Putting the CIS2018 Into Context

Putting the CIS2018 into context Robbin te Velde José Cervera Pim den Hertog December 2017 Project reference: ESTAT/G/2015/006 Compiled by: Dialogic (Utrecht, the Netherlands) Higher School of Economics (Moscow, Russia) DevStat (València, Spain) © European Union, 2017 Reproduction is authorised provided the source is acknowledged 2 Table of contents 1 Concepts for measuring and understanding innovation ..................... 5 1.1 Introduction .............................................................................................. 5 1.2 The object versus the subjective-based approach .......................................... 7 1.3 Innovative activities versus other business activities .................................... 10 2 Concepts and definitions of business innovation for measurement . 11 2.1 Total value created from improvements ...................................................... 11 2.2 Distinguishing innovation activities from other business activities .................. 14 3 CIS Variables and questions.............................................................. 16 3.1 Content and structure of CIS ..................................................................... 16 3.2 Information on the enterprise .................................................................... 18 3.3 Strategies & knowledge flows .................................................................... 19 3.4 Business and innovation activities and expenditure ...................................... 27 3.5 External factors influencing innovation ....................................................... 32 4 Data collection .................................................................................. 38 4.1 Linking CIS data with other data sources .................................................... 38 4.2 Re-using third party data .......................................................................... 40 4.3 Preconditions for linking data .................................................................... 43 4.4 Different methods to link micro data .......................................................... 47 4.5 Privacy and security ................................................................................. 52 5 Indicators and analysis of innovation data....................................... 56 5.1 Data analysis .......................................................................................... 56 5.2 Measuring innovation intensity .................................................................. 63 5.3 Enterprise profiling................................................................................... 75 6 Globalisation and innovation ............................................................. 97 6.1 Enter globalisation ................................................................................... 97 6.2 The challenge of measuring global innovation activities ................................ 98 6.3 Current approaches to measure global innovation activities ........................... 99 6.4 The international dimension in CIS2018 .................................................... 102 References ............................................................................................ 106 Technical annex .................................................................................... 113 Odds ratio ........................................................................................................ 113 Tobit model ...................................................................................................... 114 Confusion matrix ............................................................................................... 115 Machine Learning Algorithms Cheat Sheet ............................................................ 116 Globalisation matrix question .............................................................................. 117 Dialogic innovatie ● interactie 3 1 Concepts for measuring and understanding innovation 1.1 Introduction 1.1.1 Purpose of the CIS2018 User Manual This user manual is a supporting document to the Community Innovation Survey (hereafter: CIS). The focus of the document is on the actual use of the CIS data and less so on the implementation of the CIS survey. This is covered in other documentation. This manual par- ticularly deals with many aspect of the analysis of CIS survey results. The primary aim is to show how CIS data can be used, or is already being used, in the practice of innovation research and policy analysis. The manual follows the structure of CIS in broad lines. In chapters one and two the key concepts of CIS are discussed, respectively innovation and business innovation. Chapter three describes the questions of CIS2018 each and every one. Chapter four deals with the possibilities of combining CIS data with other data sources (i.e., with data linkage). Chapter five elaborates two important subsequent analyses that can be applied to CIS data, namely the calculation of innovation intensity and the profiling of specific business enterprises (i.e., innovation styles). Chapter six covers another important topic, the international dimension of innovation. 1.1.2 The users of CIS The Community Innovation Survey has many users. The four main types of users are academics, analysts, managers and policy makers. Policy makers and managers are the final users, analysts (research consultants) are intermediaries, and academics are both intermediaries and final users. In practice, there is often an overlap in roles. Nevertheless, each of the roles has specific needs and it are these user needs that drive the construction of a system for measuring and reporting innovation and the subsequent production of innovation data, statistics, indicators, and in-depth analyses of innovative activities. Ultimately it is the needs of the final users, those of the policy makers and the managers, that should drive the design of the CIS survey and the subsequent processing of the data. Although academics are also final users in their own right eventually their work is also meant to improve the understanding of the aforementioned policy makers and managers of innovation. An important observation is that neither policy makers nor manages are interested in innovation per se. They are primarily interested in the outcome of innovation, that is, in its effect on economic development, organisational change, and social transformation. A marked difference between these two types of users is that policy makers mainly work at the meso and macro level (‘the public interest’), and managers mainly at the micro level (their own organisation or benchmark organisations). The (re)use of CIS-data by managers is severely restricted by the fact that micro data cannot be disclosed by entities that collected the information on a confidential basis. This is an additional disadvantage because it is Dialogic innovatie ● interactie 5 usually these (innovation) managers who fill out the CIS survey. It is therefore not possible to provide them with tailor-made feedback on the data they have entered in return.1 This leaves policy makers as the core target for innovation data. Public policy interest in innovation is indeed extensively reflected in the literature (OECD, 2015). Since innovation is usually not a policy goal in itself, it is important to identify the primary policy goals to ensure that the data that is being collected matches policy needs. So far CIS data has been mainly used in the policy areas of industry and economic affairs. This is probably due to the fact that putting the data to use outside the core policy areas requires the linking of CIS data with existing data sources that cover the target policy areas. Linking CIS data with other data sources (especially administrative data, see chapter 4.1) has a lot of potential but it does require additional investments in existing statistical infrastructures. 1.1.3 Scope and definitions of CIS As much as user needs drive the collection and analyses of innovation data the opposite also holds. The scope and the method of the data collection and the definition of the core concepts to a large extend determines the actual (re)use of the data. For example, as already been described concerns about privacy of micro data limit the use of CIS data by managers. So- mewhat differently, the lack of investments in data linkages from CIS data with other data sources limits the use outside the conventional realm of industrial policy. This is a typical chicken-and-egg situation: there is little demand for data because there is little supply of data. The actual scope of CIS is also very much determined by the definition of the core concepts. The devil is in the detail here. Seemingly minor changes to the definitions could already give rise to substantial shifts in the scope of the use of the data. Ever since its conception in 1992 CIS has seen quite some changes in this respect. Academic researchers were the key drivers for the first efforts to measure innovation. This has has a strong influence on the first edition of CIS and the underlying Oslo Manual (Arundel & Smith, 2013). Academics have a strong interest in research that can provide predictive and causal interpretations of the effects of innovation, which requires linking innovation data to longitudinal data for variables such as value-added, employment, productivity and user/stakeholder satisfaction, as well as obtai- ning innovation data through longitudinal surveys. Up until fairly recently

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