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Evaluating the comparative performance of countries of the Middle East and North Africa: A DEA application
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Socio-Economic Planning Sciences 40 (2006) 156–167 www.elsevier.com/locate/seps
Evaluating the comparative performance of countries of the Middle East and North Africa: A DEA application Ramakrishnan Ramanathan Operations Management and Business Statistics, College of Commerce and Economics, Sultan Qaboos University, Post Box 20, Postal Code 123, Sultanate of Oman Available online 26 April 2005
Abstract Over the past few decades, countries of the Middle East and North Africa (MENA) have achieved varying levels of economic development. In this paper, data envelopment analysis (DEA) is employed to study the comparative performance of selected MENA countries. For 1999, our DEA identified four of the 18 countries studied as the most efficient: Bahrain, Jordan, Kuwait, and the United Arab Emirates. All are from the Middle East, with three being members of the Gulf Cooperation Council (GCC). Yemen was rated as the least efficient of all countries considered in the analysis. A regression analysis showed that the efficiency scores have a significant relationship with the richness of the countries (in terms of GNP per capita) but do not have a significant relationship with the size of the countries (in terms of population). Further, a time-series analysis using the Malmquist Productivity Index (MPI) indicated that the MENA countries achieved higher values of desirable attributes, and lower values of undesirable attributes, in 1999 compared to 1998. During 1998–1999, technology change contributed more to the improvement of MPI than did technical efficiency change. r 2005 Elsevier Ltd. All rights reserved. Keywords: Countries of the Middle East and North Africa; Economic and social performance; Data envelopment analysis; Malmquist productivity index
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1. Introduction Over the past few decades, countries of the Middle East and North Africa (MENA) have achieved varying levels of economic development. In recent literature, the performances of countries of the region have been studied using sophisticated methodologies. Notable among them are: (a) comparative analysis of poverty in the Mediterranean region using principal component analysis [1], (b) policy-oriented analysis of the performance of countries of the Gulf Cooperation Council (GCC) [2], (c) econometric analysis of the fiscal expenditure policy and the non-oil economic growth of the GCC countries [3], and (d) estimation of the aggregate demand for imports in the GCC countries using econometric techniques [4]. Research studies are also available on the performance of individual MENA countries, such as Saudi Arabia [5] and the United Arab Emirates (UAE) [6]. Most of these studies have used econometric methods for their analysis. To the best of our knowledge, Data Envelopment Analysis (DEA) has not been employed to study the economic performance of MENA countries, although it has been used to compare countries in other contexts [7–9]. In this paper, DEA helps provide a comparative picture of performance of selected MENA countries.
2. Data envelopment analysis DEA [10–16] is a mathematical programming methodology based on the Frontier approach [15,17]. It has been successfully employed to study the comparative performance of units that consume similar inputs and produce similar outputs. The units are generally referred to as Decision Making Units (DMUs). When assessing the performance of nations, DEA combines performances of countries in terms of several desirable and undesirable attributes into a single scalar measure, called the efficiency score. Countries that register unit efficiency scores are considered efficient in that they have the highest values of desirable attributes and the lowest values of undesirable attributes. Countries with efficiency scores less than 1 may be considered to operate sub-optimally for a given set of attributes. Two possible assumptions could be made while computing efficiency scores using DEA, namely constant returns to scale (CRS) and variable returns to scale (VRS). The assumption of CRS is said to prevail when an increase in all inputs (i.e., increase in terms of undesirable attributes) by 1% leads to an increase in all outputs (i.e., increase in terms of desirable attributes) by 1% [7]. The assumption of VRS is said to prevail when the CRS assumption is not satisfied. It has been shown that DEA efficiency scores computed with the CRS assumption (hereafter, called CRS efficiency scores) are less than or equal to the corresponding VRS efficiency scores [10,12] owing to the difference in scale size of DMUs. The VRS efficiency of a DMU measures only technical efficiency, while CRS efficiency accounts for both technical efficiency and efficiency loss when the DMU does not operate in its most productive scale size [12]. The ratio of CRS to VRS scores is called the scale efficiency [12,16]. The scale efficiency of a DMU operating in its most productive size is thus 1.
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Before detailing our study, we discuss the advantages of using DEA for a comparative performance analysis of the countries concerned. It may be noted that this is not the first study of its kind, although it is the first for MENA countries. The advantages of applying DEA for countries have been comprehensively discussed in several studies e.g. [12,17,18]. The following are selected important features and advantages of using DEA for a comparative analysis of countries. 2.1. Important features and advantages The performances of countries across the world have been compared using a variety of indices, e.g., human development index [18] and global competitiveness index [19]. Several attributes are normally considered when developing such indices. They can be termed fixed weight schemes as they combine performances in terms of various attributes using pre-fixed weights, which may be subjectively chosen. The advantage of DEA vs. fixed weight schemes is that the weights are not subjective, but determined using linear programming (LP) [8]. The approach computes these weights that maximize the efficiency score of a country subject to the efficiencies of other countries (calculated using the same set of weights) falling between 0 and 1.The weights differ for each country. Since DEA efficiency scores are obtained without requiring an a priori assignment of weights to inputs and outputs, the evaluations of social performances are ‘‘value free.’’ When applied to the performance of nations, the traditional DEA production function approach (used when a set of inputs is employed by a firm to produce a set of outputs) should be broadened. Perhaps, the underlying function could be termed an economic and social performance function [7]. When DEA is extended to evaluate social performances, terms like ‘‘inputs’’ and ‘‘outputs’’ are largely generic (to conform to DEA usages) as can be seen in earlier DEA applications [7–9]. For example, the current study uses ‘‘infant mortality rate’’ as an input and ‘‘Gross National Product (GNP) per capita’’ as an output. However, they cannot be considered input and output, respectively, in the traditional sense of DEA. That is, ‘‘infant mortality rate’’ cannot be said to produce ‘‘GNP per capita’’. In this sense, performances in terms of undesirable attributes are considered inputs and performances in terms of desirable attributes are considered outputs. Such generic usage is also true of DMUs. In a traditional DEA sense, DMUs are entities that convert inputs into outputs. Here, however, the term denotes only the entity (a country) under consideration. Nevertheless, DEA can be used to compare the performance of different countries by the scalar efficiency score. The score increases when country achievements increase in terms of desirable attributes, and decreases when achievements increase in terms of undesirable attributes.
3. Comparing performance of MENA countries using DEA In this section, the performances of 18 countries in the MENA region are analysed using DEA. Several economic, educational and health attributes, listed in Table 1, are considered. The choice of attributes and countries is influenced by issues of data consistency and reliability. Of the seven attributes, AGEDEP, ILLITER, MORTINF represent undesirable attributes while the other four represent desirable attributes.
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Table 1 Attributes used in the study Attribute/abbreviation
Description
LABOUR LIFEEXP PRITEACH GNPCAP AGEDEP ILLITER MORTINF
Ratio of total labour to population. Life expectancy at birth, total (years). Primary education, teachers (% female). GNP per capita (Current US$). Age dependency ratio (dependents to working-age population). Illiteracy rate, adult female (% of females 15+). Mortality rate, infant (per 1,000 live births).
3.1. Data issues Most of the data for MENA countries are available in public domain (e.g., United Nations Statistics, World Bank Statistics, etc.). A large portion of the data used in the present analysis was obtained from the Gender Statistics Database of the World Bank (http://devdata.worldbank.org/ genderstats/home.asp, accessed on 28 August 2004). While the initial focus is on the performance during 1999, time-series analysis of performances for 1997, 1998 and 1999 is also presented here. For want of consistent data, some countries, namely Djibouti, Iraq, Libya, Somalia and West Bank and Gaza could not be considered in the analysis. Unfortunately, Qatar, one of the rapidly growing and modernizing countries of the MENA region, is not included in the analysis as the Gender Statistics Database does not contain its GNP data. A list of countries, and the data used for the analysis, are given in Table 2. Most of the data pertaining to the year 1999 were available, though not all. Whenever not, data for the nearest available year were substituted. They are so noted in the table. 3.2. Results DEA analysis of the data presented in Table 2 was carried out using the Data Envelopment Analysis Program (DEAP) software package [20] (http://www.uq.edu.au/economics/cepa/ deap.htm, accessed on 28 August 2004). The resulting efficiency scores of those countries studied are indicated in Table 3. Note that the listed efficiencies should be viewed as relative to the best performing country (countries). Table 3 shows that four of the 18 countries are considered efficient, in terms of both CRS and VRS assumptions. They are Bahrain, Jordan, Kuwait and the UAE. It is interesting to note that all the efficient countries are from the Middle East. Except for Jordan, the other three are members of the GCC. Yemen is rated as the least efficient of the countries considered in the analysis. Table 3 also gives information about peer(s) for countries considered inefficient in the analysis. Peers are efficient countries with a performance score of 1 and all slacks zero. Algeria’s peer is the UAE, meaning that Algeria can try to emulate the UAE by achieving better values of attributes that would result in an efficiency score of 1. Note that UAE is considered a peer for many of the
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Table 2 Social and economic performance of selected MENA countries LABOUR LIFEEXP PRITEACH GNPCAP AGEDEP ILLITER MORTINF Algeria Bahrain Comoros Egypt, Arab Rep. (Egypt) Iran, Islamic Rep. (Iran) Jordan Kuwait Lebanon Mauritania Morocco Oman Saudi Arabia Sudan Syrian Arab Republic (Syria) Tunisia Turkey United Arab Emirates (UAE) Yemen, Rep.
33.10 45.00 45.60 37.80 30.70 29.50 39.70 34.70 46.10 39.70 26.60 32.80 39.70 31.40 39.10 47.50 49.50 31.60
44.76b 65.3d 20.67e 52.22a 54.35 62.08a 59.4a 48.94 24.07a 37.7a 52.25 49.91a 62.03b 65.22a 49.18a 43.57 70.14b 16.6b
70.81 72.99 60.57 66.82 71.11 71.29 76.62 70.22 53.94 67.18 73.34 72.21 55.55 69.45 72.53 69.48 75.25 56
1540 7640a 390 1380 1600 1630 19020c 3730 390 1190 5050 6900 310 1020 2090 2880 18060a 360
0.68 0.51 0.89 0.66 0.66 0.73 0.57 0.62 0.88 0.6 0.84 0.78 0.74 0.81 0.59 0.53 0.42 1.02
44.3 17.8 47.9 57.2 31.3 16.6 20.6 20.2 68.6 64.9 40.4 34.1 55.1 40.7 40.7 24.1 22 76.1
33.98 7.7 60.8 47.28 25.5 26.2 10.7 26.36 88.04 47.8 17.38 18.8 67.16 26 24.02 36.17 7.64 79
Note: All data pertain to the year 1999, except for the following: a1998 data; b1997 data; c1996 data; d1995 data; e1985 data. Source: The World Bank’s gender statistics available at the internet site: genderstats.worldbank.org
Table 3 Efficiency scores and peers for 1999 Country
CRS efficiency
VRS efficiency
Scale efficiency
Peers
Algeria Bahrain Comoros Egypt Iran Jordan Kuwait Lebanon Mauritania Morocco Oman Saudi Arabia Sudan Syria Tunisia Turkey UAE Yemen
0.581 1.000 0.435 0.565 0.638 1.000 1.000 0.842 0.444 0.625 0.513 0.574 0.502 0.496 0.686 0.844 1.000 0.306
0.618 1.000 0.472 0.636 0.67 1.000 1.000 0.873 0.477 0.7 0.523 0.59 0.568 0.53 0.712 0.852 1.000 0.412
0.941 1.000 0.921 0.888 0.952 1.000 1.000 0.965 0.931 0.893 0.981 0.973 0.884 0.936 0.964 0.991 1.000 0.744
UAE — UAE UAE Bahrain, — — Bahrain, UAE UAE Bahrain, Bahrain, UAE Bahrain, UAE Bahrain, — UAE
UAE
Jordan
UAE UAE UAE UAE
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Table 4 Results of the regression analysis with CRS efficiency score for 1999 as the dependent variable Variable
Coefficient Intercept Multiple R-squared
Independent variable Population in millions
GNP per capita in thousands
0.00162a (0.656)b 0.702 (9.64) 0.035
0.026c (3.487) 0.561 (10.920) 0.396
a
Insignificant even at 10% confidence level. Figures within the parentheses indicate the t-statistic. c Significant at 1% confidence level. b
inefficient countries. Interestingly, Kuwait is not considered a peer for any inefficient country. This might have resulted from the existence of alternate optima [13]. As expected, the CRS efficiencies are lower than the corresponding VRS efficiencies. For example, the CRS and VRS efficiencies for Algeria are, respectively, 0.581 and 0.618. This suggests that Algeria does not operate at the best possible scale size. Note further from Table 3 that Algeria’s scale efficiency is 0.941 (o1). Indeed, its current size of operations reduces pure technical (VRS) efficiency by 0.059. 3.3. Regression analysis of DEA efficiencies A study of our DEA efficiencies indicates that many of the efficient countries are oil-rich. Hence, a regression analysis was carried out to verify whether the efficiency scores are influenced by the size of the country (measured by population in millions) and the richness of the country (measured by GNP per capita in thousands). An ordinary least-squares (OLS) regression was performed separately for population and GNP per capita, as shown in Table 4, to determine their influences on CRS efficiency scores. While size of country does not seem to be an important variable, richness appears to have significantly influenced the efficiency scores. A regression analysis using VRS efficiency scores yielded similar results, but is not reported here.
4. Time series analysis of performance In general, DEA studies consider performance analysis for a particular year. However, the method can be used to analyze performances over several years using procedures such as the Window Analysis and the Malmquist Productive Index (MPI) [21]. In this section, we use the MPI to study changes that occurred in technological practices in MENA countries, as well as changes in the efficiency with which the technologies were used in two different time periods.
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4.1. Malmquist productivity index (MPI) approach to time-series analysis The output-based MPI [22] is defined as follows: t tþ1 tþ1 tþ1 tþ1 tþ1 1=2 D ðx ; y Þ D ðx ; y Þ tþ1 tþ1 tþ1 t t M ðx ; y ; x ; y Þ ¼ , Dt ðxt ; yt Þ Dtþ1 ðxt ; yt Þ where Dt is a distance function measuring the efficiency of conversion of inputs xt to outputs yt in the period t. (Note that DEA efficiency is considered a distance measure in the literature as it reflects the efficiency of converting inputs to outputs [21]). Importantly, if there is a technological change in period (t+1), then, Dtþ1 ðxt ; yt Þ ¼ Efficiency of conversion of input in period t to output in period t aDt ðxt ; yt Þ. MPI is a geometric average of the efficiency and technology changes in the two periods being considered. It can thus be written as: h tþ1 tþ1 tþ1 i h t tþ1 tþ1 i1=2 D ðx ;y Þ D ðx ;y Þ Dt ðxt ;yt Þ ; M tþ1 ðxtþ1 ; ytþ1 ; xt ; yt Þ ¼ t t t tþ1 tþ1 D ðx ;y Þ D ðxtþ1 ;ytþ1 Þ D ðxt ;yt Þ or M
¼
ET;
where E is technical efficiency change, and T is technology change. E measures the change in the CRS technical efficiency of period t+1 over that in t. If E41, there is an increase in the technical efficiency of converting inputs to outputs. T represents the average technological change over the two periods [16,21]. In addition to M, E and T, other efficiency changes over time may be defined. For example, VRS efficiency change for a country can be calculated as the ratio of its VRS efficiency in period t+1 to that in t. 4.2. MPI for performance of MENA nations Results of our analysis of performance for the 18 MENA countries in years 1998 and 1997 are presented in Table 5. Note that the performances of countries are similar to those recorded for the year 1999 (shown in Table 3). The MPI and other indices for the period 1998–1999 are shown in Table 6. Interestingly, the MPI for Algeria is 1.029. This number is more than one, indicating there has been a progress in achieving higher values of desirable attributes and lower values of undesirable attributes in 1999 compared to 1998. Both technical efficiency change (1.005) and technology change (1.024) showed progress and contributed to the MPI. It may be noted that, when DEA and MPI are applied to nations, changes in technology should be interpreted in the context of ‘‘social practices’’ and ‘‘changes in social institutions.’’ There is thus an increase in VRS efficiency (1.007) over this period, with a slight reduction in scale efficiency. Table 6 shows that all 18 countries considered in our study have registered progress in terms of MPI. The highest improvement (1.056) was registered by Iran, while the lowest came for
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Table 5 Efficiency scores of MENA countries for the years 1998 and 1997 Country
Algeria Bahrain Comoros Egypt Iran Jordan Kuwait Lebanon Mauritania Morocco Oman Saudi Sudan Syria Tunisia Turkey UAE Yemen
1998
1997
CRS efficiency
VRS efficiency
Scale efficiency
CRS efficiency
VRS efficiency
Scale efficiency
0.578 1.000 0.433 0.562 0.623 1.000 1.000 0.856 0.452 0.629 0.507 0.578 0.507 0.500 0.678 0.840 1.000 0.302
0.614 1.000 0.473 0.632 0.651 1.000 1.000 0.886 0.489 0.705 0.516 0.595 0.573 0.532 0.705 0.853 1.000 0.406
0.941 1.000 0.916 0.888 0.957 1.000 1.000 0.967 0.926 0.893 0.982 0.972 0.884 0.939 0.962 0.984 1.000 0.744
0.580 1.000 0.436 0.562 0.600 1.000 1.000 0.854 0.457 0.629 0.498 0.571 0.515 0.501 0.679 0.830 1.000 0.296
0.620 1.000 0.475 0.638 0.632 1.000 1.000 0.885 0.494 0.710 0.510 0.592 0.579 0.529 0.710 0.850 1.000 0.404
0.936 1.000 0.918 0.881 0.949 1.000 1.000 0.965 0.924 0.886 0.976 0.965 0.890 0.948 0.956 0.977 1.000 0.732
Table 6 Efficiency changes over the period 1998–1999 Country
Technical efficiency, E
Technology, T
Malmquist productivity index, M
VRS technical efficiency
Scale efficiency
Algeria Bahrain Comoros Egypt Iran Jordan Kuwait Lebanon Mauritania Morocco Oman Saudi Sudan Syria Tunisia Turkey UAE Yemen
1.005 1.000 1.005 1.005 1.024 1.000 1.000 0.984 0.982 0.994 1.012 0.993 0.990 0.992 1.012 1.005 1.000 1.013
1.024 1.055 1.020 1.025 1.031 1.047 1.039 1.049 1.019 1.023 1.033 1.032 1.024 1.036 1.024 1.036 1.018 1.025
1.029 1.055 1.025 1.031 1.056 1.047 1.039 1.032 1.001 1.017 1.045 1.025 1.014 1.027 1.036 1.041 1.018 1.039
1.007 1.000 0.998 1.006 1.029 1.000 1.000 0.985 0.975 0.993 1.014 0.992 0.991 0.996 1.010 0.999 1.000 1.015
0.999 1.000 1.007 0.999 0.995 1.000 1.000 0.998 1.007 1.001 0.998 1.001 0.999 0.996 1.002 1.006 1.000 0.998
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Table 7 Efficiency changes over the period 1997–1998 Country
Technical efficiency, E
Technology, T
Malmquist productivity index, M
VRS technical efficiency
Scale efficiency
Algeria Bahrain Comoros Egypt Iran Jordan Kuwait Lebanon Mauritania Morocco Oman Saudi Sudan Syria Tunisia Turkey UAE Yemen
0.997 1.000 0.993 1.000 1.038 1.000 1.000 1.002 0.989 1.000 1.018 1.012 0.984 0.998 0.999 1.012 1.000 1.020
1.024 1.005 1.032 1.023 1.024 1.063 1.039 1.031 1.022 1.025 1.029 1.025 1.030 1.036 1.024 1.028 0.978 1.024
1.021 1.005 1.024 1.023 1.063 1.063 1.039 1.033 1.011 1.025 1.048 1.037 1.014 1.033 1.022 1.040 0.978 1.044
0.990 1.000 0.996 0.991 1.030 1.000 1.000 1.001 0.990 0.993 1.012 1.005 0.990 1.006 0.993 1.004 1.000 1.005
1.006 1.000 0.997 1.009 1.008 1.000 1.000 1.001 0.999 1.007 1.006 1.007 0.995 0.992 1.006 1.008 1.000 1.015
Mauritania (1.001). The countries considered efficient in the DEA analysis (Bahrain, Jordan, Kuwait and UAE) showed varying levels of improvement in terms of MPI, with Bahrain registering the highest. In terms of contribution to MPI improvement, technology change was more impactive than technical efficiency change. Yet, the technology changes were all relatively modest. This suggests that change, if any, is occurring at only a modest pace in these countries. Some countries (Lebanon, Mauritania, Morocco, Saudi Arabia, Sudan and Syria) registered a decline in technical efficiency change, although all the 18 countries registered improved technology change. Finally, Table 6 also shows that the MENA countries have achieved varying levels of improvement in their VRS technical and scale efficiencies. Table 7 gives index changes for the period 1997–98. Interestingly, the results are similar to those reported for the period 1998–99 (given in Table 6) with the most notable change being the decline in MPI for the UAE during 1997–98.
5. Policy implications The results of our analyses have interesting policy implications for development of the MENA region. We wish to stress here that findings of the study are critically based on the choice of attributes, and, hence, the policy implications discussed below should be considered within this perspective.
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The current study found but 22% of the 18 countries studied as efficient. The smallest CRS efficiency score was 0.306, for Yemen. This is a significant result highlighting the wide disparity in socio-economic status amongst MENA countries. Three of the four efficient countries are members of the GCC, and net oil exporters. Our regression analysis found a significant relationship between richness of the country to its efficiency. Hence, rich countries with high oil revenues appear to perform much better than other countries. The UAE seems to be a peer for many inefficient countries. The latter could thus follow social and economic policies practiced in the UAE in efforts to improve their performances. An MPI analysis over the period 1998–1999 showed that, on average, there was improvement in the social and economic performances of the MENA countries. This is definitely a healthy trend, pointing to the overall improvement in these economies, at least over the study period. As reported earlier, in general, technology change was more impactive than technical efficiency change in terms of contribution to MPI improvement. If technology change can be interpreted similar to change in social practices, it could be concluded that healthy changes in social practices are taking place in MENA countries. However, the technology changes were all relatively modest, implying that the social reforms in these countries are occurring at only a modest pace. Some countries, such as Lebanon, Mauritania, Morocco, Saudi Arabia, Sudan and Syria, actually registered declines in technical efficiency change. This might highlight the relative ineffectiveness of social and economic policies that are not well directed in those countries. Appropriate actions would thus be necessary to reverse such a trend.
6. Summary and conclusions In this paper, the economic and social performance of 18 countries of the MENA region were compared using DEA with seven performance attributes. For 1999, four of the 18 were found to be efficient: Bahrain, Jordan, Kuwait and UAE. All are from the Middle East, with three being members of the GCC. Yemen was rated as the least efficient of all countries considered in the analysis. A regression analysis showed that the efficiency scores have a significant relationship with country richness (in terms of GNP per capita), but do not have a significant relationship with the country size (in terms of population). Further, a time-series analysis of country performance was carried out using the MPI. The analysis suggested that, for all 18 countries, there was progress in achieving higher values of desirable attributes and lower values of undesirable attributes in 1999 vs. 1998. During 1998–1999, technology change contributed more to improvements in MPI than did technical efficiency change.
Acknowledgements The author is grateful to the anonymous reviewers and to the Editor-in-Chief, Professor Barnett R. Parker, for their helpful comments and suggestions on earlier drafts of this paper.
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Ramakrishnan Ramanathan is Assistant Professor, Department of Operations Management and Business Statistics, College of Commerce and Economics, Sultan Qaboos University, Muscat, Sultanate of Oman. Previously, he was Associate Professor, Indira Gandhi Institute of Development Research, Mumbai, India, Visiting Professor, Helsinki
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University of Technology, Finland, and Senior Research Fellow, Delft University of Technology, The Netherlands. Professor Ramanthan’s research interests include rural energy and agricultural policy, environmental impact assessment, transport infrastructure. He has authored two books: An Introduction to Data Envelopment Analysis (Sage Publication, 2003), and Indian Transport towards the New Millennium: Performance, Analysis and Policy (Concept Publishing, 2003). He has more than 50 refereed publications which have appeared in such journals as International Journal of Operations & Production Management, Measuring Business Excellence, Socio Economic Planning Sciences, Ecological Economics, European Journal of Operational Research, The Journal of Multi Criteria Decision Analysis, Energy Policy, Journal of Environmental Management, Energy Economics, Transport Policy, Transportation Research, Environmental Change, IEEE Transactions on Systems, Man and Cybernetics, IEEE Transactions on Power Systems, The International Journal of Global Energy Issues, and Energy.
Socio-Economic Planning Sciences 40 (2006) 156–167 www.elsevier.com/locate/seps
Evaluating the comparative performance of countries of the Middle East and North Africa: A DEA application Ramakrishnan Ramanathan Operations Management and Business Statistics, College of Commerce and Economics, Sultan Qaboos University, Post Box 20, Postal Code 123, Sultanate of Oman Available online 26 April 2005
Abstract Over the past few decades, countries of the Middle East and North Africa (MENA) have achieved varying levels of economic development. In this paper, data envelopment analysis (DEA) is employed to study the comparative performance of selected MENA countries. For 1999, our DEA identified four of the 18 countries studied as the most efficient: Bahrain, Jordan, Kuwait, and the United Arab Emirates. All are from the Middle East, with three being members of the Gulf Cooperation Council (GCC). Yemen was rated as the least efficient of all countries considered in the analysis. A regression analysis showed that the efficiency scores have a significant relationship with the richness of the countries (in terms of GNP per capita) but do not have a significant relationship with the size of the countries (in terms of population). Further, a time-series analysis using the Malmquist Productivity Index (MPI) indicated that the MENA countries achieved higher values of desirable attributes, and lower values of undesirable attributes, in 1999 compared to 1998. During 1998–1999, technology change contributed more to the improvement of MPI than did technical efficiency change. r 2005 Elsevier Ltd. All rights reserved. Keywords: Countries of the Middle East and North Africa; Economic and social performance; Data envelopment analysis; Malmquist productivity index
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1. Introduction Over the past few decades, countries of the Middle East and North Africa (MENA) have achieved varying levels of economic development. In recent literature, the performances of countries of the region have been studied using sophisticated methodologies. Notable among them are: (a) comparative analysis of poverty in the Mediterranean region using principal component analysis [1], (b) policy-oriented analysis of the performance of countries of the Gulf Cooperation Council (GCC) [2], (c) econometric analysis of the fiscal expenditure policy and the non-oil economic growth of the GCC countries [3], and (d) estimation of the aggregate demand for imports in the GCC countries using econometric techniques [4]. Research studies are also available on the performance of individual MENA countries, such as Saudi Arabia [5] and the United Arab Emirates (UAE) [6]. Most of these studies have used econometric methods for their analysis. To the best of our knowledge, Data Envelopment Analysis (DEA) has not been employed to study the economic performance of MENA countries, although it has been used to compare countries in other contexts [7–9]. In this paper, DEA helps provide a comparative picture of performance of selected MENA countries.
2. Data envelopment analysis DEA [10–16] is a mathematical programming methodology based on the Frontier approach [15,17]. It has been successfully employed to study the comparative performance of units that consume similar inputs and produce similar outputs. The units are generally referred to as Decision Making Units (DMUs). When assessing the performance of nations, DEA combines performances of countries in terms of several desirable and undesirable attributes into a single scalar measure, called the efficiency score. Countries that register unit efficiency scores are considered efficient in that they have the highest values of desirable attributes and the lowest values of undesirable attributes. Countries with efficiency scores less than 1 may be considered to operate sub-optimally for a given set of attributes. Two possible assumptions could be made while computing efficiency scores using DEA, namely constant returns to scale (CRS) and variable returns to scale (VRS). The assumption of CRS is said to prevail when an increase in all inputs (i.e., increase in terms of undesirable attributes) by 1% leads to an increase in all outputs (i.e., increase in terms of desirable attributes) by 1% [7]. The assumption of VRS is said to prevail when the CRS assumption is not satisfied. It has been shown that DEA efficiency scores computed with the CRS assumption (hereafter, called CRS efficiency scores) are less than or equal to the corresponding VRS efficiency scores [10,12] owing to the difference in scale size of DMUs. The VRS efficiency of a DMU measures only technical efficiency, while CRS efficiency accounts for both technical efficiency and efficiency loss when the DMU does not operate in its most productive scale size [12]. The ratio of CRS to VRS scores is called the scale efficiency [12,16]. The scale efficiency of a DMU operating in its most productive size is thus 1.
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Before detailing our study, we discuss the advantages of using DEA for a comparative performance analysis of the countries concerned. It may be noted that this is not the first study of its kind, although it is the first for MENA countries. The advantages of applying DEA for countries have been comprehensively discussed in several studies e.g. [12,17,18]. The following are selected important features and advantages of using DEA for a comparative analysis of countries. 2.1. Important features and advantages The performances of countries across the world have been compared using a variety of indices, e.g., human development index [18] and global competitiveness index [19]. Several attributes are normally considered when developing such indices. They can be termed fixed weight schemes as they combine performances in terms of various attributes using pre-fixed weights, which may be subjectively chosen. The advantage of DEA vs. fixed weight schemes is that the weights are not subjective, but determined using linear programming (LP) [8]. The approach computes these weights that maximize the efficiency score of a country subject to the efficiencies of other countries (calculated using the same set of weights) falling between 0 and 1.The weights differ for each country. Since DEA efficiency scores are obtained without requiring an a priori assignment of weights to inputs and outputs, the evaluations of social performances are ‘‘value free.’’ When applied to the performance of nations, the traditional DEA production function approach (used when a set of inputs is employed by a firm to produce a set of outputs) should be broadened. Perhaps, the underlying function could be termed an economic and social performance function [7]. When DEA is extended to evaluate social performances, terms like ‘‘inputs’’ and ‘‘outputs’’ are largely generic (to conform to DEA usages) as can be seen in earlier DEA applications [7–9]. For example, the current study uses ‘‘infant mortality rate’’ as an input and ‘‘Gross National Product (GNP) per capita’’ as an output. However, they cannot be considered input and output, respectively, in the traditional sense of DEA. That is, ‘‘infant mortality rate’’ cannot be said to produce ‘‘GNP per capita’’. In this sense, performances in terms of undesirable attributes are considered inputs and performances in terms of desirable attributes are considered outputs. Such generic usage is also true of DMUs. In a traditional DEA sense, DMUs are entities that convert inputs into outputs. Here, however, the term denotes only the entity (a country) under consideration. Nevertheless, DEA can be used to compare the performance of different countries by the scalar efficiency score. The score increases when country achievements increase in terms of desirable attributes, and decreases when achievements increase in terms of undesirable attributes.
3. Comparing performance of MENA countries using DEA In this section, the performances of 18 countries in the MENA region are analysed using DEA. Several economic, educational and health attributes, listed in Table 1, are considered. The choice of attributes and countries is influenced by issues of data consistency and reliability. Of the seven attributes, AGEDEP, ILLITER, MORTINF represent undesirable attributes while the other four represent desirable attributes.
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Table 1 Attributes used in the study Attribute/abbreviation
Description
LABOUR LIFEEXP PRITEACH GNPCAP AGEDEP ILLITER MORTINF
Ratio of total labour to population. Life expectancy at birth, total (years). Primary education, teachers (% female). GNP per capita (Current US$). Age dependency ratio (dependents to working-age population). Illiteracy rate, adult female (% of females 15+). Mortality rate, infant (per 1,000 live births).
3.1. Data issues Most of the data for MENA countries are available in public domain (e.g., United Nations Statistics, World Bank Statistics, etc.). A large portion of the data used in the present analysis was obtained from the Gender Statistics Database of the World Bank (http://devdata.worldbank.org/ genderstats/home.asp, accessed on 28 August 2004). While the initial focus is on the performance during 1999, time-series analysis of performances for 1997, 1998 and 1999 is also presented here. For want of consistent data, some countries, namely Djibouti, Iraq, Libya, Somalia and West Bank and Gaza could not be considered in the analysis. Unfortunately, Qatar, one of the rapidly growing and modernizing countries of the MENA region, is not included in the analysis as the Gender Statistics Database does not contain its GNP data. A list of countries, and the data used for the analysis, are given in Table 2. Most of the data pertaining to the year 1999 were available, though not all. Whenever not, data for the nearest available year were substituted. They are so noted in the table. 3.2. Results DEA analysis of the data presented in Table 2 was carried out using the Data Envelopment Analysis Program (DEAP) software package [20] (http://www.uq.edu.au/economics/cepa/ deap.htm, accessed on 28 August 2004). The resulting efficiency scores of those countries studied are indicated in Table 3. Note that the listed efficiencies should be viewed as relative to the best performing country (countries). Table 3 shows that four of the 18 countries are considered efficient, in terms of both CRS and VRS assumptions. They are Bahrain, Jordan, Kuwait and the UAE. It is interesting to note that all the efficient countries are from the Middle East. Except for Jordan, the other three are members of the GCC. Yemen is rated as the least efficient of the countries considered in the analysis. Table 3 also gives information about peer(s) for countries considered inefficient in the analysis. Peers are efficient countries with a performance score of 1 and all slacks zero. Algeria’s peer is the UAE, meaning that Algeria can try to emulate the UAE by achieving better values of attributes that would result in an efficiency score of 1. Note that UAE is considered a peer for many of the
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Table 2 Social and economic performance of selected MENA countries LABOUR LIFEEXP PRITEACH GNPCAP AGEDEP ILLITER MORTINF Algeria Bahrain Comoros Egypt, Arab Rep. (Egypt) Iran, Islamic Rep. (Iran) Jordan Kuwait Lebanon Mauritania Morocco Oman Saudi Arabia Sudan Syrian Arab Republic (Syria) Tunisia Turkey United Arab Emirates (UAE) Yemen, Rep.
33.10 45.00 45.60 37.80 30.70 29.50 39.70 34.70 46.10 39.70 26.60 32.80 39.70 31.40 39.10 47.50 49.50 31.60
44.76b 65.3d 20.67e 52.22a 54.35 62.08a 59.4a 48.94 24.07a 37.7a 52.25 49.91a 62.03b 65.22a 49.18a 43.57 70.14b 16.6b
70.81 72.99 60.57 66.82 71.11 71.29 76.62 70.22 53.94 67.18 73.34 72.21 55.55 69.45 72.53 69.48 75.25 56
1540 7640a 390 1380 1600 1630 19020c 3730 390 1190 5050 6900 310 1020 2090 2880 18060a 360
0.68 0.51 0.89 0.66 0.66 0.73 0.57 0.62 0.88 0.6 0.84 0.78 0.74 0.81 0.59 0.53 0.42 1.02
44.3 17.8 47.9 57.2 31.3 16.6 20.6 20.2 68.6 64.9 40.4 34.1 55.1 40.7 40.7 24.1 22 76.1
33.98 7.7 60.8 47.28 25.5 26.2 10.7 26.36 88.04 47.8 17.38 18.8 67.16 26 24.02 36.17 7.64 79
Note: All data pertain to the year 1999, except for the following: a1998 data; b1997 data; c1996 data; d1995 data; e1985 data. Source: The World Bank’s gender statistics available at the internet site: genderstats.worldbank.org
Table 3 Efficiency scores and peers for 1999 Country
CRS efficiency
VRS efficiency
Scale efficiency
Peers
Algeria Bahrain Comoros Egypt Iran Jordan Kuwait Lebanon Mauritania Morocco Oman Saudi Arabia Sudan Syria Tunisia Turkey UAE Yemen
0.581 1.000 0.435 0.565 0.638 1.000 1.000 0.842 0.444 0.625 0.513 0.574 0.502 0.496 0.686 0.844 1.000 0.306
0.618 1.000 0.472 0.636 0.67 1.000 1.000 0.873 0.477 0.7 0.523 0.59 0.568 0.53 0.712 0.852 1.000 0.412
0.941 1.000 0.921 0.888 0.952 1.000 1.000 0.965 0.931 0.893 0.981 0.973 0.884 0.936 0.964 0.991 1.000 0.744
UAE — UAE UAE Bahrain, — — Bahrain, UAE UAE Bahrain, Bahrain, UAE Bahrain, UAE Bahrain, — UAE
UAE
Jordan
UAE UAE UAE UAE
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Table 4 Results of the regression analysis with CRS efficiency score for 1999 as the dependent variable Variable
Coefficient Intercept Multiple R-squared
Independent variable Population in millions
GNP per capita in thousands
0.00162a (0.656)b 0.702 (9.64) 0.035
0.026c (3.487) 0.561 (10.920) 0.396
a
Insignificant even at 10% confidence level. Figures within the parentheses indicate the t-statistic. c Significant at 1% confidence level. b
inefficient countries. Interestingly, Kuwait is not considered a peer for any inefficient country. This might have resulted from the existence of alternate optima [13]. As expected, the CRS efficiencies are lower than the corresponding VRS efficiencies. For example, the CRS and VRS efficiencies for Algeria are, respectively, 0.581 and 0.618. This suggests that Algeria does not operate at the best possible scale size. Note further from Table 3 that Algeria’s scale efficiency is 0.941 (o1). Indeed, its current size of operations reduces pure technical (VRS) efficiency by 0.059. 3.3. Regression analysis of DEA efficiencies A study of our DEA efficiencies indicates that many of the efficient countries are oil-rich. Hence, a regression analysis was carried out to verify whether the efficiency scores are influenced by the size of the country (measured by population in millions) and the richness of the country (measured by GNP per capita in thousands). An ordinary least-squares (OLS) regression was performed separately for population and GNP per capita, as shown in Table 4, to determine their influences on CRS efficiency scores. While size of country does not seem to be an important variable, richness appears to have significantly influenced the efficiency scores. A regression analysis using VRS efficiency scores yielded similar results, but is not reported here.
4. Time series analysis of performance In general, DEA studies consider performance analysis for a particular year. However, the method can be used to analyze performances over several years using procedures such as the Window Analysis and the Malmquist Productive Index (MPI) [21]. In this section, we use the MPI to study changes that occurred in technological practices in MENA countries, as well as changes in the efficiency with which the technologies were used in two different time periods.
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4.1. Malmquist productivity index (MPI) approach to time-series analysis The output-based MPI [22] is defined as follows: t tþ1 tþ1 tþ1 tþ1 tþ1 1=2 D ðx ; y Þ D ðx ; y Þ tþ1 tþ1 tþ1 t t M ðx ; y ; x ; y Þ ¼ , Dt ðxt ; yt Þ Dtþ1 ðxt ; yt Þ where Dt is a distance function measuring the efficiency of conversion of inputs xt to outputs yt in the period t. (Note that DEA efficiency is considered a distance measure in the literature as it reflects the efficiency of converting inputs to outputs [21]). Importantly, if there is a technological change in period (t+1), then, Dtþ1 ðxt ; yt Þ ¼ Efficiency of conversion of input in period t to output in period t aDt ðxt ; yt Þ. MPI is a geometric average of the efficiency and technology changes in the two periods being considered. It can thus be written as: h tþ1 tþ1 tþ1 i h t tþ1 tþ1 i1=2 D ðx ;y Þ D ðx ;y Þ Dt ðxt ;yt Þ ; M tþ1 ðxtþ1 ; ytþ1 ; xt ; yt Þ ¼ t t t tþ1 tþ1 D ðx ;y Þ D ðxtþ1 ;ytþ1 Þ D ðxt ;yt Þ or M
¼
ET;
where E is technical efficiency change, and T is technology change. E measures the change in the CRS technical efficiency of period t+1 over that in t. If E41, there is an increase in the technical efficiency of converting inputs to outputs. T represents the average technological change over the two periods [16,21]. In addition to M, E and T, other efficiency changes over time may be defined. For example, VRS efficiency change for a country can be calculated as the ratio of its VRS efficiency in period t+1 to that in t. 4.2. MPI for performance of MENA nations Results of our analysis of performance for the 18 MENA countries in years 1998 and 1997 are presented in Table 5. Note that the performances of countries are similar to those recorded for the year 1999 (shown in Table 3). The MPI and other indices for the period 1998–1999 are shown in Table 6. Interestingly, the MPI for Algeria is 1.029. This number is more than one, indicating there has been a progress in achieving higher values of desirable attributes and lower values of undesirable attributes in 1999 compared to 1998. Both technical efficiency change (1.005) and technology change (1.024) showed progress and contributed to the MPI. It may be noted that, when DEA and MPI are applied to nations, changes in technology should be interpreted in the context of ‘‘social practices’’ and ‘‘changes in social institutions.’’ There is thus an increase in VRS efficiency (1.007) over this period, with a slight reduction in scale efficiency. Table 6 shows that all 18 countries considered in our study have registered progress in terms of MPI. The highest improvement (1.056) was registered by Iran, while the lowest came for
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Table 5 Efficiency scores of MENA countries for the years 1998 and 1997 Country
Algeria Bahrain Comoros Egypt Iran Jordan Kuwait Lebanon Mauritania Morocco Oman Saudi Sudan Syria Tunisia Turkey UAE Yemen
1998
1997
CRS efficiency
VRS efficiency
Scale efficiency
CRS efficiency
VRS efficiency
Scale efficiency
0.578 1.000 0.433 0.562 0.623 1.000 1.000 0.856 0.452 0.629 0.507 0.578 0.507 0.500 0.678 0.840 1.000 0.302
0.614 1.000 0.473 0.632 0.651 1.000 1.000 0.886 0.489 0.705 0.516 0.595 0.573 0.532 0.705 0.853 1.000 0.406
0.941 1.000 0.916 0.888 0.957 1.000 1.000 0.967 0.926 0.893 0.982 0.972 0.884 0.939 0.962 0.984 1.000 0.744
0.580 1.000 0.436 0.562 0.600 1.000 1.000 0.854 0.457 0.629 0.498 0.571 0.515 0.501 0.679 0.830 1.000 0.296
0.620 1.000 0.475 0.638 0.632 1.000 1.000 0.885 0.494 0.710 0.510 0.592 0.579 0.529 0.710 0.850 1.000 0.404
0.936 1.000 0.918 0.881 0.949 1.000 1.000 0.965 0.924 0.886 0.976 0.965 0.890 0.948 0.956 0.977 1.000 0.732
Table 6 Efficiency changes over the period 1998–1999 Country
Technical efficiency, E
Technology, T
Malmquist productivity index, M
VRS technical efficiency
Scale efficiency
Algeria Bahrain Comoros Egypt Iran Jordan Kuwait Lebanon Mauritania Morocco Oman Saudi Sudan Syria Tunisia Turkey UAE Yemen
1.005 1.000 1.005 1.005 1.024 1.000 1.000 0.984 0.982 0.994 1.012 0.993 0.990 0.992 1.012 1.005 1.000 1.013
1.024 1.055 1.020 1.025 1.031 1.047 1.039 1.049 1.019 1.023 1.033 1.032 1.024 1.036 1.024 1.036 1.018 1.025
1.029 1.055 1.025 1.031 1.056 1.047 1.039 1.032 1.001 1.017 1.045 1.025 1.014 1.027 1.036 1.041 1.018 1.039
1.007 1.000 0.998 1.006 1.029 1.000 1.000 0.985 0.975 0.993 1.014 0.992 0.991 0.996 1.010 0.999 1.000 1.015
0.999 1.000 1.007 0.999 0.995 1.000 1.000 0.998 1.007 1.001 0.998 1.001 0.999 0.996 1.002 1.006 1.000 0.998
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Table 7 Efficiency changes over the period 1997–1998 Country
Technical efficiency, E
Technology, T
Malmquist productivity index, M
VRS technical efficiency
Scale efficiency
Algeria Bahrain Comoros Egypt Iran Jordan Kuwait Lebanon Mauritania Morocco Oman Saudi Sudan Syria Tunisia Turkey UAE Yemen
0.997 1.000 0.993 1.000 1.038 1.000 1.000 1.002 0.989 1.000 1.018 1.012 0.984 0.998 0.999 1.012 1.000 1.020
1.024 1.005 1.032 1.023 1.024 1.063 1.039 1.031 1.022 1.025 1.029 1.025 1.030 1.036 1.024 1.028 0.978 1.024
1.021 1.005 1.024 1.023 1.063 1.063 1.039 1.033 1.011 1.025 1.048 1.037 1.014 1.033 1.022 1.040 0.978 1.044
0.990 1.000 0.996 0.991 1.030 1.000 1.000 1.001 0.990 0.993 1.012 1.005 0.990 1.006 0.993 1.004 1.000 1.005
1.006 1.000 0.997 1.009 1.008 1.000 1.000 1.001 0.999 1.007 1.006 1.007 0.995 0.992 1.006 1.008 1.000 1.015
Mauritania (1.001). The countries considered efficient in the DEA analysis (Bahrain, Jordan, Kuwait and UAE) showed varying levels of improvement in terms of MPI, with Bahrain registering the highest. In terms of contribution to MPI improvement, technology change was more impactive than technical efficiency change. Yet, the technology changes were all relatively modest. This suggests that change, if any, is occurring at only a modest pace in these countries. Some countries (Lebanon, Mauritania, Morocco, Saudi Arabia, Sudan and Syria) registered a decline in technical efficiency change, although all the 18 countries registered improved technology change. Finally, Table 6 also shows that the MENA countries have achieved varying levels of improvement in their VRS technical and scale efficiencies. Table 7 gives index changes for the period 1997–98. Interestingly, the results are similar to those reported for the period 1998–99 (given in Table 6) with the most notable change being the decline in MPI for the UAE during 1997–98.
5. Policy implications The results of our analyses have interesting policy implications for development of the MENA region. We wish to stress here that findings of the study are critically based on the choice of attributes, and, hence, the policy implications discussed below should be considered within this perspective.
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The current study found but 22% of the 18 countries studied as efficient. The smallest CRS efficiency score was 0.306, for Yemen. This is a significant result highlighting the wide disparity in socio-economic status amongst MENA countries. Three of the four efficient countries are members of the GCC, and net oil exporters. Our regression analysis found a significant relationship between richness of the country to its efficiency. Hence, rich countries with high oil revenues appear to perform much better than other countries. The UAE seems to be a peer for many inefficient countries. The latter could thus follow social and economic policies practiced in the UAE in efforts to improve their performances. An MPI analysis over the period 1998–1999 showed that, on average, there was improvement in the social and economic performances of the MENA countries. This is definitely a healthy trend, pointing to the overall improvement in these economies, at least over the study period. As reported earlier, in general, technology change was more impactive than technical efficiency change in terms of contribution to MPI improvement. If technology change can be interpreted similar to change in social practices, it could be concluded that healthy changes in social practices are taking place in MENA countries. However, the technology changes were all relatively modest, implying that the social reforms in these countries are occurring at only a modest pace. Some countries, such as Lebanon, Mauritania, Morocco, Saudi Arabia, Sudan and Syria, actually registered declines in technical efficiency change. This might highlight the relative ineffectiveness of social and economic policies that are not well directed in those countries. Appropriate actions would thus be necessary to reverse such a trend.
6. Summary and conclusions In this paper, the economic and social performance of 18 countries of the MENA region were compared using DEA with seven performance attributes. For 1999, four of the 18 were found to be efficient: Bahrain, Jordan, Kuwait and UAE. All are from the Middle East, with three being members of the GCC. Yemen was rated as the least efficient of all countries considered in the analysis. A regression analysis showed that the efficiency scores have a significant relationship with country richness (in terms of GNP per capita), but do not have a significant relationship with the country size (in terms of population). Further, a time-series analysis of country performance was carried out using the MPI. The analysis suggested that, for all 18 countries, there was progress in achieving higher values of desirable attributes and lower values of undesirable attributes in 1999 vs. 1998. During 1998–1999, technology change contributed more to improvements in MPI than did technical efficiency change.
Acknowledgements The author is grateful to the anonymous reviewers and to the Editor-in-Chief, Professor Barnett R. Parker, for their helpful comments and suggestions on earlier drafts of this paper.
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Ramakrishnan Ramanathan is Assistant Professor, Department of Operations Management and Business Statistics, College of Commerce and Economics, Sultan Qaboos University, Muscat, Sultanate of Oman. Previously, he was Associate Professor, Indira Gandhi Institute of Development Research, Mumbai, India, Visiting Professor, Helsinki
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University of Technology, Finland, and Senior Research Fellow, Delft University of Technology, The Netherlands. Professor Ramanthan’s research interests include rural energy and agricultural policy, environmental impact assessment, transport infrastructure. He has authored two books: An Introduction to Data Envelopment Analysis (Sage Publication, 2003), and Indian Transport towards the New Millennium: Performance, Analysis and Policy (Concept Publishing, 2003). He has more than 50 refereed publications which have appeared in such journals as International Journal of Operations & Production Management, Measuring Business Excellence, Socio Economic Planning Sciences, Ecological Economics, European Journal of Operational Research, The Journal of Multi Criteria Decision Analysis, Energy Policy, Journal of Environmental Management, Energy Economics, Transport Policy, Transportation Research, Environmental Change, IEEE Transactions on Systems, Man and Cybernetics, IEEE Transactions on Power Systems, The International Journal of Global Energy Issues, and Energy.