PRICE DISCRIMINATION AND BIG DATA: HIGHEST PRICES EVERY TIME WE CLICK?

Abstract

First-degree price discrimination has been known to theoretically allow the firm to extract full surplus from consumers. However, it has been assumed that first-degree price discrimination is not possible, as firms do not have information on the willingness to pay at the individual level. While sound historically, this argument may no longer hold. Large datasets on individual behavior, popularly referred to as “Big Data,” are now readily available, and contain information potentially useful for person-specific pricing.

This thesis aims to allow the public to understand the impact and importance of Big Data on our daily lives when dealing with price discrimination in e-commerce and transactional platforms. It seeks to increase consciousness of how the information we provide to a website and our behavior when we have the mouse in our hand could impact our wallets.

The thesis aspires to inquire into what competition authorities could do to mitigate the risks discussed – both through regulation and competition. It proposes the issuance of a new regulation aiming to address the core market failure of perfect price discrimination which seems to fall within a regulatory gap, jeopardizing consumer’s welfare.