Download employment protection, investment, and firm growth

EMPLOYMENT PROTECTION, INVESTMENT,
AND FIRM GROWTH*
Douglas Fairhurst
Washington State University
Matthew Serfling
University of Tennessee
November 2016
ABSTRACT
We exploit the adoption of U.S. state-level labor protection laws to study the
effect of employment protection on corporate investment and growth. We find
that, following the adoption of these laws, capital expenditures decrease,
resulting in firms growing sales at a slower rate. Our findings are consistent
with theories predicting that greater employment protection discourages
investment by making projects more irreversible. Supporting this theoretical
channel, we also show that firms affected by these laws are less likely to divest
assets following negative cash flow shocks, and conditional on investing, they
invest in projects with higher expected rates of return.
Keywords: Employment protection, Investment, Capital expenditures, Sales
growth, Labor laws, Investment irreversibility
JEL Classifications: G31, G33, J63, K31
Douglas Fairhurst is from the Department of Finance and Management Science, Washington State University,
Pullman, WA, 99164 and can be reached at [email protected] and 509-335-7200. Matthew Serfling is from the
Department of Finance, University of Tennessee, Knoxville, TN, 37996 and can be reached at [email protected]
and 865-974-1952. We are grateful for the helpful comments and suggestions from Mihai Ion, J. Scott Judd, Lukas
Roth (discussant), Sarah Shaikh, Xiaofei Xing (discussant), Zhengyi Zhang (discussant), conference participants
at the 2014 European Finance Association annual meeting, the 2015 Financial Management Association annual
meeting, and the 2016 Eastern Finance Association annual meeting, and seminar participants at the University
of Tennessee and Washington State University. An earlier version of this paper was titled “Employee Firing Costs
and Corporate Investment.” All errors and omissions are our own.
*
1. Introduction
Critics of employment protection contend that it stifles corporate investment and
growth.1 Empirical work, however, does not find a consistent relation between employment
protection and capital expenditures (Autor, Kerr, and Kugler, 2007; Calcagnini, Giombini,
and Saltari, 2009; Calcagnini, Ferrando, and Giombini, 2014). Moreover, even if greater
employment protection lowers capital expenditures, prior work finds that it leads to more
innovation (Acharya, Baghai, and Subramanian, 2014; Griffith and Macartney, 2014). Thus,
the overall effect of employment protection on firm growth is unclear. In this study, we first
examine the relation between employment protection and U.S. firm-level capital
expenditures. We then study the overall effect of employment protection on sales growth.
From a theoretical perspective, the effect of employment protection on investment is
ambiguous. On one hand, greater employment protection could lead to higher investment
rates through two channels. First, if firms are less likely to discharge workers when
protection increases, a lower threat of dismissal could incentivize workers to invest in firmspecific capital, resulting in greater productivity and higher investment rates (e.g., Nickell
and Layard, 1999; Belot, Boone, and van Ours, 2007). Second, because employment
protection makes physical capital a less expensive input relative to human capital, firms may
increase investment in assets that are more capital and less labor intensive (e.g., Blanchard,
1997; Caballero and Hammour, 1998; Koeniger and Leonardi, 2007).
On the other hand, because greater employment protection makes it costlier to divest
or scale back poorly performing projects, more stringent employment protection could lower
investment rates by making projects more irreversible (Pindyck, 1991). In particular, the
discounted value of a project includes the resale or recovery price from divesting. Thus,
greater investment irreversibility due to employment protection reduces firms’ ex ante
incentives to invest by lowering the recovery value of projects and hence the number of
For example, see “European Entrepreneurs: Les Misérables,” The Economist, July 8, 2012 and “OECD
Employment Outlook: Boosting Jobs and Incomes,” OECD, 2006.
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projects with positive net present values (e.g., Bernanke, 1983; Pindyck, 1991; Bertola and
Caballero, 1994; Abel and Eberly, 1996; Abel et al., 1996).
Similar to Acharya et al. (2014), we exploit the quasi-natural experiment created by
the staggered adoption of Wrongful Discharge Laws (WDLs) by U.S. state courts to test these
competing theories. We use these law changes to identify the causal effect of employment
protection on the firm-level investment decisions of U.S. public corporations. WDLs matured
into three common laws that protect workers against different aspects of unjust dismissal.
We focus our analysis on the adoption of one particular WDL – the good faith exception. This
law applies in cases when a court determines that an employer discharged a worker out of
bad faith, malice, or retaliation. Of the three exceptions, this law represents the largest
deviation from at-will employment (e.g., Dertouzos and Karoly, 1992; Kugler and Saint-Paul,
2004). Importantly, changes in this law have a material effect on firms. For example,
employment levels, employment volatility, and firm entry decrease following its adoption
(Dertouzos and Karoly, 1992; Autor et al., 2007). Firms also experience negative cumulative
abnormal stock returns when their state adopts the law (Serfling, 2015).
For our tests, we utilize a difference-in-differences research design in which the
treatment and control groups consist of firms headquartered in states that have and have
not adopted the good faith exception, respectively. We use panel regression techniques that
control for firm and industry-year fixed effects and find that, following the adoption of this
law, capital expenditures as a fraction of book assets decrease by 5.7% relative to the sample
mean. This result holds after controlling for several firm-level characteristics as well as statelevel factors, such as per capita GDP, per capita GDP growth, and political leaning. We also
document that, following the adoption of the good faith exception, investment rates are less
sensitive to changes in investment opportunities. Overall, these results are consistent with
theories predicting that greater investment irreversibility due to an increase in employment
protection discourages corporate investment and makes firms reluctant to take full
advantage of investment opportunities.
To provide further insight on whether our findings are due to greater employment
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protection making investments more irreversible, we test two additional predictions
generated by these theories. First, if employment protection makes it more costly and difficult
to scale back poorly performing projects, we should observe that greater employment
protection reduces the sensitivity of asset divestitures to negative cash flow shocks.
Consistent with this prediction, we find that, after the adoption of the good faith exception,
firms are significantly less likely to divest assets and discharge workers following decreases
in cash flows.
Second, theories of investment irreversibility predict that a firm will invest in a
project that is costly to reverse only if its expected rate of return sufficiently exceeds the
firm’s normal internal rate of return (e.g., Pindyck, 1991; Dixit, 1992). Thus, if an increase
in employment protection reduces investment rates by making projects more irreversible,
then following the adoption of the good faith exception, investments will have higher average
expected rates of return. To test this prediction, we implement a variation of the valuation
model developed by Fama and French (1998) to derive the market value of each additional
dollar of capital expenditures. We document that, following the recognition of this law,
investors place a higher value on each incremental dollar of capital expenditures.
Our finding that investment declines following the adoption of the good faith exception
implies that greater employment protection should constrain firm growth. However, studies
find that, by increasing the enforceability of job contracts, employment protection promotes
innovative activities by solving contractual hold-up problems between employees and
employers (Acharya et al., 2014; Griffith and Macartney, 2014). For example, Acharya et al.
(2014) show that the adoption of the good faith exception leads to firms receiving more patent
grants and citations, which should accelerate firm growth. Consequently, these competing
effects make the overall effect of employment protection on firm growth unclear. We test the
net effect of employment protection on sales growth and find that firms headquartered in
states that adopt the good faith exception grow sales at a rate that is 2.7 to 3.1 percentage
points slower. Thus, the consequences of reductions in capital expenditures due to greater
employment protection appear to outweigh the benefits of increases in innovation.
3
To interpret our results as the casual effect of employment protection on investment
and growth, our experiment must satisfy the assumption that, in the absence of treatment,
the average change in capital expenditures and sales growth would have been the same for
both treatment and control firms. We show that our results are robust to accounting for a
number of econometric concerns that could threaten this parallel trends assumption. First, a
potential concern is that treatment and control firms differ along dimensions that affect
investment and growth. We address this issue by showing that our results are robust to
matching treatment to control firms based on year, industry, and several firm characteristics.
Second, it is problematic if lobbying activities influence decisions to recognize these
laws. In our setting, however, this concern is not likely a large problem because the
recognition of WDLs is based on judicial rather than legislative decisions and is therefore
more likely driven by the merits of the case than political economy considerations (Autor,
2003; Acharya et al., 2014). We also find that changes in investment and sales growth appear
only after and not before the adoption of the good faith exception, which further alleviates
this concern and provides evidence against reverse causality.
The last concern is that the adoption of the good faith exception and shrinking
investment and sales growth rates could be spuriously correlated with underlying economic
factors. While all of our results hold to controlling for per capita GDP, per capita GDP growth,
and political leaning, this set of controls is not exhaustive. Therefore, we also show that our
results are robust to controlling for several other economic factors, including unemployment
rates, unionization rates, the presence of other labor laws, and the enactment of labor laws
in other states in the same federal circuit region.
To help further address this concern of omitted correlated variables, we test whether
the recognition of the good faith exception has a larger effect on firms that operate in
industries with more volatile cash flows. Firms in these industries are more likely to need to
adjust employment in response to cash flow fluctuations (e.g., Cuñat and Melitz, 2012). Thus,
the adoption of this law should have a larger effect on the investment and sales growth rates
of these firms. In these tests, we also include state-year fixed effects, which effectively
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difference out all variables that vary at the state-year level. Therefore, these latter results
should be unaffected by omitted state-level factors. Consistent with our prediction, we find
that the decline in investment and sales growth following the law’s adoption is more
pronounced for firms operating in industries with more volatile cash flows.
Overall, our study contributes to corporate finance research on the determinants of
firms’ investment decisions. A predominant stream of research studies how financial frictions
affect investment outcomes (e.g., Fazzari, Hubbard, and Petersen, 1988; Whited, 1992;
Kaplan and Zingales, 1997; Rauh, 2006; Almeida and Campello, 2007; Gan, 2007; Duchin,
Ozbas, and Sensoy, 2010; Campello et al., 2011, among others). Our study adds to this work
by providing evidence that labor market frictions in the form of employment protection also
have a significant effect on investment activity and subsequent firm growth. Therefore, our
results demonstrate an important link between labor market frictions and real outcomes as
well as how these frictions affect capital allocation and performance.
We also contribute to work showing that employment laws can have a substantial
effect on various corporate policies and valuation. For instance, employment protection
regulations, laws governing the enforcement of non-compete agreements, and regulations
affecting unionization shape capital structure decisions, executive compensation contracts,
innovation, and investment opportunities (Matsa, 2010; Garmaise, 2011; Acharya et al.,
2014; Griffith and Macartney, 2014; Serfling, 2015; Simintzi, Vig, and Volpin, 2015; Loderer,
Waelchli, and Zeller, 2016). By documenting that employment protection laws shape real
investment decisions, our study provides further support for the notion that government
intervention outside of corporate law can influence the way managers run their firms.
While our paper is not the first to study the effect of employment protection on capital
expenditures, it is the first to show that U.S. employment protection laws lead to U.S. firms
scaling back investment, resulting in subsequent declines in firm growth. In a related paper,
Autor et al. (2007) use plant-level data and document that the capital expenditures of plants
that survived over the entire 1972 to 1999 period increase following the adoption of WDLs.
As the authors recognize, however, this research design captures expenditures to maintain
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and expand continuing plants operating in stable business climates but misses expenditures
on the creation of new plants. By studying firm-level investment and a sample that includes
both surviving and non-surviving firms, we capture both dimensions of capital expenditures
and show that the overall effect of employment protection is a reduction in investment.2
Our results are consistent with Calcagnini et al. (2009, 2014) who find a negative
relation between the capital expenditures of firms in a number of European countries and an
index of country-level employment protection legislation. However, by studying the effect of
an individual employment law in only the U.S., our empirical setting has two advantages.
First, our setting allows us to employ a relatively homogeneous sample in terms of financial
and economic development, infrastructure, and legal structure, which reduces concerns that
omitted economic factors drive our results. Second, the U.S. has relatively less stringent
employment protection laws than European countries. Consequently, although there is an
upward trend in the degree of employment protection in the U.S. (Boxold, 2008; Haider and
Plancich, 2012), there is less research on the effects of U.S. labor protection laws. Thus, our
setting provides insights on how U.S. employment protection laws can shape corporate
investment and growth.
Last, our findings provide insights for policy makers regarding the net societal effect
of increasing employment protection. Undoubtedly, employment protection laws provide a
benefit for workers by protecting them from unexpected or unfair dismissal practices despite
adequate performance. Our findings, however, suggest that these benefits should be
considered in conjunction with the potential costs of slower economic growth resulting from
reductions in corporate investment.
The remainder of the paper is organized as follows. Section 2 discusses the theoretical
motivation and institutional background on WDLs. Section 3 describes our research design
and data. Section 4 reports the empirical results. Section 5 concludes.
The sample in Autor et al. (2007) contains plant-level data for both public and private firms. Thus, to the extent
that the investment decisions of private plants are not comparable to those of public corporations, differences in
sample composition could provide another possible explanation for these opposing findings.
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2. Theoretical Motivation and Institutional Background
2.1. Theoretical Motivation
Why would greater employment protection affect investment decisions? On one hand,
greater employment protection could lead to more investment by either increasing
productivity or encouraging investment in relatively cheaper physical capital. Nickell and
Layard (1999) and Belot et al. (2007) suggest that greater employment protection could
increase productivity by mitigating hold-up problems between firms and employees. In the
model of Belot et al. (2007), workers can invest in non-contractible firm-specific knowledge.
Because workers bear the entire cost of exerting effort in learning firm-specific knowledge
but only receive a fraction of the gains from such effort, a hold-up problem is created and can
lead to underinvestment in firm-specific knowledge. Thus, employment protection can
encourage workers to accumulate firm-specific human capital by reducing the likelihood of
dismissal, which could lead to greater productivity and investment rates.
Greater employment protection can also increase investment rates through a
substitution effect in which firms substitute human capital with relatively less expensive
physical capital. Employment protection can act as a transfer of benefits from employers to
employees that is equivalent to mandated employee benefits. Under the Coase principal, if
labor markets are perfect, wages fall to cover the cost of the benefit without productivity or
employment consequences (Coase, 1960). However, labor markets are not frictionless, and in
general, employment protection likely increases firms’ labor expenses.3 For example, some
investment decisions are made after workers have been located and hired, making it costlier
to replace workers when employment protection is greater. As such, workers can generally
try to bargain for higher wages. In the long-run, firms will substitute relatively more
expensive labor-intensive assets with more capital-intensive assets, which could lead to
higher investment rates (e.g., Blanchard, 1997; Caballero and Hammour, 1998; Koeniger and
Leonardi, 2007; Cingano et al., 2010).
Consistent with this notion, Bird and Knopf (2009) document that the labor expenses of financial firms increase
following the adoption of one WDL – the implied contract exception.
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On the other hand, greater employment protection could lead to less investment. In
particular, by making it more costly to discharge workers, greater employment protection
potentially makes investments more irreversible, and there is ample evidence showing a
negative relation between investment irreversibility and investment activity (e.g., Bernanke,
1983; Pindyck, 1991; Bertola and Caballero, 1994; Abel and Eberly, 1996; Abel et al., 1996).
Investments are more irreversible if, once undertaken, they cannot be undone or made into
a different project without high costs (Bernanke, 1983). Consider an option-based approach
to calculate the net present value (NPV) of an investment. The ability to divest a project at a
later point in time creates a put option that increases the NPV of the project, and the value
of this put option is increasing in the resale or recovery price from divesting. Consequently,
greater investment irreversibility lowers the value of the put option, reducing the NPV of the
project and ex ante incentives to invest (Abel et al., 1996).
2.2. Institutional Background on Wrongful Discharge Laws
When firms dismiss workers, they can incur substantial firing costs, which are any
costs associated with discharging or firing employees. These costs include, but are not limited
to, legal fees and settlements associated with lawsuits arising from violations of employment
protection laws. Under the traditional employment “at-will” rule in the U.S., employers are
free to terminate any employee without warning and for any reason without the risk of legal
liability. However, in an attempt to protect employees from unfair dismissal practices,
legislation and common laws adopted over the last half-century have created a legal
environment that allows employees to sue employers for wrongful termination. This shift in
the legal environment has resulted in an increase in dismissal-related lawsuits and the costs
associated with discharging workers, with nearly half of surveyed public firms expressing
concerns regarding financial losses arising from such lawsuits.4,5
Boxold (2008) finds that wrongful dismissal lawsuits rose 260% over a recent 20-year period. A more recent
survey also finds that the number of federal wrongful termination lawsuits has increased substantially. Between
2005 and 2010, the frequency of these lawsuits increased 40.7% from 39,102 to 55,019 (Haider and Plancich,
2012).
5 In 2012, 46% of surveyed public firms expressed concerns regarding financial losses arising from employment
protection lawsuits (see “U.S. Public Companies’ Perceptions of Risk, and Their Risk Mitigation Strategies,”
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An important part of this shift in the legal environment came as many state courts,
beginning largely in the 1970s, recognized exceptions to the terminate at-will rule. These
common law exceptions, typically known as wrongful discharge laws, pertain to workers not
already covered by explicit contractual agreements or by federal legislation aimed at
protecting a particular class of workers, such as union members, racial minorities, women,
and the aged (Miles, 2000). These common laws evolved into three exceptions called the good
faith, implied contract, and public policy exceptions. State courts can choose to adopt none,
any, or all three of these exceptions.
The good faith exception requires that employers treat workers in a fair manner. In
its broadest sense, this law protects employees from termination for any reason other than
for a “just cause.” The implied contract exception protects employees from termination when
the employer has implicitly promised the employee not to discharge the worker without good
cause. Finally, the public policy exception protects employees from termination for refusing
to violate an established public policy or commit an illegal act.6
Of the three exceptions, the good faith exception is arguably the most far reaching, as
it represents the largest deviation from at-will employment (e.g., Dertouzos and Karoly, 1992;
Kugler and Saint-Paul, 2004). This law should therefore have the largest effect on firm
outcomes. The law applies in cases when a court determines that an employer has discharged
a worker out of bad faith, malice, or retaliation. The law also serves to prevent employers
from denying employees their contractual rights. For example, if an employer fires a
salesperson just before a commission is due to deprive the employee of his commission or
discharges an employee just before her pension vests, the employee could sue the employer
under the good faith exception.
Employees have both a contract and tort cause of action under the good faith
exception. This means that an employee can recover compensation for punitive damages and
emotional distress in addition to contractual losses. Importantly, punitive damages tend to
Chubb 2012 Public Company Risk Survey, 2012).
6 See Dertouzos and Karoly (1992), Miles (2000), and Autor, Donohue, and Schwab (2006) for a more in-depth
discussion of the implied contract and public policy exceptions.
9
be a large percentage of settlement awards and can substantially increase an employer’s
liability. Further, there is greater uncertainty associated with settlement amounts when
firms face punitive damages because a jury determines these damage awards without a clear
formula.
Prior work suggests that the public policy and implied contract exceptions may not
have material effects on firms (e.g., Miles, 2000; Autor et al., 2007). Courts typically limit
recovery under the public policy exception to dismissals in which the employer violated or
encouraged the violation of an identifiable statute or constitutional provision. In addition,
firms can largely prevent lawsuits under the implied contract exception by including
disclaimers that employment contracts are always at-will in their employee handbooks and
personnel manuals.
2.3. Wrongful Discharge Laws in Practice
An important assumption for our study is that, following the adoption of WDLs,
employment protection and expected firing costs increase due to an increase in expected
litigation. Consistent with this assumption, prior work finds that the adoption of the good
faith exception results in decreases in employment levels, employment volatility, and firm
entry (Dertouzos and Karoly, 1992; Autor et al., 2007). This assumption, however, raises
three questions worth discussing.
First, how large is the financial impact of wrongful termination lawsuits on firms?
While it is difficult to estimate the true financial impact of wrongful termination cases
because many court decisions are never published and are often settled before trial, several
studies and anecdotes suggest that these laws can impose substantial costs.7 For example,
Dertouzos, Holland, and Ebener (1988) analyze jury trials of wrongful discharge claims in
California between 1980 and 1986 and find that plaintiffs won in 68% of cases and that the
average award was $0.656 million. Similarly, Jung (1997) estimates that plaintiffs prevailed
An early study estimates that there were about 20,000 wrongful termination cases pending in state courts
(Westin and Feliu, 1988). Because the number of states recognizing WDLs has increased over time, the number
of wrongful termination cases is likely higher in more recent years.
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in 46.5% of wrongful dismissal cases that reached the trial stage in 1996 and won $1.29
million on average. Further, Boxold (2008) documents average and maximum awards of $0.59
million and $5.4 million, respectively, over the 2001 to 2007 period.
While these average settlements are arguably small for large firms, the fear of very
large settlements could alter the behaviors of risk-averse managers (Dertouzos et al., 1988).
Moreover, firms can have multiple lawsuits pending at any point in time, which can
substantially raise their legal liability. For example, in a case against Lawrence National
Security, 130 of 430 laid off employees sued the firm. The firm claimed the layoffs were
economically motivated, but the employees argued the layoffs were a “pretext to get rid of
older employees who have higher salaries, larger medical costs, and are closer to collecting
their pension.” A jury sided with five employees selected as test cases for the other 125
employees, awarding these five individuals a total of $2.7 million for breach of the good faith
exception and breach of contract.8
Second, can firms be subject to wrongful termination claims if they dismiss workers
as part of economically motivated layoffs, such as due to plant closings associated with poor
performance? In the context of discrimination, which is similar to wrongful termination,
evidence suggests that it is more difficult to prove unjust dismissal when many workers are
laid off compared to when a single worker is fired (Donohue and Siegelman, 1993; Oyer and
Schaefer, 2000). However, this point does not remove the potential for wrongful termination
lawsuits during layoffs, as highlighted in Andrews et al. v. Lawrence National Security
(above) and Robert Coelho v. Posi-Seal International, Inc.9 In this latter case, Coelho accepted
the position of manager of quality control at Posi-Seal after upper-level managers reassured
Coelho that he would not be dismissed because of ongoing conflicts between the quality
control and manufacturing division (a factor that contributed to the departure of the prior
See “Jury Awards $2.7 M Against Lawrence Livermore Lab for Wrongfully Terminating Long-Time Employees
in 2008,” PRNewswire, May 13, 2013 and Andrews, et. al. v. Lawrence Livermore National Security, LLC., Case
No. RG09453596.
9 See Coelho v. Posi-Seal International, Inc., 208 Conn. 106, 544 A.2d 170, 544 A. 2 (1988). Also, see Ewers v. Stroh
Brewery Company, 178 Mich. App. 371, 443 N.W.2d 504 (1989) for another example in which an employer faced
wrongful termination claims during layoffs.
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quality control manager). Subsequently, Posi-Seal fired Coelho, claiming it was part of
layoffs. Coelho sued Posi-Seal for breach of an implied promise of ongoing employment,
claiming he was discharged due to a dispute with a manager of manufacturing.
Posi-Seal argued that termination due to a reduction in work force is, as a matter of
law, a just cause. However, the court concluded, “an employer’s contention that some
employees were terminated as a result of a legitimate reduction in force does not necessarily
establish that all employees were discharged for the same reason.” Further, “an employer
may not use a reduction in force as a pretext to terminate other employees in violation of
contractual obligations, public policy grounds, or statutory rights.” In this lawsuit, the court
ruled in favor of Coelho because it was clear from the evidence that Posi-Seal used the layoffs
as pretext to fire Coelho.
Thus, while layoffs may reduce the risk of wrongful termination lawsuits, they do not
eliminate this risk. Further, the risk of wrongful termination lawsuits increases when
employers harm employee welfare by concealing information about closings (Rhine, 1986).
For example, if an employer knows that a plant will close, any false assurances of job security
or unreasonable delays in closure notifications could violate the good faith exception.10
The third question is whether firms can completely offset the extent of losses related
to employee lawsuits by purchasing Employment Practices Liability Insurance (EPLI). While
EPLI can offset losses arising from wrongful termination claims, the impact of EPLI on our
findings is likely limited for two reasons. First, firms’ use of EPLI was not widespread during
our sample period of 1969 to 2003. The market for EPLI largely emerged in the early 1990s
due to greater awareness of the potential for and costs of employee litigation as a result of
the Civil Rights Act of 1991 and the Clarence Thomas hearings (Klenk, 1999). Moreover,
purchasing EPLI was still the exception in the later 1990s (22% of surveyed employers had
It is worth noting that some of these situations are less common in the later years of our sample period due to
the passage of the Worker Adjustment and Retraining Notification Act (WARN Act) in 1988, which requires
employers give employees at least a 60-day advance notice before plant closings and mass layoffs.
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EPLI in 1997) but has become more widespread in recent years (68% of surveyed employers
carried EPLI in 2012).11
Second, even if a firm purchased EPLI in the later years in our sample, these early
policies typically contained “intentional acts” exclusions that limited coverage for wrongful
termination claims. These policies also often had exclusions related to downsizing and
retaliation and typically did not cover punitive damages, which can be a substantial amount
of the total settlement award. Nevertheless, to the extent that EPLI coverage reduces the
risks and costs associated with employee litigation, the presence of EPLI should only reduce
the effect of the recognition of the good faith exception on corporate investment and growth.
2.4. The Adoption of Wrongful Discharge Laws by State Courts
Our analyses rely on identifying which court cases set the precedent that a state has
adopted a particular WDL. We base our identification of the recognition of WDLs primarily
on the precedent-setting cases provided in Autor, Donohue, and Schwab (2006).12 In contrast
to Autor et al. (2006), however, we follow Walsh and Schwarz (1996) and Littler (2009) and
recognize Utah as adopting the good faith exception since 1989.13
Table 1 summarizes the dates when each state court ruled on a precedent-setting case
for each particular exception, and Figure 1 shows the number of states that have adopted
each exception in each year between 1969 and 2003. Besides California adopting the public
policy exception in 1959, all states adopted WDLs between the 1970s and 1990s. Of the 14
states that eventually recognized the good faith exception, the majority of the states
See “2012 Insurance Coverage Survey Results,” Zywave, Inc., 2012, “1997 Employment Litigation Survey,”
Society for Human Resource Management, 1997, and “U.S. Public Companies’ Perceptions of Risk, and Their Risk
Mitigation Strategies,” Chubb 2012 Public Company Risk Survey, 2012.
12 There is a degree of subjectivity in identifying precedent-setting cases. In Section 4.4.5, we show that our main
findings are robust to using the exact precedent setting cases and dates provided by Autor et al. (2006) as well as
the cases and dates provided by Walsh and Schwarz (1996), Dertouzos and Karoly (1992), and Morriss (1995).
13 Utah courts adopted the good faith exception in Berube v. Fashion Centre, Ltd., 771 p.2d at 1033 (Utah 1989).
In this case, Justice Durham concluded, “the evidence at trial established as a prima facie matter that Fashion
Centre breached the implied covenant of good faith and fair dealing. Fashion Centre terminated an experienced,
motivated, and favorably reviewed employee who refused to submit to the third polygraph examination required
of her in conjunction with a single inventory shortage, even though she had been exonerated by the previous two
exams and had requested that the third exam be rescheduled for another day. This action occurred in light of
Fashion Centre’s own employment policy which essentially limited an employee’s termination to just cause.”
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recognized the law in the 1980s. A few states also reversed their positions on enforcing the
good faith (New Hampshire in 1980 and Oklahoma in 1989) and implied contract exceptions
(Arizona in 1984 and Missouri in 1988).
3. Research Design and Sample Selection
3.1. General Empirical Methodology
To examine the relation between the recognition of the good faith exception and
corporate investment and sales growth, we adopt a difference-in-differences research design
and estimate the following panel regression model:
yi ,s,t
α1GFs,t
α2 ICs,t
β5Levi ,s,t
1
α3 PPs,t
β6 %ΔGDPs,t
β1Ln( Assets )i ,s,t
1
β7 Ln(GDP )s,t
1
1
β2TQi ,s ,t
β3CFi ,s,t
1
β8 %Dems,t
1
υi
ηi
1
β4Cashi ,s,t
ωt
εi ,s,t ,
1
(1)
where yi,s,t is either capital expenditures scaled by beginning of year book assets or the oneyear sales growth rate for firm i headquartered in state s in year t. The variables GFs,t, ICs,t,
and PPs,t are indicator variables set to one if courts in state s recognize the good faith, implied
contract, and public policy exception as of year t and zero otherwise, respectively. We control
for the natural logarithm of the beginning of year book assets (Ln(Assets)i,s,t-1), Tobin’s Q
(market value of assets scaled by book value of assets) at the beginning of the year (TQi,s,t-1),
and the beginning of year ratio of cash flow to book assets (CFi,s,t-1). We also control for
beginning of year cash holdings (Cashi,s,t-1) and book leverage (Levi,s,t-1). To help ensure that
shrinking local economic growth does not spuriously drive our results, we include the statelevel prior year’s one-year per capita GDP growth rate (%ΔGDPs,t-1) as well as the natural
logarithm of per capita GDP (Ln(GDP)s,t-1). Last, to control for local political conditions, we
include the fraction of Democrats representing their state in the U.S. House of
Representatives in a given year (%Dems,t-1).
All of our models also include firm fixed effects (υi) and industry-year fixed effects
defined at the 2-digit SIC level (ηi×ωt). The firm fixed effects control for time-invariant
omitted firm characteristics and ensure that estimates of α1 reflect average, within-firm
14
changes in investment and sales growth rates over time rather than simple cross-sectional
correlations. The industry-year fixed effects account for transitory industry- and nation-wide
factors, such as industry deregulation and macroeconomic conditions that could
simultaneously affect corporate investment activity and growth as well as the likelihood that
a state adopts the good faith exception. An advantage of our experiment is that different
states adopt this law at different times, which allows a firm headquartered in a given state
to be both in the treatment (if the state has adopted the exception by year t) and the control
group (if the state has not adopted the exception by year t). Thus, the staggered adoption of
the good faith exception implies that the control group is not restricted to only those firms
headquartered in states that never recognize these laws.
We correct estimated standard errors in all regressions for clustering at the state
level. Since the adoption of the good faith exception varies at the state level, this clustering
method accounts for the concern that residuals are serially correlated within a firm and also
correlated across firms within the same state (Bertrand, Duflo, and Mullainathan, 2004).
This methodology is therefore more general than firm-level clustering.
Employment laws typically apply to the state where an employee is working. Thus,
the measure that would best capture the degree of employment protection a firm faces
following the adoption of the good faith exception would aggregate the number of workers
who are protected by this law at each of the firm’s locations of operations. Compustat,
however, provides only a firm’s state of incorporation and headquarters. Consequently, we
follow recent studies and match these laws to the state where each firm is headquartered
(e.g., Matsa, 2010; Agrawal and Matsa, 2013; Acharya et al., 2014; Dougal, Parsons, and
Titman, 2015), which is also typically where major plants and operations are located
(Henderson and Ono, 2008). Further, Dertouzos et al. (1988) find that plaintiffs in wrongful
termination cases tend to hold executive or managerial positions (53%), who tend to be
concentrated at headquarters. Thus, using the headquarters state likely captures a large
portion of the increase in employment protection.
In addition, a limitation of Compustat is that it provides only the latest headquarters
15
locations. Therefore, we supplement Compustat headquarters data with the actual state of
headquarters extracted from WRDS SEC Analytics Suite, a machine-readable database of
text from SEC filings. We are able to obtain this data for firms beginning in 1994. We then
extend the earliest headquarters data back for each firm.
If firms relocate headquarters to a different state between the earliest year in our
sample and the first year when we are able to obtain data from the database, these firms may
be subject to different employment laws in the earlier periods. However, this measurement
error should bias us against finding an effect of the adoption of the good faith exception on
investment and sales growth. Consider the following two scenarios for investment. If a firm
was located in a state that adopted the good faith exception but we code this firm as not being
in such a state, the effect of the adoption of this law on investment will be reduced, as
investment changes despite the apparent absence of the law’s adoption. Similarly, if a firm
was not located in a state that adopted the law but we code the firm as being in such a state,
investment will fail to appear responsive to the law’s adoption. Either case will bias our tests
in favor of finding no effect of the adoption of the good faith exception on investment. The
same logic applies for sales growth.
3.2. Sample Selection
We use CRSP/Compustat Merged data for firms headquartered in the U.S. that have
non-missing data for our main variables of interest over the years 1969 to 2003. The sample
period starts five years before the earliest enactment of the good faith exception by New
Hampshire in 1974 and ends five years after the last event when Louisiana adopted the good
faith exception in 1998. We obtain data on state-level GDP from the U.S. Bureau of Economic
Analysis and data on congress members in the House of Representatives from the History,
Art & Archives, U.S. House of Representatives. For our tests, we exclude utility firms (SIC
codes 4900-4999), financial firms (SIC codes 6000-6999), and quasi-public firms (SIC codes
greater than 9900). We further require that firms have at least two years of data to estimate
the firm fixed effects and that 2-digit SIC industries have at least two observations in a given
16
year to estimate the industry-year fixed effects.
These restrictions result in a sample size of 118,314 firm-years for our main analyses.
We winsorize continuous variables, except state-level economic variables, at their 1st and
99th percentiles and express dollar values in 2009 dollars. Panel A of Table 2 presents
detailed definitions and summary statistics for the variables in our tests. Panel B compares
variable means for firms headquartered in states that eventually adopt the good faith
exception against those of firms headquartered in states that do not adopt this law during
the sample period. Firms headquartered in states that adopt the good faith exception tend to
be smaller, less profitable, have more growth opportunities, hold more cash, and have lower
debt ratios. Ideally, the treatment and control firms would be relatively similar along each
dimension. However, because they are not, we control for each variable in our regressions to
account for these differences. In Section 4.4.3, we further address this issue by using a
matched sample.
4. Empirical Results
4.1. Employment Protection and Corporate Investment
We begin by investigating whether increases in employment protection arising from
the adoption of the good faith exception affect corporate investment activity. Table 3 reports
the results from this analysis. The dependent variable in columns 1-4 is capital expenditures
scaled by beginning of year book assets. Column 1 includes the indicator variables for
whether the state where a firm is headquartered recognizes the good faith, implied contract,
and public policy exceptions, firm fixed effects, and industry-year fixed effects. The results
show a negative and statistically significant relation between investment and only the
recognition of the good faith exception. In terms of economic significance, the coefficient
estimates imply that firms reduce capital expenditures by 0.50 cents per dollar of book assets
following the adoption of this law. Given that the sample mean of the ratio of capital
expenditures to book assets is 8.72%, this finding represents a relative reduction in capital
expenditures of 5.7% (=0.50/8.72).
17
Column 2 further controls for firm size, investment opportunities, and cash flow.
Column 3 also controls for cash holdings and financial leverage. The results remain similar
to those in column 1; capital expenditures decrease by 0.44 to 0.49 percentage points following
the adoption of the good faith exception. Last, column 4 is our primary model specification
and further controls for per capita GDP growth, per capita GDP, and political balance. The
inclusion of these state-level economic variables strengthens both the statistical and
economic significance of the effect of the recognition of this law on capital expenditures. The
coefficient estimate of -0.61 on the good faith indicator variable implies that investment
declines by 7.0% (=0.61/8.72) relative to the sample mean following the adoption of this law.
We next test how the adoption of the good faith exception affects the responsiveness
of capital expenditures to changes in investment opportunities and present the results in
Table 4. If greater employment protection makes it so that firms do not invest in all profitable
projects due to the potential cost of firing workers if the project turns out poorly, we would
expect firms to respond less to changes in investment opportunities. To test this prediction,
we use our main investment regressions and interact the good faith indicator variable with
beginning of year Tobin’s Q. The results in column 1 show that a one standard deviation
increase in Tobin’s Q of 1.68 is associated with an increase in capital expenditures of 2.28
percentage points (=1.36×1.68) before the adoption of the good faith exception. Following the
law’s adoption, however, the increase in capital expenditures associated with a one standard
deviation increase in investment opportunities shrinks to 1.76 percentage points (=(1.360.31)×1.68), a 22.8% reduction.
In column 2, we rerun the regression from column 1, but replace Tobin’s Q with the
prior year’s sales growth rate as a measure of investment opportunities. The results are
similar. A one standard deviation increase in sales growth of 0.69 percentage points results
in an increase in capital expenditures of 0.70 percentage points before the adoption of the
good faith exception. This increase in investment, however, shrinks to 0.41 percentage points
following the law’s adoption, a 41.4% reduction.
18
In sum, the results in Table 3 are consistent with theories predicting that greater
employment protection makes investments more irreversible, which lowers firms’ incentives
to invest. The results in Table 4 further suggest that greater employment protection makes
firms reluctant to take full advantage of investment opportunities that may need to be
reversed in subsequent periods.
4.2. Potential Mechanisms and Additional Theoretical Predictions
If the investment irreversibility channel drives our results, theories of investment
irreversibility provide two additional empirical predictions. First, employment protection
makes it costlier to discharge workers and scale back operations if a project turns out badly.
Thus, we should observe that firms located in states that have adopted the good faith
exception are less likely to divest assets and discharge workers following negative cash flow
shocks. Second, theory predicts that a firm will invest in a project that is costly to reverse
only if its expected rate of return sufficiently exceeds the firm’s normal internal rate of return.
Therefore, we should also document that, conditional on choosing to invest, firms located in
states that have adopted the good faith exception invest in projects with higher expected
rates of return. In the following sections, we test these two predictions.
4.2.1. Employment Protection, Employment, and Divestitures
Before testing how employment protection affects firms’ divestiture decisions, we test
whether the result in Serfling (2015) that firms located in states that have adopted the good
faith exception are less likely to discharge workers following negative cash flow shocks holds
for our sample. To do so, we create an indicator variable that is set to one if a firm discharges
at least 15% of its employees over a year and zero otherwise. However, the results are also
robust to using 10% and 20% cutoffs. We also follow Hanka (1998) and Serfling (2015) and
create a continuous measure of discharges, which equals the one-year percentage decrease in
a firm’s number of employees, with employment gains (positive changes) set to zero. We then
regress these variables on a measure of decreases in cash flows and its interaction with the
19
good faith dummy. We capture declines in cash flows as the one-year change in the ratio of
cash flow to book assets from year t-1 to t. Again, increases in cash flows are set to zero.
Columns 1 and 2 of Table 5 show that firms are significantly more likely to discharge
workers following decreases in cash flows, but the adoption of the good faith exception
weakens this sensitivity. For example, the negative coefficient of 0.68 on the decrease in cash
flow variable in column 1 suggests that, before the adoption of the good faith exception, if
cash flow declines by one standard deviation (about 11.6 percentage points), the likelihood of
discharging at least 15% of workers increases by 7.9 percentage points (=0.68×0.116). Given
that firms discharge at least 15% of their workers in 13.3% of years, this relation between
decreases in cash flows and discharges is economically significant. In addition, the positive
coefficient on the interaction of the good faith dummy and the decline in cash flow variable
of 0.11 suggests that the adoption of the law reduces this sensitivity by 16.2% (=0.11/0.68).
Next, in columns 3-6, we extend this analysis to divestiture decisions. We create two
measures that capture asset divestitures. The first measure is the one-year percentage
decrease in PP&E, with increases in PP&E set to zero. To ensure that our findings are
unrelated to depreciation, the second measure equals the funds received from the sale of
PP&E, as reported in the statement of cash flows, scaled by beginning of year book value of
PP&E. We also create two analogous indicator variables that are set to one if the percentage
decrease in PP&E is at least 15% or the value of PP&E sales is at least 15% of beginning of
year PP&E. However, the results are robust to using 10% and 20% cutoffs.
Similar to the results for employee discharges, firms are significantly more likely to
divest assets following decreases in cash flows, but the adoption of the good faith exception
weakens this sensitivity. For example, the negative coefficient of 0.58 on the decrease in cash
flow variable in column 3 suggests that a one standard deviation decrease in cash flows
results in an increase in the likelihood of divesting 15% of PP&E by 6.7 percentage points
(=0.58×0.116) before the adoption of this law. This relation between decreases in cash flows
and divestitures is economically significant given that firms divest at least 15% of their PP&E
in 12.1% of years. In addition, the positive coefficient on the interaction of the good faith
20
dummy and the decline in cash flow variable of 0.15 suggests that the adoption of this law
reduces this sensitivity by 25.9% (=0.15/0.58).
4.2.1. Employment Protection and the Value of Capital Expenditures
In this section, we test whether firms allocate resources to projects with higher rates
of return when it becomes costlier to reverse investments due to greater employment
protection. To do so, we implement a variation of the valuation regressions developed by
Fama and French (1998) and Pinkowitz, Stulz, and Williamson (2006) to estimate whether
the value of each additional dollar of capital expenditures increases after the adoption of the
good faith exception. Specifically, we estimate the following panel regression model:
TQi ,s,t
β1 ΔCapexi ,s,t
β5GFs,t
β2GFs ,t
β6 ICs,t
β10 %Dems,t
1
υi
ΔCapexi ,s ,t
β7 PPs ,t
ηi
ωt
λ'X
β3Capexi ,s ,t
β8 %ΔGDPs ,t
1
1
β4 ΔCapexi ,s ,t
1
β9Ln(GDP )s ,t
1
(2)
εi ,s ,t ,
where TQi,s,t is Tobin’s Q in year t. ΔCapexi,s,t is the change in capital expenditures from year
t-1 to t, Capexi,s,t-1 is capital expenditures in year t-1, and ΔCapexi,s,t+1 is the change in capital
expenditures from year t to t+1, where all three capital expenditure variables are scaled by
book assets in year t.14 GFs,t, ICs,t, and PPs,t are indicator variables set to one if state s has
adopted the good faith, implied contract, and public policy exceptions as of year t,
respectively.
' X is the set of control variables used by Fama and French (1998) and
Pinkowitz et al. (2006) and are defined in Table 6. %ΔGDPs,t-1 is the one-year per capita state
GDP growth rate, Ln(GDP)s,t-1 is the natural logarithm of per capita state GDP, %Dems,t-1 is
the fraction of Democrats representing their state in the U.S. House of Representatives, υi
are firm fixed effects, and (ηi×ωt) are industry-year fixed effects. If investors place a higher
value on investment when employment protection increases, the estimated coefficient on β2
will be positive.
In this test, we assume that the stock market’s expectation about a firm’s capital expenditures in a given year
is equal to the firm’s capital expenditures in the previous year. Thus, to investigate whether capital expenditures
have a higher expected rate of return when employment protection increases, we examine the change in capital
expenditures relative to the previous year’s capital expenditures.
14
21
Table 6 reports the results of our value of capital expenditures analysis. In column 1,
we run the base regression that excludes ΔCapexi,s,t+1. The results show that the value of
capital expenditures is significantly higher following the recognition of the good faith
exception. In particular, a one-dollar increase in expected capital expenditures is associated
with an increase in firm value of $1.08 before the adoption of the law. Following the law’s
recognition, however, each additional dollar of expected capital expenditures is worth $1.51
(=1.08+0.43).
In column 2, we repeat the analysis in column 1 but also control for next year’s change
in capital expenditures (ΔCapexi,s,t+1). The incremental effect of the adoption of the good faith
exception on the value of capital expenditures remains similar to that in column 1 ($0.46 vs.
$0.43), but the total value of capital expenditures is higher after including the additional
control variable. Specifically, a one-dollar increase in expected capital expenditures is
associated with an increase in firm value of $1.78 before the adoption of the law. However,
following the law’s recognition, each additional dollar of expected capital expenditures is
worth $2.24 (=1.78+0.46). While the value of each additional dollar of capital expenditures
varies significantly across the model specifications, the incremental value of capital
expenditures following the adoption of the good faith exception across the models is relatively
stable. Overall, the results in Table 6 are consistent with the notion that firms invest in
projects with higher rates of return when it becomes costlier to reverse these investments
due to greater employment protection.
4.3. Employment Protection and Sales Growth
The evidence presented so far implies that increasing employment protection
discourages investment activity. This lower rate of investment could limit the firm’s ability
to grow. However, prior work finds that restrictions to employment at-will can limit a firm’s
ability to take advantage of employees that contributed significant effort to successful
innovation and in turn encourage employees to engage in more innovative activities.
Importantly, an increase in innovation due to an increase in employment protection could
22
suggest that greater employment protection accelerates firm growth. Therefore, the opposing
effects of employment protection on capital expenditures and innovation make its overall
effect on growth ambiguous. In this section, we first confirm for our sample of firms the
finding that increased employment protection leads to greater innovation (Acharya et al.,
2014) and then test the effect of employment protection on sales growth.
To confirm the innovations results, we follow Acharya et al. (2014) and create two
innovation variables using patent data from the NBER patent citation database assembled
by Hall, Jaffe, and Trajtenberg (2001) for the years 1976 to 2003. First, we count the number
of patents a firm files in a given year that are eventually granted. Second, we count the
number of citations a firm’s patents receive in subsequent years. We then regress the natural
logarithm of one plus each measure on the good faith indicator variable, industry-year fixed
effects, firm fixed effects, and the same set of control variables used in our main investment
tests. The results in the online appendix Table A1 show that, following the adoption of the
good faith exception, the number of patents filed increases by approximately 7.5% and
patents receive about 9.9% more citations.
Next, Table 7 presents the results of the analysis examining the relation between a
firm’s one-year sales growth rate and the recognition of the good faith exception. We use the
same set of control variables as in our investment regressions. Similar to Table 3, we
sequentially add firm- and state-level control variables to the model specifications. Across all
of the models, we find that firms’ sales increase at a significantly slower rate after the
enactment of this law. In particular, sales growth is 2.70 to 3.09 percentage points slower
following the law’s adoption. Overall, this finding suggests that the consequences of
reductions in capital expenditures due to greater employment protection appear to outweigh
the benefits of increases in innovation.
4.4. Econometric Concerns
Two possible sources of endogeneity could affect the interpretation of our results. In
particular, lobbying activities could influence the adoption of the good faith exception, and/or
23
an omitted economic characteristic could be correlated with both the enactment of this law
and changes in investment and sales growth rates. As a first step in determining the extent
to which the recognition of this law is exogenous, we examine the institutional details around
the adoption of the law. Because WDLs are common laws, the enactment of the good faith
exception is based on judicial rather than legislative decisions, which are more likely driven
by the merits of the case than political economy considerations (Autor, 2003; Acharya et al.,
2014). Therefore, lobbying activities should not be a major problem for our analyses.
Further, Walsh and Schwarz (1996) analyze published court decisions to investigate
cited reasons for judges adopting the good faith exception and find that these reasons include:
(1) assuring consistency with established principles of contract law, (2) enhancing fairness in
employment relationships, and (3) following other states that have already adopted WDLs.
These institutional features imply that lobbying activities and underlying economic factors
related to investment decisions and growth were not major determinants of courts’ decisions
to adopt the good faith exception and therefore provide initial evidence that the recognition
of the law likely represents an exogenous shock. Nevertheless, in the following sections, we
conduct various empirical analyses to alleviate residual endogeneity concerns.
4.4.1. The Timing of Changes in Investment and Growth
We next conduct a test to alleviate potential endogeneity concerns related to reverse
causality. The primary concern is that, during periods when corporate investment or sales
growth are declining, firms are dismissing more workers and courts may adopt the good faith
exception to protect workers from unfair dismissal. If reverse causality is an issue, then there
should be a trend of declining investment and sales growth before the enactment of the law.
Further, if a trend exists before the adoption of the law, this finding would cast doubt on the
validity of using a difference-in-differences approach because it would suggest a violation of
the parallel trends assumption.15
The “parallel trends” condition in our empirical setting means that, in the absence of treatment (the adoption
of the good faith exception), the average change in investment and sales growth would have been the same for
both the treatment (firms headquartered in states that have adopted this law) and control (firms headquartered
in states that have not adopted the law) groups. If the treatment and control groups follow different trends before
15
24
To check for pre-existing trends in capital expenditures and sales growth, we follow
Acharya et al. (2014) and replace Good Faith with the following four variables: Good Faith
(-2,-1), Good Faith (0), Good Faith (+1), and Good Faith (≥+2). These variables are indicator
variables set to one if the firm is headquartered in a state that will adopt the good faith
exception in one or two years, adopted the exception in the current year, adopted the
exception one year ago, and adopted the exception two or more years ago, respectively.16 The
coefficient on the variable Good Faith (-2,-1) is especially important because its significance
would suggest if there is any relation between investment and sales growth and the good
faith exception before the enactment of the law. Specifically, a negative and statistically
significant coefficient would suggest that the decline in investment and sales growth
preceded the law, which would cast doubt on its exogeneity.
The results in column 1 of Table 8 show that there is no trend of declining investment
before the enactment of the good faith exception. Column 1 shows that investment started to
decline slightly the year the law was adopted and this decline becomes economically and
statistically more significant two or more years after the law’s adoption. Column 2 also shows
a statistically insignificant decrease in sales growth before the enactment of the good faith
exception but a statistically significant decline two or more years after the law’s adoption.
Overall, these findings suggest that our results do not suffer from reverse causality. The
results also confirm the appropriateness of using a difference-in-differences approach, as it
shows that firms located in states that adopt and that do not adopt the good faith exception
follow parallel trends before its adoption.
4.4.2. The Effect of Potential Omitted Variables
While survey evidence suggests that judges’ rationales for adopting the good faith
exception are unrelated to factors that affect firms’ investment decisions and sales growth, it
the recognition of the law, then inferences are generally inconclusive. Specifically, the estimated effect of the
adoption of the good faith exception is biased in an unknown direction.
16 Two states reversed their previous adoption of the good faith exception. These reversals include: (1) New
Hampshire reversing the recognition of the good faith exception in 1980 and (2) Oklahoma reversing the
recognition of the good faith exception in 1989. To account for these reversals, we drop all observations for these
two states after the date of the reversal. This reduces the sample size from 118,207 to 117,241 observations.
25
is possible that judges are directly or indirectly motivated by economic factors that they do
not cite. Thus, we next explore whether several state-level variables that have been
hypothesized to affect a court’s decision to adopt WDLs are correlated with the adoption of
the good faith exception.
Dertouzos and Karoly (1992) argue that courts are more likely to adopt WDLs when
the unemployment rate in the state is higher because there is a larger fraction of workers
that could have benefited from employment protection. In addition, many of the WDLs were
adopted during the 1980s, which coincided with declining union membership. Because WDLs
protect nonunionized workers, the decline in union protection could increase demand for its
adoption. The authors also suggest that less labor friendly states are less likely to adopt labor
protection laws. Last, Bird and Smythe (2008) show that a state’s decision to adopt WDLs is
influenced by whether states that belong to the same federal circuit region have already
adopted these laws.
To test whether any of these factors predict the adoption of the good faith exception,
we follow Acharya et al. (2014) and estimate a Cox proportional hazard model with year fixed
effects, where a failure event represents the adoption of this law. Table 9 presents these
results. All predictor variables are measured as of year t-1 relative to the law’s adoption, and
all variables, except indicator variables, are standardized to have a mean of zero and a
standard deviation of one. The sample spans the years 1969 to 2003, and states are excluded
from the sample once they adopt the good faith exception.
Column 1 includes the state-level variables that we use throughout our main tests:
per capita GDP growth, per capita GDP, political balance, and whether the state has adopted
the implied contract and public policy exceptions. Only per capita GDP shows a statistically
significant relation with the adoption of the good faith exception. Column 2 controls for the
unemployment rate and the one-year change in the unemployment rate. Neither predict the
adoption of this law. In addition to these unemployment rate variables, column 3 includes an
indicator variable for whether the state has passed right-to-work laws as well as the union
26
membership rate and the change in the union membership rate.17 The results show that
courts are more likely to adopt the good faith exception following decreases in union
membership. Last, column 4 also controls for whether states in the same federal circuit court
region have already adopted WDLs by controlling for the fraction of these states that have
already adopted the good faith, implied contact, and public policy exceptions. In this model,
the adoption of the good faith exception is positively related to per capita GDP, union
membership, and the fraction of other states in the same federal circuit court that have
already adopted the good faith and public policy exceptions. The law’s adoption is no longer
significantly correlated with changes in union membership.
Next, Table 10 reports the results from our investment and sales growth regressions
that include the potential determinants of the adoption of the good faith exception from Table
9 as additional control variables. Columns 1 and 3 show that our results are robust to
controlling for the variables that significantly predict the adoption of this law, which include
the union membership rate, the change in the union membership rate, and the fraction of
states in the same federal circuit region that have already adopted the good faith and public
policy exceptions. Following the adoption of the good faith exception, capital expenditures
decreases by 0.57 percentage points and sales growth shrinks by 2.77 percentage points. In
columns 2 and 4, we also show that our results are robust to including the full set of statelevel control variables from Table 9. Overall, these results imply that our findings are robust
to controlling for a number of potential omitted variables related to local economic conditions.
To further help address the concern of omitted correlated variables, we next estimate
triple-difference regression models by testing whether the recognition of the good faith
exception has a larger effect on firms that operate in industries with more volatile cash flows.
The unemployment rate is based on data from the March Current Population Survey (CPS) each year.
Specifically, data are from the Integrated Public Use Microdata Series (IPUMS)-CPS database (King et al., 2010).
The CPS is a monthly U.S. household survey conducted jointly by the U.S. Census Bureau and the Bureau of
Labor Statistics. For missing state-years (early 1970s and late 1960s for a few states), this measure is
supplemented with data from the IPUMS-USA database (Ruggles et al., 2010). The IPUMS-USA database
compiles data from the American population federal censuses every ten years. Data on the passage of right-towork laws are from the Department of Labor. Union membership is the fraction of each state’s nonagricultural
wage and salary employees who are covered by a collective bargaining agreement. Data on state union
membership are from Hirsch, Macpherson, and Vroman (2001).
17
27
This test serves two purposes. First, if the adoption of the law causes the decline in
investment and sales growth, then the effect of the recognition of the law should be especially
strong when firms expect a larger increase in firing costs due to greater employment
protection. Because firms that operate in industries with more volatile cash flows are more
likely to need to adjust employment in response to cash flow fluctuations (e.g., Cuñat and
Melitz, 2012), firms in these industries should expect a larger increase in firing costs.
Therefore, the negative relation between the adoption of the law and investment and sales
growth should be stronger for firms operating in industries with more volatile cash flows.
Second, by identifying a group of firms within treated states that are more likely
affected by the adoption of this law and effectively comparing groups of firms within the same
state, the triple-difference estimator can alleviate the concern that unobserved factors affect
firms headquartered in states that do and do not adopt the good faith exception differently.
In addition, we include state-year fixed effects in some of these tests, which remove all timevarying omitted variables that affect all firms within the same state during a given year by
demeaning all variables by state each year. Therefore, this set of results should be unaffected
by omitted state-level factors.
Table 11 presents the results from tests examining the effect of industry cash flow
volatility on the relation between the adoption of the good faith exception and capital
expenditures and sales growth. We measure industry cash flow volatility as the average cash
flow volatility across all firms in the same 3-digit SIC industry and year. A firm’s cash flow
volatility is the standard deviation of the ratio of income before extraordinary items plus
depreciation and amortization to book assets over years t-10 to t-1. Firms must have at least
three years of data to enter the industry average calculation. We standardize industry cash
flow volatility to have a mean of zero and a standard deviation of one before interacting it
with the good faith indicator variable to ease the interpretation of coefficient estimates.
The model specifications in columns 1 and 3 include our full set of control variables,
industry-year fixed effects, and firm fixed effects. Columns 2 and 4 repeat this analysis but
also include state-year fixed effects. The results are consistent with our prediction. The
28
decrease in capital expenditures and sales growth following the adoption of the good faith
exception is larger for firms that operate in industries with more volatile cash flows.
4.4.3. The Effect of Covariate Balance
Ideally, treatment and control firms should be similar to one another along
dimensions that affect corporate investment and sales growth. As shown previously in Panel
B of Table 2, however, the mean values of firm characteristics between the two samples are
different across several dimensions. As a first pass at controlling for these differences, we
include each firm characteristic as an independent variable in all of our previous tests. Next,
we use a matched sample methodology and examine the robustness of our results to
accounting directly for these differences.
We start by creating a treatment group that consists of all firms headquartered in
states that eventually adopt the good faith exception. We keep all available observations for
treatment firms in the 5-year window around the adoption of this law.18 Our control group
consists of firms headquartered in states that never adopt the good faith exception.19 We
match each treatment firm in year t-1 to a control firm (with replacement), matching on year,
2-digit SIC industry, and book value of assets (with a max difference between book assets of
25%). We further require that a control firm have data available for each of the years that its
matched treatment firm has data available. When treatment firms have multiple control firm
matches, we retain the control firm with the closest book value of assets. Overall, the
matching procedure is successful. Panel A of Table 12 shows that, for the matched sample,
treatment and control firms are similar in terms of size, growth opportunities, cash flow, and
cash holdings.
Next, we use the model specification that includes the full set of control variables and
estimate the effect of the adoption of the good faith exception on investment and sales growth
While the purpose of this test is to match treatment firms to similar control firms, we note that by examining
the effect of the adoption of the good faith exception in the narrower window of 5 years around its adoption, this
test provides further assurance that it is the law’s adoption and not some other factor that drives our findings.
19 We exclude from both treatment and control groups any firm that relocates its headquarters, resulting in the
sample shrinking from 118,314 to 109,140 firm-year observations.
18
29
for the matched sample. Panel B shows a significant decline in both capital expenditures and
sales growth for treatment firms relative to control firms after the adoption of this law.20
4.4.4. The Effect of Geographically Dispersed Operations
Employment laws typically apply in the state where an employee works, and due to
data availability, we match WDLs to a firm’s headquarters state. Consequently, our research
design may better capture the degree of employment protection a firm faces if the firm has
geographically concentrated operations. In this section, we create four measures that proxy
for the geographic dispersion of a firm’s operations and divide the sample into firms with
more and less dispersed operations. We then estimate our main investment and sales growth
regressions for each subsample. Table 13 presents the results of these analyses. Panel A (B)
presents the results for the subsamples of firms with concentrated (dispersed) operations.
Our first measure splits the sample based on whether a firm operates in an industry
in which a large percentage of the workforce is likely geographically dispersed. These
industries include retail, wholesale, and transportation (Agrawal and Matsa, 2013). The
second measure splits the sample into firms with and without foreign operations, which we
proxy for by whether a firm reports non-missing and non-zero foreign income or foreign taxes.
The third measure assumes that smaller firms are more likely to have concentrated
operations and splits the sample based on whether a firm’s book value of assets is above or
below the sample median within its 2-digit SIC industry. The last measure splits the sample
based on whether a firm mentions an above or below median number of different states in its
10-K filing. For this measure, we follow Garcia and Norli (2012) and count the number of
different states a firm mentions in its 10-K in the following sections: “Item 1: Business,” “Item
2: Properties,” “Item 6: Consolidated Financial Data,” and “Item 7: Management’s Discussion
and Analysis.” We are able to obtain this measure from electronic SEC filings for firms
beginning in 1994. We then extend the earliest value of this measure back through the
Because book leverage is significantly different between treatment and control firms in the matched sample,
we further control for potential nonlinear effects of leverage by including higher-order polynomial terms of book
leverage (squared and cubic terms) in the investment and sales growth regressions. The results are robust.
20
30
beginning of each firm’s sample period. We note that this last measure is unavailable for
firms that have no electronic 10-K filings. Overall, the results in Table 13 show that, for the
subsamples of firms that are more likely to have geographically concentrated operations, we
continue to find a decrease in capital expenditures and sales growth following the adoption
of the good faith exception.
4.4.5. Robustness to Dating Schemes for the Enactment of Wrongful Discharge Laws
As we mentioned in Section 2.4, there is some subjectivity in determining which court
cases set the precedent that a court recognizes a particular WDL, resulting in various studies
using different dates for the adoption of each exception. In this section, we examine the
robustness of our main results to using the exact precedent-setting cases and dates provided
by Autor et al. (2006) as well as the cases and dates provided by Walsh and Schwarz (1996),
Dertouzos and Karoly (1992), and Morriss (1995). Table A2 in the online appendix shows that
the decline in investment and sales growth following the adoption of the good faith exception
is robust to using all four alternative dating schemes.
5. Conclusion
There has been a legal shift away from the employment at-will rule in the U.S. toward
providing workers with greater employment protection. This paper exploits plausibly
exogenous increases in employment protection arising from the adoption of U.S. state-level
labor protection laws to examine how greater employment protection affects corporate
investment activity and firm growth. We find that the adoption of these laws results in lower
capital expenditures and investment rates that are less sensitive to changes in investment
opportunities. Firms are also less likely to divest assets following negative cash flow shocks,
and when firms do invest, these investments tend to have higher expected rates of return.
Collectively, these investment and divestiture results are consistent with theories predicting
that greater employment protection increases investment irreversibility, which reduce ex
ante incentives to invest. In addition, we find that lower investment rates following the
adoption of these laws are also associated with lower sales growth rates.
31
Overall, our paper emphasizes the interdependence of investment policies and growth
with labor market frictions and in particular, employment protection. Thus, our study
provides insights into how labor regulations, employee adjustment costs, and litigation risk
can affect corporate investment and performance. We believe that investigating how
employment protection affects other corporate policies may be a fruitful area for future
research.
32
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Figure 1
Number of States Adopting Wrongful Discharge Laws
This figure shows the number of states that have adopted the good faith, implied contract, and public policy exceptions to the traditional employment atwill rule in each year between 1969 and 2003.
Good Faith
Implied Contract
45
40
Number of States
35
30
25
20
15
10
5
0
Year
37
Public Policy
Table 1
Adoption of State-Level Wrongful Discharge Laws
This table reports the month and year when each state adopted the good faith, implied contract, and public
policy exceptions to the traditional employment at-will rule.
State
Alabama
Alaska
Arizona
Arkansas
California
Colorado
Connecticut
Delaware
Florida
Georgia
Hawaii
Idaho
Illinois
Indiana
Iowa
Kansas
Kentucky
Louisiana
Maine
Maryland
Massachusetts
Michigan
Minnesota
Mississippi
Missouri
Montana
Nebraska
Nevada
New Hampshire
New Jersey
New Mexico
New York
North Carolina
North Dakota
Ohio
Oklahoma
Oregon
Pennsylvania
Rhode Island
South Carolina
South Dakota
Tennessee
Texas
Utah
Vermont
Virginia
Washington
West Virginia
Wisconsin
Wyoming
Month/Year
Good Faith
5/1983
6/1985
10/1980
6/1980
4/1992
Month/Year
Implied Contract
7/1987
5/1983
6/1983 (Rev. 4/1984)
6/1984
3/1972
10/1983
10/1985
Month/Year
Public Policy
8/1986
4/1977
12/1974
8/1987
11/1987
8/1984
8/1983
10/1982
4/1977
12/1978
5/1973
7/1985
6/1981
11/1983
11/1977
1/1985
5/1988
6/1980
4/1983
6/1992
1/1983 (Rev. 2/1988)
6/1987
11/1983
8/1983
8/1988
5/1985
2/1980
11/1982
7/1981
5/1980
6/1976
11/1986
7/1987
11/1985
1/1980
11/1987
1/1984
2/1974
7/1980
7/1983
2/1984
4/1982
12/1976
3/1978
5/1985
11/1987
3/1990
2/1989
6/1975
3/1974
6/1987
4/1983
11/1981
4/1985
5/1986
8/1985
9/1983
8/1977
4/1986
6/1985
8/1985
11/1985
12/1988
8/1984
6/1984
3/1989
9/1986
6/1985
7/1984
7/1978
1/1980
7/1989
8/1989
2/1986
6/1985
3/1980
9/1959
9/1985
1/1980
3/1992
1/1998
7/1977
1/1982
2/1987
2/1974 (Rev. 5/1980)
5/1985 (Rev. 2/1989)
3/1989
1/1994
38
Table 2
Summary Statistics
This table reports summary statistics for the main variables in the regression models. Panel A presents
summary statistics for the full sample. Panel B reports univariate results comparing the mean values of
variables for treatment (firms headquartered in states that eventually adopt the good faith exception) and
control firms (firms headquartered in states that never adopt this law). Standard deviations of each variable
are reported in parentheses below the corresponding mean value. In Panel B, *, **, and *** in the column
labeled Treatment Group indicate significance at the 10%, 5%, and 1% levels, respectively, for a t-test of
whether the two samples have equal means. The sample consists of Compustat industrial firms (excluding
financials and utilities) over the 1969 to 2003 period and includes 118,314 firm-year observations. Continuous
variables, except state-level economic variables, are winsorized at their 1st and 99th percentiles, and all dollar
values are expressed in 2009 dollars. Variable definitions refer to Compustat designations where appropriate.
Capext is capital expenditures scaled by beginning of year book value of assets (capxt/att-1). Sales Growtht is
the one-year sales growth rate (salet/salet-1-1). Good Faith is an indicator variable set to one if the state where
a firm is headquartered has adopted the good faith exception by year t and zero otherwise. Implied Contract
and Public Policy are indicator variables set to one if the state where a firm is headquartered has adopted the
implied contract and public policy exceptions by year t and zero otherwise, respectively. Assets is the book value
of assets (at) in millions. Tobin’s Q is the market value of assets (market value of equity plus book value of
assets minus book value of equity minus deferred taxes) divided by book value of assets ((prcc_f×csho+at-ceqtxdb)/at). Cash Flow is income before extraordinary items plus depreciation and amortization divided by book
value of assets ((ib+dp)/at). Cash Holdings is the book value of cash and short-term investments divided by
book value of assets (che/at). Book Leverage is the book value of long-term debt plus debt in current liabilities
divided by book value of assets ((dltt+dlc)/at). P.C. GDP Growth is the one-year growth rate in state-level per
capita GDP. P.C. GDP is the state-level per capita GDP (in thousands). Political Balance is the fraction of a
state’s congress members representing their state in the U.S. House of Representatives that belong to the
Democratic Party in a given year.
Panel A: Summary Statistics for Full Sample
Mean
Std. Dev.
Capext × 100
8.72
10.00
Sales Growtht × 100
20.02
56.43
0.21
0.41
0.61
0.49
P25
Median
P75
2.76
5.60
10.59
-1.46
10.19
24.92
0.00
0.00
0.00
0.00
1.00
1.00
Dependent Variables
Main Explanatory Variable
Good Faith
Control Variables
Implied Contract
Public Policy
0.62
0.49
0.00
1.00
1.00
Assetst-1
1110
3269
41.00
142.5
563.5
Tobin’s Qt-1
1.83
1.68
0.95
1.26
1.96
Cash Flowt-1
0.04
0.19
0.03
0.08
0.12
Cash Holdingst-1
0.14
0.18
0.03
0.07
0.19
Book Leveraget-1
0.24
0.20
0.07
0.22
0.36
P.C. GDP Growtht-1
0.02
0.03
0.00
0.02
0.04
P.C. GDPt-1
37.03
6.76
31.98
36.39
41.78
Political Balancet-1
0.58
0.18
0.50
0.58
0.67
39
Table 2 – (Continued)
Panel B: Comparing Sample Means for Treatment and Control Firms
Treatment Group
Obs. = 33,557
Control Group
Obs. = 84,757
8.72
(10.16)
8.72
(9.95)
22.59***
(62.52)
19.01
(53.79)
0.77***
(0.42)
0.55
(0.50)
Public Policy
0.85***
(0.36)
0.53
(0.50)
Assetst-1
873.9***
(2879)
1202.9
(3405)
Tobin’s Qt-1
2.10***
(1.97)
1.72
(1.54)
Cash Flowt-1
0.01***
(0.22)
0.05
(0.17)
Cash Holdingst-1
0.19***
(0.22)
0.13
(0.16)
Book Leveraget-1
0.22***
(0.20)
0.25
(0.20)
0.02
(0.03)
0.02
(0.03)
P.C. GDPt-1
39.68***
(7.12)
35.99
(6.32)
Political Balancet-1
0.59***
(0.22)
0.58
(0.16)
Dependent Variables
Capext × 100
Sales Growtht × 100
Control Variables
Implied Contract
P.C. GDP Growtht-1
40
Table 3
Employment Protection and Corporate Investment
This table reports the results from OLS regressions relating corporate investment to the adoption of the good
faith exception for Compustat industrial firms from 1969 to 2003. The dependent variable Capext in columns
1-4 is capital expenditures scaled by beginning of year book value of assets. Good Faith is an indicator variable
set to one if the state where a firm is headquartered has adopted the good faith exception by year t and zero
otherwise. Table 2 provides definitions of control variables. Industry fixed effects are defined at the 2-digit SIC
level. Standard errors in parentheses are clustered by state. *, **, and *** denote significance at the 10%, 5%,
and 1% levels, respectively.
Good Faith
(1)
-0.50**
(0.20)
Capext × 100
(2)
(3)
-0.44**
-0.49**
(0.21)
(0.23)
(4)
-0.61***
(0.22)
Implied Contract
-0.04
(0.17)
-0.03
(0.14)
-0.06
(0.15)
-0.07
(0.13)
Public Policy
0.07
(0.24)
0.06
(0.21)
0.12
(0.22)
0.10
(0.16)
Ln(Assets)t-1
-1.93***
(0.15)
-1.69***
(0.14)
-1.69***
(0.14)
Tobin's Qt-1
1.30***
(0.07)
1.25***
(0.07)
1.25***
(0.07)
Cash Flowt-1
6.44***
(0.75)
4.88***
(0.62)
4.86***
(0.62)
Cash Holdingst-1
1.22***
(0.42)
1.20***
(0.42)
Book Leveraget-1
-6.09***
(0.36)
-6.11***
(0.36)
P.C. GDP Growtht-1
10.14***
(1.30)
Ln(P.C. GDP)t-1
1.58*
(0.79)
Political Balancet-1
0.50
(0.32)
Industry × Year FEs
Firm FEs
Observations
Adjusted R2
Yes
Yes
118,314
0.457
Yes
Yes
118,314
0.497
41
Yes
Yes
118,314
0.503
Yes
Yes
118,314
0.503
Table 4
Employment Protection and Sensitivity to Investment Opportunities
This table reports the results from OLS regressions relating the sensitivity of investment rates to investment
opportunities to the adoption of the good faith exception for Compustat industrial firms from 1969 to 2003. The
dependent variable Capext in columns 1 and 2 is capital expenditures scaled by beginning of year book value of
assets. Good Faith is an indicator variable set to one if the state where a firm is headquartered has adopted
the good faith exception by year t and zero otherwise. Tobin’s Q is market value of assets divided by book value
of assets. Sales Growth is the one-year percentage increase in sales. Table 2 provides definitions of control
variables. Industry fixed effects are defined at the 2-digit SIC level. Standard errors in parentheses are
clustered by state. *, **, and *** denote significance at the 10%, 5%, and 1% levels, respectively.
Capext × 100
(1)
Tobin's Qt-1
1.36***
(0.08)
Good Faith × Tobin's Qt-1
-0.31***
(0.08)
(2)
Sales Growtht-1
1.01***
(0.13)
Good Faith × Sales Growtht-1
-0.42**
(0.16)
Good Faith
-0.12
(0.25)
-0.63***
(0.23)
Implied Contract
-0.06
(0.13)
-0.12
(0.14)
Public Policy
0.10
(0.16)
0.11
(0.15)
Ln(Assets)t-1
-1.68***
(0.14)
-2.03***
(0.14)
Cash Flowt-1
4.86***
(0.61)
4.74***
(0.65)
Cash Holdingst-1
1.21***
(0.41)
2.49***
(0.38)
Book Leveraget-1
-6.08***
(0.36)
-6.27***
(0.39)
P.C. GDP Growtht-1
10.24***
(1.32)
11.03***
(1.38)
Ln(P.C. GDP)t-1
1.53*
(0.80)
1.94**
(0.80)
Political Balancet-1
0.47
(0.31)
0.50
(0.33)
Yes
Yes
118,314
0.504
Yes
Yes
115,490
0.489
Industry × Year FEs
Firm FEs
Observations
Adjusted R2
42
Table 5
Employment Protection, Employment, and Divestitures
This table reports the results from OLS regressions relating asset divestitures and employment outcomes to the adoption of the good faith exception for
Compustat industrial firms from 1969 to 2003. The dependent variables in columns 1-6 are as follows: Large Decr. in Employees is an indicator variable
set to one if the one-year percentage decrease in a firm’s number of employees ((empt-empt-1)/empt-1) is greater than or equal to 15% and zero otherwise;
Decr. in Employees is the one-year percentage decrease in a firm’s number of employees, with employment gains (positive percentage changes) set to zero;
Large Decr. in PP&E is an indicator variable set to one if the one-year percentage decrease in PP&E ((ppentt-ppentt-1)/ppentt-1) is greater than or equal to
15% and zero otherwise; Decr. in PP&E is the one-year percentage decrease in PP&E, with increases in PP&E set to zero; Large Sale of PP&E is an
indicator variable set to one if the dollar value of the funds received from the sale of PP&E over year t scaled by beginning of year book value of PP&E
(sppet/ppentt-1) is greater than or equal to 15% and zero otherwise; Sale of PP&E is the dollar value of funds received from the sale of PP&E over year t
scaled by beginning of year book value of PP&E. Good Faith is an indicator variable set to one if the state where a firm is headquartered has adopted the
good faith exception by year t and zero otherwise. Decr. in Cash Flow is the one-year change in Cash Flow, with positive changes set to zero. Control
variables include Ln(Assets)t-1, Tobin’s Qt-1, Cash Flowt-1, Cash Holdingst-1, Book Leveraget-1, Implied Contract, Public Policy, P.C. GDP Growtht-1, Ln(P.C.
GDP)t-1, and Political Balancet-1. Table 2 provides definitions of all variables. Industry fixed effects are defined at the 2-digit SIC level. Standard errors in
parentheses are clustered by state. *, **, and *** denote significance at the 10%, 5%, and 1% levels, respectively.
(1)
Decr. in
Employees
× 100
(2)
(3)
(4)
(5)
(6)
Decr. in Cash Flowt
-0.68***
(0.03)
27.98***
(1.00)
-0.58***
(0.03)
27.26***
(1.38)
-0.08***
(0.01)
-3.44***
(0.39)
Good Faith × Decr. in Cash Flowt
0.11***
(0.04)
-4.21***
(1.17)
0.15***
(0.04)
-7.31***
(2.09)
0.03**
(0.01)
1.44**
(0.59)
Good Faith
<0.01
(<0.01)
-0.24
(0.17)
0.02***
(<0.01)
-0.55***
(0.18)
-0.01
(0.01)
-0.39
(0.28)
Control Variables
Industry × Year FEs
Firm FEs
Observations
Adjusted R2
Yes
Yes
Yes
112,956
0.184
Yes
Yes
Yes
112,956
0.241
Yes
Yes
Yes
117,993
0.250
Yes
Yes
Yes
117,993
0.301
Yes
Yes
Yes
89,675
0.137
Yes
Yes
Yes
89,675
0.199
Large Decr. in
Employees
Large Decr.
in PP&E
43
Decr. in PP&E
× 100
Large Sale
of PP&E
Sale of PP&E
× 100
Table 6
Employment Protection and the Value of Capital Expenditures
This table reports the results from OLS regressions relating the marginal value of capital expenditures to the
adoption of the good faith exception for Compustat industrial firms from 1969 to 2003. Variable definitions
refer to Compustat designations where appropriate. The dependent variable Tobin’s Q is the sum of market
value of equity and total debt divided by book value of assets ((prcc_f*csho+dltt+dlc)/at). Good Faith is an
indicator variable set to one if the state where a firm is headquartered has adopted the good faith exception by
year t and zero otherwise. The firm-level variables are: ΔCapext, ΔCapext+1, Capext-1, Earningst, ΔEarningst,
ΔEarningst+1, ΔAssetst, ΔAssetst+1, RDt, ΔRDt, ΔRDt+1, Interestt, ΔInterestt, ΔInterestt+1, Dividendst, ΔDividendst,
ΔDividendst+1, and ΔTobin’s Qt+1. These variables are defined as follows: Capex (capital expenditures (capx)),
Earnings (earnings before extraordinary items (ib) plus interest (xint), deferred tax credits (txdi), and
investment tax credits (itci)), Assets (book value of assets (at)), RD (research and development expenditures
(xrd)), Interest (interest expense (xint)), and Dividends (common dividends paid (dvc)). ΔXt is the change in
variable X from year t-1 to t (Xt-Xt-1). ΔXt+1 is the change in variable X from year t to t+1 (Xt+1-Xt). All firm-level
variables are deflated by book assets (at) in year t. All models also control for Implied Contract, Public Policy,
P.C. GDP Growtht-1, Ln(P.C. GDP)t-1, and Political Balancet-1. Industry fixed effects are defined at the 2-digit
SIC level. Continuous variables, except state-level economic variables, are winsorized at their 1st and 99th
percentiles, and all dollar values are expressed in 2009 dollars. Standard errors in parentheses are clustered
by state. *, **, and *** denote significance at the 10%, 5%, and 1% levels, respectively.
Dependent Variable = Tobin’s Qt
(1)
(2)
ΔCapext
1.08***
(0.16)
1.78***
(0.21)
Good Faith × ΔCapext
0.43**
(0.17)
0.46***
(0.17)
Capext-1
1.50***
(0.18)
2.20***
(0.24)
ΔCapext+1
Good Faith
Control Variables
Industry × Year FEs
Firm FEs
Observations
Adjusted R2
1.14***
(0.16)
-0.01
(0.02)
-0.01
(0.02)
Yes
Yes
Yes
105,926
0.692
Yes
Yes
Yes
105,926
0.693
44
Table 7
Employment Protection and Sales Growth
This table reports the results from OLS regressions relating sales growth to the adoption of the good faith
exception for Compustat industrial firms from 1969 to 2003. The dependent variable Sales Growtht in columns
1-4 is the one-year percentage increase in sales. Good Faith is an indicator variable set to one if the state where
a firm is headquartered has adopted the good faith exception by year t and zero otherwise. Table 2 provides
definitions of control variables. Industry fixed effects are defined at the 2-digit SIC level. Standard errors in
parentheses are clustered by state. *, **, and *** denote significance at the 10%, 5%, and 1% levels, respectively.
Sales Growtht × 100
(2)
(3)
(1)
Good Faith
(4)
-3.09***
(0.96)
-0.24
(0.94)
-2.70***
(0.92)
-0.19
(0.74)
-3.09***
(1.00)
-0.19
(0.68)
-2.99***
(1.11)
-0.06
(0.69)
-1.13
(1.03)
-1.19
(0.88)
-0.94
(0.84)
-0.95
(0.80)
Ln(Assets)t-1
-10.32***
(0.69)
-9.40***
(0.65)
-9.40***
(0.66)
Tobin's Qt-1
7.70***
(0.31)
6.98***
(0.30)
6.97***
(0.30)
Cash Flowt-1
-23.38***
(2.03)
-28.93***
(1.95)
-28.99***
(1.97)
Cash Holdingst-1
55.79***
(3.61)
55.80***
(3.60)
Book Leveraget-1
-5.81**
(2.17)
-5.83***
(2.17)
Implied Contract
Public Policy
P.C. GDP Growtht-1
17.15**
(6.40)
Ln(P.C. GDP)t-1
-2.15
(4.85)
Political Balancet-1
-1.62
(1.77)
Industry × Year FEs
Firm FEs
Observations
Adjusted R2
Yes
Yes
118,314
0.195
Yes
Yes
118,314
0.237
45
Yes
Yes
118,314
0.248
Yes
Yes
118,314
0.248
Table 8
Employment Protection and the Timing of Changes in Investment and Growth
This table reports the results from OLS regressions relating corporate investment and sales growth to the
adoption of the good faith exception for Compustat industrial firms from 1969 to 2003. The dependent variable
Capext in column 1 is capital expenditures scaled by beginning of year book value of assets. The dependent
variable Sales Growtht in column 2 is the one-year percentage increase in sales. Good Faith (-2,-1) is an
indicator variable set to one if a firm is headquartered in a state that will adopt the good faith exception in one
or two years and zero otherwise. Good Faith (0) is an indicator variable set to one if a firm is headquartered in
a state that adopts the good faith exception this year and zero otherwise. Good Faith (+1) is an indicator
variable set to one if a firm is headquartered in a state that adopted the good faith exception one year ago and
zero otherwise. Good Faith(≥+2) is an indicator variable set to one if a firm is headquartered in a state that
adopted the good faith exception two or more years ago and zero otherwise. Table 2 provides definitions of
control variables. Industry fixed effects are defined at the 2-digit SIC level. Standard errors in parentheses are
clustered by state. *, **, and *** denote significance at the 10%, 5%, and 1% levels, respectively.
Capext × 100
(1)
Sales Growtht × 100
(2)
Good Faith (-2,-1)
-0.34
(0.25)
-0.19
(1.61)
Good Faith (0)
-0.58*
(0.34)
-1.08
(2.23)
Good Faith (+1)
-0.67*
(0.38)
-3.32
(2.32)
Good Faith (≥+2)
-0.76***
(0.26)
-3.74***
(1.16)
Implied Contract
-0.10
(0.13)
-0.19
(0.67)
Public Policy
0.09
(0.16)
-0.88
(0.80)
Ln(Assets)t-1
-1.71***
(0.14)
-9.47***
(0.66)
Tobin's Qt-1
1.25***
(0.07)
6.94***
(0.31)
Cash Flowt-1
4.84***
(0.62)
-28.75***
(1.97)
Cash Holdingst-1
1.21***
(0.42)
55.76***
(3.60)
Book Leveraget-1
-6.11***
(0.36)
-5.98***
(2.18)
P.C. GDP Growtht-1
10.37***
(1.32)
16.78**
(6.29)
Ln(P.C. GDP)t-1
1.59**
(0.76)
-1.96
(4.88)
Political Balancet-1
0.50
(0.33)
-1.53
(1.79)
Yes
Yes
117,348
0.503
Yes
Yes
117,348
0.248
Industry × Year FEs
Firm FEs
Observations
Adjusted R2
46
Table 9
Determinants of Adopting the Good Faith Exception
This table reports the results from a Cox proportional hazard model analyzing the hazard of a state court
adopting the good faith exception. The sample period is from 1969 to 2003. A “failure event” is the adoption of
the good faith exception in a given state. States are excluded from the sample once they adopt the law.
Explanatory variables are measured at the state-level as of year t-1 and include the following variables: P.C.
GDP Growth is the one-year growth rate in per capita GDP; P.C. GDP is per capita GDP in thousands; Political
Balance is the fraction of a state’s congress members representing their state in the U.S. House of
Representatives that belong to the Democratic Party in a given year; Implied Contract and Public Policy are
indicator variables set to one if the state where a firm is headquartered has adopted the implied contract and
public policy exceptions by year t and zero otherwise, respectively; Unemployment Rate is the fraction of
workers in the labor force that are unemployed; Δ Unemployment Rate is the one-year change in Unemployment
Rate; Right-to-Work is an indicator variable set to one if a firm is headquartered in a state that has passed
right-to-work laws by year t and zero otherwise; Union Membership is the fraction of nonagricultural wage and
salary employees who are covered by a collective bargaining agreement; Δ Union Membership is the one-year
change in Union Membership; Circuit States’ Good Faith, Circuit States’ Implied Contract, and Circuit States’
Public Policy are the fraction of other states in the same federal circuit region that have adopted the good faith,
implied contract, and public policy exceptions by year t, respectively. All independent variables, except
indicator variables, are standardized to have a mean of zero and a standard deviation of one. Dollar values are
expressed in 2009 dollars. Standard errors in parentheses are clustered by state. *, **, and *** denote
significance at the 10%, 5%, and 1% levels, respectively.
(1)
-0.12
(0.17)
(2)
-0.13
(0.16)
(3)
0.02
(0.20)
(4)
-0.05
(0.17)
0.46***
(0.15)
0.44***
(0.16)
0.55***
(0.19)
0.60***
(0.22)
Political Balance
-0.17
(0.27)
-0.18
(0.30)
-0.12
(0.29)
0.01
(0.30)
Implied Contract
-0.04
(0.71)
-0.12
(0.68)
-0.08
(0.63)
-0.23
(0.83)
Public Policy
-0.44
(0.83)
-0.47
(0.80)
-0.45
(0.87)
-0.66
(1.08)
Unemployment Rate
0.20
(0.26)
0.30
(0.31)
0.26
(0.36)
Δ Unemployment Rate
-0.56
(0.40)
-0.43
(0.41)
-0.31
(0.37)
Right-to-Work
0.03
(0.75)
-0.01
(0.70)
Union Membership
-0.39
(0.37)
-0.82*
(0.45)
Δ Union Membership
-0.58*
(0.31)
-0.44
(0.29)
P.C. GDP Growth
Ln(P.C. GDP)
Circuit States’ Good Faith
0.42*
(0.22)
Circuit States’ Implied Contract
0.61
(0.44)
Circuit States’ Public Policy
0.80*
(0.48)
Year Fixed Effects
Observations
Pseudo R2
Yes
1,489
0.059
47
Yes
1,489
0.082
Yes
1,489
0.136
Yes
1,489
0.199
Table 10
Robustness: Controls for Economic Factors
This table reports the results from OLS regressions relating corporate investment and sales growth to the
adoption of the good faith exception for Compustat industrial firms from 1969 to 2003. The dependent variable
Capext in columns 1 and 2 is capital expenditures scaled by beginning of year book value of assets. The
dependent variable Sales Growtht in columns 3 and 4 is the one-year percentage increase in sales. Good Faith
is an indicator variable set to one if the state where a firm is headquartered has adopted the good faith exception
by year t and zero otherwise. State-level variables are defined in Table 9, and firm-level variables are defined
in Table 2. Industry fixed effects are defined at the 2-digit SIC level. Standard errors in parentheses are
clustered by state. *, **, and *** denote significance at the 10%, 5%, and 1% levels, respectively.
Good Faith
Implied Contract
Public Policy
Ln(Assets)t-1
Tobin's Qt-1
Cash Flowt-1
Cash Holdingst-1
Book Leveraget-1
P.C. GDP Growtht-1
Ln(P.C. GDP)t-1
Political Balancet-1
Union Membershipt-1
Δ Union Membershipt-1
Circuit States’ Good Faitht-1
Circuit States’ Public Policyt-1
Circuit States’ Implied Contractt-1
Unemployment Ratet-1
Δ Unemployment Ratet-1
Right-to-Work Lawst-1
Industry × Year FEs
Firm FEs
Observations
Adjusted R2
Capext × 100
(1)
(2)
-0.57**
-0.66***
(0.21)
(0.22)
-0.07
-0.06
(0.13)
(0.13)
0.03
0.07
(0.17)
(0.16)
-1.69***
-1.69***
(0.14)
(0.14)
1.25***
1.25***
(0.07)
(0.07)
4.85***
4.84***
(0.62)
(0.62)
1.20***
1.20***
(0.42)
(0.42)
-6.13***
-6.14***
(0.36)
(0.36)
9.77***
9.83***
(1.39)
(1.31)
1.83**
1.19
(0.78)
(0.86)
0.42
0.36
(0.30)
(0.30)
-2.46**
-2.22*
(1.20)
(1.18)
0.89
0.62
(1.95)
(1.95)
-0.48
-0.40
(0.39)
(0.38)
0.27
0.16
(0.27)
(0.25)
0.38*
(0.20)
-5.98**
(2.85)
5.07**
(2.21)
-0.17
(0.17)
Yes
Yes
Yes
Yes
118,314
118,314
0.503
0.503
48
Sales Growtht × 100
(3)
(4)
-2.77**
-3.23**
(1.10)
(1.28)
-0.03
-0.06
(0.66)
(0.67)
-1.22
-0.98
(0.92)
(0.86)
-9.39***
-9.39***
(0.65)
(0.65)
6.97***
6.97***
(0.30)
(0.30)
-29.01***
-29.06***
(1.97)
(1.97)
55.80***
55.80***
(3.61)
(3.60)
-5.92***
-5.96***
(2.16)
(2.16)
15.30**
15.84**
(6.58)
(6.44)
-0.79
-2.53
(4.86)
(4.91)
-1.98
-2.38
(1.78)
(1.84)
-15.38*
-16.31**
(7.69)
(7.36)
7.96
8.41
(16.39)
(16.12)
-2.08
-1.49
(1.60)
(1.78)
1.14
0.37
(1.58)
(1.54)
2.52
(1.77)
-14.79
(19.82)
18.20
(15.35)
-1.20
(1.31)
Yes
Yes
Yes
Yes
118,314
118,314
0.248
0.248
Table 11
Cross-Sectional Variation in Employment Protection
This table reports the results from OLS regressions relating corporate investment and sales growth to the
adoption of the good faith exception for Compustat industrial firms from 1969 to 2003. The dependent variable
Capext in columns 1 and 2 is capital expenditures scaled by beginning of year book value of assets. The
dependent variable Sales Growtht in columns 3 and 4 is the one-year percentage increase in sales. Good Faith
is an indicator variable set to one if the state where a firm is headquartered has adopted the good faith exception
by year t and zero otherwise. Ind. Cash Flow Vol. is the mean cash flow volatility across all firms in the same
3-digit SIC industry and year, where cash flow volatility is the standard deviation of the ratio of income before
extraordinary items plus depreciation and amortization to book assets over years t-10 to t-1 (firms must have
at least three years of data to enter the industry average calculation). Ind. Cash Flow Vol. is standardized to
have a mean of zero and a standard deviation of one to ease the interpretation of coefficient estimates. Industry
fixed effects are defined at the 2-digit SIC level. Standard errors in parentheses are clustered by state. *, **,
and *** denote significance at the 10%, 5%, and 1% levels, respectively.
Capext × 100
(1)
(2)
-0.65***
(0.21)
Sales Growtht × 100
(3)
(4)
-3.46***
(1.07)
-0.30***
(0.10)
-0.24*
(0.13)
-3.74***
(0.65)
-2.81***
(0.93)
Ind. Cash Flow Vol.
-0.02
(0.09)
-0.03
(0.09)
-1.81***
(0.49)
-1.92***
(0.54)
Implied Contract
-0.06
(0.13)
-0.02
(0.60)
0.07
(0.70)
0.83
(2.75)
Public Policy
0.09
(0.16)
0.84
(0.84)
-1.06
(0.80)
3.45
(3.05)
Ln(Assets)t-1
-1.68***
(0.14)
-1.67***
(0.14)
-9.32***
(0.65)
-9.43***
(0.63)
Tobin's Qt-1
1.24***
(0.06)
1.23***
(0.06)
6.95***
(0.30)
6.89***
(0.32)
Cash Flowt-1
4.85***
(0.61)
4.78***
(0.60)
-29.26***
(1.95)
-29.33***
(1.90)
Cash Holdingst-1
1.22***
(0.43)
1.19***
(0.43)
55.81***
(3.73)
55.81***
(3.63)
Book Leveraget-1
-6.11***
(0.36)
-6.12***
(0.36)
-5.94***
(2.18)
-5.89***
(2.16)
P.C. GDP Growtht-1
10.16***
(1.30)
17.20***
(6.15)
Ln(P.C. GDP)t-1
1.58**
(0.77)
-2.25
(4.16)
Political Balancet-1
0.54
(0.32)
-1.19
(1.76)
Good Faith
Good Faith × Ind. Cash Flow Vol.
State × Year FEs
Industry × Year FEs
Firm FEs
Observations
Adjusted R2
No
Yes
Yes
118,234
0.504
49
Yes
Yes
Yes
118,172
0.506
No
Yes
Yes
118,234
0.248
Yes
Yes
Yes
118,172
0.248
Table 12
The Effect of Using a Matched Sample
This table reports the results from OLS regressions relating corporate investment and sales growth to the
adoption of the good faith exception using a matched sample and the window ±5 years around the adoption of the
law. The treatment and control groups consist of firms headquartered in states that adopt and do not adopt the
good faith exception, respectively. We match each treatment firm in year t-1 to a control firm (with replacement),
matching on year, 2-digit SIC industry, and closest beginning of year book value of assets (with a max difference
between book assets of 25%). Panel A tabulates the means of the matched variables for the treatment and control
groups in year t-1. *, **, *** in the column labeled Treatment Group indicate significance at the 10%, 5%, and 1%
levels, respectively, for a t-test of whether the two samples have equal means. Panel B presents the results
examining the effect of the adoption of the good faith exception on capital expenditures and sales growth.
Treatment is an indicator variable set to one if the firm is headquartered in a state that adopts the law. Post is an
indicator variable set to one in the years after the adoption of the law (same for matched control firms). Control
variables include Ln(Assets)t-1, Tobin’s Qt-1, Cash Flowt-1, Cash Holdingst-1, Book Leveraget-1, Implied Contract,
Public Policy, P.C. GDP Growtht-1, Ln(P.C. GDP)t-1, and Political Balancet-1. Table 2 provides definitions of all
variables. Industry fixed effects are defined at the 2-digit SIC level. Standard errors in parentheses are clustered
by state. *, **, and *** denote significance at the 10%, 5%, and 1% levels, respectively.
Panel A: Comparison of Means across Samples
Matched Sample Year t-1
Treatment Group
Obs. = 625
Ln(Assets)
Tobin's Q
Cash Flow
Cash Holdings
Book Leverage
Control Group
Obs. = 625
4.881
1.420
0.077
0.102
0.254***
4.878
1.407
0.077
0.108
0.228
Panel B: Effect of the Adoption of the Good Faith Exception
Matched Sample
Capext × 100
(1)
Sales Growtht × 100
(2)
-0.80**
(0.32)
-5.02***
(1.66)
Post
0.28
(0.31)
3.11
(1.87)
Control Variables
Industry × Year FEs
Firm FEs
Observations
Adjusted R2
Yes
Yes
Yes
11,080
0.551
Yes
Yes
Yes
11,080
0.233
Treatment × Post
50
Table 13
The Effect of Geographically Dispersed Operations
This table reports the results from OLS regressions relating corporate investment and sales growth to the adoption of the good faith exception for
Compustat industrial firms from 1969 to 2003. The dependent variables in columns 1-4 and 5-8 are Capext and Sales Growtht, respectively. Good Faith is
an indicator variable set to one if the state where a firm is headquartered has adopted the good faith exception by year t and zero otherwise. Panel A (B)
examines the effect of the adoption of the law on samples of firms with more geographically concentrated (dispersed) operations. Columns 1 and 5 split the
sample based on whether a firm operates in a geographically dispersed industry, which includes retail, wholesale, and transportation. Columns 2 and 6
split the sample based on whether a firm has foreign operations. We classify a firm as having foreign operations if it reports non-missing and non-zero
foreign income (pifo) or foreign taxes (txfo). Columns 3 and 7 split the sample into small and large firms based on whether the firm’s book value of assets
is above or below the sample median within its 2-digit SIC industry. Columns 4 and 8 split the sample based on the number of different states a firm
mentions in its 10-K filing. We classify firms with more geographically concentrated operations as those that mention four or fewer different states (five
is the sample median). Control variables include Ln(Assets)t-1, Tobin’s Qt-1, Cash Flowt-1, Cash Holdingst-1, Book Leveraget-1, Implied Contract, Public Policy,
P.C. GDP Growtht-1, Ln(P.C. GDP)t-1, and Political Balancet-1. Table 2 provides definitions of all variables. Industry fixed effects are defined at the 2-digit
SIC level. Standard errors in parentheses are clustered by state. *, **, and *** denote significance at the 10%, 5%, and 1% levels, respectively.
Panel A: Effect of the Adoption of the Good Faith Exception on Firms with More Geographically Concentrated Operations
Capext × 100
Sales Growtht × 100
Non-Disp.
No Foreign
Small
Low Geo.
Non-Disp.
No Foreign
Small
Industries
Operations
Firms
Dispersion
Industries
Operations
Firms
(1)
(2)
(3)
(4)
(5)
(6)
(7)
Good Faith
-0.64***
-0.90***
-0.67***
-0.81*
-2.85**
-4.82***
-4.46**
(0.27)
(0.27)
(0.25)
(0.42)
(1.28)
(1.09)
(1.82)
Control Variables
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Industry × Year FEs
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Firm FEs
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Observations
95,128
78,398
58,417
36,713
95,128
78,398
58,417
Adjusted R2
0.495
0.495
0.458
0.483
0.240
0.241
0.223
Low Geo.
Dispersion
(8)
-4.06**
(1.65)
Yes
Yes
Yes
36,713
0.263
Panel B: Effect of the Adoption of the Good Faith Exception on Firms with More Geographically Dispersed Operations
Capext × 100
Sales Growtht × 100
Disp.
Foreign
Large
High Geo.
Disp.
Foreign
Large
Industries
Operations
Firms
Dispersion
Industries
Operations
Firms
(1)
(2)
(3)
(4)
(5)
(6)
(7)
Good Faith
-0.33
-0.35
-0.79***
-0.37
-2.21
-0.08
-1.42
(0.57)
(0.22)
(0.27)
(0.36)
(1.64)
(1.09)
(0.91)
Control Variables
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Industry × Year FEs
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Firm FEs
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Observations
23,045
38,839
58,417
39,511
23,045
38,839
58,417
Adjusted R2
0.548
0.578
0.612
0.526
0.548
0.578
0.612
High Geo.
Dispersion
(8)
1.14
(2.01)
Yes
Yes
Yes
39,511
0.282
51
EMPLOYMENT PROTECTION, INVESTMENT,
AND FIRM GROWTH
Online Appendix
Table A1
Employment Protection and Corporate Innovation
This table reports the results from OLS regressions relating corporate innovation to the adoption of the good
faith exception for Compustat industrial firms from 1976 to 2003. The dependent variable Ln(1 + # of Patents)t
in column 1 is the natural logarithm of one plus the number of patents a firm files in a given year that are
eventually granted. The dependent variable Ln(1 + # of Citations)t in column 2 is the natural logarithm of one
plus the number of citations a patent receives in subsequent years. Good Faith is an indicator variable set to
one if the state where a firm is headquartered has adopted the good faith exception by year t and zero otherwise.
Table 2 provides definitions of control variables. Industry fixed effects are defined at the 2-digit SIC level.
Standard errors in parentheses are clustered by state. *, **, and *** denote significance at the 10%, 5%, and
1% levels, respectively.
Ln(1 + # of Patents)t
× 100
(1)
Ln(1 + # of Citations)t
× 100
(2)
7.48***
(2.04)
9.93**
(4.14)
Implied Contract
3.81*
(2.13)
7.36
(4.41)
Public Policy
-2.32
(2.21)
-3.18
(4.09)
Ln(Assets)t-1
22.55***
(2.67)
35.13***
(3.22)
Tobin's Qt-1
2.94***
(0.38)
4.45***
(0.71)
Cash Flowt-1
-10.23***
(2.50)
-10.69**
(4.11)
Cash Holdingst-1
0.17
(3.46)
11.42*
(6.34)
Book Leveraget-1
-19.11***
(3.25)
-33.97***
(5.73)
P.C. GDP Growtht-1
36.02*
(19.21)
51.10*
(26.85)
Ln(P.C. GDP)t-1
-45.14*
(24.80)
-87.33**
(37.00)
Political Balancet-1
-3.70
(4.65)
-10.28
(8.13)
Industry × Year FEs
Firm FEs
Observations
Adjusted R2
Yes
Yes
66,441
0.823
Yes
Yes
66,441
0.725
Good Faith
1
Table A2
The Effect of Alternative Dating Schemes
This table reports the results from OLS regressions relating corporate investment and sales growth to the
adoption of the good faith exception for Compustat industrial firms from 1969 to 2003. The dependent variable
Capext in Panel A is capital expenditures scaled by beginning of year book value of assets. The dependent
variable Sales Growtht in Panel B is the one-year percentage increase in sales. Good Faith is an indicator
variable set to one if the state where a firm is headquartered has adopted the good faith exception by year t
and zero otherwise. In columns 1-4, Good Faith, Implied Contract, and Public Policy are defined using the
precedent-setting cases identified in Autor, Donohue, and Schwab (2006), Walsh and Schwarz (1996),
Dertouzos and Karoly (1992), and Morriss (1995), respectively. Because Walsh and Schwarz (1996), Dertouzos
and Karoly (1992), and Morriss (1995) are earlier studies, they do not code some states as adopting WDLs in
the 1990s. To account for this issue, we assume that these three studies would have coded these events using
the same precedent setting court cases and dates as Autor, Donohue, and Schwab (2006). Control variables
include Implied Contract, Public Policy, Ln(Assets)t-1, Tobin’s Qt-1, Cash Flowt-1, Cash Holdingst-1, Book
Leveraget-1, P.C. GDP Growtht-1, Ln(P.C. GDP)t-1, and Political Balancet-1. Table 2 provides definitions of control
variables. Industry fixed effects are defined at the 2-digit SIC level. Standard errors in parentheses are
clustered by state. *, **, and *** denote significance at the 10%, 5%, and 1% levels, respectively.
Panel A: Dependent Variable is Capital Expenditures
Good Faith
Control Variables
Industry × Year FEs
Firm FEs
Observations
Adjusted R2
ADS (2006)
(1)
-0.53**
(0.21)
Yes
Yes
Yes
118,314
0.503
Capext × 100
WS (1996)
DK (1992)
(2)
(3)
-0.42*
-0.46**
(0.23)
(0.21)
Yes
Yes
Yes
118,314
0.503
Yes
Yes
Yes
118,314
0.503
Morriss (1995)
(4)
-0.54**
(0.21)
Yes
Yes
Yes
118,314
0.503
Panel B: Dependent Variable is Sales Growth
Good Faith
Control Variables
Industry × Year FEs
Firm FEs
Observations
Adjusted R2
ADS (2006)
(1)
-2.76**
(1.16)
Yes
Yes
Yes
118,314
0.248
Sales Growtht × 100
WS (1996)
DK (1992)
(2)
(3)
-3.14***
-2.75**
(1.07)
(1.26)
Yes
Yes
Yes
118,314
0.248
2
Yes
Yes
Yes
118,314
0.248
Morriss (1995)
(4)
-2.89***
(1.03)
Yes
Yes
Yes
118,314
0.248
Online Appendix References
Autor, D.H., J.J. Donohue III, and S.J. Schwab. 2006. The costs of wrongful-discharge laws.
Review of Economics and Statistics 88:211-231.
Dertouzos, J.N., and L.A. Karoly. 1992. Labor-market responses to employer liability. Rand
Corporation R-3989-ICJ.
Morriss, A.P. 1995. Developing a framework for empirical research on the common law:
General principles and case studies of the decline of employment at-will. Case Western
Reserve Law Review 45:999-1148.
Walsh, D.J., and J.L. Schwarz. 1996. State common law wrongful discharge doctrines: Update, refinement, and rationales. American Business Law Journal 33:645-689.
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