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1 Empirische Wirtschaftsforschung Tuesday May 6 20:19: Page 1 tm / / / / / / / / / / / / Statistics/Data Analysis User: Puriya Abbassi Project: Uebung2 wagereal log: u:\empwifo\cps92_04_dummy.smcl log type: smcl opened on: 6 May 2008, 20:18: use u:\empwifo\cps92_04.dta ** Deskriptive Statistiken ** su year ahe bachelor female age ** Einschraenkung der Stichprobe auf College-Absolventen ** keep if bachelor == 1 (8986 observations deleted) ** Umwandlung der Loehne von 1992 in 2004 US-Dollar ** ge wagereal = ahe if year == 2004 (2962 missing values generated) 17. replace wagereal = ahe * / if year == 1992 (2962 real changes made) ********************* Teilaufgabe 3 *********************** ** a) Loehne su wagereal if year == 1992 wagereal su wagereal if year == 1992 & female == 0 wagereal su wagereal if year == 1992 & female == wagereal
2 Empirische Wirtschaftsforschung Tuesday May 6 20:19: Page ** b) Loehne su wagereal if year == 2004 wagereal su wagereal if year == 2004 & female == 0 wagereal su wagereal if year == 2004 & female == 1 wagereal ** Ueber Dummies 32. ge male = 1 - female 33. su female male female male ********************* Teilaufgabe 4 *********************** ** a) Lohndifferenz scalar wagediff1992 = * t-test * scalar se1992 = sqrt( ^2/ ^2/1370) 44. scalar ttest1992 = wagediff1992 / se scalar list wagediff1992 se1992 ttest1992 wagediff1992 = se1992 = ttest1992 = * p-wert scalar pvalue1992 = normal(-abs(ttest1992))*2 51. scalar list pvalue1992 pvalue1992 = 1.674e * Konfidenzintervall 54.
3 Empirische Wirtschaftsforschung Tuesday May 6 20:19: Page scalar ci1992unten = wagediff * se scalar ci1992oben = wagediff * se scalar list ci1992unten ci1992oben ci1992unten = ci1992oben = ** Ueber Dummies reg wagereal male female if year == 1992, nocons robust tsscons Linear regression Number of obs = 2962 F( 2, 2960) = R-squared = Root MSE = male female lincom male - female ( 1) male - female = 0 (1) reg wagereal female if year == 1992, robust Linear regression Number of obs = 2962 F( 1, 2960) = R-squared = Root MSE = female _cons reg wagereal male if year==1992, robust Linear regression Number of obs = 2962 F( 1, 2960) = R-squared = Root MSE = male _cons
4 Empirische Wirtschaftsforschung Tuesday May 6 20:19: Page *********************************************************** ** b) Lohndifferenz scalar wagediff2004 = * t-test * 76. scalar se2004 = sqrt( ^2/ ^2/1739) 77. scalar ttest2004 = wagediff2004 / se scalar list wagediff2004 se2004 ttest2004 wagediff2004 = se2004 = ttest2004 = * p-wert scalar pvalue2004 = normal(-abs(ttest2004))*2 84. scalar list pvalue2004 pvalue2004 = 4.463e * Konfidenzintervall scalar ci2004unten = wagediff * se scalar ci2004oben = wagediff * se scalar list ci2004unten ci2004oben ci2004unten = ci2004oben = ** Ueber Dummies reg wagereal male female if year == 2004, nocons robust tsscons Linear regression Number of obs = 3640 F( 2, 3638) = R-squared = Root MSE = male female lincom male - female ( 1) male - female = 0 (1)
5 Empirische Wirtschaftsforschung Tuesday May 6 20:19: Page reg wagereal female if year == 2004, robust Linear regression Number of obs = 3640 F( 1, 3638) = R-squared = Root MSE = female _cons reg wagereal male if year==2004, robust Linear regression Number of obs = 3640 F( 1, 3638) = R-squared = Root MSE = male _cons ********************* Teilaufgabe 5 *********************** ** Veraenderung der Lohndifferenz scalar wagediff1992_2004 = wagediff wagediff * t-test 108. scalar se1992_2004 = sqrt(se1992^2 + se2004^2) 109. scalar ttest1992_2004 = wagediff1992_2004 / se1992_ scalar list wagediff1992_2004 se1992_2004 ttest1992_2004 wagediff1992_2004 = se1992_2004 = ttest1992_2004 = * p-wert scalar pvalue1992_2004 = normal(-abs(ttest1992_2004))* scalar list pvalue1992_2004 pvalue1992_2004 =
6 Empirische Wirtschaftsforschung Tuesday May 6 20:19: Page * Konfidenzintervall scalar ci1992_2004unten = wagediff1992_ * se1992_ scalar ci1992_2004oben = wagediff1992_ * se1992_ scalar list ci1992_2004unten ci1992_2004oben ci1992_2004unten = ci1992_2004oben = ** Ueber Dummies 127. ge D1992 = year == ge D2004 = year == ge D1992_male = D1992 * male 131. ge D1992_female = D1992 * female 132. ge D2004_male = D2004 * male 133. ge D2004_female = D2004 * female reg wagereal D1992_male D1992_female D2004_male D2004_female, nocons robust tsscons Linear regression Number of obs = 6602 F( 4, 6598) = R-squared = Root MSE = D1992_male D1992_female D2004_male D2004_female lincom D1992_male - D1992_female ( 1) D1992_male - D1992_female = 0 (1) lincom D2004_male - D2004_female ( 1) D2004_male - D2004_female = 0 (1)
7 Empirische Wirtschaftsforschung Tuesday May 6 20:19: Page lincom (D2004_male-D2004_female) - (D1992_male-D1992_female) ( 1) - D1992_male + D1992_female + D2004_male - D2004_female = 0 (1) *********************************************************** */ log close log: u:\empwifo\cps92_04_dummy.smcl log type: smcl closed on: 6 May 2008, 20:18:09
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