The Relationship between Product and ProcessInnovations and Firm Performance:

Microeconometric Evidence

Bettina Peters*

Preliminary Draft2005-03-15

Abstract

A growing number of studies have recently analysed the entire link between innovation

input, innovation output and productivity (CDM model), thereby trying to shed some light

into the ”black box” of the innovation process at the firm level. One problem in previous

studies is that they only use an output indicator for product innovations. Using German data

from the CIS3 (3rd Community Innovation Survey) performed in 2001, this paper enlarges the

CDM model by including an additional equation for the output of process innovations. This

new indicator is measured as the share of reduction in unit costs due to process innovations.

Estimates have shown that the growth rate of labour productivity increases significantly with

the success of product innovations, but no similar effect pertaining to process innovations is

observed.

Keywords: Innovation, success, productivity, applied microeconometricsJEL-Classification: O33, O32, C34

Contact: * Centre for European Economic Research (ZEW), Department of Industrial Economics and InternationalManagement, P.O.Box 10 34 43, D-68034 Mannheim, Germany, E-mail: b.peters@zew.de .

I gratefully acknowledge the comments of Jacques Mairesse and Hans Lööf. The usual disclaimer applies.

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1 Introduction

Analysing and quantifying the productivity effects of innovation activities has been one

of the most challenging tasks in empirical economics for several decades. In the 1990s, this

research topic was reinforced by new theoretical underpinnings of endogenous growth theory

(see, e.g. Romer, 1990). But despite a large number of empirical studies, innovation research

has only been partly successful in explaining and even measuring the effect of product and

process innovations on productivity, at least at the firm level (see Griliches, 1995).

For a long time, empirical innovation research has focused on input-oriented innovation

indicators when analyzing the impact of innovation on productivity. The majority of these

studies have used the production-function approach including R&D-based measures as

additional input factors. However, it is well known that R&D does not capture all aspects

pertinent to innovation. Innovation activities close to the market are not captured by the

concept of R&D; such activities of small and medium-sized as well as service sector firms are

particularly heavily underestimated. Furthermore, within this simple production-function

approach, the innovation process itself -- that is, the link between the resources devoted to the

innovation process and its outcome -- is treated as a black box. Patents have been seen as a

means of overcoming this deficiency. However, patent-based indicators have been heavily

criticised as being a poor indicator of innovative outcomes (Griliches, 1990).

Since the mid-1990s, the focus of empirical innovation studies has changed in favour of

output-oriented innovation indicators when measuring aspects of innovative activities like

productivity or employment effects. The economic explanation for this is that the process of

learning involves successful implementation rather than just the resources devoted to the

innovation projects (see, e.g. Blundell et al., 1993, Crépon et al., 1998, LLorca, 2002).

Empirical innovation research has benefited from the development of the Oslo Manual

(OECD, Eurostat, 1992, 1997) and the release of new, internationally harmonized survey data

known as the Community Innovation Surveys (CIS) starting in the first half of the 1990s. The

Oslo Manual provides a unique definition of innovation and innovation output. Share of sales

due to product innovations, which can be interpreted as sales weighted innovation counts,

serves as the key output indicator.

Crépon, Duguet and Mairesse (1998) propose and estimate a model which establishes a

relationship among innovation input, innovation output and productivity. The general

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structure of this so-called CDM approach can be interpreted as a three-step model consisting

of four equations. In the first step, firms decide whether to engage in innovation activities and

on the amount of money to invest in innovation. Given a firm's decision to invest in

innovative projects, the second step defines the knowledge production function following

Pakes and Griliches (1984) in which innovation output results from innovation input and

other factors. In the third step, the augmented Cobb-Douglas production function describes

the effect of innovative output on productivity. The information provided allows a look into

the ”black box” of the innovation process at the firm level, not only to analyse the relationship

between innovation input and productivity but also to shed some light on the process in

between. By incorporating the greater structure of the innovation process and by using the

rich CIS innovation data sources, a step forward in the search for identification of

innovation’s contribution to productivity is possible (Van Leeuwen, 2002).

There are a growing number of national firm-level studies on the innovation-productivity

link using CIS data and versions of the CDM, e.g. Crépon et al. (1998), Lööf, Heshmati

(2001), Klomp, Van Leeuwen (2001, 2002), Van Leeuwen (2002), Duguet (2004) and a few

cross-country comparisons at the firm level, see, e.g. Mairesse, Mohnen (2001), Janz, Lööf,

Peters (2003) or Lööf et al. (2003).

Nevertheless, there are some problems involved in the CDM framework itself and in

applying the CIS data to this type of model. The first is related to the fact that the model only

contains an equation for product innovations as the output of innovation activities, while the

input measure (R&D or innovation expenditure) is related to product and process innovations.

Additionally, firms can obviously increase their (labour) productivity through product as well

as process innovations. Up to now, related studies have totally ignored these problems or have

merely incorporated a dummy variable to control for the process innovations issue.1 In

contrast to innovation surveys in other European countries, those conducted in Germany

1 Llorca (2002) analyses the impact of process innovations on productivity growth using number of productand process innovations. However, the exact number of process innovations is not available and is proxied bya variable taking the value 0 if the firm has not obtained a process innovation. 1 if it has introduced a newmachine or new production method and the value 2 if it has obtained both a new machine and a new method.However, Llorca (2002) does not analyse the whole innovation input, output and productivity relationship.

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include an output indicator for process innovations as well.2 The output or success of process

innovation is measured as the share of cost reduction in unit costs due to process innovations,

which can therefore be interpreted as cost-weighted innovation counts.

The objective of this paper is to enlarge the CDM model by including an output equation

for process innovations and to perform an unprecedented analysis of the determinants of the

process innovation success. The main research questions addressed in this paper are whether

different factors are more crucial to the success of process innovations as compared to product

innovations and whether firms are more successful on average in increasing their labour

productivity by means of product or process innovations.

The outline of the paper is as follows. Section 2 describes the data set used for the

empirical analysis, comprises some information on the data treatment and presents some

descriptive statistics on innovation behaviour. Section 3 presents the empirical model.

Potential factors explaining innovation input, innovation output and productivity in theoretical

models and empirical studies will be explored in Section 4; the empirical implementation of

the model is also described here. The econometric results are presented in Section 5, and

Section 6 concludes the paper.

2 Data and Descriptive Statistics

The data set used is based on the 2001 official innovation survey of German

manufacturing industries, which has been the German part of the Community Innovation

Surveys (CIS3).3 The survey covers legally independent German firms with at least five

employees and is representative of the target population. The sample of the innovation survey

is drawn as a stratified random sample and is representative. Firm size (eight size classes

according to number of employees), branch of industry (according to two-digit NACE

classes) and region (Eastern and Western Germany) serve as stratifying variables. The

innovation survey is a voluntary mail survey. For a detailed description of the survey

methodology as well as the surveyed information, see Janz et al. (2001).

2 Within the context of the actual revision of the Oslo Manual, one major aspect is the development of outputindicators for process innovations.

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The survey collected data on 2,291 firms, 1,989 of which were in manufacturing (NACE

15-37) and the rest in mining, quarrying, electricity, gas and water supply, and construction.

For this analysis, I restrict the sample to firms belonging to manufacturing industries. In

addition, outliers (for which labour productivity growth turns out to be higher than 300 %)

were eliminated and firms with incomplete data for relevant variables were dropped for

estimation purposes. The total number of observations remaining for the empirical analysis is

1,163. Due to the relatively high number of cancelled observations it seems necessary to

check whether the sample is still representative. Thus, Table A1 in the Appendix provides an

overview of the branches and their distribution across the entire population, the total sample

and the innovative sample applied for the empirical analysis. Table A2 contains

corresponding information on the distribution by size class. However, the elimination of

observations only changes the distribution by sector and size class slightly when compared to

the total sample.4 Hence, the sample used still seems to be representative.

The data set includes some general firm information (e.g. sales, employment, skill

structure of employees, main market, export, gross investments in tangible goods, training

expenditure). For those firms which had introduced a new product or process (even

uncompleted or abandoned) within the previous three years, additional information on the

kind of innovation, innovation and R&D expenditure, innovation output and the firms’

innovation behaviour (e.g. cooperation, sources, protection) is available.

Table 1 contains descriptive statistics on the above mentioned general firm characteristics

differentiated by innovation state (non-innovative versus innovative firms). For the empirical

analysis, innovative firms are defined as firms with product or process innovations and

positive innovation expenditure in the year 2000 (see Table A3 in the appendix for a detailed

definition and calculation of all variables). About 57 per cent of the manufacturing enterprises

introduced at least one product or process innovation in the reference period. Innovative firms

tend to treat global markets as their main market to a greater extent and thus received a greater

3 In Germany the survey was conducted by the Centre for European Economic Research (ZEW) on behalf ofthe German government.

4 The difference in the size distribution between the population and the total sample is due to the fact that thesampling is disproportional, i.e., the sampling probabilities vary between cells: Large firms, firms fromEastern Germany and firms from heterogeneous cells according to labour productivity are oversampled. SeeJanz et al. (2001).

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share of exports in sales. This is a well known and often found relationship between

innovation and export activities.5 Furthermore, the average share of highly skilled employees

is nearly two times greater in the innovative (16.3) compared to the non-innovative sample

(8.4). Innovative firms also spend more money on training their employees, thereby

increasing their knowledge base. Labour productivity and its corresponding nominal growth

rate between 1998 and 2000 is also higher in innovative firms (€272,700 and 10.4 per cent,

respectively) compared to non-innovators (€260,700 and 9.5 per cent). However, the averages

are not significantly different between the samples.

Table 1: Descriptive Statistics for Non-innovative and Innovative Firms

Non-innovative firms(N=501)

Innovative firms(N=662)

Mean differencetest

median mean s.d. median mean s.d. teststat. p-value Labour productivity* 183.3 260.7 257.9 221.1 272.7 207.2 -0.852 0.395 Labour productivity growth 0.060 0.095 0.263 0.068 0.104 0.305 -0.545 0.586 Highly skilled employees 0.050 0.084 0.108 0.100 0.163 0.168 -9.662 0.000 Physical capital invest./sales 0.029 0.065 0.144 0.043 0.078 0.132 -1.654 0.098 Training expend./sales 0.001 0.002 0.009 0.002 0.004 0.009 -3.043 0.002 Training expend./emp.* 0.167 0.442 1.237 0.399 0.819 1.362 -4.920 0.000 Age 12 17.91 15.55 12 18.88 17.20 -1.004 0.316 Export / sales 0.025 0.123 0.194 0.186 0.251 0.243 -9.998 0.000 Exporter 0.601 0.490 0.819 0.386 -8213 0.000 Group 0.208 0.406 0.415 0.493 -7.875 0.000 Entry 0.039 0.194 0.039 0.194 -1.285 0.199 Merger 0.030 0.171 0.060 0.238 -2.541 0.011 Closure 0.024 0.153 0.029 0.167 -0.503 0.615 Region 0.323 0.468 0.319 0.466 0.167 0.867 Market orientation - national <50km 0.253 0.435 0.077 0.267 8.003 0.000 - international <50km 0.032 0.176 0.017 0.128 1.647 0.100 - national >50km 0.385 0.487 0.349 0.477 1.269 0.205 - international >50km 0.197 0.399 0.424 0.495 -8.657 0.000

Notes: Innovative firms are defined as firms with product or process innovations and positive innovationexpenditure in the year 2000.

* in thousands of euros

Looking at the core innovation indicators, we can see that on average, 7.1 per cent of

turnover is spent on innovation projects. The median is clearly lower at 3.7 per cent.

5 However, one cannot draw any conclusions from this figure regarding the causality between innovation andexport activity. For the German service sector, Ebling und Janz (1999) show that causality runs frominnovation to export activities.

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Furthermore, the innovative firms experienced a 3.2 per cent reduction in unit costs, and an

average of 21 per cent of their sales stemmed from product innovations.

Table 2: Innovation Characteristics of Innovative FirmsInnovative Sample

median mean s.d. Innovation input / sales 0.037 0.071 0.126 Innovation input / emp. * (Innovation Input) 8.102 14.749 22.143 Innovative sales / sales 0.150 0.213 0.235 Innovative sales / emp. (Product Innovation Output) * 32.002 58.724 12.223 Cost savings / unit cost 0.000 0.034 0.058 Cost savings / emp. (Process Innovation Output) * 0.000 6.603 15.197 Product innovations 0.769 0.424 Process innovations 0.565 0.496 Cost-reducing process innovations 0.414 0.493 Continuous R&D 0.547 0.498 Public funding 0.402 0.491 Cooperation - Science 0.255 0.436 - Clients 0.137 0.345 - Competitors 0.109 0.312 - Suppliers 0.127 0.333 Sources - Science 0.644 0.479 - Clients 0.921 0.269 - Competitors 0.847 0.360 - Suppliers 0.816 0.388 - Internal 0.955 0.208 - Consultants 0.468 0.499 - Professional conferences, meetings, journals 0.850 0.357 - Fairs, exhibitions 0.912 0.283 In-house product development mainly 0.560 0.497 In-house process development mainly 0.316 0.465 Internal & external product development 0.142 0.349 Internal & external process development 0.159 0.366

Notes: Innovative firms are defined as firms with product or process innovations and positive innovationexpenditure in the year 2000.

* in thousands of euros

Another important aspect seen in Table 2 is the fact that only three out of four innovative

firms launched new products (read: reported product innovation output figures). Process

innovations are less prevalent, accounting for 57 per cent of all innovators. The introduction

of new production technologies may be motivated by several different factors. Process

innovations may seek to assure that products meet new legal requirements or quality

improvements, or firms can introduce new technologies in order to produce a new product.

Last but not least, process innovations may be intended for rationalisation in terms of

reducing average production costs. The German CIS data provided a distinction between

firms with rationalisation innovations and other process innovators. In our sample three out of

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four process innovators -- 41 per cent of all innovative firms -- introduced new production

technologies to rationalize processes. Therefore, a censoring problem occurs for both

variables and has to be taken into account in the estimation of the output equations.

3 Empirical Model

The original CDM model describes the relationship among R&D, innovation output and

productivity. Lööf and Heshmati (2001) were the first to slightly modify this model by using

innovation input rather than R&D input.6 This paper will rely on the modified version of the

CDM approach; the model will be extended by specifying an additional equation for the

outcome of process innovations.

The CDM approach recognises that not all firms engage in innovation activities. It is well

known that in this case, a restriction to the selected (innovative) sample would imply biased

estimates (see, e.g. Heckman, 1976, 1979). As a result, the selection equation (1) ascertaining

whether a firm introduced new products or processes is modelled in the first stage. Let *0iy be

a latent (unobserved) endogenous variable measuring propensity to innovate.7 The latent

variable can be interpreted as some decision criterion, such as the expected present value of a

firm’s profit accruing to innovations (see Crépon et al., 1998). If *0iy is larger than a constant

threshold (without any loss of generality we assume zero), we observe that firm i introduces a

new product or production technology. 0iy is the observed binary endogenous variable, taking

the value zero for non-innovative and 1 for innovative firms. On the condition that firm i

innovates, we can observe the amount of resources 1iy devoted to product and process

innovations (equation 2). However, information on expenditure is not available for products

and processes separately.

6 Innovation input involves expenditure for the following innovation activities: intra- and extramural R&D,acquisition of machinery and equipment related to innovations, acquisition of other external knowledge,training, market introduction of innovations, design and other preparations for production/deliveries (seeOECD and Eurostat, 1997).

7 The following convention holds: Variables with a * characterize latent variables; all other variables areobservable.

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*0 0 0 0

0 *0 0 0 0

1 if 00 if 0

i i ii

i i i

y Xy

y Xβ εβ ε

� = + >= � = + ≤�

(1)

*1 1 1 1 1 0if 1i i i i iy y X yβ ε= = + = (2)

Equation (3) and (4) describe the transformation process (knowledge production) from

innovation input to innovation output. As previously mentioned, not all innovators introduce

both new products and processes. Only on the condition that firm i launched at least one new

product can we measure the success of this activity ( 2iy ) and that of rationalisation

innovations ( 3iy ). Thus, we specify two Tobit models to explain the success of new products

and processes, respectively:

* * *2 1 1 2 2 2 1 1 2 2 2

2 *1 1 2 2 2

if 00 if 0

i i i i i i ii

i i i

y y X y Xy

y Xα β ε α β ε

α β ε� = + + + + >

= � + + ≤�

(3)

* * *3 2 1 3 3 3 2 1 3 3 3

3 *2 1 3 3 3

if 00 if 0

i i i i i i ii

i i i

y y X y Xy

y Xα β ε α β ε

α β ε� = + + + + >

= � + + ≤�

(4)

The third stage describes the link between productivity 4iy and innovation output using

an augmented Cobb-Douglas production function. According to Crépon et al. (1998) we

specify the model in latent variables.

* *4 1 2 2 3 4 4 4 0if 1i i i i i iy y y X yγ γ β ε= + + + = (5)

0iX , 1iX , 2iX , 3iX and 4iX are vectors of various variables explaining innovation

decision, innovation input, innovation output and productivity. The inverse Mills ratio

(Heckman, 1979) is included in 1iX , 2iX , 3iX and 4iX to correct for possible selection bias.

The specification of the model is explained in more detail in the next section. The β ‘s, α ‘s

and γ ‘s are the unknown parameter vectors.

0iε and 1iε are bivariate normal with zero mean, variances 20 1σ = and 2

1σ and correlation

coefficient ρ . 2iε , 3iε and 4iε are i.i.d. drawings from a normal distribution with ( )20, jN σ

for 2,3,4j = . 1iε , 2iε , 3iε and 4iε are uncorrelated. In other words, I assume a recursive

structure in model equations (2)-(5). Innovation input explains the success of new products as

well as production technologies and the output of the innovative activities are endogenous in

the productivity equation. For estimation purposes I therefore apply a three-step estimation

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procedure. In the first step the generalized Tobit model (equations (1) and (2)) is estimated by

full maximum-likelihood techniques. In the second step both innovation output equations are

separately estimated applying Tobit procedures8, using the predicted values of the input

variable as explanatory variables. In the last step the productivity equation is estimated by LS

using the predicted values from the second step.

4 Factors Explaining Innovation Input, Innovation Output as well as

Productivity and Empirical Specification

4.1 Innovation Input

As mentioned above, innovative firms are defined as firms with product or process

innovations and positive innovation expenditure in the year 2000. Innovation input is

measured by innovation intensity, which is defined as the ratio of innovation expenditure to

total sales.

Theoretical and empirical studies have identified a whole array of innovation

determinants. In accordance with the Schumpeterian tradition I include firm size and market

structure as explanatory variables in both the selection and input equations. The first

Schumpeter hypothesis claims that innovation activity increases more proportionately than

firm size9 (see Schumpeter, 1942). In contrast, later studies postulate a non-linear, U-shaped

relationship between innovation intensity and firm size (see, e.g. Kamien and Schwarz, 1975).

To allow for potential non-linearities, I add the logarithm of number of employees and the

squared logarithm of number of employees. The second Schumpeter hypothesis states that ex

8 The left censored Tobit model can be estimated efficiently by ML methods if the errors are homoscedasticand follow a normal distribution. If the errors are heteroscedastic or non-normal, it is necessary to specifytheir functional form. The ML estimator is inconsistent in the case of misspecification. I estimate ahomoscedastic as well as a heteroscedastic Tobit model, where heteroscedasticity is induced by industries. Incase of product innovations, a likelihood ratio test rejects the homoscedastic model, but not in case of processinnovations.

9 A survey of empirical studies testing the Schumpeter hypotheses can be found in Cohen (1995), Cohen andKlepper (1996) or Klette and Kortum (2002). As reported in the surveys, size has been found to be a highlysignificant determinant of firms’ engagement in innovation.

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ante market power stimulates innovation activities. The market structure is captured by the

Herfindahl index from the previous year measured on a three-digit level.10

The modern innovation literature stresses that there are additional firm-level determinants

of innovation activities other than firm size and market structure. Cohen (1995) distinguished

between firm and industry or market characteristics.

Two widely-considered firm characteristics explaining innovation activities are product

diversification (see Nelson, 1959) and availability of financial resources (see, e.g. Müller,

1967, Bond et al., 1999, Kukuk and Stadler, 2001). As the CIS3 data set does not contain

information on product diversification, we cannot take this hypothesis into account.

Availability of financial resources is proxied by an index of creditworthiness.11 Engagement

in innovation activities may also depend positively on firms’ technological capabilities (see

Dosi, 1997). The problems involved in double-counting R&D and other innovation

expenditure as innovation costs or including the variable human capital are not easily solved.

German employment data showed that about 40 of R&D wage costs goes to non-graduates.

Hence, the method of reducing the human capital variable (proxied by share of employees

with a university or college degree) by the observed share of R&D personnel is not

sufficiently exact. The second-best solution is, therefore, to exclude the human capital

variable from the equation determining size of innovation input. The variable physical capital

is also absent from this equation due to problems in distinguishing R&D-embedded from non-

R&D-embedded machinery and equipment (see Janz et al., 2003). However, human capital is

used as an explanatory variable in the selection equation, although I would have preferred a

variable totally independent of R&D personnel.

In addition, I include variables reflecting firm age, whether the firm is part of a group,

and whether it is newly established as well as indicating mergers with other firms or

10 One problem in this context is the potential endogeneity of the market structure. Innovation activities oftenaim to change existing market structures. Thus, there might be a feedback effect from innovation to marketconcentration. I try to solve the problem by using the Herfindahl-index the year before. Gottschalk and Janz(2001) analysed the relationship between innovation and market structure in a simultaneous econometricmodel for the German industry.

11 It is the credit worthiness given by Germany’s largest credit rating agency CREDITREFORM. The CIS datawere merged to the Creditreform database.

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sale/closure of parts of the enterprise. The equations also control for the important distinction

between national or international markets.

As mentioned above, market or industry characteristics - alone or in combination with

firm-specific features - may be important for innovation activities. In this context the

innovation literature stresses the importance of technological opportunities (see Cohen and

Levinthal, 1989), spillovers and effective appropriability conditions of innovation activities

(see, e.g. Spence, 1984 or Becker and Peters, 2000).

Effective appropriability conditions are important in that they allow innovators to receive

the returns on their innovation activities and thus for their success. As a result, however, they

also increase the incentives behind and amount of innovation activities, as was shown by

Spence (1984) in a theoretical model. The variables measuring appropriability and incoming

spillovers were designed in accordance with Cassiman and Veugelers (2002) or Schmidt

(2004). In other words, I distinguish between legal (patents, design patterns, trademarks,

copyrights) and strategic appropriability conditions (secrecy, complexity of design, time-

lead).12 Incoming spillovers are measured by the importance of professional conferences,

meetings and journals as well as exhibitions and fairs as sources of innovation.

The concept of technological opportunities can be summarized by the fact that the

prevailing technological circumstances in some industries are more favourable towards

innovation than in other industries. Nelson (1988) showed in a theoretical model that

improved technological opportunities increase R&D investments. Technological opportunities

are proxied by the importance of science-based information sources (universities, public or

commercial research institutes) on the one side and private sources (clients, suppliers,

competitors) on the other; see Felder et al. (1996).13 As information about firm-specific

technological opportunities, appropriability conditions and spillovers are not available to non-

12 The German CIS3 survey contains information on the importance of each protection measure. Eachprotection method is measured on a 4-Likert-scale, ranging from 0 (not used at all) to 3 (highly important).To construct, for instance, strategic appropriability the scores for all three strategic methods are summed upand divided by the maximum sum possible (9); see Cassiman and Veugelers (2002) or Schmidt (2004).

13 Based on CIS1 data, Felder et al. (1996) use a third variable, the importance of low technologicalopportunities, as a hampering factor. However, this information is not available in CIS3. Incoming spilloversand technological opportunities are constructed in an analogous manner as the appropriability indicators.

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innovators, I used corresponding industry levels from a two-digit Nace classification in the

selection equation.

The extent to which firms can exploit technological opportunities, especially scientific

findings, also depends on their absorptive capacity; see, e.g. Cohen, 1995, Cohen and

Levinthal, 1990 or Kline and Rosenberg, 1986. A dummy variable indicating whether a firm

is engaged continuously in intramural R&D proxies firms’ absorptive capacity. Furthermore, I

specify innovation intensity as a function of a dummy variable equaling 1 if the enterprise has

received any public financial support for innovation activities. Finally, innovation activities

may depend on input price changes. We try to capture this effect using growth rates of

salaries and investment goods prices on a two-digit level between 1997-1999. An increase in

both variables implies higher input costs of factors necessary for R&D and innovation and

should thus reduce the incentive to innovate. On the other hand, an increase in input costs

may have a beneficial effect on rationalisation innovations.

4.2 Innovation Output

The success of product and process innovations is measured by sales in the year 2000

stemming from new products launched in the period 1998-2000 and by cost reductions in

2000 due to new processes introduced in 1998-2000, respectively. Both innovation output

variables are scaled by number of employees.

In both innovation output equations, innovation input is used as explanatory variable. The

main drawback of the innovation input variable is that it is a flow variable and observed only

in the year 2000; in other words, the same year in which we observed innovation output. This

means that the lag between investment in research and actual innovation is ignored, along

with the lag between product innovation and market acceptance. However, Griliches (1998)

reports of some scattered evidence from questionnaire studies that such lags are rather short in

the manufacturing since most research expenditure are related to development and applied

topics.

In addition to R&D and innovation input, the success of product innovations has been

explained by several other factors: technological opportunities, technological capabilities,

absorptive capacities, appropriability conditions, market demand (e.g. Crépon et al., 1998),

network relationships, particularly with costumers (e.g. Hippel, 1998), corporate governance

structures (Czarnitzki and Kraft, 2004) knowledge capital of employees (e.g. Love and Roper,

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2001). Empirical evidence of factors explaining the success of process innovations is still

missing.

Appropriability conditions and spillovers are the same as above. To measure the impact

of firms’ external technological links on the success of product and process innovations, I

broaden the set of variables by including dummy variables for firms which cooperate (with

science, clients, competitors or suppliers) and which fall back on consultants as information

sources for innovations. Unfortunately, we cannot differentiate the importance of sources or

the involvement in cooperations by product and process innovations. Thus, all of these

variables are included in both output equations.

The data set does allow us to distinguish between new products and processes in

determining whether an innovation was developed mainly in-house, both internally and

externally or mainly externally. We use a dummy variable equaling 1 if the firm develops

mainly in-house to proxy its innovative capability instead of using the permanent R&D

variable. Firms which invest in training enhance the knowledge capital of their employees and

thus their innovative capabilities. Accordingly, a positive impact on innovation success is

expected.

Furthermore, innovation success is specified as a function of firm size, public funding,

region, and establishment.

4.3 Productivity

The final relationship is the productivity equation (5). Productivity is measured by labour

productivity, which is proxied by turnover per employee. In the empirical analysis I use levels

as well as growth rates of productivity between 1998 and 2000. Following the literature, I

control for variations in firm size, physical capital (expenditure on physical investments per

employee) and human capital in addition to both measures of innovation output. Export share

is also included. Moreover, the productivity equation controls for effects that heavily

influence turnover, including mergers and downsizing, firms’ status as “newly established”,

and being located in Eastern Germany.

One problem in identifying labour productivity effects of process innovations lie in that,

depending on competition and market power, firms pass on cost reductions to output prices,

which results in a higher product output and, ceteris paribus, in higher employment. However,

we cannot observe price changes at the firm level. Industry price deflators on a three-digit

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level were therefore used to control for indirect effects of price changes on labour

productivity. Nevertheless, the identification problem is still valid for firms which deviate

from the average industry price behaviour.

Finally, note that in all equations the intensity variables are expressed in logarithmic

terms. To control for inter-industry effects, eleven industry dummies are included in each

estimations.

5 Econometric Results

As expected and in line with other empirical findings, innovation input is heavily

dependent on firm size. Whereas the incentive to innovate is higher for larger firms, we find a

non-linear, U-shaped relationship between innovation intensity and firm size. In other words,

the highest input is realized by either small or very large firms, while intensity is relatively

low for medium-sized innovative firms. Regarding the second Schumpeterian determinant, we

do not find any significant impact of market concentration (measured by the Herfindahl

index) on innovation input. The same is valid for the proxy reflecting the availability of

financial resources.

Industry levels of technological opportunities and incoming spillovers are expected to

increase the incentive to innovate. The corresponding proxy variables show the expected

positive sign, but are not statistically significant. Moreover, the results do not confirm the

hypotheses that these variables induce a higher innovation intensity. In contrast, innovation

input is significantly larger in firms with better absorptive capacities (proxied by a permanent

intramural R&D engagement). The effects of appropriability conditions are not clear. Better

(in the sense of greater importance) strategic appropriability conditions significantly increase

innovation intensity, but surprisingly, not the incentive to innovate. Contrarily, increased

importance of legal protection methods does not stimulate innovation activities.

The probability of being an innovator is highly correlated with the skill structure of

employees. Innovative capabilities depend on the knowledge capital of employees, which is

embodied in part by formal levels of qualification. Moreover, a firm’s market orientation is an

important explanatory factor for the occurrence of innovations. Firms with a strong global

market orientation have a significantly higher probability of introducing new products or

production technologies, which is likely due to more intense competition on international

markets.

16

Table 3: Estimation Results: Innovation Input

Results of Heckman estimation

Selection Equation Input Equation

Dependent variable Innovator yes / no Innovation expenditure peremployee

Firm size 0.297*** (7.92) -0.819*** (5.49)Firm size, squared — 0.043*** (3.07)Human capital 0.022*** (6.58) —Concentration -0.367 (0.62) 0.061 (0.08)Credit rating -0.000 (0.36) -0.001 (1.41)Most significant market: regional in Germany 0.160 (1.63) -0.089 (0.63)Most significant market: international 0.394*** (3.50) -0.034 (0.22)Techn. Opp: Science sourcesa 4.060 (1.33) -0.029 (0.13)Techn. Opp: Private sourcesa 3.519 (1.54) -0.138 (0.58)Strategic appropriabilitya 0.055 (0.03) 0.529*** (3.13)Legal appropriabilitya -4.117 (1.62) 0.281 (1.18)Incoming spilloversa 2.405 (1.44) -0.084 (0.40)Salary growth rate 97-99 -1.058*** (3.21) 0.681* (1.85)Price growth investment goods 97-99 0.031 (0.51) -0.055 (0.62)Permanent R&D — 0.459*** (3.88)Public funding — 0.340*** (2.96)Process innovation — 0.349*** (3.50)Group 0.054 (0.52) 0.017 (0.14)Establishment 0.347 (1.54) -0.782*** (2.82)Merger -0.127 (0.67) —Closure -0.234 (0.95) 0.202 (0.64)Region: Eastern Germany 0.079 (0.80) -0.262** (1.96)Firm Age 0.022 (0.39) —Constant 1.728 (1.21) -5.323*** (3.30)Mills ratio -1.040 (6.27)Rho -0.736 0.081Sigma 1.414 0.072Observations 1163 662Log-likelihood -1707.86LR of overall significance 161.20 0.000LR of independenceb 16.68 0.000

Notes: Absolute value of z statistics in parentheses. Eleven industry dummies are included in each regression.Reference industry: food/tobacco.* significant at 10%; ** significant at 5%; *** significant at 1%a) In the selection equation measured by the industry level on a two-digit NACE classification.b) Wald test of independence of selection and innovation input equation (equivalent test for 0ρ = ). Test

statistic has a ( )2 1Χ distribution.

17

As can be gathered from Table 4, both product and process innovation outputs are mainly

determined by innovation intensity. For product innovations this result is line with other

empirical findings, although the estimated elasticity of product innovation output with respect

to innovation is somewhat higher compared to Crépon et al. (1998) or Lööf and Heshmati

(2001). Nonetheless, it is similar to the value found for knowledge-intensive German

manufacturing firms; see Janz et al. (2003).

Whereas innovation input depends to a large extent on firm size, no direct firm-size effect

can be detected in the context of product innovation output. However, for process innovations

we find a significantly positive size effect indicating that larger firms realise higher cost

savings per employee. The econometric results further show that newly established

enterprises experience larger cost reductions to due process innovations. This is likely due to

more pronounced learning effects in these firms.

The estimation results also highlight the role of innovative capabilities in determining the

success of product as well as process innovations. Firms that develop new products mainly in-

house enjoy significantly higher innovative sales compared to other firms. The same positive

relationship was found for process innovations. Furthermore, initiating training to increase the

knowledge capital of employees and thus their innovative capabilities seems to be crucial for

the success of product, but not of process innovations.

In line with several other empirical studies, firms using clients, suppliers or competitors

as information sources are more successful with their new products. Enterprises that even

cooperate with customers further enhance returns accruing to products innovations. In

contrast, we do not find any significant effects of sciences in the case of product innovations.

However, enterprises using universities and/or public or commercial research institutes as

information sources succeed in implementing their process innovations more often.

Additionally, cost savings are significantly higher in firms which fall back on consultants as

information sources for innovations. Appropriability conditions and incoming spillovers turn

out to be insignificant in the output equations.

18

Table 4: Estimation Results: Innovation Output

Product Innovation Output Process Innovation Output

Dependent variable Innovative sales per employee Cost reduction per employeeMethod Heteroscedastic Tobit Homoscedastic TobitInnovation input 0.619*** (4.45) 3.588*** (6.29)Size 0.036 (0.60) 1.913*** (8.04)Public funding -0.251** (2.05) -1.415*** (3.16)Training 0.047*** (2.73) 0.013 (0.20)In-house product development 0.771*** (8.03) —In-house process development — 3.149*** (8.95)Sources: Market 0.125* (1.68) -0.212 (0.81)Sources: Science 0.206 (0.90) 1.497* (1.74)Sources: Internal 0.213*** (2.60) -0.197 (0.66)Sources: Consultants -0.044 (0.39) 0.817** (1.98)Cooperation: Science -0.026 (0.18) —Cooperation: Clients 0.306** (2.13) —Cooperation: Competitors -0.135 (0.89) —Cooperation: Suppliers 0.090 (0.62) —Strategic appropriability -0.269 (1.54) -1.227* (1.85)Formal appropriability -0.041 (0.20) -1.168 (1.45)

Incoming spillovers -0.756 (1.19) -2.022 (0.84)

Concentration -0.077 (0.13) —Establishment 0.075 (0.33) 3.544*** (3.86)Region: Eastern Germany -0.092 (0.81) 1.161*** (2.63)Mills ratio -0.973*** (4.13) 0.852 (0.93)Constant 0.605 (0.74) -0.468 (0.15)Observations 662 662Non-censored observations 500 274Censored observations 162 388Log-likelihood -801.82 -918.51Mc Fadden Pseudo R2 0.12 0.13LR of overall significance 261.09 0.000 265.05 0.000

Notes: Absolute value of z statistics in parentheses. Eleven industry dummies are included in each regression.* significant at 10%; ** significant at 5%; *** significant at 1%

19

Table 5 displays results regarding the productivity effects of innovation. In contrast to

several other empirical studies, we only partially confirm significant labour productivity

effects of innovations (see e.g. Crépon et al., 1998, Lööf and Heshmati, 2001). The variable

which measures output of product innovation activities is significantly positive in the growth

rate version, but not in the level version. The variable measuring output of process innovation

activities is not significant in either version. After controlling for firm size and industrial

classification, only a few variables explain labour productivity. All in all, the growth rate

equation in particular seems to be rather poorly specified.

Table 5: Estimation Results: Productivity

Level Equation Growth Rate Equation

Dependent variable sales per employee real growth rate of sales peremployee

Product innovation output 0.068 (1.36) 0.070*** (2.69)Process innovation output -0.016 (1.40) -0.009 (1.44)Firm size 0.092*** (2.78) 0.009 (0.49)Human capital 0.002 (0.71) -0.001 (1.24)Export intensity 0.166 (1.44) 0.069 (1.15)Physical capital investment -0.048*** (3.68) —Physical capital investment growth — -0.000 (0.02)Closure 0.235* (1.69) 0.021 (0.28)Establishment -0.126 (1.02) 0.199*** (2.70)Merger -0.016 (0.16) 0.096* (1.79)Region: Eastern Germany -0.303*** (5.54) 0.017 (0.59)Mills ratio -0.159 (0.89) 0.072 (0.77)Constant -1.745*** (4.88) 0.196 (1.04)Observations 662 649R2 0.24 0.07Adjusted R2 0.21 0.03

Notes: Absolute value of t statistics in parentheses. Eleven industry dummies are included in each regression.* significant at 10%; ** significant at 5%; *** significant at 1%.

6 Conclusions

Using the approach proposed by Crépon et al. (1998) I have analysed the relationship

among innovation input, innovation output and productivity using German CIS3 data. The

model and the information provided by the data allow a look into the ”black box” of the

innovation process at the firm level, not only analysing the relationship between innovation

input and productivity, but also shedding some light on the process in between. The core CIS3

20

questionnaire does not include any output indicators for process innovations. However,

Germany’s annual innovation surveys have provided a distinction between firms with

rationalisation innovations and other process innovators since 1993; for rationalisation

innovations, cost reduction (in unit cost) measures the success of such innovations. Thus, I

extended the analysis by distinguishing between two different innovation outputs, one for

product innovations (sales stemming from new product innovations) and the other for process

innovations (cost savings due to rationalisation innovations). This might alleviate the problem

that previous studies have encountered in using only an equation for product innovations as

the output of innovation activities while the input measure (R&D or innovation expenditure)

is related to product and process innovations. Still, the best solution would involve separating

product and process innovation expenditure.

To summarize the main results: The success of product and process innovations is mainly

determined by innovation input. Furthermore, firms which develop new products or processes

mainly in-house are more successful. In contrast, appropriability conditions and incoming

spillovers turn out to be insignificant in the output equations. The empirical analysis,

however, also shows differences between the factors explaining the success of product and

process innovations. For instance, training employees to increase their knowledge capital

(read: innovative capabilities) seems to be crucial for the success of product, but not of

process innovations. Firms which use clients, suppliers or competitors as information sources

or cooperate with costumers are more successful with their new products. On the other hand,

enterprises using universities and/or public or commercial research institutes as information

sources are more likely to have success in implementing process innovations. Additionally,

cost savings are significantly higher in firms that fall back on consultants as information

sources for innovations.

In contrast to several other empirical studies, we only partially confirm significant labour

productivity effects of product innovations. The variable measuring output of process

innovation activities is not significant.

21

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Tables

Table A1: Sample by Industry

Industry NACE2- digit Population Total Sample Used Sample Innovative

Sample

# in % # in % # in % # in % Food/Tobacco 15 – 16 6021 9.8 163 8.2 96 8.3 36 5.4 Textiles 17 – 19 3462 5.6 117 5.9 69 5.9 26 3.9 Wood/Paper/Print. 20 – 22 10589 17.2 173 8.7 104 8.9 51 7.1 Chemicals/Coke 23 – 24 2214 3.6 146 7.3 74 6.4 49 7.4 Rubber/Plastic 25 4416 7.2 164 8.3 102 8.8 58 8.8 Glass/Ceramics 26 3118 5.1 104 5.2 62 5.3 29 4.4 Metals 27 – 28 11743 19.1 310 15.6 205 17.6 96 14.5 Machinery 29 8335 13.5 308 15.5 165 14.2 115 17.4 Electrical engineer. 30 – 32 4068 6.6 195 9.8 108 9.3 84 12.7 Medical instr. 33 2858 4.6 135 6.8 82 7.1 61 9.2 Vehicles 34 – 35 1464 2.4 92 4.6 47 4.0 31 4.7 Furniture 36 – 37 3252 5.3 82 4.1 49 4.7 26 3.9Total 61540 100 1989 100 1163 100 662 100

Notes: Innovative firms are defined as firms with product or process innovations and positive innovationexpenditure in the year 2000.

Table A2: Sample by Size Class

Size Class(number of employees)

Population Total Sample Used Sample InnovativeSample

# in % # in % # in % # in % 5 – 9 11989 19.5 135 7.0 86 7.4 30 4.5 10 – 19 11450 18.6 241 12.4 152 13.1 65 9.8 20 – 49 17244 28.0 407 21.0 250 21.5 107 16.2 50 – 99 9520 15.5 310 16.0 208 17.8 110 16.6 100 – 199 5539 9.0 257 13.2 153 13.2 97 14.7 200 – 499 3911 6.3 289 14.9 191 16.4 147 22.2 500 – 999 1129 1.8 136 7.0 72 6.2 60 9.11000 or more 758 1.2 166 8.6 51 4.4 46 7.0Total 61540 100 1989 100 1163 100 662 100

Notes: Innovative firms are defined as firms with product or process innovations and positive innovationexpenditure in the year 2000.

25

Table A3: Variable Definitions

Quantitative variablesInnovation input Innovation expenditure in 2000, per employee (in log.)Product innovation output Sales income from product innovations in 2000, per employee (in log.)Process innovation output Cost reduction due to product innovations in 2000, per employee (in log.)Productivity Sales per employee in 2000 (in log.)Firm size Number of employees in 2000 (in log.)Physical capital Gross investments in tangible goods in 2000, per employee (in log.)Human capital Share of employees with a university or college degree in 2000Training Training expenditure in 2000, per employee (in log.)Export Share of export per sales in 2000Concentration Herfindahl index in 1999, 3-digit NACEAge Age of firm in years (in log.)Credit rating Index of creditworthiness in 1999 (range: 100-600, best value: 600)Salary growth rate 97-99 growth rate of salaries between 1997 and 1999 on a 2-digit NACE levelPrice growth of investmentgoods 97-99

growth rate of prices for investment goods between 1997 and 1999 on a 2-digit NACE level

Qualitative variablesInnovative firm Introduction of a new or significantly improved product or process between

1998-2000 and positive innovation expenditure in 2000Product innovation Introduction of a new or sign. improved product between 1998-2000Process innovation Introduction of a new or sign. improved production process between 1998-

2000Permanent R&D Continuous engagement in intramural R&D during 1998-2000Public funding Public financial support for innovation activities during 1998-2000Strategic appropriability The scores ranging from 0 (not used at all) to 3 (highly important) for all

three strategic protection methods (complexity, secrecy, time-lead) aresummed up and divided by the maximum sum possible

Legal appropriability Analogous construction for legal protection methods (patents, design pattern,trademarks or copyrights)

Techn. opportunities: ScienceSources

Analogous construction for scientific information sources (universities,public or commercial research institutes)

Techn. opportunities: PrivateSources

Analogous construction for private information sources (clients, competitors,suppliers)

Incoming spillovers Analogous construction for professional conferences, meetings and journalsas well as exhibitions and fairs as information sources

Cooperation Cooperations on innovation activities during 1998-2000 with .... - Science ... universities, public or commercial research institutes or firms - Clients ... clients or customers - Competitors ... competitors and other firms from the same industry - Suppliers ... suppliersIn-house product development Product innovations mainly developed within a firmIn-house process development Process innovations mainly developed within a firm

26

Table A3: Variable Definitions (continued)

Qualitative variablesMarket orientation Firm’s most significant market is ... - national <50km ... local (within a distance of 50 km) within its country - international <50km ... local (within a distance of 50 km) within neighbouring countries - national >50km ... national (within a distance of more than 50 km) - international >50km ... international (within a distance of more than 50 km)Merger Sales increased by >= 10% due to merger with another firm in 1998-2000Closure Sales decreased by >= 10% due to sale or closure of part of the firm in 1998-

2000Group Firm belonging to a groupEstablishment Firm newly established during 1998-2000Region Firm located in Eastern Germany