Olga Shanks

PhD Candidate Economics

Researcher

Teacher

Stata expert

Olga Shanks

PhD Candidate Economics

Researcher

Teacher

Stata expert

About Me

Ph.D. Candidate in Economics, George Mason University                            Expected August 2022

  • Research areas: productivity, growth, innovation, and industry concentration
  • Dissertation: 3 Essays in Applied Economics

Email Address:  olga.shanks7@gmail.com

Location: London, UK

 

Working Papers

This paper estimates the elasticity of scale for different U.S. industries over the period from the 1980s to the present day using data on publicly traded companies. I apply four estimation methods: Ordinary Least Squares, Syverson’s method, Olley and Pakes’s method, and Ackerberg, Caves, and Frazer’s method. I find that the aggregate elasticity of scale has been increasing and is above one. Increasing returns to scale in turn can help explain the rising industry concentration and increases in markups for broad sectors of the economy. The aggregate markups calculated by recent literature go up as high as 1.6, while my estimates are around 1.2. The large difference stems from the classification of fixed and variable costs as well as inclusion vs. exclusion of the financial sector, which differs from other industries in substantive ways.

I advance and test a theory that in sequential auctions price rises with the number of bidders. I allow for stochastically arriving and departing bidders, so the number of bidders changes with every auction round both endogenously through the winner of the previous round dropping from future rounds and exogenously through the bidders’ stochastic arrival and departure. I test the theory on the Mecum auctions for collectible cars using the instrumental variables method. The timing of the car going to auction affects price only through the number of bidders present at the time and the number of cars still left to auction. This allows me to instrument time for the number of bidders. The empirical test shows support for the theory and provides a missing explanation for the declining price anomaly prevalent in sequential auctions.

with Dr. Thomas Stratmann

We model and empirically test the effects of citizen monitoring on services provided by bureaucrats. Monitoring by citizens is a public good. Because of collective action problems, monitoring is underprovided, allowing bureaucrats to shirk efforts to provide services. Our model shows that collective action problems in monitoring activities are associated with sub-optimal bureaucratic output. Bureaucratic output is predicted to change with the number of citizens affected and the distribution of bureaucracy-generated benefits. Utilizing income data from leases under the purview of the Bureau of Indian Affairs (BIA), we find broad support for our hypothesis that bureaucratic output is inversely related to collective action challenges of bureaucrats’ clients. These collective action problems vary with the number of owners, interests of the largest shareholder, and variations in monitoring costs due to private vs. institutional ownership.

Teaching

GEORGE MASON UNIVERSITY, Fairfax, VA

Graduate Lecturer – Introductory Econometrics (Jan 2022 – May 2022)

Teaching Assistant to Dr. Thomas Stratmann (Aug 2020 – Dec 2021)

  • Econometrics I (graduate)
  • Introductory Econometrics (undergraduate)
  • Public Economics / Public Choice (graduate)
  • Causal Inference (graduate)

Online Course Developer with Dr. Stratmann (Jan 2021 – Aug 2021)

  • Public Economics / Public Choice (graduate)
  • Causal Inference (graduate)

Teaching Philosophy

In economics as in other sciences, it is important to never lose sight of the fundamentals. It is impossible to truly master something new if it is not built on top of a strong foundation of first-order principles and concepts. That’s why I emphasize fundamental concepts in my class, so we proceed in learning starting from the same basic understanding. I tailor the review of these concepts to the level of knowledge required for the class, because “fundamental” does not need to mean “simple.” This approach empowers my students, regardless of their individual backgrounds, to participate in the future learning with confidence, and gives them a common platform to stand on.

The second principle I hold dear is active learning. I believe that it is only through the act of doing that we truly learn. Seeing a formula derived on a slide will not have the same effect as deriving it yourself, so I engage my students in active participation in the learning process by having them do derivations and calculations in class, share their results, and teach each other.

Lastly, I believe that the most effective way to understand and remember theory is through application. With that in mind, I schedule dedicated time in my class for working through examples in Stata. We work with real data sets and test how theories we learn apply in practice. Not only does it deepen the students’ understanding of theory but gives them practical technical skills they will likely need to succeed in their future careers.

Core Principles:

  1. Emphasis on the fundamentals
  2. Active learning
  3. Repeated application of theory