CASE versus BULK RESERVES: AN ISSUE OF MANIPULATION

Jill Bisco Ph.D. Candidate

Department of Risk Management/Insurance, Real Estate and Legal Studies Florida State University College of Business Rovetta Business Building, Room 125A 821 Academic Way Tallahassee, FL 32306-1110 Phone: 850-644-4070 Fax: 850.644.4077 Email: jmb06@my.fsu.edu

Kathleen McCullough, PhD Associate Professor and State Farm Professor Department of Risk Management/Insurance, Real Estate and Legal Studies Florida State University College of Business Rovetta Business Building, Room 150 821 Academic Way Tallahassee, FL 32306-1110 Phone: 850.644.8358 Fax: 850.644.5077 Email: kmccullough@cob.fsu.edu

September 4, 2012

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CASE versus BULK RESERVES: AN ISSUE OF MANIPULATION

ABSTRACT

Prior research has not considered the allocation of loss reserves between case and bulk reserves when analyzing the management of loss reserves. We argue that, due to the vast flexibility in their estimation, the management of bulk reserves will be more prevalent in contrast to the case reserves. This study analyzes both the factors related to the use of bulk loss reserves as well as the related question of whether their use impacts loss reserve manipulation. Consistent with our hypotheses, we find that there are firm differences in the use of bulk reserves as well as a significant relation between the use of bulk reserves and earning management. Further, we find that the use of bulk reserves and their relation to earnings management vary across lines of business. This is important for policyholders, stakeholders, and regulators as reserve manipulation can dramatically impact insurer pricing as well as mask financial distress and alter tax payments.

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CASE versus BULK RESERVES: AN ISSUE OF MANIPULATION  

I. Introduction  

The ability of property-liability insurance companies to estimate and manage loss reserves has been a topic of significant research over the past several decades. Since claim loss reserves are generally the largest liability on the insurer's balance sheet (e.g., Beaver, McNichols and Nelson, 2003), the impact of over- or under-estimation of loss reserves has a substantial impact on the policyholders, potential claimants, regulators, other insurers, and society. While there are some norms in estimating loss reserves, unfortunately, there appears to be no definitive definition of "reasonable" and limited guidance on what range of estimates is appropriate (Shapland, 2003). Added to the general flexibility, is the fact that reserves are estimates with amounts built in for unforeseen developments in losses and changes in interest rates. The combination creates considerable room in what are deemed appropriate reserve estimates. This potential for variation provides insurers with the opportunity to manage their loss reserves within reasonable limits to achieve certain financial goals.

Prior research indicates that insurers can use reserve manipulations to smooth income, postpone taxation, avoid reporting losses, and/or to mask insolvency concerns (e.g. Weiss, 1985; Grace, 1990; Petroni, 1992; Kazenski, Feldhaus, and Schneider, 1992; Beaver, McNichols, and Nelson 2003; Gaver and Paterson, 2004; and Grace and Leverty, 2012). While prior research indicates that insurers manage earnings through manipulating loss reserves, it does not describe “how” the insurers manage the earnings. This research expands this previous work by separating the two major types of loss reserves (i.e. case and bulk reserves) with the purpose of more clearly understanding the variation between the mix of case and bulk reserves among insurers and whether it relates to reserve manipulation. Specifically, we work to answer three questions. First, does the allocation between bulk and case loss reserves relate to an insurer’s financial, operational, and organization characteristics? Second, does the percentage of bulk reserves relate to the insurer’s management of loss reserves? Finally, does the use of bulk reserves and the relation of these reserves to reserving error vary across different lines of business? We address these issues controlling for the fact that the insurer’s decision to set the balance of bulk and case reserves is not independent from decisions about the management of loss reserves. We also control for the traditional factors known to impact loss reserve error.

To understand the potential for differences in reserve manipulation between case and bulk reserves, one must first understand the difference between these two categories of loss reserves. Case reserves are established after a claim is submitted and are based on the details of the claim and the experience of the company with similar claims. In other words, when a claim is submitted, the insurer establishes a case reserve for that specific claim. This case reserve reflects the overall expected payout for the individual claim. Case reserves are adjusted by the insurer throughout the claims process as new information is received. Total case reserves should reflect the amount that will be required to settle all reported claims less the amount already paid on such claims (Skurnick, 1973). Case reserves impact policy level and customer level loss ratios. Given that loss ratios are often used in setting future premiums, management of case reserves can directly impact a customer’s premiums. This creates concern for consumers and regulators.

In contrast, bulk reserves are not designated to specific claims and are generally established based on a line of business, rather than individual claims (i.e. a bulk amount is established for personal

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automobile and a separate bulk amount is established for homeowners/farmowners). These reserves are established to cover claims that have been incurred but that have not yet been reported (IBNR) to the insurer as well as an amount associated with potential unanticipated developments with case loss reserves.1 Accurate estimates of IBNR losses are very difficult to obtain and require some judgment (Weiss, 1985). Similarly, there is considerable judgment related to estimating unanticipated future events. Due to the greater levels of uncertainty related to setting bulk reserves, we anticipate that companies would have a greater opportunity to manage the bulk reserves without detection from outside sources.

Given the different purposes and characteristics of the case and bulk reserves, it is likely that case and bulk reserves will be used in different ways to achieve overall reserving goals (both related to accurately setting loss reserves and potential management of reserves for other purposes). This idea underlies the main research questions of whether there are significant differences in the use of case and bulk reserves among varying demographic groups of insurers and whether there are differences between case and bulk reserves with respect to the incentives and opportunities to manage loss reserves. To address these questions, the first step taken is to better understand the balance between the use of case and bulk reserves as measured by the percentage of bulk to total reserves. This is analyzed with respect to a series of firm characteristics as well as proxies for the traditional motivations for reserve management. The second stage of the analysis looks at the relation of the use of bulk versus case reserves to loss reserving error. We do this analysis at the firm level as well as for several key lines of business (i.e homeowners/farmowners, personal automobile, commercial lines multi-peril, workers compensation, commercial automobile, product liability and medical malpractice liability). This allows us to determine whether firms use bulk reserves differently based on the lines of business written or whether the strategy is uniform across the firm. This information will provide those tasked with monitoring firm’s loss reserving practices with the ability to determine whether they should focus their analysis of bulk reserves on key lines or across the firm.

We find that firms with higher percentage of bulk reserves tend to be stock companies, larger firms and those firms more focused in commercial lines. Additionally, several of the traditional motives for reserve management are significantly related to the percentage of bulk reserves including a negative relation to interest rates as well as a positive relation to tax postponement measures. With respect to the relation of overall loss reserving error and the use of bulk reserves we find some support for the hypothesis that an increase in the use of bulk reserves is positively related to reserving error. Also, consistent with prior literature, we find that the overall reserve error is inversely related to the overall combined ratio of the insurer and that holding companies have lower reserve errors. Firms with a less diversified mix of business (i.e. a higher concentration in fewer lines of business) have higher reserve errors. Premium to surplus ratios and a concentration in commercial lines have a negative relation and mutual insurers tend to have a greater overall reserve error. We also find weak evidence related to the relation of interest rates and reserve error.

Our results are not uniform across the lines of business included in this paper. Most interestingly, we find that the percentage of bulk reserves impacts reserve errors for long-tailed lines with lower levels of regulation such as commercial multi-peril, commercial automobile and product liability. The reserve errors associated with our short-tail line (homeowners/farmowners) and long-tail, regulated lines

                                                                                                                         1 It has been shown that the developments on reported cases could be favorable or unfavorable, increasing or decreasing reserves (Bornhuetter and Ferguson, 1972).

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(personal automobile, workers compensation and medical malpractice) are not significantly impacted by the percentage of bulk reserves. Also, we find that the factors impacting the percentage of bulk carried differ by line of business. Firms with a higher percentage of bulk reserves tend to be stock companies, larger firms and those firms more focused in commercial lines. In addition, for homeowners/farmowners, commercial multi-peril, workers compensation and product liability, the premium to surplus ratio is inversely related to the percentage of bulk reserves. We find weak evidence of tax postponement on most lines of business (stronger evidence of tax postponement is found on the commercial multi-peril and product liability).

The current study adds to the literature related to the management of loss reserves by considering how the use of bulk reserves impacts the insurer’s ability to manage reserves. The various reasons for the use of bulk reserves, coupled with the guidelines for setting bulk reserves, provides insurers with the potential to exercise greater amounts of discretion in setting these reserves. This opportunity may give rise to the ability to manage reserves to achieve other goals rather than just to build in reserves for unexpected development and IBNR. By separating case and bulk reserves, this study allows us to look beyond why insurers might manage reserves and focus on another aspect - how insurer's manage reserves. With this understanding, regulators with concerns of earnings management will be able to focus more directly at the bulk reserves. In addition, investors will be able to focus their attention on bulk reserves for indication of earnings management.

The remainder of this paper is arranged as follows: Section II describes the previous literature. Section III explains our hypothesis development. Section IV describes the data and methods used. The initial empirical analysis is presented in Section V. The conclusions are shown in Section VI.    

II. Literature Review  Before we can understand the roll of the allocation of case versus bulk loss reserves in potential earnings management, we must first understand the incentives insurers have to manage their loss reserves. This section provides a brief summary of the major incentives found in prior literature related to reserve management as well as the proxies used to detect the motives. Income Smoothing: Firms face the need to show relatively smooth income patterns over time. In order to please shareholders and to report at least a minor increase in profits, an insurer may manage loss reserves to redistribute income from profitable years to less- or un-profitable years, resulting in a smoothing of income (Weiss, 1985). In essence, management is able to increase or decrease their reported cash flow by under- or over-reporting their loss reserves (Grace, 1990).2 Traditionally, a measure of underwriting and investment income scaled by net earned premiums has been used to proxy for the earnings management tool of income smoothing (Grace, 1990). Similar to Grace (1990) we use the following as our proxy:

(3 yr avg Net Underwriting Income)t, t-1, t-2 + (3 yr avg Net Investment Income)t, t-1, t-2 (3 yr avg Net Premiums Earned)t, t-1, t-2

                                                                                                                         2 In the short run, this action can increase firm value. Over time, however, this shifting of income does not affect the value of the firm as it does not change the overall profitability, it simply redistributes the income over various time periods (Weiss, 1985).

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Evidence of the income smoothing measure has been mixed. For instance, Grace (1990) found that for her overall sample of 1966 to 1979 and a subsample from 1966 to 1979, the results related to smoothing were significant; however, for the subsample of 1972 to 1979, the results were insignificant.3 Using a sample period of 1988 to 1998, Beaver, McNichols, and Nelson (2003) also found that insurers smooth income. On the other hand, with a sample period of 1990-1997, Grace and Leverty (2012) found little evidence of smoothing in their research. Postpone Taxation: In her research, Grace (1990) found that management uses the variability and flexibility of loss reserves to avoid or minimize tax liabilities. Through earnings management, firms can reduce taxes in profitable years by increasing the reserves for losses. As losses are paid, the reserve error is realized and taxes that were postponed would eventually be due. Therefore, the benefit of the management of loss reserves for tax purposes is not in the avoidance of taxes but in their postponement (Grace, 1990). There are limits to the extent to which insurers can engage in this practice. If the company is over-reserved by approximately fifteen percent or more, the IRS may feel the insurer is manipulating income to avoid taxation and therefore, a penalty may be imposed (Grace, 1990). Consistent with Grace (1990), the measure used to test an insurer's intent to postpone taxes through claims reserve manipulation, TAX SHIELD, is calculated as follows:

Underwriting Income + Investment Income + Estimated Reserve Net Premiums Written

where the estimated reserve is the accident year estimate of unpaid claims.4 According to Grace (1990), insurers may over-estimate future claims costs as their taxable income increases. In the full sample and subsamples defined previously, Grace (1990) found support for the hypothesis that insurers use reserve errors for tax deductions. Mask Insolvency Concerns: There are costs to the company associated with any type of regulatory oversight. Insurers manage reserves to avoid triggering regulatory scrutiny related to increased insolvency risk and thus to reduce the associated costs which accompany this oversight (Gaver and Paterson, 2004; and Hoyt and McCullough, 2010). By under-reserving, or posting lower than expected loss reserves, insurers are able to reduce the size of their major liability category and appear stronger. Often, this allows the firm to mask financial weakness until the claims come due and ultimate payment must be made. Unfortunately, at this point the firm is often unable to make those payments due to poor financial condition (Petroni, 1992).

                                                                                                                         3 Grace (1990) did not feel it was realistic to assume that the data would remain stable over her 15-year sample. Therefore, due to a change in underwriting cycles that began in 1971 and increasing inflation rates, she separated her 15-year sample into two subsamples.

4 Grace and Leverty (2012) scale their reserve error using total assets rather than net premiums written. They also find evidence of a tax shield, however, they only find this result when considering the KFS error calculation (the Weiss error is insignificant). Petroni (1992) and Nelson (2000) control for tax postponement by using a dummy variable if an insurer is considered in a high tax bracket. The results they found utilizing the dummy variable are consistent with our results based on Grace's (1990) proxy.

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Consistent with Weiss (1985) we use the overall combined loss ratio as a proxy for a firms desire to minimize regulatory scrutiny. Underwriting results can be smoothed by over- or under-estimating loss reserves. Therefore, when the combined loss ratio is high, the firms underwriting results would appear to be more stable if loss reserves were underestimated. However, when the combined ratio is low, the underwriting results would benefit from overstating reserves. A second measure for insolvency concern used by Weiss (1985) was the Kenney Ratio (net premiums written/policyholders' surplus). Similarly, we use a premium to surplus ratio to determine if insurers utilize loss reserve management techniques to avoid the attention of regulators concerned with insolvency. When insurers premium to surplus ratio increases, insurers may reduce loss reserve levels in order to increase policyholder's surplus. When loss reserves are understated, policyholder's surplus will be overstated (Weiss, 1985). Interest Rate Impact: Interest rates may have an impact on reserve levels (Weiss, 1985). If interest rates are high, insurers may be willing to take a lower underwriting return because their investments are achieving greater gains. The investment income they receive may offset the deficiency in claims reserves (Weiss, 1985). Consistent with Weiss (1985), we use the rate of return on the three-month Treasury bills. Weiss, 1985, found that as the market rate of interest rose and investment opportunities became more profitable, insurers were less likely to over-reserve (or under-reserved less). Our empirical analysis draws on these motivations and the proxies used to test them in prior literature. For the calculation of the reserve errors, we use the two main methods created in Weiss (1990) and Kazenski, Feldhaus, and Schneider (1992) (KFS).5 The first reserve error, the Weiss error, is calculated as follows:

(Originally Reported Incurred losses)t - (Losses as Actually Developed) t+4 (Originally Reported Incurred Losses)t

The second reserve error, the KFS error, is calculated as follows:

(Originally Reported Incurred Losses)t - (Incurred Losses)t+4 (Originally Reported Incurred Losses)t

The main difference rests in the fact that the Weiss error uses the actual developed losses where the KFS error uses losses incurred, which includes the impact of outstanding reserves for open claims that have not yet been settled. According to Grace and Leverty (2012), the difference in the error measures is dependent on the development of losses (when losses are eventually paid). If claims are fully developed in the five year period there will not be a difference. However, if claims are not fully developed, the open reserve will not be incorporated in the Weiss error. The KFS error considers both paid and open reserve amounts in this case.6 The Weiss error will overstate the reserve error for

                                                                                                                         5 See Grace and Leverty (2012) for a discussion on how the loss reserve errors are calculated utilizing the data

provided on Schedule P of the statutory financial statements. 6 For a more detailed explanation on the use of Schedule P and the estimation of the Weiss and KFS errors,

refer to Grace and Leverty (2012).

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those lines with a longer settlement period (Grace and Leverty, 2012). 7 Consistent with Gaver and Patterson (2004), we scale both reserve errors by net total assets.8

III. Hypothesis Development We build on the prior loss reserving research to assess the potential roll that the allocation of case versus bulk reserves plays in earnings management. To do this, we test three hypotheses. First, we test whether the allocation of bulk reserves varies based on the operational, financial and organizational characteristics of insurers. Included in this analysis are the factors from prior literature that are typically associated with the insurer’s incentives to manage loss reserves. Due to an insurer’s flexibility in establishing estimates for IBNR, insurers have a unique opportunity to smooth income by adjusting, up or down, such estimations (Weiss, 1985). Since IBNR is a part of the bulk reserves, we expect that an insurer’s use of reserves to smooth income will be positively related to the percentage of bulk reserves. Similarly, the discretion in the use of bulk reserves may create the opportunity to achieve the other motivations for earning management such as efforts to postpone taxation and avoid insolvency concerns. We control for organization form knowing that there are differences in reserving error patterns, lines of business written and risks taken between the organizational forms (e.g. stock, mutual, and "other" which includes risk retention groups or reciprocals). Taken together we expect a positive relationship between stock and the percent of bulk reserves. We also consider the impact associated with an insurer’s concentration in commercial long- and short-tail lines as well as personal long- and short-tail lines of business.9 Insurers associated with longer-tailed lines of business are expected to carry a greater percentage of their overall reserves in bulk. In other words, we expect those lines that require longer settlement periods to have a greater percentage of their overall reserves in bulk because the longer settlement periods could make it more difficult to properly measure the anticipated development of a claim. This would lead to larger estimates for unanticipated development, which is a part of bulk reserves. In addition, longer-tailed lines of business can be associated with delays in the reporting of claims, leading to higher IBNR estimates (Grace, 1990). As a result, these long-tailed lines of business are associated with more discretion in establishing reserves (Beaver, McNichols and Nelson, 2003; and Grace and Leverty, 2012). Taken together, the short-tail line of business, homeowners/farmowners, with expected shorter settlement periods will have lower bulk percentages compared to the long-tail lines (personal automobile, commercial multi-peril, workers compensation,

                                                                                                                                                                                                                                                                                                                                                                                                                                7 Policy types differ in the length of claim settlement periods. It is unlikely that all claims are settled within a five

year period. For instance, Nelson (2000) indicates that the time to settle all claims for homeowners/farmowners is seven years and for medical malpractice it is 20 years.

8 To be consistent with the research of Weiss (1985), Petroni (1992), Grace and Leverty (2012) we tested the

results without scaling the error terms.The results are statistically similar and available from the authors upon request.

9 Commercial Long Tail (aircraft, boiler & machinery, commercial auto, commercial multi peril, workers

compensation, international, professional liability, ocean marine, product liability, reinsurance-liability); Commercial Short Tail (allied lines, burglary, inland marine, credit, earthquake, fidelity, financial guaranty, fire, group A&H, mortgage guaranty, reinsurance-property, surety); Personal Long Tail (personal auto liability); Personal Short Tail (personal auto physical damage, home, farm)

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commercial automobile, medical malpractice and product liability), which are expected to have longer settlement periods. In addition to the factors above, we also control for firm size, including firms at the top of the size distribution, as well as for the concentration of business mix. While we do not have a priori hypotheses for these variables, we do anticipate that the use of bulk reserves may vary across these firm dimensions. In our second hypothesis, we consider whether the allocation of bulk reserves is related to the insurers management of loss reserves when controlling for the traditional factors associated with loss reserving error. Due to the greater levels of uncertainty related to setting bulk reserves (i.e. setting reserves for claims that have not yet been reported (IBNR)), we anticipate that companies would have a greater opportunity to manage the bulk reserves without detection from outside sources. Therefore, we expect that we will see evidence of earnings management of loss reserves through the increased percentage of bulk reserves. As discussed in the Data and Method section to follow, we control for the fact that the allocation of bulk versus case reserves is likely made in conjunction with decisions related to reserve error. Our expectations for the remaining variables related to the motivations for managing loss reserves and other controls are consistent with prior literature, as discussed in the Literature Review section. Finally, we consider whether the patterns found in hypotheses 1 and 2 are consistent across lines of business. There are several reasons why the results may vary among lines including the level of regulation, the settlement rate of claims, and the complexity of the line of business. For regulated lines of business, an insurer would have an added incentive to more accurately determine case reserves in order to avoid a regulator’s scrutiny associated with customer complaints or audits indicating improper reserve establishment. This accuracy should lead to a reduced amount of unanticipated development for the claims associated with these lines of business and, in turn, this should minimize the use of bulk reserves. In addition, some of these regulated lines have mandatory reporting timeframes associated with major losses. For instance, workers compensation policies may contain mandatory reporting deadlines for major time-loss claims or loss of life events. These regulations would reduce the late reporting included in the IBNR portion of bulk reserves and in turn, establish case reserves for the reported claims. This would lead to a shift from bulk to case reserves. Both regulatory issues should reduce the use of bulk reserves as well as the potential link between bulk reserves and loss reserve error. Another factor may stem from the fact that losses associated with different lines of business do not settle at the same rate. For instance, Nelson (2000) indicates that the time to settle all claims for homeowners/farmowners is seven years and for medical malpractice it is 20 years. This longer settlement period could lead to a greater likelihood of unanticipated development which is part of the bulk reserves calculation. Also, we anticipate that longer-tailed lines such as product liability may tend to have a greater number of later reported claims (IBNR) which again is part of bulk reserves. Therefore, because of the unanticipated development associated with long-tailed lines (i.e. commercial automobile liability, commercial multi-peril, workers compensation, product liability, and medical malpractice) and the potential for late reported claims (IBNR) in these lines, there is a potential for a greater percentage of bulk reserves and a higher likelihood of earnings management.  

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Finally, insurance lines differ in their complexity. This includes the contract provisions as well as the insurance knowledge required by the claims adjusters in settling claims. We anticipate that more complex lines of business will have greater percentages of bulk reserves due to unanticipated development within the claims. Some lines of business are impacted by a combination of the regulation, settlement rate, and complexity issues identified above. For instance, homeowners/farmowners lines of business are generally not heavily regulated, have shorter settlement rates and are not normally considered complex lines of business. Therefore, we would anticipate that they would have a higher percentage of case reserves and provide little opportunity to manage earnings. Personal automobile is highly regulated in many states and it is not considered a complex line of insurance. The claim settlement period, however, can be short or long depending on the severity of the accident. For these reasons, the reserves will tend to be more heavily weighted in case unless the insurer is impacted by a large number of severe claims. If an insurer has more case reserves there will be less of an opportunity to manage earnings. Thus, we cannot make an a prior prediction for this line as it will depend what effect dominates. Commercial multi-peril and commercial automobile insurance are not normally highly regulated, but claims associated with these lines can have long settlement timeframes. These products are also rather complex leading to a greater need for unanticipated development, a portion of bulk reserves. This combination would lead this line to be more heavily weighted in bulk reserves which provides for an increase in the ability to manage earnings. Workers compensation and medical malpractice are heavily regulated lines of business and therefore, we would expect them to more accurately estimate their case reserves. On the other hand, there is also the issue of long reporting periods associated with the identification and filing of these types of claims (i.e. injury may not immediately present itself and the time-frame allowed to file claims, as well as the settlement period to complete such a claim, may be long). These lines of business also are very complex. Due to these reasons, there may be offsetting effects for workers compensation and medical malpractice claims. Therefore, the level of bulk and case reserves will depend on the level of regulation within the state and the impact of late reported claims for the insurer. Combined, these issues will likely impact the use of bulk reserves and thus the ability to manage earnings with bulk reserves. Finally, product liability is not generally considered a highly regulated line; however, the settlement rate can be very long and the line of business is very complex. For these reasons, the expected mix of case and bulk reserves and the link to earnings management will depend on the impact each of these issues has on an insurer. We believe that this is the first research to combine the study of case and bulk reserve issues with research on earnings management focused on claims reserves. Taken together the results will show what types of firms are most likely to utilize greater bulk reserves and whether this use is associated with more management of loss reserves. Combined with the line of business results this helps to paint a more complete picture of the role of bulk reserves in earnings management.

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IV. Data and Method  

The primary data comes from the National Association of Insurance Commissioners (NAIC) from 1996 to 2010. The initial sample includes all U.S. domiciled property-liability insurers. Consistent with prior loss reserving papers (e.g. Weiss, 1985; Grace, 1990; Petroni, 1992; Kazenski, Feldhaus, and Schneider, 1992; Beaver, McNichols, and Nelson 2003; Gaver and Paterson, 2004; Grace and Leverty, 2012) we collect the loss reserve data from the Schedule P of the statutory annual statement. We apply the same screens to prepare the loss reserving data as prior research to prevent outliers and misinformation from impacting the results.10 The reserve errors are calculated using a five year claim development period, which is consistent with works such as Weiss (1985); Petroni (1992); Gaver and Paterson (2004); and Grace and Leverty (2012). Based on our sample, reserve errors are calculated for 1996 to 2005. Applying the development period reduces our observations to 15,993. As mentioned in the previous section, it is possible that a firm’s decision with respect to the balance of bulk loss reserve to case and decisions related to under- or over-reserving are made jointly. In order to avoid simultaneity bias and potentially control for endogeneity, we perform an instrumental variable estimation procedure. By utilizing the instrumental variable approach, we are able to control for the fact that the same factors that impact the allocation of bulk and case reserves are also driving earnings management. We estimate the following system of equations:

%   Bulkit   =   α   +   β1reserve   errorit11   +   β2smoothingit   +   β3premium/surplusit   +   β4interest   rateit   +   β5tax   shieldit   +  β6combined   ratioit   +   β7mutualit   +   β8LOB   HHIit   +     β9commercial   short-­‐tailit   +   β10commercial   long-­‐tailit   +  β11personal  long-­‐tailit  +  β12large  firmit  +  β13sizeit  +  β14DPW  growthit  +  β15ratio  of  contingent  commissionsit  +  εit                                                                  Eq(1)  

Reserve   Errorit   =   α  +   β1%   bulkit   +   β2   smoothingit     +   β3premium/surplusit   +   β4interest   rateit   +     β5tax   shieldit   +  β6combined   ratioit   +   β7mutualit   +   β8LOB   HHIit   +   β9commercial   short-­‐tailit   +   β10commercial   long-­‐tailit   +    β11personal  long-­‐tailit  +  β12large  firmit  +  β13sizeit  +  εit                          Eq(2)  

Definitions of the variables are contained in Table 1. With respect to equation 1, the reserve error variable indicates whether the firm is under- or over-reserved based on the firm’s reserve errors in a given year based on the Weiss and KFS methods. The next set of variables, which includes smoothing, premiums/surplus, interest rate, tax shield, and combined ratio, relate to the incentives known to impact loss reserves discussed in the literature review. The third set of variables focus on the organizational form, concentration, business mix, and size of the firm in an effort to consider                                                                                                                          10  For example, companies that have loss reserve development greater than 50 percent in absolute value are

excluded (Grace and Leverty, 2012). To be included, the insurer must have a positive loss reserve, direct premium written, unpaid loss reserves and loss adjustment expenses. Insurers defined as reinsurers are excluded along with insurers with missing observations for the needed variables. The variables are winsorized at the 1st and 99th percentiles. Reserve errors are scaled by total assets (Gaver and Paterson, 2004). This results in a final sample of 27,852 firm-year observations.

11 The initial review considers whether the claim reserves are over- or under-estimated on the Weiss and KFS

errors.

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characteristics of the firm and/or types of business written that might drive the balance of case versus bulk reserves. The final group of variables contains the instruments used. Equation 2 follows previous research by utilizing the calculated reserve error as the dependent variable. We are estimating reserves as a function of the percentage of bulk reserves as well as the traditional variables included in previous earnings management research. These include the motivations to manage earnings through techniques to smooth income and postpone taxation, as well as the firm characteristics variables, including ownership structure, line of business concentration, and the size of the firm. Our variable of interest is the percentage of bulk reserves which is treated as endogenous. We use the Anderson-Rubin Wald and Hansen's J tests to ensure that the model is appropriately identified. All tests show that the instruments are appropriate. Variance inflation factors and correlation tests did not find evidence of multicollinearity. However, tests for heteroskedasticiy and autocorrelation indicated the presence of these problems. As a result, the system is estimated as a two-step efficient generalized method of moments system with HAC (heteroscedastic autocorrelation consistent) errors.   We estimate the models based on both overall loss reserves as well as for the two main personal lines coverages (homeowners/farmowners and personal automobile) and several commercial lines coverages (commercial lines multi-peril, workers compensation, commercial automobile, product liability and medical malpractice liability) to discover whether the balance of case versus bulk is consistent across lines of business.

V. Analysis  As indicated in the univariate statistics shown in Table 2, we see that the Weiss error is larger than the KFS error in our overall sample and in most lines of business. This is due to the fact that the KFS error considers any remaining open reserves whereas the Weiss error is calculated with paid claims only and does not include any claims with open reserves. As expected, the correlation between the KFS and the Weiss errors is relatively high at .7176. We test the KFS and Weiss errors in our models, expecting that consistent with prior literature the results will be similar.12 Prior to moving to the multivariate analysis, we first consider the general summary statistics related to the allocation of case and bulk reserves. Our working sample includes stock, mutual, and "other" (i.e. risk retention group or reciprocals) ownership types. The percentage allocation of these ownership types is 70.04 percent, 21.04 percent, and 8.92 percent, respectively. Table 3 contains a breakdown of the balance of case and bulk reserves by organizational form and key lines of business. Consistent with our hypothesis, the results indicate that stock companies carry a higher percentage of loss

                                                                                                                         12 For example, Grace and Leverty (2010) found some differences between the results using the two errors. For

example, they found that the incentive to over-reserve to postpone taxation was evident when utilizing the KFS error but found no proof when using the Weiss error.

12  

 

reserves in the form of bulk reserves.13 Further, the commercial lines of insurance (Commercial Multi-Peril, Workers Compensation, Commercial Automobile, Medical Malpractice and Product Liability) are more heavily weighted in bulk reserves. For commercial multi-peril and commercial automobile, these results reflect the longer settlement periods required for these lines and is consistent with our expectations. For workers compensation, the larger percentage of bulk reserves may indicate that the delayed reporting of such claims and the long development periods outweigh the regulation concerns regarding case reserve levels. We see a dramatic increase in bulk reserves for the professional liability coverages of Medical Malpractice and Product Liability. These lines may be associated with a greater likelihood of delayed reporting as a result of their litigious nature. In other words, losses associated with these lines may not be made for months or years following the actual loss. This delay may result in a need for increased IBNR funds, which are a portion of the bulk reserves. Also, the infrequent nature of these losses and the potential severity of these types of claims when they do occur may create some complexity when estimating for unanticipated development of the loss, another aspect of the bulk reserves. Combined, these issues could explain the large percentage of bulk reserves for Medical Malpractice and Product Liability. Interestingly, the personal lines are more heavily weighted in case reserves. Even personal automobile liability, most often thought of as a personal lines long-tail coverage, shows a majority of reserves in the case reserves. This possibly indicates that personal automobile liability claims are still settled more quickly and are more accurately reserved than commercial lines of insurance or it's possible that the regulation of personal automobile plays a part in an insurer’s level of case reserves. As previously discussed, in order to avoid a regulators scrutiny associated with customer complaints or audits, which could indicate improper reserve establishment, an insurer would have an added incentive to more accurately determine case reserves.14 This would reduce the development portion found in the bulk reserves. In other words, insurers have an incentive to attempt to accurately establish case reserves when claims occur, thus reducing the unanticipated development portion of bulk reserves.    To analyze our second hypothesis, we look to see if the allocation between bulk and case loss reserves relates to an insurer's financial, operational, and organization characteristics. Table 4 presents the results related to firm-level loss reserving patterns for the first stage of our analysis. We expect to see that insurer characteristics and the standard earning management measures will vary based on the percentage of bulk reserves. Consistent with our findings in Table 3, stock companies carry a greater percentage of their overall reserves in bulk. We also find that for the overall firm reserves, the balance of case versus bulk reserves is associated with the tax postponement motive mentioned previously. Specifically, insurers with a stronger motivation to postpone taxes by increasing loss reserves have a higher portion of bulk reserves indicating that they may be achieving this goal by increasing bulk, rather than case, reserves. This is consistent with our hypothesis that insurers will utilize bulk reserves for manipulation due to the increased flexibility in this area. In conjunction with

                                                                                                                         13 This is consistent with theories posed by researches that stock insurers should be associated with more risky

activities, including the lines of business they write and the amount of interaction required in the underwriting process (e.g. Fama and Jensen, 1983; Lamm-Tennant and Starks, 1993; Mayers and Smith, 1994).  

14 Weiss, 1985 indicates that management considers the reaction of regulators when establishing claims reserves.

13  

 

this, lower interest rates, often associated with limited investment opportunities are associated with higher levels of bulk reserves. Further, we see that many of the firm characteristic variables are also significant. Firms with a higher percentage of business in commercial lines, both long and short-tail coverage, have a greater percentage of bulk reserves relative to personal lines coverages (both short and long-tail coverage). In contrast, firms with more business in personal long-tail lines carry a lower percentage of bulk reserves relative to the commercial lines coverage mentioned above. Finally, larger firms tend to carry a higher percentage of bulk reserves.

We now look to see whether the allocation of bulk loss reserves is related to the insurers management of loss reserves when controlling for the traditional factors associated with loss reserving error. Table 5 provides the results of equation 2. In this equation, we use the framework of previous loss reserving research to determine whether reserve errors are related to the balance of case and bulk reserves. Of primary interest, is the positive and significant relation between the KFS error and the percentage of bulk reserves. This suggests that higher levels of reserving error is associated with larger percentages of bulk reserves.

Our findings related to the motives for loss reserve management and the controls are consistent with prior literature. Specifically, we find that for both reserve errors, as the combined ratio increases, the firm is more likely to under-reserve (or over-reserve less). In addition, consistent with Weiss (1985), the KFS error indicates that mutual companies are less likely to under-reserve (or over-reserve more) than stocks or said differently, stocks are more likely to under-reserve (or over-reserve less) than are mutual companies. Also, when the percentage of business written in commercial lines of business increases, the firm is more likely to over-reserve. Again, this may reflect the longer settlement periods required for these lines. These results also show that larger firms are less likely to over-reserve. The more concentrated the insurers business, the greater the likelihood of over-reserving and finally, holding companies are less likely to over-reserve than are non-holding companies.  

We analyze our final hypothesis by looking at the results to determine if the patterns found in the results discussed above are consistent across lines of business. As outlined previously, we expect that an insurer’s concentration in certain lines of business will have an impact on their overall percentage of bulk versus case reserves as well as their earnings management techniques. Our results in Tables 3, 4 and 5 indicate a need to analyze the results by line of business given the significant differences in univariate statistics across lines of business and in the business mix variables. These results underscore the importance of further dividing the sample based on the lines of business written to better understand how insurers are using bulk and case reserves in the different lines of coverage. We analyze the impact of the percentage of bulk reserves on reserving errors on a line of business basis (See Table 7). Again we see that the results vary across lines. Most interestingly, we find that the percentage of bulk reserves impacts reserve errors for long-tail, less-regulated lines of business (commercial multi-peril, commercial automobile and product liability).15 There are various reasons that this may occur. For instance, the extreme volatility of product liability (i.e. product recall) over the past

                                                                                                                         15 The commercial multi-peril and commercial auto are only significant when considering the Weiss error,

however, the product liability is significant with both the Weiss and KFS errors.

14  

 

decade along with the long settlement rates and the complexity of this line of business may allow an insurer greater flexibility in determining reserving levels, making it easier to manage reserves. The reserve errors associated with our short-tail line (homeowners/farmowners) and long-tail, regulated lines (personal automobile, workers compensation and medical malpractice) are not significantly related to the percentage of bulk reserves. Also, we find that the factors impacting the percentage of bulk carried differ by line of business (see Table 6). However, there are some consistencies that carry through most or all of the lines of business. The premium/surplus ratio, mutual ownership, interest rates, commercial and personal lines, and size all remain significant and consistent with our overall results in virtually all lines and across both Weiss and KFS errors. On the other hand, we note that the indication of a tax postponement intent remains significant for the Weiss error only and is insignificant for medical malpractice (for both error calculations). We do note that the significance associated with line of business concentration (HHI index) is not significantly related to the percentage of bulk reserves, except when considering workers compensation, medical malpractice and product liability.

VI. Conclusion  

This article jointly considers the insurers management of bulk versus case reserves and the impact of this on earnings management. We find that there are differences in the use of bulk reserves across insurers. Specifically, we see that stock companies, larger firms, and firms more focused in commercial lines have higher levels of bulk reserves. Additionally, interest rate and tax incentive proxies found in prior loss reserving literature also are significant. Perhaps more importantly, we find that there is evidence of greater use of bulk reserves associated with earnings management even when controlling for traditional factors related to management of loss reserves. The fact that larger percentages of bulk loss reserves are associated with higher levels of reserve manipulation underscores the importance of better understanding how insurers are using bulk reserves as these results impact consumers, regulators, and society. In order to understand if these patterns are uniform across lines of business, we also investigate patterns in key lines. There we find that the percentage of bulk reserves impacts reserve errors for long-tailed lines with lower levels of regulation such as commercial multi-peril, commercial automobile and product liability. Taken together these results are important for several reasons. First, the manipulation of loss reserves directly impacts consumers in the premiums they pay for insurance coverage. As shown, insurers manage reserves for numerous reasons and the impact is volatility in the premiums charged to customers. Therefore, the efforts of insurers to manage their financial cash flows by means of bulk reserve manipulation can cause hardship on consumers. Second, stakeholders of the company are also impacted by the manipulation of reserves. Whether a policyholder-owner in a mutual company or a shareholder of a stock company, those that hold an interest in an insurer need to understand the financial aspects of the firm. Clearly, an insurer’s use of bulk reserves for the smoothing of income or as a tax postponement measure should be understood by those analyzing a firm’s financial status for potential or ongoing investments. In addition, investors may want to consider the various lines of business written by the insurer and the concentration of these lines of business as a signal of whether the insurer utilizes bulk reserves for earnings management.

15  

 

The findings of this research should also be of interest to regulators, whose job it is to measure the risk of potential insolvency of insurers. Understanding how insurers manage their loss reserves will allow regulators to focus their limited resources in the appropriate area. With respect to this paper, the findings that the use of bulk reserves to manage reserves appears to be strongest in key lines gives regulators the ability to target their limited resources in these areas. Although there are few individuals that are not a consumer of insurance, the issue of reserve management is not only an individual concern. In this era of "too big to fail," the issue of insurer solvency and financial understanding has never been more important. As our research shows, large firms carry more of their overall reserves in bulk and it is the bulk reserves that are used for the earnings management techniques focused on in this paper. Understanding this will allow all concerned individuals and society as a whole to better understand the financial nuances involved in the insurance industry. While this paper provides a solid contribution to the loss reserve literature, there are still questions related to the differences in reserving patterns across lines. The current findings isolate several lines of business where the use of bulk reserves relates to reserving error. However, it is not clear whether, when insurers make the decision to increase or decrease their bulk reserves for one or more of the manipulation reasons that we have discussed in this paper, they do so with consideration as to the lines of business for allocation of the funds. This decision may be left to the accounting or actuarial departments and it is possible that there is not consistency in the allocation among policy types. To better understand the implications of these results, further analysis will take place.

16  

 

VII. References  

Beaver,  WH,  MF  McNichols,  KK  Nelson,  "Management  of  the  Loss  Reserve  Accrual  and  the  Distribution  of  Earnings  in  the  Property  Casualty  Insurance  Industry,"  Journal  of  Accounting  and  Economics,  2003,  35:  347-­‐376.    Cummins,  J.  David  and  Neil  A  Doherty,  "The  Economics  of  Insurance  Intermediaries,"  The  Journal  of  Risk  and  Insurance,  2006,  3:  359-­‐396.    Fama,  Eugene,  and  Michael  Jensen,  "Agency  Problems  and  Residual  Claims,"  Journal  of  Law  and  Economics,  1983b,  26:  327-­‐349.    Gaver,  Jennifer  J.  and  Jeffrey  S.  Paterson,  2004,  "Do  Insurers  Manipulate  Loss  Reserves  to  Mask  Solvency  Problems?"  Journal  of  Accounting  and  Economics,37:  393-­‐416.    Grace,  Elizabeth,  "Property-­‐Liability  Insurer  Reserve  Errors:  A  Theoretical  and  Empirical  Analysis,"  Journal  of  Risk  and  Insurance,  1990,  57:  28-­‐46.    Grace,  Martin  F.  and  J.  Tyler  Leverty,  "Property-­‐Liability  Insurer  Reserve  Error:  Motive,  Manipulation,  or  Mistake,"  Journal  of  Risk  and  Insurance,  2012,  79:  351-­‐380.    Harrington,  Scott  E.  and  Greg  Niehaus,  "Capital  Structure  Decisions  in  the  Insurance  Industry:  Stocks  versus  Mutuals,"  Journal  of  Financial  Services  Research,  2002,  21:  145-­‐163.    Hoyt,  Robert  E.  and  Kathleen  A.  McCullough,  “Managerial  Discretion  and  the  Impact  of  Risk-­‐Based  Capital  Requirement,”  Journal  of  Insurance  Regulation,  2010,  29:  207-­‐228.    Kazenski,  Paul,  William  Feldhaus,  and  Howard  Schneider,  "Empirical  Evidence  for  Alternative  Loss  Development  Horizons  and  the  Measurement  of  Reserve  Error."  The  Journal  of  Risk  and  Insurance,  1992,  668-­‐81.    Kunreuther,  H.,  and  M.  Pauly,  "Market  Equilibrium  with  Private  Knowledge:  An  Example,"  Journal  of  Public  Economics,  1985,  26:  269-­‐88.    Lamm-­‐Tenant,  Joan,  and  Laura  T  Starks,  "Stock  Versus  Mutual  Ownership  Structures:  The  Risk  Implications,"  Journal  of  Business,  1993,  66:  29-­‐46.    Mayers,    David,  and  Clifford  Smith,  "Managerial  Discretion,  Regulation,  and  Stock  Insurer  Ownership  Structure,"  The  Journal  of  Risk  and  Insurance,  1994,  61:  638-­‐655.    Nelson,  Karen,  "Rate  Regulation,  Competition,  and  Loss  Reserve  Discounting  by  Property-­‐Casualty  Insurers,"  Accounting  Review,  2000,  75:  115-­‐138.    Petroni,  Kathy,  "Optimistic  reporting  in  the  property-­‐casualty  insurance  industry,"  Journal  of  Accounting  and  Economics,  1992,  15  (4):  485-­‐508.    Shapland,  Mark  R.,  "Loss  Reserve  Estimates:  A  Statistical  Approach  for  Determining  'Reasonableness,'"  Casualty  Actuarial  Society  Forum,  Fall  2003:  321-­‐360.    

17  

 

Skurnick,  David,  "A  Survey  of  Loss  Reserving  Methods,"  Proceedings  of  the  Casualty  Actuarial  Seminar,  1973,  60:  16-­‐62.    Weiss,  Mary  A.,  "A  Multivariate  Analysis  of  Loss  Reserving  Estimates  in  Property-­‐Liability  Insurers,"  Journal  of  Risk  and  Insurance,  1985,  52  (2):  199-­‐221.  

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Table  1    

 

Variable Definition Reserving Error Methods

Weiss Error* (Originally Reported Incurred losses)t - (Losses as Actually Developed)t+4 (Originally Reported Incurred Losses)t

KFS Error* (Total Incurred Losses)t - (Incurred Losses)t+4 (Total Incurred Losses)t

Traditional Variables Related to Motives for Reserve Manipulation

Smoothing (3 yr avg Net Underwriting Income)t, t-1, t-2 + (3 yr avg Net Investment Income)t, t-1, t-2 (3 yr avg Net Premiums Earned)t, t-1, t-2

Premium/Surplus Ratio Net Premium Written to Surplus Ratio Interest Rate Rate of return on three month Treasury bills (average 3 month rate for the year)

Tax Shield Underwriting Income + Investment Income + Estimated Reserve Net Premiums Written

Combined Ratio Overall Combined Ratio Firm Characteristic Variables Mutual A dummy variable assigned a value of 0 for mutuals and a value of 1 otherwise. HHI Line of business Herfindahl Index Commercial Short-Tail** Percentage of direct premium written in commercial short-tail lines of business Commercial Long-Tail** Percentage of direct premium written in commercial long-tail lines of business Personal Long-Tail** Percentage of direct premium written in personal long-tail lines of business

Large Firm A dummy variable assigned a value of 1 if the net total assets are greater than the mean of all firms

Size Log of net total assets Instrumental Variables** DPW Growth Growth in direct premium written over previous year Contingent Commission Ratio Ratio of contingent commission to all commission *The Weiss and KFS errors are scaled by total assets (Gaver and Paterson, 2004).

**Commercial Long-Tail (aircraft, boiler & machinery, commercial automobile, commercial multi peril, workers compensation, international, professional liability, ocean marine, product liability, reinsurance-liability); Commercial Short-Tail (allied lines, burglary, inland marine, credit, earthquake, fidelity, financial guaranty, fire, group A&H, mortgage guaranty, reinsurance-property, surety); Personal Long-Tail (personal automobile liability); Personal Short-Tail (personal automobile physical damage, home, farm)

***Kunreuther and Pauly (1985) show that new business is less profitable than existing business, in part due to acquisition costs and part due to claims activity. We hypothesize that as insurers grow, they prepare for the potential increase in claims by increasing the percentage of reserves held in bulk. Contingent commissions are used to align incentives between the policyholder, the intermediary and the insurers and can create an efficient operation in the insurance markets. Furthermore, most of these programs are profit-based rather than volume-based (Cummins and Doherty,2006). We hypothesize that the greater the contingent commissions paid to intermediaries, the lower the overall loss ratios and therefore, the lower the percentage of bulk reserves held by the insurer.

19  

 

Table  2    

Summary Statistics Variable Mean Std. Dev. Min. Max. OVERALL ERROR KFS SIGN 0.8083 0.3937 0 1 KFS ERROR (calculated error) 0.0163 0.1123 -0.3568 0.4000 WIESS SIGN 0.8870 0.3166 0 1 WIESS ERROR (calculated error) 0.0579 0.1221 -0.3247 0.4425 HOME/FARM KFS ERROR 0.0163 0.0805 -0.2661 0.3734 WEISS ERROR 0.0236 0.0819 -0.2545 0.3954 PERSONAL AUTOMOBILE KFS ERROR 0.0158 0.0863 -0.2871 0.3424 WEISS ERROR 0.0320 0.0900 -0.2627 0.3778 COMMERCIAL MUTLI PERIL KFS ERROR -0.0081 0.1276 -0.4133 0.3889 WEISS ERROR 0.0356 0.1215 -0.3485 0.4098 WORKERS COMPENSATION KFS ERROR 0.0014 0.1498 -0.4299 0.4444 WEISS ERROR 0.1438 0.1642 -0.3571 0.4794 COMMERCIAL AUTOMOBILE KFS ERROR 0.0078 0.1260 -0.3894 0.4189 WEISS ERROR 0.0279 0.1251 -0.3704 0.4189 MEDICAL MALPRACTICE KFS ERROR 0.0616 0.2027 -0.5000 0.4718 WEISS ERROR 0.0439 0.2747 -0.5000 0.5000 PRODUCT LIABILTIY KFS ERROR -0.0015 0.2347 -0.4804 0.4706 WEISS ERROR -0.0698 0.2457 -0.5000 0.4859 SMOOTHING 0.1687 1.2814 -74.8336 39.0253 PREMIUM/SURPLUS RATIO 1.1095 2.2980 0.0002 265.3326 INTEREST RATE 3.2123 1.9653 0.1400 6.0000 TAX SHIELD 3.8809 231.6464 -345.0575 36745.9400 COMBINED RATIO 108.5396 47.6420 29.6986 422.2679 MUTUAL 0.2022 0.4016 0 1 LOB HHI 0.6515 0.3076 0.0933 1 COMMERCIAL SHORT-TAIL 0.1749 0.3028 0 1 COMMERCIAL LONG-TAIL 0.4514 0.4185 0 1 PERSONAL LONG-TAIL 0.1351 0.2267 0 1 LARGE FIRM 0.4978 0.5000 0 1 SIZE 11.0137 1.9924 5.0900 18.5308 DPW GROWTH 18.8793 71.2135 -69.4451 510.6185 RATIO OF CONTINGENT COMM 0.7609 96.4166 -19.2937 14285.5700 The  data  was  winsorized  at  the  1st  and  99th  percentile.  

20  

 

Table  3    

Bulk versus Case Reserves - Ownership Structure and Line of Business (1996 - 2010)

Percentage Case Bulk

Case Bulk Mean Std Dev Mean Std Dev By Ownership Structure Stock 0.4676 0.5324 22848.77 66062.31 29694.45 90829.32 Mutual 0.5906 0.4094 21385.01 62038.30 21185.35 74534.78 Other 0.3548 0.6452 15141.43 60612.08 18914.12 78294.34 Combined 0.4802 0.5198 21718.59 64734.82 26807.37 86569.07 By Line of Business Home/Farmowners 0.6035 0.3965 1527.56 5444.17 2075.54 23970.87 Personal Automobile 0.5766 0.4234 6036.70 22554.47 4473.64 17325.85 Commercial Multi-Peril 0.4677 0.5323 1575.38 6065.89 2108.80 8029.46 Workers Compensation 0.4151 0.5849 2648.63 9200.13 3817.57 14567.60 Commercial Automobile 0.4502 0.5498 1507.34 5371.80 1767.62 6196.64 Medical Malpractice 0.2864 0.7136 748.81 4115.57 1345.42 6970.26 Claims Made 0.3551 0.6449 664.82 3691.19 855.90 4480.52 Occurrence 0.1245 0.8755 27.29 164.53 319.07 1901.28 Product Liability 0.1681 0.8319 66.08 326.29 455.67 2162.65 Claims Made 0.2127 0.7873 4.00 28.23 19.54 133.22 Occurrence 0.1638 0.8362 54.51 266.27 416.20 1979.38

The data was winsorized at the 1st and 99th percentile.  

21  

 

Table  4      

Dependent Variable: Overall Percentage of Bulk Reserves

Sta

ndar

d R

eser

ve E

rror

Con

trols

SMOOTHING

0.002865

(0.47)

PREMIUM/SURPLUS RATIO

-0.007031

(-1.63)

INTEREST RATE

-0.011385 ***

(-9.81)

TAX SHIELD

0.011204 ***

(3.42)

COMBINED RATIO

-0.000041

(-0.40)

In

sure

r Cha

ract

eris

tic C

ontro

ls

MUTUAL

-0.107494 ***

(-21.04)

LOB HHI

0.005216

(0.59)

COMMERCIAL SHORT-TAIL

0.149644 ***

(7.53)

COMMERCIAL LONG-TAIL

0.152405 ***

(15.33)

PERSONAL LONG-TAIL

-0.040042 **

(-2.37)

LARGE FIRM

0.012067 *

(1.93)

SIZE

0.012141 ***

-6.96

Inst

rum

ents

DPW GROWTH

-0.000015

(-0.39)

RATIO OF CONTINGENT COMM

0.024363 ***

(3.54)

Coefficient and T-statistics *** significant at 1%, ** significant at 5%, * significant at 10%

   

22  

 

Table  5    

Dependent Variable: Overall Reserve Error

KFS WEISS

PERCENT BULK 0.68035 **

0.54838

(2.16)

(1.33)

S

tand

ard

Res

erve

Err

or C

ontro

ls SMOOTHING -0.00509

-0.00142

(-1.07)

(-0.34) PREMIUM/SURPLUS RATIO -0.00759

-0.00958 **

(-1.63)

(-2.30)

INTEREST RATE 0.00845 **

0.00423

(2.28)

(0.89)

TAX SHIELD -0.00348

0.00120

(-0.98)

(0.25)

COMBINED RATIO -0.00026 ***

-0.00023 ***

(-2.84)

(-3.05)

Insu

rer C

hara

cter

istic

Con

trols

HOLDING -.0151399 **

-.0132679 **

(-2.53)

(-2.53)

MUTUAL 0.08558 **

0.06249

(2.52)

(1.42)

LOB HHI 0.04725 ***

0.05873 ***

(5.38)

(8.59)

COMMERCIAL SHORT-TAIL -0.10449 **

-0.07964

(-1.98)

(-1.28)

COMMERCIAL LONG-TAIL -0.09527 *

-0.00802

(-1.85)

(-0.13)

PERSONAL LONG-TAIL 0.02004

0.00703

(1.07)

(0.36)

LARGE FIRM 0.00090

0.00024

(0.13)

(0.03)

SIZE -0.01150 ***

-0.00194

(-2.57)

(-0.37)

Coefficient and T-statistics *** significant at 1%, ** significant at 5%, * significant at 10%