The Effect of Neighborhood Poverty on Math Achievement: Evidence From a Value-Added Design

Abstract

Two trends play an increasing role in shaping the long-term prospects of children across the United States: a growing STEM (science, technology, engineering, and mathematics)-based economy and rising income segregation. Together, they suggest that the future well-being of U.S. children may be based increasingly on the contemporary interplay of mathematical learning and neighborhood environments. Drawing on data from the Panel Study of Income Dynamics and a value-added design, this article provides a rigorous test of the hypothesis that exposure to high-poverty neighborhoods exerts a depressive effect on children’s mathematics achievement growth beyond that associated with individual-, family-, and school-level characteristics. Results indicate that exposure to higher-poverty neighborhoods reduces children’s mathematical growth, a treatment effect roughly equivalent to missing three quarters of a year of schooling over the 5-year treatment period. A formal sensitivity analysis suggests that these effects are remarkably robust to unobserved residual confounding.

Gentrification and Academic Achievement: A Review of Recent Research

“…In the decades that followed, an outburst of scholarship emerged that endeavored to test the hypothesis that living in a high-poverty neighborhood was detrimental to children's academic achievement (Aaronson, 1997;Brooks-Gunn, Duncan, & Aber, 1997;Brooks-Gunn, Duncan, Klebanov, & Sealand, 1993;Jencks & Mayer, 1990;Leventhal & Brooks-Gunn, 2000;Plotnick & Hoffman, 1995;Sampson, Morenoff, & Gannon-Rowley, 2002;Sampson, Sharkey, & Raudenbush, 2008). Indeed, this scholarly fascination with the educational impacts of high poverty neighborhoods and the associated disadvantages contained therein has continued to the present (Crowder & South, 2011;Duncan, Boisjoly, & Harris, 2001;Johnson, 2010Johnson, , 2012Johnson, , 2014Pearman, 2017;Sampson et al, 2008;Sharkey & Elwert, 2011;Wodtke, Elwert, & Harding, 2016;Wodtke, Harding, & Elwert, 2011). Considered in its entirety, this body of scholarship, which became part of the so-called neighborhood effects tradition, has revealed that growing up in a high-poverty neighborhood has an adverse effect on children's educational outcomes beyond that of growing up in an economically disadvantaged family (see Durlauf, 2004;Ellen & Turner, 2003;Sastry, 2012;Sharkey & Faber, 2014, for comprehensive reviews of this literature).…”

Educación y apoyo familiar, y logro en matemáticas en dos contextos sociodemográficos diferentes

“…Este es el caso de las personas que viven en zonas rurales y pertenecen a grupos étnicos indígenas, cuyos hijos asisten a escuelas del sistema educativo indígena, y no a una escuela privada o a una escuela urbana regular; estas personas se encuentran incluso en una situación constante de exposición a segregaciones económicas y demográficas que perpetuarán las brechas educativas atribuibles a variaciones del entorno en el que residen (Reardon et al, 2017), por encima de las características individuales, familiares y escolares (Pearman, 2017).…”

“…De esta manera, la baja condición socioeconómica afecta las oportunidades de aprendizaje al reducir drásticamente las posibilidades de elección de la escuela por parte de los padres, y de contar con apoyo académico suplementario y materiales educativos adicionales, profundizando la inequidad educacional (Caro y Lenkeit, 2012;Olszewski-Kubilius y Corwith, 2018). Según Pearman (2017), la exposición constante a asentamientos humanos de alta pobreza tiene un efecto negativo en el rendimiento en matemáticas más allá del asociado a las características individuales, familiares y escolares. Por el contrario, los alumnos de condiciones socioeconómicos y culturales más favorecidos "poseen desde el inicio puntajes más altos que los demás y presentan pendientes de crecimiento más empinadas" (Muelle, 2020, p.136).…”

The Geography of Mathematical (Dis)Advantage: An Application of Multilevel Simultaneous Autoregressive (MSAR) Models to Public Data in Education Research

“…Then they estimated school and neighborhood contributions to 1-year gains in student test scores for elementary and middle school without considering performances within students’ grades, as described above. More recently, Pearman (2019) estimated the effects of neighborhood poverty across children up to age 12. Specifically, Pearman tested the hypothesis that exposure to high-poverty neighborhoods influenced students’ mathematical proficiency over and above their individual and school characteristics.…”