Access to full text is restricted to subscribers.
    ---><---

    As the access to this document is restricted, you may want to look for a different version below or search for a different version of it.

    Other versions of this item:

    References listed on IDEAS

    as
    1. David K. Lambert & William W. Wilson, 2003. "Valuing Varieties with Imperfect Output Quality Measurement," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 85(1), pages 95-107.
    2. Hausman, Jerry, 2015. "Specification tests in econometrics," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 38(2), pages 112-134.
    3. Terza, Joseph V. & Basu, Anirban & Rathouz, Paul J., 2008. "Two-stage residual inclusion estimation: Addressing endogeneity in health econometric modeling," Journal of Health Economics, Elsevier, vol. 27(3), pages 531-543, May.
    4. Abhiman Das & Subal C. Kumbhakar, 2012. "Productivity and efficiency dynamics in Indian banking: An input distance function approach incorporating quality of inputs and outputs," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 27(2), pages 205-234, March.
    5. Tim Coelli & Gholamreza Hajargasht & C.A. Knox Lovell, 2008. "Econometric Estimation of an Input Distance Function in a System of Equations," CEPA Working Papers Series WP012008, School of Economics, University of Queensland, Australia.
    6. Atkinson, Scott E. & Primont, Daniel, 2002. "Stochastic estimation of firm technology, inefficiency, and productivity growth using shadow cost and distance functions," Journal of Econometrics, Elsevier, vol. 108(2), pages 203-225, June.
    7. Caves, Douglas W & Christensen, Laurits R & Swanson, Joseph A, 1981. "Productivity Growth, Scale Economies, and Capacity Utilization in U.S. Railroads, 1955-74," American Economic Review, American Economic Association, vol. 71(5), pages 994-1002, December.
    8. Kumbhakar, Subal C., 2013. "Specification and estimation of multiple output technologies: A primal approach," European Journal of Operational Research, Elsevier, vol. 231(2), pages 465-473.
    9. Subal C. Kumbhakar & Gudbrand Lien & Ola Flaten & Ragnar Tveterås, 2008. "Impacts of Norwegian Milk Quotas on Output Growth: A Modified Distance Function Approach," Journal of Agricultural Economics, Wiley Blackwell, vol. 59(2), pages 350-369, June.
    10. Atkinson, Scott E & Cornwell, Christopher & Honerkamp, Olaf, 2003. "Measuring and Decomposing Productivity Change: Stochastic Distance Function Estimation versus Data Envelopment Analysis," Journal of Business & Economic Statistics, American Statistical Association, vol. 21(2), pages 284-294, April.
    11. Giannis Karagiannis & Peter Midmore & Vangelis Tzouvelekas, 2004. "Parametric Decomposition of Output Growth Using A Stochastic Input Distance Function," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 86(4), pages 1044-1057.
    12. Jondrow, James & Knox Lovell, C. A. & Materov, Ivan S. & Schmidt, Peter, 1982. "On the estimation of technical inefficiency in the stochastic frontier production function model," Journal of Econometrics, Elsevier, vol. 19(2-3), pages 233-238, August.
    13. COELLI, Tim, 2000. "On the econometric estimation of the distance function representation of a production technology," LIDAM Discussion Papers CORE 2000042, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    14. Fare, Rolf & Grosskopf, Shawna & Zaim, Osman, 2002. "Hyperbolic efficiency and return to the dollar," European Journal of Operational Research, Elsevier, vol. 136(3), pages 671-679, February.
    15. Daniel Muluwork Atsbeha & Dadi Kristofersson & Kyrre Rickertsen, 2012. "Animal Breeding and Productivity Growth of Dairy Farms," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 94(4), pages 996-1012.
    16. Timo Sipiläinen & Subal C. Kumbhakar & Gudbrand Lien, 2014. "Performance of dairy farms in Finland and Norway from 1991 to 2008," European Review of Agricultural Economics, Oxford University Press and the European Agricultural and Applied Economics Publications Foundation, vol. 41(1), pages 63-86, February.
    17. Cornwell, Christopher & Schmidt, Peter & Sickles, Robin C., 1990. "Production frontiers with cross-sectional and time-series variation in efficiency levels," Journal of Econometrics, Elsevier, vol. 46(1-2), pages 185-200.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Malikov, Emir, 2016. "Estimating Multi-Product Production Functions and Productivity using Control Functions," 2016 Annual Meeting, July 31-August 2, Boston, Massachusetts 235108, Agricultural and Applied Economics Association.
    2. Shabbir Ahmad, 2020. "Estimating input-mix efficiency in a parametric framework: application to state-level agricultural data for the United States," Applied Economics, Taylor & Francis Journals, vol. 52(36), pages 3976-3997, July.
    3. Roberto Mosheim & Robin C. Sickles, 2021. "Spatial effects of nutrient pollution on drinking water production," Empirical Economics, Springer, vol. 60(6), pages 2741-2764, June.
    4. Ligia Alba Melo-Becerra & Lucas Wilfried Hahn-De-Castro & Dalma Sofía Ariza-Hernández & Cristian Oswaldo Carmona-Sanchez, 2016. "Efficiency of Public Education in a Multiproduct Context: The Case of Colombian Municipalities," Borradores de Economia 979, Banco de la Republica de Colombia.
    5. Mike Tsionas & Marwan Izzeldin & Arne Henningsen & Evaggelos Paravalos, 2019. "Estimating Stochastic Ray Production Frontiers," IFRO Working Paper 2019/06, University of Copenhagen, Department of Food and Resource Economics.
    6. Ioannis Skevas, 2023. "A novel modeling framework for quantifying spatial spillovers on total factor productivity growth and its components," American Journal of Agricultural Economics, John Wiley & Sons, vol. 105(4), pages 1221-1247, August.
    7. Emir Malikov & Gudbrand Lien, 2021. "Proxy Variable Estimation of Multiproduct Production Functions," American Journal of Agricultural Economics, John Wiley & Sons, vol. 103(5), pages 1878-1902, October.
    8. Bhattacharyya, Aditi & Kutlu, Levent & Sickles, Robin C., 2018. "Pricing Inputs and Outputs: Market prices versus shadow prices, market power, and welfare analysis," Working Papers 18-009, Rice University, Department of Economics.
    9. Mike Tsionas & Marwan Izzeldin & Arne Henningsen & Evaggelos Paravalos, 2022. "Addressing endogeneity when estimating stochastic ray production frontiers: a Bayesian approach," Empirical Economics, Springer, vol. 62(3), pages 1345-1363, March.
    10. Li, Hongchang & Yu, Kemei & Wang, Kun & Zhang, Anming, 2019. "Market power and its determinants in the Chinese railway industry," Transportation Research Part A: Policy and Practice, Elsevier, vol. 120(C), pages 261-276.
    11. Melo-Becerra, Ligia Alba & Hahn-De-Castro, Lucas Wilfried & Ariza, Dalma Sofía & Carmona, Cristian Oswaldo, 2020. "Efficiency of local public education in a decentralized context," International Journal of Educational Development, Elsevier, vol. 76(C).
    12. Heshmati, Almas & C. Kumbhakar, Subal & Kim, Jungsuk, 2016. "Persistent and Transient Efficiency of International Airlines," Working Paper Series in Economics and Institutions of Innovation 444, Royal Institute of Technology, CESIS - Centre of Excellence for Science and Innovation Studies.
    13. Yongseung Han & Arthur Snow & Ronald S. Warren, 2021. "Changes in the productive efficiency of U.S. flour mills in the late nineteenth century: an input-distance-function approach," Journal of Productivity Analysis, Springer, vol. 56(2), pages 115-132, December.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Kumbhakar, Subal C., 2013. "Specification and estimation of multiple output technologies: A primal approach," European Journal of Operational Research, Elsevier, vol. 231(2), pages 465-473.
    2. Subal Kumbhakar & Frank Asche & Ragnar Tveteras, 2013. "Estimation and decomposition of inefficiency when producers maximize return to the outlay: an application to Norwegian fishing trawlers," Journal of Productivity Analysis, Springer, vol. 40(3), pages 307-321, December.
    3. Kellermann, Magnus A., 2015. "Total Factor Productivity Decomposition and Unobserved Heterogeneity in Stochastic Frontier Models," Agricultural and Resource Economics Review, Northeastern Agricultural and Resource Economics Association, vol. 44(1), pages 1-25, April.
    4. Malikov, Emir, 2016. "Estimating Multi-Product Production Functions and Productivity using Control Functions," 2016 Annual Meeting, July 31-August 2, Boston, Massachusetts 235108, Agricultural and Applied Economics Association.
    5. Baños-Pino, José F. & Boto-García, David & Zapico, Emma, 2021. "Persistence and dynamics in the efficiency of toll motorways: The Spanish case," Efficiency Series Papers 2021/03, University of Oviedo, Department of Economics, Oviedo Efficiency Group (OEG).
    6. K. Ravirajan & K.R. Shanmugam, 2021. "Efficiency of commercial banks in India after global financial crises," Theoretical and Applied Economics, Asociatia Generala a Economistilor din Romania - AGER, vol. 0(3(628), A), pages 65-82, Autumn.
    7. Getu Hailu & B. James Deaton, 2016. "Agglomeration Effects in Ontario’s Dairy Farming," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 98(4), pages 1055-1073.
    8. Farsi, Mehdi & Filippini, Massimo, 2009. "An analysis of cost efficiency in Swiss multi-utilities," Energy Economics, Elsevier, vol. 31(2), pages 306-315, March.
    9. Julio Peña & Julio Aguirre & René Cerca D'amico, 2004. "Pesca demersal en Chile: eficiencia técnica y escalas de operación," Revista de Analisis Economico – Economic Analysis Review, Universidad Alberto Hurtado/School of Economics and Business, vol. 19(1), pages 119-160, June.
    10. Kumbhakar, Subal C., 2011. "Estimation of production technology when the objective is to maximize return to the outlay," European Journal of Operational Research, Elsevier, vol. 208(2), pages 170-176, January.
    11. Emir Malikov & Gudbrand Lien, 2021. "Proxy Variable Estimation of Multiproduct Production Functions," American Journal of Agricultural Economics, John Wiley & Sons, vol. 103(5), pages 1878-1902, October.
    12. Luigi Brighi & Paolo Silvestri, 2019. "Inefficiency in Childcare Production: Evidence from Italian Microdata," Italian Economic Journal: A Continuation of Rivista Italiana degli Economisti and Giornale degli Economisti, Springer;Società Italiana degli Economisti (Italian Economic Association), vol. 5(1), pages 103-133, March.
    13. Belotti, Federico & Ilardi, Giuseppe, 2018. "Consistent inference in fixed-effects stochastic frontier models," Journal of Econometrics, Elsevier, vol. 202(2), pages 161-177.
    14. Rangkakulnuwat, Poomthan & Wang, H. Holly, 2011. "Productivity growth decomposition with FE-IV approach: Rethinking Thai commercial banks after the financial crisis," Economic Modelling, Elsevier, vol. 28(6), pages 2579-2588.
    15. Sari, Nazmi, 2003. "Efficiency outcomes of market concentration and managed care," International Journal of Industrial Organization, Elsevier, vol. 21(10), pages 1571-1589, December.
    16. Farsi, Mehdi & Filippini, Massimo & Kuenzle, Michael, 2007. "Cost efficiency in the Swiss gas distribution sector," Energy Economics, Elsevier, vol. 29(1), pages 64-78, January.
    17. Collier, Trevor & Johnson, Andrew L. & Ruggiero, John, 2011. "Technical efficiency estimation with multiple inputs and multiple outputs using regression analysis," European Journal of Operational Research, Elsevier, vol. 208(2), pages 153-160, January.
    18. Michael D. Rosko, 2001. "Cost efficiency of US hospitals: a stochastic frontier approach," Health Economics, John Wiley & Sons, Ltd., vol. 10(6), pages 539-551, September.
    19. Kristin Roll, 2013. "Measuring performance, development and growth when restricting flexibility," Journal of Productivity Analysis, Springer, vol. 39(1), pages 15-25, February.
    20. Massimiliano Piacenza, 2002. "Regulatory Constraints and Cost Efficiency of the Italian Public Transit Systems: An Exploratory Stochastic Frontier Model," CERIS Working Paper 200202, CNR-IRCrES Research Institute on Sustainable Economic Growth - Torino (TO) ITALY - former Institute for Economic Research on Firms and Growth - Moncalieri (TO) ITALY.

    More about this item

    JEL classification:

    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models
    • D24 - Microeconomics - - Production and Organizations - - - Production; Cost; Capital; Capital, Total Factor, and Multifactor Productivity; Capacity
    • Q12 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Agriculture - - - Micro Analysis of Farm Firms, Farm Households, and Farm Input Markets

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:oup:ajagec:v:97:y:2015:i:5:p:1478-1493.. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Oxford University Press (email available below). General contact details of provider: https://edirc.repec.org/data/aaeaaea.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.