The Effect of Innovation on Employment: A Panel Analysis

Transcription

1 The Effect of Innovation on Employment: A Panel Analysis Stefan Lachenmaier Ifo Institute for Economic Research at the University of Munich lachenmaier@ifo.de Prof. Dr. Horst Rottmann University of Applied Sciences Amberg-Weiden Abstract This paper analyzes the effects of innovation on employment. These effects often remain unclear in theoretical contributions: New products increase demand and therefore employment, but new processes increase labor productivity and therefore may depending on elasticities - reduce employment. Our firm panel data set allows for analyzing dynamic aspects of labor demand while controlling for unobserved firm heterogeneity. We use innovation inputs as well as weighted innovation outputs, both broken down into product and process innovations. Estimating growth and dynamic panel estimations delivers empirical evidence for the importance of these detailed breakdowns as the effects show significant differences. JEL Classification: O3, L6, C23, J23 Keywords: innovation, employment, firm size, panel data Paper is not competing to the Young Economist Award Extremely preliminary and incomplete. Do not quote nor circulate without authors explicit permission. Comments welcome.

2 The Effect of Innovation on Employment: A Panel Analysis Stefan Lachenmaier Ifo Institute for Economic Research at the University of Munich Poschingerstr Munich, Germany Phone: (+49) lachenmaier@ifo.de Internet: Prof. Dr. Horst Rottmann University of Applied Sciences Amberg-Weiden Hetzenrichter Weg Weiden, Germany Phone: (+49) Fax: (+49) h.rottmann@fh-amberg-weiden.de Internet: March 15, 2005 Extremely preliminary and incomplete. Do not quote nor circulate without authors explicit permission. Comments welcome. 2

3 The Effect of Innovation on Employment: A Panel Analysis Abstract This paper analyzes the effects of innovation on employment. These effects often remain unclear in theoretical contributions: New products increase demand and therefore employment, but new processes increase labor productivity and therefore may depending on elasticities - reduce employment. Our firm panel data set allows for analyzing dynamic aspects of labor demand while controlling for unobserved firm heterogeneity. We use innovation inputs as well as weighted innovation outputs, both broken down into product and process innovations. Estimating growth and dynamic panel estimations delivers empirical evidence for the importance of these detailed breakdowns as the effects show significant differences. JEL Classification: O3, L6, C23, J23 Keywords: innovation, employment, firm size, panel data, dynamic panel 3

4 1. Introduction This paper delivers empirical evidence for the effect of innovations on employment. It contributes to the existing research by the exploitation of a uniquely rich microeconomic panel dataset of German manufacturing firms. The dataset combines annually surveys over the last 21 years and thus delivers a unique panel data set. There are several questions which can be addressed by this data source. The overall effect of innovation on employment often remains unclear in theoretical contributions. Generally it is common opinion by now that product innovations increase employment. The basic mechanism behind this is that new products create a new demand which allows the firm to employ more people. The effect of process innovations, however, is not that obvious. On the one hand process innovations sometimes facilitate the production process by introducing new technological solutions. This may result in a replacement of manual work by technique in the short run, because the same output can be produced with less inputs. But, on the other hand, process innovations may result in an increase in employment in the long run. If the new technology is implemented in the firm, the firm can produce any given output at lower costs and if this cost advantage is passed on to the product prices, the firm can lower the prices for its products. This might - depending on demand elasticity - lead to a higher demand for the products, which in the end can also stimulate employment. All these questions of unclear effects rise the need for an empirical analysis. With our data set we can analyze the German manufacturing sector for a long time horizon, 21 years, while at the same time we can distinguish between product and process innovations. This allows us to detect dynamic processes in the innovation-employment relationship. As innovation variables we can use innovation inputs (innovation expenses) as well as different innovation outputs, (innovations introduced), including product and process innovations as well as quality measures for innovations. Using these data we will analyze the effect of innovation on employment in a dynamic panel analysis. This allows for detecting not only short-time effects but also long-time effects in firms. The use of a panel data set solves the problem of unobserved firm heterogeneity which is often a big problem in cross-sectional studies. The paper is structured as follows. Section 2 gives a short overview about the existing literature in this research field. Section 3 describes the database. In section 4 a short model is presented, from which the estimation equations are derived. The results are presented in section 5. Section 6 concludes. 4

5 2. The Literature on Innovation and Employment 2.1 Theory In theoretical contributions on the impact of innovation on employment the direction of the effect of technological progress often remains unclear. The question of the effect of technological change on firm performance is not a new one. Researchers have been analyzing this task for a long time, their analyses depending heavily on the methodology and data available. An historical overview about the evolution of this field of research is given in Petit (1995). In the theoretical literature the differentiation between product and process innovations has been proved important (Stoneman 1984, Hamermesh 1993). Whereas for product innovations it is meanwhile generally assumed that they enhance employment via a higher demand created by the introduction of new products or an improved quality of existing products it is especially the effects of process innovations which leave open questions. As Stoneman (1984) summarizes his analysis the direction of changes in employment depends on several factors. In a world with a profit-maximizing firm the technological progress will lead to increases in the output of the firm. But only if the technological progress is considered as neutral and there are decreasing returns to scale the positive effect on employment becomes clear in the model, whereas without these restricting assumptions the direction of the effect is not clear and depends on the scale economies and demand elasticities. The model does also not include the need for different skills which come along with new technology or the possible monopoly power which might at least temporarily occur in the case of introducing new products (Stoneman 1984). The resulting output effect is not unambiguous since product innovations could also reduce competition if they influence the market structure via product differentiation. Due to the higher monopolistic power this could lead to higher prices and to less output increases and employment changes than expected. The impacts of innovations on labour demand can differ significantly depending on the additional indirect effects on the produced output, which is related to the prevailing market structure and to the price elasticity of product demand. To sum up the theoretical contributions it can be stated, that product innovation should show an positive effect on employment in the empirical analysis whereas assumptions about the direction of the effect of process innovations cannot be made and the direction has to be shown by empirical analysis. 5

6 2.2 Empirical Evidence There exists a lot of empirical literature about technological progress and its impact on different economic measures. What we will concentrate on in this paper is microeconometric analysis of the effect of innovation on employment. 1 This strand of literature started mainly in the 1990s with the increasing availability of micro data on innovation behaviour. An excellent overview of microeconometric analyses in this field of research is given in Chennells / Van Reenen (1999). As suggested by the theoretical work the empirical analysis usually also distinguish between product and process innovation. In almost all analyses a positive effect of product innovations is found, for processs innovations there is also a tendency for a positive effect but the analyses are not that clear. The methods used are widespread as are the countries covered and the variables used. These include the innovation variables (or proxy variables for innovation) as well as the control variables. In terms of econometric models one can distinguish the existing literature mainly in three parts: cross-sectional analyses, analyses of the growth rates with data of two different points in time and panel data analysis. Early contributions are mainly based on cross-sectional data due to the data available at this time. Contributions in this line are Zimmermann (1991), Entorf and Pohlmeier (1991) and König et al. (1995) Zimmermann (1991) and Entorf and Pohlmeier (1991) also use ifo data, but from a different survey. Zimmermann (1991) concludes that technological progress played an important role in the decrease of employment in Entorf and Pohlmeier (1991), however, show a positive effect of product innovations on employment while process innovations showed no significant effect. König et al. (1995) also use German data, stemming from the Mannheimer Innovationspanel in 1993 and also found a positive effect of product innovations and an even stronger effect of process innovations on the expected labour demand. Newer analyses combine two surveys of different points in time and therefore are able to explain the growth rate of employment between these two points in time. Brouwer et al. (1993) are in this line of literature, who analyze data of the Netherlands of the years 1984 and Using OLS regressions the authors show a negative effect of R&D on employment growth, but a positive effect if R&D is related to creating new products. Also using OLS models Blanchflower and Burgess (1999) find a positive relation between process innovations and employment growth in the UK in 1990 and in Australia in 1989/1990. Doms et al. (1995) also show a positive relation between the use of modern 1 Topcis not to be covered include the effects on wages and skill biased technological progress. 6

7 technology and employment growth between 1987 and 1991 using firm data in the manufacturing sector together with data from a technology survey in Klette und Forre (1998) have matched different data sets for Norway. Census data was combined with several surveys between 1982 and Their, mainly descriptive, analysis did not show a clear positive relation between innovations (measured as firms conducting R&D vs. firms not conducting R&D) and employment. Using German data from the Community Innovation Survey (CIS3) Peters (2004) analyzes employment growth between 1998 and Product innovations show a significantly positive effect on employment growth whereas process innovations showed a negative effect for German manufacturing firms (but not in the Service sector). Also using CIS data and additional data for the Netherlands Blechinger at al. (1998) find positive effects of product as well as process innovation on employment growth for the Netherlands between 1988 and 1992 and for Germany between 1992 and The third type, panel studies, are the most rare ones. A first step in this direction is Greenan and Guellec (1997), who use firm panel data, but have to match it with a crosssectional innovation survey. Their results show that innovating firms (and innovative sectors) have created more new jobs than non-innovating firms (less innovative sectors). Their results suggest that, on the firm level, process innovation play the more important role whereas on the sector level it is the product innovations which are more important. Real panel analyses over a longer time horizon are the contributions of Smolny (1998), Flaig and Rottmann (1999) and van Reenen (1997). Smolny (1998) analyzes data of German firms from the ifo Konjunkturtest and the ifo Investitionstest from 1980 to Using pooled OLS regressions he shows a positive effect of product innovations as well as process innovations. Flaig and Rottmann (1999) control for unobserved firm heterogeneity and estimate a recursive equation model with output, output expectations and employment as endogenous variables. They also find positive effects for product and process innovations. Van Reenen (1997) matches firm data of firms listed at the London Stock Exchange with the English innovation database of the SPRU (Social Policy Research Unit). With this data set for he estimates also Arellano-Bond models which allows him to control for fixed effects, dynamics and endogeneity. But still he finds positive effects of innovation on employment. Rottmann and Ruschinski (1997) carried out analyses with data from the ifo Institute. The authors show in their analysis of the effects of technological change on employment growth the importance of controlling for unobserved firm heterogeneity and adjustment processes. Controlling for these effects the 7

8 authors find significantly positive effects of product innovations and significantly negative effects of process innovations on employment growth. Additional important variables in their models are labor costs, which have a negative effect and demand growth, which shows a positive effect. Building on these results the authors also use a dynamic panel method, the Anderson-Hsiao framework (Rottmann and Ruschinski 1998). The positive effect of product innovations was also found in this analysis but process innovations showed no significant impact. All these studies do not have quality measures of the innovation outputs and have no possibility to include both, innovation inputs and innovation outputs. 3. Database and Descriptive Statistics 3.1 The Ifo Innovation Survey The source for the microeconomic data used in this analysis is the Ifo Innovation Survey. The Ifo Innovation Survey is conducted yearly by the Ifo Institute for Economic Research at the University of Munich. After three years of testing it was started in Since that year the Ifo Institute collects the answers of in average 1500 respondents every year. The latest data which is used in this analysis stems from the questionnaire in 2003 which describes the innovation behavior of the year An overview about the responding behavior of the firms is given in the descriptive statistics part in section 3.3. The observation unit of this survey is not necessarily always a complete firms. For firms, which produce more than one product, the questionnaire refers only to a certain product range, i.e. for multi-product firms the survey delivers even more detailed data than firm level data. This gives us a sample of observations over 21 years from 1982 to The questionnaire offers different innovation measures. The first one is the simple information whether the firm has introduced any innovation during the last year. This information is available for product as well as for process innovations as required by the theoretical models (cf. section 2.1). As the main drawback of this variable one can argue that it does not give any hint about the importance of the innovation. An additional drawback of this innovation measure might be the subjective assessment of the term innovation. But for this problem the panel analysis offers already the solution or at least an significant improvement. As long as we assume that the definition of innovation remains unchanged in a firm this problem is similar to the unobserved firm heterogeneity problem. 8

9 This might be the case if the same person answers the questionnaire each year or if the old respondent introduces other people to this topic and the forms opinion is passed on. If this situation is given we cannot judge the firm s opinion about the term innovation itself but we rule out this problem by the fixed effects methodology which does not include time constant firm characteristics. But, on the other hand, the question for the correct measurement of innovation is still open. Other innovation variables like R&D or patents also have its advantages and disadvantages. A comparison of the ifo innovation measure with other popular measures is given in Lachenmaier/Wößmann (2004). We also try to increase the explanatory power of this innovation variable by weighting it. As weighting factors we use different other questions relating to what was required for this innovation (e.g. research) or about the importance of an innovation (e.g. Did a patent application take place?). In addition to these variable, which describe the innovation output we also include the innovation expenses of the firm. These can also be split into expenses for product innovations and expenses for process innovations. 3.2 Descriptive Statistics The dataset consists out of an unbalanced panel with observations, collected from 6826 firms over the 21 years from The survey is conducted among German Manufacturing firms. We have to admit that the number of firms decreases if we want to track certain firms over a longer time period. But still, if we only consider these firms which have answered at least in five consecutive surveys, we end up with a sample of observations from 1411 firms and if we restrict our analysis to only those firms which responded in ten consecutive years we can still analyze 339 firms (tables A1, A2 and A3). These tables also show that the attrition bias in our sample is not very severe. The first two questions of the questionnaire indicate whether the firm has innovated during the preceding year or not. Due to this construction it is often argued that only those firms tend to continue answering the questionnaire who carried out innovations. This would lead to an attrition bias in the sample. But as table A1, A2 and A3 show, this is not the case. The share of innovators does hardly change between the samples, namely from 49.4% to 52.1%. Classification as an innovator means that the firm has introduced an innovation to the market in the preceding year. This variable is further broken down into product and process innovator according to the type of innovation the firm has introduced. The average firm size changes only a little from the first sample with at least 2 consecutive observations to the sample with five consecutive observations. For the third sample the average firm size is higher, this might be an indicator for a higher probability of 9

10 staying in the sample either due to higher respondent share or due to lower mortality rate under large firms. But if we look at the employment growth variable, our dependent variable, there is almost no difference between the three samples. The employment growth variable for the subsample of innovators also remains fairly constant with values of 1.5%, 1.5% and 1.6% for innovators in the three different samples, however it is changing slightly more among non-innovators with values of 0.2%, 0% and -0.5%. The distribution of the employment variable for the sample with at least five consecutive observations is shown in figure 1. It can be seen that our samples covers all firm size categories, including small, medium-sized and large firms. 4. The Identification Strategy First, to compare our data and some of our results with the existing literature we will present in section 4.1 some employment growth regressions. The main focus then lies on the panel data methods in section 4.2, including dynamic panel data methods. The underlying equation is a reduced labour demand function of the type: EMP = F( Q, K, C) (1) EMP is the labour demand of the firm which is a function of Product Quality Q, the technological knowledge stock K and some other control variables C, including the labour costs. In this model we do not control for the output since we want to allow for changes in the output created by technological change. 4.1 Growth Models The first step in our analysis is to estimate employment growth models. This is what was also done in several other contributions and therefore allows us a comparison of our data and first results with other studies. What we estimate here is the employment growth (measured in the log difference) over the whole period for which we can observe the firm. EMPit = fi + 1 Qit + α 2 K it + α 3 ln α C + ε (2) it it [to come: include wages or labour costs: so far we unfortunately do not have the data from the German Statistical Office] 10

11 In the data we unfortunately do not have the stock value of product quality or technological knowledge, but as can be seen in equation (2) we can solve this problem by using a proxy variable for the differenced values. We can use our innovation variables as proxies for the change in product quality and technological knowledge. We will use the product innovation variable as a proxy for the change in product quality and the process innovation variable as a proxy for the change in technological knowledge. These assumptions allow us to estimate equation (2). 4.2 Panel Estimations Equation (2) in section 4.1 does not control for unobserved firm heterogeneity. But if the individual effects are correlated with the innovation activities of the firms, the estimated impact of technical progress on the employment will be biased. Therefore we apply panel estimation methods in the following. By using these methods we eliminate all effects which are constant over time for a certain firm, including those effects which can not be observed. We achieve this by taking the first difference of the estimation equation. The bias which is still present in equation (2) due to the unobserved firm heterogeneity is ruled out by this transformation. But, the analysis of the effects of technological change on employment also includes dynamic components: The short-run dynamics compound influences from adjustment costs, expectations formation and decision processes. Therefore we include in the following models the lagged value of the dependent variable, i.e. the lagged value of the first difference in employment levels. The in this equation represents the first difference of the variables. EMPit = α 0 ln EMPi, t 1α 1 Qit + α 2 K it + α 3 ln C + ε (3) it it This estimation equation involves some problems for the estimation. If the lagged value of the dependent variable appears on the right hand side of the estimation equation it is always correlated with the first differenced residual. Due to this problem OLS and the panel estimations described above would also lead to biased results. Therefore the lagged difference of the dependent variable must be instrumented. We use the Arellano-Bond- Estimation for solving this problem. Arellano and Bond (1991) suggest using the lagged values of the employment level as instruments for the difference (excluding the first lag). 11

12 Again we use the innovation variables as proxies for the difference in the stock of product quality and technological knowledge. Technically speaking, this means that the innovation variables enter the estimation equation as already differenced values. 5. Results 5.1 Growth Regressions The results presented in this section are based on equation (2). Table 1 shows the effect of different weighted innovation measures on employment growth. Employment growth is defined as the difference in logarithms between the start and the end year of the period for which the firm has answered the questionnaire. The innovation variables in this model are defined as the number of years in which innovations were carried out divided by the number of total years the respective firm is in the sample. Since we observe firms for 21 years and the years in which firms enter (or leave) the sample might differ quite a lot we control for the start year and the end year in our estimation. Both variables represent a set of dummy variables consisting out of dummy variables for each year. So for each observation the variable for the start year is one in one year and zero in all other years and the same is true for the end year, indicating in which year the firm has entered and left the sample. Specification (1) gives a first hint that product and process innovations have a significantly positive effect on employment growth. For product innovations it is especially the second weighted innovation measure, which only counts these innovations which led to a patent application, which shows a large positive effect. For process innovations patenting does not seem to be that important. Also the unweighted process innovation variable already shows a significantly positive effect on employment growth. In specification (2) we added control variables for the duration of the growth (start and end year as defined above), the industry sectors according to the EU classification NACE and the German states. The results are very similar, except for that the unweighted product innovations are now weakly significantly positive and the R&D weighted product innovations are not significant anymore. In both specifications the employment start level has a significantly negative effect. This indicates that firms which are already large as they enter our sample grow at a slower rate as small firms, which is consistent with theory. 12

13 Table 1: Employment Growth Regression, OLS Dep. Var.: Employment Growth over complete observation period of a firm (1) (2) Product Innovator (0.054) * (0.055 ) Product Innovator + R&D * (0.058) (0.058) Product Innovator + Patents *** (0.046) *** (0.047) Process Innovator *** (0.05) *** (0.050) Process Innovator + R&D (0.06) (0.06) Process Innovator + Patents ** (0.121) ** (0.117) Employment Start Level *** (0.008) *** (0.008) NACE incl. States incl. Start Year incl. End Year incl. Constant *** (0.034) *** (0.067) Observations R-squared Standard errors in parantheses *significant at 10%, **significant at 5%, ***significant at 1% ' + R&D': research or experimental development was necessary ' + Patents': innovation led to patent / petty patent application Outliers dropped if Employment Growth >3 or < Panel Estimations (preliminary) As described in section 4.2 our estimates so far do not make use of the specific characteristics of panel data. Since we have yearly data we can conduct more detailed analysis than given in section 5.1. Using panel data solves the problem of unobserved firm heterogeneity. When we are controlling for firm fixed effects, we can analyze the changes within a firm. Table 2 shows the results of fixed-effects regressions. We include in our models the dummy variables for product and process innovations and add their first, second and third lags. As further control variables we use in specifications (4) and (6) year dummy variables and in specifications (5) and (6) we add a variable which comes from the German Statistical office and which controls for the developments within the industrial sector of a firm. For each firm, we add the growth of the Gross Value Added (GVA) in its industrial sector. The R squared is increased by adding the GVA growth, but heavily increased by including year dummies. Product innovations appear to show their effect on employment growth quite rapidly, within the same reporting year. The introduction of a product innovation increases employment growth by 3 percentage points. Lags of the product innovation variable do not show any significant effects. Looking at the process innovation variables, we can see that the first lagged value shows significantly positive 13

14 effects. The size of the effect also ranges between 2 and 3 percentage points, depending on whether year dummies are included or not. These results are consistent with theory which suggests a long-term effect of process innovations and a direct effect of product innovations. The growth of GVA shows significantly positive effects in specification (5), but not in specification (6) where the year dummies are also added. Table 2: Fixed-Effects Regressions (preliminary results) Dep. Var.: Employment Growth, yearly. Fixed Effects Regression (3) (4) (5) (6) Product Innovator *** (0.011) *** (0.011) *** (0.011) *** (0.011) Product Innovator LAG (0.011) (0.011) (0.011) (0.011) Product Innovator LAG (0.010) (0.01) (0.011) (0.011) Product Innovator LAG (0.010) (0.01) (0.011) (0.01) Process Innovator (0.010) (0.01) (0.010) (0.01) Process Innovator LAG *** (0.010) ** (0.01) *** (0.010) ** (0.01) Process Innovator LAG (0.010) (0.01) (0.010) (0.01) Process Innovator LAG (0.010) (0.009) (0.010) (0.01) Year (dummy variables) incl incl. Gross Value Added Growth GVA Growth LAG * *** (0.056) (0.061) (0.056) (0.061) GVA Growth LAG (0.058) (0.064) Constant (0.010) (0.019) (0.010) (0.02) Observations Groups (firms) R-squared within Standard errors in parantheses *significant at 10%, **significant at 5%, ***significant at 1% ' + R&D': research or experimental development was necessary ' + Patents': innovation led to patent / petty patent application As described in section 4.2 the effect of innovations on employment growth may be of a more dynamic type and can therefore be estimated better by controlling for adjustment processes. This is done by a dynamic panel estimation setting. In this paper we use the Arellano-Bond framework (1991). Table 3 shows our results of dynamic panel estimations. We included in these models product and process innovations, the contemporary values in specification (7), the first lags in specification (8) and both in specification (9) and (10). All specifications include the lagged value of the dependent variable, employment growth. In specification (10) the second lag is also included. As an additional control variable we again use the growth of the sectoral gross value added. 2 In specification (10) we also 2 Turnover growth was also tested but did not show significant effects. 14

15 included dummy variables for states, year and industrial sectors according to NACE. In all regressions the lagged value shows very strong significantly positive effects on the employment growth. In the specification with contemporary innovation variables (spec. (7)) it is the product innovation which shows significant effects, in the specification with lagged values (spec. (8)), however, it is the process innovation which shows significance. As soon as both the contemporary and the lagged innovation variables are used simultaneously the significant effects disappear, except for very weak significantly positive effect for the contemporary product innovation variable in the model with the first and second lag of the dependent variable. Again these results might indicate the longer adjustment process of process innovations compared to product innovations. Table 3: Arellano-Bond Estimations (I) (preliminary results) Dep. Var.: Employment Growth, yearly. Arellano-Bond Regression (7) (8) (9) (10) Employment Growth LAG *** (0.034) *** (0.034) *** (0.033) *** (0.033) Product Innovator ** (0.007) (0.011) * (0.011) Product Innovator LAG (0.007) (0.011) (0.011) Process Innovator (0.008) (0.01) (0.01) Process Innovator LAG *** (0.008) (0.01) (0.01) Gross Value Added Growth *** (0.054) *** (0.054) *** (0.055) (0.067) Year Dummies incl. NACE Dummies incl. States Dummies incl. Constant *** (0.003) *** (0.003) *** (0.003) Observations Groups (firms) Standard errors in parantheses *significant at 10%, **significant at 5%, ***significant at 1% In Table 4 we use the weighted innovation variables. In specification 11 we use the R&D weighted innovation variables and their lags, in specification 12 we use the Patent weighted innovation variables and their lags. Again the results of specification 11 indicate that for product innovations it is the contemporary value that shows significance and for process innovations it is the lagged value. For patent weighted innovations (spec. (12)) only the lagged process innovations show significance. Again in both models the lagged dependent variable and the sectoral gross value added growth show strong significantly positive effects. 15

16 Table 4: Arellano-Bond Estimations (II) (preliminary results) Dep. Var.: Employment Growth, yearly. Arellano-Bond Regression (11) (12) Employment Growth LAG *** (0.034) *** (0.034) Product Innovator + R&D ** (0.011) Product Innovator + R&D LAG (0.011) Product Innovator + Patents (0.013) Product Innovator + Patents LAG (0.013) Process Innovator + R&D * (0.012) Process Innovator + R&D LAG *** (0.012) Process Innovator + Patents (0.029) Process Innovator + Patents LAG ** (0.029) Gross Value Added Growth *** (0.056) *** (0.056) Constant *** (0.003) *** (0.002) Observations Groups (firms) Standard errors in parantheses *significant at 10%, **significant at 5%, ***significant at 1% ' + R&D': research or experimental development was necessary ' + Patents': innovation led to patent / petty patent application [to come: include wage data in regression models, which we do not have so far] 6. Conclusions (preliminary) In this paper we analyze the effects of innovation on employment. Our long and extensive panel data set allows for different new contributions to the existing research. First we can include different innovation count measures, weighted by their importance. Second we can exploit the panel structure of the data set by analyzing innovation behavior and employment changes on a firm level with a fixed-effect framework. Third, we estimate dynamic panel regressions to account for the adjustment processes. Our results suggest that the impact of product innovations takes place faster than the impact of process innovations. This was shown in both panel specification, the fixed effects models as well as the dynamic models. The dynamic setting proved to be important as the lagged dependent variables showed strong significantly positive effects. 16

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19 Table A1 Panel with at least 2 consecutive responses N(observations)=17592 N(firms)=4098 Obs Mean Std. Dev. Min Max Dependent Variable: Employment Employment Growth if Innovator if Non-Innovator Independent Variables: Innovator Product Innovator Process Innovator Both Product and Process Innovator (Innovation expenses / Sales) Table A2: Panel with at least 5 consecutive responses N(observations)=10687 N(firms)=1411 Obs Mean Std. Dev. Min Max Dependent Variable: Employment Employment Growth if Innovator if Non-Innovator Independent Variables: Innovator Product Innovator Process Innovator Both Product and Process Innovator (Innovation expenses / Sales) Table A3: Panel with at least 10 consecutive responses N(observations)=4167 N(firms)=339 Obs Mean Std. Dev. Min Max Dependent Variable: Employment Employment Growth if Innovator if Non-Innovator Independent Variables: Innovator Product Innovator Process Innovator Both Product and Process Innovator (Innovation expenses / Sales)

20 Figure 1: Firm Size Share 40.0% 35.0% 30.0% 25.0% 20.0% 15.0% 10.0% 5.0% 0.0% < >1000 Number of Employees 20