Labor Laws and Innovation1
Viral V. Acharya
London Business School, NYU-Stern, CEPR and NBER
[email protected]
Ramin P. Baghai
Krishnamurthy V. Subramanian
London Business School
Emory University
[email protected]
[email protected]
First draft: November 18, 2008
This draft: June 8, 2009
1
We are grateful to Amit Seru, Vikrant Vig, and seminar and conference participants at American Law and
Economics Annual Meeting (2009), Cambridge University (Centre for Business Research), Emory University,
London Business School, NYU Microeconomics seminar, and NYU Stern School of Business for valuable
comments and suggestions. We thank Hanh Le for excellent research assistance.
Abstract
Labor Laws and Innovation
Can stringent labor laws be efficient? Possibly, if they provide firms with a commitment device
to not punish employees’ short-run failures and thereby spur the pursuit of value-maximizing innovative activities. In this paper, we provide empirical evidence that strong dismissal laws indeed
appear to have ex ante a positive incentive effect by encouraging the innovative pursuits of firms
and their employees. Using patents and citations as proxies for innovation and employing a timevarying index of labor laws, we find that innovation is fostered by stringent laws governing dismissal
of employees. We provide this evidence using panel regressions with fixed effects and difference-indifference regressions that exploit staggered country-level law changes. We also find that stringent
dismissal laws disproportionately influence innovation in the more innovation-intensive sectors of
the economy.
JEL: F30, G31, J5, J8, K31.
Keywords: Dismissal laws, R&D, Technological change, Law and finance, Entrepreneurship, Growth.
1
Introduction
Do legal institutions of an economy affect the pattern of its real investments, and, in turn, its
economic growth? In this paper, we focus on one specific aspect of this overarching theme, in
particular, whether the legal framework governing the relationships between employees and their
employers affects the extent of innovation in an economy.
The inefficiencies and rigidities associated with stringent labor laws — laws that prevent employers from seamlessly negotiating and/or terminating labor contracts with employees — are much
celebrated in the academic literature1 and the media. However, this discussion is generally centered around the ex post effects of labor laws.2 In particular, it is not difficult to see that once
the situation to renegotiate or terminate an employment contract has arisen, tying down an employer’s hands from doing so can lead to ex post inefficient outcomes. Much less studied, however,
is the ex ante incentive effect of such strong labor laws. Might stringent labor laws be desirable
as they provide firms a commitment device to not punish short-run failures and thereby spur their
employees to undertake activities that are value-maximizing in the long-run? Indeed, if strong
labor laws were always inefficient, their prevalence in many countries around the world would be
hard to justify based on grounds of economic efficiency, and perhaps could be rationalized only
by appealing to political economy considerations.3 In this paper, we focus on one dimension of
employment protection legislation: the ease or difficulty with which employees can be dismissed.
We provide empirical evidence that strong dismissal laws indeed appear to have an ex ante positive
incentive effect by encouraging firms and their employees to engage in more successful, and more
significant, innovative pursuits.
To provide this evidence, we use data on patents issued by the United States Patent and
Trademark Office (USPTO) to US and foreign firms and citations to these patents as constructed
by Hall, Jaffe and Trajtenberg (2001). The “industry” level classification we employ pertains to the
patent classes in this data. We measure innovation for an industry in a given year by the number
1
Botero et al. (2004), for example, claim that heavier regulation of labor leads to adverse consequences for labor
market participation and unemployment.
2
For example, strong labor market regulation is often blamed to be one of the reasons for Europe’s economic
under-performance compared to the US. For a recent study articulating this theme, see the study of France and
Germany by the McKinsey Global Institute (1997).
3
Similarly, but for their ex ante effects in encouraging innovative and high-impact research, academic employment
contracts of tenure-track type whereby new hires are awarded an explicit contract of fixed length, subject to minimum
interim standards, would be hard to rationalize.
1
of patents applied for (and subsequently granted), the number of all subsequent citations to these
patents, and the number of firms filing for patents in that year and industry.
We use the index of labor laws constructed by Deakin et al. (2007). They construct this
index by analyzing in detail the evolution of differences in employment protection legislation in
five countries — US, UK, France, Germany, and India. They analyze forty dimensions of labor
laws and group them into five components that correspond to the regulation of: (i) alternative
forms of labor contracting; (ii) working time; (iii) dismissal; (iv) employee representation; and (v)
industrial action. This index offers the advantage that it takes into account not just the formal or
positive law but also the self-regulatory mechanisms that play a functionally similar role to laws
in certain countries. While employing the Deakin et al. index forces us to limit our study to only
the five countries mentioned above, these countries account for 72% of the patents filed with the
USPTO during our sample period. Given our focus on laws that govern dismissal of employees, we
also employ the Deakin et al. (2007) dismissal law sub-index; higher values of this index represent
stricter dismissal rules, i.e., more employment protection.4
Employing this data, we test the following hypotheses:
Hypothesis 1: Stronger dismissal laws lead to greater innovation.
Since the ex ante incentives of ex post stringent dismissal laws should matter more in the
innovative sectors of the economy, we also test whether
Hypothesis 2: Stronger dismissal laws lead to relatively more innovation in the innovationintensive industries than in the traditional industries.
Finally, to identify which aspect of labor laws affects innovation more, we test whether
Hypothesis 3: Laws governing dismissal of employees influence innovation more than other
aspects of labor laws.
Our empirical investigation of these hypotheses proceeds in the following steps. First, we
employ panel regressions of the level of innovation on the level of the dismissal law index, where
we include fixed effects for country, industry (i.e., patent class) and application year. As Imbens
4
An alternative to the Deakin et al. (2007) index is the one developed by Botero et al. (2004), which covers
more countries but does not have any time-series variability. Another alternative is the EPL measure constructed
by Nicoletti and Scarpetta (2001) for a set of OECD countries for the years 1990-1998. However, this index neither
offers the cross-sectional comprehensiveness of the index constructed by Botero et al. (2004), nor the full extent of the
longitudinal advantages of the index developed by Deakin et al. (2007). Furthermore, the EPL index only measures
the aggregate stringency of a country’s labor laws, while in this study we are interested in one particular dimension
of these laws, namely dismissal rules.
2
and Wooldridge (2008) suggest, these regressions enable us to estimate a “difference-in-difference”
effect in a generalized setting with multiple treatment countries over multiple time periods. In
these tests, we find that more stringent dismissal laws positively influence the innovative activity
in a country. This effect is statistically and economically significant: an increase in the dismissal
law index by one, ceteris paribus, increases the number of annual patents and subsequent citations
in a patent class by 14.9% and 25.5% respectively.
In estimating this effect, we also control for (i) a country’s creditor rights using the Djankov et
al. (2007) index, rule of law, efficiency of judicial system, and anti-director rights as in La Porta et
al. (1998); (ii) a country’s bilateral trade with the US in each of its industries using its exports and
imports with the US in different years, a control that is necessary given our use of the US patents
to proxy innovation in these countries; (iii) a measure of the country’s comparative advantage in
an industry using the ratio of value-added of an industry in a country in a given year to the total
value-added for the country that year; and (iv) the GDP per capita of the country.
A key concern in the above tests stems from the endogeneity of the dismissal law changes.
Put simply, other changes that accompany the dismissal law changes may be accounting for our
results. Specifically, to cater to their political constituencies, leftist governments may be inclined to
strengthen labor laws. If leftist governments are more likely to invest in education and other public
services, which may also positively impact innovation in a country, then the dismissal law change
is endogenous and its empirical effect on innovation may potentially be due to the association of
innovation with omitted government-related variables.
To examine such endogeneity and omitted variable concerns, we augment our fixed effects
specification with country-specific and industry-specific trends, which enables us to identify the
effect of dismissal law changes using deviations (at the patent class level) from the average time
trends for each country and each industry. Since the above confounding factors would manifest
as country-specific and industry-specific time trends, these tests enable us to isolate better the
pure effect of dismissal law changes on innovation. We also examine directly the endogeneity
introduced by a change in government by including a time-varying proxy for the leftist political
leanings of a country’s government. We find that, within a country, innovation is greater under
leftist governments. Crucially, however, we find that the main effect of the dismissal laws stays
positive and significant even after accounting for the government’s political leanings.
3
In the second step of our empirical analysis, we examine the effects on innovation for each
country that underwent a significant dismissal law change by undertaking traditional difference-indifference tests. Here, we examine the before-after effect of a change in dismissal laws in the affected
country (the “treatment group”) vis-à-vis the before-after effect in a country where such a change
was not effected (the “control group”). In particular, we examine the effects of changes in dismissal
laws in the U.S. through the passage of the Worker Adjustment and Retraining Notification Act
(WARN) in 1989 and similar changes in U.K. and France in the 1970s. Our results remain similar
to those using the full sample.
Third, we investigate the hypothesis that the effect of dismissal laws should be disproportionately
higher in industries that exhibit a greater propensity to innovate than in other industries. To
conduct these tests, we follow Acharya and Subramanian (2008) in ranking patent classes by their
patenting intensity in the US. We interact the proxy for patenting intensity with the dismissal law
index in regressions that include fixed effects for country, patent class and application year. Using
the full sample as well as our two-country treatment-control settings, we find that the coefficient
on this interaction term is significantly positive, consistent with our starting hypothesis.
Fourth, we investigate if labor laws that affect the ex post likelihood of an employee being
dismissed from employment matter more for innovation than other categories of labor laws. To
this end, we line up the five dimensions of the labor laws as studied by Deakin et al. (2007) and
find that the “regulation of dismissal” component is the only one which has a consistently positive
and significant effect on innovation.
Finally, we complement our cross-country results with a test that employs the passage of the
WARN Act in the US in 1989. Here, we exploit the discontinuity introduced by the fact that the
WARN Act was applicable only to firms with 100 or more employees to undertake within-country
tests of the effect of changes in dismissal laws. These tests enable us to shut out any unobserved
heterogeneity that may affect our cross-country examinations. The results, which do not rely on
coded labor index data, corroborate our cross-country findings regarding the positive impact of
stricter dismissal laws on innovation.
Taken together, these tests lead us to conclude that innovation is fostered by stringent laws
governing dismissal of employees and in those sectors of the economy that are more innovationintensive. In additional tests, we confirm that the direction of causality runs from labor laws to
4
innovation rather than being the other way around.
Our results support the theoretical conclusions of Manso (2009), who extends the standard
principal-agent framework to incorporate two different ways in which tasks can be performed by an
agent: exploitation of well-known actions, which ensures predictable outcomes but may hinder the
discovery of superior outcomes, and exploration of untested actions, which if successful can lead
to superior outcomes, but if unsuccessful can result in outcomes that are inferior to predictable
ones (unless exploration is switched to exploitation after revelation of interim information). Manso
shows theoretically that in this setting, the optimal compensation scheme that induces innovation
(“exploration”) exhibits substantial tolerance or even reward for interim failure in addition to rewards for success. Reducing the threat of termination may be necessary to induce the agent to
explore new, untested approaches. Furthermore, the optimal contract that motivates innovation
(“exploration”) relies on long-term incentives. In fact, “[...] exploration may not even be implementable with a sequence of short-term contracts. The ability to commit to a long-term contract
is thus essential to motivate exploration. (Manso, 2009, p.3)”5
The intuition of Manso’s model fits naturally with our results on the ex ante incentive effect of
strong dismissal laws on innovation. Innovative pursuits are likely to be of higher value in the long
run but riskier in the short run. If the firm cannot commit to not fire its employees ex post (for
example, folding up its R&D units) when exploration of a new idea turns out to be unsuccessful,
then it may find it too costly ex ante to encourage innovation.
However, our results do beg the question as to why labor law is necessary: Why do firmlevel contracts not suffice to provide employees efficient incentives to innovate? One possibility
is that firm-level contracts lack the force of commitment that laws offer. Since the outcomes of
innovation are unpredictable, they are difficult to contract ex ante (Aghion and Tirole, 1994),
which renders firm-level contracts that motivate innovation susceptible to ex-post renegotiation.
The possibility of ex-post renegotiation dilutes the incentive effects of contracts set up ex ante. Since
laws are considerably more difficult for private parties to renegotiate than firm-level contracts, legal
protection of employees in the form of stringent dismissal laws offers time-consistency and a form
5
Note that although the principal’s resulting forbearance is high, the fact that employees usually share in the rents
of successful innovation, or in other words, the pay-for-performance nature of labor contracts on the upside keeps the
incentive to work hard intact.
5
of commitment that may be absent in firm-level contracts.6 Another reason why the law might
be necessary to protect against employee dismissals and promote innovation is that firms may be
run by short-termist or myopic top management. In such firms, poor firm-level governance of top
management actions might prevent efficient long-term contracts being written with employees. The
law can improve the so-called “internal governance” of firms (Acharya, Myers and Rajan, 2008)
by effectively lengthening the horizon of employees and indirectly inducing the top management
to provide better incentives to employees by investing for the long run. Assessing whether the
labor law is necessary for efficiency is an important topic for future research. Our results highlight
an important ex ante effect of labor laws, namely their ability to spur innovation, that must be
factored into such an assessment.
The rest of the paper is organized as follows. Section 2 reviews additional related literature.
Section 3 discusses our data and Section 4 presents all the empirical results. Section 5 provides a
robustness discussion. Section 6 contains concluding remarks.
2
Related Literature
Our paper is related to the literature on law and finance, which analyzes the impact of legal
institutions on various aspects of corporate policy and economic outcomes, such as the nature of
external financing of enterprises (La Porta, Lopez-de-Silanes, Shleifer and Vishny, 1997, 1998), the
ownership structure of firms (La Porta, Lopez-de-Silanes and Shleifer, 1999), and the mix between
market- and bank-dominated finance (Allen and Gale, 2000).
Specifically, our paper contributes to the literature that examines the effect of laws that govern
the relationships between employees and their employers. Botero et al. (2004) find that heavier
regulation of labor leads to adverse consequences for labor market participation and unemployment and conclude that government interventions in the labor market are driven primarily by
political economic considerations and not by any reasons of efficiency. Besley and Burgess (2004)
conclude from their study of manufacturing performance in Indian states that pro-worker labor
6
It is worth pointing out that the optimal contract in Manso (2009) that promotes innovation is inherently timeinconsistent, and thus also naturally explains why strong labor laws would be perceived to be rigid and inefficient in
states where they actually bind. Cremer (1995) makes a similar point but with the somewhat reverse intuition that
in certain settings committing not to punish ex post might conflict with the provision of efficient ex ante incentives.
Acharya, John and Sundaram (2000) argue, in a different context, that a certain amount of “resetting” of executive
stock options – apparently an act of forbearance on part of firms toward their management – may be efficient for
continuation outcomes even though it induces moral hazard ex ante.
6
laws are associated with lower levels of investment, productivity, and output. Atanassov and Kim
(2007) examine the interaction between labor laws and investor protection laws and find that rigid
employment laws lead to higher likelihood of value-reducing major asset sales, particularly when
investor protection is weak. They find that assets are sold to forestall layoffs, even if these asset
sales hurt performance. Bassanini, Nunziata and Venn (2009) also conclude, based on data for
OECD countries from 1982 to 2003, that mandatory dismissal regulations have a depressing effect
on productivity growth in industries where layoff restrictions are more likely to be binding.
In contrast to these studies which document the negative effects of labor laws, our study finds
that stringent labor laws seem to motivate a firm and its employees to pursue value-enhancing
innovative activities. Directly related to our study is the one by Menezes-Filho and Van Reenen
(2003), who focus on a specific aspect of labor laws — the extent to which unions are allowed to
operate — and survey the existing literature for their effects on innovation. They note that while
U.S. studies find a negative impact of unions on innovation, studies examining European countries
do not consistently support these findings. Our study pools together five representative countries
that span three different legal “origins” and account for over 70% of patents filed in the US. While
Menezes-Filho and Van Reenen (2003) focus on laws governing unions, we examine all dimensions
of labor laws and pay particular attention to laws governing dismissal of employees.
In less directly related work, Simon (1951) and Williamson, Wachter and Harris (1975) argue
that stronger labor laws may also have an ex post efficiency aspect to them. While the former study
argues that strong labor laws provide insurance to employees against risks associated with loss of
income and employment, the latter claims that strong labor laws reduce transaction costs derived
from the incompleteness of the employment contract. Our stance that strong dismissal laws may
be efficient is in line with that in the above studies.
Finally, recent findings of Lerner and Wulf (2007) also support Manso (2009)’s theoretical
prediction on the positive relation of long-term incentives and innovation. Studying a sample of
publicly traded U.S. firms over the years 1987 to 1998, Lerner and Wulf show that for firms with
centralized R&D units, more long-term incentives (e.g. restricted stock grants and stock options)
given to corporate R&D heads are associated with more patenting, as well as more heavily cited
and more original patents.
7
3
Data and Main Proxies
We describe the data and our proxies for innovation and strength of dismissal laws.
To construct proxies for innovation, we use patents filed with the US Patent Office (USPTO)
and the citations to these patents, compiled in the NBER Patents File (Hall, Jaffe and Trajtenberg, 2001). The NBER patent dataset provides among other items: annual information on patent
assignee names, the number of patents, the number of citations received by each patent, the technology class of the patent and the year that the patent application is filed. The dataset covers all
patents filed with the USPTO by firms from around 85 countries. We exploit the technological
dimension of the data generated by “patent classes”. Over the years, the USPTO has developed a
highly elaborate classification system for the technologies to which the patented inventions belong,
consisting of about 400 patent classes. During the patent examination process, patents are assigned
to detailed technologies as defined by the patent class. The USPTO performs these assignments
with care to facilitate future searches of the prior work in a specific area of technology (Kortum
and Lerner, 1999).
We date our patents according to the year in which they were applied for. This avoids anomalies
that may be created due to the lag between the date of application and the date of granting of
the patent (Hall, Jaffe and Trajtenberg, 2001). Note that although we use the application year as
the relevant year for our analysis, the patents appear in the database only after they are granted.
Hence, we use the patents actually granted (rather than the patent applications) for our analysis.7
3.1
Proxies for Innovation
We use three broad metrics to measure innovation. The first is a simple patent count of the
number of patents that were filed in a particular year in a specific patent class. As our second metric
of innovative activity, we use the citations that are made to the patents in a specific patent class.
Citations capture the importance and drastic nature of innovation. This proxy is motivated by the
7
A caveat about potential biases created by the use of application year, particularly in the case of foreign patents,
is in order. Since foreign firms usually file patents with the domestic patent office and then with the USPTO, readers
may believe that the application year recorded with the USPTO does not capture the exact timing of the innovation.
However, the Paris Convention which governs such firms filing both in the domestic and foreign country, mandates
that if the inventor files a foreign patent application in any other Paris Convention signatory state within 12 months
of the domestic filing, overseas patent-granting authorities will treat the application as if it were filed on the first
filing date. Therefore, the application year recorded with the USPTO would coincide with the application year for
the domestic patent of the foreign firm.
8
recognition that the simple count of patents does not distinguish breakthrough innovations from
less significant or incremental technological discoveries.8 Intuitively, the rationale behind using
patent citations to identify important innovations is that if firms are willing to further invest in a
project that is building upon a previous patent, it implies that the cited patent is influential and
economically significant. In addition, patent citations tend to arrive over time, suggesting that the
importance of a patent may be revealed over a period of time and may be difficult to evaluate at
the time the innovation occurs. Finally, citations help control for country-level differences arising
in the number of patents due to differences in the number and size of firms.
As our third measure of innovative activity, we employ the number of patenting firms in a patent
class. The USPTO defines “assignee” as the entity to which a patent is assigned. A simple count
of the number of assignees in a patent class in a given application year provides a measure of the
number of patenting entities.
Patents have long been used as an indicator of innovative activity in both micro- and macroeconomic studies (Griliches, 1990). Although patents provide an imperfect measure of innovation,
there is no other widely accepted method which can be applied to capture technological advances.9
Nevertheless, we are aware that using patents has its drawbacks. Not all firms patent their innovations, because some inventions do not meet the patentability criteria and because the inventor
might rely on secrecy or other means to protect its innovation. In addition, patents measure only
successful innovations. To that extent, our results are subject to the same criticisms as previous
studies that use patents to measure innovation (e.g., Griliches, 1990; Kortum and Lerner, 1999).
3.2
Dismissal Law index
In order to analyze the impact of dismissal laws on innovation, we employ an empirical proxy
for the stringency of laws governing dismissal of employees. The existing academic literature offers
two main alternatives:
Botero et al. (2004) analyze and code data on employment, collective relations, and social
8
Pakes and Shankerman (1984) show that the distribution of the importance of patents is extremely skewed, i.e.,
most of the value is concentrated in a small number of patents. Hall et al. (2005) among others demonstrate that
patent citations are a good measure of the value of innovations.
9
As an alternative to patents, R&D spending across different industries could be a potential proxy for innovation
intensity. However, in a cross-country setting, this presents several challenges. For example, accounting norms,
particularly whether R&D is capitalized or is expensed, would have a mechanical effect on R&D spending. Griliches
(1990) emphasized that there is a strong relationship in the US between R&D and the number of patents received at
the cross-sectional level, across firms and industries. The median R-squared is of the order of 0.9.
9
security laws for 85 countries as of 1997 in order to measure the degree of worker protection. This
index clearly has the advantage that it covers a wide range of countries. However, as our task
is to investigate the causal impact of labor laws on innovation, which necessitates controlling for
observable and unobservable time-varying heterogeneity, we cannot use the cross-sectional index
constructed by Botero et al. (2004).
Deakin et al. (2007) perform a “leximetric” analysis, which applies the indexing method to
analyze the evolution of differences in employment protection legislation between five countries over
time. While Deakin et al. (2007) focus their investigation on five countries only (US, UK, France,
Germany, and India), the advantage of their data is its availability at annual frequency for the timespan from 1970 to 2006, which allows us to analyze our research question in an econometrically
rigorous way by accounting for various sources of endogeneity. Furthermore, their categorization
of labor laws into five different components allows us to assess specifically the impact of dismissal
laws on innovation. Focussing on five countries in our analysis does not represent a substantial
omission, as these five countries account for 72% of patents filed with the USPTO. In addition to
its useful time-series properties, the index developed by Deakin et al. (2007) offers other advantages.
While the broad categories used to construct this index largely correspond to similar parts of the
cross-sectional index developed by Botero et al. (2004), Deakin et al. (2007) take into account not
just formal or positive laws, but also self-regulatory mechanisms, including collective agreements,
which play a functionally similar role in certain legal systems; this feature makes their index more
comprehensive than the Botero et al. (2004) index in terms of the range of rules which are analyzed.
In addition, the values reported in their index are complemented by more detailed country-level
data on the evolution of labor laws in each system.
In order to construct the labor law indices, Deakin et al. (2007) investigate employment law in
five countries over the time-span 1970-2006. They analyze forty dimensions of labor regulation and
group them into five aspects of labor and employment law: (i) the regulation of alternative forms
of labor contracting (e.g. self-employment, part-time work, and contract work); (ii) regulation of
working time; (iii) regulation of dismissal; (iv) employee representation; and (v) rules governing
industrial action. By averaging the sub-components for each group per country and year, Deakin
et al. (2007) obtain sub-indices for the five aspects of labor and employment law.10
10
More specifically, the five sub-indices are as follows: (i) Alternative Employment Contracts measures the cost
10
To examine the effect on innovation of laws governing dismissal of employees, we focus on the
“Regulation of Dismissal” sub-index. This sub-index (hereafter “the dismissal law index”) is made
up of the following components: The legally mandated notice period; the amount of mandatory
redundancy compensation; constraints on dismissal imposed by the law (such as dismissal being
lawful only in case of misconduct or serious fault of the employee); parties to be notified in case
of dismissal (this ranges from a formal communication to a state body to a simple oral statement
to the employee); redundancy selection (e.g. priority rules based on seniority, marital status etc.);
applicability of priority rules in re-employment; and rules governing unjust dismissal (i.e., the extent
of procedural constraints on dismissal imposed by the law; whether reinstatement is the normal
remedy for unfair dismissal; the period of service required for an employee to qualify for protection
against unjust dismissal). More details on these indices are provided in Appendix A.
Figure 1 shows the evolution of the dismissal law index for the five countries in our sample;
higher values represent stricter labor laws, i.e., more employment protection. As Figure 1 shows,
the index exhibits substantial time-series variation.
3.3
Summary Statistics
Table 1 lists the mean, standard deviation, minimum, and maximum for the following variables
(by country) for the five countries in our sample: number of patents filed, citations received by
these patents, the number of firms filing patents, as well as the dismissal law index. Data for the
labor law index is available from 1970 to 2006. Since the patent data ends in 2002, we terminate
our sample in 2002.
to employers of using alternatives to the “standard” employment contracts (e.g., part-time employment, fixed-term
contracts, agency work). (ii) Regulation of Working Time measures the extent to which the law protects employees’
working conditions by, for example, stipulating limits to annual leave holiday entitlements and daily working duration,
and compensation for overtime and weekend working. (iii) Regulation of Dismissal measures the cost to employers
for dismissals incurred through, for example, procedural constraints on dismissals, remedies for unfair dismissals, and
dismissal notification process. (iv) Employee Representation measures the bargaining power of employees and the
ability of employees to co-determine through the rights to board nomination or through the consultation process.
(v) Industrial Action measures the strength of legal protection for employees engaging in industrial action (among
others, strikes and lockouts).
11
4
Empirical Results
4.1
Empirical Strategy
We investigate whether stronger dismissal laws lead to greater innovation. Figure 2 illustrates
the univariate relationship between the dismissal law index and two of our innovation measures,
namely, log of patents and log of citations. On the y-axis, we plot the residuals from a regression
of our innovation measure on year, country, and industry dummies; these fixed effects enable us
to separate out country and industry specific factors as well as inter-temporal variations which
could be affecting the relationship. We notice in Figure 2 that there is a strong positive correlation
between the innovation measures and the stringency of dismissal laws. In the following pages, we
present statistical evidence to show that stringent dismissal laws lead to greater innovation.
Inferring a causal relationship between country-level dismissal laws and innovation presents the
challenge that country-level dismissal laws are expected to be largely correlated with other countrylevel unobserved factors. Therefore, we exploit the substantial time-series variation in the dismissal
law index to infer a causal relationship.
Our analysis proceeds in several steps. First, we employ fixed effects panel regressions of the
dismissal law index on our innovation proxies. By including fixed effects for the country, patent
class and year, we exploit the staggered country-level changes in dismissal laws to estimate a
“difference-in-difference” specification in a multiple treatment groups, multiple time periods setting
(see Bertrand, Duflo and Mullainathan (2004) and Imbens and Wooldridge (2008)).
Second, we utilize dismissal law changes in specific countries to undertake “traditional” differencein-difference tests, where we examine the before-after effect of a change in the laws regulating dismissal in the affected country (the “treatment group”) vis-à-vis the before-after effect in a country
where such a change was not effected (the “control group”). Dismissal laws changed in the U.S. in
1989 with the passage of the Worker Adjustment and Retraining Notification (WARN) Act11 while
Germany experienced no such change during this period. Our central hypothesis is that compared
to the changes in innovation in Germany before and after 1989, changes in innovation in the US in
the same period should be greater. Figure 3 illustrates this difference-in-difference. In this figure,
11
The federal WARN act was passed into law by US congress in August 1988 and became effective in February
1989.
12
we plot across time the ratio of realized number of patents and citations in a particular year to
that in 1989, which was the year of the US dismissal law change. We find that while the number
of patents and citations are relatively in sync for U.S. and Germany until 1989, post 1989, these
measures for the US break ahead of those for Germany. In particular, Figure 4 illustrates this
break for the U.S. by plotting a linear fit of the number of patents and citations across time for the
treated (U.S.) and control (Germany) groups before and after the WARN Act became effective.12
In our third set of tests, we investigate inter-industry differences in the effect of dismissal laws on
innovation. To understand the design for this test, consider two industries: “surgical and medical
instruments” and “textiles”. Firms in surgical and medical instruments have a higher propensity
to innovate and have riskier cash flows than firms in the textile industry. Therefore, surgical and
medical instruments serves as an example of a more-innovative industry while textiles serves as a
benchmark less-innovative industry. Hypothesis 2 predicts that the effect on innovation of the U.S.
dismissal law change in 1989 would be disproportionately higher in surgical and medical instruments
when compared with that in textiles. Figure 5 illustrates this interaction effect. In this figure, we
plot across time the ratio of realized number of patents and citations for surgical and medical
instruments relative to a benchmark less innovation-intensive industry (textiles and apparel) for
the U.S. vis-à-vis Germany. To examine the effect of the change in 1989, we normalize this ratio
to be one in 1989. We find that while the ratios for the U.S. and Germany overlap with each other
until 1990, after 1990, the ratio for the US surges ahead of that for Germany. In the econometric
variant of this visual test, we examine the effect of the interaction of the dismissal law index with
a proxy for the innovation intensity of an industry.
Fourth, to alleviate concerns that other dimensions of labor laws are providing the evidence for
the effect of dismissal laws on innovation, we line up all five dimensions of the Deakin et. al. (2007)
labor law index and examine the relative effect of dismissal laws vis-a-vis other labor laws.
Fifth, we examine concerns regarding reverse-causality in the relationship of labor laws to
innovation.
Finally, we complement our cross-country results with tests using the passage of the WARN
12
In both Figure 3 and 4, innovation trend lines based on citations are declining for both US and Germany. This
is due to the truncation of citations: All citations to a patent are attributed to that patent in its application year,
which naturally results in fewer citations to more recently issued patents, on average. In our statistical tests, we
control for this problem by employing year fixed effects.
13
Act in the U.S. Here, we exploit the discontinuity introduced by the fact that the WARN Act was
applicable only to firms with 100 or more employees to undertake within-country tests of the effect
of changes in dismissal laws. Figure 6 shows the linear fit of the number of patents and citations
across time for the treated (firms with 100 or more employees) and control (firms with less than 100
employees) before and after 1989. The presence of a break for the treated firms and the absence of
the same for the control group of firms in 1989 is quite clear from this figure. Tests formalizing this
visual effect enable us to shut out any unobserved heterogeneity that may affect multiple-country
examinations.
Next, we describe in detail results for each of these empirical strategies.
4.2
Fixed-effects panel regressions
To start with, we employ fixed-effects panel regressions of the innovation proxies on the dismissal
law index, where we include fixed effects at the country, year and industry (i.e., patent class) levels:
yict = βi + βc + βt + β1 ∗ DismissalLawsc,t + β · X + εict
(1)
where yict is the natural logarithm of a measure of innovation for the USPTO patent class i from
country c applied for in year t. βi , βc , βt denote respectively patent class, country and application
year fixed effects. DismissalLawsc,t denotes the stringency of dismissal laws based on the index
value for country c in year t. X denotes the set of control variables. The application year fixed
effects enable us to also control for the problem stemming from the truncation of citations, i.e.,
citations to patents applied for in later years would on average be lower than citations to patents
applied for in earlier years. Similarly, the patent class fixed effects also enable us to control for
time-invariant differences in patenting and citation practices across industries.
Since we employ country and time fixed effects in all our regressions, we estimate standard
errors that are robust to heteroscedasticity. As mentioned above, the primary concern with respect
to estimation (i.e., the coefficients) and inference (i.e., standard errors) stems from the unobserved
factors at the country level. Since we parametrically estimate the country-specific effects through
the country dummies, and thereby account for the autocorrelation in the error terms that stems
from these country specific factors, the usual inference adjusted for heteroskedasticity is valid.
14
Imbens and Wooldridge (2008) state: “If the explanatory variables of interest vary within group,
usual inference is valid with fixed effects, perhaps with adjustment for heteroskedasticity . . . with
small number of groups, inference based on cluster-robust statistics could be very conservative when
it need not be.” Nevertheless, we have examined our results by separately clustering the standard
errors at either the country or patent class levels, in addition to the fixed effects. Our results are
very similar to the ones that we report here.
As explained by Imbens and Wooldridge (2008), in (1) , β1 is estimated as the “differencein-difference” effect in a generalized multiple treatment groups, multiple time periods setting.
Intuitively, the country and time fixed effects lead to β1 being estimated as the within-country
differences before and after the dismissal law change vis-à-vis similar before-after differences in
countries that did not experience such a change during the same period (see Appendix B for a
formal proof). Therefore, these difference-in-difference tests are less subject to the criticism that
country or industry level unobserved factors influencing innovation are correlated with the level of
dismissal laws in a country.
Table 2 shows the results of the test of equation (1) using the logarithm of the number of patents,
number of patenting firms, and citations to patents as the dependent variables. In columns 1-3,
we first report the results from our basic test without any control variables. For each of the three
dependent variables, we find the coefficient on the dismissal law index to be positive and significant
at the 1% level. This result indicates that strong dismissal laws are positively correlated with
innovation, as suggested by Hypothesis 1.
Columns 4-6 show results after controlling for other variables that may affect innovation:
4.2.1
Creditor rights
Acharya and Subramanian (2008) provide empirical evidence that when a country’s bankruptcy
code is creditor-friendly, excessive liquidations cause levered firms to shun innovation, whereas by
promoting continuation upon failure, a debtor-friendly code induces greater innovation. Therefore,
first, we control for the extent of creditor protection in a country using the time-varying Djankov
et al. (2007) index of creditor rights. We find the coefficient on creditor rights to be negative and
significant.
15
4.2.2
Other country-level laws
Since the labor laws in a country may be correlated with its other laws, we employ the set of
(by construction time-invariant) legal variables highlighted by the law and finance literature (La
Porta et al. (1997, 1998)): Rule of Law, Antidirector Rights Index and the Efficiency of Judicial
System (all from La Porta et al. (1998)). The Rule of Law and the Efficiency of the Judicial
System are positively and (almost in every specification) significantly correlated with innovation.
The Antidirector Rights Index unexpectedly has a negative sign. The fact that this index is
constant over time for a given country could infer that the index is picking up other time-invariant
heterogeneity at the country level, not necessarily the effect of shareholder protection.13
A related concern is that the contracting and legal environments in India might be very different
from other countries in our sample, and that India might be driving the results in our cross-country
panel regressions. To alleviate this concern, as a robustness check, we also performed all tests
detailed on the previous pages without observations for India; the results are almost identical.
4.2.3
Bilateral Trade
While employing patents filed with the USPTO to proxy innovation done in non-US countries
avoids heterogeneity from employing patents filed under each country’s individual patenting system,
this strategy introduces potential biases. Given the country, patent class and application year fixed
effects in our regressions, the coefficient β1 in equation (1) would be biased only if time-varying
omitted variables at the country/ patent class level that affect these biases are also correlated with
the changes in dismissal laws.
Nevertheless, we employ non-US countries’ bilateral trade with the US as a potential determinant of the USPTO bias. Countries that export to the US would file more patents with the
USPTO. MacGarvie (2006) finds that citations to a country’s patents are correlated with the level
of exports and imports that the country has with the US. Therefore, in our regressions, we add for
each country the logarithm of the level of imports and the level of exports that the country has
with the US in each year at each 3-digit ISIC industry level, using data from Nicita and Olarreaga
13
Since the rule of law does not vary over time, we estimate its effect by aggregating the country fixed effects. In
omitted tests, we also controlled for legal origin, Logarithm of days to enforce a contract, and Estimated Cost of
Insolvency Proceedings in these regressions. These variables were dropped due to multi-collinearity. The absence of
an effect of legal origin is consistent with the finding in Deakin et al. (2007) that legal origin has no consistent effect
on labor laws.
16
(2006).14 While imports have no consistent effect, exports are negatively correlated with innovation, although this effect is only significant in column (6). Crucially, the effect of dismissal laws
stays positive and statistically significant.
4.2.4
Comparative Advantage and Economic Development
A key determinant of innovation is the comparative advantage that a country possesses in
its different industries, which could affect our interpretation of β1 . As our proxy for industry level
comparative advantage, we employ the ratio of value added in a 3-digit ISIC industry in a particular
year to the total value added by that country in that year. The data for these measures come from
the United Nations Industrial Development Organization (UNIDO)’s statistics. Relatedly, since
richer countries may innovate more and may also file more patents with the US, we also include the
logarithm of real GDP per capita. We find in Columns 4-6 of Table 2 that the ratio of value added
has no significant effect on innovation; this is largely because country level comparative advantages
do not change significantly over time and our country fixed effects absorb any time-invarying effects.
Notably, in these specifications, we find that the overall effect of dismissal laws stays positive and
significant for all three innovation proxies.
4.2.5
Economic magnitudes
In addition to being statistically significant, the economic magnitude of the impact of dismissal
laws on innovative activity is also large. In particular, if we use Columns 1-3 of Table 2 to estimate
these economic magnitudes, we find that an increase in the dismissal law index by one would, ceteris
paribus, result in a rise in the number of patents issued by 14.9%.15 Compared to the economic
effects for patents, the effect for the number of citations and for the number of firms is even larger:
An increase in the dismissal law index by one would result in an increase in innovative activity by
18.9% and 25.5% as measured by the number of patenting firms and citations, respectively.
14
We match the patent classes to the 3-digit ISIC using a two-step procedure: first, the updated NBER patent
dataset (patsic02.dta on Brownwyn Hall’s homepage) assigns each patent to a 2-digit SIC. We then employed the
concordance from 2-digit SIC to 3-digit ISIC codes. Since every patent is already assigned to a patent class in the
original NBER patent dataset, this completes our match from the patent class to the 3-digit ISIC code.
15
Using Column 1 of Table 2, we find that ln(P atents) = −3.824 + 0.139 ∗ DismissalLaws. Therefore, for example,
going from a Dismissal Law index of 1 to an index value of 2 would result in an increase in the number of patents
filed of exp(0.139) − 1 = 14.9%.
17
4.2.6
Endogeneity of dismissal law changes
Panel regressions with country-specific and industry-specific trends
To examine whether
other changes that accompany the dismissal law changes account for our above results, we incorporate country-specific and industry-specific time trends using the following specification:
yict = tβi + tβc + βt + β1 ∗ DismissalLawsc,t + β · X + εict
(2)
where tβi denotes a time trend for industry (patent category) i and tβc denotes a time trend for
country c; the other variables are as defined in equation (1).
By accounting for these country-specific and industry-specific time trends, we identify the intended effect using deviations (at the patent class level) from the average time trend for each
country and the average time trend for each industry. Since other country or industry level changes
accompanying the dismissal law changes could lead to country-specific as well as industry-specific
time trends, these tests enable us to isolate better the pure effect of dismissal law changes on
innovation.
The results of these tests are shown in Table 3. Even accounting for country and patent category
trends, the coefficient of the dismissal law index remains positive and significant at the 1% level.
Correlation of dismissal law changes with changes in government
An important concern
stems from the fact that changes in a country’s labor laws are likely to be correlated with changes
in elected governments in a country. In particular, to cater to their political constituencies, leftist
governments may be inclined to strengthen labor laws. If leftist governments are more likely to
invest in education and other public services, which may have a positive impact on innovation in a
country, it is possible that the effect of dismissal laws on innovation documented above is, in fact,
caused by other factors coinciding with changes in government rather than changes in dismissal
laws. Our tests employing country-specific trends should at least partially alleviate such a concern
since the effect of dismissal law changes over and above the country-specific trend should account
for such confounding factors.
Nevertheless, we examine this concern directly by using a time-varying proxy for the political
leanings of a country’s government. We use the variable Government from Armingeon et al. (2008),
18
which captures the balance of power between left and right-leaning parties in a given country’s
parliament.16 This variable takes on values from one to five, with one denoting a hegemony of
right-wing (and centre) parties, and five denoting a hegemony of social-democratic and other left
parties. The variable Government is available for all countries in our sample, except for India.
As expected, the variable Government is positively correlated with the dismissal law index (the
correlation is 0.52), which implies that stricter dismissal laws are indeed enacted in a country when
the government is leftist in its political leanings.
To control for the endogeneity in dismissal law changes stemming from changes in government,
we include Government as an additional control variable in our fixed effect panel regressions described in Equation (1). The results are shown in Table 4, columns 1-3. We find that the coefficient
of Government is positive and statistically significant, which suggests that within a country, innovation is greater under leftist governments. As suggested before, this is possibly because leftist
governments may emphasize investments in education and other basic public services, which may
in turn be positively correlated with innovation. Crucially, however, we observe that the coefficient
on the dismissal law index remains positive and significant (at the 1% level) for all three innovation
proxies.
4.2.7
Other robustness checks
In Table 4 we address two additional concerns. Is it the case that our results are driven by the
dismissal law change in the US? Second, are the results driven by a possible increase in German
patenting activity owing to the re-unification of East and West Germany in 1990? To examine
these alternative stories, we restrict our sample period from 1993 to 2002. The three year time
lag after the German re-unification and the four year time lag after the US WARN Act became
effective ensure that the effect of either event is minimal during this sample period. Columns 4-6 of
Table 4 provide evidence that identification in our tests of Equation (1) does not rely on the 1988
WARN Act alone or on the effect of the German re-unification.
16
Armingeon et al. (2008) construct a Comparative Political Data Set, which is a collection of annual political and
institutional data for 23 democratic countries for the period of 1960 to 2006. Our variable Government is denoted
“govparty” in Armingeon et al. (2008).
19
4.3
Traditional difference-in-difference tests
Given the small sample of countries in our analysis, a relevant question to ask is whether the
overall effects of labor laws hold in the time-series for each of the five countries. However, in
running these regressions, we cannot control for general macroeconomic factors and technological
shocks through year dummies since the year dummies capture all the variation in the index for a
country. Not controlling for general macroeconomic factors and technological shocks represents a
severe omission since technological shocks have historically arrived at common times in different
countries (Kortum and Lerner, 1999). Given the importance of such global technological shocks,
we cannot draw any meaningful inference from such country-by-country regressions.
Instead, we use each country which underwent a significant dismissal law change to undertake
traditional difference-in-difference tests, where we examine the before-after effect of a change in
dismissal laws in the affected country (the “treatment group”) vis-à-vis the before-after effect in
a country where such a change was not effected (the “control group”). By including another
country as a control group, these difference-in-difference tests largely neutralize the effect of global
technology shocks. Examining Figure 1 makes it clear that laws affecting dismissal underwent
changes primarily in three different instances: in the UK and France in the early 1970s and in
the US in 1989.17 We therefore examine the effect of each of these three changes in traditional
difference-in-difference tests. As mentioned before in Section 4.1, Figure 3 illustrates this differencein-difference.
The regression specification we implement is identical to that in Equation (1), except that we
restrict the sample to a treatment and a control country:
yict = βi + βc + βt + β1 ∗ DismissalLawsc,t + εict
(3)
Note that DismissalLawsc,t is constant for the “control” group. As shown in Appendix B, given
the country and year dummies, the coefficient β1 estimates the difference-in-difference.
Notice that compared to the usual difference-in-difference specification, which contains dummies
for treatment groups and treatment periods only, including dummies for all the application years as
17
India was the only other country which had significant changes in dismissal laws during our sample period.
However, the number of observations for India are quite limited, which precludes us from being able to undertake a
difference-in-difference test using the changes in India.
20
well as the patent classes leads to a much stronger test since we are able to control for time-invariant
country and patent class specific determinants of innovation as well as time-varying effects that are
common to all countries and all patent classes. The application year fixed effects enable us to also
control for the problem stemming from the truncation of citations, i.e., citations to patents applied
for in later years would on average be lower than citations to patents applied for in earlier years.
Similarly, the patent class fixed effects also enable us to control for time-invariant differences in
patenting and citation practices across industries.
Table 5 shows the results of these difference-in-difference tests. In our first natural experiment,
we exploit the dismissal law change in the US in 1989; the “control group” is Germany, which
did not experience such a law change in the sample period (see Figure 7). Results are reported
in columns 1-3 of Table 5 and clearly corroborate the hypothesis that tougher dismissal laws have
a favorable impact on innovation: The coefficient β1 , capturing the causal effect of the dismissal
law change in the US, is positive and significant at the 1% level for all three innovation proxies.
Our second difference-in-difference test examines the impact of dismissal law changes in the UK
in the early 1970s; the “control group” is the US, which did not experience such a law change in
that time interval (see Figure 8). The results from this test are reported in columns 4-6 of Table 5;
the change in dismissal laws had a positive and significant impact on two of the three innovation
proxies (number of patents and patenting firms); the impact on the other proxy (citations) is also
positive, but not significant. Our third difference-in-difference test looks at the impact of dismissal
law changes in France in the early 1970s; the “control group” is again the US (see Figure 9). Results
are reported in columns 7-9 of Table 5; the coefficient β1 , capturing the causal effect of the dismissal
law change in France, is positive and significant at the 1% level for all three innovation proxies.
Overall, the evidence presented in Table 5 lends strong support to the hypothesis that tougher
dismissal laws lead ex ante to greater innovation. The economic effects of these law changes are
quite large. In the US, for example, the dismissal index increased from 0 to 0.167 in 1988/1989. The
quantitative effect of this strengthening in employment protection was an increase in innovative
activity by 15.3%, as measured by the number of patents.18 The effect is similar or even larger
when the other two innovation proxies are considered; for instance, using the number of citations
18
Using column 1 of Table 5, we find that ln(P atents) = 4.119 + 0.854 ∗ DismissalLaws. Therefore, going
from a dismissal law index of 0 to an index value of 0.167 results in an increase in the number of patents filed of
exp(0.854 ∗ 0.167) − 1 = exp(0.143) − 1 = 15.3%.
21
to proxy for innovation would imply that the strengthening in dismissal laws in the U.S. increased
innovative activity by 31% per year.
4.3.1
Discussion
These difference-in-difference tests have several attractive features. First, apart from providing
evidence using specific natural experiments, these tests also have the advantage of easier interpretation due to the existence of specific treatment and control groups.
Second, the difference-in-difference tests address concerns that the results obtained above are
a spurious combination of (i) a general trend of labor laws, in particular laws governing dismissal,
becoming stricter over time; and (ii) a rising trend in USPTO patent applications (and grants)
since the year 1985 (see, for example, Kortum and Lerner (1999)). As seen in columns 4-9 of Table
5, the difference-in-difference tests for UK-vs-US and France-vs-US employ samples until 1978 and
1985 respectively. Given these time periods, the sample excludes years containing the rising trend
in USPTO patent applications.
Finally, by examining the effect of changes in one particular law in one particular country, the
difference-in-difference tests provide point estimates of the effect of specific changes in labor laws on
innovation using experiments of greatest relevance to policies concerned with promoting innovation.
4.4
Inter-industry differences based on Innovation Intensity
We now examine our Hypothesis 2 that the effect of dismissal laws should be disproportionately
stronger in industries that exhibit a greater propensity to innovate than in other industries. As
explained before in Section 4.1, Figure 5 illustrates this interaction effect. We examine these interindustry differences using (i) the fixed effect panel regressions employing the full sample; and (ii)
the two-country settings.
4.4.1
Fixed Effects Panel Regressions
To test Hypothesis 2, we interact the time-varying country level index of dismissal laws with a
time-varying, industry-level measure of Innovation Intensity. Again, we include country, application
year, and patent class dummies to control for time-invariant heterogeneity at these levels. The
22
regression specification is as follows:
yict = βi + βc + βt + β1 · (DismissalLawsct ∗ InnovationIntensityi,t−1 )
(4)
+β2 · DismissalLawsct + β3 · InnovationIntensityi,t−1 + βX + εict ,
where InnovationIntensityi,t−1 denotes the Innovation Intensity for patent class i in year (t − 1) .
We follow Acharya and Subramanian (2008) in measuring InnovationIntensityi,t−1 as the median
number of patents applied for by US firms in patent class i in year (t − 1). Since the proxy
for Innovation Intensity is time-varying, it captures the inter-temporal changes in the propensity
to innovate caused by technological shocks. Note that the interaction term (DismissalLawsct ∗
InnovationIntensityi,t−1 ) exhibits variation at the level of patent class i in country c in application
year t. Since our dependent variable, yict , also varies at the level of patent class i in country c and
application year t, the coefficient β1 is well-identified and measures the relative effect of dismissal
laws across industries that vary in their innovation intensity. Note that despite the country fixed
effects, the coefficient on dismissal laws (β2 ) is identified too since the dismissal law index exhibits
variation across time. Similarly, innovation intensity exhibits time variation as well, and therefore
its coefficient (β3 ) can be identified despite the presence of patent class fixed effects.
The principal coefficient of interest is that of the interaction between country level dismissal laws
and industry (i.e., patent class) level patenting intensity – β1 . Hypothesis 2 predicts that β1 > 0. As
the variable InnovationIntensity is constructed using U.S. patents, we avoid mechanical correlation
of the dependent variable with InnovationIntensity by using the innovation proxies based on the
number of patenting firms and the number of citations in this set of tests as the dependent variables.
The results of the basic tests are reported in columns 1-2 of Table 6, where we find that the
coefficient of the interaction term is indeed positive and statistically significant. As in our previous
tests, we control for other determinants of innovation in Columns 3-4. We find that the coefficient
of the interaction term stays positive and statistically significant.
The economic magnitude of the effect of the interaction term is also quite significant. Using
Column 3 in Table 6 with patents as the innovation measure and our usual set of control variables,
23
we find that
ln(P atents) = 0.353 ∗ (DismissalLaws) ∗ (InnovationIntensity) +
+0.520 ∗ DismissalLaws − 0.123 ∗ InnovationIntensity + β ∗ X
Consider now two patent classes which differ in the median number of patents issued to US firms
by one; then the marginal effect of labor laws on our proxy for innovation is greater by 68%
(=0.353/0.520) for the more innovative patent class than the less innovative one. Based on citations (using results documented in Column 4 of Table 6), the corresponding number is 18%
(=0.175/0.977).
4.4.2
Traditional difference-in-difference regressions
We also examine Hypothesis 2 using our difference-in-difference setup discussed in section 4.3.
We implement the difference-in-difference test as before, but this time we also interact the dismissal
law index with the variable InnovationIntensity. Again, to avoid mechanical correlation between
the dependent variable and the variable InnovationIntensity, we limit our innovation proxies to
those based on the number of firms and citations, respectively.
The results from this exercise are given in Table 7 and are in line with the results from the
fixed effects panel regressions (Table 6). For all three “natural experiment” settings (dismissal law
changes in UK, France, and US, respectively) the coefficient on the interaction between country level
dismissal laws and industry (i.e., patent class) level patenting intensity is positive and significant.
4.5
Effect of Other Dimensions of Labor Laws
Our next set of tests is designed to shed light on our Hypothesis 3 that labor laws that affect
the ex post likelihood of an employee being dismissed from employment matter more for innovation
than other categories of labor laws. To examine the relative significance of the laws regulating
dismissal vis-a-vis other categories of laws, we run the following regression:19
yict = βi + βc + βt + β1 ∗ lAc,t + β2 ∗ lBc,t + β3 ∗ lCc,t + β4 ∗ lDc,t + β5 ∗ lEc,t + βX + εict
19
(5)
Note that while the correlation between different labor law components is positive and significant, the tests do
not encounter any multi-collinearity problem.
24
where β1 - β5 measure the impact on innovation of the five components of the Deakin et al. (2007)
labor law index: Alternative employment contracts (lAc,t ), Regulation of working time (lBc,t ),
Regulation of dismissal (lCc,t ), Employee representation (lDc,t ), and Industrial action (lEc,t ).
Table 8 presents results of these tests. Columns 1-3 document the results from tests of equation
(5) without control variables, while the tests documented in Columns 4-6 account for the whole
range of control variables discussed in section 4.2. As can be seen from Table 8, the only dimension
of labor laws which has a consistently positive and significant impact on innovation is the “regulation
of dismissal”.
4.6
Within-country evidence using the WARN Act
In the previous sections, we provided evidence of the impact of labor laws on innovation in a
cross-country setting. In this section, we present tests of our main hypothesis based on U.S. data
only. These tests exploit a discontinuity introduced by the passage of the federal U.S. Worker
Adjustment and Retraining Notification (WARN) Act and do not rely on labor law index data.
A key advantage of these tests is that they remove any concerns about country-level unobserved
factors driving our results thus far.
In section 4.6.1, we describe the general features of the WARN Act. In section 4.6.2, we discuss
our empirical strategy. Section 4.6.3 focusses on the data sources and variable construction, and
section 4.6.4 presents the main test results.
4.6.1
An Overview of the WARN Act
The Worker Adjustment and Retraining Notification Act is a federal law (P.L. 100-379).20 It
was enacted by the U.S. Congress on August 4, 1988, and became effective on February 4, 1989.
The provisions of the WARN Act do not supersede any laws or collective bargaining agreements
that bestow additional rights on workers.21
Under the conditions outlined below, the WARN Act requires employers to give written notice
60 days before the date of a mass layoff or plant closing to affected workers, to the local govern20
The details on the WARN Act reported in this section are drawn from the following two sources, unless otherwise noted: United States Department of Labor – Employment & Training Administration (http :
//www.doleta.gov/layof f /warn.cf m); and Levine (2007).
21
For example, several states have passed similar statutes on the state level (see e.g. De Meuse and Dobrovolsky
(2004)); furthermore, during the 1970s and 1980s, courts in several of U.S. states adopted common-law exceptions to
the “employment-at-will” principle (see e.g. Autor et al. (2004)).
25
ment’s chief elected official where the employment site is located and to the State Rapid Response
Dislocated Worker Unit. The intent of the law is to provide affected workers with sufficient time for
retraining and for the search of alternative employment. Subject to the law are private employers
with 100 or more full-time employees, or with 100 or more employees who work at least a combined
4,000 hours a week. Only layoffs classified as “mass layoffs” or “plant closings,” or layoffs of 500 or
more full-time workers at a single site of employment, are covered.22 U.S. employees working at a
foreign facility of a U.S. employer count towards the 100 employee threshold, but are not entitled
to advance notice as specified under the WARN Act. Some exceptions to and exemptions from the
60 day notice period under WARN exist; for details, see Levine (2007).
In the case of non-compliance, employees, their representatives, and units of local government
can bring individual or class action suits in federal district courts against employers. Employers
who violate the WARN Act are liable for damages in the form of back pay and benefits to affected
employees.
The WARN Act applies to U.S. firms irrespective of their industry. For example, in 2002,
WARN Act notices received by the Employment Development Department in California included
the following companies23 : Lucent Technologies, Inc.; Virgin Records America; Bank of America;
Ericsson, Inc.; United Airlines, Inc.; Sony Pictures Entertainment, Inc.; Boeing Satellite Systems,
Inc.; IBM; Sony Electronics, Inc.; Arthur Andersen LLP; Daniel Free Freeman Hospital; Science
Applications International Corp.; and many others.
4.6.2
Test Design
We exploit the passage of the U.S. WARN Act as a quasi-natural experiment in order to
investigate the impact of the strengthening of dismissal laws via the WARN Act on innovation by
U.S. firms. The key to implementing this test is that only employers with more than 100 employees
are affected by the law (the “treatment group”), while firms with less than 100 employees are not
(the “control group”).
We measure the effect of the strengthening of dismissal laws via WARN on the treatment firms
22
A “plant closing” is defined as a closure of a facility within a single site of employment involving layoffs of at
least 50 full-time workers. In the case of a “mass layoff,” an employer lays off either between 50 and 499 full-time
workers at a single site of employment, or 33% of the number of full-time workers at a single site of employment.
23
Source: http : //www.edd.ca.gov/jobs and training/warn/eddwarncn02.pdf
26
vis-a-vis the control firms using the following specification:
yit = βi + βt + β1 ∗ Over100it ∗ Af ter1988t + β2 ∗ Over100it
(6)
+ β3 ∗ Af ter1988t + βX + it
where y is a proxy for innovation by firm i in year t, Af ter1988t is a dummy taking the value of
one after the passage of the WARN Act (i.e., for the years 1989-1994), and Over100it is a dummy
taking the value of one if a firm has ≥ 100 employees in a given year, and zero otherwise.24 βi and
βt are firm and year fixed effects, respectively. X is a set of control variables.
β1 measures the difference-in-difference effect on innovation of the strengthening of dismissal
laws via the WARN Act. The sample covers twelve years around the passage of the WARN Act
(from 1983-1994).
In addition to controlling for the time-invariant heterogeneity of firms via firm dummies and
for general macro-economic factors via year dummies, we include firm size to account for the fact
that larger firms might innovate more, and Tobin’s Q to control for investment opportunities. We
also control for R&D expenditures as an important input into innovation. Furthermore, as Sapra
et al. (2009) show that innovation is fostered by either an unhindered market for corporate control
or strong anti-takeover laws that significantly deter takeovers, we employ a control for the external
takeover pressure a firm faces. Finally, we cluster all standard errors at the firm level.
4.6.3
Data and Variable Construction
As before, to construct proxies for innovation, we use patents filed with the US Patent Office
(USPTO) and the citations to these patents, compiled in the NBER Patents File (Hall, Jaffe and
Trajtenberg, 2001). Each assignee in the NBER dataset is given a unique and time-invariant
identifier. We match the assignee names in the NBER patent dataset to the names of divisions
and subsidiaries belonging to a corporate family from the Directory of Corporate Affiliations. We
then match the name of the corporate parent to Compustat, which we use to obtain firm-level
24
The number of employees obtained from Compustat (data item emp) is the number of all employees of consolidated
subsidiaries, both domestic and foreign; this also includes U.S. employees working at a foreign facility of a U.S.
employer, who do count towards the 100 employee threshold. However, foreign workers at foreign sites of U.S. firms
do not count towards the threshold, but are included in the number of employees obtained from Compustat. We
might therefore include some companies within the treatment group sample even though they are not affected by the
WARN Act. However, this is of limited concern in our tests since we only consider patents filed by inventors who are
not only working for U.S. firms, but also residing in the U.S.
27
accounting data.
The variables are constructed as follows (Compustat data items are given in parentheses): We
approximate Tobin’s Q via the Market-to-Book ratio, which is the market value of assets to total
book assets. Market value of assets is total assets (at) plus market value of equity minus book
value of equity. The market value of equity is calculated as common shares outstanding (csho)
times fiscal-year closing price (prccf). Book value of equity is defined as common equity (ceq) plus
balance sheet deferred taxes (txdb). Size is the natural logarithm of sales (sale). R&D/TA is the
ratio of research and development expenditures (xrd) to total assets; missing values of research
and development expenditures are set to zero. Anti-Takeover Index is the state-level index of antitakeover statutes from Bebchuk and Cohen (2003). In order to eliminate the impact of outliers,
we winsorize the variables Market-to-Book and Size at the 1% and 99% levels; we winsorize the
number of employees (Compustat item: emp) and R&D/TA only at the 99% level, as both variables
are naturally bounded by zero.
Table 9 describes the main variables used in the WARN sample.
4.6.4
Results
Table 10 shows the results from the difference-in-difference tests. Columns 1–2 implement
equation 6 without control variables, while columns 3–4 include the control variables described
above. In line with Hypothesis 1, we find that the strengthening of dismissal laws via the WARN
Act had a positive and significant impact on U.S. firm-level innovation. To be specific, in column
3 of Table 10 (specification with control variables) we estimate that the passage of the WARN Act
increases innovation as measured by patents by exp(0.236)-1=27% in the treatment group (firms
with ≥ 100 employees) vis-a-vis the control group (firms with < 100 employees). The effect is of
similar magnitude when innovation is measured by the number of citations (31%).
5
Discussion
It is important to further examine the direction of causality from dismissal laws to innovation.
Was it the case that dismissal laws changed for reasons other than promoting innovation, so that
our evidence above can be interpreted truly as a causal effect of the change on innovation? Or, was
it the case that the dismissal law changes were part of an overall package to promote or give an
28
extra boost to growth and innovation, so that the evidence above exhibits some reverse causality?
Note that in either of these cases, the evidence lends support to our claim that dismissal laws can
affect the extent of innovative activity. Nevertheless, we examine reverse causality in our tests
below. In these tests, we also examine the dynamic effect of the dismissal law changes to infer
whether or not the effect of these law changes persisted after a few years. Since it is also important
to understand what caused the changes in the dismissal laws, we discuss the political economy of
the changes in labor laws. Finally, we discuss the relative merits of employing US patents to proxy
innovation internationally.
5.1
Causality or reverse-causality?
If the dismissal law changes were effected to provide an extra boost to innovation already
occurring due to some other changes in the economy, then we might see an “effect” of the change
even prior to the change itself. Another possible caveat is due to the political economy of what
brought about the dismissal law change. In particular, the dismissal laws could have been made
stronger in some countries precisely due to lobbying by innovative industries in these countries as
they happened to be more innovative than similar industries in other countries (due to technological
or comparative advantage reasons).
The unique dismissal law change in the US via the WARN Act lends itself well to address such
a concern; the “control group” employed in this test is again Germany, which did not experience
such a major dismissal law change in the sample period (1970-1995). We follow Bertrand and
Mullainathan (2003) in decomposing the change in dismissal laws into three separate time periods:
(i) Dismissal Law Change (-2,0), which captures any effects from two years before to the year of
the change; (ii) Dismissal Law Change (1,2), which captures the effects in the year after the change
and two years after the change; and (iii) Dismissal Law Change (≥3), which captures the effect
three years after the change and beyond.
Table 11 shows the results of these regressions. A positive and significant coefficient on Dismissal
Law Change (-2,0) would be symptomatic of reverse causation. However, we find that while this
coefficient is negative and statistically significant in Columns 1-2, it is statistically insignificant in
Column 3. As seen in the coefficients of Dismissal Law Change (1,2) and Dismissal Law Change
(≥3) in Columns 1-3, we note that while the dismissal law change has an effect on the innovation
29
proxies in the first two years, the effect of the law change lasts three years and beyond; in fact,
this “long-run” effect is economically greater than the effect in the first two years. The effect in
the first two years of the law change is consistent with evidence in Kondo (1999) that there is
about a one-and-a-half year lag between patent applications and R&D investment. Furthermore,
evidence that compared to the effect in the first two years, the effect of the dismissal law change
is economically larger for the period after three years is consistent with the long gestation periods
involved with innovative projects.
5.2
Political Economy of Changes in Labor Laws
Botero et al. (2004) find evidence that labor market regulation is often driven by political
considerations: countries with a longer history of leftist governments have more stringent labor
regulation. The evidence in Deakin et al. (2007) supports the evidence in Botero et al. (2004) that
the primary motivation for labor market (de)regulation is political. Deakin et al. (2007) find that a
rapid decline in the intensity of labor market regulation in the UK coincided with the election of a
Conservative government committed to a policy of labor market deregulation. Similarly, a limited
revival of regulation of the labor markets in the UK coincided with the return to office in 1997 of
a Labor government which ended the UK’s opting out of the EU Social Charter. Similarly, they
find that in France, the election of the socialist government in 1981 led to a series of labor law
reforms, the ‘Auroux laws’, which were enacted in 1982 and affected a wide range of issues in both
individual and collective labor law. Since that time, French labor law has tracked the changing
political fortunes of the main parties. The change in the regulation of dismissal laws in the US was
effected in 1989 when the Worker Adjustment and Retraining Notification Act of 1988 (WARN)
was passed. Brugemann (2007) examines various articles in the business press that document the
events preceding and following the WARN Act. He does not find any of these articles arguing that
this Act was aimed at improving any specific aspect of the economy.25
Finally, as we discussed in section 4.2.6 and as documented in Table 4, our results are robust
to inclusion of a time-varying country-specific proxy of political cabinet composition.
25
Most of the business press articles focus on firms accelerating layoffs before the law’s passage in an effort to avoid
being subject to the new law.
30
5.3
USPTO Patents as a Proxy for Innovation
To compare innovation done by firms across countries, it is crucial to employ patents filed in a
single jurisdiction by firms from these countries. Since enforcement of intellectual property protection may vary across jurisdictions, comparing domestic patents filed in the various countries would
not accurately measure differences in ex post innovation or the ex ante incentives for innovation in
these countries. In contrast, comparing patents granted in one jurisdiction alleviates such concerns
of heterogeneity and provides standardization across patents in the strength of patent protection,
the duration of protection, the penalties for patent infringement and therefore the nature of patent
enforcement, and the patenting practices followed by the jurisdiction’s patent office for all firms
filing in the jurisdiction irrespective of which country the firms belong to.
Given its status as the technological leader, the US is the natural single jurisdiction of choice.
Lall (2003) notes that “most researchers on international technological activity use US patent data,
for two reasons. First, practically all innovators who seek to exploit their technology internationally
take out patents in the USA, given its market size and technological strength. Second, the data
are readily available and can be taken to an extremely detailed level.” Furthermore, the US has
the most advanced patenting system in the world (Kortum and Lerner, 1999) and most innovating
firms internationally file patents in the US (Cantwell and Hodson, 1991).26 Finally, US patents are
a high quality indicator of international technological activity.27
However, using patents filed with the USPTO introduces potential biases since it is likely that
foreign firms file patents with the USPTO because they need to sell their products in the US.28
Hence, the variables we employed in our tests to control for such systematic biases for comparative
advantages and bilateral trade patterns were quite important.
26
Cantwell and Hodson (1991) found in their study of patenting practices of the world’s largest firms (from the
Fortune listings) that over 85% of all these firms had recorded patenting in the US.
27
Cantwell and Anderson (1996) note that the pattern of patenting in the US is a good indicator of technological
activity in all industrialized and newly industrializing countries. Soete and Wyatt (1983) also note that although
international patenting propensities remain lower than domestic patenting propensities, international patents are on
average of higher ‘quality’.
28
Paci, Sassu, and Usai (1997) find that firms apply for a patent abroad mainly to: (i) protect goods to be
exported to the countries concerned; (ii) protect goods that may be subsequently produced in the foreign country;
(iii) guarantee the payment of royalties from the granting of production licences; and (iv) exchange know-how and
other technological information.
31
6
Conclusion
In the corporate sector, some of the most successful innovators like 3M and Google have a
culture that encourages employees to take risks and which tolerates failure (Hindo, 2007). We also
know from the tenure-track system for academic appointments that there is a trade-off between
promoting innovative research by granting faculty a certain period over which their job is guaranteed
and entrenching them for too long. This paper presented empirical evidence that this relationship
between innovation and ease with which employees can be dismissed by firms exists on a broad
scale. Using patents and citations as proxies for innovation and a time-varying index of labor laws,
we find that innovation is fostered by stringent dismissal laws.
Overall, we conclude that labor laws are an important part of the policy toolkit for promoting
innovative growth. This conclusion complements the evidence offered by Acharya and Subramanian
(2008) who focus on the effect on innovation of another aspect of the legal environment – the creditor
or debtor friendliness of the bankruptcy code. They find that debtor-friendly codes, by giving firms
a “fresh start” when they falter, promote more innovative pursuits. It is an interesting, open
question as to whether creditor rights and labor laws interact in their effects, whether they are
substitutes or complements, and indeed, which of the two matters more for innovation.
32
Appendix A – Description of the Labor Law Index
This section briefly describes the five components of the labor law index, as detailed in Deakin, Lele
and Siems (2007), namely Alternative Employment Contracts, Regulation of Working time, Regulation of
Dismissal, Employee Representation, and Industrial Action.
Alternative Employment Contracts.
This sub-index measures the cost of using alternatives to the
“standard” employment contract, computed as an average of the following eight variables: 1. Stringency
as to the determination of the legal status of the worker (equal 1 if the law mandates such a status; 0.5
if the law allows the status to be determined by the contract nature; and 0 if the parties have complete
freedom in stipulating the status); 2. Equal treatment of part-time workers relative to full-time ones (equal
1 if part-time workers are legally recognized a right to equal treatment with full-time workers; 0.5 if this
right is more limited; and 0 otherwise); 3. Cost of dismissing part-time workers relative to that for full-time
workers (equal 1 if part-time workers enjoy proportionate rights to full time workers regarding dismissal
protection; and 0 otherwise); 4. Substantive constraints on the conclusion of a fixed-term contract (equal 1
if there is such a constraint; and 0 otherwise); 5. The right to equal treatment of fixed-term workers relative
to permanent workers (equal 1 if such a right is present, 0.5 if such a right is more limited, and 0 otherwise);
6. Maximum duration of fixed-term contracts before the employment is deemed permanent (taking scores
between 0 and 1, with higher scores indicating a lower allowed duration); 7. Stringency as to the use of
agency work (equal 1 if the use of agency labor is prohibited, 0.5 if this use is limited and 0 otherwise); and
8. Equal treatment of agency workers relative to permanent ones (equal 1 if the right to this equal treatment
is legally recognized, an intermediate score between 0 and 1 if this right is limited, and 0 otherwise).
Regulation of Working Time. This sub-index measures how employee-focused the law on working
time is. The sub-index is computed as an average score of the following seven variables: 1. Annual leave
entitlements, which measures the standardized normal length of annual paid leave (taking values between 0
and 1, with higher values indicating longer leave entitlements); 2. Public holiday entitlements (taking values
between 0 and 1, with higher values indicating longer public holiday entitlements); 3. Overtime premia
(equal 1 if the premium if double time, 0.5 if it is time and a half, and 0 if there is no overtime premium); 4.
Weekend working (equal 1 if the normal premium for weekend working is double time, or if weekend working
is prohibited or strictly controlled, 0.5 if it is time and a half, and 0 if there is no premium); 5. Limits to
overtime working (equal 1 if there is a limit to the number of weekly working hours, including overtime, 0.5
if such limits can be averaged out over a period longer than a week, and 0 if there is no such a limit); 6.
Duration of the weekly normal working hours, exclusive of overtime (equal 1 for 35 hours or less, 0 for 50
hours or more, and intermediate values between 0 and 1 for the rest); and 7. Maximum daily working time
(scores are normalized to be on a 0-1 scale, with a limit of 8 hours scoring 1, and a limit of 18 hours or more
scoring 0).
Regulation of Dismissal.
This sub-index measures the extent to which the regulation of dismissal
favors the employee. The sub-index is an average score of the following nine variables: 1. Legally mandated
notice period (values are normalized to be between 0 and 1, with 12 weeks = 1 and 0 weeks = 0); 2. Legally
mandated redundancy compensation made to a worker who is made redundant after 3 years of employment
(values are normalized to be between 0 and 1, with 12 weeks = 1 and 0 weeks = 0); 3. Minimum qualifying
period of service for normal case of unjust dismissal (values are normalized to be between 0 and 1, with 0
months = 1 and 3 years or more = 0); 4. Procedural constraints on dismissal (taking values of 1, 0.67, 0.33
and 0; the higher of which suggests higher costs of the employer’s failure to follow procedural requirements
prior to dismissal); 5. Substantive constraints on dismissal (taking values of 1, 0.67, 0.33 and 0; the higher
of which suggests stricter requirements on the part of the employer to establish reasons for dismissal); 6.
Reinstatement as a normal remedy for unfair dismissal (taking values of 1, 0.67, 0.33 and 0; which suggest,
as the remedy for unfair dismissal, respectively reinstatement, a choice of reinstatement or compensation,
compensation, no remedy); 7. Notification of dismissal (taking values of 1, 0.67, 0.33 and 0; higher values
of which imply more complicated procedure for dismissal notification); 8. Redundancy selection (equal 1 if
redundancy dismissal must be based on priority rules, and 0 otherwise); and 9. Priority in re-employment
33
(equal 1 if re-employment must be based on priority rules, 0 otherwise).
Employee Representation.
This sub-index measures the strength of employee representation. The
sub-index is an average score of the following seven variables: 1. Right to Unionization (taking values of
1, 0.67, 0.33 and 0; higher values indicate better protection of the right to form trade unions); 2. Right to
collective bargaining (taking values of 1, 0.67, 0.33 and 0; higher values indicate better protection of the
right to collective bargaining); 3. Duty to bargain (equal 1 if the employer has the legal duty to reach an
agreement with worker organizations; and 0 otherwise); 4. Extension of collective agreements (equal 1 if
collective agreements are legally extended to third parties at the national or sectoral level, and 0 otherwise);
5. Closed shops (equal 1 if both pre-entry and post-entry closed shops are permitted, 0.5 if pre-entry closed
shops are prohibited but post-entry ones are permitted; and 0 if neither type of closed shops is permitted); 6.
Codetermination via board membership (equal 1 if unions/ workers have the legal right to nominate directors
in companies of a certain size; and 0 otherwise); and 7. Codetermination and information/ consultation of
workers (taking values of 1, 0.67, 0.5, 0.33 and 0; higher values of which suggest higher degree of participation
by workers in the determination process through work councils and enterprise committees).
Industrial Action. This sub-index measures the strength of legal protection for industrial action. The
sub-index is calculated as the average of the following nine variables: 1. Unofficial industrial action (equal 1
if strikes are conditionally not unlawful, and 0 otherwise); 2. Political industrial action (equal 1 if politicaloriented strikes are permitted, and 0 otherwise); 3. Secondary industrial action (taking values of 1, 0.5 and
0 if secondary or sympathy strike action is respectively unconstrained, permitted under certain conditions,
and prohibited); 4. Lockouts (equal 1 if permitted and 0 otherwise); 5. Right to industrial action (taking
values of 1, 0.67, 0.33 and 0; higher values of which suggest better protection of the right to industrial
action); 6. Waiting period prior to industrial action (equal 1 if strikes can occur without mandatory prior
notification/waiting period, and 0 otherwise); 7. Peace obligation (equal 1 if existence of a collective agreement does not render a strike unlawful, and 0 otherwise); 8. Compulsory conciliation or arbitration (equal
1 if alternative dispute resolution mechanisms before the strike are not mandatory, and 0 otherwise); and
9. Replacement of striking workers (equal 1 if employers are prohibited from dismissing striking workers
engaging in a non-violent or non-political strike, and 0 otherwise).
Appendix B – “Difference-in-Difference” Interpretation for the Fixed
Effect Panel Regressions
In this Appendix, we show that the fixed effects panel regressions employed in equation (1) estimate a
“difference-in-difference” in a generalized multiple treatment groups, multiple time period setting.
We begin with the model specification used in equation (1):
yict = βi + βc + βt + β1 · DismissalLawsct + εict
(7)
During the sample period 1970-2002, suppose the Dismissal Law Index for country c, DismissalLawsct ,
changes n times in years t1 , ..., tn where 1 < ... < n and tl denotes the year in which the lth change occured
for country c. Denote ml = [tl + 1, tl+1 ] as the time interval during which the lth change has occured but
not the (l + 1)th . Let DismissalLawsc (ml ) denote the value of the Dismissal Law index during the period
ml . Thus, DismissalLawsct = DismissalLawsc (ml ) for any t ∈ ml .
Therefore,
yict
yict0
= βi + βc + βt + β1 · DismissalLawsc (ml ) + εict , t ∈ ml
0
= βi + βc + βt0 + β1 · DismissalLawsc (ml+1 ) + εict0 , t ∈ ml+1
(8)
(9)
Subtracting (8) from (9), we obtain
yict0 − yict = (βt0 − βt ) + β1 · ∆DismissalLawsc,l + εict0 − εict
where
∆DismissalLawsc,l = DismissalLawsc (ml+1 ) − DismissalLawsc (ml )
34
(10)
denotes the magnitude of the lth change in the Dismissal Law Index in country c.
Let c0 denote a country that did not change its Dismissal Laws over the time intervals ml or ml+1 or
equivalently the time period [tl + 1, tl+2 ].
yic0 t
yic0 t0
= βi + βc + βt + β1 · DismissalLawsc0 (ml ) + νict , t ∈ ml
0
= βi + βc + βt0 + β1 · DismissalLawsc0 (ml+1 ) + νict0 , t ∈ ml+1
(11)
(12)
Because the Dismissal Law index is unchanged over the time period [tl + 1, tl+2 ],
DismissalLawsc0 (ml ) = DismissalLawsc0 (ml+1 )
(13)
Subtracting (11) from (12) and using (13), we obtain
yic0 t0 − yic0 t = (βt0 − βt ) + νict0 − νict
(14)
Subtracting (14) from (10), we obtain
[yict0 − yict ] − [yic0 t0 − yic0 t ] = β1 · ∆DismissalLawsc,l + [(εict0 − νict0 ) − (εict − νict )]
Assuming that
E [{(εict0 − νict0 ) − (εict − νict )} |∆DismissalLawsc,l ] = 0
(15)
we get after taking expectations
β1 · ∆DismissalLawsc,l =
E [yict0 − yict ]
{z
}
|
Before-after difference for Treatment
−
E [yic0 t0 − yic0 t ]
{z
}
|
Before-after difference for Control
Thus, the coefficient β1 in (1) estimates the “difference-in-difference” in a multiple treatment groups, multiple
time periods setting.
The identifying assumption for the “difference-in-difference” (equation (15)) is that the error term
[(εict0 − νict0 ) − (εict − νict )] , which captures unexplained factors influencing changes in innovation in the
treatment country vis-à-vis the unexplained factors influencing changes in innovation in the control country, should be uncorrelated with the changes in the Dismissal Laws, ∆DismissalLawsc,l . This identifying
assumption is weaker than assuming that εict in (1) , unexplained factors influencing levels of innovation, is
uncorrelated with the level of the Dismissal Law index, DismissalLawsct .
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[39] Kondo M., 1999, “R&D dynamics of creating patents in the Japanese industry,” Research Policy, 28(6),
587-600.
[40] Nicita, A., and M. Olarreaga, 2006, “Trade, Production and Protection: 1976-2004,” World Bank
Economic Review , 21(1).
[41] Nicoletti, G., and S. Scarpetta, 2001, “Interaction between Product and Labour Market Regulations:
Do They Affect Employment? Evidence from the OECD Countries,” Conference on “Labour Market
Institutions and Economic Outcomes.” Lisbon: Bank of Portugal, June 3–4, 2001.
[42] Paci R., Sassu A., Usai S., 1997, “International patenting and national technological specialization,”
Technovation, 17(1), pp. 25-38.
[43] Pakes, A., and M. Shankerman, 1984, “The rate of obsolescence of patents, research gestation lags, and
the private rate of return to research resources,” in Zvi Griliches, ed., R&D, Patents and Productivity,
University of Chicago Press, 98–112.
[44] Sapra, H., A. Subramanian and K. Subramanian, 2009, “Corporate Governance and Innovation,” Working paper, Chicago GSB.
[45] Simon, H.A., 1951, “A Formal Theory of the Employment Relationship,” Econometrica, 19, 293–305.
[46] Soete, L. and S. Wyatt, 1983, “The use of foreign patenting as an internationally comparable science
and technology output indicator,” Scientometrics, January: 31-54.
[47] Williamson, O. E., M. L. Wachter, and J. E. Harris, 1975, “Understanding the Employment Relation:
The Analysis of Idiosyncratic Exchange,” The Bell Journal of Economics, 6(1), 250–278.
37
Figure 1: Regulation of Dismissal.
The figure shows the strength of the “Regulation of Dismissal” for a given country and year. Higher values
indicate more employment protection / stricter laws. The dismissal index data is from Deakin et al. (2007).
38
Figure 2: Innovation Proxies vs. Dismissal Law Index.
The figure shows the univariate relationship between the strength of dismissal regulation and measures of
innovation, after accounting for year, country, and industry fixed effects. On the y-axis of each graph, we
plot the residuals from a regression of our innovation measure (log of patents and citations, respectively) on
year, country, and industry dummies; the x-axis of each graph displays the Deakin et al. (2007) dismissal
law index.
39
Figure 3: Aggregate Innovation: U.S. vs Germany (1).
The figure shows a plot across time of the ratio of the realized number of patents and citations in a particular
year to that in 1989, the year the US WARN Act became effective. The continuous line shows the ratio for
the US while the discontinuous line shows the same for Germany, which experienced no dismissal law change
in the time interval under examination. The vertical line indicates the year the US WARN Act became
effective.
40
Figure 4: Aggregate Innovation: U.S. vs Germany (2).
This figure shows the linear fit of the two innovation measures patents and citations for the treated (US;
continuous line) and control (Germany; discontinuous line) groups before and after the WARN Act became
effective (1989).
41
Figure 5: Differences in Innovation between Innovation-intensive and Non-intensive
industries for US vis-à-vis Germany.
This figure plots the time series of the ratio of the realized number of patents and citations in an innovationintensive sector (Surgery and Medical Instruments) relative to a non-intensive sector (Textiles and Apparel)
for the US vis-a-vis Germany. The continuous line shows the trend for the US while the discontinuous line
shows the same for Germany. The vertical line indicates the year 1989, when the US WARN Act became
effective. For each country, the ratio is normalized to 1 in 1989.
42
Figure 6: WARN Act and Innovation by US Firms.
This figure shows the linear fit of the number of patents and citations for the treated (firms with ≥ 100
employees; continuous line) and control (firms with < 100 employees; discontinuous line) groups before and
after the WARN Act became effective (1989).
Figure 7: Regulation of Dismissal, U.S. and Germany.
The figure shows the index representing the regulation of dismissal for the U.S. and Germany from 1970-1995.
43
Figure 8: Regulation of Dismissal, U.S. and U.K.
The figure shows the index representing the regulation of dismissal for the U.S. and U.K. from 1970-1978.
Figure 9: Regulation of Dismissal, U.S. and France.
The figure shows the index representing the regulation of dismissal for the U.S. and France from 1970-1985.
44
45
Variable
Number of patents
Number of patenting firms
Number of citations
Dismissal Law Index
Variable
Number of patents
Number of patenting firms
Number of citations
Dismissal Law Index
Variable
Number of patents
Number of patenting firms
Number of citations
Dismissal Law Index
Variable
Number of patents
Number of patenting firms
Number of citations
Dismissal Law Index
Variable
Number of patents
Number of patenting firms
Number of citations
Dismissal Law Index
Panel A: United States
Observations
Mean Standard
13291 120.518
13291
49.122
13291 820.045
13291
0.070
Panel B: United Kingdom
Observations
Mean Standard
10383
8.152
10383
5.501
10383
44.474
10383
0.377
Panel C: Germany
Observations
Mean Standard
11722
18.615
11722
9.550
11722
83.339
11722
0.431
Panel D: France
Observations
Mean Standard
10277
8.085
10277
5.157
10277
38.271
10277
0.699
Panel E: India
Observations
Mean Standard
661
1.852
661
1.390
661
4.080
661
0.782
Deviation
2.222
1.088
8.125
0.040
Deviation
11.700
5.366
57.678
0.150
Deviation
24.462
9.931
121.727
0.018
Deviation
12.630
6.090
72.760
0.094
Deviation
168.881
59.590
1317.006
0.082
Min
1
1
0
0.610
Min
1
1
0
0.281
Min
1
1
0
0.407
Min
1
1
0
0.049
Min
1
1
0
0
Max
20
10
88
0.797
Max
262
64
767
0.782
Max
365
113
1360
0.488
Max
297
90
1353
0.444
Max
3172
728
16726
0.167
The table gives summary statistics for the following variables per country and year: number of patents, number of patenting firms, number of citations,
and regulation of dismissal. Patent data is from the NBER Patents File (Hall, Jaffe and Trajtenberg, 2001); the labor law index data is from Deakin
et al. (2007).
Table 1: Summary Statistics.
46
US Patent class dummies
Country dummies
Application year dummies
Observations
R-squared
Constant
Log of per capita GDP
Ratio of Value Added
Log Exports
Antidirector
Rights Index
Efficiency of
Judicial System
Log Imports
Rule of Law
Creditor Rights Index
Dependent Variable is
Logarithm of
Dismissal Law Index
-3.824***
(0.084)
Y
Y
Y
46334
0.83
(1)
Number of
Patents
0.139***
(0.047)
-3.246***
(0.072)
Y
Y
Y
46334
0.83
(2)
Number of
Patenting Firms
0.173***
(0.039)
-4.841***
(0.12)
Y
Y
Y
42918
0.80
(3)
Number of
Citations
0.227***
(0.063)
(4)
Number of
Patents
0.808***
(0.089)
-0.109***
(0.021)
0.155
(0.14)
-0.365***
(0.027)
0.716***
(0.045)
0.034
(0.057)
-0.075
(0.058)
0.020
(0.037)
0.019
(0.23)
-6.324***
(1.22)
Y
Y
Y
32941
0.84
(5)
Number of
Patenting Firms
0.927***
(0.072)
-0.072***
(0.017)
0.174
(0.11)
-0.279***
(0.022)
0.619***
(0.037)
0.036
(0.047)
-0.062
(0.047)
0.008
(0.030)
0.003
(0.18)
-6.018***
(0.97)
Y
Y
Y
32941
0.84
(6)
Number of
Citations
1.156***
(0.11)
-0.114***
(0.028)
0.505***
(0.18)
-0.320***
(0.036)
0.843***
(0.062)
-0.053
(0.084)
-0.243***
(0.084)
0.010
(0.055)
-0.492
(0.30)
-8.761***
(1.58)
Y
Y
Y
30159
0.82
The OLS regressions below implement the following model:
yict = βi + βc + βt + β1 ∗ DismissalLawsc,t + βX + εict
where yict is the natural logarithm of a measure of innovation for the USPTO patent class i from country c in application year t. βi , βc , βt denote patent class,
country and application year fixed effects. β1 measures the impact of dismissal laws on our innovation proxies. X denotes a set of control variables. The Creditor
Rights Index is from Djankov, McLiesh, and Shleifer (2007). Rule of Law, Antidirector Rights Index and the Efficiency of Judicial System are time-invariant legal
variables (all from La Porta et al. (1998)). Log Imports is the log of a country’s imports from the US in a given 3-digit ISIC industry in a given year; Log Exports
is the log of a country’s exports to the US in a given 3-digit ISIC industry in a given year (export and import data are from Nicita and Olarreaga, 2006). Ratio
of Value Added is the ratio of value added in the 3-digit ISIC in a year to the total value added by that country in that year (from UNIDO). Log of per capita
GDP is the logarithm of real GDP per capita. The dismissal index data is from Deakin et al. (2007). Robust standard errors are given in parentheses. ***, **,
and * denote significance at the 1%, 5%, and 10% levels, respectively.
Table 2: Fixed Effects Regressions using Dismissal Law Index.
47
Patent class, country, application year dummies
Patent Category Trend (Application Year
* Patent Category Dummies)
Country Trend (Application Year
* Country Dummies)
Observations
R-squared
Constant
Log of per capita GDP
Ratio of Value Added
Log Exports
Antidirector
Rights Index
Efficiency of
Judicial System
Log Imports
Rule of Law
Creditor Rights Index
Dependent Variable is
Logarithm of
Dismissal Law Index
158.0***
(3.65)
Y
Y
Y
46212
0.84
Y
46212
0.84
(2)
Number of
Patenting Firms
0.328***
(0.040)
194.6***
(4.22)
Y
Y
(1)
Number of
Patents
0.266***
(0.047)
42874
0.81
Y
407.8***
(11.3)
Y
Y
(3)
Number of
Citations
0.280***
(0.066)
32839
0.84
Y
(4)
Number of
Patents
1.855***
(0.17)
-0.009
(0.029)
20.84***
(5.72)
-1.824*
(1.00)
11.54***
(4.42)
0.053
(0.057)
-0.061
(0.058)
0.024
(0.037)
-0.060
(0.26)
-12.35
(36.4)
Y
Y
32839
0.85
Y
(5)
Number of
Patenting Firms
1.409***
(0.14)
0.018
(0.025)
14.92***
(4.29)
-0.492
(0.80)
1.841
(3.52)
0.040
(0.046)
-0.065
(0.047)
0.012
(0.030)
-0.179
(0.21)
70.18**
(28.0)
Y
Y
30122
0.82
Y
(6)
Number of
Citations
2.804***
(0.23)
-0.074*
(0.043)
23.91***
(8.04)
-2.498*
(1.42)
26.35***
(6.40)
-0.017
(0.084)
-0.212**
(0.083)
0.014
(0.054)
0.140
(0.36)
125.8**
(52.5)
Y
Y
The OLS regressions below implement the following model:
yict = tβi + tβc + βt + β1 ∗ DismissalLawsc,t + β · X + εict
where tβi denotes a time trend for industry (patent category) i and tβc denotes a time trend for country c; β1 measures the impact of dismissal laws on our
innovation proxies. X denotes a set of control variables. The Creditor Rights Index is from Djankov, McLiesh, and Shleifer (2007). Rule of Law, Antidirector
Rights Index and the Efficiency of Judicial System are time-invariant legal variables (all from La Porta et al. (1998)). Log Imports is the log of a country’s
imports from the US in a given 3-digit ISIC industry in a given year; Log Exports is the log of a country’s exports to the US in a given 3-digit ISIC industry in a
given year (export and import data are from Nicita and Olarreaga, 2006). Ratio of Value Added is the ratio of value added in the 3-digit ISIC in a year to the
total value added by that country in that year (from UNIDO). Log of per capita GDP is the logarithm of real GDP per capita. The dismissal index data is from
Deakin et al. (2007). Robust standard errors are given in parentheses. ***, **, and * denote significance at the 1%, 5%, and 10% levels, respectively.
Table 3: Fixed Effects Regressions using Dismissal Law Index - Robustness: Country and Patent Category Trends.
48
US Patent class dummies
Country dummies
Application year dummies
Observations
R-squared
Constant
Log of per capita GDP
Ratio of Value Added
Log Exports
Antidirector
Rights Index
Efficiency of
Judicial System
Log Imports
Rule of Law
Creditor Rights Index
Government
Dependent Variable is
Logarithm of
Dismissal Law Index
Data from 1978-2002
(1)
(2)
(3)
Number of
Number of
Number of
Patents Patenting Firms
Citations
0.939***
1.012***
1.225***
(0.088)
(0.071)
(0.11)
0.021***
0.012***
0.037***
(0.004)
(0.003)
(0.005)
-0.051**
-0.034**
-0.062**
(0.020)
(0.017)
(0.028)
2.471***
1.992***
2.903***
(0.089)
(0.071)
(0.12)
0.0130
0.0150
0.0685**
(0.020)
(0.016)
(0.028)
0.703***
0.628***
0.846***
(0.045)
(0.037)
(0.062)
0.021
0.023
-0.050
(0.057)
(0.047)
(0.084)
-0.082
-0.060
-0.220***
(0.058)
(0.047)
(0.084)
0.027
0.016
0.011
(0.037)
(0.030)
(0.054)
-1.244***
-0.784***
-1.798***
(0.22)
(0.18)
(0.30)
-15.46***
-15.40***
-18.32***
(1.69)
(1.36)
(2.34)
Y
Y
Y
Y
Y
Y
Y
Y
Y
32609
32609
29988
0.84
0.84
0.82
Data from 1993-2002
(4)
(5)
(6)
Number of
Number of Number of
Patents Patenting Firms
Citations
1.827***
1.792***
4.658***
(0.63)
(0.51)
(1.06)
0.038***
0.024***
0.082***
(0.007)
(0.006)
(0.011)
0.361***
0.334***
0.669***
(0.063)
(0.051)
(0.11)
3.279***
2.829***
4.597***
(0.25)
(0.20)
(0.40)
0.219***
0.215***
0.392***
(0.035)
(0.028)
(0.053)
.
.
.
(.)
(.)
(.)
0.022
0.051
-0.165
(0.094)
(0.075)
(0.15)
-0.094
-0.036
-0.336**
(0.094)
(0.076)
(0.15)
0.108**
0.070
0.136
(0.055)
(0.044)
(0.084)
-0.239
-0.280
-0.036
(0.60)
(0.49)
(0.91)
-27.53***
-23.34***
-48.50***
(5.79)
(4.80)
(9.53)
Y
Y
Y
Y
Y
Y
Y
Y
Y
12374
12374
9960
0.85
0.86
0.83
The OLS regressions below implement the following model:
yict = βi + βc + βt + β1 ∗ DismissalLawsc,t + βX + εict
where yict is the natural logarithm of a measure of innovation for the USPTO patent class i from country c in application year t. βi , βc , βt denote patent class,
country and application year fixed effects. β1 measures the impact of dismissal laws on our innovation proxies. X denotes a set of control variables. Government,
from the Comparative Political Data Set by Armingeon et al. (2008), captures the balance of power between left and right-leaning parties in a given country’s
parliament (variable denoted “govparty” in Armingeon et al. (2008)). The Creditor Rights Index is from Djankov, McLiesh, and Shleifer (2007). Rule of Law,
Antidirector Rights Index and the Efficiency of Judicial System are time-invariant legal variables (all from La Porta et al. (1998)). Log Imports is the log of a
country’s imports from the US in a given 3-digit ISIC industry in a given year; Log Exports is the log of a country’s exports to the US in a given 3-digit ISIC
industry in a given year (export and import data are from Nicita and Olarreaga, 2006). Ratio of Value Added is the ratio of value added in the 3-digit ISIC in a
year to the total value added by that country in that year (from UNIDO). Log of per capita GDP is the logarithm of real GDP per capita. The dismissal index
data is from Deakin et al. (2007). Robust standard errors are given in parentheses. ***, **, and * denote significance at the 1%, 5%, and 10% levels, respectively.
Table 4: Fixed Effects Regressions using Dismissal Law Index - Robustness: Controlling for Political Cabinet Composition.
49
US Patent class dummies
Country dummies
Application year dummies
Observations
R-squared
Constant
Dependent Variable is
Logarithm of
Dismissal Law Index
(1)
(2)
(3)
Germany & US; US dismissal
law change in 1989;
data from 1970-1995
Number of
Number of
Number of
Patents
Patenting Firms
Citations
0.854***
0.692***
1.619***
(0.13)
(0.10)
(0.16)
4.119***
3.362***
6.188***
(0.021)
(0.017)
(0.026)
Y
Y
Y
Y
Y
Y
Y
Y
Y
20039
20039
19875
0.85
0.86
0.83
(4)
(5)
(6)
UK & US; UK dismissal
law changes in early 1970s;
data from 1970-1978
Number of
Number of
Number of
Patents Patenting Firms
Citations
0.149*
0.222***
0.187
(0.085)
(0.072)
(0.12)
1.397***
1.135***
3.008***
(0.031)
(0.026)
(0.039)
Y
Y
Y
Y
Y
Y
Y
Y
Y
6633
6633
6568
0.92
0.92
0.89
(7)
(8)
(9)
France & US; France dismissal
law change in early 1970s;
data from 1970-1985
Number of
Number of Number of
Patents
Patenting Firms
Citations
0.376***
0.422***
0.339***
(0.051)
(0.043)
(0.073)
4.307***
3.524***
2.665***
(0.021)
(0.018)
(0.052)
Y
Y
Y
Y
Y
Y
Y
Y
Y
11623
11623
11474
0.91
0.91
0.88
The OLS regressions below implement the following model:
yict = βi + βc + βt + β1 ∗ DismissalLawsc,t + εict
where yict is the natural logarithm of a measure of innovation for the USPTO patent class i from country c in application year t. βi , βc , βt denote
patent class, country and application year fixed effects. DismissalLawsc,t denotes the index of laws governing dismissal in country c in year t. β1
measures the difference-in-difference effect of the change of the regulation of dismissal. In this table, we focus on regressions examining “large” changes
in the regulation of dismissal in three countries. Columns 1-3 report the results examining the impact of the dismissal law change in the U.S. in 1989;
the “control group” is Germany, which did not experience such a law change in the sample period. Columns 4-6 report the results examining the
impact of dismissal law changes in the U.K. in the early 1970s; the “control group” is the U.S. Finally, Columns 7-9 report the results examining the
impact of dismissal law changes in France in the early 1970s; the “control group” is again the U.S, which did not experience such a law change in
that time interval. The dismissal index data is from Deakin et al. (2007). Robust standard errors are given in parentheses. ***, **, and * denote
significance at the 1%, 5%, and 10% levels, respectively.
Table 5: Difference-in-Difference Tests using the Regulation of Dismissal Index.
Table 6: Relative Impact of Dismissal Laws on Aggregate Innovation in Different Industries based on their Innovation Intensity.
The OLS regressions below implement the following model:
yict = βi + βc + βt + β1 · DismissalLawsct ∗ InnovationIntensityi,t−1 + β2 · DismissalLawsct + β3 ·
InnovationIntensityi,t−1 + βX + εict
where yict is the natural logarithm of a measure of innovation for the USPTO patent class i from country c in application year t. βi , βc , βt denote patent class, country and application year fixed effects. The Innovation Intensity
for patent class i in year (t − 1) , InnovationIntensityi,t−1 , is measured as the median number of patents applied by
US firms in patent class i in year (t − 1). X denotes a set of control variables. The Creditor Rights Index is from
Djankov, McLiesh, and Shleifer (2007). Rule of Law, Antidirector Rights Index and the Efficiency of Judicial System
are time-invariant legal variables (all from La Porta et al. (1998)). Log Imports is the log of a country’s imports from
the US in a given 3-digit ISIC industry in a given year; Log Exports is the log of a country’s exports to the US in
a given 3-digit ISIC industry in a given year (export and import data are from Nicita and Olarreaga, 2006). Ratio
of Value Added is the ratio of value added in the 3-digit ISIC in a year to the total value added by that country in
that year (from UNIDO). Log of per capita GDP is the logarithm of real GDP per capita. The labor law index data
is from Deakin et al. (2007). Robust standard errors are given in parentheses. ***, **, and * denote significance at
the 1%, 5%, and 10% levels, respectively.
Dependent Variable is
Logarithm of
(Dismissal Law Index)
* (Innovation Intensity)
Dismissal Law Index
Innovation Intensity
(1)
Number of
Patenting Firms
0.336***
(0.044)
-0.126*
(0.065)
-0.124***
(0.020)
(2)
Number of
Citations
0.195***
(0.072)
0.138
(0.10)
-0.040
(0.030)
-3.088***
(0.092)
Y
Y
Y
41609
0.83
-2.545***
(0.096)
Y
Y
Y
38890
0.81
Creditor Rights Index
Rule of Law
Antidirector
Rights Index
Efficiency of
Judicial System
Log Imports
Log Exports
Ratio of Value Added
Log of per capita GDP
Constant
US Patent class dummies
Country dummies
Application year dummies
Observations
R-squared
50
(3)
Number of
Patenting Firms
0.353***
(0.056)
0.520***
(0.094)
-0.123***
(0.025)
-0.066***
(0.018)
0.257**
(0.12)
-0.265***
(0.024)
0.596***
(0.040)
0.023
(0.048)
-0.073
(0.048)
0.025
(0.031)
-0.164
(0.20)
-4.719***
(1.12)
Y
Y
Y
31087
0.84
(4)
Number of
Citations
0.175*
(0.096)
0.977***
(0.16)
-0.052
(0.039)
-0.087***
(0.030)
1.498***
(0.16)
0.437***
(0.019)
.
(.)
-0.109
(0.085)
-0.274***
(0.085)
0.025
(0.055)
-0.965***
(0.32)
-6.269***
(1.77)
Y
Y
Y
28741
0.82
51
US Patent class dummies
Country dummies
Application year dummies
Observations
R-squared
Constant
Innovation Intensity
Dismissal Law Index
Dependent Variable is
Logarithm of
Innovation Intensity* Dismissal Law Index
(1)
(2)
Germany & US; US dismissal
law change in 1989;
data from 1970-1995
Number of
Number of
Patenting Firms
Citations
0.320***
0.280**
(0.074)
(0.11)
0.363***
1.352***
(0.13)
(0.20)
-0.070***
-0.047
(0.021)
(0.031)
3.471***
3.265***
(0.029)
(0.076)
Y
Y
Y
Y
Y
Y
18651
18550
0.86
0.83
(3)
(4)
UK & US; UK dismissal
law changes in early 1970s;
data from 1970-1978
Number of
Number of
Patenting Firms
Citations
0.226*
0.524**
(0.12)
(0.22)
-0.035
-0.213
(0.15)
(0.28)
-0.034
-0.049
(0.033)
(0.049)
1.217***
3.109***
(0.051)
(0.073)
Y
Y
Y
Y
Y
Y
5587
5549
0.92
0.89
(5)
(6)
France & US; France dismissal
law change in early 1970s;
data from 1970-1985
Number of
Number of
Patenting Firms
Citations
0.324***
0.195***
(0.050)
(0.073)
0.028
0.049
(0.074)
(0.12)
-0.087***
-0.030
(0.023)
(0.033)
3.542***
2.801***
(0.029)
(0.072)
Y
Y
Y
Y
Y
Y
10272
10188
0.91
0.88
The OLS regressions below implement the following model:
yict = βi + βc + βt + β1 · DismissalLawsct ∗ InnovationIntensityi,t−1 + β2 · DismissalLawsct + β3 · InnovationIntensityi,t−1 + εict
where yict is the natural logarithm of a measure of innovation for the USPTO patent class i from country c in application year t. βi , βc , βt denote
patent class, country and application year fixed effects. DismissalLawsc,t denotes the index of laws governing dismissal in country c in year t. The
Innovation Intensity for patent class i in year (t − 1) , InnovationIntensityi,t−1 , is measured as the median number of patents applied by US firms
in patent class i in year (t − 1). β1 measures the difference-in-difference effect of the change of the regulation of dismissal. Columns 1-2 report the
results examining the impact of dismissal law change in the U.S. in 1989; the “control group” is Germany, which did not experience such a law change
in the sample period. Columns 3-4 report the results examining the impact of dismissal law changes in the U.K. in the early 1970s; the “control
group” is the U.S. Finally, Columns 5-6 report the results examining the impact of dismissal law changes in France in the early 1970s; the “control
group” is again the U.S, which did not experience such a law change in that time interval. The dismissal index data is from Deakin et al. (2007).
Robust standard errors are given in parentheses. ***, **, and * denote significance at the 1%, 5%, and 10% levels, respectively.
Table 7: Difference-in-Difference Tests documenting the Impact of the Regulation of Dismissal on Different Industries
based on their Innovation Intensity.
52
US Patent class dummies
Country dummies
Application year dummies
Observations
R-squared
Constant
Log of per capita GDP
Ratio of Value Added
Log Exports
Antidirector
Rights Index
Efficiency of
Judicial System
Log Imports
Rule of Law
Creditor Rights Index
Industrial action
Employee representation
Alternative employment contracts
Regulation of working time
Dependent Variable is
Logarithm of
Dismissal Law Index
-3.741***
(0.13)
Y
Y
Y
46334
0.83
(1)
Number of
Patents
0.092*
(0.048)
-0.037
(0.097)
-0.190***
(0.059)
0.709***
(0.15)
-0.181
(0.16)
-3.099***
(0.11)
Y
Y
Y
46334
0.83
(2)
Number of
Patenting Firms
0.167***
(0.041)
-0.028
(0.082)
-0.287***
(0.050)
0.640***
(0.12)
-0.221
(0.14)
-5.658***
(0.20)
Y
Y
Y
42918
0.80
(3)
Number of
Citations
0.153**
(0.067)
0.684***
(0.14)
0.193**
(0.089)
-0.571**
(0.22)
1.136***
(0.24)
(4)
Number of
Patents
1.070***
(0.10)
-0.488***
(0.13)
0.020
(0.069)
0.817***
(0.16)
-0.663***
(0.21)
-0.104***
(0.032)
0.096
(0.15)
-0.297***
(0.032)
0.501***
(0.080)
0.0275
(0.057)
-0.081
(0.058)
0.021
(0.037)
0.231
(0.25)
-6.225***
(1.31)
Y
Y
Y
32941
0.84
(5)
Number of
Patenting Firms
1.048***
(0.081)
-0.426***
(0.11)
-0.124**
(0.058)
0.717***
(0.14)
-0.529***
(0.18)
-0.057**
(0.027)
0.133
(0.12)
-0.220***
(0.026)
0.365***
(0.067)
0.028
(0.047)
-0.068
(0.047)
0.008
(0.030)
0.181
(0.20)
-5.269***
(1.05)
Y
Y
Y
32941
0.84
(6)
Number of
Citations
1.247***
(0.13)
0.298
(0.20)
0.290***
(0.10)
-0.333
(0.26)
0.480
(0.32)
-0.073
(0.046)
0.441**
(0.19)
-0.339***
(0.044)
1.040***
(0.12)
-0.050
(0.084)
-0.240***
(0.084)
0.011
(0.055)
-0.421
(0.32)
-11.18***
(1.73)
Y
Y
Y
30159
0.82
The OLS regressions below implement the following model:
yict = βi + βc + βt + β1 ∗ lAc,t + β2 ∗ lBc,t + β3 ∗ lCc,t + β4 ∗ lDc,t + β5 ∗ lEc,t + βX + εict
where yict is the natural logarithm of a measure of innovation for the USPTO patent class i from country c in application year t. β1 - β5 measure the impact
on measures of innovation of the labor law for the five components of the labor law index: Alternative employment contracts (lAc,t ), Regulation of working time
(lBc,t ), Regulation of dismissal (lCc,t ), Employee representation (lDc,t ), and Industrial action (lEc,t ). The labor index data is from Deakin et al. (2007). X
denotes the following set of control variables. The Creditor Rights Index is from Djankov, McLiesh, and Shleifer (2007). Rule of Law, Antidirector Rights Index
and the Efficiency of Judicial System are time-invariant legal variables (all from La Porta et al. (1998)). Log Imports is the log of a country’s imports from the
US in a given 3-digit ISIC industry in a given year; Log Exports is the log of a country’s exports to the US in a given 3-digit ISIC industry in a given year (export
and import data are from Nicita and Olarreaga, 2006). Ratio of Value Added is the ratio of value added in the 3-digit ISIC in a year to the total value added by
that country in that year (from UNIDO). Log of per capita GDP is the logarithm of real GDP per capita. Robust standard errors are given in parentheses. ***,
**, and * denote significance at the 1%, 5%, and 10% levels, respectively.
Table 8: Regressions using Other Dimensions of Labor Laws.
Table 9: WARN Act: Summary Statistics.
The table gives summary statistics for the main variables used to test equation (6). Market-to-Book ratio
is the market value of assets to total book assets. Market value of assets is total assets plus market value of
equity minus book value of equity. The market value of equity is calculated as common shares outstanding
times fiscal-year closing price. Book value of equity is defined as common equity plus balance sheet deferred
taxes. Size is the natural logarithm of sales. R&D/TA is the ratio of research and development expenditures
to total assets; missing values of research and development expenditures are set to zero. Anti-Takeover Index
is the state-level index of anti-takeover statutes from Bebchuk and Cohen (2003). Patent data is from the
NBER Patents File (Hall, Jaffe and Trajtenberg, 2001). Firm-level data is from Compustat. The sample
spans 1983-1994.
Variable
Number of patents
Number of citations
Number of employees (thousand)
Market-to-Book
Size
R&D/TA
Anti-Takeover Index
Observations
12763
12763
12763
11413
12613
12763
12263
53
Mean
17.606
182.526
12.257
2.002
5.333
0.073
1.395
Std. Dev.
61.335
727.855
26.787
1.843
2.513
0.120
1.508
Min
1
0
0
0.598
-1.415
0
0
Max
1612
21042
172
12.623
10.261
0.797
5
Table 10: Difference-in-difference tests: Impact of WARN Act on US firm-level innovation.
The OLS regressions below implement the following model:
yit = βi + βt + β1 ∗ (Over100)it ∗ (Af ter1988)t + β2 ∗ (Over100)it + β3 ∗ (Af ter1988)t + βX + it
where y is a proxy for firm-level and time-varying innovation (the natural logarithm of patents or citations), and βi
and βt are firm and year fixed effects, respectively. The sample covers twelve years around the passage of the WARN
Act (from 1983-1994). (Af ter1988)t is a dummy taking the value of one after the passage of the WARN Act (i.e., the
years 1989-1994), and (Over100)it is a dummy taking the value of one if a firm has ≥ 100 employees in a given year,
and zero otherwise. β1 measures the difference-in-difference effect on innovation of the strengthening of dismissal
laws via the WARN Act. X is a set of control variables. Market-to-Book ratio is the market value of assets to total
book assets. Size is the natural logarithm of sales. R&D/TA is the ratio of research and development expenditures to
total assets. Anti-Takeover Index is the state-level index of anti-takeover statutes from Bebchuk and Cohen (2003).
Patent data is from the NBER Patents File (Hall, Jaffe and Trajtenberg, 2001). Firm-level data is from Compustat.
Robust standard errors (clustered at the firm level) are given in parentheses. ***, **, and * denote significance at
the 1%, 5%, and 10% levels, respectively.
Dependent Variable is
Logarithm of
(Over100)*(After1988)
(Over100)
(After1988)
(1)
Number of
Patents
0.143**
(0.059)
0.127**
(0.051)
0.063
(0.063)
(2)
Number of
Citations
0.281***
(0.088)
-0.077
(0.093)
-0.164*
(0.095)
1.093***
(0.050)
Y
Y
12763
0.87
3.515***
(0.089)
Y
Y
12487
0.80
Size
Market-to-Book
R&D/TA
Anti-Takeover Index
Constant
Firm dummies
Application Year dummies
Observations
R-squared
54
(3)
Number of
Patents
0.236***
(0.067)
-0.186***
(0.065)
(-)
0.275***
(0.029)
-0.005
(0.009)
0.770***
(0.16)
-0.040***
(0.015)
0.045
(0.13)
Y
Y
10856
0.87
(4)
Number of
Citations
0.270***
(0.10)
-0.218**
(0.11)
(-)
0.216***
(0.040)
0.011
(0.013)
0.620**
(0.24)
-0.030
(0.021)
1.855***
(0.23)
Y
Y
10641
0.80
Table 11: Tests of Reverse Causality.
This table reports the results examining the impact of dismissal law change in the U.S. in 1989; the “control group”
is Germany, which did not experience such a law change in the sample period (from 1970-1995).
Dismissal Law Change (-2,0) is a dummy that takes the value of one for the years 1987-1989 for the U.S., zero
otherwise; Dismissal Law Change (1,2) is a dummy that takes the value of one for the years 1990-1991 for the U.S.,
zero otherwise; finally, Dismissal Law Change (≥3) is a dummy that takes the value of one for the years 1992 and
thereafter for the U.S., zero otherwise.
The labor law index data is from Deakin et al. (2007). Robust standard errors are given in parentheses. ***, **, and
* denote significance at the 1%, 5%, and 10% levels, respectively.
Dependent variable is
Dismissal Law Change(-2,0)
Dismissal Law Change(1,2)
Dismissal Law Change(≥3)
Constant
Country dummies
Year dummies
Patent class dummies
Observations
R-squared
(1)
ln(Patents)
-0.132***
(0.027)
0.071**
(0.034)
0.173***
(0.027)
4.278***
(0.030)
Y
Y
Y
20039
0.85
55
(2)
ln(Firms)
-0.119***
(0.022)
0.071***
(0.027)
0.144***
(0.022)
3.492***
(0.023)
Y
Y
Y
20039
0.86
(3)
ln(Citations)
-0.014
(0.035)
0.218***
(0.042)
0.309***
(0.033)
5.695***
(0.037)
Y
Y
Y
19875
0.83