Chapter 1 - The Nature of Econometrics and Economic Data - Computer Exercises
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name: SNlog: ~Wooldridge\intro-econx\iproblem1.smcllog type: smclopened on: 23 Jan 2019, 01:20:58. **********************************************. * 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
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| 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 femalestorage display value
variable name type format label variable label
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female byte %8.0g =1 if female. count if female==1252. count if female==0 274. **Problem.C2. use bwght.dta, clear. *i. Women in the sample? how many women smoking during pregnancy?. ddescribe cigsstorage display value
variable name type format label variable label
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cigs byte %8.0g cigs smked per day while preg. count if cigs>0212. display _N1388. *ii. Average cigs. mean cigs
Mean estimation Number of obs = 1,388
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| 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 _N1823. *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
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| 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 | 1.0000read4 | 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 trainstorage 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
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| 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==145. count if train==1185. display 45/85.52941176. count if train==0 & unem78==192. count if train==0260. 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==1611. 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
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| Mean Std. Err. [95% Conf. Interval]
-------------+------------------------------------------------
children | 1.898527 .0729547 1.755254 2.0418
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. mean children if electric==0
Mean estimation Number of obs = 3,747
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| 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 _N2197. 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 | 1.0000execs | 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 closename: SNlog: ~Wooldridge\intro-econx\iproblem1.smcllog type: smclclosed on: 23 Jan 2019, 01:20:58
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