Innovation and Reputation:

The Effect of Spin-outs on the Performance of Progenitors

in the Hard Disk Drive Industry, 1963 to 1998

Jonathan Jaffee Marshall School of Business

University of Southern California

David G. McKendrick Durham Business School

University of Durham

January 2005

PRELIMINARY DRAFT—OK to cite but please do not quote without permission. • This research has been supported by a grant from the Alfred P. Sloan Foundation to

the Information Storage Industry Center at the University of California at San Diego.

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ABSTRACT Reputation is an intangible asset that affects organizational performance. It is conferred

on an organization by external audiences such as customers, competitors, analysts, or the

media. Potential employees are also a salient external audience for organizations that

want to have a positive reputation in the labor market. We argue that spin-outs (i.e., new

ventures created by (former) employees of an incumbent firm) help an organization

create a positive reputation in the technical labor market. We theorize that although spin-

outs initially lower the innovativeness of progenitors (i.e., the “parent” firm of the spin-

out) due to the loss of key personnel and disruption of existing routines, over time, they

improve their innovative capabilities by creating a reputation as an incubator of talented

entrepreneurs, which in turn attracts even more talented technical employees. We test our

theory in the context of firm innovation in the hard disk drive industry from 1963 to

1998. Consistent with our theorizing, we find strong statistical support that spin-outs

reduce rates of innovation for the progenitor for the first couple of years after a spin-out

event, but that this initial decline is outweighed over time by the improved innovation

that comes from having had spin-outs.

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INTRODUCTION

In December 1967, twelve engineers and managers left IBM’s storage products

group to form Information Storage Systems, Inc., a hard disk drive manufacturer (Pugh,

et al., 1991: 490). This was the first time in company history that employees quit en

masse, and their IBM colleagues labeled them the “dirty dozen.” ISS became one of

IBM’s most significant competitors in the high-speed plug-compatible disk drive market.

As one might expect, these defections hit IBM’s development group hard, and the

company suffered an immediate technological disruption. Yet, IBM’s disk drive

development group rebounded to maintain its technological leadership. How?

Commenting on the surprising departure of the dirty dozen, Pugh et al. (1991) note that

“such a strong sense of loyalty existed that even the voluntary departure of one engineer

from the closely knit development team was unexpected” because IBM provided a

challenging yet munificent working environment. Because of its reputation, IBM was

able to replenish its technical labor pool and went on to be the fountainhead for an

enormous number of technical professionals and entrepreneurs in different segments of

the electronics industry, including computing and data storage.

We think this portrait is emblematic of a more general phenomenon in industrial

evolution—that an organization’s reputation can play a large role in enhancing or

preserving its technological leadership. In this paper, we argue that spin-outs—new

ventures created by (former) employees of an incumbent firm—are one important signal

that organizations (perhaps unwittingly) transmit to the broader labor market, and spin-

outs enhance the technical reputation of the progenitor. Although employee defections

may initially hurt an organization—and also reflect poor employee morale—spin-outs

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imply a taken-for-granted assumption that the progenitor organization (i.e., the “parent”

firm of the spin-out) is a desirable place to work, continues to attract technical talent, and

is an incubator of entrepreneurs. The effect over the long run is that spin-outs benefit the

progenitor by attracting the technical talent necessary to improve technological

performance.

We test our theory by studying the effects of spin-outs on the innovativeness of

their progenitors. Our context is the hard disk drive industry from 1963 to 1998.

THEORY

Research in economics, strategy, and organization theory has emphasized the

important consequences of intangible resources for understanding performance

consequences among firms (Itami, 1987; Barney, 1991; Peteraf, 1993; Rao 1994; Peteraf

and Shanley, 1997; Ferguson, et al. 2000). For strategy researchers, intangible assets are

rare, complex and hard to trade and imitate. These include reputation (Dierickx and Cool,

1989; Fombrun and Shanley, 1990; Hall, 1992), trade secrets and engineering experience

(Teece et al. 1997), and customer service climate and managerial information technology

knowledge (Ray, et al., 2004). While agreeing that reputation confers advantages on

organizations, Rao (1994) offers a more sociological explanation for the source of

organizational reputation: It is socially constructed and the outcome of legitimation

processes. In his study of the early automobile industry, Rao (1994) finds that automakers

that win auto races (“certification contests”) increase their survival rates. According to

Rao (1994), victories in auto races acted as a signal or proxy for an automobile

company’s competence, which helped increase the firm’s reputation, leading to lower

failure rates.

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Reputations imply an external audience that confers legitimacy or status on an

organization or organizational form. It is the audience that must be impressed by the

signals or symbols that an organization sends or represents. Who is the audience? For

Rao (1994), in his study of the legitimation of the U.S. automobile industry, the relevant

external audience was consumers, producers, financiers, writers for automobile

magazines, and those of the business press. All were influenced by the results of

certification contests, which ranked products and organizations. As Rao (1994: 32) tells

it, “Victories in certification contests legitimate organizations and validate their

reputation because of the taken-for-granted axiom that winners are ‘better’ than losers

and the belief that contests embody the idea of rational and impartial testing. Contests

structure search in crowded and confused markets and circumvent the issue of measuring

capabilities.” Thus, visibility in the product market is an important source of cognitive

legitimation, identity, and reputation for the organization because audiences often

categorize organizations indirectly, through direct interaction with their products

(Zuckerman, 1999). This is generally consistent with organizational ecology’s

characterization of organizational forms in terms of externally perceived product markets

(Carroll and Hannan, 2000; McKendrick et al., 2003).

But product market identities are not the only, or necessarily the most important,

source of organizational reputation; nor are consumers, producers, financiers, and

journalists the only salient external audience. Our interest here is in an organization’s

ability to gain or preserve its reputation in the labor market. Organizations not only

compete in product markets; they also engage in “recruitment-based” competition

(Sørensen, 2004: 150). The key underpinnings of organizational (and form) identity are

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likely to be different for employees than for consumers, producers, and financial analysts

(Sørensen, 2004; Baron, 2004). Because organizations compete for the skills and efforts

of employees, their identities as perceived by the labor market can be critical. In many

industries, organizations depend on developing and retaining the intellectual capital of

their core employees. They also must recruit new intellectual capital to replenish their

technical cores. The employment system and culture affect the ability to respond to the

loss of technical talent.

For workers, the job search process is highly uncertain. They examine as many

opportunities as possible and compare the immediate and future rewards of these

opportunities with their current work (Greve and Fujiwara-Greve, 2003). Because of this

uncertainty, workers form images about the employment conditions at other

organizations. Organizations that have similar labor market identities find it difficult to

avoid competing with each other for the best talent (Sørensen, 2004). But where

organizations differ markedly in their labor market identities, these differences can be

fateful. When human capital is key to competitive advantage and the labor market is

highly competitive, then organizational leaders could devote more time to creating a

distinctive labor market identity: “Developing and sustaining a distinctive labor market

identity is, in essence, the construction of a reputation” (Baron, 2004: 19). If its

reputation is an organization-specific endowment inaccessible to rivals, then it should

confer a performance advantage.

Progenitor Reputation and Spin-outs

How is an organization’s reputation in the labor market created and sustained?

Reputations are likely to develop where there are mechanisms for transferring reputations

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across generations (Baron, 2004). Friends, company advertisements, labor unions, and

media coverage of employment practices are all mechanisms that broadcast an image of

an organization (Gatewood, et al., 1993; Baron, 2004). Small organizations, for example,

may have to build a reputation among specialized segments of the labor market, such as

professional networks, through some of these mechanisms. By contrast, Greve and

Fujiwara-Greve (2003) suggest that certain organizational characteristics can serve as

proxies for reputations—that the quality of jobs may correlate with observable

organizational characteristics. One such characteristic is organizational size: Large

organizations are said to carry high-wage reputations in the labor market (Greve and

Fujiwara-Greve, 2003).

We argue here that spin-outs similarly serve to project an organization’s

reputation to the labor market. Employees always engage in direct interaction with

organizations and provide insider assessments. Given their familiarity with the

progenitor, when employees leave to start a new venture, what does that signal? We

answer by first describing the advantages that spin-outs may have for their parent firm

(their progenitor). We then discuss the negative signals it sends below.

Although there has been virtually no research on the effects of spin-outs on

progenitor firms, there has been some empirical work on the effects of being a spin-out

firm itself (Phillips, 2002; Klepper, 2002; Agarwal et al., 2004).1 According to Phillips

(2002), compared to other new organizations, spin-outs are better able to implement

routines that overcome the liability of newness, have more effective and understood

1 We focus here on the small literature on spin-outs from commercial firms. Garvin (1983) provides an early overview. There is a much larger descriptive literature that analyzes spin-outs from universities and government research laboratories. See Smilor et al. (1990), Radosevitch (1995), and Steffensen et al. (1999).

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compensation systems and divisions of labor, and are better embedded in a network of

social relations within a population. They have these advantages because of what the

founders accumulated as employees of the progenitor. In his study of Silicon Valley law

firms, Phillips (2002) found that spin-outs have greater survival rates than other law

firms. Similarly, in the automobile industry, Klepper (2002) found that spin-off auto

producers had lower failure rates than firms with other types of industry origin. In the

context of the hard disk drive industry, Agarwal et al. (2004) found that spin-outs have

higher survival rates than other types of new entrants into the industry, and, additionally,

that spin-outs inherit some of the resources and capabilities of their progenitor: the

increased capabilities of the spin-out are positively correlated with the technological

capabilities of the progenitor.

In our view, the success of the spin-out reflects well upon the progenitor. Spin-

outs thus contribute to status ordering among organizations and signal the performance

and reputation of the parent firm in the labor market. Having a reputation as a good place

to work is an important underlying cultural premise and value in labor market identity.

Spin-outs transmit information about these identities and, in particular, that the

organization is an incubator of talented entrepreneurs. They tell the labor market that the

progenitor cultivates and trains top technical talent, although some of that talent

eventually wants to control its own technical work. This is especially true in

environments that show a tolerant attitude toward job hopping and the sharing of

technical information (Garvin, 1983).

By having spin-outs, the progenitor can create a reputation as both a good place to

be an employee and as a potential springboard for subsequent entrepreneurial activity. By

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attaining such a reputation, the parent-firm has the ability to attract talented

organizational members. This whole process can turn into a virtuous cycle whereby the

progenitor’s initial attraction of better talent leads to more spin-outs, which then attracts

even more talented organizational members. Moreover, this kind of reputation tends to be

rare, socially complex, and difficult to imitate and, therefore, can translate into

competitive advantage for a firm.

In sum, we believe that the cumulative number of spin-outs for a hard drive

producer (the progenitor) will enrich its reputation as an incubator of talent, increasing its

ability to attract greater human capital, and thereby leading to increased firm innovation

over time. We formalize this thinking in our first hypothesis:

Hypothesis 1: A progenitor’s cumulative number of spin-outs will lead to increased innovation rates.

Hypothesis 1 highlights the positive effects of having spin-outs for the progenitor.

However, prior research suggests that spin-outs may also reflect poorly on the parent and

reduce its life chances. In the technology management literature, for example, internal

inertia prevents established firms from undertaking innovation and thus encourages spin-

outs, which end up displacing their progenitors (Tushman and Anderson, 1986). By this

view, the established firm is too close to existing customers and hesitant to approve a new

technology program that may not prove profitable in its established markets. Disgruntled

or disappointed engineers are thus forced to leave the parent in order to exploit the new

opportunity (Christensen 1997). Garvin (1983) similarly underscores how frustrations at

work can motivate a select few employees to start a new venture. Many economists also

point to top management’s role in pushing employees to start companies (e.g., Scherer

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and Ross, 1990). In particular, top management’s aversion to risk generally applies to

ideas generated by employees.

This loss to the parent-firm would seem especially problematic in knowledge-

intensive fields where tacit knowledge resides in key individuals. A progenitor’s reservoir

of such tacit knowledge thus becomes depleted when individuals leave to start new

ventures. Such departures immediately hit product development groups and disrupt the

innovation process. If product cycles are short, then the loss of key personnel means that

a firm may miss a development cycle (and thus pay a revenue penalty), or even fail if

customers no longer trust the firm’s ability to meet their deadlines and requirements.

Very little research has focused on the important issue of what happens to a

progenitor after it has a spin-out. In one of the few (if only) study on this issue, Phillips

(2002) found that law firms that generated spin-outs had higher failure rates than firms

that did not. Phillips focused on the negative consequences that progenitors suffer due to

the loss of firm-specific skills and resources, such as the social capital of departing

employees, ties to important external constituents, as well as the disruption of their

existing routines—especially the innovation process—and general social organization.

However, Phillips (2002: 492) also found that the negative impact of a spin-out for the

progenitor law firm was greatest at the initial time of the spin-out and then diminished

substantially over time as the parent-firm began to rebuild its resources, routines,

capabilities, and the like.

We believe that the characterization of the rebuilding process understates the

important positive consequences of spin-outs for the progenitor involving the creation of

a reputation as an incubator of talented entrepreneurs in an industry, as we discussed for

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Hypothesis 1. More specifically, we suggest that there is an important temporal issue

involving the duration and extent of these negative effects of spin-outs for the progenitor

vis-à-vis the positive reputational effects that may accrue to the progenitor due to spin-

outs. Although we expect spin-outs to have some deleterious consequences for the

progenitor due to the loss of key personnel and disruption of existing routines, we see this

negative effect as being only temporary because a firm with a positive reputation in the

labor market will be able to replenish its technical talent rather expeditiously.

Coupling our first hypothesis that the cumulative number of spin-outs for a

progenitor will increase innovation rates with the potentially negative consequences of

spin-outs discussed above, especially over the short-run, we offer a more nuanced view

of the temporal dynamics of spin-outs on progenitor innovation rates: spin-outs initially

hurt the technological abilities of progenitors due to the loss of key personnel and the

disruption in existing firm routines but over time lead to greater innovation due to the

enhanced reputation in the industry that comes from being known as an incubator of

talent. We formalize this logic in the following hypotheses:

Hypothesis 2A: Spin-outs will initially reduce a progenitor’s rate of innovation. Hypothesis 2B: After the initial reduction in innovation due to having a spin-out, the progenitor will have increased rates of innovation.

INDUSTRY BACKGROUND

There are several good reasons for examining our theory in the context of the hard

disk drive industry. First, as Agarwal et al. (2004) suggest, spin-outs are an important

element of the evolution of the hard disk drive industry. Moreover, since spin-outs are

among the most successful firms in the industry (McKendrick et al., 2000), to discover

that the innovation rates of the progenitor increases as a result of generating spin-outs

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would be a surprising and important finding. Since Agarwal et al. (2004) do not address

the effects of spin-outs on parent-firms, we believe that our paper offers an important

contribution to this burgeoning area of research. Finally, Phillips’ research (2002) is the

only published study of the effect of spin-outs on the performance of progenitors, and

future research is needed to see whether his results generalize outside the context of

Silicon Valley law firms (Phillips: 500).

The defining characteristic of competition in the hard disk drive industry has been

the ongoing race to deliver higher storage capacities on ever-smaller devices at less cost

and in less time (McKendrick et al., 2000). Capacity is determined by how many bits can

be stored on a square inch of disk, otherwise known as the hard disk drive’s areal density.

Since IBM shipped the first movable-head disk drive in 1956, the industry has undergone

tremendous technological change. Until 1991, areal density increased at an annual rate of

30 percent, but grew by an astounding 60 percent per year from 1992-1997, a faster rate

of progress than semiconductors, and an amazing 125 percent in 1998, our last year of

study. Thus firms that increased the capacities of their disk drives by 30-40 percent each

year—the average rate between 1956 and 1991—would merely be standing still in

relative terms. To be a technological leader, a firm would have to exceed that rate.

Rapid technological change has segregated the market into three segments. One

segment is composed of firms that offer the highest capacity drives. A second segment

consists of firms that are the early leaders into a “capacity point” in demand by the largest

computer manufacturers, which typically excludes the highest capacity drives. The third

segment encompasses the technological laggards that serve the secondary market of

second-tier mainframe and minicomputer makers and the hundreds of small and medium

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sized microcomputer companies. Firms late to market with a new drive thus suffer a

severe revenue penalty; if too late, they may find no customers and be forced either to

absorb their development costs and start developing at an even a higher capacity drive or

exit the industry. Even so, first-to-market innovators typically hold only the slimmest of

leads as other manufacturers generally introduce comparable products within a relatively

short time. The likelihood of rapidly decreasing profitability over the life cycle of any

given product provides a strong incentive for firms to innovate rapidly. But the result is

extremely short product cycles, estimated in 2003 to be from six to nine months. Most

firms have had trouble keeping up with continuous product introductions.

Not only have firms needed to keep up with changes in areal density, they have

needed to keep pace with the reductions in the physical size of hard disk drives — from

using disks with diameters of 39, 31 and 24 inches to 14-, 8-, 5.25-, 3.5-, 2.5-, and 1.8-

inch drives. These changes in “form factor” have proven problematic for many drive

manufacturers, and most firms have not survived the transition. For some firms, the

inability to introduce smaller form factors was due to technological factors; scaling down

components and getting designs to work properly were engineering challenges. In other

cases, as Christensen (1997) has pointed out, new form factors sometimes served new

markets, and managers of many incumbent firms were slow to recognize the necessity of

change.

The volatility of this organizational population has limited the ability of market

leaders to control pricing or the length of product life cycles. Periodic oversupply and

constant price erosion are ways of life for disk drive producers. Disk drive manufacturers

might attempt to avoid the pitfalls of the price-sensitive, high-volume, low-end of the

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market by differentiating their products into higher capacity segments, such as drives for

file servers and network storage. But ongoing innovation has made it impossible for

firms to sustain a product differentiation strategy. In this context, then, technological

advance has been crucial to organizational success.

METHODS

Data

We collected data on every organization ever to manufacture a hard disk drive

anywhere in the world through 1998 from a variety of sources, including market research

reports, publicly available financial information, industry participants, and an extensive

search of the business press. From these disparate sources, the life history of each disk

drive company was compiled. These histories cover entry and mortality/exit dates, sales,

presence in a given form factor, product technical specifications, acquisition history, and

nationality for each company that made a hard disk drive since the first known

manufacture of a disk in 1956 through 1998. The resulting database includes 171

organizations that manufactured hard disk drives at any time or place over the period.

Nearly all organizations (155) failed or exited the industry by the end of 1998.

The primary source of data for this study is the Disk/Trend Report, published

annually since 1977 by Disk/Trend, Inc., a market research company. These reports track

every known company that made hard disk drives, list detailed product specifications and

shipment dates, and publishes revenue and unit shipment information by form factor and

capacity range.2 We believe that these data are more comprehensive than those analyzed

2 The Disk/Trend Report does not list the date of first entry for many companies that produced disk drives before 1976. In order to collect information for these companies, we contacted surviving companies for information, spoke with retired engineers involved in the development of a company’s first disk drive for both surviving and former disk drive producers and often received copies of written technical and market

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in the prior research on the industry by Lerner (1997), Christensen (1997), Christensen et

al. (1998), King and Tucci (2002), Chesbrough (2003), and Agarwal et al. (2004). While

their data are left-censored (using market research reports that begin in 1977), ours cover

the entire organizational population dating back to the beginning of the industry in 1956.

Only Barnett and McKendrick (2004) and Carroll et al. (2004) analyze data on this

industry of comparable coverage.

Although we have data on the entire population, our analysis begins with the

introduction of the first 14-inch disk drive in 1963 because there were too few

observations in earlier form factors (i.e., disk diameters), as well as more recent ones

such as the 2.5-inch and 1.8-inch drives, for meaningful quantitative analysis (in terms of

sample size). Although we focus on form factors subsequent to the beginning of the hard

drive industry are analyses are not left-censored because we start with the beginning of

the 3.5-, 5.25-, 8-, and 14-inch disk drive sizes. Moreover, for those firms who existed

prior to 1963, we control for their prior history in terms of firm age; in unreported

analyses, we also included a variable that captures the year that a firm first produced any

type of hard drive (akin to a first-mover or order-of-entry measure) with similar estimates

to those that we report.

To investigate the technological evolution of this industry, we coded for each firm

whether it shipped a product in a particular form factor and that product's maximum

capacity (in terms of areal density) in any given year. As explained below, we conduct

most of our analysis by form factor, investigating changes over time in a firm's product's

information in their possession, reviewed company documents in the files of Disk/Trend, Inc., and researched company histories in books, financial reports, and the business press. Reports by two market research firms in particular were basic sources of information on product specifications and shipment dates

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maximum areal density. In those cases where a firm produced more than one product in a

particular form factor, we used the one with the highest capacity for these analyses.

Dependent Variable

In this study, we focus on rates of innovation for hard disk driver producers,

specifically a firm’s highest areal density in a given form factor each year. As described

above, improvement in areal density is the central technological metric in the evolution of

the industry. In this regard, our dependent variable is a firm’s areal density in the 3.5-,

5.25-, 8-, and 14-inch form factors.

Key Explanatory Variables

Spin-Out Count Measures

We define a spin-out as occurring when an employee from an incumbent hard

driver producer is a founder of a new entrant in the industry.3 We use several different

measures in order to test our predictions about the effects of spin-outs on the innovation

rates of progenitor hard drive producers. First, in order to measure the effects of having

multiple spin-outs, we count a hard drive producer’s cumulative or total number of spin-

outs at a given point of time (updated annually). For our reported models, we use the

natural logarithm of this measure, though we receive similar estimates with a non-logged

measure of cumulative spin-outs.

Spin-Out “Clock” Measures

of firms operating during the 1960s and early 1970s (High Technology West, 1970; Modern Data Systems, 1972). 3 Some spin-outs have multiple parents (i.e., employees leave from more than one incumbent firm). From the standpoint of the spin-out itself, single versus multiple parents may well have important differential effects on spin-out life chances. However, for our purposes, since we focus on progenitors, not spin-outs, the distinction between single and multiple parents is not as relevant – any departure by an employee to form a new venture has significant consequences for the progenitor, irrespective of whether that new venture is formed from single or multiple parents. For this reason, we treat single and multiple parent spin-outs the same for our purposes.

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We test the temporal effects of having spin-outs by creating a spin-out “clock”

that captures the amount of time (in years) since the progenitor hard drive producer has

had a spin-out. If a hard drive producer has had more than one spin-out, the spin-out

clock is reset to zero and the clock begins counting until its next spin-out. For example, in

1972, in its third year of existence, Pertec had its first spin-out (one of its employees

founded Western Dynex). In the following year (1973), we start Pertec’s spin-out clock at

1 year. Pertec’s spin-out clock continues to increase annually until 1981 when Pertec had

its second spin-out (four of its employees leave to found Computer Memories). We then

re-set Pertec’s spin-out clock back to zero and continue counting annually until its next

spin-out.

In order to model our theory about the temporal effects of having spin-outs, we

use our spin-out clock to create separate spin-out clock dummy variables for each of the

first nine years after a hard drive producer has had a spin-out. That is, we create a 1/0

dummy variable for whether a firm is in the first year since it had a spin-out, a 1/0

dummy for whether a firm is in the second year since it had a spin-out, and so on until the

ninth year after a spin-out.

In exploratory models, we examined how many spin-out clock dummy variables

created best-fitting firm innovation models (in terms of Wald Chi-Squared tests). For the

3.5-, 5.25-, and 8-inch drives, the best fitting models involved the nine spin-out clock

dummies that we use in our reported models. For the 14-inch drive, having ten spin-out

clock dummy variables represented the best fitting model. Since the substantive results

for the 14-inch drive were the same with either nine or ten clock dummy variables, we

report the results with the nine spin-out clock dummies for the sake of consistency across

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form factors. It should be noted that the estimates of the spin-out clock dummies do not

substantially change if we use either a smaller number of clock dummies (e.g., dummies

representing just five years since a spin-out) or a wider cut-off (e.g., dummies

representing up to 15 years since a spin-out). Based on our knowledge of the hard drive

industry, we believe that nine years since a spin-out represents a reasonable amount of

time to assess the reputational benefits of having spin-outs.

We use these spin-out clock dummy variables rather than just the linear spin-out

clock itself as a covariate because our theory does not predict a linear effect of time on

innovation rates, but, rather, a reduction in innovation rates over the first couple of years

after a spin-out, followed by a subsequent increase in innovation. Similarly, using both

the spin-out clock and its squared-term would not (and does not) capture the temporal

nuances of our theory.

Baseline Measures

Besides our key explanatory variables, we expect several other firm-specific and

population-level variables to affect hard drive innovation rates. We start by including the

age of a hard drive producer since a firm’s age has been found to affect firm innovation

(Sørensen & Stuart, 2000). Our main measure of organizational size is a categorical

measure that distinguishes between large and small firms, defined relatively for each

year, and updated from year to year. For years prior to 1976, this designation was made

by examining historical documents to identify major players in the industry in each year:

firms with substantial hard drive sales were coded with a “1,” while firms with little to no

sales were coded with a “0”. For observations after 1976, we relied on the data source

Disk/Trend for this distinction: hard drive producers for whom Disk/Trend reported sales

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figures, representing the largest 20 to 30 firms in the industry in a given year, were coded

with a “1,” while all other firms were coded with a “0”. Our results do not hinge on the

inclusion of our measure of organizational size – in exploratory models, we ran models

without the inclusion of organizational size with similar estimates to those reported in the

paper.

Following Carroll et al. (1996), we also include a dummy variable to distinguish

between de novo firms—those that entered the industry as a start up—and de alio entrants

who moved or expanded into the industry from some other industry. In addition, we

include a variable that measures order-of-entry or first-mover status in a given form

factor (i.e., 3.5-, 5.25-, 8-, and 14-inch drive drives): this is a non-time varying variable

that captures the initial year the hard drive producer entered a given form factor with “1”

being the first year of any production in that form factor. Since a higher number for this

variable represents a late-entrant, if there are first-mover advantages, this variable should

have a negative effect on innovation rates.

We also take into account whether a hard drive producer engaged in any so-called

“captive” production of disk drives for their own computers. In a similar manner, we also

control for the number of low-, medium-, and high-capacity form factors produced by

each firm in our sample. Since we are concerned that a hard driver producer’s rate of

innovation in one form factor may be affected by its presence (or lack thereof) in another

size of disk drive, we use dummy variables for whether a firm produces in any of the

other disk drive sizes (excluding the one we are modeling).

In terms of controls operating at the population-level, we include a time-trend in

our models for the year of production. In exploratory models, we tried linear, curvilinear

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(linear and quadratic terms), and the natural log of year with similar effects on

innovation; we report the natural log of year since the natural log of year fits our data the

best.

Estimation

We estimate a hard drive producer’s rate of innovation using models similar to

classic firm growth models based on Gibrat’s law (see, e.g., Barron, West, and Hannan,

1994; Carroll and Hannan, 2000). Let ADit represent the maximum areal density of a hard

drive producer, i, in a given year, t. In our models, “growth” or change in innovation

involves regressing a hard driver producer’s maximum areal density at time t, ADit on its

areal density at time t - 1, ADi,t-1 and other covariates (e.g., our key explanatory measures

involving spin-outs and our baseline covariates, usually at time t).

In order to simplify estimation, growth models of this kind often use a natural log

transformation of both maximum areal density at time t, the dependent variable, and areal

density at time t - 1, a key lagged regressor-term. This leads to the following model of

innovation:

Ln(ADit) = ωln(ADi,t-1) + β1SOit + β2χit + εit,

where ADi,t-1 represents a one-year lag of maximum areal density, SOit denotes our key

spin-out explanatory variables, χit are our baseline control measures, and εit represents a

random disturbance term.

We use random-effect within regression models based on standard models in the STATA

statistical package.

21

RESULTS

Table 1 contains the descriptive statistics of the key explanatory variables used to

estimate hard drive innovation rates in the 3.5-, 5.25-, 8-, and 14-inch drives. In the

Appendix of this paper, we include correlations among the key explanatory variables for

the four sizes of hard drives.

[Insert Table 1 about here]

Table 2 presents models testing Hypothesis 1, the effects of a hard drive firm’s

logged cumulative number of spin-outs on innovation rates. Models 1 through 4 offer

estimates for the four different sizes of disk drives that we study. Consistent with

Hypothesis 1, we find that a firm’s logged cumulative number of spin-outs has a

statistically significant effect of increasing innovation rates for 3.5-, 5.25-, and 14-inch

disk drives. We do not find any support for Hypothesis 1 for 8-inch drives: a firm’s

logged cumulative number of spin-outs has a non-significant negative effect on

innovation rates. Several of our baseline (control) variables have significant effects on

firm innovation. Being a large firm (statistically significant in 3 of 4 disk sizes), a firm’s

number of medium or high form factors (statistically significant in all sizes), and the

time-trend (natural log of year) (statistically significant in all sizes) are all correlated with

increased firm innovation. Being a late entrant in a given form factor reduces innovation

rates (statistically significant in 3 of 4 disk sizes), suggesting the benefits of first-mover

advantages in these markets. De novo firms have lower innovation rates than de alio

firms (statistically significant in 2 of 4 disk sizes).

[Insert Table 2 about here]

22

Table 3 presents our models testing Hypothesis 2A and 2B, the temporal effects

of having a spin-out on a hard drive firm’s innovation rates. Models 5 through 8 offer

estimates for the four different sizes of disk drives that we study.

[Insert Table 3 about here]

In Model 5, for 3.5-inch disk drives, the dummy variable that captures whether a

firm has had a spin-out is significant and positive, suggesting that as a main effect firms

that have had spin-outs have increased innovation. This positive main effect of having a

spin-out is (significantly) negatively reduced in the 1st, 2nd, and 9th years after the firm has

had a spin-out. However, in Model 5, since the negative size of the coefficients for the 1st,

2nd, and 9th years after a spin-out are not as large as the positive main effect of having had

a spin-out—compare -.406, -.348, -.296, and .489, respectively—this suggests that having

a spin-out never hurts firm innovation in an absolute sense, contrary to Hypothesis 2A.

In contrast to Model 5, Models 6 through 8 offer strong support for both

Hypotheses 2A and 2B. In Model 6, for 5.25-inch drives, the (statistically significant)

negative size of the coefficients for the 1st, 2nd, and 3rd years after a spin-out are larger

than the (statistically significant) positive main effect of having had a spin-out —

compare -.450, -.465, -.569, and .433, respectively — suggesting that innovation is

reduced for the first few years after a spin-out but then is subsequently increased. Model

7, 8-inch drives, also supports Hypotheses 2A and 2B: the (statistically significant)

negative size of the coefficients for the 1st and 2nd years after a spin-out are larger than the

(statistically significant) positive main effect of having had a spin-out (compare -.393, -

.353, and .296, respectively). In addition, in Model 8, for 14-inch drives, the (statistically

significant) negative size of the coefficient for the 1st year is larger than the (statistically

23

significant) positive main effect of having had a spin-out (compare -.454 and .420). In

this model (14-inch drives), by the 2nd year after a spin-out, innovation rates are

unchanged: the (statistically significant) negative size of the coefficient for the 2nd year is

the same size as the main effect of having a spin-out.

The models in Table 3 include the same baseline (control) variables as in Table 2

with similar substantive effects on innovation rates (e.g., late entrants in a form factor

have reduced innovation rates).

DISCUSSION AND CONCLUSION

Although researchers have begun to examine the phenomenon of spin-outs, most

of this research has focused on the effects on the spin-out itself. Our paper represents one

of the most detailed studies of the important flipside of this organizational dynamic: what

happens to the progenitor after it has a spin-out. Prior to our paper, what little research

that has been done on progenitors suggests the negative effects of spin-outs for these

parent firms. In contrast, we offer a more nuanced portrait of the effects of spin-outs on

progenitor firms, pointing out that in certain circumstances spin-outs may increase

innovation for progenitors if the latter can develop a reputation in their industry as an

incubator of talented entrepreneurs.

We start by finding that a firm’s (logged) cumulative number of spin-outs

significantly increases innovation rates. Based on estimates in Table 2, Figure 1 plots the

substantive effects of a firm’s (logged) cumulative number of spin-outs on innovation

rates using a standard multiplier rate analysis. As Figure 1 shows, a firm’s (logged)

cumulative number of spin-outs substantially increases innovation rates, consistent with

Hypothesis 1. For example, for 3.5-inch drives, going from a mean value of (logged)

24

cumulative number of spin-outs (.3) to a one standard deviation increase above the mean

(.8) leads to a 13% increase in innovation rates. In a similar way, a one standard deviation

increase in (logged) cumulative number of spin-outs above the mean increases innovation

by 5% for 5.25-inch drives and by 7% for 14-inch drives.

[Insert Figure 1 about here]

We are cognizant, however, of the potentially negative effects of spin-outs on

their parent firms. The spin-out process may hurt the progenitor firm through the loss of

key personnel and the disruption in existing firm routines, among other ways. We

reconcile these conflicting standpoints (spin-outs increase a progenitor’s reputation in the

industry as an incubator of talent but also has material, deleterious consequences) by

suggesting a nuanced temporal effect of spin-outs on progenitor innovation rates: due to

the loss of key personnel and the disruption in existing firm routines, among other

factors, spin-outs hurt progenitors in the first few years after a spin-out, but, over time,

this negative effect is more than made up in increased innovation due to the enhanced

industry reputation that comes from being known as an incubator of talented

entrepreneurs.

In order to better illustrate the temporal effects of having spin-outs, Figure 2 plots

the rate of innovation (using a multiplier plot) over time for our four sizes of disk drives

based on the models in Table 3. For Figure 2, we only plot spin-out covariates that are

statistically significant (e.g., in Model 5, the dummy variable for the 3rd year after a spin-

out is not included in our plot).4 As Figure 2 demonstrates, innovation is significantly

4 In order to create our multiplier plot in Figure 2, we simply combine the effect of a firm’s dummy variable for having had a spin-out with the effect of each of the different time since spin-out clock dummies. For example, in Model 6, after 1 year, a firm would have innovation reduced by .017 (.433 - .450 = -.017). Similarly, in Model 6, in the 2nd year after a spin-out, innovation would be reduced by .032 (.433 - .465 = -.032). We then take the exponent of this sum (due to the non-linear specification of the model) and plot over time. A value of 1 represents the baseline or no change in

25

reduced (i.e., a value less than 1) in years 1 through 3 after a spin-out for 5-inch drives, in

years 1 and 2 after a spin-out for 8-inch drives, and only after year 1 for 14-inch drives.

These substantive effects offer strong support for Hypotheses 2A and 2B: for progenitors,

spin-outs initially reduce innovation in the first few years, but, over time, spin-outs

increase innovation rates.

[Insert Figure 2 about here]

Although our paper pushes research on progenitors forward, there are still some

important unanswered questions. For example, we treat all spin-outs alike in terms of

their effects on progenitor firms. However, it may be the case that more successful spin-

outs (in terms of survival or innovation) have stronger effects in terms of a progenitor’s

reputation. On a different note, we only focus on single-generation spin-outs, where the

new venture is directly founded by an employee from an incumbent firm. One could also

examine the effects on progenitors of multiple-generation spin-outs (e.g., ‘grand-parent’

progenitors) where these ‘ancestor’ progenitors get credit for the spin-outs of their spin-

outs. It is unclear whether these ancestor progenitors would receive the same enhanced

reputation as we find for parent firms: on the one hand, the direct link between the

ancestor and the new venture is moderated by the new venture’s parent firm (the

ancestor’s initial spin-out); on the other hand, a multiple-generation spin-out could signal

a particularly fertile progenitor.

In a similar manner, there is a fair amount of employee mobility among

incumbent firms in the hard drive industry. A spin-out may well be founded by an

employee who was most recently at one incumbent firm but had earlier come from

innovation, while a value greater than 1 means significantly increased innovation, and a value less than 1 means significantly reduced innovation.

26

another incumbent firm. As with ancestor progenitors, the question remains whether

these types of progenitors receive any ‘credit’ for the spin-out. In this respect, our present

analysis of progenitors is a fairly conservative test because we only include direct spin-

outs at the first-generational link. Including multiple-generation spin-outs or incumbent-

mobility-based spin-outs may well show even greater effects of spin-outs on progenitors.

An additional implication of our research relates to the legal infrastructure and

public policy. At the beginning of the paper, we said that organizations may unwittingly

transmit a positive signal to the broader labor market by generating spin-outs. We

emphasize the unintended consequences of spin-outs because so many firms try to keep

employees from using their tacit knowledge to compete against them. Intellectual

property law that covers trade secrets, employment covenants, non-compete contracts and

the like all seek to restrain employees from trading on the knowledge they accumulated

while working with their employers. Moreover, litigation by the progenitor against the

founders of spin-outs risks the imposition of labor market reputation penalties against the

progenitor. In other words, it can actually harm the prestige of the progenitor. If our

results hold in other commercial contexts, then these efforts are misguided and even

counterproductive: progenitors gain by having spin-outs.

27

Table 1. Descriptive Statistics for Hard Disk Drive Producers: Split-Spell File3.5-Inch Drives 5.25-Inch Drives

Variable Mean S.D. Min. Max Mean S.D. Min. MaxLn(Areal Density) 3.99 1.42 1.67 7.90 2.56 1.28 1 7.41Dummy: Large Firm .674 .469 0 1 .598 .491 0 1Year (1 = 1956; 42 = 1998) 34.6 3.67 26 42 31.4 3.88 24 42Ln(Year) 3.58 .097 3.33 3.76 3.49 .111 3.26 3.76Firm Age 11.4 9.82 1 42 9.55 8.33 1 38Form Factor age at firm entry (t0) 23.7 9.28 0 39 22.4 7.69 0 39Dummy: Denovo Firm (t0) .351 .478 0 1 .342 .475 0 1Number of Low Capacity Form Factors 1.43 .893 0 4 1.27 .895 0 4Number of Med / High Cap. Form Factors 1.50 1.1797 0 5 1.30 1.21 0 5Dummy: Firm has In 3.5-Inch Drives . . . . .373 .484 0 1Dummy: Firm has In 5.25-Inch Drives .447 .498 0 1 . . . .Dummy: Firm has In 8-Inch Drives .188 .392 0 1 .279 .449 0 1Dummy: Firm has In 14-Inch Drives .070 .256 0 1 .150 .357 0 1Dummy: Captive Production .319 .467 0 1 .350 .478 0 1Dummy: Firm has had a spin-out .272 .445 0 1 .208 .406 0 1Ln(Cumulative Number of Spin-outs) .259 .463 0 1.61 .205 .438 0 2.30Dummy: 1 Year After Spin-out .032 .176 0 1 .042 .202 0 1Dummy: 2 Years After Spin-out .022 .148 0 1 .029 .168 0 1Dummy: 3 Years After Spin-out .019 .137 0 1 .022 .148 0 1Dummy: 4 Years After Spin-out .026 .158 0 1 .018 .133 0 1Dummy: 5 Years After Spin-out .019 .137 0 1 .013 .115 0 1Dummy: 6 Years After Spin-out .022 .148 0 1 .013 .115 0 1Dummy: 7 Years After Spin-out .016 .126 0 1 .013 .115 0 1Dummy: 8 Years After Spin-out .016 .126 0 1 .011 .105 0 1Dummy: 9 Years After Spin-out .019 .137 0 1 .009 .094 0 1N of firms = 59 ; N of firm-year spells = 313 N of firms = 83 ; N of firm-year spells = 448

8-Inch Drives 14-Inch DrivesVariable Mean S.D. Min. Max Mean S.D. Min. MaxLn(Areal Density) 2.26 1.08 0.0751 4.54 .312 1.62 -3.772 5.5932Dummy: Large Firm .799 .401 0 1 .627 .484 0 1Year (1 = 1956; 42 = 1998) 29.3 4.01 22 41 22.8 6.28 7 39Ln(Year) 3.43 .122 3.1781 3.74 3.16 .267 2.20 3.69Firm Age 13.6 9.10 1 38 8.60 6.81 1 39Form Factor age at firm entry (t0) 16.4 7.43 0 34 14.7 5.79 0 28Dummy: Denovo Firm (t0) .249 .433 0 1 .154 .361 0 1Number of Low Capacity Form Factors 1.28 1.11 0 4 .606 .762 0 4Number of Med / High Cap. Form Factors 1.82 1.25 0 5 .816 .977 0 4Dummy: Firm has In 3.5-Inch Drives .293 .456 0 1 .056 .231 0 1Dummy: Firm has In 5.25-Inch Drives .570 .496 0 1 .137 .344 0 1Dummy: Firm has In 8-Inch Drives . . . . .174 .379 0 1Dummy: Firm has In 14-Inch Drives .357 .480 0 1 . . . .Dummy: Captive Production .631 .484 0 1 .543 .498 0 1Dummy: Firm has had a spin-out .386 .488 0 1 .236 .425 0 1Ln(Cumulative Number of Spin-outs) .415 .612 0 2.40 .244 .499 0 2.40Dummy: 1 Year After Spin-out .072 .259 0 1 .056 .231 0 1Dummy: 2 Years After Spin-out .056 .231 0 1 .037 .188 0 1Dummy: 3 Years After Spin-out .044 .206 0 1 .030 .172 0 1Dummy: 4 Years After Spin-out .036 .187 0 1 .026 .159 0 1Dummy: 5 Years After Spin-out .028 .166 0 1 .020 .139 0 1Dummy: 6 Years After Spin-out .020 .141 0 1 .017 .128 0 1Dummy: 7 Years After Spin-out .020 .141 0 1 .015 .123 0 1Dummy: 8 Years After Spin-out .028 .166 0 1 .012 .110 0 1Dummy: 9 Years After Spin-out .028 .166 0 1 .009 .095 0 1N of firms = 42 ; N of firm-year spells = 249 N of firms = 66 ; N of firm-year spells = 657

28

Table 2. Hard Drive Innovation: Cumulative Spinout Models Random Effect Within Regressions of Hard Drive Areal Density ("AD")

Ln(3.5-Inch AD) Ln(5.25-Inch AD) Ln(8-Inch AD) Ln(14-Inch AD)Variables Model 1 Model 2 Model 3 Model 4Ln(AD3), t-1 .641*** (.053)

Ln(AD5), t-1 .782*** (.037)

Ln(AD8), t-1 .563*** (.066)

Ln(AD14), t-1 .765*** (.027)

Dummy: Large Firm .303*** (.064) .241*** (.060) -.061 (.093) .224*** (.049)

Ln(Year) 6.42*** (.754) 1.65*** (.462) 11.8*** 3.917 1.54*** (.496)

Firm Age -.008 (.006) -.003 (.005) -.306** (.123) -.035 (.023)

Form Factor age at firm entry (t0) -.046*** (.014) -.207 (.108) -.318** (.123) -.049* (.023)

Dummy: Denovo Firm (t0) -.196*** (.074) .038 (.056) .041 (.107) -.159*** (.055)

Number of Low Capacity Form Factors by Firm -.069 (.037) -.092*** (.032) -.012 (.042) -.081** (.036)

Number of Med. Cap. Form Factors by Firm .186*** (.032) .116*** (.032) .212*** (.047) .148*** (.038)

Dummy: Firm has In 3.5-Inch Drives .042 (.061) -.124 (.103) .021 (.111)

Dummy: Firm has In 5.25-Inch Drives -.071 (.064) -.213* (.095) -.014 (.092)

Dummy: Firm has In 8-Inch Drives -.233** (.091) .190*** (.067) -.205** (.080)

Dummy: Firm has In 14-Inch Drives .071 (.109) -.116 (.073) .024 (.087)

Dummy: Captive Production .069 (.090) -.002 (.060) .146 (.104) .019 (.045)

Ln (Cumulative Number of Spinouts) .237*** (.076) .113* (.057) -.102 (.073) .099* (.048)

Model Constant -21.1*** (2.50) -5.09*** (1.54) -30.0*** (9.77) -3.66*** (1.07)Firm-Year Spells 313 448 249 657Wald Chi2 (df) 4227.8 (13) 3726.9 (13) 898.6 (13) 6664.4 (13)All variables are not lagged unless otherwise stated.* p <.05; ** p <..025; *** p <.01. Standard errors in parentheses

29

Table 3. Hard Drive Innovation: Time Since Spinout Models Random Effect Within Regressions of Hard Drive Areal Density ("AD")

Ln(3.5-Inch AD) Ln(5.25-Inch AD) Ln(8-Inch AD) Ln(14-Inch AD)Variables Model 5 Model 6 Model 7 Model 8Ln(AD3), t-1 .605*** (.055)

Ln(AD5), t-1 .778*** (.037)

Ln(AD8), t-1 .851*** (.051)

Ln(AD14), t-1 .778*** (.027)

Dummy: Large Firm .299*** (.067) .239*** (.062) .003 (.071) .240*** (.049)

Ln(Year) 6.59*** (.775) 4.57 (3.14) .410 (.533) 1.50*** (.500)

Firm Age -.006 (.006) -.094 (.093) .007 (.004) -.039 (.023)

Form Factor age at firm entry (t0) -.042*** (.015) -.090 (.092) -.040** (.017) -.050* (.023)

Dummy: Denovo Firm (t0) -.208** (.082) .027 (.055) -.074 (.064) -.180*** (.055)

Number of Low Capacity Form Factors by Firm -.076* (.039) -.099*** (.031) .024 (.034) -.078* (.036)

Number of Med. Cap. Form Factors by Firm .185*** (.033) .122*** (.032) .102*** (.037) .148*** (.037)

Dummy: Firm has In 3.5-Inch Drives .013 (.062) -.108 (.087) -.024 (.111)

Dummy: Firm has In 5.25-Inch Drives -.084 (.067) -.129 (.072) .017 (.092)

Dummy: Firm has In 8-Inch Drives -.213** (.094) .205*** (.067) -.190** (.080)

Dummy: Firm has In 14-Inch Drives .048 (.112) -.129 (.073) -.030 (.064)

Dummy: Captive Production .056 (.097) .014 (.059) .045 (.053) .025 (.044)

Dummy: Firm has had a spinout .489*** (.131) .433*** (.119) .296* (.144) .420** (.182)

Dummy: 1 Year After Spin-out -.406** (.164) -.450*** (.148) -.393** (.163) -.454** (.193)

Dummy: 2 Years After Spin-out -.348* (.172) -.465*** (.163) -.353* (.168) -.420* (.199)

Dummy: 3 Years After Spin-out -.337 (.180) -.569*** (.176) -.380 (.256) -.188 (.203)

Dummy: 4 Years After Spin-out -.149 (.188) -.150 (.187) -.186 (.197) -.024 (.204)

Dummy: 5 Years After Spin-out -.314 (.178) -.146 (.206) -.178 (.216) -.306 (.213)

Dummy: 6 Years After Spin-out -.017 (.141) -.120 (.177) -.155 (.389) -.274 (.218)

Dummy: 7 Years After Spin-out -.193 (.172) .198 (.233) -.123 (.215) -.263 (.223)

Dummy: 8 Years After Spin-out -.260 (.186) -.152 (.217) -.442 (.429) -.398 (.234)

Dummy: 9 Years After Spin-out -.296* (.138) -.371* (.175) -.525 (.394) -.377 (.250)

Model Constant -21.6*** (2.57) -12.4 (8.02) -1.05 (1.92) -3.48*** (1.07)Firm-Year Spells 313 448 249 657Wald Chi2 (df) 4026.2 (22) 3890.4 (22) 2006.9 (22) 6886.6 (22)All variables are not lagged unless otherwise stated.* p <.05; ** p <..025; *** p <.01

30

Figure 1. Innovation Rates Due to Ln (Cumulative Number of Spinouts)

0.8

0.9

1

1.1

1.2

1.3

1.4

1.5

1.6

1.7

1.8

0.00 0.50 1.00 1.50 2.00 2.50

Ln (Cumulative Number of Spinouts)

Rat

e of

Inno

vatio

n (A

real

Den

sity

)

AD3"AD5"AD14"

31

Figure 2. Innovation Rates Due to Time Since Spinout

0.80

0.90

1.00

1.10

1.20

1.30

1.40

1.50

1.60

1.70

1.80

0 1 2 3 4 5 6 7 8 9

Years Since Spinoff

Rat

e of

Inno

vatio

n (A

rrea

l Den

sity

)

AD3"AD5"AD8"AD14"

32

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APPENDIX: Correlations of VariablesCorrelations for 3.5-Inch DrivesVariable 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22

1 Ln(Areal Density)2 Dummy: Large Firm .183 Ln(Year) .93 -.024 Firm Age .41 .38 .335 Industry Age at Firm Entry -.08 -.40 .03 -.936 Dummy: Denovo Firm .00 .11 -.05 -.33 .337 Number of Low Capacity Form Factors by Firm .13 .26 .13 .36 -.34 .088 Number of Med. Cap. Form Factors by Firm .13 .48 -.01 .57 -.61 .00 .559 Dummy: Firm has In 5.25-Inch Drives -.14 .38 -.21 .42 -.53 .05 .46 .56

10 Dummy: Firm has In 8-Inch Drives -.16 .28 -.18 .48 -.58 -.18 .51 .63 .5211 Dummy: Firm has In 14-Inch Drives -.18 .14 -.19 .32 -.42 -.18 .24 .43 .31 .5112 Dummy: Captive Production .11 .33 .06 .69 -.71 -.50 .17 .42 .28 .49 .3213 Dummy: Firm has had a spin-out .30 .33 .20 .16 -.09 .48 .25 .17 .19 -.06 .00 -.1614 Ln(Cumulative Number of Spin-outs) .34 .33 .23 .31 -.24 .37 .24 .23 .18 .00 .07 -.05 .9215 Dummy: 1 Year After Spin-out .01 .13 -.02 -.07 .07 .21 .07 .01 .09 -.09 -.05 -.12 .30 .2616 Dummy: 2 Years After Spin-out .00 .11 -.01 -.06 .07 .16 .02 .01 .04 -.07 -.04 -.10 .25 .19 -.0317 Dummy: 3 Years After Spin-out .00 .05 .00 -.06 .06 .14 .06 -.02 .01 -.07 -.04 -.10 .23 .17 -.03 -.0218 Dummy: 4 Years After Spin-out .01 -.06 -.01 -.03 .03 .14 .01 .05 .02 .03 .03 -.07 .27 .18 -.03 -.02 -.0219 Dummy: 5 Years After Spin-out .01 .10 .00 .01 .00 .09 .06 .00 .01 .05 .05 -.05 .23 .21 -.03 -.02 -.02 -.0220 Dummy: 6 Years After Spin-out .03 .11 .02 .01 .00 .12 .02 .01 .04 -.02 .04 -.06 .25 .24 -.03 -.02 -.02 -.02 -.0221 Dummy: 7 Years After Spin-out .04 .09 .02 .04 -.03 .12 .02 .05 .04 .07 .16 -.03 .21 .23 -.02 -.02 -.02 -.02 -.02 -.0222 Dummy: 8 Years After Spin-out .02 .09 .01 .06 -.06 .07 .00 .05 .04 .07 .06 .02 .21 .19 -.02 -.02 -.02 -.02 -.02 -.02 -.0223 Dummy: 9 Years After Spin-out .05 .05 .05 .07 -.05 .09 .11 .04 .06 -.01 -.04 .00 .23 .20 -.03 -.02 -.02 -.02 -.02 -.02 -.02 -.02

Correlations for 5.25-Inch DrivesVariable 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22

1 Ln(Areal Density)2 Dummy: Large Firm .123 Ln(Year) .83 -.204 Firm Age .46 .42 .395 Industry Age at Firm Entry -.09 -.54 .06 -.896 Dummy: Denovo Firm .13 .02 .03 -.34 .397 Number of Low Capacity Form Factors by Firm .28 .27 .29 .48 -.38 .008 Number of Med. Cap. Form Factors by Firm .34 .56 .08 .63 -.64 -.07 .519 Dummy: Firm has In 3.5-Inch Drives .48 .41 .35 .52 -.39 .02 .45 .57

10 Dummy: Firm has In 8-Inch Drives .06 .37 -.08 .48 -.56 -.15 .51 .64 .2211 Dummy: Firm has In 14-Inch Drives -.12 .27 -.18 .39 -.51 -.20 .34 .46 .03 .4612 Dummy: Captive Production .02 .41 -.07 .55 -.63 -.50 .23 .43 .21 .46 .3513 Dummy: Firm has had a spin-out .20 .31 .10 .21 -.18 .25 .25 .15 .23 .12 .06 .0314 Ln(Cumulative Number of Spin-outs) .17 .31 .08 .28 -.26 .18 .25 .20 .21 .17 .13 .10 .9215 Dummy: 1 Year After Spin-out -.04 .15 -.09 -.02 -.01 .13 .07 .07 .00 .04 .07 .01 .41 .4316 Dummy: 2 Years After Spin-out -.04 .11 -.07 -.02 -.01 .07 .13 .02 -.02 .07 .08 .01 .34 .30 -.0417 Dummy: 3 Years After Spin-out -.06 .09 -.07 .02 -.05 .05 .06 .00 .01 .11 -.02 .05 .30 .26 -.03 -.0318 Dummy: 4 Years After Spin-out .00 .01 -.02 .04 -.06 .05 .03 .05 .07 .07 .04 .01 .26 .20 -.03 -.02 -.0219 Dummy: 5 Years After Spin-out .00 .02 -.01 .03 -.05 .04 .05 -.01 .07 .06 .01 .00 .23 .19 -.02 -.02 -.02 -.0220 Dummy: 6 Years After Spin-out .04 .06 .02 .06 -.05 .04 -.01 -.01 .07 -.03 .01 .00 .23 .19 -.02 -.02 -.02 -.02 -.0121 Dummy: 7 Years After Spin-out .04 .06 .05 .07 -.05 .04 -.01 -.01 .03 .01 .06 .00 .23 .19 -.02 -.02 -.02 -.02 -.01 -.0122 Dummy: 8 Years After Spin-out .07 .09 .07 .11 -.09 .06 .02 .06 .09 .08 .07 .01 .21 .18 -.02 -.02 -.02 -.01 -.01 -.01 -.0123 Dummy: 9 Years After Spin-out .12 .08 .10 .11 -.07 .03 .10 .06 .12 -.01 -.04 .03 .19 .18 -.02 -.02 -.01 -.01 -.01 -.01 -.01 -.01

37

APPENDIX: Correlations of VariablesCorrelations for 8-Inch DrivesVariable 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22

1 Ln(Areal Density)2 Dummy: Large Firm 0.023 Ln(Year) 0.85 -0.14 Firm Age 0.65 0.25 0.615 Industry Age at Firm Entry -0.4 -0.3 -0.2 -0.96 Dummy: Denovo Firm -0.1 -0.2 -0 -0.4 0.457 Number of Low Capacity Form Factors by Firm 0.39 0.25 0.42 0.34 -0.2 -08 Number of Med. Cap. Form Factors by Firm 0.46 0.37 0.3 0.44 -0.4 -0.1 0.699 Dummy: Firm has In 3.5-Inch Drives 0.63 0.23 0.62 0.59 -0.4 -0.2 0.55 0.6

10 Dummy: Firm has In 5.25-Inch Drives 0.38 0.29 0.38 0.3 -0.2 -0.1 0.65 0.67 0.5411 Dummy: Firm has In 14-Inch Drives -0.1 0.27 -0.2 0.15 -0.3 -0.1 0.25 0.41 -0.1 0.1612 Dummy: Captive Production 0.17 0.43 0.17 0.56 -0.6 -0.6 0.1 0.15 0.26 0.19 0.113 Dummy: Firm has had a spin-out 0.01 0.11 0.09 0.12 -0.1 0.15 -0.2 -0.2 -0.1 -0.2 -0.1 0.1614 Ln(Cumulative Number of Spin-outs) -0 0.1 0.08 0.23 -0.3 0.04 -0.1 -0.2 -0.1 -0.1 0.01 0.19 0.8615 Dummy: 1 Year After Spin-out -0.2 0.1 -0.2 -0.1 -0 0.02 -0.1 -0.1 -0.2 -0 0.05 0.05 0.35 0.4216 Dummy: 2 Years After Spin-out -0.2 0.08 -0.1 -0.1 0.05 0.02 -0.1 -0.1 -0.2 -0 -0 0.11 0.31 0.3 -0.117 Dummy: 3 Years After Spin-out -0.1 0.11 -0.1 -0 0 0.01 -0.1 -0.1 -0.1 -0.1 -0 0.12 0.27 0.22 -0.1 -0.118 Dummy: 4 Years After Spin-out -0 0.04 0.01 0.03 -0 -0 -0.1 -0 0.02 -0 0.04 0.1 0.24 0.18 -0.1 -0 -019 Dummy: 5 Years After Spin-out -0.1 0.02 0.03 0.01 0 0.01 -0.1 -0.1 -0 -0 -0 0.08 0.21 0.11 -0 -0 -0 -020 Dummy: 6 Years After Spin-out 0.11 0.07 0.05 0.07 -0.1 0.05 -0.1 -0 -0 -0.1 0.01 -0 0.18 0.13 -0 -0 -0 -0 -021 Dummy: 7 Years After Spin-out 0.13 0 0.08 0.09 -0.1 0.05 -0.1 -0 -0 -0 0.01 -0 0.18 0.13 -0 -0 -0 -0 -0 -022 Dummy: 8 Years After Spin-out 0.19 -0 0.15 0.07 -0 0.07 -0.1 -0 0.05 0 -0 -0 0.21 0.15 -0 -0 -0 -0 -0 -0 -023 Dummy: 9 Years After Spin-out 0.2 -0.1 0.19 0.09 -0 0.07 -0 -0 0.05 -0 -0.1 -0 0.21 0.15 -0 -0 -0 -0 -0 -0 -0 -0

Correlations for 14-Inch DrivesVariable 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22

1 Ln(Areal Density)2 Dummy: Large Firm .473 Ln(Year) .83 .324 Firm Age .67 .34 .565 Industry Age at Firm Entry .09 -.11 .38 -.556 Dummy: Denovo Firm -.05 .08 -.02 -.11 .097 Number of Low Capacity Form Factors by Firm .27 .19 .31 .41 -.14 .008 Number of Med. Cap. Form Factors by Firm .46 .24 .34 .48 -.18 -.01 .549 Dummy: Firm has In 3.5-Inch Drives .43 .16 .32 .57 -.28 -.10 .43 .56

10 Dummy: Firm has In 5.25-Inch Drives .44 .22 .40 .50 -.13 -.05 .67 .72 .5411 Dummy: Firm has In 8-Inch Drives .41 .31 .36 .41 -.11 -.01 .54 .75 .38 .6112 Dummy: Captive Production .13 .29 -.03 .31 -.39 -.25 .03 .05 .20 .14 .1113 Dummy: Firm has had a spin-out .34 .09 .29 .38 -.10 .13 -.10 -.06 .05 .02 .04 .0714 Ln(Cumulative Number of Spin-outs) .40 .14 .31 .47 -.19 .02 -.04 -.02 .11 .08 .09 .11 .8815 Dummy: 1 Year After Spin-out .06 .07 .03 .03 .00 .02 -.01 .00 -.06 .02 .05 .04 .44 .4216 Dummy: 2 Years After Spin-out .04 .05 .05 .05 .00 .05 -.01 -.05 -.05 .02 .02 .03 .35 .30 -.0517 Dummy: 3 Years After Spin-out .03 .01 .06 .06 -.01 .05 -.05 -.05 .00 -.02 .04 .04 .32 .25 -.04 -.0318 Dummy: 4 Years After Spin-out .09 -.03 .09 .11 -.03 .06 -.04 -.02 .04 -.01 .00 -.02 .29 .23 -.04 -.03 -.0319 Dummy: 5 Years After Spin-out .12 .02 .10 .10 -.01 .06 -.08 -.05 .01 -.02 -.01 .02 .26 .21 -.03 -.03 -.03 -.0220 Dummy: 6 Years After Spin-out .15 .03 .13 .12 .00 .04 -.06 -.02 .02 -.02 .00 .00 .23 .21 -.03 -.03 -.02 -.02 -.0221 Dummy: 7 Years After Spin-out .14 .02 .13 .13 .01 .05 -.05 .00 .02 .02 .01 -.01 .22 .17 -.03 -.02 -.02 -.02 -.02 -.0222 Dummy: 8 Years After Spin-out .12 .03 .12 .14 -.02 .03 -.03 .04 .03 .04 .02 -.01 .20 .16 -.03 -.02 -.02 -.02 -.02 -.01 -.0123 Dummy: 9 Years After Spin-out .10 -.03 .10 .15 -.06 .05 .01 .00 .05 .01 .00 .02 .17 .16 -.02 -.02 -.02 -.02 -.01 -.01 -.01 -.01