Nick Hart on Foundations for Evidence-Based Policymaking Act

Robert Hahn:

Hello, and welcome to the Technology Policy Institute’s podcast, Two Think Minimum. I’m your host, Robert Hahn, and today we are delighted to speak with our colleague and friend Nick Hart on evidence-based policy, which I’m sure is very near and dear to all of your hearts.

Nick is one of the world’s experts in telling people how to get evidence-based policy to work in real-time. He’s also the CEO of the Data Coalition. He helped shape the Foundations for Evidence-Based Policymaking Act, and those of us in the know sometimes call it “the Evidence Act,” and he’s worked with numerous agencies and Congress to improve data evaluation and privacy policies.

Before that, Nick directed the Bipartisan Policy Center’s Evidence Project and was also a civil servant at the Office of Management and Budget. Nick, welcome, and thanks for joining us today.

Dr. Nick Hart:

Hey, thanks for the invitation. Happy to be here.

Robert Hahn:

And Happy New Year! To get the ball rolling, I was wondering if you might tell us a little bit about your interest in evidence-based policy and what you mean by the term?

Dr. Nick Hart:

Sure. You know, it’s actually an interesting concept because evidence-based policymaking means different things to different people. There are individuals out there who hear that term and think it means that all decision-making in government will be dictated by what a particular study or finding suggests. And in reality, that’s not typically what we mean when we say, “evidence-based policymaking.”

The Commission, Bob, that you were actually a member of, defined it slightly differently in texts. And that’s because there’s a lot of complexities to making decisions in government. And especially in democratic society, we make decisions with lots of individuals providing inputs based on values and evidence. So, I actually, just in my own work, prefer the term “evidence-informed policy” because it tends to be more often how we actually operate. And so, what we mean by that is taking the best science, the best data, and the best analysis, and I can talk more about what I mean by those specific terms too, but taking that body of knowledge and that body of information and using it to apply a decision-making framework to build reasonable and responsible policy.

Yeah, I like to say that there’s no study that’s ever going to tell a member of Congress or a president or a civil servant across the executive branch, how to specifically make a decision, you know? Do you have this piece of information about a program working? What does that mean for the budget? Do you give more money or less money in a budgetary context?

Oh, there’s a lot of other things that drive how we make budget decisions, including how much money we want to spend. Well, that’s a value decision. So, the evidence is really just one among many inputs, but it should be a really important input for how we make decisions.

Dr. Nick Hart:

We have a great body of knowledge about things that work and things that don’t work when it comes to anti-poverty programs, and that should inform how we deploy systems around child welfare, child support, employment services, et cetera, et cetera. And we have the capability to do much of that today. There’s still tons of gaps out there. And this is the part, to my interest, the part that makes me really excited about this work is we know that we don’t know everything about how government programs work. We wish we did in many cases, but the programs are constantly evolving as the population changes, the economy changes, society changes. And so we have this continuous need to learn and keep improving these programs. If we’re going to keep improving them, we also have to stay up to date with what’s happening.

So, when I think about evidence-based policymaking, that’s a full spectrum of knowledge, everything from basic descriptive statistics… like the unemployment rate, it’s a descriptive statistic. It’s a basic indicator of what’s happening in the economy for a portion of the labor force… all the way through different types of analysis, to things that we call impact evaluations, really robust studies often with random samples and random assignment where we can actually measure the impact of whether a program works, and in what context. We need all of that, everything in between those two types of analysis to inform good decision-making on government.

So, we have tons of work to do. We don’t have the capabilities to do all of those things across the federal government or many state and local governments today, but we could, and I think we’re slowly getting to a better place where we can deploy those different forms of analytics for good decision-making.

Robert Hahn:

So, let me ask you a question related to COVID, which is obviously top of mind in many spheres, you know, where people have to deal with it on a daily basis. I heard, and maybe I’m mistaken, but I heard on TV, you know, people talking about, “following the science.” And when I listened to you sort of in your introductory remarks, what I sort of hear reading between the lines is, “Science isn’t going to solve these problems for you, but it can inform the discussion.”

So, what’s your take sort of when people say, “follow the science,” and what’s your reaction to that either generally or with respect to COVID?

Dr. Nick Hart:

Yeah, I mean, this is a question that really comes up quite often, and there’s a whole field of science communication that’s really built around this idea of, “How do you take the information that scientists are producing as quickly as they can, and effectively relay that to not just decision-makers, but in some cases the American public?” And COVID has been an interesting experience in the context of science communication because there’s so much good information but also misinformation and disinformation that can quickly circulate. I mean, I don’t know how many times I’ve been on social media and I’ve seen somebody take a quote out of a peer-reviewed article and, you know, figure out ways to contort it, to fit some preconceived narrative. That’s not really what we’re talking about. The piece that is absolutely essential, especially when it comes to public health, is considering not just a single study, but a body of knowledge, which could be multiple studies.

Sometimes, it’s expert opinions based on the people that are working on those studies, when they have to interpret in the gray areas of how to figure out what is the policy that makes the most sense based on what knowledge and the resources that we actually have. I spent a lot of my career working with the Environmental Protection Agency, and Bob, I know you’ve worked a lot with EPA as well, and one of the things that EPA, the US EPA, is among the best in the world at doing is this thing called, “risk assessment.” And risk is a concept that I would say most of the American public… I mean, including my own household sometimes… we struggle with it. Because it’s not a binary. It’s not this deterministic like, “Yes, you do this thing or no, you don’t do that thing.” They’re often gradations of how we think about the severity of consequence. So, whether it comes to public health hazards or cancer risk, those are things that are not particularly binary when we make regulations and when we make laws. And so decision-makers, policymakers have to be able to process a lot of information to figure out what is the acceptable level of risk.

Well, that relates directly to this question about science. How do we incorporate science in decision making? Because, what I said at the beginning, I will very much stand by, that no piece of science is ever going to give you an absolute to say, “Do this thing,” but it can help set up the guard rails, and it can guide us down a path of the things that are generally in the right direction. And the more we build that body of knowledge, the more clarity we should be developing about whether that’s right.

Sometimes, we get it wrong. I mean, the EPA, since I used that as an example, has a long history of making some mistakes in its regulatory processes. I think there’s an interesting example of the regulation of saccharin. It’s a sweetener, and when EPA initially issued its hazardous waste regulations, it plied one of the most onerous regulatory infrastructures in the country, perhaps the world, to this chemical called saccharin. And it’s an incredibly expensive regulatory infrastructure to comply with, but it was based on studies that we now know were not really appropriate for understanding the risks of that particular chemical. So, the mode of action for cancer that was identified in these studies for rats, doesn’t actually apply to humans. Okay. So, what did that mean? That meant that the whole basis of the regulatory framework didn’t apply to this particular chemical, and it took years to change that regulation for that particular chemical. That’s an example where fortunately we eventually got it right, but it was literally decades before we did.

That’s a suggestion that we have to be willing to also continue to learn as science develops. And as we understand more about in this case, toxicology, epidemiology, we’re going to continue to learn a lot about COVID within the next several years, particularly as there are mutations and evolutions, a better understanding of public health and behaviors that are happening across society, things around schools, what’s happening in schools around different mitigation behaviors and reopenings as well as the concepts around things like learning loss, the other consequences that happen beyond the direct public health that might affect our broad population. Those are all things that we need to apply science to. Social science research, natural science in some cases, but we have to be able to continue learning what’s happening out there in the real world, so we can apply that information to policymaking.

Robert Hahn:

So, I hadn’t thought about saccharin in I can’t say how many years. I can remember those little tablets or whatever that you use to put in your cup of coffee or whatever, but I have a question for you related to evidence. So that’s sort of a negative-positive story, but are there positive stories that you can come up with or that you’re aware of where some sort of scientific evidence-building process has made a positive difference for public policy?

Dr. Nick Hart:

Yeah. I think we actually have a lot of really good examples, and I worked on a book last year called Evidence Works that highlighted a number of case studies where this is actually the specific question we sought to delve into. And one of the things that we’ve learned in that process is that finding these examples can often be very difficult because a lot of us, myself included, will gravitate to talk about the study, what the research says. And we sort of forget about what it means to apply the evidence and the policy-making context because it’s often a black box. You know, even for as transparent as governments can be on different decision-making processes at the end of the day, a lot of what happens for actual decision-making using evidence is inside this black box. So, what are some examples we can talk about? Lots in the anti-poverty space, particularly since the mid-nineties welfare reform, we have learned an incredible amount about what works and what doesn’t when it comes to anti-poverty programs.

And that’s because they have this really powerful authority to test and to use government funding, to test and experiment with new ideas across the country, across the States. When it comes to child support, for example, we have a major national program on child support enforcement. It’s intended to identify non-custodial parents and ensure that they’re paying child support that is due to benefit the kids. Well, we had a great deal of evidence that was built over a number of years that identified not just particular problems, but one really specific one around what happens to non-custodial parents who are incarcerated. It turned out a lot of incarcerated parents would have child support arrears, and then they would get out of jail. They couldn’t pay them, and then they would get put right back in jail. And this sounds like a really obvious thing now, but if you get put back in jail, you’re not going to pay child support.

Well, we did some really good experimentation about alternatives to that approach, and I’m happy to say that many of those things are now embodied in a regulation that the Obama administration put forward at the end of the second term. And they were solutions that were identified from some of this experimentation. In a different context, we have learned a lot about the benefits of public housing through some really good work in partnerships between the Federal Department of Housing and Urban Development and a part of the Health and Human Services Department called the National Center for Health Statistics; it’s one of our federal statistical agencies. They did something that was really kind of basic. This was not a really rigorous sophisticated research project; it was literally combining datasets that each of those different departments collected, and what they were able to do was look at blood lead levels in children, in public housing.

And what they were able to identify was that actually one of the unstated benefits of public housing provided and funded by the federal government was an incredible reduction, relative to other housing, for kids and blood lead levels, and that affects cognitive development. So, it’s a major factor for childhood development that as we understand that, and now we know that that’s a benefit. It also helps us figure out how we target funding for things that might actually have health benefits. In addition to it’s like literally just providing the housing for individuals that was built on evidence that our statistical system developed.

And then I guess maybe one other example that I like to think about is a major evaluation study that was also conducted by HUD called the Family Options Study. This was towards the end of the Obama administration when it was actually completed, but it was a decade-long project, and actually one of the staff members of the Commission on Evidence-Based Policymaking worked on this project for an entire decade in her career. And it was basically an attempt to look at the different methods for engaging in public housing, and there’s a lot of information out there about what works best and what reduces reliance on government services, and what benefits children the most and leads to the best long-term outcomes and employment and earnings and things of that sort.

Well, HUD tested that. They spent a lot of money over a decade to test these concepts, and it turns out we know that one of the most effective things, and as an economist, this is not going to be surprising, but the direct subsidy is one of the most effective strategies for improving the cohort of long-term outcomes that they were interested in, and that manifested, when it comes to the use, the Obama administration made some pretty pivotal changes in the design of housing programs, including a massive budget request to really double down on the fact that this is what the evidence showed, and this is what it meant.

I realized I’ve just given three examples that were largely from the Obama administration. There are examples from more recent time as well, but I think the more distant we get from some of these examples, the more we come to understand what the real impact was for using the evidence, which is sometimes a hard question to answer because we don’t see that use happening all the time. It’s not always one-to-one, you’ve got this study and somebody immediately writes a law or put something in a regulation or policy. Sometimes this is about changing perceptions, and sometimes it’s eventually about changing those policies directly.

Robert Hahn:

Great. I have a question for you about the Evidence Act since you helped shepherd it through Congress and deserve some notion of paternity, I think. Can you tell us a little bit about the politics of that act? Anything you could share with us based on your experience there, putting you on your Ph.D. hat, and also whether you think there’s likely to be bipartisan support for this idea going forward I mean, one of the conversations that you and I had some time ago was they passed a nice act that had a lot of the Commission’s recommendations, but the funding wasn’t necessarily there to do what we might have hoped.

Dr. Nick Hart:

Yeah. The whole story of the manifestation, and then final enactment of the Foundations for Evidence-Based Policymaking Act, I find fascinating, but I was also involved in that story; so, I’m probably a little bit biased. You know, when it comes to the politics, one of the really fascinating things from watching how that process unfolded was there was this Commission, which you were part of, that issued this set of recommendations, and that was really the basis for this entire law. The fact that that Commission was comprised of members appointed by both Republicans and Democrats, and then, in turn, issued unanimous recommendations. The fact that those recommendations were unanimous and bipartisan can not be understated as the starting point of this entire endeavor. I think that was really powerful when the conversations with Congress were beginning and even in the late stages. And after we’d seen the bill passed the House the first time, and it was weaving its way through the Senate, there were a lot of times where that bipartisanship in both the Commission, but also some of the dialogues that were happening behind the scenes with Congressional staff and members, that was really central to the success.

So, why was this so bipartisan? It’s hard to say to be totally honest because there is inevitably going to be politics when it comes to using evidence. When it comes to using evidence, people will always apply their value systems and their ideologies to inform how they apply that information, and I’d say that’s actually a good thing in democratic society. I think the reason there was so much bipartisanship around this particular legislation was because it started that way. It started in a very bipartisan framework and much of what was established here was actually about building the evidence and ensuring that the supply or the production of knowledge could happen.

That’s not the part that people typically want to debate, right? I think there’s generally a consensus that we need to know and have as much good information as we possibly can. So, there’s a lot in this law about the infrastructure, the capacity, the leadership positions, and in some cases, the assessments, but also providing you with the authorities for engaging in data sharing while appropriately protecting privacy. Those are all things that for the most part members of Congress got behind. The legislation went through the Senate under unanimous consent, not a single objection. It was passed in the House under what’s called suspension, which means that it was overwhelmingly supported. At the end of the process. There were only 17 no votes on the final legislation. That’s an incredible achievement when it comes to legislation because there’s often something that folks can nitpick at that might stall a bill out of the gate. And this was an example of legislation achieving that overwhelming bipartisan support, but also moving incredibly fast. You know, from start to finish, it was under two years of consideration.

So, what does that mean for all this work going forward? I think that it suggests that we can expect to continue seeing some bipartisan support when it comes to using data, managing, navigating data analytics across government. It does not mean that there will be bipartisan support on what the evidence says or bipartisan agreement on what the evidence says, and there will be healthy and spirited debates about that for generations to come. But when it comes to this need for knowledge, I think it’s really, particularly in the contemporaneous environment, it’s very encouraging that this legislation could move along the trajectory that it did and that it’s actually being implemented today.

Civil servants across government are really prioritizing this. We now have Chief Data Officers, evaluation officers that are not just taking the positions in name, but really leaning in and engaging on types of activities that were intended both by the Commission, but also the legal framework that’s now in place.

You asked about money, Bob. You know, money is like the critical piece of the puzzle that in some cases just doesn’t exist yet. The resources, which are not just about money, it’s also people and capacity inside the institutions of government. And some agencies, some departments, this was not a problem. They figured it out. They found resources. They reallocated because the leaders wanted to make it a priority. There are other departments that today, we know, unless they get an influx of resources, this is going to be a challenge. This is new to them. They don’t have a history of building the kind of evidence that we’re talking about, particularly evaluation. You know, it’s a relatively new concept in many federal government agencies and many state and local agencies for that matter. But it’s something that’s gained a lot of steam, a lot of enthusiasm because of the power of the knowledge that it generates, and frankly, the usefulness of the recommendations that come out of evaluation activities. But without new funding, some agencies just aren’t going to get there.

So, this has got to be a priority for Congress. It must be a priority for the new Biden-Harris administration in developing budget proposals for the next fiscal year. If we’re serious about making evidence-based policy, making it happen in this country… or evidence-informed policymaking, data-driven government, whatever you want to call it. If we want the data to be available, accessible, and useful for researchers or even for folks inside the government itself, we have to be able to allocate the resources to make it all happen. And OMB can provide great leadership here. Congressional appropriators can provide great leadership here, but however it happens, we definitely need to have a really honest conversation about where the resources are and making sure they show up.

Robert Hahn:

Okay. So my next question for you is, is it bigger than a bread box? I mean, what sort of resources are we talking about? Are we talking about millions, billions, trillions, what are we talking about in terms of getting this to work well and you know, what improvements would we see for what kind of investments?

Dr. Nick Hart:

Yeah, so what I’m envisioning here is not billions or trillions, and I should clarify that. A lot can be done with relatively small investments. When it comes to managing data, improving data quality, these are things that if we plan well, we can actually do a lot with existing resources. A lot of the evaluation activities that have happened in recent decades are in the thousands or millions, depending on the scope and scale that is of interest. So, national scale evaluations, for example, tend to cost a bit more. Particularly when you add big benefit changes for anti-poverty programs, let’s say. The Family Option study that I described a bit ago, I think was roughly a $10 million evaluation program over a decade.

So, you know, depending on where you sit and your perception of government spending, that might be a lot or a little, but you know, if we’re spending billions of dollars on some of these programs, investing in the evaluation and the knowledge generation activities, it can be a blip in the radar for actually making sure that that bigger investment is used well.

The Social Security Disability Insurance Program is a $140 billion a year program. We spend a pittance on studying how to ensure those benefits are maximizing outcomes for the American public who receives benefits from that program. We should be investing much more and testing new ideas for things like disability insurance and other major spending items across the federal budget. This isn’t to say, “This is all about money.” Government effectiveness is a major piece of this puzzle, but when we look at the amount of money the government spends, the amount that we invest in research and evaluation is just an incredibly small number, and it should be much more.

Robert Hahn:

Okay, so let me just follow up on that. How much more? I mean, I know there’s no magic number, but there have been proposals floating around from long before you got your Ph.D. that we ought to put some fraction of the total budget into the evaluation. So, we can learn from that. Do you have any numbers in mind or want to float some numbers that academics have put forward for what we should do in the evaluation sphere to do better policy over time?

Dr. Nick Hart:

Yeah. I mean, there’s a lot of estimates that are out there. If you remember, the Obama administration had a goal of 3% of GDP, that was their total R&D investments across a whole bunch of functions, not just evaluation and data things.

But there’s an idea that’s been floated, including by the Evidence Commission. It was in the final report, along with the recommendations that were kind of buried in the details. And some other non-profits have talked about this for a while, that there should be a set percentage that we think about for government programs allocating to research and evaluation activities. Depending on who you talk to, it generally ranges from about half a percent to 1% of the spending on that program. I always put an asterisk on that number though, because I just gave the example of Social Security, Disability Insurance, $140 billion a year. Well, 1% of $140 billion is a lot of money, and I don’t know that I would quite recommend we spend that much on SSDI evaluation, because I’m not sure we would have enough capacity to execute it. But the point being that if we can think about evaluation and research activities as a necessity of good program administration, just like you spend money doing performance reviews for your staff and managing the team and doing strategic planning, assessing your ability to accomplish those goals should just also be a natural part of that equation.

Today, we don’t think about it that way in government. There are some exceptions to what I’m saying here, but by and large, that’s not the framework a lot of managers, political leaders, or civil servants approach this discussion with, and we need to change that. And that was really one of the tenors that I think you can take out of the Evidence Commission’s report, or even the Evidence Act was sort of hitting a reset on that baseline assumption and saying, “This is actually really important, and we need to figure out the strategies for doing this.”

All that to say, since you asked me about what the magic number is, the Evidence Commission did not offer that specific number. It’s Recommendation 5.5 that said there should be resources. So, we know this is an area that will often happen on a case by case basis, based on the size, the scope, the scale, the interest in particular programs. And frankly, there are programs that are doing this well today. So, I wouldn’t say they necessarily need more money, but across the whole government infrastructure, it’s gotta be a priority.

Robert Hahn:

So, this relates to a broader question that I know you’ve given some serious thought to and the Commission talked about as well, which is how does one introduce what I’ll call a culture of experimentation or evaluation into our government? Or does it, you know, is it there in part now and related to that, as I recall, one of the items in the Evidence Act that you had a role in was promoting this idea of a learning agenda, and I think that agencies are supposed to come up with some kind of learning agenda on a semi-regular basis. Are they doing that? And you know, what’s your assessment of how things are going there?

Dr. Nick Hart:

Yeah. So, the question of how we build the culture for learning is super complicated. There is no magic recipe for putting something in law to say, “You shall be a learning organization. You shall be a learning entity or institute or institution.” That’s really the motivation for this idea behind the learning agenda. That again, this was an idea that came out of the Evidence Commission. There are a number of federal agencies that had been doing it prior to the Commission’s work. So, the Commission was informed actually by what agencies were doing, but it’s now a requirement for the twenty-four largest federal departments, and this whole idea is that if we can be honest about the informational needs for major decisions and transparently share that information about what the informational needs are with researchers and the evidence-building community, we can also provide a positive feedback loop for decision-makers actually having that information in the time that they need it and the relative framing that they need it, or relevant framing I should say, that they can make it an input to the whole decision-making process.

So, the learning agenda is not the silver bullet. It’s not going to solve all of these cultural problems around learning, but it’s a starting point for acknowledging that if we can be honest about what those questions are, we can design the production function or the production capabilities in the research and evidence building community to actually successfully make that information available. But this question around how you establish a learning culture is one that I’ve, as you said, I’ve been thinking about for years, and one of the things that we struggle with in government is the concept of failure. We set up a lot of really negative incentive structures around failure, and that is not how the scientific enterprise is built. We fail an incredible amount in science because we use that to continuously learn and redesign models, redesign studies, converge on knowledge. It might be that we do an evaluation of a program and it doesn’t produce the expected impact, but it’s also conceivable that if you look really deep, that that analysis might be somewhat difficult to interpret because it could be that that program is already the best thing out there, and so you’re trying to measure an incremental improvement instead of the program relative to it not existing. But we’re not going to just turn off antipoverty benefits. We’re not going to turn off the UI system in its entirety or make Social Security Disability Insurance go away to run an experiment. So, we’re always trying to build this knowledge in real-time while these programs are operating, but to this concept of failure, I mean, federal civil servants, I think need to be open and willing to not just acknowledge when things aren’t working as well as they can, but receive support from political leadership, members of Congress, congressional staff, that sometimes failure is okay if you can use it as a learning opportunity, and I’m not saying let’s go waste hundreds of billions of dollars. So, please don’t interpret this the wrong way, but if we had a program that was not operating optimally, how do you fix it? How do you identify the strategies to make it work better? And that’s really what we’re talking about is this incremental approach to learning, applying that information, recognizing that something might not be working as well as intended. It might not be achieving the outcomes that are desired, at least as fully expected. But if we apply our knowledge, our science, our evidence, then not only are we building this insight over time, you can actually improve the way government functions. And that’s what I see and hope for is the potential of evidence-based policymaking, but it’s really hinging on this idea that sometimes it’s okay to fail, and if we can admit that and not rake everybody through the coals for making a mistake or not achieving the outcomes that might be what we really want and have that honest conversation about how we do achieve those outcomes and then be open to fixing the programs to do just that. I mean, that’s really what this is all about.

Robert Hahn:

Yeah, so what I hear you saying is that trial and error can be a good thing if you can learn from it. And I also hear a note of optimism that as you and I, and others who are interested in this area, try to move the ball forward that we may actually do better over time.

Dr. Nick Hart:

You know, I’m the eternal optimist.

Robert Hahn:

All right, let me put you on the spot with one final question. Imagine that you had the ear of President-elect Biden for 10 minutes, either in the Oval Office or now. What would you be advising or whispering to him in terms of how to make a real difference in this area if he wanted to spend some political capital here or just do some things that were constructive?

Dr. Nick Hart:

Well, I think there are a number of things in the Evidence Act that the Trump administration did not fully implement, some things that were not even initiated in implementation, and really prioritizing some of those regulatory actions or guidance documents. And this includes new authorities around data sharing that would actually help us respond to the current coronavirus pandemic if they were enabled. I’ve made an appeal to try to get OMB to issue an interim direct final rule on some of these data-sharing capabilities, because they would honestly matter for developing the insights in real-time that much of the American public is currently asking for. So, implementing the Evidence Act well, including those authorities that don’t yet exist is going to be really key.

Prioritizing the work of these new Chief Data Officers would be a second major theme. This is a new function in government. Most agencies did not have somebody intentionally focused on data governance and data management. And that sounds really silly, but it’s true. We spend so much time on the data systems and the IT infrastructure, the quality of the information sometimes gets lost in the vortex. So, we now have an opportunity to prioritize the quality and make sure the information that we’re collecting is actually useful, and then let’s use it.

I think a third thing is really about how we engage in this strategy government-wide. I’m very much a supporter of finding the agencies where you think you can make progress and just laser focus on them, make some good progress while you can, but we really need to improve capabilities government-wide. The Trump administration has something called the Federal Data Strategy, and I really hope the Biden-Harris administration can find a way to continue a coherent national data strategy. Working with the private sector, working with academics, and importantly, working with state and local governments, that are actually collecting a lot of the information that the federal government uses. We need to have that coherence because if we’re not actually using that infrastructure and network effectively, we’re doing a lot of duplication. We’re wasting a lot of resources, and we’re never going to get to this optimal ideal state of evidence-based policymaking. So, that national strategy and the coherent strategy is going to be just a really central way to make all of the changes that we can envision, things around data standards, data quality, data sharing. It’s a way to make it all happen. So, those would be my three out of the gate suggestions.

Robert Hahn:

I think you used up your 10 minutes with the president, but no, that’s great. Nick, I wanted to thank you for joining us today. We’ve been listening to or speaking with Dr. Nick Hart, who’s CEO of the Data Coalition, and I hope you’ll come back in a suitable timeframe and give us an update on how we’re doing on evidence-based policy. Thank you!

Dr. Nick Hart:

Always a pleasure, Bob. Thank you.