Beyond Binary Indicators: Measuring Socioeconomic Status and Capturing Socioeconomic Heterogeneity in High-Poverty Contexts

• In Event: Defining and Measuring Advantage and Disadvantage in Education

Abstract

The fourth author elaborates on the limitations and consequences of blunt and binary measures of (dis)advantage (e.g., free or reduced-price lunch). Many states are linking administrative data sets to more accurately identify students as economically disadvantaged, creating a timely opportunity to inform the construction of new measures (e.g., Blagg et al., 2021). Yet, old binary indicators are being replaced with new binary indicators (Harwell, 2019). Thus, while binary measures help distinguish between lower- and higher-income students, schools, or districts, they are problematic for differentiating among low-income students or between high-poverty schools or districts.

The author uses data from a representative survey of students in Detroit, which includes rich measures of SES and is linked to administrative data and questions about student and parent experiences during the COVID-19 pandemic. 90% of Detroit students are “economically disadvantaged” based on the state’s binary measure, and over 80% of students are Black. The author highlights substantial SES heterogeneity among Detroit’s low-income and racially minoritized students, and shows how those SES differences relate to different educational experiences and outcomes. The author concludes with implications for adequate funding, equitable school policies and practices, and the representation of low-income and racially minoritized students in educational politics; and highlights other districts where inadequate measures of SES likely pose similar problems. This research highlights SES differences among an often homogenized population and the importance of attending to those differences in educational policymaking, practice, and research. The author concludes by discussing how the issue observed in Detroit applies throughout the United States, for example by mapping rates of concentrated poverty nationwide, and offering recommendations for constructing better measures of poverty and SES through the use of linked state administrative data sets.

Author

• Jeremy L. Singer, Michigan State University
  Presenting Author