IDEAS home Printed from https://ideas.repec.org/a/wly/soecon/v89y2023i3p885-923.html
   My bibliography  Save this article

Proxy variable estimation of productivity and efficiency

Author

Listed:
  • Mike G. Tsionas
  • Subal C. Kumbhakar

Abstract

We model productivity and inefficiency jointly, instead of modeling and estimating either only productivity or only inefficiency with many variable and quasi‐fixed inputs. In the first model, we use a multi‐step procedure. We use the proxy variable method based on the first‐order condition (FOC) of expected profit maximization with respect to the single variable input to take care of the endogeneity problem arising from both productivity and inefficiency. To separate mean inefficiency from mean productivity we assume them nonparametric functions of different sets of exogenous variables. In the second model, we consider a novel system consisting of the production function and the FOCs of expected profit maximization for the multiple variable inputs. Distributional assumptions are made on all the random errors associated with the production function, the FOCs, productivity, and inefficiency functions in the second model. We use the Colombian food manufacturing data as an application of our model.

Suggested Citation

  • Mike G. Tsionas & Subal C. Kumbhakar, 2023. "Proxy variable estimation of productivity and efficiency," Southern Economic Journal, John Wiley & Sons, vol. 89(3), pages 885-923, January.
  • Handle: RePEc:wly:soecon:v:89:y:2023:i:3:p:885-923
    DOI: 10.1002/soej.12608
    as

    Download full text from publisher

    File URL: https://doi.org/10.1002/soej.12608
    Download Restriction: no

    File URL: https://libkey.io/10.1002/soej.12608?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Massimo Filippini & William Greene, 2016. "Persistent and transient productive inefficiency: a maximum simulated likelihood approach," Journal of Productivity Analysis, Springer, vol. 45(2), pages 187-196, April.
    2. Amit Gandhi & Salvador Navarro & David A. Rivers, 2020. "On the Identification of Gross Output Production Functions," Journal of Political Economy, University of Chicago Press, vol. 128(8), pages 2973-3016.
    3. Yair Mundlak, 1961. "Empirical Production Function Free of Management Bias," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 43(1), pages 44-56.
    4. Amil Petrin & Jagadeesh Sivadasan, 2013. "Estimating Lost Output from Allocative Inefficiency, with an Application to Chile and Firing Costs," The Review of Economics and Statistics, MIT Press, vol. 95(1), pages 286-301, March.
    5. Olley, G Steven & Pakes, Ariel, 1996. "The Dynamics of Productivity in the Telecommunications Equipment Industry," Econometrica, Econometric Society, vol. 64(6), pages 1263-1297, November.
    6. Hu, Yingyao & Huang, Guofang & Sasaki, Yuya, 2020. "Estimating production functions with robustness against errors in the proxy variables," Journal of Econometrics, Elsevier, vol. 215(2), pages 375-398.
    7. Train,Kenneth E., 2009. "Discrete Choice Methods with Simulation," Cambridge Books, Cambridge University Press, number 9780521747387.
    8. 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.
    9. Zach Flynn, 2020. "Identifying productivity when it is a factor of production," RAND Journal of Economics, RAND Corporation, vol. 51(2), pages 496-530, June.
    10. Daniel A. Ackerberg & Kevin Caves & Garth Frazer, 2015. "Identification Properties of Recent Production Function Estimators," Econometrica, Econometric Society, vol. 83, pages 2411-2451, November.
    11. Fuss, Melvyn & McFadden, Daniel (ed.), 1978. "Production Economics: A Dual Approach to Theory and Applications," Elsevier Monographs, Elsevier, edition 1, number 9780444850133.
    12. Huang, Minjie & Zhao, Shunan & Kumbhakar, Subal C., 2022. "Decomposition of Output, Productivity and Market Structure Changes," European Journal of Operational Research, Elsevier, vol. 303(1), pages 422-437.
    13. Christopher F. Parmeter & Hung-Jen Wang & Subal C. Kumbhakar, 2017. "Nonparametric estimation of the determinants of inefficiency," Journal of Productivity Analysis, Springer, vol. 47(3), pages 205-221, June.
    14. Fuss, Melvyn & McFadden, Daniel, 1978. "Production Economics: A Dual Approach to Theory and Applications (I): The Theory of Production," History of Economic Thought Books, McMaster University Archive for the History of Economic Thought, volume 1, number fuss1978.
    15. Shunan Zhao & Bing Qian & Subal C. Kumbhakar, 2020. "Estimation of productivity and markups with price dispersion: Evidence from Chinese manufacturing during economic transition," Southern Economic Journal, John Wiley & Sons, vol. 87(2), pages 666-699, October.
    16. Gonçalves, Sílvia & Kaffo, Maximilien, 2015. "Bootstrap inference for linear dynamic panel data models with individual fixed effects," Journal of Econometrics, Elsevier, vol. 186(2), pages 407-426.
    Full references (including those not matched with items on IDEAS)

    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. Bournakis, Ioannis & Tsionas, Mike G., 2023. "A Non-Parametric Estimation of Productivity with Idiosyncratic and Aggregate Shocks: The Role of Research and Development (R&D) and Corporate Tax," MPRA Paper 118100, University Library of Munich, Germany.
    2. Aguiar, Victor H. & Kashaev, Nail & Allen, Roy, 2023. "Prices, profits, proxies, and production," Journal of Econometrics, Elsevier, vol. 235(2), pages 666-693.
    3. Maican, Florin & Orth, Matilda, 2021. "Entry Regulations and Product Variety in Retail," CEPR Discussion Papers 15992, C.E.P.R. Discussion Papers.
    4. Pontus Mattsson & Jonas Månsson & William H. Greene, 2020. "TFP change and its components for Swedish manufacturing firms during the 2008–2009 financial crisis," Journal of Productivity Analysis, Springer, vol. 53(1), pages 79-93, February.
    5. Bruno Merlevede & Angelos Theodorakopoulos, 2018. "Productivity Effects of Internationalisation Through the Domestic Supply Chain: Evidence from Europe," Working Papers of VIVES - Research Centre for Regional Economics 627689, KU Leuven, Faculty of Economics and Business (FEB), VIVES - Research Centre for Regional Economics.
    6. Mertens, Matthias, 2019. "Micro-mechanisms behind declining labour shares: Market power, production processes, and global competition," IWH-CompNet Discussion Papers 3/2019, Halle Institute for Economic Research (IWH).
    7. Mike G. Tsionas & Subal C. Kumbhakar, 2023. "Productivity and Performance: A GMM approach," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 85(2), pages 331-344, April.
    8. Emir Malikov & Jingfang Zhang & Shunan Zhao & Subal C. Kumbhakar, 2023. "Accounting for Cross-Location Technological Heterogeneity in the Measurement of Operations Efficiency and Productivity," Papers 2302.13430, arXiv.org.
    9. Chen Yeh & Claudia Macaluso & Brad Hershbein, 2022. "Monopsony in the US Labor Market," American Economic Review, American Economic Association, vol. 112(7), pages 2099-2138, July.
    10. Amit Gandhi & Salvador Navarro & David Rivers, 2017. "How Heterogeneous is Productivity? A Comparison of Gross Output and Value Added," University of Western Ontario, Centre for Human Capital and Productivity (CHCP) Working Papers 201727, University of Western Ontario, Centre for Human Capital and Productivity (CHCP).
    11. Bang, Minji & Gao, Wayne Yuan & Postlewaite, Andrew & Sieg, Holger, 2023. "Using monotonicity restrictions to identify models with partially latent covariates," Journal of Econometrics, Elsevier, vol. 235(2), pages 892-921.
    12. Victor Aguirregabiria & Margaret Slade, 2017. "Empirical models of firms and industries," Canadian Journal of Economics, Canadian Economics Association, vol. 50(5), pages 1445-1488, December.
    13. Maican, Florin & Orth, Matilda, 2021. "Determinants of economies of scope in retail," International Journal of Industrial Organization, Elsevier, vol. 75(C).
    14. Mike Tsionas & Christopher F. Parmeter & Valentin Zelenyuk, 2021. "Bridging the Divide? Bayesian Artificial Neural Networks for Frontier Efficiency Analysis," CEPA Working Papers Series WP082021, School of Economics, University of Queensland, Australia.
    15. Michael L. Polemis & Mike G. Tsionas, 2022. "Endogenous productivity: a new Bayesian perspective," Annals of Operations Research, Springer, vol. 318(1), pages 425-451, November.
    16. Tsionas, Mike G., 2022. "Convex non-parametric least squares, causal structures and productivity," European Journal of Operational Research, Elsevier, vol. 303(1), pages 370-387.
    17. Dibyendu Maiti & Chiranjib Neogi, 2020. "Endogeneity Corrected Stochastic Frontier with Market Imperfections," Working papers 313, Centre for Development Economics, Delhi School of Economics.
    18. Roman Fossati & Heiko Rachinger, 2021. "Total Factor Productivity: Exploring firms’ dynamics and heterogeneity over the business cycle," Asociación Argentina de Economía Política: Working Papers 4471, Asociación Argentina de Economía Política.
    19. Daniel A. Ackerberg & Kevin Caves & Garth Frazer, 2015. "Identification Properties of Recent Production Function Estimators," Econometrica, Econometric Society, vol. 83, pages 2411-2451, November.
    20. Emir Malikov & Shunan Zhao & Jingfang Zhang, 2024. "A System Approach to Structural Identification of Production Functions with Multi-Dimensional Productivity," Advances in Econometrics, in: Essays in Honor of Subal Kumbhakar, volume 46, pages 211-263, Emerald Group Publishing Limited.

    More about this item

    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:wly:soecon:v:89:y:2023:i:3:p:885-923. 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: Wiley Content Delivery (email available below). General contact details of provider: https://doi.org/10.1002/(ISSN)2325-8012 .

    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.