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Decomposition of the Gender Wage Gap using the LASSO Estimator. (2020). Böheim, René ; Stollinger, Philipp ; Boheim, Rene.
In: Economics working papers.
RePEc:jku:econwp:2020-03.

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  1. Locus of control and Human Capital Investment Decisions: The Role of Effort, Parental Preferences and Financial Constraints. (2020). Szabo-Morvai, Agnes ; Kiss, Hubert Janos.
    In: CERS-IE WORKING PAPERS.
    RePEc:has:discpr:2055.

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  2. Appendix Table 1: Descriptive Statistics by Sex, 2006 and 2016.
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  21. Note: Weighted data for 2016 for heads and their spouses who were between 25 and 64 years of age, who earned an hourly wage of at least US$2, and who worked for at least 26 weeks. Non-farming, non-military, non-self-employed wage and salary workers. Excluding all persons with missing values for any of the explanatory variables of the wage regressions. Table 2: Comparison of Different Regression Models.
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  24. Significance codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘◦ ’ 0.1, significance codes for POSTLASSO estimates calculated by the method proposed by Belloni, Chernozhukov and Kato (2014).
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  27. Table 3: Oaxaca-Blinder Decomposition for 2016 - Grouped Variables.
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  28. Table 4: Smith-Welch Decomposition of the Change in the Gender Wage Gap between 2006 and 2016.
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  34. Weighted data for 2016 for heads and their spouses who were between 25 and 64 years of age, who earned an hourly wage of at least US$2, and who worked for at least 26 weeks. Non-farming, non-military, non-self-employed wage and salary workers. Excluding all persons with missing values for any of the explanatory variables of the wage regressions.
    Paper not yet in RePEc: Add citation now
  35. Weighted data for 2016 for heads and their spouses who were between 25 and 64 years of age, who earned an hourly wage of at least US$2, and who worked for at least 26 weeks. Non-farming, non-military, non-self-employed wage and salary workers. Excluding all persons with missing values for any of the explanatory variables of the wage regressions. N = 3,390 women and 2,985 men.
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  36. Weighted data for 2016 for heads and their spouses who were between 25 and 64 years of age, who earned an hourly wage of at least US$2, and who worked for at least 26 weeks. Non-farming, non-military, non-self-employed wage and salary workers. Excluding all persons with missing values for any of the explanatory variables of the wage regressions. N = 3,390 women and 2,985 men.
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  37. Women Men OLSall POSTLASSO OLSall POSTLASSO # observations 3,390 3,390 2,985 2,985 # coefficients 73 57 73 57 σ̂2 MPE 0.2013 0.2003 0.2321 0.2302 adj. R2 0.5014 0.4983 0.5291 0.5262 Note: The table shows number of non-zero coefficients generated by different models, the error variance estimated based on the mean squared prediction error generated by cross-validation, and the adjusted coefficient of determination for different models by gender. OLSall is based on an OLS specification that uses all explanatory variables. POSTLASSO is a re-estimation by OLS-regression of the wage regressions including only the explanatory variables selected by the LASSO-estimator according to the one standard error rule.
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  38. Year Women Men Women − Men # of observations 2006 2,756 2,451 305 2016 3,390 2,985 405 Source: Authors’ calculations. Data from PSID.
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