IDEAS home Printed from
   My bibliography  Save this paper

Returns to Scale in Electricity Generation: Revisited and Replicated


  • David H. Bernstein

    (University of Miami)

  • Christopher F. Parmeter

    (University of Miami)


We replicate the findings of two in uential studies on returns to scale in the electricity generation market in the United States. The main results are also contrasted using local linear nonparametric regression, a technique robust to functional form assumptions. While the quantitative findings differ somewhat regarding the magnitude of returns to scale, we find that there is a substantial shift in returns to scale across the electricity generation market of 1955 to that of 1970.

Suggested Citation

  • David H. Bernstein & Christopher F. Parmeter, 2017. "Returns to Scale in Electricity Generation: Revisited and Replicated," Working Papers 2017-08, University of Miami, Department of Economics.
  • Handle: RePEc:mia:wpaper:2017-08

    Download full text from publisher

    File URL:
    File Function: First version, 2017
    Download Restriction: no

    References listed on IDEAS

    1. RITTER, Christian & SIMAR, Léopold, 1994. "Another Look at the American Electrical Utility Data," LIDAM Discussion Papers CORE 1994007, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    2. Henderson, Daniel J. & Kumbhakar, Subal C. & Li, Qi & Parmeter, Christopher F., 2015. "Smooth coefficient estimation of a seemingly unrelated regression," Journal of Econometrics, Elsevier, vol. 189(1), pages 148-162.
    3. David C. Wheelock & Paul W. Wilson, 2012. "Do Large Banks Have Lower Costs? New Estimates of Returns to Scale for U.S. Banks," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 44(1), pages 171-199, February.
    4. Henderson,Daniel J. & Parmeter,Christopher F., 2015. "Applied Nonparametric Econometrics," Cambridge Books, Cambridge University Press, number 9780521279680.
    5. Hayfield, Tristen & Racine, Jeffrey S., 2008. "Nonparametric Econometrics: The np Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 27(i05).
    6. Greene, William H., 1990. "A Gamma-distributed stochastic frontier model," Journal of Econometrics, Elsevier, vol. 46(1-2), pages 141-163.
    Full references (including those not matched with items on IDEAS)


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

    Cited by:

    1. Sun, Kege & Wu, Libo, 2020. "Efficiency distortion of the power generation sector under the dual regulation of price and quantity in China," Energy Economics, Elsevier, vol. 86(C).

    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. Bernstein, David H. & Parmeter, Christopher F., 2019. "Returns to scale in electricity generation: Replicated and revisited," Energy Economics, Elsevier, vol. 82(C), pages 4-15.
    2. Jun Cai & William C. Horrace & Christopher F. Parmeter, 2020. "Density Deconvolution with Laplace Errors and Unknown Variance," Center for Policy Research Working Papers 225, Center for Policy Research, Maxwell School, Syracuse University.
    3. Gavoille, Nicolas & Verschelde, Marijn, 2017. "Electoral competition and political selection: An analysis of the activity of French deputies, 1958–2012," European Economic Review, Elsevier, vol. 92(C), pages 180-195.
    4. Das, Sonali & Racine, Jeffrey S., 2018. "Interactive nonparametric analysis of nonlinear systems," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 510(C), pages 290-301.
    5. Christopher F. Parmeter & Valentin Zelenyuk, 2019. "Combining the Virtues of Stochastic Frontier and Data Envelopment Analysis," Operations Research, INFORMS, vol. 67(6), pages 1628-1658, November.
    6. Daniel J. Henderson & Anne-Charlotte Souto, 2018. "An Introduction to Nonparametric Regression for Labor Economists," Journal of Labor Research, Springer, vol. 39(4), pages 355-382, December.
    7. Mariam Camarero & Jesús Peiró-Palomino & Cecilio Tamarit, 2017. "External imbalances and growth," Working Papers 2017/02, Economics Department, Universitat Jaume I, Castellón (Spain).
    8. Diego Restrepo-Tobón & Subal Kumbhakar, 2015. "Nonparametric estimation of returns to scale using input distance functions: an application to large U.S. banks," Empirical Economics, Springer, vol. 48(1), pages 143-168, February.
    9. Christopher F. Parmeter & Jeffrey S. Racine, 2018. "Nonparametric Estimation and Inference for Panel Data Models," Department of Economics Working Papers 2018-02, McMaster University.
    10. Subal C. Kumbhakar & Christopher F. Parmeter & Valentin Zelenyuk, 2017. "Stochastic Frontier Analysis: Foundations and Advances," Working Papers 2017-10, University of Miami, Department of Economics.
    11. Oikawa, Koki, 2016. "A microfoundation for stochastic frontier analysis," Economics Letters, Elsevier, vol. 139(C), pages 15-17.
    12. Nuno Baetas da Silva & João Sousa Andrade, 2016. "The relationship between social transfers and poverty reduction: A nonparametric approach for the EU-27," GEMF Working Papers 2016-09, GEMF, Faculty of Economics, University of Coimbra.
    13. Behr, Andreas & Tente, Sebastian, 2008. "Stochastic frontier analysis by means of maximum likelihood and the method of moments," Discussion Paper Series 2: Banking and Financial Studies 2008,19, Deutsche Bundesbank.
    14. G. Ardizzi & F. Crudu & C. Petraglia, 2015. "The Impact of Electronic Payments on Bank Cost Efficiency: Nonparametric Evidence," Working Paper CRENoS 201517, Centre for North South Economic Research, University of Cagliari and Sassari, Sardinia.
    15. Wilson, Paul W., 2018. "Dimension reduction in nonparametric models of production," European Journal of Operational Research, Elsevier, vol. 267(1), pages 349-367.
    16. Gong, Binlei, 2018. "Agricultural reforms and production in China: Changes in provincial production function and productivity in 1978–2015," Journal of Development Economics, Elsevier, vol. 132(C), pages 18-31.
    17. Spierdijk, Laura & Shaffer, Sherrill & Considine, Tim, 2017. "How do banks adjust to changing input prices? A dynamic analysis of U.S. commercial banks before and after the crisis," Journal of Banking & Finance, Elsevier, vol. 85(C), pages 1-14.
    18. Diego Restrepo-Tobón & Subal Kumbhakar & Kai Sun, 2015. "Obelix vs. Asterix: Size of US commercial banks and its regulatory challenge," Journal of Regulatory Economics, Springer, vol. 48(2), pages 125-168, October.
    19. McCloud, Nadine & Parmeter, Christopher F., 2020. "Determining the Number of Effective Parameters in Kernel Density Estimation," Computational Statistics & Data Analysis, Elsevier, vol. 143(C).
    20. Manthos D. Delis & Sotirios Kokas & Steven Ongena, 2016. "Foreign Ownership and Market Power in Banking: Evidence from a World Sample," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 48(2-3), pages 449-483, March.

    More about this item


    System Estimation; Shepard's Lemma; Seemingly Unrelated Regression; Nonparametric. Publication Status: Submitted;
    All these keywords.

    JEL classification:

    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General

    NEP fields

    This paper has been announced in the following NEP Reports:


    Access and download statistics


    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:mia:wpaper:2017-08. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Christopher Parmeter). General contact details of provider: .

    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 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.

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

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.