Gender and the Vote in WA

Updated on 10/10/2018.  Still under construction. 

Voter Histories for Primary 2018 are due first week in September and will allow for Primary 2018 to be incorporated into this post. The tables below want a wide screen. Gender is a such a widespread and binary classifier that understanding it's significance in terms of blue/red, young/old, white/POC appears difficult. That women register and vote more than men is an easy insight. Who women vote for in what counties and why they vote is more difficult.

To give a simple example relevant to the 2018 midterms: It is possible that the new stampless ballot could increase conservative votes among women of low motility and busy schedules in red counties. There is some evidence that partisanship of gender  is more clearly defined when gender is considered as a function of location, age, religion, race, class, income, party allegiance and education levels. Peering into a wide scale 'binary classifier' like gender and gender's relationship to other electoral co-variants is proving to be more that a weekend's task.  The tables below want a wide screen.

It is important to concentrate on relevant statistics.  Women outnumber men among registered voters in WA State:

    Gender   Count
1:    Male 2037855
2:  Female 2212194
3: Unknown   10846

If we look at the "LastVoted" field in the August VRDB (active only) grouped by Gender we see women "outvoting" men in every category but those who have no "LastVote":

     LastVoted      F      M    U
 1: 08/07/2018 923225 824432 2968
 2: 11/08/2016 501485 442435 2150
 3:            284125 306161 4220
 4: 11/07/2017 129252 121454  464
 5: 02/13/2018 107158  93998  445
 6: 11/06/2012  54931  51405   63
 7: 04/24/2018  34114  29916   23
 8: 08/01/2017  31293  27221  154
 9: 02/14/2017  21506  18033   95
10: 11/04/2008  16528  15170   20

But please notice the the second and third largest groups above are those that last voted in GE2016 and those that have no record of voting. Notice also that 25.4% of the current active voter database have "last voted" only in the last three Presidential elections:

    LastVoted      N
1: 11/08/2016 946070
2: 11/06/2012 106399
3: 11/04/2008  31718

sum last Presidential Voters / Total Active
1084187/4260895 = 0.25445

If we look at last year voted (active voters September VRDB) by Gender divided 'greater than or equal to' 50 (e.g >= 50) and 'less than 50' (e.g. > 50), we see big difference in the ages groups of those who have 'last voted' in 2018 and those who have not.  We also large overhangs of  'last year voted'= = 2016 for both genders but especially those less than 50.

    LYV Female50GTE Female50LT Male50GTE Male50LT
1: 2018      718920     343699    638732   308017
2: 2016      207823     313440    183614   278760
3: 2017       90525      93832     83099    85827
4:   NA       61033     238443     61480   260146

5: 2012       21219      35746     19283    34522

Regardless of Gender, the bulk of the 1.7M primary votes were cast by those over 50. Many 2016 and 2017 voters will vote in GE 2018. But how many?

    LYV All_50GTE All_50LT
1: 2018   1359428   653389
2: 2016    392161   593725
3: 2017    173914   180117
4:   NA    123634   502283

5: 2012     40523    70316

The table two paragraphs below looks at currently active voters and their voting histories in 2017 and 2018 for Primary 2017, General Election or Primary 2018 only.  These are blocks of voting groups segmented by their voting histories. A sample of the raw data looks like this:

         Gender Age PRI2017 GE2017 PRI2018
      1:      M  53       0      0       1
      2:      F  61       0      0       0
      3:      M  78       0      0       0
      4:      F  47       0      1       1
      5:      F  36       0      0       1

It is unsurprising to find women voting at a higher percentage than men in any single or any grouped combination of these elections. What is surprising is that the participatory totals for both genders for those who voted in PRI2018 only vs those that voted in GE2017 only nearly doubled. This is doubtless the effect of the the new stampless ballot more than any change in gender percentage. The second most surprising data is that 2M plus men or women have not voted in any single or combination of Primary 2017, General Election or Primary 2018 inside the last two years. Since there are 4.3M active registered  voters, we can state that nearly 49% of the registered voters in WA state have not voted in any major election in the last two years.  It is also worth noting that only 17% of the active, registered voter database have voted in all three of Primary 2017, General Election or Primary 2018 inside the last two years.

                   Elections  Female    Male  Fpct  Mpct
1: None.in.Pri.Gen.2017.2018 1059498 1003486 51.36 48.64
2:                   PRI2018  246778  222565 52.58 47.42
3:                    GE2017  128077  121352 51.35 48.65
4:            GE2017.PRI2018  244116  225840 51.94 48.06
5:                   PRI2017   40716   35003 53.77 46.23
6:           PRI2017.PRI2018   49312   41039 54.58 45.42
7:            PRI2017.GE2017   60675   53576 53.11 46.89
8:    PRI2017.GE2017.PRI2018  383022  334994 53.34 46.66

These are Whatcom County Totals for Mail vs. Dropbox
Notice the increase in mail totals vs the decrease in drop total. GE2017 and PRI2018 had roughly the same number of votes.

        Type Pri2017 GE2017 Pri2018
1: MailTotal   13099  19177   31054
2: DropTotal   22035  46778   35209
3:  MailGood   10751  17048   27642
4:  DropGood   21928  46558   35065

For more on statewide dropbox vs mail-in metrics, please my post here.

WA voter active and registered for the previous five years. 

All totals from end of year except 2018 (July):

   Year  Female    Male Unknown   Total pctF pctM pctU
1: 2014 2048184 1872884    2277 3923345 52.2 47.7  0.1
2: 2015 2072658 1897010    2651 3972319 52.2 47.8  0.1
3: 2016 2224698 2042803    8990 4276491 52.0 47.8  0.2
4: 2017 2215647 2039814    9314 4264775 52.0 47.8  0.2
5: 2018 2232141 2056350   10904 4299395 51.9 47.8  0.3


Taken from Collection of ~15M voter histories from ~4.8M voters from 2014 - 2018 . These are active and once active voters from VRDB voter histories before the Primary 2018:

      Election  Female    Male Unknown FminusM FdivMF FdiffMF

 1: 2015-02-10  153839  133103     251   20736   53.6     7.2
 2: 2015-04-06      78      65       1      13   54.5     9.0
 3: 2015-04-28  233624  206687     153   26937   53.1     6.2
 4: 2015-08-04  407243  352293     392   54950   53.6     7.2
 5: 2015-11-03  755260  682315     880   72945   52.5     5.0
 6: 2016-02-09  372580  324207     606   48373   53.5     7.0
 7: 2016-04-04      55      43       0      12   56.1    12.2
 8: 2016-04-26  162157  141572     128   20585   53.4     6.8
 9: 2016-05-24  735603  608070    1543  127533   54.7     9.4
10: 2016-08-02  722975  633278    1289   89697   53.3     6.6
11: 2016-11-08 1677072 1485433    5270  191639   53.0     6.0
12: 2017-02-14  162439  140334     565   22105   53.7     7.4
13: 2017-04-17     178     139       0      39   56.2    12.4
14: 2017-04-25   65404   55448     373    9956   54.1     8.2
15: 2017-08-01  538272  468729    1327   69543   53.5     7.0
16: 2017-11-07  821959  741396    2110   80563   52.6     5.2
17: 2018-02-13  473456  416577    1483   56879   53.2     6.4
18: No History  449410  468259    4790  -18849   49.0    -2.0

Current WA voter  database (active status) by 'Generations':

   GTEAge LTAge   Male Female MaleMeanAge FemaleMeanAge MaleMedianAge FemaleMedianAge

1:     18    38 620563 656464    28.64927      28.70344            29              29
2:     38    58 690152 729129    47.70612      47.69196            48              48
3:     58    78 621679 685044    66.27935      66.31080            66              66
4:     78    98 122893 158515    83.43901      84.22610            82              83
5:     98   118   1059   2987    99.73088      99.75226            99              99

GE 2016 County based M/F Correlations from Precinct Results. These are actually Clinton-Stein/Trump-Johnson Female/Male correlations. The GE2016 Results are merged with a current VRDB (July 2018) for gender and precincts.

     sc Clinton-Female Clinton-Male Trump-Female Trump-Male
 1: AD          0.948        0.758        0.902      0.826
 2: AS          0.926        0.873        0.894      0.930
 3: BE          0.918        0.948        0.900      0.961
 4: CH          0.910        0.936        0.893      0.940
 5: CM          0.924        0.876        0.880      0.903
 6: CR          0.760        0.692        0.729      0.736
 7: CU          0.837        0.897        0.791      0.934
 8: CZ          0.864        0.704        0.819      0.775
 9: DG          0.945        0.950        0.933      0.968
10: FE          0.742        0.794        0.565      0.927
11: FR          0.681        0.676        0.666      0.709
12: GA          0.955        0.965        0.949      0.973
13: GR          0.864        0.871        0.860      0.899
14: GY          0.937        0.937        0.928      0.942
15: IS          0.760        0.577        0.640      0.715
16: JE          0.884        0.612        0.812      0.723
17: KI          0.661        0.557        0.674      0.525
18: KP          0.677        0.775        0.614      0.825
19: KS          0.865        0.907        0.807      0.942
20: KT          0.831        0.896        0.829      0.900
21: LE          0.949        0.944        0.911      0.968
22: LI          0.976        0.995        0.979      0.995
23: MA          0.932        0.938        0.923      0.975
24: OK          0.718        0.517        0.691      0.629
25: PA          0.983        0.971        0.974      0.984
26: PE          0.906        0.940        0.877      0.968
27: PI          0.850        0.737        0.808      0.789
28: SJ          0.985        0.944        0.989      0.932
29: SK          0.892        0.889        0.865      0.920
30: SM          0.865        0.908        0.860      0.947
31: SN          0.820        0.747        0.781      0.799
32: SP          0.680        0.569        0.612      0.641
33: ST          0.866        0.936        0.827      0.964
34: TH          0.778        0.786        0.731      0.828
35: WK          0.839        0.917        0.772      0.944
36: WL          0.897        0.897        0.872      0.919
37: WM          0.619        0.412        0.578      0.474
38: WT          0.923        0.820        0.926      0.816
39: YA          0.853        0.668        0.788      0.730
    sc Clinton-Female Clinton-Male Trump-Female Trump-Male

Added a Correlation Chart

Click on this chart to enlarge.  The 10 (top) counties in the chart make up ~80% or more of WA's state registered voters. It appears that the more blue your county, the tighter and higher the correlations between presidential candidates and men and women in GE2016. The converse appears to be true for red.  I am picking up a rural/urban split here: the more denser counties have bluer votes and less significant difference between gender correlations.  The redder counties have male/female votes with a greater spread between the correlations of the number of votes by gender and blue/red candidates. To be honest,  I don't have the math chops to understand if my data has any real significance or whether I am just seeing irrelevant patterns. Gender may not be a strong macro scale predictor in WA state. It may have significance inside smaller segmentation (e.g. precinct groups).

Chart of  Top Ten Counties Primary 2018

These are Voting History charts  from the Primary 2018 vote for Maria Cantwell (D) and Susan Hutchison (R) and of Female and Males votes for Primary 2018.  King County is so large and so liberal it votes like a separate entity from the rest of WA counties. Among the other top nine counties, King, Thurston, Whatcom, and Kitsap have the heavy Maria Cantwell vote (top blue bar) that outdistances the female vote (second blue bar) which is probably indicative of strong male support for Cantwell. Counties like Pierce, Snohomish, Benton ,Yakima appear to have greater support for Hutchison and female votes appear to exceed those who voted for Cantwell . This probably indicative of stronger female support for Hutchison. Click to enlarge charts.

Contrary 2016 Precincts

Table 1 contains precincts dominated by female voters, but votes overwhelmingly for Trump or Johnson. Table 2 contains precincts dominated by male voters, but votes overwhelmingly for Clinton or Stein.

Table 1

      StateCode  CS  TJ   F   M      pctF     pctCS

 1:      OK_203  25  73  97  44 0.6879433 0.2551020

 2:       WL_68  24  63  63  39 0.6176471 0.2758621

 3:      CU_103  62 137 147  97 0.6024590 0.3115578

 4:       FR_47  10  20  26  13 0.6666667 0.3333333

 5:     BE_1676 196 378 421 272 0.6075036 0.3414634

 6:      LE_206 187 347 428 280 0.6045198 0.3501873

 7:      LE_112 226 403 532 310 0.6318290 0.3593005

 8:      CH_100   4   7  10   6 0.6250000 0.3636364

 9:      YA_142 141 227 303 181 0.6260331 0.3831522

10:      YA_307  18  27  44  29 0.6027397 0.4000000

11:      OK_214  27  40  60  35 0.6315789 0.4029851

12:     SP_7036 230 309 413 250 0.6229261 0.4267161

13:     KI_3517 100 133 300 172 0.6355932 0.4291845

14: SN_23811572 131 173 489 287 0.6301546 0.4309211

15:      OK_147  25  33  45  28 0.6164384 0.4310345

16:      YA_139 312 405 614 328 0.6518047 0.4351464

17:      OK_107  24  31  43  23 0.6515152 0.4363636

18:      OK_108  35  42  64  37 0.6336634 0.4545455

Table 2

    StateCode  CS TJ   F   M      pctF     pctCS

 1:   KI_3416 300 74 133 421 0.2400722 0.8021390

 2:   KI_2987 208 51 205 330 0.3831776 0.8030888

 3:   KI_1823  83 19  59 116 0.3371429 0.8137255

 4:    KI_568  57 13  36  56 0.3913043 0.8142857

 5:   KI_2081 161 32 139 250 0.3573265 0.8341969

 6:   KI_3497 389 75 224 394 0.3624595 0.8383621

 7:   KI_3665 417 78 265 419 0.3874269 0.8424242

 8:   KI_1975 120 22 126 195 0.3925234 0.8450704

 9:   KI_2089 170 29 108 211 0.3385580 0.8542714

10:   KI_3647 229 31 152 402 0.2743682 0.8807692

11:   KI_1928 229 26 144 221 0.3945205 0.8980392

12:   KI_3571 286 29 209 341 0.3800000 0.9079365

13:   KI_2549 558 50 296 471 0.3859192 0.9177632

14:   KI_2852 444 37 219 331 0.3981818 0.9230769

15:   KI_1860 786 51 314 493 0.3890954 0.9390681

16:   KI_1847 511 27 256 398 0.3914373 0.9498141

17:   KI_2541 260 11 122 208 0.3696970 0.9594096

18:   KI_1859 497 20 244 385 0.3879173 0.9613153

APPENDICES

Statistical Concepts

A 'Correlation Coefficient' is a measure of the strength of the relationship between two sets of data. The 'Pearson correlation coefficient'  is defined as the covariance of X,Y over the product of the standard deviations of X,Y. The base R language correlation coefficient function is 'cor'.   'Cor' returns a data point between 1 : -1.  For practical usage:

1 =  absolute correlation
0 = no correlation
-1 = absolute negative correlations

There are no universally agreed upon definitions for the strength of correlations, only generally accepted guidelines.  Correlation coefficient functions produces a  statistical measure that shows the mathematical strength of a numerical relationship. An oft repeated warning is "Correlation does not imply Causation". All that being said, covariance and correlation are some the best measures of relationships between linear data. The Pearson correlation coefficient normalizes covariance with standard deviation .