Last Updated on December 2, 2021
In a previous post, I argued that the black-white cognitive ability gap is responsible for many of the important social disparities we find between blacks and whites, e.g. disparities regarding income, crime, education, occupational prestige, etc. Therefore, it is important that we determine how to resolve this gap (if possible). In order to resolve this gap, we need to investigate the cause of the gap. I started this investigation in another post where I considered and rejected the idea that differences in school quality or parental income and education can adequately account for the gap. I also considered some indirect arguments for a genetic explanation of the gap. I later considered direct evidence regarding the effect of genetics on the gap and concluded that genetic differences likely do not account for a significant portion of the gap.
In this post, I will review studies that have attempted to account for the cognitive ability gap among children by controlling for a variety of factors, including parental socioeconomic status (SES), birth weight, home environment, parenting practices, etc. I will use these studies to determine what seem to be the most plausible candidate explanations of the gap. I end by reviewing some important issues for future investigation.
Studies attempting to explain the gap
In a previous post, I only considered parental income, parental education, parental marital status, and school quality as possible explanations of the gap. I concluded that these factors do not adequately explain the gap. While these factors are often considered important components of environment (and they should be good proxies for any other important environmental factors), there may be other important environmental factors not captured by these specific factors. Several studies have employed broader measures of environmental factors. I will consider these studies below.
Explanations of the gap in elementary school
Fryer and Levitt (2005) [archived] analyzed data from the Early Childhood Longitudinal Study Kindergarten Cohort (ECLS–K), which is “a nationally representative sample of over 20,000 children entering kindergarten in 1998” (page 5). Children were administered standardized tests in the fall and spring of kindergarten, spring of first grade, and spring of third grade.
- Measures: mathematics and reading scores as measured by the ECLS–K (page 6).
- Results: About 60% of the gap was explained in grade 3. Fryer and Levitt find that, contrary to many prior studies, a variety of controls can account for the entirety of the black-white gap in kindergarten. Yeung and Pfeiffer (2009) state that one reason for this unique finding may be that the “ECLS-K uses different test instruments that produce smaller raw gaps to start with than those used in other national studies” (page 413). Regardless, the controls by Fryer and Levitt cannot account for the entirety of the gap in third grade. The math gap in third grade decreases from 0.882 (page 7) to 0.382 (page 10) standard deviations after including the controls, a 57% reduction.
- Controls: The controls include gender, age, birth weight, a composite indicator of SES constructed by the ECLS, books in the home, a proxy for maternal age at birth, and WIC participation (see page 9).
Also see Rothstein and Wozny (2013) [archived] which challenged the findings of the Fryer and Levitt study. They present data showing that a greater portion of the racial test score gap can be explained by using permanent income as opposed to current income used in Fryer and other studies.
Murnane, Willett, Bub, and McCartney (2006) [archived] examined the black-white test score gap using two longitudinal datasets – the kindergarten cohort of the Early Childhood Longitudinal Study (ECLS-K) and the National Institute of Child Heath and Human Development Study of Early Child Care and Youth Development (NICHD SECCYD).
- Measures: mathematics and English Language Arts (ELA) skills as measured by the ECLS–K and NICHD.
- Results: 45-50% of the gap was explained at grade 3. Controlling for family background accounted for about 30-35% of the mathematics gap and about 15-25% of the ELA gap in third grade (table 5). Including maternal sensitivity in the controls accounts for about one quarter of the remaining gap (page 122). In total, about 45-50% the gaps are accounted for after controlling for maternal sensitivity and family background (table 5).
- Controls: The family background controls were family SES, birth weight, a composite indicator SES status constructed by the ECLS, books in the home, whether the mother was a teen at first birth, whether the mother was aged 30+ at first birth, and whether the mother received assistance (WIC or AFDC) (table 3). Family SES includes parental income, occupation, and education (page 111).
Todd and Wolpin (2007) examined the relative contribution of home inputs, school inputs, and mother’s abilities in accounting for racial/ethnic test score gaps for children drawn from the National Longitudinal Surveys of Labor Market Experience–Children Sample (NLSY79-CS). The NLSY79-CS is a sample of all children ever born to the women respondents of the NLSY79, which is itself a nationally representative sample of individuals who were aged 14–21 as of January 1, 1979. Researchers analyzed the performance of children aged 6-13 on the PIAT mathematics and reading tests.
- Measures: Scores on the Peabody Individual Achievement Test in mathematics (PIAT-M) and the Peabody Individual Achievement Test in reading recognition (PIAT-R). Both raw test scores and age-normed percentile scores were measured (page 101).
- Results: Differences in mother’s AFQT test scores account for roughly half of the black-white test score gap, and differences in home inputs account for another 10–20 percent. Differences in school inputs and in mother’s schooling account for only very small portions of the gap (page 127).
- Controls: Home inputs are based on a battery of questions about the home environment of the child, some based on self-report from the parent and some based on interview observation. The questions concern the number of books that the child has, whether the family takes the child to museums, etc. School inputs include pupil-teacher ratios and teacher salaries.
Burchinal et al. (2011) used longitudinal data from the NICHD Study of Early Child Care and Youth Development (SECCYD) to study academic achievement gaps of 314 low income black and white children from 4.5 years of age to grade 5. The researchers examined the degree to which family, neighborhood, and school characteristics can explain test score gaps among these low income youths.
- Measures: Academic achievement was measured using four subtests of the Woodcock–Johnson Psycho–Educational Battery–Revised. Reading achievement was assessed with the Letter–Word Identiﬁcation (54 month and grade 1) and Broad Reading (grades 3 and 5) subtests. Mathematics achievement was assessed with the Applied Problems (54 month and grade 1) and Broad Math (grades 3 and 5) subtests (page 1407).
- Results: 43-64% of the gap was explained at grade 5. The raw black-white math and reading gaps in grade 5 were 0.56 and 0.67 SDs, respectively (table 4). After controlling for family, neighborhood, and school characteristics, the respective gaps at 5th grade reduced to 0.32 and 0.24 SDs in grade 5 (table 4). Thus, inclusions of the controls accounted for about 43% and 64% of the math and reading gaps, respectively, at grade 5.
- Controls: Family characteristics were gender, whether the child was firstborn, maternal education, maternal childrearing attitudes, whether the mother was a single parent, family income-to-needs ratio, and parenting quality. Neighborhood characteristics are based on Census block indices of household income, employment status, and marital status. School characteristics were school demographic risk and classroom quality. See pages 1408-1409 for more info on these controls.
In summary, the above studies seem to be able to account for about half of the black-white cognitive ability gap by controlling for a variety of factors, including parental SES, parenting practices, home environment, etc. However, these studies should be interpreted with caution because the children tested were quite young. Both the math and reading gaps in the Fryer study (page 10) and the ELA gaps in the Murnane study (table 5) widened over time even after including the controls. Additionally, Yeung and Pfeiffer (2009) find that accounting for various covariates “can explain the gaps in very early years but not gaps as children move to higher grades” (page 424). Therefore, it is possible that the gaps found in the studies mentioned in this section will widen as children age. Why do gaps after controlling for these variables widen over time? We do not know for sure at the moment. But Fryer and Levitt (2013) note that there are “at least three possible explanations for the emergence of racial differences with age” (page 982):
- The first possible explanation is that the tests given to young children may not test the same abilities as the tests given to older children. The researchers note the possibility that “the skills tested in one-year-olds are not the same as those required of older children, and there are innate racial differences only in the skills measured in the older subjects.”
- The second possible explanation offerred is that there are racial differences in rates of cognitive development. They note that “If black infants mature earlier than whites, then black performance on early tests may be artificially inflated relative to their long-term levels.” Another possible reason for racial differences in rates of development is given byFryer (2010): “if blacks are less likely to be cognitively stimulated at home or more likely to be reared in environments that Shonkoff (2006) would label as characterized by “toxic stress,” disruptions in brain development may occur which may significantly retard cognitive growth” (page 9).
- The last possible explanation is that “the relative importance of genes and environmental factors in determining test outcomes varies over time.” This may occur because the heritability of intelligence increases as children age (Neisser et al. 1996, page 85; Bouchard 2013; Plomin et al. 2016). For example, Plomin and Deary 2015 [archived] report that “for intelligence, heritability increases linearly, from (approximately) 20% in infancy to 40% in adolescence, and to 60% in adulthood. Therefore, if the test score gaps between black and white children are partially due to genetic differences, then one would expect widening test score gaps over time.
As far as I know, we do not have enough data to determine which of these explanations is correct or most plausible. Regardless, we should now consider studies that study test-score gaps of older children to see which variables can account for the gap for older children. For the sake of completion, if one is interested in studies that investigate the cause of cognitive ability gaps before children reach elementary school, see Phillips et al. (1998), Lee and Burkam (2002), Brooks–Gunn et al. (2003), Fryer and Levitt (2013), and Gibbs and Downey (2020).
Limited explanations of the gap beyond elementary school
Now, consider studies that examine children at later ages (during middle school and high school). Similar to the recently cited studies, the following studies control for a more encompassing set of environmental variables. Unfortunately, these controls can typically only explain a limited portion (25% to 45%) of the gap:
Clotfelter, Ladd, and Vigdor (2006) [archived] examined five consecutive cohorts of North Carolina public school students as they progressed from 3rd to 8th grade. They authors find “test-score gaps between black and white students are sizable, even after controlling for several important student covariates” (page 2).
- Measures: Scores from standardized math and reading tests required for all students. Data was maintained by the North Carolina’s Department of Public Instruction (page 8).
- Results: 40-44% of the gap was explained at grade 8. The raw math and reading gaps were 0.814 standard deviations (SDs) and 0.776 SDs, respectively (page 11 and table 2). For grade 8 mathematics scores, school fixed effects accounted for 0.093 SDs (11%) of the gap and student covariates accounted for 0.267 SDs (33%) of the gap, accounting for 44% of the gap in total (table 3). For grade 8 reading scores, school fixed effects accounted for 0.055 SDs (7%) of the gap and student covariates accounted for 0.256 SDs (33%) of the gap (table 3), accounting for 40% of the gap in total.
- Controls: The student covariates include gender, age, parental education, eligibility for free or reduced price lunch, and indicator variables signifying type of district and region within the state (page 16).
Yeung and Pfeiffer (2009) examined data from the Panel Study of Income Dynamics (PSID) and its two waves of Child Development Supplements (CDS). Yeung and Pfeiffer used used a sample of 1794 children, including 856 blacks and 938 whites (page 417). The Researcher studied three cohorts of children who were tested at two points in time – once in 1997 and once in 2003.
- Measures: Cognitive skills are measured with the Woodcock Johnson achievement test-revised. Math skills were measured using the applied problem test score. Verbal skills were measured using the letter–word test score (page 417).
- Results: 25-45% of the gap was explained at grades 10-12. The raw black-white gaps in the applied problem and letter-word tests in grades 10-12 were 0.78 and 0.74 standard deviations, respectively. Accounting for a variety of controls reduces these gaps to 0.58 and 0.40 standard deviations, respectively (table 2) – accounting for about 25% and 45% of the total math and verbal gaps. Also, see Yeung and Pfeiffer (2005) and Yeung and Conley (2008) for earlier analyses of the same dataset.
- Controls: The controls used are grandparents’ education, mother’s characteristics at birth (whether received AFDC while pregnant, whether a teenage mother), child’s characteristics (gender, low birthweight, birth order), parental SES (income, education, occupation, wealth), number of children at family, family structure, urbanicity index, whether the child ever attended a private school, parenting behavior and values, and mother’s verbal test scores. (see page 417 for more details about the variables)
Substantial explanations of the gap beyond elementary school
There are just two studies (of which I am aware) explain the entirety of the black-white test score gap among adolescents, i.e. studies which eliminate the gap entirely or which render the gap statistically insignificant after accounting for a variety of covariates.
Mandara, Varner, Greene, and Richman (2009) [archived] examined intergenerational family predictors of the black–white achievement gap among 4,406 adolescents from the National Longitudinal Survey of Youth (NLSY). This study only considered test assessments between the ages of 10-11 and 13-14.
- Measures: Academic achievement was measured with the reading recognition, reading comprehension, and mathematical reasoning subtests of the Peabody Individual Achievement Test (PIAT) (page 871).
- Results: The black-white test score gap was completely eliminated after statistically controlling for intergenerational family factors (page 874). The intergenerational family factors were grouped into 4 categories: grandparent SES, mother’s achievement test scores, parental SES, and parenting practices. The authors constructed a conceptual model which found that the only variables that had a direct effect on adolescent achievement were parenting practices and maternal cognitive ability (table 4). The effect of grandparent SES was fully mediated by maternal achievement and parental SES (page 875), and the effect of parental SES was completely mediated by parenting practices (page 876). Further, the study finds that “parenting explained most of the achievement gap” (page 877).
- Controls: Intergenerational family factors were grandparent SES (education, occupational prestige, literary resources), mother’s achievement test scores (AFQT), parental SES (occupational prestige, poverty status, wealth) and parenting practices (maternal warmth, parental monitoring, school-oriented home environment, frequency of parent-offspring arguments about rules, house chores, and the freedom the child had in decision making). The variables that had a statistically significant effect on offspring test scores were school-oriented home environment (β = 0.32), the frequency of arguments about rules (β = –0.28), offspring decision making (β = 0.22), mother’s achievement (β = 0.17), and house chores (β = –0.10) (Table 4). Black households were rated worse than white households for each of these variables (Table 2). Parenting monitoring (–0.00) and maternal warmth (–0.01) were also measured, but these variables did not have a statistically significant effect.
Cottrell, Newman, and Roisman (2015) [archived] examined cognitive ability of children of 1,364 families who participated in the National Institute of Child Health and Human Development (NICHD) Study of Early Child Care and Youth Development (SECCYD). Tests were administered to children at 54 months of age, first grade, third grade, fifth grade, and age 15 (page 7).
- Measures: The math, vocabulary, and reading ability facets of the Woodcock-Johnson Psycho-Educational Battery-Revised (WJ-R) were used to measure general cognitive ability/knowledge (g).
- Results: Large black-white cognitive test score gaps are found at every point of assessment with gap sizes ranging between 1.24 and 1.39 standard deviations (page 8). The authors constructed a model to explain the test score gap by accounting for a wide variety of explanatory variables. On this model, the relationship between race and test scores was no longer statistically significant (page 24).
- Controls: The variables in their full model that were statistically significant in explaining the test score gap were maternal verbal ability/knowledge (uniquely explained 33.5% of the gap), maternal sensitivity (25.2%), learning materials in the home (6.8%), safe physical environment (5.4%), and birth order (5.1%) (table B1 and page 24). The other variables included in the model – maternal acceptance (2.7%), birth weight (1.5%), income (0.5%), and maternal education (3.2%) – were not statistically significant in the full model (table B1, page 24). Altogether, the percent of the black-white test score gap explained by their model ranged from 89% (at 54 months) to 85% (at first grade) to 79-80% (at third grade, fifth grade, and 15 years) (page 11).
Direct and Indirect causes
Since the previous two studies are the only studies (of which I am aware) to erase the gap between non-adopted black and white children after elementary schools, I believe that the variables used in these studies are good candidate explanations of the black-white gap. Based on those studies, it seems that the strongest candidate direct explanations of the gap can be split into the following broad categories:
- Maternal cognitive ability. Both studies found that maternal cognitive ability played a strong role in explaining the gap. Mandara (2009) measured maternal achievement using the arithmetic reasoning, reading recognition, and reading comprehension sections of the ASVAB (page 870). Cottrell (2015) measured maternal verbal ability/knowledge using the Peabody Picture Vocabulary Test–Revised.
- Parenting practices. The components of parenting practices found to be significant in the previous two studies are maternal sensitivity (Cottrell 2015), frequency of arguments (Mandara 2009), and decision-making freedom afforded to the child (Mandara 2009).
- Home environment. The components of home environment found to be significant in the previous two studies are learning materials in the home (Cottrell 2015), safety of physical environment (Cottrell 2015), and school-oriented home environment (Mandara 2009).
Note that I emphasized that these are the best candidates for direct explanations of the gap. I note this because there may be other factors which explaining the test score gap, not because they directly cause the cognitive ability gap, but because they cause the gaps in parenting practices and home environment. In other words, there may be other factors that cause the cognitive ability gap, but the causal effect is mediated by maternal cognitive ability, parenting practices, and home environment. These factors would be indirectly responsible for the gap. Consider a few examples of factors that may play this indirect role:
- Parental SES. Parental SES was associated with test scores and the test score gap in both Cottrell (2015) and Mandara (2009), but this association was eliminated after controlling for maternal cognitive ability, parenting practices, and home environment. This may be because parental SES has no causal association with black-white test scores (it may be merely correlated with parenting practices and home environment). Or it could be that differences in parental SES cause the differences in, e.g., parenting practices and home environment which then causes the black-white gap in test scores. The data doesn’t support or refute either of these possibilities. Therefore, we cannot dismiss the possibility that differences in parental SES are ultimately (and indirectly) responsible for the gap (though I will argue in another post that the totality of evidence suggests that this is not the case).
- Genetics. It may be that genetic differences between blacks and whites are ultimately what determine the racial differences in, e.g., maternal cognitive ability and parenting practices. While none of the studies necessarily give support to this hypothesis, none of them refute this possibility either. Therefore, from the data in this post, we cannot rule out the possibility that genetic differences are ultimately (and indirectly) responsible for the gap (although I argue in a separate post that genetic differences don’t seem to explain a large portion of the black-white differences in cognitive ability).
In fact, it is obvious that differences in parenting practices and home environment – while they may be direct causes of the gap – cannot be ultimate causes of the gap. There needs to be an explanation as to why blacks and whites differ with respect to their parenting practices and home environment (i.e. such differences cannot be due to random chance). Parental SES and genetics attempt to provide the missing explanation. Another possible explanation is cultural differences between blacks and whites. I will explore these possibilities in a later post.
Predictors or causes?
As I just explained, one issue with these candidate explanations is that they may not be ultimate causes of the gap. Another issue is that they may not be causes at all. They may simply be robust predictors that are not mediated by any of the variables involved in the models within which they were tested. In other words, there may be a spurious, rather than causal, relationship between differences in cognitive ability and differences in, e.g., parenting practices. There are a possible few reasons why this might be a spurious relationship.
One reason may be that there is a hidden variable that confounds the relationship between, say, parenting practices and childhood IQ. Parents with higher IQs are more likely to exhibit superior parenting practices. Therefore, if the IQ gap were due to some hidden variable, then controlling for parenting practices implicitly controls for that other hidden variable. So any reduction in the IQ gap after controlling for parenting practices might be due to controlling for that hidden variable rather than controlling for parenting practices. For example, if the entirety of the IQ gap were directly caused by genetic differences in cognitive ability, then we would expect the IQ gap to reduce after controlling for parenting practices, since controlling for parenting practices also controls for the genetics of the parents (and therefore the children as well). This point has been made by prominent “hereditarians” Rushton and Jensen (2005) (page 267):
The most frequently stated culture-only hypothesis is that the mean IQ differences are due to SES. In fact, controlling for SES only reduces the mean Black–White group difference in IQ by about a third, around 5 IQ points. The genetic perspective does not regard this control for SES as being entirely environmental. It holds that the parents’ socioeconomic level in part reflects their genetic differences in intelligence.
The possibility of genetic confounding is especially important regarding differences in cognitive ability because there is ample data showing that the association between family SES and children’s cognitive development is substantially confounded by genetic factors (Trzaskowski et al. 2014, Plomin et al. 2017). In other words, much of the association between parental SES and children’s cognitive development is not due to a direct causal relationship. Rather, much of the association is due to a third factor – i.e. the shared genetics of the parent and offspring – which causes both parental SES and children’s cognitive development.
Another reason is that the direction of causation might be in the unexpected direction. For example, it could be that differences in childhood cognitive ability cause differences in parenting. Mandara, et al. (2009) [archived] discuss this possibility: “it is still possible that children’s achievement influences parenting as much as parenting influences achievement. Parents may respond to poor achievement in the early grades by being more involved and increasing the educational experiences in the home” (page 876).
It should be acknowledged, however, that at present there is no way of knowing how much of the IQ advantage for children with excellent environments is due to the environments per se and how much is due to the genes that parents creating those environments pass along to their children. In addition, some of the IQ advantage of children living in superior environments may be due to the superior genetic endowment of the child producing a phenotype that rewards the parents for creating excellent environments for intellectual development (Braungart, Plomin, DeFries, & David, 1992; Coon, Fulker, DeFries, & Plomin, 1990; Plomin, Loehlin, & DeFries, 1985). To the extent that such processes play a role, the IQ advantage of children in superior environments might be due to their own superior genes rather than to the superior environments themselves.
In a report of top replicated findings from behavioral genetics, Plomin et al. (2016) have also emphasized these point. They find that “most associations between environmental measures and psychological traits are significantly mediated genetically”. They even specifically address the problem with inferring causation from the discovered correlation of parenting practices and offspring cognitive ability:
Rather than assuming that correlations between parenting and children’s behavior are caused by the environmental effect of parenting on children’s behavior, it is important to consider the possibility that the correlation is in part due to genetic factors that influence both parenting and children’s behavior. Individual differences in parenting might reflect genetically driven differences in children’s behavior or differences in parenting might be due to genetically driven propensities of parents that are inherited directly by their children.
Indeed, in a review of literature concerning the role of family SES on the black-white score gap among young children, Magnuson and Duncan (2006) [archived] also note the need to consider genetic confounding when analyzing the association between racial differences in SES and racial differences in cognitive test scores (page 386):
Simply documenting that SES accounts for about .4–.5 of standard deviation of the black–white test score gap does not prove that differences in SES have caused differences in children’s test scores. For example, although Fryer and Levitt (2004) are able to account for virtually all of the racial and ethnic gaps in kindergarten achievement using measures of family background, they lack any measure of genetic endowments and are thus unable to discount the possibility that what appear to be family socioeconomic effects are really caused by other family characteristics.
Duncan and Magnuson (2005) [archived] have reiterated this same point elsewhere to state that these studies cannot demonstrate causation. They argue that these studies, at best, can demonstrate an upper-bound for the effect of the variables controlled (page 47):
On average, when black and Hispanic children begin school, their academic skills lag behind those of whites. Accounting studies find that differences in socioeconomic status explain about half a standard deviation of the initial achievement gaps. But because none of the accounting studies is able to adjust for a full set of genetic and other confounding causes of achievement, we regard them as providing upper-bound estimates of the role of family socioeconomic status.
Therefore, even though the specific environmental controls mentioned here may substantially reduce the cognitive ability gap, we cannot assume that they are causal. This mistaken inference has been called the “sociologist’s fallacy” by many “hereditarians” (i.e. those who believe that genetic differences play a role in explaining racial differences in cognitive ability). In a review of intelligence research by experts in the field, Neisser et al. (1996) [archived] (page 94) also mentions this mistake as well:
Several considerations suggest that this [SES explanations of the black-white IQ gap] cannot be the whole explanation. For one thing, the Black/White differential in test scores is not eliminated when groups or individuals are matched for SES (Loehlin et al., 1975). Moreover, the data reviewed in Section 4 suggest that–if we exclude extreme conditions–nutrition and other biological factors that may vary with SES account for relatively little of the variance in such scores. Finally, the (relatively weak) relationship between test scores and income is much more complex than a simple SES hypothesis would suggest. The living conditions of children result in part from the accomplishments of their parents: If the skills measured by psychometric tests actually matter for those accomplishments, intelligence is affecting SES rather than the other way around. We do not know the magnitude of these various effects in various populations, but it is clear that no model in which “SES” directly determines “IQ” will do.
This is not to say that these candidate explanations – maternal cognitive ability, parenting practices, and home environment – are useless. On the contrary, they are rather useful. Because we know that controlling for these variables accounts for the test gap, this conveys useful information on possible causes of the gap. The possible causes are either (a) the variables themselves, or (b) other factors that are significantly correlated with those variables. Further inquiry is required to tease apart these possibilities. I will investigate this issue in a later post.
Search methods for academic sources
[google scholar searches were performed on September 2020]
I searched “black white test score gap” in google scholar which returns the book The Black-White Test Score Gap by Jencks and Phillips (1998) (cited by 2615) as the first result. The second result is Fryer and Levitt (2005) (cited by 566), which seems to be a seminal work to which most following works refer. I used Google Scholar to find studies that cited the Fryer study, which led me to Rothstein and Wozny (2013) which challenges the methods of Fryer and also cites Phillips et al. (1998) as a prior work of importance.
Using Google Scholar, I then searched for other studies that cited Rothstein and Wozny (2013). The only two studies within the first two pages that seemed related to my topic were “Patterns and trends in racial/ethnic and socioeconomic academic achievement gaps” by Reardon et al. (2014) (cited by 217) and “A two decade examination of historical race/ethnicity disparities in academic achievement by poverty status” by Paschall et al. (2018) (cited by 33). These two studies had great literature reviews of other studies that were directly related to my topic of interest. These literature reviews provided the bulk of the academic studies found in this post:
- Reardon et al. (2014) has a section titled “How Much of Racial/Ethnic Achievement Gaps Can Be Explained by Socioeconomic Status?” (page 14) which cites Phillips et al. (1998), Fryer and Levitt (2005), Murnane et al. (2006), Clotfelter et al. (2006), Rothstein and Wozny (2013), and Mandara, et al. (2009).
- Paschall et al. (2018) gives a review of literature in the section titled “Black-White Gaps”. Cited studies that were relevant to my topic of interest were Fryer and Levitt (2005), Murnane et al. (2006), Burchinal et al. (2011).
Because the results in Mandara, et al. (2009) were so interesting – it was the first study I found that completely eliminated the test score gap with their model – I used Google Scholar to search for studies that cited this study. One result that stood out was “Explaining the black–white gap in cognitive test scores: Toward a theory of adverse impact” by Cottrell, Newman, and Roisman (2015) (cited by 44). The appendix of this study listed several past studies that attempted to explain the black-white gap in cognitive test scores: Brooks–Gunn et al. (2003), Fryer and Levitt (2005), Murnane et al. (2006), Burchinal et al. (2011), Mandara et al. (2009), Yeung and Pfeiffer (2009), and Burchinal et al. (2011). I then used Google scholar to search for any new studies that cited the Cottrell study. The search didn’t bring up any prominent studies (i.e., studies cited by 10+ other studies) that were related to my topic.
Recently, I found a paper titled “The role of family socioeconomic resources in the black–white test score gap among young children” by Magnuson and Duncan (2006). This paper had a fairly comprehensive literature review on previous studies that attempted to explain the test score gap between black and white children. The studies cited that were relevant to my search were Phillips et al. (1998), Brooks–Gunn et al. (2003), Fryer and Levitt (2005), Yeung and Pfeiffer (2005), Murnane et al. (2006), and Todd and Wolpin (2007).