The purpose of this post is to provide a comprehensive overview of racial and ethnic disparities on cognitive and academic tests in the United States. The primary focus is on black and white Americans because most data focuses on comparing these groups, but I’ll also mention disparities for other groups (mainly Hispanics and Asians) when such data is available. I start by reviewing data on the magnitude of racial disparities in cognitive ability. Next, I consider racial disparities in other kinds of tests, including college admissions and academic achievement tests, finding that these disparities are about as large as disparities in cognitive ability. Then, to better contextualize the magnitude of racial disparities in test scores, I compare racial gaps to gaps between other groups, such as students from different countries or different levels of socioeconomic status. Finally, I present data on the ubiquity of test score gaps, showing that the gaps persist through all levels of education, across all geographical units of analysis, and across all socioeconomic levels.
Category: Social Issues
In a previous post, I cited several studies showing that racial disparities in many important social outcomes are largely driven by racial disparities in cognitive ability. This post will expand on those findings by demonstrating similar patterns in 3 nationally representative datasets that I have not yet considered elsewhere. The datasets include data on socioeconomic outcomes from the early 1990s to early 2010s. I will examine how racial disparities in educational attainment, occupational prestige, and income (the three primary measures of socioeconomic status, as explained here) are related to various factors such as parental income, high school academic achievement, and family structure. My main focus is on disparities between blacks and whites, where I find that the vast majority (over 90%) of the adulthood income gap is explained by some combination of the aforementioned factors, and virtually all of the disparity in educational attainment and occupational prestige are explained by high school achievement.
The purpose of this post is to cite predictors for academic achievement. I will focus on merely listing effect sizes rather than synthesizing the research.
In this post, I explore racial disparities in intergenerational mobility, i.e. racial disparities in offspring outcomes after controlling for parental achievement on the same outcome. The primary focus is on black-white disparities in income mobility, i.e. the finding that black children have lower incomes than white children with similar parental incomes. However, other racial groups and socioeconomic outcomes will be considered when data is available. I start by documenting racial disparities in various socioeconomic outcomes, such as income, educational attainment, and wealth. I also show that there are also large racial disparities in mobility for each of these outcomes. Next, I document some of the patterns of income mobility gaps in more detail, by showing the history of the gap, how the gaps vary by sex, and making comparisons with racial groups other than blacks and whites. Following that, I explain why differences in income mobility are pivotal to explaining persistent income gaps between blacks and whites. I then consider a number of different factors that might explain black-white gaps in income mobility. Finally, I end by considering what I take to be important implications of these findings.
In a previous post, I cited data showing that cognitive ability is significantly correlated with various important outcomes, such as academic achievement, occupational performance, socioeconomic status, anti-social behavior, and health. However, that data only establishes that there is a statistical association between cognitive ability and these outcomes. The data does not establish that cognitive ability has a causal influence on any of these outcomes. In this post, I will provide evidence that cognitive ability has a causal influence on academic achievement, occupational performance, socioeconomic success, and anti-social behavior.
The purpose of this post is to provide some guidelines to aid in inferring causation in social science research. At the moment, the post just defines confounding variables, distinguishes them from other third-variables (e.g. mediators and colliders), and provides some examples of statistical techniques that can be used to control for confounding variables. In the future, I hope to cover in more detail regression analysis, causal diagrams, multicollinearity, and other concepts important to understand to infer causation in the social sciences.
There is overwhelming evidence showing the predictive validity of cognitive ability for important life outcomes. Cognitive ability measured as early as age 6 has a strong association with one’s future success in a number of important outcomes, including academic achievement, occupational performance, income, educational attainment, occupational prestige, criminality, self-control, and health. The associations are typically large, often making cognitive ability the best predictor for such outcomes. In this post, I will cite research showing this evidence. I will begin with some background on cognitive ability, including definitions, the distributions of IQ test scores, the stability of cognitive ability test scores, and expert consensus on the validity of cognitive ability. Finally, I will cite data demonstrating the predictive validity of cognitive ability in academic achievement, occupational performance, socioeconomic success, anti-social behavior, and health.
Note: this post is a work in progress. The effect size is a statistic that quantifies the magnitude of the association between two variables. It is arguably the most important statistic reported in any study attempting to report the relationship between different variables. This purpose of this post is to aid in interpreting effect sizes, particularly from a social science perspective. There are several different kinds of statistics that are used to report effect sizes, […]
There is overwhelming evidence demonstrating the undeniable predictive validity of cognitive ability. Cognitive ability measured as early as age 6 has a strong association with one’s future success in a number of important outcomes, including income, educational attainment, academic performance, occupational prestige, occupational performance, criminality, etc. These associations are robust, persisting even after controlling for a number of plausible confounding variables, including parental socioeconomic status, race, job training and job experience, and other risk factors for the relevant life outcomes. The totality of evidence heavily implies that this association is causal, indicating that early cognitive ability is a powerful factor in determining a person’s chances of achieving conventional measures of success in Western societies.
In previous posts, I have emphasized the predictive power of IQ on a variety of outcomes such as education academic achievement, educational attainment, occupational prestige, income, and crime. I referenced studies showing that IQ is a better predictor of many of these outcomes compared to other metrics traditionally assumed to predict success. For example, many studies show that IQ predicts education, occupation, and income better than many metrics that people assume to be predictors of success – e.g. parental SES, parental income, parental education, etc. Such data might lead some to believe that IQ is by far the single best predictor of conventional measures of success within Western societies. I wish to challenge that idea in this post. I do not necessarily deny that IQ is generally the best predictor of certain measures of success. Rather, I insist that there are a variety of personality traits that are better predictors for certain measures of success. There are many personality traits that I could use to support my argument, such as conscientiousness or locus of control. In this post, I will focus on self-regulation. I will present data showing that self-regulation predicts a variety of important outcomes independent of various confounders (including IQ and parental SES).