Employment Discrimination - Critique of Neoclassical Approach

Critique of Neoclassical Approach

Neoclassical economics ignores logical explanations of how self-fulfilling prophecy by the employers affect the motivation and psychology of women and minority groups and thus it alters the decision making of individuals regarding human capital. This is the feedback explanation that correlates with the drop in human capital investment (such as more schooling or training) attainment by women and minorities.

Moreover, power and social relationships link discrimination to sexism and racism, which is ignored in the neoclassical theory. Furthermore, along with the classical and Marxist theory of competition, racial-gender structure of the job is related to the bargaining power and thus wage differential. Therefore, discrimination persists since racial and gender characteristics shape who gets the higher paying jobs, both within and between occupations. In short, the power relationships are embedded in the labor market, which are neglected in the neoclassical approach,.

In addition, critics have argued that the neoclassical measurement of discrimination is flawed. As Figart points out, conventional methods do not put gender or race into the heart of the analysis and they measure discrimination as the unexplained residual. As a result, we are not informed about the causes and nature of discrimination. She argues that gender and race should not be marginal to the analysis but at the center and suggests a more dynamic analysis for discrimination. Figart argues gender is more than a dummy variable since gender is fundamental to the economy. Moreover, the segmentation in the labor market, institutional variables and non-market factors affect wage differentials and women dominate low-paid occupations. Again, none of these is because of productivity differentials nor are they the outcome of voluntary choices. Figart also indicates how women’s jobs are associated with unskilled work. For that reason, men don’t like association of “their” jobs with women or femininity, skills are engendered.

Although empirical evidence is a tool to use to prove discrimination, it is important to pay attention to the biases involved in using this tool. The biases might cause under or over-estimation of labor market discrimination. There is lack of information on some individual qualifications which indeed affect their potential productivity. The factors such as motivation or work effort, which affects incomes, are difficult to be scaled. Moreover, information regarding the type of college degree may not be available. In short, all the job qualification related factors are not included to study gender wage gap.

An example for underestimation is the feedback effect of labor market discrimination. That is, women may choose to invest less in human capital such as pursuing a college degree based on the current wage gap, which is also a result of discrimination against women. Another reason may be the childbearing responsibilities of women standing as a negative impact on women's careers since some women may choose to withdraw from the labor market with their own will. By doing so, they give up opportunities, such as the firm-specific training that would have potentially helped with their job promotion or reduction in the wage gap. An example of over-estimation of gender discrimination is men might have been more motivated at work. Therefore, it is wrong to equate unexplained wage gap with discrimination, although most of the gap is a result of discrimination, but not all.

Furthermore, empirical evidence can also be twisted to show that discrimination does not exist or it is so trivial that it can be ignored. This was seen in the results and interpretation of the results of Armed Forces Qualifying Test, (AFQT). Neal and Johnson claimed the economic differences in the black and white labor markets were due to the "pre-market factors," not to discrimination. Darity and Mason’s study of the same case disagrees with the findings of Neal and Johnson’s . They take into account factors such as age family background, school quality and psychology into consideration to make the adjustments.