This paper makes the first empirical contribution to investigating the relationship between migration status and remittance behavior among Egyptian migrants. Using new individual-level data from the 2023 wave of the Egypt Labor Market Panel Survey and a probit regression, this paper estimates the impact of having legal entry to and work authorization in the host country on the likelihood of sending remittances. Additionally, the paper utilizes a supervised machine learning approach, the Random Forest algorithm, to identify the most influential variables in predicting remittance behavior. The analysis finds that legal entry into a host country plays a significant role in remittance behavior, with an increase in remittance by 16 percentage points compared to those with no legal entry. However, the Random Forest analysis suggests that being a married woman is the most influential factor associated with a higher likelihood of remitting.
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