action costs associated with remittance transfers, and encourage greater remittances (particularly through the formal sector) (Freund and Spatafora, 2008).<sup>10</sup> In addition, the propensity of migrants to remit (again, particularly through formal channels) may depend on the institutional, cultural, and informational gaps between migrants and the host province’s financial system (Albareto and Mistrulli, 2011). We proxy these gaps using a measure of the functional distance between banks and local economies, based on whether banks are headquartered in the relevant province ( _FUNCTIONAL DISTANCEj,t_ ). Intuitively, when banks are headquartered in an area, they are better able to collect local information, and as a result are more likely to serve the economic needs of the area (Alessandrini _et al._ , 2009), including the needs of resident migrant workers.

Since the dependent variable _REMi,j,t_ has a significant share of non-randomly distributed zeros (that is, many empty country-province cells), equation 1 and 2 are estimated using the Fixed Effects Poisson estimator. Despite deriving originally from the analysis of count data, the Poisson estimator can also be applied to non-negative continuous variables (Wooldridge, 2010). Poisson regression estimates are consistent in the presence of heteroskedasticity and reasonably efficient, especially when considering large samples. Thanks to its multiplicative form, the Poisson specification also provides a natural way to deal with zero observations in the dependent variable instead of either transforming or excluding them from the sample (Silva and Tenreyro, 2006; Burger _et al._ , 2009). In the baseline estimates, we include country-province pair and time fixed effects and additionally control for the potential correlation of errors at the bilateral level by clustering standard errors by country-province pairs.<sup>11</sup>

# **3.2 Data sources**

The variables used in equations 1 and 2 are constructed using data collected from many sources. Here we provide an overview; a precise definition of each variable and of its sources is in Table 1.

The main data source is a detailed panel dataset on bilateral outward remittances from 103 Italian provinces to 87 developing countries, providing annual data at constant prices for the period 2005-11, compiled by the Bank of Italy (see Table 2 for a list of recipient countries included in the sample).<sup>12</sup> The dataset covers remittances sent through formal channels, and predominantly reflects transfers carried out through money-transfer operators and the postal system. The banking system has been included in the survey only since 2010, and accounts for 5 to 10 percent of total remittances. All formal transactions are reported, regardless of the amount. As a _caveat_ , the dataset does not include remittances sent through informal channels.

> 10 Ideally, we should rely on a more precise measure of transaction costs, such as the service fees charged by banks and money transfer operators for international transfers, as done by Freund and Spatafora (2008). However, those data are not available at the provincial level and for the time span of our analysis. We employ bank branches penetration as a proxy for transaction costs and for the level of financial development at the provincial level.

> 11In the robustness section, we control for unobservables by separately including country and province fixed effects instead of country-province pair fixed effects, so that we can also identify the effect of specific time invariant variables at bilateral level.

> 12Data on remittance flows to 204 destination countries are collected as part of a monthly survey carried out by the Bank of Italy on a provincial basis since 2005. The dataset is publicly available at: www.bancaditalia.it/statistiche/rapp_estero. Our sample is limited to the 87 developing countries where data on the control variables for the extended model specification are available.

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