MNREGA’s impact on rural labour markets
14 Mar 2016
Poverty & Inequality
In this article, Laura Zimmermann, Assistant Professor of Economics at the University of Georgia, provides an overview of the research on the impact of the initial phase of MNREGA on rural labour markets in India. The evidence suggests that the programme has served as an important short- and long-term safety net, and has had some employment generation effects during the agricultural off-season. However, the effect on rural casual wages is less clear.
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Since the introduction of the Mahatma Gandhi National Rural Employment Guarantee Act (MNREGA) in the first districts about 10 years ago, the assessment of its economic impacts has been at the heart of the debate about programme benefits1. As the world’s largest public-works programme and due to a couple of ambitious features (such as the legal guarantee of employment and the absence of detailed eligibility criteria), there has been considerable interest in how MNREGA has affected rural labour markets in a variety of areas2. These include employment generation, wage increases, and the provision of a safety net3.
Employment generation and wage increases
As a public works programme, one of the main intended benefits of MNREGA is the creation of additional employment: workers are hired to perform manual work on local public sector projects and paid the minimum wage. This means that the employment guarantee scheme should be especially attractive during times when there is little alternative employment available, such as during the agricultural off-season. MNREGA is also set up to be attractive for women, who get paid the same wage as men, whereas there is a substantial gender wage gap in private employment4.
Since MNREGA is meant to be a demand-driven programme that workers can take advantage of at any time of the year, the scheme also has the potential to affect rural labour markets when alternative job opportunities are available. Especially in areas where the minimum wage laws are not well enforced, the programme could lead to wage increases in private casual employment if the availability of MNREGA jobs increases workers’ bargaining power. At the same time, this could lead to a crowding out of private sector jobs and counteract the overall employment creation effects. The net benefits of the programme are therefore unclear without a data-driven analysis.
Short- and long-term safety net
In addition to the potential impacts during normal times, MNREGA can also be used as a short- and long-term safety net. The programme can be taken up for short periods after adverse economic shocks such as a bad harvest season, and can help households to smooth consumption more effectively. The programme may also be helpful in the long term: Since households know that MNREGA work is available should they fall on hard times, the existence of this safety net may allow them to re-optimise time allocation and to spend more time on rewarding but more risky forms of employment such as self-employment.
How important these different effects are in practice is an empirical question. Poor implementation quality, for example in the form of job rationing or corruption, will attenuate all of these effects5.
Empirical evidence on labour market effects
Over the last couple of years, a number of research papers have analysed the labour market effects of MNREGA using different datasets, empirical estimation strategies and outcome variables. What they have in common is a focus on the first years of MNREGA when the programme was rolled out in phases. This is driven by research design motives. In order to causally estimate the impact of the programme on the rural labour market, researchers need to be able to compare employment and wages in MNREGA districts to what the situation would have been if they had not received the programme. In practice, this is usually done by comparing MNREGA districts to similar districts without access to the scheme. Hence, researchers take advantage of the phasing-in of MNREGA between 2006 and 2008.
Some of my own work uses this phasing-in of the programme to study the labour-market impacts of MNREGA by exploiting the government algorithm that was used to allocate districts to implementation phases (Zimmermann 2014a
. According to the government algorithm, the 200 “most backward” rural districts in India received MNREGA in 2006, the next 130 in 2007, and the remaining districts in 20087
. This means that during the first two years of the programme, it is possible to compare similar districts in terms of socioeconomic characteristics which received or did not receive the programme.In my research, I find no evidence of substantial employment-generation effects, wage increases or crowding out of private-sector employment, although the effects are a bit larger during the agricultural off-season. There is also no significant variation across gender in the results. Instead, MNREGA seems to be much more effective as a safety net: MNREGA employment increases substantially after a negative rainfall shock, and workers move from casual private employment into self-employment, which is consistent with long-term safety net considerations8
A number of other papers in the literature use different empirical estimation approaches that do not fully exploit the existence of the algorithm but find results that also support many of these empirical patterns: They find higher MNREGA take-up in the agricultural off-season, after negative rainfall shocks, or among particularly vulnerable subgroups, for example (Azam 2012
, Johnson 2009
, Imbert and Papp 2015
). The main differences occur in the analysis of the wage effects, where some papers find substantial wage increases for men and especially for women (Azam 2012
, Berg et al. 2012
, Imbert and Papp 2015
). In addition to different estimation strategies, one potential explanation for this discrepancy is the time needed for MNREGA to have an influence on private sector wages, since Berg et al. (2012)
find that it takes 6-11 months (as well as a high level of implementation quality) for the wage effects to materialise9
What we know and future research questions
Overall, the existing literature on the rural labour market impacts of MNREGA suggests that the programme has generated important short- and long-term safety net benefits along with some employment generation effects during the agricultural off-season. There is no evidence of a large crowding-out effect of private sector employment. These are important benefits of the employment guarantee scheme despite widespread implementation quality challenges especially in the early days of the programme. However, it is less clear how exactly the programme has influenced rural casual wages.
What is needed now is a long-term assessment of the programme in order to estimate how the labour market effects of MNREGA have developed since 2008 once districts had time to learn how to implement the programme. There have also been government initiatives aimed at improving the effectiveness of the scheme which may have influenced employment and wage impacts: for example, biometric smartcards in Andhra Pradesh
allow direct transfers to workers’ bank accounts and thereby reduce the scope for corruption (Muralidharan et al. 2015
- Implemented in 2005, MNREGA guarantees each rural household in India 100 days of manual public-sector employment at the minimum wage. In contrast to almost all other anti-poverty programmes in developing countries, MNREGA is based on a law, which makes the scheme enforceable in court and increases the hurdles for discontinuing the programme. MNREGA does not specify any eligibility criteria other than rural residency and allows households to self-select into the programme. This is supposed to improve the targeting of the programme to households in need who are willing to work in public works projects to receive the minimum wage.
- In contrast to almost all other anti-poverty programmes in developing countries, MNREGA is based on a law, which makes the scheme enforceable in court and increases the hurdles for discontinuing the programme. MNREGA does not specify any eligibility criteria other than rural residency and allows households to self-select into the programme. This is supposed to improve the targeting of the programme to households in need who are willing to work in public works projects to receive the minimum wage.
- See, for example, Ravallion (1991) and Subbarao et al. (2013) for a comprehensive overview of the potential impacts of public works programmes and evidence from developing countries worldwide. In Zimmermann (2014b), I provide a short overview of the recent empirical evidence on public works programmes in developing countries.
- MNREGA is also supposed to provide childcare at the worksite, although in practice it is rarely available. During the agricultural year 2004-05, before the introduction of MNREGA in any district, the daily private casual wage was about Rs. 53 for men and Rs. 38 for women in Phase 2 MNREGA districts (Zimmermann 2014a). Phase 2 districts received MNREGA in 2007.
- Job rationing refers to the phenomenon of excess demand for MNREGA work. In theory, the programme is meant to be completely demand-driven, which means that every worker applying for work will receive a MNREGA job. In practice, rationing of jobs to a subset of people desiring employment is widespread.
- See also Planning Commission (2003) and Planning Commission (2009).
- The government algorithm ranked districts within states on a ‘backwardness index’ created in Planning Commission (2003), which uses district-level information on agricultural wages, agricultural productivity, and the proportion of Scheduled Caste and Scheduled Tribe individuals from early to mid-1990s. There is some discrepancy between the districts that should have received MNREGA in a given implementation phase and the districts that actually received the programme in practice: 84% of predicted districts in Phase 1 and 82% of predicted districts in Phase 2 actually received MNREGA in the respective implementation phase.
- Hari and Raghunathan (2015) find that when there is access to MNREGA, households move towards growing riskier crops. This is consistent with the idea that the existence of a long-term safety net allows households to take on rewarding but riskier employment strategies.
- My empirical estimation strategy is limited to comparing adjacent implementation phases (example, Phase 2 and Phase 3) for districts that are similar to each other (the last few districts to be eligible for MNREGA are compared to the first few ineligible districts based on the government algorithm). Since MNREGA was rolled out to the next group of districts after a year, this method will have problems with picking up wage effects if these take a couple of months to materialise (especially if it takes 11 months). Some of the other papers are able to look at a longer time frame by comparing Phase 1 and Phase 3 districts, which gives them about two years for the analysis, so wage effects can be observed over a longer time period. But this comes at the (considerable) cost of no longer being able to compare similar districts, since the Phase 1 districts are substantially poorer than the Phase 3 districts by design.
- Azam, M (2012), ´The Impact of Indian Job Guarantee Scheme on Labor Market Outcomes: Evidence from a Natural Experiment´, IZA Discussion Paper 6548.
- Berg, E, S Bhattacharyya, R Durgam and M Ramachandra (2012), ‘Can Rural Public Works Affect Agricultural Wages? Evidence from India’, CSAE Working Paper WPS/2012-05.
- Hari, S and K Raghunathan (2015), ‘Providing More than just Employment? Evidence from the NREGA in India´ Mimeo.
- Imbert, Clement and John Papp (2015), “Labor Market Effects of Social Programs: Evidence of India´s Employment Guarantee”, American Economic Journal: Applied Economics, 7(2): 233-63.
- Johnson, D (2009), ‘Can Workfare Serve as a Substitute for Weather Insurance? The Case of NREGA in Andhra Pradesh´, Institute for Financial Management and Research, Centre for Micro Finance, Working Paper 32.
- Muralidharan, K, P Niehaus, and S Sukhtankar (2014), ‘Building state capacity for better programme implementation: Lessons from the Andhra Pradesh Smartcard Programme’, Ideas for India, 3 December 2014.
- Muralidharan, K, P Niehaus, and S Sukhtankar (2015), ‘Building State Capacity: Evidence from Biometric Smartcards in India’, NBER Working Paper w19999.
- Planning Commission (2003), ‘Report of the Task Force: Identification of Districts for Wage and Self Employment Programmes’.
- Planning Commission (2009), ‘Report of the Expert Group to Review the Methodology for Estimation of Poverty’.
- Ravallion, Martin (1991), “Reaching the Rural Poor through Public Employment: Arguments, Evidence and Lessons from South Asia”, The World Bank Research Observer, 6(2): 153-175.
- Subbarao, K, C del Ninno, C Andrews and C Rodriguez-Alas (2013), ‘Public Works as a Safety Net - Design, Evidence and Implementation’, World Bank, Washington DC.
- Zimmermann, Laura (2014a) ‘Why Guarantee Employment? Evidence from a Large Indian Public-Works Program´, Mimeo.
- Zimmermann, Laura (2014b), ‘Public-Works Programs in Developing Countries’, IZA World of Labor.