The Summary and the Policy Suggestions

  • Arpita Ghose
Part of the SpringerBriefs in Economics book series (BRIEFSECONOMICS)


The summary results show most of the States/UT is technically inefficient considering both output-oriented technical-efficiency (OUTTE) and input-oriented technical-efficiency (INPTE). Also not all the States/UT depicts improved performance over the sample 2005–06 to 2010–11. Higher literacy-rate or educational-development-index does not imply better OUTTE. The extents of underutilization of inputs are estimated. The determinants of both OUTTE and INPTE vary between General-Category, Special-Category States and between primary and upper-primary level, highlighting importance of policy, infrastructure, State-specific and social-indicator variables. At policy level, both INPTE and OUTTE can be enhanced by increasing (i) the availability and utilization of central grant (AGM), (ii) the proportion of girls to boys getting free text book, para-teachers having qualification graduate and above, school with drinking water facility, SC teacher, (iii) State’s service sectors income, population-density and by reducing (i) without-building school, (ii) inequality of income-distribution. OUTTE can further be stimulated by increasing percentage of (i) schools getting school-development-grant, having common-toilet, (ii) SC-enrollment and by reducing proportion of (i) single-teacher-school, (ii) single-classroom-school, and (iii) below the poverty line population. INPTE can also be enhanced by increasing the proportion of students getting free text book. Effect of AGM on INPTE also operates through its joint interaction with other variables. For SCS&UT-primary effect of AGM on INPTE is positive up to a limit. Although employment of average input-bundle in SCS&UT-school produces less benefit as compared to GCS-school both for primary and upper primary, elasticity of OUTTE with respect to AGM is higher for SCS&UT-school.


Technical Efficiency Efficiency Score Primary Level Union Territory OUTTE Score 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Copyright information

© The Author(s) 2017

Authors and Affiliations

  1. 1.Department of EconomicsJadavpur UniversityKolkataIndia

Personalised recommendations