John List
speaker
84 appearances
2 recordings
2 series
first heard Dec 2024
last heard Apr 2025
John List’s voice in public audio — every appearance, attributed to the second.
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Appearances
Good job! She's smiling. Oh, that's great!
That's John List. He's an economist at the University of Chicago. They actually have to go and get the medicines, which a lot of people have a very hard time doing, even though it's sitting next to your bed every night. People don't take it. And they don't take it because they forget. They don't take it because the side effect is a lot worse than the benefit they think they're getting.
All of these types of problems, as humans, including myself, we do a really bad job in trying to solve.
If you turn back the clock to the 1990s, there was a credibility revolution in economics, focusing on what data and modeling assumptions are necessary to go from correlation to causality. List responded by running dozens and dozens of field experiments.
Now, my contribution in the credibility revolution was instead of working with secondary data, I actually went to the world and used the world as my lab and generated new data to test theories and estimate program effects.
I think moving our work into policymaking circles and having a very strong impact has just not been there. And I think one of the most important questions is, how are we going to make that natural progression of field experiments within the social sciences to more keenly talk to policymakers, the broader public, and actually the scientific community as a whole?
In a past life, I worked in the White House advising the president on environmental and resource issues within economics.
A harsh lesson that I learned was you have to evaluate the effects of public policy as opposed to its intentions.
When you step back and look at the amount of policies that we put in place that don't work...
So down in Chicago Heights, I ran a series of interventions, and one of the more powerful interventions was called the Parent Academy. That was a program that brought in parents every few weeks, and we taught them everything.
What are the best mechanisms and approaches that they can use with their three-, four-, and five-year-old children to push both their cognitive skills and their executive function skills, things like self-control? What we found was within three to six months, we can move a child in very short order to have very strong cognitive test scores and very strong executive function skills.
So, of course, we're very optimistic after getting this type of result, and we want the whole world to now do parent academies. The UK approaches us and said, we want to roll it out across London and the boroughs around London. What we found is that it failed miserably. It wasn't that the program was bad. It failed miserably because no parents actually signed up.
So if you want your program to work at higher levels, you have to figure out how to get the right people and all the people, of course, into the program.
The main problem is we just don't understand the science of scaling.
I do think it's a crisis in that if we don't take care of it as scientists, I think everything we do can be undermined in the eyes of the policymaker and the broader public. We don't understand how to use our own science to make better policies.
So Dana and I met back in 2012. And we were introduced by a mutual friend. And we did the usual ignore each other for a few years because we're too busy. And push came to shove. Dana and I started to work on early childhood research. And after that, research turned to love.
You can kind of put what we've learned into three general buckets that seem to encompass the failures. Bucket number one is that the evidence was just not there to justify scaling the program in the first place. The Department of Education did this broad survey on prevention programs attempting to attenuate youth substance and crime and aspects like that.
And what they found is that only 8% of those programs were actually backed by research evidence. Many programs that we put in place really don't have the research findings to support them. And this is what a scientist would call a false positive. So are we talking about bad research? Are we talking about cherry picking? Are we talking about publication bias?
So here we're talking about none of those. We're talking about a small-scale research finding that was the truth in that finding But because of the mechanics of statistical inference, and it just won't be right, what you were getting into is what I would call the second bucket of why things fail, and that's what I call the wrong people were studied.
You know, these are studies that have a particular sample of people that shows really large program effect sizes. But when your program is gone to general populations, that effect disappears. So essentially, we were looking at the wrong people and scaling to the wrong people.
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