QUESTION 4 You have data on 4,290 men. The data contain an indicator for whether the individual has ever spent time in jail (jail), an indicator for completing compulsory school (compulsory), as well as age and an dummy variable indicating residence in an urban area. You want to estimate the causal effect of completing compulsory education on the likelihood of spending time in jail. You are concerned that the indicator for compulsory school is correlated with the error term. The data set contains an indicator for being born in the first quarter of the year (quarter1). Birth month determines age of first entry into school in the USA. Individuals born in the first quarter of the year start school after turning 6. Since it is legal to drop out of school at age 16, this implies that these individuals can drop out of school before completing 10 years of school. You obtain the following IV regression results: . ivregress 2sls jail c.age##c.age i.urban (compulsory = quarter1) Instrumental variables (2SLS) regression Number of obs 4,290 %3D Wald chi2(4) 747.82 Prob > chi2 0.0000 R-squared 0.1011 Root MSE .46831 jail | Coef. Std. Err. P>|z| [95% Conf. Interval] compulsory | age | -.4936852 .2143447 -2.30 0.021 -.9137931 -.0735774 .1410853 .0062064 22.73 0.000 .1289209 .1532497 c.age#c.age I -.0021272 .0000905 -23.50 0.000 -.0023046 -.0019498 1.urban | .0478431 .0307336 1.56 0.120 -.0123935 .1080798 „cons | -1.205115 1216564 -9.91 0.000 -1.443557 -.9666725 Instrumented: compulsory age c.age#c.age 1.urban quarter1 Instruments: Based on the number of instruments used in this regression, the model is over-identified. O True O False
QUESTION 4 You have data on 4,290 men. The data contain an indicator for whether the individual has ever spent time in jail (jail), an indicator for completing compulsory school (compulsory), as well as age and an dummy variable indicating residence in an urban area. You want to estimate the causal effect of completing compulsory education on the likelihood of spending time in jail. You are concerned that the indicator for compulsory school is correlated with the error term. The data set contains an indicator for being born in the first quarter of the year (quarter1). Birth month determines age of first entry into school in the USA. Individuals born in the first quarter of the year start school after turning 6. Since it is legal to drop out of school at age 16, this implies that these individuals can drop out of school before completing 10 years of school. You obtain the following IV regression results: . ivregress 2sls jail c.age##c.age i.urban (compulsory = quarter1) Instrumental variables (2SLS) regression Number of obs 4,290 %3D Wald chi2(4) 747.82 Prob > chi2 0.0000 R-squared 0.1011 Root MSE .46831 jail | Coef. Std. Err. P>|z| [95% Conf. Interval] compulsory | age | -.4936852 .2143447 -2.30 0.021 -.9137931 -.0735774 .1410853 .0062064 22.73 0.000 .1289209 .1532497 c.age#c.age I -.0021272 .0000905 -23.50 0.000 -.0023046 -.0019498 1.urban | .0478431 .0307336 1.56 0.120 -.0123935 .1080798 „cons | -1.205115 1216564 -9.91 0.000 -1.443557 -.9666725 Instrumented: compulsory age c.age#c.age 1.urban quarter1 Instruments: Based on the number of instruments used in this regression, the model is over-identified. O True O False
Glencoe Algebra 1, Student Edition, 9780079039897, 0079039898, 2018
18th Edition
ISBN:9780079039897
Author:Carter
Publisher:Carter
Chapter4: Equations Of Linear Functions
Section4.5: Correlation And Causation
Problem 11PPS
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![QUESTION 4
You have data on 4,290 men. The data contain an indicator for whether the individual has ever
spent time in jail (jail), an indicator for completing compulsory school (compulsory), as well as
age and an dummy variable indicating residence in an urban area.
You want to estimate the causal effect of completing compulsory education on the likelihood of
spending time in jail. You are concerned that the indicator for compulsory school is correlated
with the error term. The data set contains an indicator for being born in the first quarter of the
year (quarter1). Birth month determines age of first entry into school in the USA. Individuals
born in the first quarter of the year start school after turning 6. Since it is legal to drop out of
school at age 16, this implies that these individuals can drop out of school before completing
10 years of school. You obtain the following IV regression results:
. ivregress 2sls jail c.age##c.age i.urban (compulsory = quarter1)
Instrumental variables (2SLS) regression
Number of obs
4,290
%3D
Wald chi2(4)
747.82
Prob > chi2
0.0000
R-squared
0.1011
Root MSE
.46831
jail |
Coef.
Std. Err.
P>|z|
[95% Conf. Interval]
compulsory |
age |
-.4936852
.2143447
-2.30
0.021
-.9137931
-.0735774
.1410853
.0062064
22.73
0.000
.1289209
.1532497
c.age#c.age I
-.0021272
.0000905
-23.50
0.000
-.0023046
-.0019498
1.urban |
.0478431
.0307336
1.56
0.120
-.0123935
.1080798
„cons |
-1.205115
1216564
-9.91
0.000
-1.443557
-.9666725
Instrumented:
compulsory
age c.age#c.age 1.urban quarter1
Instruments:
Based on the number of instruments used in this regression, the model is over-identified.
O True
O False](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2F21c69e4a-662f-4e66-806f-5b90389d8382%2F54059558-be73-4927-bbe6-a77099e086f0%2F9ezxb7q_processed.png&w=3840&q=75)
Transcribed Image Text:QUESTION 4
You have data on 4,290 men. The data contain an indicator for whether the individual has ever
spent time in jail (jail), an indicator for completing compulsory school (compulsory), as well as
age and an dummy variable indicating residence in an urban area.
You want to estimate the causal effect of completing compulsory education on the likelihood of
spending time in jail. You are concerned that the indicator for compulsory school is correlated
with the error term. The data set contains an indicator for being born in the first quarter of the
year (quarter1). Birth month determines age of first entry into school in the USA. Individuals
born in the first quarter of the year start school after turning 6. Since it is legal to drop out of
school at age 16, this implies that these individuals can drop out of school before completing
10 years of school. You obtain the following IV regression results:
. ivregress 2sls jail c.age##c.age i.urban (compulsory = quarter1)
Instrumental variables (2SLS) regression
Number of obs
4,290
%3D
Wald chi2(4)
747.82
Prob > chi2
0.0000
R-squared
0.1011
Root MSE
.46831
jail |
Coef.
Std. Err.
P>|z|
[95% Conf. Interval]
compulsory |
age |
-.4936852
.2143447
-2.30
0.021
-.9137931
-.0735774
.1410853
.0062064
22.73
0.000
.1289209
.1532497
c.age#c.age I
-.0021272
.0000905
-23.50
0.000
-.0023046
-.0019498
1.urban |
.0478431
.0307336
1.56
0.120
-.0123935
.1080798
„cons |
-1.205115
1216564
-9.91
0.000
-1.443557
-.9666725
Instrumented:
compulsory
age c.age#c.age 1.urban quarter1
Instruments:
Based on the number of instruments used in this regression, the model is over-identified.
O True
O False
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