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AUDIT COMMITTEE
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Audit committee characteristicsand loss reserve error
Fang SunDepartment of Economics, CUNY, New York, New York, USA
Xiangjing WeiDepartment of Business and Economics, Wilson College, Chambersburg,
Pennsylvania, USA, and
Yang XuSchool of Business, University of Kansas, Lawrence, Kansas, USA
Abstract
Purpose – The purpose of this paper is to investigate two audit committee characteristics –independence and expertise of the audit committee – and the property-liability insurers’ financialreporting quality, which is proxied by loss reserve error.
Design/methodology/approach – The authors’ hypotheses are tested using multivariate analysiswhere the loss reserve error is the dependent variable, and audit committee independence, and fourtypes of audit committee financial expertise (accounting, finance, supervisory, and insuranceexpertise) are the testing variables.
Findings – It is found that accounting, finance, and insurance financial expertise are associated withmore accurate loss reserve estimate. In contrast, a supervisory financial expertise and an independenceaudit committee are not found to be associated with better loss reserve quality.
Research limitations/implications – The sample includes publicly-held property-liabilityinsurers. Although the results from publicly-held insurers could provide a good laboratory for suchinvestigation in all insurers, they might be limited due to different organization structures of public vsprivate insurers.
Practical implications – The implications of the study are important for the SEC and NAIC. Theresults suggest that the requirements on the audit committee financial expertise would be necessary,even in highly regulated industry, such as property-casualty insurance.
Originality/value – The paper contributes to the extant literature by studying audit committeecharacteristics in the insurance industry. It also contributes to the extant literature on audit committeeeffectiveness by decomposing the financial expertise into four types of financial expertise (accounting,finance, supervisory, or insurance expertise) and investigates which (if any) of these four types ofexpertise really drives the improvement of loss reserve quality.
Keywords United States of America, Audit committees, Insurance companies, Financial reporting,Loss reserve error, Audit financial expertise, Audit accounting expertise, Audit insurance expertise,Property-liability insurer
Paper type Research paper
1. IntroductionIn this paper, we investigate the relationship between the audit committee compositionindependence and expertise and the property-liability insurers’ financial reportingquality, which is proxied by loss reserve error. In June 2006, the National Associationof Insurance Committee (NAIC) revised the Annual Financial Reporting ModelRegulation (FRMR) in response to the Sarbanes-Oxley act (SOX) enacted in 2002.
The current issue and full text archive of this journal is available at
www.emeraldinsight.com/0268-6902.htm
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Received 6 January 2011Reviewed 31 March 2011
Accepted 1 May 2011
Managerial Auditing JournalVol. 27 No. 4, 2012
pp. 355-377q Emerald Group Publishing Limited
0268-6902DOI 10.1108/02686901211217978
Under the new standards, all insurance companies are imposed to designate an auditcommittee and, for insurers with more than $500 million in premiums, 75 percent ormore audit committee members must be independent. These SOX-like requirementsanticipate that the audit committee and their individual directors take a more effectiverole in managing and monitoring the financial condition of insurers.
While SOX requirements could facilitate non-insurance industry in improving thequality of financial reporting (Beasley, 1996; Dechow et al., 1996; Klein, 2002a, b; Carcelloand Neal, 2003; Abbott et al., 2004), there are a number of reasons to suspect thatimposing the SOX-like requirements on insurers is appropriate. Chief among them is thestatutory accounting principle (SAP) which is more conservative than generallyaccepted accounting principles. Furthermore, compared with the non-insurance SOXcompliant entity, an insurance company is subject to a far greater degree of oversightand financial regulation by insurance commissioners. For example, state insurancecommissioners have the authority to require any information they find necessary toprotect policyholders and to ensure a company’s solvency. Thus, more regulations forsuch an already heavy regulated industry could add un-necessary cost while create lessbenefit. Besides the SAP and heavy regulation, another issue arises from the differentcorporate structures the insurance industry holds. The insurance industry includes notonly publicly held companies but also the mutual and private companies. The distinctivecorporate structures between publicly held companies and private firms imply differenttechnology of corporate governance control. For example, the non-transferableownership rights of a mutual and a private firm restricts the effectiveness of such controlmechanisms as managerial equity ownership and stock based compensation, etc. Thereduced effectiveness of the publicly held firms related control implies a relative moreimportant control role of independence member and financial expertise in committees ofnon-stock firms. However, compared with public-held insurers, mutual and privateinsurers have less agency problem between owners and policyholders because, for themutual, the owners are also policyholders, leading to a projection that a mutual insurerrequires less monitoring mechanism than the publicly held firm.
Thus, far, the literature has almost no analysis, either theoretical or empirical, aboutthe relation between audit committee composition (independence and expertise) andthe property-liability insurers’ loss reserve estimate. The purpose of our research is tofill out this gap. Although FRMR applies to both public insurance companies andmutual and privately owned companies[1], due to data availability, we focus our studyon the publicly held insurers. Since all of firms including private and mutuals belong toone special industry – insurance industry which subjects to heavier regulation,complex business operation and strict accounting system, we project that publicly heldinsurers could provide a good laboratory for testing the effectiveness of FRMR.
Using a sample of 98 publicly traded property-liability insurers in the years 2003, wefirst examine the association between audit committee independence and loss reserveerror of insurers. We then examine the relation between financial expertise anddiscretionary loss reserve error of insurers. We find that accounting, finance, andinsurance financial expertise are associated with more accurate or conservative lossreserve estimate. In contrast, we find that a supervisory financial expertise, or aindependence audit committee are not associated with better the loss reserve quality.These findings are consistent with prior non-insurance industry research suggestingthat firms with specialist auditors in its audit committee have lower level of discretionary
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accruals compared with those whose audit committees have no specialist (Balsam et al.,2003). These findings suggest that audit committees with financial expertise withhands-on experience of preparing financial statements, or industry experience, are betterable to monitor the financial reporting than those have no such experience. Thesefindings are consistent with prior literatures (Dhaliwal et al., 2006; Carcello et al., 2006)suggesting that adopting narrower versions of the definition that capture accountingand finance expertise.
Our study makes the following primary contributions to the existing literature.First, we evaluate the association between the audit committee characteristics and
accuracy of loss reserve estimate in property-liability insurance industry. Although agrowing number of literatures studied the relation between the audit committeecomposition and earnings quality, to our best knowledge, the literature has almost noanalysis, either theoretical or empirical, on such a relation in the heavily regulatedinsurance industry.
Second, SAP accounting system and restrictive regulation environments ofinsurance industry provide a more powerful setting in which to test the effectiveness ofthe role of audit committees as mechanisms to enhance financial reporting. Differentfrom GAAP, losses in SAP are not discounted, biasing the results against finding largeerror. This makes our results more robust.
Third, we decompose the financial expertise into four types of financial expertise:accounting, finance, supervisory, and industry expertise and investigate which (if any)of these four types of financial expertise really drives the improvement of loss reservequality. Our paper provides evidence that audit committee members with directexperience in creating financial reports, and members with finance industry experienceare better able to curb the discretionary loss reserve error of insurers than the membersthat have no such backgrounds.
Finally, examining a single industry-insurance industry, and hence a homogeneoussample of firms, avoids the potentially confounding effects that industry-specificfactors could have on research results.
The remainder of the paper is organized as follows. Section 2 provides backgroundinformation. Section 3 provides a review of related literature and developed hypotheses.Section 4 presents research methodology. Section 5 explains empirical results. Section 6concludes.
2. BackgroundInspired by SOX, in June 2006, NAIC revised FRMR and set particular requirements forthe audit committee in Section 14. In the revised FRMR, NAIC requires all insurerswith certain premium standard designate an “audit committee”. Besides that, identicalto the requirement imposed by SOX, NAIC has independence requirements on thecommittee members. Although the requirements are not as strict as those listed bySOX that require the audit committee be entirely composed of independent, NAIC setthe percentage of the audit independence based on the prior year direct written andassumed premium. For example, for insurers with more than $500 million in prior yeardirect written and assumed premium, a super majority (75 percent or more) of membersmust be independent, for insurers with less than $500 million but greater than$300 million in prior year direct written and assumed premium, a majority (50 percentor more) of members must be independent, and for insurers with less than $300 million
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in prior year direct written and assumed premium, no requirements are imposed on thenumber of independent members. In order to be considered independent, as NAICdefined in Section 14C:
[. . .] a member of the Audit committee may not, other than in his or her capacity as a memberof the Audit committee, the board of directors, or any other board committee, accept anyconsulting, advisory or other compensatory fee from the entity or be an affiliated person ofthe entity or any subsidiary thereof.
These FRMR requirements are similar to those imposed on publicly held companies in2002 by SOX except that SOX applies to all public firms while FRMR applies to allinsurers which include mutuals and private insurers.
The provision has caused much controversy (Andreason, 2005; Fitzgerald, 2004).Observers argue that SOX was legislatively imposed in response to very specific abusesby public companies outside of the insurance industry, and it is improper to impose SOXon the insurance industry through non-legislative action, particularly in view of theheavy financial regulation that already exists in this industry (Fitzgerald, 2004). Furtherthe “independence” requirement for audit committee directors might pose seriousproblems for non-profits and mutual entities that often are required by statute or theircorporate articles and bylaws to have customers or service providers as directors(Fitzgerald, 2004). Finally, the distinctive corporate structure between publicly heldcompanies and private firms clouds the effectiveness of the sox-like requirements.Insurance industry includes both private firms and publicly held firm which havedistinctive corporate structure. Distinctive corporate structure implies differenttechnology of corporate governance control. For example, the non-transferableownership rights of a mutual or a private firm restrict the effectiveness of such controlmechanisms as managerial equity ownership, etc. The reduced effectiveness of thepublicly held firms related control implies a relative more importance control role ofindependent member and financial expertise in committees of non-stock firms. However,compared with public-held insurers, mutual and private insurers has less agencyproblem between owners and policyholders because for the mutual, the owners are alsopolicyholders, leading to a projection that a mutual insurer requires less monitoringmechanism than the publicly held firm.
2.1 Loss reservesUnder SAP accounting, loss reserves are insurers’ estimated liability for unpaid claimson all losses that occurred prior to the balance sheet date. Loss reserves are collectivelythe largest liability on a property-liability insurance company’s balance sheet. Gaverand Paterson (2004) report that loss reserves account for 53 percent of total liabilities.Loss reserves could “adversely affect the financial strength of the insurer and possiblylead to insolvency” (Weiss, 1985), thus accuracy of loss reserves is an importantconcern to both regulators and stockholders of property-liability insurers. However,estimation of loss reserves is highly subjective because not all claims for current periodlosses are file by the balance sheet date. Even for claims filed in the current period, theultimate cash settlement could be quite different in amount or delayed for severalyears. The accounting matching principle requires insurers to match claim losses withrelated premium revenues in order to report profitability during a special time interval.Although the premiums are recognized in the year incurred, the majority claims will
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remain outstanding for several years. To estimate the amount an individual claim willultimately cost, typically an insurer’s actuaries generate predictions about future losspayments and expenses and recommend a range to management, who then chooses theactual loss reserve levels to be reported. Therefore, the unique aspect of the loss reserveallows insight into the reserve bias. For example, Petroni (1992) examines whetherinsurers that are financially weak underestimate their reserves. She finds thatfinancially troubled insurers underestimate reserve in order to avoid detection byregulatory authorities. Beaver et al. (2003) also provide empirical evidence thatproperty-liability insurers use reserve estimation practices to manipulate reportedearnings. Using a methodology similar to Beaver et al. (2003) and Gaver and Paterson(2004) further improves the loss reserve manipulation by showing the empirical resultsthat weak insurers adjust their loss reserve to evade regulatory intervention. In a morerecent study, Gaver and Perterson (2007) also proves that insurance companiesintentionally bias reported loss reserves to conceal financial distress. In this study, weextend the prior research by addressing the impact of certain audit committeecharacteristics identified by FRMR (2006) and SOX (2002) on the audit committees’effectiveness in improving the property-liability insurers’ financial reporting quality.The financial reporting quality is proxied by loss reserve errors and the auditcommittee quality is measured with the committee size, committee independence, andthe committee financial expertise.
3. Literature review and hypotheses3.1 IndependenceReforms introduced by the FRMR require greater audit committee independence. Theintuition behind this is that an independent auditor committee makes better monitors.For example, NYSE Listing Guide stated that “[. . .] comprised solely of directorsindependent of management and free from any relationship that would interfere withthe exercise of independent judgment as a committee member”[2]. However, empiricalevidence on whether independent members play an effective monitoring role is mixed:while some studies report a positive association between the two, others conclude theopposite. For example, Beasley (1996) and Dechow et al. (1996) show that a higher levelof outside directors on the board decrease the likelihood of fraudulent information inthe firm’s financial statements. In addition, Klein (2002a, b) finds that independentaudit committees are less likely to be associated with the abnormal accruals. Further,Carcello and Neal (2003) document a positive relation between audit committeeindependence, governance expertise, and lower stockholdings and the auditordismissals following new going-concern reports. Finally, Abbott et al. (2004) found asignificant and negative association between the occurrence of restatement and thecorporate audit committee independence.
Whereas those papers have reported evidence supporting the independent directorto facilitate the internal monitoring, nonetheless, many other studies persist in a boardwith high insider representation and some studies support this strategy. For example,Fama and Jensen (1983) proposed that inside directors provide valuable information toboards because inside directors possess more firm-specific industry visions. Consistentwith Fama and Jensen (1983) and Bhagat and Black (2002) suggest that “inside andaffiliated directors play valuable roles that may be lost in a single-minded drive forgreater board independence”. Further, Klein (2002a, b) found a positive market reaction
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to whose boards significantly increase their percentages of insiders on some specialcommittees. The absence of monitoring effectiveness of the independence of its boardmembers has also been confirmed by Agrawal and Chadha (2005). When Agrawal andChadha (2005) investigate the relation between the governance mechanisms and theincidence of an earnings restatement, they found that the independence of boards isessentially unrelated to the probability of a company restating earnings.
A popular explanation of the inconsistent results is the reverse-causality effect: firmsthat perform poorly increase the proportion of outsiders in the board instead of boardindependency causing poor financial outcomes. However, Bhagat and Black (2002) donot support the reverse-causality effects. Bhagat and Black (2002) found thatlow-profitability firms with more independent board members do not achieve improvedprofitability when they respond to their business troubles by “following conventionalwisdom and increasing the proportion of independent directors on their boards”. Theabsence of clear evidence on the role of independence board could also be partlyexplained by the intrinsic difficulty in defining the independence. For example, theseemly independent directors might not be independent. Again, Bhagat and Black(2002), dividing independent directors into outsiders with and without affiliation withthe company, find a negative association between the board dependent and firmperformance.
As the sign of the coefficient on independence is not clear a priori, we test thefollowing hypothesis in null:
H1. There is no relation between the proportion of independent audit committeemembers and loss reserve accruals.
3.2 Financial expertiseBoth NAIC and SOX do not require a firm to include a financial expert on its auditcommittee. But SOX mandates that the firm discloses in its filings whether thecommittee has such an expert, and if not to explain why. As well, in Model Section 14Aof FRMR, the audit committee of an insurer is required to:
[. . .] be directly responsible for the appointment, compensation and oversight of the work ofany accountant (including resolution of disagreements between management and theaccountant regarding financial reporting) for the purpose of preparing or issuing the Auditedfinancial report or related work pursuant to this regulation.
Obviously, the implication of FRMR is that audit committee member’s financialbackground is an important factor impacting the financial reporting quality. Theintuition behind this is that in order to fulfill their responsibilities for monitoringinternal control and financial reporting, audit committee members should possess thenecessary expertise. Thus, in our second hypothesis, we test whether a financial expertwill improve the monitoring effect of an insurer.
Previous research on the monitoring effect of financial expertise provides mixedresults. For example, Carcello and Neal (2003) examine the relation between an auditcommittee with greater financial expertise and the likelihood that the company willdismiss its auditor for issuing a going-concern report. They fail to find evidence thatthe percentage of audit committee members with financial expertise impacts theauditor dismissals. However, other literatures, for example, Bryan et al. (2004) amongothers, proved that an audit committees with financially literate are significantly likely
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to be associated with the earnings informativeness and transparency. Further,Krishnan (2005) reports a positive relation between the audit committees with financialexpertise and the incidence of internal control problems when she tests the associationbetween audit committee quality and the internal control. Other literatures such asAbbott et al. (2004) and Farber (2005) also document negative relation between thefinancial expertise in audit committees and the instances of earnings restatements andlower occurrence of financial fraud.
Carcello et al. (2006) indicate that the controversial definition of “financial expert”could partly explain the inconsistent results. Some studies provide evidencesupporting this argument. Krishnan (2005) and Dhaliwal et al. (2006) report thatfinancial expertise, measured using a strict definition based on accounting/auditingexperience, is associated with less earnings management. Further, DeFond et al. (2005)report positive market effects of accounting financial experts appointments. Bycontrast, they find that the market does not significantly react to the appointment ofnon-accounting financial experts, including persons with experience as CEO and/orpresident. Davidson et al. (2004) also document that the market significantly rewardscompanies for the appointment of accounting financial experts, but shows marginal orno reaction to appointment of audit committee members with corporate financialmanagement or financial statement analysis expertise.
Dhaliwal et al. (2006) divided the financial expertise into three types: accounting,finance, and supervisory expertise and investigate the association between the threetypes of audit committee financial expertise and accruals quality, they find accountingexpertise is significant positively related to the accruals quality but no significantassociation between accruals quality and the presence of finance or supervisoryexpertise in audit committees. We follow Dhaliwal et al. (2006) to define accountingexpert, financial expert, and supervisory expert:
. Accounting expert – all directors who currently have (or have previously had)work experience as certified public accountants, chief financial officers, vicepresidents of finance, treasurer, financial controllers, or any other majoraccounting positions.
. Financial expert – all directors who have work experience as investmentbankers, financial analysts, or any other financial management roles.
. Supervisory expert – all directors who have work experience as chief executiveofficers or company presidents; a current or retired senior manager in another firm(i.e. a CEO, chairman of the board, president, chief operating officer, or vicepresident).
Besides accounting, finance, and supervisory expertise, we further recognize one morecategory financial expertise: insurance expertise. We define insurance expert asfollowing:
. Insurance expert – all directors who have experience with insurance industry,a fellow of the Society of Actuaries, a member of the American Academy of Actuaries,MD, Insurance Commissioner, Chartered Underwriter and, Claims Committee.
We propose that the accounting experts and insurance experts could help preventfinancial scandals and ensure a better monitoring effect, however, we have nopredication on the monitoring effect of supervisory and financial experts.
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Our second set of hypotheses is stated as follows:
H2a. There is no relation between the presence of a finance expert in the auditcommittee and the loss reserve error.
H2b. There is a negative relation between the presence of an accounting financialexpert in the audit committee and the loss reserve error.
H2c. There is a no relation between the presence of a supervisory expert in theaudit committee and the loss reserve error.
H2d. There is a negative relation between the presence of an insurance expert in theaudit committee and the loss reserve error.
4. Research methodology4.1 Sample and dataThe sample for this study draws from the pubic traded stock property-casualty insurancecompanies operating in the USA in 2003. The sample includes only insurers domiciled inthe USA since the behavior of foreign insurers is likely to be confounded by foreignregulation. The primary source of data for loss reserve error is the regulatory annualstatements schedule P filed by insurers with the NAIC. The unique aspect of this dataset isthat it contains each insurers’ gradual settlement of claims over time and records allrevisions of the loss reserve estimate. Revisions, known as “development”, provide anindication of wither the previously reported amount was under- or over-stated. Dataavailability limits the loss reserve analysis to the four-year period 2003-2007. To beincluded in the sample, we use the similar selection criteria as Beaver et al. (2003):
. Firm is a domestic public traded stock company. We classify an insurer as apublicly held firm if the ultimate owner is included in the CRSP database or theEDGAR system of Securities and Exchange Commission filings.
. Firm is not primarily a reinsurer (i.e. direct premiums written are greater thanpremiums assumed).
. Net income, cash from operations, and total (admitted) assets are available.
. The firm does not cede all of its premiums to other insurers (i.e. the firm reportspositive loss reserves).
After imposing the screen, 189 public insurers were initially selected. Data aboutboards and board audit committees were hand-collected from SEC-filed proxystatements. We use SIC code 6331 to obtain the property-liability firms listed on theSEC in year 2003 through year 2005. The SEC-filed proxy statement discloses whetherfirms have a standing audit committee and if a committee exists. Firms are required todisclose its members, and the number of times the committee met during the last fiscalyear. In addition, the proxy statement also disclose each director’s name, businessbackground, other current directorships, family relationships between any director,nominee or executive officer, significant current or proposed transactions withmanagement, significant business relationships with the firm, and number of sharesheld. We gather the financial ratings for each insurer from Best’s Insurance Reports –Property-Casualty. After matching with these three dataset, the final sample includes98 firms.
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4.2 Variable construction4.2.1 Loss reserve error measurement. In the literature, loss reserve error is mostcommonly calculated as the difference between insurers’ revised estimate of thecumulative claim losses outstanding disclosed in year t þ j and the originally reportedestimate of cumulative claim loss reserve at the end of year t, e.g. Petroni (1992),Petroni and Beasley (1996), Beaver et al. (2003) and Grace and Leverty (2010). Sopositive reserve errors are associated with under-reserving and negative reserve errorswith over-reserving. Due to data availability, we examine four years of reservedevelopment, so j is four.
In order to reduce problems of heteroskedasticity to allow cross-sectionalcomparability, and more importantly to reflect the errors’ importance relative to thefinancial statements taken as a while, we follow Petroni (1992), Petroni and Beasley(1996), Beaver et al. (2003) and Grace and Leverty (2010), to scale loss reserve errors bytotal admitted assets[3]:
LossReserveErrori;t ¼Loss Reservei;tþj 2 Loss Reservei;t
TotalAssetsi;t
where Loss Reservei,t is the loss reserve for insurer I reported in yeart t, and LossReservei,tþ j is the revised estimate of the year t loss reserve reported in year t þ j.Details on how to calculate the loss reserve error using actual firm data are available inTable I.
Table I, excerpted from the Statutory Annual Statement of Tower InsuranceCompany of New York, provides an example of how to calculate the loss reserve errors.The loss reserve error that we examine in this paper is measured as the differencebetween total losses incurred in a given calendar year and a revised estimate of totallosses incurred fours calendar years in the future. The estimate of total incurred lossesfor a given calendar year is the sum of the losses in the column of that year. For TowerInsurance Company of New York, at the end of 2003, the estimated losses for all yearsup to and including 2003 totaled $47.881 million (the sum of the italicized values incolumn 7 – 2003). By the end of 2007, the estimate for the same loss period had beenincreased to $59.173 million (the sum of the italicized values in column 11 – 2007).Accordingly, Tower Insurance Company of New York’s loss reserve error for is$11.292 million ($59.173 million 2 $47.881 million), indicating that Tower InsuranceCompany of New York under-estimated their reserves by $11.292 million.
4.2.2 Independent variables. The primary variable of interest is audit committeeindependence and its four types of financial expertise. To mitigate biased coefficientsand the mis-estimation of the impact of different characteristics of audit committees,we include control variables that are related to determining loss reserves from priorresearch. The control variables are meant to capture influences on loss reserve errorsthat are unrelated to testing variables.
Studies indicate that loss reserve errors are related to the complexity of insurer’stype of business written, e.g. Weiss (1985). Petroni (1992) and Petroni and Beasley(1996) find that insurers with long-tailed product lines tend to have more pronouncedreserved errors. Following them, we include LENGTH and MAL two control variables.LENGTH is included because the longer the claim cycle, the more difficult it is toforecast total claims. It is defined as claim loss reserves expressed as a percentage oftotal liabilities. MAL is the percentage of net premiums written for malpractice.
Audit committeecharacteristics
363
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Excerpt from the 2007Annual Statement ofTower InsuranceCompany of New York
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364
Beasley and Petroni (1996) suggest that the amount of malpractice insurance written ispositively related to the loss reserve estimation error.
Prior research has also identified other variables that potentially affect loss reserveestimation error. The first is insurer size; Aiuppa and Trieschmann (1987) argue thatlarger insurers should report more accurate reserves because they have strongeractuarial staffs and are more diversified. But both Pentroni (1992) and Weiss (1985) findno relation between insurer size and loss reserve estimation error. The second is financialsolvency. Harrington and Danzon (1994) find that firms with weak safety (solvency)incentives under-report reserves to increase firm growth. They also discover that thesefirms attempt to hide their under-reserving with reinsurance. We account for growthusing the one-year percent increase in net premiums written and reinsurance usage withthe percent of gross premiums written ceded to reinsurers. We include financial distressmeasured as the A.M. Best ratings as our control variable. A.M. Best has six level ratingsto insurers with different financial conditions: A þ and A (excellent), B þ (very good),B (good), C þ (fairly good), and C (fair). Following [. . .] we assign 1 to all firms with arating of A 2 , A, A þ , or Aþþ , 2 to Bþþ , B þ , B or B 2 , and 3 to C þ , or C orbelow. Following Grace and Leverty (2010), we also account for differences in productand geographical diversification. Product diversification is measured using the productline Herfindahl Index, which is calculated as the sum of the squared percentage ofpremiums earned by an insurer in each of the 26 lines of P/L insurance. Geographicaldiversification is gauged using the geographical Herfindahl Index, the sum of thesquared percentage of business written in each of the 50 states and the District ofColumbia. Petroni and Beasley (1996) find that financially troubled insurers with BigEight auditors are associated with significantly more conservative reserve estimates.
4.2.3 Multivariate models. We use five multivariate regression models to test ourtwo sets of hypotheses. Specifically, we use model 1 to test the relation between lossreserve error and audit committee independence, models 2-5 to test audit committeefinancial expertise:
LossReserveErrori¼b0þb1AudIndiþb2LENGTHiþb3MALi
þb4ReInsuranceiþb5Big4i*Distressi
þb6GROWTHiþb7GeoHerfindahliþb8ProdHerfidahliþb9SIZEiþ1i ðModel 1Þ
LossReserveErrori ¼b0 þ b1AudIndi
þ b2TestingVariable
ðAudAcctExpi;AudFinExpi;AudSupExpi;AudInsExpiÞ
þ b3LENGTHi þ b4MALi þ b5ReInsurancei
þ b6Big4i*Distressi þ b7GROWTHi þ b8GeoHerfindahli
þ b9ProdHerfidahli þ b10SIZEi þ 1i
ðModels 2 ~5Þwhere:
LossReserveError – the difference between insurer i’s revised estimate of thecumulative claim losses outstanding disclosed in year t þ 4(2007) and the originally reported estimate of cumulative
Audit committeecharacteristics
365
claim loss reserve at the end of year t ¼ 2003. Positive reserveerrors are associated with under-reserving and negativereserve errors with over-reserving.
AudInd – indicative variable, which equals to 1 if the percentage of insureri’s independent members in its audit committee is greater than50 percent, 0 otherwise. The member (director) is identified asindependent if she/he is not a current/former employee ofinsurer i, free of relative relationships with its employee, and hasno any business dealings with the insurer i, including acceptingany consulting, advisory or other compensatory frees.
AudAcctExp – indicative variable, which equals to 1 if there is at least oneaccounting expert in insurer i’s audit committee, 0 otherwise.An accounting expert is defined as who currently has(or previously had) work experience as certified publicaccountants, chief financial officers, vice presidents offinance, treasurer, financial controllers, or any other majoraccounting positions.
AudFinExp – indicative variable, which equals to 1 if there is at least onefinance expert in insurer i’s audit committee, 0 otherwise.A finance expert is defined as who currently has (or previouslyhad) work experience as investment bankers, financialanalysts, or any other financial management roles.
AudSupExp – indicative variable, which equals to 1 if there is at least onesupervisory expert in insurer i’s audit committee, 0 otherwise.A supervisory expert is defined as who currently has(or previously had) work experience as chief executiveofficers or company presidents; a current or retired seniormanager in another firm (i.e. a CEO, chairman of the board,president, chief operating officer, or vice president).
AudInsExp – indicative variable, which equals to 1 if there is at least oneinsurance expert in insurer i’s audit committee, 0 otherwise. Aninsurance expert is defined as who currently has (or previouslyhad) work experience with insurance industry, a fellow of theSociety of Actuaries, a member of the American Academy ofActuaries, MD, Insurance Commissioner, CharteredUnderwriter and, Claims Committee.
LENGTH – claim loss reserves expressed as a percentage of total liabilities.
MAL – the percentage of net premiums written for malpractice.
ReInsurance – the percentage of gross premiums written ceded to reinsurers.
Big4 – indicative variable, which equals to 1 if insurer i engaged one ofthe largest four audit firms (KPMG, Ernst & Young, Deloitteand Touche, Pricewaterhouse Coopers), 0 otherwise.
MAJ27,4
366
Distress – based on best’ letter rating for insurer i, we assign 1 for ratingA 2 , A, or Aþþ , 2 for B 2 , B, B þ , or Bþþ , and 3 for C þ ,or C or below.
GROWTH – one-year percent increase in net premiums written for insurer i.
GeoHerfindahl – the sum of the squared percentage of premiums earned byinsurer i in each of the 26 lines of P/L insurance.
ProdHerfindahl – the sum of the squared percentage of business written byinsurer i in each of the 50 states and the District of Columbia.
SIZE – the natural logarithm of insurer i’s total admitted assets.
5. Empirical results5.1 Descriptive statisticsTable II summarizes for the descriptive statistics for our sample. All the continuousvariables are presented after winsorizing all the variables at the first and the99th percentile to remove the effect of outliers. The results indicate that loss reservesare over-stated a mean (median) of 0.31 (0.29) percent of total assets during thesample period. However, there is substantial cross-sectional variation; loss reservesare over-stated 2.57 percent at the 25th percentile but understated 1.68 percent at the75th percentile. In terms of firm characteristics, mean or median total assets (SIZE) areapproximately about $200 million, indicating that the size distribution of the sample isnot skewed by some particularly large insurers. Approximately, two-thirds of thesample write lines of business with a settlement period, or length, of three years.Relative to total premiums, 2.48 percent of premium revenue, on average, isattributable to malpractice, and 38.97 percent ceded to reinsurers. The mean (median)premium growth rate in 2003 is 31.32 (11.13) percent in the sample. The average firmhas a product line Herfindahl of 0.51, which translates to an average of 1.96 lines ofbusiness. The mean geographical Herfindahl is 0.52, indicating that the average firmoperates in approximately two states.
Turning to audit committee characteristics, only three out of 98 insurers’independent members in their audit committee is less than 50 percent. About56.12 percent insurers in the sample have at least one accounting expert on its auditcommittee, 64.29 percent have at least one finance expert, 75.51 percent have at leastone supervisory export, and 61.22 percent have at least one insurance expert.
Table III presents the Pearson (above the diagonal) and Spearman (below thediagonal). The Pearson correlations between loss reserve error and AudInd,AudAcctExp, AudInsExp are negative and significant (at the 0.05 level), which areconsistent with our prediction. Nevertheless, the univariate relationships betweenthe loss reserve error and the testing variables are not of central importance,as pairwise correlations may lead to inappropriate inferences since other firmcharacteristics are not appropriately acknowledged. The correlations, however, displaythe associations between the various independent variables. There is no largecorrelation coefficient between independent variables, therefore, the inclusion of allthe independent variables in the multivariate models is feasible and accurate. Moreimportantly, the variance inflation factors reported in our regressions are less than twofor all independent variables, indicating that multicollinearity is not a problem.
Audit committeecharacteristics
367
5.2 Multivariate resultsTo test our hypotheses, a total of four models are run by using the sample of98 property-casualty insurers. Table IV contains the estimation results.
As observed from Table IV, all of the five the models are significant, with F-statisticsof 5.95 ( p , 0.0001), 7.64 ( p , 0.0001), 5.82 ( p , 0.0001), 5.30 ( p , 0.0001), and6.17 ( p , 0.0001), and the adjusted R 2 of 31.48, 40.64, 33.21, 30.73, and 34.77 percent.
Model 1 of Table IV reports the results for our H1. From model 1, the coefficient onaudit committee size is negative while insignificantly different from 0 at 0.05 level,indicating that firms with independent committee members are more effective inmonitoring the internal financial reporting quality of an insurer. This result supportsSOX and NAIC’s independence requirements on the committee member. However,we need to interpret this finding with caution as almost all insurers’ audit committeeare independent in our sample displayed in Table II.
Variables n Mean First quartile Median Third quartile
LossReserveError 98 20.0031 20.0257 20.0029 0.0168AudInd 98 0.9694 1.0000 1.0000 1.0000AudAcctExp 98 0.5612 0.0000 1.0000 1.0000AudFinExp 98 0.6429 0.0000 1.0000 1.0000AudSupExp 98 0.7551 1.0000 1.0000 1.0000AudInsExp 98 0.6122 0.0000 1.0000 1.0000LENGTH 98 0.4489 0.3611 0.4703 0.5417MAL 98 0.0248 0.0000 0.0000 0.0000ReInsurance 98 0.3897 0.1590 0.3765 0.6111Big4_Distress 98 1.7143 1.0000 1.0000 2.0000GROWTH 98 0.3132 20.0043 0.1113 0.3428Prod_Herfindahl 98 0.5081 0.2366 0.4710 0.7672Geo_Herfindahl 98 0.5230 0.0862 0.2131 0.5103SIZE 98 19.1046 17.8087 19.1014 20.3590
Notes: This table reports summary statistics for the year 2003-2007; LossReserveError is thedifference between insurer i’s revised estimate of the cumulative claim losses outstanding disclosed inyear t þ 4 (2007) and the originally reported estimate of cumulative claim loss reserve at the end ofyear t ¼ 2003; positive reserve errors are associated with under-reserving and negative reserve errorswith over-reserving; AudInd is an indicative variable, which equals to 1 if the percentage of insurer i’sindependent members in its audit committee is greater than 50 percent, 0 otherwise; AudAcctExp is anindicative variable, which equals to 1 if there is at least one accounting expert in insurer i’s auditcommittee, 0 otherwise; AudFinExp is an indicative variable, which equals to 1 if there is at least onefinance expert in insurer i’s audit committee, 0 otherwise; AudSupExp is an indicative variable, whichequals to 1 if there is at least one supervisory expert in insurer i’s audit committee, 0 otherwise;AudInsExp is an indicative variable, which equals to 1 if there is at least one insurance expert ininsurer i’s audit committee, 0 otherwise; LENGTH is claim loss reserves expressed as a percentage oftotal liabilities; MAL is the percentage of net premiums written for malpractice; ReInsurance is thepercentage of gross premiums written ceded to reinsurers; Big4 is an indicative variable, which equalsto 1 if insurer i engaged one of the largest four audit firms (KPMG, Ernst & Young, Deloitte andTouche, Pricewaterhouse Coopers), 0 otherwise; distress is 1 for rating A 2 , A, or Aþþ , 2 for B 2 ,B, B þ , or Bþþ , and 3 for C þ , or C or below; GROWTH is one-year percent increase in netpremiums written; GeoHerfindahl is the sum of the squared percentage of premiums earned by insureri in each of the 26 lines of P/L insurance; ProdHerfindahl is the sum of the squared percentage ofbusiness written by insurer i in each of the 50 states and the District of Columbia
Table II.Descriptive statistics
MAJ27,4
368
Var
iab
le1
23
45
67
89
1011
1213
1415
1.L
ossR
eser
veE
rror
20.227
20.403
20.1
59
20.0
63
20.302
0.0
35
20.1
92
0.1
26
20.0
70
20.0
97
0.381
0.1
15
20.256
20.0
01
2.A
ud
Ind
20.
114
0.0
82
20.0
09
0.0
37
0.1
02
0.1
29
0.0
40
0.1
00
0.228
20.0
62
20.0
71
20.230
0.0
44
0.1
20
3.A
ud
Acc
tEx
p2
0.567
0.08
20.0
28
0.1
18
0.605
20.0
90
20.0
22
20.1
10
20.0
18
0.0
22
20.1
39
20.4
30
0.0
45
0.1
37
4.A
ud
Fin
Ex
p2
0.14
82
0.00
90.
028
0.0
21
0.1
94
20.0
07
20.206
0.229
0.0
21
0.298
20.1
69
20.1
37
0.0
60
20.0
36
5.A
ud
Su
pE
xp
20.
065
0.03
70.
118
0.02
10.229
20.1
92
20.0
07
0.0
40
0.1
91
0.1
36
20.1
23
20.1
78
20.1
83
0.1
66
6.A
ud
InsE
xp
20.458
0.10
20.605
0.19
40.229
20.0
51
0.1
74
20.0
55
0.0
06
0.0
60
0.0
33
20.290
0.0
62
0.0
44
7.L
EN
GT
H0.
059
0.09
32
0.08
00.
043
20.
183
20.
031
0.248
20.1
75
0.219
20.0
80
20.0
34
0.1
02
0.224
20.0
13
8.M
AL
0.10
10.
097
20.
036
20.
135
20.
065
0.18
90.207
20.1
69
0.0
52
20.380
20.0
43
0.291
20.0
16
0.1
59
9.R
eIn
sura
nce
0.16
60.
115
20.
119
0.220
0.04
72
0.06
72
0.13
22
0.06
00.1
51
0.294
20.0
73
20.222
20.0
39
20.0
72
10.
Big
42
0.03
20.228
20.
018
0.02
10.
191
0.00
60.245
0.12
70.
169
0.293
0.0
29
20.0
81
0.0
10
0.240
11.
Dis
tres
s0.
004
20.
066
0.04
20.308
0.14
80.
082
20.
056
20.
037
0.310
0.333
20.1
38
20.289
0.0
10
0.0
64
12.
GR
OW
TH
0.282
20.
037
20.224
20.
058
20.
065
20.247
20.240
20.
039
0.03
72
0.01
62
0.00
10.1
84
20.0
16
20.1
39
13.
Pro
d_
Her
fin
dah
l0.
180
20.215
20.447
20.
126
20.
190
20.304
0.10
62
0.05
32
0.258
20.
093
20.308
0.07
40.0
54
20.1
74
14.
Geo
_H
erfi
nd
ahl
0.15
80.
152
20.234
20.
080
20.
137
20.
092
0.16
52
0.00
12
0.06
12
0.10
32
0.210
0.04
10.366
20.1
48
15.
SIZ
E2
0.10
30.
118
0.14
32
0.01
80.
159
0.08
60.
013
0.253
20.
018
0.252
0.10
02
0.06
42
0.248
20.572
Notes:
Th
ista
ble
pro
vid
esp
airw
ise
corr
elat
ion
sfo
rth
esa
mp
le;P
ears
onco
rrel
atio
ns
are
inth
eu
pp
ertr
ian
gle
(ita
lize
d)
and
Sp
earm
anco
rrel
atio
ns
are
inth
elo
wer
tria
ng
le(u
nit
aliz
ed);
corr
elat
ion
sig
nifi
can
tat
the
0.05
lev
elar
ein
bol
dfi
gu
res;
Los
sRes
erv
eErr
oris
the
dif
fere
nce
bet
wee
nin
sure
ri’s
rev
ised
esti
mat
eof
the
cum
ula
tiv
ecl
aim
loss
esou
tsta
nd
ing
dis
clos
edin
yea
rtþ
4(2
007)
and
the
orig
inal
lyre
por
ted
esti
mat
eof
cum
ula
tiv
ecl
aim
loss
rese
rve
atth
een
dof
yea
rt¼
2003
;p
osit
ive
rese
rve
erro
rsar
eas
soci
ated
wit
hu
nd
er-r
eser
vin
gan
dn
egat
ive
rese
rve
erro
rsw
ith
over
-res
erv
ing
;A
ud
Ind
isan
ind
icat
ive
var
iab
le,w
hic
heq
ual
sto
1if
the
per
cen
tag
eof
insu
rer
i’sin
dep
end
ent
mem
ber
sin
its
aud
itco
mm
itte
eis
gre
ater
than
50p
erce
nt,
0ot
her
wis
e;A
ud
Acc
tEx
pis
anin
dic
ativ
ev
aria
ble
,wh
ich
equ
als
to1
ifth
ere
isat
leas
ton
eac
cou
nti
ng
exp
ert
inin
sure
ri’s
aud
itco
mm
itte
e,0
oth
erw
ise;
Au
dF
inE
xp
isan
ind
icat
ive
var
iab
le,
wh
ich
equ
als
to1
ifth
ere
isat
leas
ton
efi
nan
ceex
per
tin
insu
rer
i’sau
dit
com
mit
tee,
0ot
her
wis
e;A
ud
Su
pE
xp
isan
ind
icat
ive
var
iab
le,
wh
ich
equ
als
to1
ifth
ere
isat
leas
ton
esu
per
vis
ory
exp
ert
inin
sure
ri’s
aud
itco
mm
itte
e,0
oth
erw
ise;
Au
dIn
sEx
pis
anin
dic
ativ
ev
aria
ble
,w
hic
heq
ual
sto
1if
ther
eis
atle
ast
one
insu
ran
ceex
per
tin
insu
rer
i’sau
dit
com
mit
tee,
0ot
her
wis
e;L
EN
GT
His
clai
mlo
ssre
serv
esex
pre
ssed
asa
per
cen
tag
eof
tota
lli
abil
itie
s;M
AL
isth
ep
erce
nta
ge
ofn
etp
rem
ium
sw
ritt
enfo
rm
alp
ract
ice;
ReI
nsu
ran
ceis
the
per
cen
tag
eof
gro
ssp
rem
ium
sw
ritt
ence
ded
tore
insu
rers
;B
ig4
isan
ind
icat
ive
var
iab
le,
wh
ich
equ
als
to1
ifin
sure
ri
eng
aged
one
ofth
ela
rges
tfo
ur
aud
itfi
rms
(KP
MG
,E
rnst
&Y
oun
g,
Del
oitt
ean
dT
ouch
e,P
rice
wat
erh
ouse
Coo
per
s),0
oth
erw
ise;
dis
tres
sis
1fo
rra
tin
gA2
,A,o
rAþþ
,2fo
rB2
,B,B
þ,o
rBþþ
,an
d3
for
Cþ
,or
Cor
bel
ow;
GR
OW
TH
ison
e-y
ear
per
cen
tin
crea
sein
net
pre
miu
ms
wri
tten
;G
eoH
erfi
nd
ahl
isth
esu
mof
the
squ
ared
per
cen
tag
eof
pre
miu
ms
earn
edb
yin
sure
ri
inea
chof
the
26li
nes
ofP
/Lin
sura
nce
;Pro
dH
erfi
nd
ahl
isth
esu
mof
the
squ
ared
per
cen
tag
eof
bu
sin
ess
wri
tten
by
insu
rer
iin
each
ofth
e50
stat
esan
dth
eD
istr
ict
ofC
olu
mb
ia
Table III.Correlation matrix
Audit committeecharacteristics
369
Model 1 Model 2 Model 3 Model 4 Model 5
InterceptCoefficient 20.0811 20.0252 20.0655 20.0831 20.0373t-value (21.00) (20.33) (20.81) (21.01) (20.46)p-value 0.3224 0.7448 0.4201 0.3159 0.6490VIF 0.0000 0.0000 0.0000 0.0000 0.0000AudIndCoefficient 20.0785 20.0790 20.0822 20.0784 20.0763t-value (22.22) * * (22.40) * * (22.36) * * (22.21) * * (22.22) * *
p-value 0.0287 0.0183 0.0207 0.0297 0.0293VIF 1.1142 1.1143 1.1181 1.1143 1.1151AudAcctExpCoefficient 20.0480t-value (23.82) * * *
p-value 0.0003VIF 1.3561AudFinExpCoefficient 20.0230t-value (21.81) *
p-value 0.0734VIF 1.1436AudSupExpCoefficient 0.0034t-value (0.23)p-value 0.8202VIF 1.2114AudInsExpCoefficient 20.0302t-value (22.33) * *
p-value 0.0220VIF 1.2606LENGTHCoefficient 0.0954 0.0808 0.0980 0.0972 0.0841t-value (2.79) * * * (2.52) * * (2.90) * * * (2.76) * * * (2.50) * *
p-value 0.0064 0.0135 0.0047 0.0071 0.0145VIF 1.2330 1.2509 1.2352 1.2989 1.2592MALCoefficient 20.1834 20.1627 20.1992 20.1850 20.1377t-value (23.05) * * * (22.89) * * * (23.32) * * * (23.04) * * * (22.23) * *
p-value 0.0030 0.0048 0.0013 0.0031 0.0285VIF 1.3135 1.3258 1.3419 1.3313 1.4597ReInsuranceCoefficient 0.0619 0.0446 0.0685 0.0625 0.0551t-value (2.68) * * * (2.03) * * (2.97) * * * (2.68) * * * (2.43) * *
p-value 0.0087 0.0453 0.0038 0.0088 0.0173VIF 1.2233 1.2782 1.2550 1.2366 1.2442Big4*DistressCoefficient 20.0162 20.0182 20.0147 20.0168 20.0152t-value (21.90) * (22.29) * * (21.74) * (21.88) * (21.82) *
p-value 0.0611 0.0246 0.0859 0.0640 0.0715VIF 1.3043 1.3101 1.3166 1.4121 1.3076
(continued )Table IV.Empirical results
MAJ27,4
370
Model 1 Model 2 Model 3 Model 4 Model 5
GROWTHCoefficient 0.0051 0.0048 0.0047 0.0051 0.0054t-value (4.21) * * * (4.25) * * * (3.91) * * * (4.19) * * * (4.55) * * *
p-value 0.0001 0.0001 0.0002 0.0001 0.0000VIF 1.0614 1.0664 1.0900 1.0713 1.0760GeoHerfindahlCoefficient 20.0114 20.0100 20.0111 20.0113 20.0105t-value (23.15) * * * (22.95) * * * (23.09) * * * (23.07) * * * (22.94) * * *
p-value 0.0022 0.0041 0.0027 0.0028 0.0042VIF 1.0839 1.0968 1.0867 1.1035 1.0981ProdHerfindahlCoefficient 0.0290 20.0129 0.0279 0.0296 0.0067t-value (1.24) (20.53) (1.21) (1.25) (0.27)p-value 0.2188 0.5994 0.2307 0.2146 0.7868VIF 1.3032 1.6332 1.3041 1.3207 1.5311SIZECoefficient 0.0064 0.0069 0.0062 0.0064 0.0057t-value (1.70) * (1.99) * (1.67) * (1.68) * (1.55)p-value 0.0925 0.0502 0.0989 0.0956 0.1240VIF 1.2118 1.2139 1.2128 1.2126 1.2194n 98 98 98 98 98R 2 0.3784 0.4676 0.4010 0.3787 0.4150Adj. R 2 0.3148 0.4064 0.3321 0.3073 0.3477Model F-value 5.9513 7.6400 5.8237 5.3037 6.1707Model Pr . F 0.0000 0.0000 0.0000 0.0000 0.0000
Notes: Significance at: *0.10, * *0.05, and * * *0.01 levels, respectively; multivariate regression testsof loss reserve error on audit committee characteristics and control variables; sample consists of 98firms:
LossReserveErrori ¼ b0 þ b1AudIndi þ b2LENGTH i þ b3MALi þ b4ReInsurancei
þ b5Big4i*Distressi þ b6GROWTHi þ b7GeoHerfindahliþ b8ProdHerfidahli þ b9SIZEi þ 1i ðModel 1Þ
LossReserveErrori ¼b0 þ b1AudIndi þ b2TestingVariable
ðAudAcctExpi; AudFinExpi; AudSupExpi; AudInsExpiÞ
þ b3LENGTHi þ b4MALi þ b5ReInsurancei
þ b6Big4i*Distressi þ b7GROWTHi þ b8GeoHerfindahli
þ b9ProdHerfidahli þ b10SIZEi þ 1i
ðModels 2 ~ 5Þ
This table provides results of multivariate regressions examining the magnitude of loss reserve error;the dependent variable is loss reserve error scaled by total admitted assets; LossReserveError is thedifference between insurer i’s revised estimate of the cumulative claim losses outstanding disclosed inyear t þ 4 (2007) and the originally reported estimate of cumulative claim loss reserve at the end ofyear t ¼ 2003; positive reserve errors are associated with under-reserving and negative reserve errorswith over-reserving; AudInd is an indicative variable, which equals to 1 if the percentage of insurer i’sindependent members in its audit committee is greater than 50 percent, 0 otherwise; AudAcctExp is anindicative variable, which equals to 1 if there is at least one accounting expert in insurer i’s auditcommittee, 0 otherwise; AudFinExp is an indicative variable, which equals to 1 if there is at least one Table IV.
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Models 2-5 present separate tests on relationship between the magnitude of insurer’sloss reserve errors and its audit committee’s accounting expertise, finance expertise,supervisory financial expertise, and insurance financial expertise. Model 2 shows thatthe coefficient on accounting financial experts is 20.0480 which is significant at1 percent level, suggesting that accounting financial experts, having hands-onexperience with preparing financial statements, are better able to monitor the lossreserve management. Model 3 shows that the coefficient on the finance expertsis 20.0230 is also negative and significant at 10 percent level, which indicates thepresence of finance experts in audit committee also improves loss reserve estimate.Further, model 5 shows that the coefficient on the insurance financial experts is20.0302which is significant at 5 percent level, indicating that financial experts with insuranceknowledge improve the effectiveness in monitoring of the insurer’s financial reportingprocess. In contrast, model 4 shows that the coefficient on supervisory experts isinsignificantly different from 0, indicating no systematic relation between loss reserveerror and supervisory expertise.
Of the control variables, the variables LENGTH, GROWTH, and ReInsurance are allpositive and statistically significant (at least at 5 percent level) across all five models,while GeoHerfindahl is negatively related to loss reserve error (at the 1 percent level).These findings are consistent with Grace and Leverty (2010). The coefficient onBig4*Distress is negative and significant different from 0, indicating that within thesubset of financially troubled insurers Big4 auditors are associated with significantlymore conservative reserve estimates. These findings are consistent with Pentroni andBeasley (1996). Turning to the remaining control variables, we find no statisticallysignificant association between ProdHerfindahl and loss reserve error, and SIZE arepositively related to loss reserve error in four out of the five models, all at 10 percentlevel. As the results in Pentroni (1992), MAL is used to control for the amount ofmalpractice insurance written and its coefficient is allowed to vary across years. In oursample, we find that MAL is negatively associated with loss reserve error at least5 percent level in the five models.
In summary, while our empirical results in models 2, 3, and 5 support ourhypotheses that financial expertise with accounting, finance or insurance backgroundis negatively associated with loss reserve error. However, the result from model 4 fails
finance expert in insurer i’s audit committee, 0 otherwise; AudSupExp is an indicative variable, whichequals to 1 if there is at least one supervisory expert in insurer i’s audit committee, 0 otherwise;AudInsExp is an indicative variable, which equals to 1 if there is at least one insurance expert in insureri’s audit committee, 0 otherwise; LENGTH is claim loss reserves expressed as a percentage of totalliabilities; MAL is the percentage of net premiums written for malpractice; ReInsurance is thepercentage of gross premiums written ceded to reinsurers; Big4 is an indicative variable, which equalsto 1 if insurer i engaged one of the largest four audit firms (KPMG, Ernst & Young, Deloitte andTouche, Pricewaterhouse Coopers), 0 otherwise; Distress is 1 for rating A 2 , A, or Aþþ , 2 for B 2 , B,B þ , or Bþþ , and 3 for C þ , or C or below; GROWTH is one-year percent increase in net premiumswritten; GeoHerfindahl is the sum of the squared percentage of premiums earned by insurer i in each ofthe 26 lines of P/L insurance; ProdHerfindahl is the sum of the squared percentage of business writtenby insurer i in each of the 50 states and the District of Columbia; SIZE is the natural logarithm of insureri’s total admitted assets; each of the continuous variables is winsorized at 1 percent and 99 percent tomitigate outliersTable IV.
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to prove that supervisory experts are better able to monitor loss reserve error for aninsurer. Those findings are consistent with our expectations.
5.3 Supplemental analysisIn addition to above OLS tests, we follow Koenker and Bassett (1982) and Grace andLeverty (2010) to adopt quantile regression for supplemental robust checks. As Graceand Leverty (2010) indicate the incentives for reserve management may vary acrossreserve error levels. Quantile regression provides snapshots of the relationshipbetween regressors and the dependent variable at different points of its conditionaldistribution. Thus, quantile regression techniques are not sensitive to outliers orskewness in the dependent variable, as in the following equation:
QtðLossReserveErrorjX ; Z Þ ¼ at þ btX þ gtZ þ 1
where bt is the vector of coefficients for the control variables X at the tth percentileand gt is the coefficient of testing variable at the tth percentile.
Table V presents the quantile regression results at the 25th, 50th, and 75thpercentiles. We report coefficient as well as Wald test statistic for each interest variable.The quantile regression results show that AudInd variable is not statistically significantat these three percentile. Suggesting that the OLS may be biased due to extreme values.The sign and statistical significance of AudAcctExp are consistent across thedistribution of loss reserve error. However, the majority of the AudIndsExp’s responseto loss reserve error occurs at the lower quantiles of the loss reserve error distribution,and the coefficient on AudFinExp is statistically significant at the 50th percentiles, butnot at the 25th and 75th percentile. Finally, nowhere on the conditional distribution is
Quantiles0.25 coefficient 0.5 coefficient 0.75 coefficient
AudIndWald Test 20.0073 20.0078 20.2327Statistic (0.0026) (0.0012) (1.4732)AudAcctExpWald test 20.0473 20.0347 20.0389Statistic (7.1030) * * * (14.0435) * * * (9.7472) * * *
AudFinExpWald test 20.0284 20.0202 20.0052Statistic (2.2126) (5.5542) * * (0.0966)AudSupExpWald test 20.0131 20.0095 20.0024Statistic (1.1173) (0.3238) (0.0632)AudInsExpWald test (20.0396) (20.0267) (20.0224)Statistic (5.6050) * * (7.6026) * * * (2.1140)
Notes: Significant at: *0.10, * *0.05 and * * *0.01 levels, respectively; this table provides results ofmultivariate quantile regressions examining the association of the magnitude of loss reserve error andaudit committee characteristics; the regressions include a constant term and a set of control variablesas in OLS models in Table IV, which are not reported to conserve space
Table V.Quantile regression
results
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significant for AudSupExp. Overall the quantile regression estimate confirms ourfindings in OLS models.
6. ConclusionIn this paper, we investigate the relation between two audit committee characteristics –independence and expertise of the audit committee – and the accuracy ofproperty-liability insurers’ loss reserve estimate. Our work is motivated by theregulation set by SOX requiring such characteristics for publicly held insurer companiesand by Section 14 of FRMR set by NAIC imposing similar requirements on mutuals andprivate insurers.
Using a sample of 98 publicly traded property-liability insurers in the years 2003,we find evidence that accounting, finance, and insurance financial expertise isassociated with more accurate or conservative loss reserve estimate. In contrast, wefind that the presence of a supervisory financial expertise is not associated with betterthe loss reserve quality. These findings suggest that audit committees withfinancial expertise with hands-on experience of preparing financial statements, orindustry experience, are better able to monitor the financial reporting than those haveno such experience. These findings are consistent with prior literatures (Dhaliwal et al.,2006; Carcello et al., 2006) suggesting that adopting narrower versions of the definitionthat capture accounting and finance expertise.
When it comes to the audit committee independence, although the model coefficientis negative and significant at 5 percent level, due to the limitation of our sample, we failto conclude a statistically significant association exists between these audit committeeindependence and the loss reserve error.
The implications of our study are important for the SEC and NAIC. Our resultssuggest that the requirements on the audit committee financial expertise would benecessary, even in highly regulated industry, such as property-casualty insurance.Also, our findings, consistent with prior research, show the need for the SEC and NAICto provide a narrower definition for the financial expert emphasizes the importance ofaudit committee directors who have hands-on experience in major accounting positionsand who has insurance industry knowledge.
Our paper is subject to a number of limitations. First, because of data availability,we limited our sample in publicly held property-liability insurers. Although the resultsfrom publicly held insurers could provide a good laboratory for such investigation inall insurers, they might be limited due to different organization structures of differenttypes of insurers. Second, our categorization of audit committee members as the fourtypes of audit financial expertise is dependent on firms’ public disclosures. Althoughfirms are required to disclose this information, the quality and transparency of thisdisclosure is likely to vary across firms.
Notes
1. Insurers having direct premiums written less than $1,000,000 in any calendar year and lessthan 1,000 policyholders or certificate holders of direct written policies nationwide at the endof the calendar year shall be exempt from this regulation for the year (NAIC, 2006a, b).
2. NYSE Listing Guide, at 6. The American Stock Exchange recommends an audit committee.Starting in 1989, the NASDAQ requires US listed firms to have an audit committee with amajority of its members being independent of management.
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3. There are a number of scaling values observed in the literature: total assets, net premiumsearned, or 1.6 £ (the larger of total admitted assets and net premium written)2/3. Our resultsare robust to all scaling techniques.
References
Abbott, L.J., Parker, S. and Peters, G.F. (2004), “Audit committee characteristics andrestatements”, Auditing: A Journal of Practice & Theory, Vol. 23, pp. 69-87.
Agrawal, A. and Chadha, P. (2005), “Corporate governance and accounting scandals”, Journal ofLaw and Economics, Vol. XLVIII, pp. 371-405.
Aiuppa, T.A. and Treieschmann, J.S. (1987), “An empirical analysis of the magnitude andaccuracy of incurred-but-not-reported reserves”, Journal of Risk and Insurance, March,pp. 100-18.
Andreason, C. (2005), “Will the NAIC knock the SOX off insurers?”, Washington Perspective:The Changing Climate of Insurance Regulation, April.
Balsam, S., Krishnan, J. and Yang, J. (2003), “Auditor industry specialization and earningsquality”, Auditing: A Journal of Practice & Theory, Vol. 22, pp. 71-97.
Beasley, M. (1996), “An empirical analysis of the relation between the board of directorcomposition and financial statement fraud”, The Accounting Review, Vol. 71, pp. 443-65.
Beasley, M. and Petroni, K. (1996), “Errors in accounting estimates and their relation to audit firmtype”, Journal of Accounting Research, Spring, pp. 151-72.
Beaver, W.H., McNichols, M.F. and Nelson, K.K. (2003), “Management of the loss reserve errorand the distribution of earnings in the property-casualty insurance industry”, Journal ofAccounting and Economics, Vol. 35, pp. 347-76.
Bhagat, S. and Black, B. (2002), “The non-correlation between board independence and long termfirm performance”, Journal of Corporation Law, Vol. 27, pp. 231-73.
Bryan, D., Liu, M. and Tiras, S. (2004), “The influence of independent and effectiveaudit committee on earnings quality”, working paper, State University of New York,Buffalo, NY.
Carcello, J.V. and Neal, T.L. (2003), “Audit committee characteristics and auditor dismissalsfollowing ‘new’ going-concern reports”, The Accounting Review, Vol. 78, pp. 95-117.
Carcello, J.V., Hollingsworth, C.W., Klein, A. and Neal, T.L. (2006), “Audit committee financialexpertise, competing corporate governance mechanisms, and earnings management”,working paper, University of Tennessee, Knoxville, TN.
Davidson, W.N. III, Xie, B. and Xu, W. (2004), “Market reaction to voluntary announcements ofaudit committee appointments: the effect of financial expertise”, Journal of Accounting andPublic Policy, Vol. 23, July, pp. 279-93.
Dechow, P.M., Sloan, R.G. and Sweeney, A.P. (1996), “Causes and consequences of earningsmanipulation: an analysis of firms subject to enforcement actions by the SEC”,Contemporary Accounting Research, Vol. 13, pp. 1-36.
DeFond, M.L., Hann, R.N. and Hu, X. (2005), “Does the market value financial expertise on auditcommittees of boards of directors?”, Journal of Accounting Research, Vol. 43, pp. 153-93.
Dhaliwal, S.D., Naiker, V. and Navissi, F. (2006), “Audit committee financial expertise, corporategovernance and accruals quality: an empirical analysis”, working paper, University ofArizona, Tucson, AZ.
Fama, E.F. and Jensen, M.C. (1983), “Separation of ownership and control”, Journal of Law andEconomics, Vol. 26, pp. 301-25.
Audit committeecharacteristics
375
Farber, D. (2005), “Restoring trust after fraud: does corporate governance matter?”,The Accounting Review, Vol. 80, pp. 539-61.
Fitzgerald, K. (2004), “Sarbanes-Oxley initiatives at the NAIC”, Foley & Lardner LLP.s LegalNews – Insurance Law Update.
Gaver, J.J. and Paterson, J.S. (2004), “Do insurers manipulate loss reserves to mask solvencyproblems?”, Journal of Accounting and Economics, Vol. 37, pp. 393-416.
Gaver, J.J. and Paterson, J.S. (2007), “The influence of large clients on office-level auditoroversight: evidence from the property-casualty insurance industry”, Journal of Accountingand Economics, Vol. 43, pp. 299-320.
Grace, M.F. and Leverty, J.T. (2010), “Political cost incentives for managing the property-liabilityinsurer loss reserve”, Journal of Accounting Research, Vol. 48 No. 1, pp. 21-49.
Harrington, S.E. and Danzon, P.M. (1994), “Price cutting in liability insurance markets”, Journalof Business, Vol. 67 No. 4, pp. 511-38.
Klein, A. (2002a), “Audit committee, board of director characteristics, and earningsmanagement”, Journal of Accounting and Economics, Vol. 33 No. 3, pp. 375-400.
Klein, A. (2002b), “Economic determinants of audit committee independence”, The AccountingReview, Vol. 77, pp. 435-52.
Koenker, R. and Bassett, G. (1982), “Robust tests for heteroskedasticity based on regressionquantiles”, Econometrica, Vol. 50, pp. 43-61.
Krishnan, J. (2005), “Audit committee quality and internal control: an empirical analysis”,The Accounting Review, Vol. 80, pp. 649-75.
National Association of Insurance Commissioners (2006a), Annual Financial Reporting ModelRegulation, NAIC, Kansas City, MO, 11 June.
National Association of Insurance Commissioners (2006b), Implementation Guide for the AnnualFinancial Reporting Model Regulation, NAIC, Kansas City, MO, 11 June.
Petroni, K.R. (1992), “Optimistic reporting in the property-casualty insurance industry”, Journalof Accounting and Economics, Vol. 15 No. 4, pp. 485-508.
Petroni, K.R. and Beasley, M. (1996), “Errors in accounting estimates and their relation to auditfirm type”, Journal of Accounting Research, Vol. 34, pp. 151-71.
SOX (2002), “Public Law 107-204”, 107th Congress of the United States, 30 July.
Weiss, M. (1985), “A multivariate analysis of loss reserving estimates in property-liabilityinsurers”, Journal of Risk and Insurance, Vol. 52, pp. 199-221.
Further reading
Beaver, W.H. and McNichols, M.F. (1998), “The characteristics and valuation of loss reserves ofproperty casualty insurers”, Review of Accounting Studies, Vol. 3, pp. 73-95.
Bedard, J., Chtourou, S.M. and Courteau, L. (2004), “The effect of audit committee expertise,independence, and activity on aggressive earnings management”, Auditing: A Journal ofPractice and Theory, Vol. 23, pp. 13-35.
Carcello, J.V. and Neal, T.L. (2000), “Audit committee composition and auditor reporting”,The Accounting Review, Vol. 75, pp. 453-67.
DeFond, M.L. and Francis, J.R. (2005), “Audit research after Sarbanes-Oxley”, Auditing:A Journal of Practice & Theory, Vol. 24, pp. 5-30.
Eisenberg, T., Sundgren, S. and Wells, M. (1998), “Larger board size and decreasing firm value insmall firms”, Journal of Financial Economics, Vol. 48, pp. 35-54.
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Fama, E.F. (1980), “Agency problems and the theory of the firm”, Journal of Political Economy,Vol. 88, pp. 288-307.
Felo, A.J., Krishnamurthy, S. and Solieri, S.A. (2003), “Audit committee characteristics and theperceived quality of financial reporting: an empirical analysis”, working paper, Penn StateGreat Valley, Malvern, PA.
Gaver, J.J. (2000), “Earnings management under changing regulatory regimes: state accreditationin the insurance industry”, Journal of Accounting and Public Policy, Vol. 19, pp. 399-420.
Gaver, J.J. and Paterson, J.S. (1999), “Managing insurance company financial statements to meetregulatory and tax reporting goals”, Contemporary Accounting Research, Vol. 16,pp. 207-41.
Grace, E. (1990), “Property-liability insurer reserve errors: a theoretical and empirical analysis”,Journal of Risk and Insurance, Vol. 57, pp. 28-46.
Mayers, D., Shivdasani, A. and Smith, C.W. (1997), “Board composition and corporate control:evidence from the insurance industry”, Journal of Business, Vol. 70, pp. 33-62.
Nelson, K.K. (2000), “Rate regulation, competition, and loss reserve discounting byproperty-casualty insurers”, The Accounting Review, Vol. 75, pp. 115-38.
Petroni, K.R., Ryan, S.G. and Wahlen, J.R. (2000), “Discretionary and non-discretionary revisionsof loss reserves by property-casualty insurers: differential implications for futureprofitability, risk and market value”, Review of Accounting Studies, Vol. 5, pp. 95-125.
Xie, B., Davidson, W.N. and DaDalt, P.J. (2003), “Earnings management and corporategovernance: the role of the board and the audit committee”, Journal of Corporate Finance,Vol. 9, pp. 295-316.
Corresponding authorFang Sun can be contacted at: [email protected]
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