Consider the following model of wage determination log(wage,) = 3o+Biedu; + B₂zp. + B3 female; +34 female, x edu, + 3s female, x zp; + Beage, +₂ where edu, is educational attainment (measured in years of education completed), zp, is potential work experience (defined as work experience one could have if they had continuous employment immediately after completing their education, minus six years to account for the time before they started formal schooling), and female, is an indicator variable. The data you are working with codes gender as binary. Can you estimate this equation using OLS (ignoring, for now, the endogeneity present in the model)? O Yes No, because there is multicollinearity present O No, because there is perfect collinearity present
Consider the following model of wage determination log(wage,) = 3o+Biedu; + B₂zp. + B3 female; +34 female, x edu, + 3s female, x zp; + Beage, +₂ where edu, is educational attainment (measured in years of education completed), zp, is potential work experience (defined as work experience one could have if they had continuous employment immediately after completing their education, minus six years to account for the time before they started formal schooling), and female, is an indicator variable. The data you are working with codes gender as binary. Can you estimate this equation using OLS (ignoring, for now, the endogeneity present in the model)? O Yes No, because there is multicollinearity present O No, because there is perfect collinearity present
Algebra: Structure And Method, Book 1
(REV)00th Edition
ISBN:9780395977224
Author:Richard G. Brown, Mary P. Dolciani, Robert H. Sorgenfrey, William L. Cole
Publisher:Richard G. Brown, Mary P. Dolciani, Robert H. Sorgenfrey, William L. Cole
Chapter2: Working With Real Numbers
Section2.3: Rules For Addition
Problem 8P
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Transcribed Image Text:Consider the following model of wage determination
log(wage;)
Bo + Biedui + B₂xpi + 33 female; +34 female; x edu; + B5 female, x xpi + Boage; + uj
where edu, is educational attainment (measured in years of education completed), xp, is potential work experience (defined as work experience one could have if they had continuous employment immediately after completing their education, minus six
years to account for the time before they started formal schooling), and female; is an indicator variable. The data you are working with codes gender as binary. Can you estimate this equation using OLS (ignoring, for now, the endogeneity present in the
model)?
Ⓒ Yes
No, because there is multicollinearity present
O No, because there is perfect collinearity present
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