Endogenous Firm Heterogeneity, ICT and
R&D Incentives∗
Daniel CERQUERA†
Gordon J. KLEIN‡ZEW Mannheim
Germany
April 17, 2009

Abstract
Firm heterogeneity explains the productivity driven selection mechanism that determines
aggregate productivity growth within industries (i.e. creative destruction). This paper
empirically demonstrates that ICT has a robust impact on firm heterogeneity only when
ICT is used intensively and jointly with specific ICT applications. Moreover, ICT induced
heterogeneity exhibits a significant and positive, albeit small, effect on the decision toinvest in R&D personnel, suggesting a relevant role of ICT in the process of creative
destruction. The results are shown to be robust to different empirical strategies that
semiparametrically account for endogenous regressors and censored dependent variables.

Keywords: Firm Heterogeneity, Information and Communication Technologies, Creative
Destruction, R&D Incentives, Firm Level Data.
JEL Classification: D21, L25, O32.
∗

We would like to thank Irene Bertschek for valuable comments. We also gratefully acknowledge the comments and suggestions from participants at the 30th Hohenheimer Oberseminar at the University of Nuremberg
2008, the EU KLEMS Final Conference at the Universtiy of Groningen 2008 and the 35th Conference of theEuropean Association of Research in Industrial Economics (EARIE), Toulouse 2008. All errors are ours.
†
cerquera@zew.de. Centre for European Economic Research (ZEW), Mannheim, Germany. ResearchGroup Information and Communication Technologies. Corresponding author.
‡
klein@zew.de. Centre for European Economic Research (ZEW), Mannheim, Germany. Research Group
Information and Communication Technologies.

1

1

Introduction

The economic literature has provided robust evidence indicating that the adoption of information and communication technologies (ICT) has positively affected productivity at aggregate
and firm levels.1 Given the rapid technological progress observed in the production of ICT
and the uncertainty and costs associated with their implementation, it is not surprising thatthe benefits of ICT are not equally distributed among adopting firms. As some firms reap
those benefits sooner and better than others, the adoption of ICT represents a source offirm heterogeneity that might generate competitive advantages, affect firm strategies and/or
influence aggregate productivity growth.2
This paper empirically studies how the adoption of ICT affects firm heterogeneity and how
such (ICT induced) heterogeneity impacts R&D incentives. The analysis is based on two
established results from the empirical literature on the analysis of productivity at the firmlevel. The first result shows that there exist high and persistent firm level productivity
differences within industries (i.e. firm heterogeneity).3 Moreover, those differences explain
the process of creative destruction where more productive firms grow faster, exhibit a higher
probability of survival and displace low productivity firms.4 The second result in the literature
has documented how the adoption of ICT has a significant, positive impact on productivity
at the firm level.5 Although the latter suggests that ICT impacts firm heterogeneity, themain contribution of this paper is to account for the role of ICT on specific measures of firm
heterogeneity.
If the adoption of ICT is expected to affect firm heterogeneity, and firm heterogeneity inturn explains the process of creative destruction within industries, then the estimated ICT
induced heterogeneity should be also related to additional firm strategies relevant for firm
survival. The present analysis empirically assess the impact of the recovered ICT induced
heterogeneity on one of such strategies: firm specific R&D incentives. Even though firmlevel competitiveness and survival depends on a wide range of factors, it has been widely
recognized that innovative efforts are at the core of successful firm level strategies to tackle
competition.6 In the literature, R&D incentives are viewed as strategies that allow firms
to differentiate themselves with respect to their competitors through their impact on firm
1

See Draca et al. (2007), van Ark et al. (2008) and Jorgenson et al. (2008).
See Chun et al. (2008).
3
See Bartelsman and Doms (2000) for a survey.
4
See Foster et al. (2008) for a discussion on the role of productivity on the process of creative destruction.
5
See Draca et al. (2007) for a survey.
6
See Aghion et al. (2005) and Aghion and Griffith (2005).
2

1

level productivity.7 This paper investigates whether R&D incentives react to ICT induced
heterogeneity.
The present analysis exploits a detailed database on the economic performance of a representative sample of the German manufacturing and service sector (ZEW ICT Survey). This data
set contains information on the economic characteristics, performance, ICT use and innovative activity of the sampled firms for the years 2003 and 2006. ICT intensity is captured by
the percentage of employees that work mainly with a PC (PCW). A set of dichotomous variables showing the adoption of different ICT applications such as enterprise resource planningsystems (ERP), supply chain management (SCM) and customer relationship management
software (CRM) is also available in the data. Additionally, the data include information on
the innovative inputs (i.e. R&D) and outputs (i.e. innovations introduced to the market)of the sampled firms, as well as ICT related strategies such as ICT related consulting andoutsourcing activities.
Defining firm heterogeneity as the deviation of a firm’s productivity level from a given industry
benchmark, this paper shows that ICT has a robust, positive impact on firm heterogeneity
only when ICT is used intensively and jointly with specific ICT applications. That is, throughtheir use of ICT, firms are able to differentiate themselves (positively and negatively) withrespect to other firms belonging to the same economic sector. This result is shown to berobust to different empirical strategies. In addition, the analysis also shows that ICT induced
heterogeneity is not innocuous: it has a significant and positive, albeit small, impact on the
incentives to innovate. In particular, ICT induced heterogeneity is shown to positively affectthe decision to invest in R&D personnel.
The intuition behind this result states that firms are able to deviate from their competitors
in terms of productivity and through ICT only when the adopted infrastructure is large
enough (i.e. intensive use of PC accompanied by specific ICT applications). Given the costs
and resources required for the adoption of such infrastructure, positive deviations result
from the positive organizational impact of successful implementation of ICT (e.g. optimized
internal processes), while negative deviations are associated with adoption costs that are
not compensated by the benefits attached to the ICT infrastructure implemented (e.g. longterm benefits not yet obtained). As firms in practice might employ several market strategies
to differentiate themselves with respect to their competitors, it is not surprising that ICT
induced heterogeneity is positively correlated with other market strategies such as R&D
7

See Griliches (1995), Crepon et al. (1998) and Hall et al. (2008).

2

investments.
The paper is organized as follows. The second section discusses related literature. The third
section presents the details of the empirical strategy. The fourth section summarizes the main
results of the paper. Finally, the fifth section concludes.

2

Related LiteratureThis paper is related with two strands of literature. First, there is a voluminous literaturethat documents the existence and persistence of firm heterogeneity in terms of productivity.
This literature has been reviewed in Bertelsman and Doms (2000), and highlights the roleof several supply-side production factors that determine productivity at the firm level. For
instance, technology shocks, management skills, R&D investments, among others, are shown
to affect firm level productivity in traditional production function estimation approaches.
In addition, Syverson (2004) studies the impact of demand-side factors that determine differences in observed productivity levels. In particular, he finds evidence of the negative impact
of product substitutability within an industry on the dispersion of its productivity distribution. The lower the product substitutability within an industry, the more low productivity
firms are able to stay in business and the higher the corresponding dispersion of the industry’s
productivity distribution.
This literature has also considered the role of ICT in determining firm level heterogeneity.8
In that literature, the estimated positive impact of ICT on productivity is recovered as thecoefficient on a proxy for ICT capital from a production function estimation. That is, ICT
is shown to increase the central tendency of the conditional productivity distribution but issilent about the effect of ICT on the dispersion of this distribution. Moreover, negative andpositive impact of ICT are averaged out, making impossible to identify the potential negative
impact of ICT and its consequences for firm strategies.
This paper presents a measure of firm heterogeneity that explicitly accounts for the dispersion of the productivity distribution, allowing the analysis of the role of ICT use on firm
heterogeneity. In particular, firm heterogeneity is defined as the deviation of a firm’s productivity level from a given industry benchmark. In consequence, the adopted measure exhibits
two main advantages with respect to the existing literature. On the one hand, it permits to
8

See Bresnahan et al. (2002), Brynjolfsson and Hitt (2003), and Bloom et al. (2008).

3directly account for the role of ICT on firm heterogeneity in a way that cannot be inferredfrom existing production function estimation. On the other hand, the economic literature
has shown how firm heterogeneity (as previously defined) actually explains a great variety ofstrategic decisions at the firm level as the following literature review shows.
The second strand of literature related with the present paper analyzes the impact of firm
heterogeneity on firm strategies. Although there is a voluminous literature in this area, therelevant work for the present paper corresponds to the analyses that considered similar measures of firm heterogeneity from an empirical perspective. For instance, firm decentralization
decisions are theoretically and empirically explained by the role of firm heterogeneity (Acemoglu et al., 2007) and the nature of the relationship between innovation and competition
depends on the level of firm heterogeneity (Aghion et al., 2005).
More specifically, defining the distance to the technological frontier as the difference between
a firm’s productivity level and the highest productivity level observed in the same industry(i.e. a measure of firm heterogeneity), Acemoglu et al. (2007) develop a model to analyze
the relationship between the diffusion of new technologies and the decentralization of firms.
They show, theoretically and empirically, that firms closer to the technological frontier are
more likely to choose decentralization. The intuition behind this result states that these firms
deal with newer technologies about which there is less information available. In consequence,a decentralized structure allows them to benefit from better informed managers (as opposed
to principals).
In addition, and developing additional measures of firm heterogeneity, the paper shows how
firms in more heterogeneous environments are also more likely to be decentralized because firm
heterogeneity makes learning (i.e. how to exploit a new technology given the experience ofothers) more difficult. In a sequence of papers, Acemoglu et al. (2003), Aghion et al. (2005),
Acemoglu et al. (2006) and Aghion et al. (2006) use similar measures of heterogeneity in orderto study, among other topics, the relationship between innovation, entry, credit constraints
and competition. Essentially, these papers highlight the role of firm heterogeneity as a main
driver of industry evolution.9
In a different approach, Chun et al. (2008) directly estimate the impact of ICT use on
firm heterogeneity for a panel of U.S. firms from 1971 to 2000. They find that elevatedheterogeneity in firm performance (i.e. variability in labor productivity) is positively and9

See Bartelsman et al. (2006) for an analysis of industry evolution using similar measures of firm heterogeneity.

4

significantly correlated with the use of ICT (i.e. ICT capital stock). The results also show thatfirm heterogeneity is associated with faster productivity growth. They argue that the results
provide evidence of creative destruction (i.e. increased competition) at the firm level. Thatis, through their use of ICT, more productive firms displace less productive firms. However,their results can only be recovered at the industry level and do not permit the analysisof their impact on firm strategies. This paper contributes to this literature by explicitly
considering the impact of ICT on productivity heterogeneity at the firm level, analyzing theconsequences for specific firm strategies relevant to the process of creative destruction (i.e.
R&D incentives).

3

Empirical Analysis

3.1

Data

The analysis is based on two waves of a business survey carried out by the Centre for European
Economic Research (ZEW) corresponding to the years 2003 and 2006 (ZEW ICT Survey).
The data set is a representative sample of the German manufacturing and service sector,
and contains detailed information on the economic characteristics, performance and ICT use
for 4,400 firms in each wave. Table 1 provides some descriptive statistics for the year 2003.
In general, and in line with similar data sets at the firm level, the surveyed firms evidence
a great variability with respect to sales (in millions euros), number of employees and laborproductivity calculated as the ratio between sales and number of employees. In addition, theempirical distribution of the reported sales, as well as the number of employees appear to beleft skewed. The median size of the surveyed firm corresponds to 50 employees, whereas theaverage is about 337.7 employees. This indicates the presence of few very large companies inthe data.
Analogously, the median value of sales is 7 millions with an average of 131.7 millions. The
same also holds for the distribution of labor productivity where the median and the mean are
0.13 and 0.24 millions per employee, respectively. This skewness present in the distributions of
the reported data is consistent with empirical analyses at the firm level. Table 1 also includes
information about the use and intensity of ICT. The intensity in the use of ICT is measured
by the percentage of employees working mainly with a PC (PCW) and is nearly equallydistributed around the different percentiles. In addition, the data also provides information

5

about different ICT software applications, namely enterprise resource planning (ERP), supply
chain management (SCM) and customer relationship management (CRM).
Table 2 shows how the use of these different ICT applications are related with different firm
characteristics. These firm characteristics (e.g. sales and number of employees) vary if thefirm uses ICT more intense than the median (i.e. PCW greater than 50%) or introduced
ICT applications. Specifically, the table shows that the intense use of ICT, as well as theintroduction of different ICT applications, is correlated with higher firm performance interms of sales, number of employees and labor productivity. However, it should be noted thatstandard deviations are high through all the data, which indicates a high level of heterogeneity.
For example, the surveyed firms evidence 338 employees on average, whereas companies using
ERP, SCM or CRM tend to be larger with 518, 518 and 627 average number of employees,
respectively. The same can be observed in terms of sales. Firms using ICT applications orusing ICT more intensively were more likely to exhibit higher sales. The average level of sales
over all firms is 131.7 millions euros, whereas companies using CRM, SCM or ERP evidence
205.9, 222.8 and 209.2 millions, respectively. In general, these statistics show that firms using
ICT more intensively tend to be larger than their counterparts.
Moreover, firms that use ICT more intensively and that introduce ICT applications do not
only tend to be larger, but more efficient than other firms. In terms labor productivity (i.e.
ratio between sales and number of employees), it can be seen that the average is 0.24 million
euros per employee. This is higher for firms using ICT more extensively. Specifically, companies using ICT more intensively (i.e. PCW greater than 50%) exhibit a labor productivityof 0.31 millions euro per employee. Moreover, companies using CRM exhibit on average 0.27
millions euro per employee, while those using SCM and ERP evidence 0.26 millions and 0.27
millions per employee, respectively.
In addition to these general characteristics, the data also contain information regarding theinnovation activities of the sampled firms. For instance, as a proxy for innovation incentives,the data contain information on the fraction of employees working on R&D activities in 2006
(i.e. R&D intensity). The mean value of this variable is 0.17 with a median equal to 0.09,
indicating a left-skewed distribution.
In addition, information on innovation outputs is also available. This includes dummy variables indicating whether the firm introduced product or process innovations during the periods 2001-2003 and 2004-2006, the number of such innovation introduced during 2004-2006,6the percentage of sales reported in 2006 that are derived from the product innovations introduced during the period 2004-2006 (mean: 26.32, median: 20) and the percentage of cost
reductions achieved in 2006 from the introduction of process innovations during the period
2004-2006 (mean: 9.49, median: 8).
The measure of ICT intensity, percentage of employees working with a PC (PCW), is positively correlated to all measures of innovation. This does not hold for the ICT applications
considered. For instance, ERP use is related to a slightly higher amount of the value ofprocess innovations, while the value of product innovations slightly decreases from 27.4% to25.8%. The fraction of employees working on R&D activities (i.e. R&D intensity), however,is decreasing from 21.7% to 14.9% for firms that adopted ERP. This is different for the caseof CRM use. The value of product innovations, the value of process innovations and R&D
intensity are positively related with CRM adoption.
From the data set, the measure of firm heterogeneity at the firm level is constructed as
the absolute value of the deviation of a given firm labor productivity, with respect to themedian labor productivity in its corresponding industry. Deviations are taken in absolutevalues to derive a measure of heterogeneity associated with the dispersion of the productivity
distribution. Labor productivity is computed as the ratio between sales and number of
employees reported by the firms for a given year.10
Note that by taking absolute values positive, as well as negative deviations are treated equally.
Table 3 presents a quantile regression analysis of the firms’ specific deviations that further
motivates the adopted measure of firm heterogeneity previously defined. That is, the dependent variable corresponds to the deviation of a given firm labor productivity with respect tothe median labor productivity in its corresponding industry. In general, the analysis showsthat when used intensively, ICT induce firm heterogeneity by exerting a stronger positiveeffect on higher quantiles.
More specifically, Table 3 shows that the marginal effect of ERP for firms at the 10th percentile is equal to 0.22 (i.e. column 1) and 0.33 at the 90th percentiles (i.e. column 5).
Even though the size of the impact of ICT use on industry-specific productivity deviationsat intermediate quantiles is dependent on the econometric model adopted, the previous result is robust to alternative specifications. This suggests that high productivity firms canappropriate the benefits of ICT faster and better than low productivity firms, inducing firm
10

The sampled firms are classified into 14 industries. The banking and insurance sector was excluded fromthe analysis given the difficulties measuring sales. Firms with no obvious classification were also excluded.

7

heterogeneity.

3.2

Empirical Strategy

As mentioned before, the measure of firm heterogeneity at the firm level is constructed as theabsolute value of the deviation of a given firm labor productivity, with respect to the medianlabor productivity in its corresponding industry. Moreover, ICT intensity is captured by thepercentage of employees that work mainly with a PC (PCW). A set of dichotomous variables showing the adoption of different ICT applications such as enterprise resource planningsystems (ERP), supply chain management (SCM) and customer relationship management
software (CRM) is also available.
Additionally, the data includes information on the innovative inputs (i.e. R&D) and outputs(i.e. innovations introduced to the market) of the sampled firms. In the present analysis,
the fraction of total employees that is engaged in R&D activities is used as a measure of
the incentives to invest in R&D personnel (i.e. innovative activity). Additional general
information at the firm and sectoral level is also included.
The empirical analysis follows a two stage strategy. In the first stage the direct effect of
ICT on firm heterogeneity is estimated. This stage is implemented using OLS regressions,although the results are robust to different methods. In the second stage, the analysis follows
an instrumental variables approach, where firm heterogeneity is instrumented by different
measures of ICT use (providing a measure of ICT induced heterogeneity) and then relatedwith the firms’ R&D incentives.
Equations (1) and (2) describe the general relationship between ICT, firm heterogeneity (H )and innovation incentives (I ) and highlight the two stage instrumental variable strategy.
The key identification assumption corresponds to the exclusion of ICT from equation (2).
As discussed in the introduction, ICT generates firm heterogeneity, which in turn affect firm
strategies. Therefore, it is assumed that ICT affects innovation incentives only through theirimpact on firm heterogeneity. Different statistical tests support this assumption.

Hi,t = f (ICTi,t−1 , Xi,t−1 )(1)Ii,t = g(fi,t , Xi,t−1 )(2)8Specifically, Hi,t is the measure of firm heterogeneity for firm i in t, which corresponds to theyear 2006. ICTi,t−1 corresponds to the variables capturing the intensity of ICT use, as wellas the adoption of ICT applications (i.e. ERP, SCM and CRM) with t − 1 corresponding to
the year 2003. Ii,t represents firm i’s innovation incentives captured by the fraction of total
employees engaged in R&D activities. And Xi,t−1 is a matrix with control variables such as
the economic sector a firm belongs to (14 classes), the geographic location of the firm (eastor west Germany), as well as firm’s age up to 2007, the presence of exporting activities in
2003, among others.
The analysis considers alternative functional forms for f (·) and g(·). In particular, the firststage assumes a linear specification for f (·) and the main results are presented in Table 4.
The results are robust to nonlinear considerations of f (·). In order to consider this possibility,
a nonparametric analysis was performed. The analysis of the second stage also considered alinear specification for g(·). However, given the censoring present in the R&D data available,a (nonlinear) Tobit model was also considered. In addition, in order to check the robustnessof the results, an alternative semiparametric approach with a nonparametric first stage wasalso implemented (see Appendix A and B). The results of the second stage are presented inTable 5. Alternative robustness tests considering different samples (as it will be clear below),
as well as different types of information regarding innovation activities were also performed.

4

Results

The analysis of the first stage is presented in Table 4. In particular, the objective of the firststage is to consider the relationship between the intensity of ICT use and firm heterogeneity,controlling for different firm characteristics that might influence such relationship. Table 4
present the analysis by means of ordinary least squares regression, which amount to assume
a liner functional form for f (·) in equation (1). The results presented in Table 4 are robust
to nonlinear considerations of f (·). In order to consider this possibility, a nonparametricanalysis was performed.
In particular, the specification considered in column 1 of Table 4 estimates the direct impact of
the intensity of ICT use in 2003 on the firm heterogeneity observed in 2006. The coefficienton ICT shows no impact on the observed firm heterogeneity, suggesting no independent
impact of the intensity of ICT use. In order to account for the potential persistence in firm
heterogeneity derived by the persistence in productivity differences, column 2 includes a set
9

of dummy variables that locate each firm into the corresponding quartile of its sector specificproductivity distribution. As can be observed, the result of column 1 is maintained with thespecification presented in column 2.
However, as highlighted by the economic literature, the adoption of ICT implies reorganization at the firm level in order to exploit the benefits of the ICT implementation. One
dimension of potential complementary investments required to exploit the gains of ICT corresponds to the introduction of ICT (software) applications. By introducing specific ICT
applications, a given firm might achieve a minimal ICT infrastructure needed to reap the
benefits of the investments in computers and software.
In order to consider this possibility, the specification presented in column 3 includes specific ICT applications adopted by the sampled firms. In particular, EPR and SCM systems
described previously are included. The hypothesis behind this specification states that the
impact of ICT on firm heterogeneity does not only depend on the presence of ICT equipment,
but also on the way such infrastructure is used. However, as the results shown in columns 1
and 2, the coefficients on ICT intensity and ICT applications show no independent impact
on ICT on firm heterogeneity.
Column 4 extends the specifications presented in columns 1-3 to consider the interactions term
between ICT and ERP. It analyzes whether the impact of ICT is associated with complementarities between the different ICT components. The results show positive and significant
coefficients for the interaction term (coeff.: 0.19, std. error: 0.10), suggesting an importantcomplementarity between the different components of the ICT infrastructure adopted at thefirm level. That is, the impact of the intensity of ICT on firm heterogeneity is conditional on
the presence of ERP systems. In other words, there is a critical infrastructure needed in orderfor ICT to differentiate a firm with respect to his competitors (i.e. induce firm heterogeneity).
Note that if ICT is not used intensively, the marginal effect of the ERP applications on firm
heterogeneity is negative because the independent impact of ERP is negative (coeff: -0.11,
std. error: 0.05).
Column 5 considers and alternative hypothesis to the one presented in column 4. In particular,
it investigates the existence of complementarities in the introduction of ICT, but on different
ICT applications. It considers the interaction term between PCW and SCM. As can be clear
from the results presented in the table, the complementarity argument does not hold forthis type of application. Moreover, additional results not reported in Table 4 show that theoutcome of column 4 also extends to the consideration of the interaction term between PCW
10and CRM.
This result is not surprising at least for two reasons. First, the ERP application is a genericgeneral purpose software in comparison to the SCM and CRM applications. In consequence,
the impact of SCM and CRM might be related with particular activities of the sampled firmsthat are not captured with the available data. These activities might be related with thefirm specific relations with their suppliers or with costumers for the case of SCM and CRM,
respectively. Second, SCM and CRM applications tend to be adopted by firms after a basic
ICT infrastructure in general, and ERP systems in particular, are adopted successfully.
However, the data permit a testable hypothesis of the previous argument. That is, if there
is any complementarity between ICT components that is related with their characteristics
and/or timing of adoption, then the interaction terms to be considered simultaneously should
be PCW and ERP on the one hand, and ERP and SCM (or CRM) on the other hand.
In this manner, a regression analysis can capture the role of the intensity of ICT use asa determinant to adopt ERP system and, subsequently, provided that ERP systems wereadopted successfully, the impact of SCM or CRM should be conditional on the adoption of
ERP. This hypothesis is tested in column 6.
The result presented in column 6 shows that, indeed, the impact of ICT not only depends
on the introduction of ICT applications, but the complementarity between ICT components
is a determinant of firm heterogeneity. This is highlighted by the significant coefficient of
the interaction terms of PCW and ERP, and ERP and SCM (coeff: 0.19, std. error: 0.10
and coeff: 0.13, std. error: 0.07, respectively). Moreover, the observed complementarity is
consistent with the previous argument suggesting that such complementarities depend on thecharacteristics of the considered applications (e.g. timing of adoption). Note again that if
ICT is not used intensively, the marginal effect of the particular ICT applications on firm
heterogeneity is negative.
In sum, the results of Table 4 show that ICT affects firm heterogeneity only when ICT is used
intensively and jointly with particular ICT applications. These results are robust to nonparametric specifications. If ICT impacts productivity positively and induces heterogeneity, it canbe argued that ICT represent a source of volatility that stimulates the process of creative
destruction. If this is the case, firm strategies should react accordingly, specially strategies
that can provide a competitive advantage such as R&D initiatives. This is analyzed in Table
5 for a subsample of firms that reported ICT consulting activities in 2003. This selection
was performed in order to consider firms for which ICT infrastructure is important. Table 6
11

shows that the value and significance level of the coefficients using the full sample are very
close to the values reported in Table 5. However, the specification tests are inconclusive.
Table 5 presents the main results of the analysis and performs the two stage approach taken
into account the specification presented in column 4 of Table 4. Column 1 presents thebenchmark case considering an OLS specification. Following a instrumental variable approach, column 2 considers the impact of firm heterogeneity on R&D incentives where ICT
variables act as instruments. Column 2 shows a positive impact of ICT induced heterogeneity
on the fraction of employees that perform R&D (coeff: 0.06, std. error: 0.03). The reported
p-values of the endogeneity and overidentification tests (0.10 and 0.83, respectively) suggest
the validity of the IV approach and the exclusion restriction for the considered firms. In
order to verify the robustness of this result, column 3 and 4 consider a Tobit and IV Tobit
specification due to the censoring present in the R&D information. Interestingly, the results
are maintained, including the required specification tests.
In order to account for the parametric shortcomings associated with the estimations of Tobit models, column 5 presents the results of a semiparametric model proposed by Blundell
and Powell (2007). This approach accounts for censoring and endogenous regressors in asemiparametric setting (nonparametric first stage). Interestingly, the impact is still present
although its magnitude is clearly reduced. These results suggest that firm heterogeneity is afactor explaining firms’ strategic decisions and that ICT exert an important and independent
impact in generating such heterogeneity.

5

Robustness Checks

This section presents some alternative specifications in order to check the robustness of themain results. In particular, two different alternative specification of the two stage procedure
were investigated. First, the specifications presented in Table 5 were estimated for the wholeavailable sample and not only for firms that reported ICT consulting activities in 2003,
revealing the importance of ICT infrastructure in their operations. The results are presented
in Table 6. Second, alternative measures of innovations were also taken into account under
the same first stage specification. The alternative innovation measures correspond to thepercentage of sales reported in 2006 that are derived from the product innovations introduced
during the period 2004-2006, as well as the percentage of cost reductions achieved in 2006
from the introduction of process innovations during the period 2004-2006.
12

Consistent with the results reported in Table 5, Table 6 shows that the main results withrespect to the impact of ICT induced heterogeneity on the ratio of R&D employees and total
employees are maintained when the full sample is considered for the parametric approaches
presented in columns 1-4. Although the endogeneity tests suggest the validity of the twostage approach, the overidentification tests are inconclusive in the IV GMM and IV Tobit
specifications presented in columns 2 and 4 of Table 6 (i.e. rejection is achieved only at 10%,
while in Table 5 no rejection was possible). Within the analysis of this paper, rejection of theoveridentification test implies that the use of ICT does independently affect R&D incentives,
as well as firm heterogeneity. If rejection is achieved with high confidence, which is not thecase in the present analysis, the proposed two stage approach would be invalid.
In addition, instead of analyzing the impact of ICT induced heterogeneity on R&D incentives,
the analysis also considers the impact on the percentage of sales reported in 2006 that arederived from the product innovations introduced during the period 2004-2006 as a measureof innovative output. As the analysis presented in Table 5, the sample used corresponds
only to firms that reported ICT consulting activities in 2003 in order to consider the impact
of ICT induced heterogeneity on ICT intensive firms. In this case, the coefficients of theparametric specifications were significant although the specification test rejected the validityof the approach. This result is not surprising given the demand side factors (not consideredin the analysis) that might affect the relationship between firm heterogeneity and the benefitsof product innovation.
Similarly, the analysis also considers the case of a different measure of innovative output.
In this case, the percentage of cost reductions achieved in 2006 from the introduction ofprocess innovations during the period 2004-2006 was investigated. As before, the analysis
was performed for ICT intensive firms. The parametric results are consistent with the main
results of the paper reported in Table 5, including the specification tests. This result suggeststhat the adoption of ICT is a useful tool to differentiate a given firm from its competitors(i.e. first stage results are significant) and this ICT induced heterogeneity has an independent
impact on the firm innovative output (i.e. second stage results).

6

Conclusions

This paper studies how the adoption of information and communication technologies (ICT)
affects firm heterogeneity and thereby contributes to the productivity driven selection mech13

anism that determines aggregate productivity growth within industries. Given the well documented existence of high and persistent productivity differences within industries wheremore productive firms grow faster, exhibit a higher probability of survival and displace low
productivity firms, this paper attempts to specifically estimate the role of ICT on such firm
heterogeneity. Moreover, to explore the role of ICT induced heterogeneity on the ”creative
destruction” process, the results are related to observed firm strategies relevant to that process (i.e. R&D incentives and innovation outputs). The results show that ICT has a robust,
positive impact on firm heterogeneity only when ICT is used intensively and jointly withspecific ICT applications. In addition, the analysis showed that ICT induced heterogeneity
is not innocuous: it has a significant and positive, albeit small, impact on the incentives toinnovate. These results suggest that firm heterogeneity is a factor explaining firms’ strategic decisions and that ICT exert an important and independent impact in generating such
heterogeneity. Although the results are focused on the impact of ICT infrastructure on firm
heterogeneity, current work (in progress) explores the impact of ICT related strategies (i.e.
consulting and outsourcing) on such heterogeneity.

14

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and the Productivity Miracle, Working Paper, Stanford University.
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16

Appendix A: Blundell and Powell (2007)
The previous analysis included a semiparametric estimator proposed by Blundell and Powell(2007). This estimator takes simultaneously into account the presence of censoring in thedependent variable, as well as the existence of endogenous variables. The proposed estimatorfollows a two-stage approach, which estimates the censored model:0yi = max(0, xi β0 + ui )(3)0

The variable xi contains a set of regressors including an endogenous regressor, as well as anscalar error term ui . The endogenous regressor can be represented as a possibly nonparametricfunction of its instruments zi and its corresponding error term vi :

(e)

xi

= π(zi ) + vi

(4)

The estimation procedure follows in two stages. First, the conditional quantile qi ≡ Qi [yi |xi , zi ]of the dependent variables is estimated used a nonparametric quantile regression (See Appendix B). In the same stage, a control variable vi corresponding to the error of the nonpara(e)metric estimation of the endogenous regressor vi = xi − π(zi ) is derived (See Appendix B).
Subsequently, in a second stage the coefficients for βi are derived in a weighted least-squares
regression following a ”pairwise differencing” argument. The estimator is given by:Pvˆi −vˆjˆˆ
i<j Kv ( hn ) · ti tj (xi − xj ) · (qˆi − qˆj )(e)βi = Pvˆi −vˆj0
ˆˆ
i<j Kv ( hn ) · ti tj (xi − xj ) · (xi − xj )(5)

The variable Kv is the kernel function chosen. In this paper, an Epanechikov kernel was
employed. The term hn corresponds to the optimized bandwidth derived by a cross-validation
method and ti represents a trimming term ensuring that censoring is taken into account.
Standard Errors are derived by bootstrapping with 500 iterations.

17Appendix B: Multinominal Kernel regression
Estimation of the nonparametric kernel regression and nonparametric quantile regression are
described in Li and Racine (2007). Both procedures used kernel estimations with mixed data
continuous and categorical data to allow the use of discrete regressors. Importantly, bandwidths for the smoothing parameters are derived by data driven cross-validation methods.

Nonparametric Kernel regression
Li and Racine (2007) propose a method for nonparametric regression that includes both,
continuous and discrete regressors. They apply a kernel method based on work of Aitchison
and Aitkens (1976). The nonparametric function consider is:

Y i = g(Xi ) + ui

(6)

The vector Xi = (Xc , Xd ) contains continuous, as well as discrete variables. The modified
kernel estimator Kh,ix = (Wh,ix Lλ,ix ) contains an estimator for each, continuous variables
Wh,ix and discrete variables Lλ,ix . The resulting kernel function for both variables is definedas:Kh,ix =(X c − xc )1X
Wh0 ( i) · L(Xid , xd , λ)
n
h

(7)

with h and λε[0, c − 1/c] being non-stochastic smoothing parameter that are derived by across-validation bandwidth estimation. Wh0 is a kernel function for continuous variables
and L(Xid , xd , λ) for discrete variables. The continuous kernel can contain any usual kernel
available for continuous data (i.e. Epanechikov or Gaussian), while for discrete variables is
Aitchisons and Aitkens (1976).

181 − λ, if X d = xdd diil(X i , xi ) = λ ,otherwise.
c−1

(8)

The resulting Kernel estimator for both kinds of variables, the joint density function of(Xc , Xd ), is therefore:

1fˆ(x) =
n

X

Kh,ix

(9)

Estimation of gˆx is accounting for the joint density function and derived easily by:P
n−1 Yi Kh,ixfˆ(y, x)=gˆx = yfˆ(x)dyfˆ( x)(10)

Nonparametric quantile estimation
Li and Racine (2007) provide an estimator to derive conditional cumulative distribution function (CDF) nonparametrically by a kernel estimator and to determine the conditional quantile
of a function of continuous and dichotomous regressors. Estimation uses the same kernel function as in the nonparametric kernel regression also provided by Li and Racine (2007). In afirst step, the conditional cumulative distribution function (CDF) of Y is estimated:F̂ (y, x) =

n−1

P

i
G( y−Yh0 )Kh (Xi , x)
fˆx

(11)

Importantly, G is a kernel chosen to smooth Yi . After estimation of the CDF the conditional
quantile is given by the inverted conditional CDF. Therefore:q̂α (x) = inf {y : F (y|x) ≥ α} ≡ F̂ −1 (α|x)

19

(12)

In practice, q̂α (x) is derived by computation of the following objective function, minimizingq:q̂α (x) = arg min |α − F̂ (q|x)
q

20

(13)

48.89
0.61
0.52
0.41
0.61
0.17
26.32
9.49

% of Employees working (mainly) with PC

Use of Enterprise Resource Planning

Use of Customer Relationship Management Software

Use of Supply Chain Management Software

Heterogeneity (Absolute Deviation of median sector productivity)

R&D Employees (% of Employees)

Value of Product Innovations 2006 (% of Sales)

Value of Process Innovations 2006 (% of Cost Reductions)

Notes: The sample includes 8,800 observations from the ZEW-ICT Survey of 2003 and 2006.

0.24

337.73

Total Employees 2003

Labor Productivity (yi /li ) 2003

131.66

Mean

Sales (in millions Euro) 2003

Variable:

9.14

22.51

0.23

0.56

0.49

0.50

0.49

33.70

0.45

1,775.48

1,333.95

Standard
Deviation

Table 1: Descriptive Statistics

3

10

0.03

0.20

0

0

0

20

0.08

15.00

1.80

25%

8

20

0.09

0.45

0

1

1

40

0.13

50.00

7.00

50%

75%

15

30

0.20

0.84

1

1

1

80

0.23

200.0035.00

Percentiles

20

60

0.50

1.36

1

1

1

100

0.41

600.00130.0090%0.61(0.56)
48.89(33.70)17.36(23.26)26.32(22.51)
9.49(9.14)

Heterogeneity% of Employees working with PC

R&D Employees (% of Employees)

Value of Product Innovations 2006

Value of Process Innovations 2006

8.72
(8.37)

22.48(20.15)12.01(17.65)

20.46(12.10)

0.57
0.50

0.18
(0.27)

355.87(1693.77)

128.65(1574.16)

Below Median

10.50(10.08)31.08(24.63)24.66(27.73)86.49(14.86)

0.68
0.64

0.31
( 0.62)307.94(2000.08)

140.86(1096.37)

Above MedianNotes: The sample includes 8,800 observations from the ZEW-ICT Survey of 2003 and 2006.

0.24(0.45)

337.73(1,775.48)

Total Employees 2003

Labor Productivity (yi /li ) 2003

131.66(1,333.95)

EntireSampleSales (in millions Euro) 2003

Variable:% of employees working with a PC

9.57
(9.10)

25.80(22.04)

14.94(21.50)50.80(31.44)

0.59
0.56

0.26
(0.48)

518.45(2258.80)

205.91(1693.69)

With

9.34
(9.25)

27.40(23.44)21.72(25.57)45.92(36.75)

0.65
0.57

0.20
(0.39)

60.81(197.39)

10.53(33.04)

Without

By ERP use

Table 2: Descriptive Statistics - Means and Standard Deviations

9.61(8.82)

27.46(23.16)17.50(23.49)55.90(32.68)0.63(0.58)

0.27(0.53)

518.42(2368.738)

209.26(1751.40)

With

9.34
(9.56)

24.74(21.50)17.21(23.01)

41.25(33.15)0.59(0.55)

0.20(0.35)

143.17(660.20)44.63(582.04)

Without

By CRM use

9.51(8.75)

24.79(20.64)

14.56(21.14)49.15(30.49)0.59(0.55)

0.26(0.42)

626.89(2620.72)

222.28(1288.88)

With

9.48
(9.48)

27.54(23.85)19.51(24.56)48.71(35.83)0.62(0.62)

0.22(0.47)

133.59(655.76)

63.59(1368.33)

Without

By SCM use

Table 3: Quantile Regression: The Impact of ICT on Firm-Specific Deviation
Quantile Regression
10th

25th

50th

75th

90th

(1)

(2)

(3)

(4)

(5)

Labor 2003 (in logs)

0.0070(0.0410)-0.0236(0.0263)-0.0140(0.0211)-0.0467*(0.0269)-0.0156(0.0355)

Investment 2003 (in logs)

0.0465(0.0288)

0.0538***(0.0200)

0.0372**(0.0152)0.0487**(0.0195)

0.0435*(0.0245)% of Employees working with PC (PCW ) 0.4196**(0.1715)0.4473***(0.1077)

0.2456**(0.1012)0.2050*(0.1214)0.0500(0.1937)

Enterprise Resource Planning (ERP )0.2180*(0.1286)0.2674***(0.0853)

0.1705**(0.0721)0.0182(0.0839)-0.0939(0.1055)

PCW × ERP

-0.1465(0.1963)-0.3104**(0.1228)-0.0976(0.1175)

0.0864(0.1425)

0.3288*(0.1976)

1st Quartile (Labor Productivity)-0.4684***(0.1259)-0.8847***(0.0908)-1.1622***(0.0780)-1.4467***(0.0836)-1.6768***(0.1445)

2nd Quartile-0.0626(0.1152)-0.4618***(0.0868)-0.8179***(0.0768)-1.1144***(0.0788)-1.4276***(0.1298)

3rd Quartile

0.0976(0.1187)-0.2194**(0.0921)-0.4781***(0.0723)-0.7484***(0.0805)-1.0302***(0.1342)

Constant-1.0263***(0.2833)-0.0845(0.1843)

0.5231***(0.1358)

1.4416***(0.1749)

2.0767***(0.2586)

1027

1027

1027

1027

1027

0.20060.22010.25960.30950.3339Number of Observations
Pseudo-R

2

Bootstrapped standard errors with 800 replications are reported in parenthesis.
Industry dummies, location and exporting behavior included.
*** Significant at 1%.** Significant at 5%. * Significant at 10%.

Table 4: Impact of ICT on Firm-Specific Heterogeneity
Dependent Variable:

Ordinary Least Squares

Firm Heterogeneity(Absolute deviation from Median

(1)

(2)

(3)

(4)

(5)

(6)

Labor 2003

0.0033(0.0108)0.0015(0.0098)

0.0058(0.0117)0.0073(0.0117)0.0063(0.0117)0.0055(0.0117)

Firm Age-0.0010(0.0010)-0.0010(0.0010)-0.0010(0.0010)-0.0010(0.0010)-0.0010(0.0010)-0.0009(0.0010)% of Employees working with PC (PCW ) 0.1074(0.0668)

0.0315(0.0610)0.0396(0.0616)-0.0650(0.0832)0.0101(0.0709)-0.0635(0.0831)Enterprise Resource Planning (ERP )-0.0203(0.0370)-0.1077**(0.0536)-0.0189(0.0371)-0.1424**(0.0572)

Supply Chain Management (SCM )-0.0172(0.0325)-0.0145(0.0324)-0.0574(0.0515)-0.1051*(0.0557)

of Labor Productivity 2006 (in Logs.))

PCW × SCM

0.0865(0.0985)

PCW × ERP

0.1893*(0.0976)0.1869*(0.0974)

ERP × SCM

0.1283**(0.0654)

1st Quartile (Labor Productivity)-0.2499***(0.0454)-0.2522***(0.0455)-0.2562***(0.0455)-0.2519***(0.0455)-0.2555***(0.0455)

2nd Quartile-0.5423***(0.0426)-0.5439***(0.0425)-0.5416***(0.0426)-0.5402***(0.0428)-0.5405***(0.0426)

3rd Quartile-0.4371***(0.0443)-0.4357***(0.0444)-0.4345***(0.0443)-0.4339***(0.0446)-0.4352***(0.0444)0.6819***(0.0763)0.9892***(0.0808)

0.9856***(0.0816)

1.0225***(0.0831)0.9949***(0.0817)0.2973(0.1915)

Number of Observations

1251

1251

1251

1251

1251

1251

F-statistic

2.62

11.67

10.75

10.54

10.37

10.60

0.04900.18820.18770.19140.18920.1935Constant

R

2

Robust standard errors are reported in parenthesis. Industry dummies, location and exporting behavior included.
*** Significant at 1%.** Significant at 5%. * Significant at 10%.

Table 5: Impact of Firm-Specific Heterogeneity on R&D Intensity - ICT Intensive Firms
OLS

IV GMMTobit

IV Tobit

Censored
Semiparametric

(1)

(2)

(3)

(4)

(5)

First Stage(Dep. Var.: Firm Heterogeneity 2006)% of Employees working with PC (PCW )-0.0872(0.1935)-0.0872(0.1566)

0.0000(0.3033)

Enterprise Resource Planning (ERP )-0.1971**(0.0992)-0.1971**(0.0938)0.0000(0.4912)

PCW × ERP

0.3387*(0.1986)

0.3387**(0.1703)0.0405*(0.0175)

Labor 2003 (in Logs.)

0.0052(0.0155)

0.0052(0.0158)

Firm Age-0.0023**(0.0010)-0.0023**(0.0011)

1st Quartile (Labor Productivity)-0.2637***(0.0738)-0.2637***(0.0692)-0.0431***(0.0000)

2nd Quartile-0.5740***(0.0603)-0.5740***(0.0602)-0.1442***(0.0000)

3rd Quartile-0.4519***(0.0631)-0.4519***(0.0587)-0.1148***(0.0000)

Second Stage(Dep. Var.: R&D Intensity 2006)

Firm Heterogeneity (2006)

0.0130(0.0162)

0.0628*(0.0334)

0.0141(0.0159)

0.0720**(0.0352)

0.0026***(0.0009)

Labor 2003 (in Logs.)-0.0402***(0.0069)-0.0388***(0.0067)-0.0406***(0.0056)-0.0402***(0.0057)-0.2219***(0.0044)

Product Innovation

0.0437**(0.0192)

0.0416**(0.0195)

0.0509**(0.0211)0.0475**(0.0214)0.0033***(0.0005)

Firm Age

0.0009**(0.0004)

0.0009**(0.0004)

0.0009**(0.0004)

0.0011**(0.0004)-0.5311***(0.0277)

505

505

505

Number of Observations

505

505

Hansen’s J-Test (p-value)

0.83

Hausman Test (p-value)

0.10

Amemiya-Lee-Newey Test (p-value)

0.75

Wald Test (p-value)

0.06

Robust standard errors are reported in parenthesis. Industry dummies, location and exporting behavior included.
First stage in column 5 reports estimated gradients evaluated at the median of the explanatory variable. p-values
from a significance tests are reported in parenthesis. Second stage estimations in column 5 are in thousands andare calculated from a subsample of the large data set after pairwise differencing. See Blundell and Powell (2007).
For the second stage in column 5 bootstrapped standard errors with 500 replications are reported in parenthesis.
*** Significant at 1%.** Significant at 5%. *Significant at 10%.

Table 6: Impact of Firm-Specific Heterogeneity on R&D Intensity - Full Sample
OLS

IV GMMTobit

IV Tobit

Censored
Semiparametric

(1)

(2)

(3)

(4)

(5)

First Stage(Dep. Var.: Firm Heterogeneity 2006)% of Employees working with PC (PCW )-0.1242(0.1006)-0.1242(0.0922)0.0012***(0.0000)

Enterprise Resource Planning (ERP )-0.1567**(0.0631)-0.1567**(0.0651)-0.0025(0.1930)

PCW × ERP

0.27361**(0.1162)

0.2736**(0.1070)0.0000(0.3634)

Labor 2003 (in Logs.)

0.0069(0.0117)0.0069(0.0122)

Firm Age-0.0022***(0.0008)-0.0022**(0.0009)

1st Quartile (Labor Productivity)-0.2645***(0.0512)-0.2645***(0.0501)-0.0485***(0.0000)

2nd Quartile-0.5279***(0.0472)-0.5279***(0.0447)-0.1404***(0.0000)

3rd Quartile-0.4316***(0.0481)-0.4316***(0.0460)-0.1026***(0.0000)

Second Stage(Dep. Var.: R&D Intensity 2006)

Firm Heterogeneity (2006)-0.0076(0.0131)0.0588*(0.0304)

0.0062(0.0124)0.0497*(0.0306)

0.0005(0.0004)

Labor 2003 (in Logs.)-0.0490***(0.0051)-0.0466***(0.0050)-0.0497***(0.0045)-0.0497***(0.0045)-0.2647***(0.0046)

Product Innovation

0.0563***(0.0142)0.0540***(0.0139)0.0655***(0.0163)0.0647***(0.0162)0.0003(0.0004)

Firm Age

0.0008**(0.0004)

0.0009**(0.0004)

0.0009**(0.0004)

0.0010**(0.0004)-0.6045***(0.0173)

922

922

922

Number of Observations

922

922

Hansen’s J-Test (p-value)

0.09

Hausman Test (p-value)

0.09

Amemiya-Lee-Newey Test (p-value)

0.01

Wald Test (p-value)

0.12

Robust standard errors are reported in parenthesis. Industry dummies, location and exporting behavior included.
First stage in column 5 reports estimated gradients evaluated at the median of the explanatory variable. p-values
from a significance tests are reported in parenthesis. Second stage estimations in column 5 are in thousands andare calculated from a subsample of the large data set after pairwise differencing. See Blundell and Powell (2007).
For the second stage in column 5 bootstrapped standard errors with 500 replications are reported in parenthesis.
*** Significant at 1%.** Significant at 5%. *Significant at 10%.