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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
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]
2
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.
3
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
4
(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.
5
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
6
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
7
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)
8
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
9
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).
10
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.
11
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.
12
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
13
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
14
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.
15
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.
16
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.
17
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
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
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
Food, Beverage, Tobacco
Remaining Industries
Chemical and
Rubber Metal Construction Auto
Wholesale Trade
Retail Trade
Transportation Communicat
ions Financial Services
Real Estate and
Rental
Hotel and
Catering
Business Services
Remaining Services
Parameter estimates (b):
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***
Employees dedicated 0,18*** 0,158*** 0,176*** 0,204*** 0,295*** 0,262*** 0,095*** 0,102*** 0,23*** 0,105*** 0,11*** 0,144*** 0,141*** 0,096*** 0,091*** 0,214*** 0,156***
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
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
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
Predicted Likelihood to Innovate
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
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 .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
Predicted Likelihood to Innovate
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
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
Parameter estimates (b):
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***
Employees dedicated 0,27*** 0,296*** 0,222*** 0,278*** 0,183*** 0,172*** 0,376*** 0,3*** 0,242*** 0,315*** 0,3*** 0,409*** 0,167*** 0,296*** 0,307*** 0,23*** 0,284***
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
Predicted Likelihood to Innovate
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
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
Parameter estimates (b):
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