The document summarizes a research paper that examines hypotheses for property-liability insurer reserve errors. It analyzes two measurements of reserve error and tests four main hypotheses from the literature - taxes, income smoothing, rate regulation incentives, and financial weakness. The results found that the choice of reserve error measurement impacted the findings. There was little evidence for income smoothing. Evidence supported overreserving for tax purposes under one measurement but not the other. Rate regulation incentives were supported. Financial weakness was linked to underreserving rather than deception.

1. Property–Liability Insurer Reserve
Error: Motive, Manipulation, Or Mistake
Paper by Martin F. Grace, J. Tyler Leverty
Presentation by Michael-Paul James
1

2. Table of contents
Introduction Loss Reserves
Results
Empirical Methodology And Results
Data
01 02
04 05
Hypothesis
Discussion of four main hypotheses in
the literature
Conclusion
03
06
2

3. Introduction
01
story, questions, context, issues
3

4. Studying Managerial Discretion
● Property liability insurance industry ideal candidate for studying
managerial discretion
○ Homogeneous sample of firms
■ Decomposition of discretionary and nondiscretionary
○ Disclosure requirements to material accrual (loss reserves)
■ Direct measure of managerial bias
● Existing research is wanting due to lack of comprehensive control
○ Excluding alternative motivations impact econometric inference
○ Excluding controls for important nondiscretionary components.
● Measuring reserve error (RE)
○ Weiss RE compares initial estimate to actual claims 5 years later.
○ Kazenski, Feldhaus, and Schneider (KFS) RE compares initial
estimate to revised estimate 5 years later (Post 1992)
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Michael-Paul James

5. Research Objective
● Compare results from the two measurements of Reserve Error
● Examine the hypothesized rationales for loss reserve management
○ Financial weakness
○ Income Smoothing
○ Tax Deferment
○ Rate regulation incentives
● Previous literature failed to jointly test these hypotheses.
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Michael-Paul James

6. Research Findings
● Choice of reserve error measurement fundamentally changed results
● Income smoothing
○ Little evidence found for income smoothing
● Tax Deferment
○ Under Weiss error, no evidence of overreserving for tax purposes
○ Under KFS error, strong support for overreserving for tax purposes
● Rate regulation incentives
○ Evidence of overreserving in high rate regulation areas
● Financial weakness
○ Evidence of underreserving by weak insurers
○ Authors challenge the blind acceptance of IRIS ratio violations.
○ Authors use probability of failure using insolvency model- CGP 1999
○ Inference change: underreserve due to weakness not deception
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Michael-Paul James

7. Loss Reserves
02
7

8. Mechanics
● Loss reserves are largest liability on the balance sheet
● Process
○ Collect information about firm and industry losses
○ Actuaries predict future losses within a range
■ Settlement delays and information changes require revisions.
■ Not accounting for all available information lead to RE
○ Management chooses a precise quantity reported
■ Frequent changes (development) to reserve estimation tend to
cause concern
■ Frequent changes result from new information
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9. Table
1:
NAIC
P/L
Schedule
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Michael-Paul James
TABLE 1: NAIC Property-Liability Statement Schedule P—Parts 2
Excerpt from the 1998 Annual Statement of SAFECO Insurance Company of America
NAIC Property-Liability Annual Statement: Schedule P—Part 2—Summary
Incurred Losses and Allocated Expenses Reported at Year End ($000 Omitted)
1 2 3 4 5 6 7 8 9 10 11
Accident Year 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998
1 Prior 613807 619,111 626,645 633,669 667,040 680,480 709,191 726,018 740,511 737,793
2 1989 859996 861,977 860,853 853,520 840,964 837,136 841,741 841,928 845,506 844,414
3 1990 927,152 919,939 916,973 900,421 893,866 889,022 892,222 890,531 887,493
4 1991 945,398 943,325 913,047 894,198 887,248 877,437 869,604 865,323
5 1992 916,327 890,524 869,005 849,558 840,140 833,768 822,648
6 1993 869,368 846,296 823,720 804,527 796,746 798,123
7 1994 892,188 874,558 848,851 843,170 831,341
8 1995 852,069 846,539 843,353 836,748
9 1996 875,159 898,167 891,808
10 1997 860,228 868,741
11 1998 938,478
Losses estimated in year incurred plus subsequent estimate adjustments as the claims are settled.

10. Table
1:
NAIC
P/L
Schedule
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Michael-Paul James
TABLE 1: NAIC Property-Liability Statement Schedule P—Part 3
Excerpt from the 1998 Annual Statement of SAFECO Insurance Company of America
NAIC Property-Liability Annual Statement: Schedule P—Part 3—Summary
Incurred Losses and Allocated Expenses Reported at Year End ($000 Omitted)
1 2 3 4 5 6 7 8 9 10 11
Accident Year 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998
1 Prior – 197,666 322,953 395,671 448,406 482,269 510,634 553,540 554,847 568,396
2 1989 364924 565,754 628,366 721,471 756,825 777,872 792,062 802,055 807,898 813,526
3 1990 395,581 611,339 709,163 771,301 804,031 825,585 843,122 853,545 857,431
4 1991 404,863 607,519 705,731 759,363 793,760 810,811 822,795 831,065
5 1992 390,671 587,541 680,319 730,513 754,882 772,928 783,622
6 1993 395,337 571,740 654,346 699,145 726,014 743,622
7 1994 417,342 609,674 698,049 746,226 773,467
8 1995 426,727 618,442 705,059 754,385
9 1996 462,398 680,138 767,068
10 1997 462,957 649,424
11 1998 518,433
Cumulative losses in year incurred plus subsequent losses as the claims are settled.

11. Table
1:
NAIC
P/L
Schedule
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Michael-Paul James
Percent change in cumulative losses as the claims are settled.
TABLE 1: NAIC Property-Liability Statement Schedule P—Parts 3: Percentage Change
Excerpt from the 1998 Annual Statement of SAFECO Insurance Company of America
NAIC Property-Liability Annual Statement: Schedule P—Part 3—Summary
Incurred Losses and Allocated Expenses Reported at Year End ($000 Omitted)
1 2 3 4 5 6 7 8 9 10 11
Accident Year 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998
1 Prior - - 63.38% 22.52% 13.33% 7.55% 5.88% 8.40% 0.24% 2.44%
2 1989 - 55.03% 11.07% 14.82% 4.90% 2.78% 1.82% 1.26% 0.73% 0.70%
3 1990 - 54.54% 16.00% 8.76% 4.24% 2.68% 2.12% 1.24% 0.46%
4 1991 - 50.06% 16.17% 7.60% 4.53% 2.15% 1.48% 1.01%
5 1992 - 50.39% 15.79% 7.38% 3.34% 2.39% 1.38%
6 1993 - 44.62% 14.45% 6.85% 3.84% 2.43%
7 1994 - 46.08% 14.50% 6.90% 3.65%
8 1995 - 44.93% 14.01% 7.00%
9 1996 - 47.09% 12.78%
10 1997 - 40.28%
11 1998 -

12. Error Measurement
● Weiss (W) RE compares initial estimate to actual claims 5 years later.
Wi,t
= Incurred Lossesi,t
- Developed Losses Paidi,t-j
● Kazenski, Feldhaus, and Schneider (KFS) RE compares initial estimate
to revised estimate 5 years later (Post 1992)
○ Strength: not dependent on loss development
○ Weakness: reserve manipulation in both measurements
KFSi,t
= Incurred Lossesi,t
- Incurred Lossesi,t-j
■ i: firm ■ t: year ■ t-j: future year
■ Incurred losses: losses known plus estimated losses
■ Developed Losses Paid: Losses actually paid
■ Positive if overestimated; negative if underestimated
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13. Table
2:
RE
Summary
Statistics
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Michael-Paul James
TABLE 2: Reserve Error Summary Statistics
Year Obs. Mean Std. Dev. Min. Max.
Panel A: Weiss Reserve Error
1989 806 0.057 0.123 −0.694 0.861
1990 778 0.065 0.13 −0.657 1.836
1991 828 0.071 0.142 −0.974 1.963
1992 849 0.086 0.141 −0.538 1.355
1993 864 0.084 0.145 −1.033 1.6
1994 855 0.083 0.156 −1.000 1.373
1995 899 0.085 0.141 −0.855 1.398
1996 894 0.083 0.133 −0.909 1.082
1997 887 0.076 0.115 −0.857 0.622
Total 7,660 0.077 0.137 −1.033 1.963
Panel B: KFS Reserve Error
1989 806 −0.011 0.11 −0.767 0.532
1990 778 −0.005 0.117 −1.091 0.414
1991 828 0.004 0.12 −1.068 0.533
1992 849 0.012 0.106 −0.759 0.889
1993 864 0.013 0.115 −1.046 0.498
1994 855 0.014 0.12 −1.241 0.587
1995 899 0.019 0.112 −0.913 0.401
1996 894 0.022 0.107 −0.921 0.433
1997 887 0.017 0.105 −0.966 0.373
Total 7,660 0.01 0.113 −1.241 0.889
Note. Weiss Reserve Error is
the difference between
current loss reserves and
claims paid 5 years later. KFS
Reserve Error is the
difference between the loss
reserve in the current period
and a revised estimate 5
years in the future. Both
reserve errors are scaled by
total assets. Positive reserve
errors indicate that the firm
initially overreserved, while
negative reserve errors
indicate underreserving.

14. Table
3:
Percentage
Losses
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Michael-Paul James
TABLE 3: Percentage of Total Losses Paid by the End of Each Accident Year-by-Year of Payment
% Paid 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 Mean Std Dev.
Year t0
40.60% 40.70% 40.70% 41.00% 40.70% 42.00% 41.20% 43.60% 43.40% 44.50% 44.80% 44.60% 44.40% 44.60% 42.60% 0.018
Year t1
64.00% 63.10% 63.20% 63.90% 63.20% 64.50% 64.30% 66.10% 66.40% 67.80% 68.20% 66.50% 67.10% 65.30% 0.018
Year t2
74.40% 74.20% 74.40% 75.10% 74.50% 75.50% 75.10% 76.70% 77.60% 78.60% 76.60% 77.10% 75.80% 0.015
Year t3
82.20% 82.10% 82.40% 83.30% 82.80% 83.60% 83.60% 84.70% 85.50% 84.10% 84.20% 83.50% 0.011
Year t4
87.50% 87.40% 87.60% 88.10% 87.90% 88.60% 88.70% 89.60% 88.50% 88.40% 88.20% 0.007
Year t5
91.00% 90.90% 90.80% 91.40% 91.30% 92.10% 92.10% 91.50% 91.30% 91.40% 0.005
Year t6
93.30% 93.10% 93.00% 93.60% 93.70% 94.40% 93.60% 93.20% 93.50% 0.004
Year t7
94.70% 94.70% 94.60% 95.20% 95.30% 94.80% 94.80% 94.90% 0.003
Year t8
95.80% 95.80% 95.80% 96.20% 95.90% 95.60% 95.90% 0.002
Year t9
96.70% 96.80% 96.70% 96.70% 96.70% 96.70% 0.001
Source: NAIC Annual Statements, 1989–2002. Year t0
is the current accident year. Year t1
to Year t9
is 1 to 9 years later.
● W error overestimates reserve error for the average firm.
○ Overestimation more severe for firms with long tail business.
● KFS error can be manipulated in both estimates but much more difficult to manipulate the revised estimate.

15. Hypotheses
03
Discussion of four main hypotheses in the literature
15

16. Taxes
● Tax hypothesis
○ Overestimating reserves allows firms to shelter earnings,
■ Greater the tax savings the higher the incentive
○ Two measurement methods
■ Measured with the following formula (Grace 1990):
Tax Shield = (Net Incomet
+ Estimated Reservet
)/ (Total Assett
)
■ Dummy variable for high tax rate firms
● Low tax rate if firm has a Net Operating Loss (NOL)
Carryforward
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Michael-Paul James

17. Income Smoothing (IS)
● Agency problems potentially lead to income smoothing
○ Managers may modify rate of return to maximize personal utility.
○ Regulators are triggered by sharp changes in surplus, endorsing IS
○ Managers prefer to keep earnings in line with expectations
■ Authors criticize 3 year average return on assets (ROA) method
○ Managers prefer to avoid reporting losses.
● Authors use smoothing measurement method by reported earning
○ Small loss as the first 5% in losses
○ Loss identifies firms with earnings in the top 90% of losses
○ Small profit as the first 5% in gains
○ Profit identifies firms with earnings in the top 90% of gains
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Michael-Paul James

18. Financial Weakness (FW) and the IRIS ratio
● Weak P/L firms underreserve more often than strong P/L firms
○ Weak firms traditionally measured by IRIS ratios
● Hypotheses
○ Firms underreserve to avoid costs of regulatory scrutiny
○ Firms with weak solvency incentives (limited liability, risk
insensitive guaranty funds) underreport to increase growth (Moral
Hazard Hypothesis)
● IRIS ratios and reserve reporting
○ Underreserving improves 8 ratios ○ 1 ratio is indeterminate
○ Overreserving improves 1 ratio ○ 2 ratios are unaffected
● Paper estimates each firms probability of failure
Ii
= α +βXi
rst
+ δXi
fc
+ εi
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Michael-Paul James

19. Table
4:
Compare
IRIS
Ratios
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Michael-Paul James
TABLE 4: Comparison of Reported and Premanaged IRIS Ratios
Panel A: Sum of IRIS Ratios Outside of the NAIC Acceptable Boundaries
Percentiles
Obs. Mean 25th 50th 75th Std. Dev. T-Testa
1 1989–1992
Reported 3,235 0.850 0 0 1 1.174
Premanaged with Weiss Error 3,127 1.497 0 1 2 1.271 ∗∗∗
Premanaged with KFS Error 3,127 1.311 0 1 2 1.326 ∗∗∗
2 1993–1997
Reported 4,381 1.258 0 1 2 1.451
Premanaged with Weiss Error 4,255 2.033 1 2 3 1.471 ∗∗∗
Premanaged with KFS Error 4,255 1.770 1 2 3 1.554 ∗∗∗
3 1989–1997
Reported 7,616 1.085 0 1 2 1.356
Premanaged with Weiss Error 7,382 1.776 1 2 3 1.411 ∗∗∗
Premanaged with KFS Error 7,382 1.553 0 1 2 1.477 ∗∗∗
Panel B: Replication of Gaver and Paterson’s (2004) Table 5, Panel C
1 Gaver and Paterson (2004)
1988–1993 (N=6233) Reported Violations < 4 Reported Violations >= 4
Premanaged Violations < 4 0.059 (80.6%) 0.224 (1.1%)
Premanaged Violations > = 4 −0.200 (12.0%) −0.123 (6.3%)
2 Our results
1989–1993 (N = 5111) Reported Violations < 4 Reported Violations >= 4
Premanaged Violations < 4 0.02 (79.0%) 0.006 (1.3%)
Premanaged Violations > =4 −0.080 (7.7%) −0.004 (11.9%)
Note: Premanaged IRIS
ratios are the reported IRIS
ratios purged of the effect of
the loss reserve error (Gaver
and Paterson, 2004). The
sample is partitioned into
subsets based on the
violation of more than three
IRIS ratios. The median KFS
Error is reported in the body
of the table with the
percentage of firms falling
within each subset reported
in parentheses. In
accordance with Gaver and
Paterson (2004), the KFS
Error is scaled by reported
reserves.
a
T-test for differences
between the reported and
premanaged IRIS ratios.
● More insolvencies
happened in 1989 -1992
when less errors were
reported.
● After 1993 incentives to
manipulate ratios lessened
but premanaged rates
remain constant (

20. Calculating the Probability of Failure
● Paper estimates each firms probability of failure with a logistic
regression model
Ii
= α +βXi
rst
+ δXi
fc
+ εi
● I: unobserved propensity to fail
● i: insurer
● α: intercept
● Xfc
: vector of firm characteristics
● Xrst
: vector of regulatory solvency tools
● ε: error term
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Michael-Paul James

21. Rate Regulation
● Competing Hypotheses
○ P/L Firms operating in strict rate regulatory environments
understate reserves to convince regulators they can charge lower
rates
○ When regulation pushes rates below economic cost, then stringent
rate regulation incentivizes managers overstate reserve estimates
to reduce rate suppression.
% Reg = Σ(Premiums Writtenistl
* Stringent Reg Lawstl
) / Σ Premiums Writtenistl
● firm (i), year (t), in line of business (l), states (s)
● Stringent rate regulation: 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
● Not stringent rate regulation: file-and use, use-and-file, filing only, or had flex rating with a
large flex band
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22. Data
04
22

23. Sources
● NAIC annual statement database
○ 1987 to 2002
○ 5 year resolution period
○ 3 year smoothing metric
● After cleaning, sample represents 60% of total industry assets.
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24. Table
5:
Summary
Statistics
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Michael-Paul James
Note: The table presents summary statistics for the years
1990 to 1997. There are 5,459 observations. Weiss Error is
the difference between current reserves and claims paid 5
years later. KFS Error is the difference between the loss
reserve in the current period and a revised estimate 5
years in the future. Both reserve errors are scaled by total
assets. Positive reserve errors indicate that the firm initially
overreserved, while negative reserve errors indicate
underreserving. Mutual is an indicator variable of whether
the firm has a mutual organizational structure. Stock is an
indicator variable of whether the firm has a stock
structure. Public is an indicator of whether the firm is
publicly traded. Group is an indicator if the firm is a
member of a group. Direct is an indicator of whether the
firm is a direct writer of insurance. Growth is the 1-year
percent increase in net premiums written. Reinsurance is
the percentage of gross premiums written ceded to
reinsurers. Short-Tail is the percentage of losses incurred
in short-tail lines of insurance. Long-Tail is the percentage
of losses incurred in long-tail lines of insurance. Product
Herf is the line of business Herfindahl index. Geo Herf is
the geographical Herfindahl index. Tax Indicator is a
dummy variable set equal to one if an insurer has a high
tax rate, and zero otherwise (Petroni, 1992). Tax Shield is
the sum of net income and the estimated reserve (5 years
prior to resolution) over total assets (Grace, 1990). Smooth
is the previous 3 years’ average return on assets (Grace,
1990). Small Profit is an indicator for insurers with
earnings in the bottom 5 percent of the positive earnings
distribution. Profit is an indicator for firms with earnings in
the top 90 percent of the positive earnings distribution.
Small Loss is an indicator for insurers with earnings in the
top 5 percent of the negative earnings distribution. Loss is
an indicator for firms with earnings in the bottom 90
percent of the negative earnings distribution. PrFail is the
estimated probability of failure based on reported IRIS and
FAST ratios. % Reg is the percent of business written
subject to stringent rate regulation (Grace and Leverty,
2010).
TABLE 5: Summary Statistics
Mean Std. Dev. Min. Max.
Weiss Error (overestimated) 0.080 0.134 -1.033 1.963
KFS Error 0.011 0.107 -1.241 0.889
Mutual 0.276 0.447 0.000 1.000
Stock 0.724 0.447 0.000 1.000
Public 0.121 0.326 0.000 1.000
Group 0.747 0.435 0.000 1.000
Direct (to customers) 0.184 0.387 0.000 1.000
Total Assets (millions) 428.665 1,192.410 0.836 9,054.830
Growth 0.090 0.294 −0.903 1.148
Reinsurance 0.246 0.265 0.000 1.000
Short-Tail 0.296 0.197 0.000 1.000
Long-Tail 0.702 0.200 0.000 1.000
Product Herf (3 lines) 0.332 0.240 0.027 1.000
Geo Herf (2 states) 0.510 0.375 0.033 1.000
Tax Indicator (high tax) 0.471 0.499 0.000 1.000
Tax Shield 0.028 0.054 −0.182 0.191
Smooth 0.023 0.042 −0.201 0.240
Small Profit 0.041 0.197 0.000 1.000
Profit 0.739 0.439 0.000 1.000
Small Loss 0.010 0.099 0.000 1.000
Loss 0.159 0.366 0.000 1.000
PrFail 0.049 0.068 0.000 0.920
%Reg (stringent rate reg) 0.211 0.279 0.000 1.000

25. Table
6:
Correlation
Matrix
25
Michael-Paul James
TABLE 6: Pairwise Correlations
(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11)
(1) Weiss Error 0.635∗∗∗ 0.082∗∗∗ 0.068∗∗∗ 0.024 −0.043∗∗∗ 0.074∗∗∗ −0.028∗∗∗ −0.049∗∗∗ −0.144∗∗∗ 0.049
(2) KFS Error 0.604 0.07 0.169 0.129∗∗∗ −0.042∗∗∗ 0.097∗∗∗ −0.013 −0.056∗∗∗ −0.054∗∗∗ −0.008
(3) Tax Indicator 0.071∗∗∗ 0.071∗∗∗ 0.287∗∗∗ 0.121∗∗∗ −0.075∗∗∗ 0.322∗∗∗ −0.058∗∗∗ −0.306∗∗∗ −0.075∗∗∗ 0.023∗
(4) Tax Shield 0.070∗∗ 0.146∗∗ 0.291 0.536∗∗∗ −0.195∗∗∗ 0.718∗∗∗ −0.105∗∗∗ −0.610∗∗∗ −0.103∗∗∗ −0.004
(5) Smooth 0.031 0.102 0.125 0.499 −0.094 0.348 −0.036 −0.297 −0.029∗∗ 0.01
(6) Small Profit −0.036 −0.027 −0.075 −0.115 −0.085 −0.346 −0.021 −0.090 0.021 0.006
(7) Profit 0.063 0.094 0.322 0.687 0.32 −0.346 −0.169 −0.732 −0.140 0.028∗∗
(8) Small Loss −0.017 −0.004 −0.058 −0.069 −0.030∗∗ −0.021 −0.169 −0.044 0.011 0.029∗∗
(9) Loss −0.046 −0.071 −0.306 −0.679 −0.282 −0.090 −0.732 −0.044 0.139 −0.042
(10) PrFail −0.133 −0.136 −0.091 −0.163 −0.091 0.008 −0.143 −0.003 0.158 −0.096
(11) % Reg −0.024∗ 0.024∗ 0.002 0.002 0.009 0.002 0.021 0.034∗∗ −0.028∗∗ −0.025∗
Note: This table provides pairwise correlations for the years 1990 to 1997. Pearson correlations are in the lower triangle (unitalized) and Spearman
correlations are in the upper triangle (italicized). There are 5,321 observations. Reserve error is scaled by total assets. All variables are defined in
Table 5. ∗∗∗, ∗∗, and ∗ indicate significance at 0.01, 0.05, and 0.10 levels, respectively.
● Pearson coefficient examines a linear relationship between the two variables using raw data
● Spearman Coefficient examines both the linear and monotonic relationships using rank-ordered variables.

26. Results
05
26

27. Empirical Method
● The following Feasible Generalized Least Squares model investigates
the magnitude of reserve error and evaluates the hypothesized
incentives while controlling for nondiscretionary variables
yit
= αit
+βXit
+ λZit
+ eit
● i indexes firms
● t indexes time periods
● yit
is reserve error scaled by total assets
● Xit
is the institutional and firm characteristic variables
● Zit
is the hypothesized incentives.
27
Michael-Paul James

28. Table
7:
Magnitude
of
Weiss
RE
28
Michael-Paul James
Note: The table reports the
results of cross-sectional
time-series feasible
generalized least squares
regressions. The dependent
variable is theWeiss reserve
error scaled by total assets.
Size is the natural logarithm
of total assets. All remaining
variables are defined in
Table 5. Year indicators are
included in the model but
not reported to conserve
space. Reported standard
errors are bias-corrected
bootstrap standard errors.
∗∗∗, ∗∗, and ∗ indicate
significance at 0.01, 0.05,
and 0.10 levels, respectively.
● Hausman test statistic
indicate preference for
fixed effects.
● Wald test shows
groupwise
heteroskedasticity.
● Wooldridge test shows
no first order serial
correlation.
TABLE 7: Magnitude of Weiss Reserve Error
(1) (2) (3) (4)
Variable Coef. Std Err. Coef. Std Err. Coef. Std Err. Coef. Std Err.
Intercept −0.2656 0.0405∗∗∗ −0.2431 0.0385∗∗∗ −0.2785 0.0382∗∗∗ −0.2766
0.0490
∗∗∗
Mutual
−0.0331 0.0041∗∗∗ −0.0334 0.0047∗∗∗ −0.0352
0.0049
∗∗∗ −0.0355 0.0052∗∗∗
Public 0.0026 0.0034 0.0020 0.0035 0.0017 0.0036 0.0017 0.0034
Group 0.0073 0.0055 0.0059 0.0057 0.0058 0.0056 0.0081 0.0062
Direct
−0.0125 0.0048 −0.0142
0.0049
∗∗∗ −0.0160 0.0043∗∗∗ −0.0171 0.0047∗∗∗
Size 0.0172 0.0020∗∗∗ 0.0168 0.0019∗∗∗ 0.0180 0.0018∗∗∗ 0.0180 0.0025∗∗∗
Growth −0.0110 0.0065∗∗ −0.0108 0.0067 −0.0132 0.0065∗∗ −0.0131 0.0066∗∗
Reinsurance 0.0127 0.0083 0.0144 0.0082∗ 0.0170 0.0089∗ 0.0171 0.0099∗
Long-Tail
0.0310
0.0090
∗∗∗ 0.0304 0.0086∗∗∗ 0.0307 0.0093∗∗∗ 0.0326 0.0096∗∗∗
Product Herf 0.0160 0.0106 0.0142 0.0103 0.0152 0.0118 0.0130 0.0105
Geo Herf 0.0128 0.0069∗ 0.0087 0.0067 0.0135 0.0064∗∗ 0.0162 0.0066∗∗
Tax Indicator 0.0031 0.0022 0.0044 0.0028
Tax Shield 0.0058 0.0298 0.0174 0.0355
Smooth −0.0598 0.0437 −0.0825 0.0447∗
Small Profit −0.0055 0.0046 −0.0042 0.0048
Profit −0.0040 0.0034 −0.0036 0.0039
Small Loss −0.0028 0.0068 −0.0028 0.0072
PrFail −0.1922 0.0553∗∗∗ −0.1855 0.0524∗∗∗ −0.1753 0.0533∗∗∗ −0.1921 0.0559∗∗∗
%Reg 0.0012 0.0058 −0.0017 0.0060 0.0027 0.0058 0.0012 0.0060

29. Table
8:
Magnitude
of
KFS
RE
29
Michael-Paul James
Note: The table reports the
results of cross-sectional
time-series feasible
generalized least squares
regressions. The
dependent variable is the
KFS reserve error scaled by
total assets. Size is the
natural logarithm of total
assets. All remaining
variables are defined in
Table 5. Year indicators
are included in the model
but not reported to
conserve space. Reported
standard errors are
bias-corrected bootstrap
standard errors. ∗∗∗, ∗∗, and
∗ indicate significance at
0.01, 0.05, and
0.10 levels, respectively.
TABLE 8: Magnitude of KFS Reserve Error
(1) (2) (3) (4)
Variable Coef. Std Err. Coef. Std Err. Coef. Std Err. Coef. Std Err.
Intercept −0.0539 0.0286∗ −0.0366 0.0256 −0.0474 0.0293 −0.0504 0.0312
Mutual 0.0059 0.0042 0.0067 0.0040∗ 0.0067 0.0041 0.0087 0.0044∗∗
Public
0.0128
0.0046
∗∗∗ 0.0095 0.0044∗∗ 0.0163
0.0047
∗∗∗ 0.0105 0.0051∗∗
Group −0.0020 0.0044 −0.0015 0.0048 0.0004 0.0043 −0.0002 0.0047
Direct 0.0075 0.0042∗ 0.0093 0.0042∗∗ 0.0081 0.0039∗∗ 0.0094 0.0043∗∗
Size 0.0015 0.0014 0.0012 0.0013 0.0008 0.0014 0.0014 0.0015
Growth −0.0069 0.0048 −0.0073 0.0047 −0.0067 0.0045 −0.0063 0.0042
Reinsurance
−0.0452 0.0075∗∗∗ −0.0525 0.0072∗∗∗ −0.0521
0.0083
∗∗∗ −0.0512 0.0081∗∗∗
Long-Tail 0.0160 0.0072∗∗ 0.0176 0.0071∗∗ 0.0184 0.0070 0.0174 0.0070∗∗
Product Herf
0.0401
0.0087
∗∗∗ 0.0310
0.0088
∗∗∗ 0.0429
0.0096
∗∗∗ 0.0328 0.0101∗∗∗
Geo Herf
0.0201 0.0056∗∗∗ 0.0170 0.0055∗∗∗ 0.0143
0.0054
∗∗∗ 0.0179 0.0059∗∗∗
Tax Indicator
0.0063
0.0020
∗∗∗ 0.0042 0.0022∗
Tax Shield 0.0939 0.0307∗∗∗ 0.1035 0.0414∗∗
Smooth −0.0097 0.0379 −0.0044 0.0356
Small Profit 0.0037 0.0044 −0.0008 0.0049
Profit 0.0041 0.0031 −0.0031 0.0041
Small Loss 0.0032 0.0067 −0.0008 0.0068
PrFail
−0.1153 0.0394∗∗∗ −0.1053
0.0400
∗∗∗ −0.1037 0.0433∗∗ −0.1005 0.0421∗∗

30. Table
7
&
8
30
Michael-Paul James
Note: The table reports the
results of cross-sectional
time-series feasible
generalized least squares
regressions. The
dependent variable is the
KFS reserve error scaled by
total assets. Size is the
natural logarithm of total
assets. All remaining
variables are defined in
Table 5. Year indicators
are included in the model
but not reported to
conserve space. Reported
standard errors are
bias-corrected bootstrap
standard errors. ∗∗∗, ∗∗, and
∗ indicate significance at
0.01, 0.05, and
0.10 levels, respectively.
TABLE 7 & 8 Weiss Reserve Error KFS Reserve Error
(1) (2) (1) (2)
Variable Coef. Std Err. Coef. Std Err. Coef. Std Err. Coef. Std Err.
Intercept −0.2656
0.0405
∗∗∗ −0.2431 0.0385∗∗∗ −0.0539 0.0286∗ −0.0366 0.0256
Mutual −0.0331 0.0041∗∗∗ −0.0334 0.0047∗∗∗ 0.0059 0.0042 0.0067 0.0040∗
Public 0.0026 0.0034 0.0020 0.0035 0.0128 0.0046∗∗∗ 0.0095 0.0044∗∗
Group 0.0073 0.0055 0.0059 0.0057 −0.0020 0.0044 −0.0015 0.0048
Direct
−0.0125 0.0048 −0.0142
0.0049
∗∗∗ 0.0075 0.0042∗ 0.0093 0.0042∗∗
Size
0.0172
0.0020
∗∗∗ 0.0168 0.0019∗∗∗ 0.0015 0.0014 0.0012 0.0013
Growth −0.0110 0.0065∗∗ −0.0108 0.0067 −0.0069 0.0048 −0.0073 0.0047
Reinsurance 0.0127 0.0083 0.0144 0.0082∗ −0.0452 0.0075∗∗∗ −0.0525 0.0072∗∗∗
Long-Tail
0.0310
0.0090
∗∗∗ 0.0304
0.0086
∗∗∗ 0.0160 0.0072∗∗ 0.0176 0.0071∗∗
Product Herf
0.0160 0.0106 0.0142 0.0103 0.0401
0.0087
∗∗∗ 0.0310
0.0088
∗∗∗
Geo Herf
0.0128 0.0069∗ 0.0087 0.0067 0.0201
0.0056
∗∗∗ 0.0170 0.0055∗∗∗
Tax Indicator
0.0031 0.0022 0.0063
0.0020
∗∗∗ 0.0042 0.0022∗
Tax Shield 0.0058 0.0298 0.0939 0.0307∗∗∗
Smooth −0.0598 0.0437 −0.0825 0.0447∗ −0.0097 0.0379 −0.0044 0.0356
Small Profit
Profit
Small Loss

31. Table
7
&
8
31
Michael-Paul James
Note: The table reports the
results of cross-sectional
time-series feasible
generalized least squares
regressions. The
dependent variable is the
KFS reserve error scaled by
total assets. Size is the
natural logarithm of total
assets. All remaining
variables are defined in
Table 5. Year indicators
are included in the model
but not reported to
conserve space. Reported
standard errors are
bias-corrected bootstrap
standard errors. ∗∗∗, ∗∗, and
∗ indicate significance at
0.01, 0.05, and
0.10 levels, respectively.
TABLE 7 & 8 Weiss Reserve Error KFS Reserve Error
(3) (4) (3) (4)
Variable Coef. Std Err. Coef. Std Err. Coef. Std Err. Coef. Std Err.
Intercept −0.2785 0.0382∗∗∗ −0.2766
0.0490
∗∗∗ −0.0474 0.0293 −0.0504 0.0312
Mutual
−0.0352
0.0049
∗∗∗ −0.0355 0.0052∗∗∗ 0.0067 0.0041 0.0087 0.0044∗∗
Public
0.0017 0.0036 0.0017 0.0034 0.0163
0.0047
∗∗∗ 0.0105 0.0051∗∗
Group 0.0058 0.0056 0.0081 0.0062 0.0004 0.0043 −0.0002 0.0047
Direct −0.0160 0.0043∗∗∗ −0.0171 0.0047∗∗∗ 0.0081 0.0039∗∗ 0.0094 0.0043∗∗
Size 0.0180 0.0018∗∗∗ 0.0180 0.0025∗∗∗ 0.0008 0.0014 0.0014 0.0015
Growth −0.0132 0.0065∗∗ −0.0131 0.0066∗∗ −0.0067 0.0045 −0.0063 0.0042
Reinsurance
0.0170 0.0089∗ 0.0171 0.0099∗ −0.0521
0.0083
∗∗∗ −0.0512 0.0081∗∗∗
Long-Tail
0.0307 0.0093∗∗∗ 0.0326
0.0096
∗∗∗ 0.0184 0.0070 0.0174 0.0070∗∗
Product Herf
0.0152 0.0118 0.0130 0.0105 0.0429
0.0096
∗∗∗ 0.0328 0.0101∗∗∗
Geo Herf
0.0135 0.0064∗∗ 0.0162 0.0066∗∗ 0.0143
0.0054
∗∗∗ 0.0179 0.0059∗∗∗
Tax Indicator 0.0044 0.0028
Tax Shield 0.0174 0.0355 0.1035 0.0414∗∗
Smooth
Small Profit −0.0055 0.0046 −0.0042 0.0048 0.0037 0.0044 −0.0008 0.0049
Profit −0.0040 0.0034 −0.0036 0.0039 0.0041 0.0031 −0.0031 0.0041
Small Loss −0.0028 0.0068 −0.0028 0.0072 0.0032 0.0067 −0.0008 0.0068

32. Conclusion
06
32

33. Conclusion
● Controlling for relevant variables matters
○ Results are mixed depending on control variables
● Do not find evidence that insurers use the claim loss reserve to smooth
income
● Do find mixed evidence that insurers use the claim loss reserve for tax
purposes
● Do find strict rate regulation lead to overreserving on average
● Do find weak firms underreserve on average
○ Do not find evidence of solvency manipulation
○ Reject IRIS ratios for solvency measure
● Understating loss reserves reduces the number of IRIS ratio violations.
○ Largely mechanical due to 8 of 12 ratios improving with
underreserving.
33
Michael-Paul James

34. You are Amazing
Ask me all the questions you desire. I will do my best to answer honestly
and strive to grasp your intent and creativity.
34