Revealing Inequities in MGNREGA: What Data and Field Insights Tell Us
The Mahatma Gandhi National Rural Employment Guarantee Act (MGNREGA), enacted in 2005, was designed as a powerful tool to ensure livelihood security and promote social justice in rural India. With a built-in focus on prioritizing the marginalized, MGNREGA holds transformative potential. Yet, our recent research combining data-driven analysis with field-level insights from Bihar and Jharkhand reveals a disquieting reality: deep-rooted inequities, particularly caste-based, continue to shape access to the scheme’s benefits. On the upside, we show that careful data analysis can reveal these underlying patterns and give hints towards specific policy actions that can be taken at the local level.
Our recently published paper analyzed publicly available Management Information System (MIS) data from the MGNREGA portal and conducted field visits across diverse panchayats in Bihar and Jharkhand. The findings confirmed what the data had begun to suggest—persistent inequities that disproportionately impact Scheduled Castes (SC), Scheduled Tribes (ST), and other marginalized communities. Despite the program’s intentions, these groups often remain at the margins of MGNREGA’s implementation.
Brief presentation. Dvara Research, InSPIRE talk #83. May, 2025.
Field report: Caste-based Inequities in MGNREGA Implementation: Precision Policy Advisory for Participatory Planning Processes. May, 2025.
Paper: An In-depth Analysis of Caste-based Inequity in Welfare Allocation of the National Rural Employment Guarantee Scheme in India - A. Mittal, C. Sheth, and A. Seth. ICTD, 2024.
We will soon be releasing maps that identify these pockets of inequity, at the state, district, and panchayat levels. The exciting aspect with this work though is that the patterns and underlying processes through which inequity seems to be operating and getting amplified, spotted through data, can make it possible to identify precise policy actions in an area, at the state-level, district-level, and panchayat-level. These actions may span administrative sensitization on caste issues to be undertaken in some areas, or greater awareness and proactive demand generation in other areas, or greater attention to fund allocation, etc.
Inequity towards marginalized castes at state-level (Orange color indicates higher inequity)
Inequity towards marginalized castes at district-level (Orange color indicates higher inequity)
Inequity towards marginalized castes at panchayat-level for the Maheshwar block, West Nimar district, Madhya Pradesh, as an example.
Structural Gaps and Social Barriers in MGNREGA
Despite its inclusive intent, MGNREGA’s impact is hindered by several systemic issues. Complex guidelines are hard to interpret, giving an edge to proactive leaders—often from dominant castes—while leaving marginalized groups behind. Local elites frequently influence fund allocation and beneficiary selection, perpetuating inequity. Many disadvantaged families are excluded from asset-related work due to lack of land or capital, especially when reimbursements are delayed. Persistent delays in wage payments further discourage participation among the poorest. Additionally, increasing digitization without adequate infrastructure or digital literacy in remote areas widens the gap, unintentionally excluding those it aims to support.
How do these issues intersect with caste? Our interactions through interviews, focus group discussions, and participatory observations showed that in some panchayats, power asymmetries between castes were evident in selective approval of individual MGNREGA assets, work assignment, timely wage payment, and participation in MGNREGA planning process. In others, misinterpretation of MGNREGA guidelines, lack of access to digital technology, and limited digital literacy resulted in exclusion of intended beneficiaries of the scheme, i.e. the workers from scheduled castes and scheduled tribes. These field insights demonstrate that inequities in MGNREGA are not incidental but deeply embedded in local socio-political structures.
But the interesting aspect is that our data analysis methods can identify which of these specific issues seem to be dominant in which location.
Precise policy action
We built a rich set of methods to identify what kind of issues are likely to be dominant in which location. We first developed a machine-learning method to project what the MGNREGA demand is likely to be in a panchayat, since the registered demand under MGNREGA may not express the complete needs of the community. We then built a set of indicators to understand the actual registered demand to the projected demand, the number of days of employment generated relative to the projected demand, and the number of households given employment relative to the projected demand. We also built a propensity-score based matching method to identify pairs of panchayats with one panchayat having a high SC/ST population and one panchayat with a low SC/ST population, to study whether caste as a factor was responsible for any differences. We further nuanced the analysis by studying structurally different panchayats in terms of how the SC/ST populations were distributed across the villages in the panchayat – whether the populations are segregated in some villages or mixed with other castes across villages. A combination of various indicators at the panchayat, district, and state levels can help infer several interesting patterns like the ones outlined below.
The Way Forward:
Our work shows that by using MIS data to pinpoint where disparities emerge and why, targeted interventions can be designed and can turn data into actionable steps for precision policy and inclusive governance. But ultimately real change requires more than data—it needs grassroots participation, especially from women and marginalized communities, simplified processes, timely payments, digital inclusion, and strong accountability. Without these deliberate structural reforms, MGNREGA risks reinforcing the very inequalities it was meant to challenge. The future must be built on equity, participation, and localized decision-making to ensure no household is left behind.