Ultimately, we think these findings make a strong case for increased research & policy attention to understand which aspects of the transition may be most amenable to policy intervention. Lots more detail in the paper, hope you’ll give it a read! 10/10

Our descriptive evidence is consistent w/ intuitive conclusion: low-SES grads appear disadvantaged in the first job transition, regardless of the broader economic context, and this has consequences for earnings gaps years later. 9/

Overall, we find that diffs in first job transitions can explain *nearly two-thirds* of the year 5 residual earnings gap between high- and low-SES graduates (i.e., the gap that remains after controlling for other observable differences at graduation, including major, GPA, test scores, etc.) 8/

Interestingly, the SES gap in the first firm’s *average* pay is substantially bigger than the SES gap in grads’ own starting salaries (even in percentage terms). In other words, low-SES grads start out at firms where they may have less “room to grow” 7/

Descriptively, low SES-grads are less likely to have already started working with their first post-college employer prior to graduation (34% vs. 40%), have lower starting salaries ($38K vs $43K) and work at lower-paying firms ($53K average vs $64K average) than high-SES grads 6/

To describe first job transitions, we look at time to first job, starting salary, industry, industry-major match, firm size and average pay (and a few other things too) - these are all predictive of earnings at year five 5/

We don’t examine these constraints directly, but begin by documenting large SES gaps in post-college earnings: even after controlling for a ton of other info on students’ background and grades, high-SES grads earn almost $5,000 (8%) more than similar low-SES grads five years post-grad 4/

But what if some groups have persistently rockier transitions to the labor market, even in boom times? E.g. what if low-SES students struggle more to land a good first job, not b/c of their school, major, or grades, but b/c informational, financial, or structural constraints get in the way? 3/

Context: While earnings bump for BAs remains strong, unemployment for recent grads has risen faster than other groups since 2022. Rigorous research shows economic conditions at graduation have long-run impacts, in part b/c it affects quality of grads’ first jobs 2/ www.newyorkfed.org/research/col...

How about this? drive.google.com/file/d/1QdMA...

Unarchiver was able to get it to extract for me.

Blast! I may have it on an old external hard drive - I can check later tonight

Does the file at this link work for you? web.archive.org/web/20210213...

Certainly not arguing the status quo (where all sorts of award letter shenanigans can and do occur) is optimal, but just as a matter of calculation, how would you calculate the value of IDR, PSLF, borrower protections such as hardship forbearances, in school deferments, closed school discharge, etc.

Thanks for all you do for this package!

Also could it handle “negative” contributions (variables that increase the coefficient) in a way that is visually intuitive?

This looks great!! Think it would scale well to many covariates?