---------------------------------------------------------------------------------------------------------------------------------
      name:  <unnamed>
       log:  C:/Users/paserman/Dropbox/Research/GenderCooperativeness/EJ/3 replication package/Log/AppendixC_simulations_May2021.
> log
  log type:  text
 opened on:  10 May 2021, 18:23:16

. 
. 
. 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 =  2.162e-28  
Iteration 1:   EE criterion =  4.110e-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 =  2.033e-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 =  1.407e-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 =  6.756e-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 =  2.312e-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 =  2.425e-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 =  3.917e-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)  |   .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 =  4.790e-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 =  8.458e-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
------------------------------------------------------------------------------

. 
. fulijhkjhk
command fulijhkjhk is unrecognized
r(199);

end of do-file
r(199);

end of do-file

r(199);

. rdrobust z voteshare_female, c(0.5)  kernel(uniform)

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
--------------------------------------------------------------------------------

. rdrobust z voteshare_female, c(0.5)  kernel(uniform) masspoints(off) stdvars(on)

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 |      2279        2405            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) |     0.495       0.495

Outcome: z. Running variable: voteshare_female.
--------------------------------------------------------------------------------
            Method |   Coef.    Std. Err.    z     P>|z|    [95% Conf. Interval]
-------------------+------------------------------------------------------------
      Conventional |  .01803     .00924   1.9510   0.051   -.000083      .036141
            Robust |     -          -     2.0378   0.042    .000813      .041726
--------------------------------------------------------------------------------

. adopath
  [1]              "C:/ado/plus/r/rd_2021"
  [2]  (BASE)      "C:\Program Files (x86)\Stata15\ado\base/"
  [3]  (SITE)      "C:\Program Files (x86)\Stata15\ado\site/"
  [4]              "."
  [5]  (PERSONAL)  "c:\ado\personal/"
  [6]  (PLUS)      "c:\ado\plus/"
  [7]  (OLDPLACE)  "c:\ado/"

. doedit

. pwd
C:\Users\paserman\Dropbox

. doedit "C:\Users\paserman\Dropbox\Research\GenderCooperativeness\EJ\3 replication package\Dofiles\master.do" 

. exit, clear