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Did the nation-wide implementation of e-FMS in MGNREGS result in reduced expenditures? A re-examination of the evidence

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  • Goel, Deepti
  • Meenakshi, J. V.
  • De Souza, Zaeen

Abstract

This paper revisits a part of the analysis by Banerjee et al. (2020), in which they examine the consequences of the nation-wide scale up of reforms to the funds management system (e-FMS) in India's national workfare programme, using a two-way fixed effects specification. They report a substantial 19 percent reduction in labour expenditures. We exploit the recent literature that highlights the limitations of the TWFE estimator in the presence of staggered roll out and effect a Goodman-Bacon decomposition of the TWFE coefficient, to pinpoint sources of identifying variation. We undertake a detailed examination of subsamples of six constituent and valid DiDs based on timing of treatment that are averaged into the TWFE coefficient to identify heterogeneity in treatment effects. This disaggregated subsample analysis does not support the conclusion of any reductions in MGNREGS labour expenditures, suggesting that the TWFE coefficient based on the full sample is indeed biased.

Suggested Citation

  • Goel, Deepti & Meenakshi, J. V. & De Souza, Zaeen, 2022. "Did the nation-wide implementation of e-FMS in MGNREGS result in reduced expenditures? A re-examination of the evidence," GLO Discussion Paper Series 1146, Global Labor Organization (GLO).
    • Handle: RePEc:zbw:glodps:1146
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    1. Bharadwaj, Prashant & Fenske, James & Kala, Namrata & Mirza, Rinchan Ali, 2020. "The Green revolution and infant mortality in India," Journal of Health Economics, Elsevier, vol. 71(C).
    2. Douglas Gollin & Casper Worm Hansen & Asger Mose Wingender, 2021. "Two Blades of Grass: The Impact of the Green Revolution," Journal of Political Economy, University of Chicago Press, vol. 129(8), pages 2344-2384.
    3. Goodman-Bacon, Andrew, 2021. "Difference-in-differences with variation in treatment timing," Journal of Econometrics, Elsevier, vol. 225(2), pages 254-277.
    4. Abhijit Banerjee & Esther Duflo & Clément Imbert & Santhosh Mathew & Rohini Pande, 2020. "E-governance, Accountability, and Leakage in Public Programs: Experimental Evidence from a Financial Management Reform in India," American Economic Journal: Applied Economics, American Economic Association, vol. 12(4), pages 39-72, October.
    5. Sun, Liyang & Abraham, Sarah, 2021. "Estimating dynamic treatment effects in event studies with heterogeneous treatment effects," Journal of Econometrics, Elsevier, vol. 225(2), pages 175-199.
    6. Khera, Reetika, 2021. "Some Questions of Ethics in RCTs," SocArXiv fgxun, Center for Open Science.
    7. Callaway, Brantly & Sant’Anna, Pedro H.C., 2021. "Difference-in-Differences with multiple time periods," Journal of Econometrics, Elsevier, vol. 225(2), pages 200-230.
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    Cited by:

    1. Jeong, Dahyeon & Shenoy, Ajay & Zimmermann, Laura V., 2023. "De Jure versus De Facto transparency: Corruption in local public office in India," Journal of Public Economics, Elsevier, vol. 221(C).

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    More about this item

    Keywords

    e-governance; public funds management; MGNREGS;
    All these keywords.

    JEL classification:

    • H53 - Public Economics - - National Government Expenditures and Related Policies - - - Government Expenditures and Welfare Programs
    • H75 - Public Economics - - State and Local Government; Intergovernmental Relations - - - State and Local Government: Health, Education, and Welfare
    • D73 - Microeconomics - - Analysis of Collective Decision-Making - - - Bureaucracy; Administrative Processes in Public Organizations; Corruption
    • D78 - Microeconomics - - Analysis of Collective Decision-Making - - - Positive Analysis of Policy Formulation and Implementation
    • I38 - Health, Education, and Welfare - - Welfare, Well-Being, and Poverty - - - Government Programs; Provision and Effects of Welfare Programs

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