Student Presentations

Abstracts of Best Papers Presented at
CSU, SSRIC Social Science Student Symposium
May 5, 2016

Charles McCall Award for Best Undergraduate Paper
Is Education the Great Equalizer? Comparing the Wages of Native and Immigrant Latinos and Asians in California: 2000-2012

Maria Pimentel, California State University Channel Islands

This study analyzes the socioeconomic outcomes of Latino and Asian young-adults in California across two decades (2000-2012). Specifically, I examine how the income gap between immigrants and natives is influenced by educational attainment. For example, does education have the same pay-off regardless of nativity status? The immigrant generation is disaggregated by age of arrival into two groups: 1.0 and 1.5 generation. Two prevalent theories of immigrant assimilation, and theory of human capital are tested by utilizing individual level data from the 2000 U.S. census and 2008-2012 American Community Survey. Considering the continued growth of the immigrant population in California, this study of Latino and Asian young-adults provides insight to the state’s future workforce.

Betty Nesvold Award for Best Graduate Paper
Privacy and False Identification Risk in Geomasking Techniques
Dara Siedl, San Diego State University

Recent years have seen an uptick in location privacy research, including the application of geomasking procedures. Masking aims to protect privacy and preserve spatial information through the slight displacement of point data. False identification, or the mistaken association of data with the incorrect person, is an unexplored issue in geomasking, despite legal protections against false association. This study examines the risk of false identification in three masking techniques: random perturbation, donut masking, and Voronoi masking. Voronoi masking is found to best protect against false identification and preserve clustering properties of a San Francisco foreclosure data set.

Gloria Rummels Award for Best Use of Quantitative Data
Analyzing Factors that Predict Alumni Giving at a Public University in California
Ginger Hashimoto, Sacramento

Using data from a California State University institution, this study conducted a two-part regression analysis to examine factors that predict alumni giving. The first stage uses logistic regression to determine donor likelihood and the second stage uses ordinary least squares regression to estimate gift amounts. Corroborating previous research, the study identified higher educational attainment, student involvement, age, median household income, and proximity to campus as statistically significant determinants for alumni giving. While not surprising, given the lack of literature specific to the CSU system, it is significant that the study validated such findings for public, non-research based institutions in California.