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Measurement of the efficiency and productivity ofnational oil companies and its determinantsChidi Basil Ikea & Hyunjung Leeb

a Department of Petroleum Resources, 7 Kofo Abyomi Street, Victoria, Lagos, Nigeriab Asian Development Bank, 6 ADB Avenue, Mandaluyong 1550, Manila, PhilippinesPublished online: 24 Mar 2014.

To cite this article: Chidi Basil Ike & Hyunjung Lee (2014) Measurement of the efficiency and productivity of national oilcompanies and its determinants, Geosystem Engineering, 17:1, 1-10, DOI: 10.1080/12269328.2014.887045

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Measurement of the efficiency and productivity of national oil companies and its determinants

Chidi Basil Ikea1 and Hyunjung Leeb*

aDepartment of Petroleum Resources, 7 Kofo Abyomi Street, Victoria, Lagos, Nigeria; bAsian Development Bank, 6 ADB Avenue,Mandaluyong 1550, Manila, Philippines

(Received 27 September 2013; accepted 17 January 2014)

Most countries, both oil-producing and oil-consuming countries, have established national oil companies (NOCs) that aresaddled with the responsibility of protecting and managing their respective governments’ interests. As NOCs’ productioninfluence and market power have risen, they become major players competing with international oil companies (IOCs) in theglobal petroleum market. However, the performance of NOCs is considered lower than IOCs because of the perceivedinefficiency due to the structure of the petroleum industry, government regulation and policy, and government’s otherinterests through NOCs. Since oil exports account for a majority of their foreign incomes in most oil-rich developingcountries and those incomes are the indispensable resource for the country’s economic and social development, it is criticalto find a way to improve the performance of NOCs. Against this backdrop, we measured the relative efficiency andproductivity of 38 NOCs and IOCs in total, which belong to the world’s largest 50 oil companies in the period of 2003–2010sourced from the Energy Intelligence Petroleum Industry Weekly. In this process, the data envelopment analysis method wasused, which enables the comparison of a firm’s performance with those of other firms relatively. In addition, random-effectsregression model was used for the second stage analysis on the environmental factors which influence the efficiency andproductivity level. The empirical results showed that Organization of the Petroleum Exporting Countries NOCs are the lowperformers while big IOCs are the high performers. Based on secondary analysis, specific policies were suggested such asreviewing the size and percentage of its government ownership in NOCs, diversifying its crude oil and gas export supplymarket, reducing the level of government interference in its technical management, and granting greater autonomy to itssubsidiaries.

Keywords: efficiency; petroleum industry; NOC; IOC; data envelopment analysis

1. Introduction

The petroleum industry is very crucial today because of the

major role of oil and gas in the world’s energy mix. The

industry is technologically driven and requires constant

upgrade and advancement to gain competitive advantage.

The industry is plaguedwith huge capital investment and risk

throughout the value chain, starting from exploration to the

refining and marketing of the products. These have

contributed to the separation of the industry players into a

small number of large-size operators and numerous small-

size service providers. The emergence of cartels such as

Organization of the Petroleum Exporting Countries (OPEC)

has also shaped theway the industry is structured. According

to the OPEC Statute (2008), its principal aim is the

coordination and unification of the petroleum policies of its

member countries and the determination of the best means

for safeguarding their interests, individually andcollectively.

Due to this and by controlling the major share of the world’s

petroleum reserves and production, OPEC has made a

significant influence on the world petroleum market.

The operators, the ones popularly referred to as oil

companies, play the most important roles in the industry.

They design the business model, provide directions,

coordinate activities, procure oil fields, engage in

negotiations, and come up with strategies. The operators

can be divided into two groups such as the international oil

companies (IOCs) and national oil companies (NOCs).

NOCs are oil companies that are totally or partially owned

and controlled by the government while the IOCs are large

private oil companies operating in more than one country.

The two groups jointly hold a substantial amount of the

world’s petroleum reserves, production, and marketing.

Most of the existing IOCs were founded in the late

nineteenth and early twentieth centuries, the early period

of expanding oil discovery and utilization. Prior to the

1970s, there were very few NOCs in existence, and the

IOCs controlled and dictated the pace of the world’s

petroleum market. The major IOCs used to collectively

hold a large share of the world’s petroleum market.

However, their shares continued to decline with time,

especially with the nationalization policies of the oil-

producing states and the subsequent emergence of NOCs.

As the production influence and market power of

NOCs have risen, they become major players in the global

petroleum market competing with IOCs. With the wave of

privatization of state-owned enterprises in the last two

decades, however, some NOCs have now become partially

owned by the government. More recently, not only have

q 2014 Taylor & Francis

*Corresponding author. Email: hjlee@adb.org

Geosystem Engineering, 2014

Vol. 17, No. 1, 1–10, http://dx.doi.org/10.1080/12269328.2014.887045

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NOCs risen to protect their respective national reserves

and policies, but there have been growing collaborations

between NOCs to explore and produce petroleum in what

seems to be the sidelining of the IOCs. This may have

partly led to the different patterns of investments among

the IOCs. Jaffers and Soligo (2007) noted that the “big

five” IOCs, despite having larger access to operating cash

flow, continued to see a decline in their exploration

expenditure from 1996 to 2005, compared with the 20 next

privately traded American oil companies; this implies that

after a while, a large percentage of the non-OPEC oil

production being held by the big five will drop drastically.

It is believed that there has been a continuous decrease in

trust between the IOCs and their host countries due to

information asymmetry, thereby making the IOCs’ host

nations more receptive to other NOCs, reducing the risk

and associated cost through NOC-to-NOC cooperation.

NOCs from oil-consuming countries and even in some oil-

producing countries are now bidding for oil reserves and

signing agreements with other IOC host countries, cutting

across the entire value chain of the petroleum industry,

which also includes the oil-refining business and other

downstream activities.

With the increasing dominance of the NOCs in the

petroleum market, their efficiency and productivity are put

into question. Some of the earlier studies have shown that

the NOCs are less efficient than the IOCs, especially the

ones owned by oil-producing countries. This is especially

important not only as a result of revenue losses, but there is

also a concern about the dearth of expertize in the industry

as it is believed that the IOCs have always provided the

technical and managerial competence needed in the highly

sophisticated industry. Al-Obaidan and Scully (1991)

found that ceteris paribus, state firms (NOCs) could satisfy

the demand for their output with something less than half

of their current resource inputs simply by being converted

into private enterprises. As such, the performance of

NOCs could affect the industry performance as a whole as

well. In addition, oil exports account for a majority of their

foreign incomes in most oil-rich developing countries and

those incomes are the indispensable resource for the

country’s economic and social development. Therefore,

NOCs’ performance can influence the national competi-

tiveness and well-being.

Against this backdrop, we measured the relative

efficiency and productivity of 38 NOCs and IOCs in total

among the world’s largest 50 oil companies in the period of

2003–2010 sourced from the Energy Intelligence Petroleum

IndustryWeekly (PIW). In this process, the data envelopment

analysis (DEA) method was used, which enables the

comparison of a firm’s performancewith those of other firms

relatively. In addition, random-effects regression model was

used for the second stage analysis on the environmental

factors which influence the efficiency and productivity level.

Based on those results, specific policies were suggested.

2. Literature review

The existing literature on the DEA application to the

measurement of the efficiency and productivity of firms or

units are quite large and comprehensive, but the literature

on the study of NOCs and IOCs are relatively very scarce.

Nevertheless, those studies generally have shown that IOCs

are more efficient than NOCs, which is partly attributed to

the fact that most NOCs have some other objectives other

than profit maximization (see, for example, Al-Obaidan &

Scully, 1991; Eller, Hartley, & Medlock III, 2011; Victor,

2007). These objectives include securing national energy

reserves, creating employment, providing subsidized

products for the citizens, and other politically motivated

reasons. These reasons make it imperative for NOCs to

continue to exist in the nearest future. This is against the

insinuation that countries may abandon the idea of having

an NOC due to the observed inefficiency associated with it.

One of the very first attempts to compare the efficiency

and productivity of IOCs and NOCs was carried out by Al-

Obaidan and Scully (1991), when they estimated the

technical (managerial), scale, and allocative efficiency

differences between 44 private and state-owned firms in

the international petroleum industry. In testing the

property rights theory, they employed the parametric

approach,2 and their empirical findings suggest, ceteris

paribus, that state-owned firms (NOCs) can satisfy the

demand for their output with something less than half of

their resource inputs, simply by being converted into

private, for-profit enterprises (IOCs). Their sample

companies, however, were basically firms that were

vertically integrated and did not include some important

and influential NOCs from oil-producing countries that

hold and produce a substantial amount of the world’s oil

reserves and production, respectively, like Saudi Aramco.

More recently, some literature on the behavior of NOCs

and model for evaluating NOCs have appeared, noting the

complex issues facing NOCs, even more complex than

those facedbyother state-owned companieswithin the same

government (Frankel, 1978; Hartley & Medlock III, 2008;

Stevens, 2008). This is related to the primary necessity and

high importance of oil for both the oil-producing and oil-

consuming countries. Oil-producing countries concern

aboutmaximizing their benefits from their natural resources

while oil-consuming countries care about their energy

supplies and security, among other issues.

To add more insight into the related literature, Wolf

(2009) investigated the existence of ownership effects in

the global oil and gas industry – whether there are

systematic performance and efficiency differentials

between NOCs and IOCs over the period 1987–2006 –

using multivariate regression analysis. Grouping the

companies according to four distinct ownership types and

using four indicators, the study concluded that “ownership

matters” (i.e. private ownership encourages better per-

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formance and greater efficiency than state ownership

does).3 Wolf also found that NOCs and the OPEC NOCs in

particular produce a much lower annual percentage of

upstream reserves than the private sector. He attributed this

to the more conservative depletion policy by the respective

companies, whether intentional or not. To date, only Eller

et al. (2009) have used the DEA method to evaluate the

efficiency differences between IOCs and NOCs and

obtained consistent results with another method. However,

they used only three-year data (2002–2004), and their

analysis focused on only two groups: the NOCs and IOCs.

In our study, we expanded the time horizon and divided

groups considering OPEC membership, with which we

were able to draw important policy implications.

3. Methodology: DEA

The DEA method allows for multiple inputs and outputs to

be aggregated and is used as one measure: the output-to-

input ratio, a common measure of efficiency. The method

has now become widely used for the evaluation of the

efficiency and performance of decision-making units

(DMUs) since its introduction in the 1970s. Farrell (1957)

made foundations for the measurement of productive

efficiency. Then, Charnes, Cooper, and Rhodes (CCR)

(1978) came up with the first published article describing

the method and labeling the approach “DEA” and their

model is namedCCRmodel. Since the CCRmodel assumes

the constant returns to scale (CRS), in 1984, Banker,

Charnes, and Cooper (BCC) expanded this model for

variable returns to scale, which is named as BCC now.

Among other extensions is the slacks-basedmethod (SBM),

which is used in this research. The CCRmodel is described

as follows. Let X, an (m £ n) matrix, and Y, an (s £ n)

matrix, denote the input and output matrices for the DMUs.

X ¼

x1;1 x1;2 · · · x1;n

x2;1 x2;2 · · · x2;n

: : : :

: : : :

xm;1 xm;2 · · · xm;n

2666666664

3777777775; ð1Þ

Y ¼

y1;1 y1;2 · · · y1;n

y2;1 y2;2 · · · y2;n

: : : :

: : : :

ys;1 ys;2 · · · ys;n

2666666664

3777777775; ð2Þ

where m and s are the number of inputs and outputs,

respectively, and n is the number of DMUs in the sample. If

the input/output data of n DMUs with m and s inputs and

outputs, respectively, are arranged as in Equations (1) and

(2), the efficiency of each DMU can be evaluated using n

optimizations for each of them. Let the efficiency of DMU j

be evaluated by an optimization based on n several trials.

Let the trial be designated as DMUj, where j ranges over 1

through n. Equations (3)–(6) outline the fractional

programming problem to obtain values for the input

weights (vi; i ¼ 1; . . . ;m) and the output weights

(ur; r ¼ 1; . . . ; s).

maxv;u

u ¼ u1y1j þ u2y2j þ u3y3j þ · · ·usysj

v1x1j þ v2x2j þ v3x3j þ · · ·vmxmj: ð3Þ

Subject tou1y1j þ u2y2j þ u3y3j þ · · ·usysj

v1x1j þ v2x2j þ v3x3j þ · · ·vmxmj# 1

ð j ¼ 1; . . . ; nÞ;ð4Þ

v1; v2; . . . vm $ 0; ð5Þu1; u2; . . . us $ 0: ð6Þ

The constraints are added to ensure that the ratio of the

output to the input does not exceed 1 for every DMU. The

objective is to obtain weights that maximize ratio u of

DMUj. The optimal value of u, u* is at most 1. The

fractional programming of Equations (3)–(6) can be

converted into the equivalent linear programming, as in

Equations (7)–(11).

maxv;u

u ¼ u1y1j þ u2y2j þ u3y3j þ · · ·usysj: ð7Þ

Subject to v1x1j þ v2x2j þ v3x3j þ · · ·vmxmj ¼ 1; ð8Þ

u1y1j þ u2y2j· · ·usysj # v1x1j þ v2x2j þ · · ·vmxmj

ð j ¼ 1; . . . ; nÞ;ð9Þ

v1; v2; . . . vm $ 0: ð10Þu1; u2; . . . us $ 0; ð11Þ

ðu*; v*Þ was obtained as an optimal solution for the linear

programming results in a set of optimal weights for DMUj.

The ratio scale is evaluated using the following equation:

u* ¼ Ssr¼1u

*r yrj

Smi¼1v

*i x1j

; ð12Þ

ðu*; v*Þ is the set of most favorable weights for DMUj

as it maximizes u*. The value of u* is independent of the

units in which the inputs and outputs are measured

provided these units are the same for every DMU. This is

known as “unit invariance.” An evaluated DMU is CCR-

efficient if u* ¼ 1 and if there exists at least one optimal

ðu*; v*Þ, with u* . 0 and v* . 0. Otherwise, the DMU is

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CCR-inefficient, and the value of u*, which is less than 1,

reflects the level of relativity to the efficient DMUs.

In matrix form, the dual-problem linear programming

of Equations (7)–(11) is expressed with a real variable, u,and with the non-negative vector l ¼ ðl1; . . . ; lnÞTof thevariables, as follows:

minu;l

u: ð13Þ

Subject to uxj 2 xl $ 0; ð14Þ

Yl $ yj; ð15Þ

l $ 0: ð16Þ

When the optimal value of u, u* is less than 1,

ðXl; YlÞ outperforms ðuxj; yjÞ. Due to this, the input

excesses and the output shortfalls can be defined as s2 [Rm and sþ [ Rs, respectively, and can be said to be the

slack vectors as below. The input excesses and output

shortfalls can be obtained by solving another linear

programming as a next stage. In addition to the previous

condition for a DMU to be CCR-efficient, all the slacks

must be zero for that DMU.

s2 ¼ uxj 2 xl& sþ ¼ Yl2 yj: ð17Þ

The slacks-based model (SBM) is a non-radial

augmented additive model that incorporates the unit

invariance property. As such, the efficiency measure is

independent of the units in which the inputs and outputs are

measured, provided these units are the same for every DMU

in the sample. SBM was introduced in response to the

shortfall of the radial models. The advantage of the non-

radial or slacks-based model over the radial DEA model is

that the non-zero slacks are not neglected in the direct

computation of the efficiency scores; uSBM incorporates all

the input excesses and output shortfalls, as indicated by

Equation (17), therefore, requiring no additional solution

stage, as in the case of the radialmodels.When the slacks are

freely disposable and are not pronounced, however, this

advantage is diminished.4 SBM is important because the free

disposability of the slacks is rarelypracticable in applications

with real data. uSBM is always less than uBCC or uCCR, and assuch, it is more conservative in assigning efficiency scores

and status to DMUs. This is because of the additional effect

of the slacks considered in the computation. To calculate the

SBM efficiency, the following mathematical program was

formulated:

minx;s2;sþ

uSBM ¼ 12 ð1=mÞSmi¼1ðs2i =xijÞ

1þ ð1=sÞSsr¼1ðsþr =yrjÞ

: ð18Þ

Subject to xj ¼ Xlþ s2; ð19Þ

yj ¼ Yl2 sþ; ð20Þ

l $ 0; s2 $ 0; sþ $ 0; ð21Þwhere l; s2; sþ, m, n, and other variables are as earlier

defined. SBM efficiency, as defined by the program,

measures the product of the input and output efficiencies.

To measure the output-oriented SBM, the numerator of

Equation (18) is removed, s2 is neglected, and Equation

(19) is reduced to xj $ Xl. Also, for the input-oriented

SBM, the denominator of Equation (18) is removed, sþ is

neglected, and Equation (20) is reduced to yj # Yl.For the productivity measurement, this research used

the DEA Malmquist index, which is a combination of the

DEA method and the widely known Malmquist index. The

DEA productivity measurement is a product of two

effects: the catch-up and frontier shift effects.

MIcatch-up¼EfficiencyofX2withrespect toperiod2frontier

EfficiencyofX1withrespect toperiod1frontier

¼ ðZB=ZX2ÞðYD=YX1Þ :

ð22ÞThe catch-up index measures the relative change in the

efficiency scores of the two periods. A catch-up index

value of greater (less) than 1 signifies progress (regress)

while an index value of 1 signifies a no-change status from

period 1 to period 2.

The frontier shift indexmeasures the effect of the change

in the reference frontier of the evaluated DMU fromperiod 1

toperiod2.That is, the technologyor innovation shifts across

the periods, as can be seen in the evaluated DMU. For

instance, reference point D of X1 at period 1 moved to point

A at period 2.Also, reference pointB ofX2 at period 2moved

from point E at period 1. Again, in the case of Figure 1, the

frontier shift effect of X at period 1 is given as

MI frontier 1 ¼ YD

YA: ð23Þ

On the other hand, the frontier shift effect of X at

period 2 is given as

MI frontier 2 ¼ ZE

ZB: ð24Þ

Equations (26) and (27) are combined to give one

frontier shift index of DMU X for the two periods, as

follows:

MI frontier ¼ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiMIfrontier 1 £MIfrontier 2

p: ð25Þ

A frontier index value of greater (less) than 1 signifies

progress (regress) in the frontier technology around DMU

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X, while an index value of 1 signifies a no-change status

from period 1 to period 2.

Finally, the DEAMalmquist index, uMI, is a product of

the two indices, as follows:

uMI ¼ MIcatch-up £MIfrontier

¼ ZB=ZX2

YD=YX1

� �£ YD

YA£ ZE

ZB

� �1=2: ð26Þ

In general, a Malmquist index of greater (less) than 1

signifies progress (regress) in the productivity of a DMU,

while an index value of 1 signifies no change in

productivity from one period to another. The Malmquist

index can be computed using the different DEA models

(radial or non-radial; output-, input-, or non-oriented, etc.).

4. Analysis and results

This study used the data for 38 oil and gas companies

primarily sourced from the PIW ranking of the world’s 50

largest oil companies for the operational years 2003–

2010. PIW’s ranking is based on operational data from

over 130 firms. The 38 firms in the sample were chosen

because they appeared consistently throughout the 8-year

study period, and because they had complete data,

as reported by the Energy Intelligence Group. PIW’s

unique system uses oil and gas reserves, oil and gas

production, refinery distillation capacity, and product sales

volume as ranking criteria. The data available for each

company per period are the oil production (0000 b/d), gas

production (MMcf/d), oil reserves (million Bbl), gas (Bcf),

distillation capacity (0000 b/d), product sales (0000 b/d),

revenue (US$ million), net income (US$ million), total

assets (US$ million), employees, country in which the

company is located or headquartered, and government

ownership percentage (%).

4.1 Selection of input and output variables

Three input variables and two output variables were

selected. Oil reserves, gas reserves, and number of

employees were chosen as the inputs. The number of

employees represents the labor input, and as stated by Eller

et al. (2009), the reserves remain a substantial part of the

total assets of most oil and gas firms and are likely to be

measured much more accurately than the other assets.

It was also noted in their study that reserves were used as a

proxy for capital primarily because data on total assets were

not available formanyNOCs, especially theOPECmember

countries.

It is common to use profit and/or revenue as a measure

of the performance output. Given that most NOCs are

forced to subsidize petroleum products and to carry out

other social tasks that may affect their revenue/profits,

financial figures are not used as indicators. Also, the

primary concern of this research was not to capture the

effect of directing NOCs to carry out social responsibilities

such as providing subsidy. The main concern here was to

see how efficient companies are in converting their input

resources into the maximum obtainable outputs, using the

best practice. Therefore, oil and gas production were

chosen as outputs. Physical output quantities are also more

likely to be measured and reported more correctly by most

companies compared to financial figures.

4.2 Selection of DEA method

The DEA SBM method was adopted so that the non-zero

slacks will be fully accounted for. In the petroleum

industry, where the operational costs and other associated

expenditures of companies vary across countries and

locations, it is more appropriate to evaluate the efficiency

differences using the output-oriented model (i.e. observing

the ability of firms to increase their current output to the best

possible level, given their input resources). TheCCRmodel

was selected for the computations because the frontiers are

most likely to exhibit CRS in the petroleum industry.

Therefore, the output-oriented SBM-CRS was used to

calculate the static DEA scores for each year. The same

assumptions were also applied for the computation of the

Malmquist index, which allows for dynamic efficiency

measurement. SAITECH DEA-Solver (professional ver-

sion 8.0) was used to compute the DEA results.

4.3 Categories of companies

For comparison purposes, the companies were divided into

four categories, namely (i) category 1: OPEC NOCs –

NOCs owned by the governments of the OPEC member

countries; (ii) category 2: NOPEC NOCs – NOCs not

owned by the governments of the OPEC member

countries; (iii) category 3: IOCs – all companies fully

Figure 1. Trend of the DEA static mean scores for the differentcategories of companies (2003–2010).

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owned by the private sector, except the big six, as listed in

category 4; and (iv) category 4: Big IOCs – the six biggest

IOCs by size, namely BP, Chevron, ConocoPhillips, Royal

Dutch Shell, ExxonMobil, and Total.

4.4 DEA static scores

The relative efficiency of the firms for each year, the

geometric mean of the scores for all the companies, and

the different categories of companies were computed. The

average scores ranged from 0 to 1 as shown in Figure 1.

As expected, the mean score for the big IOCs remained at

the frontier, followed closely by the mean of the IOCs;

their average scores ranged from 0.7 to 0.8. The poor

performers throughout the period of analysis were the

NOCs owned by the governments of the OPEC member

countries; their average scores ranged from <0.3–0.4.

Eller et al. (2009) found the average for only 3 years and

considered only two groups in their analysis5; their

reported DEA scores were 0.73 for the IOCs and 0.28 for

the NOCs. It is interesting to note that their research was

about revenue efficiency, using slightly different input and

output variables, as used in the present study, yet the

results of both studies are similar and are consistent with

the general efficiency hypothesis of NOCs and IOCs.

The present research showed, however, that OPEC

membership has a significant effect on the performance of

the companies. The production quota policy of OPEC may

be responsible for the negative influence of the average

efficiency scores of the OPEC NOCs. This policy implies

that companies are restricted to a certain level of

production even though they may have capabilities to

produce more, given their input resources. Saudi Aramco,

however, which is an OPEC NOC, consistently remained

at the frontier for the whole study period, and performed

relatively much better than the other companies within the

same category, remaining an outlier in the category.

4.5 DEA Malmquist index

DEA Malmquist analysis involves the computation of

three indices: the catch-up, frontier, and Malmquist

indices. Figure 2 shows the mean catch-up index for the

different categories of companies. The catch-up index

explains the magnitude with which companies change in

their efficiency values between two periods.

This negative change in all the categories may be

attributed to the general economic crisis that plagued most

economies during that peak period. On average, except for

the non-OPEC NOCs, which slightly made a positive

catch-up, and the non-OPEC companies that registered no

change, all the other categories of companies regressed in

their efficiency scores from 2008 to 2009. The results also

indicate that the IOCs were the slowest in terms of

recovery, as evident from their average catch-up index of

less than 1.

The frontier index indicates the magnitude of change

in the frontier technology with respect to the company

being evaluated. The averages for the different categories

are shown in Figure 3. With the exception of the two

periods 2004–2005 and 2005–2006, all the categories of

companies reported identical reference frontier technology

changes on average. This implies that there are relatively

similar applications of production technologies and

innovations in petroleum across the world’s oil industry.

The Malmquist index combines the effects of both the

catch-up and frontier index to give the total productive

change over a certain period. The trends for the mean

indices of the different categories of companies are shown

in Figure 4. As on average, the different categories of

companies seem to operate with similar frontier

technologies, a company’s ability to catch up will

dominate the efficiency and productivity change.

Figure 2. Trend of the DEAMalmquist index (Catch-up) for thedifferent categories of companies.

Figure 3. Trend of the DEA Malmquist index (frontier) for thedifferent categories of companies.

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4.6 Environmental effects on efficiency

In order to identify the key factors that influence the

efficiency and productivity level of oil companies,

environmental effect analysis was performed using

econometric analysis. The environmental and external

variables were chosen as government ownership/shares,

the degree of vertical integration of the company, the

human resources factor, the experience of the company,

and the company’s international exposure/operations. For

the model, we used the fixed- and random-effects

regression models. Hoff (2007) claimed that the Tobit

and OLS approaches sufficiently represented DEA second-

stage analysis in a case study for the Danish fishery.

Banker and Natarajan (2008)6 also argued that OLS yields

consistent estimates when the DEA scores are regressed on

the environmental factors. Barros and Dieke (2008)

measured the economic efficiency of airports and

concluded that the truncated bootstrapped second-stage

regression proposed by Simar and Wilson (2011) better

describes the efficiency scores. In this study, the steps

outlined by Dougherty (2007) were followed in selecting

the appropriate panel regression model. First, the

observations can be said to be those of a random sample

from a given population because the 38 companies were

spatially drawn from across the regions of the world and

fairly represent the top and major players in the world’s oil

industry. The Durbin–Wu–Hausman test was carried out

based on which the random-effects model was selected.

Furthermore, the Breusch and Pagan Lagrangian multi-

plier test was carried out to determine the presence of

random effects; it also discriminates between the selection

of a pooled OLS and random-effects regression. The test

results led to the selection of time series random-effects

regression for the analysis. The robust standard errors of

the coefficients were calculated to address the detected

heteroskedastic problem with the data. Equation (27)

shows the regression equation for estimating the effect of

the environmental variables on the efficiency of the

companies, using the random-effects model.

DEAit ¼ aþ b1GOit þ b2VIit þ b3HRit þ b4CAit

þ b5IOit þ 1it; ð27Þ

where DEAit is the yearly DEA score, GOit is the

government share, VIit is the vertical-integration index of

a company, HRit is the human resources factor, CAit is the

age of a company, and IOit is the international operations

(IO) factor. b1;b2; . . .b5 are the respective coefficients

while 1it is the disturbance term, which consists of two

components, as follows:

1it ¼ ui þ vit; ð28Þwhere ui is the unobserved-error effect and vit is the specific-

error term. The usual-error term covariance assumption for

the random-effects model is as follows: CovðXitj; uiÞ ¼0; whereXj; j ¼ 1; 2ðvariablesÞ; t ¼ 1; 2; . . . ; 8 (years) andi ¼ 1; 2; . . . ; 38 (companies).

The results displayed in Table 1 show that the

coefficients of the government share and vertical-

integration variables were statistically significant at a

99% confidence level. The magnitudes of the two

coefficients were practically significant and had the

expected sign, as earlier hypothesized. The constant,

which is a very important parameter in the model, was also

highly significant and had the expected sign. Although

they were statistically insignificant, the three other

variables boosted the R2 and adjusted the coefficients of

the significant variables.

The results imply that government ownership affects

the efficiency of companies negatively, holding other

affecting variables constant. Changing government own-

ership by one unit will affect the DEA efficiency score of a

company by as much as 0.0035 units. Put differently, the

efficiency score of a fully government-owned NOC in

the industry is likely to increase by 0.175 points if the

Figure 4. Trend of the DEA Malmquist index mean for thedifferent categories of companies.

Table 1. Second-stage regression results.

(1) DEA (2) DEA

GO –0.0039 (4.76)** –0.0035 (3.72)**

VI –0.0238 (3.89)** –0.0254 (3.79)**

HR – 0.0114 (0.12)CA – 0.0028 (1.57)IO – –0.0020 (1.05)Constant 0.8815 (16.77)** 0.7490 (5.36)**

R 2 overall 0.2557 0.2950Observations 304 304No. of coy 38 38

Note: *5%; **1%.

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company is partially privatized by divesting 50% of the

government share to private holdings.

The interpretation of the VI variable is similar to that

of the GO variable. The impact of VI, however, is

dominated by GO but can significantly affect the

efficiency of a company. The value of the VI variable in

the sample ranged from 0 to 8.69.7 Based on the results,

increasing the VI status of a company by one unit will

likely reduce the company’s efficiency value by 0.0254.

This implies that becoming more downstream-(upstream)-

oriented in the industry will likely reduce (increase) the

efficiency of a vertically integrated oil company. As earlier

explained, this may be due to the too many objectives8 of

oil companies engaged in downstream activities, which

can limit their production abilities and can also lead to

overstaffing, among others, which will make them appear

inefficient.

Based on the equation and value of the constant, themost

efficient companies are those that are fully privately owned

and are purely upstream-oriented. In other words, the

maximumobtainableDEA score is attainedwhenGOandVI

are both 0; this score corresponds to the value of the constant

(0.7490). This corresponds to the average DEA score of the

big IOCs, as earlier found.On theother hand, the less efficient

companies are those that are fully owned by the government

and that tend to have more downstream activities.

The coefficient of HR is positive, signifying that easier

access to higher quality manpower enhances performance.

The oil companies do not have control over this effect,

which can be attributed to the education sector of the larger

economy where the company is located or headquartered.

Many NOCs, especially the OPEC NOCs, suffer from the

problem of low HR value more than the IOCs do. To

overcome this challenge, onemay source the required skills

from foreign countries, if necessary (e.g. employment of

foreign expatriates by some OPEC NOCs).9 The study

results also indicate that the number of years that a company

has been in operation also influences the company’s

performance positively. This is consistent with the

hypothesis regarding tapping from experience within an

organization (Stuart & Podolny, 1996).Most of the existing

IOCs have a very long history compared to theNOCs. Some

of them have been in existence for more than a decade and

have already established a pool of managerial and technical

experts. In fact, most of the OPEC NOCs were created in

around the 1970s, and continued to rely on IOC expertize

for a long time before building their own capacities. This

CA variable helps explain the advantage enjoyed by some

companies, especially the big IOCs. The coefficient of IO

appears as unimportant but useful for control in the model,

obviously due to the non-regular and curvilinear

hypothesis.

5. Policy implications and conclusion

Based on the study findings and analysis, we draw several

policy implications as follows. First, government owner-

ship is a key factor in determining the level of efficiency of

oil companies. Higher government ownership breeds

inefficiency, as in the case of the OPEC NOCs. The oil

industry is characterized by high risks and uncertainties as

well as huge capital requirements, and thus requires

adequate long-term planning and managerial stability.

Wolf and Pollitt (2008) found that privatizing NOCs

improved their efficiency and revenue. This can help

reduce or minimize bureaucracies that serve as obstacles to

the decision-making process associated with government

enterprises. New ideas, good management practices, and

assuming responsibilities are the elements that can be

brought in by the private sector to increase efficiency and

productivity.

Vertical integration is another important strategy for

oil companies. Al-Moneef (1998) noted that the scramble

for markets to sell their crude oil and gas has driven NOCs

to become fully integrated companies. Another reason for

vertical integration by oil companies is to expand their

reach and to gain more profit. Therefore, it seems that it is

good for oil companies to be vertically integrated. Our

study, however, showed that a higher degree of vertical

integration tends to reduce a firm’s production efficiency.

As NOC-to-NOC cooperation is becoming increas-

ingly popular, this strategy can be adopted to gain more

experience and know-how from other oil companies

with similar mandates. The main issue here lies in the

quality of skills associated with expatriate workers.

Therefore, it is imperative to rapidly and continually

develop the skills of the employees of NOCs to make up

for the deficiencies that may exist due to the lack of

expatriates in the company.

Another point of difference is on the issue of

managerial independence. Most NOCs from resource-

rich countries are characterized by excessive government

control. Hertog (2010), however, provides possible

reasons for the moderate and lower government inter-

ference in the NOCs of some Gulf states, including Qatar

and Saudi Arabia. The present chief executive of Saudi

Aramco assumed office in 2009, taking over from his

predecessor, who stayed in that position from 1995 to

2009. The stability of an NOC’s management is required

to provide consistent policy directions and implemen-

tation. This involves working toward minimal interference

from political interests and control over the company’s

business decisions. Much difference can be made in the oil

industry if long-term decisions will be made based on

economic rather than political reasons, which can yield

more efficient results.

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Diversifying crude oil and gas supplies to different

regions is necessary to reduce the effect of petroleum

market volatility. Apart from this, which can be achieved

via vertical integration, deliberate policies toward product

diversification are required. Also, new countries are

joining the league of petroleum-producing countries,

thereby providing stiffer market competition for supplies.

Policy strategies that will address these concernswill greatly

enhance the future stability and efficiency of NOCs.

As such, the enumerated policy implications indicate

that the efficiency and productivity of NOCs can be

enhanced by addressing some of its specific policy issues,

such as reviewing the size and percentage of its

government ownership, granting greater autonomy to its

subsidiaries, diversifying its crude oil and gas export

supply market, rapidly and continually developing the

skills of its employees, and reducing the level of

government interference in its technical management.

Acknowledgement

This research was conducted while Dr Hyunjung Lee was onspecial leave from the Asian Development Bank and worked asVisiting Professor in the International Energy Policy Program ofSeoul National University in Korea.The views expressed in this paper are those of the authors and donot necessarily reflect the views and policies of the Departmentof Petroleum Resources, Nigeria and the Asian DevelopmentBank or its Board of Governors or the governments theyrepresent.

Notes

1. basilike@gmail.com2. They estimated the Aigner–Chu deterministic frontiers,

maximum likelihood stochastic frontiers, and maximumlikelihood gamma frontiers in the analysis.

3. The four groups were “own-1” (fully-state-owned NOCs,with no less than 100% public ownership); “own-2”(majority-state-owned companies, with public-voting own-ership in excess of 50%); “own-3” (minority-state-ownedcompanies); and “own-4” (fully private companies). Theindicators that were used were upstream production, outputefficiency, revenue generation, and profitability (Wolf, 2009).

4. Cooper et al. (2007).5. They used 3-year data (2002–2004), and their analysis

focused on only two groups: the NOCs and IOCs.6. As cited in Simar and Wilson (2011).7. The regression results suggest that the maximum feasible VI

value for a company is <20, of which the DEA score willremain non-negative and less than 1. A VI value of 20 meansthat the quantity of oil produced by a company is 20 timesless than that processed, marketed, and distributed by thecompany. As it is uncommon to find vertically integrated oilcompanies with a VI value of more than 20, companies withVI . 20 were regarded in this study as pure downstream oilcompanies and were thus excluded from the analysis. Asearlier indicated, pure downstream oil companies were notincluded in this study as they lack oil reserves andproduction. The non-vertically integrated companies thatwere included in the study were the pure upstream oilcompanies with a VI value of 0.

8. A vertically integrated oil company cuts across the entirevalue chain of the oil industry: the upstream and downstreamsectors. The downstream sector involves numerous activi-ties, such as oil refining and processing as well as marketingand distribution, which can cover a wide range of areas andlocations.

9. Some OPEC NOCs, such as QP and Saudi Aramco, havesizeable number of foreign expatriates as employees. Theyappeared at the frontier for some years.

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Notes on contributors

Chidi Basil Ike is an ElectricalEngineer of the Department of Pet-roleum Resources, Federal Republic ofNigeria. He holds a BS degree in Elect/Computer Engineering from FederalUniversity of Technology, Minna NigerState and an MS in TechnologyManagement Economics and Policyfrom Seoul National University.(Email: basilike@gmail.com)

Hyunjung Lee is an energy economistof the Asian Development Bank. Sheholds a BS degree in Electronics andElectrical Engineering from KoreaAdvanced Institute of Science andTechnology and MS and Ph.D. degreesin Economics from Seoul NationalUniversity. (E-mail: hjlee@adb.org)

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