Debt as a Bonding Mechanism: Evidence from the Relations Between Employee Productivity, Capital Structure, and Outside Employment Opportunities

Jayant R. Kale

Harley E. Ryan, Jr.

Lingling Wang

This version: October 16, 2007

Abstract We investigate the disciplining role of debt in the publicly held firm by examining the relation between employee productivity and financial leverage. The unique feature of our study is that we incorporate outside employment opportunities for employees into the analysis. Consistent with the notion that the debt serves as a bonding mechanism because agents exert additional effort to avoid the personal costs of financial distress, we find a positive concave relation between employee productivity and financial leverage; employee productivity initially increases with debt and ultimately decreases. This relation is robust to controls for endogeneity and alternate measures of productivity. We also find that as outside employment opportunities increase (decrease), the positive concave productivity-leverage relation becomes weaker (stronger). Our results suggest that employees rationally trade off the costs of leaving the firm against the expected personal costs of financial distress and the disutility of additional effort. JEL classification: G30; G32; G38 Keywords: Debt Bonding, Agency Theory, Outside Employment Opportunities, Employee Productivity

All authors are at the Department of Finance, Robinson College of Business, Georgia State University, Atlanta, GA – 30302. Contact info for Kale: +1 404 413 7345 (phone) and jkale@gsu.edu (e-mail), for Ryan: +1 404 413 7337 (phone) and cryan@gsu.edu (e-mail), and for Wang: +1 404 413 7310 (phone) and fncllwx@langate.gsu.edu (e-mail). We thank Vikas Agarwal, Gerry Gay, Atul Gupta, Kathleen Weiss Hanley, Iftekhar Hasan, Marcin Kacperczyk, Omesh Kini, Ron Masulis, George Morgan, Gordon Phillips, Husayn Shahrur, Steve Smith, Anand Venkateswaran, and seminar participants at Georgia State University and the Steve Smith Memorial Conference at the Federal reserve bank of Atlanta for comments. We thank the Bureau of Labor Statistics for providing some of the data used in our analysis. We alone are responsible for remaining errors.

Debt as a Bonding Mechanism: Evidence from the Relations Between Employee Productivity, Capital Structure, and Outside Employment Opportunities

Abstract We investigate the disciplining role of debt in the publicly held firm by examining the relation

between employee productivity and financial leverage. The unique feature of our study is that we

incorporate outside employment opportunities for employees into the analysis. Consistent with

the notion that the debt serves as a bonding mechanism because agents exert additional effort to

avoid the personal costs of financial distress, we find a positive concave relation between

employee productivity and financial leverage; employee productivity initially increases with debt

and ultimately decreases. This relation is robust to controls for endogeneity and alternate

measures of productivity. We also find that as outside employment opportunities increase

(decrease), the positive concave productivity-leverage relation becomes weaker (stronger). Our

results suggest that employees rationally trade off the costs of leaving the firm against the

expected personal costs of financial distress and the disutility of additional Effort.

Debt as a Bonding Mechanism: Evidence from the Relations Between Employee Productivity, Capital Structure, and Outside Employment Opportunities

1. Introduction

The separation of ownership and control in a publicly held firm can result in value-

reducing inefficiencies since the manager may maximize her own utility and not that of the

shareholders (e.g., Alchian, 1969; Alchian and Demsetz, 1972; Jensen and Meckling, 1976).

Grossman and Hart (1982) and Jensen (1986) propose that the presence of debt in the firm’s

capital structure can serve as a costly bonding mechanism to reduce these inefficiencies. Since

debt introduces the likelihood that managers will lose their jobs due to financial distress, it

induces the manager to exert greater effort to increase firm value and reduce the possibility of

financial distress. The empirical support for the disciplining role of debt comes primarily from

studies of firms that either are targets of leveraged buyouts or engage in highly levered

recapitalizations.1 Since firms seldom conduct such highly-leveraged recapitalizations, the

sample sizes in these studies are rather small.

We investigate the disciplining role of debt in a large panel data set of more than 144,000

firm-years over a span of 36 years. We also investigate whether outside employment

opportunities affect the efficacy of debt as a disciplining mechanism. If employees can obtain

substitute employment, they might rationally choose to exit the firm rather than remain in the

firm and bear the expected costs of losing their jobs and the disutility of additional effort. To our

knowledge, our study is the first large-sample examination of the role of debt as a disciplining

1 For evidence on leveraged buyouts in which the firm goes private see Kaplan (1989), Lehn and Poulsen (1989), Marais, Schipper, and Smith (1989), Lehn, Netter, and Poulsen (1990), Muscarella and Vetsuypens (1990), Smith (1990), Opler (1992), Opler and Titman (1993), and Matsa (2006). Denis and Denis (1993) study 39 firms that engage in highly levered recapitalizations and remain publicly traded, albeit with more concentrated inside ownership.

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mechanism in the firm as well as the first one to explicitly incorporate the role of outside

employment opportunities into this analysis. Our empirical findings offer strong support for the

disciplining role of debt and demonstrate that an increase in the outside employment

opportunities for employees reduces the effectiveness of debt as a disciplining mechanism.

Grossman and Hart (1982) illustrate the role of debt as a costly bonding mechanism that

provides the manager with an incentive to increase firm value. In the spirit of Grossman and

Hart, we argue that a manager derives utility from wealth and disutility from effort and that the

manager can improve firm value by exerting greater effort. The presence of risky debt in the

firm’s capital structure introduces the possibility that the firm goes bankrupt. The manager bears

significant personal costs in the event of bankruptcy but receives a portion of the firm value in

the non-bankrupt state, which provides an incentive to increase her effort to minimize the

possibility of default. In equilibrium, the manager chooses an optimal level of debt as a bonding

mechanism where the manager commits to improving performance by exerting greater effort by

choosing a higher debt level.

Oyer (2004) notes that principal-agent models that derive optimal conditions to maximize

agent effort implicitly assume that agents are constrained to remain within the firm. In reality,

however, employees often have alternative employment opportunities outside the firm. In the

bonding-with-debt framework, an employee will optimally choose to accept a job in another firm

if it is less costly for him to leave the firm than to bear the disutility of extra effort resulting from

a higher debt level.2 We posit that the existence of outside employment opportunities affects the

efficacy of debt as a bonding mechanism in two ways. First, the presence of outside employment

opportunities lead to a positive concave relation between effort and financial leverage. The

2 For the manager, there may be other costs associated with debt than the disutility from additional effort and personal bankruptcy costs. The presence of debt may also reduce the ability of the manager to consume perks and/or invest in “pet” projects, and increase the disutility from additional monitoring. See, for instance, Stulz (1990).

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intuition is that, at each level of debt, the manager compares the benefits of lowering the

probability of financial distress by working harder with the current firm to the payoffs from

obtaining employment outside the firm, which entails search costs and possibly lower

remuneration.3 At sufficiently low levels of debt, the former effect dominates resulting in a

positive relation between debt level and the effort expended by the employee. Beyond a certain

debt level, however, the probability of distress is large enough to induce the employee to

consider taking a job with another firm, which leads to a negative relation between leverage and

expended effort. The possibility that higher-ability employees, who have better outside

opportunities, are more likely to leave the firm first is also consistent with the negative

productivity-leverage relation at high debt levels.

The positive concave relation between productivity and leverage can also obtain in the

framework where an employee optimally chooses the level of relation-specific investment. If

employees can make relationship specific investments, then the presence of career concerns due

to bankruptcy leads to a positive concave relation between leverage and productivity. At lower

debt levels, when the threat of bankruptcy is negligible, the employee makes greater investments

in relation-specific investments as compared to when the level of debt is high and the bankruptcy

probability is significant. The assumption that relation-specific investments are productivity-

enhancing then leads to a positive concave relation between debt and productivity.

Our second main hypothesis relates to the influence of outside employment opportunities

on the effectiveness of debt as a disciplining mechanism. At any level of debt, an increase in

outside employment opportunities will reduce the effectiveness of debt as a disciplining

mechanism since greater outside employment opportunities increase the likelihood an employee

3 Evidence suggests that displaced works typically take jobs in a new firm for lower pay (see, e.g., Neal, 1995). Addison and Portugal (1989) estimate that wages decline by 5.4% to 13.9% when workers change occupations and 16.1% to 19.8% when the new job is in a different industry.

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will leave the firm rather then exert more effort. Consider the extreme case when there are no

outside opportunities. In this case, the agent will exert the most effort since there is nothing to

offset the personal costs in the event of financial distress, and the disciplining role of debt will be

the greatest. With outside employment opportunities, the agent will exert less effort at any debt

level than what she would have in the extreme case of no outside employment opportunities. As

the external employment opportunities become better, the agent will exert even less effort at any

given level of debt.

We test the bonding role of debt by using employee productivity, that is, the productivity

of managers and workers together, as the measure of the effort exerted by the agents. Although

bonding models focus on the effort levels chosen by the manager (CEO), we consider the

productivity of all employees for two reasons. First, a large portion of the CEO’s effort goes

towards ensuring the productivity of lower-level employees. Debt provides the top management

with the incentive to exert more effort and more closely monitor middle managers. As a result,

middle-level managers expend greater effort and, in turn monitor lower-level employees more

closely, and so forth. Therefore, the presence of debt in the firm’s capital structure should

induce all employees to expend greater effort. Second, the possibility of bankruptcy from the

presence of debt also creates job-loss concerns for all employees. At the top management level,

research (e.g., Gilson, 1989, 1990; Gilson and Vetsuypens, 1993) shows that a large percentage

of CEOs lose their jobs when their firms experience financial distress so CEOs have a strong

incentive to take preemptive actions to avoid financial distress. Such actions could reasonably

include layoffs and downsizing, which results in job losses for lower-level employees.

Supporting this view, Sharpe (1994) finds that the cyclicality of the labor force in manufacturing

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firms is positively related to financial leverage. Thus, all employees bear the costs imposed by

debt and have the incentive to increase their productivity to keep their jobs.4

We examine a panel data set of over 144,000 firm-years encompassing 16,482 firms from

1970-2005 and find a positive relation between employee productivity and leverage at lower

levels of leverage. Beyond a sufficiently high level of debt, we find that the relation between

productivity and leverage becomes negative. In the analysis, we control for a variety of factors

that could influence employee productivity including the number of employees, the age of the

firm, asset intensity, the past profitability of the firm, a measure of operating leverage, labor cost,

union membership in the firm’s industry, a proxy for employee working conditions, and industry

concentration. Thus, our empirical findings support the hypothesized positive concave relation

between productivity and leverage. The positive concave relation is robust to controls for

endogeneity using instrumental variables for leverage and a two-stage least squares estimation

technique as well as for alternative measures of employee productivity. We also conduct these

tests over several different time periods. In summary, our empirical analysis indicates that the

positive concave relation between employee productivity and leverage obtains regardless of the

specification, proxies for labor productivity, control variables, or time period.

Next, we examine the influence of outside employment opportunities on the relation

between employee productivity and leverage. To measure outside employment opportunities we

use (i) the voluntary quit rate (the number of employees in an industry that voluntarily quit their

jobs), (ii) the hire rate (the number of new hires in an industry divided by total employees), (iii)

the Parrino (1997) measure for industry homogeneity, and (iv) the products of the homogeneity

proxy and the quit rate and hire rate, respectively. To measure the cost to the employee of

4 Moreover, Hallock (1998) shows that even when firms are not in financial distress, CEOs typically continue to receive large compensation packages even as lower level employees lose their jobs due to layoffs and firings.

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leaving the firm, we use (i) a dummy variable to indicate that the firm’s labor cost per employee

exceeds the industry median by at least 20% and (ii) the firm’s industry median adjusted labor

cost per employee. These measures capture the compensation and benefits the employee

foregoes by leaving the firm. Finally, since studies indicate that the 1994 North American Free

Trade Agreement (NAFTA) reduced job opportunities in the manufacturing sector, we also use

the implementation of NAFTA as an exogenous shock to employment opportunities in the

manufacturing sector.

The results for all of the outside employment opportunity proxies tell a consistent story.

When outside opportunities are greater, the relation between employee productivity and debt is

significantly weaker. When outside opportunities are fewer or the cost to the employee of

leaving the firm is higher, the influence of debt on employee productivity is significantly

stronger. Taken together, our findings strongly support the premise that debt serves as a costly

bonding mechanism as proposed by Grossman and Hart (1982) and Jensen (1986). Furthermore,

in the spirit of Oyer (2004), these results establish empirically the effects of outside employment

opportunities on the disciplining role of debt.

Our paper contributes to the literature in two areas. First, our finding of a positive

concave relation between employee productivity and debt adds to the empirical evidence on the

trade-off explanations of capital structure. We document that increased employee productivity is

an added benefit of financial leverage for a significantly high level of debt (up to approximately

45%), which includes a large portion of the firms in our sample. Our finding of a decline in

employee productivity at high levels of debt complements Matsa (2006) who finds that

supermarkets that went through leveraged buyouts (LBOs) had a significant increase in product

stockouts following the LBO. Second, our results regarding the influence of outside

employment opportunities on the relation between employee productivity and debt support the

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participation constraint arguments in Oyer (2004). In particular, they suggest that principal-agent

models that derive optimal conditions to maximize the agent’s effort should be modified to

include the availability of outside employment options. Parrino (1997) provides evidence that

outside employment opportunities influence the decision to fire and hire CEOs, and Rajgopal,

Shevlin, and Zamora (2006) find that outside employment opportunities influence the structure

of CEO incentive compensation.

Our approach differs from previous studies in the finance literature by considering the

productivity of all the employees of the firm and not just the management or the labor force. We

argue, and our evidence suggests, that the debt provides an incentive to all firm employees to

exert greater effort. Several studies (e.g., Bronars and Deere, 1991, Dasgupta and Sengupta,

1993, Perotti and Spier, 1993, Hanka, 1998, and Kale and Shahrur, 2007) find that management

can use debt as bargaining tool in to extract greater concessions from labor unions in wage and

productivity negotiations. These studies imply that debt will affect the productivity of all the

employees of the firm, which further supports our approach. Therefore, we test the hypothesis

that debt encourages all agents to exert more effort by examining the effect of debt on total

employee productivity.

The rest of the paper is organized as follows. The next section describes our data sources

and the variables we use in the analysis. Section 3 presents our empirical findings that support

the positive concave relation between employee productivity and debt level. The following

section investigates the impact of changes in outside employment opportunities on the

productivity-leverage relation and presents the evidence showing that the relation becomes

weaker (stronger) as outside employment opportunities improve (worsen). The last section

concludes the paper.

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2. Data, Model Specification and Variable Construction

2.1 Data

Our initial sample includes all firms in the Compustat Industrial Annual database from

year 1970 to 2005. We exclude financial firms (SIC codes 6000 to 6999) and utilities (SIC codes

4900 to 4999).5 To be included in the sample, we require firms to have information on number

of employees, total assets, debt, sales and previous year’s profitability. We eliminate firms with

sales or asset growth more than 200% a year since extremely high growth usually signals major

corporate events, such as merger or acquisition activities, that could also affect employee

productivity (see Campello, 2006). As we discuss later, we adjust the variables used in the

analysis by subtracting their respective industry medians. Therefore, we require a minimum of 5

firms in the firm’s 4 digit SIC code.6 In total, 16,482 firms and 144,444 firm-years comprise our

sample, with the number of firms ranging from 2,550 to 5,750 during any particular year.

We obtain data on annual quit rates (the percentage of employees in an industry who

voluntarily quit their jobs), hire rates (the percentage of new hires in an industry), and the annual

employee working hours by industry from the Job Opening and Labor Turnover Survey (JOLTS)

published by the Bureau of Labor Statistics. JOLTS provides data from 2000 onward and covers

all nonagricultural industries.7 Stock returns come from the Center for Research in Securities

Prices (CRSP) database. We obtain union membership information from the Current Population

Survey (CPS) union membership database compiled by Hirsch and Macpherson

(http://www.unionstats.com/) which provides information on union membership from 1983-2005

5 We use the firm’s historical SIC code to identify its industry. Since historical SIC code is only available in Compustat from 1987 onward, we use the 1987 historical SIC code for years prior to 1987. 6 As an alternative, we require a minimum of ten firms in an industry and obtain similar results to those reported.. 7 The BLS also provides a dataset with quit ratios from 1970 to 1981. However, the data only cover the manufacturing industry and are not directly comparable with the JOLTS data. Therefore, we only use the quit rates and hire rates from JOLTS data.

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based on CIC (Census/CPS Industry Codes) classifications. We use the matching table provided

by Hirsch and Macpherson to match union coverage at the 4- or 3-digit SIC code level.8 To proxy

for working conditions, we obtain Fortune Magazine’s list of “100 best companies to work for”

from 1998 to 2006. We adjust all dollar values to 2003-dollar levels by using the Consumer

Price Index (CPI-U) compiled by the BLS. To control for the influence of extreme values, we

winsorize all variables and set values that exceed the 99th percentile or fall below the first

percentile to the 99% and 1% values, respectively,

2.2 Model Specification for Employee Productivity

We use the standard Cobb-Douglas production function to specify employee productivity

as follows.

Yit = λLita Kit

b (1)

where Yit, Lit, and Kit are the total output, labor input, and capital input, respectively, for firm i at

time t. We divide all factors in (1) by Lit, and then apply a log transformation to obtain:

Ln(Yit/ Lit) = Lnλ + bLn(Kit/Lit) + cLn(Lit) (2)

where c = a + b - 1, and return to scale = a + b = c + 1.

2.3 Variable Construction

2.3.1 Employee Productivity, Labor Input, and Capital Intensity measures

We measure a firm’s employee productivity (Yit/ Lit) as the firm’s total output divided by

number of employees (labor input). Specifically, firm level employee productivity, Output Per

Employee, equals sales (Compustat data item 12) plus changes in inventories (work in progress,

8 For a detailed description on this union membership database, refer to Hirsch and Macpherson (2003).

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data item 77 and finished goods, data item 78) divided by the number of employees (data item

29) in the firm. 9 We also use two alternative measures of employee productivity, Output Per

Labor Hour and EBITA Per Employee. Output Per Labor Hour equals sales plus changes in

inventories divided by the number of hours that a typical employee in the firm’s industry works

in one year. EBITDA Per Employee is the ratio of operating income before depreciation and

amortization to the number of employees. We use the number of employees (date item 6) to

proxy for labor input in equation (1). Following Hanka (1998), we compute Capital Intensity as

total assets (data item 6) divided by the number of employees.

2.3.2 Leverage measures

We measure Leverage as the book value of long-term debt plus debt in current liabilities

(data item 9 + data item 34) divided by book value of debt plus the market value of equity (data

item 9 + data item 34 + data item 25*data item 199). We repeat our tests using a leverage ratio

based on the book value of equity and obtain similar results to those reported in the tables.

2.3.3 Outside Employment Opportunities

We use five different proxies to measure outside employment opportunities. These

variables are: (1) Quit Rate by industry; (2) Hire Rate by industry, (3) Industry Homogeneity, (4)

Excess Labor Cost Per Employee, and (5) High-Pay Dummy. We also consider an exogenous

event, the North American Free Trade Agreement (NAFTA), which shocked the level of

employment opportunities for manufacturing firms. We investigate how the NAFTA, which

came into effect on January 1st, 1994, affects the debt-productivity relation.

9 Schoar (2002) and Brynjolfsson and Hitt (2003) use sales plus changes in inventories as measures of firm output.

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The BLS defines Quit Rate as the number of employees who leave their jobs voluntarily,

with exceptions for retirements or transfers to other locations with the same firm, divided by the

total number of employees in an industry for the annual survey period. A higher Quit Rate

implies higher outside employment opportunities for employees in that industry, but we

recognize that it could result from a general downturn in the industry. Thus we also use the Hire

Rate, which the BLS defines as the number of new hires in the industry during the annual survey

period divided by the number of employees in the industry over the same period. The Quit Rate

and Hire Rate are available for broadly defined industries (2-digit SIC codes) and we assign the

respective Quit Rate and Hire Rate to a firm based on its 2-digit SIC industry code.

For our third measure of outside employment opportunities, we use Parrino’s (1997)

Industry Homogeneity proxy. This proxy is based on the premise that a CEO’s skills can be

transferred more easily to another firm in the industry when the firms are more homogeneous.

We argue that a homogenous industry offers better employment opportunities to all employees

and not just the CEO since firms in a homogenous industry have similar technologies and

compete in similar product markets. To account for changes in industries over time, we estimate

industry homogeneity for four periods: 1970 to 1979, 1980 to 1989, 1990 to 1997 and 1998 to

2005.10 Industry Homogeneity affects employees’ outside employment opportunities in two

ways. Firms in a homogenous industry likely require similar skill sets for employees, providing

employees more mobility to move from one firm to another. However firms also have a

relatively larger supply of labor with similar skills, which increases labor market competition.

10 Following Parrino (1997), we calculate Industry Homogeneity as follows. First, we create an equally weighted return index for the 2-digit SIC industry using CRSP monthly returns and estimate a regression of the monthly return for each firm in the index on the equally weighted market index and the industry index. Then, we take the partial correlation coefficient for the industry return index and average it across all firms in the industry to obtain the homogeneity proxy. We require a minimum of 20 firms in a 2-digit SIC code. We obtain similar results when we require a minimum of 35 firms (as in Parrino (1997)). Out of our sample of 144,444 firm-years, we are able to compute Industry Homogeneity for 141,249 firm-years.

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Whether the mobility effect or the competition effect dominates is an empirical question. To

address this issue we create two interaction variables: the product of Industry Homogeneity and

Quit Rate, and the product of Industry Homogeneity and Hire Rate. The mobility effect is more

likely to dominate when both Industry Homogeneity and Quit/Hire Rates are high.

Motivated by Becker (1962), we base our fourth and fifth outside employment

opportunity measures on the premise that employees who have more firm-specific investments in

a firm are less likely to quit their jobs. Employees who make firm-specific investment become

more valuable to their current firm and can bargain for a percentage of the firm’s surplus, which

should lead to higher wages (see Rajan and Zingales, 1998). Thus, employees who make firm-

specific investments will receive higher salaries, be less likely to quit the firm when faced with

financial distress, and have larger incentives to work hard to avoid financial distress. Because

other firms in the industry will be unlikely to pay the amount of salaries these employees are

currently making, Becker suggests that the level of firm-specific investment made by an

employee can be measured as the employee’s wage minus the alternative wage in the industry

and job search costs. We proxy for the level of firm-specific investment by an employee, Excess

Labor Cost Per Employee, as the industry median adjusted natural log value of Labor Cost Per

Employee, which we measure as the firm’s labor expense divided by number of employees (data

item 42 divided by data item 29).11 For a cleaner measure less susceptible to noise and

measurement error, we construct a dummy variable (High-Pay Dummy) that equals one if the

firm’ labor cost per employee is 20% or more than the industry median. Approximately 10% of

the firms in Compustat report their labor and related expenses and, therefore, we conduct the

analysis related with labor pay on smaller sample than for other measures.

11 After subtracting the industry median, half the values are negative, which creates a problem when we use the natural log transformations in the analysis. Therefore, in the analysis, we adjust for industry median as follows: Log(Labor Cost Per Employee) – industry median(Log(Labor Cost Per Employee)).

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We also examine the effect of an exogenous shock to employment opportunities in

manufacturing firms due to the implementation of NAFTA in January of 1994. According to

Scott (2001), NAFTA “eliminated 766,030 actual and potential U.S. jobs between 1994 and

2000 because of the rapid growth in the net U.S. export deficit with Mexico and Canada.” We

create a dummy variable, NAFTA, which equals zero if the firm-year is before 1994, and one if

the firm-year is 1994 or later.

2.3.4 Descriptive Statistics for Test Variables

In Table 1, we present descriptive statistics for our proxies for leverage, employee

productivity, and outside employment opportunities. As mentioned earlier, the sample size for

any particular test depends upon the data availability for the variables. Thus, the sample size

varies from 144,444 firm-years for Leverage and Output Per Employee to 16,193 for Excess

Labor Cost Per Employee. The mean (median) value for Leverage is 0.269 (0.203), which is

comparable to that in prior studies in the literature. The mean (median) Output Per Employee is

$230,108 ($159,108). On an hourly basis, an employee produces an average (median) of $660

($58) of sales. On a value-added basis, each employee produces an average (median) of $16,772

($13,627) of operating income before depreciation and amortization. The mean (median) values

for the Quit Rate and Hire Rate are 1.567% (1.308%) and 3.029% (2.550%), respectively. About

15% firms pay their employees 20% or more than the typical salary in an industry.

Industries differ according to their product characteristics, technology, and the

sophistication (e.g. education levels) of workers. Therefore, we expect significant variation in

employee productivity across industries. Table 2 presents the average values for the three

measures of employee productivity in each 2-digit SIC code. For expositional ease, we sort

industries by descending values for Output Per Employee. As expected, we find considerable

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variation in productivity across industries. Output Per Employee ranges from a high of $871,040

in the Petroleum and Coal Products group to $42,420 in the Social Services industry. Similarly,

Output Per Labor Hour ranges from $6,030 in the Petroleum and Coal Products group to $130

for Miscellaneous Manufacturing Industries. Petroleum and Coal Products group has the highest

EBITDA Per Employee of $121,280 and Agricultural Services the lowest at -$26,950.

Given the considerable variation in employee productivity across industries, we adjust all

variables except leverage by subtracting the industry median from the firm value. We do not

adjust leverage because, the possibility of financial distress is determined by the level of debt in

the firm, and our hypothesis is that leverage affects productivity because of the employment

concerns in financial distress.12

In Table 3, we report correlations between our leverage, productivity, and outside

employment opportunity variables. Consistent with the bonding role of debt, we find a positive

correlation between Leverage and all the industry-median-adjusted employment productivity

measures. In unreported results, we find that the correlations with unadjusted productivity

measures are also positive and generally have higher values for the correlation coefficient. The

measures of outside employment opportunity, Quit Rate, Hire Rate, and Industry Homogeneity

are positively correlated with each other. Higher Excess Labor Cost Per Employee implies lower

mobility, which is consistent with the negative correlation with Hire Rate and Industry

Homogeneity. High-Pay Dummy, however, has a small positive correlation with Hire Rate.

2.3.5 Other control variables

In addition to the number of Employees, and Asset Intensity, which arise directly from the

Cobb-Douglas production function, we control for a number of other factors that could influence

12 We repeat all our analysis using industry median adjusted values for leverage and obtain similar results.

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productivity. These additional factors are wages, unionization, the work environment and non-

pecuniary compensation, firm age, past profitability, operating leverage, and industry

concentration. Higher wages may induce employees to exert more effort and therefore improve

employee productivity. We use Labor Cost Per Employee, which we defined earlier, as the proxy

for the wages. Many studies suggest a relation between unionization and employee productivity.

Baldwin (1983) argues that firms keep inefficient plants to discourage unions from bargaining

for higher wages. Doucouliagos and Laroche (2003) find a positive relation between

unionization and productivity for U.S. firms. Chen, Kacperczyk, and Ortiz-Molina (2007) argue

that unions reduce the agency costs of debt financing. In view of this literature, we include the

variable Union Membership, which is the fraction of workers in an industry who belong to a

union, as a control variable in our analysis. A good work environment and non-pecuniary

compensation could encourage greater employee productivity. To control for employee-friendly

characteristics, we define a Best Company dummy variable that equals one if the firm is in the

list of “100 Best Companies to Work for in America” compiled annually by Fortune magazine.

Productivity could increase along a learning curve as firms mature. To control for this

possibility, we include a proxy for Firm Age, the number of years a firm has been in the

Compustat Annual database, in our tests. The Compustat database contains data on firms dating

to 1950, so the maximum age Firm Age in our sample is 55 years. We control for persistence in

firm performance by including lagged Profitability, which we measure as operating earnings

over assets (data item 18 plus data item 14 divided by data item 6). We recognize that operating

leverage typically relates negatively to financial leverage. A negative relation between operating

leverage and employee productivity may lead to a spurious relation between financial leverage

and employee productivity. To rule out such a possibility, we include the variable Operating

Leverage, which is the gross value of property, plant & equipment (data item 7) divided by total

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assets.13 Research (Jensen, 1986; Maksimovic, 1988; Philips, 1995; Mackay and Philips, 2004)

suggests that product market competition can affect the relations between debt and firm

efficiency. We control for the effects of product markets competition by including the industry’s

Herfindahl Index (HHI) as a control variable. For each four-digit SIC code, we calculate the

Herfindahl Index as the sum of squared market share of each firm in that industry and then

assign the Herfindahl Index to every firm in that industry.

Table 4 presents summary statistics for our control variables. We note that the minimum

number of employees in a firm is 3. We repeat our analysis on a sample that requires a minimum

of 100 employees per firm and obtain similar results to those reported.

3. Evidence on the Relation between Employee Productivity and Financial Leverage

We hypothesize that the relation between employee productivity and financial leverage is

initially increasing and ultimately decreasing. For sufficiently low levels of debt, we expect

employee productivity to increase with debt. When the debt level becomes too high, we expect

productivity to decrease as debt increases. To test this proposed non-linear relation between

productivity and leverage, we include leverage and its squared value as independent variables in

the regression. Specifically, we extend equation (2) to include leverage and other firm

characteristics as determinants of employee productivity as follows:

yit = α + β1kit + β2lit + β3debtit-1 + β4debtit-12 + control variables + εit (3)

where yit = Ln(Yit/ Lit), kit = Ln(Kit/ Lit), and lit = Ln(Lit).

It is possible that unobserved variables, such as industry characteristics, year effects, and

manager’s type, could affect Leverage and Leverage2, the independent variables, as well as the 13 We have also used the net value of property, plant & equipment divided by total sales as an alternative proxy for operating leverage. The results are very similar to using the gross value of property, plant & equipment. In fact, the correlation between these two proxies of operating leverage is 0.838.

17

dependent variable, employee productivity. In our empirical analysis, we control for endogeneity

as follows. First, we adjust all our firm level variables (except Leverage and Leverage2) for the

industry median value. Campello (2006) argues that removing industry effects from all the

variables mitigates the omitted variable problem since it would be difficult to identify any

variable that could “be correlated with the deviation of the included variables’ realization from

their mean for each industry in each year”. Second, we include year dummies to control for

potential year fixed effects. Third, we estimate two-stage least squares (2SLS) regression using

instrumental variables for Leverage and Leverage2. As we will show, our tests do indicate

endogeneity, but it appears that the presence of endogeneity does not affect the nature and

structure of the relations among the variables of interest.

3.1 Results from OLS Estimation

We present the findings from the OLS estimations of equation (3) in Table 5. Since we

do not have data for all control variables in all years, we present the results from five different

models. Model 1 has the fewest explanatory variables but has the largest sample size (144,444

firm years). We include all variables in model 5, which has the smallest sample size of 3,798

firm-years. In all five models, the coefficient on Leverage is positive and statistically significant.

The p-value is less than 0.00 in models 1through 4, respectively, and 0.06 in model 5. The

coefficient on the Leverage2 term is negative in all five models with p-values less than 0.00 in

models 1 through 4 and a p-value of 0.03 in model 5. Taken together, these findings support the

hypothesized positive concave relation between employee productivity and leverage.

Calculations using the estimates from these models indicate that the inflection point occurs at a

debt level of approximately 45% to 56%. These results offer strong support for the hypothesis

that debt serves as a costly bonding mechanism to encourage greater effort by employees.

18

We find that productivity relates positively to the number of Employees (models 1 and 5),

and Firm Age (all models). The positive relation with Firm Age indicates the presence of a

learning curve. We also find performance persistence; the coefficient on lagged Profitability is

positive and significant in four of five models. The coefficient on Union Membership is

significantly positive in model 2, but insignificant in models 3 and 5. The coefficient on the

variable Best Company Dummy is positive as hypothesized as in models 3 and 5 (the p-value is

less than 0.00 and equals 0.104, respectively). When we include Excess Labor Cost Per

Employee as an additional determinant in models 4 and 5, we find that it relates positively to

productivity as expected (p-values are less than 0.00).

3.2 Results from 2SLS Estimation

To address the possibility that removing industry medians may not fully control the

endogeneity issue, we identify instrumental variables for Leverage and Leverage2 that are

uncorrelated with the employee productivity and estimate the model using two stage least

squares (2SLS).. The instruments in our analysis are the median values of Leverage and

Leverage2 for firms in the same four-digit SIC code. Prior research (e.g., Leary and Roberts,

2005) shows that firms adjust their leverage such that it is similar to other firms in their industry,

which provides the economic rationale for using industry median values for leverage as

instruments. As suggested by Wooldridge (2002) and Ortiz-Molina (2007), we use the square of

the industry median debt level as an instrument for the square term of debt. We present evidence

on the relevance of these instruments in Table 6. Panel A of Table 6 presents the estimated

coefficients for the instruments in the first-stage regressions for Leverage and Leverage2. The

coefficients on both the instruments are statistically significant, which implies that our

instruments are individually relevant. Additionally, the p-values for the F-statistic and the partial

19

R-squared values provide significant support for the joint relevance of all our instruments in the

first stage. The significant values of Durbin-Wu-Hausman statistics reported in Panel B of Table

6 indicate the presence of endogeneity.

The results from estimating the second-stage in the 2SLS are in Table 7. As in Table 5,

we report results from five models, where model 1 has the fewest explanatory variables but the

largest sample size, and model 5 has all the determinants but a significantly smaller sample size.

The coefficient on Leverage is positive and significant in all the five specifications, which is

similar to the findings for the OLS estimation. The coefficients on Leverage2 are significantly

negative in all but the last model, where the p-value on the coefficient is 0.151. Thus, the

positive concave relation between employee productivity and leverage continues to hold when

we correct for the endogeneity in the relation between leverage variables and employee

productivity. The structure of the relation remains reasonably similar to that established in Table

5. For example, calculations indicate that the implied points of inflection for leverage levels

vary from 45% to 60%, which is similar to the values computed using OLS estimates.

3.3 Relation of Leverage with Alternative Measures of Employee Productivity

We investigate the robustness of the positive concave relation between leverage and two

alternate measures of employee productivity. These measures are (i) EBITDA Per Employee

defined as the level of operating income before depreciation per employee, and (ii) Output Per

Labor Hour, which is the level of sales generated by an employee in one hour. As before, all

variables except leverage are adjusted for industry median values. We present findings from both

the OLS and the 2SLS estimations of model 1 in Table 8. Results for our test variables from

models 2 through 5 are similar to those reported.

20

The first two columns of Table 8 present the results for EBITDA Per Employee. The

coefficients on Leverage and Leverage2 are respectively positive and negative for both OLS and

2SLS specifications and are statistically significant. The positive concave relation between

leverage and productivity attains a maximum at leverage levels of 46% and 48% in the OLS and

2SLS, respectively. As is evident from the reported findings in the last two columns of the table,

the findings are similar when the dependent variable is Output Per Labor Hour. The relation

between productivity and leverage is positive concave and the inflection points are 48% and 58%

for OLS and 2SLS, respectively. Thus the initially increasing and ultimately decreasing relation

between leverage and productivity is robust to alternative measures of productivity. Given the

similarity in the inflection points, it also appears that the basic structure of this relation is

reasonably stable across the various specifications reported in Tables 5, 7, and 8.

4. The Effects of Outside Employment Opportunities on the Efficacy of Debt Bonding

Oyer (2004) notes that principal-agent models that establish optimal conditions to

maximize the agent’s effort implicitly assume that the agent is constrained to remain in the firm.

It is reasonable to expect that the incentives that debt provides to employees to expend more

effort will be less effective (more effective) when it is easier (more difficult) for the employees

to find employment outside their firms. To test this premise, we examine if changes in outside

employment opportunities affect the relation between debt and employee productivity.

Specifically, we create interactions between the different measures for outside employment

opportunity and Leverage and Leverage2, respectively. We estimate is an extended version of

equation (3) as follows:

yit = α + β1kit + β2lit + β3debtit-1 + β4debtit-12 + β5debtit-1*OutsideEmp.Opp.

+ β6debtit-12 *OutsideEmp.Opp. + OutsideEmp.Opp. + Control Variables + εit (4)

21

where yit = Ln(Yit/ Lit), kit = Ln(Kit/ Lit), lit = Ln(Lit). If the measure of outside employment

opportunity is positively (negatively) related to employee mobility, then the relation between

leverage and productivity will become weaker (stronger) and flatter (steeper). More precisely,

the coefficient β5 on the interaction between Leverage and the outside employment measure will

be negative (positive) and the coefficient β6 on the interaction with Leverage2 will be positive

(negative).

4.1 The Effects of High Outside Employment Opportunities

In order to investigate the impact of improved outside employment opportunities on the

leverage-productivity relation, we use the three measures of outside opportunities described

earlier, namely, the industry Quit Rate, the industry Hire Rate, and Industry Homogeneity.

Higher values of all these three variables imply better employment opportunities and thus should

result in making the leverage productivity relation weaker. We present our findings from the

OLS estimation of equation (4) using each of these three measures in the first three columns of

Table 9. The coefficients on Leverage and Leverage2 in all the columns of the table indicate that

inclusion of the two interaction terms into the specifications does not alter the significantly

positive concave relation between productivity and leverage.

In the first two columns, the coefficients on the interaction term with Leverage are

negative and significant in the models where Quit Rate and Hire Rate are the measures of

employee productivity. The coefficients on the interaction terms with Leverage2 are positive and

significant. These findings imply that as outside employment opportunities improve, the

leverage-productivity relation becomes weaker which is consistent with our prediction. In

column three, where Industry Homogeneity is the measure of outside opportunities, the

coefficients on the two interaction terms are not statistically significant. Since a more

22

homogenous industry offers better opportunities for employees as well as larger labor pool for

the employer, we allow for interactions between Industry Homogeneity with Quit rate and Hire

Rate and use these products as the measures of outside opportunities. The rationale is that when

the industry is more homogenous and the Quit/Hire Rate is also high, better employee mobility

is more likely to be the case than a better labor pool. We report findings from using these two

measures in columns four and five of table 9. Consistent with our conjecture, we find that the

coefficients on the interaction terms with Leverage and Leverage2 are negative and positive,

respectively, but are significant only for the product with Quit Rate.

In order to address endogeneity concerns, we estimate 2SLS specifications for all the five

models where we consider Leverage, Leverage2, and the two product terms of these variables

with outside employment opportunities as being endogenously determined. To obtain

instruments for the two interaction terms, we first predict the values for Leverage and Leverage2

using the instruments described earlier. Then we use the products of the predicted Leverage and

Leverage2 values and the proxy for outside employment opportunities as the instruments for the

interaction terms between Leverage and Leverage2 and the proxy for outside employment

opportunities. The rationale is to generate instruments that are more relevant in the first stage

regressions (see Wooldridge, 2002).14 From unreported first-stage regression, we note that these

instruments easily satisfy all the tests for relevance. We present the results from the 2SLS

estimation of all the five models in table 10. First, the positive concave leverage- productivity

relation continues to obtain. The coefficients on the interaction terms with Leverage and

Leverage2 are negative and significant as predicted in all the five cases. These findings offer

14 We also use an alternative approach suggested by Woolridge (2002) based on the following instruments: industry median Leverage, industry median Leverage2, the product of industry median Leverage and the proxy for outside employment opportunities, and the product of industry median Leverage2 and the proxy for outside employment opportunities. The coefficients on the interaction terms with Leverage are negative and significant as predicted in all the five cases. The coefficients on the interaction terms with Leverage2 are positive and significant at the 10% level or better in three of the five models and with p-values of 0.12 for the quit rate and hire rate, respectively.

23

fairly strong support to our conjecture that when employees have better employment

opportunities outside their firm, debt is less effective as a bonding mechanism.

4.2 The Effects of High Costs of Leaving the Firm

We next consider the cases where it is more costly for employees to leave the firm. We

use the two measures of employee mobility, Excess Labor Cost Per Employee and High-Pay

Dummy, as proxies for firm-specific investment and the cost of leaving the firm. We

hypothesize that when it is more costly for employees to leave the firm, debt will be more

effective as a bonding mechanism and the positive concave relation between employee

productivity and financial leverage will be stronger.

We present OLS and 2SLS estimates for both measures of excess pay in Table 11. The

first two columns present the findings using Excess Labor Cost Per Employee and the last two

columns using the High pay Dummy. We again find a positive concave relation between leverage

and productivity. In the 2SLS estimation, the coefficients on the interaction term with Leverage

are positive and significant, and the coefficients on the interaction term with Leverage2 are

negative and significant. These findings support our hypothesis that as employment opportunities

become scarce, debt is more effective as a bonding mechanism. The signs on the relevant

coefficients from the OLS estimation are also consistent with this interpretation, but generally

not statistically significant at the 10% level or better.

4.3 An External Shock to Outside Employment Opportunities

We next consider a case where the employment opportunities in an industry are affected

by an external shock and investigate the impact of this shock on the leverage-productivity

relation. The North American Free Trade Agreement became effective on January 1, 1994.

24

Evidence suggests that NAFTA changed the employment prospects for workers in many

industries in the US. To investigate the effect of NAFTA, we create a dummy variable NAFTA

that equals one in the years 1994 and after and zero for prior years. As before, we interact both

Leverage and Leverage2 with the variable NAFTA and estimate equation (4) using the previously

described method for both OLS and 2SLS. We present the results from this analysis in Table 12.

The first two columns of the table present our findings from estimating OLS and 2SLS on the

total sample. The coefficients on the interaction of NAFTA dummy with Leverage and Leverage2

are positive and negative, respectively, which supports our hypothesis that the passage of

NAFTA made debt more effective as a bonding mechanism by reducing the employment

opportunities for employees (p-values are less than 0.01 for 2SLS). The coefficients from the

OLS estimation offer weaker support for this claim.

Research (see Scott, 2005, and Hottenrott and Blank, 1998) suggests that the impact of

NAFTA on employment was considerably greater in manufacturing firms. Therefore, any

influence of NAFTA on the efficacy of debt as a disciplining mechanism should be more evident

for manufacturing firms. To investigate this possibility, we divide our sample into subsamples of

manufacturing and non-manufacturing firms (SIC codes 2000 – 3999). We report the findings

from OLS and 2SLS estimations on these two subsamples in the last four columns of table 12.

Columns three and four present the findings for manufacturing firms and columns five and six

for non-manufacturing firms. The findings are quite striking and offer strong support to our

hypothesis that the passage of NAFTA affected the leverage-productivity relation through its

impact on external employment opportunities. In the sample of manufacturing firms, the

coefficients on the interaction terms of NAFTA with Leverage and with Leverage2 are

respectively positive and negative and statistically significant for both OLS and 2SLS

estimations. The coefficients on the same interaction terms in the non-manufacturing sample are

25

of the opposite sign and statistically not significantly different from zero. Thus, the passage of

NAFTA affected the relation between productivity and leverage only in the manufacturing

sector, where external employment opportunities for employees were adversely affected by

NAFTA.

4.4 Robustness of Results for Over-Identified Systems

As an additional robustness test, we estimate our model using an overidentified 2SLS

specification. We first estimate the base model of Table 7 by adding a third instrument Firm

Age2 in the first stage and report the findings in the first column of Table 13.15 First-stage

statistics, not reported to save space, suggest that all instruments are relevant. Additionally, the

Hansen-J statistic is not significant, which indicates that our instruments are valid. The positive

concave shape of the leverage-productivity relation is again evident in this specification.

We next investigate the robustness of our findings on the effect of varying outside

employment opportunities on the leverage-productivity relation. For space considerations, we

report findings from 2SLS estimation of equation (4) for only two measures of outside

employment opportunity. The first, higher values of which imply better mobility, is Industry

Homogeneity and the second, the higher values of which imply scarcer outside opportunities, is

Excess Labor Cost Per Employee. The second column of the table reports the results with

Industry Homogeneity. We again note from unreported results that the instruments are relevant

and the insignificant value of the Hansen-J statistic reported in Panel B indicates that the

15 As suggested by Wooldridge (2002), natural instruments for squared terms are the square of other included or excluded instruments. In our case, the square terms of all independent variables are potential candidates for instruments of leverage2. However, for the base model and the model with homogeneity in Table 13, only Firm Age2 together with the aforementioned instruments in the exactly identified system satisfy the Hansen-J test as valid instruments. For the model with Excess Labor Cost Per Employee in Table 13, only Firm Age2, (Lag(Profitability))2, together with the aforementioned instruments in the exactly identified system satisfy the Hansen-J test as valid instruments.

26

instruments are valid. Again, our results are similar to those previously reported. A higher value

of Industry Homogeneity leads to a weaker leverage-productivity relation as predicted. The third

column of Table 13 reports our findings for the second measure of outside opportunities, Excess

Labor Cost Per Employee. In this specification, we add two more instruments: Firm Age2 and

(Lag (Profitability))2. Again, we note without reporting the statistics that these instruments are

relevant and the Hansen-J Statistic of 0.869 indicates that the instruments are valid. The

coefficients on the interaction terms with Leverage and Leverage2 are respectively positive and

negative and statistically significant, indicating that increasing the cost to employees of leaving

the firm makes the positive concave relation between productivity and leverage stronger as

predicted.

5. Conclusion

As a direct test of the debt bonding hypothesis, we analyze the relation between

employee productivity and financial leverage in a large sample of publicly held firms over a span

of 36 years. Supporting the premise that debt serves as a costly bonding mechanism to mitigate

agency conflicts, financial leverage exerts a positive influence on employee productivity up to

some critical value where disincentives from the costs of financial distress begin to outweigh the

bonding incentives. Below this critical value, our evidence indicates that employees increase

their productivity as the leverage ratio increases, consistent with the argument that they exert

more effort to lessen the likelihood that they lose their jobs due to financial distress. Beyond this

critical level of debt, we find a negative relation between productivity and leverage, which we

interpret as evidence that employees reduce their effort or firm-specific human capital

investment because they rationally infer that their actions will not influence the outcome. This

concave relation between productivity and financial leverage also adds to the evidence on the

27

tradeoff explanations of capital structure and highlights lost employee productivity as an indirect

cost of financial distress.

We also find that the influence of financial leverage on employee productivity is weaker

when employees have more outside employment opportunities. This result suggests that

employees compare the costs that they incur to lessen the likelihood of financial distress, for

instance additional effort, to the transaction costs of leaving the firm. As outside employment

opportunities increase, the relative costs of leaving the firm become less than the costs of

additional effort, and debt becomes less effective as a bonding device. Thus, our results for the

influence of outside employment opportunities on the relation between productivity and financial

leverage emphasize the fact that bonding costs are borne by the agent. Collectively, the positive

concave relation between employee productivity and financial leverage strongly support the debt

bonding arguments of Grossman and Hart (1982) and Jensen (1986).

Our study has implications for researchers who study mechanisms that minimize

principal-agent conflicts and policymakers and activists who seek to improve governance in

publicly held firms. Governance and other control mechanisms minimize agency conflicts

because they impose costs on agents for behavior that is inconsistent with shareholder wealth

maximization and reward behavior that is aligned with the objective of maximizing shareholder

wealth. Our results for the influence of outside employment opportunities on the efficacy of debt

as a bonding mechanism suggest that researchers should control for outside employment

opportunities when they examine the usefulness of other governance and incentive alignment

mechanisms. Our results also suggest that the constrained optimal equilibrium level of any

governance mechanism will vary across industries and over time with the outside employment

opportunities available to the managers and employees of a firm. Finally, we find that the

passage of NAFTA in 1994 increased the efficacy of debt as a disciplining mechanism in the

28

manufacturing sector. While this finding supports our hypothesis regarding the effect of

changing employment opportunities, we wonder whether the government anticipated this indirect

effect in its policy deliberations.

29

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Table 1. Descriptive statistics for Test Variables This table presents descriptive statistics for our proxies for leverage, employee productivity, and outside employment opportunities . Leverage is the book value of debt divided by the sum of book value of debt plus market value of common equity. Output Per Employee equals the ratio of sales plus changes in inventories to the number of employees. Output Per Labor Hour equals the ratio of sales plus changes in inventories to the annual number of labor hours per employee for the firm’s industry. EBITDA Per Employee equals the ratio of operating income before depreciation and amortization to the number of employees. Quit Rate is the number of employees in an industry that voluntarily quit their jobs divided by the total number of employees in the industry. Hire rate is the total number of new hires in an industry divided by the total number of employees. Quit Rate and Hire Rate are available for 2000-2006. Industry Homogeneity measures the correlation between common stock returns within two-digit SIC industries (as in Parrino (1997)). Excess Labor Cost Per Employee is Log(Labor Cost Per Employee)- Median(Log(Labor Cost Per Employee)). High-Pay Dummy equals one if a firm is paying its employees 20% or more than the typical wage in the firm’s industry. The sample of firms is from the Compustat database and covers the period 1970-2005 All firm-level variables are winsorized at the 1% and 99% tails. Variables Obs. Mean Median Maximum Minimum Std. Dev.

Leverage 144,444 0.269 0.203 0.939 0 0.252

Employee Productivity

Output Per Employee ($k) 144,444 230.108 159.108 1733.150 8.090 253.595 Output Per Labor-Hour ($k) 59,967 0.660 0.058 15.271 0.000 2.070 EBITDA Per Employee ($k) 144,205 16.772 13.627 447.426 -344.326 84.965 Outside Employment Opportunity Quit Rate (%) 27,625 1.567 1.308 4.917 0.500 0.572 Hire Rate (%) 27,625 3.029 2.550 7.217 1.000 1.129 Industry Homogeneity 141,249 0.168 0.147 0.488 0.038 0.078 Excess Labor Cost Per Employee 16,193 -0.040 0 1.191 -2.027 0.425 High-Pay Dummy (0/1) 16,193 0.152

Table 2.Median Employee Productivity by Industry The sample of firms is from the Compustat database and covers the period 1970-2005. Leverage is the book value of debt divided by the sum of book value of debt plus market value of common equity. Output Per Employee equals the ratio of sales plus changes in inventories to the number of employees. Output Per Labor Hour equals the ratio of sales plus changes in inventories to the annual number of labor hours per employee for the firm’s industry. EBITDA Per Employee equals the ratio of operating income before depreciation and amortization to the number of employees. The industry descriptions for two-digit SIC codes are from U.S. Census Bureau.

SIC Industry Description Output Per

Employee ($k)Output Per

Labor Hour ($k) EBITDA Per

Employee ($k)29 Petroleum and coal products 871.04 6.03 121.28 51 Wholesale trade--nondurable goods 605.18 1.02 26.88 15 General building contractors 581.64 na 44.80 13 Oil and gas extraction 464.50 0.20 113.40 50 Wholesale trade--durable goods 437.62 0.35 16.49 47 Transportation services 361.99 na 45.43 44 Water transportation 344.88 na 78.43 78 Motion pictures 338.27 0.59 56.27 02 Agricultural production- livestock 324.46 na 1.69 12 Coal mining 310.87 0.39 36.05 55 Automotive dealers and gasoline service stations 289.89 0.50 10.85 21 Tobacco manufactures 289.57 5.00 62.06 20 Food and kindred products 275.42 1.41 22.59 33 Primary metal industries 264.01 0.78 26.07 28 Chemicals and allied products 240.70 0.71 -12.28 14 Nonmetallic minerals, except fuels 240.10 0.31 32.18 10 Metal mining 238.59 0.22 25.69 48 Communications 232.06 1.36 51.24 52 Building materials, hardware, garden supply, & mobile 225.20 1.01 12.30 59 Miscellaneous retail 224.98 0.47 3.66 16 Heavy construction contractors 221.39 na 11.95 39 Miscellaneous manufacturing industries 209.64 0.13 13.89 26 Paper and allied products 209.00 1.20 25.72 54 Food stores 208.36 2.83 7.99 57 Furniture, home furnishings and equipment stores 203.60 0.58 7.00 31 Leather and leather products 200.31 0.16 15.44 23 Apparel and other textile products 199.68 0.29 12.17 01 Agricultural production- crops 198.13 na 24.36 24 Lumber and wood products 194.81 0.54 18.04 35 Industrial machinery and equipment 194.51 0.53 8.13 27 Printing and publishing 194.19 0.50 20.62 73 Business services 192.31 0.16 3.06 32 Stone, clay, glass, and concrete products 190.93 0.48 25.72 45 Transportation by air 188.59 1.96 17.18 30 Rubber and miscellaneous plastics products 187.67 0.27 15.71 37 Transportation equipment 183.58 1.97 14.76

Table 2 Continued 75 Automotive repair, services, and parking 182.94 0.51 58.26 42 Motor freight transportation and warehousing 178.92 0.67 15.85 36 Electrical and electronic equipment 176.85 0.45 7.13 34 Fabricated metal products 157.95 0.29 14.32 17 Special trade contractors 157.56 na 3.11 38 Instruments and related products 157.46 0.25 -0.10 22 Textile mill products 145.53 0.31 11.60 25 Furniture and fixtures 137.73 0.43 13.53 87 Engineering and management services 136.62 na -13.31 53 General merchandise stores 135.64 3.22 7.82 76 Miscellaneous repair services 130.64 na 7.44 82 Educational services 128.10 na 2.27 07 Agricultural services 121.15 na -26.95 79 Amusement and recreational services 119.80 na 14.55 56 Apparel and accessory stores 115.06 0.72 8.04 80 Health services 111.42 na 8.43 72 Personal services 90.02 0.26 9.29 70 Hotels, rooming houses, camps, and other lodging places 83.49 0.22 13.01 41 Local and interurban passenger transit 67.76 0.34 9.95 58 Eating and drinking places 55.38 0.37 4.58 83 Social services 42.42 na 4.30

Table 3. Pearson Correlations Between Test Variables This table presents correlations between our measures of leverage, employee productivity, and outside employment opportunities. Leverage is the book value of debt divided by the sum of book value of debt plus market value of common equity. Output Per Employee equals the ratio of sales plus changes in inventories to the number of employees. Output Per Labor Hour equals the ratio of sales plus changes in inventories to the annual number of labor hours per employee for the firm’s industry. EBITDA Per Employee equals the ratio of operating income before depreciation and amortization to the number of employees. Quit Rate is the number of employees in an industry that voluntarily quit their jobs divided by the total number of employees in the industry. Hire rate is the total number of new hires in an industry divided by the total number of employees. Quit Rate and Hire Rate are available for 2000-2006. Industry Homogeneity measures the correlation between common stock returns within two-digit SIC industries (as in Parrino (1997)). Excess Labor Cost Per Employee is Log(Labor Cost Per Employee)- Median(Log(Labor Cost Per Employee)). High-Pay Dummy equals one if a firm is paying its employees 20% or more than the typical wage in the firm’s industry. The sample of firms is from the Compustat database and covers the period 1970-2005. All firm-level variables are winsorized at the 1% and 99% tails.

Leverage Output Per Employee($k)

Output Per Labor

Hour($k)

EBITDA Per Employee($k) Quit Rate Hire Rate Industry

Homogeneity

Excess Labor Cost Per

Employee Leverage 1

Output Per Employee($k) 0.041*** 1

Output Per Labor Hour($k) 0.035*** 0.076*** 1

EBITDA Per Employee($k) 0.041*** 0.480*** 0.095*** 1

Quit Rate 0.106*** -0.014*** 0.011 0.012*** 1

Hire Rate -0.005 0.003 -0.023* -0.005 0.858*** 1

Industry Homogeneity 0.140*** -0.004* 0.100*** 0.049*** 0.126*** 0.172*** 1

Excess Labor Cost Per Employee -0.014*** 0.415*** 0.139*** 0.063*** 0.002 -0.038*** -0.029*** 1

High-Pay Dummy -0.047*** 0.220*** 0.045*** 0.015*** -0.009 0.066*** -0.010 0.539

*** significant at 1% level; ** significant at 5% level; * significant at 10% level.

Table 4. Summary Statistics for Control Variables The sample of firms is from the Compustat database and covers the period 1970-2005. Union Membership is the fraction of workers in an industry who belong to a union and is available for 1983-2005. Best Company dummy equals one if the firm is in the list of “100 best companies to work for in America” by Fortune magazine during 1998 - 2005. Excess Labor Cost Per Employee is Log(Labor Cost Per Employee)- Median(Log(Labor Cost Per Employee)).. Employees is the number of employees reported in Compustat. Asset Intensity equals total assets divided by the number of employees. Firm Age is the number of years the firm has appeared in Compustat. Profitability is the ratio of operating earnings to assets. Operating Leverage is the gross value of Property, Plant & Equip divided by total assets. The Herfindahl Index for a 4-digit SIC code classified industry is the sum of the squared market share of each firm in that industry. We winsorize all firm-level variables at the 1% and 99% tails. Variables Obs. Mean Median Maximum Minimum Std. Dev.

Union Membership (%) 101,701 11.900 7.9 83.6 0 12.209

Best Co. (0/1) 38,917 0.008

Employees (k) 144,444 6.740 0.890 118.900 0.003 17.730

Asset Intensity ($k) 144,444 297.218 141.678 3505.390 14.818 501.860

Firm Age 144,444 22.934 21 52 4 10.821

Profitability 144,225 0.009 0.078 0.317 -2.096 0.284

Operating Leverage 144,444 0.553 0.475 2.052 0.023 0.380

Herfindahl Index 144,444 0.214 0.177 0.987 0.035 0.148

Table 5. The Influence of Financial Leverage on Employee Productivity: OLS This table presents pooled panel data OLS regression results. The dependent variable is the natural log value of Output Per Employee, which is sales plus changes in inventories divided by the number of employees. Leverage is the ratio of book value of debt divided by the sum of book value of debt plus market value of common equity. Union Membership is the fraction of workers in an industry who belong to a union and is available for 1983-2005. Best Company dummy equals one if the firm is in the list of “100 best companies to work for in America” by Fortune magazine during 1998 - 2005. Excess Labor Cost Per Employee is Log(Labor Cost Per Employee)- Median(Log(Labor Cost Per Employee)).. Employees is the number of employees reported in Compustat. Asset Intensity equals total assets divided by the number of employees. Firm age is the number of years the firm has appeared in Compustat. Profitability is the ratio of operating earnings to assets. Operating Leverage is the gross value of Property, Plant & Equip divide by total assets. The Herfindahl Index for a 4-digit SIC code industry is the sum of the squared market share of each firm in that industry. Except for Leverage and Leverage2, all firm-level variables have been adjusted for the respective industry median. The sample of firms is from the Compustat database and covers the period 1970-2005. We present p-values, adjusted for heteroskedasticity and firm clustering, in parentheses. (1) (2) (3) (4) (5) Intercept -0.082 *** -0.115 *** 1.830*** -0.064 *** 2.551 *** (0.00) (0.00) (0.00) (0.01) (0.00) Leverage 0.414 *** 0.437 *** 0.454*** 0.369 *** 0.306 * (0.00) (0.00) (0.00) (0.00) (0.06) Leverage2 -0.460 *** -0.450 *** -0.405*** -0.419 *** -0.340 ** (0.00) (0.00) (0.00) (0.00) (0.03) Union Membership 0.001 ** 0.001 0.000 (0.01) (0.35) (0.99)Best Co. (0/1) 0.197*** 0.155 (0.00) (0.10)Excess Labor Cost Per Employee 0.289 *** 0.229 *** (0.00) (0.00)Ln(Employees) 0.027 *** 0.036 0.035 0.025 *** 0.025 *** (0.00) (0.41) (0.00) (0.00) (0.01)Ln(Asset Intensity) 0.532 *** 0.522 *** 0.520*** 0.509 *** 0.576 *** (0.00) (0.00) (0.00) (0.00) (0.00)Ln(Firm Age) 0.043 *** 0.043 *** 0.055*** 0.046 *** 0.111 *** (0.00) (0.00) (0.00) (0.00) (0.00)Lag(Profitability) 0.028 *** 0.026 *** 0.028*** 0.005 0.010 *** (0.00) (0.00) (0.01) (0.81) (0.73)Ln(Operating Lev.) 0.078 *** 0.106 *** 0.127*** 0.048 0.018 (0.00) (0.00) (0.00) (0.19) (0.75)Herfindahl Index 0.024 0.047 * 0.069* -0.140 *** -0.230 ** (0.23) (0.05) (0.07) (0.01) (0.04)Year Dummies Yes Yes Yes Yes YesR-square 0.287 0.271 0.274 0.420 0.473Observations 144,444 101,701 37,984 16,193 3,798

*** significant at 1% level; ** significant at 5% level; * significant at 10% level.

Table 6. Results and Related Test Statistics for the First Stage of the 2SLS Regression Leverage is the ratio of book value of debt divided by the sum of book value of debt plus market value of common equity. Models (1) to (5) are models from OLS regressions in Table 5. For example, Model (3) is model (3) in Table 5. The instrumental variable for Leverage is the median leverage level of the industry that the firm belongs to. The instrumental variable for Leverage2 is the square of the median debt level of the industry that the firm belongs to. Industries are classified based on 4 digit SIC codes. The p-value for joint significance in Panel B is based on the F-statistic for the joint significance of the two instrumental variables.

Panel A: First Stage Coefficients and Joint Significance of Instrumental Variables

Base Model Union

MembershipBest

Company Excess Labor Cost

Per Employee All (1) (2) (3) (4) (5) Dependent Variable: Leverage Industry Median Leverage 0.898*** 0.923*** 0.928*** 0.740*** 0.828*** (0.00) (0.00) (0.00) (0.00) (0.00) Industry Median Leverage2 -0.178*** -0.237*** -0.244*** 0.016 -0.137*** (0.00) (0.00) (0.00) (0.87) (0.00) P-value for Joint Significance 0.00 0.00 0.00 0.00 0.00 Partial R-square 0.254 0.225 0.229 0.247 0.180 Dependent Variable: Leverage Squared Industry Median Leverage 0.339*** 0.378*** 0.400*** 0.200*** 0.314*** (0.00) (0.00) (0.00) (0.00) (0.00) Industry Median Leverage2 0.375*** 0.315*** 0.292*** 0.525*** 0.392** (0.00) (0.00) (0.00) (0.00) (0.02) P-value for Joint Significance 0.00 0.00 0.00 0.00 0.00 Partial R-square 0.207 0.181 0.18 0.202 0.137

Panel B: Test of Endogeneity

Base Model Union

MembershipBest

Company Excess Labor Cost

Per Employee All (1) (2) (3) (4) (5)

Durbin-Wu-Hausman Test Statistic 46.48*** 34.64*** 18.51*** 7.14*** 2.48* P-value 0.00 0.00 0.00 0.00 0.08 *** significant at 1% level; ** significant at 5% level; * significant at 10% level.

Table 7. The Influence of Financial Leverage on Employee Productivity: 2SLS This table presents 2SLS regression results. The dependent variable is the natural log value of Output Per Employee, which is sales plus changes in inventories divided by the number of employees. Leverage and Leverage2 are the predicted values from the first stage. Union Membership is the fraction of workers in an industry who belong to a union and is available for 1983-2005. Best Company dummy equals one if the firm is in the list of “100 best companies to work for in America” by Fortune magazine during 1998 - 2005. Excess Labor Cost Per Employee is Log(Labor Cost Per Employee)- Median(Log(Labor Cost Per Employee)). Employees is the number of employees reported in Compustat. Asset Intensity equals total assets divided by the number of employees. Firm Age is the number of years the firm has appeared in Compustat. Profitability is the ratio of operating earnings to assets. Operating Leverage is the gross value of Property, Plant & Equip divide by total assets. The Herfindahl Index for a 4-digit SIC code industry is the sum of the squared market share of each firm in that industry. Except for Leverage and Leverage2, all firm-level variables have been adjusted for the respective industry median. The sample of firms is from the Compustat database and covers the period 1970-2005. We present p-values, adjusted for heteroskedasticity and firm clustering, in parentheses. (1) (2) (3) (4) (5) Intercept -0.139 *** -0.156 *** -0.146 *** -0.153 *** -0.155 **

(0.00) (0.00) (0.00) (0.00) (0.04)

Predicted Leverage 0.974 *** 1.060 *** 0.719 *** 0.982 *** 1.322 **

(0.00) (0.00) (0.00) (0.00) (0.03)

Predicted (Leverage2) -1.092 *** -1.165 *** -0.607 ** -1.017 *** -1.110

(0.00) (0.00) (0.02) (0.01) (0.15)

Union Membership 0.000 0.000 -0.002 **

(0.76) (0.46) (0.04)

Best Co. (0/1) 0.218 *** 0.222 **

(0.00) (0.04)

Ln(Labor Cost) 0.295 *** 0.235 ***

(0.00) (0.00)

Ln(Employees) 0.024 *** 0.032 *** 0.034 *** 0.022 *** 0.022 **

(0.00) (0.00) (0.00) (0.00) (0.03)

Ln(Asset Intensity) 0.534 *** 0.524 *** 0.520 *** 0.504 *** 0.564 ***

(0.00) (0.00) (0.00) (0.00) (0.00)

Ln(Firm Age) 0.031 *** 0.041 *** 0.054 *** 0.045 *** 0.095 ***

(0.00) (0.00) (0.00) (0.00) (0.00)

Lag(Profitability) 0.026 *** 0.024 *** 0.026 ** 0.002 0.010

(0.00) (0.00) (0.01) (0.93) (0.75)

Operating Leverage 0.066 *** 0.090 *** 0.117 *** 0.027 -0.017

(0.00) (0.00) (0.00) (0.47) (0.77)

Herfindahl Index 0.018 0.035 0.061 -0.124 ** -0.228 **

(0.39) (0.16) (0.11) (0.02) (0.04)

Year Dummies Yes Yes Yes Yes Yes

R-square 0.282 0.266 0.272 0.413 0.453

Observations 144,444 101,701 37,984 16,193 3,798 *** significant at 1% level; ** significant at 5% level; * significant at 10% level.

Table 8. Alternative Measures of Employee Productivity and Leverage This table presents OLS/2SLS regression results based on alternative productivity measures. The dependent variable is the natural log value of Output Per Labor Hour and EBITDA Per Employee, respectively. Output Per Labor Hour equals the ratio of sales plus changes in inventories to the annual number labor hours per employee for the firm’s industry. EBITA Per Employee equals the ratio of operating income before depreciation and amortization to the number of employees. Leverage is the ratio of book value of debt divided by the sum of book value of debt plus market value of common equity. For 2SLS regressions, Leverage and Leverage2 are the predicted values from the first stage. Employees is the number of employees reported in Compustat. Asset Intensity equals total assets divided by the number of employees. Firm Age is the number of years the firm has appeared in Compustat. Profitability is the ratio of operating earnings to assets. Operating Leverage is the gross value of Property, Plant & Equip divide by total assets. The Herfindahl Index for a 4-digit SIC code industry is the sum of the squared market share of each firm in that industry. Except for Leverage and Leverage2, all firm-level variables have been adjusted for the respective industry median. The sample of firms is from the Compustat database and covers the period 1970-2005. We present p-values, adjusted for heteroskedasticity and firm clustering, in parentheses.

EBITDA Per Employee Output Per Labor Hour OLS 2SLS OLS 2SLS Intercept -5.632*** -19.459*** -0.134*** -0.183*** (0.00) (0.00) (0.00) (0.00)Leverage 22.460*** 147.552*** 0.545*** 0.800*** (0.00) (0.00) (0.00) (0.00)Leverage2 -24.470*** -154.455*** -0.579*** -0.690*** (0.00) (0.00) (0.00) (0.01)Ln(Employees) 11.563*** 10.874*** 1.025*** 1.024*** (0.00) (0.00) (0.00) (0.00)Ln(Asset Intensity) 25.112*** 25.431*** 0.542*** 0.543*** (0.00) (0.00) (0.00) (0.00)Ln(Firm Age) -2.388*** -2.528*** 0.047*** 0.045*** (0.00) (0.00) (0.00) (0.00)Lag(Profitability) 4.019*** 3.330*** 0.031*** 0.029*** (0.00) (0.00) (0.00) (0.01)Operating Leverage 20.077*** 17.126*** 0.099*** 0.091*** (0.00) (0.00) (0.00) (0.00)Herfindahl Index -3.125 -4.579* 0.056* 0.040 (0.19) (0.07) (0.09) (0.22)Year Dummies Yes Yes Yes YesR-square 0.118 0.097 0.262 0.214Observations 144,205 144,205 59,967 59,967

*** significant at 1% level; ** significant at 5% level; * significant at 10% level.

Table 9. The Influence of High Outside Employment Opportunities on Debt Bonding: OLS This table presents OLS regression results. The dependent variable is the natural log value of Output Per Employee, which we define as sales plus changes in inventories divided by the number of employees. Leverage is the ratio of book value of debt divided by the sum of book value of debt plus market value of common equity. Proxies for Outside Employment Opportunity include: Quit Rate, Hire Rate, Industry Homogeneity, the products of Quit Rate and Industry Homogeneity, and Hire Rate and Industry Homogeneity. Quit Rate is the number of employees in an industry that voluntarily quit their jobs divided by the total number of employees in the industry. Hire Rate is the total number of new hires in an industry divided by the total number of employees. Quit Rate and Hire Rate are available for 2000-2006. Industry Homogeneity measures the correlation between common stock returns within two-digit SIC industries (as in Parrino (1997)). Leverage*Outside Emp. Opp. and Leverage2*Outside Emp. Opp. are the products of Leverage, Leverage2 and the corresponding proxy for outside employment opportunities respectively. Best Company dummy equals one if the firm is in the list of “100 best companies to work for in America” by Fortune magazine during 1998 - 2005. Employees is the number of employees reported in Compustat. Asset Intensity equals total assets divided by the number of employees. Firm Age is the number of years the firm has appeared in Compustat. Profitability is the ratio of operating earnings to assets. Operating Leverage is the gross value of Property, Plant & Equip divide by total assets. Except for Best Company dummy and Leverage related variables, all firm-level variables have been adjusted for the respective industry median. We present p-values, adjusted for heteroskedasticity and firm clustering, in parentheses.

Quit Ratio Hire Rate Homogeneity Quit Ratio * Homogeneity

Hire Rate * Homogeneity

(1) (2) (3) (4) (5) Intercept -0.199 *** -0.188 *** -0.127 *** -0.154 *** -0.147 ***

(0.00) (0.00) (0.00) (0.00) (0.00) Leverage 0.838 *** 0.806 *** 0.445 *** 0.595 *** 0.559 ***

(0.00) (0.00) (0.00) (0.00) (0.00) Leverage2 -0.841 *** -0.788 *** -0.368 ** -0.552 *** -0.491 ***

(0.00) (0.00) (0.03) (0.00) (0.00) Leverage * Outside Emp. Opp. -0.248 *** -0.119 ** 0.140 -0.498 * -0.196

(0.01) (0.02) (0.85) (0.08) (0.25) 0.297 *** 0.139 *** -0.279 0.584 * 0.208 Leverage2 * Outside Emp. Opp. (0.00) (0.01) (0.73) (0.05) (0.25)

Outside Employment Opp. 0.041 ** 0.018 * 0.004 0.077 0.028 (0.03) (0.06) (0.97) (0.14) (0.37)

Best Co. (0/1) 0.203 *** 0.206 *** 0.184 *** 0.204 *** 0.206 *** (0.00) (0.00) (0.00) (0.00) (0.00)

Ln(Employees) 0.035 *** 0.035 *** 0.034 *** 0.036 *** 0.035 *** (0.00) (0.00) (0.00) (0.00) (0.00)

Ln(Asset Intensity) 0.518 *** 0.518 *** 0.522 *** 0.519 *** 0.518 *** (0.00) (0.00) (0.00) (0.00) (0.00)

Ln(Firm Age) 0.051 *** 0.051 *** 0.054 *** 0.051 *** 0.051 *** (0.00) (0.00) (0.00) (0.00) (0.00)

Lag(Profitability) 0.020 * 0.020 * 0.024 ** 0.018 0.018 (0.08) (0.08) (0.02) (0.11) (0.11)

Operating Leverage 0.135 *** 0.135 *** 0.128 *** 0.137 *** 0.136 *** (0.00) (0.00) (0.00) (0.00) (0.00)

Herfindahl Index 0.090 ** 0.088 ** 0.064 * 0.086 * 0.082 * (0.03) (0.04) (0.1) (0.04) (0.05)

Year Dummies Yes Yes Yes Yes Yes R-square 0.277 0.277 0.275 0.277 0.276 Observations 27,625 27,625 38,238 27,403 27,403 *** significant at 1% level; ** significant at 5% level; * significant at 10% level.

Table 10. The Influence of High Outside Employment Opportunities on Debt Bonding: 2SLS This table presents 2SLS regression results. The dependent variable is the natural log value of Output Per Employee, which we define as sales plus changes in inventories divided by the number of employees. Leverage, Leverage2, and the products of Leverage*Outside Emp. Opp. and Leverage2*Outside Emp. Opp. are the predicted values from the first stage. Outside Employment Opportunity proxies included in this table are: Quit Rate, Hire Rate, Industry Homogeneity, the product of Quit Rate and Industry Homogeneity, and the product of Hire Rate and Industry Homogeneity. Quit Rate is the number of employees in an industry that voluntarily quit their jobs divided by the total number of employees in the industry. Hire rate is the total number of new hires in an industry divided by the total number of employees. Quit Rate and Hire Rate are available for 2000-2006. Industry Homogeneity measures the correlation between common stock returns within two-digit SIC industries (as in Parrino (1997)). Best Company dummy equals one if the firm is in the list of “100 best companies to work for in America” by Fortune magazine during 1998 - 2005. Employees is the number of employees reported in Compustat. Asset Intensity equals total assets divided by the number of employees. Firm Age is the number of years the firm has appeared in Compustat. Profitability is the ratio of operating earnings to assets. Operating Leverage is the gross value of Property, Plant & Equip divide by total assets. Except for Best Company dummy and Leverage related variables, all firm-level variables have been adjusted for the respective industry median. We present p-values, adjusted for heteroskedasticity and firm clustering, in parentheses.

Quit Ratio Hire Rate HomogeneityQuit Ratio * Homogeneity

Hire Rate * Homogeneity

(1) (2) (3) (4) (5) Intercept -0.361 *** -0.348 *** -0.224 *** -0.225 *** -0.210 *** (0.00) (0.00) (0.00) (0.00) (0.00) Predicted Leverage 2.219 *** 2.385 *** 1.923 *** 1.528 *** 1.477 *** (0.00) (0.00) (0.00) (0.00) (0.00) Predicted (Leverage2) -2.371 *** -2.658 *** -2.328 *** -1.608 *** -1.600 *** (0.00) (0.00) (0.00) (0.00) (0.00)

-0.697 ** -0.411 *** -6.281 *** -2.767 *** -1.277 *** Predicted (Leverage * Outside Emp. Opp.) (0.01) (0.01) (0.01) (0.00) (0.01)

0.773 ** 0.492 *** 8.622 *** 3.293 *** 1.611 *** Predicted (Leverage2 * Outside Emp. Opp.) (0.02) (0.01) (0.00) (0.00) (0.01) Outside Employment Opp. 0.096 *** 0.045 *** 0.441 * 0.263 *** 0.105 ** (0.00) (0.00) (0.07) (0.00) (0.05) Best Co. (0/1) 0.216 *** 0.225 *** 0.201 *** 0.223 *** 0.227 *** (0.00) (0.00) (0.00) (0.00) (0.00) Ln(Employees) 0.031 *** 0.031 *** 0.034 *** 0.034 *** 0.034 *** (0.00) (0.00) (0.00) (0.00) (0.00) Ln(Asset Intensity) 0.485 *** 0.485 *** 0.525 *** 0.521 *** 0.521 *** (0.00) (0.00) (0.00) (0.00) (0.00) Ln(Firm Age) 0.044 *** 0.043 *** 0.050 *** 0.048 *** 0.047 *** (0.00) (0.00) (0.00) (0.00) (0.00) Lag(Profitability) 0.018 0.018 0.023 ** 0.017 0.018 (0.12) (0.12) (0.03) (0.13) (0.13) Operating Leverage 0.090 *** 0.090 *** 0.123 *** 0.127 *** 0.127 *** (0.00) (0.00) (0.00) (0.00) (0.00) Herfindahl Index 0.190 *** 0.184 *** 0.051 0.068 0.064

(0.00) (0.00) (0.19) (0.11) (0.13) Year Dummies Yes Yes Yes Yes Yes R-square 0.256 0.255 0.269 0.273 0.273 Observations 27,625 27,625 38,238 27,403 27,403 ***: significant at 1% level; **: significant at 5% level; *: significant at 10% level.

Table 11. The Influence of High Costs of Leaving the Firm on Debt Bonding The table presents OLS and 2SLS regression results. The dependent variable is the natural log value of Output Per Employee, which we define as sales plus changes in inventories divided by the number of employees. Leverage is the ratio of book value of debt divided by the sum of book value of debt plus market value of common equity. Outside Employment Opportunity proxies include: Excess Labor Cost Per Employee and High-Pay Dummy. Excess Labor Cost Per Employee is Log(Labor Cost Per Employee)- Median(Log(Labor Cost Per Employee)). High-Pay Dummy equals one if the firm’s Labor Cost Per Employee is 20% or higher than the industry median Labor Cost Per Employee. Leverage*Outside Emp. Opp. is the product of Leverage and the corresponding proxy for outside employment opportunities. Leverage2*Outside Emp. Opp. is the product of Leverage2 and the corresponding proxy for outside employment opportunities. For 2SLS regressions, Leverage, Leverage2, and the products of Leverage*Emp. Opp. and Leverage2*Emp. Opp. are the predicted values from the first stage. Employees is the number of employees reported in Compustat. Asset Intensity equals total assets divided by the number of employees. Firm age is the number of years the firm has appeared in Compustat. Profitability is the ratio of operating earnings to assets. Operating Leverage is the gross value of Property, Plant & Equip divide by total assets. Except for Leverage related variables, all firm-level variables have been adjusted for the respective industry medians. We present p-values, adjusted for heteroskedasticity and firm clustering, in parentheses.

Excess Labor Cost Per Employee High-Pay Dummy OLS 2SLS OLS 2SLS Intercept -0.066 *** -0.159 *** -0.098 *** -0.188 *** (0.01) (0.00) (0.00) (0.00) Predicted Leverage 0.388 *** 1.087 *** 0.309 *** 0.853 *** (0.00) (0.00) (0.00) (0.01) Predicted (Leverage2) -0.431 *** -1.095 *** -0.372 *** -0.832 ** (0.00) (0.01) (0.00) (0.04)

0.310 * 2.735 *** 0.257 1.486 ** Predicted (Leverage * Excess Pay.) (0.07) (0.00) (0.19) (0.02) -0.118 -3.375 *** -0.179 -1.676 ** Predicted (Leverage2 * Excess Pay) (0.53) (0.00) (0.40) (0.03)

Outside Employment Opp. 0.236 *** 0.056 0.113 *** 0.001 (0.00) (0.44) (0.00) (0.99) Ln(Employees) 0.024 *** 0.021 *** 0.027 *** 0.024 *** (0.00) (0.00) (0.00) (0.00) Ln(Asset Intensity) 0.508 *** 0.505 *** 0.571 *** 0.566 *** (0.00) (0.00) (0.00) (0.00) Ln(Firm Age) 0.046 *** 0.046 *** 0.064 *** 0.063 *** (0.00) (0.00) (0.00) (0.00) Lag(Profitability) 0.004 0.008 -0.003 -0.003 (0.83) (0.7) (0.9) (0.88) Operating Leverage 0.049 0.023 0.081 ** 0.051 (0.18) (0.55) (0.04) (0.21) Herfindahl Index -0.142 *** -0.142 ** -0.125 ** -0.096 *

(0.01) (0.01) (0.02) (0.1) Year Dummies Yes Yes Yes Yes R-square 0.422 0.385 0.388 0.372 Observations 16,193 16,193 16,193 16,193 ***: significant at 1% level; **: significant at 5% level; *: significant at 10% level.

Table 12. NAFTA, Employee Productivity, and Financial Leverage (2SLS and OLS) This table presents OLS/2SLS regressions results. The dependent variable is the natural log value of Output Per Employee, which we define as sales plus changes in inventories divided by the number of employees. Leverage is the ratio of book value of debt divided by the sum of book value of debt plus market value of common equity. NAFTA dummy equals one for observations in year 1994 and onwards, and equals zero otherwise. Leverage*NAFTA is the product of Leverage and the NAFTA dummy. Leverage2*NAFTA is the product of Leverage2 and the NAFTA dummy. For 2SLS regressions, Leverage, Leverage2, and the products of Leverage*NAFTA and Leverage2*NAFTA are the predicted values from the first stage. Based on U.S. Census Bureau’s classification, manufacturing firms include all firms with SIC codes between 2000 and 3999. Non-manufacturing firms include all firms with SIC codes smaller than 2000, and larger than 3999. Employees is the number of employees reported in Compustat. Asset Intensity equals total assets divided by the number of employees. Firm age is the number of years the firm has appeared in Compustat. Profitability is the ratio of operating earnings to assets. Operating Leverage is the gross value of Property, Plant & Equip divide by total assets. Except for NAFTA and Leverage related variables, all firm-level variables have been adjusted for the respective industry medians. We present p-values, adjusted for heteroskedasticity and firm clustering, in parentheses. All Firms Manufacturing Firms Non-Manufacturing Firms OLS 2SLS OLS 2SLS OLS 2SLS Intercept -0.039 *** -0.088*** -0.028** -0.069** -0.052** -0.109***

(0.00) (0.00) (0.03) (0.02) (0.02) (0.00) Leverage 0.335 *** 0.640*** 0.276*** 0.508** 0.415*** 0.779***

(0.00) (0.00) (0.00) (0.02) (0.00) (0.00) Leverage2 -0.412 *** -0.704*** -0.362*** -0.534* -0.487*** -0.846***

(0.00) (0.00) (0.00) (0.08) (0.00) (0.00) Leverage * NAFTA 0.158 *** 0.589*** 0.310*** 1.576*** -0.018 -0.144

(0.01) (0.00) (0.00) (0.00) (0.84) (0.60) Leverage2 * NAFTA -0.074 -0.682** -0.261*** -2.155*** 0.114 0.269

(0.28) (0.01) (0.01) (0.00) (0.26) (0.46) NAFTA -0.067 *** -0.075*** -0.104*** -0.158*** -0.027 0.006

(0.00) (0.00) (0.00) (0.00) (0.31) (0.88) Ln(Employees) 0.027 *** 0.024*** 0.027*** 0.022*** 0.030*** 0.028***

(0.00) (0.00) (0.00) (0.00) (0.00) (0.00) Ln(Asset Intensity) 0.533 *** 0.533*** 0.497*** 0.501*** 0.554*** 0.553***

(0.00) (0.00) (0.00) (0.00) (0.00) (0.00) Ln(Firm Age) 0.032 *** 0.032*** 0.036*** 0.038*** 0.029*** 0.028***

(0.00) (0.00) (0.00) (0.00) (0.00) (0.00) Lag(Profitability) 0.027 *** 0.025*** 0.024** 0.020** 0.031*** 0.029***

(0.00) (0.00) (0.02) (0.05) (0.00) (0.01) Operating Leverage 0.078 *** 0.068*** -0.008 -0.026 0.137*** 0.130***

(0.00) (0.00) (0.64) (0.16) (0.00) (0.00) Herfindahl Index 0.023 0.016 0.018 0.001 0.038 0.036

(0.27) (0.43) (0.48) (0.98) (0.28) (0.30) Year Dummies Yes Yes Yes Yes Yes Yes R-square 0.288 0.283 0.235 0.217 0.336 0.334 Observations 144,444 144,444 80,485 80,485 63,959 63,959 ***: significant at 1% level; **: significant at 5% level; *: significant at 10% level.

Table 13. The Influence of Outside Employment Opportunities on Debt Bonding: Overidentified 2SLS This table presents 2SLS regressions results. The dependent variable is the natural log value of Output Per Employee, which we define as sales plus changes in inventories divided by the number of employees. Leverage is the ratio of book value of debt divided by the sum of book value of debt plus market value of common equity. In this table, proxies for outside employment opportunities include: Industry Homogeneity and Excess Labor Cost Per Employee. We calculate Industry Homogeneity as in Parrino (1997). Excess Labor Cost Per Employee is Log(Labor Cost Per Employee)- Median(Log(Labor Cost Per Employee)). Industry Homogeneity measures the correlation between common stock returns within two-digit SIC industries (as in Parrino (1997)). Best Company dummy equals one if the firm is in the list of “100 best companies to work for in America” by Fortune magazine during 1998 - 2005. Employees is the number of employees reported in Compustat. Asset Intensity equals total assets divided by the number of employees. Firm Age is the number of years the firm has appeared in Compustat. Profitability is the ratio of operating earnings to assets. Operating Leverage is the gross value of Property, Plant & Equip divide by total assets. Except for Leverage related variables, all firm-level variables have been adjusted for the respective industry medians. We present p-values, adjusted for heteroskedasticity and firm clustering, in parentheses. Base Model Outside Employment Opportunity

Industry Homogeneity Excess Labor Cost

Per EmployeeIntercept -0.139*** -0.225*** -0.160***

(0.00) (0.00) (0.00)Leverage 0.974*** 1.919*** 1.101***

(0.00) (0.00) (0.00)Leverage2 -1.091*** -2.319*** -1.117***

(0.00) (0.00) (0.01)Leverage * Outside Emp. Opp. -6.290*** 2.706***

(0.01) (0.00)Leverage2 * Outside Emp. Opp. 8.607*** -3.339***

(0.00) (0.00)Outside Employment Opp. 0.447** 0.059

(0.07) (0.41)Best Co. (0/1) 0.201***

(0.00)Ln(Employees) 0.024*** 0.034*** 0.021***

(0.00) (0.00) (0.00)Ln(Asset Intensity) 0.534*** 0.525*** 0.505***

(0.00) (0.00) (0.00)Ln(Firm Age) 0.031*** 0.050*** 0.046***

(0.00) (0.00) (0.01)Lag(Profitability) 0.026*** 0.023** 0.008

(0.00) (0.03) (0.70)Operating Leverage 0.066*** 0.123*** 0.023

(0.00) (0.00) (0.55)Herfindahl Index 0.018 0.052 -0.142**

(0.39) (0.18) (0.01)

Additional Instrumental Variables Firm Age2 Firm Age2 Firm Age2 (Lag(Profitability))2

Hansen-J Statistic 1.316 0.978 0.774 (p-value) (0.25) (0.32) (0.68) Year Dummies Yes Yes Yes R-square 0.282 0.269 0.385 Observations 144,444 38,238 16,193 ***: significant at 1% level; **: significant at 5% level; *: significant at 10% level.