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SPECIALIZING IN GENERALITY:
FIRM STRATEGIES WHEN FACTOR MARKETS WORK
Raffaele ContiCatolica Lisbon School of Business and Economic
Alfonso GambardellaDepartment of Management & Technology and CRIOS
Bocconi University, [email protected]
Elena NovelliCass Business School, City University of London
March 2016
PRELIMINARY – COMMENTS WELCOME
ABSTRACT
We study the interdependence between two strategic decisions of firms in vertical industries in which an
asset is used to produce downstream products but can also be sold in intermediate markets to final
producers. Our theory suggests that when intermediate factor markets are efficient, the strategic decision
to specialize in selling upstream assets without entering downstream markets is complementary to the
strategic decision to invest in making the assets fungible to many different downstream markets. We test
our predictions using a sample of firms in the US laser industry between 1993 and 2001. A regulatory
shock that affects the cost of downstream entry provides the setting for a quasi-natural experiment that
corroborates our prediction. Apart from providing a direct test of the complementarity between two
strategic choices (no-entry and investment in fungible assets), our study highlights the potential for
exploiting economies of scope through markets rather than internal organizations. The traditional view by
Penrose (1959) or Nelson (1959) is that fungible assets are the domain of larger firms that invest in
fungibility because they can exploit the asset internally. Interestingly, their prediction would be opposite
to what we find because in their case higher downstream entry costs ought to reduce fungibility of
internal assets. Our result suggests that exploiting economies of scope inside an organization is bounded
by the lack of well-functioning vertical markets.
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INTRODUCTION
Valuable resources are at the core of firms’ competitive advantage (Barney 1991; Penrose, 1959). They
constitute the essence of the “product opportunity set” of a firm, encompassing “all of the productive
possibilities that its `entrepreneurs´ see and take advantage of” (Penrose 1959, p. 28). Drawing on this
opportunity set, firms undertake strategic actions possibly resulting in greater profits. However, resources
differ in their ability to be deployed in multiple uses, and so, in providing firms with strategic options – a
characteristic referred to as the generality or general-purpose nature of the resource, or “fungibility” (e.g.,
Kim & Bettis, 2014). Indeed, some resources are inherently specific to some applications while others are
more fungible and so can be more easily reconfigured into alternative uses (e.g., Helfat & Eisenhardt,
2004).
The leading argument underlying research in this area is that the more fungible a resource is, the
higher the likelihood that it can be re-used in a new business, thus determining entry into new markets
(e.g. Nelson, 1959; Penrose, 1959). More in detail, Penrose (1959) argues that firms grow by exploiting
internal synergies, which arise from the possibility of redeploying fungible resources into new businesses
at low or even zero cost. In a similar vein, Nelson (1959) suggests that diversified firms are most likely to
internalize the externalities arising from the breadth of opportunities created by investments in basic
research, which is conducted without a specific application context in mind and so might lead to inventive
outcomes applicable across multiple settings. This view is still at the core of the RBV theory of entry and
diversification (e.g., Nason & Wilklund, 2015).
Yet, one of the core assumptions of the RBV theory for internal expansion is that “strategic
factors markets” – where both resources and their deriving services can be traded (Dierickx & Cool,
1989) – are not perfect or at least do not operate smoothly. In fact, while scholars in this area emphasize
that “without market failure due to high transactions costs or imperfect mobility, the firm could simply
sell the services of their redundant resources” (Peteraf, 1993, p. 183), they also tend to believe that market
for resources are inherently imperfect (e.g., Teece, 1980) and, as such, firm growth is actually the only
option firms have for exploiting their fungible resources. This paper contributes to the RBV theory in two
ways: (a) it argues that in industries with well-functioning strategic factor markets, investment in fungible
resource might be complementary to trading those resources in intermediate markets; (b) it explores the
contingencies that determine the strength of this complementarity.
When strategic factor markets do not work, the only choice of firms to profit from their idle,
fungible, resources is to expand into a new market. The incentive to do so is contingent on the level of
economies of scope experienced by the firm. The lower the adaptation costs required for employing a
resource in an additional downstream market, the higher is the incentive to enter, i.e. the stronger is the
complementarity between investing in fungible resource and entering downstream. But the incentive to
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employ a fungible resource in a new market also depends on the costs of downstream operations in each
market after entry. It follows that if a firm faces an increase in these costs, it reduces the complementarity
between resource fungibility and entry. Hence, in the Penrosian world, firms would be likely to respond
to an increase in downstream costs by reducing the fungibility of their resources.
However, when strategic factor markets work, the outcome may be just the opposite. Firms can
trade their excess resources in intermediate markets. This implies that the net benefits arising from using a
fungible resource to enter in a downstream market have to be weighed against the net benefits from
selling that resource in the intermediate markets. To the extent that the latter benefits overcome the
former, investing in more fungible upstream resources and trading those resources – rather than entering
downstream – may become complementary strategic choices. Hence, when the option of selling resources
in intermediate markets is available, we discuss the conditions under which reduced opportunities to
operate downstream (or greater opportunities to trade in intermediate markets) encourage firms to
specialize upstream and increase the fungibility of their resources. We also explore the conditions under
which we are more likely to observe this phenomenon. In particular, we show that the complementarity
between investing in fungible resources and trading them in intermediate markets is stronger for firms
that (1) have not yet entered into any downstream market or (2) face a homogeneous – as opposed to
skewed – distribution of potential buyers of their services in the downstream markets.
Our framework evokes some important strategic decisions of firms in recent years, which we
briefly discuss in the next section as a motivation to our analysis. For example, it echoes one of the most
significant strategic turnaround at the turn of the last century: the transformation of IBM from a
mainframe producer to a service-based firm. As suggested Louis Gerstner (2002), the CEO who
engineered this transformation between 1993 and 2002, the shock that triggered the change was the rise of
UNIX and the PC, and the fact that IBM did not have a comparative advantage in these new businesses.
However, over the years, it had developed a significant general capability of applying technologies to
solve business problems. This turned IBM into a company that served “… customers [who] had a
problem with a product from Digital, Compaq, or Amdahl” (p.128) – that is, IBM’s competitors in the PC
business. Similarly, IBM realized that its considerable stock of technologies could be licensed making
USD 1.5 billion in income in 2001 from $500 million in 1994, that the technological components that it
used to make for itself could be used by many other firms, and that it could provide technological
solutions to a wide range of problems of its business clients.
We test our predictions using a sample of firms in the US laser industry between 1993 and 2001.
This is an ideal setting for our theoretical framework for several reasons. First, the crucial resource in this
industry is the laser technology itself, and each laser technology might be more or less fungible: that is, it
may have a lower or higher number of applications, each one linked to a specific submarket. In the
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particular time period taken into account, the applications of laser – and the corresponding submarkets –
expanded considerably. Furthermore, the laser industry is vertically disintegrated: it is populated by laser
producers – which produce the laser as a standalone technology – and laser system manufacturers – which
embed the lasers into a “laser system” ready-to-use. This implies that intermediate strategic factor
markets for the crucial resource (that is, laser technology as a standalone component) exist and work
smoothly.
Finally, in the time period of our sample we can exploit an exogenous regulatory shock that
increased the cost of downstream operations for firms operating in some US States, which allows for
testing complementarity in investment in fungible resources (lasers) and in trading them. In so doing, we
follow Brynjolfsson and Milgrom’s (2012, p. 58) suggestion that “legal and institutional changes are
often ideal candidates to … [estimate complementarities in organizations] … because a change in a law or
government policy can provide a precise date and specific geographic area or jurisdiction for the change
to occur.” Our difference-in-difference analysis tests whether firms located in states enacting the
regulation (treatment group) – so increasing their cost of entry into new downstream markets – are more
likely both to invest in fungible technological resources and trade them (rather than enter in new
downstream markets), compared to firms in the other states (control group). We employ both bivariate
probit and linear probability models for the four combinations of investing/not investing in fungible
resources and entry/trade, and find results consistent with our predictions.
In the following sections, after a discussion of the background of our research, we present an
intuitive framework for our theory. We then present our data and methodology, discuss our results
(including robustness checks), and conclude. The Appendix provides a full-fledged formal model
capturing our main ideas.
THEORETICAL FRAMEWORK
Background
Anecdotal evidence is consistent with the increasing importance of the strategy to invest in fungible
resources to be traded in intermediate markets when firms face high downstream production costs. The
most valuable resource of IDEO, a leading design company and known for pioneering a new business
model, is the overall creative process for designing new ideas. This resource is extremely fungible as it
can be applied for designing new products in multiple market domains, including, for instance,
electronics, robotics, and apparel. However, the production costs to operate in several of these potential
markets are high. Hence, taking advantage of corporate downsizing in the 90’s and the creation of
“markets for designing”, IDEO decided to trade the services deriving from its creative process to
companies in several different markets – such as Apple Computer, AT&T, Samsung, Philips, Amtrak,
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Steelcase, Baxter International, and NEC Corp. Similarly, Echelon, an industrial automation company,
developed a universal automated control system (Lonwork) with applications in sectors as different as
elevators, manufacturing processes, cars, or utilities (Thoma, 2009). Because of the high entry costs in
these markets, largely due to scale requirements, the company deliberately chose to not enter downstream,
focusing instead on increasing the fungibility of its controller and expanding its span of applications (see
also Gambardella & McGahan, 2010).
As noted in the introduction, the transformation of IBM from a mainframe and computer producer
to a general-purpose technology service company is another example of our story. The shock came from
the rise of UNIX, the open system environment developed by Sun and HP, and the PC. Not only did this
produced a tough competitive environment for IBM’s core business, but IBM was unable to react, or at
least it did not have a comparative advantage with respect to its competitors. It is not uncommon that even
successful companies like IBM, once under attack by new disruptive technologies, lose their war
(Christensen, 1997). However, IBM sought a different strategy from the two natural reactions that we
most often think of: resist to the competitive threats by improving the core business under attack, or
trying to enter in the new business. As Gerstner (2002) puts it:
It would have been easy to follow HP and UNISYS and all the rest down this path. All of
the pundits who followed the industry saw the dominance of this model inevitable. It
would also have been easy simply to be stubborn and say that the changeover wasn’t
going to happen, then fight a rear-guard action based on our historical view of a
centralized computing model. What happened, however, is that we did neither. … we
decided to stake the company’s future on a totally different view of the industry.” (p.123)
This different view was to become a supplier of “solutions” that integrate different technologies.
Instead of serving customers, typically firms, with the PC or other specific products, IBM served them
with general-purpose technologies and services to improve their processes. In so doing, IBM built upon
its quintessential asset: “In services you don’t make a product and then sell it. You sell a capability. You
sell knowledge.” (p.133) While the company applied these service capabilities only internally or to its
own product, now it supplies them to many clients and for many products. Similarly, IBM has opened the
“company store,” as Gerstner puts it, ranging from technology licensing to the sale of standard
components or sophisticated services. With this strategy, IBM has reached the very same clients who use
the products of companies in the software, networking, computing, or communication business in which
IBM could have entered if it had bet on the idea that this was, inevitably, the way to go. In our framework
firms that decide to specialize in generality serve the downstream firms that enter the new markets. More
than serving these firms IBM serves the clients of these firms in the many downstream markets that it can
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reach with its fungible capability. But this is a detail. The thrust of our argument is that there is
complementarity between the strategic decision not to enter downstream markets and the strategic
decision to expand the fungibility of the firm’s resources to serve different markets – that is, the decision
to increase the scale of operations by going broadly across markets than deeply with a large volume of
activities in one market.
Why not all companies do so then? For one reason, you need a shock that triggers the decision to
change business model – from entry and operations downstream to serving many markets with a fungible
resource and focusing upstream. Moreover, the change in business model is not without costs. Gerstner
notes that the change hit the traditional culture of IBM, and it was not easy to implement in practice,
eliciting a lot of the diseconomies of scope, between different business cultures, highlighted for example
by Bresnahan et al. (2011).
An Italian firm specialized in producing military aircrafts provides a good example of a company
that had the option to change, but did not. An engineer found that a technology for Ground Support
Equipment (GSE, the support equipment used to service aircrafts between flights) could also be used,
with proper modifications (e.g., modifying the fluids’ capacity or the hydraulic pumps) as a general
technology for a larger variety of military aircrafts of similar size. The company was using the technology
in-house and it was only selling the GSE as a bundle to its own product. It considered seriously the
opportunity to make GSE more fungible to sell it to many aircraft producers, possibly pulling out or at
least reducing its stakes in the downstream market. However, there was no reason to abandon its
traditional business, there was no trigger or shock. As a result, the company decided to stay in the
downstream market without investing in the fungibility of the technology. This helps us to emphasize that
our paper makes no claim that specialization in generality is a better strategy, but only that, under some
conditions, no-entry and generality are complementary strategic choices.
We would also like to note a few other ingredients of our framework. First, we focus on a shock
to the downstream production costs of some firms as our trigger to switch between the two business
models. This is the shock that we observe in our empirical analysis and we find it convenient to use it to
illustrate our theoretical framework. However, we can think of other shocks, particularly factors that
increase the efficiency of vertical market transactions. Second, our framework elucidates other aspects of
the choice between transactions and integration. In particular, transaction cost economics suggests that
stronger bargaining power of downstream buyers induce upstream suppliers to integrate downstream to
reduce the hold-up problem. We show that staying upstream and investing in fungibility is another
potential response of upstream firms. In so doing, our framework highlights another angle of the problem.
Hold-up depends on asset-specificity, which transaction cost economics often takes as a given. We argue
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that asset-specificity can be a matter of choice, and firms, particularly upstream firms, can choose to make
their assets less idiosyncratic to specific applications.
Predictions
Following Penrose (1959) firms can be conceived as “a collection of productive resources the disposal of
which between different uses and over time is determined by administrative decisions” (p. 21). For each
of the tasks taking place inside it, the firm has to possess the appropriate amount of resources in line with
its scale of operations. However, if resources are fungible – and so they can be applied to several contexts
not necessarily related to the firms’ current operations – expansion in new markets is an opportunity for
the firm to find an application for the unused productive services of the fungible resource (Penrose,
1959). In other words, employing fungible resources is likely to lead firms to obtain economies of scope
(Panzar & Willig, 1975), since most of the multiple applications provided by a fungible resource are
“free” for a firm that has acquired the resource only to employ it in one or few applications (Penrose,
1959). This also implies that exploiting excess resources by entering multiple markets can translate into a
source of competitive advantage for the focal firm (Penrose, 1959). In addition, the opportunity offered
by these resources would be even greater when the fungible resource is completely scale-free – i.e. when
the value of the resource is not reduced according to the magnitude of operations to which the resource is
applied (Levinthal & Wu, 2010) – and, as such, the resource does not produce any trade-off among its
multiple potential applications.
One of the core assumptions underlying this logic is that of imperfect or even absent strategic
factor markets (Mahoney & Pandian, 1982; Penrose, 1959; Teece, 1982). In other words, it is commonly
held that it is not possible or profitable for firms to trade their fungible resources – or the services
deriving from those resources. Such assumption is at the basis of several streams of research on firm
diversification and growth. For example, fungible resource co-deployment has generally been considered
as a factor enhancing performance of a multi-business firm (e.g. Rumelt, 1974; 1982; Tanriverdi &
Venkataraman, 2005; Wrigley, 1970). Similarly, the transfer of fungible resources over time between
multiple businesses of the same corporation has been seen as a source of competitive advantage (e.g.
Anand & Singh, 1997; Helfat & Eisenhardt, 2004). To be sure, prior research has recognized the
possibility of diseconomies of scope, even in the presence of fungible resources (Helfat & Eisenhardt,
2004; Teece, 1980). For example, human capital might in principle be applied to different settings but, as
Teece (1980) suggests, there might be costs of “overextended scientists, engineers and managers” – when
human resources allocate their time across too many businesses, reducing the quality of their work and
productivity.
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However, the RBV literature has traditionally investigated in a limited way the possibility of
exploiting a fungible resource other than using it to enter into a new market. In other words, this literature
has mostly seen organic growth as a superior choice for exploiting fungible resources, due to the
imperfections in strategic factor markets (e.g. Helfat & Eisenhardt, 2004; Teece, 1980). In fact, these
imperfections have become less and less pronounced in the past few decades, and vertical disintegration
has become more and more common (e.g. Baldwin & Clark, 2000; Jacobides & Winter, 2005; Kapoor &
Adner, 2012). For instance, an increasing number of firms operate as “upstream specialists”, profiting
only by selling technological resources in markets (e.g. Arora et al., 2001; Jacobides, 2005). Moreover,
other firms, despite being vertically integrated, continue to sell their technologies in intermediate market
as a further source of profits (Kapoor, 2013).
These patterns of vertical disintegration call for additional research in investigating the conditions
under which firms can exploit their fungible resources via intermediate markets versus direct entry into
downstream markets – which is the main objective of this paper. Few studies have explored the trade-off
between using a resource for entry vs. trade. For instance, Silverman (1999) show that some resources,
due to some inherent features (e.g., being associated with tacit knowledge) are more likely to be exploited
through entry rather than contracting. Rawley and Simcoe (2010) show that firms owning upstream
resources applicable to multiple markets and facing downstream diseconomies of scope might choose to
trade some of their resources. However, even those studies consider firm resources – and their
characteristics – as an exogenous endowment, rather than as an endogenous variable in which firms
purposefully choose to invest. By contrast, not only do we consider the degree of resource fungibility as a
firm choice variable, but we also advance that the decision about the degree of resource fungibility is
strictly interrelated with the choice of how fungibility should be exploited – which might be either
through direct entry into new downstream business or by selling the resource in the corresponding
intermediate market.
To be sure, the firm’s optimal choice on whether to invest in fungibility and how exploit it
depends on several variables. For instance, the existence of firm internal economies of scope would,
ceteris paribus, make the option to trade a fungible resource less profitable than the option of entering into
a market (Teece, 1980; 1982). By contrast, when markets operate efficiently and it is relatively easy to
find a downstream buyer that can draw substantial value out of the intermediate resource, the option to
trade becomes more profitable (Teece, 1980; 1982). We study the choice of the firm whether to set-up
production assets to enter one or more new markets, and possibly selling the resource in others, or not to
enter any downstream markets and specialize in trading the resource. This is line with what we observe in
many markets and particularly in the laser industry that is the focus of the empirical analysis of this study
– most notably, firms that integrate in downstream markets sometimes also sell the services from their
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resources to other firms, possibly in different market niches, while other firms specialize as upstream
suppliers. To fix ideas we call the first strategy integration strategy, and the second one trading strategy,
or specialization in trading. Our main claim is that the options of (i) investing in a fungible resource and
(ii) choosing an integration vs upstream specialization strategy are present simultaneously in the minds of
managers and affect each other.
If so, any factor that increases the production costs of specific firms to operate in downstream
markets (or that reduces the relative costs of trading in intermediate markets vis-à-vis entry) should have
the straight implication that it reduces the incentives of these firms to enter into new markets. Does this
increase the fungibility of the stock of the firm resources? The answer requires a few additional
ingredients, and the Appendix provides a formal model that clarifies this point. To summarize our
framework, firms choose between an integration and a trading strategy, and whether they want to increase
the fungibility of their stock of resources. If they choose to integrate they preserve the option to produce
downstream in any of the downstream markets in which they can operate with their resources, and they
can still trade if in a particular market profits from entry are lower than profits from trading in that
market. If they specializing in trading, they give up the option to enter, and can only trade in the
downstream markets. This is a natural set-up since downstream production requires some ex-ante
preparation (e.g., set-up of manufacturing operations) that the firms may have to give up if not prepared
with a sufficient lead time. Therefore, unless firms commit, they can only trade, and this will produce
lower expected gross profits because the option of future entry implies that firms trade whenever trading
is a superior choice in a particular market and produce when this is instead the superior option. The trade-
off arises because the costs of preserving the option to enter in all the downstream markets that can be
reached with the resources of the firm is higher than the costs of just trading in them. Again, it is natural
to expect that trading implies fewer costs than early investments in manufacturing or similar
commitments to preserve the option to enter.
An increase in downstream production costs lowers the profits of a firm from entering a new
market. Profits from integration could then fall below the profits from trading. However, the increase in
fungibility occurs only if it generates a higher increase in profits when the firm specializes in trading as
opposed to integration. This depends on the benefits and costs of expanding fungibility under trading or
integration. The cost to expand fungibility is typically lower when the firm specializes in trading for the
very reasons noted above: production requires investments in manufacturing and other downstream
activities to cover the new applications of the resource that the firm does not incur if it does not plan to
enter. Benefits of trading will also be higher if there are enough gains from trade so that buyers and
sellers earn more than without concluding the deal. In other words, the focal firm should not be
significantly more efficient than potential buyers at using the resource in the new applications.
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To clarify our logic further, consider the case in which the firm is much better than other firms in
the market to exploit the resource downstream. This would be the classical Penrosian corporation or the
Chandlerian firm with significant assets and capabilities to undertake what Chandler (1990) calls the
“three-pronged investments” in research, production and marketing. An increase in production costs
would reduce the profits of this firm from entering new markets. However, for a large profitable
corporation, profits may still be higher in all the markets in which the firm can enter than the opportunity
cost of not entering, and if the firm is much better than other firms to exploit the resource, the profits from
trading it will be too low, and likely lower than the profits from integration. As a result, the firm will still
integrate after the increase in costs. At the same time, because the increase in downstream costs reduces
the benefits from expanding fungibility, the firm will reduce the fungibility of its assets. The story that we
tell in this paper is different. With profitable users and lower costs of expanding fungibility under trading
vs integration, higher costs of downstream production encourage firms to switch from integration to trade,
which is what produces the increase in fungibility in our framework.
H1. When strategic factor markets work, and other firms can profitably use its resources, an increase in
downstream production costs leads a firm to (i) invest in fungible resources and (ii) trade these resources
in intermediate markets – that is activities (i) and (ii) are complementary strategic choices.
Our next question is – for which firms are the strategies of investing in fungible resources and selling
resources in intermediate markets complementary? We identify two conditions under which this occurs.
First, this is likely to be the case for firms that have not yet entered any downstream market; second, this
is likely to be the case when firms face a set of downstream markets with similar rather than a skewed
number of firms in each markets. We explain the logic behind these statements below.
We first compare upstream firms (i.e. firms with no stake in downstream markets) with integrated
firms (i.e. firms that operate in some downstream markets), and predict that, following an increase in
downstream production costs, an upstream firm is (i) less likely to enter any new markets (specialization
in trading) and (ii) more likely to increase the fungibility of its stock of resources. Suppose that an
upstream and an integrated firm have to decide whether to invest in the same fungible resource with
potential applicability in the same set of downstream markets. The decision to invest in this resource
depends on the number of applications in which the firm can exploit the resource. The integrated firm can
apply the same fungible resource to fewer new markets than the upstream firm. It already operates in
some markets, and therefore – unlike the upstream specialist – it most likely operates in some of the
markets in which the resource can be used. Similarly, an integrated firm might be reluctant to sell a
10
resource to a competitor in the downstream market because this may reduce its competitive advantage
from using the resource in other markets (e.g. Arora & Nandkumar, 2012).
If so, consider for simplicity the case in which the use of the new fungible resource to enter
markets in which the firm operates does not produce more profits than what the firm already earns. An
increase in production costs then only affects the profits in the markets in which the firm does not operate
because in the others the firm will continue to use the old resources, and the value of the new resource in
these markets will be zero. In contrast, as shown by our model in the Appendix, a firm that can enter all
the markets in which the fungible resource can be used will experience a higher decline in profits from
entry because the increase in production costs affects all the markets in which it can enter. As a result, this
firm will experience a stronger reduction in the profits of an integration strategy, and it is more likely to
switch to a trading strategy, which is our mechanism that increases fungibility. The logic is not different
under the milder assumption that the firm earns more profits even in the markets in which it operates. In
this case, the benefit of using the new resource in these markets will be net of the opportunity cost of
using the old resource. If we make the reasonable assumption that the increase in costs does not hit the
profits from using the new resource far more than when the firm uses the old resource, the increase in
costs will reduce profits from entry in these markets by a smaller amount than if the firm was earning the
full profits from entry with the new resource (i.e., if the opportunity cost was zero.)
H2. When strategic factor markets work, and other firms can profitably use its resources, an increase in
the cost of downstream production induces firms with no downstream stakes to (i) invest in fungible
resources and (ii) trade these resources in intermediate markets, more than firms with downstream
stakes.
In downstream markets with a higher number of firms, the seller enjoys more bargaining power and
therefore earns more rents from selling. In turn, this implies that if the firm implements an integration
strategy it will integrate only in markets in which profits from entry are sufficiently high to overcome the
rents from trading. Suppose that the fungible resource can be applied to one market with many potential
buyers and several other markets with very few buyers. In a trading strategy, the marginal benefits of
increasing fungibility will be small for all the markets after the first one. As a result, a trading strategy
does not induce a strong increase in fungibility because the additional benefits from higher fungibility are
small. In a set of markets with similar number of firms, a trading strategy exhibits a higher marginal
increase in fungibility, which raises it. Therefore, in a set of markets with a more similar number of firms,
the switch to a trading strategy after an increase in downstream production costs is associated with a
larger increase in fungibility.
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H3 When strategic factor markets work, and other firms can profitably use its resources, an increase in
the cost of downstream production induces firms facing a homogenous distribution of potential buyer
firms in the markets to (i) invest in fungible resources and (ii) trade these resources in intermediate
markets, more than firms facing a skewed distribution.
DATA AND EMPIRICS
Empirical setting
To test our predictions we build a novel longitudinal dataset containing information about a sample of US
firms active in the laser manufacturing industry over a nine-year period (1993–2001) that we
complemented with interviews to managers and industry experts. The term laser – i.e. “light amplification
by stimulated emission of radiation” – is used to refer to devices that emit light through a process of
optical amplification based on the stimulated emission of electromagnetic radiation. Based on theoretical
work by Charles Hard Townes and Arthur Leonard Schawlow, the first laser was built in 1960 by
Theodore H. Maiman at Hughes Laboratories (Hecht, 2011).
All laser technologies are composed by a set of standard components that include a lasing
material (i.e. the gain medium), a pump source and a laser cavity. The atoms of a material such as crystal,
glass, liquid, dye or gas are excited by the pump source to a semi-stable state so that lasing can be
achieved. Usually, the pump source is constituted by another light source (for instance, a laser diode or
flash lamp) or an electric discharge. The light emitted by an atom interacts with the excited atoms nearby
as it drops back to the ground state. Identical pairs of photons are released in the process called stimulated
emission. The process is further duplicated while the photons bounce back and forth in the cavity from
mirrors or other reflective cavity structures. In this way the light emission is further amplified and beams
of light at specific frequencies are produced (Hecht, 2011).
Lasers differ regarding their power and the wavelength of light they emit and this has
implications regarding the applications for which they can be used. Possible applications range from
biomedical/medical (e.g. medical imaging, dermatology), to information processing (e.g. scanning,
optical disk reading), telecommunications (e.g. data transmission, pulse generation), military (e.g. target
designation) and industrial (e.g. cutting, welding, marking) applications.
As noted in the introduction, the laser industry constitutes an ideal setting for testing our theory
for several reasons. It has a clear vertical structure, with producers of laser technologies that are critical
components of the laser systems, which are instead the goods employed for applications as varied as
industrial, medical, military and many others. As a result, the industry has many downstream applications
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(laser systems) and many upstream technologies (lasers) that can be dedicated to one or few applications,
or that are fungible because they can be used in a large number of laser systems for different sub-markets.
At the same time, the industry features upstream specialist producers, downstream specialist firms and
integrated firms. The integrated firms do not necessarily use the technology only internally, but
sometimes also offer it to other downstream firms.
Another valuable aspect of this setting is that, as confirmed by numerous interviews, in the laser
industry, fungibility does not imply that the laser is an inherently superior or inferior technology. Firms
choose lasers according to the application that they have in mind. For example, an expert whom we
interviewed for this project said that “in the laser industry… the application is most important factor …a
technology is simply good for one or another application, and this is fairly orthogonal to whether it has
multiple applications or not,” and we have similar quotes from other interviewees. At the same time,
there are enough laser technologies around, typically more than one for each application, and more
generally when firms mention their strategies about developing laser products with broader applications
they think primarily of the flexibility that this gives them, with no mention about whether this implies a
superior or inferior technology. As some of the managers that we interviewed put it: “We sell to as many
market as possible. This is a way to address uncertainty… “; or “We are trying to make a generic part at
the moment. ... We are developing (…) a module (…) to give people a taster and we would then modify it
up, change it, depending on their market (…).”
In order to define the laser industry and its boundaries we rely on an industry directory, which
lists all companies active in the laser context. We select all U.S. companies listed in the directory as
active in the laser industry between 1993 and 2001. The sample includes both private and public firms,
and so it is generally representative of the different categories of firms active in high-technology contexts.
It also includes firms that enter or exit the industry during the period, limiting any survival bias. We
extract information on their characteristics (e.g., independence status, size, age, location) for each year.
We use the same directory to collect information on the laser types in which the firm is active as well as
on their entry and exit. We pick our time window for empirical reasons: first, during 1993-2001 many US
states enacted laser safety regulations that increased the costs of operating in downstream markets – and
we use those enactments as exogenous shocks. Second, during the time period taken into account, the
number of possible laser applications has increased considerably due to the dramatic diffusion of Internet.
In order to obtain data on these firms’ patents, we use firms’ names and locations and match them
to patent assignee’s names in the National Bureau of Economic Research (NBER) patents database. The
NBER data set provides patent data consolidated at the parent portfolio level for public firms. For private
firms, we use the D&B Who Owns Whom database to build a list of their worldwide subsidiaries for each
year of the study. We match this list with the NBER data set to obtain the list of patents filed by each of
13
the firm’s subsidiaries, and consolidate the list of patents at the parent firm level. This procedure yields a
sample of 204 firms corresponding to 783 firm-year observations.
Methodology
Our analysis is at the firm-year level. We predict that, after an increase in production costs, we should
observe complementarity between investments in fungible resources and selling those technologies in
intermediate markets, but less so for vertically integrated firms and when the distribution of downstream
market size is skewed. Unfortunately, to assess complementarity between two choices or activities we
cannot just analyze the impact of the former choice on the latter. This would determine an obvious case of
endogeneity, and uncovering the correlation between the two strategies would only be suggestive of what
we are trying to establish. To address the endogeneity issue, we take advantage of the fact that in the
period under investigation, some US states enacted laser safety regulations that increased the costs of
downstream operations by establishing new rules laser system manufacturers have to follow in order to
reduce the risk of accidents.
In general, the laser industry is heavily regulated because laser technologies present potential
hazards for individual users. In the US, both the Federal Government and some voluntary standards
regulate the safety requirements – i.e. The Laser Product Performance Standard of the Center for Devices
and Radiological Health; the American National Standards Institute; the Occupational Safety and Health
Administration; the Federal Aviation Administration. In addition to the federal and industry regulations,
in the 1990s some US states enacted some local regulations to increase laser safety controls further
(Rockwell & Parkinson, 1999). These regulations require, for instance, that laser systems users and
manufacturers register each laser system and maintain it compliant with these rules; that they establish
and supervise programs of laser radiation safety; that they keep a record of all calibration and incidents.
For example, an expert in laser compliance told us: “The State requirements (…) can be problematic and
they can produce added costs and reduction of commerce and things (…) they come at it from a safety of
use standpoint. (…). The State monitors very carefully, for the safety of the patients, that the equipment is
compliant, they know where it is, they come and test it now and then, etc.”
The introduction of a state regulation increases the costs of producing downstream because: (i) it
adds costly activities that the firms in the state have to comply to (ii) it implies a tighter control by the
states on the use of the laser machines – with a higher chance of being sued in the case of an accident.
Importantly, these regulations likely affect the production of laser systems more than lasers, i.e. the
technologies embedded into these systems. Moreover, while the firms operating in this industry sell lasers
or laser systems throughout the country, or even internationally, they are mostly small-medium sized
firms that would hardly move manufacturing location away from their own area or town. The new
14
regulations were introduced by the States of New York (in 1994), Arizona (in 1996), Florida (in 1996),
Massachusetts (in 1997), Illinois (in 1997) and Texas (in 1999). Because the regulation is introduced in
different years, in our panel the shock is not a mere chronological threshold. Other states introduced the
regulation outside our time window; moreover, account of the regulation enactment suggests it was
exogenous to the economic and political conditions of the state (Rockwell & Parkinson, 1999). We also
corroborate the exogeneity of our shock in our robustness checks.
We assess the effect of our shock by comparing firms located in states that enacted the regulation
– our treatment group – to firms in states that did not introduce the regulation – our control group. In
particular, our treatment is the variable ProductionCost, a dummy equal to 1 for firms operating in a State
that introduced the regulation after its enactment, and 0 otherwise. Since we control for year fixed effects
and we introduce state dummies – besides clustering the error at the state level as suggested by Bertrand
and Mullainathan (2003) – our approach is a classical diff-in-diff regression, where the coefficient of
ProductionCost produces us unbiased estimate of the impact of an increase in downstream entry costs on
the outcome of interests.
In particular, our dependent variable is the joint occurrence of two events: (1) the firm invests in
more fungible resources and (2) it does not enter into a downstream market and sells the technology in the
intermediate market. The most appropriate way for estimating a joint likelihood of two events is through
bivariate probit – but as a robustness check we also use a linear probability model. To measure entry in a
downstream market, we use DownstreamEntry which is a dummy equal to 1 if in year t the firm enters a
sub-market in which it did not operate among the six main downstream sub-markets of this industry –
communication, information processing, industrial, medical, military and miscellaneous; and 0 otherwise.
The construction of the dummy for investment in fungibility, InvestmentTechnologyFungibility, is
slightly elaborate. We take advantage of the fact that laser technology has several possible applications
across the six core sub-markets mentioned above. The breadth of application of a laser technology varies
depending on the laser medium. Based on the laser medium, lasers can be classified in the following
categories: Alexandrite ; ArF ; Argon-Ion ; Co2; CO2 TEA ; Metal Vapor ; Diode; Dye ; Er:Glass ;
Er:YAG ; Excimer ; HeNe ; Krypton-Ion ; Nd:YAG ; Ruby ; Thulium; HeCd; Krf; Lead Salt; Nd: Glass;
Ti:Sapphire ; Color-Center; HF/DF ; Holmium YAG. Each of these laser categories can be used in a
broader versus narrower range of applications. For instance, a KrF laser can be applied for industrial
drilling but not for applications in dermatology. On the contrary a Er:Glass laser technology is
appropriate to be used in dermatology but not in laser drilling. A third alternative, the laser Alexandrite,
can be used for applications in both dermatology and in industrial drilling. Therefore, laser Alexandrite is
a technology more fungible than the KrF or the Er:Glass lasers. In order to measure the fungibility of the
firm technology we first measure the individual laser’s degree of fungibility by calculating the ratio
15
between the number of uses to which that specific laser type can be applied to and the total number of
applications across all laser types. We then compute the degree of firm’s technology fungibility in each
year by considering the average degree of fungibility of the lasers in the firm’s portfolio. Finally, we
measure InvestmentTechnologyFungibility as a dummy equal to 1 if the firm increases its average
fungibility from year t-1 to year t, and 0 otherwise
In order to test H1, we estimate the effect of ProductionCost on the joint likelihood of investment
in fungible resources and downstream entry through the bivariate probit. To test H2 and H3, we interact
the ProductionCost variable with, respectively, a measure of whether the firm supplying laser is vertically
integrated rather than an upstream specialist, and a measure of the extent to which the distribution of the
size of downstream markets is skewed rather than being homogeneous.
The former variable – Vertically integrated supplier – distinguishes between firms that are
vertically integrated laser suppliers before the shock (i.e. producing and selling both lasers and laser
systems) and firms that, before the shock, are upstream specialist laser suppliers (i.e. only selling lasers).
Accordingly, the dummy takes the value 1 if the firm selling laser is already vertically integrated. To limit
potential endogeneity between the regulatory change and the decision to vertically integrate, for firms
based in states in which the regulation is issued, we consider the year immediately before the regulatory
change; for firms located in the states in which the regulation has never been issued, we take the year of
entry in the database.
We measure the latter variable – SkewnessoOfMarketSizeDistribution – at the level of firms and
years. For each firm in the sample that supplies lasers, we consider the sub-markets in which those lasers
are potentially applicable and the number downstream firms operating in these sub-markets. Our measure
is the Herfindhal of the concentration of these downstream firms across sub-markets. ProductionCost
Moreover, in all specifications we include as additional variables the number of lasers, which
controls for the number of different types of lasers produced by the firm. We also control for the number
of patents applied and granted by the firm in the five years prior the focal year, the size of the firm
(number of employees) and the firm age.
RESULTS
Main results
Table 1 shows some descriptive statistics concerning the population of firms in the laser industry between
1993 and 2001. During this time period about 56 per cent of suppliers are vertically integrated, that is,
besides selling their technologies to other downstream companies, they embed them into the final
systems; 44 per cent are instead upstream specialists. On average, each laser supplier sells two types of
lasers and employs about 300 hundred employees – even though the distribution of employees is skewed.
16
As noted, six states enacted new regulations that increase the costs of operating downstream; in our
sample, this enactment affects about 20 per cent of our firm-year observations. Moreover, more than 6 per
cent of the suppliers enter into some downstream market and almost 16 per cent of them invest in more
fungible resources during our time window.
------
Insert Table 1 about here
-----
Our shock is relevant. The increase in production costs has pushed fewer upstream firms to
integrate downstream and it has reduced the number of downstream manufacturers. We compute the
probabilities that in year t a firm operates upstream selling lasers (measured by a dummy 1,0) or
downstream selling laser systems (measured by a dummy 1,0) as a function of whether the firm operates
upstream or downstream in year t-1, the compliance shock, and the interaction between these variables.
Note that we cover all possible firm types because in a particular year a firm can be an upstream specialist
(1,0), a downstream specialist (0,1), an integrated firm (1,1) or out of the industry (0,0). Our bivariate
probit in Table 2 and the marginal effects in Table 3 show that the shock prevents the upstream specialists
from entering downstream: the probability that an upstream specialist integrates downstream decreases by
about 5%. To some extent, the change also stops the downstream entry of brand-new companies (firms
that were outside the industry earlier) – even if the effects are not statistically significant at the
conventional levels. All in all, this suggests that the regulatory change determines an increase in
production cost that acts mainly as a barrier to entry. As a matter of fact, even though it has not induced
the exit of firms that already operated downstream, the shock has lowered the number of downstream
producers. Table 4 presents the findings of a Poisson regression in which the dependent variable is the
number of companies selling laser systems in any state, sub-market and year. After the new regulation,
the number of downstream firms in the state affected by the regulation diminishes considerably, by about
16 per cent.
------
Insert Tables 2, 3 & 4 about here
-----
According to H1 and H2, increases in downstream costs induce firms to trade (no entry) and
invest in fungible resources. In particular, this combination of strategies is less likely to be observed in the
case of integrated firms compared to upstream specialists and when the market size distribution is
skewed. The results of the bivariate probit in Table 5 show that the increase in downstream production
costs discourages downstream entry (first row of columns 2-4-6) and induce firms to invest in more
fungible resources (first row of columns 1-3-5). Furthermore, the signs of the two interactions comply
17
with our theoretical predictions. On the one hand, the positive impact of the increase in downstream
production costs on the probability of investing in more fungible resources while trading them diminishes
when the firm is integrated (Table 5, columns 3-4). On the other hand, it decreases when downstream
firms are more concentrated in the same sub-markets (Table 5, columns 5-6).
The marginal effects in Table 6 are also consistent with our theory. The increase in production
costs raises the joint probability of trading and investing in fungible resources by a technology supplier by
about 5 per cent (p=0.12) in line with Hypothesis 1. Moreover, consistently with Hypothesis 2, this effect
is much larger for upstream specialists than integrated firms. The probability that upstream specialists
trade and invest in fungible resources increases by more than 10 per cent (p=0.059), while the probability
of the integrated firms does not change. Our results also imply that more than half of the upstream
specialist firms would enter the market without the increase in downstream production costs: the joint
probability of entering with or without investing in a more fungible resources reduces respectively by
about 4 and 1 per cent. As suggested by H3, the likelihood that firms invest in more fungible resources
and trade decreases when the market size distribution is more skewed. In particular, when the variable
SkewnessofMarketSizeDistribution is at the 25th percentile (approximately equal to 0.266, corresponding
to a more homogenous market distribution in our setting), the probability that a technology supplier
pursues a fungible resources strategy increases, after the regulatory change, by about 13 per cent
(p=0.001). It becomes, instead, negative when the variable SkewnessofMarketSizeDistribution is at the
75th percentile (approximately equal to 0.288, corresponding to a more skewed market distribution in our
setting): in particular, the probability of investing in a fungible resources while trading decreases by about
8.5 percent (p=0.003).
------
Insert Tables 5 & 6 about here
-----
To better understand these results, in Figure 1 and 2 we report graphically the effect of an
increase/decrease in the skewness of the market size distribution on the strategy of investing in fungibility
while not entering. The results are plotted separately for integrated firms and for upstream suppliers and
show that not only upstream firms but also integrated firms seem to switch to a strategy of “fungibility &
no entry” as the skewness of the market size distribution decreases, i.e. as the number of viable
submarkets likely increases. This suggests that if the opportunity of profitably selling resources to many
markets is available, also for integrated firms the marginal value of a “fungibility and no-entry strategy”
is higher than that of a “fungibility and entry” strategy.
Further on this point, in additional analyses (available upon request) we investigated which firms,
among the vertically integrated ones, are more likely to choose the “fungibility and no-entry” versus the
18
“fungibility and entry” strategy in the presence of high costs of downstream entry. Interestingly, the
results indicated that the age of the firm matters, in that younger firms (those with an age below the
median age of firms in the sample) tend to choose the former strategy, unlike older firms. Conversely, we
did not find a similar effect associated to the size of the firm. These additional results suggest the
intriguing possibility – which should possibly be investigated by future research – that entry costs (or the
benefits of division of labor) induce new firms that are still experimenting with their strategies and have
possibly made a lower amount of commitments to change their business model, specializing in fungibility
and moving upstream. This also speaks more generally to the fact that young firms still experiment with
their business model. In this respect, not only do upstream specialists consider whether to move
downstream, but also integrated firms that have not yet undergone a long-standing commitment and
experience in the industry, may consider moving back upstream. In doing so, the opportunity that they see
is that they can exploit the fungibility of their resources and bet on a business model that does not commit
to any specific downstream market but on the ability of the firm to be able to reach many of these
markets. Some authors have emphasized the importance of entrepreneurial experimentation (e.g., Kerr,
Nanda, & Rhodes-Kropf, 2014) or pivoting (Marx, Gans & Hsu, 2015). We argue that this
experimentation or pivoting may not just regard the choice of which downstream market to operate in, but
also whether to become an integrated firm or a “specialist in generality.”
------
Insert Figures 1 & 2 about here
-----
Robustness checks
Comparison between treatment and control group. An important assumption of any experimental and
quasi-experimental methodology is that the treatment is exogenous and therefore not systematically
correlated with the characteristics of the firms. As we argued, this is likely to be the case. At any rate, we
also verify this assumption empirically by analyzing whether firms in the treatment and control group
differ along all variables included in our analysis. Specifically, we compare the means of the groups in the
first year of our sample, before the regulatory changes are enacted. Table 7 shows that, overall, the two
groups are similar, which corroborates our identification assumption. The only difference is in the number
of patents. However, about two-thirds of the companies in both the treatment and control group have zero
patents, and the difference is produced by a few outliers.
------
Insert Tables 7 about here
-----
19
Political economy of laser regulation enactment. A further assumption of the quasi-experimental
methodology is that the change in regulation is not associated with any other economic and political
characteristics of the states that could affect the business environment in fungible, and the probability of
entering into a downstream market and/or investing in fungible resources. We run a simple linear
probability model predicting the likelihood of a State enacting a laser regulation as a function of State
GDP per capita, the overall taxation level, and the State political orientation – as proxied by having a Red
Republican Governor or having voted for a Red US president in the last presidential elections. Table 8
shows that no predictor shows any significant correlation with the probability of enacting the new
regulation, which reinforces our identification strategy.
------
Insert Tables 8 about here
-----
Relaxing the parallel path assumption. A specific assumption of the quasi-experimental diff-in-
diff approach is the so-called “parallel path assumption.” This assumption implies that, in order for the
diff-in-diff estimation to be indicative of the real impact of the treatment, the outcome variable should
exhibit a similar trend for individuals in the treatment and control group. As indicated by Angrist and
Pischke (2015) a simple way of relaxing this assumption is to introduce an interaction term in the diff-in-
diff regression, that is, the interaction between a dummy for treated units and a time trend. This
interaction captures any differential trend between treated and control units before the treatment. We
adopt this approach in order to check if our results are robust to the inclusion of a different time trend for
each state. Unfortunately, including this variable in the bivariate probit makes the estimation impossible.
Hence, we use a linear probability model, where the dependent variables are the four possible strategies
resulting from the combinations of investing in more fungible resources (or not) and entering into a
downstream market (or not). The results are again largely consistent with our theory. The likelihood that a
technology supplier invests in more fungible resources and avoids entry (which is the strategy considered
in columns 1-3of Table 9) increases, but less so for vertically integrated supplier and when downstream
firms are concentrated in few markets.
------
Insert Table 9 about here
-----
Inclusion of firm fixed effects. An additional concern of the main analyses is the presence of
some firm-unobserved characteristics, which we do not control for in the bivariate probit model. Overall,
we believe this is not a major concern as we show that our shock is arguably exogenous – and so
uncorrelated with both time-variant and time-invariant firm characteristics. Moreover, a fixed effect
20
model would only rely on within firm variation in the likelihood of entering in a downstream market
and/or investing in fungible resources, whereas most variation is indeed across firms. However, we also
check whether our results change when we introduce firm fixed effect in previous linear probability
model. Table 10 shows that the main results are robust to the inclusion of firm-specific dummies
(columns 1-3). There is only one significant difference between the model with and without fixed effect
though. In Table 9 (without firm fixed effects), the increase in the production cost reduces the likelihood
of all suppliers – be them vertically integrated or upstream specialists – to enter downstream (Table 9,
column 8). Instead, in Table 10, this is true only for vertically integrated firms (Table 10, column 8). It
seems that, despite the greater costs, few upstream specialists prefer to integrate vertically.
In fact, even if puzzling, not only does this result have a logical explanation, but it also reinforces
our theory. As shown earlier (Table 4) the production costs reduce the number of downstream firms.
Transaction cost economics suggests that this raises the bargaining power of the remaining firms when
they negotiate with upstream suppliers. A standard response of the suppliers is then to integrate forward.
As shown by our results, the most pronounced effect of the increase in production costs is to discourage
entry by the upstream suppliers, and by our logic of strategic complementarity this encourages the
investment in more fungible resources. However, as we are showing here, it may be that for a few
upstream suppliers the net effect of the increase in manufacturing costs and in the bargaining power of the
downstream firms is that of encouraging rather than discouraging entry. Yet, as column 8 of Table 10
show, these firms invest in dedicated rather than fungible resources, which is consistent with our theory.
To summarize, most upstream suppliers see the increase in downstream production costs as an
opportunity to delay entry, and invest in more fungible resources. For a few of them, this is instead an
opportunity to enter, and they invest in dedicated technologies, as implied by our predicted
complementarity between fungibility and trade. We did not find this result in our main bivariate probit in
Table 5; more generally these results, based only on the longitudinal variation in the likelihood of
entering, should be interpreted with caution. It is reassuring however that, if true, it would only strengthen
the logic of our story.
------
Insert Table 10 about here
-----
CONCLUSIONS
The thrust of our analysis is that the strategic decision of a firm to specialize upstream in an industry is
complementary with the decision to invest in the fungibility of its resources in order to reach different
markets. We suggested in the initial sections of this paper that quite a few companies see this
21
complementarity as a way to escape potential limitations in the growth of the firm in the downstream
markets. The market development manager of a large international laser company summarizes our
approach: “Our lasers can be applied to any type of industry. What I tell to our customers is ‘I do not
care what you need to make with it: I have the laser for you!’ (…) We have developed this skill through
the years. We do not need to move into systems. Some companies do but it is not our business concept.”
We then argue that this approach has interesting implications for strategy scholars and managers.
For example, while we focus on an increase in downstream production costs as the trigger to reconsider
the firm’s business model, any other trigger could produce a similar response, like more efficient vertical
markets, the rise of new final markets that compete with the firm’s focal products and that the firm is
unable to counter, as suggested by our IBM example, or the supplier’s response to hold-up following an
increase in bargaining power of the downstream buyers. More importantly, our analysis speaks to
question of how firms respond to the potential disruption of new businesses or technologies (Christensen,
1997). The standard responses seem to be to jump in the arena and fight in the newly arising markets, or
to hold tightly to the core business of the firm and counter-attack. Most often these strategies have just
delayed the downfall, or the firm’s scaling down. A potential alternative is to refocus upstream and
exploit fungible capability. This may not be easy in that firms may have to cope with “cultural”
diseconomies of scope of the kind that Bresnahan et al. (2011) have highlighted. However, as IBM and
possibly many of the laser firms in our sample, have shown, it may not be impossible.
Our paper is also defined by its limitations. First, as in any quasi-experimental setting, we cannot
argue that our treatment is completely exogenous and so uncorrelated with other factors potentially
affecting our outcome of interests. However, the several robustness checks we conducted tend to
corroborate the idea that we can consider the treatment as if it was exogenous.
Second, considering one single industry might generate some concerns about the generalizability
of our results. In particular, one might argue that some characteristics of the laser industry – e.g., the
division of labor between upstream firms providing laser technologies and downstream firms embedding
those technologies into laser systems – might not extend to other sectors. However, several of the laser
industry characteristics – including the division of innovative labor – are increasingly common across
knowledge-intensive industries; for this reason, other scholars have also chosen the laser industry as
empirical setting for their theoretical predictions (e.g., Klepper & Sleeper, 2005).
Third, a longer timeframe may produce more robust results. However, the time period that we
have taken into account allows us to capture sufficient variations in both the main independent and
dependent variables. In fact, not only does the timeframe covered by our dataset allow us to compare
states enacting a regulation that increase downstream production costs with states not enacting the
22
regulation, but it also allows us to observe a wide variance among lasers in terms of their number of
potential applications.
Despite these limitations, this paper provides important implications for practitioners. In
particular, “specialization in generality” is a natural prescription of our discussion. George Stigler’s
(1951) used the term “general-specialties” to refer to activities “… (like shipping, railroads, banking,
etc.), which could not be attached to anyone industries” (p.192) and that gave England of the XIX century
a big head start in terms of development of many other downstream businesses. Translating Stigler’s
intuition into the business environment, we argue that, under certain contingencies, the main strategy
managers should pursue is to serve different downstream markets with a fungible intermediate resource,
rather than entering in anyone of these markets. In this regard, while Penrose (1959), Chandler (1990) or
Nelson (1959) saw economies of scope largely as accruing within – usually large – organizations, in this
paper we suggest that the benefits of economies of scope can be achieved (also) through markets and the
division of innovative labor (Arora et al., 2001). Studying the extent to which firms can take advantage of
internal economies of scope, by entering multiple markets, rather than exploiting external economics of
scope, by selling in these markets, is an interesting avenue for future research.
23
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26
APPENDIX
We develop a formal model that captures the key elements of our theory. Suppose that for a given firm
the value of integrating downstream in a given market to produce an application of an upstream resource
is x. This value is uncertain and distributed as F(x) with support [a, X]. While X > 0, a could be negative –
that is, the firm may not find it profitable to enter. If the firm trades the resource in the market it earns r.
For our focal firm, the applications are ranked in the sense that the expected value of xi declines with the
index i = 1, 2, …, n, and so does ri. Again, ri could be negative, in which case the firm does not sell in
these markets. We assume that ri increases with the bargaining power of the focal firm in the transaction
as well as with the efficiency of the potential buyers of the services of the resource for that application
(more efficient users are willing to pay more.) For simplicity ri is known with certainty.
The firm has to decide whether to adopt an integration strategy, which we label the I-strategy, or a
trading specialization strategy, which we label the T-strategy. Contemporaneously, it decides whether to
invest in a fungible resource that can be used in n potential application markets. We assume for simplicity
that the returns x are independent across application markets. If the firm adopts the I-strategy, it invests
downstream in the market that produces the best expected x among the n applications of the resource.
This investment also provides the firm with the opportunity to elicit signals about the returns x from
producing in the other markets. The firm can then observe the signals and decide whether to enter or trade
in the other markets. If the firm adopts the T-strategy, it does not invest in downstream production, and
can only trade in all the n markets earning ri.
If the firm adopts the I-strategy, it invests K to produce the application with the highest expected
x, that is x1, and spends KIn to obtain the signals si, i = 2, 3, … n, positively correlated with the other xi.
The signals are iid and distributed as G(s). For each application i = 2, 3, … n, the firm chooses a threshold
si¿ such that whenever the signal is greater than the threshold the firm enters the ith market to produce,
otherwise it trades the services of the resource. In addition, the firm chooses n, the fungibility of the
resource. The I-strategy problem of the firm is
maxs∗,n
−K+∫0
X
xdF ( x|1 )−K I (n−1 )+∑i=2
n [∫0X
xdF (x|i , s≥ s¿)(1−G (s¿))+riG ( s¿ )]where the lower bound of the integral implies that the firm will not produce if the true x turns out to be
negative. After integrating by parts, the integral in the square brackets can be written as
( X−∫0
X
∫s¿
.
F( x∨i , s) dG1−G(s¿)
dx) (1−G(s¿)), and the problem becomes
maxs∗,n
−K I (n−1 )+∑i=2
n [ X (1−G(s¿))−∫0
X
∫s¿
.
F(x∨i , s )dGdx+r iG ( s¿ )]27
where we dropped the first market because it does not affect the optimization problem.
Maximization with respect to s¿ yields the following first order condition
−Xg ( s¿ )+∫0
X
F ( x|i , s¿) g (s¿) dx+¿ ri g ( s¿)=0¿
where g(s¿) is the density of G. Since the signal is positively correlated with x, F diminishes as s¿
increases, which establishes the second order condition for a maximum. Since we ranked the market
opportunities so that higher i correspond to less profitable projects, F increases with i, and it is easy to see
that the first order condition increases with ri. Standard comparative statics then implies that the optimal
threshold increases for less profitable projects (higher i) and for projects with higher ri. The intuition is
straightforward: the firm needs a high signal to believe that it is worth producing in low-ranked markets
or that yield good returns from selling the services of the resource.
Maximization with respect to n yields the following first order condition
−K I+vnI=0
where vnI ≡[ X (1−G (s¿ ) )−∫
0
X
∫s¿
.
F ( x|i , s ) dGdx+riG (s¿ )] is the expected marginal benefit of the least
profitable application. This first order condition yields nI, which is the fungibility of the firm’s resource
under the I-strategy.
If the firm pursues the trading-specialization strategy, which we label the T-strategy, it foregoes
the opportunity to produce. The T-strategy problem is
maxn
−KT n+∑i=1
n
ri
where KTn is the cost of trading the services of a resource with n applications. To help the comparison
with the I-strategy problem, we can rewrite the T-strategy problem as
maxn
−KT n+r1+ ∑i :s>s¿
n
ri ¿¿¿
Apart from r1, which we can easily assume to be smaller than the best expected x, the returns ri from
trading in all the markets in which the firm also trades under the I-strategy (which correspond to signals
lower than the threshold) are equal to the equivalent returns in the I-strategy. All the returns ri from
trading in markets in which the firm does not trade under the I-strategy (which correspond to signals
lower than the threshold) are instead smaller than the expected returns from production in the I-strategy –
otherwise the firm would have picked them under the I-strategy. This also implies that the expected gross
benefits of the T-strategy are smaller than the expected gross benefits of the I-strategy. However, it is
natural to assume that KT < KI. Under the I-strategy, the firm investigates the production of opportunities
28
of the n – 1 markets. This may even require some initial manufacturing investments and capital costs,
which you do not incur when you only look at trading opportunities. As a result, the net benefits of the T-
strategy can be smaller or larger than the I-strategy.
The first order condition of the T-strategy problem is
−KT+rn=0
Note that rn ≤ vnI because either rn is equal to the returns from trading in the I-strategy, or it corresponds to
a market in which under the I-strategy the firm finds it profitable to produce, earning more than trading.
Since KT < KI the optimal level of fungibility under the T-strategy, nT, can be higher or lower than nI. It
will be higher the lower KT or the more efficient the potential buyers, which raises rn.
We represent increases in production costs of the focal firm as a first order stochastic dominant
effect that increases F, and therefore reduces the expected profits of the I-strategy. The increase in
production costs does not affect the returns from trading. Therefore, the expected profits of the I-strategy
may drop below the expected profits of the T-strategy, in which case the firm switches from the I- to the
T-strategy. If KT is sufficiently low, or there are enough efficient buyers raising rn, nT is higher than nI. If
so, the firms that switch increase fungibility after the increase in costs. For all the other firms fungibility
does not change (if the firm adopts a T-strategy before and after the increase in production costs) or it
decreases (if the firms adopts an I-strategy before and after the increase in costs.)
For our other two propositions about upstream suppliers and skewed distribution of firms in
markets, consider first a firm that can exploit fewer applications of the same resource that can be used in
n markets. Compared to a firm that can enjoy full profits from any of the applications of this resource, for
this firm some of the applications with profits higher or equal to the marginal application will be lower.
As a result, under a T-strategy, this firm only needs a resource that can be used in the fewer applications
in which it earns full profits (lower fungibility). Therefore, as production costs increase, if this firm finds
it profitable to switch it will seek a less fungible resource than a firm that can earn full profits in a larger
number of the potential applications of the resource. Similarly, if the profitability of the marginal
application is small, a firm does not raise fungibility as much as a firm facing more profitable marginal
applications.
We can also represents our results graphically. In the figure below, the horizontal axis represents
the choice of fungibility (n). In the vertical axis, we represent the marginal benefits and costs of the
increasing n under the I- and T-strategy. The marginal costs KI and KT are constant, while the marginal
benefits vI and vT decline with n. The firm chooses the levels nI and nT, where the marginal benefits of the
strategy equals the marginal costs (A and B). Note that in order to have that nT > nI, the vT marginal
benefit curve has to be higher than the v0T
curve, or KT has to be sufficiently small compared to KI (point C
29
in the figure.) This mirrors our earlier statement that the focal firm cannot be far superior to other firms in
the market in exploiting the applications of the fungible resource, or the marginal costs of the T-strategy
ought to be sufficiently small. An increase in downstream production costs will shift the vI curve to the
left to v1I
which reduces nI to n1I
(A1). The question is whether this will also make the net profits of the
T-strategy higher than the net profits of the I-strategy (which we cannot establish from the figure): if so,
fungibility will increase from nI and nT (from A to B); if not, it will decrease from nI and n1I
(from A to
A1). Also, the increase in costs shifts the marginal benefit vI to the left whether the firms already occupies
some downstream application markets or not, and it shifts vT to the left only if the firm already occupies
some downstream markets, and similarly for resources such that the benefits of the marginal application
declines sharply. In both cases, the expected increase in fungibility, following the increase in downstream
production costs, will be less pronounced.
30
A1 A
C B
v0T
v1I
Increase in production costs
Switch
KI
KT
n1I
nI nI
nT
vT
vI
n
TABLES
Table 1 Descriptive statistics and pairwise correlation between variables
Observations Mean S.D. Min Max
Investment in fungible resources 783 0.157 0.364 0.000 1.000 1.000
Downstream entry 783 0.068 0.251 0.000 1.000 0.037 1.000
Production cost 783 0.202 0.402 0.000 1.000 0.037 -0.072 1.000
Number of lasers (t-1) 783 1.927 1.514 1.000 12.000 0.169 0.110 -0.058 1.000
Number of patents (from t-5 to t-1) 783 46.147 338.339 0.000 4941.858 0.058 -0.030 -0.066 -0.057 1.000
Number of employees (t-1) 783 336.648 1379.587 1.000 21760.000 0.164 0.013 -0.032 0.326 0.264 1.000
Age (t-1) 783 20.789 18.831 2.000 134.000 0.031 -0.006 0.038 0.094 0.195 0.363 1.000
Skewness of market size distribution 783 0.310 0.120 0.251 1.000 -0.126 -0.042 -0.088 -0.148 -0.017 -0.029 -0.095 1.000
Vertically integrated supplier 783 0.561 0.497 0.000 1.000 -0.085 0.064 -0.017 0.138 -0.019 0.034 -0.042 -0.002 1.000
31
Table 2 Impact of increased compliance costs on the probability of being a downstream laser system supplier and/or an upstream laser supplier: Bivariate Probit estimation
(1) (2)VARIABLES Upstream laser
supplier (t)Downstream laser systems supplier (t)
Production Cost(t-1) 0.026 -0.072
(0.132) (0.102)Production costs(t-1) X Downstream laser systems supplier (t-1) 0.072 0.350***
(0.153) (0.125)Production Cost(t-1) X Upstream laser supplier (t-1) -0.016 -0.194
(0.176) (0.146)Downstream laser systems supplier (t-1) -0.362*** 1.770***
(0.066) (0.047)Upstream laser supplier (t-1) 2.507*** -0.090
(0.073) (0.066)Year fixed effects Included IncludedState dummies Included IncludedConstant -1.984*** -1.356***
(0.382) (0.318)
Observations 5,533 5,533Note: Robust standard errors clustered by state in parentheses *** p<0.01, ** p<0.05, * p<0.1
32
Table 3 Impact of increased production costs on the probability of being a downstream laser system supplier and/or an upstream laser supplier: Marginal effect estimation (based on the Bivariate Probit estimation in Table 2)
MARGINAL EFFECT OF BEING, at t-1: ON THE PROBABILITY OF BEING, at t: Effect size when production costs=0
Effect size when production costs=1
Difference between marginal effects
Prob > chi2
Outside the industry U(t-1)=0 D(t-1)=0 Outside the industry U(t)=0 D(t)=0 0.825 *** 0.837 *** 0.012 0.607
Outside the industry U(t-1)=0 D(t-1)=0 Upstream specialist U(t)=1 D(t)=0 0.027 *** 0.03 *** 0.003 0.6669
Outside the industry U(t-1)=0 D(t-1)=0 Downstream specialist U(t)=0 D(t)=1 0.115 *** 0.1 *** -0.015 0.4053
Outside the industry U(t-1)=0 D(t-1)=0 Vertically integrated U(t)=1 D(t)=1 0.033 *** 0.032 *** -0.001 0.9141
Upstream specialist U(t-1)=1 D(t-1)=0 Outside the industry U(t)=0 D(t)=0 0.202 *** 0.202 *** 0 0.992
Upstream specialist U(t-1)=1 D(t-1)=0 Upstream specialist U(t)=1 D(t)=0 0.669 *** 0.716 *** 0.047 0.3158
Upstream specialist U(t-1)=1 D(t-1)=0 Downstream specialist U(t)=0 D(t)=1 0.005 *** 0.003 *** -0.002 * 0.0829
Upstream specialist U(t-1)=1 D(t-1)=0 Vertically integrated U(t)=1 D(t)=1 0.124 *** 0.079 *** -0.045 * 0.057
Downstream specialist U(t-1)=0 D(t-1)=1 Outside the industry U(t)=0 D(t)=0 0.242 *** 0.166 *** -0.076 *** 0.0093
Downstream specialist U(t-1)=0 D(t-1)=1 Upstream specialist U(t)=1 D(t)=0 0 *** 0 ** 0 0.1489
Downstream specialist U(t-1)=0 D(t-1)=1 Downstream specialist U(t)=0 D(t)=1 0.729 *** 0.799 *** 0.07 ** 0.0173
Downstream specialist U(t-1)=0 D(t-1)=1 Vertically integrated U(t)=1 D(t)=1 0.029 *** 0.035 *** 0.006 0.5718
Vertically integrated U(t-1)=1 D(t-1)=1 Outside the industry U(t)=0 D(t)=0 0.162 *** 0.142 *** -0.02 0.5573
Vertically integrated U(t-1)=1 D(t-1)=1 Upstream specialist U(t)=1 D(t)=0 0.109 *** 0.103 *** -0.006 0.7676
Vertically integrated U(t-1)=1 D(t-1)=1 Downstream specialist U(t)=0 D(t)=1 0.151 *** 0.146 *** -0.005 0.8432
Vertically integrated U(t-1)=1 D(t-1)=1 Vertically integrated U(t)=1 D(t)=1 0.577 *** 0.61 *** 0.033 0.5739
33
Table 4 Impact of increased production costs on the number of laser system downstream suppliers: Poisson estimation
(1)
VARIABLES Number of Downstream Suppliers
Production cost(t-1) -0.154**
(0.057)
Sub-market dummies Included
Year fixed effects Included
State dummies Included
Constant 0.009
(0.059)
Observations 3,330
R-squared 0.887
Note: Robust standard errors clustered by state in parentheses
*** p<0.01, ** p<0.05, * p<0.1
34
Table 5 Impact of increased production costs on the probability of investing in more fungible resources and/or entering in a downstream market: Bivariate Probit estimation
(1) (2) (3) (4) (5) (6)VARIABLES Investment
in fungible resources
Downstream entry
Investment in fungible resources
Downstream entry
Investment in fungible resources
Downstream entry
Production cost(t-1) 0.137 -0.419* 0.408* -5.122*** 14.909*** -0.537
(0.176) (0.215) (0.224) (0.240) (2.972) (0.339)Vertically integrated supplier -0.236* 0.237
(0.123) (0.167)
Production cost(t-1) X Vertically integrated supplier
-0.455 4.892***
(0.320) (0.238)Skewness of market size distribution -5.970*** -1.033***
(2.227) (0.356)Production cost(t-1) X Skewness of market size distribution
-54.161*** 0.490
(11.196) (0.957)Number of lasers (t-1) 0.188*** -0.019 0.206*** -0.022 0.186*** -0.025
(0.052) (0.029) (0.055) (0.030) (0.067) (0.031)Number of patents (from t-5 to t-1) 0.000*** -0.001*** 0.000*** -0.001*** 0.000*** -0.001***
(0.000) (0.000) (0.000) (0.000) (0.000) (0.000)Number of employees (t-1) 0.000*** 0.000 0.000*** 0.000 0.000** 0.000
(0.000) (0.000) (0.000) (0.000) (0.000) (0.000)Age (t-1) -0.009* 0.006 -0.009* 0.005 -0.009** 0.005
(0.005) (0.010) (0.005) (0.010) (0.004) (0.010)Year fixed effects Included Included Included Included Included IncludedState dummies Included Included Included Included Included IncludedConstant -0.834*** -0.265 -0.829*** -0.254 0.818 0.051
(0.289) (0.298) (0.307) (0.293) (0.573) (0.268)
Observations 783 783 783 783 783 783Note: Robust standard errors clustered by state in parentheses. *** p<0.01, ** p<0.05, * p<0.1
35
Table 6 Impact of increased production costs on the probability of investing in more fungible resources and/or entering in a downstream market: Bivariate Probit marginal effect estimation
MARGINAL EFFECT OF: ON THE JOINT PROBABILITIES OF:
Increased production costs Investment in fungible resources
Downstream entry
Effect size P-value
1 0 0.045 0.1220 0 -0.004 0.8790 1 -0.032 *** 0.0001 1 -0.009 ** 0.014
WHEN:vertically integrated supplier is equal to:
0 1 0 0.105 * 0.0590 0 0 -0.048 0.4050 0 1 -0.043 *** 0.0000 1 1 -0.014 *** 0.007
1 1 0 -0.002 0.9351 0 0 0.029 0.4401 0 1 -0.022 0.1161 1 1 -0.005 0.289
WHEN:Skewness of market size distribution is at the percentile:
25% 1 0 0.128 *** 0.00125% 0 0 -0.088 ** 0.01425% 0 1 -0.037 *** 0.00125% 1 1 -0.003 0.624
75% 1 0 -0.085 *** 0.00375% 0 0 0.122 *** 0.00075% 0 1 -0.026 * 0.08575% 1 1 -0.011 *** 0.001
Note: Robust standard errors clustered by state in parentheses *** p<0.01, ** p<0.05, * p<0.1
36
Table 7 Comparison between treated and control group
Average Control group Average treated group Difference p-value
Number of lasers (t-1) 1.660 1.817 -0.157 0.4161
Number of patents (from t-5 to t-1) 67.159 1.364 65.795 0.1886
Number of employees (t-1) 305.535 247.511 58.024 0.7282
Age (t-1) 19.271 16.483 2.788 0.3596
Vertically integrated manufacturer 0.576 0.517 0.060 0.4362
Skewness of market size distribution 0.334 0.309 0.025 0.2834
Note: *** p<0.01, ** p<0.05, * p<0.1
37
Table 8 Impact of state economic and political characteristics on the probability of enacting a new laser regulation
(1) (2) (3)
VARIABLES New laser regulation
New laser regulation
New laser regulation
GDP per capita 0.000 0.000 0.000
(0.000) (0.000) (0.000)
Red Presidential Elections 0.023 0.021
(0.018) (0.017)
Red Governor -0.003 -0.002
(0.009) (0.009)
Taxation level -0.001
(0.001)
Year fixed effects Included Included Included
State dummies Included Included Included
Observations 481 471 471
R-squared 0.034 0.038 0.039
Number of stateid 51 50 50
Note: Robust standard errors clustered by state in parentheses *** p<0.01, ** p<0.05, * p<0.1
38
Table 9 Impact of increased production costs on the probability of investing in more fungible resources and entering downstream: OLS estimation including time trend
(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12)VARIABLES Investment in fungible resources=1,
Downstream entry =0Investment in fungible resources=0, Downstream entry =0
Investment in fungible resources=0, Downstream entry =1
Investment in fungible resources=1, Downstream entry =1
Production cost(t-1) 0.041 0.134* 0.449*** 0.036 -0.057 -0.340*** -0.070** -0.076* -0.092** -0.011 -0.006 -0.031(0.052) (0.070) (0.064) (0.053) (0.058) (0.089) (0.035) (0.043) (0.043) (0.010) (0.009) (0.021)
Vertically integrated supplier -0.037* 0.001 0.028* 0.009(0.022) (0.034) (0.017) (0.011)
Production cost(t-1) X Vertically integrated supplier -0.155* 0.156
0.009 -0.008
(0.089) (0.099) (0.029) (0.012)
Skewness of market size distribution -0.286*** 0.388***-
0.074***-0.052
(0.059) (0.066) (0.022) (0.051)Production cost(t-1) X Skewness of market size distribution -1.404*** 1.286***
0.080 0.074
(0.191) (0.212) (0.050) (0.062)
Number of lasers (t-1) 0.035*** 0.037*** 0.031*** -0.033** -0.034** -0.028*-
0.011***-
0.012***-
0.012***0.010 0.010 0.009
(0.011) (0.012) (0.012) (0.015) (0.016) (0.016) (0.003) (0.004) (0.003) (0.008) (0.008) (0.008)
Number of patents (from t-5 to t-1) 0.000 0.000 0.000 -0.000 -0.000 -0.000-
0.000***-
0.000***-
0.000***-0.000 -0.000 -0.000
(0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000)Number of employees (t-1) 0.000*** 0.000*** 0.000*** -0.000*** -0.000*** -0.000*** 0.000*** 0.000* 0.000*** -0.000 -0.000 -0.000
(0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000)Age (t-1) -0.002*** -0.002*** -0.002*** 0.001 0.001 0.002 -0.000 -0.000 -0.000 0.001 0.001 0.001
(0.001) (0.001) (0.001) (0.001) (0.001) (0.001) (0.001) (0.001) (0.001) (0.001) (0.001) (0.001)Year fixed effects Included Included Included Included Included Included Included Included Included Included Included IncludedStates dummies Included Included Included Included Included Included Included Included Included Included Included IncludedTime trend X States dummies Included Included Included Included Included Included Included Included Included Included Included IncludedConstant -0.126 -7.471*** 0.015 0.275 0.266 0.745*** 1.140*** 1.214*** 0.211*** 0.143** 0.124** 0.111***
(0.191) (0.280) (0.037) (0.305) (0.324) (0.045) (0.133) (0.160) (0.022) (0.060) (0.058) (0.021)
Observations 783 783 783 783 783 783 783 783 783 783 783 783Number of firms 204 204 204 204 204 204 204 204 204 204 204 204
Note: Robust standard errors clustered by state in parentheses. *** p<0.01, ** p<0.05, * p<0.1
39
Table 10 Impact of increased production costs on the probability of investing in more fungible resources and entering downstream: OLS estimation including time trend and firm fixed effects
(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12)VARIABLES Investment in fungible resources=1,
Downstream entry =0Investment in fungible resources=0, Downstream entry =0
Investment in fungible resources=0, Downstream entry =1
Investment in fungible resources=1, Downstream entry =1
Production cost(t-1) 0.018 0.308*** 0.810** 0.030 -0.374*** -0.668* -0.039 0.059** -0.132* -0.009 0.007 -0.010(0.051) (0.107) (0.349) (0.061) (0.124) (0.360) (0.045) (0.028) (0.066) (0.010) (0.008) (0.033)
Production cost(t-1) X Vertically integrated supplier -0.495*** 0.690*** -0.168*** -0.027
(0.179) (0.187) (0.049) (0.020)Skewness of market size distribution -0.517** 0.318 0.188** 0.011
(0.253) (0.331) (0.084) (0.014)Production cost(t-1) X Skewness of market size distribution -2.871** 2.527* 0.338** 0.006
(1.204) (1.250) (0.154) (0.109)Number of lasers (t-1) 0.023 0.023 0.022 0.003 0.004 0.003 -0.036*** -0.036*** -0.035*** 0.009 0.009 0.009
(0.021) (0.019) (0.020) (0.024) (0.023) (0.024) (0.007) (0.007) (0.006) (0.014) (0.014) (0.014)Number of patents (from t-5 to t-1) -0.000 -0.000 -0.000 0.000 0.000 0.000 -0.000*** -0.000** -0.000** 0.000 0.000 0.000
(0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000)
Number of employees (t-1) 0.000* 0.000** 0.000* -0.000*** -0.000*** -0.000*** -0.000 -0.000 -0.000-
0.000*** -0.000** -0.000***(0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000)
Age (t-1) 0.100*** 0.100*** 0.099*** -0.104*** -0.103*** -0.103*** 0.008*** 0.007*** 0.008*** -0.004 -0.004 -0.004(0.007) (0.006) (0.007) (0.006) (0.005) (0.006) (0.002) (0.002) (0.002) (0.004) (0.004) (0.004)
Firm fixed effects Included Included Included Included Included Included 0.000 0.000 0.000 0.000 0.000 0.000Year fixed effects Included Included Included Included Included Included (0.000) (0.000) (0.000) (0.000) (0.000) (0.000)States dummies Included Included Included Included Included Included -0.010 -0.013 -0.010 -0.020 -0.021 -0.020Time trend X States dummies Included Included Included Included Included Included (0.042) (0.041) (0.043) (0.018) (0.019) (0.018)
Constant 136.364*** 134.423*** 133.208***-
179.520***-
176.814***-
176.713***42.569**
* 41.909*** 42.914*** 1.587 1.482 1.591(11.973) (13.666) (8.650) (10.848) (11.835) (10.279) (4.761) (4.716) (4.452) (6.085) (6.056) (6.151)
Observations 783 783 783 783 783 783 783 783 783 783 783 783R Squared 0.169 0.191 0.185 0.120 0.150 0.126 0.064 0.070 0.067 0.073 0.074 0.073Number of firms 204 204 204 204 204 204 204 204 204 204 204 204
Note: Robust standard errors clustered by state in parentheses. *** p<0.01, ** p<0.05, * p<0.1
40
FIGURES
Figure 1
min 10% 25% 50% 75% 90% max
-0.200
-0.150
-0.100
-0.050
0.000
0.050
0.100
0.150
0.200
0.250
0.300
Fungibility, no entry
UpstreamIntegrated
Skewness of market distribution
Effec
t
Figure 2
min 10% 25% 50% 75% 90% max
-0.080
-0.060
-0.040
-0.020
0.000
0.020
0.040
0.060
0.080
No fungibility, entry
UpstreamIntegrated
Skewness of market distribution
Effec
t
41
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