Tag: Joe Stone

Signaling for Attention: Mobility and Student Performance in United Way’s Promise Neighborhoods

Presenters: Neil Cronkrite and Ian O’Gorman

Mentor: Joe Stone

AM Session Oral Presentation

Panel Name: M4 Transforming Education

Location: Metolius Room

Time: 11:00am – 12:00pm

A fixed effects linear leastsquares statistical regression model was used to explore the relationship between student academic performance and student mobility in the Bethel School District in Eugene, Oregon. Our United Way of Lane County, as struggled with student mobility as the organization refines its new Promise Neighborhoods project, aimed at distressed neighborhoods in Lane County. Student mobility may limit United Way’s ability to improve the educational and developmental outcomes of students. We use voter registration data to estimate total mobility in Lane County and in the Promise N4eighborhoods. We also use Bethel School District student transfer codes and statewide state test scores as data. Due to the structure of our data, we cannot draw a definitive conclusion regarding the direction of causality between mobility and learning. However, we can say with confidence that, at a minimum, there is a significant relationship between disruption to learning and high levels of mobility – a good starting point for United Way as they continue to explore mobility and refine its Promise Neighborhood project.

ALICE (Asset Limited, Income Constrained, Employed) Population in Lane County- A Project with United Way of Lane County

Presenter: Man Nguyen (Economics)

Co-Presenters: Eric Wittkop and Emily White

Mentor: Joe Stone

Oral Presentation

Panel C: “Technology and Government” Coquille/Metolius Rooms

Concurrent Session 1: 9:00-10:15am

Facilitator: Melina Pastos

Many US households earn an income greater than that specified by the Federal Poverty Level (FPL), a measure of poverty that does not vary across the 48 contiguous states, however, many households in the U.S who stand above the FPL still struggle to meet their basic needs and be financially self-sufficient. Although the FPL does not take into account the actual quantity of money required to meet the basic cost of living expenses across the United States, many financial assistance programs are designed solely to assist people below this line, especially federally administered programs. As there exists a percentage of population who stands above the 100% FPL but still not able to be self-sufficient, it is the ALICE (Asset Limited, Income Constrained, Employed) population. We are working on the research with United Way of Lane County who seeks a way to calculate the percentage population of ALICE and its distribution in Lane County. It is important to know the ALICE population as ALICEs has been suffering without sufficient income that will lead to a short and long-term suffering to the whole community. Our methodology is focused on meeting two separate objectives. The first objective is to calculate the number and percentagage of ALICE population in Lane County. The second objective is to create a predictive model that will give United Way a tool to estimate future fluctuations in the size (but not the distribution) of ththe Lane County ALICE population so that they can better direct their programming to serve this group.

United Way of Lane County’s Promise Neighborhoods and the Benefits of Reading Readiness

Presenter: Jacob McGrew (Economics, Music)

Mentor: Joe Stone

Oral Presentation

Panel A: “Enhancing Learning” Maple Room

Concurrent Session 1: 9:00-10:15am

Facilitator: Nedzer Erilus

In this paper, we measure statistical relationships between defining characteristics of incoming kindergartners and their initial literacy scores. Our analysis focuses on four elementary schools in Oregon’s Springfield School District: two Promise Neighborhood schools and two comparable non-Promise Neighborhood schools. Using scores from the literacy benchmark tests each incoming student takes upon entering kindergarten—controlling for variables such as family income, English language learners, gender, special education, and ethnicity—we find the defining characteristics with the most significant relationships that influence literacy scores. In the absence of a fully randomized experimental design, we give policy suggestions to United Way of Lane County to more effectively increase early literacy in the Lane County, as well as offer advice on the kinds of additional information that would permit a more definitive future study of the Promise Neighborhoods.