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Replication do-file for:
Verge, Tania & Sílvia Claveria (2016). "Gendered political resources: The case of party office". 
Party Politics, online first August 16th, DOI: 10.1177/1354068816663040.
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Data source: CCS (2014) Comparative Candidates Survey Module I, 2005-2012 [Dataset cumulative file]. Distributed by FORS, Lausanne, 2014.
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Variables used:

Elected recent:	*A4b1. Elected in the most recent national election: Yes/No

ELECTED RECENT INCLUDES 12 COUNTRIES (T1) FOR WHICH PARTY OFFICE DATA IS RECORDED: 
Belgium, Czech Republic, Denmark, Finland, Germany, Greece, Hungary, Netherlands, Norway, Portugal, Sweden and Switzerland 

Incumbent: A4b2. Elected in the second most recent national election: Yes/No

Party office (at any level)) 
A8a. How many years local party office: Yes/No
A8b. How many years regional party office: Yes/No
A8c. How many years national party office: Yes/No

Length of party office (at any level)
A8a. How many years local party office
A8b. How many years regional party office
A8c. How many years national party office

A12. Number of hours devoted to party activities in an average month

Sex: E1. Gender

Age: E2. Year of birth

Educational level: E6a. Level of education (1=Incomplete primary; 2=Primary completed; 3=Incomplete secondary; 4=Secondary completed; 5=Post secondary trade/vocational school; 6=University incomplete; 7=University completed)


****RECODE VD**** (candidates / gender=E1)
gen sex=E1
recode sex (1=0) (2=1) (else=.)

gen elected_recent=A4b1
recode elected_recent (1=1)(2=0)(else=.)

gen candidate_recent=A4a1
recode candidate_recent (1=1)(2=0)(else=.)

gen incumbent=A4b2
recode incumbent (1=1)(2=0)(else=.)

****RECODE VI****

gen localoffice=A8a
recode localoffice (-1 99 =.)

gen localoffice_dic=localoffice
recode localoffice_dic (0=0) (1/55=1)(else=.)

gen regoffice=A8b
recode regoffice (-1 99 =.)

gen regoffice_dic=regoffice
recode regoffice_dic (0=0) (1/98=1)(else=.)

gen natloffice=A8c
recode natloffice (-1 99 =.)

gen natloffice_dic=natloffice
recode natloffice_dic (0=0) (1/98=1)(else=.)

gen anyoffice=localoffice_dic==1 | regoffice_dic==1 | natloffice_dic==1
replace anyoffice=. if localoffice_dic==. & regoffice_dic==. & natloffice_dic==.

gen hoursparty=A12
recode hoursparty (-1 999 =.)

gen edulevel=E6a
recode edulevel (-1 9 =.)

gen child_under5=E12a
recode child_under5(1=0) (2=1) (3=1) (4=1) (5=1)(else=.)

gen child1=E12a
recode child1(1=0) (2=1) (3=2) (4=3) (5=4) (else=.)

gen age=2015-E2
replace age=. if age>94

gen log_hoursparty= ln(1+hoursparty)

egen lenghtpo=rowtotal(A8*)
gen log_lenghtpo=ln(1+lenghtpo)


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TABLE 2. THE DETERMINANTS OF VIABLE CANDIDACY (I) (ANYOFFICE) & FIGURE 1A
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logit  elected_recent sex anyoffice incumbent, cluster (T1)
logit  elected_recent i.anyoffice##i.sex incumbent, cluster (T1)
tab T1 if e(sample)
logit  elected_recent i.anyoffice##i.sex log_hoursparty incumbent, cluster (T1)
gen anysex=anyoffice*sex
logit  elected_recent anyoffice sex anysex log_hoursparty incumbent, cluster (T1)
lincom anyoffice+anysex
logit  elected_recent anyoffice c.log_hoursparty##i.sex incumbent, cluster (T1)
gen hourssex=log_hoursparty*sex
logit  elected_recent anyoffice sex hourssex log_hoursparty incumbent, cluster (T1)
lincom log_hoursparty+hourssex

logit  elected_recent i.anyoffice##i.sex incumbent c.log_hoursparty##i.sex edulevel age, cluster (T1)
logit  elected_recent anyoffice sex anysex incumbent c.log_hoursparty##i.sex edulevel age, cluster (T1)
lincom anyoffice+anysex
tab T1 if e(sample)
margins, at(anyoffice=(0 1) sex=(0 1))
marginsplot

margins, at(log_hoursparty=(0 (1) 7) sex=(0 1))
marginsplot

margins, dydx(anyoffice) at(sex=(0 1))


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TABLE 3. THE DETERMINANTS OF VIABLE CANDIDADCY (II)(LOG_LENGHTPO) & FIGURE 1B
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logit  elected_recent sex log_lenghtpo incumbent, cluster (T1)
logit  elected_recent c.log_lenghtpo##i.sex incumbent, cluster (T1)
logit  elected_recent log_lenghtpo sex log_lenghtposex incumbent, cluster (T1)

logit  elected_recent c.log_lenghtpo##i.sex log_hoursparty incumbent, cluster (T1)
gen log_lenghtposex=log_lenghtpo*sex
logit  elected_recent log_lenghtpo sex log_lenghtposex incumbent log_hoursparty, cluster (T1)
lincom log_lenghtpo+log_lenghtposex

logit  elected_recent log_lenghtpo c.log_hoursparty##i.sex incumbent, cluster (T1)
logit  elected_recent c.log_lenghtpo##i.sex incumbent c.log_hoursparty##i.sex edulevel age, cluster (T1)


logit  elected_recent log_lenghtpo sex log_lenghtposex incumbent c.log_hoursparty##i.sex edulevel age, cluster (T1)
lincom log_lenghtpo+log_lenghtposex

gen log_hourspartysex=log_hoursparty*sex
logit  elected_recent log_lenghtpo sex log_hoursparty log_hourspartysex incumbent c.log_lenghtpo##i.sex edulevel age, cluster (T1)
lincom log_hourspartysex+log_hoursparty

margins, at(log_lenghtpo=(0 (1) 5) sex=(0 1))
marginsplot

margins, dydx(log_lenghtpo) at(sex=(0 1))



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Data source: Claveria (2014) - See excel file in www.taniaverge.cat 

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TABLE 4. THE DETERMINANTS OF PORTFOLIO ALLOCATION & FIGURE 2A
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***pm=1 discarded since she or he is the top selector in ministerial appointments*** 

logit portfolio_allocation p_office sex if pm==0, cluster (country)
logit portfolio_allocation p_office sex recruitment if pm==0, cluster (country)
logit portfolio_allocation i.p_office##i.sex if pm==0, cluster (country)
logit portfolio_allocation i.p_office##i.sex recruitment if pm==0, cluster (country)
logit portfolio_allocation i.edu i.pol_expertise c.pol_experience i.p_office##i.sex if pm==0, cluster (country)
logit portfolio_allocation i.edu i.pol_expertise c.pol_experience i.p_office##i.sex recruitment if pm==0, cluster (country)

margins, at(p_office=(0 1) sex=(0 1))
marginsplot

margins, dydx(p_office) at(sex=(0 1))


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TABLE 5. THE DETERMINANTS OF POST-MINISTERIAL OCCUPATION & FIGURE 2B
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***pm=1 discarded since she or he has specific post-tenure options***

logit post_ministerial p_office sex recruitment c.patronage if pm==0, cluster (country)
logit post_ministerial i.p_office##i.sex recruitment c.patronage if pm==0, cluster (country)
logit post_ministerial i.p_office##i.sex c.pol_experience i.pol_expertise recruitment c.patronage if pm==0, cluster (country)

margins, dydx(p_office) at(sex=(0 1))

margins, at(p_office=(0 1))
margins, at(p_office=(0 1) sex=(0 1))
marginsplot