A Study of How the
Return to Education and
   the Gender Gap Have
   Changed: 2000-2010
                            Colleen Cahill
              University of South Florida
             Econometrics II / ECO 6425
                    November 14, 2011
                         Dr. Beom S. Lee
   Equal Pay Act of 1963
    ◦ 1960: Wage disparity approximately 60%
    ◦ 2010: Wage disparity approximately 77%

   General Expectation that More Education
    and Experience Equals Higher Income
    ◦ Women surpassed men in educational
      attainment in the 1990’s
    ◦ The ranking of U.S. education compared to
      other OECD countries has fallen in the past
      decade



Motivation for the Study
   Factors that Contribute to the Wage Gap
    ◦ Personal Choices
    ◦ Male-Female Differences in Skills
    ◦ Differences in the Treatment of Equally
      Qualified Men and Women

   Disparity in the Return to Education

   Potential Problems with the Use of the
    Basic Wage Equation



In the Literature
   Personal Choices Regarding Labor Force
    Participation
    ◦ Having two or more children
      Human capital depreciation
      Less work force experience
    ◦ “Sexist Family Decision Rules”
      Housework time
      Wives who follow their husbands to new
       geographic locations
    ◦ Choice of Occupation



In the Literature
   Male-Female Differences in Skills
    ◦ Human capital is rewarded differently for men
      and women
      Perceived or actual differences in the quality of
       capital accumulated including years of schooling
       and experience
      Initial increases in women’s labor force were
       associated with a declining skill level of employed
       women relative to men




In the Literature
   Differences in the Treatment of Equally
    Qualified Men and Women
    ◦ Wage growth among young women found to be less
      than that of young men
    ◦ Employers with imperfect information about
      potential employees
       Use sex to predict future work commitment and the
        likelihood that a worker will quit or take time off
       Women must have greater ability to be promoted
       Women hold a lower proportion of high paying jobs




In the Literature
   Disparity in the Return to Education
    ◦ Individual variations in human capital imply
      differences in earnings power
    ◦ The return to education increased sharply in
      the 1980s
      Shift to service-oriented production rather than
       industrialized production
      The return to a college education increased more
       for men than for women
    ◦ Family decisions to invest in men’s education
      over women’s




In the Literature
   Potential Problems with the Basic Wage
    Equation
    ◦ Summary measures may be inadequate
      controls for work experience
    ◦ Failure to control for ability may lead to an
      upward bias in the return to schooling
    ◦ Endogeneity of experience and tenure controls
      when number of children is included in the
      equation




In the Literature
   Collected from the Current Population
    Survey
    ◦ Cross-sectional Data
      Primary source of information on the labor force
       characteristics of the civilian non-institutional U.S.
       population
    ◦ U.S. Census Bureau for the Bureau of Labor
      Statistics
    ◦ DataFerrett Tool
    ◦ March 2000 – 2010 Surveys



The Data
The Data
The Data
The Data
The Data
The Data
The Data
The Data
The Data
The Data
The Models:




  The Estimation Method:
  • Ordinary Least Squares Estimation
  • Robust Standard Errors
  • Stata



The Models and Methods
   The Return to Education
    ◦ Initial Samples: 2000 Return Approximately 12%
    ◦ Revised Samples: 2000 Return Approximately 6%

    ◦ Change in the Return to Education: Less than 1
      percentage point for any period
      Initial Samples: Statistically different from 0 at less
       than a 10% significance level in periods 2008-2010
      Revised Samples: Statistically different from 0 at less
       than a 10% significance level in 2007 where it falls
       slightly




Results and Interpretations
   The Gender Gap
    ◦ Initial Samples: 2000 Gender Gap Approximately 42% in
      ln(wages) and 34% in weekly wages
    ◦ Revised Samples: 2000 Gender Gap Approximately 25%
      in ln(wages) and 22% in weekly wages

    ◦ Change in the Gender Gap: Approximately 5 percentage
      points from 2000-2010
      Initial Samples: Shown to have fallen against the positive
       one sided alternative at less than a 10% significance level
       in periods 2001,2003,2005-2007, 2009-2010
      Revised Samples: Shown to have fallen against the
       positive one sided alternative at less than a 10%
       significance level in periods 2003-2010




Results and Interpretations
Results and Interpretations
Results and Interpretations
   For Each Year 2001-2010
    ◦ Generated 10 Random Error Terms, Normally Distributed with
      Mean 0 and Variance the Square of the Mean Standard Error

    ◦ Generated 10 New Dependent Variables Using the Estimated
      Coefficients, the Existing Data for the Independent Variables and
      the Random Error Terms

    ◦ Regressed the New Dependent Variables on the Existing Data for
      the Independent Variables

    ◦ Calculated the Mean of the Estimated Coefficients for Each Year

    ◦ Compared the Mean Coefficient Estimates to the Original
      Coefficient Estimates




Monte Carlo Simulation
Monte Carlo Simulation
Monte Carlo Simulation
   The estimated return to education is practically small
    and primarily insignificant

   The actual gender gap is a number between those
    estimated
    ◦ The estimations using the larger, less controlled samples
      estimate a larger gap
    ◦ The estimations using the smaller, more controlled samples
      estimate a smaller gap

   For the purposes of this study, a basic wage equation
    seems adequate, although better data collection may
    lead to results closer to what has been reported in
    the population




Conclusions

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Colleen P Cahill Econometrics II Presentation

  • 1. A Study of How the Return to Education and the Gender Gap Have Changed: 2000-2010 Colleen Cahill University of South Florida Econometrics II / ECO 6425 November 14, 2011 Dr. Beom S. Lee
  • 2. Equal Pay Act of 1963 ◦ 1960: Wage disparity approximately 60% ◦ 2010: Wage disparity approximately 77%  General Expectation that More Education and Experience Equals Higher Income ◦ Women surpassed men in educational attainment in the 1990’s ◦ The ranking of U.S. education compared to other OECD countries has fallen in the past decade Motivation for the Study
  • 3. Factors that Contribute to the Wage Gap ◦ Personal Choices ◦ Male-Female Differences in Skills ◦ Differences in the Treatment of Equally Qualified Men and Women  Disparity in the Return to Education  Potential Problems with the Use of the Basic Wage Equation In the Literature
  • 4. Personal Choices Regarding Labor Force Participation ◦ Having two or more children  Human capital depreciation  Less work force experience ◦ “Sexist Family Decision Rules”  Housework time  Wives who follow their husbands to new geographic locations ◦ Choice of Occupation In the Literature
  • 5. Male-Female Differences in Skills ◦ Human capital is rewarded differently for men and women  Perceived or actual differences in the quality of capital accumulated including years of schooling and experience  Initial increases in women’s labor force were associated with a declining skill level of employed women relative to men In the Literature
  • 6. Differences in the Treatment of Equally Qualified Men and Women ◦ Wage growth among young women found to be less than that of young men ◦ Employers with imperfect information about potential employees  Use sex to predict future work commitment and the likelihood that a worker will quit or take time off  Women must have greater ability to be promoted  Women hold a lower proportion of high paying jobs In the Literature
  • 7. Disparity in the Return to Education ◦ Individual variations in human capital imply differences in earnings power ◦ The return to education increased sharply in the 1980s  Shift to service-oriented production rather than industrialized production  The return to a college education increased more for men than for women ◦ Family decisions to invest in men’s education over women’s In the Literature
  • 8. Potential Problems with the Basic Wage Equation ◦ Summary measures may be inadequate controls for work experience ◦ Failure to control for ability may lead to an upward bias in the return to schooling ◦ Endogeneity of experience and tenure controls when number of children is included in the equation In the Literature
  • 9. Collected from the Current Population Survey ◦ Cross-sectional Data  Primary source of information on the labor force characteristics of the civilian non-institutional U.S. population ◦ U.S. Census Bureau for the Bureau of Labor Statistics ◦ DataFerrett Tool ◦ March 2000 – 2010 Surveys The Data
  • 19. The Models: The Estimation Method: • Ordinary Least Squares Estimation • Robust Standard Errors • Stata The Models and Methods
  • 20. The Return to Education ◦ Initial Samples: 2000 Return Approximately 12% ◦ Revised Samples: 2000 Return Approximately 6% ◦ Change in the Return to Education: Less than 1 percentage point for any period  Initial Samples: Statistically different from 0 at less than a 10% significance level in periods 2008-2010  Revised Samples: Statistically different from 0 at less than a 10% significance level in 2007 where it falls slightly Results and Interpretations
  • 21. The Gender Gap ◦ Initial Samples: 2000 Gender Gap Approximately 42% in ln(wages) and 34% in weekly wages ◦ Revised Samples: 2000 Gender Gap Approximately 25% in ln(wages) and 22% in weekly wages ◦ Change in the Gender Gap: Approximately 5 percentage points from 2000-2010  Initial Samples: Shown to have fallen against the positive one sided alternative at less than a 10% significance level in periods 2001,2003,2005-2007, 2009-2010  Revised Samples: Shown to have fallen against the positive one sided alternative at less than a 10% significance level in periods 2003-2010 Results and Interpretations
  • 24. For Each Year 2001-2010 ◦ Generated 10 Random Error Terms, Normally Distributed with Mean 0 and Variance the Square of the Mean Standard Error ◦ Generated 10 New Dependent Variables Using the Estimated Coefficients, the Existing Data for the Independent Variables and the Random Error Terms ◦ Regressed the New Dependent Variables on the Existing Data for the Independent Variables ◦ Calculated the Mean of the Estimated Coefficients for Each Year ◦ Compared the Mean Coefficient Estimates to the Original Coefficient Estimates Monte Carlo Simulation
  • 27. The estimated return to education is practically small and primarily insignificant  The actual gender gap is a number between those estimated ◦ The estimations using the larger, less controlled samples estimate a larger gap ◦ The estimations using the smaller, more controlled samples estimate a smaller gap  For the purposes of this study, a basic wage equation seems adequate, although better data collection may lead to results closer to what has been reported in the population Conclusions