Tertiary Education Sector: How Efficient are Malaysia and the OIC Countries?

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1 Tertiary Education Sector: How Efficient are Malaysia and the OIC Countries? Dr. Mohd Nahar Mohd Arshad Centre for Islamic Economics International Islamic University Malaysia Abstract The purpose of this study is to investigate the level of technical efficiency of tertiary education in a selected OIC and ASEAN countries, as well as Israel. Education efficiency has become an important issue given the pressing levels of public deficit and debt of many countries. With budget constraints and high expectation by the public to see a continuous improvement in tertiary educational sector, waqf or endowment is viewed as a potential alternative avenue for resource generation and maintenance. Before such alternative is further explored, this study probes into the current state of resource utilization in realizing the goals of tertiary education. The technique used to calculate the level of technical efficiency is Data Envelopment Analysis (DEA). Among the 43 selected OIC countries, 35 of them are technically inefficient in utilising their tertiary educational resources to achieve better number of graduates and research quality and quantity. The findings ring the alarm regarding the way educational resources have been managed. Further concern is raised particularly when waqf becomes the way how resources are generated to run the institutions of higher learning. Keywords: Technical efficiency; OIC countries; tertiary education; Data Envelopment Analysis; Malaysia. 1.0 Introduction The purpose of this study is to analyse the trends of investment in tertiary education in Malaysia, in comparison with selected OIC and ASEAN countries and to assess the efficiency level of the investment made in higher education. Education efficiency has become an important issue given the pressing levels of public deficit and debt of many countries. In a case of tertiary educational sector, managing the operations of institutions of higher learning requires substantial budget allocations. The public, expect to see a continuous improvement and better educational outcomes 1

2 from such huge investment made. These situations have put governments and universities management under pressure to deliver the expected educational outputs. Have the investment made in tertiary education in Malaysia, the OIC and ASEAN countries, been technically efficient? Perhaps, countries have invested more than necessary for the current level of their achievements; or may be, some countries have underinvested in higher education, thus unable to realize the set goals. This study probes into the issues. Its findings allow for more informed debate in policies related to higher education planning. Focus of this study is on tertiary education. Since the dawn of the 21 st century, many developing countries had considered tertiary education as a strategic tool for their human capital development. Schultz (1963) points that while primary education might suffice for basic production of goods and services, workers with secondary education can use technology in the workplace, and tertiary education is considered important in the process to invent and to innovate technology. For developing countries to transform their economies, a proportionally qualified workforce are needed. Tertiary education remains as a crucial stage for developing countries to develop their human capital as a natural progression towards becoming a developed economy. For that matter, an evaluation of whether the investments made in these countries tertiary education are efficient merits further scrutiny. The presentation of the paper is as follows. In the next section, the performance of tertiary education sector in Malaysia is compared with the other selected countries. A review of the theories and models of Data Envelopment Analysis (DEA) is presented in Section 3. In the section, the application of DEA models in education is offered. In Section 4, I present findings from the analysis. Further discussions of the results in relation to waqf are discussed in Section 5. Some concluding remarks are presented in Section 6. 2

3 2.0 How Malaysia Fare Internationally? Seminar on Sustainable Higher Education Model based on Waqf To evaluate trends of tertiary education investment and performance of Malaysia at the international level, a cross-sectional dataset comprising of OIC and ASEAN countries, as well as Israel, are compared across one another. Two educational input variables employed for the analysis are each country s educational expenditure in tertiary education as a percentage of total current educational expenditure (both public and private) and the ratio of each country s total tertiary enrolment to teaching staff (public and private). Each respective variable represents the financial investment and human resource capacity of each country. Performance of tertiary educational sector for each country, on the other hand, is represented by two output variables, namely, total graduates to tertiary age population (public and private) and H-index of each country. The first variable measures the quantity of graduates produced and the second variable measures the quantity and quality of published research papers produced. All variables are based on year 2010, for the year consists almost complete cells of data for each country. Sources of the data are from the World Bank s database on Education Statistics 1, SCImago Journal and Country Rank database 2 and the Statistical, Economic and Social Research and Training Centre for Islamic Countries (SESRIC). 3 The analysis is limited to the 49 countries because of the following factors: i) To make a comparable analysis, countries with similar socioeconomic conditions with Malaysia are considered. All the selected countries are considered as developing countries, with the exception of Israel. 1 The database is retrievable for free at 2 The database is available at: 3 Input data for the OIC countries are obtained from SESRIC database at 3

4 ii) Seminar on Sustainable Higher Education Model based on Waqf The selected OIC countries are included for religious similarity. Israel is included for geopolitical issues that for so long have sparked controversies with Muslim nations. The selected ASEAN countries are included for regional economic similarity with Malaysia. iii) The absence of several OIC and ASEAN countries are due to data discrepancies. Countries are dropped from the sample when none, or one, or at the most two, data cells from the four variables considered are available. Singapore, for instance, is dropped for this reason. Summary statistics of the selected countries input-output variables are shown in Table 1. As shown in the table, the average scores of the OIC countries in both total graduates to tertiary age population and in H-index score are lower than the entire sample averages. Further, a more concerning point can be observed between the performance of the OIC and the ASEAN countries. The performance of universities from the OIC countries is on average significantly lower than their ASEAN counterparts despite of having slightly better educational inputs (as represented by educational expenditure in tertiary as a percentage of total educational expenditure and total tertiary enrolment to teaching staff in tertiary education). The gap is a clear indication of how far behind is the OIC countries in terms of meeting the global education standard. 4

5 Table 1: Summary statistics of output and input variables Sample Statistics Mean Std dev Min Max Obs. All Total graduates to tertiary age population H-Index Educational expenditure in tertiary as % of total educational expenditure Total tertiary enrolment to teaching staff OIC Total graduates to tertiary age population H-Index Educational expenditure in tertiary as % of total educational expenditure Total tertiary enrolment to teaching staff ASEAN Total graduates to tertiary age population H-Index Educational expenditure in tertiary as % of total educational expenditure Total tertiary enrolment to teaching staff

6 Countries Seminar on Sustainable Higher Education Model based on Waqf Bahrain Thailand Jordan Turkey Lebanon Albania Malaysia Kyrgyz Egypt Azerbaijan Brunei Philippines Saudi Arabia Tajikistan Oman Indonesia Algeria Vietnam Kuwait Libya Maldives Suriname Syria Myanmar Namibia Morocco Uzbekistan Cambodia Cameroon Bangladesh Benin Qatar Gambia Djibouti Burkina Faso Mozambique Afghanistan Kazakhstan Guinea Niger Israel Iran Gabon Togo Sierra Leone Uganda Mauritania Somalia Mali Figure 1: Total graduates to tertiary age populatiom of selected countries (2010) Ratio

7 In Figure 1, each country s total graduates produced to tertiary aged population is graphed from the highest to the lowest ratios. Malaysia is ranked 7 th with the ratio of total graduates to tertiary aged population of This ratio is higher than the overall sample average, the OIC average and the ASEAN average, as shown in Table 1. There is however, gaps between Malaysia and the highest scoring country, Bahrain (11.16), suggesting potential area for improvement. Those countries with higher ratios, such as Bahrain and Thailand, as compared to Malaysia could be the potential exemplars for Malaysia to learn from in order to improve its tertiary education sector. In terms of research quantity and quality, Figure 2 illustrates the positions of countries based on their H-index, from the highest to the lowest. Malaysia is ranked 6 th in the figure, after Israel, Turkey, Thailand, Iran and Egypt. In general, OIC countries perform poorly in producing quality research outputs. As illustrated in Figure 2, 11 of the selected OIC countries are at the lower side of the chart with Somalia got the lowest score (13). Figure 2 also shows a clear gap between the OIC and ASEAN countries with Israel. Despite of geopolitical turmoil in the Middle East for decades, Israel has managed to achieve significantly higher H-index. Efforts to produce more research with high level of quality should be the focus of universities in OIC and ASEAN countries in order to transform their economies. Research and development are the core areas considered as vital in universities and nation development. Serious efforts to reduce this gap need to be undertaken if the OIC and ASEAN countries are serious to change their future into a better one. 7

8 Countries Seminar on Sustainable Higher Education Model based on Waqf Figure 2: H-index of selected countries (2010) Index Israel Turkey Thailand Iran Egypt Malaysia Saudi Arabia Philippines Indonesia Vietnam Bangladesh Uganda Lebanon Morocco Jordan Kuwait Algeria Gambia Cameroon Oman Burkina Faso Gabon Syria Namibia Mozambique Qatar Kazakhstan Mali Uzbekistan Cambodia Benin Niger Azerbaijan Mauritania Brunei Darussalam Bahrain Libya Myanmar Albania Guinea Kyrgyz Republic Togo Suriname Afghanistan Tajikistan Sierra Leone Maldives Djibouti Somalia

9 For the input variables, as shown in Table 1, the average allocation of educational expenditure for tertiary education as a percentage of total current education expenditure for the 49 selected countries is per cent with a standard deviation of In Figure 3, I present each country s tertiary educational expenditure as a percentage of total current education expenditure in a bar chart. Albania is the country with the highest allocation to tertiary education at 36.4 per cent and Algeria is the country with the lowest allocation of only 3.6 per cent refer to Figure 3. Malaysia, on the other hand, has an allocation of 9.9 per cent, lower than the overall average, the OIC average and the ASEAN average. Caution however needs to be exercised when comparing countries using this percentage data. High income countries (with high GDP), for example, may have a small percentage allocation but in terms of absolute value, the figures can be higher than a low income country with high percentage allocation. By using the percentage figure for the analysis, countries educational allocations are normalized and more comparable. To evaluate the student-teacher ratio, total tertiary enrolment to teaching staff in tertiary education ratio is employed. As presented in Table 1, the overall average ratio is 22.08, while the OIC countries ratio is much lower at ASEAN countries ratio (24.30), on the other hand, is higher than the overall average. The range of minimum and maximum student-teacher ratio is between 0.45 to 69 students per teacher. As shown in Figure 4, the level of student-teacher ratio for Malaysia is 4.88 students per teacher. Togo has the highest student-teacher ratio and Qatar has the lowest ratio. Lower ratio signifies a better learning and teaching conditions because instructors/teachers can give more focus to the small number of students. 9

10 Countries Seminar on Sustainable Higher Education Model based on Waqf Figure 3: Educational expenditure in tertiary as % of total current educational expenditure of selected countries (2010) Per cent Albania Morocco Oman Mali Azerbaijan Turkey Sierra Leone Bangladesh Lebanon Myanmar Kazakhstan Syria Brunei Vietnam Jordan Thailand Burkina Faso Gambia Benin Libya Israel Saudi Arabia Djibouti Indonesia Afghanistan Egypt Qatar Somalia Suriname Uzbekistan Cameroon Kyrgyz Republic Uganda Kuwait Maldives Niger Philippines Gabon Bahrain Guinea Tajikistan Malaysia Iran Mozambique Mauritania Namibia Cambodia Togo Algeria

11 Countries Seminar on Sustainable Higher Education Model based on Waqf Figure 4: Total tertiary enrolment to teaching staff in tertiary education of selected countries (2010) Ratio Togo Mali Morocco Cameroon Kyrgyz Myanmar Tajikistan Syria Thailand Afghanistan Turkey Vietnam Indonesia Algeria Egypt Bangladesh Philippines Libya Gambia Iran Djibouti Bahrain Somalia Guinea Saudi Arabia Cambodia Burkina Faso Benin Kuwait Namibia Kazakhstan Mauritania Gabon Uzbekistan Albania Niger Suriname Brunei Lebanon Sierra Leone Azerbaijan Jordan Mozambique Malaysia Israel Maldives Uganda Oman Qatar

12 3.0 Data Envelopment Analysis (DEA) Seminar on Sustainable Higher Education Model based on Waqf Two basic DEA models have been widely applied: (i) the constant returns to scale (CRS) model of Charnes, Cooper and Rhodes (1978), and (ii) the variable returns to scale (VRS) model of Banker, Charnes and Cooper (1984). In Figure 5, I illustrate the theoretical idea behind the two principal approaches to DEA frontier analysis and the derivation of technical efficiency measures based on the DEA frontier. The figure is constructed based on a single-input, single-output case. The simplification enables the production process to be described in a simple two-dimensional diagram. In Figure 5, points A, B, C and D represent the observed performance of four decision making units (called DMUs the DMUs in this study are those selected countries), given their level of input and output and production technology. The CRS model is represented by the thin line extending from the origin of Figure 5 through point B, where the DMU B is chosen to maximise the angle of the ray. The thin line is the production frontier as identified under the CRS model. Based on the CRS model, the DMU B is identified as the most efficient DMU since it lies on the frontier. Point B is therefore, CRS-efficient. Other DMUs (A, C and D), which lie below the frontier, are inefficient under the CRS model. Still referring to Figure 5, the VRS model is illustrated by the solid thick lines that connect points A and B, and B and C. The solid lines depict the so-called VRS production frontier. The VRS model has its production frontier spanned by the convex hull of the DMUs (from point A to B, and B to C). The frontier is piecewise linear and concave. The VRS-frontier assumes variable returns to scale where: (i) increasing returns to scale occurs in the first solid line (AB) segment, and (ii) decreasing returns to scale in the second segment (BC) (Cooper et al., 2006, pp ). Note that points A, B and C are on the frontier and are therefore VRS- 12

13 efficient. Point D, on the other hand, is the inefficient DMU because it lies below the frontier (Cooper et al., 2000). Output (Y) Figure 5: The best-practice reference frontier U CRS-efficient frontier B S C VRS-efficient frontier P Q R D A T 0 * Source: Based on Coelli et al. (2005, p. 174) Input (X) Given the CRS-efficient and VRS-efficient frontiers, an inefficient DMU has two major projection paths to improve its performance, namely, (i) an input-oriented path, and (ii) an output-oriented path (Cooper et al., 2000). The input-oriented path aims at reducing the input amounts by as much as possible while keeping the present output levels unchanged. The outputoriented path aims at maximising output levels under the given input consumption. For the study, both output- and input-oriented paths are adopted for a better comparison of the countries performance. Referring to Figure 5, the output-oriented model identifies technical efficiency as a proportional augmentation of output for a given level of input. Under the VRS model, the 13

14 inefficient DMU D can improve its performance by a movement to point S. The movement to point S means DMU D needs to increase its output level given the amount of inputs it has. As such, the VRS technical efficiency of DMU D under the output-oriented path (OTEVRS) is given by: OTEVRS = SD/ST (1) With the understanding of the theoretical concept of DEA in mind, I present results of the DEA analysis in the next section. 4.0 Results The efficiency scores based on output- and input-oriented DEA were obtained using a software package called DEAP Summary statistics of the DEA results based on the output- and input-oriented DEA for the selected 49 countries are presented in Table 2. The range of efficiency scores are from 0 (for the least efficient) to 1 (or 100 per cent for the most efficient). As shown in the table, on average, the CRS-technical efficiency is 0.40 or 40 per cent for both output- and input-oriented DEA. The average technical efficiency under the VRS assumption, on the other hand, is 53 per cent with a standard deviation of 33 per cent for the output-oriented DEA. Under the input-oriented DEA, however, the VRS-technical efficiency is 61 per cent with a standard deviation of 28 per cent. These average efficiency scores are considered low, and should raise concerns for serious efforts to be made to improve the competitiveness of many countries involved in the sample. The higher VRS efficiency under the input-oriented DEA as compared to VRS efficiency under output-orientation, suggests that countries are more efficient at reducing the input amounts by as much as possible (input conservation) while keeping the 4 The software package is downloadable for free from The Centre of Efficiency and Productivity Analysis (CEPA) at 14

15 present output levels unchanged rather than utilizing the existing inputs for achieving much higher outputs of education (output augmentation). In Table 3, details of each country s scores based on output-oriented DEA are shown. Somalia is the country with the lowest technical efficiency scores under both the CRS and VRS assumptions with only 3 per cent CRS and VRS technical efficiency scores. In other words, due to technical inefficiency, Somalia has failed to realise its potential output more than the other countries. Sic countries have recorded a CRS-technical efficiency score of 100 per cent, namely, Algeria, Bahrain, Israel, Malaysia, Oman and Qatar. Ten countries have the maximum score of 100 per cent under the VRS assumption Algeria, Bahrain, Israel, Malaysia, Oman, Qatar, Jordan, Thailand, Namibia and Mozambique (refer to Table 3). These countries form the VRS frontier against which the performance of the other countries are evaluated. Tertiary education in Malaysia has been found to be technically efficient in terms of output-oriented DEA. Indonesia, on the other hand, only 53.2 per cent technically efficient under the VRS assumption. For Indonesia, given its 2010 tertiary educational inputs, the country could have actually performed better in producing more graduates and scoring higher H-index. Table 2: Summary statistics of DEA efficiencies for all 49 selected countries Output-oriented Input-oriented Statistics CRS VRS Scale CRS VRS Scale Mean Std dev Min Max

16 Table 3: Efficiency scores based on output-oriented DEA Countries CRS VRS Scale Returns to scale Algeria cons Bahrain cons Israel cons Malaysia cons Oman cons Qatar cons Jordan drs Thailand drs Namibia irs Mozambique irs Turkey drs Lebanon drs Cambodia irs Uganda irs Albania drs Egypt drs Philippines drs Iran irs Kyrgyz drs Maldives irs Saudi Arabia drs Azerbaijan drs Indonesia drs Brunei drs Tajikistan drs Kuwait drs Vietnam drs Mauritania irs Morocco drs Togo irs Bangladesh drs Suriname drs Cameroon drs Syria drs Uzbekistan drs Libya drs Myanmar drs Gambia drs Benin drs 16

17 Gabon irs Burkina Faso drs Niger cons Kazakhstan drs Guinea drs Mali drs Afghanistan drs Djibouti drs Sierra Leone drs Somalia cons Note: cons stands for constant returns to scale; irs stands for increasing returns to scale and drs stands for decreasing returns to scale In terms of input-oriented DEA, the efficiency scores of each country are shown in Table 4. The same countries identified as CRS and VRS efficient under the output-oriented DEA (Table 3), are also efficient under the input-oriented DEA. Malaysia, also has been technically efficient under the input orientation approach. Turkey, for example, has 92.5 per cent level of technical efficiency under the VRS assumption. In other words, given its 2010 tertiary educational outputs, Turkey could have improved its efficiency by another 7.5 per cent with a strategy of using much lower educational inputs. Table 4: Efficiency scores based on input-oriented DEA Country CRS VRS Scale Returns to scale Algeria cons Bahrain cons Israel cons Malaysia cons Oman cons Qatar cons Jordan drs Thailand drs Namibia irs Mozambique irs Cambodia irs Togo irs Uganda irs Maldives irs 17

18 Turkey drs Mauritania irs Iran irs Lebanon drs Philippines irs Gabon irs Guinea irs Egypt drs Kuwait irs Niger irs Saudi Arabia irs Suriname irs Tajikistan irs Kyrgyz irs Brunei irs Uzbekistan irs Indonesia irs Azerbaijan irs Benin irs Libya irs Somalia irs Djibouti irs Albania drs Gambia irs Sierra Leone irs Afghanistan irs Burkina Faso irs Vietnam irs Cameroon irs Kazakhstan irs Bangladesh irs Syria irs Myanmar irs Morocco irs Mali irs cons stands for constant returns to scale; irs stands for increasing returns to scale and drs stands for decreasing returns to scale 18

19 5.0 Discussions of findings Seminar on Sustainable Higher Education Model based on Waqf In an effort to develop a sustainable waqf model for higher education, especially for Malaysia and the other OIC countries, concern about how tertiary educational resources have been managed stands as a critical issue to be addressed. Countries identified as inefficient, in particular, should learn from their efficient counterparts (superior peers). Experience of the superior peers in utilizing their educational resources should be carefully studied, particularly in areas to better enhance teaching and learning processes, to improve quantity and quality of research, to provide more effective educational technologies and to adopt best-practice education management system. Findings from this study should raise concerns, in particular, for those countries with low scores of efficiency, regarding their resource utilization and management. This concern should be addressed in an urgent manner especially when religious scheme such as waqf is involved in the resource generation process. Even though Malaysia has been identified to be technically efficient, rooms for improvement are many. It is vital to note here that, one reason for the highest efficiency scores for Malaysia has been due to the comparison sets employed for the study. Putting Malaysia alongside OECD countries, South Korea and Singapore, for instance, may lead to a different result. This kind of analysis is a potential area for future investigation, provided that careful treatment is done in controlling for the different environmental factors between Malaysia with those countries. In addition, investigating the performance of higher learning institutions within Malaysia is another plausible avenue for future research. With such study, more details information could be gathered to understand best-practice institutions within the country. Furthermore, analyzing the positions of Malaysia based on Figures 1, 2, 3 and 4, for instance, points to a clear indication that Malaysia is just average in its tertiary education 19

20 achievements and resource allocations. The DEA analyses suggest that Malaysia is efficient in utilizing the existing resources. To improve, more resources are required and waqf is a possible source for the purpose of resource generation and maintenance. 6.0 Conclusion The study investigated the level of technical efficiency of tertiary education of 49 countries, involving some selected OIC and ASEAN countries as well as Israel. Central to the analysis was the assessment of OIC countries technical efficiency in utilizing the allocated educational resources. The technique employed for the analysis was Data Envelopment Analysis (DEA). For the DEA analysis, each country s total graduates to tertiary aged population ratio and H-index score in 2010 were employed as the educational outputs while the inputs were represented by the percentage distribution of current expenditure (public and private) to the tertiary education and student-teacher ratio of tertiary education. From the analysis, low average efficiency scores were found. The low efficiency scores, especially among the OIC countries, raise a serious concern about the way educational resources had been managed. This issue is more concerning particularly when waqf resources are involved. To improve, the identified inefficient countries may learn from the identified superior peers in terms of enhancing teaching and learning processes, improving quantity and quality of research, providing more effective educational technologies and adopting best-practice education management system. 20

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