Loss Reserves and In-House Actuary Certification Jiang Cheng*, Mary A. Weiss, and Tzuting Lin

Transcription

1 Loss Reserves and In-House Actuary Certification By Jiang Cheng*, Mary A. Weiss, and Tzuting Lin *Jiang Cheng is Associate Professor at the School of Finance, Shanghai University of Finance and Economics. Mary A. Weiss is the Deaver Professor at the Department of Risk, Insurance and Healthcare Management at Temple University. Tzuting Lin is Assistant Professor at the Department of Finance, National Taiwan University. The authors can be contacted via and 1

2 Loss Reserves and In-House Actuary Certification Abstract This research investigates the relationship between property-casualty insurer reserving errors and the use of an in-house actuary to certify reserves. Further, this research investigates whether property-casualty insurers with different organizational forms systematically under- or over-reserve and whether the interaction of organizational form and the use of an in-house actuary is associated with differing reserve errors. The effect of the Sarbanes-Oxley Act (SOX) on insurers using an in-house actuary to estimate loss reserves is examined as well. The sample period studied is 1999 to 2006, and the data are an unbalanced panel. The results suggest that using an in-house actuary to certify loss reserves is associated with a larger reserve error than if an outside firm is used and that loss reserving errors also vary among organizational form for those insurers using an in-house actuary. Reserving errors diminished more significantly for insurers using in-house actuaries post-sox. 2

3 1.Introduction Loss reserves are the largest liability of a property-casualty (P-C) insurer. In 2012, these reserves accounted for 57 percent of total liabilities (A. M. Best Co., Best s Aggregates and Averages, P/C edition, 2013). 1 Given their importance to P-C insurers, a paramount issue for gauging financial solvency is accuracy in reserve estimation. Regulators are concerned with insurer solvency, and hence, loss reserve and loss adjustment expense estimation. So much so, that in 1980, the National Association of Insurance Commissioners (NAIC) adopted a requirement that a qualified loss reserve specialist certify, for purposes of adequacy, an insurer s loss reserves and loss adjustment expense reserves. This opinion is included in the P-C annual statement. Estimation of loss reserves is not a cut and dried process; instead there is ample opportunity for informed disagreement among experts and even the possibility of reserve manipulation. If the actuary rendering the opinion on loss reserves is an employee of the insurer, questions arise as to the independence of the certification provided. The possibility exists that insurer management may subtly or not so subtly influence the judgement of the actuary or that unintentional biases may enter the reserve estimation process (Gustavson and Schultz 1983). 2 Although independence of the qualifying actuary was discussed as a potential requirement for loss reserve certification, the final rule adopted did not contain this requirement. 3 The American Academy of Actuaries argued that independence did not need to be required, because the reputation of the actuary rendering an opinion on loss reserves was at stake; further, deliberate mis-estimation of loss reserves could subject the certifying actuary to disciplinary procedures of their professional organization (Gustavson and Schultz 1983). Thus, a strong incentive for objectivity on the part of the certifying 1 This includes loss adjustment expense reserves. 2 In extreme cases, disagreement with management over the loss reserve estimate could result in dismissal of the employee/certifying actuary. 3 However, any relationship between the insurer and the certifying loss reserve specialist must be disclosed. 3

4 actuary exists in theory. Another, different argument favoring use of an in-house actuary to certify reserves is that the actuary would understand the nuances of the insurer s business better than an outside consulting firm. 4 The only prior research on this subject is Kelly, Kleffner, and Li (2010); they find no systematic differences in the reserve error for Canadian P-C insurers using in-house actuaries versus outside consulting companies. However, important differences exist between U.S. and Canadian P-C insurer data used to analyze loss reserve errors. For example, Kelly, Kleffner, and Li (2010) use a three year period to estimate their reserve error while the extant U.S. literature uses a five year period. Using a five year period allows for more time for the loss reserve error to be discovered. The screening process used by Kelly, Kleffner, and Li (2010) for purposes of determining the sample of insurers analyzed is different than for U.S. studies also. The purpose of this research is to investigate whether systematic biases exist when an in-house actuary performs the loss reserve certification, as opposed to having an (independent) firm do the certification. This research recognizes that pressure to possibly manipulate loss reserves varies by organizational form (i.e., publicly-held stock insurers, closely-held stock insurers and mutuals). That is, all types of insurers are regulated for solvency, but otherwise the degree of scrutiny received among these organizational forms varies. For example, mutual insurers have weaker governance mechanisms than other organizational forms. In contrast, publicly-traded stock insurers are heavily scrutinized by the capital markets and may be significantly concerned with such details as the pattern of earnings. These different degrees of scrutiny may affect the motivation for loss reserve manipulation in general and when an in-house actuary is used to certify reserves. Implementation of the Sarbanes-Oxley Act (SOX) appears to have a general mitigating effect on 4 However, Kelly, Kleffner and Li (2010) also argue that outside consultants are better able to see the big picture and react more quickly to changes such as changes in the underwriting cycle, for example. 4

5 earnings management behavior and errors in loss reserve reporting on publicly-traded stocks (Iliev 2010; Eckles et al. 2011). Thus the extent to which loss reserves are manipulated should have been reduced after SOX; and the extent of this reduction from SOX may be further related to the use of an in-house actuary to certify reserves and the organizational form of the insurer. Investigation of these issues, too, form part of the purpose. The research proceeds by conducting regression analysis in which the loss reserve error is used as the dependent variable. The loss reserve error is defined in terms of the difference in the original report of incurred losses in year t and an updated or developed estimate of the same incurred losses five years later (Petroni 1992; Gaver and Paterson 2004, 2014; Grace and Leverty 2010, 2012). Control variables include whether an in-house actuary is used to certify loss reserves and organizational form variables. Interactions of these variables are used also. A distinction is made in the pre- versus post- SOX periods. The sample period is 1999 to 2006, 5 and regression analysis is performed for all insurers in the sample over this period. Because prior research has shown that weak insurers tend to underreserve while healthy insurers tend to over-reserve, additional, separate regressions are estimated for weak insurers and for healthy insurers. We recognize that selection bias may exist in the choice of using an in-house actuary; therefore all analyses are carried out using a propensity score matching (PSM) analysis. This study contributes to the literature by determining whether loss reserve errors made by inhouse actuaries in U.S. P-C insurers is systematically different from when an outside, independent firm performs the loss reserve certification. We also contribute by analyzing the effect of SOX on nonpublicly traded firms, such as mutuals. We investigate for those firms that use an in-house actuary whether organizational form matters and whether the pattern of reserving errors was affected by SOX. 5 Future years estimates of loss reserves are required, limiting the sample to

6 No prior studies have considered whether the loss reserve error associated with using in-house actuaries varies by organizational form or whether the loss reserve error associated with using in-house actuaries varies with SOX. The results indicate that use of an in-house actuary to certify reserves is associated with a larger reserve error than if an outside firm is used. A one standard deviation increase in the use of an in-house actuary to certify reserves would lead to an estimated $1.1 to $1.6 million increase in the reserve error at the mean. Some results indicate that mutuals have larger reserve errors relative to publicly-held stocks and that closely-held stocks have even larger reserve errors compared to mutuals. Weak firms are found to have larger reserve errors than healthier insurers. Further, weak insurers using an in-house actuary to certify reserves tend to be under-reserved, while healthy firms using an in-house actuary are more likely to be over-reserved. This research is important because the financial health of P-C insurers is important. Loss reserve errors result in a misspecification of loss reserves and the financial position of the company. Underreserving is of special importance to an insurer s regulators and stakeholders (policyholders and stockholders) because under-reserving is an important cause of insolvencies for P-C insurers (Kelly, Kleffner, and Li 2010). Over-reserving is a concern also, as it results in lower reported earnings and lower current income taxes. 6 If certification of loss reserves by in-house actuaries misrepresents loss reserves in a systematic way, then the (lack of an) independence requirement for loss reserve certification indicates an important agency problem for U.S. P-C insurers. The remainder of this paper is organized as follows. The next section contains hypothesis development; and this is followed by the methodology section. Section 4 discusses the data and sample. Section 5 contains a discussion of the results, while the last section concludes. 6 Of course, income tax will be paid on all income; over-reserving merely defers the tax until a future period. 6

7 2. Hypotheses Insurers may gain certification of their loss reserves by using an in-house qualified actuary, an outside consultant such as a (Big N) auditing firm or an actuarial services firm. Arguments exist for and against using an in-house actuary. An in-house actuary has more detailed, first-hand knowledge of the firm s operations; this can form the basis for an accurate assessment of loss reserve adequacy. On the other hand, independence of the in-house actuary may be compromised because of the actuary s employment relationship with the firm an in-house actuary may be pressured to report results that management favors because of the employment relationship. Even if the actuary does not feel pressure from management, the in-house actuary-employee could unintentionally produce biased reserve estimates (Gustavson and Schultz 1983). This leads to Hypothesis 1: Hypothesis 1: Use of an in-house actuary to certify loss reserves leads to larger reserving errors than when an outside consultant is used. Partly due to agency costs considerations, insurers are organized primarily as mutuals or stocks. Mutuals eliminate agency costs between owners and policyholders by combining the role of the two parties. However this comes at the expense of higher agency costs between owners and managers. The inalienability of ownership rights gives mutual owners much weaker governance mechanisms to control and monitor managers than owners of stocks (Mayer and Smith, 1988, 2005; Cheng, Cummins, and Lin, 2013). Thus it could be easier for managers in mutuals to manipulate reserves than for stock insurers. Alternatively, many mutuals operate in less complex lines so it should be possible to estimate reserves with less error. If the nature of the business varies among stocks and mutuals in this way, then more accurate reserve estimation should occur in mutuals. On balance, we believe the argument that mutuals operate in less complex lines to be the dominant one. Thus Hypothesis 2 states, Hypothesis 2: Mutual insurers report lower reserve errors than stock insurers. Stock insurers can be further classified as publicly-traded and closely-held. The owners of 7

8 closely-held stock insurers are usually comprised of a limited number of large shareholders. These stakeholders have incentives and have the ability to monitor managers directly, preventing or at least limiting manager opportunistic behavior (in the form of reserve manipulation). 7 Further, relative to publicly-held stocks, management of closely-held stocks should be less motivated by market signaling, and they face less pressure from the capital market to report favorable results (e.g., beat the financial analyst s prediction). 8 These arguments suggest that reserve errors are expected to be smaller in closelyheld stocks than in publicly-traded stocks. Alternatively, publicly-traded stock insurers are already under strict monitoring from the capital market and may have relatively superior financial reporting quality than other ownership structures. On balance, we expect the former argument to dominate. Hence Hypothesis 3 states, Hypothesis 3: insurers. Closely-held stock insurers report lower reserve errors than publicly-traded stock Given the significant differences in agency costs, types of business written, and public versus private oversight that exist among the different organizational forms, using an in-house actuary may produce reserve errors that interact with organizational form. For example, since publicly-held insurers may be more concerned with how they appear to the market, the in-house actuary may face greater pressure to report specific financial results in a publicly-held stock firm than in a mutual or a closelyheld stock firm. Therefore, Hypothesis 4 states, Hypothesis 4: Loss reserving errors vary among organizational forms when an in-house actuary is used to certify loss reserves. Implementation of SOX may have a bearing on loss reserving practices. Technically, SOX applies to publicly-traded firms. However, after the passage of SOX, many insurers voluntarily adopted 7 Owner-manager agency conflicts may still exist in closely-held stock insurers if owners do not directly run the company (Ang et al. 2000). 8 Beaver, McNichols, and Nelson (2003) find that unlike publicly-traded stock insurers and mutuals, private insurers do not manage loss reserves to avoid reporting losses. 8

9 provisions of SOX, probably out of reputational concerns. Further, several SOX provisions are incorporated into the Model Law, Model Regulation Requiring Annual Audited Financial Reports (Model Audit Rule 205) which went into effect in As argued previously, insurers using in-house actuaries to certify loss reserves should have larger reserving errors due to management pressure, everything else held equal. After SOX, however, managers are expected to have reduced any opportunistic behavior regarding financial reporting due to increasing legal liability and enhanced regulatory requirements. Increasing legal liability after SOX is expected to make the board of directors more vigilant in monitoring managers with respect to financial reporting as well. If in-house actuarial loss reserve certification resulted in larger loss reserve errors pre-sox, then imposition of SOX-like regulatory requirements should have had more of an impact on insurers using in-house actuaries. Hypothesis 5 states, Hypothesis 5: Reserving errors should have diminished more significantly for insurers using an inhouse actuary after SOX than for insurers using an outside firm to certify reserves. Finally, the impact of SOX on financial reporting might be different among the three ownership structures. Publicly-traded stock insurers employing in-house actuaries should have reduced errors more significantly after SOX than for other ownership structures. This is because publicly-traded stock insurers may have had more of a motive for market signaling and faced market pressure to report favorable results and because in-house actuaries were likely to receive more pressure from management in reporting reserves than outside firms. Alternatively, SOX may only have had a limited impact on publicly-traded stock insurers compared to mutual and closely-held insurers because they already received strict scrutiny by capital markets. In this case, mutuals and closely-held stock insurers using in-house actuaries would be expected to reduce reserve errors more significantly after SOX than for publicly-traded stocks. On balance, we expect that publicly-traded stock insurers employing in-house actuaries reacted 9

10 more significantly to the implementation of SOX: Hypothesis 6: Publicly-held insurers reduced reserving errors more significantly after SOX than other organizational forms. 3. Methodology To determine the relationship between the magnitude of the loss reserve error and the use of an in-house actuary to certify reserves, we regress our measure of the loss reserve error on a set of variables designed to test our hypotheses. Additional variables are included in the model to control for other factors which prior studies have shown to be related to the loss reserve error. The remainder of this section describes the model, the estimation strategy and variables more fully. 3.1 Regression Model The following general model is estimated: Yit = α + βxit + δzit + γyeart +εit, (1) where i represents firm i and t references time. The dependent variable, Yit, is the absolute value of the scaled loss reserve error. (This is explained more fully below.) The Xit, are variables used to test the hypotheses concerning loss reserve errors. Zit is a vector of institutional and firm characteristics variables; and Yeart indexes time periods. The error term is εit. 3.2 Specification of Regression Variables Specification of Dependent Variable As indicated above, the loss reserve error is used as the basis of the dependent variable in model (1). The loss reserve error is defined as the difference between the estimate of total incurred losses for a firm as of a given calendar year t and a future revised estimate of the same losses in calendar year t+5, following Petroni (1992), Gaver and Paterson (2004, 2014) and Grace and Leverty (2010, 2012). Table 1 provides a detailed explanation on how the loss reserve error is calculated using an excerpt of actual firm data. The five year loss reserve error is scaled by admitted assets, following Petroni (1992), Beaver, 10

11 McNichols, Nelson (2003) and Gaver and Paterson (2014). The loss reserve error is positive (negative) if the original estimate of incurred losses is overestimated (underestimated). 9 The absolute value of the scaled loss reserve error is the dependent variable in model (1). The absolute value of this variable is used because the hypotheses are stated in terms of a reserve error, with no distinction between positive and negative errors. However, further regressions are conducted using the actual value of the 5 year scaled reserve error (as explained below) Specification of the Independent Variables for Hypothesis Testing The organizational form variables are indicator variables. If the insurer is a mutual, the mutual variable is set equal to one and is zero otherwise. According to Hypothesis 2, loss reserve errors should be lower for mutuals because of the less complex nature of the business they write; hence the sign of this variable should be negative in the (absolute value of the) loss reserve error equation. If the insurer is a closely-held stock (publicly-traded stock) the closely-held stock (publicly traded stock) variable is set equal to one and to zero otherwise. Hypothesis 3 suggests that the coefficient of the closely-held stock indicator variable should also be negative in the (absolute value of the) loss reserve error regression, signifying lower loss reserve errors than for publicly-traded stock insurers. The publicly-traded stock indicator variable is the omitted variable. We specify an indicator variable, INTERNAL, which is set equal to one if the insurer uses an actuary who is an employee to certify loss reserves; this variable is set equal to zero otherwise. This indicator variable is expected to have a positive coefficient in the regression model if Hypothesis 1 holds. This variable is also interacted with the mutual indicator and with the Closely-Held Stock Indicator to test Hypothesis 4. (The omitted variable is Publicly-Held Stock Indicator*INTERNAL Indicator.) The coefficients of the variables interacted with INTERNAL should be significantly different from zero with 9 An experiment was conducted in which the five year developed loss reserve was used as the scalar, as in Gaver and Paterson (2004). The results are not sensitive to the choice of scaling variable. 11

12 significantly different coefficients. An indicator variable is created which is set equal to one after the enactment of SOX, and zero otherwise. Previous research indicates that earnings manipulation magnitudes diminished after enactment of SOX (e.g., Khurana and Raman 2004). Hence the sign for this variable is expected to be negative. This variable is interacted with the INTERNAL actuary variable to create a new variable, INTERNAL Indicator*Post-SOX Indicator. The expected coefficient for this variable is negative if Hypothesis 5 holds, signifying that loss reserve errors decreased more after implementation of SOX for firms using in-house actuaries to certify reserves. Interaction variables with the Post-SOX indicator variable are created, Post-SOX Indicator*Mutual Indicator and Post-SOX Indicator*Closely-held Stock Indicator to test Hypothesis 6. (The omitted variable is Post-SOX Indicator*Publicly-Held Stock Indicator.) Hypothesis 6 indicates that relative to publicly-held stock insurers, mutual and closely-held stock insurers should have larger reserve errors after SOX, so the interaction terms for mutuals and closely-held stock insurers should be positive Specification of Control Variables The main control variables are used to test four principal hypotheses that exist in the literature as to why firms may under- or over-state loss reserves. These hypotheses relate to (1) taxes, (2) income smoothing, (3) financial weakness, and (4) price regulation. Other control variables found to be significantly related to loss reserve errors in the literature are also used, as described below. Insurers deduct estimates of losses incurred (i.e., losses that the firm is responsible for) that have not been paid yet from taxable income. Overestimates of these losses understate total net income of the insurer, thereby reducing its income tax liability. Of course, overestimation of incurred losses in the current year does not mean that the insurer will not pay taxes on the underreported income; it merely 12

13 defers these taxes to the future. So, everything else held equal, the tax hypothesis is that firms will overestimate losses and the incentive to do so increases with the potential tax savings from having income categorized as reserves. The tax shield is used to measure this incentive (Grace 1990): Tax Shieldt = (Net Incomet + Estimated Reservet)/Total Assetst (2) Grace (1990) indicates that insurers have an incentive to overstate incurred losses as taxable income increases. 10 Therefore, the Tax Shield variable is expected to be directly related to overstating loss reserves in the loss reserve equation with a positive coefficient. Various explanations exist as to why an insurer would want to smooth income. Agency problems are one explanation (see Weiss 1985; Grace 1990). For example, the variability of a firm s income might be interpreted as riskiness of the firm to bondholders, stockholders, or other stakeholders. Thus, measures the firm takes to reduce this variability in income may lower the cost of external financing (Froot, Scharfstein, and Stein 1993). Also, if managers performance is evaluated by the rate of return of the insurer, managers have an incentive to manipulate reserves (Grace 1990). But even if agency problems did not exist, P-C insurers might smooth income for regulatory reasons. Net income affects the surplus (capital) of the insurer, and large increases or decreases in surplus can easily trigger regulatory attention. More specifically, Beaver, McNichols, and Nelson (2003) hypothesize that insurers, in an effort to avoid accounting losses, may manage loss reserves. The extent of this management varies across the income distribution. For example, Beaver, McNichols, and Nelson (2003) found that management of reserves is the most income increasing for insurers with small positive earnings. Insurers that had the highest incomes also overestimated loss reserves to reduce reported income. Therefore, following Grace and Leverty (2010, 2012) and Beaver, McNichols, and Nelson 10 Grace and Leverty (2012) also use this variable to control for the tax motive concerning loss reserve estimation. 13

14 (2003), four indicator variables are created to identify firms reported earnings. The Small Profit (Small Loss) indicator variable is set equal to one for insurers with reported earnings in the first 5% of the distribution to the right (left) of zero, and is set equal to zero otherwise. A Profit indicator variable and a Loss indicator variable are created to represent insurers with earnings in the top 90% of the positive and negative earnings distributions, respectively. The Loss dummy variable is omitted in the regressions to avoid collinearity. We have no priors on the relative size of these variables in the (absolute value of the) loss reserve error equation. Financially weak firms are expected to have a tendency to underestimate loss reserves. The financial condition of each insurer is measured in two alternative ways in this study. First, an indicator variable, (WEAK) is specified which takes on the value of one if the firm has four or more unusual IRIS (Insurance Regulatory Information System) 11 ratios, and is zero otherwise, following Petroni and Beasley (1996), and Gaver and Paterson (2004, 2007, and 2014). 12 The WEAK dummy variable is expected to be positively related to the absolute value of the reserve error. The second variable to gauge financial weakness is the z-score for the insurer (Roy 1952; Laeven and Levine 2009). The z-score is defined as (the return on assets plus the capital asset ratio) divided by the standard deviation of asset returns, and is interpreted as distance to default. 13 A small z score indicates a shorter distance from default (i.e., a weak firm). This variable should be inversely related to 11 For a description of the financial ratios used in IRIS and the benchmarks against which they are evaluated, see Gaver and Paterson (2014) or the NAIC s Insurance Regulatory Information System, IRIS Ratio Manual (2010). 12 The IRIS system is still used today. However, in response to a record setting number of insolvencies in the late 1980s and early 1990s, new solvency tools were put in place to overcome the ineffectiveness of the IRIS ratios in identifying firms headed to insolvency. These new tools include the Financial Analysis Solvency Tools (FAST) and Risk-Based-Capital (RBC) requirements; these new tools were designed to supplement the information provided by the IRIS Ratios. One perceived problem with the IRIS ratios was that insurers knew the ratio definitions and the benchmarks used to evaluate the ratios, so some troubled insurers could manipulate their results to meet the benchmarks. In contrast, FAST also uses financial ratio analysis (with a greater number of ratios) to evaluate insurers, but insurers do not know exactly what ratios are used and how the ratios are evaluated (i.e., the ratio benchmarks). This makes manipulation of financial results to avoid regulatory scrutiny much more difficult. 13 The standard deviation of ROA is measured as the standard deviation of the statutory value of return on assets over the sample period. Return on assets is defined as net income divided by assets. 14

15 the absolute value of the reserve error, if weak insurers tend to under-reserve. The logarithm of this variable is used in the analysis, as is typical for this variable (Laeven and Levine 2009). Two competing hypotheses exist as to how rate regulation affects loss reserving errors. Nelson (2000) hypothesizes that insurers are interested in convincing regulators that they can charge low rates so that insurers have an incentive to understate reserves. Grace and Leverty (2010), based on many prior insurance studies, hypothesize that rate regulation results in rate suppression. They believe that insurers have an incentive to over-reserve in an attempt to convince regulators that the regulated price is too low. To measure the extent of rate regulation for each insurer, we adopt the rate regulation variable used in Grace and Leverty (2010): Rate Regulationit = ( Prem. Writtenistl * Stringent Reg Lawstl)/ Prem. Writtenistl, i,s,t,l i,s,t,l where i indicates firm i, s indicates state s, l indicates line l, and t indicates year t. A state is considered to have a stringent rate regulatory law if it had state-made rates, a prior approval law, or a file and use law that required the insurer to file for prior approval if the insurer wanted to charge a rate that deviated from that filed by a rate advisory organization (Harrington 2002; Grace and Leverty 2010). States that had file and use, use or file, filing only, or flex rating (with a large rating band) are considered not to be stringently regulated. According to Grace and Leverty (2010) this variable should be positively related to the loss reserve error Specification of Control Variables The control variables (Zit) used in the analysis are board of directors composition and size, insurer size, geographic and line Herfindahl indices, reinsurance usage, types of lines written, growth and group ownership (Grace and Leverty 2010, 2012; Cheng, Lin, and Weiss 2014). If the managers of an insurer are manipulating loss reserves, then it falls at least partly to the independent board members to identify and rectify reporting errors. Thus boards with more independent members are expected to 15

16 place greater emphasis on loss reserve accuracy. Following Weisbach (1988) and Denis, Denis, and Sarin (1997), a board independence indicator variable is created. Firms in which outsiders make up more than 60% of the directors are classified as outsider-dominated firms, and the indicator variable, Board Independence, is set equal to one for these firms; it is set to zero otherwise. The coefficient for this variable is expected to be less than zero in the (absolute value of the) loss reserve error equation, signifying that boards with more independent directors are associated with lower reserve errors. Another board of directors variable is used in the analysis, the size of the board. Larger boards are sometimes considered to be less effective monitors of managers (Jensen 1993; Cheng 2008) suggesting that loss reserve errors are directly related to board size. However, larger boards are more likely to have more directors with special expertise in accounting. The latter would argue for lower loss reserve errors associated with larger boards. Therefore, we expect this variable to be significant in the loss reserve error equation, although we have no priors on the sign of this variable. Insurer size is estimated as the logarithm of net premiums written. Grace and Leverty (2012) find this variable to be positively related to the (absolute value of the reserve error) in their sample. Previous research indicates that loss reserving errors are more likely to occur in long-tail lines so the proportion of business in long-tail commercial business lines is used as a control variable, and the expected sign of this variable is positive (Petroni and Beasley 1996; Beaver, McNichols, and Nelson 2003; Grace and Leverty 2010, 2012). The fraction of business in personal lines is also used as a control variable for the types of business written by the insurer. Compared to commercial lines, personal lines are simpler; therefore the expected sign of the coefficient for the personal lines variable is negative. Harrington and Danzon (1994) indicate that reinsurance usage and growth are related to underreserving. Reinsurance usage is measured as the percentage of gross premiums written ceded to 16

17 reinsurers. Growth is measured as the percentage increase in net premiums written from the previous to the current year. According to Harrington and Danzon (1994), weak firms are expected to underreport loss reserves to increase firm growth suggesting the coefficient of growth should be positive. Further, weak firms tend to hide their financial condition through reinsurance. Therefore reinsurance usage should be positively related to the (absolute value of the) loss reserve error. The geographic and line Herfindahl indices are also used as control variables. The product line Herfindahl index is measured as the sum of the squared percentage of premiums earned in each of the lines written in the P-C insurer while the geographic Herfindahl index is measured using the sum of the squared percentage of business written in each of the 50 states and the District of Columbia by the insurer. We have no priors on the sign of these variables in the regressions, although Grace and Leverty (2012) found these variables to be positively related to the reserve error. Group affiliation is associated with intragroup reinsurance, which may increases the complexity of the insurer and lead to a positive association with the (absolute value of the) loss reserve error. 3.3 Estimation Strategy Model 1 is estimated using feasible generalized least squares (FGLS) with a panel-specific AR(1) autocorrelation structure. 14 Grace and Leverty (2012) use the same procedure. Year dummies are included in the model to control for exogenous economic factors related to reserving decisions that change over time and have not otherwise been controlled for in the model The Breusch-Pagan Lagrangean multiplier tests suggest that fixed/random effects models are preferred to a pooled crosssectional model. Hausman tests indicated that fixed effects are preferred to random effect models. Modified Wald tests reject the null hypothesis that there is no existence of group-wise heteroscedasticity (p<0.0001). Wooldridge (2002) tests also indicate serious autocorrelation in our panel data. Beaver and McNichols (1998) also report positive serial correlation in reserve errors indicating multiperiod reserve management. We also estimate models adjusting for clustered standard errors across both firms and time (Peterson 2009). Results are not materially different from results obtained using FGLS and are available upon request from the authors. A few firms are dropped from the estimation in various models because they are present for only one year of the sample period. 15 Exogenous economic factors include unexpected inflation rate, regulatory changes, changes in court attitudes, and jury verdicts which are out of management control and knowledge (Weiss 1985; Petroni 1992). Gaver, Paterson and Pacini (2012) document that the P-C industry as a whole over-reserved from 1993 to 1997, under-reserved from 1998 to 2002, and returned 17

18 It is possible that insurers may systematically choose in-house actuaries. For example, insurers with high levels of earnings management might tend to select in-house actuaries. Also, high-quality insurers may prefer independent external certifying firms. Thus the results might be influenced by selection bias. Following Lawrence, Minutti-Meza, and Zhang (2011), a propensity-score matching model (PSM) is used to control for differences in insurer characteristics between insurers using an in-house actuary for actuarial reserve certification and those that do not (Rosenbaum and Rubin 1983; Lennox, Francis, and Wang 2012). 16 That is, PSM is a statistical tool that attempts to estimate the effect of a treatment by controlling for the variables that predict obtaining the treatment. More specifically we obtained our matched pairs of observations in three steps. First, we separate the full sample into three subsamples, mutuals, publicly-traded stocks, and closely-held stocks. A logistic propensity score model (PSM) for in-house actuary choice was estimated to yield the probability of an insurer employing an in-house actuary in each of the three subsamples. All of the control variables (except the INTERNAL indicator and the interaction terms with the INTERNAL Indicator) from the (absolute value of the) loss reserve error equation were used in the propensity score estimation. In the second step, for each insurer employing in-house actuaries, we identify an insurer with the closest five digits of the propensity score that did not use an in-house actuary among the three subsamples. 17 This approach enables us to form matched pairs with the smallest propensity score differences (i.e., most to over-reserving from 2003 to Reserve errors for our sample by year are , , , , , , , and from 1999 to 2006, respectively. 16 There are two primary ways in which to address the self-selection issue of using an in-house actuary to certify reserves. The first methodology uses a Heckman procedure to control for selection bias. Lennox, Francis and Wang (2012) suggest that the selection models are fragile and can yield opposite inferences due to small changes in model specification. The most challenging part is to find a valid instrumental variable that is unrelated to the error term in the second stage model. The second methodology is a sample matching methodology which is used in this study. Lennox, Francis, and Wang (2012) suggest that PSM has an advantage over the Heckman method because it does not necessarily impose exclusion restrictions in the second stage model. 17 If the selected insurer employs external independent firms, we find a match. If not, we next try to match on four digits of the propensity score. This process continues down to a one-digit match on propensity score for those that remain unmatched. Some observations could not be matched. 18

19 similar along a set of firm characteristics) but the greatest difference in the in-house actuary choice among the three subsamples. Third, we combine observations employing in-house actuaries and their matched observations for all three subsamples. We are able to identify 880 observations employing inhouse actuaries and 880 matched observations. After PSM, we estimate the (absolute value of the) loss reserve error models to control for any potential remaining differences in insurer client characteristics between the treatment and control groups. 18 Gaver and Paterson (2014) control for WEAK insurers in a different way than described above for loss reserve errors. Separate regressions were estimated for WEAK and Healthy insurers. This helps to delineate more clearly the incentives of weak from more healthy insurers (i.e., under-reserving versus over-reserving). Some of the control variables, in particular, have predicted coefficients that are related specifically to under-reserving or over-reserving. Therefore, we follow Gaver and Paterson (2014) by also estimating our regression (model (1)) separately for insurers categorized as Weak (WEAK indicator =1) versus Healthy (WEAK indicator =0). The dependent variable in these regressions is the actual 5-year scaled reserve error, not its absolute value. Regressions are estimated using the full samples of weak and healthy insurers and a PSM sample. 4. Sample Selection and Data 4.1 Sample Selection Following Petroni (1992), Gaver and Paterson (2014), and Grace and Leverty (2010, 2012), firms that have extreme errors in their loss reserves (observations with an original loss reserve estimate that differs from the revised estimate by greater than 50% in absolute value) are eliminated from the sample. In addition, firms that cede all premiums to reinsurers and/or write greater than 25% of their 18 A simple univariate t-test (Wilcoxon rank-sum test) of the differences in means (medians) suggests that there are no significant differences in most variables between matched pairs except that insurers employing in-house actuaries have a lower geographical Herfindahl index and write more commercial lines of business than those that do not (at the 10% and 5% significance levels, respectively). 19

20 premiums in workers compensation, accident and health, surety, credit, and/or reinsurance are also excluded. The sample firms are either mutual or stocks (publicly-traded or closely-held) domiciled in the U.S. for which we also had the associated control variable values. After performing the procedures mentioned above, an unbalanced final sample of 4,919 observations are obtained. The sample has 369 mutuals, 221 closely-held stocks, and 359 publiclytraded stocks. The sample period is 1999 to The sample period stops in 2006 because 5 future years of data are needed to estimate the loss reserve error. Table 2 indicates the proportion of the sample using an in-house actuary to certify loss reserves. The proportion of the sample using an in-house actuary to certify loss reserves is 28% for our sample and remains stable throughout our sample period. This ratio is 52% for publicly-traded stocks, compared to 18% for mutuals and 9% for closely-held stocks. 4.2 Data The main data sources for this research are Best s Insurance Reports, Property/Casualty Editions, Best s Key Rating Guide, 19 the NAIC annual statement database, and proxy statements of the publiclytraded insurers. Actuary information is obtained from Best s Insurance Reports: Property/Casualty, various years, and this information is cross-checked with the General Interrogatories page of the insurer s annual statement. The loss reserve errors are determined from Schedule P, Part 2 of the insurers annual statements. Data to determine whether a state had stringent regulation was obtained from the NAIC s Compendium of State Laws and Regulations on Insurance Topics. Data for organizational form and ownership structure were obtained from Best s Insurance Reports, Property/Casualty editions, various years and the NAIC annual statement database. If these sources did not reveal the ultimate owner of an insurance company, we further checked the company s 19 Both Best s Insurance Reports, Property/Casualty editions and Best s Key Rating Guide are published annually by the A. M. Best Company (Oldwick, New Jersey). 20

21 website and news sources on the Internet. For each firm-year in the sample, we identify the composition of the board of directors from Best s Insurance Reports, Property/Casualty for non-publicly-traded insurance companies and from proxy statements for publicly-traded companies. 20 Following the independence requirement by NYSE, outside directors in the non-publicly-traded insurers are defined as those who are not listed as executives in the company or in the same insurance group, are not retired CEOs, and do not have the same last name as any executive listed in the Management section of Best s Insurance Reports Property/Casualty edition. All other remaining variables were obtained from the NAIC annual statement database. 5. Results 5.1 Descriptive Statistics Descriptive statistics are contained in Table 3. Panel A shows the mean and related statistics for the full sample of insurers. The table indicates that the mean reserve error is very small Approximately 44, 36, and 20 percent of the sample consists of mutual insurers, publicly-traded insurers, and closely-held stock insurers, respectively. The results in Table 3, Panel B contain the means and medians for the samples of weak firms and healthy firms. 21 All of the means and medians for the weak versus healthy firms are significantly different at the 1 percent level except for the variables Firm Size (in Net Premiums Written), the Herfindahl Index of Lines of Business and the Proportion of Premiums from Personal Lines. The mean and median loss reserve errors of weak firms are less than zero while they are positive for healthy firms. At the mean, weak firms are more likely to be closely-held stock or publicly-traded stock insurers compared to mutual insurers. Weak firms, at the mean, are less likely to have an independent board of 20 For some publicly-traded firm year observations for which proxy statements are unavailable, we take other board information from the proxy statement in the nearest available year. Weibasch (1988) indicates that this approximation is reasonably accurate since board composition remains stable from one year to the next. 21 Weak firms are defined to be firms failing 4 or more of the IRIS ratio tests. 21

22 directors, have a smaller board size, write more business in commercial long tail lines, use more reinsurance, have larger growth in net premiums written and are more likely to be affiliated with a group. 5.2 Regression Results Regression Results for Full Sample Regression results for the full sample (columns 1 and 2) and the PSM sample (columns 3 and 4) are in Table 4. The absolute value of the scaled reserve error is the dependent variable. In column 1 (3), financial weakness is determined by failing four IRIS tests, while in column 2 (4), it is assessed using the (logged) distance to default (i.e., z-score). We focus primarily on the PSM results as they control for endogeneity of the firm s decision to use in-house actuaries. Hypothesis 1 indicates that insurers that use in-house (INTERNAL) actuaries to generate loss reserves should have larger reserve errors. The coefficients for the in-house actuary indicator variable are positive and significant at the 1 percent level in columns 3 and 4, consistent with Hypothesis 1. Therefore, insurers using in-house actuaries tend to have larger (absolute value of) reserve errors, suggesting that perhaps independence is compromised when an in-house actuary certifies reserves. A one standard deviation increase in in-house actuary use for the mean firm results in a 13.23% (column 3) or 18.8% (column 4) increase in reserve errors as a percentage of total admitted assets. In monetary terms, the (absolute value of the) loss reserve error would increase by roughly $1.1 million to $1.6 million with a one standard deviation increase in the usage of an in-house actuary at the mean of the sample. 22 The coefficients are positive and significant for the mutual indicator variable in the PSM results. These results are not completely consistent with Hypothesis 2 which states that mutuals should have lower reserve errors relative to stock insurers. The results indicate that the (absolute value of) loss 22 This number is comparable to Grace and Leverty (2012) which find high-tax firms over-reserve by $1.8 to 2.7 million and insurers subject to higher price regulation over-reserve by $1.8 to 2.0 million. 22

23 reserve errors are significantly higher for mutual insurers in two of the four equations in Table 4 compared to publicly-held stock insurers (the omitted variable). However, the results in columns 1, 2 and 4 do suggest that the absolute value of the reserve errors are higher for closely-held stock insurers compared to mutuals (i.e., the coefficients are significantly higher for the closely-held stock versus the mutual indicator variable). Thus, the results for Hypothesis 2 are mixed. Hypothesis 3 considers the relative (absolute value of) reserve errors of closely-held stock insurers compared to publicly-held stock insurers. The results in columns 1, 2 and 4 suggest that closely-held stocks have higher (absolute value of) reserve errors compared to publicly-held stock insurers (the omitted variable), contrary to Hypothesis 3. The insignificant result for the coefficient of the closely-held stock variable in column 3 is not consistent with Hypothesis 3. Therefore, the results are not consistent with Hypothesis 3. Overall, the interaction terms between organizational form and the in-house actuary indicator variable are significant in the PSM results in Table 4. In particular, in the PSM results in columns 3 and 4, mutuals using an in-house actuary tend to have lower (absolute value of) reserve errors compared to publicly-held stock insurers using an in-house actuary as signified by the negative and significant coefficients for Mutual Indicator*INTERNAL. Thus it appears that in-house actuaries in mutual insurers are associated with lower (absolute value of) reserve errors than for publicly-held stock insurers that use in-house actuaries. These results support Hypothesis 4. Two of the four coefficients for the interaction of in-house actuary and closely-held stock insurer are negative and significant in Table 4. The latter results support the hypothesis that publicly-held stock insurers (the omitted indicator variable) that use an in-house actuary are associated with larger (absolute value of) loss reserve errors than for closely-held stock insurers. However, in the PSM results, only the result in column 3 is significant, and it is positive. Therefore little support in favor of Hypothesis 4 exists with respect to closely-held stock 23

24 insurers. Hypothesis 5 indicates that reserving errors should have diminished more significantly for insurers using in-house actuaries post-sox. The coefficients for the interaction term INTERNAL*Post- SOX Indicator are negative and significant in all of the equations in Table 4, supporting Hypothesis 5. The last hypothesis concerns interaction terms between the Post-SOX indicator and the organizational form indicator variables. The coefficients for the interaction of Post-SOX with mutual are positive and significant in the PSM results in columns 3 and 4 supporting Hypothesis 6. This suggests that mutuals are associated with higher (absolute value of) loss reserve errors after SOX compared to publicly-held stock insurers after SOX, ceteris paribus. The coefficients for the interaction of the closely-held organizational form variable and Post-SOX are not significant in the PSM results, while they are significant in columns 1 and 2. This suggests that once endogeneity is controlled for, reserving behavior of closely-held stock insurers after SOX is not significantly different from publiclyheld insurers Post-SOX, contrary to Hypothesis 6. Overall, then, limited support exists for Hypothesis 6 with respect to closely-held stocks. Some other results with respect to the control variables in Table 4 are notable. The WEAK Firm indicator variable is positive and significant at the 1 percent level suggesting weak firms have larger reserve errors, ceteris paribus. This is consistent with the result for the distance to default variable (or z- score). The coefficient for the distance to default variable is negative and significant in all regressions, signifying that the (absolute value of) reserve errors are negatively associated with the distance to default (i.e., firms with higher distance to defaults have lower (absolute value of) reserve errors). Board independence is negatively related to reserve errors; thus firms with boards consisting of at least 60 percent outsiders are associated with lower (absolute value of) reserve errors. Therefore board independence is associated with lower reserving errors. The results of the PSM analysis suggest 24