Date of Graduation
Spring 5-15-2020
Document Type
Thesis
Degree Name
Master of Science in International and Development Economics (MSIDEC)
College/School
College of Arts and Sciences
Department/Program
Economics
First Advisor
Bruce Wydick
Abstract
There are currently over 65 million individuals that have been forcibly displaced globally. The cumulative trauma that comes from the refugee experience and exposure to violence has proven to have long-term negative psychological outcomes and thus negative impacts on human capital in the long run. Given that over 50% percent of the global refugee population are children, the ability to efficiently and accurately assess their mental well-being is of critical importance. Using data from over 2000 refugee children in Jordan, I use machine learning techniques to find key predictors of psychological distress, PTSD, and exposure to violence found in children’s drawings. Results show that there are multiple consistencies across the predictors chosen and indicators highlighted in the psychology literature. This provides empirical evidence for the possibility of children’s drawings to be used as a low-cost assessment tool of mental well-being for a refugee population.
Recommended Citation
Smith, Stephanie, "Estimating Predictors of Mental Well-Being Through Analysis of Children’s Drawings: The Case of Syrian Refugees" (2020). Master's Theses. 1281.
https://repository.usfca.edu/thes/1281
Included in
Behavioral Economics Commons, Child Psychology Commons, Developmental Psychology Commons, Econometrics Commons, Experimental Analysis of Behavior Commons, Growth and Development Commons, Health Economics Commons
