## **Table 1: Sample distribution by State**

||**Jharkhand**|**Madhya Pradesh**|**Maharashtra**|**Total**|
|---|---|---|---|---|
|Districts|4|12|7|23|
|Blocks|10|27|13|50|
|Villages|212|208|307|727|
|Self-help Groups|858|550|893|2301|
|Households|1260|1237|1819|4316|
|SC/ST Households|1051|946|1069|3066|

# **3.1 Sampling Strategy**

The survey followed a multi-stage sampling strategy at the block, village and household levels. We first identified potential treated blocks where the program takes up rate was at least 50 percent. Then we listed potential comparison blocks in consultation with the National and various state level rural livelihood missions as it involved identification of those blocks where the program had just entered in 2016 or was about to take off in the following year. The treatment and comparison blocks were randomly selected from the list of potential blocks.<sup>8</sup> This block selection strategy reduced the program placement bias to some extent at the sampling stage itself.

After block selection, propensity scores using standard logistic specification were estimated in order to construct the group of matched treatment and control villages using village-level variables from the pre-intervention period. Variables such as literacy, availability of infrastructure, composition of caste, access to land, remoteness and indicators associated with household amenities, were drawn from the Socio Economic and Case Census (SECC)2013. We used data on other village-level characteristics such as the Population Census 2011. Several variables were identified in consultation with state missions because implementation agencies have recently used these for identification and mobilization of target groups. The SECC data were given priority over the census data wherever data on a characteristic were available from both sources, as household level data were available for the SECC and it was a more recent survey. The choice of other variables in the village selection model was based on the literature review of similar interventions. Results from the village selection model for each state are given in the Annex (Tables A1-A3).

Once the matched list of treatment and control villages was identified, 6 households were randomly selected from each village from the SECC’s household list. Due to the nature of the rollout of the program, the sample was not evenly distributed across states. Program placement bias owing to state specific rollout heterogeneity was therefore addressed by specifying the village selection model at the state level. However, the household selection model was proposed for the entire sample. This was done to mimic power calculations and ensure availability of a larger group of comparison households to match from.

# **3.2 Survey Data**

In this section we present the summary statistics on key characteristics of the surveyed sample. The average age of the household head in the sample is approximately 47 years, and there are 10 percent female-headed households. The average education of the household head is approximately 4 years. Approximately 49 percent of household heads are married, with an average household size of 4.6. Only 33.1 percent of households have access to a

> 8 In a few cases, there were not enough sample blocks, so all the blocks were taken.

6