A few studies use bilateral data but adopt a wider geographical perspective. Lueth and RuizArranz (2008) use a panel dataset on bilateral remittances for 11 European and Asian recipient countries during the period 1980-2004. Frankel (2011) merges their data with other bilateral data on remittances from the Inter-American Development Bank and the European Commission (JimenezMartin _et al._ , 2007). Docquier _et al._ (2012) merges the sources used by Frankel (2011) with a database from the European Central Bank and a Romanian database; the resulting dataset include 89 sending countries but is still limited to 46 receiving countries, both developing and developed.

As remittances represent a nonnegligible share of GDP in many recipient countries, the results of existing studies could be severely biased by reverse causality from remittances to GDP, with the exception of the few studies that focus on exogenous income shocks (see, for instance Yang, 2008). In cross-country aggregate level analyses this issue is seldom addressed by means of instrumental variables (Singh _et al._ , 2011) or GMM techniques (Cooray and Mallick, 2013). In the context of bilateral data, Frankel (2011) addresses endogeneity issues concerning the size of migrant stocks, but disregards the possible bias related to the receiving country’s GDP. Lueth and Ruiz-Arranz (2008) acknowledge the endogeneity problem and maintain that GMM estimates that use lagged values of growth in the recipient countries confirm their results about the procyclical pattern of remittances. However, it remains unclear whether GMM estimates that use lagged values of growth address the issue satisfactorily, as concerns about the capacity of GMM to address causality are mounting, for instance because of weak instruments and the over-fitting of endogenous variables (Roodman, 2009; Bazzi and Clemens, 2013).<sup>8</sup>

# **3 Empirical Strategy and Data**

## **3.1 The empirical model**

To identify the effect of business cycle fluctuations and financial development on remittances we estimate a model in which bilateral remittances are a function of a set of independent variables constructed by exploiting information on both migrants’ origin countries and their host Italian provinces. In the baseline specification, total bilateral remittances between the source province _i_ and the recipient country _j_ at time _t_ ( _REMi,j,t_ ) are a function of the logarithm of actual GDP per capita over potential GDP per capita in the source province ( _CY CLEi,t_ ) and in the recipient country ( _CY CLEj,t_ ), the log of trend GDP per capita ( _TRENDi,t_ , _TRENDj,t_ ), the log of 1 + the bilateral stocks of migrants ( _MIGRANTSi,j,t_ ), and the log of population levels ( _POPi,t_ , _POPj,t_ ): 8 Results on specific remittance corridors might also suffer from reverse causality. Sayan (2004) and Durdu and Sayan (2010) investigate the cross-correlations between remittances from Germany to Turkey and cyclical fluctuations in Turkish and German GDP without discussing the direction of causality. Akkoyunlu and Kholodilin (2008) estimate a VAR model and find no evidence in favor of Granger causality from remittances from Germany to Turkish GDP.

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