Product Innovation and Survival in a High-Tech
Industry
Roberto Fontana, Lionel Nesta

Paper to be presented at the 2009 Comparative Analysis of Enterprise Data
(CAED) Conference, Hitotsubashi University, Tokyo, October 2 - 4, 2009

R. Fontana
Department of Economics, University of Pavia, Via San Felice 5, 27100, Pavia, Italy and KITeS - Bocconi
University, Via Sarfatti 25, 20139, Milan, Italy. Tel: +39 02 58 36 30 37; Fax: +39 02 58 36 33 99.
e-mail: roberto.fontana@unibocconi.it

L. Nesta
Observatoire Français des Conjonctures Economiques, Departement de Recherche sur l'Innovation et laConcurrence, 250, rue Albert Einstein, 06560 Valbonne - France. Tel: +33 (0) 4 93 95 42 39; Fax: +33 (0)4 93 65
37 98.
e-mail: lionel.nesta@ofce.sciences-po.fr

Abstract

We investigate the relationship between product innovation and firm survival

for a sample of 121 firms in a high-tech industry. We find that location near the technologicalfrontier is an important determinant of firm survival. Firms that are located near the frontier are
also more likely to be acquired than to exit by liquidation if they cannot survive as free-standing
enterprises. This suggests that product location in the technology space acts as a signal of firm
quality. Greater R&D efforts increase the probability of surviving; in the event that the firm does
exit, however, its R&D efforts do not significantly influence whether it exits via acquisition orexits via liquidation.

Keywords

Market positioning, Quality frontier, Firm survival, Competing risk
model, High-technology industries

JEL Classification

L10 – O33 – D49

1 Introduction
Why do innovators exit the market? And how do they exit, by liquidation or by being
acquired? Our answer is based on the dual role of product innovation in shaping thesurvival rate of firms. On the one hand, the dominant effect of successful productinnovation should be to increase a firm's survival rate. On the other hand, successful
product innovation may provide an incentive for competitors to acquire the innovator,
either because the latter is a threat to the incumbents, or because it represents anopportunity to assimilate rapidly a bundle of technical competencies that otherwise
would be time consuming to develop in-house. Therefore, the second order effect of
successful product innovation should be ultimately to shorten a firm's life duration.

We focus on 121 firms that form the core of the worldwide local area networking (LAN)switch equipment industry. We define successful product innovation as improvementthat is located at or near the technological frontier of the market. Having information on
the firms' mode of exit, by liquidation or by acquisition, we run a competing risk model
to investigate the relationship between distance to the technological frontier and exit byliquidation or by acquisition. We find that successful innovation has a positive impact
on the firm's life expectancy. However if a successful innovator exits the market, it doesso (as would be expected) by being acquired rather than by liquidation. This result
suggests that mergers and acquisitions are key to molding the boundaries of firms,
determining market competition, and ultimately shaping the dynamics of industries.

The paper is organized as follows: Section 2 provides a review of the literature. In
Section 3 we provide some background information on the LAN switch industry,
describe the data sources and the variables that will be used in the empirical analysis,

and develop the econometric model. Importantly, Subsection 3.2 presents the method of
hedonic prices that are used to measure the distance to the technological frontier as aproxy for successful innovations and firm quality. Section 4 discusses the results. Section
5 concludes.

2 Literature Review
Schumpeterian competition as a process of innovation and selection is increasingly
viewed as the key to achieve sustained aggregate economic growth, by screening out theleast innovative firms and promoting the most agile ones (Caves, 1998). Wheninnovation is understood in terms of productivity growth, it has been shown that exiting
firms are mostly concentrated in the lowest part of the productivity distribution,suggesting that markets contribute to aggregate productivity growth by rightly selecting
against inefficient firms (Baily, Hulten, and Campbell, 1992).1

When innovation is understood more directly, for example, in terms of R&D
investments or new product innovation, a more complex pattern emerges. In fastchanging industries, soon after entry firms have a lower probability of survival, but once
the initial period has been passed, their life expectancy increases significantly(Audretsch, 1995; Audretsch and Mahmood, 1999). Cefis and Marsili (2006) find that
innovative firms are more likely to survive than are non-innovative firms. Finally, thetiming of innovation (Christensen, Suarez, and Utterback, 1998), commercial strategy
and relatedness among business lines (Mitchell, 1991; Willard and Cooper, 1985), and
1 A non-exhaustive list of contributions includes, among others, Haltiwanger (1997), Foster, Haltiwanger,

Krizan, Hulten, Dean, and Harper (2001) for the United States, Griliches and Regev (1995) for Israel, and
Aw, Chen, and Roberts (2001) for South Korea and Taiwan.

the nature of the technological regime (Audretsch, 1991) have a strong influence on thelife duration of new firms. On the whole, the idea common to all of these contributions isthat innovative firms should grow faster, be more profitable, and ultimately survive forlonger (Geroski, 1995).

Alongside this neat stylized fact, however, there are many examples of innovative firmsthat fail to survive. Exit is far from being a homogeneous event. Although most firms
exit by liquidation, some exit as a result of acquisitions. Exit by acquisition may be theresult of innovators’ being “too successful”, at least from a technological viewpoint. For
competitors, they represent a threat and also an opportunity to acquire valuable
intangible capital and distinctive skills.

But as of today, few analyses have investigated the case of firms that are innovative but
fail (Schary, 1991), and even fewer have examined the links between innovation andmode of exit (Siegfried and Evans, 1994; Perez, Llopis, and Llopis, 2005). To ourknowledge, the contribution by Cockburn and Wagner (2007) is the only one to address
this issue explicitly. It looks at the survival of a sample of 356 Internet-related firms in
the aftermath of the dot-com bubble. Employing patent applications as an indicator ofinnovativeness, they find that possessing a large patent portfolio both increases theprobability of survival and decreases the probability of exiting via merger or acquisition.
Only those firms with a large share of highly cited patents are more likely to exit byacquisition. Given that citations are a good indication of patent quality (Hall, Jaffe, andTrajtenberg, 2005), this switch in the sign reveals that acquired companies provide
intangible capital of economic value.2

In this paper, we focus on product innovation. Product innovation is interesting forseveral reasons. On the one hand, product innovation may confer to firms a competitive
advantage, which boosts the life duration of successful innovators. A substantial
theoretical literature (Bonanno, 1987; Brander and Eaton, 1984; Schmalensee, 1978)
explains how firms strategically locate their products with respect to competitors. Less
evidence has been provided on the relationship between such strategies and firm
survival.

Stavins (1995), for instance, analyzed the determinants of entry and exit in a sample ofpersonal computer (PC) models and firms between 1976 and 1988. She found thatmodels that are produced by old firms are more likely to survive and that moreinnovative firms experience higher survival rates. Greenstein and Wade (1998) carriedout a similar study on a sample of computer mainframes between 1968 and 1982.
Employing duration analysis, they found that older models had a lower chance ofsurviving in the market. Interestingly they also found that the hazard rate of the single
model increases in the number of models in the adjacent market segment, whichsuggests, therefore, that location in crowded markets negatively affects the likelihood of
product survival.
2 Yet this important result is only weakly significant from a statistical viewpoint, and hard to interpret froman economic one. As the authors acknowledge, the correlation between patenting and firm duration may
reflect unobserved firm characteristics such as the quality of the firm's products and intangible assets otherthan the technology itself.

On the other hand, product innovation is also a signal of firm quality (Bontems andMeunier, 2006). Think, for example, of a market in which products are distinguished on
the basis of their quality. High quality products are those located near the technologicalfrontier; thus, their market positioning reveals the quality of firms. In this context, highquality firms are very likely to become acquisition targets for at least two reasons. First,
acquiring high quality firms represents an opportunity for competitors to improve andexpand their own productive resources quickly (Karim and Mitchell, 2000). Second,acquisition may be used to exclude potential rivals from preferential positions in themarket. In this case, the goal of the merger is not to increase productivity or researchefficiency, but to increase market power (Salant, Switzer, and Reynolds, 1983), or even topreempt mergers by rival companies (Brito, 2003).

The above remarks become even more relevant for highly dynamic sectors. In suchsectors, young firms are more likely to be acquired by incumbents because of the threat
they pose to them (Grandstrand and Sjolander, 1990). Filson and Gretz (2004) looked at
product innovation in the case of the hard disk drive industry. They found that productgeneration and the maturity of the firm are important determinants of firm success.
Moreover, they found that the type of outcome (i.e., firm growth versus acquisition) also
depends on the type of product innovation done by the firm. This paper provides an
empirical investigation of the firm-level attributes that determine a firm’s exit from themarket. By measuring product innovation on a vertical scale, we characterize each firmby its distance to the technological frontier in a rapidly changing industry: the LAN
switch industry.

3 Survival and Innovation in the LAN Switch Industry
3.1 Schumpeterian Competition in the LAN Switch Industry
The Local Area Networking (LAN) switch equipment industry began in 1990 with theinvention of the first switch for data communication. The LAN switch industry isinteresting because its evolution provides the typical characteristics of Schumpeteriancompetition.

First, entry in the industry was initially slow, but it dramatically increased after 1993.
The swarm of entrants consisted of three different types of firms: Incumbents fromLAN-based markets (i.e., routers and hubs) entered the switch industry. Second, there
was participation from incumbents outside the industry, but with experience intelecommunications, in semiconductors, or in the computer industry. Finally, there were
new firms searching for new opportunities. These firms were generally highly
innovative and founded by entrepreneurs who were either former academics or former
employees in the LAN industry.

Second, entry was mainly driven by the evolution of the technology itself. This evolution
opened up opportunities for establishing new firms in emerging new segments (Kenneyand von Burg, 2000) near the technological frontier and/or away from incumbents(Fontana and Nesta, 2006), which led to the opening of two segments. The high-end
segment included products that were characterized by high performance, targeted tocustomers with large networks. The low-end segment encompassed lower performanceswitches that were targeted to customers with small networks. Hence in the low-end
segment, manufacturers competed mainly on price, whereas in the high-end segment,

competition was mainly based on the constant search for technical excellence andincreased performance.

Third, exit was the result of a severe process of selection. Indeed, market polarization led
to consolidation and to an increase in the rate of exit. However, among the firms thatexited the industry, the vast majority were new firms, which were ultimately acquiredby incumbents. For many of the new firms the chances of turning a new venture into alarge firm were very few. In many cases, new companies usually revolved around asingle innovative product or technology. Acquisition usually entailed the purchase of
the new firm in a stock swap, followed by integration of the product as well as thetechnology (Kenney and von Burg, 2000).

In this paper we take the quality of a firm as the distance of its product(s) from thetechnological frontier. Our empirical work is based on the exhaustive list of 536 switch
products that were introduced by 121 companies. These firms were identified as activein the industry precisely for having introduced at least one switch in the LAN switch
market.3 For each new switch that was introduced we have information on its list price,
its technical characteristics, and the date of market introduction. Data on technicalcharacteristics and prices for switches, and also for hubs and routers, were obtained
from an original dataset of 1,825 LAN products (536 switches, 535 hubs, and 754 routers)that were marketed between 1990 and 1999. This dataset was constructed using
information from specialized trade journals (Network World and Data Communications),
which periodically publish Buyers' Guides and details on new product introductions.

3 Companies that failed to introduce a switch product are thus ignored.

The set of 121 companies forms the population of firms that were active in the LAN
switch industry. For each firm in our dataset we have information on the date of entry
into and the date of exit from the switch industry and the number of switches that wereintroduced. Information on the firms’ entry and exit dates was gathered from a variety
of sources, such as the D&B Million Dollar Database and Lexis-Nexis. Additional
information on the firms' backgrounds and founders was gathered by searching publicly
available databases that aggregate news and press releases, such as ABI-Inform, andannual reports gathered from the Thomson Research (Global Access) database.
Information on the type of exit (i.e., whether a firm survived or exited either byacquisition or liquidation by the end of the period) was obtained from a review ofannouncements in the specialized trade press and information contained in the
CORPTECH database.4 After consolidation we have a sample of 121 firms that together
marketed a total of 503 switch products, including 33 switches for which we lack sometechnical characteristics.

The rapid growth of the LAN switch industry in the 1990s gave rise to a large number ofboth entries and exits. Of the 121 companies that entered the industry, only 38 still
existed in 2005; 15 had exited through liquidation (i.e., they failed) and 68 had beenacquired by a third party.
3.2 The Three Components of Observed Product Prices
The goal of this section is to provide a measure of firm quality, price strategies, and firm
reputation. We assume that firm quality is captured by the quality of its products and4This information was double checked, with press communications released by manufacturers.

synthesized by the distance of its product(s) to the technological frontier. Our
methodology is based on the decomposition of the observed price into threecomponents: distance to technological frontier, firm reputation, and price strategies all
being conditional on product characteristics. Table 1 provides the list of productcharacteristics together with their summary statistics.

[Table 1 about here.]

Our procedure follows Stavins (1995) and reduces the multi-attribute structure -- i.e., thetechnological characteristics -- to a single-dimensional measure of product quality.
Assuming independence across product technical characteristics, we project them onto alinear scale as follows:

qm = ∑ β j z jm(1)j

Eq. 1 suggests that quality q of model m can be measured as the weighted sum of its
characteristics. The weights βj represent the marginal value of characteristic j that bothconsumers and producers place on the jth attribute. These weights are approximated byregressing observed prices (deflated to 1996 US dollars using the sector-specific deflator
for telecommunication equipment provided by the US Department of Commerce,
Bureau of Economic Analysis) on characteristics, as follows:

pmit = α + ∑ β j z jm + α t + ε mit(2)jwhere pmit is the log is the observed price for model m introduced in the market by firm i
at time t, α is a constant, and αt is a time fixed effect. Table 2 provides the results fromthe hedonic regression. With almost 70% of the variance of prices explained, the overall
fit is satisfactory although a substantial part of the observed prices (30%) is due tofactors other than those introduced in the regression. This may in turn be due to omitted
product attributes and pricing that reflects changes in demand.

[Table 2 about here.]

Whereas the observed prices embody measurement errors reflecting various factors suchas changes in demand, promotional discounts and other non-quality components(Stavins, 1995), the predicted price p̂ reflects (by construction) the quality q of the
product. Thus we posit:

qmit = pˆ mit(3)

Eq. 3 states that ranking predicted prices is tantamount to ranking products according totheir quality. However, in order to account more properly for product quality, weamend Eq. 2 in two ways. First in Eq. 2 the estimated weights are constrained to beconstant over time, whereas the technology in the switch market is likely to have
evolved over time. This suggests that, given significant changes in product quality in the
1990s, the pooled regression may produce inexact weights. Therefore, we interact all
explanatory variables with annual dummy variables, in order to allow the weights βj tovary with time. Second we include a firm fixed effect μi to control for heterogeneity inthe firms' pricing practices.
For example, positive values of μi can be interpreted as persistent (apparent) overpricing: a firm mark-up beyond and above comparable values. The important point hereis that values of μi provide information on the firms' pricing practices, not on productquality, which may be related to reputation, market power, and other unobserved
persistent firm characteristics. Therefore, we amend Eq. 2 as follows:

p′mit = α + ∑ ∑ β tj (ztjm × α t ) + α t + μt + ε mit
t

(4)

j

Including the full vector of explanatory variables as specified in Eq. 4 yields an increased
R2 of 0.85, implying that accounting for changes in the marginal values of productcharacteristics and firm mark-ups explains a significant share of the variance ofobserved prices in the LAN switch market.

We use Eq. 4 to decompose the observed price into three components. The firstcomponent is based on product quality and is used to compute the firm's distance to thefrontier. Amending Eq. 3 by subtracting μi from the predicted price p̂ yields:

q′mit = pˆ ′mit − μi

(5)

We use estimated product quality q′ to compute distances of products from the frontier;that is, we rank products in a vertical product space. To do so, for every product wecompute its distance to the frontier as follows:
f
′
d mit
= max(qt ) − qmit

(6)f
′ is the quality of model m by firm i in year t. The higher is d mit
where qmit
, the farther isthe product from the frontier. Because firms can introduce several products in a given
year, we retrieve for each multi-product company the minimum distance to

[ ]

f
.
technological frontier, that is, we compute DISTANCE TO FRONTIER as: d itf = min d mit
itThe second component of the observed price is the firm fixed effect μi. Indeed, a positive
FIRM EFFECT is likely to reveal a persistent apparent overpricing strategy by firms, which
may indicate market power, reputation, unmeasured quality attributes, and the like.5
The last component of the observed price exploits the price residual of product m
introduced by firm i at time t, ε mit , as a measure of pricing strategy. Provided that all of
the relevant quality attributes are accounted for by the explanatory variables, theresiduals reveal which firms are offering apparent "bargains" (i.e., negative residuals)
and which firms are offering apparently over-priced products (i.e., positive residuals).
This makes a strong case for including the residuals among the explanatory variables inthe duration analysis. For multi-product firms, we use the mean PRICE RESIDUAL:

ε it = mean[emit ]it .
All three components -- DISTANCE TO FRONTIER d itf , FIRM EFFECT μi and PRICE RESIDUAL

ε mit -- are then included in the vector of explanatory variables for firm survival. Basedon previous results (Fontana and Nesta, 2006), we assume a non linear relationshipbetween product quality and survival, and we enter both DISTANCE TO FRONTIER and its
squared value. Importantly, we do not have a strong prior belief as to whether toinclude or exclude higher order effects for other price components. A preliminary
exploration of the data suggested that both DISTANCE TO FRONTIER d itf and its squared

5

Note that mono-product companies are not included in the vector of firm dummy variables. In this case,

we do not estimate μi instead, we set it arbitrarily to 0. Of the 121 companies in our sample, this concerns 46
firms.

value should be used as explanatory variables, whereas higher order effects for otherprice components (μi and ε mit ) were not useful.

3.3 Control Variables
Recent studies have stressed that firm survival is also related to the mode of entry(Buenstorf, 2007; Thompson, 2005). We use information on founders' backgrounds andfirms' main activities to assign to firms a status defining their mode of entry. We define
SPIN-OUTS as those firms whose main line of business is the LAN industry. This includes
cases where the founder(s) were already employed in the LAN industry in the year prior
to the founding of the new company, and university spin-offs. We define DIVERSIFIERS as
firms whose founder(s) had no prior experience in the LAN industry and whose mainline of business was outside the LAN industry (i.e., computer, semiconductor, etc.) at thetime of entry into the switch market.

We identify three possible fates for firms: Failure (i.e., liquidation), Buy-out (i.e.,acquisition), and Survival. More than two thirds (69%) of the firms in our sample exited
the LAN switch industry after entry. Spin-outs exit mostly as a result of acquisition.
Among the survivors, Spin outs are the largest share of the total, although almost half ofDiversifiers survive. This preliminary evidence suggests that, although firms withgreater pre-entry experience constitute most of the survivors, those that exit generally
tend to be bought-out. This indicates that their fate may be linked to their status and thatexit should not be treated as a homogeneous event.

In addition to pre-entry experience, the literature on firm survival highlights that firm
size and age are among the most important determinants of firm selection (Dunne,Roberts, and Samuelson, 1988; Mata and Portugal, 1994; Audretsch, 1997). The size
variable (SIZE) is constructed as the logarithm of the total number of employees at thetime of entry. We define AGE AT ENTRY as the number of years as of the time of entry
into the switch market, since the firm’s institutional birth.6

We also add R&D Intensity (R&D INTENSITY) as a measure of innovative capital. Thisvariable is constructed as the share of R&D expenditures in total revenues, at the time ofentry, and may be a proxy for knowledge capital at the time of entry, similar to
Cockburn and Wagner (2007) patent stock measure.7 Finally, to capture the effect of theindustry’s life cycle on firm survival (Klepper and Simmons, 2005), we add two industry
level variables: ENTRY RATE and EXIT RATE are the number of new firms and the numberof firms exiting the switch industry respectively. Both variables are lagged one year to
avoid endogeneity biases.

[Table 3 about here.]

Summary descriptive statistics for these explanatory variables are reported in Table 3. In
all regressions, we consider 121 firms, of which 83 eventually exit the industry. All firm

6This is different from studies that use census data, in which the age of the firm is generally taken as thenumber of years of presence in the census (i.e., the dataset).
7

It is important to note that after accounting for revealed firm quality through product differentiation, modeof entry, intangible capital and firm size, the variable AGE AT ENTRY captures two effects: (i) the combined
influence of availability of financial resources and diversification in the firm's product portfolio; (ii) a
residual unobserved heterogeneity effect, related essentially to the firm's past experience in doing business,
implying that older firms are more able to survive in their economic environment.

level variables take values at the time when the firm enters the industry. All duration
models include a full vector of entry-year dummy variables. Expanding the dataset bytime intervals yields a total of 600 observations.

3.4 Modelling Firm Survival
We develop two sets of econometric models to evaluate the factors that affect the fate ofinnovative firms. First, we estimate a discrete time duration model to explain theprobability of exit (Prentice and Gloeckler, 1978). Suppose there are firms i = 1, … ,N,that enter the industry at time t = 0. The hazard rate function for firm i at time t and t =1,,T is assumed to take the proportional hazard form: θ it = θ 0 (t ) ⋅ X it′ β , where θ 0 (t ) is thebaseline hazard function and Xit is a series of time-varying covariates summarizing
observed differences among firms. The discrete time formulation of the hazard of exit
for firm i in time interval t is given by a complementary log logistic function such as:

ht ( X it ) = 1 − exp{− exp( X it′ β + θ (t ))}(7)

where θ (t ) is the baseline hazard function relating the hazard rate ht(Xit) at the tth
interval with the spell duration (Jenkins, 1995).

Second, we apply a competing risk model to account for heterogeneity in firm exit. In
this second set of models, we relax the assumption of homogeneous exit by accounting
for the mode of exit, namely firm liquidation, or firm acquisition. The extension of thestandard pooled duration model to two exit forms is referred to as the Competing Risks
Model (CRM) (Jenkins, 2004). The two destinations are treated as independent, so thatthe probability of exit by liquidation is assumed not to depend on the probability of exitby acquisition. In practical terms, the independent competing risk framework treats both

exits as right censored (Lancaster, 1990; Jenkins, 2004) allowing the full set of parameters
to vary according to the different destinations:ht (X ijt ) = 1 − exp{− exp(X it′ β j + θ j (t ))}(8)

where, in our case j = 1 or 2 respectively, depending on the mode of exit (liquidation
versus acquisition). We test whether these two forms of exit can in fact be treated as
independent. This is equivalent to testing the null hypothesis of equality for all[parameters. The test statistic is 2 ln (LCR ) − ln (LSR ) −∑ n ln( p )], where ln(L ) is the
j

j

j

CR

maximized log-likelihood from the competing risk model (the sum of those from thecomponent models), ln(LSR) is the maximized log-likelihood from the single-risk model,
nj is the number of exits to state j, and p j = n j∑ n , where there are j = 1, … , jjj

destination states. This test statistic is Chi-squared distributed with degrees of freedom
equal to the number of restrictions. Additionally, we perform the Hausman test forindependence of irrelevant alternatives test (IIA) to test whether each exit can be treatedas independent from all other alternatives.

4 The Determinants of Market Selection in the LAN Industry
We start our empirical investigation by accounting for different types of exit: exit byliquidation, by acquisition (being acquired), or by either liquidation or acquisition. Table
4 reports the results.

[Table 4 about here.]In Column (1), we account for exit by liquidation versus survival and acquisition. The
striking result lies in the pronounced effect of distance to frontier on liquidation. The
first order effect of DISTANCE TO FRONTIER is positive, indicating that firms capable ofbeing close to the frontier have a relatively higher probability of surviving. However, thenegative and significant coefficient of the squared value suggests that the relation is nonlinear and that exits mainly occur among firms located in the middle of the market. We
note a similar and significant - albeit less pronounced - effect of DISTANCE TO FRONTIER
on the probability of being acquired (Column 2) and on the probability of liquidation or
acquisition (Column 3).

The extrema are found at the 30th, 25th and the 26th percentiles for Column (1) to (3)respectively. This implies that for more than two thirds of the firms in the industry,competition based on quality - rather than price – will prove harmful. This suggests thatcompeting in the high end of the market is a very risky strategy, for only a fraction ofthese companies will actually benefit from innovation. This is a good reflection of thesituation in the switch market during the 1990s, which was polarized between high-end
and low-end. At the high end of the market firms compete to be at the frontier and thosethat lag behind do not survive. At the low end competition occurs at the boundaries
with the high-end of the market, where firms struggle to survive, while firms serving
niches at the bottom of the low end have a higher probability of surviving.

The non-monotonicity of DISTANCE TO FRONTIER is puzzling. In the end, why don't
medium-quality firms locate in the low-quality end of the market and outperform "lowquality" firms? To answer this, one should take a broader look at the LAN industry,
which also includes routers and hubs. Whereas routers are more technologicallysophisticated than switches, hubs constitute products of lower technological quality thatcompete directly with low-quality switch products. Medium firms who are strong in the
hub market do not enter because they do not want to cannibalize their own hub
business. This, in turn, constitutes an incentive for firms to move up the quality ladderin the switch market, where indeed selection is fiercer.

It is worth noting that the other price components convey little information on firm
liquidation. The price residual displays the expected sign in Column (1): firms with
overpriced products are more likely to quit the scene. A significant and negative firm
effect is observed on the probability of being acquired (Column 2). One plausible
interpretation is that firms with high reputation and/or market power are more
profitable and hence less exposed to acquisition. On the whole, these two components ofobserved prices seem to be only weakly connected with a firm’s fate.

The remaining variables behave as expected: the variable SPIN-OUT enters negatively
and significantly in all types of exit, indicating that firms with higher pre-entry
experience have a relatively lower probability of exiting the switch industry by either
liquidation or acquisition. For both cases, the marginal effect is quite large, since itcorresponds to a reduction by a factor of 3 (Column 1) and 4 (Column 2) of theprobability of exiting. R&D INTENSITY, SIZE, and AGE AT ENTRY are all significant
determinants of the firm's fate. Notably, R&D INTENSITY enters with a significant
negative sign in all types of exit, suggesting that possessing a higher stock of innovative
capital reduces the probability of leaving the industry. AGE AT ENTRY indicates that older
firms have a lower probability of leaving the industry by liquidation or being acquired.

Thus three important conclusions can be drawn: (i) the effect of product quality on firm
survival is very large; (ii) the effect of product quality on firm survival is highly non
linear, in line with the idea of a two-tier market structure where firms located in the
middle of the market are the least likely to survive as free-standing enterprises; and (iii)
there is only weak evidence of the role of the price residual and the firm effect on the
firm's fate. Altogether the sample is behaving as expected and is robust to alternative
specifications of the hazard rate function and to the inclusion of firms’ unobserved
heterogeneity.8

Still, the above results remain somewhat disappointing. Although the magnitudes of all
variables are similar across types of exit, they do not reveal strong differences in thedeterminants of exit by liquidation and exit by acquisition. Moreover, they do notaccount for the fact that the sum of the probability of each type of exit, conditional on all
right-hand-side variables, should add up to unity. In fact, one should analyze therelationship between firm strategic positioning and exit types simultaneously. We
provide this type of analysis using a multinomial logistic competing risk model. Table 5
displays the results of the competing risk model. Column (4) reports the results of thecomparisons between the alternatives of exiting by liquidation and surviving. Results of
8

These results are robust to alternative specifications of the hazard rate function and to the inclusion offirm’ unobserved heterogeneity. Moreover, the second-order polynomial of DISTANCE TO FRONTIER wastested against higher order polynomials. The results clearly indicate that the second-order polynomial is themost suited for the analysis. Lastly, we measured the distance to the frontier using observed prices andpredicted price with firm fixed effects. The result suggests that with the exception of observed prices, whichcome out not significant, the direction of the relationship between distance-to-frontier and survival is robust
across measures.

the comparison between exit by acquisition, and survival are reported in Column (5).
Column (6) compares the two alternatives of exiting by acquisition and exiting byliquidation.

[Table 5 about here.]
Before we examine the results, it is important to test for the existence of significant
differences in exit types. The Hausman test of independence of irrelevant alternatives
implies that outcomes "liquidation" and "survival" (Column 4) and outcomes"acquisition" and "survival" (Column 5) are independent of the third alternatives. Thisreveals that all outcomes are independent of other alternatives and each binomial choicemust be investigated for its own sake. Next, the LR test examines the null hypothesisthat any pair of outcome categories can be combined, due to the lack of significance inparameter estimates (all estimates are nil). Clearly, the results are in line with theHausman test, since for all alternatives being examined, we reject the null hypothesisthat coefficients associated with a given pair of alternatives are 0.

As a consequence, there are significant differences in the factors that drive the various
outcomes in general, and between liquidation and acquisition outcomes especially. Now
looking at the parameter estimates in Column (4), DISTANCE TO FRONTIER has a positiveand significant coefficient, suggesting that firms that are located relatively farther awayfrom the technological frontier do in fact have a higher probability of exiting byliquidation than of surviving. The relationship is non-linear as indicated by the negativeand significant coefficient of (DISTANCE TO FRONTIER)2. The same applies to exit byacquisition (Column 5), although as reported in Table 5, the effect is less large. Choosing
to locate in the middle of the quality scale is the most dangerous strategy for firms,

while positioning at either end - near the frontier or far from the frontier - may be theright strategy to survive as a free-standing enterprise.

A striking result emerges from the negative significance of DISTANCE TO FRONTIER in
Column (6). The negative parameter estimate suggests that firms that are located close to
the frontier have a higher probability of being acquired than of exiting by liquidation.
This is confirmed by the coefficient (DISTANCE TO FRONTIER)2, which is positive andindicates that the probability of being acquired is higher for firms that are located very
close to the frontier, decreases as distance increases, and then increases again for thosefirms located farthest away. Overall, both of these results confirm that acquisitions aremainly triggered by the need to appropriate the technology of rival firms, enrich firm
product portfolios, and improve research activities. The other price components are
insignificant, although in Columns (4) and (5) they are consistent with Table 4 in theirmagnitude and significance. The important conclusion here is that pricing decisions byfirms disclose different sorts of information about the companies. But of the threecomponents that we have identified in this paper, only the firm's capacity to locate near
the technological frontier plays a significant role in shaping the firm's fate.

Coefficients for AGE AT ENTRY and SIZE are all negative and significant (Columns 4 and5), indicating that bigger firms, endowed with better availability of financial capital havea relatively higher probability of surviving. The coefficient of SPIN-OUT is also negativeand significant, indicating that possessing pre-entry experience decreases the probabilityof exiting by liquidation rather than surviving. Finally, the direction of the effect of R&D
intensity is as expected (i.e., positive on survival and negative on both acquisition andliquidation) and supports the general idea that a large stock of intangible capital isimportant for firm survival. Note that although not significant, firms with a larger R&D
effort seem more attractive for acquisition relative to liquidation. However possessing alarge stock of intangible capital also makes potential targets more expensive to purchase(Hall, Jaffe, and Trajtenberg, 2005). In a context where knowledge obsolescence isextremely rapid, this may in turn inhibit acquisitions.

Altogether, these estimates provide new results that enrich our analysis and shed somelight on our initial hypotheses. When exit is treated as a homogeneous event, strategic
product positioning is an important determinant of firm survival, in the sense that being
located close to the frontier increases the probability of surviving. However, among
exiting firms, those that are located close to the frontier are more likely to be acquired;thus, position relative to the technological frontier seems to act as a signal of firm
quality.

Table 6 displays the predicted probability of each mode of exit.9 We observe that for the
median firm with average entry year and entry type, the overall liquidation rate is 5%,
the probability of acquisition is 21% and the survival probability is 74%.10 At first sight,
the overall probability of exit, either by liquidation or by acquisition, seems low: themedian firm has three chances out of four of surviving as a stand-alone firm.

9

We first computed the predicted probability using the median values of the continuous variables (time,age, size, distance to frontier, R&D intensity) and the mean values of the dichotomous variables (entry-year
dummy variables and entry type). All marginal effects were computed as discrete change, holding all otherindependent variables constant at their mean or median values.
10

Note that the sum of the predicted probabilities equals unity.

Comparison of this result with the observed overall exit rate for the whole period 19902005 suggests the presence of large effects at the margin for each independent variable.

We therefore compute the marginal effects of all significant variables. We compute thediscrete change in the predicted probability by imputing a variation of two quintiles
around the median value of each continuous variable - i.e. from the 30th to the 70th
percentile - holding all other variables constant. Because of the non-linearity of the effectof distance from the frontier on survival, we compute the change in the predictedprobabilities while at the frontier, at the 30th and at the 50th percentiles. For the
dichotomous variable SPIN-OUT, we computed the discrete change from being adiversifier to being a spin-out. All changes are reported as absolute and relative change
in probability. We have two important observations:[Table 6 about here.]First, we observe that Age acts differentially on the mode of exit from the industry.
Although the marginal effect of Age on survival confirms the role of post-entry
experience, the mode of exit (acquisition versus liquidation) matters. Firms that exit as aresult of liquidation have survived longer in the market than have firms that are
acquired. Our interpretation is related to the presence of sunk costs and assumes that, on
average, exit by acquisition has lower sunk exit costs than does exit by liquidation.
Based on this interpretation, firms that are struggling in the market should always preferto exit by acquisition than by liquidation. These firms would enter the mergers andacquisitions market, and offer their quality at a given price to potential buyers. In otherwords, they enter a market in which firms compete to be acquired, the choice of exit byliquidation being a last resort.

Second, the presence of a non-linear relationship between firm location (relative to thefrontier) and survival hints at the possibility that there are some regions in the quality
space where firms are more at risk. In particular, firms that are located at the 30th
percentile have to cope with the highest probability of exiting by any mode, and of
exiting through liquidation in particular. Firms that are located close to the frontier facean increasing probability of being acquired, since the risk of being acquired peaks at the30th percentile. Thus, competition in the LAN industry is a race in which most players
commit resources to innovate along the quality ladder. But only one firm will make it to
the frontier, whereas others will innovate with products of substantial but lower quality.
Our analysis reveals that these good, but not-good-enough innovators are more exposedto acquisition.

Our analysis has the following important implications. From the viewpoint of theliterature on industrial dynamics, our results stress that firms with higher pre-entry
experience are relatively more likely to be acquired, especially immediately after
innovation has occurred. Recent studies have highlighted the importance of pre-entry
experience for firm survival (Klepper, 2002; Thompson, 2005). Our study, however,provides insights that are relevant to highly innovative sectors.

Our analysis also has implications for the empirical literature on aggregate economic
growth at the industry level (see Baily, Hulten, and Campbell, 1992; Haltiwanger, 1997;
Baily, Bartelsman, and Haltiwanger, 2001, among others). These empirical analyses seekto decompose sectoral - or aggregate – economic growth into an internal firm effect(firms increase their own levels of productivity), an external effect (changes in market
shares across incumbents), and a market selection effect (the impact of firm entry andexit on economic growth). In particular when entrants outperform exiting companies,
market selection contributes positively to aggregate economic growth.11 Because
information on modes of exit is not readily available in large datasets, exit is treated as ahomogeneous event, such that exit by acquisition equates with exit by liquidation. But
since acquired companies are located closer to the frontier than are firms that exit byliquidation, the benefits of market selection may have been underestimated.

Finally, our analysis of innovativeness and mode of exit has also important policy
implications. In dynamic industries, acquisitions are mainly finalized at strengthening
market positions at the expenses of the closest competitors. Our results highlight thattargeted firms are generally young and are endowed with experience that is inheritedfrom skilled founders. This suggests that acquisitions are a means of acquiringknowledge and innovative assets that the buyers do not possess. Evaluating these
implications in terms of welfare gains/losses from these mergers is not straightforward
and beyond the scope of this paper. However, acquired assets are generally costly toreplicate and require time to be developed. The occurrence of these mergers allows theexisting resources to be kept within the economic system while at the same time
avoiding duplication of costs. This is an important aspect that should be taken into

11

With few exceptions (Nishimura, Nakajima, and Kiyota, 2005), the contribution of this market selection is

generally positive. According to textbook economics, this is to be expected since it implies that only the mostprofitable companies stay in the industry.

consideration by antitrust authorities, which usually are more concerned with theanticompetitive effects of mergers and acquisitions.
5 Conclusion
This paper has investigated the relationship between product innovation and firm
survival in a high-tech industry. First, we looked at the hazard rate of firms byconsidering exit as a homogeneous event. We found that firm quality, as captured byfirm location with respect to the technological frontier, innovative capital, pre-entry
experience, age, and size are important determinants of firm survival. Second we haveextended the analysis to the case of heterogeneous exit. We found that, when controllingfor firm level attributes, among firms that exited, those located close to the frontier weremore likely to be acquired, suggesting that position with respect to the technologicalfrontier acts as a signal of firm quality. We also found support for the presence of apositive relationship between the firm’s R&D effort and the probability of surviving.
However, in the event that the firm does exit, its R&D effort does not significantly
influence whether it exits via liquidation or exits via acquisition.

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Table 1: Summary Statistics on the Technological Characteristics of the LAN Switch
Products (N = 503)
Variable
Constant Price
Log of Price
Backplane Capacity
Number of Ethernet Ports
Number of Fast Ethernet Ports
Number of FDDI Ports
Number of Token Ring Ports
Number of 100VG-AnyLAN Ports
Number of ATM Ports
Number of Gigabit Ethernet Ports
VLANs Capability
ChassisFixed Configuration

Mean

Std. Dev.

Min

Max

38257.970
9.490
7.168
2.061
1.396
0.268
0.260
0.044
0.730
0.360
0.797
0.268
0.390

72790.430
1.415
1.808
1.643
1.540
0.732
0.912
0.307
1.285
0.976
0.402
0.444
0.488

402.178
5.997
2.398
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000

52282413.16713.236
6.645
6.645
4.575
5.820
4.174
5.198
4.875
1.000
1.000
1.000

Table 2: OLS Regression on Observed Prices
Backplane Capacity

0.236[0.036]***
Number of Ethernet Ports
0.09[0.028]***
Number of Fast Ethernet Ports
0.04[0.037]
Number of FDDI Ports
0.024[0.060]
Number of Token Ring Ports
0.132[0.046]***
Number of 100VG-AnyLAN Ports
0.248[0.122]**
Number of ATM Ports
0.112[0.042]***
Number of Gigabit Ethernet Ports
0.361[0.055]***
VLANs Capability
0.394[0.099]***Chassis0.899[0.130]***
Fixed Configuration-0.222[0.088]**Constant
8.37[0.389]***Observations503
R-squared
0.699
Dependent Variable: Deflated Product Price.
Robust standard errors in brackets. * significant
at 10%; ** significant at 5%; *** significant at 1%.
Year dummy variables omitted for clarity.

Table 3: Summary Statistics
Variable

Obs

Mean

Std. Dev.

Min

Max

Failure

121

0.12

0.33

0.00

1.00

Bought-Out

121

0.56

0.50

0.00

1.00

Survivor

121

0.31

0.47

0.00

1.00

Spin-Out

121

0.83

0.37

0.00

1.00

Diversifier

121

0.17

0.37

0.00

1.00

Age at Entry

121

9.61

12.54

1.00

84.00

Size (log)

121

6.16

2.16

2.71

13.30

Dist. Frontier

121

2.42

1.09

0.00

4.69

Price Residual ( ε it )

121

-.030

.229

-1.415

1.051

Firm Effect ( μi )

121

-.310

.668

-2.913

2.077

R&D Intensity

121

0.11

0.20

0.00

0.70

Entry Rate ( t − 1 )

600

1.23

2.00

0.00

6.80

Exit Rate ( t − 1 )

600

0.44

0.58

0.00

2.00

Table 4: Firm Entry and the Hazard Rate of Exit in the LAN Switch Industry
Liquidation(1)

Acquisition(2)

Both (1) & (2)(3)-0.228-1.137-1.037[0.378][0.286]***[0.254]***-0.048-0.056-0.062[0.019]**[0.022]***[0.019]***-0.175-0.319-0.275[0.132][0.090]***[0.080]***

4.109

1.346

1.549

[1.295]***[0.575]**[0.555]***-1.028-0.414-0.471[0.314]***[0.146]***[0.141]***

1.433

0.109

0.015

[0.787]*[0.532][0.474]0.182-0.453-0.406[0.400][0.239]*[0.228]*-5.781-1.637-2.048[2.398]**[0.889]*[0.845]**-1.171-1.405-1.474[0.643]*[0.523]***[0.467]***-0.036-0.204-0.237[0.171][0.126][0.121]**-0.074

0.066

0.104

[0.530][0.361][0.349]-0.255

3.17

3.179

[1.901][1.122]***[1.041]***

Number of firms

121

121

121

Number of firm exit

15

68

83

P (Y | X ) = 1

.021

.062

.082

Log Likelihood-97.031-170.486-195.218

Chi-square

44.2***

83.1***

91.9***

Time (Log)Age at Entry
Size (Log)Dist. Frontier(Dist. Frontier)2

Price Residual ( ε it )Firm Effect ( μi )R&D Intensity
Spin-Out
Entry Rate (Lagged)
Exit Rate (Lagged)
Constant

Number of observations: 600. Discrete Time Duration Model with Weibull
Hazard Function. Standard errors in brackets. * significant at 10%; **significant at 5%; *** significant at 1%. All duration models include a full
vector of entry-year dummy variables, not reported here for clarity.

Table 5: Competing Risk Model. The Determinants of the Exit Forms in the LAN Switch
Industry
Time (Log)Age at Entry

Size (Log)Dist. Frontier(Dist. Frontier)2

Price Residual ( ε it )Firm Effect ( μi )R&D Intensity

Spin-Out
Entry Rate ( t − 1 )Exit Rate ( t − 1 )Constant

(4)

(5)

(6)

-0.780-1.354-0.576[0.462]*[0.350]***[0.545]-0.076-0.0750.000[0.024]***[0.024]***[0.031]-0.418-0.451-0.033[0.157]***[0.115]***[0.176]

4.894

1.962

-2.931[1.370]***[0.689]***[1.462]**-1.213-0.571

0.642[0.339]***[0.175]***[0.360]*

1.302

0.202

-1.100[0.835][0.614][0.957]0.090-0.456-0.546[0.424][0.279][0.464]-6.366-2.5223.844[2.681]**[1.034]**[2.780]-2.337-2.1080.230[0.821]***[0.660]***[0.896]-0.291-0.2570.034[0.225][0.170][0.262]

0.224

0.077

-0.147[0.593][0.441][0.680]

4.398

5.583

1.185

[2.341]*[1.682]***[2.578]Log Likelihood-256.3Hausman Test (IAA)

0.0

21.5

LR Test (Combined)

57.9***

93.4***

28.2**

Number of observations: 600. Competing Risk Duration Model. Standard
errors in brackets. * significant at 10%; ** significant at 5%; *** significant at
1%. All duration models include a full vector of entry-year dummy
variables, not reported here for clarity.
(4) Liquidation vs. Survival(5) Acquisition vs. Survival(6) Acquisition vs. FailureTable 6: Marginal Effects of Firm-Level Variables on the Mode of Exit
Liquidation

Acquisition

Surviving

Predicted Probability

0.050

0.210

0.740

Time
th
Predicted at the 30 percentile

0.064

0.393

0.543

Predicted at the 70 percentile
Absolute Change

0.041

0.135

0.824

-0.023-0.2580.281

Relative Change-35.9%-65.6%

51.7%

Age at Entryth
Predicted at the 30 percentile

0.063

0.259

0.678

th

Predicted at the 70 percentile
Absolute Change

0.036

0.148

0.817

-0.027-0.111

0.139

Relative Change-42.9%-42.9%

20.5%

Size
th
Predicted at the 30 percentile

0.062

0.265

0.673

th

Predicted at the 70 percentile
Absolute Change

0.026

0.104

0.87

-0.036-0.161

0.197

Relative Change-58.1%-60.8%

29.3%

0.001

0.077

0.922

th

Distance to FrontierstPredicted at the 1 percentile
Predicted at the 30thpercentile

0.056

0.279

0.665

Predicted at the 50thpercentile

0.040

0.212

0.748

st

th

0.065

0.175

-0.240stth+∞

227.3%-26.0%

percentile

0.063

0.224

0.713

Predicted at the 70 percentile
Absolute Change

0.038

0.192

0.77

-0.025-0.032

0.057

Relative Change-39.7%-14.3%

8.0%

Spin-Out
Predicted for SPIN-OUT=0

0.15

0.517

0.333

Predicted for SPIN-OUT=1

0.035

0.153

0.812

Absolute Change-0.115-0.364

0.479

Relative Change-76.7%-70.4%

143.8%

Absolute Change ( 1 vs 30 )Relative Change ( 1 vs 30 )R&D Intensity
Predicted at the 30thth

The predicted probability has been computed using the median values of continuous
variables (time, age, size, distance to frontier, R&D intensity) and the mean values ofthe dichotomous variables (entry-year dummy variables and entry type). All marginal
effects have been computed as discrete change, while holding all other independent
variables constant at their mean or median values.