Inverse Selection

Brunnermeier, Markus K.; Lamba, Rohit; Segura-Rodríguez, Carlos Luis

doi:10.2139/ssrn.3584331

Cited by 8 publications

(4 citation statements)

References 26 publications

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“…In some applications, private information on the side of the market is possible if the market has access to data on past outcomes for other agents with similar covariates. (This view of big data is embedded in, for instance, the “inverse selection” model of Brunnermeier, Lamba, and Segura‐Rodriguez (2021). ) We show in Section 4.2 that our results continue to hold under this sort of informational asymmetry, so long as measurement of a new covariate leads the agent to believe that the market has gained new information about his type or shock.…”

Section: Modelmentioning

confidence: 99%

“…66 Our approach complements recent work focusing on how data collection impacts markets shaped by asymmetric information. See, for instance, Bergemann, Bonatti, and Gan (2022), Elliott, Galeotti, and Koh (2022), Yang (2022) for price discrimination; Ichihashi (2019), Hidir and Vellodi (2021), Gomes and Pavan (2022) for matching on platforms; and Braverman and Chassang (2022), Brunnermeier, Lamba, and Segura‐Rodriguez (2021) for insurance pricing. …”

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confidence: 99%

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Data and incentives

Liang,

Madsen

2024

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“Big data” gives markets access to previously unmeasured characteristics of individual agents. Policymakers must decide whether and how to regulate the use of this data. We study how new data affects incentives for agents to exert effort in settings such as the labor market, where an agent's quality is initially unknown but is forecast from an observable outcome. We show that measurement of a new covariate has a systematic effect on the average effort exerted by agents, with the direction of the effect determined by whether the covariate is informative about long‐run quality versus a shock to short‐run outcomes. For a class of covariates satisfying a statistical property that we call strong homoskedasticity, this effect is uniform across agents. More generally, new measurements can impact agents unequally, and we show that these distributional effects have a first‐order impact on social welfare.

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Section: Modelmentioning

confidence: 99%

mentioning

confidence: 99%

Data and incentives

Liang,

Madsen

2024

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“…In an insurance setup with two dimensional types, Brunnermeier et al. (2021) consider an insurer who obtains private information about the correlation of a consumer's two dimensional risk type. In contrast to the current article, the authors study the effects of these correlation savvy insurers on boundedly rational consumers and competition.…”

Section: Related Literaturementioning

confidence: 99%

Correlation‐savvy sellers

Strausz

2024

The RAND J of Economics

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A multi‐product monopolist sells sequentially to a buyer who privately learns his valuations. Using big data, the monopolist learns the intertemporal correlation of the buyer's valuations. Perfect price discrimination is generally unattainable—even when the seller learns the correlation perfectly, has full commitment, and in the limit where the consumption good about which the buyer has ex ante private information becomes insignificant. This impossibility is due to informational externalities that require information rents for the buyer's later consumption. These rents induce upward and downward distortions, violating the generalized no distortion at the top principle of dynamic mechanism design.

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“…In a broader context, it is also important to highlight the impact of digitization on insurability and, consequently, on sustainability in risk management. For instance, Brunnermeier et al (2021) discuss the concept of "inverse selection" in the insurance industry, illustrating that technology can reverse traditional information asymmetries between customers and insurers. Due to technology and big data, insurers can now better assess the customer and their risk, shifting the advantage away from the customer.…”

Section: The Journal Of Risk Financementioning

confidence: 99%

Is the insurance industry sustainable?

Eling

2024

JRF

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PurposeThis study aims to develop a comprehensive framework for discussing sustainability within the insurance industry, extending the traditional Environmental, Social, and Governance (ESG) dimensions to include economic and technological considerations. This inclusion is vital, recognizing that financial stability and the adoption of innovative technologies are fundamental to meeting other sustainability targets.Design/methodology/approachWe base our findings on an extensive literature review, case studies, and interactive workshops with key stakeholders in the insurance industry. Our analytical framework employs Porter's (1985) insurance-specific value chain, complemented by Berliner's (1982) insurability criteria, to distinguish between insurable and non-insurable risks.FindingsOur results show that the insurance industry is sustainable because it actively incorporates and contributes to sustainability goals across environmental, social, economic, and technological dimensions. This is illustrated through the identification of 50 distinct contributions across the insurance value chain, showcasing the sector’s unique position to significantly influence the sustainability discourse.Practical implicationsAddressing the pressing challenges of sustainability and insurability necessitates a strategic, collective response from the global insurance and risk management community. This paper proposes several policy recommendations, including enhancing risk assessment methodologies, diversifying insurance product offerings, encouraging cross-sectoral collaboration, and prioritizing investments in resilience and preventive measures.Originality/valueBy broadening the sustainability discussion to encompass economic and technological facets, this paper enriches the dialogue surrounding the insurance industry’s role in sustainability. It aims to inform decision-makers across the industry, political spheres, and broader society about the necessity of sustainability, fostering pertinent political discussions and highlighting avenues for future research.

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Inverse Selection

Cited by 8 publications

References 26 publications

Data and incentives

Data and incentives

Correlation‐savvy sellers

Is the insurance industry sustainable?

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