Overhead Grants’ Usage and Educational Institutions in Nigeria: Data Envelopment Analysis Perspective
DOI:
https://doi.org/10.14738/abr.1003.11932Keywords:
Decision Making Units, Federal Educational Institutions, Data Envelopment Analysis, Overhead Grants’ UtilizationAbstract
This study appraised the utilization of overhead grants’ efficiency among federal educational institutions in Nigeria between2011-2019. Data for the study were sourced from the Annual Audited Financial Statements of the public sector entities. The sampled size for the study comprised (25) federal educational institutions out of 69 federal educational institutions drawn across the country among four (4) geo-political zones. Data were analyzed using Data Envelopment Analysis. The results of the average efficiency scores from both CCR and BBC models showed that the entities were averagely efficient in overhead grants’ utilization. Overall results showed that federal educational institutions have high capacity to absorb sufficient overhead grants from the center. The study therefore concluded that there is need for an improved overhead grants releases to the federal educational institutions to achieve effective service deliveries of their core mandates. The study recommended a continuous assessment and periodic appraisal of the overhead grants’ utilization among the institutions by their supervising ministry to achieve full efficiency.
References
Abdulkareem, A. Y., & Oyeniran, S. (2011). Managing the Performance of Nigerian universities for suitable development using data envelopment analysis. International Journal of Academic Research in Business and Social Science, 15(1), 54 – 67.
Agasisti, T., & Johnes, G. (2009). Comparing the efficiency of higher education decision making units across more than one country. Education Economics, 17, 59 -79.
Ahn, T. Charnes A.& Cooper W. (1988). Some statistical and DEA evaluations of relative efficiencies of public and private institutions of higher learning. Socio-economic Planning sciences. 22(6), 259 -269.
Banker, R.O. &Park, H. U. (2021). Three-Stage approach to analyze managerial ability. Data Envelopment Analysis Journal,5(1),27-54.
Bogetoft P., & Otto, L. (2011). Benchmarking with DEA & SFA New York: Springer.
Calvalho, J, Gomes, P.& Fernandes, M, J. (2012). The main determinants of the use of the cost accounting system in Portuguese local government. Journal of financial accountability and management,28(3),306-334.
Charnes, A., Cooper, W. W., & Rhodes, E. (1978). Measuring the efficiency of decision- making units. European Journal of Operation Research, 2(2), 429 -444.
Cheng, Z., Cai, M., Tao, H; He, Z; Lin, X, Lin, H; & Zuo, Y. (2016). Efficiency and productivity measurement of rural township hospitals in China: a bootstrapping data envelopment analysis. BMJ Open, 6(11), 1 – 11.
Cvetkoska, V & Savic, G. (2021). DEA in banking: analysis and visualization of bibliometric data. Data Analysis Journal, 6(2), 22-45.
Cvetkoska, V &Fotova Cikovic, K. (2020). Assessing the relative efficiency of commercial banks in the Republic of North Macedonia: DEA window analysis. Croatian Operational Research Review,11(2),217-227.
Cyrek, M. (2017). Social Efficiency of employment in three sectors – a comparison of Polish regions. Equilibrium. Quarterly Journal of Economics and Economic Policy, 12(3), 417 – 432.
Enofe, A. O, Afiangbe, S. E, &Agha, D. I. (2017). Financial management reform and corruption in Nigeria public sector. International journal of advanced academic research in social and management sciences,3(7),1-22
Elsawy, I., Hosny, H, Razek, M.A. (2011). Neural Network model for construction projects site overhead estimates in Egypt. International Journal of computer science issues,8(3),273-283.
Farrel, M. J. (1957). The measurement of productive efficiency. Journal of royal statistical society. 120(3), 253 –270.
Globalson, S. (2017). Using the earned value approach for controlling overhead costs in construction projects. Journal of modern project management,5(2),50-55.
Harnandez, A. R., & San, S. M. (2014). Assessing the technical efficiency of health posts in rural Guatemala: a data envelopment analysis. Global Health Action, 2(1), 1- 12
Huijben, M., Geurtsen, A., & VanHelden, G. J. (2014). Managing overhead costs in public sector organization through benchmarking. Public Money and Management, 34(1), 27-34.
Jia, T., & Yuan, H. (2017). The application of DEA (data envelopment analysis) window analysis in the assessment of influence on operational efficiencies after the establishment of branched hospitals. BMC Health Service Research; 17(1), 265 – 289. rd
Juszezyk, M., Lesniak, A. (2016). Site overhead cost index prediction using RBF Neutral Networks, 3 International conference on economics and management (ICEM,2016), Suzhou, Jiangsu, China.
Kazley, A. S., & Ozcan, Y. A. (2008). Electronic medical record use and efficiency: A DEA and windows analysis of hospitals. Socio Econ. Plan Sci., 43(3), 209 – 221.
Kwon, H. B., & Lee, J. (2015). Two-stage production modeling of large U. S. Banks: A DEA-neural network approach. Expert Systematic Application, 42, 6758 -6785
Rezaee, M. J., & Karimdadei A. (2015). Do geographical locations affect hospital performance? A multi-group data envelopment analysis. Journal of medical system, 39(9), 1- 11
Odewole, P. O. (2020). Adequacy of DEA in measuring the efficiency of public sector entities in Nigeria: A comparative analysis approach. Journal of economics and Behavioural studies,12(5),13-22.
Odewole, P.O. & Salawu, R. O. (2021). Assessment of the efficiency of selected public sector entities in Nigeria. Ilorin Journal of economic policy,8(1),18-30.
Odewole, P.O., Salawu, M. K. & Salawu, R.O. (2021). Evaluation of financial sustainability of the federal health institutions in Nigeria. European journal of sustainable development,10(1),267-280.
Odewole, P. O. & Oladejo, T.M. (2020). Evaluation of the efficiency of capital grants’ utilization among the federal health institutions in Nigeria: The non-parametric approach. Hallmark University Journal of Management and Social Sciences,2(3),64-73.
Paradi, J. C., David, H.S., & Fai, K. T. (2018). Data envelopment analysis in the financial services industry: A guide for practitioners and analysis working in operations research using DEA. Cham: Springer international publishing AG
Salawu, R.O.& Odewole, P.O. (2020). The efficiency of personnel costs utilization among MDAs in Nigeria: The Data envelopment analysis approach. International journal on government financial management,10(1),64-82.
Samut, P. K., & Cafri, R. (2016). Analysis of the efficiency determinants of health systems in OECD countries by DEA and panel. Tobit Social indicators Research, 2(1), 1- 20. Section 22(2) of the Fiscal Responsibility Act, FRA
Sendek, S., & Svitalkova, K. (2015). Efficiency evaluation of hospitals in environment of the Czech and Slovak Republic. Acta Universities, 63(6), 2109 – 2118.
Stanickova, M. (2017). Can the implementation of the Europe 2020 strategy goals be efficient? The challenge for achieving social equality in the European Union. Equilibrium Quarterly Journal of Economics and Economic Policy, 12(3), 383 – 398.
Tao, L., Liu, X. & Chen, Y. (2013). Online banking performance evaluation using data envelopment analysis and axiomatic fuzzy set clustering. Qualitative and quantitative Journal, 47, 1259 – 1273.
Tsolas, I. E. and Charles, V. (2015). Incorporating risk into bank efficiency: A satisficing DEA approach to access the Greek banking crisis. Expert system Application Journal, 42, 3491 – 3500.
Verbeeten, F.H.M. (2011). Public sector costs management practices in Netherlands. International Journal of Public Sector Management, 24(6), 492-506.
Yu, Yaw-Shun; Barros, A; Tsai, Chih-Hung & Liao, Kuo Hsiung (2014). A comparison of Ratio and Data Envelopment Analysis: Efficiency Assessment of Taiwan Public Listed Companies. International Journal of Academic Research in Accounting, Finance and Management Sciences, 4(1), 212 – 219.
Yilmaz, B., & Yurdusev, M. A. (2011). Use of Data Envelopment Analysis as a multi criteria decision tool – A case of irrigation management. Mathematical and computational Application, 16(3), 669 -679.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2022 Babatunde Moses Ololade, Philip Olawale Odewole, Adesola Adebayo Akande

This work is licensed under a Creative Commons Attribution 4.0 International License.