Yeah, you use a log scale with negative values 🤪

I am partially red-green colour blind. It's not a binary condition. I a setting like this I have no problem identifying the colours. Centering the colour spectrum on 0 would waste half the spectrum. Negative values are not that common.

Yes, it did.

Someone who freely admits they are wrong! Online?! I never thought I'd live to see the day. Thank you for the civil discourse. :-D

Thank you for the feedback! I think it's a trade-off: you really lose out on the details on the rest of the chart that is already predominantly green as it is. Consider this: to go to 20% you would probably not only need to introduce one but two more primary colours (eg. one at 15% and one at 20%).

Introducing a new colour makes it noisier. In my opinion, adding on a whole rainbow of shades to differentiate 3% of the data (time and country/wise) is not worth it.

You have the link to the code. Feel free to try it out!

The factors you point out definitely have a balancing influence. However, you have different consumption baskets, some countries import more of their energy/oil or goods in general. To your point: the European countries are more homogenous.

These are the countries that OECD tracks. It's interesting to see the differences between say Germany and Frace or outliers like Greece or Switzerland.

If you extend the colour scale range to dark red=20% inflation, most of the chart is just uniformly green.

Well, the Swiss Franc appreciated materially against most other currencies (which dampens the impact of inflation), they have the highest share of regulated prices in Europe and a lot of their power is produced from hydropower and nuclear. That all helps.

It's a close tie between the Argies and Turkey. Both are in the OECD dataset but I removed them from the chart because it detracts from a meaningful comparison between the other countries. Also, if you try to fit the range of the colour scale to include their values, the rest is just a uniform sea of green. :-)

Chart created in Python using Altair / data from OECD

Full code & link to data here.

How often does the BLS quote changes in inflation in percent and how often in index points in that article?

I'm getting it more than you realise. The index values (that's the technical term) are in fact totally arbitrary. All the information is in the relative changes. Once you have those, you can arbitrarily rebase. In fact the indices are regularly rebased (current one has 1982-84 = 100). There are series with other bases which are still published by the BLS because other bases are referenced in contracts.

Every announcement of CPI quotes the changes in percentage points. Take the BLS word for it, not mine: "Calculating Index Changes: Movements of the indexes from 1 month to another are usually expressed as percent changes rather than changes in index points, because index point changes are affected by the level of the index in relation to its base period, while percent changes are not."

Sure, she is reporting a CPI. It even says so in the graphic. And thank you for agreeing that quoting rates of change in per cent is more common.

Alright, then please point me to one central bank or data vendor that quotes CPI changes not as per cent but in index points.

Look, there's a fair amount of established economic theory around inflation, how the numbers are usually presented and what they mean.

It's highly misleading to say that a dollar devalued by 20 cents in 2022 because precisely only a 1989 dollar devalued 20 cents. A 2021 dollar devalued 9 cents and a 1960 dollar probably devalued 50 cents. See how bothersome this is? That's why people refer to the changes in per cent and not absolute terms.

Yes, and kudos for having the grit to finish your chart and the guts to post it here. You will have a more meaningful understanding if you think about inflation/purchasing power changes in relative terms. Your 1960s dollars probably devalued by 50 cents in the last year. :-)

OK, I see what you are getting at. But consider this: your 2021 money is devaluing by 21 cents on a $2.19 basis. This is a relative (~9%; there are significant rounding errors) devaluation by approximately 9%. It's meaningless to look at absolute devaluation if the basis is changing from $1 to $2.19 from 1989 to 2021. You are comparing 1989 apples to 2021 oranges. What you want to look at is year-on-year percentage changes, those are on a relative (previous-year power of purchasing) basis.

OK, one dollar became 9.1 cents less valuable year-on-year from July 2021 to July 2022. There was no 1-year period in 2022, where one dollar became worth 20 cents less.

If you don't believe me, please tell me during which one-year period the value decreased by 20 cents.

The technical term for what you are trying to show is the [YoY (year-on-year) CPI inflation](https://www.investing.com/economic-calendar/cpi-733). This peaked at 9.1% in July 2022. It was never anywhere near 20%.

Sure, I just hacked together a blog for myself and you can find it there:

The data is available as a .csv file and you can also see the Python/altair code there.

Please promise me to YOLO any Costa Rica winnings into deep OTM calls 😁

Just to clarify where the probabilities come from: The probabilities are the implied probabilities from the betting markets for each of those events (p = 1 / odds). This is purely based on the quotes from the market and does not involve any modelling/forecasting.

Just to clarify where the probabilities come from: The probabilities are the implied probabilities from the betting markets for each of those events (p = 1 / odds). This is purely based on the quotes from the market and does not involve any modelling/forecasting.