Spend on the poor now and do not worry much about fiscal cost

Amartya Sen, Raghuram Rajan, Abhijit Banerjee write in The Indian Express that a combination of loss of livelihoods and interruptions in standard delivery mechanisms will push a huge number of people into dire poverty. The core message is that the government should expand social protection measures (food subsidy, cash transfers) and not worry about cost right now. Perhaps, as Paul Samuelson said "every good cause is worth some inefficiency". 

Here is excerpt from their article:

[...]As it becomes clear that the lockdown will go on for quite a while, in a total or a more localized version, the biggest worry right now, by far, is that a huge number of people will be pushed into dire poverty or even starvation by the combination of the loss of their livelihoods and interruptions in the standard delivery mechanisms. That is a tragedy in itself and, moreover, opens up the risk that we see large-scale defiance of lockdown orders — starving people, after all, have little to lose. We need to do what it takes to reassure people that the society does care and that their minimum well-being should be secure.

We have the resources to do this; the stocks of food at the Food Corporation of India stood at 77 million tons in March 2020 — higher than ever at that time of the year, and more than three times the “buffer stock norms”. This is likely to grow over the next weeks as the Rabi crop comes in. The government, recognizing the disruptions to the agricultural markets from the lockdown, is more than usually active in buying the stocks that the farmers need to get rid of. Giving away some of the existing stock, at a time of national emergency, makes perfect sense; any sensible public accounting system should not portray it as inordinately costly.

[...]More importantly, a substantial fraction of the poor are excluded from the PDS rolls, for one reason or another (such as identification barriers to get a ration card that turn out to be hard to overcome), and this supplementary provision only applies to those who are already on it. For example, even in the small state of Jharkhand, there are, we are told, 7 lakh pending applications for ration cards. There is also evidence that there are a lot of bona fide applications (for example of old-age pensioners) held up in the verification process, partly because the responsible local authorities try to avoid letting anybody in by mistake to avoid any appearance of malfeasance. Such punctiliousness has its merits, but not in the middle of a crisis. The correct response is to issue temporary ration cards — perhaps for six months — with minimal checks to everyone who wants one and is willing to stand in line to collect their card and their monthly allocations. The cost of missing many of those who are in dire need vastly exceeds the social cost of letting in some who could perhaps do without it.

[...]Starvation is just one of the worries; the unexpected loss of income and savings can have serious consequences, even if the meals are secured for now: farmers need money to buy seeds and fertilizer for the next planting season; shopkeepers need to decide how they will fill their shelves again; many others have to worry how they would repay the loan that is already due. There is no reason why, as a society, we should ignore these concerns.

[...] as a part of the commitment to not miss the needy, there has to be funding available that state and local governments can use to find effective ways to reach those who suffer from extreme deprivation.

Did India’s National Rural Employment Guarantee Scheme Improve Welfare and Reduce Poverty?

Abstract from a recent paper by Deininger and Liu, who show that NREGA contributed to increase in consumption in the short run and and brought net benefits to SCs and STs. Furthermore, it even created productive assets.

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This paper uses a three-round 4,000-household panel from Andhra Pradesh together with administrative data to explore short and medium-term poverty and welfare effects of the National Rural Employment Guarantee Scheme. Triple difference estimates suggest that participants significantly increase consumption (protein and energy intake) in the short run and accumulate more nonfinancial assets in the medium term. Direct benefits exceed program-related transfers and are most pronounced for scheduled castes and tribes and households supplying casual labor. Asset creation via program-induced land improvements is consistent with a medium-term increase in assets by nonparticipants and increases in wage income in excess of program cost.

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More on NREGA here. Here is a recent co-authored research report.

How effective are awareness campaigns?

Ravallion, van de Walle, Dutta and Murgai looked at the impact of awareness generation schemes for employment guarantee scheme in Bihar and found that there was no employment gains on average. But, it did show gains in knowledge and relatively favorable positive perceptions about the impact of the scheme.

Abstract from the paper:

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Public knowledge about India's ambitious Employment Guarantee Scheme is low in one of India's poorest states, Bihar, where participation is also unusually low. Is the solution simply to tell people their rights? Or does their lack of knowledge reflect deeper problems of poor people's agency and an unresponsive supply side? This paper reports on an information campaign that was designed and implemented in the form of an entertaining movie to inform people of their rights under the scheme. In randomly-assigned villages, the movie brought significant gains in knowledge and more positive perceptions about the impact of the scheme. But objectively measured employment showed no gain on average, suggesting that the movie created a "groupthink," changing social perceptions about the scheme but not individual efficacy in accessing it. The paper concludes that awareness generation needs to go hand-in-hand with supply-side changes.

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Book chapter: Assessment of India’s MGNREGA public works program

Here is our contribution (co-authored) to a new book on social protection, growth and employment published by the UNDP. It is about the assessment of India’s Mahatma Gandhi National Rural Employment Guarantee Act (MGNREGA), the national employment guarantee scheme that has already provided employment to over 50 million households. It guarantees a minimum of 100 days a year of paid employment (at state fixed minimum wage rate for unskilled manual labor) to all rural households. More about the program here.

The chapter in the book is an abridged version of an earlier comprehensive assessment(Employing India: Guaranteeing Jobs for Rural Poor) jointly published by Carnegie Endowment and the UNDP.

Below are some of the main points adapted from Chapter 6 (Guaranteeing Jobs for the Rural Poor: An Assessment of India’s MGNREGA Public Works Programme) of the book:

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Introduction

[…]we address the macroeconomic and distributional implications of running an employment generation programme such as the NREGA, including the indirect employment effects it has through its secondary effects on other sectors. Second, we look at the programme’s impact on prices and hence on the cost of living of rural households. Third, we consider the programme’s economic and distributional effects when land productivity increases. Finally, we briefly discuss the extent to which leakages, in the form of hiring non-poor workers, would change the programme’s economy-wide impact, as well as the impact of changing the size of NREGA through a contraction/expansion of its budget.

Overall, the modelling exercise reported in this chapter indicates that, since its inception, the programme has had a positive impact on economic activity beyond the immediate and direct impact of wage payments to the poor people who participate in it. It finds both that the economy as a whole benefits from the programme and also that each major population group does as well. Poor workers in rural areas benefit the most through direct employment creation. Secondary welfare impacts through the creation of demand in other sectors of the economy are larger for higher-income groups than for poor people. In that sense, the programme’s distributional effects are not positive. The impacts are significant yet small due to the programme’s relatively small size in relation to the Indian labour markets. This negative distributional impact, however, is very small and does not modify the programme’s overall progressive redistribution of income in the Indian economy.

[…]Overall programme performance according to the various sources varies significantly according to the specific feature under scrutiny and the geographical location. Independent studies and social audits detail weaknesses and failures, which, although in many cases they raise concerns about the implementation of the programme, also suggest that on the whole it is lowering poverty. The 2009–2010 National Household Survey data confirm that the programme is reaching poor people and is contributing to social inclusion by increasing the participation of deprived social groups and women; it also confirms a strong variance across states in the degree to which the programme accomplishes its objectives.

[…]Given the potentially far-reaching effects of employment generation programmes, economy- wide modelling is a particularly useful tool to analyse their impact in the economy at large. The section below presents the results obtained from an economy-wide model that replicates the characteristics of the NREGA. We identify the beneficiaries according to those defined by the programme, using the coverage and actual composition of workers identified by the programme in 2009–2011, and assuming that the programme only hires low-skilled workers. We also run a separate simulation to look at the impact of an increase in agricultural productivity associated with the implementation of the Act. The data used in this model correspond to a national Social Accounting Matrix (SAM) built for 2003, before the inception of the Act.

Modeling

[…]We first model an employment generation programme with a budget equivalent to 0.65 percent of GDP, which approximately corresponds to the actual size of the NREGA in fiscal year 2009–2010. The simulation consists of an increase in public expenditures equivalent to 0.65 percent of GDP to hire workers and pay for the necessary materials for construction and the wages of the administrative and few technical staff required by the projects. The simulated increase in public expenditures closely follows the actual composition of the budget in 2009–2010. The payment of wages to workers under the Act is equivalent to 0.43 percent of GDP, expenditures in intermediate inputs are equal to 0.19 percent of GDP, and the payment for government services is equivalent to 0.03 percent of GDP. In proportional terms, 66 percent of programme expenditures go directly to wages for beneficiaries, 5 percent to administration expenses and 29 percent to purchase inputs for the implementation of projects.

In the model we draw labour from rural households in proportions approximating those in the Act in 2009–2010. We assume that the labour hired is divided in equal parts between illiterate male and female workers and that these workers belong to the poorest 60 percent of rural households of SCs and STs and to the poorest 30 percent of households of OBCs and others. The payment of wages to these workers is further inputted into the income of the households from which workers are drawn. The cost of intermediate input materials and administrative expenses, which are assumed to include payments to school- and college-educated workers, represent about one third of the budget, in accordance with the actual budget reported by the programme in the period under consideration.

Results

[…]Results indicate that running an employment generation programme such as the one under the NREGA has a positive macroeconomic impact. An allocation of resources equivalent to 0.65 percent of GDP increases GDP by about 0.4 percent. The programme’s overall expansionary effect is in accordance with the basic notion of a Keynesian balanced budget multiplier, plus the additional demand generated from the shift in income towards poor households with a high marginal propensity to consume.

The programme’s distributional impact is also positive. Simulation results indicate an increase in welfare among poor rural households and a marginal increase among poor urban households. The implementation of the programme carries a cost, which comes in the form of a decline in welfare for rich people in both urban and rural areas, since the programme is financed through income taxes and ‘forced’ savings.

Overall the Act generates an expansion of activity and changes in the composition of production across sectors that result in a progressive redistribution of income towards poor people. The main effects are a sizable redistribution from rich people in urban and rural areas to poor people in rural areas, a marginal redistribution from the same groups to poor people in urban areas and an overall redistribution from urban to rural areas.

[…]Through indirect channels the benefits from the programme extend well beyond its direct beneficiaries. In our simulations, these positive effects extend to 90 percent of the rural population and 30 percent of the urban population. Although the effects are small, partly due to the small size of the programme relative to the size of the Indian labour market, they are nonetheless noticeable.

[…]the increase in labour income triggered by the programme — i.e. the indirect rise in labour income — is in the order of 0.11 percent. The small size of this effect is mainly because the direct wage payments of the programme represent no more than 8 percent of the total income earned by illiterate workers and 2 percent of the total annual income of the rural labour force. This result serves as a reminder that, as impressive as the NREGA programme is, it is still a small fraction of labour income in India.

[…]The simulated 1.5 percent increase in land productivity produces an appreciable rise in GDP, final demand and trade, ranging between 0.02 and 0.03 percent. The increase in agricultural productivity has a less positive impact on income distribution. Although the rise in productivity increases welfare across all household groups, the increase is larger for rich people and larger in urban than in rural areas.

[…]The effect on prices is so strong that even if the productivity hike decreases poor people’s income and, hence, consumption expenditures, the fall in the price of food implies such a strong rise in purchasing power that they end up better off than before. After the increase in productivity, poor people can buy more food and perhaps even increase purchases of other goods.

[…]Our simulation increasing land productivity has the expected result of expanding economic activity and enhancing aggregate welfare. It increases welfare and consumption expenditures across all households, both rural and particularly urban, and it amplifies activity in the agriculture, food-processing and service sectors.

[…]In the simulation results, however, increasing productivity also has a negative distributional impact due to a larger increase in the welfare of rich households. This result is driven by the high concentration of land in the hands of rich people. This result should not be interpreted as a criticism of the important objective of programmes such as the NREGA to increase productivity in agriculture.

[…]Our analysis suggests that the effects of leaks do not visibly change the programme’s macroeconomic impact. The presence of leakage in the implementation of a programme such as the NREGA should not be a major concern for policy makers considering its macroeconomic and distributive effects.

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Employing India: Guaranteeing jobs for the rural poor

Here are highlights from one of the latest published reports (Employing India: Guaranteeing Jobs for the Rural Poor) yours truly was involved in. It is about Mahatma Gandhi National Rural Employment Guarantee Act (MGNREGA), probably the largest social protection programs in the world -- directly employing members of around 50 million rural households at a cost of less than 0.5% of GDP.

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• India faces persistent poverty and inequality despite burgeoning growth. Between 1988 and 2005 the country’s GDP almost tripled, but its poverty rate only decreased by 30 percent, underscoring the need for poverty reduction policies.
• Under the program, members of 50 million households worked a total of 2.5 billion days in 2011.
• The act looks to empower women, widen opportunities for marginalized population groups, and reinvigorate community decision-making bodies.
• The program is meant to operate transparently and fight corruption, but corruption has been seen in the act’s implementation.
• The act is having a significant impact on the lives of the poor with rural wages increasing, but its effectiveness varies according to activity and location.
• Simulations using an economy-wide model indicate that the act has a positive macroeconomic impact, leading to increases in GDP and trade.
• As the program shifts purchasing power from the urban rich to the rural poor, the structure of demand changes. Economic activity in agriculture, processed food, and light manufacturing increases and activity in heavy manufacturing and services declines. Likewise, the demand for unskilled labor in urban and especially rural areas increases, while the demand for mainly urban skilled labor decreases.
• Poor households benefit from added employment opportunities, while high-income households might suffer from weaker demand.
• The act is likely increasing land productivity, which boosts GDP and opens the opportunity to introduce incentives to investment while keeping tax rates constant.
• Given India’s weak institutional setting, a new way of doing business is necessary to implement the act’s detailed and ambitious procedures. Institutions must solidify processes for making information transparently available, and communities need to be involved in creating and managing projects.
• The act is a work in progress that needs ongoing evaluation to fully succeed and keep the corruption affecting the program’s implementation in check.

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Below is a brief of the report:

I will have more from the report in another blog post.

India to launch nationwide direct cash transfer scheme

After the hugely popular MGNREGA, which guarantees 100 days of employment per year to an adult member of  a below poverty line (BPL) household at a stipulated wage rate (equal to wage of manual agriculture worker), launched in 2006, the UPA government is launching another social protection program of a similarly massive scale: direct cash transfer of Rs 32,000 per family per year. This amount is almost three times the average annual earning of a BPL household.

The cash transfer will replace subsidies for kerosene, LPG, pension payments as well as wages from job guarantee schemes. Anyone familiar with the burden of subsidies and social protection payments in the developing world will realize how big and far-reaching the new reform initiative is going to be.

The welfare program is big on all counts: expected big multiplier impact as a result of cash transfers directly to poor household’s account, big central and state governments spending bill, big administration to implement the program, and big political step ahead of the election in 2014. Compared to MGNREGA, the new program is much, much more bigger (annually IRs 40,000 crore for MGNREGA against Rs 400,000 crore for cash transfers). The Indian government says that the cash transfer scheme will be "fiscally neutral" since existing indirect subsidies is to be replaced by direct cash transfers. Sounds incredible. Lets see how close they can get to the target of fiscal neutrality (India’s fiscal deficit is increasing and is a cause for concern for the MoF and the markets).

The program is to be launched on 1 January 2013 and expected to be completed by April 2014. The basis for cash transfer to BPL households is the Aadhar, which provides unique identification number to each resident and will enable them to, among others, open bank accounts where the government will directly transfer cash-- cutting middlemen, administration hassles and leakages.

Below is an infographic from The Times of India:

The upcoming challenge would be to ensure that the money is spent by BPL households to uplift their living standards or to move to a higher productive and efficient consumption and productive activities. Else spending the transferred money (usually by male head of household) in something that is not related to activities that contribute to enhancing welfare of the BPL household members is going to result in less than desired outcome from the massive social welfare intervention.

MGNREGA Sameeksha: Excellent anthology of research studies on the world’s largest public works program

The Indian government has come out with a comprehensive review of the available reports and papers on MGNREGA, the largest public works program in the world. Below are excerpts from an article based on the MGNREGA Sameeksha—An anthology of research studies on the Mahatma Gandhi National Rural Employment Guarantee Act, 2006-2012.

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[…] Has the MGNREGA really built assets, or has it just been a compendium of useless earth work? Has it created a lazy workforce that is affecting our work culture? Has it negatively affected agriculture by drying up the labour market? Has the MGNREGA become the biggest source of corruption in rural India? Has it failed to arrest distress migration? Has it helped household income, and reduced hunger in the poorest households?

[…] the oft-repeated aggressive assertion that MGNREGA does not build useful assets has been made without the support of any study to justify this claim. These assertions arise very often from fleeting visits to roadside worksites, with insufficient time for anything more than an anecdote. This off-the-cuff dismissal of “useless earth works” arises from a group which often lives on the other side of a fractured India, for whom mud and dirt become synonymous! It also raises the pertinent question of what indeed is a productive asset — a village tank that recharges 40 wells, or only a work of brick and mortar.

[…] by and large, show that sustainable assets have been created. A study of the best performing water harvesting assets in Bihar, Gujarat, Rajasthan, and Kerala for instance show the potential of these works where a majority of the assets studied had a return on investment of well over 100 per cent, with investment costs recovered in less than one year! Perception-based surveys, including those carried out by the National Sample Survey Organisation (NSSO) in three States showed that the vast majority of assets were being used, and the people found them useful.

[…] it has provided livelihood and income security, decreased the incidence of poverty, increased food intake, reduced mental depression, positively affected health outcomes, and been successful as a self targeting scheme — as the poorest and most marginalised communities have sought work. In many States, it has decreased gender differential in wages, increased real wages accompanied by an increase in agricultural productivity and growth. This increase in agricultural productivity could be due to the watershed and water harvesting works, as well as the land development work on the fallow private lands of SC, ST and BPL families to make them productive. The studies do not bear out the assertion that MGNREGA has caused a shortage of farm labour.

[…] poor implementation in many places. Average wages paid are lower than minimum wages; there is a distressing delay in the payment of wages; demand is not properly captured (an NSSO survey found 19 per cent of people who wanted work did not get it); dated receipts for work applications are not properly given; and the payment of unemployment allowance is a rarity. There is a shortage of staff, and there are many instances of irregular flow of funds. Non-compliance with proactive disclosure provisions such as muster rolls being available at worksites continues to be a problem in some States. As a result, leakages and corrupt practices continue to exist. While social audits in Andhra Pradesh have significantly increased awareness and identified fraud, Sameeksha notes that social audits are a facade in most other States.

[…] Ten crore bank and post office accounts have been opened, bringing about financial inclusion, and reduced corruption in wage payments, but the delay in payments through such accounts is a major cause of distress.

[…] thanks to the MGNREGA offering alternative work, hundreds of bonded labour (Saheriya adivasis) in Rajasthan freed from generations of bondage. People have been saved from destitution in Uttar Pradesh, Bihar, Orissa, and Chhattisgarh, women have been empowered and are participating in huge numbers in Tamil Nadu, and the programme is even showing very positive results in “non-NREGA” States like Himachal Pradesh and Kerala.

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For more on MGNREGA, see previous blog posts here.

MGNREGA: Self-targeting, food prices, poverty, local capacity and jobs

Summary of latest papers on MGNREGA’s self-targeting ability, impact of food prices on poverty vis-à-vis income effect, capacity of local authorities to create enough jobs and workers’ incentives to take up MGNREGA jobs.

Jha, Bhattacharyya, Gaiha, & Shankar (2009) find that overall the size of landholdings is a negative predictor of participation in MGNREGA. A one standard deviation increase in landholdings (4.5 hectares) reduces the odds of MGNREGA participation by 1.3 fold (p.6). Specifically, they find a positive relation between size of landholdings and participation in Andhra Pradesh, and but the case is opposite in Rajasthan. They argue that program capture might be prevalent in Andhra Pradesh because of land inequality, political interference, and geographical remoteness.

Jha, Gaiha, & Pandey (2010) argue that the ratio of NREGS wage to agriculture wage, marital status, age, gender, and education determines employment in the rural employment guarantee program. Their conclusion is based on household level survey data from three states: Rajasthan, Andhra Pradesh, and Maharashtra.

• While it is broadly true that the selection of workers for NREGS favors illiterate workers and those from deprived backgrounds, female workers appear to have a lower chance of being selected. In two of the three states, the ratio of NREGS wage to agricultural wage has significant effects. Marital status and age also affect the chances of getting employment in NREGS. Within each state, workers in some districts have higher chances of being employed in NREGS.
• Once employed in NREGS, the duration of such employment is affected by social background or educational status. Factors relevant for selection for NREGS are not necessarily so for the duration of employment.

Ghose (2011) finds that MGNREGS, despite problems in implementation, has succeeded in providing substantial additional wage employment to the rural poor at a wage no lower than what prevails. It has thereby increased money incomes for this group of workers quite significantly. Yet, the program has not made a significant contribution to reduction of rural poverty. The reason is food price inflation to which the program has ended up contributing. While the MGNREGS increased the demand for food, this was not met by an increase in the supply of food in the short run. Ghose finds that the increase in wage income of rural households attributable to MGNREGA was 22.2 percent in 2009-10, up from 7.4 percent in 2006-07 (p.5).

In a case study of Birghum district in West Bengal, Mukherjee & Ghosh (2009) find:

• High inter-block variations in terms of average person-days created and utilization of NREGA funds. The blocks which have performed better also show significant variation across the Gram Panchayats within the block. There seems to be no clear relation between utilization of available funds and average person-days created either at the GP level or at the block level.
• The weak correlation observed between number of households with job card and availability of NREGA funds at the GP level suggests that GPs are not able to come up with adequate number of NREGA schemes to absorb the laborers demanding employment.
• There is also no evidence of NREGA getting better implemented in blocks with higher share of agricultural laborers or higher percentage of BPL households, which one would expect. Rather, blocks with higher share of BPL households show lower average person-days created under NREGA.
• Lack of technical skills and human resource seem to be the major reasons why the GPs are not able to develop adequate number of schemes under NREGA.
• Though NREGA allows scope for creating various types of durable productive assets at the community level (such as roads, improving rural infrastructure, drought-proofing, watershed development, water conservation etc), focus has remained on types of works which are easy to design (such as road construction and pond excavation).
• The GPs lack the capacity to design adequate number of schemes under NREGA which can be meaningfully linked with the livelihood and infrastructural development of the local economy. Therefore, greater efforts should be given for the capacity building of the GPs, especially the backward GPs.

Dutta, Murgai, Ravallion, & van de Walle (2012) argue that poorer families tend to have more demand for work on the scheme, and that (despite the un-met demand) the self-targeting mechanism allows it to reach relatively poor families and backward castes.

• Participation rates on the scheme are higher for poor people than others.
• Targeting performance varies across states. Some of those living above the official poverty line in better-off states will no doubt be relatively poor, and need help from the scheme. The overall participation rate seems to be an important factor in accounting for these inter-state differences in targeting performance, with the scheme being more pro-poor and reaching scheduled tribes and backward castes more effectively in states with higher overall participation rates.
• While the scheme is clearly popular with women—who have a participation rate that is double their participation rate in the casual labor market—the rationing process does not appear to be favoring them. We also find evidence of a strong effect of relative wages on women‘s participation—both wages on the scheme relative to the market wage and the male-female differential in market wages. As one would expect, poor families often choose whether it is the man or the woman who goes to the scheme according to relative wages.
• For India as a whole, we find that the scheme‘s average wage rate was roughly in line with the casual labor market in 2009/10. This might look like competitive labor market equilibrium, but that view is hard to reconcile with the extensive rationing we find. Interestingly, we do find a significant negative correlation between the extent of rationing and the wage rate in the casual labor market relative to the wage rate on the scheme. Although this is suggestive, on closer inspection we are more inclined to think that other economic factors are at work. Indeed, the correlation largely vanishes when we control for the level of poverty. Poorer states tend to see both more rationing of work on the scheme and lower casual wages—possibly due to a greater supply of labor given the extent of rural landlessness.

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References:

Dutta, P., Murgai, R., Ravallion, M., & van de Walle, D. (2012). Does India's Employment Guarantee Scheme Guarantee Employment? World Bank Policy Research Working Paper 6003, 1-34.

Ghose, A. K. (2011). Addressing the Employment Challenge: India's MGNREGA. ILO Employment Working Paper No. 105, 1-40.

Jha, R., Bhattacharyya, S., Gaiha, R., & Shankar, S. (2009). Capture of Anti-Poverty Programs: An Analysis of the National Rural Employment Guarantee Program in India. Journal of Asian Economics 20(4), 456-464.

Jha, R., Gaiha, R., & Pandey, M. K. (2010). Determinants of Employment in India’s National Rural Employment Guarantee Scheme. ASARC Working Paper 2010/17, 1-34.

Mukherjee, S., & Ghosh, S. (2009). What Determines the Success and Failure of 100 Days Work at the Panchayat Level? A Study of Birbhum District in West Bengal. IDSK Occasional Paper 16, 1-19.

Employment Guarantee Act in Nepal

[It was published in Republica, May 16, 2012, p.8.]

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Work on it

While a cycle of political deadlock, hope and uncertainty continue to plague political sphere, Prime Minister Dr.Baburam Bhattarai’s government quietly approved an Employment Guarantee Act (EGA) and sent it to the parliament for endorsement. The EGA, modeled along India’s Mahatma Gandhi National Rural Employment Guarantee Act (MGNREGA), will guarantee jobs to households below poverty line (BPL). If endorsed by the parliament and implemented from next fiscal year, this demand-driven scheme —originally drafted by National Planning Commission (NPC)— will help in reducing unemployment and under-employment, which is estimated to be around 46 percent of population, reduce distress migration from rural to urban areas, boost agriculture productivity, and enhance livelihood opportunities. Unfortunately, this hugely consequential public works program is not adequately discussed and consulted with relevant stakeholders.

The EGA guarantees 100 days of employment per year to one adult member of BPL households. Apart from providing jobs, the scheme seeks to create infrastructure that would help in boosting productivity and livelihood opportunities. According to NPC officials jobs will be provided in work related to construction, infrastructure, industries and other development projects. If the government is unable to provide jobs within a stipulated deadline to those who are eligible and demand one, then it will have to pay unemployment allowance equivalent to around 50 to 60 percent of minimum wage. It will have grievance redressal mechanism set up at grassroots level. In an economy with 83 percent of the population still living in rural areas, time-tested and successful public works program like employment guarantee will be a vital policy tool for social protection. That being said, without proper planning, monitoring and executive agencies at grassroots level, there is also a danger that the scheme could be a huge fiscal burden and a major instrument for elite capture, i.e. wealthy and those in power capture funds and works meant for the needy and poor.

In India, MGNREGA came into force on February 2, 2006 with an aim to directly touch the lives of the poor and promote inclusive growth. This flagship rural employment scheme of the Congress government is credited for partly helping the party get an astounding electoral victory in 2005. MGNREGA is an extension of Employment Guarantee Scheme (EGS) implemented in Maharashtra in 1979 in response to debilitating impact of famine and drought on poor households. The scheme guarantees employment within 15 days if a qualified person submits employment application at a local administrative authority. The wage rate is equal to the unskilled agriculture labor wage, which is now adjusted for inflation as well. Currently, the MGNREGA wage is highest in Haryana (IRs 191 per day) and lowest in Bihar and Jharkhand (IRs 122 per day). The average wage across all states and union territories is IRs 145 per day for seven hours of manual unskilled work. The work has to be provided within 5 km radius of the village a person is residing or else extra wage of 10 percent is payable. Moreover, working and living facilities (safe drinking water, shade for children and periods of rest for workers, first-aid box for emergency treatment and minor injuries, and safety equipment and measures for health hazards connected with work) have to be provided. At least one-third of the work is reserved for women and the while designing work by the Gram Panchayats 60:40 wage to material ratio has to be maintained. The permissible work is related to water management, land management and rural connectivity in and around the areas where the workers reside.

The program is running into its sixth year of operation and its assessment thus far has largely been positive. In its first year, the program was implemented in the 200 most backward districts. The next year it covered an additional 130 districts and now it covers all districts of India. In 2010/11, it provided employment to 55 million households and of the 2.6 billion persons-days of work provided, 47.7 percent was taken up by women and 51 percent by Scheduled Castes (SCs) and Scheduled Tribes (STs). Latest estimate shows that the cost of MGNREGA is about 0.45 percent of GDP, 3.19 percent of expenditure and 5.08 percent of revenue.

 

Preliminary cost estimate of employment guarantee scheme
Number of BPL population (million)	6.71
Minimum agri wage per day (NRs)	195
Average household size	4.90
Number of households BPL	1369028.57
Potential employment demand (15-59 years)	742013.49
Wage cost per day, billion	0.14
Wage cost per year (100 days of employment), billion	14.47
Administrative cost (25 percent of wage cost), billion	3.62
Total cost (wage plus administrative), billion	18.09
Total cost (share of budget for FY 2011-12)	4.70
Total cost (share of GDP in constant prices, 2011-12)	2.69

Source: Own estimate based on Census 2011 and NLSS III data

A quick estimate of the potential cost of the proposed EGA reveals that to cover all adults of BPL households (as per NLSS III) it would cost at most 2.69 percent and 4.7 percent of 2011/12 GDP and budget respectively. It includes wage and administrative costs of providing employment to around 0.74 million adults from 1.37 million BPL households. However, implementing the program is not as easy as it sounds because of the need to generate additional revenue to fund the program, the need to finalize central-state power division to execute and share cost burden, and the inherent administrative hassles and shortcomings.

First, implementing EGA without additional sources of funding or without cutting unproductive and untargeted subsidies would be a challenge in the face of rising budget deficit, which is expected to reach about 3.8 percent of GDP this year. Finding an additional Rs 18 billion for EGA when the MoF is struggling to secure enough funds for retired PLA combatants along with additional expenses required for Nepal Army in the post-integration era is a big headache for MoF officials. The problem compounds when revenue growth fails to keep pace with expenditure growth.

Second, NPC’s hurry in getting it implemented from next fiscal year would invite operational trouble because EGA typically mandates federal states to cover some portion of the cost as seen in the case of MGNREGA in India. EGA designed now without taking into account this political aspect is going to run into serious trouble. Furthermore, all other employment schemes need to be either scrapped or incorporated in EGA to ensure smooth operation, plug leakages, and avert duplicates. The required homework on this front is dismal.

Third, without strong local level institutions (VDCs) and representatives manning local offices, leakages will be extremely high. Implementing the scheme with weak grassroots institutions would result in fudging of muster rolls, manipulation of account by contractors and elite capture of the employment slots meant for BPL households. This is precisely one of the reasons why Karnali Employment Guarantee Program failed to yield satisfactory result. The EGA should be allowed to be used as yet another tool for political parties and their supporters to legally plunder state coffers without adding any value to productive capacity of the nation.

Fourth, the work under EGA should be strictly related to rural sector, including land management, water management and rural road connectivity—all of which will help boost agriculture production and productivity. The program is meant for BPL households and public works should be carried out in places where most of them reside, i.e. rural areas. Also, the assets created should be fairly long lasting so that they not only help agriculture sector, but also link this with the industrial sector.

Fifth, since large-scale social protection schemes like EGA create temporary distortions in the agriculture labor market, the government should be able to adequately deal with such byproducts. For instance, the MGNREGA wage rate put upward pressure on overall wage rate in India and created a shortage of labor in major agriculture states like Punjab and Haryana, where workers from poor states like Bihar and Uttar Pradesh migrated during lean agriculture season. This will also happen in our economy and temporarily affect both agriculture and industrial sectors.

Overall, while it is commendable that the NPC is pushing for the implementation of EGA, the government should realize that the scheme would be a waste of resources if it is implemented without adequate institutional set up and clear cost sharing mechanism between central and state governments. Importantly, it should be ensured that the scheme is demand-driven rather than supply-driven and adequate initiatives are instituted beforehand to plug loopholes and inefficiency.

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Latest on India’s largest poverty alleviation and rural employment generation program: MGNREGS is working

In a new working paper, Dutta, Murgai, Ravallion, and van de Walle argue that poorer families tend to have more demand for work on the scheme, and that (despite the un-met demand) the self-targeting mechanism allows it to reach relatively poor families and backward castes. The extent of the un-met demand is greater in the poorest states — ironically where the scheme is needed most. Labor-market responses to the scheme are likely to be weak. The scheme is attracting poor women into the workforce, although the local-level rationing processes favor men.

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We do not find that the local-level processes determining who gets work amongst those who want it are generally skewed against the poor. There are sure to be places where this is happening (and qualitative field reports have provided examples). But it does not appear to stand up as a generalization. We do find evidence that the poor fare somewhat less well when it comes to the total number of days of work they manage to get on the scheme. However, despite the pervasive rationing we find, it is plain that the scheme is still reaching poor people and also reaching the scheduled tribes and backward castes.

Participation rates on the scheme are higher for poor people than others. This holds at the official poverty line, but the scheme is also reaching many families just above the official line. It is only at relatively high consumption levels that participation drops off sharply. This should not be interpreted as indicating that well-off families in rural India are turning to MGREGS. There may well be shocks that are not evident in the household consumption aggregates. And there may be individual needs for help that are not evident in those aggregates.

Targeting performance varies across states. Some of those living above the official poverty line in better-off states will no doubt be relatively poor, and need help from the scheme. The overall participation rate seems to be an important factor in accounting for these inter-state differences in targeting performance, with the scheme being more pro-poor and reaching scheduled tribes and backward castes more effectively in states with higher overall participation rates.

While the allocation of work through the local-level rationing process is not working against the poor, there are clearly many poor people who are not getting help because the employment guarantee is not in operation almost anywhere (Himachal Pradesh, Rajasthan and Tamil Nadu could be counted as the exceptions, where 80% or more of those who want work got it). And other potential benefits of the scheme to poor people are almost certainly undermined by the extensive rationing, notably the empowerment gains and the insurance benefits. The first-order problem for MGNREGS is the level of un-met demand.

While the scheme is clearly popular with women—who have a participation rate that is double their participation rate in the casual labor market—the rationing process does not appear to be favoring them. We also find evidence of a strong effect of relative wages on women‘s participation—both wages on the scheme relative to the market wage and the male-female differential in market wages. As one would expect, poor families often choose whether it is the man or the woman who goes to the scheme according to relative wages.

It has been claimed by some observers that the scheme is driving up wages for other work, such as in agriculture; some observers see this as a good thing, others not. For India as a whole, we find that the scheme‘s average wage rate was roughly in line with the casual labor market in 2009/10. This might look like a competitive labor market equilibrium, but that view is hard to reconcile with the extensive rationing we find. Interestingly, we do find a significant negative correlation between the extent of rationing and the wage rate in the casual labor market relative to the wage rate on the scheme. Although this is suggestive, on closer inspection we are more inclined to think that other economic factors are at work. Indeed, the correlation largely vanishes when we control for the level of poverty. Poorer states tend to see both more rationing of work on the scheme and lower casual wages—possibly due to a greater supply of labor given the extent of rural landlessness.

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NREGA is a flagship rural employment generation and livelihood program of the UPA government in India. This social welfare program guarantees one hundred days of employment per year at the prevailing minimum wage rate for unskilled labor.

The Act came into force on February 2, 2006 with an aim to “directly touch lives of the poor and promote inclusive growth.” Along with the objectives of boosting rural economy and enhancing overall (inclusive) economic growth, this public works program was also designed to prop up purchasing power of poor people; stabilize their household income; assure livelihood security to the most marginalized groups; accelerate the pace of meeting the MDGs; and strengthen natural resource management through works that address causes of chronic poverty like drought, deforestation and soil erosion. One of the objectives of the program is to make the process of employment generation sustainable.

It started with a pilot project in the state of Maharashtra in 1965 with an aim to provide relief to poor farmers during famine and drought. An Employment Guarantee Scheme (EGS) Act was passed in 1979 by the state legislature, widening the reach of the pilot program to the entire state. The federal government picked upon the success of the program and implemented (under Phase I) it in 200 of the most backward districts on February 2, 2006. It was expanded to cover an additional 130 districts in 2007/2008 (under Phase II) and the remaining (under Phase III) 285 districts (in total 615 rural districts) on April 1, 2008. In 34 states, a total of 45,019,215 households (as of September 2, 2009) were provided employment in 2008/09.

For more on NREGA, see this. It looks like cost of Mahatma Gandhi National Rural Employment Guarantee Act (MGNREGA), the largest employment guarantee public works program in the world, is coming down. Its cost as a share of GDP, total expenditure and revenue receipts is decreasing and is expected to be 0.45 percent, 3.19 percent, and 5.08 percent respectively in fiscal year 2011-2012. Here is more on MGNREGA.

NREGA budget (Rs Crore)
	2006-07	2007-08	2008-09**	2009-10**	2010-2011*	2011-2012*
GDP, current prices#	4,293,672	4,986,426	5,582,623	6,550,271	7,877,947	8,980,860
Total expenditure	583,387	712,671	900,953	1,020,838	1,108,749	1,257,729
Revenue receipts	434,387	541,864	562,173	614,497	682212	789892
NREGA allocated budget	11,300	12,000	30,000	39,100	40,100	40,100
NREGA/GDP	0.26	0.24	0.54	0.60	0.51	0.45
NREGA/Exp	1.94	1.68	3.33	3.83	3.62	3.19
NREGA/Rev	2.60	2.21	5.34	6.36	5.88	5.08

Source: Calculation based on data from Union Budgets; *estimate; **revised estimate'; # Economic Survey 2010-11

In FY 2010-2011, 5.49 crore households were provided employment (100 days employment  on demand to each household during lean season). The total persondays of employment created was 257.15 persondays (crore). Of this, the share of SCs, STs, and women accounted for 30.63%, 20.85%, and 47.73% respectively.

Employment guarantee scheme in Nepal

The Nepalese policymakers have drafted Employment Guarantee Act promising to provide a job to all the households living below the poverty line (BPL), reports Ashok Thapa in Republica daily.

• Guaranteed job as a critical part of socio-economic security and fundamental rights of citizens, and promises a job of at least 100 days per year to at least a member of poor families.
• At least one member of families living below the poverty line will enjoy a job, fetching income equivalent to minimum wage fixed by the government.
• Jobs will be provided in sectors like construction, infrastructure and other development projects.
• In case the state failed to provide jobs, the draft says the government will pay unemployment allowance to those families.
• Based on VDC-level data, the committee, which is drafting the Act, has suggested the government to issue cards to the beneficiary households.
• In order to ensure the effectiveness of the program, the draft Act asks the local bodies to hold public hearings every four months to dig out grievances and other anomalies.
• VDC, DDC and NPC to control possible leakage.

It seem the existing plan is almost fully in line with India’s Mahatma Gandhi National Rural Employment Guarantee Act (MGNREGA), the largest employment guarantee public works program in the world.Its cost as a share of GDP, total expenditure and revenue receipts is decreasing and is expected to be 0.45 percent, 3.19 percent, and 5.08 percent respectively in fiscal year 2011-2012. This social welfare program guarantees one hundred days of employment per year at the prevailing minimum wage rate for unskilled labor. When NREGA was implemented in 2006, eleven states saw a rise in minimum wages. The new revised wages, to be adjusted with CPI, is set to increase wages in twenty states.

Similar programs (public works) have been launched in the US, Ethiopia, Sengeal, Ireland and Bangladesh among other countries. I have batted for this kind of program to solve unemployment problems in the rural areas and to boost infrastructure work. Few things to keep in mind before enacting such program:

• Without strong local level institutions (VDCs) and representatives manning the local offices leakages will be pretty high. Else, fudging of muster rolls and manipulation of accounts will occur.
• It should be demand-driven by nature.
• It is necessary to identify the households below poverty line and then provide employment to one adult members from each household for a maximum of three months (usually during lean agriculture season).
• Public works should be carried out to boost local level infrastructure that can increase agriculture productivity and promoting local markets.
• The fiscal cost is a major issues. In India it costs around 0.5 percent of GDP. Almost 53 million households were provided employment in 2009-10. Around 2826 million persondays of employment was created. Women and those from indigenous and backward groups secured most of the employment.

Below I have calculated a rough cost estimate of employment guarantee scheme in Nepal. Based on the latest NLSS III results and assuming population of 30 million, the number of BPL population in Nepal is 3.9 million. If an adult member (working age population of 15-59 years) of BPL household is given employment, then around 4.3 lakhs persons will demand employment at the minimum agriculture wage rate of NRs 170 per day. The wage cost of such scheme per day would be at least Rs 0.07 billion. If employment is provided only during lean agriculture season (which means three months), then wage cost per year would be at least Rs 6.60 billion. If we assume that administrative cost to carrying out such scheme to be 25 percent of total wage cost, then the total cost (wage plus administrative) would be around Rs 8.25 billion. Administrative cost is aimed at 20 percent of total cost in India. In Nepal, it should be higher as we lack many functioning local level institutions and representatives.

Preliminary cost estimate of employment guarantee scheme
Number of BPL population (million)	3.9
Minimum agri wage per day (NRs)	170
Average household size	4.9
Number of households BPL	795,918
Potential employment demand (15-59 years)	431,388
Wage cost per day, billion	0.07
Wage cost per year (3 months of employment), billion	6.60
Administrative cost (25 percent of wage cost), billion	1.65
Total cost (wage plus administrative), billion	8.25
Total cost (share of budget for FY 2011-12)	2.14
Total cost (share of real GDP in producers prices, 2010-11)	1.29

This would be around 2.14 percent of budget (considering FY 2011-12 budget) and 1.29 percent of GDP in producers prices of 2010-11. This cost is higher than in India, where the cost is below one percent of GDP. The program should cost lower if administrative cost is lowered. Considering budget deficit of about 3.8 percent of GDP in 2010-11, enacting employment guarantee scheme in Nepal would increase deficit to 4.83 percent of GDP.

[Note that if you assume at least one member of each household, regardless of working age population, would get guaranteed employment, then the cost would be approximately double of what is estimated above.]

Cost of MGNREGA is decreasing

It looks like cost of Mahatma Gandhi National Rural Employment Guarantee Act (MGNREGA), the largest employment guarantee public works program in the world, is coming down. Its cost as a share of GDP, total expenditure and revenue receipts is decreasing and is expected to be 0.45 percent, 3.19 percent, and 5.08 percent respectively in fiscal year 2011-2012. Here is more on MGNREGA.

NREGA budget (Rs Crore)
	2006-07	2007-08	2008-09**	2009-10**	2010-2011*	2011-2012*
GDP, current prices#	4,293,672	4,986,426	5,582,623	6,550,271	7,877,947	8,980,860
Total expenditure	583,387	712,671	900,953	1,020,838	1,108,749	1,257,729
Revenue receipts	434,387	541,864	562,173	614,497	682212	789892
NREGA allocated budget	11,300	12,000	30,000	39,100	40,100	40,100
NREGA/GDP	0.26	0.24	0.54	0.60	0.51	0.45
NREGA/Exp	1.94	1.68	3.33	3.83	3.62	3.19
NREGA/Rev	2.60	2.21	5.34	6.36	5.88	5.08

Source: Union Budgets; *estimate; **revised estimate'; # Economic Survey 2010-11

The main reason for the expected decline is that GDP, expenditure and revenue are estimated to increase but the budget allocation for MGNREGA is kept the same as during the previous fiscal year.

In FY 2010-2011, 5.49 crore households were provided employment (100 days employment  on demand to each household during lean season). The total persondays of employment created was 257.15 persondays (crore). Of this, the share of SCs, STs, and women accounted for 30.63%, 20.85%, and 47.73% respectively.