Strategic Interaction and Empirical Models: An Application of Statistical Backward Induction to the U.S. Special 301 Report





Published date: 

June, 2015


Wen-chin Wu


While conventional statistical methods usually assume that the error  term in the models are independent and identically distributed (i.i.d.), this assumption is usually violated when observations are interdependent due to the strategic interactions among players. The violation of the i.i.d assumption results in the inefficient estimation of standard errors that can further invalidate the hypothesis testing. This paper discusses the method of statistical backward induction (SBI) developed by Curtis S. Signorino and his coauthors that can be used to analyze different kinds of strategic interactions in politics, such as electoral competitions, party coalitions, and international conflicts. After demonstrating how to derive the SBI estimator, this paper applies SBI to analyze how the U.S. government uses the Special 301 Report to coerce its trade partners into protecting the intellectual property rights (IPR) of American products. It shows that one country’s trade surplus with the U.S. is a key determinant for the U.S. to nominate this trade partner in the Special 301 Report. Meanwhile, it is the dependence on the U.S. market that affects the nominated country’s decision to ignore or comply with the U.S. threat of trade retaliation implied by the Special 301 Report.