Abstract
The traditional narrative posits that differences in school quality are an important source of inequality in the stratification system. Improving the schools attended by disadvantaged children, therefore, is key to reducing inequality. But what if this view is wrong? We discuss the results of seasonal comparison studies that analyze how achievement gaps change when school is in versus out. Contrary to most education research, these studies suggest that the traditional narrative may be partly wrong in some cases and entirely misplaced in others. Indeed, when it comes to understanding socioeconomic-based gaps in math and reading skills, the evidence indicates that achievement gaps are mostly formed prior to formal schooling and that schools probably reduce the growth in gaps that we would observe in their absence. If this is correct, then the implications for battling inequality are profound. School reform efforts are likely to have limited influence; the primary source of the problem is the level of inequality in broader society.
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Notes
- 1.
We are not persuaded by this conclusion because in their study children in the “school-only” condition enjoyed many benefits typically not available to children at school, such as free medical, dental, and mental health services.
- 2.
Economist Eric Hanushek (1992, p. 106) explains the focus on schools: “While family inputs to education are indeed extremely important, the differential impacts of schools and teachers receive more attention when viewed from a policy viewpoint. This reflects simply that the characteristics of schools are generally more easily manipulated than what goes on in the family.”
- 3.
Burkam et al. (2004) predicted summer learning (fall first grade score minus spring kindergarten score) with socioeconomic status, race, gender, age, repeat kindergarten status, family structure, home language (English or not), summer trips, summer literacy activities, computer for educational use, and summer school attendance. They explained 0.079%, 0.136%, and 0.131% of the variation in literacy, math, and general knowledge learning respectively. Clearly, the vast majority of why some children learn faster than others during the summer is not captured by the information typically available in large data sets.
- 4.
It is important to recognize that with this kind of study design we do not look to the treatment period alone for our estimate of the treatment effect. We should not make the mistake, therefore, of simply observing the school-year patterns as a way of understanding how schools matter. If we just focus on the school year we would mistakenly conclude that high- and low-SES children learn at roughly the same rate, and so schools play a mostly neutral role. But the proper way to understand how schools matter is to compare the treatment (school year) period to the control period (summer). When we make that proper comparison, we learn that schools are compensatory with respect to SES-based gaps in math and reading because they reduce the magnitude of the gaps we would observe in their absence (Downey et al. 2004; Entwisle and Alexander 1992). See Downey and Condron (2016) for further discussion on this point.
- 5.
We would note, however, that this issue is an awkward explanation for seasonal patterns because it needs to be applied selectively—the problem exists during the school year but not the summers.
- 6.
Studying the kindergarten and elementary school years may offer an additional methodological advantage. Some evidence suggests that children learn more rapidly during these early years, about four times faster than during high school (LoGerfo et al. 2006). It is hard to know if young children actually learn faster or if this pattern is merely an artifact of the tests—early tests focus on more basic skills while later tests focus on the development of subject-specific course knowledge. Regardless of whether these patterns are real or an artifact, they have consequences for our ability to distinguish the learning “signal” from the “noise” produced by test measurement error. This issue becomes especially important when we estimate children’s summer learning rates that rely on test scores only a few months apart. Given the tests that are currently available, high school students only demonstrate modest learning gains, making it difficult to estimate learning accurately during the 9-month school year and even more difficult to confidently estimate summer patterns. In contrast, young children demonstrate much faster learning growth on currently available tests, producing a clearer picture of schools’ role.
- 7.
One caveat to the general SES pattern is that NWEA extracts do not always produce consistent results. In the most extensive analysis of seasonal data sets to date, von Hippel and Hamrock (2016) compared patterns across the BSS, ECLS-K: 1998, and an NWEA extract covering 14 states and concluded that “The preschool years are the period of fastest gap growth; after school starts, it is hard to say unequivocally whether gaps grow faster during school or during summer.” This impressive analysis reinforces previous findings that most of the gap develops during the early childhood years, but raises questions about whether the SES gaps grow faster during the summers or school periods, once schooling begins. In von Hippel and Hamrock’s (2016) study, ECLS-K patterns were consistent with the notion that SES gaps grow fastest when school is out, but the patterns from the NWEA extract were at times contradictory. There are challenges interpreting the NWEA patterns, however. For example, the NWEA lacks an individual-level socioeconomic indicator, and so von Hippel and Hamrock (2016) had to compare school-level gaps across Title 1 and non-Title 1 schools. Another challenge interpreting the NWEA patterns is that various scholars typically analyze unique subsets of the larger Research Growth Database, making replication difficult.
- 8.
For our purposes, it would be better if this study had estimated how the gaps increase from the beginning of kindergarten rather than first grade, but we know from other studies that the Black–White gap increases only slightly during kindergarten, and so these estimates would only increase slightly.
- 9.
Confusingly, utilizing the same ECLS-K: 1998 data set, one study finds that Black students experience summer setbacks in math (Burkam et al. 2004). Nevertheless, Quinn (2015) clarifies that these contradictory findings result from variation in modeling strategy, test metric, and assumptions about measurement error. Burkam et al. (2004) explored conditional growth and found that Black students who had the same spring scores as White students made slower math gains during the summer, but overall, Black and White students learn at similar rates during the summer and there is little evidence to show that the summer period contributes to the growing Black–White gaps (Quinn 2015).
- 10.
It is possible that the patterns we report here, emphasizing kindergarten and the next couple years, are unique and do not apply to later stages of the educational career. Some scholars have questioned whether seasonal patterns persist into high school, for example, where tracking mechanisms may produce greater school-based inequality (Gamoran 2016). It is worth noting, however, that prior to seasonal comparison analysis, most scholars assumed that schools increase achievement gaps, even among young children. Given that seasonal analysis reversed this view, we think it is important to refrain from making a similar mistake before we have seasonal analysis of high schoolers.
- 11.
It is worth noting that when seasonal comparisons are applied to other dependent variables we also tend to come away with more favorable views of schools. For example, children’s body mass index tends to grow about twice as fast during the summer versus school year (von Hippel et al. 2007), and there does not seem to be any consistent pattern to how SES, racial/ethnic, and gender gaps in social-behavioral skills change when school is in versus out (Downey et al. 2016b).
- 12.
The three demographic characteristics studied here (socioeconomic status, race/ethnicity, and gender) all produced different seasonal patterns. It is worth noting that socioeconomic status is an indicator of diverse home and neighborhood resources while gender is a socially constructed status largely uncorrelated with these non-school conditions and race/ethnicity has characteristics of both. This distinction may explain why we see the clearest seasonal patterns for socioeconomic status, the weakest for gender, and patterns somewhat in between for race/ethnicity.
- 13.
We would worry, of course, about whether racial gaps would increase.
- 14.
Growth models constructed with 9-month data remove summer noise and correlate only around 0.50 with traditional growth models using 12-month data, demonstrating that summer noise is a nontrivial problem (Atteberry 2011).
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Downey, D.B., Yoon, A., Martin, E. (2018). Schools and Inequality: Implications from Seasonal Comparison Research. In: Schneider, B. (eds) Handbook of the Sociology of Education in the 21st Century. Handbooks of Sociology and Social Research. Springer, Cham. https://doi.org/10.1007/978-3-319-76694-2_3
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