Evidence on Discrimination in Employment: Codes of Color, Codes of Gender

There is substantial racial and gender disparity in the American economy. As we will demonstrate, discriminatory treatment within the labor market is a major cause of this inequality. Yet, there appear to have been particular periods in which racial minorities, and then women, experienced substantial reductions in economic disparity and discrimination. Some questions remain: Why did the movement toward racial equality stagnate after the mid-1970s? What factors are most responsible for the remaining gender inequality? What is the role of the competitive process in elimination or reproduction of discrimination in employment? How successful has the passage of federal antidiscrimination legislation in the 1960s been in producing an equal opportunity environment where job applicants are now evaluated on their qualifications? To give away the answer at the outset, discrimination by race has diminished somewhat, and discrimination by gender has diminished substantially; neither employment discrimination by race or by gender is close to ending. The Civil Rights Act of 1964 and subsequent related legislation has purged American society of the most overt forms of discrimination, while discriminatory practices have continued in more covert and subtle forms. Furthermore, racial discrimination is masked and rationalized by widely-held presumptions of black inferiority

Detecting differences between contrast groups.

In medical research, doctors must evaluate the effectiveness of a new medicine B against a specified disease. This evaluation is often carried out by comparing B with an old medicine A, which has been used to treat the disease for many years. This comparison should include two important statistical summaries: mean and distribution function differences between A and B. The datasets of applied/tested A and B are referred to contrast groups, and the mean and distribution differences are referred to group differences. Because the datasets to be contrasted are only two samples obtained by limited applications or tests on A and B, the differences derived from the datasets are inevitably uncertain. This generates a need of measuring the uncertainty of differences. In this paper, an efficient strategy is designed for identifying confidence intervals for measuring the uncertainty of the differences between two contrast groups. This approach is suitable for most of those applications for which we have no prior knowledge about the underlying distribution of the data. We experimentally evaluate the proposed approach using the UCI, datasets against the bootstrap resampling method and the traditional method, and demonstrate that our method is efficient in measuring the structural differences between contrast groups