MGNREGA: The Guaranteed Refuge for Returning Migrants During COVID-19 Lockdown in India

I. Introduction

This study presents evidence on how millions of seasonal migrants who returned to their native villages could cope up and secure a part of their lost income by working under the Mahatma Gandhi National Rural Employment Guarantee Act (MGNREGA) during the nationwide lockdown amid the COVID-19 ¹ pandemic. The crisis has once again highlighted the importance of social protection measures in anticipation of the risks (Gentilini et al., 2020) that are emerging from the transmission of global volatility, increasing inequity and heightened macroeconomic risks, especially in the developing world (Loayza et al., 2007). Using the decadal growth rate of 2001–2011 on Census 2011 data, ² we estimate that currently there are around 660 million migrants, of which about 56 million migrated with the reason for work/employment. Nearly 23 million migrants fall under the category of seasonal migrants, usually above the age of 15 years, who migrate temporarily from rural or urban areas within or outside the States for non-farm-based casual work. India followed the global response to the COVID-19 emergency, which saw lockdown as the only possible solution to curb the spread of disease. The immediate fallout of the lockdown strategy was the large-scale loss of jobs, especially in the informal sector, affecting the sources of the daily income of the casual workers in it. On 26 March 2020 (the second day of Phase 1 lockdown), the government announced an economic relief package of ₹1.7 lakh crore, which was a mix of food security and direct cash transfer, primarily for daily wage earners and to curb the reverse migration of the seasonal migrant workers. ³ But despite government’s best efforts, millions of them chose to walk back to their native villages. In response to the migrant’s exodus, the government had to administer the prescriptions from the policy realm to go full-on with existing social security schemes (Duflo & Banerjee, 2020; Rangarajan & Dev, 2020) and issued a revised consolidated guideline ⁴ on 15 April 2020 (the first day of Phase 2 lockdown) to open up the industries operating in rural areas, agricultural-cum-allied activities and works under MGNREGA, ⁵ all with adequate safeguards and mandatory standard operating protocols. The rationale for this decision amid the COVID-19 crisis was to provide an impetus to the rural economy by creating job opportunities for rural workers as well as for the returning seasonal migrants. Following the order for the partial lifting of the lockdown in rural areas, several State governments initiated MGNREGA activities with additional guidelines and measures for workers. On average, annually, the programme employs over 75 million rural workers. ⁶ With the seasonal migrant workers across the country returning to villages, their dependence on the programme as the source of income was inevitable, and hence the increase in demand for work under it was anticipated. Seeing the need, the government allocated an additional ₹40,000 crore under the MGNREGA as part of the ₹20 lakh crore economic stimulus in the wake of the COVID-19 pandemic. ⁷ The government’s decision, which allowed the programme to resume its operation, was supposed to bring economic relief not only to millions of rural workers but also to millions of returning seasonal migrants during the lockdown. To check the efficacy of this decision, this study presents a research design that tries to find evidence of a surge over the usual demand for work under MGNREGA after government’s order for opening it up, estimate the aggregate income lost per day by the seasonal migrants and the proportion of it secured by those among them who chose to work under the programme during the lockdown.

II. Research Design

For the estimates and analyses, we use population and migrant’s data of censuses for the year 2001 and 2011 and administrative data of MGNREGA for the financial year 2018–2019, 2019–2020 and 2020–2021. India’s administrative network comprises over 725 districts, covering more than 7,000 blocks and 6.5 lakh villages spread across 28 States and 8 Union Territories (UTs). ⁸ However, we could compile the data for 595 districts across 28 States because many of them were bifurcated and reorganised after the year 2011, adding nearly 125 new districts to the total administrative units. ⁹ We adopt the following steps in our analysis.

Step 1: Estimating the Incidence of Seasonal Migrants in the District’s Population

We run linear extrapolation (using the decadal growth rates of 2001–2011) on Census 2011 data to estimate the figures for population, total migrants, migrants for work/employment and seasonal migrant for the year 2021 in each of 595 districts. We assume here that the 2001–2011 growth rates have not changed significantly for the current decade (2011–2021), and, hence, the estimates are supposed to be a good approximation to proceed with the further analysis. For each district, the ratio of the number of seasonal migrants to its population gives us an estimate of the incidence of seasonal migrants in the district’s population.

Step 2: Estimating the Usual Demand for Work Under MGNREGA Before Lockdown

We estimate the average total number of persons employed under MGNREGA in 2018–2019 and 2019–2020 in each of 595 districts. The average value for each district serves as its baseline value of the usual demand for work under the programme.

Step 3: Identify Districts with Surge over Usual Demand for Work Under MGNREGA After Lockdown

Similarly, we estimate the total number of persons employed under the programme for the period of five months, that is, from 1 April 2020 to 31 August 2020 in each of 595 districts. These initial five-month of 2020–2021 also were the ones when the government imposed nationwide lockdown (in April and May) and unlocking (in June, July and August). We identify districts with a surge over the usual demand for work under MGNREGA by subtracting the baseline value of each district from their respective estimates for 2020–2021. The difference is either positive or negative. It is evident that the districts with a negative difference are the ones that are following the usual trajectory of demand for work under the programme. However, the districts with a positive difference are the ones that have already surpassed the baseline of the usual demand for work under MGNREGA. The value of the positive difference in a particular district can be considered as good approximation of number of seasonal migrants who have returned to their native villages in it. We pinpoint such districts since they faced the influx of returning seasonal migrants who chose to work under the programme during the lockdown. For the purpose of distinction, we categorise 595 districts into two groups. The districts with a negative difference are placed in ‘Group N’ and those with a positive difference in ‘Group P’.

Step 4: Scrutiny of Employment Under MGNREGA in ‘Group N’ and ‘Group P’ Districts Before and After Lockdown

We now consolidate the administrative data of MGNREGA for Group N and Group P districts as well as for a total of 595 districts for 2018–2019, 2019–2020 and 2020–2021 on four crucial indicators of employment. We estimate the aggregate number of persons employed under the programme, person-days generated, the average number of days of work provided per person and the amount of wage bill (nominal and real ¹⁰ ) generated. Scrutiny of each of these indicators reveals that with reference to the average of aggregates for 2018–2019 and 2019–2020, the aggregates of ‘Group P’ districts are inflated compared to aggregates of ‘Group N’ districts. Further analysis of Group P districts helps us estimate the total number of seasonal migrants who returned and took refuge under MGNREGA for securing part of their income, which otherwise they would have lost due to lockdown.

Step 5: Estimate the Aggregate Per Day Income Lost, Gained and Secured by the Seasonal Migrants During Lockdown

Using the State-wise schedule of the wage rate ¹¹ for MGNREGA, we compile the data on the minimum wage rate (nominal and real) for each of 595 districts. We assume here that in the pre-COVID-19 period, every seasonal migrant in each of these districts is at least earning daily income equivalent to or perhaps more than the minimum wage entitled under MGNREGA. Hence, the product of the total number of seasonal migrants in a district and the applicable minimum wage rate in it provides us a crude estimate of minimum income lost per day in that district. The aggregate for 595 districts is an approximate figure for the least total income lost per day by seasonal migrants during the lockdown. Similarly, we now estimate the total minimum income gained per day by seasonal migrants who returned and worked under MGNREGA in each of ‘Group P’ districts. The aggregate is the least total income gained per day by a chunk of seasonal migrants. The ratio of aggregate minimum income gained to aggregate minimum income lost by seasonal migrants is the proportion of income secured through provisioning of work to them under MGNREGA.

Results and Discussion

Concluding Remarks

One of the income risk coping strategies adopted by the population in rural regions of the country is to temporarily migrate to an economically developed area in search of daily wage-based casual work. The lockdown imposed due to the covid-19 pandemic severely affected those following this strategy and resulted in an exodus of migrants to return home. During this crisis, our study finds that nearly 7.5 million seasonal migrant workers took refuge under MGNREGA during the lockdown, found work for around 23 days, and secured about 28% of the income they used to earn daily in the pre-COVID-19 period. Though MGNREGA proved to be a successful safety net for one-third of returning seasonal migrants, it could not address the income issues faced by the remaining two-third of them. Our analysis of district-wise incidence of seasonal migrants shows that regions in the northern plain, especially in the ‘BIMARU’ States ¹³ of the past, are still not able to generate adequate non-farm-based employment opportunities during the agricultural off season for its rural population. The overdependence on MGNREGA in these regions during the present crisis is a symptom of heavy economic distress persisting within them and, hence, needs redressal through interventions of policies focussing on development of human capabilities. Though the research approach in this study is exploratory and estimates are approximate, it does provide small evidence of the great importance the MGNREGA holds in India’s development policy discourse. But for more robust analysis and addressing of socio-economic concerns faced by seasonal migrants and other informal sector workers, establishing a formal comprehensive data repository of them is the need of hour.