DATA, PROGRAMS, AND EXPANDED DESCRIPTION OF THE RESULTS FROM MARVELL & MOODY, "THE IMPACT OF OUT-OF-STATE PRISON POPULATION ON STATE HOMICIDE RATES: DISPLACEMENT AND FREE-RIDER EFFECTS." Criminology, August, 1998. Thomas B. Marvell, Justec Research, 155 Ridings Cove, Williamsburg, VA 23185 757 229 9772 (fax) 220 2462 tmarvell@erols.com Carlisle Moody, Department of Economics, College of William and Mary, Williamsburg, VA 23185. 757 221 2373 (fax) 221 2390 cemood@morton.wm.edu Please feel free to contact us if you have questions. This document contains 1) STATE DATA 2) NATIONAL DATA 3) PROGRAMS FOR CREATING SAS DATA SETS 4) PROGRAM FOR THE STATE-LEVEL REGRESSION 5) PROGRAM FOR THE REGION-LEVEL REGRESSION 6) PROGRAM FOR THE IMPACT OF NEARBY STATES 7) EXPANDED RESULTS SECTION 1) STATE DATA contains the state level data. Missing data are periods. The variables are: YEAR year POP state population (in 1000's) PRCYE prison population at year's end VTMUR homicides, from vital statistics TPI total personal income P15T17 persons 15 to 17 years old (in 1000's) P18T24 persons 18 to 24 years old (in 1000's) P25T34 persons 25 to 34 years old (in 1000's) EXEC executions 2) NATIONAL DATA contains national level data. YEAR year POPUS population PRUS nationwide state prison population at year's end MILPER number of persons in the military UNRATE unemployment rate CPI consumer price index (1967 = 1). 3) contains two programs that can be used to create a SAS data set. 4) BYSTATE PROGRAM is a SAS program for the regressions summarized in Table 1 and in Table 3, part 2. 5) BYREGION REGION is a SAS program for the regressions in Table 2. 6) NEARBY STATE PROGRAM is a SAS program for the regressions summarized in Table 3, parts 1 and 3. 7) EXPANDED RESULTS describes the regression results in more detail than that given in the Criminology article. The regression results using these data and programs differ slightly from the results reported in the article because the population age variables are rounded in STATE.DAT (in the original data set the age variables on are taken to three decimal places, but only starting in 1970). 1) STATE DATA 1 27 . 3152 . . . . . 6 1 28 . 3674 . . . . . 5 1 29 2644 . 513 843884 172 377 353 3 1 30 2647 . 523 694941 173 382 351 5 1 31 2649 . 601 582234 171 380 356 4 1 32 2653 . 581 421901 170 380 361 4 1 33 2661 . 671 435392 169 379 368 3 1 34 2685 . 713 557785 170 380 377 5 1 35 2719 . 611 582693 172 383 387 2 1 36 2743 . 622 681776 174 383 395 17 1 37 2762 . 577 728658 175 382 403 4 1 38 2787 . 529 669373 176 383 412 9 1 39 2814 7248 505 698109 176 386 420 10 1 40 2845 6446 466 788651 174 390 429 9 1 41 2902 5166 422 1073631 172 394 436 4 1 42 2950 4096 381 1511434 170 396 443 9 1 43 2887 4036 304 1878662 163 382 433 5 1 44 2799 3044 324 2051291 155 364 420 2 1 45 2770 3646 334 2159259 149 356 415 3 1 46 2906 3948 411 2179308 151 369 434 10 1 47 2944 4432 450 2353932 151 366 439 2 1 48 2969 4679 451 2619109 147 365 441 2 1 49 3000 5036 422 2500149 143 364 444 5 1 50 3058 4454 427 2782749 142 365 450 4 1 51 3059 4416 355 3193008 144 359 444 0 1 52 3068 4646 400 3383545 146 353 438 4 1 53 3053 4879 364 3539199 147 345 428 2 1 54 3014 5255 365 3425020 146 333 416 4 1 55 3050 5222 381 3870601 150 331 413 0 1 56 3071 5407 351 4146284 152 326 406 1 1 57 3109 5224 363 4393770 156 323 400 2 1 58 3163 5543 352 4608036 165 317 398 1 1 59 3204 5449 319 4859969 167 316 392 2 1 60 3274 5369 335 5057751 168 320 392 1 1 61 3316 5540 329 5210248 165 340 391 1 1 62 3323 5521 356 5478191 177 339 386 1 1 63 3358 5083 355 5838286 187 343 386 0 1 64 3395 4586 368 6337981 199 348 388 1 1 65 3443 4377 405 6911716 192 372 392 1 1 66 3464 4056 373 7430868 192 386 396 0 1 67 3458 3881 396 7856397 192 395 398 0 1 68 3446 4017 429 8579497 196 400 404 0 1 69 3440 4140 436 9413187 198 407 409 0 1 70 3450 3790 438 10210439 200 417 414 0 1 71 3497 3823 506 11155413 220 432 416 0 1 72 3540 3632 490 12381420 225 439 439 0 1 73 3581 3693 507 14008856 227 451 460 0 1 74 3628 4259 544 15563657 229 462 483 0 1 75 3681 4420 581 17278117 230 476 506 0 1 76 3737 5192 519 19515378 231 491 531 0 1 77 3783 6096 533 21566061 230 500 553 0 1 78 3834 5529 502 24461449 230 508 570 0 1 79 3869 5464 540 27332354 227 511 587 0 1 80 3903 6368 605 30127696 222 516 603 0 1 81 3918 7199 555 33375283 214 519 621 0 1 82 3925 8581 502 35074714 204 517 622 0 1 83 3934 9641 413 37377474 195 513 628 1 1 84 3952 10246 423 41132600 192 505 636 0 1 85 3973 10749 448 44234912 194 493 646 0 1 86 3992 11504 461 46929752 200 478 654 1 1 87 4016 12602 455 49772196 201 466 660 1 1 88 4024 12357 466 53443563 196 457 658 0 1 89 4030 13575 514 57511576 187 450 653 4 1 90 4048 15365 551 61637066 180 448 652 1 1 91 4087 16400 580 65137162 179 448 646 0 1 92 4131 16938 541 69807198 181 450 640 2 2 27 . . . . . . . 0 2 28 . . . . . . . 0 2 29 0 . . . . . . 1 2 30 0 . . . . . . 0 2 31 0 . . . . . . 0 2 32 0 . . . . . . 0 2 33 0 . . . . . . 0 2 34 0 . . . . . . 0 2 35 0 . . . . . . 0 2 36 0 . . . . . . 0 2 37 0 . . . . . . 0 2 38 0 . . . . . . 0 2 39 0 . . . . . . 1 2 40 0 . . . . . . 0 2 41 0 . . . . . . 0 2 42 0 . . . . . . 0 2 43 0 . . . . . . 0 2 44 0 . . . . . . 0 2 45 0 . . . . . . 0 2 46 0 . . . . . . 0 2 47 0 . . . . . . 0 2 48 0 . . . . . . 1 2 49 0 . . . . . . 0 2 50 135 . . 310503 . . . 1 2 51 158 . . 430194 . . . 0 2 52 189 . . 470142 . . . 0 2 53 205 . . 494050 . . . 0 2 54 215 . . 475386 . . . 0 2 55 222 . . 484736 . . . 0 2 56 224 . . 529666 . . . 0 2 57 231 . . 517703 . . . 0 2 58 224 . . 508793 . . . 0 2 59 224 . 11 546239 . . . 0 2 60 229 . 20 644768 14 27 36 0 2 61 238 . 23 645110 14 29 36 0 2 62 246 . 10 677294 16 30 37 0 2 63 256 . 22 736592 17 31 38 0 2 64 263 . 25 831698 18 32 39 0 2 65 271 . 24 903077 18 35 40 0 2 66 271 . 26 979416 18 36 40 0 2 67 278 . 26 1080853 18 37 41 0 2 68 285 . 27 1175667 19 39 42 0 2 69 296 . 29 1374143 20 41 45 0 2 70 304 . 31 1543123 20 42 46 0 2 71 316 191 27 1682135 19 46 53 0 2 72 326 183 32 1839538 20 45 57 0 2 73 333 174 27 2159219 21 47 60 0 2 74 345 175 35 2664537 22 49 64 0 2 75 371 207 27 3745222 24 56 71 0 2 76 393 255 53 4503230 25 62 76 0 2 77 397 419 44 4631797 25 64 79 0 2 78 402 490 60 4722485 25 63 82 0 2 79 399 532 52 4992448 24 58 87 0 2 80 405 571 34 5610523 22 59 92 0 2 81 418 713 53 6488143 22 58 98 0 2 82 450 872 58 7761312 22 59 106 0 2 83 488 1072 54 8757903 23 61 115 0 2 84 514 1293 50 9271138 24 62 119 0 2 85 533 1530 41 10054139 25 62 120 0 2 86 544 1666 43 10042945 26 62 118 0 2 87 539 1767 48 9704370 26 60 113 0 2 88 542 1862 29 9980834 24 58 113 0 2 89 547 1908 34 10902659 23 58 113 0 2 90 553 1851 31 11641891 23 56 112 0 2 91 569 1840 45 12271337 24 56 110 0 2 92 587 1944 41 12924836 25 58 108 0 3 27 . 483 . . . . . 1 3 28 . 534 . . . . . 4 3 29 430 509 67 254188 25 54 69 0 3 30 434 539 52 222041 25 55 68 2 3 31 429 587 54 181093 24 54 67 1 3 32 426 677 63 134071 24 54 67 0 3 33 426 615 52 128094 24 54 67 0 3 34 428 572 48 152053 24 54 68 4 3 35 434 613 46 177350 25 55 69 0 3 36 443 688 57 201538 25 56 70 4 3 37 453 696 40 224627 26 57 72 4 3 38 466 709 44 218686 27 59 74 1 3 39 484 801 39 232524 28 61 76 2 3 40 499 796 46 247903 28 64 79 1 3 41 492 726 34 308873 27 62 78 0 3 42 530 653 40 479692 28 66 83 0 3 43 661 603 52 655823 34 81 104 5 3 44 618 603 34 643983 32 74 97 0 3 45 591 645 44 661060 29 70 93 3 3 46 618 784 43 684363 30 72 97 0 3 47 652 941 39 765151 31 75 102 0 3 48 690 964 55 908110 31 78 108 0 3 49 714 894 43 925632 31 80 112 0 3 50 756 878 48 1026652 32 83 118 1 3 51 785 900 50 1266456 34 85 121 1 3 52 842 937 74 1438374 37 89 127 0 3 53 894 994 54 1530553 40 94 133 0 3 54 933 1007 72 1576844 42 96 137 0 3 55 987 1055 81 1721365 45 100 142 2 3 56 1053 1118 61 1937785 49 106 148 0 3 57 1125 1238 93 2119562 54 111 154 1 3 58 1193 1392 83 2235769 59 114 160 2 3 59 1261 1493 85 2478632 63 119 165 1 3 60 1321 1516 91 2705941 65 125 169 1 3 61 1407 1592 83 2944631 68 140 176 1 3 62 1471 1679 106 3169252 76 146 181 0 3 63 1521 1728 88 3332386 83 152 184 2 3 64 1556 1627 85 3573068 89 156 186 0 3 65 1584 1694 96 3807715 87 169 188 0 3 66 1614 1627 101 4165059 89 178 191 0 3 67 1646 1596 102 4524118 91 187 195 0 3 68 1682 1692 116 5192639 95 195 202 0 3 69 1737 1714 114 6016374 100 206 211 0 3 70 1795 1461 157 6839321 104 218 218 0 3 71 1896 1401 157 7774010 115 238 236 0 3 72 2009 1529 180 8929000 124 252 263 0 3 73 2125 1691 200 10346783 132 270 289 0 3 74 2224 2101 244 11670664 139 286 312 0 3 75 2286 2647 231 12405506 141 299 330 0 3 76 2348 2850 210 13942568 143 310 349 0 3 77 2427 3229 244 15717777 145 323 371 0 3 78 2518 3450 264 18580990 146 337 391 0 3 79 2639 3737 248 22055139 147 354 419 0 3 80 2736 4360 306 25518703 146 371 451 0 3 81 2810 5199 282 28959347 142 378 480 0 3 82 2890 6048 278 30477088 138 382 499 0 3 83 2969 6743 254 33475369 136 383 520 0 3 84 3067 7646 279 37813081 138 383 544 0 3 85 3184 8273 278 42203528 144 384 569 0 3 86 3309 9038 351 46334045 151 387 597 0 3 87 3438 10558 309 50211358 154 391 619 0 3 88 3536 12063 334 53753137 153 395 633 0 3 89 3622 12843 331 57368844 149 398 637 0 3 90 3679 13833 321 60851440 150 391 630 0 3 91 3747 14892 370 63767896 151 386 630 0 3 92 3836 15945 375 67952138 155 385 629 1 4 27 . 1241 . . . . . 3 4 28 . 1124 . . . . . 4 4 29 1852 1153 285 565779 118 258 251 0 4 30 1859 1312 256 415597 118 261 250 10 4 31 1848 1374 327 388167 116 258 250 1 4 32 1836 916 286 283092 115 256 251 2 4 33 1854 1417 306 285732 115 257 257 4 4 34 1878 1699 334 343750 116 259 264 3 4 35 1890 1872 291 385006 116 259 268 9 4 36 1892 1859 246 462531 117 257 272 5 4 37 1903 1917 200 481177 117 257 276 5 4 38 1928 1896 187 438573 118 259 282 7 4 39 1948 2015 199 477499 118 260 287 7 4 40 1955 1904 194 500121 117 261 290 4 4 41 1966 1611 197 664129 113 259 290 5 4 42 1983 1509 178 944528 111 257 291 5 4 43 1839 1425 151 1017621 100 234 269 2 4 44 1771 1100 121 1209507 94 221 258 4 4 45 1758 1140 132 1295527 90 216 254 2 4 46 1805 1252 188 1358422 89 218 259 4 4 47 1835 1446 150 1353377 89 216 262 6 4 48 1825 1442 145 1622550 85 211 259 2 4 49 1844 1595 151 1506452 82 210 259 4 4 50 1908 1541 147 1626596 83 212 266 3 4 51 1901 1471 138 1827178 83 208 260 5 4 52 1838 1483 124 1891582 82 197 246 2 4 53 1780 1470 122 1902525 80 188 234 0 4 54 1734 1665 132 1866062 79 179 223 1 4 55 1725 1751 121 2031526 79 175 217 0 4 56 1704 1721 116 2097151 79 170 208 1 4 57 1733 1776 120 2160575 82 169 205 0 4 58 1726 1849 123 2270349 85 163 199 0 4 59 1756 1911 126 2484956 86 163 196 6 4 60 1789 2016 130 2517278 86 165 194 8 4 61 1806 2076 123 2726922 85 174 194 0 4 62 1853 2066 137 2910454 93 178 197 0 4 63 1875 2000 140 3096508 99 181 198 0 4 64 1897 1992 147 3358660 105 183 200 1 4 65 1894 1970 146 3566170 100 193 200 0 4 66 1899 1864 107 3968335 99 200 203 0 4 67 1901 1651 153 4223791 100 205 205 0 4 68 1902 1554 161 4572615 102 209 210 0 4 69 1913 1573 164 5011317 104 214 216 0 4 70 1930 1633 181 5483850 106 221 221 0 4 71 1972 1658 192 6068621 118 228 224 0 4 72 2018 1619 196 6839559 122 236 242 0 4 73 2058 1679 172 8123115 124 241 257 0 4 74 2100 1938 238 9082015 127 247 274 0 4 75 2158 2162 203 9910477 129 257 291 0 4 76 2169 2431 196 11011036 129 259 301 0 4 77 2207 2386 188 12328840 130 265 314 0 4 78 2241 2578 209 14367066 131 271 323 0 4 79 2269 2980 232 15849804 131 274 332 0 4 80 2290 2911 257 17076818 129 279 337 0 4 81 2293 3328 244 19263181 122 282 345 0 4 82 2294 3922 227 20086719 115 283 343 0 4 83 2306 4226 219 21355953 110 284 347 0 4 84 2320 4482 193 23773271 109 280 351 0 4 85 2327 4726 208 25394005 110 273 354 0 4 86 2332 5159 226 26629382 113 265 358 0 4 87 2343 5491 209 27728602 113 257 362 0 4 88 2343 5859 250 29577792 111 248 362 0 4 89 2346 6546 246 31293952 107 242 361 0 4 90 2354 7274 266 33035000 105 239 359 2 4 91 2371 7722 291 35060049 105 240 354 0 4 92 2395 8195 301 37961553 107 242 348 2 5 27 . 4234 . . . . . 7 5 28 . 4771 . . . . . 5 5 29 5531 4950 335 5373200 275 602 987 12 5 30 5711 4981 379 4960152 283 626 1009 15 5 31 5824 5261 406 4256562 289 641 1028 9 5 32 5894 5833 456 3326407 291 651 1041 6 5 33 5963 6329 466 3163041 295 660 1056 11 5 34 6060 6409 435 3551523 300 672 1075 9 5 35 6175 6005 393 3973532 308 685 1098 17 5 36 6341 5676 366 4773456 319 702 1128 17 5 37 6528 5676 424 5068833 331 722 1162 8 5 38 6656 6033 355 5006536 338 738 1184 11 5 39 6785 6103 300 5178760 343 755 1204 4 5 40 6950 5727 337 5737428 348 779 1230 6 5 41 7269 5041 314 7218845 352 805 1283 10 5 42 7814 4213 352 9888628 367 854 1375 9 5 43 8623 4010 385 13156557 395 927 1515 3 5 44 9261 3997 450 14484743 415 977 1624 7 5 45 9619 4640 508 15031603 417 1000 1682 13 5 46 9727 5161 535 16021991 408 996 1695 6 5 47 9912 5983 586 16552347 409 993 1721 7 5 48 10064 6694 470 17446445 400 994 1739 11 5 49 10337 7264 484 17748579 395 1006 1777 11 5 50 10677 7739 466 19762065 397 1018 1826 7 5 51 11134 8025 405 22843408 422 1053 1872 6 5 52 11635 8828 497 25340582 452 1090 1921 9 5 53 12251 9356 486 27230321 484 1139 1985 8 5 54 12746 10200 479 28064318 515 1174 2026 9 5 55 13133 10661 445 30929493 542 1197 2044 9 5 56 13713 10872 533 33869242 577 1239 2080 5 5 57 14264 11843 552 36373793 616 1273 2104 9 5 58 14880 13441 567 38194177 673 1297 2138 6 5 59 15467 13509 563 41916307 709 1338 2160 6 5 60 15870 15162 720 44247375 722 1378 2160 9 5 61 16497 16749 674 46944250 737 1522 2204 8 5 62 17072 16822 734 50631738 828 1588 2241 11 5 63 17668 18293 711 54153263 911 1671 2288 1 5 64 18151 18538 809 58749641 995 1741 2327 0 5 65 18585 18428 988 62890959 983 1904 2367 0 5 66 18858 19227 966 68410502 1002 2014 2400 0 5 67 19176 19419 1159 73810915 1033 2122 2450 1 5 68 19394 19905 1319 80934135 1078 2202 2512 0 5 69 19711 19275 1495 89097011 1119 2305 2579 0 5 70 20023 17523 1422 95657078 1156 2415 2635 0 5 71 20346 17474 1707 101537744 1139 2587 2793 0 5 72 20585 16970 1850 111026111 1165 2608 2980 0 5 73 20868 19794 1935 122512768 1191 2684 3126 0 5 74 21173 21897 2071 136840232 1212 2772 3264 0 5 75 21537 17296 2221 150232454 1220 2880 3409 0 5 76 21935 18113 2266 168637365 1241 2980 3570 0 5 77 22350 17338 2559 187808973 1256 3065 3736 0 5 78 22839 19550 2678 215186403 1265 3141 3894 0 5 79 23255 21260 3027 246647494 1250 3214 4064 0 5 80 23797 23264 3476 280600585 1227 3266 4301 0 5 81 24285 27913 3248 314131913 1184 3301 4545 0 5 82 24820 33583 2908 335230326 1148 3316 4693 0 5 83 25362 38025 2738 361323980 1131 3315 4862 0 5 84 25847 41652 2859 402017450 1141 3299 5009 0 5 85 26444 48326 2875 436510009 1173 3296 5165 0 5 86 27106 57725 3160 468316213 1215 3292 5317 0 5 87 27781 64812 3023 504641233 1214 3314 5447 0 5 88 28468 73780 3075 547466485 1187 3357 5558 0 5 89 29218 84338 3272 588412175 1152 3427 5649 0 5 90 29905 94122 3786 636593480 1168 3419 5673 0 5 91 30416 98515 4136 651224307 1156 3329 5712 0 5 92 30910 105467 4201 683397888 1174 3253 5731 1 6 27 . 1258 . . . . . 0 6 28 . 1209 . . . . . 2 6 29 1008 1250 90 631837 55 121 154 0 6 30 1040 1331 88 593569 57 126 157 7 6 31 1056 1382 87 491739 58 128 160 4 6 32 1066 1348 85 371555 58 129 162 2 6 33 1071 1257 81 371587 58 130 163 2 6 34 1075 1312 77 389794 58 130 165 1 6 35 1078 1321 64 472543 59 131 166 3 6 36 1090 1359 82 583554 60 132 168 1 6 37 1104 1437 64 580769 61 133 171 1 6 38 1112 1639 57 555639 61 134 173 0 6 39 1120 1749 46 571079 61 135 174 4 6 40 1130 1556 52 607572 61 137 176 0 6 41 1124 1536 25 720262 59 135 175 2 6 42 1133 1386 40 1003381 58 135 176 2 6 43 1151 1303 55 1183180 58 135 179 2 6 44 1124 1174 39 1189698 55 130 175 0 6 45 1113 1211 55 1315884 53 128 173 3 6 46 1196 1331 64 1435970 56 136 186 0 6 47 1236 1389 52 1662790 57 138 192 2 6 48 1263 1456 59 1818097 56 140 196 0 6 49 1295 1362 65 1838013 56 142 201 2 6 50 1325 1490 44 2000697 56 143 205 0 6 51 1326 1446 35 2364767 57 141 202 1 6 52 1365 1520 45 2550879 59 143 206 0 6 53 1431 1597 68 2582636 63 147 212 0 6 54 1493 1783 52 2635489 66 151 218 0 6 55 1546 1800 104 2878377 70 154 222 0 6 56 1625 1912 51 3170547 74 159 229 1 6 57 1664 2006 42 3485383 77 159 229 1 6 58 1667 1972 69 3577027 80 155 224 0 6 59 1710 2012 82 3855106 83 156 225 0 6 60 1769 2078 69 4121588 84 161 228 1 6 61 1844 2149 81 4439156 87 179 234 1 6 62 1899 2356 95 4666935 97 186 238 2 6 63 1936 2594 95 4890553 105 193 240 0 6 64 1970 2666 88 5173112 114 200 243 1 6 65 1985 2766 77 5549318 111 216 244 0 6 66 2007 2540 78 5982738 113 228 247 0 6 67 2053 2514 88 6466751 118 242 255 1 6 68 2120 2338 124 7173105 126 257 268 0 6 69 2166 2107 120 8030521 132 271 278 0 6 70 2224 2066 146 9024542 138 288 288 0 6 71 2304 1957 123 10132690 137 317 311 0 6 72 2405 1925 190 11457715 144 335 346 0 6 73 2496 1894 175 13159755 151 354 377 0 6 74 2541 1968 159 14766028 155 362 403 0 6 75 2586 2039 198 16195688 156 374 427 0 6 76 2632 2239 188 17995320 158 385 452 0 6 77 2696 2311 190 20165036 159 395 481 0 6 78 2767 2474 180 23348637 159 405 508 0 6 79 2849 2658 159 27063870 157 416 541 0 6 80 2910 2609 208 31162711 152 418 577 0 6 81 2978 2770 253 35834905 147 416 607 0 6 82 3062 3286 196 39223197 143 412 625 0 6 83 3134 3455 196 42155255 141 404 640 0 6 84 3170 3390 185 46154585 142 390 646 0 6 85 3209 3631 196 48798529 146 378 651 0 6 86 3238 4147 241 50470155 149 364 650 0 6 87 3261 5016 199 52590333 147 353 643 0 6 88 3263 6086 195 55298840 139 345 630 0 6 89 3276 7318 160 59107081 131 341 618 0 6 90 3304 7671 155 63517598 130 335 604 0 6 91 3370 8392 208 67592785 131 334 596 0 6 92 3463 8997 231 72634510 138 337 588 0 7 27 . 1005 . . . . . 2 7 28 . 1097 . . . . . 0 7 29 1594 1210 41 1613610 86 189 255 1 7 30 1613 1328 52 1469141 88 193 255 2 7 31 1628 1342 45 1286797 89 197 259 0 7 32 1637 1366 48 997744 89 199 261 0 7 33 1642 1267 30 939650 90 201 264 0 7 34 1650 1238 40 1060388 91 203 267 0 7 35 1666 1299 33 1159805 93 206 271 0 7 36 1672 1243 46 1329570 94 207 274 1 7 37 1678 1204 34 1423586 96 209 276 1 7 38 1684 1174 36 1275178 96 211 278 1 7 39 1696 1166 22 1396838 97 214 281 0 7 40 1708 1146 31 1545623 97 218 283 2 7 41 1751 1055 37 1978188 95 218 291 0 7 42 1798 1163 44 2520021 94 219 299 0 7 43 1797 1175 26 2833747 91 213 300 2 7 44 1801 1115 34 2855160 88 207 302 1 7 45 1782 1023 25 2767666 83 199 299 1 7 46 1911 1045 30 2993100 85 208 321 3 7 47 1969 1078 38 3321895 85 207 332 0 7 48 2014 1084 35 3408518 83 205 339 1 7 49 2032 1087 35 3332066 79 201 342 0 7 50 2016 1020 29 3761530 75 192 339 0 7 51 2028 1081 39 4328879 76 190 336 0 7 52 2081 1093 33 4729416 79 192 339 0 7 53 2168 1173 31 5141569 83 196 347 0 7 54 2249 1188 26 5245989 88 200 353 0 7 55 2300 1260 30 5662962 91 200 354 3 7 56 2316 1263 48 6183752 92 198 348 0 7 57 2359 1329 36 6590870 96 198 345 0 7 58 2446 1565 36 6642910 103 198 348 0 7 59 2523 1500 37 7115422 107 201 350 2 7 60 2544 1497 51 7405327 106 202 344 1 7 61 2586 1639 33 7838076 105 218 342 0 7 62 2647 1653 44 8368020 117 225 343 0 7 63 2727 1652 51 8836442 128 235 347 0 7 64 2798 1716 54 9500133 140 245 351 0 7 65 2857 1642 60 10247586 138 267 355 0 7 66 2903 1599 70 11270576 141 283 359 0 7 67 2935 1587 74 12384521 144 297 363 0 7 68 2964 1444 79 13235031 151 307 370 0 7 69 3000 1630 95 14542915 156 321 377 0 7 70 3039 1568 115 15428007 160 335 382 0 7 71 3061 1938 102 16141793 172 344 386 0 7 72 3070 1818 94 17401518 176 346 407 0 7 73 3069 1663 107 19004184 180 341 422 0 7 74 3076 1464 106 20715362 182 357 434 0 7 75 3085 1849 128 22053268 181 366 447 0 7 76 3086 1923 104 24036670 181 374 457 0 7 77 3089 1647 136 26534481 179 378 467 0 7 78 3095 1863 128 29603605 178 381 472 0 7 79 3100 2139 129 33438050 176 384 481 0 7 80 3112 2750 153 38108160 175 387 495 0 7 81 3129 3348 152 42713873 171 392 510 0 7 82 3139 3129 161 45995447 163 394 511 0 7 83 3163 3577 124 49290682 156 396 521 0 7 84 3180 3748 142 55014510 152 394 531 0 7 85 3201 4043 125 59264952 151 391 542 0 7 86 3224 4326 149 64069864 151 385 553 0 7 87 3248 4637 156 70109600 144 378 564 0 7 88 3272 4723 180 77418620 134 371 575 0 7 89 3283 6309 196 83320147 123 360 582 0 7 90 3289 7771 171 86749295 117 345 584 0 7 91 3291 8585 190 87944151 115 328 575 0 7 92 3279 8794 176 92945381 115 309 560 0 8 27 . . . . . . . 1 8 28 . . . . . . . 0 8 29 236 332 16 241305 13 28 37 0 8 30 239 332 34 202994 13 28 37 2 8 31 242 340 25 185404 13 29 38 0 8 32 245 204 20 143050 13 29 38 0 8 33 248 238 31 138328 13 30 39 0 8 34 250 510 34 159354 14 30 40 0 8 35 252 470 23 174949 14 31 41 3 8 36 253 525 24 217644 14 31 41 1 8 37 254 521 14 238552 14 31 42 2 8 38 257 491 14 201300 14 31 43 0 8 39 263 435 17 233120 15 32 44 0 8 40 269 386 12 272438 15 33 45 0 8 41 276 168 21 317129 15 34 46 1 8 42 281 151 16 358242 14 34 47 1 8 43 281 130 8 406808 14 33 47 0 8 44 286 135 17 426425 14 33 48 0 8 45 286 153 14 433302 13 32 48 1 8 46 300 169 28 465991 14 33 50 1 8 47 306 189 14 504252 13 33 51 0 8 48 312 178 18 514539 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549 0 47 65 4411 4553 342 11208410 258 499 553 0 47 66 4456 4220 315 12144849 259 521 560 0 47 67 4508 4033 399 13260086 264 542 570 0 47 68 4558 4126 420 14679908 273 558 585 0 47 69 4614 4407 363 16345556 280 578 600 0 47 70 4660 4648 426 17573782 286 597 611 0 47 71 4753 4981 453 19234234 277 638 638 0 47 72 4828 4946 494 21420042 286 636 681 0 47 73 4907 5100 536 24156600 294 654 714 0 47 74 4978 5032 566 26953451 300 674 742 0 47 75 5056 5497 607 29520650 301 699 773 0 47 76 5133 6820 496 32938024 303 718 806 0 47 77 5206 8146 501 36591400 302 732 841 0 47 78 5284 9056 477 41584554 303 745 871 0 47 79 5325 9011 502 46712657 299 746 899 0 47 80 5368 8581 490 53244099 294 746 937 0 47 81 5444 9013 496 59917893 285 758 976 0 47 82 5493 9715 435 64393634 273 754 983 1 47 83 5565 9855 416 69954239 264 754 1004 0 47 84 5644 10665 462 78462883 262 750 1028 1 47 85 5716 11717 425 85158388 265 740 1048 2 47 86 5812 12545 446 92221526 272 735 1074 1 47 87 5933 12931 472 100150273 271 736 1100 1 47 88 6038 13928 486 108937753 262 731 1120 1 47 89 6120 16273 531 117672539 247 727 1131 1 47 90 6214 17418 575 124252496 239 722 1143 3 47 91 6287 19660 600 128998291 236 704 1145 2 47 92 6390 20989 582 136191353 240 694 1146 4 48 27 . 1484 . . . . . 1 48 28 . 1621 . . . . . 3 48 29 1555 1741 80 1138329 83 182 253 1 48 30 1568 1765 80 1017619 84 185 251 4 48 31 1581 1911 71 832442 84 187 253 1 48 32 1584 1801 82 624903 84 187 254 2 48 33 1592 1811 93 586303 84 188 256 1 48 34 1610 1812 92 700966 85 190 260 2 48 35 1629 2012 73 785149 86 192 264 1 48 36 1653 1986 60 927049 88 194 268 3 48 37 1681 2166 72 991712 90 197 273 0 48 38 1698 2299 66 971754 91 199 276 4 48 39 1715 2292 44 1036687 92 201 279 5 48 40 1740 2312 61 1126713 92 205 283 2 48 41 1793 2256 40 1526944 91 209 291 3 48 42 1904 1771 54 2251035 94 219 309 0 48 43 2022 1778 56 2936085 97 229 328 2 48 44 2138 2033 80 3232127 101 238 347 2 48 45 2238 2002 79 3141848 102 245 363 1 48 46 2300 1957 82 3186527 102 248 372 2 48 47 2225 2044 74 3312416 97 235 359 1 48 48 2255 1944 74 3633650 94 235 363 0 48 49 2294 2053 80 3630976 93 236 368 3 48 50 2387 2290 62 4080103 94 240 382 0 48 51 2424 2151 58 4510780 97 241 381 2 48 52 2448 2224 61 4799115 100 240 377 0 48 53 2466 2331 79 5066942 102 239 372 2 48 54 2516 2438 86 5202098 106 241 372 0 48 55 2604 2443 71 5484659 111 245 376 0 48 56 2668 2468 71 5766394 116 248 375 1 48 57 2724 2422 69 6127558 120 249 371 1 48 58 2773 2623 61 6290452 128 247 367 0 48 59 2821 2876 71 6685060 131 248 362 0 48 60 2855 2455 70 6925133 131 251 357 1 48 61 2882 2401 79 7275332 131 270 354 0 48 62 2942 2341 89 7840778 145 278 356 0 48 63 2955 2539 98 8041968 155 285 353 1 48 64 2961 2960 76 8423138 166 290 351 0 48 65 2967 3202 91 9106489 160 311 350 0 48 66 3057 3098 87 10328493 166 334 361 0 48 67 3174 2738 123 11304015 175 360 377 0 48 68 3270 2599 130 12566467 187 381 395 0 48 69 3343 2765 135 13665238 195 402 409 0 48 70 3417 2864 150 14310350 203 424 421 0 48 71 3447 2782 154 15066789 203 446 450 0 48 72 3447 2608 146 16298492 204 435 476 0 48 73 3477 2632 172 18437364 206 445 504 0 48 74 3548 2989 191 20886226 210 460 538 0 48 75 3619 3369 206 23400244 211 477 571 0 48 76 3691 3771 179 26199849 213 492 605 0 48 77 3772 4272 171 29138278 215 504 637 0 48 78 3886 4487 195 34111550 217 520 671 0 48 79 4013 4342 206 39435961 216 537 708 0 48 80 4155 4399 237 44685567 214 555 753 0 48 81 4236 5336 229 49828227 206 553 787 0 48 82 4277 6264 200 52338474 197 541 793 0 48 83 4301 6658 217 55516793 192 526 800 0 48 84 4344 6821 205 59629773 194 512 811 0 48 85 4401 6919 231 63507720 199 500 820 0 48 86 4453 6603 218 68041695 203 488 829 0 48 87 4533 6131 253 72251746 200 484 836 0 48 88 4641 5816 282 78263336 192 486 846 0 48 89 4746 6928 234 86344645 185 487 848 0 48 90 4901 7995 247 95980485 190 489 851 0 48 91 5018 9156 232 102386507 194 487 856 0 48 92 5146 9959 294 110461026 204 489 856 0 49 27 . 1762 . . . . . 1 49 28 . 1931 . . . . . 3 49 29 1717 2174 176 781322 102 224 239 4 49 30 1733 2344 234 699148 103 227 239 2 49 31 1740 2571 240 611323 103 229 241 3 49 32 1747 2642 220 442864 104 232 244 2 49 33 1752 2338 216 447660 104 234 247 3 49 34 1771 2198 267 546676 106 237 253 0 49 35 1794 2294 202 598215 108 241 258 2 49 36 1808 2322 216 698130 110 243 262 0 49 37 1813 2396 166 749718 112 244 265 4 49 38 1828 2507 128 668050 113 248 269 4 49 39 1868 2643 137 715679 116 255 276 0 49 40 1907 2691 160 766593 117 263 283 2 49 41 1885 2689 140 928094 112 256 280 0 49 42 1832 2736 127 1112131 106 246 272 0 49 43 1741 2431 78 1276487 98 230 259 0 49 44 1708 2208 78 1391813 94 221 254 0 49 45 1707 2044 92 1507329 91 218 254 2 49 46 1828 2178 136 1681354 94 230 271 1 49 47 1882 2309 133 1927400 95 231 279 1 49 48 1899 2484 144 2101452 92 230 281 4 49 49 1930 2776 140 1968146 90 230 285 1 49 50 2006 2904 120 2113747 92 234 296 0 49 51 1984 2974 101 2341768 91 227 288 3 49 52 1957 2697 108 2435322 91 219 279 0 49 53 1929 2316 111 2459595 90 211 270 1 49 54 1905 2220 93 2331426 89 204 262 1 49 55 1880 2269 80 2473508 89 196 254 0 49 56 1857 2101 97 2736550 88 189 245 1 49 57 1843 2191 79 2930793 89 183 237 0 49 58 1845 2406 95 2864043 92 176 231 2 49 59 1855 2492 74 2963207 92 173 226 1 49 60 1853 2407 68 3007298 90 171 221 0 49 61 1828 2207 75 3044498 86 177 214 0 49 62 1809 2031 75 3171404 91 175 208 0 49 63 1796 1912 98 3308161 95 175 203 0 49 64 1797 1722 67 3524110 100 176 201 0 49 65 1786 1477 63 3772815 95 185 199 0 49 66 1775 1189 68 4006651 94 190 197 0 49 67 1769 1208 93 4251542 94 194 197 0 49 68 1763 1124 83 4506983 96 197 199 0 49 69 1746 1001 87 4859005 97 199 199 0 49 70 1747 938 96 5411578 98 204 201 0 49 71 1770 1063 109 5936106 107 214 194 0 49 72 1797 1058 110 6576768 108 220 210 0 49 73 1805 1086 97 7218105 109 222 223 0 49 74 1814 989 96 8023553 108 222 237 0 49 75 1841 1271 118 9070265 108 227 253 0 49 76 1877 1278 132 10181045 107 232 271 0 49 77 1906 1250 125 11389049 106 237 285 0 49 78 1920 1237 153 12703763 105 238 294 0 49 79 1939 1251 138 14208705 105 240 303 0 49 80 1953 1257 159 15704820 103 243 307 0 49 81 1954 1565 129 17008695 100 241 313 0 49 82 1950 1547 141 17988916 96 236 310 0 49 83 1945 1611 114 18300180 93 230 308 0 49 84 1928 1579 105 19558379 93 221 304 0 49 85 1907 1725 91 20290732 92 212 300 0 49 86 1883 1472 117 21063492 93 202 294 0 49 87 1858 1449 112 21603260 92 195 286 0 49 88 1830 1557 111 22672072 89 188 277 0 49 89 1807 1638 137 23755037 84 183 268 0 49 90 1792 1667 112 25410664 81 182 262 0 49 91 1799 1789 133 26685089 81 186 254 0 49 92 1807 1912 137 28381569 82 188 246 0 50 27 . 1595 . . . . . 0 50 28 . 1617 . . . . . 0 50 29 2934 1933 66 1951663 162 354 450 0 50 30 2950 2381 91 1709416 161 357 450 0 50 31 2990 2584 105 1382475 163 362 457 0 50 32 3021 2696 83 1075585 164 366 463 0 50 33 3040 2402 55 995831 164 368 468 0 50 34 3054 2302 71 1142384 165 370 472 0 50 35 3070 2331 41 1398439 166 371 477 0 50 36 3082 2819 49 1578350 168 371 480 0 50 37 3088 2791 64 1681652 169 370 483 0 50 38 3098 2775 59 1548877 170 371 485 0 50 39 3121 2332 43 1579829 170 374 489 0 50 40 3143 2330 41 1693957 169 379 492 0 50 41 3140 2139 42 2081711 163 373 490 0 50 42 3066 1929 51 2623922 155 359 476 0 50 43 3007 1706 38 3132849 148 346 465 0 50 44 2971 1415 26 3265550 142 336 459 0 50 45 2961 1439 48 3468591 137 329 455 0 50 46 3168 1588 31 3802004 142 347 485 0 50 47 3250 1705 44 4172504 143 348 495 0 50 48 3314 1915 26 4706692 140 349 502 0 50 49 3391 1991 44 4651963 138 351 510 0 50 50 3438 2017 39 5136664 136 348 514 0 50 51 3439 1880 39 5919979 137 343 505 0 50 52 3469 1932 58 6183104 141 339 501 0 50 53 3506 2216 42 6392648 143 337 497 0 50 54 3608 2210 36 6351193 149 341 501 0 50 55 3679 2281 38 6840017 154 341 501 0 50 56 3742 2336 45 7387470 158 340 496 0 50 57 3791 2282 52 7744126 163 337 489 0 50 58 3843 2617 44 7904610 172 331 483 0 50 59 3891 2640 52 8591709 175 329 475 0 50 60 3962 2784 58 8860122 175 334 472 0 50 61 4009 2953 69 9138769 174 359 469 0 50 62 4049 2887 46 9649873 191 366 465 0 50 63 4112 2811 73 9986525 206 378 466 0 50 64 4165 2844 57 10752294 222 389 468 0 50 65 4232 2830 59 11676315 218 422 472 0 50 66 4274 2709 90 12781661 221 445 477 0 50 67 4303 2607 92 13527454 226 464 482 0 50 68 4345 2172 101 14783859 236 482 493 0 50 69 4378 2768 98 16166247 243 500 502 0 50 70 4426 2973 108 17315857 250 522 511 0 50 71 4460 2493 137 18559907 270 543 519 0 50 72 4498 2036 148 20259424 276 556 553 0 50 73 4518 2147 142 22679194 280 568 579 0 50 74 4538 2587 149 24907688 285 579 606 0 50 75 4570 2992 164 26947005 287 595 634 0 50 76 4585 3299 153 29808633 290 605 661 0 50 77 4613 3347 141 33431322 290 614 688 0 50 78 4632 3432 122 37544022 287 623 705 0 50 79 4666 3677 156 42288715 280 635 731 0 50 80 4714 3980 145 46542782 272 644 755 0 50 81 4726 4385 160 50546802 260 646 778 0 50 82 4729 4714 156 53048341 247 639 781 0 50 83 4722 4845 148 55706928 234 627 789 0 50 84 4736 4974 139 61233968 228 611 800 0 50 85 4748 5395 154 64527418 225 592 810 0 50 86 4756 5717 168 68125382 227 566 817 0 50 87 4779 6126 180 71778430 220 547 822 0 50 88 4823 6325 160 76170092 211 535 828 0 50 89 4857 6775 185 81793624 200 523 825 0 50 90 4902 7438 234 86869298 200 511 818 0 50 91 4949 7775 238 90598983 203 502 810 0 50 92 4997 7992 241 97151864 210 494 798 0 51 27 . 301 . . . . . 0 51 28 . 318 . . . . . 0 51 29 223 357 19 148542 13 28 38 0 51 30 226 338 24 130249 13 28 38 2 51 31 229 352 19 107278 13 29 38 0 51 32 230 312 22 84461 13 29 38 0 51 33 230 311 22 83500 13 29 38 1 51 34 233 313 18 93726 13 30 39 0 51 35 237 322 17 115585 14 30 39 0 51 36 240 363 7 130439 14 31 40 0 51 37 243 364 7 145250 14 31 40 1 51 38 246 351 6 135423 14 31 41 0 51 39 248 387 13 143069 14 32 41 0 51 40 250 363 14 148074 14 32 41 1 51 41 249 337 10 191222 14 32 40 0 51 42 249 274 11 232271 14 32 40 0 51 43 246 276 17 279939 13 31 40 0 51 44 240 265 7 291447 13 30 39 1 51 45 240 278 16 299864 12 30 39 1 51 46 253 323 11 341518 13 31 41 0 51 47 256 366 14 381353 13 31 41 0 51 48 269 356 14 429299 13 32 43 0 51 49 277 411 14 451488 13 33 44 0 51 50 290 410 4 492905 13 34 46 0 51 51 291 368 9 564762 13 33 45 0 51 52 293 299 12 555559 13 32 45 0 51 53 290 268 12 556462 13 31 44 0 51 54 293 326 8 542211 13 31 43 0 51 55 307 287 14 583995 14 31 44 0 51 56 312 326 10 622526 14 31 44 0 51 57 314 272 13 665086 15 30 43 0 51 58 315 384 10 680761 15 29 42 0 51 59 320 387 15 726559 15 28 42 0 51 60 331 338 18 761915 15 29 42 0 51 61 337 329 12 800556 15 31 42 0 51 62 333 328 11 828318 16 31 41 0 51 63 336 327 10 845988 17 32 41 0 51 64 339 339 18 869374 18 32 41 0 51 65 332 336 12 904741 17 34 40 1 51 66 323 279 13 932656 17 34 39 0 51 67 322 254 14 1000197 17 35 39 0 51 68 324 257 13 1066353 18 36 39 0 51 69 329 246 22 1168371 18 38 40 0 51 70 334 231 14 1282062 19 39 41 0 51 71 340 263 11 1426623 22 41 42 0 51 72 347 262 10 1595538 22 44 46 0 51 73 353 278 16 1867270 22 46 49 0 51 74 365 269 22 2198768 23 49 53 0 51 75 380 307 27 2475383 23 53 58 0 51 76 395 340 27 2760683 24 56 64 0 51 77 412 400 17 3240112 24 60 68 0 51 78 431 433 31 3915998 25 63 76 0 51 79 452 504 36 4620816 25 67 83 0 51 80 475 534 31 5417436 24 69 90 0 51 81 492 556 25 6151484 24 67 95 0 51 82 506 702 37 6345687 24 64 98 0 51 83 510 721 26 6277219 24 60 98 0 51 84 505 724 21 6525321 23 56 95 0 51 85 500 762 24 6817804 24 52 93 0 51 86 496 861 28 6685737 25 48 90 0 51 87 477 916 10 6478829 24 45 84 0 51 88 465 945 13 6654240 22 43 80 0 51 89 458 1026 21 7048809 21 42 77 0 51 90 453 1110 21 7735811 21 42 73 0 51 91 458 1099 14 8368589 22 43 70 0 51 92 464 1063 17 8653446 23 45 66 1 2) NATIONAL DATA 27 119035 108364 249 3.3 0.52 28 120509 114799 251 4.2 0.513 29 121769 118090 255 3.2 0.513 30 123075 127988 256 8.7 0.5 31 124038 136554 253 15.9 0.456 32 124839 137014 245 23.6 0.409 33 125580 137057 244 24.9 0.388 34 126372 137361 247 21.7 0.401 35 127251 141037 252 20.1 0.411 36 128054 141452 291 16.9 0.415 37 128822 146719 312 14.3 0.43 38 129824 152773 323 19 0.422 39 130884 157472 334 17.2 0.416 40 131955 151991 458 14.6 0.42 41 133417 144814 1801 9.9 0.441 42 134670 131956 3859 4.7 0.488 43 134697 119388 9045 1.9 0.518 44 134075 112604 11452 1.2 0.527 45 133387 113023 12123 1.9 0.539 46 140638 120245 3030 3.9 0.585 47 143665 131594 1583 3.9 0.669 48 146091 136780 1446 3.8 0.721 49 148666 143568 1615 5.9 0.714 50 151871 145714 1460 5.3 0.721 51 153970 144846 3249 3.3 0.778 52 156369 146435 3636 3 0.795 53 158946 150206 3555 2.9 0.801 54 161881 158526 3302 5.5 0.805 55 165058 161123 2935 4.4 0.802 56 168078 164771 2806 4.1 0.814 57 171178 169761 2796 4.3 0.843 58 174143 178333 2601 6.8 0.866 59 177124 179823 2504 5.5 0.873 60 179954 183237 2475 5.5 0.887 61 182960 189275 2483 6.7 0.896 62 185708 187676 2806 5.5 0.906 63 188423 186315 2699 5.7 0.917 64 191063 184682 2686 5.2 0.929 65 193451 181957 2654 4.5 0.945 66 195486 172169 3092 3.8 0.972 67 197360 166995 3375 3.8 1 68 199297 166988 3546 3.6 1.042 69 201298 170959 3458 3.5 1.098 70 203799 170559 3065 4.9 1.163 71 206818 177113 2713 5.9 1.213 72 209275 174379 2322 5.6 1.253 73 211349 181396 2252 4.9 1.331 74 213334 196105 2162 5.6 1.477 75 215457 216462 2128 8.5 1.612 76 217554 235853 2082 7.7 1.705 77 219761 257465 2075 7.1 1.815 78 222098 269249 2062 6.1 1.954 79 224564 279724 2027 5.8 2.174 80 227255 295563 2051 7.1 2.468 81 229457 332499 2083 7.6 2.724 82 231669 373692 2109 9.7 2.891 83 233806 395293 2123 9.6 2.984 84 235847 419695 2138 7.5 3.111 85 237950 451147 2151 7.2 3.222 86 240162 490368 2169 7 3.284 87 242321 526342 2174 6.2 3.404 88 244534 572044 2138 5.5 3.543 89 246820 645777 2130 5.3 3.713 90 249402 698038 2044 5.5 3.913 91 252131 753368 1986 6.7 4.078 92 255028 803752 1807 7.4 4.201 3) PROGRAMS FOR CREATING SAS DATA SETS These two SAS programs create a SAS data set from the STATE.DAT and USA.DAT data sets. YYY is the user's account. //CREATE JOB (0000,AAAA,2,2),YY,NOTIFY=YYY // EXEC SAS //A DD DSN=YYY.XXX.SAS, // DISP=(NEW,CATLG,DELETE), // SPACE=(TRK,(100,20)),UNIT=SYSDA //STATE DD DSN=YYY.STATE.DATA,DISP=SHR DATA STATE; INFILE STATE; INPUT STATE YEAR POP PRCYE VTMUR TPI P15T17 P18T24 P25T34 EXEC; PROC SORT; BY STATE YEAR; DATA A.XXX; SET STATE; //ADDUSA JOB (0000,AAAA,1,1),YY,NOTIFY=YYY // EXEC SAS //A DD DSN=YYY.XXX.SAS,DISP=OLD //USA DD DSN=YYY.USA.DATA,DISP=SHR DATA USA; INFILE USA; INPUT YEAR POPUS PRUS MILPER UNRATE CPI; PROC SORT; BY YEAR; DATA A.XXX; SET A.XXX; PROC SORT; BY YEAR STATE; DATA A.XXX; MERGE USA A.XXX; BY YEAR; PROC SORT; BY STATE YEAR; 4) PROGRAM FOR THE STATE-LEVEL REGRESSION DATA XXX; SET A.XXX; IF STATE NE 9; IF STATE NE 2; IF STATE NE 12; *DC,AK,HI OUT; PRUSQ=PRUS-PRCYE; PRCYE=PRCYE/POP*100; PRCYEZ=(PRCYE+LAG(PRCYE))/2; PRUSQ=PRUSQ/(POPUS-POP)*100; PRUSQZ=(PRUSQ+LAG(PRUSQ))/2; PRDIF=(PRUSQZ/PRCYEZ); RTPI=TPI/CPI; RTPI=RTPI/POP; MILPER=MILPER/POPUS*100; IF VTMUR=0 THEN VTMUR=.1; ARRAY FORPOP VTMUR P15T17 P18T24 P25T34; DO OVER FORPOP; FORPOP=FORPOP/POP*100; END; EXEC=EXEC/POP*10000; EXECZ=(EXEC+LAG(EXEC))/2; EXECZ=LOG(EXECZ+1); PRJDUM=0; IF YEAR GT 76 THEN PRJDUM=1; WAR =0; IF YEAR GT 41 THEN WAR =1; IF YEAR GT 45 THEN WAR =0; WAR2=0; IF YEAR GT 42 THEN WAR2=1; IF YEAR GT 44 THEN WAR2=0; CRACK = 0; IF YEAR GT 84 THEN CRACK = YEAR-84; ARRAY FORLOG VTMUR PRCYEZ PRUSQZ PRDIF P15T17 P18T24 P25T34 RTPI MILPER CPI UNRATE; DO OVER FORLOG; FORLOG=LOG(FORLOG); END; DO OVER FORLOG; FORLOG=DIF(FORLOG); END; ARRAY DD WAR WAR2 PRJDUM CRACK EXECZ; DO OVER DD; DD=DIF(DD); END; VTMUR1=LAG(VTMUR); VTMUR2=LAG2(VTMUR); VTMUR3=LAG3(VTMUR); VTMUR4=LAG4(VTMUR); VTMUR5=LAG5(VTMUR); IF STATE=33 THEN STSIZE60=01;IF STATE= 5 THEN STSIZE60=02; IF STATE=39 THEN STSIZE60=03;IF STATE=14 THEN STSIZE60=04; IF STATE=36 THEN STSIZE60=05;IF STATE=44 THEN STSIZE60=06; IF STATE=23 THEN STSIZE60=07;IF STATE=31 THEN STSIZE60=08; IF STATE=22 THEN STSIZE60=09;IF STATE=10 THEN STSIZE60=10; IF STATE=15 THEN STSIZE60=11;IF STATE=34 THEN STSIZE60=12; IF STATE=26 THEN STSIZE60=13;IF STATE=47 THEN STSIZE60=14; IF STATE=50 THEN STSIZE60=15;IF STATE=11 THEN STSIZE60=16; IF STATE=43 THEN STSIZE60=17;IF STATE=24 THEN STSIZE60=18; IF STATE= 1 THEN STSIZE60=19;IF STATE=19 THEN STSIZE60=20; IF STATE=21 THEN STSIZE60=21;IF STATE=18 THEN STSIZE60=22; IF STATE=48 THEN STSIZE60=23;IF STATE=16 THEN STSIZE60=24; IF STATE= 7 THEN STSIZE60=25;IF STATE=41 THEN STSIZE60=26; IF STATE=37 THEN STSIZE60=27;IF STATE=17 THEN STSIZE60=28; IF STATE=25 THEN STSIZE60=29;IF STATE=49 THEN STSIZE60=30; IF STATE= 4 THEN STSIZE60=31;IF STATE=38 THEN STSIZE60=32; IF STATE= 6 THEN STSIZE60=33;IF STATE=28 THEN STSIZE60=34; IF STATE= 3 THEN STSIZE60=35;IF STATE=20 THEN STSIZE60=36; IF STATE=32 THEN STSIZE60=37;IF STATE=45 THEN STSIZE60=38; IF STATE=40 THEN STSIZE60=39;IF STATE= 9 THEN STSIZE60=40; IF STATE=42 THEN STSIZE60=41;IF STATE=27 THEN STSIZE60=42; IF STATE=13 THEN STSIZE60=43;IF STATE=35 THEN STSIZE60=44; IF STATE=12 THEN STSIZE60=45;IF STATE=30 THEN STSIZE60=46; IF STATE= 8 THEN STSIZE60=47;IF STATE=46 THEN STSIZE60=48; IF STATE=51 THEN STSIZE60=49;IF STATE=29 THEN STSIZE60=50; IF STATE= 2 THEN STSIZE60=51; A=STSIZE60; IF A=1 OR A=2 OR A=3 OR A=4 OR A=6 OR A=9 OR A=10 OR A=12 OR A=13 OR A=14 OR A=16 OR A=17 OR A=18 OR A=19 OR A=20 OR A=21 OR A=22 OR A=25 OR A=26 OR A=27 OR A=30 OR A=31 OR A=38 THEN DO; VTMUR2=0; VTMUR3=0; VTMUR4=0; VTMUR5=0; END; IF A=8 OR A=11 OR A=15 OR A=28 OR A=32 OR A=34 OR A=35 OR A=36 OR A=37 OR A=47 OR A=50 THEN DO; VTMUR3=0; VTMUR4=0; VTMUR5=0; END; IF A=5 OR A=23 OR A=24 OR A=39 OR A=42 OR A=43 OR A=48 OR A=49 THEN DO; VTMUR4=0; VTMUR5=0; END; IF A=7 OR A=29 OR A=41 OR A=44 OR A=46 THEN DO; VTMUR5=0; END; PROC SORT; BY STSIZE60 YEAR; PROC REG; WEIGHT POP; BY STSIZE60; MODEL VTMUR= PRUSQZ PRCYEZ RTPI CPI UNRATE P15T17 P18T24 P25T34 EXECZ MILPER WAR WAR2 CRACK PRJDUM VTMUR1-VTMUR5 /ACOV; 5) PROGRAM FOR THE REGION-LEVEL REGRESSION DATA XXX; SET A.XXX; IF STATE NE 9; IF STATE NE 2; IF STATE NE 12; IF STATE NE 11; ARRAY STVAR VTMUR PRCYE TPI P15T17 P18T24 P25T34 POP EXEC; ARRAY REGVAR VTMURR PRCYER TPIR P15T17R P18T24R P25T34R POPR EXECR; DO OVER REGVAR; REGVAR=STVAR; END; *TRANSFORMING STATES INTO REGIONS 1,2=NORTHEAST 3,4=MIDWEST 5,6,7=SOUTH 8,9=WEST; IF STATE=20 THEN REGION=1;IF STATE=30 THEN REGION=1; IF STATE=46 THEN REGION=1;IF STATE=22 THEN REGION=1; IF STATE=40 THEN REGION=1;IF STATE= 7 THEN REGION=1; IF STATE=33 THEN REGION=2;IF STATE=31 THEN REGION=2; IF STATE=39 THEN REGION=2;IF STATE=36 THEN REGION=3; IF STATE=15 THEN REGION=3;IF STATE=14 THEN REGION=3; IF STATE=23 THEN REGION=3;IF STATE=50 THEN REGION=3; IF STATE=24 THEN REGION=4;IF STATE=16 THEN REGION=4; IF STATE=26 THEN REGION=4;IF STATE=35 THEN REGION=4; IF STATE=42 THEN REGION=4;IF STATE=28 THEN REGION=4; IF STATE=17 THEN REGION=4;IF STATE= 8 THEN REGION=5; IF STATE=21 THEN REGION=5;IF STATE= 9 THEN REGION=5; IF STATE=47 THEN REGION=5;IF STATE=49 THEN REGION=5; IF STATE=34 THEN REGION=5;IF STATE=41 THEN REGION=5; IF STATE=11 THEN REGION=5;IF STATE=10 THEN REGION=5; IF STATE=18 THEN REGION=6;IF STATE=43 THEN REGION=6; IF STATE= 1 THEN REGION=6;IF STATE=25 THEN REGION=6; IF STATE= 4 THEN REGION=7;IF STATE=19 THEN REGION=7; IF STATE=37 THEN REGION=7;IF STATE=44 THEN REGION=7; IF STATE=27 THEN REGION=8;IF STATE=13 THEN REGION=8; IF STATE=51 THEN REGION=8;IF STATE= 6 THEN REGION=8; IF STATE=32 THEN REGION=8;IF STATE= 3 THEN REGION=8; IF STATE=45 THEN REGION=8;IF STATE=29 THEN REGION=8; IF STATE=48 THEN REGION=9;IF STATE=38 THEN REGION=9; IF STATE= 5 THEN REGION=9;IF STATE= 2 THEN REGION=9; IF STATE=12 THEN REGION=9; /*FOR FOUR REGIONS IF REGION=2 THEN REGION=1; IF REGION=4 THEN REGION=3; IF REGION=6 THEN REGION=5; IF REGION=7 THEN REGION=5; IF REGION=9 THEN REGION=8; IF STATE NE 1; IF STATE NE 25; END OF FOUR REGIONS - ALSO CHANGE STATE NUMBERING BELOW */ PROC SORT; BY REGION YEAR; PROC SUMMARY; BY REGION YEAR; VAR VTMURR PRCYER TPIR P15T17R P18T24R P25T34R POPR EXECR; OUTPUT OUT=OUT SUM=; DATA OUT; SET OUT; DATA XXX; SET XXX; IF STATE=20 OR STATE=33 OR STATE=15 OR STATE=24 OR STATE=21 OR STATE=18 OR STATE=19 OR STATE=27 OR STATE=48; /* FOR FOUR REGIONS IF STATE=20 OR STATE=15 OR STATE=21 OR STATE=27; END OF FOUR REGIONS */ DROP VTMURR PRCYER TPIR P15T17R P18T24R P25T34R POPR EXECR; PROC SORT; BY REGION YEAR; DATA TEMP; MERGE XXX OUT; BY REGION YEAR; IF REGION=6 THEN DO; IF YEAR LT 39 THEN VTMURR=.;END; PRUSRQ=PRUS-PRCYER; PRCYER=PRCYER/POPR*100; PRCYERZ=(PRCYER+LAG(PRCYER))/2; PRUSRQ=PRUSRQ/(POPUS-POPR)*100; PRUSRQZ=(PRUSRQ+LAG(PRUSRQ))/2; PRDIFR=(PRUSRQZ/PRCYERZ); RTPIR=TPIR/CPI; RTPIR=RTPIR/POPR; MILPER=MILPER/POPUS*100; ARRAY FORPOPR VTMURR P15T17R P18T24R P25T34R; DO OVER FORPOPR; FORPOPR=FORPOPR/POPR*100; END; EXECR=EXECR/POPR*10000; EXECRZ=(EXECR+LAG(EXECR))/2; EXECRZ=LOG(EXECRZ+1); PRJDUM=0; IF YEAR GT 76 THEN PRJDUM=1; WAR =0; IF YEAR GT 41 THEN WAR =1; IF YEAR GT 45 THEN WAR =0; WAR2=0; IF YEAR GT 42 THEN WAR2=1; IF YEAR GT 44 THEN WAR2=0; CRACK = 0; IF YEAR GT 84 THEN CRACK = YEAR-84; ARRAY FORLOG VTMURR PRCYERZ PRUSRQZ PRDIFR P15T17R P18T24R P25T34R RTPIR MILPER CPI UNRATE; DO OVER FORLOG; FORLOG=LOG(FORLOG); END; DO OVER FORLOG; FORLOG=DIF(FORLOG); END; ARRAY DD WAR WAR2 PRJDUM CRACK EXECRZ; DO OVER DD; DD=DIF(DD); END; VTMURR1=LAG(VTMURR); VTMURR2=LAG2(VTMURR); VTMURR3=LAG3(VTMURR); IF REGION=1 OR REGION=2 OR REGION=4 OR REGION=5 OR REGION=6 OR REGION=7 THEN DO; VTMURR2=0; VTMURR3=0; END; IF REGION=8 OR REGION=9 THEN DO; VTMURR3=0; END; PROC SORT; BY REGION YEAR; PROC REG; WEIGHT POPR; BY REGION; MODEL VTMURR= PRUSRQZ PRCYERZ RTPIR CPI UNRATE P15T17R P18T24R P25T34R EXECRZ MILPER WAR WAR2 CRACK PRJDUM VTMURR1-VTMURR3 /ACOV; 6) PROGRAM FOR THE IMPACT OF NEARBY STATES DATA DATA1; SET A.XXX; *MUST DO IN 2 STEPS DUE TO SPACE LIMITATIONS. FOR THE SECOND STEP CHANGE TO 26-51 AT THE KEEP LIST AT ZZZZ1, AND CHANGE TO "IF STATE LT 27" AT ZZZZ2; *MUST DO IN SAS6 BECAUSE OF LONG LAGS; *BECAUSE OF THE LAGS, THE PROGRAM ASSUMES 76 OBS PER STATE, THEREFORE MUST ADD 10 YEARS (WITH MISSING DATA) TO THE DATA SET PROVIDED; *THE FOLLOWING PUTS PRISON AND POPULATION VARIABLES FOR ALL STATES INTO STATE 51; PPP=POP; PPP51=PPP;PPP50=LAG76(PPP);PPP49=LAG152(PPP);PPP48=LAG228(PPP); PPP47=LAG304(PPP); PPP46=LAG380(PPP); PPP45=LAG456(PPP); PPP44=LAG532(PPP); PPP43=LAG608(PPP); PPP42=LAG684(PPP); PPP41=LAG760(PPP); PPP40=LAG836(PPP); PPP39=LAG912(PPP); PPP38=LAG988(PPP); PPP37=LAG1064(PPP);PPP36=LAG1140(PPP); PPP35=LAG1216(PPP);PPP34=LAG1292(PPP);PPP33=LAG1368(PPP); PPP32=LAG1444(PPP);PPP31=LAG1520(PPP);PPP30=LAG1596(PPP); PPP29=LAG1672(PPP);PPP28=LAG1748(PPP);PPP27=LAG1824(PPP); PPP26=LAG1900(PPP);PPP25=LAG1976(PPP);PPP24=LAG2052(PPP); PPP23=LAG2128(PPP);PPP22=LAG2204(PPP);PPP21=LAG2280(PPP); PPP20=LAG2356(PPP);PPP19=LAG2432(PPP);PPP18=LAG2508(PPP); PPP17=LAG2584(PPP);PPP16=LAG2660(PPP);PPP15=LAG2736(PPP); PPP14=LAG2812(PPP);PPP13=LAG2888(PPP);PPP12=LAG2964(PPP); PPP11=LAG3040(PPP);PPP10=LAG3116(PPP);PPP9 =LAG3192(PPP); PPP8 =LAG3268(PPP);PPP7 =LAG3344(PPP);PPP6 =LAG3420(PPP); PPP5 =LAG3496(PPP);PPP4 =LAG3572(PPP);PPP3 =LAG3648(PPP); PPP2 =LAG3724(PPP);PPP1 =LAG3800(PPP); PZZ=PRCYE; PZZ51=PZZ;PZZ50=LAG76(PZZ);PZZ49=LAG152(PZZ);PZZ48=LAG228(PZZ); PZZ47=LAG304(PZZ);PZZ46=LAG380(PZZ);PZZ45=LAG456(PZZ); PZZ44=LAG532(PZZ);PZZ43=LAG608(PZZ);PZZ42=LAG684(PZZ); PZZ41=LAG760(PZZ);PZZ40=LAG836(PZZ);PZZ39=LAG912(PZZ); PZZ38=LAG988(PZZ);PZZ37=LAG1064(PZZ);PZZ36=LAG1140(PZZ); PZZ35=LAG1216(PZZ);PZZ34=LAG1292(PZZ);PZZ33=LAG1368(PZZ); PZZ32=LAG1444(PZZ);PZZ31=LAG1520(PZZ);PZZ30=LAG1596(PZZ); PZZ29=LAG1672(PZZ);PZZ28=LAG1748(PZZ);PZZ27=LAG1824(PZZ); PZZ26=LAG1900(PZZ);PZZ25=LAG1976(PZZ);PZZ24=LAG2052(PZZ); PZZ23=LAG2128(PZZ);PZZ22=LAG2204(PZZ);PZZ21=LAG2280(PZZ); PZZ20=LAG2356(PZZ);PZZ19=LAG2432(PZZ);PZZ18=LAG2508(PZZ); PZZ17=LAG2584(PZZ);PZZ16=LAG2660(PZZ);PZZ15=LAG2736(PZZ); PZZ14=LAG2812(PZZ);PZZ13=LAG2888(PZZ);PZZ12=LAG2964(PZZ); PZZ11=LAG3040(PZZ);PZZ10=LAG3116(PZZ);PZZ9 =LAG3192(PZZ); PZZ8 =LAG3268(PZZ);PZZ7 =LAG3344(PZZ);PZZ6 =LAG3420(PZZ); PZZ5 =LAG3496(PZZ);PZZ4 =LAG3572(PZZ);PZZ3 =LAG3648(PZZ); PZZ2 =LAG3724(PZZ);PZZ1 =LAG3800(PZZ); IF STATE = 51; *THE FOLLOWING MAKES THE NEARBY STATE PRISON VARIABLES; NEPR1 =(PZZ11 +PZZ25 +PZZ10 +PZZ43); NEPOP1 =(PPP11 +PPP25 +PPP10 +PPP43); NEPR2 =.; NEPOP2=.; NEPR3 =(PZZ5 +PZZ32 +PZZ29 +PZZ45 +PZZ6); NEPOP3 =(PPP5 +PPP32 +PPP29 +PPP45 +PPP6); NEPR4 =(PZZ26 +PZZ19 +PZZ37 +PZZ44+ PZZ25 +PZZ17 +PZZ18+PZZ43); NEPOP4 =(PPP26 +PPP19 +PPP37 +PPP44+ PPP25 +PPP17 +PPP18+PPP43); NEPR5 =(PZZ3 +PZZ29 +PZZ38 ); NEPOP5 =(PPP3 +PPP29 +PPP38 ); NEPR6 =(PZZ45 +PZZ17 +PZZ32 +PZZ28+PZZ51+PZZ3 +PZZ44+PZZ37); NEPOP6 =(PPP45 +PPP17 +PPP32 +PPP28+PPP51+PPP3 +PPP44+PPP37); NEPR7 =(PZZ33 +PZZ22 +PZZ40 +PZZ31); NEPOP7 =(PPP33 +PPP22 +PPP40 +PPP31); NEPR8 =(PZZ21 +PZZ39 +PZZ31 +PZZ47); NEPOP8 =(PPP21 +PPP39 +PPP31 +PPP47); NEPR9 =.; NEPOP9=.; NEPR10 =(PZZ11 +PZZ1 +PZZ25); NEPOP10=(PPP11 +PPP1 +PPP25); NEPR11 =(PZZ10 +PZZ1 +PZZ41 +PZZ34+ PZZ43); NEPOP11=(PPP10 +PPP1 +PPP41 +PPP34+ PPP43); NEPR12 =(PZZ5 +PZZ38 +PZZ48); NEPOP12=(PPP5 +PPP38 +PPP48); NEPR13 =(PZZ38 +PZZ48 +PZZ45 +PZZ27+PZZ29+PZZ51); NEPOP13=(PPP38 +PPP48 +PPP45 +PPP27+PPP29+PPP51); NEPR14 =(PZZ15 +PZZ50 +PZZ26 +PZZ16 +PZZ18 +PZZ23 +PZZ43); NEPOP14=(PPP15 +PPP50 +PPP26 +PPP16 +PPP18 +PPP23 +PPP43); NEPR15 =(PZZ14 +PZZ23 +PZZ36 +PZZ18); NEPOP15=(PPP14 +PPP23 +PPP36 +PPP18); NEPR16 =(PZZ14 +PZZ26 +PZZ28 +PZZ24+PZZ42+PZZ50 +PZZ17); NEPOP16=(PPP14 +PPP26 +PPP28 +PPP24+PPP42+PPP50 +PPP17); NEPR17 =(PZZ26 +PZZ6 +PZZ37 +PZZ28 +PZZ4 +PZZ16 +PZZ44); NEPOP17=(PPP26 +PPP6 +PPP37 +PPP28 +PPP4 +PPP16 +PPP44); NEPR18 =(PZZ15 +PZZ43 +PZZ36 +PZZ14+PZZ47+PZZ49+PZZ26+PZZ4); NEPOP18=(PPP15 +PPP43 +PPP36 +PPP14+PPP47+PPP49+PPP26+PPP4); NEPR19 =(PZZ44 +PZZ25 +PZZ4 +PZZ37); NEPOP19=(PPP44 +PPP25 +PPP4 +PPP37); NEPR20 =(PZZ30 +PZZ22 +PZZ46); NEPOP20=(PPP30 +PPP22 +PPP46); NEPR21 =(PZZ39 +PZZ8 +PZZ47 +PZZ31+PZZ49); NEPOP21=(PPP39 +PPP8 +PPP47 +PPP31+PPP49); NEPR22 =(PZZ7 +PZZ30 +PZZ40 +PZZ33+PZZ46 +PZZ20); NEPOP22=(PPP7 +PPP30 +PPP40 +PPP33+PPP46 +PPP20); NEPR23 =(PZZ36 +PZZ15 +PZZ50 +PZZ14); NEPOP23=(PPP36 +PPP15 +PPP50 +PPP14); NEPR24 =(PZZ50 +PZZ16 +PZZ35 +PZZ42); NEPOP24 =(PPP50 +PPP16 +PPP35 +PPP42); NEPR25 =(PZZ1 +PZZ19 +PZZ4 +PZZ10+PZZ43); NEPOP25=(PPP1 +PPP19 +PPP4 +PPP10+PPP43); NEPR26 =(PZZ14 +PZZ16 +PZZ17 +PZZ4 +PZZ37 +PZZ43 +PZZ18+PZZ28); NEPOP26=(PPP14 +PPP16 +PPP17 +PPP4 +PPP37 +PPP43 +PPP18+PPP28); NEPR27 =(PZZ51 +PZZ13 +PZZ35 +PZZ42+PZZ48); NEPOP27=(PPP51 +PPP13 +PPP35 +PPP42+PPP48); NEPR28 =(PZZ16 +PZZ6 +PZZ17 +PZZ26+PZZ42 +PZZ51); NEPOP28=(PPP16 +PPP6 +PPP17 +PPP26+PPP42 +PPP51); NEPR29 =(PZZ5 +PZZ45 +PZZ3 +PZZ13+PZZ38); NEPOP29=(PPP5 +PPP45 +PPP3 +PPP13+PPP38); NEPR30 =(PZZ22 +PZZ20 +PZZ46 +PZZ33); NEPOP30=(PPP22 +PPP20 +PPP46 +PPP33); NEPR31 =(PZZ33 +PZZ39 +PZZ7 +PZZ8 +PZZ21); NEPOP31=(PPP33 +PPP39 +PPP7 +PPP8 +PPP21); NEPR32 =(PZZ44 +PZZ3 +PZZ6 +PZZ45+PZZ37); NEPOP32=(PPP44 +PPP3 +PPP6 +PPP45+PPP37); NEPR33 =(PZZ39 +PZZ31 +PZZ7 +PZZ22+PZZ30 +PZZ36 +PZZ46); NEPOP33=(PPP39 +PPP31 +PPP7 +PPP22+PPP30 +PPP36 +PPP46); NEPR34 =(PZZ47 +PZZ41 +PZZ43 +PZZ11+PZZ49); NEPOP34=(PPP47 +PPP41 +PPP43 +PPP11+PPP49); NEPR35 =(PZZ24 +PZZ42 +PZZ27); NEPOP35=(PPP24 +PPP42 +PPP27); NEPR36 =(PZZ23 +PZZ15 +PZZ39 +PZZ18+PZZ33 +PZZ49); NEPOP36=(PPP23 +PPP15 +PPP39 +PPP18+PPP33 +PPP49); NEPR37 =(PZZ44 +PZZ17 +PZZ4 +PZZ26+PZZ19 +PZZ6 +PZZ32); NEPOP37=(PPP44 +PPP17 +PPP4 +PPP26+PPP19 +PPP6 +PPP32); NEPR38 =(PZZ5 +PZZ48 +PZZ13 +PZZ29); NEPOP38=(PPP5 +PPP48 +PPP13 +PPP29); NEPR39 =(PZZ33 +PZZ31 +PZZ36 +PZZ21+PZZ47 +PZZ8 +PZZ49); NEPOP39=(PPP33 +PPP31 +PPP36 +PPP21+PPP47 +PPP8 +PPP49); NEPR40 =(PZZ22 +PZZ7); NEPOP40=(PPP22 +PPP7); NEPR41 =(PZZ34 +PZZ11); NEPOP41=(PPP34 +PPP11); NEPR42 =(PZZ24 +PZZ28 +PZZ35 +PZZ51+PZZ16 +PZZ27); NEPOP42=(PPP24 +PPP28 +PPP35 +PPP51+PPP16 +PPP27); NEPR43 =(PZZ18 +PZZ1 +PZZ34 +PZZ4 +PZZ49 +PZZ14+PZZ11+PZZ25+PZZ26); NEPOP43=(PPP18 +PPP1 +PPP34 +PPP4 +PPP49 +PPP14+PPP11+PPP25+PPP26); NEPR44 =(PZZ19 +PZZ37 +PZZ32 +PZZ4 +PZZ6 +PZZ17); NEPOP44=(PPP19 +PPP37 +PPP32 +PPP4 +PPP6 +PPP17); NEPR45 =(PZZ6 +PZZ3 +PZZ13 +PZZ29+PZZ51 +PZZ32); NEPOP45=(PPP6 +PPP3 +PPP13 +PPP29+PPP51 +PPP32); NEPR46 =(PZZ33 +PZZ30 +PZZ22 +PZZ20); NEPOP46=(PPP33 +PPP30 +PPP22 +PZZ20); NEPR47 =(PZZ34 +PZZ21 +PZZ49 +PZZ39+PZZ18 +PZZ8); NEPOP47=(PPP34 +PPP21 +PPP49 +PPP39+PPP18 +PPP8); NEPR48 =(PZZ38 +PZZ13 +PZZ27); NEPOP48=(PPP38 +PPP13 +PPP27); NEPR49 =(PZZ47 +PZZ36 +PZZ39 +PZZ21+PZZ18 +PZZ34 +PZZ43); NEPOP49=(PPP47 +PPP36 +PPP39 +PPP21+PPP18 +PPP34 +PPP43); NEPR50 =(PZZ14 +PZZ24 +PZZ16 +PZZ23); NEPOP50=(PPP14 +PPP24 +PPP16 +PPP23); NEPR51 =(PZZ6 +PZZ27 +PZZ13 +PZZ45+PZZ28 +PZZ42); NEPOP51=(PPP6 +PPP27 +PPP13 +PPP45+PPP28 +PPP42); ARRAY SSS OP1-OP51; ARRAY RRR NEPR1-NEPR51; ARRAY TTT NEPOP1-NEPOP51; DO OVER SSS; SSS=RRR/TTT*100; END; ARRAY UUU OPX1-OPX51; ARRAY VVV PPP1-PPP51; ARRAY WWW PZZ1-PZZ51; DO OVER UUU; UUU=(PRUS-WWW-RRR)/(POPUS-TTT-VVV)*100; END; KEEP YEAR OP1-OP26 OPX1-OPX26; *ZZZZZ1; PROC SORT; BY YEAR; DATA DATA2; SET A.XXX; KEEP STATE YEAR POPUS POP P15T17 P18T24 P25T34 CPI TPI VTMUR MILPER UNRATE PRCYE EXEC; PROC SORT; BY YEAR STATE; DATA DATA3; MERGE DATA1 DATA2; BY YEAR; PROC SORT; BY STATE YEAR; DATA DATA3; SET DATA3; IF STATE NE 9; IF STATE NE 2; IF STATE NE 12; IF STATE LT 27; *ZZZZZ2; PRUSQ=PRUS-PRCYE; PRCYE=PRCYE/POP*100; PRCYEZ=(PRCYE+LAG(PRCYE))/2; PRUSQ=PRUSQ/(POPUS-POP)*100; PRUSQZ=(PRUSQ+LAG(PRUSQ))/2; IF STATE = 1 THEN OP=OP1 ;IF STATE = 2 THEN OP=OP2 ; IF STATE = 3 THEN OP=OP3 ;IF STATE = 4 THEN OP=OP4 ; IF STATE = 5 THEN OP=OP5 ;IF STATE = 6 THEN OP=OP6 ; IF STATE = 7 THEN OP=OP7 ;IF STATE = 8 THEN OP=OP8 ; IF STATE = 9 THEN OP=OP9 ;IF STATE =10 THEN OP=OP10 ; IF STATE =11 THEN OP=OP11 ;IF STATE =12 THEN OP=OP12 ; IF STATE =13 THEN OP=OP13 ;IF STATE =14 THEN OP=OP14 ; IF STATE =15 THEN OP=OP15 ;IF STATE =16 THEN OP=OP16 ; IF STATE =17 THEN OP=OP17 ;IF STATE =18 THEN OP=OP18 ; IF STATE =19 THEN OP=OP19 ;IF STATE =20 THEN OP=OP20 ; IF STATE =21 THEN OP=OP21 ;IF STATE =22 THEN OP=OP22 ; IF STATE =23 THEN OP=OP23 ;IF STATE =24 THEN OP=OP24 ; IF STATE =25 THEN OP=OP25 ;IF STATE =26 THEN OP=OP26 ; IF STATE =27 THEN OP=OP27 ;IF STATE =28 THEN OP=OP28 ; IF STATE =29 THEN OP=OP29 ;IF STATE =30 THEN OP=OP30 ; IF STATE =31 THEN OP=OP31 ;IF STATE =32 THEN OP=OP32 ; IF STATE =33 THEN OP=OP33 ;IF STATE =34 THEN OP=OP34 ; IF STATE =35 THEN OP=OP35 ;IF STATE =36 THEN OP=OP36 ; IF STATE =37 THEN OP=OP37 ;IF STATE =38 THEN OP=OP38 ; IF STATE =39 THEN OP=OP39 ;IF STATE =40 THEN OP=OP40 ; IF STATE =41 THEN OP=OP41 ;IF STATE =42 THEN OP=OP42 ; IF STATE =43 THEN OP=OP43 ;IF STATE =44 THEN OP=OP44 ; IF STATE =45 THEN OP=OP45 ;IF STATE =46 THEN OP=OP46 ; IF STATE =47 THEN OP=OP47 ;IF STATE =48 THEN OP=OP48 ; IF STATE =49 THEN OP=OP49 ;IF STATE =50 THEN OP=OP50 ; IF STATE =51 THEN OP=OP51 ; IF STATE = 1 THEN OPX=OPX1 ;IF STATE = 2 THEN OPX=OPX2 ; IF STATE = 3 THEN OPX=OPX3 ;IF STATE = 4 THEN OPX=OPX4 ; IF STATE = 5 THEN OPX=OPX5 ;IF STATE = 6 THEN OPX=OPX6 ; IF STATE = 7 THEN OPX=OPX7 ;IF STATE = 8 THEN OPX=OPX8 ; IF STATE = 9 THEN OPX=OPX9 ;IF STATE =10 THEN OPX=OPX10 ; IF STATE =11 THEN OPX=OPX11 ;IF STATE =12 THEN OPX=OPX12 ; IF STATE =13 THEN OPX=OPX13 ;IF STATE =14 THEN OPX=OPX14 ; IF STATE =15 THEN OPX=OPX15 ;IF STATE =16 THEN OPX=OPX16 ; IF STATE =17 THEN OPX=OPX17 ;IF STATE =18 THEN OPX=OPX18 ; IF STATE =19 THEN OPX=OPX19 ;IF STATE =20 THEN OPX=OPX20 ; IF STATE =21 THEN OPX=OPX21 ;IF STATE =22 THEN OPX=OPX22 ; IF STATE =23 THEN OPX=OPX23 ;IF STATE =24 THEN OPX=OPX24 ; IF STATE =25 THEN OPX=OPX25 ;IF STATE =26 THEN OPX=OPX26 ; IF STATE =27 THEN OPX=OPX27 ;IF STATE =28 THEN OPX=OPX28 ; IF STATE =29 THEN OPX=OPX29 ;IF STATE =30 THEN OPX=OPX30 ; IF STATE =31 THEN OPX=OPX31 ;IF STATE =32 THEN OPX=OPX32 ; IF STATE =33 THEN OPX=OPX33 ;IF STATE =34 THEN OPX=OPX34 ; IF STATE =35 THEN OPX=OPX35 ;IF STATE =36 THEN OPX=OPX36 ; IF STATE =37 THEN OPX=OPX37 ;IF STATE =38 THEN OPX=OPX38 ; IF STATE =39 THEN OPX=OPX39 ;IF STATE =40 THEN OPX=OPX40 ; IF STATE =41 THEN OPX=OPX41 ;IF STATE =42 THEN OPX=OPX42 ; IF STATE =43 THEN OPX=OPX43 ;IF STATE =44 THEN OPX=OPX44 ; IF STATE =45 THEN OPX=OPX45 ;IF STATE =46 THEN OPX=OPX46 ; IF STATE =47 THEN OPX=OPX47 ;IF STATE =48 THEN OPX=OPX48 ; IF STATE =49 THEN OPX=OPX49 ;IF STATE =50 THEN OPX=OPX50 ; IF STATE =51 THEN OPX=OPX51 ; DROP OP1-OP51 OPX1-OPX51; OPZ= (OP+LAG(OP))/2; OPXZ=(OPX+LAG(OPX))/2; OPDIF=(OPZ/PRCYEZ); OPXDIF=(OPXZ/PRCYEZ); IF VTMUR=0 THEN VTMUR=.1;; RTPI=TPI/CPI; RTPI=RTPI/POP; MILPER=MILPER/POPUS*100; ARRAY FORPOP VTMUR P15T17 P18T24 P25T34; DO OVER FORPOP; FORPOP=FORPOP/POP*100; END; EXEC=EXEC/POP*10000; EXECZ=(EXEC+LAG(EXEC))/2; EXECZ=LOG(EXECZ+1); PRJDUM=0; IF YEAR GT 76 THEN PRJDUM=1; WAR =0; IF YEAR GT 41 THEN WAR =1; IF YEAR GT 45 THEN WAR =0; WAR2=0; IF YEAR GT 42 THEN WAR2=1; IF YEAR GT 44 THEN WAR2=0; CRACK = 0; IF YEAR GT 84 THEN CRACK = YEAR-84; ARRAY FORLOG VTMUR CRMUR PRCYEZ PRUSQZ P15T17 P18T24 P25T34 RTPI MILPER CPI UNRATE OPZ OPXZ OPDIF OPXDIF; DO OVER FORLOG; FORLOG=LOG(FORLOG); END; DO OVER FORLOG; FORLOG=DIF(FORLOG); END; ARRAY DUMDIF CRACK PRJDUM WAR WAR2 EXECZ; DO OVER DUMDIF; DUMDIF=DIF(DUMDIF); END; VTMUR1=LAG(VTMUR); VTMUR2=LAG2(VTMUR); VTMUR3=LAG3(VTMUR); VTMUR4=LAG4(VTMUR); VTMUR5=LAG5(VTMUR); IF STATE=33 THEN STSIZE60=01;IF STATE= 5 THEN STSIZE60=02; IF STATE=39 THEN STSIZE60=03;IF STATE=14 THEN STSIZE60=04; IF STATE=36 THEN STSIZE60=05;IF STATE=44 THEN STSIZE60=06; IF STATE=23 THEN STSIZE60=07;IF STATE=31 THEN STSIZE60=08; IF STATE=22 THEN STSIZE60=09;IF STATE=10 THEN STSIZE60=10; IF STATE=15 THEN STSIZE60=11;IF STATE=34 THEN STSIZE60=12; IF STATE=26 THEN STSIZE60=13;IF STATE=47 THEN STSIZE60=14; IF STATE=50 THEN STSIZE60=15;IF STATE=11 THEN STSIZE60=16; IF STATE=43 THEN STSIZE60=17;IF STATE=24 THEN STSIZE60=18; IF STATE= 1 THEN STSIZE60=19;IF STATE=19 THEN STSIZE60=20; IF STATE=21 THEN STSIZE60=21;IF STATE=18 THEN STSIZE60=22; IF STATE=48 THEN STSIZE60=23;IF STATE=16 THEN STSIZE60=24; IF STATE= 7 THEN STSIZE60=25;IF STATE=41 THEN STSIZE60=26; IF STATE=37 THEN STSIZE60=27;IF STATE=17 THEN STSIZE60=28; IF STATE=25 THEN STSIZE60=29;IF STATE=49 THEN STSIZE60=30; IF STATE= 4 THEN STSIZE60=31;IF STATE=38 THEN STSIZE60=32; IF STATE= 6 THEN STSIZE60=33;IF STATE=28 THEN STSIZE60=34; IF STATE= 3 THEN STSIZE60=35;IF STATE=20 THEN STSIZE60=36; IF STATE=32 THEN STSIZE60=37;IF STATE=45 THEN STSIZE60=38; IF STATE=40 THEN STSIZE60=39;IF STATE= 9 THEN STSIZE60=40; IF STATE=42 THEN STSIZE60=41;IF STATE=27 THEN STSIZE60=42; IF STATE=13 THEN STSIZE60=43;IF STATE=35 THEN STSIZE60=44; IF STATE=12 THEN STSIZE60=45;IF STATE=30 THEN STSIZE60=46; IF STATE= 8 THEN STSIZE60=47;IF STATE=46 THEN STSIZE60=48; IF STATE=51 THEN STSIZE60=49;IF STATE=29 THEN STSIZE60=50; IF STATE= 2 THEN STSIZE60=51; A=STSIZE60; IF A=1 OR A=2 OR A=3 OR A=4 OR A=6 OR A=9 OR A=10 OR A=12 OR A=13 OR A=14 OR A=16 OR A=17 OR A=18 OR A=19 OR A=20 OR A=21 OR A=22 OR A=25 OR A=26 OR A=27 OR A=30 OR A=31 OR A=38 THEN DO; VTMUR2=0; VTMUR3=0; VTMUR4=0; VTMUR5=0; END; IF A=8 OR A=11 OR A=15 OR A=28 OR A=32 OR A=34 OR A=35 OR A=36 OR A=37 OR A=47 OR A=50 THEN DO; VTMUR3=0; VTMUR4=0; VTMUR5=0; END; IF A = 5 OR A=23 OR A=24 OR A=39 OR A=42 OR A=43 OR A=48 OR A=49 THEN DO; VTMUR4=0; VTMUR5=0; END; IF A=7 OR A=29 OR A=41 OR A=44 OR A=46 THEN DO; VTMUR5=0; END; PROC SORT; BY STSIZE60 YEAR; PROC REG; BY STSIZE60; WEIGHT POP; MODEL VTMUR= OPZ OPXZ PRCYEZ RTPI CPI UNRATE P15T17 P18T24 P25T34 EXECZ MILPER WAR WAR2 CRACK PRJDUM VTMUR1-VTMUR5/ACOV; 7) EXPANDED RESULTS SECTION EXPANDED DESCRIPTION OF THE RESULTS FROM MARVELL & MOODY, "THE IMPACT OF OUT-OF-STATE PRISON POPULATION CHANGES ON STATE HOMICIDE RATES: AN APPLICATION OF DISPLACEMENT AND FREE-RIDER THEORY." Criminology, August, 1998. T. Marvell & C. Moody (Note: This memo is a longer version of the results from the article. The results section in the article was cut down at the request of reviewers. The major difference is that this version includes alternate analysis to test the sensitivity of the results.) Table 1 summarizes the 48 individual state regressions. The states are listed in descending order of population size in 1960, near the midpoint of data series. The two left columns give the mean number of homicides for 1930-92 and the mean absolute annual percent change in homicides. The latter illustrates the greater variation for small states and states with fewer homicides. Standard errors are much larger for small states; the correlation between homicide means and the standard errors for out-state prison coefficients is -.60 (prob. = .001). Small states, likewise, tend to have more significant lagged dependent variables (which almost always have negative coefficients). The overall finding is that in-state prison has less impact than out-state prison, and the first prediction made above is confirmed. Of the 48 out-state prisoner coefficients, 94% are negative. Eleven are significant to the .05 level, and only one positive coefficient is significant. Many other coefficients are large, but are not significant due to the unevenness of homicide data. In contrast, only 65% of the in-state prison coefficients are negative, four significant (again one positive coefficient is significant). A good measure for comparison is coefficient means, given at the end of Table 1. The out-state prisoner mean is -.76 (t = 6.33),[1] more than three times the in-state prison mean of -.22 (t = 3.67). The erratic nature of homicide trends in small states, however, leads to some extreme values which can greatly affect the mean. We address this problem in two ways. First, we eliminate extreme outlier values by dropping the two largest positive and two largest negative coefficients. (The number dropped is necessarily somewhat arbitrary; dropping the three highest and three lowest produces almost the same results.) This has little impact on the in-state prison mean, but it increases the out-state mean to -.83. Second, we drop the smallest states, presenting means for the 40 and 24 largest states (the latter figure is half the states in the study). These produce lower in-state means of -.18 and -.17, respectively. The out-state means increase, and the largest is -.94 for 40 states, nearly five times the in-state mean. Table 1 suggests that the elasticity for out-state prison averages roughly four times the elasticity for in-state prison. The results for out-state prison are closest to (but still smaller than) studies that estimate the impact of national prison levels on national crime, and the results for in-state prison are similar to studies that estimate the impact of state prisons on state homicide. Again, the elasticity is a measure of percent change in crime due to a given percent change in prison population. Each additional state prisoner has a much bigger impact in the state than each additional prisoner at the national level, because the former is a much larger percent change than the latter. The crime- reduced-per-additional-prisoner measure, however, would be misleading here because in practice the typical change in the national level involves many more prisoners than the typical change at the state level. In contrast, the percent changes at the national and state levels are comparable in practice. Therefore, the elasticity is the better measure. Still, comparing the size of in-statate and out-state elasticities somewhat understates the impact of in-state prison. First, the elasticity of in-state prison might be underestimated because, as discussed above, state-level data are not available for several control variables that tend to affect homicide and prison population the same way. Second, because in-state prison varies more then out-state prison, in practice it has had more impact for a given elasticity. The mean absolute change for in-state and out- state prison over 1930-92 were 5.9% and 3.7% respectively. Because the former is 1.6 times larger, out-state prison trends are roughly two or three (rather than four times) as important as in- state trends in affecting state homicide rates. The magnitude of the free-rider effect suggests that potential murderers move in and out of states at a high rate. Estimating the rate requires that we first calculate the ratio of out-state prison impact to in-state prison impact. For Table 1, the ratio averages approximately four to one. This suggests that, of the inmates who are prevented from killing because of incapacitation, 80% would be outside the state if on the street. This estimate, of course, is sensitive to the estimates of in-state and out-state prison coefficients, and as will be seen these vary with different regression procedures. Also, the regressions might underestimate the elasticity of in-state prisons. Most control variables push homicide and prison population in the same direction, and if the controls are incomplete the regression will produce low prison coefficients. As discussed above, only national-level data are available for several controls; to the extent that these do not reflect state trends, the in-state prison coefficient is probably too small. ALTERNATE MODELS. We tested the sensitivity of the Table 1 results by varying the regression design, the variables entered, and the time period. [2] Table 2 summarizes the results of several alternate regressions. Model 1 is the basic model, repeated here to facilitate comparisons. The other models are the same as used in Table 1 except for the modifications indicated. Models 2 and 3 are presented because they give a likely range for out-state prison elasticities. Entering a linear trend variable increases out-state prison coefficients to approximately -1.10, but it has little impact on in-state prison. Because it is added to a differenced equation, the linear trend represents quadratic growth. It controls for factors, not represented by other independent variables, that change at an ever-increasing or ever-decreasing rate. An example might be the number of young men who grew up in families without fathers. The quadratic trend was not entered in the basic model because it is controversial, but recently Black and Nagin (1997) found that it greatly helps when modeling homicide and violent crime time series. In Model 3 the variables are not differenced. This reduces the out-state prison coefficients to approximately half those in Model 1. The likely reason is that the long-term homicide and prison trends are upward, thus obscuring a negative relationship between the two variables. (The regression includes a linear trend, which takes the place of the intercept in the differenced equation. Without the trend, the out-state coefficients are higher, similar to those in Table 1. The trend, however, only controls for linear factors which cause homicide and out-state prison trends to be similar; when a quadratic term is entered, the results for out-state prison are very similar to those for the differenced equation with a trend, see Table 2, Model 2, but in- state prison coefficients are smaller.) The next two models use generalized least squares (GLS), which does not include lagged dependent variables but incorporates autoregressive corrections in the error term. Model 4 uses a basic autoregressive process. This analysis is conducted with SAS PROC AUTOREG, maximum likelihood method, using backstep autoregression with an initial order of five (SAS Institute, 1993:183-254). The backstep procedure eliminates autoregressive lags that are not significant; and when none are significant the procedure is the same as OLS. Model 5 uses generalized autoregressive conditional heteroscedasticity (GARCH), which is designed for data series, such as interest rates, where variability is very irregular (Greene, 1993:568-577; SAS Institute, 1993:197-221). The resulting coefficients are a bit smaller than those in Table 1, perhaps because the regressions are not weighted, and early years may have excessive influence. Models 6 and 7 are time series for the early and late parts of the data. Model 6 ends in 1965 and starts in 1931 to 1935 depending on the number of lagged dependent variables. Model 7 is for the years 1960-1992. The regressions usually have only 18 degrees of freedom, about the lower limit for time series regression. Out-state prison coefficients change little, but in- state prison coefficients change from small and not significant in the early part to at least -.32 for 1960-92. The most likely reason is that, as discussed later, the displacement impact is much greater in the later period, and this increases the homicide- reduction impact of in-state prison. [3] One commentator suggested that the Table 1 results might be due to the fact that the state-level prison data might be less accurate than the national level data, such that in Table 1 the national data are acting as surrogates for the state data. However, this effect could only occur if state prison data were inaccurate for only some of the states, otherwise the national level data would its self be in accurate (unless by odd coincidence the state level errors canceled out). Thus, under this line of argument, in-state prison would have a larger impact than out-state prison in many states, which is not the case (see Table 1). To our mind, the most likely source of bias in Table 1 with respect to the prison variables is that homicide might be affected by some nation-wide trend that is highly correlated with nation- wide prison. We are not aware of any such variable, at least one that is distinct from prison population and, thus, would render the results spurious. Almost all factors that one would expect to affect state crime are state-level factors, and thus even if not included as control variables they would have little effect on the results concerning out-state prison. It is quite likely, on the other hand, that the out-state prison coefficient reflects broader nation-wide trends in criminal justice punitiveness. Most important here is that the out-state prison impact is only part of the total national effort to incapacitate criminals around the nation, including use of juvenile facilities and jails and even greater numbers on probation and parole, which offer some partial incapacitation. As we discuss in the Appendix, it seems that the sum of the incapacitation efforts nationwide produce a greater impact then prison population alone. But this does not detract from the Table 1 findings concerning the importance of out-state prison. REGIONAL LEVEL. In Table 3 we aggregate the state data into regions, summing the state-level continuous variables and dividing by regional population. Aggregation reduces variability of the homicide data, standard errors, and negative autocorrelation. Thus, Table 3 presents t-ratios rather than standard errors. It also presents the results for all independent variables. The second prediction made earlier, that out-region prison has a smaller impact then out-state prison, is confirmed, but only marginally so. Using the nine Census regions (the states in each are listed in Table 3), the mean coefficients are -.78 (t = 4.21) for out-region prison populations and -.24 (t = 1.80) for in-region prison. The out-region prison mean is slightly larger than the out-state prison mean coefficient of -.76 for 48 states, but slightly smaller than the -.83 when outliers are deleted (Table 1). Perhaps the best comparison is with the -.82 mean for the 24 largest states (Table 1), which dominate the regional data. The third prediction is that the in-region prison has a larger impact than in-state prison, because the former include criminals that would move from state to state in the region if on the street. Again, this is barely confirmed by Table 3. The mean of -.24 for the nine regions is barely larger than the 48-state mean of -.22, but is substantially larger than the -.17 for the largest 24 states. Table 3, in summary, supports the second and third predictions, but the differences between the regional and state- level results are not large. It is likely, therefore, that migration is largely between regions of the country and, by implication, over substantial distances. The fourth prediction, that aggregation into larger regions reduces the impact of out-region prison and increases the impact of in-region prison, receives strong support. Using the four large Census regions, out- and in-region prison have similar impacts, with mean coefficients of -.50 (t = 2.50) and -.44 (t = 2.62), respectively (Table 3). The former is substantially lower than the mean coefficients with nine regions and with individual states, and the latter is substantially higher. The final step is aggregation into a single unit, that is to the national level (Table 4, last column), where "in-region" prison is the sum of all state prison populations (divided by the national population). The prison population coefficient is -.85, which is quite a bit smaller than the estimates in Devine et al. (1988), perhaps because our control variables are less complete. Finally, we duplicate the Table 2 models at the regional level. When the results in Table 4 are compared to the right column of Table 2 (for the 24 largest states), the second through fourth predictions are confirmed for 25 of the 30 coefficients in the alternate models (Models 2-7, Table 4). [4] Again, the differences between the state level and the nine-region level are not large. COEFFICIENT HETEROGENEITY. Overall, Tables 1 to 4 are substantial evidence that the incapacitation impact of prisons on state homicides comes mainly from prisons in other states. There is, however, considerable heterogeneity in the state and regional results that should be explored. Much is due to chance variation. The coefficients on out-state prison in Table 1, for example, are within one standard error of the mean coefficient for 71% of the states and within two standard errors for 94%, and the corresponding figures for in-state prison coefficients are 60% and 94%. These are close to the 68% and 95% expected by chance. Of the 26 in-state and out-state prison coefficients in the two regional levels, 62% are within one standard error and all are within two standard errors. Thus, the wide range of coefficients in Tables 1 and 4 can be attributed largely to statistical uncertainty. This uncertainty also makes it difficult to discern patterns in the coefficients. In general, however, the impacts of in-area and out-area prison populations appear to be negatively related. At the state level, the correlation between in-state and out-state prison coefficients is a modest -.32 (prob. = .03). The correlations are -.14 (prob. = .39) and -.58 (prob. = .004) for the 40 and 24 largest states respectively. At the nine-region level the correlation is -.66 (prob. = .06). The reason might be that the impact of out-state incapacitation is positively associated with the amount of migration, while the impact of in-state incapacitation is negatively associated. The absence of information about migration habits of murderers, however, prevents a test of this suggestion. NEARBY-STATE PRISON POPULATIONS. The impact of prisons in nearby states is a complex issue, involving the length of migration as well as balancing the free- riding and displacement effects. If the out-state prison incapacitation (that is, free riding) impact is primarily due to short-distance migration or to temporary trips across state lines, then the prisons in nearby states have more impact than prisons elsewhere in the nation. Likewise, if the out-state prison impact is due primarily to long-distance migration, then nearby-state prisons have comparatively little impact. As for displacement (criminals moving to states with less harsh penal policies), Table 1 suggests that it is much weaker than the free-rider impact. The impact, however, might be stronger with respect to nearby states, and thus counteract any free-riding. Eck (1993) argues that territorial displacement is a short-distance phenomenon because criminals tend to move to familiar areas. Criminals wishing to escape punitive prison policies in a state, thus, are likely to pick nearby states because the penal policies there are best known. To measure nearby prison we add prison populations in states that either touch the subject state or are within 50 miles of its boarders. (Like other prison variables, nearby prison is a per capita variable, divided by the total population of the nearby states.) The latter are included because quite often states that one might expect to affect each other do not actually touch, such as Michigan-Illinois, Maine-Massachusetts, and Maryland-New Jersey. Even with this broad definition, the nearby states average only 11% of the national population (the extremes are 2% for Washington and 24% for Ohio). (Because prison trends are similar throughout the nation, any larger grouping of states would probably present collinearity problems. Correlations between yearly changes in nearby-state prison and changes in rest-of-the-nation prison are fairly large, averaging .77 for the 48 states with a range of .52 to .94, but not large enough to suggest that the results in Table 5 are due to collinearity.) As seen in Table 5 nearby-state prison has relatively little impact. Model 1 is the same as the regressions in Table 1, except that out-state prison is divided into nearby states and the rest of the nation. The coefficients on nearby prison are not significant, and they are essentially zero without the smallest states. The nearby-state coefficients are slightly larger with other specifications, but still not significant (Table 3, Models 2-4). Because the impact of rest-of-the-nation prison continues to be very large, Table 5 suggests that potential murderers migrate considerable distances and that the Table 1 results are not due to border-crossing escapades. This is consistent with the regional analysis, where the sizeable out-region prison impact implies long- distance migration. As for nearby-state prison, a possible reason for the small coefficients is that chances of a criminal's migrating to a state are not closely related to the distance of the state. In that case, few potential murders would come from nearby states simply because such states contain only a small portion of the out-state population. Another likely explanation is that migration from nearby states is substantial, but the impact is counterbalanced by displacement, as discussed above. The next section gives some support for this explanation. EVIDENCE OF DISPLACEMENT. The prison coefficients in Tables 1 to 5 reflect the net impact of displacement and free-riding. The fact that free riding dominates does not rule out displacement, which might be obscured by the free-rider effect. We searched for evidence of displacement by replacing the out-state and in-state prison variables with a ratio of the two (that is, out-state prison divided by in-state prison, where the prison variables are per-capita variables). [5] If potential murderers do in fact move to states with more lenient penal policies, this variable has a positive coefficient. As seen in Table 6, Model 1, that result does occur, with the coefficients averaging about .2. The results are similar at the regional level (analysis not reported here), where the ratio of out-region to in- region prison has a mean coefficient of .23 with nine regions and .25 with four regions (neither is significant, however). It is possible that this result, an apparent displacement impact, is only due to the fact that in-state prison varies more than out-state prison, as was discussed above. That is, the positive coefficient on the prison ratio variable might reflect the negative association between homicides and the in-state prison, the denominator of the ratio. The smallness of the displacement impact is consistent with other research on displacement (e.g., Eck, 1993). The significance levels in Table 6 are not large, with t-ratios below three, and the coefficients are modest. If a state increases its prison population by ten percentage points more than nation-wide increases, homicides would decline roughly 2% in the state due to displacement. (Additional declines might occur due to deterrent and incapacitation effects.) Models 2 and 3 break down the time series into early and late years. The evidence for displacement is limited to the late years, which might explain why in-state prison has a greater impact then (Table 2, Models 6 and 7). Model 4 of Table 6 uses the ratio of nearby-state prison to in-state prison, and Model 5 uses the ratio of rest-of-the-nation prison to in-state prison. The coefficients are positive, although not always significant. Unlike in Table 5, nearby-state prison has an impact that is nearly as large as rest-of-the-nation prison. Because nearby states average only 11% of the out-state prison population, the implication is that nearby states contribute far more than their share of the displacement effect. As discussed earlier, this might explain why nearby-state prison has little impact in Table 5. CONCLUSIONS. The evidence for free riding is strong, and on average out- state prison has much more impact on state homicide rates than in- state prison. Prior state-level studies suffer from specification bias due to disaggregation and, thus, understate the impact of prisons on homicides. A state usually benefits more from prison expenditures elsewhere than from its own expenditures, and the benefits of the latter are largely felt outside the state. This study is limited to homicide because it is the easiest crime to analyze. State homicide data are available for more years than other crime data, and simultaneity problems are less likely. There is evidence, however, that the free-rider effect exists for other crimes as well. Devine et al. (1988) and Cohen and Land (1987) estimated much larger impacts of prisons on robbery, burglary, and auto theft at the national level than Marvell and Moody (1994), Zimring and Hawkins (1995), and Levitt (1996) estimated at the state level. Also, in general, robbery and burglary trends are similar to those for homicide (Blumstein, 1995), and characteristics of homicide are often the same as robbery and assault except for, of course, the death (Cook, 1987; Harries, 1990). Finally, in other research in progress, we found that out-state prison growth reduces other crimes as well, with the greatest impact on robbery and burglary. These similarities also explain why enlarging prison populations can reduce homicides even though most prisoners are not convicted murderers. Incarceration for robbery or assault, especially, should have an incapacitation impact on homicide; the facts that murderers are similar to other major criminals and that criminals usually do not specialize (e.g., Kempf, 1987) suggests that the sheer volume of prisoners can affect homicide rates. When we speak of the association between crime and prison population, we assume that the latter is the actual causal factor. It is possible that part of the association is due not to prison population but to other aspects of criminal justice systems that affect prisons. For example, higher arrest rates and stricter prosecution of felony charges might deter criminals directly or cause more jail incapacitation, in addition to their impacts on prison populations. Thus prison population might be in part a surrogate measure of the general effectiveness and punitiveness of the criminal justice system in a state, but we do not know how large a part. This does not affect our conclusions concerning the relative importance of displacement and free riding, but it does hinder attempts to estimate the impact of prisons and to make cost- benefit calculations. Our findings have several broader implications. They support Eck's (1997) suggestion that criminology theory and research should give more attention to free riding. At the very least, researchers who search for displacement should look as thoroughly for free- riding. This is especially true when researchers use control sites that might be free-riding beneficiaries. Second, the findings are consistent with the general consensus that displacement effects are usually small, and we agree with Miethe (1991) that this is evidence against the importance of the rational criminal model. Third, the relatively small impact of in-state prison supports the common assumption that the incapacitation impact of prisons is much larger than the deterrent impact. In one respect our findings depart from what most criminologists probably believe. The magnitude of the free-rider effect implies that potential murderers (and other major criminals) move in and out of states at a high rate. Prior research provides some limited support for this implication, and to our knowledge no research refutes it. This topic deserves much more attention. APPENDIX. IN-STATE AND OUT-STATE HOMICIDE As part of our search for sensitivity of the results to alternate specifications and procedures, we added out-state homicide (nationwide homicide less homicide in the state) to the state-level homicide regressions in Table 1. (Like all continuous variables, this is divided by population, here the out-state population, logged, and differenced.) The result is so interesting and complex that it deserves separate appendix. As summarized in Model 1 of Table 7, the out-state homicide has an extremely large association with in-state homicide, with mean coefficients of approximately .8 for current year out-state homicide and .5 for the prior year. When the second lag of out- state homicide is entered, it is not significant. The current-year out-state homicide coefficient is virtually the same when the lagged variable is not entered. The correlation between out-state and in-state homicide averages .49 for the 48 states. That is, the current and prior year elasticities sum to more than one. The significance levels are very high, with t-ratios approximately ten for the current year. Importantly, the coefficient on out-state prison population becomes negligible. (When a trend term is added, the out-state prison coefficients are larger, reaching -.25 for 40 states, but they are not significant.) Furthermore, substituting nationwide violent crime (rape, robbery, and assault, for which data start in 1935) for out-state homicide produces similar results, although less extreme (Table 7, Model 2). Even property crime (burglary and motor vehicle theft) is significant, and the out-state prison coefficients are much lower than in Table 1 (Model 3). The results in Models 2 and 3 are consistent with research on criminal specialization, which generally finds low levels of specializations, but with some tendency to specialize in violent or property crime (e.g., Chaiken and Chaiken, 1982; Gottfredson and Gottfredson, 1994; Kempf, 1987). Why is out-state homicide so important, and why does it eliminate the impact of out-state prison? These questions lead to difficult conceptual issues and, consequently, complex statistical analyses. We first present two possible interpretations that bring into question the large estimate of out-state prison found in Table 1, and then we present two interpretations that are consistent with that estimate. The evidence overwhelmingly supports the latter. Scenario A. A possible, but unlikely, explanation is a contagion or brutalizing effect, whereby persons commit murder because they learn about murders elsewhere. It seems inconceivable that this could have enough impact to produce the huge elasticities for out-state homicide; that would imply a magnifying effect. Also if these effects are important, the existence or absence of a lagged impact would be the same at the state and national levels (a lagged effect is possible if it takes time for information about homicides to travel). The lags, however, are substantial at the state-level (Table 7) and negligible at the national level (research not reported here). Finally, contagion and brutalizing effects cannot explain why entering out-state homicide eliminates the impact of out-state prison in Model 1 of Table 7. Scenario B. The association between in-state homicide and out-state prison might be due, not to incapacitation, but to intervening factors that are not represented by control variables in Table 1. If such factors exist, they must have opposite impacts on in-state homicide and out-state prison (because of the negative coefficient on out-state prison in Table 1), and they must have impacts on in-state and out-state homicides in the same direction (because of the positive coefficient on out-state homicide in Table 7). To our knowledge, the only candidates are factors that pertain to the effectiveness of the criminal justice system generally. The number of prisoners is determined by many facets of the criminal justice system - including the effectiveness of the police and prosecution in capturing and convicting criminals, the willingness of citizens to cooperate with the police and prosecution, legislative sentencing policy, judges' punitiveness when sentencing, and parole board release policies. We do not have usable variables to control for these trends over the time period studied. Some or all might affect homicide independently of the incapacitation impact of imprisonment. For simplicity sake, we present the argument in terms of one element of the criminal justice system, police effectiveness. Perhaps greater effectiveness deters homicide and also increases prison populations because arrests increase, producing a negative association between out-state prison and homicide. If police effectiveness increases at similar rates throughout the nation, the deterrent impact would operate both inside and outside the state, leading to a positive association between in-state and out-state homicide (thus, indirectly leading to the negative association between in-state homicide and out-state prison). This scenario, it is important to stress, pertains to the deterrent impact of police effectiveness only, but as a practical matter it would be very difficult to distinguish such deterrence from an incapacitation impact due to the fact that greater police effectiveness implies more incarceration in jails and prisons. An obvious objection to this explanation is its complexity and, thus, the need to presume several large, specific causal assumptions. Also, it relies totally on deterrence, and to our knowledge no studies have found deterrent impacts from criminal justice system effectiveness as large as is implied by the huge elasticities for out-state prison and by the fact that entering out-state homicide eliminates the association between in-state homicide and out-state prison. Also, scenario B is not consistent with the association between in-state homicide and lagged out-state homicide. If the relationship between in-state and out-state homicide were due to common deterrence resulting from nation-wide criminal justice activities, then the relationship between in-state and out-state homicide should be instantaneous only. On the other hand, the scenario is consistent with the absence of an association between homicide and lagged prison at the national level. Scenario C. One argument that the results in Table 7, model 1, support the results in Table 1 is based on the assumption, discussed in the text, that major criminals migrate greatly. More out-state murders indicate that there is a greater pool of potential migrants who might commit murder in the state. This is consistent with the findings in Table 7, Models 2 and 3, because those committing other crimes are likely later to commit murder. It is hard to imagine, however, how this could account for elasticities as large as in Model 1 (except in conjunction with the incapacitation scenario, discussed below). Scenario C is also consistent with the lagged impact of out-state homicide on in-state homicide (migration is not instantaneous) and with the absence of lagged impact at the national level (where aggregation washes out any impact of migration). The migration assumption can also explain why the impact of out-state prison disappears in Model 1: its impact is mediated by the migration of potential murders. Scenario D. The explanation most consistent with the information at hand is that the out-state homicide variable absorbs the impact of out-state prison because the latter is an incomplete measure of the incapacitation impact. That is, the full effect of incapacitation on homicide is reflected in the out-state homicide variable more than in the out-state prison variable. The impact of the prison variable on in-state homicide is less than, and included in, the association with out-state homicide, eliminating the coefficient on the out-state prison variable. The prison variable is an imperfect measure of incapacitation because many criminals are incapacitated in jails, juvenile facilities, and even hospitals and mental health facilities. Also, our measure of annual prison population, the average of year-end data for the current and prior years, might not accurately reflect trends, especially if seasonal variation changes over time. This explanation is consistent with all regression results. The elasticities for out-state homicide are similar to those for out-state prison (Table 1). Scenario D is perhaps the only credible reasons why the coefficient on out-state prison in Table 7, Model 1, completely disappears. The lagged impact of out-state homicide, again, would be expected if migration is important and not instantaneous. To test Scenario D, we use residuals (error terms) from national-level homicide regressions (again using first differences of logged, per-capita variables). The residuals are what is left of the homicide variable after deleting the impact of the independent variables. The residual is then substituted for homicide in regressions equivalent to Model 1 of Table 7. We produced three such residuals, using different combinations of independent variables in the national regression. Residual A (Table 7, model 4) is taken from an equation with all independent variables except prison, such that the residual represents the impact of prison population plus the miscellaneous factors otherwise in the error term. Residual A acts vary much like out- state homicide: it has a large coefficients for the current and prior year, and out-state prison is not significant. Second, we took the residual from a nation-wide regression with all independent variables, including prison. When entered into the state-level regressions, Residual B has almost the same coefficients as the first residual, but it has little or no effect on out-state prison, which retain large negative coefficients (Model 5). Third, we took the residual from a nation-wide regression that includes only prison and the dummy variables on the right-hand side (Model 6). (The dummies again are the World War II variables, the crack variable, and the 1977 dummy for the change in prison definition.) Here again the residual has substantial impacts, and the out-state prison coefficients are similar to those in Table 1. Because residuals B and C are the homicide variable with impact of prison population deleted, Models 5 and 6 are strong evidence that Scenario D dominates. To the best of our knowledge, in conclusion, the credible explanations for the association between in-state and out-state homicide and its impact on the association between in-state crime and out-state prisons are 1) criminal justice system variables, not entered in the equation, are confounding factors, and 2) the out- state homicide variable absorbs the incapacitation impact of the out-state prison variable. The first is based on deterrence only and the second is based on incapacitation. Again, the best evidence is that incapacitation has a much larger impact than deterrence. FOOTNOTES 1. The standard error of the mean coefficients given in Table 1 is mean of the standard errors for the 48 states divided by the square root of the number of states. It assumes that the regression coefficients are independently distributed across states. An alternate measure is to divide standard deviation of the mean divided by the square root of the number of states. Standard errors produced by the two measures average roughly the same, but differ substantially in individual cases. We use the first measure upon the advice of Clive Granger of the University of San Diego. It is preferable because it takes into account the fact that standard errors for the various coefficients differ. 2. We went to considerable effort to explore the sensitivity of the Table 1 results, a difficult task here because each alternative requires 48 separate regressions. Many of these are described later in the text and footnotes. Others include not using logged variables, not using per capita variables, and using a random one- half sample of states. All alternative regressions produced results similar to Table 1, except that in three situations alternate regressions produced much lower out-state elasticities than in Table 1, although for reasons that are readily explained. The first occurs when in-state prison is regressed on the prison variables only, without the control variables. This is expected because most controls affect homicides and prison populations in the same direction, as discussed above. The impact of World War II is especially important; out-state prison becomes large and significant if the analysis starts after the war or if the military variables are entered. Second, when the state data are pooled into a single regression the results are similar to Table 1; but when year effects (separate dummies for each year) are added they are highly significant, and out-state prison is no longer significant. The reason is that the year effects, which estimate the impact of factors that operate nationwide, are almost completely collinear with out-state prison, which is essentially a nationwide trend and is almost the same for each state. Third, similarly, when out- state homicide is added to the regression in Table 1, it is highly significant, with an elasticity approaching one, and out-state prison is no longer significant. The reason is that the out-state homicide variable reflects incapacitation more comprehensively than out-state prison because it includes the impact of jails, juvenile facilities, and hospitals in addition to the impact of prisons. This is shown by the fact that when we take the impact of prisons out of the out-state homicide variable (that is, we use the residual from a notion-level regression of homicide on prison and the controls) the coefficient on out-state homicide remains close to one, and the coefficient on out-state prisons is similar to that in Table 1. The appendix explains this analysis in more detail. 3. Several other reasons are possible. Because the time series are short, the difference might only reflect statistical uncertainty, which would account for the fact that elasticities in Model 7 are higher than Marvell and Moody (1994) and Levitt (1996) found with post-1970 data. Second, the impact of incomplete controls, discussed above, is greater in the early years, when homicide and prison trends are buffeted by the depression and World War II. Third, migration of potential murderers might have declined over the years. We were able to eliminate one explanation, the fact that prison populations were quite steady in the first part but rose greatly during the second. Perhaps homicides respond to increases in in- state prison more than declines. We constructed prison (growth rate) variables that are 1) zero except when prisons rose, and 2) zero except when prisons declined. When substituting these for the prison variables in Table 1, however, the coefficients differed little (for both out-state and in-state prison variable pairs). We also tested whether the rapid prison growth in recent decades could explain the result in Model 7, Table 2, by entering the square of in-state prison, but it is not significant. 4. The exceptions are the level analysis with nine regions (both coefficients), and analysis ending in 1965 for four regions (out- state prison), and the 1960-92 analysis for 9 regions (both coefficients). 5. These variables, as usual, are then logged and differenced. An alternative measure is the percent by which out-state prison exceeds in-state prison. Because this has negative values, we had to forgo log transformations. The results are the same as in Table 6 in that the coefficients are always positive and nearly always significant, but the coefficient size is not readily interpretable. Table 1 Relationship Between State Homicide and In- and Out-State Prison Populations, 1930-92 Homicide Prison Population mean mean % D.V. Outside State Inside State change lags coef. s.e. coef. s.e. New York 1112.5 8.0 1 -.34 .37 -.35 .23 California 1371.8 9.5 1 -.98* .23 -.38* .13 Pennsylvania 475.6 8.5 1 -1.12* .43 .07 .25 Illinois 768.6 9.2 1 -1.37 .71 .54 .35 Ohio 513.7 8.1 3 -1.29* .45 -.25 .21 Texas 1287.3 7.6 1 -.46 .45 -.45 .29 Michigan 546.3 9.4 4 -.77 .42 -.31 .21 New Jersey 257.7 11.5 2 -1.51* .68 .02 .31 Massachusetts 129.2 12.6 1 -1.23 .66 .29 .49 Florida 763.3 8.7 1 -.75 .53 -.39 .29 Indiana 231.2 11.4 2 -.95 .51 -.61* .23 North Carolina 497.5 7.6 1 -.81* .30 .34 .24 Missouri 353.9 10.7 1 -.54 .85 -.82 .49 Virginia 379.0 9.1 1 -.48 .46 -.25 .34 Wisconsin 94.3 19.7 2 -.67 .90 -.24 .40 Georgia 647.2 8.4 1 -.93* .44 -.12 .30 Tennessee 431.9 7.9 1 -.76 .47 -.28 .34 Minnesota 71.5 18.1 1 -2.04 1.08 .43 .69 Alabama 461.6 6.9 1 -.80* .40 .03 .26 Louisiana 431.0 9.0 1 -.12 .53 -.22 .26 Maryland 269.2 9.7 1 -.27 .47 -1.03* .40 Kentucky 305.5 8.4 1 -.05 .51 -.08 .30 Washington 125.1 12.7 3 -.84 .64 -.40 .27 Iowa 49.9 17.2 3 -.70 .97 .28 .60 Connecticut 77.2 17.8 1 -.17 .57 -.43 .28 South Carolina 294.8 9.5 1 -.76 .40 -.03 .31 Oklahoma 193.9 10.6 1 -.75 .77 -.39 .29 Kansas 90.8 19.2 2 -.60 .89 -.65 .34 Mississippi 325.6 10.7 4 -.89* .43 .82* .22 West Virginia 124.0 14.0 1 -.23 .57 -.11 .33 Arkansas 192.9 9.9 1 -.77 .56 .00 .30 Oregon 75.3 19.7 2 -.67 .80 -.36 .42 Colorado 112.0 19.6 5 -.23 .98 -1.04 .63 Nebraska 39.8 26.3 2 -3.17* 1.31 -1.38* .69 Arizona 138.7 14.8 2 -.71 .51 -.22 .26 Maine 19.0 31.3 2 -1.76 1.24 -.36 .68 New Mexico 86.3 19.5 2 -3.50* .82 .27 .28 Utah 31.7 26.3 1 -.66 1.21 .14 .58 Rhode Island 22.2 33.6 3 -.71 1.19 .62 .43 South Dakota 15.7 47.1 4 -2.19 2.36 .26 .69 Montana 30.3 20.2 3 -.59 .86 -.49 .32 Idaho 23.8 32.1 3 -1.27 1.27 .77 .72 North Dakota 9.4 56.2 4 5.31* 1.95 -.36 .77 New Hampshire 11.6 78.8 4 2.26 2.05 -1.43 1.05 Delaware 29.0 29.7 2 -3.91* 1.36 .22 .31 Vermont 7.6 76.3 3 -1.23 1.79 -1.19 1.05 Wyoming 16.4 32.8 3 1.74 1.66 -1.23 .84 Nevada 54.8 29.0 2 -1.54 .86 .09 .41 Mean for 48 states -.76* .12 -.22* .06 Mean without four outliers -.83* .12 -.22* .06 Mean for 40 largest states -.94* .11 -.18* .06 Mean for 24 largest states -.82* .12 -.17* .07 The mean percent change is the mean absolute annual percent change. D.V. lags refer to the number of dependent variable lags in the regressions. The prison population columns summarize the results of 48 separate regressions, presenting the coefficients and standard errors for the two prison variables. Asterisks indicate that the coefficient is significant to the .05 level. Eleven control variables, listed in Table 3, are not reported here. Continuous variables in the regressions are first differences of logged per capita variables. Table 2. Alternate Regression Models, State Level Means of Prison Population Coefficients All Without Largest Largest Model 48 states 4 outliers 40 states 24 states 1) From Table 1 Out-state prison -.76* -.83* -.94* -.82* In-state prison -.22* -.22* -.18* -.17* 2) With Trend Out-state prison -1.07* -1.11* -1.23* -1.10* In-state prison -.23* -.22* -.17* -.19* 3) Levels Out-state prison -.47* -.58* -.67* -.37* In-state prison -.19* -.17* -.14* -.15* 4) Autoregression (GLS) Out-state prison -.71* -.81* -.83* -.76* In-state prison -.21* -.19* -.16* -.12 5) GARCH (GLS) Out-state prison -.67* -.77* -.80* -.73* In-state prison -.22* -.19* -.15 -.12 6) Years through 1965 Out-state prison -1.09* -1.13* -1.31* -1.36* In-state prison -.16 -.13 .01 .05 7) Years 1960-92 Out-state prison -1.01* -1.11* -1.21* -1.05* In-state prison -.35* -.38* -.32* -.47* See the notes to Table 1. The figures are the mean coefficients for the 24 to 48 state-level regressions. Model 1 is the same as Table 1, and the other models differ from it as indicated. Table 3 Relationship Between Region Homicide and In- and Out-Region Prison Populations 1 2 3 4 Middle East North West North New England Atlantic Central Central coef. t coef. t coef. t coef. t Out-area prison pop. -1.09* 2.19 -.18 .55 -.93* 2.52 -1.33 1.83 In-area prison pop. .20 .49 -.52* 2.05 .00 .02 -.20 .37 Personal income -.14 .42 .12 .57 -.24 .72 -.15 .51 Consumer prices .38 1.10 .53* 2.83 .59* 2.46 .84* 2.56 Unemployment rate -.07 1.05 .09* 2.24 -.08 1.50 .13* 2.25 Population 15-17 .30 .65 .43 1.38 -.14 .54 .42 1.03 Population 18-24 1.40* 2.40 .69 1.84 1.56* 4.01 .56 1.02 Population 25-34 -.14 .24 -.27 .62 -.50 1.16 .40 .66 Executions -.04 .91 -.03 .76 .04 .96 .03 .60 Military personnel -.17* 3.99 .02 .66 -.04 1.16 -.05 .90 War 1942-45 .06 1.07 .05 1.50 -.06 1.22 .00 .02 War 1943-44 -.13 1.33 -.18* 3.53 -.20* 5.60 -.08 1.54 Age-race-heroin .11* 2.31 .09* 2.71 .11* 4.37 .16* 4.50 Prison count change .01 .22 .03 1.51 -.02 1.45 .14* 4.20 Homicide lag 1 -.28* 2.20 .21 1.87 .04 .34 -.27* 2.09 Homicide lag 2 -- -- -- -- -.13 1.34 -- -- Homicide lag 3 -- -- -- -- .25* 2.44 -- -- 5 6 7 8 9 South East South West South Atlantic Central Central Mountain Pacific coef. t coef. t coef. t coef. t coef. t Out pri. -.20 .69 -1.12* 2.34 -.39 .69 -.81 1.27 -1.00* 4.28 In pri. -.73* 1.98 .43 1.25 -.40 1.05 -.55 1.37 -.43* 2.84 Per. in. .31 1.50 .16 .59 -.22 .77 -.28 .66 -1.12* 3.08 CPI .20 .87 .91* 3.06 .60* 2.08 .85* 2.67 .56 1.63 Unemp. .10* 2.29 .13* 3.76 .10* 2.05 .14 1.54 -.04 .60 15-17 .36 1.22 1.02* 3.58 .32 1.14 .98* 2.61 .50 1.30 18-24 .48 1.11 1.65* 3.74 .59 1.03 .28 .53 1.18* 2.55 25-34 .58 1.46 .12 .21 .17 .28 1.72* 2.05 1.30* 2.41 Exec. .06 1.38 -.01 .48 -.07 1.68 -.03 .81 .05 1.11 Mil. -.09* 5.00 -.06* 2.10 -.02 .59 -.14* 2.27 -.10 1.49 WW1 -.02 .82 -.07* 2.32 .02 .49 .09 1.14 .07 .82 WW2 -.11* 2.39 -.15* 3.23 -.12* 3.71 -.02 .14 -.03 .62 A-R-H .07* 2.54 .16* 4.58 .07* 2.10 .14* 2.79 .14* 4.67 Pr. cnt. .03 .60 -.03 1.73 .11* 4.79 .08* 2.63 .09* 4.71 Homo. 1 .13 .84 -.51* 6.03 -.07 .48 -.68* 5.82 -.40* 3.94 Homo. 2 -- -- -- -- -- -- -.32* 2.83 -.23* 2.20 1 & 2 3 & 4 5 to 7 8 & 9 1 to 9 Northeast Midwest South West USA coef. t coef. t coef. t coef. t coef. t Out pri. -.40 1.39 -.85* 2.52 -.04 .12 -.72* 3.04 -- -- In pri. -.32 1.25 -.13 .83 -.75 1.92 -.53* 2.77 -.85* 5.21 Per. in. .15 .72 -.27 .88 -.02 .08 -.77* 2.33 -.24 1.12 CPI .48* 2.70 .54* 2.38 .32 1.55 .48 1.82 .39* 2.24 Unemp. .07* 2.02 -.03 .68 .08* 2.16 -.02 .36 .03 .83 15-17 .38 1.39 .03 .15 .34 1.44 .42 1.26 .26 1.55 18-24 .75* 2.28 1.37* 4.83 .79 1.81 .68 1.66 .89* 3.03 25-34 -.40 1.02 -.22 .55 .28 .62 .99 1.99 .20 .54 Exec .00 .11 .03 .59 .00 .01 .03 .54 .00 .02 Mil. -.01 .48 -.04 .90 -.05* 3.11 -.08 1.55 -.03 1.40 WW1 .05 1.72 -.04 1.07 -.03 1.58 .05 .67 -.01 .60 WW2 -.18* 5.53 -.18* 5.24 -.13* 2.94 -.07 1.30 -.14* 9.28 A-R-H .09* 2.70 .11* 4.58 .07* 2.48 .12* 4.07 .09* 4.07 Pr. cnt. .05* 2.32 .04* 2.86 .05 1.48 .08* 3.93 .06* 3.28 Homo. 1 .28* 2.79 .01 .04 .04 .30 -.30* 2.49 .05 .37 These regressions are the same as those in Table 1 except that data are aggregated to the regional and national levels. The nine regions are 1) New England: Connecticut, Maine, Massachusetts, New Hampshire, Rhode Island, and Vermont. 2) Middle Atlantic: New Jersey, New York and Pennsylvania. 3) North East Central: Illinois, Indiana, Michigan, Ohio, and Wisconsin. 4) West North Central: Iowa, Kansas, Minnesota, Missouri, Nebraska, North Dakota, and South Dakota. 5) South Atlantic: Delaware, Florida, Maryland, North Carolina, South Carolina, Virginia, and West Virginia (Georgia is dropped). 6) East South Central: Alabama, Kentucky, Mississippi, and Tennessee. 7) West South Central: Arkansas, Louisiana, Oklahoma, and Texas. 8) Mountain: Arizona, Colorado, Idaho, Montana, Nevada, New Mexico, Utah, and Wyoming. 9) Pacific: California, Oregon, and Washington. Data start in 1930 (after differencing) for all analyses, except for East South Central were Alabama and Mississippi are not available before 1939. These two states (as well as Georgia) are deleted from the four-region and nationwide analyses. Table 4. Alternate Regression Models, Regional and National Levels Means of Prison Population Coefficients Model 9 Regions 4 Regions National 1) From Table 3 Out-area prison -.78* -.51* -- In-area prison -.24* -.42* -.85* 2) With Trend Out-area prison -1.03* -.76* -- In-area prison -.29* -.52* -1.13* 3) Levels Out-area prison -.59* -.18 -- In-area prison -.08 -.23* -.85* 4) Autoregression (GLS) Out-area prison -.73* -.52* -- In-area prison -.24* -.44* -.87* 5) GARCH (GLS) Out-area prison -.71* -.52* -- In-area prison -.25 -.44 -.87* 6) Years, through 1965 Out-area prison -1.31* -1.33* -- In-area prison -.07 -.25 -1.02* 7) Years, 1960-92 Out-area prison -1.27* -.87* -- In-area prison -.41 -.67* -1.45* See the notes to Table 1. The figures are mean coefficients for nine regions and four regions and the coefficient in the single nation-wide regression. Table 5 Prisoners in Nearby States Means of Prison Population Coefficients All Without Largest Largest Model 48 states 4 outliers 40 states 24 states 1) Basic OLS Out-state prison nearby states -.25 -.19 -.06 -.08 other states -.66* -.80* -1.04* -.90* In-state prison -.20* -.20* -.16 -.11 2) With Trend Out-state prison nearby states -.24 -.20 -.06 -.10 other states -1.06* -1.18* -1.42* -1.33* In-state prison -.21* -.20* -.16* -.12 3) Autoregression Out-state prison nearby states -.24 -.18 -.24 -.16 other states -.50* -.69* -.62* -.64* In-state prison -.20* -.16* -.14* -.05 4) Years 1960-92 Out-state prison nearby states -.13 -.07 -.12 -.11 other states -.78* -.94* -.98* -.96* In-state prison -.41* -.42* -.39* -.35* See the notes to Table 1. The figures are the mean coefficients for the 24 to 48 state-level regressions. Nearby states are within fifty miles of the subject state, and the other states are the rest of the nation. Model 1 is the same as the model in Table 1, except that out-state prison is divided into nearby state prison and other state prison. Table 6 Ratios of Prison Population Rates Means of Prison Population Coefficients All Without Largest Largest Model 48 states 4 outliers 40 states 24 states 1) Basic Ratio of out- to in-state prison .24* .23* .18* .17* 2) Basic through 65 Ratio of out- to in-state prison .06 .04 -.12 -.20 3) Basic 1960-92 Ratio of out- to in-state prison .19* .23* .21* .27* 4) Nearby Prison Ratio of nearby to in-state prison .14* .15* .11 .08 5) Prison Elsewhere Ratio of non-nearby to in-state prison .22* .21* .16* .14 See notes to Table 2. The figures are the mean coefficients for the 24 to 48 state-level regressions. The basic model is the same as in Table 1 except that the out- and in-state prison variables are replaced by the ratio of the two. In models 4 and 5 all three prison variables in Model 1 of Table 5 are replaced by the ratio indicated. Table 7 Adding Out-State Crime Means of Prison Population Coefficients All Without Largest Largest Model 48 states 4 outliers 40 states 24 states 1) With Out-State Homicide Out-state prison .12 .04 -.11 -.09 In-state prison -.16* -.15* -.11 -.08 Out-state homicide .81* .83* .83* .83* Out-state homicide lag .58* .55* .50* .39* 2) With National Violent Crime Out-state prison -.28 -.34* -.36* -.12 In-state prison -.18* -.20* -.19* -.18* Violent crime .39* .44* .49* .66* Violent crime lag .28* .27* .28* .21* 3) With National Property Crime Out-state prison -.38* -.44* -.44* -.31* In-state prison -.20* -.21* -.21* -.20* Property crime .29* .35* .37* .43* Property crime lag .18* .15 .17* .15 4) With Residual A Out-state prison -.08 -.15 -.29 -.09 In-state prison -.16* -.17* -.10 -.10 Residual B .77* .81* .78* .87* Residual B lag .32* .34* .28* .35* 5) With Residual B Out-state prison -.53* -.63* -.75* -.60* In-state prison -.17* -.22* -.12 -.12 Residual B .89* .86* .90* 1.00* Residual B lag .46* .51* .42* .46* 6 With Residual C Out-state prison -.33 -.43* -.56* -.41* In-state prison -.16* -.18* -.12 -.11 Residual C 1.02* 1.01* 1.01* 1.02* Residual C lag .39* .41* .34* .41* See the notes to Table 1. The figures are the mean coefficients for the 24 to 48 state-level regressions. The models are the same as Table 1 except for the following modifications: 1) Homicide in the rest of the country is added as a regressor. 2 & 3) National level violent and property crime are added, and the regression begins in 1937. 4, 5 & 6) Residuals are added. The residuals are from national time series homicide regressions, with A) all regressors except out-state prison, B) all regressors including prison variables, and C) only prison variables and the dummy variables as regressors.