THE ROLE OF SUNK COSTS IN THE DECISION TO INVEST IN R&D *
JUAN A. MÁÑEZ-CASTILLEJOa, MARÍA E. ROCHINA-BARRACHINAb,
AMPARO SANCHIS-LLOPISb AND JUAN A. SANCHIS-LLOPISb
Abstract
This paper tests the existence of sunk costs in the firm decision to engage in
R&D activities, taking into account that these costs may differ between small
and large firms, and among different technological regimes. We estimate a
multivariate dynamic discrete choice model using firm-level data of Spanish
manufacturing for 1990-2000. Our results support the existence of sunk R&D
costs. Large firms operating in high technological intensity industries exhibit
the highest sunk R&D costs. Firm characteristics such as export intensity,
labour quality, size, demand, foreign capital equipment, advertising intensity
and legal structure, have a significant influence on the R&D decision.
Key words: R&D, sunk costs, size, technological intensity, multivariate
dynamic discrete choice model.
JEL classification: C23, L60, O33
We appreciate comments and suggestions from participants at the ESEM-EEA Conference
(Madrid, 2004), EARIE Annual Conference (Berlin, 2004), the VII Encuentro de Economía
Aplicada (Vigo, 2004) and the XXVIII Jornadas de Economía Industrial (Granada, 2004) and
helpful comments from Pilar Beneito. Financial support from the Spanish Ministry of Science
and Technology, Project number SEC2002-03812, and IVIE is gratefully acknowledged. We
would also like to thank Fundación SEPI for providing the data.
a Corresponding author: Juan A. Máñez-Castillejo, Universidad de Valencia and LINEEX,
Facultad de Economía, Departamento de Economía Aplicada II, Avda. de los Naranjos s/n,
46022 Valencia (Spain); telephone: 0034 963828356, fax: 0034 963828354, e-mail address:
[email protected].
b Universidad de Valencia and LINEEX, Facultad de Economía, Departamento de Economía
Aplicada II, Avda. de los Naranjos s/n, 46022 Valencia (Spain).
*
1
I. INTRODUCTION.
In spite of the believe that sunk costs exist in R&D activities, the large number
of studies analyzing the determinants of the firm decision to undertake R&D
have not yet explicitly analysed the role of sunk costs in shaping this decision
(see, among others, the works of Levin et al. [1985], Cohen et al. [1987], Cohen
and Levin [1989], or the survey by Cohen [1995]). The development of R&D
activities may involve creating an R&D department, purchasing specific
physical assets and hiring or training specialized workforce, which are startup costs that in turn may be considered, at least partly, as sunk costs.1
According to Stiglitz [1987, p. 889], “most expenditures on R&D are, by their
nature, sunk costs. The resources spend on a scientist to do research cannot
be recovered. Once this time is spent, it is spent”. Martin [1993] argues that
investment in R&D creates an asset –knowledge- which value, if highly specific
and tied to the operations of the firm, will be largely lost upon exit. Thus, the
specificity of investments in R&D activities suggests the existence of sunk
costs associated with these activities.
The theory of contestable markets has emphasized the role of sunk
costs as a barrier to entry the market and so as a key determinant of industry
structure (see, e.g., Baumol and Willig [1981], and Baumol, Panzar and Willig
[1982], and more recently, Martin [2002] and Schmalensee [2004], among
others). However, this theory has not explicitly modelled sunk R&D costs.
Sutton [1991, 1998] has also extensively analysed the relationship between
sunk costs and market structure. Sutton’s theory distinguishes between
exogenous and endogenous sunk costs. Exogenous sunk costs are those
1
In general terms, Tirole [1988, p. 307] defines sunk cost as “specific investment that has no
intrinsic value to other firms (and therefore has no value on a second hand market) and cannot
be allocated to another use within the firm”.
2
outside a firm control, such as the cost of developing a manufacturing plant of
minimum efficient scale (MES). By contrast, endogenous sunk costs are
determined strategically by firms with the aim to enhance consumers’
willingness to pay for their products. Sutton considers R&D spending as a key
endogenous sunk cost determining market structure. The importance of sunk
costs for market structure has been empirically tested in Kessides [1990],
Sutton [1991, 1998] and Robinson and Chiang [1996], among others.2
Within the literature of game theory, a number of papers have modelled
firm R&D competition taking into account the role of sunk costs. These models
consider firms as players in a game with sunk costs and payoffs given by
future discounted profits associated with their decision on R&D. There are two
strands in this literature, i.e., the so-called auction models (e.g. Gilbert and
Newbery [1982)) and the patent races models (Lee and Wilde [1980],
Reinganum [1983, 1985], Dasgupta [1986] and Kaplan et al. [2003], among
others). However, these game theoretical models on R&D competition lie upon
very restrictive assumptions and are highly stylized, so that, together with the
lack of suitable data, there is no empirical testing on their relevance for real
life. A first attempt to empirically test these models of innovation competition
is Czarnitzki and Kraft [2004].
Although it is generally accepted that R&D activities entail sunk costs,
there is a lack of empirical evidence on this relationship. To the best of our
knowledge, the only empirical work providing evidence on the existence of
sunk costs associated with innovating activities is Astrebo [2004]. Using
national survey data on US plants from twenty metalworking industries,
2
The role of sunk costs on firm survival is analyzed by Ghosal [2002], and an empirical test on
the relationship between firm productivity and sunk costs can be found in Fariñas and Ruano
[2005].
3
Astrebo [2004] analyses sunk learning costs associated with the adoption of
new technology.3
The aim of this paper is to empirically assess the role of sunk costs in
the firm decision to undertake R&D activities and to test whether sunk R&D
costs differ by firm size group and industry technological intensity, using firm
level panel data. In order to properly identify the role of sunk costs, we use an
econometric framework that allows controlling for other competing sources of
persistence in the decision to undertake R&D activities, such as underlying
firm heterogeneity, or serial correlation in exogenous shocks. In particular, we
use a multivariate dynamic discrete choice model, where the firm decision to
engage in R&D activities is a function, among other factors, of its previous
history in performing R&D. This model is estimated by pseudo simulated
maximum-likelihood techniques. The data used is a panel of Spanish
manufacturing firms drawn from the Encuesta sobre Estrategias Empresariales
(ESEE, hereafter), for the period 1990-2000.
Our work is the first attempt to empirically analyze the role of sunk
costs in the firm decision to invest in R&D using a dynamic approach and
accounting for a wide range of firm characteristics. There is an extensive
literature on the relationship between size and R&D (see, e.g. Cohen [1995],
Cohen and Klepper [1996], Lee and Sung [2005], and references therein). Most
studies conclude that larger firms carry out larger R&D investments. As a
substantial part of the R&D investment is sunk, we expect that larger firms
will incur larger sunk R&D costs as compared to small firms. Therefore, we
allow the sunk R&D costs to differ between small and large firms. In addition,
we consider firm sunk R&D costs to be different according to the technological
3
These sunk costs refer to collecting information on the new technology, skill acquisition,
organizational changes and adjusting the technology to the idiosyncrasies of operations.
4
intensity of the industry in which the firm operates. We group firms into low,
medium and high technological content, following the traditional and revised
OECD industry classification (OECD, 2002).4 This classification groups
industries according to their patterns of generation and acquisition of
technology. We consider that differences in the technological content of these
industries will be associated with different levels of sunk R&D costs.5
To anticipate our results, we find evidence on the existence of sunk
R&D costs in the decision to undertake R&D activities and that these costs are
higher for large firms and/or firms in high technological intensity industries,
as compared to small firms and/or firms in low-medium technological
intensity industries. We also find that those firms that cease performing R&D
activities suffer a rapid depreciation of their experience, independently of the
size group and/or the technological intensity regime. Furthermore, we find
that firm/market characteristics such as export intensity, labour quality, size,
demand, foreign capital equipment, advertising intensity and being a limited
liability corporation are factors that increase the firm propensity to undertake
R&D activities.
The rest of the paper is organized as follows. Section II describes the
data and the patterns of R&D activities for Spanish manufacturing. In section
III, we introduce a model of entry and exit in R&D activities with explicit
consideration of the role of sunk costs, we discuss our estimation methodology
and examine the determinants of the firm decision to invest in R&D. The
estimation results are summarized in section IV. Finally, section V concludes.
4
In the rest of the paper and for convenience, we refer to this classification as high, med and
low tech industries.
5
See section III for further discussion.
5
II. DATA AND R&D PATTERNS.
We use data drawn from the ESEE, a representative annual survey of
Spanish manufacturing firms that collects exhaustive information at the firm
level.6 We classify a firm in a given period as an R&D firm when it claims to
undertake R&D activities.
We select a panel of continuously operating firms from 1990 to 2000.
The choice of a continuous panel is motivated by two reasons. First, to analyze
firm R&D trajectories for the maximum length of time, we sample out those
firms that fail to supply R&D information in any year. Secondly, to estimate a
dynamic specification with lagged endogenous variables, we need to build up a
panel as long as possible. After applying these criteria, we end up with a
balanced panel of 756 firms.
Table I reports the percentages of R&D firms in our data for the period
1990-2000. By size group, the proportion of R&D firms is always higher for
large firms as compared to small firms (66.46% and 17.15%, respectively, on
average over the period). By industry group according to the technological
intensity content, the lower proportion of R&D firms corresponds to low-tech
industries (20.94%), followed by med-tech industries (35.06%) and, lastly,
high-tech industries (48.79%).
[Insert Table I about here]
To evaluate the importance of continuity in the performance of R&D
activities, we analyze firm transition rates (Table II). For small firms, the
average exit rate (21.2%) exceeds the average entry rate (4.7%), suggesting a
high rate of turnover. For large firms, we observe the opposite since the
6
This survey is carried out by the Ministry of Industry and the Spanish Foundation Fundación
SEPI since 1990.
6
average entry rate (13.3%) is larger than the average exit rate (8.7%); this
uncovers a trend of incorporation and stay in R&D activities. Moreover, we
observe a strong persistence both in the R&D and non R&D statuses.
By industry technological intensity, exit rates always exceed entry rates.
However, it is important to note two remarkable differences: (i) the entry rate is
higher in high-tech industries than in med-tech ones, and higher in med-tech
industries than in low-tech ones; and (ii) the exit rate is lower in high-tech
industries than in med-tech ones, and in med-tech industries lower than in
low-tech ones. As a consequence, the average entry rate in high-tech
industries is much closer to the average exit rate than in med and low-tech
industries. Therefore, we observe a larger turnover in low and med-tech
industries, and a trend of entry and stay in high-tech industries.
[Insert Table II about here]
Columns 1 and 2 of Tables IIIa and IIIb present the proportion of R&D
and non-R&D firms in 1990 that had the same status in one of the
subsequent ten years. Table IIIa shows that the percentage of small firms that
performed R&D in 1990 and were also performing R&D in 2000 is 62.65%.
R&D persistence is higher for large firms as compared to small firms, since
76.76% of the large firms performing R&D in 1990 also performed R&D in
2000. The higher persistence in the R&D status for large firms may be
indicating that sunk R&D costs are higher for large firms. For small firms,
persistence in the non-R&D status is more intense than in the R&D status.
Approximately 89.50% of small firms that did not carried out R&D in 1990 did
not carried out R&D in 2000. The opposite is true for large firms, as
persistence in the non-R&D status is lower than persistence in the R&D
status. This lower rate of persistence for large firms in the non-R&D status
confirms the trend of incorporation to R&D activities detected above. With
7
respect to technological regime, columns 1 and 2 of Table IIIb show that
persistence in the R&D status is much higher for firms in high-tech industries
than for firms in medium and low-tech ones. Whereas a 77.56% of the firms
performing R&D in 1990 in high-tech industries were also performing R&D in
2000, for med-tech and low-tech industries these percentages are 57.61% and
58.74%, respectively. The higher persistence for firms in high-tech industries
in the R&D status may be suggesting higher sunk R&D costs in high-tech
industries. In addition, persistence in the non R&D status is always higher
than persistence in the R&D status. Furthermore, persistence in the non-R&D
status is inversely related with industry technological intensity.
[Insert Tables IIIa and IIIb about here]
Columns 3 and 4 of Tables IIIa and IIIb report the predicted rates of
persistence in each of the two statuses. These are calculated using
accumulatively the annual transition rates given by the data and reported in
Table II. Over the whole sampling period, and regardless of size or industry,
predicted
persistence
is
lower
than
actual
persistence.
The
general
implications of these patterns are twofold: first, lower figures in column 3
compared to column 1 indicate that there is a high rate of re-starting by
former R&D firms (i.e., performing R&D in the past affects positively to the
probability of performing R&D in the future); secondly, the fact that figures in
column 4 are smaller than in column 2 suggests that firms performing R&D
without R&D experience have a higher probability of ceasing their R&D
activities. In addition, some key issues can be derived from the comparison of
the patterns of actual and expected persistence among groups. For instance,
the difference between actual and expected persistence in the R&D status is
much higher for small than for large firms, suggesting that R&D re-entry is
much more important for small than for large firms and R&D persistence
8
higher for large firms. This could suggest lower sunk costs for small firms.
Furthermore, the higher the technological intensity of the industry, the
smaller the difference between actual and expected persistence in the R&D
status, indicating that re-entry is more intense in industries with lower
technological intensity. Easier R&D re-entry could be considered evidence of
lower sunk R&D costs in these industries.
The aim of next section is to present an econometric model to
investigate the role of sunk costs and firm/market characteristics in
explaining observed R&D status persistence. Furthermore, the observed
differences between the patterns of persistence by size group and technological
sector suggest testing whether sunk costs differ accordingly.
III. MODELLING AND ESTIMATION.
III(i). Modelling the R&D decision.
We model the decision to invest in R&D by a rational, profit-maximizing firm
following Roberts and Tybout [1997], who model the firm exporting decision.
We assume that firms consider expected profits derived from the decision to
perform R&D (net of sunk costs of starting-up or ceasing R&D activities). In
each period t the variation in gross profits adjusted for sunk R&D costs is
given by
(1)
Ji
πˆit = yit ⎡⎣π it ( pt , sit ) − Fit0 (1 − yi ,t −1 ) − ∑ ( Fitj − Fit0 ) y i ,t − j ⎤⎦ − Git yi ,t −1(1 − yit )
j =2
where yit takes the value of 1 if the firm performs R&D in period t and 0
otherwise. π it is the current increase to gross profits associated with the
decision to perform R&D; firm and market characteristics are included in sit ;
and other factors, such as R&D policies and macro conditions are included in
(
pt . Ji is the age of the firm and y i ,t − j = yi ,t − j ∏ k =1 (1 − yi ,t −k )
j −1
)
summarizes the
9
firm recent R&D experience and takes the value of 1 if the last period that firm
i performed R&D was period t − j and 0 otherwise.
To account for sunk R&D costs the following three assumptions are
made. First, a firm that has never undertaken R&D would face a sunk startup cost of Fit0 and its profits the first year doing R&D would be given by
π it ( pit , sit ) − Fit0 . Thus, Fit0 represents those costs associated with starting–up
R&D activities that may be considered sunk (i.e. cannot be recovered by the
firm), related to, e.g., purchasing specific physical assets, creating an R&D
department and hiring or training specialized workforce. Secondly, a firm that
invested in R&D in the previous year, i.e. yi,t-1=1, would not have to pay the
sunk R&D start-up cost in t and would earn profits given by π it ( pt , sit ) , but if
this firm decides to cease R&D activities this period it would incur in a sunk
R&D ceasing cost represented by −Git . We assume that ceasing R&D activities
entails a loss of physical and human capital related to R&D investments, such
as closing down the R&D department or the cost of firing or reallocating R&D
employees. Thirdly, firms that abandoned R&D activities in previous periods (t
- j with j ≥ 2 ) and decide to re-start those activities again are also considered.
In this case, we assume that the firm would face a sunk R&D re-starting cost
of
Fitj
(with
Fitj < Fit0 ), so that the firm earnings would be given by
π it ( pi , sit ) − Fitj . The j subscript indicates that sunk R&D re-starting costs
depend on the length of time a firm has been away from R&D activities. This
could reflect the depreciation of knowledge and experience accumulated
during the period in which the firm was undertaking R&D activities, or the
10
increasing cost of updating the firm to the “changing” conditions in the
performance of R&D activities.7
We assume that in period t managers plan the firm R&D trajectory that
maximises the expected current and discounted future profits net of R&D
related sunk costs.8 This maximised payoff is,
(2)
⎛ ∞ s −t ⎞
Vit = max
E
πˆis ⎟
t ⎜ ∑δ
∞
yis s =t
⎝ s =t
⎠
where Et is an expectations operator conditional on the set of firm information
at time t and δ is a time discount rate. Firm i chooses the current yit value that
satisfies the Bellman´s equation:
(3)
Vit = max πˆit + δ E t ⎡Vi ,t +1 yit − j
⎢⎣
yit
Ji
j =0
⎤.
⎥⎦
A firm that decides to perform R&D in t obtains the expected present
value of payoffs given by
(4)
(
π it + δ Et Vi ,t +1 yit = 1, yit − j
Ji
j =1
)
Ji
− Fit0 (1 − yi ,t −1 ) − ∑ ( Fitj − Fit0 )y i ,t − j
j =2
and one that decides not to do it
(5)
(
δ Et Vi ,t +1 yit = 0, yit − j
Ji
j =1
) −G y
it
i ,t −1
.
The ith firm will decide to perform R&D during period t whenever (4)
minus (5) is positive, i.e.
(6)
7
On measuring knowledge stocks, Bitzer [2005] argues that R&D activities generate knowledge
independently of whether an R&D project is ultimately successful or not. Knowledge is
generated gradually over the years as R&D activities (projects) take place, and do not only
emerge upon the complexion of a given R&D project. Once the firm ceases its R&D activities, its
accumulated knowledge depreciates.
8
We assume that the firm also chooses the profit-maximizing level of R&D expenditures when
deciding to undertake R&D.
11
Ji
π it + δ ⎡⎣E t (Vi ,t +1 yit = 1) − Et (Vi ,t +1 yit = 0 ) ⎤⎦ − Fit0 + ( Fit0 + Git ) yi ,t −1 − ∑ ( Fitj − Fit0 )y i ,t − j ≥ 0.
j =2
Our empirical specification is derived from equation (6). Defining the
latent variable π it* as current gross operating profits plus the discounted
expected future returns from being an R&D firm in year t,
π it* = π it + δ ⎡⎣Et (Vi ,t +1 yit = 1) − Et (Vi ,t +1 yit = 0 ) ⎤⎦
(7)
the decision to invest in R&D is then given by the following dynamic discrete
choice process:
(8)
⎧
⎪1 if
yit = ⎨
⎪0
⎩
Ji
π it* − Fit0 + ( Fit0 + Git ) yi ,t −1 − ∑ ( Fitj − Fit0 )y i ,t − j ≥ 0
j =2
otherwise.
We approximate π it* − Fit0 as a reduced-form expression on firm/market
characteristics (Xit), macro conditions ( µt ), and noise ( ε it ).9 Therefore,
(9)
π it* − Fit0 = µt + β X it + ε it .
We also consider three identifying assumptions in relation to sunk R&D
costs. First, we assume that sunk R&D costs do not vary across time.
Secondly, we allow for the sunk R&D costs to be different for small and large
firms. When deciding to engage in R&D activities, large firms may have a
number of advantages over small firms (see, e.g. Cohen [1995], Cohen and
Klepper [1996], Lee and Sung [2005], among others): larger firms may spread
the fixed costs of R&D activities over a larger volume of sales; larger firms may
have an advantage in financial markets in order to obtain the required funds
needed to invest in R&D; and larger firms may be able to exploit more easily
economies of scale and scope in R&D activities. These advantages may imply
higher R&D investments for large firms and therefore higher sunk R&D costs.
9
All of them, with the exception of
ε it , are assumed to be observable to the firm in period t.
12
Thirdly, we consider firm sunk R&D costs to be different according to the
industry technological content in which the firm operates. Sunk R&D costs are
expected to be low in low–tech industries, such as leather, wood or textiles.
According to Pavitt [1984], the generation of technology by firms in these
industries takes place mainly by acquisition from other firms. Med-tech
industries are a heterogeneous group, ranging from rubber and plastic to
machinery. Firms in these industries are usually both technology acquirers
and R&D performers. Sunk R&D costs are likely to be higher in med-tech as
compared to low-tech industries. Finally, high-tech industries, such as
chemicals or electronic products, are usually very active in the generation of
their own technology by engaging in R&D activities, usually highly specific to
the firm and in their own R&D departments. Sunk R&D costs are therefore
expected to be higher in these industries.
Thus, conditional to size group and industry technological intensity
group, we assume that sunk R&D starting-up costs for firms that did not
perform R&D for at least J years are the same, that firms that did not perform
R&D for j < J years incur in the same re-starting sunk costs, and that firms
currently engaged in R&D activities have the same R&D ceasing cost.
Incorporating the above assumptions, and substituting (9) into (8), we
have the following estimation equation:
(10)
J
J
⎧
j
0
0
1
if
µ
β
X
γ
y
γ
y
γ
y
τ
γ l j−s y i ,t − jτ
+
+
+
+
+
∑
∑
t
it
s ,L i ,t −1
s ,L i ,t − j
l −s i ,t −1
⎪
j =2
j =2
⎪
J
J
⎪⎪
yit = ⎨
+ γ M0 − L yi ,t −1dM + ∑ γ Mj − L y i ,t − j dM + γ H0 − L yi ,t −1dH + ∑ γ Hj − L y i ,t − j dH + ε it ≥ 0
j =2
j =2
⎪
⎪0 otherwise.
⎪
⎪⎩
where subscripts l and s stand for large and small group firms, respectively,
and subscripts H, M and L stand for high, med and low-tech industries,
respectively; τ is an indicator variable taking value 1 for large firms and 0 for
13
small ones; dH is an indicator variable taking value 1 for high-tech industries
and 0 otherwise; and dM is an indicator variable taking value 1 for med-tech
industries and 0 otherwise. The reference category is a firm belonging to the
small size group and to a low-tech intensity industry. Equation (10) re-defines
Fit0 + Git and Fitj − Fit0 (for j = 2,…,J) in (8) as follows. For small firms, Fit0 + Git
and Fitj − Fit0 are, respectively, γ s0,L and γ sj,L in low-tech industries; γ s0,L + γ M0 − L
and γ sj,L + γ Mj − L in med-tech industries; and γ s0,L + γ H0 − L and γ sj,L + γ Hj − L in hightech industries. For large firms, Fit0 + Git
γ s0,L + γ l0−s
and
γ sj,L + γ l j−s
in
low-tech
and Fitj − Fit0 are, respectively,
industries;
γ s0,L + γ l0−s + γ M0 − L
and
γ sj,L + γ l j−s + γ Mj − L in med-tech industries, and γ s0,L + γ l0−s + γ H0 −L and γ sj,L + γ l j−s + γ Hj − L
in high-tech industries.
The specification in (10) allows testing for the importance of sunk R&D
starting-up and ceasing costs by testing whether the coefficients for yi ,t −1 are
equal to zero. It is also possible to analyse the reduction in the full sunk R&D
starting-up costs endured by a firm that last performed R&D in period t-j (for j
= 2,…, J), as compared to a firm starting these activities, by testing whether
the coefficients associated to y i ,t − j are equal to zero. Furthermore, we may also
analyse the rate of depreciation of experience and accumulated knowledge in
R&D activities by looking at the evolution of the y i ,t − j coefficients from j = 2 to j
= J.
III(ii). Estimation issues.
Given that we are interested in identifying the effects of sunk R&D costs in the
decision to invest in R&D activities, it is crucial to control for other sources of
14
persistence. Most of this task is accomplished by including the vector of
observable characteristics X it in (10). However, it could be argued that there
may be unobserved factors causing persistence, such as managerial ability or
the length of R&D projects.10 Since these factors are potentially permanent, or
highly serially correlated for a firm, in practice we assume that ε it in (10) has
two components, a permanent firm-specific effect ( α i ) and a transitory
component ( uit ). Hence, we allow for two sources of serial correlation in ε it ,
the first arising from the permanent component and the second arising from
serial correlation in transitory shocks to R&D profits. This is an important
issue since, whether or not uit are independent across t, ε it will always be
serially correlated because of α i . We further assume that the variance of ε it is
σ t2 . Note that the ε it are allowed having different variances in different time
periods.
We also need to address an “initial conditions” problem. We observe a
firm R&D status in years 1 through T, and our lag structure reaches back J
periods. Values corresponding to the first J years ( yi1,..., yiJ ) cannot be treated
as exogenous determinants of yit, when t > J, because each one depends on α i
and previous realizations of uit, both of which are correlated with ε it . Heckman
[1981] suggests dealing with this initial conditions problem by using an
approximate representation for yit when t ≤ J. Specifically, let us suppose that
10
R&D activities may involve long-term projects which run several years, and this could lead to
persistence in R&D activities just by the nature of the schedule of R&D programs. However,
given the lack of information about the length of R&D projects and assuming that this length
nature is rather persistent at the firm level we control for its effects in persistence by including
firm-specific effects in the model. Additionally, industry dummies would control for any industry
specific length nature of R&D projects.
15
expected profits from R&D during the J pre-sample years can be represented
by the equation
π it* − Fit0 = λ X itp + ε itp
(11)
where X itp is a distributed lag in pre-sample realizations on X it variables.11
Then, pre-sample R&D-participation is described by
if λ X itp + ε itp ≥ 0
otherwise
⎧1
yit = ⎨
⎩0
(12)
instead of equation (10). We assume that ε itp has the same properties than ε it .
Furthermore, it is assumed that the joint distribution of ε iP1 ,..., ε iJP , ε iJ +1,..., ε iT is
multivariate standard normal, and its full correlation matrix is characterised
by
{(T × T ) − T }/2
free distinct (and estimable) correlations, 12 with ones on the
diagonal and ρts = ρst as off-diagonal elements.13
Positive (negative) signs in the set of correlation coefficients between the
disturbances of the first J years and the disturbances in every other year,
indicate that firms that were more likely to perform R&D in the initial
conditions years are more (less) likely to remain doing so during sample years
compared to the non R&D firms. If these correlation coefficients are jointly
equal to zero, there is no initial conditions problem and the model reduces its
dimension to a T - J multivariate probit model. And if ρts , ∀t ≠ s , are all jointly
equal to zero, then R&D equations may be estimated using simple univariate
11
In our empirical work firm characteristics ( X it ) are included as explanatory variables in X itp .
We also include two-year lagged values of the firm variables.
12
13
In our empirical work J=2 and T=10.
ρts = ρ(ε
t
σ t )( ε s σ s )
. Roberts and Tybout [1997] impose an AR(1) on the serial correlation of the
transitory components of
ε it
and
ε
P
t
, but we leave it fully unrestricted.
16
probit models for each period. In our empirical work we estimate the general
model with free correlations and test for special cases.
Our general model is a dynamic multivariate probit that we estimate
using the mvprobit Stata program14 developed by Cappellari and Jenkins
[2003]. This program uses simulated maximum likelihood techniques (SML) to
solve the computational problem of evaluating T-dimensional integrals.15 In
addition to including all the possible correlations ( ρts ) between the composed
errors, the program allows implementing a pseudo simulated maximum
likelihood estimator (PSML), by adjusting the estimates of the parameter
covariance matrix to account for arbitrary correlations between all panel
observations of a given firm (see Huber [1967] and White [1982]).
III(iii). Explanatory variables.
To parameterise the reduced-form model given by equation (10) describing the
firm R&D decision, we assume that variation in R&D profitability and sunk
R&D costs (other than unobserved characteristics) may arise from the
following sources: time-specific effects (µt), firm and market characteristics
(Xit), and previous R&D history. In order to assess the importance of sunk
R&D costs, we use a specific lag structure for past firm R&D experience that
reaches back two periods and takes into account the possibility of different
sunk R&D costs according to size group and technological regime. As noticed
14
This
program
can
be
obtained
either
at
SSC
public
domain
software
archive
(http://fmwww.bc.edu/RePEc/bocode/m) or inside Stata, typing “ssc install mvprobit”.
15
In particular, it uses the Geweke-Hajivassiliou-Keane (GHK) simulator to replace multivariate
standard normal probability distribution functions by their simulated counterparts, see
Hajivassiliou and Ruud [1994] and Gourieroux and Monfort [1996].
17
earlier, if sunk R&D costs matter the current R&D firm decision will depend
upon the firm R&D history.
We include time-specific effects in order to capture macro-level changes
in R&D conditions and institutional factors that are common across firms,
such as R&D policy variations, the business cycle, credit-market conditions,
etc.
Regarding firm/market characteristics, we classify them into the
following
groups,
according
to
the
relevant
literature:16
economic
opportunities, technological opportunities, measures of firm success, variables
capturing the market structure in which firms operate, R&D appropriability
conditions, spillovers, and other firm characteristics.
[Insert Table IV about here]
Economic opportunities
The incentives to invest in R&D depend primarily on the economic
opportunities faced by firms, that is, the market possibilities to exploit
innovation
results
(Schmoockler
[1962]).
To
measure
firms
economic
opportunities we consider first firms EXPORT INTENSITY, since exporting
firms may need to innovate to face a higher competitive pressure in
international markets (Kleinschmidt and Cooper [1990] and Kotable [1990]). In
addition, according to Cohen and Levinnthal [1989], foreign markets may
facilitate the transfer of technology and so stimulate firm R&D activities.
Secondly, we also consider the evolution of the firm main market and include
a set of dummy variables capturing whether the firm claims to face an
EXPANSIVE, STABLE or RECESSIVE DEMAND. We expect the incentives to
undertake R&D activities to be higher for firms facing an expanding demand
as they have more possibilities to exploit innovation results.
16
Note that some of the firm/market characteristics could be classified in more than one group.
18
Technological Opportunities
Technological opportunities refer to the possibility of converting
research resources into new products or better production techniques (Cohen
and Levinthal [1989], Lunn and Martin [1986]).
Although it is generally
accepted that industries differ in the opportunities they face for technical
progress, there is no consensus on how to properly measure technological
opportunities. We proxy for them by including the INDUSTRIAL SECTOR in
which firms operate17, and firm LABOUR QUALITY, which may facilitate the
implementation
and
development
of
innovation
activities
(Bartel
and
Lichtenberg [1987]).
Firm Success
The usual claim here is that better performing firms are more prone to
undertake R&D activities. To proxy for firm success we include two variables
that are standard in the literature as indicators of firm success: age and size.
Firm AGE captures firm experience and knowledge accumulation, and it
usually proxies for efficiency differences at the firm level (Jovanovic [1982],
Ericson and Pakes [1995]).18 Relating to the association between firm SIZE and
R&D investment, there is a considerable amount of literature (see, e.g. Cohen
[1995], Cohen and Levin [1996], Lee and Sung [2005], and references therein).
The exploitation of economies of scale and scope, larger market size, lower
17
The industry classification can be found in Table V. As already mentioned, industry dummies
could also be capturing the industry specific length nature of R&D projects.
18
In order to capture possible non-linearities in the relationship between firms age and the
probability of performing R&D activities, in the estimation we include a quadratic term in age
(see Table IV).
19
risk, higher appropriability possibilities, etc, are the usual arguments used to
support a positive association between firm size and innovative activities.
Empirical results are mixed but in general they suggest a positive association,
although not necessarily linear.19
Market Structure
The decision to invest in R&D may also be influenced by the degree of
product market competition. The literature on industrial organization remains
controversial on whether market power encourages or inhibits firms from
innovating. According to Schumpeter [1942], ex ante market power generates
financial means to innovate and reduces risk levels. However, following Arrow
[1962] the incentives to innovate are higher in competitive markets because
the expected incremental returns from innovating are higher as compared to
monopoly conditions. In order to capture the degree of product market
competition,
we
use
two
variables,
namely,
the
firm
NUMBER
OF
COMPETITORS, and whether the firm declares to enjoy a significant MARKET
SHARE.
Appropriability Conditions
Appropriability conditions refer to the extent to which the results from
innovative activities can be appropriated by the firm or easily diffused within
or across industries (Levin 1988]). To proxy for them we follow Beneito [2003]
and calculate the ratio between total number of patents granted and the total
number of firms that claim to have achieved innovations in the firm industry.
19
In order to check for non-linearities in the relation between size and the probability to invest
in R&D, we measure size using a set of three dummy variables according to the number of
employees (see Table IV for details).
20
Spillovers
The theoretical literature on R&D has stressed the importance of
spillovers for the decision to innovate. According to Griliches [1992], spillovers
may be understood as ideas borrowed by research teams of firm/industry i
from the results obtained by firm/industry j. Interaction allows collecting
information about technologies and markets, and facilitates the acquisition of
other inputs to complement the internal learning process (Rothwell and
Dogson [1991], Lundvall [1992]). Geographical proximity may also generate
positive externalities, market linkages and possibilities for collaboration that
in
turn
encourage
technological
improvements
and
innovations.
We
approximate spillovers considering R&D activities undertaken by other firms
in the same industry or region, distinguishing among three separate forms of
SPILLOVERS: industry-specific, region-specific and local.
Other Firm Characteristics
Other firm characteristics suggested in the literature as factors
influencing the decision to invest in R&D activities are related to horizontal
product differentiation and firm ownership. As a proxy for the degree of
horizontal product differentiation we consider firm ADVERTISING INTENSITY.
We expect that firms with large budgets on advertising will be more prone to
invest in R&D so as to maintain their image and reputation. Regarding firm
ownership, we control for two variables. We consider FOREIGN CAPITAL
PARTICIPATION, as foreign ownership may have a discipline effect on the
innovation activities of the firm. However, existing studies are inconclusive as
whether the nationality of ownership affects R&D activities (see Baldwin et al.
[2002], and references therein). Secondly, to control for the legal structure of
21
the firm we include the variable CORPORATE, which takes value one when the
firm is a limited liability company. The hypothesis here is that these firms are
relatively less risk averse (as compared to owned-managed firms) and thus
more willing to undertake risky investments such as R&D activities (Love et al.
[1996]).
We also include a variable to control for the foreign content of the firm
physical capital (FOREIGN PHYSICAL EQUIPMENT) in order to check whether
foreign technology incorporated in machinery increases technology absorption
and stimulates R&D activities.
Finally, it should be noted that all nominal variables in Table IV have
been deflated using specific industry deflators according to the twenty sectors
of the two digit NACE-93 classification. It is also important to remark that in
the estimation we lag all firm and market characteristics one year in order to
avoid potential simultaneity problems.20
IV. ESTIMATION RESULTS.
As explained in section II, the sample used to estimate equation (10) includes
8316 observations, corresponding to eleven annual observations for 756 firms
that cover the 1990-2000 period. The years 1991 and 1992 are treated as the
J = 2 pre-sample years and are used to control for the initial conditions
problem. The observations for 1990 are used as regressors for the 1991 initial
conditions set. Finally, years 1993 to 2000 are used to estimate the relevant
parameters in equation (10).
[Insert Table V about here]
20
Therefore we cannot use in the estimation the information relating to the first year of the
sample, 1990.
22
In Table V we report the PSML estimation results. This estimation
includes the past participation history in R&D activities (with a structure of
two lags), allows for serially correlated errors and individual effects, and
controls for endogeneity of initial conditions. Testing the joint significance of
all the ρ -correlation coefficients leads to the rejection of the null hypothesis
that they are jointly equal to zero. This suggests that the estimation method
should be a multivariate probit model, since using univariate probit models
would imply ignoring two possible sources of persistence: unobserved
individual heterogeneity and serially correlated error terms. Furthermore, we
also perform a test for the endogeneity of the initial conditions by testing the
joint significance of the ρ -correlation coefficients between initial conditions
errors (1990< t ≤1992) and sample years errors (1992< t ≤ 2000). Exogeneity of
initial conditions is strongly rejected, indicating that initial conditions should
not be treated as exogenous.21
IV(i). Time dummies and firm/market characteristics.
We analyze the impact of time dummies and firm/market characteristics on
the expected profits net of sunk R&D costs ( π it* − F 0 ) of a firm with no previous
experience in undertaking R&D activities. We obtain that only the 1998
dummy is significant. We reject the hypothesis that all time dummies are
jointly equal to zero at a 6 percent level of significance (the χ 72 test is 13.52
and the corresponding p-value is approximately 0.06).
We
further
analyze
the
influence
of
observable
firm/market
characteristics on the net profitability of undertaking R&D activities. In
relation to variables proxying for economic opportunities, we find that EXPORT
21
These tests are reported at the bottom of Table V.
23
INTENSITY has a very significant and positive effect on the decision to invest
in R&D, suggesting that firms have incentives to innovate in order to face
more competitive international markets. This result is consistent with existing
literature (see, e.g., Cassiman and Veugelers [1999] and Beneito [2003],
among others). We also obtain that firms facing an EXPANSIVE DEMAND have
a significant higher probability to undertake R&D activities, as they may enjoy
higher possibilities to exploit their innovation results.
As for technological opportunities, we obtain positive and significant
effects for a set of two-digit INDUSTRY dummies. Interestingly, when the
coefficients are significant, they are higher for med and high-tech industries
(especially remarkable are the results for rubber and plastic, machinery and
mechanical equipment, and chemical products; see Table V). The omitted
industrial sector in estimation is Meat industry, a typically low-tech industry.
The coefficient for the variable proxing for LABOUR QUALITY is positive
and very significant indicating that those firms with a high degree of qualified
workforce may find easier to implement innovation activities (Bartel and
Lichtenberg [1987]) and/or have a higher “absorptive capacity” and so greater
incentives to perform R&D activities (Cohen and Levin [1989]).
The coefficients for the variables related to the firm age are non
significant statistically. As regards firm SIZE (measured by the number of
workers), our results show that firms with more than 100 employees are more
prone to undertake R&D activities (a particularly strong effect is found for
firms with more than 200 employees, since the coefficient for SIZE3 is
significantly higher than the coefficient for SIZE2). This positive and nonlinear association between firm size and R&D is consistent with existing
empirical literature (see, e.g., Beneito [2003], Cassiman and Veugelers [1999]
or Kamien and Schwartz [1982], among others).
24
None of the coefficients for the variables included to capture market
structure, appropriability and spillovers is statistically significant.
Finally, among other variables that may influence the decision to invest
in R&D, our results suggests that firms participated by FOREIGN CAPITAL
could be mainly productive platforms of parent foreign companies as they are
less likely to carry out R&D activities. However, the higher the FOREIGN
content of firms PHYSICAL EQUIPMENT, the higher the probability to invest in
R&D, suggesting that the two sources of technology are complementary.
ADVERTISING INTENSITY and CORPORATE also affect positively the chances
to perform R&D activities.
IV(ii). Sunk R&D costs parameters.
For small low-tech firms (our reference category), the estimated coefficient for
yi,t-1 ( γˆs0, L = 1.832 ) is large, positive and significant (at a 1% level). This
coefficient is an estimate of the sum of sunk R&D starting-up costs (for a firm
that has never performed R&D) and sunk R&D ceasing costs (for a current
R&D firm ceasing R&D activities). This result clearly reveals that sunk costs
are an important determinant in the firm decision to perform R&D activities.
Regarding to the effect of size, the sum of sunk R&D starting-up and
ceasing costs is significantly higher for large firms than for small firms (the
estimate of γˆl0−s is 0.289 and significant at a 5% level). Turning to the
technological regime effect, this sum is also higher for firms in high-tech
industries (the estimate of γˆH0 − L is 0.369 and significant at a 5% level), as
compared to firms in med and low-tech industries, and there is no difference
between firms in med and low-tech industries ( γˆM0 −L
is non-significant).
Moreover, the effect of being large is statistically indistinguishable from the
25
effect of operating in a high-tech industry (the null that γˆl0−s is equal to γˆH0 −L
cannot be rejected).22 These findings confirm both our hypothesis and
descriptive results on the effect of size on sunk R&D costs and shed light on
the relationship between sunk R&D costs and industry technological intensity.
Sunk R&D costs are only significantly higher for firms in high-tech industries
where to keep pace of competitors requires performing high R&D investments,
usually in their own departments, to generate new technologies, technologies
that are usually highly specific to the firm. This R&D competition results in an
escalation of R&D expenditures that increases the endogenous R&D sunk
costs that the firm has to incur to survive in high-tech industries (Sutton,
1998). Such R&D competition does not seem to operate in med-tech
industries, since the sunk R&D related costs are not significantly different
form those in low-tech industries.
If we take into account firms classification according to both size and
technological regime we can establish the following firm taxonomy according
to their sunk R&D (starting plus ceasing) costs. The highest sunk costs
correspond to large firms operating in high-tech industries (with an estimated
value of 2.489 and significant at a 1% level); the lowest sunk costs correspond
to small firms operating in low and med-tech industries (with an estimated
value of 1.832 and significant at a 1% level). Sunk costs for large firms
operating in low and med-tech industries and for small firms operating in
high-tech industries have an intermediate value (they rank between 2.12 and
2.20, and are not statistically different from each other).
As regards the coefficient of y i ,t −2 , measuring the reduction in the sunk
R&D starting-up costs (enjoyed by those firms that last performed R&D
22
The p-value of the test is 0.698.
26
activities two years ago, as compared to the sunk R&D starting-up costs faced
by a firm that performs R&D activities for the first time) we get that the
coefficient for the reference category,
2
(γˆs,L
) , is 0.445 and statistically
significant at a 1% level. This finding suggests that R&D history matters: firms
with past R&D experience have a higher probability to perform R&D activities
today. However, we do not find that either size or technology regime has an
additional effect on this reduction.
To sum up, large firms and/or firms producing in a high technological
intensity industry have higher sunk R&D starting-up plus leaving costs, being
statistically equivalent the effects of producing in a high technological
intensity industry or being a large firm. However, the reduction in the sunk
R&D starting costs is independent of size or technological regime.
We also estimated a model including
y i ,t −3 , but the associated
coefficients were non significant. These results indicate a rapid depreciation of
experience in performing R&D activities: whilst the sunk R&D re-starting-up
costs faced by a firm that last performed R&D activities two years ago is
smaller than the one that must be incurred by a beginner in R&D activities,
there is no significant difference between the sunk re-starting costs of a firm
that last performed R&D activities three or more years ago and a firm that
never did it before. Furthermore, the lack of significance of y i ,t −3 suggests that
our choice of a two-year lag structure seems to be appropriate to capture the
relevant R&D history of a firm.
Table VI presents the predicted probabilities of performing R&D for
firms classified by size and technological regime, using the model estimates
given by Table V. In Table VI, we compare firms with β xit moving from the 25th
to the 75th percentile. We consider simultaneously three R&D histories
27
corresponding to firms which did not perform R&D in the past ( yt −1 = y t −2 = 0 ),
firms which last performed R&D two years ago ( yt −1 = 0, y t −2 = 1 ), and firms
which performed R&D last year ( yt −1 = 1, y t −2 = 0 ).
The results obtained in Table VI are as follows. First, the probability of
performing R&D today if the firm was not an R&D firm in the past is the
lowest in the table, especially for small firms. Secondly, the probability of restarting R&D activities today when the firm last performed R&D two years ago
is higher than the probability of new performers. Furthermore, for small firms
the probability of re-starting today is much lower than for large firms, what
contributes to higher persistence of large firms in these activities. Thirdly, the
highest probabilities of performing R&D activities today are found for firms
performing R&D the previous year, being higher for large firms than for small
ones and for firms in high-tech industries as compared to firms in med and
low-tech industries. Moreover, for firms performing R&D the previous year, the
probability of performing R&D in the current year compared to new performers
can increase in an order of [0.43, 0.74]. This suggests that if firms were
somehow given R&D experience it would make them continue in R&D
activities with a probability in most cases higher than 0.5. Fourthly,
conditioning on the type of firm (by size and technological regime), higher
percentiles of expected profits from R&D activities ( β xit ) increase the
probability of performing R&D. Finally, independently of the R&D history, the
higher probabilities of undertaking R&D are for large high-tech firms.
[Insert Table VI about here]
IV (iii). Goodness of fit.
28
To evaluate the goodness of fit of our model we compare actual and predicted
firms R&D trajectories. Given the eight-year period 1993-2000, there are 256
(28) possible R&D trajectories for an individual firm. For the 756 firms of our
sample, some of these trajectories either are never observed or are quite
unusual. Hence, to simplify the comparison of actual and predicted
trajectories, we group the 256 possible trajectories into 6 categories based on
two criteria: firm R&D status in 1993 and whether the firm changes R&D
status once or more between 1994 and 2000. Table VII shows that actual and
predicted
frequencies
for
these
six
categories
are
rather
similar.23
Furthermore, the results of a chi-square contingency table test, comparing
actual and predicted frequencies ( χ 62 =4.497 with p-value of 0.480), indicate
that there are not significant differences between both of them. These results
suggest that our functional form, lags structure and error structure are
appropriate and that our model predicts rather accurately observed R&D
patterns.
[Insert Table VII about here]
V. CONCLUDING REMARKS.
In this paper, we have tested for the existence of sunk costs in the firm
decision to undertake R&D activities and whether these costs are different
between large and small firms, and firms in different technological intensity
regimes. We have presented and estimated a dynamic discrete choice model
where a firm decision to undertake R&D activities is a function, among other
factors, of its previous history in performing R&D activities. We have used a
23
Actual and predicted frequencies for the complete 256 possible trajectories are shown in the
Appendix.
29
panel of Spanish manufacturing firms drawn from the ESEE for the period
1990-2000.
Our results, by showing that prior R&D experience matters in the
current decision to invest in R&D, provide evidence on the existence of sunk
costs in such decision. Additionally, we also find that large firms and/or firms
operating in high-tech industries have significantly higher sunk R&D costs as
compared to small firms and/or firms in low and med-tech industries. Finally,
those firms that cease R&D activities suffer a rapid depreciation of their
accumulated experience or knowledge, independently of the size group and/or
the technological intensity regime. Sunk R&D re-starting costs faced by a firm
that last performed R&D three or more years ago are not significantly different
from those faced by a firm that has never performed R&D activities before.
This finding is in line with the view that R&D activities develop in a rapidly
changing environment and that the ability to work in this environment
depreciates fairly quickly once a firm quits these activities.
Firm heterogeneity has shown to be also an important factor explaining
R&D activities given that many of the observed firm characteristics are
relevant in explaining firm R&D trajectories. Regarding to the firm economic
opportunities, the probability of performing R&D increases with export
intensity
and
with
an
expansive
demand.
Among
firm
technological
opportunities, firms in med and high-tech industries and with a high degree of
labour qualification also are more prone to perform R&D activities. Firm size,
the foreign content of firm physical capital and advertising intensity are found
to be positively associated with the propensity to invest in R&D. Finally, our
results also suggest that firms participated by foreign capital are mainly
productive platforms as they are less likely to carry out R&D activities.
30
Our findings contribute to a better understanding of the firm decision to
engage in R&D activities. Such understanding is crucial when designing R&D
policies aimed at encouraging and fostering innovative activities by firms. The
combined relevance of sunk R&D costs and firm characteristics in the
probability of investing in R&D suggest possible R&D promotion policies. On
the one hand, policies directed at providing information and access to R&D
activities or creating R&D networks could reduce the sunk costs of starting
innovative activities. On the other hand, our findings on the rapid depreciation
of knowledge suggest the convenience of policies aimed not only at stimulating
firms to initiate R&D activities, but also at encouraging them to perform those
activities in a continuous way.
Even though the relationship between R&D
activities and innovation results is complex, it is generally accepted that
technological advances cannot take place without a systematic engagement in
R&D activities.
31
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37
TABLE I: PERCENTAGES OF R&D FIRMS.
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
Average
Small firms
Large firms
15.37%
65.44%
16.91%
62.91%
17.20%
60.62%
18.28%
64.41%
17.45%
66.85%
16.96%
62.70%
17.57%
66.48%
15.43%
67.36%
17.58%
71.65%
18.20%
72.25%
17.65%
70.41%
17.15%
66.46%
Low-tech industries
19.46%
23.84%
20.00%
20.00%
20.98%
19.02%
20.00%
19.27%
23.17%
22.44%
22.20%
20.94%
Medium-tech industries
37.61%
30.73%
33.79%
34.70%
34.70%
33.79%
35.16%
34.70%
36.53%
37.44%
36.53% 35.06%
High-tech industries
49.22%
47.66%
45.31%
48.44%
47.66%
47.66%
49.22%
48.44%
49.22%
52.34%
51.56% 48.79%
Notes: (i) The sampling procedure of the ESEE is the following. In the base year, 1990, firms were chosen using a selective sampling scheme with different
participation rates depending on firm size. All firms with more than 200 employees were requested to participate and the participation rate reached approximately
70% of the number of firms in the population. Firms that employed between 10 and 200 were randomly sampled, holding around 5% of the population. Firms with
less than 10 employees in 1990 were not included in the survey. (ii) For the computation of this Table by technological classification, the number of firms in the
sample has been upgraded to population figures by using the aforementioned percentages, usually applied when analysing the data in the ESEE.
38
TABLE II: FIRMS TRANSITION RATES IN THE R&D STATUS BY SIZE AND TECHNOLOGICAL INTENSITY, 1990-2000.
SMALL FIRMS
Year t
status
Year t+1
status
1990-91
1991-92
1992-93
1993-94
1994-95
1995-96
1996-97
1997-98
1998-99
1999-2000
Average
Non-R&D
Non-R&D
R&D
Non-R&D
R&D
0.941
0.059
0.205
0.795
0.955
0.045
0.258
0.742
0.956
0.044
0.202
0.798
0.949
0.051
0.275
0.725
0.962
0.038
0.225
0.775
0.960
0.040
0.151
0.849
0.964
0.036
0.220
0.780
0.943
0.057
0.161
0.839
0.948
0.052
0.232
0.768
0.955
0.045
0.192
0.808
0.953
0.047
0.212
0.788
R&D
LARGE FIRMS
Year t
status
Year t+1
status
1990-91
1991-92
1992-93
1993-94
1994-95
1995-96
1996-97
1997-98
1998-99
1999-2000
Average
Non-R&D
Non-R&D
R&D
Non-R&D
R&D
0.827
0.173
0.155
0.845
0.917
0.083
0.113
0.887
0.880
0.120
0.056
0.944
0.818
0.182
0.085
0.915
0.940
0.060
0.090
0.910
0.822
0.178
0.094
0.906
0.872
0.128
0.092
0.908
0.851
0.149
0.031
0.969
0.833
0.167
0.072
0.928
0.907
0.093
0.085
0.915
0.867
0.133
0.087
0.913
1995-96
1996-97
1997-98
1998-99
1999-2000
Average
R&D
LOW-TECH INDUSTRIEs
Year t
status
Year t+1
status
Non-R&D
Non-R&D
R&D
Non-R&D
R&D
R&D
1990-91
0.934
0.066
0.200
0.800
1991-92
0.971
0.029
0.354
0.646
1992-93
0.956
0.044
0.322
0.678
1993-94
0.956
0.044
0.320
0.680
1994-95
0.975
0.025
0.322
0.678
0.960
0.040
0.254
0.746
0.973
0.027
0.241
0.759
0.942
0.058
0.174
0.826
0.958
0.042
0.296
0.704
0.959
0.041
0.219
0.781
0.958
0.042
0.270
0.730
MED-TECH INDUSTRIES
Year t
status
Year t+1
status
Non-R&D
Non-R&D
R&D
Non-R&D
R&D
R&D
1990-91
0.949
0.051
0.320
0.680
1991-92
0.924
0.076
0.207
0.793
1992-93
0.972
0.028
0.107
0.893
1993-94
0.937
0.063
0.254
0.746
1994-95
1995-96
0.948
0.052
0.246
0.754
0.971
0.029
0.080
0.920
1996-97
0.949
0.051
0.239
0.761
1997-98
0.940
0.060
0.177
0.823
1998-99
0.938
0.062
0.232
0.768
1999-2000
0.940
0.060
0.203
0.797
Average
0.947
0.053
0.206
0.794
HIGH-TECH INDUSTRIES
Year t
status
Year t+1
status
Non-R&D
Non-R&D
1990-91
0.945
1991-92
0.944
1992-93
0.907
1993-94
0.923
1994-95
0.944
1995-96
0.921
1996-97
0.940
1997-98
0.941
1998-99
0.903
1999-2000
0.959
Average
0.933
R&D
0.055
0.056
0.093
0.077
0.056
0.079
0.060
0.059
0.097
0.041
0.067
R&D
Non-R&D
0.080
0.112
0.103
0.162
0.038
0.095
0.150
0.098
0.095
0.091
0.102
R&D
0.920
0.888
0.897
0.838
0.962
0.905
0.850
0.902
0.905
0.909
0.898
Notes: (i) Each one of the entries is the proportion of firms in each of the year t statuses that chooses each of the two possible statuses in year t+1. (ii) As in
Table I, for the computation of transitions by technological regime, the number of firms in the sample has been upgraded to population figures.
39
TABLE IIIA: PERSISTENCE OF R&D STATUSES BY SIZE.
(1)
R&D Firms
Actual
(2)
Non-R&D Firms
Actual
79.52%
72.29%
74.70%
65.06%
66.27%
62.65%
57.83%
65.06%
68.67%
62.65%
94.09%
93.65%
93.65%
92.34%
93.22%
91.47%
91.47%
90.15%
90.81%
89.50%
(3)
(4)
R&D Firms Non-R&D Firms
Expected
Expected
Small
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
79.52%
59.00%
47.07%
34.14%
26.46%
22.46%
17.53%
14.71%
11.29%
9.12%
94.09%
89.88%
85.90%
81.50%
78.42%
75.31%
72.63%
68.46%
64.89%
61.95%
Large
1991
84.51%
82.67%
84.51%
82.67%
1992
79.58%
84.00%
74.98%
75.78%
1993
80.28%
80.00%
70.78%
66.72%
1994
81.69%
76.00%
64.74%
54.59%
1995
76.06%
74.67%
58.94%
51.30%
1996
76.06%
69.33%
53.37%
42.18%
1997
76.06%
70.67%
48.49%
36.78%
1998
78.87%
64.00%
46.99%
31.29%
1999
79.58%
61.33%
43.61%
26.07%
2000
76.76%
62.67%
39.93%
23.64%
Notes: (i) Figures in columns (1) and (2) represent the percentage of R&D
(non-R&D) firms in 1990 that were in the same status in the listed years. We
do not distinguish between firms that perform R&D (do not perform R&D)
continuously and firms that change status. For example, in the percentage
of 1996 in column (1) we include both firms that performed R&D every year
from 1990 to 1996 and firms that performed R&D in 1990 and 1996, but not
in one or more of the years in between. (ii) Figures in columns (3) and (4)
show the expected percentages if firms were chosen randomly with annual
transition rates given by the data (see Table II). For instance, from Table II
we know that 79.5% of small R&D firms in 1990 were doing R&D in 1991. Of
these 79.5% of firms, only 74.2% (that is 59% of those doing R&D in 1990)
will also be doing R&D in 1992.
40
TABLE IIIB: PERSISTENCE OF R&D STATUSES BY INDUSTRY TECHNOLOGICAL INTENSITY
(1)
R&D Firms
Actual
(2)
Non-R&D Firms
Actual
(3)
R&D Firms
Expected
(4)
Non-R&D Firms
Expected
Low-tech
1991
79.96%
93.44%
79.96%
93.44%
1992
62.48%
94.54%
51.68%
90.70%
1993
65.03%
94.86%
35.03%
86.74%
1994
54.42%
93.42%
23.81%
82.90%
1995
59.14%
95.84%
16.15%
80.78%
1996
53.44%
94.04%
12.05%
77.54%
1997
44.99%
93.37%
9.15%
75.47%
1998
59.14%
91.52%
7.56%
71.10%
1999
61.89%
92.59%
5.32%
68.14%
2000
58.74%
91.57%
4.15%
65.35%
Med-tech
1991
68.02%
94.95%
68.02%
94.95%
1992
71.83%
92.39%
53.95%
87.73%
1993
75.63%
93.17%
48.19%
85.31%
1994
68.53%
91.44%
35.97%
79.93%
1995
57.36%
89.78%
27.11%
75.75%
1996
57.61%
88.89%
24.93%
73.58%
1997
61.17%
90.55%
18.99%
69.82%
1998
61.42%
88.83%
15.62%
65.62%
1999
64.97%
89.54%
12.00%
61.55%
2000
57.61%
87.11%
9.56%
57.85%
High-tech
1991
92.02%
94.49%
92.02%
94.49%
1992
84.29%
90.90%
81.75%
89.20%
1993
84.54%
87.18%
73.35%
80.88%
1994
81.05%
87.18%
61.49%
74.67%
1995
84.04%
85.38%
59.17%
70.52%
1996
80.55%
81.67%
53.53%
64.92%
1997
73.82%
81.67%
45.49%
61.01%
1998
77.56%
83.46%
41.03%
57.40%
1999
81.30%
81.54%
37.12%
51.82%
2000
77.56%
81.41%
33.72%
49.71%
Notes: (i) Figures in columns (1) and (2) represent the percentage of R&D (non-R&D)
firms in 1990 that were in the same status in the listed years. We do not distinguish
between firms that perform R&D (do not perform R&D) continuously and firms that
change status. For example, in the percentage of 1996 in column (1) we include both
firms that performed R&D every year from 1990 to 1996 and firms that performed
R&D in 1990 and 1996, but not in one or more of the years in between. (ii) Figures in
columns (3) and (4) show the expected percentages if firms were chosen randomly
with annual transition rates given by the data (see Table II). (iii) The number of firms
in the sample has been upgraded to population numbers, as in Tables I and II.
41
TABLE IV: VARIABLES DEFINITION.
yi,t
yi,t-1
y i ,t − 2
yi ,t −1τ
y i ,t − 2τ
yi ,t −1dM
y i ,t − 2dM
yi ,t −1dH
y i ,t − 2dH
Year dummies
Export intensity
Expansive demand
Stable demand
Recessive demand
Industry dummies
Labour quality
Age
Age2/10
Size1
Size2
Size3
Number of competitors 0-10
Number of competitors 10-25
Number of competitors > 25
Market share
Dummy variable taking value 1 if firm i performs R&D
activities in year t, and 0 otherwise.
Dummy variable taking value 1 if the firm i performed R&D in
year t -1, and 0 otherwise.
Dummy variable taking value 1 if the last time that the firm i
performed R&D was two years ago, and 0 otherwise.
Dummy variable taking value 1if the firm has more than 200
workers ( τ = 1) and the firm performed R&D in year t –1, and 0
otherwise.
Dummy variable taking value 1if the firm has more than 200
workers ( τ = 1) and the firm was last time performing R&D two
years ago, and 0 otherwise.
Dummy variable taking value 1if the firm is from a medium
technological intensity industry ( dM = 1 ) and the firm
performed R&D in year t –1, and 0 otherwise.
Dummy variable taking value 1if the firm is from a medium
technological intensity industry ( dM = 1 ) and the firm was last
time performing R&D two years ago, and 0 otherwise.
Dummy variable taking value 1if the firm is from a high
technological intensity industry ( dH = 1 ) and the firm
performed R&D in year t –1, and 0 otherwise.
Dummy variable taking value 1if the firm is from a high
technological intensity industry ( dH = 1 ) and the firm was last
time performing R&D two years ago, and 0 otherwise.
Dummy variables taking value 1 for the corresponding year,
and 0 otherwise.
Exports to sales ratio (in %).
Dummy variable taking value 1 if the firm claims to face an
expansive demand, and 0 otherwise.
Dummy variable taking value 1 if the firm claims to face a
stable demand, and 0 otherwise.
Dummy variable taking value 1 if the firm claims to face a
recessive demand, and 0 otherwise.
Industry dummies accounting for 20 industrial sectors of the
NACE-93 classification. See Table V for the classification of
industries.
Ratio of the number of highly qualified workers to total
employment (R&D workforce not included) (in %).
Number of years since the firm was born.
Number of years since the firm was born to the square divided
by 10.
Dummy variable taking value 1 if the number of workers of the
firm is below or equal to 100, and 0 otherwise (R&D workforce
not included).
Dummy variable taking value 1 if the number of workers of the
firm is above 100 and below or equal to 200, and 0 otherwise
(R&D workforce not included).
Dummy variable taking value 1 if the number of workers of the
firm is above 200, and 0 otherwise. (R&D workforce not
included).
Dummy variable taking value 1 if the firm asserts to have less
than (or equal to) 10 competitors with significant market share
in its main market, and 0 otherwise.
Dummy variable taking value 1 if the firm asserts to have
more than 10 and less than (or equal to) 25 competitors with
significant market share in its main market, and 0 otherwise.
Dummy variable taking value 1 if the firm asserts to have
more than 25 competitors with significant market share in its
main market, and 0 otherwise.
Dummy variable taking value 1 if the firm asserts to account
for a significant market share in its main market, and 0
otherwise.
42
TABLE IV: VARIABLES DEFINITION (continued).
Appropriability
Region-specific spillovers
Industry-specific spillovers
Local-spillovers
Advertising intensity
Foreign capital participation
Corporate
Foreign Physical Equipment
Ratio of the total number of patents over the total number of
firms that assert to have achieved innovations in the firms
industrial sector (50 sectors of the three-digit NACE-93
classification) (in %).
Percentage of R&D investment over total sales for firms in
the same region but outside the corresponding NACE-93
industry (20 industries).
Percentage of R&D investment over total sales for firms in
the same NACE-93 industry (20 industries) but outside a
given region.
Percentage of R&D investment over total sales for firms in
the same region and in the same NACE-93 industry.
Advertising expenditure normalized by sales (in %).
Dummy variable taking value 1 if more than 25% of the firm
shares are foreign owned, and 0 otherwise.
Dummy variable taking value 1 if the firm is a limited
liability corporation, and 0 otherwise.
Firm’s average percentage of foreign physical equipment.
43
TABLE V: DYNAMIC MULTIVARIATE PROBIT MODEL FOR THE DECISION TO UNDERTAKE R&D ACTIVITIES.
Sunk costs parameters
Coefficient
Standard Error
1.832***
(0.200)
0
yi,t-1→
( γ s ,L )
y i ,t − 2 →
2
0.445***
(0.155)
0
0.289**
(0.120)
-0.187
(0.235)
0.098
(0.131)
-0.298
(0.225)
0.369**
(0.162)
( γ s ,L )
yi,t-1 τ → ( γ l − s )
2
y i ,t − 2 τ → ( γ l − s )
0
yi,t-1 d M → ( γ M − L )
2
y i ,t − 2 d M →( γ M − L )
0
yi,t-1 d H → ( γ H − L )
-0.127
2
y i ,t − 2 d H → ( γ H − L )
Time dummies
Year 1994
0.011
Year 1995
-0.115
Year 1996
0.071
Year 1997
-0.030
Year 1998
0.199**
Year 1999
0.058
Year 2000
-0.010
Economic opportunities
Export intensity
0.100***
Expansive demand
0.109**
Recessive demand
0.084
Technological opportunities
Low technological intensity industries
Beverages
0.250
Textiles
0.123
Leather and shoes
0.383*
Wood
-0.132
Paper
-0.018
Printing
-0.347*
Non metallic miner
0.184
Metallic products
0.181
Furniture
-0.039
Other manufacturing goods
-0.046
Medium technological intensity industries
Food and tobacco
-0.016
Rubber and plastic
0.353**
Metallurgy
0.251
Machinery and mech. eq.
0.537***
Motors and cars
0.408*
High technological intensity industries
0.034
Chemical products
0.559**
Office machines
0.112
Electronic
0.228
Other transport material
-0.308
Labour quality
0.017***
Firm success
Age
0.003
Age2/10
-0.0002
Size2
0.448***
Size3
0.617***
Market structure
Number competitors 10-25
-0.026
Number competitors >25
-0.013
Market share
0.085
Appropriability
0.019
Spillovers
Regional Spillovers
0.065
Industry Spillovers
0.062
Local Spillovers
0.010
Others
Advertising intensity
0.016**
Foreign capital participation
-0.183**
Corporate
0.120*
Foreign physical equipment
0.002***
Intercept
-2.417***
***, **, *, indicate significance at the 1%, 5% and 10%, respectively
1. Test Ho: ρts , ∀t ≠ s , jointly equal to zero:
2 = 92.43
χ 45
;
p − value = 0.000
(0.274)
(0.100)
(0.102)
(0.097)
(0.101)
(0.098)
(0.100)
(0.102)
(0.024)
(0.053)
(0.066)
(0.287)
(0.166)
(0.226)
(0.228)
(0.204)
(0.204)
(0.177)
(0.160)
(0.237)
(0.218)
(0.162)
(0.180)
(0.248)
(0.207)
(0.234)
(0.250)
(0.243)
(0.322)
(0.238)
(0.427)
(0.005)
(0.003)
(0.0002)
(0.103)
(0.113)
(0.067)
(0.091)
(0.053)
(0.018)
(0.043)
(0.078)
(0.019)
(0.007)
(0.079)
(0.065)
(0.0008)
(0.178)
.
2. Test Ho: ρts , ∀t (1990 <t ≤1992) and s (1992<s ≤2000 ) , jointly equal to zero:
2
χ16 = 97.019
;
p − value = 0.000
.
44
TABLE VI: PREDICTED PROBABILITY OF PERFORMING R&D (BASED ON ESTIMATES IN TABLE V).
25th percentile of
β xit
50th percentile of
β xit
75th percentile of
β xit
Firm by size group and
tech. intensity sector
(0,0)
(0,1)
(1,0)
(0,0)
(0,1)
(1,0)
(0,0)
(0,1)
(1,0)
Small-low tech.
Small-med tech.
Small-high tech.
0.026
0.033
0.066
0.068
0.046
0.118
0.457
0.539
0.757
0.042
0.067
0.102
0.100
0.089
0.170
0.542
0.668
0.823
0.071
0.124
0.164
0.153
0.157
0.254
0.641
0.780
0.889
Large-low tech.
Large-med tech.
Large-high tech.
0.163
0.207
0.217
0.235
0.196
0.257
0.873
0.920
0.956
0.208
0.282
0.286
0.289
0.269
0.332
0.904
0.950
0.973
0.265
0.352
0.401
0.356
0.338
0.452
0.932
0.967
0.987
Note: Each table entry is the predicted probability of performing R&D for a given combination of
R&D trajectory ( yt −1 , yt −2 ) and R&D profitability (percentile of β xit ).
TABLE VII: OBSERVED VS. PREDICTED FREQUENCIES OF yit TRAJECTORIES.
Observed
Predicted
Trajectory type
frequencies frequencies
Always non R&D firm
0.471
0.500
Begins as non R&D firm , switches once
0.073
0.087
Begins as non R&D firm, switches at least twice
0.131
0.103
Always an R&D firm
0.204
0.192
Begins as R&D firm, switches once
0.041
0.038
Begins as R&D firm, switches at least twice
0.081
0.079
45
Appendix.
Actual and Predicted Frequencies
R&D activities status
93 94
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
95
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
96
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
97
1
1
1
1
1
1
1
1
0
0
0
0
0
0
0
0
1
1
1
1
1
1
1
1
0
0
0
0
0
0
0
0
1
1
1
1
1
1
1
1
0
0
0
0
0
0
0
0
1
1
1
1
1
1
1
1
0
0
0
0
0
0
0
0
98
1
1
1
1
0
0
0
0
1
1
1
1
0
0
0
0
1
1
1
1
0
0
0
0
1
1
1
1
0
0
0
0
1
1
1
1
0
0
0
0
1
1
1
1
0
0
0
0
1
1
1
1
0
0
0
0
1
1
1
1
0
0
0
0
99
1
1
0
0
1
1
0
0
1
1
0
0
1
1
0
0
1
1
0
0
1
1
0
0
1
1
0
0
1
1
0
0
1
1
0
0
1
1
0
0
1
1
0
0
1
1
0
0
1
1
0
0
1
1
0
0
1
1
0
0
1
1
0
0
00
1
0
1
0
1
0
1
0
1
0
1
0
1
0
1
0
1
0
1
0
1
0
1
0
1
0
1
0
1
0
1
0
1
0
1
0
1
0
1
0
1
0
1
0
1
0
1
0
1
0
1
0
1
0
1
0
1
0
1
0
1
0
1
0
R&D activities status
Actual
Freq.
0.204
0.007
0.003
0.001
0.001
0.000
0.001
0.003
0.001
0.001
0.000
0.000
0.004
0.000
0.000
0.005
0.003
0.001
0.000
0.000
0.000
0.000
0.000
0.000
0.003
0.001
0.000
0.000
0.003
0.000
0.000
0.003
0.003
0.001
0.000
0.000
0.000
0.000
0.000
0.001
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.003
0.001
0.000
0.000
0.000
0.000
0.000
0.000
0.001
0.001
0.000
0.001
0.003
0.001
0.001
0.003
0.005
Expected
Freq.
0.192
0.003
0.001
0.004
0.001
0.000
0.004
0.003
0.004
0.000
0.000
0.000
0.001
0.000
0.003
0.003
0.004
0.000
0.000
0.000
0.000
0.000
0.000
0.003
0.001
0.000
0.000
0.000
0.000
0.001
0.003
0.005
0.009
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.003
0.000
0.000
0.001
0.004
0.000
0.000
0.000
0.001
0.001
0.001
0.001
0.001
0.000
0.000
0.000
0.001
0.000
0.001
0.009
93
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
46
94
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
95
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
96
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
97
1
1
1
1
1
1
1
1
0
0
0
0
0
0
0
0
1
1
1
1
1
1
1
1
0
0
0
0
0
0
0
0
1
1
1
1
1
1
1
1
0
0
0
0
0
0
0
0
1
1
1
1
1
1
1
1
0
0
0
0
0
0
0
0
98
1
1
1
1
0
0
0
0
1
1
1
1
0
0
0
0
1
1
1
1
0
0
0
0
1
1
1
1
0
0
0
0
1
1
1
1
0
0
0
0
1
1
1
1
0
0
0
0
1
1
1
1
0
0
0
0
1
1
1
1
0
0
0
0
99
1
1
0
0
1
1
0
0
1
1
0
0
1
1
0
0
1
1
0
0
1
1
0
0
1
1
0
0
1
1
0
0
1
1
0
0
1
1
0
0
1
1
0
0
1
1
0
0
1
1
0
0
1
1
0
0
1
1
0
0
1
1
0
0
00
1
0
1
0
1
0
1
0
1
0
1
0
1
0
1
0
1
0
1
0
1
0
1
0
1
0
1
0
1
0
1
0
1
0
1
0
1
0
1
0
1
0
1
0
1
0
1
0
1
0
1
0
1
0
1
0
1
0
1
0
1
0
1
0
Actual
Freq.
0.005
0.003
0.000
0.000
0.000
0.000
0.000
0.001
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.001
0.000
0.000
0.000
0.000
0.000
0.000
0.001
0.005
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.003
0.000
0.000
0.001
0.001
0.000
0.000
0.003
0.001
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.001
0.000
0.000
0.001
0.000
0.003
0.000
0.023
Expected
Freq.
0.001
0.003
0.001
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.001
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.003
0.000
0.000
0.000
0.000
0.000
0.001
0.000
0.001
0.000
0.000
0.000
0.000
0.001
0.000
0.000
0.001
0.000
0.000
0.000
0.001
0.000
0.000
0.001
0.005
0.001
0.000
0.000
0.000
0.000
0.000
0.015
Appendix.
Actual and Predicted Frequencies (cont.)
R&D activities status
93 94
0
1
0
1
0
1
0
1
0
1
0
1
0
1
0
1
0
1
0
1
0
1
0
1
0
1
0
1
0
1
0
1
0
1
0
1
0
1
0
1
0
1
0
1
0
1
0
1
0
1
0
1
0
1
0
1
0
1
0
1
0
1
0
1
0
1
0
1
0
1
0
1
0
1
0
1
0
1
0
1
0
1
0
1
0
1
0
1
0
1
0
1
0
1
0
1
0
1
0
1
0
1
0
1
0
1
0
1
0
1
0
1
0
1
0
1
0
1
0
1
0
1
0
1
0
1
0
1
95
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
96
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
97
1
1
1
1
1
1
1
1
0
0
0
0
0
0
0
0
1
1
1
1
1
1
1
1
0
0
0
0
0
0
0
0
1
1
1
1
1
1
1
1
0
0
0
0
0
0
0
0
1
1
1
1
1
1
1
1
0
0
0
0
0
0
0
0
98
1
1
1
1
0
0
0
0
1
1
1
1
0
0
0
0
1
1
1
1
0
0
0
0
1
1
1
1
0
0
0
0
1
1
1
1
0
0
0
0
1
1
1
1
0
0
0
0
1
1
1
1
0
0
0
0
1
1
1
1
0
0
0
0
99
1
1
0
0
1
1
0
0
1
1
0
0
1
1
0
0
1
1
0
0
1
1
0
0
1
1
0
0
1
1
0
0
1
1
0
0
1
1
0
0
1
1
0
0
1
1
0
0
1
1
0
0
1
1
0
0
1
1
0
0
1
1
0
0
00
1
0
1
0
1
0
1
0
1
0
1
0
1
0
1
0
1
0
1
0
1
0
1
0
1
0
1
0
1
0
1
0
1
0
1
0
1
0
1
0
1
0
1
0
1
0
1
0
1
0
1
0
1
0
1
0
1
0
1
0
1
0
1
0
R&D activities status
Actual
Freq.
0.011
0.003
0.004
0.000
0.003
0.000
0.001
0.003
0.001
0.000
0.000
0.001
0.001
0.000
0.000
0.000
0.001
0.000
0.000
0.001
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.007
0.000
0.000
0.000
0.000
0.000
0.000
0.001
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.001
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.001
0.001
0.000
0.000
0.012
Expected
Freq.
0.015
0.001
0.000
0.000
0.001
0.000
0.000
0.003
0.000
0.000
0.000
0.000
0.001
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.001
0.000
0.003
0.000
0.001
0.001
0.000
0.000
0.001
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.001
0.000
0.000
0.001
0.000
0.000
0.000
0.000
0.000
0.001
0.001
0.009
93
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
47
94
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
95
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
96
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
97
1
1
1
1
1
1
1
1
0
0
0
0
0
0
0
0
1
1
1
1
1
1
1
1
0
0
0
0
0
0
0
0
1
1
1
1
1
1
1
1
0
0
0
0
0
0
0
0
1
1
1
1
1
1
1
1
0
0
0
0
0
0
0
0
98
1
1
1
1
0
0
0
0
1
1
1
1
0
0
0
0
1
1
1
1
0
0
0
0
1
1
1
1
0
0
0
0
1
1
1
1
0
0
0
0
1
1
1
1
0
0
0
0
1
1
1
1
0
0
0
0
1
1
1
1
0
0
0
0
99
1
1
0
0
1
1
0
0
1
1
0
0
1
1
0
0
1
1
0
0
1
1
0
0
1
1
0
0
1
1
0
0
1
1
0
0
1
1
0
0
1
1
0
0
1
1
0
0
1
1
0
0
1
1
0
0
1
1
0
0
1
1
0
0
00
1
0
1
0
1
0
1
0
1
0
1
0
1
0
1
0
1
0
1
0
1
0
1
0
1
0
1
0
1
0
1
0
1
0
1
0
1
0
1
0
1
0
1
0
1
0
1
0
1
0
1
0
1
0
1
0
1
0
1
0
1
0
1
0
Actual
Freq.
0.004
0.001
0.001
0.003
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.001
0.001
0.001
0.001
0.001
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.001
0.000
0.000
0.003
0.000
0.000
0.001
0.001
0.012
0.000
0.000
0.000
0.001
0.001
0.001
0.001
0.003
0.000
0.000
0.001
0.000
0.001
0.000
0.009
0.007
0.005
0.001
0.005
0.001
0.000
0.000
0.007
0.016
0.005
0.001
0.008
0.012
0.008
0.019
0.471
Expected
Freq.
0.011
0.001
0.001
0.004
0.001
0.001
0.000
0.000
0.001
0.001
0.000
0.000
0.000
0.000
0.000
0.003
0.000
0.000
0.000
0.001
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.001
0.001
0.001
0.007
0.004
0.004
0.000
0.000
0.000
0.000
0.003
0.003
0.001
0.000
0.000
0.000
0.001
0.001
0.000
0.004
0.009
0.000
0.003
0.003
0.001
0.000
0.000
0.005
0.011
0.003
0.001
0.008
0.020
0.007
0.019
0.500