Proving You Can Regionalize Baseline Earnings in California

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I’m developing some better models of the data that will be used in the OBBBA institutional accountability, focusing on the regionality problem noted last week.

Yesterday I described the likely impact being place-based schools. Regional universities, community colleges, small non-elite private institutions.

Rather than just complain that there should be better baseline / comparison data, I wanted to prove that it is possible with the same data sources used by FVT & GE, College Futures / HEA Group, and OBBBA - the Census Bureau’s American Community Survey 5-year estimates. I am using 2018 - 2022 in 2022 dollars to match College Futures / HEA Group, but I also have 2015 - 2019 in 2019 dollars to match GE and the latest College Scorecard.

There are already Public Use MicroData Areas (PUMAs) in the data that fit within states and better capture regionality. I calculated the median earnings for high school graduates (or GED holders) with no college, ages 25-34, no longer enrolled, and working (which the OBBBA uses) and in the workforce (working or unemployed and looking, which GE and HEA Group use). I calculated these median earnings across the state (as they all do) and at a PUMA level. And I calculated an earnings_var variable that simply subtracts the state_earnings from the puma_earnings, to show the variance for each PUMA. In other words, by how much would the baseline measure change if you took into account this regionality.

Admittedly, the 5-digit PUMA is too granular and could be aggregated to a more reasonable level. But here is the first view of California, chosen so that we can further explore the College Futures / HEA Group metrics. Green = areas with median earnings above the state median, and red = areas with median earnings below the state median.

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