1. Paige Hehl, UW Eau Claire Faculty Mentor: Dr. David Schaffer

2. Previous Research Some economic researchers have concluded that gender discrimination in the U.S. is essentially gone. Schaffer’s previous research suggested otherwise. Our research using a different set of statistical techniques and an enormous database supports the idea of continuing discrimination against women in the labor market.

3. Data  Data was obtained from the Current Population Survey (CPS) for the years 1971- 2006. (http://www.census.gov/cps/)http://www.census.gov/cps/  We have approximately 60,000 observations for each year.  Used Stata 10 & 11

5. Oaxaca Decomposition  Regression of wage rate onto years of schooling and potential experience: Differential due to Discrimination Differential due to differences in human capital

6. Method 1: Extending Oaxaca  Decomposing further to obtain differential due to gender segregation  We used three types of occupations  A: Occupations with less than 30% female workers  B: Occupations with 30-70% female workers  C: Occupations with more than 70% female  Actual wage gap calculated as A∆= ∑[(N iM /N M )lnW iM – (N iF /N F )lnW iF ]

7.  A∆= H+D+S  H= differential due to differences in human capital  D= differential due to discrimination  S= differential due to gender segregation H= ∑ β iM (X iM – X iF ) (N i /N) D= ∑(β iM -β iF )X iF (N i /N) S= ∑ {[(N iM /N M )-(N i /N)]lnW iM – [(N iF /N F )-(N i /N)]lnW iF } Need program for more categories

8. Results from Decomposing  1971  A∆= 0.546149 (actual logwage gap)  H= 0.017139  D= 0.453878  S= 0.075132 S is about 13.8% of A∆  2002  A∆= 0.281517 (actual logwage gap)  H= -0.008705  D= 0.225145  S= 0.06507 S is about 23.1% of A∆

9. Method 2:Regression Analysis with Additional Variables  Regressed certain variables against the natural log of wages  Used years of education, potential experience, fraction-female, average occupation education, and others  Restricted the wages between $2 - $200 (an hour) to eliminate some of the variance  Used weighted averages

10. Additional Variables  500 occupation categories determined by the Census Bureau  Fraction-female (within each occupation)  Average education (within each occupation) Fraction-Female Coefficients 2006 Fraction FemaleMalesFemales 0-.100.000.11-.20-0.131-0.132.21-.30-0.099-0.132.31-.40-0.130-0.157.41-.50-0.174-0.265.51-.60-0.219-0.279.61-.70-0.275-0.341.71-.80-0.272-0.316.81-.90-0.293-0.318.91-1.00-0.343-0.322

13. Interpretations  Types of Discrimination  Pure Discrimination  Gender Segregation Penalty  It has always been the case that wages decrease as you move to a more female job  The size of the wage gap has increased over time  Jobs have become less segregated, but the wage penalty has gotten larger for being in the more female segregated jobs.

14. Citations  Borjas, George. Labor Economics. 5th. New York, NY: McGraw-Hill/Irwin, 2008. Print.  Fluckiger, Yves, and Jacques Silber. The Measurement of Segregation in the Labor Force. Germany: Physica-Verlag Heidelberg, 1999. Print.