The key to a nation’s long-run prosperity is increased productivity. If workers can produce more in an hour, day, or week then they can collectively work less or enjoy more of the fruits of their labor: More pickleball courts and more time to play pickleball.

With all the astonishing improvements that we see on our computers and smartphones, it might seem that productivity is about to explode, that our concern will soon be how to distribute income and manage leisure when machines do all the work that people used to do. The reality is the opposite. The annual rate of increase of productivity in the United States averaged nearly 3% between 1870 and 1970 but has since slowed to less than 1%, according to data from the Bureau of Labor Statistics. That 2-percentage-point drop isn’t much in the short run, but it is everything in the long run. If productivity continues to increase at 1% annual rate instead of 3%, workers will produce a third less output in 20 years, 50% less in 35 years.

A 2020 study published in the American Economic Review, the flagship journal of the American Economics Association, focused on research productivity because this is a key driver of overall productivity improvements. (Ideas found and developed by researchers are extraordinarily valuable because, unlike oil, timber, and other natural resources, they can be used simultaneously by many different people without depletion.)

They looked at specific industries and concluded that, “Our robust finding is that research productivity is falling sharply everywhere we look. Taking the US aggregate number as representative, research productivity falls in half every 13 years: ideas are getting harder and harder to find. Put differently, just to sustain constant growth in GDP per person, the United States must double the amount of research effort every 13 years to offset the increased difficulty of finding new ideas.”

The semiconductor industry, long governed by Moore’s law, offers one example of this declining productivity. The “law” refers to the empirical regularity that the number of transistors that can fit onto a computer chip doubles about every two years; it’s thought to be one of the key drivers of economic growth over the past decades. But the authors estimate that the number of researchers required to double chip density is now more than 18 times larger than the number required in the early 1970s. Meanwhile, the number of researchers needed to maintain the past rate of increase in crop yields of corn, soybeans, cotton, and wheat is now between 6 to 24 times larger than it was in 1970. The number of researchers needed to develop new drugs has increased more than fivefold.

The authors don’t offer a compelling explanation for the drop in research productivity. We will suggest several possibilities. The title of the article, “Are Ideas Harder to Find?,” suggests that productive ideas are increasingly scarce and illusive—that the low-ranking fruit have been harvested. It is certainly true that Moore’s law is just an empirical observation that may be running into physical restraints, but it is also true, by definition, that it is nigh impossible to assess how many productive ideas are waiting to be discovered. After all, in 1899, the Director of the U.S. Patent Office said, “Everything that can be invented has been invented.”

Consider, for example, the sport of soccer in the United States. The U.S. famously underperforms in soccer, given its population and resources, but one explanation is that many of its best athletes are busy playing other sports. Something similar may be true here, in that the problem is not the elusiveness of what is being looked for but how the search is being conducted.

One issue is administrative bloat, what David Graeber calls “bullshit jobs”: “a form of paid employment that is so completely pointless, unnecessary, or pernicious that even the employee cannot justify its existence even though, as part of the conditions of employment, the employee feels obliged to pretend that this is not the case.” (Some of the examples he mentions include Flunkies, Goons, Box tickers, Taskmasters, and Duct tapers, who are people who temporarily fix problems that could be fixed permanently.) We agree, though we will refrain from giving examples from the businesses and universities that we have worked and consulted for.

For people who have seemingly non-BS jobs, they are too often working on BS products. “The best minds of my generation are thinking about how to make people click ads,” former Facebook engineer Jeff Hammerbacher lamented in 2011. More recently, many of the best minds are working on large language models like ChatGPT. These may eventually lead to great productivity gains but, so far, their greatest successes seem to be in promoting fake-it-till-you-make-it schemes and polluting the internet with disinformation.

An enormous amount of otherwise productive resources have also been devoted to the development, improvement, and deployment of online games and social media which, if anything, reduce productivity. Surely, the minds behind such questionable distractions could be better employed.

Another factor is Goodhart’s law, named after the British economist Charles Goodhart, which states that, “When a measure becomes a target, it ceases to be a good measure.” Applied to research, publications and patents are too often the basis of job security, promotions, pay increases, and status. As predicted by Goodhart’s law, the number of patents granted and papers published has exploded even while productivity growth has slowed.

Much of this tsunami of dodgy research is fueled by two deceptive research practices: p-hacking and HARKing. P-hackers use multiple tests to try to find support for their questionable theories and then report only the results that are most supportive. HARKers ransack large databases looking for statistical patterns and then pretend that this is what they were looking for all along. Both deceptions contribute to the replication crisis in science, in which peer-reviewed published research can’t be replicated with fresh data. The initial researchers waste their time trying to find publishable results; then other researchers waste their time trying to replicate the original nonsense.

For instance, a $2 million, eight-year effort to replicate 193 experiments from 53 top cancer papers was only able to replicate a quarter of these experiments, and of the 50 experiments that were replicated, the effect sizes were, on average, 85% lower than had been initially reported. Again, to the extent the original research was flawed, the researchers misspent their time—as did the researchers who tried to replicate the flawed research.

An extreme version of the ill effects of publication pressure is the proliferation of “editing services” (aka, “paper mills”) that some researchers use to buy publishable papers or to buy coauthorship on publishable papers. It has been estimated that thousands of such papers have been published; it is known that hundreds have been retracted after being identified by research-integrity sleuths.

Gary recently received an invitation to be part of this scam. Someone going by the name Carlee emailed Gary with this proposition:

Dear Editor，

Good morning.

Sorry to bother you, but we really need your help.We are a domestic translation agency in China that specializes in polishing papers for publication in journals. If you can help us publish articles in your journal, we will give you a nice reward.

Are you interested in establishing long-term cooperation with us?

Contact me！

Gary has published more than 100 peer-reviewed research papers, but he is not the editor of any journal. “Gary Smith” is a fairly common name so perhaps he had been confused with someone of the same name who is a journal editor. Out of curiosity, Gary replied, “What are the details?” and received a quick response:

Thank you for your quickly reply
Let me introduce our company to you first.
We are a scientific research agency engaged in paper polishing&publishing services. We have a large number of clients in different areas,
If you think you can cooperate with us, our cooperation mode is as follows

1. You provide some titles to me, if a customer likes and chooses your title, then you finish the writing, and we will pay you when the article is published
2. We provide the article, you are responsible for polishing it for us, and you also get paid after the article is published
I wonder if you can accept this mode of cooperation?
By the way, is it convenient to learn about your field of study

Gary then asked, “How large a payment?” and received this response:

I am very glad that you are interested in talking with me.
The amount of remuneration depends on your research field, whether you are a journal editor, and what section of JCR the journal belongs to.
If convenient, please add me on wechat or WhatsApp so that we can communicate in detail.

and a follow-up email,

Have you had a good time? How are you thinking about the cooperation with us? Look forward to working with you.

Gary continued to play along: “I need to know the renumeration.” And got some numbers:

Our prices range from $1500 to $6000, the exact amount you will get, still need to communicate more details.

A la Goodhart’s law, it is understandable that people would pay thousands of dollars to be credited with publications, even if the published research is BS. It is also a drag on society that people who are intelligent and capable of doing productive things instead spend their time paying for and supplying papers for such scams.

Perhaps the productivity slowdown is not due to the shrinking number of bullseyes, but to a declining number of sharpshooters focused on the right targets.