*******Verdienststrukturerhebung 2010********* ********************************************** ********************************************** ********* Syntax 3: Descriptives ************* ********************************************** clear clear matrix set more off, perm set scrollbufsize 50000 set matsize 11000, perm numlabel _all, add set rmsg on use "${orig}\vse_work.dta", clear capture log using ${log}\3_desc, text replace *Descriptive tab empl_firm tab bargaining, gen(barg_) tab ef9, gen(perf_) * Table 1 sum wage, d sum wage if female==1, d sum wage if female==0, d sum wage if female==1 & ost==1, d sum wage if female==0 & ost==1, d sum wage if female==1 & ost==0, d sum wage if female==0 & ost==0, d sum wage if female==1 & public==1, d sum wage if female==0 & public==1, d * Table 2 bysort female: sum ost educ exp ft_gen empl_firm_* public schicht barg_* besch craftsman perf_* list wage besch female isco ef18 public exp educ if wage > 100 list ef1 wage hours exp educ ft_gen isco if wage >100 *Figure 2 twoway (histogram wage if female==1 & wage <61, width(1) color(gray)) /// (histogram wage if female==0 & wage <61, width(1) /// fcolor(none) lcolor(black)), legend( order(1 "Female" 2 "Male" )) graphr(fcolor(white) lc(white) lwidth(large)) graph export wagehist.eps, replace *Quantile Plots (Figure 3) ****Full sample preserve pctile eval1=ln_wage if female==1, nq(100) pctile eval2=ln_wage if female==0, nq(100) *Density functions with gaussian kernel kdensity ln_wage if female==0, at(eval2) gen(evalm densm) width(0.10) kernel(gau) kdensity ln_wage if female==1, at(eval1) gen(evalf densf) width(0.10) kernel(gau) *Wage Gaps over wage distribution, mean gap as horizontal line gen qtau=_n/100 if _n<100 gen qdiff=evalm-evalf if _n<100 reg ln_wage female //obtain mean gap and CIs graph twoway (line qdiff qtau if qtau>0.0 & qtau<1.0, connect(l) /// m(i) lw(medium) lc(black) ) , yline(.215, lpattern(solid) lcolor(grey)) /// yline(.217 .213, lpattern(dash) lcolor(grey) ) /// xlabel(0.0 0.2 0.4 0.6 0.8 1.0) ylabel(-0.1 0.0 0.1 0.2 0.3 0.4 0.5) /// xtitle("Quantile") ytitle("Log Wage Differential") graphr(fcolor(white) lc(white)) graph export rifplotfull.eps, replace restore ****East preserve keep if ost==1 pctile eval1=ln_wage if female==1, nq(100) pctile eval2=ln_wage if female==0, nq(100) *Density functions with gaussian kernel kdensity ln_wage if female==0, at(eval2) gen(evalm densm) width(0.10) kernel(gau) kdensity ln_wage if female==1, at(eval1) gen(evalf densf) width(0.10) kernel(gau) *Wage Gaps over wage distribution, mean gap as horizontal line gen qtau=_n/100 if _n<100 gen qdiff=evalm-evalf if _n<100 reg ln_wage female //obtain mean gap and CIs graph twoway (line qdiff qtau if qtau>0.0 & qtau<1.0, connect(l) /// m(i) lw(medium) lc(black) ) , yline(.107, lpattern(solid) lcolor(grey)) /// yline(.109 .105, lpattern(dash) lcolor(grey) ) /// xlabel(0.0 0.2 0.4 0.6 0.8 1.0) ylabel( -0.1 0.0 0.1 0.2 0.3 0.4 0.5) /// xtitle("Quantile") ytitle("Log Wage Differential") graphr(fcolor(white) lc(white)) graph export rifploteast.eps, replace restore ****West preserve keep if ost==0 pctile eval1=ln_wage if female==1, nq(100) pctile eval2=ln_wage if female==0, nq(100) *Density functions with gaussian kernel kdensity ln_wage if female==0, at(eval2) gen(evalm densm) width(0.10) kernel(gau) kdensity ln_wage if female==1, at(eval1) gen(evalf densf) width(0.10) kernel(gau) *Wage Gaps over wage distribution, mean gap as horizontal line gen qtau=_n/100 if _n<100 gen qdiff=evalm-evalf if _n<100 reg ln_wage female //obtain mean gap and CIs graph twoway (line qdiff qtau if qtau>0.0 & qtau<1.0, connect(l) /// m(i) lw(medium) lc(black) ) , yline(.230, lpattern(solid) lcolor(grey)) /// yline(.228 .232, lpattern(dash) lcolor(grey) ) /// xlabel(0.0 0.2 0.4 0.6 0.8 1.0) ylabel(-0.1 0.0 0.1 0.2 0.3 0.4 0.5) /// xtitle("Quantile") ytitle("Log Wage Differential") graphr(fcolor(white) lc(white)) graph export rifplotwest.eps, replace restore ****Public preserve keep if public==1 pctile eval1=ln_wage if female==1, nq(100) pctile eval2=ln_wage if female==0, nq(100) *Density functions with gaussian kernel kdensity ln_wage if female==0, at(eval2) gen(evalm densm) width(0.10) kernel(gau) kdensity ln_wage if female==1, at(eval1) gen(evalf densf) width(0.10) kernel(gau) *Wage Gaps over wage distribution, mean gap as horizontal line gen qtau=_n/100 if _n<100 gen qdiff=evalm-evalf if _n<100 reg ln_wage female //obtain mean gap and CIs graph twoway (line qdiff qtau if qtau>0.0 & qtau<1.0, connect(l) /// m(i) lw(medium) lc(black) ) , yline(.156, lpattern(solid) lcolor(grey)) /// yline(.152 .160, lpattern(dash) lcolor(grey) ) /// xlabel(0.0 0.2 0.4 0.6 0.8 1.0) ylabel(-0.1 0.0 0.1 0.2 0.3 0.4 0.5) /// xtitle("Quantile") ytitle("Log Wage Differential") graphr(fcolor(white) lc(white)) graph export rifplotpublic.eps, replace restore log close