Unfair to be smarter

This is what the Unfaircampaign comes down to. Or it would, had the anti-racists not won their war against truth, specifically against IQ realism, a victory which allows douchebag academics to make bald-faced lies about the etiology of social outcome differences(e.g., Income, Health, Home ownership, Employment, and so on.)

All of this has been found repeatedly. Below, for example, is Fryer (2010)’s discussion. To note, Fryer’s investigation, like so many others, has two major failings. First, in determining the extent to which IQ can explain outcome differences, he fails to take into account Spearman’s fact. Jensen and Nyborg (1999) demonstrated the importance of regressing outcomes on extracted g scores, as opposed to mere IQ scores, which makes sense, as the Black-White gap is more pronounced on g and as g is the predictive backbone of an IQ test. And second, he fails to take into account population level effects. The latter is why, despite the perverse amount of institutional discrimination for Blacks (e.g., reducing “adverse impact”; affirmative action; diversity programs), controlling for IQ does not completely reverse the outcome differences. That is, the differences are a result of aggregated individual level differences plus population level effects and are mitigated by societal discrimination for Blacks). That said, it’s worth the read:

One of the most important developments in the study of racial inequality has been the quantication of the importance of pre-market skills in explaining differences in labor market outcomes between blacks and whites (Neal and Johnson, 1996; O’Neill, 1990). Using the National Longitudinal Survey of Youth 1979 (NLSY79), a nationally representative sample of 12,686 individuals aged 14 to 22 in 1979, Neal and Johnson (1996) and that educational achievement among 15- to 18-year-olds explains all of the black-white gap in wages among young women and 70 percent of the gap among men. Accounting for pre-market skills also eliminates the Hispanic-white gap. Important critiques such as racial bias in the achievement measure (Darity and Mason, 1998; Jencks, 1998), labor market dropouts, or the potential that forward-looking minorities underinvest in human capital because they anticipate discrimination in the market cannot explain the stark results. We begin by replicating the seminal work of Neal and Johnson (1996) and extending their work in four directions. First, the most recent cohort of NLSY79 is between 42 and 44 years old (15 years older than in the original analysis), which provides a better representation of the lifetime gap. Second, we perform a similar analysis with the National Longitudinal Survey of Youth 1997 cohort (NLSY97). Third, we extend the set of outcomes to include unemployment, incarceration, and measures of physical health. Fourth, we investigate the importance of pre-market skills among graduates of thirty-four elite colleges and universities in the College and Beyond database, 1976 cohort.

Table 1 presents racial disparities in wage and unemployment for men and women, separately. The odd-numbered columns present racial differences on our set of outcomes controlling only for age. The even-numbered columns add controls for the Armed Forces Qualifying Test (AFQT) {a measure of educational achievement that has been shown to be racially unbiased (Wigdor and Green, 1991) {and its square. Black men earn 39.4 percent less than white men; black women earn 13.1 percent less than white women. Accounting for educational achievement drastically reduces these inequalities { 39.4 percent to 10.9 percent for black men and 13.1 percent lower than whites to 12.7 percent higher for black women. An eleven percent difference between white and black men with similar educational achievement is a large and important number, but a small fraction of the original gap. Hispanic men earn 14.8 percent less than whites in the raw data { 62 percent less than the raw black-white gap { which reduces to 3.9 percent more than whites when we account for AFQT. The latter is not statistically significant. Hispanic women earn six percent less than white women (not significant) without accounting for achievement. Adding controls for AFQT, Hispanic women earn sixteen percent more than comparable white women and these differences are statistically significant. Labor force participation follows a similar pattern. Black men are more than twice as likely to be unemployed in the raw data and thirty percent more likely after controlling for AFQT. For women, these differences are 3.8 and 2.9 times more likely, respectively. Hispanic-white differences in unemployment with and without controlling for AFQT are strikingly similar to black-white gaps. Table 2 replicates Table 1 using the NLSY97.7 The NLSY97 includes 8,984 youths between the ages of 12 and 16 at the beginning of 1997; these individuals are 21 to 27 years old in 2006-2007, the most recent years for which wage measures are available. In this sample, black men earn 17.9 percent less than white men and black women earn 15.3 percent less than white women. When we account for educational achievement, racial differences in wages measured in the NLSY97 are strikingly similar to those measured in NLSY79 { 10.9 percent for black men and 4.4 percent for black women. The raw gaps, however, are much smaller in the NLSY97, which could be due either to the younger age of the workers and a steeper trajectory for white males (Farber and Gibbons, 1996) or to real gains made by blacks in recent years. After adjusting for age, Hispanic men earn 6.5 percent less than white men and Hispanic women earn 5.7 percent less than white women, but accounting for AFQT eliminates the Hispanic-white gap for both men and women. Black men in the NLSY97 are almost three times as likely to be unemployed, which reduces to twice as likely when we account for educational achievement. Black women are roughly two and a half times more likely to be unemployed than white women, but controlling for AFQT reduces this gap to seventy percent more likely. Hispanic men are twenty-five percent more likely to be unemployed in the raw data, but when we control for AFQT, this difference is eliminated. Hispanic women are fifty percent more likely than white women to be unemployed and this too is eliminated by controlling for AFQT. Similar to the NLSY79, controlling for AFQT has less of an impact on racial differences in unemployment than on wages.

Table 3 employs a Neal and Johnson specification on two social outcomes: incarceration and physical health. The NLSY79 asks the \type of residence” in which the respondent is living during each administration of the survey, which allows us to construct a measure of whether the individual was ever incarcerated when the survey was administered across all years of the sample. The NLSY97 asks individuals if they have been sentenced to jail, an adult corrections institution, or a juvenile corrections institution in the past year for each yearly follow-up survey of participants. In 2006, the NLSY79 included a 12-Item Short Form Health Survey (SF-12) for all individuals over age 40. The SF-12 consists of twelve self-reported health questions ranging from whether the respondent’s health limits him from climbing several fights of stairs to how often the respondent has felt calm and peaceful in the past four weeks. The responses to these questions are combined to create physical and mental component summary scores. Adjusting for age, black males are about three and a half times and Hispanics are about two and a half times more likely to have ever been incarcerated when surveyed. Controlling for AFQT, this is reduced to about eighty percent more likely for blacks and fifty percent more likely for Hispanics. Again, the racial differences in incarceration after controlling for achievement is a large and important number that deserves considerable attention in current discussions of racial inequality in the United States. Yet, the importance of educational achievement in the teenage years in explaining racial differences is no less striking. The final two columns of Table 3 display estimates from similar regression equations for the SF-12 physical health measure, which has been standardized to have a mean of zero and standard deviation of one for ease of interpretation. Without accounting for achievement, there is a black-white disparity of 0.15 standard deviations in self-reported physical health for men and 0.23 standard deviations for women. For Hispanics, the differences are -0.140 for men and 0.030 for women. Accounting for educational achievement eliminates the gap for men and cuts the gap in half for black women [-0.111 (0.076)]. The remaining difference for black women is not statistically significant. Hispanic women report better health than white women with or without accounting for AFQT. Extending Neal and Johnson (1996) further, we turn our attention to the College and Beyond (C&B) Database, which contains data on 93,660 full-time students who entered thirty-four elite colleges and universities in the fall of 1951, 1976, or 1989. We focus on the cohort from 1976.The C&B data contain information drawn from students’ applications and transcripts, Scholastic Aptitude Test (SAT) and the American College Test (ACT) scores, standardized college admissions exams that are designed to assess a student’s readiness for college, as well as information on family demographics and socioeconomic status in their teenage years. The C&B database also includes responses to a survey administered in 1995 or 1996 to all three cohorts that provides detailed information on post-college labor market outcomes. Wage data were collected when the respondents were approximately 38 years old, and reported as a series of ranges. We assigned individuals the midpoint value of their reported income range as their annual income.12 The response rate to the 1996 survey was approximately 80 percent. Appendix Table 3 contains summary statistics used in our analysis. Table 4 presents racial disparities in income for men and women from the 1976 cohort of the C&B Database.13 The odd numbered columns present raw racial differences. The even-numbered columns add controls for performance on the SAT and its square.14 Black men from this sample earn 27.3 percent less than white men, but when we account for educational achievement, the gap shrinks to 15.2 percent. Black women earn more than white women by 18.6 percent, which increases to an advantage of 28.6 percent when accounting for SAT scores. There are no differences in income between Hispanics and whites with or without accounting for achievement. Hispanic men earn 3.8 percent less than similarly aged white men (not statistically significant) and one percent less when one accounts for pre-college scores. (Racial Inequality in the 21st Century: The Declining Signi_cance of Discrimination)

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One Response to Unfair to be smarter

  1. James says:

    i couldn’t sit through the unfair campaign video all the way through. its the most condescending, nsulting video ive seen in a while.

    i feel nothing but loathing for the white people who took part in and (probably) created that video, as well. why don’t we, as white people, collectively have feelings that deserve to be considered as well?

    has anybody here experienced or benefited from their ‘white privilege’ lately? i haven’t.

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