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Chapter 8: Evidence on Gender Gap in Earnings

Chapter 8: Evidence on Gender Gap in Earnings. Already have established that gender earnings gap exists. Sources: Human capital: partially from different lifetime work experience; Discrimination: hard to measure Quantitative methods : 1) Regression analysis

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Chapter 8: Evidence on Gender Gap in Earnings

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  1. Chapter 8: Evidence on Gender Gap in Earnings • Already have established that gender earnings gap exists. • Sources: • Human capital: partially from different lifetime work experience; • Discrimination: hard to measure • Quantitative methods: • 1) Regression analysis • 2) Oaxaca decomposition (extension of regression)

  2. Regression Analysis • Review Chapter 1 discussion on your own. • Earnings regression equation: • Y =  + 1YrsEduc + 2WorkExp + 3AA + 4Female +  Y = earnings = dependent variable 4 independent variables (Xs) • and s : regression coefficients 1 = effect of  education by one year on earnings, holding other Xs fixed.  (mew): effect of unobserved factors on Y

  3. Regression continued • See 0-1 variables (called dummy variables): 4 measures impact of being female on earnings. • Data: imagine a lotus spreadsheet, with 5 columns. • Person 1’s info is in row 1: include info on earnings and all four Xs. • Next row is person 2. • OLS regression: • Regression through the mean so gives an average estimate of impact of one unit change X on Y. • Gives “best” values of  and s: ones that make predicted value of Y closest to actual value of Y.

  4. Continued • Other Xs could include: • Region of country; • Urban/rural residence; • Try to avoid including factors that might be result of discrimination: • Occupation; • Union status dummy variable. • Other details: • Remember to look for statistical significance. • Data sources: CPS, NLSY, SIPP, NSAF.

  5. Measured vs Unmeasured Factors • The estimated coefficients pick up effects of measured characteristics. • The error term picks up effect of unmeasured factors, which are often important. • Discrimination: some researchers use this error term as estimate of role of discrimination but it can under-state or over-state discrimination. • Some measured Xs themselves are results of discrimination. • There are unobserved factors that legitimately affect wages, such as motivation, quality of training, etc.

  6. Example • Revise regression equation so that dependent variable is the natural log of the hourly wage: •  associated with years of education is an estimate of the returns to education. • If  = 0.08: increasing education by one year results in an 8% increase in hourly wages, ceteris paribus. • Looking at  associated with being female: • If  = -0.10 then women earn 10% less per hour than men, ceteris paribus.

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