Investigating external and private benefits from investment in skills and training: UK innovators study

Author: Emanuele Giovannetti
Institution: Anglia Ruskin University
Type of case study: Research

About the research

Investment in intangible assets is growing: "The current transition towards a knowledge-based economy goes hand in hand with the shift of strategic investment from tangible to intangible assets. These intangibles define the knowledge base of firms and industries. They emerge as major factors in the shaping of competitive advantages and innovativeness." Best Practices as to How to Support Investment in Intangible AssetsWWWforEurope, Working Paper no. 101

As part of its commitment "to using a strong evidence base to support the development and implementation of its policies" BIS research papers, the Department for Business Innovation and Skills (BIS) commissioned Professors Emanuele Giovannetti and Claudio Piga to examine micro-evidence on UK innovation activity and to assess the cumulative effects of firms’ decisions on intangible innovation activities, such as investing in internal and external R&D activities, training and advertising for the purpose of innovation. The main research question was to assess whether and how these investments in innovation activities not only affect the outcomes of the investing firm, the internal effects, but also generate knowledge spillovers affecting the innovation performance of other firms in the economic systems, the external effects. The research findings were published in December 2014 in the BIS Research Paper no. 203.

The paper acknowledges that innovation plays an essential role in UK productivity and economic stability: firms which encourage and support innovation are more productive and maintain sustainable growth. However, between 2006 and 2010 the authors observe that innovation activities declined across all sectors and regions, a trend which was further fuelled by the financial crisis. They conclude that starting and maintaining the virtuous cycle of innovation activities, leading to economic growth, would be an essential step in UK firms regaining lost productivity and competing successfully in the global market.

BIS was keen that the researchers understand not just the benefits of innovation activities which lead to directly improved productivity and the other outcomes of traditional research and development programmes, but also whether additional benefits resulted from increasing innovation activities, for example improved processes, products or services.  

The report acknowledges that these benefits not only play a key role in influencing a firm's innovation output and productivity, but also generate knowledge spillovers (flows of an intangible commodity, innovation knowledge that is difficult to define and quantify) and benefit the performance of other firms in the sector. In the past these spillover benefits have been challenging to map and record, and this project is one of the first attempts to model this intangible diffusion of innovation through the economy. 

Writing for the Cambridge Network Professor Giovannetti said: “Using original estimation methods, our findings show that innovations are positively influenced by network effects or R&D ‘spillovers’. We considered three main types of innovations: product, process and organisational. The positive effect of process innovations on productivity has an immediate interpretation: firms decide to introduce such innovations to reduce costs and increase efficiencies, leading to a direct increase in productivity." 

“This is perhaps no surprise, but our research also discovered a positive association between productivity and organisational innovations as these may provide a ‘defensive business strategy’, and are adopted by firms to reduce costs when revenues are threatened by adverse macroeconomic conditions."


Professors Giovannetti and Piga used three datasets available from the UK Data Service to analyse new variables on spillover benefits and distinguish between the external and internal effects of intangible investments in innovation activities. The survey data also allowed the researchers to complete a geographic analysis of innovation and training activities to demonstrate how these activities are distributed across the UK. The datasets showed that research and development activities were clustered in particular areas of the UK and that this tendency persisted across the time period studied. The research used data from the Community Innovation Survey, the Business Expenditure on Research and Development and the Annual Respondent Database.

  • The Community Innovation Study (CIS) provides the main source of information on business innovation in the UK. The survey is carried out by the ONS every two years and records details about firms’ characteristics and their innovation activities.
  • The Annual Respondents Database (ARD) is one of the most comprehensive surveys of businesses in the UK, covering over 100 key economic variables and approximately two-thirds of the UK economy.
  • The Business Expenditure on Research and Development (BERD) is an annual survey which supplies data for science and technology for policy purposes. BERD provides evidence on total research and development expenditure in the UK, differentiating between in-house R and D projects and externally funded development.  

Using these datasets, the researchers created a merged dataset on innovation activities which allowed them to explore relations between firms’ characteristics and innovative activity during the period 2002-2010.

The network element of these decisions is provided by two dimensional coordinates: a firm’s geographic location and sector of production. This analysis is based on four main categories of information collected at individual firm level: investment in intangibles and innovation activities; introduction of innovation outcomes; firm characteristics, behaviour, motivations and cooperation relations; and knowledge spillovers, based on proximity in both geographic and production spaces.

The report focused on the interrelations between the variables populating these four categories, with the objective of achieving a better understanding of the complex set of relations underlying firm’s innovative activities, not only affecting the outcomes of the investing firm, the internal effects, but also knowledge spillovers, affecting the innovation performance of other firms in the economic systems, the external effects. The researchers constructed a set of new variables capturing the spillovers taking place along different dimensions of the innovation activities. They then introduced an econometric model to assess the role played by different innovative activities and their sector and spatial spillovers, in determining both innovation outcomes and productivity.

The model is divided into a three-stage estimation procedure: In the first stage, four separate estimations, one for each of the intangible innovation activities namely Research and Development (R and D), training and advertising (for the purposes of innovation). The second stage utilises the predicted values obtained from the first stage estimation, together with more covariates, for predicting the outcomes of a firm’s three possible innovation outcomes: product, process and organisational innovations. Finally, in the last stage the researchers used the estimated firm’s joint probabilities of introducing process, product and organisational innovations to estimate their impact on productivity.

The research found that innovation activities have a clear impact on a firm’s success, with productivity being significantly related to the introduction of product, process and organisational innovations. The direct effect of innovation was an improvement in production processes, reduction in costs and increased efficiency. However, there were also clear indirect effects of research and development spillovers which have a positive impact on process innovations.

The relationship between innovation activities and the effect on firms was found to be complex, with cooperation in innovation between firms in the same group, suppliers or customers having a positive economic benefit, whilst active cooperation in innovation with competitors had a negative effect. However, passive cooperation, through spillovers arising from the same sector, had a positive impact on innovation and productivity. This highlights the benefits of competition, not only in increasing efficiency within firms, but in also revitalising the sector a firm operates in.

Findings for policy



Findings for policy

This project demonstrates that innovation plays a key role in economic growth and should be encouraged through policy decisions. Policy makers can promote UK economic growth by maximising the wider spillover effects through targeted subsidies and incentives for innovation for key sectors. Whilst individual firms benefit from innovation, encouraging sector targeted knowledge spillovers would have a positive economic impact across the economy. In addition, understanding that geographic proximity between innovators helps local productivity and assessing where R&D incentives are geographically distributed, can make firms more efficient, with greater benefit to the economy as a whole.

Professor Giovannetti continued: “The relevance of these spillovers effects indicates a clear role for policy intervention in incentivising R&D whenever private incentives are insufficient. It is necessary to identify the key sectors, the most central within the supply chain exchanges, where subsidies and incentives for R&D would maximise the wider spillover effects.”

“In addition to the need to identify the key sectors – the ones at the heart of the supply chain – where subsidies and incentives for R&D maximise the effects of spillovers, geographic proximity to innovators helps local productivity.  Therefore a careful assessment of the geographic distribution of R&D incentives is necessary.”

The full report is available via:

This site uses cookies

Some of these cookies are essential, while others help us to improve your experience by providing insights into how the site is being used.

For more detailed information please check our Cookie notice

Necessary cookies

Necessary cookies enable core functionality. This website cannot function properly without these cookies.

Cookies that measure website use

If you provide permission, we will use Google Analytics to measure how you use the website so we can improve it based on our understanding of user needs. Google Analytics sets cookies that store anonymised information about how you got to the site, the pages you visit, how long you spend on each page and what you click on while you’re visiting the site.