Product Innovation and Export Performance

Transcription

1 Product Innovation and Export Performance Rachel Bocquet Patrick Musso April 12, 2010 Very preliminary and incomplete draft Abstract This paper examines the impact of product innovation on export behavior of French manufacturing firms, with a particular emphasis on the endogeneous link between innovation and exporting. Using a merged dataset of the fourth Community Innovation Survey (CIS4) and the Enquete Annuelle d Entreprises (EAE) for the period , we find that product innovation is a main factor driving export behavior (both in terms of export participation and export intensity) while there is no such evidence for process innovation. Results also show that engaging in product innovation significantly increases the probability to start exporting. These findings regarding the decisive role of product innovations are in line with recent theoretical models that provide a more nuanced characterization of the firm self-selection process. Keywords: Exporting behavior, Product innovation, Firm-level data JEL classification: D21, F10, Introduction A fast growing empirical literature based on firm-level data has revealed extensive heterogeneity across plants, even within narrowly defined sectors (Bartelsman and Doms, 2000). Regarding firm export behavior, several stylized facts are now well established. Exporters are larger than non-exporters, they are more productive and pay higher wages. From the theoretical side, Melitz (2003) provided a general equilibrium trade model with heterogeneous firms that quickly became the workhorse framework to study firm export behavior. In this model, firms incur a sunk cost to enter a foreign market. Only the most productive firms can afford to pay for such fixed costs and expand their production abroad. Less productive firms will find it rational to concentrate exclusively on the domestic market. Although this model parsimoniously explain the salient facts underlined by the empirical literature, the prediction of a threshold level for firm size and total factor productivity for export is clearly at odds with the data (Hallak and Sivadasan, 2009). The main purpose of this paper is to show empirically that, beside total factor productivity, product innovation can also play a key role in the self-selection process embodied in the Melitz s model. Product innovation allows the firm to raise its monopolistic rent. This, in turn, can allow the firm to pay for the sunk cost of export. This story could explain the lack of a one-to-one relationship between firm size, productivity and export status University of Savoie. Rachel.Bocquet@univ-savoie.fr University of Savoie and SKEMA Business School. Patrick.Musso@univ-savoie.fr 1

2 predicted by single attribute models and is compatible with recent models of two-factor heterogeneous-firms (Hallak and Sivadasan, 2009; Fasil, 2009). Using firm-level data on French manufacturing firms on the period , we show that product innovation is a main factor driving export behavior (both in terms of export participation and export intensity) while no such evidence can be found for process innovation when total factor productivity is controlled for. We also show that engaging in product innovation significantly increases the probability to start exporting. The rest of the paper is organized as follows. Section 2 provides a short review of the theoretical literature, followed, in Section 3, by a description of the dataset. Section 4 presents some preliminary evidence on exporting behavior of French manufacturing firms. Section 5 presents our econometrics results on the link between product innovation and export participation (5.1) and on the determinants of export intensity (5.4). Section 6 concludes. 2 Theoretical Background Our study is related to the literature on trade and firm heterogeneity. Our interest is on the role of firms innovation on their export performance. Observing a large heterogeneity in export behavior across firms, recent theoretical models of trade have modeled comparative advantage as an attribute of the firm (Melitz, 2003; Bernard et al., 2003; Yeaple, 2005; Melitz and Ottaviano, 2005). To explain differences in patterns of export participation, there is a common position in this literature to consider productivity heterogeneously distributed across firms. These models predict that only the more productive firms, and hence larger, start to supply goods to foreign markets in presence of fixed cost of exporting. They also predict a threshold firm size above which, all firms export, and below which, none do. Though consistent, the cross-country evidence on self-selection in exporting and high persistence of exporting status (Roberts and Tybout, 1997; Bernard and Jensen, 1999; Greenaway and Kneller, 2007) stays somewhat short to explain why some firms are initially more productive. As an answer to this limitation, the importance of firm s other investment activities for productivity and exporting has been recently brought to the forefront. A few theoretical papers provide a more nuanced characterization of the self-selection process by including endogenous investment in R&D or innovation and export decisions. Costantini and Melitz (2007) elaborate a dynamic model of firm-level adjustment to trade liberalization. This model of heterogeneous firms incorporates both idiosyncratic firm uncertainty and forward looking decisions (concerning entry, exit, export and innovation) subject to sunk costs. They show that anticipation of trade liberalization may lead firms to bring forward the decision to innovate, in order to be ready for future participation in the export market. Therefore, investment in innovation such as the adoption of a new technology or a major product quality upgrade/redesign may be a key factor to explain a firm productivity and its decision to enter a market. Another contribution comes from Aw et al. (2009). They build a structural dynamic model of a firm s decision to invest in R&D and to participate in the export market. In the model, firms differ in their past export market experience, capital stocks, productivity, and export demand, thus determining their short-run profits in the domestic and export market. Firms can affect their future productivity and profits by investing in R&D or participating in the export market. Results show that both R&D and exporting have a positive effect on the plant s future productivity, reinforcing therefore the selection effect. They also find that the interdependence between R&D and exports is not a very important factor for firm s decision. As a matter of fact, once they control 2

3 for the initial productivity, they find little evidence of investments in R&D on the export decision as well as differences in the return to R&D between exporters and non exporters. The model of Fasil (2009) provides deeper insights about the relation between innovation and exports including a clear distinction between product and process innovations. Building on the works of Melitz (2003) and Hopenhayn (1992), she develops an endogenous growth model with two dimensions of firm heterogeneity, production efficiency and product quality. At the firm level, both factors evolve through permanent shocks but firms endogenously affect their evolution through innovation investments in product and/or process. The model shows that costly trade generates the selection of inefficient firms and innovators but the share of firms that undertake only product innovation increase due to a better cost-benefit ratio of their R&D investments. Another important result is that small firms can have an easier access to the export market when their product quality is high. In the same way, Hallak and Sivadasan (2009) elaborate a model of international trade with two sources of firm heterogeneity: productivity and caliber defined as the ability to product outputs using fewer fixed outlays. They predict that in the presence of quality constraints, the size of the firm, taken alone, can not explain its export status. Moreover, conditional on firm size, exporters produce higher quality and sell at higher prices than non-exporters. Finally, since the production of quality goods requires more skilled labor and capital, exporters pay higher average wages and are more capital intensive. According to these models, a key underlying mechanism for the selection of firms into exporting is related to firm s innovation/product upgrading. While theoretically such heterogeneity is still difficult to handle, an interesting feature of the model of Fasil (2009) is to take into account the endogeneous determination of process and product innovations and exports. Hence, it is possible to hypothesize that there is a distinct role to play for product and process innovations on firm decision to export. More precisely, this model predicts a dominant effect of product innovation compared to process innovation on exporting. However, we have to admit that empirical evidences are still lacking. If some studies try to capture the heterogeneous effects of product and process innovations, very few of them provide robust results with respect to potential endogeneity between innovation and export decision. Using a panel data set of Spanish firms, Caldera (2009) shows that product innovations have a larger effect on the firm export participation than the introduction of cost-saving innovations. In line with Bernard and Jensen (2004), she concludes that product upgrading is a necessary step to serve foreign markets as firms seek to meet consumer preferences and to differentiate themselves from competitors. This result is generally robust when controlling for firm unobserved heterogeneity 1 and for the potential endogeneity between innovation and exporting. Becker and Egger (2007) also demonstrate the dominant role of product innovation compared to process innovation on the export propensity of German firms. They use matching techniques to control for firms self-selection into product and/or process innovations when estimating their impact on export decision. Interestingly, they show a larger effect of process innovation on the export intensity when it is accompanied by product innovation. This result contrasts with the findings of Harris and Li (2009) that show no innovation effect on export intensity 2. Finally, using a matching approach on Slovenian microdata, Damijan et al. (2008) find 1 In one of the three models used to control for firm unobserved heterogeneity (the fixed-effect linear probability model), the estimates of R&D intensity and product innovation do not significantly impact the probability of exporting. 2 Using a plant-level data set for the UK, they estimate a simultaneous probit model that treat exports and R&D as jointly endogeneous variables. They find that (endogenous) R&D plays an important role on export propensity, but conditional on having entered export markets (continuous) R&D does not increase export intensity levels when such R&D is treated as endogenous 3

4 no empirical evidence that either product or process innovations improve the probability of entering foreign markets. After controlling for potential endogeneity of the innovation activities, Van Beveren and Vandenbussche (2009) also fail to find a significant relationship between product and process innovations and export participation in the case of Belgium firms. In the absence of a clear consensus, it is still necessary to get right to the bottom of the relationship between innovation and exporting. 3 Data Description 3.1 Data Sources We use data from two main sources. Both collect information on French firms, though their coverage is somehow different. The first (Enquete Annuelle d Entreprises EAE) is a survey that gathers balance sheets information for all manufacturing firms with at least 20 employees, from 1990 to The second (the Fourth Community Innovation Survey CIS4 ), carried out by the SESSI (French Ministry of Economics, Finance and Industry), is based on the Oslo Manual drawn up by OECD (see OECD and Eurostat, 2007). The reference period is This survey provides detailed information about French firms innovation activities including direct and indirect measures of innovation performance and a wide variety of factors influencing innovation. The French CIS4 was addressed to firms with more than 9 employees belonging to all sectors. In manufacturing, the sample of 8000 firms is representative in terms of sectors and number of employees. The response rate was good (88%). For our cross-sectional analysis, the sample is restricted to manufacturing firms with more than 19 employees, with large firms getting over-represented. After merging these two surveys (CIS4 with EAE), we obtained a sample of about 4600 French manufacturing firms. We get an original database, which provides variables related to both innovation and exports. Since our motivation is to assess the role of (product) innovation on exports, several variables measuring export and innovation activities are chosen based on the related literature on the determinants of exports. All variables used in our estimations are described in appendix A. Firstly, we implement three variables to measure export decisions. Exporting dummy is included to understand what determines the probability of exporting. It equals one if the firm exported in 2004 and zero otherwise. Export intensity, calculated as ratio of export sales over total sales, provides us a different perspective on the study of firms export behavior. The focus is no longer on the probability of entry but on the probability to expand into foreign markets. The last dependent variable is Starters dummy. It indicates if the firm starts exporting during the observed period (zero otherwise). This allows us to deal with time-dependent behavior that is crucial to test the self-selection hypothesis. From this perspective, a firm self-selects into export participation on the basis of its relative performance before export starts. With a sample of over one thousand starters, the size is still large enough to conduct reliable estimations. Secondly, various other measures for firm innovation activities have been used. Earlier studies use quite narrow input measures of innovation processes such as R&D and/or training expenditure or expenditure related to the acquisition of machinery, equipment and software (Aw et al., 2007; Girma et al., 2008). In that way, we include the dummy variables R&D, Training and Equip. for innovation as indirect measures of innovation 3 The survey is conducted by the French Ministry of Industry. The surveyed unit is the legal (not the productive) unit, which means that we are dealing with firm level data. To investigate the role of innovation on export performance, firm level data, closer to strategic decision making, are more appropriate than plant level data (Wakelin, 1998). 4

5 activity. At the same time, it is often argued that such measures do not capture innovation efforts properly (Lachenmaier and Woessmann, 2004). Some authors have also shown that export performance is more likely to be affected by the output of innovation processes (Ebling and Janz, 1999). Moreover, as demonstrated in the motivation of our paper, we need using more sophisticated innovation variables to capture potential heterogeneous effects between product and process innovations. Product and process innovations are therefore the variables of main interest of firms innovation activities in our study. Product innovation dummy equals one if the firm reports to introduce new or significantly improved products over the period (zero otherwise). Process innovation dummy equals one when the firm reports to introduce changes in the process over the period , and zero otherwise 4. Finally, other variables control for a set of firm characteristics which have been shown to affect the export behavior. One such additional variable is Log Empl. which measures the size of the firm as the logarithm of the total number of employees. Firm size is expected to have a positive impact on entry decision into export markets since larger firms have more resources to pay for fixed costs (Roper and Love, 2002; Barrios et al., 2003). When the firm is part of a Group, it can benefit from additional resources necessary to export such as finance, marketing, physical and human capital (Basile, 2001). This is particularly the case if the firm has foreign capital participation. Foreign owned firms are more likely to enter foreign markets because they may exploit export channels and contacts of other firms belonging to the same group (Caldera, 2009). Some other studies of industrial dynamics show that efficient firms are more likely to survive and grow (Bartelsman and Doms, 2000). In that way, the variable firm age may have a positive impact on export participation. Following the empirical literature on wages in exporting firms initiated by Bernard and Jensen (1995), a high average wage, log (W ages/empl.), indicates a firm with a large degree of accumulated human capital that may lower entry costs. To give an indication of the level of physical assets influencing the marginal cost of the output of the firm, we include the real capital stock variable, log K. As a result, we expect to find a positive effect of log K on exports. Finally, since a major focus of this paper is to investigate how the productivity - export causal link may be explained by a firm s product innovation activity, two additional explanatory variables are of main interest. Export t 1 indicates the lagged export status which is usually interpreted in the literature as evidence of sunk costs of entry into export markets. Following Roberts and Tybout (1997), if sunk costs are relevant, firms may continue to export even if foreign sales are no longer profitable in the current period in order to avoid re-entry costs. Then, exporting in current period should be strongly correlated with exporting in the previous one. To account for the self-selection of more efficient firms into exporting (Bernard and Jensen, 2004; Aw et al., 2007), a measure of firm-level Total Factor Productivity (TFP) has also been computed. Initial performance should be important to explain why some firms enter foreign markets. The methodology is presented in the next subsection. 3.2 Productivity Measures To measure firm productive efficiency, we apply two complementary indicators, namely Labour Productivity (LP) and Total Factor Productivity (TFP). Labour Productivity is defined as the log-ratio of real value added on labour (hours worked): 4 Such as the introduction of new machinery, new distribution methods, new maintenance systems or operation for purchasing, accounting or computing. Purely organizational innovations are excluded from the definition. 5

6 ln LP it = ln ( Vit L it ) (1) where V it denotes the value added of the firm deflated by the sectoral price indexes published by INSEE (French System of National Accounts). We compute Total Factor Productivity using the so-called Multilateral Productivity Index first introduced by Caves et al. (1982) and extended by Good et al. (1997). This methodology consists of computing the TFP index for firm i at time t as follows: ln T F P it = ln Y it ln Y t + t ( ) ln Yτ ln Y τ 1 τ=2 N 1 2 (S nit + S nt ) (ln X nit ln X nt ) n=1 + t N τ=2 n=1 1 2 (S nτ + S nτ 1 ) (ln X nτ ln X nτ 1 ) (2) where Y it denotes the real gross output of firm i at time t using the set of N inputs X nit, where input X is alternatively capital stocks (K), labour in terms of hours worked (L) and intermediate inputs (M). S nit is the cost share of input X nit in the total cost (Appendix A provides a full description of the variables). Subscripts τ and n are indices for time and inputs, respectively. Symbols with an upper bar correspond to measures for the reference point (the hypothetical firm), computed as the means of the corresponding firm level variables, for all firms, in year t. Note that Eq.(2) implies that reference points ln Y and ln X are the geometric means of the firm s output and input quantities respectively, whereas the cost shares of inputs for the representative firms S are computed as the arithmetic means of the cost shares for all firms in the dataset. This methodology is particularly well suited to comparisons of within firm-level panel datasets across industries in that it guarantees the transitivity of any comparison between two firm-year observations by expressing each firm s input and output as deviations from a single reference point. 4 The Characteristics of Exporters: the Case of French Manufacturing Firms We start by looking at some descriptive statistics of variables considered for the groups of exporting-firms (exporters and starters), non-exporting firms as well as for all firms (see Table 1) [Table 1 about here.] For qualitative variables, we report the number of firms with the realization of value 1 for the binary variable considered and the mean for quantitative variables. As can be seen in Table 1, exporters are more product and/or process innovators than non-exporters. This is also true for starters compared to non-exporters, though the difference is not so important. The same must be said of R&D expenditure for innovations: exporters and starters invest more in R&D than non-exporters. 6

7 Consistent with the self-selection hypothesis, the differences in size, age, capital intensity, Total Factor Productivity, Labor Productivity are significant across groups. Exporters are found larger, more capital intensive, more productive and pay higher wages than non exporters. Similar arguments hold for starters. They have relatively desirable performance characteristics compared to non-exporters. Beyond export participation, Figure 1 shows that it is important to pay attention on export intensity. This figure shows that nearly 25% of exporters have an export market share less than 2,5% of their turnover meaning that the export decision does not always match with a large expansion into export markets. The real motivation of these firms may be questionable: they may be more driven by the access to subsidies for exportation than a true status of well performing firms on foreign markets. From this point of view, the study of export intensity can provide us a complementary perspective on export behavior. This can be particularly useful to suggest more nuanced conclusions for public policy makers. [Figure 1 about here.] Other interesting features of our data are given by the transition matrix (Table 2). This table presents an overview of the probabilities for an exporter/non exporter in t of being an exporter/non exporter in t + 1. A firm stands as an exporter as soon as it has declared export sales during this period. The results show a high persistence of export status, largely due to the presence of sunk cost of entry into export markets. We show in Table 2 that a firm which paid for such fixed costs will not be incited to exit its foreign market(s). More precisely, an exporter in t is more likely to export in t + 1 by almost 70% compared to a non exporter. [Table 2 about here.] Figure 2 provides other descriptive statistics on the magnitude of the export performance gap for a variety of firms attributes. This figure plots the fraction of exporters in 5 quintiles for size, labor productivity and TFP. Clearly, we observe that our data are not consistent with the prediction of self-selection models that state the existence of a threshold level above which, all firms export, and below which none of them do. We do not find any threshold effect but rather a gradual effect of the various firms attributes on export performance. These stylized facts highlight the limits of single attribute models to explain export status. Beside productivity, other investment activities should be taken into account to provide a full understanding of firms exporting behavior. The remainder of the paper examines the role of product innovation on export performance. [Figure 2 about here.] However, this relationship must be taken cautiously since many variables may be positively correlated with product innovation and export performance. It is then necessary to investigate more thoroughly this relationship with a particular emphasis on the problem of endogeneity. This will be explored in more detail in the subsequent section of econometric results. 7

8 5 On the Importance of Product Innovation As suggested by the literature on trade and firm heterogeneity, the endogeneous link between innovation and exporting may be due to several factors. First, the decision to innovate in products and the probability of exporting are likely to be both affected by common elements of unobservable heterogeneity. For example, top management s predispositions toward risk or profitability are unobservable firm specific factors which may affect export and innovative performance. Second, the decision to enter foreign markets and the decision to innovate are probably simultaneous. This results from the fact that firms innovation and export decision are taken at the same time. Third, a causality bias can arise when past export experience is not properly controlled for. In this context, a mechanism of learning by exporting occurs when exporting firms can benefit from knowledge inputs that are not available for domestic firms. Then, it can be easier for exporters to innovate. 5.1 Export Participation Given that export status and innovation activity can be highly correlated, we model exporting and innovation as joint decisions by estimating a recursive bivariate probit using maximum likelihood techniques. Let us remind that our data does not only contain information on investments in innovation resources (such as R&D, training, equipment) but also on the actual outcome of firms innovation activity (and especially product innovation). This allows us to assess the direct effect of product innovation on the probability of exporting given that each variable is likely to affect the other one. The decision to export (Ei ) depends on the determinants emphasized by the literature on trade and firm heterogeneity and on a binary variable I i taking the value of 1 if the firm i undertakes product innovation and 0 otherwise. The decision to innovate in product (Ii ) depends on the traditional determinants of innovation. We define a latent variable model accounting for these relations as: { E i = βi i + X 1i γ 1 + u 1i Ii = (3) X 2i γ 2 + u 2i The vector X i includes explanatory variables that allow to control for a set of firm characteristics and industry dummies. We define the binary variables E i and I i such as: { Ei = 1 if E i > 0 E i = 0 if E i 0 (4) { Ii = 1 if I i > 0 I i = 0 if I i 0 (5) Errors terms in Eq.(3) are assumed to be independently and identically distributed as bivariate normal: ( ) ([ ] [ ]) u1i 0 1 ρ Φ, (6) 0 ρ 1 u 2i 8

9 The correlation coefficient ρ between the disturbances accounts for the possible existence of unobservable factors that affect simultaneously the decisions to innovate and to export. If ρ = 0, E i is not correlated with the error term u 1i and the two equations could be estimated separately. In contrast, if ρ 0, a joint estimation is required to obtain consistent estimates. The results obtained through estimation of Eq.(3) are presented below in Table 3. The five models in this table are the results of running different specifications of the determinants of export participation. The estimation shows a strong correlation between the error terms of the exporting equation and the innovation equation in models 1, 2 and 3. This implies that the two decisions should be modelled simultaneously. In contrast, the error terms of the two equations are not correlated in models 4 and 5, meaning that the estimation of a simple probit model should not lead to a loss of efficiency for these two specifications. The first part of the table summarizes the estimates of the innovation equation while the estimates of the exporting equation are reported in the second part. We first comment the results of the exporting equation. Model 1 corresponds to the self-selection hypothesis of the more productive firms into export markets. Consistent with the standard approach, we find that TFP, log K and firm Size increase the probability of exporting. This result confirms the importance of high productivity, scale-effects and financial resources to come up sunk costs for entering foreign markets. Prior to exporting, Foreign ownership and Age are also important factors driving export behavior. More importantly, when controlling for the potential effect of product innovation for trade performance, the positive and significant effects of all these variables on the probability of exporting remain unchanged, except for the size variable (Model 2). As expected, Product innovation has a positive and significant impact on export participation. This result gives support to the recent models that predict a dominant role of product attributes on export decision. Firms with new or significantly improved products appear to be more able to enter foreign markets as the goods they produce are more adapted to foreign markets characteristics and demand preferences (Bernard and Jensen 2004). In model 3, the average wage variable [log (W ages/empl.)] has also a positive impact on export participation suggesting that firms with a large degree of accumulated human capital are more likely to export. In Model 4, we show that past export status (Export t 1 ) positively affects the probability of exporting meaning that the presence of sunk costs induces persistence in export participation. Not surprisingly, younger firms are more likely to begin to export (the estimate for age is negative): there is no reason that an old firm would decide to export during the observed period while it has never done it before. Finally, Model 6 contains estimates for specifications to distinguish between product and process innovations. Product innovations may have productivity effects when they are not associated with process innovations. For example, the introduction of new or improved products can imply changes in production methods. In order to control for this effect, the process innovation dummy is included. The coefficient on process innovation is never significant. This suggests that the introduction of cost-reducing process innovations does not determine the probability of exporting. Consistent with the results of Becker and Egger (2007) on German firms, we find that product innovation exerts a significant and positive impact on export decision while there is no such evidence for process innovations. [Table 3 about here.] 9

10 The second part of the table shows the estimates of the innovation equation. Not surprisingly, results show that all the traditional determinants of innovation have a positive impact on the probability to innovate in product. Firms that invest in innovation inputs such as R&D, training or machinery and equipment are more likely to be product innovators. Size, Group, TFP and average wage also appear to be key attributes for product innovation activities. 5.2 Starting to export The second step of our analysis concerns a sub-sample of manufacturing firms which excludes firms exporting before This allows us to properly control for the potential causality bias due to past exporting experience. The results are shown in Table 4 below. We have estimated the choice system defined in Eq 3 using a recursive biprobit model. The first point to note is that the correlation coefficient ρ is never significantly different from zero, implying that the two decisions could be modelled separately when starters are considered 5. In the product innovation equation, we see that input innovation variables have a positive and significant impact on the probability to innovate in product. These results do not differ from those obtained on the full sample except for size and Group. The effect of these variables is no more significant. This indicates that the ability of starters to produce new goods adapted to foreign markets characteristics is not facilitated by the access to important or additional resources. Results of the second equation show that the determinants of the decision to start exporting are quite similar to those obtained on the full sample. The effect of product innovation is still significant while process innovation has no impact on starters export decision. Younger and capital intensive firms are more likely to start exporting. Compared to previous results, the main difference concerns TFP which has now no significant impact on the probability to start exporting. This result is in line with previous studies on the decision to start exporting. Bellone et al. (2008) show that French manufacturing firms experience a relative decrease in their TFP level a few years before starting exporting. Their TFP bounces back to a level significantly higher than the one of non-exporters only one or two years after entering the foreign market. This U-shaped pattern is attributed to the fact that the sunk cost of entry on foreign markets takes, at least partly, the form of new investments. This increase in the real capital stock induces mechanically a drop in TFP. [Table 4 about here.] To challenge this hypothesis, we decided to replace our measure of TFP by the level of labor productivity (LP) computed as described by Eq.(1). Labor productivity is usually strongly correlated with TFP. However, because LP is not affected by investment, this measure should be less polluted by the sunk entry cost incurred by starters. We should then find back a positive impact of LP on the probability to start exporting compatible with the self-selection hypothesis. The results of this specification are provided by Table 5. Notice that, because the correlation coefficient ρ is not significantly different from zero 5 We found the same result in the exporting equation (full sample) when we controled for Export t 1 (see Models 4 and 5) 10

11 in our favorite specifications, these new estimates are computed thanks to a simple probit model. The results obtained support our hypothesis. Productivity and product innovation are the two main factors driving the decision to start exporting. 5.3 Marginal Effects [Table 5 about here.] We show in the previous sections of this paper that product innovation has a significant and positive effect on export participation while there is no such evidence for process innovation. Still, we do not know the magnitude of the effect of product innovation. In this way, the marginal effects calculated in this section will give us useful information (See Table 6). The marginal effect of a qualitative variable is measured by the difference between the conditional probabilities Thus, the marginal effect of product innovation on the likelihood to export is given by: P = P (E = 1 I = 1, X 1 ) P (E = 1 I = 0, X 1 ) (7) The computation of these conditional probabilities in the case of the recursive bivariate probit model described by Eq. (3) is a bit involved (Greene, 2003). However, because the correlation coefficient ρ between the disturbances is not statistically significant in our favorite specifications (Model (5) in Table 3 and Table 4), the two equations can (and actually, have to) be estimated separately. The marginal effects presented below are then computed from the estimates of the simple probit models for all firm and starters. [Table 6 about here.] The main result put forward by Table 6 is that product innovation increases very significantly the probability to start exporting by 44%. The impact of product innovation is far less important for firms that were exporting before 2004 (13%). This result gives evidence that product innovation is particularly determinant for firms that have not yet paid for sunk costs of entering into foreign markets. This can be interpreted as follows: when creating new products that meet consumer requirements and help them to differenciate from competitors, starters can raise their monopolistic rent and therefore can pay for these sunk costs. Focusing now on the probability to start exporting, we can go further Table 6 by looking at the distribution of marginal effects for starters (Figure 3). We observe that for half of the sample of starters marginal effects of product innovation are strong, greater than 42%. [Figure 3 about here.] Figure 4 plots individuals marginal effects against the probability to export when the firm does not benefit from a product innovation. A clear result is that a marginal increase in product innovation augments all the more the probability of exporting when this latter is inially weak. As we can see, when the probability of exporting is near to 0, product innovation increases this initial probability by almost 90% whereas the increase is only of 11

12 9% when the initial probability is This stresses again that product innovation acts as a booster to become an exporter. [Figure 4 about here.] Figure 5 presents the results of a series of simulation exercises. For all these simulations, the value of the independent variables are all set to their sample mean, except for a particular variable that varies from the minimum to the maximum of its sample values. The panels of Figure 5 show the corresponding effects for different variables of interest (Employment, TFP, Real Capital Stock and Age). 5.4 Export intensity [Figure 5 about here.] In the final step of our analysis, we have estimated a Tobit model to identify the determinants of the export intensity (see Table 7). This allows us to get deeper insights on the firm export behavior and to test the robustness of our results. [Table 7 about here.] Interestingly, the results reveal that the determinants of export intensity are quite similar as those included in the determination of export participation. Firstly, product innovation has a positive and significant impact on export intensity in all specifications (as found in probit estimations) while process innovation is not significant. A positive and significant impact of process innovation on export intensity is only found in model 4. It is no longer significant when the variable capturing the average capital intensity is included. Let us recall that process innovation was never found significant when dealing with export participation. Secondly, we find that firms attributes such as age, foreign ownership, average wage and lagged export status well explained both export participation and export intensity. In contrast, TFP has a negative impact on the export intensity. The effect of size also differs from the probit estimations. Here, larger firms are more likely to increase their export participation while this variable has no impact on the probability of exporting. 6 Conclusion Using a rich dataset on French manufacturing firms, this paper shows that product innovation plays a key role in explaining firm heterogeneity in export behavior. The impact of this type of innovation is particularly strong when considering the decision to start exporting. These results are robust even when we carefully control for the potential endogeneity of innovation. Acknowledgments The authors blame each other for any remaining mistake. They nevertheless agree on the need to thank Mareva Sabatier for very useful comments and discussions. 12

13 A Variables definition [Table 8 about here.] B Main Variables Used in TFP Computation All nominal output and inputs variables are available at firm level. Industry level data are used for price indexes, hours worked and depreciation rates. Output Gross output deflated using sectoral price indexes published by INSEE (French System of National Accounts). Labour Labour input is obtained by multiplying the number of effective workers (i.e. number of employees plus number of outsourced workers minus workers taken from other firms) by the average hours worked. The annual series for hours worked are available at the 2-digit industry level and provided by INSEE. Note that a large drop in hours worked occurs between 1999 and 2000 because of the specific French 35 hours policy (On average, worked hours fell from in 1999 to in 2000). Capital input Capital stocks are computed from investment and book values of tangible assets following the traditional perpetual inventory method (PIM): K t = (1 δ t 1 ) K t 1 + I t (A-1) where δ t is the depreciation rate and I t is real investment (deflated nominal investment). Both investment price indexes and depreciation rates are available at the 2-digit industrial classification from INSEE data series. Intermediate inputs Intermediate inputs are defined as purchases of materials and merchandise, transport and travel, and miscellaneous expenses. They are deflated using sectoral price indexes for intermediate inputs published by INSEE (French System of National Accounts). Input cost shares With w, c and m representing respectively wage rate, user cost of capital and price index for intermediate inputs CT kt = w kt L kt + c It K kt + m It M kt represents the total cost of production of firm k at time t. Labour,capital and intermediate inputs cost shares are then respectively given by s Lkt = w ktl kt CT kt ; s Kkt = c ItK kt CT kt ; s Mkt = m ItM kt CT kt (A-2) To compute labour cost share, we rely on the variable labour compensation in the EAE survey. This value includes total wages paid to salaries plus income tax withholding, and is used to approximate the theoretical variable w kt L kt. To compute the intermediate inputs cost share, we use variables for intermediate goods consumption in the EAE survey and the price index for intermediate inputs in industry I provided by INSEE. 13

14 We computed the user cost of capital using Hall s (1988) methodology in which the user cost of capital (i.e. the rental price of capital) in the presence of a proportional tax on business income and of a fiscal depreciation formula, is given by ( ) 1 c It = (r t + δ It πt e τt z I ) p IKt (A-3) 1 τ t where πt e is the expected inflation rate for investment computed as a 3 periods moving average of the past inflation rate in investment price index. τ t is the business income tax in period t and z I denotes the present value of the depreciation deduction on one nominal unit investment in industry I. Complex depreciation formulae can be employed for tax purposes in France. To simplify this, we chose to rely on the following depreciation formula z I = n t=1 (1 δ I ) t 1 δ (1 + r) t 1 where δ I is a mean of the industrial depreciation rates and r is the mean of the nominal interest rate on the period References Aw, B. Y., Roberts, M. J. and Winston, T. (2007), Export market participation, investments in r&d and worker training, and the evolution of firm productivity., World Economy 30(1), Aw, B. Y., Roberts, M. J. and Xu, D. Y. (2009), R&d investment, exporting, and productivity dynamics, Working Paper 14670, National Bureau of Economic Research. Barrios, S., Grg, H. and Strobl, E. (2003), Explaining firms s export behaviour: R&d, spillovers and the destination market, Oxford Bulletin of Economics and Statistics 65(4), Bartelsman, E. and Doms, M. (2000), Understanding productivity: Lessons from longitudinal microdata, Journal of Economic Literature 38(3), Basile, R. (2001), Export behaviour of italian manufacturing firm over the nineties: The role of innovation, Research Policy 30, Becker, S. O. and Egger, P. H. (2007), Endogeneous product versus process innovation and a firm s propensity to export, Working Paper 1906, CESIFO. Bellone, F., Musso, P., Nesta, L. and Quere, M. (2008), The u-shaped productivity dynamics of french exporters, Review of World Economics/Weltwirtschaftliches Archiv 144(4), Bernard, A. B. and Jensen, J. (1995), Exporters, jobs and wages in us manufacturing: , Brooking papers on economic activity, microeconomics. Bernard, A. B. and Jensen, J. B. (1999), Exporting and productivity, Working Paper 7135, National Bureau of Economic Research. 14

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17 Table 1: Descriptive Statistics Firm Status Exporters Non-Exporters Starters All Dummies R&D Process innovation Product innovation Process & Product Foreign Ownership Mean log Empl log K log (Wages/Empl.) log LP log TFP Age Obs

18 Table 2: Transition Matrix Export Status 0 1 Total Total

19 Table 3: The Probability to Export: a Recursive Bivariate Probit Model Product innovation (1) (2) (3) (4) (5) log Empl [0.023]*** [0.023]*** [0.023]*** [0.023]*** [0.023]*** log TFP [0.142]* [0.140] [0.140] [0.145]* [0.145]* log (Wages/Empl.) [0.324]** [0.328]*** [0.325]** [0.333]*** [0.333]*** R&D [0.065]*** [0.065]*** [0.065]*** [0.067]*** [0.067]*** Training [0.066]*** [0.065]*** [0.065]*** [0.067]*** [0.067]*** Equip. for innov [0.064]*** [0.064]*** [0.064]*** [0.065]*** [0.066]*** Group [0.062]*** [0.062]** [0.062]** [0.064]** [0.064]** Constant [0.458]*** [0.462]*** [0.459]*** [0.497]*** [0.497]*** Export log Empl [0.034]** [0.034] [0.035] [0.046] [0.046] log TFP [0.164]*** [0.154]*** [0.156]*** [0.159]** [0.158]** log K [0.024]*** [0.024]*** [0.024]*** [0.031]*** [0.032]*** Foreign [0.070]*** [0.071]*** [0.072]*** [0.088]*** [0.088]*** Age [0.003]* [0.003]** [0.003]** [0.005]** [0.005]** Product innovation [0.070]*** [0.071]*** [0.102]* [0.127]** log (Wages/Empl.) [0.345]*** [0.455] [0.457] Export t [0.070]*** [0.071]*** Process innovation [0.091] Constant [0.316]*** [0.335]*** [0.583]*** [0.724]*** [0.729]*** Observations ρ [0.043]*** [0.056]** [0.056]** [0.079] [0.089] LL Models include a vector of sector dummies. Robust standard errors in brackets * significant at 10%; ** significant at 5%; *** significant at 1% 19