Federal education policy has targeted children who are disadvantaged in order to improve their academic performance. The most recent federal education policy is the No Child Left Behind law signed by President Bush in 2001. Indicators often used to identify an at-risk youth range from economic, personal, family, and neighborhood characteristics. A probit model is used in this study to estimate the probability that a student graduates from high school as a function of 8th grade variables. Students are classified as at-risk of dropping out of high school or non at-risk based on having one or more risk factor. The main measures of academic outcomes are high school completion and post-secondary academic achievements. The main measures of labor market outcomes are short-term and long-term earnings. The results show that a student who comes from a low income family, has a sibling who dropped out, has parents with low education, is home alone after school for three hours or more, or comes from a step family in the eighth grade is at-risk of dropping out of high school. At-risk students are less likely than non at-risk students to graduate from high school. They appear to be more sensitive to existing conditions that may impair/assist their academic progress while they are in high school. At-risk students are also less likely to select a bachelor’s degree. When they are compared to comparable non at-risk students, a greater percentage of at-risk students select a bachelor’s degree or post-graduate degrees than non at-risk students. At-risk individuals face long-term disadvantage in the labor market, receiving lower wage offers than the non at-risk group. Comparing only those without post secondary education shows that the average earnings offered to at-risk individuals were lower than those offered to non at-risk individuals. At-risk college graduates also receive lower earnings than non at-risk college graduates. The wage differential is largely due to the disadvantage at-risk individuals face in the labor market.

During the 1960s and early 1970s, the black – white wage gap narrowed significantly, but has remained constant since the late 1980s. The black – white wage gap in the recent period may reflect differences in human capital. A key component of human capital is labor market experience. The first chapter of this dissertation examines how differences in the returns and patterns of experience accumulation affect the black – white wage gap. Accounting for differences in the nature of experience accumulation does not explain the very large gap in wages between blacks and whites. Instead, the wage gap seems to be driven by constant differences between blacks and whites which may represent unobserved differences in skill or the effects of discrimination. The second chapter of the dissertation examines the role of discrimination in explaining the wage gap by asking whether statistical discrimination by employers causes the wages of never incarcerated blacks to suffer when the incarceration rate of blacks in an area increases. I find little evidence that black incarceration rates negatively affect the wages of never incarcerated blacks. Instead, macroeconomic effects in areas with higher incarceration rates play a more important role in explaining the variation in black wages. The third and final chapter of the dissertation examines the black – white wage gap and its determinants across the entire wage distribution to determine if the factors that are driving the wage gap vary across the distribution. I find that at the top of the conditional distribution, differences in the distribution of characteristics explain relatively more of the black – white wage gap than differences in the prices of characteristics. At the bottom of the conditional distribution, differences in the distribution of characteristics explain relatively more of the wage gap—although this finding varies across different specifications of the model.

There was a well documented surge of wage inequality in the US that started from mid-70s and continued in 80s, slowing down by mid-90s, caused by increased dispersion both between and within groups of people with similar personal characteristics and skills. We analyze the US wage inequality in the more recent years to see if this trend continues. We apply the decomposition technique of Juhn, Murphy and Pierce (1993) and quantile regression to March Current Population Survey data of the US Bureau of Labor Statistics data and Luxembourg Income Study data for a few selected European countries. We find that the increase in wage inequality continues during the 90s, especially in the second half. In addition, the focus of wage inequality shifts into the upper half of the wage distribution after mid-80s. The European countries do not show a common trend in the direction of wage inequality during the 90s. However, the focus of their wage inequality seems to be shifting towards the lower half of the wage distribution as opposed to that of US.