Innovation

Innovation is the fundamental driver of economic growth (Romer, 1990), but input activities of innovation—Research and Development (R&D)—have externalities due to the public goods nature of knowledge. Traditional economic wisdom suggests that government interventions (e.g., subsidies) should be used in such cases. However, all economic resources are scarce and government subsidies are ultimately paid by taxes. Are R&D subsidies worth it?

To answer this question, we conducted two related studies. One is to evaluate the policy effects of subsidy policies in the EU (Foreman-Peck & Zhou, 2022, Economic Systems), and the other is to estimate the spillover effects of R&D (Foreman-Peck & Zhou, 2023, Research Policy). The former discusses how much government interventions can affect productivity via R&D and innovation, while the latter investigates whether they are economically justifiable. 

Our basic conclusion is that: R&D subsidies can be effective espeically for emerging economies, but the crowding out effect and the small spillover effect can make it economically inefficient. All key results support one general principle—specialisation precedes diversification. Instead of widely spreading the limited resources, firms and governments should identify where the use of resources is more effective, either to R&D activities with greater effects or to R&D firms with specialised expertise.

Data

Our data consist of national Community Innovation Surveys (CIS), with the majority of questions standardised across Europe. The survey is conducted at the enterprise level every two years, with certain questions covering a period of three years, such as “During the three years from xxx to xxx, did your enterprise receive any public financial support….?” Firms that organise their business activities into separate legally defined units can be sampled several times. According to the CIS2018 questionnaire, R&D is broadly defined to cover the creation of new knowledge and the solving of scientific or technical problems (including software development that meets this requirement). Nonetheless, most firms do not engage in any R&D activities (only 18% of our sample do such intramural R&D). Furthermore, for those firms that do engage (RRDIN=1), there is another decision to make on how much R&D to undertake, R&D intensity (RRDINX). On average, the turnover ratio of intramural R&D is 7.8% among R&D active firms in our sample. Enterprises in Bulgaria in 2006 had a ratio of 4.9% and those in Germany in 2018 achieved a similar ratio, indicating far higher R&D expenditures because of the discrepancy in turnovers and extensity. The ratio measure has the merit of approximately controlling for price changes. 13.31% of firms in our sample opt for an eclectic approach of cooperative R&D to share the responsibilities and costs of R&D. In our data, we can distinguish collaborators from enterprises of the same group, suppliers, clients, competitors, consultants, universities, and governments. Our key variables are:

Model

To model the effects of R&D on productivity, we adapt the famous CDM model (Crepon et al., 1998) to include an additional selection equation for subsidy (Foreman-Peck & Zhou, 2022) and various forms of R&D effects (Foreman-Peck & Zhou, 2023). The first model focuses on subsidies, so we distinguish among three types of subsidies: (i) EU funding, (ii) central government funding, and (iii) local government funding. The second model focuses on R&D effects, so we distinguish among four types of effects: (i) intramural R&D effect, which is planned and undertaken internally by the firm, (ii) cooperative R&D effect, which is intentional and undertaken partially outside the firm, (iii) extramural R&D effect, which is intentional and performed entirely externally, and (iv) spillover effect of R&D, which is unplanned by the donor and absorbed from outside the recipient enterprise. Further more, the spillover effect can be different at different levels of "spillover pools": the industry-level, the country-level, and the EU-level. The model is estimated using the endogenous treatment effect model.

Results

Subsidy Effects: Crowding Out vs. Crowding In

We have estimated innovation subsidy policy impact, at the level of the firm and the economy, for eight members of the EU that joined in the first decade of the 21st century and for comparison, three western European economies. The baseline results are shown in Table 1. We find impact within each group varied substantially, with that of the Bulgarian EU subsidy being minimal (as Tevdovski et al (2017) also discovered) especially compared with those of Hungarian, Lithuanian and Estonian firms. Hungary’s impact contrasts with the Maroshegyi and Nagy (2010) evaluation for an earlier period. 

For nationally funded policies, again there was considerable heterogeneity within the groups, with Estonia, the Czech Republic and Romania having bigger firm-level innovation policy effects among new members. For EU and central government policy impact jointly Estonia was prominent, as implied by the earlier Hartsenko and Sauga (2012) and Masso and Vahter (2008) evaluations. On average the Western three economies have greater innovation policy effectiveness (both at the individual enterprise and the economy levels) than the new members for central and local government. All new members have lower economy-wide impact than Portugal, the poorest western sample nation. The Czech Republic was the most local innovation policy effective among the new members, contrary to Bachtrögler and Hammer (2018), though the marginal effect was nonetheless very small.

We find an inverse relationship between EU and national innovation policy impacts across Old and New EU members. This is consistent with substantial external EU funded innovation initiatives crowded out nationally supported innovation projects. The correlation pattern between subsidy proportions also support this crowding out pattern, which might be explained by the limited administrative and other resources of the new. By contrast, the subsidy correlation at the enterprise level indicates that the grant of an EU innovation project encouraged the awarding of national and local innovation subsidies crowding-in.

Table 1: Baseline results Foreman-Peck & Zhou (2022)

Table 2: Baseline results Foreman-Peck & Zhou (2023)

R&D Effects: Smaller Spillover Hypothesis (H1)

The baseline results are presented in Table 2. Our first finding is that the "smaller spillover hypothesis" (H1) is confirmed. To see this, we calculate productivity effects of a typical support  via intramural R&D and spillovers respectively to make a fair comparison. For intramural R&D effects, we need to multiply the effects of support on R&D extensity/intensity with the effects of R&D extensity/intensity on productivity. The results are that a typical support boosts productivity by 7.26% via extensity and 163.9% via intensity over a two-year period . In contrast, spillover effects of a typical support are smaller than the intended effects. 

We provide a numerical calculation to demonstrate this (Figure 1). To compare like with like, we evaluate the two effects based on a typical government support. This way, we can see how much effect of the support is due to the intended intramural mechanism and how much is due to the unintended spillover effect. The intended intramural effect for the supported firm is equal to 12.595% (extensity) plus 2.443*21.283% (intensity) = 64.58%. The total unintended spillover effect is equal to 0.436%*(N-1)/(1%*N)+2.443*0.582%*(N-1)/N. As N rises, the aggregate spillover effect (extensity) rises. However, the scale effect converges to a fixed level because (N-1)/N converges to 1. The intended and unintended effects have the following empirical relationship—the intramural effect of one typical support on one firm’s productivity is greater than the spillover effects on all other firms combined. The gap is smaller as N gets bigger, but the scale effect diminishes and converges to around 45% which is still substantially lower than the intended effect 64.58% as shown in Figure 3. The spillover effect lies between 22% to 45% (boundaries), smaller than the intramural effect (H1). This finding is in line with the survey of Ugur et al. (2020).

The hypothesis holds despite a diminishing intramural R&D effect. As shown in the scatter plot of Figure 2, R&D effects are nonlinear with respect to the intensity of R&D expenditure. Specifically, the productivity return to intramural R&D expenditure declines only after the sample average (at turning point 2.987% compared with 1.8% sample average). This finding suggests that most sample firms underinvest in intramural R&D and have yet to reach the optimal level.

Figure 1: Smaller spillover effect hypothesis.

Figure 2: Diminishing intramural R&D effect.

R&D Effects: Dampening Hypothesis (H2)

Separating the sample into “old” EU members (Germany, Spain, and Portugal) and “new” EU members (former centrally planned economies), we can see some differences: there are cross-sectional heterogeneities in both R&D intensity and R&D quality. The “old” are more effective in intramural R&D extensity, presumably because from longer market experience they know better which firms to pull into subsidy regimes. The two groups are about equally effective in intramural R&D intensity. For the average enterprise in both groups a 1% increase in the intramural R&D ratio raises productivity by about 16%. The “old” have more positive spillover effects; their R&D is complementary with other firms’ R&D. The “new” have insignificant or even negative spillover effects, most likely because their R&D is substitutable for other firms’ R&D.

A multinational corporation (MNC) plant is here identified as a member of a larger group of enterprises with a headquarters in another country. There is strong intra-group financing of R&D due to a large presence of multinationals based in the “new” members of the EU (European Investment Bank, 2018 p13). We find no difference between “old” and “new” in intramural R&D effects on productivity, consistent with Vujanović et al. (2022). There are more MNCs in the new (13% of enterprises in the “new” members of the EU are MNCs and 8% in the “old”) and MNCs are more intensity productive than non-MNC enterprises. These findings confirm the dampening hypothesis (H2).

For the “old”, a wider distribution of firms doing R&D raises productivity by more than for “new” EU member countries; the old are more extensity productive. “New” economies are much less extensity productive than “old” which is also explained by a greater contribution of MNCs because MNCs are less extensity productive. Because the “new” are generally less productive across their economies, extensity productivity is much greater for the “old”. As relative GDP per capita suggests, Germany (“old”) is more directly R&D productive than the average of the rest (mainly “new”). 

R&D Effects: Absorptive Capacity Hypothesis (H3)

Enterprises able to derive a competitive advantage from knowledge of their environment have a strong absorptive capacity (Cohen & Levinthal, 1990). Such businesses may be in a better position to utilise spillovers (Estrada et al., 2010; Harris et al., 2021). We measure two types of enterprise absorptive capacity: the influence of a firm’s R&D intensity on spillover effects of other firms’ R&D intensity and the influence of R&D extensity on spillover effects of other firms’ R&D extensity. With few firms to copy from close to the frontier of technology the possibility of absorbing spillovers is low with limited benefits and the spillovers are likely expensive to adapt. Hence, the costs of absorption can exceed the benefits; the estimated R&D coefficient can be negative. 

To quantify the magnitudes of absorptive capacity effect, we regress the estimated spillover effects on the average intramural R&D activities (extensity or intensity). Thanks to the heterogeneity along country, industry, and year in the structural model (interactive terms or slope dummies), we can construct a spillover effect for each country-industry pair in each year. To explain the variations of these spillover effects, RRDIN (extensity) and RRDINX (intensity) are averaged over country-industry pairs and years to construct a pseudo-panel (Guillerm, 2017) . Compared to the original firm-level data which do not have observations of past R&D activities, the pseudo-panel data can capture lag effects of the same country-industry unit. 

The pseudo-panel regression with the country-industry pair fixed effects with an allowance for a lag is reported. A negative coefficient means the absorptive possibility dom-inates the capacity effect. The mixed results suggest that the absorptive capacity hypothesis (H3) holds with conditions. If a firm is already at the technological frontier, it is less likely to copy or absorb from others, i.e., the negative absorptive possibility effect dominates the positive absorptive capacity effect. We show that the impact of R&D intensity and extensity on the spillover marginal effects are small compared to direct effects and the largest numbers are negative. The intensity coefficients mean intramural R&D more than two years ago  helps absorb spillovers in the same country and in the same industry but not EU spillovers. Generally absorptive capacity does not matter much if at all for spillover, compared with direct effects.

Conclusions