Analysis of Influential Factors and Short-term Forecast of International Migration Flows in Asia Using Eigenvector Space-Time Filtering Models
With the continuous advancement of the globalization process,communication and cooperation among countries and regions around the world are becoming increasingly closer,and the scale of international migration flows is also expanding.Asia stands out as an active region for international migration,with a large portion of migratory movements occurring within its borders.In addition to the social and economic factors of the origin and destination regions,spatial and temporal dependence among migration flows is crucial in understanding international migration dynamics,indicating that migration is influenced by neighboring and past migration flows.Different from other kinds of data(e.g.,regional GDP),migration flows between different regions often contain many zero values,necessitating specific methods for handling them.Additionally,spatial and temporal dependence among migration flows can be categorized into space-time contemporaneous and lagged structures,with the former reflecting the links to the preceding location and the instantaneous neighboring locations,and the latter pertaining to the preceding location and the preceding neighboring locations.Based on the bilateral migration data of Asian countries in six periods from 1990 to 2020,this study utilizes eigenvector space-time filtering models,along with contemporaneous and lagged dependent structures,as well as eigenvector spatial filtering models and zero-inflated negative binomial regression models,to explore the influential factors of the international migration flows within Asia and their changes during 1990-2020.Finally,this study aims to forecast international flows within Asia between 2020 and 2025 based on two types of space-time filtering models.Preliminary results indicate significant space-time autocorrelation of international migration flows within Asia,with neighboring migration flows exerting a greater influence over the same time period compared to the past.Incorporating eigenvectors to represent spatial and temporal dependence effectively improves the goodness-of-fit of the models.Main factors affecting international migration flows within Asia include population size,economic level,war situation,and proximity.During the 30 years(1990-2020),the influence of population size fluctuated,economic disparities initially increased before weakening,wars continued to drive emigration,geographical barriers decreased,and factors like language proximity and economic cooperation significantly influenced migration.Looking ahead from 2020 to 2025,migration trends are evident between Pakistan and India,as well as from India to Saudi Arabia,from Pakistan to Afghanistan and from Syria to Jordan.Combining the forecasting results of the two eigenvector space-time filtering models,the mean value of the total volume of international migration flows within Asia from 2020 to 2025 is projected to be approximately 1.8×107.India emerges as a major country for international migration.Understanding the spatial and temporal dependence and other characteristics of international migration within Asia is crucial for accurately forecasting future migration flows and providing scientific reference for policy making.
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