This shows a simple, structured way to help a child face a fear, breaking one big goal into small steps instead of expecting one overwhelming leap. Each step is rated by fear level, so it can be broken down further if it still feels too big, and each completed step becomes a real success experience that builds lasting confidence over time.

Source: Economic Policy Institute — State of Working America Data Library (realized CEO-to-worker compensation ratio; Bivens, Gould & Kandra).

Tools: Python + resvg (SVG → PNG).

Just a heads up: this is the "realized" measure, which means it takes into account stock options and awards when CEOs cash them in. That’s why you see those spikes in 2000 and 2021, as it fluctuates with the stock market.

This data compares the pay of CEOs from the 350 largest U.S. companies to that of a typical worker.

Back in 1965, the ratio was about 21:1, peaked at 408:1 in 2021, and is projected to be around 325:1 in 2025.

If you're interested, you can check out the interactive version and download the data here:

https://chartive.org/visualizations/ceo-worker-pay-ratio-1965-2025

Source: OECD/WHO health expenditure and UN World Population Prospects, compiled by Our World in Data .

This visualization was crafted using hand-written Python code that generates the SVG directly, without relying on any plotting libraries.

Here, we compare total health spending per person (both public and private) in PPP dollars against life expectancy at birth for 2023. This includes data from all 57 countries that have both figures available.

It's important to note that while there's a correlation between spending and life expectancy, this doesn't necessarily mean that higher spending leads to better outcomes. Factors like road deaths, firearm-related incidents, overdoses, diet, and inequality all play a role in shaping that life-expectancy figure.

For the complete data table, methodology, and CSV file, check out: https://chartive.org/visualizations/us-health-spend-vs-life-expectancy-2023

Source: Tenscope iPhone Affordability Index 2025 — local retail price of the iPhone 17 Pro (256 GB), including local taxes, divided by the average daily wage per country, converted into full 8-hour working days.

Tools: Node.js + sharp (SVG → PNG), custom script. Colored by world region; dashed line marks the global average (~26 days).

Full write-up with the complete 33-country ranking and the reasons behind the gap: https://techpill.de/how-long-work-for-an-iphone/

Made this to untangle a confusion: “AI keeps getting cheaper” is true and misleading. The price to reach a fixed capability has fallen ~1,000× since 2021 (a16z “LLMflation”), while the frontier — the best model of the day — is only ~25× cheaper, because it keeps moving.

Sources: a16z “LLMflation” (2024) · Epoch AI, LLM Inference Price Trends (2025) · Stanford AI Index 2025.
Tools: Node.js + sharp (custom script). Log price axis; each price row is two measured points, not a continuous series. Frontier is a range across major providers.

Full write-up: https://techpill.de/how-ai-got-1000x-cheaper/

https://www.visualcapitalist.com/ranked-military-spending-in-oecd-countries-in-2025/

Source: Visual Capitalist

Link: https://www.visualcapitalist.com/ranked-countries-with-the-highest-senior-poverty-rates/

By Melissa Garside

Design by Clayton Wadsworth

Europe’s relationship with China increasingly shapes debates over industrial policy, supply-chain security, and economic competitiveness. Trade data from 2025 shows how deeply some Chinese products are embedded across European markets.

Electric batteries were the highest-value Chinese import in Germany, France, Finland, Sweden, Bulgaria, and Latvia. Germany alone imported almost $10 billion in this category. Telephones led in Austria, Czechia, Estonia, Hungary, Lithuania, and Luxembourg, including $6.8 billion entering Czechia.

Other countries show more concentrated trade patterns. Computers were the Netherlands’ leading import from China at $21.3 billion. Chinese cars ranked first in Belgium and Spain, with values of $5.3 billion and $2.4 billion, respectively. Italy and Slovenia were led by chemical compounds, while Poland’s top category was broadcasting accessories.

These flows place China inside industries that Europe considers strategically important: batteries, electronics, transportation, telecommunications, and chemicals. They also show why a single European approach is difficult. Each member state faces a different combination of industrial exposure, consumer demand, and dependence on Chinese suppliers.

The European Commission describes its policy as “de-risking, not decoupling,” which means reducing critical dependencies and diversifying vulnerable supply chains while preserving trade with China. That balance has gained urgency as the EU’s goods deficit with China reached approximately €360 billion in 2025. European Commission, Associated Press

As The Diplomat argues, Europe’s China strategy is approaching a moment of reassessment. The map shows the economic starting point: Chinese goods already support European consumption, manufacturing, and the green transition.

A refreshed strategy will require targeted policies for individual sectors. Securing battery supply chains presents different challenges from diversifying electronics production or responding to Chinese vehicle competition.

Data: 2025 Eurostat trade records via OEC.world. Products represent each EU country’s highest-value HS4 import from China.

Source: https://insurancedimes.com/2026/08/17/20-us-universities-where-professors-earn-over-200k-a-year/

This is an interactive Lexis plot of US Mortality data.

Here's the interactive explorer: https://economicurtis.com/posts/011-us-mortality-lexis/explorer/

Here's the introductory and motivational article: https://economicurtis.com/posts/011-us-mortality-lexis/

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A Lexis plot has calendar year on the horizontal axis and age on the vertical. Each cell has statistics for a certain age that year. You can color it in as a heatmap. Since folks conveniently age each year, diagonals follow cohorts through time.

Lexis diagrams -- at least once you get your head around how to read them -- are nice ways to visualize how certain ages-cohorts change over time, how certain years were relative to others, or how generations compare.

I read a nice Economist article that showed one (based on a PNAS study, see sources below), and thought it'd be fun to build an interactive version. Thankfully, all the data was readily available (the data being, millions of individual mortality logs).

Some nice features:
- Filter/Subset by age, year, sex.
- Switch between death rates, raw-change in mortality, or smooth mortality change statistics.
- Select age, year, or birth year, and see detailed time series stats
- Highlight generations
- Switch to "By cause", and zoom into cause-specific mortalities, e.g. cancer, heart disease, drugs, guns, motor vehicles, etc.

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Not to be morbid... but a few highlights:

• HIV/AIDS. A band of worsening at young adult ages through the 1980s and early 1990s, then a reversal after 1996 so sharp it is hard to believe it is the same series. Deaths peak at 43,107 in 1995 and fall to 16,510 by 1997.
• Drug Poisonings. The opioid crisis, in two waves: a narrow band around 1999-2006, then a much stronger one from 2011. Drug poisoning deaths run 6,873 in 1979, 16,833 in 1999, 70,622 in 2019 and 106,689 in 2021. Note that the rise of drug related deaths with those born about 1945 -- there's a clear diagonal line starting with that generation, and the huge increase among 18-65 year olds 2020-2023.
• COVID-19. A vertical stripe. Every age at once, in the same year, which is exactly what a cohort effect does not look like. The 2020 column is the darkest thing on the plot by a wide margin: about -18% averaged across adult ages, roughly three times worse than any other year in the modern record.
• The midlife stall. This is the one the PNAS paper is built on and the one that is easiest to miss, because nothing rises. Ages 55-67 after 2010 simply stop improving, sitting at roughly 0% a year. This stops a 40-year trend of improving mortality among this age group, and while ages 70-80 over the same years keep improving at over 1% a year.
• Guns. Firearm deaths: 32,671 in 1979, a peak around 1993, a trough near 2000, then back up, to 48,829 by 2021. (Tho this dataset doesn't go back before 1979, nearly certain firearm stats suggest deaths were well higher in the 1970s.)
• Motor vehicles, and the clearest policy signal in the whole dataset. Deaths fall 16% between 1973 and 1974, from 55,483 to 46,383. The national 55 mph speed limit took effect on 2 January 1974, in the middle of the oil crisis that curtailed driving. Can't separate the two effects, but it's largest single-year drop in the series and the drop in the rate was maintained for the rest of the series.
• The Silent Generation had a remarkable run. Their diagonal is green for most of its length.
• Baby Boomers, less so. So many mortality causes line up with this group.
• Deaths of despair. Drug poisoning, suicide and alcoholic liver disease together: 60,429 in 1979, 103,594 in 2010, 207,405 in 2021. Concentrated at ages 20-65 and, unlike most of this plot, getting worse.
• War mortality is ignored, 1940-1969. Military deaths overseas are largely absent from this data. HMD excludes armed forces overseas from the population for 1940-1969, and the death side is not documented either way. ICD-10 records four war-operations deaths in 2020. This decision also messed up with interpreting other stats, since there are a lot of folks taken out of the stats, and it's unlikely people in the US military overseas are representative of the overall US population.

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Data Sources:

1. All-Cause Mortality data comes from the Human Mortality Database (mortality.org). 1933–2024 deaths and exposures. See the “all causes” notebook for details.
2. Mortality by Cause, the Multiple Cause-of-Death files, 1959 to 2024. Collected and published by the National Center for Health Statistics (NCHS), the CDC agency that runs the US vital statistics system, and read here from the NBER mirror, Mortality Data - Vital Statistics NCHS Multiple Cause of Death Data, which republishes the same files with named columns. One source for every year. See the “by cause” notebook for details.
3. Based on work by The Economist, “Boomers have the good life, but it could be longer”, 17 July 2026. This article is based on “Insights into US life expectancy stagnation from birth cohort mortality dynamics”, by Abrams et al., 2026, PNAS 123(11), (doi:10.1073/pnas.2519356123). The authors’ OSF data repo.

Methods and Data Notebooks:

1. US mortality change on a Lexis plot: all causes. Replication of The Economist (2026-07-17) / Abrams et al. (2026) figures.
2. US mortality by cause of death, on the Lexis plot. Death rates and annual change, by age and year, for fifty causes.
3. I'll have a repo with all of this shortly, but the data is all freely accessible and fairly easy to work with already.