The Remittance Blueprint: Data-driven Intelligence for Sri Lanka
For policymakers in Sri Lanka, this provides data-driven insights to design exchange rate policies and migration strategies for economic resilience.
This study analyzes 32 years of Sri Lankan migration and remittances, finding that external macroeconomic variables (exchange rates and oil prices) drive inflows more than domestic indicators. Ridge Regression improves prediction accuracy by 73.8% over SARIMA, projecting 2026 remittances at USD 9,001 million.
This study analyzes Sri Lankan migration and remittances over 32 years (1994-2025). Using a 384-month harmonized dataset, we apply exploratory data analysis, stationarity corrected time-series modeling (ADF, Johansen, VAR/VECM), and supervised learning. Results reveal remittance inflows are primarily driven by external macroeconomic variables, specifically exchange rate dynamics and global oil prices, rather than domestic indicators. Impulse response analysis confirms the asymmetric impact of currency depreciation and oil price shocks. Predictively, multivariate machine learning models outperform traditional univariate approaches; Ridge Regression achieves a 73.8% accuracy improvement over SARIMA (Annualized RMSE: USD 494.8 Mn). The optimized framework projects 2026 remittances at USD 9,001 million under stable conditions. These findings highlight the structural dependence of remittances on global economies, emphasizing the need for robust exchange rate policies, skilled migration, and formal financial channels to enhance long-term economic resilience.