This analysis compares National Free and Reduce-Price School Lunch Program Eligibility data with NAEP Reading and Math score data to examine correlations between poverty and student performance.
Gibson, S. (2009). Are Student Achievement and Poverty Related? Retrieved from are-student-achievement-and.
As rich and poor families have increasingly moved into separate communities, the character of neighborhood life in the United States has changed. This paper attempt to outlined several concrete steps to reduce economic segregation, rebuild communities, and narrow the opportunity gap.
Closing the Opportunity Gap. (2016). A Project of the Saguaro Seminar. Retrieved from https://theopportunitygap.com/wp-content/uploads/2016/04/april25.pdf
This report reviews the research and strategies for achieving high levels of student performance in high poverty schools.
Center for Public Education. (2005, August 22). High-performing, high-poverty schools: Research review. Retrieved December 8, 2016, from Center for Public Education, http://www.centerforpubliceducation.org/Main-Menu/Organizing-a-school/High-performing-high-poverty-schools-At-a-glance-/High-performing-high-poverty-schools-Research-review.html
This study examined how uncertainty, both about students and the context in which they are taught, remains a persistent condition of teachers’ work in high-poverty, urban schools. Their conclusion: Traditional public schools are open systems and require systematic organizational responses to address the uncertainty introduced by their environments. Uncoordinated individual efforts alone are not sufficient to meet the needs of students in high-poverty urban communities.
Kraft, M. A., Papay, J. P., Johnson, S. M., Charner-Laird, M., Ng, M., & Reinhorn, S. (2015). Educating amid uncertainty. Educational Administration Quarterly, 51(5), 753–790. doi:10.1177/0013161X15607617
This study examined the complex linkages between teacher quality and socio-economic-based disparities in student achievement. The gap in teacher quality appears to arise from the lower payoff to teacher qualifications in high-poverty schools. In particular, the experience-productivity relationship is weaker in high-poverty schools and is not related to teacher mobility patterns. Recruiting teachers with good credentials into high-poverty schools may be insufficient to narrow the teacher quality gap. Policies that promote the long-term productivity of teachers in challenging high-poverty schools appear key.
Sass, T., Hannaway, J., Xu, Z., Figlio, D., & Feng, L. (2016, June). Value added of teachers in high-poverty schools and lower-poverty schools. Retrieved from http://www.urban.org/research/publication/value-added-teachers-high-poverty-schools-and-lower-poverty-schools
This overview examines the best available evidence from a wide range of descriptive and correlational analyses executed by various state and city education departments, research groups, and academic researchers. Fortunately, the data paint an unequivocal picture. The results are overwhelmingly consistent across levels of analysis (school, students), units of measurement (achievement tests, graduation rates, dropout rates), areas of focus (reading, math, social indicators), units of education (grades, schools), and students of all demographics. Additionally, each analysis shows a linear relationship between absences and performance; the greater the number of absences, the worse the performance.
Alexander, K. L., Entwisle, D. R., & Olson, L. S. (2001). Schools, achievement, and inequality: A seasonal perspective. Educational Evaluation and Policy Analysis, 23(2), 171–191.
Allison, M. A., Attisha, E., & AAP Council on School Health. (2019). The link between school attendance and good health. Pediatrics, 143(2). e20183648
Anguiano, M., Eastin D., Fine, M, Lockyer, B., Robles, D., Santana, M.,…Wong, K. (2015). Los Angeles Unified School District Report of the Independent Financial Review Panel. Los Angeles, CA: Los Angeles Unified School District.
Applied Survey Research. (2011). Attendance in early elementary grades: Associations with student characteristics, school readiness, and third grade outcomes. San Francisco, CA: Attendance Works.
Ashby, C. M. (2010). K–12 education: Many challenges arise in educating students who change schools frequently.Report to Congressional Requesters (GAO-11-40). Washington, DC: Government Accountability Office.
Attendance Works. (2018).3 tiers of intervention.Retrieved from https://www.attendanceworks.org/chronic-absence/addressing-chronic-absence/3-tiers-of-intervention/
Attendance Works and Everyone Graduates Center. (2017). Portraits of change: Aligning school and community resources to reduce chronic absence.Retrieved from https://www.attendanceworks.org/portraits-of-change/
Balfanz, R., & Byrnes, V. (2012). The importance of being in school: A report on absenteeism in the nation’s public schools. Baltimore, MD: Johns Hopkins University Center for Social Organization of Schools.
Balfanz, R., & Byrnes, V. (2013). Meeting the challenge of combating chronic absenteeism: Impact of the NYC mayor’s interagency task force on chronic absenteeism and school attendance and its implications for other cities. Baltimore, MD: Johns Hopkins School of Education.
Balfanz, R., Durham, R., & Plank, S. (2008). Lost days: Patterns and levels of chronic absenteeism among Baltimore City public school students 1999-00 to 2005-06. Baltimore, MD: Baltimore Education Research Consortium.
Balfanz, R., Herzog, L., & Mac Iver, D. J. (2007). Preventing student disengagement and keeping students on the graduation path in urban middle-grades schools: Early identification and effective interventions. Educational Psychologist, 42(4), 223–235.
Baltimore Education Research Consortium. (2011). Destination graduation: Sixth grade early warning indicators for Baltimore city schools. Their prevalence and impact. Baltimore, MD: Author.
Bauer, L., Liu, P., Whitmore Schanzenbach, D., & Shambaugh, J. (2018). Reducing chronic absenteeism under the every student succeeds act. The Hamilton Project. Washington, DC: Brookings Institute. Retrieved from http://www.hamiltonproject.org/assets/files/reducing_chronic_absenteeism_under_the_every_student_succeeds_act.pdf
Buehler, M. H., Tapogna, J., Chang, H. N., & ECO Northwest, Ltd. (2012). Why being in school matters: Chronic absenteeism in Oregon Public Schools. Attendance Works.
Burkam, D. T., Ready, D. D., Lee, V. E., & LoGerfo, L. (2004). Social-class differences in summer learning between kindergarten and first grade: Model specification and estimation. Sociology of Education, 77(1), 1–31.
Chang, H. N., Bauer, L., & Byrnes, V. (2018). Data matters: Using chronic absence to accelerate action for student success.Attendance Worksand Everyone Graduates Center.
Chang, H. N., & Romero, M. (2008). Present, engaged, and accounted for: The critical importance of addressing chronic absence in the early grades.New York, NY: National Center for Children in Poverty.
Chen, C., & Stevenson, H. W. (1995). Motivation and mathematics achievement: A comparative study of Asian‐American, Caucasian‐American, and East Asian high school students. Child Development, 66(4), 1215–1234.
Chingos, M., & Blagg, K. (2017). Making sense of state school funding policy.Washington, DC: Urban Institute.
Coelho, R., Fischer, S., McKnight, F., Matteson, S., & Schwartz, T. (2015). The effects of early chronic absenteeism on third-grade academic achievement measures.Madison, WI: Robert M. La Follette School of Public Affairs, University of Wisconsin.
Connell, J. P., Spencer, M. B., & Aber, J. L. (1994). Educational risk and resilience in African‐American youth: Context, self, action, and outcomes in school. Child Development, 65(2), 493–506.
da Costa Nunez, R., Erb-Downward, J., & Shaw-Amoah, A. (2015). Empty seats: The epidemic of absenteeism among homeless elementary students. New York, NY: Institute for Children, Poverty & Homelessness. Retrieved from https://www.attendanceworks.org/wp-content/uploads/2017/12/ICPH-Policy-Report_Empty-Seats_Chronic-Absenteeism.pdf
Digest of Education Statistics. (2017). Homeless students enrolled in public elementary and secondary schools, by grade, primary nighttime residence, and selected student characteristics: 2009-10 through 2015–16.Table 204.75a.Washington, DC: U.S. Department of Education, National Center for Education Statistics.
Downey, D. B., von Hippel, P. T., & Broh, B. A. (2004). Are schools the great equalizer? Cognitive inequality during the summer months and the school year. American Sociological Review, 69(5), 613–635.
Durán-Narucki, V. (2008). School building condition, school attendance, and academic achievement in New York City public schools: A mediation model. Journal of environmental psychology, 28(3), 278–286.
Epstein, J. L. & Sheldon, S. B. 2002. Present and accounted for: Improving student attendance through family and community involvement. Journal of Educational Research 95(5): 308–318.
Fantuzzo, J. W., LeBoeuf, W. A., Chen, C. C., Rouse, H. L., & Culhane, D. P. (2012). The unique and combined effects of homelessness and school mobility on the educational outcomes of young children. Educational Researcher, 41(9), 393–402.
Fiester, L. (2010). Early warning! Why reading by the end of third grade matters.Kids Count special report. Baltimore, MD: Annie E. Casey Foundation.
GAO, (1994) Elementary school children: Many change schools frequently, harming their education,GAO/HEHS-94-45 (Washington, D.C.: Feb. 4, 1994).
Ginsburg, A., Jordan, P., & Chang, H. (2014). Absences Add Up: How School Attendance Influences Student Success. Attendance Works.
Gottfried, M. A. (2010). Evaluating the relationship between student attendance and achievement in urban elementary and middle schools: An instrumental variables approach. American Educational Research Journal, 47(2), 434–465.
Gottfried, M. A. (2014). Chronic absenteeism and its effects on students’ academic and socio-emotional outcomes. Journal of Education for Students Placed at Risk (JESPAR), 19(2), 53–75.
Gottfried, M. A. (2015). Chronic absenteeism in the classroom context: Effects on achievement. Urban Education,54(1), 3–34.
Gottfried, M. A. & Hutt, E.L. (Eds.). (2019). Absent from school: Understanding and addressing student absenteeism.Cambridge, MA: Harvard Education Press.
Harris, Kamala. (2016). In School + On Track 2016.Sacramento, CA: Office of the Attorney General, State of California Department of Justice.
Henderson, T., Hill, C., & Norton, K. (2014). The connection between missing school and health: A review of chronic absenteeism and student health in Oregon.Portland, OR: Upstream Public Health.
Hernandez, D. (2011). Double jeopardy: How third-grade reading skills and poverty influence high school graduation.Baltimore, MD: Annie E. Casey Foundation.
Hess, F. M., & McShane, M. Q. (2018), Bush-Obama school reform: Lessons learned.Cambridge, MA: Harvard Education Press.
Johnson, G. M. (2005). Student alienation, academic achievement, and WebCT use. Journal of Educational Technology & Society, 8(2), 179–189.
Kearney, C. A. (2016). Managing school absenteeism at multiple tiers: An evidence-based and practical guide for professionals. New York City, NY: Oxford University Press.
Kearney, C. A., & Graczyk, P. (2014). A response to intervention model to promote school attendance and decrease school absenteeism. Child & Youth Care Forum,43(1), 1–25.
Lawrence, E. M., Rogers, R. G., & Zajacova, A. (2016). Educational attainment and mortality in the United States: Effects of degrees, years of schooling, and certification. Population Research and Policy Review, 35(4), 501–525.
London, R, A., Sanchez, M., & Castrechini, S. (2016). The dynamics of chronic absence and student achievement. Education Policy Analysis Archives, 24(112), 1–27.
Losen, D. J., & Whitaker, A. (2018). 11 million days lost: Race, discipline, and safety at U.S. public schools. A joint report by the Center for Civil Rights Remedies of UCLA’s Civil Rights Project and the American Civil Liberties Union of Southern California.
Mac Iver, M. A., & Messel, M. (2012). Predicting high school outcomes in the Baltimore city public schools.The Senior Urban Education Research Fellowship Series. Volume VII. Washington, DC: Council of the Great City Schools.
NAEP Data Explorer, 2015 and 2017 mathematics and reading assessments. Washington, DC: U.S. Department of Education, National Center for Education Statistics, National Assessment of Educational Progress (NAEP). Retrieved from https://nces.ed.gov/nationsreportcard/naepdata/
NAEP Data Explorer, 2015 and 2017 reading and mathematics scale scores of 4th, 8th, and 12th graders and percentage absent from school, by selected characteristics and number of days absent in the last month. Table 227.50. Washington, DC: U.S. Department of Education, National Center for Education Statistics, National Assessment of Educational Progress (NAEP). Retrieved from https://www2.ed.gov/datastory/chronicabsenteeism.html#one
National Forum on Education Statistics. (2009). Every school day counts: The forum guide to collecting and using attendance data (NFES 2009–804). Washington, DC: U.S. Department of Education, National Center for Education Statistics.
Nowicki,J. M. (2018). K–12 Education: Discipline disparities for Black students,boys, and students with disabilities. Report to Congressional Requesters. GAO-18-258. Washington, DC: Government Accountability Office.
Olsen, L. S. (2014). Why September matters: Improving student attendance.Policy brief. Baltimore, MD: Baltimore Education Research Consortium.
Office for Civil Rights. (2016). 2013–2014 civil rights data collection: A first look.
Railsback J. (2004). Increasing student attendance: Strategies from research and practice.Northwest Regional Educational Laboratory. Portland, OR.
Ready, D. D. (2010). Socioeconomic disadvantage, school attendance, and early cognitive development: The differential effects of school exposure. Sociology of Education 83(4): 271–286.
Robertson, A. A., & Walker, C. S. (2018). Predictors of justice system involvement: Maltreatment and education. Child Abuse & Neglect, 76, 408–415.
Robinson, C. D., Lee, M. G., Dearing, E., & Rogers, T. (2018). Reducing student absenteeism in the early grades by targeting parental beliefs. American Educational Research Journal, 55(6), 1163–1192.
Rogers, T., & Feller, A. (2018). Reducing student absences at scale by targeting parents’ misbeliefs. Nature Human Behaviour, 2(5), 335.
Romero, M., & Lee, Y. (2007). A national portrait of chronic absenteeism in the early grades.New York, NY: National Center for Children in Poverty, the Mailman School of Public Health at Columbia University.
Rumberger, R. W. (2015). Student mobility: Causes, consequences, and solutions. Boulder, CO: National Education Policy Center, University of Colorado.
Snell L., Smith, G. A., Koteskey, T., Joffe, M., & Bui, T. (2018). A 2018 evaluation of LAUSD’s fiscal outlook: Revisiting the findings of the 2015 Independent Financial Review Panel.Los Angeles, CA: Los Angeles Unified School District.
Telfair, J., & Shelton, T. L. (2012). Educational attainment as a social determinant of health. North Carolina Medical Journal, 73(5), 358–365.
Tourangeau, K., Nord, C., Lê, T., Sorongon, A. G., & Najarian, M. (2009). Early Childhood Longitudinal Study, Kindergarten Class of 1998-99 (ECLS-K): Combined User's Manual for the ECLS-K Eighth-Grade and K-8 Full Sample Data Files and Electronic Codebooks. NCES 2009-004. National Center for Education Statistics.
U.S. Department of Education. (2008). A uniform, comparable graduation rate: How the final regulations for Title I hold schools, districts, and states accountable for improving graduation rates. Retrieved from https://www2.ed.gov/policy/elsec/reg/proposal/uniform-grad-rate.html
U.S. Department of Education, Office for Civil Rights, 2013–2014 Civil Rights Data Collection. A First Look; Key Data Highlights on Equity and Opportunity Gaps in Our Nation’s Public Schools.Retrieved from https://www2.ed.gov/about/offices/list/ocr/docs/2013-14-first-look.pdf
U.S. Department of Education, Office for Civil Rights, 2015–2016 Civil Rights Data Collection. Chronic absenteeism in the nation’s schools: A hidden educational crisis. Retrieved from https://www2.ed.gov/datastory/chronicabsenteeism.html#one
Utah Education Policy Center [UEPC]. (2012). Research brief: Chronic absenteeism.Retrieved from https://www.schools.utah.gov/file/31291767-087c-4edb-8042-87f272507c1d
One of the most critical issues facing K-12 education is the impact that poverty has on school performance. This study first examines school performance using traditional metrics for school poverty levels (percent of student body that qualify for free and reduced lunch: FRL) and school performance (school achievement based on the aggregate test scores of its student body). The results support prior research documenting the negative relationship between the level of poverty in a school and student achievement (the higher the poverty the lower the achievement). However, when replacing the student achievement metric with a student growth metric, the relationship is significantly different.
Alexander, K. L., Entwisle, D. R., & Olson, L. S. (2001). Schools, achievement, and inequality: A seasonal perspective. Educational Evaluation and Policy Analysis, 23, 171–191.
The purpose of this study was to evaluate the efficacy of a classroom-teacher-delivered reading intervention for struggling readers called the Targeted Reading Intervention (TRI), designed particularly for kindergarten and first-grade teachers and their struggling students in rural, low-wealth communities.
Amendum, S. J., Vernon-Feagans, L., & Ginsberg, M. C. (2011). The effectiveness of a technologically facilitated classroom-based early reading intervention: The targeted reading intervention. The Elementary School Journal, 112(1), 107-131.
This report provides extensive data on the high-poverty schools and the students who attend them. It also provides information on principals, teachers, and staff who work in them.
Aud, S., Hussar, W., Planty, M., Snyder, T., Bianco, K., Fox, M., Frohlich, L., Kemp, J., Drake, L. (2010). The Condition of Education 2010 (NCES 2010-028). National Center for Education Statistics, Institute of Education Sciences, U.S. Department of Education. Washington, DC.
Rich opportunities for learning are important for all teachers. Whatever expertise they
acquire in their pre-service program, teachers continue to need ongoing professional learning in order to meet additional responsibilities and the evolving needs of their students and schools. Continuous learning is especially vital for teachers who work in the dynamic and demanding environments of high-poverty, urban schools.
Charner-Laird, M., Ng, M., Johnson, S. M., Kraft, M. A., Papay, J. P., & Reinhorn, S. K. (2016). Gauging Goodness of Fit: Teachers’ Assessments of their Instructional Teams in High-Poverty Schools. Retrieved from http://projectngt.gse.harvard.edu/files/gse-projectngt/files/gauging_goodness_of_fit_0622916.pdf
Using longitudinal data on teachers, we estimate hazard models that identify the impact of this differential pay by comparing turnover patterns before and after the program’s implementation, across eligible and ineligible categories of teachers, and across eligible and barely-ineligible schools.
Clotfelter, C. T., Glennie, E., Ladd, H. F., & Vigdor. J. L. (2008). Would higher salaries keep teachers in high-poverty schools? Evidence from a policy intervention in North Carolina. Journal of Public Economics, 92(5), 1352–1370.
This study examined a teacher incentive policy in Washington State that awards a financial bonus to National Board Certified Teachers who teach in high-poverty schools. It found that the bonus policy increased the proportion of National Board Certified Teachers in bonus-eligible schools, through increases in both the number of existing NBCTs hired and the probability that teachers at these schools apply for certification. However, it do not find evidence that the bonus resulted in detectible effects on student test achievement.
Cowan, J., & Goldhaber, D. (2015). Do bonuses affect teacher staffing and student achievement in high-poverty schools? Evidence from an Incentive for National Board Certified Teachers in Washington State. Center for Education Data & Research.
In this study, researchers studied the ways in which daily exchanges between a parent and child shape language and vocabulary development. After four years these differences in parent-child interactions produced significant discrepancies in not only children’s knowledge, but also their skills and experiences with children from high-income families being exposed to 30 million more words than children from families on welfare.
Hart, B., & Risley, T. (2003). The early catastrophe. American Educator, 27(4), 6-9.
This study examined the relationships between poverty and a school's academic performance (both student achievement and growth).
Hegedus, A. (2018). Evaluating the Relationships between Poverty and School Performance. NWEA Research. NWEA.
This report provides descriptive information on traditional public, charter, and private school principals over the period of 1987-88 through 2011-12. It includes comparative data on number of principals, gender, race/ethnicity, age, advance degrees, principal experience, teaching experience, salaries, hours worked, focus of work, experience and tenure at current schools, etc.
Hill, J., Ottem, R., & DeRoche, J. (2016). Trends in Public and Private School Principal Demographics and Qualifications: 1987-88 to 2011-12. Stats in Brief. NCES 2016-189. National Center for Education Statistics.
This report investigates the possibility that the characteristics and conditions of schools are behind the teacher shortage crisis. The data indicate that school staffing problems are not primarily due to teacher shortages, in the sense of an insufficient supply of qualified teachers. Rather, the data indicate that school staffing problems are primarily due to a “revolving door” – where large numbers of qualified teachers depart from their jobs long before retirement. The data show that much of the turnover is accounted for by teacher job dissatisfaction and teachers pursuing other jobs. Significant numbers of those who depart from their jobs in these schools report that they are hampered by inadequate support from the school administration, too many intrusions on classroom teaching time, student discipline problems and limited faculty input into school decision-making.
Ingersoll, R. M. (2004). Why do high-poverty schools have difficulty staffing their classrooms with qualified teachers? (Report prepared for Renewing Our Schools, Securing Our Future—A National Task Force on Public Education). Washington, DC: The Center for American Progress and the Institute for America’s Future. Retrieved from https://scholar.gse.upenn.edu/rmi/files/ingersoll-final.pdf.
This analysis examines the influence of poverty on student reading performance across grade levels.
Keyworth, R. (2015). How does reading proficiency correlate with a student's socio-economic status? Oakland, CA: The Wing Institute. Retrieved from https://www.winginstitute.org/how-does-reading-proficiency
This study examined teachers need for organizational responses that addressed the environmental uncertainty of working with students from disadvantaged neighborhoods. It described four types of organizational responses — coordinated instructional supports, systems to promote order and discipline, socio-emotional supports for students, and efforts to engage parents — and illustrate how these responses affected teachers’ ability to manage the uncertainty introduced by their environment.
Kraft, M. A., Papay, J. P., Johnson, S. M., Charner-Laird, M., Ng, M., & Reinhorn, S. (2015). Educating Amid Uncertainty The Organizational Supports Teachers Need to Serve Students in High-Poverty, Urban Schools. Educational Administration Quarterly, 51(5), 753-790.
This report provides a detailed analysis of long-term dropout and completion trends and student characteristics of high school dropouts and completers. The first measure examined was the “event dropout rate” which is the percent of students who drop out in grades 10-12 without a high school diploma or alternative credential. The event dropout rate for SY 2015-16 was 4.8%, which translated into 532,000 students.
McFarland, J., Cui, J., Rathbun, A., and Holmes, J. (2018). Trends in High School Dropout and Completion Rates in the United States: 2018 (NCES 2019-117). U.S. Department of Education. Washington, DC: National Center for Education Statistics. Retrieved December 14, 2018 from http://nces.ed.gov/pubsearch.
This study found that one aspect of segregation in particular—the disparity in average school poverty rates between white and black students’ schools—is consistently the single most powerful correlate of achievement gaps. This implies that high-poverty schools are, on average, much less effective than lower-poverty schools, and suggests that strategies that reduce the differential exposure of black, Hispanic, and white students to poor classmates may lead to meaningful reductions in academic achievement gaps.
Reardon, S.F. (2015). School Segregation and Racial Academic Achievement Gaps (CEPA Working Paper No.15-12). Retrieved from Stanford Center for Education Policy Analysis: http://cepa.stanford.edu/wp15-12
This meta-analysis looked at socioeconomic status and race statistics to determine whether there were relationships among socioeconomic status, race, and fathers absence from the home. The results of the meta-analysis appear to indicate that father-absence effects are independent of socioeconomic status or race.
Salzman, S. A. (1988). Father Absence, Socioeconomic Status, and Race: Relations to Children's Cognitive Performance.
This paper reviews evidence from six recent studies, which collectively suggest that teachers who leave high-poverty schools are not fleeing their students, but rather the poor working conditions that make it difficult for them to teach and their students to learn. They include school leadership, collegial relationships, and elements of school culture.
Simon, N. S., & Johnson, S. M. (2013). Teacher turnover in high-poverty schools: What we know and can do. Teachers College Record, 117, 1-36
This meta-analysis reviewed the literature on socioeconomic status (SES) and academic achievement in journal articles published between 1990 and 2000. The results showed a medium to strong SES–achievement relation.
Sirin, S. R. (2005). Socioeconomic status and academic achievement: A meta-analytic review of research. Review of educational research, 75(3), 417-453.
For the first time in recent history, a majority of the schoolchildren attending the nation’s public schools come from low income families. The latest data collected from the states by the National Center for Education Statistics (NCES), evidence that 51 percent of the students across the nation’s public schools were low income in 2013.
Suitts, Steve. A New Majority Research Bulletin: Low Income Students Now a Majority in the Nation's Public Schools. Southern Education Foundation. (2015).
This research looked at test score gaps for a range of populations: between boys and girls; between black, white, and Hispanic children; between the children and the mother’s education; between children in poor and nonpoor families; and the gaps between high-poverty and low-poverty schools. They wanted to know whether gaps grow faster during summer or the school year. They were unable to answer this question as the results were inconclusive. Although, von Hippel and Hamrock did find the total gap in performance from kindergarten to eighth grade, is substantially smaller than the gap that exists at the time children enter school. The conclusion is that gaps happen mostly in the first five years of life. study suggests students who are behind peers at the time they enter kindergarten should receive early remedial instruction as the most efficacious way to improve overall performance.
von Hippel, P. T., & Hamrock, C. (2019). Do test score gaps grow before, during, or between the school years? Measurement artifacts and what we can know in spite of them. Sociological Science, 6, 43-80.
This meta-analysis of almost 200 studies that considered the relation between SES and academic achievement were examined. Results indicated that as SES is typically defined (income, education, and/or occupation of household heads) and typically used (individuals as the unit of analysis), SES is only weakly correlated (r = .22) with academic achievement.
White, K. R. (1982). The relation between socioeconomic status and academic achievement. Psychological bulletin, 91(3), 461.