facilitate such analysis. In the next section, we describe the resulting challenges and our empirical strategy, including the key assumptions and caveats underlying our findings.

# **3.  Empirical Strategy**

In the absence of data formally collected for purposes of evaluation, we use three crosssections of household survey data collected over a period that spans program implementation by the National Family Health Survey (NFHS).<sup>4</sup> The NFHS is a widelyused nationally-representative survey of maternal and child health. The first wave of the NFHS was carried out before the reform in 1992/1993 (NFHS-1) and repeated, with some modification in 1998/1999 (NFHS-2) and in 2006 (NFHS-3). The survey covers women of reproductive age (15-49), including a complete birth history and retrospective health histories for recent births. The timing of the NFHS allows us to view the Haryana sample within the NFHS as follows: the 1992/1993 data may be regarded as a baseline survey and the following two surveys as follow-ups, where the 1998/1999 data covers the shortterm and 2006 covers the medium-term.

Even with these data, impact evaluation presents substantial challenges. Clean identification of program effects is not straightforward for two reasons: Firstly, we do not have explicit individual or household-level measures of actual program participation from any household survey data to the best of our knowledge. By virtue of this, we are limited to estimating an intent-to-treat effect, relying on the statutory eligibility criteria for the program. Secondly, Haryana introduced ABAD in all districts simultaneously. As a result, we are unable to exploit any time variation in the program’s introduction to identify effects, as there is no possibility of observing “treatment” and “control” areas within Haryana at the same time<sup>5</sup> . We discuss our final identification strategy, noting important caveats and assumptions that should be taken into account when interpreting the findings. We also further note that matching the available data to these criteria depends on several key assumptions, and result in some unavoidable limitations imposed by the nature of the surveys. These are also highlighted below.

# **3.1 Identification**

Identification of treatment effects depends on the assumption that outcomes such as parental investment in health are exogenous to a jointly-determined measure of program eligibility. For both women and children, our measure of ABAD treatment at the time of each survey is determined by the combination of interview date, family composition as of October 1994, gender, family economic status, and caste affiliation.

We first construct proxy measures of poverty criteria (described in the following section) and combine them with caste affiliation to identify eligible and non-eligible households across all rounds of the NFHS. We then classify women and children within these

> 4 The NFHS follows the format of Demographic and Health Surveys (Macro International). 5 We also note that using other states as a control group is not feasible: owing to demographic and economic differences between the states of India, the only likely candidate for a control would be Punjab, but a similar program was introduced in Punjab at the same time.

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