Hannah Druckenmiller joins the Caltech faculty this year as an assistant professor of economics. An environmental economist, Druckenmiller focuses on governmental policies concerned with preserving natural resources: whether the policies succeed or fail, how their costs and benefits can be better conceptualized for decision-makers, and how the effects of climate change can best be ameliorated through improved regulation.
Druckenmiller earned her PhD in economics at UC Berkeley and most recently worked for Resources for the Future (RFF), a nonprofit in Washington, D.C., focused on environmental economics and policy research.
What environmental policies are you most interested in?
Two of the most significant environmental regulations in the United States at the federal level are the Clean Water Act and the Clean Air Act. Although these regulations have been in place for more than 50 years, they’re constantly being reassessed and reinterpreted. The scope of the Clean Water Act, for example, is repeatedly debated and redefined by the Supreme Court, presidential administrations, and state litigation. In fact, the Environmental Protection Agency has had different interpretation of which waters are regulated by the Clean Water Act under Presidents Obama, Trump, and Biden. And the change in the scope of environmental regulation is significant—we estimate that 30–40 percent of regulated waters lost federal protection when the Clean Water Act was reinterpreted by the Trump administration.
How can the scope of the Clean Water Act change so dramatically?
The regulation is written in an ambiguous way. It’s not clear exactly what is protected. The Clean Water Act protects the “Waters of the United States.” This phrase clearly includes navigable waterways like the Mississippi River, and clearly excludes a small puddle in your backyard. Debates center around whether the law protects intermediate cases like isolated wetlands or ephemeral streams that flow a few days per year. In order to decide whether a water resource—like a lake or a river—is regulated, the government sends out an engineer from the Army Corps. These are case-by-case decisions, and we don’t have even a ballpark figure for what percent of streams or wetlands in the United States are regulated. One of my projects is trying to use machine learning to map this. Basically, we’re creating an algorithm trained on all these case-by-case decisions that have been made historically to decide what the possibility is of any particular resource being regulated. We’re trying to understand what was originally regulated, how that scope changes when you get a narrower interpretation of what’s protected, and then what the downstream consequences are.
What types of downstream consequences have you looked at?
I’m interested in how ecosystem services change when you alter environmental protections. Under the Trump administration’s interpretation of the “Waters of the United States,” a significant share of wetlands was deregulated. I wanted to understand how this would impact the flood protection services that wetlands provide. We found that converting 1 hectare of wetlands (roughly the size of 2.5 football fields) to built-up land increases property damages from flooding by more than $12,000 per year. These costs are rarely borne by the developer who converted the wetlands to another use—they are mostly borne by downstream community members who no longer benefit from the wetlands’ ability to trap and slowly release water that would otherwise cause flooding.
How do you look at effects of climate change?
I recently did a project on how to disincentivize development in areas that are most likely to be affected by climate change. We already have high levels of development in places that are at risk from coastal flooding and sea level rise. One big question is, how can we manage retreat from those places? A second question is how we can stop future development in those risky places.
A longstanding hypothesis in economics is that, intentionally or not, the government subsidizes development in environmentally fragile places by providing risk management tools like insurance, or funding for infrastructure such as roads, water lines, and sewage systems, and by providing disaster assistance when problems arise. When there’s a hurricane and the federal government comes in and gives the locality or individuals money to recover, that’s indirectly subsidizing them for living there in the first place.
So our question was, if you got rid of government subsidies in these fragile places, could that alone prevent development there? Or are these such desirable places—they are along beautiful coastlines—that governmental subsidies, or their lack, wouldn’t make a significant dent in the amount of development you see? We looked at this program from the 1980s called the Coastal Barrier Resources System, which was a policy that removed these types of subsidies for development in designated areas along the Atlantic and Gulf coasts. We wanted to see the long-term effect of the policy. Forty years later, do we see much lower levels of development in these places?
The challenge to studying the Coastal Barrier Resources System is that designated areas were not randomly assigned along the coast. They were intentionally selected by land-use planners because they were considered risky or because the land was thought to have environmental value. We were able to find controls—similar places not affected by the Coastal Barrier Resources System program—by running a machine-learning procedure intended to mimic the process by which land-use planners designated target areas in the 1980s. This allowed us to find places that were statistically indistinguishable from treated areas in the 1980s, but which did not enter the program. Then we compared outcomes in the treatment and the control areas. What we found is that this policy did a lot to reduce development. On average, the protected areas had 85 percent lower development levels.
What I found most interesting is that we also found evidence that the areas without federal incentives for development created spillover benefits to surrounding communities. By conserving natural land, ensuring that it wasn’t converted into built-up area, we saw flood-protection benefits in the areas surrounding wetlands. We even saw higher property values in those areas because they’re next to a natural amenity: namely, these pristine coastal areas.
That was interesting to me as an economist. It implies that federal programs to reduce disaster exposure don’t need to conflict with local interests in maintaining the tax base. A lot of localities don’t want these protected areas in their jurisdiction because they think that if they kill development it will lower their property tax revenue. But what we found is that restricting development in these coastal areas has no net effect on property tax revenue across the counties they’re located in. There are higher property values in surrounding areas and lower property values in the designated protected area, so most often these two effects cancel each other out.
Do you think removing subsidies for development in wildfire prone areas could work the same way?
My coauthors at Resources for the Future and I are hoping to work more on this. Some of my collaborators do a lot of work on wildfires, and we’re really interested to see if you could institute a similar policy in the wildland-urban interface. There are differences between coastal areas and wildland-urban interfaces, so it’s not obvious that the same policy instrument would work, but that’s something we’re interested in looking at.
How did you become interested in environmental policy?
I grew up in New York, and we spent a lot of time by the ocean. I always wanted to do something related to marine biology. When I was in high school, I attended a program in the Bahamas called The Island School. You go there for a semester, and all of your academics are place-based: your science class is marine biology, your math class is focused on celestial navigation, and in the humanities all the literature we read was written by authors from the Caribbean.
The Island School is an amazing place, and it really solidified my desire to study marine biology. So when I went to Stanford, I chose an interdisciplinary major in environmental science with a focus on oceans. The major required several classes in economics since economics is central to understanding environmental policy. I took my first economics class and fell in love with it as a framework for understanding why the environment is regulated, when regulation is successful, and when it’s not.
What are you looking forward to at Caltech?
The thing I’m most excited about is that people here really think differently. It seems like you’re not constrained to your disciplinary box as much as you might be at other institutions. There’s access at Caltech to world-class scientists who are interested in collaborating with social scientists. I hear ideas here that I haven’t heard before.
Will you be participating in the Center for Science, Society, and Public Policy (CSSPP)?
Yes, the CSSPP was a big draw for me at Caltech. The center supports engagement between science and policy, with priority research areas including climate change and sustainability and artificial intelligence (AI). I’m increasingly interested in how we can use AI systems to improve the monitoring and enforcement of environmental regulations.
For example, the Clean Air Act is enforced based on a system of 900 ground-based monitors across the United States. That’s less than one per county, so the law is not binding in many places that have noncompliant air quality where there simply isn’t a monitor. I have a new project that asks whether there’s a path to using satellite data directly for enforcement of the Clean Air Act. And if not, is there a path to using satellite data to at least inform the placement of new monitors?
So there were no satellite monitors when the Clean Air Act was first written?
Correct, but it’s more complicated than that. Satellites cannot directly measure chemicals or pollutants as you would ordinarily think of them. They measure things that are correlated with pollution, like aerosol optical depth. That’s basically just a fancy word for how hazy the atmosphere is. Then you can use a statistical model to convert that into an estimate of a specific pollutant, like particulate matter. It’s hard to enforce a regulation based on something this uncertain, especially when the regulation is very costly. I mean, if you’re noncompliant with the Clean Air Act, your county has to curb pollution-generating activities like industrial production or traffic. This basically means less economic activity in your region.
What are you going to be teaching at Caltech?
I’m starting with environmental economics at the undergraduate level this year. I’ve met some of the PhD students in economics since I arrived. It seems like the students here are really bright and curious. I also hope to work with postdocs coming through CSSPP.