When the spread of COVID-19 had started to move the U.S. into action in early 2020, many states had to make decisions about policies regarding masks, school closures, stay at home orders, and numerous other topics. In many cases this decision making was made without strong experiments or evaluations of their effects tailored to the unique state dynamics. So how did they make these decisions? Did these decisions spread to other states?
In their new book Why bad policies spread (and good one’s don’t), Charles R. Shipan and Craig Volden draw from a wide range of policy domains to examine whether states learn from another to improve the spread of good or effective policies, which policies spread for which reasons, and which conditions lead to good or bad policies to spread, among other core questions. As they note:
“Evidence-based policymaking is so crucial to states learning from one another and to the spread of policies that are more beneficial than costly. There is a reason why states are called “policy laboratories.” They are experimenting with policies constantly. And the scientific community – both social scientists such as policy analysts, and natural scientists in individual areas impacted by policy choices – can benefit from evaluating those experiments and their effects.”
In many ways this book illustrates with clarity the immense complexity of policy, in particular, the chapter illustrating eighteen problems for the learning-based spread of good policies. Craig kindly answered some questions about their book below.
why bad policies spread (and good one’s don’t)
shipan and volden
Why did you write this book?
So many important public policies are being addressed by state governments these days, from how to confront the pandemic to voting rights issues to abortion. And none of the states is acting in isolation. They are all eyeing one another for new ideas or to discern how well various policies work, with costs and benefits for the public as well as for politicians.
On the one hand, such experimentation could lead to dramatic benefits for American federalism as a system of “states as policy laboratories.” On the other hand, so many of these policies are being heavily criticized and contested – often on partisan grounds, and often without waiting to see how well the policies actually work when put into practice.
We wrote this book to help sort out – in our own minds first, and then for our readers – just how these two views can be reconciled. When does the system work as it should for the spread of good policies, and when does it go (sometimes horribly) wrong?
How does your book contribute to our understanding of how scientific evidence might be more effectively utilized in policy?
Evidence-based policymaking is so crucial to states learning from one another and to the spread of policies that are more beneficial than costly. There is a reason why states are called “policy laboratories.” They are experimenting with policies constantly. And the scientific community – both social scientists such as policy analysts, and natural scientists in individual areas impacted by policy choices – can benefit from evaluating those experiments and their effects.
For example, during the pandemic, epidemiologists had access to so much more data than ever before about the effect of vaccines, mask mandates, social distancing in schools, and so many other topics. And the results of their studies played a major role in policy decisions – leading some states to adopt best practices and to abandon those that were more costly than beneficial.
Of course, no system is going to be perfect along these lines. In many cases, there was not enough time to learn before action was needed in the face of rising infections and deaths. In other cases, political considerations seemed to outweigh scientific evidence. And in still other instances, public attention was drawn to costs that could be easily measured – COVID cases, for example – rather than costs that are tougher to see, such as long-term learning losses among students forced into online environments.
What core lessons can policymakers and others take away from your book to improve the likelihood of learning-based spread of “good” policies and lessen the likelihood of the spread of “bad” policies?
We highlight three ingredients needed for the learning-based spread of good policies: observable experiments, time to learn from others, and expertise/incentives to learn. Policymakers, nongovernment organizations, and others can help create environments in which these ingredients are readily available.
For example, clearinghouses that highlight the experiments taking place across the states – especially if linked to evaluative criteria on how those experiments are performing – are enormously helpful. We discuss how the federal government set up such reporting for youth antismoking programs, how media outlets served this role with respect to pandemic policies, and how the National Conference of State Legislatures highlights various policy innovations on a regular basis.
In terms of expertise and incentives, significant evidence suggests that “professional” state legislatures outperform “citizen legislatures” in their ability to learn from others. Paying legislators more than a low, part-time salary attracts a more diverse and capable set of lawmakers on average. Experienced legislative staff help draw attention to experiments found elsewhere, with less deference to interest groups that may present biased views.
Beyond these three ingredients, the book documents numerous ways this recipe for successful policymaking can go wrong. And we conclude with nine lessons to help avoid these pitfalls and to more closely achieve the promises of experimentation, learning, and the spread of good public policies.
