Yes please

If there's room for one more :)

Previous RAs have gone onto graduate programs at Columbia, Berkeley, and positions at the U.S. Census Bureau (you don't have to end up doing a PhD, a pre doc is a great way to find out if it's something you really want to do!)

You'll be joining a growing team of other pre docs and RAs and other env econ researchers (@ajsw.info, currently at Stanford, and Jenna Anders, currently at Berkeley, are joining UVA too!) Heavy focus on mentoring and training. V competitive salary & lower cost of living.

Brilliant, thanks Rei! We're certainly refining as we go and are happy to incorporate new ideas and suggestions to make them useful for decision-makers.

And I forgot to add... because I'm still used to writing on Twitter where we did a longer thread earlier before Milton made landfall that provided this context, that this is work is in partnership with the EIF team at the U.S. Census Bureau led by @johnvoorheis.bsky.social!

The population characteristics of those exposed to excessive rainfall and tropical storm-force winds were similar to the population that was forecasted to be exposed.

Approximately 8.9 million people received more than 5 inches of rainfall within 24 hours, an amount high enough to trigger flash flooding. The affected population had a higher proportion of older, Hispanic, and low-income individuals, compared to national averages.

Approximately 21.5 million people experienced tropical storm-force winds. The affected population had a higher proportion of older, Hispanic, non-Hispanic Black, and low-income individuals compared to national averages.

The population exposed to hurricane-force winds included a larger share of Hispanic, non-Hispanic Black, under-18, and low-income individuals than the population that was forecasted to be exposed with a high probability.

Approximately 3.6 million people were exposed to hurricane-force winds. The affected population had a higher proportion of older, Hispanic, and low-income individuals compared to national averages.

Identifying who is exposed is crucial to developing targeted, efficient, and equitable responses. We have now finalized our post-landfall report for Hurricane Milton (took us longer than expected, but we'll be quicker next time).

www.environmental-inequality-lab.org/real-time-an...

This is the data we're using drive.google.com/file/d/14b8H...

We're looking into highways in our analysis of mobility to see where people move. The benefit of what we're doing over the earlier work is that we data on individuals, where they live and their income. We're not relying on data on the characteristics of places, e.g., median income of a tract etc.

Have you come across the wonder weeks? We found it was quite a cute for our two.

We do analysis looking at variation in exposure throughout the wealth distribution as well as the income distribution and find very similar results. One way of thinking about the findings is that income/wealth isn't isn't sufficient to explain differences in exposure.

Many questions remain! For example, it is possible, likely even, that income could affect env' quality in ways not captured in this study. e.g. aggregate increases in income for a community could affect env' quality through collective action. Someone should look into that.