Industry dynamics

As a last point, we analyse the temporal structure of the impact of R&D and capital expenditures across sectors. Our sample covers a number of sectors ranging from manufacturing to services, which are characterised by very different R&D processes. In Hypothesis 5 we set up the natural expectation: The temporal structure of the impact of R&D activities and capital expenditures on firm growth varies across industries. Therefore, we compare the temporal structure of the impact of R&D and capital expenditures in different industries. To gain a clearer picture of the temporal structure and dynamics within the industries, we repeat our analyses for several kinds of manufacturing industries and services such as real estate. For the detailed analysis we choose those industries for which our firm sample contains a high number of firms. Again, we only discuss the findings for the regressions considering temporal autocorrelation.

Table 3.6 Estimates for turnover growth for high-tech and low-tech firms

Estimates for turnover growth for high-tech and low-tech firms regressed on different variables of R&D and capital expenditures (see appendix 3.25 and 3.26; standard errors in parentheses).

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variable High-tech Low-tech R&Dexp 0.000291*** (0.00009) -0.00187 (0.00789)

Capex -0.0000003* (0.0000001) 0.0000104 (0.000040) R&Dexp 0.000109 (0.0001) -0.00328 (0.00509)

Capex05 0.00127** (0.000626) -0.0104** (0.00482) R&Dexp 0.000147 (0.000116) -0.00268 (0.00506)

Capex04 0.000589 (0.000806) -0.0123** (0.00515) R&Dexp 0.000225** (0.00001) -0.00284 (0.00511) Capex03 -0.000162 (0.000413) -0.0103* (0.00579) R&Dexp05 0.000454*** (0.000110) -0.000164 (0.00808) Capex -0.0000004** (0.0000001) -0.000023 (0.00006) R&Dexp04 0.000263*** (0.00005) -0.00105 (0.00789) Capex -0.0000002* (0.0000001) -0.000009 (0.000121) R&Dexp03 0.000009 (0.00008) -0.00236 (0.00753) Capex -0.00000004 (0.0000001) 0.00003 (0.000177)

Standard errors in parentheses

*** p<0.01, ** p<0.05, * p<0.1

Before we examine various individual industries, we compare high-tech and low-tech manufacturing firms (see Table 3.6). For firms in the high-tech manufacturing we find results similar to the ones above. On the one hand, firm growth is positively related to the average R&D expenditures and to one-time R&D expenditures one and two years before the measurement of growth. On the other hand, permanent capital expenditure is negatively related with growth in high-tech manufacturing industries, while we find also a significant positive coefficient for capex in the previous year. The same has been found for the total firm population and was comprehensively discussed above.

For firms in low-tech manufacturing industries different results are obtained. Most coefficient estimates are insignificant. Only for capital expenditures one, two and three years before the measurement of growth we find significant negative estimates. Capital investment in one year is related to lower growth rates in the successive years in low-tech industries. R&D expenditures do not seem to play a positive role in low-tech industries. All estimates are negative, although not significantly.

Let us now considered those industries separately for which we have sufficient firm numbers in our data base. The results in Table 3.7 as well as the results before clearly confirm our Hypothesis 4 stating that the relevance of R&D and capital expenditures depends on the industry.

Table 3.7 Estimates for turnover growth for firms by industry

Estimates for turnover growth for firms, sorted by industries, regressed on different variables of R&D and capital expenditures (see appendix 3.27 to 3.32; standard errors in parentheses).

variable Food and beverages Chemicals Metal R&Dexp -0.0209 (0.0132) 0.000332*** (0.000117) 0.00757 (0.0120)

Capex 0.0198** (0.00921) -0.0000003* (0.0000002) -0.00421 (0.00533) R&Dexp -0.0123 (0.0121) 0.000128 (0.000122) -0.000130 (0.00722) Capex05 0.0110* (0.00582) 0.00210 (0.00143) -0.00669 (0.00865) R&Dexp -0.00740 (0.0126) 0.000181 (0.000177) -0.000312 (0.00725) Capex04 0.00244 (0.00628) 0.000703 (0.00209) -0.00326 (0.00790) R&Dexp -0.00793 (0.0126) 0.00304** (0.000118) -0.000158 (0.00725) Capex03 -0.00123 (0.00673) -0.00130 (0.00109) 0.00108 (0.00980) R&Dexp05 -0.0161 (0.0120) 0.000461*** (0.000137) 0.0130 (0.0119) Capex 0.0184* (0.00920) -0.0000003** (0.0000002) -0.00584 (0.00490) R&Dexp04 -0.0237 (0.0146) 0.000292*** (0.00005) 0.00394 (0.0119)

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Capex 0.0195* (0.00908) -0.0000002* (0.0000001) -0.00273 (0.00488) R&Dexp03 -0.0318** (0.0142) 0.0000003 (0.000125) 0.00331 (0.0104) Capex 0.0209** (0.00862) -0.00000004 (0.0000002) -0.00323 (0.00627) variable Electrical and optical

equipment Transport Real estate R&Dexp 0.000490* (0.000249) 0.0430*** (0.00699) 0.00007 (0.000280)

Capex -0.0000007 (0.000003) -0.000351*** (0.00005) -0.00000002 (0.00000002) R&Dexp 0.000537* (0.000287) -0.00181** (0.000685) -0.000300 (0.000418) Capex05 -0.000291 (0.000817) 0.0429*** (0.00357) 0.00243 (0.00323) R&Dexp 0.000855* (0.000467) -0.00669*** (0.00144) -0.000102 (0.000322) Capex04 -0.00133 (0.00143) 0.0509*** (0.0105) 0.000884 (0.00312) R&Dexp 0.000889*** (0.000339) -0.0125*** (0.00158) -0.00004 (0.000239) Capex03 -0.000929 (0.000592) 0.0429*** (0.00541) -0.00006 (0.000668) R&Dexp05 0.00130*** (0.000294) 0.0117 (0.0139) 0.00006 (0.000123) Capex -0.000007** (0.000004) -0.00009 (0.00008) -0.00000002 (0.00000002) R&Dexp04 0.00111*** (0.000223) 0.0183*** (0.00127) 0.00002 (0.000226) Capex -0.00002*** (0.000005) -0.000305*** (0.00002) -0.00000001 (0.00000002) R&Dexp03 0.000543*** (0.000193) 0.0116*** (0.00115) 0.00003 (0.000251) Capex -0.0000004 (0.000003) -0.000547*** (0.00005) -0.00000002 (0.00000002)

Standard errors in parentheses

*** p<0.01, ** p<0.05, * p<0.1

First, we consider the chemical industry and the electrical and optical equipment industry because these industries shows results similar to those we found and discussed for the high-tech industries and the complete firm sample. Hence, we can refer to the discussion above and repeat here only that growth is positively related to R&D activities one, two and - in the case of electrical and optical equipment - three years before and also to the average R&D activity.

Furthermore, we also find a negative significant relationship between permanent capital expenditures and growth, at least in some regression. In contrast to the findings for all firms, we do not find a significant positive relationship between one-time capital expenditures and later growth rates. In the case of chemicals the respective estimates are mainly positive, so that the insignificance might be a result of the smaller number of firms. In the case of electrical and optical equipment the negative estimates suggest that one-time capital expenditures are, at least, not followed by higher growth rates.

In contrast, for the food, beverages and tobacco industry we find positive coefficients for average capital expenditures. This holds for permanent capital investment as well as for capital investment in the year before measuring growth. Hence, in this industry capital expenditures and growth are clearly positively related. In contrast, for R&D expenditures mainly insignificant estimates are found and in one case even a negative significant estimate is obtained. In the food, beverages and tobacco industry it seems not to pay off to invest in intangible assets such as R&D, maybe because it is a low-tech industry.

The transport equipment industry shows very distinguished results. Again, permanent capital expenditures show a significantly negative relationship with growth. This negative relationship turns into a positive relationship if we consider one-time values of capital investment. This means, as discussed above, that firms with high capital expenditures show, on average, lower growth rates, which might be caused by differences within the industry, e.g.

between suppliers and car manufacturers. In contrast, one-time capital investment is related to successive growth. For the permanent R&D expenditures we find the significant positive relationship with growth that is also found for high-tech industries. Looking at one-time R&D investments, the same positive relationship is found, decreasing with the time gap. Hence, R&D seem to have a positive impact on growth in this industry. However, the coefficients for average R&D expenditures turn significantly negative if one-time capital investments are

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considered in the regressions instead of average capital investments. This is another indication that our firm sample in the transport equipment industry is far from homogeneous. Therefore, the finding for this industry should be interpreted with care. A further separation of the firms in this industry would provide further insights.

For the metal industry we do not find any positive or negative coefficient for the independent variables. This means, R&D efforts do not play a key role for growth in the metal industry.

This is in line with the findings on the aggregated level for low-tech industries (see Table 3.10 in the appendix). The same is true for firms in real estate.

To sum up, Hypothesis 4 is confirmed by our results: the relationship between R&D and capital expenditures and firm growth and their temporal structure varies across industries. In general, we find that R&D investments are positively related to growth in high-tech industries. Permanent capital investments are positively related to growth in one industry (food, beverages and tobacco), while they are negatively related to growth in many other industries.