D Question 15 Recoming Part V: Omitted Variable Bias You are given a sample of 600 working individuals, with data on their sex and earnings. You construct the following model: wage Bo+Bimale + u, where the dependent variable is the annual earnings (wage) in $1,000. The explanatory variable is male (which equals one if a worker is male and equals zero if a worker is female). An OLS regression yields Bo=25.2, B₁ = 4.3. As we have learned in class, education matters a lot in explaining wages-more years of education lead to higher wages. In the above econometric model, education belongs to the error term, u. We also know that in our population of interest, males have lower drop-out rates than females. These facts imply that B₁ is biased and inconsistent because the estimation is subject to omitted variable bias. The correct model should be: wage Bo+Bimale + B₂educ + e, = where educ denotes the number of years of education. The OLS estimate of B₁ from our incorrect model presented at the beginning of this question can be approximated by B1B1+ B2cov(male,educ) var(male) Using the information from above, we can deduct that [Select] B1. You are given a sample of 600 working individuals, with data on their location and earnings. You construct the following model: wage=Bo+Binorth+u, where the dependent variable is the annual earnings (wage) in $1,000. The explanatory variable is north (which equals one if a worker lives in the north, and equals zero otherwise). An OLS regression yields B 24.8. B₁ = 2.1. = As we have learned in class, education matters a lot in explaining wages-more years of education lead to higher wages. In the above econometric model, education belongs to the error term, u. We also know that in our population of interest, workers in the north have lower drop-out rates than workers in the south. These facts imply that B₁ is biased and inconsistent because the estimation is subject to omitted variable bias. The correct model should be: wage Bo+Binorth + B₂educ + e, where educ denotes the number of years of education. The OLS estimate of B1 from our incorrect model presented at the beginning of this question can be approximated by B1B1+ B2 cov(north,educ) var(north) Using the information from above, we can deduct that [Select] B1.
D Question 15 Recoming Part V: Omitted Variable Bias You are given a sample of 600 working individuals, with data on their sex and earnings. You construct the following model: wage Bo+Bimale + u, where the dependent variable is the annual earnings (wage) in $1,000. The explanatory variable is male (which equals one if a worker is male and equals zero if a worker is female). An OLS regression yields Bo=25.2, B₁ = 4.3. As we have learned in class, education matters a lot in explaining wages-more years of education lead to higher wages. In the above econometric model, education belongs to the error term, u. We also know that in our population of interest, males have lower drop-out rates than females. These facts imply that B₁ is biased and inconsistent because the estimation is subject to omitted variable bias. The correct model should be: wage Bo+Bimale + B₂educ + e, = where educ denotes the number of years of education. The OLS estimate of B₁ from our incorrect model presented at the beginning of this question can be approximated by B1B1+ B2cov(male,educ) var(male) Using the information from above, we can deduct that [Select] B1. You are given a sample of 600 working individuals, with data on their location and earnings. You construct the following model: wage=Bo+Binorth+u, where the dependent variable is the annual earnings (wage) in $1,000. The explanatory variable is north (which equals one if a worker lives in the north, and equals zero otherwise). An OLS regression yields B 24.8. B₁ = 2.1. = As we have learned in class, education matters a lot in explaining wages-more years of education lead to higher wages. In the above econometric model, education belongs to the error term, u. We also know that in our population of interest, workers in the north have lower drop-out rates than workers in the south. These facts imply that B₁ is biased and inconsistent because the estimation is subject to omitted variable bias. The correct model should be: wage Bo+Binorth + B₂educ + e, where educ denotes the number of years of education. The OLS estimate of B1 from our incorrect model presented at the beginning of this question can be approximated by B1B1+ B2 cov(north,educ) var(north) Using the information from above, we can deduct that [Select] B1.
Chapter1: Making Economics Decisions
Section: Chapter Questions
Problem 1QTC
Related questions
Question
![D
Question 15
Recoming
Part V: Omitted Variable Bias
You are given a sample of 600 working individuals, with data on their sex and earnings. You construct
the following model:
wage Bo+Bimale + u,
where the dependent variable is the annual earnings (wage) in $1,000. The explanatory variable is
male (which equals one if a worker is male and equals zero if a worker is female). An OLS regression
yields Bo=25.2, B₁ = 4.3.
As we have learned in class, education matters a lot in explaining wages-more years of education lead
to higher wages. In the above econometric model, education belongs to the error term, u. We also know
that in our population of interest, males have lower drop-out rates than females. These facts imply that
B₁ is biased and inconsistent because the estimation is subject to omitted variable bias.
The correct model should be:
wage Bo+Bimale + B₂educ + e,
=
where educ denotes the number of years of education. The OLS estimate of B₁ from our incorrect model
presented at the beginning of this question can be approximated by
B1B1+ B2cov(male,educ)
var(male)
Using the information from above, we can deduct that [Select]
B1.](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2F2fc0ee15-0f91-464a-9c89-ff2e773a521f%2Fe6149954-5baa-418b-b192-0eb724eaa034%2Fihoff9_processed.jpeg&w=3840&q=75)
Transcribed Image Text:D
Question 15
Recoming
Part V: Omitted Variable Bias
You are given a sample of 600 working individuals, with data on their sex and earnings. You construct
the following model:
wage Bo+Bimale + u,
where the dependent variable is the annual earnings (wage) in $1,000. The explanatory variable is
male (which equals one if a worker is male and equals zero if a worker is female). An OLS regression
yields Bo=25.2, B₁ = 4.3.
As we have learned in class, education matters a lot in explaining wages-more years of education lead
to higher wages. In the above econometric model, education belongs to the error term, u. We also know
that in our population of interest, males have lower drop-out rates than females. These facts imply that
B₁ is biased and inconsistent because the estimation is subject to omitted variable bias.
The correct model should be:
wage Bo+Bimale + B₂educ + e,
=
where educ denotes the number of years of education. The OLS estimate of B₁ from our incorrect model
presented at the beginning of this question can be approximated by
B1B1+ B2cov(male,educ)
var(male)
Using the information from above, we can deduct that [Select]
B1.
![You are given a sample of 600 working individuals, with data on their location and earnings. You
construct the following model:
wage=Bo+Binorth+u,
where the dependent variable is the annual earnings (wage) in $1,000. The explanatory variable is
north (which equals one if a worker lives in the north, and equals zero otherwise). An OLS regression
yields B 24.8. B₁ = 2.1.
=
As we have learned in class, education matters a lot in explaining wages-more years of education lead
to higher wages. In the above econometric model, education belongs to the error term, u. We also know
that in our population of interest, workers in the north have lower drop-out rates than workers in the
south. These facts imply that B₁ is biased and inconsistent because the estimation is subject to omitted
variable bias.
The correct model should be:
wage Bo+Binorth + B₂educ + e,
where educ denotes the number of years of education. The OLS estimate of B1 from our incorrect model
presented at the beginning of this question can be approximated by
B1B1+ B2
cov(north,educ)
var(north)
Using the information from above, we can deduct that [Select]
B1.](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2F2fc0ee15-0f91-464a-9c89-ff2e773a521f%2Fe6149954-5baa-418b-b192-0eb724eaa034%2Fy0624p_processed.jpeg&w=3840&q=75)
Transcribed Image Text:You are given a sample of 600 working individuals, with data on their location and earnings. You
construct the following model:
wage=Bo+Binorth+u,
where the dependent variable is the annual earnings (wage) in $1,000. The explanatory variable is
north (which equals one if a worker lives in the north, and equals zero otherwise). An OLS regression
yields B 24.8. B₁ = 2.1.
=
As we have learned in class, education matters a lot in explaining wages-more years of education lead
to higher wages. In the above econometric model, education belongs to the error term, u. We also know
that in our population of interest, workers in the north have lower drop-out rates than workers in the
south. These facts imply that B₁ is biased and inconsistent because the estimation is subject to omitted
variable bias.
The correct model should be:
wage Bo+Binorth + B₂educ + e,
where educ denotes the number of years of education. The OLS estimate of B1 from our incorrect model
presented at the beginning of this question can be approximated by
B1B1+ B2
cov(north,educ)
var(north)
Using the information from above, we can deduct that [Select]
B1.
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