# **A Tale of Two Programs: Assessing Treatment and Control in NREGA Studies**

## **Girish Bahal**

#### **JEL classification:** H53, I38, J38

**Keywords:** Impact evaluation, Treatment-Control, Public workfares, SGRY, NREGA

### **1. Introduction**

Often, similar older policy initiatives precede “new” anti-poverty programs implemented by governments across the world. For example, Brazil’s flagship anti-poverty program Bolsa Escola, a conditional cash transfer program, was replaced with Bolsa Familia in 2003. In 2011, Bolsa Familia was extended to Brasil Sem Mis ́eria (Brazil without poverty). In Mexico, Prospera was preceded by Oportunidades, which in turn was built on Progresa. Similarly, when India introduced the largest public works program in the world in 2006 – the National Rural Employment Guarantee Act (henceforth NREGA) – it replaced an older program – Sampoorna Grameen Rozgar Yojana (henceforth SGRY). Such government interventions naturally raise important questions for research and policy. In an environment where randomization is not feasible, quasi-experimental pretest-posttest designs often identify the impact of a change in economic policy. For example, a difference-in-differences model identifies the policy impact by comparing the average change over time in the outcome variable in the treatment and control groups.

This article highlights an important aspect of impact evaluation studies on NREGA. While estimating the general equilibrium impact of the program, it is crucial to control for a similar older workfare that was exclusively present in the “control” districts that received NREGA late. This article shows how ignoring the continued existence of the SGRY program has first-order effects on estimating the employment policy’s real impact. I show how ignoring SGRY overestimates the gap in employment provision (or the treatment dosage) between the treatment and control districts and underestimates the effect on various relevant socio-economic outcomes studied in the literature.<sup>1</sup> Specifically, I analyze the general equilibrium impact of NREGA on i) agricultural wages, ii) casual earnings, iii) casual employment in the public and private labor market (for men and women), and iv) human capital outcomes in terms of children’s school enrollments and child labor. For all outcomes, the impact of NREGA is more pronounced when we control for SGRY. The extent of underestimation of program Girish Bahal is Assistant Professor at the University of Western Australia, Perth, Australia; his email address is girish.bahal@gmail.com. The author is very grateful to the editor in charge of this article, Eric Edmonds, and three anonymous referees for thoughtful and constructive comments. The author thanks Toke Aidt, Tiago Cavalcanti, Giancarlo Corsetti, Douglas Gollin, Rema Hanna, Cl ́ement Imbert, Sriya Iyer, Michael Jetter, Kaivan Munshi, Anand Shrivastava, Laura Zimmermann, and seminar participants at Cambridge University, Delhi School of Economics, Indian Statistical Institute Delhi and International Association for Applied Econometrics for helpful comments and suggestions. The author also thanks Thiemo Fetzer for sharing TRMM rainfall data and Manisha Shah and Bryce Millett Steinberg for sharing data and codes for their study on NREGA’s impact on human capital outcomes. Finally, the author gratefully acknowledges the Ministry of Rural Development, Government of India, for help in data collection. A supplementary online appendix is available with this article at The World Bank Economic Review website. 1 See Sukhtankar (2017) for a thorough review of the literature on NREGA.

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