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Underwriting Strategy and Underwriting Cycle in the Medical Malpractice

Insurance Industry

Yu Lei Barney School of Business

University of Hartford 200 Bloomfield Ave. West Hartford, CT

Phone: 860-768-4682 Email: lei@hartford.edu

Mark J. Browne

975 University Avenue Madison, WI 53706-1323

Phone: (608) 263-3030 Fax: (608) 265-4195

Email: mbrowne@bus.wisc.edu

July 2012 To be Presented at the 2012 American Risk and Insurance Association Meeting

Preliminary draft. Please do not quote without permission.

mailto:lei@hartford.edumailto:mbrowne@bus.wisc.edu

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Underwriting Strategy and Underwriting Cycle in the Medical Malpractice Insurance Industry

ABSTRACT

Even though underwriting cycles have been extensively studied, one area seems to receive little

attention. This article fills the gap by examining whether medical malpractice insurers underwriting strategy

exhibits any cyclical behavior. Our analysis of the NAIC data indicates that some aspects of malpractice

carriers underwriting strategy do show certain degrees of cyclical nature and they display trend that seem to be

opposite that of the combined loss ratio in medical malpractice insurance, which we use in this study as a

measure of the underwriting cycle. We find that when insurers underwriting performance worsens, there are

fewer insurers offering medical malpractice, there are more exits than entries, insurers are less geographically

concentrated in selling malpractice, and the significance of malpractice in terms of this lines premium share

declines. Moreover, when we look at which states in which malpractice carriers do business, we see that the

percentage of safer states (states that have caps on general damages or patient compensation funds) in which

insurers write malpractice and the percentage of insurers that choose to do business only in safer states are both

negatively associated with the combined loss ratio of the medical malpractice insurance industry. Taken all

together, it seems that at the industry level, insurers underwriting performance has a negative association with

their risk taking behavior in terms of how much to focus on malpractice line of business and where to write such

business. Less focus on malpractice and wider distribution of malpractice products are seen to accompany

worsened underwriting performance.

We also test whether the capacity constraint theory can help explain the cyclical nature of medical

malpractice insurers underwriting strategy. We find that when the total surplus of all single-line insurers (those

only selling medical malpractice) shrinks, insurers are less likely to go single-line. We observe the same trend

when we examine single-state insurers (those selling medical malpractice in just one state) and ONLY-CAP-

State insurers (those selling medical malpractice only in states with caps on general damages). In other words,

our results provide some support for the capacity constraint theory which predicts an inadequate capacity will

shrink insurance supply.

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INTRODUCTION

It is well recognized that many property/liability insurance markets exhibit cyclical nature. Soft market

periods, where prices are low and coverage is abundant, are followed by hard markets, where prices are high

and coverage is scarce. Medical malpractice insurance, which provides coverage against professional liability

for health-care providers, is a great example of the recurring soft and hard markets. Over the past several

decades medical malpractice insurance has experienced periodic performance crises evidenced by rising

premiums and decreasing supply of malpractice carriers.

Even though underwriting cycles have been extensively studied, previous literature usually focuses on

the cyclical behavior of prices, premium growth, underwriting performance (loss ratios or combined loss ratios)

or insurance availability. On the other hand, most research on medical malpractice insurance crisis concentrates

on the causes of price volatility during hard markets.

This paper intends to examine one little-studied area of the medical malpractice insurance market. We

will examine malpractice insurers underwriting strategy during the underwriting cycle and see if it exhibits any

cyclical behavior. If so, we want to see whether the capacity constraint theory can help explain such

phenomenon.

This paper makes contribution to both the underwriting cycle study and the medical malpractice

insurance literature by focusing on various aspects of insurers underwriting strategy. When the insurance

industry swings from soft (or hard) to hard (or soft) markets, it is natural for insurers to re-evaluate and adjust

their underwriting strategy to gain a competitive hold. It is likely the underwriting cycle causes changes in

underwriting strategy, but it is also plausible for the modified underwriting strategy to have an impact on the

depth and length of the underwriting cycle. It is not this papers intention to discuss how the two-way feedback

works. Well instead try to identify if there is any cyclical pattern in insurers underwriting strategy during the

medical malpractice insurance cycle, which we will measure using the malpractice industrys combined loss

ratios.

Insurers underwriting strategy could encompass many aspects. For instance, in response to medical

malpractice crises, do insurers establish tighter claims frequency and severity standards for potential insured

health care providers? Do they increase deductible amount and/or decrease the policy limit theyre willing to

insure? Do they choose to exclude certain high-risk specialties to cover? Ideally, wed like to explore how

insurers adjust their underwriting strategy in reality. Unfortunately, we do not have such information available.

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Instead, well utilize the National Association of Insurance Commissioners (NAIC) database and focus on the

following things which we call underwriting strategy in our paper.

First, do insurers choose to enter or exit the medical malpractice market? Lei and Browne (2008) study

malpractice insurers entry and exit during the period of 1994-2006 and find that exits are less frequent in states

where there are caps on general (noneconomic) damages. We extend their study by looking at how insurers

move in and out of the market in accordance with the underwriting cycle.

Second, when insurers do choose to enter the malpractice market, how much do they want to focus on

the malpractice line of business? Do they want to devote the entire business to malpractice or do they also write

other lines of business? In other words, we want to examine how the significance of medical malpractice (which

will be measured by malpractice lines premium share) changes in accordance with the underwriting cycle.

Third, where do insurers sell medical malpractice? Do they write malpractice in just one state or

multiple states? When they go multi-state, how do they allocate malpractice premiums across states?

Fourth, do insurers choose to sell malpractice in safer states? In response to malpractice crises, many

states enacted tort reforms (such as caps on awards for non-economic damages) and/or created alternative

mechanisms (such as joint underwriting associations and patients compensation funds that provide coverage

for substandard risks or limit an insurers loss exposure on catastrophic claims). These efforts are intended to

reduce the claims cost as well as the uncertainty associated with them. In this paper, we call states with either

caps on general damages or patient compensation funds safer states. Viscusi and Born (2005) find that many

tort reforms help reduce losses, lower premiums, and enhance insurer profitability, with limits on noneconomic

damages being the most influential in affecting insurance market outcomes.

Lastly, do insurers choose to insure more physicians or hospitals? Or do they choose to specialize in

covering just one type of health care providers since different policyholders have different risk implications?

It is not hard to imagine that these various aspects of insurers underwriting strategy, namely, entry and

exit, geographic concentration of malpractice business, significance of malpractice line of business, distribution

of malpractice business between safer states and less safe states (those without tort reform measures in place),

and choice of prospective policyholders to cover, will have different implications on firms performance.

Different strategies may have their own comparative advantages and will likely affect insurers differently.1

1 There is not much study on the underwriting strategy mentioned here yet. The few available studies on geographic diversification and product diversification produce mixed results. Liebenberg and Sommer (2008) find that single-line property-liability insurers consistently outperform multiline insurers. Elango et al. (2008) discover that performance advantages associated with product diversification are contingent upon an insurers degree of geographic diversification. Their results indicate that a highly diversified product profile with low geographic diversification is associated with the highest performance. Insurers that have relatively low

We

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do not intend to evaluate the effectiveness of insurers underwriting strategy in this paper, but rather we will

show how they change in the underwriting cycle.

In the next section, we discuss our data and definitions of medical malpractice insurers. We generate two

samples for our empirical study and we offer a brief overview of the samples in the same section. In the next

five sections that follow, we show how the above-mentioned five aspects of insurers underwriting strategy,

namely, entry and exit, geographic concentration, significance of malpractice line of business, distribution of

malpractice business between safer states and less safe states, and choice of prospective policyholders to cover,

evolve as the underwriting cycle unfolds. We then test the capacity constraint theory in the subsequent section.

In the last section, we summarize our findings and conclude the paper.

DATA AND DEFINITION OF MEDICAL MALPRACTICE INSURERS

We utilize the 1992-2010 NAIC property/casualty data to conduct our research. Since our focus is the

underwriting strategy of medical malpractice insurers, we need to define such carriers in the first place. A

natural response is to include all insurers that report positive direct premiums written in medical malpractice.

We call the resulting sample Large Sample. This sample includes all possible medical malpractice insurers,

yet some of them report to the NAIC even after they have stopped selling new policies. They continue to report

positive premiums from existing relationships, but are not truly active in the market. To account for this issue,

we also follow Nordman, Cermak and McDaniel (2004) and define a medical malpractice insurer as one that

wrote at least 2 percent of the medical malpractice premium in at least one state in that year. We call the

resulting sample Small Sample.

Since we need to examine insurers geographic concentration, we make use of the state-level financial

information in the NAIC database. The major financial statement we rely on is Exhibit of Premiums and

Losses in different states, which we refer to as the Stage Page throughout the paper. The Stage Page provides

information on premiums written/earned, losses incurred/unpaid/paid and loss adjustment and other expenses by

line of business for each firm in all 50 states and Washington D.C. each year. With such information, we can

analyze the underwriting performance of medical malpractice insurers both at the state-level and at the country-

level.

product and geography diversification have medium level performance. Lei and Schmit (2008) find no significant impact of geographic diversification on firm performance of malpractice insurers, but show that more product diversification is associated with stronger firm performance.

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Using our two definitions of medical malpractice insurers and the Stage Page, we generate our Large

Sample and Small Sample. Table 1 provides a snapshot of the two samples. Table 1: Comparison of Large Sample and Small Sample

Large Sample Small Sample

Year N of MM

Insurers

Median Loss Ratio

Median Expense

Ratio

Median Combined

Ratio

N of MM

Insurers

Country-level

Premium Share in Large

Sample

State-level

Average Premium Share in Large

Sample

Median Loss Ratio

Median Expense

Ratio

Median Combined

Ratio

1992 297 0.73 0.11 0.87 117 0.823 0.852 0.88 0.05 0.95 1993 294 0.74 0.11 0.85 121 0.801 0.834 0.89 0.06 0.94 1994 268 0.70 0.10 0.80 108 0.822 0.851 0.77 0.05 0.83 1995 264 0.77 0.10 0.86 108 0.817 0.846 0.82 0.05 0.89 1996 273 0.72 0.09 0.81 109 0.806 0.836 0.79 0.05 0.85 1997 275 0.72 0.10 0.86 105 0.789 0.827 0.86 0.05 0.92 1998 268 0.71 0.11 0.85 108 0.800 0.831 0.90 0.06 0.97 1999 268 0.82 0.11 0.93 114 0.798 0.831 0.87 0.06 0.93 2000 257 0.81 0.11 0.91 114 0.788 0.816 1.04 0.06 1.07 2001 243 0.96 0.11 1.02 106 0.795 0.825 1.08 0.08 1.12 2002 251 0.82 0.09 0.92 104 0.809 0.829 0.98 0.07 1.02 2003 276 0.73 0.08 0.82 112 0.803 0.824 0.88 0.06 0.94 2004 301 0.65 0.07 0.73 113 0.793 0.816 0.71 0.06 0.78 2005 310 0.63 0.07 0.71 116 0.787 0.817 0.66 0.05 0.73 2006 324 0.55 0.06 0.63 123 0.781 0.811 0.57 0.05 0.62 2007 323 0.50 0.07 0.59 124 0.773 0.800 0.52 0.05 0.58 2008 337 0.49 0.07 0.59 125 0.761 0.789 0.51 0.06 0.62 2009 337 0.51 0.07 0.60 122 0.745 0.782 0.53 0.07 0.61 2010 345 0.49 0.07 0.57 124 0.737 0.775 0.50 0.07 0.59 Source: authors analysis of NAIC data.

As we can see from Table 1, from 1992 to 2010, insurers that report positive premiums in medical

malpractice business number from a low 243 in 2001 to an all time high of 345 in 2010. When we require that

insurers must write at least 2% of medical malpractice in at least one state, the sample size drops significantly.

Though the small sample is less than 45% of the large sample in terms of its size, its insurers are very active and

meaningful malpractice writers, as evidenced by the premium shares they have when compared to the large

sample. For instance, in year 2010, the small samples total premiums account for 73.7% of the large samples

total premiums at the country level. At the state-level, we see that on average in each state, the small sample

writes about 77.5% of the large samples premiums. In other words, the small sample is very representative of

the entire medical malpractice industry. For all the analyses we do, we use both samples as a robustness test to

each other and we can also see how the entire industry and the major active writers differ or behave similarly in

various aspects of their underwriting strategy.

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In this paper we use the medical malpractice insurance industrys loss ratios as a proxy to the

underwriting cycle. The State Page allows us to calculate three ratios for each malpractice carrier both at the

state-level and at the country-level. Loss ratios are losses and loss adjustment expenses incurred divided by

premiums earned. Expense ratios are commissions, taxes and fees divided by premiums written. Combined ratio

is the sum of loss ratio and expense ratio. Since here we are not doing any other sample selection besides

imposing definitions of medical malpractice insurers, we do have insurers that report negative premiums and

losses. As a result, the mean values of loss ratios are not reliable. Instead, we use median values to show the

trend of the underwriting cycle. Table 1 also shows the ratios for both samples over time. Figure 1 presents the

same information in a more visual form.

Figure 1: Comparison of Large and Small Samples Loss Ratios

As we can see, expense ratios are relatively stable over time for both samples, with the small sample

enjoying lower expense ratios. Volatility in combined ratios is thus largely driven by changes in loss ratios. The

small sample tends to have higher loss ratios and combined loss ratios (except for 2006-2007). Both samples

reached their peak in 2001 with the highest loss ratios during our study period. Overall, the two samples follow

very similar pattern in terms of their loss ratios movement. In the analyses that follow, we use the combined

ratio as a measure of the underwriting cycle. We next show how insurers underwriting strategy evolves in the

underwriting cycle.

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NUMBER OF TOTAL INSURERS, ENTRANTS AND EXITERS

The first aspect of the underwriting strategy we study is whether or not an insurer chooses to enter or

exit medical malpractice line of business. For this purpose, we study the movement both at the country-level

and at the state-level. We define an insurer as entering the market in a state in a given year if its direct

premiums written (DPW) for medical malpractice insurance in that state exceeded the 2% threshold for Small

Sample (or 0% threshold for Large Sample) for the first time in that year. Similarly, we define a firm as exiting

a state in a particular year if it wrote malpractice coverage in a particular state in a particular year, but in no

subsequent years wrote 2% or more for Small Sample of the direct premiums in that state (or wrote no positive

premium for Large Sample). Country-level entry and exit are similarly defined, with national entrant of a

certain year being one that had positive premiums in medical malpractice for the first time in that year (for

Large Sample), or that wrote at least 2% of premium in at least one state in that year (for Small Sample). Table

2 reports the total number of medical malpractice insurers, entrants or exiters at the country level. It also shows

the mean values of total number of insurers, entrants and exiters at the state level.

Table 2: Total Number of Medical Malpractice Insurers, Entrants and Exiters

Year Country-level Mean Values at State-level

Large Sample Small Sample Large Sample Small Sample Total Entry Exit Total Entry Exit Total Entry Exit Total Entry Exit

1992 297 - 32 117 - 8 65.53 - 6.43 7.82 0.88 1993 294 29 55 121 12 26 70.37 11.27 22.53 7.86 0.92 3.06 1994 268 29 22 108 13 10 55.29 7.45 4.88 6.75 1.94 0.67 1995 264 18 10 108 10 10 60.80 10.39 8.69 7.37 1.29 0.94 1996 273 19 21 109 11 13 59.69 7.57 5.94 7.67 1.24 1.22 1997 275 23 26 105 9 8 64.12 10.37 6.90 7.82 1.37 1.14 1998 268 19 23 108 11 9 66.94 9.73 7.61 8.06 1.37 1.43 1999 268 23 31 114 15 11 69.61 10.27 12.47 8.02 1.39 1.69 2000 257 20 35 114 11 17 65.61 8.47 10.35 7.78 1.45 1.76 2001 243 21 27 106 9 17 64.71 9.45 12.53 8.39 2.37 2.22 2002 251 35 29 104 15 11 62.14 9.96 9.92 8.57 2.39 1.96 2003 276 54 39 112 19 17 62.63 10.41 10.24 8.18 1.57 1.71 2004 301 64 20 113 18 7 64.63 12.24 8.69 7.71 1.24 0.88 2005 310 29 21 116 10 10 63.12 7.18 7.02 7.96 1.14 1.14 2006 324 35 23 123 17 11 62.33 6.24 5.16 7.71 0.88 1.12 2007 323 22 16 124 12 8 63.33 6.16 3.75 7.45 0.86 0.65 2008 337 30 19 125 9 12 68.02 8.43 4.84 7.55 0.75 0.82 2009 337 19 17 122 9 3 71.16 7.98 3.67 7.61 0.88 0.61 2010 345 25 - 124 5 - 76.51 9.02 - 7.94 0.94 - Source: authors analysis of NAIC data.

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Figures 2 and 3 provide a visual description of how the total number of insurers, entrants and exiters

correspond to the combined loss ratios in the medical malpractice industry. State-level average values, though

not graphed, show similar patterns.

Figure 2: Large Sample: Total N. of Firms, Entrants and Exiters

As we can see the combined ratio seems to be moving in opposite direction of the total number of

insurers. Around the year of 2001 when the combined ratios worsened for both small and large samples, we see

a dip in the total number of insurers. When loss ratios improved in recent years, we see gradual increase in the

total number of malpractice insurers.

During our study period, year 1993 saw the most exits in both large sample and small sample. In Large

Sample, we notice more exits than entries leading up to the 2001 crisis period. In Small Sample, such

phenomenon coincides with the worsened 2001 combined ratio. In general, we notice that when loss ratios are

high, there tend to be more exits than entries (though there may be a time lag). When loss ratios improve, we

see more entries than exits in both samples, contributing to the increased size of the malpractice market.

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Figure 3: Small Sample: Total N. of Firms, Entrants and Exiters

GEOGRAPHIC CONCENTRATION OF MALPRACTICE BUSINESS

The second aspect of the underwriting strategy we examine is how insurers spread out their malpractice

business across states. We first look at the number of states in which insurers sell medical malpractice. Table 3

reports the median values of this information for both samples over time. Though the number of states in which

insurers write malpractice ranges from 1 to 51, the median values are pretty low in both samples. In Large

Sample, half of the insurers write malpractice in less than 4 states. The small sample insurers write in even

fewer states, with 1 or 2 being the median values. In order to see how insurers allocate their malpractice

premiums across states, we also calculate a geographic Herfindahl-Hirschman Index (HHI) for each firm, which

is defined as the sum of the squares of its premium share in each state 2

. A higher HHI indicates more

geographic concentration. Table 3 also reports the median values of geographic HHI over time for both samples.

As we can see more clearly from Figure 4, the geographic HHI shows an opposite trend to that of the combined

loss ratios. In other words, higher loss ratios are shown to be associated with lower geographic HHI. When loss

ratios improve, we see higher geographic HHI. In other words, a worsening (improving) underwriting

performance seems to be linked with less (more) geographic concentration of malpractice business.

2 Premium share is the firms malpractice premium in each state divided by its country-level total malpractice premiums.

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Table 3: Analysis of Geographic Concentration of Medical Malpractice Insurers

Year

Large Sample Small Sample Small Sample Large Sample

Geographic HHI

N of States

Insurers Sell MM

Geographic HHI

N of States

Insurers Sell MM

N of SS Insurers

% of SS Insurers

% of SS Premium

N of SS Insurers

% of SS Insurers

% of SS Premium

1992 0.893 3 0.943 1 74 0.633 0.409 116 0.391 0.278 1993 0.777 3 0.888 1 80 0.661 0.410 105 0.357 0.258 1994 0.871 2 0.962 1 71 0.657 0.456 99 0.369 0.312 1995 0.816 3 0.951 1 65 0.602 0.418 84 0.318 0.287 1996 0.841 3 0.886 1 62 0.569 0.405 95 0.348 0.259 1997 0.828 3 0.744 1 55 0.524 0.389 90 0.327 0.249 1998 0.769 4 0.661 1 57 0.528 0.341 87 0.325 0.223 1999 0.706 4 0.650 1 61 0.535 0.334 80 0.299 0.231 2000 0.696 4 0.602 1 62 0.544 0.347 70 0.272 0.206 2001 0.614 4 0.496 2 46 0.434 0.226 71 0.292 0.147 2002 0.691 4 0.610 2 48 0.462 0.198 80 0.319 0.133 2003 0.855 3 0.591 1 59 0.527 0.245 103 0.373 0.149 2004 0.917 2 0.778 1 62 0.549 0.243 121 0.402 0.146 2005 0.964 2 0.848 1 68 0.586 0.329 132 0.426 0.177 2006 0.970 2 0.893 1 78 0.634 0.384 144 0.444 0.224 2007 0.961 2 0.922 1 80 0.645 0.422 139 0.430 0.232 2008 0.919 2 0.849 1 78 0.624 0.400 143 0.424 0.243 2009 0.919 2 0.799 1 73 0.598 0.384 138 0.410 0.239 2010 0.931 2 0.774 1 70 0.565 0.385 146 0.423 0.244 Source: authors analysis of NAIC data.

Figure 4: Dynamics of Geographic Concentration

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We next look at an extreme case of geographic concentration, given that many medical malpractice

insurers operate in just one state. Table 3 also shows how many (and what percentage of) insurers sell medical

malpractice in just one state, and the premium share these single-state insurers have when compared to the

entire malpractice industry. Figure 5 graphs the same information. Again, we notice similar pattern. When

insurers underwriting performance worsens, we see fewer insurers that sell malpractice in just one state.

Improved loss ratios are shown to be associated with more insurers selling malpractice in just one state.

Figure 5: Analysis of Single-state MM Insurers

Single-state (SS) and multi-state (MS) insurers each have their own competitive advantage. Operating

in just one state may gain insurers superior knowledge in dealing with state legal and regulatory environments

and thus enable them to have better loss control. On the other hand, multi-state insurers may enjoy the benefits

of diversification should a certain state suddenly changes its legal or regulatory environments in a way thats

detrimental to the firms. Table 4 shows that usually multi-state insurers have higher expense ratios than single-

state insurers (except in year 2001 when in large sample, MS insurers have a higher median value of expense

ratio than SS insurers). Figure 6 presents the same information in a more straightforward way.

We also notice that single-state insurers have lower combined ratio than multi-state insurers from 1995

to 2004, but in other times they underperform. We suspect it is the comparative advantage of different

underwriting strategies that are at play. Figures 7-8 provide a better presentation of the loss ratio information.

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Table 4: Median Loss Ratios of Single-state vs Multi-state Insurers

year Small Sample MS Insurers Small Sample SS Insurers Large Sample MS Insurers Large Sample SSInsurers

Exp Ratio

Loss Ratio

Comb Ratio

Exp Ratio

Loss Ratio

Comb Ratio

Exp Ratio

Loss Ratio

Comb Ratio

Exp Ratio

Loss Ratio Comb Ratio

1992 0.102 0.888 0.987 0.035 0.873 0.907 0.138 0.718 0.883 0.057 0.735 0.833

1993 0.095 0.812 0.931 0.042 0.901 0.942 0.131 0.733 0.867 0.075 0.754 0.798

1994 0.069 0.754 0.815 0.038 0.789 0.841 0.122 0.702 0.814 0.060 0.676 0.750

1995 0.074 0.849 0.933 0.046 0.808 0.850 0.117 0.789 0.902 0.055 0.687 0.769

1996 0.070 0.861 0.912 0.039 0.746 0.776 0.103 0.732 0.848 0.048 0.675 0.752

1997 0.066 0.868 0.955 0.038 0.795 0.874 0.110 0.750 0.873 0.069 0.638 0.687

1998 0.064 0.910 0.991 0.053 0.898 0.915 0.122 0.770 0.942 0.085 0.506 0.638

1999 0.074 0.936 0.998 0.059 0.802 0.870 0.110 0.821 0.927 0.089 0.851 0.932

2000 0.066 1.069 1.132 0.053 1.010 1.030 0.108 0.833 0.962 0.135 0.670 0.826

2001 0.079 1.131 1.189 0.056 0.908 0.916 0.117 0.993 1.106 0.089 0.834 0.916

2002 0.075 1.047 1.101 0.050 0.866 0.910 0.101 0.962 1.010 0.067 0.674 0.775

2003 0.071 0.903 0.986 0.041 0.807 0.887 0.084 0.745 0.826 0.064 0.710 0.750

2004 0.062 0.758 0.833 0.049 0.651 0.733 0.077 0.665 0.751 0.044 0.612 0.693

2005 0.068 0.666 0.716 0.041 0.659 0.731 0.080 0.627 0.718 0.045 0.643 0.696

2006 0.065 0.575 0.620 0.046 0.545 0.625 0.082 0.553 0.636 0.046 0.530 0.617

2007 0.070 0.453 0.553 0.046 0.556 0.624 0.084 0.475 0.582 0.044 0.528 0.603

2008 0.071 0.445 0.523 0.045 0.587 0.651 0.095 0.473 0.591 0.041 0.513 0.593

2009 0.076 0.515 0.605 0.043 0.545 0.632 0.094 0.495 0.613 0.047 0.518 0.567 2010 0.078 0.530 0.592 0.045 0.487 0.574 0.100 0.479 0.574 0.041 0.496 0.559 Source: authors analysis of NAIC data.

Figure 6: Expense Ratio: Single-state vs Multi-state Insurers

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Figure 7: Loss Ratio: Single-state vs Multi-state Insurers

Figure 8: Combined Ratio: Single-state vs Multi-state Insurers

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SIGNIFICANCE OF MEDICAL MALPRACTICE IN INSURERS PORTFOLIO

The third aspect of the underwriting strategy we examine is the significance of malpractice insurance in

insurers entire portfolio. We first look at the percentage of insurers total premiums written in malpractice line

of business. Table 5 reports both mean and median values of such percentages over time. We observe that since

2003 theres been increased significance of malpractice business. By 2010, on average medical malpractice

accounts for about 68.7% of total property/liability insurance premiums in Large Sample, and 84.9% in Small

Sample. When we turn to median values of such premium percentages, we notice that half of malpractice

insurers write more than 98% of premiums in this particular line. When we graph such information in Figures 9

and 10, we see that overall the percentage of malpractice premiums shares a negative association with the

combined loss ratios. When insurers underwriting performance worsens, there is less significance of

malpractice insurance (meaning insurers are writing less malpractice). When performance improves, we see

insurers focus more on malpractice. This makes intuitive sense since its natural for profit-driven firms to move

away from less profitable business.

Table 5: Analysis of Significance of Medical Malpractice Business

Year

Large Sample Small Sample N of SL

% of SL

% of SL Premiums

% of MM Premiums

(Mean)

% of MM Premiums (Median)

N of SL

% of SL

% of SL Premiums

% of MM Premiums

(Mean)

% of MM Premiums (Median)

1992 62 0.209 0.206 0.444 0.150 41 0.350 0.218 0.778 0.995 1993 62 0.211 0.196 0.447 0.187 36 0.298 0.210 0.741 0.979 1994 56 0.209 0.230 0.464 0.256 34 0.315 0.238 0.812 0.992 1995 57 0.216 0.194 0.493 0.381 26 0.241 0.194 0.786 0.983 1996 55 0.202 0.094 0.482 0.341 21 0.193 0.078 0.793 0.980 1997 46 0.167 0.069 0.465 0.243 17 0.162 0.060 0.795 0.967 1998 37 0.138 0.106 0.443 0.198 15 0.139 0.096 0.776 0.920 1999 35 0.131 0.108 0.440 0.157 14 0.123 0.099 0.736 0.909 2000 29 0.113 0.111 0.416 0.145 14 0.123 0.109 0.723 0.915 2001 30 0.124 0.110 0.427 0.167 14 0.132 0.115 0.737 0.950 2002 38 0.151 0.066 0.489 0.461 13 0.125 0.065 0.781 0.961 2003 60 0.217 0.081 0.527 0.626 18 0.161 0.072 0.798 0.965 2004 103 0.342 0.113 0.641 0.969 25 0.221 0.100 0.828 0.977 2005 110 0.355 0.150 0.659 0.973 32 0.276 0.134 0.849 0.983 2006 133 0.411 0.198 0.692 0.981 38 0.309 0.180 0.846 0.981 2007 139 0.430 0.212 0.704 0.989 42 0.339 0.188 0.839 0.985 2008 143 0.424 0.228 0.696 0.983 43 0.344 0.202 0.854 0.985 2009 138 0.410 0.222 0.682 0.975 41 0.336 0.198 0.850 0.981 2010 139 0.403 0.223 0.687 0.976 42 0.339 0.199 0.849 0.983 Source: authors analysis of NAIC data.

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Figure 9: Significance of MM in Large Sample

Figure 10: Significance of MM in Small Sample

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We also examine an extreme case where insurers devote its entire business to medical malpractice.

Table 5 also shows that the total number and percentage of single-line (SL) insurers that only sell medical

malpractice, as well as the premium share these single-line insurers have among all medical malpractice

insurers. From 1996 to 2002, we see fewer single-line insurers only selling medical malpractice. The number

and percentage picked up since 2002. In Large Sample, 40.3% of insurers are single-line insurers in 2010,

contributing 22.3% to total medical malpractice premiums. In Small Sample, we see a slightly lower percentage

of single-line insurers representing 19.9% of the medical malpractice market.

Figure 11 shows that for the most part the percentage of the number and premium share of single-line

insurers move in opposite direction to that of the combined loss ratios. When loss ratios improve, we see more

single-line insurers focusing on malpractice.

Figure 11: Analysis of Single-line (SL) Insurers

Table 6 shows how single-line (SL) insurers fare as opposed to multi-line (ML) insurers. Figure 12

indicates that SL insurers usually have lower expense ratios. The only exception is in year 2005 when ML

insurers have lower expense ratios in Small Sample.

17

Table 6: Median Loss Ratios of Single-line vs Multi-line Insurers

Year Large Sample SL Insurers Large Sample ML Insurers Small Sample SL Insurers Small Sample ML Insurers Exp

Ratio Loss Ratio

Comb Ratio

Exp Ratio

Loss Ratio

Comb Ratio

Exp Ratio

Loss Ratio

Comb Ratio Exp Ratio

Loss Ratio

Comb Ratio

1992 0.024 0.800 0.823 0.151 0.717 0.882 0.028 0.891 0.914 0.080 0.870 0.974

1993 0.032 0.851 0.913 0.144 0.683 0.827 0.033 0.942 0.960 0.075 0.827 0.930

1994 0.031 0.709 0.743 0.141 0.701 0.813 0.024 0.723 0.749 0.061 0.803 0.857

1995 0.030 0.724 0.817 0.125 0.795 0.904 0.026 0.750 0.831 0.064 0.875 0.931

1996 0.034 0.756 0.862 0.103 0.694 0.799 0.024 0.812 0.831 0.061 0.784 0.852

1997 0.029 0.652 0.709 0.112 0.734 0.869 0.026 0.718 0.735 0.067 0.872 0.928

1998 0.036 0.692 0.788 0.122 0.715 0.872 0.031 0.900 0.926 0.065 0.904 0.977

1999 0.030 0.759 0.867 0.119 0.859 0.947 0.030 0.842 0.900 0.074 0.868 0.938

2000 0.025 0.766 0.812 0.121 0.816 0.918 0.021 1.121 1.145 0.071 1.034 1.059

2001 0.026 0.975 1.002 0.125 0.960 1.026 0.024 1.005 1.015 0.085 1.081 1.133

2002 0.032 0.718 0.785 0.105 0.860 0.987 0.029 1.001 1.021 0.069 0.976 1.028

2003 0.045 0.648 0.710 0.086 0.798 0.887 0.055 0.719 0.825 0.064 0.899 0.986

2004 0.038 0.613 0.688 0.077 0.658 0.753 0.055 0.754 0.856 0.060 0.704 0.773

2005 0.042 0.645 0.716 0.077 0.630 0.703 0.058 0.682 0.756 0.048 0.644 0.691

2006 0.044 0.552 0.620 0.085 0.540 0.643 0.049 0.522 0.615 0.058 0.595 0.632

2007 0.046 0.526 0.584 0.089 0.473 0.585 0.051 0.438 0.493 0.058 0.563 0.649

2008 0.047 0.515 0.603 0.093 0.454 0.567 0.056 0.501 0.622 0.065 0.514 0.614

2009 0.051 0.505 0.605 0.092 0.505 0.593 0.055 0.542 0.613 0.069 0.524 0.605 2010 0.048 0.512 0.555 0.093 0.481 0.578 0.051 0.542 0.593 0.071 0.487 0.579 Source: authors analysis of NAIC data.

Figure 12: Expense Ratio: Single-line (SL) vs Multi-line (ML) Insurers

18

Figure 13: Loss Ratio: Single-line (SL) vs Multi-line (ML) Insurers

Figure 14: Combined Ratio: Single-line (SL) vs Multi-line (ML) Insurers

19

Figures 13-14 show that in terms of loss ratios and combined ratios, SL and ML insurers have their

comparative advantage at different times of the underwriting cycle. From 1997 to 2004, single-line insurers

perform better. Multi-line insurers have better results at other times.

DISTRIBUTION OF MALPRACTICE INSURANCE BETWEEN SAFER AND LESS SAFE STATES

The fourth aspect of the underwriting strategy we study is how insurers allocate their malpractice

business between safer states (those that have caps on general damages and/or patient compensation funds) and

less safe states (states that do not have tort reform measures in place). Table 7 counts how many firms sell

malpractice in CAP- or PCF- states (those that have caps on general damages and/or patient compensation

funds), and how many in less safe states. Since a firm may sell in both safer and less safe states, the number of

firms selling in CAP states and the number of firms selling in NO-CAP states do not add up to the total number

of firms. We see some sharp increase in the number of firms operating in PCF states in recent years.

Table 7: Analysis of Number of Firms in States of Different Regulatory Environments

Year

Large Sample Small Sample

N of Firms

N of Firms in

PCF States

N of Firms in

CAP states

N of Firms in No-PCF States

N of Firms in No-CAP

States

N of Firms

N of Firms in PCF States

N of Firms in CAP states

N of Firms in No-PCF States

N of Firms in No-CAP

States

1992 297 149 195 280 260 117 39 63 102 89 1993 294 146 198 280 256 121 36 66 107 89 1994 268 129 183 251 228 108 36 52 94 82 1995 264 134 189 249 226 108 39 62 93 80 1996 273 140 199 260 229 109 37 62 99 82 1997 275 144 205 265 236 105 37 65 96 78 1998 268 146 210 257 222 108 33 68 101 78 1999 268 148 207 258 232 114 37 70 106 87 2000 257 140 196 245 225 114 41 73 105 84 2001 243 131 187 238 211 106 44 72 97 80 2002 251 148 184 233 220 104 44 68 92 80 2003 276 170 199 243 227 112 53 73 94 85 2004 301 185 219 257 242 113 48 74 97 81 2005 310 186 232 268 236 116 49 74 97 78 2006 324 193 242 270 239 123 46 78 104 79 2007 323 191 234 279 248 124 48 78 106 78 2008 337 196 242 292 260 125 47 78 109 84 2009 337 197 241 292 266 122 49 75 106 83 2010 345 208 253 296 266 124 55 82 105 81 Source: authors analysis of NAIC data.

20

Next we examine how many CAP- or PCF-states each firm sells malpractice in. Table 8 shows the

average values across insurers. For instance, in 2010, insurers in Large Sample write malpractice in 4.27 PCF

states, which represents 45.4% of the states in which firms sell malpractice. Similarly we find that insurers sell

malpractice in an average number of 8.07 CAP states, which account for 66.8% of total states in which insurers

have malpractice business.

Table 8: Average Number of States of Different Regulatory Environments Insurers Sell Medical Malpractice

Year Large Sample Small Sample

N of PCF States

% of PCF States

N of CAP States

% of CAP States

N of PCF States

% of PCF States

N of CAP States

% of CAP States

1992 3.91 0.321 5.70 0.495 1.897 0.558 2.381 0.692 1993 4.21 0.297 6.02 0.489 1.972 0.575 2.242 0.716 1994 3.77 0.338 5.11 0.510 2.000 0.579 2.173 0.700 1995 4.01 0.315 5.88 0.514 2.051 0.570 2.274 0.687 1996 3.81 0.304 5.76 0.533 2.135 0.483 2.403 0.680 1997 3.97 0.278 6.35 0.522 2.108 0.474 2.492 0.680 1998 4.08 0.278 6.59 0.540 2.485 0.473 2.529 0.688 1999 4.22 0.275 6.93 0.517 2.243 0.492 2.429 0.662 2000 4.14 0.284 6.81 0.504 1.976 0.477 2.370 0.685 2001 4.43 0.254 7.08 0.509 2.068 0.467 2.431 0.634 2002 4.28 0.341 6.80 0.511 2.227 0.513 2.632 0.641 2003 4.29 0.423 7.22 0.598 2.094 0.577 2.575 0.665 2004 4.04 0.451 7.04 0.620 2.042 0.565 2.581 0.706 2005 3.90 0.439 7.54 0.690 2.061 0.593 3.095 0.792 2006 3.85 0.482 6.96 0.696 2.130 0.639 2.782 0.808 2007 3.92 0.452 7.30 0.687 1.958 0.588 2.718 0.823 2008 4.07 0.451 7.49 0.673 1.936 0.586 2.718 0.792 2009 4.17 0.449 7.90 0.666 1.918 0.574 2.813 0.788 2010 4.27 0.454 8.07 0.668 1.855 0.590 2.622 0.778 Source: authors analysis of NAIC data.

Figure 15 shows that the average percentage of CAP- or PCF- states in which firms sell medical

malpractice insurance moves in opposite direction to the combined loss ratios. Overall, we notice that higher

loss ratios are associated with fewer CAP- or PCF-states in which insurers write malpractice. In other words,

underwriting performance worsens when insurers write in fewer safer states. It is likely that operating in riskier

states lead to worsened loss ratios in the first place. Since its hard to identify cause and effect, we can only

conclude that less business in safer states is associated with higher loss ratios.

21

Figure 15: % of PCF/CAP States Insurers Sell MM In

We now turn to an extreme case where insurers choose to write malpractice only in safer states (CAP- or

PCF- states). For simplicity purpose, we call such firms Only-CAP firms or Only-PCF firms. Table 9 shows the

number, percentage and premium share of Only-CAP and Only-PCF firms. We notice since 2003 theres been

an increase in the number of insurers that choose to sell malpractice only in PCF-states, or CAP-states. When

we graph such information in Figures 16 and 17, we observe that the percentage of Only-CAP firms and Only-

PCF firms share a negative relationship between the loss ratios. Such firms premium shares also seem to move

in opposite direction to the combined ratios, though not as closely as the percentage of the number of firms. In

other words, when loss ratios are high, we have fewer insurers that choose to sell malpractice only in safer

states. When loss ratios improve, we see more firms preferring to write malpractice only in CAP- or PCF- states.

This is consistent with our earlier observation in that fewer firms operating in safer states may have caused the

underwriting performance to decline in the first place.

22

Table 9: Number, Percentage and Premium Share of Insurers that Only Sell MM in Cap-/PCF- States

Year

Large Sample Small Sample Only-CAP Firms Only-PCF Firms Only-CAP Firms Only-PCF Firms

N % of

N % of

Premium N % of

N % of

Premium N % of

N % of

Premium N % of

N % of

Premium 1992 37 0.125 0.073 17 0.057 0.018 28 0.239 0.107 15 0.128 0.035 1993 38 0.129 0.071 14 0.048 0.019 32 0.264 0.107 14 0.116 0.037 1994 40 0.149 0.104 17 0.063 0.025 26 0.241 0.144 14 0.130 0.048 1995 38 0.144 0.097 15 0.057 0.023 28 0.259 0.138 15 0.139 0.037 1996 44 0.161 0.080 13 0.048 0.011 27 0.248 0.139 10 0.092 0.038 1997 39 0.142 0.083 10 0.036 0.007 27 0.257 0.156 9 0.086 0.038 1998 46 0.172 0.076 11 0.041 0.007 30 0.278 0.133 7 0.065 0.018 1999 36 0.134 0.070 10 0.037 0.008 27 0.237 0.117 8 0.070 0.020 2000 32 0.125 0.058 12 0.047 0.006 30 0.263 0.122 9 0.079 0.020 2001 32 0.132 0.063 5 0.021 0.005 26 0.245 0.107 9 0.085 0.015 2002 31 0.124 0.052 18 0.072 0.016 24 0.231 0.089 12 0.115 0.034 2003 49 0.178 0.054 33 0.120 0.021 27 0.241 0.091 18 0.161 0.043 2004 59 0.196 0.071 44 0.146 0.037 32 0.283 0.114 16 0.142 0.049 2005 74 0.239 0.086 42 0.135 0.041 38 0.328 0.144 19 0.164 0.055 2006 85 0.262 0.089 54 0.167 0.043 44 0.358 0.152 19 0.154 0.052 2007 75 0.232 0.088 44 0.136 0.029 46 0.371 0.158 18 0.145 0.050 2008 77 0.228 0.087 45 0.134 0.028 41 0.328 0.149 16 0.128 0.049 2009 71 0.211 0.076 45 0.134 0.032 39 0.320 0.136 16 0.131 0.048 2010 79 0.229 0.082 49 0.142 0.035 43 0.347 0.144 19 0.153 0.051 Source: authors analysis of NAIC data.

23

Figure 16: Large Sample: % and P-Share of Only Cap- or PCF- Firms

Figure 17: Small Sample: % and P-Share of Only Cap- or PCF- Firms

24

Table 10: Median Loss Ratios: Only-CAP Firms vs. Others

Year

Large Sample Only-CAP Firms

Large Sample All Other Firms

Small Sample Only-CAP Firms

Small Sample All Other Firms

Exp Ratio

Loss Ratio

Comb Ratio

Exp Ratio

Loss Ratio

Comb Ratio

Exp Ratio

Loss Ratio

Comb Ratio

Exp Ratio

Loss Ratio

Comb Ratio

1992 0.049 0.701 0.766 0.128 0.742 0.889 0.037 0.864 0.921 0.057 0.888 0.971

1993 0.058 0.798 0.892 0.125 0.733 0.845 0.059 0.940 0.954 0.053 0.848 0.931

1994 0.057 0.749 0.812 0.111 0.693 0.799 0.049 0.789 0.841 0.046 0.766 0.815

1995 0.046 0.625 0.691 0.113 0.779 0.902 0.048 0.693 0.747 0.047 0.896 0.937

1996 0.050 0.699 0.769 0.097 0.719 0.828 0.038 0.739 0.805 0.054 0.816 0.882

1997 0.070 0.639 0.794 0.107 0.727 0.863 0.070 0.813 0.827 0.047 0.864 0.916

1998 0.081 0.553 0.686 0.119 0.759 0.902 0.067 0.796 0.868 0.060 0.908 0.987

1999 0.065 0.732 0.845 0.114 0.854 0.958 0.063 0.806 0.847 0.063 0.900 0.963

2000 0.102 0.537 0.616 0.113 0.816 0.928 0.081 0.850 0.952 0.058 1.097 1.132

2001 0.082 0.847 0.905 0.117 0.986 1.063 0.070 1.101 1.103 0.077 1.074 1.120

2002 0.075 0.670 0.695 0.097 0.857 0.936 0.032 0.809 0.839 0.069 1.027 1.086

2003 0.084 0.711 0.741 0.077 0.741 0.825 0.047 0.807 0.834 0.065 0.892 0.975

2004 0.075 0.564 0.685 0.067 0.659 0.750 0.054 0.653 0.724 0.058 0.722 0.785

2005 0.064 0.580 0.647 0.069 0.659 0.734 0.050 0.634 0.661 0.055 0.682 0.749

2006 0.067 0.478 0.565 0.059 0.580 0.663 0.072 0.505 0.564 0.049 0.612 0.683

2007 0.068 0.489 0.584 0.070 0.506 0.586 0.076 0.488 0.556 0.051 0.529 0.600

2008 0.056 0.454 0.563 0.075 0.498 0.600 0.084 0.487 0.599 0.053 0.541 0.628

2009 0.064 0.436 0.513 0.073 0.520 0.618 0.064 0.500 0.548 0.068 0.545 0.657

2010 0.061 0.390 0.455 0.074 0.524 0.611 0.070 0.428 0.480 0.069 0.548 0.650 Source: authors analysis of NAIC data.

To gain some basic understanding of potential comparative advantage insurers that choose to write

malpractice only in safer states, we compare the underwriting performance of Only-Cap firms to that of their

counterparts. Table 10 reports the median values over time. We notice that firms that only sell medical

malpractice insurance in CAP-states have lower expense ratios for most years in Large Sample, but such

advantage is not as evident in Small Sample. In terms of loss ratios, from 1995 to 2010, firms that operate in

only CAP states have lower loss ratios than their counterparts. This is consistent with our observation when we

compare CAP-states versus No-CAP states and find that states that impose limits on general damages on

average have better underwriting performance in their jurisdictions than states with no such limits on awards.

This shows that caps on general damages indeed have effects on mitigating the crisis. The same pattern

regarding loss ratio and combined ratio is also observed in Small Sample, as evidenced in Figures 18 and 19.

25

Figure 18: Large Sample Loss Ratios: Only-CAP Firms vs Others

Figure 19: Small Sample Loss Ratios: Only-CAP Firms vs Others

26

CHOICE OF PROSPECTIVE POLICYHOLDERS TO COVER

The last aspect of the underwriting strategy we study is the types of health care providers insurers

choose to cover. Ideally, we want to find out what kind of high- or low-risk specialties carriers tend to cover

less or more. However, we do not have such information. In this study we utilize the best available data to do

some preliminary analysis. For this purpose we turn to Supplement A To Schedule T Exhibit Of Medical

Malpractice Premiums Written Allocated By States And Territories, which is an exhibit the NAIC didnt start

providing until 2001. This exhibit shows premiums and losses each insurer incurs in medical malpractice in

each state each year for each of the following four policyholder types defined by NAIC: PH (= physicians); OP

(= other health care professionals); HS (= hospitals); OF (= other health care facilities). In Table 11 we show

the total number of malpractice insurers each year3

and the percentage of premiums written to cover each type

of health care providers. We also show the median loss ratios of these providers. For instance, in 2001, 64% of

premiums are written to cover physicians who as a group have a median loss ratio of 0.83. Note that the loss

ratios discussed in this section are losses incurred divided by premiums earned since there is no information on

loss adjustment expense and underwriting expense by types of providers.

Table 11: Mean Premium Share and Median Loss Ratios to Cover Each Type of Provider

year

Small Sample Large Sample % of Premiums Written to

Cover Median Loss Ratios of % of Premiums Written to

Cover Median Loss Ratios of

N PH HS OP OF PH HS OP OF N PH HS OP OF PH HS OP OF 2001 87 0.64 0.22 0.09 0.06 0.83 0.94 0.33 0.85 188 0.45 0.17 0.30 0.09 0.71 0.84 0.35 0.55 2002 90 0.57 0.25 0.09 0.09 0.66 0.81 0.48 0.56 208 0.42 0.18 0.30 0.10 0.60 0.72 0.35 0.52 2003 97 0.58 0.28 0.06 0.08 0.66 0.61 0.30 0.46 227 0.45 0.17 0.57 -0.18 0.54 0.62 0.34 0.41 2004 102 0.60 0.28 0.05 0.07 0.50 0.54 0.31 0.51 251 0.49 0.21 0.18 0.12 0.47 0.49 0.31 0.48 2005 106 0.62 0.26 0.06 0.07 0.44 0.47 0.29 0.36 254 0.51 0.23 0.14 0.12 0.44 0.44 0.27 0.43 2006 109 0.61 0.26 0.06 0.07 0.38 0.49 0.38 0.31 269 0.49 0.21 0.15 0.15 0.37 0.43 0.32 0.31 2007 115 0.61 0.24 0.08 0.07 0.32 0.41 0.23 0.13 289 0.51 0.17 0.17 0.15 0.35 0.40 0.20 0.12 2008 115 0.61 0.25 0.06 0.08 0.31 0.34 0.28 0.37 309 0.52 0.17 0.17 0.14 0.31 0.33 0.22 0.27 2009 120 0.57 0.28 0.08 0.06 0.33 0.42 0.34 0.36 326 0.51 0.18 0.12 0.20 0.32 0.40 0.31 0.20 2010 122 0.58 0.28 0.08 0.06 0.33 0.33 0.24 0.23 327 0.50 0.19 0.17 0.14 0.33 0.32 0.19 0.33 Source: authors analysis of NAIC data.

As we can see from both samples, the majority of premiums are written to cover physicians, followed by

hospitals. Over the years, there is some fluctuation in physicians premium share, though hospitals premium

3 Note the total numbers somehow differ from our earlier analysis based on the State Page. The reason is that not every firm provides information on both the State Page and the Supplement A page.

27

share remains relatively stable. Also hospitals tend to have higher loss ratios which explain why insurers cover

less of them. As a matter of fact, many hospitals formed self-insured entities and do not report to NAIC.

Since there are no earlier years of premiums/losses breakdown by types of health care providers, we do

not observe significant trend during the years 2001-2010 by examining providers premium share and loss ratios.

We next turn to specialists that cover only one type of health care providers. Table 12 shows the percentage of

such specialist-insurers as well as their premium shares. We graph the same information in Figure 20.

Table 12: Analysis of Specialists Covering Only One Type of Health Care Providers

Year

Large Sample Small Sample % of Number of Firms

Covering Only Premium Share of Firms

Covering Only % of Number of Firms

Covering Only Premium Share of Firms

Covering Only HS PH OP OF S HS PH OP OF S HS PH OP OF S HS PH OP OF S

2001 0.04 0.13 0.17 0.03 0.36 0.09 0.04 0.03 0.00 0.16 0.03 0.12 0.02 0.01 0.18 0.08 0.05 0.02 0.00 0.14 2002 0.05 0.15 0.17 0.03 0.40 0.08 0.05 0.03 0.01 0.17 0.07 0.13 0.03 0.01 0.23 0.08 0.06 0.03 0.00 0.16 2003 0.07 0.18 0.12 0.05 0.41 0.09 0.02 0.01 0.01 0.14 0.07 0.13 0.01 0.02 0.23 0.09 0.02 0.01 0.01 0.12 2004 0.06 0.21 0.07 0.07 0.41 0.13 0.02 0.01 0.01 0.16 0.06 0.16 0.01 0.02 0.25 0.12 0.02 0.00 0.00 0.14 2005 0.06 0.22 0.06 0.06 0.40 0.13 0.02 0.00 0.01 0.17 0.07 0.20 0.01 0.03 0.30 0.12 0.02 0.00 0.00 0.15 2006 0.08 0.21 0.07 0.08 0.44 0.18 0.03 0.01 0.01 0.23 0.08 0.21 0.01 0.02 0.32 0.17 0.03 0.00 0.00 0.20 2007 0.06 0.25 0.09 0.08 0.48 0.16 0.03 0.02 0.01 0.22 0.07 0.21 0.02 0.02 0.32 0.14 0.03 0.00 0.00 0.17 2008 0.06 0.26 0.09 0.08 0.51 0.15 0.04 0.02 0.01 0.21 0.06 0.20 0.02 0.02 0.29 0.13 0.04 0.00 0.00 0.17 2009 0.07 0.29 0.10 0.10 0.57 0.17 0.07 0.03 0.01 0.27 0.08 0.23 0.03 0.02 0.36 0.14 0.07 0.01 0.00 0.23 2010 0.08 0.28 0.11 0.08 0.55 0.13 0.06 0.04 0.01 0.24 0.08 0.22 0.03 0.02 0.35 0.12 0.07 0.01 0.00 0.20 Note: S refers to specialists that cover only one type of health care providers. Source: authors analysis of NAIC data.

Figure 20: Analysis of % of Specialist-insurers

28

In 2001, 4% of firms in Large Sample cover only hospitals (HS), representing 9% of the total premiums

written in medical malpractice. Similarly, 36% of firms in Large Sample are specialists covering only one type

of health care providers in 2001, yet on average their premium share is 16% among all medical malpractice

insurers. In both samples, we see a largely increasing trend in the number of specialist-insurers over time, yet

their premium shares do not go up that much. Overall, were seeing more and more insurers covering only

physicians in their malpractice business, close to 28% in the Large Sample and about 22% in Small Sample by

2010, though their premium shares are still relatively small and stable. Figure 20 shows that for most years

(except 2010), the percentage of insurers only covering physicians and that of all specialist-insurers display an

opposite trend to that of the combined loss ratios. When we see insurers underwriting performance improve in

recent years, we also observe an increasing percentage of specilist insurers.

Test of Capacity Constraint Theory

Having discovered some aspects of insurers underwriting strategy display certain cyclical nature, we

now test whether the capacity constraint theory can explain such phenomenon in the medical malpractice

industry. The causes of insurance cycles have been extensively studied and in this section we want to focus on

the capacity constraint theory, while keeping in mind that tests of other cycle theories could be future research

topics.

Weiss and Chung (2004) summarize the capacity constraint theory well, The capacity constraint

hypothesis postulates that capital does not flow freely into and out of the insurance indutry due to market

imperfections As a result, insurers will be inclined to hold on to seemingly excess capital in profitable years, so

as to have capital available to pursue opportunities in lean years. The capacity constraint hypothessi assumes

also that claims are uncetain and correlated across policies, and insurers (combined) equity determins industry

supply. Because losses are correlated, all insurers will be affected similarly by a loss chock (e.g., adverse

interpretation of tort law for general liability insurers in the 1980s leading to a general liability crisis). Thus

industry-wide soft and hard markets will occur. In soft markets capital is plentiful, while in hard markets it is

relatively scarce. As a result, in periods of excess capacity, insurance price is relatively low, and it is relatively

high in periods with relatively low capitabization. Therefore prices are hypothesized to be sensitive to industry-

wide capital. (p442)

In our study we do not intend to examine how malpractice prices change in response to capacity change.

Instead, we will test the capacity constraint theory by examining whether an increase (or decrease) in capacity

29

will expand (or shrink) insurance supply. More specifically, we will use the following general form of

regression equation:

Y = f (lagged total surplus, lagged overall loss ratio, lagged firm-specific loss ratio).

We have the following four dependent variables, which are all binary variables.

Single_line: equals 1 if an insurer only sells medical malpractice in a particular year.

Single_state: equals 1 if an insurer sells medical malpractice in one single state in a particular year.

Only_CAP_state: equals 1 if an insurer only sells medical malpractice in states that have caps on general

damanges.

Specialist: equals 1 if an insurer only covers one type of health care providers.

The major independent variable is the capacity, which we measure using insurers surplus. Since we

have four different dependent variables, we have also four corresponding surplus variables, which are

constructed in the same manner though.

When we analyze how capacity affects insurers decision to go single-line, the surplus we are looking at

is the total surplus of all single-line insurers in a particular year. Similary, the surplus variable for the dependent

variable of Single_state (Only_CAP_state) is the total surplus of all single-state (Only_CAP_state) insurers.

The loss ratio variables in the regression equation refer to combined loss ratios when the dependent

variables are Single_line, Single_state or Only_CAP_state. When the dependent variable is Specialist, the loss

ratios are simply loss incurred divided by premiums earned.

The overall loss ratio variables are constructed in a similar manner to that of surplus variables. When the

dependent variable is Single_line, the overall loss ratio is the combined loss ratio of all single-line insurers.

Lastly, firm-specific loss ratios are firm-year level loss ratios.

Given the binary nature of our dependent variables, we run four firm-year level logistic regressions and

report our results in Tables 13 and 14, respectively. Table 13 shows the regression results of dependent

variables Single_line, Single_state and Only_CAP_state. We group them together because the relevant

information is all from the State Page. We have 4807 observations in Large Sample and 1891 in Small Sample.

The probability modeled here is Y equal to 1, which is the probability that an insurer is a single_line insurer,

single-state insurer or Only_CAP_state insurer. To account for potential endogeneity issue, we use lagged

values for all independent variables.

Our results show that lagged surplus has a positive relationship with the three dependent variables

derived from the State Page. A lower surplus in the previous year will reduce the probability that a firm will be

30

a single-line insurer, single-state insurer or Only_CAP_state insurer. This is consistent with the capacity

constraint theory in that a shrinking capacity will suppress the insurance supply. Our innovation here is that

instead of studying the entire malpractice industrys surplus, we look at segments of the industry and see how

capacity in a particular segment affects supply of insurance in that segment. When we divide the industry into

single-line and multi-line insurers, we have segments of single-line insurers and multi-line insurers. Similarly,

when we study geographic concentration and distribution of malpractice insurance between safer and less safe

states, we have different segementations. We show that capacity of a particular segment does positively affect

supply of insurance in that segement. Our empirical study provides evidence to support the capacity constraint

theory in the malpractice market.

Table 13: Logistic Regression of Underwriting Strategy: Probability modeled is Y=1

Large Sample (N=4807)

Y Results Intercept Lagged Total Surplus

Lagged Overall Loss Ratio

Lagged Firm-specific Loss Ratio

Single_line Estimate 0.3463

31

In the large sample, we also find that overall loss ratio of single-line insurers has a negative impact on

firms decision to go single-line. In other words, if loss ratios for single-line insurers go up, insurers are less

likely to just sell medical malpractice. This makes sense since insurers tend to move away from less profitable

business. In our small sample, we find that firm-specific loss ratios play a more significant role in determining

insurers likelihood of becoming single-state or only_CAP_state firms.

Table 14: Logistic Regression: Y=Specialist, Probability modeled is Y=1

Variables Large Sample N=2424 Small Sample N=947

Estimate ProbChiSq Estimate ProbChiSq Intercept 0.6348 0.0849 -0.3988 0.2755 Lagged Total Surplus

32

Moreover, when we look at which states in which malpractice carriers do business, we see that the percentage

of safer states (states that have caps on general damages or patient compensation funds) in which insurers write

malpractice and the percentage of insurers that choose to do business only in safer states are both negatively

associated with the combined loss ratio of the medical malpractice insurance industry. Taken all together, it

seems that at the industry level, insurers underwriting performance has a negative association with their risk

taking behavior in terms of how much to focus on malpractice line of business and where to write such business.

Less focus on malpractice and wider distribution of malpractice products are seen to accompany worsened

underwriting performance.

We also test whether the capacity constraint theory can help explain the cyclical nature of medical

malpractice insurers underwriting strategy. We find that when the total surplus of all single-line insurers (those

only selling medical malpractice) shrinks, insurers are less likely to go single-line. We observe the same trend

when we examine single-state insurers (those selling medical malpractice in just one state) and ONLY-CAP-

State insurers (those selling medical malpractice only in states with caps on general damages). In other words,

our results provide some support for the capacity constraint theory which predicts an inadequate capacity will

shrink insurance supply.

References 1. Elango B, Y Ma, and N Pope, 2008, An Investigation Into The Diversification-Performance Relationship In The

U.S. Property-Liability Insurance Industry, Journal of Risk and Insurance, 75 (3): 567-591.

2. Lei, Yu and Mark Browne, Fall 2008, Medical Malpractice Insurance Market Entry and Exit: 1994-2006,

Journal of Insurance Regulation, 27 (1), 47-71.

3. Lei, Yu and Joan Schmit, 2010, Influences of Organizational Structure and Diversification on Medical

Malpractice Insurer Performance, Journal of Insurance Issues, 33 (2): 152177.

4. Liebenberg AP and DW Sommer, 2008, Effects Of Corporate Diversification: Evidence From The Property-

Liability Insurance Industry, Journal of Risk and Insurance, 75 (4): 893-919.

5. Nordman, E., D. Cermak and K. McDaniel, 2004, Medical Malpractice Insurance Report: A Study of Market

Conditions and Potential Solutions to the Recent Crisis. Kansas City, Mo.: National Association of Insurance

Commissioners.

6. Viscusi, W Kip and Born, Patricia H, 2005, Damages Caps, Insurability, and the Performance of Medical

Malpractice Insurance, Journal of Risk and Insurance, 72 (1): 23-43.

7. Weiss, Mary A. and Joon-Hai Chung, 2004, U.S. Reinsurance Prices, Financial Quality, and Global Capacity, Journal of risk and Insurance, 71 (3): 437-467.