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Reimagining the Economy brought together Nell Abernathy, Director of the Office of Policy and Strategic Planning, Department of Commerce; Alex Jones, Director of the Build Back Better Regional Challenge (BBBRC), Economic Development Administration (EDA); and Dan Kim, Chief Economist of the CHIPS program office, Department of Commerce, to speak with RtE Faculty Co-Director Gordon Hanson about place-based and industrial policies.

The goal of this event was to consider place-based and industrial policies from multiple perspectives: in the long term, looking back and looking ahead multiple decades, and in the moment, drawing lessons from the outgoing Biden administration as the Trump administration begins. One of the biggest takeaways is that these policies require hard work. Writing and passing bills always takes effort, but implementing them takes just as much effort — if not more. As Jones said, they aren’t just “set it and forget it.” However, all the speakers agreed that there are reliable methods to make this effort worthwhile.

Make and use goals

Perhaps the most important method is to choose a big picture goal to motivate a program. Each speaker emphasized the importance of their goals: for CHIPS, to build capacity, capability, and competitiveness in the American semiconductor industry; for the BBBRC, to accelerate local economic development; and for the Commerce Department as a whole to increase American economic competitiveness.

Big picture goals like these provide essential direction for policy implementation and can be built into the design of programs. For example, the BBBRC’s goal of acceleration was built into the fast pace and short term deadlines of the challenge. Moreover, a single big picture goal can generate intermediate goals which are useful in their own right:

• Concrete goals: While the big picture goals are somewhat abstract, they permit more concrete, near-term goals. This can create timelines, as in the BBBRC’s aim to achieve “a generation’s worth of economic development within 5 years.” It can also create measurable outcomes. As Abernathy pointed out, the concrete goal of connecting every American to broadband is easy to observe; by 2030, it will be obvious if it’s succeeded or not (and in the years before that, it will be relatively easy to see what’s been done and what still needs doing). 
   
• Flexible goals: Although the big picture won’t change, many specifics will. Kim quoted Mike Tyson: “Everybody has a plan until they get punched in the face.” As lessons are learned and circumstances change, goals and plans must change with them.
   
• A portfolio of goals: Within a large goal, some objectives will be at odds. For example, cutting edge technological research and production tends to happen in affluent communities, so directing technology and fighting inequality can’t easily be done with one program. Jones recommended using a “portfolio approach” where each goal gets its own program: Tech Hubs targets technology and Recompete targets inequality. 
  
  Using a portfolio also helps mitigate risk. Even when goals are perfectly aligned, some programs involve uncertainty and risky choices like investments. Although one investment can fail, a portfolio can still succeed as a whole.

Learn and adapt

Policymakers bring a lot of expertise to the table. As the CHIPS team has grown, Kim has been pleasantly surprised to see a group of nearly 200 people as passionate and knowledgeable about semiconductors (and public service) as he is — especially since he was one of only two people in the entire federal government dedicated to semiconductors earlier in his career. However, policymakers can’t possibly know everything. Learning and adaptation must be core components of any place-based or industrial policy.

The speakers underlined two key principles: iteration and engagement.

• Iteration: A lot of learning comes from trial and error. Besides open-mindedness toward “failure” and trying new things, this requires constant monitoring and evaluation. Abernathy mentioned that some of this work is outsourced to universities and think tanks, but it remains a fundamental part of their programs — they are actively monitoring and evaluating themselves every step of the way.
   
• Engagement: Most expertise — especially on business topics — is in the private sector. Policymakers need to maintain a two-way street of communication with the private sector to make programs work: learning from those with the most knowledge and clearly explaining what the goals are. This, too, involves some iteration. Jones described repeated back-and-forth communication between Challenge applicants and EDA leaders: constantly learning from each other and refining their objectives.

Iteration and engagement help build the “institutional muscle memory” to keep existing programs going and make new programs easier to implement.

Be bigger than politics

Kim warned about policies becoming a “victim of politics.” Many of the methods the speakers discussed go against common political instincts. The most important challenge is the risk of failure. Clearly stating goals (especially concrete, short-term goals) is politically risky. It’s obvious if they fail. Maintaining a portfolio of programs is useful as a whole, but some will inevitably fail. Learning through iteration means failures can’t be hidden; they must be used to effect change. High profile failures can ruin an individual’s career, so, as Abernathy says, making these policies work doesn’t just require competence — it requires courage.