Appendix I. Descriptive Statistics and List of Countries
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We are grateful to Erik Lueth and Marta Ruiz-Arranz for kindly sharing their dataset and to Simone Bertoli for insightful comments. We also thank seminar participants at the 2012 Conference (Oxford), CERDI-University of Auvergne (France), and IMF-APD departmental seminar. The usual disclaimers applied.
See, for example, Chami et al. (2003, 2008); Glytsos (2005); Giuliano and Ruiz-Arranz (2009); Catrinescu et al. (2009); Barajas et al. (2009), Bhaskara Rao and Mainul Hassan (2011); Abdih et al. (2011); Combes and Ebeke (2011); Ebeke (2012a).
Controlling for trade and financial openness also ensures that the coefficient associated with remittance inflows captures the direct effect, which does not work through the positive impact of remittances on these variables. This point is particularly relevant given the growing literature that points to a positive impact of remittance inflows on trade and financial openness (Beine et al., 2011; Abdih et al., 2012).
Henceforth, and unless otherwise noted, “sending and receiving countries” will refer to “remittance-sending and remittance-receiving countries, respectively.
This paper takes advantage of the recent bilateral migration data provided by the World Bank in its World Bilateral Migration dataset. Bilateral migration stock data are available every 10 years, thus allowing us to obtain time-varying weights. Given that these weights change slowly over time, this ensures a relative exogeneity of the weighting structures in the computation of the host countries business cycles.
The Baxter and King band-pass filter is used with arguments 2 and 8.
The smoothness parameter of the Hodrik-Prescott filter is set equal to 6.25, following Ravn and Uhlig’s (2002) recommendation for annual data.
In the bilateral models, this problem does not arise since the dependent variable records the bilateral correlation coefficient of business cycles within each potential country-pair.
Recently, Frankel (2011) used the same dataset to analyze the countercyclical properties of remittances at the bilateral level. Beine et al. (2011) also used the same dataset in a recent study examining the role of immigration policies on the relationship between remittances and education.
Given that original data in the Chinn and Ito dataset can contain negative values, the financial openness variable has been rescaled so that all the values are positive.
Chami et al. (2006, 2012), among others, show that remittances effect on macroeconomic variables is nonlinear.
Unless remittances are sent for investment purposes in the country of origin, there is no established result suggesting that remittance outflows from migrant host countries during booms rise more than proportionally. These issues are discussed in Ebeke (2012b).
The banking crisis dummy comes from Laeven and Valencia (2010) dataset. The Natural disasters variable is defined as the ratio of people affected by a natural disasters normalized by the lagged value of the total population (data are drawn from the EM-DAT dataset). All the remaining additional control variables are drawn from the World Development Indicators dataset.
The real GDP growth in the remittance sending country is taken as exogenous conditional on the presence of the globalization variables (trade and financial openness).
See Brückner (2011) for the proof of the robustness of this two-step approach and the efficiency of the technique.
Column  in each Table presents the first-stage results of the instrumentation of the log of real GDP of the remittance-receiving economy.