IDEAS home Printed from https://ideas.repec.org/a/gam/jresou/v14y2025i2p32-d1593650.html
   My bibliography  Save this article

Measuring the Economic Impact of Pre-Salt Layer on the Productivity of the Oil and Natural Gas Sector

Author

Listed:
  • Mario Jorge Cardoso de Mendonca

    (Energy Planning Programme, Institute for Postgraduate Studies and Research in Engineering, Federal University of Rio de Janeiro, Rio de Janeiro 21941-909, Brazil)

  • Amaro Olimpio Pereira Junior

    (Energy Planning Programme, Institute for Postgraduate Studies and Research in Engineering, Federal University of Rio de Janeiro, Rio de Janeiro 21941-909, Brazil)

  • Jose Francisco Moreira Pessanha

    (Institute of Mathematics and Statistics, State University of Rio de Janeiro, Rio de Janeiro 20550-000, Brazil)

  • Rodrigo Mendes Pereira

    (Institute of Applied Economic Research, Brasília 70076-900, Brazil)

  • Julian David Hunt

    (Biological and Environmental Science and Engineering Division, King Abdullah University of Science and Technology, Thuwal 23955-6900, Saudi Arabia)

Abstract

Based on productivity and efficiency indicators, we investigated the performance of the Brazilian oil and gas exploration industry, comparing the performance of this sector with other industrial sectors. We associate productivity with the concept of total factor productivity (TFP), while efficiency is measured using the stochastic frontier production model. Our sample was assembled from the Annual Industrial Survey (PIA) for 29 Brazilian industrial sectors from 2007 to 2019, period of data availability. The results derived from both methods allow us to affirm that the policies resulting from the Pre-Salt have significantly boosted the oil and natural gas extraction sector in terms of technological progress and efficiency. Between 2007 and 2009, the sector was among the least efficient, ranking 29th. However, in 2019 it reached first place in terms of efficiency. This structural change, which began in 2010 as a result of the technological innovations resulting from investments in R&D, has undergone a change since 2010, reflected in the upward trend towards pre-salt exploration promoted by Petrobras, in Rio de Janeiro, Brazil, as well as the new regulatory framework and government incentives for oil exploration in Brazil. Un-fortunately, these productivity gains have not been exported to other branches of industry connected to the oil industry.

Suggested Citation

  • Mario Jorge Cardoso de Mendonca & Amaro Olimpio Pereira Junior & Jose Francisco Moreira Pessanha & Rodrigo Mendes Pereira & Julian David Hunt, 2025. "Measuring the Economic Impact of Pre-Salt Layer on the Productivity of the Oil and Natural Gas Sector," Resources, MDPI, vol. 14(2), pages 1-18, February.
  • Handle: RePEc:gam:jresou:v:14:y:2025:i:2:p:32-:d:1593650
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2079-9276/14/2/32/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2079-9276/14/2/32/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Efthymios G. Tsionas, 2006. "Inference in dynamic stochastic frontier models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 21(5), pages 669-676, July.
    2. Efthymios G. Tsionas, 2002. "Stochastic frontier models with random coefficients," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 17(2), pages 127-147.
    3. 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.
    4. 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.
    5. Jim Griffin & Mark Steel, 2007. "Bayesian stochastic frontier analysis using WinBUGS," Journal of Productivity Analysis, Springer, vol. 27(3), pages 163-176, June.
    6. Aline Souza Magalhães & Edson Paulo Domingues, 2014. "Blessing or curse: Impacts of the Brazilian Pre-Salt oil exploration," Economia, ANPEC - Associação Nacional dos Centros de Pós-Graduação em Economia [Brazilian Association of Graduate Programs in Economics], vol. 15(3), pages 343-362.
    7. Battese, George E. & Corra, Greg S., 1977. "Estimation Of A Production Frontier Model: With Application To The Pastoral Zone Of Eastern Australia," Australian Journal of Agricultural Economics, Australian Agricultural and Resource Economics Society, vol. 21(3), pages 1-11, December.
    8. Vasconcelos, Rafael da Silva, 2017. "Misallocation in the Brazilian Manufacturing Sector," Brazilian Review of Econometrics, Sociedade Brasileira de Econometria - SBE, vol. 37(2), November.
    9. Managi, Shunsuke & Opaluch, James J. & Jin, Di & Grigalunas, Thomas A., 2006. "Stochastic frontier analysis of total factor productivity in the offshore oil and gas industry," Ecological Economics, Elsevier, vol. 60(1), pages 204-215, November.
    10. Berlemann Michael & Wesselhöft Jan-Erik, 2014. "Estimating Aggregate Capital Stocks Using the Perpetual Inventory Method: A Survey of Previous Implementations and New Empirical Evidence for 103 Countries," Review of Economics, De Gruyter, vol. 65(1), pages 1-34, April.
    11. Douglas Gollin, 2002. "Getting Income Shares Right," Journal of Political Economy, University of Chicago Press, vol. 110(2), pages 458-474, April.
    12. Fernandez, Carmen & Osiewalski, Jacek & Steel, Mark F. J., 1997. "On the use of panel data in stochastic frontier models with improper priors," Journal of Econometrics, Elsevier, vol. 79(1), pages 169-193, July.
    13. George E. Battese & Greg S. Corra, 1977. "Estimation Of A Production Frontier Model: With Application To The Pastoral Zone Of Eastern Australia," Australian Journal of Agricultural and Resource Economics, Australian Agricultural and Resource Economics Society, vol. 21(3), pages 169-179, 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. de Mendonça, Mário Jorge Cardoso & Pereira, Amaro Olimpio & Bellido, Marlon Max H. & Medrano, Luis Alberto & Pessanha, José Francisco Moreira, 2023. "Service quality performance indicators for electricity distribution in Brazil," Utilities Policy, Elsevier, vol. 80(C).
    2. 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.
    3. Kamil Makieła & Błażej Mazur, 2020. "Bayesian Model Averaging and Prior Sensitivity in Stochastic Frontier Analysis," Econometrics, MDPI, vol. 8(2), pages 1-22, April.
    4. Goto, Mika & Makhija, Anil K., 2007. "The Impact of Competition and Corporate Structure on Productive Efficiency: The Case of the U.S. Electric Utility Industry, 1990-2004," Working Paper Series 2007-10, Ohio State University, Charles A. Dice Center for Research in Financial Economics.
    5. Luis R. Murillo‐Zamorano, 2004. "Economic Efficiency and Frontier Techniques," Journal of Economic Surveys, Wiley Blackwell, vol. 18(1), pages 33-77, February.
    6. Arabinda Das, 2015. "Copula-based Stochastic Frontier Model with Autocorrelated Inefficiency," Central European Journal of Economic Modelling and Econometrics, Central European Journal of Economic Modelling and Econometrics, vol. 7(2), pages 111-126, June.
    7. Tom Kompas, 2004. "Market reform, productivity and efficiency in Vietnamese rice production," International and Development Economics Working Papers idec04-4, International and Development Economics.
    8. Martini, Gianmaria & Scotti, Davide & Viola, Domenico & Vittadini, Giorgio, 2020. "Persistent and temporary inefficiency in airport cost function: An application to Italy," Transportation Research Part A: Policy and Practice, Elsevier, vol. 132(C), pages 999-1019.
    9. Kristof De Witte & Laura López-Torres, 2017. "Efficiency in education: a review of literature and a way forward," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 68(4), pages 339-363, April.
    10. García-Suárez, Federico & Pérez-Quesada, Gabriela & Molina, Carlos, 2022. "Rangeland cattle production in Uruguay: Single-output versus multi-output efficiency measures," Economia Agraria y Recursos Naturales, Spanish Association of Agricultural Economists, vol. 22(01), June.
    11. Antonio Ramos Andrade & Julian Stow, 2017. "Assessing the efficiency of maintenance operators: A case study of turning railway wheelsets on an under-floor wheel lathe," Journal of Risk and Reliability, , vol. 231(2), pages 155-163, April.
    12. Galina Besstremyannaya, 2011. "Managerial performance and cost efficiency of Japanese local public hospitals: A latent class stochastic frontier model," Health Economics, John Wiley & Sons, Ltd., vol. 20(S1), pages 19-34, September.
    13. Jorge Galán & Helena Veiga & Michael Wiper, 2014. "Bayesian estimation of inefficiency heterogeneity in stochastic frontier models," Journal of Productivity Analysis, Springer, vol. 42(1), pages 85-101, August.
    14. Pavlos Almanidis & Mustafa U. Karakaplan & Levent Kutlu, 2019. "A dynamic stochastic frontier model with threshold effects: U.S. bank size and efficiency," Journal of Productivity Analysis, Springer, vol. 52(1), pages 69-84, December.
    15. Sickles, Robin C. & Hao, Jiaqi & Shang, Chenjun, 2015. "Panel Data and Productivity Measurement," Working Papers 15-018, Rice University, Department of Economics.
    16. Kamil Makie{l}a & B{l}a.zej Mazur, 2020. "Stochastic Frontier Analysis with Generalized Errors: inference, model comparison and averaging," Papers 2003.07150, arXiv.org, revised Oct 2020.
    17. Pavlos Almanidis, 2013. "Accounting for heterogeneous technologies in the banking industry: a time-varying stochastic frontier model with threshold effects," Journal of Productivity Analysis, Springer, vol. 39(2), pages 191-205, April.
    18. repec:cte:wsrepe:ws121007 is not listed on IDEAS
    19. Li, Hong-Zhou & Kopsakangas-Savolainen, Maria & Xiao, Xing-Zhi & Tian, Zhen-Zhen & Yang, Xiao-Yuan & Wang, Jian-Lin, 2016. "Cost efficiency of electric grid utilities in China: A comparison of estimates from SFA–MLE, SFA–Bayes and StoNED–CNLS," Energy Economics, Elsevier, vol. 55(C), pages 272-283.
    20. Otieno, David Jakinda & Hubbard, Lionel J. & Ruto, Eric, 2011. "Technical efficiency and technology gaps in beef cattle production systems in Kenya: A stochastic metafrontier analysis," 85th Annual Conference, April 18-20, 2011, Warwick University, Coventry, UK 108947, Agricultural Economics Society.
    21. Tom Kompas & Tuong Nhu Che & R. Quentin Grafton, 2004. "Technical efficiency effects of input controls: evidence from Australia's banana prawn fishery," Applied Economics, Taylor & Francis Journals, vol. 36(15), pages 1631-1641.

    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:jresou:v:14:y:2025:i:2:p:32-:d:1593650. 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.