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<p>This paper aims to analyse the efficiency and productivity change of primary schools in the states and union territories of India using dual layered non-parametric model [Data Envelopment Analysis (DEA) and Malmquist Index methodology] on a panel dataset for 2017 to 2022 period. This study used both Charnes-Cooper-Rhodes (CCR) and Banker, Charnes and Cooper (BCC) models to calculate input oriented technical efficiency. To compliment the static analysis, this study employs Malmquist Index for tracking the productivity changes and its decomposition in efficiency change and technical change overtime. The major contributor of increased productivity is found to be technological change, as technical efficiency change showed a slight decline of 0.4 percent. This combined approach enables clear distinction between efficiency and productivity, offering a more comprehensive understanding of sectoral performance. Dividing the states into three categories based on efficiency score of starting year, highlights marked disparities: some states are consistently efficient, while others show productivity stagnation or decline. Combining the efficiency scores and productivity change with the achievement scores in language reveals whether shortcomings stem from insufficient resources or from lack of innovation. It is also observed that on average the states having higher efficiency are also the states which demonstrate higher achievement scores and higher number of private schools. However, the study lacks second-stage regression DEA analysis to identify factors influencing state efficiency, highlighting a key area for future research with the same data. It also overlooks recent advances in efficiency measurement techniques, suggesting that future studies should use more sophisticated methods.</p>

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Efficiency and productivity analysis of the primary education in India based on the Malmquist-DEA technique

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  • 发表日期：

    2026年6月20日

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  • Iqra Choudhary,
  • Aas Mohammad

摘要

This paper aims to analyse the efficiency and productivity change of primary schools in the states and union territories of India using dual layered non-parametric model [Data Envelopment Analysis (DEA) and Malmquist Index methodology] on a panel dataset for 2017 to 2022 period. This study used both Charnes-Cooper-Rhodes (CCR) and Banker, Charnes and Cooper (BCC) models to calculate input oriented technical efficiency. To compliment the static analysis, this study employs Malmquist Index for tracking the productivity changes and its decomposition in efficiency change and technical change overtime. The major contributor of increased productivity is found to be technological change, as technical efficiency change showed a slight decline of 0.4 percent. This combined approach enables clear distinction between efficiency and productivity, offering a more comprehensive understanding of sectoral performance. Dividing the states into three categories based on efficiency score of starting year, highlights marked disparities: some states are consistently efficient, while others show productivity stagnation or decline. Combining the efficiency scores and productivity change with the achievement scores in language reveals whether shortcomings stem from insufficient resources or from lack of innovation. It is also observed that on average the states having higher efficiency are also the states which demonstrate higher achievement scores and higher number of private schools. However, the study lacks second-stage regression DEA analysis to identify factors influencing state efficiency, highlighting a key area for future research with the same data. It also overlooks recent advances in efficiency measurement techniques, suggesting that future studies should use more sophisticated methods.