Forecasting the Return Home of Non-US Citizens with US Ph.D.'s: Trends over time and why Non-US Citizens with US earned doctorates choose to return home

Arizona State University, Scottsdale AZ

Investigators

Abstract

The number of U.S. doctoral students with temporary visas has steadily increased over the last two decades; approximately half of these individuals stay in the U.S. after receipt of their doctorate. However, the roles of economic, nationalistic or scientific influences on the decision to stay or to leave are not widely understood. The objectives of this project are to 1) generate a baseline forecast of temporary U.S. visa holders with U.S. doctoral degrees who return home, 2) build an explanatory model for the decision to return home by these individuals, and 3) use the results of the model to enhance our ability to predict future trends about temporary visa holders intention to either stay in the U.S. or return home. A major focus of this work is to provide policy relevant information and tools on how the changing status of economic and political activity abroad influences individual decisions to leave the U.S. Further the work will contribute to our understanding of how early scientific training and the quality of training also influence the decision to return home. Data from the Survey of Earned Doctorates (SED) and the Survey of Doctorate Recipients (SDR) will be used for the study. The development of a forecast from this data will provide evidence as to whether or not the return phenomena is stable, growing or in decline over time. The explanatory model will provide a mechanism for linking policy relevant variables (e.g. nature, amount and conditions of financial support for doctoral education, quality of training, role of foreign governments) to individual likelihoods of return after receiving a U.S. degree. Finally, the results can help enhance data quality for regularly collected information from the SED by quantification of bias. It is also useful to note that this project will support one full time doctoral student who will become proficient using SED and SDR data.