1|Page
The Nature of Econometrics and Economic Data
SOLUTION AMANUAL LATEST EDITION
2025/2026
C1.1 (i) The average of educ is about 12.6 years. There are two people reporting zero years of
education and 19 people reporting 18 years of education.
(ii) The average of wage in the sample is about $5.90, which seems low.
(iii) Using Table B-60 in the 2004 Economic Report of the President, the CPI was 56.9 in
1976 and 233 in 2013.
(iv) To convert 1976 dollars into 2013 dollars, we use the ratio of the CPIs, which is
.9 ≈ 4.09. Therefore, the average hourly wage in 2013 dollars is roughly
4.09($5.90) ≈ $24.13, which is a reasonable figure.
(v) The sample contains 252 women (the number of observations with female = 1) and 274
men.
C1.3 (i) The largest is 100, the smallest is 0.
(ii) 289 out of 1,823, or about 15.85 percent of the sample.
(iii) 17
(iv) The average of math4 is about 71.9 and the average of read4 is about 60.1. So, at least
in 2001, the reading test was harder to pass.
(v) The sample correlation between math4 and read4 is about .843, which is a very high
degree of (linear) association. Not surprisingly, schools that have high pass rates on one test
have a strong tendency to have high pass rates on the other test.
,2|Page
(vi) The average of exppp is about $5,194.87. The standard deviation is $1,091.89, which
shows rather wide variation in spending per pupil. [The minimum is $1,206.88 and the
maximum is $11,957.64.]
(vii) The percentage by which school A outspends school B is
(vii) The percentage by which school A outspends school B is
(6,000 − 5,500)
100 ∙ 5,500 ≈ 9.09%.
,3|Page
When we use the approximation based on the difference in the natural logs we get a somewhat
smaller number:
100 ∙ [log(6,000) − log(5,500)] ≈ 8.71%.
C1.5 (i) The smallest and largest values of children are 0 and 13, respectively. The average is
about 2.27.
(ii) Out of 4,358 women, only 611 have electricity in the home, or about 14.02 percent.
(iii) The average of children for women without electricity is about 2.33, and for those with
electricity it is about 1.90. So, on average, women with electricity have .43 fewer children than
those who do not.
(iv) We cannot infer causality here. There are many confounding factors that may be
related to the number of children and the presence of electricity in the home; household
income and level of education are two possibilities. For example, it could be that women with
more education have fewer children and are more likely to have electricity in the home (the
latter due to an income effect).
C1.7 (i) The percentage of men in the sample report abusing alcohol is 9.9. The employment
rate is 24.3.
(ii) The employment rate of men who abuse alcohol is 22.6.
(iii) The employment rate who do not abuse alcohol is 24.5.
(iv) The employment rates of men who abuse alcohol and who do not are 22.6 and 24.5,
respectively. The difference in these employment rates is very less, which means that alcohol
abuse does not cause unemployment.
, 4|Page
CHAPTER 2
The Simple Regression Model
SOLUTIONS TO PROBLEMS
2.1 (i) Income, age, and family background (such as number of siblings) are just a few
possibilities. It seems that each of these could be correlated with years of education. (Income
and education are probably positively correlated; age and education may be negatively
The Nature of Econometrics and Economic Data
SOLUTION AMANUAL LATEST EDITION
2025/2026
C1.1 (i) The average of educ is about 12.6 years. There are two people reporting zero years of
education and 19 people reporting 18 years of education.
(ii) The average of wage in the sample is about $5.90, which seems low.
(iii) Using Table B-60 in the 2004 Economic Report of the President, the CPI was 56.9 in
1976 and 233 in 2013.
(iv) To convert 1976 dollars into 2013 dollars, we use the ratio of the CPIs, which is
.9 ≈ 4.09. Therefore, the average hourly wage in 2013 dollars is roughly
4.09($5.90) ≈ $24.13, which is a reasonable figure.
(v) The sample contains 252 women (the number of observations with female = 1) and 274
men.
C1.3 (i) The largest is 100, the smallest is 0.
(ii) 289 out of 1,823, or about 15.85 percent of the sample.
(iii) 17
(iv) The average of math4 is about 71.9 and the average of read4 is about 60.1. So, at least
in 2001, the reading test was harder to pass.
(v) The sample correlation between math4 and read4 is about .843, which is a very high
degree of (linear) association. Not surprisingly, schools that have high pass rates on one test
have a strong tendency to have high pass rates on the other test.
,2|Page
(vi) The average of exppp is about $5,194.87. The standard deviation is $1,091.89, which
shows rather wide variation in spending per pupil. [The minimum is $1,206.88 and the
maximum is $11,957.64.]
(vii) The percentage by which school A outspends school B is
(vii) The percentage by which school A outspends school B is
(6,000 − 5,500)
100 ∙ 5,500 ≈ 9.09%.
,3|Page
When we use the approximation based on the difference in the natural logs we get a somewhat
smaller number:
100 ∙ [log(6,000) − log(5,500)] ≈ 8.71%.
C1.5 (i) The smallest and largest values of children are 0 and 13, respectively. The average is
about 2.27.
(ii) Out of 4,358 women, only 611 have electricity in the home, or about 14.02 percent.
(iii) The average of children for women without electricity is about 2.33, and for those with
electricity it is about 1.90. So, on average, women with electricity have .43 fewer children than
those who do not.
(iv) We cannot infer causality here. There are many confounding factors that may be
related to the number of children and the presence of electricity in the home; household
income and level of education are two possibilities. For example, it could be that women with
more education have fewer children and are more likely to have electricity in the home (the
latter due to an income effect).
C1.7 (i) The percentage of men in the sample report abusing alcohol is 9.9. The employment
rate is 24.3.
(ii) The employment rate of men who abuse alcohol is 22.6.
(iii) The employment rate who do not abuse alcohol is 24.5.
(iv) The employment rates of men who abuse alcohol and who do not are 22.6 and 24.5,
respectively. The difference in these employment rates is very less, which means that alcohol
abuse does not cause unemployment.
, 4|Page
CHAPTER 2
The Simple Regression Model
SOLUTIONS TO PROBLEMS
2.1 (i) Income, age, and family background (such as number of siblings) are just a few
possibilities. It seems that each of these could be correlated with years of education. (Income
and education are probably positively correlated; age and education may be negatively
