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Precautionary Cash: The Role of Key Human Capital

Bektemir Ysmailov*

* Doctoral Student at the College of Business, University of Nebraska-Lincoln, 730 N. 14th Street, Lincoln, NE 68588; phone: 402-472-3450. E-mail: bysmailov2@unl.edu.

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Precautionary Cash: The Role of Key Human Capital

Abstract

This paper studies the relation between precautionary cash and the risk that firms’ key employees may depart at any time, or the key human capital risk. I hypothesize that the exposed firms will build precautionary cash reserves to fund unexpected increases in key employee compensation to prevent them from leaving. Consistent with the hypothesis, I find that the exposed firms have 2.4 percentage points higher cash ratios. Additionally, once a firm hedges the risk of one of the two forms of key employee departure (i.e., through death) by carrying key man insurance policy its associated precautionary cash holdings are offset.

Keywords: Corporate Cash, Precautionary Cash, Skilled Labor, Key Human Capital, Key Man Insurance

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1. Introduction

The relation between corporate financial policies and human capital has recently attracted more

attention from researchers.2 As pointed out by Zingales (2000), human capital is emerging as the

most crucial asset for the “new firm” and understanding the implications of this trend for corporate

financial policies is one of many critical steps for “further advancement in corporate finance”. In

this paper, I study the relation between corporate cash holdings and the risk of key employee

departure, or the key human capital risk.

This risk arises because the replacement of key employees is difficult while training is both

costly and often ineffective when a key employee departs (Israelsen and Yonker, 2017).

Additionally, Eisfeldt and Papanikolaou (2013) argue that organization capital is embodied in key

talent and that the share of cash flows from organization capital that shareholders can capture

varies systematically with the outside option of the firm’s key talent. When the efficiency of

organization capital in new firms improves, shareholders must offer higher compensation to induce

key talent to remain with the firm. Therefore, I hypothesize that firms exposed to the risk of key

employee departure will have higher precautionary demand for cash. However, once a firm hedges

this risk, I expect its associated cash buffer to be offset.

To identify the exposed firms, I follow Israelsen and Yonker (2017) who utilize the U.S.

Securities and Exchange Commission (SEC) filings disclosures of key man life insurance, which

is a life insurance policy on a key employee that lists the employee’s firm as the beneficiary.3

There are two types of key employee departure: voluntary and through death. Firms that disclose

2 Recent papers examine the relation between key talent and capital structure (Baghai et al., 2016; Klasa et al., 2017); corporate hedging (Qiu, 2016), compensation policies (Qiu and Wang, 2017), and corporate investment (Xu, 2017). 3 I’d like to thank Israelsen and Yonker (2017) for making their data available at https://sites.google.com/site/ryandisraelsen/

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that they do carry key man insurance are exposed to the risk of voluntary departure but not to the

risk of departure through death whereas firms that disclose that they do not carry key man

insurance are exposed to both types of risks. Thus, the disclosure itself is used as a proxy for the

key human capital risk.

The suitability of the disclosure of key man insurance as a proxy for the key human capital

risk is underscored by several findings reported by Israelsen and Yonker (2017). First, they find

that the exposed firms have 5%-20% higher total and idiosyncratic return volatilities than non-

exposed firms. Second, the departure announcements of key employees are followed by negative

abnormal returns of 8%. Thus, the risk of key employee departures is recognized by the market

and may induce firms to address it through their corporate policies.

I utilize the following variables to test my hypotheses. An indicator variable called Key

Human Capital is equal to 1 if a firm discloses whether it carries key man insurance (yes or no)

and 0 if no mention of key man insurance is made in the SEC filings. An indicator variable called

Insure is equal to 1 for firms that disclose that they carry key man insurance and thus hedge key

employee departure through death and 0 for firms that disclose that they do not. In a regression

with the cash to assets ratio as the dependent variable, I expect a positive coefficient on Key Human

Capital and a negative coefficient on Insure.

The results are consistent with the main hypotheses. First, firms exposed to the key human

capital risk have 2.4 percentage points higher cash ratios, which is approximately 13% of the

sample mean. Second, precautionary cash holdings of firms exposed to the key human capital risk

that hedge one of the two forms of departure (i.e., through death) by carrying key man insurance

are completely offset: they have 2.9 percentage points lower cash ratios than the exposed firms

that do not insure.

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One of the takeaways from the analyses up to this point is that cash holdings and key man

insurance are substitute mechanisms for addressing key human capital risk. To quantify this

relation beyond the binomial characterization, for a subset of hedging firms that report the size of

their key man insurance policies, I test whether the precautionary cash demand declines as the size

of the key man insurance policies grows. Israelsen and Yonker (2017) define a variable called Key

Human Capital Intensity that is equal to the ratio of total key man insurance policy amounts to

book value of total assets. In line with expectations, I find that the higher the policy amount, the

lower the precautionary cash demand arising from the risk of key employee departure through

death.

Although most firms hold life insurance policies on their key employees because they

recognize their dependence on them, in some cases, lenders require firms to carry key man

insurance policies as part of a loan covenant. This suggests that hedging firms may be financially

constrained and that the positive relation between cash and key human capital risk simply reflects

a well-documented positive relation between financial constraints and corporate cash (Opler et al.,

1999; Almeida et al., 2004; Bates et al., 2009). However, the results above show that it is the

disclosure rather than the act of holding of key man insurance that drives the positive relation

between cash and key human capital risk. In fact, cash holdings of firms that insure are lower than

those of firms that do not insure.

To further address this issue, I re-run the main regression in the subsamples of constrained

and unconstrained firms based on three widely used financial constraints measures: the Whited

and Wu (2006) (WW) index, the Size and Age (SA) index (Hadlock and Pierce, 2010), and the

Kaplan and Zingales (1997) (KZ) index. I confirm that the main results hold in the subsamples of

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both constrained and unconstrained firms, i.e. the coefficients on the main variables of interest

remain statistically and economically significant and have similar magnitudes.

Firms in competitive industries face higher wage bills as well as higher search and hiring

costs. This is because the departure of key employees can hurt firms through dissemination of

proprietary information which can erode their competitive advantages over rivals (Brown and

Petersen, 2011; Klasa et al., 2017). Therefore, I hypothesize that firms in competitive industries

will hold more precautionary cash to prevent their key employees from leaving compared to firms

in non-competitive industries. Using text-based proxies for industry competitiveness and product

market threats from Hoberg and Phillips (2016) and Hoberg et al. (2014), I confirm my conjecture.

In a closely related study, Ghaly et al. (2017) show that firms that rely more on skilled

labor hold more precautionary cash reserves. The relation arises because firms that rely more on

skilled labor face higher labor adjustment costs which reduces their ability to mitigate the impact

of future negative cash flow shocks. This paper builds upon these findings in two ways. First, while

Ghaly et al. (2017) utilize an industry-level index that measures the weighted-average skill level

of all occupations within an industry, I use a firm-level measure that considers the risk arising from

departure of only a handful of key employees. I further discuss the difference and present

supporting evidence in Section 4.2. Second, I show how firms hedge one form of key human

capital risk by carrying key man insurance and thus offset their precautionary cash demand.

The rest of the paper is organized as follows. Section 2 discusses the source of the

precautionary demand for cash among firms with key human capital and develops the main

hypotheses. Section 3 outlines sample selection and data. Section 4 presents the main results.

Section 5 concludes.

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2. Hypothesis Development

The main hypotheses in this paper derive from the theoretical framework of Eisfeldt and

Papanikolaou (2013) who build upon the models of Atkeson and Kehoe (2005) and Lustig et al.

(2011). The underlying proposition in these papers is that the investment in human capital is risky

because key talent can leave the firm at any time. Eisfeldt and Papanikolaou (2013) suggest that

the probability of key talent leaving the firm varies with their outside option, which, in turn, is

determined by the entrance of new firms (in response to technology shocks) who may pay a

premium for such workers. To retain key talent, shareholders must offer higher compensation.

Therefore, I hypothesize that firms that are exposed to the risk of key employee departure will hold

more precautionary cash to fund unexpected increases in compensation.

Hypothesis 1: There is a positive relation between corporate cash holdings and the firm’s exposure

to the key human capital risk.

Anecdotal evidence suggests that there is indeed a risk that a firm’s key employees may be

poached by rival firms. For example, in 2016, XPO Logistic sued its close competitor YRC

Worldwide for hiring several former employees with the goal of obtaining proprietary information

such as customer and pricing secrets.4 Additionally, Kim (2014) shows both theoretically and

empirically that financially strong firms can offer higher wages to poach a rival’s employees who

know its trade secrets and deprive the rival of its competitive advantages. This evidence highlights

the possibility that firms will build up cash reserves to address key human capital risk.

If there is indeed precautionary cash demand arising from the key human capital risk, then

the cash holdings of the exposed firms that hedge this risk should be offset. The data allow me to

4 See “XPO Logistics Sues Trucker YRC, Charging Rival ‘Poached’ Executives, Trade Secrets” article published in the Wall Street Journal on February 5, 2016.

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identify the firms that carry key man insurance and, thus, hedge the risk of key employee departure

due to death. Importantly, these firms remain exposed to the risk that their key employees may

leave voluntarily.

Hypothesis 2: The precautionary cash holdings of firms exposed to the key human capital risk that

hedge one form of it by carrying key man insurance should be offset.

3. Data

The sample is constructed as in Israelsen and Yonker (2017) and includes all firms listed on

NYSE/AMEX/NASDAQ excluding financial firms (Standard Industry Classification (SIC) codes

between 6000 and 6999) from 1998 through 2009. To identify firms exposed to the key human

capital risk, I use the data made available by Israelsen and Yonker (2017) who search through the

SEC filings disclosures of key man life insurance. Although firms are not required to disclose

whether they carry key man insurance, they often do to comply with the Item 503(c) (“Risk

Factors”) of Regulation S-K that instructs filers to “provide under the caption ‘Risk Factors’ a

discussion of the most significant factors that make the offering speculative or risky.”

Israelsen and Yonker (2017) construct the following three variables to capture key human

capital risk. First, a binary variable called ‘Key Human Capital’ is equal to 1 if a firm discloses

whether it carries a life insurance policy on its key employees. This variable includes firms that

disclose that they do and firms that disclose that they do not carry key man insurance, thus, the

disclosure itself reveals whether a firm is subject to the key human capital risk. Second, a variable

called ‘Insure Key Employee’ is equal to 1 if a firm actually carries key man insurance. This

variable identifies firms that hedge the risk of key employee departure due to death but remain

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exposed the risk of voluntary departure. Third, a subset of firms that carry key man insurance also

disclose the names and positions of key employees as well as the face value of the policy amounts.

A variable called ‘Key Human Capital Intensity’ is the ratio of total key man policy amounts to

total assets.

Firm-level control variables are constructed from Compustat and include size, cash flow

volatility, market to book ratio, cash flow, net working capital, capital expenditures, leverage,

research and development (R&D) expense, dividend dummy, and acquisitions. Variable

definitions are provided in the Appendix.

Summary statistics are presented in Table 1. Approximately 19% of firm-year observations

indicate the exposure to key human capital risk, of which, slightly less than half indicate the

ownership of key man life insurance. Firms that disclose the size of their key man insurance

policies place a dollar value on key employees of approximately 9% of total assets. Israelsen and

Yonker (2017) report that over two-thirds of the key employees hold the position of CEO while

the rest are primarily scientists and researchers. Additionally, 26% of key employees hold either a

PhD or MD, whereas only 5%-6% of executives do.

4. Results

Table 2 presents the main results, the dependent variable in all models is the cash to assets ratio.

Consistent with the precautionary cash demand arising from the key human capital risk, the

coefficient on Key Human Capital binary variable is positive and significant. Further, Column 2

of Table 2 shows that there is negative relation between corporate cash holdings and a binary

variable indicating whether a firm carries key man insurance. This result suggests that once a firm

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hedges the key human capital risk by carrying a life insurance policy, its precautionary demand

for cash declines.

Both Key Human Capital Risk and Insure Key Employee variables are included in the same

regression model in Column 3, Table 2. When looking at the economic significance of results, an

interesting pattern emerges. The coefficient on Key Human Capital indicates that firms exposed

to the key man risk (Key Human Capital = 1) have a 2.4 percentage point higher cash to assets

ratio, which is approximately 13% of the sample mean cash to assets ratio. At the same time, the

coefficient on Insure Key Employee implies that the cash to assets ratio of firms that are exposed

to the key human capital risk but carry key man insurance to hedge it is 2.9 percentage points

lower. Taken together, these estimates suggest that there is indeed precautionary demand for cash

among firms exposed to the key man risk but, once this exposure is hedged, in this case, by carrying

key man insurance, this demand is offset and eliminated.

Firms that carry key man insurance remain exposed to the risk that their key employees

may leave voluntarily. Therefore, it is somewhat surprising that the precautionary cash demand of

the exposed firms that insure are offset entirely rather than partially. There are three possibilities

as to why this might be the case: (1) the proxy for key human capital risk is biased towards key

employee departure through death; (2) firms carrying key man insurance are better at preventing

voluntary departure of key talent through means other than liquidity management and rationally

choose to not hold precautionary cash; or (3) key man insurance gives a false sense of security for

carrying firms whereas they acknowledge the risk of voluntary departure of key employees but

choose not to precaution against it.

The last model in Table 2 quantifies the relation between key man insurance and corporate

cash beyond the binomial characterization for a subsample of firms that disclose the key man

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insurance policy amounts in their SEC filings. The results are consistent with expectations: there

is a negative relation between the policy amounts and corporate cash, i.e. the more intensively a

firm hedges the risk of key employee departure through death, the lower its precautionary cash

holdings. Specifically, a one standard deviation in key human capital intensity is associated with

an increase in the cash to assets ratio of 1.35 percentage points.

An immediate concern with the analyses up to this point is that some firms hold key man

life insurance to satisfy loan covenants and, therefore, it is possible that firms exposed to the key

man risk are financially constrained and that is what really drives the main results (Israelsen and

Yonker, 2017). To address this possibility, I perform the following test. I separate firms into

constrained and unconstrained subsamples based on the three widely-used financial constraints

measures: the Whited and Wu (2006) (WW) index, the Size and Age (SA) index (Hadlock and

Pierce, 2010), and the Kaplan and Zingales (1997) (KZ) index. Specifically, firms with above

(below)-median scores on the SA, KZ, and WW indices are classified as constrained

(unconstrained). I then run the main regression for the two subsamples based on each financial

constraints measure. The results are presented in Table 3.

The positive relation between key human capital risk and cash holds among both

constrained and unconstrained firms based on all three measures of financial constraints and the

precautionary cash savings of exposed firms that choose to insure their key employees are offset

as shown by the negative coefficient on the Insure Key Employee variable. Moreover, the

magnitude of the coefficients on the two key variables is similar across constrained and

unconstrained subsamples suggesting that the main results are not primarily driven by the financial

constraints.

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4.1 Product Market Structure and Key Human Capital Risk

Losing key talent should be more costly for firms operating in competitive industries. The cost of

losing key talent is exacerbated by the possibility of dissemination of proprietary information in

case of voluntary departure, which can quickly erode firms’ competitive advantages (Brown and

Petersen, 2011; Klasa et al., 2017). According to the survey sponsored by the U.S. Chamber of

Commerce, firms lose over $50 billion annually due to divulgence of their trade secrets and CEOs

report that former employees are the greatest source of risk associated with the loss of proprietary

information.5 Therefore, I expect the precautionary savings motive to be more pronounced among

firms in competitive industries and facing predatory threats.

To identify competitive industries, I use a text-based network industry classification

Herfindahl index (TNIC HHI) developed by Hoberg and Phillips (forthcoming). Firms with TNIC

HHI above (below) the median are defined as belonging to concentrated (competitive) industries.

Similarly, firms with product similarity (Hoberg and Phillips, 2016) and product market fluidity

(Hoberg et al., 2014) above (below) the median are defined as facing high (low) predatory threats.

The results of these tests are presented in Table 4. Consistent with expectations, Columns

1 and 2 show that the relation between corporate cash holdings and key human capital risk is more

pronounced among firms in competitive industries. The rest of the table shows that the effect of

key human capital risk is more pronounced on the cash holdings of firms facing higher predatory

threats. Taken together, these results are consistent with my conjecture.

5 See “Trends in Proprietary Information Loss,” ASIS International, September 2002.

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4.2 Labor Skill Index and Key Human Capital Risk

Ghaly et al. (2017) show that firms that rely more on skilled labor and face higher labor adjustment

costs have more precautionary cash savings. Although somewhat related to the one used in this

paper, the measure used in Ghaly et al. (2017) called Labor Skill Index (LSI) is different in that

(1) it is industry-level rather than firm-level; (2) takes into account the required skill level for all

rather than a handful of key occupations; and (3) is weighted by the ratio of the number of all

employees within an occupation to the total number of employees within an industry. Whereas

two-thirds of key employees are executives, they are practically ignored in the construction of LSI

when weighted by the total number of employees within an industry because, by definition, there

are few employees within this occupation.

In Table 5, I report the result of running the main regressions with a control for LSI.

Consistent with Ghaly et al. (2017), the coefficient on LSI is positive and significant in all four

specifications. More importantly, the magnitude, the sign, and the economic significance of the

coefficients on Key Human Capital and Insure Key Employee are indistinguishable from the main

specification presented in Table 2.

5. Conclusion

In this paper, I study the relation between the risk of key employee departure and precautionary

cash savings. I posit that firms build up precautionary cash reserves to be able to fund unexpected

increases in key employee compensation to prevent them from leaving (Eisfeldt and Papanikolaou,

2013). Consistent with this hypothesis, I find that there is a positive relation between corporate

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cash and key human capital risk. Moreover, once a firm hedges the risk of key employee departure

through death by carrying key man insurance, its associated precautionary cash savings are offset.

The traditional precautionary motive states that firms accumulate cash to avoid passing up

profitable investment opportunities in case of an internal cash flow shortfall when external

financing is costly. However, as Bolton et al. (2017) point out, “even when there are no capital

market frictions, corporations add value by optimally managing risk and liquidity because this

allows them to reduce the cost of key-man risk to investors.” This paper taps into this idea by

showing how firms manage their cash and hedging policies to address key human capital risk.

Appendix

Variable Definitions

Compustat data items are in parentheses.

• Acquisitions: ratio of acquisitions [#129] to total book assets [#6].

• Capex/Assets: ratio of capital expenditures [#128] to total book assets [#6].

• Cash/assets: ratio of the sum of cash and short-term investments [#1] to total book assets

[#6].

• Cash flow/assets: ratio of operating income before depreciation [#13], after interest [#15],

dividends [#21], and taxes [#16] to total book assets [#6].

• Dividend dummy: indicator variable equal to 1 if a firm paid a common dividend in a given

year (i.e., #21 is positive).

• Cash flow volatility: volatility of cash flow to assets within the two-digit SIC group of a

firm. As in Bates, Kahle, and Stulz (2009), for a given year and two-digit SIC group, I

calculate the standard deviation of cash flow / assets over the previous 10 years for each

firm within that group. A firm must have at least three observed cash flow / assets over the

previous 10 years to be counted. Industry sigma for a two-digit SIC group is the average

of the standard deviations of cash flow / assets across all firms in the group.

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• Leverage: ratio of the sum of long-term debt [#9] and debt in current liabilities [#34] to

total book assets [#6].

• Market to book: ratio of the market value of the firm to total book asset value [#6]. Market

value is computed as book value of assets [#6] plus market value of equity (equal to the

stock price at fiscal year close [#199] times the number of common shares outstanding

[#25]) less book value of common equity [#60].

• NWC/assets: ratio of net working capital, net of cash and short-term investments [#179-

#1], to total book assets [#6].

• R&D/sales: ratio of R&D expenditures [#46] to sales [#12]. When missing from

Compustat, R&D is set equal to 0.

• Size: book value of total assets [#6].

To limit the effect of outliers, I winsorize the data as follows: leverage is between zero and one;

R&D/sales, acquisitions, cash flow volatility, NWC/assets, cash flow/assets, capital

expenditures/assets, and market-to-book ratio are winsorized at the 1% level.

References

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Bolton, P., Wang, N. and Yang, J., 2016. Liquidity and Risk Management: Coordinating Investment and Compensation Policies. Working Paper.

Brown, J.R. and Petersen, B.C., 2011. Cash Holdings and R&D Smoothing. Journal of Corporate Finance 17(3), 694-709.

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Ghaly, M., Dang, V.A. and Stathopoulos, K., 2017. Cash Holdings and Labor Heterogeneity: The Role of Skilled Labor. The Review of Financial Studies 30, 3636–3668.

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Table 1. Summary Statistics

Variables Mean Median St. Dev. N Key Human Capital 0.19 0.00 0.39 36950 Insure Key Employee 0.09 0.00 0.29 36950 KHC Intensity 0.00 0.04 0.15 2599 Cash / Assets 0.19 0.10 0.22 36950 Market to book 2.00 1.44 1.69 36950 Size 2365 265 9456 36950 Cash Flow / Assets -0.01 0.06 0.26 36950 NWC / Assets 0.05 0.04 0.20 36950 Capex / Assets 0.05 0.04 0.06 36950 Leverage 0.23 0.19 0.23 36950 Ind. CF Volatility 0.55 0.21 0.84 36950 Dividend Dummy 0.28 0.00 0.45 36950 R&D / Sales 0.33 0.00 1.56 36950 Acquisitions 0.02 0.00 0.06 36950 The sample includes all firms listed on NYSE/AMEX/NASDAQ excluding financial firms (Standard Industry Classification (SIC) codes between 6000 and 6999) from 1998 through 2009. Variable definitions are provided in the Appendix.

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Table 2. Key Human Capital Risk and Corporate Cash

(2) (3) (4) (5) Variables Cash/Assets Key Human Capital 0.010*** 0.024***

(0.002) (0.003) Insure Key Employee -0.008** -0.029***

(0.003) (0.004) Key Human Capital Intensity -0.090**

(0.037) CF Volatility 0.011*** 0.011*** 0.011*** 0.012*

(0.002) (0.002) (0.002) (0.007) R&D / Sales 0.030*** 0.030*** 0.030*** 0.023***

(0.001) (0.001) (0.001) (0.003) Market to book 0.021*** 0.021*** 0.021*** 0.022***

(0.001) (0.001) (0.001) (0.002) Size -0.006*** -0.006*** -0.006*** 0.009***

(0.001) (0.001) (0.001) (0.003) Cash Flow / Assets -0.006 -0.007 -0.006 -0.011

(0.006) (0.006) (0.006) (0.017) NWC / Assets -0.263*** -0.264*** -0.262*** -0.248***

(0.006) (0.006) (0.006) (0.021) Capex / Assets -0.474*** -0.474*** -0.476*** -0.348***

(0.017) (0.017) (0.017) (0.067) Leverage -0.327*** -0.327*** -0.327*** -0.361***

(0.005) (0.005) (0.005) (0.019) Dividend Dummy -0.052*** -0.054*** -0.052*** -0.039***

(0.002) (0.002) (0.002) (0.014) Acquisitions -0.298*** -0.296*** -0.298*** -0.383***

(0.011) (0.010) (0.010) (0.043) Constant 0.298*** 0.302*** 0.300*** 0.278***

(0.005) (0.005) (0.005) (0.021)

Industry FE? Yes Yes Yes Yes Year FE? Yes Yes Yes Yes Observations 36,950 36,950 36,950 2,599 R-squared 0.511 0.511 0.512 0.557 The sample includes all firms listed on NYSE/AMEX/NASDAQ excluding financial firms (Standard Industry Classification (SIC) codes between 6000 and 6999) from 1998 through 2009. Missing explanatory variables reduce the sample to 36,950 observations for 6,433 unique firms for the OLS regressions. Variable definitions are provided in the Appendix. Robust standard errors in parentheses. Note: *** p<0.01, ** p<0.05, * p<0.1

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Table 3. The Impact of Financial Constraints on the Relation between Corporate Cash and Key Human Capital Risk

WW index SA index KZ index Const. Unconst. Const. Unconst. Const. Unconst.

Variables (1) (2) (3) (4) (5) (6) Key Human Capital 0.021*** 0.021*** 0.019*** 0.022*** 0.018*** 0.020***

(0.005) (0.004) (0.004) (0.004) (0.004) (0.004) Insure Key Employee -0.024*** -0.027*** -0.028*** -0.015** -0.014*** -0.024***

(0.006) (0.006) (0.005) (0.006) (0.005) (0.006)

CF Volatility 0.011*** 0.008*** 0.014*** 0.007*** 0.010*** 0.011*** (0.003) (0.002) (0.003) (0.002) (0.003) (0.002)

R&D / Sales 0.026*** 0.023*** 0.024*** 0.046*** 0.027*** 0.023*** (0.001) (0.003) (0.001) (0.006) (0.001) (0.001)

Market to book 0.019*** 0.031*** 0.018*** 0.037*** 0.033*** 0.049*** (0.001) (0.002) (0.001) (0.001) (0.001) (0.001)

Size 0.013*** -0.014*** 0.016*** -0.014*** 0.006*** -0.017*** (0.001) (0.001) (0.001) (0.001) (0.001) (0.001)

Cash Flow / Assets -0.031*** -0.121*** -0.032*** -0.225*** -0.042*** -0.241*** (0.007) (0.029) (0.007) (0.019) (0.007) (0.011)

NWC / Assets -0.265*** -0.267*** -0.262*** -0.280*** -0.092*** -0.443*** (0.008) (0.009) (0.008) (0.009) (0.007) (0.010)

Capex / Assets -0.591*** -0.354*** -0.562*** -0.371*** -0.214*** -0.675*** (0.025) (0.020) (0.024) (0.021) (0.018) (0.028)

Leverage -0.386*** -0.222*** -0.406*** -0.203*** -0.081*** -0.419*** (0.008) (0.006) (0.008) (0.006) (0.007) (0.014)

Dividend Dummy -0.044*** -0.035*** -0.042*** -0.034*** -0.050*** -0.058*** (0.005) (0.002) (0.004) (0.002) (0.002) (0.003)

Acquisitions -0.442*** -0.218*** -0.437*** -0.209*** -0.183*** -0.403*** (0.020) (0.011) (0.019) (0.011) (0.010) (0.022)

Constant 0.274*** 0.278*** 0.264*** 0.270*** 0.059*** 0.390*** (0.007) (0.007) (0.007) (0.007) (0.006) (0.007)

Industry FE? Yes Yes Yes Yes Yes Yes Year FE? Yes Yes Yes Yes Yes Yes Observations 18,363 18,587 18,477 18,473 18,477 18,473 R-squared 0.475 0.489 0.501 0.482 0.555 0.607 The sample includes all firms listed on NYSE/AMEX/NASDAQ excluding financial firms (Standard Industry Classification (SIC) codes between 6000 and 6999) from 1998 through 2009. Missing explanatory variables reduce the sample to 36,950 observations for 6,433 unique firms for the OLS regressions. Variable definitions are provided in the Appendix. Robust standard errors in parentheses. Note: *** p<0.01, ** p<0.05, * p<0.1

20

Table 4. The Impact of Product Market Structure on The Relation Between Cash and Key Human Capital Risk.

Market Concentration: TNIC HHI

Predatory Threats: Product Market Similarity

Predatory Threats: Product Market Fluidity

High Low High Low High Low Variables (1) (2) (3) (4) (5) (6) Key Human Capital 0.015*** 0.024*** 0.017*** 0.006 0.018*** 0.011***

(0.005) (0.004) (0.004) (0.004) (0.004) (0.004) Insure Key Employee -0.022*** -0.026*** -0.027*** -0.015*** -0.027*** -0.022***

(0.006) (0.006) (0.006) (0.006) (0.006) (0.005)

CF Volatility -0.004 0.017*** 0.010*** 0.006** 0.011*** 0.003 (0.003) (0.003) (0.003) (0.002) (0.003) (0.002)

R&D / Sales 0.032*** 0.025*** 0.024*** 0.028*** 0.025*** 0.034*** (0.002) (0.001) (0.001) (0.002) (0.001) (0.003)

Market to book 0.021*** 0.019*** 0.018*** 0.018*** 0.018*** 0.018*** (0.001) (0.001) (0.001) (0.001) (0.001) (0.001)

Size -0.007*** -0.010*** -0.011*** -0.009*** -0.006*** -0.009*** (0.001) (0.001) (0.001) (0.001) (0.001) (0.001)

Cash Flow / Assets -0.015* 0.011 0.024*** -0.014 0.011 -0.006 (0.009) (0.008) (0.008) (0.009) (0.008) (0.010)

NWC / Assets -0.231*** -0.261*** -0.280*** -0.206*** -0.249*** -0.235*** (0.008) (0.010) (0.011) (0.007) (0.010) (0.007)

Capex / Assets -0.432*** -0.504*** -0.523*** -0.371*** -0.507*** -0.433*** (0.026) (0.021) (0.024) (0.022) (0.025) (0.022)

Leverage -0.343*** -0.280*** -0.262*** -0.333*** -0.290*** -0.330*** (0.007) (0.008) (0.008) (0.007) (0.008) (0.006)

Dividend Dummy -0.030*** -0.059*** -0.054*** -0.020*** -0.063*** -0.023*** (0.002) (0.003) (0.003) (0.002) (0.004) (0.002)

Acquisitions -0.228*** -0.355*** -0.391*** -0.203*** -0.430*** -0.174*** (0.014) (0.015) (0.018) (0.012) (0.017) (0.012)

Constant 0.280*** 0.339*** 0.364*** 0.267*** 0.342*** 0.275*** (0.007) (0.007) (0.007) (0.006) (0.008) (0.006)

Industry FE? Yes Yes Yes Yes Yes Yes Year FE? Yes Yes Yes Yes Yes Yes Observations 17,342 19,608 17,343 19,607 17,157 19,793 R-squared 0.440 0.573 0.587 0.411 0.528 0.435 The sample includes all firms listed on NYSE/AMEX/NASDAQ excluding financial firms (Standard Industry Classification (SIC) codes between 6000 and 6999) from 1998 through 2009. Missing explanatory variables reduce the sample to 36,950 observations for 6,433 unique firms for the OLS regressions. Variable definitions are provided in the Appendix. Robust standard errors in parentheses. Note: *** p<0.01, ** p<0.05, * p<0.1

21

Table 5. Controlling for the Labor Skill Index

Variables (1) (2) (3) (4) LSI 0.066*** 0.066*** 0.066*** 0.065***

(0.003) (0.003) (0.003) (0.003) Key Human Capital 0.010*** 0.023***

(0.003) (0.003) Insure Key Employee -0.009** -0.029***

(0.003) (0.004) CF Volatility 0.008*** 0.008*** 0.008*** 0.008***

(0.002) (0.002) (0.002) (0.002) R&D / Sales 0.029*** 0.029*** 0.029*** 0.029***

(0.001) (0.001) (0.001) (0.001) Market to book 0.021*** 0.021*** 0.021*** 0.021***

(0.001) (0.001) (0.001) (0.001) Size -0.006*** -0.006*** -0.006*** -0.006***

(0.001) (0.001) (0.001) (0.001) Cash Flow / Assets -0.005 -0.005 -0.006 -0.005

(0.007) (0.007) (0.007) (0.007) NWC / Assets -0.264*** -0.263*** -0.264*** -0.263***

(0.007) (0.007) (0.007) (0.007) Capex / Assets -0.466*** -0.466*** -0.467*** -0.468***

(0.018) (0.018) (0.018) (0.018) Leverage -0.323*** -0.323*** -0.322*** -0.322***

(0.006) (0.006) (0.006) (0.006) Dividend Dummy -0.049*** -0.048*** -0.050*** -0.048***

(0.002) (0.002) (0.002) (0.002) Acquisitions -0.317*** -0.318*** -0.316*** -0.318***

(0.012) (0.012) (0.012) (0.012) Constant 0.144*** 0.142*** 0.146*** 0.144***

(0.009) (0.009) (0.009) (0.009)

Industry FE? Yes Yes Yes Yes Year FE? Yes Yes Yes Yes Observations 31,980 31,980 31,980 31,980 R-squared 0.523 0.523 0.523 0.524 The sample includes all firms listed on NYSE/AMEX/NASDAQ excluding financial firms (Standard Industry Classification (SIC) codes between 6000 and 6999) from 1998 through 2009. Variable definitions are provided in the Appendix. Robust standard errors in parentheses. Note: *** p<0.01, ** p<0.05, * p<0.1