# **3. Data**

## Program Expenditure Data

Data on program expenditure is collected from the Ministry of Rural Development (MoRD). Although the information on NREGA is publicly available, data on SGRY was specially sourced from MoRD and Datanet (India).<sup>8</sup> MoRD reports district-wise annual physical and financial statements for both the programs. Physical statements provide information on the number of public works completed and employment generated, while financial statements give statistics on the availability of funds and actual expenditure. I use data on “actual expenditure” when referring to program expenditure in the empirical models below. Data on SGRY is from its implementation in 2001 to 2007, its last operational year. Data on NREGA is from 2006 to 2010. In total, I use ten years (20012010) of district-level data on employment expenditure. The data used in this article is the most disaggregated and detailed data on program expenditure used in any comparable analysis on public workfares.

## Agricultural Wage Data

I use data on agricultural wages from the Agricultural Wages in India (AWI) series published by the Ministry of Agriculture. The AWI data has been extensively used for time series analysis on agricultural wages in India (see for example, Ravallion et al. 1993, Ozler<sup> ̈</sup> et al. 1996, and Berg et al. 2018). The AWI district-level wage data provides monthly information on daily wage rates. I construct a measure of the agricultural wage rate from 2001 to 2010 by taking the average daily wage rates for men and women for agricultural activities like plowing, sowing, reaping, and weeding.

_Matching AWI wage data with expenditure data:_ The AWI data, unlike the program expenditure data, is not reported for all districts. Data for nearly 40 percent of the districts are available for less than six out of ten years. To improve the signal-to-noise ratio of agricultural wages, I convert the monthly AWI data to annual frequency by taking 12-month averages in the Indian fiscal year format to match the program expenditure data. I further improve the quality of the wage data by focusing on a balanced panel of 134 districts from 12 major Indian states for ten years. For the analysis of agricultural wages, I have a total of 1340 observations.

Since I lose districts with incomplete wage data, a possible objection can be the correlation of a district’s backwardness with the unavailability of wage data. If this is true, then restricting the analysis to 134 districts should result in the proportion of early phase NREGA districts being substantially lower in this sub-sample than the aggregate sample. Encouragingly, this is not the case. The percentage of phase-I and phase-II districts in the full sample – approximately 37 percent and 58 percent, respectively – closely matches the sub-sample proportion of 42 percent and 63 percent, respectively.

Both the wage and expenditure data are deflated to 2001 prices using the labor bureau’s consumer price index for rural laborers (CPI-RL). Unless otherwise mentioned, all variables in the empirical analysis are in real, percapita terms.

## National Sample Survey Data

_Casual earnings and employment outcomes_ : An alternative data used in the literature is from the nationally representative employment and unemployment surveys carried out by the National Sample Survey Organization (NSSO). The nationally representative rounds with a large sample size usually happen every three to five years. This study also employs NSSO data to compare the impact of NREGA on daily casual earnings with and without controlling for SGRY. Since both programs apply only to persons living in rural areas, I restrict the analysis to persons in rural areas and persons aged between 18 and 60. The sample includes 440 districts within the 17 largest states of India. I use data from Jul 2004 to Jun 2005 (round 61) to form the pre-NREGA period. For the post-NREGA period, I use data from Jul 2007 to Jun 2008 (round 64). Consistent with the methodology used in Imbert and Papp (2015), all statistics and estimates computed using the NSS data are adjusted using sampling weights.

> 8 To check the reliability of the SGRY district-level data, I aggregate district-level expenditure estimates to state-level. Apart from minor differences, the state-level figures match with the official state-level estimates published in MoRD reports.