That must be a maximum entropy estimator. If one has no data or relevant prior information Laplaces principle of indifference leads to, "meh, prolly 50/50". Good that we're kept informed on the latest whale statistics.
There's a whale stranded on the German Baltic coast and the intensity of news reporting matches the crises of past years. The live news ticker borders on sarcasm.
Scatter plot with a fitted regression line illustrating the near-perfect negative linear relationship between year-on-year relative changes in life expectancy and year-on-year relative changes in total death counts for Italian males, covering the period 1950 to 2023. The horizontal axis is labeled "Δ log D" (change in log total deaths), ranging from approximately −0.15 to beyond 0.15. The vertical axis is labeled "Δ log e₀" (change in log life expectancy at birth), ranging from below 0.00 to above 0.02. Reference lines cross at the origin (0.0, 0.00). Dark teal circular data points are tightly clustered along a descending pink regression line, confirming a strong inverse correlation: years with rising death counts correspond to falling life expectancy, and vice versa. The majority of points fall near the origin, reflecting small year-on-year fluctuations. Two notable outliers appear in the upper-left quadrant (large life expectancy gain, large death count drop). One prominent outlier is labeled "2020" in pink, located far to the lower right, representing a large spike in deaths and a sharp drop in life expectancy during the COVID-19 pandemic. A second unlabeled point sits near it, slightly above and to the left. The title reads: "A near-perfect linear link between changes in life expectancy and total death counts." The subtitle reads: "Year-on-year relative changes in life expectancy VS year-on-year relative changes in total death counts, Italy, males, 1950–2023." The data source is credited to UN World Population Prospects 2024 (wpp2024 R package), with attribution to Ilya Kashnitsky @ikashnitsky.phd, part of the #30DayChartChallenge 2026, Day 16, theme: causation.
💡 Changes life expectancy follow changes in total death counts @araksha.bsky.social ✨
📝 doi.org/10.31219/osf...
🔗 #rstats code: github.com/ikashnitsky/...
🧙♂️ no ai jumpstarter this time, I worked off @jschoeley.com's code, all here github.com/ikashnitsky/...
DAY 16 -- causation 💫 #30DayChartChallenge
Illustration-style painting on a white background featuring a central hexagonal frame surrounded by colorful paint splashes and drips, displaying numerous Linux distribution logos arranged across the composition. At the center of the hexagon, a group of five painted penguins — three adults and two smaller ones — represent the Linux mascot (Tux), rendered in a realistic watercolor style in black, white, and yellow. Below the penguins, bold black text reads **LinuxColors**. Surrounding the central frame, the following Linux distribution logos are visible, each rendered in a painterly, dripping style: - **Fedora** — blue circular logo, upper center-left - **Arch Linux** — cyan/teal upward triangle with "tm" mark, upper center - **EndeavourOS** — purple and red triangle with text "ENDEAVOUROS", upper center - **openSUSE** — green chameleon logo with text "openSUSE", upper right - **Void Linux** — dark green circular logo with text "VOID", center-left - **Pop!\_OS** — yellow circular logo with "P!" symbol, lower center-left - **Linux Mint** — green "lm" stylized logo, lower center - **Ubuntu** — orange circular logo with three dots, lower right - **Kali Linux** — grey dragon/kite shape, left side - **Zorin OS** — blue circular "Z" logo, lower left - **Artix Linux** — blue circular "A" logo, bottom left - **Nix / NixOS** — blue snowflake-style logo, upper left Additional unidentified logos appear in dark navy (an "X" triangle, lower center) and teal/green shapes on the right side. Paint splatters in red, yellow, blue, green, and purple are scattered throughout.
🎨 {linuxcolors} a small #rstats package with the identity colors of the most popular #Linux distros 🐧
💎 #ggplot2 ready with scale_{color/fill}_linux() functions
🔗: github.com/ikashnitsky/... 📦
DAY 13 -- ecosystems 🌍 #30DayChartChallenge
✨ #FOSS world is a unique human #ecosystem
We are hiring a tenure track (!) senior researcher in political economy!
This is obviously a great job (permanent without teaching obligation) and I hope you all apply.
However, I would like to take a moment to share just how significant this is in the German academic context ⬇️
On a substantial level it will be relevant to study the share of extremely vulnerable people in the population (high-dependency, high number of co-comorbidities) 2020 to 25. Country differences in the dynamics of this share may explain differences between group 1 and 3.
Statistical uncertainty comes from the counterfactual: what would life expectancy trends have looked like without the pandemic? This uncertainty is large enough that we can't safely say that Sweden did better than e.g. Norway or Denmark although we can safely say that it did better than most.
bsky.app/profile/jsch...
"Low birth rates are simply not a problem," and other profoundly important observations from @lesja.bsky.social, to redirect the left away from the myth of progressive pronatalism.
I think it is a very smart signal. Certainly makes you stand out. People will develop authenticity signaling strategies. As for physical effort as an am-i-human captcha – why not?
Let's return to hand written letters to the editor as a publication hurdle.
1/ Has life expectancy fully recovered from the COVID-19 pandemic? In a new pre-print, we find that 31 of 34 high-income countries had still not returned to their expected life expectancy trajectories five years after the onset of the COVID-19 pandemic. www.medrxiv.org/content/10.6... #demography
Thank you. This is excellent. Subjective health expectancies may be even trickier, given that they refer to the future. Would you say age is among the strongest factors modifying a person's latent->scale transfer function?
I wonder if these findings translate to self rated health.
After 5 years I can finally share a full WP of our project conducting cognitive interviews of life satisfaction reporting.
Main findings:
1. LS scales are psychometrically valid, but...
2. Standard statistical assumptions made when analysing LS data are not credible.
osf.io/gv5e3/files/...
Call for abstracts: Nordic Yearbook of Population Research 2026 ➡️ Abstract submission deadline 15 March 2026.
Thank you for sharing the call @populationeu.bsky.social
#PopulationStudies
Weaponizing Kinship: A Demographic Analysis of Bereavement in the Colombian Conflict
Thank you. Working with gif as output format for animated data viz in the past I realized that a separate video editing step would help in getting the details right. I'll try your toolchain next time.
Five years after the pandemic began, have countries returned to their pre-COVID life expectancy trajectories?
New research shows 31 of 34 high-income countries still have life expectancy deficits in 2024, suggesting lasting effects on population health.
www.demography.ox.ac.uk/news/new-lcd...
Very effective and informative. Thank you for sharing. Just out of curiosity: Why was there a need for a custom video player?
I made an animated map of ship movements in the Straights of Hormuz using data from marinetraffic.com.
Ship track frames rendered using #ggplot, base map created using #gdal, video editing using #ffmpeg, video player written in #svelte.
🎁 www.zeit.de/wirtschaft/2...
The age groups were too coarse for us to do the necessary calculations. Same for Canada. We may add these regions though based on their official reported statistics. github.com/jschoeley/e0...
Got flashbacks to me using yaml 1.1. Parsed Norwegian country codes as boolean and age groups 01, 02, 03, etc. as octal. Have been a fan ever since.
Thank you. I'd expect the magnitude of this to be lower than the evident excess but I have not looked closely into this particular explanation.
FYI, I had a look at the Dutch data not long ago and it is infuriating
1/2
bsky.app/profile/jean...
Fitting period is 2000-2019. Longer compared to the weekly analyses as Lee-Carter estimates a random walk with drift for the trend and needs a double digit number of data points for a good variance estimate. Shorter periods make a difference for single countries (US) but not for the 4 group pattern.
Great to know. The puzzling countries are those with a step change in 2020, Netherlands for example.
You got it. The question on our mind is to distinguish between the 3 stylized scenarios below. We've seen a and b in the data, but have not found strong evidence for c, which would be the most consequential one.
We define the e0 deficit as "below the pre-pandemic trend". So by 2024 countries were lagging their pre-pandemic trend, but they did exceed 2019 levels in many cases. Thank you for pointing that out.
Data by @hmdatabase.bsky.social.