International Journal of Energy Sector Management
Inter-group performance of oil producing countries: a meta and global frontier
analysis
Kwaku Ohene-Asare, Victor Sosu Gakpey, Charles Turkson,
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IJESM
12,3 Inter-group performance of oil
producing countries: a meta and
global frontier analysis
426 Kwaku Ohene-Asare
Department of Operations and Management Information Systems,
University of Ghana Business School, Legon, Ghana
Victor Sosu Gakpey
Airport Operations, Ghana Airports Company Limited, Accra, Ghana, and
Charles Turkson
Department of Operations and Management Information Systems,
University of Ghana Business School, Legon, Ghana
Abstract
Purpose – The purpose of this study is to compare the production efficiencies and frontiers differences of
oil-producing countries (OPCs) in four inter-governmental organizations (IGOs) in the international petroleum
industry with the aim of providing such countries understanding of group characteristics that help maximize
their supply interests.
Design/methodology/approach – The empirical analysis is based on 14 years of panel data covering
the period from 2000 to 2013. In all 46 unique countries who are members of four IGOs relevant to the
international petroleum industry are examined on individual and group bases. The authors use both
metafrontier analysis and global frontier difference in examining the group average and group frontiers,
respectively.
Findings – Groups with high inter and intra-group collaborations which ensure exchange of information,
organizational learning and innovation tend to do better than groups with even higher hydro-carbon
endowment. Additionally, hydro-carbon resource endowment may not be the solution to group inefficiency
without higher endowment in human capital, economic stability, technology and infrastructure.
Practical implications – Choice of inter-governmental organizational membership should be based on
the level of inter- and intra-group collaborations, human capital endowment among others and not mere
historic links or even resource endowment.
Originality/value – This is among the few studies to compare and rank IGOs. Specifically, it is among the
first studies to analyze the petroleum production efficiencies of IGOs involved in the international petroleum
industry. This study assesses the performance differences among OPCs with the aim of identifying for OPCs
the characteristics of inter-governmental groups that are beneficial to efficiency in upstream petroleum
activities.
Keywords Data envelopment analysis, Petroleum products, Global frontier differences,
Intergovernmental organizations, Technology gap ratios
Paper type Research paper
International Journal of Energy 1. Introduction
Sector Management
Vol. 12 No. 3, 2018 The oil and gas industry plays an important role in the economic growth and development
pp. 426-448
© EmeraldPublishingLimited of most economies and have become an integral part of global economic life (Kashani, 2005;
1750-6220
DOI 10.1108/IJESM-07-2017-0006 Barros and Assaf, 2009; Murphy and Hall, 2011; Ramachandra et al., 2005). The sector
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supports such industries as energy, transportation, industrialized agriculture, steel and Meta and
plastic pipe production, health care and chemical industries that generate income and global frontier
employment (Francisco et al., 2012; Ismail et al., 2013). The importance of oil and gas means
oil-producing countries (OPCs) play crucial roles in the supply of crude oil and natural gas analysis
(Managi et al., 2006). OPCs, through the production and export of oil and gas, hold
substantial amount of the world petroleum reserves, production and marketing (Ike and Lee,
2014). Out of the 1,492,164 million barrels of world-proven crude oil reserves in 2016, for
example, 81.5 per cent was owned by Organization of the Petroleum Exporting Countries 427
(OPEC) member states alone (OPEC, 2017).
At the forefront of the state involvement in the industry are inter-governmental
organizations (IGOs) which are coalitions of countries aimed at improving the bargaining
power of member countries. In the industry, IGOs like the OPEC, Organization of Arab
Petroleum Exporting Countries (OAPEC) and International Energy Agency (IEA) are
influential in forging global policy pertaining to the industry. Though these IGOs are
influential parties, few studies have assessed the performance of IGOs in terms of petroleum
supply (Dike, 2013; Ike and Lee, 2014; Ramcharran, 2002), as well as with the consideration
that OPCs in different groups may not have the same production technologies in petroleum
production and exploration and hence may not be easily comparable. Productive efficiencies
of different OPCs may not be comparable because of differences in production opportunities
as a result of membership of different IGOs. Group policies and restrictions, like production
quotas, differences in stock of human, physical or financial capital, as well as differences in
economic infrastructure and resource endowment, have the potential of affecting efficiencies
of member states (Ike and Lee, 2014; O’Donnell et al., 2008).
Even in the general literature on IGOs, not necessarily in the petroleum industry, most
studies on IGOs concentrate on one particular IGO in their assessment. Taylor et al. (2010),
Shahabinejad et al. (2013), Muldoon et al. (2011) and Gorton and Davidova (2004) have
concentrated on intra-group performance assessment considering efficiency only in that
particular IGO of interest. A few studies like those of Abu-Alkheil et al. (2012), Drakos (2003)
and Selowsky and Martin (1997) have based their research arguments on samples
comprising two or more IGOs. However, among these studies whose samples cut across
various IGOs, there have been little inter-group comparison to identify and rank IGOs with
respect to a particular phenomenon under study. There is therefore room for this study to
build literature by conducting an inter-group performance assessment which is lacking in
literature. We do not yet know what kinds of IGOs thrive in the oil and gas industry and
why they do.
This study contributes to the oil and gas efficiency literature by examining performance
differences between different IGOs in petroleum supply. It examines differences in the
production opportunities available to OPCs in different IGOs with the aim of identifying
whether membership of particular groups provide some advantages over others. This is
done by assessing inter-group meta-efficiency, technology gap ratios (TGR) and frontier
differences of OPCs using a bootstrap-based data envelopment analysis (DEA) (Charnes
et al., 1978) based on metafrontier analysis (Battese et al., 2004; O’Donnell et al., 2008) and the
global frontier differences – GFD (Asmild and Tam, 2007). Next, for robustness check, the
study tests the differences in the distribution of meta-efficiency levels and frontier estimates
(Li, 1996; Simar and Zelenyuk, 2006). The rest of the study is organized as follows: Section 2
presents theoretical and empirical literature, this is followed by Section 3 which is dedicated
to the methods and data from which findings are presented in Section 4. Section 5 is for
discussions and conclusions. It makes policy prescriptions on the essence of inter and intra
group collaborations in enhancing productive efficiencies of member states.
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IJESM 2. Literature review
12,3 2.1 Theories on inter-group performance
The theories on inter-group performance help explain why the performance of different
blocs of countries may differ. Two main theoretical views are used to explain inter-group
performance. These are the game theory (Von Neumann and Morgenstern, 1944) and
resource-based theory (RBT) (Penrose, 1959).
428 2.1.1 Game theory. The theory explains decisions individuals or a group of players take
to win a game when competing with one or more opponents (Von Neumann and
Morgenstern, 1944). This theory describes the strategies employed by competitors in their
choices of action that enhances their chances of gain or loss by considering the action being
taken by their opponents (Miles, 2012). It basically examines the actions players make that
decides the outcomes, gains or the optimal decision (Madhani, 2010; Rasmussen, 1989). The
game-theoretic approach studies systems with multiple self-interested parties with the aim
of predicting the likely outcomes of the system under rational behavior of the players with
mutual and possibly conflicting interests (Trestian et al., 2012; Yin et al., 2012). In other
words, game theory studies games that are mathematical models of relationships and
interactions among multiple players, each trying to advance their self-interest by choosing
among a set of strategies (Yin et al., 2012; Weibull, 1997).
In the application of game theory to such governmental-backed inter-governmental
institutions, both inter- and intra-group dynamics of collaboration and competition among
members can be explained. This has been summarized in Table I that show various levels of
collaboration among members of a particular IGO and between other IGOs. This has been
adapted from Rigby et al. (2013) benchmarking options matrix.
The theoretical views of game theorists are therefore very important in assessing and
understanding the competition and collaboration between various IGOs in the international
petroleum industry. IGOs use alternative strategies that may be a winning strategy with
higher outcome in a particular situation it faces in the competitive environment. Individual
member states tend to benefit or suffer in efficiency depending on not only the level of intra-
group learning and cooperation, but also inter-group collaboration.
Inter-group collaboration
Intra-group
collaboration Low High
Low Low exchange of information within I Low exchange of information within II
and between groups results in: group renders between group
Organizational learning not collaboration irrelevant leading to:
supported Organizational learning not
Information asymmetries not supported
addressed Information asymmetries not
addressed
High High exchange of information within III High exchange of information within IV
group and between group results in:
but low exchange between group Organizational learning/innovation
results in: Information asymmetries addressed
Table I. Organizational learning/innovationInformation asymmetries not
Outcomes of inter addressed
and intra group
collaboration Source:Adapted from Rigby et al. (2013)
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2.1.2 Resource-based theory. RBT studies the differences in outcomes in respect of resources Meta and
owned (Peteraf and Barney, 2003). Theory defines the organization’s uniqueness and global frontier
position in competitive situations in the environment (Hoopes et al., 2003). Its emphasis is on
differences in efficiency (Peteraf and Barney, 2003). The focus of the theory is how the analysis
organization acts against competitors on their strength, competence and resources that
shows performance differences in the environment (Barney, 1991; Wernerfelt, 1984; Miles,
2012). The RBT of strategy (RBT) hinges on the argument that entities with valuable, rare,
special and inimitable resources have the potential of achieving superior performance 429
(Wiklund and Shepherd, 2003; Bharadwaj, 2000; Barney, 1991; Barney, 1995; Amit and
Schoemaker, 2012). RBT uses the internal characteristics and resources of entities to explain
their heterogeneity in strategy and performance (Camis
on and Villar-L
opez, 2014). Basically,
RBT assumes that there are underlying production heterogeneities or differences across
entities (Dobbin and Baum, 2000; Peteraf, 1993; Barney, 1991).
Thus, production processes and resources are different from a group to another group.
Therefore, entities endowed with such superior resources are able to produce more economically
and/or better satisfy customer wants (Peteraf, 1993). Heterogeneity in this context also implies
that entities of varying capabilities are able to compete in the same marketplace and, at least,
breakeven (Dobbin and Baum, 2000; Peteraf, 1993). Accordingly, the main assumption of RBT is
that only entities with certain resources and capabilities with special characteristics will gain
competitive advantage and, therefore, achieve superior performance (Camis
on and Villar-L
opez,
2014). Therefore, in the context of this study, when an IGO is considered as the unit of analysis, it
stands to reason that the unique competencies and resources of members of the particular IGO
will give it competitive advantages over other relevant IGOs in the international petroleum
industry. Size of oil and gas reserves; human, technical and technological competencies of
member states; and political and economic bargaining powers are all relevant tangible and
intangible resources that an IGO may use to out-compete others. RBT therefore believes that
IGOswith such higher levels of resources will bemore efficient.
2.2 Efficiency in the petroleum industry
There has been quite a number of efficiency-related research in the petroleum industry,
substantially among the issues of ownership (Ike and Lee, 2014; Wolf, 2009; Ohene-Asare
et al., 2017), privatization (Hawdon, 2003; Kashani, 2005) and environmental efficiency
(Ismail et al., 2013; Sueyoshi and Goto, 2012). Additionally, as this study does international
comparison of the oil and gas supply activities of various countries, it is encouraging that
some studies have previously attempted such international comparison. Kim et al. (1999)
and Hawdon (2003) have all attempted such comparison, except that these studies focused
on gas distribution to the neglect of crude oil. Similarly, these papers that have attempted
international comparison only focused on the downstream activities of the industry.
Although the body of literature in the petroleum industry is substantial, there is rarely done
an international benchmarking and in a more aggregated context or mainly geared towards
the upstream operations. However very few studies are dedicated to cross country and
regional efficiency in the downstream. These include Hawdon (2003) who analyzed the
efficiency of the gas industry of 33 countries and Kim et al. (1999) who examined 28 natural
gas transmission and distribution companies operating in eight countries.
2.3 Group performance and efficiency
Studies regarding group performance and efficiency of IGOs have been heavily skewed
toward banking, energy efficiency and health. For example, whereas Abu-Alkheil et al.
(2012) concentrated on bank efficiency of OAPEC and Gulf Cooperation Countries (GCC),
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IJESM Marius Andrie
s and Capraru (2012), Casu and Molyneux (2003), Claeys and Vander Vennet
12,3 (2008) and Košak et al. (2009) looked at bank efficiency in the European Union (EU). Others
worthy of mention are Behname (2012) who examined banking efficiency among OPEC
countries and Drakos (2003) who considered same in the Former Soviet Union (FSU) and
Central and Eastern European (CEE) countries. Just like banking, several studies can be
cited for energy efficiency (Adetutu, 2014; Al-Rashed and Le
on, 2015; Goldthau and Witte,
430 2011; Filippini and Hunt, 2011) and health (Adler-Milstein et al., 2014; Retzlaff-Roberts et al.,
2004; Oderkirk et al., 2013; Al-Essa et al., 2015). Other research issues like education (Afonso
and St Aubyn, 2005; Afonso and St. Aubyn, 2006), agriculture, (Vlontzos et al., 2014; Gorton
and Davidova, 2004), insurance (Donni and Fecher, 1997), railways (Oum and Yu, 1994),
postal service, environment and policy (Selowsky and Martin, 1997) have also been
considered by other studies.
Although these studies have recorded differences in the level of efficiency of individual
members of various IGOs (Vlontzos et al., 2014; Oderkirk et al., 2013), together, some particular
IGOs have been seen to experience modest improvements in efficiency. For example Adetutu
(2014) observed that some selected OPEC countries have modest energy efficiency arising from
subsidy effect and artificially low energy prices. This notwithstanding, little is known about
efficiency of these IGOs with respect to oil and gas production and supply efficiency. Even
studies that purposely targeted energy-focused IGOs like OPEC, OAPEC and IEA (Adetutu,
2014; Al-Rashed and Le
on, 2015; Behname, 2012; Goldthau and Witte, 2011) rather looked at
issues like banking efficiency and energy efficiency. Studies that have come close to examining
supply efficiency include Dike (2013) who looked at security in energy exportation of OPEC
countries and Ramcharran (2002) who examined efficiency and production responses to price
changes in the international petroleum industry. It is also evident in literature that most studies
on IGOs concentrate on one particular IGO in their assessment, thus concentrating on intra-
group performance (Taylor et al., 2010; Gorton and Davidova, 2004). A few studies like those of
Abu-Alkheil et al. (2012), Aristovnik (2012), Selowsky and Martin (1997) and Drakos (2003)
have based their research arguments on samples comprising two or more IGOs. However,
among these studies whose samples cut across various IGOs, there has been little inter-group
comparison to identify and rank IGOs with respect to a particular phenomenon under study.
There is therefore room for this study to build literature in conducting an inter-group
performance comparison.
Finally, the focus is on the methods used in the assessment and whether these methods
adequately cater for group heterogeneities. In assessing the performance and efficiency of
groups (IGOs), several models relating to the issue of international comparisons and frontier
efficiency have been applied. Abu-Alkheil et al. (2012) used DEA to determine banks
efficiency in OAPEC and GCC countries between 2005 and 2008. Arestis et al. (2006) applied
it to 26 Organization for Economic Corporation and Development (OECD) countries from
1963 to 1992. Regression-based estimation approaches like the Translog Cost Function
(Adetutu, 2014; Claeys and Vander Vennet, 2008) and Auto Regressive Distributed Lag (Sari
and Soytas, 2009) have also seen some considerable use. Whereas all these techniques have
their own advantages and disadvantages, it is important that papers that compare various
groups use models that can adequately cater for group differences in estimating the
efficiency. From the review, only Krishnasamy and Ahmed (2009) used the metafrontier
approach to measure the efficiency and productivity of the economies of 26 OECD countries
from 1980 to 2008. Their paper does not conduct inter-IGO comparison, as the focus was
only on OECD. Most of the reviewed literature used in this study have applied a number of
models, some in combination to estimate efficiency of groups or IGOs; however, there is rare
evidence regarding global frontier or metafrontier analysis in the international oil and gas
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industry. Hence, there is enough gap for this paper to situate in efficiency assessment the Meta and
use of models that adequately cater for such group differences in the estimation of efficiency. global frontier
analysis
3. Methods and data
In this section, we present our methods used in making deductions about inter-group
performance differences among IGOs in the production and supply of petroleum resources.
We primarily use DEA-based metafrontier and global frontier analysis which are
subsequently discussed in the subsections. We present in Figure 1 a summary of the 431
analysis process
The first step is the data, the input and output variables that are used to model the
production processes of these OPCs and which will be the basis for the comparison. We present
the variables used and the OPCs in 3.4.2 and 3.4.3. In all, three input variables and two output
variables on each of the 46 unique OPCs that belong to the four IGOs are gathered for the
purpose of this study. The next step is the metafrontier analysis. This is presented in Section
3.1. It involves three main steps. First the efficiencies of all OPCs are estimated based on a
pooled frontier that does not distinguish between the groups: this is the meta efficiency scores.
The second step is to separate the OPCs into their unique groups and group efficiencies
computed for each OPC. The ratio of the meta efficiency scores to the group efficiency scores is
the Technology Gap Ratio which measures the similarity between group performance and best
practice frontiers in the industry. The meta efficiency scores and Technology Gap Ratios of the
various IGOs are compared statistically to see if differences exist.
Input and Output 
Variables
Compute Meta 
Efficiencies Equation (2)
Metafrontier Analysis Compute Group 
Efficiencies Equation (3)
Compute Technology 
Equation (4)
Global Frontier Analysis Gap Ratios
Compute Technology 
Index for Group k Equation (5)1
Compute Technology 
Indices for all groups Equation (5)
Figure 1.
Compute Global Frontier 
Equation (6) Summary of analysisDifferences process
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IJESM The next process is the global frontier analysis which is presented in section 3.2. While the
12,3 metafrontier analysis allows for the examination of the average technology gap ratios at the
state level, the GFDs allow for examining differences in production possibility frontiers at
the group level. With this, efficiencies of all OPCs irrespective of group is computed relative
to the frontier of only one group k. The geometric mean of this score becomes the
Technology Index for that group k. This process is repeated for all K groups. The GFD
432 between group k1 and k2 is the ratio of the technology indices (TIs) of these two groups.
3.1 Metafrontier analysis
Metafrontier analysis in DEA, attributed to the works of Battese et al. (2004), O’Donnell et al.
(2008), allows for comparison across heterogeneous groups (Battese and Rao, 2002; O’Donnell
et al., 2008). This involves the measurement of efficiency scores for individual groups (group
efficiency), as well as for the entire dataset irrespective of group (
meta-efficiency). Therefore, on
the assumption thatN
countries producem non-negative outputs denoted by y 2 <mþ ; using
s non-negative inputs denoted by x 2 <sþ ; the production possibility set can be defined as:
T ¼ fðx; yÞ 2 <mþsþ jx can produce yg (1)
Now, based on the assumptions of DEA, such as ray unboundedness, monotonicity and
convexity, the CRS output-oriented Farrell (1957) technical efficiency score, TE0 (x, y), of a
given OPC (x0, y0) relative to metafrontier can be computed by solving the following linear
programming problem:
>8 >  XN
>>  l jxij # xi0 8i ¼ 1; 2; . . .; s<  j¼1
TEM0 ðx; yÞ ¼ Max M>w 0 ðx

; yÞ  XN (2)
>>  l jyrj  w yr0 8r ¼ 1; 2; . . .;m>:  j¼1 l j  0 j ¼ 1; 2; . . .;N
Therefore, TEM0 ðx; yÞ is the output-oriented efficiency score that measures the proportional
increment in the output of a DMU necessary to be efficient given the level of input. For
output-orientation, an OPC is considered inefficient if TEM0 ðx; yÞ > 1, but it is considered
efficient if and only if TEM0 ðx; yÞ ¼ 1 (Fare et al., 1994). However, because of the existence of
sub-groups K in the meta-technology and the possibility of technological heterogeneities
across groups, k group-specific technical efficiency, TEk0ðx; yÞ, can be formulated for each
OPC relative to its group frontier. This is defined in equation (3) as:
8 
>>  Xnk>  l k k>  j xij# xi0 8i ¼ 1; 2; . . .; s<>
¼ 
 j¼1
TEk0ðx; yÞ Max >w
k
0ðx; yÞ  Xnk>  l kyk
(3)
>  j rj
 w yr0 8r ¼ 1; 2; . . .;m
>>: 
 j¼1
 l kj  0 ; j ¼ 1; 2; . . .; nk; k ¼ 1; 2; . . .;K
Based on the meta-efficiency and the group efficiency scores, a technology gap ratio of OPCo
in group k can be computed as the ratio of TEM0 ðx; yÞ toTEk0ðx; yÞ.
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TEM
TGRkðx; yÞ ¼ 0 ðx; yÞ Meta and0 k (4)TE0ðx; yÞ global frontier
analysis
This measure, which takes values between zero and unity, measures the diversion from the
available technology, irrespective of group because of membership of the particular group k.
433
3.2 Global frontier analysis
The GFDs index is a component of the global Malmquist productivity index and is the
primary model used in comparing the frontier differences between various groups
(Asmild and Tam, 2007). This examines the overall rather than the individual changes
in the frontier of various groups (Asmild et al., 2013). This means that this model can
draw conclusions about performance differences for the entire sample. It is better, as
aggregating other performance indices can be problematic using other methods in
sparsely populated data set and unbalanced panel (Otsuki, 2013). The global frontier
shift index therefore performs better than traditional frontier shift indices. It can also
cater for overlapping frontiers.
In estimating the GFDs, it is important to define a technology index for each group k
(TIk), which is a geometric mean of the efficiency scores of all countries relative to the
frontier of a particular group k. This is defined as:
0 Y  11=N
TIkðx ; y Þ ¼ TEk xK ; yKj j @ j j (5)
j ¼ A1; . . . ;N
k ¼ 1; . . . ;K
The global frontier shift index or global technical change or the GFD between different
groups k1 and k2 is defined as:
k ;k ¼ TI
k2ðxj; yjÞ
GFD 1 2
TIk0 1Yðxj; yjÞ  1
TEk2 xK@ j ; y
K 1=N
j
j ¼ A1; . . . ;N (6)
¼ 0 k ¼ 1; . . . ;KY  1
@ TEk1 x
K
j ; y
K 1=N
j
j ¼ A1; . . . ;N
k ¼ 1; . . . ;K
This is the ratio of the geometric mean of the efficiencies of all countries relative to k2
frontier to the geometric mean of the efficiencies of all countries relative to k1 frontier. The
efficiency scores are computed using similar model formulation to that already presented in
equation (2). The GFD > 1 indicate that Group 1 frontier is on average better that Group 2
frontier. When GFD = 1 then Group 1 frontier is not better than Group 2 frontier. Finally, if
GFD< 1, then Group 1 frontier is worse than Group 2. Frontier shift is used where changes
in time are being assessed. However, where the frontiers are of different groups, then the
term GFD is preferred.
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IJESM 3.3 Testing of differences in the distribution of efficiency scores
12,3 The study uses the Simar–Zelenyuk-adapted Li test (SZAL) to statistically explore
differences in the distribution of efficiency or frontier estimates between different IGOs in
the oil and gas industry (Li, 1996; Simar and Zelenyuk, 2006). This nonparametric test
effectively compares the equality of distributions of efficiency estimates using kernel
density estimations. The non-parametric kernel density estimator is largely gaining more
434 significance in research (Simar and Zelenyuk, 2006) and is very useful since there are no
distributional assumptions imposed on the efficiency scores across the groups.
In comparing the density of distribution of efficiency scores between two random groups
for which the random samples {TEA,j : j = 1,. . ., N } and {TEB,j : j = 1,. . ., N } represent the
efficiencies of the two subgroups A and B in a population. Now, given that fl denotes the
density of the distribution of the efficiency TEl (l = A,B) our null and alternative hypotheses
would be:
H : f ðTEA0 A Þ ¼ fBðTEBÞ
ð Þ ð Þ (7)Ha : fA TEA 6¼ f BB TE
The null hypothesizes the existence of equal distribution of scores across the two groups.
The alternate hypothesis expects there to be significant differences in the distribution of
scores across the two groups. It should be noted that this test is a pairwise test. The true
technical efficiency scores in each subgroup, {TEA,j : j = 1,. . .,n} and {TEB,j : j= 1,. . .,n}, are
independently and identically distributed (i.i.d.) within each subgroup with densities fA(.)
and fB(.), respectively. A bootstrap algorithm is used to generate p-values for this test based
on the empirical distribution of scores. This bootstrap algorithm, as well as the test statistic
of the Simar–Zelenyuk-adapted Li-test, can be seen by reference to Kenjegalieva et al. (2009),
Simar and Zelenyuk (2006).
3.4 Data description
3.4.1 Inter-governmental organizations. Intergovernmental organizations bring together
complementary skills and create platforms for innovation, cooperation and creativity and
make full use of the available resources to provide sustainable development for the member
nations (Dorussen and Ward, 2008; Holland, 1998). As the oil and gas industry continues to
play a major role in meeting the world’s growing economic demands, there have been a
number of such IGOs with policy direction toward the petroleum industry. In this study, we
focus on four IGOs, three of which are at the forefront of forging global policy. These are
OPEC, OAPEC and IEA. The FSU is also included in this study to capture the dynamics of
such IGOs without clear collaboration and formality in organization.
OPEC is a permanent intergovernmental organization headquartered in Vienna, Austria
(OPEC, 2012). Currently, OPEC comprises 14 members (OPEC, 2018) with their main
objective to co-ordinate and unify petroleum policies among member countries to secure fair
and stable prices for petroleum producers (OPEC, 2012; Fuinhas et al., 2015). The
organization aims to ensure an efficient, economic and regular supply of petroleum to
consuming nations; and a fair return on capital to those investing in the industry (OPEC,
2012). The organization phases a challenge of member states not respecting the output
policies established (Goldthau and Witte, 2011). OPEC continues to actively engage in
international cooperation and dialogue which has become an important industry event an
effort in exchanging views and outlooks with other energy stakeholders (OPEC, 2014).
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OAPEC is a regional IGO concerned with the development of the petroleum industry by Meta and
fostering cooperation among its members (OAPEC, 2015). Membership comprises 11 Arab global frontier
oil exporting countries. OAPEC contributes to the effective use of the resources of member
countries through sponsoring joint ventures. The main goals of OAPEC are: member states analysis’
cooperation in various forms of the economic activity, development of close links between
them, safeguarding the legitimate interests of its members under fair and reasonable terms
in the petroleum industry (OAPEC, 2015). The organization is guided by the belief of
building an integrated petroleum industry as a cornerstone for future economic integration 435
among Arab countries (OAPEC, 2015).
The IEA was established as an autonomous organization of the OECD after an
agreement of the International Energy Program in 1974 with membership of 16 countries
and its secretariat in Paris (Colgan et al., 2012). Currently, IEA membership stands at 29.
They work to ensure reliable, affordable and clean energy for its member countries andmost
importantly for oil importing countries and beyond. The IEAwas founded in response to the
1973-1974 oil crisis, the IEA’s initial role was to help countries co-ordinate a collective
response to major disruptions in oil supply through the release of emergency oil stocks to
the markets (IEA, 2014). The IEA works with other international organizations and forums
in the energy field. It engages in active discussions with producer countries and other IGOs
in the oil and gas industry particularly at the International Energy Forum (IEA, 2014). In
addition, the IEA collaborates with the International Renewable Energy Agency and
engages with partner countries and other international agencies to provide all stakeholders
including business leaders a true global perspective of the world’s energy system.
The post-Soviet states, also collectively known as the FSU, are the 15 independent states
that emerged from the Union of Soviet Socialist Republic in its dissolution in December 1991
(Minescu et al., 2008). The dissolution of the Soviet Union took place as a result of general
economic stagnation, even regressing the inter-republic economic connections, leading to
even more serious breakdown of the post-Soviet economies (Easterly and Fischer, 1994).
Although the FSU is not a traditional IGO like OPEC and OAPEC, the close historical links,
the share of oil and gas reserves and production and collaboration in trade have made their
influence critical in such comparison. The FSU holds a sizeable quantity of natural resources
in oil and gas that can be economically produced to meet the global demand for energy
(Aguilera, 2012; Bhattacharyya, 2007). Together, the FSU region and the Middle East
controls about 2/3 of the global conventional oil (Rogner et al., 2012). In view of their
importance, OPEC and US energy information administration (EIA) recognize their
influence in their annual statistical reports.
Without IGOs, it will be difficult to monitor performance over time, set up international
regulations and restrictions for countries, as IGOs help to create and bind international
administrations (Biermann and Bauer, 2004).
3.4.2 Inputs and outputs. Three inputs and two outputs are selected for the efficiency
estimation process. Oil reserves, gas reserves and total labor force employed were chosen as
inputs to generate natural resources of oil and gas. Choice of these variables was motivated
by previous literature that has considered reserves, labor and production levels as a good
representation of the production process in the upstream petroleum industry (Eller et al.,
2011; Ohene-Asare et al., 2017; Kashani, 2005). The two outputs, oil and gas production are
physically generated from the oil and gas reserves using human resources. These variables
are selected because the issue under consideration is how OPCs are achieving maximum
outputs obtainable given their available inputs. Oil and gas production is the quantity of oil
and gas that have been recovered in a given time period. This is primarily output from
operations of drilling from the oil and gas reserves as an end product of the upstream
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IJESM industry activities (Wolf, 2009). By this, increasing the production output is an essential
12,3 pointer for improved economic performance of OPCs.
Oil and gas reserves are commercially identified volumes of oil and gas that can be
recovered in the future. Oil reserves are volumes of an estimated quantity of crude oil
identified in a specific area through geographic analysis and data from demonstrated
engineering surveys (EIA, 2013). The resources are converted into outputs that generate
436 significant revenue (Wolf, 2009). These two reserves are measured separately. Whereas oil
reserves are measured in billions of barrels (bbls), natural gas reserves are measured in
trillions of cubic feet (tcf). Finally, labor is a critical resource in the production process. It is a
key factor of production in production theory. The petroleum industry is both labor- and
capital-intensive process. These variables were sourced from the US EIA and the World
Development Indicators database of World Bank. The labor force is estimated as the labor
force of a country as a proportion of the contribution of the industry to Gross Domestic
Product.
3.4.3 Data. In all, 53 OPCs across the period from 2000 to 2013 are used to draw
inferences. All 12 members of OPEC as at the study period, 20 of the 29 members of IEA, 10
of the 15 members of FSU and all 11 members of OAPEC qualified for the study. They were
selected because of data availability for both oil and natural gas operations. A few members
of IEA and FSU were either exclusively oil producing or natural gas extracting not both. We
focused on OPCs with both oil and gas operations. Additionally, seven members of OPEC
were also members of OAPEC, therefore, if the duplicates are corrected the effective number
of IGOs becomes 46 unique countries. After data cleaning because of missing data, 737
observations were useful. Descriptive statistics and correlations of the pooled dataset are
presented in Table II.
From Table II, all inputs from the table are positively and significantly associated with
both outputs. Correlations between inputs and outputs are significant at the 1 per cent level
therefore the isotonicity property of DEA which requires that an output should not decrease
with an input increase (Dyson et al., 2001; Honma and Hu, 2008; Wanke et al., 2015) is not
violated. The intuition for the positive associations is that employing more inputs is
expected to lead to higher production levels. Finally, there are relatively weaker correlations
among the inputs. Even the correlation between the oil reserves and labor force is not
statistically significant (r = -0.032, p> 0.05). The weak correlations among the inputs is also
an encouraging sign in DEA estimations, as it provides evidence of the discriminatory
power of the inputs used (Dyson et al., 2001). This means that the inputs actually measure
different dimensions in the production process. Also, test of the returns to scale property of
the production frontier using the mean of ratios as proposed by Simar and Wilson (2002)
revealed that the production frontier exhibits constant returns to scale for all 14 years.
Variable Symbol Mean SD y1 y2 x1 x2 x3
Oil produced y1 1628.59 2679.15 1
Table II. Gas produced y2 2309.03 4749.99 0.673** 1
Descriptive statistics Oil reserves x1 33.87 63.28 0.749** 0.157** 1Gas reserves x
and correlations 2
133.63 296.81 0.526** 0.618** 0.261** 1
Labor force x3 15561972 25022196 0.435** 0.764** 0.032 0.280** 1
between inputs and
outputs Note: **p< 0.01
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4. Findings Meta and
4.1 Metafrontier analysis global frontier
Starting with the metafrontier analysis, the average meta-efficiency, group-efficiencies and
TGRs of OPCs in each IGO are presented and ranked in Table III. IGOs are ranked based on analysis
their TGRs to identify the best performing ones in order of importance. As the group
efficiencies are computed relative to different production frontiers, it is not appropriate to
compare the group’s specific efficiency scores of these different groups (Canhoto and
Dermine, 2003; Dietsch and Lozano-Vivas, 2000). It can however be noted that, whereas 437
OAPEC member states are on average producing at about 75 per cent (1/1.3332 = 0.75) of
their potential capacity, FSU states are only producing at 44 per cent (1/2.2928 = 0.44) of
their potential production capacity. IEA and OPEC states are producing at 61 and 66 per
cent of their potential production capacities respectively.
Meta-efficiencies are quite high indicating more inefficiencies, as most scores are away
from the efficiency score of 1. IEA (1.9163) is the only IGO with an average meta-efficiency
score that is quite similar to their average group efficiency score. The differences between
the average meta-efficiencies and group efficiencies of OAPEC and OPEC are fairly high but
not too far away from each other. Again, scores for FSU seem to show quite high differences
between the two average efficiency scores. This is an indication that, as compared to other
IGOs, IEA member states are producing using the best state of knowledge in the industry
since their TGR is 0.8486. OAPEC and OPEC are producing, to some extent, close to the best
state of knowledge in the industry with TGRs slightly above 0.60. FSU could only manage
0.3817 of the state of knowledge available in the international petroleum industry. Also, the
TGRs of all IGOs except FSU (0.3817) are above 0.6057. This means that, given the inputs,
IEA is producing at 84.86 per cent close to the available state of production technology in the
international petroleum industry. OAPEC and OPEC are producing at 63.27 and 60.57
per cent respectively.
What is not obvious in these inter-group comparisons is whether differences in the meta-
efficiencies and TGRs of these IGOs are statistically significant. This is the basis for the
pairwise comparisons presented in Table IV. The performances of the four IGOs are first
compared using traditional point estimate statistical techniques. Independent t-test and
Mann Whitney U tests are used to conduct pairwise comparison of the means and ranks of
the various IGOs. While these tests are well known, it only compare point estimates and
neglect the distribution of the entire dataset (Li, 1996; Simar and Zelenyuk, 2006). To cater
for this weakness, the SZAL test, which uses kernel density estimators to compare the
distribution of the scores are also used here.
The first pairwise comparison is between the scores of FSU and IEA. This is a
comparison of the two extremes, as previous results from Table III revealed that FSU has
the highest meta-inefficiency scores of 6.0071 and lowest TGR of 0.3817, while IEA had the
best meta-efficiency score and TGR of 1.9163 and 0.8486, respectively. Statistical
comparisons for all three tests reveal significant differences in the meta-efficiencies and
TGRs of FSU and IEA at the 0.1 per cent significance level. IEA member states significantly
IGO Meta efficiencies Group efficiencies TGR Rank
FSU 6.0071 2.2928 0.3817 4
IEA 1.9163 1.6262 0.8486 1 Table III.
OAPEC 2.1071 1.3332 0.6327 2 Inter-IGO
OPEC 2.5092 1.5197 0.6057 3 metafrontier results
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IJESM
12,3 IGOs Index t-test Mann–Whitney SZAL
FSU - IEA Meta Eff. 6.28 (0.000)*** 31737 (0.000)*** 27.47 (0.000)***
TGR 22.01 (0.000)*** 3064 (0.000)*** 66.89 (0.000)***
FSU – OAPEC Meta Eff. 6.12 (0.000)*** 16437 (0.000)*** 16.13 (0.000)***
TGR 9.83 (0.000)*** 3560 (0.000)*** 33.02 (0.000)***
FSU – OPEC Meta Eff. 5.71 (0.000)*** 16255 (0.000)*** 17.46 (0.000)***
438 TGR 8.65 (0.000)*** 5318 (0.000)*** 19.18 (0.000)***
IEA – OAPEC Meta Eff. 2.49 (0.013)* 19648 (0.188) 2.60 (0.005)**
TGR 11.98 (0.000)*** 34324 (0.000)*** 33.26 (0.000)***
IEA – OPEC Meta Eff. 5.99 (0.000)*** 18716 (0.001)** 2.89 (0.002)**
Table IV. TGR 12.18 (0.000)*** 37122 (0.000)*** 34.41 (0.000)***OAPEC - OPEC Meta Eff. 3.81 (0.000)*** 10848.5 (0.038)* 2.30 (0.011)*
Pairwise TGR 0.87 (0.383) 13311 (0.345) 13.38 (0.000)***
comparisons of inter-
IGO performance Notes: ***p< 0.001; **p< 0.01; *p< 0.05; values in parenthesis () are the p-values
outperform their FSU counterparts on both meta-efficiency and TGR. FSU states are next
compared with OAPEC member states. Conclusions on both the meta-efficiencies and the
TGR are similar to that revealed when FSU and IEA were compared. OAPEC states, on
average, outperform FSU states on both indicators and on all three estimators of difference.
OPEC member states also significantly outperform FSU states on both meta-efficiency and
TGR. Results from the metafrontier analyses therefore reveal that FSU is the least
performing IGO in the international petroleum industry.
Next, results for IEA are compared with that of OAPEC. Starting with the meta-
efficiency scores, mixed results are observed between the results of the parametric t-test and
its nonparametric counterpart –Mann–Whitney test. Whereas results from the t-test reveal
that IEA states have significantly lower average meta-inefficiencies than OAPEC states (t =
2.49, p < 0.05), Mann–Whitney shows no statistically significant differences in the ranks
of these two IGOs (U= 19648, p= 0.188). It is amidst these disparities in conclusions that the
utility of the SZAL test is seen. SZAL results show statistically significant differences in the
distribution of meta-efficiency scores of IEA and OAPEC states (l = 2.60, p < 0.01). Results
of the SZAL test is more reliable since it compares all members of one group against all
members in the other. It is also based on nonparametric techniques which are important
because of the nonparametric nature of DEA technique. For the TGR, all three estimation
techniques observe significant differences in the TGRs of IEA and OAPEC states. IEA
states therefore significantly outperform OAPEC counterparts. This can be graphically
observed with reference to Figure 2 which shows the kernel density distribution of the TGRs
of the four IGOs. From Figure 2 it is clear that whereas the distribution of scores for IEA
seem to gain more density toward the score of 1.0, that of OAPEC seem to peak between 0.4
and 0.6.
IEA states are then statistically compared with OPEC states. Results are quite
straightforward. There are statistically significant differences in the means, ranks and
distributions of these two IGOs for both the meta-efficiency scores and TGRs. IEA has
significantly lower meta-inefficiencies than OPEC and higher TGRs than OPEC on average.
IEA states therefore significantly outperform OPEC states on both meta-efficiency scores
and TGRs.
From deductions made, it is clear that FSU is the worst performing IGO based on the
metafrontier analyses, whiles IEA is the best IGO. What is not clear is whether any
differences exist between the performances of OAPEC and OPEC. Both IGOs were seen in
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5 Meta and
global frontier
4 analysis
3
439
2
1
0 Figure 2.
–0.1 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 1.1 1.2 1.3 Distribution of
technological gap
FSU IEA OAPEC OPEC ratios
Table IV to have meta-efficiencies slightly below 2.60 and TGRs slightly above 0.6.
Although OAPEC has larger TGR of 0.6389 compared to OPEC’s of 0.6026 and smaller
meta-efficiency of 2.1083 compared with OPEC’s of 2.5228, questions remain whether there
are statistically significant differences. Just like previous comparisons, scores of OAPEC
and OPEC are statistically compared on all three tests of differences. For the meta-efficiency
scores, all three statistical approaches revealed significant differences between the scores of
OAPEC and OPEC. OAPEC therefore has lower meta-inefficiencies compared with OPEC
states on average. The result is not that straightforward when the TGRs are compared. For
both point estimators, no statistically significant differences were observed between the
scores of these two IGOs. There are no statistically significant differences in the means (t =
0.87, p = 0.383) and ranks (U = 13311, p = 0.345) of TGRs of OAPEC and OPEC member
states. This notwithstanding, there is a statistically significant difference between the
distribution of OAPEC and OPEC states (l = 13.38, p < 0.001). This can be inferred from
Figure 2 where it is seen that whereas OAPEC gathers greater mass between 0.4 and 0.6 as
well as between 0.8 and 1.0, OPEC states seem to be distributed relatively evenly across a
wider range of scores. There are even quite a number of OPEC states with scores lower than
the 0.4 mark, while only few OAPEC members fall in this sector. OAPEC states therefore
seem to outperform OPEC states in this regard. Statistical tests therefore reveal that IEA is
the best performing IGO followed by OAPEC. OAPEC is closely followed by OPEC states.
FSU is, however, in a distant fourth place on the ranking.
4.2 Global frontier differences
This measures performance based on the production frontiers rather than average
efficiencies. First, TI are estimated for all the IGOs. This is then followed by an assessment
of the frontier differences. If the TI is less than 1, it means that the frontier is worse than
most observations since on average other DMUs are superefficient (score < 1 in output-
oriented model). In short, higher TIs signify better frontier on average. The average TI for
each IGO is presented in Table V for each year from 2000 to 2013. The average for the
pooled dataset is also included.
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Density
IJESM
12,3 Year Tech indices (OPEC) Tech indices (IEA) Tech indices (FSU) Tech indices (OAPEC)
2000 0.76285 1.95127 0.60600 1.02614
2001 0.75169 1.98658 0.61070 1.00069
2002 0.76011 2.01982 0.62169 0.98068
2003 0.84802 2.03534 0.64190 1.00340
2004 0.79012 1.96082 0.60276 0.95621
440 2005 0.77051 1.90790 0.59017 0.91812
2006 0.76774 1.93495 0.59833 0.91415
2007 0.77331 2.15939 0.63381 0.98010
2008 0.74753 2.18157 0.63885 0.98017
2009 0.78701 2.13726 0.64222 0.96278
2010 0.77807 2.12755 0.65562 0.96216
2011 0.83750 2.26267 0.72035 1.04981
Table V. 2012 0.80934 2.15908 0.69886 0.98903
Technology indices 2013 0.85030 2.26530 0.72382 1.04325
for the IGOs Pooled 0.78744 2.07394 0.64022 0.98254
It is obvious from Table V that, for each year, IEA has had the highest TI. The TI for
IEA has consistently been greater than 1, signifying that most observations (especially
from the other IGOs) have been inefficient relative to the IEA frontier. IEA’s worse TI of
1.9079 is even higher than OAPEC’s best of 1.04981. OAPEC seems to have
technological indices fluctuating around 1.00. It has in effect had periods where its
frontier is better than most observations, on average and periods where its frontier has
not been that good on average. While the TI of OPEC ranges from 0.7475 to 0.8503, FSU
have had the lowest levels of the technological indices having achieved their highest
index of 0.7238 in 2013. FSU’s largest TI is even lower than the lowest of OPEC. For the
pooled dataset, on average, whereas IEA has the highest average of 2.07394 and
OAPEC has a score of 0.98254, OPEC and FSU have scores of 0.78744 and 0.64022,
respectively.
While the TI gives an indication of how good the frontier is, it provides no real indication
of how well a particular group’s frontier is as compared to that of another specific group.
This is where the GFD approach of Asmild and Tam (2007) gains utility. The TIs are used in
the estimation of the GFDs between the different groups. Results of the GFD are presented
subsequently. GFDmeasures and provides the overall conclusions about whether one group
frontier is superior to the other (Asmild and Tam, 2007). This approach provides an overall
estimation of the differences between two frontiers or more importantly between groups.
The frontier can differentiate between the efficiencies of two frontiers or two groups without
considering shifts overtime. The GFD tells by how much a particular IGO is far away from
or close to another IGO. Mathematical notations of the index are presented in equation (6).
Results are presented in Table VI.
IGO OPEC IEA FSU OAPEC
OPEC 1.0000
Table VI. IEA 2.6338 1.0000
Global frontier FSU 0.8130 0.3087 1.0000
differences OAPEC 1.2478 0.4738 1.5347 1.0000
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These scores are based on the pooled scores although GFDs can be computed for individual Meta and
years. It was observed that the pooled results are not that different from the annual GFDs. global frontier
From Table VI, it is evident that when compared to the OPEC frontier, IEA and OAPEC are
163.38 per cent ([2.6338  1]  100) and 25.78 per cent ([1.2478  1] 100), respectively, analysis
better than OPEC frontier, whereas FSU on average is 18.7 per cent ([1  0.8130] 100)
worse than OPEC frontier. Compared to the IEA frontier, FSU recorded the worse overall
performance with respect to the IEA frontier. FSU frontier was, on average, 69.13 per cent
([1  0.3087] 100) worse than IEA’s frontier. This was closely followed by OPEC which 441
had a GFD of 0.3797 signifying that OPEC frontier was on average 62.03 per cent worse than
IEA’s frontier. The closest group to the state of technology employed by IEA was OAPEC,
which had a frontier 52.62 per cent worse than IEA’s. GFD results relative to the FSU
frontier shows it is the least performing one since all other IGOs have better performing
production frontiers. The final of the GFD comparisons is relative to the OAPEC frontier.
These scores are reported in Table VI. OAPEC’s frontier shows some interesting results.
Whereas on average, OAPEC’s frontier seems better than the OPEC (0.8014) and FSU
(0.6516) frontiers, it is not better than that of IEA (2.1106).
5. Discussions and conclusions
5.1 Discussions
From both the metafrontier and GFD assessments, IEA is seen to have higher efficiencies
and production frontier relative to the three other IGOs. This may be because of fact that the
IEA is made up of industrialized Western countries with much higher resource in terms of
human capital, technology, infrastructure and capital for investment, political and economic
stability, bargaining power and collaborations with many more IGOs (Bamberger and Scott,
2004; Jollands et al., 2010; Colgan et al., 2011; Duffield, 2012). The RBT purports that the way
an organization is structured combined with its resources, can better enhance its
performance. This is justified by the empirical works which give credence to the importance
of these resource characteristics for performance (Sirmon et al., 2011; Crook et al., 2008).
With respect to game theory, the IEA strategy of maintaining minimum oil and gas stocks
in the face of production cuts and price hikes (Bamberger and Scott, 2004), as well as the
strong inter and intra group collaboration, is consistent with the theory. When there is high
inter and intra group collaborations it results in high exchange of information within and
between groups. It then leads to organizational learning, innovation and information
asymmetries are addressed (Rigby et al., 2013). IEA can be situated in quarter IV of Table I
which sees strong intra and inter-group collaboration resulting in organizational learning,
innovation and disruption of information asymmetries.
OAPEC’s frontier showed a better score as compared with OPEC and FSU. This is an all
Arab organization and perhaps this is the reason for their good performance. The group is
seen to share similar cultural and political values because of their close links with each other
and their occupation of the same geographical location. OAPEC sponsored ventures also
help them to keep pace with developments and succeed in enhancing their performance in
the industry (OAPEC, 2010). Stronger and more strategic group collaboration within and
with other groups, the higher the levels of learning and innovation and this leads to
improved performance. OAPEC’s oil reserves have been estimated at about 713 billion
barrels about 43 per cent of the proven world’s reserves in 2014 (OAPEC, 2015). The large oil
and gas resource endowment may be another factor influencing their level of performance.
There is a link between organization resources and performance. The more resource
endowed an organization is, the better its performance. Unlike IEA, OAPEC and OPEC seem
to experience comparative challenge in their collaboration efforts although they have high
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IJESM resource endowment. Institutional bottlenecks, failure of members to conform to production
12,3 quotas and other policy initiatives can have a negative effect on group performance.
OPEC’s frontier follows OAPEC as the next best production frontier with scores better
than that of FSU. The composition of membership cuts across countries from Africa, South
America and Asia. OPEC’s coordination between major producers and consumers and their
participation in international energy forums show the extent of engagement within the
442 group and among other groups in the international oil and gas industry (Goldthau and
Witte, 2011). It stands to reason that their better performance as compared with FSUmay be
a result of this collaborations, solidarity and other factors favoring the group in the coalition
among member countries (Mikdashi, 1974). Good strategic collaboration within a particular
group, as well as between that group and others, allows for group learning and innovation
which can enhance the group’s performance.
The inadequate level of cooperation among governments of FSU countries probably
underscores the reasons for its inefficiencies. There is no agreement coupled with differences
in political and economic interest in the development of the oil resource in their respective
countries (Aguilera, 2012). These essential ties, cooperation or networks as required for
positive utility to countries in the petroleum industry, by influencing access to resources,
reducing transactional costs and building interest based on coalitions (Lauber et al., 2008).
However, absence of higher levels of such cooperation may be detrimental to the group
interest, as differences in the individual goals may not stimulate group performance.
Another issue that probably impacts on the higher inefficiency of members in this IGO is the
concentration on internal use of the oil and gas produced by the FSU countries (EIA, 2013).
Excessive government subsidies on the oil and gas supplied for domestic market can be a
disincentive to higher production levels, as economic benefits from higher production levels
may not be realized by the producing organizations. Additionally, it is possible that because
most oil produced in this region is heavy (Goldemberg, 2000; EIA, 2013), it is a contributory
factor to their inefficiencies. Heavy oil requires enhanced oil recovery techniques. This
stands to reason that because higher production technology is required for exploring heavy
oil, intra-group collaborations in terms of technology and research, as well as reducing the
transactional cost, will be beneficial. Therefore, the poor performance can be associated with
lack of collaboration in technology and research in the IGO (Goldemberg, 2000). FSU is
clearly placed in quarter I of Table I where there is low collaboration both within and with
other IGOs. The finding is that as was expected.
5.2 Conclusions
On the basis of little inter-country and inter-IGO comparison literature on the international
petroleum industry, this study examined four IGOs in the industry in terms of production
efficiencies and GFDs of member states. IEA states, on average, were the best performers
followed by OAPEC, OPEC and FSU in that order. The average levels of meta-efficiency and
technology gap ratios of IEA were seen to be significantly larger than the averages of the
other IGOs. This was also confirmed from the Global Frontier analysis since the best
performing countries in the IEA are seen to significantly outperform even the best
performing countries in the other three IGOs. IEA and OAPEC production frontiers were
seen to be consistently better than both the OPEC and FSU frontiers for all 14 years. IEA’s
performance could be as a result of both the high intra-group collaboration and even higher
inter-group (external) collaborations together with strong Western-industrialized market
economy structures. Therefore, high exchange of information that results in better
organizational learning and innovation by members is achieved and all information
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asymmetries and bottlenecks that may hinder the progress of the production and supply Meta and
capabilities of member states addressed. global frontier
Therefore, IGOs like FSU should put in more efforts to formalize their association with
clearer policy guidelines that enshrine better collaboration among member states. Close analysis
association with higher performing states and organizations can better streamline their
activities and reduce their inefficiencies. Whereas OAPEC and OPEC were among the best
performers, individual members experienced different levels of performance. Organizational
policies and guidelines should be better institutionalized. Efforts should be directed toward 443
ensuring that member countries adhere to these policies that work.
While theory supports the view that common background among actors ensures close
collaboration, for FSU states, this common ancestry does not seem to improve their
performance. Further research can explore which kinds of ties can improve efficiencies in
the international petroleum industry and how countries with adversarial histories can be
joined toward a common productive goal. Further research can explore how production
quotas and other regulatory guidelines of some IGOs, such as OPEC, affect the performance
of individual countries. OPCs were examined at the composite country level. It would be
interesting to examine how domestic dynamics and macroeconomic conditions translate to
performance. Further research can explore how various IGOs in the industry handle price
volatilities in the industry and how low-price environments affect the performances of
different IGOs.
This study makes a number of contributions, this notwithstanding there are some
limitations in terms of data and scope. Countries used for assessment were drawn from the
list of members of four IGOs – OPEC, IEA, OAPEC and FSU. However, a few were
eliminated because they produced either only oil or only gas. While this is a limitation,
majority of members of these IGOs produce both oil and gas. Additionally, a larger sample
covering all OPCs in the world would have been appropriate but it is difficult to access
because of data scarcity. Finally, there are several other IGOs like European Union (EU),
Gulf Cooperation Council (GCC) and Organizaci
on Latinoamericana de Energia (OLADE)
whose activities may influence the oil supply decisions of member countries. This
notwithstanding, most of the petroleum producing members of these other IGOs are also
members of the four IGOs that are currently under study.
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Corresponding author
Kwaku Ohene-Asare can be contacted at: kohene-asare@ug.edu.gh
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