7. PUBLIC SECTOR SUPPORT AND FIRM INNOVATION OUTPUTS
7.4. Estimation of relationship between policy instruments and innovativenessinstruments and innovativeness
7.4. Estimation of relationship between policy instruments and innovativeness
The main results for technological innovations are in Table 7.3 and for non-technological innovations in Table 7.4. I will introduce the model results for technological innovations and subgroups first, and then for non-technological in-novations and subgroups.
These models in Tables 7.3 and 7.4 are presented with subgroups. Estimates can-not be compared between models, as in comparing the size of coefficients for in-vestment instruments between new products and new process models. However, estimates can be compared within the model, such as comparing investment in-struments with consulting inin-struments in the new product innovation model. This
3Innovation Growth Lab in the UK also hosts a database of recent RCT based trials worldwide
4A good example of a Heckman selection model for multiple grant possibility is in Hottenrott et al.
(2017).
refers only to coefficient size. The significance of the coefficient suggests that a positive or negative relationship is not random.
Estimates for technological innovations suggest that investment, innovation and R&D, marketing and export promotion, and training and skills development in-struments are positively linked with technological innovation outputs. Consulting, financial guarantees, mixed support and others are not.
Within product innovations and its subgroups, new products and new services, some aspects are highlighted. For example, investments, training and skill de-velopment, and marketing instruments are positively related with product inno-vations. However, within this category, they are only positively related with new products and not with new services. This suggests that firms with innovative ac-tivities mainly related to the creation or improvement of services are not more likely to be beneficiaries in these instruments.
Investment schemes usually consist of buying new machinery and other equip-ment, which can provide some explanation to the link with new products and not with new services. With marketing and export promotion instruments, it is dif-ficult to ascertain. Firms with new products and services should both be equally likely beneficiaries of these instruments. Similarly, training and skills develop-ment instrudevelop-ments are often dealing with soft skills such as marketing, promotion and management, which are not unique to either products or services.
Mixed support is a relatively rare instrument in this dataset, and it is positively correlated with only new products in the product innovation categories. Most firms in this instrument are part of early phase support programs, which might explain the link with new products.
Other instruments, which are a combination of collaboration programs and direct subsidies, are positively correlated with only new services in the product innova-tion category. However, these instruments are very different from each other and very rare. It is difficult to ascertain the exact relationship between the innovation output and instruments.
Process innovations and its subgroups, new processes, new distribution systems and new support systems, show similarly that policy instruments are not uniformly related with firms. Innovation and R&D instruments are not related with any type of process innovations at all. This suggests that firms that participate in these types of instruments are more focused on developing novel products or services.
Investment instruments are positively related to new processes and new support systems but not with new distribution systems. Investments are mostly acquisi-tion of new machinery and equipment, which should almost always yield process
innovations. The lack of effect on new distribution systems suggests that firms are not seeking extra funding to create these innovations.
Marketing and export promotion instruments are positively correlated with all types of process innovations. This suggests that firms actively seeking new mar-kets or new methods for marketing their products are also actively developing internal processes. The direction of causality is not established with this model.
Training and skills development instruments are positively correlated with new processes and new support systems but not with new distribution systems. Train-ing and skills development instruments are mostly soft instruments, so there should not be any distinction here.
Estimation coefficient sizes in a random effects model, where dependent and in-dependent variables are both binary, are log odds. Log odds are not very intuitive to interpret. They can be transformed to probabilities, but it is not really neces-sary. The coefficient estimate is not so exact that it could be interpreted as valid for every situation. Log odds relate to probabilities in a s-curve relationship: log odds 0 is 50 percent probability of the situation; above that is higher and negative is less. A simple takeaway is that if the log odds (coefficient estimate) is positive, there is a positive relationship and, if negative, vice versa.
It is interesting to note that coefficient estimates indicate that firms participating in marketing and export promotion instruments have a higher probability to have product innovations than firms participating in investment instruments. A con-verse effect is seen for process innovations. If it would be clear that causality is from instruments to innovation outputs, it would suggest that marketing instru-ments are much more effective for creating product innovations, not to mention the massive savings it could produce. However, this is a good example that causal-ity can run both ways in this type of regression model.
Estimates for non-technological innovations suggests that innovation and R&D, marketing and export promotion and training and skill development instruments are positively related with non-technological innovation outputs. Other instrument types are not. Since non-technological innovation subcategories were changed several times between the five CIS waves used in this analysis, only wider cate-gories for organisational and marketing innovations can be compared.
Innovation and R&D instruments, marketing instruments, training and skill de-velopment instruments and mixed support instruments are positively correlated with marketing innovations. Marketing innovations suggests that firms develop new marketing strategies or significantly improve their design or packaging. A direct relationship could be assumed from marketing and export promotion instru-ments. For other instrument types, the relationship is less clear. Training and skill development instruments are often for soft skills such as marketing, promotion
and management, which could improve a firm’s choice of strategy. Mixed sup-port instruments are mostly early phase development instruments meant for new ventures, suggesting that young firms have to create new marketing strategies at an early stage. However, firms participating in innovation and R&D instruments could have an indirect link. They are also positively correlated with new products, which might be linked with marketing innovations.
Training and skills development instruments are the only ones that positively cor-relate with firms developing organisational innovations. These instruments are mainly teaching new skills, managerial among them, which could help create or-ganisational innovations.
In most cases, positive and significant relationships are logical, suggesting that firms participating in certain instruments are also innovative in similar outputs.
Some instruments, such as consulting, labour support and financial guarantees are not related with any type of innovation outputs at all. These instruments are either not supporting any innovative activities, such as labour support, or non-innovative firms self-select into these instruments. As mentioned before, this type of model does not give any indication of causality.
Control variables for these models are mostly significant, and coefficients are logi-cal. For example, as explained in Chapter 2, exporting firms and foreign firms tend to be more innovative. Both Schumpeter Mark I and Mark II patterns of innova-tion were introduced in Chapter 2.1. Mark I suggests that young, small firms are more innovative, and Mark II suggests that older, larger, more corporation type firms are more innovative. Control variables in models estimated here suggest that, for Estonian firms, larger firms are more innovative on average. However, firm age is negative and significant in all models, suggesting that older firms are not more innovative.
Controls for technological regimes are compared to their base values for the scale and information intensive technological regime. The values for control variables are logical and aligned with concepts described in Chapter 2.1. Science based firms are more innovative than scale and information intensive firms. Supplier based and specialised suppliers are less innovative. For supplier based firms, it is to be expected that they are relatively less innovative than other categories. How-ever, specialised suppliers should not be less innovative. This might be a speciality of Estonian firms, which could be investigated further in other research. Finally, there is a group of firms supporting infrastructure, mostly municipal owned in-Policy instruments have less positive and significant relationships with non-techno-logical innovations than with technonon-techno-logical innovations. There are no significant negative relationships for any type of policy instrument among these 11 models.
frastructure firms such as waste treatment plants or water providers. They are also less innovative than scale and information intensive firms, which is to be expected.
The constant in the models is the mean intercept for every firm. The random effects model estimates a separate intercept for every firm. The intercept in Tables 7.3 and 7.4 is the mean log odds based on all firms odds to be innovative when all independent variables are zero. However, this is nonsensical, since firm age or workers cannot be zero. The mean intercept for technological innovations is log odds -1.477, which is roughly a 0.18 percent probability of a firm with zero age, workers, instruments, etc. There are 3502 unique firms in the models, and all intercepts are not printed in this thesis, since they add little value to understanding the innovation process.
Technological and non-technological innovation output and policy instrument mod-els suggest that there is some relevant variation in the relationships. Instruments that support more acquisition of machinery and equipment are positively corre-lated with innovative outputs, whereas instruments that support innovation are with new products but not processes. Marketing and export promotion and train-ing and skills development instruments — both more akin to soft skills develop-ment — are very likely to have innovative firms participating. The question is whether these relationships are causal in a way that turns non-innovative firms into innovative ones.
Other investigations into the instrument mix should expect non-uniform relation-ships between instruments and innovation outputs. Since this thesis is not mainly focused on the effects of instrument mix, interaction effects are not investigated further. These could be, for example, about firms with investments and marketing and export promotion, together. This is one possible avenue for further research based on the same dataset.
The models here also suggest that researchers should be cautious when modelling innovation outputs. There is variation, even among technological innovations, between new products and services. These should be taken into account, and research hypotheses should be more exact.
Results suggest that there are instruments that are positively related with outputs and instruments that are not. For example, firms participating in consulting instru-ments display no positive or negative significant relationships with any innovation output. With these instruments, no effect might be detected when investigating in-struments with innovation strategies.
Innovation strategies also differ in terms of output, as seen in Chapter 6.3. The anticipated effect from policy instruments to certain types of innovation strategies is expected to be correlating with outputs. For example, firms with product
inno-vations and more science based strategy are also expected to be more related with investment or innovation and R&D instruments.
The overall impression from the models presented in this chapter suggests that policy instruments and innovation activities can have significant positively corre-lating relationships. This also suggests that policy instruments can have a possible influence on the choice of innovation strategies. In the next chapter, 8, the rela-tionship between the dynamics of innovation strategies and policy instruments is investigated. If it would turn out that there are no significant relationships between innovation outputs and policy instruments, it would be plausible to suggest that they have no effect on innovation strategies as well.
These models indicate that policy instruments are related to innovative activities, which was the aim of this chapter. Policy instruments, at least some of them, are related to innovation outputs. In further chapters, the relationship between policy instruments and strategies is tested.
Table7.3:Technologicalinnovations.Modelresults. Dependentvariable: TechnologicalProductNewproductsNewservicesProcessNewprocessNewdistributionNewsupport innovationsinnovationsinnovationssystem Generalizedlinearmixedmodelwithbinaryoutcomeandrandomintercepts (1)(2)(3)(4)(5)(6)(7)(8) Consulting0.1280.1620.1250.1680.0580.0570.109−0.098 (0.161)(0.155)(0.172)(0.204)(0.149)(0.157)(0.221)(0.166) Financial0.2030.0430.173−0.3180.2020.2470.0660.143 (0.240)(0.231)(0.252)(0.342)(0.217)(0.226)(0.320)(0.237) Innovation0.598∗∗∗0.565∗∗∗0.533∗∗∗0.509∗∗∗0.0620.006−0.1530.058 (0.158)(0.147)(0.163)(0.171)(0.139)(0.147)(0.208)(0.147) Investments0.983∗∗∗ 0.391∗∗∗ 0.501∗∗∗ −0.1440.835∗∗∗ 0.843∗∗∗ 0.1570.365∗∗∗ (0.135)(0.123)(0.137)(0.163)(0.115)(0.115)(0.154)(0.112) Labour−0.0470.0570.049−0.0800.045−0.066−0.0300.091 (0.102)(0.107)(0.125)(0.139)(0.100)(0.108)(0.147)(0.110) Marketing0.632∗∗∗0.645∗∗∗0.851∗∗∗−0.0130.348∗∗∗0.326∗∗∗0.357∗∗0.332∗∗∗ (0.127)(0.112)(0.122)(0.144)(0.109)(0.111)(0.143)(0.109) Training0.478∗∗∗0.357∗∗∗0.400∗∗∗0.1210.377∗∗∗0.286∗∗∗0.1690.322∗∗∗ (0.082)(0.081)(0.091)(0.099)(0.076)(0.079)(0.107)(0.078) Mixed0.914∗0.869∗1.533∗∗∗0.1731.346∗∗∗1.733∗∗∗0.074−0.098 (0.480)(0.445)(0.479)(0.532)(0.431)(0.436)(0.654)(0.526) Continued.
Dependentvariable: TechnologicalProductNewproductsNewservicesProcessNewprocessNewdistributionNewsupport innovationsinnovationsinnovationssystem Generalizedlinearmixedmodelwithbinaryoutcomeandrandomintercepts (1)(2)(3)(4)(5)(6)(7)(8) Other1.446∗ 0.8590.9681.519∗∗ 0.3320.6280.9920.574 (0.854)(0.737)(0.793)(0.674)(0.632)(0.624)(0.702)(0.608) Sciencebased0.453∗∗∗ 0.699∗∗∗ 0.331∗∗ 0.744∗∗∗ 0.1480.1580.2340.062 (0.149)(0.140)(0.161)(0.150)(0.130)(0.136)(0.168)(0.126) Specialisedsupplier−0.499∗∗∗−0.350∗∗∗−0.522∗∗∗−0.149−0.352∗∗∗−0.220∗−0.629∗∗∗−0.262∗∗ (0.125)(0.125)(0.152)(0.142)(0.115)(0.122)(0.178)(0.116) Supplierdominated−0.663∗∗∗−0.487∗∗∗−0.259∗∗−0.545∗∗∗−0.500∗∗∗−0.537∗∗∗0.002−0.373∗∗∗ (0.097)(0.096)(0.113)(0.112)(0.089)(0.094)(0.119)(0.087) Supportinginfras.−0.570∗∗∗−1.187∗∗∗−1.245∗∗∗−0.821∗∗∗−0.129−0.023−0.449∗−0.161 (0.157)(0.177)(0.230)(0.202)(0.146)(0.156)(0.231)(0.149) Exporter0.362∗∗∗ 0.314∗∗∗ 0.705∗∗∗ −0.1320.274∗∗∗ 0.400∗∗∗ 0.226∗ 0.200∗∗ (0.080)(0.084)(0.105)(0.097)(0.078)(0.086)(0.121)(0.085) Workers—log0.539∗∗∗ 0.318∗∗∗ 0.262∗∗∗ 0.218∗∗∗ 0.601∗∗∗ 0.563∗∗∗ 0.371∗∗∗ 0.426∗∗∗ (0.038)(0.037)(0.043)(0.043)(0.035)(0.036)(0.043)(0.033) Firmage−0.040∗∗∗ −0.057∗∗∗ −0.031∗∗∗ −0.069∗∗∗ −0.036∗∗∗ −0.044∗∗∗ −0.036∗∗∗ −0.025∗∗∗ (0.007)(0.007)(0.008)(0.008)(0.006)(0.007)(0.009)(0.007) Foreignowned0.299∗∗∗0.286∗∗∗0.235∗∗∗0.195∗∗0.263∗∗∗0.133∗0.397∗∗∗0.283∗∗∗ Continued.
Dependentvariable: TechnologicalProductNewproductsNewservicesProcessNewprocessNewdistributionNewsupport innovationsinnovationsinnovationssystem Generalizedlinearmixedmodelwithbinaryoutcomeandrandomintercepts (1)(2)(3)(4)(5)(6)(7)(8) (0.076)(0.075)(0.086)(0.089)(0.070)(0.074)(0.093)(0.070) CIS20060.129−0.172∗∗ −0.232∗∗ 0.0210.274∗∗∗ 0.451∗∗∗ −0.252∗∗ −0.169∗ (0.081)(0.081)(0.091)(0.099)(0.079)(0.085)(0.128)(0.087) CIS20080.257∗∗∗−0.249∗∗∗−0.427∗∗∗0.0690.362∗∗∗0.644∗∗∗0.371∗∗∗−0.050 (0.082)(0.083)(0.095)(0.101)(0.080)(0.086)(0.118)(0.087) CIS2010−0.074−0.677∗∗∗−0.982∗∗∗−0.184−0.1250.1090.158−0.374∗∗∗ (0.097)(0.102)(0.119)(0.123)(0.095)(0.104)(0.141)(0.105) CIS2012−0.611∗∗∗−1.006∗∗∗−1.297∗∗∗−0.536∗∗∗−0.659∗∗∗−0.420∗∗∗−0.349∗∗−0.751∗∗∗ (0.108)(0.115)(0.138)(0.142)(0.109)(0.121)(0.170)(0.122) Constant−1.477∗∗∗ −1.155∗∗∗ −2.127∗∗∗ −1.561∗∗∗ −2.502∗∗∗ −3.038∗∗∗ −3.766∗∗∗ −2.713∗∗∗ (0.168)(0.166)(0.198)(0.192)(0.158)(0.170)(0.219)(0.159) Observations9,1559,1559,1559,1559,1559,1559,1559,155 LogLikelihood−5,326.172−4,989.220−4,180.573−3,530.397−5,245.679−4,709.826−2,533.937−4,144.359 AkaikeInf.Crit.10,698.34010,024.4408,407.1467,106.79410,537.3609,465.6535,113.8758,334.718 BayesianInf.Crit.10,862.15010,188.2508,570.9537,270.60110,701.1709,629.4605,277.6828,498.526 Source:InnovationData(2018);∗ p<0.1;∗∗ p<0.05;∗∗∗ p<0.01
Table 7.4: Non-technological innovations. Model results.
Dependent variable:
Non-technological Organisational Marketing innovations innovations innovations GLMM with binary outcome and random intercepts
(1) (2) (3)
Consulting −0.053 0.128 −0.084
(0.143) (0.150) (0.151)
Financial 0.195 0.253 0.235
(0.208) (0.217) (0.217)
Innovation 0.270∗∗ 0.140 0.342∗∗
(0.135) (0.139) (0.140)
Investments 0.155 0.017 0.160
(0.112) (0.115) (0.117)
Labour 0.031 0.007 0.053
(0.094) (0.102) (0.102)
Marketing 0.497∗∗∗ 0.045 0.710∗∗∗
(0.106) (0.108) (0.106)
Training 0.432∗∗∗ 0.485∗∗∗ 0.211∗∗∗
(0.073) (0.075) (0.078)
Mixed 0.625 0.183 1.074∗∗∗
(0.410) (0.435) (0.415)
Other 0.340 0.311 1.005
(0.645) (0.631) (0.648)
Science based 0.120 0.215∗ −0.074
(0.125) (0.127) (0.130)
Specialised supplier −0.627∗∗∗ −0.325∗∗∗ −1.066∗∗∗
(0.110) (0.113) (0.125)
Supplier dominated −0.459∗∗∗ −0.374∗∗∗ −0.442∗∗∗
(0.084) (0.086) (0.088)
Supporting infras. −0.673∗∗∗ −0.445∗∗∗ −1.118∗∗∗
(0.141) (0.147) (0.166)
Exporter 0.312∗∗∗ 0.230∗∗∗ 0.317∗∗∗
(0.073) (0.077) (0.082)
Workers — log 0.404∗∗∗ 0.419∗∗∗ 0.270∗∗∗
(0.033) (0.034) (0.035)
Continued.
Dependent variable:
Non-technological Organisational Marketing innovations innovations innovations GLMM with binary outcome and random intercepts
(1) (2) (3)
Firm age −0.031∗∗∗ −0.038∗∗∗ −0.013∗
(0.006) (0.006) (0.007)
Foreign owned 0.224∗∗∗ 0.263∗∗∗ 0.162∗∗
(0.067) (0.069) (0.072)
CIS2006 0.063 0.037 0.403∗∗∗
(0.075) (0.075) (0.082)
CIS2008 −0.538∗∗∗ −0.758∗∗∗ 0.031
(0.077) (0.079) (0.085)
CIS2010 −0.693∗∗∗ −0.975∗∗∗ −0.135
(0.090) (0.095) (0.100)
CIS2012 −0.983∗∗∗ −1.235∗∗∗ −0.365∗∗∗
(0.100) (0.108) (0.113)
Constant −1.041∗∗∗ −1.421∗∗∗ −1.887∗∗∗
(0.147) (0.151) (0.159)
Observations 9,155 9,155 9,155
Log Likelihood −5,573.855 −5,156.260 −4,893.234 Akaike Inf. Crit. 11,193.710 10,358.520 9,832.468 Bayesian Inf. Crit. 11,357.520 10,522.330 9,996.275
Source: Innovation Data (2018);∗p<0.1;∗∗p<0.05;∗∗∗p<0.01