Causality in empirical analyses with emphasis on asymmetric information and risk management

Georges Dionne ()

No 23-4, Working Papers from HEC Montreal, Canada Research Chair in Risk Management

Abstract: We discuss the difficult question of measuring causality effects in empirical analyses, with applications to asymmetric information and risk management. It is now well documented in the economic literature that policy analysis must be causal. Hence, the measurement of its effects must also be causal. After having presented the main frameworks for causality analysis, including instrumental variable, difference-in-differences, and generalized method of moments, we analyze the following questions: Does risk management affect firm value and risk? Do we face a moral hazard problem in the insurance data? How can we separate moral hazard from adverse selection and asymmetric learning? Is liquidity creation a causal factor for reinsurance demand? We show that residual information problems are often present in different markets, while risk management may increase firm value when appropriate methodologies are applied.

Keywords: Asymmetric information; moral hazard; adverse selection; risk learning; risk management; causality test; dynamic data; essential heterogeneity; difference-in-differences; instrumental variable; propensity scor; generalized method of moments (search for similar items in EconPapers)
JEL-codes: C12 C18 C23 C25 C26 D80 G11 G22 (search for similar items in EconPapers)
Pages: 70 pages
Date: 2023-10-31
New Economics Papers: this item is included in nep-cfn and nep-rmg
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Chapter: Causality in Empirical Analyses with Emphasis on Asymmetric Information and Risk Management (2025)
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Persistent link: https://EconPapers.repec.org/RePEc:ris:crcrmw:2023_004

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