Olga Shanks

PhD Candidate Economics



Stata expert

Olga Shanks

PhD Candidate Economics



Stata expert

About Me

Ph.D. in Economics, George Mason University

  • Joining Southeastern Louisiana University as an Assistant Professor of Economics in August 2024
  • Areas of interest: productivity, innovation, industrial organization, political economy
  • Dissertation: Three Essays in Industrial Organization and Political Economy

My name is Olga Shanks. I hold a Ph.D. in Economics from George Mason University. Prior to my doctoral studies, I had a career in corporate finance, which inspired my research interests in industrial organization, productivity, and innovation. My job market paper “Increasing Returns to Scale and Markups” has been published in Structural Change and Economic Dynamics. Additionally, I have a keen interest in political economy and plan to pursue research questions that integrate methodologies from both industrial organization and political economy.

Location: Washington, DC

Email: ostaradu@gmu.edu

Working Papers

I estimate the aggregate and industry-specific elasticities of scale and markups for the U.S. economy over the period from 1980 to 2019 using data on publicly traded companies. I apply Olley-Pakes and Ackerberg-Caves-Frazer estimation methods and find that the aggregate elasticity of scale for the U.S. economy is 1.1 and has been rising. The elasticity of scale in turn serves as an input for calculating industry markups. Increasing returns to scale help explain observed increases in markups over the last decades for broad sectors of the economy. My estimate of 1.2 for the aggregate markup is significantly lower than the estimate of 1.6 found in recent literature. The large disparity in markup estimates stems from differences in the treatment of fixed and variable costs and the methodological approach to the calculation of markups.

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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 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.

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Graduate Lecturer

  • ECON 306 Intermediate Microeconomics (Spring 2023)
  • ECON 306 Intermediate Microeconomics (Fall 2022)
  • ECON 345 Introductory Econometrics (Spring 2022)

Teaching Assistant

  • ECON 695 Public Economics / Public Choice (Fall 2021)
  • ECON 695 Causal Inference (Fall 2021)
  • ECON 637 Econometrics I (Spring 2021)
  • ECON 345 Introductory Econometrics (Fall 2020)

Online Course Developer with Thomas Stratmann

  • ECON 695 Public Economics / Public Choice
  • ECON 695 Causal Inference

Teaching is an important reason why I want to pursue a career in academia. I have a passion for learning, and I want to impart that passion to others. When I got the opportunity to teach during my graduate studies, I had a distinct feeling that I had found my calling. Designing new courses, serving as a teaching assistant, and being an independent instructor gave me invaluable experience and a new perspective on teaching.

First, it was eye-opening that good professors are incredibly well-prepared professors. Years of experience teaching the same class may replace thorough preparation before each lecture, but until then, you need to over-prepare to do well. The course must have a comprehensive design before the semester starts, with detailed plans for lectures, recitations, homework assignments, and exams. A thorough design does not mean the course must be rigid, quite the opposite. A well-thought-out syllabus and schedule enable the instructor to make changes throughout the course to adapt it to the needs of the students, and to do it seamlessly and with ease. The preparation does not stop with a good course design, however. As an instructor, I must rigorously prepare for each class to deliver the best learning experience to my students.

When teaching, my goal is to make the class stimulating for all the students, regardless of their individual backgrounds. To empower my students to participate in learning with confidence, I start the course with a review of the fundamental concepts needed for the course. I believe it is impossible to truly master something new if it is not built on top of a strong foundation of first-order principles. I tailor this review to the level of knowledge required for the class, because “fundamental” does not need to mean “simple.” This approach gives the students a common platform to stand on, so we proceed in learning from the same basic understanding. And when hard topics come up, I can always revert to the fundamentals to ground the class and help us understand the links that connect the new knowledge to our shared foundation.

Another 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. It is much easier to do when the course is taught in person than online. To engage students in active learning online, I utilize team assignments, breakout sessions, and discussion boards.

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 Excel and 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 of Teaching Philosophy:

  1. Rigorous instructor preparation
  2. Emphasis on the fundamentals
  3. Active learning
  4. Application of theory using Excel and Stata