Working Paper No. 1/2008

June 2008

Revised April 2013

Corporate Insurance and Managers'

and Owners' Risk Aversion

Paul Ehling

© Paul ehling 2013. All rights reserved. Short sections of text, not to exceed two paragraphs, may be quoted without explicit permission, provided that full credit, including © notice, is given to the source. This paper can be downloaded without charge from the CCGR website http://www.bi.edu/ccgr

Corporate Insurance and Managers' and Owners' Risk

Aversion ∗

Paul Ehling†

BI Norwegian Business School

Current Draft: April 2013

∗I would like to thank Janis Berzins, Carsten Bienz, Barbara Bukhvalova, Øyvind Bøhren,Alexander Mürmann, Giovanna Nicodano, Per Pundsnes, Ibolya Schindele, Karin Thorburn, Char-lotte Ostergaard, participants at the �rst, second, and third CCGR workshop and conference par-ticipants at Corporate Finance and Governance of Privately Held Firms at BI and at the EuropeanWinter Finance Summit 2009 for helpful comments and suggestions. Jon Ånestad, William Birke-land, Jonas Carlsen, Hermann Overlie, Pål Rydland, and Emily Zhan provided excellent researchassistance. I thank Alexandra Coiculescu, Raphael Robin, Ling Yue, and Jing Yu for dedicatedresearch assistance. Financial support from Finansmarkedsfondet (project 178972) is gratefully ac-knowledged. I also thank the Center for Corporate Governance Research (CCGR) at BI for �nancialsupport and for extensive help with the CCGR database. Last but not least, I am grateful to AonGrieg Norway for sharing their data with me. Previous drafts of the paper circulated under thetitle: Risk Management with Cash and Insurance in Non-Listed Firms.†Tel.: +47 464 10 505; fax: +47 210 48 000. E-mail address: paul.ehling@bi.no. (P. Ehling)

Abstract

Using corporate property insurance contracts of small and medium-sized private �rms, Idocument negative relations between various ownership measures �CEO salary, ownershipconcentration and aggregate female ownership� and insurance use as well as a positiverelation between the number of family owners and insurance use. None of these relationsseem consistent with the risk aversion motive to insure or to hedge. Next, I show that changesin insurance premium and lagged changes in earnings are positively related, consistent withthe view that insurers raise premium after �rms' earnings soar. If mangers and owners withsigni�cant stakes reduce coverage at the margin or relative to the average �rm to counteractrent extraction by brokers or insurers, then the positive association between changes inearnings and changes in premium might help explain why proxies for managers' and owners'risk aversion show negative instead of positive relation with property insurance. Further, Iprovide evidence of strong causal relations between insurance, leverage and cash. Speci�cally,insurance and cash are risk management complements since insurance use exerts a positivein�uence on corporate cash holdings and cash exerts a positive in�uence on insurance use.

Keywords: Cash; Corporate Risk Management; Leverage; Managers' and Owners' RiskAversion; Private Firms; Property Insurance

JEL Classi�cation: G22; G32; G33; G35

1 Introduction

I present empirical evidence of a robust and signi�cant negative relation between owner-

ship variables �CEO salary, ownership concentration and aggregate female ownership�

and corporate property insurance use, and thereby apparently I reject the hypothesis that

managers' and owners' risk aversion provide incentives to increase corporate hedging policies.

One plausible interpretation of these relations is that �rms with high CEO salary, ownership

concentration and aggregate female ownership reduce insurance coverage, relative to the av-

erage �rm, since insurers exercise market power. This interpretation seems plausible since

changes in insurance premium and changes in lagged earnings show positive relation in the

data.

Small and medium-sized private �rms are most sensitive to changes in risk factors such as

exchange rates, interest rates and other shocks, for instance catastrophic events or causality

losses. Despite the fact that one single event can wipe out a small business and, thus, small

�rms have stronger incentives to hedge than large �rms, empirical work on corporate risk

management has basically overlooked small �rms. In addition, nearly all of the empirical

literature on corporate risk management focuses on derivative use. However, small or private

�rms are not signi�cant users of derivatives.

Therefore, I exploit a unique database from an international insurance broker on property

insurance purchases by small and medium-sized private Norwegian �rms to study insurance

use.1 The insurance data is merged with exceptionally detailed accounting data for these

private �rms. Signi�cantly, in Norway, all limited liability �rms, listed and private, face

an identical accounting and reporting environment. Therefore, the quality of the data is

likely high. Further, ownership variables are computed from family relations between two

1Motives for corporate hedging were initially introduced to justify derivatives use but are often equallyvalid for insurance use. Note, however, that derivatives can be used to control underinvestment and freecash �ow problems (Morellec and Smith (2007); Stulz (1990)) while insurance can control underinvestmentproblems only. Further, insurance can come with real services through the expertise of insurers in evaluatingrisks and managing claims settlement procedures (Mayers and Smith (1982). Derivatives brokers or banksdo usually not o�er such transfer of expertise through trade or other real services.

individuals, including relationships like sisters, cousins, uncle/aunt and nephew/niece.2

Importantly, insurance use data o�ers one crucial advantage over derivative use data,

namely, that exposures and the extent of hedging are easy to measure. According to the

accounting data, the average value of the insured assets is forty percent of the total assets.

As a result, risk management with insurance is signi�cant to the survival of the �rms in my

sample.

A crucial empirical question is whether managers' and owners' risk aversion (Amihud

and Lev (1981), Stulz (1984) and Smith and Stulz (1985), among others), provide incentives

to implement corporate hedging policies. E�ectively, the risk aversion hypothesis predicts

that ownership variables and the extent of hedging or insurance use are positively related.

However, the empirical evidence on the risk aversion hypothesis is mixed. Indeed, Mayers

and Smith (1982) argue that for large corporations with di�use ownership risk aversion as

the source of the demand for insurance is largely eliminated. Unlike in public �rms, owners

of small and medium sized �rms have, in general, tied their wealth to the �rm. In addition,

family �rms stress survival and the welfare of stakeholders as important concerns and, thus,

need to engage in risk management. A speci�c version of the risk aversion hypothesis is,

therefore, that the predicted positive relation between ownership variables and the extent

of insurance use is stronger or more relevant for private �rms. I, however, provide empirical

evidence of strong, statistically signi�cant, negative relations between ownership variables

�CEO salary, ownership concentration and aggregate female ownership� and corporate

property insurance use in private �rms. CEO ownership also shows a negative relation with

insurance use. Because CEO ownership substantially reduces sample size, it is excluded from

the main analysis. All these relations are inconsistent with the risk aversion hypothesis. I

also provide empirical evidence of a positive relation between the number of family owners

and insurance use. This relation is also inconsistent with the risk aversion hypothesis since

2Accounting as well as ownership data from Scandinavia have received increased attention recently. See forexample Bennedsen et al. (2007) employing data from Denmark, Sundgren and Wells (1998) studying Finnish�rms, and Thorburn (2000) using data from Sweden. For an overview of the accounting and ownership dataemployed in this study, see Berzins et al. (2008).

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the risk aversion motive is expected to diminish in importance as the number of owners,

within a family, increases. Importantly, one cannot attribute these relations to an unob-

servable adverse private �rm e�ect or to some peculiarity of Norwegian corporations since

Aunon-Nerin and Ehling (2008), employing data on U.S. public �rms, also �nd negative

relations between managerial ownership and property insurance coverage and between the

share holdings of 5% block owners and property insurance coverage. Aunon-Nerin and Ehling

(2008), however, do not elaborate on or further investigate their counter-intuitive �nding.

The above relations between ownership variables and insurance use are consistent with

over-insuring by �rms with low or below average CEO salary and ownership variables, as well

as with self-insurance among CEO or family controlled �rms, given insurers exercise market

power. Indeed, there exists anecdotal as well as empirical evidence on insurers possessing and

exercising market power. Aunon-Nerin and Ehling (2008), for example, observe increases in

property insurance premiums up to 300% within one year. Notably, Dafny (2010) documents

that health insurers charge higher premiums to more pro�table �rms. In my sample, I �nd

that in a regression of changes in premium on a constant, lagged changes in earnings, changes

in property, lagged changes in property plus other controls, the coe�cient on lagged changes

in earnings is signi�cantly positive. The positive relation is consistent with insurers raising

property premiums to �rms that experience an increase or a series of increases in earnings.

Indeed, changes in earnings together with lagged changes in earnings also show signi�cantly

positive coe�cient estimates possibly suggesting that insurers extract rents speci�cally by

leveraging on earnings persistence.

While the positive relation between the dynamics of earnings and property insurance

premium is also consistent with the view that �rms raise coverage, and therefore premium,

in response to an increase in earnings, it appears controversial whether such a relation is

economically sound. Certainly, it is less clear why such a relation should simultaneously

imply a negative relation between ownership variables and corporate property insurance use.

Speci�cally, managers' and owners' risk aversion over catastrophic events is unlikely to relate

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to earnings or pro�tability. In addition, purchasing more coverage or wasting premium even

without raising coverage do not appear to be fruitful routes through which managers and

owners can misappropriate a �rm's surplus. Even in a catastrophic event with a major

causality loss there is nothing to be gained for managers and owners since no insurance

contract covers the entire loss.3 In the end, misappropriating a �rm's surplus via insurance

would require fraud. Even attempted fraud can be ruled out to drive my empirical results

since fraud and increases in earnings or pro�tability seem an odd couple. Hence, although I

cannot empirically rule out that earnings drives insurance premium, I do rule it out based

on lack of incentives for such an economic mechanism.

I analyse insurance use, leverage and liquidity jointly by employing simultaneous-equation

regressions. This is an important aspect of corporate insurance, or more generally corporate

hedging, as property insurance may allow �rms to increase debt capacity. A second reason

for why it is important to empirically allow for simultaneity between insurance and debt is

that debt contracts frequently stipulate levels of insurance coverage, Smith (1995). Not sur-

prisingly, I �nd that private �rms insure to increase their debt capacity. For the �rms in my

sample, it is also true that the debt ratio exerts a positive in�uence on insurance use. These

two results show that private �rms do not di�er from public �rms in this respect (Graham

and Rogers (2002) and Aunon-Nerin and Ehling (2008)). Perhaps surprisingly, I �nd that

insurance use exerts a positive in�uence on corporate liquidity. I also �nd the converse, that

corporate liquidity exerts a positive in�uence on insurance use. To my knowledge, this is

the �rst empirical evidence that insurance and liquidity are complements. The view that

cash and insurance are complements is consistent with Rochet and Villeneuve (2011) who

prove, in a model where the �rm's cash management interacts with Brownian risk (hedged

with derivatives) and with Poisson risk (hedged with insurance), that cash-rich �rms should

3Property insurance contracts de�ne coverage limits per causality and have with annual aggregates.Aggregates may be eliminated at the insurers discretion. If so, the market value of the property is themaximum coverage. Further, even if all losses due to business interruption are insured too it is still expectedthat some clients move on to competitors since it takes time to rebuild. This time to rebuild risk relating tothe customer base prior to a causality loss cannot be entirely insured.

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insure but cash-poor �rms should not. Lastly, although insurance and cash are complements,

�rms do not hold cash to increase debt capacity. Cash exerts a negative in�uence on long-

term debt and long-term debt exerts a negative in�uence on cash. Hence, long-term debt or

leverage and cash are substitutes.

The last two points are related to Acharya et al. (2007) who argue that cash is held as a

hedging instrument rather than as negative debt by �nancially constrained �rms. They show

theoretically �and provide empirical evidence supporting the theory� that constrained

�rms with high hedging needs prefer higher cash to lower debt, but �rms with low hedging

needs prefer lower debt to higher cash. Although the aims of this paper di�er from the

ones in Acharya et al. (2007), I nevertheless �nd that cash serves two purposes: it is a

hedging instrument (complement with insurance), but may also be interpreted as negative

debt (substitute for long-term debt). Evidence for the substitutability between cash or

liquidity and long-term debt is provided in Opler et al. (1999); concretely, they show that

�rm characteristics known to be empirically associated with low debt are also associated

with high cash.

The plan of the paper is as follows: Section 2 contains a brief literature review. Section

3 develops hypotheses regarding managers' and owners' risk aversion. Section 4 describes

the data, the explanatory variables used and the motivation for their inclusion. Section 5

contains the main empirical analysis. Section 6 contains empirical evidence on the negative

relation between ownership variables and insurance use. Section 7 contains robustness checks,

and conclusions are provided in Section 8. Appendix A provides a detailed description of

the data, while Appendix B contains sign predictions.

2 Literature Review

This paper relates to four strands of literature. First, in the derivative use literature Tufano

(1996), Schrand and Unal (1998) and Graham and Rogers (2002) �nd that hedging increases

5

with managerial share ownership. Second, Tufano (1996) and Haushalter (2000) �nd that

hedging is decreasing in managerial option ownership. However, it has been argued, Rogers

(2002), that the lack of evidence for the risk aversion motive in previous literature (see

Gezcy et al. (1997) or Allayannis and Ofek (2001)) may be a result of poor proxy variables

for executive option holdings. Assuming that managerial compensation schemes are jointly

determined enables Coles et al. (2006) to show that option delta and vega a�ect �rm policy.

The somewhat con�icting or at least weak �ndings regarding a relation between managerial

stock and option ownership and corporate hedging with derivatives, with the exception of

Coles et al. (2006), suggest that the evidence for the risk aversion motive to hedge or insure

can be di�cult to identify in the data even when it matters for risk management.

Second, another smaller strand of the literature studies corporate risk management with

insurance (Mayers and Smith (1982), Mayers and Smith (1987), Mayers and Smith (1990),

Yamori (1999), Hoyt and Kang (2000), Zou et al. (2003), Aunon-Nerin and Ehling (2008),

and Ehling et al. (2013)).4 Within this strand, only Aunon-Nerin and Ehling (2008) ana-

lyze managerial ownership, institutional ownership and block-owners' in�uence on corporate

insurance use.5 They report, consistent with the results presented in this paper, negative

relations between block owners and insurance coverage and between managerial ownership

and insurance coverage. I provide evidence, especially in form of more detailed ownership

variables, that complements Aunon-Nerin and Ehling (2008). In addition, I provide an ex-

planation for the negative relation between proxies for managers' and owners' risk aversion

and property insurance use that does not rule out that managers' and owners' risk aversion

and property insurance use have positive relation. My explanation, however, may rule in

4Mayers and Smith (1990) study reinsurance purchases by property/casualty insurance companies. Theirevidence is consistent with the hypothesis that risk aversion provides incentives to increase corporate hedg-ing/insurance. Speci�cally, they �nd that the less diversi�ed the owners' portfolios, the greater the rein-surance purchases. Market power is likely to be much larger in the reinsurance market than within theinsurance industry. Precisely therefore the results of this paper stand not necessarily in contrast to theresults in Mayers and Smith (1990). Speci�cally, if reinsurers extract monopolistic rents from all insurers,they then might not be able to further discriminate against pro�table insurers.

5Ehling et al. (2013) employ the data in Aunon-Nerin and Ehling (2008) but use only managerial ownershipas explanatory variable.

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that insurers extract rents from �rms.

Third, Vickery (2008) is �rst to study risk management practices in private �rms by

investigating how these �rms adjust their interest rate exposure via �xed-rate or variable-

rate loans. He provides evidence for why �rms engage in risk management through standard

arguments, such as due to �nancial constraints, and draws on two surveys. According to

Vickery (2008), owners do not have a signi�cant e�ect on the risk management decision.

Speci�cally, Vickery (2008) employs, among other ownership-related variables, the owner's

age and the concentration of ownership, but �nds no relation. The only evidence supporting

the risk aversion motive is that adjustable-rate loans are more common among �rms with

wealthier owners, which is consistent with the notion that risk aversion is declining with

increased wealth. This paper complements Vickery (2008) in that it shows how capital

structure and cash policies in private �rms relate to the management practices with property

insurance.

Fourth, this paper also relates to the growing literature on corporate liquidity. Recent

contributions include Bates et al. (2009) who show that cash ratios increase over time because

cash �ows of �rms become riskier and because �rms change, as well as Lins et al. (2010),

who show that �rms hold more cash in countries with greater agency problems.6 Again,

Acharya et al. (2007) argue that cash is a hedging instrument for �nancially constrained

�rms with growth opportunities if their cash �ows show low or even negative correlation

with the investment opportunity set. I add to this literature by showing that cash and

insurance are complements.

3 Managers' and Owners' Risk Aversion

I test the hypotheses that managers' and owners' risk aversion drive corporate risk manage-

ment with property insurance. For a given level of risk aversion, the hypothesis implies that

6Other notable papers addressing corporate liquidity include Kim et al. (1998), Opler et al. (1999),Dittmar et al. (2003), Almeida et al. (2004) and Faulkender and Wang (2006).

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the more a manager or owner is exposed to �rm risk the more coverage will be demanded

through the �rm.

3.1 Hypothesis Development

CEO salary and share holdings In untabulated analysis I �nd that small �rms show

higher variability of CEO salary and dividends than large �rms in my sample as well as

in the population. A likely reason for the high variability of CEO salary and dividends in

small private �rms is that CEO salary and dividends absorb cash �ow shocks. Risk averse

managers, however, value smooth salary and payout. In trading o� higher premium or even

excessive premium versus states of nature with particularly low cash �ows, i.e. when there

is a severe causality loss and therefore with particularly low salary and payout, risk averse

managers with above average exposure to �rm risk are expected to purchase above average

coverage.7

Owners I employ ownership concentration, family size and female share holdings to mea-

sure various aspects of owners' risk aversion: The higher the ownership concentration the

less likely is that owners are well diversi�ed. Therefore, the risk aversion motive to insure

predicts a positive relation between coverage or premium and ownership concentration.

Females are often considered to be more risk averse. Overall, the empirical evidence

certainly supports this view, Gerdes and Gransmark (2010). Even though, some of the

evidence on high risk aversion for females is likely to su�er from an endogeneity problem, see

for instance Alesina et al. (2013). One stylized example for how endogeneity a�ects outcomes

that are not related to risk aversion might be a male banker who o�ers less risky portfolios

to female clients because of his misguided perception of higher female risk aversion. Still,

there are only few studies that refute the hypothesis that women are more risk averse than

7Note that both risk aversion channels, salary and share holdings, are self-enforcing. The higher the CEOsalary, CEO shareholdings, or both, keeping everything else constant, the more powerful is the CEO relativeto other stakeholders. A more powerful CEO will always �nd it easier to persuade other stakeholders topurchase more property insurance.

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men. I, therefore, predict a positive relation between female share holdings and coverage or

premium.

Keeping everything the same, the larger a family of owners is the lower is each family

members share in a given �rm. Lower share holdings increase the likelihood for each family

member to have other signi�cant investments or that they are well diversi�ed. I, therefore,

predict a negative relation between family size and coverage or premium.

3.2 Controls

Other incentives for corporate hedging or insurance as well as �rm characteristics that a�ect

insurance include bondholders motives (Mayers and Smith (1982), Smith and Stulz (1985),

Shapiro and Titman (1986), Stulz (1996), and Aunon-Nerin and Ehling (2008)), equityhold-

ers motives (Main (1983), Smith and Stulz (1985), and Chen and PonArul (1989)), under-

investment problems (Myers (1977), Mayers and Smith (1987), Shapiro and Titman (1986),

Lessard (1990), Stulz (1990), and Froot et al. (1993)), dividend yield or payout (Aunon-

Nerin and Ehling (2008)), informational asymmetries (DeMarzo and Du�e (1991), Grace

and Rebello (1993), DeMarzo and Du�e (1995), and Breeden and Viswanathan (1998)), as

well as industry, regulatory, size, and year e�ects (Hoyt and Kang (2000)). I use standard

proxies for these controls. Sign predictions are found at Appendix B.

4 The Data

The insurance data are property insurance contracts between Norwegian private limited

liability �rms and Aon Grieg, an international insurance broker. Note that, liabilities are

not covered by the property insurance contracts. Further, business interruption contracts are

separately available and I, therefore, exclude these contracts from the analysis. The original

panel data set contains more than 1, 855 �rm-year observations, ranging from January, 2003

through May, 2006. The database contains a few listed companies, but the vast majority of

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the data is for private �rms. Therefore, I focus the study on private �rms. Each insurance

contract is matched with accounting and ownership data from the CCGR database when

available.8 A brief account of the selection and matching procedure is provided in Appendix

A.

Table 1 shows the descriptive statistics of sample �rm's property insurance premium data.

Premium is the annual insurance premium in Norwegian Kroner (NOK). The �nal sample

contains insurance premiums for 933 �rms with 1, 855 �rm-year observations. The mean

(median) for the insurance premium is NOK 192, 432 (45, 848).9 The premium data show

wide variation across �rms and to a lesser extent over time as evidenced by the di�erence

between the minimum (164 NOK) and maximum (15, 281, 813 NOK) observation. Firms

with positive long-term debt ratio show a mean (median) for the insurance premium of

235, 523.60 (65, 490) while �rms with no long-term debt show a mean (median) for the

insurance premium of 127, 210.70 (29, 240.50). Although �rms with positive long-term debt

ratio show a substantially higher mean and median premium as well as lower skew in premium

than �rms with no long-term debt, both groups of �rms exhibit a similar wide variation in

premiums.

I follow Mayers and Smith (1982), Mayers and Smith (1987), Mayers and Smith (1990),

Yamori (1999), Hoyt and Kang (2000) and Zou et al. (2003) in interpreting the insurance

premium as a proxy for the extent of property coverage. Indeed, Aunon-Nerin and Ehling

(2008) argue that their main results are practically unchanged when replacing coverage with

premium. The analysis below assumes that the leasing of property, which is unobservable,

does not systematically bias insurance coverage.

The insurance ratio, which represents the dependent variable employed in this study,

is property insurance scaled by property, plant and equipment plus inventories (PP&E+I).

Because of extreme outliers of the insurance ratio, the smallest and largest one percent are

8The accounting and ownership database are maintained at the Centre for Corporate Governance Research(CCGR) at BI Norwegian Business School.

9The USD/NOK exchange rate during the sample period ranges from roughly 6.11 to 7.41 (monthlyaverages), source: www.norges-bank.no.

10

excluded from the analysis and from Table 1. Appendix A contains further information

regarding outliers.

Table 1 also reports the property insurance premium to dividends and the property

insurance premium to earnings ratio. Both measures suggest that corporate spending on

insurance premiums to cover losses of property is economically signi�cant. The value of

the insured assets is economically signi�cant too as the mean of PP&E+I to total assets is

greater than thirty percent, reaching forty percent for the subsample with positive long-term

debt ratio. This implies that risk management with insurance is signi�cant to the survival

of these �rms.

Firms with zero long-term debt are smaller (untabulated) and pay smaller insurance

premium than �rms with positive long-term debt. Another di�erence between the two sub-

samples in Table 1, namely the mean of the insurance ratio, which is statistically signi�cant,

may be interpreted as implying that �rms with zero long-term debt purchase more insurance

per unit of property. This interpretation is plausible since most property insurance contracts

cover replacement costs: other �rm characteristics, such as �rm age or the average age of

property, are less likely to cause the higher insurance ratio for �rms with zero long-term

debt. An alternative and certainly plausible interpretation is that brokers or insurers or

both systematically discriminate against smaller �rms.

Table 2 reports the distribution of sample �rms across industries, the number of obser-

vations over time, and the percent of �rms with negative and positive earnings as well as

negative and positive equity. To simplify, I use a one digit industry classi�cation system

that relies on 8 industries instead of the standard approach using 10 industries.10 Firms that

are active in multiple industries are assigned to the Multi Group industry.11 Approximately

10Of the 1,618 �rm-years for which the insurance ratio is available, 299 observations have a missing value forthe industry classi�cation code. However, I note that, based on the data available for the period 1994-2006,no �rm appears to change the industry classi�cation code in my subsample of the population. Therefore, I�ll in missing values with the industry code for the same �rm if it is available for at least one year. Thisresults in only 6 cases with missing industry classi�cations for the sample of �rms with available insuranceratio.

11Derivative use work excludes �nancial �rms from the analysis because hedging and sales of derivativesare indistinguishable and because banks are regulated. Initially, I do not exclude �nancial �rms since the

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one third of the data are from the year 2004 and the year 2005. The remaining one third

of the �rm-year observations are almost equally from years 2003 and 2006. The table shows

that almost seven percent of the �rm-year observations are from �rms with negative equity.

Table 2 also shows characteristics for the subsamples of �rms with positive long-term debt

and for �rms with zero long-term debt. Overall, the two subsamples, zero long-term debt

versus positive long-term debt, do not di�er substantially on the reported characteristics of

Table 2.

Table 3 summarizes the descriptive statistics (mean, standard deviation, minimum, me-

dian, and maximum) of the corporate accounting variables which, according to corporate

�nance theory, motivate the purchase of insurance. I employ the following explanatory vari-

ables related to incentives for corporate insurance use. CASH is cash and equivalents divided

by assets. CEOSALARY is the CEO salary divided by assets times 1,000. CONCENTRA-

TION is the Her�ndahl index of equity ownership.12 DIV is dividends scaled by total equity.

FAMILYSIZE is number of family owners of the largest family. LTD stands for the book

value of long-term debt divided by assets. FEMALE is aggregate female ownership divided

by 100. INSTOWN is institutional ownership divided by 100. INTANGASSETS is intan-

gible assets scaled by total assets. OPEX is operating expenses scaled by sales. PP&E is

property, plant, and equipment as a percentage of assets. ROA is the return on assets (oper-

ating earnings over assets). SALESGROWTH is the three-year moving average percentage

growth in sales. SALES denotes the logarithm of sales. SIZE stands for the logarithm of

assets.

One can see from Table 3 that �rms with zero long-term debt substantially di�er from

�rms with positive long-term debt. For instance, �rms with zero long-term debt show a

higher mean for CASH, higher mean for CEOSALARY, higher mean for DIV, etc. These

�rms in my sample do not engage in sales of insurance contracts. In the regressions below, I always excludethe �nancial industry dummy and the multi group industry dummy since both have small sample size. Inthe end, �nancial �rms show several missing sales data and are, thus, excluded from the reported regressions.

12The �ndings presented below are robust to various other ways (percentage of the largest owner andlogarithm of the number of owners) of calculating ownership concentration.

12

di�erences between means are highly statistically signi�cant.

In previous versions of this paper, I employed various other standard corporate �nance,

risk management or ownership related variables. These variables did not add explanatory

power to the regressions or represent an alternative proxy for the same hedging motive.13 I

note that several slightly modi�ed regression speci�cations lead to results that are qualita-

tively similar to the ones presented below.

Untabulated correlation coe�cients of the explanatory variables, along with the insurance

ratio, are low, mostly insigni�cant, and thus suggest that colinearity problems will not a�ect

the regressions presented below. The only exception is the rather high correlation, 0.7,

between SALES and SIZE. As I argue in Section 7, the high correlation between these two

�rm characteristics raises the hurdle for �nding robust evidence for the various hedging

theories and therefore does not drive my results.

5 Empirical Findings

In this section, I study whether corporate property insurance purchases of private �rms are

explained by standard proxies, with focus on proxies for managers' and owners' risk aversion,

that aim at measuring corporate �nance hedging motives. I further focus on the simultaneity

between capital structure, cash, and insurance. I present results of linear GMM simultaneous

equations with heteroskedasticity and autocorrelation consistent p-values.

Table 4 presents the �rst insights into the relation between ownership variables and

corporate property insurance purchases. The dependent variables are insurance premium

scaled by PP&E+I, LTD, and CASH. The LTD equation is motivated by Graham and

Rogers (2002) and the related literature, while the CASH equation is motivated by Bates

13Variables excluded from the main analysis of the paper include: average family owner size, cash dividedby current liabilities, CEO is member of largest family owner dummy, CEO ownership, chair is memberof largest family owner dummy, convertible loans divided by the book value of assets, debt equity ratio,direct ownership of all family owners, direct ownership of largest family, dividends per share to earnings pershare, logarithm of number of employees, number of seats of largest family owner, regional dummy variables,ultimate ownership of all family owners and ultimate ownership of largest family.

13

et al. (2009) and also by Lins et al. (2010).14

CEOSALARY, CONCENTRATION, and FEMALE show negative and signi�cant coe�-

cient estimates in the INSURANCE equation of Table 4. These results are inconsistent with

the risk aversion motive put forward in Amihud and Lev (1981), Stulz (1984) and Smith and

Stulz (1985).

The relation between the dividend yield and insurance use is negative and signi�cant.

This result complements the evidence in Aunon-Nerin and Ehling (2008) on public �rms.

They argue that if dividends and free cash �ow are positively related, then a �rm with high

dividends is less likely to fail to rebuild when a casualty loss occurs. This is likely to be true

for private �rms too. The above relation is also consistent with the view that if dividends

are high, then the under-investment problem is small due to the negative relation between

dividends and the investment opportunity set (Smith and Watts (1992)).

According to Grace and Rebello (1993), favorable information may be signaled through

a high level of insurance coverage and vice versa. DeMarzo and Du�e (1991), DeMarzo and

Du�e (1995) and Breeden and Viswanathan (1998) also provide models of informational

asymmetry that motivate hedging. Informational asymmetries are expected to be lower

for �rms with high institutional ownership than for �rms with low institutional ownership.

However, the empirical evidence for this hedging incentive is weak or even inconsistent with

the predictions. Aunon-Nerin and Ehling (2008) �nd no relation between institutional own-

ership and property insurance coverage. Purnanandam (2008) �nds that derivative hedgers

have signi�cantly higher institutional shareholdings than non-hedgers. Further, Graham and

Rogers (2002) and Rogers (2002) �nd a positive and signi�cant relation between institutional

ownership and the extent of derivative hedging.

The negative relation between INSTOWN and INSURANCE, which is insigni�cant, in

Table 4 appears, at �rst, to support the informational asymmetry hedging motive. However,

it is not clear to whom the �rms may signal through insurance. Since private �rms rarely

14Regression speci�cations are adapted to private �rms and data availability.

14

attract signi�cant investments by institutional investors, it is more likely that institutional

investors either directly or indirectly in�uence the �rms in my sample to reduce insurance.

To control for pro�tability, I use ROA. In Table 4, ROA shows a signi�cant coe�cient

estimate with negative sign, which is consistent with the sign of DIV. It is also consistent with

the view that high dividends imply greater free cash �ow and that dividends are typically

paid by pro�table �rms.

SIZE shows predicted sign but is insigni�cant. The insigni�cant coe�cient may be caused

by the rather small variability of �rm size in the data. Recall that the �rms in the database

need to reach a certain size to justify an insurance broker. This excludes very small �rms.

Focusing on �rms with positive LTD also removes smaller �rms. Since insurance data on

listed �rms is scarce in my sample (and excluded from the analysis), many large �rms were

automatically excluded. Hence, the nature of sample �rms and the selection procedure

reduce cross-sectional variation in �rm size relative to the population.

The variable SALESGROWTH, a proxy for growth options, shows a negative sign and

is insigni�cant. This hedging argument is based on the idea that it is optimal for �rms

with growth options to reduce cash �ow variability. Note that Aunon-Nerin and Ehling

(2008) argue that insurance only a�ects cash �ow variability indirectly, via the insurance

deductible. Since I use insurance premium, which proxies for insurance coverage rather

than for the insurance deductible, it appears that the insigni�cant coe�cient estimate for

SALESGROWTH is plausible.

The simultaneous-equation regressions setting allows for controlling for insurance use

due to debt demand. This is an important matter because hedging or insurance allows �rms

to increase debt capacity. Too, �rms with high debt may be contracted with debtors to

insure. Indeed, sample �rms insure, as suggested by Table 4, to increase debt capacity. For

the �rms in my sample, it is also true that the debt ratio exerts a positive in�uence on

insurance use. This shows that private �rms do not di�er from public �rms in this respect,

see Graham and Rogers (2002) for corporate derivative use and Aunon-Nerin and Ehling

15

(2008) for corporate property insurance use. Next, note that INSURANCE exerts a positive

in�uence on corporate liquidity, CASH. Signi�cantly, I also �nd that the insurance-liquidity

relation exists in reverse, in that CASH exerts a positive in�uence on INSURANCE. This

is the �rst evidence that insurance and liquidity, both can be viewed as a form of risk

management, are complements. Although insurance and liquidity are complements, �rms

do not hold cash to increase debt capacity. Speci�cally, Table 4 shows that liquidity exerts

a negative in�uence on long-term debt, and that long-term debt exerts a negative in�uence

on liquidity. Therefore, long-term debt and liquidity behave as substitutes.

The view that liquidity serves two purposes, namely to hedge as well as to directly or

indirectly reduce debt, is consistent with Acharya et al. (2007). They argue that cash is held

as a hedging instrument rather than as negative debt by �nancially constrained �rms with

high hedging needs.

Next, I turn to the LTD and CASH regression results in Table 4. The coe�cient estimates

in the LTD equation, in Table 4, are consistent with the results in Graham and Rogers

(2002) with the exception of SALESGROWTH. Graham and Rogers (2002) employ R&D

expenditure15 as a proxy for growth options and report a signi�cantly negative coe�cient

estimate. I, however, document a signi�cantly positive coe�cient estimate for sales growth.

The di�erence might be due to a di�erence in the timing of the growth options embedded in

the two measures. The coe�cient estimates in the CASH regression are also consistent with

the �ndings reported in Bates et al. (2009). Note, however, that Bates et al. (2009) make

their prediction for a dividend payout dummy and, thus, their results cannot be directly

compared to the results reported in Table 4. DIV, in Table 4, is highly signi�cant and shows

a positive sign whereas Bates et al. (2009) predict and �nd a negative coe�cient for the

dividend payout dummy variable.

To the extent that insurance use, leverage and liquidity are simultaneously determined,

these �rm policies are likely to be a�ected by the same explanatory variables. Speci�cally,

15R&D expenditures are not available from the CCGR database.

16

what is the in�uence of managerial and owners' risk aversion for leverage and liquidity? The

simultaneous-equation regression model in Table 5 re-estimates the model in Table 4 using

CONCENTRATION and FEMALE also in the LTD and in the CASH equation.

In Table 5, the coe�cients yield slightly di�erent results in signi�cance in comparison

to those obtained in Table 4. CEOSAL is now insigni�cant and INSTOWN is only slightly

insigni�cant, whereas in Table 4, INSTOWN shows a p-value of 0.22 in the INSURANCE

regression. In the LTD regression, there is almost no change in signi�cance in comparison

to those obtained in Table 4, except that the p-value of OPEX drops from 0.14 to 0.11.

The coe�cients in the CASH regression of Table 5 yield identical results in signi�cance in

comparison to those obtained in Table 4. Note also that there is not one single sign change in

Table 5 relative to Table 4. Interestingly, CONCENTRATION and FEMALE show positive

relations with LTD although CONCENTRATION is insigni�cant and, as well, positive rela-

tions with CASH. The latter relations are consistent with a risk aversion motivated hedge.

I conclude from this evidence that risk aversion matters for �rm policies but often it is hard

to detect in the data for instance because other incentives cover it up.

I also study the in�uence of family ownership variables.16 However, family ownership

variables including ultimate ownership of all family owners and ultimate ownership of largest

family show no signi�cant relations with INSURANCE. The only exception is the number of

owners in the largest family. Recall that the risk aversion hypothesis for owners is expected

to become less important when the number of owners increases. FAMILYSIZE, however,

shows a signi�cantly positive relation with INSURANCE in Table 6. This result underscores

the coe�cient estimates of CONCENTRATION and FEMALE, which also show unexpected

sign in Tables 4-6. Further, FAMILYSIZE shows signi�cantly negative relation with CASH,

which is consistent with a risk aversion motivated hedge and consistent with the CASH

16The database contains any family's ownership in a �rm. Family ownership is classi�ed as a pair interms of a basic family relation as of the year end 2007. Family relations are parents, grandparents, great-grandparents, great-great-grandparents and marriage. 18 types of family relations between two individuals,including relationships like sisters, cousins, uncle/aunt and nephew/niece are included in the database.

17

equation in Table 5.17

To sum up, the empirical results suggest that corporate insurance use is signi�cantly

a�ected by ownership structure and the CEO's private motives. However, the coe�cient

estimates show the wrong sign. In addition, private �rms insure to increase their long-term

debt capacity; insurance and liquidity are complements; and liquidity and long-term debt

are substitutes.

6 Explaining the Negative Relation between Ownership

Variables and Insurance

In this section, I present empirical evidence that may explain the strong negative relation

between ownership variables �CEO salary, ownership concentration and aggregate female

ownership� and corporate property insurance use.

Dafny (2010) argues that health insurers exploit more pro�table �rms. If this is also a

common practice in the property insurance industry, then, on the one hand, it is conceivable

that �rms with low CEO salary, low ownership concentration or low female ownership ignore

or even facilitate monopolistic insurance premium pricing practices. On the other hand, �rms

with high levels of CEO salary, ownership concentration and female ownership, in one way

or another, probably respond to overpriced insurance contracts by cutting back on coverage

and thus also on premium. If the motive to cut back on coverage, relative to the coverage

that would prevail without insurers trying to exploit pro�table �rms, is marginally stronger

in the regressions or in general than the risk aversion hedging motive then we may have an

explanation for the negative relation.

17Ozkan and Ozkan (2004) document a nonlinear relation between ownership and cash holdings using asample of UK �rms. Harford et al. (2008) also document a nonlinear relation between ownership and cashholdings in US �rms. In contrast to these studies, I do not �nd that there is a nonlinear relation betweenownership and cash when replicating their analysis in my linear GMM simultaneous equation regressions.One possible reason for the lack of nonlinearity in my data is the short time series that does not allow toidentify such a relation although it might be present. Another reason could be that in private �rms theownership structure does not permit a nonlinear relation between ownership variables and cash.

18

Results in Table 7 support the view that insurers exploit �rms that experience increased

earnings. The table contains regressions of changes in premium on a constant, changes in

earnings, lagged changes in earnings, both changes in earnings and lagged earnings, changes

in PP&E+I and lagged changes in PP&E+I plus year and industry dummy variables. Lagged

changes in earnings and both changes in earnings and lagged changes in earnings show

signi�cantly positive coe�cients, possibly implying that insurers raise property premiums to

�rms that experience an increase in earnings or a series of increases in earnings.

Of course, an alternative interpretation, which I cannot rule out empirically, is that prof-

itable �rms raise coverage or incorporate other, potentially expensive provisions, into their

property insurance contracts. Importantly, both interpretations help explain why I �nd a

strong negative relation between ownership variables �CEO salary, ownership concentra-

tion and aggregate female ownership� and corporate property insurance use. In any case,

if pro�table �rms do raise coverage, this practice would be less common among �rms with

high ownership concentration.

Overall, the evidence in Table 7 supports the view that managers' and owners' risk

aversion matter, albeit with a twist: The negative relation between ownership variables and

insurance use may be due to �rms response to monopolistic insurance premium that is more

signi�cant at �rms that have high managers' and owners' risk aversion.

7 Robustness

The results presented above are robust to various ways of treating outliers. For example,

dropping observations of the insurance variable at 5% (on both sides of the distribution),

instead of 1%, or keeping outliers of the explanatory variables in the sample, see Appendix

A, does not alter the main �ndings of this paper.

Next, I address the high correlation between SALES and SIZE. Note that the correlation

between these two �rm characteristics a�ects only �rst-stage estimates since only one of the

19

two variables appears in the three second-stage regressions. This, however, only raises the

hurdle for �nding robust �that is, signi�cant� evidence for the various hedging theories

that are tested for in this paper. Moreover, when I exclude SALES from the �rst-stage

regressions, I then obtain coe�cient estimates in the second-stage which are almost identical

to the reported results.

The exogenous variables in Tables 4 to 6 are obtained by using data for the �scal year-end

prior to the start of the insurance contract. First, when the data from the �scal year-end

after the insurance policy was initiated is used, similar results are obtained.

Second, I also construct an additional sample by selecting stock data from the �nancial

year before the insurance contract was initiated and �ow data from the same year as the

insurance contract. This speci�cation assumes that management relies on past stock data

but has good estimates available for current �ow data. Gezcy et al. (1997), for example, use

this procedure. Overall, I �nd that the qualitative results are una�ected by changes in the

matching and selection criteria between the insurance data and the exogenous variables.

Third, the main qualitative results also hold if the three-year moving average is used

instead of the lagged values for the exogenous variables, or if, alternatively, all variables

are averaged across all years for which insurance data is available, resulting in a purely

cross-sectional model.

To show that the sample is representative, I compare the descriptive statistics of my

data to the population for the relevant years. The �rms in the Aon Grieg database show

somewhat higher sales and size than the average Norwegian private �rm. This is, of course,

not surprising since smaller �rms do not require an insurance broker. Another notable

di�erence between the sample of �rms in the study and the population is that sales growth

is lower for �rms in this study than in the population, and yet, the median sales growth in

my sample and the population are comparable. Overall, I �nd that the �rms in my sample do

not di�er in an economically signi�cant way from the population of private �rms in Norway.

Next, I re-estimate the models in Tables 4 to 6, but include the CEO ownership variable

20

and �ll in missing CEO ownership data with zeros.18 One among the reasons why CEO

ownership data is missing is the possibility that CEO ownership is too small to be recorded.

I, therefore, �nd it plausible to replace missing data with zero. The results in Tables 4 to

6 are practically unchanged and CEO ownership shows a signi�cantly negative coe�cient,

which is consistent with the results presented in Section 5.

Finally, the lagged data and the simultaneous-equation regressions reduce biases but

likely cannot eliminate them entirely. Indeed, lagging the data only imperfectly reduces the

time-series dependency in the explanatory variables. Another potential problem is that prop-

erty insurance use depends on forecasted �rm characteristics. Unfortunately, to su�ciently

address such concerns one would need a much larger sample than I have.

8 Conclusions

I study corporate risk management with property insurance in private small and medium-

sized �rms. Risk management in private �rms must be signi�cant since owners, including

the CEO, of small and medium-sized �rms tie their wealth to the �rm at least as tightly

as in public �rms. Further, it is expected that the risk management motives of owners and

managers are much more aligned in small and medium-sized �rms than in large public com-

panies. In addition, family �rms stress survival and, thus, need to rely on risk management.

I, therefore, predict that the expected positive relation between ownership variables and the

extent of insurance use is stronger or more relevant for private �rms.

Surprisingly, I document negative relations between the following ownership variables:

CEO salary, ownership concentration and aggregate female ownership and between insur-

ance use. I also document a positive relation between the number of family owners and

insurance use. These relations are inconsistent with the risk aversion motive to hedge.

However, the relations are consistent with self-insurance among CEO-controlled �rms, �rms

18I have, in addition, hand-collected CEO ownership data via a questionnaire and replaced missing datawith zero when industrial ownership equals 100 percent. Nevertheless, the models in Tables 4 to 6 cannotbe identi�ed without �lling in missing CEO ownership data with zeros.

21

with high ownership concentrations, �rms with above average female owners and �rms with a

small number of family owners, given monopolistic insurance premiums. I provide empirical

evidence that supports this view by showing that insurers raise property insurance premiums

for �rms that experience increases in earnings.

Moreover, I analyse insurance, capital structure, and liquidity choices jointly by em-

ploying simultaneous-equation regressions. The results suggest that private �rms insure to

increase their debt capacity. The results also suggest that insurance and liquidity are comple-

ments. Although insurance and liquidity are complements, �rms do not hold cash to increase

debt capacity. Speci�cally, I �nd that long-term debt and liquidity behave as substitutes.

22

A Data Description

This appendix contains details about the construction of the variables employed in this study.

A.1 Insurance Data

The data from Aon Grieg are insurance property premiums. The �le contains the �rm name,

gross premium, net premium, and a few other items. The insurance data are merged with

the accounting data by matching on the �rm name. Firms that cannot be uniquely identi�ed

are removed.

A.2 Variables De�nition

Item numbers are CCGR variable de�nitions.

ASSETS: Assets is the sum of current asset and �xed asset. Item 63 + Item 78

CASH: CASH is cash and equivalents divided by assets. (Item 75 + Item 76) / Assets

CEOSALARY: CEO is the CEO salary divided by assets multiplied by 1000. Item 114

/ Assets * 1000

CEOSHARE: CEOSHARE is shares owned by the CEO divided by 100. Item 13601 /

100

CEOSHARE2: CEOSHARE2 is shares owned by the CEO divided by 100. Missing

data is replaced by zero. Item 13601 / 100

CONCENTRATION: Concentration is the Her�ndahl index for ownership. Item 225

DIV: DIV is total paid-out dividends scaled by total equity. Dividends are calculated

from Item 105 and replaced with Item 41 if missing. Item 105 / Item 87

EARNINGS: EARNINGS is the operating income after tax and before extraordinary

items. Item 35

FAMILYSIZE: FAMILYSIZE is the number of family owners of the largest family.

FEMALE: FEMALE is aggregate female ownership divided by 100. Item 221 / 100

INSTOWN: INSTOWN is shares owned by institutional investors divided by 100. Item

218 / 100

INVENTORY: INVENTORY is �rm's inventory in current year. Item 64

LTD: LTD is the long-term debt ratio, which is given by the book value of long-term

debt divided by total assets. Item 98 / Assets

ROA: ROA is the return on assets; it is calculated as the net income divided by assets.

Item 39 / Assets

23

SALES GROWTH: SALES GROWTH is the three-year backward-moving average

percentage growth in sales (SALES).

SIZE: Size is the logarithm of total assets. Log(Item 63 + Item 78)

INTANGASSETS: INTANGASSETS is intangible assets scaled by total assets. Item

46 / Assets

OPEX: OPEX is operating expenses scaled by sales, with the sales series transformed

as explained under SALES GROWTH above. (Item 12 + Item 13 + Item 14 + Item 15 +

Item 16 + Item 17 + Item 18)/Item 9

PP&E: PP&E is property, plant, and equipment as a percentage of total assets. Item

51 / Assets

SALES: SALES is the logarithm of sales revenue. log(Item 9)

A.3 Outliers

The insurance ratio (INSURANCE) data contains outliers and I, therefore, remove data

below 1 percent values and above 99 percent values. Moreover, because of the small sample,

for the explanatory variables, it is more convenient to remove outliers individually for each

series, as follows:

CASH: I eliminate the 6 cases in which CASH is higher than 1.

DIV: I allow the dividend-to-equity ratio to be less than 4, removing a total of 18

observations.

INTANGASSETS: Negative intangible assets are removed.

LTD: I eliminate the 16 observations for which LTD is larger than 3. Cases with long-

term debt ratio higher than 1 (but lower than 3) are kept in the sample, to capture the

e�ects of �nancial distress.

OPEX: I remove operating expenses to sales ratio at 99 percent as well as negative

operating expenses.

ROA: I remove one observation with an ROA of -28.

SALES GROWTH: Observations for which the percentage growth in sales in a given

year is higher than 10 (1000 percent) are eliminated. This reduces the number of observations

of the SALES GROWTH variable by one percent.

B Sign Predictions

This appendix contains the predictions for the variables used in the study.

24

B.1 Insurance equation

Sign predictions for the insurance equation are as follows:

CEOSALARY: positive in�uence (risk aversion motive)

DIV: negative in�uence

FAMILYSIZE: negative in�uence (risk aversion motive)

FEMALE: positive in�uence (risk aversion motive)

INSTOWN: negative in�uence

CEOSHARE: positive in�uence (risk aversion motive)

CONCENTRATION: positive in�uence (risk aversion motive)

LTD: positive in�uence

CASH: positive in�uence

ROA: negative in�uence

SALES GROWTH: positive in�uence

SIZE: negative in�uence

B.2 LTD equation

Sign predictions for the LTD equation are as follows:

CASH: no prediction, variable is not included in LTD regressions in Aunon-Nerin and

Ehling (2008) and Graham and Rogers (2002)

INSURANCE: positive in�uence, see Aunon-Nerin and Ehling (2008)

SALES GROWTH: negative in�uence, see Graham and Rogers (2002)

INTANGASSETS: positive in�uence, see Graham and Rogers (2002)

OPEX: no prediction

SALES: negative in�uence, see Graham and Rogers (2002)

PP&E: positive in�uence, see Graham and Rogers (2002)

B.3 CASH equation

Sign predictions for the CASH equation are as follows:

DIV: negative in�uence for dividend dummy variable, see Bates et al. (2009)

LTD: negative in�uence, see Bates et al. (2009)

INSURANCE: no prediction

SALES GROWTH: positive in�uence, see Bates et al. (2009)

25

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29

Table 1: Insurance Data. The table summarizes descriptive statistics of property insurancepremium for 933 private Norwegian �rms. The sample consists of 1855 �rm-year observations(All �rms), 1117 �rm-year observations (Firms with positive long-term debt), and 738 �rm-year observations (Firms with zero long-term debt) ranging from January, 2003 throughMay, 2006. The premium is the annual insurance premium in Norwegian Kroner (NOK).The insurance ratio is premium scaled by property, plant and equipment plus inventory(PPE&I in NOK). Insurance data are from AON Grieg Norway; accounting data are fromCCGR.

N Mean Std. Dev. Min. Median Max All firms Premium in NOK 1855 192432.00 732789.80 164.00 45848.00 15281813.00 Insurance ratio 1618 0.03 0.10 0.00 0.01 1.69 Premium to dividends 347 0.16 1.20 0.00 0.01 20.94 Premium to earnings 1730 0.14 5.91 -146.61 0.01 155.83 (PP&E+I) / Assets 1732 0.33 0.29 0.00 0.27 1.00 Firms with positive long-term debt Premium in NOK 1117 235523.60 783591.10 164.00 65490.00 13654878.00 Insurance ratio 946 0.02 0.07 0.00 0.00 1.69 Premium to dividends 192 0.11 0.47 0.00 0.02 5.88 Premium to earnings 997 0.03 5.20 -146.61 0.01 58.84 (PP&E+I) / Assets 994 0.41 0.30 0.00 0.42 1.00 Firms with zero long-term debt Premium in NOK 738 127210.70 643418.00 211.00 29240.50 15281813.00 Insurance ratio 672 0.04 0.13 0.00 0.01 1.50 Premium to dividends 155 0.22 1.72 0.00 0.01 20.94 Premium to earnings 733 0.28 6.76 -72.16 0.01 155.83 (PP&E+I) / Assets 738 0.22 0.24 0.00 0.13 0.99

30

Table2:IndustryClassi�cations,SampleYears,andNegativeEarningsandEquity.Thistablesummarizesdescriptive

statisticsofindustry

classi�cations,sampleyearsandnegativeearnings

andequityfor933privateNorwegian�rm

s.Thesample

consistsof

1618

�rm

-yearobservations(A

ll�rm

s)

946�rm

-yearobservations(Firmswithpositive

long-term

debt)

and

672

�rm

-yearobservations(Firmswithzero

long-term

debt)

rangingfrom

January,

2003

through

May,

2006.MultiGroupcontains

�rm

sin

multipleindustries.Accountingdataarefrom

CCGR.

All f

irm

s Fi

rms w

ith p

ositi

ve lo

ng-te

rm d

ebt

Firm

s with

zero

long

-term

deb

t In

dust

ry

N

% o

f sam

ple

(N=1

618)

N

%

of s

ampl

e (N

=946

)

% o

f sam

ple

(N=6

72)

Agr

icul

ture

, For

estry

, Fis

hing

, Min

ing

67

4.14

%

54

5.71

%

13

1.93

%

Man

ufac

turin

g 52

3 32

.32%

30

6 32

.35%

21

7 32

.29%

En

ergy

49

3.

03%

42

4.

44%

7

1.04

%

Con

stru

ctio

n 59

3.

65%

35

3.

70%

24

3.

57%

Se

rvic

e 42

0 25

.96%

25

8 27

.27%

16

2 24

.11%

Fi

nanc

ial

11

0.68

%

8 0.

85%

3

0.45

%

Trad

e 38

8 23

.98%

17

8 18

.82%

21

0 31

.25%

Tr

ansp

ort

50

3.09

%

30

3.17

%

20

2.98

%

Mul

ti G

roup

50

3.

09%

34

3.

59%

16

2.

38%

M

issi

ng in

dust

ry c

lass

ifica

tion

1 0.

06%

1

0.11

%

0 0.

00%

Su

m

1618

10

0.00

%

946

100.

00%

67

2 10

0.00

%

Yea

rs

2003

21

7 13

.41%

13

5 14

.27%

82

12

.20%

20

04

555

34.3

0%

313

33.0

9%

242

36.0

1%

2005

54

9 33

.93%

32

7 34

.57%

22

2 33

.04%

20

06

297

18.3

6%

171

18.0

8%

126

18.7

5%

Earn

ings

and

equ

ity

Neg

ativ

e ea

rnin

gs

0 0.

00%

0

0.00

%

0 0.

00%

N

egat

ive

equi

ty

76

4.70

%

55

5.81

%

21

3.13

%

Posi

tive

earn

ings

and

equ

ity

1356

83

.81%

77

5 81

.92%

58

1 86

.46%

31

Table3:SummaryofExplanatoryVariables.

Thistablesummarizes

descriptive

statistics

of�nancial

characteristicsfor

933privateNorwegian�rm

s,PanelA.Thesampleconsistsof

1618

�rm

-yearobservations(A

ll�rm

s),

946�rm

-yearobservations

(Firmswithpositive

long-term

debt),and

672�rm

-yearobservations(Firmswithzero

long-term

debt)

rangingfrom

January,

2003

through

May,

2006,withwellde�ned

insurance

ratio(Table1).PanelBcontainsthedescriptive

statistics

of�nancial

characteristicsforthepopulation

ofprivate

Norwegian�rm

sfrom

January,

2003

through

Decem

ber,

2006.CASHiscash

and

equivalents

divided

byassets.CEOSALARY

istheCEO

salary

divided

byassets

times

1000.CONCENTRATIO

Nisthe

Her�ndahlindex

ofequityow

nership.DIV

isdividendsscaled

bytotalequity.

FEMALEisaggregatefemaleow

nership

divided

by100.

INSTOWN

isinstitutional

ownership

divided

by100.

INTANGASSETSisintangible

assets

scaled

bytotalassets.

FAMILYSIZEisthenumber

offamilyow

nersoflargestfamily.

LTDstandsforbook

valueoflong-term

debtdivided

byassets.

OPEX

isoperatingexpensesscaled

bysales.

PPEisproperty,plantandequipmentscaled

byassets.ROA

isthereturn

onassets(operatingearnings

over

assets).SALESisthelogarithm

ofsales.SALESGROWTHisthethree-year

backwardmoving

averagepercentage

grow

thin

sales.SIZEstandsforthelogarithm

ofassets.Allvariablesarewinsorizedat

thebottom

andtop

10%

values.Accountingdataarefrom

CCGR.

Pane

l A

All s

ampl

e fir

ms

Sam

ple

firm

s with

pos

itive

long

-term

deb

tSa

mpl

e fir

ms w

ith ze

ro lo

ng-te

rm d

ebt

Var

iabl

e N

M

ean

STD

M

in.

Med

.M

ax.

N

Mea

nST

DM

in.

Med

.M

ax.

N

Mea

nST

DM

in.

Med

.M

ax.

CA

SH

1617

0.

13

0.14

0.

00

0.07

0.

43

946

0.10

0.

120.

00

0.05

0.

43

671

0.17

0.

160.

00

0.12

0.

43

CEO

SAL

1285

0.

03

0.03

0.

00

0.01

0.

09

732

0.02

0.

020.

00

0.01

0.

09

553

0.04

0.

030.

00

0.02

0.

09

CO

NC

ENTR

ATI

ON

15

45

0.82

0.

28

0.28

1.

00

1.00

91

4 0.

78

0.29

0.28

1.

00

1.00

63

1 0.

88

0.24

0.28

1.

00

1.00

D

IV

1609

0.

04

0.09

0.

00

0.00

0.

29

944

0.04

0.

080.

00

0.00

0.

29

665

0.05

0.

100.

00

0.00

0.

29

FEM

ALE

15

45

0.00

0.

00

0.00

0.

00

0.00

91

4 0.

00

0.00

0.00

0.

00

0.00

63

1 0.

00

0.00

0.00

0.

00

0.00

IN

STO

WN

15

45

0.00

0.

00

0.00

0.

00

0.00

91

4 0.

00

0.00

0.00

0.

00

0.00

63

1 0.

00

0.00

0.00

0.

00

0.00

IN

TAN

GA

SSET

S 14

32

0.03

0.

04

0.00

0.

02

0.12

83

0 0.

03

0.04

0.00

0.

01

0.12

60

2 0.

03

0.04

0.00

0.

02

0.12

FA

MIL

YSI

ZE

733

1.97

1.

18

1.00

1.

00

4.00

52

8 2.

02

1.21

1.00

1.

00

4.00

20

5 1.

83

1.11

1.00

1.

00

4.00

LT

D

1618

0.

17

0.21

0.

00

0.04

0.

59

946

0.29

0.

200.

00

0.26

0.

59

672

0.00

0.

000.

00

0.00

0.

00

OPE

X

1409

-1

.01

0.13

-1

.32

-0.9

8-0

.85

793

-1.0

20.

13-1

.32

-0.9

8-0

.85

616

-1.0

00.

13-1

.32

-0.9

7-0

.85

PPE

1432

0.

19

0.20

0.

01

0.10

0.

62

830

0.25

0.

220.

01

0.19

0.

62

602

0.10

0.

130.

01

0.05

0.

62

RO

A

1431

0.

06

0.08

-0

.08

0.05

0.

19

829

0.05

0.

07-0

.08

0.05

0.

19

602

0.07

0.

09-0

.08

0.07

0.

19

SALE

S 14

32

15.3

9 5.

95

0.00

17

.48

19.6

283

0 14

.93

6.48

0.00

17

.40

19.6

260

2 16

.03

5.08

0.00

17

.60

19.6

2SA

LESG

RO

WTH

12

51

0.05

0.

17

-0.2

10.

03

0.37

70

1 0.

06

0.18

-0.2

1 0.

04

0.37

55

0 0.

05

0.16

-0.2

10.

03

0.37

SI

ZE

1432

17

.44

1.44

15.

1817

.49

19.7

583

0 17

.71

1.46

15.1

8 17

.74

19.7

560

2 17

.07

1.33

15.1

817

.05

19.7

5Pa

nel B

20

03 –

200

6 Po

pula

tion

CA

SH

2331

10

0.23

0.

20

0.01

0.

17

0.62

13

6860

0.17

0.

170.

01

0.11

0.

62

9625

00.

33

0.22

0.01

0.

32

0.62

C

EOSA

L 19

8397

0.

00

0.00

0.

00

0.00

0.

00

1189

240.

00

0.00

0.00

0.

00

0.00

79

473

0.00

0.

000.

00

0.00

0.

00

CO

NC

ENTR

ATI

ON

208

750

0.68

0.

29

0.28

0.

56

1.00

12

5632

0.68

0.

290.

28

0.55

1.

00

8311

80.

70

0.29

0.28

0.

63

1.00

D

IV

2330

22

0.18

0.

37

0.00

0.

00

1.10

13

6932

0.15

0.

330.

00

0.00

1.

10

9609

00.

22

0.40

0.00

0.

00

1.10

FE

MA

LE

2089

12

0.09

0.

18

0.00

0.

00

0.50

12

5727

0.09

0.

180.

00

0.00

0.

50

8318

50.

09

0.18

0.00

0.

00

0.50

IN

STO

WN

20

8920

0.

00

0.00

0.

00

0.00

0.

00

1257

300.

00

0.00

0.00

0.

00

0.00

83

190

0.00

0.

000.

00

0.00

0.

00

INTA

NG

ASS

ETS

2331

10

0.02

0.

03

0.00

0.

00

0.08

13

6860

0.02

0.

030.

00

0.00

0.

08

9625

00.

01

0.03

0.00

0.

00

0.08

FA

MIL

YSI

ZE

1862

98

1.45

0.

70

1.00

1.

00

3.00

11

4208

1.48

0.

721.

00

1.00

3.

00

7209

01.

41

0.68

1.00

1.

00

3.00

LT

D

2331

10

0.20

0.

23

0.00

0.

09

0.65

13

6860

0.34

0.

210.

00

0.31

0.

65

9625

00.

00

0.00

0.00

0.

00

0.00

O

PEX

23

3528

-0

.98

0.13

-1

.25

-0.9

7-0

.78

1372

78-0

.99

0.13

-1.2

5 -0

.97

-0.7

896

250

-0.9

80.

14-1

.25

-0.9

7-0

.78

PPE

2331

10

0.21

0.

23

0.00

0.

11

0.68

13

6860

0.29

0.

250.

00

0.21

0.

68

9625

00.

10

0.16

0.00

0.

04

0.68

R

OA

23

3110

0.

06

0.12

-0

.15

0.06

0.

27

1368

600.

05

0.11

-0.1

5 0.

04

0.27

96

250

0.09

0.

14-0

.15

0.09

0.

27

SALE

S 23

3528

15.

40

1.32

13.

0315

.48

17.3

313

7278

15.5

31.

2613

.03

15.6

217

.33

9625

015

.21

1.37

13.0

315

.26

17.3

3SA

LESG

RO

WTH

17

0899

0.

11

0.17

-0

.09

0.06

0.

46

1038

560.

11

0.16

-0.0

9 0.

07

0.46

67

043

0.10

0.

17-0

.09

0.06

0.

46

SIZE

20

1294

0.

09

0.24

-0

.24

0.04

0.

59

1210

560.

09

0.24

-0.2

4 0.

04

0.59

80

238

0.08

0.

25-0

.24

0.03

0.

59

32

Table 4: Simultaneous Analysis of Debt Ratio, Insurance Ratio, and Liquidity

I This table reports linear GMM coe�cient estimates from a simultaneous equation modelfor debt (LTD), insurance (INSURANCE), and cash (CASH), with a total of 663 �rm-yearobservations with non-zero LTD. The p-values are heteroskedasticity and autocorrelationconsistent. CASH is cash and equivalents divided by assets. CEOSAL is the CEO salarydivided by assets times 1000. CONCENTRATION is the Her�ndahl index of equity own-ership. DIV is dividends scaled by total equity. FEMALE is aggregate female ownershipdivided by 100. INSTOWN is institutional ownership divided by 100. INSURANCE is theinsurance premium over property, plant and equipment plus inventories. INTANGASSETSis intangible assets scaled by total assets. LTD stands for book value of long-term debtdivided by assets. OPEX is operating expenses scaled by sales. PPE is property, plant, andequipment as a percentage of assets. ROA is the return on assets (operating earnings overassets). SALES is the logarithm of sales. SALESGROWTH is the three-year moving averagepercentage growth in sales. SIZE is the logarithm of assets. Industry dummy variables arede�ned in Table 2. Endogenous variables, with superscript (*), are measured as of �scalyear-ends after the starting date of the insurance contract. Insurance data are from AONGrieg Norway, accounting and ownership data are from CCGR.

INSURANCE LTD CASH Variable Coefficient p-value Coefficient p-value Coefficient p-value Constant -0.0190 0.63 0.2718 0.04 0.0919 0.00 CASH* 1.1285 0.00 -0.2718 0.06 CEOSAL (t-1) -0.3071 0.03 CONCENTRATION (t-1) -0.0206 0.03 DIV (t-1) -0.0552 0.00 0.0609 0.00 FEMALE (t-1) -0.0833 0.01 INSTOWN (t-1) -0.1262 0.22 INSURANCE* 3.6656 0.00 0.5009 0.00 INTANGASSETS (t-1) 0.5380 0.00 LTD* 0.1463 0.00 -0.1655 0.00 OPEX (t-1) -0.0104 0.29 PPE (t-1) 0.5182 0.00 ROA (t-1) -0.0821 0.01 SALES (t-1) -0.0100 0.14 SALESGROWTH (t-1) -0.0295 0.00 0.0454 0.03 0.0264 0.01 SIZE (t-1) -0.0018 0.32 Dummy variables Industry dummies Y Y Y Year dummies Y Y Y

33

Table 5: Simultaneous Analysis of Debt Ratio, Insurance Ratio, and Liquidity

II This table reports linear GMM coe�cient estimates from a simultaneous equation modelfor debt (LTD), insurance (INSURANCE), and cash (CASH), with a total of 663 �rm-yearobservations with non-zero LTD. The p-values are heteroskedasticity and autocorrelationconsistent. CASH is cash and equivalents divided by assets. CEOSAL is the CEO salarydivided by assets times 1000. CONCENTRATION is the Her�ndahl index of equity own-ership. DIV is dividends scaled by total equity. FEMALE is aggregate female ownershipdivided by 100. INSTOWN is institutional ownership divided by 100. INSURANCE is theinsurance premium over property, plant and equipment plus inventories. INTANGASSETSis intangible assets scaled by total assets. LTD stands for book value of long-term debtdivided by assets. OPEX is operating expenses scaled by sales. PPE is property, plant,and equipment as a percentage of assets. ROA is the return on assets (operating earningsover assets). SALES is the logarithm of sales. SALESGROWTH is the three-year movingaverage percentage growth in sales. SIZE is the logarithm of assets. Industry dummy vari-ables are as in Table 2. Endogenous variables, with superscript (*), are measured as of �scalyear-ends after the starting date of the insurance contract. Insurance data are from AONGrieg Norway, accounting and ownership data are from CCGR.

INSURANCE LTD CASH Variable Coefficient p-value Coefficient p-value Coefficient p-value Constant -0.0193 0.65 0.2786 0.04 0.0675 0.00 CASH* 1.0769 0.00 -0.5094 0.06 CEOSAL (t-1) -0.1959 0.21 CONCENTRATION (t-1) -0.0436 0.03 0.0313 0.34 0.0293 0.11 DIV (t-1) -0.0515 0.00 0.0571 0.00 FEMALE (t-1) -0.1903 0.00 0.1828 0.01 0.1905 0.00INSTOWN (t-1) -0.1473 0.14 INSURANCE* 3.6464 0.00 0.5681 0.00 INTANGASSETS (t-1) 0.5060 0.00 LTD* 0.1551 0.00 -0.1724 0.00 OPEX (t-1) -0.0068 0.46 PPE (t-1) 0.4967 0.00 ROA (t-1) -0.0689 0.02 SALES (t-1) -0.0106 0.11 SALESGROWTH (t-1) -0.0297 0.00 0.0535 0.01 0.0270 0.01 SIZE (t-1) -0.0009 0.64 Dummy variables Industry dummies Y Y Y Year dummies Y Y Y

34

Table 6: Simultaneous Analysis of Debt Ratio, Insurance Ratio, and Liquidity IIIThis table reports linear GMM coe�cient estimates from a simultaneous equation modelfor debt (LTD), insurance (INSURANCE), and cash (CASH), with a total of 526 �rm-year observations with zero and non-zero LTD. The p-values are heteroskedasticity andautocorrelation consistent. CASH is cash and equivalents divided by assets. CEOSAL isthe CEO salary divided by assets times 1000. CONCENTRATION is the Her�ndahl indexof equity ownership. DIV is dividends scaled by total equity. FAMILYSIZE is the numberof family owners of largest family. FEMALE is aggregate female ownership divided by 100.INSURANCE is the insurance premium over property, plant and equipment plus inventories.INTANGASSETS is intangible assets scaled by total assets. LTD stands for book value oflong-term debt divided by assets. OPEX is operating expenses scaled by sales. PPE isproperty, plant, and equipment as a percentage of assets. ROA is the return on assets(operating earnings over assets). SALES is the logarithm of sales. SALESGROWTH isthe three-year moving average percentage growth in sales. SIZE is the logarithm of assets.Industry dummy variables are as in Table 2. Endogenous variables, with superscript (*), aremeasured as of �scal year-ends after the starting date of the insurance contract. Insurancedata are from AON Grieg Norway. Accounting data are from CCGR.

INSURANCE LTD CASH Variable Coefficient p-value Coefficient p-value Coefficient p-value Constant -0.6951 0.02 -0.3046 0.41 0.1331 0.00 CASH* 2.2285 0.00 -0.7958 0.00 CEOSAL (t-1) -0.5007 0.00 CONCENTRATION (t-1) 0.0106 0.22 DIV(t-1) -0.0722 0.00 0.0415 0.00 FAMILYSIZE (t-1) 0.0199 0.01 -0.0111 0.24 -0.0094 0.04FEMALE(t-1) -0.2603 0.00 0.2740 0.00 0.1737 0.00 INSURANCE* 3.6174 0.00 0.4028 0.09 INTANGASSETS(t-1) 0.4662 0.00 LTD* 0.2566 0.01 -0.1512 0.00 OPEX(t-1) -0.0143 0.21 PPE(t-1) 0.7709 0.00 ROA(t-1) -0.2393 0.00 SALES(t-1) 0.0216 0.22 SALESGROWTH(t-1) -0.0154 0.03 -0.0040 0.83 0.0126 0.19 SIZE(t-1) 0.0227 0.02 Dummy variables Industry dummies Y Y Y Year dummies Y Y Y

35

Table 7: Changes in Insurance Premium and Earnings The table reports coe�cientestimates from three di�erence in di�erence models for changes in property insurance pre-mium. The top panel shows the three regression models for (All �rms) while the bottompanel shows these regressions for (Firms with positive long-term debt). Earnings is resultsfrom operations. PPE&I is property, plant, equipment and inventory. ∆ denotes changesfrom accounting year to accounting year, where (−1) stands for lag. t-statistics are in paren-theses, where * p < 0.1, ** p < 0.05, *** p < 0.01. Insurance data are from AON GriegNorway. Accounting data are from CCGR.

Δ INSURANCE Δ INSURANCE Δ INSURANCE Variable Coefficient t-statistics Coefficient t-statistics Coefficient t-statistics All firms Constant Y Y Y Δ EARNINGS 0.746 (1.31) 0.588*** (4.54) Δ EARNINGS (-1) 0.608** (2.02) 0.566*** (4.40) Δ PPE & I -2.811*** (-2.60) -2.920** (-2.59) Δ PPE & I (-1) -1.232 (-1.38) -0.401 (-1.63) Industry dummies Y Y Y Year dummies Y Y Y N 303 303 303 adj. R-squared 0.010 -0.011 0.014 Firms with positive long-term debt Constant Y Y Y Δ EARNINGS 0.768 (1.32) 0.577*** (4.57) Δ EARNINGS (-1) 0.620** (2.01) 0.581*** (4.43) Δ PPE & I -2.862** (-2.09) -3.003** (-2.05) Δ PPE & I (-1) -1.257 (-1.23) -0.277 (-0.85) Industry dummies Y Y Y Year dummies Y Y Y N 172 172 172 adj. R-squared 0.040 -0.007 0.054

36

The CCGR Working Paper Series: Contents The papers may be downloaded without charge from our website http://www.bi.edu/ccgr 2007

1/2007 Ole-Kristian Hope and John Christian Langli:

Auditor Independence in a Private Firm and Low Litigation Risk Setting

Revised April 2009

Accepted for publication in the Accounting Review June 2009

2008

1/2008 Paul Ehling:

Corporate Insurance and Managers' and Owners' Risk Aversion

Revised April 2013

2009

1/2009 Øyvind Norli, Charlotte Ostergaard and Ibolya Schindele:

Liquidity and Shareholder Activism

Revised April 2010

2010

1/2010 Roland E. Kidwell and Arne Nygaard:

The Dual-Agency Problem Reconsidered: A Strategic Deviance Perspective

on the Franchise Form of Organizing

Revised September 2010

2/2010 Ole-Kristian Hope, John Christian Langli and Wayne B. Thomas:

Agency Conflicts and Auditing in Private Firms

March 2010

Revised December 2011

3/2010 Mohammad Abdolmohammadi, Erlend Kvaal and John Christian Langli:

Earnings Management Priorities of Private Family Firms

November 2010

4/2010 Sturla Lyngnes Fjesme, Roni Michaely and Øyvind Norli:

Using Brokerage Commissions to Secure IPO Allocations

November 2010

2011

1/2011 Charlotte Ostergaard, Amir Sasson, and Bent E. Sørensen:

The Marginal Value of Cash, Cash Flow Sensitivities, and Bank-Finance

Shocks in Nonlisted Firms

January 2011

2/2011 Sturla Lyngnes Fjesme:

Laddering in Initial Public Offering Allocations

January 2011

3/2011 Charlotte Ostergaard and David C. Smith:

Corporate Governance Before There Was Corporate Law

April 2011

4/2011 Sturla Lyngnes Fjesme and Øyvind Norli:

Initial Public Offering or Initial Private Placement?

April 2011

5/2011 Janis Berzin, Øyvind Bøhren and Bogdan Stacescu:

Dividends and Stockholder Conflicts: A Comprehensive Test for Private

Firms

December 2011

6/2011 Paul Ehling and David Haushalter:

When Does Cash Matter?

Revised March 2012

2013

1/2013 John Christian Langli and Tobias Svanström:

Audits of private firms

January 2013

2/2013 Janis Berzins, Øyvind Bøhren and Bogdan Stacescu:

Tax concerns and agency concerns in dividend policy: Holding companies

as a separating device

January 2013

The Centre for Corporate Governance Research (CCGR) conducts research on the relationship between corporate governance, firm behavior, and stakeholder welfare. Our projects pay particular attention to the governance of closely held firms and family firms, and the research teams come from different disciplines in several countries. Financing is provided by private sponsors and the Research Council of Norway. The CCGR is organized by the Department of Financial Economics at BI Norwegian Business School in Oslo, Norway (http://www.bi.edu) Centre for Corporate Governance Research BI Norwegian Business School Nydalsveien 37 N-0442 OSLO Norway http://www.bi.edu/ccgr