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Weights Restrictions and Super-Efficiency Measure

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Weights Restrictions and Super-Efficiency Measure ASSESSMENT OF UNIVERSITIES EFFICIENCY USING DATA ENVELOPMENT ANALYSIS: WEIGHTS RESTRICTIONS AND SUPER-EFFICIENCY MEASURE Sourour Ramzi, PhD student Mohamed Ayadi, PhD, Professor Higher School of Management University of Tunis, Tunisia 40 Introduction In order to occupy their place on the labor market, individuals must possess technical, theoretical and relational skills adequate to skills required by the job and corresponding to socio-economic needs of the country. Unfortunately, higher education in Tunisia has undergone over the past decade several changes and reforms that led to a loss of efficiency overlooked the socio-economic needs of the country. With an unemployment share of university graduates over 30% of the total number of unemployed in Tunisia, the higher education has become a mass education system and not a selective one. This deterioration in the quality of higher education has made our universities less competitive at international level. It is then important to assess the efficiency of these universities, to detect their shortcomings and propose solutions for the improvement. DEA models have been widely used to evaluate the efficiency of higher education. They represent a linear programming models proposed by Farrell (1957) and developed by Charnes et al. (1978). In this paper we evaluate the efficiency of 11 homogenous Tunisian universities in 2009, 2012 and 2013 using two input variables “the number of students enrolled in Letter and Human Sciences” and “the number of students enrolled in Computer Sciences, Media and Telecom”. The output variable describing research activity is the number of research units and laboratories. Teaching activity is measured by the number of graduates from Fundamental and Applied Licence. We have introduced in the analysis two fields of DEA development “weights restrictions and super-efficiency measure” to evaluate the performance of universities and to distinguish between efficient and inefficient DMUs. To study the importance of each input and output variables in the efficiency of universities, we have conducted different DEA model specifications. The main results show that the application of DEA in higher education has motivated the use of weights restrictions to avoid the complete freedom of weights’ variation allowed by the original DEA model. The introduction of weights restrictions into DEA model in order to analyze the efficiency of universities increases the discrimination of estimated results. The super-efficiency measure reveals its usefulness for the ranking of efficient universities by attributing to them scores greater than one. The estimation of the different specifications models demonstrate the significant effect of the input variable, the number of students enrolled in Computer sciences media and telecom and the output variable, the number of graduates from Fundamental and Applied science on the efficiency of Tunisian higher education. This paper is organized as follows: in next section we present a description of the general characteristics of higher education in Tunisia. Following section presents the literature review on the related existing literature on the evaluation of the efficiency of higher education using DEA approach with weights restrictions and super-efficiency analysis. Than we present the methodology applied in our analysis. In next section we describe the data collection, variables selection and results estimated. Finally, last section provides the conclusion. Higher Education in Tunisia Since 1959, the Tunisian education has experienced an important progress. The Tunisian government was interested in its development in order to produce a strong 41 human capital able to deal with the change of a nation. Several measures have been set to reduce the centralized government control and bureaucracy, to increase the university’ autonomy and to enhance the employability of higher education graduates. Other specific measures were focused on the vocational training of graduates. Several reforms have been pursued to improve the quality of higher education in Tunisia and to ensure the scientific value of Tunisian diplomas and all they overlap like skills. The principal reform that experienced the higher education is the adoption of the LMD (Licence-Master-Doctorate) system and the technical accreditation that must emanate from an independent organization and address the required quality criteria. The objective of this reform is to adopt the educational system to European Standards. The adoption of the LMD system by the Tunisian authorities was performed 7 years after France and 4 years after Morocco did it. The first wave of academic institutions has adopted the LMD system in 2006-2007 and the second wave was in 2007-2008. It was known under the name of BAC+3+5+8 which means the apportionment of the university cycles. The first cycle is a Licence that lasts 3 years and it includes 180 credits, spread over two semesters; the second cycle consists on a research or professional master degree with duration of 2 years (120 credits). Finally the doctoral degree takes three years. However, Engineering, medicine and architecture studies are not concerned with the LMD reform. The diplomas from old regime such as the “maîtrise” have continued to be awarded provisionally. The graduates from LMD system have passed from 2063 for fundamental Licence and 6128 for Applied Licence in 2009 to 19925 and 32597 respectively in 2011, while graduates from old system have decreased by 99% between 2009 and 2011. Several initiatives have been introduced to train a pool of Tunisian university teachers and professors in the quality-based approach to improve the quality of teaching activity ensured within universities. Tempus, through many projects has contributed to enhance the internal quality assurance and to introduce certification procedures in the Tunisian higher education system. In 2011, the Ministry of Higher education prepared a quality assurance program PAQ (programme d’appui à la qualite) in the objective of enhancing the teaching standards and introducing a new decentralized system. Higher education in Tunisia is provided by Universities, Higher Institutions of Technological Studies (ISET) and Higher Institutes of Teachers Training (IGH). During last years, the number of public universities and the number of university institutions have been increased despite a decrease in enrollment students in Higher education (Figure 1, 2 and 3). 42 Figure 1. Evolution of the Number of Tunisian Public Universities Source: Ministry of Higher education Figure 2. Evolution of the Number of University Institutions Source: Ministry of Higher Education Figure 3. Evolution of Enrolled Students in Higher Education Source: Ministry of Higher Education Figure 4. Expenditure on Higher Education as Percentage of 43 Government Expenditure Source: Institute for Statistics, UNESCO Figure 5. Government Expenditure per Student as % of GDP per capita Source: Institute for Statistics, UNESCO Figure 6. Evolution of the Government Expenditure per Student in PPP$ Source: Institute for Statistics, UNESCO The resources allocated to higher education are mostly budgetary. The Tunisian government spends the equivalent of 1.7% of national GDP for the financing of higher education. This confirms the strong commitment and the high priority accorded by the Tunisian Government on the funding of education (Figure 4). Public spending is to a large extent devoted to operating expenses and mainly the payment of salaries of functionary, whose share in the expenses increased by 74% in 2003 to over 79% in 2012. The evolution of the educational expenditure per student 44 shows the limits of public financing deal with the “massification” of universities (Figures 5 and 6). Literature Review In recent years, several studies have evaluated the efficiency of higher educational institutions using DEA approach. From one study to another, decision making units (DMUs), variables and types of DEA models differ depending on the objective of the research aimed by the authors and the availability of data. Original DEA models (constant returns to scale, variable returns to scale, input oriented and output oriented DEA models) were used to measure the relative efficiency scores attributed to Universities, departments, etc. Abbott and Doucouliagous (2003) apply this approach to evaluate technical and scale efficiency of the population composed by 36 Australian government universities in 1995. For teaching activity, the authors used the number of equivalent full time students, number of post-graduate and under-graduate degrees enrolled, the number of post-graduate conferred and the number of under-graduate degrees conferred. Research activity is measured by Research Quantum Allocation that each university receives as well as medical and non-medical research income. The input variables used are the total number of academic and non-academic staff and expenditure used on energy, non-salary academic and administration service. The results show that the relative efficiency scores estimated indicated a high level of technical efficiency in Australian universities but this doesn’t neglect the presence of margin for improvement in several inefficient universities. Some other studies have focused on increasing the distinction between efficient DMUs with Data Envelopment Analysis. Two types of methods can be used to achieve this goal. Those that require a priori information provided by the decision maker or analyst on the importance of variables
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