Sign in

CJ Libassi

@clibassi.bsky.social
940 followers 539 following 91 posts

phd student in econ and ed at EPSAatTC. formerly: SMPAGWU, College Board, CAPhighered, edpolicyford, ComunidadMadrid, pgcps.

PostsRepliesMedia
CJ Libassi @clibassi.bsky.social · 13/10/2025
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/
Horizontal bar chart showing how controlling for first job characteristics reduces earnings gaps between low- and high-socioeconomic status college graduates five years after graduation. Three horizontal bars extend leftward from zero, representing negative dollar amounts. The top gray bar shows an initial gap of $4,948 for observably similar graduates. The middle gray bar shows the gap reduced to $2,251 after controlling for first job salary, with a horizontal bracket and whiskers indicating a 55% reduction. The bottom blue bar shows the gap further reduced to $1,716 after controlling for all first job features, with a second horizontal bracket indicating a 65% reduction from the middle bar. Title states 'The Role of First Job Transitions in Explaining Earnings Gaps for Similar Low- vs. High-SES Grads, Five Years After Graduation.' Subtitle indicates data is from traditionally aged BA graduates from 2010-17 from a large urban public university system. X-axis shows dollar amounts from -$5,000 to $0.
110
CJ Libassi @clibassi.bsky.social · 31/07/2025
I think these look great! Very logical way to put things together. The challenge in my mind is how to handle many vars? One thing I have toyed with for this is trying to plot the top N vars decomposition results. Something like this toy example I just had Claude code whip up on simulated data.
220
CJ Libassi @clibassi.bsky.social · 30/06/2025
On top of tracking debt-to-earnings over a longer period, we look at different parts of the distribution of earnings. Here for example is the interquartile range of earnings for all of the students in the PSEO data, which captures a recurring fact in the report: things are just different in medicine
Chart showing earnings ranges for professional programs 5 and 10 years after graduation. Horizontal bars display interquartile ranges with median lines. Programs from lowest to highest 10-year median earnings: Rehabilitation/Therapeutic Professions, Veterinary Medicine, Optometry, Law/JD, Pharmacy, Dentistry, and Medicine/MD. Medicine shows dramatically higher earnings than other programs, with 10-year median above $300k and upper range extending to over $500k. Most other programs cluster between roughly $100k-$150k for 10-year median earnings
100
CJ Libassi @clibassi.bsky.social · 30/06/2025
New at @pseocoalition.bsky.social, @julia-turner.bsky.social & I have a new report on grad school debt & earnings over the medium term. For some key professional fields (🩺⚖️🦷💊🐾), we show the varied patterns both within & across areas of study, looking at the first decade of earnings after graduation
Scatter plot comparing median program debt (x-axis, $50k-$300k) vs 10-year cumulative earnings (y-axis, $0.5M-$2M) for professional programs. Medicine MD programs (pink squares) cluster in upper right with high debt ($150k-$250k) and high earnings ($1.4M-$1.8M). Law programs (blue circles) cluster in the bottom left corner with lower debt and lower earnings, though some elite programs show medicine-level earnings and somewhat higher debt than other law schools. Veterinary Medicine (green diamonds), Dentistry (plus signs) and Pharmacy (X marks) are distributed across middle ranges of earnings, but across wide ranges of the distribution of debt, with almost all pharmacy programs having lower debt than almost all dentistry programs. Physical therapy and veterinary programs have law-like earnings (between half a million and a million over 10 years), and while PT has law-like debt as well, veterinary debt is generally much higher and closer to medical school.
32312
CJ Libassi @clibassi.bsky.social · 23/04/2025
The Pope didn't die without performing one last miracle.
Screenshot of an email from Alex Smith on April 23, 2025, announcing an update to the Integrated Postsecondary Education Data System (IPEDS) data on the College Scorecard website. The update includes more recent data values from a new collection year, a new metric called "SCORECARD_SECTOR" to classify institutions by dominant award and ownership, and updated data derived from Federal Student Aid sources for various metrics like Operating Status Flag and Cohort Default Rate.
020
CJ Libassi @clibassi.bsky.social · 17/01/2025
Lastly, a view into where the growth in annual graduate loan volume is happening over the last 9 years
120
CJ Libassi @clibassi.bsky.social · 17/01/2025
Next up cumulative debt vs. earnings at 3-years for law schools with at least 50 federal borrowers.
Scatter plot of law school graduate earnings vs. cumulative debt. Outside top programs, most graduates earn $75k-$100k regardless of debt level. Top 15 programs labeled, showing some correlation between higher earnings ($200k+) and debt, but relationship is noisy. Most programs cluster between $100k-$200k in debt.
100
CJ Libassi @clibassi.bsky.social · 17/01/2025
Here's a look at completion rates within 2-years of program length, by credential and institutional control.
Bar chart comparing graduate program completion rates across public, private non-profit, and for-profit institutions. Public and private non-profits show similar completion rates around 70-80% for most programs. For-profit completion rates are consistently lower, around 40-50% across all credential types.
100
CJ Libassi @clibassi.bsky.social · 17/01/2025
Next, the same thing but for cumulative borrowing. In both this and the above graph, we present the top 25 fields in terms of volume and the distributions are sorted by the average borrowing.
A stacked density plot showing cumulative debt distribution for 2019 graduates by program. Professional degrees show highest cumulative debt - dentistry graduates commonly owing $200k-400k. Master's program graduates generally have much lower debt, with education degrees typically under $100k. Clear stratification between professional and master's programs.
100
CJ Libassi @clibassi.bsky.social · 17/01/2025
Wanted to highlight a new report we have just released at the Office of the Chief Economist at ED: "An Overview of Graduate Borrowing and Outcomes." Below are a few graphs that might entice you to read more. First: distributions of annual borrowing for top 25 credentials in terms of annual volume
A stacked density plot showing distribution of annual graduate program borrowing in 2019 across different programs. Professional programs like dentistry and medicine have highest borrowing ($80k-120k), while master's programs in education and business typically borrow much less ($20k-40k). Programs ordered by average borrowing amount.
1156
CJ Libassi @clibassi.bsky.social · 17/01/2025
Any device can be a mobile device if you believe in yourself.
020
CJ Libassi @clibassi.bsky.social · 15/12/2024
Maybe this from B&B on Powerstats is what you need? Table code: ifctbp
010
CJ Libassi @clibassi.bsky.social · 10/12/2024
Gordon, Zettelmeyer, Bhargava, and Chapsky (2018) is a very impressive version of this - their big data lets them do many types of comparisons (look at the attached table!) across different research designs and populations: www.kellogg.northwestern.edu/faculty/gord...
120
CJ Libassi @clibassi.bsky.social · 26/11/2024
And to be clear, those are the custom instructions I use in a Claude project, and then use this prompt
A prompt that says: “Please provide a comprehensive summary and analysis of the attached research paper using the standardized template format. Ensure the summary is thorough, accurate, and follows all the guidelines in your custom instructions, paying particular attention to formatting rules for titles and references. Double-check that you've avoided using 'et al.' in any titles or references, including in the Related Papers section. For the Related Papers section, make sure to list each paper with its standardized title in double square brackets, followed by a 1-2 sentence explanation of how it relates to the current study, all on a single line. Present the result in a code block for easy copying to Obsidian.​​​​​​​​​​​​​​​​“
040
CJ Libassi @clibassi.bsky.social · 18/10/2023
But I would actually also be interested in papers working with the multivalued treatment case!
100
CJ Libassi @clibassi.bsky.social · 18/10/2023
Here's a snippet from Imbens and Rubin (1997) "Estimating outcome distributions for compliers in instrumental variables models" - does anyone know of any papers where someone actually does extend the estimation of complier distributions to the multi-valued treatment case?
241