Changes in the Operational Efficiency of National Oil Companies

1. Changes in the Operational Efficiency of National Oil Companies Peter Hartley George & Cynthia Mitchell Professor, Economics Department, Rice University,Rice Scholar in Energy Studies , James A Baker III Institute for Public Policy and Kenneth B. Medlock III James A Baker, III and Susan G Baker Fellow in Energy and Resource Economics& Deputy Director, Baker Institute Energy Forum andJames A Baker III Institute for Public PolicyAdjunct Assistant Professor, Economics Department, Rice University

2. Overview • We examine asample of firms that includes fully government owned National Oil Companies (NOCs), partly government owned national oil companies (pNOCs) and shareholder owned oil companies (SOCs) • We reaffirm previous evidence that NOCs and pNOCS tend to be less revenue efficient than SOCs • Excessive employment and retail subsidies are again found to be significant causes of the reduced revenue efficiency of NOCs and pNOCs • We also find that partial privatization, and mergers and acquisitions, are likely to increase efficiency • Also, while oil and gas firms as a whole tended to become more efficient at producing revenue from 2001–09, NOCs and pNOCs on average improved more than SOCs

3. Data • Primary source “Ranking the World’s Oil Companies” by Energy Intelligence • Company annual reports also used to check and revise, and provide missing, data • We began with almost 150 firms but the methods require a balanced panel • This constrained both the number of years and the number of firms • The inputs and revenues of merger partners were combined in years prior to a merger • Nippon Oil was dropped as an outlier in specialization in downstream activities • Ultimately, the data set covered 61 firms for the period 2001-2009 • We added data on oil and natural gas prices from the EIA and average retail fuel prices from the Metschies surveys

4. Model • We focus on revenue technical efficiency for several reasons: • The different products sold are most naturally aggregated using prices • Political pressure is likely to force a NOC to subsidize sales to domestic consumers • Revenue is a key objective for both public and private firms • For many firms, revenue figures are more readily available than physical outputs of different commodities • Crude or natural gas output: Q = F(L)×Rsv×G(E), where E = cumulative output • Marketed products: R = H(K,L,Q) • Revenue is then p(1–s)R for a vector of product prices p and corresponding percentage subsidies s for each component of R

5. Variables used in the analysis • Rev = Revenue in $ million • Emp = Number of employees • OilRsv = Oil reserves in millions of barrels • NGRsv = Natural gas reserves in billions of cubic feet • Refcap = Refining capacity in thousands of barrels per day • Oilp = Average US import oil price, average OPEC and non-OPEC oil prices (in $/barrel) • NGp = Henry Hub, Japan LNG import prices, EU pipeline and LNG import prices • year = 1, 2, … 9 for 2001–2009 respectively • GovSh = Government ownership share; GovSh+ = I(GovSh > 0) • VertInt = Vertical integration measure (product sales/liquids production both in thousands of barrels per day) • Premerge = 1 in the years before firms merged, 0 in years following a merger; 0 for non-merging firms • RetSubs = percentage deviation of gasoline and diesel retail prices in the headquarter country below the average US retail prices; 0 if average retail prices are greater than or equal to US retail prices

6. Methods overview • We use two methods to calculate revenue efficiency: data envelopment analysis (DEA) and stochastic frontier analysis (SFA) • DEA is non-parametric but does not allow for measurement error or other sources of variation across firms unrelated to inputs or relative efficiency • SFA is parametric, permitting a structural interpretation of the results • However, if the assumptions, including those relating to the structure of the error terms, are invalid the interpretation may be misleading • SFA also allows for other sources of random variation and tests of goodness of fit • The aim is not to compare the methods but to check robustness of the results

7. Annual DEA efficiency scores

8. Some observations • The efficiency scores for most of the firms increased over the nine-year period • Notable exceptions among SOCs: Occidental, Chesapeake, BG, EOG, CNR, Devon, Talisman, Noble and Plains • PDV from Venezuela followed a rising then falling pattern ending with a lower score in 2009 than any earlier year • Partially privatized PDO from Oman also follows a rising then falling pattern • TNK starts with a score of 0.077, jumps to 0.78 when TNK-BP is formed in 2003, stays on the frontier until 2007, but then drops back to 0.73 in 2009 • Like the major IOCs, the most efficient pNOCs – Statoil-Hydro, Sinopec and PTT – are on the frontier every year • ENI, CNOOC and Petrobrasgenerally improve with ENI and CNOOC attaining the frontier • Saudi Aramco is on the frontier from 2005–09, Sonangol from 2004–07, QP in 2009 and Kuwait’s KPC in 2007

9. Average DEA scores by category • Trend in SOC scores is not statistically significantly different from zero • Both NOC and pNOC scores trend up at the same rate of around 0.015/year • NOC and pNOC scores vary more year to year, partly because a larger fraction of SOCs have a maximum score of 1 each year

10. Panel Tobit models of DEA scores • Government ownership and time: • After allowing for firm-specific effects (> 80% of residual variance), partial government ownership has the same effect as full ownership • NOCs and pNOCs start with 30% lower DEA scores in 2001, but by 2009 their scores are only 8% lower • Allowing for retail subsidies, efficiency changes from mergers, and an effect of vertical integration we find:

11. Technical change measures

12. Comments on technical change measures • The average of all technical change measures across years and firms is 1.129, implying that on average the frontier expanded over the decade • The average measure across firms exceeded 1 for all pairs of years except 2001–2002 • Years 2006–07 and 2008–09 also showed somewhat weaker technical progress • 2002–03 had highest average measure, although only in 2004–05 were all measures greater than 1 • Across all years, the average technical change measure for SOCs is 1.141 compared to 1.123 for NOCs and 1.111 for pNOCs • With the exceptions of CNOOC and PDO in 2008–09, all the large technical change expansions, and the majority of the frontier contractions, occur for SOCs

13. Comments on technical change measures • Measures for SOCs vary more than measures for pNOCs and NOCs • Among the five SOCs that remain on the frontier every year: • ExxonMobil stands out with technical change measures very close to 1 in all years, although the measures for BP are only slightly more dispersed • Wintershalland BHPBilliton have the largest dispersions in technical change • The dispersion in technical change measures for Marathon is intermediate • Among the pNOCs on the frontier every year (StatoilHydro, Sinopec and PTT) • Dispersions in StatoilHydroand PTT measures are quite similar to those of Marathon • Except for 2006-07, the measures for Sinopec are about as dispersed as those for ExxonMobil, but their average is substantially above 1, implying that Sinopec had to make changes to remain on the frontier each year

14. Panel regression on Malmquist total productivity change measures • Mergers and a reduction in retail fuel subsidies both raise total productivity • The insignificance of ΔVertInt and the GovSh measures is consistent with the DEA results

15. Stochastic frontier model 1 • For a Cobb-Douglas production function, log of revenue will depend linearly on the logs of the input variables and the log of prices • Error terms:vitand ui are distributed independently of each other and the regressorswith estimated values μ = 1.506 (0.2657), η= 0.0291 (0.0047), (0.0888) and (0.0031) so more than 89% of the variation in the composite error term is due to the one-sided systematic efficiency differences or other sources of firm heterogeneity

16. Efficiency measures compared Panel Tobit regression:

17. SFA model 2 • We would expect ln(1–s) to appear in the revenue function • Relative inefficiency of NOCs is also likely to be manifest in reduced labor productivity, or an interaction between GovSh and Empin the revenue function • We also allowed for a structural model of the one-sided inefficiency error term using the specification of Battese and Coelli (1995) whereby the mean of the firm-specific inefficiency measures uitdepends on firm-specific covariates zlitwhere wit is a truncated normal with mean zero and variance such that the point of truncation is wit ≥ –zitδ • We allowed GovSh, Premergeand VertIntas potential z variables • The non-efficiency error component vitis iid and independently distributed from the uit, which also are independently distributed from each other

18. SFA model 2 estimates • The best estimated modelwith estimated error structureand with

19. Conclusions • The two inefficiency measures were highly positively correlated • Retail subsidies were a major source of reduced efficiency for many NOCs and pNOCs • Government ownership also tends to produce a lower productivity of labor • Partial privatization reduced this effect on average, but it did not decline over time • There was also an additional residual negative impact of government ownership that was not affected by partial privatization and disappeared over the decade • The DEA and Malmquist analyses found evidence that mergers tended to raise the efficiency of the merging firms, but we did not find a similar effect in the SFA • SFA, but not DEA, found that higher final product sales/liquids production was a significant source of firm heterogeneity • More variable technical change measures for SOCs may indicate more innovation occurs in such firms • The DEA and Malmquist analyses provided some interesting details on the relative efficiencies and efficiency changes of particular firms over the decade

20. Additional slides

21. Contributions to dominating composite firms