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

  • Arpita GhoseEmail author


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.



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


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© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Department of EconomicsJadavpur UniversityKolkataIndia

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