clear
input str33 fiel prop_women prop_women_se ci_low ci_hi str3 test_
"Education" 0.7365 0.0029 0.7308 0.7422 No
"Social and behavioral sciences" 0.5251 0.0163 0.4931 0.5569 Yes
"Psychology" 0.8282 0.0183 0.7921 0.8639 No
"Nursing and caring" 0.9298 0.0046 0.9208 0.9388 No
"Therapy and rehabilitation" 0.8598 0.0336 0.7939 0.9257 No
"Child care and youth service" 0.9517 0.0178 0.9168 0.9866 No
"Social work and counseling" 0.8419 0.0238 0.7953 0.8885 No
end
// Transform from proportion to percentage
replace prop_women = prop_women * 100
replace ci_low = ci_low * 100
replace ci_hi = ci_hi * 100
// Sort educational fields by % females
egen order_ = rank(-prop_women), unique
labmask order_, val(fiel)
twoway (dot prop_women order_, horizontal) ///
(rcap ci_low ci_hi order_, horizontal) ///
, legend(off) ylabel(1/7, valuelabels) ///
ytitle("Educational fields") ///
xtitle("% Females") ///
xline(50) xlabel(40 (10) 100, format(%6.0f)) ///
note("{it: Source:} European Labor Force Survey 2009" ///
"{it: Note:} Error bars denote 95% CI's", span)
clear
input str44 fiel prop_women prop_women_se ci_low ci_hi hard
"Science, mathematics, and computing" 0.445 0.0114 0.4226 0.4673 1
"Physical science" 0.383 0.0069 0.3694 0.3964 1
"Physics" 0.390 0.0288 0.3336 0.4464 1
"Mathematics" 0.571 0.0193 0.5331 0.6089 1
"Computer science" 0.252 0.0051 0.1460 0.3580 1
"Engineering, manufacturing, and construction" 0.161 0.0012 0.1586 0.1634 1
"Engineering and engineering trades" 0.199 0.0182 0.1633 0.2347 1
"Humanities" 0.717 0.0096 0.698 0.7358 0
"Social and behavioral science" 0.525 0.0163 0.4931 0.5569 0
"Psychology" 0.828 0.0183 0.7921 0.8639 0
"Sociology and cultural studies" 0.663 0.0354 0.5936 0.7323 0
end
// Transform from proportion to percentage
replace prop_women = prop_women * 100
replace ci_low = ci_low * 100
replace ci_hi = ci_hi * 100
// Sort educational fields by % females and by hard/soft
egen orderhard = rank(-prop_women) if hard, unique
labmask orderhard, val(fiel)
egen ordersoft = rank(-prop_women) if hard == 0, unique
labmask ordersoft, val(fiel)
twoway (dot prop_women orderhard, horizontal) ///
(rcap ci_low ci_hi orderhard, horizontal) ///
, legend(off) ylabel(1/7, valuelabels) ///
ytitle("Hard educational fields") ///
xtitle("% Females") ///
xline(50) xlabel(20 (10) 100, format(%6.0f)) ///
xscale(off) /// // remove x-axis
name(hard, replace)
twoway (dot prop_women ordersoft, horizontal) ///
(rcap ci_low ci_hi ordersoft, horizontal) ///
, legend(off) ylabel(1/4, valuelabels) ///
ytitle("Soft educational fields") ///
xtitle("% Females") ///
graphregion(margin(l=28)) /// Account for different y-axis label length
xline(50) xlabel(20 (10) 100, format(%6.0f)) ///
name(soft, replace)
graph combine hard soft, col(1) imargin(b = 2 t = 1) ///
note("{it: Source:} European Labor Force Survey 2009" ///
"{it: Note:} Error bars denote 95% CI's", span)
graph export Graph.png
