to plan and prepare for their departure may have all their documents together and they may move to a location with better economic opportunities and denser social network. Contrast that to households with little time for preparation because their property was damaged or destroyed. They might lose access to the PDS because of missing documents, and at the same time have fewer options to work or informal support network. Families’ choice of destination itself, therefore, can have important implications in having access to the PDS. A household’s ration card is linked to a specific local ration agent. When moving to a new location, the household is required to establish an official residency, and only then can it start the process of transferring the linkage to a new agent in a new location and start receiving the assistance. A household that values the PDS highly may choose a destination where it is easy to establish a residency, and which has uninterrupted supply of rations. To control for both the observed and unobserved geography-specific association with PDS accessibility, we modify equation 1 as follows: where, subscripts i , j , and m index a household i , resident in census block j and district m . The SWIFT survey covered 106 qhadas (districts), which are added as fixed-effects, ηm , to account for qhada-level variation in the availability of ration and other characteristics that influence households’ welfare and the PDS accessibility. Census blocks (EA) served as the primary sampling units for the survey which were selected with a probability proportional to size. The standard errors, hence, are clustered at this level to allow for the correlation among error terms within these blocks.<sup>10</sup> Equation 2 teases out the confounding factors between the PDS and outcomes of interest at the specified geographic level. The non-random assignment of the treatment, however, entails that the heterogeneity in a household’s selection into the PDS program within qhadas, if present, will bias the coefficient estimates. We try to overcome such biases by employing the propensity score matching (PSM) technique, which is a popular method to adjust for confounding in the context of non-random treatment assignment in observational studies to establish causal inference.<sup>11</sup> An underlying conjecture of all the matching estimators is the conditional independence assumption (CIA). The assumption states that treatment allocation is random conditional on a set of observable covariates X that are not influenced by treatment, i.e., unconfoundedness. A counterfactual household for each program participant is constructed by matching units from the untreated group exclusively on the observed X . The downside, however, is that conditioning on all the relevant covariates can be limited, specifically, in the context of high dimensional vector X (“curse of dimensionality”). Rosenbaum & Rubin (1983) show that if potential outcomes are independent of treatment conditional on covariates X , the outcomes are also independent of treatment conditional on the propensity score P(X) = P(PDS= 1|X) , i.e., the probability of one’s participation ( PDS= 1 ) given her observed characteristics X . The additional “overlap and common support” condition, 0 < P(PDS= 1|X) < 1 , rules out the perfect predictability of treatment status and ensures units with the same values of X have a positive probability of being both participants and non-participants (Heckman, Lalonde and Smith 1999). Given both the conditions hold,

> 10 Only 327 displaced households were sampled for the “long-form” questionnaire (Table A1). Therefore, we use governorate fixed-effects and cluster standard errors are at the nahiya (sub-district) level when analyzing the outcomes from the “long-form”.

> 11 PSM is used widely across the disciplines – from labor market research see (Dehejia and Wahba 1999, 2002, R. Dehejia 2005) or (Heckman, Ichimura and Smith, et al. 1996, Heckman, Ichimura and Todd 1998, Smith and Todd 2001, 2005a), to cardiovascular research (Deb, et al. 2016), or pharmacoepidemiology (Perkins, et al. 2000) etc.

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