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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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This literature has been reviewed in Bertelsman and Doms (2000), and highlights the role of several supply-side production factors that determine productivity at the firm level. For instance, technology shocks, management skills, R&D investments, among others, are shown to affect firm level productivity in traditional production function estimation approaches.

In addition, Syverson (2004) studies the impact of demand-side factors that determine differences in observed productivity levels. In particular, he finds evidence of the negative impact of product substitutability within an industry on the dispersion of its productivity distribution. The lower the product substitutability within an industry, the more low productivity firms are able to stay in business and the higher the corresponding dispersion of the industry’s productivity distribution.

This literature has also considered the role of ICT in determining firm level heterogeneity.8 In that literature, the estimated positive impact of ICT on productivity is recovered as the coefficient on a proxy for ICT capital from a production function estimation. That is, ICT is shown to increase the central tendency of the conditional productivity distribution but is silent about the effect of ICT on the dispersion of this distribution. Moreover, negative and positive impact of ICT are averaged out, making impossible to identify the potential negative impact of ICT and its consequences for firm strategies.

This paper presents a measure of firm heterogeneity that explicitly accounts for the dispersion of the productivity distribution, allowing the analysis of the role of ICT use on firm heterogeneity. In particular, firm heterogeneity is defined as the deviation of a firm’s productivity level from a given industry benchmark. In consequence, the adopted measure exhibits two main advantages with respect to the existing literature. On the one hand, it permits to directly account for the role of ICT on firm heterogeneity in a way that cannot be inferred See Bresnahan et al. (2002), Brynjolfsson and Hitt (2003), and Bloom et al. (2008).

from existing production function estimation. On the other hand, the economic literature has shown how firm heterogeneity (as previously defined) actually explains a great variety of strategic decisions at the firm level as the following literature review shows.

The second strand of literature related with the present paper analyzes the impact of firm heterogeneity on firm strategies. Although there is a voluminous literature in this area, the relevant work for the present paper corresponds to the analyses that considered similar measures of firm heterogeneity from an empirical perspective. For instance, firm decentralization decisions are theoretically and empirically explained by the role of firm heterogeneity (Acemoglu et al., 2007) and the nature of the relationship between innovation and competition depends on the level of firm heterogeneity (Aghion et al., 2005).

More specifically, defining the distance to the technological frontier as the difference between a firm’s productivity level and the highest productivity level observed in the same industry (i.e. a measure of firm heterogeneity), Acemoglu et al. (2007) develop a model to analyze the relationship between the diffusion of new technologies and the decentralization of firms.

They show, theoretically and empirically, that firms closer to the technological frontier are more likely to choose decentralization. The intuition behind this result states that these firms deal with newer technologies about which there is less information available. In consequence, a decentralized structure allows them to benefit from better informed managers (as opposed to principals).

In addition, and developing additional measures of firm heterogeneity, the paper shows how firms in more heterogeneous environments are also more likely to be decentralized because firm heterogeneity makes learning (i.e. how to exploit a new technology given the experience of others) more difficult. In a sequence of papers, Acemoglu et al. (2003), Aghion et al. (2005), Acemoglu et al. (2006) and Aghion et al. (2006) use similar measures of heterogeneity in order to study, among other topics, the relationship between innovation, entry, credit constraints and competition. Essentially, these papers highlight the role of firm heterogeneity as a main driver of industry evolution.9 In a different approach, Chun et al. (2008) directly estimate the impact of ICT use on firm heterogeneity for a panel of U.S. firms from 1971 to 2000. They find that elevated heterogeneity in firm performance (i.e. variability in labor productivity) is positively and significantly correlated with the use of ICT (i.e. ICT capital stock). The results also show that See Bartelsman et al. (2006) for an analysis of industry evolution using similar measures of firm heterogeneity.

firm heterogeneity is associated with faster productivity growth. They argue that the results provide evidence of creative destruction (i.e. increased competition) at the firm level. That is, through their use of ICT, more productive firms displace less productive firms. However, their results can only be recovered at the industry level and do not permit the analysis of their impact on firm strategies. This paper contributes to this literature by explicitly considering the impact of ICT on productivity heterogeneity at the firm level, analyzing the consequences for specific firm strategies relevant to the process of creative destruction (i.e.





R&D incentives).

3 Empirical Analysis

–  –  –

The analysis is based on two waves of a business survey carried out by the Centre for European Economic Research (ZEW) corresponding to the years 2003 and 2006 (ZEW ICT Survey).

The data set is a representative sample of the German manufacturing and service sector, and contains detailed information on the economic characteristics, performance and ICT use for 4,400 firms in each wave. Table 1 provides some descriptive statistics for the year 2003.

In general, and in line with similar data sets at the firm level, the surveyed firms evidence a great variability with respect to sales (in millions euros), number of employees and labor productivity calculated as the ratio between sales and number of employees. In addition, the empirical distribution of the reported sales, as well as the number of employees appear to be left skewed. The median size of the surveyed firm corresponds to 50 employees, whereas the average is about 337.7 employees. This indicates the presence of few very large companies in the data.

Analogously, the median value of sales is 7 millions with an average of 131.7 millions. The same also holds for the distribution of labor productivity where the median and the mean are

0.13 and 0.24 millions per employee, respectively. This skewness present in the distributions of the reported data is consistent with empirical analyses at the firm level. Table 1 also includes information about the use and intensity of ICT. The intensity in the use of ICT is measured by the percentage of employees working mainly with a PC (PCW) and is nearly equally distributed around the different percentiles. In addition, the data also provides information about different ICT software applications, namely enterprise resource planning (ERP), supply

chain management (SCM) and customer relationship management (CRM).

Table 2 shows how the use of these different ICT applications are related with different firm characteristics. These firm characteristics (e.g. sales and number of employees) vary if the firm uses ICT more intense than the median (i.e. PCW greater than 50%) or introduced ICT applications. Specifically, the table shows that the intense use of ICT, as well as the introduction of different ICT applications, is correlated with higher firm performance in terms of sales, number of employees and labor productivity. However, it should be noted that standard deviations are high through all the data, which indicates a high level of heterogeneity.

For example, the surveyed firms evidence 338 employees on average, whereas companies using ERP, SCM or CRM tend to be larger with 518, 518 and 627 average number of employees, respectively. The same can be observed in terms of sales. Firms using ICT applications or using ICT more intensively were more likely to exhibit higher sales. The average level of sales over all firms is 131.7 millions euros, whereas companies using CRM, SCM or ERP evidence 205.9, 222.8 and 209.2 millions, respectively. In general, these statistics show that firms using ICT more intensively tend to be larger than their counterparts.

Moreover, firms that use ICT more intensively and that introduce ICT applications do not only tend to be larger, but more efficient than other firms. In terms labor productivity (i.e.

ratio between sales and number of employees), it can be seen that the average is 0.24 million euros per employee. This is higher for firms using ICT more extensively. Specifically, companies using ICT more intensively (i.e. PCW greater than 50%) exhibit a labor productivity of 0.31 millions euro per employee. Moreover, companies using CRM exhibit on average 0.27 millions euro per employee, while those using SCM and ERP evidence 0.26 millions and 0.27 millions per employee, respectively.

In addition to these general characteristics, the data also contain information regarding the innovation activities of the sampled firms. For instance, as a proxy for innovation incentives, the data contain information on the fraction of employees working on R&D activities in 2006 (i.e. R&D intensity). The mean value of this variable is 0.17 with a median equal to 0.09, indicating a left-skewed distribution.

In addition, information on innovation outputs is also available. This includes dummy variables indicating whether the firm introduced product or process innovations during the periods 2001-2003 and 2004-2006, the number of such innovation introduced during 2004-2006, the percentage of sales reported in 2006 that are derived from the product innovations introduced during the period 2004-2006 (mean: 26.32, median: 20) and the percentage of cost reductions achieved in 2006 from the introduction of process innovations during the period 2004-2006 (mean: 9.49, median: 8).

The measure of ICT intensity, percentage of employees working with a PC (PCW), is positively correlated to all measures of innovation. This does not hold for the ICT applications considered. For instance, ERP use is related to a slightly higher amount of the value of process innovations, while the value of product innovations slightly decreases from 27.4% to 25.8%. The fraction of employees working on R&D activities (i.e. R&D intensity), however, is decreasing from 21.7% to 14.9% for firms that adopted ERP. This is different for the case of CRM use. The value of product innovations, the value of process innovations and R&D intensity are positively related with CRM adoption.

From the data set, the measure of firm heterogeneity at the firm level is constructed as the absolute value of the deviation of a given firm labor productivity, with respect to the median labor productivity in its corresponding industry. Deviations are taken in absolute values to derive a measure of heterogeneity associated with the dispersion of the productivity distribution. Labor productivity is computed as the ratio between sales and number of employees reported by the firms for a given year.10 Note that by taking absolution values positive, as well as negative deviations are treated equally. Table 3 presents a quantile regression analysis of the firms’ specific deviations (i.e.

without taking absolute values) that further motivate the adopted measure of firm heterogeneity previously defined. The table shows that when used intensively, ICT induce positive deviations for quantiles above the median. That is, the marginal effect of ERP, conditional on high values of PCW, is positive for quantiles above the median (i.e. columns 4-5), increasing the firm specific’s deviation. In contrast, for quantiles below the median, ICT use reduces such deviation (i.e. column 2).



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