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Aleksandra Babii, Ms. Alina Carare, Dmitry Vasilyev, and Mr. Yorbol Yakhshilikov
Traditional models relying on standard variables like the U.S. Hispanic unemployment rate fared well in explaining remittances to CAPDR and Mexico during the pre-pandemic period. However, they fail to predict the sustained growth in remittances since June 2020, including the significant increase in the average amount remitted. Using data from over 300 remittances corridors (from 23 U.S. states to 14 Salvadoran departments), we find that this increase is primarily explained by the dynamics of U.S. states real wages, as well as more temporary factors like U.S. unemployment relief (including the extraordinary pandemic support), U.S. states mobility, and COVID-19 infections at home. The paper also analyses what role the change in the modes of transmission of remittances, additional U.S. fiscal stimulus and U.S. labor market developments, especially in the sectors were CAPDR and Mexican migrants preponderantly work, play in explaining aggregate remittances growth.
Aleksandra Babii, Ms. Alina Carare, Dmitry Vasilyev, and Mr. Yorbol Yakhshilikov

aggregate remittances are much more elastic to the U.S. Hispanic unemployment rate. Additional robustness exercises include using a different method (see next subsection). B. Panel Vector Autoregression Model of Remittances As a second step, we used the panel vector autoregression method developed by Abrigo and Love (2016) to further control for endogeneity . This method applies the generalized method of moments estimator and allows all variables to be treated as endogenous as well as cross-sectional dynamics. While this model does not impose long