or more members is a household strategy to increase income through remittances. Let the parameter θ�,� be an unobserved measure of household welfare generated by individual _i_ ’s leisure time (i.e., non-market activities including production in the household). If the household’s decision for a member to migrate, and subsequently send remittances, depends on this function of household welfare, then R� is itself a function of θ�,� (Equation II).

Under this scenario, there are two econometric problems in determining the relationship between Y� and R�: omitted variable bias arising from the unobservable nature of θ�,� and a violation of the ignorability assumption since selection into the group of beneficiaries of remittances is non-random.

To address the impact of θ�,� on the estimates, access to either panel data, which can be used to remove individual and household fixed effects, or a strong instrumental variable are ideal. Yet no household panel data exist for the Northern Triangle with sufficient observations for this type of analysis. At the same time, a lack of geographical disaggregation in the regions’ household surveys limits the extent to which surveybased geographical and temporal variation can be exploited for instrumental variables. Other potential data sources that could be mined for instrumental variables – like municipal or departmental migration rates based on census or administrative data, are not complete or available for the three countries.

Given the lack of exogenous information that can be used as a strong instrumental variable in the three countries, an alternative approach is to maximize the similarity between the treated group (households who receive remittances) and the control group (households that did not receive remittances). This can be accomplished through the use of propensity score matching. This methodology has been widely used to look at the impact of remittances internationally, including Acosta (2011) for El Salvador; Berloti and Marchetta (2014) for Ecuador; Bouoiyour and Miftah (2015) for Morocco; Clement (2011) for Tajikistan; Cox-Edwards and Rodriguez-Oreggia (2009) and Esquivel and Huerta-Pineda (2007) for Mexico; De and D. Ratha (2012) for Sri Lanka; Dey (2015) for India; Garip (2014) for Thailand; Jimenez-Soto and Brown (2012) for Tonga; Kahn (2008) for Bangladesh; and Siddiqui (2013) for Pakistan. This approach identifies a sample of households that do not receive remittances that are similar to households that receive remittances by matching across invariant household socioeconomic features. This reduces biases that are due to systematic differences between receiving and non-receiving households (for example, Dehejia and Whaba 2002). In other words, this approach eliminates confounding effects related to differences in _observable_ characteristics between recipients and non-recipients.<sup>18</sup> Propensity score matching depends on the strong ignorability condition, i.e., the outcome and treatment are independent conditional on the observable feature. This is violated by the inclusion of R��θ�,�� in the model. However, to the extent that unobservable characteristics are correlated with observable characteristics, propensity score matching attenuates potential endogeneity (see Stuart 2010). That is, suppose that θ�,� is correlated with observable characteristics in the vectors X�, Z�, R�. This could be the case if, for example, the value of home production is higher in households with higher dependence ratios or in rural households with lower access to basic services. Matching across households with similar sets of these characteristics would thus reduce the difference in θ between treated and untreated households. �,�

> 18 In addition, using propensity score matching may also reduce the potential for bias due to survey undercount. This is a concern since the decrease in the number of households reporting the receipt of remittances in the 2014 surveys compared to 2006 surveys suggests a potential measurement issue in the survey instruments.

7