who are you?

I am an Assistant Professor of Economics at Middlebury College, working mostly on outer space-related issues.

I received a BS in Business Administration in 2012 from the University of California, Riverside, an MA in Economics in 2016 from the University of Colorado Boulder, and a PhD in Economics in 2019 from the University of Colorado Boulder; my PhD adviser was Dan Kaffine. Before graduate school, I worked on motion sensor data analysis for action sports.

what is your work about?

This year I’m teaching statistics (ECON 210) and environmental economics (ECON 265). Pedagogically, I’m very interested in the use of games and experiential learning to teach mathematical and economic concepts.

My research is broadly concentrated in two agendas: first, the economics of outer space; second, computational economics. My research is motivated by a desire to see us make better use of limited resources and fragile environments.

the economics of outer space

Most of my research is on the economics of orbit use. Earth’s orbits are the world’s largest common-pool resource, and as humans launch more satellites the risk of collisions between orbiting objects increases. Paths in low-Earth orbit are under “open access”—firms are unable to secure property rights over orbits. Open access to a common-pool resource typically causes over-exploitation, and sometimes collapse, of the resource. In the orbital case, expect to see more satellite-destroying collisions and a higher risk of Kessler Syndrome in low-Earth orbit than would be socially optimal.

In my job market paper I derived economic principles governing the choice of space traffic control policies and the effects of active debris removal technologies. Since physical uncertainty over collisions is symmetric between regulators and firms, space traffic control policies can be efficient as prices or quantities. What matters for efficiency is whether policies are imposed on the stock of active satellites in an orbit (e.g. a satellite tax) or the flow of satellites entering an orbit (e.g. a launch tax). Policies targeting satellite launches deter entry into the orbital commons, creating rents for incumbent satellite owners. These rents cause inefficient spikes in the launch rate, collision risk, and risk of Kessler Syndrome, and limit the regulator’s ability to induce socially but not privately optimal satellite deorbits. Policies targeting satellite ownership, on the other hand, can smoothly reduce the collision risk and induce deorbits when necessary. Though active debris removal technologies can reduce the risk of Kessler Syndrome, under open access they will only reduce the risk of satellite-destroying collisions to the extent that satellite owners bear the cost of debris removal.

While I focus on commercial orbit use, national security space use is an important piece of the story. Historically, the most accurate space situational awareness data has been provided by the US military as a global public good, and the US national security establishment has been one of the largest space users globally. This is changing now as commercial use takes off and other nations are also starting to demonstrate anti-satellite weapons capabilities. The latter is particularly dangerous for orbit management, as a kinetic conflict in space could trigger Kessler Syndrome in valuable orbits. It’s still unclear to me how effective orbit management policy should account for this.

computational economics

I’m also interested in computational modeling and assessing the economic policy consequences of computational tools and limitations.

My work in computational modeling has mostly been about ways to speed up dynamic programming algorithms and simulating large congestion games with heterogeneous agents. Agent-based simulations are particularly interesting to me because their computational limitations often suggest issues which real humans and organizations need to work around.

My work on the economic policy consequences of computation has been focused on applying tools from information elicitation to externality problems in environmental economics. Many of externality problems require a regulator to elicit reports from parties generating externalities to assess compliance and liability. Using these reports as the basis of corrective policy gives externality creators an incentive to manipulate their reports to reduce their liability. In ongoing work, my coauthor and I uncover some features of economically-efficient elicitation and corrective taxation mechanisms which use independently-obtained aggregate or ambient data to verify marginal emissions reports from polluters. We numerically analyze a specific scheme, the Brier-Pigou tax, and consider its robustness to strategic collusion in reporting. I think it would be useful for policy design if this could be generalized to an understanding of how the elicitation complexity of marginal damages or abatements scales with uncertainty and convexity in the damage or abatement cost functions. Ultimately, every regulator must query some information and perform some calculations to do their job. Their ability to do so has economic implications for the systems they query and regulate. This agenda connects to broader questions about the policy design implications of information requirements and bounded computational abilities.

what is your play about?

In my free time I make silly Twitter bots, play video games (online RTS and single-player RPGs), and think about the economics of fictional worlds. I like bike rides, snowboarding, sci-fi/fantasy and historical fiction, tea, rock climbing, and judo. I take care of a cat who thinks I’m kind of slow.


Akhil Rao
Department of Economics
Middlebury College
Warner Hall 502A
Middlebury, VT 05753

Email: akhIilrdon't@wantmiddspam! leSopleasebuleave ryme.alonee!du
Phone: 802-443-2192