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Knowledge, competition and entry in the evolution of vertically related industries

January 2020

Franco Malerba

Pamela Adams

Roberto Fontana

Gianluca Capone

ABSTRACT

We build on previous research on vertically-related industries by exploring how the density of an upstream, supplier industry affects the entry and survival of new and independent startup firms in a related downstream industry. We develop predictions concerning the effect of rising density upstream on the related downstream population of new entrants and test these predictions with data describing the semiconductor and telecommunications equipment industries from 1999 to 2008. In a second stage we further test our results by developing a (history-friendly) simulation model that incorporates the framework developed for this study. We find that the exit rate of startups in the telecommunications equipment industry rises with increases in the density of semiconductor firms. However, startups that spinout from firms in the semiconductor industry to enter the telecommunications industry (supplier-industry spinouts) are more likely to survive than other types of independent startups. These findings hold for both the empirical analysis and the model. The study underlines the important role of competition and market structure in the vertical relationship among industries and the double-edged sword of firm density. While upstream density may have a positive effect on downstream survival in early phases of industry evolution due to mechanisms related to price and variety, it may negatively affect survival, especially of new entrants, in later phases of industry evolution due to mechanisms related to entry and increased competition. Much depends on the characteristics of the context in which these dynamics play out.

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KEYWORDS: vertical chain, firm density, entry, entrepreneurship, semiconductors, telecommunications, history friendly model

INTRODUCTION

The relationship between vertically-related industries has long been a topic of interest for both

theoretical scholarship and empirical analyses in industry dynamics and industry evolution

(Jacobides and Winter, 2005; Malerba et al., 2016). Vertical relatedness refers to upstream

(backward) and downstream (forward) industries that define the stages of the vertical chain of

production of a specific product or service. Scholarship on vertical relatedness has been advanced

over the past decades by research that explores the relationship between supplier and buyer

industries along a value chain (Bonnacorsi and Giuri, 2000; Negro and Sorenson, 2006). Building

on these studies, DeFigueiredo and Silverman (2012) examine the impact of vertical relatedness by

analyzing how density in an upstream industry may affect the survival rate of firms in a related,

downstream industry. They find that greater firm density in a core supplier industry enhances the

survival rate of firms in the related, buyer industry.

While this work provides initial evidence of a strong relationship between the dynamics in

related upstream and downstream industries, important gaps remain in our understanding of how

these dynamics work across different contexts. In particular, one important gap concerns how the

nature of these dynamics may change as two related industries evolve over time. As industries pass

from birth through maturity and eventual decline, the technological, demand and resource

conditions in which firms enter and compete also change. More work needs to be done, therefore,

to understand whether the dynamics uncovered in one period of time may hold in competitive

environments that characterize different phases of industry evolution. In the study cited above, in

fact, the relationship concerning firm density upstream and firm survival downstream is found in

two related industries that are both in the early phases of their evolution. We may ask if similar

dynamics are found in industry contexts more characteristic of later phases of industry evolution.

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To examine the relationship between upstream firm density and firm survival downstream,

we further draw on the literatures on industry dynamics and entrepreneurship to propose that entry

may play a critical role in this. Our focus on entry underlines the relevance of new firms in the

growth of industries (Agarwal, Echambadi, Franco, & Sarkar, 2004; Audretsch, 1995; Bartelsman,

Scarpetta, & Schivardi, 2005; Decker, Haltiwanger, Jarmin, & Miranda, 2016) and opens a new

perspective on the knowledge resources of new ventures that cross industry boundaries (Adams,

Fontana, & Malerba, 2015, 2019; Agarwal & Shah, 2014). We distinguish between three different

types of independent, new ventures according to the origins of their founders: spinouts from the

focal industry, spinouts from vertically-related industries and de novo entrants whose founders’

origins are from outside of these industries1. We then explore how the density of upstream

suppliers affects both the entry and the survival rates of these different categories of new entrants in

the downstream industry. The context of our study involves two vertically-related industries,

semiconductors (a core supplier industry) and telecommunications equipment (the downstream,

buyer industry), between the years of 1999 and 2008 when both industries were in more established,

rather than early, phases of their evolution.

We begin our analysis by developing a set of predictions and testing them empirically with

data collected on both industries. We then build a simulation model of entry, competition and

survival in two vertically related industries, which aims at illustrating formally the main

mechanisms that are at the base of our empirical analysis. The model presented takes inspiration

from the history-friendly models of industry evolution (Malerba, Nelson, Orsenigo and Winter,

2016), and allows us to explore the extent to which our empirical findings might be due to

mechanisms specific to the context of our analysis.

1 The terminology related to start-ups is often confusing. New and independent ventures created by the ex-employees of incumbent firms in an industry are called both spinoffs (Klepper, 2001, 2009) and spinouts (Agarwal et al., 2004). Some studies also call them ‘spawns’ (Chatterji, 2009). We have chosen to use the term, spinouts here, in order to underline the fact that these new firms are founded in industries different from that of their founders’ origins. The use of the term spinouts is also consistent with previous work on user-industry spinouts (Adams, et al., 2016). It should be noted that ventures founded and/or partially controlled by established firms from either the same or a different industry are excluded from this category.

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We find that the greater density in a core supplier industry density of the upstream industry

affects survival rates for downstream startups in a nuanced way. The exit rate of startups in the

downstream industry rises with increases in the density of core supplier firms. However, startups

that spinout from firms in the supplier industry to enter the downstream industry (supplier industry

spinouts) are more likely to survive than other types of independent startups. These findings hold

for both the empirical analysis and the model. We explain our findings, which contrast somewhat

with those of previous studies on density and survival in vertically related industries (DeFigueiredo

and Silverman, 2012), by differences in the competitive context of the later phase of industry

evolution with respect to the early phase.

Our study makes several contributions to the literature. First, it contributes to scholarship on

industrial dynamics by providing further evidence of the interplay between vertically related

industries. It also underlines the important role of competition and market structure in this

relationship and the double-edged sword of firm density. While upstream density may have a

positive effect on downstream survival in early phases of evolution due to price and variety

mechanisms, it may negatively affect survival, especially of new entrants, in later phases of industry

evolution due to mechanisms related to entry and increased competition. Much depends on the

characteristics of the context in which these dynamics play out. Second, we further extend the

literatures on knowledge and entrepreneurship by showing that knowledge from a vertically related,

upstream industry may represent both a source of innovation and a foundation successful startup

ventures. Our study shows, in fact, that spinouts that originate from vertically related industries are

more likely to survive in a new industry context than other de novo entrants. Finally, we add to the

literature on firm strategy by examining the competitive dynamics between vertically related

industries. Our findings suggest, in fact, that knowledge-sharing relations with core supplier firms

may offer incumbent firms in a downstream buyer industry a competitive advantage as firm density

increases in the upstream industry. By the same token, however, knowledge related to a core

component for a downstream industry may offer spinouts from a supplier industry the competitive

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advantage needed to cross industry boundaries and enter a downstream industry through an

independent startup venture. The unique knowledge base that such startups inherit through their

experience in a core component supplier firm, in fact, may constitute an alternative type of

competitive advantage with respect to the accumulated knowledge of incumbents in the

downstream, buyer industry. The changing dynamics between vertically related industries as they

evolve over time are therefore important for both incumbents and potential spinout ventures to

understand.

The paper is organized into the following sections. In the following section, divided into

three subsections, we discuss the theoretical background for our analysis and build our hypotheses.

In Section 3 we present the industry context of the study as well as the data used and methodology

followed in our empirical analysis. Section 4 contains the main results of the empirical analysis,

while Section 5 presents the model and the findings of the model. The final section, Section 6,

presents a discussion of the findings within a broader framework regarding the dynamics and

evolution of vertically related industries.

THEORETICAL BACKGROUND

The Evolution of Vertically Related Industries

An emerging area for empirical research in industrial dynamics regards the relationship between

vertically related industries. Malerba et al. (2016) show how the vertical scope of computer

producers between the 1950’s and 1980’s was largely determined by the co-evolution of firms in

the semiconductor industry. Sturz (2014) explores the impact of upstream suppliers on the survival

of downstream firms in the piano industry across three centuries. Adopting a network approach,

Bonaccorsi and Giuri (2001) show how the long-term structural evolution of the engine industry

was affected by developments in two of its downstream industries (jet aircraft and commercial

turboprop aircraft) in the latter half of the 20th century.

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While vertical relatedness generally refers to links between upstream and downstream

industries along a value chain, the impact of this relationship is most pronounced for core

components. Core components represent critical assets for downstream producers. They are often

produced by specialized suppliers and may be customized to meet the needs of specific buyers or

even co-created together with the customers themselves (Prahalad and Ramaswamy, 2004;

Williamson, 1985). Compared to standardized or commodity inputs, core components thus may

promote substantial variety and innovation among downstream products and allow downstream

producers to differentiate their offerings against those of competitors. As core components often

represent a significant share of the cost of a downstream product, the price of supply may also

impact the ability of downstream producers to achieve a better cost position and become more price

competitive. The strategic importance of core components for downstream manufacturers,

therefore, makes the relationship between core upstream supplier industries and their downstream

buyer industries of particular interest for an analysis of the dynamics between vertical related

industries.

The extent to which variety in the supply of core components is valued by downstream

producers, however, will depend in part on the nature of the downstream market. In markets where

demand is relatively homogeneous or where a dominant design has emerged, variety in the

availability of upstream inputs is less relevant. By contrast, in markets where demand is relatively

heterogeneous and fragmented into multiple, independent submarkets, the ability of downstream

producers to access greater variety among core upstream resources matters more (Malerba et al.,

2016; Bhaskarabhatla and Klepper, 2014). If different core assets are important in separate

submarkets, and if resource transfer across submarkets is difficult, then variety in the supply of core

components will be highly valued among downstream buyer firms. Therefore, demand conditions

play an important role in the dynamics between core supplier industries and their related

downstream industries (Adams, et al., 2013). We use these distinguishing features of vertically

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related industries to explore the effects of firm density in a core, upstream industry on the survival

of firms in a related, downstream industry.

In one recent study on the laser engine industry (the core supplier industry) and the

downstream laser printer industry between 1984 and 1996, it is suggested that increases in the

density of upstream supplier firms enhance the survival rate of firms in the downstream industry

(De Figueiredo and Silverman, 2012). The authors draw on three complimentary mechanisms to

explain their findings: price, variety and innovation. First, increases in density imply greater

competition upstream and, therefore, greater price competition among supplier firms. Yet greater

competition also induces increased specialization in upstream resources and thus, greater variety in

supply. Such variety provides downstream firms with a greater opportunity to differentiate their

product offerings from those of their competitors. Finally, greater variety in supply acts to expose

downstream firms to new ideas and new products, thus promoting downstream innovation. As a

result, increases in the number of upstream suppliers expand the resource space that buyers may

access and use to improve their chances of success in the downstream context. (more citations from

their article to show where they got ideas)

While the above study provides initial evidence of a positive relationship between the

density of firms upstream and the survival of firms in a related downstream industry, the data

examined refers to the early years of both the laser engine and laser printer industries, from the

moment of their near simultaneous births through the first 13 years of their histories. A key question

thus remains concerning how this relationship develops over time as the two industries further

evolve. Scholarship on both the industry life cycle (Abernathy and Utterback, 1978; Anderson

and Tushman, 1990; Suarez and Utterback, 1995) and evolutionary theory (Nelson and Winter,

1982; Malerba et al, 2016) suggests that industries generally pass through different periods in their

evolution from birth through to maturity and eventual decline (Gort and Klepper, 1982; Klepper,

1996). The transition from one period to another signifies a change in the technological, demand

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and resource conditions in which firms enter and compete in an industry. Along similar lines,

models of organizational ecology argue that the dynamics of populations change over time with

increasing levels of density and competition (Hannan and Carroll, 1992). These observations

suggest that examining the relationship between industries and the related cohorts of firms at

different points in time might reveal different results due to discontinuities in environmental

conditions. As a consequence, a full understanding of the relationship between two vertically

related industries requires a dynamic approach that considers changes over time in the competitive

and sectoral context (Agarwal, Sakar, Echambadi, 2002; Malerba and Adams, 2013). We thus

draw on the literatures on industry evolution to explore if and how the relationship between firm

density upstream and survival downstream discussed above for the early years on industry evolution

holds in a competitive environment that is more characteristic of a later period of two related

industries.

For analytic convenience, we distinguish between two different periods of industry

evolution: an initial period and a subsequent, established period. As implicit in our previous

discussion of the specific features of vertically related industries linked by a core component, in this

general discussion we consider an industry characterized by heterogeneous demand. The initial

period of industry evolution begins with the introduction of a new product by the first producer and

continues as many firms, most of which are newly founded, enter to compete for a growing demand

(Klepper, 1982; 1996). Relatively low levels of firm density and relatively low barriers to entry

characterize this period. As user preferences are still uncertain, the potential for new firms to bring

new knowledge into the industry and to benefit from multiple technological opportunities to

develop product innovations is high (Gort and Klepper, 1982; Abernathy and Utterback, 1978;

Winter, 1984). As firms exploit these opportunities, new submarkets are created (Klepper and

Thompson, 2006). Given that both R&D and capital requirements are still relatively low,

opportunities also exist for competition from low cost product variants. As a result, in the initial

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period of industry evolution, increasing density is associated with greater product variety and

increasing price competition.

As the industry transitions into the subsequent, established period of industry evolution, firm

density reaches higher levels and the entry of new firms diminishes (Klepper, 1982). As most of

the needs and requirements of users have been met, fewer opportunities in the form of new

submarkets exist for firms to exploit (Malerba et al. 2016). The focus of competition thus moves to

existing submarkets and is characterized by incremental improvements in current products and by

cost reductions through process innovation. This shift favors incumbent firms as accumulated

assets and scale become key to competitive advantage (Malerba and Orsenigo, 1996). Entry is also

made more difficult by increasing barriers in terms of knowledge: increasing levels of product and

market expertise are required to develop successful product innovations in a crowded market

(Winter, 1984; Nelson and Winter, 1982). Therefore higher overall levels of firm density intensify

competition and raise the hazard rate for new firms (Agarwal and Gort, 2002; Carroll and Hannan,

1989). As a result, compared to the initial period of evolution, during the established period,

increases in density will have a more muted effect on both variety and price.

But what do such differences in the competitive context of a single industry characterized by

heterogeneous demand mean for the relationship between two vertically related industries? Does

the effect of increasing density in an upstream industry on firm survival in a downstream industry

change as both industries transition into a later, established period of evolution? Following the

arguments presented above, the effects of increases in firm density upstream on price and variety

will be more muted in the established period as compared to the initial period of industry evolution.

As a result, the downstream industry will gain fewer advantages in terms of product variation and

cost reduction to promote differentiation in its own product offerings. In addition, these effects will

take hold in a downstream industry that is, in turn, experiencing increasing competition, higher

barriers to entry in terms of both scale and knowledge, and lower entry. The more muted effects of

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changes in the upstream industry thus compound the existing challenges for entry and firm survival

in the downstream industry. As a result, increasing density upstream is less likely to have a strong

positive effect on the survival rate of firms in the downstream industry during the established period

of industry evolution as compared to the initial period.

From the discussion thus far, it is clear that entry plays a critical role in the relationship

between firm density and firm survival in these two periods of industry evolution. Entry underlies

increases in density, product innovation, and competition in both the upstream and the downstream

industry contexts. While the central role of entry is clear, however, our understanding about how

the specific characteristics of the new firms that enter and compete in these different periods affect

the dynamics between two related industries is less clear. This is especially true for new startup

ventures, which are the main drivers of entry. If the barriers to entry in terms of both scale and

knowledge are higher in the established period of industry evolution as compared to the initial

period, does this change the type of startups that are able to enter both the upstream and the

downstream industries? If so, does this also change the relationship between increasing density

upstream and new firm survival downstream?

To answer these questions, we turn to the extant literatures on innovation and

entrepreneurship, which provide a foundation for theorizing about the relationship between the

characteristics and resources of startup ventures and their ability to compete and survive in different

industry contexts. Evolutionary theory suggests, in fact, that the relative advantages of new entrants

depend on the source of knowledge at the base of their innovative activity and its fit with the

opportunities, resources and incentives to innovate in any one stage of the evolutionary process

(Nelson and Winter, 1982; Helfat and Lieberman, 2002). Knowledge is viewed as both a

wellspring of new firm entry (Klepper, 2001) and a critical source of competitive advantage

(Barney, 1991; Grant, 1996). We therefore draw on these literatures to develop hypotheses linking

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the knowledge inheritance of different types of startups to density, entry and survival across

vertically related industries. .

Knowledge, entry and survival across vertically related industries

The knowledge base that sustains the innovative activities of firms and provides the

foundation for new firm entry and survival changes over time throughout an industry’s evolution

(Breschi, Malerba, Orsenigo, 2000). In the initial period of evolution, new knowledge emanates

mainly from outside the industry, from universities and public research organizations or from

related industries (Gort and Klepper, 1982). In fact, the relatively small number of producers in

early years of growth, and their young average age, means that the accumulated stock of experience

and knowledge available within the industry is limited in both breadth and depth. Yet, as user

preferences are still uncertain, opportunities exist for firms to pursue multiple product innovations

and technological trajectories. As a result, external knowledge acts to widen the scope of innovation

and encourage entry.

As an industry evolves, both the relative number of firms active in the industry and the

average age of active firms increase. The base of cumulative knowledge expands and deepens as

firms invest in R&D and gain market-based experience with customers, distribution networks and

regulatory agencies. The accumulated stock of knowledge and experience available within the

industry provides incumbents with competitive advantage and acts as a barrier to entry against new

firms (Malerba and Orsenigo, 1996). Although demand may continue to create opportunities for

new entrants to address the needs of customer niches that value innovative product attributes

(Christensen and Bower, 1996), increasing levels of product expertise are needed to develop

significant innovations. Entry from outside is therefore more difficult and restricted to firms that

offer significant value over existing alternatives.

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The accumulated stock of knowledge and experience within existing firms, however,

provides new opportunities for entry from within the industry itself. From the moment of their

founding and over the course of their evolution, incumbent firms accumulate knowledge and

capabilities that allow them to pursue innovations (Nelson and Winter, 1982; Winter, 1984). Two

types of accumulated knowledge are of particular relevance: technological knowledge, which

reflects a firm’s ability to generate new technological breakthroughs and innovative solutions

(Cattani, 2005), and market knowledge, which contributes to the firm’s ability to gain commercial

success over competitors (Kohli and Jaworski, 1990; Roy and Sarkar, 2016). Market knowledge

includes information on and contacts with customers, distribution networks and other market related

organizations and agencies (Chatterji, 2009; Sorenson and Audia, 2000 go to their original sources).

As incumbent firms grow and accumulate both technological and market knowledge, they become

hotbeds for spinout ventures, defined as new and independent firms founded by the ex-employees

of existing firms (Agarwal et al., 2004; Franco and Filson, 2006; Klepper 2009). Studies show, in

fact, that the pre-entry experience in knowledge rich firms provides the employees of incumbent

firms in an industry with differential informational advantages that may facilitate opportunity

recognition and encourage spinout formation (Helfat and Lieberman, 2002; Shane, 2000; add

more). As result, in the later period of industry evolution, the greater the number of knowledge-rich

firms in an industry, the greater the potential for entry through spinouts (Capone et al., 2019).

Yet extant scholarship on spinout behavior also suggests that the likelihood that employees

will act on the advantages offered by their pre-entry experience in a parent firm will depend the

strategies adopted by the parent (Agarwal et al., 2004; Klepper and Thompson, 2010; Thompson

and Klepper, 2005). In the later period of industry evolution, the advantages of both knowledge and

scale controlled by incumbents mean that they face greater pressure to focus on cost and lower

incentives to pursue all customer niches with product innovations (Capone et al, 2019; Klepper,

1996). As a result, they may underutilize either their technological or their market knowledge and

leave unexploited opportunities in the market. This may frustrate employees and motivate them to

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use the knowledge that they have gained in the parent firm to form a new venture of their own to

address these gaps in market coverage (Klepper, 2001; Agarwal et al., 2004). In the later period of

industry evolution, therefore, both knowledge accumulation within incumbent firms and changes in

the competitive environment facing these firms create greater opportunities for entry by spinout

ventures.

Spinouts that have origins in the same industry in which they enter are referred to as ‘focal

spinouts’. Recent research shows, however, that both downstream and upstream industries also

represent rich contexts from which spinouts may originate and cross into a vertically related

industry (Adams et al. 2013; 2016; 2019). Relevant here are cases in which the ex-employees of an

upstream parent firm in a supplier industry to form a new and independent venture in a downstream

industry. These are referred to as ‘supplier-industry’ spinouts (Adams, et al., 2019). Such spinouts

enter the downstream industry equipped with knowledge regarding specific components or

technologies gained from their pre-entry experience in a parent firm in a core supplier industry

(Alacer and Oxley, 2014).

While the founding of supplier-industry spinouts may be understood with the same factors

as other spinouts, their choice to enter the downstream industry may be explained by their distinct

knowledge inheritance. It is likely that, as ex-employees of a core supplier firm, the founders of

these spinouts will have worked closely with buyer firms to integrate their components into specific

applications or final products. This experience provides such founders with a unique and

idiosyncratic combination of technological knowledge from the supplier industry and

product/market knowledge from the buyer industry. These employees may have also been exposed

to market opportunities left unaddressed by the parent firm due to diverging strategic priorities or a

lack of specific resources. The founders of supplier-industry spinouts thus inherit relevant

contextual knowledge that will enhance their ability to recognize potential opportunities for

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innovation and entrepreneurial profit in a downstream industry characterized by multiple

submarkets and differentiated customer needs.

Combining theories concerning industry evolution and firm density with those of knowledge

accumulation and spinout behavior, we may thus formulate hypotheses regarding entry across two

vertically related industries in the established period of industry evolution. As density increases in

an upstream, core supplier industry in the later period of industry evolution, the likelihood of

spinout formation increases, thus raising the likelihood of supplier-industry spinouts to form as

well. We therefore propose:

Hypothesis 1a: In the established period of industry evolution, greater density in an upstream, core supplier industry increases the likelihood of entry by supplier- industry spinouts in the downstream industry.

Consequently, due to the greater number of supplier-industry spinouts that enter the downstream

industry, as density increases upstream, the overall number of entrants in the downstream industry

will increase. It therefore follows:

Hypothesis 1b: In the established period of industry evolution, greater density in an upstream, core supplier industry increases the likelihood of entry by startup ventures in the downstream industry.

Survival among new entrants in vertically related downstream industries

We now consider the effect of density in an upstream industry on the survival of new

entrants in a vertically related, downstream industry when both industries find themselves in a later,

established period of industry evolution. Following the arguments presented in the above section

on industry evolution, the effects of increases in firm density in a core, upstream industry on price

and variety will be much less robust in the established period as compared to the initial period of

industry evolution. New ventures downstream will thus have more limited opportunities to innovate

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and differentiate their products through new variations in core components offered by supplier

firms. As a result, increasing density upstream will have a more muted effect on the survival rate of

firms in the downstream industry during the established period of industry evolution. Yet, if

increasing density in the upstream industry also results in greater entry downstream by the

formation of supplier-industry spinouts, another major effect of increasing density will be greater

competition downstream, leading to lower survival rates for firms. These effects will be most

pronounced for new, startup ventures that lack the scale and the resources of incumbents to compete

in such a competitive context. As a result, we would expect that the hazard rate for startup ventures

downstream should rise with increasing density upstream during the established period of industry

evolution. While in the initial period, the benefits of greater density are able to promote sustainable

differentiation and innovation downstream, in this later period the more muted advantages of

greater density upstream are of less support to new firms in the downstream industry in overcoming

the pressures of increasing competition. We thus propose:

Hypothesis 2. In the established period of industry evolution, greater density in an upstream, core supplier industry decreases the likelihood of survival of startup ventures in the downstream industry.

Yet it is also likely that not all types of startup ventures will be affected in the same way. As

stated above, greater density upstream is likely to increase the number of supplier-industry spinouts

that enter a downstream industry. Such spinouts face competition from both de novo entrants and

focal spinouts from incumbent firms in the downstream industry. Drawing on the literature on

spinout behavior and performance, we may expect that supplier-industry spinouts have differential

advantages with respect to these other types of new entrants. Pre-entry experience in a supplier

context may provide the founders of spinout ventures with information advantages over other new

entrants from which to develop capabilities to compete in a downstream industry. They may inherit

such capabilities from their pre-entry experience working to customize or even co-create products

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with customers. Confronted with the need for specialized inputs, in fact, buyers are often forced to

share basic technological, production and design know-how with their suppliers (Bönte and

Wiethaus, 2007). Through their experience in a supplier firm and their interactions with customers,

such founders may also gain knowledge about how the upstream core product is used in

downstream industries, and how downstream processes are managed (Arruñada and Vazquez, 2006;

Alcácer and Oxley, 2014). In the same way, suppliers gain marketing knowledge about downstream

distribution channels and customer segments as well as business knowledge about industry

networks, associations and regulatory agencies that govern the downstream industry. While such

advantages have been associated with suppliers that integrate vertically downstream to compete

with their former customers (Chatain, 2011; Wan and Wu, 2016; Helfat, 2015; Jacobides and

Winter, 2005), they may similarly be associated with individuals that leave their current

employment in a supplier firm to start an independent, new venture in a downstream industry (i.e. a

supplier-industry spinout). Moreover, because the knowledge resources gained through pre-entry

experience in a supplier firm cannot be easily acquired on the market, and may need more time to

develop without direct access to strategic assets (Dierickx and Cool, 1989), these knowledge

resources may represent a strong and sustainable advantage for supplier-industry spinouts as they

enter the downstream industry. Finally, once they enter the downstream industry, supplier-industry

spinouts will have knowledge advantages in terms of how to search effectively and rapidly for the

best supplier to meet their needs once they are operational in the new downstream context (Chen,

Williams and Agarwal, 2012). In turn, they may also have better capabilities than other startups to

absorb new knowledge from innovative suppliers and to integrate this knowledge into their products

and applications (Cohen and Levinthal, 1990). As a result, we may expect that supplier-industry

spinouts have more likelihood to survive than other types of startups in the downstream industry.

DATA AND METHODOLOGY

Industry context and variables

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The context of our empirical analysis refers to two vertically related industries:

semiconductors and telecommunications equipment. Their relatedness derives from the fact that

semiconductor devices are a core component of telecommunications equipment. The period of our

analysis is the decade between 1997 and 2007. This decade represents an appropriate context for

the study as both industries had entered what we characterize as a later stage of industry evolution.

The semiconductor industry was born in the 1950’s following the invention of the transistor

by Bell Labs in 1947 and the integrated circuit in 1958 (Braun and MacDonald, 1982). Sales of

semiconductor chips grew steadily from the 1970’s through to the year 2000. In 2001 there was a

sharp dip in sales due to the dot-com crash, but sales regained a consistent growth pattern between

2002 and 2007 (WSTS, 2019). By the 1980’s barriers to entry into semiconductor production in

terms of both scale and R&D were high and the top players controlled significant market power

(Platzer and Sargent, 2016). Yet two developments opened the door to the entry of newcomers into

the industry in the late 1990’s (Brown and Linden, 2009). The first was the emergence of the

‘fabless’ model in which firms with design capabilities could outsource production to

manufacturers with excess capacity (Balconi and Fontana, 2011). This allowed smaller firms with

fewer financial resources to invest in innovation and avoid the excessively high cost of

semiconductor production. The second development was the diffusion of EDA software to design

integrated circuits. This gave rise to new business models for firms involved in the design of

customized chips for different end markets. Both of these developments had a significant impact on

the industry during the later part of the 1990’s and into the first decade of the new millennium as

firms began to enhance competitiveness by specializing in specific segments of the value chain

(SIA Study, 2016). In the decade under examination, therefore, the semiconductor industry was

characterized by a relatively high density of firms, many of which were large players with extensive

experience and knowledge in the industry. At the same time, technological developments opened

opportunities for entry by newcomers.

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For the purposes of the present analysis, it should also be noted that semiconductors is an

archetypal example of core component upstream supplier industry. The industry produces a

plethora of products and services that are used in a wide variety of applications in many highly

differentiated submarkets (i.e. telecommunications, automobiles, consumer electronics, defense).

Semiconductor chips may be highly specialized, thereby offering downstream producers a potential

basis for product differentiation. Competitors in the same submarket may try to differentiate their

products in terms of multiple criteria such as performance, speed, reliability, and power

consumption. Customization and design factors make them relatively expensive inputs,

representing a significant share of overall cost for many final product categories, including

telecommunications equipment.

The telecommunications equipment industry has a long history that may be traced back to the

invention of the telephone in the late 1800’s. In order to put the telephone to use, an extensive

infrastructure needed to be constructed and more robust technology developed to handle long

distance communications. By the end of the next century, the telecommunications industry was

characterized by a series of layers vertically related and hierarchically organized (Fransman, 2002).

Telecommunications equipment firms design and manufacture the hardware components (i.e.

routers, switches, base stations, servers etc.) that are used for voice, video, and data transmission in

telecommunication networks and systems. They are located at the interface with the semiconductor

industry from which they buy specific semiconductors components that are used in their devices.

The liberalization of regulations that had formerly restricted competition in the industry,

together with the rapid pace of privatization and technological innovation in telecommunications

services, led to a rapid growth of the U.S. telecommunications equipment industry in the 1990’s

(Olley and Pakes, 1996). It was at this time that the Internet also became the new paradigm to

respond to the rising demand for information and communication services. The advent of computer

networking and then the commercialization of the Internet marked a ‘new start’ for the industry

with the entry of a large number of firms (see Greenstein, 2015, Chapter 9). These new firms

19

increasingly focused their attention on the provision of data communication equipment away from

voice.2 Four different periods followed, each of them characterized by the creation of a specific

relationship between firms in the industry and upstream suppliers of semiconductor core

components. The first phase, from the beginning of 1980s till the end of the decade, was

characterized by steady growth and the development of data communication standards (e.g.

Ethernet and Token Ring). During this phase manufacturers relied mostly on traditional

semiconductor incumbents (e.g. AMD, National Semiconductor, Intel and Texas Instruments) for

the development and manufacturing of chipsets for their equipment (e.g. routers, hubs and bridges).

The second phase, the first half of the 1990s, witnessed the opening up of new market segments

(e.g. switches) and the emergence of Fast Ethernet as the dominant standard (Fontana, 2008). The

growth of the switch market was accompanied by developments in the semiconductor industry as

equipment manufacturers started relying more and more on Application Specific Integrated Circuits

(ASICs) as opposed to software based architectures revolving around Reduced Instruction Set

Computers (RISC) design.

The switch market mainly grew and became the dominant one in the industry during the third

phase (e.g. from mid-1990 to the end of the decade). Two events occurred during this phase

(Fontana and Nesta, 2009). The first event was the entry of three types of manufacturers. There

were incumbents from adjacent markets. Cisco Systems, the future dominant firm in the switch

market, came from the router market and was among the first firms to enter. There were start-ups

searching for new opportunities. There were firms from outside the industry but with previous

experience in the upstream semiconductor industry (e.g. Marvell Technology). The second event

was the opening up of two segments in the switch market. High-end switches, characterized by high

2 There exist many studies of the changes that occurred in the Telecommunication industry as computer and telephone technologies merged. Some of these studies have focused on the role played by competitive and cooperative business strategies and technology strategies for innovation (Lee, 2007). Other studies considered instead the role of corporate venture capital for fueling innovation (von Burg and Kenney, 2000; Wadhwa and Khota, 2006). To our knowledge no prior study has explicitly looked at the effect that the convergence of computing and phone technologies had on the joint evolution of the semiconductor and Telecommunication equipment industries.

20

performance, were targeted to customers with large networks. Low-end switches were low cost,

generally less performing, and targeted to customers with small networks.

The nature of the competition in the two types of market segments was different. In the low-

end segment, manufacturers competed mainly on price. In the high-end segment, competition was

on quality and usually influenced by the presence of substantial switching costs for customers. A

final phase started at the beginning of 2000 with the advent of wireless (Wi-Fi) standards for

communications. During this phase the relationships between the semiconductor and the

telecommunication equipment industry strengthened. More and more semiconductor firms directly

entered into the industry by acquiring equipment manufacturers (e.g. the acquisition of Sorrento

Networks by Zhone Technologies or the acquisition of SysKonnect by Marvell Technology). Many

equipment manufacturers developed close relationships and partnerships with chip designer and/or

producers (e.g. the 2006 agreement between Cisco Systems and Cortina a fabless firm is an

example of this trend). Finally, a number of independent firms entered the industry. These new

firms were founded by prior employees in the upstream semiconductor industry who relied upon

their specialized knowledge in the design and development of key core components to enter and

compete downstream (e.g. DowsLake Microsystems corporation manufactures TLC equipment but

was spun out by an upstream semiconductor company that produced optical components and

amplifiers).

Our empirical analysis combines two types of data: population level data and detailed

information on new entrants. Aggregate data for population density and sales are taken from the US

Census. More specifically, UPSTREAM (SCD) DENSITY refers to the total number of establishments

belonging to ‘Semicondutor and related device manufacturing’ (NAICS 334413) class. UPSTREAM

(SCD) SALES refers instead to the total value of shipments as reported by the same source. Data for

Telecommunication equipment manufacturing density (DOWNTREAM TLC DENSITY) and sales

(DOWNSTREAM TLC SALES) refer instead to establishments and shipments included in NAICS class

334210.

21

In terms of new entrants, our analysis is based on a sample of 766 semiconductors firms and

90 telecommunications equipment firms. These firms are all new independent start-ups that entered

into their respective industries between 1996 and 2008.3 The lists of firms were extracted from two

different, but consistent, directories. The data on semiconductor firms come from Semiconductor

Times, a monthly publication that profiles new start-ups in the industry and provides a description

of their product offerings and activities, as well as information about the team of founders.

Telecommunications data come, instead, from Telecom Trend, an analogous publication that

focuses on telecommunication start-ups.4 Both sources are created and managed by Pinestream

Communication, a private consultancy specialized in monitoring start-ups in hi-tech industries. The

information contained in the lists was integrated with additional information on the founders

collected from a variety of public sources.5

We used the industry of origin of the founders to classify the firms in our sample into three

categories: focal spinouts, vertical spinouts, and other de novo entrants. Focal industry spinouts are

defined as new firms founded by entrepreneurs previously active in the Telecommunication

equipment industry. Supplier industry spinouts are defined as entrants that originate from the

upstream industry (i.e. former employees of semiconductor firms). De novo entrants are those start-

ups whose founders have no prior experience either in the focal industry or in vertically related

sectors. These entrants may have been employed in other unrelated businesses.6 On the basis of

this classification we have constructed our most important explanatory variable FIRM ORIGIN, which

is equal to one if the firm is a vertical entrant, two if the firm is a focal spinout, and three if it is a de

novo entrant. To estimate the effect of firm origin, when possible we include in our regressions two

dummy variables: a SUPPLIER INDUSTRY SPINOUT dummy, which is equal to one if the firm is a

3 The time span is 1997-2007 in the case of semiconductor firms. 4 The Telecom Trend directory includes a total of 1,193 firms after cleaning and consolidation. In this article we restrict our analysis to a subset of 265 firms, those who entered into the telecommunications equipment and telecommunications network/connectivity industry for which we were able to find complete information on all variables of interest. 5 For more details on the sources please see: Adams et al. (2016) and Fontana et al. (2016). 6 We excluded from the sample firms whose founders were previously employed in academia (i.e. university spinouts). Additional details on the classification of firms with multiple founders can be found in Adams et al. (2016).

22

vertical spinout, and zero otherwise; and a FOCAL INDUSTRY SPINOUT dummy, which is equal to one

if the firm is a focal spinout, and zero otherwise. DE NOVO ENTRANT is treated as reference category.

The survival analysis of telecommunications equipment firms also include a set of control

variables: FOUNDER CEO which is a dummy equal to one if the role of the CEO is held by a founder

and zero if the CEO has been hired externally, after the foundation of a firm; CEO PHD DEGREE

which is equal to one if the founder’s highest academic degree at the time of founding is a Ph.D.

and zero otherwise; SERIAL ENTREPRENEUR, which is equal to one if the founder, or a member of

the founding team, had previously founded another firm; FOUNDING TEAM, which is equal to one if

the firm was founded by a team of employees, and zero otherwise.

RESULTS

Entry analysis

The analysis of entry is carried out through a binary response model, with a standard logit

specification.7 The main issue in the empirical analysis of entry at firm level is the definition of a

reference category of potential entrants to which actual entrants can be compared. In this work, we

exploit the availability of data about vertically-related industries and about different types of

entrants to define different reference categories.

Results are reported in Table 1. In Model (1), we estimate the probability of entry in the

Telecommunication equipment (downstream industry), under the assumption that all firms entering

the vertically related semiconductor industry were potential entrants of the Telecommunication

equipment industry. Therefore, the dependent variable is equal to one if the firm enters the TLC

industry, and 0 if it enters the SCD industry. Results show that UPSTREAM (SCD) DENSITY has a

positive effect on entry in Telecommunication equipment, whereas DOWNSTREAM (TLC) DENSITY

has a negative effect. These results hold controlling for sales in both industries and for type of

entrant (Supplier Industry and Focal Industry Spinouts). Overall, they provide support for

7 Results are robust to alternative specifications, such as Probit and linear probability model.

23

Hypothesis 1b. However, the results should be carefully interpreted: they show the effect of

upstream density on downstream entry, compared to upstream entry, that is upstream density has

also a negative effect on (upstream) entry in semiconductors.

--------------------------------------------------- INSERT TABLE 1 ABOUT HERE

---------------------------------------------------

Our theory suggests that this increase in entry is due to Supplier Industry spinouts. Therefore,

in Models 2 to 4 we estimate the probability of entry by Supplier Industry spinouts (firms

originating in the semiconductor industry) into the Telecommunication equipment (downstream)

industry. In all cases, the dependent variable is equal to one when the firm is a Supplier Industry

spinout. The three models differ because of the reference category (i.e. the zeros): in Model (2) all

other firms in the sample (De novo entrants and Focal Industry spinouts in TLC and all entrants in

SCD) are included as potential entrants; in Model (3), De novo entrants in SCD are excluded from

the analysis; in Model (4), only Supplier Industry and Focal Industry spinouts in SCD are included

as reference category. In all the specifications, the probability of entry of Supplier Industry spinouts

in the Telecommunication equipment industry increases with upstream density, therefore supporting

Hypothesis 1a.

Survival Analysis

To analyze the survival of new entrants in the Telecommunication equipment industry, we

employ a standard discrete time duration model. Given N firms entering the industry at time t0, the

hazard rate function for a firm i, at a time t = 1,… T can be expressed as a function of two

components: the baseline hazard function (𝜃𝜃𝑡𝑡0) and a function of covariates (𝑋𝑋𝑖𝑖𝑡𝑡) summarizing

observed differences among firms. We adopt the complementary log-logistic specification (Jenkins,

2005), that determines the following expression for the hazard of exit of firm i at time t:

ℎ𝑡𝑡(𝑋𝑋𝑖𝑖𝑡𝑡) = 1 − 𝑒𝑒𝑒𝑒𝑒𝑒[− exp(𝑋𝑋𝑖𝑖𝑡𝑡′ 𝛽𝛽 + 𝜃𝜃𝑡𝑡0)]

24

For the baseline hazard function, we use the logarithmic specification. Results are robust to

alternative specifications (polynomial, fully non parametric, and piece-wise constant). In addition to

the explanatory and control variables described above, we also include in each regression a full

vector of entry year dummies.

Results are reported in Table 2. Model (1) includes only the control variables. Estimated

coefficients for SUPPLIER INDUSTRY SPINOUT and FOCAL INDUSTRY SPINOUT are negative and

significant indicating that both types of firms have a lower hazard of exit than DE NOVO entrants. A

test of coefficient equality shows a significant difference between the coefficient estimates. Some

founders characteristics (FOUNDER CEO, CEO PHD DEGREE, FOUNDING TEAM) reduce the hazard

rate, whereas the coefficient estimate of SERIAL ENTREPRENEUR is positive, suggesting higher

hazard of exit. We also observe a negative, but statistically not significant, time duration

dependence of the hazard rate. Industry density, measured both at entry and in each year, increases

the hazard of exit, indicating a strong level of competition. Overall, these results confirm previous

findings by Adams et al. (2016; 2019) that new entrants with pre-entry experience either in the

same industry or in a vertically related industry survive longer than de novo entrants.

--------------------------------------------------- INSERT TABLE 2 ABOUT HERE

---------------------------------------------------

In Model (2) we introduce UPSTREAM (SCD) DENSITY as explanatory variable: the estimated

coefficient is positive and significant, indicating that the hazard of exit of new entrants in the

Telecommunication equipment (downstream) industry increases as density in Semiconductors

(upstream) increases. This result is robust to the inclusion of UPSTREAM (SCD) SALES in the

analysis (Model 3). These findings are consistent with Hypothesis 2. In both Model (2) and Model

(3), Supplier Industry spinouts still show a better performance when compared to De novo entrants,

whereas the coefficient estimate for Focal Industry spinouts is still negative, but statistically not

significant.

25

A MODEL OF ENTRY, COMPETTION AND SURVIVAL IN TWO VERTICALLY

RELATED INDUSTRES

The empirical analysis of entry and survival in the Telecommunications equipment industry

has showed that density in the upstream Semiconductors industry has a negative effect on the

survival of downstream firms, in contrast with previous findings by the literature (De Figueiredo

and Silverman, 2012; Sturz, 2014). The theory we have developed suggests that this negative effect

can be attributed to the different competitive environment prevailing in the established period of the

industry, when increases in upstream density have a more muted effect on both variety and price

and have a higher probability to induce entry by Supplier Industry spinouts. However, we cannot

exclude that this result is due to other mechanisms, that are specific to the empirical context of our

analysis.

In order to explore in depth the idea that the role of upstream density in downstream survival

changes as an industry enters a more established phase, we have developed a simulation model of

industrial dynamics that reproduces the main features of two related industries (a downstream, focal

industry and its upstream ‘core component’ supplier industry) and includes three types of entrants

in the downstream industry, as identified in the previous sections. The model presented here takes

inspiration from the history-friendly models of industry evolution (Malerba, Nelson, Orsenigo and

Winter, 2016), and more specifically it builds on and integrates insights from two previous works.

First, following Malerba, Nelson, Orsenigo and Winter (2008), it features the dynamics of two

vertically related industries: here the two industries are Semiconductors and Telecommunication

equipment. Second, as done in Capone, Malerba and Orsenigo (2019), it distinguishes different

types of entrants, including not only de novo firms and Focal Industry spinouts, but also Supplier

Industry spinouts.

In the model two industries are represented. In the upstream industry, specialized firms

produce a core component and sell it only to firms in the downstream industry. In the downstream

26

industry, firms use the core component to produce multiple products that are sold to final

consumers. In this section we provide a description of the model, highlighting the main features of

both industries in terms of demand, technology, and industry dynamics.

The Upstream Industry

Overview. In the upstream industry a number of firms compete by producing a firm-specific

core component that can be incrementally improved through innovation activities. Each core

component has an individual profile of relatedness to downstream products: therefore, its

contribution to these products depends not only on its overall technical quality, but also on the

extent to what it satisfies the needs of the specific downstream product to which it is applied.

Demand. Firms in the downstream industry are the customers of firms in the upstream

industry. Downstream firms pay a search cost to select the best supplier among the available ones,

considering both the technical quality and the fitness to their needs. Once they select a supplier,

they keep it if for a certain number of periods. In each period, a downstream firm buys a number of

components equal to the number of products that it sells to the final consumers in the downstream

market. Therefore, the success of an upstream core component supplier depends on both the number

and the size of the downstream firms that constitute its customers.

Technological landscape. The relatedness profile is specific to each core component and is

determined once and for all when the component is launched by the firm. On the other hand, the

quality of a component depends on the component-specific knowledge that the firm has

accumulated. The relation between knowledge and quality represents the conpoenent’s

technological trajectory. Let 𝑘𝑘𝑖𝑖 be the knowledge base of the component produced by firm i, the

resulting quality 𝑞𝑞𝑖𝑖 is given by:

𝑞𝑞𝑖𝑖(𝑘𝑘𝑖𝑖) =𝐴𝐴𝑖𝑖

[1 + 𝑋𝑋𝑖𝑖𝑒𝑒−𝑌𝑌𝑖𝑖∙𝑘𝑘𝑖𝑖]1𝑍𝑍𝑖𝑖

(1)

27

where 𝐴𝐴𝑖𝑖 (ranging between 0 and 1) represents the maximum quality that a conponent can

reach. The shape of the technological trajectory depends on three component-specific parameters:

𝑋𝑋𝑖𝑖 represents quality when knowledge is zero (we impose that 𝑞𝑞𝑖𝑖(0) is strictly positive); 𝑍𝑍𝑖𝑖

determines the symmetry of the function (there is no constraint on its value); 𝑌𝑌𝑖𝑖 is the average

growth rate of quality with respect to knowledge (we restrict the range of possible values so that the

growth of quality is not too fast).

Firm activities: innovation. In each period all firms perform their innovation activities

according to their own set of routines and capabilities. Innovation activities aim at improving the

quality of the components by increasing their knowledge base. The probability of generating a new

piece of knowledge (𝑛𝑛𝑗𝑗,𝑡𝑡) depends on the amount of resources invested in this activity: for the sake

of simplicity, all firms use all their profits to finance innovation activities. If a new piece of

knowledge is generated, its value is a positive function of the technological capabilities of the firm

(θf). This is formally expressed by the following condition:

𝑛𝑛𝑗𝑗,𝑡𝑡 =

⎩⎨

⎧0, 𝑖𝑖𝑖𝑖 𝑈𝑈(0,1) <1

1 + �𝑏𝑏𝑖𝑖,𝑡𝑡 𝐶𝐶𝑗𝑗,𝑡𝑡𝐿𝐿⁄

Exp(θf), otherwise

; (2)

where 𝐶𝐶𝑗𝑗,𝑡𝑡𝐿𝐿 is the unit cost of improving the existing component j at time t and 𝑏𝑏𝑖𝑖,𝑡𝑡 are the

resources invested by firm i at time t. The new level of knowledge (kn,t) is given by the combination

of the new piece of knowledge and the existing knowledge, and can be expressed formally as:

𝑘𝑘𝑛𝑛,𝑡𝑡 = 𝛾𝛾 ∙ 𝑘𝑘𝑗𝑗,𝑡𝑡−1 + 𝑛𝑛𝑗𝑗,𝑡𝑡 (3)

where 𝛾𝛾 is a parameter representing the cumulativeness of technical change. The firm uses the

new knowledge only if this determines an increase in component quality.

Firm activities: pricing. Firms set prices applying a markup on marginal production cost,

which is constant and equal for all firms. The level of markup (𝑤𝑤𝑖𝑖,𝑡𝑡) depends on the past market

share of the firm, and is given by the following expression:

28

𝑤𝑤𝑖𝑖,𝑡𝑡 =𝑚𝑚𝑖𝑖.𝑡𝑡−1

𝜂𝜂 − 𝑚𝑚𝑖𝑖.𝑡𝑡−1 (4)

where 𝑚𝑚𝑖𝑖.𝑡𝑡 is the market share of firm i at time t and 𝜂𝜂 is the price elasticity of demand, equal

for all customer firms and constant over time.

The Downstream Industry

Overview. In the downstream industry firms compete by producing multiple products, using

core components that are produced by upstream firms. At any time, firms can discover new

products or can improve their existing ones through innovation activities. Each product targets a

specific submarket, but it can be appealing also for customers in other submarkets.

Demand. The demand for downstream products comes from final customers. When buying a

product, customers consider two elements, price and quality: for all customers, the lower the price

and the higher the quality, the higher the probability of buying a product. Customers are also

grouped in submarkets, on the basis of their preferences for specific features of the product. They

can buy products targeting other submarkets, but only if their high quality allows them to meet

satisfactorily their needs.

Firms activities: innovation. The technological landscape is analogous to the one described

for the upstream industry. The innovation activities of firms aimed at improving the quality of

existing products follow the same logic as before (see Equations 2 and 3). However, in the

downstream industry firms can also introduce new products. The probability of introducing a new

product depends on the resources invested by the firm.

Firms activities: resources allocation. As in the upstream industry, firms invest all their

profits in innovation. However, given the presence of multiple products, a key activity of the firm is

to choose how to allocate its resources to the different types of innovation activities. Each product

of a firm is associated to a team, which carries on its research activity independently, and randomly

chooses whether to use its financial resources in the current period to improve an existing product

29

or to develop new ones. Therefore, firms can pursue both innovation activities at the same time,

provided that they have multiple teams. The allocation of financial resources between different

teams depends on their past performance: a team has more resources if the associated product earns

a higher share of the firm’s profits and if its innovation activities generate quality improvements or

the discovery of new products. Let 𝑞𝑞𝑗𝑗,𝑡𝑡′ be the increase in the quality of product j that has occurred

from the previous period. Then the budget of team j for its innovative activities in period t (bj,t) is

determined according to the following rule:

𝑏𝑏𝑗𝑗,𝑡𝑡 = �𝜆𝜆𝑓𝑓,𝑡𝑡 ∙𝑞𝑞𝑗𝑗,𝑡𝑡−1′

∑ 𝑞𝑞𝑗𝑗,𝑡𝑡−1′𝐽𝐽𝑓𝑓

𝑗𝑗=1

+ �1 − 𝜆𝜆𝑓𝑓,𝑡𝑡� ∙Π𝑗𝑗,𝑡𝑡−1

∑ Πj,t−1𝐽𝐽𝑓𝑓𝑗𝑗=1

� · Π𝑓𝑓,𝑡𝑡−1 (5)

which means that the share of resources that can be used by team j depends on both the share

of profits earned by the corresponding product and the team innovative performance in the last

period vis-à-vis the other teams of the firm. Moreover, the weight that is assigned to earned profits

and innovation performance varies over time, according to the overall innovative performance

obtained by the firm:

𝜆𝜆𝑓𝑓,𝑡𝑡 = 1 −1

1 + ∑ 𝑞𝑞𝑗𝑗,𝑡𝑡−1′𝐽𝐽𝑓𝑓

𝑗𝑗=1

(6)

Firms activities: pricing. Also downstream firms set prices applying a markup on marginal

production cost, constant and equal for all firms. However, the markup (𝑤𝑤𝑖𝑖,𝑡𝑡) here takes into

account the markets share of all products in the portfolio of the firm, and is given by the following

expression:

𝑤𝑤𝑗𝑗,𝑡𝑡 =𝑚𝑚𝑗𝑗,𝑡𝑡−1∗

𝜂𝜂 − 𝑚𝑚𝑗𝑗,𝑡𝑡−1∗ (7)

where m*j,t is analogous to a market share, referring to the whole downstream industry, but

taking into account that customers outside the target submarket might not be interested at all in

buying the product. So, m*j,t is the ratio between the number of consumers actually purchasing the

product in all submarkets and the number of potential customers in the industry. The latter is

30

obtained by subtracting to the total number of customers in the industry both the customers whose

minimum quality requirements are higher than the current quality of the product (as a change in the

price would not affect their purchasing behavior) and the customers that are currently buying other

products of the firm (so to avoid cannibalization).

Model Dynamics

A simulation run represents the evolution of the two related industries over time. The

downstream industry is composed by several submarkets that appear, grow and disappear at regular

intervals over time. The simulation starts as soon as one downstream firm enters the industry and it

ends T periods after the last downstream submarket has appeared. The first entrant is followed by

other firms that find an appealing technological opportunity. The number of these firms entering at

the beginning is set exogenously and acts as a seed for the entry of all other firms in later periods.

These firms can be classified into three distinct groups. First, there are “De novo” firms: each

period, potential firms look at the performance of recent entrants in the downstream industry and

choose whether entry is feasible. Second, there are “Focal Industry” spinouts: whenever an

incumbent firm discovers a new product, a new firm might actually be created to commercialize it.

The probability that a “Focal Industry” spinout appears is higher if the new product is distant from

the existing products of the incumbent and if the product is located in a submarket new to the

industry or to the incumbent firm. “Focal Industry” spinouts inherit some of the knowledge of their

parent firm. Third, there are “Supplier Industry” spinouts, which are generated by firms operating in

the upstream industry. Each upstream firm has a fixed, exogenous probability to spawn a “Supplier

Industry” spinout in the downstream industry. “Supplier Industry” spinouts have a better knowledge

of the upstream industry and therefore have lower search costs for suppliers.

The evolution of the downstream industry has two distinct phases: an early phase and an

established phase. There are two relevant differences between the two phases, that involve the

nature of submarkets and the characteristics of new entrants. First, although new submarkets arrive

31

at the same rate both in the early and in the established phase, there is a difference in the way core

component knowledge can be accessed. Specifically, we assume that the knowledge about the

supplier industry becomes less accessible in the established phase, and therefore the search costs for

components are lower (and normalized to zero) in submarkets emerging in the early phase and

higher (strictly positive) in submarkets emerging in the established phase. Second, we also assume

that the probability of entry of “Supplier Industry” spinouts changes between the two phases, and

goes from zero in the early phase to a strictly positive value in the established phase. Due to their

peculiar knowledge of the core component supplier industry, these spinouts have low (normalized

to zero) search costs for components also in submarkets emerging in the established phase.

The number of firms in the upstream industry is fixed exogenously. To keep the number

constant over time, replacement entry occurs any time an upstream firms exits. Upstream firms exit

if they do not have any customer from the downstream industry. Downstream firms exit if they do

not have any product to sell on the market. Downstream firms withdraw their products that do not

reach a minimum market share.

Model Results

We study the outcomes of the model at different levels of density in the upstream industry.

For analytic convenience, we consider here only two values of density: a low value (10 firms) and a

high value (100 firms). Replacement entry in the upstream industry guarantees that the number of

firms is constant to the specified value throughout the whole simulation.

For each density value (Low vs High), we run 1000 simulations letting initial conditions vary

randomly over meaningful values and keeping fixed the values of all other parameters. We analyze

the simulation-generated data by looking at survival patterns in the downstream industry,

distinguishing between entrants in the early phase and entrants in the established phase.

--------------------------------------------------- INSERT FIGURE 1 ABOUT HERE

---------------------------------------------------

32

Figure 1 reports the Kaplan-Maier estimates of survival rates of entrants in the early phase,

distinguishing between low and high density values. For all values of age, survival rates are always

higher in the case of high density, suggesting that increasing density in the upstream market

increases survival chances for downstream firms, in line with the prediction of the previous

literature about the early phase of vertically related industries (De Figuiredo and Silverman, 2012).

--------------------------------------------------- INSERT FIGURE 2 ABOUT HERE

---------------------------------------------------

Figure 2 reports analogous estimates for entrants in the established phase. Here, the pattern is

reverted, as survival rates are higher in the case of low density. This result suggests that the increase

in the number of firms and competition that the entry of “Supplier Industry” spinouts determines in

the downstream industry outweighs the positive effects on downstream survival generated by lower

prices and more tailored components.

33

CONCLUSIONS AND DICUSSION In recent years, a growing literature has emerged to explore the relationship between vertically

related industries. This study builds on this literature by examining how the density of vertically

related industries affects the entry and exit of independent startups in the downstream industry. The

analysis is conducted in two stages. In the first stage we develop predictions concerning the effect

of rising density upstream on the downstream population of new entrants and test these predictions

with data describing the semiconductor and telecommunications equipment industries from 1999 to

2008. In the second stage we explain our results by developing a simulation model that formally

represents the main mechanisms that drive entry, competition and survival in the two vertically

related industries.

Our first contribution is to the literature on industry dynamics and vertical relatedness.

Consistent with our predictions, we find that greater density upstream increases the likelihood of

entry by new startup ventures in the downstream industry. This entry is driven by the entry of new

firms that spinout from existing companies in the upstream, supplier industry and enter into the

downstream industry. This finding indicates that two vertically related industries are not just linked

through the exchange of products, collaborative agreements or the presence of vertically related

firms, but also by the entry of new startup firms (i.e. supplier-industry spinouts from the upstream

industry). These types of entrants represent another major link between these industries that affect

their coupled dynamics and that the analysis of industry evolution need to consider in future

research.

Our second contribution is to the literature on industry evolution and resource based theory.

We develop two opposing predictions concerning the relationship between greater density in the

upstream, supplier industry and the hazard rate of failure of new startup ventures in the downstream

industry. In the first hypothesis, consistent with previous studies, we predict that greater density

will increase the likelihood of survival for these new firms. In the second hypothesis, we predict

34

that greater density upstream will lead to a decrease in the survival rate of new firms in the

downstream industry due to increasing entry and competition. We find confirmation for the second

hypothesis. But we also find that startups from the supplier industry (supplier-industry spinouts)

are more likely to survive than other types of new startups, including spinouts from the focal

industry and de novo entrants. These findings suggest that the direct economic effects of increasing

density upstream on a downstream industry may not necessarily the dominant mechanism in the

link between vertically related industries. Although all downstream firms were potentially able to

benefit from the advantages of lower prices from supplier firms (as the result of increased density),

the survival rate of new startups downstream decreased with increasing density upstream. Our

results suggest that knowledge resources play a critical role in this relationship. In the upstream

industry, greater density not only results in an increase in the number of spinouts that enter the

downstream industry, but also in spinouts that have a resource advantage with respect to the other

entrants in the downstream industry. We argue, in fact, that the differential survival rate of supplier-

industry spinouts with respect to focal spinouts and de novo entrants is due to the knowledge

resources that they inherit through their founders from the supplier industry. These resources allow

them both to screen potential suppliers better and select the best supplier for their needs and to

absorb more effectively new and innovative ideas from the upstream industry for use in their own

products and applications. This study, therefore, provides further evidence that knowledge

inherited from one industry may represent both a resource for entrepreneurship in a vertically

related industry, but also a base upon which to build competitive advantage.

A third contribution of our study concerns the use of history-friendly modeling to test the

logic of our theoretical framework and to formally explore the basic mechanisms that explain our

findings. In fact, in order to represent and test in a transparent and rigorous way the theoretical

framework proposed in the initial part of the paper, a parsimonious model of the dynamics of two

vertically related industries has been developed. With this model we have shown that the

introduction of supplier-industry spinouts undermines the positive effects of increasing density in

35

the upstream industry on the survival of new entrants in the downstream industry. The model does

so by focusing on two opposite mechanisms. One, highlighted by the previous literature, is the

expansion of the final demand due to the introduction of lower priced and better quality final

products. This is possible, in turn, due to the supply of cheaper, better and more differentiated

components from the upstream industry. The second mechanism, proposed in this paper, is the

increased competition in the downstream industry due to the entry of supplier industry spinouts

with increasing upstream density. The model shows that the increasing competition effect more

than outhweighs the demand expansion effect thus resulting in a decrease in the overall survival

rates of new entrants. The model opens the way for future theoretical explorations in at least two

directions. One is the rigorous examination of the conditions in which the market structure of the

downstream industry may end up being concentrated, eventually being dominated by supplier-

industry spinouts. The second is a finer grained examination of the role of other factors that may

provide the competitive advantage of supplier-industry spinouts, for example the capability to better

customize or integrate upstream components into final products.

When considering the results of this study, it is important to note several limitations. We

believe, however, that these limitations also represent interesting avenues for future research. To

begin, our data does not include information concerning incumbent firms in the industry, both

vertically integrated and non-integrated firms. Our work suggests, however, that a comparison

between these types of firms may provide important insights into the role of knowledge resources

across industry boundaries. Vertical spinouts represent a distinct form of organizational structure

with respect to both vertical integration and vertical specialization. In the case of vertical

integration, firms benefit from internal integrative capabilities that allow them to bring together

specific knowledge from related industries into an incumbent organization that operates across two

industries (Langlois, 1992). Integrated knowledge is therefore shared within an integrated

organization. In the case of vertical specialization, specific knowledge remains separated in

specialized organizations that operate in one or the other vertically related industry. By contrast, in

36

the case of vertical spinouts, specific knowledge from two vertically related industries is integrated

into a specialized organization that operates in only one or the other vertically related industry.

Integrated knowledge is therefore not shared within an established vertically integrated firm, but

within a new and independent specialized firm whose founders bring knowledge across industry

boundaries. Second, the article does not examine the change and transformation in knowledge,

capabilities and products that vertical spinouts may experience once they have been active in the

focal industry for some time. The long-term survival rate of new entrants is therefore another

interesting area for further research. Third, in this article we rely on data from only two vertically

related industries. Further research is needed to explore the relevance and role of vertical spinouts

in other vertical chains. Finally, this research invites further study into how vertically related

industries co-evolve over time in terms of the interrelated dynamics of the two industries.

37

38

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43

TABLES

Table 1. Probability of Entry in TLC Equipment. Logit Model. Model (1) (2) (3) (4)

DV = 1 Entrants in TLC

Vert Spinouts in TLC

Vert Spinouts in TLC

Vert Spinouts in TLC

DV = 0 Entrants in SCD All other entrants All other entrants excl DN in SCD

Vert & Focal in SCD

Upstream (SCD) Density

0.0378** [0.0076]

0.0421** [0.011]

0.0396** [0.011]

0.0637** [0.0143]

Upstream (SCD) Sales

0.0871** [0.0211]

0.0729** [0.0271]

0.065* [0.0274]

0.114* [0.0311]

Downstream (TLC) Density

-0.0335** [0.0083]

-0.0429** [0.0106]

-0.0426** [0.0107]

-0.0605** [0.0154]

Downstream (TLC) Sales

-0.0838* [0.0339]

-0.035 [0.0696]

-0.0163 [0.071]

-0.0931 [0.0569]

Vertical Spinout

1.767** [0.3813]

Focal Spinout

0.7281* [0.3955]

Constant -29.5071** [6.046]

-30.5384** [10.5032]

-27.82** [10.6323]

-45.14** [10.7078]

Observations 682 682 508 463 Wald / F 44.11** 26.68** 25.84** 25.18** log-likelihood -201.02 -103.11 -94.61 -87.87 Pseudo R2 0.1338 0.1174 0.1269 0.1686 Robust standard errors in brackets. ***: significant at .01; **: significant at .05.

44

Table 2. Hazard of exit in the TLC equipment industry. Cloglog model. Model (1) (2) (3)

Upstream (SCD) Density 0.3530*** 0.3400*** [0.0727] [0.0684]

Upstream (SCD) Sales 0.7976*** [0.2001]

Downstram (TLC) Density 2.2323*** 2.7148*** 2.8176*** [0.6066] [0.3474] [0.3742]

Downstream (TLC) Density Sq. -0.1816*** -0.2602*** -0.2629*** [0.0663] [0.0434] [0.4575]

Downstream (TLC) Density Delay 0.2615*** 0.2572* 0.2579* [0.0877] [0.1503] [0.1499]

Downstream (TLC) Sales -0.3005 -0.3492*** -0.4316*** [0.4211] [0.1070] [0.1001]

Firm origin Denovo Entrant Ref. Ref. Ref. Vertical Spinout -1.3239*** -1.3259** -1.2981**

[0.2402] [0.6015] [0.5984] Focal Spinout -0.4028** -0.4087 -0.3988

[0.1984] [0.4858] [0.4838] Founder CEO -0.3114* -0.3192 -0.3200

[0.1669] [0.4857] [0.4842] CEO Ph.D Degree -1.5701*** -1.5652*** -1.5458***

[0.2060] [0.5414] [0.5391] Serial Entrepreneur 0.5120*** 0.5080* 0.4988*

[0.1272] [0.2933] [0.2927] Founding Team -0.7001*** -0.7010 -0.6905

[0.1787] [0.4968] [0.4936] Age (Ln) -0.0932 0.0177 0.0464

[0.2624] [0.1364] [0.1487] Constant -10.8023** -14.1945* -22.5557***

[4.8665] [5.5356] [5.6635] Observations 504 504 504 Number of firms 90 90 90 Number of firms exit 70 70 70 log likelihood -212.57 -211.70 -210.72 Chi-square 149.40*** 258.86*** 222.89*** Robust standard errors in brackets. ***: significant at .01; **: significant at .05; *: significant at .1. All specifications include a full vector of entry year dummies.

45

FIGURES

Figure 1. Higher upstream density improves survival of downstream entrants in the early phase.

Figure 2. Higher upstream density worsens survival of downstream entrants in the established phase.