IDEAS home Printed from https://ideas.repec.org/a/spr/empeco/v58y2020i6d10.1007_s00181-018-1589-2.html
   My bibliography  Save this article

Estimation of the production profile and metafrontier technology gap: a quantile approach

Author

Listed:
  • Hung-pin Lai

    (National Chung Cheng University)

  • Cliff J. Huang

    (Vanderbilt University)

  • Tsu-Tan Fu

    (Soochow University)

Abstract

In this paper, a quantile function is suggested as an alternative description of a production technology. Since the quantile function may not share the same functional properties as the frontier function, it is argued that the quantile-based production function serves as a better benchmark for a firm’s production structure analysis. This argument is extended to the metafrontier analysis. The quantile metafrontier is defined as the envelopment of all groups’ quantile frontier at the same quantile level. The quantile technology gap serves as a more relevant indicator of efficiency in the adopted technology than the traditional measure of the metafrontier technology gap. The quantile approach is illustrated using survey data to estimate the earning profiles for men, and the impact of human capital on the industrial wage distributions in the service industry, the manufacturing industry, and all other industries in Taiwan.

Suggested Citation

  • Hung-pin Lai & Cliff J. Huang & Tsu-Tan Fu, 2020. "Estimation of the production profile and metafrontier technology gap: a quantile approach," Empirical Economics, Springer, vol. 58(6), pages 2709-2731, June.
    • Handle: RePEc:spr:empeco:v:58:y:2020:i:6:d:10.1007_s00181-018-1589-2
    DOI: 10.1007/s00181-018-1589-2
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s00181-018-1589-2
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s00181-018-1589-2?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
    ---><---

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

    References listed on IDEAS

    as
    1. Behr, Andreas, 2010. "Quantile regression for robust bank efficiency score estimation," European Journal of Operational Research, Elsevier, vol. 200(2), pages 568-581, January.
    2. Yujiro Hayami, 1969. "Sources of Agricultural Productivity Gap Among Selected Countries," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 51(3), pages 564-575.
    3. Wheelock, David C. & Wilson, Paul W., 2009. "Robust Nonparametric Quantile Estimation of Efficiency and Productivity Change in U.S. Commercial Banking, 1985–2004," Journal of Business & Economic Statistics, American Statistical Association, vol. 27(3), pages 354-368.
    4. Cliff Huang & Tai-Hsin Huang & Nan-Hung Liu, 2014. "A new approach to estimating the metafrontier production function based on a stochastic frontier framework," Journal of Productivity Analysis, Springer, vol. 42(3), pages 241-254, December.
    5. Cristina Bernini & Marzia Freo & Attilio Gardini, 2004. "Quantile estimation of frontier production function," Empirical Economics, Springer, vol. 29(2), pages 373-381, May.
    6. Hung-jen Wang & Peter Schmidt, 2002. "One-Step and Two-Step Estimation of the Effects of Exogenous Variables on Technical Efficiency Levels," Journal of Productivity Analysis, Springer, vol. 18(2), pages 129-144, September.
    7. Christopher O’Donnell & D. Rao & George Battese, 2008. "Metafrontier frameworks for the study of firm-level efficiencies and technology ratios," Empirical Economics, Springer, vol. 34(2), pages 231-255, March.
    8. Walheer, Barnabé, 2018. "Aggregation of metafrontier technology gap ratios: the case of European sectors in 1995–2015," European Journal of Operational Research, Elsevier, vol. 269(3), pages 1013-1026.
    9. Aragon, Y. & Daouia, A. & Thomas-Agnan, C., 2005. "Nonparametric Frontier Estimation: A Conditional Quantile-Based Approach," Econometric Theory, Cambridge University Press, vol. 21(2), pages 358-389, April.
    10. Wang, Yongqiao & Wang, Shouyang & Dang, Chuangyin & Ge, Wenxiu, 2014. "Nonparametric quantile frontier estimation under shape restriction," European Journal of Operational Research, Elsevier, vol. 232(3), pages 671-678.
    11. Hayami, Yujiro & Ruttan, Vernon W, 1970. "Agricultural Productivity Differences Among Countries," American Economic Review, American Economic Association, vol. 60(5), pages 895-911, December.
    12. Cliff J. Huang & Tsu-Tan Fu & Hung-Pin Lai & Yung-Lieh Yang, 2017. "Semiparametric smooth coefficient quantile estimation of the production profile," Empirical Economics, Springer, vol. 52(1), pages 373-392, February.
    13. Polachek, Solomon W. & Robst, John, 1998. "Employee labor market information: comparing direct world of work measures of workers' knowledge to stochastic frontier estimates," Labour Economics, Elsevier, vol. 5(2), pages 231-242, June.
    14. Koenker, Roger W & Bassett, Gilbert, Jr, 1978. "Regression Quantiles," Econometrica, Econometric Society, vol. 46(1), pages 33-50, January.
    15. George Battese & D. Rao & Christopher O'Donnell, 2004. "A Metafrontier Production Function for Estimation of Technical Efficiencies and Technology Gaps for Firms Operating Under Different Technologies," Journal of Productivity Analysis, Springer, vol. 21(1), pages 91-103, January.
    16. Daouia, Abdelaati & Simar, Leopold, 2007. "Nonparametric efficiency analysis: A multivariate conditional quantile approach," Journal of Econometrics, Elsevier, vol. 140(2), pages 375-400, October.
    17. Afsharian, Mohsen & Podinovski, Victor V., 2018. "A linear programming approach to efficiency evaluation in nonconvex metatechnologies," European Journal of Operational Research, Elsevier, vol. 268(1), pages 268-280.
    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. Mohammed, Sadick & Abdulai, Awudu, 2021. "Extension Participation and Improved Technology Adoption: Impact on Efficiency and Welfare of Farmers in Ghana," 2021 Conference, August 17-31, 2021, Virtual 315362, International Association of Agricultural Economists.
    2. Radovanović, Sandro & Savić, Gordana & Delibašić, Boris & Suknović, Milija, 2022. "FairDEA—Removing disparate impact from efficiency scores," European Journal of Operational Research, Elsevier, vol. 301(3), pages 1088-1098.

    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. Walheer, Barnabé, 2023. "Meta-frontier and technology switchers: A nonparametric approach," European Journal of Operational Research, Elsevier, vol. 305(1), pages 463-474.
    2. Abebayehu Girma Geffersa & Frank Wogbe Agbola & Amir Mahmood, 2022. "Modelling technical efficiency and technology gap in smallholder maize sector in Ethiopia: accounting for farm heterogeneity," Applied Economics, Taylor & Francis Journals, vol. 54(5), pages 506-521, January.
    3. Afsharian, Mohsen & Ahn, Heinz & Harms, Sören Guntram, 2019. "Performance comparison of management groups under centralised management," European Journal of Operational Research, Elsevier, vol. 278(3), pages 845-854.
    4. Tsionas, Mike G., 2020. "Quantile Stochastic Frontiers," European Journal of Operational Research, Elsevier, vol. 282(3), pages 1177-1184.
    5. Thanh Pham Thien Nguyen & Son Hong Nghiem & Eduardo Roca & Parmendra Sharma, 2016. "Efficiency, innovation and competition: evidence from Vietnam, China and India," Empirical Economics, Springer, vol. 51(3), pages 1235-1259, November.
    6. Zhang, Hui & Zhou, Peng & Sun, Xiumei & Ni, Guanqun, 2024. "Disparities in energy efficiency and its determinants in Chinese cities: From the perspective of heterogeneity," Energy, Elsevier, vol. 289(C).
    7. Alexandros Maziotis & Ramon Sala-Garrido & Manuel Mocholi-Arce & Maria Molinos-Senante, 2021. "Changes to The Productivity of Water Companies: Comparison of Fully Private and Concessionary Water Companies," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(10), pages 3355-3371, August.
    8. Shalander Kumar & Abhishek Das & Michael Hauser & Geoffrey Muricho & Tulu Degefu & Asnake Fikre & Chris Ojiewo & Setotaw Ferede & Rajeev K. Varshney, 2022. "Estimating the potential to close yield gaps through increased efficiency of chickpea production in Ethiopia," Food Security: The Science, Sociology and Economics of Food Production and Access to Food, Springer;The International Society for Plant Pathology, vol. 14(5), pages 1241-1258, October.
    9. Khanal, Uttam & Wilson, Clevo & Shankar, Sriram & Hoang, Viet-Ngu & Lee, Boon, 2018. "Farm performance analysis: Technical efficiencies and technology gaps of Nepalese farmers in different agro-ecological regions," Land Use Policy, Elsevier, vol. 76(C), pages 645-653.
    10. Wang, Yongqiao & Wang, Shouyang & Dang, Chuangyin & Ge, Wenxiu, 2014. "Nonparametric quantile frontier estimation under shape restriction," European Journal of Operational Research, Elsevier, vol. 232(3), pages 671-678.
    11. Kumar, Surender & Jain, Rakesh Kumar, 2019. "Carbon-sensitive meta-productivity growth and technological gap: An empirical analysis of Indian thermal power sector," Energy Economics, Elsevier, vol. 81(C), pages 104-116.
    12. Galina Besstremyannaya, 2014. "The efficiency of labor matching and remuneration reforms: a panel data quantile regression approach with endogenous treatment variables," Working Papers w0206, Center for Economic and Financial Research (CEFIR).
    13. Besstremyannaya, Galina, 2017. "Heterogeneous effect of the global financial crisis and the Great East Japan Earthquake on costs of Japanese banks," Journal of Empirical Finance, Elsevier, vol. 42(C), pages 66-89.
    14. Zarkovic, Maja, 2020. "Cap-and-trade and produce at least cost? Investigating firm behaviour in the EU ETS," Working papers 2020/12, Faculty of Business and Economics - University of Basel.
    15. Jin, Qianying & Kerstens, Kristiaan & Van de Woestyne, Ignace, 2020. "Metafrontier productivity indices: Questioning the common convexification strategy," European Journal of Operational Research, Elsevier, vol. 283(2), pages 737-747.
    16. Li, Hong-Zhou & Kopsakangas-Savolainen, Maria & Xiao, Xing-Zhi & Lau, Sim-Yee, 2017. "Have regulatory reforms improved the efficiency levels of the Japanese electricity distribution sector? A cost metafrontier-based analysis," Energy Policy, Elsevier, vol. 108(C), pages 606-616.
    17. Mei-Ying Huang & Jia-Ching Juo & Tsu-tan Fu, 2015. "Metafrontier cost Malmquist productivity index: an application to Taiwanese and Chinese commercial banks," Journal of Productivity Analysis, Springer, vol. 44(3), pages 321-335, December.
    18. Phuc Trong Ho & Pham Xuan Hung & Nguyen Duc Tien, 2023. "Effects of varieties and seasons on cost efficiency in rice farming: A stochastic metafrontier approach," Asian Journal of Agriculture and Rural Development, Asian Economic and Social Society, vol. 13(2), pages 120-129.
    19. Huang, Tai-Hsin & Chiang, Dien-Lin & Tsai, Chao-Min, 2015. "Applying the New Metafrontier Directional Distance Function to Compare Banking Efficiencies in Central and Eastern European Countries," Economic Modelling, Elsevier, vol. 44(C), pages 188-199.
    20. Ng’ombe, John N., 2017. "Technical efficiency of smallholder maize production in Zambia: a stochastic meta-frontier approach," Agrekon, Agricultural Economics Association of South Africa (AEASA), vol. 56(3), December.

    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:spr:empeco:v:58:y:2020:i:6:d:10.1007_s00181-018-1589-2. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.