Innovation Studies Utrecht (ISU) Working Paper Series · Innovation Studies Utrecht (ISU) Working Paper Series . ... SME innovation in the Netherlands, 1999-2009 . Neil Thompson and

Innovation Studies Utrecht (ISU)

Working Paper Series

Macroeconomic Dynamics and Innovation: SME innovation in the Netherlands, 1999-2009

Neil Thompson and Erik Stam

ISU Working Paper #10.03

http://www.uu.nl/uupublish/homeuu/homeenglish/1757main.html�

Macroeconomic Dynamics and Innovation

SME innovation in the Netherlands, 1999-2009

Neil Thompson, MSc*

&

Dr. Erik Stam**

*Utrecht University, Utrecht, NL ** Utrecht School of Economics, Utrecht University, Utrecht, NL University of Cambridge, Cambridge, UK

Abstract: While numerous academic studies sufficiently bond the emergence of (radical) innovations to macroeconomic growth (Plosser (1989); Freeman and Perez (1988); Mansfield (1983); Mensch (1979); Jovanovic and Lach (1997); Giedeman and Simons (2006)), the competitive mechanisms that influence small firm innovation activity are under-theorized and empirically under-represented (see Heger (2004)). Moreover, policy-maker tend to assume a “one-size-fits-all” stimulus agenda can be implemented nation-wide to enhance innovation activity in small firms, i.e. suggesting that supportive policies for the macroeconomic climate will have the same effect on all industries, while in reality, firm and industry innovativeness results in different effects from the macro-economy. Therefore, our main research question asks to what extent and how do macroeconomic dynamics affect product innovations. We take a quantitative approach by examining innovation survey responses from small and medium sized enterprises (SMEs) from 1999-2009 in the Netherlands. Methodologically, we utilize logistic regressions on the pooled cross-section dataset to examine statistically significant effects at an aggregate, innovativeness, and sector level using macroeconomic indicators such as Real GDP, domestic consumption, unemployment rates, and long-term interest rates. Findings suggest that innovativeness of firms and industries results in varying significant effects from the macroeconomic condition. Policy should account for sector specifics and innovativeness when considering future innovation stimulus objectives.

Correspondence Address: Neil Thompson, Innovation Studies Group, Department of Innovation Studies, Heidelberglaan 2, 3584 CS Utrecht, NL Email: [email protected]

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1. Introduction While numerous academic studies sufficiently bond the emergence of (radical)

innovations to macroeconomic growth (Mensch 1979; Mansfield 1983; Freeman and

Perez 1988; Plosser 1989; Thurik 1996; Jovanovic and Lach 1997; Giedeman and Simons

2006), empirical studies into the effects of macroeconomic dynamics on innovation

activity are scarce. Innovation researchers generally agree that where technology

opportunities are present, innovations should and do flourish, but as Geroski and Gregg

(1997) note, “this is not the whole story”. User demand certainly plays a major role in the

introduction of innovation whether to stimulate internal capabilities, information

receiving and evaluating changing rival market competition. This question of what ‘right’

setting for innovation activity is touches upon “one of the longest and least satisfactory

debates in economics” (Geroski and Gregg 1997: 15).

Considering that the majority of the businesses in an economy are small and

medium-sized enterprises (SMEs), the cyclical changes and shocks to their business

environments is of particular interest. Policy in the USA (for example the Small Business

Innovation Research Program) and the EU (for example the Small Business Act) is

increasingly focusing on SMEs as drivers of economic growth and societal

transformation to a knowledge and entrepreneurial society (Audretsch 2009). It is of

some considerable interest then to examine innovation behavior whilst observing their

inherent heightened sensitivity to macroeconomic shocks relative to large firms. Our

main interest of this study is to test how macroeconomic dynamics affect product

innovation activities in SMEs. We define product innovations as the introduction of new

marketable product and/or service new to the industry. This inclusion of ‘new-to-the-

industry’ refines the definition to only major (radical) innovations thereby excluding

innovations that are mere imitations1. We aim to improve insights into the mechanisms

through which the macroeconomic environment effects SMEs’ innovations in the form of

radical products. Therefore, the main research question is, to what extent and how do

macroeconomic dynamics affect product innovations?

1 The definition of process innovations is the introduction of a new method or process of production to the firm. Most importantly, product innovations are likely to be more labor and resource intensive, and involve a longer timeframe than process innovations, which will most likely be financed by retained earnings.

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To answer this question we take a quantitative approach by examining innovation

survey responses from SMEs from the 1999-2009 in the Netherlands. Strengths of this

dataset lie in the large number of observations over the decade long business cycle, SME

innovation characteristics, and size and industry characteristics. We measure

macroeconomic dynamics in a variety of indicators including real GDP growth and its

decomposition into financial market, labor market and consumer demand elements.

Additionally, in contrast to most previous studies, this dataset not only includes

manufacturing, but also is stratified across sixteen industries representing the entire

Dutch economy. The main findings of this study are the general positive effect of

consumption on product innovation (in most sectors) suggesting that small firms innovate

when consumer spending and confidence is increasing. However, we find evidence of

small firms in industries of the most innovative quartile (manufacturing and trade)

utilizing innovation as a strategic process. Lastly, positive links of a labor market effect

in the aggregate suggest that as the labor market becomes more competitive the

likelihood of product innovating increases due to the access of skilled employee capital.

The structure of the paper is as follows. Section 2 reviews the relevant literature on the

macroeconomic effects on innovation, and formulates propositions for the pro-cyclical

and counter-cyclical effects on product innovation. Section 3 presents the data and

research method including the macroeconomic climate and SME innovativeness in the

Netherlands from 1999 to 2009. Section 4 reports the results of our quantitative analyses.

Section 5 summarizes and interprets these findings.

2. Macroeconomic dynamics and innovation

In the wake of the global financial crisis, there is a resurfacing debate amongst

economists questioning the most conducive macroeconomic climate for innovation

activity. In early theorizing on innovation and the business cycle (Schumpeter 1934;

Schmookler 1966), economists created a demand-pull and supply-push vocabulary to

conceptualize the fundamental directions of causality of innovation activity. Demand-pull

terminology very broadly recognizes that demand conditions of consumers in terms of

preferences and incomes have a large affect on prevalence of innovation activity

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(Schmookler 1966; Plosser 1989), while the supply-push doctrine argues the introduction

of new technology in society ultimately influences the emergence of innovations in an

economy (Rosenberg and Frischtak 1983; Mansfield 1983; Jaffe 1988). The

contemporary belief is that while supply-push is observably important, demand-pull does

indeed influence innovation activity (Geroski and Walters 1995; Geroski and Gregg

1997), but the cyclical direction and temporality underlying the effects, particularly for

SMEs, is not well- represented in research2. Furthermore, policy-makers often assume

that a “one-size-fits-all” stimulus agenda can be adopted nation-wide to boost innovation

activity in small firms, i.e. suggesting that supportive policies for the macroeconomic

climate will have the same effect on all industries. However, little empirical research

investigates the different macroeconomic dynamic’s affects on innovation across

different industries resulting inconspicuous effects from policy.

It is commonly argued in economic literature that competitive pressures sharpen

incentives to innovate however this is likely only to a certain degree. Extreme adversity

and competition, on the other hand, may be a hindrance to a firm’s ability to innovate

successfully (Geroski and Gregg, 1997). Realizing the inherent dynamic composition of

macro- competitive structures, theorizing about innovation in equilibrium, static

environment is erroneous. An incorrect inert assumption deters conceptualization of the

macro-economies agency upon competition and innovation activity. One can allude to the

fact that competitive markets are far from stationary in late 1990s to 2009 citing the

dynamic nature of the macro-economy during this period but importantly the question of

causality and innovation is subtle. Following, Mowery and Rosenberg’s (1979: 231)

argument:

When we ask why a particular innovation came at a particular point in time, it is never enough to say that it was “market demand”. The question is why innovation did not come years earlier or later. The answer to such a question therefore has to deal with changes in demand- or supply- conditions. It is not sufficient to say that demand conditions “stimulated” or “triggered” an event; rather one must demonstrate changes in demand conditions. To establish the primacy of demand-side factors one has to show that demand conditions changed in ways more significant or decisive than changes in supply conditions e.g., in cost.

2 An exception is Heger (2004) where she investigates the decision to innovate in manufacturing SMEs in Germany.

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Hence, we do not set out to describe demand-pull’s importance but rather to

delineate the mechanisms in which the macro-economy influences innovation activity in

small firms. Changing macroeconomics, therefore, could either have a propensity to

increase, decrease, or have no effects on innovation activity dependent to some degree of

sector specific and firm subjectivity. To reiterate the dynamic nature of competitive

markets and its effects on innovation activity we conceptualize several possible effects

from a variety of indicators of the macro-economy.

Consumption effect Demand-pull in the form of changing consumption rates (e.g. changing preferences,

incomes, relative prices, and competition structures including expectations of future

prices, incomes, and technological developments) may influence innovation activity in a

number of ways. First, there may be a limited ability of markets to absorb new products

(Geroski and Walters 1995). During instances of economic growth, increasing consumer-

spending power may add scope to consumer preferences for innovative products and

services reflected in consumer consumption. A plausible strategy for full market

absorption, avoiding deconstructive imitation products or services, is choosing to

introduce the innovation during this limited window of opportunity at its highest

probability of being successful. Inversely, during periods of recession where consumer-

spending power and scope of preferences is weakened, it maybe that the likelihood of a

successful launch of a new good or service decreases. Secondly, expectations on behalf

of SME managers and consumers of future economic growth and increasing consumer’s

willingness to buy new products may influence the rates of innovations (Geroski and

Gregg 1997). This leads to the first hypothesis:

Hypothesis 1: There is a positive relation between consumption and product innovation

Labor market effect

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Kleinknecht (1998) argues that as innovators develop new products they accumulate a

‘unique’ firm-specific knowledge, or ‘tacit’ knowledge. This knowledge is difficult to

imitate owing to its subjective accumulation through people and their practical

experiences (Dosi, 1988). For radical innovations it may be that tacit knowledge acts as

an entry barrier for imitators allowing the innovating firm to capture profits. This

rationale implies that skilled employees the firm is able to attract are critical to the

creation and success of innovations in the marketplace. Therefore, the condition of the

labor market during economic growth and decline may have sufficient abilities to direct

the timing of product innovations. Heger (2004) cites and finds evidence that a significant

barrier to innovation for small firms has been found to be the variable access to highly

skilled employees. One would expect that as the unemployment rate increases and the

supply of highly skilled personnel enter the labor market, however temporally lagging,

there will be a positive effect on the capabilities of firms, especially small firms, to

innovate new products and services. Therefore, for product innovations, given that access

to more highly skilled labor increases as the unemployment rate increases, we expect a

positive relationship with labor market on product innovations in the Dutch economy.

Hypothesis 2: There is a positive relation between unemployment and product innovation

Finance effect Another indicator of the macro-economy that may have an influence on innovation

activity is the amount and cost of financing available. Problems with gaining finance are

the most often cited deterrent for innovative small firms (Baldwin and Gellatly 2003) due

to deficient financial structures and/or under-capitalization. Small firms, similar to all

firms, utilize a mix of debt and equity financing to fund innovative activities but differ,

generally, in that they rely more on the availability of the external financing supply

disproportional to their (limited) retained earnings and (relatively small) balance sheet

buffers (Himmelberg and Petersen 1994). In financing innovative projects, it is likely that

the cost of capital will play a role. The cost of capital, as long-term interest rates, may

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influence the innovation activity among small firms.3 We expect that as the cost of the

finance available to finance product innovation increases the propensity to innovate

decreases. Therefore, the next two hypotheses are:

Hypothesis 3: There is a negative relation between the cost of capital and product

innovation.

“Pit-stop” theory of recession “Pit-stop” theory (Mensch, 1979), or opportunity cost theory (Kleinknecht, 1987),

proclaims an increase in investments in innovations during recessions. Contrary to the

aforementioned effects, these rationales observe operations from a one-sum viewpoint,

that is, innovative operations of managers and labor come at a cost to daily ‘normal’

operations of the firm. Innovations, thus, requires intensive factors of production

(typically management and labor) could otherwise be used for ‘normal’ daily operations.

During periods of stagnation or recession, an incentive to invest into innovative projects

may increase due to the decreasing opportunity cost of diverting factors of production

elsewhere. Therefore, one is likely to observe more product innovations during a

recession than in a boom (Aghion and Saint Paul 1993) effectively innovating out of

recession. Empirical research by Geroski and Gregg (1997) suggests that although

investments in all forms of capital typically falls during recessions a large number of

firms bring forward investments in R&D, and product innovations providing evidence in

favour of the “pit-stop” theory of recession.4 We thus construct the next hypothesis:

3 However this may not be so straightforward. Owing to government monetary policy during crises and inflationary targeting, interest rates may fall drastically during periods of crisis, and may be relatively stable when under ‘normal’ conditions. For example, during non-crisis, non-recessionary periods, market rates typically reflect the quantity of demand and the quantity of supply of capital. An increasing (decreasing) market interest rate makes innovations too (in) expensive for small firms, reflecting a negative relationship. There may be a non-linear dimension present. That is, during persistent stagnation and decline, that reduces financing supply, monetary policy may lower interest rates to increase investment while actual financing costs, to some degree, may be realistically higher than market interest rates due to increasing asymmetric information. Thus, the relationship of interest rates and innovation activity is not as clear as in a relatively unregulated competitive market. 4 A more critical assessment of “pit-stop” theory comes from Freeman et al. (1982)

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Hypothesis 4: There is a negative relation between real GDP growth and product

innovation

Innovation as a strategic process Lastly, macroeconomic dynamics may in fact have no effect on the introduction of

innovations. It is conceivable that small innovative firms may choose to approach

innovation as an ongoing strategic process resulting in product and process innovations

that are independent from macroeconomic dynamics (Heger 2004). It is certainly possible

that independency exist for several reasons. First, the innovative process (value creation)

timeframe often surpasses the duration of the macroeconomic fluctuation (Heger 2004).

For instance, a successful firm may realize innovation as a core competency for survival

and growth and continually try to even innovation expenditures despite fluctuations in the

macro-economy (Baldwin and Gellatly 2003). Secondly, small firms may regard

expenditures into innovative projects as “sunk costs” with damaging adjustment costs. To

avoid these costs in the innovation process, firms will choose, to some degree, that

continuing with operations during macroeconomic decline is too costly giving the

disincentive to discontinue projects.

Hypothesis 5: There is no relation between GDP growth and product innovation

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Table 1: Hypothesized effects of macroeconomic dynamics on innovation activities

Macroeconomic Effect

Product innovation

Positive (pro-cyclical)

H1: Consumption effect H2: Labor market effect

Neutral

H5: Strategic Process

Negative (counter-cyclical)

H3: Cost of capital effect H4: “Pit-stop” theory

In summary, we outline several hypotheses on the direction the macro-economy

may have on SME innovation activities. Next, we present the data and empirical

methodology followed by results and discussion.

3. Data and methods

This study investigates the effects of macroeconomic dynamics on product innovation

activities in Dutch SMEs. We use a comprehensive innovation dataset to investigate

systematically the proposed hypotheses. The dataset contains a random sample of surveys

from the year 1999 to 2009 (with the exclusion of 2001). The sample does not survey the

same organizations from year to year, thus excluding longitudinal methods. Data

gathering utilized a computer-assisted telephone interviewing (CATI) system.

Respondents to the questionnaire are business managers, entrepreneurs, or general

managers responsible for day-to-day operations. The sample, has an average of 3,383

respondents, with 7,593 being the most responses in year 2006 and 1,612 being the least

in the year 2000 (response rates vary from 50 to 60 percent). As controls, the firm profile

includes a stratified sample across sixteen industries and firm size (employee numbers).

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Although classifying SMEs into industries by lumping diverse organizations together

may be somewhat inhibiting, the large number of industries and firms is a strength of this

sample.

We employ real percent change GDP growth as a primary indicator of the

macroeconomic environment then decompose this indicator into domestic consumption

rates5, long-term interest rates, and unemployment rates. To adjust temporally for the

innovation period of three years (implicit in the questionnaire) we use a three-year

average of all indicators6. All data was acquired from the Centraal Bureau voor de

Statistiek (CBS) in the Netherlands who supply national macroeconomic indicators. The

macroeconomic performance in the Netherlands over the most recent decade has been

anything but static. Figure 1 displays the simplest and most familiar picture of cyclical

changes using data of unemployment rates and real GDP growth. In the late 1990s, a

period of stagnation and eventual economic decline of 4.4% well into 2002 results in an

increasing unemployment rate to a high of 6.3% in 2004 largely due to slowing trade, the

Dot.com bubble burst, and the September 11th attacks. After a period of economic

growth, the global financial crisis in 2008-2009 drops macroeconomic performance to a

dismal -4.9% and results in the first signs of a lagging, rising unemployment rate to 4.9%.

At the time of research, the condition has been longer than two quarters of steep GDP

decline with the combination of extremely low interest rates, contrasting to the stagnated

growth in the early 2000s. Unlike unemployment rates, GDP growth show less signs of

hysteresis (low or high growth does not persist over long periods of time).

5 We also examined Consumer Confidence to capture consumer sentiments but yielded very similar results to domestic consumption. 6 Lags of each variable also explored temporality but yielded no improved results. We further examined each indicator using a Hodrick-Prescott filter enabling us to examine deviations from the trend, but did not increase explanatory power.

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Figure 1: Real GDP growth and unemployment rates in the Netherlands, 1997-2009

-6

-4

-2

0

2

4

6

8

1999 2000 2002 2003 2004 2005 2006 2007 2008 2009

Real GDP Grow th Unemployment Rate

Radical product innovations (defined as new products or services new to the

industry) average to about 23%, a relatively high number for SMEs over the decade. The

volatility of percent ‘yes’ responses to product innovations over the years seem to be

minor and suggests, if an effect can be established, influence from aggregate economic

conditions will be on a small scale. For example, in 2006, a period of macroeconomic

growth correlates with the highest percent ‘yes’ responses. Meanwhile 2002, 2008 and

2009, periods of economic decline concur with the lowest percentages.

To examine the affects of the macro-economy, we control for a number of small

firm characteristics including use of external resources, inter-firm cooperation,

innovation intensity, size and industry by using previous literature7. The innovation

database accounts for two variables at the firm level to indicate SMEs’ external

orientation. First, a small firm’s usage of external networks, e.g. a university, a research

7 The changes in of SME innovation characteristics (use of external networks, inter-firm cooperation, and innovation intensity) over the ten-year period are relatively stable. The volatility of percent ‘yes’ answers suggests that inter-firm cooperation differs from year to year with the highest percentage occurring in 1999 and the lowest more recently in 2008. Deviations from the mean suggest that inter-firm cooperation is relatively stable over the years. Half of the surveyed SMEs responded ‘yes’ on average to the use of external networks. The volatility suggests that responses vary from year to year, but deviations from the mean are minimal. The highest percentage occurs in 2006, and the lowest during the 2008 and 2009 period. Further investigations into the predictive power indicators are further analyzed in the next section. The correlation matrix is presented in the Appendix.

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institute, suppliers, or any other knowledge source, is indicated by business manager’s

response to the question: “has the firm coordinated or sought assistance from a university,

research business, suppliers or any other outside knowledge source?” Freel (2000, 2003),

Hoffman et al. (1998) and Romijn and Albaladejo (2002) find the usage of external

resources allow firm’s to expand their knowledge base and suggest that SMEs rely

heavily on the ability to gain knowledge from internal and external networks.

The second variable capturing innovation knowledge accumulation for small

firms is “inter-firm cooperation”. Managers, entrepreneurs, or general managers indicate

their reliance on firm cooperation by responding ‘yes’ to; “has your firm cooperated in a

renewal project with another firm?” This indicator permits the inclusion of formal

contracts and/or informal agreements with cooperating companies to assist in innovative

projects. Brouwer and Kleinknecht (1996) find that these strategic partnerships can assist

SMEs to overcome a lack in available resources, risk diversification, and knowledge

accumulation. Although these measures correlate, the structure of the question avoids

perfect correlation and can be used in regression analysis.

The database collects one question that captures innovation intensity in the

innovation process over the ten year period. Innovation intensity measures the dedication

of employees in their daily work towards innovation processes (Vermeulen,

O'Shaughnessy, and de Jong, 2003). Sundbo (1996) finds that SMEs rely on personnel to

contribute to the innovation process, filling the void of a structured R&D department. By

answering ‘yes’ to the question “does your firm have employees, including

mangagement, whose daily work is dedicated to renewal projects?” Indicators for

innovation intensity typically have been monetary, through R&D expenditures, owing to

its relative ease in cross organization comparisons, measurement, and collection.

However, R&D expenditures as a measure of innovation intensity likely leaves out a

majority of small firms that do not have formal R&D structures (Loof, Heshmati,

Asplund, and Naas, 2001). According to Davenport and Bibby (1999), by empowering

employees and involving them in innovation procedures, a better view of costumer needs

can be met. Moreover, this indicator is more appropriate for service industries.

Size classes are created in accordance with the European Commission definitions

of SMEs (Glancey and McQuaid, 2000) and are used as controls in this study. Firms with

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0 to 5 employees are classified as micro-sized firms, 5 to 19 employees are very small, 20

to 49 are small, and 50 to 249 are classifed as medium-sized. The size of the firm is an

important determinate towards innovation (see Arrow 2000, Acs and Audretsch 1988,

Kleinknecht 1989, Braaksma and Meijaard 2007, Brouwer and Kleinknecht 1996, Link

and Bozeman, 1991). For this study, the size control is of particular importance as it is

likely to capture many characteristics of small firm innovation motivators that are not

included in the survey. For example, Baldwin and Gellatly (2003) state that financing

innovation activities for small firm is a large barrier due to small collateralizable net

worth. The size control also likely accounts for other organizational characteristics of the

firm, such as resources invested in innovation, business structure, and business culture

(Wang and Costello 2009, De Jong 2006). De Jong and Vermeulen (2006) also provide

evidence that the effect of firm size is not linear. For example, one additional employee

to a small firm has more of an impact than one addition employee to a large firm.

The last control variable includes sixteen industries in the Dutch economy. The

innovation survey collects industry classifications at the time of the interview. The

sample consists of sixteen industries represented over the ten-year period and is slightly

bias towards business services with 6,308 responses (18.65% of entire sample), with

Communications being the least represented with 363 responses (1.07% of the sample).

Of the industries interviewed, the majority of respondents are from Construction,

Transportation, Financial Services, Wholesale Trade and Business Services consistently

over the ten years. Industry-level analysis of the effects of aggregate economic

fluctuations on SME product and process innovations coincides with De Jong and

Vermeulen (2006) findings that determinates of innovation differ by industry. In full

regression equations, industry dummies capture the technological opportunities and

indursty-specific appropriateness (Heger, 2004).

The Econometric Model

Using a linear probability model in OLS leads to bias estimates attributable to the

linear nature of the model. Therefore, we utilize binary logistic models using maximum

likelihood estimators that are better suited to model the dependent variables. Primary

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interest lies in the response probability where x are the explanatory variables. Using a

binary response model with the form:

where G is a function, using a latent variable model, taking a value strictly between zero

and one. The logit model used in analysis is the logistic cumulative distribution function

of:

For regressions, the full set of explanatory variables is used; that is, the specified

macroeconomic indicator, external resources, inter-firm cooperation, employees

dedicated towards innovation, industry, and size class. The natural log of size

classifications is taken to ensure a linear path of the variable, as it is expected that one

additional employee to a micro-sized firm has more of an impact than one additional

employee to a medium sized firm. The marginal effects of the coefficients indicate the

marginal impact of independent variables on the probability that the dependent variable

equals to one. Therefore, the interpretation of the marginal effects are relatively

uncomplicated; a rise in a marginal unit of the independent variable produces a percent

increase (decrease) in the dependent variable if the marginal effect is positive (negative).

A possible limitation from the data available could be the existence and inability to

correct for survival bias. Survival bias is most likely to occur during random sampling in

the years 2008 -2009; those years that small firms exit rates are highest. It is possible that

the high exit rates during this time period results in the sample being biased towards

surviving innovative firms. Due to the inability to track individual firms’ survival or

failure, we are unable to examine the exact rates of failure during booms and recessions.

However, this may not necessarily be the case. Innovative firms may reveal more risky

behavior and are thus more likely to fail. Therefore due to our large sample selection that

improves measurability we may not know the net result of these two mechanisms i.e. a

sensible effect from survival bias on the (mis)interpretation of our pooled cross-section

results remains unobservable.

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5. The effect of macroeconomic dynamics on innovation activity Our research aims to test the effects of macroeconomic dynamics on product innovation

activity of SMEs in the Netherlands from 1999-2009. From a background of demand-pull

theory (Schmookler 1966; Geroski and Walters 1995; Geroski and Gregg 1997), we

hypothesize several effects that various macroeconomic indicators may have on the

cyclicality of product innovation activity. To take into account Geroski and Gregg’s

(1997) argument that innovation activity is likely to be highly subjective across sectors

and firms; we not only investigate at an aggregate population level but also expand

analysis to quartiles depending on innovativeness: the four most innovative

manufacturing/trade industries, the four most innovative service industries, and all other

industries. Lastly, we look for effects at the sectoral-level (sectoral results in the

Appendix)8. We display results over the aggregate, by innovativeness, and by sectors in

that order.

Table 2 shows evidence for consumption, unemployment, and cost of capital

effects from the macro-economy on product innovation. First, we find that real GDP

growth is positively significant to product innovation in the SME population (p< .01).

This is strong evidence that SME product innovations are pro-cyclical to the

macroeconomic environment. The explanatory power of typical SME innovation

characteristics is also positively significant (p<.01) to innovation activity ceteris paribus

and continues to be in all regressions. Moreover, the control variables (size and industry)

are also significant to explain product innovations as well, in the expected direction. In

GDP decomposition, we interestingly find no evidence of domestic consumption effects

(H1), but do find evidence of a cost of capital influence on product innovations (H3).

8 Limitations of these regressions: For better measurement, a model ideally would be built for each industry using specific industry characteristics e.g. product life cycle, market structure, competition, etc. Unfortunately, the innovation database only includes the regressed explanatory variables over the entire ten-year period. Conclusions on the impact of extra-organizational variables may be erroneous as well. For instance, it is plausible that a reverse causation scenario violates the independent variable exogeneity assumption, where firms decide to innovate then find external resources. Therefore, a conclusion on the impact of input variables is cautioned.

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Table 2: Logistic Regression Analysis for product innovations at the aggregate-level

(1) (2) (3) (4)

VARIABLES marginal effects

marginal effects

marginal effects

marginal effects

Real GDP 0.009*** Domestic Consumption 0.003 Unemployment 0.007** Interest Rates -0.015** Assistance from External Networks 0.055*** 0.055*** 0.055*** 0.055*** Inter-firm Cooperation 0.137*** 0.137*** 0.137*** 0.137*** Innovation Workers 0.180*** 0.181*** 0.181*** 0.181*** Size Classes 0.047*** 0.047*** 0.050*** 0.050*** 20 Sectors -0.001** -0.001** -0.001** -0.001** Observations 27733 27733 27733 27733 Pseudo R-squared 0.128 0.128 0.128 0.128 Robust standard errors in parentheses

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

Lastly, we find evidence that there is a pro-cyclical link to unemployment rates

(H2) and new products and services suggesting that increasing access to employees

supports product innovations. This is first evidence that supports our hypotheses into the

affects of the macro-economy on innovation; however, it may be that at the aggregate

level we do not get a clear picture of how the hypothesized mechanisms. For this reason,

we separate industries in three categories by innovativeness: the Top 4 most innovative

manufacturing/trade (Metal, Chemical, Wholesale Trade, Food and Beverage), the Top 4

most innovative service (Business Services, Financial Services, Real Estate, remaining

services), and all remaining industries.

In Table 3, the macro-economy affects on product innovation a clearer picture of

innovation as a strategic process effect. We find no effect from any of the macro

variables for these industries (p>.10), even though they have the highest innovation rates

each year and over the ten-year period (average 33%). This provides evidence to

hypothesis 5 suggesting that most innovative industries will choose to continue

innovating despite the current macroeconomic conditions due to core business strategies

and/or ‘sunk costs’. Next, we investigate the most innovative service industries in order

to establish their (in)dependence of the macro-economy.

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Table 3: Top 4 Most Innovative Manufacturing/Trade Industries

(1) (2) (3) (4) (5)

VARIABLES marginal effects

marginal effects

marginal effects

marginal effects

marginal effects

Real GDP 0.005 Domestic Consumption 0.000 Unemployment 0.006 Interest Rates -0.011 Assistance from External Networks 0.072*** 0.072*** 0.072*** 0.072*** 0.072*** Inter-firm Cooperation 0.148*** 0.148*** 0.149*** 0.149*** 0.148*** Innovation Workers 0.241*** 0.242*** 0.241*** 0.241*** 0.240*** Size Classes 0.042*** 0.042*** 0.044*** 0.044*** 0.042*** 20 Sectors -0.001 -0.001 -0.001 -0.001 -0.001 Observations 6829 6829 6829 6829 6829 Pseudo R-squared 0.117 0.117 0.117 0.117 0.117 Robust standard errors in parentheses

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

From Table 4 we illustrate the effects from the macro-economy on highly

innovative service firms. Unlike the most innovative manufacturing/trade industries, we

find evidence for a positive link with Real GDP growth and domestic consumption rates

(H1) (b=.013, p<.01), but do not find a labor market effect (H2). This suggests a rebuttal

to the hypothesis that these industries use constant innovation as a strategic process (H5),

to the “pit-stop” theory of recession (H4), as well as the labor market (H2) and cost of

capital (H3) effects. In order to compare the results from the most innovative

manufacturing/trade and service industries, we investigate the macroeconomic indicators’

effects using a population of the remaining industries. Results are displayed in Table 5.

18

Table 4: Top 4 most innovative service industries

(1) (2) (3) (4) VARIABLES marginal effects marginal effects marginal effects marginal effects Real GDP 0.013*** Domestic Consumption 0.013*** Unemployment -0.005 Interest Rates 0.013 Assistance from External Networks 0.055*** 0.056*** 0.056*** 0.056*** Inter-firm Cooperation 0.159*** 0.159*** 0.160*** 0.160*** Innovation Workers 0.186*** 0.188*** 0.189*** 0.189*** Size Classes 0.080*** 0.078*** 0.080*** 0.080*** 20 Sectors 0.012*** 0.013*** 0.012*** 0.012*** Observations 9036 9036 9036 9036 Pseudo R-squared 0.123 0.123 0.122 0.122 Robust standard errors in parentheses

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

In Table 5 logistic regression we find similar positive effects from the aggregate

investigations; a positive effect from Real GDP growth (b=.012, p<.01) and domestic

consumption (b=.01, p<.01) (H1). Interestingly, we also find evidence for a labor market

effect (H2) although in the opposite direction as hypothesized. A negative sign suggests

that as the unemployment rate increases, product innovations decrease and vice versa (b=

-.005, p<.10), contrary to expectations. Next, to further investigate the mechanisms

through which the macroeconomic environment effects product innovation, we explore

the sector specificity.

19

Table 5: All remaining industries (least innovative)

(1) (2) (3) (4) VARIABLES marginal effects marginal effects marginal effects marginal effects Real GDP 0.012*** Domestic Consumption 0.010*** Unemployment -0.006* Interest Rates 0.008 Assistance from External Networks 0.031*** 0.031*** 0.037*** 0.030*** Inter-firm Cooperation 0.088*** 0.088*** 0.098*** 0.088*** Innovation Workers 0.120*** 0.121*** 0.118*** 0.123*** Size Classes 0.037*** 0.036*** 0.030*** 0.039*** 20 Sectors -0.005*** -0.005*** -0.006*** -0.005*** Observations 11868 11868 11867 11868 Pseudo R-squared 0.124 0.124 0.121 0.122 Robust standard errors in parentheses

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

For product innovations and using GDP growth as the macro indicator, we find

evidence of the consumption effect for the Agriculture and Fisheries, Construction, Retail

Trade, Transportation, Financial Services, Hotel and Catering, and Business Services

industries. When domestic consumption is an indicator, we find the same industries (plus

Real Estate) have positive linkages. Investigating the labor market effect (H5) for product

innovations by industry, we find evidence to support this hypothesis for Agriculture,

Construction, and Financial Services sectors whereas we had a positive effect in the

aggregate9 10.

9 We extended analysis into the effects from the macro-economy on process innovations in the aggregate, innovativeness, and sectors as well. Whereas product innovations were quite complicated depending on innovativeness and industry, we find strong evidence that process innovations are positively linked to domestic consumption rates in the aggregate, innovativeness, and all sectors. This results in concluded that consumer demand explains the introductions of product innovations. Moreover, we extend analysis to include the labor market effects on process innovations and find them negatively linked to the access of employees. This is most likely the case because as the unemployment rate increases, the competitiveness of the labor market increases thus increasing the access to skilled employees. Small firms choose to invest employment instead of new processes in this case. Oppositely, as the labor market becomes less competitive, small firms invest into process innovations using retained earning. 10 All other logistic regressions are in the appendix.

20

6. Conclusions

Grounded in a history of demand-pull theory, this research seeks to uncover the effects of

macroeconomic dynamics on SME innovations in the Netherlands from 1999-2009.

Confirming early research, demand conditions do indeed ‘matter’, but most importantly,

the mechanisms through which these conditions influence SME product innovations lead

to more explanatory results. We use binary logistic regressions and control for size,

industry, and innovation inputs to examine the various effects that macroeconomic

conditions have on innovation outputs. We indentify real GDP growth as the primary

indicator of the aggregate economic condition and its decomposition into domestic

consumption rates, long-term interest rates, and unemployment rates as most impacting

macroeconomic variables.

Empirical tests reveal several interesting conclusions. When taking real GDP

growth as the indicator of macroeconomic dynamics, evidence suggests that product

innovations are positively linked. In addition, long-term interest rates and unemployment

figures seem to explain rates of innovation introductions in the aggregate. Nevertheless,

when we extend analysis to quartiles using innovativeness and by industry, a more

refined view emerges for product innovations.

First, we find no evidence of a “pit-stop” theory of recession, in any of our

analyses rather we find domestic consumption rates (capturing changing consumer

confidence, preferences, and incomes), long-term interest rates, and unemployment rates

(capturing access to labor) seemingly influence product innovations positively. We do

find evidence to support innovation as a strategic process by examining the top four most

innovative manufacturing and trade industries. Innovation in these industries seems to be

insensitive to macro-economic dynamics. It is thus likely that firms in these industries

view innovation as a central strategy to the success of business operations; to not

innovate would to not be in business. It may also be that these industries are more

resource intensive and discontinuing ongoing innovative projects would lead to damaging

adjustment costs. On the other hand, the top four most innovative service industries

(Business Services, Financial Services, Real Estate, and remain services) are positive to

domestic consumption rates suggesting that they are more sensitive to changing

21

22

competition structures. Possibly, innovative service industries that are not resource

intensive choose to discontinue a project or launch a new product in limited window of

opportunity dependent on the competition structure of the market. All industries that are

least innovative are positively linked as well, most likely because when they do innovate,

they prefer to launch products at the highest (perceived) probability of success. In

addition, the positive labor market effect implies that during a more competitive labor

market, the access to more skilled employees increases the probability of product

innovations.

Lastly, policy-makers in the Netherlands often assume a “one-size-fits-all”

stimulus plan can increase product innovations in small firms for the benefit of regaining

economic growth, however, we our research gives evidence that changing

macroeconomic dynamics and competitions structures influence small firms differently

depending on their innovativeness. While most industries do innovative along with the

macroeconomic growth cycle, some do not, and policy aimed at augmenting further

innovations may not be applied to correct levels for industry specific innovativeness.

Further research into the industry specific effects of (tailored) macroeconomic indicators

is needed to fully understand the various impacts that the macro-economy has on SME

product innovation activity.

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Appendices

Correlation Matrix

Real GDP

Growth

Domestic Consump

tion Unemployment

Long-term

interest rates

Consumer Confidence

External Resources

Inter-firm Cooperation

Innovation Workers

Size (employees)

20 Sectors

Real GDP Growth 1 Domestic Consumption 0.7059 1 Unemployment -0.0083 -0.6842 1 Long-term interest rates 0.2121 0.805 -0.9568 1 Consumer Confidence 0.8076 0.8534 -0.4004 0.4784 1 External Resources 0.0246 0.0174 0.0141 -0.0052 0.01 1 Inter-firm Cooperation 0.0517 0.0522 -0.0008 0.0175 0.0355 0.3809 1 Innovation Workers 0.1164 0.0826 0.044 0.0157 0.0659 0.2939 0.3723 1 Size (employees) 0.0845 0.1459 -0.1188 0.1416 0.1052 0.1765 0.1885 0.2908 1 20 Sectors 0.0242 0.1052 -0.1158 0.1241 0.0586 0.0572 0.0478 0.0378 -0.0288 1

Process innovations and macroeconomic dynamics in the aggregate

(1) (2) (3) VARIABLES marginal effects marginal effects marginal effects Real GDP 0.020*** Domestic Consumption 0.046*** Unemployment Rate -0.042*** Assistance from External Networks 0.097*** 0.097*** 0.096*** Inter-firm Cooperation 0.103*** 0.102*** 0.104*** Workers Dedicated Towards Innovation Activities 0.268*** 0.271*** 0.278*** Size Classes 0.221*** 0.214*** 0.218*** 20 Sectors -0.001** -0.002*** -0.002*** Observations 27558 27558 27558 Pseudo R-squared 0.199 0.205 0.201 Robust standard errors in parentheses

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

Sector specifics

Product innovations GDP

Total

Sample

Agriculture and

Fisheries

Food, Beverage, Tobacco

Remaining Industries

Chemical and

Rubber Metal Construction Auto

Wholesale Trade

Retail Trade

Transportation Communications Financial Services

Real Estate and

Rental

Hotel and

Catering

Business Services

Remaining Services

Real GDP Growth .010*** .029*** -0,03 0,009 -0,006 0,0002 0,021*** -0,0004 0,013 0,012* 0,02*** 0,019 0,025** 0,016 0,024*** 0,018** 0,015

External Resources .055*** -.024*** 0,063** 0,056* 0,12*** 0,087*** 0,032*** -0,003 0,042** 0,043** 0,032** 0,05 0,04 0,061** 0,055*** 0,052*** 0,066***

Inter-firm Cooperation .137*** .114*** 0,16*** 0,116*** 0,154*** 0,155*** 0,094*** 0,05** 0,131*** 0,078*** 0,085*** 0,101** 0,14*** 0,076*** 0,037** 0,2*** 0,086***

Employees dedicated .181*** .152*** 0,176*** 0,2*** 0,302*** 0,268*** 0,09*** 0,103*** 0,23*** 0,103*** 0,101*** 0,142*** 0,134*** 0,092*** 0,085*** 0,218*** 0,154***

log(size) .046*** .079*** 0,041*** 0,087*** -0,058 0,058*** 0,009 0,057*** 0,045** 0,029** 0,003 0,09*** 0,092*** 0,122*** 0,022 0,091*** 0,041**

Industry -.001***

observations 27733 1130 1135 1801 680 2541 2341 869 2455 2038 1863 320 1477 673 1506 5204 1682

pseudo R2 0.1298 0,1907 0,0992 0,1421 0,1117 0,1393 0,1425 0,0844 0,1013 0,0834 0,1438 0,1766 0,0856 0,1297 0,0902 0,1461 0,0897

Predicted Likelihood to Innovate

.196 0,109 0,256 0,204 0,405 0,298 0,083 0,102 0,282 0,134 0,081 0,132 0,26 0,147 0,1 0,262 0,198

Note: *** p < .01, **p<.05, * p<.10

Product Domestic Consumption

Total Sample

Agriculture and

Fisheries



Chemical and


Wholesale Trade

Retail Trade


Real Estate and

Rental

Hotel and

Catering

Business Services

Remaining Services

Parameter estimates (b):

Domestic consumption 0,003 0,03*** 0,001 0,001 -0,014 -0,006 0,014*** -0,002 0,009 0,007 0,012*** 0,009 0,022** 0,019* 0,015*** 0,013** 0,007

External Resources 0,055*** -0,023 0,063** 0,056*** 0,121*** 0,087*** 0,032*** -0,004 0,042** 0,043*** 0,031** 0,048 0,04 0,06** 0,054*** 0,053*** 0,065***

Inter-firm Cooperation 0,137*** 0,114*** 0,16*** 0,115*** 0,155*** 0,156*** 0,095*** 0,05** 0,129*** 0,079*** 0,084*** 0,104** 0,138*** 0,077*** 0,037** 0,198*** 0,086***

Employees dedicated 0,184*** 0,148*** 0,175*** 0,202*** 0,3*** 0,268*** 0,088*** 0,104*** 0,23*** 0,102*** 0,102*** 0,142*** 0,134*** 0,09*** 0,086*** 0,219*** 0,155***

log(size) 0,047*** 0,078*** 0,041 0,088*** -0,056 0,061*** 0,01 0,057*** 0,045** 0,029** 0,003 0,091*** 0,09*** 0,12*** 0,022 0,091*** 0,04**

Industry -0,001***

observations 27733 1130 1153 1801 680 2541 2341 869 2455 2038 1863 320 1477 673 1506 5204 1682

pseudo R2 0,1278 0,1927 0,0992 0,1414 0,112 0,1394 0,1401 0,0845 0,1011 0,0826 0,1399 0,1743 0,0864 0,1321 0,086 0,1459 0,0888 Predicted Likelihood to

Innovate 0,196 0,11 0,256 0,204 0,405 0,298 0,084 0,102 0,283 0,134 0,082 0,133 0,26 0,146 0,1 0,262 0,198 Note: *** p < .01, **p<.05, *

p<.10

Product innovations and market interest rates

Total Sample

Agriculture and

Fisheries



Chemical and


Wholesale Trade

Retail Trade

Transportation Communicat

ions Financial Services

Real Estate and

Rental

Hotel and

Catering

Business Services

Remaining Services


Market interest rates -0,015** 0,056** 0,005 0,018 -0,052 -0,0405 0,057*** 0,022 0,001 0,047** 0,008 -0,055 0,064* 0,024 0,022 -0,026 0,007

External Resources 0,055*** -0,029 0,0633** 0,056*** 0,12*** 0,087*** 0,0305** -0,001 0,04** 0,042 0,031** 0,043 0,038 0,061** 0,049*** 0,052*** 0,064***

Inter-firm Cooperation 0,136*** 0,117*** 0,159*** 0,116*** 0,153*** 0,155*** 0,094*** 0,05** 0,129*** 0,078*** 0,085*** 0,103** 0,138*** 0,075*** 0,037** 0,197*** 0,086***


log(size) 0,049*** 0,081*** 0,041 0,088*** -0,058 0,062*** 0,011 0,056*** 0,048*** ,028** 0,005 0,096*** 0,092*** 0,126*** 0,025 0,095*** 0,04**

Industry -0,001**

observations 27733 1130 1153 1801 680 2541 2341 869 2455 2038 1863 320 1477 673 1506 5204 1682

pseudo R2 0,1278 0,185 0,0992 0,1419 0,1124 0,1399 0,1391 0,0851 0,1008 0,0844 0,1335 0,1767 0,0843 0,1277 0,08 0,1456 0,0883

Predicted Likelihood to Innovate 0,196 0,11 0,256 0,204 0,405 0,298 0,083 0,103 0,283 0,134 0,083 0,131 0,26 0,146 0,102 0,262 0,199 Note: *** p < .01, **p<.05, *

p<.10

product unemployment

Total Sample

Agriculture and Fisheries



Chemical and Rubber

Metal Construction Auto Wholesale Trade

Retail Trade


Real Estate and Rental

Hotel and Catering

Business Services

Remaining Services

Average unemployment .007** -0,034** -0,007 0,002 0,016 0,016 -0,018*** -0,004 -0,002 -0,012 -0,001 0,025 -0,038** -0,025 -0,003 0,001 0,004

External Resources .055*** -0,028 0,063** 0,057*** 0,12*** 0,087*** 0,031** -0,003 0,042** 0,043** 0,031** 0,044 0,037 0,061** 0,049*** 0,053*** 0,064***

Inter-firm Cooperation .137*** 0,119*** 0,159*** 0,115*** 0,154*** 0,156*** 0,096*** 0,05** 0,129*** 0,079*** 0,084*** 0,102** 0,137*** 0,077*** 0,038** 0,198*** 0,085***

Employees dedicated .181*** 0,154*** 0,178*** 0,201*** 0,295*** 0,263*** 0,093*** 0,102*** 0,23*** 0,104*** 0,109*** 0,148*** 0,143*** 0,096*** 0,091*** 0,216*** 0,156***

log(size) .05*** 0,08*** 0,04 0,089*** -0,058 0,062*** 0,013 0,057*** 0,048*** 0,03** 0,006 0,095*** 0,086*** 0,125*** 0,027* 0,093*** 0,042**

Industry -.001***

observations 27733 1130 1153 1801 680 2541 2341 869 2455 2038 1863 320 1477 673 1506 5204 1682

pseudo R2 0,1279 0,1859 0,099 0,1417 0,1121 0,1398 0,137 0,0845 0,1008 0,0828 0,1334 0,1769 0,0864 0,1304 0,0793 0,1453 0,0883


0,1962 0,11 0,256 0,204 0,405 0,298 0,084 0,102 0,283 0,134 0,083 0,131 0,259 0,146 0,102 0,262 0,199

Note: *** p < .01, **p<.05, * p<.10

27

process innovations GDP

Total Sample

Agriculture and Fisheries



Chemical and Rubber

Metal Construction Auto Wholesale Trade

Retail Trade

Transportation Communications

Financial Services

Real Estate and Rental

Hotel and Catering

Business Services

Remaining Services

Real GDP growth .041*** 0.015 0,05*** 0,022* 0,063*** 0,03***

0,051*** -0.008 0,044*** 0,033*** 0,06*** 0,083** 0,036*** 0,069*** 0,029** 0,046*** 0,049***

External Resources .097*** 0,141*** 0,09*** 0,081*** 0,084*** 0,081***

0,138*** 0,067* 0,104*** 0,118*** 0,122*** 0,167** 0,078*** 0,115*** 0,06** 0,064*** 0,088***

Inter-firm Cooperation

.102*** 0,093** 0,076** 0,09*** 0,075** 0,128***

0,151*** 0,118*** 0,099*** 0,186*** 0,066*** 0,162** 0,06*** 0.003 0,123*** 0,063*** 0,084***

Employees dedicated .271*** 0,297*** 0,216*** 0,276*** 0,158*** 0,168***

0,38*** 0,3*** 0,245*** 0,318*** 0,31*** 0,419*** 0,168*** 0,301*** 0,311*** 0,225*** 0,284***

log(size) .218*** 0,204*** 0,194*** 0,226*** 0,135*** 0,174***

0,24*** 0,25*** 0,209*** 0,244*** 0,22*** 0,219*** 0,182*** 0,161*** 0,189*** 0,241*** 0,183***

Industry -.002***

observations 27558 1081 1156 1802 681 2538 2317 854 2433 2018 1853 319 1470 666 1505 5186 1679

pseudo R2 0.2017 0.1821 0.1445 0.2328 0.1984 0.1813 0.2637 0.1787 0.2032 0.214 0.2258 0.3067 0.2411 0.1944 0.1951 0.1608 0.1659


0.712 0.625 0.705 0.757 0.85 0.789 0.609 0.644 0.751 0.576 0.686 0.643 0.852 0.728 0.693 0.729 0.669

Note: *** p < .01, **p<.05, * p<.10

process innovations Domestic consumption

Total Sample Agriculture and

Fisheries



Chemical and


Wholesale Trade

Retail Trade

Transportation

Communications

Financial Services

Real Estate and

Rental

Hotel and

Catering

Business Services

Remaining Services


Domestic consumption 0,046*** 0,04** 0,072*** 0,039*** 0,085*** 0,033*** 0,053*** -0.004 0,053*** 0,033*** 0,061*** 0,088*** 0,038*** 0,085*** 0,033*** 0,057*** 0,041***

External Resources 0,097*** 0,142*** 0,088*** 0,078*** 0,079** 0,078*** 0,138*** 0,069* 0,103*** 0,119*** 0,1189*** 0,161** 0,076*** 0,113*** 0,063** 0,064*** 0,088***

Inter-firm Cooperation 0,102*** 0,091** 0,07** 0,09*** 0,069** 0,127*** 0,152*** 0,118*** 0,096*** 0,186*** 0,066** 0,17** 0,057*** 0.001 0,124*** 0,062*** 0,088***


log(size) 0,214*** 0,202*** 0,186*** 0,223*** 0,13*** 0,172*** 0,238*** 0,248*** 0,207*** 0,242*** 0,218*** 0,229*** 0,175*** 0,157*** 0,185*** 0,237*** 0,181***

Industry -0,002***

observations 27558 1081 1156 1802 681 2538 2317 854 2433 2018 1853 319 1470 666 1505 5186 1679

psuedo R2 0.2047 0.1841 0.1491 0.2363 0.2082 0.1826 0.2674 0.1786 0.2059 0.2146 0.2296 0.3089 0.2485 0.2005 0.1973 0.1644 0.1664


0.714 0.6262 0.708 0.759 0.855 0.79 0.61 0.644 0.753 0.576 0.688 0.645 0.855 0.734 0.694 0.731 0.669

Note: *** p < .01, **p<.05, * p<.10

28

29

process innovations Unemployment

Total

Sample

Agriculture and

Fisheries



Chemical and


Wholesale Trade

Retail Trade


Real Estate and

Rental

Hotel and Catering

Business Services

Remaining Services


unemployment rate .001*** -0.01 -0,054*** -0,051*** -0,048*** -0,024** -0,071*** -0.002 -0,026** -0.002 -0,058*** -0.079 -0,0457*** -0,049** -0,054*** -0,034*** -0,036**

External Resources .097*** 0,142*** 0,087*** 0,074*** 0,096*** 0,078*** 0,133*** 0,069* 0,104*** 0,119*** 0,112*** 0,158** 0,074*** 0,117*** 0,057** 0,068*** 0,084***

Inter-firm Cooperation .103*** 0,094** 0,075** 0,087*** 0,075*** 0,128*** 0,154*** 0,117*** 0,096*** 0,191*** 0,063** 0,174** 0,056*** -0.005 0,125*** 0,067*** 0,093***

Employees dedicated .271*** 0,298*** 0,232*** 0,292*** 0,1876*** 0,178*** 0,384*** 0,299*** 0,25*** 0,319*** 0,32*** 0,418*** 0,183*** 0,316*** 0,315*** 0,229*** 0,289***

log(size) .221*** 0,204*** 0,193*** 0,226*** 0,1408*** 0,178*** 0,242*** 0,248*** 0,214*** 0,247*** 0,223*** 0,229*** 0,178*** 0,175*** 0,182*** 0,242*** 0,176***

Industry -.001**

observations 27558 1081 1156 1802 681 2538 2317 854 2433 2018 1853 319 1470 666 1505 5186 1679

pseudo R2 0.1985 0.1818 0.1444 0.2376 0.1947 0.1806 0.2656 0.1786 0.1999 0.212 0.2217 0.3011 0.2408 0.1859 0.1976 0.1583 0.1613

Predicted Likelihood to Innovate 0.711 0.625 0.705 0.757 0.849 0.788 0.608 0.644 0.749 0.574 0.6855 0.642 0.851 0.722 0.694 0.729 0.668

Note: *** p < .01, **p<.05, * p<.10

Documents

Innovation Studies Utrecht (ISU) Working Paper Series · Innovation Studies Utrecht (ISU) Working Paper Series . ... SME innovation in the Netherlands, 1999-2009 . Neil Thompson and