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Published in: Review of Quantitative Finance and Accounting 2/2018

02-05-2017 | Original Research

Forestalling capital regulation or masking financial weakness? Evidence from loss reserve management in the property–liability insurance industry

Authors: Yi-hsun Lai, Wen-chang Lin, Liang-wei Kuo

Published in: Review of Quantitative Finance and Accounting | Issue 2/2018

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Abstract

This study examines the extent to which capital thresholds induce insurers to strategically exert accounting discretion to forestall regulatory actions. Using a sample of US property–liability insurers during 1994–2009, we find that when managing their claim loss reserves, the average insurers are insensitive to the pressure of capital regulation as measured by the distance of their RBC ratio to the action threshold. Yet, when the insurers are virtually partitioned by their reserving tendency, the effect of regulatory pressure is significantly related to the downward reserve bias in the under-reserving insurer cohorts. This finding continues to hold even after we utilize the number of ratio violations in the insurance regulatory information systems to purge the financial weakness effect embedded in the distance to RBC bound ratio. Hence, our empirical evidence suggests that insurers that are about to trigger the regulatory threshold will have the incentives to understate their loss reserves to preclude the impending authorized preventive actions. Finally, our analyses also shed light on the heterogeneity of incentives to managing loss reserves among over- and under-reserving insurers.

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Appendix
Available only for authorised users
Footnotes
1
Another closely related reason to manage loss reserves is to avert downward ratings from rating agencies (Klein and Wang 2009; Grace and Leverty 2012a, b). We do not consider this incentive in our model because it is very similar to, and hence inseparable from, the financial-weakness-masking incentive.
 
2
Grace and Leverty (2010) also use the quantile regression to examine the motives for insurers to manage loss reserves due to the non-normal characteristic of reserve distribution. Besides, due to the appealing features of quantile regression, some recent studies in the areas of corporate finance, risk management, and asset pricing employ this approach to address their research questions (Bikki et al. 2009; Du et al. 2013; Huang 2013; Du and Zhao 2017).
 
3
Eckles and Halek (2010) find that managers may manage loss reserves to maximize their compensation. We do not consider this incentive in this study because it is relevant to manager interest rather than shareholder interest. Other incentives will be discussed in detail later in the study.
 
4
For instance, Grace and Leverty (2012a, b) document that during 1989–2002, the average insurer paid about 43% of claims by the end of the accident year, 83.5% after three years, and 91.4% after 5 years.
 
5
For example, the KFS error was used by Beaver et al. (2003), Gaver and Paterson (2004), and Grace and Leverty (2010), while the Weiss error was adopted by Grace (1990) and Petroni and Beasley (1996).
 
6
Since the two error measures are standard in the insurance studies, for the sake of brevity we do not illustrate the computations through sample Schedule P—Part 2 and Part 3. Interested readers can refer to Grace and Leverty (2012a, b), among others, for the explanations.
 
7
Incurred losses are the losses recognized by the insurers plus the estimated losses that have been incurred but not yet recognized by the insurers. In contrast, losses paid are those losses actually paid up to a given year.
 
8
Quantile regression estimates the coefficient of the explanatory variable based on a pre-specified quantile. Different quantiles are often characterized by distinct coefficient estimates. The coefficients estimated by quantile regression are typically more efficient than those estimated by OLS for each of the sub-samples because it takes into account the full sample. For more details, see Koenker and Hallock (2001).
 
9
Mata and Machado (1996) show that the results based on the quantile regression are relatively robust even if the conditional distribution heavily departs from normality and/or is highly skewed. For more details, see Koenker and Bassett (1978).
 
10
We exclude those insurers with RBC ratios below 200% because they have been on the watch list and therefore are less likely to manipulate their loss reserves.
 
11
These three principles of rate regulation are explicitly stated in the All-Industry model statutes adopted by the NAIC in 1945.
 
12
An alternative proxy for earnings-smoothing incentives is the moving average of the ROA over the past three years. The empirical results based on these two measures are, however, qualitatively similar.
 
13
Both personal and commercial liability lines are included.
 
14
The skewness of the 5-year reserve error distributions is also found to be greater than that of the 3-year error distributions.
 
15
Using the average ROA over the past 3 years as a proxy for the income smoothing incentive yields similar results.
 
16
The average RBC ratio for insurers at the 10% quantile is 485%, while that at the median (the 50% quantile) is 744%.
 
17
The reason we do not use the pre-managed RBC ratio in the model is that calculating the pre-managed RBC ratio requires information as to developed losses on a line-by-line basis, which is not available in Schedule P of the NAIC statements.
 
18
We find that the average number of reported IRIS violations is generally lower (larger) than the average pre-managed IRIS violations for under-reserving (over-reserving) insurers. This result is consistent with the finding of Gaver and Paterson (2004) that weaker insurers under-reserve to a greater extent than healthy insurers.
 
19
It is possible that firms that use in-house actuarial certification have more incentives to forestall regulatory preventative actions by underestimating loss reserves. Following recent studies (e.g., Grace and Leverty 2011, 2012a, b; Kamiya and Milidonis 2016) that examine the effect of in-house versus external actuaries on loss reserve errors, we construct an in-house actuary variable (In-house) and include this factor and its interaction with ln(TCB) in the OLS and quantile regression models. The sign, magnitude, and significant level of the coefficient estimate of ln(TCB) in all quantiles remain the same. We also find that the coefficient of In-house × ln(TCB) is negative but insignificant in the below-median quantiles, suggesting that the incentive to forestall regulatory pressure is not limited to those that appoint an in-house actuary. All of these findings are robust to including year effects and to subsamples of hard and soft markets. We thank the anonymous referee for pointing out the in-house actuary effect. The untabulated results of the in-house actuarial certification are available upon request.
 
20
This outcome might be caused by the high skewness of the reserve error distribution.
 
21
The F test statistic can be obtained by calculating the bootstrapped standard errors from over 100 replications (see e.g., Koenker and Bassett 1978).
 
22
The test rejects the hypothesis that the coefficients of all the explanatory variables between over-reserving and under-reserving insurers are equal (see Panel F in Table 9).
 
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Metadata
Title
Forestalling capital regulation or masking financial weakness? Evidence from loss reserve management in the property–liability insurance industry
Authors
Yi-hsun Lai
Wen-chang Lin
Liang-wei Kuo
Publication date
02-05-2017
Publisher
Springer US
Published in
Review of Quantitative Finance and Accounting / Issue 2/2018
Print ISSN: 0924-865X
Electronic ISSN: 1573-7179
DOI
https://doi.org/10.1007/s11156-017-0636-y

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