MNREGA – the world’s largest workfare programme - formed the backbone of the UPA government’s anti-poverty programme, and may well represent its most important legacy in the long run. This column reviews various studies on its performance, and provides a perspective on its broader macroeconomic effects. It argues that while MNREGA was far from perfect in terms of implementation, it was much more effective than other existing schemes in benefitting the poor.
Nine years ago, the United Progressive Alliance (UPA) government implemented the Mahatma Gandhi National Rural Employment Guarantee Act (MNREGA), the world’s largest workfare programme. Rolled out in three successive stages starting in 2005, it covered the entire country by 2008. The scale of the programme is staggering, providing employment to a third of India’s rural population, at an annual cost of 0.3-0.4% of Gross Domestic Product (GDP). It formed the backbone of the UPA government’s anti-poverty and rural safety net programme, and may well represent its most important legacy in the long run.
Views concerning the impact of MNREGA are varied, predictably along partisan lines. Critics argue that it is populist, politically motivated and manipulated by the UPA for its own electoral advantage, beset with corruption and leakages. It is said to be responsible for runaway fiscal deficits, inflation and slowdown of growth. Supporters argue the programme has succeeded in reducing rural poverty, building infrastructure and strengthening local governance. Which of these two views is closer to the truth?
To answer this, it is essential to review ground-level evidence concerning how MNREGA has actually performed. As it turns out, an impressive amount of research has appeared recently, appraising functioning and impacts of MNREGA on various dimensions. Over the past month, I managed to find over 20 articles based on econometric analysis of large scale household surveys, besides a few books, field studies and government reports. Written by academic scholars both in India and abroad, mostly using National Sample Survey (NSS) data, and aimed for publication in academic journals, the papers are free of partisan bias. Each paper focuses on a specific question, using a particular dataset and research methodology. Trying to evaluate the programme based on these analyses, I was reminded of the ancient fable in which six blind men try to assess an elephant from six different angles. Nevertheless, the following broad picture emerges.
The broad facts are the following. Launched in February 2006, the programme was rolled out in three successive phases, with the first phase comprising 200 of the poorest districts, and all 618 rural districts covered by 2009-10. Each rural household is entitled to 100 days of work per year, at a guaranteed minimum wage, to be provided within 15 days of application (failing which the household qualifies for unemployment benefits) and within 5 km of the household’s residence. Since 2009-10, it has provided employment to over 50 million households annually, with an average of 42 days per year per household, at a cost of around Rs 37,000 crores ($6.16 bn approx.). Gram panchayats are given the responsibility to administer the projects, which typically involve construction of local infrastructure aimed at providing water security, soil conservation, flood control and raising land productivity. The projects are required to be decided following Gram Sabha meetings. A 60:40 split between labour and material costs is mandated Government of India 2012).
The evidence shows wide variations across states with regard to implementation of these provisions: lower income states with limited administrative capacities have tended to lag noticeably behind five ‘star’ states (Kerala, Tamil Nadu, Rajasthan, Himachal Pradesh and Andhra Pradesh (AP)). Less than half the rural population was aware about the work-on-demand feature, and less than a fifth of their unemployment benefit entitlement beyond the 15 day waiting period. Gram Sabhas are held infrequently, with low participation rates. There is substantial rationing, particularly in Bihar, Odisha and Jharkhand - in 2009-10 while 25% of rural households were provided work, 19% sought work but did not get employed. A lot of the employment tends to be provided in the spring (slack) season, because it becomes difficult to undertake construction projects during the monsoon, and Gram Panchayats do not want to create labour market shortages during peak harvest seasons.
Household surveys and social audits reveal numerous complaints, involving non-issuance of dated receipts, non-payment of unemployment allowance, payment of less than full wages, and especially, delayed payments. In some regions, resistance from the local elite has prevented social audits being carried out. In states such as AP where audits have been carried out, there is no sign of abatement of these complaints across successive years. A rising proportion (ranging from 25-50%) concern non-payment or delayed payment of wages, besides bribes (14-20%), benami
payments (16-19%) and missing records (4-6%). There were fewer complaints regarding material irregularities, but these are intrinsically harder to detect. Follow-up disciplinary action following discovered irregularities was largely lacking, with major or medium actions taken in only 3.4% of cases (Afridi and Iversen 2013
). A recent study in eight AP districts carefully estimates the overall extent of leakage of funds from the programme at 30%, which went down substantially as a result of recent use of smartcards to disburse payments (Muralidharan et al. 2014). Surveys in districts in other states (Bihar, Chattisgarh, Odisha and Rajasthan) indicate leakage rates ranging from 5 to 40%1
To assess anti-poverty impacts, many studies have examined relative effects on growth of wages, employment, consumption, savings, child labour, schooling between households exposed to the scheme across different phases. One methodology (Difference-In-Difference (DID)2) compares these measures of well-being for households before and after coverage, after accounting for pre-existing trends and various household characteristics. The DID estimates show a rise of daily wages of approximately 5% that can be attributed to the programme, an estimate that rises to 9% in the ‘star’ states (Imbert and Papp 2012). Different studies using this methodology find corresponding positive effects on food and non-food consumption, calorie and protein intakes, and on savings. Rural-urban migration rates have dropped, owing largely to reduction in ‘distress migration’, and urban unemployment rates fell by 7%. Child labour fell by approximately 10%, with no corresponding effects on school enrolment, and significant positive effects on grade progression and test scores (larger even than observed effects of conditional cash transfer schemes in Latin America) (Mani et al. 2014). These benefits were typically larger for lower castes, illiterates and women.
An alternative methodology (Regression Discontinuity (RD)) examines differences between districts that just missed belonging to one phase of the rollout with those that just made it. This approach yields more conservative estimates of the anti-poverty impacts, with no average impact on wages (Zimmerman 2013). Yet even this approach shows strong positive effects - a 50% drop in poverty - for the most vulnerable sections of the population (Scheduled Castes (SCs) and Scheduled Tribes (STs)) during the slack season (Klonner and Oldiges 2014). The effects are more pronounced in periods of low rainfall. The stronger safety net was accompanied by a move out of employment to self-employment and non-agricultural employment for males, while women’s labour force participation rates rose. Field studies corroborate these statistical results (Carswell and de Neve 2013).
There is comparatively less evidence concerning the broader macroeconomic effects of MNREGA on growth, fiscal deficits or inflation. These are intrinsically harder to estimate. Nevertheless, my own broad assessment is that NREGA had relatively little impact on macro aggregates, compared to other key determinants. The adverse macroeconomic events set in only after 2009-10, by which time MNREGA had already been fully implemented. Slowing of growth rates owe to many different factors, such as ‘policy paralysis’, problems with environmental clearances, land acquisition, recession in the world economy, appreciation of the real exchange rate, and fiscal and monetary contraction since 2010. The rise in inflation is also a post-2010 phenomenon, especially marked in food articles such as milk, edible oils, sugar, fish and vegetables in which rising rural wages are unlikely to have played an important role (Mishra and Roy 2011). Moreover, the RD-based econometric evidence fails to find positive effects on average rural wages, with most of the positive effect concentrated in the agricultural slack season.
Safety net for the most vulnerable, but poorly implemented
Summing up the evidence, what seems most striking is the effect of MNREGA in providing a safety net and reducing poverty for the most vulnerable sections of the rural population. Providing employment to rural unskilled labour is without doubt the single most direct and effective way of reducing poverty amongst the most vulnerable sections of the population. Undoubtedly the price tag is large, around 0.4% of GDP. But on the other hand the scheme provides employment to one third of the rural population who are the poorest. With regard to targeting success, it beats hollow the other big-ticket subsidy items in government budgets - food, fertiliser and petroleum subsidies, each of which account for 0.8% of GDP, and benefit mainly the middle class rather than the poor. A leakage rate of 30% or less seems a lot better than the 90% leakage rates associated with food subsidies in India (Jha and Ramaswami 2011). MNREGA is far from perfect, but a lot more effective than any other existing scheme in benefitting the poor.
It is no surprise therefore that MNREGA helped UPA gain re-election in the 2009 general elections. There is detailed RD-based evidence showing that UPA reaped electoral benefits from rolling out MNREGA in 2009, and that MNREGA budgetary allocations across districts and blocks have been manipulated in certain states to increase these benefits. But there is also recent evidence that the electoral benefits which arose for the Indian National Congress (INC) in early stages of implementation, have turned into a liability in later stages (Zimmerman 2012). As time goes by, citizen expectations from the scheme have risen, while problems of implementation have become more evident. Failure to implement MNREGA properly may thus have been a political liability for the INC in the recent election. Herein lies a cautionary tale for parties in power when they introduce populist schemes - failure to implement them properly will turn out to be a political liability in the long run.
A shorter version of this column appeared in the Indian Express.
- Ministry of Rural Development (2012), ‘MNREGA Sameeksha: An Anthology of Research Studies’, Government of India, Pg. 66.
- In the simplest set-up, Difference-In-Differences (DID) compares the outcomes of two groups for two time periods. One of the groups is exposed to a treatment (for example, a policy change) in the second period but not the first. The second group is not exposed to the treatment in either period. To provide a clean measure of the impact of the treatment, the DID technique subtracts the average gain of the second (control) group over the two periods from that of the first (treatment) group. This removes biases arising from permanent differences between the two groups as well as biases arising due to general (trend) changes in the treatment group over
- C. Imbert and J. Papp (2012), ’Labor Market Effects of Social Programs: Evidence from India’s Employment Guarantee’, Working Paper, Department of Economics, Princeton University.
- F. Afridi and V. Iversen (2013), ’Social Audits and MGNREGA Delivery: Lessons from Andhra Pradesh’, Indian Policy Forum.
- G. Carswell and G. de Neve (2013), “Women at the Crossroads: Implementation of Employment Guarantee Scheme in Rural Tamilnadu”, Economic and Political Weekly, 28 December.
- K. Muralidharan, P. Niehaus and S. Sukhtankar (2014), ’Payment Infrastructure and the Performance of Public Programs: Evidence from Biometric Smartcards in India’, Working Paper, Department of Economics, University of California San Diego.
- L. Zimmerman (2012), ’Jai Ho? The Impact of a Large Public Works Program on the Government’s Election Performance in India’, Working Paper, Department of Economics, University of Michigan.
- L. Zimmerman (2013), ’Why Guarantee Employment? Evidence from a Large Public Works Program’, Working Paper, Department of Economics, University of Michigan.
- Ministry of Rural Development (2012), ’MNREGA Sameeksha: An Anthology of Research Studies’, Government of India.
- P. Mishra and D. Roy (2011), ‘Explaining Inflation in India: The Role of Food Prices’, in S. Shah, B. Bosworth and A. Panagariya (eds.), India Policy Forum 2011-12, vol. 8, Sage, New Delhi.
- S. Jha and B. Ramaswami (2011), ‘The Percolation of Public Expenditures: Food Subsidies and the Poor in India and Philippines’, in S. Shah, B. Bosworth and A. Panagariya (eds.), India Policy Forum 2011-12, vol. 8, Sage, New Delhi.
- S. Klonner and C. Oldiges (2014), ‘Can an Employment Guarantee Alleviate Poverty? Evidence from India’s National Rural Employment Guarantee Act’, Working Paper, South Asia Institute, University of Heidelberg.
- S. Mani, J. Behrman, S. Galab and P. Reddy (2014), ‘Impact of the NREGS on Schooling and Intellectual Human Capital’, Population Study Centre, University of Pennsylvania Scholarly Commons.