Question diveded to three parts, so asked in three different questions:  part 1:  This problem is inspired by a study of the “gender gap” in earnings in top corporate jobs. The study compares total compensation among top executives in a large set of U.S. public corporations in the 1990s. a) Let Female be an indicator variable that is equal to 1 for females and 0 for males. A regression of the logarithm of earnings onto Female yields ln(Earnings)=6.48-0.44Female+uˆ, SER = 2.65. (0.01) (0.05) i) The estimated coefficient on Female is –0.44. Explain what this value means. ii) The SER is 2.65. Explain what this value means.

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Question diveded to three parts, so asked in three different questions: 

part 1: 

This problem is inspired by a study of the “gender gap” in earnings in top corporate jobs. The study compares total compensation among top executives in a large set of U.S. public corporations in the 1990s.
a) Let Female be an indicator variable that is equal to 1 for females and 0 for males. A
regression of the logarithm of earnings onto Female yields ln(Earnings)=6.48-0.44Female+uˆ, SER = 2.65.
(0.01) (0.05)
i) The estimated coefficient on Female is –0.44. Explain what this value means.
ii) The SER is 2.65. Explain what this value means.

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