7. Imagine you regress earnings of individuals on a constant, a binary variable ("Male") which takes on the value 1 for males and is 0 otherwise, and another binary variable ("Female ") which takes on the value 1 for females and is 0 otherwise Because females typically earn less than males, you would expect a) the coefficient for Male to have a positive sign, and for Female a negative sign b) the coefficient for Male to have a negative sign, and for Female a positive sign. c) none of the OLS estimators to exist because there is perfect multicollinearity. d) both the coefficient for Male and that for Female to have positive signs

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7. Imagine you regress earnings of individuals on a constant, a binary variable
("Male") which takes on the value 1 for males and is 0 otherwise, and another binary
variable ("Female ") which takes on the value 1 for females and is 0 otherwise
Because females typically earn less than males, you would expect
a) the coefficient for Male to have a positive sign, and for Female a negative sign
b) the coefficient for Male to have a negative sign, and for Female a positive sign.
c) none of the OLS estimators to exist because there is perfect multicollinearity.
d) both the coefficient for Male and that for Female to have positive signs
Transcribed Image Text:7. Imagine you regress earnings of individuals on a constant, a binary variable ("Male") which takes on the value 1 for males and is 0 otherwise, and another binary variable ("Female ") which takes on the value 1 for females and is 0 otherwise Because females typically earn less than males, you would expect a) the coefficient for Male to have a positive sign, and for Female a negative sign b) the coefficient for Male to have a negative sign, and for Female a positive sign. c) none of the OLS estimators to exist because there is perfect multicollinearity. d) both the coefficient for Male and that for Female to have positive signs
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