---------------------------------------------------------------------------------------------------------------------------------------------------------------------
      name:  <unnamed>
       log:  /Users/stefanogagliarducci/Dropbox/various/published/womenpart/EJ/3 replication package/Log/AppendixC_simulations.log
  log type:  text
 opened on:  12 May 2021, 00:03:12

. 
. 
. cap prog drop rd_sim

. prog def rd_sim, rclass
  1.         version 15.1
  2.         syntax [, nobs(integer 10000) beta_a(real 1.0) beta_b(real 1.0) rho_x(real 0.7) /*
>         */        zbar_R(real 0.5) zbar_D(real -0.5) /*
>         */        alpha_R(real 0.5) alpha_D(real 0.5) /*
>         */        kappa_ksi_R(real 0.0) beta_ksi_R(real 10.0) kappa_ksi_D(real 0.0) beta_ksi_D(real 10.0) /*
>         */        phi0_R(real -1.0) phi1_R(real -1.0) phi0_D(real -1.0) phi1_D(real -1.0)   /*
>         */        kappa_u(real 0.0) beta_u(real 1.0)   /*
>         */        gamma0(real 0.0)  gamma1(real -5.0) gamma2(real 0.0) gamma3(real 0.0)/*
>         */        tau0(real 0.3) tau1(real -1.0)  tau2(real 0.0)]
  3.         drop _all
  4.         set obs `nobs'
  5.         
.         * Overall district ideology
.     gen z = 2*(rbeta(`beta_a',`beta_b')-0.5)
  6.         gen x = z + (sqrt((1-`rho_x'^2)/`rho_x'^2))*(2*(rbeta(`beta_a',`beta_b')-0.5))
  7.         
.         * Ideology of R and candidates: weighted average of national party and local ideology, plus noise       
.         gen z_R = `alpha_R'*`zbar_R' + (1-`alpha_R')*z + `kappa_ksi_R'*(rbeta(`beta_ksi_R',`beta_ksi_R')-0.5)
  8.         gen z_D = `alpha_D'*`zbar_D' + (1-`alpha_D')*z + `kappa_ksi_D'*(rbeta(`beta_ksi_D',`beta_ksi_D')-0.5)
  9. 
.         * Gender is correlated with candidate ideology
.         gen byte female_D = rnormal(`phi0_D' + `phi1_D'*z_D)>0
 10.         gen byte female_R = rnormal(`phi0_R' + `phi1_R'*z_R)>0
 11.         
.         * Voteshare depends on ideology of the candidates plus noise
.         gen u = `kappa_u'*(rbeta(`beta_u', `beta_u')-0.5)
 12.         gen voteshare_D = (exp(`gamma0' + `gamma1'*(z - (z_D+z_R)/2) + `gamma2'*female_D - `gamma3'*female_R + u)/    /*
>         */                (1+ exp(`gamma0' + `gamma1'*(z - (z_D+z_R)/2) + `gamma2'*female_D  - `gamma3'*female_R + u )))
 13. 
.         gen voteshare_female = voteshare_D  if female_D==1 & female_R==0
 14.         replace voteshare_female = (1-voteshare_D) if female_D==0 & female_R==1
 15.         
.         * Outcome: depends on who is elected
.         gen y = `tau0' + `tau1'*abs(z_D) + `tau2'*female_D + rnormal() if voteshare_D>=0.5
 16.         replace y = `tau0' + `tau1'*abs(z_R) +`tau2'*female_R + rnormal() if voteshare_D<0.5
 17.         
.         * Now four types of RD analyses
.     * (1) Density test
.         rddensity voteshare_female, c(0.5)
 18.         local denstest_pval_all = e(pv_q)
 19. 
.         rddensity voteshare_female if voteshare_D>=0.5, c(0.5)
 20.         local denstest_pval_D = e(pv_q)
 21.     
.         rddensity voteshare_female if voteshare_D<0.5, c(0.5)
 22.         local denstest_pval_R = e(pv_q)
 23. 
.         * (2) is ideology continuous at the threshold
.         rdrobust z voteshare_female, c(0.5)  kernel(uniform)
 24.         mat b = e(b)
 25.         mat V = e(V)
 26.         local b_ideology_all = b[1,1]
 27.     local se_ideology_all = sqrt(V[1,1])
 28.         
.         rdrobust z voteshare_female if voteshare_D>=0.5, c(0.5)  kernel(uniform)
 29.         mat b = e(b)
 30.         mat V = e(V)
 31.         local b_ideology_D = b[1,1]
 32.     local se_ideology_D = sqrt(V[1,1])
 33. 
.         rdrobust z voteshare_female if voteshare_D<0.5, c(0.5)  kernel(uniform)
 34.         mat b = e(b)
 35.         mat V = e(V)
 36.         local b_ideology_R = b[1,1]
 37.     local se_ideology_R = sqrt(V[1,1])
 38. 
.         * (3) Estimate treatment effect with simple RD
.         rdrobust y voteshare_female, c(0.5) kernel(uniform)
 39.         mat b = e(b)
 40.         mat V = e(V)
 41.         local b_rd_all = b[1,1]
 42.     local se_rd_all = sqrt(V[1,1])
 43.         local band_all = e(h_l)
 44.         
.         rdrobust y voteshare_female if voteshare_D>=0.5, c(0.5)  kernel(uniform)
 45.         mat b = e(b)
 46.         mat V = e(V)
 47.         local b_rd_D = b[1,1]
 48.     local se_rd_D = sqrt(V[1,1])
 49.         local band_D = e(h_l)
 50.         
.         rdrobust y voteshare_female if voteshare_D<0.5, c(0.5)  kernel(uniform)
 51.         mat b = e(b)
 52.         mat V = e(V)
 53.         local b_rd_R = b[1,1]
 54.     local se_rd_R = sqrt(V[1,1])
 55.         local band_R = e(h_l)
 56.         
.         * (4-5) Estimate the treatment effect with weighted RD
.         gen byte female = female_D if voteshare_D>=0.5
 57.         replace female = female_R if voteshare_D<0.5
 58.         gen voteshare_female_adj = voteshare_female-0.5
 59.         
.         * (4) using x
.         probit female x if abs(voteshare_female_adj)<=`band_all'
 60.         predict pscore if e(sample)==1
 61.         gen wt =1/pscore if female==1
 62.         replace wt = 1/(1-pscore) if female==0
 63.     reg y female voteshare_female_adj i.female#c.voteshare_female_adj [aw=wt] if abs(voteshare_female_adj)<=`band_all'
 64.     local b_rdwt_all = _b[female]
 65.         local se_rdwt_all = _se[female]
 66.     drop pscore wt
 67. 
.         probit female x if voteshare_D>=0.5 & abs(voteshare_female_adj)<=`band_D'
 68.         predict pscore if e(sample)==1
 69.         gen wt =1/pscore if female==1
 70.         replace wt = 1/(1-pscore) if female==0
 71.     reg y female voteshare_female_adj i.female#c.voteshare_female_adj [aw=wt] if voteshare_D>=0.5 & abs(voteshare_female_adj)<=`band_D'
 72.     local b_rdwt_D = _b[female]
 73.         local se_rdwt_D = _se[female]
 74.     drop pscore wt
 75.         
.         probit female x if voteshare_D<0.5 & abs(voteshare_female_adj)<=`band_R'
 76.         predict pscore if e(sample)==1
 77.         gen wt =1/pscore if female==1
 78.         replace wt = 1/(1-pscore) if female==0
 79.     reg y female voteshare_female_adj i.female#c.voteshare_female_adj [aw=wt] if voteshare_D<0.5 & abs(voteshare_female_adj)<=`band_R'
 80.     local b_rdwt_R = _b[female]
 81.         local se_rdwt_R = _se[female]
 82.     drop pscore wt
 83.         
.         
.         
.         * (5a) using ideology of the district
.         probit female z if abs(voteshare_female_adj)<=`band_all'
 84.         predict pscore if e(sample)==1
 85.         gen wt =1/pscore if female==1
 86.         replace wt = 1/(1-pscore) if female==0
 87.     reg y female voteshare_female_adj i.female#c.voteshare_female_adj [aw=wt] if abs(voteshare_female_adj)<=`band_all'
 88.     local b_rdwtideodistrict_all = _b[female]
 89.         local se_rdwtideodistrict_all = _se[female]
 90.     drop pscore wt
 91. 
.         probit female z if voteshare_D>=0.5 & abs(voteshare_female_adj)<=`band_D'
 92.         predict pscore if e(sample)==1
 93.         gen wt =1/pscore if female==1
 94.         replace wt = 1/(1-pscore) if female==0
 95.     reg y female voteshare_female_adj i.female#c.voteshare_female_adj [aw=wt] if voteshare_D>=0.5 & abs(voteshare_female_adj)<=`band_D'
 96.     local b_rdwtideodistrict_D = _b[female]
 97.         local se_rdwtideodistrict_D = _se[female]
 98.     drop pscore wt
 99.         
.         probit female z if voteshare_D<0.5 & abs(voteshare_female_adj)<=`band_R'
100.         predict pscore if e(sample)==1
101.         gen wt =1/pscore if female==1
102.         replace wt = 1/(1-pscore) if female==0
103.     reg y female voteshare_female_adj i.female#c.voteshare_female_adj [aw=wt] if voteshare_D<0.5 & abs(voteshare_female_adj)<=`band_R'
104.     local b_rdwtideodistrict_R = _b[female]
105.         local se_rdwtideodistrict_R = _se[female]
106.     drop pscore wt
107. 
.         * (5b) using ideology of the elected representative
.         gen z_elected = z_D if voteshare_D>=0.5
108.         replace z_elected = z_R if voteshare_D<0.5
109.         
.         probit female z_elected  if abs(voteshare_female_adj)<=`band_all'
110.         predict pscore if e(sample)==1
111.         gen wt =1/pscore if female==1
112.         replace wt = 1/(1-pscore) if female==0
113.     reg y female voteshare_female_adj i.female#c.voteshare_female_adj [aw=wt] if abs(voteshare_female_adj)<=`band_all'
114.     local b_rdwtideoelected_all = _b[female]
115.         local se_rdwtideoelected_all = _se[female]
116.     drop pscore wt
117. 
.         probit female z_elected if voteshare_D>=0.5 & abs(voteshare_female_adj)<=`band_D'
118.         predict pscore if e(sample)==1
119.         gen wt =1/pscore if female==1
120.         replace wt = 1/(1-pscore) if female==0
121.     reg y female voteshare_female_adj i.female#c.voteshare_female_adj [aw=wt] if voteshare_D>=0.5 & abs(voteshare_female_adj)<=`band_D'
122.     local b_rdwtideoelected_D = _b[female]
123.         local se_rdwtideoelected_D = _se[female]
124.     drop pscore wt
125.         
.         probit female z_elected if voteshare_D<0.5 & abs(voteshare_female_adj)<=`band_R'
126.         predict pscore if e(sample)==1
127.         gen wt =1/pscore if female==1
128.         replace wt = 1/(1-pscore) if female==0
129.     reg y female voteshare_female_adj i.female#c.voteshare_female_adj [aw=wt] if voteshare_D<0.5 & abs(voteshare_female_adj)<=`band_R'
130.     local b_rdwtideoelected_R = _b[female]
131.         local se_rdwtideoelected_R = _se[female]
132.     drop pscore wt
133.         
.         * (6-7) Propensity score methods
.         gen absMV = abs(voteshare_D-0.5)
134. 
.         * (6a) pscore - x
.         cap teffects ipw (y) (female x absMV, probit), pstolerance(1e-6) osample(osample)
135.         teffects ipw (y) (female x absMV, probit) if osample==0, pstolerance(1e-6)
136.         drop osample
137.         mat b = e(b)
138.         mat V = e(V)
139.         local b_pscorex_all = b[1,1]
140.         local se_pscorex_all = sqrt(V[1,1])
141. 
.         cap teffects ipw (y) (female x absMV, probit) if voteshare_D>=0.5, pstolerance(1e-6) osample(osample)
142.         teffects ipw (y) (female x absMV, probit) if voteshare_D>=0.5 & osample==0, pstolerance(1e-6)
143.         drop osample
144.         mat b = e(b)
145.         mat V = e(V)
146.         local b_pscorex_D = b[1,1]
147.         local se_pscorex_D = sqrt(V[1,1])
148. 
.         cap teffects ipw (y) (female x absMV, probit) if voteshare_D<0.5, pstolerance(1e-6) osample(osample)
149.         teffects ipw (y) (female x absMV, probit) if voteshare_D<0.5 & osample==0, pstolerance(1e-6)
150.         drop osample
151.         mat b = e(b)
152.         mat V = e(V)
153.         local b_pscorex_R = b[1,1]
154.         local se_pscorex_R = sqrt(V[1,1])
155. 
. 
.         * (7a) pscore - district ideology
.         cap teffects ipw (y) (female z absMV, probit), pstolerance(1e-6) osample(osample)
156.         teffects ipw (y) (female z absMV, probit) if osample==0, pstolerance(1e-6)
157.         drop osample
158.         mat b = e(b)
159.         mat V = e(V)
160.         local b_pscoreideodistrict_all = b[1,1]
161.         local se_pscoreideodistrict_all = sqrt(V[1,1])
162. 
.         cap teffects ipw (y) (female z absMV, probit) if voteshare_D>=0.5, pstolerance(1e-6) osample(osample)
163.         teffects ipw (y) (female z absMV, probit) if voteshare_D>=0.5 & osample==0, pstolerance(1e-6)
164.         drop osample
165.         mat b = e(b)
166.         mat V = e(V)
167.         local b_pscoreideodistrict_D = b[1,1]
168.         local se_pscoreideodistrict_D = sqrt(V[1,1])
169. 
.         cap teffects ipw (y) (female z absMV, probit) if voteshare_D<0.5, pstolerance(1e-6) osample(osample)
170.         teffects ipw (y) (female z absMV, probit) if voteshare_D<0.5 & osample==0, pstolerance(1e-6)
171.         drop osample
172.         mat b = e(b)
173.         mat V = e(V)
174.         local b_pscoreideodistrict_R = b[1,1]
175.         local se_pscoreideodistrict_R = sqrt(V[1,1])
176. 
.         
.         * (7b) pscore - elected representative ideology
.         cap teffects ipw (y) (female z_elected absMV, probit), pstolerance(1e-6) osample(osample)
177.         teffects ipw (y) (female z_elected absMV, probit) if osample==0, pstolerance(1e-6)
178.         drop osample
179.         mat b = e(b)
180.         mat V = e(V)
181.         local b_pscoreideoelected_all = b[1,1]
182.         local se_pscoreideoelected_all = sqrt(V[1,1])
183. 
.         cap teffects ipw (y) (female z_elected absMV, probit) if voteshare_D>=0.5, pstolerance(1e-6) osample(osample)
184.         teffects ipw (y) (female z_elected absMV, probit) if voteshare_D>=0.5 & osample==0, pstolerance(1e-6)
185.         drop osample
186.         mat b = e(b)
187.         mat V = e(V)
188.         local b_pscoreideoelected_D = b[1,1]
189.         local se_pscoreideoelected_D = sqrt(V[1,1])
190. 
.         cap teffects ipw (y) (female z_elected absMV, probit) if voteshare_D<0.5, pstolerance(1e-6) osample(osample)
191.         teffects ipw (y) (female z_elected absMV, probit) if voteshare_D<0.5 & osample==0, pstolerance(1e-6)
192.         drop osample
193.         mat b = e(b)
194.         mat V = e(V)
195.         local b_pscoreideoelected_R = b[1,1]
196.         local se_pscoreideoelected_R = sqrt(V[1,1])
197. 
.                 
.         * (8) OLS
.         reg y female 
198.         local b_ols_all =_b[female]
199.         local se_ols_all = _se[female]
200.         
.         reg y female if voteshare_D>=0.5
201.         local b_ols_D = _b[female]
202.     local se_ols_D = _se[female]
203. 
.         reg y female if voteshare_D<0.5
204.         local b_ols_R = _b[female]
205.     local se_ols_R = _se[female]
206. 
.         * (9) Return 
.         return scalar denstest_pval_all = `denstest_pval_all' 
207.         return scalar denstest_pval_D = `denstest_pval_D' 
208.         return scalar denstest_pval_R = `denstest_pval_R'
209.         
.         return scalar b_ideology_all = `b_ideology_all'
210.         return scalar se_ideology_all = `se_ideology_all'
211.         return scalar b_ideology_D = `b_ideology_D'
212.         return scalar se_ideology_D = `se_ideology_D'
213.         return scalar b_ideology_R = `b_ideology_R'
214.         return scalar se_ideology_R = `se_ideology_R'
215.         
.         return scalar b_rd_all = `b_rd_all'
216.         return scalar se_rd_all = `se_rd_all'
217.         return scalar b_rd_D = `b_rd_D'
218.         return scalar se_rd_D = `se_rd_D'
219.         return scalar b_rd_R = `b_rd_R'
220.         return scalar se_rd_R = `se_rd_R'
221. 
.         return scalar b_rdwt_all = `b_rdwt_all'
222.         return scalar se_rdwt_all = `se_rdwt_all'
223.         return scalar b_rdwt_D = `b_rdwt_D'
224.         return scalar se_rdwt_D = `se_rdwt_D'
225.         return scalar b_rdwt_R = `b_rdwt_R'
226.         return scalar se_rdwt_R = `se_rdwt_R'
227.         
.         return scalar b_rdwtideodistrict_all = `b_rdwtideodistrict_all'
228.         return scalar se_rdwtideodistrict_all = `se_rdwtideodistrict_all'
229.         return scalar b_rdwtideodistrict_D = `b_rdwtideodistrict_D'
230.         return scalar se_rdwtideodistrict_D = `se_rdwtideodistrict_D'
231.         return scalar b_rdwtideodistrict_R = `b_rdwtideodistrict_R'
232.         return scalar se_rdwtideodistrict_R = `se_rdwtideodistrict_R'
233.         
.         return scalar b_rdwtideoelected_all = `b_rdwtideoelected_all'
234.         return scalar se_rdwtideoelected_all = `se_rdwtideoelected_all'
235.         return scalar b_rdwtideoelected_D = `b_rdwtideoelected_D'
236.         return scalar se_rdwtideoelected_D = `se_rdwtideoelected_D'
237.         return scalar b_rdwtideoelected_R = `b_rdwtideoelected_R'
238.         return scalar se_rdwtideoelected_R = `se_rdwtideoelected_R'
239. 
.         return scalar b_pscorex_all = `b_pscorex_all'
240.         return scalar se_pscorex_all = `se_pscorex_all'
241.         return scalar b_pscorex_D = `b_pscorex_D'
242.         return scalar se_pscorex_D = `se_pscorex_D'
243.         return scalar b_pscorex_R = `b_pscorex_R'
244.         return scalar se_pscorex_R = `se_pscorex_R'
245.         
.         return scalar b_pscoreideodistrict_all = `b_pscoreideodistrict_all'
246.         return scalar se_pscoreideodistrict_all = `se_pscoreideodistrict_all'
247.         return scalar b_pscoreideodistrict_D = `b_pscoreideodistrict_D'
248.         return scalar se_pscoreideodistrict_D = `se_pscoreideodistrict_D'
249.         return scalar b_pscoreideodistrict_R = `b_pscoreideodistrict_R'
250.         return scalar se_pscoreideodistrict_R = `se_pscoreideodistrict_R'
251. 
.         return scalar b_pscoreideoelected_all = `b_pscoreideoelected_all'
252.         return scalar se_pscoreideoelected_all = `se_pscoreideoelected_all'
253.         return scalar b_pscoreideoelected_D = `b_pscoreideoelected_D'
254.         return scalar se_pscoreideoelected_D = `se_pscoreideoelected_D'
255.         return scalar b_pscoreideoelected_R = `b_pscoreideoelected_R'
256.         return scalar se_pscoreideoelected_R = `se_pscoreideoelected_R'
257. 
.         return scalar b_ols_all = `b_ols_all'
258.         return scalar se_ols_all = `se_ols_all'
259.         return scalar b_ols_D = `b_ols_D'
260.         return scalar se_ols_D = `se_ols_D'
261.         return scalar b_ols_R = `b_ols_R'
262.         return scalar se_ols_R = `se_ols_R'
263.         
. end

. 
. 
. ********************************************************************************
. ********************************************************************************
. ********************************************************************************
. 
. * Now actually run the simulations
. 
. set seed 1234567

. 
. * Run one simulation as a test
. rd_sim, beta_a(5) beta_b(5) kappa_ksi_R(0.4) kappa_ksi_D(0.4) kappa_u(1) tau1(-5) rho_x(0.6) nobs(100000) /*
> */       gamma2(0.0) gamma3(0.6) phi1_D(-1) phi1_R(-1)
number of observations (_N) was 0, now 100,000
(79,715 missing values generated)
(8,263 real changes made)
(53,038 missing values generated)
(53,038 real changes made)
Computing data-driven bandwidth selectors.

Point estimates and standard errors have been adjusted for repeated observations.
(Use option nomasspoints to suppress this adjustment.)

RD Manipulation test using local polynomial density estimation.

     c =     0.500 | Left of c  Right of c          Number of obs =        28548
-------------------+----------------------          Model         = unrestricted
     Number of obs |     11425       17123          BW method     =         comb
Eff. Number of obs |      4542        4450          Kernel        =   triangular
    Order est. (p) |         2           2          VCE method    =    jackknife
    Order bias (q) |         3           3
       BW est. (h) |     0.096       0.083

Running variable: voteshare_female.
------------------------------------------
            Method |      T          P>|T|
-------------------+----------------------
            Robust |   -0.2327      0.8160
------------------------------------------

P-values of binomial tests. (H0: prob = .5)
-----------------------------------------------------
 Window Length / 2 |       <c         >=c |     P>|T|
-------------------+----------------------+----------
             0.000 |        5          15 |    0.0414
             0.000 |       19          29 |    0.1934
             0.001 |       31          40 |    0.3425
             0.001 |       42          54 |    0.2615
             0.001 |       51          69 |    0.1203
             0.001 |       64          83 |    0.1374
             0.002 |       69          97 |    0.0358
             0.002 |       83         112 |    0.0447
             0.002 |      100         127 |    0.0842
             0.002 |      115         132 |    0.3086
-----------------------------------------------------
Computing data-driven bandwidth selectors.

Point estimates and standard errors have been adjusted for repeated observations.
(Use option nomasspoints to suppress this adjustment.)

RD Manipulation test using local polynomial density estimation.

     c =     0.500 | Left of c  Right of c          Number of obs =        13774
-------------------+----------------------          Model         = unrestricted
     Number of obs |      2457       11317          BW method     =         comb
Eff. Number of obs |      1615        6442          Kernel        =   triangular
    Order est. (p) |         2           2          VCE method    =    jackknife
    Order bias (q) |         3           3
       BW est. (h) |     0.141       0.173

Running variable: voteshare_female.
------------------------------------------
            Method |      T          P>|T|
-------------------+----------------------
            Robust |    9.3618      0.0000
------------------------------------------

P-values of binomial tests. (H0: prob = .5)
-----------------------------------------------------
 Window Length / 2 |       <c         >=c |     P>|T|
-------------------+----------------------+----------
             0.000 |        5          15 |    0.0414
             0.000 |       19          29 |    0.1934
             0.001 |       31          40 |    0.3425
             0.001 |       42          54 |    0.2615
             0.001 |       51          69 |    0.1203
             0.001 |       64          83 |    0.1374
             0.002 |       69          97 |    0.0358
             0.002 |       83         112 |    0.0447
             0.002 |      100         127 |    0.0842
             0.002 |      115         132 |    0.3086
-----------------------------------------------------
Computing data-driven bandwidth selectors.

Point estimates and standard errors have been adjusted for repeated observations.
(Use option nomasspoints to suppress this adjustment.)

RD Manipulation test using local polynomial density estimation.

     c =     0.500 | Left of c  Right of c          Number of obs =        14774
-------------------+----------------------          Model         = unrestricted
     Number of obs |      8968        5806          BW method     =         comb
Eff. Number of obs |      6647        2312          Kernel        =   triangular
    Order est. (p) |         2           2          VCE method    =    jackknife
    Order bias (q) |         3           3
       BW est. (h) |     0.211       0.148

Running variable: voteshare_female.
------------------------------------------
            Method |      T          P>|T|
-------------------+----------------------
            Robust |  -12.2100      0.0000
------------------------------------------

P-values of binomial tests. (H0: prob = .5)
-----------------------------------------------------
 Window Length / 2 |       <c         >=c |     P>|T|
-------------------+----------------------+----------
             0.000 |        5          15 |    0.0414
             0.000 |       19          29 |    0.1934
             0.001 |       31          40 |    0.3425
             0.001 |       42          54 |    0.2615
             0.001 |       51          69 |    0.1203
             0.001 |       64          83 |    0.1374
             0.002 |       69          97 |    0.0358
             0.002 |       83         112 |    0.0447
             0.002 |      100         127 |    0.0842
             0.002 |      115         132 |    0.3086
-----------------------------------------------------

Sharp RD estimates using local polynomial regression.

     Cutoff c = .5 | Left of c  Right of c            Number of obs =      28548
-------------------+----------------------            BW type       =      mserd
     Number of obs |     11425       17123            Kernel        =    Uniform
Eff. Number of obs |      2282        2410            VCE method    =         NN
    Order est. (p) |         1           1
    Order bias (q) |         2           2
       BW est. (h) |     0.046       0.046
       BW bias (b) |     0.092       0.092
         rho (h/b) |     0.497       0.497

Outcome: z. Running variable: voteshare_female.
--------------------------------------------------------------------------------
            Method |   Coef.    Std. Err.    z     P>|z|    [95% Conf. Interval]
-------------------+------------------------------------------------------------
      Conventional |  .01855     .00924   2.0073   0.045    .000437      .036665
            Robust |     -          -     2.0807   0.037    .001262      .042242
--------------------------------------------------------------------------------

Sharp RD estimates using local polynomial regression.

     Cutoff c = .5 | Left of c  Right of c            Number of obs =      13774
-------------------+----------------------            BW type       =      mserd
     Number of obs |      2457       11317            Kernel        =    Uniform
Eff. Number of obs |       882        2485            VCE method    =         NN
    Order est. (p) |         1           1
    Order bias (q) |         2           2
       BW est. (h) |     0.065       0.065
       BW bias (b) |     0.118       0.118
         rho (h/b) |     0.552       0.552

Outcome: z. Running variable: voteshare_female.
--------------------------------------------------------------------------------
            Method |   Coef.    Std. Err.    z     P>|z|    [95% Conf. Interval]
-------------------+------------------------------------------------------------
      Conventional |   .2107     .00961   21.9190  0.000    .191862      .229544
            Robust |     -          -     18.9995  0.000    .191055      .235007
--------------------------------------------------------------------------------

Sharp RD estimates using local polynomial regression.

     Cutoff c = .5 | Left of c  Right of c            Number of obs =      14774
-------------------+----------------------            BW type       =      mserd
     Number of obs |      8968        5806            Kernel        =    Uniform
Eff. Number of obs |      2933        1286            VCE method    =         NN
    Order est. (p) |         1           1
    Order bias (q) |         2           2
       BW est. (h) |     0.083       0.083
       BW bias (b) |     0.151       0.151
         rho (h/b) |     0.551       0.551

Outcome: z. Running variable: voteshare_female.
--------------------------------------------------------------------------------
            Method |   Coef.    Std. Err.    z     P>|z|    [95% Conf. Interval]
-------------------+------------------------------------------------------------
      Conventional | -.19744      .0082   -24.0813 0.000   -.213511     -.181372
            Robust |     -          -     -20.4594 0.000   -.214516     -.177009
--------------------------------------------------------------------------------

Sharp RD estimates using local polynomial regression.

     Cutoff c = .5 | Left of c  Right of c            Number of obs =      28548
-------------------+----------------------            BW type       =      mserd
     Number of obs |     11425       17123            Kernel        =    Uniform
Eff. Number of obs |      4751        5448            VCE method    =         NN
    Order est. (p) |         1           1
    Order bias (q) |         2           2
       BW est. (h) |     0.101       0.101
       BW bias (b) |     0.190       0.190
         rho (h/b) |     0.530       0.530

Outcome: y. Running variable: voteshare_female.
--------------------------------------------------------------------------------
            Method |   Coef.    Std. Err.    z     P>|z|    [95% Conf. Interval]
-------------------+------------------------------------------------------------
      Conventional |  .17272     .04322   3.9964   0.000    .088013      .257427
            Robust |     -          -     3.2304   0.001    .063486       .25937
--------------------------------------------------------------------------------

Sharp RD estimates using local polynomial regression.

     Cutoff c = .5 | Left of c  Right of c            Number of obs =      13774
-------------------+----------------------            BW type       =      mserd
     Number of obs |      2457       11317            Kernel        =    Uniform
Eff. Number of obs |       880        2484            VCE method    =         NN
    Order est. (p) |         1           1
    Order bias (q) |         2           2
       BW est. (h) |     0.065       0.065
       BW bias (b) |     0.117       0.117
         rho (h/b) |     0.555       0.555

Outcome: y. Running variable: voteshare_female.
--------------------------------------------------------------------------------
            Method |   Coef.    Std. Err.    z     P>|z|    [95% Conf. Interval]
-------------------+------------------------------------------------------------
      Conventional |  .31188     .07973   3.9117   0.000     .15561      .468147
            Robust |     -          -     3.3740   0.001    .131626      .496528
--------------------------------------------------------------------------------

Sharp RD estimates using local polynomial regression.

     Cutoff c = .5 | Left of c  Right of c            Number of obs =      14774
-------------------+----------------------            BW type       =      mserd
     Number of obs |      8968        5806            Kernel        =    Uniform
Eff. Number of obs |      3622        1619            VCE method    =         NN
    Order est. (p) |         1           1
    Order bias (q) |         2           2
       BW est. (h) |     0.104       0.104
       BW bias (b) |     0.184       0.184
         rho (h/b) |     0.566       0.566

Outcome: y. Running variable: voteshare_female.
--------------------------------------------------------------------------------
            Method |   Coef.    Std. Err.    z     P>|z|    [95% Conf. Interval]
-------------------+------------------------------------------------------------
      Conventional |  .38413     .06763   5.6797   0.000    .251574      .516685
            Robust |     -          -     4.6972   0.000    .218021      .530242
--------------------------------------------------------------------------------
(53,038 missing values generated)
(53,038 real changes made)
(71,452 missing values generated)

Iteration 0:   log likelihood =  -7045.573  
Iteration 1:   log likelihood = -7014.2341  
Iteration 2:   log likelihood = -7014.2334  
Iteration 3:   log likelihood = -7014.2334  

Probit regression                               Number of obs     =     10,199
                                                LR chi2(1)        =      62.68
                                                Prob > chi2       =     0.0000
Log likelihood = -7014.2334                     Pseudo R2         =     0.0044

------------------------------------------------------------------------------
      female |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
           x |  -.2260739   .0286094    -7.90   0.000    -.2821473   -.1700006
       _cons |   .0707624   .0125909     5.62   0.000     .0460847      .09544
------------------------------------------------------------------------------
(option pr assumed; Pr(female))
(89,801 missing values generated)
(94,552 missing values generated)
(4,751 real changes made)
(sum of wgt is 20,398.6936738491)

      Source |       SS           df       MS      Number of obs   =    10,199
-------------+----------------------------------   F(3, 10195)     =     51.96
       Model |  193.062182         3  64.3540607   Prob > F        =    0.0000
    Residual |  12626.5639    10,195  1.23850554   R-squared       =    0.0151
-------------+----------------------------------   Adj R-squared   =    0.0148
       Total |  12819.6261    10,198  1.25707258   Root MSE        =    1.1129

-----------------------------------------------------------------------------------------------
                            y |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
------------------------------+----------------------------------------------------------------
                       female |   .1739132   .0439874     3.95   0.000     .0876893    .2601372
         voteshare_female_adj |   3.538998   .5419161     6.53   0.000     2.476736     4.60126
                              |
female#c.voteshare_female_adj |
                           1  |  -6.258399   .7649045    -8.18   0.000    -7.757763   -4.759036
                              |
                        _cons |  -1.052096   .0305361   -34.45   0.000    -1.111952   -.9922388
-----------------------------------------------------------------------------------------------

Iteration 0:   log likelihood = -1933.3477  
Iteration 1:   log likelihood = -1878.7605  
Iteration 2:   log likelihood = -1878.6371  
Iteration 3:   log likelihood = -1878.6371  

Probit regression                               Number of obs     =      3,364
                                                LR chi2(1)        =     109.42
                                                Prob > chi2       =     0.0000
Log likelihood = -1878.6371                     Pseudo R2         =     0.0283

------------------------------------------------------------------------------
      female |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
           x |   .5759299    .055828    10.32   0.000     .4665091    .6853507
       _cons |    .720536   .0252009    28.59   0.000     .6711433    .7699288
------------------------------------------------------------------------------
(option pr assumed; Pr(female))
(96,636 missing values generated)
(97,516 missing values generated)
(880 real changes made)
(sum of wgt is 6,733.80004513264)

      Source |       SS           df       MS      Number of obs   =     3,364
-------------+----------------------------------   F(3, 3360)      =     40.81
       Model |  139.293765         3  46.4312551   Prob > F        =    0.0000
    Residual |  3822.60101     3,360  1.13767887   R-squared       =    0.0352
-------------+----------------------------------   Adj R-squared   =    0.0343
       Total |  3961.89478     3,363  1.17808349   Root MSE        =    1.0666

-----------------------------------------------------------------------------------------------
                            y |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
------------------------------+----------------------------------------------------------------
                       female |   .2406377   .0730094     3.30   0.001     .0974903    .3837851
         voteshare_female_adj |   6.792886   1.412536     4.81   0.000     4.023369    9.562403
                              |
female#c.voteshare_female_adj |
                           1  |  -9.439579   1.989195    -4.75   0.000    -13.33973   -5.539424
                              |
                        _cons |  -1.285744   .0501982   -25.61   0.000    -1.384166   -1.187322
-----------------------------------------------------------------------------------------------

Iteration 0:   log likelihood = -3240.1233  
Iteration 1:   log likelihood = -3133.1627  
Iteration 2:   log likelihood = -3132.9107  
Iteration 3:   log likelihood = -3132.9107  

Probit regression                               Number of obs     =      5,241
                                                LR chi2(1)        =     214.43
                                                Prob > chi2       =     0.0000
Log likelihood = -3132.9107                     Pseudo R2         =     0.0331

------------------------------------------------------------------------------
      female |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
           x |   -.629661   .0437244   -14.40   0.000    -.7153593   -.5439628
       _cons |  -.5177479   .0184832   -28.01   0.000    -.5539742   -.4815216
------------------------------------------------------------------------------
(option pr assumed; Pr(female))
(94,759 missing values generated)
(98,381 missing values generated)
(3,622 real changes made)
(sum of wgt is 10,475.5064911842)

      Source |       SS           df       MS      Number of obs   =     5,241
-------------+----------------------------------   F(3, 5237)      =     80.33
       Model |  290.368848         3  96.7896161   Prob > F        =    0.0000
    Residual |  6310.12588     5,237  1.20491233   R-squared       =    0.0440
-------------+----------------------------------   Adj R-squared   =    0.0434
       Total |  6600.49473     5,240   1.2596364   Root MSE        =    1.0977

-----------------------------------------------------------------------------------------------
                            y |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
------------------------------+----------------------------------------------------------------
                       female |   .3231791   .0614265     5.26   0.000     .2027576    .4436006
         voteshare_female_adj |    4.05668    .722778     5.61   0.000     2.639734    5.473627
                              |
female#c.voteshare_female_adj |
                           1  |   -5.84145   1.017686    -5.74   0.000    -7.836538   -3.846362
                              |
                        _cons |  -.8815513   .0424921   -20.75   0.000    -.9648534   -.7982492
-----------------------------------------------------------------------------------------------

Iteration 0:   log likelihood =  -7045.573  
Iteration 1:   log likelihood = -6830.0923  
Iteration 2:   log likelihood = -6829.9941  
Iteration 3:   log likelihood = -6829.9941  

Probit regression                               Number of obs     =     10,199
                                                LR chi2(1)        =     431.16
                                                Prob > chi2       =     0.0000
Log likelihood = -6829.9941                     Pseudo R2         =     0.0306

------------------------------------------------------------------------------
      female |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
           z |  -1.538549   .0747452   -20.58   0.000    -1.685046   -1.392051
       _cons |  -.0209974   .0136641    -1.54   0.124    -.0477786    .0057838
------------------------------------------------------------------------------
(option pr assumed; Pr(female))
(89,801 missing values generated)
(94,552 missing values generated)
(4,751 real changes made)
(sum of wgt is 20,472.7413574457)

      Source |       SS           df       MS      Number of obs   =    10,199
-------------+----------------------------------   F(3, 10195)     =     70.48
       Model |   262.88843         3  87.6294767   Prob > F        =    0.0000
    Residual |  12675.1194    10,195  1.24326821   R-squared       =    0.0203
-------------+----------------------------------   Adj R-squared   =    0.0200
       Total |  12938.0078    10,198   1.2686809   Root MSE        =     1.115

-----------------------------------------------------------------------------------------------
                            y |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
------------------------------+----------------------------------------------------------------
                       female |   .1943662   .0436797     4.45   0.000     .1087454    .2799871
         voteshare_female_adj |   3.946968   .5368838     7.35   0.000     2.894571    4.999366
                              |
female#c.voteshare_female_adj |
                           1  |  -6.460541   .7641782    -8.45   0.000    -7.958481   -4.962601
                              |
                        _cons |  -1.069798   .0301335   -35.50   0.000    -1.128866   -1.010731
-----------------------------------------------------------------------------------------------

Iteration 0:   log likelihood = -1933.3477  
Iteration 1:   log likelihood = -1286.2133  
Iteration 2:   log likelihood = -1267.5265  
Iteration 3:   log likelihood = -1267.4815  
Iteration 4:   log likelihood = -1267.4815  

Probit regression                               Number of obs     =      3,364
                                                LR chi2(1)        =    1331.73
                                                Prob > chi2       =     0.0000
Log likelihood = -1267.4815                     Pseudo R2         =     0.3444

------------------------------------------------------------------------------
      female |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
           z |   7.444313   .2623512    28.38   0.000     6.930114    7.958512
       _cons |    1.78103   .0537058    33.16   0.000     1.675768    1.886291
------------------------------------------------------------------------------
(option pr assumed; Pr(female))
(96,636 missing values generated)
(97,516 missing values generated)
(880 real changes made)
(sum of wgt is 6,608.81980001926)

      Source |       SS           df       MS      Number of obs   =     3,364
-------------+----------------------------------   F(3, 3360)      =    112.97
       Model |  469.699399         3  156.566466   Prob > F        =    0.0000
    Residual |  4656.54364     3,360  1.38587608   R-squared       =    0.0916
-------------+----------------------------------   Adj R-squared   =    0.0908
       Total |  5126.24304     3,363  1.52430658   Root MSE        =    1.1772

-----------------------------------------------------------------------------------------------
                            y |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
------------------------------+----------------------------------------------------------------
                       female |   -.793492   .0774302   -10.25   0.000    -.9453071   -.6416768
         voteshare_female_adj |   26.62825   1.637998    16.26   0.000     23.41667    29.83982
                              |
female#c.voteshare_female_adj |
                           1  |  -31.34425   2.256755   -13.89   0.000      -35.769    -26.9195
                              |
                        _cons |  -.2816657   .0479712    -5.87   0.000    -.3757214   -.1876099
-----------------------------------------------------------------------------------------------

Iteration 0:   log likelihood = -3240.1233  
Iteration 1:   log likelihood = -2172.2326  
Iteration 2:   log likelihood = -2152.4686  
Iteration 3:   log likelihood = -2152.4172  
Iteration 4:   log likelihood = -2152.4172  

Probit regression                               Number of obs     =      5,241
                                                LR chi2(1)        =    2175.41
                                                Prob > chi2       =     0.0000
Log likelihood = -2152.4172                     Pseudo R2         =     0.3357

------------------------------------------------------------------------------
      female |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
           z |  -7.049271   .1911968   -36.87   0.000     -7.42401   -6.674532
       _cons |  -.7631799   .0241643   -31.58   0.000    -.8105411   -.7158187
------------------------------------------------------------------------------
(option pr assumed; Pr(female))
(94,759 missing values generated)
(98,381 missing values generated)
(3,622 real changes made)
(sum of wgt is 9,903.25282597542)

      Source |       SS           df       MS      Number of obs   =     5,241
-------------+----------------------------------   F(3, 5237)      =     22.54
       Model |  81.2014589         3   27.067153   Prob > F        =    0.0000
    Residual |  6288.68375     5,237  1.20081798   R-squared       =    0.0127
-------------+----------------------------------   Adj R-squared   =    0.0122
       Total |   6369.8852     5,240  1.21562695   Root MSE        =    1.0958

-----------------------------------------------------------------------------------------------
                            y |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
------------------------------+----------------------------------------------------------------
                       female |  -.1379332   .0656739    -2.10   0.036    -.2666815   -.0091849
         voteshare_female_adj |   5.627017   .7120137     7.90   0.000     4.231173    7.022861
                              |
female#c.voteshare_female_adj |
                           1  |  -7.189718    1.03427    -6.95   0.000    -9.217319   -5.162118
                              |
                        _cons |   -.680452   .0396242   -17.17   0.000     -.758132   -.6027719
-----------------------------------------------------------------------------------------------
(53,038 missing values generated)
(53,038 real changes made)

Iteration 0:   log likelihood =  -7045.573  
Iteration 1:   log likelihood = -6040.1188  
Iteration 2:   log likelihood = -6037.2237  
Iteration 3:   log likelihood = -6037.2236  

Probit regression                               Number of obs     =     10,199
                                                LR chi2(1)        =    2016.70
                                                Prob > chi2       =     0.0000
Log likelihood = -6037.2236                     Pseudo R2         =     0.1431

------------------------------------------------------------------------------
      female |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   z_elected |  -1.958926   .0449516   -43.58   0.000     -2.04703   -1.870823
       _cons |   .0135668   .0133481     1.02   0.309    -.0125949    .0397285
------------------------------------------------------------------------------
(option pr assumed; Pr(female))
(89,801 missing values generated)
(94,552 missing values generated)
(4,751 real changes made)
(sum of wgt is 20,703.5746251345)

      Source |       SS           df       MS      Number of obs   =    10,199
-------------+----------------------------------   F(3, 10195)     =    156.58
       Model |  586.245291         3  195.415097   Prob > F        =    0.0000
    Residual |  12723.9437    10,195  1.24805725   R-squared       =    0.0440
-------------+----------------------------------   Adj R-squared   =    0.0438
       Total |   13310.189    10,198   1.3051764   Root MSE        =    1.1172

-----------------------------------------------------------------------------------------------
                            y |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
------------------------------+----------------------------------------------------------------
                       female |   .3725144    .044218     8.42   0.000     .2858384    .4591904
         voteshare_female_adj |   3.852766   .5269349     7.31   0.000      2.81987    4.885662
                              |
female#c.voteshare_female_adj |
                           1  |  -6.090878   .7661041    -7.95   0.000    -7.592593   -4.589164
                              |
                        _cons |  -1.201598   .0296283   -40.56   0.000    -1.259675    -1.14352
-----------------------------------------------------------------------------------------------

Iteration 0:   log likelihood = -1933.3477  
Iteration 1:   log likelihood = -1587.7928  
Iteration 2:   log likelihood = -1582.0926  
Iteration 3:   log likelihood = -1582.0776  
Iteration 4:   log likelihood = -1582.0776  

Probit regression                               Number of obs     =      3,364
                                                LR chi2(1)        =     702.54
                                                Prob > chi2       =     0.0000
Log likelihood = -1582.0776                     Pseudo R2         =     0.1817

------------------------------------------------------------------------------
      female |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   z_elected |   7.410054   .3114873    23.79   0.000      6.79955    8.020558
       _cons |   3.042828   .1071286    28.40   0.000      2.83286    3.252796
------------------------------------------------------------------------------
(option pr assumed; Pr(female))
(96,636 missing values generated)
(97,516 missing values generated)
(880 real changes made)
(sum of wgt is 6,813.62821555138)

      Source |       SS           df       MS      Number of obs   =     3,364
-------------+----------------------------------   F(3, 3360)      =     44.01
       Model |  168.894906         3  56.2983019   Prob > F        =    0.0000
    Residual |  4298.15433     3,360   1.2792126   R-squared       =    0.0378
-------------+----------------------------------   Adj R-squared   =    0.0369
       Total |  4467.04924     3,363  1.32829296   Root MSE        =     1.131

-----------------------------------------------------------------------------------------------
                            y |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
------------------------------+----------------------------------------------------------------
                       female |  -.4509505   .0758094    -5.95   0.000    -.5995877   -.3023132
         voteshare_female_adj |    16.0325   1.518248    10.56   0.000     13.05572    19.00929
                              |
female#c.voteshare_female_adj |
                           1  |  -19.80716   2.132745    -9.29   0.000    -23.98877   -15.62555
                              |
                        _cons |  -.6497326   .0491586   -13.22   0.000    -.7461164   -.5533488
-----------------------------------------------------------------------------------------------

Iteration 0:   log likelihood = -3240.1233  
Iteration 1:   log likelihood = -2594.5471  
Iteration 2:   log likelihood = -2583.7661  
Iteration 3:   log likelihood = -2583.7327  
Iteration 4:   log likelihood = -2583.7327  

Probit regression                               Number of obs     =      5,241
                                                LR chi2(1)        =    1312.78
                                                Prob > chi2       =     0.0000
Log likelihood = -2583.7327                     Pseudo R2         =     0.2026

------------------------------------------------------------------------------
      female |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   z_elected |  -7.805294   .2423385   -32.21   0.000    -8.280269    -7.33032
       _cons |    1.29987   .0573838    22.65   0.000       1.1874    1.412341
------------------------------------------------------------------------------
(option pr assumed; Pr(female))
(94,759 missing values generated)
(98,381 missing values generated)
(3,622 real changes made)
(sum of wgt is 10,372.1911814213)

      Source |       SS           df       MS      Number of obs   =     5,241
-------------+----------------------------------   F(3, 5237)      =     16.72
       Model |  60.8606207         3  20.2868736   Prob > F        =    0.0000
    Residual |  6354.50433     5,237  1.21338635   R-squared       =    0.0095
-------------+----------------------------------   Adj R-squared   =    0.0089
       Total |  6415.36495     5,240  1.22430629   Root MSE        =    1.1015

-----------------------------------------------------------------------------------------------
                            y |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
------------------------------+----------------------------------------------------------------
                       female |  -.1872573   .0632442    -2.96   0.003    -.3112422   -.0632723
         voteshare_female_adj |   5.046656   .7215085     6.99   0.000     3.632198    6.461113
                              |
female#c.voteshare_female_adj |
                           1  |  -5.702727    1.02625    -5.56   0.000    -7.714604   -3.690849
                              |
                        _cons |  -.6934209   .0411359   -16.86   0.000    -.7740645   -.6127773
-----------------------------------------------------------------------------------------------

Iteration 0:   EE criterion =  2.168e-28  
Iteration 1:   EE criterion =  4.132e-33  

Treatment-effects estimation                    Number of obs     =    100,000
Estimator      : inverse-probability weights
Outcome model  : weighted mean
Treatment model: probit
------------------------------------------------------------------------------
             |               Robust
           y |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
ATE          |
      female |
   (1 vs 0)  |    .185551    .009143    20.29   0.000     .1676311    .2034709
-------------+----------------------------------------------------------------
POmean       |
      female |
          0  |   -1.51444   .0040466  -374.25   0.000    -1.522371   -1.506509
------------------------------------------------------------------------------

Iteration 0:   EE criterion =  5.545e-18  
Iteration 1:   EE criterion =  2.076e-33  

Treatment-effects estimation                    Number of obs     =     46,962
Estimator      : inverse-probability weights
Outcome model  : weighted mean
Treatment model: probit
------------------------------------------------------------------------------
             |               Robust
           y |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
ATE          |
      female |
   (1 vs 0)  |  -.0620271   .0113967    -5.44   0.000    -.0843642     -.03969
-------------+----------------------------------------------------------------
POmean       |
      female |
          0  |  -1.499856   .0061075  -245.58   0.000    -1.511827   -1.487886
------------------------------------------------------------------------------

Iteration 0:   EE criterion =  2.364e-23  
Iteration 1:   EE criterion =  4.823e-32  

Treatment-effects estimation                    Number of obs     =     53,038
Estimator      : inverse-probability weights
Outcome model  : weighted mean
Treatment model: probit
------------------------------------------------------------------------------
             |               Robust
           y |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
ATE          |
      female |
   (1 vs 0)  |   .4934254    .015274    32.30   0.000     .4634889    .5233619
-------------+----------------------------------------------------------------
POmean       |
      female |
          0  |  -1.519821   .0053757  -282.72   0.000    -1.530357   -1.509284
------------------------------------------------------------------------------

Iteration 0:   EE criterion =  7.198e-22  
Iteration 1:   EE criterion =  6.742e-33  

Treatment-effects estimation                    Number of obs     =    100,000
Estimator      : inverse-probability weights
Outcome model  : weighted mean
Treatment model: probit
------------------------------------------------------------------------------
             |               Robust
           y |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
ATE          |
      female |
   (1 vs 0)  |   .1960373   .0094185    20.81   0.000     .1775775    .2144971
-------------+----------------------------------------------------------------
POmean       |
      female |
          0  |  -1.527443    .004071  -375.20   0.000    -1.535422   -1.519464
------------------------------------------------------------------------------

Iteration 0:   EE criterion =  4.047e-18  
Iteration 1:   EE criterion =  2.245e-32  

Treatment-effects estimation                    Number of obs     =     46,962
Estimator      : inverse-probability weights
Outcome model  : weighted mean
Treatment model: probit
------------------------------------------------------------------------------
             |               Robust
           y |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
ATE          |
      female |
   (1 vs 0)  |  -.0158669   .0108021    -1.47   0.142    -.0370386    .0053048
-------------+----------------------------------------------------------------
POmean       |
      female |
          0  |   -1.51214   .0060289  -250.82   0.000    -1.523956   -1.500323
------------------------------------------------------------------------------

Iteration 0:   EE criterion =  2.427e-27  
Iteration 1:   EE criterion =  1.682e-31  

Treatment-effects estimation                    Number of obs     =     52,919
Estimator      : inverse-probability weights
Outcome model  : weighted mean
Treatment model: probit
------------------------------------------------------------------------------
             |               Robust
           y |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
ATE          |
      female |
   (1 vs 0)  |  -.1946729   .0624661    -3.12   0.002    -.3171042   -.0722417
-------------+----------------------------------------------------------------
POmean       |
      female |
          0  |   -1.45934   .0055571  -262.61   0.000    -1.470232   -1.448448
------------------------------------------------------------------------------

Iteration 0:   EE criterion =  1.047e-24  
Iteration 1:   EE criterion =  5.030e-32  

Treatment-effects estimation                    Number of obs     =    100,000
Estimator      : inverse-probability weights
Outcome model  : weighted mean
Treatment model: probit
------------------------------------------------------------------------------
             |               Robust
           y |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
ATE          |
      female |
   (1 vs 0)  |   .2416772   .0092623    26.09   0.000     .2235235    .2598309
-------------+----------------------------------------------------------------
POmean       |
      female |
          0  |  -1.519808   .0040542  -374.88   0.000    -1.527754   -1.511862
------------------------------------------------------------------------------

Iteration 0:   EE criterion =  3.676e-18  
Iteration 1:   EE criterion =  2.042e-33  

Treatment-effects estimation                    Number of obs     =     46,962
Estimator      : inverse-probability weights
Outcome model  : weighted mean
Treatment model: probit
------------------------------------------------------------------------------
             |               Robust
           y |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
ATE          |
      female |
   (1 vs 0)  |   -.000495   .0106006    -0.05   0.963    -.0212719    .0202818
-------------+----------------------------------------------------------------
POmean       |
      female |
          0  |  -1.515822   .0060067  -252.36   0.000    -1.527595   -1.504049
------------------------------------------------------------------------------

Iteration 0:   EE criterion =  9.684e-16  
Iteration 1:   EE criterion =  8.628e-28  

Treatment-effects estimation                    Number of obs     =     53,038
Estimator      : inverse-probability weights
Outcome model  : weighted mean
Treatment model: probit
------------------------------------------------------------------------------
             |               Robust
           y |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
ATE          |
      female |
   (1 vs 0)  |  -.1050439   .0401988    -2.61   0.009     -.183832   -.0262557
-------------+----------------------------------------------------------------
POmean       |
      female |
          0  |  -1.458219   .0054927  -265.48   0.000    -1.468984   -1.447453
------------------------------------------------------------------------------

      Source |       SS           df       MS      Number of obs   =   100,000
-------------+----------------------------------   F(1, 99998)     =    115.72
       Model |  155.270531         1  155.270531   Prob > F        =    0.0000
    Residual |  134179.437    99,998   1.3418212   R-squared       =    0.0012
-------------+----------------------------------   Adj R-squared   =    0.0011
       Total |  134334.707    99,999  1.34336051   Root MSE        =    1.1584

------------------------------------------------------------------------------
           y |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      female |   .0988518   .0091894    10.76   0.000     .0808406    .1168629
       _cons |  -1.501196   .0040908  -366.97   0.000    -1.509214   -1.493178
------------------------------------------------------------------------------

      Source |       SS           df       MS      Number of obs   =    46,962
-------------+----------------------------------   F(1, 46960)     =     81.37
       Model |  107.755058         1  107.755058   Prob > F        =    0.0000
    Residual |   62185.463    46,960  1.32422196   R-squared       =    0.0017
-------------+----------------------------------   Adj R-squared   =    0.0017
       Total |  62293.2181    46,961  1.32648832   Root MSE        =    1.1507

------------------------------------------------------------------------------
           y |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      female |  -.1088381   .0120654    -9.02   0.000    -.1324866   -.0851897
       _cons |  -1.487199   .0061843  -240.48   0.000    -1.499321   -1.475078
------------------------------------------------------------------------------

      Source |       SS           df       MS      Number of obs   =    53,038
-------------+----------------------------------   F(1, 53036)     =    886.34
       Model |  1182.47024         1  1182.47024   Prob > F        =    0.0000
    Residual |  70755.5305    53,036  1.33410383   R-squared       =    0.0164
-------------+----------------------------------   Adj R-squared   =    0.0164
       Total |  71938.0008    53,037  1.35637387   Root MSE        =     1.155

------------------------------------------------------------------------------
           y |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      female |   .4290223   .0144105    29.77   0.000     .4007775     .457267
       _cons |  -1.511833   .0054114  -279.38   0.000    -1.522439   -1.501226
------------------------------------------------------------------------------

. 
. 
. 
. 
. set seed 1234567

. 
. forvalues j = 1/4 {
  2.         if `j'==1 {
  3.                 * BASELINE: Everything balanced
.                 local gamma2 = 0    // No preference for female D
  4.                 local gamma3 = 0        // No preference for female R
  5.                 local phi1_D = 0                // No relationship between gender and ideology
  6.                 local phi1_R = 0
  7.                 local DataDescr`j' = "Baseline - Everything Balanced"
  8.         }
  9.         else if `j'==2 {
 10.                 * Variant 1: Women are more left-wing, no preference for female candidates
.                 local gamma2 = 0    // No preference for female D
 11.                 local gamma3 = 0        // No preference for female R
 12.                 local phi1_D = -1               // No relationship between gender and ideology
 13.                 local phi1_R = -1
 14.                 local DataDescr`j' = "Variant 1 - Women are more left-wing, no preference for female candidates"
 15.         }
 16.         else if `j'==3 {
 17.                 * Variant 2: Women are more left-wing, equal preference for female candidates
.                 local gamma2 = 0.3    // No preference for female D
 18.                 local gamma3 = 0.3      // No preference for female R
 19.                 local phi1_D = -1               // No relationship between gender and ideology
 20.                 local phi1_R = -1
 21.                 local DataDescr`j' = "Variant 2 - Women are more left-wing, equal preference for female candidates"
 22.         }
 23. 
.         else if `j'==4 {
 24.                 * Variant 3: Women are more left-wing, only D's prefer female candidates
.                 local gamma2 = 0.6    // No preference for female D
 25.                 local gamma3 = 0        // No preference for female R
 26.                 local phi1_D = -1               // No relationship between gender and ideology
 27.                 local phi1_R = -1
 28.                 local DataDescr`j' = "Variant 3 - Women are more left-wing, only Ds prefer female candidates"
 29.         }
 30.         
.         simulate denstest_pval_all=r(denstest_pval_all) denstest_pval_D=r(denstest_pval_D) denstest_pval_R=r(denstest_pval_R) /*
>         */       b_ideology_all=r(b_ideology_all) se_ideology_all=r(se_ideology_all) /*
>         */       b_ideology_D=r(b_ideology_D) se_ideology_D=r(se_ideology_D)  /*
>         */       b_ideology_R=r(b_ideology_R) se_ideology_R=r(se_ideology_R)  /*
>         */       b_rd_all=r(b_rd_all) se_rd_all=r(se_rd_all) /*
>         */       b_rd_D=r(b_rd_D) se_rd_D=r(se_rd_D)  /*
>         */       b_rd_R=r(b_rd_R) se_rd_R=r(se_rd_R)  /*
>         */       b_rdwt_all=r(b_rdwt_all) se_rdwt_all=r(se_rdwt_all) /*
>         */       b_rdwt_D=r(b_rdwt_D) se_rdwt_D=r(se_rdwt_D)  /*
>         */       b_rdwt_R=r(b_rdwt_R) se_rdwt_R=r(se_rdwt_R)  /*
>         */       b_rdwtideodistrict_all=r(b_rdwtideodistrict_all) se_rdwtideodistrict_all=r(se_rdwtideodistrict_all)  /*
>         */       b_rdwtideodistrict_D=r(b_rdwtideodistrict_D) se_rdwtideodistrict_D=r(se_rdwtideodistrict_D)  /*
>         */       b_rdwtideodistrict_R=r(b_rdwtideodistrict_R) se_rdwtideodistrict_R=r(se_rdwtideodistrict_R)  /*
>         */       b_rdwtideoelected_all=r(b_rdwtideoelected_all) se_rdwtideoelected_all=r(se_rdwtideoelected_all)  /*
>         */       b_rdwtideoelected_D=r(b_rdwtideoelected_D) se_rdwtideoelected_D=r(se_rdwtideoelected_D)  /*
>         */       b_rdwtideoelected_R=r(b_rdwtideoelected_R) se_rdwtideoelected_R=r(se_rdwtideoelected_R)  /*
>         */       b_pscorex_all=r(b_pscorex_all) se_pscorex_all=r(se_pscorex_all)  /*
>         */       b_pscorex_D=r(b_pscorex_D) se_pscorex_D=r(se_pscorex_D)  /*
>         */       b_pscorex_R=r(b_pscorex_R) se_pscorex_R=r(se_pscorex_R)  /*
>         */       b_pscoreideodistrict_all=r(b_pscoreideodistrict_all) se_pscoreideodistrict_all=r(se_pscoreideodistrict_all)  /*
>         */       b_pscoreideodistrict_D=r(b_pscoreideodistrict_D) se_pscoreideodistrict_D=r(se_pscoreideodistrict_D)  /*
>         */       b_pscoreideodistrict_R=r(b_pscoreideodistrict_R) se_pscoreideodistrict_R=r(se_pscoreideodistrict_R)  /*
>         */       b_pscoreideoelected_all=r(b_pscoreideoelected_all) se_pscoreideoelected_all=r(se_pscoreideoelected_all)  /*
>         */       b_pscoreideoelected_D=r(b_pscoreideoelected_D) se_pscoreideoelected_D=r(se_pscoreideoelected_D)  /*
>         */       b_pscoreideoelected_R=r(b_pscoreideoelected_R) se_pscoreideoelected_R=r(se_pscoreideoelected_R)  /*
>         */       b_ols_all=r(b_ols_all) se_ols_all=r(se_ols_all)  /*
>         */       b_ols_D=r(b_ols_D) se_ols_D=r(se_ols_D)  /*
>         */       b_ols_R=r(b_ols_R) se_ols_R=r(se_ols_R)  /*
>         */       , reps(1000) saving("$AppendixC_simulations/rd_simulations`j'.dta", replace): /*
>         */         rd_sim, beta_a(5) beta_b(5) kappa_ksi_R(0.4) kappa_ksi_D(0.4) kappa_u(1) tau1(-5) rho_x(0.6) nobs(10000) /*
>         */                 gamma2(`gamma2') gamma3(`gamma3') phi1_D(`phi1_D') phi1_R(`phi1_R')
 31. 
.         gen gamma2 = `gamma2'
 32.         gen gamma3 = `gamma3'
 33.         gen phi1_D = `phi1_D'
 34.         gen phi1_R = `phi1_R'
 35.         label data "`DataDescr`j''"
 36.         save "$AppendixC_simulations/rd_simulations`j'.dta", replace
 37.         
.         foreach type in "rd" "rdwt" "rdwtideodistrict" "rdwtideoelected" "pscorex" "pscoreideodistrict" "pscoreideoelected" "ols" {
 38.                 di ""
 39.                 di ""
 40.                 di in ye "type = `type'"
 41.                 foreach party in "all" "D" "R" {
 42.                         gen byte hit_`type'_`party' = b_`type'_`party' - 1.96*se_`type'_`party'<=0 & /*
>                         */                            b_`type'_`party' + 1.96*se_`type'_`party'>=0
 43.                 }
 44.                 sum b_`type'_D se_`type'_D hit_`type'_D b_`type'_R se_`type'_R hit_`type'_R
 45.         }
 46. }

         command:  rd_sim, beta_a(5) beta_b(5) kappa_ksi_R(0.4) kappa_ksi_D(0.4) kappa_u(1) tau1(-5) rho_x(0.6) nobs(10000) gamma2(0) gamma3(0) phi1_D(0) phi1_R(0)
denstest_pval_~l:  r(denstest_pval_all)
 denstest_pval_D:  r(denstest_pval_D)
 denstest_pval_R:  r(denstest_pval_R)
  b_ideology_all:  r(b_ideology_all)
 se_ideology_all:  r(se_ideology_all)
    b_ideology_D:  r(b_ideology_D)
   se_ideology_D:  r(se_ideology_D)
    b_ideology_R:  r(b_ideology_R)
   se_ideology_R:  r(se_ideology_R)
        b_rd_all:  r(b_rd_all)
       se_rd_all:  r(se_rd_all)
          b_rd_D:  r(b_rd_D)
         se_rd_D:  r(se_rd_D)
          b_rd_R:  r(b_rd_R)
         se_rd_R:  r(se_rd_R)
      b_rdwt_all:  r(b_rdwt_all)
     se_rdwt_all:  r(se_rdwt_all)
        b_rdwt_D:  r(b_rdwt_D)
       se_rdwt_D:  r(se_rdwt_D)
        b_rdwt_R:  r(b_rdwt_R)
       se_rdwt_R:  r(se_rdwt_R)
b_rdwtideodist~l:  r(b_rdwtideodistrict_all)
se_rdwtideodis~l:  r(se_rdwtideodistrict_all)
b_rdwtideodist~D:  r(b_rdwtideodistrict_D)
se_rdwtideodis~D:  r(se_rdwtideodistrict_D)
b_rdwtideodist~R:  r(b_rdwtideodistrict_R)
se_rdwtideodis~R:  r(se_rdwtideodistrict_R)
b_rdwtideoelec~l:  r(b_rdwtideoelected_all)
se_rdwtideoele~l:  r(se_rdwtideoelected_all)
b_rdwtideoelec~D:  r(b_rdwtideoelected_D)
se_rdwtideoele~D:  r(se_rdwtideoelected_D)
b_rdwtideoelec~R:  r(b_rdwtideoelected_R)
se_rdwtideoele~R:  r(se_rdwtideoelected_R)
   b_pscorex_all:  r(b_pscorex_all)
  se_pscorex_all:  r(se_pscorex_all)
     b_pscorex_D:  r(b_pscorex_D)
    se_pscorex_D:  r(se_pscorex_D)
     b_pscorex_R:  r(b_pscorex_R)
    se_pscorex_R:  r(se_pscorex_R)
b_pscoreideodi~l:  r(b_pscoreideodistrict_all)
se_pscoreideod~l:  r(se_pscoreideodistrict_all)
b_pscoreideodi~D:  r(b_pscoreideodistrict_D)
se_pscoreideod~D:  r(se_pscoreideodistrict_D)
b_pscoreideodi~R:  r(b_pscoreideodistrict_R)
se_pscoreideod~R:  r(se_pscoreideodistrict_R)
b_pscoreideoel~l:  r(b_pscoreideoelected_all)
se_pscoreideoe~l:  r(se_pscoreideoelected_all)
b_pscoreideoel~D:  r(b_pscoreideoelected_D)
se_pscoreideoe~D:  r(se_pscoreideoelected_D)
b_pscoreideoel~R:  r(b_pscoreideoelected_R)
se_pscoreideoe~R:  r(se_pscoreideoelected_R)
       b_ols_all:  r(b_ols_all)
      se_ols_all:  r(se_ols_all)
         b_ols_D:  r(b_ols_D)
        se_ols_D:  r(se_ols_D)
         b_ols_R:  r(b_ols_R)
        se_ols_R:  r(se_ols_R)

Simulations (1000)
----+--- 1 ---+--- 2 ---+--- 3 ---+--- 4 ---+--- 5 
..................................................    50
..................................................   100
..................................................   150
..................................................   200
..................................................   250
..................................................   300
..................................................   350
..................................................   400
..................................................   450
..................................................   500
..................................................   550
..................................................   600
..................................................   650
..................................................   700
..................................................   750
..................................................   800
..................................................   850
..................................................   900
..................................................   950
..................................................  1000
file /Users/stefanogagliarducci/Dropbox/various/published/womenpart/EJ/3 replication package/Output/AppendixC_simulations_output/rd_simulations1.dta saved


type = rd

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
      b_rd_D |      1,000     .000474    .2158104  -.7346113   .9701019
     se_rd_D |      1,000    .2066569    .0209705   .1547102   .2945822
    hit_rd_D |      1,000        .948    .2221381          0          1
      b_rd_R |      1,000    .0061781    .2194184  -.6579666    .882883
     se_rd_R |      1,000    .2077052    .0213403   .1590595   .2819441
-------------+---------------------------------------------------------
    hit_rd_R |      1,000        .944    .2300368          0          1


type = rdwt

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
    b_rdwt_D |      1,000     .000563     .215728   -.760636   .9889148
   se_rdwt_D |      1,000    .2074663    .0194472   .1614645   .2764145
  hit_rdwt_D |      1,000        .952    .2138732          0          1
    b_rdwt_R |      1,000    .0063396     .218869  -.6742517   .8876668
   se_rdwt_R |      1,000    .2079266    .0202564   .1640088   .2907166
-------------+---------------------------------------------------------
  hit_rdwt_R |      1,000        .949    .2201078          0          1


type = rdwtideodistrict

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
b_rdwtid~t_D |      1,000    .0007777    .2130281  -.7609494   .9757128
se_rdwti~t_D |      1,000    .2074916    .0194821   .1614185   .2770738
hit_rdwtid~D |      1,000        .955    .2074079          0          1
b_rdwtid~t_R |      1,000    .0070163    .2142945  -.7047111   .8283938
se_rdwti~t_R |      1,000    .2079609    .0202332   .1639686   .2897356
-------------+---------------------------------------------------------
hit_rdwtid~R |      1,000         .95     .218054          0          1


type = rdwtideoelected

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
b_rdwtid~d_D |      1,000   -.0000482    .2120022  -.7777959   .9878977
se_rdwti~d_D |      1,000    .2075136    .0194996   .1613577   .2767253
hit_rdwt~d_D |      1,000        .954    .2095899          0          1
b_rdwtid~d_R |      1,000    .0071389     .211381  -.6772436   .8140548
se_rdwti~d_R |      1,000    .2079869    .0202312   .1639532   .2900819
-------------+---------------------------------------------------------
hit_rdwt~d_R |      1,000         .95     .218054          0          1


type = pscorex

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
 b_pscorex_D |      1,000    .0016244    .0410007  -.1189567   .1347485
se_pscorex_D |      1,000    .0417603    .0010802   .0389149   .0450724
hit_pscore~D |      1,000        .964    .1863833          0          1
 b_pscorex_R |      1,000    .0001079    .0414081  -.1207875   .1166156
se_pscorex_R |      1,000     .041873    .0010361   .0387544   .0453656
-------------+---------------------------------------------------------
hit_pscore~R |      1,000        .952    .2138732          0          1


type = pscoreideodistrict

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
b_pscore~t_D |      1,000    .0011411    .0389529  -.1209979    .135818
se_pscor~t_D |      1,000    .0394981    .0010252   .0365711   .0428945
hit_psco~t_D |      1,000        .956    .2051977          0          1
b_pscore~t_R |      1,000   -.0000255    .0389256  -.1365211   .1169339
se_pscor~t_R |      1,000    .0396001    .0009805   .0368675   .0428658
-------------+---------------------------------------------------------
hit_psco~t_R |      1,000        .949    .2201078          0          1


type = pscoreideoelected

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
b_pscore~d_D |      1,000    .0009746    .0382667  -.1189793   .1366088
se_pscor~d_D |      1,000     .038707    .0010097   .0358897   .0423216
hit_psco~d_D |      1,000        .959    .1983894          0          1
b_pscore~d_R |      1,000    .0001725    .0384444  -.1271377   .1249728
se_pscor~d_R |      1,000    .0387925    .0009651   .0358581   .0419615
-------------+---------------------------------------------------------
hit_psco~d_R |      1,000        .941    .2357426          0          1


type = ols

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
     b_ols_D |      1,000    .0016925    .0436147  -.1204213   .1329182
    se_ols_D |      1,000    .0444561    .0007614   .0424713   .0471137
   hit_ols_D |      1,000         .96    .1960572          0          1
     b_ols_R |      1,000   -.0000372    .0443401  -.1468209   .1455884
    se_ols_R |      1,000    .0445274    .0007684   .0422679    .047389
-------------+---------------------------------------------------------
   hit_ols_R |      1,000        .948    .2221381          0          1

         command:  rd_sim, beta_a(5) beta_b(5) kappa_ksi_R(0.4) kappa_ksi_D(0.4) kappa_u(1) tau1(-5) rho_x(0.6) nobs(10000) gamma2(0) gamma3(0) phi1_D(-1)
                       phi1_R(-1)
denstest_pval_~l:  r(denstest_pval_all)
 denstest_pval_D:  r(denstest_pval_D)
 denstest_pval_R:  r(denstest_pval_R)
  b_ideology_all:  r(b_ideology_all)
 se_ideology_all:  r(se_ideology_all)
    b_ideology_D:  r(b_ideology_D)
   se_ideology_D:  r(se_ideology_D)
    b_ideology_R:  r(b_ideology_R)
   se_ideology_R:  r(se_ideology_R)
        b_rd_all:  r(b_rd_all)
       se_rd_all:  r(se_rd_all)
          b_rd_D:  r(b_rd_D)
         se_rd_D:  r(se_rd_D)
          b_rd_R:  r(b_rd_R)
         se_rd_R:  r(se_rd_R)
      b_rdwt_all:  r(b_rdwt_all)
     se_rdwt_all:  r(se_rdwt_all)
        b_rdwt_D:  r(b_rdwt_D)
       se_rdwt_D:  r(se_rdwt_D)
        b_rdwt_R:  r(b_rdwt_R)
       se_rdwt_R:  r(se_rdwt_R)
b_rdwtideodist~l:  r(b_rdwtideodistrict_all)
se_rdwtideodis~l:  r(se_rdwtideodistrict_all)
b_rdwtideodist~D:  r(b_rdwtideodistrict_D)
se_rdwtideodis~D:  r(se_rdwtideodistrict_D)
b_rdwtideodist~R:  r(b_rdwtideodistrict_R)
se_rdwtideodis~R:  r(se_rdwtideodistrict_R)
b_rdwtideoelec~l:  r(b_rdwtideoelected_all)
se_rdwtideoele~l:  r(se_rdwtideoelected_all)
b_rdwtideoelec~D:  r(b_rdwtideoelected_D)
se_rdwtideoele~D:  r(se_rdwtideoelected_D)
b_rdwtideoelec~R:  r(b_rdwtideoelected_R)
se_rdwtideoele~R:  r(se_rdwtideoelected_R)
   b_pscorex_all:  r(b_pscorex_all)
  se_pscorex_all:  r(se_pscorex_all)
     b_pscorex_D:  r(b_pscorex_D)
    se_pscorex_D:  r(se_pscorex_D)
     b_pscorex_R:  r(b_pscorex_R)
    se_pscorex_R:  r(se_pscorex_R)
b_pscoreideodi~l:  r(b_pscoreideodistrict_all)
se_pscoreideod~l:  r(se_pscoreideodistrict_all)
b_pscoreideodi~D:  r(b_pscoreideodistrict_D)
se_pscoreideod~D:  r(se_pscoreideodistrict_D)
b_pscoreideodi~R:  r(b_pscoreideodistrict_R)
se_pscoreideod~R:  r(se_pscoreideodistrict_R)
b_pscoreideoel~l:  r(b_pscoreideoelected_all)
se_pscoreideoe~l:  r(se_pscoreideoelected_all)
b_pscoreideoel~D:  r(b_pscoreideoelected_D)
se_pscoreideoe~D:  r(se_pscoreideoelected_D)
b_pscoreideoel~R:  r(b_pscoreideoelected_R)
se_pscoreideoe~R:  r(se_pscoreideoelected_R)
       b_ols_all:  r(b_ols_all)
      se_ols_all:  r(se_ols_all)
         b_ols_D:  r(b_ols_D)
        se_ols_D:  r(se_ols_D)
         b_ols_R:  r(b_ols_R)
        se_ols_R:  r(se_ols_R)

Simulations (1000)
----+--- 1 ---+--- 2 ---+--- 3 ---+--- 4 ---+--- 5 
..................................................    50
..................................................   100
..................................................   150
..................................................   200
..................................................   250
..................................................   300
..................................................   350
..................................................   400
..................................................   450
..................................................   500
..................................................   550
..................................................   600
..................................................   650
..................................................   700
..................................................   750
..................................................   800
..................................................   850
..................................................   900
..................................................   950
..................................................  1000
file /Users/stefanogagliarducci/Dropbox/various/published/womenpart/EJ/3 replication package/Output/AppendixC_simulations_output/rd_simulations2.dta saved


type = rd

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
      b_rd_D |      1,000    .0012576    .2228617  -.7331437   1.009145
     se_rd_D |      1,000    .2176755    .0255152   .1618408   .3236289
    hit_rd_D |      1,000        .946    .2261308          0          1
      b_rd_R |      1,000    .0330576     .243948  -.7533619   .8312749
     se_rd_R |      1,000    .2278799    .0267695   .1654119   .3677094
-------------+---------------------------------------------------------
    hit_rd_R |      1,000        .933    .2501471          0          1


type = rdwt

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
    b_rdwt_D |      1,000     .000469    .2242803  -.7151504    1.00985
   se_rdwt_D |      1,000    .1968521    .0196471   .1512845   .2650954
  hit_rdwt_D |      1,000        .922    .2683058          0          1
    b_rdwt_R |      1,000    .0330745    .2432499  -.7427425   .8059599
   se_rdwt_R |      1,000    .2059705     .020458   .1560483   .2731172
-------------+---------------------------------------------------------
  hit_rdwt_R |      1,000        .902    .2974634          0          1


type = rdwtideodistrict

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
b_rdwtid~t_D |      1,000    -.006175    .2212031  -.7455772   .9364876
se_rdwti~t_D |      1,000     .196769    .0196699   .1512361   .2634379
hit_rdwtid~D |      1,000         .93    .2552747          0          1
b_rdwtid~t_R |      1,000     .028441    .2407647  -.8496327   .8543036
se_rdwti~t_R |      1,000    .2060652    .0204876   .1575227   .2727789
-------------+---------------------------------------------------------
hit_rdwtid~R |      1,000         .91     .286325          0          1


type = rdwtideoelected

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
b_rdwtid~d_D |      1,000    .0090432    .2212611  -.6531096    .982204
se_rdwti~d_D |      1,000    .1969396    .0196544   .1515357   .2642587
hit_rdwt~d_D |      1,000        .925    .2635231          0          1
b_rdwtid~d_R |      1,000    .0109837    .2401174  -.8600007   .7876767
se_rdwti~d_R |      1,000    .2062539    .0205667   .1576751   .2730821
-------------+---------------------------------------------------------
hit_rdwt~d_R |      1,000        .914    .2805043          0          1


type = pscorex

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
 b_pscorex_D |      1,000   -.0550926    .0353542  -.1679334   .0592917
se_pscorex_D |      1,000     .034824    .0006266   .0327949   .0370951
hit_pscore~D |      1,000        .632    .4825027          0          1
 b_pscorex_R |      1,000    .0663517    .0536153  -.1293039   .2506798
se_pscorex_R |      1,000    .0543453    .0021536   .0482796   .0620071
-------------+---------------------------------------------------------
hit_pscore~R |      1,000        .774    .4184484          0          1


type = pscoreideodistrict

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
b_pscore~t_D |      1,000   -.0138341    .0335508  -.1293545   .0784498
se_pscor~t_D |      1,000    .0330559    .0006057   .0309695   .0349889
hit_psco~t_D |      1,000        .926    .2619019          0          1
b_pscore~t_R |      1,000    .0170442     .051773  -.1677895   .2020657
se_pscor~t_R |      1,000    .0520056    .0021865   .0455246   .0609398
-------------+---------------------------------------------------------
hit_psco~t_R |      1,000        .938    .2412762          0          1


type = pscoreideoelected

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
b_pscore~d_D |      1,000    .0006646    .0329737  -.1116275   .0922345
se_pscor~d_D |      1,000    .0324259    .0006012   .0302777    .034353
hit_psco~d_D |      1,000        .946    .2261308          0          1
b_pscore~d_R |      1,000    .0002771     .050832  -.1800453   .1714571
se_pscor~d_R |      1,000    .0511972    .0021817   .0452904   .0606324
-------------+---------------------------------------------------------
hit_psco~d_R |      1,000        .958    .2006895          0          1


type = ols

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
     b_ols_D |      1,000   -.1063805    .0375849  -.2240475   .0198816
    se_ols_D |      1,000    .0369016    .0005005   .0353002   .0384732
   hit_ols_D |      1,000        .186    .3893014          0          1
     b_ols_R |      1,000    .1257383    .0563577  -.0464704   .3107925
    se_ols_R |      1,000    .0573451    .0013656   .0529088   .0618195
-------------+---------------------------------------------------------
   hit_ols_R |      1,000        .406    .4913302          0          1

         command:  rd_sim, beta_a(5) beta_b(5) kappa_ksi_R(0.4) kappa_ksi_D(0.4) kappa_u(1) tau1(-5) rho_x(0.6) nobs(10000) gamma2(.3) gamma3(.3) phi1_D(-1)
                       phi1_R(-1)
denstest_pval_~l:  r(denstest_pval_all)
 denstest_pval_D:  r(denstest_pval_D)
 denstest_pval_R:  r(denstest_pval_R)
  b_ideology_all:  r(b_ideology_all)
 se_ideology_all:  r(se_ideology_all)
    b_ideology_D:  r(b_ideology_D)
   se_ideology_D:  r(se_ideology_D)
    b_ideology_R:  r(b_ideology_R)
   se_ideology_R:  r(se_ideology_R)
        b_rd_all:  r(b_rd_all)
       se_rd_all:  r(se_rd_all)
          b_rd_D:  r(b_rd_D)
         se_rd_D:  r(se_rd_D)
          b_rd_R:  r(b_rd_R)
         se_rd_R:  r(se_rd_R)
      b_rdwt_all:  r(b_rdwt_all)
     se_rdwt_all:  r(se_rdwt_all)
        b_rdwt_D:  r(b_rdwt_D)
       se_rdwt_D:  r(se_rdwt_D)
        b_rdwt_R:  r(b_rdwt_R)
       se_rdwt_R:  r(se_rdwt_R)
b_rdwtideodist~l:  r(b_rdwtideodistrict_all)
se_rdwtideodis~l:  r(se_rdwtideodistrict_all)
b_rdwtideodist~D:  r(b_rdwtideodistrict_D)
se_rdwtideodis~D:  r(se_rdwtideodistrict_D)
b_rdwtideodist~R:  r(b_rdwtideodistrict_R)
se_rdwtideodis~R:  r(se_rdwtideodistrict_R)
b_rdwtideoelec~l:  r(b_rdwtideoelected_all)
se_rdwtideoele~l:  r(se_rdwtideoelected_all)
b_rdwtideoelec~D:  r(b_rdwtideoelected_D)
se_rdwtideoele~D:  r(se_rdwtideoelected_D)
b_rdwtideoelec~R:  r(b_rdwtideoelected_R)
se_rdwtideoele~R:  r(se_rdwtideoelected_R)
   b_pscorex_all:  r(b_pscorex_all)
  se_pscorex_all:  r(se_pscorex_all)
     b_pscorex_D:  r(b_pscorex_D)
    se_pscorex_D:  r(se_pscorex_D)
     b_pscorex_R:  r(b_pscorex_R)
    se_pscorex_R:  r(se_pscorex_R)
b_pscoreideodi~l:  r(b_pscoreideodistrict_all)
se_pscoreideod~l:  r(se_pscoreideodistrict_all)
b_pscoreideodi~D:  r(b_pscoreideodistrict_D)
se_pscoreideod~D:  r(se_pscoreideodistrict_D)
b_pscoreideodi~R:  r(b_pscoreideodistrict_R)
se_pscoreideod~R:  r(se_pscoreideodistrict_R)
b_pscoreideoel~l:  r(b_pscoreideoelected_all)
se_pscoreideoe~l:  r(se_pscoreideoelected_all)
b_pscoreideoel~D:  r(b_pscoreideoelected_D)
se_pscoreideoe~D:  r(se_pscoreideoelected_D)
b_pscoreideoel~R:  r(b_pscoreideoelected_R)
se_pscoreideoe~R:  r(se_pscoreideoelected_R)
       b_ols_all:  r(b_ols_all)
      se_ols_all:  r(se_ols_all)
         b_ols_D:  r(b_ols_D)
        se_ols_D:  r(se_ols_D)
         b_ols_R:  r(b_ols_R)
        se_ols_R:  r(se_ols_R)

Simulations (1000)
----+--- 1 ---+--- 2 ---+--- 3 ---+--- 4 ---+--- 5 
..................................................    50
..................................................   100
..................................................   150
..................................................   200
..................................................   250
..................................................   300
..................................................   350
..................................................   400
..................................................   450
..................................................   500
..................................................   550
..................................................   600
..................................................   650
..................................................   700
..................................................   750
..................................................   800
..................................................   850
..................................................   900
..................................................   950
..................................................  1000
file /Users/stefanogagliarducci/Dropbox/various/published/womenpart/EJ/3 replication package/Output/AppendixC_simulations_output/rd_simulations3.dta saved


type = rd

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
      b_rd_D |      1,000     .491752    .2494523  -.3531313    1.51067
     se_rd_D |      1,000    .2283538    .0280412   .1690641   .3394082
    hit_rd_D |      1,000        .405    .4911377          0          1
      b_rd_R |      1,000    .5173595    .2292981  -.2692255   1.492157
     se_rd_R |      1,000    .2289896    .0260837   .1711041   .3486938
-------------+---------------------------------------------------------
    hit_rd_R |      1,000        .375    .4843652          0          1


type = rdwt

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
    b_rdwt_D |      1,000    .4388141    .2610048  -.5014127   1.687006
   se_rdwt_D |      1,000    .2074678    .0232727   .1568016   .2954484
  hit_rdwt_D |      1,000        .437    .4962633          0          1
    b_rdwt_R |      1,000    .4646391    .2379002  -.2868821   1.428946
   se_rdwt_R |      1,000    .2171307    .0218101   .1686809   .3166831
-------------+---------------------------------------------------------
  hit_rdwt_R |      1,000        .429     .495181          0          1


type = rdwtideodistrict

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
b_rdwtid~t_D |      1,000    .0013756    .4101497  -2.066836   2.024154
se_rdwti~t_D |      1,000     .203602    .0271368   .1319862   .3706861
hit_rdwtid~D |      1,000        .705     .456271          0          1
b_rdwtid~t_R |      1,000      .11661    .3633796  -1.787883   1.752881
se_rdwti~t_R |      1,000     .230485     .025827   .1736978   .3815075
-------------+---------------------------------------------------------
hit_rdwtid~R |      1,000        .779    .4151281          0          1


type = rdwtideoelected

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
b_rdwtid~d_D |      1,000    .0325854    .3558411  -1.752627   1.752868
se_rdwti~d_D |      1,000    .2076113    .0264597   .1428789   .3456056
hit_rdwt~d_D |      1,000         .78    .4144536          0          1
b_rdwtid~d_R |      1,000    .0705305    .3363157  -1.379199   1.892265
se_rdwti~d_R |      1,000    .2250768    .0251694   .1710656   .3803324
-------------+---------------------------------------------------------
hit_rdwt~d_R |      1,000         .82    .3843797          0          1


type = pscorex

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
 b_pscorex_D |      1,000    .1519819    .0346478   .0358244   .2736447
se_pscorex_D |      1,000    .0338529    .0006006   .0321172   .0360062
hit_pscore~D |      1,000        .007    .0834144          0          1
 b_pscorex_R |      1,000    .2756086    .0518065   .1026839   .4199368
se_pscorex_R |      1,000    .0503297    .0018679   .0446273    .057132
-------------+---------------------------------------------------------
hit_pscore~R |      1,000           0           0          0          0


type = pscoreideodistrict

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
b_pscore~t_D |      1,000   -.0714853    .0360254  -.1921055   .0466862
se_pscor~t_D |      1,000    .0359764    .0015933   .0323575   .0454675
hit_psco~t_D |      1,000        .487    .5000811          0          1
b_pscore~t_R |      1,000   -.0686628    .0793611  -.3470891    .156843
se_pscor~t_R |      1,000     .076294     .015689   .0545845   .1807837
-------------+---------------------------------------------------------
hit_psco~t_R |      1,000        .869    .3375692          0          1


type = pscoreideoelected

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
b_pscore~d_D |      1,000   -.0001303     .032578  -.1045127    .108526
se_pscor~d_D |      1,000    .0323971     .000756   .0304841   .0350485
hit_psco~d_D |      1,000        .944    .2300368          0          1
b_pscore~d_R |      1,000   -.0178836    .0604781  -.2088141   .1564328
se_pscor~d_R |      1,000    .0605834    .0081455   .0476141   .1530132
-------------+---------------------------------------------------------
hit_psco~d_R |      1,000        .945    .2280943          0          1


type = ols

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
     b_ols_D |      1,000    .0217441     .035059  -.0793685   .1615416
    se_ols_D |      1,000    .0348545    .0004235   .0336369    .036255
   hit_ols_D |      1,000        .908    .2891706          0          1
     b_ols_R |      1,000    .2789744    .0533577   .1072924   .4430476
    se_ols_R |      1,000    .0517018    .0010793   .0486151   .0550177
-------------+---------------------------------------------------------
   hit_ols_R |      1,000           0           0          0          0

         command:  rd_sim, beta_a(5) beta_b(5) kappa_ksi_R(0.4) kappa_ksi_D(0.4) kappa_u(1) tau1(-5) rho_x(0.6) nobs(10000) gamma2(.6) gamma3(0) phi1_D(-1)
                       phi1_R(-1)
denstest_pval_~l:  r(denstest_pval_all)
 denstest_pval_D:  r(denstest_pval_D)
 denstest_pval_R:  r(denstest_pval_R)
  b_ideology_all:  r(b_ideology_all)
 se_ideology_all:  r(se_ideology_all)
    b_ideology_D:  r(b_ideology_D)
   se_ideology_D:  r(se_ideology_D)
    b_ideology_R:  r(b_ideology_R)
   se_ideology_R:  r(se_ideology_R)
        b_rd_all:  r(b_rd_all)
       se_rd_all:  r(se_rd_all)
          b_rd_D:  r(b_rd_D)
         se_rd_D:  r(se_rd_D)
          b_rd_R:  r(b_rd_R)
         se_rd_R:  r(se_rd_R)
      b_rdwt_all:  r(b_rdwt_all)
     se_rdwt_all:  r(se_rdwt_all)
        b_rdwt_D:  r(b_rdwt_D)
       se_rdwt_D:  r(se_rdwt_D)
        b_rdwt_R:  r(b_rdwt_R)
       se_rdwt_R:  r(se_rdwt_R)
b_rdwtideodist~l:  r(b_rdwtideodistrict_all)
se_rdwtideodis~l:  r(se_rdwtideodistrict_all)
b_rdwtideodist~D:  r(b_rdwtideodistrict_D)
se_rdwtideodis~D:  r(se_rdwtideodistrict_D)
b_rdwtideodist~R:  r(b_rdwtideodistrict_R)
se_rdwtideodis~R:  r(se_rdwtideodistrict_R)
b_rdwtideoelec~l:  r(b_rdwtideoelected_all)
se_rdwtideoele~l:  r(se_rdwtideoelected_all)
b_rdwtideoelec~D:  r(b_rdwtideoelected_D)
se_rdwtideoele~D:  r(se_rdwtideoelected_D)
b_rdwtideoelec~R:  r(b_rdwtideoelected_R)
se_rdwtideoele~R:  r(se_rdwtideoelected_R)
   b_pscorex_all:  r(b_pscorex_all)
  se_pscorex_all:  r(se_pscorex_all)
     b_pscorex_D:  r(b_pscorex_D)
    se_pscorex_D:  r(se_pscorex_D)
     b_pscorex_R:  r(b_pscorex_R)
    se_pscorex_R:  r(se_pscorex_R)
b_pscoreideodi~l:  r(b_pscoreideodistrict_all)
se_pscoreideod~l:  r(se_pscoreideodistrict_all)
b_pscoreideodi~D:  r(b_pscoreideodistrict_D)
se_pscoreideod~D:  r(se_pscoreideodistrict_D)
b_pscoreideodi~R:  r(b_pscoreideodistrict_R)
se_pscoreideod~R:  r(se_pscoreideodistrict_R)
b_pscoreideoel~l:  r(b_pscoreideoelected_all)
se_pscoreideoe~l:  r(se_pscoreideoelected_all)
b_pscoreideoel~D:  r(b_pscoreideoelected_D)
se_pscoreideoe~D:  r(se_pscoreideoelected_D)
b_pscoreideoel~R:  r(b_pscoreideoelected_R)
se_pscoreideoe~R:  r(se_pscoreideoelected_R)
       b_ols_all:  r(b_ols_all)
      se_ols_all:  r(se_ols_all)
         b_ols_D:  r(b_ols_D)
        se_ols_D:  r(se_ols_D)
         b_ols_R:  r(b_ols_R)
        se_ols_R:  r(se_ols_R)

Simulations (1000)
----+--- 1 ---+--- 2 ---+--- 3 ---+--- 4 ---+--- 5 
..................................................    50
..................................................   100
..................................................   150
..................................................   200
..................................................   250
..................................................   300
..................................................   350
..................................................   400
..................................................   450
..................................................   500
..................................................   550
..................................................   600
..................................................   650
..................................................   700
..................................................   750
..................................................   800
..................................................   850
..................................................   900
..................................................   950
..................................................  1000
file /Users/stefanogagliarducci/Dropbox/various/published/womenpart/EJ/3 replication package/Output/AppendixC_simulations_output/rd_simulations4.dta saved


type = rd

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
      b_rd_D |      1,000    .4820324    .2371305  -.3588133   1.248664
     se_rd_D |      1,000    .2197392    .0262706   .1618011   .3616504
    hit_rd_D |      1,000        .389    .4877673          0          1
      b_rd_R |      1,000    .5221546    .2740097  -.7637966   1.467739
     se_rd_R |      1,000     .257724    .0312845   .1895249    .435447
-------------+---------------------------------------------------------
    hit_rd_R |      1,000        .457    .4983968          0          1


type = rdwt

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
    b_rdwt_D |      1,000     .430918    .2455162  -.6153042   1.289431
   se_rdwt_D |      1,000    .2067505    .0224016   .1520135   .3382523
  hit_rdwt_D |      1,000        .445    .4972145          0          1
    b_rdwt_R |      1,000    .4733341    .2820927  -.7657607   1.432925
   se_rdwt_R |      1,000    .2504693    .0251529   .1840224   .3487194
-------------+---------------------------------------------------------
  hit_rdwt_R |      1,000        .497    .5002412          0          1


type = rdwtideodistrict

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
b_rdwtid~t_D |      1,000    .0175561    .3773986   -1.54672   1.429458
se_rdwti~t_D |      1,000    .2034587    .0255179    .151171   .3283263
hit_rdwtid~D |      1,000        .723    .4477404          0          1
b_rdwtid~t_R |      1,000    .1003379    .3997651  -1.323882   1.491106
se_rdwti~t_R |      1,000    .2610874    .0296607   .1934556    .382313
-------------+---------------------------------------------------------
hit_rdwtid~R |      1,000        .798    .4016931          0          1


type = rdwtideoelected

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
b_rdwtid~d_D |      1,000    .0435866    .3157486  -1.321281   1.114188
se_rdwti~d_D |      1,000    .2070375    .0249135   .1524316   .3418367
hit_rdwt~d_D |      1,000        .819    .3852108          0          1
b_rdwtid~d_R |      1,000    .0720104    .3748857  -1.244581    1.60306
se_rdwti~d_R |      1,000     .257405    .0284439   .1913853   .3689896
-------------+---------------------------------------------------------
hit_rdwt~d_R |      1,000        .819    .3852108          0          1


type = pscorex

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
 b_pscorex_D |      1,000    .3748557    .0352603   .2518727   .5018864
se_pscorex_D |      1,000    .0347588     .001049   .0321233   .0393144
hit_pscore~D |      1,000           0           0          0          0
 b_pscorex_R |      1,000    .0688194    .0572938  -.0997917   .2288021
se_pscorex_R |      1,000    .0579885    .0024276   .0505837   .0686423
-------------+---------------------------------------------------------
hit_pscore~R |      1,000        .792    .4060799          0          1


type = pscoreideodistrict

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
b_pscore~t_D |      1,000   -.0609244    .0622255   -.466482   .1154791
se_pscor~t_D |      1,000    .0582026    .0183329   .0385205   .3128201
hit_psco~t_D |      1,000        .802    .3986916          0          1
b_pscore~t_R |      1,000    .0148012    .0548574   -.134412   .1764981
se_pscor~t_R |      1,000    .0553551    .0025051   .0480639   .0661574
-------------+---------------------------------------------------------
hit_psco~t_R |      1,000        .942    .2338604          0          1


type = pscoreideoelected

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
b_pscore~d_D |      1,000    .0200114    .0447027  -.1660888    .141097
se_pscor~d_D |      1,000    .0436844    .0076024   .0329284    .119063
hit_psco~d_D |      1,000        .911    .2848862          0          1
b_pscore~d_R |      1,000   -.0019193    .0540542  -.1474438   .1564901
se_pscor~d_R |      1,000    .0544823    .0025367   .0468632   .0659097
-------------+---------------------------------------------------------
hit_psco~d_R |      1,000        .951     .215976          0          1


type = ols

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
     b_ols_D |      1,000    .1312602    .0340315   .0222765   .2691881
    se_ols_D |      1,000    .0330522    .0003831   .0318416   .0342806
   hit_ols_D |      1,000        .024    .1531256          0          1
     b_ols_R |      1,000    .1232987    .0598521  -.0781653   .3008979
    se_ols_R |      1,000    .0610299    .0015038   .0564883   .0665193
-------------+---------------------------------------------------------
   hit_ols_R |      1,000        .477    .4997206          0          1

. 
. 
. 
. ********************************************************************************
. ********************************************************************************
. ********************************************************************************
. 
. * From here on, display results
. clear

. 
. foreach j of numlist 1 2 4 {
  2.         if `j'<=2 {
  3.                 local jtex = `j'
  4.         }
  5.         else if `j'==4 {
  6.                 local jtex = `j'-1
  7.         }
  8.         use "$AppendixC_simulations/rd_simulations`j'.dta", clear
  9.         foreach type in "ideology" "rd" "rdwt" "rdwtideodistrict" "rdwtideoelected" "pscorex" "pscoreideodistrict" "pscoreideoelected" "ols" {
 10.                 di ""
 11.                 di ""
 12.                 di in ye "type = `type'"
 13.                 foreach party in "all" "D" "R" {
 14.                         gen byte rejrate_`type'_`party' = b_`type'_`party' - 1.96*se_`type'_`party'>0 | /*
>                         */                            b_`type'_`party' + 1.96*se_`type'_`party'<0
 15.                 }
 16.         }
 17.         foreach party in "all" "D" "R" {
 18.                 gen denstest_reject_`party' = denstest_pval_`party'<0.05
 19.         }
 20.         
.         foreach esttype in "ideology" "rd" "rdwt" "rdwtideodistrict" "rdwtideoelected" "pscorex" "pscoreideodistrict" "pscoreideoelected" "ols"{
 21.                 ren b_`esttype'_all `esttype'1
 22.                 ren rejrate_`esttype'_all `esttype'2
 23.                 ren b_`esttype'_D `esttype'3
 24.                 ren rejrate_`esttype'_D `esttype'4
 25.                 ren b_`esttype'_R `esttype'5
 26.                 ren rejrate_`esttype'_R `esttype'6
 27.         }
 28. 
.         ren denstest_pval_all denstest1
 29.         ren denstest_reject_all denstest2
 30.         ren denstest_pval_D denstest3
 31.         ren denstest_reject_D denstest4
 32.         ren denstest_pval_R denstest5
 33.         ren denstest_reject_R denstest6
 34.         
.         gen iterno=_n
 35.         drop se_*
 36.         reshape long denstest ideology rd rdwt rdwtideodistrict rdwtideoelected pscorex pscoreideodistrict pscoreideoelected ols, i(iterno) j(stype)
 37.         label def stype 1 "All - estimate" 2 "All - Rej.Rate" 3 "D - estimate" 4 "D - Rej.Rate" 5 "R - estimate" 6 "R - Rej.Rate"
 38.         label value stype stype
 39.         
.         label var denstest "Density test (p-value)"
 40.         label var ideology "Discontinuity in ideology"
 41.         label var rd "RD - simple"
 42.         label var rdwt "P-score weighted RD - x"
 43.         label var rdwtideodistrict "P-score weighted RD - district ideology"
 44.         label var rdwtideoelected "P-score weighted RD - ideology of elected representative"
 45.         label var pscorex "P-score weighted, x"  
 46.         label var pscoreideodistrict "P-score weighted, district ideology" 
 47.         label var pscoreideoelected "P-score weighted, ideology of elected representative" 
 48.         label var ols "OLS"
 49. 
.         estpost tabstat denstest ideology rd rdwt rdwtideodistrict rdwtideoelected /*
>         */              pscorex pscoreideodistrict pscoreideoelected ols, by(stype) column(statistics) nototal
 50.         esttab using "$AppendixC_simulations/results_AppendixTableC`jtex'.tex", main(mean %6.3f) unstack tex replace noobs nomtitles nonumbers nonotes label    
 51. }
(Baseline - Everything Balanced)


type = ideology


type = rd


type = rdwt


type = rdwtideodistrict


type = rdwtideoelected


type = pscorex


type = pscoreideodistrict


type = pscoreideoelected


type = ols
(note: j = 1 2 3 4 5 6)

Data                               wide   ->   long
-----------------------------------------------------------------------------
Number of obs.                     1000   ->    6000
Number of variables                  65   ->      16
j variable (6 values)                     ->   stype
xij variables:
      denstest1 denstest2 ... denstest6   ->   denstest
      ideology1 ideology2 ... ideology6   ->   ideology
                        rd1 rd2 ... rd6   ->   rd
                  rdwt1 rdwt2 ... rdwt6   ->   rdwt
rdwtideodistrict1 rdwtideodistrict2 ... rdwtideodistrict6->rdwtideodistrict
rdwtideoelected1 rdwtideoelected2 ... rdwtideoelected6->rdwtideoelected
         pscorex1 pscorex2 ... pscorex6   ->   pscorex
pscoreideodistrict1 pscoreideodistrict2 ... pscoreideodistrict6->pscoreideodistrict
pscoreideoelected1 pscoreideoelected2 ... pscoreideoelected6->pscoreideoelected
                     ols1 ols2 ... ols6   ->   ols
-----------------------------------------------------------------------------

Summary statistics: mean
     for variables: denstest ideology rd rdwt rdwtideodistrict rdwtideoelected pscorex pscoreideodistrict pscoreideoelected ols
  by categories of: stype

       stype |   e(mean) 
-------------+-----------
1            |           
    denstest |  .5455671 
    ideology |  .0001327 
          rd |  .0024096 
        rdwt |  .0026502 
rdwtideodi~t |  .0025334 
rdwtideoel~d |  .0024999 
     pscorex |  .0009188 
pscoreideo~t |  .0009269 
pscoreideo~d |   .000916 
         ols |  .0008537 
-------------+-----------
2            |           
    denstest |      .031 
    ideology |      .055 
          rd |      .059 
        rdwt |      .056 
rdwtideodi~t |      .054 
rdwtideoel~d |      .059 
     pscorex |      .053 
pscoreideo~t |      .052 
pscoreideo~d |      .053 
         ols |      .046 
-------------+-----------
3            |           
    denstest |  .5292896 
    ideology | -.0008158 
          rd |   .000474 
        rdwt |   .000563 
rdwtideodi~t |  .0007777 
rdwtideoel~d | -.0000482 
     pscorex |  .0016244 
pscoreideo~t |  .0011411 
pscoreideo~d |  .0009746 
         ols |  .0016925 
-------------+-----------
4            |           
    denstest |      .031 
    ideology |      .057 
          rd |      .052 
        rdwt |      .048 
rdwtideodi~t |      .045 
rdwtideoel~d |      .046 
     pscorex |      .036 
pscoreideo~t |      .044 
pscoreideo~d |      .041 
         ols |       .04 
-------------+-----------
5            |           
    denstest |  .5135349 
    ideology |  .0007091 
          rd |  .0061781 
        rdwt |  .0063396 
rdwtideodi~t |  .0070163 
rdwtideoel~d |  .0071389 
     pscorex |  .0001079 
pscoreideo~t | -.0000255 
pscoreideo~d |  .0001725 
         ols | -.0000372 
-------------+-----------
6            |           
    denstest |      .032 
    ideology |      .059 
          rd |      .056 
        rdwt |      .051 
rdwtideodi~t |       .05 
rdwtideoel~d |       .05 
     pscorex |      .048 
pscoreideo~t |      .051 
pscoreideo~d |      .059 
         ols |      .052 

category labels saved in macro e(labels)
(output written to /Users/stefanogagliarducci/Dropbox/various/published/womenpart/EJ/3 replication package/Output/AppendixC_simulations_output/results_AppendixTableC
> 1.tex)
(Variant 1 - Women are more left-wing, no preference for female candidates)


type = ideology


type = rd


type = rdwt


type = rdwtideodistrict


type = rdwtideoelected


type = pscorex


type = pscoreideodistrict


type = pscoreideoelected


type = ols
(note: j = 1 2 3 4 5 6)

Data                               wide   ->   long
-----------------------------------------------------------------------------
Number of obs.                     1000   ->    6000
Number of variables                  65   ->      16
j variable (6 values)                     ->   stype
xij variables:
      denstest1 denstest2 ... denstest6   ->   denstest
      ideology1 ideology2 ... ideology6   ->   ideology
                        rd1 rd2 ... rd6   ->   rd
                  rdwt1 rdwt2 ... rdwt6   ->   rdwt
rdwtideodistrict1 rdwtideodistrict2 ... rdwtideodistrict6->rdwtideodistrict
rdwtideoelected1 rdwtideoelected2 ... rdwtideoelected6->rdwtideoelected
         pscorex1 pscorex2 ... pscorex6   ->   pscorex
pscoreideodistrict1 pscoreideodistrict2 ... pscoreideodistrict6->pscoreideodistrict
pscoreideoelected1 pscoreideoelected2 ... pscoreideoelected6->pscoreideoelected
                     ols1 ols2 ... ols6   ->   ols
-----------------------------------------------------------------------------

Summary statistics: mean
     for variables: denstest ideology rd rdwt rdwtideodistrict rdwtideoelected pscorex pscoreideodistrict pscoreideoelected ols
  by categories of: stype

       stype |   e(mean) 
-------------+-----------
1            |           
    denstest |   .526024 
    ideology | -.0004892 
          rd | -.0180679 
        rdwt | -.0177774 
rdwtideodi~t | -.0186809 
rdwtideoel~d |  .0115203 
     pscorex |  .0054515 
pscoreideo~t | -.0098912 
pscoreideo~d |  .0004067 
         ols | -.0367354 
-------------+-----------
2            |           
    denstest |      .033 
    ideology |      .052 
          rd |      .051 
        rdwt |      .049 
rdwtideodi~t |      .048 
rdwtideoel~d |      .082 
     pscorex |      .053 
pscoreideo~t |      .069 
pscoreideo~d |       .05 
         ols |      .228 
-------------+-----------
3            |           
    denstest |  .0253585 
    ideology |  .0030011 
          rd |  .0012576 
        rdwt |   .000469 
rdwtideodi~t |  -.006175 
rdwtideoel~d |  .0090432 
     pscorex | -.0550926 
pscoreideo~t | -.0138341 
pscoreideo~d |  .0006646 
         ols | -.1063805 
-------------+-----------
4            |           
    denstest |      .889 
    ideology |       .07 
          rd |      .054 
        rdwt |      .078 
rdwtideodi~t |       .07 
rdwtideoel~d |      .075 
     pscorex |      .368 
pscoreideo~t |      .074 
pscoreideo~d |      .054 
         ols |      .814 
-------------+-----------
5            |           
    denstest |  .0245342 
    ideology | -.0022047 
          rd |  .0330576 
        rdwt |  .0330745 
rdwtideodi~t |   .028441 
rdwtideoel~d |  .0109837 
     pscorex |  .0663517 
pscoreideo~t |  .0170442 
pscoreideo~d |  .0002771 
         ols |  .1257383 
-------------+-----------
6            |           
    denstest |      .901 
    ideology |      .058 
          rd |      .067 
        rdwt |      .098 
rdwtideodi~t |       .09 
rdwtideoel~d |      .086 
     pscorex |      .226 
pscoreideo~t |      .062 
pscoreideo~d |      .042 
         ols |      .594 

category labels saved in macro e(labels)
(output written to /Users/stefanogagliarducci/Dropbox/various/published/womenpart/EJ/3 replication package/Output/AppendixC_simulations_output/results_AppendixTableC
> 2.tex)
(Variant 3 - Women are more left-wing, only Ds prefer female candidates)


type = ideology


type = rd


type = rdwt


type = rdwtideodistrict


type = rdwtideoelected


type = pscorex


type = pscoreideodistrict


type = pscoreideoelected


type = ols
(note: j = 1 2 3 4 5 6)

Data                               wide   ->   long
-----------------------------------------------------------------------------
Number of obs.                     1000   ->    6000
Number of variables                  65   ->      16
j variable (6 values)                     ->   stype
xij variables:
      denstest1 denstest2 ... denstest6   ->   denstest
      ideology1 ideology2 ... ideology6   ->   ideology
                        rd1 rd2 ... rd6   ->   rd
                  rdwt1 rdwt2 ... rdwt6   ->   rdwt
rdwtideodistrict1 rdwtideodistrict2 ... rdwtideodistrict6->rdwtideodistrict
rdwtideoelected1 rdwtideoelected2 ... rdwtideoelected6->rdwtideoelected
         pscorex1 pscorex2 ... pscorex6   ->   pscorex
pscoreideodistrict1 pscoreideodistrict2 ... pscoreideodistrict6->pscoreideodistrict
pscoreideoelected1 pscoreideoelected2 ... pscoreideoelected6->pscoreideoelected
                     ols1 ols2 ... ols6   ->   ols
-----------------------------------------------------------------------------

Summary statistics: mean
     for variables: denstest ideology rd rdwt rdwtideodistrict rdwtideoelected pscorex pscoreideodistrict pscoreideoelected ols
  by categories of: stype

       stype |   e(mean) 
-------------+-----------
1            |           
    denstest |   .516915 
    ideology |   .000854 
          rd |  .6216279 
        rdwt |  .6209797 
rdwtideodi~t |   .620556 
rdwtideoel~d |  .5385951 
     pscorex |  .3820008 
pscoreideo~t |  .3955427 
pscoreideo~d |  .3258178 
         ols |  .1589017 
-------------+-----------
2            |           
    denstest |      .047 
    ideology |      .052 
          rd |      .961 
        rdwt |      .961 
rdwtideodi~t |      .958 
rdwtideoel~d |      .897 
     pscorex |         1 
pscoreideo~t |         1 
pscoreideo~d |         1 
         ols |         1 
-------------+-----------
3            |           
    denstest |  .2114761 
    ideology |  .2113024 
          rd |  .4820324 
        rdwt |   .430918 
rdwtideodi~t |  .0175561 
rdwtideoel~d |  .0435866 
     pscorex |  .3748557 
pscoreideo~t | -.0609244 
pscoreideo~d |  .0200114 
         ols |  .1312602 
-------------+-----------
4            |           
    denstest |      .359 
    ideology |         1 
          rd |      .611 
        rdwt |      .555 
rdwtideodi~t |      .277 
rdwtideoel~d |      .181 
     pscorex |         1 
pscoreideo~t |      .198 
pscoreideo~d |      .089 
         ols |      .976 
-------------+-----------
5            |           
    denstest |  .1600951 
    ideology |  -.209657 
          rd |  .5221546 
        rdwt |  .4733341 
rdwtideodi~t |  .1003379 
rdwtideoel~d |  .0720104 
     pscorex |  .0688194 
pscoreideo~t |  .0148012 
pscoreideo~d | -.0019193 
         ols |  .1232987 
-------------+-----------
6            |           
    denstest |      .434 
    ideology |         1 
          rd |      .543 
        rdwt |      .503 
rdwtideodi~t |      .202 
rdwtideoel~d |      .181 
     pscorex |      .208 
pscoreideo~t |      .058 
pscoreideo~d |      .049 
         ols |      .523 

category labels saved in macro e(labels)
(output written to /Users/stefanogagliarducci/Dropbox/various/published/womenpart/EJ/3 replication package/Output/AppendixC_simulations_output/results_AppendixTableC
> 3.tex)

. 
. 
end of do-file
. 
. 
. 
. program error:  matching close brace not found
r(198);

end of do-file

r(198);
. exit, clear