Skip to main content
SpringerLink
Log in

Empirical evidence on the operational efficiency of National Oil Companies

  • Published:
Empirical Economics Aims and scope Submit manuscript

Abstract

On the basis of application of both data envelopment analysis and stochastic frontier estimation applied to a panel of 78 firms, we present empirical evidence on the revenue efficiency of National Oil Companies (NOCs) and private international oil companies (IOCs). We find that with few exceptions, NOCs are less efficient than IOCs. In addition, much of the inefficiency can be explained by differences in the structural and institutional features of the firms, which may arise due to different firms’ objectives.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

Abbreviations

NOC:

National Oil Company

NOCs:

National Oil Companies

IOC:

International oil company

IOCs:

International oil companies

EIA:

Energy Information Administration of United States Department of Energy

DEA:

Data envelopment analysis

SFA:

Stochastic frontier analysis

CRS:

Constant returns to scale

VRS:

Variable returns to scale

OPEC:

Organization of the petroleum exporting countries

References

  • Afriat SN (1972) Efficiency estimation of production functions. Int Econ Rev 13: 568–598

    Article  Google Scholar 

  • Aigner DJ, Chu SF (1968) On estimating the industry production function. Am Econ Rev 58(4): 826–839

    Google Scholar 

  • Aigner DJ, Lovell CAK, Schmidt P (1977) Formulation and estimation of stochastic frontier production function models. J Econ 6(1): 21–37

    Google Scholar 

  • Alchian AA (1965) Some economics of property rights. Il Politico 30(4): 816–829

    Google Scholar 

  • Al-Obaidan AM, Scully GW (1991) Efficiency differences between private and state-owned enterprises in the international petroleum industry. Appl Econ 23: 237–246

    Article  Google Scholar 

  • Banker RD (1993) Maximum likelihood, consistency and data envelopment analysis: a statistical foundation. Manag Sci 39(10): 1256–1273

    Article  Google Scholar 

  • Banker RD, Charnes A, Cooper WW (1984) Some Models for estimating technical and scale inefficiencies in data envelopment analysis. Manag Sci 30(9): 1078–1092

    Article  Google Scholar 

  • Battese GE, Coelli TJ (1992) Frontier production functions, technical efficiency and panel data: with application to paddy farms in India. J Prod Anal 3(1–2): 153–169

    Article  Google Scholar 

  • Battese GE, Coelli TJ (1995) A model for technical inefficiency effects in a stochastic frontier production function for panel data. Empir Econ 20: 325–332

    Article  Google Scholar 

  • Boles JN (1966) Efficiency squared—efficient computation of efficiency indexes. Proceedings of the 39th annual meeting of the western farm economic association, pp 137–142

  • Charnes A, Cooper WW, Rhodes E (1978) Measuring the efficiency of decision making units. Eur J Oper Res 2: 429–444

    Article  Google Scholar 

  • Coelli TJ (1996a) A guide to DEAP version 2.1: a data envelopment analysis (computer) program. CEPA working paper 96/8, Department of Econometrics, University of New England, Armidale NSW Australia

  • Coelli TJ (1996b) A guide to FRONTIER version 4.1: a computer program for stochastic frontier production and cost function estimation. CEPA working paper 96/7, Department of Econometrics, University of New England, Armidale NSW Australia

  • Coelli T, Rao DSP, Battese GE (1999) An introduction to efficiciency and productive analysis. Kluwer Academic Publishers, Boston

    Google Scholar 

  • Cooper WW, Tone K (1997) Measures of inefficiency in data envelopment analysis and stochastic frontier estimation. Eur J Oper Res 99(1): 72–88

    Article  Google Scholar 

  • Cornwell C, Schmidt P, Sickles RC (1990) Production frontiers with cross-sectional and time-series variation in efficiency levels. J Econ 46(1–2): 185–200

    Google Scholar 

  • Debreu G (1951) The coefficient of resource utilization. Econometrica 19(3): 273–292

    Article  Google Scholar 

  • Desai A, Ratick SJ, Schinnar AP (2005) Data envelopment analysis with stochastic variations in data. Socioecon Plan Sci 39: 147–164

    Article  Google Scholar 

  • Energy Intelligence (2004, 2005, 2006) The energy intelligence top 100: ranking the world’s oil companies

  • Energy Intelligence: (2006) PIW’s top 50: how the firms stack up. Petroleum Intelligence Weekly Special Suppl 45(51): 2–3

    Google Scholar 

  • Energy Information Administration (2006) International energy annual, “Table 2.2 world crude oil production, 1980–2004”

  • Energy Information Administration (2007) “World proved crude oil reserves, January 1, 1980–January 1, 2007 estimates”

  • Farrell MJ (1957) The measurement of productive efficiency. J R Stat Soc 120(3): 11–48

    Google Scholar 

  • Gong BH, Sickles RC (1992) Finite sample evidence on the performance of stochastic frontiers and data envelopment analysis using panel data. J Econ 51(1–2): 259–284

    Google Scholar 

  • Grosskopf S (1996) Statistical inference and nonparametric efficiency: a selective survey. J Prod Anal 7: 161–176

    Article  Google Scholar 

  • Harris M, Raviv A (1978) Some results on incentive contracts with applications to education and employment, health insurance, and law enforcement. Am Econ Rev 68(1): 20–30

    Google Scholar 

  • Hartley PR, Medlock KB III (2008) A model of the operation and development of a national oil company. Energy Econ 30(5): 2459–2485

    Article  Google Scholar 

  • Jensen CM, Meckling WH (1976) Theory of the firm: managerial behavior, agency costs and ownership structure. J Financ Econ 3(4): 305–360

    Article  Google Scholar 

  • Jondrow J, Lovell CAK, Materov IS, Schmidt P (1982) On the estimation of technical inefficiency in the stochastic frontier production function model. J Econ 9(2–3): 233–238

    Google Scholar 

  • Koopmans TC (1951) An analysis of production as an efficient combination of activities. In: Koopmans Activity analysis of production and allocation. Cowles commission for research in economics, Monograph 13. Wiley, New York

  • Kumbhakar SC, Lovell CAK (2000) Stochastic frontier analysis. Cambridge University Press, Cambridge

    Google Scholar 

  • Laffont JJ, Tirole J (1991) Privatization and incentives. J Law Econ Org 7(Special issue):84–105

    Google Scholar 

  • Metschies GP (2003) International fuel prices 2003, 3rd edn. www.internationalfuelprices.com

  • Metschies GP (2005) International fuel prices 2005, 4th edn. www.internationalfuelprices.com

  • Meeusen W, van dan Broeck J (1977) Efficiency estimation from Cobb–Douglas production functions with composed error. Int Econ Rev 18(2): 435–444

    Article  Google Scholar 

  • Pitt M, Lee LF (1981) The measurement and sources of technical inefficiency in the Indonesian weaving industry. J Dev Econ 9: 715–723

    Article  Google Scholar 

  • Ruggiero J (2007) A comparison of DEA and stochastic frontier model using panel data. Int Trans Oper Res 14(3): 259–266

    Article  Google Scholar 

  • Schmidt KM (1996) The cost and benefits of privatization: an incomplete contracts approach. J Law Econ Org 12(1): 1–24

    Google Scholar 

  • Schmidt P, Sickles RC (1984) Production frontiers and panel data. J Bus Econ Stat 2(4): 367–374

    Article  Google Scholar 

  • Shephard RW (1953) Cost and production functions. Princeton University Press, Princeton

    Google Scholar 

  • Tran KC, Tsionas EG (2009) Estimation of nonparametric inefficiency effects stochastic frontier models with an application to British manufacturing. Econ Model 26: 904–909

    Article  Google Scholar 

  • Tsionas EG (2003) Combining DEA and stochastic frontier models: an empirical Bayes approach. Eur J Oper Res 147: 499–510

    Article  Google Scholar 

  • Villalonga B (2000) Privatization and efficiency: differentiating ownership effects from political, organizational, and dynamic effects. J Econ Behav Org 42: 43–74

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. James A. Baker III Institute for Public Policy, Rice University, Houston, TX, USA

    Stacy L. Eller & Kenneth B. Medlock III

  2. Department of Economics, Rice University, Houston, TX, USA

    Peter R. Hartley

Authors
  1. Stacy L. Eller
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Peter R. Hartley
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Kenneth B. Medlock III
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Peter R. Hartley.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Eller, S.L., Hartley, P.R. & Medlock, K.B. Empirical evidence on the operational efficiency of National Oil Companies. Empir Econ 40, 623–643 (2011). https://doi.org/10.1007/s00181-010-0349-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00181-010-0349-8

Keywords

JEL classification codes

Search

Navigation