UCSC ECON 104 - MALE - FEMALE WAGE DIFFERENTIALS (18 pages)

Previewing pages 1, 2, 3, 4, 5, 6 of 18 page document View the full content.
View Full Document

MALE - FEMALE WAGE DIFFERENTIALS



Previewing pages 1, 2, 3, 4, 5, 6 of actual document.

View the full content.
View Full Document
View Full Document

MALE - FEMALE WAGE DIFFERENTIALS

41 views

Lecture Notes


Pages:
18
School:
University of California, Santa Cruz
Course:
Econ 104 - Truth in Numbers

Unformatted text preview:

MALE FEMALE WAGE DIFFERENTIALS nlshs72 dta and nlsy dta This course is devoted to finding empirical answers to sometimes controversial issues Controversy means that there are at least two sides to an issue Whatever finding you discover there will always be someone ready to dismiss your findings You should anticipate all the arguments counter arguments and counters to the counter arguments A Choice of Variables This section begins with a discussion on the choice of variables what kind of biases they might introduce and how we may want to search for new or different measures to control for the potential bias In trying to detect job market discrimination researchers typically regress the log of wages against characteristics of the wage earner including gender and or ethnic background 1 The dependent variable may itself be biased For example if the variable is wage and the main effect of job discrimination is not to hire women in the first place but once they are hired they get nearly the same wage as men then fewer women are working and making a wage The regression results would show little or no discrimination Fringe benefits are often 25 of salary but are unlikely to be included in the variable called wage If fringe benefits do not systematically vary with any of the independent variables that is our least squares assumptions are not violated then there will not be any systematic bias in the estimates of the coefficients only a greater variability But if for example men are more likely than women to have jobs with extensive fringe benefits then discrimination against women is likely to be underestimated Wage or salary is a function of education the type of industry marital status and region The more experience that people have the more they tend to be paid But a common proxy variable for experience age education 6 has certain inherent biases Women tend to be in the labor market for shorter periods and instead specialize in domestic production having and raising



View Full Document

Access the best Study Guides, Lecture Notes and Practice Exams

Loading Unlocking...
Login

Join to view MALE - FEMALE WAGE DIFFERENTIALS and access 3M+ class-specific study document.

or
We will never post anything without your permission.
Don't have an account?
Sign Up

Join to view MALE - FEMALE WAGE DIFFERENTIALS and access 3M+ class-specific study document.

or

By creating an account you agree to our Privacy Policy and Terms Of Use

Already a member?