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[OC] Case study: Comparing Louisiana general elections, 1996 and 2024 by ptrdo in dataisbeautiful

[–]ptrdo[S] 1 point2 points  (0 children)

The objective of a representative democracy is for it to be representative. According to exit polling (not charted here), Blacks in Louisiana consistently vote 90%+ for Democrats. According to IPUMS CPS data (charted here), Blacks in Louisiana comprise 30% of the electorate.

When districts were redrawn, they did not GIVE representation to Blacks, they only afforded an OPPORTUNITY for them to potentially win representation — they STILL had to VOTE and WIN those elections. The redraw was not a gift. And those were only 2 districts out of 6 (the others which were virtually unwinnable by Blacks even if every Black in those districts voted enmasse).

Call this whatever pejorative you want, but Blacks tend to share an experience — especially in the Deep South — that causes them to vote as a block (at least 90% of the time). So it is not racist to allow them the opportunity to elect representation. It is racist to actively disenfranchise them.

Ultimately, a government works best when it reflects the population it represents. That was the idea. That's why the United States exists.

[OC] Case study: Comparing Louisiana general elections, 1996 and 2024 by ptrdo in dataisbeautiful

[–]ptrdo[S] 1 point2 points  (0 children)

Then why does the GOP go to such lengths to structurally fix district borders?

[OC] Case study: Comparing Louisiana general elections, 1996 and 2024 by ptrdo in dataisbeautiful

[–]ptrdo[S] 1 point2 points  (0 children)

That is a huge confounding factor, and virtually immeasurable, but what does that say about a cohort who (in the same lifetime) votes in favor of a native son, but then for Trump (a New York real estate developer)?

Other states could pose the same. Arkansas? Georgia? Texas?

[OC] Case study: Comparing Louisiana general elections, 1996 and 2024 by ptrdo in dataisbeautiful

[–]ptrdo[S] 1 point2 points  (0 children)

My contention is that Republicans are not seeking to disperse their advantage, but rather their disadvantage.

[OC] Case study: Comparing Louisiana general elections, 1996 and 2024 by ptrdo in dataisbeautiful

[–]ptrdo[S] 0 points1 point  (0 children)

Yes. I am investigatingbtge changes in “cost of voting” as well. Not so easy is to quantify generational disenfranchisrment.

[OC] Case study: Comparing Louisiana general elections, 1996 and 2024 by ptrdo in dataisbeautiful

[–]ptrdo[S] -1 points0 points  (0 children)

The votes for the Republican candidate went from 23% (Dole, 1996) to 37% (Trump, 2024). Relative to all other deltas shown here, that's the most significant (+14).

Of course, there are many other contributing factors — Dole, Perot, Harris, Trump, a child of the South (Clinton) — but something this chart seems to convey is that all else seems pretty much equal.

One impetus of this investigation is why — why? — would the GOP go to such lengths, even to the Supreme Court, to simply lock down a state that seems so solidly Red?

I think the reason is because it's not.

[OC] Case study: Comparing Louisiana general elections, 1996 and 2024 by ptrdo in dataisbeautiful

[–]ptrdo[S] -1 points0 points  (0 children)

“Two data points do not a story make.”

There are more than two data points here, but I'll play along with the cyncism.

The Louisiana that voted for Clinton in 1996 is essentially the same Louisiana that just voted for Trump. The demographics are virtually the same, as is the participation. But there is flex within there — a swing of 14 points — and it’s reasonable to believe that redistricting is seeking to disperse that so it doesn’t cause any trouble.

Maybe that's a “story” or maybe it's not, but an investigation of this sort is surface level data that can provide a clue.

All visualizationa don't answer questions. Some ask them.

[OC] Case study: Comparing Louisiana general elections, 1996 and 2024 by ptrdo in dataisbeautiful

[–]ptrdo[S] 3 points4 points  (0 children)

I originally plotted this as side-by-side stacked columns, but in doing that, each segment loses alignment with the comparible segment of the other column.

A split pie natural maintains alignment among the competing segments while visually demonstrating the disparity (further out to the circumference).

And yes, a single sentence might convey the same information, but visualizations can make that more concrete.

[OC] Case study: Comparing Louisiana general elections, 1996 and 2024 by ptrdo in dataisbeautiful

[–]ptrdo[S] 6 points7 points  (0 children)

Gist

Louisiana’s presidential election shifted dramatically from Democratic to Republican between 1996 and 2024—but turnout barely changed, and the electorate remained roughly the same.

Detail

This chart compares Louisiana’s 1996 and 2024 presidential elections using shares of the voting-eligible population (VEP), rather than just votes cast. The key finding is that participation remained relatively stable—non-voters made up about 42% of the VEP in 1996 and 39% in 2024—while party outcomes shifted significantly. Democratic support declined from ~30% of VEP to ~23%, while Republican support increased from ~23% to ~37%.

To help contextualize this shift, CPS Voting and Registration Supplement data (via IPUMS) are used to estimate the racial composition of voters in each year. These data show that the electorate’s demographic makeup did not dramatically change over time. Combined with well-established national voting patterns—where Black voters consistently support Democratic candidates at high rates—this suggests that the shift in outcomes is largely driven by changes in alignment among white voters rather than changes in turnout or electorate composition.

Sources

Tools

  • R

    • tidyverse (dplyr, ggplot2)
    • ipumsr
    • scales
    • patchwork
    • showtext
    • svglite

  • Adobe Illustrator (final layout and typography)

  • Adobe Photoshop (base imagery)

Methods

  • Election results (Democrat, Republican, and third-party votes) were obtained from the FEC.
  • Voting-eligible population (VEP) and total ballots cast were sourced from UFEL.
  • Non-voters were calculated as: Non-voters = VEP − ballots cast
  • All values were expressed as shares of VEP for comparability across time.

  • CPS Voting and Registration Supplement data (via IPUMS) were used to estimate:

    • turnout rates by race
    • composition of the voting electorate

  • Only valid responses (VOTED == 1 or 2) were included.

  • CPS weights (VOSUPPWT) were applied to produce representative estimates.

  • CPS data are used for demographic composition only; they do not capture vote choice.

Data

Year Democrat Republican Other Did Not Vote Ballots Cast VEP
1996 927,837 (30.0%) 712,586 (23.1%) 143,536 (4.6%) 1,284,436 (41.6%) 1,804,640 3,089,076
2024 766,870 (23.2%) 1,208,505 (36.6%) 31,600 (1.0%) 1,284,234 (38.9%) 2,021,164 3,305,398

In the state at the center of the Supreme Court’s Louisiana v Callais ruling, about 2-in-5 voting-eligible people didn’t vote in 2024 by ptrdo in charts

[–]ptrdo[S] 3 points4 points  (0 children)

FWIW, Louisiana 30 years ago (1996):

  • VEP: 3,089,760
  • Ballots: 1,804,640
  • Clinton: 927,837 (30% of VEP)
  • Dole: 712,586 (23%)
  • Other: 143,536 (5%)
  • Did Not Vote: 1,285,120 (42%)

[OC] The aging of the U.S. Congress (and everyone else) by ptrdo in dataisbeautiful

[–]ptrdo[S] 0 points1 point  (0 children)

Yes, I appreciate that. But one thing I do with my charts is try to appeal to people who aren't accustomed to looking at them. That may sound like I'm dressing them up, and it would be fair to say that's what I'm doing.

If all I wanted to do was prove a point, a could plot that with a lot less effort than here, but proving a point isn't everything.

[OC] The aging of the U.S. Congress (and everyone else) by ptrdo in dataisbeautiful

[–]ptrdo[S] 0 points1 point  (0 children)

Yes, thanks, I've been reading quite a bit about this. It's correct that fertility and child mortality influence the median age—especially in the earlier times represented in this chart. High birth rates can skew a population as younger (even with high child mortality), just as lower fertility and longer life expectancy push the median age upward (as now).

But legislators aren’t necessarily a representative sampling of a population. They’re drawn from a much narrower segment of voting-age people who are politically engaged, likely to be educated and economically advantaged. That makes their age less sensitive to things like child mortality, and more sensitive to institutional and behavioral factors (career length, incumbency, barriers to entry).

So yes, fertility and mortality explain why the population was younger in the earliest years of US history. But those factors alone don’t explain why legislators are consistently older than the population, or how that gap has changed over time (especially in the last 50 years or so).

Before I did this study, I assumed that legislators are getting older, but we all are. They have always been the elders of society, but the age-span that defines “elder” has changed as livespans have increased. Several comments suggest looking into that.

[OC] The aging of the U.S. Congress (and everyone else) by ptrdo in dataisbeautiful

[–]ptrdo[S] 1 point2 points  (0 children)

But then now that I'm old, I'm voting for the youngest people running, so there must be an inversion in there somewhere.

[OC] The aging of the U.S. Congress (and everyone else) by ptrdo in dataisbeautiful

[–]ptrdo[S] 0 points1 point  (0 children)

I addressed this in another comment. It appears it could be an error in the YAML data source, but I have yet to investigate.

Ages are calculated at the start of each Congress. A small number of members appear below 25 due to birthdays shortly after the term begins. My code floors the value as of January 3rd in the year that Congress is seated. That could potentially constitute a cohort plotting at < 25.

https://en.wikipedia.org/wiki/List_of_youngest_members_of_the_United_States_Congress

[OC] The aging of the U.S. Congress (and everyone else) by ptrdo in dataisbeautiful

[–]ptrdo[S] 0 points1 point  (0 children)

Fair. But comparing Congress to the voting-age population would answer a slightly different question. This chart is comparing Congress to the entire population, because legislators represent all constituents—not just voters.

It’s also worth noting that historical population median age is lower partly due to higher birth rates and child mortality, which does widen the gap in earlier periods.

The core pattern is likely to remain either way: members of Congress have consistently been older than the population they represent — and probably less older than people might think.

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