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Targeting Accuracy of the NREG: Evidence from Madhya Pradesh and Tamil Nadu

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Abstract

This article focuses on the targeting accuracy of National Rural Employment Guarantee Scheme (NREG) in two Indian states, Madhya Pradesh (MP) and Tamil Nadu (TN), based on household data for 2009–2010. To overcome difficulties arising with the use of a specific poverty threshold, stochastic dominance tests are used. MP demonstrated much better targeting than TN in terms of the FGT class of poverty indices over a wide range of poverty thresholds. This is significant as the proportion of poor is twice as high in MP compared with TN. It raises doubts about prevailing views that there is greater under-provision of jobs under NREG in the poorer states. The self-selection of the poor was undermined and (relatively) affluent crowded in because of high NREG wage (relative to agricultural wage). Transfer benefits in the form of additional income to the poor were small mainly due to short spells of work and daily wages lower than minimum wage.

Abstract

Cet article s’intéresse à la précision du ciblage du NREG (Programme National de Garantie de l’Emploi Rural) dans deux États indiens, le Madhya Pradesh (MP) et le Tamil Nadu (TN), et s’appuie, pour le faire, sur des données de 2009–2010 sur les ménages. Pour surmonter les difficultés associées à l’utilisation d’un seuil de pauvreté spécifique, nous effectuons des tests de dominance stochastique. L’État de MP s’avère effectuer des ciblages plus précis que l’État du TN, en termes des indices FGT de pauvreté, et ceci pour un large éventail de seuils de pauvreté. Ce constat est important car la proportion de pauvres est deux fois plus élevée dans l’État du MP que dans celui du TN. Il remet en question l’idée dominante selon laquelle l’insuffisance de création d’emplois, dans le cadre du NREG, est plus forte dans les états les plus pauvres. Les taux de salaires élevés (relatifs aux salaires agricoles) offerts dans le cadre du NREG ont entravé l’auto-sélection des pauvres et le nombre de ménages (relativement) plus aisés a fortement augmenté. Les prestations de transfert sous la forme de revenus supplémentaires pour les pauvres étaient peu élevées en raison, essentiellement, des brèves périodes de travail et des salaires journaliers inférieurs au salaire minimum.

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Notes

  1. Consider, for example, the fact that assignment of funds under the individual beneficiary component of NREGS in Madhya Pradesh is mostly on the basis of political connections. What is more worrying is that some of the Sarpanchs (heads of Panchayats or village councils) told us that that they were planning to assign 80 per cent of the NREGS funds to such schemes. For details, see Shankar and Gaiha (2012).

  2. About 42 per cent of Indians lived below the poverty line of $1.25 a day in 2005 prices, as compared with 16 per cent in China and 8 per cent in Brazil (Ravallion, 2009). The Tendulkar Committee Report (2009) using a new poverty line (lower than $1.25) arrived at a slightly lower estimate of 37 per cent in 2004–2005. For a comment, see Gaiha and Kulkarni (2011).

  3. Using a political economy perspective, Kohli (2012) argues persuasively that state-business alliance has resulted in greater emphasis on growth and neglect of poverty.

  4. According to the 61st round of the NSS, the percentage of rural poor in the three states was 8 per cent (AP), 14 per cent (Rajasthan) and 22 per cent (Maharashtra). As our sample frame comprised NREG districts in the first two phases – mostly the poorest – our poverty estimates are not directly comparable to these.

  5. Section 17 of the NREGA provides for a social audit of all project work in a village by the village assembly (gram sabha). The village governing council (gram panchayat) has to provide requisite details to the auditors. For a critique of the social audit process in AP, Rajasthan and Madhya Pradesh, see Shankar and Gaiha (2012).

  6. Another assessment (Dreze and Khera, 2009) comes to a similar conclusion. ‘As things stand, NREGA meets a fraction of this demand: only 13 per cent of the respondents had actually secured 100 days of NREGA work during the preceding 12 months. There were, of course, wide inter-state variations in this respect. While the proportion of sample workers who had completed 100 days of work was particularly low in Chhattisgarh (1 per cent), Bihar (2 per cent), Uttar Pradesh (3 per cent) and Jharkhand (7 per cent), it was considerably higher in Madhya Pradesh (17 per cent), and as high as 35 per cent in Rajasthan’ (p. 3). See also Ambastha et al (2008) for a balanced and comprehensive assessment.

  7. For a more detailed corroboration of sensitiveness of excess demand to district-level poverty, see Gaiha et al (2010). For a more recent corroboration of this finding, see Dutta et al (2012).

  8. The nine districts chosen in Madhya Pradesh were Sheopur, Tikamgarh, Satna, Shahdol, Sidhi, Jhabua, West Nimar (Khargone), East Nimar (Khandwa) and Dindori.

  9. In Tamil Nadu, the three districts chosen were Tiruvannamalai, Viluppuram and Sivaganga.

  10. Poverty cut-off points remain a contentious subject. Following the Tendulkar Committee Report (2009) in which the then poverty cut-off point for rural India was updated using the urban consumption basket and appropriate prices, there was another revision in 2012 using prices for 2009–2010 that resulted in a marked reduction in poverty over the period 2004–2005 and 2009–2010. A serious flaw in the Tendulkar report (of which one of us was a dissenting member) is the delinking of poverty lines from calorie norms on the specious ground that the latter are not measurable in a precise manner. Instead, we have relied on state-specific poverty cut-off points used in Sen and Himanshu (2004) with appropriate price adjustments based on Consumer Price Index for Agricultural Laborers (CPIAL). These were also the official poverty lines. For a comment on the fragility of poverty estimates, see Gaiha and Kulkarni (2011).

  11. For the cut-off points used for this classification, see Table A1.

  12. Our cut-off points for acutely poor and (relatively) affluent are admittedly arbitrary. As the poverty cut-off points are extremely low, a cut-off 25 per cent lower allows for a bare subsistence. The (relatively) affluent are those with consumption expenditure 50 per cent higher than the poverty cut-off point as most of these households own TVs, telephones and pucca houses. We could have used a higher cut-off for the (relatively) affluent but decided against it because of underreporting of income (and consumption) in the upper tail of the income distribution. Details will be furnished on request.

  13. Differences in shares of participation of poor and non-poor were partly attributable to political and social networks. Specifically, levels of awareness of the scheme and its components were much lower in MP than in TN. For details of econometric and ethnographic awareness, see Shankar and Gaiha (2011, 2012).

  14. This could be due to the way work is measured: combinations of time and piece rates were used, and a collective measurement method is used (total output on a worksite is measured once a fortnight). Lower wages could also be due to corruption-wages recorded are higher than the wages paid. There were also irregularities in payments: barely 11 percent of MP beneficiaries, for example, were paid weekly, 23 per cent fortnightly, 9 per cent monthly and over 50 per cent were paid when the funds were available (Shankar and Gaiha, 2012).

  15. See, for example, Ravallion and Datt (1995).

  16. Household income is defined as net of NREG earnings.

  17. A reviewer's comment that SD tests reflect essentially the fact that MP is poorer than TN is not persuasive as different poverty indices in the FGT class do not necessarily move in tandem.

  18. Note that expenditure is not adjusted for extra income through NREG as it must allow for endogeneity of both NREG earnings and consumption.

  19. For details of the variables used, see Table A2.

  20. In the neo-classical model of household economics, fertility and number of children are endogenous to maximization of utility subject to budget and other constraints. For an exposition, see Behrman and Deolalikar (1988).

  21. For details of the probit model, see Wooldridge (2006).

  22. This contrasts with a positive relationship between participation and land owned in Andhra Pradesh. Together with greater land distribution inequality (relative to Rajasthan), remoteness of villages in Andhra Pradesh and a positive effect of higher NREG wage to agricultural wage, Jha et al (2009) argue that this evidence implies NREG ‘capture’ by the better endowed.

  23. We interacted the wage ratio and distance to worksite but the coefficients of distance and the interaction terms were non-significant. In this specification square of the wage ratio was dropped. Details are available on request.

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Acknowledgements

We gratefully acknowledge financial support from Australian Research Council–AusAID Linkage grant LP0775444 and Raj Bhatia for expert statistical assistance. We are grateful to two anonymous reviewers for their incisive and constructive suggestions and the associate editor for encouragement. The usual caveat applies.

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Authors and Affiliations

  1. Australian National University, Canberra, Australia

    Raghbendra Jha & Manoj K Pandey

  2. Australian National University and MIT, Cambridge, MA

    Raghav Gaiha

  3. Centre for Policy Research, New Delhi, India

    Shylashri Shankar

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  1. Raghbendra Jha
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  2. Raghav Gaiha
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  3. Shylashri Shankar
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  4. Manoj K Pandey
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Appendix

Appendix

Table A1

Table A1 Disaggregation of households by poverty status
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Table A2

Table A2 Definitions of variables used in probit analysis
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Jha, R., Gaiha, R., Shankar, S. et al. Targeting Accuracy of the NREG: Evidence from Madhya Pradesh and Tamil Nadu. Eur J Dev Res 25, 758–777 (2013). https://doi.org/10.1057/ejdr.2012.33

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