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:
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.
Similarly, the targets for population with access to drinking water, sanitation, and electricity are set at 96%, 91% and 87%, respectively. They were 85%, 32% and 67%, respectively, in FY2013. Furthermore, the government targets to add 668 MW by FY2016 (major contribution coming from the completion of 456 MW Upper Tamakoshi hydropower project). It is targeting cent percent telephone (including mobile) access by FY2016. While aiming to construct 3,000 kms of road, the government is also targeting to link all district headquarter by road transport.
Expenditure and revenue: The government is targeting average annual revenue growth (FY2013 constant price) of 13.8%. Based on the assumption that at the end of FY2013, revenue will be 17% of GDP, the government is targeting revenue of 21.1% of GDP by FY2016. However, looking at the numbers provided in the draft approach paper, the revenue target is set at 18.4% of GDP in FY2014, 19.6% of GDP in FY2015 and 20.8% of GDP in FY2016. It indeed is ambitious and even if the revenue growth targets (growth at current prices are usually higher than growth at constant prices) are met, it will be hinged not on domestic economic activities, but on taxes (customs, excise, VAT) on imported goods and services, which in turn are a function of remittances. Foreign grants are expected to be between 3.28% of GDP and 3.33% of GDP.
The size of budget is estimated at 26.9% of GDP in FY2014, 28.0% of GDP in FY2015 and 29.1% of GDP in FY2016. Recurrent expenditure is estimated at 17.1% of GDP in FY2014, 17.1% of GDP in FY2015 and 16.6% of GDP in FY2016 (surprising that it would decline in FY2016 without justification!). Meanwhile, capital expenditure is estimated at 5.0% of GDP in FY2014, 5.5% of GDP in FY2015, and 4.1% of GDP in FY2016 (not expected to recover to the level reached in FY2009) . Given the delays in unveiling a full budget and implementation bottlenecks, capital spending target is already ambitious.
Domestic borrowing is expected to be below 2.3% of GDP and foreign loans 2.8% of GDP. To finance expenditure, the government is aiming to seek more loans from major multilateral and bilateral development partners and reduce the share of domestic borrowing. It will have implications on debt sustainability.
