Documentation of the IDA-DISKO Database - CiteSeerX

Documentation of the IDA-DISKO Database Toke Reichstein & Anker Lund Vinding Department of Business Studies - IKE Group Aalborg University [email protected] & [email protected] http//:www.business.auc.dk/˜tr/ & http//:www.business.auc.dk/˜alv/

July 23, 2003

Abstract The following pages are a documentation of the IDA-DISKO data located at the Danish Statistical Bureau in Aarhus/Copenhagen. The pages are thought as guidance to the variables in the database as well as the use of the formed data files. As will become apparent in this document, the datasets are formed using four different sources. The data as well as some statistical regularities are presented.

Contents 1 Introduction

2

2 Construction of Data Files - Background Materials and Output

2

3 The Data - Structure and Important Considerations

9

4 The Programming

10

5 Written and Planned Papers Using This Data

13

Appendix A - Questionnaires of the DISKO Surveys

i

Appendix B - Respons Rates of the DISKO Surveys

xxviii

Appendix C - Variable List of the Datasets

xxxvii

Keywords: IDA, DISKO, PIE, Accounting Statistics, Databases




The data sets described was constructed by Peter Nielsen, Toke Reichstein and Anker Lund Vinding. They are of cause responsible for any errors that may exist. Any error that may appear in this manual is the responsibility of the authors alone.

1

1 Introduction The purpose of these few pages is to describe a number of related datasets at the firm level as well as the level of each employees employed in these firms. Although the datasets are restrictedin access, the basic idea is to enable interested researchers to do not only simple firm statistical analysis in the traditional sense, but also a statistical analysis which has ’time’ as a core issue in doing industrial and microeconomic statistical analysis. This is in accordance with the Austrian and Schumpeterian school of thought which was the point of departure in the DISKO and PIE1 research projects. Technical and organizational change is the main variables of interest in the survey based materials from these projects. It should be noted though that some of the datasets described here are used in these projects and readers are encouraged to take a look at thses publikations to get a more in-debt understanding of the structure of the datasets as well as get a feeling with the many possibilities the dataset brings out. In the following pages it will become fairly apparent to the reader that many more variables are present than the ones just shortly referred to above. As technical and organizational change are issues central to economists doing research in industrial economics, many other rather uncommon variables are also included that may prove important. Uncommon in the sense that they are very rarely seen in statistical data. Many of these variables that could be of interest to researcher of industrial dynamics or business management as well as labour market economists are in some sense present in the data sets presented in this document. The Document is structured as follows. Section 2 describes the Databases used in constructing the dat sets presented in the paper. Also the construction method as well as the outcome is presented. Section 3 presents some key variables as well as the distribution of the observations. The general structure of data is shortly presented. Section 4 presents an example as to how one might go about programming and combining the data. Section 5 concerns the already existing literature using this data as well as planned publications. Additionally an appendix is added with the two questionnaires referred to in the document (see appendix A). Tables of dataset contents are also included (see table C-1 and C-2). The appendix hopefully give the reader an understanding of how one may use the data besides the few examples given throughout the text.

2 Construction of Data Files - Background Materials and Output The data sets covered by this document are constructed by using four separate, but compatible, databases. These databases are: DISKO - A Questionnaire Survey concerned with technological and organizational changes. PIE - A second questionnaire following similar ideas as those present in DISKO but with additional features included. IDA & FIDA - Integrated Database for Labour Market Research Accounting Statistics. 1 Sometimes

the PIE project has been refered to as DISKO. The questionaires and research agendas are fairly similar to each other.

2

The DISKO Data DISKO is a SAS database which combines survey data and register data. 2 It is designed to analyse organizational and technical change in private business firms. Data collection took place during April-June 1996 by postal questionnaire followed up by telephone interviews with those who didn’t respond by post. The respondents are primarily high-level executives. The survey data covers first, changes within the period of 1994-96 regarding issues such as major organizational change, training and education, demand for qualifications, work organisation principles, content of work tasks, intensity of competition, the effort of developing qualifications, technical and market innovation and relations with important external actors. Second, questions which covers state of things in 1996; demand for qualifications, work organisation principles and the effort of developing qualifications.3 The data contains private business sector - traditional manufacturing and construction and service sector which can be disaggregated into 117 industries or 7 industrial clusters (the so called resource areas). The data set contains data from 1900 firms. The original sample was 3993 which leaves us with a response rate at 48%. The cut-off point was 20 in manufacturing and 10 in other sectors. 4 Besides the questionnaire, the dataset contains firm level register data - accounting data and IDA data. Basically the data set is relevant for exploring multiple number and types of research questions, including labour market research, intra-organizational research, industrial dynamics, innovation based analysis etc. 5 The PIE Data PIE is basically a follow-up on DISKO.6 It is survey data addressed to all firms in the private sector with 25 or more employees, supplemented with a stratified proportional sample of firms with 20-25 employees. The survey was carried out in the winter of 2001 by postal questionnaire followed up by telephone interviews with those who didn’t respond by post. The respondents are again primarily high-level executives. As shown below, PIE contains many of the same variables as those found in DISKO. But several additional variables have been added. The survey was carried out in 2001 as part of the LOK-Project.7 The data collected was done in collaboration with Center for Labour Market Analysis at Aalborg University. The general aim of the data collection was to have a data set that covered some of the same firms as those found in the DISKO survey. It was possible to find about 1363 of these in the statistics. As to make sure the sample drawn from the population would be big enough additional responses was included. Apart from asking the firm white collars as a representative for the firm, blue collar employees was also asked to give comments on many of the same questions as well as some additional ones (an IRApproach).8 The questions posed to the management of the firms covered issues on organisation and management, internal and external collaboration, use and management of personnel, competence requirements and – development, the labour market, produc2 See

appendix A page ii for the questionnaire DRUID WP 96-16 & 96-17 for details 4 Respons rates at a more disaggregate level may be seen in Appandix B on page xxix. 5 Further information on the dataset may be found on http://www.druid.dk/databases/disko1.pdf. 6 See appendix A page xiv for the questionnaire. 7 For an introduction to PIE see http://www.business.auc.dk/pie/. In this research longitudinal studies are carried out of specific product development in 11 firms. The aim of the project is to combine these qualitative studies with quantitative generalisations. 8 the questionnaire for the employee representative about organisation, employee skills and development of new products may be send on request. 3 See

3

tion with reference to conditions and results as well as innovation. The questionnaire send to the representative employee of the same firms covered questions relating to issues as organisation and management, internal collaboration, competence requirements and – development, participation in innovation and finally use, planning and management of personnel. The purpose by incorporating representatives from the employees was to highlight the issues from both sides in order to get a better perspective. Two samples were carried out. The first sample consisted of 4078 firms and included 1363 of the DISKO firms which were present in 1998. All firms with more than 100 employees at the upper end and firms with at least 20 employees at the lower end were selected. However, the number of responses was not satisfying. For that reason a second sample with 2897 firms representing the rest of the firms with more than 25 to 100 employees were carried out. The response rates for the first sample was 38% and for the second sample 16%. The total sample of the survey was 6975 firms. 2007 firms responded to the survey from management and 473 responses form employee representatives.9 This makes the overall response rate of the survey 29% which is not satisfying. However, a closer response analysis broken down on industries and size show acceptable variations on response rates. Moreover, in order to achieve information of non-response, 145 firms who have participated in DISKO 1 were interviewed by phone. Although the responses from the non-response are biased compared to the firms who have participated in the survey, the number of firms in the non-response category is too few in order to calculate weights for the non-response. By ignoring non-response answers, the PIE survey do not indicate unacceptable bias.10 Hence, in the stratifying of the firms, specific criteria had to be met. These can be summarised as: 1. Still existing firms from DISKO Survey - 1369 firms 2. Additional firm including all firms with more than 100 employees 3. Firms who meet criteria 1 and 2 and who respond to a large survey from autumn 2000 on the use of ICT 4. As the criteria 1-3 may cause the sample to be skewed, a selection of firms were included intended to adjust the sampling to the total population 5. In order to achieve a reasonable number of answers for the questionnaire presented to the management, the representatives of the employees and answers which contain firms who have answered the questionnaire to both the management and the employee representatives, additional firms including all firms between 25 and 100 employees were selected. Thus, all firms with more than 25 employees were selected. To take the highly possible skewness in the data set compared to the population into account, weights have been implemented. These have been calculated on the grounds of firm size and sectoral distribution. To the PIE survey data from Danish integrated database of labour market research (IDA) and the register of business data from 1998, 1999 and 2000, the latter updated when available, is merged together with the PIE survey. 9 The reason for a relatively low number of answers regarding employee representatives is due to the fact that the management of the firm has to approve participation of the employee representative 10 Further information on respons rates at a more disaggregate level may be seen in Appendix B on page xxx.

4

The panel data (DISKO and PIE) One of the purposes of launching PIE was establishment of panel data for longitudinal analysis. Of those 1369 firms who have survived from the DISKO survey, 637 or 47% participated in PIE. For these firms it may be interesting to emphasize the questions which are more or less similar for both surveys. These are: q. 27 in DISKO and q. 2 in PIE concerning subcontractor relationship q. 21 in DISKO and q. 5 in PIE concerning organizational changes q. 2 in DISKO and q. 6 in PIE concerning objectives of org. changes q. 3 in DISKO and q. 7 in PIE concerning employee education due to org. changes q. 6 in DISKO and q. 8 in PIE concerning ways of organising work q. 9 in DISKO and q. 10 in PIE concerning factors furthered/hampered org. development q. 4 in DISKO and q. 11 in PIE concerning changes in management q. 5 in DISKO and q. 12 in PIE concerning organising and follow-up upon work done by employees q. 12 in DISKO and q. 17 in PIE concerning possibilities to ensure personal resources q. 15 in DISKO and q. 26 in PIE concerning how to secure of development of employees skills q. 14 in DISKO and q. 28 in PIE concerning competitiveness and employees development of skills q. 20 in DISKO and q. 35 in PIE concerning introduction of product innovation q. 21 in DISKO and q. 36 in PIE concerning market location of the new product innovation q. 25 in DISKO and q. 41 in PIE concerning pressure of competitiveness q. 26 in DISKO and q. 42 in PIE concerning development of closer relationship with external actors q. 23 in DISKO and q. 44 in PIE concerning implementation of ICT The IDA & FIDA Data Two databases concerning register data on each individual employee employed in the firms (IDA) and register data on firm accounting (FIDA) has been merged together for all three surveys mentioned above. For DISKO these data goes from 1990-1997 but the number of firms decreases to 1544 if the firm has to be included in the whole period. For PIE register data for 1998-99 are included and finally the panel data of 637 firms included register data for 1990-1999. IDA is a all-inclusive longitudinal and integrated database which includes both establishments and employees. It is foremost useful in relation to labour market analysis. 5

but as it also has variables concerning the dynamics of firm it is well linked to industrial features and hence useful in relation to industrial structural change. Mainly it is used to analyse the flows of labour as it contains data on for instance transition of the labour market states. 20 variables have been implemented from IDA & FIDA.11 These are: Yearly full time employees The Individuals A- income during leave of absence due to illness Age of the individual Does the Individual leave one work place for another the following year Have the Individual left one work place for another the past year The degree of unemployment during the year The Individuals B-income during leave of absence due to illness The work experience of the individual The Highest degree of education of the individuals Changes in the identity of the workplace The title of the individual as a municipality employee The sex of the individual Has the individual participated in AMU-courses (vocational training) Periods of unemployment of the individual Salaries for the individual The type of the job the individual posses Primary work title of the individual Citizenship for the individual An aggregation of degrees of unemployment from 1980 The hourly wage rate for the individual The quality of the valuation of the individuals hourly wage rate Address code of the educational institution to which the individual is associate 1/10-1980 Geographic code of the educational institution 11 To see a more detailed

description of the database see http://www.druid.dk/databases/ida.pdf.

6

Accounting Data Accounting data is yearly distributed files covering data on firm specific data. As many of these variables already was included in the DISKO database up till 1994 by contract, the files was not necessary before 1995. As will become apparent in subsequent sections we have updated data until 1999. The data is distributed by The Danish Bureau of Statistics. Among the variables are Rate of return on investments Fixed assets Other fixtures and fittings, tools and equipment Capital and reserves Yield on equity ownership Outside financing Yield on outside financing Export volume Investments in plant and machinery Rate of investment Expenses for raw materials and consumables Staff costs Profit/loss before tax Turnover Production value Total assets Value added No. of man years employed No. of employees Danish industry classification NACE classification, 6 digits Classification by county

7

Raw Firm Data sets

Created Firm Data sets

Created Person Data sets

Raw Person Data sets

DISKO 1

DI19094

PE19094

FIRMA9094

DI19097

PE19097

IDA9094

FIRMA9597

DI19099

PE19099

IDA9597

FIRMA9899

DI29899

PE29899

IDA9899

DISKO II

DI129099

PE129099

Figure 1: Graphical presentation of the formation of the datasets The Combination of the Five The combination of the five databases gives us a wide range of possibilities of combining data at different levels of aggregation. IDA has observations on both the workplace and the individual level. Both DISKO and Accounting Statistics are constructed on the firm level. The years in question are 1990 to 1999. The databases are combined by using the firm registration codes, which is present or has been implemented in all four databases. Figure 2 illustrates graphically the formation of the datasets. In each side the raw data is presented. The lines with arrows illustrates how the data is combined in forming new datasets. As can be seen the process is rather cumulative in the sense that newly formed datasets often is used in additional data sets. Dividing the data sets up into two groups we may refer to either firm based data sets (left side) and employee based data sets (right side). The firm based are the two DISKO datasets with survey data and Accounting data (register data) from 1990 to 1999. The Accounting data has been divided up into three boxes (1990-94, 1995-97 and 199899) as they were used at three different points in time. As can be seen the firm based data sets were used to construct four datasets in which the unit of counting is the firm and the data covers different years and different combinations of the questionnaires of DISKO and PIE. On the right hand side we see a similar pattern. Here we have three different boxes with data sets from IDA (1990-94, 1995-97 and 1998-99). They are used in constructing the employee based datasets. As can be seen from the two middle rows of figure 2 the result of the database combinations are eight separate data files:

8

The original DISKO with accounting data from 1990-94 as well as some IDA work place variables (Filename: DIS11900)(Number of observations: 1900/1610) A employee based dataset constructed using the original DISKO data as well as IDA data 1990-94 (Filename: PER9094)(In total 246146 employees has partly or throughout the whole period been employed in at least on of the 1610 participating firms) The DIS1900 dataset updated to 1997 both with respect to accounting data as well as workplace data (Filename: DIS11544)(number of observations:1544) A employee based dataset constructed by using DISKO11544 and PER9094 as well as IDA data from 1995-97 (Filename: PER9097)(In total 315795 employees has partly or throughout the whole period been employed in at least on of the 1544 participating firms) A firm based dataset constructed using the PIE survey and firm data for 1998-99 (Filename: DIS22738)(Number of observations: 2738) A employee based data set constructed using DIS22738 and IDA data for 199899 (Filename:PER9899)(In total 271154 employees has partly or throughout the whole period been employed in at least on of the 2738 participating firms) A firm based dataset combining the DISKO survey (DIS11544) and the PIE survey (DIS22738). The overlap between the two are combined and forms a dataset covering 1990-99 (Filename: PANEL637)(Number of observations: 637) A employee based data set that is based on the 637 observations of PANEL637 and employee data from PER9097 and PER9899 (Filename: PP9099)(In total 151873 employees has partly or throughout the whole period been employed in at least on of the 637 participating firms) The general reason for using the firm based datasets in creating the employee based datasets are of cause to make sure the specific employees who are associated with these firms are drawn out and kept in the dataset. Every firm based datasets therefore have a corresponding employee dataset that combined may be used to describe several key structural issues in the specific situations. The employees present say in PER9899 correspond to the ones working in the firms which is in DIS22738. The reason for the lower number of firms in DIS11544 than in DIS11900 (366 less) is simply due to the non-existence of the firm in the statistics. This may be due to several factors, but one of these is surely exits from the market between 1994 and 1997.

3 The Data - Structure and Important Considerations As the two DISKO surveys are the ’dominating’ we should put some extra attention on these. It is the original 1900 firms of the DISKO survey that has been drawn from the databases. The DISKO data was collected in 1996 as a part of the DISKO-project (DISKO is a Danish Acronym for ’The Danish Innovation System - A Comparative Analysis). As some of the firms were not present in the IDA database only 1610 firms are covered in the employee data. Since 1996 some of the firms analysed has had to exit 9

their markets for some unknown reasons. As a consequence the two databases holds statistics for only 1544 firms. This causes the workplace combination to have 1544 observations each year and the individual combination to have 315795 observations over the 8 year period. While the 1544 firms are the same from year to year, the individuals included may change from year to year. Table 1: Number of individuals in the employee based data sets Year 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999

PER9094 142745 143290 141413 138019 145469 . . . . .

PER9097 138974 139374 137475 133983 141677 141657 139459 140939 . .

PER9899 . . . . . . . . 223717 222265

PP9099

63006 61001 62696 57199 56277

Table 1 shows how many individuals there are each year. The number of employees in the 1544 firms varies from approximately 134000 to just about 145000 which is approximately 17% of the workforce in the population of DISKO firms. The changes in the number of employees, illustrated in the bar chart (figure 2), should correspond to the general business cycles of the economy. This is by no means the case. Even though the Danish unemployment rate was at its peak in the year 1993 as you would expect looking at the bars, the other deviations does not follow the general deviations. The most appealing explanation for this surprising result is that we have not either exit firm nor entry firms as a part of the database. In the case of the 1993 unemployment peak of the danish economy we would expect an even larger fluctuations in the number of employees in the scedule if we had not required the firms to be existing throughout the timespans of the data sets. Also we could argue that the sample isn’t large enough as to give a clear-cut picture of the economy as a whole without using different weights in the calculations. this could correct some of the ’strange’ pattern in the depiction of employment deviations.

4 The Programming The files are both located on the DST intra net. The path to which the files are associ˚ ated is /raid2/701855/GEM/ at the Arhus terminals while it is /701855/data/ at an external terminal logging on to the DST server called Sonny. In the following ex˚ ample we are showing it as though the user is sitting in Arhus. Please do remember that UNIX environments distinguishes between small and capital letters. Therefore you must make sure that you are using the appropriate small letters. In the use of the data files you need to open SAS (Statistical Analysis System). By 10

Figure 2: Deviations From Last Year Change

7694 7450

8000 6000 4000

1695 1480

2000 545 400

-20

0 -2000 -4000

-922 -1452

-1877 -1899

-2005 -2198

-3394 -3492 -5497

-6000 -8000

Year (1990-1999) using the programming shown here you will be able to refer to the specific variables of interest directly. The firm specific data; libname data ’/raid2/701855/GEM’; data x; set data.panel637 (keep=[....]); The person specific data; libname data ’/raid2/701855/disko/GEM’; data x; set data.pp9099 (keep=[....]); The first line gives the computer information concerning the specific location of the data files. The second line creates a work dataset. The third line refers to the specific file of which the work data set x will be created. In the first example it is the program line you should use if you want to use the firm specific data. In the second example it is the program lines you should use if you want to make an analysis on the person aggregation level. By implementing the keep statement in the ordinary brackets you are able just to draw out the variables you are interested in. These should be written instead of the square brackets and dots. It would be a good idea to use this statement as it will shorten the run-time of the program you are about to submit. After these lines you may write your own program depending on the things you are interested in. If you for example wish to analyse the dynamics of the work patterns of the individuals with respect to some of the DISKO questions it is necessary to combine the two 11

dataset. Even though the variables that refers to the DISKO questionnaire are present in the Person2 dataset they will not be directly usable as they refer to the firm the individuals are employed in 1997. Therefore it is required to use both datasets if a dynamic picture are in question. The following program lines are an example of how you can make an analysis over time using both datasets. dm ’clear log; clear out’; libname sasdata ’/raid2/701855/GEM’; options pagesize=70 linesize=78; data test; set sasdata.pp9099 (keep=antnov90 antnov91 antnov92 antnov93 antnov 94 antnov95 antnov96 antnov97 lbnrr); Proc sort; by lbnrr; run; proc univariate noprint data=test; var antnov90 antnov91 antnov92 antnov93 antnov 94 antnov95 antnov96 antnov97; output n=ant90 n=ant91 n=ant92 n=ant93 n=ant94 n=ant95 n=ant96 n=ant97 out=test1; by lbnrr; run; data test2; set sasdata.panel637 (keep=fleks lbnrr); proc format; value fleks 1=’static’ 2=’flexible’ 3=’Innovative’ 4=’Dynamic’; run; data test3; merge test1 test2; by lbnrr; run; proc tabulate format=8.0; title ’number of employees in november’; class fleks; var ant90 ant91 ant92 ant93 ant94 ant95 ant96 ant97; format fleks fleks.; label ant90=’employ. nov90’ ant90=’employ. nov91’ ant90=’employ. nov92’ ant90=’employ. nov93’ ant90=’employ. nov94’ ant90=’employ. nov95’ 12

ant90=’employ. nov96’ ant90=’employ. nov97’ fleks=’firm characteristic’; keylabel all=’Total’ sum=’Sum’; table fleks all, (ant90*(sum)), (ant91*(sum)), (ant92*(sum)), (ant93*(sum)), (ant94*(sum)), (ant95*(sum)), (ant96*(sum)), (ant97*(sum)) /rts=20; run; quit; The first three lines are standard lines making sure the SAS LOG- and OUTPUT window are cleared, the libname is correctly specified to the path in which the data are located and the size of the lines and the page in the OUTPUT window is set to a appropriate value. The following ten lines are the core of the individual analysis. It refers to the person2.sd01 file which is the dataset containing information for each individual. The observations are sorted with respect to the firm in which they are employed. Finally the PROC UNIVARIATE procedure counts the number of observations in each firm (number of employees) and spits out a figure for each year. It is the ANTNOV variable that is used in the counting process. The figures are saved in the test1 dataset. Notice that we only keep the variables that are necessary in the processes. This limits the run-time of the program. The next section of the program is on the firm level. In this example we only need the flexibility indicator drawn from the DISKO analysis and the firm specific number. A format is attached to the different values the fleks variable may take. Now we are ready to put the two datasets together. This is done using the MERGE procedure. In order to be sure that the values in the test1 dataset are attached to the right observations in the test2 dataset, we merge by the firm specific number (lbnrr). Finally we are ready to make the analysis in question. We are going to analyse how many people there are employed in the four different kinds of firms from 19901997. This is done using the TABULATE procedure. Leaving aside the labelling, the formats and the title we can see that the TABULATE procedure counts the number of observations of each class. In this case the flexibility index is used as a class variable. These are summed up and used in the table as such. The result is a table showing how many employees there were in each year in each of the four different kinds of firms (Static, Flexible, Innovative and Dynamic). We have made an analysis of the dynamics of employees across time across firm characteristics.

5 Written and Planned Papers Using This Data As to give the reader a feeling with the literature already written using this data we have here listed some of the publications. Apart from these we have pointed out some for the planned publications. Apart from given the reader a general overview of the possibilities hidden in the data, it might also bring forth new ideas and perspectives as to further work. ’Finished’ Papers: 13

Gjerding, A.N. (1996), Organisational Innovation in the Danish Private Business Sector, DRUID WP No. 96-16, Aalborg, Department of Business Studies, Aalborg University Gjerding, A.N. (1997), Udvikling i nordjysk virksomheders konkurrenceevne (The Development of Competitiveness in North Jutland Firms), NEP Publication No. 3, Aalborg, Center for International Studies, Aalborg University Gjerding A.N. (1998), International Competitiveness by ’orgfensive’ Means, International Business Economics WP No. 26, Aalborg: Center for International Studies, Aalborg University Gjerding A.N. et al. (1997), Den Flexible Virksomhed (The Flexible Firm), Kbenhavn, Erhvervsudviklingsrdet Joergensen, K.M. (1998), Information Technology and Change in Danish Organizations - Results from a Survey, DRUID WP No. 98-8, Aalborg: Department of Business Studies, Aalborg University Laursen, K. and N.J. Foss (forthcoming), ’New HRM Practices, Complementarities, and the Impact on Innovation Performance’, Cambridge Journal of Economics. Laursen, K. (forthcoming, 2002), ’The Importance of Sectoral Differences in the Application of Complementary HRM Practices for Innovation Performance’, International Journal of the Economics of Business, Vol. 9(1). Laursen, K. and V. Mahnke (2001), ’Knowledge strategies, firm types, and complementarity in human-resource practices’, Journal of Management and Governance, Vol. 5(1), pp. 1-27. Lund, R & Gjerding, A.N. (1996), The Flexible Company, Innovation, Work Organization and Human Resource Management, DRUID WP No. 96-17, Aalborg: Department of Business Studies, Aalborg University Lundvall, B.-., Christensen, Jesper L. (2002), Broadening the analysis of innovation systems competition, organizational change and employment dynamics in the Danish system, forthcoming in Conceicao, P., Heitor, M., Lundvall, B.-. (eds.), Innovation, competence building, and social cohesion in Europe, Edward Elgar. Lundvall, B.-. and Nielsen, P., ’Competition and transformation in the learning economy - illustrated by the Danish case’, Revue d’Economie Industrielle, No.88, 1999, pp.67-90. ˚ & Kristensen, F.S. (1997), Organisational change, Innovation Lundvall, B.-A. and Human Resource Development as a Response to Increased Competition, DRUID WP No. 97-16, Aalborg: Department of Business Studies, Aalborg University Planned Papers:

14

Knowledge creation, Learning Organizations and industrial relations. How do firms select and combine organizational standards or dimensions framing probability of innovative behaviour? Building knowledge organisations, in which way does indirect and/or direct employee participation matter? How important are workplace industrial relations in modern work organisations? Persistence of Product Innovation. To what degree does persistence in innovative activities exists and what characterise these firms in relation to internal competencies (formal competencies and work experience), patterns of external interaction and Organisational setting? Absorptive capacity and Innovative Performance. What does a broader definition of absorptive capacity (social capital and absolute absorptive capacity)tell us in terms of innovative performance. Firms and knowledge Institutions: The Innovative Potential in Small Firms and Low-tech Sectors. Are academics and their relation to knowledge institutions and universities in particular conducive to development of less imitative product innovations? Knowledge and Technology Structures of Exit Firms: It is the intension to find some empirical regularities considering those firms that we may consider as exit firms in the sense they have left the market. Technological perspectives and general educational level in the firms are specifically in focus. Persistence of Firm Performance in Terms of Productivity: We test the hypothesis that there is a considerable persistence in the productivity level of firms. What is the probability that best-practise firms at present time also are best-practise in the future. Firm Growth and Product Development: We test the hypothesis that firms that may be categorised as product innovative also seems to be those with the highest growth rates. We specifically highlight the double causality by employing a simultaneous regression analysis.

15

Appendix A Questionnaires associated with the DISKO Surveys. DISKO 1 Questionnaire from page ii. DISKO 2 Questionnaire from page xiv.

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No

Don’t know

52,1

46,7

1,3

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Some extent

Small extent

Not at all

Don’t know

a. The effectiveness of the daily work

63,3

29,2

2,4

2,5

2,5

b. Co-operation and coordination across the organisation

50,5

32,7

6,5

5,5

4,7

c. The ability to adapt to more turbulent surroundings

49,2

31

10,3

4,9

4,6

d. The ability continuously to develop new products/services

28,9

36,4

17,4

11,5

5,9

e. The ability continuously to strengthen and renew knowledge and know-how

27,7

37,8

17,3

11,3

6,0

f. Other objectives

14,7

9,9

4,4

15,5

55,4

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43,6

55,8

0,6

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Mark with X. More answers are allowed

The employee her/himself

Supervisor/ Middle manager

Top management

a. Daily planning of work

50,3

38,5

10,4

b. Weekly planning of work

27,9

54,6

15,2

c. Follow-up upon working tasks

21,5

57,7

18,8

d. New working areas

10,5

34,6

51,8

iii

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Mark with X

Yes

No Kindly mark how many of the firm’s employees who are covered Below 25%

25-50%

Above 50%

Don’t know

a. Interdisciplinary workgroups

45,2

27,4

13

9,2

5,1

b. Quality circles/groups

54,9

19,1

9,0

9,9

7,2

7,3

19

8,0

c. Systems for the collection of proposals from employees (not quality circles/groups)

47,6

18,1

d.Planned job rotation

58,3

22,2

7,1

6,6

5,7

e.Delegation of responsibility

11,6

22,3

23,3

39,5

3,3

f.Integration of functions (e.g. sales, production/service, finance)

34,7

29,4

14,4

13,2

8,3

g. Performance related pay (not piece work)

54,3

16,4

7,0

15,6

6,3

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