Academic assessment of the scheme appears far more favourable than evident from the public discourse
Ten years after it was launched, the Mahatma Gandhi National Rural Employment Guarantee Scheme (MGNREGS), which promises 100 days of employment to every rural household, is back in the news. More people in rural India are seeking employment through the programme across the country, with job numbers scaling a five-year peak.
Although the MGNREGS seems to be reaching many more rural households than before, urban opinion on the programme is sharply divided, both in the mainstream and social media. Even the Narendra Modi government seems divided on the programme, with the ministry of rural development declaring that the 10th anniversary of the programme was a matter of “national pride" barely a year after Modi had derided the programme as a ditch-digging exercise on the floor of Parliament.
Academic opinion on the MGNREGS, however, appears far more favourable than is evident from the public discourse on the issue. A growing body of research on the MGNREGS suggests that it has helped dent poverty, reduced distress migration and raised the bargaining power of rural labourers, especially among lower castes and women, the biggest beneficiaries of the programme.
The latest UN Development Programme report on human development hailed the programme as a “milestone", which had raised living standards of the poorest of households by offering them a safety net. In a 2014 paper analysing the impact of the programme, economists Stefan Klonner and Christian Oldiges of the University of Heidelberg found that it had reduced poverty by almost half during the agricultural lean season, by helping smoothen seasonal spikes in the consumption of the poorest families.
One of the chief attractions of the scheme, according to economists, is the self-selection mechanism of choosing beneficiaries. As Pranab Bardhan, emeritus professor of economics at the University of California, Berkeley, pointed out in an interview to Mint about a year ago, the MGNREGS is effectively a conditional cash-transfer programme.
The condition is that the beneficiary has to work manually, which immediately rules out the rich and the middle class. And in the absence of credible data on poor households, this mechanism seems to be effective in reaching those who need it the most. As the ministry’s press release pointed out, the proportion of scheduled castes and tribes (SCs and STs) among those the programme has reached is greater than their share in the overall population of India.
The MGNREGS has been instrumental in providing a safety net to the poor because it “attracts mainly poor and vulnerable people such as agricultural wage labourers, scheduled tribes, scheduled castes and small, marginal farmers", the NCAER report pointed out.
Given that the key objective of the programme was to provide livelihood security (especially during the agricultural lean season) and thereby act as a safety net for the poor, it seems to have met that goal.
Nonetheless, the programme continues to face four main criticisms:
1. It is not actually a demand-driven programme, and its success depends on the willingness of the respective state governments and local bodies.
2. It has failed to create durable assets in rural areas.
3. It has contributed significantly to wage growth and stoked the fires of inflation.
4. It has led to a massive leakage of public resources, and led to unintended consequences in rural areas such as on educational outcomes.
The first point is perhaps the most potent among the main criticisms of the programme. Although it was launched as a demand-driven “workfare" programme, in reality, the MGNREGS remains supply-driven with its reach and impact determined by central, state and local government functionaries, and varying widely across states.
Research by Deepta Chopra of the Institute of Development Studies, Sussex, shows that it is the government’s inability or rather unwillingness to award jobs under the MGNREGS that has led to the decline of the programme in Rajasthan.
In Rajasthan, the early success of the programme turned out to be its biggest weakness, Chopra argues. She points out that the involvement of grassroots organizations in the implementation of the programme threatened local power brokers, who resented the inability to award jobs according to their discretion.
This led them to sabotage the process of demand-driven work schemes, and the frontline workers charged with accepting applications for work refused to accept them.
Economists Abhiroop Mukhopadhyay of the Indian Statistical Institute, New Delhi, Himanshu of Jawaharlal Nehru University and M.R. Sharan of Harvard University found significant rationing of work by village headmen in Rajasthan. In many villages, people did not demand work because they were told that “they can request work only when it is available".
Supply-side issues in the MGNREGS are so important that a group of World Bank economists found that even after mitigating the information asymmetry, participation may not increase. Martin Ravallion and others of the World Bank ran a randomized experiment in Bihar, where they showed a group of villagers an informative video about the MGNREGS.
While the perception of the programme certainly improved among those who viewed the video, the impact on seeking and finding work through the programme remain modest.
Given the supply-side nature of the programme, better-functioning states such as those in south India have made better use of the programme and received more funds compared to poorer northern states such as Bihar and Uttar Pradesh, an analysis by former bureaucrat N.C. Saxena shows. Saxena suggests that pre-fixing state-wise MGNREGS allocations based on need would have been far more equitable.
While creating durable assets was not the main objective of the programme, it became a key aim in later years, and led to convergence with other schemes. The evidence on this count is mixed, but the perception that it has just been an empty ditch-digging exercise may be an urban myth.
While there are anecdotal examples of poor assets created under the programme, there is no systematic evidence suggesting that most or even a majority of the assets are useless. A 2014 study by a team led by economist Sudha Narayanan of the Indira Gandhi Institute of Development Research (IGIDR) shows that most assets recorded under the programme in Maharashtra exist in reality and not just on paper.
Furthermore, an overwhelming majority of rural households surveyed found the assets created under the programme such as bunds, ponds, embankments, etc., to be useful for them. Seventy-five per cent of the assets created are directly or indirectly linked to agriculture, the study found.
Another criticism that has been prevalent is that MGNREGS wages increase agricultural wages and, hence, the cost of cultivation rises, which has inflationary effects. In a 2012 paper, Mehtabul Azam of Oklahoma State University analysed the impact of the MGNREGS and found that the programme drove up wages of casual female labour by 8%.
However, this is indicative of a reduction in the gender-wage gap in India’s labour market more than of a wage-inflation spiral. There is very little macroeconomic evidence to suggest that the MGNREGS has been a key driver of inflation. A 2014 Reserve Bank of India report suggests that MGNREGS may not have had any significant impact on food inflation.
A new paper by Manisha Shah of UCLA and Bryce Steinberg of Brown University argues that the MGNREGS has undesirable effects on educational outcomes, particularly for adolescents. Using the Annual Status of Education Report (ASER) survey data, the duo finds that children in districts with more MGNREGS exposure perform worse in math, and are more likely to drop out of school.
“We examine the effect of MGNREGS, one of the largest workfare programmes in the world, on human capital investment. Since MGNREGS increases labour demand, it could increase the opportunity cost of schooling, lowering human capital investment even as incomes increase. Using a household survey of test scores and schooling outcomes for approximately 2.5 million rural children in India, we show that each year of exposure to MGNREGS decreases school enrolment by 2 percentage points and math scores by 2% of a standard deviation amongst children aged 13-16. In addition, while the impacts of MGNREGS on human capital are similar for boys and girls, adolescent boys are primarily substituting into market work when they leave school while adolescent girls are substituting into unpaid domestic work."
However, the negative effects of older children dropping out of school could be compensated for by the greater investment made on younger children in participating households. In a forthcoming paper in the IZA Journal of Labour & Development, economists Farzana Afridi, Abhiroop Mukhopadhyay and Soham Sahoo of the Indian Statistical Institute show that a mother’s participation in the MGNREGS not only raises the odds of the child attending school but is also associated with better academic performance.
The issue of corruption is not unique to the MGNREGS but afflicts most state-run programmes, including those that provide for health or infrastructure. As Bardhan pointed out, the leakages from the MGNREGS are a small fraction of the subsidies to the better-off sections of society, and it is, therefore, important not to lose a sense of proportion in evaluating the programme.
A fundamental issue that plagues all well-intentioned public programmes is the lack of state capacity or political commitment to implement it effectively. So far, the focus has been on building state capacity through technocratic solutions (biometric payment systems, for instance).
The evidence on automated payment systems has been mixed. In Andhra Pradesh, a smart card-based payment system has been found to have reduced the problems of delayed payments and helped plug leakages.
Karthik Muralidharan of the University of California, San Diego, who along with his colleagues conducted the experiment on smart card payments for MGNREGS wages in Andhra Pradesh found that “despite the incomplete implementation, beneficiaries in carded mandals experienced a faster, more reliable, and less corrupt payment experience. The smart card system reduced the lag between working on an MGNREGS project and collecting payment by 29%, and reduced the unpredictability in the lag by 39%. Further, it reduced by 19% the time workers spent collecting MGNREGS payments."
While the Andhra Pradesh experiment has been hailed as a success, news from Chhattisgarh, another state that has done well in the implementation of the MGNREGS, does not bode well for biometric payments. Supriya Sharma of Scroll noted that the Aadhaar-based payment system is facing problems in Chhattisgarh, with many enrolment centres charging bribes while enrolling people.
In summary, the MGNREGS program seems to have been reasonably successful in meeting its goals. It may not have single-handedly transformed rural India, but then it was never meant to do that. It was meant to be a safety net for the poorest and most marginalized sections of society, whose incomes went through sharp fluctuations across seasons.
Although it suffers from implementation challenges and leakages, the MGNREGS has reached the target population more effectively than most other government schemes.
This is not to deny that the programme needs reforms to perform better. While better use of technology can solve certain problems, they are not adequate to fix design bugs or issues of political accountability for the programme.
Given the regional imbalances in fund allocation for the programme, it may be worth considering allocating resources to states and districts that require this programme the most, based on the levels of poverty and exposure to drought, as Saxena argues.
It may also be worthwhile to decentralize decision-making on the implementation of the scheme once the allocation is based on a fair and transparent criterion.
Rather than micromanage each aspect of the programme, the central government should perhaps focus more on monitoring key outcomes such as generation of employment and assets, and on publicizing data relating to these aspects to make states and local bodies accountable for the funds they receive.
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