As Budget Day draws near, economic policy makers in the country are likely to shift their attention to the allocation of funds to various programmes planned for the upcoming year. MGNREGA is one such programme. The Mahatma Gandhi National Rural Employment Guarantee Act, which aims to provide at least 100 days employment to the rural unemployed and underemployed by engaging them in rural infrastructure building, was launched in 2006 and has now attained a semblance of permanence. While well-intentioned, our research based on careful study of employment and operating data from factories show that MGNREGA has led to some interesting consequences: factory jobs have declined by more than 10% and mechanisation has increased by 22.3% as a result of implementing MGNREGA, i.e., MGNREGA may have led to the migration of workers from higher value productive factory work to digging pits and filling them, with factories choosing to mechanise faster instead of replacing these workers.
Like most well-meaning social sector programmes, MGNREGA’s intention to provide income to the rural unemployed is definitely laudable. MGNREGA belongs to a class of social sector programmes, known as workfare programmes, where beneficiaries are required to exert some effort in order to earn a minimum wage. The purpose of such a requirement is to prevent those who are otherwise gainfully employed from crowding out these jobs and to ensure that only the truly unskilled and unemployed take up these jobs.
Our research reveals that what transpired in practice was very different from what the policy makers had envisaged when launching the programme. We looked into how implementing MGNREGA in a region adversely impacts the availability of labour for nearby factories, using factory-level data from the Annual Survey of Industries across the period 2002-10. Since our data includes time periods before and after MGNREGA was introduced, we are able to perform a pre-post analysis to understand the effects of MGNREGA. Our results show that nearly 10% of the permanent factory workers jettison their factory jobs to join the MGNREGA bandwagon.
Why would a factory worker prefer 100 days a year of minimum wage MGNREGA work building rural infrastructure over an entire year’s work at wages higher than minimum in a factory? We conjecture and show that MGNREGA work is unlikely to involve a lot of effort. More importantly, workers may prefer the 100 days of guaranteed and effortless work near home to a high-effort high-risk factory job far from home. Having a job close to home also means less out-of-pocket expenses on travel, clothing and other requirements of factory work. It is quite possible that such workers either work as contract workers in factories or do odd jobs in their villages during non-MGNREGA days. There was no decline in contract workers so it is plausible that the outflow of contract workers to MGNREGA was offset by erstwhile permanent workers turning into contract workers.
How did factories respond? It is quite natural that when workers have better options, they demand higher wages. However, this demand is not always supported by an increase in productivity – the worker is unlikely to work doubly hard or be able to work doubly hard and double his output every time his wages are doubled. Hence, at the inflated wage level, it is quite possible that factories may find mechanization more profitable. Consider a factory whose cost of production is Rs 100 per unit if it employs workers and Rs 110 per unit if it employs machines to do a job. Naturally, the factory is likely to employ workers. Now if because of the availability of the MGNREGA option, workers demand Rs 120 per unit, the factory is likely to find using machines cheaper and let the workers go. This is indeed what we found. Note that if the increase in wages is a result of increased productivity, per unit cost remains unchanged and hence factories are likely to continue employing workers as before.
One might think that there could be other explanations. We performed a battery of econometric tests to rule out alternative explanations. For example, we find that low-wage workers are more likely to leave factory work for MGNREGA. In another test, we find that such migration is likely to be higher in states that have employer-friendly labour laws with less job security. In yet another test, we find that the propensity to mechanize is higher in regions with relatively higher access to finance. All these results support our main point that MGNREGA leads to the migration of productive workers from factories to digging pits and filling them. Not surprisingly, we find that factory input costs increase and output declines as result of the above phenomenon.
Why is this migration of factory workers to MGNREGA undesirable? First, it defeats the very purpose of workfare, which is to prevent the already employed from cornering workfare jobs at the expense of truly unemployed. Second, this phenomenon negatively impacts both the Make in India and Skill Development programmes launched by the central government. Factories that do not have access to ample financing to mechanise and cope with the labour shock engineered by MGNREGA may be forced out of business. Third and most importantly, MGNREGA may be turning skilled factory workers into unskilled pit fillers (since MGNREGA discourages the use of machines of any form) while the really unskilled continue to be unemployed. The impact on skill development is severe and may have deeper consequences for human capital development in India as a country.
Despite its imperfections, MGNREGA has benefited many in the country and our intention is to help improve the programme so that India fully reaps its potential. In this article, we point out a potentially important cost which has so far gone unnoticed by academic researchers and policy analysts. Our findings show that it is important to not just to focus on allocation but also on programme design. From a societal point of view, there is a need to redesign the program to encourage skill development and discourage entry of gainfully employed productive workers. Imposing quantity and quality based output targets for the programme could be a first step in this direction.
Note : Above write-up is taken from a leading online source where it is contributed by Sumit Agarwal, Professor of Finance at Georgetown University, Shashwat Alok, Assistant Professor of Finance at Indian School of Business, Yakshup Chopra, Research Associate and Prasanna L Tantri, Senior Associate Director at the Center for Analytical Finance, Indian School of Business. Views expressed above are views of contributor’s and SkillReporter is nowhere related to the study.