Istock/peterhowell How Policy Schools Can Narrow the Gap
At the height of the pandemic, when tens of millions of Americans lost their jobs, unemployment systems across the country crashed at precisely the moment they were needed most. The chorus of the media was about failed technology and wasted money: how could we spend billions of dollars on websites and systems that crumble under pressure? They were not wrong, it absolutely was a failure of technology. However, if you peek under the hood, it is just one symptom of a deeply dysfunctional system with warped incentives-a system that perceives and treats implementation as subordinate to policy.
“Successful organizations understand the importance of implementation, not just strategy, and, moreover, recognize the crucial role of their people in this process.”—Jeffrey Pfeffer
Preparing the next generation of policymakers means acknowledging that public policy and technology are inextricably entwined.
Implementation is ultimately about how a goal is achieved—how something is accomplished—and the conditions that enable success. While there are countless organizational, behavioral, and strategic factors of implementation, technology is now an inextricable dependency on almost everything we do. From paying bills and making appointments to booking travel and driving your car, to voting and engaging with public services, today almost everything we do depends on [often invisible] technology. Success depends on implementation, and implementation depends on technology.
In her book Recoding America, Jen Pahlka demonstrates how accountability, procurement, and the gap between policy and implementation set the U.S. government on a path for decades of failure. While Pahlka offers suggestions, I would argue that they are necessary but not sufficient, because they are (rightfully) focused on issues within the walls of government. If we want to close this gap for good, we must simultaneously focus on academia—which is at the root of the policy-implementation gap. Overlooking the role of academia in this gap all but guarantees that the challenge will persist because much of what happens in government is influenced—either directly or indirectly— by academia.
As it turns out, I have been oddly fixated on this issue over the past several years. My time as a founding member of the U.S. Digital Service and Senior Advisor to the U.S. Chief Technology Officer brought into sharp focus the outsized role that technology plays (or does not play) in the delivery of even the least technical policy outcomes. I went to academia to explore questions related to building capacity in government and recently dove into the role of policy schools as a Siegel Research Fellow [1].
Grounded in the understanding that there are different flavors of policy schools, I examined core requirements from six of the top graduate policy schools in the United States to assess if and how implementation surfaced in their core curricula. In short, it mostly does not. Students learn how to decide what to do, often based on multiple variables, as well as how to measure impact. As I explored why, it became clear that policy means something entirely different in the academy than it does in practice. In short:
The discipline of public policy is largely about the process and prioritization of decision-making and is deeply rooted in economics. It is about how to decide what to do or accomplish, based on countless variables and inputs.
Naturally, schools of public policy have curricula that are focused on and prioritize economics and associated skills, like data science, statistics, and other quantitative approaches to measurement. In some ways, they are schools of economics in a government context. This is not a critique. It is an explanation.
Each school I looked at is clearly thoughtfully and intentionally designed. Schools of Public Policy cannot be everything to everyone, and we should not want them to be.
Opportunities That Fit the Current Model.
I See Three Opportunities To Fold Key Learning Objectives Into Existing Programs, without Major Changes.
- Acknowledge and explore the entanglements between technology and public policy—ideally within the core curriculum.
- Teach students how to sense make and navigate in a complex, ambiguous, and changing environment.
- Provide opportunities to learn the fundamentals of digital products and systems.
There is a lot that can be done by simply drawing attention to the entanglements, providing a new mental model for problem-solving, and providing some foundational knowledge in a way that connects to the policy realm.
Surface How Policy and Technology Are Entangled
When Jen Pahlka’s book Recoding America came out last year, Ezra Klein described it [2] as one of the best policy books he’s ever read.
Reflecting on Ezra’s take, Pahlka shared [3] that “…most people who saw the proposal thought of it as a technology book.” And this (incorrect) perception of technology as separate from policy is a recurrent theme of the book. It is also an incorrect perception across the policy arena, from government institutions to academia, to civil society. They are not separate. Technology (visible or invisible) is essentially a dependency of public policy: if the tech does not work, nothing works.
The discipline of public policy is largely about the process and prioritization of decision-making.
However, this does not mean policy students need to be technology experts. What they need to understand is how and why policy and technology are entangled, how to think about the tradeoffs that new technologies present, and why technology projects in government fail [4]. More specifically:
1. Policy and its implementation cannot be separated
Policy depends on implementation, and implementation depends on [often invisible] technology. Implementation has long been treated as subordinate to policy, but the degree to which even our most basic institutional operations depend on digital technology has made that separation untenable. Policy people simply need to ask: where does what I want to accomplish touch or depend on technology? They do not need to be the experts-they need to ask the question and be prepared to listen to the answer.
2. What we pay attention to grows, and what we focus on is failure + procedures-not outcomes
As Pahlka lays out beautifully in her book, there are two distinct systems of accountability in government: 1) does it work? versus 2) did you follow procedures? The second (compliance) is the one you can be punished for. You will not be punished if it does not work. You will be punished for not following the procedure/process. This means that compliance is prioritized over competency and outcomes, creating convoluted incentives across government that warp our ability to do anything effectively.
3. Implementation is outsourced
In the realm of digital technology, government employees largely do not design or build products or systems, they “manage” implementation done by contractors or consultants. This means that implementation has been outsourced, with major implications for policy and policymaking. As my colleague Eddie Hartwig says: “Procurement is the convergence of everything that is broken.”
In some ways, these are learning objectives that could be slotted into existing classes or other programming, as most appropriate for each institution. And the shift must go beyond pedagogy—scholars and researchers must recognize this as well. This is all the more urgent with artificial intelligence (AI) surging onto the scene, and being prematurely integrated into government systems and services [5].
Ideas to try.
- Find ways to engage with practitioners who are geographically proximate to your university. Discover experts near you through networks of practitioners like the U.S. Digital Response [6], the Digital Service Network [7], the Digital Benefits Network [8], and the Public Tech Leadership Collaborative [9].
- Design a session during orientation that introduces this entanglement and key learning objectives. Students and faculty can call back to those throughout the program.
- Do a landscape analysis of required classes and identify places that implementation naturally connects to.
Build Collective Sense-Making and Wayfinding Muscles
Sense-making is exactly what it sounds like. It is the act of making sense of an environment enough that you can make a reasonable decision. It is not the decision-making itself; as R. J. Cordes says “… it is the necessary precursor to effective action” [10].
Policymakers do not need to be technology experts.
We live in a time of massive and foundational change and upheaval. Everywhere we look, our most fundamental assumptions are being challenged, and how our world works is shifting beneath us daily. This is precisely the kind of moment sense-making and wayfinding skills that are needed-especially among those in positions of public trust.
In a recent FP article, Stephen Walt said…
”… in a world of increasingly rapid and interconnected disruption, some of the familiar verities, principles, and practices that we have taken for granted (and confidently taught to our students) may not be all that helpful. In these circumstances, what will matter is a leader’s ability to adapt, to jettison old ideas, to discriminate between sound science and snake oil, and to invent new ways of meeting public needs. Teaching students how things worked in the past and instilling timeless truths derived from earlier epochs may not be that helpful-it might even be counterproductive” [11].
I could not agree more. To confront this challenge, policy schools must create space for students to explore new ways of sense-making and wayfinding, rather than relying on mental models from a past century. I will not pretend to have the answer to how policy schools could accomplish this, but I have one answer: design.
Design is inherently a process of sense-making and problem-solving. Design research, in particular, might be a practical and valuable way to build sense-making muscles within the existing highly quantitative core curricula of policy schools. Design research is an approach to understanding the world that does not aim to land on a single generalizable truth, but instead identifies meaningful patterns in individual and subjective experiences. It is immersive and bears a resemblance to ethnography but in an applied context on a much shorter timescale. Quantitative research, which makes up an enormous part of policy curricula, is necessary but not sufficient. As Tricia Wang said nearly a decade ago: “big data needs thick data” [12].
While students do learn about qualitative methods, all of those methods require a structure for the research as a starting point. Design research breaks the structural requirement-it allows the researcher to immerse themselves in the problem space, and out of that land on a hypothesis. That then enables the researcher to create a structure as a starting point for more traditional social science research. In other words: design research can create a strong foundation for empirical research.
Design methods are inherently about solving a problem for someone who is not you-a mindset that is extraordinarily valuable to the policy world, where there is an analogous goal. Ultimately design methods of all kinds are extraordinarily useful lenses, tactics, and mental models for sense-making in ambiguity.
Ideas to try.
- Add design research to required methods classes.
- Integrate design research into thesis and capstone projects. Because of its sensemaking nature, design research is an extraordinarily valuable precursor to more traditional academic research methods.
Introduce Basic Domain Knowledge
Policymakers do not need to be technology experts. The goal is not to be fluent-it is to be conversant. A pool of short courses on technology is an approach with potential because it allows students to explore different hard skills and domains, without having the burden of another high-intensity course. This could entail domain knowledge like: What is a tech stack and how does it affect what organizations can do, change, and create? What is a digital product and how does it come to be? It could also entail hard skills like UX/design research, data visualization, usability testing, and product management.
A crash course that I tested out at Georgetown [13] during the pandemic provides one way of thinking about learning objectives for the basics of digital products. The Teaching Public Service Digital team has also identified competencies [14] that are useful to people working in public service today. While what exactly is useful depends on the role of the individual and their goals, these examples are great starting points for identifying what information might be most valuable to your audience.
We have entered an unnervingly complex, entangled, and ambiguous era. I think the challenge for all of us, but especially for those in positions of public trust, is beautifully summed up by Nancy Leveson, a professor of aeronautics and astronautics at the Massachusetts Institute of Technology who has been studying software safety for nearly 40 years: “The problem is that we are attempting to build systems that are beyond our ability to intellectually manage.”
People working in the public trust must have the tools and experiences needed to navigate the complex and ambiguous path ahead—schools and scholars of public policy are well-positioned to lead the way.
Author Information
Evagelia Emily Tavoulareas is the managing chair at the Tech & Society Initiative, Georgetown University, Washington, DC 20057 USA. Email: Evagelia.Tavoulareas@georgetown.edu.
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