Ph.D. Job Market Candidates
Several Pardee RAND students and recent alumni are actively pursuing employment opportunities. Prospective employers or executive recruiters are encouraged to contact them directly by email.
To view the profiles of all Pardee RAND students, visit our Students page.
For more information on recruiting at Pardee RAND, please contact our Career Development staff at (310) 393-0411, ext. 6742 or email email@example.com.
As part of his dissertation, Gursel developed behavioral interventions to reduce tax evasion. While his research mostly focuses on behavioral decision-making, he is interested and experienced in survey research methodology, social policy issues and health economics too. He has also taught Statistics and Microeconomics for over 10 years.
Taxpayers Misperceptions and Two Novel Behavioral Interventions to Counter Tax Evasion
Gabriela has conducted experimental and qualitative evaluations, as well as applied machine learning algorithms to improve programs targeting vulnerable groups, particularly on the topic of social determinants of health. Her current work includes a randomized controlled trial to assess the impact of a food security intervention for people living with HIV in the Dominican Republic, and research in Mexico to understand the factors predicting cognitive decline and impacting mental health in old age.
Essays on the Mental Health of Three Vulnerable Populations in Latin America
The World Health Organization estimates that close to 1 billion people lived with a mental disorder in 2020. Vulnerable groups, such as women and those living in poverty, are particularly prone to experiencing mental health disorders. My doctoral dissertation studies the factors affecting the mental health of three vulnerable populations in two Latin American countries, and whether gender plays a role. The first paper uses multivariate regression analysis to examine the factors associated with stigma and depression for people living with HIV and food insecurity in the Dominican Republic. The second paper uses logistic regression, difference-in-differences, and the triple difference estimator, to assess the impact of a supplemental income program on major depressive episodes of older adults in Mexico, and potential heterogeneous program effects by gender. The third paper uses machine learning, including random forests and artificial neural networks, to identify the factors predicting cognitive impairment for people ages 50 years and older in Mexico. The results from these studies will provide policy recommendations to help improve the mental health of older adults, and people living with HIV and food insecurity.