We all want to know if COVID lockdowns actually work. They’re a huge price to pay even if they do and all the more painful if they don’t.

To understand a relationship between any cause and effect, both must be measured. We need to understand the severity of lockdowns — the cause — before we can do any regression or correlation analysis with death toll or economic impact — the effects. Metrics for these effects exist, but understanding the cause poses a challenge. Until now.

The index I present here — created from raw data with thousands of lines of code — is a useful measure of COVID lockdown policy severity.

A visually-simple measure of political lockdown strength for the United States.

I’ve created this from the ground up, using eight categories of lockdown measures for each of the fifty sates and DC. This provides an unprecedented level of detail in addition to the geographically- and categorically-aggregated nation-wide index.

8 categories comprise the index, some of which are themselves aggregates of granular categories.
California drives the most severe and Texas dumped all at once.

This is completely open-source and available for regressions on any effect you may wish to measure. You can read more about the data, methodology, and implications in the project’s readme file:

Please contact me at interesting@michaelboerman.com with questions or comments. If you or someone you know is in need of a data scientist, please contact me at hireme@michaelboerman.com.

To see a full portfolio of my work, visit michaelboerman.com.

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