| |
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| | name: <unnamed>
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| | log: C:/Users/paserman/Dropbox/Research/GenderCooperativeness/EJ/3 replication package/Log/AppendixC_simulations_May2021.
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| | > log
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| | log type: text
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| | opened on: 10 May 2021, 18:23:16
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| |
|
| | .
|
| | .
|
| | . cap prog drop rd_sim
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| |
|
| | . prog def rd_sim, rclass
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| | 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)]
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| | 3. drop _all
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| | 4. set obs `nobs'
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| | 5.
|
| | . * Overall district ideology
|
| | . gen z = 2*(rbeta(`beta_a',`beta_b')-0.5)
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| | 6. gen x = z + (sqrt((1-`rho_x'^2)/`rho_x'^2))*(2*(rbeta(`beta_a',`beta_b')-0.5))
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| | 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
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| | 10. gen byte female_R = rnormal(`phi0_R' + `phi1_R'*z_R)>0
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| | 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
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| | 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
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| | 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)
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| | 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)
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| | 24. mat b = e(b)
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| | 25. mat V = e(V)
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| | 26. local b_ideology_all = b[1,1]
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| | 27. local se_ideology_all = sqrt(V[1,1])
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| | 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)
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| | 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)
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| | 35. mat V = e(V)
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| | 36. local b_ideology_R = b[1,1]
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| | 37. local se_ideology_R = sqrt(V[1,1])
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| | 38.
|
| | . * (3) Estimate treatment effect with simple RD
|
| | . rdrobust y voteshare_female, c(0.5) kernel(uniform)
|
| | 39. mat b = e(b)
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| | 40. mat V = e(V)
|
| | 41. local b_rd_all = b[1,1]
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| | 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])
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| | 49. local band_D = e(h_l)
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| | 50.
|
| | . rdrobust y voteshare_female if voteshare_D<0.5, c(0.5) kernel(uniform)
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| | 51. mat b = e(b)
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| | 52. mat V = e(V)
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| | 53. local b_rd_R = b[1,1]
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| | 54. local se_rd_R = sqrt(V[1,1])
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| | 55. local band_R = e(h_l)
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| | 56.
|
| | . * (4-5) Estimate the treatment effect with weighted RD
|
| | . gen byte female = female_D if voteshare_D>=0.5
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| | 57. replace female = female_R if voteshare_D<0.5
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| | 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]
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| | 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
|
| | |
