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Spatial data analysis of Mahatma Gandhi national rural employment guarantee scheme and its influencing factors

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Abstract

As a part of the Mahatma Gandhi National Rural Employment Guarantee Act (MGNREGA) program in India, durable assets under various work categories are created in rural areas by employing adult members belonging to the marginal communities. Taking the Prakasam district in Andhra Pradesh as an example, we analyzed the spatial clustering of the various work implemented under MGNREGA using kernel density estimation (KDE). We also analyzed the spatial clustering of villages in terms of overall assets and their statistical significance using hotspot analysis (Getis-Ord). The socio-economic factors influencing village-level clustering are analyzed using a machine learning model. Results show significant spatial variations in kernel density and village hotspots, thus, indicating the demand-driven nature of the program as influenced mainly by the marginal worker population female followed by the main other worker population. The study thus offers a methodological framework that may help improve and complement the empirical indicators currently employed in mapping the performance of the MGNREGA and similar programs.

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References

  1. Ghasemaghaei, M., & Calic, G. (2020). Assessing the impact of big data on firm innovation performance: Big data is not always better data. Journal of Business Research, 108, 147–162. https://doi.org/10.1016/j.jbusres.2019.09.062

    Article  Google Scholar 

  2. Caiaffa, E. (2005). May. Geographic Information Science for geo-knowledge-based governance. In 8th AGILE Conference on Geographic Information Science (pp. 659–664). Estoril Portugal: IGP Istituto Geografico Portugues

  3. Scharl, A. (2007). Towards the geospatial web: Media platforms for managing geotagged knowledge repositories. The geospatial web (pp. 3–14). London: Springer

    Chapter  Google Scholar 

  4. Bonney, R., Cooper, C. B., Dickinson, J., Kelling, S., Phillips, T., Rosenberg, K. V., & Shirk, J. (2009). Citizen science: a developing tool for expanding science knowledge and scientific literacy. BioScience, 59(11), 977–984

    Article  Google Scholar 

  5. Burrough, P. A. (1986). Principles of geographical. Information systems for land resource assessment. Clarendon Press, Oxford

  6. Butler, D. (2006). Virtual globes: The web-wide world. Nature, 439(7078), 776–779

    Article  Google Scholar 

  7. Goodchild, M. F., Yuan, M., & Cova, T. J. (2007). Towards a general theory of geographic representation in GIS. International journal of geographical information science, 21(3), 239–260

    Article  Google Scholar 

  8. Neutens, T., Delafontaine, M., Scott, D. M., & De Maeyer, P. (2012). A GIS-based method to identify spatiotemporal gaps in public service delivery. Applied Geography, 32(2), 253–264

    Article  Google Scholar 

  9. Schindler, S., & Kanai, J. M. (2021). Getting the territory right: Infrastructure-led development and the re-emergence of spatial planning strategies. Regional Studies, 55(1), 40–51

    Article  Google Scholar 

  10. Manoharan, A. P., Melitski, J., & Holzer, M. (2022). Digital Governance: An Assessment of Performance and Best Practices. Public Organiz Rev. https://doi.org/10.1007/s11115-021-00584-8

    Article  Google Scholar 

  11. Anselin, L. (1989). What is special about spatial data? alternative perspectives on spatial data analysis. Symposium on Spatial Statistics, Past, Present, and Future, Department of Geography, Syracuse University

  12. Murray, A. T., & Estivill-Castro, V. (1998). Cluster discovery techniques for exploratory spatial data analysis. International Journal of Geographical Information Science, 12, 431–443

    Article  Google Scholar 

  13. Anselin, L. (2006). How (not) to lie with spatial statistics. American journal of preventive medicine, 30(2), S3–S6

    Article  Google Scholar 

  14. Bhat, B. B., & Mariyappan, P. (2015). MGNREGA: A new hope to reduce rural poverty. International Journal of Innovative Research and Development, 4(8), 347–351

    Google Scholar 

  15. Padma, K. (2015). MGNREGA and rural distress in India. International Journal of Humanities and Social Science Invention, 4(8), 67–76

    Google Scholar 

  16. IILS (2012, April 26). World of work report: Better jobs for a better economy. International Labour Office, International, Institute for Labour Studies. Geneva. XVII: 108 retrieved August 25, 2021, from, https://www.ilo.org/global/research/global-reports/world-of-work/WCMS_179453/lang--en/index.htm

  17. Saroniya & Singh. (2016). The Ecological and Social Benefits of MGNREGA in India. International Journal of Current Research, 8(11), 42077–44208

    Google Scholar 

  18. Chandramouli, C., & General, R. (2011). Census of India. Rural urban distribution of population, provisional population total. New Delhi: Office of the Registrar General and Census Commissioner. India. https://censusindia.gov.in

    Google Scholar 

  19. NRSC Indian Geoplatform of ISRO. https://bhuvan.nrsc.gov.in/governance/mgnrega/

  20. Rao, S. S., Banu, V., Tiwari, A., Bahuguna, S., Uniyal, S., Chavan, S. B., Nagaraja, R., Sharma, J. R., Murthy, M. V. R., & Arya, V. S. (2014). Application of Geo-Spatial Techniques for Precise Demarcation of Village/Panchayat Boundaries. ISPRS Annals of Photogrammetry, Remote Sensing & Spatial Information Sciences. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2 (8), 123–132. https://www.isprs-ann-photogramm-remote-sens-spatial-inf-sci.net/II-8/123/2014/

  21. Wang, R. Y., Kon, H. B., & Madnick, S. E. (1993). Data quality requirements analysis and modeling,“ Proceedings of IEEE 9th International Conference on Data Engineering, 1993. 670–677, doi: https://doi.org/10.1109/ICDE.1993.344012

  22. Silverman, B. W. (1986). Density estimation for statistics and data analysis

  23. Getis, A., & Ord, J. K. (1996). Local spatial statistics: an overview. Spatial analysis: modeling in a GIS environment. 374, 261–277

  24. Kotsiantis, S. B. (2013). Decision trees: a recent overview. Artificial Intelligence Review, 39(4), 261–283

    Article  Google Scholar 

  25. Afridi, F. (2017). Governance and public service delivery in India. Discussion Paper, Institute of Labor Economics. IZA DP No. 10856 SSRN: https://ssrn.com/abstract=2998965

  26. Pellissery, S., & Jalan, S. K. (2011). Towards transformative social protection: A gendered analysis of the Employment Guarantee Act of India (MGNREGA). Gender & Development, 19(2), 283–294

    Article  Google Scholar 

  27. Bhagat, R. B. (2010). Internal migration in India: are the underprivileged migrating more. Asia-Pacific Population Journal, 25(1), 27–45

    Article  Google Scholar 

  28. Roy, I. (2015). Class politics and social protection: The implementation of India’s MGNREGA. ESID Working Paper No 46, Available at SSRN: https://ssrn.com/abstract=2597964

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Acknowledgements

We gratefully acknowledge the support received from the Director NRSC and Deputy Director RSA for constant support and motivation. Our sincere thanks are to the Ministry of Rural Development, Government of India for their support throughout the project implementation. We also thank our colleagues at Bhuvan Geoportal and Data Dissemination Team for providing the geotagged data. Thanks are also to the anonymous reviewers for their insight and valuable contributions, which helped us significantly, improve this manuscript.

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Gupta, S., Reddy, A., Reddy, S.C. et al. Spatial data analysis of Mahatma Gandhi national rural employment guarantee scheme and its influencing factors. Spat. Inf. Res. 31, 125–133 (2023). https://doi.org/10.1007/s41324-022-00475-0

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