Government support programs targeted toward innovative SMEs have become more common in recent years, and these programs are generally considered to be important in increasing innovative activities, and consequently employment growth, among growth-oriented SMEs (Bradley et al., 2021).
A challenge when evaluating these targeted R&D grant programs is how to estimate the counterfactual effect, i.e., the development of firms that were supported in the absence of receiving any government R&D grant. SMEs are not randomly selected by the programs; rather, R&D grants are often awarded to the most promising growth-oriented firms based on a combination of criteria. Hence, assessments might conclude that government support programs have been highly effective in increasing firms’ labor demand, even though targeted SMEs would have increased their number of employees and workers with higher education regardless of whether they received the R&D grant or not.
This selection problem is often handled using a matching technique, thereby comparing firms that received support with similar firms that did not receive any targeted R&D grants. We rely on Coarsened Exact Matching to investigate the effects of two growth-oriented support programs in Sweden targeted toward innovative SMEs, making it possible to provide a more robust approach to matching. Our analyses are made possible due to access to a unique micro database on government firm support programs, compiled by the Swedish Government Agency for Growth Policy Analysis. This database alleviates the previous data acces-based concerns by finding appropriate matching firms.
The most striking result of our analyses is the absence of statistically significant effects. We find no robust evidence that the government support programs had any positive and statistically significant effects on the number of employees brought into these growth-oriented SMEs. Additionally, there is not any robust evidence of an impact of the grants on the skill composition of the labor force.
The lack of statistically significant findings is troublesome considering that government support programs require a positive impact to cover the administrative costs that are associated with these programs. When the expected return of engaging in nonproductive entrepreneurship is high, entrepreneurs might also use time and resources to apply for government firm support programs instead of developing their businesses (Baumol, 1990). Firm support programs can thus crowd out more productive investments.
Our findings complement recent papers (e.g., Autio & Rannikko, 2016; Howell, 2017; Stevenson et al., 2021; Söderblom et al., 2015) that found significant positive effects of government subsidies toward innovative SMEs. Their approach of using firms that applied for, but did not receive, funding as a control group has led to varying outcomes. We cannot exclude that their results are due to an inherent selection bias in their analysis since the treatment group has been judged as more promising than the control group. This suggests that the treatment group would have performed better than the control group even without a grant.
The lack of significant employment effects of the government support programs that we investigate is troublesome considering that policymakers often justify targeted R&D grants with the need to correct market failures and promote job growth. We believe that the lack of significant results points toward the challenges involved in using targeted R&D grants as a way of promoting future growth among SMEs. Coad et al. (2014), for example, noted that it is very difficult to point out which firms are going to be fast growers in the future, suggesting that government support programs are unlikely to target potential high-growth firms that would not grow without support. This is consistent with previous evidence that firm growth, to a large extent, can be considered random (Coad et al., 2013), thereby making it extremely hard for policymakers to determine what characterizes SMEs that need an R&D grant to promote job growth and demand for highly skilled labor.
Our findings may also reflect the heterogeneous nature of SMEs, even highly innovative ones. Reflecting the variety of innovative firms may begin to lessen the randomness of the next stages of development, by including truly new ventures with their initial product offering, developing, or commercializing a new-to-the-world technology vs. leveraging innovation from elsewhere, younger ventures that are highly technical but which may not rely on traditional R&D functions, or even more established small firms looking to expand. One notion advanced by Mason and Brown (2013) is to focus on outcomes that help support retaining winners rather than simply picking winners. This approach would at a minimum remove some sources of variance among firms applying for these types of growth grants.
As an alternate explanation, it may be that the highly influential interviews and expert evaluations of those firms under consideration are ineffective. There is currently a dearth of empirical evidence that scrutinizes the questions asked, of whom they are asked, and how the answers are analyzed as part of the application process—or what objective metrics they employ to rate the attractiveness of these potential grant recipients. Even when professional investors have tremendous difficulties in predicting the future outcomes of high-potential but risky ventures, policymakers maybe even less equipped to make these evaluations or provide monitoring of the funding over time (Lerner, 2009). The opacity of this evaluation process and the inconclusive results of their benefit brings into question whether and how robust and objective decision rubrics can effectively be employed.
More broadly, the absence of positive results in our study brings into question whether government support programs toward SMEs can be justified, given that they are associated with high administrative costs, increased incentives for rent-seeking behavior among entrepreneurs, and crowding-out effects on alternative investments that could be more beneficial for society (Bradley et al., 2021). The incentive and ability for researchers to publish results that are statistically significant (Møen & Thorsen, 2017) might also have led to an overconfidence in policymakers’ abilities to influence the future growth and human capital of SMEs, when the non-findings of policy effects are rarely published.
Our study does not come without limitations, however. Even though we use a matching method that is at the research frontier, our results might still be biased if unmeasured variables are correlated with job growth and the likelihood of receiving a targeted R&D grant. We believe that the approach used by Söderblom et al. (2015) can provide more reliable estimates if the policymakers in the last stage of the decision-making process would randomize which firms receive an R&D grant. The advantage of using a randomized field experiment is that the outcome variable cannot affect the probability of receiving an intervention, which means that we know that it is not the intervention that affects the outcome variable. Randomization also implies that there is no systematic connection between the probability of belonging to the intervention group and observable and non-observable factors (Burtless, 1995).
Randomized control trials (RCT) have recently been used in the UK to evaluate the effects of targeted R&D grants (Bakhshi et al., 2015; Roper, 2020), and McKenzie (2017) provides fascinating evidence from an RCT in Nigeria on the effects of public grants following a national business program competition. We believe that more such studies are needed to provide more robust evidence on the effectiveness of government support programs, although we recognize that introducing randomization may be a challenge for policymakers to justify. But it may also potentially remove concerns regarding implicit bias (or crony capitalism at worst; Klein et al., 2021) from selecting from among a group of SMEs that otherwise meet or exceed the criteria for a grant.
Another fruitful area for future research is to more closely evaluate whether the effects of the grants are related to underlying unobservable or difficult-to-quantify factors, such as differences in how well companies are integrated into local business conditions, or the presence of positive spillovers from other companies. These factors are found to be important for growth among innovative firms but create potential challenges to identify and categorize a priori. More research is also needed on whether certain types of targeted R&D grants are more effective than others. Certainly, innovative activities among SMEs come in many shapes and sizes (McKelvie et al., 2017), where many of the most impactful aspects that lead to growth do not appear as formal R&D activity. As such, a heterogeneity analysis could deepen the understanding of the conditions under which the opportunities for positive effects of government support programs are greatest across different aspects of innovative activities beyond R&D.
Instead of focusing on a small group of growth-oriented R&D-intensive SMEs, it may also be more important to focus research and policy measures on what is needed to stimulate growth among SMEs that do not grow, or at best grow marginally. As noted by Bornhäll et al. (2015), the existence of growth barriers is likely to prevent these firms from growing, while potential high-growth firms might grow despite the existence of such growth barriers. General policy measures aimed at low-growth SMEs (e.g., simplification of rules, reduced labor costs, more liberal employment protection legislation, etc.) can thus be more effective in promoting job growth than targeted R&D grants toward SMEs that are considered potential high-growth firms. This corresponds to the analogy in Coad et al. (2014, p. 92), in which tourists on safaris are focused on beautiful gazelles but fail to see the importance of the dung beetle in maintaining the health of the ecosystem.
Note also that we have been investigating the effects of two Swedish R&D grants that are administrated by Vinnova and targeted toward growth-oriented SMEs. The lack of significant effects does not mean that R&D grants never work. As noted in our literature review, there is substantial evidence that grants can indeed be helpful for SMEs when looking at different outcome variables. Other types of programs can also be successful in promoting employment growth, while similar R&D grants might be efficient under other institutional contexts (e.g., in other countries). The unique nature of growth-oriented ventures in Sweden has been noted in the literature (McKelvie et al., 2021).
Another possible interpretation of our non-significant results is that the R&D grants under study usually work, but that the government agency administering the programs is not designing or executing the programs adequately. The R&D grants might, for example, work better if they were given to larger firms or a smaller set of higher-quality applicants. Our study does not currently investigate these alternate models, but we do encourage others to take up this task. We have furthermore focused on the employment effects of targeted R&D grants; it is possible that such programs have a higher impact on other outcome variables (see e.g., Howell, 2017).Footnote 6 The external validity of our findings is thus low, and interpretations in other contexts should be made with care. This highlights the importance of gathering more robust evidence on the effects of government support programs that are targeted toward SMEs with growth potential.
While we fully accept that the development of growth-oriented SMEs is important to the economy, we also recognize that more transparent and methodologically sophisticated tools are needed to more fully evaluate the effectiveness of current practices, such as R&D grant programs. Our intent with this study is to illustrate two programs that are well-intended but that do not seem to have the desired impact, and to offer thoughts on the conditions through which we as scholars can better make these determinations of effectiveness. In doing so, we hope to contribute to a more robust and systematic understanding of how government policies further—or fail to further—entrepreneurship.