Category: Data Analytics and AI
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20-10-2026
For the life of me, I couldn't get Ctrl+Shift+M to work for inserting pipes (|>) when editing a Quarto document in Positron visual mode. After countless hours fiddling with keybindings.json, I developed a functional, but obtuse workaround for Windows. It's not pretty, but it works. This post outlines the approach in case it helps others facing the same problem.
28-09-2026
In 2019 I wrote a post with seven reasons policy professionals should learn to code. Now that AI can write reasonable code, I’ve had a number of people ask me if it’s still worth learning to program. Based on the evidence I’ve seen, AI appears to be a an amplifier, rather than a substitute for human intelligence in the policy space: meaning the people that get the most out of AI are likely to be those that know how to instruct AI to tackle a problem and tell good output from bad. So my answer is yes, learning to code still makes sense. Not necessarily so you can write the code yourself, but to expand the quantity, scope and quality of what you're capable of.
21-09-2026
The last post identified potential transition pathways for truck drivers based on how closely other occupations matched them on skills, abilities and knowledge. This post follows the same basic approach across a wider range of occupations and asks whether two proxies could stand in for the ratings where no O*NET exists: a small language model comparing the meaning of job descriptions and the distance between two jobs' US Standard Occupational Classification (SOC) codes.
This post estimates potential labor market transition pathways based on the similarity of skills, abilities and knowledge required by occupations. The approach broadly follows Bratanova et al. (2026). Like their research, the considered transition pathways are for workers currently employed as truck drivers. Unlike their approach, we've used a more recent version of the O*NET database and a different SOC-to-ANZSCO crosswalk. Although the point wasn't to precisely replicate their results, our approach largely did, with differences in similarity scores likely stemming from our different mapping of the SOC to the ANZSCO rather than from the method itself.
There's no official crosswalk between the ONET's occupational taxonomy and ANZSCO, which is awkward, because Australian researchers use ONET data all the time. This post builds one, explains why you should be suspicious of relying on it and then suggests a better methodology is probably what was applied by the European Commission.
The first post established that the WGI and WJP rule of law measures agree on how they rank countries. This follow-up tests the stricter question of whether the WGI can predict the WJP’s levels, rather than just the relative ranking / relative position of countries. It can: forecasting errors appear to be minimal and don’t vary significantly over time.
This post examines whether the Worldwide Governance Indicators' (WGI) rule of law measure provides a reasonable proxy for the World Justice Project's (WJP) Rule of Law Index, such as when analysis might benefit from the WGI's availability for more countries over a longer period. Results suggest the two measures broadly agree with one another when making cross-country comparisons, but care is warranted for country-level estimates where it's likely to be more difficult to differentiate measurement noise from genuine changes in the quality of institutions.