Information Equalisation and Competition in Selection Markets: Evidence From Auto Insurance

Why It Matters

The findings illustrate that mandated data sharing can dramatically boost consumer surplus and price competition, but may also dampen incentives for insurers to invest in advanced risk‑prediction technologies.

Key Takeaways

• Full transparency raises consumer surplus 16.9% in Italy.
• Centralised risk bureau yields similar 15.7% surplus increase.
• Privacy‑limited regime improves surplus only 3.6%.
• Low‑risk drivers gain up to 78% surplus; high‑risk lose.
• Data sharing cuts premiums 21‑26% and reshapes profits.

Pulse Analysis

European regulators are increasingly pushing data‑sharing rules through the Digital Markets Act, the Data Act and PSD2, aiming to level the informational playing field in data‑driven markets. The Italian auto‑insurance sector, with over 31 million contracts, serves as a microcosm for these policies. Converting the typical €6 million minimum coverage to roughly $6.6 million underscores the scale of exposure, while the average cost savings of €32 per policy (about $35) highlight tangible consumer benefits when information asymmetries are reduced.

The study’s counterfactual analysis shows that granting insurers full knowledge of each driver’s true risk type can lift consumer surplus by 16.9% and slash premiums between 21.6% and 25.7%. A centralised risk bureau—essentially a public data pool—delivers almost the same welfare boost (15.7% surplus increase) at a fraction of the administrative burden. By contrast, a privacy‑focused regime that limits firms to public data yields a modest 3.6% surplus gain, illustrating how restrictive policies can blunt competition. Low‑risk drivers reap the biggest gains, with surplus jumps exceeding 78%, while high‑risk drivers face higher prices, reflecting intensified price discrimination under richer data environments.

Policymakers must balance these consumer gains against the potential erosion of incentives for insurers to develop superior risk‑assessment algorithms. While data‑sharing can enhance market efficiency—reducing average costs by about 3.7% (≈$35 per contract) and improving matching between insurers and risk profiles—it may also dampen private investment in predictive technology. The Italian case suggests that a hybrid approach, such as a centralised risk bureau, can capture most of the welfare upside while preserving some competitive edge for innovators, a lesson applicable to other selection markets like credit and health insurance.