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What are the contentious political determinants of the largest workfare program in the world, the Mahatma Gandhi National Rural Employment Guarantee Act (MGNREGA) of India? We argue that the Indian government uses this program as a counter-insurgency strategy against the Maoist unrest. Our argument is based on a quantitative analysis that is conducted using a novel dataset that we created by merging at the district level a nationally representative household survey (Indian Human Development Survey-II) and Maoist incidents data (Dasgupta et al. 2017). In our multivariate analysis, we examine the effect of the number of Maoist incidents on our dependent variable, household level income generated from MGNREGA participation. Using the district-level variation of violent incidents, we show that higher intensity of violent conflicts leads to higher MGNREGA benefits. This result is robust when we control for many program-related factors and state level fixed effects as well as when we cluster the standard errors at the district level. Also, different models with similar dependent variables and independent variable yields similar results. In addition, propensity score matching analysis shows that Maoist violence is associated with higher MGNREGA provision. This finding provides strong support for the claim that MGNREGA is used by the state to contain the Maoist insurgency. This finding is an important contribution to the growing literature on anti-poverty programs in the Global South. This literature suffers from a predominance of structuralism, materialized by an overemphasis on demographic and economic explanations and by an under-exploration of contentious political factors. If politics is considered, it is in most cases related to electoral dynamics. Some scholars consider how anti-poverty programs diminish the level of contentious politics, such as terrorism or radicalism, which appear as dependent variables. Our analysis is one of few studies that illustrated the reverse relationship, i.e., how contentious politics affects anti-poverty programs.