The number of economics papers is not exploding on arXiv
On the importance of measurement
You can see the computer age everywhere but in the productivity statistics.
-Robert Solow (1987)
Will AI, with its ability to autonomously write papers from start to finish, cause an explosion in research papers and the collapse of the academic publishing pipeline?
Agentic tools are incredible but human society is complex and hard to automate. It’s not clear whether people see these tools and say “Oh yes, I need to write twice as many papers.” They may work more intensely on papers, but they may also choose to spend more leisure time as it becomes easier to get work done! Or they may work through ideas quickly and find the better papers to focus on. It’s an empirical question.
I am going to provide four data sources to try to answer the question: “are people producing more working papers?”1
arXiv’s working papers for economics, statistics, and machine learning (a CS category)
the NBER’s working papers
SSRN’s working paper series (by far the hardest-to-get and least complete data)
AEA journal submissions
The key question will be exactly how to measure this. Across almost every data source, the number of papers is rising; this was true long before AI. So instead, I’ll focus on the change in the growth rate of working papers, which will capture the acceleration due to AI:
The punchline: growth rates did tick up, but by far less than a true deluge would imply. SSRN is the one venue with a clear acceleration.2 And in the field where I’d have bet on the biggest jump — machine learning — the growth rate didn’t move at all: 18% per year over 2022–2024, and 18% again over 2024–2026.
Journal submissions at the AEA are a partial exception — they’ve risen recently — but as I’ll show, that’s partly a rebound to pre-2019 levels rather than new growth. More on that below.
arXiv
arXiv is a public working paper repository that hosts for a variety of scientific topics, including economics (although it is not nearly as important as SSRN right now). Papers can be posted on the site, and then updated anytime they are changed. Arxiv has the useful feature that every version of a paper is time-stamped (very useful for fields like math, where precedence is very important).
An extremely important cautionary note about arXiv — it is easy to accidentally use the “last-updated” date for papers when indexing. However this will bias your results towards finding a large recent increase in papers. Below, I plot the number of new papers posted per month in the three economics arXiv mailing lists.
In economics, we see that when we correctly measure the dates, there is a slight recent increase in papers, but nothing outrageous. Comparing the annualized rate of change between 2022-2024 to 2024-2026, we see a slight increase in the rate from 14% to 19% per year.
This story looks similar in other arXiv fields. In Statistics, the rate from 2022-2024 was 9% per year, and in 2024-2026, it grew to 14% per year.
In Machine Learning (a computer science mailing list), posting rates have grown at the same steady rate since 2022!
National Bureau of Economic Research
An alternative mailing list for working papers is the National Bureau of Economic Research (NBER). This mailing list is unique in that the number of people who can post to it is held in very restricted quantity.3 Hence, any changes here on production would really be an extensive margin story among very research-active scholars.
In this setting, we see a similar change from 2022-2024 (5%) to 2024-2026 (9%) as before, but with lower growth rates in both periods.
SSRN
The Social Science Research Network (SSRN) is the dominant social science platform for economics and finance working papers. It’s also owned by Elsevier and hides all its information. It requires all sorts of insane logins and clickthroughs to use and is overall a terrible service. You should stop using it.
That being said, it would be unfair to do any analysis of the state of working papers in economics without looking at it. So, I had to hack together a horrible solution.
Using the Wayback Machine, I pulled SSRN’s own reported totals — the cumulative stock of papers — for its two main categories, the Economics Research Network (ERN) and the Financial Economics Network (FEN), at a handful of snapshots over time. There weren’t many, but enough for a rough read.
From 2020 through 2025 the stock grew at roughly 5% a year, with a recent acceleration to about 10%. Treat this as the softest evidence in the piece, though: it’s by far the sparsest and least reliable of the four sources and, ironically, the only one showing a clear pickup in working papers.
AEA Journal Submissions
I collected data from the Report of the Editor for each major AEA Journal (AER + 4 AEJs). We definitely see an increase over the past few years in submissions, but what is strange is how much submissions were initially down. Why? One candidate is price: the cost of submitting to the journal went up! In 2015, the cost was $200 dollars for non-members and 100 for members. Between 2019 and 2020, it had increased to 300/200.
This stands out because even in 2025, we still haven’t reach the peak of 2018 for the AER. It is difficult, however, looking at the graph, to suss out exactly how much of this is rebounding after some declines post-Covid, or a true increase. The real increase appears to be in the AEJ: EP and AEJ:Applied, have significant and recent growth in submissions.
So what now? The real question is whether submission rates will continue to grow. Will it be concentrated in things like AEJ:EP and AE? Or will micro theory start to have more submissions? AI adoption is still diffusing, so if there’s a real effect we’d expect it to build over time — which is exactly why this is worth tracking. And if price screens submissions as effectively as it appears to, editors hold a powerful lever.
Personally, I think the AEA, and finance bodies like the SFS and AFA, would do us a real service by publishing submission data regularly. Right now there’s a lot of hand-wringing and very little data, which makes it hard to know whether AI is deluging us with papers or we’re just projecting our fears.
For now, you can see the AI age everywhere except in the submission statistics.
A repo for replicating the analysis in this piece is available here: https://github.com/paulgp/ai-working-paper-deluge
This is really the only quantity that is not artificially constrained, since journal slots will likely stay fixed in the short-run.
This is also the data source with the least reliable data.
Only NBER Research Associates and Faculty Research Fellows are able to post their papers there. Disclosure: I am an NBER FRF
I would have though that the RePEc network would be the most comprehensive and easy to reach source for working papers in Economics.