Challenges in Statistically Rejecting the Perfect Competition Hypothesis Using Imperfect Competition Data

TL;DR

This paper explores the difficulties in statistically rejecting perfect competition using imperfect data, highlighting the influence of sample size and market conditions on test power and the implications for empirical research.

Contribution

It provides a theoretical proof of the challenges in rejecting perfect competition and evaluates the finite sample performance of conduct parameter tests in market data.

Findings

Statistical power increases with more markets, higher conduct parameters, and stronger demand instruments.

Rejecting perfect competition remains difficult with moderate markets and five firms, regardless of instrument strength.

Empirical failures to reject perfect competition are mainly due to limited market data, not methodological flaws.

Abstract

We theoretically prove why statistically rejecting the null hypothesis of perfect competition is challenging, known as a common problem in the literature. We also assess the finite sample performance of the conduct parameter test in homogeneous goods markets, showing that statistical power increases with the number of markets, a larger conduct parameter, and a stronger demand rotation instrument. However, even with a moderate number of markets and five firms, rejecting the null hypothesis of perfect competition remains difficult, irrespective of instrument strength or the use of optimal instruments. Our findings suggest that empirical results failing to reject perfect competition are due to the limited number of markets rather than methodological shortcomings.