New Congressional Maps, New Dem Strategy: Our TX Analysis

1. Dem-lean by district in TX: our demographic model vs. historical data

The new Texas map has 38 Congressional districts (+2 from 2020). Our “demographic” model forecasts the potential Democratic lean of each new district in TX based on attributes like race, education, age, and population density. In the graph and table below, we compare our predictions to a “historical” model (from the excellent Dave’s Redistricting (DR) web-site) built up from precinct-level results in prior elections¹. (See methods at the end of this post for more details.) The axes show the projected 2-party Dem vote share with each model. The diagonal line represents where districts would fall on this scatter-plot if the two models agreed precisely. In districts to the left of the line, our demographic model thinks the D vote share is higher than historical results, and to the right of the line, we think it’s lower than the historical model predicts².

NB: For this and all scatter charts to follow, you can pan & zoom by dragging with the mouse or moving the scroll wheel. To reset the chart, hold shift and click with the mouse.

We generally focus our attention on districts that fall in the 45-55% range of Dem share in our demographic model. That’s because we think a 3-4 point gap is one that either party could potentially close with some focused energy, resources, and strategic thinking and all models have some uncertainty which we acknowledge by widening the range an extra point on both sides. Our methodology for using our model and the historical data to classify districts is explained in this post.

TX-specific findings: With those preliminaries in mind, here are a few observations on the new TX districts:

1. Unexpected findings – high-priority pickup opportunities for Dem donors (2)

   • The most surprising and consequential disagreements our model and the historical one are in TX-24 and TX-38. We see TX-24 and TX-38 as lean D (D+2) but the historical model sees them as R+10 or more. These are both relatively dense, majority-white districts near cities (TX-24 contains parts of Dallas and TX-38 contains parts of Houston). We believe these may be worthy of consideration by donors as “plausible long-shots,” despite their historical GOP bent – more on them later.

2. Unexpected findings – potential longer-term defense opportunities for Dem donors (2)

   • We also found two districts that we think need more defense than suggested by prior results. The historical model sees TX-34 as D+12 but our model is much less sure (tossup), the historical model estimates TX-28 as D+6 where we also see a tossup. Both districts are majority Hispanic and the model has significant uncertainty about the preference of Hispanic voters. This uncertainty stems from the large range of election outcomes in majority Hispanic districts throughout the country and TX.

3. Expected findings – known high-priority races for Dem Donors (1)

   • TX-15 is rated by historical models as lean D (D+2), whereas our model sees it as a R+4. We think this district is extremely vulnerable. But since it is conventionally rated as a tossup, we also think it will get plenty of attention from all sorts of donors.

4. Unexpected findings – but low-priority for Dem donors (8)

   • Five districts look competitive in our model (TX-2, TX-3, TX-12, TX-22, TX-26), at R+5 or closer, but for all of them the historical model looks like safe R (R+10 or more). Although we think these may be far less GOP-leaning than historical results suggest, they all seem much harder to flip than TX-24 and TX-38. We’re keeping an eye on them, but recommend de-prioritizing them for now.

   • 3 Districts look vulnerable in our model but very safe historically. For all three we are keeping an eye on the margins but we expect them to be safe D and thus not useful places for donors to focus on.

     • TX-16 is D+20 historically but only D+5 in our model
     • TX-35 is D+23 historically but only D+5 in our model
     • TX-37 is D+26 historically but only D+4 in our model

5. Expected findings – safe D and safe R districts (25)

   • Several TX districts are clearly far outside the range and don’t merit serious investment by Dems looking to maximize their impact.

   • Both the demographic model and the historical model agree that there are 8 safe D districts (TX-7, TX-9, TX-16, TX-18, TX-20, TX-29, TX-30, TX-32, TX-33), though our model sees them as slightly less safe than the historical model.

   • Both models also agree that there are 17 safe R districts (TX-1, TX-4, TX-5, TX-6, TX-8, TX-10, TX-11, TX-13, TX-14, TX-17, TX-19, TX-21, TX-23, TX-25, TX-27, TX-31, TX-36), though our model sees some of them as somewhat more competitive than the historical model.

This might be clearer in a table, here sorted by the Dem share in our demographic model.

2. Districts worthy of Dem donor support

Based on the results above, we think there are five good options for Dem donors in TX: - TX-15 (BR: R+4): We think this current Dem seat needs resources for Team Blue to hold it, particularly since it became tighter after redistricting. The incumbent, Vincente Gonzalez, opted to run in TX-34, which looks nominally safer for Dems (see below). Six Dems and nine Republicans have filed to compete in the primary.

• TX-28 (BR: tossup) was won by 18 points by incumbent Dem Henry Cuellar in 2020, but is now significantly more competitive and needs defensive support. Cuellar is being primaried from the left by Jessica Cisneros, who almost unseated him in 2020; seven Republicans have also thrown their hats in the ring.

• TX-34 (BR: tossup) is being viewed as relatively safe by Dems (see above), but we believe it may be tighter than pundits appreciate, and in need of support. Retiring Dem incumbent Filemon Vela has thrown his support behind Vincente Gonzalez in a race with multiple GOP hopefuls.

TX-24 (BR: D+2) and TX-38 (BR: D+2) appear to be “plausible long-shots” in our demographic model. Both are relatively dense, majority-white districts near cities. In TX-24, which contains parts of Dallas, GOP incumbent Beth Van Duyne eked out a 1.5-point victory in 2020 under the old map, although the new lines should tilt things more in the Republican’s favor. The newly-created Houston-area TX-38 was designed to be Republican leaning, and the GOP’s Wesley Hunt, who lost a tight contest in nearby TX-7 to Lizzie Pannill Fletcher in 2020, is well-funded and may be hard to beat. Although historical data and pundits would call both of these out of reach for Team Blue, we believe organizing resources could mobilize latent Dem-leaning voters.

A few points on “plausible long-shots”: (Feel free to skip if you’ve read our discussion of these sorts of districts in other analyses.) Our findings in TX-24 and TX-38 deserve particular discussion. These look to be "safe R" given the voting patterns in the precincts within them, but our demographic model suggests they should be much more Dem-leaning, and maybe even Dem-winnable. What might this mean? One way to answer this question is to consider the difference between the two models. Our demographic model asks: if the voting-age citizens of this district turned out and voted like similar people in other parts of the country and state, what would we expect the outcome of this election to be? In contrast, the historical model asks how we'd expect the election to turn out if the voters in this district turn out and vote as they have in previous elections. This points to a few possible reasons why a historically deep-red district like TX-38 might look flippable in our model -- including, but not limited to, the following:

• Our model may be wrong about how we define "similar" voters. We've incorporated factors like education and race, but maybe we've missed key things that make voters in TX-38 different from superficially "similar" voters in other districts nationwide.

• Location-specific factors may depress Dem voting. It's possible that barriers related to voter access in TX-38 depress Dem vote share below what it would be in other districts.

• Dems may, in fact, have underperformed relative to their potential among these voters. It's possible that more effective candidate recruitment, campaigning, or organizing could, in fact, yield a win for Dems in TX-38 or TX-24.

We don't know which (if any) of these explanations is correct. But our model suggests that if you want to support Team Blue in TX, and are open to the idea of supporting some long-shot candidates, then donating to a Dem in TX-24 or TX-38 might fit the bill.

A few overarching observations: (Again, we’ve made similar points in prior analyses.) As with many gerrymandered maps, this one makes the minority party very safe in their small number of districts and the majority somewhat less safe in theirs. Although the historical analysis has most of the R districts about R+15 to R+20 our demographic model sees some of them as much closer, between R+10 and R+5. That means that in a wave election favoring Dems–admittedly not likely in 2022, but you never know!–this map might be surprisingly risky for Republicans. And for Dem donors, it points to several giving opportunities that could help move the needle in 2022 or future elections.

3. Coming next from Blue Ripple

Here’s where we’re planning to take these analyses over the next few months:

• We’re going to do the same type analysis in many (all?) of the states, in order to identify the best options for Dem donors in 2022 on both offense and defense nationwide. Here are our takes on Arizona, Michigan, North Carolina, and Pennsylvania.

• We’re going to continue to refine and improve our demographic model–we’ll update this post and others as we do so. Feel free to contact us if you want more details on the mechanics, or if you’d like to propose changes or improvements.
• As maps get solidified, we’ll set up ActBlue donation links for candidates (after the primaries) to make it easy for you to donate.

If you want to stay up-to-date, please sign up for our email updates! We’re also on Twitter, Facebook, and Github.

4. Coda #1: Demographics of new TX districts

One thing we haven’t seen discussed very much is how redistricting in TX has changed the demographics in each district. As a way of putting the demographic model results in context, let’s look at the underlying population two different ways:

• The first chart below shows each of TX’s proposed 2022 districts, with the population broken down by race/ethnicity (Black, Hispanic, Asian, white-non-Hispanic and other) and education (College Grad and non-College Grad). Each bar also has a dot representing the (logarithmic) population density³ of the district. The scale for that dot is on the right-side axis of the chart. For reference, a log density of 5 represents about 150 people per square mile and a log density of 8 represents about 3000 people per square mile. We’ve ordered the districts by D-share based on our demographic model, which is helpful for understanding how the model responds to demographics and density.

• In the second chart, we look at these demographics a different way, placing each TX district according to its proportion of college graduates and non-white citizens of voting age. We also indicate (logarithmic) population density via the size of the circle and modeled D-edge (D-share minus 50%) via color. This makes it easier to see that the model predicts larger D vote-share as the district becomes more educated, more non-white and more dense.

It’s hard to see anything specific from these charts, though we are continuing to examine them as we try to understand what might be happening in each specific district. Overall, our impression is that this new map in TX has made the safe D districts safer by adding voters-of-color (mostly Hispanic and Black voters but also some Asian voters) and thus made the rest of the districts easier for Republicans to win by removing those same voters from places where they might have made districts competitive.

5. Coda #2: Brief intro to our methods (for non-experts)

This part of the post contains a general summary of the math behind what we’re doing here intended for non-experts. If you want even more technical details, check out the links at the end of this section, visit our Github page, or contact us directly.

Our model is demographic. We use turnout data from the 2020 CPS voter supplement (a self-reported survey); voting and turnout data from the 2020 CES (a validated survey); and election result data from the 2020 presidential, senate and house elections. The survey data from the CPS and CES is broken down by several demographic categories, including sex, education and race/ethnicity.

The election results are trickier to use in the model since we don’t have demographic information paired with with turnout or vote choice. What we do know is the overall demographics of the state or house district. So we use the election-data to assign a likelihood to the post-stratification of our parameters across the demographics of the relevant region (from the micro-data ACS).

Then we look at the demographics of a particular house or state-legislative district (using tract-level census data from the ACS), breaking it down into the same categories and then apply our model of turnout and voter preference to estimate the 2-party vote share we expect for a Democratic candidate.

This is in contrast to what we call the historical model: a standard way to predict “partisan lean” for any district, old or new: break it into precincts with known voting history (usually a combination of recent presidential, senate and governors races) and then aggregate those results to estimate expected results in the district.

The historical model is likely to be a pretty accurate “predictor” if you think the same people will vote the same way in subsequent elections, regardless of where the district lines lie. So why did we build a demographic model? Three reasons:

1. We’re interested in places where the history may be misleading, either because of the specific story in a district or because changing politics or demographics may have altered the balance of likely voters.⁴

2. Our demographic analysis is potentially more useful when the districts are new, since voting history may be less “sticky” there. For example, if I’m a Dem-leaning voter in a strong-D district, I might not have bothered voting much in the past because I figured my vote didn’t matter. But if I now live in a district that’s more competitive in the new map, I might be much more likely to turn out.

3. We’re not as interested in predicting what will happen in each district, but what plausibly could happen in each district if Dems applied resources in the right way, or fail to when the Republicans do. The historical model is backward-looking, whereas our demographic model is forward-looking making them complementary when it comes to strategic thinking.

Two final points. First, when it comes to potential Dem share in each district, we’re continuing to improve and refine our demographic model. The Blue Ripple web-site contains more details on how it works and some prior results of applying a similar model to state legislative districts, something we will also do more of in the near future. Second, for the historical model comparator, we use data from the excellent “Dave’s Redistricting”, which is also the source of our maps for the new districts.

Want to read more from Blue Ripple? Visit our website, sign up for email updates, and follow us on Twitter and FaceBook. Folks interested in our data and modeling efforts should also check out our Github page.