External Monitor Quality and Managerial Discretion

Transcription

1 External Monitor Quality and Managerial Discretion Martin F. Grace + and J. Tyler Leverty ++ December 15, 2012 Abstract We investigate the extent to which external monitors limit managerial discretion. We study the property-liability insurance industry because it provides an objective measure of managerial bias and a unique mechanism to separate external monitor quality into its two components: technical knowledge and independence. We find managers use their discretion to reduce the impact of rate regulation, to smooth earnings, and for tax purposes. The extent of the managerial discretion, however, is reduced by high quality external monitors. We find that both the independence and the technical knowledge of the external monitor matter, but independence only matters if it is coupled with technical knowledge. JEL classification: M41, G22 Keywords: Accounting Discretion; Insurance; Reserve Management; External Monitor; Audit Firm Fixed Effects; Actuarial Firm Fixed Effects + James S. Kemper Professor, Department of Risk Management & Insurance, Georgia State University, PO Box 4035, Atlanta, Georgia ; mgrace@gsu.edu ++ Corresponding Author: Associate Professor, TRISTAR Risk Management Fellow, Department of Finance, Henry B. Tippie College of Business, University of Iowa, Iowa City, Iowa ; Phone: ; ty-leverty@uiowa.edu

2 External Monitor Quality and Managerial Discretion Abstract We investigate the extent to which external monitors limit managerial discretion. We study the property-liability insurance industry because it provides an objective measure of managerial bias and a unique mechanism to separate external monitor quality into its two components: technical knowledge and independence. We find managers use their discretion to reduce the impact of rate regulation, to smooth earnings, and for tax purposes. The extent of the managerial discretion, however, is reduced by high quality external monitors. We find that both the independence and the technical knowledge of the external monitor matter, but independence only matters if it is coupled with technical knowledge. JEL classification: M41, G22 Keywords: Accounting Discretion; Insurance; Reserve Management; External Monitor; Audit Firm Fixed Effects; Actuarial Firm Fixed Effects

3 Managers have incentives to influence their firm s reported results. An important question is whether external oversight limits this behavior. The quality of external oversight is the joint outcome of the monitor s technical knowledge and independence (DeAngelo, 1981). The probability that a monitor detects a problem is determined by its technical capability. The conditional probability that the monitor discloses a detected problem is determined by its independence. The bigger the external monitor the less dependent it is on retaining any one client and the more likely it will be to report their findings (DeAngelo, 1981; Watts and Zimmerman, 1981). Because size is a good proxy for the independence of the external monitor, the extant literature on external monitor quality mainly focuses on independence. 1 We investigate the extent to which both components of external monitor quality, independence and technical knowledge, limit managerial discretion. We do this by studying the loss reserves of U.S. P/L insurers (see e.g., Petroni, 1992; Petroni and Beasley, 1996; Gaver and Paterson, 2004; Gaver and Paterson, 2007), which are overseen by two external monitors, actuaries and auditors, with different technical knowledge. To sign statements of actuarial opinion, actuaries must be Fellows of the Casualty Actuarial Society (CAS) and have at least three years of full-time work experience under the review of an actuary who is qualified to sign statements of actuarial opinion. Fellowship in the CAS requires the successful completion of nine exams and a professionalism course. Three of the exams cover the estimation of claim liabilities. Thus, certifying actuaries are required to have extensive technical knowledge about loss reserving best practices. 3 In contrast, 1 DeAngelo (1981) assumes technical competence is fixed. Becker et al. (1998) find fewer income-increasing discretionary accruals are reported by the clients of Big Six auditors. Francis et al. (1999) find smaller absolute magnitudes of discretionary accruals for firms with Big Six auditors. Other studies examine whether factors that may impair the independence of the external monitor are related to earnings management. Frankel et al. (2002) find a positive association between the purchase of non-monitoring services and earnings management. Reynolds and Francis (2001) and Gaver and Paterson (2007) find that external monitors allow less accounting discretion to their larger clients. Myers et al. (2003) find that longer external monitor tenure results in less earnings management. 3 Moreover, one of the main job functions of the certifying P/L actuary is to accurately estimate reserves and their reputation is largely based on how well they do it. 1

4 external auditors are not. The divergence in required technical knowledge about loss reserves allows us to identify both components of external monitor quality: technical capability and independence. 4 Another advantage to studying P/L insurer loss reserves is that insurers are required to disclose revisions to their loss reserves, offering an objective measure of managerial discretion. Most prior studies (e.g., Becker et al., 1998; Francis et al., 1999; Frankel et al., 2002; Reynolds and Francis, 2001; and Myers et al., 2003) use Jones (1991) model residuals to evaluate the impact of external monitor independence on managerial discretion. Jones model residuals, however, can result in erroneous inferences concerning the extent of discretion (see e.g., Dechow et al., 1998; Hribar and Collins, 2002; Kothari et al., 2005). McNichols (2000) recommends avoiding the Jones model approach and instead concentrate on a specific accrual that is material to the firm and subject to substantial discretion on the part of management. This is exactly what we do. Moreover, studying a single industry provides a homogeneous sample of firms which allows for easier decomposition of discretionary and nondiscretionary components. In the literature, managerial bias of the P/L loss reserve is measured in two ways: (1) comparing the originally reported loss reserve to a revised estimate five years in the future (e.g., Petroni, 1992; Kazenski, Feldhaus, and Schneider, 1992; Beaver et al., 2003); and (2) comparing the originally reported loss reserve to future claims paid five years in the future (e.g., Weiss, 1985, Grace, 1990). Both measures require five years of data to calculate the reserve error (a 2004 reserve error is calculated using 2009 data). Since loss reserves are likely to be revised as new information arises over time (e.g., with changes costs or litigation), there is potential measurement bias in these measures. Moreover, the definitions are essentially point estimates of reserve adequacy that are not available to managers in real time. The long time lag also creates a practical concern for our study. Even though 4 If external auditors have technical knowledge on loss reserves (or can obtain it by walking across the hall to an actuary in their firm), then it will bias our results away from identifying whether the technical capability of external monitors matters. Thus, to the extent we find evidence that technical capability is important, it will be statistically robust. 2

5 we have P/L insurer data for the years 1989 to 2009, we only have information on the identities of the actuaries and auditors for 2005 to 2009, making it impossible to use these definitions in our study. We extend the literature and develop a new measure of reserve error based on the stochastic loss reserving models promoted in the actuarial science literature (see e.g., Taylor, 2000; Wuthrich and Mertz, 2008; and Kaas, Goovaerts, Dhaene, and Denuit, 2008). The technique uses all the available information about a P/L insurer s loss reserve but only requires two years of data, years t-1 and t (e.g., a 2005 reserve error is calculated using 2004 and 2005 data), allowing us to study all five years of the actuary and auditor data. The shorter time-window also means there is less of a chance that intervening events will bias our measure. Moreover, the stochastic reserve model has a forecast error that allows us to control for the relative uncertainty in each firm s reserve estimate. Another advantage of our approach is that it is the state of the art in the technical practice of loss reserving and it is related to how insurers model and assess the adequacy of their reserves. Plus, it is available in real time and managers can and do make decisions using models like the one we use. Other studies also examine the role of external monitor quality in the P/L insurance industry. Petroni and Beasley (1996) study the role of auditor size and find no systematic differences in P/L insurer claim loss reserve estimation accuracy between Big Eight and non-big Eight audit firms. Kelly, Kleffner and Li (2012) investigate whether actuary independence reduces managerial discretion in a sample of Canadian insurers. They find no evidence of systematic differences between in-house and consultant actuaries. Using a sample of 465 P/L insurers in 1993, Gaver and Paterson (2001) investigate the interaction of actuaries and auditors. They find less under-reserving by weak insurers when the auditor and actuary are both affiliated with a Big Six accounting firm, but no significant reduction when the auditor is affiliated with a Big Six accounting firm but the actuary 3

6 is not. They conclude that the quality of auditors is diminished when the audit firm relies on third party actuaries to evaluate the loss reserve estimates of struggling insurance clients. Our study extends the existing literature in a number of dimensions. First, using a sample of 5,498 firm-years from 2005 to 2009 we separately investigate the role of actuaries and auditors. This allows us to examine whether both components of external monitor quality, technical knowledge and independence, reduce managerial discretion. 6 We find that larger actuaries reduce the extent of managerial discretion, suggesting that more independent external monitors limit managerial discretion. However, similar to Petroni and Beasley (1996), we find no evidence that larger auditors reduce managerial discretion. The results suggest that technical knowledge and independence are both important determinants of external monitor quality. However, independence only matters if it is coupled with technical knowledge. Second, we are the first study to investigate whether there are systematic differences in the estimation of reserves by individual actuarial and audit firms. The annual statements of P/L insurers report the identity of the certifying actuary and auditor and the firm they are employed by for each insurer from 2005 to We use this information to construct actuarial firm-insurer and auditorinsurer matched panel data sets, which allow us to track actuarial and audit firms across different insurers in a given year and over time, enabling us to determine the extent reserve errors are linked to actuarial and audit firms above and beyond firm, group, and year effects. Our results show actuarial firm fixed effects are an empirically and economically important determinant of reserve estimates. The inclusion of actuarial firm fixed effects increases the adjusted R-squared. Moreover, the proportion of actuarial firm fixed effects that are statistically related to insurer reserve error is greater than the proportion of firm and group fixed effects. This evidence indicates that actuaries 6 We also extend the literature by accounting for the possibility that insurers and actuaries (auditors) may not match randomly. Actuaries (auditors) may choose to work for higher quality insurers out of reputation concerns. Moreover, insurers may choose to work with high quality actuaries (auditors) out of reputation concerns. Thus, there is potential endogeneity in the matching of insurers and actuaries (auditors). We, however, find no evidence of endogeneity. 4

7 play an economically and statistically important role in influencing insurer reserve estimates. We, however, do not find this to be true for audit firms. These results demonstrate the importance of accounting for the technical capability of an external monitor. Finally, we investigate the role of external monitor quality, while controlling for all of the hypothesized rationales for why insurers might manage loss reserves (Grace and Leverty, 2012). Similar to Beaver, et al. (2003), we find evidence that firms manage earnings with their reserve estimates. Consistent with Grace and Leverty (2010), we see that the managers of insurers subject to stringent rate regulation bias their reserves upward to a greater extent than other insurers. We also find evidence of managerial discretion for tax purposes (see e.g., Grace, 1990; Gaver and Paterson, 1999, 2000). Prior studies also hypothesize that weak insurers under-reserve to a greater extent than healthy firms (Petroni, 1992; Harrington and Danzon, 1994; Petroni and Beasley, 1996; Penalva, 1998; Petroni, Ryan, and Wahlen, 2000; Gaver and Paterson, 2004). We do not find evidence that weak insurers under-reserve more than other firms. This suggests that the finding of under-reserving with the traditional reserve error measures (which requires a five year data lag to calculate) may not be related to managerial manipulation to avoid regulatory scrutiny, but rather that weak firms have less resources to devote to reserves. This is an important distinction because it has implications for causality. Low reserve levels are frequently cited as one of the major causes of insurer failure (A.M. Best, 2004). Our results indicate under-reserving may only be a symptom of weakness and not the cause of failure. The remainder of the paper proceeds as follows. Section II provides background information on reserve estimation, including a discussion of the traditional definitions of reserve error and our definition of reserve error. Section III describes our data. Section IV discusses our empirical strategy and describes the results. Section V concludes. 5

8 II. LOSS RESERVES Loss reserves are the largest liability on a P/L insurer s balance sheet. The establishment of loss reserves ordinarily begins with the collection of information about an insurer s loss experience as well as information about the industry s loss experience (through entities like Insurance Services Organization). Once this information has been compiled, an insurer s actuaries generate predictions about future loss payments and expenses. Actuaries usually recommend a range and then management chooses the actual loss reserve levels to be reported. Estimation of claims is difficult as not all claims for current period losses are filed by the balance sheet date. Moreover, even when claims are filed in the current period, the ultimate settlement date is often delayed several years. Estimates based solely on past claims may not yield accurate predictions of future claims, and thus reserves are likely to be revised as new information about claims arises over time. Given the uncertainties involved in estimating loss reserves it is conceivable that reserve errors result from a failure to account for all the available information. Developments in litigation as well as costs can change significantly and give the perception that managers are cooking the books when, in fact, different expectations of future loss development result from new information. It is also possible that reserve errors result from managers exercising discretion. Under statutory accounting principles (SAP) the loss reserve is the firm s estimated liability for all losses that occurred prior to the balance sheet date. The unique aspect of the insurer loss reserve is that firms must disclose the gradual settlement of claims over time and record all revisions of the loss reserve estimate. Revisions, known as development, provide an indication of whether the previously reported amount was under- or over-stated. Schedule P of the National Association of Insurance Commissioner s Annual Statement records the insurer s description of their accident year loss development over a ten-year period. All U.S. P/L insurers must compile Schedule P as part of their regulatory annual filings. 6

9 Table 1 shows an insurer s Schedule P - Parts 2 and 3 for year The data in the table are excerpted from the Statutory Annual Statement of State Farm Mutual Automobile Insurance Company (henceforth State Farm). Schedule P Part 2 reports losses estimated in the year incurred as well as subsequent adjustments. It documents how insurers provide extensive disclosures about loss estimates over time. In particular, it shows incurred losses by the year in which the losses were incurred, the accident year (rows 1 thru 11), and the evaluation date, the calendar year (columns 1 thru 11). For instance, in calendar year 2002, State Farm estimated that $23,515 million (M) of losses occurred during the accident year 2002 (row 6, column 6, in bold and italics). This estimate of 2002 accident year losses was revised downward to $22,275 M by calendar year 2007 (row 6, column 11, in bold and italics). Schedule P Part 3 reports cumulative losses paid at the end of a year. It shows cumulative losses paid by the accident year (rows 1 thru 11) and the evaluation date, the calendar year (columns 1 thru 11). For example, in calendar year 2002, State Farm paid $14,826 M for losses associated with accidents that occurred in 2002 (row 6, column 6, in bold and italics). Of the estimated $23,515 M in losses that occurred during the accident year 2002, percent are paid in The cumulative amount of losses paid for accident year 2002 increased to $21,950 M in calendar year 2007 (row 6, column 11, in bold and italics). As a result, given the 2007 revised estimate of $22,275 M in losses that occurred during the accident year 2002, 98.5 percent of estimated losses are paid by In the literature on managerial discretion in the P/L insurance industry there are two methods for calculating reserve error. The first reserve error, which we call the Weiss error (Weiss, 1985), is the difference between total incurred loss for firm i as of a given calendar year t and cumulative developed losses paid in a future calendar year t+j. Wit, = Incurred Lossesit, Developed Losses Paid it, + j (1) 7

10 Incurred losses are losses that are known to the insurer plus those that are estimated to have occurred. Developed losses paid are those losses actually paid. The subscript t denotes the end of the year valuation of the loss and the subscript j denotes some future year s accumulated loss payment. The Weiss error is positive (negative) if the originally reported reserve is overestimated (underestimated) relative to what is eventually paid on these losses. This definition of reserve error is also used in other studies (see e.g. Grace, 1990; Diacon et al., 2004; Browne et al., 2008). The second reserve error, which we call the Petroni error (Petroni, 1992), is the difference between total incurred losses for firm i as of a given calendar year t and a revised estimate of incurred losses in calendar year t+j: Pit, = Incurred Lossesit, Incurred Losses it, + j (2) The Petroni error is positive if the originally reported reserve is overestimated. This error measure has also been used in other studies (see e.g., Kazenski, et al., 1992; Eckles and Halek, 2010). We further describe these two reserve errors using the data in Table 1. A majority of the extant literature (e.g., Petroni 1992; Beaver et al. 2003; Gaver and Paterson 2004, Grace and Leverty 2012), examine reserve error five years prior to resolution, so j is five. The Petroni error is the difference between total losses incurred in a given calendar year and a revised estimate of total losses incurred five calendar years in the future (Beaver et al., 2003; Gaver and Paterson, 2004). It relies upon information reported in Schedule P Part 2. The estimate of total incurred losses for a given calendar year is the sum of the losses in the column of that year. For State Farm, at the end of 2002, estimated losses for all years up to and including 2002 totaled $105,560 M (the sum of the italicized values in Part 2, column ). By the end of 2007, the estimate for the same accident years had been reduced to $103,820 M (the sum of the italicized values in Part 2, column ). Initial over- (under) reserving yields a positive (negative) reserve error since the revised estimate of total losses incurred five years in the future is greater (less) than the initial estimate. Accordingly, the 8

11 Petroni error for State Farm in 2002 is $1,740 M ($105,560 M minus $103,820 M), indicating that State Farm over-estimated their reserves by $1,740 M. The strength of the Petroni error is that it is not dependent on the development of losses, i.e., when losses are eventually paid. The weakness of the Petroni error is it is affected by possible reserve manipulation in the initial estimate (in year t) and in the revised estimate (in year t+5). Nevertheless, the potential for error in the revised estimate is considerably less than in the initial estimate. Insurers cannot manipulate paid claims and more claims are paid each year. The Weiss error is the difference between total losses incurred in a given calendar year and total cumulative losses paid five calendar years in the future (Weiss, 1992). It relies upon information reported in Parts 2 and 3 of Schedule P. The estimate of total incurred losses for a given calendar year is the same as in the Petroni error. For State Farm, at the end of 2002, estimated losses for all years up to and including 2002 totaled $105,560 M (the sum of the italicized values in Part 2, column ). Total cumulative losses paid five calendar years in the future is the sum of cumulative losses paid in the column of that year. By the end of 2007, total losses paid by State Farm are $102,618 M (the sum of the italicized values in Part 3, column ). Accordingly, State Farm s Weiss error for 2002 is $2,942 M ($105,560 M minus $102,618 M), indicating that State Farm overestimated their reserves by $2,942 M. The strength of the Weiss error is that it uses losses paid to determine the error. Error is determined by comparing the initial estimate of reserves with claims paid. The weakness of the Weiss error is that these are not ultimate claims paid (where 100% of claims are paid), but rather claims paid 5-years later. Few lines of insurance have 100% of losses paid within 5 years. Nelson (2000) reports the average time to claims development exhaustion (i.e., when all outstanding claims are paid) is 7-years for homeowners insurance and 20-years for medical malpractice insurance. If the time between when a claim is declared and the payment is long (i.e., greater than five years), then the 9

12 Weiss error will overstate the reserve error. A weakness of both reserve errors is that there is a great deal of loss development information that is not used. Another weakness is that they require 5-years of data. We propose a full information definition of reserve error that accounts for all the available information in the loss reserve schedule (Schedule P Part 2) to make a rationale forecast about the ultimate reserve using stochastic reserving estimation techniques. It requires only two years of data. The assumption behind the full information reserve error is that firms are using best actuarial practices to develop their estimates of loss reserves. The extent to which they deviate from best practices is measured as reserve error. We now describe the details behind estimating the full information reserve error. First, we transform the information in Schedule P Part 2 into a loss development triangle. A loss development triangle reports estimated losses by the year they were incurred (i.e., the accident year), as well as subsequent adjustments in these estimates over time (development years). It documents how insurers update information about losses over time. Second, we use this information to estimate a stochastic loss reserve model. In particular, we estimate the following model for each firm and year: (3) x ( ft) = α + λr+ δ C+ ε ij i j ij i= 2 j= 2 where x ij (ft) are the cells from the loss development triangle for a given firm (f) in a given year (t); i indexes the rows in the loss development triangle; j indexes the columns; α is an intercept term, λi andδ are the coefficients on the indices for the rows (R) and columns (C), respectively. We estimate j (3) for each firm and year for 1989 to

13 Third, we use the fitted values from (3) to forecast next year s loss development, ˆx ij(ct)for year t+1. We then compare ˆx ij (ct)for year t+1 to true x(ct)in ij year t+1: Δ i = ˆx ij (t+ 1) x(t ij + 1). Finally, we sum the Δ i to obtain the full information reserve error, Θ (ct) = Δ 10 i(ct). i= 2 We further describe the full information reserve error using the data in Table 2, which shows the 2001 Schedule P Part 2 for State Farm. To calculate the 2002 reserve error using the Petroni or Weiss reserve error annual statement data from 2007 is used. In contrast, to calculate the 2002 reserve error using the full information reserve error, the 2001 annual statement is used. We transform the information in the Schedule P Part 2 (shown in Table 2) into a loss development triangle, which is shown in Table 3. Each cell in Table 2 corresponds to an x ij, where i indexes the columns (the loss development) and j indexes the rows (the accident year). Using these values, we estimate equation (3) for State Farm in The results are reported in Table 4. We use these parameter estimates to forecast next year s loss development. The predicted values, ˆx ij (ct), are highlighted in yellow in Panel A of Table 5. Panel B shows State Farm s actual loss development in 2002, highlighted in green. The full information reserve error,θ, is $467 M ($161,596 M (the sum of the green, double bordered cells) minus $161,129 M (the sum of the yellow, dotted-lined bordered cells)), indicating that State Farm over-estimated their reserves in 2002 by $467 M. Table 6 shows the descriptive statistics of the full information reserve error scaled by total assets for 1990 to It also shows the descriptive statistics of the explanatory power (i.e. the R-squared) of the stochastic loss reserving model (equation 3). In contrast to prior studies that use the Weiss and Petroni reserve errors, we find the average insurer slightly under-estimates reserves. The mean scaled full information reserve error is The average firm in this sample has $604 million in total assets. Thus, the average firm under-estimates reserves by approximately $624,000. The explanatory power of the stochastic loss reserving model is high with the average R 2 over 95 percent. 11

14 III. DATA We use data from the NAIC annual statement database, which are prepared using Statutory Accounting Principles (SAP). 7 Our reported findings are based on affiliated and unaffiliated single insurers. We also examine a sample of group and unaffiliated single insurers. The results for this sample are qualitatively similar to the reported results. Data for Schedule P of the NAIC Annual Statement is collected for the years 1989 to We estimate the stochastic loss reserve model using this sample, which gives us the full information reserve error for the years 1990 to Our main objective is to investigate the role of external monitor quality on the accuracy of P/L insurer reserve estimates to determine whether there are systematic differences between high and low quality firms. The NAIC annual statements report the identity of the certifying actuary and auditor and the firm they are employed by for each insurer from 2005 to We use this information to calculate actuarial and audit firm market share and to construct actuary-insurer and auditor-insurer matched panel data sets, which allows us to track actuaries and auditors across different insurers over time. Thus, even though we estimate the full information reserve error for 1990 to 2009, our main analysis is for the 2005 to 2009 period. In all of our tests we control for the other hypotheses for why insurers manage reserves that are investigated in the literature (Grace and Leverty, 2012). These hypotheses include: financial weakness (e.g., Petroni, 1992; Harrington and Danzon, 1994; Gaver and Paterson, 2004), tax (e.g, Grace, 1990), income smoothing (e.g., Weiss, 1985; Beaver, et al., 2003), and rate regulation incentives (e.g., Nelson, 2000; Grace and Leverty, 2010). Studies hypothesize that weak insurers under-reserve to a greater extent than stronger firms (see e.g., Petroni, 1992; Harrington and Danzon, 1994; Petroni and Beasley, 1996, Penalva, 1998, Petroni, 7 A majority of the prior research on P/L insurer reserve estimates relies on statutory accounting data (e.g., Weiss, 1985; Grace, 1990; Petroni, 1992; Penalva, 1998; Gaver and Paterson, 1999; Nelson, 2000; Beaver, et al., 2003; and Gaver and Paterson, 2004). 12

15 et al., 2000, Gaver and Paterson, 2000, 2004). We measure financial weakness similar to Grace and Leverty (2012) and estimate each firm s probability of failure. In particular, we follow the P/L insurer insolvency literature (e.g., Cummins et al., 1999) and estimate the following model: I = α + β X + δ X + ε (4) rst fc it it it it where for insurer i and data year t: Iit is the unobserved propensity to fail, α is an intercept term, rst X it is a vector of time-varying regulatory solvency tools, fc X it is a vector of time-varying firm characteristics, andε it is an error term. We estimate equation (4) using a discrete-time hazard model (Shumway, 2001). The dependent variable is equal to one if the insurer is insolvent in either year t+1 or t+2. 8 The firm characteristics are the natural logarithm of total assets and a mutual indicator. The regulatory solvency tools are fourteen balance sheet and income statement ratios in the FAST system. 9 We use all insurers for which we have data to calculate the fourteen FAST ratios. The only insurers we omit are those who do not have data available in at least one of the two years prior to their year of insolvency. In an effort to include as many insolvent observations in the analysis, we include insurers who report data two years prior to their insolvency if they do not report in the year prior to their insolvency. We identify 191 P/L insurers that fail between 1990 and The results are shown in Panel A of Appendix A. The explanatory power of the model is reasonable. For parsimony we do not discuss the hazard model results in detail. We do note, however, that our results are consistent with those reported by other studies (e.g., Doherty et al., 8 We define the year of insolvency as the year of the first formal regulatory action taken against a troubled insurer (Grace et al., 1998). In prior studies (e.g., Leverty and Grace, 2012), insurers are classified as subject to formal regulatory proceedings for conservation of assets, rehabilitation, receivership, or liquidation. In contrast, we classify insurers as subject to formal regulatory proceedings for liquidations only. Our definition, therefore, is more conservative as we focus only on insolvencies rather than just troubled companies and as a result our sample includes a smaller number of insolvencies than found in previous studies. The source for this information is the NAIC s Global Receivership Information Database. 9 Grace et al. (1998) list 25 FAST ratios. There is no guarantee the NAIC still uses these same ratios. Using the full set of FAST ratios considerably reduces the number of observations without significantly improving the fit. 13

16 2012; Grace and Leverty, 2010). Panel B of Appendix A reports the estimated one-year probabilities of failure for healthy and insolvent insurers. The mean (median) probability of insolvency for the solvent firms is 0.47 (0.12) percent and it is (3.92) percent for the insolvent insurers in the year before they fail. The average (median) estimated one-year probability of failure for firms that fail in the next year is over 24 (33) times larger than the average (median) probability for solvent firms. Overall, the model appears to adequately distinguish weak insurers from healthy insurers. The hypothesis for taxes is that over-estimating reserves provides an opportunity for a firm to shelter earnings, ceteris paribus (see e.g., Grace, 1990). By overestimating future losses attributable to current premiums, the insurer increases its reserves and incurred losses and reduces its current tax liability. The insurer does not eliminate any taxes with reserve errors, but it does postpone the tax payments until future periods when ultimate claim costs are realized. Similar to a majority of the prior literature (e.g., Petroni, 1992; Penalva, 1998; Nelson, 2000), we measure the tax incentive using an indicator variable. The tax indicator takes the value of one if the insurer has a high tax rate, and zero otherwise. This proxy is based on the tax-paying status of the firm (Scholes et al., 1990). Firms are assumed to have a low tax rate if they have an NOL carryforward, which is identified by the insurer not currently paying any taxes or receiving a refund of prior-year taxes. According to the income smoothing hypothesis, a firm may manage earnings to avoid reporting accounting losses (Weiss, 1985; Grace, 1990; Beaver, et al., 2003). Beaver, et al. (2003) find that insurers manage reserves across the distribution of earnings. In particular, they document that firms with small positive earnings report the most income-increasing reserve levels, while firms with the highest earnings report the most income-decreasing reserves. We also investigate the incentive to smooth earnings along the earnings distribution. Similar to Beaver et al. (2003), we create indicator variables that identify the position of an insurer in the earnings distribution (small profit, profit, small loss, and loss) in each year of our sample. Small Profit identifies insurers with reported 14

17 earnings in the first 10 percent of the distribution to the right of zero. Small Loss classifies insurers with earnings in the first 10 percent of the distribution to the left of zero, i.e., the top 10 percent of the negative earnings distribution. Profit and Loss identify insurers with earnings in the top 90 percent of the positive and negative earnings distribution, respectively. Nelson (2000) hypothesizes that insurers operating in strict rate regulatory environments will under-state reserves to convince regulators they can charge lower rates. Since the evidence on rate regulation in the insurance industry suggests regulation has often held rates below the economic cost of writing business since the mid-1970s (e.g., Cummins and Harrington, 1987; Harrington, 2002; Cummins, et al., 2001; Grace and Phillips, 2008; Weiss, Tennyson, and Regan, 2010), Grace and Leverty (2010) hypothesize the opposite. If regulation suppresses rates below the economic cost of writing business, then stringent rate regulation will create an incentive for managers to bias reserve estimates upward in an attempt to reduce the effect of rate suppression. To examine these two hypotheses we need a measure of rate regulation. Because rate regulation varies by state and by line of business and few insurers operate only in states and lines with strict rate regulation, we create a rate regulation index variable (% Reg) that weights the stringency of rate regulation. Using the description of the lines of business subject to rate regulation in the NAIC s Compendium of State Laws on Insurance Topics (National Association of Insurance Commissioners, various years), we calculate for each firm the percent of total premiums written subject to these regulations. The rate regulation variable for firm i, in year t, in line of business l, operating in states indexed by s is given by: % Reg = istl,,, Premiums Written istl,,, istl * Stringent Reg Law ( PremiumsWritten ) istl stl (5) 15

18 where Stringent Reg Law is an indicator variable set equal to one if state s has a stringent rate regulation law that applies to line l in year t, and zero otherwise. Similar to Harrington (2002) a state is classified as having a stringent rate regulation law if it had either state-made rates, a prior approval law, or a file-and-use law that required prior approval of deviations from rates filed by a rate advisory organization. A state is classified as not having a stringent rate regulation law if it permitted file-and-use, use-and-file, filing only, or had flex rating with a large flex band. Since states maintain their rate regulation laws for extended periods of time, there is little concern that reserve decisions arise endogenously with state rate regulation. Our main objective is to determine the extent to which the independence and technical knowledge of external monitors limits managerial discretion. The independence of the monitor determines the probability that it discloses a detected problem. The bigger the external monitor the less dependent it is on retaining any one client and the more likely it will be to report their findings (DeAngelo, 1981). Therefore, the size of the actuarial or audit firm is a good measure of the independence of the external monitor. We measure the size of actuarial and audit firms using each firm s market share in the P/L insurance industry by year (Actuary M.S. and Auditor M.S.). The technical capability of the monitor affects the probability that it detects a problem. Actuaries receive extensive technical training in evaluating P/L insurer loss reserves, while auditors do not. Therefore, we can evaluate the extent to which technical knowledge limits managerial discretion by measuring the differential impact of auditors and actuaries on reserve accuracy. Table 7 shows the top ten auditing and actuarial firms by market share for the period 2005 to PricewaterhouseCoopers has the greatest auditor and actuarial market share at 21 and 8 percent, respectively. After the top four firms, the market share of auditing firms drops precipitously. Accordingly, we also use a dichotomous measure of whether or not the firm is a member of one of the top four actuarial or audit firms by market share (Top Four Actuary and Top 16

19 Four Auditor). A dichotomous measure of external monitor size (e.g. the Big Eight, Six, Five or Four), is the norm in the accounting literature (see e.g., Francis and Wilson, 1988). To be included in the sample, firms must have positive reserves, losses incurred, and direct premiums written. To allow comparability with other studies (e.g., Petroni, 1992; Beaver, et al., 2003), we implement a similar firm screen. 10 All regressions are performed using the full and the screened samples. The results of the two samples are qualitatively identical. For ease of exposition, we tabulate and discuss the results of only the screened sample. Table 8 shows the descriptive statistics of our main sample for the years 2005 to The final sample includes 5,498 firm-years. The mean loss reserve is under-estimated by 1.3 percent of total assets. The average actuarial firm has 1.5 percent of the market and the average audit firm 8.6 percent. Twenty-six percent of the sample uses a top four actuarial firm and 60.6 percent use a top four audit firm. Turning to the other hypothesized incentives, 47.6 percent of the sample is identified as having a high tax rate (Tax Indicator). Profit and Loss firms comprise 77 and 15 percent of the sample, respectively. Roughly 7 percent of firms report earnings in the first 10 percent of the earnings distribution to the right of zero (Small Profit), while 1.4 percent report earnings in the first 10 percent to the left of zero (Small Loss). The average firm has a probability of failure (PrFail) of 0.1 percent and 18.3 percent of business subject to stringent rate regulation (% Reg). The research design for our multivariate analyses includes measures for both discretion and nondiscretion. This methodology mitigates biased coefficients and the mis-estimation of the reserve components in the case that the determinants of non-discretion and discretion are interrelated (Petroni, et al., 2000). The set of non-discretionary variables reflects the firm s economic condition, 10 Firms that have extreme errors in their loss reserves (observations with an original loss reserve estimate that differs from the revised estimated by greater than 50 percent in absolute value) are eliminated from the full sample. In addition, firms which cede all premiums to reinsurers and/or write greater than twenty-five percent of their premiums in workers compensation, accident and health, surety, credit, and/or reinsurance are also excluded. 17

20 organizational structure, and strategic choices. These economic factors may also impact the incentives and ability of management to exercise discretion over reserves. Studies indicate that reserve mis-estimation results from managerial mistakes related to the difficulty of determining an insurer s liabilities (e.g., Weiss, 1985; Grace, 1990). The difficultly is related to the type(s) of business written, the incentives inherent in different ownership structures (e.g., Lamm-Tennant and Starks, 1993; Mayers and Smith, 2001) and distribution systems (e.g, Berger, et al., 1997; Regan and Tennyson, 2001), and product and geographical diversification (e.g, Comment and Jarrell, 1995; Berger and Ofek, 1995; Berger, et al., 2000). Failure to include these variables in our econometric models may result in omitted variables bias. We identify organizational structure using three variables. The first two variables are indicators of whether the firm is a mutual or stock insurer (He and Sommer, 2010; Mayers and Smith, 2010). We categorize firms according to the ownership structure of their ultimate owner (Mayers and Smith, 1994). Mutual insurers make up a quarter of our sample; the other three-quarters are stock. The third variable is an indicator of whether the firm is part of a group of insurers under common ownership (see e.g., Powell, Sommer, and Eckles, 2008). Roughly seventy-four percent of the firms are associated with a group. Prior research finds insurers underwriting long-tailed lines of business have more discretion over their reserves (e.g., Petroni and Beasley, 1996; Beaver et al., 2003). Accordingly, we account for the percent of losses incurred in long-tail lines of insurance (as defined by Phillips et al., 1998). The average firm has 68.4 percent of its losses in long-tail lines and 33.4 percent in short-tail lines. We also account for differences in product and geographical diversification. Product diversification is measured using the product line Herfindahl Index, which is calculated as the sum of the squared percentage of premiums earned in each of the 26 lines of P/L insurance. The average firm has a product line Herfindahl of 0.418, which is equivalent to approximately 2 lines of business. 18

21 Geographical diversification is gauged using the geographical Herfindahl Index, the sum of the squared percentage of business written in each of the 50 states and the District of Columbia. The mean geographical Herfindahl is 0.546, indicating the average firm operates in roughly two states. We also use other control variables. Harrington and Danzon (1994) find that firms with weak safety (solvency) incentives under-report reserves to increase firm growth. They also discover that these firms attempt to hide their under-reserving with reinsurance. We account for growth using the one-year percent increase in net premiums written and reinsurance usage with the percent of gross premiums written ceded to reinsurers. The mean firm has a growth rate of 2.9 percent and cedes 30 percent of its business to reinsurers. We also account for firm size (total assets). The mean firm has approximately $ million in total assets. Finally, we control for whether the firm directly sells its products. Roughly nine percent of the firms distribute their products directly. IV. EMPIRICAL STRATEGY AND RESULTS A. Magnitude of Full Information Reserve Error To investigate the magnitude of reserve error and determine how it relates to auditor and actuary independence while controlling for other hypothesized incentives and non-discretionary components, we estimate the following model: y = α + δ + βx + λz + e (6) it t i it it it where i indexes firms, t indexes time periods, yit is the full information reserve error scaled by total assets, αt is year fixed effects; δ i is firm fixed effects; X it is the institutional and firm characteristic variables, and Zit is the hypothesized incentives. The standard errors are adjusted for firm clustering. A potential concern for our analysis is the endogeneity of actuarial (audit) firm independence. In particular, actuaries (auditors) may choose to work for higher quality insurers out of reputation 19

22 concerns. Moreover, insurers may choose to work with more independent actuaries (auditors) out of reputation concerns. Thus, there is potential endogeneity in the matching of insurers and actuaries (auditors). Accordingly, we undertake a Hausman test of endogeneity. We instrument Top Four Actuary and Top Four Auditor with the insurer s A.M. Best rating, as this is a common measure of an insurer s quality, and return on assets. In a separate analysis, we also instrument Top Four Actuary and Top Four Auditor with their lagged values. Inclusion in the top four firms is highly persistent over time, making the previous year s inclusion in the group a powerful predictor of the current year s inclusion. Hausman tests fail to reject the null hypothesis that Top Four Actuary and Top Four Auditor are exogenous. In our multivariate analysis of the magnitude of reserve error, we examine three samples: (1) the full sample of firms; (2) the subsample of over-reserving firms; and (3) the subsample of underreserving firms. We split the sample into over- and under-reserving firms to determine whether larger quality audit and actuarial firms are associated with more accurate reserves, i.e., less overreserving and under-reserving. 11 To allow the regression results to be interpreted in this way, the dependent variable in the under-reserving subsample is the absolute value of the full information reserve error scaled by total assets. A positive coefficient indicates greater under-reserving and a negative coefficient less under-reserving. For each sample, we estimate two specifications. The first uses the continuous measures of external monitor independence (Actuary M.S. and Auditor M.S.) and the second uses the dichotomous measures (Top Four Actuary and Top Four Auditor). 11 An alternative to splitting the sample into over- and under-reserving samples is to use the full sample and interact the independent variables with over- and under-reserving indicator variables: abs( yit ) = αt + δi + Over *( βxit + λzit ) + Under *( βxit + λzit ) + e it A model of this form, however, is expected to be a misspecification of the over/under error generating process to the degree that over-reserving errors are generated by a different function than under-reserving errors. Accordingly, by estimating this alternative model we are constraining the result. The preference of equation (6) over this alternative specification is verified using a Wald Test. The null hypothesis that the restricted model dominates the unconstrained model (6) is rejected at the 0.01 level. 20

23 Our estimation results are shown in Table 9. In the full sample, there is no observed relationship between actuary size and reserve error. The over- and under-reserving subsamples, however, show that larger actuaries are associated with more accurate reserves. In the over-reserving sample, larger actuaries are significantly related to less over-reserving, i.e., more precise reserve estimates. The coefficient on Actuary M.S. is (p<0.01) (regression (3)). In the regression sample, the standard deviation of actuary market share is and the mean of reserve error is Thus, a one standard deviation increase in actuary market share corresponds to 58.6 percent reduction in overreserving ((0.025*-1.50)/0.064)). The coefficient on Top Four Actuary is (p<0.05) (regression (4)). A top four actuary is associated with a 53.1 percent reduction in over-reserving (-0.034/0.064). In monetary terms, a top four actuary reduces over-reserving by $3.9 M to $4.3 M for the average over-reserving firm. In the under-reserving sample, larger actuaries are significantly related to less under-reserving. The coefficient on Actuary M.S. is (p<0.01) (regression (5)). The standard deviation of actuary market share in the regression sample is and the mean reserve error is A one standard deviation increase in actuary market share corresponds to 22.6 percent reduction in under-reserving ((0.024*-0.442)/abs(-0.047)). The coefficient on Top Four Actuary is (p<0.01) (regression (6)), indicating that a top four actuary reduces the magnitude of under-reserving by 36.2 percent (-0.017/abs(-0.047)). For the mean firm, a top four actuary reduces under-reserving by $1.9 - $3.0 M. In untabulated regressions we examine whether switching to or from a top four actuary is associated with less managerial discretion. We find that firms that switch to a top four actuary report less over-reserving error and less under-reserving error, while the firms that switch away from a top four actuary report more reserve error. The results suggest that more independent actuaries limit managerial discretion. 21