Variable creation: winnability of seat (margin by which candidate's party won or lost the previous election)

[Gill Steel]

Dear Stata Forum Users,

I would be extremely grateful if anyone could help with this.

I would like to check if female newcomer candidates are selected for less winnable seats than male newcomer candidates.

For winnability, I would like to use the margin by which a candidate’s opposing party won or lost the previous election in the district. I was then planning to conduct a ttest.
I would also like a variable to measure the margin by which a candidate’s own party won or lost the previous election.
But, I cannot correctly create these variables.

I have the following variables: sex, year, prev_election, seito_r (party), vote (%), district_smd (district), status (newcomer, incumbent, previously elected).
The three parties in the analysis:
LDP 1996-2024
2000-2012 DPJ
2017-2024 CDP

I will post my latest (failed) do file below and data below.
I am using Stata19.

Sincerely,
Gill Steel

* Example generated by -dataex-. For more info, type help dataex
clear
input byte sex int year float prev_election long(seito_r vote district_smd) byte status
0 2005 2003 46  82486  1 0
0 2012 2009 46  77215  1 0
0 1996    . 46  72178  2 0
0 2000 1996 46  57047  2 0
0 2024 2021 46  59529  2 0
0 2005 2003 46 105631  3 0
0 2012 2009 46  91164  3 0
0 2012 2009 46  91816  4 0
0 1996    . 46  80629  5 0
0 2012 2009 46  98670  5 0
0 2021 2017 46 120203  5 0
0 2005 2003 46  78561  6 0
0 2012 2009 46  71881  6 0
0 2000 1996 46  96086  7 0
0 2012 2009 46  73521  7 0
0 2000 1996 46  47269  9 0
0 2003 2000 46  56472  9 0
0 2005 2003 46  87804  9 0
1 2009 2005 46  58225  9 0
0 2012 2009 46  67086  9 0
0 2021 2017 46  79418  9 0
0 2005 2003 46  86426 10 0
0 2012 2009 46  77700 10 0
0 2024 2021 46  54199 10 0
0 2003 2000 46  79749 11 0
1 2005 2003 46  87824 11 0
0 2012 2009 46  63932 11 0
0 1996    . 46  46485 12 0
0 2009 2005 46  88964 12 0
0 2012 2009 46  67218 12 0
0 1996    . 46  59631 13 0
0 2003 2000 46  80700 13 0
0 2005 2003 46 126670 13 0
0 1996    . 46  49727 14 0
0 2000 1996 46  83601 14 0
0 2000 1996 46  84641 15 0
0 2005 2003 46 123280 15 0
0 1996    . 46  58059 16 0
0 2012 2009 46  93757 16 0
0 2012 2009 46  73356 17 0
0 2005 2003 46  53555 18 0
0 2009 2005 46  92600 18 0
0 2024 2021 46  65200 18 0
0 2012 2009 46  73237 20 0
0 1996    . 46  63672 21 0
0 2021 2017 46 126137 21 0
0 2017 2014 46 128740 22 0
0 1996    . 46  70590 23 0
0 2009 2005 46  94820 24 0
0 2012 2009 46  69927 24 0
0 2024 2021 46  69563 25 0
0 2012 2009 46  75152 28 0
0 2021 2017 46 100227 28 0
0 2024 2021 46  51723 29 0
0 2012 2009 46 100551 30 0
0 1996    . 46  73254 31 0
0 2000 1996 46  95311 31 0
0 1996    . 46  73792 32 0
0 2000 1996 46  76067 32 0
0 2003 2000 46  80051 32 0
0 2005 2003 46 128890 32 0
0 2014 2012 46  67600 32 0
0 2017 2014 46  61804 32 0
0 2003 2000 46  64393 33 0
0 2005 2003 46 132691 33 0
0 1996    . 46  60801 34 0
0 1996    . 46  71306 35 0
0 2003 2000 46  83899 35 0
0 2005 2003 46 118801 35 0
0 2009 2005 46 104262 35 0
0 1996    . 46  69539 36 0
0 2024 2021 46  59070 36 0
0 1996    . 46  75667 37 0
0 2012 2009 46  80024 37 0
0 2024 2021 46  65963 37 0
0 2021 2017 46 119633 38 0
0 2009 2005 46  79924 40 0
0 2012 2009 46  71033 40 0
0 2021 2017 46  76263 40 0
0 2021 2017 46  81015 41 0
0 2000 1996 46  61707 42 0
1 2005 2003 46  51242 42 0
0 1996    . 46  48762 44 0
0 2000 1996 46  81698 44 0
0 1996    . 46  56748 45 0
0 2000 1996 46  68483 45 0
0 2003 2000 46  53611 45 0
0 2005 2003 46  92891 45 0
0 2012 2009 46  96706 45 0
0 2024 2021 46  54186 46 0
0 1996    . 46  42152 47 0
0 2000 1996 46  65838 47 0
0 2012 2009 46 105493 48 0
0 2012 2009 46 118299 49 0
0 1996    . 46  86765 50 0
0 2012 2009 46  86039 50 0
0 2024 2021 46 105563 51 0
0 1996    . 46  71742 52 0
0 2012 2009 46  96172 53 0
0 2005 2003 46 171507 56 0
end
label values sex gender_label
label def gender_label 0 "Male", modify
label def gender_label 1 "Female", modify
label values seito_r seito_r
label def seito_r 46 "自民", modify
label values district_smd district_smd
label def district_smd 1 "aichi_1", modify
label def district_smd 2 "aichi_10", modify
label def district_smd 3 "aichi_11", modify
label def district_smd 4 "aichi_12", modify
label def district_smd 5 "aichi_13", modify
label def district_smd 6 "aichi_14", modify
label def district_smd 7 "aichi_15", modify
label def district_smd 9 "aichi_2", modify
label def district_smd 10 "aichi_3", modify
label def district_smd 11 "aichi_4", modify
label def district_smd 12 "aichi_5", modify
label def district_smd 13 "aichi_6", modify
label def district_smd 14 "aichi_7", modify
label def district_smd 15 "aichi_8", modify
label def district_smd 16 "aichi_9", modify
label def district_smd 17 "akita_1", modify
label def district_smd 18 "akita_2", modify
label def district_smd 20 "aomori_1", modify
label def district_smd 21 "aomori_2", modify
label def district_smd 22 "aomori_3", modify
label def district_smd 23 "aomori_4", modify
label def district_smd 24 "chiba_1", modify
label def district_smd 25 "chiba_10", modify
label def district_smd 28 "chiba_13", modify
label def district_smd 29 "chiba_14", modify
label def district_smd 30 "chiba_2", modify
label def district_smd 31 "chiba_3", modify
label def district_smd 32 "chiba_4", modify
label def district_smd 33 "chiba_5", modify
label def district_smd 34 "chiba_6", modify
label def district_smd 35 "chiba_7", modify
label def district_smd 36 "chiba_8", modify
label def district_smd 37 "chiba_9", modify
label def district_smd 38 "ehime_1", modify
label def district_smd 40 "ehime_3", modify
label def district_smd 41 "ehime_4", modify
label def district_smd 42 "fukui_1", modify
label def district_smd 44 "fukui_3", modify
label def district_smd 45 "fukuoka_1", modify
label def district_smd 46 "fukuoka_10", modify
label def district_smd 47 "fukuoka_11", modify
label def district_smd 48 "fukuoka_2", modify
label def district_smd 49 "fukuoka_3", modify
label def district_smd 50 "fukuoka_4", modify
label def district_smd 51 "fukuoka_5", modify
label def district_smd 52 "fukuoka_6", modify
label def district_smd 53 "fukuoka_7", modify
label def district_smd 56 "fukushima_1", modify
label values status status_label
label def status_label 0 "Newcomer", modify

************************************************** *
* party labels
************************************************** *
gen str3 party = ""
replace party = "LDP" if seito_r == 46
replace party = "DPJ" if seito_r == 42
replace party = "CDP" if seito_r == 35

************************************************** *
* opposition vote share in previous election by district
************************************************** *
gen prev_opp_voteshare = .

* Assign previous opposition vote share based on the election year
foreach year in 2000 2003 2005 2009 2012 2014 2017 2021 2024 {
* Determine opposition party (DPJ or CDP)
local opp = cond(`year' < 2017, "DPJ", "CDP")

* previous opposition vote share for the relevant district and year
bysort str_district (year): replace prev_opp_voteshare = voteshare if year == `year' & party == "`opp'"
}

************************************************** *
* Keep only LDP newcomers (status == 0)
************************************************** *
keep if seito_r == 46 & status == 0

************************************************** *
* confirm `prev_opp_voteshare`
************************************************** *
summ prev_opp_voteshare

[Clyde Schechter]

Well, a serious error made is keeping only the records for LDP newcomers. In order to calculate how the opposition did in the previous election, you need the full records of that election, not just the records of an LDP newcomer in the previous election.

Also, what do you mean by "margin the opposing party won by." Is that the difference in vote share between the previous winner and the party of the person in the observation, or is it the difference in vote share between the vote share between the previous winner and all others in that election? Or something else?

Please repost a new data example that includes all of the observations from a series of several consecutive elections in just a few districts--that will make it possible to try to help you.

[Gill Steel]

Clyde Schechter
Thank you very much for the quick reply. I would like to look at newcomers and also at incumbents.
I was trying to do this in stages, although that may not be necessary.
First, are LDP newcomer women and men run in equally winnable seats. Second, are opposition newcomer women and men run in equally winnable seats. Third, are LDP candidates overall run in equally winnable seats and fourth, are opposition candidates overall run in equally winnable seats.
Winnability: The relative winnability of the seat for each candidate, calculated as the percentage margin by which their party won or lost in that district in the previous election (relative to the runner up).
The larger the margin of victory between the winner and the runner up in the previous election for the candidate’s party, the more winnable the seat would be.

Please find below observations 3 elections (there are 10 in my dataset, but that exceeds the character limit here).
I would really appreciate any suggestions.

[CODE]
* Example generated by -dataex-. For more info, type help dataex
clear
input int year byte sex long seito_r byte(win_smd status) long(vote district_smd)
2014 0 35 0 2 48796 95
2014 0 35 0 2 75715 236
2014 0 35 0 2 66037 13
2014 1 35 0 1 74073 189
2014 0 35 0 1 85591 84
2014 0 35 0 0 49494 68
2014 0 35 0 1 69543 243
2014 0 35 0 1 65810 117
2014 1 35 0 1 82612 128
2014 0 35 0 2 58599 46
2014 0 35 0 2 77234 72
2014 0 35 0 2 77179 103
2014 0 35 0 0 15981 307
2014 0 35 0 0 48389 194
2014 0 35 0 0 62766 191
2014 0 35 0 1 68564 37
2014 0 35 0 2 47315 120
2014 0 35 0 2 80014 281
2014 0 35 0 1 89232 267
2014 1 35 0 0 43040 269
2014 0 35 0 2 64940 112
2014 0 35 0 1 82383 225
2014 1 35 0 0 40000 77
2014 0 35 0 0 56701 18
2014 0 35 0 1 82857 227
2014 0 35 0 2 49230 1
2014 0 35 0 2 87584 277
2014 0 35 0 2 49862 66
2014 0 35 0 2 42690 181
2014 0 35 0 1 63332 259
2014 0 35 0 1 82351 280
2014 0 35 0 0 63078 251
2014 0 35 0 2 79788 133
2014 0 35 0 1 56951 197
2014 0 35 0 0 85120 108
2014 0 35 0 2 63558 174
2014 0 35 0 1 54900 155
2014 0 35 0 2 43464 141
2014 0 35 0 2 51362 279
2014 1 35 0 0 73348 295
2014 0 35 0 0 38346 247
2014 0 35 0 2 65809 25
2014 0 35 0 1 50323 34
2014 0 35 0 2 43265 208
2014 1 35 0 0 97630 56
2014 1 35 0 2 84573 186
2014 1 35 0 2 49142 22
2014 0 35 0 1 62103 6
2014 1 35 0 2 44123 268
2014 1 35 0 0 29656 31
2014 0 35 0 2 83485 130
2014 0 35 0 0 23127 170
2014 0 35 0 2 41407 230
2014 0 35 0 2 71871 55
2014 0 35 0 2 48957 166
2014 0 35 0 0 38237 145
2014 1 35 0 2 39178 157
2014 1 35 0 2 74508 38
2014 0 35 0 0 46029 305
2014 0 35 0 1 74218 178
2014 0 35 0 0 58358 57
2014 1 35 0 1 73513 240
2014 0 35 0 1 63397 175
2014 1 35 0 0 62035 81
2014 0 35 0 0 42562 146
2014 0 35 0 0 40902 246
2014 0 35 0 2 77074 63
2014 1 35 0 2 65395 49
2014 0 35 0 2 70049 85
2014 0 35 0 2 52602 40
2014 0 35 0 1 60041 60
2014 1 35 0 2 32797 169
2014 0 35 0 0 46595 177
2014 0 35 0 1 53111 65
2014 0 35 0 1 90420 306
2014 0 35 0 2 69188 264
2014 0 35 0 1 75338 199
2014 0 35 0 2 57500 33
2014 0 35 0 2 41507 106
2014 0 35 0 2 38331 212
2014 0 35 0 2 71212 283
2014 0 35 0 0 36285 11
2014 0 35 0 2 63706 282
2014 0 35 0 0 64480 7
2014 0 35 0 2 73805 51
2014 0 35 0 0 37654 256
2014 1 35 0 0 32687 284
2014 0 35 0 2 66255 292
2014 0 35 0 2 51816 176
2014 0 35 0 0 61405 80
2014 0 35 0 2 48861 291
2014 1 35 0 2 59590 110
2014 0 35 0 2 39357 100
2014 0 35 0 2 60671 233
2014 0 35 0 2 50199 138
2014 0 35 0 0 24402 96
2014 1 35 0 1 81113 164
2014 0 35 0 2 87800 252
2014 0 35 0 2 66549 73
2014 0 35 0 2 66897 126
2014 0 35 0 2 42050 185
2014 0 35 0 2 57782 17
2014 0 35 0 2 42960 45
2014 0 35 0 2 58407 278
2014 1 35 0 0 38402 61
2014 0 35 0 0 42032 43
2014 0 35 0 0 62251 262
2014 0 35 0 2 54095 244
2014 1 35 0 2 35242 167
2014 0 35 0 1 44916 231
2014 0 35 0 0 38106 23
2014 0 35 0 2 86252 90
2014 0 35 0 1 64725 28
2014 0 35 0 2 57814 310
2014 0 35 0 2 29329 311
2014 0 35 0 0 94975 86
2014 0 35 0 1 111353 289
2014 1 35 0 0 46593 245
2014 0 35 0 2 61711 241
2014 0 35 0 2 85967 16
2014 0 35 0 2 99058 15
2014 0 35 0 2 61216 207
2014 0 35 0 0 61902 99
2014 1 35 0 0 43028 271
2014 0 35 0 0 71219 79
2014 0 35 0 2 33010 215
2014 0 35 0 2 45238 250
2014 1 35 0 0 43672 2
2014 0 35 0 0 50511 142
2014 0 35 0 1 70487 187
2014 0 35 0 2 58692 156
2014 0 35 0 2 62062 237
2014 0 35 0 2 83535 48
2014 0 35 0 0 65924 179
2014 1 35 0 1 72056 88
2014 0 35 0 2 58884 163
2014 0 35 0 2 63377 272
2014 0 35 0 2 49297 298
2014 0 35 0 2 66771 253
2014 0 35 0 1 89877 276
2014 0 35 1 1 96333 173
2014 0 35 1 1 103116 132
2014 0 35 1 1 103918 239
2014 0 35 1 1 102058 9
2014 0 35 1 2 97745 89
2014 0 35 1 1 64534 144
2014 0 35 1 1 84226 12
2014 0 35 1 1 143012 254
2014 0 35 1 1 90030 238
2014 0 35 1 1 79265 182
2014 0 35 1 1 116652 5
2014 0 35 1 1 94462 58
2014 0 35 1 1 116398 78
2014 0 35 1 1 104422 294
2014 0 35 1 1 66227 154
2014 0 35 1 1 67740 302
2014 0 35 1 2 94893 192
2014 0 35 1 1 102111 313
2014 0 35 1 1 120950 161
2014 1 35 1 1 61725 206
2014 0 35 1 1 114161 255
2014 0 35 1 1 85903 224
2014 0 35 1 1 119193 32
2014 0 35 1 1 80339 115
2014 0 35 1 1 90182 93
2014 0 35 1 1 62439 260
2014 0 35 1 1 60688 107
2014 0 35 1 1 84755 24
2014 0 35 1 1 82422 10
2014 1 35 1 2 113474 14
2014 0 35 1 1 126498 3
2014 0 35 1 2 104595 87
2014 0 35 1 1 64753 168
2014 0 35 1 1 76787 113
2014 0 35 1 1 102030 158
2014 0 35 1 1 78797 118
2014 0 35 1 2 82619 188
2014 0 35 1 1 91676 160
2021 0 42 0 1 91354 64
2021 0 42 0 2 96690 227
2017 0 42 0 1 78019 125
2021 0 42 0 0 80408 177
2021 0 42 0 1 118619 140
2021 0 42 0 0 107920 268
2021 0 42 0 1 71600 40
2021 0 42 0 1 65627 31
2021 1 42 0 0 65909 21
2021 1 42 0 0 38786 92
2021 0 42 0 0 112535 84
2021 0 42 0 0 49935 50
2021 0 42 0 1 72786 11
2021 0 42 0 2 88914 14
2021 0 42 0 0 116273 130
2021 0 42 0 1 82718 79
2021 0 42 0 1 101258 48
2021 0 42 0 0 86923 231
2017 0 42 0 2 65281 34
2021 1 42 0 0 71048 35
2021 0 42 0 1 50033 145
2021 1 42 0 1 111366 86
2017 0 42 0 0 67070 271
2021 0 42 0 0 55577 181
2021 0 42 0 0 24490 214
2017 0 42 0 1 56757 196
2017 1 42 0 0 48018 216
2021 0 42 0 0 85501 57
2017 0 42 0 0 62894 33
2017 0 42 0 0 45313 219
2017 0 42 0 2 52018 31
2021 0 42 0 1 62555 197
2017 0 42 0 2 65780 212
2021 1 42 0 1 100963 236
2021 0 42 0 1 91707 1
2021 0 42 0 1 80776 7
2017 1 42 0 2 53480 291
2021 0 42 0 0 39826 261
2021 1 42 0 0 68615 65
2017 0 42 0 2 91073 281
2021 0 42 0 1 77347 99
2021 0 42 0 0 53974 250
2021 1 42 0 1 47487 216
2021 1 42 0 1 99090 280
2017 1 42 0 0 73250 236
2021 1 42 0 0 31467 200
2021 1 42 0 2 92880 136
2017 0 42 0 1 71405 274
2021 1 42 0 0 53476 97
2017 0 42 0 0 87819 142
2021 0 42 0 1 67689 68
2021 0 42 0 1 72966 151
2021 0 42 0 0 73872 305
2021 1 42 0 1 66943 206
2021 0 42 0 1 76912 13
2021 0 42 0 1 80971 25
2021 0 42 0 1 109165 160
2017 0 42 0 0 67456 68
2021 1 42 0 0 64817 100
2021 0 42 0 0 41252 300
2021 0 42 0 1 72366 17
2021 0 42 0 1 60726 256
2021 0 42 0 1 90422 278
2021 0 42 0 1 60281 207
2021 0 42 0 0 38458 222
2021 0 42 0 0 35654 303
2021 0 42 0 1 73159 194
2017 1 42 0 0 65285 247
2021 0 42 0 2 28254 218
2021 0 42 0 1 99758 255
2021 0 42 0 0 70939 72
2017 0 42 0 0 54035 30
2017 1 42 0 0 47191 136
2021 0 42 0 0 23316 198
2021 1 42 0 0 29073 311
2021 0 42 0 0 33681 74
2017 0 42 0 0 22684 314
2021 0 42 0 1 72571 212
2017 0 42 0 0 38531 250
2021 0 42 0 1 113570 108
2021 0 42 0 0 66689 114
2021 0 42 0 2 62414 98
2021 0 42 0 2 63947 298
2017 0 42 0 2 94772 49
2021 0 42 0 0 50684 309
2021 0 42 0 0 83842 148
2021 0 42 0 1 98304 49
2021 0 42 0 0 71845 42
2021 0 42 0 0 14563 299
2021 0 42 0 0 53375 2
2021 0 42 0 0 77091 38
2017 0 42 0 2 66536 220
2021 0 42 0 1 88744 188
2021 0 42 0 0 52016 248
2021 0 42 0 0 55820 53
2021 0 42 0 0 53755 45
2021 0 42 0 1 79687 28
2021 0 42 0 0 72031 204
2021 1 42 0 0 17730 208
2021 0 42 0 1 59191 220
2021 0 42 0 0 55321 81
2021 0 42 0 1 76747 112
2017 1 42 0 0 76283 295
2021 0 42 0 2 89837 133
2021 1 42 0 0 54326 183
2017 1 42 0 0 35961 311
2021 1 42 0 0 108630 85
2021 0 42 0 1 80496 203
2021 0 42 0 0 80756 242
2021 0 42 0 0 38578 52
2021 0 42 0 1 109362 115
2021 0 42 0 2 64870 20
2017 1 42 0 0 44884 284
2017 0 42 0 2 90540 277
2021 0 42 0 1 71958 233
2021 0 42 0 2 41737 217
2021 1 42 0 0 60237 166
2017 0 42 0 0 70168 268
2021 0 42 0 1 70843 109
2021 0 42 0 1 68405 179
2021 0 42 0 2 90390 134
2021 0 42 0 1 107722 16
2021 0 42 0 0 48491 110
2021 1 42 0 1 32108 157
2021 0 42 0 2 93616 63
2021 0 42 0 0 74995 12
2021 0 42 0 0 55189 180
2021 0 42 0 0 109179 175
2021 0 42 0 2 64507 159
2021 0 42 0 0 23137 94
2021 0 42 0 0 24952 221
2021 0 42 0 0 61884 95
2021 0 42 0 0 48629 61
2021 0 42 0 1 87880 141
2017 0 42 0 0 91230 278
2017 0 42 0 0 51776 35
2021 0 42 0 0 45520 77
2021 0 42 0 1 112393 281
2021 0 42 0 1 78779 193
2021 0 42 0 2 80932 272
2017 0 42 0 0 60291 269
2017 1 42 0 0 135948 86
2021 0 42 0 1 50325 67
2021 1 42 0 1 93419 240
2017 0 42 0 2 84133 194
2017 0 42 0 0 26140 205
2021 0 42 0 0 71032 251
2021 0 42 0 0 89991 284
2021 0 42 0 2 56747 27
2021 0 42 0 1 69984 43
2021 0 42 0 1 90043 267
2021 1 42 0 0 45563 88
2021 0 42 0 2 64119 244
2021 0 42 0 1 126732 282
2021 0 42 0 0 56682 69
2017 0 42 0 0 14568 209
2021 0 42 0 0 41311 162
2021 1 42 0 0 44441 314
2017 0 42 0 0 57431 28
2021 0 42 0 0 72116 246
2017 0 42 0 0 96282 79
2021 0 42 0 0 66700 259
2021 1 42 0 0 53143 73
2017 0 42 0 0 31197 222
2021 1 42 0 0 68397 274
2021 1 42 0 1 66847 247
2021 0 42 0 2 120649 15
2021 0 42 0 0 68457 138
2021 0 42 0 1 118223 313
2021 0 42 0 0 93403 87
2017 0 42 0 2 57780 1
2017 0 42 0 0 26383 256
2021 0 42 0 1 83859 199
2017 0 42 0 0 78704 164
2021 1 42 0 0 43473 265
2021 0 42 0 0 65499 196
2021 0 42 0 0 63796 22
2017 0 42 0 2 99182 292
2017 0 42 0 0 69878 99
2021 0 42 0 0 28477 205
2021 0 42 0 0 49094 229
2021 0 42 0 0 63674 105
2021 0 42 0 0 51965 254
2021 0 42 0 2 61103 104
2021 0 42 0 1 89232 120
2021 0 42 0 1 121962 173
2021 1 42 0 2 58978 273
2021 0 42 0 0 59462 6
2021 0 42 0 2 69583 30
2021 1 42 0 1 96649 164
2021 0 42 0 0 107043 262
2021 0 42 0 1 87635 269
2021 0 42 0 0 78665 271
2017 0 42 0 2 89279 151
2021 0 42 0 2 90214 228
2021 0 42 0 0 69887 33
2017 0 42 0 0 61441 283
2017 0 42 0 2 90619 85
2017 0 42 0 2 51063 45
2021 0 42 0 0 41788 75
2021 0 42 0 1 55214 146
2021 0 42 0 1 124524 142
2021 0 42 0 2 32193 215
2021 1 42 1 1 91538 80
2021 0 42 1 2 78657 91
2017 1 42 1 1 128045 186
2021 0 42 1 1 113512 90
2021 0 42 1 1 122091 276
2021 1 42 1 1 95013 128
2021 0 42 1 1 114210 257
2017 0 42 1 1 86291 141
2021 0 42 1 1 90457 58
2017 0 42 1 2 76699 120
2021 0 42 1 1 118286 78
2021 0 42 1 1 130925 143
2021 0 42 1 1 121400 10
2021 0 42 1 1 81033 168
2021 0 42 1 1 134033 5
2017 0 42 1 1 136312 87
2021 0 42 1 1 101391 174
2021 0 42 1 1 144688 161
2021 0 42 1 1 105807 83
2017 0 42 1 1 117118 294
2017 1 42 1 0 98214 80
2021 0 42 1 1 124541 294
2017 0 42 1 1 100400 293
2021 1 42 1 1 123620 56
2021 0 42 1 2 104053 122
2017 1 42 1 1 86550 128
2021 0 42 1 1 124961 289
2021 0 42 1 1 142536 4
2021 0 42 1 1 137558 132
2021 0 42 1 0 60719 170
2021 0 42 1 0 109489 296
2021 0 42 1 1 85361 46
2021 0 42 1 0 130124 129
2021 0 42 1 1 154412 32
2021 0 42 1 1 102627 230
2017 0 42 1 1 119091 238
2021 0 42 1 1 106608 225
2021 0 42 1 1 73593 260
2021 0 42 1 1 128556 24
2021 0 42 1 1 134281 239
2021 0 42 1 1 92452 224
2021 0 42 1 1 111246 292
2021 1 42 1 1 97494 189
2021 0 42 1 1 76683 59
2021 0 42 1 1 83796 76
2017 1 42 1 1 75788 206
2021 0 42 1 1 116111 158
2021 0 42 1 0 135125 36
2017 0 42 1 1 91081 12
2021 1 42 1 0 125315 51
2017 0 42 1 0 139110 78
2021 0 42 1 0 90679 191
2021 1 42 1 2 116320 165
2021 0 42 1 1 83847 144
2021 0 42 1 1 81845 18
2017 0 42 1 1 141680 84
2021 0 42 1 1 112857 89
2021 1 42 1 0 137341 295
2017 0 42 1 1 96713 276
2021 0 42 1 1 110169 293
2021 1 42 1 1 127365 186
2021 0 42 1 1 87017 113
2017 1 42 1 0 67020 139
2021 0 42 1 2 112464 252
2021 1 42 1 1 66841 139
2017 0 42 1 2 96255 267
2021 0 42 1 1 93050 182
2021 0 42 1 1 107322 37
2021 0 42 1 1 100118 125
2021 0 42 1 1 113615 238
2021 0 42 1 1 89259 155
2017 0 42 1 1 98595 10
2021 0 42 1 1 111267 277
2021 0 42 1 1 90267 117
2017 0 46 0 1 57111 302
2017 0 46 0 1 96978 3
2014 0 46 0 1 61479 139
2017 0 46 0 1 65445 260
2014 0 46 0 0 67600 32
2014 0 46 0 1 63187 160
2017 0 46 0 1 101243 89
2021 0 46 0 1 81556 36
2017 1 46 0 1 87524 189
2021 0 46 0 1 64542 202
2021 0 46 0 1 105381 295
2017 1 46 0 1 93234 267
2021 0 46 0 1 68958 174
2021 0 46 0 1 80469 121
2021 0 46 0 1 99489 10
2021 0 46 0 1 79158 76
2017 0 46 0 1 80049 178
2014 0 46 0 1 57824 116
2021 0 46 0 1 100775 252
2021 0 46 0 1 72835 218
2014 1 46 0 1 46409 113
2021 0 46 0 1 109131 277
2014 0 46 0 1 52156 202
2017 0 46 0 1 105876 313
2021 0 46 0 1 73945 18
2021 0 46 0 1 91159 128
2021 0 46 0 0 69053 178
2021 0 46 0 1 73784 59
2017 0 46 0 1 70819 144
2021 0 46 0 1 76481 55
2017 0 46 0 1 104780 160
2014 0 46 0 1 46165 115
2014 0 46 0 1 72353 10
2021 0 46 0 1 80937 216
2014 0 46 0 1 67681 9
2021 1 46 0 0 61410 168
2021 0 46 0 1 106842 90
2021 0 46 0 1 59649 170
2017 0 46 0 0 74831 120
2021 0 46 0 1 56061 213
2014 0 46 0 1 34293 168
2021 0 46 0 1 53878 107
2021 1 46 0 0 75932 192
2014 0 46 0 0 82460 214
2017 0 46 0 1 71600 9
2017 0 46 0 0 60006 58
2017 0 46 0 1 118961 84
2014 0 46 0 1 72791 91
2017 0 46 0 1 92222 239
2017 1 46 0 1 69366 207
2017 0 46 0 1 64193 202
2021 0 46 0 1 98224 225
2014 0 46 0 1 74137 173
2021 0 46 0 1 81882 46
2014 0 46 0 1 76937 24
2021 0 46 0 1 52843 206
2021 0 46 0 1 124595 129
2021 1 46 0 1 70568 207
2014 0 46 0 1 56365 175
2014 0 46 0 0 51223 215
2021 0 46 0 1 117963 143
2014 0 46 0 1 59937 302
2021 0 46 0 0 96591 186
2014 0 46 0 0 37180 154
2014 0 46 0 1 71616 12
2014 1 46 0 1 68109 314
2017 0 46 0 1 96571 115
2021 1 46 0 2 85336 80
2014 0 46 0 1 65494 76
2014 0 46 0 1 118165 4
2021 0 46 0 1 61608 302
2017 0 46 0 1 85460 173
2017 0 46 0 1 108157 255
2021 0 46 0 1 105842 292
2021 0 46 0 1 99895 24
2017 1 46 0 1 66527 203
2021 0 46 0 1 90549 191
2014 0 46 0 1 84574 254
2014 0 46 0 1 86636 238
2014 0 46 0 0 71648 205
2017 0 46 0 0 40336 154
2021 0 46 0 0 81209 161
2021 0 46 0 0 89745 83
2014 0 46 0 1 97167 3
2017 0 46 0 1 85305 294
2017 0 46 0 1 57833 215
2021 0 46 0 1 92319 224
2017 0 46 0 2 74119 143
2021 0 46 0 1 80775 156
2021 0 46 0 1 89110 122
2014 0 46 0 0 63219 213
2021 0 46 0 2 82004 158
2014 0 46 0 1 62240 260
2021 0 46 0 1 115749 165
2021 0 46 0 1 110706 51
2017 0 46 0 1 88225 280
2021 0 46 0 1 87502 99
2021 0 46 0 1 96791 103
2021 0 46 0 1 83776 223
2014 0 46 0 1 101748 87
2021 0 46 0 0 59304 208
2014 1 46 0 1 57647 198
2017 0 46 0 1 50496 168
2021 0 46 0 0 79418 9
2017 0 46 0 1 80198 214
2017 0 46 0 1 67210 174
2014 0 46 0 1 105918 78
2014 0 46 0 1 91351 89
2017 0 46 0 2 53910 40
2014 1 46 0 1 101026 313
2017 0 46 0 1 74722 175
2014 0 46 0 1 57318 118
2021 0 46 0 1 118074 56
2021 1 46 0 1 62666 113
2017 1 46 0 1 82096 80
2017 0 46 0 1 109581 5
2021 1 46 0 1 71592 221
2017 0 46 0 1 58534 213
2014 0 46 0 1 65838 204
2021 0 46 0 0 53877 222
2017 1 46 0 1 62617 109
2021 0 46 0 1 84813 32
2021 0 46 0 1 61337 254
2017 0 46 0 1 83924 128
2021 0 46 0 1 85321 209
2014 0 46 0 0 49174 58
2017 0 46 0 1 59949 118
2021 0 46 0 1 105186 293
2014 0 46 0 1 108151 14
2014 0 46 0 0 53659 161
2021 0 46 0 1 106985 78
2014 0 46 0 1 101144 5
2017 0 46 0 1 72651 12
2021 0 46 0 1 95284 296
2017 0 46 0 1 54204 201
2021 0 46 0 1 47511 139
2017 0 46 0 1 95667 276
2014 0 46 0 1 102714 255
2017 0 46 0 0 61804 32
2017 0 46 0 1 56483 206
2014 0 46 0 0 53160 206
2017 0 46 0 2 120987 78
2021 0 46 0 0 97256 189
2014 0 46 0 1 94303 239
2014 0 46 0 0 74562 93
2014 0 46 0 1 83476 294
2017 0 46 0 1 76220 10
2017 0 46 0 1 77023 238
2021 0 46 0 1 54455 201
2017 0 46 0 1 78972 224
2021 0 46 0 1 83718 182
2021 0 46 0 1 115881 276
2017 0 46 0 1 71553 4
2021 0 46 0 1 61597 214
2017 0 46 0 1 81705 187
2021 0 46 0 1 68918 144
2021 0 46 0 1 70827 117
2017 0 46 0 1 127329 14
2021 0 46 0 1 67145 205
2014 0 46 0 0 56741 122
2014 0 46 0 1 59991 144
2021 0 46 0 1 52052 215
2021 0 46 0 0 120203 5
2017 0 46 0 1 113045 186
2017 0 46 0 1 113514 56
2021 0 46 0 0 67887 211
2014 0 46 0 1 101627 132
2014 0 46 0 2 76555 210
2017 0 46 0 1 69209 76
2021 0 46 0 1 60091 149
2021 0 46 0 1 77398 264
2021 0 46 0 1 60837 190
2017 0 46 0 1 113851 87
2014 0 46 0 1 53276 229
2021 0 46 0 2 54334 118
2021 0 46 0 2 101379 89
2021 0 46 0 1 114396 132
2017 0 46 0 1 95594 188
2021 0 46 0 1 90408 257
2014 0 46 0 1 56440 59
2021 0 46 0 1 128083 4
2014 1 46 0 1 59491 203
2021 1 46 0 1 61674 155
2021 0 46 0 1 107532 238
2021 0 46 0 1 98493 230
2017 0 46 0 1 99067 225
2014 0 46 0 1 83421 224
2014 0 46 0 1 73032 143
2017 0 46 0 1 56534 155
2021 0 46 0 0 64253 260
2021 0 46 0 1 105433 239
2017 1 46 0 1 57381 113
2014 0 46 0 1 67473 182
2014 0 46 0 1 53732 107
2017 0 46 0 1 71029 146
2021 1 46 0 1 54930 198
2017 0 46 0 1 55700 139
2014 0 46 0 1 94736 158
2017 0 46 0 1 98422 293
2021 0 46 0 1 43291 154
2014 0 46 0 1 85714 273
2017 0 46 0 1 63406 161
2017 0 46 0 2 92467 254
2014 1 46 0 1 65638 109
2021 0 46 0 1 64202 91
2021 0 46 0 1 102741 37
2021 0 46 0 1 70029 210
2014 0 46 0 1 88507 192
2021 0 46 0 1 81087 294
2014 0 46 0 1 74319 188
2021 0 46 0 1 76302 58
2014 0 46 0 1 53241 201
2021 0 46 0 1 116753 289
2017 0 46 1 1 99596 162
2017 1 46 1 0 102820 108
2021 0 46 1 1 119555 169
2014 0 46 1 1 100081 284
2014 1 46 1 1 107015 267
2017 0 46 1 1 81675 145
2021 0 46 1 1 99430 45
2014 0 46 1 1 110273 279
2017 1 46 1 1 124508 183
2017 0 46 1 1 104825 312
2014 0 46 1 1 107608 277
2014 1 46 1 1 81109 221
2017 0 46 1 1 74876 299
2021 0 46 1 1 94156 171
2014 0 46 1 1 92873 149
2017 0 46 1 1 86499 230
2014 0 46 1 1 66041 20
2021 0 46 1 1 83822 25
2017 0 46 1 1 96892 57
2021 0 46 1 1 121714 15
2017 0 46 1 1 81129 47
2014 0 46 1 1 94725 162
2014 0 46 1 1 59437 155
2014 0 46 1 1 91089 177
2021 0 46 1 1 102870 259
2021 0 46 1 1 81107 2
2014 0 46 1 1 123584 248
2021 0 46 1 1 120365 134
2014 0 46 1 1 84530 67
2017 0 46 1 1 154761 127
2021 0 46 1 1 110688 305
2014 0 46 1 1 98508 305
2017 0 46 1 1 93622 99
2021 0 46 1 1 78253 74
2021 0 46 1 1 131371 148
2014 0 46 1 1 97805 90
2014 0 46 1 1 92166 264
2021 0 46 1 0 86238 153
2014 0 46 1 1 102153 69
2017 0 46 1 1 88106 153
2014 0 46 1 1 70085 299
2014 0 46 1 1 128938 235
2017 0 46 1 1 105953 130
2017 0 46 1 1 129463 256
2021 0 46 1 1 87434 75
2014 0 46 1 1 103954 278
2017 0 46 1 1 95558 100
2014 0 46 1 1 101809 77
2014 0 46 1 1 111544 97
2017 0 46 1 1 71614 208
2017 0 46 1 1 112993 278
2014 0 46 1 1 108257 304
2014 0 46 1 1 85277 31
2017 0 46 1 1 104726 50
2021 0 46 1 1 92233 53
2014 0 46 1 1 155388 131
2017 0 46 1 1 74371 261
2014 0 46 1 1 81176 190
2014 0 46 1 1 71167 303
2014 0 46 1 1 107986 236
2021 1 46 1 1 136171 42
2014 0 46 1 1 82046 18
2021 0 46 1 1 125158 150
2017 0 46 1 1 97923 252
2021 0 46 1 1 125500 236
2014 1 46 1 1 90167 231
2017 1 46 1 1 109453 70
2014 0 46 1 1 85311 73
2017 0 46 1 1 122026 102
2021 0 46 1 1 131082 251
2014 0 46 1 1 129311 251
2014 0 46 1 1 120500 105
2014 0 46 1 1 110965 26
2014 0 46 1 1 91189 199
2014 0 46 1 1 102241 48
2014 0 46 1 1 98485 257
2017 0 46 1 1 103919 26
2021 0 46 1 1 104178 255
2021 0 46 1 1 133704 71
2021 0 46 1 1 125325 313
2021 0 46 1 1 110538 26
2021 0 46 1 1 86021 68
2014 0 46 1 1 105536 103
2021 1 46 1 1 101868 250
2021 0 46 1 1 68693 157
2014 0 46 1 1 91670 121
2017 0 46 1 1 92952 241
2014 0 46 1 1 86869 134
2014 0 46 1 1 105347 252
2021 0 46 1 0 96153 228
2014 0 46 1 1 71384 112
2014 0 46 1 1 134037 27
2017 1 46 1 1 103453 61
2014 0 46 1 1 86104 163
2021 0 46 1 1 123210 27
2014 0 46 1 1 97915 306
2021 0 46 1 0 76263 40
2021 0 46 1 1 89052 199
2021 0 46 1 0 98958 240
2017 0 46 1 1 104612 269
2017 0 46 1 1 81566 117
2014 0 46 1 1 76281 119
2014 0 46 1 1 129587 269
2017 0 46 1 1 112014 284
2017 0 46 1 1 110522 282
2014 0 46 1 1 100376 247
2017 1 46 1 1 116969 42
2014 0 46 1 1 108051 172
2021 0 46 1 1 95140 100
2014 0 46 1 1 96294 28
2014 0 46 1 1 60460 246
2017 0 46 1 1 93081 28
2014 0 46 1 1 110439 98
2021 0 46 1 1 94437 119
2017 0 46 1 1 95568 7
2021 0 46 1 1 153017 30
2014 0 46 1 1 114328 262
2014 0 46 1 1 70789 245
2014 0 46 1 1 68773 167
2017 0 46 1 1 108747 90
2017 0 46 1 1 87674 46
2017 0 46 1 1 130857 251
2017 0 46 1 1 74835 18
2021 0 46 1 1 115122 268
2014 0 46 1 1 86359 179
2021 0 46 1 1 88799 67
2017 0 46 1 0 91855 190
2021 0 46 1 1 97482 243
2017 0 46 1 1 86027 149
2017 0 46 1 1 115239 291
2017 0 46 1 1 129312 150
2017 0 46 1 1 101977 158
2014 0 46 1 1 83837 68
2021 0 46 1 1 133206 282
2017 0 46 1 1 122341 184
2021 0 46 1 1 105426 187
2021 0 46 1 0 119633 38
2017 0 46 1 1 111167 262
2014 1 46 1 1 87546 272
2017 0 46 1 1 111285 134
2021 0 46 1 1 120856 227
2021 0 46 1 1 82398 261
2021 1 46 1 1 103805 61
2021 0 46 1 1 72861 39
2017 0 46 1 1 80083 218
2017 0 46 1 1 109949 306
2017 0 46 1 1 96243 253
2014 0 46 1 1 112963 140
2014 0 46 1 1 76733 157
2021 0 46 1 1 101964 164
2017 0 46 1 1 107708 289
2014 0 46 1 1 102323 138
2014 0 46 1 1 138991 301
2021 0 46 1 0 128753 162
2021 0 46 1 1 86762 246
2017 0 46 1 1 74662 209
2017 0 46 1 1 140803 301
2017 0 46 1 1 89403 263
2021 0 46 1 0 95271 179
2014 1 46 1 0 79872 307
2014 0 46 1 1 73003 1
2017 0 46 1 1 81616 265
2017 0 46 1 1 71861 91
2014 0 46 1 1 104139 64
2014 0 46 1 1 66213 11
2017 0 46 1 1 82998 73
2014 0 46 1 1 62814 261
2017 0 46 1 1 106425 297
2021 0 46 1 1 68957 96
2017 0 46 1 1 84457 274
2014 0 46 1 1 67376 120
2017 0 46 1 2 90558 182
2017 0 46 1 1 101157 138
2017 0 46 1 1 73150 197
2017 0 46 1 0 107473 64
2017 0 46 1 1 131244 52
2017 0 46 1 1 94086 300
2017 0 46 1 1 127214 129
2017 0 46 1 1 90281 264
2017 0 46 1 1 100708 200
2014 0 46 1 1 119116 108
2014 0 46 1 1 126491 102
2021 1 46 1 1 74362 109
2017 0 46 1 1 86193 75
2017 0 46 1 1 104824 83
2017 0 46 1 1 80589 41
2014 0 46 1 1 90116 65
2017 0 46 1 0 60277 157
2014 0 46 1 1 97783 25
2021 0 46 1 0 117810 146
2014 0 46 1 1 132698 256
2021 0 46 1 1 77960 17
2014 1 46 1 2 73931 207
2017 0 46 1 1 64911 74
2014 0 46 1 1 101223 169
2014 0 46 1 1 77690 85
2017 1 46 1 1 104137 272
2014 1 46 1 1 88730 237
2021 0 46 1 1 102638 57
2017 0 46 1 1 108964 30
2014 0 46 1 1 96236 71
2017 0 46 1 1 90841 240
2017 0 46 1 1 78724 245
2014 0 46 1 1 75744 156
2017 0 46 1 1 99863 295
2021 0 46 1 1 105441 297
2014 0 46 1 1 89792 92
2014 0 46 1 1 106143 276
2021 0 46 1 1 96023 50
2021 0 46 1 1 109326 85
2014 0 46 1 1 105941 33
2014 0 46 1 1 78326 216
2017 0 46 1 1 89219 67
2014 0 46 1 1 111316 242
2017 0 46 1 1 111559 165
2017 0 46 1 1 104227 305
2014 0 46 1 1 83764 146
2017 0 46 1 1 93172 199
2021 0 46 1 1 96210 166
2017 0 46 1 1 120744 271
2017 0 46 1 1 122279 296
2017 0 46 1 1 109098 48
2021 0 46 1 1 79061 92
2014 0 46 1 1 108234 62
2021 1 46 1 1 141858 183
2014 0 46 1 1 93105 297
2017 0 46 1 1 118421 111
2017 0 46 1 1 127632 275
2014 0 46 1 1 93126 241
2014 0 46 1 1 98594 16
2021 0 46 1 1 131430 284
2017 0 46 1 1 94205 92
2021 0 46 1 1 144725 14
2014 0 46 1 1 114093 49
2014 0 46 1 1 88219 159
2021 0 46 1 1 135197 130
2014 0 46 1 1 116193 295
2014 0 46 1 1 89813 100
2014 0 46 1 1 102929 56
2017 0 46 1 1 93262 69
2014 0 46 1 1 96799 310
2014 0 46 1 1 91222 50
2021 0 46 1 0 89341 300
2017 0 46 1 1 94780 170
2017 0 46 1 1 103177 20
2014 0 46 1 1 79334 184
2014 0 46 1 1 92416 253
2021 0 46 1 1 88758 274
2017 0 46 1 1 101858 257
2017 0 46 1 1 82838 24
2017 0 46 1 1 73694 244
2017 0 46 1 1 113824 126
2021 1 46 1 0 87710 203
2021 0 46 1 1 135031 49
2014 0 46 1 1 91686 57
2021 0 46 1 1 117002 242
2021 0 46 1 2 116917 84
2014 0 46 1 1 106887 13
2017 0 46 1 1 91146 268
2021 0 46 1 0 126137 21
2014 0 46 1 1 142238 104
2014 0 46 1 1 66954 41
2017 1 46 1 1 96500 250
2021 0 46 1 1 122772 159
2017 0 46 1 1 76323 34
2014 1 46 1 1 89544 250
2021 0 46 1 1 84154 253
2017 0 46 1 1 61450 107
2017 0 46 1 1 104183 104
2017 0 46 1 1 96229 277
2021 0 46 1 1 155572 151
2014 0 46 1 1 102424 292
2021 0 46 1 1 134734 19
2014 0 46 1 0 91931 209
2017 0 46 1 1 95200 88
2017 0 46 1 1 109381 93
2021 0 46 1 1 136168 13
2021 0 46 1 1 104924 54
2021 0 46 1 1 118230 22
2014 0 46 1 1 85488 47
2017 0 46 1 1 108477 15
2021 0 46 1 1 94656 98
2021 0 46 1 1 91011 20
2014 0 46 1 1 79156 23
2021 0 46 1 1 120023 175
2017 0 46 1 1 92113 65
2014 0 46 1 1 83544 228
2017 0 46 1 1 98008 172
2017 0 46 1 1 109584 159
2014 0 46 1 1 109408 130
2017 0 46 1 1 136499 49
2021 0 46 1 1 97730 177
2017 0 46 1 1 73899 181
2021 0 46 1 1 84320 12
2014 0 46 1 1 116413 52
2017 0 46 1 0 74287 237
2014 0 46 1 1 97152 7
2017 0 46 1 1 100123 164
2021 1 46 1 1 108681 272
2017 0 46 1 1 98166 94
2017 0 46 1 1 110493 281
2017 0 46 1 1 96185 233
2021 0 46 1 1 102564 188
2014 0 46 1 1 61927 66
2017 0 46 1 1 101314 292
2021 0 46 1 1 111262 232
2021 0 46 1 1 79365 303
2017 0 46 1 0 80114 246
2021 0 46 1 1 118734 115
2014 0 46 1 1 131394 86
2017 0 46 1 1 98443 232
2021 0 46 1 0 96983 311
2021 0 46 1 1 149152 283
2021 0 46 1 1 107135 237
2021 0 46 1 1 97634 81
2017 0 46 1 1 109139 259
2014 0 46 1 1 101514 263
2021 0 46 1 1 147634 127
2021 0 46 1 1 92761 93
2017 0 46 1 1 123431 148
2021 1 46 1 1 131284 133
2021 0 46 1 0 92405 141
2021 0 46 1 1 128708 291
2017 0 46 1 1 133545 21
2014 0 46 1 1 73684 153
2021 0 46 1 1 119281 278
2014 0 46 1 1 67055 222
2014 0 46 1 1 83327 128
2021 0 46 1 1 81888 245
2014 0 46 1 1 78567 243
2021 0 46 1 1 118882 309
2021 0 46 1 1 133126 72
2017 0 46 1 1 113068 105
2021 0 46 1 1 90939 196
2014 0 46 1 1 71436 197
2021 0 46 1 1 119384 275
2014 0 46 1 1 86297 211
2021 0 46 1 0 115669 271
2017 0 46 1 1 85453 228
2021 0 46 1 1 127548 35
2014 0 46 1 1 147084 136
2014 0 46 1 1 75718 174
2021 0 46 1 1 102807 194
2014 0 46 1 1 92649 84
2021 0 46 1 1 137032 111
2017 0 46 1 1 97874 179
2021 0 46 1 0 88321 110
2014 0 46 1 1 98569 36
2021 0 46 1 1 149168 114
2017 0 46 1 1 93475 223
2017 0 46 1 0 128740 22
2014 0 46 1 1 59280 22
2017 0 46 1 1 109488 304
2021 0 46 1 1 101251 120
2017 0 46 1 1 129884 114
2014 0 46 1 1 94977 124
2014 1 46 1 1 87118 80
2014 0 46 1 1 109377 291
2014 0 46 1 1 83638 191
2017 0 46 1 1 73298 167
2021 0 46 1 1 106500 31
2021 0 46 1 1 104204 7
2017 0 46 1 1 115731 35
2014 0 46 1 1 120084 309
2017 0 46 1 1 123758 51
2021 0 46 1 0 110327 248
2014 0 46 1 1 85092 37
2014 0 46 1 1 109723 15
2017 0 46 1 1 95093 236
2014 0 46 1 1 101088 142
2014 0 46 1 1 74115 117
2014 0 46 1 1 113844 125
2021 0 46 1 1 128870 142
2014 0 46 1 1 82538 218
2021 0 46 1 1 110244 66
2017 0 46 1 0 133221 309
2014 0 46 1 1 77066 34
2021 1 46 1 1 125703 108
2014 0 46 1 1 115623 289
2014 0 46 1 1 105969 200
2017 0 46 1 1 100875 103
2014 1 46 1 1 118537 133
2021 0 46 1 1 108380 263
2017 0 46 1 1 91477 53
2017 0 46 1 1 72838 96
2014 0 46 1 1 65102 244
2014 0 46 1 1 110583 111
2021 0 46 1 1 132357 63
2014 0 46 1 1 87892 55
2017 0 46 1 1 106699 227
2021 0 46 1 1 71696 299
2017 0 46 1 1 135334 54
2014 0 46 1 1 126024 283
2014 0 46 1 1 90059 196
2017 0 46 1 1 75772 303
2017 0 46 1 1 85461 31
2014 0 46 1 1 93345 164
2014 0 46 1 1 88667 83
2014 0 46 1 1 82898 300
2017 0 46 1 1 120189 97
2021 0 46 1 0 110155 160
2021 0 46 1 0 100227 28
2017 0 46 1 1 92180 37
2021 0 46 1 1 111985 33
2017 0 46 1 1 112168 110
2021 0 46 1 1 130024 256
2014 0 46 1 1 76579 298
2014 0 46 1 1 70589 187
2017 0 46 1 1 122331 283
2017 0 46 1 1 127308 63
2021 0 46 1 1 81705 43
2021 0 46 1 1 74721 167
2017 0 46 1 1 113239 71
2014 0 46 1 1 99900 38
2014 1 46 1 1 77137 189
2017 0 46 1 1 113012 310
2014 0 46 1 1 81567 46
2017 0 46 1 1 79442 17
end
label values sex gender_label
label def gender_label 0 "Male", modify
label def gender_label 1 "Female", modify
label values seito_r partyE_label1
label def partyE_label1 35 " DPJ", modify
label def partyE_label1 42 "CDP", modify
label def partyE_label1 46 "LDP", modify
label values win_smd win_smd
label def win_smd 0 "Other", modify
label def win_smd 1 "Won SMD", modify
label values status status_label
label def status_label 0 "Newcomer", modify
label def status_label 1 "Incumbent", modify
label def status_label 2 "Previously elected", modify
label values district_smd district_smd
label def district_smd 1 "aichi_1", modify
label def district_smd 2 "aichi_10", modify
label def district_smd 3 "aichi_11", modify
label def district_smd 4 "aichi_12", modify
label def district_smd 5 "aichi_13", modify
label def district_smd 6 "aichi_14", modify
label def district_smd 7 "aichi_15", modify
label def district_smd 9 "aichi_2", modify
label def district_smd 10 "aichi_3", modify
label def district_smd 11 "aichi_4", modify
label def district_smd 12 "aichi_5", modify
label def district_smd 13 "aichi_6", modify
label def district_smd 14 "aichi_7", modify
label def district_smd 15 "aichi_8", modify
label def district_smd 16 "aichi_9", modify

[Gill Steel]

Clyde Schechter
Thank you very much for the quick reply. I'll try to do a better job of explaining what I would like to do.
My apologies for not explaining fully. I would like to look at newcomers and also at incumbents.
Of course, you are exactly right on the LDP newcomers - I was trying to do this in stages, although that may not be necessary.
First, are LDP newcomer women and men run in equally winnable seats (I had thought of creating a temporary file and then inserting that into the original dataset, but realize this isn’t the best way to do that). Second, are opposition newcomer women and men run in equally winnable seats. Third, are LDP candidates overall run in equally winnable seats and fourth, are opposition candidates overall run in equally winnable seats.
Winnability:
The relative winnability of the seat for each candidate, calculated as the percentage margin by which their party won or lost in that district in the previous election (relative to the runner up).
The larger the margin of victory between the winner and the runner up in the previous election for the candidate’s party, the more winnable the seat would be.
Please find below a subset of data (I have observations for the 10 elections and 9000 candidates, too many too post here)

I would really appreciate any help

[CODE]
* Example generated by -dataex-. For more info, type help dataex
clear
input int year byte sex long seito_r byte(win_smd status) long(vote district_smd)
2014 0 35 0 2 48796 95
2014 0 35 0 2 75715 236
2014 0 35 0 2 66037 13
2014 1 35 0 1 74073 189
2014 0 35 0 1 85591 84
2014 0 35 0 0 49494 68
2014 0 35 0 1 69543 243
2014 0 35 0 1 65810 117
2014 1 35 0 1 82612 128
2014 0 35 0 2 58599 46
2014 0 35 0 2 77234 72
2014 0 35 0 2 77179 103
2014 0 35 0 0 15981 307
2014 0 35 0 0 48389 194
2014 0 35 0 0 62766 191
2014 0 35 0 1 68564 37
2014 0 35 0 2 47315 120
2014 0 35 0 2 80014 281
2014 0 35 0 1 89232 267
2014 1 35 0 0 43040 269
2014 0 35 0 2 64940 112
2014 0 35 0 1 82383 225
2014 1 35 0 0 40000 77
2014 0 35 0 0 56701 18
2014 0 35 0 1 82857 227
2014 0 35 0 2 49230 1
2014 0 35 0 2 87584 277
2014 0 35 0 2 49862 66
2014 0 35 0 2 42690 181
2014 0 35 0 1 63332 259
2014 0 35 0 1 82351 280
2014 0 35 0 0 63078 251
2014 0 35 0 2 79788 133
2014 0 35 0 1 56951 197
2014 0 35 0 0 85120 108
2014 0 35 0 2 63558 174
2014 0 35 0 1 54900 155
2014 0 35 0 2 43464 141
2014 0 35 0 2 51362 279
2014 1 35 0 0 73348 295
2014 0 35 0 0 38346 247
2014 0 35 0 2 65809 25
2014 0 35 0 1 50323 34
2014 0 35 0 2 43265 208
2014 1 35 0 0 97630 56
2014 1 35 0 2 84573 186
2014 1 35 0 2 49142 22
2014 0 35 0 1 62103 6
2014 1 35 0 2 44123 268
2014 1 35 0 0 29656 31
2014 0 35 0 2 83485 130
2014 0 35 0 0 23127 170
2014 0 35 0 2 41407 230
2014 0 35 0 2 71871 55
2014 0 35 0 2 48957 166
2014 0 35 0 0 38237 145
2014 1 35 0 2 39178 157
2014 1 35 0 2 74508 38
2014 0 35 0 0 46029 305
2014 0 35 0 1 74218 178
2014 0 35 0 0 58358 57
2014 1 35 0 1 73513 240
2014 0 35 0 1 63397 175
2014 1 35 0 0 62035 81
2014 0 35 0 0 42562 146
2014 0 35 0 0 40902 246
2014 0 35 0 2 77074 63
2014 1 35 0 2 65395 49
2014 0 35 0 2 70049 85
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2014 0 46 1 1 102323 138
2014 0 46 1 1 138991 301
2021 0 46 1 0 128753 162
2021 0 46 1 1 86762 246
2017 0 46 1 1 74662 209
2017 0 46 1 1 140803 301
2017 0 46 1 1 89403 263
2021 0 46 1 0 95271 179
2014 1 46 1 0 79872 307
2014 0 46 1 1 73003 1
2017 0 46 1 1 81616 265
2017 0 46 1 1 71861 91
2014 0 46 1 1 104139 64
2014 0 46 1 1 66213 11
2017 0 46 1 1 82998 73
2014 0 46 1 1 62814 261
2017 0 46 1 1 106425 297
2021 0 46 1 1 68957 96
2017 0 46 1 1 84457 274
2014 0 46 1 1 67376 120
2017 0 46 1 2 90558 182
2017 0 46 1 1 101157 138
2017 0 46 1 1 73150 197
2017 0 46 1 0 107473 64
2017 0 46 1 1 131244 52
2017 0 46 1 1 94086 300
2017 0 46 1 1 127214 129
2017 0 46 1 1 90281 264
2017 0 46 1 1 100708 200
2014 0 46 1 1 119116 108
2014 0 46 1 1 126491 102
2021 1 46 1 1 74362 109
2017 0 46 1 1 86193 75
2017 0 46 1 1 104824 83
2017 0 46 1 1 80589 41
2014 0 46 1 1 90116 65
2017 0 46 1 0 60277 157
2014 0 46 1 1 97783 25
2021 0 46 1 0 117810 146
2014 0 46 1 1 132698 256
2021 0 46 1 1 77960 17
2014 1 46 1 2 73931 207
2017 0 46 1 1 64911 74
2014 0 46 1 1 101223 169
2014 0 46 1 1 77690 85
2017 1 46 1 1 104137 272
2014 1 46 1 1 88730 237
2021 0 46 1 1 102638 57
2017 0 46 1 1 108964 30
2014 0 46 1 1 96236 71
2017 0 46 1 1 90841 240
2017 0 46 1 1 78724 245
2014 0 46 1 1 75744 156
2017 0 46 1 1 99863 295
2021 0 46 1 1 105441 297
2014 0 46 1 1 89792 92
2014 0 46 1 1 106143 276
2021 0 46 1 1 96023 50
2021 0 46 1 1 109326 85
2014 0 46 1 1 105941 33
2014 0 46 1 1 78326 216
2017 0 46 1 1 89219 67
2014 0 46 1 1 111316 242
2017 0 46 1 1 111559 165
2017 0 46 1 1 104227 305
2014 0 46 1 1 83764 146
2017 0 46 1 1 93172 199
2021 0 46 1 1 96210 166
2017 0 46 1 1 120744 271
2017 0 46 1 1 122279 296
2017 0 46 1 1 109098 48
2021 0 46 1 1 79061 92
2014 0 46 1 1 108234 62
2021 1 46 1 1 141858 183
2014 0 46 1 1 93105 297
2017 0 46 1 1 118421 111
2017 0 46 1 1 127632 275
2014 0 46 1 1 93126 241
2014 0 46 1 1 98594 16
2021 0 46 1 1 131430 284
2017 0 46 1 1 94205 92
2021 0 46 1 1 144725 14
2014 0 46 1 1 114093 49
2014 0 46 1 1 88219 159
2021 0 46 1 1 135197 130
2014 0 46 1 1 116193 295
2014 0 46 1 1 89813 100
2014 0 46 1 1 102929 56
2017 0 46 1 1 93262 69
2014 0 46 1 1 96799 310
2014 0 46 1 1 91222 50
2021 0 46 1 0 89341 300
2017 0 46 1 1 94780 170
2017 0 46 1 1 103177 20
2014 0 46 1 1 79334 184
2014 0 46 1 1 92416 253
2021 0 46 1 1 88758 274
2017 0 46 1 1 101858 257
2017 0 46 1 1 82838 24
2017 0 46 1 1 73694 244
2017 0 46 1 1 113824 126
2021 1 46 1 0 87710 203
2021 0 46 1 1 135031 49
2014 0 46 1 1 91686 57
2021 0 46 1 1 117002 242
2021 0 46 1 2 116917 84
2014 0 46 1 1 106887 13
2017 0 46 1 1 91146 268
2021 0 46 1 0 126137 21
2014 0 46 1 1 142238 104
2014 0 46 1 1 66954 41
2017 1 46 1 1 96500 250
2021 0 46 1 1 122772 159
2017 0 46 1 1 76323 34
2014 1 46 1 1 89544 250
2021 0 46 1 1 84154 253
2017 0 46 1 1 61450 107
2017 0 46 1 1 104183 104
2017 0 46 1 1 96229 277
2021 0 46 1 1 155572 151
2014 0 46 1 1 102424 292
2021 0 46 1 1 134734 19
2014 0 46 1 0 91931 209
2017 0 46 1 1 95200 88
2017 0 46 1 1 109381 93
2021 0 46 1 1 136168 13
2021 0 46 1 1 104924 54
2021 0 46 1 1 118230 22
2014 0 46 1 1 85488 47
2017 0 46 1 1 108477 15
2021 0 46 1 1 94656 98
2021 0 46 1 1 91011 20
2014 0 46 1 1 79156 23
2021 0 46 1 1 120023 175
2017 0 46 1 1 92113 65
2014 0 46 1 1 83544 228
2017 0 46 1 1 98008 172
2017 0 46 1 1 109584 159
2014 0 46 1 1 109408 130
2017 0 46 1 1 136499 49
2021 0 46 1 1 97730 177
2017 0 46 1 1 73899 181
2021 0 46 1 1 84320 12
2014 0 46 1 1 116413 52
2017 0 46 1 0 74287 237
2014 0 46 1 1 97152 7
2017 0 46 1 1 100123 164
2021 1 46 1 1 108681 272
2017 0 46 1 1 98166 94
2017 0 46 1 1 110493 281
2017 0 46 1 1 96185 233
2021 0 46 1 1 102564 188
2014 0 46 1 1 61927 66
2017 0 46 1 1 101314 292
2021 0 46 1 1 111262 232
2021 0 46 1 1 79365 303
2017 0 46 1 0 80114 246
2021 0 46 1 1 118734 115
2014 0 46 1 1 131394 86
2017 0 46 1 1 98443 232
end
label values sex gender_label
label def gender_label 0 "Male", modify
label def gender_label 1 "Female", modify
label values seito_r partyE_label1
label def partyE_label1 35 " DPJ", modify
label def partyE_label1 42 "CDP", modify
label def partyE_label1 46 "LDP", modify
label values win_smd win_smd
label def win_smd 0 "Other", modify
label def win_smd 1 "Won SMD", modify
label values status status_label
label def status_label 0 "Newcomer", modify
label def status_label 1 "Incumbent", modify
label def status_label 2 "Previously elected", modify
label values district_smd district_smd
label def district_smd 1 "aichi_1", modify
label def district_smd 2 "aichi_10", modify
label def district_smd 3 "aichi_11", modify
label def district_smd 4 "aichi_12", modify
label def district_smd 5 "aichi_13", modify
label def district_smd 6 "aichi_14", modify
label def district_smd 7 "aichi_15", modify
label def district_smd 9 "aichi_2", modify
label def district_smd 10 "aichi_3", modify
label def district_smd 11 "aichi_4", modify
label def district_smd 12 "aichi_5", modify
label def district_smd 13 "aichi_6", modify
label def district_smd 14 "aichi_7", modify
label def district_smd 15 "aichi_8", modify
label def district_smd 16 "aichi_9", modify
label def district_smd 17 "akita_1", modify
label def district_smd 18 "akita_2", modify

[Clyde Schechter]

If I have correctly understood the problem, I believe the following does what you want. I notice that in the example data, there are never more than two parties running in any election. I don't assume that this is necessarily true throughout your data and have written the code for N-party elections generally. I also note that many elections have only a single candidate, in which case the winner's margin is 100% and there are no loser margins.

//    IDENTIFY WINNER AND CALCULATE WINNING AND LOSING MARGINS IN EACH ELECTION

frame put district_smd year seito_r vote, into(previous)
frame change previous
by district_smd year (vote), sort: egen pct_votes = pc(vote)
by district_smd year (pct_votes), sort: gen previous_winner = seito_r[_N]
by district_smd year (pct_votes): gen previous_winner_margin = pct_votes[_N]-pct_votes[_N-1] ///
    if _N >= 2
by district_smd year (pct_votes): gen previous_loser_margin = pct_votes - pct_votes[_N] ///
    if _N >= 2 & _n < _N
frame change default


//    IDENTIFY YEAR OF PREVIOUS ELECTION
rangestat (max) previous_year = year, interval(year . -1) by(district_smd)

//    LINK EACH YEAR'S DATA TO PREVIOUS YEARS WINNER AND MARGINS
frlink 1:1 district_smd previous_year seito_r, ///
    frame(previous district_smd year seito_r)
frget previous_winner *_margin, from(previous)
label values previous_winner partyE_label1
sort district_smd year seito_r
gen margin = cond(seito_r == previous_winner, previous_winner_margin, ///
    previous_loser_margin)

The variable called margin contains the margin by which the party won or lost in the previous election. The other created variables may or may not be needed for your purpose and could be dropped at that point if you want to keep the data set more compact.

The -rangestat- command is written by Robert Picard, Nick Cox, and Roberto Ferrer and is available from SSC.

[Gill Steel]

Dear Clyde Schechter ,
Thank you very much for this code.
You're right - this is a subset of the main parties only, but I do also want to look at all parties.
I was not familiar with frames so I have just downloaded the introductory pdf.
I really appreciate this (to avoid bothering list members, I had already asked ChatGPT, which failed miserably, repeatedly at this task).
Again, many thanks,
Gill

[Nick Cox]

Clyde Schechter is better at Stata than ChatGPT!

[Gill Steel]

Nick Cox Heh heh. Indeed. Think we should have t-shirts made?