Abstract
This paper attempts to address the impact of the MGNREGA on the rural agricultural sector, focusing on cropping patterns, irrigated area, crop yields, wages and rural employment. The analysis is based on two data sources: the first is a unique district-season level panel dataset that we construct using multiple sources, and the second is unit record data from the NSS Employment Unemployment Surveys. To identify causal effects, we employ a difference-in-difference matching procedure, where districts are matched based on propensity scores; the use of propensity scores represents a novel aspect of this paper. We also examine pre-programme trends for each outcome variable to provide a check on the validity of our estimates. Our results indicate modest changes in cropping patterns that are state and period specific; however, they do not indicate any improvements in crop yields that were expected given the MGNREGA’s focus on investments in irrigation, although there is some evidence that irrigated area may have expanded after a lag. We also find that there is no systematic evidence of impact on wages and therefore no evidence that public works employment in MGNREGA crowded out casual labour in agriculture.
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Notes
Rangarajan et al. (2011) find that between 1999/2000 and 2004/2005 about 19 million people were added to the agricultural work force, while between 2004/2005 and 2009/2010 about 21 million people moved out of it. They also note a greater fall in share of agricultural employment in the total work force between 2004/2005 and 2009/2010 as compared to 1999/2000 to 2004/2005.
Unless noted otherwise, these are all crop years, beginning in July and ending in June.
It would also be useful to look at the impact on volume of water for irrigation as this may be a mechanism via which yields are affected. However, we are unable to study this as, to the best of our knowledge, data on irrigation volumes are not available.
It is also possible that the scheme may have led to mechanization that could potentially lower agricultural wages, as noted in Bhargava (2014).
See Appendix Table A9.
In 2004/5, labour force participation rate for males in rural India was 545 persons per thousand persons, while the corresponding figure for females was 287 (NSSO 2006).
Some district boundaries were redrawn during this period, and new districts created; the analysis accounts for these changes.
Although implementation in Phase III districts was officially initiated in April 2008, as noted in Imbert and Papp (2015), effective employment creation is likely to have been weak in the initial months since implementation. Therefore, in the empirical analysis, Phase III districts are assumed to be immune to the scheme in the last 3 months of 2007/2008.
These states together cover 97% of the country’s rural population in 2004/2005.
We change terminology to be consistent with other literature, in particular with Imbert and Papp 2015. The rainy season roughly corresponds to the agricultural peak season because in most states it includes the sowing and harvesting of kharif crops, and the sowing of rabi crops, all of which are highly labour intensive. On the other hand, the dry season may be considered as the agricultural off-peak season as the only labour-intensive operation during this period is the harvesting of rabi crops.
We restrict ourselves to a standard DID for the analyses of cropping pattern and crop yields as these are carried out at the state-season level and the number of districts is not large enough to implement matching sensibly.
Note that this outcome variable is not in logarithms as for a given individual many categories take the value 0.
In specifications for the crop-wage dataset, the subscript i is not applicable as the unit of observation is a district and not an individual.
Again, this variable is not in logarithms because even within state-season strata, there are several districts which do not grow a particular major crop and therefore have 0 values for those crops.
For the two outcomes analysed at the state-season level, namely, share of crop acreage in total cropped area and crop yield, AEZs are replaced by Agro-Ecological Zone Production Systems (AEZPSs). Each AEZPS is a homogenous group of districts with similar cropping pattern that falls within a single AEZ (Saxena et al. 2001).
For specifications using the crop-wage dataset, the covariates are shares of: SC/STs, illiterates, currently married, Muslims and average age and age squared, all at the district level.
For the agricultural wage outcome in the crop-wage dataset, Z also includes the proportion of SC/ST population and the literacy rate, both at the district level.
Note that generating a balanced panel across all 3 years would have resulted in a loss of several observations, and for this reason, the sample sizes are smaller for 2004/2005. This means that impact estimates computed later for partial implementation are based on a smaller sample size than those for full implementation; the estimation sample under each is however a balanced panel.
Our results are robust to choice of specification and other weighting and matching methods.
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Acknowledgements
We would like to thank the Ministry of Agriculture, Government of India, for providing us with the seasonal area, production and yield data. We would also like to thank K.L.Krishna, S.C.Panda, Ashwini Deshpande, Uday Bhanu Sinha, Anirban Kar, Abhiroop Mukhopadhyaya and an anonymous referee for their input and suggestions to improve the paper. We are also grateful to participants at the IZA young scholar programme, the Indira Gandhi Institute of Development Research (IGIDR) conference on MGNREGA and the Indian Statistical Institute annual conference on economic growth and development, for their feedback. Any errors are our own.
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Varshney, D., Goel, D. & Meenakshi, J.V. The Impact of MGNREGA on Agricultural Outcomes and the Rural Labour Market: A Matched DID Approach. Ind. J. Labour Econ. 61, 589–621 (2018). https://doi.org/10.1007/s41027-019-0151-x
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DOI: https://doi.org/10.1007/s41027-019-0151-x