Employment guarantee and the dialectics of the state: navigating welfare, market and efficiency in the neoliberal age through machine learning

In the context of neoliberal developmentalism, the state’s retreat from direct economic intervention has paradoxically coincided with its expanding responsibility for managing the social risks generated by market-led growth. This paper examines whether India’s employment guarantee scheme, MGNREGA, represents a genuine recalibration of state welfare through institutionalised employment security and labour decommodification or a market-compatible instrument that optimises technical efficiency while side-lining equity. Drawing on the concepts of allocative and productive efficiency, the study uses a non-parametric benchmarking approach (Data Envelopment Analysis) and a productivity index (Malmquist Productivity Index) to assess how effectively 28 Indian states convert resources (labour, funds, administrative and infrastructural inputs) into employment generation and rural asset creation between 2016–17 and 2023–24. To understand what drives these efficiency patterns, the analysis integrates interpretable machine learning models (Random Forests, Gradient Boosting, and Artificial Neural Networks) as diagnostic tools to identify which programme dimensions, such as infrastructure works, ecological assets, and the participation of women and SC/ST workers, most strongly shape state-level performance. The results reveal a critical tension: states can attain high technical efficiency primarily by prioritising infrastructure- and employment-intensive works, while the inclusion of marginalised groups has limited influence on efficiency scores. This equity–efficiency decoupling suggests that MGNREGA, in practice, risks being implemented as a market-compatible welfare mechanism rather than a fully rights-based, inclusive employment guarantee. The paper contributes to debates on welfare, state capacity, and neoliberal governance by showing how evaluation through efficiency metrics can reconfigure the rural social contract, simultaneously consolidating state legitimacy and constraining the transformative potential of public employment schemes.

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Publication Details

Journal
Humanities and Social Sciences Communications
Published
2026-04-27
DOI
https://doi.org/10.1057/s41599-026-07374-x
Primary Topic
Digital Economy and Work Transformation
Type
article
Field-Weighted Citation Impact
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article

Employment guarantee and the dialectics of the state: navigating welfare, market and efficiency in the neoliberal age through machine learning

Pushpender Yadav, Sandeep Tripathi
Humanities and Social Sciences Communications
Digital Economy and Work Transformation
article

Employment guarantee and the dialectics of the state: navigating welfare, market and efficiency in the neoliberal age through machine learning

Pushpender Yadav, Sandeep Tripathi
article en

Abstract

In the context of neoliberal developmentalism, the state’s retreat from direct economic intervention has paradoxically coincided with its expanding responsibility for managing the social risks generated by market-led growth. This paper examines whether India’s employment guarantee scheme, MGNREGA, represents a genuine recalibration of state welfare through institutionalised employment security and labour decommodification or a market-compatible instrument that optimises technical efficiency while side-lining equity. Drawing on the concepts of allocative and productive efficiency, the study uses a non-parametric benchmarking approach (Data Envelopment Analysis) and a productivity index (Malmquist Productivity Index) to assess how effectively 28 Indian states convert resources (labour, funds, administrative and infrastructural inputs) into employment generation and rural asset creation between 2016–17 and 2023–24. To understand what drives these efficiency patterns, the analysis integrates interpretable machine learning models (Random Forests, Gradient Boosting, and Artificial Neural Networks) as diagnostic tools to identify which programme dimensions, such as infrastructure works, ecological assets, and the participation of women and SC/ST workers, most strongly shape state-level performance. The results reveal a critical tension: states can attain high technical efficiency primarily by prioritising infrastructure- and employment-intensive works, while the inclusion of marginalised groups has limited influence on efficiency scores. This equity–efficiency decoupling suggests that MGNREGA, in practice, risks being implemented as a market-compatible welfare mechanism rather than a fully rights-based, inclusive employment guarantee. The paper contributes to debates on welfare, state capacity, and neoliberal governance by showing how evaluation through efficiency metrics can reconfigure the rural social contract, simultaneously consolidating state legitimacy and constraining the transformative potential of public employment schemes.

Humanities and Social Sciences Communications
Maulana Azad National Institute of Technology (IN)
Decent work and economic growth
Openalex Percentile: Top 4%
Digital Economy and Work Transformation
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