Month: December 2025
Large Randomized Controlled Trial Finds Little Benefit in Free Money to Poor, Undermining Case for Universal Basic Income (UBI)
A variety of arguments have been made in support of a Universal Basic Income (UBI). I am most interested in the argument that says that technology will destroy the jobs of the worst off, and so for them to survive society would be justified in giving them a basic income. I do not believe that in a free society technological progress will on balance destroy the jobs of the worst off. If innovative entrepreneurs are free to innovate, especially in labor markets, they will find ways to employ the worst off.
Others have argued that giving a basic income to the worst off will make them better parents, measurable by better child outcomes in terms of language skills and better behavior and cognition. Several years ago these advocates setup a big, expensive randomized controlled trial to test their argument. The results? None of their hypotheses were supported. The passages quoted below are from a front page New York Times article in which they express their surprise, and for some, their incredulity.
(p. A1) If the government wants poor children to thrive, it should give their parents money. That simple idea has propelled an avid movement to send low-income families regular payments with no strings attached.
Significant but indirect evidence has suggested that unconditional cash aid would help children flourish. But now a rigorous experiment, in a more direct test, found that years of monthly payments did nothing to boost children’s well-being, a result that defied researchers’ predictions and could weaken the case for income guarantees.
After four years of payments, children whose parents received $333 a month from the experiment fared no better than similar children without that help, the study found. They were no more likely to develop language skills, avoid behavioral problems or developmental delays, demonstrate executive function or exhibit brain activity associated with cognitive development.
“I was very surprised — we were all very surprised,” said Greg J. Duncan, an economist at the University of California, Irvine and one of six researchers who led the study, called Baby’s First Years. “The money did not (p. A15) make a difference.”
The findings could weaken the case for turning the child tax credit into an income guarantee, as the Democrats did briefly four years ago in a pandemic-era effort to fight child poverty.
. . .
Though an earlier paper showed promising activity on a related neurological measure in the high-cash infants, that trend did not endure. The new study detected “some evidence” of other differences in neurological activity between the two groups of children, but its significance was unclear.
While researchers publicized the earlier, more promising results, the follow-up study was released quietly and has received little attention. Several co-authors declined to comment on the results, saying that it was unclear why the payments had no effect and that the pattern could change as the children age.
For the full story see:
(Note: ellipsis added.)
(Note: the online version of the story has the date July 28, 2025, and has the title “Study May Undercut Idea That Cash Payments to Poor Families Help Child Development.”)
The academic presentation of the research discussed above, can be found in:
AI Cannot Know What People Think “At the Very Edge of Their Experience”
The passages quoted below mention “the advent of generative A.I.” From previous reading, I had the impression that “generative A.I” meant A.I. that had reached human level cognition. But when I looked up the meaning of the phrase, I found that it means A.I. that can generate new content. Then I smiled. I was at Wabash College as an undergraduate from 1971-1974 (I graduated in three years). Sometime during those years, Wabash acquired its first minicomputer, and I took a course in BASIC computer programming. I distinctly remember programming a template for a brief poem where at key locations I inserted a random word variable. Where the random word variable occurred, the program randomly selected from one of a number of rhyming words. So each time the program was run, a new rhyming poem would be “generated.” That was new content, and sometimes it was even amusing. But it wasn’t any good, and it did not have deep meaning, and if what it generated was true, it was only by accident. So I guess “the advent of generative A.I.” goes back at least to the early 1970s when Art Diamond messed around with a DEC.
This is not the main point of the passages quoted below. The main point is that the frontiers of human thought are not on the internet, and so cannot be part of the training of A.I. So whatever A.I. can do, it can’t think at the human “edge.”
(p. B3) Dan Shipper, the founder of the media start-up Every, says he gets asked a lot whether he thinks robots will replace writers. He swears they won’t, at least not at his company.
. . .
Mr. Shipper argues that the advent of generative A.I. is merely the latest step in a centuries-long technological march that has brought writers closer to their own ideas. Along the way, most typesetters and scriveners have been erased. But the part of writing that most requires humans remains intact: a perspective and taste, and A.I. can help form both even though it doesn’t have either on its own, he said.
“One example of a thing that journalists do that language models cannot is come and have this conversation with me,” Mr. Shipper said. “You’re going out and talking to people every day at the very edge of their experience. That’s always changing. And language models just don’t have access to that, because it’s not on the internet.”
For the full story see:
(Note: ellipsis added.)
(Note: the online version of the story has the date May 21, 2025, and has the title “Will Writing Survive A.I.? This Media Company Is Betting on It.”)
If AI Takes Some Jobs, New Human Jobs Will Be Created
In the passage quoted below, Atkinson makes a sound general case for optimism on the effects of AI on the labor market. I would add to that case that many are currently overestimating the potential cognitive effectiveness of AI. Humans have a vast reservoir of unarticulated common sense knowledge that is not accessible to AI training. In addition AI cannot innovate at the frontiers of knowledge, not yet posted to the internet.
(p. A15) AI doomsayers frequently succumb to what economists call the “lump of labor” fallacy: the idea that there is a limited amount of work to be done, and if a job is eliminated, it’s gone for good. This fails to account for second-order effects, whereby the saving from increased productivity is recycled back into the economy in the form of higher wages, higher profits and reduced prices. This creates new demand that in turn creates new jobs. Some of these are entirely new occupations, such as “content creator assistant,” but others are existing jobs that are in higher demand now that people have more money to spend—for example, personal trainers.
Suppose an insurance firm uses AI to handle many of the customer-service functions that humans used to perform. Assume the technology allows the firm to do the same amount of work with 50% less labor. Some workers would lose their jobs, but lower labor costs would decrease insurance premiums. Customers would then be able to spend less money on insurance and more on other things, such as vacations, restaurants or gym memberships.
In other words, the savings don’t get stuffed under a mattress; they get spent, thereby creating more jobs.
For the full commentary, see:
(Note: the online version of the commentary has the date June 5, 2025, and has the same title as the print version.)
Nick Gillespie Calls Openness to Creative Destruction “Fantastic”
This episode of the Reason Roundtable podcast was posted on Mon., Nov. 24, 2025.