The DOI matches its Crossref record; 26 of the 26 references it cites were found in a registry or an index.
Do Credit‐Based Insurance Scores Proxy for Income in Predicting Auto Claim Risk?
Abstract
Property and casualty insurers often use credit‐based insurance scores in their underwriting and rating processes. The practice is controversial—many consumer groups oppose it, and most states regulate it, in part out of concern that insurance scores proxy for policyholder income in predicting claim risk. We offer new evidence on this issue in the context of auto insurance. Prior studies on the subject suffer from the limitation that they rely solely on aggregate measures of income, such as the median income in a policyholder's census tract or zip code. We analyze a panel of households that purchased auto and home policies from a U.S. insurance company. Because we observe the households’ home policies as well as their auto policies, we are able to employ two measures of income: the median income in a household's census tract, an aggregate measure, and the insured value of the household's dwelling, a policyholder‐level measure. Using these measures, we find that insurance scores do not proxy for income in a standard actuarial model of auto claim risk.
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In the citing paper
“However, as emphasized by Morris, Schwarcz, and Teitelbaum (2017), insurance scores are regulated because they potentially correlate with suspect classifications, in particular race and income.”
From The Discriminating (Pricing) Actuary (2023) · cites this paper as Morris, Schwarcz, and Teitelbaum (2017)
In the citing paper
“According to FICO, in the absence of state prohibitions, 95 percent of automobile insurers and 85 percent of homeowners insurers employ insurance scores in either the underwriting or rating process (NAIC 2012a), see also Morris, Schwarcz, and Teitelbaum (2017).”
From The Discriminating (Pricing) Actuary (2023) · cites this paper as Morris, Schwarcz, and Teitelbaum (2017)
In the citing paper
“However, from Morris, Schwarcz, and Teitelbaum (2017), this is inconsistent with the fact that two of the major drivers of credit risk are unemployment and health problems, neither of which seems to reflect irresponsible behavior such as reckless driving or lack of fire safety.”
From The Discriminating (Pricing) Actuary (2023) · cites this paper as Morris, Schwarcz, and Teitelbaum (2017)
In the citing paper
“So, although both sources re-affirm that credit-based insurance scores provide a sound platform for predicting insurance losses, the causal nature remains unclear.”
From The Discriminating (Pricing) Actuary (2023) · cites this paper as Morris, Schwarcz, and Teitelbaum (2017)
Citations
Cites
(2001) · RePEc: Research Papers in Economics · Cited by 27,577
Regression Analysis of Count Data
Cameron, A. C., & Trivedi, P. K. (2013) · doi:10.1017/cbo9781139013567 · Cited by 6,640
Regression Analysis of Count Data
Cameron, A. C., & Trivedi, P. K. (1998) · Cambridge University Press eBooks · doi:10.1017/cbo9780511814365 · Cited by 3,147
Beyond Testing: Empirical Models of Insurance Markets
Liran Einav, B., Finkelstein, A. N., & Levin, J. (2010) · Annual Review of Economics · doi:10.1146/annurev.economics.050708.143254 · Cited by 228
Actuarial Modelling of Claim Counts
Denuit, M., Maréchal, X., Pitrebois, S., & Walhin, J. (2007) · Wiley eBooks · doi:10.1002/9780470517420 · Cited by 204
Actuarial Modelling of Claim Counts: Risk Classification, Credibility and Bonus-Malus Systems
Denuit, M. M., Walhin, J., Pitrebois, S., & Maréchal, X. (2007) · Cited by 174
The Political Economy of Regulation: The Case of Insurance
Weiss, M. A., & Meier, K. J. (1989) · Journal of Risk & Insurance · doi:10.2307/253003 · Cited by 134
Squires, G. D. (2003) · Journal of Urban Affairs · doi:10.1111/1467-9906.t01-1-00168 · Cited by 110
Finkelstein, A. N., & Poterba, J. M. (2014) · Journal of Risk & Insurance · doi:10.1111/jori.12030 · Cited by 96
Implementing Anti-discrimination Policies in Statistical Profiling Models
Pope, D. G., & Sydnor, J. R. (2011) · American Economic Journal: Economic Policy · doi:10.1257/pol.3.3.206 · Cited by 87
Research works printed but not linked: 3
- Avraham Ronen, 2014, Understanding Insurance Discrimination, Southern California Law Rev, 87, 195
- Hunter J. Robert TomFeltner &DouglasHeller(2013) “What Works: A Review of Auto Insurance Rate Regulation in America and How Best Practices Save Billions of Dollars ” Consumer Federation of America http://www.consumerfed.org/pdfs/whatworks-report_nov2013_hunter-feltner-heller.pdf.
- National Conference of Insurance Legislators (NCOIL)(2009) “Model Act Regarding Use of Credit Information in Personal Insurance ”http://www.ncoil.org/Private/2009/Docs/07172009CreditScoringModel.pdf.
Cited by
The Discriminating (Pricing) Actuary
Frees, E. W. (., & Huang, F. (2023) · North American Actuarial Journal · doi:10.1080/10920277.2021.1951296 · Cited by 56
Tschantz, M. C. (2022) · 2022 ACM Conference on Fairness Accountability and Transparency · doi:10.1145/3531146.3533242 · Cited by 25
Metadata
- Authors
- Morris, D. S., Schwarcz, D., & Teitelbaum, J. C.
- Venue
- Journal of Empirical Legal Studies
- Publisher
- Wiley
- Published
- 2017-04-25
- Volume
- 14
- Issue
- 2
- Pages
- 397-423
- ISSN
- 1740-1453
- 1740-1461
- Cited by
- 9
- References
- 26
- Language
- en
- Licence
- Funders
- National Science Foundation (SES‐1031136)
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- Morris, D. S., Schwarcz, D., & Teitelbaum, J. C. (2017). Do Credit‐Based Insurance Scores Proxy for Income in Predicting Auto Claim Risk? Journal of Empirical Legal Studies. https://doi.org/10.1111/jels.12151
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.