Code for these posts is available on request. County code index at bottom.
The latest (June 6th) WA State voterdb give us this data:
Total of all active registrants:
mat2[,sum(AllRegistrants),]
[1] 4086653
Total of all active registrants born after 1980 (36 years or younger):
mat2[,sum(Born1980orGTR),]
[1] 1148586
PCT of those born after 1980 Active and Registered:
mat2[,sum(Born1980orGTR),] / mat2[,sum(AllRegistrants),]
[1] 0.281
Counties
Ten Counties have over 100K registrants. All of them are flush with Millennials:
mat2[AllRegistrants > 100000,][order(-AllRegistrants)]
County AllRegistrants Born1980orGTR PCT
1: KI 1233259 378975 30.7
2: PI 460443 132886 28.9
3: SN 436022 123381 28.3
4: SP 296079 85279 28.8
5: CR 259991 69358 26.7
6: TH 167751 45639 27.2
7: KP 158800 41011 25.8
8: WM 133361 41222 30.9
9: YA 109824 33407 30.4
10: BE 102367 28889 28.2
These ten counties make up 82% of the registered voters in the state of WA.
85% of all voters 36 years or younger come from those ten counties.
mat2[AllRegistrants >= 100000,sum(AllRegistrants),]
[1] 3357897
mat2[AllRegistrants >= 100000,sum(AllRegistrants),] / mat2[,sum(AllRegistrants),]
[1] 0.822
mat2[AllRegistrants >= 100000,sum(Born1980orGTR),] / mat2[,sum(Born1980orGTR),]
[1] 0.853
Legislative Districts
There is an exceptional wide range difference in Legislative Districts in the percentage of Millennials:
ldat2[LD != 0,][order(-PCT)]
LD AllRegistrants Born1980orGTR PCT
1: 43 100223 47180 47.1
2: 36 107957 39812 36.9
3: 37 85558 29165 34.1
4: 3 75159 25469 33.9
5: 29 64524 21713 33.7
6: 15 55927 18482 33.0
7: 11 73640 23008 31.2
8: 27 79392 24664 31.1
9: 9 70547 21825 30.9
10: 46 98725 30435 30.8
11: 47 77821 23105 29.7
12: 30 70426 20658 29.3
13: 44 90116 26399 29.3
14: 21 81937 23928 29.2
15: 33 69021 20136 29.2
16: 25 81056 23601 29.1
17: 38 74264 21525 29.0
18: 2 84734 24541 29.0
19: 42 90534 26115 28.8
20: 8 81368 23396 28.8
21: 22 93305 26646 28.6
22: 6 88486 25154 28.4
23: 28 75230 21330 28.4
24: 40 88953 25209 28.3
25: 49 77448 21840 28.2
26: 1 92033 25951 28.2
27: 17 81415 22813 28.0
28: 32 87118 24161 27.7
29: 16 65657 18185 27.7
30: 48 77193 21050 27.3
31: 34 94321 25697 27.2
32: 13 66025 17931 27.2
33: 4 91012 24652 27.1
34: 31 86554 23217 26.8
35: 39 83178 22188 26.7
36: 45 90620 23586 26.0
37: 5 96335 24897 25.8
38: 14 74398 18785 25.2
39: 41 92603 22875 24.7
40: 23 89040 21961 24.7
41: 18 91843 22649 24.7
42: 26 89393 21837 24.4
43: 20 84407 19659 23.3
44: 12 75252 17514 23.3
45: 19 78195 17879 22.9
46: 35 87848 19657 22.4
47: 7 89193 19478 21.8
48: 10 93265 19918 21.4
49: 24 93344 16688 17.9
Men and Women
The 2,128,842 women make up 52% of the WA registered voter database.
More importantly, women tend to be more Democratic than Republican:
sat2[,sum(Female),]
[1] 2128842
sat2[,sum(Female),] / mat2[,sum(AllRegistrants),]
[1] 0.521
Counties
Women are also 52% of all registrants in the top ten counties,quite possibly making them the
most important demographic group in WA state:
sat2[AllRegistrants >= 100000,sum(Female)]
[1] 1749240
sat2[AllRegistrants >= 100000,sum(Female)] / sat2[AllRegistrants >= 100000,sum(AllRegistrants)]
[1] 0.521
Millennial women outnumber Millennial men in all of the top ten most registered counties.
In some counties like King, Pierce, Thurston, Whatcom, Yakima and Benton, the percentage difference
is more than 1%.
sat4[AllRegistrants >= 100000,][order(-AllRegistrants)]
County AllRegistrants Female_lte_36years Male_lte_36years PCT_F PCT_M
1: KI 1233259 193865 184849 15.7 15.0
2: PI 460443 69236 63641 15.0 13.8
3: SN 436022 63339 60017 14.5 13.8
4: SP 296079 43540 40999 14.7 13.8
5: CR 259991 36213 33140 13.9 12.7
6: TH 167751 23682 21922 14.1 13.1
7: KP 158800 20732 20271 13.1 12.8
8: WM 133361 21416 19805 16.1 14.9
9: YA 109824 17986 15419 16.4 14.0
10: BE 102367 14533 13426 14.2 13.1
...
Last Year Voted
The key glean from this set of data is the difference in percentage for 'youth' between those who have never
voted before and those who have recently voted.
is.na(LastYearVoted)
The voter database contains the field "LastVoted". The years 'last voted' look like this:
Year n PCT
1: 2016 1817882 44.48340
2: NA 654807 16.02306
3: 2015 493697 12.08072
4: 2012 423789 10.37008
5: 2014 399086 9.76560
6: 2013 103269 2.52698
7: 2008 66325 1.62297
8: 2010 44035 1.07753
9: 2011 19837 0.48541
10: 2004 19417 0.47513
11: 2009 10683 0.26141
12: 2006 7052 0.17256
13: 2000 5742 0.14051
14: 2005 4079 0.09981
15: 2007 2974 0.07277
16: 1997 2253 0.05513
17: 2002 2211 0.05410
18: 1996 2135 0.05224
19: 1998 1398 0.03421
20: 2003 1298 0.03176
21: 2001 1211 0.02963
22: 1992 1172 0.02868
23: 1999 1154 0.02824
24: 1995 510 0.01248
25: 1994 378 0.00925
26: 1993 259 0.00634
Year n PCT
Here is how the 654,807 that have no last year voted play out by top BirthYears, Counties, Age groups:
BirthYear N
1: 1995 28434
2: 1996 26952
3: 1997 26261
4: 1991 25296
5: 1992 25056
6: 1990 24322
7: 1994 23890
8: 1993 23824
9: 1989 23237
10: 1988 22132
11: 1987 21146
...
CountyCode N
1: KI 198160
2: PI 76333
3: SN 66782
4: SP 47685
5: CR 47644
6: TH 29374
7: KP 22910
8: YA 21001
9: WM 19170
10: BE 17386
11: CZ 10172
...
lat8[order(-AllRegistrants)][1:12]
County AllRegistrants is.na_LYV BY_gte_1980 BY_gte_1990 PCT_NA PCT_NA_1980 PCT_NA_1990
1: KI 1233259 198160 126707 61931 16.1 10.27 5.02
2: PI 460443 76333 47702 25596 16.6 10.36 5.56
3: SN 436022 66782 40238 21639 15.3 9.23 4.96
4: SP 296079 47685 30458 16899 16.1 10.29 5.71
5: CR 259991 47644 27016 14423 18.3 10.39 5.55
6: TH 167751 29374 17840 9323 17.5 10.63 5.56
7: KP 158800 22910 14362 7726 14.4 9.04 4.87
8: WM 133361 19170 12949 8207 14.4 9.71 6.15
9: YA 109824 21001 13992 8467 19.1 12.74 7.71
10: BE 102367 17386 10764 5852 17.0 10.52 5.72
11: SK 71577 9907 5915 3296 13.8 8.26 4.60
...
LastYeaVoted == 2016
Here is how the 1,817,882 whose last year voted == 2016 play out by top BirthYears, Counties, Age groups:
lat2016_1
BirthYear N
1: 1947 46015
2: 1952 45177
3: 1953 45065
4: 1954 44394
5: 1951 44186
6: 1948 43667
7: 1949 43239
8: 1950 42980
9: 1955 42846
10: 1956 42686
11: 1957 41264
...
lat2016_2[N >= 10000,][order(-N)]
CountyCode N
1: KI 508514
2: PI 192838
3: SN 181653
4: SP 126651
5: CR 119243
6: KP 82551
7: TH 81226
8: WM 71261
9: BE 48701
10: YA 42453
11: SK 34685
...
lat2016_8[order(-AllRegistrants)][1:12]
County AllRegistrants LYV_2016 BY_gte_1980 BY_gte_1990 PCT_2016 PCT_2016_1980 PCT_2016_1990
1: KI 1233259 508514 96893 32820 41.2 7.86 2.66
2: PI 460443 192838 31446 11915 41.9 6.83 2.59
3: SN 436022 181653 30870 12085 41.7 7.08 2.77
4: SP 296079 126651 19600 7991 42.8 6.62 2.70
5: CR 259991 119243 17579 6957 45.9 6.76 2.68
6: TH 167751 81226 12393 4637 48.4 7.39 2.76
7: KP 158800 82551 12417 4906 52.0 7.82 3.09
8: WM 133361 71261 13903 6251 53.4 10.43 4.69
9: YA 109824 42453 6656 2969 38.7 6.06 2.70
10: BE 102367 48701 7553 2924 47.6 7.38 2.86
11: SK 71577 34685 4509 1791 48.5 6.30 2.50
12: CZ 60749 25029 3153 1331 41.2 5.19 2.19
Merging these two 'Last Year' groups:
merge8 <- span=""> merge(lat8,lat2016_8, by="County",all=TRUE)
merge8[,.(County,AllRegistrants.x,is.na_LYV,LYV_2016,PCT_NA_1980,PCT_2016_1980),][order(-AllRegistrants.x)][1:12]
County AllRegistrants.x is.na_LYV LYV_2016 PCT_NA_1980 PCT_2016_1980
1: KI 1233259 198160 508514 10.27 7.86
2: PI 460443 76333 192838 10.36 6.83
3: SN 436022 66782 181653 9.23 7.08
4: SP 296079 47685 126651 10.29 6.62
5: CR 259991 47644 119243 10.39 6.76
6: TH 167751 29374 81226 10.63 7.39
7: KP 158800 22910 82551 9.04 7.82
8: WM 133361 19170 71261 9.71 10.43
9: YA 109824 21001 42453 12.74 6.06
10: BE 102367 17386 48701 10.52 7.38
11: SK 71577 9907 34685 8.26 6.30
12: CZ 60749 10172 25029 9.46 5.19
Appendix
Code,County
AD,Adams
AS,Asotin
BE,Benton
CH,Chelan
CM,Clallam
CR,Clark
CU,Columbia
CZ,Cowlitz
DG,Douglas
FE,Ferry
FR,Franklin
GA,Garfield
GR,Grant
GY,Grays Harbor
IS,Island
JE,Jefferson
KI,King
KP,Kitsap
KS,Kittitas
KT,Klickitat
LE,Lewis
LI,Lincoln
MA,Mason
OK,Okanogan
PA,Pacific
PE,Pend Oreille
PI,Pierce
SJ,San Juan
SK,Skagit
SM,Skamania
SN,Snohomish
SP,Spokane
ST,Stevens
TH,Thurston
WK,Wahkiakum
WL,Walla Walla
WM,Whatcom
WT,Whitman
YA,Yakima
No comments:
Post a Comment