Detection of Uncompetitive Behavior in Natural Resource Auctions Using Regression Analysis
Open Access
Author:
Jung, In Kyo
Area of Honors:
Economics
Degree:
Bachelor of Science
Document Type:
Thesis
Thesis Supervisors:
Sung Jae Jun, Thesis Supervisor Russell Paul Chuderewicz, Thesis Honors Advisor
Keywords:
Regression auction theory New Hampshire Timber Auction
Abstract:
The following thesis will look at some solid quantitative methods to detecting collusive actions in an American Natural Resource action, specifically timber. Collusion has been outlawed in the United States since the late 1800s. However, enforcement has been a legal matter that has proved to be rather complicated.
While various statistical measures have been introduced to combat collusive behavior, they vary with respect to the specific action cartels are taking. This thesis will look at the linear regression approach comparing a “problematic”, or collusive, model and a competitive model.
Even though limitations are introduced due to the dataset being strictly of US origin, this analysis provides a solid framework as to how this method can be performed on a smaller scale auction performed in the state of New Hampshire.
