1 Introduction
This paper explores the impact of creative, technical and management workforce skills, and their combinations, on firm growth. There is a widely accepted link between levels of human capital and economic performance at the geographical level (e.g. Toner
2011). Consequently, policymakers have sought to encourage the development of specific types of workforce skills. In particular, recent decades have seen a substantial emphasis on the encouragement and promotion of science, technology, engineering and mathematics (STEM) education (Atkinson and Mayo
2010). Indeed, growing evidence suggests that the presence of STEM workers is associated with increased productivity, rising wages and higher levels of innovation at the regional level (Atkinson and Mayo
2010; Peri et al.
2015; Winters
2014). Concurrent but distinct to the STEM phenomenon, there has been an increasing recognition of the economic importance of creative workers. This originally stemmed from Florida’s work (2002; 2004), which argued that the presence of ‘bohemians’, or artists and related cultural amenities, were drivers of innovation and regional growth. Subsequent research has explored the impact of these workers’ skills on performance at a regional level (McGranahan and Wojan
2007; Wojan et al.
2007; Wedemeier
2010; Huggins and Clifton
2011; Marrocu and Paci
2012; Lee and Rodríguez-Pose
2014; Wojan and Nichols
2018). In parallel, the role played by management skills (or lack thereof) in explaining persistent stagnation and declines in productivity in OECD economies has been debated (Bloom et al.
2019). While debates on STEM, creative and management skills remained separate for some time, the growing realisation that there may be complementary effects between these skills led them to increasingly converge over recent years. This has manifested in the rise of a movement advocating to add arts to STEM education, resulting in ‘STEAM’—science, technology, engineering,
arts, and mathematics—curricula (Daugherty
2013), which has coincided with growing evidence about the interactive effects of STEM and creative occupations at the regional level (Brunow et al.
2018).
While the association between skills and regional growth is now well-established, there has been comparatively less work on the impact of skills, and skills combinations, on firm performance. Therefore, evidence on the benefits firms can attain by investing in STEM (see Leiponen
2005; Coad et al.
2014) and creative skills (Mollick
2012) is limited. By contrast, recent contributions to the literature on the economics of management practices (Bloom and van Reenen
2007; Bloom et al.
2019) point to the importance of management skills for firm performance. Despite the relative shortage of evidence on the performance implications of firms’ investments in specific workforce skills, there is growing interest in understanding the impact of skills
combinations. In their research on a UK creative cluster, Sapsed et al. (
2013) find that firms combining STEM and creative skills outperform those that specialise in just one of those types of skills. These findings found further support in a study that investigated the broader population of UK firms in Siepel et al. (
2016).
The aim of this paper is, therefore, to contribute evidence on the impact of investments in STEM, creative and management skills—and various combinations thereof—on firm performance. In particular, following an exploratory approach, we aim at understanding the impact of utilising, and combining, these skills on turnover growth. We do so by analysing a panel dataset of firms obtained by combining data from two official UK datasets. We use three waves of the UK Innovation Survey (2008–10, 2010–12 and 2012–14) and combine these with annual turnover data from the administrative Business Structure Database. Using fixed effects and pooled OLS methods, we model the impact of the use of skills and skill combinations on future firm performance. While we find limited effects on performance when we consider creative or STEM skills when deployed on their own, we find that the performance benefits associated with both STEM and creative skills are only revealed when used in combination with each other, with management skills, or with the combination of all three types of skills together.
This paper makes two contributions to the literature. It is the first study—to our knowledge—to compare the differential impacts of STEM, creative and management skills on firm performance. Second, it is the first to examine the impact of various combinations of these skills. In doing so, it sheds light on which combinations of skills are particularly valuable to firms, informing policies on education and training. The paper highlights that the performance benefits of the use of creative and STEM skills are better explained by the presence of skills in combinations—with each other and with management skills. Consequently we offer support to policies encouraging investments in the combination of skills as a means to expedite growth. The remainder of the paper is organized as follows: in Section
2, we review extant literature on skills and firm growth, as well as studies on skill combinations. We then illustrate data and methodology in Section
3, before presenting results in Section
4. Finally, Section
5 discusses the implications of our findings, highlighting the key limitations of the study and possible avenues for future research.
4 Results
As discussed above, our main research question asks whether use of STEM, creative and management skills and all their possible combinations are associated with a higher firm performance, measured by sales growth. We begin by presenting descriptive analysis to show which uses and combination of skills are the most prevalent in our sample. Our main econometric analysis takes the form of panel regressions with FE using different specifications and pooled OLS regressions.
4.1 Descriptive results
The descriptive results are presented in Tables
4 and
5. Table
4 shows the prevalence of the skills seen in the sample, regardless of the individual use or combination of these skills. Overall, 73.38% of the firms used at least one of the three types of skills considered in this study (creative, STEM and management) in the observation period. The use of different types of skills is quite balanced in the case of creative (50.72%) and STEM skills (51.40%), while management skills are less prevalent (39.32%), and 26.62% of firms did not report using any of the types of skills considered. However, these categories described the use of these skills
tout court, without considering whether these skills are used individually or in combination with other skills. When we consider the range of possible skill combinations, we get a better picture of the distribution of these combinations. Table
5 shows the breakdown of firms by use of skills.
Table 4
Use of skills (individually or in combination with other skills) (N = 6842)
ANY skill | 73.38% |
No skills reported | 26.62% |
CREAT skills | 50.72% |
STEM skills | 51.40% |
MGMT skills | 39.32% |
Table 5
Breakdown of firms by type of skills (N = 6842)
No skills reported | 26.62% |
Specialized in one type of skill: 24.87% | CREAT only | 7.94% |
STEM only | 7.50% |
MGMT only | 9.43% |
Combining two types of skills: 28.99% | CREAT & STEM | 18.63% |
CREAT & MGMT | 4.62% |
STEM & MGMT | 5.74% |
Combining all three skills:19.52% | CREAT, STEM & MGMT | 19.52% |
Total | 100.00% |
Among the firms that used some types of skills, 24.87% specialized in only one type of skills, 28.99% combined two skills and 19.52% used all three types of skills. Among the specialized firms, the most frequently used skills are the management ones (9.43%), followed by creative (7.94%) and STEM (7.50%). If we consider the firms combining two types of skills, 18.63% of firms used creative and STEM skills, while 4.62% used creative and management skills, and 5.74% STEM and management. Quite interestingly, the two most frequently combined skills are creative and STEM ones, by themselves (18.63%), or together with management skills (19.52%).
4.2 Multivariate results
Our main multivariate results are presented in Table
6, which is reporting the results of the panel FE regressions, and Table
7, which reports the results of pooled OLS. Model 1 represents our FE baseline results, including only the control variables. We note that most of the variables are associated with turnover growth. First, smaller firms in terms of turnover are more likely to experience a higher growth. The coefficients of
log age and
log age squared, both significant at the 10% level, appear to describe a U-shaped relationship between firm’s age and growth.
7 Also R&D Intensity seems to have a nonlinear relationship with firm growth (inverted u-shape).
8 Finally, the coefficient of
capital investments is positive and significant, while the one of
human capital is not significant. These variables retain the same level of significance and about the same magnitude in models 2, 3 and 4 where the variables related with skills and skills combinations are added. These results are broadly consistent with the same regressions performed using pooled OLS (model 5), with the exception of
R&D intensity and c
apital investments which are not significant in this case, and human capital which is significant at 10% level.
Table 6
Results of the panel regressions with fixed effects
ANY Skill i,t - 1 | | 0.133** | | |
| | (0.051) | | |
CREAT Skills i,t - 1 | | | 0.034 | |
| | | (0.042) | |
STEM Skills i,t - 1 | | | − 0.004 | |
| | | (0.036) | |
MGMT Skills i,t - 1 | | | 0.059 | |
| | | (0.041) | |
CREAT Only i,t - 1 | | | | 0.086 |
| | | | (0.055) |
STEM Only i,t - 1 | | | | 0.101 |
| | | | (0.078) |
MGMT Only i,t - 1 | | | | 0.242*** |
| | | | (0.092) |
CREAT & STEM i,t - 1 | | | | 0.115** |
| | | | (0.056) |
CREAT & MGMT i,t - 1 | | | | 0.169* |
| | | | (0.092) |
STEM & MGMT i,t - 1 | | | | 0.067 |
| | | | (0.087) |
CREAT, STEM & MGMT i,t - 1 | | | | 0.133** |
| | | | (0.063) |
Log Turnover i,t - 1 | − 0.813*** | − 0.813*** | − 0.814*** | − 0.814*** |
| (0.080) | (0.079) | (0.079) | (0.079) |
Log Age i,t - 1 | − 1.131* | − 1.138* | − 1.113* | − 1.178** |
| (0.619) | (0.606) | (0.610) | (0.594) |
Log Age Squared i,t - 1 | 0.357* | 0.368* | 0.355* | 0.385* |
| (0.206) | (0.200) | (0.204) | (0.197) |
R&D Intensity i,t - 1 | 0.149** | 0.150** | 0.147** | 0.149** |
| (0.067) | (0.067) | (0.067) | (0.065) |
R&D Intensity Squared i,t - 1 | − 0.003* | − 0.003* | − 0.002* | − 0.003* |
| (0.001) | (0.001) | (0.001) | (0.001) |
Capital Investments i,t - 1 | 0.110*** | 0.111*** | 0.110*** | 0.110*** |
| (0.015) | (0.015) | (0.015) | (0.014) |
Human Capital i,t - 1 | − 0.060 | − 0.076 | − 0.059 | − 0.078 |
| (0.126) | (0.126) | (0.126) | (0.126) |
Constant | 7.426*** | 7.243*** | 7.357*** | 7.219** |
| (0.889) | (0.885) | (0.884) | (0.871) |
Firms | 1267 | 1267 | 1267 | 1267 |
Observations | 2759 | 2759 | 2759 | 2759 |
R2 | 0.311 | 0.315 | 0.313 | 0.317 |
Table 7
Results of the pooled OLS regressions
ANY Skill i,t - 1 | | 0.082*** | | |
| | (0.016) | | |
CREAT Skills i,t - 1 | | | 0.030* | |
| | | (0.015) | |
STEM Skills i,t - 1 | | | 0.033** | |
| | | (0.016) | |
MGMT Skills i,t - 1 | | | 0.065*** | |
| | | (0.014) | |
CREAT Only i,t - 1 | | | | 0.030 |
| | | | (0.022) |
STEM Only i,t - 1 | | | | 0.037 |
| | | | (0.027) |
MGMT Only i,t - 1 | | | | 0.097*** |
| | | | (0.026) |
CREAT & STEM i,t - 1 | | | | 0.079*** |
| | | | (0.020) |
CREAT & MGMT i,t - 1 | | | | 0.087** |
| | | | (0.037) |
STEM & MGMT i,t - 1 | | | | 0.088** |
| | | | (0.037) |
CREAT, STEM & MGMT i,t - 1 | | | | 0.128*** |
| | | | (0.023) |
Log Turnover i,t - 1 | − 0.025*** | − 0.028*** | − 0.030*** | − 0.030*** |
| (0.005) | (0.005) | (0.005) | (0.005) |
Log Age i,t - 1 | − 0.383*** | − 0.390*** | − 0.384*** | − 0.384*** |
| (0.091) | (0.091) | (0.090) | (0.090) |
Log Age Squared i,t - 1 | 0.062*** | 0.064*** | 0.063*** | 0.063*** |
| (0.017) | (0.017) | (0.016) | (0.017) |
R&D Intensity i,t - 1 | 0.012 | 0.010 | 0.009 | 0.009 |
| (0.043) | (0.043) | (0.043) | (0.043) |
R&D Intensity Squared i,t - 1 | 0.000 | 0.000 | 0.000 | 0.000 |
| (0.001) | (0.001) | (0.001) | (0.001) |
Capital Investments i,t - 1 | 0.031 | 0.031 | 0.031 | 0.031 |
| (0.023) | (0.022) | (0.022) | (0.022) |
Human Capital i,t - 1 | 0.072* | 0.042 | 0.033 | 0.034 |
| (0.037) | (0.037) | (0.037) | (0.037) |
Constant | 0.771*** | 0.763*** | 0.766*** | 0.764*** |
| (0.149) | (0.148) | (0.147) | (0.147) |
Observations | 6842 | 6842 | 6842 | 6842 |
R2 | 0.053 | 0.057 | 0.059 | 0.059 |
Model 2 tests whether using any type of skill (
ANY Skill) without making any distinction between different kinds of skills is associated with sales growth, using an FE approach. The results show that this variable is positive and significant.
9 Similarly,
ANY Skill is also positively and significantly associated with turnover growth also in the case of pooled OLS (model 6).
Model 3 adds the skill variables that include any use of any of the three skills considered (and not their combinations). Here, we see that none of the skills is associated to a higher or lower growth. This seems to suggest that is not the use per se of these skills which may drive firm growth, but the specialization in one of these skills or the combinations of two or more of them. Quite interestingly, if we compare these results with model 7, which uses pooled OLS as method of estimation, we notice that in this case, all skills variables display positive and significant coefficients. This is an interesting result, which suggests that estimations of the impact of each of the skills on their own may be better explained by skills combinations when we use more robust panel analysis techniques.
The effects of a simultaneous or individual use of these skills are explored in model 4. In this model, we estimate the effect of all the possible specialization strategies (using only one type of skill) or combinations of skills on firm growth, with respect to the baseline category (i.e. firms which do not use any of the three types of skills considered). While the only specialization strategy with a significant coefficient is to use management skills only (MGMT Only), several skills combinations are associated with a higher firm growth. In particular, the combination of creative and STEM skills (CREAT & STEM), and of creative and management skills (CREAT & MGMT) is associated with a positive effect in terms of turnover growth. Finally, the firms combining all three skills (CREAT, STEM & MGMT) are more likely to report higher growth rates.
Specifically, the coefficient of the variable CREAT & STEM is 0.115, which means that the combination of creative and STEM skills corresponds to an increase in the growth rate of the firm of approximately 11.5% with respect to the baseline category. This increase in turnover growth is about 16.9% in the case of CREAT & MGMT, about 13.3% for CREAT, STEM & MGMT, and about 24.2% for MGMT Only. Although the effects related with investments in skills (and their combinations) presented above appear of a relevant magnitude, tests on differences among their regression coefficients show that there are no significant differences between them.
Model 8, based on pooled OLS regressions, provides very similar results. All the skills combinations that had an effect on turnover growth in the case of FE also do it in the case of pooled OLS (generally with higher level of significance), which also shows a significant and positive effect of STEM & MGMT.
Overall, these results seem to indicate that more than the mere use of skills is the combination of these skills which brings growth dividends to firms. With the exclusion of management skills that are also significant if used by themselves, several combinations of skills are associated with higher returns in terms of firm growth, including the simultaneous use of all three types of skills. Moreover, creative skills seem to play an important role, since they are not significant in if taken on their own, but they are always significant and with a positive effect on growth if they are combined with other skills.
4.3 Robustness checks
To further validate our findings, we carried out a number of robustness checks. First of all, we took into consideration the presence of size effects. The use of skills by the firms in our sample is measured using dichotomous variables, which are capturing the presence of skills and not their quantity. However, there is an argument which may suggest that the presence of positive returns from combining skills could be mainly derived by the role of large firms, which may be more likely to mix diverse types of skills simply because they are large. Therefore, further elaborating on our findings, we considered different size effects in Table
8, which presents the FE model with all skills combinations (model 4 in Table
6) run on three categories of firms grouped according to their size measured in terms of number of employees
10 (defined here as firms with between 10–49 employees; 50–249 employees and above 250 employees) to see if the effect identified above is limited to particular sizes of firms. The analysis of the first size group (10–49 employees) shows a significant effect for
MGMT Only, CREAT & STEM,
CREAT & MGMT, and
CREAT, STEM & MGMT, mimicking the result of the regressions performed on the entire sample. Interestingly, the second size group (50–249 employees) find no significant effects at all. Finally, in the case of the third group of firms (with more than 250 employees), we also find some significant and positive effects, though here, we are considerably more cautious about the results, given the comparatively small sample size and low R2, which suggest that there are more unexplained sources of variation.
Table 8
Regressions for size categories
CREAT Only i,t - 1 | 0.148** | 0.015 | − 0.032 |
| (0.073) | (0.121) | (0.065) |
STEM Only i,t - 1 | 0.159 | − 0.063 | 0.102 |
| (0.137) | (0.110) | (0.070) |
MGMT Only i,t - 1 | 0.198** | 0.194 | 0.175 |
| (0.099) | (0.199) | (0.187) |
CREAT & STEM i,t - 1 | 0.186** | 0.072 | 0.001 |
| (0.086) | (0.106) | (0.072) |
CREAT & MGMT i,t - 1 | 0.226** | − 0.032 | 0.337** |
| (0.088) | (0.244) | (0.134) |
STEM & MGMT i,t - 1 | 0.054 | − 0.004 | 0.202* |
| (0.145) | (0.123) | (0.103) |
CREAT, STEM & MGMT i,t - 1 | 0.220** | 0.071 | 0.005 |
| (0.097) | (0.116) | (0.064) |
Log Turnover i,t - 1 | − 0.722*** | − 0.999*** | − 0.721*** |
| (0.098) | (0.150) | (0.100) |
Log Age i,t - 1 | − 1.726 | − 0.804 | − 0.532 |
| (1.102) | (0.778) | (0.446) |
Log Age Squared i,t - 1 | 0.555 | 0.314 | 0.203 |
| (0.369) | (0.285) | (0.239) |
R&D Intensity i,t – 1 | 0.127** | 0.237** | 2.086* |
| (0.064) | (0.101) | (1.217) |
R&D Intensity Squared i,t - 1 | − 0.002 | − 0.008*** | − 2.882** |
| (0.001) | (0.002) | (1.378) |
Capital Investments i,t - 1 | 0.093*** | 1.141*** | − 0.188** |
| (0.015) | (0.283) | (0.092) |
Human Capital i,t - 1 | − 0.046 | − 0.313 | − 0.218 |
| (0.166) | (0.207) | (0.199) |
Constant | 5.644*** | 8.824*** | 7.467*** |
| (0.885) | (1.986) | (1.662) |
Firms | 534 | 434 | 299 |
Observations | 1141 | 939 | 679 |
R2 | 0.442 | 0.336 | 0.257 |
This suggests that the results of the regressions carried out on the entire sample presented in Table
6 do not seem to equally apply to all size categories of firms, but mainly to the case of small firms with less than 50 employees (though excluding the firms with less than 10 employees which are not included in the sample). One potential explanation for this finding is that small firms, given their size and limited amount of resources, find it difficult to diversify the skillsets of their employees, and therefore experience greater returns than other, larger firms.
While we deem that the FE estimation is the most appropriate for our analysis (further confirmed by the Hausman test), it could be argued that random effects could also be appropriate (see for instance the literature on the links between human resource practices and firm performance, e.g. Huselid and Becker
1998). On this basis, as a robustness check, we estimate random effects models for our panel and present the results in Appendix Table
12. The results are consistent with our main findings.
There are a number of further robustness checks that we do not present for concision but are available upon request. As highlighted above, a potential issue associated with the use of UKIS data for panel analysis is that these data are not deliberately designed to be used as a panel. This means that only a fraction of our firms have surveyed in multiple waves of UKIS. In particular, for the purpose of this paper, in order to create a panel dataset, we included firms which appeared in two of three waves considered or more. This specific characteristics of UKIS data, plus the existence of missing values in the survey, reduced the number of observations-per-firm in our panel analysis. We addressed this problem by providing pooled OLS regressions (Table
7) along with the FE results. In addition to this, we have also made several further checks. First, we run the pooled OLS regressions including all the information available, i.e. even the firms that have participated in only one single survey wave. Then, we run separate OLS regressions on each of the three waves. Finally, we run our fixed effects panel regressions only considering the firms that appeared in all three waves. Our main results held across all these attempts.
We also tested whether the effects of skills combinations and turnover growth still hold if we shorten or lengthen the period in which the turnover growth is measured. We notice that if we measure, firm growth is measured on a shorter period of time (for instance, 2010–11 instead of 2010–12, in the case of regressors in the period 2008–10), we obtain consistent results with our main estimates. However, if you calculate growth on a longer period of time (e.g. 2010–13), we do not obtain significant results indicating that combining different types of skills produce benefits in terms of future turnover growth, but these effects tend not to persist over a too long period of time, suggesting a decaying effect.
Finally, we estimate a version of the model excluding firms that replied to the survey saying that they used all skills, as a means of avoiding potential response-style bias (i.e. firms answering ‘yes’ to all questions), but the results are again consistent.
5 Discussion and conclusions
This paper explores the impact of the use of workforce skills on firm performance. Certain types of skills—particularly STEM (science, technology, engineering and mathematics) skills—have been recently emphasized as being drivers of economic growth (e.g. Atkinson and Mayo
2010; Winters
2014), while other research has, following Florida (
2002), separately highlighted the importance of creative skills (defined here as skills associated with creative occupations, as opposed to skills explicitly linked to creativity), and management skills and practices (Bloom and van Reenen
2007; Bloom et al.
2019). Extant research has mainly explored the role of these skills at the regional level, with only a few studies examining their impact—on their own and in combination—at the firm level. This paper represents an attempt to address this gap. Using panel and pooled cross-sectional data derived from the UK Innovation Survey and UK Business Structure Database, it explores the impact of the combination of STEM, creative and management skills on firms’ future turnover growth.
Our results strongly point to the combination of skills as a factor contributing to firm growth. We find no evidence that STEM or creative skills, on their own, are associated with significantly higher levels of performance. We do, however, find that the benefits of both STEM and creative skills arise only when these skills are combined with another type, or types, of skill(s). This has a number of implications. First, management plays a crucial role in driving firms’ turnover growth. Our results show that introduction of management skills in a firm that had previously not used these skills was associated with a subsequent 24.2% increase in turnover. This is congruent with the literature on the implementation of management practices
11 (e.g. Bloom and van Reenen
2007; Bloom et al.
2019). More importantly, we find that the benefits from STEM and creative skills only emerge in the presence of management skills, in the presence of each other (i.e. STEM and creative), or if all three types of skills are used. For instance, firms that combine STEM and management skills see an 11.5% increase in turnover, firms combining creative and management skills see a 16.9% increase and firms combining all three see a 13.3% increase in turnover. However, we found no statistically significant differences between these coefficients (that is, the skills combinations are significant, but no single combination has a coefficient that is significantly higher than the others). We therefore cannot conclude that the primary effect on turnover growth stems from the combination of two or more specific skills. The implication of this is that the specific mix of skills that has an impact on growth may vary depending on other factors, such as firm’s sector, type of activity or business model.
These findings are broadly consistent with the sizeable body of literature that points to the benefits of knowledge recombination (Fleming
2001; Yayavaram and Ahuja
2008; Stark
2011). However, whereas much of this literature focuses on the recombination of technological knowledge, we consider not just technological skills but also creative and management skills, and find that besides contributing to firms’ innovation, these skill combinations generate positive effects on growth. Building on previously identified complementarities between creative and STEM skills for innovation (Sapsed et al.
2013; Siepel et al.
2016), we present a broadly Schumpeterian story in which innovations require managerial skills to be successfully exploited.
The contribution of our study is twofold. First, it is the first study, to our knowledge, to examine the impact of three distinct types of skills (STEM, creative and management) on firm performance. We show that while using cross-sectional techniques, we find a positive association between the use of STEM, creative and management skills individually and firm growth (supporting evidence provided by previous studies), when we adopt more robust estimation techniques, which better control for possible endogeneity issues; this positive association holds only for managements skills. Importantly, previous studies have found positive associations between STEM skills and growth, and creative skills and growth, but we find this effect is explained by the combination of each of these skills with other skills, rather than their presence on their own. The other positive findings for STEM and creative skills are better explained by skills combinations. This leads us to our second contribution, where we show that the benefits of both STEM and management skills are largely realised in combination with creative skills. This represents a novel and valuable finding that extends previous related work, which found a positive impact of STEM and creative skills (Brunow et al.
2018; Sapsed et al.
2013; Siepel et al.
2016,) and STEM and management skills (Siepel et al.
2017). By showing that superior performance is achieved by those firms that invest in skills combinations, we recognise the importance of the breadth of knowledge for firms, particularly smaller firms. Our findings hold for firms with between 10 and 49 employees, which suggests that investment in these skills among small firms is more likely to generate growth. It is likely that these smaller firms require a more diverse array of skills to be able to scale up.
Some policy implications may also be drawn from our findings. In particular, our evidence provides a cautionary message to efforts to solely develop STEM skills at the expense of arts and humanities skills. Also, our findings challenge the received wisdom that there is a direct association between STEM skills and growth. We present a much more complicated picture that shows that the impact of STEM skills themselves is rather limited, but that it is through the combination with other types of skills that it unlocks growth. In so doing, our evidence supports the burgeoning global STEAM education movement, (adding an ‘A’ for Arts to the familiar STEM acronym). Our findings also hold across industries, thereby pointing to the importance of creative skills also for firms operating in sectors outside those traditionally associated with the ‘creative milieu’. This adds further support to the view that policymakers should broaden their focus to creative activities in the wider ‘creative economy’—not just in the creative industries—to fully capture the impact of creative skills throughout the economy.
As with all research, ours has some important limitations. We acknowledge that the relationship between skills and firm growth may be affected by endogeneity issues. While we tried to address this by using panel fixed-effects models and we tried to account for reverse causality and unobserved heterogeneity, experimental or instrumental variable techniques could provide better approximations of causality. It is worth emphasizing, however, that we do not make statements of causality in this paper but only point to associations among variables. Further limitations are in large part due to the nature of the data. The UKIS is not exclusively a longitudinal study so the panel element is limited, meaning that our preferred specification is an unbalanced panel. We have done our best to address these issues through the various robustness checks previously described. Moreover, given the structure of the data, we only know whether a firm used a skill but we are not able to capture the magnitude of this use, which prevents the identification of threshold effects whereby firm performance is affected. Finally, common to all studies based on survey data, measurement error is a further source of concern. We feel that this issue is not more important here than in other surveys, and the repeated and standardised nature of the UKIS (as part of the European Community Innovation Survey), which has been running biannually since the 1990s (see ONS
2017), gives us some confidence. With this said, the risk of respondents’ misunderstanding or misreporting is present.
This paper presents a number of avenues for future research. As previously highlighted, we refrain from explaining the effects that we observe as being truly causal. Studies able to identify means to address causality using other methods would be valuable. Moreover, valuable contributions could be provided by research exploring the interaction between different types and levels of skills, and from longitudinal studies investigating the performance implications of the accumulation of skills over time. Interesting findings could also come from the analysis of the use of internally developed skills versus externally acquired ones. The presence in the UKIS of questions asking about the use of skills accessed inside and outside the firm makes this a viable option. Future research could also examine what particular mix of skills is more beneficial depending on firm’s characteristics, demography, sector of activity or business model. We hope that our core finding regarding the positive link between the combination of STEM, creative and management skills and firm performance provides the basis for future research into skills combination and firm performance.
Acknowledgements
This work is based on data from UK Community Innovation Survey (UKIS), produced by the Office for National Statistics (ONS) and supplied by the Secure Data Service at the UK Data Archive. The data are Crown Copyright and reproduced with the permission of the controller of HMSO and Queen’s Printer for Scotland. The use of the data in this work does not imply the endorsement of ONS or the Secure Data Service at the UK Data Archive in relation to the interpretation or analysis of the data. This work uses research datasets which may not exactly reproduce National Statistics aggregates. This paper draws upon research originally funded by Nesta and the AHRC Brighton Fuse2 project, as well as the AHRC Creative Industries Policy and Evidence Centre. We are grateful to Alberto Marzucchi, Hasan Bakhshi, John Davies, Juan Mateos Garcia, Paul Nightingale, Gabriele Pellegrino, Jonathan Sapsed, David Storey, Chris Tucci and participants at the following conferences: Eu-SPRI 2016 in Lund, Uddevalla 2016 in London, SPRU 50th in Brighton, DRUID 2017 in New York, UKIS Users Group 2017, EURAM 2018 in Reykjavik and R&D Management 2018 in Milan for very helpful feedback. We are grateful to the editor and three anonymous reviewers for their very helpful comments. Data statement: The data in this paper were accessed using the Secure Lab of the UK Data Service. Details about the data and how it may be accessed can be found at http://doi.org/10.5255/UKDA-SN-6697-9 (Business Structure Database) and http://doi.org/10.5255/UKDA-SN-6699-6 (UK Innovation Survey). The usual disclaimers apply.
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