Due to the COVID-19 crisis, the past few months have seen large flows of migrant labour from urban centres back to their home states. To reduce the hardship of these workers the central government launched the Garib Kalyan Rozgar Abhiyan (GKRA) on June 20, with the twin objectives of providing employment and benefits to villages through the development of rural infrastructure using returning migrants’ skills. The GKRA covers 116 districts in six states over 125 days, each with a large concentration of returning migrant workers. But it is basically an umbrella scheme of 25 different government schemes under 12 ministries with the aim of better implementation via a centralised chain of command (the Ministry of Rural Development). Moreover, the allocation of Rs 50,000 crore represents a front loading of the outlays which had been planned over the next financial year. Interestingly, out of 25 work types under the GKRA scheme, 11 of them are already covered under MGNREGA.
While the objectives of the scheme are laudable, especially in a climate of uncertainty where migrant workers’ livelihood is under severe stress, is this expenditure likely to yield results in terms of the main objectives? If yes, we should expect that the choice of districts was guided by: One, the number of migrants from that district; two, how poor those districts are relative to others and; three, how efficient these districts are in terms of delivery of the programme objectives.
The first criterion is difficult to test because of the paucity of recent data on district wise migrant workers. While the GKRA scheme only applies to districts which had at least 25,000 returning migrants, we do not really know with any certainty the universe of such districts in the country. However, based on existing estimates, it is highly likely that the states of Uttar Pradesh and Bihar account for a large part of the out migration and the GKRA has 63 out of 116 districts in UP and Bihar.
Regarding the second criterion, we compare the GKRA districts with non-GKRA districts in the country (across 19 major states) on some socioeconomic measures: GKRA districts are on average less developed based on night lights data (a proxy for GDP per district), poverty rates (from the NSS, 68th round, 2011-12), literacy rates and the proportion of Scheduled Castes and Scheduled Tribes population (from the Census 2011), and hence the emphasis on these districts seems justified.
In order to say something about the third criterion, we use existing publicly available administrative data on past district-level performance indicators in the MGNREGA (the National Rural Employment Guarantee Scheme) over the period 2011-19 from the Ministry of Rural Development. We divided our performance indicators into two broad categories: Coverage (the degree to which the programme reached the intended beneficiaries) and intensity (how much each programme participant benefitted from the programme). Thus, “demographic coverage” is the yearly average employment as a proportion of the 2011 Census rural adult population below the poverty line and “financial coverage” is the inflation-adjusted average yearly expenditure per rural adult below the poverty line on the programme. The “demographic/financial intensity” is the average number of days worked/annual (real) payment received per programme participant.
The pattern that emerges is that the GKRA districts fare worse on average on an overall composite index of coverage and intensity than the other districts over the period 2011-2019. However, they fare better on demographic intensity, and no worse than non-GKRA districts on financial intensity. Hence, the better overall performance of the non-GKRA districts is being driven by their better performance in coverage. In terms of SC/ST coverage too, the GKRA districts do worse on demographic coverage. One interpretation of this analysis is that in the GKRA districts, benefits of MGNREGA are highly concentrated on some selected beneficiaries to the detriment of the majority of the targeted poor population. It could also be that some districts have more unionised workers.
The patterns of higher demographic and financial intensity and lower coverage are replicated across states — while poorer states like Bihar and UP are amongst the worst performers in access measured as person days generated, they implement MGNREGA with as much intensity as richer states. It is widely accepted that state capacity influences the delivery of public programmes. However, it can also be that corruption is higher in these states. Figures on irregularities across all districts are notoriously hard to obtain due to social audits results not being reported by states. But some estimates of irregularities based on the NSS data, suggest that states like Jharkhand, Bihar, Orissa, UP and Uttarakhand reveal larger gaps between officially reported employment generation figures and the NSS-based estimates which may reflect irregularities. On the other hand, states like Andhra Pradesh, Himachal Pradesh, Tamil Nadu and Rajasthan have well matched figures. It may not be a coincidence that social audit processes have been documented to work especially well in Andhra Pradesh and Rajasthan.
Together with other evidence on state level implementation of MGNREGA, state level irregularities and administrative capacity, the success of GKRA schemes may be tightly linked to past performance of MGNREGA, and based on that, the prognosis is not good. By the end of August 2020, a tracker released by the Peoples Action for Employment Guarantee (PAEG) showed that in the state of Bihar, there was no observable difference between MGNREGA employment generation in the GKRA districts as against the others. Moreover, out of 12 districts that reported a higher materials-to-labour expenditure on MGNREGA (the materials to labour ratio is mandated by the government to be below 40 per cent), nine out these were the GKRA districts. Our analysis shows that the poorest districts with the largest number of migrant workers are precisely the ones that need to generate employment, but have the least capacity to deliver. The Centre has created a bureaucratic structure accountable to the Ministry of Rural Development and that may deliver the right outcomes in the short run, but it remains to be seen how to manage this in the longer run.
This article first appeared in the print edition on October 7, 2020 under the title “The elusive promise of work”. Afridi is associate professor at Indian Statistical Institute, New Delhi, Dhillon is professor at King’s College, London and Chaudhuri is assistant professor at Shiv Nadar University
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