The Data

At the start of the PhD project, a report from the Norwegian research council (NRC) was released, which argued that what appeared to be lacking in the study of geography in Norway is “research that makes use of more comprehensive

empirical data covering a wider range of regions and localities in Norway and that may be able to generate input for the more general debate on regional and urban development and policy” (NRC, 2011, p. 67). Based on this, the general research design that was chosen was to take advantage of comprehensive empirical data that cover a wide range of regions and localities in Norway. The four different papers take advantage of four distinct sources of data. This provides a platform for studying innovation, space, and diversity from different angles and at the same time holds the benefits of using different and unique data. It also generates input on the more general debate on regional development and policy in Norway. This provided an important backdrop for designing the study and important design consideration in this study of innovation, space, and diversity. It furthermore strengthens the thesis as it offers a broader platform on which to draw conclusions. All of the individual papers are based on data within the period 2000-2013. Thus, the data are relatively new and shed “up-to-date” light on these important aspects of innovation in Norwegian firms.

This thesis is concerned with innovation in private firms. In Norway, like in many other advanced economies in the developed world, it is most common that private firms undertake the role of commercialization of innovation (Fagerberg, Mowery, and Verspagen 2009).

Papers I and III use Linked Employer Employee Data (LEED) that encompasses information on all employees and all employers in Norwegian private firms. These are gathered annually by Statistics Norway and it is a unique and large database that provides many interesting opportunities to study the individual characteristics (LEED tracks individuals’ career paths) in relation to firm performance. The benefits of using these large datasets besides its reliability are many, one being that we may generalize from the results to a larger degree than with smaller samples. Since these data are gathered annually for all firms, we do not have to deal with sample selection bias to the same degree as in more narrow designs.

The LEED is then merged with the Community Innovation Survey (CIS).¹⁷ This merge was made by Statistics Norway by using unique identifiers for firms and

17The Norwegian Innovation Survey is conducted biannually in combination with the business enterprise Research & Development (R&D) survey. It is carried out as part of the pan-European

employees ensuring individual and firm anonymity. The CIS gives important information about the Norwegian business enterprise sector and “provides core indicators on Norwegian enterprises’ ability to face transitions, introduce new products and processes, and create growth” (Wilhelmsen 2012, 3). Smith (2005, 148) argues that the CIS has taken up the challenge pointed out by e.g. Arrow (1984, 51) and Griliches (1987, 824) that “far too little fresh economic data is collected” and that ”too much juice” has been made based on old data that were collected for different purposes than those they are then used for. Smith refers to the CIS as the most important development within new survey-based indicators.

It is mandatory for Norwegian firms to participate in this survey, and prior CIS surveys in Norway have not shown any indication of non-response bias.

Wilhelmsen (2012, 9) demonstrates that the response rate has been very high for the CIS survey in Norway, “~95 percent or more, and there does not seem to be any clear pattern amongst non-respondents”. A methodological issue related to this CIS dataset is that innovation activity is self-reported. This could lead to measurement bias, and to give an example, firms that do not wish to spend a lot of time answering the questionnaire could under-report their innovation activity, which in turn corresponds to them having to answer fewer questions. On the other hand, people could overestimate their own innovation activity, which could lead to a higher number of innovations being reported than what is actually the case.

Paper II builds on firm-level data from a survey of 533 Norwegian firms, gathered in 2013. The survey was developed by the authors, drawing on indicators from the Community Innovation Survey (CIS), in particular for the dependent variables. The data have been combined with firm-level register data on firm size and industry classification.¹⁸ The survey was conducted in two stages: first, through a telephone interview, in which 2002 firms participated.

These firms were sampled from a larger population of all firms with more than ten employees registered in the Norwegian Register of Business Enterprises

Community Innovation Survey (CIS) that is coordinated by Eurostat (the EU statistics agency).

The survey is based on Guidelines for collecting and interpreting innovation data, colloquially called the Oslo Manual (Wilhelmsen 2012, 3).

18 Nomenclature generale des Activites economoniques dans les Communautes europeennes (NACE).

according to quotas for five different regions: Oslo (500 firms), Stavanger (350), Bergen (300), Trondheim (250), and the rest of Norway (600). The “rest of Norway” category responds to peripheral regions in Norway. Referring back to the gap identified by the Norwegian Research Council of few quantitative studies of the geography and development in Norway, this was part of the backdrop in forming this study aiming at involving firms in both core and peripheral regions. The overall response rate for the telephone survey was 20 percent. During the telephone interview, respondents were invited to fill in a follow-up web questionnaire containing further questions, which 533 managers did. The dependent variables on innovation, collaboration, and the organization of innovation processes are all drawn from the telephone interviews, as are several of the control variables, while the data on foreign workers are based on the web questionnaire. Consequently, we limited the study to the firms that participated in both stages of the survey for the models concerned with foreign workers, while the association between international networking and innovation is analyzed on the full sample of 2002 firms. There could be a potential bias of the ones answering the web survey as being more innovative, and that is a trend that we also observed. This could then be considered somewhat of a sample selection bias, and that is also why we ran models following Heckman (1979).

Paper IV uses data that have been gathered by Brønnøysundregistrene, which develops and operates many of Norway’s most important registers for companies and organizations. Firms are obliged by law to provide audited balance sheet and profit/loss statements to the Brønnøysund Register Centre (Sasson and Blomgren 2011). In addition to the balance sheets and the financial declarations (including profit and loss statements), the data set comprises information e.g. year of establishment, geographical location, and industrial affiliation (Sasson and Blomgren 2011, 127). The data used were from 2000-2009. There are many benefits from using these data; firstly, they are based on

“hard facts” and “real” numbers, such as profit, loss statements, revenue, and number of employees. With these data, one does not have the same issues as with self-reported data as mentioned above. The source and the data are highly reliable, and it holds the benefit of using large sample data.

The research design set out to take advantage of different types of data (see table 3 for overall view). This has several benefits; firstly, the data used are

reliable by themselves, but also the weaknesses of one type of data are alleviated by the use of other data sources. Together, they provide a broader perspective on how different types of diversity and space affect different measures of innovation.

Paper Data Source Additional

Centre for Innovation Research, University of Stavanger.

Paper IV

Panel Data. 1500 firms within the Norwegian oil and gas industry (2000-2009).

Statistics Norway/Brønnøysund Register Centre. IRIS (c/o Atle Blomgren).

Table 3: Data sources used in the various paper.