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name: <unnamed> |
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log: /Users/stefanogagliarducci/Dropbox/various/published/womenpart/EJ/3 replication package/Log/AppendixC_simulations.log |
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log type: text |
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opened on: 12 May 2021, 00:03:12 |
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. |
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. |
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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 |
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2. syntax [, nobs(integer 10000) beta_a(real 1.0) beta_b(real 1.0) rho_x(real 0.7) |
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zbar_R(real 0.5) zbar_D(real -0.5) |
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alpha_R(real 0.5) alpha_D(real 0.5) |
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kappa_ksi_R(real 0.0) beta_ksi_R(real 10.0) kappa_ksi_D(real 0.0) beta_ksi_D(real 10.0) |
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phi0_R(real -1.0) phi1_R(real -1.0) phi0_D(real -1.0) phi1_D(real -1.0) |
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kappa_u(real 0.0) beta_u(real 1.0) |
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gamma0(real 0.0) gamma1(real -5.0) gamma2(real 0.0) gamma3(real 0.0) |
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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. |
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. * Overall district ideology |
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. 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. |
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. * Ideology of R and candidates: weighted average of national party and local ideology, plus noise |
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. gen z_R = `alpha_R'*`zbar_R' + (1-`alpha_R')*z + `kappa_ksi_R'*(rbeta(`beta_ksi_R',`beta_ksi_R')-0.5) |
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8. gen z_D = `alpha_D'*`zbar_D' + (1-`alpha_D')*z + `kappa_ksi_D'*(rbeta(`beta_ksi_D',`beta_ksi_D')-0.5) |
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9. |
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. * Gender is correlated with candidate ideology |
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. 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. |
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. * Voteshare depends on ideology of the candidates plus noise |
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. gen u = `kappa_u'*(rbeta(`beta_u', `beta_u')-0.5) |
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12. gen voteshare_D = (exp(`gamma0' + `gamma1'*(z - (z_D+z_R)/2) + `gamma2'*female_D - `gamma3'*female_R + u)/ |
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(1+ exp(`gamma0' + `gamma1'*(z - (z_D+z_R)/2) + `gamma2'*female_D - `gamma3'*female_R + u ))) |
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13. |
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. 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 |
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15. |
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. * Outcome: depends on who is elected |
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. 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 |
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17. |
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. * Now four types of RD analyses |
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. * (1) Density test |
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. rddensity voteshare_female, c(0.5) |
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18. local denstest_pval_all = e(pv_q) |
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19. |
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. rddensity voteshare_female if voteshare_D>=0.5, c(0.5) |
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20. local denstest_pval_D = e(pv_q) |
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21. |
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. rddensity voteshare_female if voteshare_D<0.5, c(0.5) |
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22. local denstest_pval_R = e(pv_q) |
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23. |
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. * (2) is ideology continuous at the threshold |
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. 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. |
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. rdrobust z voteshare_female if voteshare_D>=0.5, c(0.5) kernel(uniform) |
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29. mat b = e(b) |
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30. mat V = e(V) |
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31. local b_ideology_D = b[1,1] |
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32. local se_ideology_D = sqrt(V[1,1]) |
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33. |
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. rdrobust z voteshare_female if voteshare_D<0.5, c(0.5) kernel(uniform) |
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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. |
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. * (3) Estimate treatment effect with simple RD |
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. rdrobust y voteshare_female, c(0.5) kernel(uniform) |
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39. mat b = e(b) |
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40. mat V = e(V) |
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41. local b_rd_all = b[1,1] |
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42. local se_rd_all = sqrt(V[1,1]) |
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43. local band_all = e(h_l) |
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44. |
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. rdrobust y voteshare_female if voteshare_D>=0.5, c(0.5) kernel(uniform) |
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45. mat b = e(b) |
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46. mat V = e(V) |
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47. local b_rd_D = b[1,1] |
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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. |
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. 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. |
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. * (4-5) Estimate the treatment effect with weighted RD |
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. 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 |
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59. |
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. * (4) using x |
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. probit female x if abs(voteshare_female_adj)<=`band_all' |
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60. predict pscore if e(sample)==1 |
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61. gen wt =1/pscore if female==1 |
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62. replace wt = 1/(1-pscore) if female==0 |
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63. reg y female voteshare_female_adj i.female#c.voteshare_female_adj [aw=wt] if abs(voteshare_female_adj)<=`band_all' |
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64. local b_rdwt_all = _b[female] |
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65. local se_rdwt_all = _se[female] |
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66. drop pscore wt |
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67. |
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. probit female x if voteshare_D>=0.5 & abs(voteshare_female_adj)<=`band_D' |
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68. predict pscore if e(sample)==1 |
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69. gen wt =1/pscore if female==1 |
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70. replace wt = 1/(1-pscore) if female==0 |
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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' |
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72. local b_rdwt_D = _b[female] |
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73. local se_rdwt_D = _se[female] |
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74. drop pscore wt |
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75. |
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. probit female x if voteshare_D<0.5 & abs(voteshare_female_adj)<=`band_R' |
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76. predict pscore if e(sample)==1 |
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77. gen wt =1/pscore if female==1 |
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78. replace wt = 1/(1-pscore) if female==0 |
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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' |
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80. local b_rdwt_R = _b[female] |
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81. local se_rdwt_R = _se[female] |
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82. drop pscore wt |
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83. |
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. * (5a) using ideology of the district |
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. probit female z if abs(voteshare_female_adj)<=`band_all' |
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84. predict pscore if e(sample)==1 |
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85. gen wt =1/pscore if female==1 |
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86. replace wt = 1/(1-pscore) if female==0 |
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87. reg y female voteshare_female_adj i.female#c.voteshare_female_adj [aw=wt] if abs(voteshare_female_adj)<=`band_all' |
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88. local b_rdwtideodistrict_all = _b[female] |
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89. local se_rdwtideodistrict_all = _se[female] |
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90. drop pscore wt |
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91. |
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. probit female z if voteshare_D>=0.5 & abs(voteshare_female_adj)<=`band_D' |
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92. predict pscore if e(sample)==1 |
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93. gen wt =1/pscore if female==1 |
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94. replace wt = 1/(1-pscore) if female==0 |
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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' |
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96. local b_rdwtideodistrict_D = _b[female] |
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97. local se_rdwtideodistrict_D = _se[female] |
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98. drop pscore wt |
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99. |
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. probit female z if voteshare_D<0.5 & abs(voteshare_female_adj)<=`band_R' |
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100. predict pscore if e(sample)==1 |
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101. gen wt =1/pscore if female==1 |
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102. replace wt = 1/(1-pscore) if female==0 |
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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' |
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104. local b_rdwtideodistrict_R = _b[female] |
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105. local se_rdwtideodistrict_R = _se[female] |
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106. drop pscore wt |
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107. |
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. * (5b) using ideology of the elected representative |
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. gen z_elected = z_D if voteshare_D>=0.5 |
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108. replace z_elected = z_R if voteshare_D<0.5 |
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109. |
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. probit female z_elected if abs(voteshare_female_adj)<=`band_all' |
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110. predict pscore if e(sample)==1 |
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111. gen wt =1/pscore if female==1 |
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112. replace wt = 1/(1-pscore) if female==0 |
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113. reg y female voteshare_female_adj i.female#c.voteshare_female_adj [aw=wt] if abs(voteshare_female_adj)<=`band_all' |
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114. local b_rdwtideoelected_all = _b[female] |
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115. local se_rdwtideoelected_all = _se[female] |
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116. drop pscore wt |
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117. |
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. probit female z_elected if voteshare_D>=0.5 & abs(voteshare_female_adj)<=`band_D' |
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118. predict pscore if e(sample)==1 |
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119. gen wt =1/pscore if female==1 |
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120. replace wt = 1/(1-pscore) if female==0 |
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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' |
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122. local b_rdwtideoelected_D = _b[female] |
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123. local se_rdwtideoelected_D = _se[female] |
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124. drop pscore wt |
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125. |
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. probit female z_elected if voteshare_D<0.5 & abs(voteshare_female_adj)<=`band_R' |
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126. predict pscore if e(sample)==1 |
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127. gen wt =1/pscore if female==1 |
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128. replace wt = 1/(1-pscore) if female==0 |
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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' |
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130. local b_rdwtideoelected_R = _b[female] |
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131. local se_rdwtideoelected_R = _se[female] |
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132. drop pscore wt |
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133. |
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. * (6-7) Propensity score methods |
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. gen absMV = abs(voteshare_D-0.5) |
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134. |
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. * (6a) pscore - x |
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. cap teffects ipw (y) (female x absMV, probit), pstolerance(1e-6) osample(osample) |
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135. teffects ipw (y) (female x absMV, probit) if osample==0, pstolerance(1e-6) |
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136. drop osample |
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137. mat b = e(b) |
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138. mat V = e(V) |
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139. local b_pscorex_all = b[1,1] |
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140. local se_pscorex_all = sqrt(V[1,1]) |
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141. |
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. cap teffects ipw (y) (female x absMV, probit) if voteshare_D>=0.5, pstolerance(1e-6) osample(osample) |
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142. teffects ipw (y) (female x absMV, probit) if voteshare_D>=0.5 & osample==0, pstolerance(1e-6) |
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143. drop osample |
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144. mat b = e(b) |
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145. mat V = e(V) |
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146. local b_pscorex_D = b[1,1] |
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147. local se_pscorex_D = sqrt(V[1,1]) |
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148. |
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. cap teffects ipw (y) (female x absMV, probit) if voteshare_D<0.5, pstolerance(1e-6) osample(osample) |
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149. teffects ipw (y) (female x absMV, probit) if voteshare_D<0.5 & osample==0, pstolerance(1e-6) |
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150. drop osample |
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151. mat b = e(b) |
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152. mat V = e(V) |
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153. local b_pscorex_R = b[1,1] |
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154. local se_pscorex_R = sqrt(V[1,1]) |
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155. |
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. |
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. * (7a) pscore - district ideology |
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. cap teffects ipw (y) (female z absMV, probit), pstolerance(1e-6) osample(osample) |
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156. teffects ipw (y) (female z absMV, probit) if osample==0, pstolerance(1e-6) |
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157. drop osample |
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158. mat b = e(b) |
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159. mat V = e(V) |
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160. local b_pscoreideodistrict_all = b[1,1] |
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161. local se_pscoreideodistrict_all = sqrt(V[1,1]) |
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162. |
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. cap teffects ipw (y) (female z absMV, probit) if voteshare_D>=0.5, pstolerance(1e-6) osample(osample) |
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163. teffects ipw (y) (female z absMV, probit) if voteshare_D>=0.5 & osample==0, pstolerance(1e-6) |
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164. drop osample |
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165. mat b = e(b) |
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166. mat V = e(V) |
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167. local b_pscoreideodistrict_D = b[1,1] |
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168. local se_pscoreideodistrict_D = sqrt(V[1,1]) |
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169. |
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. cap teffects ipw (y) (female z absMV, probit) if voteshare_D<0.5, pstolerance(1e-6) osample(osample) |
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170. teffects ipw (y) (female z absMV, probit) if voteshare_D<0.5 & osample==0, pstolerance(1e-6) |
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171. drop osample |
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172. mat b = e(b) |
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173. mat V = e(V) |
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174. local b_pscoreideodistrict_R = b[1,1] |
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175. local se_pscoreideodistrict_R = sqrt(V[1,1]) |
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176. |
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. |
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. * (7b) pscore - elected representative ideology |
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. cap teffects ipw (y) (female z_elected absMV, probit), pstolerance(1e-6) osample(osample) |
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177. teffects ipw (y) (female z_elected absMV, probit) if osample==0, pstolerance(1e-6) |
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178. drop osample |
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179. mat b = e(b) |
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180. mat V = e(V) |
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181. local b_pscoreideoelected_all = b[1,1] |
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182. local se_pscoreideoelected_all = sqrt(V[1,1]) |
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183. |
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. cap teffects ipw (y) (female z_elected absMV, probit) if voteshare_D>=0.5, pstolerance(1e-6) osample(osample) |
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184. teffects ipw (y) (female z_elected absMV, probit) if voteshare_D>=0.5 & osample==0, pstolerance(1e-6) |
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185. drop osample |
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186. mat b = e(b) |
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187. mat V = e(V) |
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188. local b_pscoreideoelected_D = b[1,1] |
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189. local se_pscoreideoelected_D = sqrt(V[1,1]) |
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190. |
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. cap teffects ipw (y) (female z_elected absMV, probit) if voteshare_D<0.5, pstolerance(1e-6) osample(osample) |
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191. teffects ipw (y) (female z_elected absMV, probit) if voteshare_D<0.5 & osample==0, pstolerance(1e-6) |
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192. drop osample |
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193. mat b = e(b) |
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194. mat V = e(V) |
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195. local b_pscoreideoelected_R = b[1,1] |
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196. local se_pscoreideoelected_R = sqrt(V[1,1]) |
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197. |
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. |
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. * (8) OLS |
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. reg y female |
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198. local b_ols_all =_b[female] |
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199. local se_ols_all = _se[female] |
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200. |
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. reg y female if voteshare_D>=0.5 |
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201. local b_ols_D = _b[female] |
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202. local se_ols_D = _se[female] |
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203. |
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. reg y female if voteshare_D<0.5 |
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204. local b_ols_R = _b[female] |
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205. local se_ols_R = _se[female] |
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206. |
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. * (9) Return |
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. return scalar denstest_pval_all = `denstest_pval_all' |
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207. return scalar denstest_pval_D = `denstest_pval_D' |
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208. return scalar denstest_pval_R = `denstest_pval_R' |
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209. |
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. return scalar b_ideology_all = `b_ideology_all' |
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210. return scalar se_ideology_all = `se_ideology_all' |
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211. return scalar b_ideology_D = `b_ideology_D' |
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212. return scalar se_ideology_D = `se_ideology_D' |
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213. return scalar b_ideology_R = `b_ideology_R' |
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214. return scalar se_ideology_R = `se_ideology_R' |
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215. |
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. return scalar b_rd_all = `b_rd_all' |
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216. return scalar se_rd_all = `se_rd_all' |
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217. return scalar b_rd_D = `b_rd_D' |
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218. return scalar se_rd_D = `se_rd_D' |
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219. return scalar b_rd_R = `b_rd_R' |
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220. return scalar se_rd_R = `se_rd_R' |
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221. |
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. return scalar b_rdwt_all = `b_rdwt_all' |
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222. return scalar se_rdwt_all = `se_rdwt_all' |
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223. return scalar b_rdwt_D = `b_rdwt_D' |
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224. return scalar se_rdwt_D = `se_rdwt_D' |
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225. return scalar b_rdwt_R = `b_rdwt_R' |
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226. return scalar se_rdwt_R = `se_rdwt_R' |
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227. |
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. return scalar b_rdwtideodistrict_all = `b_rdwtideodistrict_all' |
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228. return scalar se_rdwtideodistrict_all = `se_rdwtideodistrict_all' |
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229. return scalar b_rdwtideodistrict_D = `b_rdwtideodistrict_D' |
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230. return scalar se_rdwtideodistrict_D = `se_rdwtideodistrict_D' |
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231. return scalar b_rdwtideodistrict_R = `b_rdwtideodistrict_R' |
|
|
232. return scalar se_rdwtideodistrict_R = `se_rdwtideodistrict_R' |
|
|
233. |
|
|
. return scalar b_rdwtideoelected_all = `b_rdwtideoelected_all' |
|
|
234. return scalar se_rdwtideoelected_all = `se_rdwtideoelected_all' |
|
|
235. return scalar b_rdwtideoelected_D = `b_rdwtideoelected_D' |
|
|
236. return scalar se_rdwtideoelected_D = `se_rdwtideoelected_D' |
|
|
237. return scalar b_rdwtideoelected_R = `b_rdwtideoelected_R' |
|
|
238. return scalar se_rdwtideoelected_R = `se_rdwtideoelected_R' |
|
|
239. |
|
|
. return scalar b_pscorex_all = `b_pscorex_all' |
|
|
240. return scalar se_pscorex_all = `se_pscorex_all' |
|
|
241. return scalar b_pscorex_D = `b_pscorex_D' |
|
|
242. return scalar se_pscorex_D = `se_pscorex_D' |
|
|
243. return scalar b_pscorex_R = `b_pscorex_R' |
|
|
244. return scalar se_pscorex_R = `se_pscorex_R' |
|
|
245. |
|
|
. return scalar b_pscoreideodistrict_all = `b_pscoreideodistrict_all' |
|
|
246. return scalar se_pscoreideodistrict_all = `se_pscoreideodistrict_all' |
|
|
247. return scalar b_pscoreideodistrict_D = `b_pscoreideodistrict_D' |
|
|
248. return scalar se_pscoreideodistrict_D = `se_pscoreideodistrict_D' |
|
|
249. return scalar b_pscoreideodistrict_R = `b_pscoreideodistrict_R' |
|
|
250. return scalar se_pscoreideodistrict_R = `se_pscoreideodistrict_R' |
|
|
251. |
|
|
. return scalar b_pscoreideoelected_all = `b_pscoreideoelected_all' |
|
|
252. return scalar se_pscoreideoelected_all = `se_pscoreideoelected_all' |
|
|
253. return scalar b_pscoreideoelected_D = `b_pscoreideoelected_D' |
|
|
254. return scalar se_pscoreideoelected_D = `se_pscoreideoelected_D' |
|
|
255. return scalar b_pscoreideoelected_R = `b_pscoreideoelected_R' |
|
|
256. return scalar se_pscoreideoelected_R = `se_pscoreideoelected_R' |
|
|
257. |
|
|
. return scalar b_ols_all = `b_ols_all' |
|
|
258. return scalar se_ols_all = `se_ols_all' |
|
|
259. return scalar b_ols_D = `b_ols_D' |
|
|
260. return scalar se_ols_D = `se_ols_D' |
|
|
261. return scalar b_ols_R = `b_ols_R' |
|
|
262. return scalar se_ols_R = `se_ols_R' |
|
|
263. |
|
|
. end |
|
|
|
|
|
. |
|
|
. |
|
|
. ******************************************************************************** |
|
|
. ******************************************************************************** |
|
|
. ******************************************************************************** |
|
|
. |
|
|
. * Now actually run the simulations |
|
|
. |
|
|
. set seed 1234567 |
|
|
|
|
|
. |
|
|
. * Run one simulation as a test |
|
|
. rd_sim, beta_a(5) beta_b(5) kappa_ksi_R(0.4) kappa_ksi_D(0.4) kappa_u(1) tau1(-5) rho_x(0.6) nobs(100000) /* |
|
|
> */ gamma2(0.0) gamma3(0.6) phi1_D(-1) phi1_R(-1) |
|
|
number of observations (_N) was 0, now 100,000 |
|
|
(79,715 missing values generated) |
|
|
(8,263 real changes made) |
|
|
(53,038 missing values generated) |
|
|
(53,038 real changes made) |
|
|
Computing data-driven bandwidth selectors. |
|
|
|
|
|
Point estimates and standard errors have been adjusted for repeated observations. |
|
|
(Use option nomasspoints to suppress this adjustment.) |
|
|
|
|
|
RD Manipulation test using local polynomial density estimation. |
|
|
|
|
|
c = 0.500 | Left of c Right of c Number of obs = 28548 |
|
|
-------------------+---------------------- Model = unrestricted |
|
|
Number of obs | 11425 17123 BW method = comb |
|
|
Eff. Number of obs | 4542 4450 Kernel = triangular |
|
|
Order est. (p) | 2 2 VCE method = jackknife |
|
|
Order bias (q) | 3 3 |
|
|
BW est. (h) | 0.096 0.083 |
|
|
|
|
|
Running variable: voteshare_female. |
|
|
------------------------------------------ |
|
|
Method | T P>|T| |
|
|
-------------------+---------------------- |
|
|
Robust | -0.2327 0.8160 |
|
|
------------------------------------------ |
|
|
|
|
|
P-values of binomial tests. (H0: prob = .5) |
|
|
----------------------------------------------------- |
|
|
Window Length / 2 | <c >=c | P>|T| |
|
|
-------------------+----------------------+---------- |
|
|
0.000 | 5 15 | 0.0414 |
|
|
0.000 | 19 29 | 0.1934 |
|
|
0.001 | 31 40 | 0.3425 |
|
|
0.001 | 42 54 | 0.2615 |
|
|
0.001 | 51 69 | 0.1203 |
|
|
0.001 | 64 83 | 0.1374 |
|
|
0.002 | 69 97 | 0.0358 |
|
|
0.002 | 83 112 | 0.0447 |
|
|
0.002 | 100 127 | 0.0842 |
|
|
0.002 | 115 132 | 0.3086 |
|
|
----------------------------------------------------- |
|
|
Computing data-driven bandwidth selectors. |
|
|
|
|
|
Point estimates and standard errors have been adjusted for repeated observations. |
|
|
(Use option nomasspoints to suppress this adjustment.) |
|
|
|
|
|
RD Manipulation test using local polynomial density estimation. |
|
|
|
|
|
c = 0.500 | Left of c Right of c Number of obs = 13774 |
|
|
-------------------+---------------------- Model = unrestricted |
|
|
Number of obs | 2457 11317 BW method = comb |
|
|
Eff. Number of obs | 1615 6442 Kernel = triangular |
|
|
Order est. (p) | 2 2 VCE method = jackknife |
|
|
Order bias (q) | 3 3 |
|
|
BW est. (h) | 0.141 0.173 |
|
|
|
|
|
Running variable: voteshare_female. |
|
|
------------------------------------------ |
|
|
Method | T P>|T| |
|
|
-------------------+---------------------- |
|
|
Robust | 9.3618 0.0000 |
|
|
------------------------------------------ |
|
|
|
|
|
P-values of binomial tests. (H0: prob = .5) |
|
|
----------------------------------------------------- |
|
|
Window Length / 2 | <c >=c | P>|T| |
|
|
-------------------+----------------------+---------- |
|
|
0.000 | 5 15 | 0.0414 |
|
|
0.000 | 19 29 | 0.1934 |
|
|
0.001 | 31 40 | 0.3425 |
|
|
0.001 | 42 54 | 0.2615 |
|
|
0.001 | 51 69 | 0.1203 |
|
|
0.001 | 64 83 | 0.1374 |
|
|
0.002 | 69 97 | 0.0358 |
|
|
0.002 | 83 112 | 0.0447 |
|
|
0.002 | 100 127 | 0.0842 |
|
|
0.002 | 115 132 | 0.3086 |
|
|
----------------------------------------------------- |
|
|
Computing data-driven bandwidth selectors. |
|
|
|
|
|
Point estimates and standard errors have been adjusted for repeated observations. |
|
|
(Use option nomasspoints to suppress this adjustment.) |
|
|
|
|
|
RD Manipulation test using local polynomial density estimation. |
|
|
|
|
|
c = 0.500 | Left of c Right of c Number of obs = 14774 |
|
|
-------------------+---------------------- Model = unrestricted |
|
|
Number of obs | 8968 5806 BW method = comb |
|
|
Eff. Number of obs | 6647 2312 Kernel = triangular |
|
|
Order est. (p) | 2 2 VCE method = jackknife |
|
|
Order bias (q) | 3 3 |
|
|
BW est. (h) | 0.211 0.148 |
|
|
|
|
|
Running variable: voteshare_female. |
|
|
------------------------------------------ |
|
|
Method | T P>|T| |
|
|
-------------------+---------------------- |
|
|
Robust | -12.2100 0.0000 |
|
|
------------------------------------------ |
|
|
|
|
|
P-values of binomial tests. (H0: prob = .5) |
|
|
----------------------------------------------------- |
|
|
Window Length / 2 | <c >=c | P>|T| |
|
|
-------------------+----------------------+---------- |
|
|
0.000 | 5 15 | 0.0414 |
|
|
0.000 | 19 29 | 0.1934 |
|
|
0.001 | 31 40 | 0.3425 |
|
|
0.001 | 42 54 | 0.2615 |
|
|
0.001 | 51 69 | 0.1203 |
|
|
0.001 | 64 83 | 0.1374 |
|
|
0.002 | 69 97 | 0.0358 |
|
|
0.002 | 83 112 | 0.0447 |
|
|
0.002 | 100 127 | 0.0842 |
|
|
0.002 | 115 132 | 0.3086 |
|
|
----------------------------------------------------- |
|
|
|
|
|
Sharp RD estimates using local polynomial regression. |
|
|
|
|
|
Cutoff c = .5 | Left of c Right of c Number of obs = 28548 |
|
|
-------------------+---------------------- BW type = mserd |
|
|
Number of obs | 11425 17123 Kernel = Uniform |
|
|
Eff. Number of obs | 2282 2410 VCE method = NN |
|
|
Order est. (p) | 1 1 |
|
|
Order bias (q) | 2 2 |
|
|
BW est. (h) | 0.046 0.046 |
|
|
BW bias (b) | 0.092 0.092 |
|
|
rho (h/b) | 0.497 0.497 |
|
|
|
|
|
Outcome: z. Running variable: voteshare_female. |
|
|
-------------------------------------------------------------------------------- |
|
|
Method | Coef. Std. Err. z P>|z| [95% Conf. Interval] |
|
|
-------------------+------------------------------------------------------------ |
|
|
Conventional | .01855 .00924 2.0073 0.045 .000437 .036665 |
|
|
Robust | - - 2.0807 0.037 .001262 .042242 |
|
|
-------------------------------------------------------------------------------- |
|
|
|
|
|
Sharp RD estimates using local polynomial regression. |
|
|
|
|
|
Cutoff c = .5 | Left of c Right of c Number of obs = 13774 |
|
|
-------------------+---------------------- BW type = mserd |
|
|
Number of obs | 2457 11317 Kernel = Uniform |
|
|
Eff. Number of obs | 882 2485 VCE method = NN |
|
|
Order est. (p) | 1 1 |
|
|
Order bias (q) | 2 2 |
|
|
BW est. (h) | 0.065 0.065 |
|
|
BW bias (b) | 0.118 0.118 |
|
|
rho (h/b) | 0.552 0.552 |
|
|
|
|
|
Outcome: z. Running variable: voteshare_female. |
|
|
-------------------------------------------------------------------------------- |
|
|
Method | Coef. Std. Err. z P>|z| [95% Conf. Interval] |
|
|
-------------------+------------------------------------------------------------ |
|
|
Conventional | .2107 .00961 21.9190 0.000 .191862 .229544 |
|
|
Robust | - - 18.9995 0.000 .191055 .235007 |
|
|
-------------------------------------------------------------------------------- |
|
|
|
|
|
Sharp RD estimates using local polynomial regression. |
|
|
|
|
|
Cutoff c = .5 | Left of c Right of c Number of obs = 14774 |
|
|
-------------------+---------------------- BW type = mserd |
|
|
Number of obs | 8968 5806 Kernel = Uniform |
|
|
Eff. Number of obs | 2933 1286 VCE method = NN |
|
|
Order est. (p) | 1 1 |
|
|
Order bias (q) | 2 2 |
|
|
BW est. (h) | 0.083 0.083 |
|
|
BW bias (b) | 0.151 0.151 |
|
|
rho (h/b) | 0.551 0.551 |
|
|
|
|
|
Outcome: z. Running variable: voteshare_female. |
|
|
-------------------------------------------------------------------------------- |
|
|
Method | Coef. Std. Err. z P>|z| [95% Conf. Interval] |
|
|
-------------------+------------------------------------------------------------ |
|
|
Conventional | -.19744 .0082 -24.0813 0.000 -.213511 -.181372 |
|
|
Robust | - - -20.4594 0.000 -.214516 -.177009 |
|
|
-------------------------------------------------------------------------------- |
|
|
|
|
|
Sharp RD estimates using local polynomial regression. |
|
|
|
|
|
Cutoff c = .5 | Left of c Right of c Number of obs = 28548 |
|
|
-------------------+---------------------- BW type = mserd |
|
|
Number of obs | 11425 17123 Kernel = Uniform |
|
|
Eff. Number of obs | 4751 5448 VCE method = NN |
|
|
Order est. (p) | 1 1 |
|
|
Order bias (q) | 2 2 |
|
|
BW est. (h) | 0.101 0.101 |
|
|
BW bias (b) | 0.190 0.190 |
|
|
rho (h/b) | 0.530 0.530 |
|
|
|
|
|
Outcome: y. Running variable: voteshare_female. |
|
|
-------------------------------------------------------------------------------- |
|
|
Method | Coef. Std. Err. z P>|z| [95% Conf. Interval] |
|
|
-------------------+------------------------------------------------------------ |
|
|
Conventional | .17272 .04322 3.9964 0.000 .088013 .257427 |
|
|
Robust | - - 3.2304 0.001 .063486 .25937 |
|
|
-------------------------------------------------------------------------------- |
|
|
|
|
|
Sharp RD estimates using local polynomial regression. |
|
|
|
|
|
Cutoff c = .5 | Left of c Right of c Number of obs = 13774 |
|
|
-------------------+---------------------- BW type = mserd |
|
|
Number of obs | 2457 11317 Kernel = Uniform |
|
|
Eff. Number of obs | 880 2484 VCE method = NN |
|
|
Order est. (p) | 1 1 |
|
|
Order bias (q) | 2 2 |
|
|
BW est. (h) | 0.065 0.065 |
|
|
BW bias (b) | 0.117 0.117 |
|
|
rho (h/b) | 0.555 0.555 |
|
|
|
|
|
Outcome: y. Running variable: voteshare_female. |
|
|
-------------------------------------------------------------------------------- |
|
|
Method | Coef. Std. Err. z P>|z| [95% Conf. Interval] |
|
|
-------------------+------------------------------------------------------------ |
|
|
Conventional | .31188 .07973 3.9117 0.000 .15561 .468147 |
|
|
Robust | - - 3.3740 0.001 .131626 .496528 |
|
|
-------------------------------------------------------------------------------- |
|
|
|
|
|
Sharp RD estimates using local polynomial regression. |
|
|
|
|
|
Cutoff c = .5 | Left of c Right of c Number of obs = 14774 |
|
|
-------------------+---------------------- BW type = mserd |
|
|
Number of obs | 8968 5806 Kernel = Uniform |
|
|
Eff. Number of obs | 3622 1619 VCE method = NN |
|
|
Order est. (p) | 1 1 |
|
|
Order bias (q) | 2 2 |
|
|
BW est. (h) | 0.104 0.104 |
|
|
BW bias (b) | 0.184 0.184 |
|
|
rho (h/b) | 0.566 0.566 |
|
|
|
|
|
Outcome: y. Running variable: voteshare_female. |
|
|
-------------------------------------------------------------------------------- |
|
|
Method | Coef. Std. Err. z P>|z| [95% Conf. Interval] |
|
|
-------------------+------------------------------------------------------------ |
|
|
Conventional | .38413 .06763 5.6797 0.000 .251574 .516685 |
|
|
Robust | - - 4.6972 0.000 .218021 .530242 |
|
|
-------------------------------------------------------------------------------- |
|
|
(53,038 missing values generated) |
|
|
(53,038 real changes made) |
|
|
(71,452 missing values generated) |
|
|
|
|
|
Iteration 0: log likelihood = -7045.573 |
|
|
Iteration 1: log likelihood = -7014.2341 |
|
|
Iteration 2: log likelihood = -7014.2334 |
|
|
Iteration 3: log likelihood = -7014.2334 |
|
|
|
|
|
Probit regression Number of obs = 10,199 |
|
|
LR chi2(1) = 62.68 |
|
|
Prob > chi2 = 0.0000 |
|
|
Log likelihood = -7014.2334 Pseudo R2 = 0.0044 |
|
|
|
|
|
------------------------------------------------------------------------------ |
|
|
female | Coef. Std. Err. z P>|z| [95% Conf. Interval] |
|
|
-------------+---------------------------------------------------------------- |
|
|
x | -.2260739 .0286094 -7.90 0.000 -.2821473 -.1700006 |
|
|
_cons | .0707624 .0125909 5.62 0.000 .0460847 .09544 |
|
|
------------------------------------------------------------------------------ |
|
|
(option pr assumed; Pr(female)) |
|
|
(89,801 missing values generated) |
|
|
(94,552 missing values generated) |
|
|
(4,751 real changes made) |
|
|
(sum of wgt is 20,398.6936738491) |
|
|
|
|
|
Source | SS df MS Number of obs = 10,199 |
|
|
-------------+---------------------------------- F(3, 10195) = 51.96 |
|
|
Model | 193.062182 3 64.3540607 Prob > F = 0.0000 |
|
|
Residual | 12626.5639 10,195 1.23850554 R-squared = 0.0151 |
|
|
-------------+---------------------------------- Adj R-squared = 0.0148 |
|
|
Total | 12819.6261 10,198 1.25707258 Root MSE = 1.1129 |
|
|
|
|
|
----------------------------------------------------------------------------------------------- |
|
|
y | Coef. Std. Err. t P>|t| [95% Conf. Interval] |
|
|
------------------------------+---------------------------------------------------------------- |
|
|
female | .1739132 .0439874 3.95 0.000 .0876893 .2601372 |
|
|
voteshare_female_adj | 3.538998 .5419161 6.53 0.000 2.476736 4.60126 |
|
|
| |
|
|
female#c.voteshare_female_adj | |
|
|
1 | -6.258399 .7649045 -8.18 0.000 -7.757763 -4.759036 |
|
|
| |
|
|
_cons | -1.052096 .0305361 -34.45 0.000 -1.111952 -.9922388 |
|
|
----------------------------------------------------------------------------------------------- |
|
|
|
|
|
Iteration 0: log likelihood = -1933.3477 |
|
|
Iteration 1: log likelihood = -1878.7605 |
|
|
Iteration 2: log likelihood = -1878.6371 |
|
|
Iteration 3: log likelihood = -1878.6371 |
|
|
|
|
|
Probit regression Number of obs = 3,364 |
|
|
LR chi2(1) = 109.42 |
|
|
Prob > chi2 = 0.0000 |
|
|
Log likelihood = -1878.6371 Pseudo R2 = 0.0283 |
|
|
|
|
|
------------------------------------------------------------------------------ |
|
|
female | Coef. Std. Err. z P>|z| [95% Conf. Interval] |
|
|
-------------+---------------------------------------------------------------- |
|
|
x | .5759299 .055828 10.32 0.000 .4665091 .6853507 |
|
|
_cons | .720536 .0252009 28.59 0.000 .6711433 .7699288 |
|
|
------------------------------------------------------------------------------ |
|
|
(option pr assumed; Pr(female)) |
|
|
(96,636 missing values generated) |
|
|
(97,516 missing values generated) |
|
|
(880 real changes made) |
|
|
(sum of wgt is 6,733.80004513264) |
|
|
|
|
|
Source | SS df MS Number of obs = 3,364 |
|
|
-------------+---------------------------------- F(3, 3360) = 40.81 |
|
|
Model | 139.293765 3 46.4312551 Prob > F = 0.0000 |
|
|
Residual | 3822.60101 3,360 1.13767887 R-squared = 0.0352 |
|
|
-------------+---------------------------------- Adj R-squared = 0.0343 |
|
|
Total | 3961.89478 3,363 1.17808349 Root MSE = 1.0666 |
|
|
|
|
|
----------------------------------------------------------------------------------------------- |
|
|
y | Coef. Std. Err. t P>|t| [95% Conf. Interval] |
|
|
------------------------------+---------------------------------------------------------------- |
|
|
female | .2406377 .0730094 3.30 0.001 .0974903 .3837851 |
|
|
voteshare_female_adj | 6.792886 1.412536 4.81 0.000 4.023369 9.562403 |
|
|
| |
|
|
female#c.voteshare_female_adj | |
|
|
1 | -9.439579 1.989195 -4.75 0.000 -13.33973 -5.539424 |
|
|
| |
|
|
_cons | -1.285744 .0501982 -25.61 0.000 -1.384166 -1.187322 |
|
|
----------------------------------------------------------------------------------------------- |
|
|
|
|
|
Iteration 0: log likelihood = -3240.1233 |
|
|
Iteration 1: log likelihood = -3133.1627 |
|
|
Iteration 2: log likelihood = -3132.9107 |
|
|
Iteration 3: log likelihood = -3132.9107 |
|
|
|
|
|
Probit regression Number of obs = 5,241 |
|
|
LR chi2(1) = 214.43 |
|
|
Prob > chi2 = 0.0000 |
|
|
Log likelihood = -3132.9107 Pseudo R2 = 0.0331 |
|
|
|
|
|
------------------------------------------------------------------------------ |
|
|
female | Coef. Std. Err. z P>|z| [95% Conf. Interval] |
|
|
-------------+---------------------------------------------------------------- |
|
|
x | -.629661 .0437244 -14.40 0.000 -.7153593 -.5439628 |
|
|
_cons | -.5177479 .0184832 -28.01 0.000 -.5539742 -.4815216 |
|
|
------------------------------------------------------------------------------ |
|
|
(option pr assumed; Pr(female)) |
|
|
(94,759 missing values generated) |
|
|
(98,381 missing values generated) |
|
|
(3,622 real changes made) |
|
|
(sum of wgt is 10,475.5064911842) |
|
|
|
|
|
Source | SS df MS Number of obs = 5,241 |
|
|
-------------+---------------------------------- F(3, 5237) = 80.33 |
|
|
Model | 290.368848 3 96.7896161 Prob > F = 0.0000 |
|
|
Residual | 6310.12588 5,237 1.20491233 R-squared = 0.0440 |
|
|
-------------+---------------------------------- Adj R-squared = 0.0434 |
|
|
Total | 6600.49473 5,240 1.2596364 Root MSE = 1.0977 |
|
|
|
|
|
----------------------------------------------------------------------------------------------- |
|
|
y | Coef. Std. Err. t P>|t| [95% Conf. Interval] |
|
|
------------------------------+---------------------------------------------------------------- |
|
|
female | .3231791 .0614265 5.26 0.000 .2027576 .4436006 |
|
|
voteshare_female_adj | 4.05668 .722778 5.61 0.000 2.639734 5.473627 |
|
|
| |
|
|
female#c.voteshare_female_adj | |
|
|
1 | -5.84145 1.017686 -5.74 0.000 -7.836538 -3.846362 |
|
|
| |
|
|
_cons | -.8815513 .0424921 -20.75 0.000 -.9648534 -.7982492 |
|
|
----------------------------------------------------------------------------------------------- |
|
|
|
|
|
Iteration 0: log likelihood = -7045.573 |
|
|
Iteration 1: log likelihood = -6830.0923 |
|
|
Iteration 2: log likelihood = -6829.9941 |
|
|
Iteration 3: log likelihood = -6829.9941 |
|
|
|
|
|
Probit regression Number of obs = 10,199 |
|
|
LR chi2(1) = 431.16 |
|
|
Prob > chi2 = 0.0000 |
|
|
Log likelihood = -6829.9941 Pseudo R2 = 0.0306 |
|
|
|
|
|
------------------------------------------------------------------------------ |
|
|
female | Coef. Std. Err. z P>|z| [95% Conf. Interval] |
|
|
-------------+---------------------------------------------------------------- |
|
|
z | -1.538549 .0747452 -20.58 0.000 -1.685046 -1.392051 |
|
|
_cons | -.0209974 .0136641 -1.54 0.124 -.0477786 .0057838 |
|
|
------------------------------------------------------------------------------ |
|
|
(option pr assumed; Pr(female)) |
|
|
(89,801 missing values generated) |
|
|
(94,552 missing values generated) |
|
|
(4,751 real changes made) |
|
|
(sum of wgt is 20,472.7413574457) |
|
|
|
|
|
Source | SS df MS Number of obs = 10,199 |
|
|
-------------+---------------------------------- F(3, 10195) = 70.48 |
|
|
Model | 262.88843 3 87.6294767 Prob > F = 0.0000 |
|
|
Residual | 12675.1194 10,195 1.24326821 R-squared = 0.0203 |
|
|
-------------+---------------------------------- Adj R-squared = 0.0200 |
|
|
Total | 12938.0078 10,198 1.2686809 Root MSE = 1.115 |
|
|
|
|
|
----------------------------------------------------------------------------------------------- |
|
|
y | Coef. Std. Err. t P>|t| [95% Conf. Interval] |
|
|
------------------------------+---------------------------------------------------------------- |
|
|
female | .1943662 .0436797 4.45 0.000 .1087454 .2799871 |
|
|
voteshare_female_adj | 3.946968 .5368838 7.35 0.000 2.894571 4.999366 |
|
|
| |
|
|
female#c.voteshare_female_adj | |
|
|
1 | -6.460541 .7641782 -8.45 0.000 -7.958481 -4.962601 |
|
|
| |
|
|
_cons | -1.069798 .0301335 -35.50 0.000 -1.128866 -1.010731 |
|
|
----------------------------------------------------------------------------------------------- |
|
|
|
|
|
Iteration 0: log likelihood = -1933.3477 |
|
|
Iteration 1: log likelihood = -1286.2133 |
|
|
Iteration 2: log likelihood = -1267.5265 |
|
|
Iteration 3: log likelihood = -1267.4815 |
|
|
Iteration 4: log likelihood = -1267.4815 |
|
|
|
|
|
Probit regression Number of obs = 3,364 |
|
|
LR chi2(1) = 1331.73 |
|
|
Prob > chi2 = 0.0000 |
|
|
Log likelihood = -1267.4815 Pseudo R2 = 0.3444 |
|
|
|
|
|
------------------------------------------------------------------------------ |
|
|
female | Coef. Std. Err. z P>|z| [95% Conf. Interval] |
|
|
-------------+---------------------------------------------------------------- |
|
|
z | 7.444313 .2623512 28.38 0.000 6.930114 7.958512 |
|
|
_cons | 1.78103 .0537058 33.16 0.000 1.675768 1.886291 |
|
|
------------------------------------------------------------------------------ |
|
|
(option pr assumed; Pr(female)) |
|
|
(96,636 missing values generated) |
|
|
(97,516 missing values generated) |
|
|
(880 real changes made) |
|
|
(sum of wgt is 6,608.81980001926) |
|
|
|
|
|
Source | SS df MS Number of obs = 3,364 |
|
|
-------------+---------------------------------- F(3, 3360) = 112.97 |
|
|
Model | 469.699399 3 156.566466 Prob > F = 0.0000 |
|
|
Residual | 4656.54364 3,360 1.38587608 R-squared = 0.0916 |
|
|
-------------+---------------------------------- Adj R-squared = 0.0908 |
|
|
Total | 5126.24304 3,363 1.52430658 Root MSE = 1.1772 |
|
|
|
|
|
----------------------------------------------------------------------------------------------- |
|
|
y | Coef. Std. Err. t P>|t| [95% Conf. Interval] |
|
|
------------------------------+---------------------------------------------------------------- |
|
|
female | -.793492 .0774302 -10.25 0.000 -.9453071 -.6416768 |
|
|
voteshare_female_adj | 26.62825 1.637998 16.26 0.000 23.41667 29.83982 |
|
|
| |
|
|
female#c.voteshare_female_adj | |
|
|
1 | -31.34425 2.256755 -13.89 0.000 -35.769 -26.9195 |
|
|
| |
|
|
_cons | -.2816657 .0479712 -5.87 0.000 -.3757214 -.1876099 |
|
|
----------------------------------------------------------------------------------------------- |
|
|
|
|
|
Iteration 0: log likelihood = -3240.1233 |
|
|
Iteration 1: log likelihood = -2172.2326 |
|
|
Iteration 2: log likelihood = -2152.4686 |
|
|
Iteration 3: log likelihood = -2152.4172 |
|
|
Iteration 4: log likelihood = -2152.4172 |
|
|
|
|
|
Probit regression Number of obs = 5,241 |
|
|
LR chi2(1) = 2175.41 |
|
|
Prob > chi2 = 0.0000 |
|
|
Log likelihood = -2152.4172 Pseudo R2 = 0.3357 |
|
|
|
|
|
------------------------------------------------------------------------------ |
|
|
female | Coef. Std. Err. z P>|z| [95% Conf. Interval] |
|
|
-------------+---------------------------------------------------------------- |
|
|
z | -7.049271 .1911968 -36.87 0.000 -7.42401 -6.674532 |
|
|
_cons | -.7631799 .0241643 -31.58 0.000 -.8105411 -.7158187 |
|
|
------------------------------------------------------------------------------ |
|
|
(option pr assumed; Pr(female)) |
|
|
(94,759 missing values generated) |
|
|
(98,381 missing values generated) |
|
|
(3,622 real changes made) |
|
|
(sum of wgt is 9,903.25282597542) |
|
|
|
|
|
Source | SS df MS Number of obs = 5,241 |
|
|
-------------+---------------------------------- F(3, 5237) = 22.54 |
|
|
Model | 81.2014589 3 27.067153 Prob > F = 0.0000 |
|
|
Residual | 6288.68375 5,237 1.20081798 R-squared = 0.0127 |
|
|
-------------+---------------------------------- Adj R-squared = 0.0122 |
|
|
Total | 6369.8852 5,240 1.21562695 Root MSE = 1.0958 |
|
|
|
|
|
----------------------------------------------------------------------------------------------- |
|
|
y | Coef. Std. Err. t P>|t| [95% Conf. Interval] |
|
|
------------------------------+---------------------------------------------------------------- |
|
|
female | -.1379332 .0656739 -2.10 0.036 -.2666815 -.0091849 |
|
|
voteshare_female_adj | 5.627017 .7120137 7.90 0.000 4.231173 7.022861 |
|
|
| |
|
|
female#c.voteshare_female_adj | |
|
|
1 | -7.189718 1.03427 -6.95 0.000 -9.217319 -5.162118 |
|
|
| |
|
|
_cons | -.680452 .0396242 -17.17 0.000 -.758132 -.6027719 |
|
|
----------------------------------------------------------------------------------------------- |
|
|
(53,038 missing values generated) |
|
|
(53,038 real changes made) |
|
|
|
|
|
Iteration 0: log likelihood = -7045.573 |
|
|
Iteration 1: log likelihood = -6040.1188 |
|
|
Iteration 2: log likelihood = -6037.2237 |
|
|
Iteration 3: log likelihood = -6037.2236 |
|
|
|
|
|
Probit regression Number of obs = 10,199 |
|
|
LR chi2(1) = 2016.70 |
|
|
Prob > chi2 = 0.0000 |
|
|
Log likelihood = -6037.2236 Pseudo R2 = 0.1431 |
|
|
|
|
|
------------------------------------------------------------------------------ |
|
|
female | Coef. Std. Err. z P>|z| [95% Conf. Interval] |
|
|
-------------+---------------------------------------------------------------- |
|
|
z_elected | -1.958926 .0449516 -43.58 0.000 -2.04703 -1.870823 |
|
|
_cons | .0135668 .0133481 1.02 0.309 -.0125949 .0397285 |
|
|
------------------------------------------------------------------------------ |
|
|
(option pr assumed; Pr(female)) |
|
|
(89,801 missing values generated) |
|
|
(94,552 missing values generated) |
|
|
(4,751 real changes made) |
|
|
(sum of wgt is 20,703.5746251345) |
|
|
|
|
|
Source | SS df MS Number of obs = 10,199 |
|
|
-------------+---------------------------------- F(3, 10195) = 156.58 |
|
|
Model | 586.245291 3 195.415097 Prob > F = 0.0000 |
|
|
Residual | 12723.9437 10,195 1.24805725 R-squared = 0.0440 |
|
|
-------------+---------------------------------- Adj R-squared = 0.0438 |
|
|
Total | 13310.189 10,198 1.3051764 Root MSE = 1.1172 |
|
|
|
|
|
----------------------------------------------------------------------------------------------- |
|
|
y | Coef. Std. Err. t P>|t| [95% Conf. Interval] |
|
|
------------------------------+---------------------------------------------------------------- |
|
|
female | .3725144 .044218 8.42 0.000 .2858384 .4591904 |
|
|
voteshare_female_adj | 3.852766 .5269349 7.31 0.000 2.81987 4.885662 |
|
|
| |
|
|
female#c.voteshare_female_adj | |
|
|
1 | -6.090878 .7661041 -7.95 0.000 -7.592593 -4.589164 |
|
|
| |
|
|
_cons | -1.201598 .0296283 -40.56 0.000 -1.259675 -1.14352 |
|
|
----------------------------------------------------------------------------------------------- |
|
|
|
|
|
Iteration 0: log likelihood = -1933.3477 |
|
|
Iteration 1: log likelihood = -1587.7928 |
|
|
Iteration 2: log likelihood = -1582.0926 |
|
|
Iteration 3: log likelihood = -1582.0776 |
|
|
Iteration 4: log likelihood = -1582.0776 |
|
|
|
|
|
Probit regression Number of obs = 3,364 |
|
|
LR chi2(1) = 702.54 |
|
|
Prob > chi2 = 0.0000 |
|
|
Log likelihood = -1582.0776 Pseudo R2 = 0.1817 |
|
|
|
|
|
------------------------------------------------------------------------------ |
|
|
female | Coef. Std. Err. z P>|z| [95% Conf. Interval] |
|
|
-------------+---------------------------------------------------------------- |
|
|
z_elected | 7.410054 .3114873 23.79 0.000 6.79955 8.020558 |
|
|
_cons | 3.042828 .1071286 28.40 0.000 2.83286 3.252796 |
|
|
------------------------------------------------------------------------------ |
|
|
(option pr assumed; Pr(female)) |
|
|
(96,636 missing values generated) |
|
|
(97,516 missing values generated) |
|
|
(880 real changes made) |
|
|
(sum of wgt is 6,813.62821555138) |
|
|
|
|
|
Source | SS df MS Number of obs = 3,364 |
|
|
-------------+---------------------------------- F(3, 3360) = 44.01 |
|
|
Model | 168.894906 3 56.2983019 Prob > F = 0.0000 |
|
|
Residual | 4298.15433 3,360 1.2792126 R-squared = 0.0378 |
|
|
-------------+---------------------------------- Adj R-squared = 0.0369 |
|
|
Total | 4467.04924 3,363 1.32829296 Root MSE = 1.131 |
|
|
|
|
|
----------------------------------------------------------------------------------------------- |
|
|
y | Coef. Std. Err. t P>|t| [95% Conf. Interval] |
|
|
------------------------------+---------------------------------------------------------------- |
|
|
female | -.4509505 .0758094 -5.95 0.000 -.5995877 -.3023132 |
|
|
voteshare_female_adj | 16.0325 1.518248 10.56 0.000 13.05572 19.00929 |
|
|
| |
|
|
female#c.voteshare_female_adj | |
|
|
1 | -19.80716 2.132745 -9.29 0.000 -23.98877 -15.62555 |
|
|
| |
|
|
_cons | -.6497326 .0491586 -13.22 0.000 -.7461164 -.5533488 |
|
|
----------------------------------------------------------------------------------------------- |
|
|
|
|
|
Iteration 0: log likelihood = -3240.1233 |
|
|
Iteration 1: log likelihood = -2594.5471 |
|
|
Iteration 2: log likelihood = -2583.7661 |
|
|
Iteration 3: log likelihood = -2583.7327 |
|
|
Iteration 4: log likelihood = -2583.7327 |
|
|
|
|
|
Probit regression Number of obs = 5,241 |
|
|
LR chi2(1) = 1312.78 |
|
|
Prob > chi2 = 0.0000 |
|
|
Log likelihood = -2583.7327 Pseudo R2 = 0.2026 |
|
|
|
|
|
------------------------------------------------------------------------------ |
|
|
female | Coef. Std. Err. z P>|z| [95% Conf. Interval] |
|
|
-------------+---------------------------------------------------------------- |
|
|
z_elected | -7.805294 .2423385 -32.21 0.000 -8.280269 -7.33032 |
|
|
_cons | 1.29987 .0573838 22.65 0.000 1.1874 1.412341 |
|
|
------------------------------------------------------------------------------ |
|
|
(option pr assumed; Pr(female)) |
|
|
(94,759 missing values generated) |
|
|
(98,381 missing values generated) |
|
|
(3,622 real changes made) |
|
|
(sum of wgt is 10,372.1911814213) |
|
|
|
|
|
Source | SS df MS Number of obs = 5,241 |
|
|
-------------+---------------------------------- F(3, 5237) = 16.72 |
|
|
Model | 60.8606207 3 20.2868736 Prob > F = 0.0000 |
|
|
Residual | 6354.50433 5,237 1.21338635 R-squared = 0.0095 |
|
|
-------------+---------------------------------- Adj R-squared = 0.0089 |
|
|
Total | 6415.36495 5,240 1.22430629 Root MSE = 1.1015 |
|
|
|
|
|
----------------------------------------------------------------------------------------------- |
|
|
y | Coef. Std. Err. t P>|t| [95% Conf. Interval] |
|
|
------------------------------+---------------------------------------------------------------- |
|
|
female | -.1872573 .0632442 -2.96 0.003 -.3112422 -.0632723 |
|
|
voteshare_female_adj | 5.046656 .7215085 6.99 0.000 3.632198 6.461113 |
|
|
| |
|
|
female#c.voteshare_female_adj | |
|
|
1 | -5.702727 1.02625 -5.56 0.000 -7.714604 -3.690849 |
|
|
| |
|
|
_cons | -.6934209 .0411359 -16.86 0.000 -.7740645 -.6127773 |
|
|
----------------------------------------------------------------------------------------------- |
|
|
|
|
|
Iteration 0: EE criterion = 2.168e-28 |
|
|
Iteration 1: EE criterion = 4.132e-33 |
|
|
|
|
|
Treatment-effects estimation Number of obs = 100,000 |
|
|
Estimator : inverse-probability weights |
|
|
Outcome model : weighted mean |
|
|
Treatment model: probit |
|
|
------------------------------------------------------------------------------ |
|
|
| Robust |
|
|
y | Coef. Std. Err. z P>|z| [95% Conf. Interval] |
|
|
-------------+---------------------------------------------------------------- |
|
|
ATE | |
|
|
female | |
|
|
(1 vs 0) | .185551 .009143 20.29 0.000 .1676311 .2034709 |
|
|
-------------+---------------------------------------------------------------- |
|
|
POmean | |
|
|
female | |
|
|
0 | -1.51444 .0040466 -374.25 0.000 -1.522371 -1.506509 |
|
|
------------------------------------------------------------------------------ |
|
|
|
|
|
Iteration 0: EE criterion = 5.545e-18 |
|
|
Iteration 1: EE criterion = 2.076e-33 |
|
|
|
|
|
Treatment-effects estimation Number of obs = 46,962 |
|
|
Estimator : inverse-probability weights |
|
|
Outcome model : weighted mean |
|
|
Treatment model: probit |
|
|
------------------------------------------------------------------------------ |
|
|
| Robust |
|
|
y | Coef. Std. Err. z P>|z| [95% Conf. Interval] |
|
|
-------------+---------------------------------------------------------------- |
|
|
ATE | |
|
|
female | |
|
|
(1 vs 0) | -.0620271 .0113967 -5.44 0.000 -.0843642 -.03969 |
|
|
-------------+---------------------------------------------------------------- |
|
|
POmean | |
|
|
female | |
|
|
0 | -1.499856 .0061075 -245.58 0.000 -1.511827 -1.487886 |
|
|
------------------------------------------------------------------------------ |
|
|
|
|
|
Iteration 0: EE criterion = 2.364e-23 |
|
|
Iteration 1: EE criterion = 4.823e-32 |
|
|
|
|
|
Treatment-effects estimation Number of obs = 53,038 |
|
|
Estimator : inverse-probability weights |
|
|
Outcome model : weighted mean |
|
|
Treatment model: probit |
|
|
------------------------------------------------------------------------------ |
|
|
| Robust |
|
|
y | Coef. Std. Err. z P>|z| [95% Conf. Interval] |
|
|
-------------+---------------------------------------------------------------- |
|
|
ATE | |
|
|
female | |
|
|
(1 vs 0) | .4934254 .015274 32.30 0.000 .4634889 .5233619 |
|
|
-------------+---------------------------------------------------------------- |
|
|
POmean | |
|
|
female | |
|
|
0 | -1.519821 .0053757 -282.72 0.000 -1.530357 -1.509284 |
|
|
------------------------------------------------------------------------------ |
|
|
|
|
|
Iteration 0: EE criterion = 7.198e-22 |
|
|
Iteration 1: EE criterion = 6.742e-33 |
|
|
|
|
|
Treatment-effects estimation Number of obs = 100,000 |
|
|
Estimator : inverse-probability weights |
|
|
Outcome model : weighted mean |
|
|
Treatment model: probit |
|
|
------------------------------------------------------------------------------ |
|
|
| Robust |
|
|
y | Coef. Std. Err. z P>|z| [95% Conf. Interval] |
|
|
-------------+---------------------------------------------------------------- |
|
|
ATE | |
|
|
female | |
|
|
(1 vs 0) | .1960373 .0094185 20.81 0.000 .1775775 .2144971 |
|
|
-------------+---------------------------------------------------------------- |
|
|
POmean | |
|
|
female | |
|
|
0 | -1.527443 .004071 -375.20 0.000 -1.535422 -1.519464 |
|
|
------------------------------------------------------------------------------ |
|
|
|
|
|
Iteration 0: EE criterion = 4.047e-18 |
|
|
Iteration 1: EE criterion = 2.245e-32 |
|
|
|
|
|
Treatment-effects estimation Number of obs = 46,962 |
|
|
Estimator : inverse-probability weights |
|
|
Outcome model : weighted mean |
|
|
Treatment model: probit |
|
|
------------------------------------------------------------------------------ |
|
|
| Robust |
|
|
y | Coef. Std. Err. z P>|z| [95% Conf. Interval] |
|
|
-------------+---------------------------------------------------------------- |
|
|
ATE | |
|
|
female | |
|
|
(1 vs 0) | -.0158669 .0108021 -1.47 0.142 -.0370386 .0053048 |
|
|
-------------+---------------------------------------------------------------- |
|
|
POmean | |
|
|
female | |
|
|
0 | -1.51214 .0060289 -250.82 0.000 -1.523956 -1.500323 |
|
|
------------------------------------------------------------------------------ |
|
|
|
|
|
Iteration 0: EE criterion = 2.427e-27 |
|
|
Iteration 1: EE criterion = 1.682e-31 |
|
|
|
|
|
Treatment-effects estimation Number of obs = 52,919 |
|
|
Estimator : inverse-probability weights |
|
|
Outcome model : weighted mean |
|
|
Treatment model: probit |
|
|
------------------------------------------------------------------------------ |
|
|
| Robust |
|
|
y | Coef. Std. Err. z P>|z| [95% Conf. Interval] |
|
|
-------------+---------------------------------------------------------------- |
|
|
ATE | |
|
|
female | |
|
|
(1 vs 0) | -.1946729 .0624661 -3.12 0.002 -.3171042 -.0722417 |
|
|
-------------+---------------------------------------------------------------- |
|
|
POmean | |
|
|
female | |
|
|
0 | -1.45934 .0055571 -262.61 0.000 -1.470232 -1.448448 |
|
|
------------------------------------------------------------------------------ |
|
|
|
|
|
Iteration 0: EE criterion = 1.047e-24 |
|
|
Iteration 1: EE criterion = 5.030e-32 |
|
|
|
|
|
Treatment-effects estimation Number of obs = 100,000 |
|
|
Estimator : inverse-probability weights |
|
|
Outcome model : weighted mean |
|
|
Treatment model: probit |
|
|
------------------------------------------------------------------------------ |
|
|
| Robust |
|
|
y | Coef. Std. Err. z P>|z| [95% Conf. Interval] |
|
|
-------------+---------------------------------------------------------------- |
|
|
ATE | |
|
|
female | |
|
|
(1 vs 0) | .2416772 .0092623 26.09 0.000 .2235235 .2598309 |
|
|
-------------+---------------------------------------------------------------- |
|
|
POmean | |
|
|
female | |
|
|
0 | -1.519808 .0040542 -374.88 0.000 -1.527754 -1.511862 |
|
|
------------------------------------------------------------------------------ |
|
|
|
|
|
Iteration 0: EE criterion = 3.676e-18 |
|
|
Iteration 1: EE criterion = 2.042e-33 |
|
|
|
|
|
Treatment-effects estimation Number of obs = 46,962 |
|
|
Estimator : inverse-probability weights |
|
|
Outcome model : weighted mean |
|
|
Treatment model: probit |
|
|
------------------------------------------------------------------------------ |
|
|
| Robust |
|
|
y | Coef. Std. Err. z P>|z| [95% Conf. Interval] |
|
|
-------------+---------------------------------------------------------------- |
|
|
ATE | |
|
|
female | |
|
|
(1 vs 0) | -.000495 .0106006 -0.05 0.963 -.0212719 .0202818 |
|
|
-------------+---------------------------------------------------------------- |
|
|
POmean | |
|
|
female | |
|
|
0 | -1.515822 .0060067 -252.36 0.000 -1.527595 -1.504049 |
|
|
------------------------------------------------------------------------------ |
|
|
|
|
|
Iteration 0: EE criterion = 9.684e-16 |
|
|
Iteration 1: EE criterion = 8.628e-28 |
|
|
|
|
|
Treatment-effects estimation Number of obs = 53,038 |
|
|
Estimator : inverse-probability weights |
|
|
Outcome model : weighted mean |
|
|
Treatment model: probit |
|
|
------------------------------------------------------------------------------ |
|
|
| Robust |
|
|
y | Coef. Std. Err. z P>|z| [95% Conf. Interval] |
|
|
-------------+---------------------------------------------------------------- |
|
|
ATE | |
|
|
female | |
|
|
(1 vs 0) | -.1050439 .0401988 -2.61 0.009 -.183832 -.0262557 |
|
|
-------------+---------------------------------------------------------------- |
|
|
POmean | |
|
|
female | |
|
|
0 | -1.458219 .0054927 -265.48 0.000 -1.468984 -1.447453 |
|
|
------------------------------------------------------------------------------ |
|
|
|
|
|
Source | SS df MS Number of obs = 100,000 |
|
|
-------------+---------------------------------- F(1, 99998) = 115.72 |
|
|
Model | 155.270531 1 155.270531 Prob > F = 0.0000 |
|
|
Residual | 134179.437 99,998 1.3418212 R-squared = 0.0012 |
|
|
-------------+---------------------------------- Adj R-squared = 0.0011 |
|
|
Total | 134334.707 99,999 1.34336051 Root MSE = 1.1584 |
|
|
|
|
|
------------------------------------------------------------------------------ |
|
|
y | Coef. Std. Err. t P>|t| [95% Conf. Interval] |
|
|
-------------+---------------------------------------------------------------- |
|
|
female | .0988518 .0091894 10.76 0.000 .0808406 .1168629 |
|
|
_cons | -1.501196 .0040908 -366.97 0.000 -1.509214 -1.493178 |
|
|
------------------------------------------------------------------------------ |
|
|
|
|
|
Source | SS df MS Number of obs = 46,962 |
|
|
-------------+---------------------------------- F(1, 46960) = 81.37 |
|
|
Model | 107.755058 1 107.755058 Prob > F = 0.0000 |
|
|
Residual | 62185.463 46,960 1.32422196 R-squared = 0.0017 |
|
|
-------------+---------------------------------- Adj R-squared = 0.0017 |
|
|
Total | 62293.2181 46,961 1.32648832 Root MSE = 1.1507 |
|
|
|
|
|
------------------------------------------------------------------------------ |
|
|
y | Coef. Std. Err. t P>|t| [95% Conf. Interval] |
|
|
-------------+---------------------------------------------------------------- |
|
|
female | -.1088381 .0120654 -9.02 0.000 -.1324866 -.0851897 |
|
|
_cons | -1.487199 .0061843 -240.48 0.000 -1.499321 -1.475078 |
|
|
------------------------------------------------------------------------------ |
|
|
|
|
|
Source | SS df MS Number of obs = 53,038 |
|
|
-------------+---------------------------------- F(1, 53036) = 886.34 |
|
|
Model | 1182.47024 1 1182.47024 Prob > F = 0.0000 |
|
|
Residual | 70755.5305 53,036 1.33410383 R-squared = 0.0164 |
|
|
-------------+---------------------------------- Adj R-squared = 0.0164 |
|
|
Total | 71938.0008 53,037 1.35637387 Root MSE = 1.155 |
|
|
|
|
|
------------------------------------------------------------------------------ |
|
|
y | Coef. Std. Err. t P>|t| [95% Conf. Interval] |
|
|
-------------+---------------------------------------------------------------- |
|
|
female | .4290223 .0144105 29.77 0.000 .4007775 .457267 |
|
|
_cons | -1.511833 .0054114 -279.38 0.000 -1.522439 -1.501226 |
|
|
------------------------------------------------------------------------------ |
|
|
|
|
|
. |
|
|
. |
|
|
. |
|
|
. |
|
|
. set seed 1234567 |
|
|
|
|
|
. |
|
|
. forvalues j = 1/4 { |
|
|
2. if `j'==1 { |
|
|
3. * BASELINE: Everything balanced |
|
|
. local gamma2 = 0 // No preference for female D |
|
|
4. local gamma3 = 0 // No preference for female R |
|
|
5. local phi1_D = 0 // No relationship between gender and ideology |
|
|
6. local phi1_R = 0 |
|
|
7. local DataDescr`j' = "Baseline - Everything Balanced" |
|
|
8. } |
|
|
9. else if `j'==2 { |
|
|
10. * Variant 1: Women are more left-wing, no preference for female candidates |
|
|
. local gamma2 = 0 // No preference for female D |
|
|
11. local gamma3 = 0 // No preference for female R |
|
|
12. local phi1_D = -1 // No relationship between gender and ideology |
|
|
13. local phi1_R = -1 |
|
|
14. local DataDescr`j' = "Variant 1 - Women are more left-wing, no preference for female candidates" |
|
|
15. } |
|
|
16. else if `j'==3 { |
|
|
17. * Variant 2: Women are more left-wing, equal preference for female candidates |
|
|
. local gamma2 = 0.3 // No preference for female D |
|
|
18. local gamma3 = 0.3 // No preference for female R |
|
|
19. local phi1_D = -1 // No relationship between gender and ideology |
|
|
20. local phi1_R = -1 |
|
|
21. local DataDescr`j' = "Variant 2 - Women are more left-wing, equal preference for female candidates" |
|
|
22. } |
|
|
23. |
|
|
. else if `j'==4 { |
|
|
24. * Variant 3: Women are more left-wing, only D's prefer female candidates |
|
|
. local gamma2 = 0.6 // No preference for female D |
|
|
25. local gamma3 = 0 // No preference for female R |
|
|
26. local phi1_D = -1 // No relationship between gender and ideology |
|
|
27. local phi1_R = -1 |
|
|
28. local DataDescr`j' = "Variant 3 - Women are more left-wing, only Ds prefer female candidates" |
|
|
29. } |
|
|
30. |
|
|
. simulate denstest_pval_all=r(denstest_pval_all) denstest_pval_D=r(denstest_pval_D) denstest_pval_R=r(denstest_pval_R) |
|
|
b_ideology_all=r(b_ideology_all) se_ideology_all=r(se_ideology_all) |
|
|
b_ideology_D=r(b_ideology_D) se_ideology_D=r(se_ideology_D) |
|
|
b_ideology_R=r(b_ideology_R) se_ideology_R=r(se_ideology_R) |
|
|
b_rd_all=r(b_rd_all) se_rd_all=r(se_rd_all) |
|
|
b_rd_D=r(b_rd_D) se_rd_D=r(se_rd_D) |
|
|
b_rd_R=r(b_rd_R) se_rd_R=r(se_rd_R) |
|
|
b_rdwt_all=r(b_rdwt_all) se_rdwt_all=r(se_rdwt_all) |
|
|
b_rdwt_D=r(b_rdwt_D) se_rdwt_D=r(se_rdwt_D) |
|
|
b_rdwt_R=r(b_rdwt_R) se_rdwt_R=r(se_rdwt_R) |
|
|
b_rdwtideodistrict_all=r(b_rdwtideodistrict_all) se_rdwtideodistrict_all=r(se_rdwtideodistrict_all) |
|
|
b_rdwtideodistrict_D=r(b_rdwtideodistrict_D) se_rdwtideodistrict_D=r(se_rdwtideodistrict_D) |
|
|
b_rdwtideodistrict_R=r(b_rdwtideodistrict_R) se_rdwtideodistrict_R=r(se_rdwtideodistrict_R) |
|
|
b_rdwtideoelected_all=r(b_rdwtideoelected_all) se_rdwtideoelected_all=r(se_rdwtideoelected_all) |
|
|
b_rdwtideoelected_D=r(b_rdwtideoelected_D) se_rdwtideoelected_D=r(se_rdwtideoelected_D) |
|
|
b_rdwtideoelected_R=r(b_rdwtideoelected_R) se_rdwtideoelected_R=r(se_rdwtideoelected_R) |
|
|
b_pscorex_all=r(b_pscorex_all) se_pscorex_all=r(se_pscorex_all) |
|
|
b_pscorex_D=r(b_pscorex_D) se_pscorex_D=r(se_pscorex_D) |
|
|
b_pscorex_R=r(b_pscorex_R) se_pscorex_R=r(se_pscorex_R) |
|
|
b_pscoreideodistrict_all=r(b_pscoreideodistrict_all) se_pscoreideodistrict_all=r(se_pscoreideodistrict_all) |
|
|
b_pscoreideodistrict_D=r(b_pscoreideodistrict_D) se_pscoreideodistrict_D=r(se_pscoreideodistrict_D) |
|
|
b_pscoreideodistrict_R=r(b_pscoreideodistrict_R) se_pscoreideodistrict_R=r(se_pscoreideodistrict_R) |
|
|
b_pscoreideoelected_all=r(b_pscoreideoelected_all) se_pscoreideoelected_all=r(se_pscoreideoelected_all) |
|
|
b_pscoreideoelected_D=r(b_pscoreideoelected_D) se_pscoreideoelected_D=r(se_pscoreideoelected_D) |
|
|
b_pscoreideoelected_R=r(b_pscoreideoelected_R) se_pscoreideoelected_R=r(se_pscoreideoelected_R) |
|
|
b_ols_all=r(b_ols_all) se_ols_all=r(se_ols_all) |
|
|
b_ols_D=r(b_ols_D) se_ols_D=r(se_ols_D) |
|
|
b_ols_R=r(b_ols_R) se_ols_R=r(se_ols_R) |
|
|
, reps(1000) saving("$AppendixC_simulations/rd_simulations`j'.dta", replace): |
|
|
rd_sim, beta_a(5) beta_b(5) kappa_ksi_R(0.4) kappa_ksi_D(0.4) kappa_u(1) tau1(-5) rho_x(0.6) nobs(10000) |
|
|
gamma2(`gamma2') gamma3(`gamma3') phi1_D(`phi1_D') phi1_R(`phi1_R') |
|
|
31. |
|
|
. gen gamma2 = `gamma2' |
|
|
32. gen gamma3 = `gamma3' |
|
|
33. gen phi1_D = `phi1_D' |
|
|
34. gen phi1_R = `phi1_R' |
|
|
35. label data "`DataDescr`j''" |
|
|
36. save "$AppendixC_simulations/rd_simulations`j'.dta", replace |
|
|
37. |
|
|
. foreach type in "rd" "rdwt" "rdwtideodistrict" "rdwtideoelected" "pscorex" "pscoreideodistrict" "pscoreideoelected" "ols" { |
|
|
38. di "" |
|
|
39. di "" |
|
|
40. di in ye "type = `type'" |
|
|
41. foreach party in "all" "D" "R" { |
|
|
42. gen byte hit_`type'_`party' = b_`type'_`party' - 1.96*se_`type'_`party'<=0 & |
|
|
b_`type'_`party' + 1.96*se_`type'_`party'>=0 |
|
|
43. } |
|
|
44. sum b_`type'_D se_`type'_D hit_`type'_D b_`type'_R se_`type'_R hit_`type'_R |
|
|
45. } |
|
|
46. } |
|
|
|
|
|
command: rd_sim, beta_a(5) beta_b(5) kappa_ksi_R(0.4) kappa_ksi_D(0.4) kappa_u(1) tau1(-5) rho_x(0.6) nobs(10000) gamma2(0) gamma3(0) phi1_D(0) phi1_R(0) |
|
|
denstest_pval_~l: r(denstest_pval_all) |
|
|
denstest_pval_D: r(denstest_pval_D) |
|
|
denstest_pval_R: r(denstest_pval_R) |
|
|
b_ideology_all: r(b_ideology_all) |
|
|
se_ideology_all: r(se_ideology_all) |
|
|
b_ideology_D: r(b_ideology_D) |
|
|
se_ideology_D: r(se_ideology_D) |
|
|
b_ideology_R: r(b_ideology_R) |
|
|
se_ideology_R: r(se_ideology_R) |
|
|
b_rd_all: r(b_rd_all) |
|
|
se_rd_all: r(se_rd_all) |
|
|
b_rd_D: r(b_rd_D) |
|
|
se_rd_D: r(se_rd_D) |
|
|
b_rd_R: r(b_rd_R) |
|
|
se_rd_R: r(se_rd_R) |
|
|
b_rdwt_all: r(b_rdwt_all) |
|
|
se_rdwt_all: r(se_rdwt_all) |
|
|
b_rdwt_D: r(b_rdwt_D) |
|
|
se_rdwt_D: r(se_rdwt_D) |
|
|
b_rdwt_R: r(b_rdwt_R) |
|
|
se_rdwt_R: r(se_rdwt_R) |
|
|
b_rdwtideodist~l: r(b_rdwtideodistrict_all) |
|
|
se_rdwtideodis~l: r(se_rdwtideodistrict_all) |
|
|
b_rdwtideodist~D: r(b_rdwtideodistrict_D) |
|
|
se_rdwtideodis~D: r(se_rdwtideodistrict_D) |
|
|
b_rdwtideodist~R: r(b_rdwtideodistrict_R) |
|
|
se_rdwtideodis~R: r(se_rdwtideodistrict_R) |
|
|
b_rdwtideoelec~l: r(b_rdwtideoelected_all) |
|
|
se_rdwtideoele~l: r(se_rdwtideoelected_all) |
|
|
b_rdwtideoelec~D: r(b_rdwtideoelected_D) |
|
|
se_rdwtideoele~D: r(se_rdwtideoelected_D) |
|
|
b_rdwtideoelec~R: r(b_rdwtideoelected_R) |
|
|
se_rdwtideoele~R: r(se_rdwtideoelected_R) |
|
|
b_pscorex_all: r(b_pscorex_all) |
|
|
se_pscorex_all: r(se_pscorex_all) |
|
|
b_pscorex_D: r(b_pscorex_D) |
|
|
se_pscorex_D: r(se_pscorex_D) |
|
|
b_pscorex_R: r(b_pscorex_R) |
|
|
se_pscorex_R: r(se_pscorex_R) |
|
|
b_pscoreideodi~l: r(b_pscoreideodistrict_all) |
|
|
se_pscoreideod~l: r(se_pscoreideodistrict_all) |
|
|
b_pscoreideodi~D: r(b_pscoreideodistrict_D) |
|
|
se_pscoreideod~D: r(se_pscoreideodistrict_D) |
|
|
b_pscoreideodi~R: r(b_pscoreideodistrict_R) |
|
|
se_pscoreideod~R: r(se_pscoreideodistrict_R) |
|
|
b_pscoreideoel~l: r(b_pscoreideoelected_all) |
|
|
se_pscoreideoe~l: r(se_pscoreideoelected_all) |
|
|
b_pscoreideoel~D: r(b_pscoreideoelected_D) |
|
|
se_pscoreideoe~D: r(se_pscoreideoelected_D) |
|
|
b_pscoreideoel~R: r(b_pscoreideoelected_R) |
|
|
se_pscoreideoe~R: r(se_pscoreideoelected_R) |
|
|
b_ols_all: r(b_ols_all) |
|
|
se_ols_all: r(se_ols_all) |
|
|
b_ols_D: r(b_ols_D) |
|
|
se_ols_D: r(se_ols_D) |
|
|
b_ols_R: r(b_ols_R) |
|
|
se_ols_R: r(se_ols_R) |
|
|
|
|
|
Simulations (1000) |
|
|
|
|
|
.................................................. 50 |
|
|
.................................................. 100 |
|
|
.................................................. 150 |
|
|
.................................................. 200 |
|
|
.................................................. 250 |
|
|
.................................................. 300 |
|
|
.................................................. 350 |
|
|
.................................................. 400 |
|
|
.................................................. 450 |
|
|
.................................................. 500 |
|
|
.................................................. 550 |
|
|
.................................................. 600 |
|
|
.................................................. 650 |
|
|
.................................................. 700 |
|
|
.................................................. 750 |
|
|
.................................................. 800 |
|
|
.................................................. 850 |
|
|
.................................................. 900 |
|
|
.................................................. 950 |
|
|
.................................................. 1000 |
|
|
file /Users/stefanogagliarducci/Dropbox/various/published/womenpart/EJ/3 replication package/Output/AppendixC_simulations_output/rd_simulations1.dta saved |
|
|
|
|
|
|
|
|
type = rd |
|
|
|
|
|
Variable | Obs Mean Std. Dev. Min Max |
|
|
|
|
|
b_rd_D | 1,000 .000474 .2158104 -.7346113 .9701019 |
|
|
se_rd_D | 1,000 .2066569 .0209705 .1547102 .2945822 |
|
|
hit_rd_D | 1,000 .948 .2221381 0 1 |
|
|
b_rd_R | 1,000 .0061781 .2194184 -.6579666 .882883 |
|
|
se_rd_R | 1,000 .2077052 .0213403 .1590595 .2819441 |
|
|
|
|
|
hit_rd_R | 1,000 .944 .2300368 0 1 |
|
|
|
|
|
|
|
|
type = rdwt |
|
|
|
|
|
Variable | Obs Mean Std. Dev. Min Max |
|
|
|
|
|
b_rdwt_D | 1,000 .000563 .215728 -.760636 .9889148 |
|
|
se_rdwt_D | 1,000 .2074663 .0194472 .1614645 .2764145 |
|
|
hit_rdwt_D | 1,000 .952 .2138732 0 1 |
|
|
b_rdwt_R | 1,000 .0063396 .218869 -.6742517 .8876668 |
|
|
se_rdwt_R | 1,000 .2079266 .0202564 .1640088 .2907166 |
|
|
|
|
|
hit_rdwt_R | 1,000 .949 .2201078 0 1 |
|
|
|
|
|
|
|
|
type = rdwtideodistrict |
|
|
|
|
|
Variable | Obs Mean Std. Dev. Min Max |
|
|
|
|
|
b_rdwtid~t_D | 1,000 .0007777 .2130281 -.7609494 .9757128 |
|
|
se_rdwti~t_D | 1,000 .2074916 .0194821 .1614185 .2770738 |
|
|
hit_rdwtid~D | 1,000 .955 .2074079 0 1 |
|
|
b_rdwtid~t_R | 1,000 .0070163 .2142945 -.7047111 .8283938 |
|
|
se_rdwti~t_R | 1,000 .2079609 .0202332 .1639686 .2897356 |
|
|
|
|
|
hit_rdwtid~R | 1,000 .95 .218054 0 1 |
|
|
|
|
|
|
|
|
type = rdwtideoelected |
|
|
|
|
|
Variable | Obs Mean Std. Dev. Min Max |
|
|
|
|
|
b_rdwtid~d_D | 1,000 -.0000482 .2120022 -.7777959 .9878977 |
|
|
se_rdwti~d_D | 1,000 .2075136 .0194996 .1613577 .2767253 |
|
|
hit_rdwt~d_D | 1,000 .954 .2095899 0 1 |
|
|
b_rdwtid~d_R | 1,000 .0071389 .211381 -.6772436 .8140548 |
|
|
se_rdwti~d_R | 1,000 .2079869 .0202312 .1639532 .2900819 |
|
|
|
|
|
hit_rdwt~d_R | 1,000 .95 .218054 0 1 |
|
|
|
|
|
|
|
|
type = pscorex |
|
|
|
|
|
Variable | Obs Mean Std. Dev. Min Max |
|
|
|
|
|
b_pscorex_D | 1,000 .0016244 .0410007 -.1189567 .1347485 |
|
|
se_pscorex_D | 1,000 .0417603 .0010802 .0389149 .0450724 |
|
|
hit_pscore~D | 1,000 .964 .1863833 0 1 |
|
|
b_pscorex_R | 1,000 .0001079 .0414081 -.1207875 .1166156 |
|
|
se_pscorex_R | 1,000 .041873 .0010361 .0387544 .0453656 |
|
|
|
|
|
hit_pscore~R | 1,000 .952 .2138732 0 1 |
|
|
|
|
|
|
|
|
type = pscoreideodistrict |
|
|
|
|
|
Variable | Obs Mean Std. Dev. Min Max |
|
|
|
|
|
b_pscore~t_D | 1,000 .0011411 .0389529 -.1209979 .135818 |
|
|
se_pscor~t_D | 1,000 .0394981 .0010252 .0365711 .0428945 |
|
|
hit_psco~t_D | 1,000 .956 .2051977 0 1 |
|
|
b_pscore~t_R | 1,000 -.0000255 .0389256 -.1365211 .1169339 |
|
|
se_pscor~t_R | 1,000 .0396001 .0009805 .0368675 .0428658 |
|
|
|
|
|
hit_psco~t_R | 1,000 .949 .2201078 0 1 |
|
|
|
|
|
|
|
|
type = pscoreideoelected |
|
|
|
|
|
Variable | Obs Mean Std. Dev. Min Max |
|
|
|
|
|
b_pscore~d_D | 1,000 .0009746 .0382667 -.1189793 .1366088 |
|
|
se_pscor~d_D | 1,000 .038707 .0010097 .0358897 .0423216 |
|
|
hit_psco~d_D | 1,000 .959 .1983894 0 1 |
|
|
b_pscore~d_R | 1,000 .0001725 .0384444 -.1271377 .1249728 |
|
|
se_pscor~d_R | 1,000 .0387925 .0009651 .0358581 .0419615 |
|
|
|
|
|
hit_psco~d_R | 1,000 .941 .2357426 0 1 |
|
|
|
|
|
|
|
|
type = ols |
|
|
|
|
|
Variable | Obs Mean Std. Dev. Min Max |
|
|
|
|
|
b_ols_D | 1,000 .0016925 .0436147 -.1204213 .1329182 |
|
|
se_ols_D | 1,000 .0444561 .0007614 .0424713 .0471137 |
|
|
hit_ols_D | 1,000 .96 .1960572 0 1 |
|
|
b_ols_R | 1,000 -.0000372 .0443401 -.1468209 .1455884 |
|
|
se_ols_R | 1,000 .0445274 .0007684 .0422679 .047389 |
|
|
|
|
|
hit_ols_R | 1,000 .948 .2221381 0 1 |
|
|
|
|
|
command: rd_sim, beta_a(5) beta_b(5) kappa_ksi_R(0.4) kappa_ksi_D(0.4) kappa_u(1) tau1(-5) rho_x(0.6) nobs(10000) gamma2(0) gamma3(0) phi1_D(-1) |
|
|
phi1_R(-1) |
|
|
denstest_pval_~l: r(denstest_pval_all) |
|
|
denstest_pval_D: r(denstest_pval_D) |
|
|
denstest_pval_R: r(denstest_pval_R) |
|
|
b_ideology_all: r(b_ideology_all) |
|
|
se_ideology_all: r(se_ideology_all) |
|
|
b_ideology_D: r(b_ideology_D) |
|
|
se_ideology_D: r(se_ideology_D) |
|
|
b_ideology_R: r(b_ideology_R) |
|
|
se_ideology_R: r(se_ideology_R) |
|
|
b_rd_all: r(b_rd_all) |
|
|
se_rd_all: r(se_rd_all) |
|
|
b_rd_D: r(b_rd_D) |
|
|
se_rd_D: r(se_rd_D) |
|
|
b_rd_R: r(b_rd_R) |
|
|
se_rd_R: r(se_rd_R) |
|
|
b_rdwt_all: r(b_rdwt_all) |
|
|
se_rdwt_all: r(se_rdwt_all) |
|
|
b_rdwt_D: r(b_rdwt_D) |
|
|
se_rdwt_D: r(se_rdwt_D) |
|
|
b_rdwt_R: r(b_rdwt_R) |
|
|
se_rdwt_R: r(se_rdwt_R) |
|
|
b_rdwtideodist~l: r(b_rdwtideodistrict_all) |
|
|
se_rdwtideodis~l: r(se_rdwtideodistrict_all) |
|
|
b_rdwtideodist~D: r(b_rdwtideodistrict_D) |
|
|
se_rdwtideodis~D: r(se_rdwtideodistrict_D) |
|
|
b_rdwtideodist~R: r(b_rdwtideodistrict_R) |
|
|
se_rdwtideodis~R: r(se_rdwtideodistrict_R) |
|
|
b_rdwtideoelec~l: r(b_rdwtideoelected_all) |
|
|
se_rdwtideoele~l: r(se_rdwtideoelected_all) |
|
|
b_rdwtideoelec~D: r(b_rdwtideoelected_D) |
|
|
se_rdwtideoele~D: r(se_rdwtideoelected_D) |
|
|
b_rdwtideoelec~R: r(b_rdwtideoelected_R) |
|
|
se_rdwtideoele~R: r(se_rdwtideoelected_R) |
|
|
b_pscorex_all: r(b_pscorex_all) |
|
|
se_pscorex_all: r(se_pscorex_all) |
|
|
b_pscorex_D: r(b_pscorex_D) |
|
|
se_pscorex_D: r(se_pscorex_D) |
|
|
b_pscorex_R: r(b_pscorex_R) |
|
|
se_pscorex_R: r(se_pscorex_R) |
|
|
b_pscoreideodi~l: r(b_pscoreideodistrict_all) |
|
|
se_pscoreideod~l: r(se_pscoreideodistrict_all) |
|
|
b_pscoreideodi~D: r(b_pscoreideodistrict_D) |
|
|
se_pscoreideod~D: r(se_pscoreideodistrict_D) |
|
|
b_pscoreideodi~R: r(b_pscoreideodistrict_R) |
|
|
se_pscoreideod~R: r(se_pscoreideodistrict_R) |
|
|
b_pscoreideoel~l: r(b_pscoreideoelected_all) |
|
|
se_pscoreideoe~l: r(se_pscoreideoelected_all) |
|
|
b_pscoreideoel~D: r(b_pscoreideoelected_D) |
|
|
se_pscoreideoe~D: r(se_pscoreideoelected_D) |
|
|
b_pscoreideoel~R: r(b_pscoreideoelected_R) |
|
|
se_pscoreideoe~R: r(se_pscoreideoelected_R) |
|
|
b_ols_all: r(b_ols_all) |
|
|
se_ols_all: r(se_ols_all) |
|
|
b_ols_D: r(b_ols_D) |
|
|
se_ols_D: r(se_ols_D) |
|
|
b_ols_R: r(b_ols_R) |
|
|
se_ols_R: r(se_ols_R) |
|
|
|
|
|
Simulations (1000) |
|
|
|
|
|
.................................................. 50 |
|
|
.................................................. 100 |
|
|
.................................................. 150 |
|
|
.................................................. 200 |
|
|
.................................................. 250 |
|
|
.................................................. 300 |
|
|
.................................................. 350 |
|
|
.................................................. 400 |
|
|
.................................................. 450 |
|
|
.................................................. 500 |
|
|
.................................................. 550 |
|
|
.................................................. 600 |
|
|
.................................................. 650 |
|
|
.................................................. 700 |
|
|
.................................................. 750 |
|
|
.................................................. 800 |
|
|
.................................................. 850 |
|
|
.................................................. 900 |
|
|
.................................................. 950 |
|
|
.................................................. 1000 |
|
|
file /Users/stefanogagliarducci/Dropbox/various/published/womenpart/EJ/3 replication package/Output/AppendixC_simulations_output/rd_simulations2.dta saved |
|
|
|
|
|
|
|
|
type = rd |
|
|
|
|
|
Variable | Obs Mean Std. Dev. Min Max |
|
|
|
|
|
b_rd_D | 1,000 .0012576 .2228617 -.7331437 1.009145 |
|
|
se_rd_D | 1,000 .2176755 .0255152 .1618408 .3236289 |
|
|
hit_rd_D | 1,000 .946 .2261308 0 1 |
|
|
b_rd_R | 1,000 .0330576 .243948 -.7533619 .8312749 |
|
|
se_rd_R | 1,000 .2278799 .0267695 .1654119 .3677094 |
|
|
|
|
|
hit_rd_R | 1,000 .933 .2501471 0 1 |
|
|
|
|
|
|
|
|
type = rdwt |
|
|
|
|
|
Variable | Obs Mean Std. Dev. Min Max |
|
|
|
|
|
b_rdwt_D | 1,000 .000469 .2242803 -.7151504 1.00985 |
|
|
se_rdwt_D | 1,000 .1968521 .0196471 .1512845 .2650954 |
|
|
hit_rdwt_D | 1,000 .922 .2683058 0 1 |
|
|
b_rdwt_R | 1,000 .0330745 .2432499 -.7427425 .8059599 |
|
|
se_rdwt_R | 1,000 .2059705 .020458 .1560483 .2731172 |
|
|
|
|
|
hit_rdwt_R | 1,000 .902 .2974634 0 1 |
|
|
|
|
|
|
|
|
type = rdwtideodistrict |
|
|
|
|
|
Variable | Obs Mean Std. Dev. Min Max |
|
|
|
|
|
b_rdwtid~t_D | 1,000 -.006175 .2212031 -.7455772 .9364876 |
|
|
se_rdwti~t_D | 1,000 .196769 .0196699 .1512361 .2634379 |
|
|
hit_rdwtid~D | 1,000 .93 .2552747 0 1 |
|
|
b_rdwtid~t_R | 1,000 .028441 .2407647 -.8496327 .8543036 |
|
|
se_rdwti~t_R | 1,000 .2060652 .0204876 .1575227 .2727789 |
|
|
|
|
|
hit_rdwtid~R | 1,000 .91 .286325 0 1 |
|
|
|
|
|
|
|
|
type = rdwtideoelected |
|
|
|
|
|
Variable | Obs Mean Std. Dev. Min Max |
|
|
|
|
|
b_rdwtid~d_D | 1,000 .0090432 .2212611 -.6531096 .982204 |
|
|
se_rdwti~d_D | 1,000 .1969396 .0196544 .1515357 .2642587 |
|
|
hit_rdwt~d_D | 1,000 .925 .2635231 0 1 |
|
|
b_rdwtid~d_R | 1,000 .0109837 .2401174 -.8600007 .7876767 |
|
|
se_rdwti~d_R | 1,000 .2062539 .0205667 .1576751 .2730821 |
|
|
|
|
|
hit_rdwt~d_R | 1,000 .914 .2805043 0 1 |
|
|
|
|
|
|
|
|
type = pscorex |
|
|
|
|
|
Variable | Obs Mean Std. Dev. Min Max |
|
|
|
|
|
b_pscorex_D | 1,000 -.0550926 .0353542 -.1679334 .0592917 |
|
|
se_pscorex_D | 1,000 .034824 .0006266 .0327949 .0370951 |
|
|
hit_pscore~D | 1,000 .632 .4825027 0 1 |
|
|
b_pscorex_R | 1,000 .0663517 .0536153 -.1293039 .2506798 |
|
|
se_pscorex_R | 1,000 .0543453 .0021536 .0482796 .0620071 |
|
|
|
|
|
hit_pscore~R | 1,000 .774 .4184484 0 1 |
|
|
|
|
|
|
|
|
type = pscoreideodistrict |
|
|
|
|
|
Variable | Obs Mean Std. Dev. Min Max |
|
|
|
|
|
b_pscore~t_D | 1,000 -.0138341 .0335508 -.1293545 .0784498 |
|
|
se_pscor~t_D | 1,000 .0330559 .0006057 .0309695 .0349889 |
|
|
hit_psco~t_D | 1,000 .926 .2619019 0 1 |
|
|
b_pscore~t_R | 1,000 .0170442 .051773 -.1677895 .2020657 |
|
|
se_pscor~t_R | 1,000 .0520056 .0021865 .0455246 .0609398 |
|
|
|
|
|
hit_psco~t_R | 1,000 .938 .2412762 0 1 |
|
|
|
|
|
|
|
|
type = pscoreideoelected |
|
|
|
|
|
Variable | Obs Mean Std. Dev. Min Max |
|
|
|
|
|
b_pscore~d_D | 1,000 .0006646 .0329737 -.1116275 .0922345 |
|
|
se_pscor~d_D | 1,000 .0324259 .0006012 .0302777 .034353 |
|
|
hit_psco~d_D | 1,000 .946 .2261308 0 1 |
|
|
b_pscore~d_R | 1,000 .0002771 .050832 -.1800453 .1714571 |
|
|
se_pscor~d_R | 1,000 .0511972 .0021817 .0452904 .0606324 |
|
|
|
|
|
hit_psco~d_R | 1,000 .958 .2006895 0 1 |
|
|
|
|
|
|
|
|
type = ols |
|
|
|
|
|
Variable | Obs Mean Std. Dev. Min Max |
|
|
|
|
|
b_ols_D | 1,000 -.1063805 .0375849 -.2240475 .0198816 |
|
|
se_ols_D | 1,000 .0369016 .0005005 .0353002 .0384732 |
|
|
hit_ols_D | 1,000 .186 .3893014 0 1 |
|
|
b_ols_R | 1,000 .1257383 .0563577 -.0464704 .3107925 |
|
|
se_ols_R | 1,000 .0573451 .0013656 .0529088 .0618195 |
|
|
|
|
|
hit_ols_R | 1,000 .406 .4913302 0 1 |
|
|
|
|
|
command: rd_sim, beta_a(5) beta_b(5) kappa_ksi_R(0.4) kappa_ksi_D(0.4) kappa_u(1) tau1(-5) rho_x(0.6) nobs(10000) gamma2(.3) gamma3(.3) phi1_D(-1) |
|
|
phi1_R(-1) |
|
|
denstest_pval_~l: r(denstest_pval_all) |
|
|
denstest_pval_D: r(denstest_pval_D) |
|
|
denstest_pval_R: r(denstest_pval_R) |
|
|
b_ideology_all: r(b_ideology_all) |
|
|
se_ideology_all: r(se_ideology_all) |
|
|
b_ideology_D: r(b_ideology_D) |
|
|
se_ideology_D: r(se_ideology_D) |
|
|
b_ideology_R: r(b_ideology_R) |
|
|
se_ideology_R: r(se_ideology_R) |
|
|
b_rd_all: r(b_rd_all) |
|
|
se_rd_all: r(se_rd_all) |
|
|
b_rd_D: r(b_rd_D) |
|
|
se_rd_D: r(se_rd_D) |
|
|
b_rd_R: r(b_rd_R) |
|
|
se_rd_R: r(se_rd_R) |
|
|
b_rdwt_all: r(b_rdwt_all) |
|
|
se_rdwt_all: r(se_rdwt_all) |
|
|
b_rdwt_D: r(b_rdwt_D) |
|
|
se_rdwt_D: r(se_rdwt_D) |
|
|
b_rdwt_R: r(b_rdwt_R) |
|
|
se_rdwt_R: r(se_rdwt_R) |
|
|
b_rdwtideodist~l: r(b_rdwtideodistrict_all) |
|
|
se_rdwtideodis~l: r(se_rdwtideodistrict_all) |
|
|
b_rdwtideodist~D: r(b_rdwtideodistrict_D) |
|
|
se_rdwtideodis~D: r(se_rdwtideodistrict_D) |
|
|
b_rdwtideodist~R: r(b_rdwtideodistrict_R) |
|
|
se_rdwtideodis~R: r(se_rdwtideodistrict_R) |
|
|
b_rdwtideoelec~l: r(b_rdwtideoelected_all) |
|
|
se_rdwtideoele~l: r(se_rdwtideoelected_all) |
|
|
b_rdwtideoelec~D: r(b_rdwtideoelected_D) |
|
|
se_rdwtideoele~D: r(se_rdwtideoelected_D) |
|
|
b_rdwtideoelec~R: r(b_rdwtideoelected_R) |
|
|
se_rdwtideoele~R: r(se_rdwtideoelected_R) |
|
|
b_pscorex_all: r(b_pscorex_all) |
|
|
se_pscorex_all: r(se_pscorex_all) |
|
|
b_pscorex_D: r(b_pscorex_D) |
|
|
se_pscorex_D: r(se_pscorex_D) |
|
|
b_pscorex_R: r(b_pscorex_R) |
|
|
se_pscorex_R: r(se_pscorex_R) |
|
|
b_pscoreideodi~l: r(b_pscoreideodistrict_all) |
|
|
se_pscoreideod~l: r(se_pscoreideodistrict_all) |
|
|
b_pscoreideodi~D: r(b_pscoreideodistrict_D) |
|
|
se_pscoreideod~D: r(se_pscoreideodistrict_D) |
|
|
b_pscoreideodi~R: r(b_pscoreideodistrict_R) |
|
|
se_pscoreideod~R: r(se_pscoreideodistrict_R) |
|
|
b_pscoreideoel~l: r(b_pscoreideoelected_all) |
|
|
se_pscoreideoe~l: r(se_pscoreideoelected_all) |
|
|
b_pscoreideoel~D: r(b_pscoreideoelected_D) |
|
|
se_pscoreideoe~D: r(se_pscoreideoelected_D) |
|
|
b_pscoreideoel~R: r(b_pscoreideoelected_R) |
|
|
se_pscoreideoe~R: r(se_pscoreideoelected_R) |
|
|
b_ols_all: r(b_ols_all) |
|
|
se_ols_all: r(se_ols_all) |
|
|
b_ols_D: r(b_ols_D) |
|
|
se_ols_D: r(se_ols_D) |
|
|
b_ols_R: r(b_ols_R) |
|
|
se_ols_R: r(se_ols_R) |
|
|
|
|
|
Simulations (1000) |
|
|
|
|
|
.................................................. 50 |
|
|
.................................................. 100 |
|
|
.................................................. 150 |
|
|
.................................................. 200 |
|
|
.................................................. 250 |
|
|
.................................................. 300 |
|
|
.................................................. 350 |
|
|
.................................................. 400 |
|
|
.................................................. 450 |
|
|
.................................................. 500 |
|
|
.................................................. 550 |
|
|
.................................................. 600 |
|
|
.................................................. 650 |
|
|
.................................................. 700 |
|
|
.................................................. 750 |
|
|
.................................................. 800 |
|
|
.................................................. 850 |
|
|
.................................................. 900 |
|
|
.................................................. 950 |
|
|
.................................................. 1000 |
|
|
file /Users/stefanogagliarducci/Dropbox/various/published/womenpart/EJ/3 replication package/Output/AppendixC_simulations_output/rd_simulations3.dta saved |
|
|
|
|
|
|
|
|
type = rd |
|
|
|
|
|
Variable | Obs Mean Std. Dev. Min Max |
|
|
|
|
|
b_rd_D | 1,000 .491752 .2494523 -.3531313 1.51067 |
|
|
se_rd_D | 1,000 .2283538 .0280412 .1690641 .3394082 |
|
|
hit_rd_D | 1,000 .405 .4911377 0 1 |
|
|
b_rd_R | 1,000 .5173595 .2292981 -.2692255 1.492157 |
|
|
se_rd_R | 1,000 .2289896 .0260837 .1711041 .3486938 |
|
|
|
|
|
hit_rd_R | 1,000 .375 .4843652 0 1 |
|
|
|
|
|
|
|
|
type = rdwt |
|
|
|
|
|
Variable | Obs Mean Std. Dev. Min Max |
|
|
|
|
|
b_rdwt_D | 1,000 .4388141 .2610048 -.5014127 1.687006 |
|
|
se_rdwt_D | 1,000 .2074678 .0232727 .1568016 .2954484 |
|
|
hit_rdwt_D | 1,000 .437 .4962633 0 1 |
|
|
b_rdwt_R | 1,000 .4646391 .2379002 -.2868821 1.428946 |
|
|
se_rdwt_R | 1,000 .2171307 .0218101 .1686809 .3166831 |
|
|
|
|
|
hit_rdwt_R | 1,000 .429 .495181 0 1 |
|
|
|
|
|
|
|
|
type = rdwtideodistrict |
|
|
|
|
|
Variable | Obs Mean Std. Dev. Min Max |
|
|
|
|
|
b_rdwtid~t_D | 1,000 .0013756 .4101497 -2.066836 2.024154 |
|
|
se_rdwti~t_D | 1,000 .203602 .0271368 .1319862 .3706861 |
|
|
hit_rdwtid~D | 1,000 .705 .456271 0 1 |
|
|
b_rdwtid~t_R | 1,000 .11661 .3633796 -1.787883 1.752881 |
|
|
se_rdwti~t_R | 1,000 .230485 .025827 .1736978 .3815075 |
|
|
|
|
|
hit_rdwtid~R | 1,000 .779 .4151281 0 1 |
|
|
|
|
|
|
|
|
type = rdwtideoelected |
|
|
|
|
|
Variable | Obs Mean Std. Dev. Min Max |
|
|
|
|
|
b_rdwtid~d_D | 1,000 .0325854 .3558411 -1.752627 1.752868 |
|
|
se_rdwti~d_D | 1,000 .2076113 .0264597 .1428789 .3456056 |
|
|
hit_rdwt~d_D | 1,000 .78 .4144536 0 1 |
|
|
b_rdwtid~d_R | 1,000 .0705305 .3363157 -1.379199 1.892265 |
|
|
se_rdwti~d_R | 1,000 .2250768 .0251694 .1710656 .3803324 |
|
|
|
|
|
hit_rdwt~d_R | 1,000 .82 .3843797 0 1 |
|
|
|
|
|
|
|
|
type = pscorex |
|
|
|
|
|
Variable | Obs Mean Std. Dev. Min Max |
|
|
|
|
|
b_pscorex_D | 1,000 .1519819 .0346478 .0358244 .2736447 |
|
|
se_pscorex_D | 1,000 .0338529 .0006006 .0321172 .0360062 |
|
|
hit_pscore~D | 1,000 .007 .0834144 0 1 |
|
|
b_pscorex_R | 1,000 .2756086 .0518065 .1026839 .4199368 |
|
|
se_pscorex_R | 1,000 .0503297 .0018679 .0446273 .057132 |
|
|
|
|
|
hit_pscore~R | 1,000 0 0 0 0 |
|
|
|
|
|
|
|
|
type = pscoreideodistrict |
|
|
|
|
|
Variable | Obs Mean Std. Dev. Min Max |
|
|
|
|
|
b_pscore~t_D | 1,000 -.0714853 .0360254 -.1921055 .0466862 |
|
|
se_pscor~t_D | 1,000 .0359764 .0015933 .0323575 .0454675 |
|
|
hit_psco~t_D | 1,000 .487 .5000811 0 1 |
|
|
b_pscore~t_R | 1,000 -.0686628 .0793611 -.3470891 .156843 |
|
|
se_pscor~t_R | 1,000 .076294 .015689 .0545845 .1807837 |
|
|
|
|
|
hit_psco~t_R | 1,000 .869 .3375692 0 1 |
|
|
|
|
|
|
|
|
type = pscoreideoelected |
|
|
|
|
|
Variable | Obs Mean Std. Dev. Min Max |
|
|
|
|
|
b_pscore~d_D | 1,000 -.0001303 .032578 -.1045127 .108526 |
|
|
se_pscor~d_D | 1,000 .0323971 .000756 .0304841 .0350485 |
|
|
hit_psco~d_D | 1,000 .944 .2300368 0 1 |
|
|
b_pscore~d_R | 1,000 -.0178836 .0604781 -.2088141 .1564328 |
|
|
se_pscor~d_R | 1,000 .0605834 .0081455 .0476141 .1530132 |
|
|
|
|
|
hit_psco~d_R | 1,000 .945 .2280943 0 1 |
|
|
|
|
|
|
|
|
type = ols |
|
|
|
|
|
Variable | Obs Mean Std. Dev. Min Max |
|
|
|
|
|
b_ols_D | 1,000 .0217441 .035059 -.0793685 .1615416 |
|
|
se_ols_D | 1,000 .0348545 .0004235 .0336369 .036255 |
|
|
hit_ols_D | 1,000 .908 .2891706 0 1 |
|
|
b_ols_R | 1,000 .2789744 .0533577 .1072924 .4430476 |
|
|
se_ols_R | 1,000 .0517018 .0010793 .0486151 .0550177 |
|
|
|
|
|
hit_ols_R | 1,000 0 0 0 0 |
|
|
|
|
|
command: rd_sim, beta_a(5) beta_b(5) kappa_ksi_R(0.4) kappa_ksi_D(0.4) kappa_u(1) tau1(-5) rho_x(0.6) nobs(10000) gamma2(.6) gamma3(0) phi1_D(-1) |
|
|
phi1_R(-1) |
|
|
denstest_pval_~l: r(denstest_pval_all) |
|
|
denstest_pval_D: r(denstest_pval_D) |
|
|
denstest_pval_R: r(denstest_pval_R) |
|
|
b_ideology_all: r(b_ideology_all) |
|
|
se_ideology_all: r(se_ideology_all) |
|
|
b_ideology_D: r(b_ideology_D) |
|
|
se_ideology_D: r(se_ideology_D) |
|
|
b_ideology_R: r(b_ideology_R) |
|
|
se_ideology_R: r(se_ideology_R) |
|
|
b_rd_all: r(b_rd_all) |
|
|
se_rd_all: r(se_rd_all) |
|
|
b_rd_D: r(b_rd_D) |
|
|
se_rd_D: r(se_rd_D) |
|
|
b_rd_R: r(b_rd_R) |
|
|
se_rd_R: r(se_rd_R) |
|
|
b_rdwt_all: r(b_rdwt_all) |
|
|
se_rdwt_all: r(se_rdwt_all) |
|
|
b_rdwt_D: r(b_rdwt_D) |
|
|
se_rdwt_D: r(se_rdwt_D) |
|
|
b_rdwt_R: r(b_rdwt_R) |
|
|
se_rdwt_R: r(se_rdwt_R) |
|
|
b_rdwtideodist~l: r(b_rdwtideodistrict_all) |
|
|
se_rdwtideodis~l: r(se_rdwtideodistrict_all) |
|
|
b_rdwtideodist~D: r(b_rdwtideodistrict_D) |
|
|
se_rdwtideodis~D: r(se_rdwtideodistrict_D) |
|
|
b_rdwtideodist~R: r(b_rdwtideodistrict_R) |
|
|
se_rdwtideodis~R: r(se_rdwtideodistrict_R) |
|
|
b_rdwtideoelec~l: r(b_rdwtideoelected_all) |
|
|
se_rdwtideoele~l: r(se_rdwtideoelected_all) |
|
|
b_rdwtideoelec~D: r(b_rdwtideoelected_D) |
|
|
se_rdwtideoele~D: r(se_rdwtideoelected_D) |
|
|
b_rdwtideoelec~R: r(b_rdwtideoelected_R) |
|
|
se_rdwtideoele~R: r(se_rdwtideoelected_R) |
|
|
b_pscorex_all: r(b_pscorex_all) |
|
|
se_pscorex_all: r(se_pscorex_all) |
|
|
b_pscorex_D: r(b_pscorex_D) |
|
|
se_pscorex_D: r(se_pscorex_D) |
|
|
b_pscorex_R: r(b_pscorex_R) |
|
|
se_pscorex_R: r(se_pscorex_R) |
|
|
b_pscoreideodi~l: r(b_pscoreideodistrict_all) |
|
|
se_pscoreideod~l: r(se_pscoreideodistrict_all) |
|
|
b_pscoreideodi~D: r(b_pscoreideodistrict_D) |
|
|
se_pscoreideod~D: r(se_pscoreideodistrict_D) |
|
|
b_pscoreideodi~R: r(b_pscoreideodistrict_R) |
|
|
se_pscoreideod~R: r(se_pscoreideodistrict_R) |
|
|
b_pscoreideoel~l: r(b_pscoreideoelected_all) |
|
|
se_pscoreideoe~l: r(se_pscoreideoelected_all) |
|
|
b_pscoreideoel~D: r(b_pscoreideoelected_D) |
|
|
se_pscoreideoe~D: r(se_pscoreideoelected_D) |
|
|
b_pscoreideoel~R: r(b_pscoreideoelected_R) |
|
|
se_pscoreideoe~R: r(se_pscoreideoelected_R) |
|
|
b_ols_all: r(b_ols_all) |
|
|
se_ols_all: r(se_ols_all) |
|
|
b_ols_D: r(b_ols_D) |
|
|
se_ols_D: r(se_ols_D) |
|
|
b_ols_R: r(b_ols_R) |
|
|
se_ols_R: r(se_ols_R) |
|
|
|
|
|
Simulations (1000) |
|
|
|
|
|
.................................................. 50 |
|
|
.................................................. 100 |
|
|
.................................................. 150 |
|
|
.................................................. 200 |
|
|
.................................................. 250 |
|
|
.................................................. 300 |
|
|
.................................................. 350 |
|
|
.................................................. 400 |
|
|
.................................................. 450 |
|
|
.................................................. 500 |
|
|
.................................................. 550 |
|
|
.................................................. 600 |
|
|
.................................................. 650 |
|
|
.................................................. 700 |
|
|
.................................................. 750 |
|
|
.................................................. 800 |
|
|
.................................................. 850 |
|
|
.................................................. 900 |
|
|
.................................................. 950 |
|
|
.................................................. 1000 |
|
|
file /Users/stefanogagliarducci/Dropbox/various/published/womenpart/EJ/3 replication package/Output/AppendixC_simulations_output/rd_simulations4.dta saved |
|
|
|
|
|
|
|
|
type = rd |
|
|
|
|
|
Variable | Obs Mean Std. Dev. Min Max |
|
|
|
|
|
b_rd_D | 1,000 .4820324 .2371305 -.3588133 1.248664 |
|
|
se_rd_D | 1,000 .2197392 .0262706 .1618011 .3616504 |
|
|
hit_rd_D | 1,000 .389 .4877673 0 1 |
|
|
b_rd_R | 1,000 .5221546 .2740097 -.7637966 1.467739 |
|
|
se_rd_R | 1,000 .257724 .0312845 .1895249 .435447 |
|
|
|
|
|
hit_rd_R | 1,000 .457 .4983968 0 1 |
|
|
|
|
|
|
|
|
type = rdwt |
|
|
|
|
|
Variable | Obs Mean Std. Dev. Min Max |
|
|
|
|
|
b_rdwt_D | 1,000 .430918 .2455162 -.6153042 1.289431 |
|
|
se_rdwt_D | 1,000 .2067505 .0224016 .1520135 .3382523 |
|
|
hit_rdwt_D | 1,000 .445 .4972145 0 1 |
|
|
b_rdwt_R | 1,000 .4733341 .2820927 -.7657607 1.432925 |
|
|
se_rdwt_R | 1,000 .2504693 .0251529 .1840224 .3487194 |
|
|
|
|
|
hit_rdwt_R | 1,000 .497 .5002412 0 1 |
|
|
|
|
|
|
|
|
type = rdwtideodistrict |
|
|
|
|
|
Variable | Obs Mean Std. Dev. Min Max |
|
|
|
|
|
b_rdwtid~t_D | 1,000 .0175561 .3773986 -1.54672 1.429458 |
|
|
se_rdwti~t_D | 1,000 .2034587 .0255179 .151171 .3283263 |
|
|
hit_rdwtid~D | 1,000 .723 .4477404 0 1 |
|
|
b_rdwtid~t_R | 1,000 .1003379 .3997651 -1.323882 1.491106 |
|
|
se_rdwti~t_R | 1,000 .2610874 .0296607 .1934556 .382313 |
|
|
|
|
|
hit_rdwtid~R | 1,000 .798 .4016931 0 1 |
|
|
|
|
|
|
|
|
type = rdwtideoelected |
|
|
|
|
|
Variable | Obs Mean Std. Dev. Min Max |
|
|
|
|
|
b_rdwtid~d_D | 1,000 .0435866 .3157486 -1.321281 1.114188 |
|
|
se_rdwti~d_D | 1,000 .2070375 .0249135 .1524316 .3418367 |
|
|
hit_rdwt~d_D | 1,000 .819 .3852108 0 1 |
|
|
b_rdwtid~d_R | 1,000 .0720104 .3748857 -1.244581 1.60306 |
|
|
se_rdwti~d_R | 1,000 .257405 .0284439 .1913853 .3689896 |
|
|
|
|
|
hit_rdwt~d_R | 1,000 .819 .3852108 0 1 |
|
|
|
|
|
|
|
|
type = pscorex |
|
|
|
|
|
Variable | Obs Mean Std. Dev. Min Max |
|
|
|
|
|
b_pscorex_D | 1,000 .3748557 .0352603 .2518727 .5018864 |
|
|
se_pscorex_D | 1,000 .0347588 .001049 .0321233 .0393144 |
|
|
hit_pscore~D | 1,000 0 0 0 0 |
|
|
b_pscorex_R | 1,000 .0688194 .0572938 -.0997917 .2288021 |
|
|
se_pscorex_R | 1,000 .0579885 .0024276 .0505837 .0686423 |
|
|
|
|
|
hit_pscore~R | 1,000 .792 .4060799 0 1 |
|
|
|
|
|
|
|
|
type = pscoreideodistrict |
|
|
|
|
|
Variable | Obs Mean Std. Dev. Min Max |
|
|
|
|
|
b_pscore~t_D | 1,000 -.0609244 .0622255 -.466482 .1154791 |
|
|
se_pscor~t_D | 1,000 .0582026 .0183329 .0385205 .3128201 |
|
|
hit_psco~t_D | 1,000 .802 .3986916 0 1 |
|
|
b_pscore~t_R | 1,000 .0148012 .0548574 -.134412 .1764981 |
|
|
se_pscor~t_R | 1,000 .0553551 .0025051 .0480639 .0661574 |
|
|
|
|
|
hit_psco~t_R | 1,000 .942 .2338604 0 1 |
|
|
|
|
|
|
|
|
type = pscoreideoelected |
|
|
|
|
|
Variable | Obs Mean Std. Dev. Min Max |
|
|
|
|
|
b_pscore~d_D | 1,000 .0200114 .0447027 -.1660888 .141097 |
|
|
se_pscor~d_D | 1,000 .0436844 .0076024 .0329284 .119063 |
|
|
hit_psco~d_D | 1,000 .911 .2848862 0 1 |
|
|
b_pscore~d_R | 1,000 -.0019193 .0540542 -.1474438 .1564901 |
|
|
se_pscor~d_R | 1,000 .0544823 .0025367 .0468632 .0659097 |
|
|
|
|
|
hit_psco~d_R | 1,000 .951 .215976 0 1 |
|
|
|
|
|
|
|
|
type = ols |
|
|
|
|
|
Variable | Obs Mean Std. Dev. Min Max |
|
|
|
|
|
b_ols_D | 1,000 .1312602 .0340315 .0222765 .2691881 |
|
|
se_ols_D | 1,000 .0330522 .0003831 .0318416 .0342806 |
|
|
hit_ols_D | 1,000 .024 .1531256 0 1 |
|
|
b_ols_R | 1,000 .1232987 .0598521 -.0781653 .3008979 |
|
|
se_ols_R | 1,000 .0610299 .0015038 .0564883 .0665193 |
|
|
|
|
|
hit_ols_R | 1,000 .477 .4997206 0 1 |
|
|
|
|
|
. |
|
|
. |
|
|
. |
|
|
. ******************************************************************************** |
|
|
. ******************************************************************************** |
|
|
. ******************************************************************************** |
|
|
. |
|
|
. * From here on, display results |
|
|
. clear |
|
|
|
|
|
. |
|
|
. foreach j of numlist 1 2 4 { |
|
|
2. if `j'<=2 { |
|
|
3. local jtex = `j' |
|
|
4. } |
|
|
5. else if `j'==4 { |
|
|
6. local jtex = `j'-1 |
|
|
7. } |
|
|
8. use "$AppendixC_simulations/rd_simulations`j'.dta", clear |
|
|
9. foreach type in "ideology" "rd" "rdwt" "rdwtideodistrict" "rdwtideoelected" "pscorex" "pscoreideodistrict" "pscoreideoelected" "ols" { |
|
|
10. di "" |
|
|
11. di "" |
|
|
12. di in ye "type = `type'" |
|
|
13. foreach party in "all" "D" "R" { |
|
|
14. gen byte rejrate_`type'_`party' = b_`type'_`party' - 1.96*se_`type'_`party'>0 | |
|
|
b_`type'_`party' + 1.96*se_`type'_`party'<0 |
|
|
15. } |
|
|
16. } |
|
|
17. foreach party in "all" "D" "R" { |
|
|
18. gen denstest_reject_`party' = denstest_pval_`party'<0.05 |
|
|
19. } |
|
|
20. |
|
|
. foreach esttype in "ideology" "rd" "rdwt" "rdwtideodistrict" "rdwtideoelected" "pscorex" "pscoreideodistrict" "pscoreideoelected" "ols"{ |
|
|
21. ren b_`esttype'_all `esttype'1 |
|
|
22. ren rejrate_`esttype'_all `esttype'2 |
|
|
23. ren b_`esttype'_D `esttype'3 |
|
|
24. ren rejrate_`esttype'_D `esttype'4 |
|
|
25. ren b_`esttype'_R `esttype'5 |
|
|
26. ren rejrate_`esttype'_R `esttype'6 |
|
|
27. } |
|
|
28. |
|
|
. ren denstest_pval_all denstest1 |
|
|
29. ren denstest_reject_all denstest2 |
|
|
30. ren denstest_pval_D denstest3 |
|
|
31. ren denstest_reject_D denstest4 |
|
|
32. ren denstest_pval_R denstest5 |
|
|
33. ren denstest_reject_R denstest6 |
|
|
34. |
|
|
. gen iterno=_n |
|
|
35. drop se_* |
|
|
36. reshape long denstest ideology rd rdwt rdwtideodistrict rdwtideoelected pscorex pscoreideodistrict pscoreideoelected ols, i(iterno) j(stype) |
|
|
37. label def stype 1 "All - estimate" 2 "All - Rej.Rate" 3 "D - estimate" 4 "D - Rej.Rate" 5 "R - estimate" 6 "R - Rej.Rate" |
|
|
38. label value stype stype |
|
|
39. |
|
|
. label var denstest "Density test (p-value)" |
|
|
40. label var ideology "Discontinuity in ideology" |
|
|
41. label var rd "RD - simple" |
|
|
42. label var rdwt "P-score weighted RD - x" |
|
|
43. label var rdwtideodistrict "P-score weighted RD - district ideology" |
|
|
44. label var rdwtideoelected "P-score weighted RD - ideology of elected representative" |
|
|
45. label var pscorex "P-score weighted, x" |
|
|
46. label var pscoreideodistrict "P-score weighted, district ideology" |
|
|
47. label var pscoreideoelected "P-score weighted, ideology of elected representative" |
|
|
48. label var ols "OLS" |
|
|
49. |
|
|
. estpost tabstat denstest ideology rd rdwt rdwtideodistrict rdwtideoelected |
|
|
pscorex pscoreideodistrict pscoreideoelected ols, by(stype) column(statistics) nototal |
|
|
50. esttab using "$AppendixC_simulations/results_AppendixTableC`jtex'.tex", main(mean %6.3f) unstack tex replace noobs nomtitles nonumbers nonotes label |
|
|
51. } |
|
|
(Baseline - Everything Balanced) |
|
|
|
|
|
|
|
|
type = ideology |
|
|
|
|
|
|
|
|
type = rd |
|
|
|
|
|
|
|
|
type = rdwt |
|
|
|
|
|
|
|
|
type = rdwtideodistrict |
|
|
|
|
|
|
|
|
type = rdwtideoelected |
|
|
|
|
|
|
|
|
type = pscorex |
|
|
|
|
|
|
|
|
type = pscoreideodistrict |
|
|
|
|
|
|
|
|
type = pscoreideoelected |
|
|
|
|
|
|
|
|
type = ols |
|
|
(note: j = 1 2 3 4 5 6) |
|
|
|
|
|
Data wide -> long |
|
|
|
|
|
Number of obs. 1000 -> 6000 |
|
|
Number of variables 65 -> 16 |
|
|
j variable (6 values) -> stype |
|
|
xij variables: |
|
|
denstest1 denstest2 ... denstest6 -> denstest |
|
|
ideology1 ideology2 ... ideology6 -> ideology |
|
|
rd1 rd2 ... rd6 -> rd |
|
|
rdwt1 rdwt2 ... rdwt6 -> rdwt |
|
|
rdwtideodistrict1 rdwtideodistrict2 ... rdwtideodistrict6->rdwtideodistrict |
|
|
rdwtideoelected1 rdwtideoelected2 ... rdwtideoelected6->rdwtideoelected |
|
|
pscorex1 pscorex2 ... pscorex6 -> pscorex |
|
|
pscoreideodistrict1 pscoreideodistrict2 ... pscoreideodistrict6->pscoreideodistrict |
|
|
pscoreideoelected1 pscoreideoelected2 ... pscoreideoelected6->pscoreideoelected |
|
|
ols1 ols2 ... ols6 -> ols |
|
|
|
|
|
|
|
|
Summary statistics: mean |
|
|
for variables: denstest ideology rd rdwt rdwtideodistrict rdwtideoelected pscorex pscoreideodistrict pscoreideoelected ols |
|
|
by categories of: stype |
|
|
|
|
|
stype | e(mean) |
|
|
|
|
|
1 | |
|
|
denstest | .5455671 |
|
|
ideology | .0001327 |
|
|
rd | .0024096 |
|
|
rdwt | .0026502 |
|
|
rdwtideodi~t | .0025334 |
|
|
rdwtideoel~d | .0024999 |
|
|
pscorex | .0009188 |
|
|
pscoreideo~t | .0009269 |
|
|
pscoreideo~d | .000916 |
|
|
ols | .0008537 |
|
|
|
|
|
2 | |
|
|
denstest | .031 |
|
|
ideology | .055 |
|
|
rd | .059 |
|
|
rdwt | .056 |
|
|
rdwtideodi~t | .054 |
|
|
rdwtideoel~d | .059 |
|
|
pscorex | .053 |
|
|
pscoreideo~t | .052 |
|
|
pscoreideo~d | .053 |
|
|
ols | .046 |
|
|
|
|
|
3 | |
|
|
denstest | .5292896 |
|
|
ideology | -.0008158 |
|
|
rd | .000474 |
|
|
rdwt | .000563 |
|
|
rdwtideodi~t | .0007777 |
|
|
rdwtideoel~d | -.0000482 |
|
|
pscorex | .0016244 |
|
|
pscoreideo~t | .0011411 |
|
|
pscoreideo~d | .0009746 |
|
|
ols | .0016925 |
|
|
|
|
|
4 | |
|
|
denstest | .031 |
|
|
ideology | .057 |
|
|
rd | .052 |
|
|
rdwt | .048 |
|
|
rdwtideodi~t | .045 |
|
|
rdwtideoel~d | .046 |
|
|
pscorex | .036 |
|
|
pscoreideo~t | .044 |
|
|
pscoreideo~d | .041 |
|
|
ols | .04 |
|
|
|
|
|
5 | |
|
|
denstest | .5135349 |
|
|
ideology | .0007091 |
|
|
rd | .0061781 |
|
|
rdwt | .0063396 |
|
|
rdwtideodi~t | .0070163 |
|
|
rdwtideoel~d | .0071389 |
|
|
pscorex | .0001079 |
|
|
pscoreideo~t | -.0000255 |
|
|
pscoreideo~d | .0001725 |
|
|
ols | -.0000372 |
|
|
|
|
|
6 | |
|
|
denstest | .032 |
|
|
ideology | .059 |
|
|
rd | .056 |
|
|
rdwt | .051 |
|
|
rdwtideodi~t | .05 |
|
|
rdwtideoel~d | .05 |
|
|
pscorex | .048 |
|
|
pscoreideo~t | .051 |
|
|
pscoreideo~d | .059 |
|
|
ols | .052 |
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category labels saved in macro e(labels) |
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(output written to /Users/stefanogagliarducci/Dropbox/various/published/womenpart/EJ/3 replication package/Output/AppendixC_simulations_output/results_AppendixTableC |
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> 1.tex) |
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(Variant 1 - Women are more left-wing, no preference for female candidates) |
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type = ideology |
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type = rd |
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type = rdwt |
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type = rdwtideodistrict |
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type = rdwtideoelected |
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type = pscorex |
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type = pscoreideodistrict |
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type = pscoreideoelected |
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type = ols |
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(note: j = 1 2 3 4 5 6) |
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Data wide -> long |
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Number of obs. 1000 -> 6000 |
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Number of variables 65 -> 16 |
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j variable (6 values) -> stype |
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xij variables: |
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denstest1 denstest2 ... denstest6 -> denstest |
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ideology1 ideology2 ... ideology6 -> ideology |
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rd1 rd2 ... rd6 -> rd |
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rdwt1 rdwt2 ... rdwt6 -> rdwt |
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rdwtideodistrict1 rdwtideodistrict2 ... rdwtideodistrict6->rdwtideodistrict |
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rdwtideoelected1 rdwtideoelected2 ... rdwtideoelected6->rdwtideoelected |
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pscorex1 pscorex2 ... pscorex6 -> pscorex |
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pscoreideodistrict1 pscoreideodistrict2 ... pscoreideodistrict6->pscoreideodistrict |
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pscoreideoelected1 pscoreideoelected2 ... pscoreideoelected6->pscoreideoelected |
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ols1 ols2 ... ols6 -> ols |
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Summary statistics: mean |
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for variables: denstest ideology rd rdwt rdwtideodistrict rdwtideoelected pscorex pscoreideodistrict pscoreideoelected ols |
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by categories of: stype |
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stype | e(mean) |
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1 | |
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denstest | .526024 |
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ideology | -.0004892 |
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rd | -.0180679 |
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rdwt | -.0177774 |
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rdwtideodi~t | -.0186809 |
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rdwtideoel~d | .0115203 |
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pscorex | .0054515 |
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pscoreideo~t | -.0098912 |
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pscoreideo~d | .0004067 |
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ols | -.0367354 |
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2 | |
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denstest | .033 |
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ideology | .052 |
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rd | .051 |
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rdwt | .049 |
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rdwtideodi~t | .048 |
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rdwtideoel~d | .082 |
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pscorex | .053 |
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pscoreideo~t | .069 |
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pscoreideo~d | .05 |
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ols | .228 |
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3 | |
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denstest | .0253585 |
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ideology | .0030011 |
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rd | .0012576 |
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rdwt | .000469 |
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rdwtideodi~t | -.006175 |
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rdwtideoel~d | .0090432 |
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pscorex | -.0550926 |
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pscoreideo~t | -.0138341 |
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pscoreideo~d | .0006646 |
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ols | -.1063805 |
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4 | |
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denstest | .889 |
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ideology | .07 |
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rd | .054 |
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rdwt | .078 |
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rdwtideodi~t | .07 |
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rdwtideoel~d | .075 |
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pscorex | .368 |
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pscoreideo~t | .074 |
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pscoreideo~d | .054 |
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ols | .814 |
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5 | |
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denstest | .0245342 |
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ideology | -.0022047 |
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rd | .0330576 |
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rdwt | .0330745 |
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rdwtideodi~t | .028441 |
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rdwtideoel~d | .0109837 |
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pscorex | .0663517 |
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pscoreideo~t | .0170442 |
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pscoreideo~d | .0002771 |
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ols | .1257383 |
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6 | |
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denstest | .901 |
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ideology | .058 |
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rd | .067 |
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rdwt | .098 |
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rdwtideodi~t | .09 |
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rdwtideoel~d | .086 |
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pscorex | .226 |
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pscoreideo~t | .062 |
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pscoreideo~d | .042 |
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ols | .594 |
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category labels saved in macro e(labels) |
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(output written to /Users/stefanogagliarducci/Dropbox/various/published/womenpart/EJ/3 replication package/Output/AppendixC_simulations_output/results_AppendixTableC |
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> 2.tex) |
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(Variant 3 - Women are more left-wing, only Ds prefer female candidates) |
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type = ideology |
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type = rd |
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type = rdwt |
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type = rdwtideodistrict |
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type = rdwtideoelected |
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type = pscorex |
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type = pscoreideodistrict |
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type = pscoreideoelected |
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type = ols |
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(note: j = 1 2 3 4 5 6) |
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Data wide -> long |
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Number of obs. 1000 -> 6000 |
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Number of variables 65 -> 16 |
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j variable (6 values) -> stype |
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xij variables: |
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denstest1 denstest2 ... denstest6 -> denstest |
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ideology1 ideology2 ... ideology6 -> ideology |
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rd1 rd2 ... rd6 -> rd |
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rdwt1 rdwt2 ... rdwt6 -> rdwt |
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rdwtideodistrict1 rdwtideodistrict2 ... rdwtideodistrict6->rdwtideodistrict |
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rdwtideoelected1 rdwtideoelected2 ... rdwtideoelected6->rdwtideoelected |
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pscorex1 pscorex2 ... pscorex6 -> pscorex |
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pscoreideodistrict1 pscoreideodistrict2 ... pscoreideodistrict6->pscoreideodistrict |
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pscoreideoelected1 pscoreideoelected2 ... pscoreideoelected6->pscoreideoelected |
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ols1 ols2 ... ols6 -> ols |
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Summary statistics: mean |
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for variables: denstest ideology rd rdwt rdwtideodistrict rdwtideoelected pscorex pscoreideodistrict pscoreideoelected ols |
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by categories of: stype |
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stype | e(mean) |
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1 | |
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denstest | .516915 |
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ideology | .000854 |
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rd | .6216279 |
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rdwt | .6209797 |
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rdwtideodi~t | .620556 |
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rdwtideoel~d | .5385951 |
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pscorex | .3820008 |
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pscoreideo~t | .3955427 |
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pscoreideo~d | .3258178 |
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ols | .1589017 |
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2 | |
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denstest | .047 |
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ideology | .052 |
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rd | .961 |
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rdwt | .961 |
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rdwtideodi~t | .958 |
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rdwtideoel~d | .897 |
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pscorex | 1 |
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pscoreideo~t | 1 |
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pscoreideo~d | 1 |
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ols | 1 |
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3 | |
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denstest | .2114761 |
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ideology | .2113024 |
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rd | .4820324 |
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rdwt | .430918 |
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rdwtideodi~t | .0175561 |
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rdwtideoel~d | .0435866 |
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pscorex | .3748557 |
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pscoreideo~t | -.0609244 |
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pscoreideo~d | .0200114 |
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ols | .1312602 |
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4 | |
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denstest | .359 |
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ideology | 1 |
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rd | .611 |
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rdwt | .555 |
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rdwtideodi~t | .277 |
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rdwtideoel~d | .181 |
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pscorex | 1 |
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pscoreideo~t | .198 |
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pscoreideo~d | .089 |
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ols | .976 |
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5 | |
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denstest | .1600951 |
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ideology | -.209657 |
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rd | .5221546 |
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rdwt | .4733341 |
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rdwtideodi~t | .1003379 |
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rdwtideoel~d | .0720104 |
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pscorex | .0688194 |
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pscoreideo~t | .0148012 |
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pscoreideo~d | -.0019193 |
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ols | .1232987 |
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6 | |
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denstest | .434 |
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ideology | 1 |
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rd | .543 |
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rdwt | .503 |
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rdwtideodi~t | .202 |
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rdwtideoel~d | .181 |
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pscorex | .208 |
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pscoreideo~t | .058 |
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pscoreideo~d | .049 |
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ols | .523 |
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category labels saved in macro e(labels) |
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(output written to /Users/stefanogagliarducci/Dropbox/various/published/womenpart/EJ/3 replication package/Output/AppendixC_simulations_output/results_AppendixTableC |
|
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> 3.tex) |
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. |
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. |
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end of do-file |
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. |
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. |
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. |
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. program error: matching close brace not found |
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r(198); |
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end of do-file |
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r(198); |
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. exit, clear |
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