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«Der Open-Access-Publikationsserver der ZBW – Leibniz-Informationszentrum Wirtschaft The Open Access Publication Server of the ZBW – Leibniz ...»

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In addition to a positive correlation between productivity and ICT, there are also correlations between the intensity of ICT and firm heterogeneity. If firms use ICT intensively, they tend to deviate more from the median sector productivity (i.e. evidence more heterogeneity). Firm heterogeneity is also correlated to other firm characteristics. For instance, innovation tend to be positively correlated with firm heterogeneity.

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

3.2 Empirical Strategy

As mentioned before, the measure of firm heterogeneity at the firm level is constructed as the absolute value of the deviation of a given firm labor productivity, with respect to the median labor productivity in its corresponding industry. Moreover, ICT intensity is captured by the percentage of employees that work mainly with a PC (PCW). A set of dichotomous variables showing the adoption of different ICT applications such as enterprise resource planning systems (ERP), supply chain management (SCM) and customer relationship management software (CRM) is also available.

Additionally, the data includes information on the innovative inputs (i.e. R&D) and outputs (i.e. innovations introduced to the market) of the sampled firms. In the present analysis, the fraction of total employees that is engaged in R&D activities is used as a measure of the incentives to invest in R&D personnel (i.e. innovative activity). Additional general information at the firm and sectoral level is also included.

The empirical analysis follows a two stage strategy. In the first stage the direct effect of ICT on firm heterogeneity is estimated. This stage is implemented using OLS regressions, although the results are robust to different methods. In the second stage, the analysis follows an instrumental variables approach, where firm heterogeneity is instrumented by different measures of ICT use (providing a measure of ICT induced heterogeneity) and then related with the firms’ R&D incentives.

Equations (1) and (2) describe the general relationship between ICT, firm heterogeneity (H ) and innovation incentives (I ) and highlight the two stage instrumental variable strategy.

The key identification assumption corresponds to the exclusion of ICT from equation (2).

As discussed in the introduction, ICT generates firm heterogeneity, which in turn affect firm strategies. Therefore, it is assumed that ICT affects innovation incentives only through their impact on firm heterogeneity. Different statistical tests support this assumption.

–  –  –

Specifically, Hi,t is the measure of firm heterogeneity for firm i in t, which corresponds to the year 2006. ICTi,t−1 corresponds to the variables capturing the intensity of ICT use, as well as the adoption of ICT applications (i.e. ERP, SCM and CRM) with t − 1 corresponding to the year 2003. Ii,t represents firm i’s innovation incentives captured by the fraction of total employees engaged in R&D activities. And Xi,t−1 is a matrix with control variables such as the economic sector a firm belongs to (14 classes), the geographic location of the firm (east or west Germany), as well as firm’s age up to 2007, the presence of exporting activities in 2003, among others.

The analysis considers alternative functional forms for f (·) and g(·). In particular, the first stage assumes a linear specification for f (·) and the main results are presented in Table 4.

The results are robust to nonlinear considerations of f (·). In order to consider this possibility, a nonparametric analysis was performed. The analysis of the second stage also considered a linear specification for g(·). However, given the censoring present in the R&D data available, a (nonlinear) Tobit model was also considered. In addition, in order to check the robustness of the results, an alternative semiparametric approach with a nonparametric first stage was also implemented (see Appendix A and B). The results of the second stage are presented in Table 5. Alternative robustness tests considering different samples (as it will be clear below), as well as different types of information regarding innovation activities were also performed.

4 Results

The analysis of the first stage is presented in Table 4. In particular, the objective of the first stage is to consider the relationship between the intensity of ICT use and firm heterogeneity, controlling for different firm characteristics that might influence such relationship. Table 4 present the analysis by means of ordinary least squares regression, which amount to assume a liner functional form for f (·) in equation (1). The results presented in Table 4 are robust to nonlinear considerations of f (·). In order to consider this possibility, a nonparametric analysis was performed.

In particular, the specification considered in column 1 of Table 4 estimates the direct impact of the intensity of ICT use in 2003 on the firm heterogeneity observed in 2006. The coefficient on ICT shows no impact on the observed firm heterogeneity, suggesting no independent impact of the intensity of ICT use. In order to account for the potential persistence in firm heterogeneity derived by the persistence in productivity differences, column 2 includes a set of dummy variables that locate each firm into the corresponding quartile of its sector specific productivity distribution. As can be observed, the result of column 1 is maintained with the specification presented in column 2.

However, as highlighted by the economic literature, the adoption of ICT implies reorganization at the firm level in order to exploit the benefits of the ICT implementation. One dimension of potential complementary investments required to exploit the gains of ICT corresponds to the introduction of ICT (software) applications. By introducing specific ICT applications, a given firm might achieve a minimal ICT infrastructure needed to reap the benefits of the investments in computers and software.

In order to consider this possibility, the specification presented in column 3 includes specific ICT applications adopted by the sampled firms. In particular, EPR and SCM systems described previously are included. The hypothesis behind this specification states that the impact of ICT on firm heterogeneity does not only depend on the presence of ICT equipment, but also on the way such infrastructure is used. However, as the results shown in columns 1 and 2, the coefficients on ICT intensity and ICT applications show no independent impact on ICT on firm heterogeneity.

Column 4 extends the specifications presented in columns 1-3 to consider the interactions term between ICT and ERP. It analyzes whether the impact of ICT is associated with complementarities between the different ICT components. The results show positive and significant coefficients for the interaction term (coeff.: 0.19, std. error: 0.10), suggesting an important complementarity between the different components of the ICT infrastructure adopted at the firm level. That is, the impact of the intensity of ICT on firm heterogeneity is conditional on the presence of ERP systems. In other words, there is a critical infrastructure needed in order for ICT to differentiate a firm with respect to his competitors (i.e. induce firm heterogeneity).

Note that if ICT is not used intensively, the marginal effect of the ERP applications on firm heterogeneity is negative because the independent impact of ERP is negative (coeff:

-0.11, std. error: 0.05).

Column 5 considers and alternative hypothesis to the one presented in column 4. In particular, it investigates the existence of complementarities in the introduction of ICT, but on different ICT applications. It considers the interaction term between PCW and SCM. As can be clear from the results presented in the table, the complementarity argument does not hold for this type of application. Moreover, additional results not reported in Table 4 show that the outcome of column 4 also extends to the consideration of the interaction term between PCW and CRM.

This result is not surprising at least for two reasons. First, the ERP application is a generic general purpose software in comparison to the SCM and CRM applications. In consequence, the impact of SCM and CRM might be related with particular activities of the sampled firms that are not captured with the available data. These activities might be related with the firm specific relations with their suppliers or with costumers for the case of SCM and CRM, respectively. Second, SCM and CRM applications tend to be adopted by firms after a basic ICT infrastructure in general, and ERP systems in particular, are adopted successfully.

However, the data permit a testable hypothesis of the previous argument. That is, if there is any complementarity between ICT components that is related with their characteristics and/or timing of adoption, then the interaction terms to be considered simultaneously should be PCW and ERP on the one hand, and ERP and SCM (or CRM) on the other hand.

In this manner, a regression analysis can capture the role of the intensity of ICT use as a determinant to adopt ERP system and, subsequently, provided that ERP systems were adopted successfully, the impact of SCM or CRM should be conditional on the adoption of ERP. This hypothesis is tested in column 6.

The result presented in column 6 shows that, indeed, the impact of ICT not only depends on the introduction of ICT applications, but the complementarity between ICT components is a determinant of firm heterogeneity. This is highlighted by the significant coefficient of the interaction terms of PCW and ERP, and ERP and SCM (coeff: 0.19, std. error: 0.10 and coeff: 0.13, std. error: 0.07, respectively). Moreover, the observed complementarity is consistent with the previous argument suggesting that such complementarities depend on the characteristics of the considered applications (e.g. timing of adoption). Note again that if ICT is not used intensively, the marginal effect of the particular ICT applications on firm heterogeneity is negative.

In sum, the results of Table 4 show that ICT affects firm heterogeneity only when ICT is used intensively and jointly with particular ICT applications. These results are robust to nonparametric specifications. If ICT impacts productivity positively and induces heterogeneity, it can be argued that ICT represent a source of volatility that stimulates the process of creative destruction. If this is the case, firm strategies should react accordingly, specially strategies that can provide a competitive advantage such as R&D initiatives. This is analyzed in Table 5 for a subsample of firms that reported ICT consulting activities in 2003. This selection was performed in order to consider firms for which ICT infrastructure is important. Table 6 shows that the value and significance level of the coefficients using the full sample are very close to the values reported in Table 5. However, the specification tests are inconclusive.

Table 5 presents the main results of the analysis and performs the two stage approach taken into account the specification presented in column 4 of Table 4. Column 1 presents the benchmark case considering an OLS specification. Following a instrumental variable approach, column 2 considers the impact of firm heterogeneity on R&D incentives where ICT variables act as instruments. Column 2 shows a positive impact of ICT induced heterogeneity on the fraction of employees that perform R&D (coeff: 0.06, std. error: 0.03). The reported p-values of the endogeneity and overidentification tests (0.10 and 0.83, respectively) suggest the validity of the IV approach and the exclusion restriction for the considered firms. In order to verify the robustness of this result, column 3 and 4 consider a Tobit and IV Tobit specification due to the censoring present in the R&D information. Interestingly, the results are maintained, including the required specification tests.

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