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precinct analysis

Precinct analysis: County Clerk 2020 and 2018

Introduction
Congressional districts
State Rep districts
Commissioners Court/JP precincts
Comparing 2012 and 2016
Statewide judicial
Other jurisdictions
Appellate courts, Part 1
Appellate courts, Part 2
Judicial averages
Other cities
District Attorney
County Attorney
Sheriff
Tax Assessor

We weren’t supposed to have a County Clerk race on the ballot in 2020, but we did following the health-related resignation of Diane Trautman in May. That gave us a battle of Stan Stanart, former County Clerk whom Trautman had deposed in 2018, and Teneshia Hudspeth, former chief elections person under Stanart. Hudspeth won easily, and though her 835K total votes were on the lower end for Democratic countywide candidates, her 53.76% of the vote was pretty close to Trautman’s 54.60% from two years before. The 2018 election was a non-Presidential year, with record turnout for such a contest, and the 2018 Clerk race also featured a Libertarian candidate, so comparisons are a bit tricky. My advice is to look at Hudspeth’s percentages compared to Trautman’s. Here’s the 2020 race:


Dist  Stanart Hudspeth Stanart% Hudspeth%
=========================================
CD02  181,707  151,509   54.53%    45.47%
CD07  153,335  147,437   50.98%    49.02%
CD08   26,037   14,710   63.90%    36.10%
CD09   37,941  119,087   24.16%    75.84%
CD10  103,442   58,506   63.87%    36.13%
CD18   60,497  178,172   25.35%    74.65%
CD22   22,018   19,747   52.72%    47.28%
CD29   50,483   99,634   33.63%    66.37%
CD36   83,484   47,160   63.90%    36.10%
				
SBOE4 108,536  332,265   24.62%    75.38%
SBOE6 389,609  343,285   53.16%    46.84%
SBOE8 220,799  160,413   57.92%    42.08%
				
SD04   56,013   22,252   71.57%    28.43%
SD06   58,816  115,690   33.70%    66.30%
SD07  237,989  168,687   58.52%    41.48%
SD11   77,992   45,722   63.04%    36.96%
SD13   38,148  158,482   19.40%    80.60%
SD15  115,748  191,422   37.68%    62.32%
SD17  118,870  122,163   49.32%    50.68%
SD18   15,368   11,547   57.10%    42.90%
				
HD126  39,346   32,856   54.49%    45.51%
HD127  54,464   34,684   61.09%    38.91%
HD128  48,497   21,457   69.33%    30.67%
HD129  48,407   34,399   58.46%    41.54%
HD130  70,686   31,495   69.18%    30.82%
HD131  10,184   44,299   18.69%    81.31%
HD132  51,079   47,460   51.84%    48.16%
HD133  51,079   35,518   58.98%    41.02%
HD134  49,424   56,156   46.81%    53.19%
HD135  36,914   36,293   50.42%    49.58%
HD137  10,430   20,635   33.57%    66.43%
HD138  32,119   30,383   51.39%    48.61%
HD139  15,914   44,364   26.40%    73.60%
HD140   9,567   21,385   30.91%    69.09%
HD141   7,122   35,961   16.53%    83.47%
HD142  14,114   41,357   25.44%    74.56%
HD143  12,295   23,775   34.09%    65.91%
HD144  13,990   16,257   46.25%    53.75%
HD145  15,404   26,341   36.90%    63.10%
HD146  11,411   43,173   20.91%    79.09%
HD147  15,494   52,686   22.73%    77.27%
HD148  22,919   35,897   38.97%    61.03%
HD149  21,718   30,328   41.73%    58.27%
HD150  56,366   38,803   59.23%    40.77%
				
CC1    94,155  277,561   25.33%    74.67%
CC2   152,576  141,645   51.86%    48.14%
CC3   229,070  206,538   52.59%    47.41%
CC4   243,143  210,221   53.63%    46.37%
				
JP1    94,708  161,313   36.99%    63.01%
JP2    34,728   47,948   42.00%    58.00%
JP3    52,202   67,235   43.71%    56.29%
JP4   236,302  181,977   56.49%    43.51%
JP5   205,591  211,174   49.33%    50.67%
JP6     8,522   26,546   24.30%    75.70%
JP7    18,695   99,939   15.76%    84.24%
JP8    68,196   39,833   63.13%    36.87%

Nothing we haven’t seen before by this point. It’s possible Stanart did a little better than expected because of name recognition, but who can tell. The 2018 analysis was part of a package deal. Here’s the County Clerk’s race on its own:


Dist  Stanart Trautman  Gomez  Under Stanart%   Traut%  Gomez%
==============================================================
CD02  135,427  116,744  6,717  6,221   52.31%   45.09%   2.59%
CD07  116,383  116,488  5,648  6,706   48.79%   48.84%   2.37%
CD08   17,784   10,221    679    520   62.00%   35.63%   2.37%
CD09   23,329   93,625  2,504  2,376   19.53%   78.37%   2.10%
CD10   71,172   39,707  2,623  1,970   62.71%   34.98%   2.31%
CD18   39,159  138,311  4,892  4,087   21.47%   75.84%   2.68%
CD22   15,265   15,184    857    711   48.76%   48.50%   2.74%
CD29   30,313   82,449  3,916  2,627   25.98%   70.66%   3.36%
CD36   60,467   35,918  2,452  2,036   61.18%   36.34%   2.48%

SBOE6 287,300  269,837 14,477 15,045   50.26%   47.21%   2.53%

HD126  29,277   24,586  1,293  1,074   53.08%   44.58%   2.34%
HD127  41,017   25,198  1,634  1,260   60.45%   37.14%   2.41%
HD128  34,735   15,876  1,142    915   67.12%   30.68%   2.21%
HD129  35,567   26,799  1,739  1,582   55.48%   41.80%   2.71%
HD130  51,064   22,942  1,722  1,365   67.43%   30.30%   2.27%
HD131   6,110   34,855    864    717   14.61%   83.33%   2.07%
HD132  32,579   32,090  1,680  1,023   49.10%   48.37%   2.53%
HD133  40,721   28,089  1,552  2,192   57.87%   39.92%   2.21%
HD134  37,977   47,211  2,090  3,692   43.51%   54.09%   2.39%
HD135  26,584   27,712  1,379  1,033   47.75%   49.77%   2.48%
HD137   7,257   16,167    678    552   30.11%   67.08%   2.81%
HD138  23,336   23,515  1,257  1,100   48.51%   48.88%   2.61%
HD139  10,545   35,238  1,128    961   22.48%   75.12%   2.40%
HD140   5,269   17,569    722    490   22.36%   74.57%   3.06%
HD141   3,921   26,852    622    438   12.49%   85.53%   1.98%
HD142   8,579   30,125    850    662   21.69%   76.16%   2.15%
HD143   7,405   20,178    952    699   25.95%   70.71%   3.34%
HD144   8,949   13,629    786    450   38.30%   58.33%   3.36%
HD145   9,596   21,809  1,226    834   29.41%   66.84%   3.76%
HD146   8,082   34,044    931  1,065   18.77%   79.07%   2.16%
HD147  10,013   42,972  1,576  1,316   18.35%   78.76%   2.89%
HD148  15,587   29,671  1,907  1,695   33.05%   62.91%   4.04%
HD149  14,042   23,985    859    785   36.11%   61.68%   2.21%
HD150  41,087   27,535  1,699  1,354   58.43%   39.16%   2.42%

CC1    61,603  218,965  6,875  6,563   21.43%   76.18%   2.39%
CC2   105,901  114,124  6,772  5,028   46.69%   50.32%   2.99%
CC3   164,601  157,515  7,843  8,035   49.89%   47.74%   2.38%
CC4   177,194  158,043  8,798  7,628   51.50%   45.94%   2.56%

I included undervotes in the county candidates’ analyses in 2018 because I was trying to analyze the effects of straight ticket voting as well. As I said, if you compare just the Democratic candidates’ percentages, you see that Hudspeth and Trautman had fairly similar performances, with the drops we have noted before in some of the Latino districts. Trautman knocked it out of the park in HD134, which was more Republican in 2018. Hudspeth had among the higher scores this year in HDs 131 and 141. I fully expect she’ll build on her performance in 2022, when she will be the incumbent running for re-election, though as always the first question is what will the national atmosphere look like.

Precinct analysis: Presidential results by Congressional district

From Daily Kos Elections, the breakdown of how Presidential voting went in each of Texas’ 36 Congressional districts:

Two districts did in fact flip on the presidential level: Trump lost the 24th District in the Dallas-Fort Worth suburbs while recapturing the 23rd District along the border with Mexico. Biden, however, made major gains in a number of other suburban districts and nearly won no fewer than seven of them. Trump, meanwhile, surged in many heavily Latino areas and likewise came close to capturing three, but except for the 24th, every Trump seat is in GOP hands and every Biden seat is represented by Democrats. The 24th, which includes the suburbs north of Dallas and Fort Worth, is a good place to start because it saw one of the largest shifts between 2016 and 2020. The district began the decade as heavily Republican turf—it backed Mitt Romney 60-38—but Trump carried it by a substantially smaller 51-44 margin four years later.

Biden continued the trend and racked up a 52-46 win this time, but the area remained just red enough downballot to allow Republican Beth Van Duyne to manage a 49-47 victory in an expensive open-seat race against Democrat Candace Valenzuela.

Biden fell just short of winning seven other historically red suburban seats: the 2nd, 3rd, 6th, 10th, 21st, 22nd, and 31st, where Trump’s margins ranged from just one to three points and where the swings from 2016 ranged from seven points in the 22nd all the way to 13 points in the 3rd, the biggest shift in the state. However, as in the 24th, Biden’s surge did not come with sufficient coattails, as Republicans ran well ahead of Trump in all of these seats. (You can check out our guide for more information about each district.)

Two seats that Democrats flipped in 2018 and stayed blue last year also saw large improvements for Biden. The 7th District in west Houston, parts of which were once represented by none other than George H.W. Bush from 1967 to 1971, had swung from 60-39 Romney to 48-47 Clinton, and Biden carried it 54-45 in 2020. Democratic Rep. Lizzie Fletcher won by a smaller 51-47 spread against Wesley Hunt, who was one of the House GOP’s best fundraisers. The 32nd District in the Dallas area, likewise, had gone from 57-41 Romney to 49-47 Clinton. This time, Biden took it 54-44 as Democratic Rep. Colin Allred prevailed 52-46.

Biden’s major gains in the suburbs, though, came at the same time that Trump made serious inroads in predominantly Latino areas on or near the southern border with Mexico. That rightward shift may have cost Team Blue the chance to flip the open 23rd District, which stretches from San Antonio west to the outskirts of the El Paso area.

A full breakdown by county and district is here, and a comparison of percentages from 2016 and 2020 is here. CD23 went from being a Romney district to a Clinton district to a Trump district, though in all cases it was close. The red flags are in CDs 15, 28, and 34. In CD15, incumbent Vicente Gonzalez won by only three points, in a district Biden carried by one point, a huge drop from Clinton’s 57-40 win in 2016. Everyone’s least favorite Democrat Henry Cuellar had an easy 19-point win, but Biden only carried CD28 by four points, down from Clinton’s 20-point margin. It’s not crazy to think that Jessica Cisneros could have lost that race, though of course we’ll never know. This wasn’t the scenario I had in mind when I griped that CD28 was not a “safe” district, but it does clearly illustrate what I meant. And Filemon Vela, now a DNC Vice Chair, also had a relatively easy 55-42 win, but in a district Biden carried 52-48 after Clinton had carried it 59-38. Not great, Bob.

We don’t have the full downballot results – we’ll probably get them in March from the Texas Legislative Council – but the Harris County experience suggests there will be some variance, and that other Dems may do a little better in those districts. How much of this was Trump-specific and how much is long-term is of course the big question. The Georgia Senate runoffs, coupled with the 2018 results, suggest that having Trump on the ballot was better for Republicans than not having him on the ballot. On the other hand, 2022 will be a Democratic midterm year, and the last couple of them did not go well. On the other other hand, Trump is leaving office in complete disgrace and with approval levels now in the low 30s thanks to the armed insurrection at the Capitol, and for all the damage he did to the economy and the COVID mitigation effort, Biden is in a position to make big progress in short order. It’s just too early to say what any of this means, but suffice it to say that both Ds and Rs have challenges and opportunities ahead of them.

There are some very early third-party efforts at drawing new Congressional districts – see here and here for a couple I’ve come across. We still need the actual Census numbers, and as I’ve said before, the Republicans will have to make decisions about how much risk they want to expose themselves to. The way these maps are drawn suggests to me that “pack” rather than “crack” could be the strategy, but again this is all very early. There is also the possibility that the Democratic Congress can push through voting rights reform that includes how redistricting can be done, though the clock and potentially the Supreme Court will be factors. And if there’s one thing we should have learned over the last 20 years, it’s that due to Texas’ rapid growth, the districts you draw at the beginning of the decade may look quite a bit different by the end of the decade. We’re at the very start of a ten-year journey. A lot is going to happen, and the farther out we get the harder it is to see the possibilities.

Precinct analysis: Tax Assessor 2020 and 2016

Introduction
Congressional districts
State Rep districts
Commissioners Court/JP precincts
Comparing 2012 and 2016
Statewide judicial
Other jurisdictions
Appellate courts, Part 1
Appellate courts, Part 2
Judicial averages
Other cities
District Attorney
County Attorney
Sheriff

Tax Assessor Ann Harris Bennett is the third incumbent from 2016 running for re-election. Like Sheriff Ed Gonzalez, she improved her performance pretty significantly from four years ago. Unlike either Gonzalez or DA Kim Ogg, she came off a close race – she was actually trailing after early voting, and did just well enough on Election Day to pull out a eight thousand vote victory. In 2020, she won by ten points, with a Libertarian candidate also in the mix. Here’s how 2020 looked for Bennett:


Dist    Daniel  Bennett     Lib Daniel%Bennett%   Lib%
======================================================
CD02   174,454  151,148  11,516  51.15%  44.32%  3.38%
CD07   148,007  146,906   9,535  47.97%  47.62%  3.09%
CD08    24,960   14,786   1,419  59.88%  35.47%  3.40%
CD09    35,972  117,815   4,676  22.43%  73.47%  2.92%
CD10    98,983   58,837   5,631  59.77%  35.53%  3.40%
CD18    57,057  175,920   8,077  23.44%  72.28%  3.32%
CD22    20,650   19,913   1,660  48.18%  46.46%  3.87%
CD29    46,205  101,024   4,961  30.09%  65.80%  3.23%
CD36    79,503   48,053   4,570  59.41%  35.91%  3.42%
						
SBOE4  100,919  330,636  13,852  22.66%  74.23%  3.11%
SBOE6  374,836  342,677  24,239  50.53%  46.20%  3.27%
SBOE8  210,036  161,090  13,954  54.54%  41.83%  3.62%
						
SD04    53,982   22,540   2,570  68.25%  28.50%  3.25%
SD06    53,863  117,046   5,997  30.45%  66.16%  3.39%
SD07   227,833  169,249  13,705  55.46%  41.20%  3.34%
SD11    74,156   46,328   4,608  59.28%  37.04%  3.68%
SD13    36,043  156,250   5,976  18.18%  78.81%  3.01%
SD15   110,239  189,765  10,747  35.48%  61.07%  3.46%
SD17   115,088  121,733   7,376  47.13%  49.85%  3.02%
SD18    14,587   11,494   1,066  53.73%  42.34%  3.93%
						
HD126   37,713   32,939   2,327  51.68%  45.13%  3.19%
HD127   52,360   34,525   3,193  58.13%  38.33%  3.54%
HD128   46,291   22,223   2,192  65.47%  31.43%  3.10%
HD129   46,005   34,465   3,291  54.92%  41.15%  3.93%
HD130   67,940   31,860   3,420  65.82%  30.87%  3.31%
HD131    9,557   43,780   1,586  17.40%  79.71%  2.89%
HD132   48,284   47,303   3,782  48.59%  47.60%  3.81%
HD133   49,924   35,385   2,408  56.91%  40.34%  2.75%
HD134   48,604   55,747   2,949  45.30%  51.95%  2.75%
HD135   34,905   36,408   2,567  47.25%  49.28%  3.47%
HD137    9,845   20,352   1,178  31.38%  64.87%  3.75%
HD138   30,750   30,377   2,169  48.58%  47.99%  3.43%
HD139   14,994   44,096   1,832  24.61%  72.38%  3.01%
HD140    8,661   21,724   1,000  27.60%  69.22%  3.19%
HD141    6,617   35,561   1,217  15.25%  81.95%  2.80%
HD142   13,268   41,110   1,631  23.69%  73.40%  2.91%
HD143   11,211   24,369   1,121  30.55%  66.40%  3.05%
HD144   12,895   16,646   1,072  42.12%  54.38%  3.50%
HD145   14,110   26,467   1,630  33.43%  62.71%  3.86%
HD146   10,878   42,506   1,661  19.76%  77.22%  3.02%
HD147   14,762   51,621   2,518  21.42%  74.92%  3.65%
HD148   21,733   35,555   2,479  36.36%  59.49%  4.15%
HD149   20,767   30,361   1,522  39.44%  57.67%  2.89%
HD150   53,716   39,022   3,300  55.93%  40.63%  3.44%
						
CC1     89,315  274,496  11,676  23.79%  73.10%  3.11%
CC2    143,799  143,691  10,434  48.27%  48.23%  3.50%
CC3    220,064  206,206  14,217  49.96%  46.81%  3.23%
CC4    232,613  210,012  15,718  50.75%  45.82%  3.43%
						
JP1     90,963  160,043   8,734  35.02%  61.62%  3.36%
JP2     32,249   48,712   2,804  38.50%  58.15%  3.35%
JP3     49,382   67,843   3,512  40.90%  56.19%  2.91%
JP4    226,115  182,066  14,185  53.54%  43.11%  3.36%
JP5    196,782  210,577  13,981  46.70%  49.98%  3.32%
JP6      7,542   26,611   1,383  21.22%  74.88%  3.89%
JP7     17,840   98,244   3,456  14.92%  82.19%  2.89%
JP8     64,918   40,309   3,990  59.44%  36.91%  3.65%

Bennett’s 834K vote total was the lowest among the non-judicial countywide candidates, and only ahead of five judicial candidates. Thanks in part to the 52K votes that the Libertarian candidate received, however, she led challenger and former District Clerk Chris Daniel by over 148K votes, which is one of the bigger margins. If you want to examine the belief that Libertarian candidates mostly take votes away from Republicans, look at some of the district totals, especially HDs like 132, 135, and 138. We can’t know for sure how Daniel might have done in a two-person race, but it seems reasonable to me to say he’d have improved at least somewhat. Bennett did about as well as you’d expect someone who got 53% of the vote would do. If the final score would have been closer in a two-person race, it’s not because she’d have received fewer votes or gotten a lower percentage.

Here’s the 2016 comparison, in which Bennett knocked off incumbent Mike Sullivan. She trailed by about five thousand votes when the totals were first displayed on Election Night, with Sullivan having slight leads in both mail ballots and in person early votes – yes, that’s right, Republicans used to try to compete on mail ballots – but got nearly 52% of the Election Day vote, which was a big enough part of the vote to push her over the top.


Dist  Sullivan  Bennett  Sullivan%  Bennett%
============================================
CD02   168,936  105,778     61.50%    38.50%
CD07   147,165  106,727     57.96%    42.04%
CD09    29,855  103,511     22.39%    77.61%
CD10    83,213   34,795     70.51%    29.49%
CD18    53,558  148,586     26.49%    73.51%
CD29    41,555   88,942     31.84%    68.16%
				
SBOE6  357,083  249,953     58.82%    41.18%
				
HD126   37,003   24,186     60.47%    39.53%
HD127   50,028   23,460     68.08%    31.92%
HD128   42,659   16,238     72.43%    27.57%
HD129   44,072   24,777     64.01%    35.99%
HD130   60,429   20,277     74.88%    25.12%
HD131    8,121   37,906     17.64%    82.36%
HD132   39,094   29,321     57.14%    42.86%
HD133   50,116   25,241     66.50%    33.50%
HD134   49,352   39,410     55.60%    44.40%
HD135   33,528   26,112     56.22%    43.78%
HD137    9,664   17,099     36.11%    63.89%
HD138   28,827   22,096     56.61%    43.39%
HD139   13,707   38,266     26.37%    73.63%
HD140    7,556   19,790     27.63%    72.37%
HD141    5,934   32,109     15.60%    84.40%
HD142   11,599   33,182     25.90%    74.10%
HD143   10,372   22,294     31.75%    68.25%
HD144   11,810   15,188     43.74%    56.26%
HD145   12,669   21,519     37.06%    62.94%
HD146   11,323   36,903     23.48%    76.52%
HD147   14,119   43,254     24.61%    75.39%
HD148   20,434   26,999     43.08%    56.92%
HD149   16,639   26,389     38.67%    61.33%
HD150   50,472   25,358     66.56%    33.44%
				
CC1     82,916  231,040     26.41%    73.59%
CC2    134,067  117,084     53.38%    46.62%
CC3    202,128  149,943     57.41%    42.59%
CC4    220,415  149,294     59.62%    40.38%

Again, there’s nothing here we haven’t seen before, but as Mike Sullivan nearly hung on, you can see what an almost-successful Republican looked like in 2016. Note the margins he had in CDs 02 and 07, and the various now-competitive State Rep districts. I mean, Sullivan won HD134 by eleven points. He won CC4 by almost 20 points, and CC3 by fifteen. We don’t live in that world now.

Precinct analysis: Sheriff 2020 and 2016

Introduction
Congressional districts
State Rep districts
Commissioners Court/JP precincts
Comparing 2012 and 2016
Statewide judicial
Other jurisdictions
Appellate courts, Part 1
Appellate courts, Part 2
Judicial averages
Other cities
District Attorney
County Attorney

Behold your 2020 vote champion in Harris County: Sheriff Ed Gonzalez, running for his second term in office. I’ll get into the details of Gonzalez’s domination in a minute. Here are the numbers for 2020:


Dist     Danna  Gonzalez    Danna%  Gonzalez%
=============================================
CD02   170,422   166,902    50.52%     49.48%
CD07   141,856   162,417    46.62%     53.38%
CD08    24,788    16,406    60.17%     39.83%
CD09    35,308   122,871    22.32%     77.68%
CD10    98,458    65,239    60.15%     39.85%
CD18    54,869   186,236    22.76%     77.24%
CD22    20,466    21,710    48.53%     51.47%
CD29    43,503   109,304    28.47%     71.53%
CD36    79,327    52,648    60.11%     39.89%
				
SBOE4   96,435   349,282    21.64%     78.36%
SBOE6  363,916   378,161    49.04%     50.96%
SBOE8  208,646   176,291    54.20%     45.80%
				
SD04    53,758    25,277    68.02%     31.98%
SD06    50,944   126,617    28.69%     71.31%
SD07   224,433   186,884    54.56%     45.44%
SD11    74,078    50,852    59.30%     40.70%
SD13    35,054   162,823    17.72%     82.28%
SD15   106,009   204,899    34.10%     65.90%
SD17   110,189   133,749    45.17%     54.83%
SD18    14,532    12,635    53.49%     46.51%
				
HD126   36,979    36,165    50.56%     49.44%
HD127   51,960    38,105    57.69%     42.31%
HD128   46,345    24,235    65.66%     34.34%
HD129   45,743    37,938    54.66%     45.34%
HD130   67,658    35,780    65.41%     34.59%
HD131    9,271    45,531    16.92%     83.08%
HD132   47,705    51,772    47.96%     52.04%
HD133   47,629    39,951    54.38%     45.62%
HD134   44,590    62,513    41.63%     58.37%
HD135   34,389    39,591    46.48%     53.52%
HD137    9,680    21,648    30.90%     69.10%
HD138   30,004    33,385    47.33%     52.67%
HD139   14,623    46,351    23.98%     76.02%
HD140    8,109    23,412    25.73%     74.27%
HD141    6,449    36,900    14.88%     85.12%
HD142   12,684    43,278    22.67%     77.33%
HD143   10,463    26,455    28.34%     71.66%
HD144   12,685    17,965    41.39%     58.61%
HD145   13,322    29,035    31.45%     68.55%
HD146   10,562    44,351    19.23%     80.77%
HD147   13,955    54,824    20.29%     79.71%
HD148   20,375    39,637    33.95%     66.05%
HD149   20,574    32,068    39.08%     60.92%
HD150   53,242    42,844    55.41%     44.59%
				
CC1     85,139   289,925    22.70%     77.30%
CC2    141,416   156,934    47.40%     52.60%
CC3    214,450   226,063    48.68%     51.32%
CC4    227,992   230,814    49.69%     50.31%
				
JP1     84,929   174,954    32.68%     67.32%
JP2     31,274    52,644    37.27%     62.73%
JP3     48,485    72,207    40.17%     59.83%
JP4    223,758   199,021    52.93%     47.07%
JP5    191,671   229,696    45.49%     54.51%
JP6      6,846    28,930    19.14%     80.86%
JP7     17,135   102,122    14.37%     85.63%
JP8     64,899    44,162    59.51%     40.49%

Only Joe Biden (918,193) got more votes than Sheriff Ed (903,736) among Dems that had a Republican opponent; District Court Judge Michael Gomez (868,327) was next in line. Gonzalez’s 235K margin of victory, and his 57.46% of the vote were easily the highest. He carried SBOE6, HD132, HD138, and all four Commissioners Court precincts, while coming close in CD02 and HD126. He even made SD07, HD133, and JP4 look competitive.

How dominant was Ed Gonzalez in 2020? He got more votes in their district than the following Democratic incumbents:

CD07: Gonzalez 162,417, Lizzie Fletcher 159,529
CD18: Gonzalez 186,236, Sheila Jackson Lee 180,952
SD13: Gonzalez 162,823, Borris Miles 159,936
HD135: Gonzalez 39,591, Jon Rosenthal 36,760
HD142: Gonzalez 43,278, Harold Dutton 42,127
HD144: Gonzalez 17,965, Mary Ann Perez 17,516
HD145: Gonzalez 29,035, Christina Morales 27,415
HD149: Gonzalez 32,068, Hubert Vo 31,919
JP1: Gonzalez 174,954, Eric Carter 166,759

That’s pretty damn impressive. Gonzalez is the incumbent, he’s in law enforcement and may be the most visible county official after Judge Hidalgo, he had a solid term with basically no major screwups, he’s well liked by the Democratic base, and he ran against a frequent flyer who had no apparent base of support. At least in 2020, this is as good as it gets.

Obviously, Gonzalez did better than he did in 2016, but let’s have a quick look at the numbers anyway.


Dist   Hickman  Gonzalez  Hickman%  Gonzalez%
=============================================
CD02   162,915   111,689    59.33%     40.67%
CD07   139,292   113,853    55.02%     44.98%
CD09    26,869   106,301    20.18%     79.82%
CD10    81,824    36,293    69.27%     30.73%
CD18    48,766   153,342    24.13%     75.87%
CD29    35,526    95,138    27.19%     72.81%
				
SBOE6  341,003   265,358    56.24%     43.76%
				
HD126   36,539    24,813    59.56%     40.44%
HD127   48,891    24,516    66.60%     33.40%
HD128   41,694    17,117    70.89%     29.11%
HD129   41,899    26,686    61.09%     38.91%
HD130   59,556    21,256    73.70%     26.30%
HD131    7,054    38,887    15.35%     84.65%
HD132   38,026    30,397    55.57%     44.43%
HD133   47,648    27,378    63.51%     36.49%
HD134   44,717    43,480    50.70%     49.30%
HD135   32,586    27,180    54.52%     45.48%
HD137    8,893    17,800    33.32%     66.68%
HD138   27,480    23,366    54.05%     45.95%
HD139   12,746    39,223    24.53%     75.47%
HD140    6,376    20,972    23.31%     76.69%
HD141    5,485    32,573    14.41%     85.59%
HD142   10,801    33,924    24.15%     75.85%
HD143    9,078    23,689    27.70%     72.30%
HD144   10,765    16,194    39.93%     60.07%
HD145   10,785    23,462    31.49%     68.51%
HD146   10,144    37,991    21.07%     78.93%
HD147   12,100    45,136    21.14%     78.86%
HD148   17,701    29,776    37.28%     62.72%
HD149   15,702    27,266    36.54%     63.46%
HD150   49,904    26,142    65.62%     34.38%
				
CC1     74,178   239,211    23.67%     76.33%
CC2    125,659   125,416    50.05%     49.95%
CC3    193,214   158,164    54.99%     45.01%
CC4    213,519   156,417    57.72%     42.28%

Gonzalez ran against Ron Hickman, former Constable in Precinct 4, who was appointed following Adrian Garcia’s resignation to run for Mayor of Houston in 2015. Hickman had been well respected as Constable and wasn’t a controversial selection, but he was quickly dogged with a scandal involving lost and destroyed evidence from his Constable days, as well as the usual bugaboo of jail overcrowding; his opposition to misdemeanor bail reform did not help with that. With all that, Gonzalez got “only” 52.84% of the vote in 2016, which was ahead of most judicial candidates but behind both Kim Ogg and Vince Ryan. My thought at the time was that Gonzalez maxed out the Democratic vote, but didn’t get many crossovers. Clearly, he knocked that second item out of the park this year. I’m not going to go into a more detailed comparison – I’ll leave that to you this time – but it should be obvious that Gonzalez built on his performance from 2016. We’ll see what he can do with the next four years.

Precinct analysis: County Attorney 2020 and 2016

Introduction
Congressional districts
State Rep districts
Commissioners Court/JP precincts
Comparing 2012 and 2016
Statewide judicial
Other jurisdictions
Appellate courts, Part 1
Appellate courts, Part 2
Judicial averages
Other cities
District Attorney

The office of County Attorney gets less attention than District Attorney, but as we have seen it’s vitally important. Vince Ryan held the office for three terms before being ousted in the primary by Christian Menefee. Menefee’s overall performance was similar to Ryan’s in 2016 – I’ll get to that in a minute – but as we saw in the previous post that doesn’t mean there can’t be a fair bit of variance. Let’s see where that takes us. Here’s the 2020 breakdown:


Dist     Nation  Menefee  Nation% Menefee%
==========================================
CD02    178,265  154,520   53.57%   46.43%
CD07    149,139  151,213   49.65%   50.35%
CD08     25,809   14,986   63.27%   36.73%
CD09     37,016  119,594   23.64%   76.36%
CD10    102,438   59,410   63.29%   36.71%
CD18     58,121  179,867   24.42%   75.58%
CD22     21,591   20,074   51.82%   48.18%
CD29     48,935  100,744   32.69%   67.31%
CD36     82,457   48,040   63.19%   36.81%
				
SBOE4   104,688  334,552   23.83%   76.17%
SBOE6   380,793  351,322   52.01%   47.99%
SBOE8   218,290  162,575   57.31%   42.69%
				
SD04     55,522   22,733   70.95%   29.05%
SD06     56,939  117,097   32.72%   67.28%
SD07    235,108  171,376   57.84%   42.16%
SD11     76,866   46,710   62.20%   37.80%
SD13     36,807  159,259   18.77%   81.23%
SD15    112,115  194,216   36.60%   63.40%
SD17    115,210  125,384   47.89%   52.11%
SD18     15,204   11,676   56.56%   43.44%
				
HD126    38,751   33,320   53.77%   46.23%
HD127    53,950   35,101   60.58%   39.42%
HD128    48,046   21,796   68.79%   31.21%
HD129    47,571   35,152   57.51%   42.49%
HD130    69,976   32,109   68.55%   31.45%
HD131     9,822   44,446   18.10%   81.90%
HD132    50,540   47,980   51.30%   48.70%
HD133    49,624   36,901   57.35%   42.65%
HD134    46,775   58,410   44.47%   55.53%
HD135    36,489   36,696   49.86%   50.14%
HD137    10,191   20,871   32.81%   67.19%
HD138    31,535   30,924   50.49%   49.51%
HD139    15,325   44,753   25.51%   74.49%
HD140     9,241   21,586   29.98%   70.02%
HD141     6,943	  35,992   16.17%   83.83%
HD142    13,733   41,540   24.85%   75.15%
HD143    11,934   24,039   33.17%   66.83%
HD144    13,762   16,387   45.65%   54.35%
HD145    14,777   26,896   35.46%   64.54%
HD146    11,016   43,379   20.25%   79.75%
HD147    14,738   53,266   21.67%   78.33%
HD148    21,758   36,937   37.07%   62.93%
HD149    21,400   30,636   41.13%   58.87%
HD150    55,873   39,332   58.69%   41.31%
				
CC1      90,530  280,069   24.43%   75.57%
CC2     149,810  143,859   51.01%   48.99%
CC3     224,601  210,646   51.60%   48.40%
CC4     238,830  213,877   52.76%   47.24%
				
JP1      90,035  165,193   35.28%   64.72%
JP2      33,965   48,473   41.20%   58.80%
JP3      51,412   67,741   43.15%   56.85%
JP4     233,642  184,203   55.92%   44.08%
JP5     201,673  214,852   48.42%   51.58%
JP6       7,971   26,993   22.80%   77.20%
JP7      17,824  100,329   15.09%   84.91%
JP8      67,249   40,667   62.32%   37.68%

Menefee scored 54.66% of the vote, better than Ogg by almost a point, and better than Ryan’s 53.72% in 2016 by slightly more. Ryan was consistently an upper echelon performer in his three elections, and that was true in 2016 as well, as only Ogg, Hillary Clinton, and judicial candidate Kelly Johnson had more votes than his 685,075, with those three and Mike Engelhart being the only ones with a larger margin of victory than Ryan’s 95K. Menefee, who collected 848,451 total votes and won by a margin of 145K, was also top tier. His vote total trailed all of the statewide candidates except Chrysta Castaneda and Gisela Triana (one better than Kim Ogg), though his percentage was better than everyone except Joe Biden and Tina Clinton. He outpaced three of the four appellate court candidates (he trailed Veronica Rivas-Molloy) and all but four of the local judicial candidates. His margin of victory was eighth best, behind Biden, Castaneda, two statewide judicials, and three local judicials. (And Ed Gonzalez, but we’ll get to him next.)

Here’s my 2016 precinct analysis post for the County Attorney race, and here’s the relevant data from that year:


Dist    Leitner     Ryan  Leitner%   Ryan%
==========================================
CD02    158,149  113,363    58.25%  41.75%
CD07    135,129  116,091    53.79%  46.21%
CD09     25,714  106,728    19.42%  80.58%
CD10     80,244   36,703    68.62%  31.38%
CD18     46,062  154,354    22.98%  77.02%
CD29     35,312   93,732    27.36%  72.64%
				
SBOE6   331,484  269,022    55.20%  44.80%
				
HD126    34,999   25,571    57.78%  42.22%
HD127    47,719   24,876    65.73%  34.27%
HD128    40,809   17,464    70.03%  29.97%
HD129    41,206   26,677    60.70%  39.30%
HD130    58,268   21,630    72.93%  27.07%
HD131     6,719   39,011    14.69%  85.31%
HD132    37,294   30,571    54.95%  45.05%
HD133    46,509   28,002    62.42%  37.58%
HD134    42,937   44,634    49.03%  50.97%
HD135    31,651   27,468    53.54%  46.46%
HD137     8,661   17,869    32.65%  67.35%
HD138    26,893   23,486    53.38%  46.62%
HD139    11,874   39,721    23.01%  76.99%
HD140     6,316   20,762    23.33%  76.67%
HD141     4,969   32,887    13.13%  86.87%
HD142    10,179   34,249    22.91%  77.09%
HD143     8,745   23,486    27.13%  72.87%
HD144    10,725   16,024    40.09%  59.91%
HD145    10,858   22,921    32.14%  67.86%
HD146     9,532   38,323    19.92%  80.08%
HD147    11,719   45,087    20.63%  79.37%
HD148    17,529   29,206    37.51%  62.49%
HD149    15,405   27,290    36.08%  63.92%
HD150    48,085   26,950    64.08%  35.92%
				
CC1      70,740  240,579    22.72%  77.28%
CC2     123,739  124,368    49.87%  50.13%
CC3     188,415  160,213    54.04%  45.96%
CC4     206,707  158,990    56.52%  43.48%

Kim Ogg did slightly better in the districts in 2016 than Vince Ryan did (most notably in CD02, though Ryan outdid her in HD134), which is what you’d expect given her overall better performance. In a similar fashion, Menefee did slightly better in the districts than Ogg did, as expected given his superior totals. He won CD07 by a thousand more votes than Ogg did, and carried HD135 where Ogg did not. He lost CC2 by two points and 6K votes, while Ogg lost it by four points and 12K votes. His lead in CD29 was 6K smaller than Ryan’s was, while Ogg lost 10K off of her lead in CD29 from 2016.

Overall, Menefee improved on Ryan’s 2016 totals, and made larger gains than Ogg did over her 2016 numbers. Like Ogg, he lost ground in the Latino districts – CD29, HD140, HD143, HD144, CC2 – but not by as much. He had higher vote totals in the Latino State Rep districts, though by small amounts in HDs 140, 143, and 144, and increased the lead over what Ryan had achieved in HDs 145 and 148. Like Ogg, he also lost ground in HD149, going from a 12K lead to a 9K lead, and in HD128, going from a 23K deficit to a 27K deficit (Ogg went from down 21K to down 27K). He gained ground in HD127 (from down 23K to down 19K; Ogg stayed roughly the same) and lost only about a thousand net votes in HD130 as Ogg went from down 34K to down 39K. He posted strong gains in HD126 (down 9K to down 5K), HD133 (down 18K to down 13K), and HD150 (down 21K to down 16K).

On the whole, a very strong initial performance by Menefee. As I said, County Attorney is generally a lower-profile job than District Attorney and Sheriff, but between bail reform, the multiple election lawsuits, and the forthcoming Republican legislative assault on local control, there should be many chances for Menefee to make statements about what he does and can do. He’ll have a solid chance to build on what he did this year when he’s next up for election.

Precinct analysis: District Attorney 2020 and 2016

Introduction
Congressional districts
State Rep districts
Commissioners Court/JP precincts
Comparing 2012 and 2016
Statewide judicial
Other jurisdictions
Appellate courts, Part 1
Appellate courts, Part 2
Judicial averages
Other cities

We move on now to the county executive office races for Harris County in 2020, which will be the end of the line for Harris County precinct analyses. I do have a copy of the Fort Bend canvass, though they do theirs in an annoyingly weird way, and will try to put something together for them after I’m done with this batch. With the four executive offices that were on the ballot for their regular election in 2020 – District Attorney, County Attorney, Sheriff, and Tax Assessor – we can not only view the data for this year, but do a nice comparison to 2016, since three of the four Democrats were running for re-election. We begin with the office of District Attorney:


Dist   Huffman      Ogg   Huffman%    Ogg%
==========================================
CD02   181,395  153,831     54.11%  45.89%
CD07   151,171  152,168     49.84%  50.16%
CD08    26,099   14,788     63.83%  36.17%
CD09    38,774  118,363     24.68%  75.32%
CD10   104,070   58,639     63.96%  36.04%
CD18    61,750  177,517     25.81%  74.19%
CD22    21,915   20,050     52.22%  47.78%
CD29    51,805   98,693     34.42%  65.58%
CD36    83,428   47,862     63.54%  36.46%
				
SBOE4  112,135  329,155     25.41%  74.59%
SBOE6  386,230  351,903     52.33%  47.67%
SBOE8  222,042  160,854     57.99%  42.01%
				
SD04    56,181   22,546     71.36%  28.64%
SD06    60,192  114,828     34.39%  65.61%
SD07   238,787  169,996     58.41%  41.59%
SD11    77,642   46,770     62.41%  37.59%
SD13    39,376  157,461     20.00%  80.00%
SD15   116,146  192,255     37.66%  62.34%
SD17   116,482  126,617     47.92%  52.08%
SD18    15,601   11,441     57.69%  42.31%
				
HD126   39,478   33,020     54.45%  45.55%
HD127   55,071   34,468     61.51%  38.49%
HD128   48,573   21,680     69.14%  30.86%
HD129   48,042   35,285     57.65%  42.35%
HD130   70,936   31,731     69.09%  30.91%
HD131   10,680   43,720     19.63%  80.37%
HD132   51,619   47,325     52.17%  47.83%
HD133   50,014   37,668     57.04%  42.96%
HD134   47,324   59,450     44.32%  55.68%
HD135   37,256   36,324     50.63%  49.37%
HD137   10,453   20,788     33.46%  66.54%
HD138   31,908   30,922     50.78%  49.22%
HD139   16,318   44,125     27.00%  73.00%
HD140    9,831   21,145     31.74%  68.26%
HD141    7,624   35,399     17.72%  82.28%
HD142   14,736   40,758     26.55%  73.45%
HD143   12,636   23,549     34.92%  65.08%
HD144   14,258   16,030     47.07%  52.93%
HD145   15,480   26,476     36.90%  63.10%
HD146   11,608   43,070     21.23%  78.77%
HD147   15,669   52,711     22.91%  77.09%
HD148   22,652   36,721     38.15%  61.85%
HD149   21,576   30,596     41.36%  58.64%
HD150   56,664   38,952     59.26%  40.74%
				
CC1     95,557  277,035     25.65%  74.35%
CC2    153,715  141,830     52.01%  47.99%
CC3    227,974  210,631     51.98%  48.02%
CC4    243,161  212,418     53.37%  46.63%
				
JP1     93,091  164,781     36.10%  63.90%
JP2     35,099   47,838     42.32%  57.68%
JP3     53,148   66,595     44.39%  55.61%
JP4    238,031  181,915     56.68%  43.32%
JP5    204,724  214,657     48.82%  51.18%
JP6      8,739   26,466     24.82%  75.18%
JP7     19,549   99,068     16.48%  83.52%
JP8     68,026   40,594     62.63%  37.37%

Here’s the same data from 2016. I’m going to reprint the table below and then do some comparisons, but at a macro level, Kim Ogg was the second-most successful candidate in Harris County in 2016. Her 696,955 votes and her 108,491-vote margin of victory were second only to Hillary Clinton. Ogg received 54.22% of the vote in 2016. She fell a little short of that percentage in 2020, garnering 53.89% of the vote this year, while increasing her margin to 121,507 votes. She was more middle of the pack this year, as the overall Democratic performance was up from 2016. She trailed all of the statewide candidates in total votes except for Gisela Triana, who was less than 300 votes behind her, though her percentage was higher than all of them except Joe Biden and the three Court of Criminal Appeals candidates. She had fewer votes than three of the four appellate court candidates (she was exactly nine votes behind Jane Robinson), but had a higher percentage than three of the four. Among the district and county court candidates, Ogg had more votes and a higher percentage than seven, more votes but a lower percentage than two, and fewer votes and a lower percentage than six.

(Writing all that out makes me think it was Republicans who were skipping judicial races more than Democrats. In the race immediately above DA, Democrat Julia Maldonado got 3,354 more votes than Ogg, but Republican Alyssa Lemkuil got 17,325 fewer votes than Mary Nan Huffman. In the race immediately after DA, Democrat Lesley Briones got 14,940 more votes than Ogg, but Republican Clyde Leuchtag got 30,357 fewer votes than Huffman. That sure looks like less Republican participation to me.)

Here’s the district breakdown for the DA race from 2016. It’s not as comprehensive as this year’s, but it’s good enough for these purposes.


Dist  Anderson      Ogg  Anderson%    Ogg%
==========================================
CD02   156,027  117,810     56.98%  43.02%
CD07   135,065  118,837     53.20%  46.80%
CD09    26,881  106,334     20.18%  79.82%
CD10    78,602   38,896     66.90%  33.10%
CD18    47,408  154,503     23.48%  76.52%
CD29    36,581   93,437     28.14%  71.86%
				
SBOE6  328,802  277,271     54.25%  45.75%
				
HD126   34,499   26,495     56.56%  43.44%
HD127   46,819   26,260     64.07%  35.93%
HD128   39,995   18,730     68.11%  31.89%
HD129   40,707   27,844     59.38%  40.62%
HD130   57,073   23,239     71.06%  28.94%
HD131    7,301   38,651     15.89%  84.11%
HD132   36,674   31,478     53.81%  46.19%
HD133   46,242   29,195     61.30%  38.70%
HD134   43,962   45,142     49.34%  50.66%
HD135   31,190   28,312     52.42%  47.58%
HD137    8,728   18,040     32.61%  67.39%
HD138   26,576   24,189     52.35%  47.65%
HD139   12,379   39,537     23.84%  76.16%
HD140    6,613   20,621     24.28%  75.72%
HD141    5,305   32,677     13.97%  86.03%
HD142   10,428   34,242     23.34%  76.66%
HD143    9,100   23,434     27.97%  72.03%
HD144   10,758   16,100     40.06%  59.94%
HD145   11,145   22,949     32.69%  67.31%
HD146   10,090   38,147     20.92%  79.08%
HD147   12,156   45,221     21.19%  78.81%
HD148   17,538   29,848     37.01%  62.99%
HD149   15,352   27,535     35.80%  64.20%
HD150   47,268   28,160     62.67%  37.33%
				
CC1     73,521  240,194     23.44%  76.56%
CC2    123,178  126,996     49.24%  50.76%
CC3    187,095  164,487     53.22%  46.78%
CC4    204,103  164,355     55.39%  44.61%

The shifts within districts are perhaps more subtle than you might think. A few stand out – CD07 goes from a 6.4 point win for Devon Anderson in 2016 to a narrow Ogg win in 2020, powered in large part by a ten-point shift in Ogg’s favor in HD134. On the flip side, Ogg carried CC2 by a point and a half in 2016 but lost it by four points in 2020, as her lead in CD29 went from 43 points to 31 points. Overall, Ogg saw modest gains in Republican turf – CD02, HD126, HD133, HD150, CC3, CC4 – and some Democratic turf – CD18, HD146, HD147, HD148, CC1 – and some modest losses in each – CD10, CD29, HD128, HD140, HD143, HD144, HD145, CC2.

In a lot of places, the percentages went one way or the other, but the gap in total votes didn’t change. CD09 is a good example of this – Ogg won it by 80K votes in each year, but with about 24K more votes cast in 2020, split evenly between her and Huffman, that lowered her percentage by four points. Same thing in HD127, which Ogg lost by 20,559 in 2016 and 20,603 in 2020, but added three percentage points because 16K more votes were cast. In the three Latino State Rep districts cited above, Ogg had more votes in 2020 in HD140, HD143, and HD145 than she did in 2016 – she had 70 fewer votes in HD144 – but her improvements in the first two districts were in the hundreds, while Huffman outperformed Anderson by 2,300 in HD140, by 3,500 in HD143, and by 3,500 in HD144; Huffman improved by 4,300 in HD145 while Ogg added 3,500 votes. As we’ve discussed before, it will be interesting to see how these districts perform going forward, and in lower-turnout scenarios.

So we see some changes in where the vote was, with Ogg building a bit on 2016, in the same way that Joe Biden built a bit on what Hillary Clinton did in 2016. As I write this, I haven’t actually taken this close a look at the district changes in the other county races, so we’ll learn and discover together. I think we can expect that some of this behavior is mirrored elsewhere, but this is the only race with an incumbent running for re-election who did basically as well as they had done before, so the patterns may be a little harder to discern. But that’s what makes this exercise so interesting each cycle. Let me know what you think.

Precinct analysis: Other cities

Introduction
Congressional districts
State Rep districts
Commissioners Court/JP precincts
Comparing 2012 and 2016
Statewide judicial
Other jurisdictions
Appellate courts, Part 1
Appellate courts, Part 2
Judicial averages

I mentioned in an earlier post that I might look at election results from other cities that had their own races in November. Turns out there were quite a few of them that had their elections conducted by Harris County, and thus had their results in the spreadsheet I got. Let’s have a look.


City            Trump  Biden  Lib  Grn  Trump%  Biden%   Lib%   Grn%
====================================================================
Baytown         3,879  2,394   55   21  61.10%  37.71%  0.87%  0.33%
Bellaire        4,553  6,565  115   29  40.43%  58.29%  1.02%  0.26%
Deer Park      11,192  3,622  167   39  74.51%  24.11%  1.11%  0.26%
Friendswood     5,312  4,357  144   24  54.00%  44.29%  1.46%  0.24%
Galena Park     1,026  1,614   18    9  38.47%  60.52%  0.67%  0.34%
Humble          5,084  6,274  107   53  44.14%  54.47%  0.93%  0.46%
Katy            4,373  1,918   82   17  68.44%  30.02%  1.28%  0.27%
La Porte       11,561  5,036  201   69  68.54%  29.86%  1.19%  0.41%
League City     1,605  1,196   38    4  56.45%  42.07%  1.34%  0.14%
Missouri City     457  2,025    8    8  18.29%  81.06%  0.32%  0.32%
Nassau Bay      1,433  1,003   32    4  57.97%  40.57%  1.29%  0.16%
Pearland        5,397  7,943   84   32  40.11%  59.03%  0.62%  0.24%
Seabrook        5,532  2,768  104   21  65.66%  32.85%  1.23%  0.25%
Webster         4,594  4,850  159   33  47.68%  50.33%  1.65%  0.34%

City           Cornyn  Hegar  Lib  Grn Cornyn%  Hegar%   Lib%   Grn%
====================================================================
Baytown         3,814  2,255  119   49  61.15%  36.16%  1.91%  0.79%
Bellaire        5,312  5,762   93   48  47.37%  51.38%  0.83%  0.43%
Deer Park      11,098  3,355  269   90  74.93%  22.65%  1.82%  0.61%
Friendswood     5,380  4,009  221   74  55.56%  41.40%  2.28%  0.76%
Galena Park       892  1,408   40   42  37.45%  59.11%  1.68%  1.76%
Humble          5,098  5,927  233   98  44.89%  52.19%  2.05%  0.86%
Katy            4,401  1,749  129   40  69.65%  27.68%  2.04%  0.63%
La Porte       11,361  4,743  365  108  68.53%  28.61%  2.20%  0.65%
League City     1,654  1,099   39   18  58.86%  39.11%  1.39%  0.64%
Missouri City     458  1,934   38   25  18.66%  78.78%  1.55%  1.02%
Nassau Bay      1,471    928   43   12  59.94%  37.82%  1.75%  0.49%
Pearland        5,432  7,551  190  113  40.89%  56.83%  1.43%  0.85%
Seabrook        5,561  2,545  190   43  66.69%  30.52%  2.28%  0.52%
Webster         4,625  4,541  230   82  48.80%  47.91%  2.43%  0.87%

City           Wright  Casta  Lib  Grn Wright%  Casta%   Lib%   Grn%
====================================================================
Baytown         3,681  2,306  129   51  59.02%  36.97%  2.07%  0.82%
Bellaire        5,227  5,444  142  115  46.61%  48.54%  1.27%  1.03%
Deer Park      10,894  3,355  294  109  73.55%  22.65%  1.98%  0.74%
Friendswood     5,216  3,901  253  155  53.86%  40.28%  2.61%  1.60%
Galena Park       801  1,478   45   42  33.63%  62.05%  1.89%  1.76%
Humble          4,872  5,962  247  156  42.90%  52.50%  2.18%  1.37%
Katy            4,365  1,677  141   74  69.08%  26.54%  2.23%  1.17%
La Porte       11,057  4,773  393  175  66.70%  28.79%  2.37%  1.06%
League City     1,616  1,069   49   38  57.51%  38.04%  1.74%  1.35%
Missouri City     421  1,944   38   34  17.15%  79.19%  1.55%  1.38%
Nassau Bay      1,417    898   60   28  57.74%  36.59%  2.44%  1.14%
Pearland        5,205  7,571  189  172  39.18%  56.98%  1.42%  1.29%
Seabrook        5,477  2,439  232   83  65.68%  29.25%  2.78%  1.00%
Webster         4,488  4,416  283  165  47.35%  46.59%  2.99%  1.74%

A few words of caution before we begin. Most of these city races were at large – they were for Mayor or were citywide propositions (some of these towns had literally an entire alphabet’s worth of props for the voters), a few were At Large City Council races. Baytown, Katy, and Webster were City Council races that did not appear to be at large; League City had a Council race that didn’t give any indication one way or the other. Some of these cities – Friendswood, Katy, League City, Missouri City, and Pearland – are not fully contained within Harris County, so these are just partial results. As with the city of Houston, there’s no guarantee that Harris County precinct boundaries match city boundaries, or that precincts are contained entirely within that city, so the results from the other races may contain voters who aren’t in the city specified. Basically, consider these all to be approximations, and we’ll be fine.

I had no idea what to expect from these numbers. With the exception of Bellaire and Galena Park, all of these place are on the outer edges of Harris County, so generally in the red zone, but not exclusively. I expected Galena Park and Missouri City to be blue, I expected Baytown and Deer Park and Friendswood to be red, and the rest I either didn’t have any preconceived notions or was a little surprised. I wouldn’t have expected Bellaire or Humble to be blue – Bellaire is squarely in the CD07/HD134 part of town, so while it’s not all that shocking, I feel quite confident saying that if I did this same exercise in 2012, I’d have gotten a different result. The Katy area is getting bluer, which is how Dems won HD132 in 2018, but apparently that is not the case for the city of Katy proper, or at least the Harris County part of it. I’d guess the Brazoria County part of Pearland is redder than the Harris County part. As for La Porte, it’s not that I’m surprised that it’s red, it’s more that I’d never thought much about it.

I don’t have a whole lot more to say here – I don’t have past data handy, so I can’t make any comparisons, but even if I did we already mostly have the picture from earlier posts. It’s the same geography, just different pieces of it. There’s been a push by the TDP lately to get more local officials elected in towns like these, which is often a challenge in low-turnout May elections. There clearly some opportunities, though, and we should look to support candidates who put themselves out there in places where they’re not the norm. I have a friend who ran for Humble ISD in 2017, and while she didn’t win, that’s the sort of effort we need to get behind. Keep an eye out for what you can do this May, and find some good people to work with.

Precinct analysis: The judicial averages

Introduction
Congressional districts
State Rep districts
Commissioners Court/JP precincts
Comparing 2012 and 2016
Statewide judicial
Other jurisdictions
Appellate courts, Part 1
Appellate courts, Part 2

As you know, I use the average totals and percentages from local judicial races as my go-to metric for determining partisan indexes for each district. That’s because these are two-candidate races, and generally speaking people vote in them on the party label and not on detailed knowledge of the individual candidates. I’ve looked at this data in various ways over the years – in 2018, it was all about undervoting, as my contribution to the deeply annoying great straight-ticket voting debate. This year, I just want to provide as comprehensive a look as I can at what the partisan index of each district is, so without further ado here are the averages and minimum/maximum values for each district:


Dist    Avg R    Avg D  Avg R%  Avg D%
======================================
CD02  180,657  152,260  54.26%  45.74%
CD07  152,705  147,943  50.79%  49.21%
CD08   25,930   14,830  63.62%  36.38%
CD09   37,855  119,136  24.11%  75.89%
CD10  103,043   58,975  63.60%  36.40%
CD18   59,751  178,574  25.07%  74.93%
CD22   21,796   19,965  52.19%  47.81%
CD29   49,285  100,975  32.80%  67.20%
CD36   82,990   47,534  63.58%  36.42%
				
SBOE4 106,801  333,572  24.25%  75.75%
SBOE6 387,513  345,132  52.89%  47.11%
SBOE8 219,698  161,490  57.64%  42.36%
				
SD04   55,837   22,370  71.40%  28.60%
SD06   57,502  117,156  32.92%  67.08%
SD07  236,992  169,822  58.26%  41.74%
SD11   77,482   46,126  62.68%  37.32%
SD13   38,020  158,384  19.36%  80.64%
SD15  114,322  192,386  37.27%  62.73%
SD17  118,535  122,335  49.21%  50.79%
SD18   15,323   11,618  56.88%  43.12%
				
HD126  39,112   33,088  54.17%  45.83%
HD127  54,309   34,783  60.96%  39.04%
HD128  48,197   21,688  68.97%  31.03%
HD129  48,127   34,606  58.17%  41.83%
HD130  70,364   31,748  68.91%  31.09%
HD131  10,092   44,290  18.56%  81.44%
HD132  50,934   47,797  51.59%  48.41%
HD133  50,892   35,660  58.80%  41.20%
HD134  49,172   56,015  46.75%  53.25%
HD135  36,694   36,599  50.07%  49.93%
HD137  10,422   20,732  33.45%  66.55%
HD138  31,922   30,597  51.06%  48.94%
HD139  15,711   44,501  26.09%  73.91%
HD140   9,326   21,677  30.08%  69.92%
HD141   7,106   35,937  16.51%  83.49%
HD142  13,933   41,496  25.14%  74.86%
HD143  11,999   24,126  33.21%  66.79%
HD144  13,786   16,469  45.57%  54.43%
HD145  14,992   26,765  35.90%  64.10%
HD146  11,408   43,008  20.96%  79.04%
HD147  15,323   52,737  22.51%  77.49%
HD148  22,392   36,300  38.15%  61.85%
HD149  21,640   30,536  41.47%  58.53%
HD150  56,160   39,038  58.99%  41.01%
				
CC1    93,365  277,707  25.16%  74.84%
CC2   150,891  143,324  51.29%  48.71%
CC3   228,295  207,558  52.38%  47.62%
CC4   241,461  211,606  53.29%  46.71%
				
JP1    93,441  162,045  36.57%  63.43%
JP2    34,172   48,572  41.30%  58.70%
JP3    51,782   67,626  43.37%  56.63%
JP4   235,236  182,956  56.25%  43.75%
JP5   204,805  212,367  49.09%  50.91%
JP6     8,152   26,921  23.24%  76.76%
JP7    18,654   99,583  15.78%  84.22%
JP8    67,769   40,125  62.81%  37.19%


Dist    Max R    Min D  Max R%  Min D%
======================================
CD02  185,931  148,006  55.68%  44.32%
CD07  159,695  144,247  52.54%  47.46%
CD08   26,439   14,393  64.75%  35.25%
CD09   40,013  116,625  25.54%  74.46%
CD10  105,177   57,133  64.80%  35.20%
CD18   63,096  174,763  26.53%  73.47%
CD22   22,436   19,262  53.81%  46.19%
CD29   55,680   94,745  37.02%  62.98%
CD36   84,840   45,634  65.02%  34.98%
				
SBOE4 117,378  322,667  26.67%  73.33%
SBOE6 401,507  336,009  54.44%  45.56%
SBOE8 224,690  156,133  59.00%  41.00%
				
SD04   56,905   21,704  72.39%  27.61%
SD06   64,474  110,326  36.88%  63.12%
SD07  242,602  164,480  59.60%  40.40%
SD11   79,333   44,482  64.07%  35.93%
SD13   40,293  155,638  20.56%  79.44%
SD15  118,813  187,188  38.83%  61.17%
SD17  124,541  119,169  51.10%  48.90%
SD18   15,619   11,279  58.07%  41.93%
				
HD126  40,053   31,945  55.63%  44.37%
HD127  55,452   33,703  62.20%  37.80%
HD128  49,089   20,798  70.24%  29.76%
HD129  49,387   33,547  59.55%  40.45%
HD130  71,729   30,669  70.05%  29.95%
HD131  11,027   43,306  20.30%  79.70%
HD132  52,228   46,423  52.94%  47.06%
HD133  53,008   34,318  60.70%  39.30%
HD134  53,200   53,340  49.93%  50.07%
HD135  37,600   35,481  51.45%  48.55%
HD137  10,831   20,255  34.84%  65.16%
HD138  32,956   29,493  52.77%  47.23%
HD139  16,700   43,426  27.78%  72.22%
HD140  10,796   20,276  34.75%  65.25%
HD141   7,844   35,148  18.25%  81.75%
HD142  15,015   40,325  27.13%  72.87%
HD143  13,599   22,554  37.62%  62.38%
HD144  14,965   15,326  49.40%  50.60%
HD145  16,455   25,318  39.39%  60.61%
HD146  11,924   42,368  21.96%  78.04%
HD147  16,147   51,800  23.76%  76.24%
HD148  23,754   35,054  40.39%  59.61%
HD149  22,315   29,713  42.89%  57.11%
HD150  57,274   37,933  60.16%  39.84%
				
CC1    98,310  271,971  26.55%  73.45%
CC2   158,199  135,874  53.80%  46.20%
CC3   236,301  201,920  53.92%  46.08%
CC4   248,120  205,046  54.75%  45.25%
				
JP1    99,574  157,709  38.70%  61.30%
JP2    36,841   45,917  44.52%  55.48%
JP3    54,016   65,253  45.29%  54.71%
JP4   240,145  177,376  57.52%  42.48%
JP5   211,698  206,389  50.63%  49.37%
JP6     9,694   25,425  27.60%  72.40%
JP7    19,825   98,162  16.80%  83.20%
JP8    69,422   38,580  64.28%  35.72%


Dist    Min R    Max D  Min R%  Max D%
======================================
CD02  175,786  157,942  52.67%  47.33%
CD07  145,575  154,644  48.49%  51.51%
CD08   25,520   15,264  62.57%  37.43%
CD09   36,275  121,193  23.04%  76.96%
CD10  101,112   61,042  62.36%  37.64%
CD18   56,673  182,314  23.71%  76.29%
CD22   21,218   20,673  50.65%  49.35%
CD29   45,744  105,745  30.20%  69.80%
CD36   81,336   49,507  62.16%  37.84%
				
SBOE4 100,933  342,178  22.78%  77.22%
SBOE6 373,961  359,113  51.01%  48.99%
SBOE8 215,025  167,034  56.28%  43.72%
				
SD04   55,047   23,216  70.34%  29.66%
SD06   53,562  122,474  30.43%  69.57%
SD07  231,452  175,578  56.86%  43.14%
SD11   75,844   48,065  61.21%  38.79%
SD13   36,086  160,806  18.33%  81.67%
SD15  109,597  198,247  35.60%  64.40%
SD17  112,679  127,956  46.83%  53.17%
SD18   15,000   11,985  55.59%  44.41%
				
HD126  38,215   34,107  52.84%  47.16%
HD127  53,344   35,933  59.75%  40.25%
HD128  47,390   22,477  67.83%  32.17%
HD129  46,964   36,012  56.60%  43.40%
HD130  69,298   32,900  67.81%  32.19%
HD131   9,584   44,980  17.56%  82.44%
HD132  49,625   49,260  50.18%  49.82%
HD133  48,359   37,729  56.17%  43.83%
HD134  45,698   59,519  43.43%  56.57%
HD135  35,662   37,653  48.64%  51.36%
HD137   9,997   21,240  32.00%  68.00%
HD138  30,912   31,792  49.30%  50.70%
HD139  14,891   45,442  24.68%  75.32%
HD140   8,496   22,687  27.25%  72.75%
HD141   6,751   36,444  15.63%  84.37%
HD142  13,366   42,296  24.01%  75.99%
HD143  11,100   25,218  30.56%  69.44%
HD144  13,029   17,345  42.90%  57.10%
HD145  14,011   28,167  33.22%  66.78%
HD146  10,824   43,630  19.88%  80.12%
HD147  14,469   53,867  21.17%  78.83%
HD148  21,053   38,031  35.63%  64.37%
HD149  20,955   31,398  40.03%  59.97%
HD150  55,070   40,198  57.81%  42.19%
				
CC1    88,636  283,723  23.80%  76.20%
CC2   146,468  149,847  49.43%  50.57%
CC3   220,181  215,729  50.51%  49.49%
CC4   234,765  219,028  51.73%  48.27%
				
JP1    87,533  168,977  34.12%  65.88%
JP2    32,564   50,632  39.14%  60.86%
JP3    50,336   69,338  42.06%  57.94%
JP4   230,567  188,394  55.03%  44.97%
JP5   197,305  219,993  47.28%  52.72%
JP6     7,269   28,198  20.50%  79.50%
JP7    17,578  100,870  14.84%  85.16%
JP8    66,324   41,925  61.27%  38.73%

There were 15 contested District or County court races, with another 12 that had only a Democrat running. All of the numbers are from the contested races. The first table is just the average vote total for each candidate in that district; I then computed the percentage from those average values. For the second and third tables, I used the Excel MAX and MIN functions to get the highest and lowest vote totals for each party in each district. It should be noted that the max Republican and min Democratic totals in a given district (and vice versa) may not belong to the candidates from the same race, as the total number of votes in each race varies. Consider these to be a bit more of a theoretical construct, to see what the absolute best and worst case scenario for each party was this year.

One could argue that Democrats did better than expected this year, given the partisan levels they faced. Both Lizzie Fletcher and Jon Rosenthal won re-election, in CD07 and HD135, despite running in districts that were tilted slightly against them. The one Republican that won in a district that tilted Democratic was Precinct 5 Constable Ted Heap, who won as his JP colleague Russ Ridgway fell; as previously noted, Dan Crenshaw clearly outperformed the baseline in CD02. The tilt in Commissioners Court Precinct 3 was too much for Michael Moore to overcome, though perhaps redistricting and four more years of demographic change will move things in the Democratic direction for 2024. As for Precinct 2, I believe Adrian Garcia would have been re-elected if he had been on the ballot despite the Republican tilt in that precinct, mostly because the Latino Democratic candidates generally carried the precinct. He will also get a hand from redistricting when that happens. I believe being the incumbent would have helped him regardless, as Jack Morman ran ahead of the pack in 2018, just not by enough to hang on.

The “Republican max” (table 2) and “Democratic max” (table 3) values give you a picture of the range of possibility in each district. At their high end for Republicans, CD02 and SBOE6 don’t look particularly competitive, while CD07 and HD135 look like they really got away, while HD144 looks like a missed opportunity, and JP5 could have maybe been held in both races. HD134 remained stubbornly Democratic, however. On the flip side, you can see that at least one Democratic judicial candidate took a majority in CD07, HD135, HD138, and CC2, while CC3 and CC4 both look enticingly close, and neither HDs 134 nor 144 look competitive at all. If nothing else, this is a reminder that even in these judicial races, there can be a lot of variance.

On the subject of undervoting, as noted in the Appellate Court posts, the dropoff rate in those races was about 4.7% – there wasn’t much change from the first race to the fourth. For the contested local judicial races, the undervote rate ranged from 5.06% in the first race to 6.54%, in the seventh (contested) race from the end. There was a downward trend as you got farther down the ballot, but it wasn’t absolute – as noted, there were six races after the most-undervoted race, all with higher vote totals. The difference between the highest turnout race to the lowest was about 24K votes, from 1.568 million to 1.544 million. It’s not nothing, but in the grand scheme of things it’s pretty minimal.

The twelve unopposed Democrats in judicial races clearly show how unopposed candidates always do better than candidates that have opponents. Every unopposed judicial candidate collected over one million votes. Kristen Hawkins, the first unopposed judicial candidate, and thus most likely the first unopposed candidate on everyone’s ballot, led the way with 1.068 million votes, about 200K more votes than Michael Gomez, who was the leading votegetter in a contested race. Every unopposed Democratic candidate got a vote from at least 61.25% of all voters, with Hawkins getting a vote from 64.44% of all. I have always assumed that some number of people feel like they need to vote in each race, even the ones with only one candidate.

I’m going to analyze the vote in the non-Houston cities next. As always, please let me know what you think.

Precinct analysis: Appellate courts, part 2

Introduction
Congressional districts
State Rep districts
Commissioners Court/JP precincts
Comparing 2012 and 2016
Statewide judicial
Other jurisdictions
Appellate courts, Part 1

Here’s the more traditional look at the Court of Appeals races. Unlike the Supreme Court and CCA, all of these races just have two candidates, so we get a purer view of each district’s partisan measure.


Dist    Chris    Robsn  Chris%  Robsn%
======================================
CD02  184,964  152,768  54.77%  45.23%
CD07  157,736  147,670  51.65%  48.35%
CD08   26,431   14,916  63.92%  36.08%
CD09   39,195  119,621  24.68%  75.32%
CD10  104,717   59,540  63.75%  36.25%
CD18   62,244  178,810  25.82%  74.18%
CD22   22,412   20,080  52.74%  47.26%
CD29   51,407  100,718  33.79%  66.21%
CD36   84,772   47,797  63.95%  36.05%
				
SBOE4 111,462  333,791  25.03%  74.97%
SBOE6 398,123  345,585  53.53%  46.47%
SBOE8 224,293  162,545  57.98%  42.02%
				
SD04   56,898   22,562  71.61%  28.39%
SD06   59,896  116,837  33.89%  66.11%
SD07  241,721  170,662  58.62%  41.38%
SD11   79,273   46,425  63.07%  36.93%
SD13   39,578  158,975  19.93%  80.07%
SD15  118,283  192,558  38.05%  61.95%
SD17  122,640  122,169  50.10%  49.90%
SD18   15,589   11,734  57.05%  42.95%
				
HD126  39,903   33,263  54.54%  45.46%
HD127  55,384   34,979  61.29%  38.71%
HD128  49,071   21,878  69.16%  30.84%
HD129  49,357   34,835  58.62%  41.38%
HD130  71,485   31,992  69.08%  30.92%
HD131  10,547   44,331  19.22%  80.78%
HD132  51,970   48,189  51.89%  48.11%
HD133  52,531   35,414  59.73%  40.27%
HD134  51,636   55,503  48.20%  51.80%
HD135  37,498   36,828  50.45%  49.55%
HD137  10,775   20,855  34.07%  65.93%
HD138  32,788   30,669  51.67%  48.33%
HD139  16,375   44,551  26.88%  73.12%
HD140   9,795   21,511  31.29%  68.71%
HD141   7,493   35,952  17.25%  82.75%
HD142  14,378   41,649  25.66%  74.34%
HD143  12,559   24,038  34.32%  65.68%
HD144  14,250   16,410  46.48%  53.52%
HD145  15,600   26,725  36.86%  63.14%
HD146  11,819   43,211  21.48%  78.52%
HD147  16,024   52,771  23.29%  76.71%
HD148  23,255   36,320  39.03%  60.97%
HD149  22,187   30,741  41.92%  58.08%
HD150  57,197   39,304  59.27%  40.73%
				
CC1    97,397  278,086  25.94%  74.06%
CC2   154,992  143,474  51.93%  48.07%
CC3   234,325  208,116  52.96%  47.04%
CC4   247,164  212,247  53.80%  46.20%
				
JP1    97,730  161,507  37.70%  62.30%
JP2    35,419   48,550  42.18%  57.82%
JP3    53,112   67,814  43.92%  56.08%
JP4   239,927  183,854  56.62%  43.38%
JP5   210,230  213,175  49.65%  50.35%
JP6     8,570   26,891  24.17%  75.83%
JP7    19,569   99,806  16.39%  83.61%
JP8    69,321   40,326  63.22%  36.78%


Dist    Lloyd   Molloy  Lloyd% Molloy%
======================================
CD02  182,465  155,019  54.07%  45.93%
CD07  155,392  149,641  50.94%  49.06%
CD08   26,105   15,215  63.18%  36.82%
CD09   38,009  120,873  23.92%  76.08%
CD10  103,826   60,311  63.26%  36.74%
CD18   59,729  181,164  24.79%  75.21%
CD22   22,012   20,440  51.85%  48.15%
CD29   47,790  104,691  31.34%  68.66%
CD36   83,738   48,699  63.23%  36.77%
			
SBOE4 105,088  340,408  23.59%  76.41%
SBOE6 392,723  350,361  52.85%  47.15%
SBOE8 221,255  165,285  57.24%  42.76%
				
SD04   56,516   22,841  71.22%  28.78%
SD06   55,876  121,303  31.54%  68.46%
SD07  238,891  173,275  57.96%  42.04%
SD11   78,393   47,111  62.46%  37.54%
SD13   38,185  160,335  19.23%  80.77%
SD15  114,913  195,701  37.00%  63.00%
SD17  120,892  123,589  49.45%  50.55%
SD18   15,400   11,900  56.41%  43.59%
				
HD126  39,359   33,787  53.81%  46.19%
HD127  54,725   35,562  60.61%  39.39%
HD128  48,591   22,310  68.53%  31.47%
HD129  48,813   35,233  58.08%  41.92%
HD130  71,017   32,409  68.66%  31.34%
HD131   9,999   44,913  18.21%  81.79%
HD132  51,123   48,982  51.07%  48.93%
HD133  52,075   35,754  59.29%  40.71%
HD134  50,815   56,050  47.55%  52.45%
HD135  36,859   37,440  49.61%  50.39%
HD137  10,494   21,131  33.18%  66.82%
HD138  32,143   31,246  50.71%  49.29%
HD139  15,702   45,174  25.79%  74.21%
HD140   8,932   22,448  28.46%  71.54%
HD141   6,966   36,461  16.04%  83.96%
HD142  13,717   42,333  24.47%  75.53%
HD143  11,615   25,061  31.67%  68.33%
HD144  13,600   17,131  44.25%  55.75%
HD145  14,768   27,651  34.81%  65.19%
HD146  11,569   43,424  21.04%  78.96%
HD147  15,344   53,409  22.32%  77.68%
HD148  22,543   37,048  37.83%  62.17%
HD149  21,838   31,134  41.23%  58.77%
HD150  56,458   39,961  58.55%  41.45%
				
CC1    93,785  281,473  24.99%  75.01%
CC2   150,775  147,845  50.49%  49.51%
CC3   231,120  210,968  52.28%  47.72%
CC4   243,386  215,770  53.01%  46.99%
				
JP1    94,795  164,261  36.59%  63.41%
JP2    33,861   50,188  40.29%  59.71%
JP3    51,723   69,237  42.76%  57.24%
JP4   236,701  186,804  55.89%  44.11%
JP5   206,960  216,197  48.91%  51.09%
JP6     7,778   27,817  21.85%  78.15%
JP7    18,795  100,517  15.75%  84.25%
JP8    68,453   41,035  62.52%  37.48%


Dist    Adams   Guerra  Adams% Guerra%
======================================
CD02  184,405  152,836  54.68%  45.32%
CD07  157,212  147,381  51.61%  48.39%
CD08   26,351   14,919  63.85%  36.15%
CD09   38,998  119,778  24.56%  75.44%
CD10  104,820   59,234  63.89%  36.11%
CD18   61,326  179,332  25.48%  74.52%
CD22   22,218   20,211  52.37%  47.63%
CD29   48,121  104,386  31.55%  68.45%
CD36   84,501   47,871  63.84%  36.16%
			
SBOE4 107,293  337,920  24.10%  75.90%
SBOE6 397,124  345,286  53.49%  46.51%
SBOE8 223,535  162,743  57.87%  42.13%
				
SD04   56,904   22,386  71.77%  28.23%
SD06   56,357  120,880  31.80%  68.20%
SD07  241,466  170,348  58.63%  41.37%
SD11   79,098   46,319  63.07%  36.93%
SD13   39,476  158,887  19.90%  80.10%
SD15  116,690  193,656  37.60%  62.40%
SD17  122,412  121,729  50.14%  49.86%
SD18   15,549   11,745  56.97%  43.03%
				
HD126  39,813   33,289  54.46%  45.54%
HD127  55,237   34,999  61.21%  38.79%
HD128  48,957   21,899  69.09%  30.91%
HD129  49,340   34,653  58.74%  41.26%
HD130  71,559   31,806  69.23%  30.77%
HD131  10,266   44,574  18.72%  81.28%
HD132  51,808   48,208  51.80%  48.20%
HD133  52,597   35,086  59.99%  40.01%
HD134  51,370   55,317  48.15%  51.85%
HD135  37,274   36,945  50.22%  49.78%
HD137  10,724   20,876  33.94%  66.06%
HD138  32,559   30,808  51.38%  48.62%
HD139  16,147   44,644  26.56%  73.44%
HD140   8,966   22,430  28.56%  71.44%
HD141   7,254   36,084  16.74%  83.26%
HD142  14,142   41,863  25.25%  74.75%
HD143  11,744   24,953  32.00%  68.00%
HD144  13,658   17,072  44.45%  55.55%
HD145  14,824   27,584  34.96%  65.04%
HD146  11,928   43,032  21.70%  78.30%
HD147  15,656   53,073  22.78%  77.22%
HD148  22,757   36,812  38.20%  61.80%
HD149  22,195   30,784  41.89%  58.11%
HD150  57,176   39,156  59.35%  40.65%
				
CC1    95,892  278,971  25.58%  74.42%
CC2   152,017  146,563  50.91%  49.09%
CC3   233,933  207,769  52.96%  47.04%
CC4   246,110  212,648  53.65%  46.35%
				
JP1    95,938  162,864  37.07%  62.93%
JP2    34,099   49,931  40.58%  59.42%
JP3    52,405   68,430  43.37%  56.63%
JP4   239,343  183,827  56.56%  43.44%
JP5   209,649  213,147  49.59%  50.41%
JP6     7,852   27,792  22.03%  77.97%
JP7    19,566   99,631  16.41%  83.59%
JP8    69,100   40,329  63.15%  36.85%


Dist     Wise    Craft   Wise%  Craft%
======================================
CD02  187,076  150,161  55.47%  44.53%
CD07  160,323  144,461  52.60%  47.40%
CD08   26,468   14,814  64.12%  35.88%
CD09   39,255  119,480  24.73%  75.27%
CD10  105,224   58,786  64.16%  35.84%
CD18   62,464  178,398  25.93%  74.07%
CD22   22,479   19,942  52.99%  47.01%
CD29   51,350  100,685  33.78%  66.22%
CD36   85,152   47,195  64.34%  35.66%
				
SBOE4 111,160  333,956  24.97%  75.03%
SBOE6 403,452  338,891  54.35%  45.65%
SBOE8 225,179  161,076  58.30%  41.70%
				
SD04   57,202   22,111  72.12%  27.88%
SD06   59,943  116,758  33.92%  66.08%
SD07  242,902  168,936  58.98%  41.02%
SD11   79,698   45,696  63.56%  36.44%
SD13   39,579  158,895  19.94%  80.06%
SD15  119,640  190,784  38.54%  61.46%
SD17  125,186  119,108  51.24%  48.76%
SD18   15,641   11,636  57.34%  42.66%
				
HD126  40,122   32,983  54.88%  45.12%
HD127  55,653   34,618  61.65%  38.35%
HD128  49,175   21,666  69.42%  30.58%
HD129  49,744   34,245  59.23%  40.77%
HD130  71,894   31,468  69.56%  30.44%
HD131  10,420   44,469  18.98%  81.02%
HD132  52,080   47,898  52.09%  47.91%
HD133  53,487   34,292  60.93%  39.07%
HD134  53,678   53,121  50.26%  49.74%
HD135  37,617   36,577  50.70%  49.30%
HD137  10,841   20,738  34.33%  65.67%
HD138  33,111   30,252  52.26%  47.74%
HD139  16,338   44,533  26.84%  73.16%
HD140   9,677   21,649  30.89%  69.11%
HD141   7,162   36,255  16.50%  83.50%
HD142  14,336   41,735  25.57%  74.43%
HD143  12,465   24,123  34.07%  65.93%
HD144  14,238   16,400  46.47%  53.53%
HD145  15,761   26,507  37.29%  62.71%
HD146  12,019   42,980  21.85%  78.15%
HD147  16,327   52,404  23.75%  76.25%
HD148  24,026   35,407  40.43%  59.57%
HD149  22,369   30,513  42.30%  57.70%
HD150  57,250   39,088  59.43%  40.57%
				
CC1    98,291  276,873  26.20%  73.80%
CC2   155,580  142,504  52.19%  47.81%
CC3   236,903  204,782  53.64%  46.36%
CC4   249,017  209,766  54.28%  45.72%
				
JP1   100,430  158,362  38.81%  61.19%
JP2    35,440   48,448  42.25%  57.75%
JP3    52,981   67,919  43.82%  56.18%
JP4   240,598  182,662  56.84%  43.16%
JP5   212,371  210,308  50.24%  49.76%
JP6     8,629   26,793  24.36%  75.64%
JP7    19,649   99,743  16.46%  83.54%
JP8    69,693   39,690  63.71%  36.29%

If you just went by these results, you might think Dems did worse overall in Harris County than they actually did. None of the four candidates carried CD07, and only Veronica Rivas-Molloy carried HD135. They all still carried Harris County, by margins ranging from 6.0 to 8.7 points and 94K to 137K votes, but it’s clear they could have done better, and as we well know, even doing a little better would have carried Jane Robinson and Tamika Craft (who, despite her low score here still lost overall by less than 20K votes out of over 2.3 million ballots cast) to victory.

I don’t have a good explanation for any of this. Maybe the Libertarian candidates that some statewide races had a bigger effect on those races than we think. Maybe the incumbents had an advantage that enabled them to get a better share of the soft partisan vote. Maybe the Chron endorsements helped the incumbents. And maybe the lack of straight ticket voting did matter. The undervote rate in these races was around 4.7%, which is pretty low, but in 2018 it was around 2.7%. Picking on the Robinson race again, had the undervote rate been 2.7% instead of the 4.68% it actually was, there would have been an additional 36,154 votes cast. At the same 53.43% rate for Robinson, she would have received another 19,317 votes, with Tracy Christopher getting 16,837. That’s a 2,480 vote net for Robinson, which would be enough for her to win, by 1,291 votes. Tamika Craft would still fall short, but Dems would have won three out of four races instead of just two.

Of course, we can’t just give straight ticket voting back to Harris County and not the other nine counties. I’m not going to run through the math for each county, but given that Christopher did better in the non-Harris Counties, we can assume she’s net a few votes in them if straight ticket voting were still in effect. Maybe it wouldn’t be enough – remember, there were far more votes in Harris than in the other nine, and the Republican advantage wasn’t that much bigger, so the net would be smaller. It’s speculation built on guesswork, and it’s all in service of making up for the fact that the Democratic candidates could have done better in Harris County with the votes that were cast than they did. Let’s not get too wishful in our thinking here.

So does this affect my advice from the previous post? Not really – we still need to build on what we’re already doing, and figure out how to do better in the places where we need to do better. Maybe a greater focus judicial races is needed, by which I mean more money spent to advertise the Democratic judicial slate. As we’ve observed, these are close races in what is clearly very swingy territory, at least for now. With close races, there’s a broad range of possible factors that could change the outcome. Pick your preference and get to work on it.

Precinct analysis: Appellate courts, part 1

Introduction
Congressional districts
State Rep districts
Commissioners Court/JP precincts
Comparing 2012 and 2016
Statewide judicial
Other jurisdictions

My next two posts in this series will focus on the 1st and 14th Courts of Appeals. These courts are a little strange electorally, as the elections cover ten counties in all, and over the past few elections they have proven to be pretty darned balanced. As we know, turnout in Harris County has gone up a lot in recent years, and the county has gone from evenly split to strongly blue, yet the balance in these ten counties persists. In this post, I’m going to do a bit of a historical review, to look at the trends and see if we can spot the underlying metrics.


2008 - 1st CoA Pl 3 (50.58%)

County   Tot Votes   Share  DemVotes    Dem%
============================================
Harris   1,111,642  70.74%   585,249  52.65%
Others     459,704  29.26%   209,510  45.57%

2012 - 14th CoA Pl 3 (47.74%)

County   Tot Votes   Share  DemVotes    Dem%
============================================
Harris   1,137,580  69.82%   580,356  51.01%
Others     491,673  30.18%   197,511  40.17%

2016 - 1st CoA Pl 4 (48.95%)

County   Tot Votes   Share  DemVotes    Dem%
============================================
Harris   1,273,638  69.00%   671,908  52.76%
Others     572,258  31.00%   231,702  40.49%

2018 - 1st CoA Pl 2 (50.93%)

County   Tot Votes   Share  DemVotes    Dem%
============================================
Harris   1,187,403  68.63%   647,398  54.52%
Others     542,765  31.37%   233,693  43.06%

2020 - 1st CoA Pl 3 (50.76%)

County   Tot Votes   Share  DemVotes    Dem%
============================================
Harris   1,575,122  68.23%   856,056  54.35%
Others     733,364  31.77%   314,644  42.90%

2020 - 1st CoA Pl 5 (50.10%)

County   Tot Votes   Share  DemVotes    Dem%
============================================
Harris   1,573,903  68.24%   845,951  53.75%
Others     732,455  31.76%   309,497  42.25%

2020 - 14th CoA Chief Justice (49.97%)

County   Tot Votes   Share  DemVotes    Dem%
============================================
Harris   1,575,801  68.23%   841,923  53.43%
Others     733,698  31.77%   312,231  42.56%

2020 - 14th CoA Pl 7 (49.57%)

County   Tot Votes   Share  DemVotes    Dem%
============================================
Harris   1,573,716  68.25%   833,925  52.99%
Others     732,057  31.75%   309,115  42.23%

A couple of points of explanation here. For 2008, 2012, 2016, and 2018, I picked the top Democratic performer among the appellate court candidates. For 2008, that meant the one Democratic winner. In 2018, as every Dem won their race, I went with the candidate with the narrowest victory, since what I’m most interested in is the threshold needed to win. For 2020, I included all four candidates.

In each table, I separated out the total votes cast in that race from Harris County, and from all the other counties. “Share” is the share of the vote that came from Harris County, so in the 2008 race 70.74% of the total vote came from Harris County. “DemVotes” is the total number of votes the Democratic candidate got, in Harris and in the other counties, and “Dem%” is the percentage of the vote that Democratic candidate got.

We see that the share of the vote from Harris County has dropped every year, from over 70% in 2008 to a bit more than 68% this year. That doesn’t appear to be predictive of anything, as Dems swept these races in 2018 and won two out of four this year, with the lowest-performing Dem having (by a tiny amount) the largest Harris County vote share. The rise of Fort Bend County as a Democratic bastion has no doubt mitigated the shrinking contribution from Harris, but that points out again the importance of counties around Harris, as the reddening of Galveston and the smaller counties has kept these races competitive. One thing I hadn’t realized till I went through this exercise was that Waller County was quite close to even in 2008, but gave Republicans a 7K vote edge in 2020. Indeed, Dem candidates in Waller in 2020 were getting about the same number of votes as Dem candidates in Waller in 2008, after two cycles of failing to meet the 2008 number, as the Republican vote steadily climbed. As we have discussed before, Jane Robinson lost her race by 0.06 percentage points, or a bit more than a thousand votes out of over 1.5 million votes cast. In a race that close, you can point to many, many ways in which a small difference would have changed the outcome.

That’s one reason why these races interest me so much. For one, the appellate courts were a place where Dems made numerous pickups in 2020, yet still fell a bit short of expectations – I at least thought we’d win all four of these, given how well we’d done in 2018. But as you can see, it wasn’t quite to be. I don’t want to downplay the races we did win – Veronica Rivas Molloy and Amparo Guerra are both terrific candidates, and they are now the only Latinas on that court – I’m just greedy enough to have wanted more.

What’s frustrating to me is that I can’t tell what I think is the magic formula here. The difference between Guerra, who won by four thousand votes and 0.20 percentage points, and Robinson is tiny enough to be rounding error. The main difference is that Guerra won Harris County by ten thousand votes more than Robinson did, while Robinson did five thousand votes better in the other counties than Guerra did (she lost them by 421K while Guerra lost them by 426K). We know that Latinx candidates generally did better in Harris County this year than their peers, but that wasn’t the case outside Harris County. And even if it was, that’s not much of a lesson to learn. It was a game of inches, and we won one and lost one.

Ultimately, I think the path here is the same as the path I’ve described in the various “key counties” posts. We’re starting to move in the right direction in Brazoria County, and if we can keep that going that could be enough to tip the scales to the blue side on a longer-term basis. Basically, if we keep doing what we’re doing we’ll likely be at least competitive in these races, and if we can step it up a bit, especially but not exclusively in Brazoria, we can do better than that. Maybe not the deepest insight you’ll ever read, but it’s what I’ve got.

(Assuming that the judicial districts don’t get redrawn, which I suppose they could. In 2004, the First and Fourteenth districts included Burleson, Trinity, and Walker Counties plus the current ten. We’d have zero chance of winning these races if those three were added back in. I have no idea what the process or criteria for defining the judicial districts is. I’m just saying that if Republicans decided to do something about this, they probably could.)

Next up, I’ll do the district breakdown for these four races in Harris County. After that, more judicial races and then on to the other county races. As always, let me know what you think.

Precinct analysis: Other jurisdictions

Introduction
Congressional districts
State Rep districts
Commissioners Court/JP precincts
Comparing 2012 and 2016
Statewide judicial

You may be wondering “Hey, how come you haven’t reported on data from SBOE and State Senate districts?” Well, I’ll tell you, since the SBOE and Senate serve four-year terms with only half of the races up for election outside of redistricting years, the results in the districts that aren’t on the ballot are not discernable to me. But! I was eventually able to get a spreadsheet that defined all of the relevant districts for each individual precinct, and that allowed me to go back and fill in the empty values. And now here I present them to you. Oh, and as a special bonus, I merged the data from the 2012 city of Houston bond elections into this year’s totals and pulled out the numbers for the city of Houston for the top races. So here you have it:


Dist     Trump    Biden    Lib    Grn  Trump%  Biden%   Lib%   Grn%
===================================================================
SBOE4  110,192  350,258  3,530  1,787  23.66%  75.20%  0.76%  0.38%
SBOE6  371,101  391,911  8,796  2,157  47.95%  50.64%  1.14%  0.28%
SBOE8  219,337  176,022  4,493  1,185  54.69%  43.89%  1.12%  0.30%
								
SD04    55,426   25,561    936    145  67.54%  31.15%  1.14%  0.18%
SD06    61,089  123,708  1,577    770  32.64%  66.10%  0.84%  0.41%
SD07   232,201  188,150  4,746  1,216  54.47%  44.13%  1.11%  0.29%
SD11    77,325   51,561  1,605    389  59.08%  39.40%  1.23%  0.30%
SD13    38,198  166,939  1,474    753  18.42%  80.51%  0.71%  0.36%
SD15   110,485  208,552  3,444  1,045  34.15%  64.46%  1.06%  0.32%
SD17   110,788  140,986  2,706    720  43.41%  55.25%  1.06%  0.28%
SD18    15,118   12,735	   331     91  53.47%  45.04%  1.17%  0.32%

Hou    285,379  535,713  8,222  2,704  34.30%  64.39%  0.99%  0.32%
Harris 415,251  382,480  8,597  2,425  51.34%  47.29%  1.06%  0.30%


Dist    Cornyn    Hegar    Lib    Grn Cornyn%  Hegar%   Lib%   Grn%
===================================================================
SBOE4  110,002  330,420  8,479  5,155  23.62%  70.94%  1.82%  1.11%
SBOE6  387,726  359,196 13,130  4,964  50.68%  46.95%  1.72%  0.65%
SBOE8  220,500  164,540  7,608  2,770  55.76%  41.61%  1.92%  0.70%
								
SD04    56,085   23,380  1,405    393  69.02%  28.77%  1.73%  0.48%
SD06    59,310  115,620  3,609  2,257  32.80%  63.95%  2.00%  1.25%
SD07   237,216  173,948  7,682  2,796  55.64%  40.80%  1.80%  0.66%
SD11    77,887   47,787  2,508    854  60.36%  37.03%  1.94%  0.66%
SD13    39,386  157,671  3,502  2,149  19.43%  77.78%  1.73%  1.06%
SD15   114,616  195,264  6,065  2,657  35.43%  60.35%  1.87%  0.82%
SD17   118,460  128,628  3,892  1,603  46.42%  50.40%  1.53%  0.63%
SD18    15,268   11,859    554    180  54.80%  42.56%  1.99%  0.65%

Hou    297,735  498,078 14,537  7,021  36.43%  60.94%  1.78%  0.86%
Harris 420,493  356,080 14,680  5,868  52.75%  44.67%  1.84%  0.74%


Dist    Wright    Casta    Lib    Grn Wright%  Casta%   Lib%   Grn%
===================================================================
SBOE4  102,521  332,324  8,247  7,160  22.01%  71.35%  1.77%  1.54%
SBOE6  379,555  347,938 16,311  9,217  50.40%  46.21%  2.17%  1.22%
SBOE8  214,771  163,095  8,573  4,631  54.92%  41.70%  2.19%  1.18%
								
SD04    54,997   22,915  1,715    685  68.48%  28.53%  2.14%  0.85%
SD06    54,732  118,635  3,389  2,751  30.49%  66.09%  1.89%  1.53%
SD07   232,729  169,832  9,084  4,902  54.59%  39.84%  2.13%  1.15%
SD11    75,580   47,284  2,906  1,454  59.41%  37.17%  2.28%  1.14%
SD13    37,009  156,577  3,653  3,306  18.45%  78.08%  1.82%  1.65%
SD15   111,109  192,351  6,833  4,347  34.34%  59.45%  2.11%  1.34%
SD17   115,654  124,174  4,931  3,219  45.32%  48.66%  1.93%  1.26%
SD18    15,037   11,590    620    344  54.50%  42.01%  2.25%  1.25%

Hou    286,759  491,191 16,625 11,553  34.47%  59.04%  2.00%  1.39%
Harris 410,088  352,168 16,506  9,455  50.71%  43.54%  2.04%  1.17%

Dist     Hecht  Meachum    Lib  Hecht% Meachum%  Lib%
=====================================================
SBOE4  104,675  334,600 10,745  23.26%  74.35%  2.39%
SBOE6  387,841  349,776 17,294  51.38%  46.33%  2.29%
SBOE8  217,760  164,210  9,466  55.63%  41.95%  2.42%
						
SD04    55,773   22,920  1,721  69.36%  28.50%  2.14%
SD06    56,313  117,884  4,832  31.45%  65.85%  2.70%
SD07   235,317  172,232  9,800  56.38%  41.27%  2.35%
SD11    77,081   47,122  3,169  60.52%  37.00%  2.49%
SD13    37,495  158,731  4,500  18.68%  79.08%  2.24%
SD15   113,248  194,232  7,612  35.94%  61.64%  2.42%
SD17   119,941  123,630  5,196  48.21%  49.70%  2.09%
SD18    15,108   11,836    675  54.70%  42.85%  2.44%

Dist      Boyd   Will's    Lib   Boyd% Will's%   Lib%
=====================================================
SBOE4  104,397  336,102  8,832  23.23%  74.80%  1.97%
SBOE6  380,861  354,806 15,618  50.69%  47.23%  2.08%
SBOE8  217,360  164,288  8,525  55.71%  42.11%  2.18%
						
SD04    55,481   22,982  1,621  69.28%  28.70%  2.02%
SD06    56,932  117,444  4,132  31.89%  65.79%  2.31%
SD07   234,080  173,025  8,683  56.30%  41.61%  2.09%
SD11    76,633   47,377  2,834  60.42%  37.35%  2.23%
SD13    36,755  160,184  3,557  18.33%  79.89%  1.77%
SD15   111,564  195,699  6,798  35.52%  62.31%  2.16%
SD17   116,011  126,731  4,723  46.88%  51.21%  1.91%
SD18    15,162   11,755    627  55.05%  42.68%  2.28%


Dist     Busby   Triana    Lib  Busby% Triana%   Lib%
=====================================================
SBOE4  104,071  335,587  9,074  23.19%  74.79%  2.02%
SBOE6  389,317  343,673 17,392  51.88%  45.80%  2.32%
SBOE8  218,278  162,376  9,125  56.00%  41.66%  2.34%
						
SD04    55,864   22,402  1,739  69.83%  28.00%  2.17%
SD06    55,719  118,801  4,006  31.21%  66.55%  2.24%
SD07   235,948  169,843  9,532  56.81%  40.89%  2.30%
SD11    77,324   46,265  3,101  61.03%  36.52%  2.45%
SD13    37,498  158,536  3,962  18.75%  79.27%  1.98%
SD15   113,780  192,651  7,220  36.28%  61.42%  2.30%
SD17   120,435  121,393  5,349  48.72%  49.11%  2.16%
SD18    15,098   11,746    682  54.85%  42.67%  2.48%


Dist    Bland    Cheng  Bland%   Cheng%
=======================================
SBOE4  112,465  336,620  25.04%  74.96%
SBOE6  401,946  350,154  53.44%  46.56%
SBOE8  225,783  164,516  57.85%  42.15%
				
SD04    57,378   22,793  71.57%  28.43%
SD06    60,243  118,418  33.72%  66.28%
SD07   243,089  172,941  58.43%  41.57%
SD11    79,757   47,134  62.85%  37.15%
SD13    40,242  160,069  20.09%  79.91%
SD15   119,474  194,619  38.04%  61.96%
SD17   124,299  123,453  50.17%  49.83%
SD18    15,712   11,864  56.98%  43.02%


Dist     BertR  Frizell  BertR% Frizell%
=======================================
SBOE4  107,445  340,670  23.98%  76.02%
SBOE6  392,514  355,217  52.49%  47.51%
SBOE8  221,860  166,900  57.07%  42.93%
				
SD04    56,609   23,176  70.95%  29.05%
SD06    57,800  120,402  32.44%  67.56%
SD07   239,113  175,071  57.73%  42.27%
SD11    78,483   47,818  62.14%  37.86%
SD13    38,419  161,433  19.22%  80.78%
SD15   115,389  197,276  36.90%  63.10%
SD17   120,576  125,566  48.99%  51.01%
SD18    15,430   12,046  56.16%  43.84%


Dist     Yeary  Clinton  Yeary%Clinton%
=======================================
SBOE4  107,727  339,999  24.06%  75.94%
SBOE6  387,309  359,489  51.86%  48.14%
SBOE8  221,725  166,780  57.07%  42.93%
				
SD04    56,405   23,323  70.75%  29.25%
SD06    58,285  119,666  32.75%  67.25%
SD07   238,608  175,225  57.66%  42.34%
SD11    78,085   48,109  61.88%  38.12%
SD13    38,214  161,577  19.13%  80.87%
SD15   114,407  197,949  36.63%  63.37%
SD17   117,277  128,438  47.73%  52.27%
SD18    15,480   11,982  56.37%  43.63%


Dist    Newell    Birm  Newell%   Birm%
=======================================
SBOE4  110,449  336,329  24.72%  75.28%
SBOE6  392,944  352,514  52.71%  47.29%
SBOE8  223,453  164,440  57.61%  42.39%
				
SD04    56,669   22,936  71.19%  28.81%
SD06    59,575  117,944  33.56%  66.44%
SD07   240,463  172,769  58.19%  41.81%
SD11    78,816   47,161  62.56%  37.44%
SD13    39,166  160,126  19.65%  80.35%
SD15   116,700  195,074  37.43%  62.57%
SD17   119,849  125,464  48.86%  51.14%
SD18    15,608   11,810  56.93%  43.07%

To be clear, “Harris” refers to everything that is not the city of Houston. It includes the other cities, like Pasadena and Deer Park and so forth, as well as unincorporated Harris County. There are some municipal results in the 2020 canvass, and maybe I’ll take a closer look at them later – I generally haven’t done that for non-Houston cities in the past, but this year, we’ll see. Please note also that there are some precincts that include a piece of Houston but are not entirely Houston – the boundaries don’t coincide. Basically, I skipped precincts that had ten or fewer votes in them for the highest-turnout 2012 referendum, and added up the rest. So those values are approximate, but close enough for these purposes. I don’t have city of Houston results for most elections, but I do have them for a few. In 2008, Barack Obama got 61.0% in Houston and 39.5% in non-Houston Harris County. In 20122018, Beto reached a new height with 65.4% in Houston; that calculation was done by a reader, and unfortunately he didn’t do the corresponding total for Harris County. Joe Biden’s 64.39% fits in just ahead of Adrian Garcia in 2012, and about a point behind Beto. Not too bad.

SBOE4 is a mostly Black district primarily in Harris County with a piece in Fort Bend as well; Lawrence Allen, son of State Rep. Alma Allen and an unsuccessful candidate for HD26 in the Dem primary this year, is its incumbent. SBOE8 is a heavily Republican district with about half of its voters in Harris County and about a third in Montgomery County. It was won this year by Audrey Young over a Libertarian opponent, succeeding Barbara Cargill. Cargill was unopposed in 2016 and beat a Dem candidate in 2012 by a 71-29 margin, getting about 66% of the vote in Harris County. Like just about everywhere else, that part of the county is a lot less red than it used to be. SBOE6 was of course the focus of attention after Beto carried it in 2018. Biden fell a tad short of Beto’s mark, though Trump also fell short of Ted Cruz. No other Dem managed to win the vote there, with the range being about four to seven points for the Republicans, which does represent an improvement over 2018. Michelle Palmer lost by two points here, getting 47.38% of the vote (there was a Libertarian candidate as well; the victorious Republican got 49.76%), as the Dems won one of the three targeted, Beto-carried seats, in SBOE5. I presume the Republicans will have a plan to make the SBOE a 10-5 split in their favor again, but for now the one gain Dems made in a districted office was there.

I don’t think I’ve ever done a full accounting of State Senate districts in previous precinct analyses. Only three of the eight districts that include a piece of Harris County are entirely within Harris (SDs 06, 07, and 15; 13 extends into Fort Bend), and only SD17 is competitive. Beto and a couple of others carried SD17 in 2018 – I don’t have the full numbers for it now, but Rita Lucido won the Harris County portion of SD17 by a 49.4-48.8 margin in 2018, and every Dem except Kathy Cheng won SD17 this year, with everyone else except Gisela Triana exceeding Lucido’s total or margin or both. An awful lot of HD134 is in SD17, so this is just another illustration of HD134’s Democratic shift.

The other interesting district here is SD07, which Dan Patrick won by a 68.4-31.6 margin in 2012, and Paul Bettencourt won by a 57.8-40.3 margin in 2018. Every Dem had a smaller gap than that this year, with most of them bettering David Romero’s percentage from 2018, and Biden losing by just over ten points. It would be really interesting to see how this district trended over the next decade if we just kept the same lines as we have now, but we will get new lines, so the question becomes “do the Republicans try to shore up SD07”, and if so how? SD17 is clearly the higher priority, and while you could probably leave SD07 close to what it is now, with just a population adjustment, it doesn’t have much spare capacity. If there’s a lesson for Republicans from the 2011 redistricting experience, it’s that they have to think in ten-year terms, and that’s a very hard thing to do. We’ll see how they approach it.

Precinct analysis: Statewide judicial

Introduction
Congressional districts
State Rep districts
Commissioners Court/JP precincts
Comparing 2012 and 2016

We’re going to take a look at the seven statewide judicial races in this post, with all of the districts considered so far grouped together. You’re about to have a lot of numbers thrown at you, is what I’m saying. I’m ordering these races in a particular way, which is to put the contests that included a Libertarian candidate first (there were no Green candidates for any statewide judicial position, or indeed any judicial position on the Harris County ballot), and then the contests that were straight up D versus R next. There were three of the former and four of the latter, and we’ll see what we can determine about the effect that a Libertarian may have had on these races as we go.


Dist    Hecht  Meachum    Lib  Hecht% Meachum%   Lib%
=====================================================
CD02  179,887  154,785  7,979  52.50%   45.17%  2.33%
CD07  154,058  149,348  6,725  49.68%   48.16%  2.17%
CD08   25,686   15,145  1,014  61.38%   36.19%  2.42%
CD09   37,479  119,471  3,516  23.36%   74.45%  2.19%
CD10  101,965   60,290  3,917  61.36%   36.28%  2.36%
CD18   58,684  179,178  5,906  24.07%   73.50%  2.42%
CD22   21,575   20,271  1,140  50.19%   47.16%  2.65%
CD29   48,349  101,662  4,049  31.38%   65.99%  2.63%
CD36   82,593   48,435  3,259  61.50%   36.07%  2.43%
						
HD126  38,883   33,427  1,726  52.52%   45.15%  2.33%
HD127  53,978   35,464  2,040  59.00%   38.77%  2.23%
HD128  48,000   22,103  1,606  66.94%   30.82%  2.24%
HD129  47,867   35,292  2,208  56.07%   41.34%  2.59%
HD130  69,884   32,443  2,440  66.70%   30.97%  2.33%
HD131   9,887   44,240  1,236  17.86%   79.91%  2.23%
HD132  50,149   48,527  2,544  49.54%   47.94%  2.51%
HD133  51,732   35,958  1,730  57.85%   40.21%  1.93%
HD134  50,646   56,804  2,018  46.27%   51.89%  1.84%
HD135  36,285   36,987  1,891  48.28%   49.21%  2.52%
HD137  10,333   20,930    827  32.20%   65.22%  2.58%
HD138  31,730   30,982  1,548  49.38%   48.21%  2.41%
HD139  15,475   44,630  1,365  25.17%   72.60%  2.22%
HD140   9,151   21,719    840  28.86%   68.49%  2.65%
HD141   6,824   35,967    981  15.59%   82.17%  2.24%
HD142  13,637   41,662  1,238  24.12%   73.69%  2.19%
HD143  11,821   24,338    938  31.87%   65.61%  2.53%
HD144  13,535   16,631    867  43.61%   53.59%  2.79%
HD145  14,758   26,918  1,255  34.38%   62.70%  2.92%
HD146  11,363   43,152  1,235  20.38%   77.40%  2.22%
HD147  14,973   53,050  1,799  21.44%   75.98%  2.58%
HD148  22,163   36,851  1,701  36.50%   60.70%  2.80%
HD149  21,616   30,814  1,133  40.36%   57.53%  2.12%
HD150  55,585   39,695  2,339  56.94%   40.66%  2.40%
					
CC1    92,529  278,828  8,580  24.35%   73.39%  2.26%
CC2   149,483  145,171  7,746  49.43%   48.01%  2.56%
CC3   228,402  210,197 10,006  50.91%   46.86%  2.23%
CC4   239,862  214,392 11,173  51.54%   46.06%  2.40%
						
JP1    93,898  163,620  6,237  35.60%   62.03%  2.36%
JP2    33,762   49,003  2,174  39.75%   57.69%  2.56%
JP3    51,276   68,138  2,733  41.98%   55.78%  2.24%
JP4   233,213  185,525  9,970  54.40%   43.28%  2.33%
JP5   204,389  214,695  9,945  47.64%   50.04%  2.32%
JP6     7,834   27,042  1,074  21.79%   75.22%  2.99%
JP7    18,495   99,632  2,600  15.32%   82.53%  2.15%
JP8    67,409   40,933  2,772  60.67%   36.84%  2.49%

Dist     Boyd Williams    Lib   Boyd%Williams%   Lib%
=====================================================
CD02  177,810  155,876  7,349  52.14%   45.71%  2.15%
CD07  149,700  152,887  5,923  48.52%   49.56%  1.92%
CD08   25,674   15,116    894  61.59%   36.26%  2.14%
CD09   37,235  120,311  2,810  23.22%   75.03%  1.75%
CD10  101,850   60,145  3,613  61.50%   36.32%  2.18%
CD18   57,552  180,778  5,054  23.65%   74.28%  2.08%
CD22   21,529   20,300  1,030  50.23%   47.36%  2.40%
CD29   48,900  101,209  3,423  31.85%   65.92%  2.23%
CD36   82,368   48,573  2,879  61.55%   36.30%  2.15% 

HD126  38,664   33,525  1,557  52.43%   45.46%  2.11%
HD127  53,700   35,556  1,891  58.92%   39.01%  2.07%
HD128  48,078   22,019  1,431  67.22%   30.78%  2.00%
HD129  47,371   35,620  2,000  55.74%   41.91%  2.35%
HD130  69,697   32,424  2,234  66.79%   31.07%  2.14%
HD131   9,814   44,580    937  17.74%   80.57%  1.69%
HD132  50,168   48,466  2,311  49.70%   48.01%  2.29%
HD133  49,946   37,393  1,520  56.21%   42.08%  1.71%
HD134  47,593   59,069  1,938  43.82%   54.39%  1.78%
HD135  36,215   37,075  1,607  48.35%   49.50%  2.15%
HD137  10,226   21,044    708  31.98%   65.81%  2.21%
HD138  31,413   31,231  1,372  49.07%   48.79%  2.14%
HD139  15,293   44,932  1,208  24.89%   73.14%  1.97%
HD140   9,270   21,715    677  29.28%   68.58%  2.14%
HD141   6,943   36,106    738  15.86%   82.46%  1.69%
HD142  13,649   41,816  1,006  24.17%   74.05%  1.78%
HD143  11,953   24,211    783  32.35%   65.53%  2.12%
HD144  13,712   16,444    757  44.36%   53.19%  2.45%
HD145  14,749   26,907  1,082  34.51%   62.96%  2.53%
HD146  10,957   43,683    985  19.70%   78.53%  1.77%
HD147  14,628   53,564  1,547  20.98%   76.81%  2.22%
HD148  21,551   37,172  1,616  35.72%   61.61%  2.68%
HD149  21,554   30,949    980  40.30%   57.87%  1.83%
HD150  55,473   39,693  2,090  57.04%   40.81%  2.15%
						
CC1    90,441  281,651  7,183  23.85%   74.26%  1.89%
CC2   149,519  144,951  6,793  49.63%   48.11%  2.25%
CC3   224,732  213,022  8,935  50.31%   47.69%  2.00%
CC4   237,926  215,574 10,064  51.33%   46.50%  2.17%
						
JP1    90,471  166,282  5,724  34.47%   63.35%  2.18%
JP2    33,968   48,891  1,877  40.09%   57.70%  2.22%
JP3    51,567   68,134  2,269  42.28%   55.86%  1.86%
JP4   232,446  185,828  8,942  54.41%   43.50%  2.09%
JP5   201,507  217,080  8,748  47.15%   50.80%  2.05%
JP6     7,848   26,989    935  21.94%   75.45%  2.61%
JP7    17,772  100,858  2,001  14.73%   83.61%  1.66%
JP8    67,039   41,136  2,479  60.58%   37.18%  2.24%

Dist    Busby   Triana    Lib  Busby%  Triana%   Lib%
=====================================================
CD02  180,619  152,062  8,019  53.01%   44.63%  2.35%
CD07  154,593  146,826  6,759  50.16%   47.64%  2.19%
CD08   25,758   14,928    955  61.86%   35.85%  2.29%
CD09   37,362  119,463  3,094  23.36%   74.70%  1.93%
CD10  102,251   59,298  3,908  61.80%   35.84%  2.36%
CD18   58,913  178,629  5,394  24.25%   73.53%  2.22%
CD22   21,575   20,090  1,118  50.43%   46.96%  2.61%
CD29   47,694  102,644  3,275  31.05%   66.82%  2.13%
CD36   82,901   47,695  3,069  62.02%   35.68%  2.30%

HD126  38,980   33,040  1,658  52.91%   44.84%  2.25%
HD127  54,112   34,934  2,025  59.42%   38.36%  2.22%
HD128  48,180   21,765  1,477  67.46%   30.47%  2.07%
HD129  47,955   34,683  2,230  56.51%   40.87%  2.63%
HD130  70,019   31,790  2,447  67.16%   30.49%  2.35%
HD131   9,827   44,382  1,012  17.80%   80.37%  1.83%
HD132  50,189   48,200  2,493  49.75%   47.78%  2.47%
HD133  51,870   35,055  1,814  58.45%   39.50%  2.04%
HD134  51,239   55,036  2,250  47.21%   50.71%  2.07%
HD135  36,361   36,664  1,790  48.60%   49.01%  2.39%
HD137  10,325   20,780    812  32.35%   65.11%  2.54%
HD138  31,761   30,656  1,497  49.69%   47.96%  2.34%
HD139  15,489   44,606  1,222  25.26%   72.75%  1.99%
HD140   8,987   21,995    659  28.40%   69.51%  2.08%
HD141   6,791   36,116    798  15.54%   82.64%  1.83%
HD142  13,605   41,732  1,042  24.13%   74.02%  1.85%
HD143  11,665   24,588    733  31.54%   66.48%  1.98%
HD144  13,471   16,721    744  43.54%   54.05%  2.40%
HD145  14,593   27,092  1,061  34.14%   63.38%  2.48%
HD146  11,412   42,928  1,129  20.57%   77.39%  2.04%
HD147  15,183   52,758  1,661  21.81%   75.80%  2.39%
HD148  22,402   36,229  1,688  37.14%   60.06%  2.80%
HD149  21,574   30,729  1,065  40.42%   57.58%  2.00%
HD150  55,675   39,155  2,284  57.33%   40.32%  2.35%
						
CC1    92,822  277,923  7,778  24.52%   73.42%  2.05%
CC2   149,446  144,793  6,922  49.62%   48.08%  2.30%
CC3   228,849  207,334  9,987  51.29%   46.47%  2.24%
CC4   240,549  211,588 10,904  51.95%   45.70%  2.35%
						
JP1    94,735  161,383  6,127  36.12%   61.54%  2.34%
JP2    33,518   49,255  1,882  39.59%   58.18%  2.22%
JP3    51,327   68,119  2,341  42.14%   55.93%  1.92%
JP4   233,635  183,442  9,668  54.75%   42.99%  2.27%
JP5   204,626  212,437  9,722  47.95%   49.78%  2.28%
JP6     7,711   27,250    875  21.52%   76.04%  2.44%
JP7    18,508   99,518  2,270  15.39%   82.73%  1.89%
JP8    67,606   40,234  2,706  61.16%   36.40%  2.45%

Dist    Bland    Cheng  Bland%   Cheng%
=======================================
CD02  186,706  154,725  54.68%   45.32%
CD07  159,574  149,326  51.66%   48.34%
CD08   26,540   15,186  63.61%   36.39%
CD09   39,465  120,736  24.63%   75.37%
CD10  105,349   60,323  63.59%   36.41%
CD18   62,985  180,105  25.91%   74.09%
CD22   22,415   20,441  52.30%   47.70%
CD29   51,670  102,080  33.61%   66.39%
CD36   85,490   48,367  63.87%   36.13%

HD126  40,209   33,586  54.49%   45.51%
HD127  55,788   35,414  61.17%   38.83%
HD128  49,423   22,087  69.11%   30.89%
HD129  49,640   35,394  58.38%   41.62%
HD130  71,946   32,493  68.89%   31.11%
HD131  10,622   44,674  19.21%   80.79%
HD132  52,183   48,781  51.68%   48.32%
HD133  53,308   35,720  59.88%   40.12%
HD134  52,985   55,899  48.66%   51.34%
HD135  37,544   37,368  50.12%   49.88%
HD137  10,776   21,212  33.69%   66.31%
HD138  32,815   31,243  51.23%   48.77%
HD139  16,488   44,881  26.87%   73.13%
HD140   9,808   21,860  30.97%   69.03%
HD141   7,537   36,159  17.25%   82.75%
HD142  14,573   41,837  25.83%   74.17%
HD143  12,622   24,375  34.12%   65.88%
HD144  14,320   16,647  46.24%   53.76%
HD145  15,721   27,079  36.73%   63.27%
HD146  12,136   43,482  21.82%   78.18%
HD147  16,299   53,306  23.42%   76.58%
HD148  23,760   36,701  39.30%   60.70%
HD149  22,218   31,229  41.57%   58.43%
HD150  57,472   39,861  59.05%   40.95%
				
CC1    98,928  280,012  26.11%   73.89%
CC2   156,101  145,437  51.77%   48.23%
CC3   236,143  210,982  52.81%   47.19%
CC4   249,022  214,861  53.68%   46.32%
				
JP1    99,802  162,942  37.98%   62.02%
JP2    35,454   49,274  41.84%   58.16%
JP3    53,615   68,275  43.99%   56.01%
JP4   241,226  186,223  56.43%   43.57%
JP5   211,577  216,054  49.48%   50.52%
JP6     8,598   27,274  23.97%   76.03%
JP7    20,093  100,384  16.68%   83.32%
JP8    69,829   40,866  63.08%   36.92%

Dist    BertR  Frizell  BertR% Frizell%
=======================================
CD02  182,683  156,878  53.80%   46.20%
CD07  154,962  152,062  50.47%   49.53%
CD08   26,171   15,356  63.02%   36.98%
CD09   38,285  121,530  23.96%   76.04%
CD10  103,856   61,112  62.96%   37.04%
CD18   60,147  182,281  24.81%   75.19%
CD22   22,094   20,602  51.75%   48.25%
CD29   49,588  103,742  32.34%   67.66%
CD36   84,033   49,223  63.06%   36.94%
				
HD126  39,527   33,961  53.79%   46.21%
HD127  54,907   35,913  60.46%   39.54%
HD128  48,755   22,498  68.43%   31.57%
HD129  48,845   35,746  57.74%   42.26%
HD130  71,099   32,881  68.38%   31.62%
HD131  10,143   45,055  18.38%   81.62%
HD132  51,129   49,476  50.82%   49.18%
HD133  51,832   36,580  58.63%   41.37%
HD134  50,395   57,371  46.76%   53.24%
HD135  36,941   37,669  49.51%   50.49%
HD137  10,540   21,336  33.07%   66.93%
HD138  32,162   31,590  50.45%   49.55%
HD139  15,861   45,360  25.91%   74.09%
HD140   9,330   22,296  29.50%   70.50%
HD141   7,087   36,609  16.22%   83.78%
HD142  14,019   42,335  24.88%   75.12%
HD143  12,089   24,821  32.75%   67.25%
HD144  13,871   17,022  44.90%   55.10%
HD145  15,087   27,539  35.39%   64.61%
HD146  11,553   43,886  20.84%   79.16%
HD147  15,480   53,890  22.32%   77.68%
HD148  22,624   37,382  37.70%   62.30%
HD149  21,970   31,301  41.24%   58.76%
HD150  56,572   40,268  58.42%   41.58%
				
CC1    94,471  283,329  25.01%   74.99%
CC2   152,430  147,946  50.75%   49.25%
CC3   231,007  213,789  51.94%   48.06%
CC4   243,911  217,725  52.84%   47.16%
				
JP1    94,825  166,188  36.33%   63.67%
JP2    34,572   49,950  40.90%   59.10%
JP3    52,322   69,282  43.03%   56.97%
JP4   237,425  188,270  55.77%   44.23%
JP5   207,011  218,653  48.63%   51.37%
JP6     8,115   27,625  22.71%   77.29%
JP7    18,911  101,267  15.74%   84.26%
JP8    68,638   41,554  62.29%   37.71%

Dist    Yeary  Clinton  Yeary% Clinton%
=======================================
CD02  181,198  157,995  53.42%   46.58%
CD07  151,549  154,946  49.45%   50.55%
CD08   26,274   15,252  63.27%   36.73%
CD09   38,213  121,550  23.92%   76.08%
CD10  103,978   60,908  63.06%   36.94%
CD18   59,656  182,560  24.63%   75.37%
CD22   21,975   20,676  51.52%   48.48%
CD29   50,071  103,069  32.70%   67.30%
CD36   83,847   49,311  62.97%   37.03%

HD126  39,406   34,008  53.68%   46.32%
HD127  54,799   35,974  60.37%   39.63%
HD128  48,866   22,330  68.64%   31.36%
HD129  48,336   36,186  57.19%   42.81%
HD130  71,143   32,784  68.45%   31.55%
HD131  10,107   45,059  18.32%   81.68%
HD132  51,349   49,189  51.07%   48.93%
HD133  50,252   37,973  56.96%   43.04%
HD134  47,809   59,740  44.45%   55.55%
HD135  36,998   37,557  49.63%   50.37%
HD137  10,513   21,328  33.02%   66.98%
HD138  31,954   31,731  50.18%   49.82%
HD139  15,775   45,409  25.78%   74.22%
HD140   9,482   22,099  30.02%   69.98%
HD141   7,189   36,455  16.47%   83.53%
HD142  14,134   42,173  25.10%   74.90%
HD143  12,173   24,673  33.04%   66.96%
HD144  13,989   16,866  45.34%   54.66%
HD145  15,119   27,441  35.52%   64.48%
HD146  11,410   43,976  20.60%   79.40%
HD147  15,255   54,067  22.01%   77.99%
HD148  22,154   37,759  36.98%   63.02%
HD149  21,889   31,344  41.12%   58.88%
HD150  56,659   40,145  58.53%   41.47%
				
CC1    93,178  284,268  24.69%   75.31%
CC2   152,526  147,534  50.83%   49.17%
CC3   228,374  215,887  51.41%   48.59%
CC4   242,683  218,581  52.61%   47.39%
				
JP1    92,164  168,445  35.36%   64.64%
JP2    34,638   49,779  41.03%   58.97%
JP3    52,563   68,943  43.26%   56.74%
JP4   237,318  188,099  55.78%   44.22%
JP5   205,042  220,128  48.23%   51.77%
JP6     8,132   27,549  22.79%   77.21%
JP7    18,576  101,549  15.46%   84.54%
JP8    68,328   41,778  62.06%   37.94%

Dist   Newell    Birm  Newell%    Birm%
=======================================
CD02  183,283  155,303  54.13%   45.87%
CD07  154,445  151,554  50.47%   49.53%
CD08   26,375   15,075  63.63%   36.37%
CD09   39,055  120,306  24.51%   75.49%
CD10  104,616   60,043  63.53%   36.47%
CD18   61,174  180,645  25.30%   74.70%
CD22   22,249   20,322  52.26%   47.74%
CD29   51,148  101,583  33.49%   66.51%
CD36   84,501   48,451  63.56%   36.44%

HD126  39,784   33,498  54.29%   45.71%
HD127  55,127   35,497  60.83%   39.17%
HD128  49,062   22,055  68.99%   31.01%
HD129  48,920   35,437  57.99%   42.01%
HD130  71,414   32,353  68.82%   31.18%
HD131  10,424   44,586  18.95%   81.05%
HD132  51,878   48,536  51.66%   48.34%
HD133  51,273   36,800  58.22%   41.78%
HD134  49,412   57,931  46.03%   53.97%
HD135  37,337   37,104  50.16%   49.84%
HD137  10,697   21,067  33.68%   66.32%
HD138  32,371   31,165  50.95%   49.05%
HD139  16,204   44,873  26.53%   73.47%
HD140   9,722   21,767  30.87%   69.13%
HD141   7,342   36,259  16.84%   83.16%
HD142  14,466   41,754  25.73%   74.27%
HD143  12,491   24,246  34.00%   66.00%
HD144  14,227   16,561  46.21%   53.79%
HD145  15,377   27,059  36.24%   63.76%
HD146  11,707   43,563  21.18%   78.82%
HD147  15,713   53,487  22.71%   77.29%
HD148  22,748   37,026  38.06%   61.94%
HD149  22,175   30,953  41.74%   58.26%
HD150  56,974   39,704  58.93%   41.07%
				
CC1    95,668  281,099  25.39%   74.61%
CC2   154,203  145,222  51.50%   48.50%
CC3   231,571  211,887  52.22%   47.78%
CC4   245,404  215,077  53.29%   46.71%
				
JP1    94,960  165,091  36.52%   63.48%
JP2    35,233   48,975  41.84%   58.16%
JP3    53,108   68,215  43.77%   56.23%
JP4   238,952  185,854  56.25%   43.75%
JP5   208,027  216,365  49.02%   50.98%
JP6     8,409   27,151  23.65%   76.35%
JP7    19,213  100,651  16.03%   83.97%
JP8    68,944   40,983  62.72%   37.28%

Another word about the order in which these races appeared. On the Harris County election returns page, they appeared in the order you’d expect: first was the Supreme Court Chief Justice race, then Places 6, 7, and 8, followed by Court of Criminal Appeals Places 3, 4, and 9. In other words, the order a random person off the streets might have put them in if they had been tasked with it. For whatever the reason, on the Secretary of State election returns page, the order is different: Chief Justice, then Supreme Court Places 8, 6, and 7, followed by CCA Places 4, 9, and 3. I have no idea why they did it this way.

What difference does it make? The answer is in the total number of votes cast. The generally accepted wisdom is that the farther down the ballot, the more likely it is that a voter will skip the race, presumably because they thought “well, that’s all the voting I have in me, I’m going to call it quits now”. This was the underpinning of the many breathless articles about the effect of not having straight ticket voting, which came with the implicit assumption that Democratic voters would have less endurance in them, thus giving Republican candidates farther down the ballot an advantage. You know how I felt about that.

That said, the dropoff effect was there, albeit in a small amount. Here are the turnout totals for each race, going by the order on the Harris County ballot, which I’m taking as the proper order for elsewhere in the state. (You can check other county election sites to check this, I’ve already spent too much time on it.)


Position      Statewide     Harris
==================================
President    11,315,056  1,640,818
Senate       11,144,040  1,614,525
RRC          11,000,982  1,594,345
SC Chief     10,997,978  1,596,369
SC Place 6   10,954,061  1,591,486
SC Place 7   10,961,811  1,590,486
SC Place 8   10,948,768  1,588,895
CCA Place 3  10,918,384  1,584,608
CCA Place 4  10,898,223  1,583,031
CCA Place 9  10,879,051  1,580,131

I included the other statewide races here for comparison. There is some dropoff, but it’s pretty small – at both the statewide and Harris County level, the last race still got more than 96% of the vote total of the Presidential race. The dropoff among just the state offices is much more minimal, which I can understand – if all you care about is who’s running the country, you’ll probably stop after President, Senate, and Congress, which will be the third race on your ballot. Note also that with one exception in each column, the totals comport with their order on the ballot. Someday I might like to meet the person who decides to get off the bus after voting in three of the four Supreme Court races, or one of the three CCA races. Today is not that day, however.

The other thing to talk about here is how the candidates in races with a Libertarian candidate did versus the ones in races without a Libertarian. My eyeball sense of it is that the Republican candidates in two-person races picked up more of the erstwhile Libertarian voters in the redder districts, and the effect was more diffuse in the Dem districts, but I can’t say that with any level of rigor. There are too many factors to consider, including the gender and race of the candidates and their campaign finances and tenure in office and who knows what else. Maybe someone with a PhD can create a viable model for this.

Beyond that, what we see in these numbers is what we’ve been seeing all along. CD07 was a slightly tougher environment than it was in 2018, with three of the seven Democratic candidates carrying it. CD02 is basically a seven- or eight-point Republican district. HD135 leaned slightly Democratic, while HDs 132 and 138 leaned slightly more Republican, and HD134 completed its journey to becoming a Democratic district. Commissioner Precincts 2, 3, and 4 were all slightly to slightly-more-than-slightly red, but it won’t take much in redistricting to flip that around, at least for precincts 2 and 3. Everyone carried Constable/JP precinct 5, while precinct 4 remains a bit of a stretch. Lather, rinse, repeat.

If you’re wondering why I haven’t included SBOE and State Senate districts in these reports before now, wonder no more. I’ll be delving into those next. Let me know what you think.

Precinct analysis: Comparing to 2012 and 2016

Introduction
Congressional districts
State Rep districts
Commissioners Court/JP precincts

I had meant to get to this last week, but SeditionPalooza took up too much of my time, so here we are. The intent of this post is to compare vote totals in each of the State Rep districts from 2012 to 2016, from 2016 to 2020, and from 2012 to 2020. The vote totals compared are from the Presidential and Railroad Commissioner races for each of these years, and for the Senate races from 2012 and 2020, as there was no Senate race in 2016.

President

								
Dist   12-16 R   12-16D   16-20R   16-20D   12-20R   12-20D
===========================================================
HD126   -3,207    5,285    6,100    9,611    2,893   14,896
HD127     -931    6,042    8,547   12,707    7,616   18,749
HD128      124    2,272    8,728    6,208    8,852    8,480
HD129   -3,226    5,992    8,844   11,033    5,618   17,025
HD130    2,216    6,749   14,229   13,325   16,445   20,074
HD131     -649    2,707    4,306    6,683    3,657    9,390
HD132    3,065   10,267   15,786   20,304   18,851   30,571
HD133   -7,791    8,688    5,592   12,018   -2,199   20,706
HD134  -10,938   15,346    6,692   17,904   -4,246   33,250
HD135   -2,571    6,505    6,664   11,473    4,093   17,978
HD137     -537    2,443    2,451    4,167    1,914    6,610
HD138   -2,804    6,451    6,537    9,433    3,733   15,884
HD139   -1,294    1,187    4,847    6,854    3,553    8,041
HD140     -733    4,416    4,146    1,855    3,413    6,271
HD141      222     -681    2,604    4,453    2,826    3,772
HD142      290    2,084    4,703    8,880    4,993   10,964
HD143   -1,042    3,226    4,500    1,495    3,458    4,721
HD144   -1,039    3,561    4,057    1,523    3,018    5,084
HD145   -1,291    5,594    5,310    5,088    4,019   10,682
HD146   -1,633     -884    2,459    6,864      826    5,980
HD147   -1,272    3,583    4,602    9,933    3,330   13,516
HD148   -1,489    8,544    5,634   10,180    4,145   18,724
HD149   -3,879    3,420    8,154    4,696    4,275    8,116
HD150      503    8,228   10,180   15,037   10,683   23,265
							
Total  -39,906  121,025  155,672  211,724  115,766  332,749

Senate

	
Dist    12-20R   12-20D
=======================
HD126    3,705   13,479
HD127    8,876   16,687
HD128    8,999    7,330
HD129    7,238   14,684
HD130   18,113   17,564
HD131    3,413    8,389
HD132   19,527   28,278
HD133    2,610   16,268
HD134    3,330   27,237
HD135    4,898   16,279
HD137    2,129    6,023
HD138    4,594   14,227
HD139    3,602    6,608
HD140    2,611    5,499
HD141    2,460    2,779
HD142    4,903    9,702
HD143    2,619    4,082
HD144    2,577    4,485
HD145    3,562   10,103
HD146    1,337    4,811
HD147    4,019   12,164
HD148    5,762   16,497
HD149    4,282    7,157
HD150   11,865   20,878
		
Total  137,031  291,210

RRC

								
Dist   12-16 R   12-16D   16-20R   16-20D   12-20R   12-20D
===========================================================
HD126   -1,676    3,559    4,735   10,131    3,059   13,690
HD127    1,006    4,180    6,933   13,217    7,939   17,397
HD128      989    1,200    7,749    6,681    8,738    7,881
HD129   -1,550    3,595    7,325   12,422    5,775   16,017
HD130    4,403    4,540   13,107   12,954   17,510   17,494
HD131     -465    1,814    3,419    6,824    2,954    8,638
HD132    4,638    8,171   14,267   19,768   18,905   27,939
HD133   -4,382    3,417    5,039   14,285      657   17,702
HD134   -5,177    6,106    5,497   23,976      320   30,082
HD135   -1,163    4,634    5,398   11,950    4,235   16,584
HD137     -132    1,538    1,929    4,571    1,797    6,109
HD138   -1,483    4,248    5,378   10,328    3,895   14,576
HD139     -551      -83    3,837    7,033    3,286    6,950
HD140     -321    2,969    2,874    2,855    2,553    5,824
HD141      181     -896    2,165    3,773    2,346    2,877
HD142      844    1,204    3,814    8,568    4,658    9,772
HD143     -550    1,586    3,148    2,910    2,598    4,496
HD144     -530    2,677    2,993    2,255    2,463    4,932
HD145     -531    3,369    3,983    7,142    3,452   10,511
HD146   -1,047   -2,256    1,853    7,402      806    5,146
HD147      104      536    3,510   11,837    3,614   12,373
HD148      665    4,416    4,945   12,352    5,610   16,768
HD149   -3,089    2,133    6,698    5,331    3,609    7,464
HD150    2,552    6,010    8,826   14,942   11,378   20,952
								
Total   -7,265   68,667  129,422  233,507  122,157  302,174

The columns represent the difference in vote total for the given period and party, so “12-16” means 2012 to 2016, “16-20” means 2016 to 2020, and “12-20” means 2012 to 2020. Each column has a D or an R in it, so “12-16R” means the difference between 2016 Donald Trump and 2012 Mitt Romney for the Presidential table, and so forth. In each case, I subtract the earlier year’s total from the later year’s total, so the “-3,207” for HD126 in the “12-16R” column for President means that Donald Trump got 3,207 fewer votes in HD126 than Mitt Romney got, and the “5,285” for HD126 in the “12-16D” column for President means that Hillary Clinton got 5,285 more votes than Barack Obama got. Clear? I hope so.

Note that there were 130K more votes cast in Harris County as a whole in 2016 than there were in 2012, and 320K more votes cast in the county in 2020 over 2016, which makes a grand total of 450K more votes in 2020 than 2012. Some districts grow faster than others, but as a general rule given the overall totals you should expect increases in each district to some extent.

I have left percentages and third party totals out of this discussion. As I have shown before, tracking changes in vote percentages can give a misleading view of whether the actual gap is growing or narrowing, and by how much. I also want to emphasize that in 2012, Harris County was very much a 50-50 proposition, and now it is very much not. Doing it this way help illustrate how and where that has happened, and by how much.

And yet, with all that said, I’m going to start with an observation about percentages. In 2012, Mitt Romney got 60% or more of the vote in eight State Rep districts – HDs 126, 127, 128, 129, 130, 133, 138, and 150. Ted Cruz, running for Senate against Paul Sadler, got 60% or more of the vote in ten State Rep districts, the same eight as Romney plus HDs 132 and 135 – yes, the same 132 and 135 that Dems won in 2018. I didn’t publish an analysis of the RRC race from that year, but a review of the spreadsheet that I created at the time confirmed that Christi Craddick, running against Dale Henry, got 60% or more of the vote in eleven State Rep districts, the same ten as Cruz plus HD134. In other words, every single Republican-held State Rep district in Harris County in 2012 was at least a 60% Republican district in the Railroad Commissioner race. Mitt Romney, it should be noted, just missed getting to 60% in HDs 132 and 135, and was over 57% in HD134, as was Cruz. (Let’s just say Cruz fell way short of that mark in 2018.)

You can see how much the vote totals shifted at the Presidential level from 2012 to 2016. Trump got nearly 40K fewer votes than Romney, a combination of crossovers, third-party and write-in voting, and just the gentle degradation of the Republican brand, as you can see by Wayne Christian’s reduced vote totals from Christie Craddick. Still, in 2016, Donald Trump scored 60% or more of the vote in three State Rep districts: HDs 127, 128, and 130. In 2016, Wayne Christian, running for RRC against Grady Yarbrough, scored 60% or more of the vote in four State Rep districts: the three that Trump got plus HD150. And finally, in 2016, Eva Guzman, running for State Supreme Court, scored 60% or more of the vote in six State Rep districts: the four Christian got plus HDs 129 and 133. HDs 132 and 135 were clearly competitive at the Presidential level – Trump won 132 by four points and 135 by two points; he also lost HD138 by a hair. He lost votes compared to Romney in 18 of 24 districts.

It is certainly true that Republicans in general and Donald Trump in particular did better in 2020 than most people expected them to do – surely, they did better than I expected them to do. Trump gained 155K votes over his 2016 total, which put 2020 Trump more than 100K votes ahead of Mitt Romney. Even though Joe Biden gained 211K votes over Hillary Clinton, for a net gain of 56K, Trump had net gains on Biden in seven districts – HDs 128, 130, 140, 143, 144, 145, and 149, with the latter five being Democratic districts and four of the five being Latino. Still, Dems had a net gain from 2012 to 2020 in every district except HD128, and some of those gains were truly huge – just look at 133 and 134, for starters. And Trump’s gains in the Dem districts largely melted away by the time you got to the RRC race, with Chrysta Castaneda coming close to matching Jim Wright’s increases in 140, 143, and 144, and far exceeding him in 145. It’s hard to say from this what if any staying power the Trump gains may have, though Dems should be paying close attention to what happened there regardless.

Anyway, back to the percentages: In 2020, Donald Trump, John Cornyn, and Jim Wright scored 60% or more of the vote in two State Rep districts: HDs 128 and 130. The only statewide Republicans to score 60% or more in a third State Rep district were the statewide judicial candidates who did not have a Libertarian opponent – Jane Bland, Bert Richardson, Kevin Patrick, and David Newell – who also reached that level in HD127. I haven’t published the statewide judicial race analysis yet so you’ll have to take my word for it for now, but in any event I trust you see the pattern. This is what I mean when I say that Republicans just don’t have any spare capacity in Harris County, and that will present problems for them in redistricting. Look at the numbers in districts like 126 and 129 and 133 and 150 in 2020, and compare them to the numbers in 132 and 135 and 138 in 2012. Where do you think things are going to be in another couple of cycles?

I’ve thrown a lot of words and numbers at you, so I’ll wrap it up here. I hope this helps illustrate what I’ve been saying, about how Dem gains have largely come from huge steps forward in formerly Republican turf, and how there’s still very much room for Dems to improve in their strongholds. We need to keep building on our gains from this past decade as we proceed into the 20s. I’ll have a look at the statewide judicial races next. Let me know what you think.

Precinct analysis: Commissioners Court and JP/Constable precincts

Introduction
Congressional districts
State Rep districts

We now zoom in for a look at various county districts, which are also called “precincts”. I don’t know why we have County Commissioner precincts and JP/Constable precincts to go along with regular voting precincts – it makes for a certain amount of either monotony or inaccuracy when I have to write about them – but it is what it is. Dems made a priority of County Commissioner Precinct 3 and didn’t get it, but did flip a longstanding Republican Justice of the Peace bench.


Dist    Trump    Biden    Lib    Grn  Trump%  Biden%   Lib%   Grn%
==================================================================
CC1    90,536  295,657  3,355  1,338  23.16%  75.64%  0.86%  0.34%
CC2   154,159  154,516  3,250  1,028  49.26%  49.37%  1.04%  0.33%
CC3   220,205  234,323  4,876  1,328  47.79%  50.86%  1.06%  0.29%
CC4   235,730  233,697  5,338  1,435  49.50%  49.08%  1.12%  0.30%

Dist    Trump    Biden    Lib    Grn  Trump%  Biden%   Lib%   Grn%
==================================================================
JP1    85,426  182,182  3,199    822  31.45%  67.07%  1.18%  0.30%
JP2    35,864   51,624    741    330  40.50%  58.29%  0.84%  0.37%
JP3    53,543   70,746  1,055    375  42.59%  56.27%  0.84%  0.30%
JP4   232,147  199,750  4,698  1,250  53.02%  45.62%  1.07%  0.29%
JP5   199,292  236,253  4,525  1,384  45.14%  53.52%  1.03%  0.31%
JP6     8,554   28,500    357    158  22.77%  75.86%  0.95%  0.42%
JP7    17,977  104,457    835    464  14.53%  84.42%  0.67%  0.38%
JP8    67,827   44,681  1,409    346  59.36%  39.10%  1.23%  0.30%

Dist   Cornyn    Hegar    Lib    Grn Cornyn%  Hegar%   Lib%   Grn%
==================================================================
CC1    94,601  278,805  6,735  3,743  24.20%  71.33%  1.72%  0.96%
CC2   152,772  144,150  6,038  2,703  48.82%  46.06%  1.93%  0.86%
CC3   229,016  214,734  7,608  3,129  49.71%  46.61%  1.65%  0.68%
CC4   241,839  216,469  8,836  3,314  50.79%  45.46%  1.86%  0.70%

Dist   Cornyn    Hegar    Lib    Grn Cornyn%  Hegar%   Lib%   Grn%
==================================================================
JP1    93,109  167,648  4,655  2,101  34.28%  61.72%  1.71%  0.77%
JP2    35,186   48,126  1,638    946  39.73%  54.34%  1.85%  1.07%
JP3    52,663   67,120  2,257  1,121  41.89%  53.39%  1.80%  0.89%
JP4   235,664  186,072  8,077  2,923  53.82%  42.50%  1.84%  0.67%
JP5   205,996  217,791  7,543  3,288  46.66%  49.33%  1.71%  0.74%
JP6     8,342   26,680    795    472  22.20%  71.02%  2.12%  1.26%
JP7    19,157   99,241  2,051  1,291  15.48%  80.21%  1.66%  1.04%
JP8    68,111   41,480  2,201    747  59.61%  36.30%  1.93%  0.65%

Dist   Wright    Casta    Lib    Grn Wright%  Casta%   Lib%   Grn%
==================================================================
CC1    90,035  276,291  7,330  5,863  23.03%  70.68%  1.88%  1.50%
CC2   146,598  145,934  6,329  3,756  46.84%  46.63%  2.02%  1.20%
CC3   223,852  208,983  9,167  5,678  48.59%  45.36%  1.99%  1.23%
CC4   236,362  212,151 10,305  5,711  49.64%  44.55%  2.16%  1.20%

Dist   Wright    Casta    Lib    Grn Wright%  Casta%   Lib%   Grn%
==================================================================
JP1    90,194  163,531  5,804  3,640  33.20%  60.20%  2.14%  1.34%
JP2    32,881   49,373  1,605  1,218  37.13%  55.75%  1.81%  1.38%
JP3    50,924   67,644  2,207  1,398  40.51%  53.81%  1.76%  1.11%
JP4   230,575  183,069  9,233  5,036  52.66%  41.81%  2.11%  1.15%
JP5   200,704  213,004  8,895  5,800  45.46%  48.25%  2.01%  1.31%
JP6     7,490   27,172    730    651  19.94%  72.33%  1.94%  1.73%
JP7    17,970   98,421  2,115  2,039  14.52%  79.54%  1.71%  1.65%
JP8    66,109   41,145  2,542  1,226  57.86%  36.01%  2.22%  1.07%

First things first, the Justice of the Peace and Constable precincts are the same. There are eight of them, and for reasons I have never understood they are different sizes – as you can see, JPs 4 and 5 are roughly the size of Commissioners Court precincts, at least as far as voting turnout goes, JP1 is smaller but still clearly larger than the rest, and JP6 is tiny. When I get to have a conversation with someone at the county about their plans for redistricting, I plan to ask if there’s any consideration for redrawing these precincts. Note that there are two JPs in each precinct – Place 1 was up for election this cycle, with Place 2 on the ballot in 2022. The Constables are on the ballot with the Place 1 JPs. I’ll return to them in a minute.

You may recall from my first pass at Harris County data, Donald Trump had a super slim lead in Commissioners Court Precinct 2, home of Adrian Garcia. That was from before the provisional ballots were cured. There were something like five or six thousand provisional ballots, and overall they were pretty Democratic – I noted before that this almost pushed Jane Robinson over the top in her appellate court race – though they weren’t uniformly pro-Dem; Wesley Hunt in CD07 and Mike Schofield in HD132 netted a few votes from the provisionals, among those that I looked at more closely. In CC2, the provisional ballots put Joe Biden ever so slightly ahead of Trump, by a teensy but incrementally larger lead than Trump had had. MJ Hegar lost CC2 by a noticeable amount, and Chrysta Castaneda missed it by a hair.

Now, in 2018 Beto won CC2 by over six points. Every statewide candidate except for Lupe Valdez carried it, and every countywide candidate except for Lina Hidalgo carried it. Oddly enough, Adrian Garcia himself just squeaked by, taking the lead about as late in the evening as Judge Hidalgo did to claim the majority on the Court for Dems. I’d have thought Garcia would easily run ahead of the rest of the ticket, but it was largely the reverse. The conclusion I drew from this was that being an incumbent Commissioner was an advantage – not quite enough of one in the end for Jack Morman, but almost.

I say that for the obvious reason that you might look at these numbers and be worried about Garcia’s future in 2022. I don’t think we can take anything for granted, but remember two things. One is what I just said, that there’s an incumbent’s advantage here, and I’d expect Garcia to benefit from it in two years’ time. And two, we will have new boundaries for these precincts by then. I fully expect that the Dem majority will make Garcia’s re-election prospects a little better, as the Republican majority had done for Morman in 2011.

The bigger question is what happens with the two Republican-held precincts. I’ve spoken about how there’s no spare capacity on the Republican side to bolster their existing districts while moving in on others. That’s not the case here for Dems with Commissioners Court. Given free rein, you could easily draw four reasonable Dem districts. The main thing that might hold you back is the Voting Rights Act, since you can’t retrogress Precinct 1. The more likely play is to dump some Republican turf from Precincts 2 and 3 into Precinct 4, making it redder while shoring up 2 for the Dems and making 3 more competitive. I wouldn’t sit around in my first term in office if I’m Tom Ramsey, is what I’m saying.

I should note that Beto also won CC3, as did Mike Collier and Justin Nelson and Kim Olson, but that’s largely it; I didn’t go back to check the various judicial races but my recollection is that maybe a couple of the Dem judicials carried it. Overall, CC3 was still mostly red in 2018, with a few blue incursions, and it remained so in 2020. I feel like it would be gettable in 2024 even without a boost from redistricting, but why take the chance? Dems can set themselves up here, and they should.

What about the office Dems flipped? That would be Justice of the Peace, Place 1, where longtime jurist Russ Ridgway finally met his match. You will note that Precinct 5 Constable Ted Heap held on by a 51.5 to 48.5 margin, almost the exact mirror of Israel Garcia’s 51.4 to 48.6 win over Ridgway. What might account for the difference? For one, as we’ve seen, candidates with Latino surnames have generally done a couple of points better than the average. For two, it’s my observation that more people probably know their Constable’s name than either of their JPs’ names. Your neighborhood may participate in a Constable patrol program, and even if you don’t you’ve surely seen road signs saying that the streets are overseen by Constable so-and-so. I think those two factors may have made the difference; I’m told Garcia was a very active campaigner as well, and that could have helped, but I can’t confirm that or compare his activity to Dem Constable candidate Mark Alan Harrison, so I’ll just leave it as a second-hand observation. Dems can certainly aim for the Place 2 JP in Precinct 5, and even though Precinct 4 was in the red I’d really like to see someone run against Laryssa Korduba, who is (as of last report, anyway) the only JP in Harris County who no longer officiates weddings following the Obergefell ruling. She’s consistent about it, and acting legally by not doing any weddings, and that’s fine by me as a personal choice, but that doesn’t mean the people of Precinct 4 couldn’t do better for themselves. I’d like to see them have that choice in 2022.

Next up, some comparisons to 2012 and 2016. Next week, we get into judicial races and county races. Let me know what you think.

Precinct analysis: State Rep districts

Introduction
Congressional districts

We move now to State Rep districts, which is my usual currency since they provide complete coverage of the county with no partial pieces. You can also get a much more nuanced view of how things have shifted over time. There are more numbers here since there are more districts, so buckle up.


Dist    Trump   Biden    Lib    Grn  Trump%  Biden%   Lib%   Grn%
=================================================================
HD126  38,651  36,031    740    264  51.07%  47.61%  0.98%  0.35%
HD127  53,644  38,409  1,024    215  57.50%  41.17%  1.10%  0.23%
HD128  49,349  23,343    742    198  67.02%  31.70%  1.01%  0.27%
HD129  47,389  38,941  1,125    246  54.03%  44.40%  1.28%  0.28%
HD130  69,369  35,958  1,298    220  64.92%  33.65%  1.21%  0.21%
HD131  10,508  45,904    331    192  18.46%  80.63%  0.58%  0.34%
HD132  50,223  51,737  1,190    360  48.52%  49.98%  1.15%  0.35%
HD133  47,038  43,262    965    201  51.43%  47.30%  1.06%  0.22%
HD134  42,523  67,811  1,356    238  37.99%  60.58%  1.21%  0.21%
HD135  36,114  39,657    862    246  46.98%  51.58%  1.12%  0.32%
HD137  10,382  22,509    308    144  31.14%  67.51%  0.92%  0.43%
HD138  31,171  34,079    703    226  47.10%  51.50%  1.06%  0.34%
HD139  15,691  46,918    511    241  24.76%  74.05%  0.81%  0.38%
HD140  10,259  22,819    227    150  30.67%  68.21%  0.68%  0.45%
HD141   7,443  37,222    289    178  16.49%  82.47%  0.64%  0.39%
HD142  14,187  43,334    469    189  24.39%  74.48%  0.81%  0.32%
HD143  13,229  25,318    282    141  33.95%  64.97%  0.72%  0.36%
HD144  14,598  17,365    308    150  45.03%  53.56%  0.95%  0.46%
HD145  15,393  28,572    462    185  34.50%  64.05%  1.04%  0.41%
HD146  10,938  45,784    439    204  19.07%  79.81%  0.77%  0.36%
HD147  14,437  56,279    734    278  20.13%  78.46%  1.02%  0.39%
HD148  20,413  41,117    901    203  32.59%  65.65%  1.44%  0.32%
HD149  22,419  32,886    428    172  40.10%  58.82%  0.77%  0.31%
HD150  55,261  42,933  1,125    287  55.48%  43.10%  1.13%  0.29%

Dist   Cornyn   Hegar    Lib    Grn Cornyn%  Hegar%   Lib%   Grn%
=================================================================
HD126  39,298  33,618  1,343    535  52.54%  44.95%  1.80%  0.72%
HD127  54,433  35,689  1,690    543  58.94%  38.64%  1.83%  0.59%
HD128  48,646  22,029  1,323    447  67.15%  30.41%  1.83%  0.62%
HD129  48,318  35,924  1,715    603  55.82%  41.50%  1.98%  0.70%
HD130  70,329  32,961  1,933    551  66.49%  31.16%  1.83%  0.52%
HD131  10,557  43,670    938    621  18.92%  78.28%  1.68%  1.11%
HD132  50,865  48,460  2,011    774  49.81%  47.46%  1.97%  0.76%
HD133  51,111  38,148  1,232    471  56.19%  41.94%  1.35%  0.52%
HD134  48,629  61,015  1,408    489  43.60%  54.70%  1.26%  0.44%
HD135  36,728  37,050  1,427    628  48.43%  48.86%  1.88%  0.83%
HD137  10,617  20,914    629    343  32.66%  64.34%  1.94%  1.06%
HD138  31,993  31,508  1,183    486  49.09%  48.35%  1.82%  0.75%
HD139  15,984  44,273  1,168    647  25.75%  71.33%  1.88%  1.04%
HD140   9,771  21,167    630    423  30.54%  66.17%  1.97%  1.32%
HD141   7,409  35,278    820    511  16.83%  80.14%  1.86%  1.16%
HD142  14,269  41,061  1,055    562  25.06%  72.10%  1.85%  0.99%
HD143  12,535  23,679    737    511  33.46%  63.21%  1.97%  1.36%
HD144  14,107  16,246    629    374  44.99%  51.81%  2.01%  1.19%
HD145  15,236  26,758    899    490  35.12%  61.68%  2.07%  1.13%
HD146  11,598  43,259    938    563  20.58%  76.76%  1.66%  1.00%
HD147  15,359  53,237  1,359    707  21.74%  75.34%  1.92%  1.00%
HD148  22,087  37,707  1,303    489  35.86%  61.23%  2.12%  0.79%
HD149  22,329  30,630    888    471  41.11%  56.39%  1.63%  0.87%
HD150  56,019  39,872  1,959    650  56.87%  40.48%  1.99%  0.66%

Dist   Wright   Casta    Lib    Grn Wright%  Casta%   Lib%   Grn%
=================================================================
HD126  38,409  32,979  1,562    942  51.98%  44.63%  2.11%  1.27%
HD127  53,034  35,348  1,948  1,026  58.05%  38.69%  2.13%  1.12%
HD128  47,576  22,153  1,382    605  66.34%  30.89%  1.93%  0.84%
HD129  46,707  35,326  2,084  1,095  54.81%  41.46%  2.45%  1.29%
HD130  69,295  31,825  2,387    981  66.32%  30.46%  2.28%  0.94%
HD131   9,786  43,714    930    899  17.69%  79.01%  1.68%  1.62%
HD132  49,947  47,483  2,288  1,389  49.40%  46.96%  2.26%  1.37%
HD133  50,069  36,455  1,636    998  56.16%  40.89%  1.83%  1.12%
HD134  47,504  57,938  2,155  1,239  43.65%  53.23%  1.98%  1.14%
HD135  35,845  36,487  1,706    988  47.78%  48.63%  2.27%  1.32%
HD137  10,168  20,606    695    589  31.72%  64.28%  2.17%  1.84%
HD138  31,201  30,796  1,377    859  48.57%  47.94%  2.14%  1.34%
HD139  15,235  44,188  1,166    895  24.78%  71.87%  1.90%  1.46%
HD140   8,840  21,955    515    509  27.78%  69.00%  1.62%  1.60%
HD141   6,885  35,470    766    654  15.73%  81.03%  1.75%  1.49%
HD142  13,584  41,134  1,041    788  24.02%  72.74%  1.84%  1.39%
HD143  11,494  24,467    657    563  30.91%  65.81%  1.77%  1.51%
HD144  13,250  16,851    603    417  42.58%  54.15%  1.94%  1.34%
HD145  14,246  27,135    903    703  33.14%  63.12%  2.10%  1.64%
HD146  10,964  42,686  1,034    947  19.71%  76.73%  1.86%  1.70%
HD147  14,711  52,289  1,554  1,199  21.09%  74.96%  2.23%  1.72%
HD148  21,527  36,656  1,580    869  35.50%  60.46%  2.61%  1.43%
HD149  21,458  30,419    976    727  40.05%  56.77%  1.82%  1.36%
HD150  55,111  38,995  2,186  1,127  56.57%  40.03%  2.24%  1.16%

There’s a lot here, and I’m going to try to limit the analysis in this post to just what’s here, since I will have a separate post that looks back at previous elections. I’m going to pick a few broad themes here and will continue when I get to that subsequent post.

It’s clear that the big districts for Republicans crossing over to vote for Biden were HDs 133 and 134. Biden basically hit Beto’s number in 134, and he made 133 nearly as competitive as 126. The same effect is visible but smaller in 126, 129, 138, and 150, but it’s more noticeable in the lower downballot Democratic total than the Republican number. Some of those votes migrate to third party candidates, some may be people just voting at the Presidential level – it’s hard to say for sure. In 2016, there were bigger third party totals at the Presidential level, but this year those numbers were more like prior norms.

However you look at this, the fact remains that Republicans don’t have a lot of areas of strength. Only HDs 128 and 130 performed consistently at a 60% level for them; as we will see with the judicial races, some candidates reached that number in HD127 as well. Spoiler alert for my future post: That’s a big change from 2012. We’ll get into that later, but what that means for now is what I was saying in the Congressional post, which is that there’s little spare capacity for Republicans to distribute. There’s some red they can slosh into HDs 132, 135, and 138 if they want, but it’s going to be hard to make more than a few Republican incumbents feel safe.

I’m still not comfortable calling HD134 a Democratic district – which is a bit meaningless anyway as we head into redistricting – but the numbers are what they are. There’s still some volatility, mostly in judicial races as you’ll see, but this district just isn’t what it used to be. After the 2016 election, when Greg Abbott went hard at Sarah Davis and the Trump effect was already obvious, I wondered what Republicans would do with that district, since they didn’t seem to care about Davis. Abbott subsequently rediscovered his pragmatic side, but Davis is now history, and this district is at least as blue as Harris County is overall, so they have a whole different problem to contemplate. If anyone reading this is of a mind to mourn Davis’ demise, I say put 100% of the blame on Donald Trump and the degeneracy he has brought forward in the GOP. Sarah Davis never took my advice to leave the Republican Party, but a lot of her former voters did. The future is always in motion, but at this point I would not expect them to come back.

On the flip side, Trump and the Republicans saw some gains in Democratic areas. The two that stand out to me are HDs 144 and 149 – Dems were well above 60% in the latter in 2016. Note how Chrysta Castaneda was the best performer in this group among Dems – her numbers in HD144 were comparable to Rep. Mary Ann Perez’s totals. As for 149, it was the inverse of HD133, more or less, without anyone making it look competitive. Here, Biden did about as well as Rep. Hubert Vo. I think this is more likely to be a Trump-catalyzed fluke than the start of a trend, but we’ll just have to see what the next elections tell us.

Finally, I should probably do a separate post on third party voting by State Rep district this cycle, but for now let me state the obvious that there was a whole lot less of it than in 2016, for a variety of reasons. I didn’t bother naming the Libertarian and Green candidates in the column headers above because honestly, even with the kerfuffle over both Republicans and Democrats trying to force them off the ballot for filing fee non-payment, there just wasn’t any attention on them this year. HD148 was the high-water mark for the Libertarian candidate in 2016 at the Presidential level, and HD134 topped the chart for Railroad Commissioner levels, with 4.53% in the former and an eye-popping 12.18% in the latter; the Chron endorsement of Mark Miller for RRC in 2016 surely helped him there. HD148 was the “winner” this year for each, though at much tamer 1.44% and 2.68%, respectively. For the Greens in 2016, it was HD137 for President (1.30%) and HD145 for RRC (6.49%), and this year it was HD144 (0.46%) for President and HD137 (1.84%) for RRC. You can say what you want about which third party affects which major party – I will note that Chrysta Castaneda outperformed Grady Yarbrough in HD134 by fifteen points, while Wayne Christian was four points better than Jim Wright in the same district. HD134 shifted strongly Dem in 2020, but the quality of the Dem also mattered.

Next up is a look at County Commissioner and JP/Constable precincts, and after that we’ll get that deeper look at 2020 versus 2016 and 2012. Let me know what you think.

Precinct analysis: Congressional districts

Introduction

All right, let’s get this party started. In the past I’ve generally done the top races by themselves, but any race involving Trump provides challenges, because his level of support just varies in comparison to other Republicans depending on where you look. So this year it felt right to include the other statewide non-judicial results in my Presidential analyses, and the only way to do that without completely overwhelming you with a wall of numbers was to break it out by district types. That seemed to also pair well with a closer look at the competitive districts of interest, of which there were more than usual this year. So let’s begin with a look at the Congressional districts in Harris County. Only CDs 02, 07, 18, and 29 are fully in Harris County – we won’t have the complete data on all Congressional districts until later – so just keep that in mind.


Dist    Trump    Biden    Lib    Grn  Trump%  Biden%   Lib%   Grn%
==================================================================
CD02  174,980  170,428  4,067    969  49.93%  48.63%  1.16%  0.28%
CD07  143,176  170,060  3,416    903  45.09%  53.55%  1.08%  0.28%
CD08   25,484   16,629    520     87  59.65%  38.93%  1.22%  0.20%
CD09   39,372  125,237  1,066    589  23.68%  75.32%  0.64%  0.35%
CD10  101,390   65,714  2,023    431  59.80%  38.76%  1.19%  0.25%
CD18   57,669  189,823  2,382    962  22.99%  75.68%  0.95%  0.38%
CD22   21,912   21,720    522    137  49.47%  49.04%  1.18%  0.31%
CD29   52,937  106,229  1,265    649  32.86%  65.95%  0.79%  0.40%
CD36   83,710   52,350  1,558    402  60.65%  37.93%  1.13%  0.29%

Dist   Cornyn    Hegar    Lib    Grn Cornyn%  Hegar%   Lib%   Grn%
==================================================================
CD02  180,504  157,923  6,215  2,164  52.37%  45.82%  1.80%  0.63%
CD07  152,741  154,670  4,939  2,161  48.90%  49.52%  1.58%  0.69%
CD08   25,916   15,259    846    221  61.67%  36.31%  2.01%  0.53%
CD09   39,404  118,424  2,725  1,677  24.54%  73.76%  1.70%  1.04%
CD10  102,919   60,687  3,168    939  61.71%  36.39%  1.90%  0.56%
CD18   60,111  178,680  4,806  2,468  24.68%  73.35%  1.97%  1.01%
CD22   21,975   20,283    898    377  50.92%  47.00%  2.08%  0.87%
CD29   51,044   99,415  3,022  1,969  33.26%  64.77%  1.97%  1.28%
CD36   83,614   48,814  2,598    913  61.92%  36.15%  1.92%  0.68%

Dist   Wright    Casta    Lib    Grn Wright%  Casta%   Lib%   Grn%
==================================================================
CD02  176,484  153,628  7,631  4,122  51.62%  44.94%  2.23%  1.21%
CD07  149,114  149,853  6,276  3,974  48.22%  48.46%  2.03%  1.29%
CD08   25,558   14,796    992    394  61.23%  35.45%  2.38%  0.94%
CD09   37,090  117,982  2,764  2,570  23.12%  73.55%  1.72%  1.60%
CD10  101,414   58,873  3,758  1,793  61.15%  35.50%  2.27%  1.08%
CD18   57,783  177,020  5,021  3,846  23.71%  72.65%  2.06%  1.58%
CD22   21,026   20,231  1,007    675  48.97%  47.12%  2.35%  1.57%
CD29   46,954  102,354  2,802  2,334  30.40%  66.27%  1.81%  1.51%
CD36   81,424   48,619  2,880  1,300  60.66%  36.22%  2.15%  0.97%

Dist      GOP      Dem    Lib    Grn    GOP%    Dem%   Lib%   Grn%
==================================================================
CD02  192,828  148,374  5,524         55.61%  42.79%  1.59%
CD07  149,054  159,529  5,542         47.75%  50.79%  1.76%
CD08   25,906   15,212    926         61.62%  36.18%  2.20%
CD09   35,634  121,576  4,799         22.00%  75.04%  2.96%
CD10  103,180   60,388  3,496         61.76%  36.15%  2.09%
CD18   58,033  180,952  4,514  3,396  23.51%  73.29%  1.83%  1.38%
CD22   20,953   19,743  2,291         48.74%  45.93%  5.33%
CD29   42,840  111,305  2,328         27.38%  71.13%  1.49%
CD36   84,721   46,545  2,579    985  62.84%  34.52%  1.91%  0.73%

The first three tables are the Presidential, Senate, and Railroad Commissioner results, in that order. Subsequent presentations with State Rep and JP/Constable precincts will be done in the same fashion. For this post, I have also included the actual Congressional results – each Congressional race had both a Dem and a Republican, which doesn’t always happen, so they provide a good point of comparison. The candidate labeled as “Green” in CD18 was actually an independent – only CD36 had an actual Green Party candidate. In the other Congressional races, there were only three candidates.

How competitive CD02 looks depends very much on how you’re looking at it. On the one hand, Joe Biden came within 1.3 points, with Trump failing to reach fifty percent. On the other hand, Dan Crenshaw won by almost thirteen points, easily exceeding his marks from 2018 while clearly getting some crossover support. In between was everything else – MJ Hegar and Chrysta Castaneda trailed by about six and a half points each, with third-party candidates taking an increasing share of the vote. As we’ll see, most of the time the spread was between seven and nine points. That doesn’t tell us too much about what CD02 will look like going forward, but it does tell us that it doesn’t have a large reserve of Republican votes in it that can be used to bolster other Republicans. One possible outcome is that the map-drawers decide that Crenshaw will punch above his weight – he certainly fundraises at a very high level – which will allow them to leave him in a seemingly-narrow district while tending to more urgent matters elsewhere. The downside there is that if and when Crenshaw decides he’s made for bigger things, this district would be that much harder to hold with a different Republican running in it.

Another possibility is that Republicans will decide that they’re better off turning CD07 into a more Dem-friendly district, and using the space Republican capacity from CD07 to bolster CDs 02 and maybe 10. Lizzie Fletcher didn’t win by much, though I will note that Wesley Hunt’s 47.75% is a mere 0.28 points better than John Culberson in 2018. (There was no Libertarian candidate in 2018; do we think that hurt Hunt or Fletcher more in this context?) But other than Biden, no Dem came close to matching Fletcher’s performance – Hegar and Castaneda were among the top finishers in CD07, as we will see going forward. Like Crenshaw, Fletcher got some crossovers as well. It’s a big question how the Republicans will approach CD07 in the redistricting process. In years past, before the big blue shift in the western parts of Harris County, my assumption had been that the weight of CD07 would continue to move west, probably poking into Fort Bend and Waller counties. I’m less sure of that now – hell, I have no idea what they will do. I have suggested that they make CD07 more Democratic, which would enable them to shore up CD02, CD10, maybe CD22. They could try to add enough Republicans to tilt CD07 red, and at least make Fletcher work that much harder if not endanger her. Or who knows, they could throw everything out and do a radical redesign, in which case who knows what happens to CD07. Harris is going to get a certain number of full and partial Congressional districts in it no matter what, and there are Republican incumbents who will want to keep various areas for themselves, and the Voting Rights Act is still in effect, so there are some constraints. But there’s nothing to say that CD07 will exist in some form as we now know it. Expect the unexpected, is what I’m saying.

None of the other districts had as large a variance in the Trump vote. He trailed Cornyn and Wright in total votes in every district except CDs 29 and 36 (he also led Wright in 22). He trailed the Republican Congressional candidate in every district except 09, 18, and 29, the three strong D districts. Conversely, Joe Biden led every Democratic candidate in every district except for Sylvia Garcia in CD29; Garcia likely got about as many crossover votes as Lizzie Fletcher did. I’m amused to see Trump beat the designated sacrificial lamb candidate in CD18, partly because he was one of the co-plaintiffs on the state lawsuit to throw out all of the drive-through votes, and partly because I saw far more yard signs for Wendell Champion in my mostly-white heavily Democratic neighborhood (*) than I did for Trump. Maybe this is what was meant by “shy Trump voters”.

One more point about redistricting. Mike McCaul won the Harris County portion of CD10 by 43K votes; he won it by 46K in 2012 and 47K in 2016. He won overall by 30K, after squeaking through in 2018 by 13K votes. He had won in 2012 by 64K votes, and in 2016 by 59K votes. Now, a big driver of that is the ginormous growth in the Travis County Dem vote – he went from a 14K deficit in Travis in 2012 to a 57K deficit in 2020. The point I’m making is that there’s not a well of spare Republican votes in CD10 that could be used to redden CD07, not without putting CD10 at risk. Again, the Republicans could throw the current map out and start over from scratch – there will be new districts to include, so to some extent that will happen anyway – it’s just that Harris County is going to be of limited, and decreasing, use to them. They have to work around Harris, not with it. It’s going to make for some interesting decisions on their part.

I’ll have a look at the State Rep districts next. Let me know what you think.

(*) The two main precincts for my neighborhood went for Biden over Trump by a combined 68-28.

2020 precinct analysis: Introduction and overview

So I finally got a full canvass of the 2020 election in a nice and convenient spreadsheet form. I spent a fair amount of the Thanksgiving week doing what I usually do with it, to generate totals for all of the political districts. I also managed to find the spreadsheets I had done in 2012 and 2016, and generated some year-over-year comparisons. I also used the city proposition data from 2012 to separate out city of Houston returns from non-Houston Harris County for 2020.

There’s a lot of data here, is what I’m saying. Generating it is actually the easy part. I’ve been doing this for a long time – in this format, since at least 2008 – and it’s just a matter of lining everything up and applying the same Excel formulas as before. (I make heavy use of the “sumif” function, if you’re curious.) The challenge for me is in how to present what I generate. Well, the first challenge is in trying to figure out what it means, what is interesting or notable, what will make for a readable blog post, and then I have to figure out how to present it.

Again, the challenge here is not technical – I’ve done this before, many times – but philosophical. What pieces belong together? What comparisons do I want to make? What’s worth my time and effort, and yours?

You can judge for yourself how well I answer those questions. Here’s a list of the topics I intend to cover, in something approximating the order in which I’ll present them:

– Results by Congressional district, for President, Senate, and Railroad Commissioner. I’m using those three races in part because they’re the top of the ticket, in part because they’re the races most affected by the presence of third-party candidates, and in part because they offer some interesting points of comparison with 2012 and 2016. I will do separate posts on the judicial races, separating out the statewide, appellate, and district/county court races. I’ve often used the averages of local judicial races to measure partisan levels in various districts, but I want to see what differences exist when we look at the other types of judicial races.

I’ve always done Congressional district results in the past, but they were more ornamentation than substance. In part that’s because there wasn’t much to say about the Congressional districts before 2016, as none of them were drawn to be competitive, and in part because only some of them are fully within Harris County. With CDs 02 and 07 becoming multi-million dollar battlegrounds (also true for CDs 10 and 22, though as noted we only have partial data for these), and with redistricting on the horizon, I wanted to take a closer look at these districts.

– Results by State Rep districts, by Commissioners Court precincts, and by JP/Constable precincts. Same as above in terms of format and intent. The State Rep districts are my main currency in these analyses, because they are entirely contained within Harris County (something I hope will still be true post-redistricting) and because there have been some massive changes in them over time. I already know I’ll have a lot to say here.

– Judicial races as noted above, by type (state, appellate, local), and for all district types. While I use the local judicial averages as my overall expression for a given district’s partisan numbers, there’s some real variance in these races, and I want to examine that in some detail.

– Comparisons with 2012 and 2016. I’ve talked about this some before, but if the only point of comparison we emphasize this year is with 2018, we’re missing a lot of the forest for the trees. I can’t stress enough how much things have changed since 2012, but I’m going to try to show you. I will focus most of this on the State Rep districts, but will include some Congressional comparisons to highlight where the redistricting challenges will be.

– Whatever else comes up along the way. I’ve got city/county numbers, which will get its own post. I’ve looked at undervoting and third-party voting in the past, and may do something on that. I always find things I didn’t notice at first when I really dig into the data. If there’s something you’d like me to try to analyze, please let me know.

That’s what I’ve got so far. This will be several weeks’ worth of posts, so sit back and relax, it’s going to take some time. Let me know what you think.

Counties of interest, part seven: West Texas

Part 1 – Counties around Harris
Part 2 – Counties around Dallas/Tarrant
Part 3 – Counties around Travis
Part 4 – Counties around Bexar
Part 5 – East Texas
Part 6 – Central Texas

Last entry in this series, and like the East Texas entry, there’s a whole lot of negative numbers to look at.


County       Romney    Obama    Trump  Clinton    Trump    Biden    Shift
=========================================================================
Ector        24,010    8,118   25,020   10,249   32,586   11,310   -5,384
Jones         4,262    1,226    4,819      936    5,621      989   -1,596
Kerr         17,274    4,338   17,727    4,681   20,858    6,510   -1,412
Lubbock      63,469   26,271   65,651   28,023   78,560   39,757   -1,605
Midland      35,689    8,286   36,973   10,025   45,463   12,258   -5,802
Potter       18,918    7,126   19,630    7,657   22,732    9,867   -1,073
Randall      41,447    7,574   43,462    7,657   50,597   12,750   -3,974
Taylor       32,904    9,750   33,250   10,085   39,439   14,489   -1,796
Tom Green    26,878    9,294   27,494    9,173   32,129   12,106   -2,439
Wichita      29,812   10,525   27,631    8,770   31,930   13,024      381

Just as a reminder, Ector County is Odessa, Jones and Taylor are Abilene, Potter and Randall are Amarillo, Tom Green is San Angelo, Kerr is Kerrville, and Wichita is Wichita Falls. Lubbock and Midland, I think you can figure out.

It’s important to keep in mind that these are some decent-sized metropolitan areas, with some fairly populous cities. Lubbock has over 250K people, Amarillo has 200K, Abilene 170K, and all of the others except Kerrville have over 100K. I obsess over this fact because I believe that we can make progress in this part of the state by working on these mid-sized urban areas. I tend to focus more on Lubbock because it’s the biggest city, with a big public university in it, and there’s already the beginning of a Democratic-friendly State Rep district in it, but I don’t believe it ends there.

Of course, the numbers themselves put a damper on my enthusiasm. Midland and Ector had big increases for Trump after moving closer to Dems in 2016. Maybe that was an oilpatch thing, it’s as good an explanation as any. Most other counties had decent increases for Biden over Clinton, they just had larger increases for Trump the second time around. It’s a start, and I’ll take it where I can find it. If you had forced me to pick one, I would not have guessed that Wichita would be the one county to move in a Democratic direction 2012, however modestly.

I don’t have any bright ideas to add to what I’ve been saying over the course of this series. Each part of the state is different, and they all have their challenges and opportunities. This part has reasonably populous metro areas, and I have to believe that if we can eventually flip Tarrant County, we can begin to make progress in at least some of these counties. That’s going to take resources, it’s going to take investment in local races (which the TDP has begun doing in recent years), and it’s going to take messaging and strategy. I’m just trying to get the conversation started. As I’ve said many times, either we figure out a way to bend the curve outside of the big metro areas, or we make the task in those big metro areas that much harder. The rest is up to us. I hope this series has been useful. As always, let me know what you think.

Counties of interest, part six: Central Texas

Part 1 – Counties around Harris
Part 2 – Counties around Dallas/Tarrant
Part 3 – Counties around Travis
Part 4 – Counties around Bexar
Part 5 – East Texas

We move on now to counties in Central Texas, which for these purposes will include a number of places along I-35, but also a couple of places that aren’t East Texas or West Texas. Try not to take these designations too seriously and just go with it.


County       Romney    Obama    Trump  Clinton    Trump    Biden    Shift
=========================================================================
Bell         49,574   35,512   51,998   37,801   67,113   56,032    2,981
Brazos       37,209   17,477   38,738   23,121   47,436   35,242    7,538
Coryell      11,220    5,158   12,225    5,064   15,397    7,542   -1,793
Grayson      30,936   10,670   35,325   10,301   43,776   14,223   -9,287
Hood         18,409    3,843   21,382    4,008   26,243    5,605   -6,072
McLennan     47,903   25,694   48,260   27,063   59,432   36,550     -673
Nueces       48,966   45,772   50,766   49,198   64,467   60,749     -524
Victoria     19,692    8,802   21,275    8,866   23,244   10,271   -2,083

There’s some clear good news here. Bell County, home of Killeen, Temple, and Belton, is part of that I-35 Corridor success story. Brazos County isn’t on I-35, but it’s an even bigger mover. Bell is 21.5% Black and has been the center of a deep-cut Dem opportunity district for some time – there were a couple of maps drawn in 2011 that would have created a Democratic State Rep district, and the current HD54 has been a potential target for a couple of cycles. Brazos, home of Bryan and College Station, was more of a surprise to me and has gone from being a fairly deep red county to a moderately purple one. I’m guessing the presence of Texas A&M is the driver of that, but I’m guessing.

McLennan County is Waco, and while it looks to have more or less held steady since 2012, it had improved in 2016 and then fell back in 2020, which is not a good sign. You know how I feel about building up Dem infrastructure in cities, including and especially the medium and smaller cities that have not yet been a key component of the resurgence. Coryell is next door and moving a little farther in the wrong direction.

The tough nuts to crack here are Grayson (home of Sherman) and Hood (home of Granbury). Both are on the outskirts of the Metroplex, with Grayson north of Collin and Denton, and Hood south and west of Parker and Johnson. They’re not close enough to the blue parts of the Metroplex to benefit from spillover. I don’t have an answer here, just noting the problem.

Nueces County is of course Corpus Christi, and it’s been more or less what it is for some time. Like McLennan, it moved towards blue in 2016, then slid back in 2020. As with McLennan, we need to figure that out and get it back on track. I included Victoria County in this collection mostly because it’s a population center and it’s a geographic fit, but it’s kind of an island, its own MSA on the way from Houston to Corpus.

Counties of interest, part five: East Texas

Part 1 – Counties around Harris
Part 2 – Counties around Dallas/Tarrant
Part 3 – Counties around Travis
Part 4 – Counties around Bexar

The next three entries in this series will look at regions, and counties of interest within them. For the sake of simplicity, I’ve labeled these regions East Texas, Central Texas, and West Texas, though in a strict sense some of the counties I’m including in them would be called something else – Jefferson County, for example, is usually considered Southeast Texas. Try not to take that too seriously, and just assume I’ve split the state into three vertical sections.

Within those sections I’ve identified counties that have enough voters in them to be worthwhile. Again, this is all arbitrary, but I’ve generally aimed for places with cities or other features of interest. We begin with East Texas:


County       Romney    Obama    Trump  Clinton    Trump    Biden    Shift
=========================================================================
Angelina     20,303    7,834   21,668    7,538   25,070    9,136   -3,465
Bowie        24,869   10,196   24,924    8,838   27,053   10,692   -1,688
Gregg        28,742   12,398   28,764   11,677   32,352   14,657   -1,351
Hardin       17,746    3,359   19,606    2,780   23,806    3,449   -5,970
Harrison     17,512    8,456   18,749    7,151   21,318    7,812   -4,450
Henderson    21,231    6,106   23,650    5,669   28,816    7,048   -6,643
Hunt         21,011    6,671   23,910    6,396   29,135    8,879   -5,916
Jefferson    43,242   44,668   42,862   42,443   47,535   46,022   -2,959
Nacogdoches  13,925    6,465   14,771    6,846   17,359    8,989     -910
Orange       23,366    6,800   25,513    5,735   29,170    6,354   -6,250
Smith        57,331   21,456   58,930   22,300   68,546   29,343   -3,328
Van Zandt    15,794    3,084   18,473    2,799   22,126    3,419   -5,997
Walker       12,140    6,252   12,884    6,091   15,368    7,875   -1,605

As you might imagine this is not friendly territory for Democrats, and it’s getting less so as we go along. These counties are pretty small for the most part, but they contribute a lot of votes to the Republicans’ bottom line. Just since 2012, that gap has grown by more than 50K in the GOP direction. This is the point I’ve been trying to make lately, because while it may seem easy to write off this part of the state, these counties collectively pack a real punch. Look again at that Michael Li chart I embedded in this post about where the vote comes from in Texas. We can either do something to reduce the growing gap we face in the smaller counties, or we can accept the fact that the hill we’re pushing this boulder up gets steeper every cycle.

Let me remind you, there are cities and metro areas in these counties. You know that Jefferson County is home to Beaumont, and Smith County is Tyler. Other cities include:

Angelina County – Lufkin
Bowie County – Texarkana
Gregg County – Longview
Harrison County – Marshall
Nacogdoches County – Nacogdoches, home of Stephen F. Austin State University
Walker County – Huntsville, home of Sam Houston State University

I see three avenues to improve performance in this part of the state. One is as I’ve noted several times an effort to organize and build infrastructure in the smaller cities in Texas. We know what we can do in the big urban areas, and the formerly-small towns that are now part of big urban areas – think of places like Katy and Sugar Land – are increasingly strong for Dems. I believe the potential exists in the smaller cities that are not proximate to the big urban areas, and that more effort needs to be made, and more resources provided, to help them reach that potential. It has to be organic to these cities – surely, a helicopter drop of volunteers and/or paid staffers from Houston and Austin would not be received very well. I know the TDP has done some work along these lines, I’m just saying we need to continue it.

Second, there are as noted above universities in some of these towns. Anything we can do to grow the Democratic student groups and help them register and turn out voters is well worth it.

Finally, we can take a page from Stacy Abrams’ playbook and recognize that there’s a substantial Black population in some of these counties, and get to registering and organizing and empowering them in local and state politics. To wit:

Jefferson – 33.7% Black
Harrison – 24.0% Black
Walker – 23.9% Black
Bowie – 23.4% Black
Gregg – 19.9% Black
Smith – 17.9% Black
Nacogdoches – 16.7% Black
Angelina – 14.2% Black

All that is from those Wikipedia pages I linked above. I will freely admit here that I don’t know what is already in place in these counties – maybe we’re already doing all we can. I kind of doubt it, though.

Again, my bottom line is that we make an effort to narrow the gap in these places, or at least keep that gap from growing ever wider, or we make the task we’re already working on in the big counties that much harder. I’m not saying any of this will be easy, but I am saying we can’t shrug it off because it might be hard. This is the choice we face.

Counties of interest, part four: Around Bexar

Part 1 – Counties around Harris
Part 2 – Counties around Dallas/Tarrant
Part 3 – Counties around Travis

Pop quiz, hotshot: Close your eyes, or cover the table below, and name for me the seven counties that border Bexar. Go ahead, I’ll wait.


County       Romney    Obama    Trump  Clinton    Trump    Biden    Shift
=========================================================================
Atascosa      7,461    5,133    8,618    4,651   12,020    5,865   -3,827
Bandera       7,426    1,864    8,163    1,726   10,050    2,503   -1,985
Comal        39,318   11,450   45,136   14,238   62,260   24,369  -10,023
Guadalupe    33,117   15,744   36,632   18,391   47,423   28,706   -1,344
Kendall      14,508    3,043   15,700    3,643   20,064    6,008   -2,591
Medina       11,079    4,784   12,085    4,634   15,599    6,731   -2,573
Wilson       12,218    4,821   13,998    4,790   18,457    6,350   -4,710

Unless you’re a true geography nerd, or just a very aware (or well-traveled) resident of the area, I’m guessing you didn’t get all seven. Comal, which you pass through on your way to Austin, and Guadalupe, to the east as you travel I-10 to or from Houston, are the gimmes. They’re also the two largest, with Comal and more recently Guadalupe blending into Bexar from a development perspective. I’ve talked a lot about Comal County, which has tripled in population since 1990 and which puts up big numbers for the Republican Party; I call it Montgomery County’s little brother, but it’s doing its best to try to catch up. I think it feels a little to me like Montgomery because it’s also this booming suburb a few miles away from the big city, with enough distance to be its own separate entity but with any remaining vacant space between them rapidly vanishing.

Guadalupe, on the other hand, feels more remote to me because for most of my time in Texas, there was very little between Seguin and Loop 1604, and even then there wasn’t much between 1604 and Loop 410. That change is more recent, and to my eyes more dramatic since I don’t travel that way all that often and had just been very used to the former emptiness. It’s really interesting to me that while Comal is still getting redder, Guadalupe is more or less holding in place, with Republican growth only slightly outpacing Democratic growth as its population has blossomed. Guadalupe feels more rural to me while Comal feels more suburban, but maybe that’s because I’ve spent much more time in New Braunfels (I have family there) than in Seguin. I’d love to hear more about this from anyone in this part of the state.

I just don’t know much about the other counties, from the north through the west and around to the south and southeast of Bexar. I’ve been to Kendall (in particular, the town of Boerne) and Bandera, but not since the 80s. Kendall and Medina seem like long-term candidates for suburban sprawl, as both have a piece of I-10 and Medina has I-35 running through it. I know nothing at all about Wilson and Atascosa. I’m going to stop here because I don’t want to babble, but again if someone reading this can tell us more about the future prospects in these counties, please do so.

Counties of interest, part three: Around Travis

Part 1 – Counties around Harris
Part 2 – Counties around Dallas/Tarrant

Travis County has been at the forefront of the Democratic renaissance in Texas, punching well above its weight with both performance and turnout. Its blue essence has been spilling over its borders into its neighbor counties, and overall the picture here is as bright as you’ll see anywhere. Let’s have a look:


County       Romney    Obama    Trump  Clinton    Trump    Biden    Shift
=========================================================================
Bastrop      14,033    9,864   16,328   10,569   20,486   15,452     -865
Blanco        3,638    1,220    4,212    1,244    5,429    1,905   -1,106
Burnet       12,843    3,674   14,638    3,797   18,721    5,615   -3,937
Caldwell      6,021    4,791    6,691    4,795    7,975    6,536     -209
Hays         31,661   25,537   33,826   33,224   47,427   59,213   17,910
Williamson   97,006   61,875  104,175   84,468  138,649  142,457   38,939

Williamson and Hays get all the ink, and they certainly present opportunities for further growth. I believe the same dynamic is here as it is in Dallas and Collin/Denton, which is that Travis County and all of its characteristics have simply expanded into the adjacent counties, making the distinction between the two, at least in the areas near the border, basically meaningless. I’ve long felt this about the southwest part of Harris County and Fort Bend. The numbers certainly bear it out.

Of great interest to me is that Bastrop and Caldwell counties took a step in the right direction in 2020, after going the wrong way in 2016. I was especially worried about Bastrop, home of Jade Helm hysteria, starting to slip away, but perhaps they too will begin to go the way of Hays as development from Travis creeps farther out along State Highway 71. Caldwell County was a pleasant surprise, as it is more of a rural county, and one I honestly hadn’t realized bordered Travis – you pass through Caldwell on I-10 between Houston and San Antonio – until I was reviewing the map I consulted for this post. Whatever happened in Caldwell in 2020 to get it moving in this direction, I approve.

That leaves Burnet and Blanco, both to the west and northwest of Travis. I haven’t been to Burnet since the 90s and may well be talking out of my ass here, but just looking at the geography, I could imagine some of the Travis overflow that had been going into Williamson going a little farther west into Burnet, and maybe that will blue it up a little. Just a guess, and even if there’s merit to it that’s likely not a short-term prospect. Until then, if Dem activist folks in Travis are looking for new worlds to conquer, I humbly suggest Burnet – and Bastrop, and Caldwell – as opportunities to consider.

Counties of interest, part two: Around the Metroplex

Part 1 – Counties around Harris

Dallas and Tarrant Counties are two big squares right next to each other, so I’m combining them into one post.


County       Romney    Obama    Trump  Clinton    Trump    Biden    Shift
=========================================================================
Collin      196,888  101,415  201,014  140,624  250,194  227,868   73,147
Denton      157,579   80,978  170,603  110,890  221,829  188,023   42,795
Ellis        39,574   13,881   44,941   16,253   56,651   27,513   -3,445
Johnson      37,661   10,496   44,382   10,988   54,523   16,418  -10,940
Kaufman      24,846    9,472   29,587   10,278   37,474   18,290   -3,810
Parker       39,243    7,853   46,473    8,344   61,584   12,789  -17,405
Rockwall     27,113    8,120   28,451    9,655   38,842   18,149   -1,700
Wise         17,207    3,221   20,670    3,412   26,986    4,953   -8,047

Most of the attention goes to Collin and Denton counties, for good reason. Even as they stayed red this year, they have shifted tremendously in a blue direction. Basically, a whole lot of Dallas has spilled over the county lines, and the result is what you’d expect. There’s not a whole lot to say here – demography, time, and continued organizing should do the trick.

But once you get past those two counties, it’s a whole lot of red. The Republicans have netted more total votes since 2012 from the other six counties than the Dems have from Denton. Parker County, west of Tarrant, home of Weatherford, ninety percent white and over eighty percent Republican, more than twice as populous now as it was in 1990, is A Problem. Johnson County, south of Tarrant and with nearly identical demographics as Parker while also growing rapidly, is right behind it.

I don’t know that there’s much to be done about those two. There does appear to be more promise in Ellis (south of Dallas, home of Waxahachie), Kaufman (southeast of Dallas), and Rockwall counties. The first two are slightly less white than Parker and Johnson, and all three saw enough growth in Democratic voters in 2020 (at least at the Presidential level; we’ll need to check back on other races) to mostly offset the growth in Republican voting. It’s almost certainly the case that proximity to Dallas County is better for Democratic prospects than proximity to Tarrant. Again, that doesn’t address a big part of the problem, but it at least provides a place to start.

I don’t have a whole lot more to offer, so I’m interested in hearing what my readers from this part of the state have to say. I’ll be honest, I had not given any thought to the geography of this before I started writing these posts. Hell, in most cases I had to do some research to know which counties to look up. I hope that by doing so I’ve helped you think about this.

Counties of interest, part one: Around Harris

There’s been so much focus in the past couple of years about the suburbs and how their traditional voting patterns have changed. I wanted to use the election results we have to take a closer look at what that means. My approach is to look at the results in the counties that surround the large urban counties in Texas, and see what we can infer from the Presidential election data since 2012. A few things to note before we get started.

– I will be looking at the counties that border Harris, Dallas/Tarrant, Travis, and Bexar. I’m skipping El Paso because there’s only one county in the state that is adjacent to it.

– I’m using Presidential results from 2012, 2016, and 2020. As we have discussed, this is only one dimension to the data, but I want to keep this fairly simple. We can discern direction from these numbers, and that’s good enough for these purposes.

– I’m going back to 2012 to provide some extra context. I could have gone back further, and maybe I will take a look at trends since 2004 in some counties at a later date, but I think keeping this study to after the 2010 election, when rural areas gave up the pretense of supporting Democrats at any level, makes more sense.

– In the chart below and in subsequent posts, “Shift” is the change in net votes from a Democratic perspective, from 2012 to 2020. A positive number means Democrats did better in 2020 than in 2012, and a negative number means Republicans did better. So for example, Obama trailed in Brazoria County by 36,431 votes, but Biden trailed by 28,159 votes, so a shift in the Democrat direction by 8,282 votes. Obama lost Chambers County by 8,997 votes, Biden lost it by 13,346 votes, so a shift of 4,329 away from Dems. Make sense?

All right. Let’s start with the seven counties that border Harris County.


County       Romney    Obama    Trump  Clinton    Trump    Biden    Shift
=========================================================================
Brazoria     70,862   34,421   72,791   43,200   89,939   61,780    8,282
Chambers     11,787    2,790   13,339    2,948   17,343    3,997   -4,349
Fort Bend   116,126  101,144  117,291  134,686  157,595  195,191   52,578
Galveston    69,059   39,511   73,757   43,658   93,306   58,247   -5,511
Liberty      17,323    5,202   18,892    4,862   23,288    5,779   -5,388
Montgomery  137,969   32,920  150,314   45,835  193,224   74,255  -13,920
Waller        9,244    6,514   10,531    5,748   14,206    8,130   -3,346

The first thing that should be clear is that just because a county borders a big urban county, that doesn’t mean it’s suburban. For sure Montgomery and Fort Bend and Brazoria and Galveston meet that definition, though all four of those counties also have some very rural areas, but I daresay no one thinks of Chambers or Liberty or Waller that way. Yet while the first four are seen as places of booming population growth, the other three are doing their share of growing, too. Chambers County has doubled in population since 1990. Waller County has more than doubled in that timespan. Liberty County is up by almost 75%.

But they’re still small. None has a city with more than ten thousand people in it, so they don’t have much in common with the other counties. Maybe it’s different for you, but while I personally know plenty of people in Brazoria, Fort Bend, Galveston, and Montgomery Counties, I know all of one in the other three. I drive through Waller now and then on my way to Austin or to Camp Allen when my daughters were going there, but I couldn’t tell you the last time I was in Chambers or Liberty.

I say all this to note that while Montgomery is the driving force behind the Republican strength in this area, with Galveston right behind it thanks to places like Friendswood and League City, the other three counties have increased the Republican bottom line over the past few elections by a significant amount as well, with far fewer people in them. Jane Robinson would be the incoming Chief Justice of the 14th Court of Appeals if Chambers County had had the same numbers in 2020 as they had in 2016. It makes a difference.

Part of the reason I’m doing this is just to highlight the places where we’re losing ground, if only so we can be aware of it. We’ve got our arms around Fort Bend County, and Brazoria is starting to head in the right direction. Montgomery and Galveston are problems, but we have infrastructure in those places, and just by virtue of being suburban I have some reason to think we’ll get to a turning point. I have no idea what exists in the other three counties to promote Democratic policies or candidates. We need a strategy for these places, and the resources to carry it out. We don’t need to win them – we’re no more likely to win Chambers than we are to win Montgomery any time soon – but we at least need to keep up with Republican voter growth.

That’s a theme I’m going to return to more than once a I proceed through these. I don’t pretend to know what the right answers are, I’m just trying to make sure we know there are problems that need to be addressed. I hope you find this helpful.

So how did my simple projection work out?

Remember this? I divided the counties up by how much their voter rolls had grown or shrunk since 2012, then used the 2016 turnout levels and 2018 results to project final numbers for the Presidential election in 2020. Now that we have those numbers, how did my little toy do? Let’s take a look.

A couple of things to acknowledge first. The most up to date voter registration numbers show that the group of counties that looked to have lost voters since 2012 have actually gained them, at least in the aggregate. Second, the actual turnout we got so far exceeded past numbers that we literally couldn’t have nailed this, at least not at a quantitative level. So with that in mind, let’s move forward.

We start with the counties that had seen growth of at least 10K voters on their rolls since 2012. There were 33 of these. Here are the numbers I had in my initial review, updated to include what happened this year.


Romney  3,270,387   Obama    2,792,800
Romney      53.9%   Obama        46.1%
Romney +  477,587

Trump   3,288,107   Clinton  3,394,436
Trump       49.2%   Clinton      50.8%
Trump  -  106,329

Cruz    3,022,932   Beto     3,585,385
Cruz        45.7%   Beto         54.3%
Cruz   -  562,453

Trump   4,119,402   Biden    4,579,144
Trump       47.4%   Biden        52.6%
Trump  -  459,742

Year  Total voters   Total votes   Turnout
==========================================
2012    10,442,191     6,157,687     59.0%
2016    11,760,590     7,029,306     59.8%
2018    12,403,704     6,662,143     53.7%
2020    13,296,048     8,765,774     65.9%

When I did the original post, there were 12,930,451 registered voters in these 33 counties. As you can see, and will see for the other groups, that increased between August and November, by quite a bit. As you can see, Trump did considerably worse than he had in 2016 with these counties, but better than Ted Cruz did in 2018. That says it all about why this race wasn’t as close as the Beto-Cruz race in 2018. My projection had assumed 2016-level turnout, but we obviously got more than that. Here’s what I had projected originally, and what we would have gotten if the 2020 results had been like the 2018 results from a partisan perspective:


Trump   3,533,711   Biden    4,198,699
Trump  -  664,988

Trump   3,975,236   Biden    4,723,310
Trump  -  748,074

Fair to say we missed the mark. We’ll see how much of a difference that would have made later. Now let’s look at the biggest group of counties, the 148 counties that gained some number of voters, from one to 9,999. Again, here are my projections, with the updated voter registration number:


Romney  1,117,383   Obama      415,647
Romney      72.9%   Obama        27.1%
Romney +  701,736

Trump   1,209,121   Clinton    393,004
Trump       75.5%   Clinton      24.5%
Trump  +  816,117

Cruz    1,075,232   Beto       381,010
Cruz        73.8%                26.2%
Cruz   +  694,222

Trump   1,496,148   Biden      501,234
Trump       74.0%   Biden        26.0%
Trump  +  994,914

Year  Total voters   Total votes   Turnout
==========================================
2012     2,686,872     1,551,613     57.7%
2016     2,829,110     1,653,858     58.5%
2018     2,884,466     1,466,446     50.8%
2020     3,112,474     2,022,490     65.0%

As discussed, there’s a whole lot of strong red counties in here – of the 148 counties in this group, Beto carried ten of them. They had 2,929,965 voters as of August. What had been my projection, and how’d it go here?


Trump   1,264,954   Biden      449,076
Trump  +  815,878

Trump   1,496,148   Biden      501,234
Trump  +  994,914

The margin is wider due to the higher turnout, but Biden actually did a little better by percentage than Clinton did, and was right in line with Beto. This is obviously an area of great need for improvement going forward, but the projection was more or less right on target, at least from a partisan performance perspective. But as you can see, even with the more optimistic projection for Biden, he’s already in the hole. Like I said, this is an area of urgent need for improvement going forward.

Now on to the last group, the 73 counties that had lost voters from 2012, at least going by the August numbers. As you can see, that turned out not to be fully true:


Romney     182,073   Obama      99,677
Romney       64.6%   Obama       35.4%
Romney +    82,396

Trump      187,819   Clinton    90,428
Trump        67.5%   Clinton     32.5%
Trump  +    97,391

Cruz       162,389   Beto       79,237
Cruz         67.2%   Beto        32.8%
Cruz   +    83,152

Trump      226,104   Biden     105,490
Trump        68.2%   Biden       31.8%
Trump  +   120,514

Year  Total voters   Total votes   Turnout
==========================================
2012       517,163       284,551     55.0%
2016       511,387       286,062     55.9%
2018       505,087       243,066     48.1%
2020       546,997       335,110     61.2%

As you can see, that decline in registrations has reversed, quite dramatically. I didn’t check each individual county – it seems likely that some of them are still at a net negative – but overall they are no longer in decline. Good for them. As you can also see, Biden performed a little worse than Clinton and Beto, but close enough for these purposes. Let’s compare the projection to the reality:


Trump      187,587   Biden      91,561
Trump +     96,026

Trump      226,104   Biden     105,490
Trump  +   120,514

Put the best-case scenario from the first group with what we got in the last two, and we could have had this:


Trump    5,697,488   Biden   5,330,034
Trump       51.67%   Biden      48.33%

Which is pretty close to what I had projected originally, just with a lot more voters now. The actual final result is 52.18% to 46.39%, so I’d say my method came closer to the real result than most of the polls did. Clearly, I missed my calling.

All this was done as an exercise in frivolity – as I said at the time, I made all kinds of assumptions in making this projection, and the main one about turnout level was way wrong. The point of this, I think, is to show that while Dems have indeed improved greatly in performance in the biggest counties, they haven’t done as well everywhere else, and while the marginal difference from Obama 2012 to Clinton 2016 and Biden 2020 isn’t much, the overall direction is wrong (even as Biden improved somewhat on the middle group over Clinton), and we’re going to have a real problem making further progress if we can’t figure out a way to improve our performance in these smaller counties. There is room to grow in the big and growing counties – these include some fast-growing and very red places like Montgomery and Comal, for instance – but we’re going to reach diminishing marginal growth soon, if we’re not already there. We need to step it up everywhere else. I’ll be returning to this theme as we go forward. Let me know what you think.

More early data from State Rep districts

From Derek Ryan on Twitter:

Couple of things, as we wait for the rest of the data – I hope to get at least a draft canvass from Harris County soon, and may look at some other counties’ data as I can; the full state data will likely be published in March or so.

– I have covered some of this, all from Harris County. Ryan’s data is around the state.

– I previously noted that HDs 31 and 74 were pretty purple already; I would expect HD34 to join them when that data is available. I should note that despite those Trump numbers, the Dems in those districts did just fine – Rep. Ryan Guillen won HD31 with 58%, Rep. Abel Herrero took HD34 with over 59%, and Eddie Morales won the open HD74 with 54%. If we’re going to argue that Democrats were too ambitious in 2020 – I would not make that argument, but I have seen others at least suggest it – then one might also argue that Republicans were not ambitious enough.

– I have no doubt that Republicans will take these numbers under serious consideration, and I won’t be surprised if they try to draw another Republican-friendly district in the Valley, to accompany HD43.

– Which doesn’t mean they’ll succeed. Someone reminded me on Twitter that Cameron County voted for George W. Bush in 2004. He also got almost 45% in Hidalgo County, and almost 57% in Nueces County. Republican Presidents running for re-election have done well in South Texas before. Perhaps we all forgot about that. We know now that was not the start of a trend.

– That said, I believe that a county or district electing a candidate from one party while supporting Presidents and Senators from another party is an unstable situation, one that sooner or later topples over. See: all of the rural districts that used to elect Democrats to Congress and the Lege while voting 60-70% Republican otherwise, and Sarah Davis in HD134. If statewide Dems do well in these districts again in 2022 and 2024, we can go back to thinking of them as blue. If not, then we do indeed have a whole new ballgame.

(The same is true, of course, for the urban/suburban districts that Republicans won but Biden carried. In those at least we have more than one election’s worth of data to contemplate.)

– And again, we should remember that the Biden/Trump numbers are just one data point. As noted, it’s entirely possible in some of these districts that Trump’s numbers will be well above, or well below, the norm. We’ll need to consider the entire range.

– The implied question in all of this is, what does this data mean for 2022? The answer is, we just don’t know. We haven’t had two elections in a row that looked the same in this state. There are plausible scenarios that make 2022 potentially good for Dems, and that make 2022 bad for Dems. Hard to believe, I know, but we have to let the things happen that will affect those possible outcomes.

More to come as we get more data. Reform Austin is also on this.

It’s a range, not a number

I don’t have full canvass data yet, but as I have said, I have Presidential data courtesy of Greg Wythe. Here are a couple of districts of interest:


Dist     Biden    Trump   Biden%   Trump%
=========================================
CD02   170,707  178,840    48.1%    50.0%
CD07   168,108  141,749    53.5%    45.1%

SBOE6  387,589  367,658    50.6%    48.0%

HD126   35,693   38,313    47.6%    51.1%
HD132   51,384   49,821    50.0%    48.5%
HD133   42,556   46,453    47.2%    51.5%
HD134   66,889   42,027    60.5%    38.0%
HD135   39,345   35,846    51.6%    47.0%
HD138   33,739   30,928    51.4%    47.2%

CC2    153,300  153,394    49.3%    49.3%
CC3    231,808  218,167    50.8%    47.8%
CC4    233,594  238,468    48.8%    49.8%

JP2     51,115   35,546    58.3%    40.5%
JP3     70,725   53,284    56.4%    42.5%
JP4    200,061  235,435    45.3%    53.3%
JP5    233,798  197,420    53.5%    45.2%

So Biden carried four districts in which the Democratic candidate lost – SBOE6, HD132, HD138, and County Commissioners Court Precinct 3. He also carried Constable/JP precinct 5, where the Democrats won the JP race but lost for Constable. He came close in CD02 and HD133, as well as some other places. Biden also lost Commissioners Court Precinct 2 by a whisker. What do we take away from this?

First, a reminder that none of these districts will be the same in 2022. All of them, including CC2, will be redrawn. CC2 was more Democratic in 2016 and 2018, but still pretty close in 2016. I’m pretty sure the Commissioners will have a long look at these numbers before they begin their decennial task.

I don’t want to go too deep into these numbers, partly because none of these districts (except the JP/Constable districts, most likely) will exist as is in 2022, but also because as the title implies, they’re only part of the story.

It is pretty much always the case that there’s a range of outcomes in each district. We saw Hillary Clinton greatly outperform Donald Trump in 2016 in basically every district. It was so pervasive, and in some cases so large, that I had a hard time taking it seriously. As you may recall, I was initially skeptical of CDs 07 and 32 as potential pickups because in other races, the Republicans carried those districts with some ease. Harris County Republican judicial candidates generally won CD07 by ten to twelve points in 2016, which meant that a lot of people who voted for Hillary Clinton also voted mostly if not entirely Republican down the ballot. Which number was real?

We know how that turned out. And in 2018, we saw a similar phenomenon with Beto O’Rourke, who quite famously carried 76 State Rep districts. He also outperformed every other Democrat on the ticket, some (like Justin Nelson and Mike Collier and Kim Olson) by a little, others (like Lupe Valdez) by a lot. Once again, what was reality? Here, I confess, I wasn’t nearly as skeptical as I was with the Clinton numbers. I – and I daresay, the entire Democratic establishment and more – saw the potential in those numbers, but we were only looking at the top end.

That doesn’t mean that number wasn’t real, any more than the Hillary Clinton number wasn’t real. But it did mean that it wasn’t the only indicator we had. I don’t have the full range of numbers yet from this election, but I think we can safely say that the Biden figures will exist in a range, the same way that the Clinton and Beto numbers did. If I had to guess, I’d say that Biden will be at the top of more Republican districts, like HD133, but maybe below the average in the Latino districts. I will of course report on that when I have that data.

So when we have new districts and we know what the partisan numbers are in them, how should we judge them? By the range, which may span a big enough distance to make each end look like something completely different. We don’t know what we’re going to get, and won’t know until we’re well into the thick of the election. It’s fine to believe in the top end, as long as we remember that’s not the only possible outcome. For more on the Harris County results, see Jasper Scherer on Twitter here and here.

A first response to the Latino voting (and polling) question

For your consideration:

It’s very much not my intent to pin blame on anyone. As I noted in my post about how voting went in these Latino counties, which includes a lot of RGV counties as well as Bexar and El Paso, I’m just showing what happened. I think Jolt has done a lot of good work, a lot of hard and necessary work, and I salute them for it.

I can’t address the specifics of the numbers cited in those tweets – I don’t have his data, and the public data is quite limited right now. I do have some limited Harris County canvass data, courtesy of Greg Wythe, so I thought I’d bring that in here to continue the discussion. Here’s what I can say about how voting went in the five predominantly Latino State Rep districts in Harris County:


Dist   Trump  Clinton  Trump%  Clinton%  Margin
===============================================
140    6,119   21,009   21.8%     75.0%  14,890
143    8,746   23,873   26.0%     70.9%  15,127
144   10,555   15,885   38.3%     57.6%   5,330
145   10,102   23,534   28.7%     66.8%  13,432
148   14,815   31,004   30.3%     63.4%  16,279

      50,337  115,305   30.4%     69.6%  64,968

Dist   Trump    Biden  Trump%    Biden%  Margin
===============================================
140   10,175   22,651   30.3%     67.4%  12,476
143   13,105   25,109   33.5%     64.1%  12,004
144   14,415   17,174   44.5%     53.0%   2,759
145   15,198   28,200   34.1%     63.4%  13,102
148   20,207   40,821   32.2%     65.0%  20,614

      73,100  133,955   35.3%     64.7%  60,855

The first table is 2016, the second is 2020. Please note that while the percentages for each candidate is their actual percentage for all voters in the district, the totals at the bottom are just the two-candidate values. I apologize for mixing apples and oranges. We should note that while these five districts are the five predominantly Latino districts in Houston, there is some variance. HDs 140 and 143 have the largest Latino population totals by percentage, while the others have a significant minority of Anglo residents. HD144 includes the Pasadena area, while HDs 145 and 148 include parts of the Heights and surrounding neighborhoods. HD148 is probably the least Latino of the five, and is currently represented by Anna Eastman, who won the special election to serve the remainder of Jessica Farrar’s term, though she was defeated in the primary by Penny Shaw.

As you can see, Trump improved on his 2016 performance in all five districts. Biden got more votes than Clinton in all five districts, but had a lower percentage in all but HD148. The reason both Trump and Biden could see an increase in percentage in HD148 is because the third-party share of the vote was so high in 2016 – it was over six percent that year, but looks to be less than three percent this year. Overall, Trump lost these five districts by about four thousand fewer votes than he did in 2016, with about 20K more votes cast.

This is not an eye-popping change like what we saw in some RGV counties was, but it’s still a decline. I don’t know how much of that is from Latinos voting for Trump, and how much is from Anglo voters in these districts turning out for Trump. Jolt’s mission is to turn out Latino voters, and in the aggregate that’s going to be good for Democrats even if there are some rough spots, and even if it’s not quite as good as we might have expected. My approach is not as granular as it could be, so we shouldn’t draw broad conclusions from it. There are plenty of Latino precincts elsewhere in Harris County – HDs 137 and 138 will have quite a few – so there’s much more to be said. This is the data I have right now. Make of it what you will.

A few observations from the final unofficial countywide data

This is still unofficial, and there will still be some overseas/military ballots to be counted as well as some provisional ballots to be cured, but the count of the votes cast by Election Day is over, and we have the current final totals, broken down by vote type for each race. So let’s have a stroll through the data and see what we come up with.

– While Republican voting strength increased on Election Day compared to mail and early in person voting, Democrats still won Election Day. As far as I can tell, every Democrat who was on the whole county’s ballot beat their Republican opponent on Election Day, except for one: Genesis Draper, the appointed and now elected Judge of County Criminal Court #12, who lost Election Day by about 6,000 votes. She still won her election by 78,000 votes, so no big deal. Te’iva Bell, now the elected Judge of the 339th Criminal District Court, won Election Day by fourteen (yes, 14) votes out of 183,492 ballots cast in that race. She won by just over 100K votes overall.

– Democrats did especially well in mail ballots – in the judicial races, the number was usually around 60% for the Democratic candidate. That staked them to an initial lead of 27-40K, with usually a bit more than 160K mail ballots being cast. It’s amazing to realize how much that has shifted from even the recent past – remember, Republicans generally won the mail ballots in 2018, though they lost them in 2016. I don’t know if they quietly walk back all the hysterical “MAIL BALLOT FRAUD” hyperbole and go back to using this tool as they had before, or if that’s it and they’re all about voting in person now.

– As far as I can tell, no one who was leading at 7 PM on November 3, when the early + mail ballot totals were posted, wound up losing when all the votes were in. No one got Ed Emmett’ed, in other words. Gina Calanni and Akilah Bacy led in mail ballots, but lost early in person votes by enough that they were trailing going into Election Day. Lizzie Fletcher, Ann Johnson, and Jon Rosenthal lost the Election Day vote, but had won both mail and early in person voting, and that lead was sufficient to see them through.

– As noted, a very small percentage of the vote was cast on Election Day – 12.28% of all ballots in Harris County were Election Day ballots. That varied by district, however:


Dist     Total   E-Day   E-Day%
===============================
CD18   251,623  33,109    13.2%
CD29   161,673  30,274    18.7%

SD04    89,122   8,385     9.4%
SD06   187,819  34,996    18.6%

HD133   91,137   8,650     9.5%
HD134  111,639   9,389     8.4%
HD137   33,344   5,035    15.1%
HD140   33,614   7,325    21.8%
HD143   39,153   6,693    17.1%
HD144   32,522   6,989    21.5%
HD145   44,514   7,774    17.5%

Definitely some later voting by Latinos. Note that Sarah Davis won Election Day with 66% of the vote. There just weren’t enough of those votes to make a difference – she netted less than 3K votes from that, not nearly enough to overcome the 10K vote lead Ann Johnson had.

– There’s a conversation to be had about turnout in base Democratic districts. Countywide, turnout was 67.84% of registered voters. Of the strong-D districts, only HD148 (68.58%) exceeded that. Every strong-R or swing district was above the countywide mark, while multiple strong-D districts – HDs 137, 140, 141, 143, 144, and 145 – were below 60%. HD140 had 51.36% turnout, with HD144 at 51.81%. Harris County is strong blue now because Democrats have done an outstanding job of expanding out into formerly deep red turf – this is how districts like HDs 132, 135, and 138 became competitive, with HD126 a bit farther behind. As we discussed in 2018, deepest red districts are noticeably less red now, and with so many votes in those locations, that has greatly shifted the partisan weight in Harris County. But it’s clear we are leaving votes on the table – this was true in 2018 as well, and it was one reason why I thought we could gain so much more ground this year, to make the state more competitive. The focus now, for 2022 and 2024 and beyond, needs to be getting more votes out of these base Democratic districts and precincts. For one thing, at the most basic level, these are our most loyal voters, and we need to pay them a lot more attention. At a practical level, we need more out of these neighborhoods and communities to really put the state in play. We’ve figured out a big part of the equation, but we’re still missing some key pieces. That needs to change.

(Yes, I know, we have just talked about how perhaps some low-propensity Latino voters are much more Republican than their higher-propensity counterparts. We do need a strategy that has some thought and nuance to it, to make sure we’re not committing a self-own. But to put this in crass marketing terms, your strongest customers are the ones who have already bought your product in the past. We need to do better with them, and we start by doing better by them.)

– I’ll have more data going forward, when I get the full canvass. But in the meantime, there was one other group of people who had a propensity for voting on Election Day – people who voted Libertarian. Get a load of this:


Race         E-Day%  Total%
===========================
President     1.89%   1.03%
Senate        3.33%   1.81%
CD02          3.18%   1.59%
CD07          3.57%   1.77%
CD09          5.82%   2.97%
CD22          8.23%   5.33%
RRC           3.62%   2.08%
SCOTX Chief   4.50%   2.35%

You can peruse the other races, but the pattern holds everywhere. Seems to be the case for Green candidates as well, there are just far fewer of them. Not sure what that means, but it’s a fun fact. By the way, the Libertarian candidate in CD22 got 3.87% overall. Not sure why he was so much more popular in Harris County.

CD31 poll: Carter 43, Imam 37

Another interesting Congressional race poll.

Donna Imam

With less than two months to go until Election Day, an increasing number of eyes are looking toward Texas, where Republicans are fighting to keep their grip on the once-reliably conservative state.

There is perhaps no better sign of Texas’ shift toward Democrats than what’s happening in the state’s 31st Congressional District. The previously deep red district north of Austin has shifted dramatically in recent years, and a new poll obtained exclusively by COURIER shows incumbent Rep. John Carter (R-Texas) is vulnerable.

The poll, conducted by Public Policy Polling (PPP), found Carter leading challenger Donna Imam by only six points, 43-37 among 831 voters in the district. Libertarian Clark Patterson and Independent Jeremy Bravo tallied 10% of the vote combined, while 11% of voters remained undecided.

Imam performs particularly well with independent voters, leading Carter 44-28. She also appears to have significant room to grow, as 53% of voters said they were unsure whether or not they had a favorable opinion about her.

The poll also surveyed voters on the presidential race and found that President Donald Trump holds a narrow one-point lead (48-47) over Democratic nominee Joe Biden, a substantial shift from 2016 when Trump won the district 54-41.

[…]

While Democrats have set their eyes on several prizes across the state, the recent blue shift in the 31st has been particularly notable. Between 2002 and 2016, Carter won each of his elections by at least 20 points. But in 2018, Carter faced the fight of his career and narrowly edged out his Democratic challenger, MJ Hegar, by only three points. Hegar is now challenging Cornyn and finds herself down only 2 points in the district (48-46), according to the PPP poll.

You can see the poll data here. It’s a solid result in a district where Beto got 48.4% of the vote. Hegar ran just a shade behind Beto – he lost to Ted Cruz 50.5 to 48.4, while Hegar lost 47.6 to 50.6 – and this district has been on the radar for the DCCC (and for the Republicans, and for the national race-raters) from the beginning of the cycle. The problem has been finding a standout candidate, as there was a rotating cast of players in the primary, with nobody raising any money or making much noise until the runoff, when Imam finally started to edge forward. She still has to establish herself as a fundraiser – the DCCC is in town, but they’ve got plenty of fish to fry. I’ll be very interested in Imam’s Q3 finance report.

This poll is reminiscent of the polling in CD21, another near-miss district from 2018 with a similar demographic profile. In 2018, Joe Kopser lost to Chip Roy 50.2 to 47.6, Beto lost the district by a tenth of a point, and in 2016 Hillary Clinton lost it to Donald Trump 52-42. These latest polls have Biden up by one in CD21 and down by one in CD31, consistent with statewide polling that has Texas as a real tossup.

They key here has been the shift in voter preferences in Williamson County, which comprises a bit more than two-thirds of the district. Here’s how the Williamson County vote has gone in recent elections:


2012       Votes    Pct
=======================
Romney    97,006  59.4%
Obama     61,875  37.9%

Cruz      92,034  57.3%
Sadler    60,279  37.5%

Carter    96,842  60.9%
Wyman     55,111  34.6%


2016       Votes    Pct
=======================
Trump    104,175  51.3%
Clinton   84,468  41.6%

Carter   112,841  56.8%
Clark     74,914  37.7%


2018       Votes    Pct
=======================
Cruz      99,857  48.0%
Beto     105,850  50.8%

Abbott   112,214  54.1%
Valdez    90,002  43.4%

Patrick  101,545  49.2%
Collier   98,375  47.6%

Paxton    98,175  47.7%
Nelson   100,345  48.7%

Carter    99,648  48.2%
Hegar    103,155  49.9%

The story of 2018 was of the huge gains Democrats made in suburban areas like Williamson, but the thing here is that Dems gained about as many votes from 2012 to 2016 as they did from 2016 to 2018, with Republicans barely growing their vote at all outside of a couple of races. It wasn’t so much a shift as an acceleration, and it took WilCo from being on the fringes of competitiveness, where you could see it off in the distance from the vantage point of 2016 but figured it was still a few cycles away, to being a true swing district just two years later. If Dems can even come close to replicating that kind of growth in 2020, then CD31 is likely being undersold as a pickup opportunity. Obviously, the pandemic and the ambient chaos and pretty much everything else is a variable we can’t easily quantify. But the numbers are right there, so if CD31 does go Dem, we can’t say we didn’t see it coming.

One more thing: That 10% total for the Libertarian and independent candidates combined is almost certainly way too high. Libertarian candidates actually do pretty well overall in this district. The Lib Congressional candidate in 2012 got 3.7%, while a couple of statewide judicial candidates in races that also had a Democrat topped five percent. In 2016, the Libertarian in CD31 got 5.2%, with Mark Miller getting 7.1% in the Railroad Commissioner’s race. They didn’t do quite as well in 2018, however, with the Congressional candidate getting 1.9%, and the high water mark of 4.1% being hit in the Land Commissioner’s race. I’d contend that’s a combination of better Democratic candidates, with more nominal Republicans moving from casting a “none of the above” protest vote to actually going Dem. My guess is 2020 will be more like 2018 than 2016 or 2012, but we’ll see. In any event, I’d put the over/under for the two “other” candidates at five, not at ten. The Texas Signal has more.

CD21 poll: Davis 48, Roy 47

Second poll in this district.

Wendy Davis

Between August 31 and September 4, Garin-Hart-Yang interviewed a representative sample of 401 likely general election voters in Texas-21st CD. The survey, which was conducted on both landlines and cell phones, was fully representative of an expected November 2020 general election by key factors such as gender, age, geography, and race. The survey’s margin of error is +5%. The following are the key findings:

1. Joe Biden slight advantage in the presidential race is basically unchanged since our mid-July poll. The Vice President leads Donald Trump by 49% to 47%, compared to the 50% to 47% margin in the last survey.

2. The mid-July survey had the congressional candidates virtually tied, with Congressman Chip Roy ahead by one point. In the latest poll we find Wendy Davis with a one-point lead. Realistically, the Davis-Roy match-up continues to be extremely competitive and likely to remain a dead-heat.

One important finding is that despite several weeks of Club for Growth negative TV ads, Wendy’s initial TV ads emphasizing her inspiring personal story and bipartisan work in the Texas Senate are resonating with voters. Since our last survey, we find an increase in voters attributing positive sentiment to Wendy, including sizable gains for her among Independent voters.

See here for some background, and here for the Patrick Svitek tweet that you knew would be the source. CD21 has been a pretty good bellwether for the state as a whole these last couple of elections:


2016      District    State
===========================
Smith        57.1%
Wakely       36.5%

Trump        51.9%    52.2%
Clinton      42.1%    43.2%

Christian    53.9%    53.1%
Yarbrough    34.6%    38.4%

Keasler      56.7%    55.0%
Burns        38.1%    40.9%


2018      District    State
===========================
Roy          50.2%
Kopser       47.6%

Cruz         49.6%    50.9%
O'Rourke     49.5%    48.3%

Abbott       55.0%    55.8%
Valdez       42.8%    42.5%

Patrick      50.6%    51.3%
Collier      46.8%    46.5%

Craddick     53.3%    53.2%
McAllen      43.4%    43.9%

Hervey       54.3%    54.2%
Franklin     45.7%    45.8%

Closer correlations in 2018 than 2016, but they’re both in the ballpark. Ted Cruz underperformed relative to his peers. Lamar Smith ran ahead of the typical Republican, both in the district and statewide, while Chip Roy ran a little behind them. Don’t know if any of this means anything for 2020, but I’ll venture that CD21 will resemble the state as a whole fairly well. I don’t think Wendy Davis needs Joe Biden to carry the state to win, but as with any of the other hot races, the better he does, the better her odds are likely to be.

Poll: Michael Moore claims large lead in Commissioners Court race

From Keir Murray:

There’s an image of the polling memo at the tweet, and you can see the whole thing here. To sum up:

– About one fifth of voters had no preference initially, not surprising since Commissioners Court is a lower-profile race. Moore led Republican Tom Ramsey 42-39 in the initial ask, likely a recapitulation of the partisan mix, with Moore having slightly higher name recognition, perhaps due to having to compete in the primary runoff.

– After a positive message about both candidates, Moore led 53-39. After a negative message about both candidates, Moore led 50-35. Joe Biden led 53-39 in the precinct.

– This is of course an internal campaign poll, and the sample appears to be likely voters, sample size 508, margin of error 4.4%.

– While the notion of “shy Trump voters” has been discredited multiple times by various investigators, I can believe that Trump might get the bulk of the non-responsive respondents here. To put it another way, I believe Moore is winning. I don’t believe he’s really winning by fourteen points. It’s not impossible by any means, but it’s very much on the high end of my expected range of outcomes.

– For comparison, Beto carried CC3 by four points in 2018. The stronger statewide Dems in 2018 carried it by a bit less, while the weaker Dems were losing it by five to seven points. Hillary Clinton lost CC3 by less than a point in 2016, but she ran well ahead of the partisan baseline, as the average Dem judicial candidate was losing it by ten points. Kim Ogg and Ed Gonzalez, the next two strongest Dems in 2016, were losing CC3 by eight or nine points. You want to talk suburban shift? This here is your suburban shift. Not too surprisingly, there’s a fair bit of CD07 overlapping CC3.

– The larger point here is that if Dems have improved on Beto’s performance in CC3, that’s another data point to suggest that Biden is doing better than Beto, and a lot better than Clinton, in 2020. You can figure out what that means at the statewide level.

Again, internal poll, insert all the caveats here. I give you data points because I care.

A very simple projection of the November vote

In my earlier post about the current state of voter registrations, I noted that you could see the county-by-county totals in the contest details for the Senate runoff. What that also means is that if you have current (till now, anyway) voter registration totals, you can do a comparison across the counties of where voter registration totals have gone up the most, and how the vote has shifted in recent elections. In doing so, you can come up with a simple way to project what the 2020 vote might look like.

So, naturally, I did that. Let me walk you through the steps.

First, I used the 2020 runoff results data to get current registration totals per county. I put that into a spreadsheet with county-by-county results from the 2012 and 2016 Presidential elections and the 2018 Senate election to calculate total voter registration changes from each year to 2020. I then sorted by net change since 2012, and grouped the 254 counties into three buckets: Counties that had a net increase of at least 10,000 voters since 2012, counties that had a net increase of less than 10,000 voters since 2012, and counties that have lost voters since 2012. From there, I looked at the top race for each year.

First, here are the 2012 big gain counties. There were 33 of these counties, with a net gain of +2,488,260 registered voters as of July 2020.


Romney  3,270,387   Obama    2,792,800
Romney      53.9%   Obama        46.1%
Romney +  477,587

Trump   3,288,107   Clinton  3,394,436
Trump       49.2%   Clinton      50.8%
Trump  -  106,329

Cruz    3,022,932   Beto     3,585,385
Cruz        45.7%   Beto         54.3%
Cruz   -  562,453

Year  Total voters   Total votes   Turnout
==========================================
2012    10,442,191     6,157,687     59.0%
2016    11,760,590     7,029,306     59.8%
2018    12,403,704     6,662,143     53.7%
2020    12,930,451     

The shift in voting behavior here is obvious. Hillary Clinton did much better in the larger, growing counties in 2016 than Barack Obama had done in 2012, and Beto O’Rourke turbo-charged that pattern. I have made this point before, but it really bears repeating: In these growing counties, Ted Cruz did literally a million votes worse than Mitt Romney did. And please note, these aren’t just the big urban counties – there are only seven such counties, after all – nor are they all Democratic. This list contains such heavily Republican places as Montgomery, Comal, Parker, Smith, Lubbock, Ector, Midland, Randall, Ellis, Rockwall, and Kaufman. The thing to keep in mind is that while Beto still lost by a lot in those counties, he lost by less in them than Hillary Clinton did, and a lot less than Obama did. Beto uniformly received more votes in those counties than Clinton did, and Cruz received fewer than Trump and Romney.

Here’s where we do the projection part. Let’s assume that in 2020 these counties have 59.8% turnout at 2018 partisan percentages, which is to say Biden wins the two-party vote 54.3% to 45.7% for Trump. At 59.8% turnout there would be 7,732,410 voters, which gives us this result:


Trump   3,533,711   Biden    4,198,699
Trump  -  664,988

In other words, Biden gains 100K votes over what Beto did in 2018. If you’re now thinking “but Beto lost by 200K”, hold that thought.

Now let’s look at the 2012 small gain counties, the ones that gained anywhere from eight voters to 9,635 voters from 2012. There are a lot of these, 148 counties in all, but because their gains were modest the total change is +243,093 RVs in 2020. Here’s how those election results looked:


Romney  1,117,383   Obama      415,647
Romney      72.9%   Obama        27.1%
Romney +  701,736

Trump   1,209,121   Clinton    393,004
Trump       75.5%   Clinton      24.5%
Trump  +  816,117

Cruz    1,075,232   Beto       381,010
Cruz        73.8%                26.2%
Cruz   +  694,222

Year  Total voters   Total votes   Turnout
==========================================
2012     2,686,872     1,551,613     57.7%
2016     2,829,110     1,653,858     58.5%
2018     2,884,466     1,466,446     50.8%
2020     2,929,965     

Obviously, very red. Beto carried a grand total of ten of these 148 counties: Starr, Willacy, Reeves, Jim Wells, Zapata, Val Verde, Kleberg, La Salle, Dimmit, and Jim Hogg. This is a lot of rural turf, and as we can see Trump did better here than Romney did, both in terms of percentage and net margin. Ted Cruz was a tiny bit behind Romney on margin, but did slightly better in percentage. The overall decline in turnout held Cruz back.

Once again, we project. Assume 58.5% turnout at 2018 partisan percentages. That gives us 1,714,030 voters for the following result:


Trump   1,264,954   Biden      449,076
Trump  +  815,878

Trump winds up with the same margin as he did in 2016, as the 2018 partisan mix helps Biden not fall farther behind. Trump is now in the lead by about 150K votes.

Finally, the counties that have had a net loss of registered voters since 2012. There were 73 such counties, and a net -17,793 RVs in 2020.


Romney     182,073   Obama      99,677
Romney       64.6%   Obama       35.4%
Romney +    82,396

Trump      187,819   Clinton    90,428
Trump        67.5%   Clinton     32.5%
Trump +     97,391

Cruz       162,389   Beto       79,237
Cruz         67.2%   Beto        32.8%
Cruz +      83,152

Year  Total voters   Total votes   Turnout
==========================================
2012       517,163       284,551     55.0%
2016       511,387       286,062     55.9%
2018       505,087       243,066     48.1%
2020       499,370    

Again, mostly rural and again pretty red. The counties that Beto won were Culberson, Presidio, Jefferson (easily the biggest county in this group; Beto was just over 50% here, as Clinton had been, while Obama was just under 50%), Zavala, Duval, Brooks, and Frio.

Assume 55.9% turnout at 2018 partisan percentages, and for 277,148 voters we get:


Trump      187,587   Biden      91,561
Trump +     96,026

Again, basically what Trump did in 2016. Add it all up, and the result is:


Trump    5,012,802   Biden    4,770,351
Trump       51.24%   Biden       48.76%

That’s actually quite close to the Economist projection for Texas. If you’re now thinking “wait, you walked me through all these numbers to tell me that Trump’s gonna win Texas, why did we bother?”, let me remind you of the assumptions we made in making this projection:

1. Turnout levels would be equal to the 2016 election, while the partisan splits would be the same as 2018. There’s no reason why turnout can’t be higher in 2020 than it was in 2016, and there’s also no reason why the Democratic growth in those top 33 counties can’t continue apace.

2. Implicit in all this is that turnout in each individual county within their given bucket is the same. That’s obviously not how it works in real life, and it’s why GOTV efforts are so critical. If you recall my post about Harris County’s plans to make voting easier this November, County Clerk Chris Hollins suggests we could see up to 1.7 million votes cast here. That’s 360K more voters than there were in 2016, and 500K more than in 2018. It’s over 70% turnout in Harris County at current registration numbers. Had Beto had that level of turnout, at the same partisan percentages, he’d have netted an additional 85K votes in Harris. Obviously, other counties can and will try to boost turnout as well, and Republicans are going to vote in higher numbers, too. My point is, the potential is there for a lot more votes, in particular a lot more Democratic votes, to be cast.

Remember, this is all intended as a very simple projection of the vote. Lots of things that I haven’t taken into account can affect what happens. All this should give you some confidence in the polling results for Texas, and it should remind you of where the work needs to be done, and what the path to victory is.

Primary precinct analysis: Where a man can still win

Judge Gisela Triana

As previously discussed, female candidates in Democratic judicial primaries kicked a whole lot of ass this year. The four statewide races that featured one female candidates against one male candidate were shockingly not close – Amy Clark Meachum and Tina Clinton both topped 80%, while Kathy Cheng and Gisela Triana were both over 70%.

I’ve said before that blowout elections usually don’t yield anything interesting to see when you take a closer look at them. When a candidate wins by a dominant margin, that dominance tends to be ubiquitous. Still, I wondered, given that Texas is such a mix of counties – large, medium, small; urban, suburban, rural; Anglo and Hispanic; Republican and Democratic – that I wondered if that might still be true in these judicial primaries.

So, I picked the closest of the four race, Gisela Triana versus Peter Kelly, which was a 73-27 win by Triana, and looked at the county by county canvass. Behold, here is every county in Texas in which Peter Kelly won or tied:


County      Kelly   Triana
==========================
Borden          4        2
Briscoe        16       15
Burleson      340      292
Carson         59       56
Coke           33       28
Collingsworth  25       17
Fisher         79       20
Glasscock       7        5
Hall           33       30
Hansford       11        8
Hardeman       53       41
Hartley        32       29
Haskell        83       59
Hudspeth      143      143
Jack           72       70
Jasper        551      494
Kent           21       12
King            2        0
Lavaca        257      213
Limestone     340      308
Loving          4        1
Madison       132      111
Morris        345      274
Motley          5        5
Newton        160      134
Oldham         18       18
Red River     208      191
Roberts         5        4
Rusk          861      776
San Augustine 219      172
Shelby        187      182
Stonewall      35       19
Wilbarger     130      129

So there you have it. Congratulations to Fisher County, in what I would call the southern end of the panhandle, for being the most pro-dude part of the state, and to Rusk County in East Texas for being the largest pro-dude county. There were two counties in which each candidate got at least a thousand votes that were fairly close:


County      Kelly   Triana
==========================
Gregg       2,028    2,159
Harrison    1,182    1,484

I did not check the other races, on the assumption that there would be fewer such examples in those less-close contests. None of this is intended as a comment on the quality of the candidates – the Dems had a robust lineup of well-qualified contenders this cycle. I don’t think that this kind of “analysis”, if one can call it that, tells us anything useful, but I do think there’s value in examining the silly side of politics now and then. I’ve also had this sitting in my drafts since mid-March and felt like it was finally time to publish it. I hope you enjoyed this little exercise in said silliness.