BEYOND LEGAL BORDERS: APPLYING PREDICTIVE MODELING TO AFFIRMATIVE ASYLUM DECISIONS USING ECONOMIC AND POLITICAL CONDITIONS

The present study investigates the objectivity of the current affirmative asylum approval decision-making process in the United States. It provides a brief history of asylum law and the relevant statutes that determine an applicant’s eligibility to be granted asylum. This study will show that the current strict criteria to be granted affirmative asylum in the United States does not reflect the reality of the decision-making process done by asylum officers and adjudicators.
Using affirmative asylum approval data by country from the Department of Homeland Security alongside computational libraries found in Python 3, the study will perform correlation and ordinary least squares regression analysis to show how external factors, such as political and economic conditions of an applicant’s country of origin, influence the decision to approve an applicant for asylum. The study will provide statistically significant results that show that nearly one-third of the variability of affirmative asylum approval decisions can be attributed to political and economic conditions of an applicant’s country of origin. These results will be presented with data visualizations, and code snipped will be provided for future researchers to replicate this study’s methods.