Review of Economics and Development Studies Vol. I, No 1, June 2015 45 Volume and Issues Obtainable at Center for Sustainability Research and Consultancy Review of Economics and Development Studies ISSN:2519-9692 ISSN (E): 2519-9706 Volume 1: Issue 1 June 2015 Journal homepage: www.publishing.globalcsrc.org/reads Efficiency Analysis of Public and Private Sector Schools of Multan District: A Non- Parametric Approach 1 Sumaira Batool, 2 Fatima Farooq, 3 Imran Abbas, 4 Muhammad Abbas 1 Lecturer, Department of Economics, The Women University Multan, Pakistan, sumairaimran@hotmail.com 2 Assistant Professor, School of Economics, Bahauddin Zakariya University, Multan, Pakistan, fatimafarooq@bzu.edu.pk 3 Ph.D. student, School of Economics, Bahauddin Zakariya University, Multan, Pakistan, imran_thaheem@hotmail.com 4 Assistant Professor, Air University Multan campus ARTICLEDETAILS ABSTRACT History Revised format: May 2015 Available online: June 2015 The purpose of this paper is to evaluate the efficiency of public and private sector secondary and higher secondary schools in Multan district. We use output oriented data envelopment analysis to measure technical and scale efficiency of a sample of 100 public and private sector schools, using data for the year 2014. DEA is employed to compare efficiency of both types of schools because it is the most popular technique used to measure the relative efficiency of non-profit organizations due to the absence of prices or relative values of educational outputs. Moreover, it can handle multiple inputs and outputs with great ease. As public and private schools are working under similar environmental conditions, we have used a single frontier, incorporating four educational inputs and four outputs. The results of the data demonstrate that public schools lag behind private schools in terms of CRS and VRS technical efficiency scores and scale efficiency scores. Our study of schools validates the dominant paradigm that private schools outperform the state-run institutes. © 2015 The authors, under a Creative Commons Attribution- NonCommercial 4.0 Keywords DEA, Efficiency, technical efficiency, scale efficiency, public schools, private schools JEL Classification I12, I15, I18 Corresponding author’s email address: sumairaimran@hotmail.com Recommended citation: Batool, S., Farooq, F., Abbas, I. and Abbas, M. (2015). Efficiency Analysis of Public and Private Sector Schools of Multan District: A Non-Parametric Approach. Review of Economics and Development Studies, 1 (1) 45-56 DOI: https://doi.org/10.26710/reads.v1i1.115 http://www.publishing.globalcsrc.org/reads mailto:sumairaimran@hotmail.com mailto:fatimafarooq@bzu.edu.pk mailto:imran_thaheem@hotmail.com mailto:sumairaimran@hotmail.com Review of Economics and Development Studies Vol. I, No 1, June 2015 46 1. Introduction Education, a basic human need as well as right, plays an important role in all spheres of human life. The importance of education in development process of any nation is quite evident and all economists have acknowledged it. Education is a vital component of human capital as it enhances labour productivity. This productive and skilled labour force plays key role in poverty alleviation and development process of a country. During last three decades or so, privatization has become a dominant paradigm in education sector. Like other parts of the world, Pakistan has witnessed mushroom growth of private educational institutions. The general perception is that private educational institutions are superior to public educational institutions for a number of reasons including better management, accountability to parents, greater scope for innovation by teachers and school management. Literature relating to South Asia almost is in favor of private educational institutions. The concept of privatization of the educational institutes is closely linked to the classical and neo- classical theory of free market economy to provide education (the service) to the students (the customer) in the most efficient way (Rutkowshi & Rutkowshi, 2009). Like all over the world, educational system in Pakistan consists of two types of institutions namely public sector educational institutes and private sector educational institutes. Even when Pakistan came into being in 1947, both types of institutes were engaged in the provision of education as public sector alone cannot fulfill the growing demand of education. In developing countries, particularly in Pakistan, government education sector is hampered by a number of problems such as poor management, non-accountability, political instability, ill qualified teachers, lack of professional competencies, shortage of funds, absence of monitoring mechanism and lack of capital investment in educational sector. The paper attempts to analyze efficiency of public and private sector schools of Multan district. The efficiency of public and private sector schools will be calculated through Data Envelopment Analysis (DEA) indexes of both types of schools. These indexes will be further decomposed to compute technical efficiency of boys and girls schools of both sectors separately. The paper comprises of five sections. The review of previous studies on the issue will be presented in section two, followed by data and methodology. Empirical results will be presented in section four, followed by conclusion. 2. Literature Review Using PISA 2000 data, Dronkers and Robert (2008) measured the differences in scholastic achievement of private and public schools in 22 comparable countries and showed that the higher gross educational outcomes are for private government dependent schools. Again in the same year 2008 Dronkers and Robert analyzed the effectiveness of various types of public and private schools in 19 OECD countries and concluded that performance of private government- dependent schools’ students was higher than the students from public schools. In 2001, Dronkers concluded that privately administered schools performed better in Flemish Belgium, France, Germany, Hungary, the Netherlands, and Scotland. Jimenez, Lockheed and Paqueo (1991) suggested a positive relationship between attending private schools and students’ performance in Colombia, the Philippines, Dominican Republic, Thailand and Tanzania. Research conducted by Asadullah (2009) concluded that Pakistan private schools appeared to be more effective than public schools in boosting students’ achievements. Kingdon (1996) found conducive to greater superiority as such schools were technologically efficient as well as cost-efficient as compared to other types of schools in Utter Pradesh. Like other studies, Chudgar and Quin (2012) also pointed dissatisfaction of parents with the performance of public schools. Using the TIMSS 2003 data, Rutkowski and Rutkowski (2009) concluded that private schools showed significantly higher Review of Economics and Development Studies Vol. I, No 1, June 2015 47 achievements. Coulson (2009) reviewed the research conducted all over the world in the past several decades and concluded that the private sector outperformed the public sector. Braun, Jenkins & Grigg (2006) also came to the same conclusion, when they used NAEP 8 th grade mathematics achievement. They had controlled the data for selected student and school variables. 3. Data and Methodology 3.1. Data Source: Data have been collected through a field survey, with stratified random sampling technique. 3.2. Sampling Size: In our research plan, only those secondary and higher secondary schools of Multan district were included, which are affiliated with Board of Intermediate and Secondary Education, Multan due to time and resource constraints. The reason for this was as BISE provides the results of only affiliated institutions by name while the students of remaining institutions, appearing in the examinations, are treated as private students. Finally, sample from schools was selected as follows: Table 1: Total Number of Schools in Multan district Schools Govt. Pvt. Total Male 115 97 212 Female 58 111 169 Total 173 208 381 Out of 381 total schools, 100 schools were selected as sample. Table 2: Sample Sizes of Schools in Multan district Schools Govt. Pvt. Total Male 30 26 56 Female 15 29 44 Total 45 55 100 3.3. Data Collection Procedure: For the collection of data, a survey was conducted in randomly selected schools of Multan district. These institution were selected from all three tehsils and keeping in view the rural-urban divide. A questionnaire was prepared for this self-administered survey. Matriculation examination results were collected from the BISE and the record of CM extra-curricular activities and some other information were collected from different education and administrative offices. 3.4. Variables of the Study: In DEA model, two types of variables i.e. input and output variables are used. 3.4.1. Input Variables: Review of Economics and Development Studies Vol. I, No 1, June 2015 48 We have taken following four input variables. Table 3: Input Variables Abbreviation Variable NT Number of Teachers NC Number of Class-Rooms ATET Average Teaching Experience of Teachers TE Total Expenditures Output Variables: We have used following four output variables. Table 4: Output Variables Abbreviation Variable NS Number of Students PR Percentage Result WAPPM Weighted Average of Passing Students’ Percentage Marks SECA Score of Extra-Curricular Activities Out of above-mentioned eight variables, six have been used in a number of previous studies. We have included two output variables in our model, which have not been used previously. WAPPM is Weighted Average of Passing Students’ Percentage Marks. This variable is constructed on the basis of the marks of the passing students of the institutes. The variable was developed to capture the percentage marks of all students in an institute. WAPPM was developed by taking the weighted average of the all grades, obtained by the students of the institute. Grades’ minimum marks were taken as the weights and they were multiplied with the number of students of the institution, falling in that grade. SECA is the Score of Extra-Curricular Activities, which is constructed with 3 extra-curricular activities including oral (Speech), written (Essay-writing etc.) and sports, each category having maximum 1 score. If any institution had participated in any level of CM Punjab’s last year competition, it was assigned 0.5 score and for wining a competition, 1 score was awarded to the institution and for nonparticipation no score was awarded. The references of the remaining variables are given in Table 5. Table 5: Input & Output Variables: Variable Name Reference NT Johnes (2005), Abbot & Doucouliagos (2003), Martin (2003), Avkiran(2001) NC Johnes & Yu (2008), Bedi & Garg (2000), Dronkers & Robert (2008) ATET Johnes & Yu (2008), Lassibille & Tan (2010), Oliver, Belluzzo & Pazello (2013) TE Castano & Cabanda (2007), Cuenca (2011), Johnes (2006), Martin(2003) NS Avkiran (2001), Dills & Mulholland (2010), Lassibille & Tan (2010), Johnes & Yu (2008), Bedi & Garg (2000), Johnes (2006) PR Chudgar & Quin (2012), Perelman & Santin (2011), Review of Economics and Development Studies Vol. I, No 1, June 2015 49 Dronker & Robert (2008), Horowitz & Spector (2005), Rutkowski & Rutkowski (2009), Cavalcanti, Guimaraes &Sampaio (2010). 3.5. Analytical Tool: Data Envelopment Analysis has been used for analysis in the study. The linear programming method of DEA is based on frontier approach. For relative performance, DEA is most suitable frontier method. Dyson, et al. (1998) suggested that sample size of DMUs should be greater than the product of number of inputs and outputs while Stern, et al. (1994) recommended that number of DMUs should be greater than thrice the sum of inputs and outputs. Max [2(m×n),3(m+n)] 3.6. Area Profile: Multan district, with an area of 3,721 square kilometres, has three tehsils including Multan, Shujabad and Jalapur Pirwala. According to 1998 census, Multan district’s population was 3,116,851, with 42 percent urban population. Now the population is estimated around 7 million. Literacy rate in Multan district is estimated to be 66 percent (BOS 2013). In Multan district, there are a total number of 1,397 public sector educational institutions. Out of which, 1,012 are located in urban areas and the remaining 385 are in rural areas. The total enrolment of students in these institutions is 350,101 (153,350 in urban area institutes and 196,751 in rural area institutions. As many as 10,227 teachers are serving in these institutions. Out of these, 5,395 are teaching in urban area institutions and 4,832 are teaching in rural area institutions. 3.7. Descriptive Analysis: Table 6: Summary Statistics of Schools’ Data Descriptive Statistics of Schools N Minimum Maximum Mean Std. Deviation NT 100 12 104 43.51 20.79165 NC 100 10 88 29.73 13.29301 ATET 100 5 22 11.96 4.02748 TE 100 6900000 78000000 24734831 14407124.55 NS 100 386 3862 1317.6 727.33279 WAPPM 100 54.55 100 83.1502 10.94726 PR 100 51.47 75.53 59.9409 5.63311 SECA 100 0 3 1.25 0.8056 Correlation Matrix Schools’ Data: Table 7: Correlation Matrix of Schools’ Data Correlation Matrix of Schools Data NT NC ATET TE NS PR WAPPM SECA NT 1 0.746027 0.282874 0.942934 0.921178 -0.15052 -0.02949 0.330323 NC 0.746027 1 -0.16624 0.741798 0.763479 0.030713 0.333782 -0.0573 ATET 0.282874 -0.16624 1 0.320228 0.241282 0.106399 -0.2899 0.75029 TE 0.942934 0.741798 0.320228 1 0.895777 -0.10834 0.042298 0.350212 NS 0.921178 0.763479 0.241282 0.895777 1 -0.11891 -0.0211 0.314621 PR -0.15052 0.030713 0.106399 -0.10834 -0.11891 1 0.634734 0.123538 Review of Economics and Development Studies Vol. I, No 1, June 2015 50 WAPPM -0.02949 0.333782 -0.2899 0.042298 -0.0211 0.634734 1 -0.10606 SECA 0.330323 -0.0573 0.75029 0.350212 0.314621 0.123538 -0.10606 1 4. Public and Private Schools’ Efficiency: An Empirical Analysis The ability to produce the output with the minimum inputs required is called efficiency (Sherman, 1988). Abbot (2003) explains that “Technical efficiency investigates how well the production process converts inputs into outputs while Scale efficiency shows the extent by which an institution can take the advantage of return to scale by altering its size towards the optimal size”. An institution can be technically efficient even if with too much or too little output. Scale efficiency provides the information about the scale of production. The results are computed using Solver and DEAP software. CRS technical efficiency represents overall efficiency and VRS technical efficiency shows pure technical efficiency while scale efficiency is measured as a ratio of CRS to VRS technical efficiency scores. For the segregation of pure technical efficiency from scale efficiency, technical efficiency is measured on both CRS and VRS models. DEAP software has been used to compute results. Table 8: DEA Results for Efficiency Comparison (Public & Private Schools) Institutions EFFECIENCY CRS TECHNICAL EFFICIENCY VRS TECHNICAL EFFICEINCY SCALE EFFICIENCY Public Mean 0.849 0.949 0.892 Median 0.865 0.98 0.886 Private Mean 0.897 0.958 0.936 Median 0.931 0.986 0.972 All Mean 0.876 0.954 0.916 Median 0.886 0.983 0.943 T test P- value 0.035 0.471 0.012 Source: Author’s estimations Figure 1: DEA (CRS) Results for Efficiency Comparison (Public & Private Schools) Review of Economics and Development Studies Vol. I, No 1, June 2015 51 Figure 2: DEA (VRS) Results for Efficiency Comparison (Public & Private Schools) Figure 3: DEA (Scale) Results for Efficiency Comparison (Public & Private Schools) 0.8 0.82 0.84 0.86 0.88 0.9 0.92 0.94 Mean Median Mean Median Mean Median Public Private All CRS (Technical Efficiency) 0.849 0.865 0.897 0.931 0.876 0.886 0.93 0.94 0.95 0.96 0.97 0.98 0.99 Mean Median Mean Median Mean Median Public Private All VRS (Technical Efficiency) 0.949 0.98 0.958 0.986 0.954 0.983 Review of Economics and Development Studies Vol. I, No 1, June 2015 52 Results of the data demonstrate that public schools lack behind private schools in terms of CRS and VRS technical efficiency scores, and scale efficiency score. There exists significant difference in CRS technical efficiency score and scale efficiency score. Table 9: DEA Results for Efficiency Comparison (Public & Private Boys’ Schools) Institutions EFFECIENCY CRS TECHNICAL EFFICIENCY VRS TECHNICAL EFFICEINCY SCALE EFFICIENCY Public Mean 0.817 0.934 0.871 Median 0.794 0.947 0.859 Private Mean 0.884 0.945 0.935 Median 0.906 0.978 0.963 All Mean 0.848 0.939 0.901 Median 0.85 0.967 0.901 T test P- value 0.04 0.001 0.404 Source: Author’s estimations Results of the data demonstrate that public boys schools lack behind private boys schools in terms of CRS and VRS technical efficiency scores, and scale efficiency score. Significant difference exist in CRS technical efficiency score and scale efficiency score. Table 10: DEA Results for Efficiency Comparison (Public & Private Girls’ Schools) Institutions EFFECIENCY 0.84 0.86 0.88 0.9 0.92 0.94 0.96 0.98 Mean Median Mean Median Mean Median Public Private All Scale Efficiency 0.892 0.886 0.936 0.972 0.916 0.943 Review of Economics and Development Studies Vol. I, No 1, June 2015 53 CRS TECHNICAL EFFICIENCY VRS TECHNICAL EFFICEINCY SCALE EFFICIENCY Public Mean 0.909 0.954 0.916 Median 0.942 1 0.942 Private Mean 0.915 0.979 0.934 Median 0.943 0.998 0.972 All Mean 0.911 0.972 0.936 Median 0.943 1 0.971 T test P- value 0.04 0.001 0.404 Source: Author’s estimations Significant difference exist in VRS technical efficiency score and scale efficiency score as the results of the data demonstrate that private girls schools are performing better in comparison with the public girls schools in terms of CRS and VRS technical efficiency scores, and scale efficiency score. Table 11: DEA Results for Efficiency Comparison (Boys & Girls Government Schools) Institutions EFFECIENCY CRS TECHNICAL EFFICIENCY VRS TECHNICAL EFFICEINCY SCALE EFFICIENCY Boys Mean 0.817 0.934 0.871 Median 0.794 0.947 0.859 Girls Mean 0.915 0.979 0.934 Median 0.942 1 0.942 All Mean 0.849 0.949 0.892 Median 0.865 0.98 0.886 T test P- value 0.04 0.001 0.404 Source: Author’s estimations Results of the data demonstrate that public girls schools have performed better as compared to public boys schools in terms of CRS and VRS technical efficiency scores, and scale efficiency score. Significant difference exist in CRS and VRS technical efficiency score and scale efficiency score. Table 12: DEA Results for Efficiency Comparison (Boys & Girls Private Schools) Institutions EFFECIENCY Review of Economics and Development Studies Vol. I, No 1, June 2015 54 CRS TECHNICAL EFFICIENCY VRS TECHNICAL EFFICEINCY SCALE EFFICIENCY Boys Mean 0.884 0.945 0.916 Median 0.906 0.978 0.963 Girls Mean 0.909 0.954 0.935 Median 0.943 0.998 0.972 All Mean 0.897 0.958 0.936 Median 0.931 0.986 0.972 T test P- value 0.04 0.001 0.404 Source: Author’s estimations Results of the data demonstrate that private girls schools’ performance is better than private boys schools in terms of CRS and VRS technical efficiency scores, and scale efficiency score and the difference is significant. 5. Conclusion and Policy Implications Using DEA, CRS Input oriented model, our findings conclude that private schools are performing better as compared to government owned schools and colleges. Our study validates the dominant paradigm that private schools outperform the state run institutes. The efficiency of private schools is attributed to a number of school and student related factors. School related factors include better educated teachers, a huge stock of physical resources and infrastructure at the disposal of private schools, their accountability to the parents of students as well as their better management practices. Other factors affecting the efficiency of private schools are related to students’ rich and educated family background. In private schools, teacher absenteeism/skipping classes is almost zero because management is accountable to the parents who pay huge amounts of fee. Private schools have low student–teacher ratio as compare to public schools which is also helpful to increase their efficiency. On the other hand, the efficiency of public schools is hampered by a number of problems such as extra duties of teachers, poor management, non-accountability, political instability, shortage of funds, absence of monitoring mechanism and lack of capital investment in educational sector. Keeping in view the results of the study, it is suggested that the government should give incentives to private sector but lower and middle classes should not be left at the mercy of private sector, which considers education as a business. Better infrastructure should be provided and strict monitoring system should be introduced to enhance public sector schools’ performance and school teachers should be exempted from all extra duties. Policy of public private partnership should be implemented. References Abbot, M., & Doucouliagos, C. (2003). The Efficiency of Australian Universities: A Data Envelopement Analysis. Economics of Education, 22, 89-97. Asadullah, M. N. (2009). 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