Summary of latest papers on MGNREGA’s self-targeting ability, impact of food prices on poverty vis-à-vis income effect, capacity of local authorities to create enough jobs and workers’ incentives to take up MGNREGA jobs.
Jha, Bhattacharyya, Gaiha, & Shankar (2009) find that overall the size of landholdings is a negative predictor of participation in MGNREGA. A one standard deviation increase in landholdings (4.5 hectares) reduces the odds of MGNREGA participation by 1.3 fold (p.6). Specifically, they find a positive relation between size of landholdings and participation in Andhra Pradesh, and but the case is opposite in Rajasthan. They argue that program capture might be prevalent in Andhra Pradesh because of land inequality, political interference, and geographical remoteness.
Jha, Gaiha, & Pandey (2010) argue that the ratio of NREGS wage to agriculture wage, marital status, age, gender, and education determines employment in the rural employment guarantee program. Their conclusion is based on household level survey data from three states: Rajasthan, Andhra Pradesh, and Maharashtra.
- While it is broadly true that the selection of workers for NREGS favors illiterate workers and those from deprived backgrounds, female workers appear to have a lower chance of being selected. In two of the three states, the ratio of NREGS wage to agricultural wage has significant effects. Marital status and age also affect the chances of getting employment in NREGS. Within each state, workers in some districts have higher chances of being employed in NREGS.
- Once employed in NREGS, the duration of such employment is affected by social background or educational status. Factors relevant for selection for NREGS are not necessarily so for the duration of employment.
Ghose (2011) finds that MGNREGS, despite problems in implementation, has succeeded in providing substantial additional wage employment to the rural poor at a wage no lower than what prevails. It has thereby increased money incomes for this group of workers quite significantly. Yet, the program has not made a significant contribution to reduction of rural poverty. The reason is food price inflation to which the program has ended up contributing. While the MGNREGS increased the demand for food, this was not met by an increase in the supply of food in the short run. Ghose finds that the increase in wage income of rural households attributable to MGNREGA was 22.2 percent in 2009-10, up from 7.4 percent in 2006-07 (p.5).
In a case study of Birghum district in West Bengal, Mukherjee & Ghosh (2009) find:
- High inter-block variations in terms of average person-days created and utilization of NREGA funds. The blocks which have performed better also show significant variation across the Gram Panchayats within the block. There seems to be no clear relation between utilization of available funds and average person-days created either at the GP level or at the block level.
- The weak correlation observed between number of households with job card and availability of NREGA funds at the GP level suggests that GPs are not able to come up with adequate number of NREGA schemes to absorb the laborers demanding employment.
- There is also no evidence of NREGA getting better implemented in blocks with higher share of agricultural laborers or higher percentage of BPL households, which one would expect. Rather, blocks with higher share of BPL households show lower average person-days created under NREGA.
- Lack of technical skills and human resource seem to be the major reasons why the GPs are not able to develop adequate number of schemes under NREGA.
- Though NREGA allows scope for creating various types of durable productive assets at the community level (such as roads, improving rural infrastructure, drought-proofing, watershed development, water conservation etc), focus has remained on types of works which are easy to design (such as road construction and pond excavation).
- The GPs lack the capacity to design adequate number of schemes under NREGA which can be meaningfully linked with the livelihood and infrastructural development of the local economy. Therefore, greater efforts should be given for the capacity building of the GPs, especially the backward GPs.
Dutta, Murgai, Ravallion, & van de Walle (2012) argue that poorer families tend to have more demand for work on the scheme, and that (despite the un-met demand) the self-targeting mechanism allows it to reach relatively poor families and backward castes.
- Participation rates on the scheme are higher for poor people than others.
- Targeting performance varies across states. Some of those living above the official poverty line in better-off states will no doubt be relatively poor, and need help from the scheme. The overall participation rate seems to be an important factor in accounting for these inter-state differences in targeting performance, with the scheme being more pro-poor and reaching scheduled tribes and backward castes more effectively in states with higher overall participation rates.
- While the scheme is clearly popular with women—who have a participation rate that is double their participation rate in the casual labor market—the rationing process does not appear to be favoring them. We also find evidence of a strong effect of relative wages on women‘s participation—both wages on the scheme relative to the market wage and the male-female differential in market wages. As one would expect, poor families often choose whether it is the man or the woman who goes to the scheme according to relative wages.
- For India as a whole, we find that the scheme‘s average wage rate was roughly in line with the casual labor market in 2009/10. This might look like competitive labor market equilibrium, but that view is hard to reconcile with the extensive rationing we find. Interestingly, we do find a significant negative correlation between the extent of rationing and the wage rate in the casual labor market relative to the wage rate on the scheme. Although this is suggestive, on closer inspection we are more inclined to think that other economic factors are at work. Indeed, the correlation largely vanishes when we control for the level of poverty. Poorer states tend to see both more rationing of work on the scheme and lower casual wages—possibly due to a greater supply of labor given the extent of rural landlessness.
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References:
Dutta, P., Murgai, R., Ravallion, M., & van de Walle, D. (2012). Does India's Employment Guarantee Scheme Guarantee Employment? World Bank Policy Research Working Paper 6003, 1-34.
Ghose, A. K. (2011). Addressing the Employment Challenge: India's MGNREGA. ILO Employment Working Paper No. 105, 1-40.
Jha, R., Bhattacharyya, S., Gaiha, R., & Shankar, S. (2009). Capture of Anti-Poverty Programs: An Analysis of the National Rural Employment Guarantee Program in India. Journal of Asian Economics 20(4), 456-464.
Jha, R., Gaiha, R., & Pandey, M. K. (2010). Determinants of Employment in India’s National Rural Employment Guarantee Scheme. ASARC Working Paper 2010/17, 1-34.
Mukherjee, S., & Ghosh, S. (2009). What Determines the Success and Failure of 100 Days Work at the Panchayat Level? A Study of Birbhum District in West Bengal. IDSK Occasional Paper 16, 1-19.
