households as individually qualified for the program based on their birth history (in the former case) and their birth order (in the latter).

Under this identification assumption, we compare eligible and non-eligible groups of women and children in the ``baseline” and “follow-up” years of the survey while controlling for relevant household and individual-level characteristics. This is a standard difference in-difference methodology, controlling for common (additive) time trends and pre-program differences between the two groups<sup>6</sup> .

# **3.2 Measuring Eligibility**

# **3.2.1. Which Households Are Poor?**

NFHS data do not provide any explicit measure of poverty. As a result we construct a proxy measure based on available household asset information. NFHS computes an asset index for the sample population of households using data on 21 asset indicators and household landownership. These variables are aggregated into a wealth score, using an approach first proposed and validated by Filmer and Pritchett (2001). For each survey wave, within the rural and urban sector, we sort individuals according to the household asset index. We then define as poor the population-weighted proportion of the sample falling below the percentile thresholds implied by appropriate measures of the poverty headcount ratio.

Two important considerations should be noted here. Firstly, the use of asset indexes are not always a suitable proxy for poverty, as they are by construction a stock measure, rather than a measure of consumption flows (Filmer and Pritchett, 2001). Misclassification could result if we classify some BPL (potential) ABAD beneficiaries as non-eligible, and non-BPL non-eligible are classified as eligible. Provided misclassification errors are independent and uncorrelated with other regressors<sup>7</sup> , this will result in attenuation bias in the estimated treatment effects.

> 6 Due to the nominally rule-based nature of this program, we also considered the potential use of regression discontinuity design (RD), where treatment is based on being above or below the threshold for a known covariate “forcing” measure (such as wealth, sibling composition or birthdate of child in this case). If the relationship between the outcome and the forcing measure is smooth, any discontinuity at the cutoff point may be interpreted as a causal program effect. In the event of a “fuzzy” RD, where treatment is in practice not perfectly consistent with a “sharp” rule of this type, the researcher accounts for this by using a twostage-least squares estimation where the rule instruments for observed treatment.  In this setting, RD analysis is generally not convincing, primarily because of our acknowledged difficulties in precisely defining treatment The eligibility criteria themselves are multiple, mostly categorical in nature and  - most importantly - based on strong assumptions we have made in the analysis, such as the threshold values for poverty. for measures that are less problematic such as number of siblings at birth, the data are such that that it is not meaningful to compare local linear regressions for any reasonable bandwidth below and above the cutoff value of 2. For covariates such as poverty which are more theoretically suited to this analysis, we do not have well-defined actual measures. Secondly, we do not observe actual treatment, making it impossible to account for any fuzziness using a 2SLS approach.

7 If there is significant concern that errors are not uncorrelated, alternative estimation strategies in Mahajan (2006) can be explored, conditional on a suitable instrument for true BPL status.

6