Freakonomics Radio - Policymaking Is Not a Science (Yet) (Ep. 405 Rebroadcast)

Episode Date: March 25, 2021

Why do so many promising solutions — in education, medicine, criminal justice, etc. — fail to scale up into great policy? And can a new breed of “implementation scientists” crack the code?

Hey there, Stephen Dubner. Most episodes of Freakonomics Radio involve or are even built around academic research. Obviously, we think this research is interesting, even important. But the sad fact is that a great deal of academic research, even the best stuff, often remains stuck in research land. Converting it into policy or behavior change is a whole other proposition. I've been thinking about this dilemma lately as we are a few months into a new presidential administration here in the U.S. Some of the Biden administration's policy ideas plainly have

a political component, but there's also a lot of proposed policy that's drawn from or at least tightly connected to academic research around things like poverty, health care, education, wages, energy, so on. We will be exploring a lot of that research-based policy over the coming months on the show. But first, I wanted to play for you a very relevant episode we made last year, just before the pandemic. it is about how rare it is for good academic research to be turned into good policy. This isn't all about blaming policymakers and politicians. Some of the failure has to be attributed to the researchers themselves. As we tried to make clear in the title of this episode, it's called Policymaking is Not a Science Yet.

And it starts right now. Usually when children are born deaf, they call it nerve deafness. But it's really not the actual nerve. It's little tiny hair cells in the cochlea. Dana Suskind is a physician scientist at the University of Chicago, and more dramatically, she is a pediatric surgeon who specializes in cochlear implants. My job is to implant this incredible piece of technology which bypasses these defective hair cells and takes the sound from the environment, the acoustic sound, and transforms it into

electrical energy, which then stimulates the nerve. And somebody who is severe to completely profoundly deaf after implantation can have normal levels of hearing. And it is pretty phenomenal. It is pretty phenomenal. It is pretty phenomenal. If you ever need a good cry, a happy cry, just type in cochlear implant activation on YouTube. You'll see little kids hearing sound for the first time and their parents flipping out with joy. Good job! She's smiling.

Oh, that's great! She's so smiling. Yeah, that's your ears. Yeah. The cochlear implant is a remarkable piece of technology, but really it's just one of many remarkable advances in medicine and elsewhere. Created by devoted researchers and technologists and sundry, smart people. You know what's even more remarkable?

How often we fail to take advantage of these advances. One of the most compelling examples is the issue of hypertension. About a third of all Americans have high blood pressure. First of all, the awareness rate is about only 80%. Of the total amount, only 50% actually are controlled. We have great drugs, right? But you can see the cascade of issues when you have to disseminate, you have to adhere, etc., and the public health ramifications of that. Prescription adherence is a very difficult nut to crack. It'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. All of us, our lives get busy. We forget. You wouldn't think you'd have an adherence issue with something like the cochlear implant. It has such an obvious upside. And yet...

When I put the internal device in, it stays there. But it actually requires an external portion as well, sort of like a hearing aid. And that is the part where you see issues related to adherence. Just because I put the internal part doesn't mean that an individual or a child will be wearing the external part. In one study, only half of the participants wore their device full-time. I mean, we have figured through randomized control trials to understand causation, real impact in the small scale. But the next step is understanding the science of how to use this science. Because, you know, how you do it on the small scale in perfect conditions is very different than the messy real world. And that is a very real issue.

Today on Freakonomics Radio, what to do about that very real issue. Because you see the same thing not just in medicine, but in education and economic policy and elsewhere. Solutions that look foolproof in the research stage are failing to scale up. People said, let's just put it out there. And then we quickly realized that's far more complicated. There might be something that you think would be great, but it's never going to be able to be implemented in the real world. We need to know what is the magic sauce.

We'll go in search of that magic sauce right after this. This is Freakonomics Radio, the podcast that explores the hidden side of everything. Here's your host, Stephen Dubner. John List is a pioneer in the relatively recent movement to give economic research more credibility in the real world. 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. Okay, so you and others moved experiments out of the lab and into the real world, but have you been able to successfully translate those experimental findings into, let's say, good policy? 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? The way List sees it, academics like him work hard to come up with evidence for some intervention that's supposed to help alleviate poverty or improve education, to help people quit smoking or take their blood pressure medicine. The academic then writes up their paper for an incredibly impressive-looking academic journal, impressive at least to fellow academics. To the rest of us, it's jargony and indecipherable. But then, with paper in hand, the academic goes out proselytizing to policymakers. He might say, you politicians always talk about making evidence-based policy. Well, here's some new evidence for an effective and cost-effective way of addressing that problem you say you care so

much about. And then the policymaker may say, well, the last time we listened to an academic like you, we did just what they told us, but it didn't work. And it cost three times what they said it would. And we got hammered in the press. And here's the thing. The politician and the academic may both be right. John List has seen this from both sides now. In a past life, I worked in the White House advising the president on environmental and resource issues within economics. This was in the early 2000s under George W. Bush. A harsh lesson that I learned was you have to evaluate the effects of public policy as opposed to its intentions. Because the intentions are obviously good. For instance, improving literacy for grade schoolers or helping low-income high schoolers get to college.

When you step back and look at the amount of policies that we put in place that don't work. It's just a travesty. List has firsthand experience with the failure to scale. 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 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. Wow. If you had asked me to guess all the ways that a program like that could fail, it would have taken me a while to guess that you simply didn't get parental uptake. The main problem is we just don't understand the science of scaling. If you were to attach a noun to what this is, the scalability blank, is it a problem? Is it a dilemma? Is it a crisis?

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 John List and Dana Susskind and some other researchers are on a quest to address this scalability crisis. They've been writing a series of papers, for instance, The Science of Using Science Towards an Understanding of the Threats to Scaling Experiments. A lot of their focus is on early education, since that is a particular passion of Suskind's. I guess you could say I'm a surgeon by day and social scientist by night.

My clinical work is about taking care of one child at a time. My research really comes out of the fact that not all children do as well as others after surgery and trying to figure out the best ways to allow all my patients and really children born into low-income backgrounds to reach their educational potentials. It is kind of like a superhero in reverse. During the day, you're doing the big dramatic stuff, and at night, you're going home to analyze the data

and figure out what's happening. I think that really the hard part is the night part. I love doing surgery. I adore my patients. But it's actually not as hard as many of the complex issues in this world. And was that a recognition that some kids after the surgery sort of zoomed up the education ladder and others didn't? Yeah. It's not simply about hearing loss. It's because language is the food for the developing brain. Before surgery, they all looked like they'd have

the same potential to, as you say, zoom up the educational ladder. After surgery, there were very different outcomes. And too often that difference fell along socioeconomic lines. That made me start searching outside the operating room for understanding why and what I could do about it. And it has taken me on a journey. 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. Dane and I started to work on early childhood research, and after that, research turned to love. I always joke that I was wooed with spreadsheets and hypotheses.

Is that true? Yes. Yeah. So, in fact, the reason I decided to marry him was because I wanted this area of scaling to be a robust area of research for him. Because it really is a major issue. Susskind started what was then called the 30 Million Words Initiative. 30 million being an estimate of how many fewer words a child from a low-income home will have heard than an affluent child by the time they turn four. But these days, the project is called the TMW Center for Early Learning and Public Health.

We've actually moved away from the term 30 million words because it's such a hot-button issue. Hot-button because it's so hard to believe that the number is legit? Well, no. I mean, some people say, look, it's a deficit mentality. You're talking about what's not there. And then the replication, somebody did another study that said, oh, it's only 4 million. And it really isn't actually even the point because it's not even about words. It's about the interaction. So I just made the decision. I'd rather be focusing on developing the research than fighting a naming battle. So you didn't make TMW stand for something else? Well, that's what everybody gives me trouble for.

It stands for 30 million words, but only I know that. Okay, now you all know it too. Anyway, they started the center with this idea. With this idea that, you know, we need to take a public health or a population level approach during the early years to optimize early foundational brain development. Because the research is pretty clear that parent talk and interaction in the first three years of life are the catalyst for brain development. And so that's basically our work. Okay, so far so good. The research is clear that heavy exposure to language is good for the developing brain. But how do you turn that research finding into action? And how do you scale it up? Initially, we started with an intensive home visiting program. But understanding that to reach population-level impact, you need to develop programs both with an eye for scaling as well as an eye for understanding where parents go regularly.

Because healthcare, unlike the education system, the first three years of life really don't have any infrastructure in which to disseminate programs. So we actually expanded our model. We have this multifaceted program that reached parents where they were, from maternity wards into pediatrics offices, into the homes as well as group sessions. Those programs that are most vulnerable to the issues of scale are the complex sort of service delivery interventions. You know, anything that takes a human service delivery.

Scaling isn't an end. It's really just a continuation. You know, it's a hard one. That's Patti Chamberlain, Science Director of the Oregon Social Learning Center. And I do research and implementation of evidence-based practices in child welfare, juvenile justice, mental health, and education systems. Chamberlain also looks at scaling as a process. So it's almost like there's stages that you have to go through. And if the first stage is research that involves an RCT, a randomized controlled trial, there's already an important choice to make.

You're far better off to situate your RCT in a real-world setting than a university clinic, so that you're learning from the beginning what's feasible and what's not feasible. There might be something that you think would be great, but it's never going to be able to be implemented in the real world. I've been at this now for, oh, probably 25 years, and I learned sort of through failing. One program Chamberlain founded is called Treatment Foster Care Oregon. Kids tend to commit crimes together. It's a team sport. But then, oddly, the way that we're set up to deal with kids who, you know, reach the level where they're really being unsafe to themselves and to the community is we put them in group homes together. We're putting kids in a situation where they're more likely to commit crimes. So we decided, what if we placed a child singly in a family that was completely devoted to

using evidence-based parenting skills to help that child do well with peers in school and in the family setting? What if we gave the parents, the biological parents of that kid, the same kind of skills that the treatment foster care family had? What if we gave the kid individual therapy, the biological family was getting family therapy, we're giving the kids support at school. So we were basically wrapping all these services around an individual child in a family home. What we found was, yeah, the kids do a lot better. They have a lot fewer arrests. They spend less days in institutions. They use fewer drugs. And guess what? It costs a lot less as well. Because you do not have a facility. You do not have 24-7 staff that you're paying in shifts.

You do not have, you know, all of the stuff that it takes to run an institution. You have a family. The success of Chamberlain's program caught the eye of researchers who were working on a program for a federal agency called the Office of Juvenile Justice and Delinquency Prevention. And so we got this call saying, you know, we want you to implement your program in 15 sites. If the program was successful at one site, how hard could it be to make it work at 15? I went in thinking that it wouldn't be that hard because we had good outcomes. We showed that we could save money. And yet? We were absolutely not ready. It wasn't because we didn't have enough data. We had, at that point, plenty of data. But we didn't

have the know-how of how to put this thing down in the real world, and it blew up. One reason, systemic complication. The three systems, child welfare, juvenile justice, and mental health, all put some money in the pot to fund this implementation. I was completely delighted. I thought, oh, this is going to be great because we have all the relevant systems buying into this. Well, what happened was when we tried to implement, we ran into tremendous barriers because if we satisfied the policies and procedures of one system, we were at odds with the policies and procedures in the other system. Patty Chamberlain had run up against something that Dana Suskin had come to see as an inherent disconnect when you try to scale up a research finding.

There's obviously the implementation, everybody focusing on adherence, but there's also sort of the infrastructure delivery mechanism, which I think is an issue, whether it's government or healthcare, that they're just not set up for interventions, which are sort of like innovations. So you've got these researchers who think of themselves as, you know, scientific entrepreneurs developing the next best thing, you know, thinking, you know, you build it and they will come. And then you've got organizations that are sort of built for efficiency rather than effectiveness that can't uptake it.

If only there were another science, a science to help these scientific entrepreneurs and institutions come together to implement this new research. Maybe something that could be called... Implementation science. Implementation science. Implementation science. Implementation science. Implementation science. Implementation science. Okay, let's define implementation science. It's the study of how programs get implemented into practice

and how the quality of that implementation may affect how well that program works or doesn't work. That's Lauren Suplee. She is the senior program officer at the William T. Grant Foundation, which supports research into reducing youth inequality. She previously worked evaluating programs within the federal government, mostly at Health and Human Services. This whole science is maybe 10 or 15 years old. It's really coming out of this movement of evidence-based policy and programs where people

said, well, we have this program. It appears to change important outcomes. Let's just put it out there. And then we quickly realized that there are a lot of issues and actually that put it out there is far more complicated. A lot of the evidence-based programs we have were designed by academic researchers who were testing it in the maybe more ideal circumstances that they had available to them. That might have included graduate students. It might have been a school district that was very amenable to research. And then you take the results of that and trying to put that into another location is where the challenge happened. So coming up after the break, can implementation science really help? You know, I want policy science not to be an oxymoron.

You're listening to Freakonomics Radio. I'm Stephen Dubner. We'll be right back. What randomized control trials tell us about an intervention is what that actual intervention does in a particular population, in a particular context. It doesn't mean that it's generalizable. That, again, is Dana Suskind from the University of Chicago. But you have to continue the science so you can understand how it's going to work in a different place, in a different context, in a different population, and have the same effect. And that's part of the scaling science.

The scaling science. That is what Susskind and her economist collaborator, John List, who's also her husband, and other researchers have been working on. They've been systematically examining why interventions that work well in experimental or research settings often fail to scale up. You can see why this is an important puzzle to solve.

Scaling up a new intervention, like a medical procedure or a teaching method, has the potential to help thousands, millions, maybe billions of people. But what if it simply fails at scale? What if it ends up costing way more than anticipated or create serious unintended consequences. That'll make it that much harder for the next set of researchers to persuade the next set of policymakers to listen to them. So List and Susskind have been looking at scaling failures from the past and trying to categorize what went wrong. 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. And when you say the wrong people, the people that are being studied then are to what? They are the people who are the fraction or the group of people who receive the largest

program benefits. So I think of some of the experiments that are done on college campuses, right, where there's a professor who's looking to find out something about, let's say, altruism, and the experimental setting is a classroom where 20 college students will come in and they're a pretty homogeneous population and they're pretty motivated. Maybe they're very disciplined and that may not represent what the world actually is. Is that what you're talking about? That's one piece of it. Another piece is who will sign their kids up for Head Start or for a program in a neighborhood that advances the reading skills of the child. Who's going to be first in line?

The people who really care about education and the people who think their child will receive the most benefits from the program. Now, another way to get it is sort of along the lines that you talked about. It could be the researcher knows something about the population that other people don't know. Like, I want to give my program its best shot of working. Okay. And what's in your third bucket of scaling failures? The third bucket is something that we call the wrong situation was used. And what I mean by that is that certain aspects of the situation change when you go from the original research to the scaled research program, we don't understand what properties of the situation or features of the environment will matter. There are a really large group of implementation scientists who have explored this question for years. Now, what they emphasize and focus on is something called voltage drop.

Voltage drop essentially means I found a really good result in my original research study, but then when they do it at scale, that voltage drop ends up being, for example, a tenth of the original result or a quarter of the original result. An example of this is when you look at Head Start's home visiting services, what they do there is this is an early childhood intervention that found huge improvements in both child and parent outcomes in the original study, except when they tried to scale that up and into home visits at a much larger scale, what they found is that, for example, home visits for at-risk families involved a lot more distractions in the house and there was less time on child-focused activities. So this is sort of

the wrong dosage or the wrong program is given at scale. There are many factors that contribute to this voltage drop, including the admirably high standards set by the original researchers. When the researcher starts his or her experiment, the inclination is, I'm going to get the best tutors in the world, so I'm going to be able to show how effective my intervention is. Dana Suskind again. You only needed 10 math tutors, and you happened to get the PhD students from the University of Chicago. And then what happens is you show this tremendous effect size, and in the scaling, all of a sudden you need 100 or 1,000.

And you no longer have that access to those individuals. And you go either down the supply chain with individuals who are not quite as well trained, or you end up having to pay a whole lot more money to maintain the trained tutor program. And one way or the other, either the impacts of the intervention go down or your costs go up significantly. Another problem in this third bucket, it's a big bucket, is when the person who designed the intervention and masterminded the initial trial can no longer be so involved once the program scales up to multiple locations. Imagine if instead of talking about an educational or medical program, we were talking about a successful restaurant and the original chef.

When you think about the chef, if a restaurant succeeds because of the magical work of the chef, and you think about scaling that, if you can't scale the magic in the chef, that's not scalable. Now, if the magic is because of the mix of ingredients, and the secret sauce, like Domino's, for example, the secret sauce or Papa John's is the actual ingredients, then that will be scalable. Now, if you are the kind of pizza eater who doesn't think Domino's or Papa John's is good pizza, well, welcome to the scaling dilemma. Going big means you have to be many things to many people. Going big means you will face a lot of trade-offs. Going big means you'll have a lot of

people asking you, do you want this done fast or do you want it done right? Once you peer inside these failure buckets that List and Susskind describe, it's not so surprising that so many good ideas fail to scale up. So what do they propose that could help? Now, our proposal is that we do not believe that we should scale a program until you're 95% certain the result is true. So essentially what that means is we need the original research and then three or four well-powered independent replications of the original findings. And how often is that already happening in the real world of, let's say, education reform research? I can't name one.

Wow. How about in the realm of medical compliance research? My intuition is that they're probably not far away from three or four well-powered independent replications. In the hard sciences, in many cases, you not only have the original research, but you have a first replication also published in science. You know, the current credibility crisis in science is a serious one that major results are not replicating. The reason why is because we weren't serious about replication in the first place. So this sort of puts the onus on policymakers and funding agencies in a sense of saying, we need to change the equilibrium. So that suggests that policymakers or decision makers, they are being what? Over eager, premature in accepting a finding that looks good to them and want to rush it

into play? Or is it that the researchers are overconfident themselves or maybe pushing this research too hard? Where is this failure really happening? Well, I think it's sort of a mix. I think it's fair to say that some policymakers are out looking for evidence to base their preferred program on. What this will do is slow that down. If you have a pet project that you want to get through, fund the replications, and let's make sure the science is correct. We think we should actually be rewarding scholars for attempting to replicate. You know, right now in my community, if I try to replicate someone else, guess what I've just made?

I've just made a mortal enemy for life. If you find a publishable result, what result is that? You're refuting previous research. Now I've doubled down on my enemy. So that's like a first step in terms of rewarding scholars who are attempting to replicate. Now, to complement that, I think we should also reward scholars who have produced results that are independently replicated. You know what I'm talking about? Tying tenure decisions, grant money, and the like to people who have given us credible research that replicates. But replication is just one component of the scaling revolution that List is proposing.

He also wants to make sure the original research is more robust. Say I'm doing an experiment in Chicago Heights on early childhood, and I find a great result. How confident should I be that when we take that result to all of Illinois or all of the Midwest or all of America, is that result still going to find that important benefit cost profile that we found in Chicago Heights? We need to know what is the magic sauce. Was it the 20 teachers you hired down in Chicago Heights, where if we go nationally, we need 20,000. So it should behoove me as an original researcher to say, look, if this scales up,

we're going to need many more teachers. I know teachers are an important input, is the average teacher in the 20,000 the same as the average teacher in the 20? This is the dreaded voltage drop that implementation scientists talk about. And the implementation scientists have focused on fidelity as a core component behind the voltage drop. Fidelity meaning that the scaled-up program reflects the integrity of the original program. Measures of fidelity. That's a really critical part of the implementation process. That, again, is Patty Chamberlain, founder of Treatment Foster Care Oregon. You've got to be able to measure, is this thing that's down in the real world the same, you know,

does it have the same components that produce the outcomes in the RCTs? Remember, it was Chamberlain's good outcomes with young people in foster care that made federal officials want to scale up her program in the first place. We got this call saying, we want you to implement your program in 15 sites. She found the scaling up initially very challenging. saying, we want you to implement your program in 15 sites. She found the scaling up initially very challenging. It wasn't the kumbaya moment that we thought it was going to be. But in time, Treatment and Foster Care Oregon became a very well-regarded program.

It's been around for roughly 25 years now, and the model has spread well beyond Oregon. One key to this success has been developing fidelity standards. So the way that we do it is we have people upload all of their sessions onto a HIPAA-secure website, and then we code those. And if they're not meeting the fidelity standards, then we offer a fidelity recovery plan. We haven't had to drop a site, but we have had to have some of the people in the site retrained or not continue. Being able to measure fidelity well from afar provides another benefit to scaling up. It allows the people who developed the original program to ultimately step back so they don't become a bottleneck, which is a common scaling problem. There can be sort of an orderly process whereby you step back in increments as people become more and more competent doing what they're

doing. And that's what you want because you don't want to have this tied to the developer forever. Otherwise, you can't get any kind of reasonable reach. That said, you also need to have some humility. When you're scaling up, you shouldn't assume your original program was perfect, that it won't need adjustment, and you need to be willing to make adjustments. that wasn't therapy per se. They needed skills because the kids had often been excluded from normal socializing, you know, sort of things like sports teams and clubs. And so we needed what we call a skills coach to help those kids learn the moves that they needed to be able to participate in, you know, these pro-social activities that are normal kind of things. So you have research, you have a theory, and then you have the implementation, and that feeds into more research, more theory, more implementation.

Look, everybody's motivation at the end of the day is about trying to do good for the people they serve. Dana Suskind, again. There are many children out there, and there are a lot of injustices, so we need to move, but I don't know. Science is slower than you'd like. People have wanted things before I thought they were ready, and finding a way to deal with that dance of people wanting information but also wanting to continue to build the evidence, I think we can figure out how to do it.

I think that's exactly right. And John List again. I think too many times, whether it's in public policy, whether it's a for-profit or a not-for-profit, we tend to only focus on one side of the market when we have problems. And you really need to take account of both sides because your optimal solutions, the best solutions are only going to come when you look at both sides of the market. I'm probably getting this wrong, or at least being way too reductive. But to me, it sounds like the chief barrier to scaling up programs to help people

is people, that people are the problem. Yeah, so I do think inherently it is about people. That said, this is not a fatal flaw that causes us to throw up our arms and say, well, this isn't physics, this isn't chemistry. We have to deal with people, so we can't use science. I think that's wrong, because there are some very, very neat advantages of scaling. Think about on the cost side, economists always talk about when things get bigger and bigger, guess what happens? The per unit cost goes down. It's called increasing returns to scale.

The problem that kind of we're thinking about is let's make sure that those policymakers who really want to do the right thing in youth science, let's make sure that they have the right programs to implement. So one of your papers includes this quote from Bill Clinton, or at least something that Clinton may have said, which is essentially that nearly every problem has been solved by someone somewhere, but we just can't seem to replicate those solutions anywhere else. So what makes you think that you've got the keys to success here where others may not have been able to do it? You know, I view what we've done as put forward a set of modest proposals is only a start to tackle what I think is the most vexing problem in evidence-based policymaking, which is scaling. I think we're just taking some small steps theoretically and empirically. But I do

think that these first set of steps are important because if you go in the right direction, what I've learned is that literature will follow that direction. If you go in the wrong direction, sometimes the literature follows that wrong direction for several years. And we really don't have the time. Right now, the opportunity cost of time is very high. You know, in the end, I want policy science not to be an oxymoron. And I think that's what this research agenda is about. The way that I would view it is that the world is imperfect because we haven't used science in policymaking. And if we add science to it, we have a chance to make an

imperfect world a little bit more perfect. If you want to read the papers that John List and Dana Suskind and their collaborators have been working on, you will find links on Freakonomics.com, as well as links to Patty Chamberlain's work with Treatment Foster Care Oregon, and much more, including, as always, a complete transcript of this episode. Coming up next time on Freakonomics Radio, as you have probably heard, healthcare in the U.S. can be very expensive. We have a three and a half trillion dollar medical system. And our best guess is that a trillion dollars a year is unnecessary.

Does anyone have any ideas for clawing back that trillion dollars? Yes, they do. That's next time. Until then, take care of yourself and if you can, someone else too. Freakonomics Radio is produced by Stitcher and Renbud Radio. We can be reached at radio at Freakonomics.com. This episode was produced by Matt Hickey. Our staff also includes Allison Craiglow, Mark McCluskey, Greg Rippin, Mary Duduk, Zach Lipinski, and Emma Terrell. We had help this week from Lyric Bowditch. Our theme song is Mr. Fortune by the Hitchhikers. All the other music was composed by Luis Guerra. The complete archive of Freakonomics Radio, more than 450 episodes, is now available on all podcast apps, and you can also hear it on many

NPR stations across the country. As always, thanks for listening. So you want to talk scaling? Wow, it's a heavy paper, right? It's great. I thought it was about scaling fish initially, so that was all my background reading. Yeah, so I don't know anything about what we're going to talk about today. Neither do I, so we can just both wing it.