IDEAS home Printed from https://ideas.repec.org/a/kap/jproda/v56y2021i2d10.1007_s11123-021-00615-y.html
   My bibliography  Save this article

Changes in the productive efficiency of U.S. flour mills in the late nineteenth century: an input-distance-function approach

Author

Listed:
  • Yongseung Han

    (University of North Georgia)

  • Arthur Snow

    (University of Georgia)

  • Ronald S. Warren

    (University of Georgia)

Abstract

The productive efficiency of the U.S. flour milling industry increased substantially between 1850 and 1880. Specifically, a typical flour mill in 1880 was able to produce the same value of output as a mill in 1850 with 25 percent fewer factor inputs. We use the concept of the cone technology, combined with an input-distance-function approach, to decompose this increase in productive efficiency into changes in technical efficiency, technological progress, and changes in scale efficiency, assuming unchanged allocative efficiency in combining inputs. We find that the average technical efficiency of flour mills was essentially constant throughout the period, implying that almost all the gains in productive efficiency were due to improvements in scale efficiency occasioned by more fully exploiting increasing returns. Furthermore, productive efficiency was positively related to mill size as larger mills were better able to take advantage of both economies of scale and technological progress. These results provide evidence in support of an important role for the increased scale of production in providing the preconditions for the emergence in the early twentieth century of an oligopolistic market structure in the U.S. flour milling industry.

Suggested Citation

  • 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.
  • Handle: RePEc:kap:jproda:v:56:y:2021:i:2:d:10.1007_s11123-021-00615-y
    DOI: 10.1007/s11123-021-00615-y
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11123-021-00615-y
    File Function: Abstract
    Download Restriction: Access to full text is restricted to subscribers.

    File URL: https://libkey.io/10.1007/s11123-021-00615-y?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. Atack, Jeremy & Bateman, Fred & Margo, Robert A., 2008. "Steam power, establishment size, and labor productivity growth in nineteenth century American manufacturing," Explorations in Economic History, Elsevier, vol. 45(2), pages 185-198, April.
    2. 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.
    3. Amsler, Christine & Prokhorov, Artem & Schmidt, Peter, 2016. "Endogeneity in stochastic frontier models," Journal of Econometrics, Elsevier, vol. 190(2), pages 280-288.
    4. 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.
    5. 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.
    6. Bert Balk, 2001. "Scale Efficiency and Productivity Change," Journal of Productivity Analysis, Springer, vol. 15(3), pages 159-183, May.
    7. Léopold Simar & Valentin Zelenyuk, 2011. "Stochastic FDH/DEA estimators for frontier analysis," Journal of Productivity Analysis, Springer, vol. 36(1), pages 1-20, August.
    8. Sickles,Robin C. & Zelenyuk,Valentin, 2019. "Measurement of Productivity and Efficiency," Cambridge Books, Cambridge University Press, number 9781107036161.
    9. Zelenyuk, Valentin, 2006. "Aggregation of Malmquist productivity indexes," European Journal of Operational Research, Elsevier, vol. 174(2), pages 1076-1086, October.
    10. Baltagi, Badi H & Griffin, James M, 1988. "A General Index of Technical Change," Journal of Political Economy, University of Chicago Press, vol. 96(1), pages 20-41, February.
    11. Atack, Jeremy, 1986. "Firm Size and Industrial Structure in the United States During the Nineteenth Century," The Journal of Economic History, Cambridge University Press, vol. 46(2), pages 463-475, June.
    12. James, John A., 1983. "Structural Change in American Manufacturing, 1850–1890," The Journal of Economic History, Cambridge University Press, vol. 43(2), pages 433-459, June.
    13. Layer, Kevin & Johnson, Andrew L. & Sickles, Robin C. & Ferrier, Gary D., 2020. "Direction selection in stochastic directional distance functions," European Journal of Operational Research, Elsevier, vol. 280(1), pages 351-364.
    14. Chang-Tai Hsieh & Peter J. Klenow, 2009. "Misallocation and Manufacturing TFP in China and India," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 124(4), pages 1403-1448.
    15. By RICHARD PERREN, 1990. "Structural change and market growth in the food industry: flour milling in Britain, Europe, and America, 1850-1914," Economic History Review, Economic History Society, vol. 43(3), pages 420-437, August.
    16. Saloutos, Theodore, 1946. "The Spring-Wheat Farmer in a Maturing Economy 1870–1920," The Journal of Economic History, Cambridge University Press, vol. 6(2), pages 173-190, November.
    17. William C. Horrace & Peter Schmidt, 2000. "Multiple comparisons with the best, with economic applications," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 15(1), pages 1-26.
    18. Banker, Rajiv D., 1984. "Estimating most productive scale size using data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 17(1), pages 35-44, July.
    19. Svend Rasmussen, 2010. "Scale efficiency in Danish agriculture: an input distance--function approach," European Review of Agricultural Economics, Oxford University Press and the European Agricultural and Applied Economics Publications Foundation, vol. 37(3), pages 335-367, September.
    20. Meeusen, Wim & van den Broeck, Julien, 1977. "Efficiency Estimation from Cobb-Douglas Production Functions with Composed Error," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 18(2), pages 435-444, June.
    21. Atkinson, Scott E & Cornwell, Christopher, 1994. "Estimation of Output and Input Technical Efficiency Using a Flexible Form and Panel Data," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 35(1), pages 245-255, February.
    22. 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.
    23. Robin Sickles & David Good & Lullit Getachew, 2002. "Specification of Distance Functions Using Semi- and Nonparametric Methods with an Application to the Dynamic Performance of Eastern and Western European Air Carriers," Journal of Productivity Analysis, Springer, vol. 17(1), pages 133-155, January.
    24. 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.
    25. Phill Wheat & Alexander D. Stead & William H. Greene, 2019. "Robust stochastic frontier analysis: a Student’s t-half normal model with application to highway maintenance costs in England," Journal of Productivity Analysis, Springer, vol. 51(1), pages 21-38, February.
    26. Stevenson, Rodney E., 1980. "Likelihood functions for generalized stochastic frontier estimation," Journal of Econometrics, Elsevier, vol. 13(1), pages 57-66, May.
    27. William J. Collins & Robert A. Margo, 2015. "Enterprising America: Businesses, Banks, and Credit Markets in Historical Perspective," NBER Books, National Bureau of Economic Research, Inc, number coll13-1, March.
    28. Atack, Jeremy, 1979. "Fact in fiction? The relative costs of steam and water power: a simulation approach," Explorations in Economic History, Elsevier, vol. 16(4), pages 409-437, October.
    29. Aigner, Dennis & Lovell, C. A. Knox & Schmidt, Peter, 1977. "Formulation and estimation of stochastic frontier production function models," Journal of Econometrics, Elsevier, vol. 6(1), pages 21-37, July.
    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. Sickles, Robin C. & Song, Wonho & Zelenyuk, Valentin, 2018. "Econometric Analysis of Productivity: Theory and Implementation in R," Working Papers 18-008, Rice University, Department of Economics.
    2. Madau, Fabio A., 2012. "Technical and scale efficiency in the Italian Citrus Farming: A comparison between Stochastic Frontier Analysis (SFA) and Data Envelopment Analysis(DEA) Models," MPRA Paper 41403, University Library of Munich, Germany.
    3. Luis R. Murillo‐Zamorano, 2004. "Economic Efficiency and Frontier Techniques," Journal of Economic Surveys, Wiley Blackwell, vol. 18(1), pages 33-77, February.
    4. 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.
    5. Binlei Gong & Robin C. Sickles, 2020. "Non-structural and structural models in productivity analysis: study of the British Isles during the 2007–2009 financial crisis," Journal of Productivity Analysis, Springer, vol. 53(2), pages 243-263, April.
    6. Tsionas, Mike & Parmeter, Christopher F. & Zelenyuk, Valentin, 2023. "Bayesian Artificial Neural Networks for frontier efficiency analysis," Journal of Econometrics, Elsevier, vol. 236(2).
    7. Christian Growitsch & Tooraj Jamasb & Michael Pollitt, 2009. "Quality of service, efficiency and scale in network industries: an analysis of European electricity distribution," Applied Economics, Taylor & Francis Journals, vol. 41(20), pages 2555-2570.
    8. Zangin Zeebari & Kristofer Månsson & Pär Sjölander & Magnus Söderberg, 2023. "Regularized conditional estimators of unit inefficiency in stochastic frontier analysis, with application to electricity distribution market," Journal of Productivity Analysis, Springer, vol. 59(1), pages 79-97, February.
    9. Oleg Badunenko & Daniel J. Henderson, 2024. "Production analysis with asymmetric noise," Journal of Productivity Analysis, Springer, vol. 61(1), pages 1-18, February.
    10. Kamil Makieła & Błażej Mazur, 2022. "Model uncertainty and efficiency measurement in stochastic frontier analysis with generalized errors," Journal of Productivity Analysis, Springer, vol. 58(1), pages 35-54, August.
    11. Mai, Nhat Chi, 2015. "Efficiency of the banking system in Vietnam under financial liberalization," OSF Preprints qsf6d, Center for Open Science.
    12. Léopold Simar & Valentin Zelenyuk, 2011. "Stochastic FDH/DEA estimators for frontier analysis," Journal of Productivity Analysis, Springer, vol. 36(1), pages 1-20, August.
    13. Glass, Anthony J. & Kenjegalieva, Karligash & Sickles, Robin C. & Weyman-Jones, Thomas, 2018. "The Spatial Efficiency Multiplier and Common Correlated Effects in a Spatial Autoregressive Stochastic Frontier Model," Working Papers 18-003, Rice University, Department of Economics.
    14. 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.
    15. Massimo Del Gatto & Adriana Di Liberto & Carmelo Petraglia, 2011. "Measuring Productivity," Journal of Economic Surveys, Wiley Blackwell, vol. 25(5), pages 952-1008, December.
    16. Russ Kashian & Nicholas Lovett & Yuhan Xue, 2020. "Has the affordable care act affected health care efficiency?," Journal of Regulatory Economics, Springer, vol. 58(2), pages 193-233, December.
    17. Orea, Luis, 2019. "The Econometric Measurement of Firms’ Efficiency," Efficiency Series Papers 2019/02, University of Oviedo, Department of Economics, Oviedo Efficiency Group (OEG).
    18. Taining Wang & Jinjing Tian & Feng Yao, 2021. "Does high debt ratio influence Chinese firms’ performance? A semiparametric stochastic frontier approach with zero inefficiency," Empirical Economics, Springer, vol. 61(2), pages 587-636, August.
    19. Phu Nguyen-Van & The Nguyen To, 2014. "Agricultural extension and technical efficiency of tea production in northeastern Vietnam," Working Papers hal-01725580, HAL.
    20. Léopold Simar & Ingrid Keilegom & Valentin Zelenyuk, 2017. "Nonparametric least squares methods for stochastic frontier models," Journal of Productivity Analysis, Springer, vol. 47(3), pages 189-204, June.

    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:kap:jproda:v:56:y:2021:i:2:d:10.1007_s11123-021-00615-y. 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.