– When the authors use the new test scores, they find that variance is substantial at the start of kindergarten and does not grow but actually shrinks over the next two to three years. This finding, which was not evident in the original Great Equalizer
study, implicates the years before kindergarten as the primary source of inequality in elementary reading and math.
– Total score variance grows during most summers and shrinks during most school years, suggesting that schools reduce inequality overall.
– Changes in inequality are small after kindergarten and do not replicate consistently across grades, subjects, or cohorts. That said, socioeconomic gaps tend to shrink during the school year and grow during the summer, while the black-white gap tends to follow the opposite pattern.
– Socioeconomic gaps tend to shrink during the school year and grow during the summer, while the black-white gap tends to follow the opposite pattern.
– Inequality in basic reading and math skill originates mainly in early childhood, before kindergarten begins.
2018 - Inequality in Reading and Math Skills Forms Mainly before Kindergarten: A Replication, and Partial Correction, of ‘‘Are Schools the Great Equalizer?’’
– The authors compare two cohorts of US children from the nationally representative Early Childhood Longitudinal Study, Kindergarten class (ECLS-K)—an older cohort that started kindergarten in 1998–1999 and a newer cohort that started kindergarten in 2010–2011.
– 1998-2000 cohort includes 16,897 observations.
– 2010-2013 cohort includes 17,733 observations.
– In both cohorts, children took tests in the fall and spring of kindergarten and the fall and spring of first grade. In the new cohort, children also took tests in the fall and spring of second grade.
– In first and second grade, fall tests were only given in a 30 percent subsample of participating schools. The reduced fall sample size reduces statistical power but does not introduce
bias since the schools were subsampled at random
– The authors used sampling weights in their descriptive statistics to compensate for oversampling and nonresponse, but they did not weight their regressions since the weights were correlated with some regressors.
-Ivs: Race, SES
-Dvs: Reading and math test scores