Assignment-log linear model with dummy variable regressor A least squares regression model explaining the sample variation of LOGEARN,, the logarithm of reported weekly earnings for individual i, yields the estimated Bivariate Regression Model LOGEARN = 5.99+ .383 gender, + U₁ where gender, is a dummy variable which is, in this case, one for females and zero for males. The issue of how to evaluate the precision of the parameter estimates in a model such as this will be discussed in Chapters 6 and 7: you may ignore these issues in answering this question. a. What interpretation can be given to this estimated coefficient on the variable gender,? In particular, what can one say about how the expected value of the logarithm of weekly earnings depends on gender? b. What can one say about how the expected value of household earnings itself depends on gender? c. Suppose that it becomes apparent that females are, on average, better educated than males. Presuming that well-educated individuals have a higher marginal product of labor, and hence earn more, than less-educated individuals, what would one expect to happen to the coefficient on gender, if an education variable were included in the model? Suppose that- due either to inadvertence or to a lack of data-the education variable is omitted from the model. How would the interpretation of the coefficient on gender, in the above Bivariate Regression Model change in that case?
Assignment-log linear model with dummy variable regressor A least squares regression model explaining the sample variation of LOGEARN,, the logarithm of reported weekly earnings for individual i, yields the estimated Bivariate Regression Model LOGEARN = 5.99+ .383 gender, + U₁ where gender, is a dummy variable which is, in this case, one for females and zero for males. The issue of how to evaluate the precision of the parameter estimates in a model such as this will be discussed in Chapters 6 and 7: you may ignore these issues in answering this question. a. What interpretation can be given to this estimated coefficient on the variable gender,? In particular, what can one say about how the expected value of the logarithm of weekly earnings depends on gender? b. What can one say about how the expected value of household earnings itself depends on gender? c. Suppose that it becomes apparent that females are, on average, better educated than males. Presuming that well-educated individuals have a higher marginal product of labor, and hence earn more, than less-educated individuals, what would one expect to happen to the coefficient on gender, if an education variable were included in the model? Suppose that- due either to inadvertence or to a lack of data-the education variable is omitted from the model. How would the interpretation of the coefficient on gender, in the above Bivariate Regression Model change in that case?
Chapter1: Making Economics Decisions
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Transcribed Image Text:Assignment-log linear model with dummy variable regressor
A least squares regression model explaining the sample variation of LOGEARN,, the
logarithm of reported weekly earnings for individual i, yields the estimated Bivariate
Regression Model
LOGEARN = 5.99+ .383 gender, + U₁
where gender, is a dummy variable which is, in this case, one for females and zero for males.
The issue of how to evaluate the precision of the parameter estimates in a model such as this
will be discussed in Chapters 6 and 7: you may ignore these issues in answering this question.
a. What interpretation can be given to this estimated coefficient on the variable gender,? In
particular, what can one say about how the expected value of the logarithm of weekly
earnings depends on gender?
b. What can one say about how the expected value of household earnings itself depends on
gender?
c. Suppose that it becomes apparent that females are, on average, better educated than males.
Presuming that well-educated individuals have a higher marginal product of labor, and
hence earn more, than less-educated individuals, what would one expect to happen to the
coefficient on gender, if an education variable were included in the model? Suppose that-
due either to inadvertence or to a lack of data-the education variable is omitted from the
model. How would the interpretation of the coefficient on gender, in the above Bivariate
Regression Model change in that case?
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