Abstract Although funding STEM graduate students in the United States is a multibillion-dollar endeavor annually, we have little information on how students' experiences and outcomes differ across funding mechanisms. The purpose of this NSF-funded research project is to understand how to spend graduate student funding wisely to ensure a variety of student experiences and an optimal set of outcomes, including equal access for all students to the financial, academic, professional and social resources that support success in graduate study. Specifically, the five-year research project is addressing the following research questions: 1. How do graduate students' funding mechanisms vary across their incoming characteristics (i.e., demographics and bachelor's or master's institutional type, location, or affiliation) and STEM discipline? 2. What is the relationship between graduate students' funding mechanisms and their post-doctoral outcomes, including time to degree, field of first job, job placement, and salary of first job? 3. How does the relationship between graduate students' funding mechanisms and their post-doctoral outcomes vary across their incoming characteristics (i.e., demographics and bachelor's or master's institutional type, location, or affiliation) and STEM discipline? 4. What do STEM graduate students, faculty members, and administrators perceive to be the benefits and drawbacks of various graduate student funding mechanisms? How does each group make decisions about offering or accepting offers of different funding mechanisms? 5. How does funding mechanism impact STEM graduate students' experiences, socialization, identity formation, and other factors previously shown to contribute to overall success? We are following a mixed methods approach to address these research questions. Our quantitative data are comprised of the complete Survey of Earned Doctorates, which is a restricted-use data set that is characterized by a greater than 90% response rate of all U.S. doctorate earners for the past several decades. The data set includes variables that will address the first three research questions. To address the final two research questions, the team is conducting a case study analysis of three STEM departments at each of eight different institutions. This qualitative aspect of the study seeks to understand how funding mechanisms enable or inhibit experiences and outcomes for students from the perspectives of department administrators, faculty advisors, and graduate students. We have been developing variables to quantify engineering graduate programs' doctoral student funding portfolios and to determine the extent to which funding portfolios vary. Our poster will illuminate how there is considerable variation in graduate student funding across engineering—institutions cluster into different “modes” of funding portfolios that do not neatly map onto common institutional categories, such as Carnegie classification and institutional control. We delve deeper into exploring this variation by comparing funding portfolios across engineering disciplines. These early research findings will inform future studies on understanding how graduate student funding corresponds to student experiences and outcomes, including access for students to the financial, academic, professional and social resources that support success.

Real estate property value analysis is used for municipal taxation and budgeting. Commercial properties make up a large percentage of the property tax base in many, if not most, taxing jurisdictions. Data constraints limit the number of analyses conducted on commercial property value patterns. This study employs a fairly extensive data set to address that problem in the context of El Paso, Texas, a large metropolitan economy located on the United States border with Mexico. The sample contains data for 105,611 commercial real estate parcels. Empirical analysis is conducted using geographically weighted regression analysis. Results confirm that parameter estimation for the commercial property data in this sample should be conducted using methodologies that allow for spatial autocorrelation and heteroscedasticity.

School districts face different costs to produce the same level of educational opportunity because of differences in student population, geographical costs of living, and district size. However, in many states, the school finance system fails to take these factors into account when distributing funds to school districts. Most prior analyses of state school finance systems focus on the relationship between district funding and the percent of low-income students in that district or the percent of emergent bilinguals, who are typically classified as English language learners (ELLs).\nWe present the first longitudinal descriptive evidence of the extent to which state school finance systems compound inequities for districts serving high concentrations of both low-income students and emergent bilinguals. We assess the extent to which high-ELL high-poverty districts are underfunded relative to otherwise similar districts in the same state and how these trends have changed leading up to and following the recession-era spending cuts.\nWe find that prior to the recession, high-ELL districts received greater funding levels than otherwise similar low-ELL districts in the same state. However, recessionary spending cuts disproportionately impacted funding for emergent bilinguals. The remaining resource advantages for high-ELL districts are concentrated in low-poverty districts. These findings are consistent across measures of funding, expenditures and staffing ratios. Finally, our cross-state analyses identify wide differences in the extent to which states allocate resources equitably across districts. We find that larger student weights for ELL and FRL students may increase funding for those students, but there is a relatively weak relationship between the size of funding weights for special populations and the degree of funding equity for those students.