free range statistics

A few specific notes on technical issues relating to a previous post. On drawing network graphs with different coloured edges; modelling strategy; different specifications of models; and accessing UN SDG and gender inequality data.

The time that men spend on domestic chores is positively related to total fertility rate. But only if you are looking at countries selected because they are rich. Overall, it's negatively related. And if you model it with both GDP per capita and gender inequality (generally, more country-level gender inequality means more children), the effect goes away altogether. At the country level, it's a statistical artefact. To look into this properly, you need individual and household-level data.

I set out to improve a Sankey plot that had been shared as an example of how bad they are, and hopefully show that some careful design decisions and polish can make these plot useful for purposes like seeing cohorts' progress (up, down, same) over time.

Some more comprehensive simulations of what happens to 'fragile' p values (those between 0.01 and 0.05), when the actual power differs from the minimum detectable difference that an 80% power calculation was depended upon to set the sample size.

What proportion of significant p values should be between 0.01 and 0.05? Turns out the answer is 'it depends'.

How to produce an animation of demographic patterns in Pacific island countries and territories from 1950 to 2050, in just a few lines of code.

I have a quick look at the latest World Economic Outlook released by the IMF, with a particular eye on the economic growth forecasts for Pacific island countries. The Pacific countries that have had the biggest revision downwards in their growth prospects over the six months since the last Outlook are the three in the Compact of Free Association with USA (Palau, Marshall Islands, and Federated States of Micronesia), plus Fiji.

I expand on my last post, to see if the relationship between depression and voting for Trump at county-level persists when you control for the racial composition of counties (it doesn't).

Multi-level modelling with spatial auto-correlation! I look at county level data on incidence of depression in 2020, and voting for Trump in the 2024 US Presidential election, and conclude that there's something there, but of course there are lots of potential explanations of what is behind the relationship.

I explore death rates by cause of death with OECD data, for the USA and other countries. Causes of death that are relatively high in the USA include assaults, accidents, suicides; diseases of the nervous systems (including Alzheimer's); and diseases of the circulatory system (including heart attacks).