# The innovation content of sales and exports: an econometric analysis at the firm level in French manufacturing

Corinne Barlet, Emmanuel Duguet, David Encaoua, Jacqueline Pradel

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Corinne Barlet, Emmanuel Duguet, David Encaoua, Jacqueline Pradel. The innovation content of sales and exports: an econometric analysis at the firm level in French manufacturing. 1996. halshs-05151536

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CAHIERS ECO & MATHS

96.92

THE INNOVATION CONTENT OF SALES AND EXPORTS :
AN ECONOMETRIC ANALYSIS AT THE FIRM LEVEL
IN FRENCH MANUFACTURING

Corinne BARLET
Emmanuel DUGUET
David ENCAOUA
Jacqueline PRADEL

CEME
The innovation content of sales and exports:
an econometric analysis at the firm level
in French manufacturing¹

C. Barlet², E. Duguet³, D. Encaoua⁴ and J. Pradel⁵

October 1996

¹We thank the participants at the Xth ADRES Conference on the Economics and Econometrics of Innovation (Strasbourg, 1996) for helpful suggestions and comments. The usual disclaimer applies.

²Université de Besançon et CEME-Université de Paris I.

³Université de Paris I-CEME and CREST-Laboratoire d'Economie Industrielle.

⁴Université de Paris I-CEME.

⁵Université de Paris I-CEME and CREST-ENSAE.
Résumé

Cet article étudie le contenu innovant des ventes totales et des exportations de l'industrie manufacturière française, sur données d'entreprises. En tenant compte des différences d'opportunités de demande, de technologie et d'innovation entre les entreprises et les secteurs d'activité, nous expliquons la part des innovations dans les ventes par les différents types d'innovations mis en œuvre par les entreprises. Nous pouvons ainsi examiner la réussite des innovation mises en œuvre par les entreprises. Nous proposons également d'évaluer la qualité des régressions sur variables qualitatives en recourant au R² du modèle latent, à partir duquel les données observables sont définies. Cette mesure possède deux avantages : elle peut être calculée facilement à partir des programmes d'estimation existants et permet les comparaisons entre des modèles différents. Nos principaux résultats sont les suivants : (i) le contenu en innovations des exportations est plus élevé que celui des ventes domestiques; (ii) le contenu en imitations des ventes totales et à l'exportation décroît avec le degré d'innovation des activités et s'annule pour le degré d'innovation le plus élevé; (iii) le contenu des ventes en produits nouveaux pour le marché s'accroît avec le degré d'innovation des activités; (iv) dans tous les cas, l'amélioration de produit reste à la base des ventes effectuées; (v) la taille affecte positivement l'output innovant dans les activités de haute technologie seulement.

Mots-Clef : enquête Innovation, exportation, opportunités de demande et de technologie, pourcentages d'innovation, probit polytomique ordonné, types d'innovation, R² latent.

Abstract

We investigate the innovation contents of total sales and exports at the firm level in French manufacturing. Controling for demand pull, technology push and innovation opportunities differences between firms and sectors, we explain the innovation percentages by the innovation types identified in the Innovation survey. Thus, we examine which innovations are successful. We also propose a new goodness of fit measures for qualitative variable models, namely to compute the *R*² of the latent model over which the truncation is defined. This measure is always computed easily from existing packages and allows for comparison *between* models. Our main results are the following: (i) the innovation content of exports is higher than the one of domestic sales; (ii) the imitation content of new products decreases with the technological level and vanishes for the high-tech firms; (iii) the content in significantly new products increases with innovation opportunities for both total and export sales; (iv) product improvement is always important; (v) size has a positive effect on innovation output in high-tech activities only.

**Keywords:** innovation, export, innovation types, goodness of fit measures, latent R-squared, demand pull, technology push, ordered probit model with known thresholds.

JEL Classification: C24, D21, F14, L60,O32.
# 1 Introduction

A large number of firms in developed countries are involved in innovation activities. This led to a large number of studies aiming to assess the importance of innovation in firms' performances. Most papers in the applied literature paid attention to the impact of the overall innovation level of the firm, measured through R&D expenditure or patents, on output, often measured as total factor productivity. Due to the lack of available data, few attention has been devoted to the decomposition of this innovation level, and similar global innovation rates can have different innovation contents. The recent Innovation surveys now allow us for identifying which types of innovations industrial firms have implemented. Thus we may wonder what is the innovation content of sales: improvement, new products or imitations? The purpose of this paper is to examine the total sales and export sales innovation content according to the different innovation types. Is this innovation level mostly made of product or process innovations? Are they rather incremental or radical? Are exports more innovative than domestic sales?

We address these issues by using the innovation percentages in total sales and export sales provided by the Innovation survey in French manufacturing. Since the data are mostly qualitative, we estimate an ordered probit model constraining these proportions to lie between 0 and 1; we also use a different measure of goodness of fit adapted to limited dependent variables. Throughout all the paper we account for size and innovation opportunities differences among firms and sectors.

The paper is organized as follows: we present the data in section 2 and the econometric model in section 3. Since the data is qualitative we discuss the estimations and goodness of fit issues in the fourth section. Then, we present the results in section 5 and conclude in the last section.

# 2 The Data

We use two sources: the Enquête Annuelle d'Entreprises (EAE census) which provides general information and the Enquête Innovation (“Innovation survey”) which provides innovation data.

## 2.1 Sources : EAE census and Innovation survey

The EAE is conducted every year by the Ministère de l'Industrie. It is a census for all the firms having at least 20 employees. For each firm, the census provides firm level accounting information. From it we extracted total sales, exports and the industrial classification of the firms. We retained firms that were present over 1986-1990, the year of the Innovation survey. This selection is necessary since our innovation measures refer to innovations implemented between 1986 and 1990. The census also benefits from a important feature of French data: each firm has a national identification code, the ‘SIREN’ code. Therefore it is easily matched
with the Innovation survey.

The Innovation Survey was also conducted by the Ministère de l'Industrie in 1991 on the whole set of 25000 French manufacturing industry firms with at least 20 employees. Its information refers to the five-years period 1986-1990 but does not provide any yearly information. For a presentation of this survey, see François (1990).

We extracted the following data :

A) *Innovation types*. The survey tells us whether firms have implemented in-
novations or not. There are eight innovation types: (1) product improvement, (2)
new product for the market, (3) product imitation, (4) process breakthrough, (5)
process improvement, (6) packaging innovation, (7) organization innovation and
(8) marketing innovation. The answers simply indicate by ‘yes’ or ‘no’ wether the
innovation had took place in the firms during 1986-1991. We use these variables
in order to detect the pattern of successful innovations in total sales and exports.

B) *Innovation determinants*. Firms are asked to evaluate the intensity of two
determinants of innovative activities. The first determinant refers to innovations
impulsed by the market, relationships with customers or competitors or *“demand
pull”*; the second determinant refers to the importance of the *“own dynamic of
technology”* or *“technology push”*. The answers to the corresponding questions
are on a four points scale: no, weakly, moderately and strongly. We will keep
these two variables in the regressions in order to detect individual departures
from the sectoral dummies. Significant coefficients will indicate wether the sec-
toral dummies account for enough heterogeneity or if there remain demand and
technological opportunities between firms from a same sector.

B) *Innovation opportunities*. Each firm is asked to assess the innovation degree of its activity. The answers are on a four point scale: not innovative, weakly innovative, moderately innovative and strongly innovative. We will see that this variable gives the strongest innovation output differences among firms, so that we will have to run separate regressions according to it, while controlling for sectoral, demand and technological effects.

D) *Share of total sales and export sales from new products.* These are the share of sales (*m.m.*, exports) from products launched between 1986 and 1990, in 1990 sales (*m.m.*, exports). Answers to these two distinct questions are given according to 4 intervals: 0-10%, 10-30%, 30-70% and 70-100%.

For both innovation determinants and innovation opportunities, we grouped
the two first classes so that our firms can be grouped in three sets according to
each variable (low, moderate and strong). Two separate analyses are performed.
The first one is on innovation in total output and is conducted on all innovative
firms. The second one is conducted on innovative exporting firms only.

## 2.2 The sample

The sample is obtained by matching the EAE and the Innovation survey, on
manufacturing. We only keep the set of firms over 20 employees for which the
main activity is in manufacturing, which answered correctly both to the EAE
census<fnref n="1" /> and to the Innovation survey and which remained active from 1986 to 1990. This last choice has been made because the Innovation survey refers to the whole period 1986-1990. For instance the data on sales from new products refer to products launched between 1986 and 1990. Thus the corresponding ratios are only comparable among the firms that were active over the same time period. We obtain a sample of 9377 manufacturing firms which satisfy all these conditions. Among these firms, 6242 have innovated over the period (all types included). It is the sample we work on. Inside this sample, 4991 firms export.

In order to study innovation output in 1990, we concentrate on the 6242 innovating firms. The reason is the following: in the questionnaire, innovations ratios, “demand pull” and “technology push” are only asked to innovative firms.

For the same reason, we kept exporting firms to analyze export output, that is 4991 firms out of 6242.

We use two measures of innovation performance. on one hand, the percentage of innovations in total sales and, on the other hand the percentage of innovations in export sales. Differences between the two indicate departures between the domestic market and foreign trade innovation contents. These ratios also give an idea of the renewal rate of products, but the quality of the innovations involved remains to be studied. Similar innovation rates can have different innovation contents.

## 3 The model

In all cases we explain a ratio representing innovation output. We can write down this relationship:

$$ \phi_i^* = \frac{\exp(x_i b + u_i)}{1 + \exp(x_i b + u_i)} \in [0, 1] $$

where $\phi_i^*$ is either the percentage of innovations in total sales or in export sales, $x_i$ the vector of explanatory variables for firm i, b the corresponding vector coefficient and $u_i$ a disturbance summarizing the remaining firm-level heterogeneity that is not accounted for by the model (i.e., by $x_i b$).

The logistic form is chosen so as to ensure that the proportion lies between its natural bound, 0 and 1. Notice that this does comes from a distributional assumption on the residual, which remains to be done since this model is heterogeneous through the disturbance $u_i$. It is a mere functional form assumption.

The logistic function is strictly increasing, so that a positive sign for the coefficient b means a positive effect on innovation output. The explanatory variables $x_i$ are:

1. Size, measured by employment logarithm;

<footnote n="1">¹Several codes are available in the EAE to check the data: they allowed us to cast out firms whose answers are incomplete or incorrect, firms resulting from automatic duplication and firms characterized by a strong subcontracting. We have also discarded firms with missing sales or negative value-added.</footnote>
2. Industry dummies (18 sectors);

3. Firm level dummies indicating the 8 innovation types;

4. Firm level dummies indicating the intensity of demand pull and technology push (2 dummies only for 'moderate' and 'strong', to avoid perfect multicolinearity).

Since the dependent variable $\phi_i^*$ is only observed by interval, we need to discuss the econometric method.

# 4 Econometrics

Our dependent variables can be observed by intervals only so that specific methods are to be used. We discuss two points: first, how to estimate the model while constraining the percentages to lie between 0 and 1 and, second, how to compute a goodness of fit measure equivalent to the $R^2$ in the standard linear model, that allows to compare the fit of the different regressions.

## 4.1 Estimation of interval ratios models

The model gives the innovation proportion denoted by $\phi_i^*$ as a function of the explanatory variables:

$$ \phi_i^* = F(x_i b + u_i) \in [0, 1] $$

The function $F$ is given, strictly increasing and insures that the proportion lies between 0 and 1. Thus, the estimation problem comes from the fact that we do not observe $\phi_i^*$ but the interval it lies in, namely $[0, 0.1]$, $[0.1, 0.3]$, $[0.3, 0.7]$ or $[0.7, 1]$.

In order to estimate the model, we assume that the residual is normally distributed :

$$ u_i \stackrel{iid}{\sim} N(0, \sigma_u^2) $$

Therefore, we have an ordered probit model with known thresholds on the transformed variable $f_i^*$:

$$ f_i^* = F^{-1}(\phi_i^*) = x_i b + u_i $$

The only departure from the standard ordered probit model is that we do not explain $\phi_i^*$ directly. The only practical difference is that we do not use the thresholds directly but their transformations by $F^{-1}$. Therefore, a standard ordered probit model can be used to estimate $b$ with any known function $F$.

In this paper, we have chosen the logistic functional form. This gives :

$$ \phi_i^* = \frac{\exp(x_i b + u_i)}{1 + \exp(x_i b + u_i)} $$
and the transformed model is

$$f_i^* = \ln \frac{\phi_i^*}{1 - \phi_i^*} = x_i b + u_i$$

We do not observe $f_i^*$ but the qualitative variable $f_i$:

$$f_i = \begin{cases} 1 & \text{if } -\infty < f_i^* \le a_1 \\ 2 & \text{if } a_1 < f_i^* \le a_2 \\ 3 & \text{if } a_2 < f_i^* \le a_3 \\ 4 & \text{if } a_3 < f_i^* \le +\infty \end{cases}$$

with $a_1 = \ln 0.1/0.9$, $a_2 = \ln 0.3/0.7$ and $a_3 = \ln 0.7/0.3$.

The probability of observing interval $k$ is given by:

$$\begin{align*} \Pr[f_i = k] &= \Pr[a_{k-1} < f_i^* \le a_k] \\ &= \Pr[a_{k-1} < x_i b + u_i < a_k] \\ &= \Phi\left[\frac{a_k - x_i b}{\sigma}\right] - \Phi\left[\frac{a_{k-1} - x_i b}{\sigma}\right] \end{align*}$$

with the convention $a_0 = \{-\infty\}$ and $a_4 = \{+\infty\}$. The the log- likelihood of the sample is given by:

$$L(f/x; b, \sigma) = \sum_{i=1}^{N} \sum_{j=0}^{3} \delta_{(f_i=j)} \times \ln \Pr[f_i=j]$$

where $\delta_{(f_i=j)} = 1$ if $f_i = j$, 0 otherwise. We make the parameter change $1/\sigma$ and $b/\sigma$ so that the log-likelihood is concave and estimate this model by maximum likelihood<fnref n="2" />.

## 4.2 The latent $R^2$ of limited dependent variable models

An original feature of this paper is to compute the $R^2$ of the latent model instead of the usual measures of fit used in the qualitative variables literature (e.g., the Mc Fadden $R^2$). We first present the measure and then justify it.

In a standard linear model, the $R^2$ is given by:

$$R^2 = \frac{V_e(\hat{f})}{V_e(\hat{f}) + V_e(\hat{u})}$$

where the subscript $e$ denotes the empirical variance and $\hat{f}$ the vector of predictions. Then $V_e(\hat{f})$ is the variance explained by the model and $V_e(\hat{u})$ the residual variance. By analogy, we define the latent $R_*^2$ as:

$$R_*^2 = \frac{V_e(\hat{f})}{V_e(\hat{f}) + (\hat{\sigma}_u)^2} = \frac{V_e(\hat{f}/\hat{\sigma}_u)}{V_e(\hat{f}/\hat{\sigma}_u) + 1}$$

<footnote n="2">²The estimation program is a variation over a previous SAS-IML macro command which is described in a working paper by Crépon and Duguet (1995).</footnote>
where $\hat{\sigma}_u$ is the maximum likelihood estimate of $\sigma_u$ (so that $(\hat{\sigma}_u)^2$ is the maximum likelihood estimate of $\sigma_u^2$). It is a consistent estimate of the $R^2$ of the underlying linear model over which the truncation is defined. This measure has three advantages compared to the other measures of 'goodness of fit' used in the limited dependent variable literature:

1. It has the same interpretation as the usual $R^2$ from the linear model; it gives the share of the total variance explained by the latent model. Therefore, it is always bounded between 0 and 1;

2. It is defined for all the models with a latent linear form, which is the case of almost all the models used in empirical applications;

3. Its definition does not depend on the truncation rule adopted for the endogenous variable and the $R^2$ are comparable across different models and truncation rules;

It is also defined for the simple probit models since $\hat{y}/\hat{\sigma}_u$ is always identified even though $\hat{\sigma}_u$ is not. This property comes from the fact that the quality of a model is unchanged if both hands of the equations are multiplied by a constant $(1/\sigma_u$, say). In a probit model, one simply divides both hands of the equation by the standard error of the residual, so that only $b/\sigma_u$ is identified. This implies that $x_i$. ($b/\sigma_u$) is also identified. Therefore, one can compute this measure even if the variance of the residual is not separately identified.

We compare our measure to the Mc Fadden $R^2$, which has a completely different interpretation. It is defined by:

$$MF = 1 - \frac{\ln L_c}{\ln L_a} = \frac{\ln L_a - \ln L_c}{\ln L_a}$$

where $L_c$ is the log likelihood of the model with an intercept only and $L_a$ the log-likelihood of the model under study. It always lies between 0 and 1 but its foundation is different from $R_*^2$. MF indicates the percentage increase in the log-likelihood, compared to the basic model with an intercept.

The problem with this measure is that the basic likelihoods of different models are not on the same scale, so that this measure cannot be used easily to compare the goodness of fit from separate regressions, or with different kinds of truncation rules. Even if the endogenous variable was the same and the only difference was the truncation rule, we would not have comparable 'goodness of fit' measures.

On the contrary, the latent $R^2$ is always defined the same way and allows for comparisons across both separate regressions and truncation rules. Moreover it is always computed easily for all limited dependent variable models based on linear latent forms equations. This will allow us to compare the quality of regressions between total and export sales.
# 5 Results

A first look at the results is provided by table 1. It presents the global regressions on the innovation sales ratios. It is important to notice that the coefficients are comparable across regressions<fnref n="3" />. We clearly see that product improvements are the most important (coefficients around 0.8-0.9) followed by new products (0.5) and imitations (0.3-0.4). Then comes process breakthrough, with a lower coefficient. Moreover, the patterns between total sales and exports do not seem to differ, except for size. There is a scale effect for exports since export innovation intensity increases with employment. This is not the case for total sales, a result similar to previous studies (Crépon, Duguet and Kabla, 1996). We also find that although sectoral dummies are introduced in both regressions, there remains a significant firm-level heterogeneity according to demand pull, technology push and innovation opportunities. A closer look at the coefficients clearly shows that the strongest differences are explained by innovation opportunities. Moderate opportunities explain as much differences than new products and strong opportunities explain more differences than product improvements. Such strong differences could be the sign that all the coefficients differ according to this variable and not only the average innovation ratio. Thus we will run separate regressions according to innovation opportunities and see that it is indeed the case.

## 5.1 Innovation contents of total sales

Product improvements are always important in innovation sales, always significant with the stronger coefficients of all innovation types (table 2). Then comes either product innovation or imitation, depending on the level of innovation opportunities in firms' activities. This variable is available at the firm level and we control for sectoral effects. When innovation opportunities are low, product imitation are more important than new products (0.5 vs 0.4) but this ranking is reversed when innovation opportunities are higher. Both types, new products and imitation, are equivalent for moderate innovative opportunities (around 0.5) and the effect of imitation vanishes for highly innovative activities.

There are then different innovation patterns depending on innovation opportunities. As the innovation level rises, imitation becomes less and less important and new products more important. In the highest level, imitation has no significant effect on innovation sales.

Process innovation also contribute to the innovation content of sales, one significant effect being found for 'breakthroughs' in highly innovative activities. Finally, additional effects of packaging and marketing are found in low innovative activities : firms that implemented packaging innovations over 1986-1990 had a lower innovation output in 1990 and the ones that implemented marketing innovations a higher one. These innovations have no significant effect for higher

<footnote n="3">³This feature comes from the fact that the logistic function we use is a mere functional form, not a distributional assumption. Thus the properties of the estimates are the same as the ordered probit model with known thresholds.</footnote>
innovative activities. Thus, only significant product and process innovations matter in strongly innovative activities.

The demand pull and technology push indicators show a significant departure from the sectoral indicators, mostly in average and low innovation activities. Most of the difference comes from the demand conditions, which seem to create more output variations inside the sectors than technology push. In strongly innovative activities, no significant firm level variations are found, which suggests that both demand and technology conditions are more homogenous within the sectors.

The size, measured by employment, is significant in strong innovative activities only. This recalls some Schumpeterian arguments. We find a significant effect of size on innovation output, once control for innovation implementations and sectoral differences. Thus firms with a higher size have a higher innovation intensity.

Last, the sectoral dummies are always significantly different. Controlling for sectoral effects appears especially useful in highly innovative activities where two biases would result from their omission: a negative effect of products improvement and of packaging. This means that sectors that implemented more process improvements and packaging, weakly innovative activities, have lower average innovation output than the other sectors. But this is not a firm-level effect.

The fit of the regression is measured in two ways. The Mc Fadden $R^2$ is around 0.09 which means that the variables (including the sectoral dummies) increase the basic log-likelihood (i.e., with an intercept only) of 9%. The latent $R^2$ is more easily interpreted and indicates that the share of explained variance is around 22-25%, which is correct for a cross section using mostly dummy variables. Introducing the sectoral dummies in the regression always lead to a higher latent $R^2$ and to a lower standard error of the residual. We can also use this measure to compare the respective fits of the total sales and exports equations.

## 5.2 Innovation contents of export sales

The results we obtain for export are close to the one obtained on total sales. But globally, we find a sharper pattern, with more important variations among firms. In general, new products are more important in exports than in total sales.

In low innovation activities, product improvement is as important as imitation (0.7-0.8), twice more important than new products. In moderate innovative activities new products (0.5) are now more important than imitations (0.3) and less than improvements (0.8). But in the most innovative activities, new products are as important as improvement themselves (0.8) and imitation is not significant (-0.01). Then we find the same kind of pattern than for total sales, but with stronger differences among innovation types.

Process 'breakthrough' also contribute to the innovation content of exports and more importantly than for total sales (0.4). The other effects are comparable to the ones on total sales as well as the necessity to control for sectoral effects.

The slight 'pro-Schumpeterian' effect we found is reinforced for exports where both moderate and strong innovative activities associate a higher innovation ex-
port output (0.2 and 0.3) to bigger firms.

Finally the fit of the export intensity equation is comparable to the one on the total sales, with a latent $R^2$ around 20-23%. The introduction of sectoral dummies leads to a stronger improvement of the regression; without the dummies the $R^2$ would be around 14% only. This suggests the presence of sectoral conditions specific to international trade.

# 6 Conclusion

The innovation content of sales differs strongly across firms and sectors. The pattern changes with the innovation level, so that comparable innovation ratios have a different meaning in different activities. Our first finding is that products improvements are the basis of innovation in all cases. The second one is that the importance of product imitations vanishes as innovation opportunities increase. Finally, the content in new products increases with innovation opportunities.

We also find some departure between total sales and export: product imitation is less important in this case and new products are as important as products improvements. Then, the innovation content of exports is higher than the one of total production. Since this last indicator includes exports, the difference between domestic and export sales is higher.

These results are interesting since they provide an econometric support to the new theories of trade, insisting on scale economies and product differentiation.

Other factors are found to play a lower but a significant role in innovation output. One the one hand, strong process innovations are always significant in the highly innovative activities. Then, there are interactions between product and process innovations, but they are limited to the most innovative activities. On the other hand, size is found to be significant in the strongest innovative activities only, recalling the Schumpeterian arguments. Since the well-known results of proportionality is found in the total regressions and not in the separate regressions, it is possible that size has a positive effect in stimulating innovation in high-tech activities only.
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[7] Scherer M. (1984): “Innovation and growth: Schumpeterian perspectives”, The MIT Press, Cambridge, Ma.

[8] Schumpeter J. (1942): “Capitalism, Socialism and Democracy”, Harper, New York
<table><caption>Table 1: Innovation Content of Total Sales and Exports</caption><caption>Left-hand variables: innovation percentages<br>Asymptotic standard errors between parentheses</caption><thead><tr><th>Innovation % in:<br>(logistic transformation)</th><th>Total<br>Sales</th><th>Exports</th></tr></thead><tbody><tr><td>Employment logarithm</td><td>0.03 (0.019)</td><td>0.21 (0.032)</td></tr><tr><td>Product innovation:</td><td></td><td></td></tr><tr><td>- improvement</td><td>0.87* (0.053)</td><td>0.82* (0.099)</td></tr><tr><td>- innovation</td><td>0.46* (0.045)</td><td>0.54* (0.081)</td></tr><tr><td>- imitation</td><td>0.39* (0.043)</td><td>0.31* (0.079)</td></tr><tr><td>Process innovation:</td><td></td><td></td></tr><tr><td>- breakthrough</td><td>0.13* (0.047)</td><td>0.21* (0.084)</td></tr><tr><td>- improvement</td><td>-0.10* (0.047)</td><td>-0.10 (0.085)</td></tr><tr><td>Other innovations:</td><td></td><td></td></tr><tr><td>- packaging</td><td>-0.14* (0.052)</td><td>-0.18 (0.093)</td></tr><tr><td>- organisation</td><td>0.12* (0.048)</td><td>0.14 (0.086)</td></tr><tr><td>- marketing</td><td>0.12* (0.055)</td><td>0.13 (0.098)</td></tr><tr><td>Demand pull:</td><td></td><td></td></tr><tr><td>- moderate</td><td>0.27* (0.070)</td><td>0.22 (0.131)</td></tr><tr><td>- strong</td><td>0.39* (0.063)</td><td>0.51* (0.118)</td></tr><tr><td>Technology push:</td><td></td><td></td></tr><tr><td>- moderate</td><td>0.16* (0.049)</td><td>0.38* (0.088)</td></tr><tr><td>- strong</td><td>0.19* (0.056)</td><td>0.34* (0.101)</td></tr><tr><td>Innovation opportunities:</td><td></td><td></td></tr><tr><td>- moderate</td><td>0.51* (0.051)</td><td>0.58* (0.095)</td></tr><tr><td>- strong</td><td>1.07* (0.068)</td><td>1.27* (0.123)</td></tr><tr><td>σ<sub>u</sub></td><td>1.33* (0.021)</td><td>2.01* (0.046)</td></tr><tr><td>Latent R<sup>2</sup></td><td>0.331</td><td>0.256</td></tr><tr><td>Mc Fadden R<sup>2</sup></td><td>0.126</td><td>0.069</td></tr><tr><td>LRT sectoral dummies</td><td>128.6</td><td>145.9</td></tr><tr><td>Number of firms</td><td>6242</td><td>4991</td></tr></tbody></table>

Maximum likelihood estimates.

All regressions include 18 sectoral dummies.

LRT: likelihood ratio test, with 17 degrees of freedom.

*: significant at the 5% level.
<table><caption>Table 2: Innovation Content of Total Sales</caption><thead><tr><th rowspan="2">Innovation opportunities:</th><th colspan="2">Low</th><th colspan="2">Moderate</th><th colspan="2">Strong</th></tr><tr><th>Constant</th><th>Dummies</th><th>Constant</th><th>Dummies</th><th>Constant</th><th>Dummies</th></tr></thead><tbody><tr><td>Employment logarithm</td><td>-0.009<br>(0.040)</td><td>-0.017<br>(0.041)</td><td>0.004<br>(0.024)</td><td>0.003<br>(0.024)</td><td>0.114*<br>(0.037)</td><td>0.108*<br>(0.041)</td></tr><tr><td><em>Product innovations:</em></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>- improvement</td><td>0.926*<br>(0.091)</td><td>0.899*<br>(0.091)</td><td>0.954*<br>(0.072)</td><td>0.889*<br>(0.071)</td><td>0.911*<br>(0.160)</td><td>0.774*<br>(0.157)</td></tr><tr><td>- innovation</td><td>0.416*<br>(0.089)</td><td>0.410*<br>(0.098)</td><td>0.495*<br>(0.057)</td><td>0.468*<br>(0.057)</td><td>0.665*<br>(0.121)</td><td>0.529*<br>(0.121)</td></tr><tr><td>- imitation</td><td>0.551*<br>(0.086)</td><td>0.519*<br>(0.086)</td><td>0.429*<br>(0.056)</td><td>0.410*<br>(0.056)</td><td>0.176<br>(0.110)</td><td>0.128<br>(0.108)</td></tr><tr><td><em>Process innovations:</em></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>- breakthrough</td><td>0.146<br>(0.109)</td><td>0.143<br>(0.108)</td><td>0.058<br>(0.060)</td><td>0.072<br>(0.060)</td><td>0.296*<br>(0.108)</td><td>0.278*<br>(0.106)</td></tr><tr><td>- improvement</td><td>-0.192*<br>(0.086)</td><td>-0.183*<br>(0.086)</td><td>-0.059<br>(0.062)</td><td>-0.008<br>(0.061)</td><td>-0.302*<br>(0.125)</td><td>-0.143<br>(0.123)</td></tr><tr><td><em>Other innovations:</em></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>- packaging</td><td>-0.260*<br>(0.111)</td><td>-0.278*<br>(0.112)</td><td>-0.113<br>(0.065)</td><td>-0.061<br>(0.066)</td><td>-0.292*<br>(0.121)</td><td>-0.223<br>(0.124)</td></tr><tr><td>- organisation</td><td>0.150<br>(0.103)</td><td>0.181<br>(0.103)</td><td>0.109<br>(0.061)</td><td>0.109<br>(0.061)</td><td>0.044<br>(0.112)</td><td>0.059<br>(0.110)</td></tr><tr><td>- marketing</td><td>0.272*<br>(0.113)</td><td>0.256*<br>(0.113)</td><td>0.095<br>(0.072)</td><td>0.091<br>(0.072)</td><td>0.023<br>(0.129)</td><td>0.023<br>(0.126)</td></tr><tr><td><em>Determinants:</em></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>- moderate demand pull</td><td>0.388*<br>(0.125)</td><td>0.256*<br>(0.113)</td><td>0.246*<br>(0.095)</td><td>0.251*<br>(0.094)</td><td>0.139<br>(0.189)</td><td>0.090<br>(0.183)</td></tr><tr><td>- high demand pull</td><td>0.580*<br>(0.114)</td><td>0.358*<br>(0.124)</td><td>0.403*<br>(0.086)</td><td>0.395*<br>(0.085)</td><td>0.103<br>(0.164)</td><td>0.060<br>(0.160)</td></tr><tr><td>- moderate technology push</td><td>0.156<br>(0.091)</td><td>0.134<br>(0.091)</td><td>0.159*<br>(0.062)</td><td>0.146*<br>(0.061)</td><td>0.292<br>(0.153)</td><td>0.242<br>(0.150)</td></tr><tr><td>- high technology push</td><td>0.062<br>(0.124)</td><td>0.071<br>(0.124)</td><td>0.213*<br>(0.072)</td><td>0.197*<br>(0.007)</td><td>0.257<br>(0.143)</td><td>0.226<br>(0.141)</td></tr><tr><td>σ̂<sub>u</sub></td><td>1.398*<br>(0.052)</td><td>1.379*<br>(0.051)</td><td>1.267*<br>(0.027)</td><td>1.245*<br>(0.027)</td><td>1.490*<br>(0.047)</td><td>1.434*<br>(0.046)</td></tr><tr><td>Latent model R<sup>2</sup></td><td>0.221</td><td>0.247</td><td>0.215</td><td>0.241</td><td>0.166</td><td>0.223</td></tr><tr><td>Mc Fadden R<sup>2</sup></td><td>0.092</td><td>0.100</td><td>0.079</td><td>0.091</td><td>0.056</td><td>0.080</td></tr><tr><td>LRT sectoral dummies<br>(17 degrees of freedom)</td><td>-</td><td>30.24</td><td>-</td><td>76.88</td><td>-</td><td>64.86</td></tr><tr><td>Number of firms</td><td>2137</td><td>2137</td><td>3064</td><td>3064</td><td>1041</td><td>1041</td></tr></tbody></table>

Maximum likelihood estimates of the ordered probit model.

Sample of innovating manufacturing firms with at least 20 employees.

Dummies: 18 sectoral dummies are included in the regression.

*: significant at the 5% level.
<table><caption>Table 3: Innovation Content of Exports</caption><thead><tr><th rowspan="2">Sectoral innovation level</th><th colspan="2">Low</th><th colspan="2">Moderate</th><th colspan="2">Strong</th></tr><tr><th>Constant</th><th>Dummies</th><th>Constant</th><th>Dummies</th><th>Constant</th><th>Dummies</th></tr></thead><tbody><tr><td>Employment logarithm</td><td>0.070<br>(0.073)</td><td>0.108<br>(0.075)</td><td>0.217*<br>(0.042)</td><td>0.204*<br>(0.043)</td><td>0.291*<br>(0.063)</td><td>0.319*<br>(0.068)</td></tr><tr><td>Product innovations:</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>- improvement</td><td>0.848*<br>(0.169)</td><td>0.767*<br>(0.169)</td><td>0.980*<br>(0.135)</td><td>0.845*<br>(0.134)</td><td>1.128*<br>(0.302)</td><td>0.827*<br>(0.296)</td></tr><tr><td>- innovation</td><td>0.345*<br>(0.167)</td><td>0.332*<br>(0.166)</td><td>0.534*<br>(0.102)</td><td>0.515*<br>(0.102)</td><td>0.979*<br>(0.221)</td><td>0.796*<br>(0.217)</td></tr><tr><td>- imitation</td><td>0.722*<br>(0.165)</td><td>0.705*<br>(0.165)</td><td>0.295*<br>(0.102)</td><td>0.264*<br>(0.101)</td><td>0.055<br>(0.191)</td><td>-0.012<br>(0.189)</td></tr><tr><td>Process innovations:</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>- breakthrough</td><td>0.187<br>(0.199)</td><td>0.210<br>(0.200)</td><td>0.125<br>(0.106)</td><td>-0.022<br>(0.110)</td><td>0.488*<br>(0.189)</td><td>0.420*<br>(0.183)</td></tr><tr><td>- improvement</td><td>-0.053<br>(0.163)</td><td>-0.059<br>(0.164)</td><td>-0.099<br>(0.111)</td><td>-0.078<br>(0.118)</td><td>-0.508*<br>(0.214)</td><td>-0.265<br>(0.209)</td></tr><tr><td>Other innovations:</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>- packaging</td><td>-0.053<br>(0.208)</td><td>-0.458*<br>(0.210)</td><td>-0.199<br>(0.116)</td><td>0.020<br>(0.109)</td><td>-0.479*<br>(0.209)</td><td>-0.285<br>(0.212)</td></tr><tr><td>- organisation</td><td>0.348<br>(0.190)</td><td>0.348<br>(0.190)</td><td>-0.009<br>(0.110)</td><td>0.089<br>(0.128)</td><td>0.320<br>(0.195)</td><td>0.286<br>(0.190)</td></tr><tr><td>- marketing</td><td>0.464*<br>(0.208)</td><td>0.443*<br>(0.206)</td><td>0.086<br>(0.130)</td><td>0.091<br>(0.072)</td><td>-0.017<br>(0.225)</td><td>-0.102<br>(0.219)</td></tr><tr><td>Dummies:</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>- moderate demand pull</td><td>0.181<br>(0.235)</td><td>0.123<br>(0.253)</td><td>0.285<br>(0.180)</td><td>0.251*<br>(0.094)</td><td>0.205<br>(0.349)</td><td>0.125<br>(0.337)</td></tr><tr><td>- high demand pull</td><td>0.587*<br>(0.212)</td><td>0.535*<br>(0.212)</td><td>0.603*<br>(0.163)</td><td>0.395*<br>(0.085)</td><td>0.370<br>(0.309)</td><td>0.218<br>(0.300)</td></tr><tr><td>- moderate technology push</td><td>0.313<br>(0.174)</td><td>0.296<br>(0.173)</td><td>0.468*<br>(0.112)</td><td>0.146*<br>(0.061)</td><td>0.271<br>(0.263)</td><td>0.213<br>(0.255)</td></tr><tr><td>- high technology push</td><td>0.268<br>(0.230)</td><td>0.282<br>(0.229)</td><td>0.553*<br>(0.131)</td><td>0.197*<br>(0.007)</td><td>-0.119<br>(0.251)</td><td>-0.139<br>(0.244)</td></tr><tr><td>σ̂<sub>u</sub></td><td>2.112*<br>(0.114)</td><td>2.077*<br>(0.112)</td><td>1.918*<br>(0.059)</td><td>1.866*<br>(0.057)</td><td>2.258*<br>(0.101)</td><td>2.146*<br>(0.096)</td></tr><tr><td>Latent model R<sup>2</sup></td><td>0.130</td><td>0.203</td><td>0.142</td><td>0.194</td><td>0.159</td><td>0.234</td></tr><tr><td>Mc Fadden R<sup>2</sup></td><td>0.054</td><td>0.067</td><td>0.050</td><td>0.070</td><td>0.053</td><td>0.082</td></tr><tr><td>LRT sectoral dummies<br>(17 degrees of freedom)</td><td></td><td>26.82</td><td></td><td>95.56</td><td></td><td>63.66</td></tr><tr><td>Number of firms</td><td>1641</td><td>1641</td><td>2474</td><td>2474</td><td>876</td><td>876</td></tr></tbody></table>

Maximum likelihood estimates of the ordered probit model.

Sample of innovating manufacturing firms with at least 20 employees.

Dummies: 18 sectoral dummies are included in the regression.

* : significant at the 5% level.