Chapter 1 - The Nature of Econometrics and Economic Data - Computer Exercises
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name: SN
log: ~Wooldridge\intro-econx\iproblem1.smcl
log type: smcl
opened on: 23 Jan 2019, 01:20:58
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. * Solomon Negash - Solutions to Computer Exercises
. * Wooldridge (2016). Introductory Econometrics: A Modern Approach. 6th ed.
. * STATA Program, version 15.1.
. * Chapter 1 - The Nature of Econometrics and Economic Data
. * Computer Exercises (Problems)
. ******************** SETUP *********************
. **Problem.C1
. use wage1.dta, clear
. *i Average, minimum and maximum years of education
. sum educ
Variable | Obs Mean Std. Dev. Min Max
-------------+---------------------------------------------------------
educ | 526 12.56274 2.769022 0 18
. *ii Average hourly wage
. mean wage
Mean estimation Number of obs = 526
--------------------------------------------------------------
| Mean Std. Err. [95% Conf. Interval]
-------------+------------------------------------------------
wage | 5.896103 .1610262 5.579768 6.212437
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. *iii. CPI_1976 = 56.9 CPI_2010 = 218.056. Source: usinflationcalculator.com accessed 071118
. *iv.
. *v. How many women? men?
. describe female
storage display value
variable name type format label variable label
-------------------------------------------------------------------------------------
female byte %8.0g =1 if female
. count if female==1
252
. count if female==0
274
. **Problem.C2
. use bwght.dta, clear
. *i. Women in the sample? how many women smoking during pregnancy?
. ddescribe cigs
storage display value
variable name type format label variable label
-------------------------------------------------------------------------------------
cigs byte %8.0g cigs smked per day while preg
. count if cigs>0
212
. display _N
1388
. *ii. Average cigs
. mean cigs
Mean estimation Number of obs = 1,388
--------------------------------------------------------------
| Mean Std. Err. [95% Conf. Interval]
-------------+------------------------------------------------
cigs | 2.087176 .1603153 1.772689 2.401663
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. *iii. Average cigs among smoking women
. sum cigs if cigs>0
Variable | Obs Mean Std. Dev. Min Max
-------------+---------------------------------------------------------
cigs | 212 13.66509 8.690907 1 50
. *iv. Average fatheduc
. sum fatheduc
Variable | Obs Mean Std. Dev. Min Max
-------------+---------------------------------------------------------
fatheduc | 1,192 13.18624 2.745985 1 18
. *v. Average and Standard deviation of Family income
. sum faminc
Variable | Obs Mean Std. Dev. Min Max
-------------+---------------------------------------------------------
faminc | 1,388 29.02666 18.73928 .5 65
. **Problem.C3
. u meap01, clear
(Written by R. )
. *i. max & min of math4
. sum math4
Variable | Obs Mean Std. Dev. Min Max
-------------+---------------------------------------------------------
math4 | 1,823 71.909 19.95409 0 100
. *ii. How many schools (%) have a perfect pass rate of math4?
. count if math4==100
38
. display _N
1823
. *iii. exactly 50%?
. count if math4==50
17
. *iv. Average pass rate for math4 & read4
. mean math4 read4
Mean estimation Number of obs = 1,823
--------------------------------------------------------------
| Mean Std. Err. [95% Conf. Interval]
-------------+------------------------------------------------
math4 | 71.909 .4673461 70.99241 72.82559
read4 | 60.06188 .44845 59.18235 60.94141
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. *v. Correlation between math4 & read4
. corr math4 read4
(obs=1,823)
| math4 read4
-------------+------------------
math4 | 1.0000
read4 | 0.8427 1.0000
. *vi. Average and SD of exppp
. sum exppp
Variable | Obs Mean Std. Dev. Min Max
-------------+---------------------------------------------------------
exppp | 1,823 5194.865 1091.89 1206.882 11957.64
. display r(mean) " and " r(sd)
5194.8655 and 1091.8896
. *vii. %age comaparison
. display 100*[(6000-5500)/5500]
9.0909091
. display 100*[ln(6000)-ln(5500)]
8.7011377
. **Problem.C4
. u jtrain2.dta, clear
. *i. Fraction of men receiving job training
. d train
storage display value
variable name type format label variable label
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train byte %9.0g =1 if assigned to job training
. mean train
Mean estimation Number of obs = 445
--------------------------------------------------------------
| Mean Std. Err. [95% Conf. Interval]
-------------+------------------------------------------------
train | .4157303 .0233895 .3697624 .4616982
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. *ii. Average re78 for men receiving and not receiving job training
. sum re78 if train==1
Variable | Obs Mean Std. Dev. Min Max
-------------+---------------------------------------------------------
re78 | 185 6.349145 7.867405 0 60.3079
. sum re78 if train==0
Variable | Obs Mean Std. Dev. Min Max
-------------+---------------------------------------------------------
re78 | 260 4.554802 5.483837 0 39.4835
. *iii.
. d unem78
storage display value
variable name type format label variable label
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unem78 byte %9.0g =1 if unem. all of 1978
. count if train==1 & unem78==1
45
. count if train==1
185
. display 45/85
.52941176
. count if train==0 & unem78==1
92
. count if train==0
260
. display 92/260
.35384615
. *iv. was the job training program effective?
. **Problem.C5
. u fertil2, clear
. *i. Max, min & mean of children
. sum children
Variable | Obs Mean Std. Dev. Min Max
-------------+---------------------------------------------------------
children | 4,361 2.267828 2.222032 0 13
. *ii. %age of Women who have electricity
. count if electric==1
611
. display 100*r(N)/_N "%"
14.010548%
. *iii. Average of children for women who have and have not electricity
. mean children if electric==1
Mean estimation Number of obs = 611
--------------------------------------------------------------
| Mean Std. Err. [95% Conf. Interval]
-------------+------------------------------------------------
children | 1.898527 .0729547 1.755254 2.0418
--------------------------------------------------------------
. mean children if electric==0
Mean estimation Number of obs = 3,747
--------------------------------------------------------------
| Mean Std. Err. [95% Conf. Interval]
-------------+------------------------------------------------
children | 2.327729 .0372054 2.254784 2.400674
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. *iv. can you infer that having electricity “causes” women to have fewer children?
. **Problem.C6
. u countymurders, clear
(Written by R. )
. keep if year==1996
(35,152 observations deleted)
. *i. How many countries in total? how many with zero murders
. display _N
2197
. count if murders==0
1,051
. *ii. Maximum murders? Maximum executions? Average executions?
. sum murders execs
Variable | Obs Mean Std. Dev. Min Max
-------------+---------------------------------------------------------
murders | 2,197 6.390077 39.76102 0 1403
execs | 2,197 .0159308 .14226 0 3
. *iii. Correlation rate between murders & execs
. corr murders execs
(obs=2,197)
| murders execs
-------------+------------------
murders | 1.0000
execs | 0.2095 1.0000
. *iv. Do you think that more executions cause more murders to occur?
. **Problem.C7
. u alcohol, clear
(Written by R. )
. *i. %age of men abusing alcohol? employment rate?
. sum abuse
Variable | Obs Mean Std. Dev. Min Max
-------------+---------------------------------------------------------
abuse | 9,822 .0991651 .2988988 0 1
. display 100*r(mean) "%"
9.9165139%
. sum unem
Variable | Obs Mean Std. Dev. Min Max
-------------+---------------------------------------------------------
unemrate | 9,822 5.569212 1.505064 2.8 10.9
. display 100-r(mean) "%"
94.430788%
. *ii. Emplyment rate for alcohol abusers?
. sum unem if abuse==1
Variable | Obs Mean Std. Dev. Min Max
-------------+---------------------------------------------------------
unemrate | 974 5.515708 1.507293 2.8 10.9
. display 100-r(mean) "%"
94.484292%
. *iii. Emplyment rate for non-abusers
. sum unem if abuse==0
Variable | Obs Mean Std. Dev. Min Max
-------------+---------------------------------------------------------
unemrate | 8,848 5.575102 1.504787 2.8 10.9
. display 100-r(mean) "%"
94.424898%
. *iv. Discuss the difference in answers to parts(ii) and (iii).
. log close
name: SN
log: ~Wooldridge\intro-econx\iproblem1.smcl
log type: smcl
closed on: 23 Jan 2019, 01:20:58
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