Creative destruction, regional competitiveness and policy - DIME

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Creative destruction, regional competitiveness and policy Paper for the DIME WP 2.3 Conference 2006 on: Normative Policy Implications from Recent Advances in the Economics of Innovation and Industrial Dynamics London, UK, December 15-16, 2006 Niels Bosma1, Erik Stam1,2,3 and Veronique Schutjens1 1

Utrecht University University of Cambridge 3 Max Planck Institute of Economics 2

Abstract: Does creative destruction improve the competitiveness of regions? If yes, is this a universal mechanism, or is it contingent on the type of industry or region in which creative destruction takes place? In the paper we analyse the effect of turbulence on regional competitiveness (measured both as total factor productivity growth and as employment growth) across 40 regions in the Netherlands over the period 1988-2002. Our analyses suggest that turbulence – especially the entry component – leads to productivity growth in services and to employment growth in manufacturing. These effects are contingent on the degree of urbanization in a region: they are reinforced in regions with high levels of related variety and regions with low population density.

Introduction In the last decades, entrepreneurship has increasingly been linked with economic growth (Wennekers and Thurik, 1999; Carree and Thurik, 2003). There is now a widespread agreement that entrepreneurship is important for competitiveness (Porter 1990). According to Krugman (2003) it makes more sense to talk about competitiveness of regions than of nations. Many authors have also argued that in the current era of globalization, regions have become more important than countries in the creation of economic growth (Castells and Hall 1994; Storper 1997; Porter 2000). Entrepreneurship is also highly sensitive to regional conditions (Feldman 2001; Stam 2008), so if its effect on economic growth is analysed, the region is a more appropriate unit of analysis than the nation. Until now, most studies have measured entrepreneurship as firm formation rates and regional competitiveness as employment growth in regions (Van Stel and Storey 2004; Acs and Armington 2004). Both indicators are open for improvement. First, these studies are inspired by Schumpeter’s (1934; 1942) work on the mechanisms of economic development, in which the role of entrepreneurship was central. Although these studies in general equate entrepreneurship with new firm formation, originally Schumpeter’s (1942) theory of “creative destruction” involved both creation (new firm formation) and destruction (firm exit). This latter aspect reflects the selection mechanism that is a crucial outcome of the process of competition and a cause of competitiveness and economic growth. A second improvement regards the measurement of regional competitiveness. Though employment growth is indeed an important element of economic development, competitiveness might better be measured with productivity growth, reflecting increasing economic efficiency within firms and regions. In addition to these two improvements regarding indicators of entrepreneurship and competitiveness, there is also a need for improving the insight in the (sectoral and regional) conditions in which creative destruction leads to competitiveness. We especially focus on the regional conditions related to urbanization as these have been said to positively affect both turbulence and regional competitiveness. This article seeks to clarify the effect of creative destruction on regional competitiveness. The new insights delivered by our empirical analyses will be valuable input in the debate on policy measures to stimulate regional competitiveness. This article addresses two research questions. (1) How does creative destruction affect regional competitiveness? (2) How do urbanization condition the effect of creative destruction on regional competitiveness? For answering the first question we will analyse the effects of entry and exit on regional competitiveness separately and combined (i.e. turbulence). While the effect of creative destruction on competitiveness has most often been measured within the manufacturing sector; we also analysed this in the services sector. Regarding the second research question, we have distinguished two kinds of urbanization effects: (a) population density, which might have positive effects (high level of competition, scale economies) and negative effects (cost levels due to scarcity and congestion); (b) related variety, which is assumed to have a positive effect on the probability of new combinations given the possibilities to combine ideas from different but related sectors (Jacobs, 1969; Frenken et al. 2002). Firm entry in regions with high population density might stimulate competitiveness because of the high levels of competition between different suppliers and the possibilities to achieve economies of scale with relatively large demand. On the other hand it might have negative effects on competitiveness because low entry barriers attract too many inefficient entrants, and because high cost levels lead to lower employment growth. The latter effect might however also stimulate entrants to be more labour productive (cf. Kleinknecht 1998; Madsen and Damasia 2001). Firm entry in regions with a relatively high level of related variety is likely to stimulate competitiveness because of its catalyzing effect on innovation (cf. Duranton and Puga 2001), which has been regarded as a source of competitiveness (Jacobs 1969; Glaeser et al. 1992; Van Oort 2004).

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This article makes three contributions to the literature. There is some evidence on the effects of turbulence on employment growth in services, and of turbulence on employment growth and productivity growth in manufacturing, but there is hardly any research on the effects of entry and exit on productivity growth in services. So our first contribution will be the analysis of the effect of turbulence on productivity growth in services. Most of the research on the effects of turbulence on competitiveness has been done in the context of industries in nations. However, it is increasingly acknowledged that regions are key in understanding the creation of competitive advantage, both as conditions for stimulating entrepreneurship (Stam, 2008) and as the appropriate unit of analysis for creating competitiveness (Storper, 1997; Porter, 2000; Camagni, 2002). Especially for entry, competition and learning, the regional level might be more relevant than the national level (Fritsch and Schmude, 2006). 1 Our second contribution will thus be a shift of focus from the national to the regional level in studying the effects of creative destruction on competitiveness. If we take the region more serious as a site where competitiveness is created, we should also take into account the specific regional conditions in which this is done most effectively. Therefore, our third contribution will be the analysis of two types of urbanization – population density and related variety – and their conditioning effect on the creation of competitiveness via the entry and exit of firms in regions. The paper is organized as follows. First, we review the literature on (elements of) creative destruction and its effect on competitiveness. Next, we examine the possible effects of regional conditions. After this, we will shortly present the data, method and outcomes of our empirical analyses. We analyse the effect of turbulence on regional competitiveness (measured both as total factor productivity growth and as employment growth) across 40 regions in the Netherlands over the period 1988-2002. Our analyses suggest that turbulence – especially the entry component – leads to productivity growth in services and to employment growth in manufacturing. These effects are contingent on the degree of urbanization in a region: they are reinforced in regions with high levels of related variety and regions with low population density. Finally, we discuss the policy implications of our findings. Creative destruction and competitiveness Many studies on competitiveness are inspired by Schumpeter’s (1934; 1942) work on the mechanisms of economic development, especially the role of entrepreneurship. These studies in general equate entrepreneurship with new firm formation, and in fact disregard the firm exit mechanism as another important aspect of entrepreneurship. Schumpeter’s (1942) theory of “creative destruction” involves both creation (new firm formation) and destruction (firm exit). Firm exit reflects the selection mechanism that is a crucial outcome of the process of competition and a cause of competitiveness. The so-called Schumpeter Mark I argument on creative destruction (‘entrepreneurial regime’) goes as follows (cf. Eliasson 1996). Entrepreneurs introduce new combinations embodied in new firms. These innovative entrants enforce incumbents to either adapt to the new efficiency standard or to exit the industry. This leads to a new situation in which the productivity of the industry has improved. This improvement is brought about by innovative entrants that are more productive than the average incumbent, and the exit of less productive incumbents via the competition process. These exits are important as resources are released that can be allocated to more productive activities. The productivity gains might be reinforced if incumbents are able to improve their productivity (cf. Aghion and Bessonova, 2006). In the end, creative destruction will lead to improved total factor productivity (TFP), but not necessarily to higher employment levels.

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Competition in product-markets, but especially in labour markets is likely to be concentrated in the home-region of the firm. Even more localized is probably the learning that takes place through knowledge spillovers (see Jaffe et al., 1993, Breschi and Lissoni 2003).

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However, if new entrants are less efficient than incumbents, the efforts involved in the emergence of entrants may even waste valuable resources. In the latter situation entrepreneurship – measured as new firm formation – is not a driver of competitiveness at all. 2 This situation has been identified in the literature as a ‘revolving door regime’: entrants that have to exit relatively soon after start-up due to an insufficient level of efficiency (Audretsch and Fritsch 2002). This revolving door regime reflects a situation with high entry rates, but with no subsequent improvement of either employment levels or productivity. There are several explanations for this phenomenon. For example, Jovanovic’s (1982) theory of passive learning assumes that individuals do not know their entrepreneurial talents in advance, and can only find out by experience in a spell of entrepreneurship. This means that many individuals start inefficient firms, only to find out that they are not successful in entering the market with a new firm. Relatively many individuals will do so if the prospects of business-ownership are perceived to be very attractive, for example in the emergence of a new industry and/or a large upturn of the economy (like in the late 1990s). Another situation in which relatively many inefficient firms enter is in times of economic depression, when individuals are pushed out of the labour force into business-ownership. A more structural view of economic change provides a different role of entrepreneurship. New entrants cause structural change when they introduce innovations that create completely new knowledge (Metcalfe 2002) and possibly new markets. In this respect, Audretsch and Keilbach (2004) have argued that there is a gap between knowledge and exploitable knowledge or economic knowledge. In their view, economic knowledge emerges from a selection process across the generally available body of knowledge. They suggest that entrepreneurship is an important mechanism in driving that selection process hence in creating diversity of knowledge, which in turn serves as a mechanism facilitating the spillover of knowledge. They provide empirical evidence that regions with higher levels of entrepreneurship indeed exhibit stronger growth in labour productivity. This kind of entry does not necessarily drive out incumbents, but might do so when the new markets substitute existing markets (e.g. the personal computers driving out typewriters, and digital cameras driving out analogue film cameras). The former situation might be called creative construction (Audretsch et al. 2005), in contrast to the latter, which reflects creative destruction. This structural change might improve both TFP, and employment if the newly created market does not cannibalise existing markets. Table 1. Proposed effects of entry and exit on employment growth and productivity growth Nature of entrants and exits Creative destruction / entrepreneurial regime Revolving door Creative construction

Innovative entrants Non-innovative entrants Innovative entrants

Inert obsolete incumbents Inefficient recent entrants 0

Effect on competitiveness Employment Productivity growth growth 0 + 0

0

+

+

Many studies have confirmed the effect of turbulence on total factor productivity (TFP) growth (at least in manufacturing; see for example: Geroski 1989; Bailey et al. 1992; Liu 1993; Carlin et al. 2001; Callejon and Segarra 1999). Some studies have revealed a positive effect of firm entry on employment growth in regions (Van Stel and Storey 2004; Acs and Armington 2004), especially in manufacturing (Fritsch and Mueller 2004; Van Stel and Suddle 2006). Studies on the effect of turbulence (thus including exit rates) on employment growth show more mixed evidence: while Reynolds (1999) found a positive effect in the US, Audretsch and Fritsch (1996) and Fritsch (1997) found a negative employment effect of 2

Perhaps only innovative entrants are stimulating competitiveness. For example, Geroski (1989) found that higher entry rates lead to higher productivity growth, which he explains by assuming that entry stimulates competition, and greater competition spurs productivity growth. But he also showed that innovation was an even more important driver of productivity (cf. Baily and Chakrabarti 1985).

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turbulence in both the manufacturing and the service sectors in Germany. Audretsch and Fritsch (1996) argued that innovative activity played a smaller role in Germany than in the US, forcing less efficient incumbents to exit more often in the latter than in the former. They indicated the dominance ‘entrepreneurial regime’ in the US and the dominance of a ‘revolving door’ regime in Germany. Audretsch and Fritsch (2002) show that the effect of turbulence on employment depends on the type of region: a positive effect in regions with an entrepreneurial regime, and a negative effect in regions with a revolving door regime. In the latter context, new relatively inefficient firms enter and also exit within a short period. There are (opportunity) costs attached to such a revolving door regime as entrepreneurial efforts waste resources, and incumbents are not triggered to improve either. In the next section we will zoom in on the regional conditions of the effects of entry and exit on competitiveness. Regional conditions Are there region-specific effects of turbulence; i.e. to what extent is urbanization relevant for the effects of entry and exit on regional competitiveness? Urbanization has both a population concentration and a related variety component. Firm entry in regions with high population density might stimulate competitiveness because of the high levels of competition between different suppliers and the possibilities to achieve economies of scale with relatively large demand. On the other hand it might have negative effects on competitiveness because low entry barriers attract too many inefficient entrants, and because high cost levels lead to lower employment growth. The latter effect might however also stimulate entrants to be more labour productive (cf. Kleinknecht 1998; Madsen and Damasia 2001). Several studies found that the effects of entry on employment growth were much stronger in densely populated regions than in rural areas (Fritsch and Mueller, 2004; Van Stel and Suddle 2006). Van Stel and Storey (2004) found no significant relationship between entry and employment creation in the 1980s in Great Britain as a whole, but a negative relationship for the ‘low enterprise’ (rural) area of the North East of England. For the 1990s, they found a significant positive relationship for Great Britain as a whole, but a negative relationship for (rural) Scotland. Population density also had a negative main effect on employment growth. These studies seem to imply that entry and exit especially stimulate employment growth in urban regions. Urbanization represents all kinds of regional influences such as availability of (relatively expensive) qualified labour, high real estate costs, traffic congestion, local demand and the level of knowledge spillovers. However, especially regarding the latter phenomenon, urbanization might better be measured with an indicator of related variety of the industrial structure than with population density alone (Frenken et al. 2002). A diverse mix of industries in an urban region improves the opportunities to interact, copy, modify, and recombine ideas, practices and technologies across industries. Variety in itself may be a source of innovation, as was suggested by Jacobs (1969). Urban regions with high levels of related variety might be fruitful breeding grounds for innovative entrants that stimulate employment growth. Firm entry in regions with a relatively high level of related variety is likely to stimulate competitiveness because of its catalyzing effect on innovation (cf. Duranton and Puga 2001), which has been regarded as a source of competitiveness (Jacobs 1969; Glaeser et al. 1992; Van Oort 2004). Jacobs (1969) argued that the most important source of ‘knowledge spillovers’ are external to the industry in which the firm operates and that cities are the source of considerable innovation because the diversity of these knowledge sources is greatest in cities. According to Jacobs, it is the exchange of complementary knowledge across diverse firms and economic agents that yields a greater return on new economic knowledge. Thus, a high level of related variety is likely to stimulate the positive effect of entry on regional competitiveness.

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Empirical analyses: Data and method Competitiveness Authors like Porter (1990; 1998) and Krugman (1990) have made a plea for using productivity as the indicator of competitiveness. A rising standard of living in the long run depends on the productivity with which resources are employed (cf. O’Mahoney and Van Ark 2003). An important empirical drawback of this indicator is that there is hardly any data available at the sub-national scale (Kitson et al. 2004), and from other industries than manufacturing (Bartelsman and Doms 2000). Another possible drawback is that it might reveal perverse effects, when labour shedding (e.g. with an extensive shakeout of workers and closure of plants) is the cause of improved (labour) productivity. Ideally, both employment growth and productivity growth should go together: increasing productivity causes an improved competitive position, which leads to higher demands of the goods and services produced, which then leads to an increased demand for labour inputs. Competitiveness is often measured with either employment growth or growth in total factor productivity (TFP). There are some notable differences between these measures of growth. For example, during recessions the efficiency measures by managers in incumbent firms might lead to employment loss and TFP growth on the short term. On the medium term, unemployment push entrepreneurship might absorb the employment loss, and decrease TFP. Entry, exit, and turbulence at the regional level In this paper we measure entry and exit separately, but also take into account turbulence and net entry as measures of entrepreneurship. As regards measuring firm dynamics, the sectors under consideration are situated in a certain territorial context. In this study we specify firm dynamics (entry and exit) relative to the stock of firms in a specific industry (i) within a specific region (j). We account for spatial spill-over effects to analyse whether the regional level is the relevant measurement scale. Dataset In this study we are interested in the effect of creative destruction on regional competitiveness. We have specified two sectors: manufacturing and services. The distinction between these two major sectors is primarily data-driven, i.e. the availability of TFP data in the Netherlands. We have used the most suitable level of territorial aggregation for the Netherlands: the Corop-level of analysis (EU Nuts 3) (cf. Van Stel and Nieuwenhuijsen 2004, Kleinknecht and Poot, 1992). The division in 40 Corop regions is based on regional commuting patterns that indicate regional labour markets. The regional panel dataset on annual entry and exit for the Netherlands in 40 regions is available for a 14 year period (1988-2002). Registrations and deregistrations are provided by the Dutch Chambers of Commerce. Entry includes independent new businesses as well as subsidiaries; exit includes bankruptcies as well as other modes of firm exit. Unfortunately we cannot distinguish between exit due to business closure (varying from simply finishing economic activity to forced liquidation) and exit due to changes in ownership (i.e. mergers or acquisitions). Figure 1 depicts turbulence and net entry rates of the 40 Dutch regions over time. There appears to be a substantial variation between these firm dynamics measures across regions, especially where turbulence is concerned (not pictured). 3 Also, the average turbulence rates during 1996-2004 are higher as compared to 1988-1995. Apart from this long-term trend we also observe a business cycle pattern, notably in the period 1996-2004. We thus experience regional and business cycle patterns, as well as a general trend of increasing firm dynamics in 3

The F-statistics with respect to variance between regions amounts to 20.7, 13.0 and 5.9 for respectively turbulence, volatility and net entry in services. In manufacturing the corresponding F-values are 9.0, 10.7 and 2.3; all significantly different from zero (p>0.95).

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the Netherlands. 4 Since business cycle effects are obviously also at play in our analysis of competitiveness (see figure 2), we will account for business cycle effects on our regression model in order to minimize the possible effects of spurious correlations. Figure 1 Turbulence and net entry rates over time, averages over Dutch regions, 1988-2004 Manufacturing (35 regions) Services (40 regions)

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Data on annual employment, value added and investment at the Corop level have been taken from Statistics Netherlands and are available for the period 1988-2002. 5 We excluded five regions from the analysis in the manufacturing sector because their regional growth rates are heavily determined by extraction (gas and electricity), which could possibly interfere with our model. The capital stock has been calculated using the Perpetual Inventory Method (PIM). Based on investments at sector and regional level, an initial capital stock level was derived. The capital stock for every following year has been calculated as the sum of the depreciated capital stock, plus investments in the current year. The depreciation rates for both sectors have been estimated using the initial levels of the capital stock in 1989 and investment levels from 1960-1976. 6 Figure 2 demonstrates that the development in TFP differs from the development in employment growth (employment is measured in full time equivalents and excludes the selfemployed). The difference is particularly striking in manufacturing in the early 1990s, where employment growth is negative and TFP growth is positive: this is a case of labour shedding in which a reduction of employment leads to a (short term) increase in TFP. In services, TFP and employment also diverge in the early 1990s but in a different way than in manufacturing: an increase in employment growth goes hand in hand with a decrease in TFP. Overall, there is hardly any employment growth in manufacturing, while TFP hardly increases in the service sector. The interregional variance appears to be smaller for services, especially for TFP. 7

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See Bosma et al. 2005 for explanations of the trendwise increase in entrepreneurship for the Netherlands. There is one noteworthy issue as regards the economic slowdown of 1991-1993. This period was also characterized by intensive start-up stimulation by the Dutch government. Specifically, in 1993 there was an important relaxation of requirements to start new ventures (see Carree and Nijkamp, 2001). This relaxation, along with the increasing importance of the ICT sector with its low barriers to entry has probably overshadowed diminishing incentives to start a business from the business cycle’s point of view 5 Value added and investments have been corrected for inflation. 6 It is conceivable that these derived depreciation rates (5.8% for manufacturing, 4.7% for services) should be adjusted for the increasing use of computers relative to total investments. 7 The interregional variance for TFP in services is weakly significant different from zero (p-value>0.90), while this variance for employment growth is significant with p>0.95. Both measures have a positive interregional variance for manufacturing (p>0.95).

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Figure 2 TFP and employment growth over time, averages over Dutch regions, 1988-2002 Manufacturing (35 regions) Services (40 regions) TFP growth

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Empirical Model Following Geroski (1989) and Calléjon and Segarra (1999) we model firm dynamics as a component of the total productivity in industry i and year t, controlling for the effects of labour and capital. For industry i and year t, the quantity of output (value added) Yit is the result of the combination of capital and labour:

Yit = F ( Ait , K it , Lit )

(1)

where output depends on the number of employees (L), the stock of physical capital (K) and a ‘productivity index’ (A) that captures the variations in production that are not attributable to changes in the use of labour and capital. Considering Hall’s proposed general value added equation (Hall, 1986), the percent change of output depends on three components. The first is the percent change in the productivity index. The second is the product of the elasticity of scale and the percentage change in capital. The third is the effect of market power, i.e. the percent change in the labour-capital ratio weighted by the price-cost ratio (mark-up μ ) and the share of labour (wages) in value added ( α ij ).

dy it = dait + γ it dk it + μα it (dlit − dk it ) + ε it ,

(2)

where the operator d reflects growth rates, expressed as first differences in logarithms. By subtracting α ij dnij + (1 − α ij )dk ij on both sides we get an expression in which the dependent variable is Solow’s residual:

θ its = da it + (γ - 1)dk it + (1 − μ )α it (dk it − dl it ) + ε it ,

(3)

Suppose that the growth of the corrected productivity index (da) can be modelled in several components for industry i and region j: percentage changes in industry productivity which are constant over time and region ( θ i ) and improvements in productivity resulting from firm dynamics (FD), regional R&D intensity (RD), the degree of related variety in the region (RV) and population density (PD). We minimise the danger of reversed causality by incorporating lagged effects of firm dynamics on TFP growth. In accordance with a previous study (Bosma

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and Nieuwenhuijsen, 2002) we model with lags of two years. 8 This extension of equation (3) leads to:

θits =θi + β1FDi,t−2 + β2 RDi + β3 RVi + β4 PDi +(γ -1)dkit +(1− μ)αit (dkit −dlit ) +εit

(4)

We control for general business cycle effects (affecting all regions) by including dummy variables representing every year of observation. Summarising, equation (4) measures total factor productivity (TFP) growth or Solow’s residual for industry i and region j as the sum of: (1) technical industrial progress in the strict sense ( θ i ), (2) additional efficiency caused by firm dynamics (elasticity β1 ), regional intensity of R&D expenditures (elasticity β 2 ), the

degree of related variety (elasticity β 3 ) and population density effects (elasticity β 4 ), (3) economies of scale measured by γ , (6) variations in the capital labour ratio weighted by the share of wages on value added, the price cost ratio (mark-up μ ). Fixed effects are induced by applying first differences over all variables, except for firm dynamics. 9 We also explicitly model the possibility that benefits of creative destruction in one region spills over to neighbouring regions. We control for spatial autocorrelation by performing regression equation (4) in two rounds. In the first round averages of the residuals in neighbouring regions are obtained. These enter the regression in the second round, so that for each region some of the unexplained variance in the neighbouring regions (in the first round) will be accounted for. To prevent multicollinearity problems, we do not model entry and exit together in one single model but use the combined measure of turbulence. A short note on our measure of related variety might be useful. Entropy statistics have been used to measure sector variety (see Frenken et al. 2002). Related variety measures the variety within each of two digit classes. Two maps on related variety and population density (the other measure of urbanization) can be found in the appendix. In our model explaining TFP growth, rates of firm dynamics are hypothesized to be a factor influencing regional growth additional to labour and capital. Basically, we investigate whether firm entry and exit, apart from growth in labour and capital stock (whether induced by existing or by new firms), invoke a certain degree of economic growth. The derived equation explaining employment growth rather than TFP growth would be the following (assuming a standard Cobb-Douglas type function rather than Hall’s equation, see Dekle 2002): 1

1

1

dpit + ε it (5a) α it α it α it Although the Cobb-Douglas model implies a negative elasticity of wage growth (dw), the sign is arguably debated; generally coefficient is estimated without specifying α (see Storey and Van Stel 2004; Van Stel and Suddle 2006). We will do the same by introducing coefficient λ . Since growth in regional prices ( dp ij ) is generally unobservable, and in the case of the dlit = −

dwit +

(θ i + β1 FDi ,t −4 + β 2 RDi + β 3 RVi + β 4 PDi ) − dk it +

Netherlands changes in prices can be assumed not to differentiate substantially across regions, the estimated equation becomes 8

We expect the effect of firm dynamics on TFP to have a shorter lag in comparison with the effect on employment. Baptista et al (2005) find a significant positive impact on employment using a lag of 4 years. Fritsch (2006) concludes that the impact of entry rates on employment growth in general takes an S-shape: direct positive returns in the first year are followed by 2-5 years of negative returns in which the new firms have to improve efficiency and the inefficient entries will exit the market. After this process, the long-term effect turns out to be positive, i.e. regions with higher entry rates reflect higher employment growth in the long run. 9 We do not apply first differences to entry and exit measures because these already capture a dynamic process. Applying fixed effects to firm dynamics by including region dummy variables (additionally to applying first differences to the other variables) does not improve the explanatory value in the used models.

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dl it = λdwit +

1

α it

(θ i + β 1 FDi ,t − 4 + β 2 RDi + β 3 RVi + β 4 PDi ) − dk it + ε it .

(5b)

When the capital stock is also unavailable at the regional level the estimated equation is the following: 1 (5c) dlit = λdwit + (θ + β1 FDi ,t −4 + β 2 RDi + β 3 RVi + β 4 PDi ) + ε it . α it i Note that, since α < 1 , model (5a) predicts that the size of the estimated effects of firm dynamics from (5c) will be larger as compared to equation 4 where TFP growth is the dependent variable. We will use equation (5c) for making comparisons with the existing studies investigating the effect of entry rates on growth in regional employment (see e.g. Van Stel and Storey 2004; Van Stel and Suddle 2006). Empirical analyses: Results Productivity growth Estimation results of equation (4) are depicted in tables 3 and 4 for manufacturing and services respectively. The first column in both tables (model I) presents the results of a basic model excluding measures of creative destruction. The second model adds entry rates, R&D and related variety and increases the performance of the model for services but not so for manufacturing. Accordingly it is seen that there is no positive effect of gross entry and turbulence on productivity growth for manufacturing, while there is a positive and significant effect for services (see table 4). One explanation for this outcome might be found in the relatively low minimum efficient size of firms in services (see Audretsch et al. 2004). This means that (often relatively small) entrants in services add more to the increase in productivity than entrants in manufacturing, that are more likely to be relatively inefficient in relation to incumbents. Table 3. Regression results for TFP growth in manufacturing. I Constant TFP (t-1) Entry (t-2) Turbulence (t-2) Exit (t) Innovation rate Related variety Population density Economies of scale (γ) Degree of market power (μ) Spatial auto-correlation Year dummies

0.04 -0.11

II ** **

-0.04 -0.14 0.04

III **

-0.01 -0.15 -0.02

IV **

-0.01 -0.15

V **

-0.03 -0.16

**

-0.04

0.69 1.20 0.28

** **

-0.08 0.09 -0.01 0.70 1.16 0.29

** ** **

-0.07 0.07 -0.00 0.79 1.44 -0.02 yes

** * **

-0.08 0.07 -0.00 0.79 1.45 -0.02 yes

Number of obs. 459 459 459 459 F statistic 9.80 5.99 5.55 5.56 Adj. R2 0.08 0.08 0.16 0.16 • p< .10, ** p< .05; for γ and μ the null hypothesis is γ=1 and μ=1

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** * **

0.04 -0.08 0.09 -0.00 0.83 1.48 0.05 yes 459 6.53 0.18

** **

Table 4. Regression results for TFP growth in services. I Constant TFP (t-1) Entry (t-2) Turbulence (t-2) Exit (t) Innovation rate Related variety Population density Economies of scale (γ) Degree of market power (μ) Spatial auto-correlation Year dummies

0.03 -0.10

0.53 1.29 0.62

II ** **

** ** **

0.00 -0.11 0.17 -0.01 0.02 -0.01 0.49 1.29 0.65

III ** **

** ** ** **

-0.02 -0.09 0.20 0.01 0.02 -0.01 0.53 1.29 -0.06 yes

IV ** ** **

* ** ** **

V

-0.02 -0.09

** **

0.14

**

0.01 0.02 -0.01 0.53 1.29 -0.07 yes

Number of obs. 459 459 459 459 F statistic 107.9 59.0 36.1 35.8 Adj. R2 0.48 0.50 0.59 0.59 • p< .10, ** p< .05; for γ and μ the null hypothesis is γ=1 and μ=1

** ** ** **

-0.01 -0.09 0.25 0.01 0.03 -0.01 0.55 1.29 -0.10 Yes

** ** ** * ** **

459 35.8 0.59

The designed spatial autocorrelation effect is, like in other empirical studies (e.g. Van Stel and Storey, 2004; Fritsch and Mueller, 2004; Van Stel and Suddle, 2006) significant. However, size and significance diminishes when we include year dummies in regression model III. This suggests that the spatial autocorrelation effect may unintentionally pick up some temporal autocorrelation as a result of business cycles. Therefore, one has to be cautious in interpreting the spatial autocorrelation as genuine regional spillover effects. 10 Still, our results suggest that in services firm turbulence positively influences TFP while controlling for the effects of economies of scale, market power and business cycles effects. Our analyses suggest that for the Netherlands, entrepreneurship (entry) and turbulence are important drivers of productivity in services, but not in manufacturing. 11 Employment growth Table 5 and 6 show the impact of creative destruction on employment growth in respectively manufacturing and services. Firm dynamics are lagged with 4 years. Wage growth is also lagged and set to the wage growth between t=t-2 and t-4. Contrary to our results on TFP growth, it is shown that firm entry and turbulence enhance employment growth in manufacturing but not so in services. This is in accordance with Van Stel and Suddle (2006) who find the largest effects in manufacturing using a similar model. 12 Interestingly, net entry does not appear to induce employment growth. It should be stressed that employment growth does not include the number of self-employed, which may partly explain the lack of evidence for positive effects of firm dynamics in services (which is more dominated by self-employed than manufacturing).

10

We have chosen for a conservative measurement of business cycle effects; if we include time dummies, the coefficient measuring the designed spatial autocorrelation effect turns out to be insignificant in most regressions. Moreover, the size of the coefficient for entry decreases when year dummies are added, suggesting that not accounting for business cycle effects may result in overestimating the impact of firm dynamics on productivity. 11 cf. Braunerhjelm and Borgman (2004) for a similar finding on the effect of entrepreneurship on labour productivity. 12 The main differences being that employment growth is not measured in logarithm, entry rates are scaled on employment rather than the stock of businesses and estimations are based on non-overlapping periods of 3 years.

11

Table 5 Regression results for employment growth in manufacturing. I Constant Empl growth (t-1) Entry (t-4) Turbulence (t-4) Exit (t) R&D Related variety Population density Change in wage rate, average (t-4/t-2) Spatial auto-correlation Year dummies

-0.01 0.17

Number of obs. F statistic Adj. R2 * p< .10, ** p< .05

389 29.13 0.18

III ** **

III

-0.07 0.11 0.32

** ** **

0.03 0.05 -0.04 0.05 0.57

** **

0.32 **

0.57

**

389 16.13 0.21

IV

-0.05 0.05 0.30

**

V

-0.04 0.07

**

0.09

**

-0.04 0.09

** *

-0.01 -0.01 0.06 -0.03

** **

**

0.02 0.05 -0.04

** **

0.01 0.06 -0.04

** **

0.34

0.36

0.12

0.07 yes

0.05 yes

0.07 Yes

389 11.63 0.30

389 11.31 0.30

389 10.67 0.28

Table 6 Regression results for employment growth in services. I Constant Empl growth (t-1) Entry (t-4) Turbulence (t-4) Exit (t) Innovation rate Related variety Population density Change in wage rate, average (t-4/t-2) Spatial auto-correlation Year dummies Number of obs. F statistic Adj. R2 * p< .10, ** p< .05

0.03 0.08

III ** *

-.02 .08 -0.20

* **

III

IV

V

-0.01 -0.07 0.01

-0.00 -0.07

-0.01 -0.07

0.02 0.11 0.07 -0.02 -0.26 0.67 389 41.51 0.24

** ** **

-0.31 **

0.63

0.10 0.06 -0.02

** ** **

0.31 **

389 17.57 0.24

0.27 yes 389 12.49 0.32

0.10 0.06 -0.02

** ** **

0.15 0.10 0.09 -0.02

0.32 **

0.26 yes 389 12.50 0.32

** ** **

0.37 **

0.26

**

Yes 389 12.35 0.33

Summarizing, the analyses show that entry and exit positively affect regional competitiveness as measured by productivity in the services sector, but not in manufacturing. In manufacturing the most spectacular improvements in TFP revealed to go hand in hand with severe decline in employment, and thus indicating labour shedding processes. In this sector, turbulence especially firm entry - has a positive impact on employment growth. However, we still have not traced the extent to which regional conditions – urbanization – influences the effect of turbulence on regional competitiveness. This will be analysed in the next sections. Urbanization, turbulence and productivity growth The positive effect of turbulence on productivity growth is highly contingent on the level of urbanization of the region. Surprisingly our two measures of urbanization have contrasting effects. On the one hand turbulence has a particular strong positive effect on productivity growth in services in regions with high levels of related variety, while it has no effect 12

whatsoever in regions with low levels of related variety. On the other hand turbulence has a particular strong positive effect on productivity growth in services in regions with low levels of population density, while it has no effect in regions with high levels of population density. For manufacturing the lack of any effect of turbulence on productivity growth is consistent in all types regions. Entry: Results regression on TFP growth by low and high related variety (using model III, table 3+4) for a selection of variables Manufacturing Services Low variety High Variety Low variety High Variety Entry (t-2) ++ R&D Population density Related variety ++ Turbulence: Results regression on TFP growth by low and high related variety (using model IV, table 3+4) for a selection of variables Manufacturing Services Low variety High Variety Low variety High Variety Turbulence (t-2) ++ R&D Population density Related variety ++ Entry: Results regression on TFP growth by low and high population density (Using model III, table 3+4) for a selection of variables Manufacturing Services Low pop. density High pop. density Low pop. density High pop. density Entry (t-2) ++ R&D Population -density Related variety + Turbulence: Results regression on TFP growth by low and high population density (Using model IV, table 3+4) for a selection of variables Manufacturing Services Low pop. density High pop. density Low pop. density High pop. density Turbulence (t-2) ++ R&D Population -density Related variety +

Urbanization, turbulence and employment growth The positive effect of turbulence on employment growth is also highly contingent on the level of urbanization of the region. In line with the productivity growth analyses, our two measures of urbanization also have contrasting effects on employment growth. On the one hand turbulence has a particular strong positive effect on productivity growth in manufacturing in regions with high levels of related variety, while it has no effect in regions with low levels of

13

related variety. On the other hand turbulence has a particular strong positive effect on productivity growth in manufacturing in regions with low levels of population density, while it has no effect in regions with high levels of population density. For services the lack of any effect of turbulence on employment growth is consistent in all regions, with one exception: a negative (!) effect on employment growth in regions with low levels of related variety. Entry: Results regression on employment growth by low and high related variety (using model III, table 5+6) for a selection of variables Manufacturing Services Low variety High Variety Low variety High Variety Entry (t-4) ++ R&D ++ ++ Urbanisation --Related variety ++ ++ Turbulence: Results regression on employment growth by low and high related variety (using model IV, table 5+6) for a selection of variables Manufacturing Services Low variety High Variety Low variety High Variety Turbulence (t-4) + R&D + ++ Urbanisation --Related variety + ++ ++ Entry: Results regression on employment growth by low and high population density (using model III, table 5+6) for a selection of variables Manufacturing Services Low pop. density High pop. density Low pop. density High pop. density Entry (t-4) ++ R&D + Urbanisation -Related variety ++ ++ Turbulence: Results regression on employment growth by low and high population density (using model IV, table 5+6) for a selection of variables Manufacturing Services Low pop. density High pop. density Low pop. density High pop. density Turbulence (t-4) ++ R&D + Urbanisation -Related variety ++ ++

Limitations Innovative entrants (level of innovativeness) vs imitative entrants We could not directly trace whether productivity growth is helped with the entry of new efficient firms and the exit of old less inefficient ones. This would require microdata (cf. Baldwin 200X; Haskel and Khawaja 2003) that makes it possible to track firms over time and calculate their productivity. One can then separate productivity growth into two effects. The first is the part of productivity growth due to productivity growth in incumbents that remain throughout the period. The second is the part of productivity growth due to the entry of new firms and closure of old ones. If entrants are above average productivity this raises

14

productivity growth, and of course if firms that exit are below average productivity this also raises productivity growth. More disaggregated industries: include the phase in the industry life cycle and the role of localization economies. Mediating effects? Related variety -> + innovative entrants -> + competitiveness High population density -> + inefficient entrants -> - competitiveness These can only be traced with micro data including the innovativeness and efficiency/productivity of firms. Policy implications If governments would initiate policy on regional competitiveness via the stimulation of entrepreneurship, in what kind of sectors and what kind of regions would this be most effective? First of all a distinction should be made between productivity growth or employment growth as an objective in competitiveness policy. One should keep in mind that in the 1988-2002 period in the Netherlands, there has hardly been any productivity growth in services, and hardly any employment growth in manufacturing on average. If productivity growth is the objective, interventions in relation to entrepreneurship in manufacturing do not seem to be effective, while stimulating entry and exit in services seems to be highly effective in achieving productivity growth. For regional policy the following implications are important. In general, related variety has a consistent positive effect, while population density has a consistent negative effect on productivity growth. More specific, stimulating entry and exit in services, especially in regions with a high level of related variety and regions with a low level of population density seems to be highly effective in achieving productivity growth. If employment growth is the objective, interventions in relation to entrepreneurship in services do not seem to be effective, while stimulating entry and exit in manufacturing seems to be highly effective in achieving employment growth. The interpretation of this sectoral difference is that due to higher entry barriers in manufacturing, individuals with relatively low abilities to start and develop businesses with employees are less likely to start a firm in manufacturing. This leads to a lower share of entrepreneurs that enter subsequently exit relatively quickly (‘revolving door’), and a higher share of new firms that turn out to be job creators in manufacturing. For regional policy the following implications are important. In general, related variety has a consistent positive effect, while population density has a consistent negative effect on employment growth. More specific, stimulating entrepreneurship in services might even turn out to be counter effective in regions with low levels of related variety. Stimulating entry and exit in manufacturing, especially in regions with a high level of related variety and regions with a low level of population density seems to be highly effective in achieving employment growth. For urban regions, in general, population density has a negative effect on employment growth, especially in manufacturing. However, in urban regions with a high level of related variety, stimulating firm entry in manufacturing is likely to improve employment growth. In order to achieve this and perhaps even trigger a process of cumulative causation spin-off firms might be stimulated that cause the development of related sectors within a region. Spinoff firms may build on the competences developed in the incubating organization (i.e. ‘regional competences’: see Lawson 1999; Best 2000). For more rural regions with low population density, stimulating entry in services is likely to improve productivity growth, while stimulating entry in manufacturing is likely to improve employment growth.

15

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APPENDIX Related variety by Corop regions in the Population density by Corop regions in the Netherlands, in quartiles Netherlands, in quartiles

19