Advertisement

Total Factor Productivity Growth of Elementary Education in India and Its Determinants: Evidence from a Non-parametric Data Envelopment Approach

  • Arpita GhoseEmail author
Chapter

Abstract

The major departure of this paper from the available literature is the approach to estimating total factor productivity growth (TFPG) for elementary education in India, by constructing two frontiers (i) for general category states (GCS) and (ii) for special category states and union territories (SCS&UT), over the period 2005–06 to 2014–15; as these two groups are not homogeneous and operate under different fiscal and economic conditions. Hence, maximum educational output producible from an input bundle by a school within a particular group may not be as high as what could be produced if the school could choose to locate in the other groups. TFPG is measured by Malmquist Productivity Index (MPI) using non-parametric data envelopment analysis for primary and upper primary levels of education and for GCS and SCS&UT in a two-output, four-input framework under variable returns to scale, considering both quantities and qualities of outputs and inputs. The outputs are net enrolment ratio and percentage of students passed with 60% and above in the examination, representing output quality. The inputs used are: (i) number of schools per lakh population, (ii) teacher–pupil ratio in the school, (iii) classroom–student ratio in the school, (iv) percentage of teachers with qualification graduate and above in the schools, indicating quality of the teacher input. The generated value of MPI is decomposed into technical change, efficiency change and scale efficiency change. The decomposition results suggest that on average productivity change is mainly facilitated by technical change and efficiency change. After obtaining MPI, a second-stage panel regression is resorted to find out its determinants, considering the effect of favourable and poor infrastructure, social and policy indicators and also the macro-indicators to see whether TFPG has been facilitated by existence of favourable infrastructure, or, existence of poor infrastructure inhibits TFPG, whether inclusion of the backward classes into the system, the provision of more public facilities can increase TFPG and whether favourable macro-indicators, i.e. favourable general economic environment of the state matters in explaining TFPG. The factors influencing the MPI are explained separately for four groups GCS-primary, GCS–upper primary; SCS&UT–primary, SCS&UT–upper primary. Results of panel regression suggest that infrastructural variables, policy variables, school-specific variables and also the state-level macro-aggregates are important in explaining MPI, and the interaction effect between different explanatory variables is also evident. Some policy suggestions for improving TFPG are highlighted.

Notes

Acknowledgements

The paper is a part of the major project done under UPE-II Programme of Jadavpur University, funded by UGC during 2016–19.

Bibliography

  1. Aigner, D. J., Lovell, C. A. K., & Schmidt, P. J. (1977). Formulation and estimation of stochastic frontier production function models. Journal of Econometrics, 6(1), 21–37.CrossRefGoogle Scholar
  2. Anand, S., & Sen, A. K. (1996). Sustainable human development: Concepts and priorities, office of development studies (pp. 1–57). Discussion Paper Series, No. 1, United Nations Development Programme, New York. (Originally published as HDRO Occasional Paper 8 for Human Development Report 1994).Google Scholar
  3. Arjomandi, A., Salleh, M. I., & Mohammadzadeh, A. (2015). Measuring productivity change in higher education: An application of Hicks–Moorsteen total factor productivity index to Malaysian public universities. Journal of the Asia Pacific Economy, 20(4), 630–643.Google Scholar
  4. Banker, R., Charnes, A., & Cooper. (1984). Some models for estimating technical and scale efficiencies in data envelopment analysis. Management Science, 30(9), 1078–1092.Google Scholar
  5. Banker, R. D., & Natarajan, R. (2008). Evaluating contextual variables affecting productivity using data envelopment analysis. Operations Research, 56(1), 48–58.CrossRefGoogle Scholar
  6. Barro, R. J. (1990). Government spending in a simple model of endogenous growth. Journal of Political Economy, 98(S5), 103–125.CrossRefGoogle Scholar
  7. Barro, R. J. (1991). Economic growth in a cross section of countries. The Quarterly Journal of Economics, 106(2), 407–443.CrossRefGoogle Scholar
  8. Bessent, A. M., Bessent, E. W., Kennington, E. W., & Regan, B. (1982). An application of mathematical programming to the productivity in the Houstan independent school district. Management Science, 28(12), 1355–1367.CrossRefGoogle Scholar
  9. Bessent, A. M., Bessent, W., Elam, J., & Long, D. (1984). Educational productivity employs management science method to improve educational quality. Interfaces, 14, 1–8.CrossRefGoogle Scholar
  10. Bonesronning, H., & Rattsq, J. (1994). Efficiency variation among Norwegian high schools: Consequences of equalization policy. Economics of Education Review, 3(4), 289–304.CrossRefGoogle Scholar
  11. Bradley, S., Johnes, J., & Little, A. (2010). Measurement and determinants of efficiency and productivity in the further education sector in England. Bulletin of Economic Research, 62(1), 0307–3378.Google Scholar
  12. Caves, D. W., Christensen, L. R., & Diewert, D. W. (1982). The economic theory of index numbers and the measurement of input, output and productivity. Econometrica, 50(6), 1393–1414.CrossRefGoogle Scholar
  13. Central Statistical Office, Office of the Registrar General & Census Commissioner, Census Data, 2011, Government of India.Google Scholar
  14. Charnes, A., Cooper, W. W., & Rhodes, E. (1978). Measuring the efficiency of decision making units. European Journal of Operational Research, 2, 429–444.CrossRefGoogle Scholar
  15. Charnes, A., Cooper, W. W., & Rhodes, E. (1981). Evaluating program and managerial efficiency: An application of DEA to program follow-through. Management Science, 27(6), 668–697.CrossRefGoogle Scholar
  16. District Information System for Education, National University of Educational Planning and Administration, State Report Cards, different issues.Google Scholar
  17. Dutta, S. (2012). Evaluating the technical efficiency of elementary education in India: An application of DEA. IUP Journal of Applied Economics, 11(2), 31–47.Google Scholar
  18. Färe, R., Grosskopf, S., Norris, M., & Zhang, P. (1994). Productivity growth, technical progress and efficiency changes in industrial countries. American Economic Review, 84(1), 66–83.Google Scholar
  19. Fare, R., Grosskopf, S., & Weder, W. L. (1989). Measuring school district performance. Public Finance Quarterly, 17, 409–428.CrossRefGoogle Scholar
  20. Farrell, M. J. (1957). The measurement of productive efficiency. Journal of Royal Statistical Society, 120(3), 253–281.CrossRefGoogle Scholar
  21. Ghose, A. (2017). Efficiency of elementary education in India: Empirical evidence using a nonparametric data envelopment Approach. Springer.Google Scholar
  22. Government of India. (1986). New Education Policy.Google Scholar
  23. Government of India. (2016). New Education Policy (NEP), 30th June 2016. www.my.gov.in.
  24. Government of India. (2019). National Education Policy.Google Scholar
  25. Johnes, J. (2008). Efficiency and productivity change in the English higher education sector from 1996–97 to 2004–05. The Manchester School, 76(6), 1463–6786, 653–674 (Check).CrossRefGoogle Scholar
  26. Johnes, J., & Li, Y. (2008). Measuring the research performance of Chinese higher education institutions using data envelopment analysis. China Economic Review, 19(4), 679–696.CrossRefGoogle Scholar
  27. Kumbhakar, S. C., & Lovell, C. A. K. (2003). Stochastic frontier analysis. Cambridge: Cambridge University Press.Google Scholar
  28. Ludwin, W., & Guthrie, T. (1989). Assessing productivity with data envelopment analysis. Public Productivity Review, 12, 361–372.CrossRefGoogle Scholar
  29. Lucas, R. E., Jr. (1988). On the mechanism of economic development. Journal of Monetary Economics, 22(1), 3–42.CrossRefGoogle Scholar
  30. Mankiw, N. G., Romer, D., & Weil, D. N. (1992). A contribution to the empirics of economic growth. Quarterly Journal of Economics, 107, 407–437.Google Scholar
  31. Parteka, A., & Wolszczak-Derlacz, J. (2013). Dynamics of productivity in higher education: Cross-European evidence based on bootstrapped Malmquist indices. Journal of Productivity Analysis, 40, 67–82.CrossRefGoogle Scholar
  32. Ray, S. C. (2004). Data envelopment analysis: Theory and technique for economics and operation research. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
  33. Ray, S. C., & Desli, E. (1997). Productivity growth, technical progress and efficiency changes in industrialised countries: Comment. American Economic Review, 85(5), 1033–1039.Google Scholar
  34. Romer, P. M. (1986). Increasing returns and long-run growth. Journal of Political Economy, 94(5), 1002–1038.CrossRefGoogle Scholar
  35. Romer, P. M. (1990). Endogenous technological change. The Journal of Political Economy, 98(5), Part 2: The Problem of Development: A Conference of the Institute for the Study of Free Enterprise Systems, S71–S102.Google Scholar
  36. Sen, A., & Anand, S. (1995). Gender inequality in human development: Theories and measurement, background papers: Human development report 1995. United Nations Development Programme (New York).Google Scholar
  37. Sengupta, A., & Pal, N. P. (2010). Primary education in India: Delivery and outcome—A district level analysis based on DISE Data. Journal of Educational Planning and Administration, XXIV(1), 5–21.Google Scholar
  38. Sengupta, A., & Pal, N. P. (2012). Assessing the primary schools multi-dimensional approach: A school level analysis based on Indian data. International Journal of Educational Development, 32(2), 264–272.Google Scholar
  39. Schultz, T. W. (1961). Investment in human capital. The American Economic Review, 51(1), 1–17.Google Scholar
  40. Tyagi, P., Yadav, S. P., Singh, S. P. (2009). Efficiency analysis of schools using DEA: A case study of Uttar Pradesh state in India, http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.536.9952.
  41. Varian, H. (1984). The non-parametric approach to production analysis. Economica, 52(3), 579–599.Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Department of EconomicsJadavpur UniversityKolkataIndia

Personalised recommendations