Data‐driven mergers and personalization

A-Tier
Journal: RAND Journal of Economics
Year: 2022
Volume: 53
Issue: 1
Pages: 3-31

Authors (4)

Zhijun Chen (not in RePEc) Chongwoo Choe (not in RePEc) Jiajia Cong (not in RePEc) Noriaki Matsushima (Osaka University)

Score contribution per author:

1.005 = (α=2.01 / 4 authors) × 2.0x A-tier

α: calibrated so average coauthorship-adjusted count equals average raw count

Abstract

This article studies tech mergers that involve a large volume of consumer data. The merger links the markets for data collection and data application through a consumption synergy. The merger‐specific efficiency gains exist in the market for data application due to the consumption synergy and data‐enabled personalization. Prices fall in the market for data collection but generally rise in the market for data application as the efficiency gains are extracted away through personalized pricing. When the consumption synergy is large enough, the merger can result in monopolization of both markets. We discuss policy implications including various merger remedies.

Technical Details

RePEc Handle
repec:bla:randje:v:53:y:2022:i:1:p:3-31
Journal Field
Industrial Organization
Author Count
4
Added to Database
2026-01-25