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Introducing race and gender into economics
Collections Currently only available at our Cape Town warehouse. Collection times vary, please wait for your Ready to Collect email before visiting the warehouse. Human participants were not directly involved in the research reported in this article; therefore, no institutional review board approval was sought. Eight outcome variables, all assessed in eighth grade, were selected to examine the study aims: For the eighth-grade data collection, children completed the item Self Description Questionnaire SDQ II [ 63 ], where they provided self-assessments of their academic skills by rating their perceived competence and interest in English and mathematics.
The SDQ also asked children to report on problem behaviors with which they might struggle. Three subscales were produced from the SDQ items: And the SDQ Internalizing Behavior subscale, which includes eight items on internalizing problem behaviors such as feeling sad, lonely, ashamed of mistakes, frustrated, and worrying about school and friendships [ 62 ]. The Self-Concept and Locus of Control scales ask children about their self-perceptions and the amount of control they have over their own lives. The seven items in the Self-Concept scale, and the six items in the Locus of Control were standardized separately to a mean of zero and a standard deviation of 1.
The scores of each scale are an average of the standardized scores [ 62 ]. Academic achievement in reading, mathematics and science was measured with the eighth-grade direct cognitive assessment battery [ 62 ]. First, children read items in a booklet and recorded their responses on an answer form.
These answer forms were then scored by the test administrator. Based on the score of the respective routing forms, the test administrator then assigned a high or low second-stage level form of the reading and mathematics assessments. For the second-stage level tests, children read items in the assessment booklet and recorded their responses in the same assessment booklet.
The routing tests and the second-stage tests were timed for 80 minutes [ 62 ]. The present analyses use the standardized scores T-scores , allowing relative comparisons of children against their peers. Latent Class Analysis, described in greater detail below, was used to classify students into classes of individual and contextual advantage or disadvantage.
Nine constructs, measuring characteristics at the individual-, school-, and neighborhood-level, were captured using 42 dichotomous variables measured across the different waves of the ECLS-K. A household-level composite index of socioeconomic status, derived by the National Center for Education Statistics, was also included at kindergarten, first, third, fifth and eighth grades.
Higher scores reflect higher levels of educational attainment, occupational prestige, and income. In the present analyses, the socioeconomic composite index was categorized into quintiles and further divided into the lowest first and second quintiles, versus the third, fourth and fifth quintiles. Two variables measured the school-level environment: Both variables were measured in the kindergarten, first, third, fifth and eighth grade data collections.
To capture the neighborhood environment, a variable was included which measured the level of safety of the neighborhood in kindergarten, first, third, fifth and eighth grades. This is a data-driven, mixture modelling technique which uses indicator variables in this case the variables described under Individual and Contextual Disadvantage Variables section to identify a number of latent classes. The first step in the 3-step procedure is to estimate the measurement part of the joint model i. Latent class analyses first evaluated the fit of a 2-class model, and systematically increased the number of classes in subsequent models until the addition of latent classes did not further improve model fit.
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For each model, replication of the best log-likelihood was verified to avoid local maxima. To determine the optimal number of classes, models were compared across several model fit criteria. The LMR-LRT can be used in mixture modeling to compare the fit of the specified class solution k -class model to a model with fewer classes k -1 class model. A non-significant chi-square value suggests that a model with one fewer class is preferred. Entropy statistics, which measure the separation of the classes based on the posterior class membership probabilities, were also examined; entropy values approaching 1 indicate clear separation between classes [ 69 ].
After determining the latent class model in step 1, the second step of the analyses used the latent class posterior distribution to generate a nominal variable N , which represented the most likely class [ 64 ]. During the third step, the measurement error for N was accounted for while the model was estimated with the outcomes and predictor auxiliary variables [ 64 ].
All analyses were conducted using MPlus v. Class characteristics are shown in Table A in S1 File. Trajectories of advantage and disadvantage were stable across ECLS-K waves, so that none of the classes identified changed in individual and contextual characteristics across time. Individually and Contextually Wealthy lived in individual and contextual privilege, with very low proportions of children in socioeconomic deprived contexts.
Individually and Contextually Disadvantaged. It also had relatively low proportions of children living in unsafe neighborhoods and low proportions of children attending diverse schools, forming a class with a mixture of individual-level deprivation, and contextual-level advantage. Whereas Black boys achieved lower scores than White boys across all classes on the math, reading and science assessments, this was not the case for Latino boys, who only underperformed White boys on the science assessment within the most privileged class Class 3: Individually and Contextually Wealthy.
Latina girls, in contrast, outperformed White boys on reading scores within Class 4 Individually Disadvantaged, Contextually Wealthy , but scored lower than White boys on science and math assessments, although only when in the two most privileged classes Class 3 and 4. For Black girls the effect of class membership was not as pronounced, and they had lower science and math scores than White boys across all but one instance.
These patterns of heightened inequality in the most advantaged classes are similar for reading and science scores see Table 2. Interestingly, racialized and gendered patterns of inequality observed in academic outcomes were not as stark in non-cognitive academic outcomes see Table 3. Black boys scored lower than White boys on internalizing behavior and higher on self-concept within Classes 2 Individually Wealthy, Contextually Disadvantaged and 4 Individually Disadvantaged, Contextually Wealthy , and Black girls scored higher than White boys on self-concept within Classes 2 and 3 Individually Wealthy, Contextually Disadvantaged, and Individually and Contextually Wealthy, respectively.
White and Latina girls, but not Black girls, scored higher than White boys on internalizing behavior within Classes 3 and 4 for White girls, and within Classes 1 and 3 for Latina girls; see Table 3. Individually Disadvantaged, Contextually Wealthy.
Introducing Race and Gender into Economics
Across gender dichotomies, Black students were more likely than White boys to be assigned to all classes of disadvantage as compared to the most advantaged class, and this was particularly strong for the most disadvantaged class, which included elements of both individual- and contextual-level disadvantage. Latino boys and girls were also more likely than White boys to be assigned to all the disadvantaged classes, but the strength of the association was much smaller than for Black students.
The measures of advantage and disadvantage captured in this study relate to characteristics afforded by parental resources, implying an intergenerational transmission of disadvantage, regardless of the presence of absolute adversity in childhood. This pattern of differential returns of affluence has been shown in other studies, which report that White teenagers benefit more from the presence of affluent neighbors than do Black teenagers [ 71 ]. Among adult populations, studies show that across several health outcomes, highly educated Black adults fare worse than White adults with the lowest education [ 72 ].
Intersectional approaches such as the one applied in this study reveal how power within gendered and racialized institutional settings operates to undermine access to and use of resources that would otherwise be available to individuals of advantaged classes [ 72 ]. In other words, racism and sexism have a direct effect on academic and non-academic outcomes among 8 th graders, independent of the effect of socioeconomic disadvantage on these outcomes. Despite this limitation, it is important to note that socioeconomic inequalities in the US are driven by racial and gender bias and discrimination at structural and individual levels, with race and gender discrimination exerting a strong influence on academic and non-academic inequalities.
Gender discrimination—another system of oppression—is apparent in this study in relation to academic subjects socially considered as typically male or female orientated. For example, results show no difference between Black girls and White boys from the most advantaged class in terms of perceived interest and competence in math but, in this same class, Black girls score much lower than White boys in the math assessment. This difference, not explained by intrinsic or socioeconomic differences, can be contextualized as a consequence of experienced intersecting racial and gender discrimination.
Growing up Black, Latino or White in the US is not the same for boys and girls, and growing up as a boy or a girl in America does not lead to the same outcomes and opportunities for Black, Latino and White children as they become adults. This includes the contrasting results found for Black and Latino boys, when compared to White boys, which show very few examples of poorer outcomes among Latino boys, but several instances among Black boys.
Results also show different racialization for Black and Latina girls. Latina girls, but not Black girls, report higher internalizing behavior than White boys, whereas Black girls, but not Latina girls, report higher self-concept than White boys. Black boys also report higher self-concept and lower internalizing behavior than White boys, findings that mirror research on self-esteem among Black adolescents [ 74 , 75 ].
For example, Black girls in all four classes score lower on science scores than White boys, but only Latina girls in the most advantaged class score lower than White boys.
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Although one can observe differences in the racialization of Black and Latino boys and girls across classes of disadvantage, findings about broad differences across Latino children compared to Black and White children should be interpreted with caution. The Latino ethnic group is a large, heterogeneous group, representing The Latino population is composed of a variety of different sub-groups with diverse national origins and migration histories [ 77 ], which has led to differences in sociodemographic characteristics and lived experiences of ethnicity and minority status among the various groups.
Differences across Latino sub-groups are widely documented, and pooled analyses such as those reported here are masking differences across Latino sub-groups, and providing biased comparisons between Latino children, and Black and White children. Stereotype threat posits that awareness of a social stereotype that reflects negatively on one's social group can negatively affect the performance of group members [ 35 ].
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Reduced performance only occurs in a threatening situation e. Findings among youth parallel findings among adult populations, which show that adult men are generally perceived to be more competent than women, but that these perceptions do not necessarily hold for Black men [ 80 ]. Children live within multiple contexts, with risk factors at the family, school, and neighborhood level contributing to their development and wellbeing.
Individual risk factors seldom operate in isolation [ 83 ], and they are often strongly associated both within and across levels [ 84 ]. Interventions to eliminate achievement gaps cannot fully succeed as long as social stratification caused by gender and racial discrimination is not addressed [ 87 , 88 ]. She would like to thank them for hosting her visit and for the support provided.
National Center for Biotechnology Information , U. Published online Oct The authors have declared that no competing interests exist. Received Jun 10; Accepted Oct 6. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. This article has been cited by other articles in PMC.
Measures Outcome Variables Eight outcome variables, all assessed in eighth grade, were selected to examine the study aims: Individual and Contextual Disadvantage Variables Latent Class Analysis, described in greater detail below, was used to classify students into classes of individual and contextual advantage or disadvantage.
Table 1 Fit indices of Latent Class Analysis.
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