IDEAS home Printed from https://ideas.repec.org/a/gam/jecomi/v13y2025i3p63-d1598871.html
   My bibliography  Save this article

Technical Progress and Sustainable Growth in the Manufacturing Sector of North American Countries, 1984–2022: A Stochastic Frontier Analysis

Author

Listed:
  • César Lenin Navarro-Chávez

    (Institute of Economic and Business Research, Universidad Michoacana de San Nicolás de Hidalgo, Morelia 58000, Mexico)

Abstract

This article presents an estimation of a stochastic frontier model using a translogarithmic production function to identify the impact of production factors—labor and capital—along with CO 2 emissions and technical progress on the value added of the manufacturing sector in North American countries over the 1984–2022 period. The model also provides estimates for technical efficiency, scale efficiency, and technological change, allowing for a comparative analysis of these indicators’ evolution within the manufacturing sectors of Canada, Mexico, and the United States. The findings indicate that capital exerts the strongest influence on manufacturing value added, followed by labor. CO 2 emissions exhibit the anticipated negative effect on the sector’s value added. Notably, the average technical efficiency of Mexico’s manufacturing sector is higher than that of Canada and the United States over the studied period. Regarding technological change, the United States demonstrates the highest values, followed by Canada, with both nations displaying an upward trend throughout the years, while Mexico shows a declining trend in this indicator.

Suggested Citation

  • César Lenin Navarro-Chávez, 2025. "Technical Progress and Sustainable Growth in the Manufacturing Sector of North American Countries, 1984–2022: A Stochastic Frontier Analysis," Economies, MDPI, vol. 13(3), pages 1-21, February.
  • Handle: RePEc:gam:jecomi:v:13:y:2025:i:3:p:63-:d:1598871
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7099/13/3/63/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2227-7099/13/3/63/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Qiang Xu & Lian Xu & Zaiyang Xie & Mufan Jin, 2021. "Does Business Group Matter for the Relationship between Green Innovation and Financial Performance? Evidence from Chinese Listed Companies," Sustainability, MDPI, vol. 13(23), pages 1-16, November.
    2. Seitz, Wesley D, 1971. "Productive Efficiency in the Steam-Electric Generating Industry," Journal of Political Economy, University of Chicago Press, vol. 79(4), pages 878-886, July-Aug..
    3. Harvey H. Millar & Suzana N. Russell, 2011. "The Adoption of Sustainable Manufacturing Practices in the Caribbean," Business Strategy and the Environment, Wiley Blackwell, vol. 20(8), pages 512-526, December.
    4. Stevenson, Rodney E., 1980. "Likelihood functions for generalized stochastic frontier estimation," Journal of Econometrics, Elsevier, vol. 13(1), pages 57-66, May.
    5. Battese, George E. & Coelli, Tim J., 1988. "Prediction of firm-level technical efficiencies with a generalized frontier production function and panel data," Journal of Econometrics, Elsevier, vol. 38(3), pages 387-399, July.
    6. Timothy J. Coelli & D.S. Prasada Rao & Christopher J. O’Donnell & George E. Battese, 2005. "An Introduction to Efficiency and Productivity Analysis," Springer Books, Springer, edition 0, number 978-0-387-25895-9, December.
    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. William Griffiths & Xiaohui Zhang & Xueyan Zhao, 2010. "A Stochastic Frontier Model for Discrete Ordinal Outcomes: A Health Production Function," Department of Economics - Working Papers Series 1092, The University of Melbourne.
    2. Mulwa, Richard & Kabubo-Mariara, Jane, 2017. "Productive Efficiency and Its Determinants in a Changing Climate: A Monotonic Translog Stochastic Frontier Analysis," EfD Discussion Paper 17-6, Environment for Development, University of Gothenburg.
    3. Adwoa Asantewaa & Tooraj Jamasb & Manuel Llorca, 2022. "Electricity Sector Reform Performance in Sub-Saharan Africa: A Parametric Distance Function Approach," Energies, MDPI, vol. 15(6), pages 1-29, March.
    4. Tim J. Coelli, 1995. "Recent Developments In Frontier Modelling And Efficiency Measurement," Australian Journal of Agricultural and Resource Economics, Australian Agricultural and Resource Economics Society, vol. 39(3), pages 219-245, December.
    5. William Griffiths & Xiaohui Zhang & Xueyan Zhao, 2014. "Estimation and efficiency measurement in stochastic production frontiers with ordinal outcomes," Journal of Productivity Analysis, Springer, vol. 42(1), pages 67-84, August.
    6. Bao Hoang Nguyen & Robin C. Sickles & Valentin Zelenyuk, 2021. "What do we know from the vast literature on efficiency and productivity in healthcare? A Systematic Review and Bibliometric Analysis," CEPA Working Papers Series WP092021, School of Economics, University of Queensland, Australia.
    7. Mai, Nhat Chi, 2015. "Efficiency of the banking system in Vietnam under financial liberalization," OSF Preprints qsf6d, Center for Open Science.
    8. Bao Hoang Nguyen & Robin C. Sickles & Valentin Zelenyuk, 2022. "Efficiency Analysis with Stochastic Frontier Models Using Popular Statistical Softwares," Springer Books, in: Duangkamon Chotikapanich & Alicia N. Rambaldi & Nicholas Rohde (ed.), Advances in Economic Measurement, chapter 0, pages 129-171, Springer.
    9. 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.
    10. Md Abdur Rouf, 2020. "Evaluation of Agricultural Projects by Parametric Cost Efficiency and Productivity-gap Approaches: An Empirical Study of Flood Control and Drainage Systems in the Southwest Coastal Area of Bangladesh," Japanese Journal of Agricultural Economics (formerly Japanese Journal of Rural Economics), Agricultural Economics Society of Japan (AESJ), vol. 22.
    11. Ali M. Oumer & Amin Mugera & Michael Burton & Atakelty Hailu, 2022. "Technical efficiency and firm heterogeneity in stochastic frontier models: application to smallholder maize farms in Ethiopia," Journal of Productivity Analysis, Springer, vol. 57(2), pages 213-241, April.
    12. repec:swn:wpaper:2023-02 is not listed on IDEAS
    13. Edward Ebo ONUMAH & Bernhard BRÜMMER & Gabriele HÖRSTGEN-SCHWARK, 2010. "Productivity of the hired and family labour and determinants of technical inefficiency in Ghana's fish farms," Agricultural Economics, Czech Academy of Agricultural Sciences, vol. 56(2), pages 79-88.
    14. J. Cummins & Hongmin Zi, 1998. "Comparison of Frontier Efficiency Methods: An Application to the U.S. Life Insurance Industry," Journal of Productivity Analysis, Springer, vol. 10(2), pages 131-152, October.
    15. Sabrina Auci & Laura Castellucci & Manuela Coromaldi, 2021. "How does public spending affect technical efficiency? Some evidence from 15 European countries," Bulletin of Economic Research, Wiley Blackwell, vol. 73(1), pages 108-130, January.
    16. Forsund, Finn R. & Sarafoglou, Nikias, 2005. "The tale of two research communities: The diffusion of research on productive efficiency," International Journal of Production Economics, Elsevier, vol. 98(1), pages 17-40, October.
    17. Henderson, Benjamin B. & Kingwell, Ross S., 2001. "An Investigation of the Technical and Allocative Efficiency of Broadacre Farmers," 2002 Conference (46th), February 13-15, 2002, Canberra, Australia 125109, Australian Agricultural and Resource Economics Society.
    18. Federico Belotti & Silvio Daidone & Giuseppe Ilardi & Vincenzo Atella, 2013. "Stochastic frontier analysis using Stata," Stata Journal, StataCorp LLC, vol. 13(4), pages 718-758, December.
    19. Yangseon Kim & Peter Schmidt, 2000. "A Review and Empirical Comparison of Bayesian and Classical Approaches to Inference on Efficiency Levels in Stochastic Frontier Models with Panel Data," Journal of Productivity Analysis, Springer, vol. 14(2), pages 91-118, September.
    20. Dhawan, Rajeev & Jochumzen, Peter, 1999. "Stochastic Frontier Production Function With Errors-In-Variables," Working Papers 1999:007, Lund University, Department of Economics.
    21. Massimiliano Piacenza & Gilberto Turati, 2014. "Does Fiscal Discipline Towards Subnational Governments Affect Citizens' Well‐Being? Evidence On Health," Health Economics, John Wiley & Sons, Ltd., vol. 23(2), pages 199-224, February.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;
    ;

    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:gam:jecomi:v:13:y:2025:i:3:p:63-:d:1598871. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.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.