PulseExploreJournal ClubDebatesTrendingResearchersJournals
Instagram
HomeExploreJournal ClubTrending
Synapse
⌘+K
Synapse
April 29, 2026Humanities and Social Sciences Communications0 citationsOpen Access

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

STSandeep TripathiPYPushpender Yadav

Key Points

  • The study aims to investigate whether MGNREGA represents a valid form of employment security or merely a market-compatible tool for efficiency.
  • Analyzed efficiency of 28 Indian states using data envelopment analysis and malmquist productivity index from 2016–17 to 2023–24.
  • Integrated machine learning models (Random Forests, Gradient Boosting, Artificial Neural Networks) to identify factors influencing state performance.
  • Focused on the relationship between resource allocation and employment generation in the context of welfare and market efficiency.
  • States achieve high technical efficiency by prioritizing infrastructure and employment-intensive works.
  • Inclusion of marginalized groups has a limited impact on efficiency scores, suggesting a decoupling of equity and efficiency.
  • MGNREGA is often implemented as a market-compatible mechanism rather than a fully inclusive employment guarantee.

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.

Ask AI
Helpful
Bookmark
Share
View Full Paper

Cite This Study

Tripathi et al. (2026) studied this question.

synapsesocial.com/papers/69f19f74edf4b468248063f4https://doi.org/10.1057/s41599-026-07374-x
Ask AI
Helpful
Bookmark
Share
View Full Paper