File size: 120,729 Bytes
57fe0a1 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 720 721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736 737 738 739 740 741 742 743 744 745 746 747 748 749 750 751 752 753 754 755 756 757 758 759 760 761 762 763 764 765 766 767 768 769 770 771 772 773 774 775 776 777 778 779 780 781 782 783 784 785 786 787 788 789 790 791 792 793 794 795 796 797 798 799 800 801 802 803 804 805 806 807 808 809 810 811 812 813 814 815 816 817 818 819 820 821 822 823 824 825 826 827 828 829 830 831 832 833 834 835 836 837 838 839 840 841 842 843 844 845 846 847 848 849 850 851 852 853 854 855 856 857 858 859 860 861 862 863 864 865 866 867 868 869 870 871 872 873 874 875 876 877 878 879 880 881 882 883 884 885 886 887 888 889 890 891 892 893 894 895 896 897 898 899 900 901 902 903 904 905 906 907 908 909 910 911 912 913 914 915 916 917 918 919 920 921 922 923 924 925 926 927 928 929 930 931 932 933 934 935 936 937 938 939 940 941 942 943 944 945 946 947 948 949 950 951 952 953 954 955 956 957 958 959 960 961 962 963 964 965 966 967 968 969 970 971 972 973 974 975 976 977 978 979 980 981 982 983 984 985 986 987 988 989 990 991 992 993 994 995 996 997 998 999 1000 1001 1002 1003 1004 1005 1006 1007 1008 1009 1010 1011 1012 1013 1014 1015 1016 1017 1018 1019 1020 1021 1022 1023 1024 1025 1026 1027 1028 1029 1030 1031 1032 1033 1034 1035 1036 1037 1038 1039 1040 1041 1042 1043 1044 1045 1046 1047 1048 1049 1050 1051 1052 1053 1054 1055 1056 1057 1058 1059 1060 1061 1062 1063 1064 1065 1066 1067 1068 1069 1070 1071 1072 1073 1074 1075 1076 1077 1078 1079 1080 1081 1082 1083 1084 1085 1086 1087 1088 1089 1090 1091 1092 1093 1094 1095 1096 1097 1098 1099 1100 1101 1102 1103 1104 1105 1106 1107 1108 1109 1110 1111 1112 1113 1114 1115 1116 1117 1118 1119 1120 1121 1122 1123 1124 1125 1126 1127 1128 1129 1130 1131 1132 1133 1134 1135 1136 1137 1138 1139 1140 1141 1142 1143 1144 1145 1146 1147 1148 1149 1150 1151 1152 1153 1154 1155 1156 1157 1158 1159 1160 1161 1162 1163 1164 1165 1166 1167 1168 1169 1170 1171 1172 1173 1174 1175 1176 1177 1178 1179 1180 1181 1182 1183 1184 1185 1186 1187 1188 1189 1190 1191 1192 1193 1194 1195 1196 1197 1198 1199 1200 1201 1202 1203 1204 1205 1206 1207 1208 1209 1210 1211 1212 1213 1214 1215 1216 1217 1218 1219 1220 1221 1222 1223 1224 1225 1226 1227 1228 1229 1230 1231 1232 1233 1234 1235 1236 1237 1238 1239 1240 1241 1242 1243 1244 1245 1246 1247 1248 1249 1250 1251 1252 1253 1254 1255 1256 1257 1258 1259 1260 1261 1262 1263 1264 1265 1266 1267 1268 1269 1270 1271 1272 1273 1274 1275 1276 1277 1278 1279 1280 1281 1282 1283 1284 1285 1286 1287 1288 1289 1290 1291 1292 1293 1294 1295 1296 1297 1298 1299 1300 1301 1302 1303 1304 1305 1306 1307 1308 1309 1310 1311 1312 1313 1314 1315 1316 1317 1318 1319 1320 1321 1322 1323 1324 1325 1326 1327 1328 1329 1330 1331 1332 1333 1334 1335 1336 1337 1338 1339 1340 1341 1342 1343 1344 1345 1346 1347 1348 1349 1350 1351 1352 1353 1354 1355 1356 1357 1358 1359 1360 1361 1362 1363 1364 1365 1366 1367 1368 1369 1370 1371 1372 1373 1374 1375 1376 1377 1378 1379 1380 1381 1382 1383 1384 1385 1386 1387 1388 1389 1390 1391 1392 1393 1394 1395 1396 1397 1398 1399 1400 1401 1402 1403 1404 1405 1406 1407 1408 1409 1410 1411 1412 1413 1414 1415 1416 1417 1418 1419 1420 1421 1422 1423 1424 1425 1426 1427 1428 1429 1430 1431 1432 1433 1434 1435 1436 1437 1438 1439 1440 1441 1442 1443 1444 1445 1446 1447 1448 1449 1450 1451 1452 1453 1454 1455 1456 1457 1458 1459 1460 1461 1462 1463 1464 1465 1466 1467 1468 1469 1470 1471 1472 1473 1474 1475 1476 1477 1478 1479 1480 1481 1482 1483 1484 1485 1486 1487 1488 1489 1490 1491 1492 1493 1494 1495 1496 1497 1498 1499 1500 1501 1502 1503 1504 1505 1506 1507 1508 1509 1510 1511 1512 1513 1514 1515 1516 1517 1518 1519 1520 1521 1522 1523 1524 1525 1526 1527 1528 1529 1530 1531 1532 1533 1534 1535 1536 1537 1538 1539 1540 1541 1542 1543 1544 1545 1546 1547 1548 1549 1550 1551 1552 1553 1554 1555 1556 1557 1558 1559 1560 1561 1562 1563 1564 1565 1566 1567 1568 1569 1570 1571 1572 1573 1574 1575 1576 1577 1578 1579 1580 1581 1582 1583 1584 1585 1586 1587 1588 1589 1590 1591 1592 1593 1594 1595 1596 1597 1598 1599 1600 1601 1602 1603 1604 1605 1606 1607 1608 1609 1610 1611 1612 1613 1614 1615 1616 1617 1618 1619 1620 1621 1622 1623 1624 1625 1626 1627 1628 1629 1630 1631 1632 1633 1634 1635 1636 1637 1638 1639 1640 1641 1642 1643 1644 1645 1646 1647 1648 1649 1650 1651 1652 1653 1654 1655 1656 1657 1658 1659 1660 1661 1662 1663 1664 1665 1666 1667 1668 1669 1670 1671 1672 1673 1674 1675 1676 1677 1678 1679 1680 1681 1682 1683 1684 1685 1686 1687 1688 1689 1690 1691 1692 1693 1694 1695 1696 1697 1698 1699 1700 1701 1702 1703 1704 1705 1706 1707 1708 1709 1710 1711 1712 1713 1714 1715 1716 1717 1718 1719 1720 1721 1722 1723 1724 1725 1726 1727 1728 1729 1730 1731 1732 1733 1734 1735 1736 1737 1738 1739 1740 1741 1742 1743 1744 1745 1746 1747 1748 1749 1750 1751 1752 1753 1754 1755 1756 1757 1758 1759 1760 1761 1762 1763 1764 1765 1766 1767 1768 1769 1770 1771 1772 1773 1774 1775 1776 1777 1778 1779 1780 1781 1782 1783 1784 1785 1786 1787 1788 1789 1790 1791 1792 1793 1794 1795 1796 1797 1798 1799 1800 1801 1802 1803 1804 1805 1806 1807 1808 1809 1810 1811 1812 1813 1814 1815 1816 1817 1818 1819 1820 1821 1822 1823 1824 1825 1826 1827 1828 1829 1830 1831 1832 1833 1834 1835 1836 1837 1838 1839 1840 1841 1842 1843 1844 1845 1846 1847 1848 1849 1850 1851 1852 1853 1854 1855 1856 1857 1858 1859 1860 1861 1862 1863 1864 1865 1866 1867 1868 1869 1870 1871 1872 1873 1874 1875 1876 1877 1878 1879 1880 1881 1882 1883 1884 1885 1886 1887 1888 1889 1890 1891 1892 1893 1894 1895 1896 1897 1898 1899 1900 1901 1902 1903 1904 1905 1906 1907 1908 1909 1910 1911 1912 1913 1914 1915 1916 1917 1918 1919 1920 1921 1922 1923 1924 1925 1926 1927 1928 1929 1930 1931 1932 1933 1934 1935 1936 1937 1938 1939 1940 1941 1942 1943 1944 1945 1946 1947 1948 1949 1950 1951 1952 1953 1954 1955 1956 1957 1958 1959 1960 1961 1962 1963 1964 1965 1966 1967 1968 1969 1970 1971 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 2031 2032 2033 2034 2035 2036 2037 2038 2039 2040 2041 2042 2043 2044 2045 2046 2047 2048 2049 2050 2051 2052 2053 2054 2055 2056 2057 2058 2059 2060 2061 2062 2063 2064 2065 2066 2067 2068 2069 2070 2071 2072 2073 2074 2075 2076 2077 2078 2079 2080 2081 2082 2083 2084 2085 2086 2087 2088 2089 2090 2091 2092 2093 2094 2095 2096 2097 2098 2099 2100 2101 2102 2103 2104 2105 2106 2107 2108 2109 2110 2111 2112 2113 2114 2115 2116 2117 2118 2119 2120 2121 2122 2123 2124 2125 2126 2127 2128 2129 2130 2131 2132 2133 2134 2135 2136 2137 2138 2139 2140 2141 2142 2143 2144 2145 2146 2147 2148 2149 2150 2151 2152 2153 2154 2155 2156 2157 2158 2159 2160 2161 2162 2163 2164 2165 2166 2167 2168 2169 2170 2171 2172 2173 2174 2175 2176 2177 2178 2179 2180 2181 2182 2183 2184 2185 2186 2187 2188 2189 2190 2191 2192 2193 2194 2195 2196 2197 2198 2199 2200 2201 2202 2203 2204 2205 2206 2207 2208 2209 2210 2211 2212 2213 2214 2215 2216 2217 2218 2219 2220 2221 2222 2223 2224 2225 2226 2227 2228 2229 2230 2231 2232 2233 2234 2235 2236 2237 2238 2239 2240 2241 2242 2243 2244 2245 2246 2247 2248 2249 2250 2251 2252 2253 2254 2255 2256 2257 2258 2259 2260 2261 2262 2263 2264 2265 2266 2267 2268 2269 2270 2271 2272 2273 2274 2275 2276 2277 2278 2279 2280 2281 2282 2283 2284 2285 2286 2287 2288 2289 2290 2291 2292 2293 2294 2295 2296 2297 2298 2299 2300 2301 2302 2303 2304 2305 2306 2307 2308 2309 2310 2311 2312 2313 2314 2315 2316 2317 2318 2319 2320 2321 2322 2323 2324 2325 2326 2327 2328 2329 2330 2331 2332 2333 2334 2335 2336 2337 2338 2339 2340 2341 2342 2343 2344 2345 2346 2347 2348 2349 2350 2351 2352 2353 2354 2355 2356 2357 2358 2359 2360 2361 2362 2363 2364 2365 2366 2367 2368 2369 2370 2371 2372 2373 2374 2375 2376 2377 2378 2379 2380 2381 2382 2383 2384 2385 2386 2387 2388 2389 2390 2391 2392 2393 2394 2395 2396 2397 2398 2399 2400 2401 2402 2403 2404 2405 2406 2407 2408 2409 2410 2411 2412 2413 2414 2415 2416 2417 2418 2419 2420 2421 2422 2423 2424 2425 2426 2427 2428 2429 2430 2431 2432 2433 2434 2435 2436 2437 2438 2439 2440 2441 2442 2443 2444 2445 2446 2447 2448 2449 2450 2451 2452 2453 2454 2455 2456 2457 2458 2459 2460 2461 2462 2463 2464 2465 2466 2467 2468 2469 2470 2471 2472 2473 2474 2475 2476 2477 2478 2479 2480 2481 2482 2483 2484 2485 2486 2487 2488 2489 2490 2491 2492 2493 2494 2495 2496 2497 2498 2499 2500 2501 2502 2503 2504 2505 2506 2507 2508 2509 2510 2511 2512 2513 2514 2515 2516 2517 2518 2519 2520 2521 2522 2523 |
---------------------------------------------------------------------------------------------------------------------------------------------------------------------
name: <unnamed>
log: /Users/stefanogagliarducci/Dropbox/various/published/womenpart/EJ/3 replication package/Log/AppendixC_simulations.log
log type: text
opened on: 12 May 2021, 00:03:12
.
.
. cap prog drop rd_sim
. prog def rd_sim, rclass
1. version 15.1
2. syntax [, nobs(integer 10000) beta_a(real 1.0) beta_b(real 1.0) rho_x(real 0.7) /*
> */ zbar_R(real 0.5) zbar_D(real -0.5) /*
> */ alpha_R(real 0.5) alpha_D(real 0.5) /*
> */ kappa_ksi_R(real 0.0) beta_ksi_R(real 10.0) kappa_ksi_D(real 0.0) beta_ksi_D(real 10.0) /*
> */ phi0_R(real -1.0) phi1_R(real -1.0) phi0_D(real -1.0) phi1_D(real -1.0) /*
> */ kappa_u(real 0.0) beta_u(real 1.0) /*
> */ gamma0(real 0.0) gamma1(real -5.0) gamma2(real 0.0) gamma3(real 0.0)/*
> */ tau0(real 0.3) tau1(real -1.0) tau2(real 0.0)]
3. drop _all
4. set obs `nobs'
5.
. * Overall district ideology
. gen z = 2*(rbeta(`beta_a',`beta_b')-0.5)
6. gen x = z + (sqrt((1-`rho_x'^2)/`rho_x'^2))*(2*(rbeta(`beta_a',`beta_b')-0.5))
7.
. * Ideology of R and candidates: weighted average of national party and local ideology, plus noise
. gen z_R = `alpha_R'*`zbar_R' + (1-`alpha_R')*z + `kappa_ksi_R'*(rbeta(`beta_ksi_R',`beta_ksi_R')-0.5)
8. gen z_D = `alpha_D'*`zbar_D' + (1-`alpha_D')*z + `kappa_ksi_D'*(rbeta(`beta_ksi_D',`beta_ksi_D')-0.5)
9.
. * Gender is correlated with candidate ideology
. gen byte female_D = rnormal(`phi0_D' + `phi1_D'*z_D)>0
10. gen byte female_R = rnormal(`phi0_R' + `phi1_R'*z_R)>0
11.
. * Voteshare depends on ideology of the candidates plus noise
. gen u = `kappa_u'*(rbeta(`beta_u', `beta_u')-0.5)
12. gen voteshare_D = (exp(`gamma0' + `gamma1'*(z - (z_D+z_R)/2) + `gamma2'*female_D - `gamma3'*female_R + u)/ /*
> */ (1+ exp(`gamma0' + `gamma1'*(z - (z_D+z_R)/2) + `gamma2'*female_D - `gamma3'*female_R + u )))
13.
. gen voteshare_female = voteshare_D if female_D==1 & female_R==0
14. replace voteshare_female = (1-voteshare_D) if female_D==0 & female_R==1
15.
. * Outcome: depends on who is elected
. gen y = `tau0' + `tau1'*abs(z_D) + `tau2'*female_D + rnormal() if voteshare_D>=0.5
16. replace y = `tau0' + `tau1'*abs(z_R) +`tau2'*female_R + rnormal() if voteshare_D<0.5
17.
. * Now four types of RD analyses
. * (1) Density test
. rddensity voteshare_female, c(0.5)
18. local denstest_pval_all = e(pv_q)
19.
. rddensity voteshare_female if voteshare_D>=0.5, c(0.5)
20. local denstest_pval_D = e(pv_q)
21.
. rddensity voteshare_female if voteshare_D<0.5, c(0.5)
22. local denstest_pval_R = e(pv_q)
23.
. * (2) is ideology continuous at the threshold
. rdrobust z voteshare_female, c(0.5) kernel(uniform)
24. mat b = e(b)
25. mat V = e(V)
26. local b_ideology_all = b[1,1]
27. local se_ideology_all = sqrt(V[1,1])
28.
. rdrobust z voteshare_female if voteshare_D>=0.5, c(0.5) kernel(uniform)
29. mat b = e(b)
30. mat V = e(V)
31. local b_ideology_D = b[1,1]
32. local se_ideology_D = sqrt(V[1,1])
33.
. rdrobust z voteshare_female if voteshare_D<0.5, c(0.5) kernel(uniform)
34. mat b = e(b)
35. mat V = e(V)
36. local b_ideology_R = b[1,1]
37. local se_ideology_R = sqrt(V[1,1])
38.
. * (3) Estimate treatment effect with simple RD
. rdrobust y voteshare_female, c(0.5) kernel(uniform)
39. mat b = e(b)
40. mat V = e(V)
41. local b_rd_all = b[1,1]
42. local se_rd_all = sqrt(V[1,1])
43. local band_all = e(h_l)
44.
. rdrobust y voteshare_female if voteshare_D>=0.5, c(0.5) kernel(uniform)
45. mat b = e(b)
46. mat V = e(V)
47. local b_rd_D = b[1,1]
48. local se_rd_D = sqrt(V[1,1])
49. local band_D = e(h_l)
50.
. rdrobust y voteshare_female if voteshare_D<0.5, c(0.5) kernel(uniform)
51. mat b = e(b)
52. mat V = e(V)
53. local b_rd_R = b[1,1]
54. local se_rd_R = sqrt(V[1,1])
55. local band_R = e(h_l)
56.
. * (4-5) Estimate the treatment effect with weighted RD
. gen byte female = female_D if voteshare_D>=0.5
57. replace female = female_R if voteshare_D<0.5
58. gen voteshare_female_adj = voteshare_female-0.5
59.
. * (4) using x
. probit female x if abs(voteshare_female_adj)<=`band_all'
60. predict pscore if e(sample)==1
61. gen wt =1/pscore if female==1
62. replace wt = 1/(1-pscore) if female==0
63. reg y female voteshare_female_adj i.female#c.voteshare_female_adj [aw=wt] if abs(voteshare_female_adj)<=`band_all'
64. local b_rdwt_all = _b[female]
65. local se_rdwt_all = _se[female]
66. drop pscore wt
67.
. probit female x if voteshare_D>=0.5 & abs(voteshare_female_adj)<=`band_D'
68. predict pscore if e(sample)==1
69. gen wt =1/pscore if female==1
70. replace wt = 1/(1-pscore) if female==0
71. reg y female voteshare_female_adj i.female#c.voteshare_female_adj [aw=wt] if voteshare_D>=0.5 & abs(voteshare_female_adj)<=`band_D'
72. local b_rdwt_D = _b[female]
73. local se_rdwt_D = _se[female]
74. drop pscore wt
75.
. probit female x if voteshare_D<0.5 & abs(voteshare_female_adj)<=`band_R'
76. predict pscore if e(sample)==1
77. gen wt =1/pscore if female==1
78. replace wt = 1/(1-pscore) if female==0
79. reg y female voteshare_female_adj i.female#c.voteshare_female_adj [aw=wt] if voteshare_D<0.5 & abs(voteshare_female_adj)<=`band_R'
80. local b_rdwt_R = _b[female]
81. local se_rdwt_R = _se[female]
82. drop pscore wt
83.
.
.
. * (5a) using ideology of the district
. probit female z if abs(voteshare_female_adj)<=`band_all'
84. predict pscore if e(sample)==1
85. gen wt =1/pscore if female==1
86. replace wt = 1/(1-pscore) if female==0
87. reg y female voteshare_female_adj i.female#c.voteshare_female_adj [aw=wt] if abs(voteshare_female_adj)<=`band_all'
88. local b_rdwtideodistrict_all = _b[female]
89. local se_rdwtideodistrict_all = _se[female]
90. drop pscore wt
91.
. probit female z if voteshare_D>=0.5 & abs(voteshare_female_adj)<=`band_D'
92. predict pscore if e(sample)==1
93. gen wt =1/pscore if female==1
94. replace wt = 1/(1-pscore) if female==0
95. reg y female voteshare_female_adj i.female#c.voteshare_female_adj [aw=wt] if voteshare_D>=0.5 & abs(voteshare_female_adj)<=`band_D'
96. local b_rdwtideodistrict_D = _b[female]
97. local se_rdwtideodistrict_D = _se[female]
98. drop pscore wt
99.
. probit female z if voteshare_D<0.5 & abs(voteshare_female_adj)<=`band_R'
100. predict pscore if e(sample)==1
101. gen wt =1/pscore if female==1
102. replace wt = 1/(1-pscore) if female==0
103. reg y female voteshare_female_adj i.female#c.voteshare_female_adj [aw=wt] if voteshare_D<0.5 & abs(voteshare_female_adj)<=`band_R'
104. local b_rdwtideodistrict_R = _b[female]
105. local se_rdwtideodistrict_R = _se[female]
106. drop pscore wt
107.
. * (5b) using ideology of the elected representative
. gen z_elected = z_D if voteshare_D>=0.5
108. replace z_elected = z_R if voteshare_D<0.5
109.
. probit female z_elected if abs(voteshare_female_adj)<=`band_all'
110. predict pscore if e(sample)==1
111. gen wt =1/pscore if female==1
112. replace wt = 1/(1-pscore) if female==0
113. reg y female voteshare_female_adj i.female#c.voteshare_female_adj [aw=wt] if abs(voteshare_female_adj)<=`band_all'
114. local b_rdwtideoelected_all = _b[female]
115. local se_rdwtideoelected_all = _se[female]
116. drop pscore wt
117.
. probit female z_elected if voteshare_D>=0.5 & abs(voteshare_female_adj)<=`band_D'
118. predict pscore if e(sample)==1
119. gen wt =1/pscore if female==1
120. replace wt = 1/(1-pscore) if female==0
121. reg y female voteshare_female_adj i.female#c.voteshare_female_adj [aw=wt] if voteshare_D>=0.5 & abs(voteshare_female_adj)<=`band_D'
122. local b_rdwtideoelected_D = _b[female]
123. local se_rdwtideoelected_D = _se[female]
124. drop pscore wt
125.
. probit female z_elected if voteshare_D<0.5 & abs(voteshare_female_adj)<=`band_R'
126. predict pscore if e(sample)==1
127. gen wt =1/pscore if female==1
128. replace wt = 1/(1-pscore) if female==0
129. reg y female voteshare_female_adj i.female#c.voteshare_female_adj [aw=wt] if voteshare_D<0.5 & abs(voteshare_female_adj)<=`band_R'
130. local b_rdwtideoelected_R = _b[female]
131. local se_rdwtideoelected_R = _se[female]
132. drop pscore wt
133.
. * (6-7) Propensity score methods
. gen absMV = abs(voteshare_D-0.5)
134.
. * (6a) pscore - x
. cap teffects ipw (y) (female x absMV, probit), pstolerance(1e-6) osample(osample)
135. teffects ipw (y) (female x absMV, probit) if osample==0, pstolerance(1e-6)
136. drop osample
137. mat b = e(b)
138. mat V = e(V)
139. local b_pscorex_all = b[1,1]
140. local se_pscorex_all = sqrt(V[1,1])
141.
. cap teffects ipw (y) (female x absMV, probit) if voteshare_D>=0.5, pstolerance(1e-6) osample(osample)
142. teffects ipw (y) (female x absMV, probit) if voteshare_D>=0.5 & osample==0, pstolerance(1e-6)
143. drop osample
144. mat b = e(b)
145. mat V = e(V)
146. local b_pscorex_D = b[1,1]
147. local se_pscorex_D = sqrt(V[1,1])
148.
. cap teffects ipw (y) (female x absMV, probit) if voteshare_D<0.5, pstolerance(1e-6) osample(osample)
149. teffects ipw (y) (female x absMV, probit) if voteshare_D<0.5 & osample==0, pstolerance(1e-6)
150. drop osample
151. mat b = e(b)
152. mat V = e(V)
153. local b_pscorex_R = b[1,1]
154. local se_pscorex_R = sqrt(V[1,1])
155.
.
. * (7a) pscore - district ideology
. cap teffects ipw (y) (female z absMV, probit), pstolerance(1e-6) osample(osample)
156. teffects ipw (y) (female z absMV, probit) if osample==0, pstolerance(1e-6)
157. drop osample
158. mat b = e(b)
159. mat V = e(V)
160. local b_pscoreideodistrict_all = b[1,1]
161. local se_pscoreideodistrict_all = sqrt(V[1,1])
162.
. cap teffects ipw (y) (female z absMV, probit) if voteshare_D>=0.5, pstolerance(1e-6) osample(osample)
163. teffects ipw (y) (female z absMV, probit) if voteshare_D>=0.5 & osample==0, pstolerance(1e-6)
164. drop osample
165. mat b = e(b)
166. mat V = e(V)
167. local b_pscoreideodistrict_D = b[1,1]
168. local se_pscoreideodistrict_D = sqrt(V[1,1])
169.
. cap teffects ipw (y) (female z absMV, probit) if voteshare_D<0.5, pstolerance(1e-6) osample(osample)
170. teffects ipw (y) (female z absMV, probit) if voteshare_D<0.5 & osample==0, pstolerance(1e-6)
171. drop osample
172. mat b = e(b)
173. mat V = e(V)
174. local b_pscoreideodistrict_R = b[1,1]
175. local se_pscoreideodistrict_R = sqrt(V[1,1])
176.
.
. * (7b) pscore - elected representative ideology
. cap teffects ipw (y) (female z_elected absMV, probit), pstolerance(1e-6) osample(osample)
177. teffects ipw (y) (female z_elected absMV, probit) if osample==0, pstolerance(1e-6)
178. drop osample
179. mat b = e(b)
180. mat V = e(V)
181. local b_pscoreideoelected_all = b[1,1]
182. local se_pscoreideoelected_all = sqrt(V[1,1])
183.
. cap teffects ipw (y) (female z_elected absMV, probit) if voteshare_D>=0.5, pstolerance(1e-6) osample(osample)
184. teffects ipw (y) (female z_elected absMV, probit) if voteshare_D>=0.5 & osample==0, pstolerance(1e-6)
185. drop osample
186. mat b = e(b)
187. mat V = e(V)
188. local b_pscoreideoelected_D = b[1,1]
189. local se_pscoreideoelected_D = sqrt(V[1,1])
190.
. cap teffects ipw (y) (female z_elected absMV, probit) if voteshare_D<0.5, pstolerance(1e-6) osample(osample)
191. teffects ipw (y) (female z_elected absMV, probit) if voteshare_D<0.5 & osample==0, pstolerance(1e-6)
192. drop osample
193. mat b = e(b)
194. mat V = e(V)
195. local b_pscoreideoelected_R = b[1,1]
196. local se_pscoreideoelected_R = sqrt(V[1,1])
197.
.
. * (8) OLS
. reg y female
198. local b_ols_all =_b[female]
199. local se_ols_all = _se[female]
200.
. reg y female if voteshare_D>=0.5
201. local b_ols_D = _b[female]
202. local se_ols_D = _se[female]
203.
. reg y female if voteshare_D<0.5
204. local b_ols_R = _b[female]
205. local se_ols_R = _se[female]
206.
. * (9) Return
. return scalar denstest_pval_all = `denstest_pval_all'
207. return scalar denstest_pval_D = `denstest_pval_D'
208. return scalar denstest_pval_R = `denstest_pval_R'
209.
. return scalar b_ideology_all = `b_ideology_all'
210. return scalar se_ideology_all = `se_ideology_all'
211. return scalar b_ideology_D = `b_ideology_D'
212. return scalar se_ideology_D = `se_ideology_D'
213. return scalar b_ideology_R = `b_ideology_R'
214. return scalar se_ideology_R = `se_ideology_R'
215.
. return scalar b_rd_all = `b_rd_all'
216. return scalar se_rd_all = `se_rd_all'
217. return scalar b_rd_D = `b_rd_D'
218. return scalar se_rd_D = `se_rd_D'
219. return scalar b_rd_R = `b_rd_R'
220. return scalar se_rd_R = `se_rd_R'
221.
. return scalar b_rdwt_all = `b_rdwt_all'
222. return scalar se_rdwt_all = `se_rdwt_all'
223. return scalar b_rdwt_D = `b_rdwt_D'
224. return scalar se_rdwt_D = `se_rdwt_D'
225. return scalar b_rdwt_R = `b_rdwt_R'
226. return scalar se_rdwt_R = `se_rdwt_R'
227.
. return scalar b_rdwtideodistrict_all = `b_rdwtideodistrict_all'
228. return scalar se_rdwtideodistrict_all = `se_rdwtideodistrict_all'
229. return scalar b_rdwtideodistrict_D = `b_rdwtideodistrict_D'
230. return scalar se_rdwtideodistrict_D = `se_rdwtideodistrict_D'
231. return scalar b_rdwtideodistrict_R = `b_rdwtideodistrict_R'
232. return scalar se_rdwtideodistrict_R = `se_rdwtideodistrict_R'
233.
. return scalar b_rdwtideoelected_all = `b_rdwtideoelected_all'
234. return scalar se_rdwtideoelected_all = `se_rdwtideoelected_all'
235. return scalar b_rdwtideoelected_D = `b_rdwtideoelected_D'
236. return scalar se_rdwtideoelected_D = `se_rdwtideoelected_D'
237. return scalar b_rdwtideoelected_R = `b_rdwtideoelected_R'
238. return scalar se_rdwtideoelected_R = `se_rdwtideoelected_R'
239.
. return scalar b_pscorex_all = `b_pscorex_all'
240. return scalar se_pscorex_all = `se_pscorex_all'
241. return scalar b_pscorex_D = `b_pscorex_D'
242. return scalar se_pscorex_D = `se_pscorex_D'
243. return scalar b_pscorex_R = `b_pscorex_R'
244. return scalar se_pscorex_R = `se_pscorex_R'
245.
. return scalar b_pscoreideodistrict_all = `b_pscoreideodistrict_all'
246. return scalar se_pscoreideodistrict_all = `se_pscoreideodistrict_all'
247. return scalar b_pscoreideodistrict_D = `b_pscoreideodistrict_D'
248. return scalar se_pscoreideodistrict_D = `se_pscoreideodistrict_D'
249. return scalar b_pscoreideodistrict_R = `b_pscoreideodistrict_R'
250. return scalar se_pscoreideodistrict_R = `se_pscoreideodistrict_R'
251.
. return scalar b_pscoreideoelected_all = `b_pscoreideoelected_all'
252. return scalar se_pscoreideoelected_all = `se_pscoreideoelected_all'
253. return scalar b_pscoreideoelected_D = `b_pscoreideoelected_D'
254. return scalar se_pscoreideoelected_D = `se_pscoreideoelected_D'
255. return scalar b_pscoreideoelected_R = `b_pscoreideoelected_R'
256. return scalar se_pscoreideoelected_R = `se_pscoreideoelected_R'
257.
. return scalar b_ols_all = `b_ols_all'
258. return scalar se_ols_all = `se_ols_all'
259. return scalar b_ols_D = `b_ols_D'
260. return scalar se_ols_D = `se_ols_D'
261. return scalar b_ols_R = `b_ols_R'
262. return scalar se_ols_R = `se_ols_R'
263.
. end
.
.
. ********************************************************************************
. ********************************************************************************
. ********************************************************************************
.
. * Now actually run the simulations
.
. set seed 1234567
.
. * Run one simulation as a test
. rd_sim, beta_a(5) beta_b(5) kappa_ksi_R(0.4) kappa_ksi_D(0.4) kappa_u(1) tau1(-5) rho_x(0.6) nobs(100000) /*
> */ gamma2(0.0) gamma3(0.6) phi1_D(-1) phi1_R(-1)
number of observations (_N) was 0, now 100,000
(79,715 missing values generated)
(8,263 real changes made)
(53,038 missing values generated)
(53,038 real changes made)
Computing data-driven bandwidth selectors.
Point estimates and standard errors have been adjusted for repeated observations.
(Use option nomasspoints to suppress this adjustment.)
RD Manipulation test using local polynomial density estimation.
c = 0.500 | Left of c Right of c Number of obs = 28548
-------------------+---------------------- Model = unrestricted
Number of obs | 11425 17123 BW method = comb
Eff. Number of obs | 4542 4450 Kernel = triangular
Order est. (p) | 2 2 VCE method = jackknife
Order bias (q) | 3 3
BW est. (h) | 0.096 0.083
Running variable: voteshare_female.
------------------------------------------
Method | T P>|T|
-------------------+----------------------
Robust | -0.2327 0.8160
------------------------------------------
P-values of binomial tests. (H0: prob = .5)
-----------------------------------------------------
Window Length / 2 | <c >=c | P>|T|
-------------------+----------------------+----------
0.000 | 5 15 | 0.0414
0.000 | 19 29 | 0.1934
0.001 | 31 40 | 0.3425
0.001 | 42 54 | 0.2615
0.001 | 51 69 | 0.1203
0.001 | 64 83 | 0.1374
0.002 | 69 97 | 0.0358
0.002 | 83 112 | 0.0447
0.002 | 100 127 | 0.0842
0.002 | 115 132 | 0.3086
-----------------------------------------------------
Computing data-driven bandwidth selectors.
Point estimates and standard errors have been adjusted for repeated observations.
(Use option nomasspoints to suppress this adjustment.)
RD Manipulation test using local polynomial density estimation.
c = 0.500 | Left of c Right of c Number of obs = 13774
-------------------+---------------------- Model = unrestricted
Number of obs | 2457 11317 BW method = comb
Eff. Number of obs | 1615 6442 Kernel = triangular
Order est. (p) | 2 2 VCE method = jackknife
Order bias (q) | 3 3
BW est. (h) | 0.141 0.173
Running variable: voteshare_female.
------------------------------------------
Method | T P>|T|
-------------------+----------------------
Robust | 9.3618 0.0000
------------------------------------------
P-values of binomial tests. (H0: prob = .5)
-----------------------------------------------------
Window Length / 2 | <c >=c | P>|T|
-------------------+----------------------+----------
0.000 | 5 15 | 0.0414
0.000 | 19 29 | 0.1934
0.001 | 31 40 | 0.3425
0.001 | 42 54 | 0.2615
0.001 | 51 69 | 0.1203
0.001 | 64 83 | 0.1374
0.002 | 69 97 | 0.0358
0.002 | 83 112 | 0.0447
0.002 | 100 127 | 0.0842
0.002 | 115 132 | 0.3086
-----------------------------------------------------
Computing data-driven bandwidth selectors.
Point estimates and standard errors have been adjusted for repeated observations.
(Use option nomasspoints to suppress this adjustment.)
RD Manipulation test using local polynomial density estimation.
c = 0.500 | Left of c Right of c Number of obs = 14774
-------------------+---------------------- Model = unrestricted
Number of obs | 8968 5806 BW method = comb
Eff. Number of obs | 6647 2312 Kernel = triangular
Order est. (p) | 2 2 VCE method = jackknife
Order bias (q) | 3 3
BW est. (h) | 0.211 0.148
Running variable: voteshare_female.
------------------------------------------
Method | T P>|T|
-------------------+----------------------
Robust | -12.2100 0.0000
------------------------------------------
P-values of binomial tests. (H0: prob = .5)
-----------------------------------------------------
Window Length / 2 | <c >=c | P>|T|
-------------------+----------------------+----------
0.000 | 5 15 | 0.0414
0.000 | 19 29 | 0.1934
0.001 | 31 40 | 0.3425
0.001 | 42 54 | 0.2615
0.001 | 51 69 | 0.1203
0.001 | 64 83 | 0.1374
0.002 | 69 97 | 0.0358
0.002 | 83 112 | 0.0447
0.002 | 100 127 | 0.0842
0.002 | 115 132 | 0.3086
-----------------------------------------------------
Sharp RD estimates using local polynomial regression.
Cutoff c = .5 | Left of c Right of c Number of obs = 28548
-------------------+---------------------- BW type = mserd
Number of obs | 11425 17123 Kernel = Uniform
Eff. Number of obs | 2282 2410 VCE method = NN
Order est. (p) | 1 1
Order bias (q) | 2 2
BW est. (h) | 0.046 0.046
BW bias (b) | 0.092 0.092
rho (h/b) | 0.497 0.497
Outcome: z. Running variable: voteshare_female.
--------------------------------------------------------------------------------
Method | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------------+------------------------------------------------------------
Conventional | .01855 .00924 2.0073 0.045 .000437 .036665
Robust | - - 2.0807 0.037 .001262 .042242
--------------------------------------------------------------------------------
Sharp RD estimates using local polynomial regression.
Cutoff c = .5 | Left of c Right of c Number of obs = 13774
-------------------+---------------------- BW type = mserd
Number of obs | 2457 11317 Kernel = Uniform
Eff. Number of obs | 882 2485 VCE method = NN
Order est. (p) | 1 1
Order bias (q) | 2 2
BW est. (h) | 0.065 0.065
BW bias (b) | 0.118 0.118
rho (h/b) | 0.552 0.552
Outcome: z. Running variable: voteshare_female.
--------------------------------------------------------------------------------
Method | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------------+------------------------------------------------------------
Conventional | .2107 .00961 21.9190 0.000 .191862 .229544
Robust | - - 18.9995 0.000 .191055 .235007
--------------------------------------------------------------------------------
Sharp RD estimates using local polynomial regression.
Cutoff c = .5 | Left of c Right of c Number of obs = 14774
-------------------+---------------------- BW type = mserd
Number of obs | 8968 5806 Kernel = Uniform
Eff. Number of obs | 2933 1286 VCE method = NN
Order est. (p) | 1 1
Order bias (q) | 2 2
BW est. (h) | 0.083 0.083
BW bias (b) | 0.151 0.151
rho (h/b) | 0.551 0.551
Outcome: z. Running variable: voteshare_female.
--------------------------------------------------------------------------------
Method | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------------+------------------------------------------------------------
Conventional | -.19744 .0082 -24.0813 0.000 -.213511 -.181372
Robust | - - -20.4594 0.000 -.214516 -.177009
--------------------------------------------------------------------------------
Sharp RD estimates using local polynomial regression.
Cutoff c = .5 | Left of c Right of c Number of obs = 28548
-------------------+---------------------- BW type = mserd
Number of obs | 11425 17123 Kernel = Uniform
Eff. Number of obs | 4751 5448 VCE method = NN
Order est. (p) | 1 1
Order bias (q) | 2 2
BW est. (h) | 0.101 0.101
BW bias (b) | 0.190 0.190
rho (h/b) | 0.530 0.530
Outcome: y. Running variable: voteshare_female.
--------------------------------------------------------------------------------
Method | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------------+------------------------------------------------------------
Conventional | .17272 .04322 3.9964 0.000 .088013 .257427
Robust | - - 3.2304 0.001 .063486 .25937
--------------------------------------------------------------------------------
Sharp RD estimates using local polynomial regression.
Cutoff c = .5 | Left of c Right of c Number of obs = 13774
-------------------+---------------------- BW type = mserd
Number of obs | 2457 11317 Kernel = Uniform
Eff. Number of obs | 880 2484 VCE method = NN
Order est. (p) | 1 1
Order bias (q) | 2 2
BW est. (h) | 0.065 0.065
BW bias (b) | 0.117 0.117
rho (h/b) | 0.555 0.555
Outcome: y. Running variable: voteshare_female.
--------------------------------------------------------------------------------
Method | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------------+------------------------------------------------------------
Conventional | .31188 .07973 3.9117 0.000 .15561 .468147
Robust | - - 3.3740 0.001 .131626 .496528
--------------------------------------------------------------------------------
Sharp RD estimates using local polynomial regression.
Cutoff c = .5 | Left of c Right of c Number of obs = 14774
-------------------+---------------------- BW type = mserd
Number of obs | 8968 5806 Kernel = Uniform
Eff. Number of obs | 3622 1619 VCE method = NN
Order est. (p) | 1 1
Order bias (q) | 2 2
BW est. (h) | 0.104 0.104
BW bias (b) | 0.184 0.184
rho (h/b) | 0.566 0.566
Outcome: y. Running variable: voteshare_female.
--------------------------------------------------------------------------------
Method | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------------+------------------------------------------------------------
Conventional | .38413 .06763 5.6797 0.000 .251574 .516685
Robust | - - 4.6972 0.000 .218021 .530242
--------------------------------------------------------------------------------
(53,038 missing values generated)
(53,038 real changes made)
(71,452 missing values generated)
Iteration 0: log likelihood = -7045.573
Iteration 1: log likelihood = -7014.2341
Iteration 2: log likelihood = -7014.2334
Iteration 3: log likelihood = -7014.2334
Probit regression Number of obs = 10,199
LR chi2(1) = 62.68
Prob > chi2 = 0.0000
Log likelihood = -7014.2334 Pseudo R2 = 0.0044
------------------------------------------------------------------------------
female | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
x | -.2260739 .0286094 -7.90 0.000 -.2821473 -.1700006
_cons | .0707624 .0125909 5.62 0.000 .0460847 .09544
------------------------------------------------------------------------------
(option pr assumed; Pr(female))
(89,801 missing values generated)
(94,552 missing values generated)
(4,751 real changes made)
(sum of wgt is 20,398.6936738491)
Source | SS df MS Number of obs = 10,199
-------------+---------------------------------- F(3, 10195) = 51.96
Model | 193.062182 3 64.3540607 Prob > F = 0.0000
Residual | 12626.5639 10,195 1.23850554 R-squared = 0.0151
-------------+---------------------------------- Adj R-squared = 0.0148
Total | 12819.6261 10,198 1.25707258 Root MSE = 1.1129
-----------------------------------------------------------------------------------------------
y | Coef. Std. Err. t P>|t| [95% Conf. Interval]
------------------------------+----------------------------------------------------------------
female | .1739132 .0439874 3.95 0.000 .0876893 .2601372
voteshare_female_adj | 3.538998 .5419161 6.53 0.000 2.476736 4.60126
|
female#c.voteshare_female_adj |
1 | -6.258399 .7649045 -8.18 0.000 -7.757763 -4.759036
|
_cons | -1.052096 .0305361 -34.45 0.000 -1.111952 -.9922388
-----------------------------------------------------------------------------------------------
Iteration 0: log likelihood = -1933.3477
Iteration 1: log likelihood = -1878.7605
Iteration 2: log likelihood = -1878.6371
Iteration 3: log likelihood = -1878.6371
Probit regression Number of obs = 3,364
LR chi2(1) = 109.42
Prob > chi2 = 0.0000
Log likelihood = -1878.6371 Pseudo R2 = 0.0283
------------------------------------------------------------------------------
female | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
x | .5759299 .055828 10.32 0.000 .4665091 .6853507
_cons | .720536 .0252009 28.59 0.000 .6711433 .7699288
------------------------------------------------------------------------------
(option pr assumed; Pr(female))
(96,636 missing values generated)
(97,516 missing values generated)
(880 real changes made)
(sum of wgt is 6,733.80004513264)
Source | SS df MS Number of obs = 3,364
-------------+---------------------------------- F(3, 3360) = 40.81
Model | 139.293765 3 46.4312551 Prob > F = 0.0000
Residual | 3822.60101 3,360 1.13767887 R-squared = 0.0352
-------------+---------------------------------- Adj R-squared = 0.0343
Total | 3961.89478 3,363 1.17808349 Root MSE = 1.0666
-----------------------------------------------------------------------------------------------
y | Coef. Std. Err. t P>|t| [95% Conf. Interval]
------------------------------+----------------------------------------------------------------
female | .2406377 .0730094 3.30 0.001 .0974903 .3837851
voteshare_female_adj | 6.792886 1.412536 4.81 0.000 4.023369 9.562403
|
female#c.voteshare_female_adj |
1 | -9.439579 1.989195 -4.75 0.000 -13.33973 -5.539424
|
_cons | -1.285744 .0501982 -25.61 0.000 -1.384166 -1.187322
-----------------------------------------------------------------------------------------------
Iteration 0: log likelihood = -3240.1233
Iteration 1: log likelihood = -3133.1627
Iteration 2: log likelihood = -3132.9107
Iteration 3: log likelihood = -3132.9107
Probit regression Number of obs = 5,241
LR chi2(1) = 214.43
Prob > chi2 = 0.0000
Log likelihood = -3132.9107 Pseudo R2 = 0.0331
------------------------------------------------------------------------------
female | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
x | -.629661 .0437244 -14.40 0.000 -.7153593 -.5439628
_cons | -.5177479 .0184832 -28.01 0.000 -.5539742 -.4815216
------------------------------------------------------------------------------
(option pr assumed; Pr(female))
(94,759 missing values generated)
(98,381 missing values generated)
(3,622 real changes made)
(sum of wgt is 10,475.5064911842)
Source | SS df MS Number of obs = 5,241
-------------+---------------------------------- F(3, 5237) = 80.33
Model | 290.368848 3 96.7896161 Prob > F = 0.0000
Residual | 6310.12588 5,237 1.20491233 R-squared = 0.0440
-------------+---------------------------------- Adj R-squared = 0.0434
Total | 6600.49473 5,240 1.2596364 Root MSE = 1.0977
-----------------------------------------------------------------------------------------------
y | Coef. Std. Err. t P>|t| [95% Conf. Interval]
------------------------------+----------------------------------------------------------------
female | .3231791 .0614265 5.26 0.000 .2027576 .4436006
voteshare_female_adj | 4.05668 .722778 5.61 0.000 2.639734 5.473627
|
female#c.voteshare_female_adj |
1 | -5.84145 1.017686 -5.74 0.000 -7.836538 -3.846362
|
_cons | -.8815513 .0424921 -20.75 0.000 -.9648534 -.7982492
-----------------------------------------------------------------------------------------------
Iteration 0: log likelihood = -7045.573
Iteration 1: log likelihood = -6830.0923
Iteration 2: log likelihood = -6829.9941
Iteration 3: log likelihood = -6829.9941
Probit regression Number of obs = 10,199
LR chi2(1) = 431.16
Prob > chi2 = 0.0000
Log likelihood = -6829.9941 Pseudo R2 = 0.0306
------------------------------------------------------------------------------
female | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
z | -1.538549 .0747452 -20.58 0.000 -1.685046 -1.392051
_cons | -.0209974 .0136641 -1.54 0.124 -.0477786 .0057838
------------------------------------------------------------------------------
(option pr assumed; Pr(female))
(89,801 missing values generated)
(94,552 missing values generated)
(4,751 real changes made)
(sum of wgt is 20,472.7413574457)
Source | SS df MS Number of obs = 10,199
-------------+---------------------------------- F(3, 10195) = 70.48
Model | 262.88843 3 87.6294767 Prob > F = 0.0000
Residual | 12675.1194 10,195 1.24326821 R-squared = 0.0203
-------------+---------------------------------- Adj R-squared = 0.0200
Total | 12938.0078 10,198 1.2686809 Root MSE = 1.115
-----------------------------------------------------------------------------------------------
y | Coef. Std. Err. t P>|t| [95% Conf. Interval]
------------------------------+----------------------------------------------------------------
female | .1943662 .0436797 4.45 0.000 .1087454 .2799871
voteshare_female_adj | 3.946968 .5368838 7.35 0.000 2.894571 4.999366
|
female#c.voteshare_female_adj |
1 | -6.460541 .7641782 -8.45 0.000 -7.958481 -4.962601
|
_cons | -1.069798 .0301335 -35.50 0.000 -1.128866 -1.010731
-----------------------------------------------------------------------------------------------
Iteration 0: log likelihood = -1933.3477
Iteration 1: log likelihood = -1286.2133
Iteration 2: log likelihood = -1267.5265
Iteration 3: log likelihood = -1267.4815
Iteration 4: log likelihood = -1267.4815
Probit regression Number of obs = 3,364
LR chi2(1) = 1331.73
Prob > chi2 = 0.0000
Log likelihood = -1267.4815 Pseudo R2 = 0.3444
------------------------------------------------------------------------------
female | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
z | 7.444313 .2623512 28.38 0.000 6.930114 7.958512
_cons | 1.78103 .0537058 33.16 0.000 1.675768 1.886291
------------------------------------------------------------------------------
(option pr assumed; Pr(female))
(96,636 missing values generated)
(97,516 missing values generated)
(880 real changes made)
(sum of wgt is 6,608.81980001926)
Source | SS df MS Number of obs = 3,364
-------------+---------------------------------- F(3, 3360) = 112.97
Model | 469.699399 3 156.566466 Prob > F = 0.0000
Residual | 4656.54364 3,360 1.38587608 R-squared = 0.0916
-------------+---------------------------------- Adj R-squared = 0.0908
Total | 5126.24304 3,363 1.52430658 Root MSE = 1.1772
-----------------------------------------------------------------------------------------------
y | Coef. Std. Err. t P>|t| [95% Conf. Interval]
------------------------------+----------------------------------------------------------------
female | -.793492 .0774302 -10.25 0.000 -.9453071 -.6416768
voteshare_female_adj | 26.62825 1.637998 16.26 0.000 23.41667 29.83982
|
female#c.voteshare_female_adj |
1 | -31.34425 2.256755 -13.89 0.000 -35.769 -26.9195
|
_cons | -.2816657 .0479712 -5.87 0.000 -.3757214 -.1876099
-----------------------------------------------------------------------------------------------
Iteration 0: log likelihood = -3240.1233
Iteration 1: log likelihood = -2172.2326
Iteration 2: log likelihood = -2152.4686
Iteration 3: log likelihood = -2152.4172
Iteration 4: log likelihood = -2152.4172
Probit regression Number of obs = 5,241
LR chi2(1) = 2175.41
Prob > chi2 = 0.0000
Log likelihood = -2152.4172 Pseudo R2 = 0.3357
------------------------------------------------------------------------------
female | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
z | -7.049271 .1911968 -36.87 0.000 -7.42401 -6.674532
_cons | -.7631799 .0241643 -31.58 0.000 -.8105411 -.7158187
------------------------------------------------------------------------------
(option pr assumed; Pr(female))
(94,759 missing values generated)
(98,381 missing values generated)
(3,622 real changes made)
(sum of wgt is 9,903.25282597542)
Source | SS df MS Number of obs = 5,241
-------------+---------------------------------- F(3, 5237) = 22.54
Model | 81.2014589 3 27.067153 Prob > F = 0.0000
Residual | 6288.68375 5,237 1.20081798 R-squared = 0.0127
-------------+---------------------------------- Adj R-squared = 0.0122
Total | 6369.8852 5,240 1.21562695 Root MSE = 1.0958
-----------------------------------------------------------------------------------------------
y | Coef. Std. Err. t P>|t| [95% Conf. Interval]
------------------------------+----------------------------------------------------------------
female | -.1379332 .0656739 -2.10 0.036 -.2666815 -.0091849
voteshare_female_adj | 5.627017 .7120137 7.90 0.000 4.231173 7.022861
|
female#c.voteshare_female_adj |
1 | -7.189718 1.03427 -6.95 0.000 -9.217319 -5.162118
|
_cons | -.680452 .0396242 -17.17 0.000 -.758132 -.6027719
-----------------------------------------------------------------------------------------------
(53,038 missing values generated)
(53,038 real changes made)
Iteration 0: log likelihood = -7045.573
Iteration 1: log likelihood = -6040.1188
Iteration 2: log likelihood = -6037.2237
Iteration 3: log likelihood = -6037.2236
Probit regression Number of obs = 10,199
LR chi2(1) = 2016.70
Prob > chi2 = 0.0000
Log likelihood = -6037.2236 Pseudo R2 = 0.1431
------------------------------------------------------------------------------
female | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
z_elected | -1.958926 .0449516 -43.58 0.000 -2.04703 -1.870823
_cons | .0135668 .0133481 1.02 0.309 -.0125949 .0397285
------------------------------------------------------------------------------
(option pr assumed; Pr(female))
(89,801 missing values generated)
(94,552 missing values generated)
(4,751 real changes made)
(sum of wgt is 20,703.5746251345)
Source | SS df MS Number of obs = 10,199
-------------+---------------------------------- F(3, 10195) = 156.58
Model | 586.245291 3 195.415097 Prob > F = 0.0000
Residual | 12723.9437 10,195 1.24805725 R-squared = 0.0440
-------------+---------------------------------- Adj R-squared = 0.0438
Total | 13310.189 10,198 1.3051764 Root MSE = 1.1172
-----------------------------------------------------------------------------------------------
y | Coef. Std. Err. t P>|t| [95% Conf. Interval]
------------------------------+----------------------------------------------------------------
female | .3725144 .044218 8.42 0.000 .2858384 .4591904
voteshare_female_adj | 3.852766 .5269349 7.31 0.000 2.81987 4.885662
|
female#c.voteshare_female_adj |
1 | -6.090878 .7661041 -7.95 0.000 -7.592593 -4.589164
|
_cons | -1.201598 .0296283 -40.56 0.000 -1.259675 -1.14352
-----------------------------------------------------------------------------------------------
Iteration 0: log likelihood = -1933.3477
Iteration 1: log likelihood = -1587.7928
Iteration 2: log likelihood = -1582.0926
Iteration 3: log likelihood = -1582.0776
Iteration 4: log likelihood = -1582.0776
Probit regression Number of obs = 3,364
LR chi2(1) = 702.54
Prob > chi2 = 0.0000
Log likelihood = -1582.0776 Pseudo R2 = 0.1817
------------------------------------------------------------------------------
female | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
z_elected | 7.410054 .3114873 23.79 0.000 6.79955 8.020558
_cons | 3.042828 .1071286 28.40 0.000 2.83286 3.252796
------------------------------------------------------------------------------
(option pr assumed; Pr(female))
(96,636 missing values generated)
(97,516 missing values generated)
(880 real changes made)
(sum of wgt is 6,813.62821555138)
Source | SS df MS Number of obs = 3,364
-------------+---------------------------------- F(3, 3360) = 44.01
Model | 168.894906 3 56.2983019 Prob > F = 0.0000
Residual | 4298.15433 3,360 1.2792126 R-squared = 0.0378
-------------+---------------------------------- Adj R-squared = 0.0369
Total | 4467.04924 3,363 1.32829296 Root MSE = 1.131
-----------------------------------------------------------------------------------------------
y | Coef. Std. Err. t P>|t| [95% Conf. Interval]
------------------------------+----------------------------------------------------------------
female | -.4509505 .0758094 -5.95 0.000 -.5995877 -.3023132
voteshare_female_adj | 16.0325 1.518248 10.56 0.000 13.05572 19.00929
|
female#c.voteshare_female_adj |
1 | -19.80716 2.132745 -9.29 0.000 -23.98877 -15.62555
|
_cons | -.6497326 .0491586 -13.22 0.000 -.7461164 -.5533488
-----------------------------------------------------------------------------------------------
Iteration 0: log likelihood = -3240.1233
Iteration 1: log likelihood = -2594.5471
Iteration 2: log likelihood = -2583.7661
Iteration 3: log likelihood = -2583.7327
Iteration 4: log likelihood = -2583.7327
Probit regression Number of obs = 5,241
LR chi2(1) = 1312.78
Prob > chi2 = 0.0000
Log likelihood = -2583.7327 Pseudo R2 = 0.2026
------------------------------------------------------------------------------
female | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
z_elected | -7.805294 .2423385 -32.21 0.000 -8.280269 -7.33032
_cons | 1.29987 .0573838 22.65 0.000 1.1874 1.412341
------------------------------------------------------------------------------
(option pr assumed; Pr(female))
(94,759 missing values generated)
(98,381 missing values generated)
(3,622 real changes made)
(sum of wgt is 10,372.1911814213)
Source | SS df MS Number of obs = 5,241
-------------+---------------------------------- F(3, 5237) = 16.72
Model | 60.8606207 3 20.2868736 Prob > F = 0.0000
Residual | 6354.50433 5,237 1.21338635 R-squared = 0.0095
-------------+---------------------------------- Adj R-squared = 0.0089
Total | 6415.36495 5,240 1.22430629 Root MSE = 1.1015
-----------------------------------------------------------------------------------------------
y | Coef. Std. Err. t P>|t| [95% Conf. Interval]
------------------------------+----------------------------------------------------------------
female | -.1872573 .0632442 -2.96 0.003 -.3112422 -.0632723
voteshare_female_adj | 5.046656 .7215085 6.99 0.000 3.632198 6.461113
|
female#c.voteshare_female_adj |
1 | -5.702727 1.02625 -5.56 0.000 -7.714604 -3.690849
|
_cons | -.6934209 .0411359 -16.86 0.000 -.7740645 -.6127773
-----------------------------------------------------------------------------------------------
Iteration 0: EE criterion = 2.168e-28
Iteration 1: EE criterion = 4.132e-33
Treatment-effects estimation Number of obs = 100,000
Estimator : inverse-probability weights
Outcome model : weighted mean
Treatment model: probit
------------------------------------------------------------------------------
| Robust
y | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
ATE |
female |
(1 vs 0) | .185551 .009143 20.29 0.000 .1676311 .2034709
-------------+----------------------------------------------------------------
POmean |
female |
0 | -1.51444 .0040466 -374.25 0.000 -1.522371 -1.506509
------------------------------------------------------------------------------
Iteration 0: EE criterion = 5.545e-18
Iteration 1: EE criterion = 2.076e-33
Treatment-effects estimation Number of obs = 46,962
Estimator : inverse-probability weights
Outcome model : weighted mean
Treatment model: probit
------------------------------------------------------------------------------
| Robust
y | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
ATE |
female |
(1 vs 0) | -.0620271 .0113967 -5.44 0.000 -.0843642 -.03969
-------------+----------------------------------------------------------------
POmean |
female |
0 | -1.499856 .0061075 -245.58 0.000 -1.511827 -1.487886
------------------------------------------------------------------------------
Iteration 0: EE criterion = 2.364e-23
Iteration 1: EE criterion = 4.823e-32
Treatment-effects estimation Number of obs = 53,038
Estimator : inverse-probability weights
Outcome model : weighted mean
Treatment model: probit
------------------------------------------------------------------------------
| Robust
y | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
ATE |
female |
(1 vs 0) | .4934254 .015274 32.30 0.000 .4634889 .5233619
-------------+----------------------------------------------------------------
POmean |
female |
0 | -1.519821 .0053757 -282.72 0.000 -1.530357 -1.509284
------------------------------------------------------------------------------
Iteration 0: EE criterion = 7.198e-22
Iteration 1: EE criterion = 6.742e-33
Treatment-effects estimation Number of obs = 100,000
Estimator : inverse-probability weights
Outcome model : weighted mean
Treatment model: probit
------------------------------------------------------------------------------
| Robust
y | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
ATE |
female |
(1 vs 0) | .1960373 .0094185 20.81 0.000 .1775775 .2144971
-------------+----------------------------------------------------------------
POmean |
female |
0 | -1.527443 .004071 -375.20 0.000 -1.535422 -1.519464
------------------------------------------------------------------------------
Iteration 0: EE criterion = 4.047e-18
Iteration 1: EE criterion = 2.245e-32
Treatment-effects estimation Number of obs = 46,962
Estimator : inverse-probability weights
Outcome model : weighted mean
Treatment model: probit
------------------------------------------------------------------------------
| Robust
y | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
ATE |
female |
(1 vs 0) | -.0158669 .0108021 -1.47 0.142 -.0370386 .0053048
-------------+----------------------------------------------------------------
POmean |
female |
0 | -1.51214 .0060289 -250.82 0.000 -1.523956 -1.500323
------------------------------------------------------------------------------
Iteration 0: EE criterion = 2.427e-27
Iteration 1: EE criterion = 1.682e-31
Treatment-effects estimation Number of obs = 52,919
Estimator : inverse-probability weights
Outcome model : weighted mean
Treatment model: probit
------------------------------------------------------------------------------
| Robust
y | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
ATE |
female |
(1 vs 0) | -.1946729 .0624661 -3.12 0.002 -.3171042 -.0722417
-------------+----------------------------------------------------------------
POmean |
female |
0 | -1.45934 .0055571 -262.61 0.000 -1.470232 -1.448448
------------------------------------------------------------------------------
Iteration 0: EE criterion = 1.047e-24
Iteration 1: EE criterion = 5.030e-32
Treatment-effects estimation Number of obs = 100,000
Estimator : inverse-probability weights
Outcome model : weighted mean
Treatment model: probit
------------------------------------------------------------------------------
| Robust
y | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
ATE |
female |
(1 vs 0) | .2416772 .0092623 26.09 0.000 .2235235 .2598309
-------------+----------------------------------------------------------------
POmean |
female |
0 | -1.519808 .0040542 -374.88 0.000 -1.527754 -1.511862
------------------------------------------------------------------------------
Iteration 0: EE criterion = 3.676e-18
Iteration 1: EE criterion = 2.042e-33
Treatment-effects estimation Number of obs = 46,962
Estimator : inverse-probability weights
Outcome model : weighted mean
Treatment model: probit
------------------------------------------------------------------------------
| Robust
y | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
ATE |
female |
(1 vs 0) | -.000495 .0106006 -0.05 0.963 -.0212719 .0202818
-------------+----------------------------------------------------------------
POmean |
female |
0 | -1.515822 .0060067 -252.36 0.000 -1.527595 -1.504049
------------------------------------------------------------------------------
Iteration 0: EE criterion = 9.684e-16
Iteration 1: EE criterion = 8.628e-28
Treatment-effects estimation Number of obs = 53,038
Estimator : inverse-probability weights
Outcome model : weighted mean
Treatment model: probit
------------------------------------------------------------------------------
| Robust
y | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
ATE |
female |
(1 vs 0) | -.1050439 .0401988 -2.61 0.009 -.183832 -.0262557
-------------+----------------------------------------------------------------
POmean |
female |
0 | -1.458219 .0054927 -265.48 0.000 -1.468984 -1.447453
------------------------------------------------------------------------------
Source | SS df MS Number of obs = 100,000
-------------+---------------------------------- F(1, 99998) = 115.72
Model | 155.270531 1 155.270531 Prob > F = 0.0000
Residual | 134179.437 99,998 1.3418212 R-squared = 0.0012
-------------+---------------------------------- Adj R-squared = 0.0011
Total | 134334.707 99,999 1.34336051 Root MSE = 1.1584
------------------------------------------------------------------------------
y | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
female | .0988518 .0091894 10.76 0.000 .0808406 .1168629
_cons | -1.501196 .0040908 -366.97 0.000 -1.509214 -1.493178
------------------------------------------------------------------------------
Source | SS df MS Number of obs = 46,962
-------------+---------------------------------- F(1, 46960) = 81.37
Model | 107.755058 1 107.755058 Prob > F = 0.0000
Residual | 62185.463 46,960 1.32422196 R-squared = 0.0017
-------------+---------------------------------- Adj R-squared = 0.0017
Total | 62293.2181 46,961 1.32648832 Root MSE = 1.1507
------------------------------------------------------------------------------
y | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
female | -.1088381 .0120654 -9.02 0.000 -.1324866 -.0851897
_cons | -1.487199 .0061843 -240.48 0.000 -1.499321 -1.475078
------------------------------------------------------------------------------
Source | SS df MS Number of obs = 53,038
-------------+---------------------------------- F(1, 53036) = 886.34
Model | 1182.47024 1 1182.47024 Prob > F = 0.0000
Residual | 70755.5305 53,036 1.33410383 R-squared = 0.0164
-------------+---------------------------------- Adj R-squared = 0.0164
Total | 71938.0008 53,037 1.35637387 Root MSE = 1.155
------------------------------------------------------------------------------
y | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
female | .4290223 .0144105 29.77 0.000 .4007775 .457267
_cons | -1.511833 .0054114 -279.38 0.000 -1.522439 -1.501226
------------------------------------------------------------------------------
.
.
.
.
. set seed 1234567
.
. forvalues j = 1/4 {
2. if `j'==1 {
3. * BASELINE: Everything balanced
. local gamma2 = 0 // No preference for female D
4. local gamma3 = 0 // No preference for female R
5. local phi1_D = 0 // No relationship between gender and ideology
6. local phi1_R = 0
7. local DataDescr`j' = "Baseline - Everything Balanced"
8. }
9. else if `j'==2 {
10. * Variant 1: Women are more left-wing, no preference for female candidates
. local gamma2 = 0 // No preference for female D
11. local gamma3 = 0 // No preference for female R
12. local phi1_D = -1 // No relationship between gender and ideology
13. local phi1_R = -1
14. local DataDescr`j' = "Variant 1 - Women are more left-wing, no preference for female candidates"
15. }
16. else if `j'==3 {
17. * Variant 2: Women are more left-wing, equal preference for female candidates
. local gamma2 = 0.3 // No preference for female D
18. local gamma3 = 0.3 // No preference for female R
19. local phi1_D = -1 // No relationship between gender and ideology
20. local phi1_R = -1
21. local DataDescr`j' = "Variant 2 - Women are more left-wing, equal preference for female candidates"
22. }
23.
. else if `j'==4 {
24. * Variant 3: Women are more left-wing, only D's prefer female candidates
. local gamma2 = 0.6 // No preference for female D
25. local gamma3 = 0 // No preference for female R
26. local phi1_D = -1 // No relationship between gender and ideology
27. local phi1_R = -1
28. local DataDescr`j' = "Variant 3 - Women are more left-wing, only Ds prefer female candidates"
29. }
30.
. simulate denstest_pval_all=r(denstest_pval_all) denstest_pval_D=r(denstest_pval_D) denstest_pval_R=r(denstest_pval_R) /*
> */ b_ideology_all=r(b_ideology_all) se_ideology_all=r(se_ideology_all) /*
> */ b_ideology_D=r(b_ideology_D) se_ideology_D=r(se_ideology_D) /*
> */ b_ideology_R=r(b_ideology_R) se_ideology_R=r(se_ideology_R) /*
> */ b_rd_all=r(b_rd_all) se_rd_all=r(se_rd_all) /*
> */ b_rd_D=r(b_rd_D) se_rd_D=r(se_rd_D) /*
> */ b_rd_R=r(b_rd_R) se_rd_R=r(se_rd_R) /*
> */ b_rdwt_all=r(b_rdwt_all) se_rdwt_all=r(se_rdwt_all) /*
> */ b_rdwt_D=r(b_rdwt_D) se_rdwt_D=r(se_rdwt_D) /*
> */ b_rdwt_R=r(b_rdwt_R) se_rdwt_R=r(se_rdwt_R) /*
> */ b_rdwtideodistrict_all=r(b_rdwtideodistrict_all) se_rdwtideodistrict_all=r(se_rdwtideodistrict_all) /*
> */ b_rdwtideodistrict_D=r(b_rdwtideodistrict_D) se_rdwtideodistrict_D=r(se_rdwtideodistrict_D) /*
> */ b_rdwtideodistrict_R=r(b_rdwtideodistrict_R) se_rdwtideodistrict_R=r(se_rdwtideodistrict_R) /*
> */ b_rdwtideoelected_all=r(b_rdwtideoelected_all) se_rdwtideoelected_all=r(se_rdwtideoelected_all) /*
> */ b_rdwtideoelected_D=r(b_rdwtideoelected_D) se_rdwtideoelected_D=r(se_rdwtideoelected_D) /*
> */ b_rdwtideoelected_R=r(b_rdwtideoelected_R) se_rdwtideoelected_R=r(se_rdwtideoelected_R) /*
> */ b_pscorex_all=r(b_pscorex_all) se_pscorex_all=r(se_pscorex_all) /*
> */ b_pscorex_D=r(b_pscorex_D) se_pscorex_D=r(se_pscorex_D) /*
> */ b_pscorex_R=r(b_pscorex_R) se_pscorex_R=r(se_pscorex_R) /*
> */ b_pscoreideodistrict_all=r(b_pscoreideodistrict_all) se_pscoreideodistrict_all=r(se_pscoreideodistrict_all) /*
> */ b_pscoreideodistrict_D=r(b_pscoreideodistrict_D) se_pscoreideodistrict_D=r(se_pscoreideodistrict_D) /*
> */ b_pscoreideodistrict_R=r(b_pscoreideodistrict_R) se_pscoreideodistrict_R=r(se_pscoreideodistrict_R) /*
> */ b_pscoreideoelected_all=r(b_pscoreideoelected_all) se_pscoreideoelected_all=r(se_pscoreideoelected_all) /*
> */ b_pscoreideoelected_D=r(b_pscoreideoelected_D) se_pscoreideoelected_D=r(se_pscoreideoelected_D) /*
> */ b_pscoreideoelected_R=r(b_pscoreideoelected_R) se_pscoreideoelected_R=r(se_pscoreideoelected_R) /*
> */ b_ols_all=r(b_ols_all) se_ols_all=r(se_ols_all) /*
> */ b_ols_D=r(b_ols_D) se_ols_D=r(se_ols_D) /*
> */ b_ols_R=r(b_ols_R) se_ols_R=r(se_ols_R) /*
> */ , reps(1000) saving("$AppendixC_simulations/rd_simulations`j'.dta", replace): /*
> */ rd_sim, beta_a(5) beta_b(5) kappa_ksi_R(0.4) kappa_ksi_D(0.4) kappa_u(1) tau1(-5) rho_x(0.6) nobs(10000) /*
> */ gamma2(`gamma2') gamma3(`gamma3') phi1_D(`phi1_D') phi1_R(`phi1_R')
31.
. gen gamma2 = `gamma2'
32. gen gamma3 = `gamma3'
33. gen phi1_D = `phi1_D'
34. gen phi1_R = `phi1_R'
35. label data "`DataDescr`j''"
36. save "$AppendixC_simulations/rd_simulations`j'.dta", replace
37.
. foreach type in "rd" "rdwt" "rdwtideodistrict" "rdwtideoelected" "pscorex" "pscoreideodistrict" "pscoreideoelected" "ols" {
38. di ""
39. di ""
40. di in ye "type = `type'"
41. foreach party in "all" "D" "R" {
42. gen byte hit_`type'_`party' = b_`type'_`party' - 1.96*se_`type'_`party'<=0 & /*
> */ b_`type'_`party' + 1.96*se_`type'_`party'>=0
43. }
44. sum b_`type'_D se_`type'_D hit_`type'_D b_`type'_R se_`type'_R hit_`type'_R
45. }
46. }
command: rd_sim, beta_a(5) beta_b(5) kappa_ksi_R(0.4) kappa_ksi_D(0.4) kappa_u(1) tau1(-5) rho_x(0.6) nobs(10000) gamma2(0) gamma3(0) phi1_D(0) phi1_R(0)
denstest_pval_~l: r(denstest_pval_all)
denstest_pval_D: r(denstest_pval_D)
denstest_pval_R: r(denstest_pval_R)
b_ideology_all: r(b_ideology_all)
se_ideology_all: r(se_ideology_all)
b_ideology_D: r(b_ideology_D)
se_ideology_D: r(se_ideology_D)
b_ideology_R: r(b_ideology_R)
se_ideology_R: r(se_ideology_R)
b_rd_all: r(b_rd_all)
se_rd_all: r(se_rd_all)
b_rd_D: r(b_rd_D)
se_rd_D: r(se_rd_D)
b_rd_R: r(b_rd_R)
se_rd_R: r(se_rd_R)
b_rdwt_all: r(b_rdwt_all)
se_rdwt_all: r(se_rdwt_all)
b_rdwt_D: r(b_rdwt_D)
se_rdwt_D: r(se_rdwt_D)
b_rdwt_R: r(b_rdwt_R)
se_rdwt_R: r(se_rdwt_R)
b_rdwtideodist~l: r(b_rdwtideodistrict_all)
se_rdwtideodis~l: r(se_rdwtideodistrict_all)
b_rdwtideodist~D: r(b_rdwtideodistrict_D)
se_rdwtideodis~D: r(se_rdwtideodistrict_D)
b_rdwtideodist~R: r(b_rdwtideodistrict_R)
se_rdwtideodis~R: r(se_rdwtideodistrict_R)
b_rdwtideoelec~l: r(b_rdwtideoelected_all)
se_rdwtideoele~l: r(se_rdwtideoelected_all)
b_rdwtideoelec~D: r(b_rdwtideoelected_D)
se_rdwtideoele~D: r(se_rdwtideoelected_D)
b_rdwtideoelec~R: r(b_rdwtideoelected_R)
se_rdwtideoele~R: r(se_rdwtideoelected_R)
b_pscorex_all: r(b_pscorex_all)
se_pscorex_all: r(se_pscorex_all)
b_pscorex_D: r(b_pscorex_D)
se_pscorex_D: r(se_pscorex_D)
b_pscorex_R: r(b_pscorex_R)
se_pscorex_R: r(se_pscorex_R)
b_pscoreideodi~l: r(b_pscoreideodistrict_all)
se_pscoreideod~l: r(se_pscoreideodistrict_all)
b_pscoreideodi~D: r(b_pscoreideodistrict_D)
se_pscoreideod~D: r(se_pscoreideodistrict_D)
b_pscoreideodi~R: r(b_pscoreideodistrict_R)
se_pscoreideod~R: r(se_pscoreideodistrict_R)
b_pscoreideoel~l: r(b_pscoreideoelected_all)
se_pscoreideoe~l: r(se_pscoreideoelected_all)
b_pscoreideoel~D: r(b_pscoreideoelected_D)
se_pscoreideoe~D: r(se_pscoreideoelected_D)
b_pscoreideoel~R: r(b_pscoreideoelected_R)
se_pscoreideoe~R: r(se_pscoreideoelected_R)
b_ols_all: r(b_ols_all)
se_ols_all: r(se_ols_all)
b_ols_D: r(b_ols_D)
se_ols_D: r(se_ols_D)
b_ols_R: r(b_ols_R)
se_ols_R: r(se_ols_R)
Simulations (1000)
----+--- 1 ---+--- 2 ---+--- 3 ---+--- 4 ---+--- 5
.................................................. 50
.................................................. 100
.................................................. 150
.................................................. 200
.................................................. 250
.................................................. 300
.................................................. 350
.................................................. 400
.................................................. 450
.................................................. 500
.................................................. 550
.................................................. 600
.................................................. 650
.................................................. 700
.................................................. 750
.................................................. 800
.................................................. 850
.................................................. 900
.................................................. 950
.................................................. 1000
file /Users/stefanogagliarducci/Dropbox/various/published/womenpart/EJ/3 replication package/Output/AppendixC_simulations_output/rd_simulations1.dta saved
type = rd
Variable | Obs Mean Std. Dev. Min Max
-------------+---------------------------------------------------------
b_rd_D | 1,000 .000474 .2158104 -.7346113 .9701019
se_rd_D | 1,000 .2066569 .0209705 .1547102 .2945822
hit_rd_D | 1,000 .948 .2221381 0 1
b_rd_R | 1,000 .0061781 .2194184 -.6579666 .882883
se_rd_R | 1,000 .2077052 .0213403 .1590595 .2819441
-------------+---------------------------------------------------------
hit_rd_R | 1,000 .944 .2300368 0 1
type = rdwt
Variable | Obs Mean Std. Dev. Min Max
-------------+---------------------------------------------------------
b_rdwt_D | 1,000 .000563 .215728 -.760636 .9889148
se_rdwt_D | 1,000 .2074663 .0194472 .1614645 .2764145
hit_rdwt_D | 1,000 .952 .2138732 0 1
b_rdwt_R | 1,000 .0063396 .218869 -.6742517 .8876668
se_rdwt_R | 1,000 .2079266 .0202564 .1640088 .2907166
-------------+---------------------------------------------------------
hit_rdwt_R | 1,000 .949 .2201078 0 1
type = rdwtideodistrict
Variable | Obs Mean Std. Dev. Min Max
-------------+---------------------------------------------------------
b_rdwtid~t_D | 1,000 .0007777 .2130281 -.7609494 .9757128
se_rdwti~t_D | 1,000 .2074916 .0194821 .1614185 .2770738
hit_rdwtid~D | 1,000 .955 .2074079 0 1
b_rdwtid~t_R | 1,000 .0070163 .2142945 -.7047111 .8283938
se_rdwti~t_R | 1,000 .2079609 .0202332 .1639686 .2897356
-------------+---------------------------------------------------------
hit_rdwtid~R | 1,000 .95 .218054 0 1
type = rdwtideoelected
Variable | Obs Mean Std. Dev. Min Max
-------------+---------------------------------------------------------
b_rdwtid~d_D | 1,000 -.0000482 .2120022 -.7777959 .9878977
se_rdwti~d_D | 1,000 .2075136 .0194996 .1613577 .2767253
hit_rdwt~d_D | 1,000 .954 .2095899 0 1
b_rdwtid~d_R | 1,000 .0071389 .211381 -.6772436 .8140548
se_rdwti~d_R | 1,000 .2079869 .0202312 .1639532 .2900819
-------------+---------------------------------------------------------
hit_rdwt~d_R | 1,000 .95 .218054 0 1
type = pscorex
Variable | Obs Mean Std. Dev. Min Max
-------------+---------------------------------------------------------
b_pscorex_D | 1,000 .0016244 .0410007 -.1189567 .1347485
se_pscorex_D | 1,000 .0417603 .0010802 .0389149 .0450724
hit_pscore~D | 1,000 .964 .1863833 0 1
b_pscorex_R | 1,000 .0001079 .0414081 -.1207875 .1166156
se_pscorex_R | 1,000 .041873 .0010361 .0387544 .0453656
-------------+---------------------------------------------------------
hit_pscore~R | 1,000 .952 .2138732 0 1
type = pscoreideodistrict
Variable | Obs Mean Std. Dev. Min Max
-------------+---------------------------------------------------------
b_pscore~t_D | 1,000 .0011411 .0389529 -.1209979 .135818
se_pscor~t_D | 1,000 .0394981 .0010252 .0365711 .0428945
hit_psco~t_D | 1,000 .956 .2051977 0 1
b_pscore~t_R | 1,000 -.0000255 .0389256 -.1365211 .1169339
se_pscor~t_R | 1,000 .0396001 .0009805 .0368675 .0428658
-------------+---------------------------------------------------------
hit_psco~t_R | 1,000 .949 .2201078 0 1
type = pscoreideoelected
Variable | Obs Mean Std. Dev. Min Max
-------------+---------------------------------------------------------
b_pscore~d_D | 1,000 .0009746 .0382667 -.1189793 .1366088
se_pscor~d_D | 1,000 .038707 .0010097 .0358897 .0423216
hit_psco~d_D | 1,000 .959 .1983894 0 1
b_pscore~d_R | 1,000 .0001725 .0384444 -.1271377 .1249728
se_pscor~d_R | 1,000 .0387925 .0009651 .0358581 .0419615
-------------+---------------------------------------------------------
hit_psco~d_R | 1,000 .941 .2357426 0 1
type = ols
Variable | Obs Mean Std. Dev. Min Max
-------------+---------------------------------------------------------
b_ols_D | 1,000 .0016925 .0436147 -.1204213 .1329182
se_ols_D | 1,000 .0444561 .0007614 .0424713 .0471137
hit_ols_D | 1,000 .96 .1960572 0 1
b_ols_R | 1,000 -.0000372 .0443401 -.1468209 .1455884
se_ols_R | 1,000 .0445274 .0007684 .0422679 .047389
-------------+---------------------------------------------------------
hit_ols_R | 1,000 .948 .2221381 0 1
command: rd_sim, beta_a(5) beta_b(5) kappa_ksi_R(0.4) kappa_ksi_D(0.4) kappa_u(1) tau1(-5) rho_x(0.6) nobs(10000) gamma2(0) gamma3(0) phi1_D(-1)
phi1_R(-1)
denstest_pval_~l: r(denstest_pval_all)
denstest_pval_D: r(denstest_pval_D)
denstest_pval_R: r(denstest_pval_R)
b_ideology_all: r(b_ideology_all)
se_ideology_all: r(se_ideology_all)
b_ideology_D: r(b_ideology_D)
se_ideology_D: r(se_ideology_D)
b_ideology_R: r(b_ideology_R)
se_ideology_R: r(se_ideology_R)
b_rd_all: r(b_rd_all)
se_rd_all: r(se_rd_all)
b_rd_D: r(b_rd_D)
se_rd_D: r(se_rd_D)
b_rd_R: r(b_rd_R)
se_rd_R: r(se_rd_R)
b_rdwt_all: r(b_rdwt_all)
se_rdwt_all: r(se_rdwt_all)
b_rdwt_D: r(b_rdwt_D)
se_rdwt_D: r(se_rdwt_D)
b_rdwt_R: r(b_rdwt_R)
se_rdwt_R: r(se_rdwt_R)
b_rdwtideodist~l: r(b_rdwtideodistrict_all)
se_rdwtideodis~l: r(se_rdwtideodistrict_all)
b_rdwtideodist~D: r(b_rdwtideodistrict_D)
se_rdwtideodis~D: r(se_rdwtideodistrict_D)
b_rdwtideodist~R: r(b_rdwtideodistrict_R)
se_rdwtideodis~R: r(se_rdwtideodistrict_R)
b_rdwtideoelec~l: r(b_rdwtideoelected_all)
se_rdwtideoele~l: r(se_rdwtideoelected_all)
b_rdwtideoelec~D: r(b_rdwtideoelected_D)
se_rdwtideoele~D: r(se_rdwtideoelected_D)
b_rdwtideoelec~R: r(b_rdwtideoelected_R)
se_rdwtideoele~R: r(se_rdwtideoelected_R)
b_pscorex_all: r(b_pscorex_all)
se_pscorex_all: r(se_pscorex_all)
b_pscorex_D: r(b_pscorex_D)
se_pscorex_D: r(se_pscorex_D)
b_pscorex_R: r(b_pscorex_R)
se_pscorex_R: r(se_pscorex_R)
b_pscoreideodi~l: r(b_pscoreideodistrict_all)
se_pscoreideod~l: r(se_pscoreideodistrict_all)
b_pscoreideodi~D: r(b_pscoreideodistrict_D)
se_pscoreideod~D: r(se_pscoreideodistrict_D)
b_pscoreideodi~R: r(b_pscoreideodistrict_R)
se_pscoreideod~R: r(se_pscoreideodistrict_R)
b_pscoreideoel~l: r(b_pscoreideoelected_all)
se_pscoreideoe~l: r(se_pscoreideoelected_all)
b_pscoreideoel~D: r(b_pscoreideoelected_D)
se_pscoreideoe~D: r(se_pscoreideoelected_D)
b_pscoreideoel~R: r(b_pscoreideoelected_R)
se_pscoreideoe~R: r(se_pscoreideoelected_R)
b_ols_all: r(b_ols_all)
se_ols_all: r(se_ols_all)
b_ols_D: r(b_ols_D)
se_ols_D: r(se_ols_D)
b_ols_R: r(b_ols_R)
se_ols_R: r(se_ols_R)
Simulations (1000)
----+--- 1 ---+--- 2 ---+--- 3 ---+--- 4 ---+--- 5
.................................................. 50
.................................................. 100
.................................................. 150
.................................................. 200
.................................................. 250
.................................................. 300
.................................................. 350
.................................................. 400
.................................................. 450
.................................................. 500
.................................................. 550
.................................................. 600
.................................................. 650
.................................................. 700
.................................................. 750
.................................................. 800
.................................................. 850
.................................................. 900
.................................................. 950
.................................................. 1000
file /Users/stefanogagliarducci/Dropbox/various/published/womenpart/EJ/3 replication package/Output/AppendixC_simulations_output/rd_simulations2.dta saved
type = rd
Variable | Obs Mean Std. Dev. Min Max
-------------+---------------------------------------------------------
b_rd_D | 1,000 .0012576 .2228617 -.7331437 1.009145
se_rd_D | 1,000 .2176755 .0255152 .1618408 .3236289
hit_rd_D | 1,000 .946 .2261308 0 1
b_rd_R | 1,000 .0330576 .243948 -.7533619 .8312749
se_rd_R | 1,000 .2278799 .0267695 .1654119 .3677094
-------------+---------------------------------------------------------
hit_rd_R | 1,000 .933 .2501471 0 1
type = rdwt
Variable | Obs Mean Std. Dev. Min Max
-------------+---------------------------------------------------------
b_rdwt_D | 1,000 .000469 .2242803 -.7151504 1.00985
se_rdwt_D | 1,000 .1968521 .0196471 .1512845 .2650954
hit_rdwt_D | 1,000 .922 .2683058 0 1
b_rdwt_R | 1,000 .0330745 .2432499 -.7427425 .8059599
se_rdwt_R | 1,000 .2059705 .020458 .1560483 .2731172
-------------+---------------------------------------------------------
hit_rdwt_R | 1,000 .902 .2974634 0 1
type = rdwtideodistrict
Variable | Obs Mean Std. Dev. Min Max
-------------+---------------------------------------------------------
b_rdwtid~t_D | 1,000 -.006175 .2212031 -.7455772 .9364876
se_rdwti~t_D | 1,000 .196769 .0196699 .1512361 .2634379
hit_rdwtid~D | 1,000 .93 .2552747 0 1
b_rdwtid~t_R | 1,000 .028441 .2407647 -.8496327 .8543036
se_rdwti~t_R | 1,000 .2060652 .0204876 .1575227 .2727789
-------------+---------------------------------------------------------
hit_rdwtid~R | 1,000 .91 .286325 0 1
type = rdwtideoelected
Variable | Obs Mean Std. Dev. Min Max
-------------+---------------------------------------------------------
b_rdwtid~d_D | 1,000 .0090432 .2212611 -.6531096 .982204
se_rdwti~d_D | 1,000 .1969396 .0196544 .1515357 .2642587
hit_rdwt~d_D | 1,000 .925 .2635231 0 1
b_rdwtid~d_R | 1,000 .0109837 .2401174 -.8600007 .7876767
se_rdwti~d_R | 1,000 .2062539 .0205667 .1576751 .2730821
-------------+---------------------------------------------------------
hit_rdwt~d_R | 1,000 .914 .2805043 0 1
type = pscorex
Variable | Obs Mean Std. Dev. Min Max
-------------+---------------------------------------------------------
b_pscorex_D | 1,000 -.0550926 .0353542 -.1679334 .0592917
se_pscorex_D | 1,000 .034824 .0006266 .0327949 .0370951
hit_pscore~D | 1,000 .632 .4825027 0 1
b_pscorex_R | 1,000 .0663517 .0536153 -.1293039 .2506798
se_pscorex_R | 1,000 .0543453 .0021536 .0482796 .0620071
-------------+---------------------------------------------------------
hit_pscore~R | 1,000 .774 .4184484 0 1
type = pscoreideodistrict
Variable | Obs Mean Std. Dev. Min Max
-------------+---------------------------------------------------------
b_pscore~t_D | 1,000 -.0138341 .0335508 -.1293545 .0784498
se_pscor~t_D | 1,000 .0330559 .0006057 .0309695 .0349889
hit_psco~t_D | 1,000 .926 .2619019 0 1
b_pscore~t_R | 1,000 .0170442 .051773 -.1677895 .2020657
se_pscor~t_R | 1,000 .0520056 .0021865 .0455246 .0609398
-------------+---------------------------------------------------------
hit_psco~t_R | 1,000 .938 .2412762 0 1
type = pscoreideoelected
Variable | Obs Mean Std. Dev. Min Max
-------------+---------------------------------------------------------
b_pscore~d_D | 1,000 .0006646 .0329737 -.1116275 .0922345
se_pscor~d_D | 1,000 .0324259 .0006012 .0302777 .034353
hit_psco~d_D | 1,000 .946 .2261308 0 1
b_pscore~d_R | 1,000 .0002771 .050832 -.1800453 .1714571
se_pscor~d_R | 1,000 .0511972 .0021817 .0452904 .0606324
-------------+---------------------------------------------------------
hit_psco~d_R | 1,000 .958 .2006895 0 1
type = ols
Variable | Obs Mean Std. Dev. Min Max
-------------+---------------------------------------------------------
b_ols_D | 1,000 -.1063805 .0375849 -.2240475 .0198816
se_ols_D | 1,000 .0369016 .0005005 .0353002 .0384732
hit_ols_D | 1,000 .186 .3893014 0 1
b_ols_R | 1,000 .1257383 .0563577 -.0464704 .3107925
se_ols_R | 1,000 .0573451 .0013656 .0529088 .0618195
-------------+---------------------------------------------------------
hit_ols_R | 1,000 .406 .4913302 0 1
command: rd_sim, beta_a(5) beta_b(5) kappa_ksi_R(0.4) kappa_ksi_D(0.4) kappa_u(1) tau1(-5) rho_x(0.6) nobs(10000) gamma2(.3) gamma3(.3) phi1_D(-1)
phi1_R(-1)
denstest_pval_~l: r(denstest_pval_all)
denstest_pval_D: r(denstest_pval_D)
denstest_pval_R: r(denstest_pval_R)
b_ideology_all: r(b_ideology_all)
se_ideology_all: r(se_ideology_all)
b_ideology_D: r(b_ideology_D)
se_ideology_D: r(se_ideology_D)
b_ideology_R: r(b_ideology_R)
se_ideology_R: r(se_ideology_R)
b_rd_all: r(b_rd_all)
se_rd_all: r(se_rd_all)
b_rd_D: r(b_rd_D)
se_rd_D: r(se_rd_D)
b_rd_R: r(b_rd_R)
se_rd_R: r(se_rd_R)
b_rdwt_all: r(b_rdwt_all)
se_rdwt_all: r(se_rdwt_all)
b_rdwt_D: r(b_rdwt_D)
se_rdwt_D: r(se_rdwt_D)
b_rdwt_R: r(b_rdwt_R)
se_rdwt_R: r(se_rdwt_R)
b_rdwtideodist~l: r(b_rdwtideodistrict_all)
se_rdwtideodis~l: r(se_rdwtideodistrict_all)
b_rdwtideodist~D: r(b_rdwtideodistrict_D)
se_rdwtideodis~D: r(se_rdwtideodistrict_D)
b_rdwtideodist~R: r(b_rdwtideodistrict_R)
se_rdwtideodis~R: r(se_rdwtideodistrict_R)
b_rdwtideoelec~l: r(b_rdwtideoelected_all)
se_rdwtideoele~l: r(se_rdwtideoelected_all)
b_rdwtideoelec~D: r(b_rdwtideoelected_D)
se_rdwtideoele~D: r(se_rdwtideoelected_D)
b_rdwtideoelec~R: r(b_rdwtideoelected_R)
se_rdwtideoele~R: r(se_rdwtideoelected_R)
b_pscorex_all: r(b_pscorex_all)
se_pscorex_all: r(se_pscorex_all)
b_pscorex_D: r(b_pscorex_D)
se_pscorex_D: r(se_pscorex_D)
b_pscorex_R: r(b_pscorex_R)
se_pscorex_R: r(se_pscorex_R)
b_pscoreideodi~l: r(b_pscoreideodistrict_all)
se_pscoreideod~l: r(se_pscoreideodistrict_all)
b_pscoreideodi~D: r(b_pscoreideodistrict_D)
se_pscoreideod~D: r(se_pscoreideodistrict_D)
b_pscoreideodi~R: r(b_pscoreideodistrict_R)
se_pscoreideod~R: r(se_pscoreideodistrict_R)
b_pscoreideoel~l: r(b_pscoreideoelected_all)
se_pscoreideoe~l: r(se_pscoreideoelected_all)
b_pscoreideoel~D: r(b_pscoreideoelected_D)
se_pscoreideoe~D: r(se_pscoreideoelected_D)
b_pscoreideoel~R: r(b_pscoreideoelected_R)
se_pscoreideoe~R: r(se_pscoreideoelected_R)
b_ols_all: r(b_ols_all)
se_ols_all: r(se_ols_all)
b_ols_D: r(b_ols_D)
se_ols_D: r(se_ols_D)
b_ols_R: r(b_ols_R)
se_ols_R: r(se_ols_R)
Simulations (1000)
----+--- 1 ---+--- 2 ---+--- 3 ---+--- 4 ---+--- 5
.................................................. 50
.................................................. 100
.................................................. 150
.................................................. 200
.................................................. 250
.................................................. 300
.................................................. 350
.................................................. 400
.................................................. 450
.................................................. 500
.................................................. 550
.................................................. 600
.................................................. 650
.................................................. 700
.................................................. 750
.................................................. 800
.................................................. 850
.................................................. 900
.................................................. 950
.................................................. 1000
file /Users/stefanogagliarducci/Dropbox/various/published/womenpart/EJ/3 replication package/Output/AppendixC_simulations_output/rd_simulations3.dta saved
type = rd
Variable | Obs Mean Std. Dev. Min Max
-------------+---------------------------------------------------------
b_rd_D | 1,000 .491752 .2494523 -.3531313 1.51067
se_rd_D | 1,000 .2283538 .0280412 .1690641 .3394082
hit_rd_D | 1,000 .405 .4911377 0 1
b_rd_R | 1,000 .5173595 .2292981 -.2692255 1.492157
se_rd_R | 1,000 .2289896 .0260837 .1711041 .3486938
-------------+---------------------------------------------------------
hit_rd_R | 1,000 .375 .4843652 0 1
type = rdwt
Variable | Obs Mean Std. Dev. Min Max
-------------+---------------------------------------------------------
b_rdwt_D | 1,000 .4388141 .2610048 -.5014127 1.687006
se_rdwt_D | 1,000 .2074678 .0232727 .1568016 .2954484
hit_rdwt_D | 1,000 .437 .4962633 0 1
b_rdwt_R | 1,000 .4646391 .2379002 -.2868821 1.428946
se_rdwt_R | 1,000 .2171307 .0218101 .1686809 .3166831
-------------+---------------------------------------------------------
hit_rdwt_R | 1,000 .429 .495181 0 1
type = rdwtideodistrict
Variable | Obs Mean Std. Dev. Min Max
-------------+---------------------------------------------------------
b_rdwtid~t_D | 1,000 .0013756 .4101497 -2.066836 2.024154
se_rdwti~t_D | 1,000 .203602 .0271368 .1319862 .3706861
hit_rdwtid~D | 1,000 .705 .456271 0 1
b_rdwtid~t_R | 1,000 .11661 .3633796 -1.787883 1.752881
se_rdwti~t_R | 1,000 .230485 .025827 .1736978 .3815075
-------------+---------------------------------------------------------
hit_rdwtid~R | 1,000 .779 .4151281 0 1
type = rdwtideoelected
Variable | Obs Mean Std. Dev. Min Max
-------------+---------------------------------------------------------
b_rdwtid~d_D | 1,000 .0325854 .3558411 -1.752627 1.752868
se_rdwti~d_D | 1,000 .2076113 .0264597 .1428789 .3456056
hit_rdwt~d_D | 1,000 .78 .4144536 0 1
b_rdwtid~d_R | 1,000 .0705305 .3363157 -1.379199 1.892265
se_rdwti~d_R | 1,000 .2250768 .0251694 .1710656 .3803324
-------------+---------------------------------------------------------
hit_rdwt~d_R | 1,000 .82 .3843797 0 1
type = pscorex
Variable | Obs Mean Std. Dev. Min Max
-------------+---------------------------------------------------------
b_pscorex_D | 1,000 .1519819 .0346478 .0358244 .2736447
se_pscorex_D | 1,000 .0338529 .0006006 .0321172 .0360062
hit_pscore~D | 1,000 .007 .0834144 0 1
b_pscorex_R | 1,000 .2756086 .0518065 .1026839 .4199368
se_pscorex_R | 1,000 .0503297 .0018679 .0446273 .057132
-------------+---------------------------------------------------------
hit_pscore~R | 1,000 0 0 0 0
type = pscoreideodistrict
Variable | Obs Mean Std. Dev. Min Max
-------------+---------------------------------------------------------
b_pscore~t_D | 1,000 -.0714853 .0360254 -.1921055 .0466862
se_pscor~t_D | 1,000 .0359764 .0015933 .0323575 .0454675
hit_psco~t_D | 1,000 .487 .5000811 0 1
b_pscore~t_R | 1,000 -.0686628 .0793611 -.3470891 .156843
se_pscor~t_R | 1,000 .076294 .015689 .0545845 .1807837
-------------+---------------------------------------------------------
hit_psco~t_R | 1,000 .869 .3375692 0 1
type = pscoreideoelected
Variable | Obs Mean Std. Dev. Min Max
-------------+---------------------------------------------------------
b_pscore~d_D | 1,000 -.0001303 .032578 -.1045127 .108526
se_pscor~d_D | 1,000 .0323971 .000756 .0304841 .0350485
hit_psco~d_D | 1,000 .944 .2300368 0 1
b_pscore~d_R | 1,000 -.0178836 .0604781 -.2088141 .1564328
se_pscor~d_R | 1,000 .0605834 .0081455 .0476141 .1530132
-------------+---------------------------------------------------------
hit_psco~d_R | 1,000 .945 .2280943 0 1
type = ols
Variable | Obs Mean Std. Dev. Min Max
-------------+---------------------------------------------------------
b_ols_D | 1,000 .0217441 .035059 -.0793685 .1615416
se_ols_D | 1,000 .0348545 .0004235 .0336369 .036255
hit_ols_D | 1,000 .908 .2891706 0 1
b_ols_R | 1,000 .2789744 .0533577 .1072924 .4430476
se_ols_R | 1,000 .0517018 .0010793 .0486151 .0550177
-------------+---------------------------------------------------------
hit_ols_R | 1,000 0 0 0 0
command: rd_sim, beta_a(5) beta_b(5) kappa_ksi_R(0.4) kappa_ksi_D(0.4) kappa_u(1) tau1(-5) rho_x(0.6) nobs(10000) gamma2(.6) gamma3(0) phi1_D(-1)
phi1_R(-1)
denstest_pval_~l: r(denstest_pval_all)
denstest_pval_D: r(denstest_pval_D)
denstest_pval_R: r(denstest_pval_R)
b_ideology_all: r(b_ideology_all)
se_ideology_all: r(se_ideology_all)
b_ideology_D: r(b_ideology_D)
se_ideology_D: r(se_ideology_D)
b_ideology_R: r(b_ideology_R)
se_ideology_R: r(se_ideology_R)
b_rd_all: r(b_rd_all)
se_rd_all: r(se_rd_all)
b_rd_D: r(b_rd_D)
se_rd_D: r(se_rd_D)
b_rd_R: r(b_rd_R)
se_rd_R: r(se_rd_R)
b_rdwt_all: r(b_rdwt_all)
se_rdwt_all: r(se_rdwt_all)
b_rdwt_D: r(b_rdwt_D)
se_rdwt_D: r(se_rdwt_D)
b_rdwt_R: r(b_rdwt_R)
se_rdwt_R: r(se_rdwt_R)
b_rdwtideodist~l: r(b_rdwtideodistrict_all)
se_rdwtideodis~l: r(se_rdwtideodistrict_all)
b_rdwtideodist~D: r(b_rdwtideodistrict_D)
se_rdwtideodis~D: r(se_rdwtideodistrict_D)
b_rdwtideodist~R: r(b_rdwtideodistrict_R)
se_rdwtideodis~R: r(se_rdwtideodistrict_R)
b_rdwtideoelec~l: r(b_rdwtideoelected_all)
se_rdwtideoele~l: r(se_rdwtideoelected_all)
b_rdwtideoelec~D: r(b_rdwtideoelected_D)
se_rdwtideoele~D: r(se_rdwtideoelected_D)
b_rdwtideoelec~R: r(b_rdwtideoelected_R)
se_rdwtideoele~R: r(se_rdwtideoelected_R)
b_pscorex_all: r(b_pscorex_all)
se_pscorex_all: r(se_pscorex_all)
b_pscorex_D: r(b_pscorex_D)
se_pscorex_D: r(se_pscorex_D)
b_pscorex_R: r(b_pscorex_R)
se_pscorex_R: r(se_pscorex_R)
b_pscoreideodi~l: r(b_pscoreideodistrict_all)
se_pscoreideod~l: r(se_pscoreideodistrict_all)
b_pscoreideodi~D: r(b_pscoreideodistrict_D)
se_pscoreideod~D: r(se_pscoreideodistrict_D)
b_pscoreideodi~R: r(b_pscoreideodistrict_R)
se_pscoreideod~R: r(se_pscoreideodistrict_R)
b_pscoreideoel~l: r(b_pscoreideoelected_all)
se_pscoreideoe~l: r(se_pscoreideoelected_all)
b_pscoreideoel~D: r(b_pscoreideoelected_D)
se_pscoreideoe~D: r(se_pscoreideoelected_D)
b_pscoreideoel~R: r(b_pscoreideoelected_R)
se_pscoreideoe~R: r(se_pscoreideoelected_R)
b_ols_all: r(b_ols_all)
se_ols_all: r(se_ols_all)
b_ols_D: r(b_ols_D)
se_ols_D: r(se_ols_D)
b_ols_R: r(b_ols_R)
se_ols_R: r(se_ols_R)
Simulations (1000)
----+--- 1 ---+--- 2 ---+--- 3 ---+--- 4 ---+--- 5
.................................................. 50
.................................................. 100
.................................................. 150
.................................................. 200
.................................................. 250
.................................................. 300
.................................................. 350
.................................................. 400
.................................................. 450
.................................................. 500
.................................................. 550
.................................................. 600
.................................................. 650
.................................................. 700
.................................................. 750
.................................................. 800
.................................................. 850
.................................................. 900
.................................................. 950
.................................................. 1000
file /Users/stefanogagliarducci/Dropbox/various/published/womenpart/EJ/3 replication package/Output/AppendixC_simulations_output/rd_simulations4.dta saved
type = rd
Variable | Obs Mean Std. Dev. Min Max
-------------+---------------------------------------------------------
b_rd_D | 1,000 .4820324 .2371305 -.3588133 1.248664
se_rd_D | 1,000 .2197392 .0262706 .1618011 .3616504
hit_rd_D | 1,000 .389 .4877673 0 1
b_rd_R | 1,000 .5221546 .2740097 -.7637966 1.467739
se_rd_R | 1,000 .257724 .0312845 .1895249 .435447
-------------+---------------------------------------------------------
hit_rd_R | 1,000 .457 .4983968 0 1
type = rdwt
Variable | Obs Mean Std. Dev. Min Max
-------------+---------------------------------------------------------
b_rdwt_D | 1,000 .430918 .2455162 -.6153042 1.289431
se_rdwt_D | 1,000 .2067505 .0224016 .1520135 .3382523
hit_rdwt_D | 1,000 .445 .4972145 0 1
b_rdwt_R | 1,000 .4733341 .2820927 -.7657607 1.432925
se_rdwt_R | 1,000 .2504693 .0251529 .1840224 .3487194
-------------+---------------------------------------------------------
hit_rdwt_R | 1,000 .497 .5002412 0 1
type = rdwtideodistrict
Variable | Obs Mean Std. Dev. Min Max
-------------+---------------------------------------------------------
b_rdwtid~t_D | 1,000 .0175561 .3773986 -1.54672 1.429458
se_rdwti~t_D | 1,000 .2034587 .0255179 .151171 .3283263
hit_rdwtid~D | 1,000 .723 .4477404 0 1
b_rdwtid~t_R | 1,000 .1003379 .3997651 -1.323882 1.491106
se_rdwti~t_R | 1,000 .2610874 .0296607 .1934556 .382313
-------------+---------------------------------------------------------
hit_rdwtid~R | 1,000 .798 .4016931 0 1
type = rdwtideoelected
Variable | Obs Mean Std. Dev. Min Max
-------------+---------------------------------------------------------
b_rdwtid~d_D | 1,000 .0435866 .3157486 -1.321281 1.114188
se_rdwti~d_D | 1,000 .2070375 .0249135 .1524316 .3418367
hit_rdwt~d_D | 1,000 .819 .3852108 0 1
b_rdwtid~d_R | 1,000 .0720104 .3748857 -1.244581 1.60306
se_rdwti~d_R | 1,000 .257405 .0284439 .1913853 .3689896
-------------+---------------------------------------------------------
hit_rdwt~d_R | 1,000 .819 .3852108 0 1
type = pscorex
Variable | Obs Mean Std. Dev. Min Max
-------------+---------------------------------------------------------
b_pscorex_D | 1,000 .3748557 .0352603 .2518727 .5018864
se_pscorex_D | 1,000 .0347588 .001049 .0321233 .0393144
hit_pscore~D | 1,000 0 0 0 0
b_pscorex_R | 1,000 .0688194 .0572938 -.0997917 .2288021
se_pscorex_R | 1,000 .0579885 .0024276 .0505837 .0686423
-------------+---------------------------------------------------------
hit_pscore~R | 1,000 .792 .4060799 0 1
type = pscoreideodistrict
Variable | Obs Mean Std. Dev. Min Max
-------------+---------------------------------------------------------
b_pscore~t_D | 1,000 -.0609244 .0622255 -.466482 .1154791
se_pscor~t_D | 1,000 .0582026 .0183329 .0385205 .3128201
hit_psco~t_D | 1,000 .802 .3986916 0 1
b_pscore~t_R | 1,000 .0148012 .0548574 -.134412 .1764981
se_pscor~t_R | 1,000 .0553551 .0025051 .0480639 .0661574
-------------+---------------------------------------------------------
hit_psco~t_R | 1,000 .942 .2338604 0 1
type = pscoreideoelected
Variable | Obs Mean Std. Dev. Min Max
-------------+---------------------------------------------------------
b_pscore~d_D | 1,000 .0200114 .0447027 -.1660888 .141097
se_pscor~d_D | 1,000 .0436844 .0076024 .0329284 .119063
hit_psco~d_D | 1,000 .911 .2848862 0 1
b_pscore~d_R | 1,000 -.0019193 .0540542 -.1474438 .1564901
se_pscor~d_R | 1,000 .0544823 .0025367 .0468632 .0659097
-------------+---------------------------------------------------------
hit_psco~d_R | 1,000 .951 .215976 0 1
type = ols
Variable | Obs Mean Std. Dev. Min Max
-------------+---------------------------------------------------------
b_ols_D | 1,000 .1312602 .0340315 .0222765 .2691881
se_ols_D | 1,000 .0330522 .0003831 .0318416 .0342806
hit_ols_D | 1,000 .024 .1531256 0 1
b_ols_R | 1,000 .1232987 .0598521 -.0781653 .3008979
se_ols_R | 1,000 .0610299 .0015038 .0564883 .0665193
-------------+---------------------------------------------------------
hit_ols_R | 1,000 .477 .4997206 0 1
.
.
.
. ********************************************************************************
. ********************************************************************************
. ********************************************************************************
.
. * From here on, display results
. clear
.
. foreach j of numlist 1 2 4 {
2. if `j'<=2 {
3. local jtex = `j'
4. }
5. else if `j'==4 {
6. local jtex = `j'-1
7. }
8. use "$AppendixC_simulations/rd_simulations`j'.dta", clear
9. foreach type in "ideology" "rd" "rdwt" "rdwtideodistrict" "rdwtideoelected" "pscorex" "pscoreideodistrict" "pscoreideoelected" "ols" {
10. di ""
11. di ""
12. di in ye "type = `type'"
13. foreach party in "all" "D" "R" {
14. gen byte rejrate_`type'_`party' = b_`type'_`party' - 1.96*se_`type'_`party'>0 | /*
> */ b_`type'_`party' + 1.96*se_`type'_`party'<0
15. }
16. }
17. foreach party in "all" "D" "R" {
18. gen denstest_reject_`party' = denstest_pval_`party'<0.05
19. }
20.
. foreach esttype in "ideology" "rd" "rdwt" "rdwtideodistrict" "rdwtideoelected" "pscorex" "pscoreideodistrict" "pscoreideoelected" "ols"{
21. ren b_`esttype'_all `esttype'1
22. ren rejrate_`esttype'_all `esttype'2
23. ren b_`esttype'_D `esttype'3
24. ren rejrate_`esttype'_D `esttype'4
25. ren b_`esttype'_R `esttype'5
26. ren rejrate_`esttype'_R `esttype'6
27. }
28.
. ren denstest_pval_all denstest1
29. ren denstest_reject_all denstest2
30. ren denstest_pval_D denstest3
31. ren denstest_reject_D denstest4
32. ren denstest_pval_R denstest5
33. ren denstest_reject_R denstest6
34.
. gen iterno=_n
35. drop se_*
36. reshape long denstest ideology rd rdwt rdwtideodistrict rdwtideoelected pscorex pscoreideodistrict pscoreideoelected ols, i(iterno) j(stype)
37. label def stype 1 "All - estimate" 2 "All - Rej.Rate" 3 "D - estimate" 4 "D - Rej.Rate" 5 "R - estimate" 6 "R - Rej.Rate"
38. label value stype stype
39.
. label var denstest "Density test (p-value)"
40. label var ideology "Discontinuity in ideology"
41. label var rd "RD - simple"
42. label var rdwt "P-score weighted RD - x"
43. label var rdwtideodistrict "P-score weighted RD - district ideology"
44. label var rdwtideoelected "P-score weighted RD - ideology of elected representative"
45. label var pscorex "P-score weighted, x"
46. label var pscoreideodistrict "P-score weighted, district ideology"
47. label var pscoreideoelected "P-score weighted, ideology of elected representative"
48. label var ols "OLS"
49.
. estpost tabstat denstest ideology rd rdwt rdwtideodistrict rdwtideoelected /*
> */ pscorex pscoreideodistrict pscoreideoelected ols, by(stype) column(statistics) nototal
50. esttab using "$AppendixC_simulations/results_AppendixTableC`jtex'.tex", main(mean %6.3f) unstack tex replace noobs nomtitles nonumbers nonotes label
51. }
(Baseline - Everything Balanced)
type = ideology
type = rd
type = rdwt
type = rdwtideodistrict
type = rdwtideoelected
type = pscorex
type = pscoreideodistrict
type = pscoreideoelected
type = ols
(note: j = 1 2 3 4 5 6)
Data wide -> long
-----------------------------------------------------------------------------
Number of obs. 1000 -> 6000
Number of variables 65 -> 16
j variable (6 values) -> stype
xij variables:
denstest1 denstest2 ... denstest6 -> denstest
ideology1 ideology2 ... ideology6 -> ideology
rd1 rd2 ... rd6 -> rd
rdwt1 rdwt2 ... rdwt6 -> rdwt
rdwtideodistrict1 rdwtideodistrict2 ... rdwtideodistrict6->rdwtideodistrict
rdwtideoelected1 rdwtideoelected2 ... rdwtideoelected6->rdwtideoelected
pscorex1 pscorex2 ... pscorex6 -> pscorex
pscoreideodistrict1 pscoreideodistrict2 ... pscoreideodistrict6->pscoreideodistrict
pscoreideoelected1 pscoreideoelected2 ... pscoreideoelected6->pscoreideoelected
ols1 ols2 ... ols6 -> ols
-----------------------------------------------------------------------------
Summary statistics: mean
for variables: denstest ideology rd rdwt rdwtideodistrict rdwtideoelected pscorex pscoreideodistrict pscoreideoelected ols
by categories of: stype
stype | e(mean)
-------------+-----------
1 |
denstest | .5455671
ideology | .0001327
rd | .0024096
rdwt | .0026502
rdwtideodi~t | .0025334
rdwtideoel~d | .0024999
pscorex | .0009188
pscoreideo~t | .0009269
pscoreideo~d | .000916
ols | .0008537
-------------+-----------
2 |
denstest | .031
ideology | .055
rd | .059
rdwt | .056
rdwtideodi~t | .054
rdwtideoel~d | .059
pscorex | .053
pscoreideo~t | .052
pscoreideo~d | .053
ols | .046
-------------+-----------
3 |
denstest | .5292896
ideology | -.0008158
rd | .000474
rdwt | .000563
rdwtideodi~t | .0007777
rdwtideoel~d | -.0000482
pscorex | .0016244
pscoreideo~t | .0011411
pscoreideo~d | .0009746
ols | .0016925
-------------+-----------
4 |
denstest | .031
ideology | .057
rd | .052
rdwt | .048
rdwtideodi~t | .045
rdwtideoel~d | .046
pscorex | .036
pscoreideo~t | .044
pscoreideo~d | .041
ols | .04
-------------+-----------
5 |
denstest | .5135349
ideology | .0007091
rd | .0061781
rdwt | .0063396
rdwtideodi~t | .0070163
rdwtideoel~d | .0071389
pscorex | .0001079
pscoreideo~t | -.0000255
pscoreideo~d | .0001725
ols | -.0000372
-------------+-----------
6 |
denstest | .032
ideology | .059
rd | .056
rdwt | .051
rdwtideodi~t | .05
rdwtideoel~d | .05
pscorex | .048
pscoreideo~t | .051
pscoreideo~d | .059
ols | .052
category labels saved in macro e(labels)
(output written to /Users/stefanogagliarducci/Dropbox/various/published/womenpart/EJ/3 replication package/Output/AppendixC_simulations_output/results_AppendixTableC
> 1.tex)
(Variant 1 - Women are more left-wing, no preference for female candidates)
type = ideology
type = rd
type = rdwt
type = rdwtideodistrict
type = rdwtideoelected
type = pscorex
type = pscoreideodistrict
type = pscoreideoelected
type = ols
(note: j = 1 2 3 4 5 6)
Data wide -> long
-----------------------------------------------------------------------------
Number of obs. 1000 -> 6000
Number of variables 65 -> 16
j variable (6 values) -> stype
xij variables:
denstest1 denstest2 ... denstest6 -> denstest
ideology1 ideology2 ... ideology6 -> ideology
rd1 rd2 ... rd6 -> rd
rdwt1 rdwt2 ... rdwt6 -> rdwt
rdwtideodistrict1 rdwtideodistrict2 ... rdwtideodistrict6->rdwtideodistrict
rdwtideoelected1 rdwtideoelected2 ... rdwtideoelected6->rdwtideoelected
pscorex1 pscorex2 ... pscorex6 -> pscorex
pscoreideodistrict1 pscoreideodistrict2 ... pscoreideodistrict6->pscoreideodistrict
pscoreideoelected1 pscoreideoelected2 ... pscoreideoelected6->pscoreideoelected
ols1 ols2 ... ols6 -> ols
-----------------------------------------------------------------------------
Summary statistics: mean
for variables: denstest ideology rd rdwt rdwtideodistrict rdwtideoelected pscorex pscoreideodistrict pscoreideoelected ols
by categories of: stype
stype | e(mean)
-------------+-----------
1 |
denstest | .526024
ideology | -.0004892
rd | -.0180679
rdwt | -.0177774
rdwtideodi~t | -.0186809
rdwtideoel~d | .0115203
pscorex | .0054515
pscoreideo~t | -.0098912
pscoreideo~d | .0004067
ols | -.0367354
-------------+-----------
2 |
denstest | .033
ideology | .052
rd | .051
rdwt | .049
rdwtideodi~t | .048
rdwtideoel~d | .082
pscorex | .053
pscoreideo~t | .069
pscoreideo~d | .05
ols | .228
-------------+-----------
3 |
denstest | .0253585
ideology | .0030011
rd | .0012576
rdwt | .000469
rdwtideodi~t | -.006175
rdwtideoel~d | .0090432
pscorex | -.0550926
pscoreideo~t | -.0138341
pscoreideo~d | .0006646
ols | -.1063805
-------------+-----------
4 |
denstest | .889
ideology | .07
rd | .054
rdwt | .078
rdwtideodi~t | .07
rdwtideoel~d | .075
pscorex | .368
pscoreideo~t | .074
pscoreideo~d | .054
ols | .814
-------------+-----------
5 |
denstest | .0245342
ideology | -.0022047
rd | .0330576
rdwt | .0330745
rdwtideodi~t | .028441
rdwtideoel~d | .0109837
pscorex | .0663517
pscoreideo~t | .0170442
pscoreideo~d | .0002771
ols | .1257383
-------------+-----------
6 |
denstest | .901
ideology | .058
rd | .067
rdwt | .098
rdwtideodi~t | .09
rdwtideoel~d | .086
pscorex | .226
pscoreideo~t | .062
pscoreideo~d | .042
ols | .594
category labels saved in macro e(labels)
(output written to /Users/stefanogagliarducci/Dropbox/various/published/womenpart/EJ/3 replication package/Output/AppendixC_simulations_output/results_AppendixTableC
> 2.tex)
(Variant 3 - Women are more left-wing, only Ds prefer female candidates)
type = ideology
type = rd
type = rdwt
type = rdwtideodistrict
type = rdwtideoelected
type = pscorex
type = pscoreideodistrict
type = pscoreideoelected
type = ols
(note: j = 1 2 3 4 5 6)
Data wide -> long
-----------------------------------------------------------------------------
Number of obs. 1000 -> 6000
Number of variables 65 -> 16
j variable (6 values) -> stype
xij variables:
denstest1 denstest2 ... denstest6 -> denstest
ideology1 ideology2 ... ideology6 -> ideology
rd1 rd2 ... rd6 -> rd
rdwt1 rdwt2 ... rdwt6 -> rdwt
rdwtideodistrict1 rdwtideodistrict2 ... rdwtideodistrict6->rdwtideodistrict
rdwtideoelected1 rdwtideoelected2 ... rdwtideoelected6->rdwtideoelected
pscorex1 pscorex2 ... pscorex6 -> pscorex
pscoreideodistrict1 pscoreideodistrict2 ... pscoreideodistrict6->pscoreideodistrict
pscoreideoelected1 pscoreideoelected2 ... pscoreideoelected6->pscoreideoelected
ols1 ols2 ... ols6 -> ols
-----------------------------------------------------------------------------
Summary statistics: mean
for variables: denstest ideology rd rdwt rdwtideodistrict rdwtideoelected pscorex pscoreideodistrict pscoreideoelected ols
by categories of: stype
stype | e(mean)
-------------+-----------
1 |
denstest | .516915
ideology | .000854
rd | .6216279
rdwt | .6209797
rdwtideodi~t | .620556
rdwtideoel~d | .5385951
pscorex | .3820008
pscoreideo~t | .3955427
pscoreideo~d | .3258178
ols | .1589017
-------------+-----------
2 |
denstest | .047
ideology | .052
rd | .961
rdwt | .961
rdwtideodi~t | .958
rdwtideoel~d | .897
pscorex | 1
pscoreideo~t | 1
pscoreideo~d | 1
ols | 1
-------------+-----------
3 |
denstest | .2114761
ideology | .2113024
rd | .4820324
rdwt | .430918
rdwtideodi~t | .0175561
rdwtideoel~d | .0435866
pscorex | .3748557
pscoreideo~t | -.0609244
pscoreideo~d | .0200114
ols | .1312602
-------------+-----------
4 |
denstest | .359
ideology | 1
rd | .611
rdwt | .555
rdwtideodi~t | .277
rdwtideoel~d | .181
pscorex | 1
pscoreideo~t | .198
pscoreideo~d | .089
ols | .976
-------------+-----------
5 |
denstest | .1600951
ideology | -.209657
rd | .5221546
rdwt | .4733341
rdwtideodi~t | .1003379
rdwtideoel~d | .0720104
pscorex | .0688194
pscoreideo~t | .0148012
pscoreideo~d | -.0019193
ols | .1232987
-------------+-----------
6 |
denstest | .434
ideology | 1
rd | .543
rdwt | .503
rdwtideodi~t | .202
rdwtideoel~d | .181
pscorex | .208
pscoreideo~t | .058
pscoreideo~d | .049
ols | .523
category labels saved in macro e(labels)
(output written to /Users/stefanogagliarducci/Dropbox/various/published/womenpart/EJ/3 replication package/Output/AppendixC_simulations_output/results_AppendixTableC
> 3.tex)
.
.
end of do-file
.
.
.
. program error: matching close brace not found
r(198);
end of do-file
r(198);
. exit, clear
|