Changes in Regional Firm Founding Activities: A Theoretical Explanation and Empirical Evidence (Routledge Studies in Global Competition)

Changes in Regional Firm Founding Activities

Much research has been dedicated recently to the analysis of entrepreneurial and start-up activities. This book concentrates on the medium- to long-term development of firm founding activity, asking if and how changes in industry start-up rates can be identified and explained. Fornahl’s aim is to develop a framework with which to focus on development and change in regional firm founding activities, using empirical evidence to analyse some processes responsible for this development. Much of the study in this area neglects considering possible changes in environmental circumstances when looking at such development, assuming a static context. Fornahl focuses in on this much overlooked area, using two research concepts: the first concentrates on changes in regional firm founding activities, looking at empirical evidence based on the analysis of 50 German regions, the second concentrates on positive examples or ‘role models’ that can lead to change in regional start-up activities, analysing their impact theoretically and empirically in the German town of Jena. This book will be of great interest to students engaged with change and development in entrepreneurial and regional start-up activities and the environmental impact of start up decisions. It will also be of interest to policy makers in this area. Dirk Fornahl is a Research Fellow at the Institute of Economic Policy Research at the University of Karlsruhe, Germany.

Routledge studies in global competition Edited by John Cantwell University of Reading, UK

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Changes in Regional Firm Founding Activities A theoretical explanation and empirical evidence

Dirk Fornahl

First published 2007 by Routledge 2 Park Square, Milton Park, Abingdon, Oxon OX14 4RN Simultaneously published in the USA and Canada by Routledge 270 Madison Ave, New York, NY 10016 Routledge is an imprint of the Taylor & Francis Group, an informa business This edition published in the Taylor & Francis e-Library, 2007. “To purchase your own copy of this or any of Taylor & Francis or Routledge’s collection of thousands of eBooks please go to www.eBookstore.tandf.co.uk.” © 2007 Dirk Fornahl All rights reserved. No part of this book may be reprinted or reproduced or utilized in any form or by any electronic, mechanical, or other means, now known or hereafter invented, including photocopying and recording, or in any information storage or retrieval system, without permission in writing from the publishers. British Library Cataloguing in Publication Data A catalogue record for this book is available from the British Library Library of Congress Cataloging in Publication Data A catalog record for this book has been requested ISBN 0-203-94678-2 Master e-book ISBN

ISBN10: 0-415-40409-6 (hbk) ISBN10: 0-203-94678-2 (ebk) ISBN13: 978-0-415-40409-9 (hbk) ISBN13: 978-0-203-94678-7 (ebk)

Contents

List of figures List of tables Acknowledgements

1

Introduction

ix xi xiii

1

1.1 Background and context 1 1.2 Structure of the study 6

2

Theoretical considerations on changes in regional firm founding activities

9

2.1 Regional factors influencing start-ups 12 2.2 Transformation and dynamics in the transmission process 20 2.3 Events and their impact on regional factors 33 2.4 Linking events, changing regional factors and founding activities 49

3

Empirical evidence

69

3.1 Background of the empirical study 70 3.2 Empirical analysis of changes in regional firm founding activities 80 3.3 How events change regional firm foundings – an empirical approach 107 3.4 Intermediate summary and discussion 123

4

Positive examples and their impact on regional start-up activities 4.1 (Shared) mental models and behavioural patterns 127 4.2 Dissemination of mental models in a regional context 134 4.3 Imitative behaviour and regional founding activities 140

126

viii

Contents 4.4 How entrepreneurial role models influence start-ups in Jena: a case study 154 4.5 Preliminary summary and discussion 168

5

Conclusions

173

5.1 Aim and structure of study 173 5.2 Summary of findings 174 5.3 Policy implications 179 5.4 Outlook 185 Appendices Notes References Index

187 243 251 264

Figures

1.1 2.1 2.2 2.3 2.4 2.5 2.6 2.7 2.8 2.9 2.10 2.11 2.12 2.13 3.1 3.2 3.3 3.4 3.5 3.6 3.7 3.8 3.9 3.10 3.11 3.12

Development of internet and e-commerce start-ups in Germany between 1991 and 2000 Structure of relationships between events, factors and start-up likelihood Stylized relationship between transformations and dynamics Linear relationship Limiting factor effect S-shaped Stepwise Delayed effect One example of the SF effect Diminishing effect S-shaped Bump Bump with contagion effect Switch Firm founding intensities in East and West Germany Average firm founding intensities in the 49 sample regions per year Absolute number of firm foundings in the sample regions and in Germany Firm founding intensity in the sample regions and in Germany Number of firm foundings in the 15 sample industries Share of sample industries in the total number of firm foundings in the sample regions Influence of different categories on (regional) firm founding activities Hypothetical example of the development of Pi,r,t and pi,r,t Calculation of mi,r,t Analysis of one hypothetical development of mi,r,t Assignment of levels in a hypothetical example Starting points of developments

4 10 22 23 23 25 26 27 28 30 30 31 31 36 74 76 77 78 79 79 81 83 84 87 89 104

x 4.1 4.2 4.3 4.4 4.5 4.6 4.7

Figures Structure of information processing and individual decision making Stage model of entrepreneurs Firm start-ups in Jena Distribution of founding decisions over time Influence of other regional founders on the own decision to start a firm Evaluation of years with regard to selected regional conditions: part 1 Evaluation of years with regard to selected regional conditions: part 2

128 145 157 158 161 163 164

Tables

2.1 2.2 2.3 2.4 2.5 2.6 2.7 2.8 2.9 2.10 2.11 2.12 2.13 2.14 3.1 3.2 3.3 3.4 3.5 3.6 3.7 3.8 3.9 3.10 3.11 3.12

Example for the suppressed-founding effect 29 Impact of changes in educational and research infrastructure on regional factors 35 Impact of financial support on regional factors 38 Impact of public demand on regional factors 39 Impact of additional infrastructure and support on regional factors 41 Impact of settlement of large firms on regional factors 41 Impact of establishment of small firms on regional factors 43 Impact of emergence of a localized cluster on regional factors 44 Impact of changes in the external environment on regional factors 46 Impact of changes in venture capital on regional factors 47 Impact of events on regional factors 50–51 Regional factors and transmission processes 57 Link between input curves, transmission processes and resulting founding dynamics 58–59 Developments of founding activities, events and transmission processes 62–66 Characterization of the identified developments 88 Level of changes 90 Classes for classification 91 Identified classes in the time series 96 Identified pure classes used in the following analysis 97 Direction of developments in the industries 98 Development classes in the different industries 98 Development classes in the different regions 100–101 Development classes in East and West Germany 102 Development classes in differently sized regions 102 Average length of the different developments 103 Summary of events and their impact on developments in start-up activities 118

xii 4.1 4.2 4.3 4.4 4.5 A.1 A.2 A.3 A.4 A.5 A.6 A.7 A.8 A.9 A.10 A.11 A.12

Tables Distribution and firm size of interviewed firms according to industry Relevance of factors for founding decision Means by which other firms influence new foundings Impact of other regional founders over time Foundings and founding decisions in relation to regional entrepreneurial climate Selected characteristics of sample regions Selected characteristics of industries Analysis of the industries in which short- to medium-term changes were identified Cross-tabulation of regions and industries with significant changes Number and types of changes according to industries Number and types of changes according to region Distribution of highest levels across industries Distribution of highest levels across regions Distribution of highest levels across regions and industries Distribution of relevant developments during the period under investigation Partners for interviews on reasons for changes in regional firm founding activities Partners for expert interviews: case study Jena

159 160 162 163 165 187–189 190 191 192–193 194 195 198 199 200–201 202 203 204

Acknowledgements

This book is based on my PhD thesis which was finished in 2005 and accepted by the economics faculty of the Friedrich Schiller University, Jena. I generated the idea of this project while working at the Max Planck Institute of Economics (MPI) in the Evolutionary Economics Group in Jena, and the long path to finish the dissertation was taken there. The work in this group provided a fruitful opportunity to meet many interesting people, to organize and attend conferences, as well as to exchange ideas with many colleagues. Thus, my thanks to all the persons at the Max Planck Institute and the Friedrich Schiller University who listened to my ideas and presentations and who provided valuable comments and suggestions. Several colleagues and friends from the MPI and from outside read pre-finished chapters of this dissertation and helped me to improve them considerably. These were: Tom Brökel, Christian Cordes, Jörg Jasper, Steffen Kolb, Veronika von Lintel, Franz Schaper, Silke Scheer, Jens-Peter Springmann, Georg von Wangenheim and Hagen Worch. Furthermore, many other members of the MPI – ranging from the administration and the library to the IT-service department and the cafeteria – supported my work and helped with all the problems that occurred again and again. The student research assistants at the MPI were probably afraid of me in the end because I consistently came up with new tasks for them. Thanks to all of you. In general, I always experienced the environment at the MPI as very productive and comfortable. Part of this book is based on interviews with many individuals in the region of Jena. Without their willingness to invest their time in answering my questions, this empirical work would not have been possible. Some of this empirical work was conducted in cooperation with Holger Graf of the Friedrich Schiller University. This joint work strongly increased the quality of both the questionnaire and the analysis. In particular, I have to and I want to thank Thomas Brenner and Ulrich Witt who supervised this PhD project. Without them overcoming the organizational and financial obstacles would have been much more complicated. Thomas Brenner was always willing to discuss new ideas, and he helped to reduce their vast amount and to put them into a convincing structure. The German Federal Ministry of Science and Education provided a two year funding for my research position. And Uwe Cantner from the Friedrich Schiller

xiv

Acknowledgements

University was, despite his tight schedule, willing to become the second referee of this dissertation. Besides the direct scientific input to the dissertation, I experienced additional personal support that was also crucial for the successful finishing of the project. Veronika von Lintel not only helped by comments on chapters of the dissertation but also was always critical with evolutionary economics. Thus, I had to prove her that it is worth following this track. Especially, Wiebke Timpe suffered from my dedication to the dissertation project which resulted in late working hours in the evenings or the weekends. Nevertheless, she always provided encouraging comments and the general emotional support to continue. Thanks for your patience and assistance. My parents and the rest of my family always listened to me when important decisions had to be taken; they gave me the freedom and encouragement to follow my own development path. Although I have to thank all these individuals and organizations that made it possible for me to finish the PhD project, this book is especially dedicated to my father, Dieter Naumann. Unfortunately, he was not able to see the final version of this book. Karlsruhe, 2007.

1

Introduction

Whether entrepreneurship increases or decreases in a society at any one particular moment in time depends on events and factors preceding it. What happens today sets the stage for the possibilities of tomorrow. What has occurred, what is occurring, what will occur, and why, can only be understood by studying the pattern of events of the past. (Gartner and Shane 1995: 298–299)

1.1 Background and context Recently, a growing share of economic research has been dedicated to the analysis of entrepreneurial and, especially, start-up activities. This study seeks to contribute to this literature by particularly focusing on ‘if’ and ‘how’ changes in regional industry-specific start-up rates can be identified and explained. This interest in understanding both new business formations in general, and specifically what factors influence their geographical distribution and changes, is theoretical and practical in nature. It reflects the fact that it has been increasingly recognized that these activities have a strong, positive impact on national and regional variables, such as the competitiveness and innovativeness of firms, the rate of unemployment and, thus, economic development in general (Audretsch and Fritsch 2003, Audretsch and Keilbach 2004). It has been found that a high rate of turbulence, meaning that many firms start up and many firms close, is positively related to the growth of the gross domestic product (Davidsson et al. 1994, Bednarzik 2000, Klomp and Thurik 1999). Furthermore, young firms have the highest increase in employment (Bednarzik 2000, Klomp and Thurik 1999).1 Moreover, empirical studies show that the regional distribution of entrepreneurial activities is uneven (e.g. Sternberg 2000, Bade and Nerlinger 2000, Fritsch and Niese 1999, Nerlinger 1998, Berger and Nerlinger 1997, Reynolds et al. 1994) and that start-up activities and changes in their intensity trigger the formation and development of localized industrial clusters (Saxenian 1994, Rosegrant and Lampe 1992, Brenner 2001, Fornahl and Menzel 2003). Several case studies in different European regions confirm this, e.g. Cambridge (Keeble et al. 1999), Oxford (Cooke 2001), Jena (Hendry et al. 2000) or Grenoble (DeBernardy

2

Introduction

1999). They show that spin-offs from existing firms or universities are important for the establishment of localized clusters and innovations in high-tech industries (Klepper and Sleeper 2002). These findings stress two aspects: first, they once more demonstrate the relevance of new business formation for development because localized industrial clusters have a positive impact on economic development. Second, they again point to the regional dimension in the analysis of start-up activities. This regional dimension encompasses two elements: • •

Start-ups mainly have an impact on their neighbouring geographic area. Certain regional variables influence the founding activities in the different regions.

These two factors together explain a large part of the variance in economic success between the regions.2 Some regions tend to predominate because they contain one of the aforementioned flourishing localized industrial clusters. Both the general impact of start-ups on economic development and the particular regional dimension, including the positive impact of successful clusters, have convinced policy makers to support (regional) start-up activities. This has been put forth, for example, by establishing chairs for entrepreneurship education at universities (Schmude 2002 analysed the impact of such chairs). Alternatively, they can assist cooperation between organizations to foster start-ups such as the German ‘EXIST promotion of university-based start-ups’ programme, whose effect was studied by Koschatzky (2003). Through such measures, policy makers try to improve regional economic development and imitate the success of other regions. In addition to such practical interests in firm foundings, a motivation exists to theoretically analyse and explain entrepreneurial behaviour and divergent regional development to gain new insights into these processes. A better understanding of why firms are started, why regions differ and how the relevant processes can be influenced helps facilitate the design of new political programmes that are aimed at increasing the number of entrepreneurs by fostering factors relevant for the start-up of firms. There are three broad strands of literature that address the issue of start-ups and entrepreneurship. These strands focus on the regional, industrial and the individual level, respectively. Factors that influence founding activities can be identified on all three levels. In the following three sections, we describe these levels, and based on them we develop the approach pursued in this study. Regional level Several studies point to the crucial role of the region for the start-up of new firms (e.g. Audretsch and Fritsch 1994, Reynolds 1994, Garofoli 1994, Keeble and Walker 1994, Steil 1997, Nerlinger 1998) and to certain locational factors relevant for start-ups or the location of firms in general (Weber 1909/1922, Salmen 2001). The central question in most research is which regional factors

Introduction

3

influence regional start-up activities. The identified local peculiarities cover a wide variety of areas ranging from demand side variables (such as growth in local population or local domestic product affecting, for example, the customer base or market size; Reynolds 1994) to supply side variables (such as the number of highly educated people or population density in general; Garofoli 1994). Additionally, the impacts of regional infrastructure (such as universities; Berger and Nerlinger 1997) or available financial capital base (such as local income; Audretsch and Fritsch 1994) are examples of factors that have been included in previous analyses. Existing regional studies have been conducted in one of three ways: first, several case studies analyse the impact of regional factors on start-up activities inside one specific region (e.g. Schätzl 1993, Fornahl and Graf 2003). Second, other investigations compare a number of case studies and the related development in the different nations or regions in regard to each other (e.g. Reynolds et al. 1994). These two approaches might lead to biased results because in most cases only successful regions with high start-up rates are analysed and compared to each other: a problem encountered in most case study research (Staber 1996, exceptions are Grabher 1993 or Seri 2003). A third kind of investigation uses a multivariate framework with many – if not all – regions of one country and many independent variables to explain the founding activities (e.g. Steil 1997, Nerlinger 1998). In these studies, the firm founding activity at a certain point in time or averages over a period of time serve as dependent variables. All these approaches have provided very detailed results shedding light on the question of how the observed disparities in the regional founding behaviour can be explained. However, the majority of studies assume a static context and leave aside possible changes of the regional conditions.3 Furthermore, they do not consider either short-term changes in the founding activities or switches of the founding activity from one level to another. In order to get a more profound understanding of the processes, we consider the results of the former studies and add some complementary elements that explain the dynamics of regional entrepreneurial behaviour. In order to do so, this approach shifts attention to the elements that have been left aside until now: the changes of regional conditions (such as human or financial capital) and related changes in the likelihood of regional firm foundings. The unit of analysis is, therefore, not the explanation of variations between founding activities in different regions, but rather we look at an inter-temporally increasing or decreasing likelihood of firm foundings in one region, possibly in the form of booms or collapses of start-up activities. Such an approach is complementary to the described empirical studies because it, indeed, examines firm founding activities on a regional level using some of the same factors as explanatory variables, but it also adds a dynamic perspective to the insights gained from the static approach of earlier studies. This approach is valuable with respect to both theoretical and political considerations: a better understanding of the processes that change the regional entrepreneurial activities yields additional knowledge about the reasons for regional differences in these activities. Furthermore, the factors that lead to these changes

4

Introduction

in founding activities can become the objectives for political interventions and support. This has two reasons: • •

Additional factors might be identified that can be supported by political measures to improve regional start-ups. The previously supported factors (such as infrastructure or general financial support) can be evaluated critically in the light of new findings.

Industrial level

Number of internet/e-commerce start-ups per year

On an industrial level, various case studies on industry life cycles have analysed founding dynamics (e.g. Klepper 1996, 1997). In most of these studies, the main variables influencing the development are market-, product- or technologicallybased factors (e.g. Utterback 1994). An example is a case study conducted by Krafft (2000) on the development of the information technology industry in Germany between 1991 and 2000. One aspect in this case study are the firm founding dynamics (Figure 1.1). Without discussing the factors relevant for this development, we can observe a strong increase in the number of foundings with an over-shooting effect and a subsequent decrease (after 1996/1997). Krafft puts forward several factors responsible for this influx, especially the growth in the relevant markets. Although he analyses the regional distribution of firm foundings and factors responsible for the locational decision of firms, he does not link the regional factors to the founding dynamics. Recently, several studies have extended this research by taking the regional dimension of these industrial dynamics into account. Especially the strong impact of spin-offs from successful firms are approved (Klepper and Sleeper 2002, Klepper and Bünstorf 2004). The present study goes in the same direction by focusing on regional variables, regional developments and changes of firm founding activities in selected 4,000 3,500 3,000 2,500 2,000 1,500 1,000 500 0 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 Year

Figure 1.1 Development of internet and e-commerce start-ups in Germany between 1991 and 2000. Source: Adapted from Krafft 2000.

Introduction

5

industries. Nevertheless, there are clear differences: first, we only consider factors with a regional impact. Thus, industry-wide or nation-wide aspects are not at the core of our analysis. Second, a more aggregated dimension with respect to industries is used: not one case study of a specific industry, such as lasers or automobiles, is utilized for the empirical study but rather aggregated ones such as ‘Manufacture of machinery and equipment’. Third, the time window for the analysis does not cover the whole life cycle of the industries but only around one decade of development. Therefore, the time window applied here is between the long-term one used for the analysis of industry life cycles and the one mostly used in studies on factors influencing regional start-up activities.4 Fourth, factors such as markets and technologies have an impact on (national and regional) start-up dynamics and many studies mainly focus on them, implying that these factors have a positive influence on opportunity discovery and on success (Roberts 1991). In our study additional individual elements, such as the willingness of agents to become entrepreneurs, are also considered. Individual level The role of the founders’ psychological characteristics, such as their motives (e.g. money or wish for independence; Kriegesmann 1999) and abilities (e.g. intelligence; van Praag 1996) are important: these factors strongly influence the likelihood that the founder starts a firm, his individual behaviour in the founding process and even the potential success of the firm. This focus on the individual agent and his characteristics is neglected in most (empirical) studies which attempt to explain differences in regional firm founding activities. On the other hand, many studies on agents’ characteristics do not take into account the context in which the agents are embedded (Van de Ven 1995). The approach utilized here seeks to unite the research concerned with the role of the agents’ personal characteristics with the work related to how regional conditions influence start-up activities. Although various factors influence the individual characteristics of agents, we only focus on the immediate social (van Praag 1996) as well as regional context and the related interactions that have a strong impact on agents’ characteristics. In this study positive regional examples or so-called role models (Simonton 1975) are the main factor representing the regional context and affecting agents’ behaviour. The effects that role models have on agents’ behaviour are confirmed in several contexts, e.g. with regard to occupational choice (Krumboltz et al. 1976, Scherer et al. 1989, King and Multon 1996, Nauta and Kokaly 2001). We examine how role models can lead to changes in the founding activities by influencing the agents’ willingness to found a firm as well as other factors relevant for agents’ individual development to become an entrepreneur. Thus, in this approach the personal and regional levels are interlinked: regional conditions influence the development of agents to become entrepreneurs and their behaviour in turn influences the regional context.

6

Introduction

1.2 Structure of the study This section first gives a brief overview of the general structure of the study and then describes the different chapters in more detail. In this study we intend to develop a framework focusing the analysis on the developmental processes and changes in regional firm founding activities, to find empirical evidence for such changes and to analyse selected processes responsible for the development. In order to fulfil this task, the study approaches the issue from two different sides and, therefore, uses two research concepts: in the first part (Chapters 2 and 3), we develop a theoretical framework to analyse changes in regional founding activities. Additionally, we present a method by which such changes in activities can be empirically identified. We then study in which regions, industries and years these changes occur and which factors lead to the changes in the activities. In the second part (Chapter 4), we take one specific event, namely the founding of a small firm, and theoretically as well as empirically analyse its founder’s impact as a positive example for other agents. One aspect has to be mentioned before we start describing the structure in detail. In general, scientific research consists of two phases: the construction of an abstract theory and the testing of its derived hypotheses using various qualitative and quantitative methods. In our study, we have taken an interactive approach, in that we enable empirical work to subsequently cause adaptations to the previously constructed abstract theory. This adapted theory can then again be tested by (other) empirical methods. Since we are opening a new field of research with this study, we start with a general theoretical description of the relevant questions (Chapter 2) and then proceed to outline specific issues in Chapter 4. Furthermore, we use qualitative research to test the stated theoretical conjectures, which enables us to receive broad feedback from experts dealing with the topic. Given the stage of the research process and the limited amount of time and space available for this study, this approach appears to us as the most promising and suitable path to pursue. In future research, other methods may be used to further elaborate on this approach. In order to illustrate what type of future steps can be taken, we present an outlook dealing with potential extensions that are directly linked to proposed theoretical elements and methods. Additionally, we discuss major future applications and enhancements in Chapter 5. In more detail, the present study is organized as follows. In Chapter 2, we present a theoretical framework to analyse the influence of specific events on regional factors which in turn affect the regional firm founding activities and cause short- to medium-term changes in regional firm founding activities.5 Based on this theoretical framework, the expected development in the number of firm foundings are presented in a stylized way and visualized by corresponding curves. In Chapter 3, we turn to the question whether one can find such changes in start-up activities in the empirical data on regional start-ups.6 After describing the used data, a method is suggested by which significant changes of the regional firm founding activities in comparison to the expected founding intensi-

Introduction

7

ties can be identified. The expected founding intensities are based on the general long-term development of start-up activities in a specific industry and the advantages or disadvantages a region has in this industry. The characteristics of the short- to medium-term developments are discussed. We classify the developments and examine in which regions, industries and years such significant changes occur. The stylized curves derived in Chapter 2 are compared to the empirically detected ones in order to figure out which developments of founding activities can be found in reality and which factors might have an impact on these developments. This question is further investigated in the last part of Chapter 3. Here qualitative interviews with representatives of the local Chambers of Commerce and Industry or other experts are presented. The aim of these questionnaires is to obtain information about the events that occurred in the regions leading to the identified start-up dynamics. The method developed to identify changes in regional founding activities is applicable to a wide range of data in order to analyse changes in these activities (e.g., in patenting activities). The second part of the study consists of Chapter 4 in which we focus on the impact of successful founders on other agents. In this chapter, we suggest a short behavioural model of an economic agent based on the impact of social-cognitive learning of mental models (Bandura 1986). We argue that these mental models moderate agents’ behaviour. Successful agents who serve as positive examples shape these social-cognitive learning processes and, thus, are an important element when it comes to influencing other agents’ behaviour. In the same chapter, we deal with the specific role of geographic and relational proximities for the creation and diffusion of mental models in a population. We apply these insights to the field of firm foundings in the second part of Chapter 4. A stylized stage model of the development process in which an agent becomes an entrepreneur is presented. The factors considered most relevant are ‘opportunity perception’, ‘willingness to found’, the ‘test of the possibilities to market the product or service’ and the ‘test of the own abilities to found a firm’. Therefore, we answer the question of how positive examples – in this case successful regional entrepreneurs – influence these factors and, thus, the likelihood of a transition from one stage of the start-up process to another one. Based on a positive example’s effect on the developmental progress through the stages, the implications for regional founding dynamics are discussed. In general, the focus lies on changing start-up patterns inside a region and less on the differences between the regions. Positive examples and the processes which lead to their dissemination in a regional population do not primarily explain the differences between the various founding activities of regions but mainly the acceleration of initial, minor changes in the founding activities. Nevertheless, in the long run, positive feedback processes result in a specific regional attitude towards entrepreneurship. Thus, the question of how the regional disparities in entrepreneurial activities come about can, at least partially, be answered by the influence of positive regional examples. The theoretical considerations are complemented by empirical research at the end of Chapter 4. Here, we describe an in-depth case study conducted in the city of Jena (Germany). The chapter

8

Introduction

contains qualitative interviews with experts in the field of firm founding as well as the results of a quantitative survey with nearly 100 firms in Jena to empirically test the impact of role models. The last chapter (Chapter 5) concludes with a summary of the findings and a discussion of the results. Some practical implications are drawn that can be useful for future political programmes to support regional start-up activities. Furthermore, in Chapter 5 we give an outlook and provide some new research questions derived from the findings in this study.

2

Theoretical considerations on changes in regional firm founding activities

As pointed out in Chapter 1, the distribution of start-up activities is uneven across regions (see for example Figure 3.2). Various studies have already analysed the influence of regional factors on start-up activities (e.g. Audretsch and Fritsch 1994, Reynolds 1994, Garofoli 1994, Keeble and Walker 1994, Steil 1997, Nerlinger 1998). In addition, nation-wide or industry-specific variables have an impact on the variation of start-up rates between countries or industries respectively (see for example Reynolds et al. 1994, Sternberg 2000 or Felder et al. 1997). Most of these studies only consider the impact of the current values of these regional variables on the current start-up rate. This means that they essentially ignore changes of the regional, national and industry variables over time. In studying these, we could tell three different versions of a regional founding story: the first one analyses how different regional stocks of human capital influence disparities in regional start-up activities. The second one simply relates increases in the human capital of a region (based on new universities, schooling or whatever not unanalysed reason) to changes in the rate of firm foundings. The third one has a more sophisticated plot: once upon a time, a new university was established in a particular region. Over time, it had an impact on human capital and competencies held by the regional population. Since the university was very successful, it grew after the first years of its establishment. Consequently, the pool of agents that had the capabilities to start a new firm enlarged, thereby increasing the number of regional firm foundings. Furthermore, the number of qualified employees that were potentially available to new firms also grew. Agents from both inside and outside the region immediately reacted to these new circumstances by founding firms in that particular region. Other agents (located elsewhere) had first to receive the information about these new circumstances. Simultaneously, the conditions that were favourable to start-ups took time to develop. The critical level of qualified potential employees was only reached after the amount of educated agents increased. Once the threshold was overcome, a rush of agents decided to become entrepreneurs and many new start-ups appeared in the next few years. Later on, the rate decreased somewhat and settled down to a certain level. In our view, the last story sounds much more vivid, interesting and is even more realistic. Many processes and interactions were described that precisely related how changes in firm founding activities are

10

Theoretical considerations

stimulated. In this study we aim to thoroughly examine these processes. While it is complementary to previous studies on regional firm foundings, it does differ in some aspects. First, the approach concentrates on changes in the regional context and claims that these changes influence the likelihood of firm founding in a region. Hence, we analyse if and how a change in the regional conditions (such as the establishment of a new start-up centre) leads to changes of the regional start-up rate. Second, in most parts of the literature the relationship between regional factors and start-up rates is the core of the analysis. We extend this approach in the present study by also taking into consideration possible events as reasons for changes in the regional factors. Thus, we enlarge the chain of causal relationships: first, certain events lead to changes of specific regional factors (left arrow in Figure 2.1) and these factors in turn influence the likelihood of regional start-ups (right arrow in Figure 2.1). Hence, we start with a theoretical perspective that examines both which events and corresponding changing regional factors influence the likelihood of regional start-ups in the short- and medium-term; and also what the resulting stylized curves of regional factors and firm founding development look like. These stylized curves show the theoretically expected development of the start-up activities and of the regional factors over a certain period of time. They do not present the exact development of these variables but classes of their idealized developments. We use these stylized curves because they enable us to predict developments and to empirically test which of these developments do indeed exist in the data. Third, this approach emphasizes several mechanisms and processes that are considered relevant for changes in the regional start-up rates, which have to be distinguished from each other. In most studies, a linear relationship between a restricted number of elements is assumed. Is this the only possible relationship? Or do other processes and relationships exist that also have an impact on the changes of regional factors or start-up activities? Such additional transmission processes can critically disturb the link between the previously studied elements. In this study, we suggest some additional transmission processes that we subdivide into two general areas (depicted in Figure 2.1): (A) transformation processes and (B) dynamics. A

The transformations can take one of two shapes: • Linear: changes in one element can be directly linked to changes in other elements by a linear relationship. This kind of relationship is applied in the majority of the existing literature, as pointed out above.

Dynamics

Event

Transformation

Change of factors

Dynamics

Transformation

Change of regional start-up likelihood

Figure 2.1 Structure of relationships between events, factors and start-up likelihood.

Theoretical considerations

11

•

B

Non-linear: the relationship between the elements may also be nonlinear. In this study, we concentrate on a s-shaped or a stepwise function. These shapes are caused by the heterogeneity of agents as we show in Section 2.2.1. Some events do result in a specific level of a regional factor and a certain level of a regional factor can lead to specific start-up likelihood. However, with regard to the possible dynamics, one has to take into account that these regional factor and start-up levels are not necessarily reached instantaneously. Rather, events and regional factors can trigger dynamic developments, which need a certain period of time to converge to the medium-term level or which can temporarily increase higher than the medium-term level. Thus, such dynamics can change the development normally resulting from the event or changes in the regional factors.

The overall intensity of regional start-up activity is thus driven by four forces: • • • •

the impact of events on changes in regional factors (1); the impact of changes in regional factors on start-up activities (2); the transformations that moderate (1) and (2); and the dynamic process describing how the regional factors or the firm founding activities adapt to the long-term level.

Such an additional perspective is relevant because, despite their importance, the available literature lacks a detailed analysis of dynamic changes and patterns in regional firm foundings. In the following, we describe and analyse changes in events and regional factors that lead to positive developments in the founding activities. However, also the opposite might happen: a change in a regional factor can decrease founding activities. We assume here that normally the effects on founding activities are symmetric, e.g. better political support leads to an improvement in the founding activities while an insufficient support results in deterioration. What happens if the two developments differ is described in the according sections. The chapter is structured as follows: first, we explain the most important regional factors that influence regional firm founding activities (Section 2.1). In Section 2.2 we study the transmission processes (i.e. transformation processes and dynamics) that affect the relationship between events, changes in regional factors and regional founding developments. First, we describe the possible linear and non-linear transformations and then we analyse the dynamics. In Section 2.3 we put forward events that influence the discussed regional factors. Thus, we address the question of why and how regional factors, which affect regional start-ups, change. We present the impact of these events on the dynamics of the regional factors over time as stylized curves. In Section 2.4 we first discuss which transmission processes affect the impacts of the regional factor dynamics on the expected development of firm foundings for each factor

12

Theoretical considerations

separately. Then we analyse how changes in the regional factors lead to changes in the potential development curves of start-up activities taking these transmission processes into consideration. In the last part we summarize the findings of the previous sections by linking the discussed events to the development curves of start-ups. We show the transformation processes and dynamics (2.2), the changes in the regional factors (2.3) and the changes in the regional founding activities (2.4) as stylized curves in the respective sections. We selected the relevant regional factors (2.1), the possible events (2.3) and the transmission processes (2.2) according to their importance for the explanation of differences in regional founding dynamics. Thus, we concentrate on a variety of events, factors and processes without being exhaustive.

2.1 Regional factors influencing start-ups In the following, the most relevant regional factors shaping regional start-up activities are presented. They are categorized in six groups to increase clarity. These groups are: (1) the pool of potential entrepreneurs, (2) local markets, (3) resources, (4) push-factors, (5) pull-factors and (6) additional regional conditions. These regional factors are relevant for different elements of the entrepreneur development process. In Section 2.3 we take these regional factors up again and analyse how they are influenced by specific events. Although most of the described factors and processes apply to all possible firm foundings, the focus is on technology-oriented or technology-based start-ups or service firms that are linked to such industries. 2.1.1 Pool of potential entrepreneurs An important factor that influences regional start-up activities are agents that have the capabilities to found a firm. This factor contains two aspects: first, the mere number of agents and, second, the qualifications of these agents. In the literature, both aspects have been identified as relevant (e.g. Guesnier 1994, Spilling 1996). We will concentrate mainly on the qualification aspect: agents need to have the professional technical and economic capabilities to found a firm. It was observed that the human capital of an agent has a strong impact on the likelihood that this agent founds a firm (Evans and Leighton 1989, van Praag 1996, Pfeiffer 1999). This prior knowledge that an agent gathers during his education or work affects whether the agent discovers any kind of entrepreneurial opportunities (Shane 2000). In order to evaluate this effect, the different kinds of human capital have to be analysed separately. Almus and Nerlinger 1998 and 1999 found that a technical education has a positive impact on firm foundings, while economic competence only positively influences the founding likelihood for non-innovative firms. Some kinds of education (e.g. in arts or languages) even have a negative impact (van Praag 1996). On a regional level, the pool of agents is often represented by the number of agents between 25 and 44 years of

Theoretical considerations

13

age because this group has the highest likelihood to found (e.g. Spilling 1996, Sternberg 2000) and by the share of highly educated people or managers (e.g. Garafoli 1994, Reynolds 1994, Spilling 1996) in a region. In the aforementioned studies these variables have a positive effect on regional firm foundings. In addition, there are two organizations that have an impact especially on the qualification of agents: the educational system (e.g. universities, research institutes and technical colleges) and established firms create human capital. Universities and research institutes have a strong positive impact on regional firm foundings, especially in high-tech/technology-based industries (Walsh et al. 1995, Berger and Nerlinger 1997, Felder et al. 1997, Bade and Nerlinger 2000, Engel and Fier 2000, Lawton Smith and Bernardy 2000). This is caused by two phenomena: first, the agent’s qualification is strongly determined by the educational system. Here, universities and research institutes play an important part (McMullan and Gillin 1998). Second, both groups of organizations serve as incubators from which mainly professors or research assistants found a firm, but not necessarily students (Kriegesmann 1999, Herrmann 1998, Wallmark 1997). Other regional firms play a unique role: they offer the possibility for employees to gather (work) experience and expertise in a certain area, although they do not directly belong to the educational system. Hence, they are incubators that strongly inspire foundings by intended or un-intended spin-off activities (Klepper and Sleeper 2002). Such firm spin-offs are one of the main factors influencing regional start-up activities (Saxenian 1994, Lindholm Dahlstrand 1999). For all types of incubators, factors specific to foundings (e.g. use of equipment, qualification, advice or information sharing, as well as general factors (e.g. involvement in research projects with firms or being a group leader), can support firm founding activities. This is caused by the fact that explicit and implicit knowledge is accumulated, opportunities discovered and the willingness to found a firm developed (Kriegesmann 1999). Implicit knowledge is particularly acquired in firms but also in universities by carrying out a repetitive activity or by observation and imitation. The absolute number of agents can change by a rise or fall in the population due to migration or by a change in the ratio between births and deaths. There is a strong link between population and qualification because many agents are attracted to a region by the educational system or firms1 (Fornahl and Graf 2003). One should keep this effect in mind as it enlarges the pool of potential entrepreneurs. Hence, positive changes in these systems have a double effect by attracting more (qualified) agents to a region and by improving the competencies of regional agents. A mere increase in the population is not considered since they are difficult to link to systematic changes in regional conditions. Furthermore, changes in population have two countervailing effects: depending on the reasons for the increases in inhabitants and the reaction of the founding rates in the population, a larger population can increase the number of foundings but, on the other hand, an increase in population can decrease start-up rates measured in foundings per inhabitant in the short term. The same holds for a decrease in the number of regional inhabitants. This is relevant since the data employed in

14

Theoretical considerations

Chapter 3 incorporates the number of firm foundings and the number of inhabitants in a region to calculate the founding likelihood of agents in specific regions. Thus, the local population is included indirectly in the analysis, but is not directly used as an explanatory variable. In conclusion, qualification or human capital and spin-off processes, based on an educational infrastructure and existing firms, are the most relevant factors influencing the pool of potential entrepreneurs. This pool of regional entrepreneurs can be accurately measured using the number of agents holding specific types of qualifications. There is one shortcoming concerning this measurement: in most cases only formal qualifications are taken into account. Nevertheless, this measurement seems to be the most appropriate one for measuring the pool of potential entrepreneurs. If detailed information on the distribution of qualifications is not available, the number of inhabitants is a proxy often used in the literature. 2.1.2 Local markets Growth and emergence of (local) markets have a strong impact on firm foundings by causing a demand side effect and, thus, offering start-up opportunities.2 New firms are attracted by signals and incentives such as positive turnover development of the regional industry, above average market growth and low number of competitors (Hause und Rietz 1984) and, thus, exploit promising market opportunities (Preisendörfer 1996). This aspect is widely analysed in the literature. On the industry level, for example, Geroski and Mazzucato (2001) or Klepper (1996) noticed that emerging or growing markets attract new firms. In regional level studies, an increase in the absolute number of local inhabitants (Guesnier 1994, Davidsson et al. 1994, Keeble and Walker 1994), growth in local gross national product (Reynolds 1994, Bade and Nerlinger 2000) and regional population density (Garofoli 1994, Davidsson et al. 1994, Spilling 1996) were used as indicators for local market growth. These demand side factors were observed to have a positive effect on regional firm foundings. Apart from the positive effects of growing regional markets, specialized regional suppliers play an important role for local markets. For many new firms, particularly in a niche market whose customers are spread globally, regional customer and supplier relations are still very important (Lissoni and Pagani 2003). They are more flexible and transaction costs are lower because of easier face-to-face interactions and common knowledge about the specific capabilities of other regional agents (e.g. Belussi and Arcangeli, 1998, Lorenzen and Foss, 2003). Porter (1990) supported these findings by claiming that local markets and competition therein have a positive impact on the emergence of local industrial clusters based on horizontal and vertical linkages between firms. Although his argument focuses on the success of incumbent firms in clusters, local markets are likely to also influence start-ups that are an important element in cluster formation. Thus, a good economic and demand side development should lead to an increase in regional firm foundings.

Theoretical considerations

15

Additionally, markets emerge or grow through policy interventions influencing the demand for specific products by providing government contracts to firms within a region, e.g. defence contracts to firms in Silicon Valley (Saxenian 1994). As pointed out in Section 2.1.1, variables linked to the regional population are disregarded in the following sections. Thus, the local market is influenced by demand side effects caused by the availability of customers’ financial resources (e.g. measured in the local spending); buyer-supplier relations (e.g. measured by the share of local supply); and local and national authorities (e.g. measured by the amount of public demand). 2.1.3 Access to resources In order to found a firm various resources such as human capital, financial capital and equipment are necessary. Since many of these resources (particularly equipment and human capital) are immobile, locally bound or costly to transport, firms needing these resources must be established in close geographic proximity to them (see, e.g. Anselin et al. 1997 for the geographic effect of universities; Fornahl and Graf 2003, Hofmann 2001). Human capital (HC) is an essential factor to entrepreneurship; but qualified and available human capital of employees is also an important factor influencing firm locations (Krugman 1991, Salmen 2001, Fornahl and Graf 2003). The accumulation of human capital, at least for technology-based industries, depends on the existence of universities, research institutes, etc., and the presence of existing firms. These relationships were already put forward above and can be measured by the number of agents holding a specific kind of qualification. Equally important for most industries is the availability of financial capital (FC) to invest in start-ups or young firms and the (opportunity) costs of this financial capital. We concentrate on three specific types of financial capital: 1 2 3

venture capital (e.g. measured in the fund of the local venture capital firms), individual disposable financial capital (e.g. measured in income and savings) public financial support (e.g. measured in the funding of public programmes).

Especially access to venture capital (1), combined with venture capitalist advice, has a positive impact on firm foundings and success (Rickne 2000). At least for firms operating in high-tech industries, a large part of their own capital is based on venture capital (Athreye and Keeble 2000). Missing venture capital is expected to have a negative impact on firm foundings (AndréossoO’Callaghan 2000, Bojnec and Konings 1999). Engel (2002) found that most firms supported by such capital are located around a limited number of cities. Although in some cases venture capital firms from outside the region finance new firms, mostly regional linkages exist (Patton and Kenney 2004). This is due

16

Theoretical considerations

to the fact that in addition to mere financial support other factors, such as advice and information transfer, are important for the interaction between start-ups and venture capital firms. Such processes operate much more easily on a local basis. Furthermore, social networks are linked to the provision of venture capital as they help overcome information asymmetries between the venture capital firm and the start-up (Shane and Cable 2002). Since social networks are strongly bound to geographic proximity (see Section 4.2.2), venture capital is also geographically bounded (Sorenson and Stuart 2001). Another analysed factor is the availability of the founders’ personal income and savings (2). It was observed that self-employment is linked to the availability of personal financial capital (van Praag 1996, Blanchflower and Oswald 1998). Individual capital encourages foundings. Alternatively a very low income also has a positive effect as the opportunity costs of starting a firm are very low (Evans and Leighton 1989; see also Section 2.1.4). Public support programmes (3) are an additional source of financial capital for start-ups. Pfeiffer (1999) noticed that the amount of start-ups out of unemployment increased after the provision of financial capital by the state and local government improved. This points to the crucial role of financial restrictions. On a regional level the results are mixed. Only the support of training and education, linked to the accumulation of human capital, was identified to have a positive impact on foundings (Felder et al. 1997). The final element that should be mentioned here is access to equipment, laboratories and machinery (Eq.). Newly founded firms in high-tech industries often need these costly resources but do not have the financial base to own them. Firms able to use these resources have a competitive advantage or retain the ability to operate. Incubators providing these resources are often universities or research institutes (Kriegesmann 1999, Fornahl and Graf 2003). This factor is difficult to measure, at least on a continuous scale, because it is hardly possible to directly aggregate various kinds of equipment. Furthermore, the measurement of capital in terms of its financial value does not do justice to the fact that it is not the quantity or size of the equipment, but rather how well it is customized to the firms’ necessities. There are two possibilities of dealing with this problem: first, the various equipment classes have to be separately analysed. In this case, it is possible to measure increases in one of these classes in discrete steps, e.g. if one piece of new equipment is bought, stock increases by one and, thus, switches to a higher level.3 Second, it is not the pure quantity of a factor that is relevant but the accessibility to this factor, e.g. based on new regulations founders are now allowed to use specific equipment. The measurement of accessibility to equipment and machinery can be discrete or continuous. Human capital, financial capital and access to equipment and machinery are important for regional start-ups because they provide the possibility to start and run a firm. Human capital is influenced through educational infrastructure and existing firms; financial capital by venture capital firms, state support, local income; and access to machinery mostly also by the educational infrastructure. Two important dimensions of resources are their existence and availability.

Theoretical considerations

17

Many resources might exist in a region, but they may be bound to firms or other organizations or are for other reasons not available to start-up firms. Thus, a change in these factors can be linked to a change in the amount of resources as well as to their availability to the potential entrepreneur. To have an impact on firm foundings, resources must both exist and be available. 2.1.4 Push-factors Push-factors – also called negative displacement effects – are linked to the unattractiveness of the agents’ current or future situation (Brockhaus 1982, Klandt 1984).4 These push-factors have an impact on the agent’s willingness to start a firm. Relevant push-factors are, for examples, unemployment or frustration concerning current working conditions, colleagues or future perspectives (Galais 1998, Pfeiffer 1999, Pleschak and Werner 1998, Valerius 1996). However, the available empirical results are mixed: •

•

•

•

Van Praag (1996) as well as Evans and Leighton (1989) could not verify that a positive relationship exists between the rate of unemployment and firm founding activities. They conclude that unemployed agents do not posses a significantly different likelihood to found a firm. On the regional level, the results are also quite unclear: some researchers claim that high unemployment has a positive effect on firm foundings because agents want to secure their income (Reynolds 1994, Audretsch and Fritsch 1994, Guesnier 1994). Others say that high unemployment leads to low foundings because recessions are daunting to entrepreneurs (Garofoli 1994, Steil 1997). Others argue that low unemployment rates cause a higher founding probability, because firms have a higher likelihood to survive and make profits in a prospering economic environment. Also, if the own start-up fails the possibility of finding a job in another firm is given (van Praag 1996). Georgellis and Wall (2000) found an inverse u-shaped relationship showing low founding activities for very low and very high rates of unemployment.

Furthermore, the effects depend on the type of industry and the qualification of the agents. Davidsson et al. (1994) found that there exists a positive correlation between the rate of unemployment and start-ups in the basic service industry; whereas Keeble and Walker (1994) concluded from their empirical analysis that unemployment has a negative effect on the manufacturing industry. It is necessary to include variables like the relevant industry and the qualification of the agents to evaluate the relationship between the push-factors and firm foundings. A mixing-up of all the agents, no matter which qualification they have, leads to unclear results. With regard to these studies, it has to be noted that unemployment is not the only push-factor, but that the unattractiveness or discontent with the current (working) situation must also be considered. For example, a highly qualified

18

Theoretical considerations

employee in a large firm who is not threatened by unemployment but unsatisfied with his current job might still think about starting a firm. In his study of firm foundings that are linked to research institutes, Kriegesmann (1999) discovered evidence for such entrepreneurial motivations. The limited duration of contracts, decreasing spending on public research and, thus, an increasing uncertain future, raise the likelihood that these researchers start their own firm. Thus, even if the results for the unemployment rate are unclear, dissatisfaction is likely to have a positive effect on start-ups. In most cases, we claim that a potential entrepreneur will start a firm if the expected net gain (measured in pecuniary and non-pecuniary terms) from this endeavour is higher than his opportunity costs (Witt 1999b). This evaluation is highly subjective and biased by various psychological mechanisms (e.g. Tversky and Kahneman 1974, Kahneman and Tversky 1979, Thaler 1980). Thus, there exists a link between unemployment and firm foundings but this link is influenced by opportunity perception and evaluation processes. We have to conclude that the effect of unemployment as a push-factor is unclear, whereas a worsening of the individual situation and dissatisfaction probably has a positive effect on firm foundings. Thus, even given a relatively good economic situation, if the situation of certain individuals worsens or they become dissatisfied, they are more likely to consider the option of founding a firm. Hence, under these conditions, changes in firms (e.g. closing down or laying off employees) and in the educational system (e.g. constraining the career) increase the likelihood that these agents found a firm. As described, there exist several push-factors and they mostly affect individual agents. We will use the term ‘regional aggregate of push-factors’ in the following, when we discuss a) the regional dimension, meaning the aggregate of the individuals, and b) the aggregated effect of several different push-factors. The proxy for a regional aggregate of push-factors that can be obtained most easily is the regional unemployment rate. However, as discussed these cover only one push-factor. The linkages between push-factors and firm foundings are described and discussed in the Section 2.3 but because of the mixed results in the empirical studies some of the linkages are questionable (see Section 2.4.1). 2.1.5 Pull-factors Pull-factors are all the events that make self-employment more attractive to economic agents and that increase the willingness to become self-employed. There are several factors related to this attractiveness and willingness such as the wish for independence, self-determination and self-realization or the willingness to take responsibilities (e.g. Klandt 1984, Bull und Willard 1993, Galais 1998, Kriegesmann 1999). These factors are based on personality and behavioural characteristics that in turn are influenced by the social context in which the agents develop (Rowe 1997). Thus, these factors change if the social context in which the agents are embedded in also changes. If such an alteration of the context takes place, the characteristics and the evaluation of the described

Theoretical considerations

19

factors adjust as well (see also Section 2.1.4 on subjective evaluation and perceptions). Push-factors (negative displacement effects) alter the regional context in a negative way and, thus, negatively influence the evaluation of alternatives. In contrast, pull-factors (positive displacement effects) are based on positive incidents, like the discovery of a business idea or the passing of an exam, which change the individual situation of an agent and make a re-orientation necessary, or at least strongly influence the behaviour of an agent (Klandt 1984). The focus of our analysis is on positive examples or so-called ‘role models’ of entrepreneurs as one pull-factor: other firm founders and their success stories serve as an example to others, thereby increasing the propensity to found. For example, Fairchild’s5 initial success in the semiconductor business exerted a powerful ‘demonstration effect’, thereby initiating a trend of entrepreneurial innovation (Saxenian 1994). In Chapter 4, we conduct an in-depth analysis of the theoretical influence of entrepreneurial role models. Here we only state that agents imitate the behaviour of other entrepreneurs and that both the social (e.g. Klandt 1984, Davidsson et al. 1994, van Praag 1996, Kriegesmann 1999) as well as the regional (e.g. Keune and Nathusius 1977, Saxenian 1994, Sternberg 2000, Moore und Davis 2004, Wagner 2003, Wagner and Sternberg 2004) context influences the founding behaviour of agents. These positive examples are the only pull-factor we consider in the following. The emergence of small firms in a region has an impact on additional firm foundings via the effect of positive examples. For the following analysis, we define a ‘regional pull-factor’ in order to evaluate the total regional impact of a positive example on the behaviour of agents in the regional population. The number of regional agents who know of the positive example is a measure of this regional pull-factor. 2.1.6 Additional regional conditions Local infrastructure consists of various elements that influence regional firm foundings. Any factor, from something as simple as a railroad station (Berger and Nerlinger 1997) to something as advanced as a technology centre/start-up centre (Engel and Fier 2000, Almus et al. 1999), has a positive impact on technology-based firms. The proximity to the nearest motorway produces unclear results. While Steil (1997) observes a positive effect of motorways in general, Engel and Fier (2000) find that it exists for low-tech firms. Conversely Felder et al. (1997) only find evidence for technology-based firms. There is some evidence that policy measures have an additional effect on regional firm settlement, start-up and spin-off activities. They also influence firm foundings via local (corporate) taxes, administrative legislation or by a broad variety of subsidies for start-ups and potential founders. •

The impact of local taxes on firm foundings was observed to be negative (Berger and Nerlinger 1997).

20

Theoretical considerations

•

The founding of a firm is a tedious process in which many administrative steps must be taken. Thus, regions that differ with regard to these necessary hurdles also differ in firm founding activity (Djankov et al. 2002). There are many possible ways in which policy makers aid firm foundings. For instance, potential entrepreneurs can be supported in their search for financial capital, potential partners and information. Koschatzky (2003) analyses the ‘EXIST promotion of university-based start-ups’ programme run by the German Federal Ministry of Education and Research that does not provide direct financial support, but assists cooperation between organizations to foster start-ups. This programme is considered to successfully impact founding activities. Also, the local Chambers of Commerce and Industry or other specialized, external service firms (e.g. tax consultancies, law firms and venture capital firms) can provide local support for new firms which lead to an increase in the number of start-ups.

•

All in all, local infrastructure is an important factor to explain regional start-ups; this includes railroads, technology and start-up centres as well as motorways. In addition local corporate taxes, administrative laws and general start-up support by policy makers and firms are relevant for founding processes.

2.2 Transformation and dynamics in the transmission process In our introductory part to this chapter and especially in our stories about regional firm founding development, we have already mentioned many different processes that alter the impact of events on regional factors and the effect of changes in regional factors on firm foundings. Taken together, as in the story, they present a relatively detailed and plausible picture of the relevant relationships. Furthermore, many of these processes and factors are discussed in different parts of economic literature, such as incomplete information (Rogers 1995), limited production functions (Varian 1992), heterogeneous agents (Gallegati and Kirman 1999), suppressed reactions (Wade et al. 1998) or social-psychological influences (Witt 1998). We apply these aspects, which will be explained in depth in Section 2.2.1 and 2.2.2 and which we summarize under the label ‘transmission processes’, to our causal chain linking events, changes in regional factors and changes in regional firm foundings. The transmission processes we consider in the following are the following: 1

2

In most parts of the literature, a linear relationship is implicitly assumed. This can be seen as a benchmark against which the other processes are compared. Limiting factor: there exist interdependencies between different regional factors, such that if one is hindered and the others change, there will be no change in the regional firm founding activities.6

Theoretical considerations 3 4

5

6

7

21

The distribution of specific characteristics among agents affects the timing and intensity of agents’ reactions to changes. Elements, e.g. a regional factor such as human capital, exhibit an initial change with additional subsequent changes based on an event that triggers these delayed developments. The change in variables is for a certain period of time stronger than expected because of a suppressed-founding effect (SF effect) where agents are hindered to found earlier, but do so after the hindrance disappears. Agents are not informed perfectly in making founding decision (e.g. on market development or financial support programmes). Thus, the diffusion of information affects their reaction and changes in the elements. As agents have to evaluate their situation, their observed behaviour does not have to be based on objective facts but is influenced by their subjective evaluation and social interaction (see also Section 4.1). This leads to a selfaugmenting process and over-shooting effects.

As we have already suggested in the introduction, the presented transmission processes are grouped into two categories; namely transformation processes and dynamics. Transformations consist of three processes (linear, limiting factor effects, distribution effects; 1 to 3) while dynamics consist of the last four (delayed effects, SF effects, information diffusion and over-shooting; 4 to 7). We discuss these transformation processes and dynamics and explain their basic mechanisms in the following. Although in reality these processes operate simultaneously and interact, they are separately examined for analytical purposes and to improve the clarity of presentation. A conceptual issue has to be mentioned: this list contains in our view the most relevant transmission processes which offer a broad spectrum of processes and which are currently discussed in economic literature. Nevertheless, the list of dynamics and transformations may not be complete but is extendable in future research in order to obtain a more comprehensive picture and to describe the transmission mechanisms in more detail. In the following, we want to show in more detail how transformation processes and dynamics affect the link between events, changes in regional factors and changes in firm foundings. In this, Figure 2.2 is a more detailed view on this link between the elements presented in Figure 2.1. In abstract terms, Figure 2.2 is described in the following way: over time an element e1 changes (Figure 2.2 (a) with a link between time t and element e1). This e1 can represent the event (if the left arrow in Figure 2.1 is analysed) or the regional factor (for the right arrow in Figure 2.1) and its development over time is the input to the processes shown in Figure 2.2. Such a change in element e1 leads to a change in element e2 (Figure 2.2 (b) with a link between element e1 and element e2). This e2 stands for a regional factor or the regional firm founding activities, respectively. Transformations, described in Section 2.2.1, operate via different functional forms of the relationship between two elements, e1 and e2. Thus, transformations translate changes in element e1 to changes in element e2. Based

22

Theoretical considerations

e1

e2

e2

Dynamics

Transformation

a)

t

b)

e1

c)

t

Figure 2.2 Stylized relationship between transformations and dynamics.

on this transformation the development of element e2 over time results (Figure 2.2 (c) with the link between time t and element e2). The result of this transformation process is a certain level of e2. This level results from all the previous changes of e1 and is fixed in the medium-term; as long as e1 does not change another time. However, it does not need to be the case that e2 instantaneously changes to this level. In addition, dynamic processes do exist, presented in Section 2.2.2, which influence the convergence of e2 to this medium-term level. Thus, the dynamics represent the short-term deviations of e2 from the mediumterm level. Depending on the kind of dynamic the short-term level can be higher or lower than the medium-term level. To sum up: the medium-term level of e2 is determined by previous changes of e1 and can alter if a new change occurs. If this is the case, the result of the transformation process is a qualitative change in e2 resulting in a new specific medium-term level of e2. The convergence to this medium-term level can be affected by the dynamics. In the end each change of e2 over time is based on a change of e1 moderated by the transmission processes. The figures presented in the following sections are stylized. Not all developments have to be as sharp and clear as shown in the figures. For example, a jump from one level of activity to another one is represented as happening at one point in time, whereas the development in reality in many cases takes longer time, but is nevertheless limited to a short time frame depicted by a sharp change. Thus, all the figures must be seen as approximations of real developments. First, we present the processes related to transformations (Section 2.2.1) and then the dynamics (Section 2.2.2). Section 2.2.3 briefly summarizes the arguments. The transmission processes are used to analyse and classify the influence of events on regional factor changes and the impact of changing factors on regional founding activities (Sections 2.3 and 2.4 respectively). 2.2.1 Transformations This section deals with the transformation processes and, thus, with the relationship between the two elements e1 and e2. The relationship has different functional forms to be discussed in the following: 1) linear, 2) limiting factor effect, 3) distribution effects based on heterogeneity or homogeneity of agents.

Theoretical considerations

23

Linear relationship The two elements e1 and e2 can be linked by a linear relationship (Figure 2.3). For example, an event (e.g. the founding of a new university with 1,000 new students) linearly affects a regional factor (e.g. leading to a proportional increase in the regional human capital) (left arrow in Figure 2.1), otherwise a change in a regional factor (e.g. human capital) increases the likelihood of firm foundings in a region (right arrow in Figure 2.1).7 As a consequence, the relationship between time t and e2 shows similar characteristics as the relationship between time t and e1: the time at which a reaction of e2 appears is the same as the time at which e1 changes. Furthermore, the general forms of e1’s development and e2’s development are the same (e.g. an s-shaped development of e1 results in an s-shaped development of e2). Differences can emerge in the strength of the reaction, which depends on the intercept and the slope of the relationship between e1 and e2. For example, a jump to a higher level in human capital results in a proportional, but smaller, jump in the regional founding activities. Most econometric studies assume such a linear relationship (e.g. Audretsch and Fritsch 1994). There, the impact of factors (the independent variables) on the start-up activities (the dependent variable) is supposed to be linear. Hence, at least for the connection between factors and start-ups (the right arrow in Figure 2.1), the analysis of this linear relationship in this study corresponds to the common approach in the literature. Limiting factor effect The relationship between e1 and e2 might have breaks or critical values (e.g. e* in Figure 2.4). If e1 increases from a value lower than e*, e2 also increases. e2

e1

Figure 2.3 Linear relationship.

e2

e1 e*

Figure 2.4 Limiting factor effect.

24

Theoretical considerations

However, after e1 exceeds e*, e2 does not change anymore. On the other hand, if e1 decreases from a value higher than e*, e2 will not react until e1 < e*.8 Again, the developments of e1 and of e2 show similar characteristics.9 If e1 only changes in the linear increasing part of the relationship the same conclusions concerning the timing of a change, curve form and e2’s reaction strength as for the linear case described above are valid. However, three additional features of the limiting factor effect are relevant. 1

2

3

It is possible that there is no effect of a change in e1 over time, if certain critical values are not overcome (i.e. if e1 > e*). In this case, a change of e1 would not lead to a change in e2 over time The timing of a reaction in e2 differs from the starting point of changes in e1. This is the case if e1 passes the critical value e* during a decrease of e1. In this case e2 reacts after the critical value is reached; and, thus, probably changing later than e1 starts to change. A development in e2 might start or stop after the critical value e* is overcome; depending on the starting point and the direction of e1’s development. This would lead to different forms of the development curves of e1 and e2.

It is supposed that many factors have an impact on firm founding activity and the mixture of these factors determines the actual founding rate. The interaction between these various factors is likely to be affected by a limiting factor effect, which is in most cases only implicitly assumed in large parts of the literature. Thus, there is the possibility that one factor, not or only partially present in the region limits firm founding developments, although other factors are present to a sufficient degree. Accordingly, an improvement in one factor (which is not the limiting one) does not lead to changes in regional start-up rates as another factor limits further development. On the other hand, also a decline in a non-limiting factor does not have an impact on the founding activities. The latter is true until this factor becomes the limiting one. For instance, in a region with vast amounts of financial and human capital, an appropriate infrastructure and all other necessary factors, but without capable or willing agents to become an entrepreneur, no start-ups occur. Such a phenomenon explains the time lags;10 between increases and decreases in regional factors (based on policy interventions); observed changes in regional start-up rates; and why some regional factor changes have no impact on founding activities. For example, policy makers invest financial capital to build regional infrastructure but without the appropriate mixture of the other necessary regional factors, the start-up rate will not increase accordingly. Although these processes are of political and theoretical interest, they are excluded, because: 1

if e1 changes but e1 > e* for all points in time, this does not lead to a change in e2 (i.e. firm founding activities). This is politically relevant and can be

Theoretical considerations

2 3

25

observed in the empirical data, but such a situation cannot be applied to the following analysis because we exactly analyse changes in e2 that take place. If e1 changes but e1 < e* for all points in time, it will have the same form as in the linear case and cannot be separated from it. If e1 changes and reaches the critical value e*, this would have the discussed effect that the development of e1 over time differs from that of e2 over time. For an increase of e1, the development of e2 would stop at a certain level. For a decrease of e1, the development would start later. The relevant parts of the developments that can be used for the following analysis, because they contain changes of e2, resemble the linear case. Thus, the limiting factor effect reacts similarly to the linear case in these parts of the development. Hence, it is subsumed under the linear case for the following analysis.

Distribution effects Agents possess different characteristics that are distributed within a population, hence, having an effect on the transformation process. In the following, two possible developments of e2 are presented, based on two different characteristics’ distributions in the population. Heterogeneity with s-shaped relationship The relationship between e1 and e2 can be s-shaped (Figure 2.5). In this case, e2 only reacts very slowly to an increase in e1. However, the development of e2 widely escalates for each additional increase of e1 until the turning point where the strongest observable increase in e2 is reached. After this turning point changes in e2 decrease until they level out. This functional relationship and the original e1 development curve over time influence e2’s development: the points of time in which changes in e1 and e2 occur are either similar or different. The curve form of e2’s development over time in most cases differs from the one of e1 (e.g. a linear development of e1 is transformed into an s-shaped one of e2). Only if e1 develops in an s-shaped way, does e2’s development look similar to the one of e1. The main reason for such an s-shaped development in the relationship between regional factors and firm founding activities is based on the heterogeneity of agents. If agents differ from one another, then they have different thresholds at e2

t t*

t**

Figure 2.5 S-shaped.

26

Theoretical considerations

which they react to changes in regional factors. In the case under discussion the reaction thresholds of each agent are assumed to be normally distributed. This means only a few agents have a very low or high threshold, and many have a medium one. According to these thresholds, agents start their firms. Figure 2.5 in this case depicts the integral of the normal distribution. For each level of e1, it shows the amount or share of agents that have started a firm. Threshold with a stepwise increasing relationship The more similar agents are to each other the steeper the slope of the s-shaped development and the smaller the variance of the normal distribution. In the most extreme case, all agents in the population are identical: the normal distribution degenerates to a distribution in which all probability is on one point and the sshaped curve changes to a stepwise increasing function (Figure 2.6) .11 This extreme case is described in the following: sometimes increases or decreases in e1 do not have a smooth impact on e2. Rather only after they have reached a specific threshold or critical value e* does such an impact take place. This time e2 does not change in an s-shaped way but switches to a different level after the threshold is reached. The relationship is described by a stepwise increasing function with a critical value of e*. For an increasing value of e1 (with a starting point of e1 < e*), e2 does not change until e* is reached. At e*, e2 switches to a higher level and stays on this level even if e1 increases further. The same development holds in the opposite direction (e2 switches to a lower level) if e1 decreases from a starting point of e1 > e* and overcomes e*. Both, such a link between e1 and e2 as well as the form of development of e1 over time, strongly influence all the characteristics of e2’s development over time: whether the timing of changes in e1 and e2 differs depends on the manner in which e1 develops over time. A linear increase of e1 over time, for example, only leads to a reaction of e2 when e1 exceeds e* and, thus, probably later than the starting point of the change of e1 (except for the limiting case of e1 starting in e*). The same holds for the form of the developments. All gradual changes of e1 translate into a different form of changing e2; namely a discrete jump to a different level. Furthermore, the strength of e2’s reaction depends on the strength of relationship between e1 and e2 which is represented by the distance between the two levels before and after e*. As long as e1 does not exceed or fall short of e*, changes in e1 are obliterated and do not lead to changes in e2. e2

e1 e*

Figure 2.6 Stepwise.

Theoretical considerations

27

An example of a threshold relationship is where agents only found a firm after a certain market size is reached. Although the market already exists no firms are founded. Even if the market size changes, start-up activities are not influenced immediately. This occurs only after a critical threshold is reached.12 As the changes of the elements depend on various distributions of agents’ characteristics,13 an s-shaped and stepwise increasing development are mutually exclusive, as are all the other transformation processes discussed. In this case, they cannot occur simultaneously because there is only one relevant distribution in a certain situation. 2.2.2 Dynamics The explanation of reactions of e2 on changes in e1 can be improved by taking processes that influence regional factor and/or start-up activity dynamics into consideration. The relevant dynamics considered in the following are delayed effects, SF effects, information diffusion processes and self-augmenting/overshooting processes. All of them cause short-term development of e2 that converges to the medium-term level. Delayed effects Some events and changing regional factors not only have an immediate influence on the development of regional factors or firm founding activities, but they also have a delayed one that follows a certain path. There exists a primary effect, followed by a secondary effect: at t* the direct effect leads to e2 switching to a higher level. Then a delayed effect occurs, in which the element e2 grows further after t*. The latter effect can last for a certain period of time depending on the concrete underlying processes (in Figure 2.7 this is represented as the time span between t* and t**) until the medium-term level is reached (after t**). Figure 2.7 shows one such example, with high growth rates shortly after t* and lower ones close to t**. Other kinds of continuous development are also possible, such as a linear one or a successive change of levels until in the end a new level is attained. Delayed dynamics are observed, for example, for all events that significantly change the regional conditions in a way that results in a further change in element e1 (e.g. the founding of a new university with its impact on various e2

t t*

t**

Figure 2.7 Delayed effect.

28

Theoretical considerations

regional factors). Such a major alteration of the regional environment is likely to entail lagged adaptation processes, resulting in the delayed changes of the element e2 (e.g. the stock of human capital). Thus, the impact of events lasts for a longer period of time, e.g. after an initial strong increase the number of students grows for some time before it reaches its final value. Suppressed-founding effect This dynamic effect is only linked to firm founding development over time and, hence, to the right arrow in Figure 2.1. The description of the suppressed-founding effect reads as follows: the element e2 (in this case the founding activities) switches to a higher level at time t* and after some time (at t**) declines to a lower medium level, still higher than the original one (Figure 2.8). Thus, the development resembles the change to a higher level but with a slight displacement at the beginning. The normal effect is amplified for a limited amount of time (between t* and t**). Such a development can be observed because at any given point in time a certain number of agents wants to found a firm. They will do so if the necessary circumstances are given (e.g. the resources needed to start). However, for example, a limiting factor hinders these founding activities: wherein some agents will establish a firm, some might desist from the idea and others who do not found at that point in time are persistent in their plan. This latter group forms a pool of agents who start their firms if the limiting factor is no longer a hindrance.14 Thus, after this event occurs (at t*), not only agents establish a firm that at that point in time would like to, but also those who were hindered in the past. Hence, founding activities are particularly high for a limited amount of time. The pool of agents who have postponed their founding activities empties as agents in this pool start their firms.15 Finally, activities drop back and reach a level that is higher than the level before the change. For illustration purposes, we assume the following situation: we have an agent population of a certain age, e.g. between 18 and 43 (Group 1 = 1,000 agents). At any point in time some agents enter Group 1 because they are becoming 18 years old (40 agents) and some exit from Group 1 because they are becoming 44 years old (again 40 agents). All the agents in Group 1 have an individual time window in which they fulfil all the necessary conditions to found a firm and are thinking about founding a firm. We assume that this time window e2

t t*

t**

Figure 2.8 One example of the SF effect.

Theoretical considerations

29

has a length of 2.5 years and that these 2.5 years are equally distributed in Group 1. Thus, we cannot say which exact agents from Group 1 also belong to Group 2 (the agents currently in their time window for foundings) but we know that it is 10 per cent of the members of Group 1. Thus, Group 2 consists of 100 agents which currently are in the time window for their founding. Since the agents stay in Group 2 for only 2.5 years, every year 40 agents enter and 40 agents leave Group 2. A share of agents in Group 2 actually does engage in founding activity (Group 3). Thus, we have agents divided into three groups. Where at each period of time some agents enter each of the pools while others leave these pools depends on the specific transition probabilities. The number of agents entering Group 3 at one specific point in time is the number of firm foundings in this period. The suppressed-founding effect now has the following impact: if for some reason, the transition probabilities between groups change the number of agents entering groups in the future change. For the example in Table 2.1, this would mean that, if the transition probability from Group 2 to Group 3 increases from 50 per cent to 75 per cent (between time = 1 and time = 2), at time = 2 40 agents enter Group 2 of which 75 per cent become founders (= 30 persons). Since the stock of agents in Group 2 consists of additional 60 agents again 75 per cent of these agents would found (= 45 persons). However, since 30 of these persons have already founded a firm (based on the transition probability of 50 per cent at time = 1), only additional 15 persons start a firm. Thus, at time = 2 a total amount of 45 persons found. At time = 3 40 new agents enter Group 2 of which again 75 per cent would found (= 30 persons). For the following periods of time (from time = 3 onwards) 30 agents would enter Table 2.1 Example for the suppressed-founding effecta Time 1

2

3

Stock Transition probability to next group Agents entering the group Agents exiting the group Stock Transition probability to next group Agents entering the group Agents exiting the group Stock Transition probability to next group Agents entering the group Agents exiting the group

Group 1

Group 2

1,000 10% 40 40 1,000 10% 40 40 1,000 10% 40 40

100 50% 40 40 100 75% 40 40 100 75% 40 40

Group 3

20

45b

30

Notes a Assuming that all agents have the same transition probability of changing from one group to the other. b In this example the suppressed-founding effect is quite strong, but this is the most extreme case in which the effect only lasts for one period of time. In Figure 2.8 it was shown that the effect can last for a longer (but limited) amount of time and the effect can split, resulting in a smaller suppressed-founding effect for each point in time.

30

Theoretical considerations

Group 3 in each period of time and, thus, would start a firm (which are 1.5 as much as before), but the effect based on the stock or pool of agents present in Group 2 is no longer operating. Information diffusion In some cases, the change of an element e2 is strongly linked to information diffusion taking place in a population. For example, the starting-up of a small firm takes place at one time but its impact on regional factors is determined by how long it takes for information about it to diffuse in the population. The same delay may reflect the impact of regional factors on the firm founding rates. Such diffusion processes follow different paths. First, information can diffuse with a diminishing effect (see Figure 2.9). Such a development results if the information is transmitted from a central source (e.g. a newspaper) (Geroski 2000). If this central source always reaches a certain percentage of the (local) population the development depicted in Figure 2.9 results. In the beginning a large proportion of agents is reached by the new information but over time fewer and fewer agents are reached who have no information about the event or changes in regional factors before. Thus, the transmission impact diminishes over time. Second, information diffuses most successfully through regional social networks, which structure interaction between agents, and through verbal transmission in face-to-face interactions (Fornahl 2003 and also Section 4.2). Diffusion through the population is represented by an s-shaped diffusion curve as initially only few agents know the information. Hence, only few agents are able to pass the information on to others. Since this number increases, the diffusion rate also increases, until finally many people in the population possess the information, e2

t

Figure 2.9 Diminishing effect. e2

t t*

t**

Figure 2.10 S-shaped.

Theoretical considerations

31

and fewer and fewer people are reached by the ‘new’ information (Geroski 2000, Geroski and Mazzucato 2001) (Figure 2.10). Self-augmenting processes and over-shooting This section focuses on agents’ interaction and the resulting dynamics in development of an element e2. Such interactive processes amplify normal effects resulting in an over-shooting effect of element e2. Thus, after a strong increase in e2, a decrease to another level is observed (see Figures 2.8,16 2.11 and 2.12). These so called ‘bubbles’ are observed in a wide range of economic fields (e.g. financial markets, diffusion of technologies, consumer choices) (Bikhchandani et al. 1998, Devenow and Welch 1996, Geroski 2000). There are two possible ways to explain this phenomenon and both are based on self-augmenting processes that lead to an over-shooting reaction. The first way is through herd behaviour, information cascades or related processes (Geroski and Mazzucato 2001). Such an approach assumes that agents are rational and react to other agents’ behaviour by imitating them (e.g. entering a market) in order to solve information problems. A second approach focuses strongly upon psychological underpinnings of human behaviour. In this latter perspective, the agent is modelled by a social-cognitive approach including agents’ over-confidence and the strong impact of subjective mental models (see Chapter 4). Based generally on the impact of social-cognitive learning and specifically positive role models agents orient their own behaviour towards others behaviour leading to an overshooting in a certain behaviour. The positive development is limited and after an initial over-shooting a decrease in element e2 is observed. In the next paragraphs we will discuss some factors that are responsible for this limitation. One area in which this over-shooting takes place are entries into new (local) e2

t*

t**

t

Figure 2.11 Bump. e2

t t* t**

Figure 2.12 Bump with contagion effect.

32

Theoretical considerations

markets or start-ups. More firms enter a market than can survive, leading to a strong decrease after the initial increase. There are two reasons for such an overshooting (which we described above): the first one is based on herd behaviour and coordination problems among agents entering a market (Klepper and Miller 1995, Dixit and Shapiro 1986). Such an approach mainly focuses on the opportunities available in founding processes. The second approach based on social-cognitive learning focuses on the willingness aspect of foundings. There are some factors which slow-down or limit the positive development in regional start-up activities. These diminish incentives or possibilities of potential firm founders and, thus, stop their entrepreneurial development process. Positive developments come to an end if the market is saturated, competition is strong and barriers to entry appear. Furthermore, start-ups (over-)absorb many potential entrepreneurs (who become real entrepreneurs), financial capital as well as human capital; space in technology centres is blocked; they increase rents and wages etc. Negative role models also decrease the propensity of other regional agents to found a firm. All this results in a levelling out of start-up activities or even in a decrease. As too many firms enter the market, leading to a lower average fitness of firms, an increase in the number of exits can be expected in the medium run. This has two important effects: first, the number of negative role models increases, potentially decreasing regional agents’ willingness to found a firm. Second, excess of resources is downsized, although it probably does not end at the optimal level but is exaggerated in the negative direction resulting in an under-supply of resources. After such a bubble, markets, particularly for financial capital, are almost always driven by a contagion effect, where even the promising and high quality ideas of potential entrepreneurs are hit by rejection due to a lack of support (Masson 1999 on contagion effects in international finance). Such negative effects damage the basis for potential new foundings, resulting in a strong decrease in the number of firm foundings; temporarily even under the original level (Figure 2.12).17 There are three relevant developments of e2 in which such an over-shooting effect can result. These three processes are considered here because they represent three different final developmental situations: an increased, an equivalent and a temporarily decreased level of e2 in comparison to the beginning. The curves are the following: • • •

after an increase at t*, a decrease to the original level of e2 follows at t** (Figure 2.11), after an increase at t*, a decrease follows at t** but founding activities stabilize at a level higher than originally (Figure 2.8), or after an increase and decrease at t* and t** respectively, e2 stabilizes at the original level but with a temporary decrease under the original level after t** (Figure 2.12).

In general the three over-shooting developments are possible for the development of the regional factor or the firm foundings. In the following, we concen-

Theoretical considerations

33

trate on Figures 2.8 and 2.11. Although Figure 2.12 represents a very interesting development, it is difficult to test empirically and to distinguish from the other two developments because of its structural characteristics. 2.2.3 Summary of transformations and dynamics By focusing on different transformations (direct, limiting factor effects, distribution effects) and dynamics (delayed effects, SF effects, information diffusion, over-shooting) in these processes it is possible to study empirically the relationship between events, changes in regional factors and changes in start-up activities including their causes in more detail. These transmission processes seem to play an important role, particularly in explaining the dynamics of regional firm foundings and in improving the empirical test of relationships by taking them into account. Furthermore, the consideration of dynamic processes and transformations can improve policy measures. Alterations in transformations and dynamics also lead to changes in the development of e2. It is interesting, for example, that changes in firm founding activities do not only result from events and changing factors but also from changes in these transformation processes and dynamics. In the following sections, we use these dynamics and transformations to analyse the relationships and developments of regional factors and founding activities.

2.3 Events and their impact on regional factors The following sections focus on changes in firm founding activities as effects of regional factor changes (described in Section 2.1), in turn caused by selected events. We assume that founding activities and regional factors remain relatively stable over time and only change if a specific event takes place. The effects of such events on regional factor changes are shown within this section (left arrow in Figure 2.1). The impact of these events on regional factor changes over time is presented as stylized curves. Before describing the impact of the selected events on such regional factors, some general topics have to be discussed. Although all analysed events have an impact on regional factors, their origin is quite heterogeneous. They originate from within the region or externally. For example, the establishment of a university in Germany is decided by the federal state government (involving local politicians), although it is likely that agents within the region design new courses and local support programmes. Furthermore, the economic system is not the only source of such changes. The political system has a strong impact on designing an institutional environment, building infrastructure or providing financial support. The most disregarded aspect is the emergence of chance events that are not the deliberate design of policy makers or other agents. Thus, for some events it is hard to forecast their emergence or their effect upon the regional factors. Hence, the role of chance should not be underestimated nor the role of policy overestimated. Nevertheless some

34

Theoretical considerations

possibilities to actively design the (regional) environment in favour of firm foundings and economic development exist (Brenner and Fornahl 2003, Koschatzky 2003). In this case the complex interactive nature of relationships in regional innovation systems has to be taken into consideration (Braczyk et al. 1998). Policy makers can directly influence regional factors by events they are able to initiate themselves and they can indirectly affect economic organizations. However, they are not able to influence chance. Only linear transformations are relevant to link an event and changes in the factors (such as building new local infrastructure) as they proportionally affect the regional factor (in this example the additional regional conditions). Characteristics distribution among agents does not play a role here as the regional factors change in most cases without agents making deliberate decisions after the event.18 However, dynamic effects are important as they influence the development of some regional factors over time. These are delayed effects, information diffusion and over-shooting processes. The suppressedfounding effect is not considered because agents do not form a pool that affect the regional factor. Each event is singularly discussed identifying important dynamic effects which describe regional factor development in addition to the direct effect. Although in reality a time lag between the event and regional factor change exists, this lag is omitted here. We assume that the event immediately influences the regional factor. As the form of stylized regional factor development curves does not change, regardless of when the effect appears, and since we focus on the curves’ form, such an approach seems justifiable. In the following, six areas of events are described which influence the regional factors presented in Section 2.1. These six areas are (1) policy interventions, (2) settlement of large firms, (3) establishment of small firms, (4) emergence of localized industrial clusters, (5) changes in the external environment and (6) changes in venture capital supply. In addition, at the end of this section two other events are put forward that could have an influence but play a less prominent role for regional development. All of these events are able to influence and change regional factors. This impact and the resulting developments of the factors are summarized in tables in Sections 2.3.1 to 2.3.6. The results of this section are summarized in Table 2.11. Before we start with the events, some aspects concerning the tables presented in the following sections have to be discussed. In the first row of the tables the transformation process linking the event with the regional factors and the dynamics that cause short-term deviations from the medium-term level of the regional factor are displayed. The stylized curves that are shown in the second row of the tables represent the development of the regional factor over time. Concerning the transformation process only a linear transformation is considered, as described above. With regard to the dynamics, we normally would have to let all the results of the transformation process that determine the medium-term level of the regional factor interact with all dynamics relevant for a specific event. In order to keep the presentations comprehensible, we concentrate only on one result of the transformation process: a switch to a higher level

Theoretical considerations

35

Table 2.2 Impact of changes in educational and research infrastructure on regional factors Event

Educational and research infrastructure

Linear effects

Qualification Equipment

Pool Res.* (HC) Push Res. (Eq.)

■

Information diffusion

Pool Res. (HC)

■

Delayed effects

Pool Res. (HC) Res. (Eq.)

Note * We use the following abbreviations in the following: ‘Res.’ = ‘Resources’, ‘HC’ = Human capital, ‘FC’ = Financial capital and ‘Eq.’ = Equipment.

in a regional factor because this is the development most often appearing in the following analysis. We will give an example of how to read the tables by using Table 2.2. 2.3.1 Policy interventions Policy interventions comprise many different events, such as educational infrastructure, financial support, additional infrastructure and general support, including changes in demand and local markets where governments are the customers. Although these events are subsumed under the heading policy because policy makers usually conduct them (e.g. the establishment of a new university), policy interventions have to be broadly understood including local Chambers of Commerce and other parastatal organizations. Furthermore, it is acknowledged that some of these events can also emerge from private activities. Changes in the educational and research (infra)structure Policy makers change the educational and research infrastructure of a region, by establishing respective organizations (including universities, research institutes or technical colleges etc.), by influencing the development of specific (new) programmes and courses or by initiating a restructuring of existing organizations. The establishment of a new university or technical college in Germany is nearly the exclusive responsibility of a federal state, while research institutes can be set up by the federal state or by the national government. Educational and research infrastructure have a strong impact on the qualification of regional agents. The qualification of regional agents is relevant for the pool of potential entrepreneurs and for human capital embodied in potential employees. In Denmark, the establishment of a new institute at the University of Aalborg led to the provision of highly qualified workers needed by firms working in telecommunication (Dalum 1995). Furthermore, the access to machinery, equipment and laboratories

36

Theoretical considerations

improves, if new universities or research institutes are established or if established organizations buy or build a particular resource. In this respect another factor is relevant, namely the legal framework for resource use. If this framework changes by political interventions, access to these resources becomes either easier or more complicated. The closing down or downsizing of organizations, as well as a general decline in the personal situation of employees at these organizations, has impacts on various factors: the pool of entrepreneurs, the resources available for new firms and the push-factors. In the short-run, provided that these agents remain in the same region, neither the number of agents forming the pool of potential entrepreneurs nor the workforce changes. However, the availability of and access to these factors is improving: resources are cheaper and agents are less bound to their employers. Actual or anticipated unemployment play a role as push-factors. More relevant, for example, for employees in scientific organizations, is a decline in job satisfaction and attractiveness. Linked to the latter aspect of public research, financial resources available at universities and research institutes (including the perspectives researchers have) influence the evaluation of job alternatives. The decrease of the current agent’s situation serves as a push-factor. The emergence of new organizations and programmes (e.g. universities or other schools of higher education) leads to a switch to a higher level in the number of agents obtaining this kind of education. Thus, the number of potential entrepreneurs in the pool and the amount of (available) human capital jump to a higher level (see Figure 2.13; Switch). However, these factors are partly bound to existing firms or universities. The same holds for technical equipment available to agents in the region. A decline of the situation through the downsizing of an organization also results in a switch to a higher level of available human capital and unemployed agents, thus influencing the pool of entrepreneurs. However, this does not positively change the amount of qualified agents, which may even decrease over time. In addition, the regional aggregate of push-factors jumps to a higher level, but simultaneously the availability of technical equipment jumps to a lower level. Apart from the effect leading to a switch to a higher level of qualification, in the availability of equipment and/or the regional aggregate of push-factors, there is an additional delayed effect. This is caused by the growth in the infrastructure or new programmes after their establishment, e.g. an increase in the number of f

t t = time f = factor

Figure 2.13 Switch.

Theoretical considerations

37

professors and students (Figure 2.7; Delayed effect). Such a process mainly influences the pool of entrepreneurs and their resources. Furthermore, before the effect of new programmes and organizations works, agents have to identify their existence. Thus, the dynamic effect of information diffusion becomes relevant (Figures 2.9; Diminishing effect and 2.10; s-shape). Some regional factors, in this case the pool of potential entrepreneurs and human capital, change just after agents receive the information that they have the possibility of learning specific competences from new programmes or organizations. Thus, information diffusion first influences the number of agents attending the educational system and, hence, indirectly the two regional factors just mentioned. Table 2.2 summarizes the effect of changes in the educational and research infrastructure on the different regional factors and displays the resulting dynamics. The table has to be read as follows: for instance, the event ‘Changes in educational and research infrastructure with regard to the qualification of agents’ (e.g. a new course at the university) shown in the first column influences the pool of potential entrepreneurs and the human capital (the internal cells). The result is a switch of these regional factors to a higher level (second row) that is based on a linear transformation (first row). Concerning the dynamics, if, for example, information diffusion plays a role (third column) the pool of potential entrepreneurs takes some time to reach the medium-term level determined by the switch of the regional factor. Financial support In addition and as an alternative to the provision of educational and research infrastructure, policy interventions can also provide financial capital. This is an important resource for new firms: new political programmes provide financial capital as a direct start-up support. However, policy also increases the financial capital available to regional agents, e.g. by reducing taxes and public charges. Both events have an impact on the availability of financial capital, which influences how much agents are able to spend on starting up a firm. It also increases market demand. Additionally, policy makers can support education and training activities by providing financial means to various organizations. This increases the pool of potential entrepreneurs as well as the human capital in the region in general. The reduction in local taxes and related costs probably has a long lasting effect and, thus, the financial resources available to the agents change to a higher level (Figure 2.13; Switch). Normally, support programmes only last for a limited amount of time, so that their influence is only temporal: the financial capital changes to a higher level for the duration of the programme and then falls back to the old level (Figure 2.11; Bump).19 The impact of training and qualification aid is less clear because they can have a long lasting effect in some circumstances; in particular, if the human capital created by such programmes is durable and stays in the region. On the other hand, it is likely that after the programme’s end the pool of human capital deteriorates over time. To deal with this problem, both possible development curves are presented in Table 2.3.

Pool Res. (HC)

Training and qualification

Pool Res. (HC)

Res. (FC)

Public financial start-up support

Local market Res. (FC)

Public financial support

Reduction of private taxes

Linear effects

Event

Table 2.3 Impact of financial support on regional factors

■

Pool Res. (HC)

Information diffusion

[Local market] [Res. (FC)]

Delayed effects

[Res. (FC)]

Overshooting

Theoretical considerations

39

As in the case of the educational infrastructure, information diffusion plays a role for the training and qualification activities because it affects the number of agents who know about these activities and may act on this knowledge (Figures 2.9; Diminishing effect and 2.10; S-shape). An interesting aspect is that other parastatal or private organizations20 might imitate the example of an initial public support. In this case these organizations themselves start to support firm foundings (in a specific industrial sector) which in turn leads to the imitation of this support by others; resulting in a self-augmenting process with an over-shooting described in Section 2.2.2 above (Figures 2.8 and 2.11). This effect is less pronounced when public organizations provide financial capital, because private firms are more likely to imitate the actions of other private firms and less those of public organizations. Hence, we put the impact in parentheses in Table 2.3. Another aspect is a delayed effect (Figure 2.7; Delayed effect). Such an effect is plausible under specific circumstances, e.g. if a reduction of private taxes increases via a multiplier effect the locally available income of the agents, or if a policy programme directly triggers other additional public or private support programmes. Only the delayed effect of a reduction of private taxes is put in parentheses in Table 2.3. The other events are not considered at all because their effect is not focused on specific regions, are hard to grasp empirically, and would have to be in many cases considered as a totally new event. Demand change and local markets (see Table 2.4) The third aspect is the impact of policy interventions on local markets. A local market grows in three ways: (1) the number of customers increases, (2) the customers substitute from one (product) market to another, or (3) the customers increase their spending. An increase in the number of customers is based on the number of births as well as on the number of people migrating to the region or conducting their purchases in the region. Shifts in the spending on different products have a comparable effect as an increase in real income. For a local setting this is of specific importance if the spending is shifted to a local market. These two aspects (1) and (2) are not considered in the following because policy makers have little influence on them. An increase in disposable income (3), based for example on higher wages or lower taxes, can result in spending Table 2.4 Impact of public demand on regional factors Event

Linear effects

Delayed effects

Public demand

Local market

Local market

40

Theoretical considerations

changes. This is partly influenced by policy makers if they change local taxes and public charges. This effect of tax reductions was described above (under ‘financial support’). This leads to an increase in the market factor (measured, e.g. in the money available to local customers as shown in Figure 2.13; Switch), depending on whether the change exists for a longer period of time so that agents are able to build relatively stable expectations on future policy. Besides indirectly influencing local markets, public organizations also can directly demand goods and services. As was described in Section 2.1.2 public intervention or policy programmes change markets for specific products or services by establishing a public demand. Regions are affected if demand is regionally focused. Such policy programmes result in an increase and a subsequent falling back of the market factor after the public demand decreases or stops totally (see Figure 2.11; Bump). Such a policy can be measured, e.g. by the increase in market demand which it brings about. A delayed effect is relevant because public demand can trigger additional demand by other public or private organizations (Figure 2.7; Delayed effect). This can be seen as a successful policy measure if initial public demand is only present for a limited time, but nevertheless generates an additional and longer lasting multiplier effect on the market. As studied earlier in Section 2.1.2 a similar process took place in Silicon Valley with an initial market generated by the government for defence-oriented products and services. Later, industry adapted to supply other products (Saxenian 1994). The other dynamic effects do not occur because the market already changes without the information diffused and an over-shooting process in market demand is also not likely to occur because agents do not demand more than they are planning to buy. Additional infrastructure and support Besides educational infrastructure, policy interventions also establish other additional infrastructure like motorways, railroads or technology and start-up centres. The impetus for such infrastructure development come from local (technology centres or start-up centres) as well as national (motorways) authorities or from private firms (railroads). Furthermore, local policy makers simplify or simply help firms with the relevant administrative processes. In addition to the other policy interventions, policy makers can also initiate a general support programme (e.g. by giving advice and assistance in the founding process) for (potential) entrepreneurs. Such changes in the regional infrastructure or in the administrative process lead to an improvement in the additional regional conditions that persist over a longer time period (see Figure 2.13; Switch). The same holds for the general support of firm foundings, but such a change can also be represented as a bump (Figure 2.11; Bump), if the political programmes only change the related factors for a limited duration. We assume that information diffusion is not important for elements such as infrastructure because the change in the regional factor, which is the unit of analysis here, occurs and makes an impact without needing previous information

Theoretical considerations

41

Table 2.5 Impact of additional infrastructure and support on regional factors Event

Linear effects

Policy – Additional support

Additional condition

Delayed effects

■

Additional condition

[Additional condition]

diffusion to change the factor. Concerning delayed effect, the government or other agents would have to continue to extend their projects after an initial change. Depending on the kind of additional condition this is possible, but in most cases not likely. We put it in parentheses in Table 2.5. Over-shooting effects are considered as not relevant for changes in additional conditions because it is unlikely that the (local) government or other agents produce and change more than they need. For some parts of the infrastructure this is in general possible, but in this case this infrastructure is not demolished afterwards to reduce the regional factor. This interaction between increase and decrease is one essential element of the over-shooting process. 2.3.2 Settlement of large firms (see Table 2.6) A large firm inside a region affects the regional economic system (see Fornahl and Menzel 2003, Steil 1997, Almus et al. 1999 for examples). There are two aspects to this effect: their impact on qualification and on the markets for products and services. As was already pointed out in Section 2.1.1 and 2.1.3, existing firms are, beside the educational infrastructure, the most important organizations in which competencies are built. Thus, a new firm changes the quantity and quality of human capital in the region. Additionally, the settlement of new firms attracts qualified employees from outside the region, which also increases the pool of potential entrepreneurs and employees. Furthermore, firms that lay-off workers, close down completely or leave a region, often leave behind a qualified workforce. This is a specific case because it influences especially the availability Table 2.6 Impact of settlement of large firms on regional factors Event

Settlement of large firm

Linear effects

Qualification Buyer-supplier

Pool Res. (HC) Push Local market

■

Delayed effects

Pool Res. (HC) Local market

42

Theoretical considerations

of resources and the pool of entrepreneurs, at least in the short-run. If new firms working with these qualifications are founded, they can directly employ their workers from this pool. This abstracts from the fact that qualified agents might leave the region and from the potential costs to train the employee for the specific need of the new firm. Otherwise, the educational system or new firms are needed to change the competence profile of the workers. All these processes influence the pool of potential entrepreneurs as well as the pool of (qualified) employees. Depending on the process, the factors can be measured by the number of agents holding a specific qualification or by the number of agents who are available to be employed from other organizations or out of unemployment. Additionally, layingoff workers or worsening the situation of employees in general are relevant pushfactors resulting in a change of the regional aggregate of push-factors. Despite globalization, where suppliers and inputs may originate from nearly anywhere, geographic proximity still plays an important role in innovation and production (Audretsch and Feldman 1996). Growing regional markets offer easy access to potential customers and specialized suppliers; the latter is especially important for large firms. These regional relations are important because ‘sophisticated regional customers’ play an active part in supply side innovation and development. Simultaneously, regional suppliers are also important for innovation and production for the (large) customer firm (Lundvall 1988, Lissoni and Pagani 2003). Thus, the settlement of a large firm strongly increases the regional demand for various products and services; especially for specialized services and technology-based products (Gray et al. 1996, Almus et al. 1999, Davidsson et al. 1994). Such local buyer-supplier relations and outsourcing activities lead to the emergence of so called hub-and-spoke districts. Here, one central firm dominates local demand and many other firms that are linked to provide the necessary supply (Markusen 1996). As was the case for educational infrastructure, a new firm in the region also shifts the qualification of regional agents to a higher level affecting the pool of potential entrepreneurs and resources (Figure 2.13; Switch). The demand factor and the regional aggregate of push-factors also change to higher levels based on new buyer-supplier relations and a worsening in the economic or social situation of the agents, respectively. The growth pattern can show the discussed delayed effect, with an additional growth of the newly established firm after its initial settlement date (Figure 2.7; Delayed effects). This has the discussed implications for the qualification and the buyer-supplier relations. However, for the large firm it is less clear whether the firm grows after the initial settlement and what this growth looks like. Furthermore, the human capital of employees in large firms increases slowly and many competencies are only established through durable learning-by-doing processes (Adler and Clark 1991). Information diffusion processes do not play a role because it is reasonable to assume that the settlement of a large firm is an event all agents know instantly about. The relevant regional factors do probably immediately change as well. An over-shooting process is very unlikely because the firm will not educate and demand more workers than it needs.

Theoretical considerations

43

2.3.3 Start-up of small firms (see Table 2.7) The number of past firm foundings in a region influences the present regional context. Small firms that start in a region have different effects on the local conditions: they influence the qualifications of local agents, the local market and they serve as role models to others. Since there is only a small effect of one single small firm on the pool of potential entrepreneurs, regionally available human capital and on the market, these effects are not considered in the following. But regional foundings have the strongest impact on the willingness of other regional agents to found their own firm. This occurs because the provision of positive examples is a pull-factor (for a more detailed analysis see Chapter 4). Note that such role models have a positive as well as a negative impact on agents’ willingness to found. Negative examples – failures of firms or bankruptcies – decrease the willingness. However, the negative impact is not as large as the positive one (see Section 4.3.3 for an explanation). It is assumed that founding a new small firm does not immediately serve as a pull-factor. Only after the information about positive examples reached an agent, the establishment of the small firm affects this agent. As we pointed out, the number of regional agents that know of the positive example measures the regional pull-factor. Hence, the development of the regional pull-factor is based on information diffusion that has a diminishing effect or an s-shaped development as shown in Figures 2.9 (Diminishing effect) and 2.10 (S-shape). In the long term, positive examples cause a positive regional entrepreneurial climate, if a number of positive examples emerge for a long period of time. At least for a certain time this process shifts to a high level the willingness of agents to found. Such a situation is the end phase of the two developmental processes described above. 2.3.4 Emergence of localized industrial clusters (see Table 2.8) The emergence of a localized industrial cluster is strongly related to firm foundings (Cooper and Folta 2000, Brenner 2004). On the other hand, localized clusters also strongly influence the regional conditions relevant for start-up processes (for this interaction see Fornahl and Menzel 2003, Markusen 1996, Feldman 2001). Researchers working in the field of New Economic Geography model how the self-augmenting processes of concentration and deconcentration Table 2.7 Impact of establishment of small firms on regional factors Event

Founding of small firm

Information diffusion

Pull

44

Theoretical considerations

Table 2.8 Impact of emergence of a localized cluster on regional factors Event

Linear effects

Emergence of cluster

Pool Local market Res. (HC, FC, Eq.)

■

Information diffusion

Pull

Over-shooting

Res. (FC)

together with the existence of critical values, lead to a geographical production system of a hierarchal nature (Krugman 1991). Foundings and relocations of firms are central mechanisms here. Brenner (2004) builds a general framework describing the establishment of localized industrial clusters that is based on market, industrial and regional requirements as well as on local self-augmenting processes. Some of these requirements have already been mentioned above, like the impact of human and financial capital, the role of research institutes and universities. Here we do not focus on the processes that lead to the cluster’s emergence. Instead, we assume in this study that a localized cluster emerges, and its impact on the developments of regional conditions for start-ups are studied. The emergence of a cluster plays a specific role here because it contains many of the other events (emergence of small firms or the settlement of large ones) or triggers these events (e.g. administrative support or new educational infrastructure). However, since the cluster’s impact cannot be fully ascribed to the other singular events, it is worth considering it as a separate event. First, the main effects of cluster emergence are on resources in general and the human capital of regional agents in particular. Second, they influence the local market through buyer-supplier relations and increase the real income due to higher wages for cluster employees. Third, based on the start-up of new small firms, positive examples are created which encourage further start-up activities. The decline of a cluster also affects regional conditions. The processes are similar to those described in Section 2.3.2 for large firms. The available human capital and, thus, the resources accessible for new firms and the pool of potential entrepreneurs are affected. Again the regional aggregate of push-factors increases to a higher level, if the situation for individual agents worsens. However, in this case, the effect of a declining cluster on regional factors is not considered. Such an effect is very hard to predict because the decline is just as complicated as the establishment and not much detailed research exists on it. Since Fortis and Maggioni (2002) show that localized industrial clusters grow logistically, we also assume that their impact on regional factors is shaped in the same way (Figure 2.10; S-shape). Additionally, the establishment of a localized industrial cluster involves dynamic effects of information diffusion and self-augmentation. These were already described above: the establishment of a small firm influences the

Theoretical considerations

45

regional pull-factor via information diffusion processes. During the establishment of a cluster especially the provision of financial capital takes place in a self-augmenting manner. The s-shaped development already describes a dynamic adaptation process to a higher level of economic activity that is similar to the delay effect. We will not explicitly consider additional delayed effects in the following analysis. 2.3.5 Changes in the external environment It was noted that changes in political systems and their economic implications have peculiar features (Felder et al. 1997, Fischer 1996). Such a rapid restructuring process took place, for example, after the German reunification leading to enormous changes in national and regional factors. The same occurs in a regional setting (see Longhi 1999 for restructuring processes in Sophia Antipolis). Furthermore, changes in the elements of the external environment of a regional system, e.g. new technologies, market developments or nation-wide processes, also influence regions differently (see also item 2 of 2.3.7, Nr. 2). This depends on the current situation in the regional system. The introduction of new technologies, for example, leads to a disintegration of specific industries because, e.g. outsourcing processes are suddenly possible. If an industry is regionally concentrated and disintegration takes place, the regional factors change considerably. There are specifically four regional factors that are most strongly influenced by these events: (1) markets (e.g. by new technologies or liberalization of the market), (2) the pool of potential entrepreneurs (e.g. by technological changes), (3) the resources (e.g. also by technological change or new financial legislation) or (4) the push-factors (e.g. by changes in unemployment benefits). In the following only the three events just mentioned (new technologies, the opening of new markets and unemployment benefit legislation) are considered. They are only a small fraction of possible events, but they cover the most important areas. New technologies influence the pool of entrepreneurs and employees. Whether the number of agents increases or decreases depends on various factors: a new technology makes some qualifications obsolete and other qualifications become more important. This affects the extent of the pool as well as the availability of the agents in the pool, but the net effect is not clear. Nevertheless, depending on the net effect, an upward or downward switch in entrepreneur pool level and resources based on new technologies can be expected (Figure 2.13; Switch). A positive development only results when some agents in the population hold the necessary competencies. Furthermore, new technologies open up new opportunities which suggest that a switch in market demand to a different level is to be expected. On the other hand, new competing technologies may have a negative effect on the use of old technologies and on old markets. One positive example for such a process is the development of the microcomputer that changed the local market and opened up new market opportunities; especially for ‘technical and management consulting firms and other providers of

46

Theoretical considerations

Table 2.9 Impact of changes in the external environment on regional factors Event

Changes of external environment

Linear effects

New technologies Opening of new markets Change in unemployment benefits

Pool Res. (HC) [Market demand] Local market

■

Delayed effects

Pool Res. (HC) [Market demand] Local market

Push

business services’ (Saxenian 1994: 19). However, the effect on the market factor is not clear because a new technology is not necessarily linked to new products or services for which an actual market demand already exists. It takes some time and effort to develop the appropriate marketable products or services, if it is at all possible. Since this effect is not clear, it is put in parentheses in Table 2.9. For all the described regional factors, a delayed effect is expected because many new technologies will lead to follow-up innovations and changes in (related) technologies. Information diffusion and over-shooting effects do not play a role for a change in the relevant regional factors. This is the case because the relevant qualifications and market demand are present instantly without the need of information diffusion and adequately sized without customers demanding more than they would buy. If new competencies are needed and the agents have to be educated, it does not fall into this category, but is considered as a new event. If new markets open, the market demand for products or services is expected to switch (Figure 2.13; Switch). These products or services are either entirely new or they were not previously sold for other reasons (e.g. market was closed to competition). Delayed effects can be expected because after an initial switch directly after the establishment of the new market, it is likely that this market grows further, e.g. based on neighbouring industries that also demand these products. Other dynamic effects are not taken into consideration for the reasons discussed before: the local demand already changes without information diffusion being necessary and an over-shooting in the demand is not likely to happen because the customers will not demand more than they need. Changes in unemployment benefits21 are a push-factor, leading also to a switch of the regional aggregate of push-factors to a different level (Figure 2.13; Switch). In this case the incentive structure and the willingness of unemployed agents or those who are threatened with unemployment in the (near) future are affected by such an event. Since the agents know how much they receive as unemployment benefits, information diffusion is not relevant.22 A delayed effect23 and an over-shooting in unemployment benefits are not possible either.

Theoretical considerations

47

2.3.6 Changes in venture capital The supply of venture capital changes due to the establishment of new local venture capital firms or via an increase of the financial capital provided by already existing local venture capital firms. Such changes in regional conditions impact the financial capital of firms. This can be measured directly by the amount of funds available to the venture capital firm. Furthermore, venture capital firms often offer general support for start-up firms. The actual impact of a local venture capital firm is less clear because, on the one hand, firms are often located near to their venture capitalist, but on the other hand, venture capitalists also invest in firms not in close geographic proximity (see also Section 2.1.3). The expected development is a switch in the level of financial resources and the general support, based on the establishment of a new firm or a new funding programme (Figure 2.13; Switch).24 Since venture capital is available immediately and, thus, the regional factor also changes immediately, information diffusion does not play a role in this step. Interesting in the field of venture capital is a self-augmenting process with an over-shooting effect. If private venture capital firms invest in new firms involved in a specific technological field, other venture capital firms imitate this investment behaviour based on the considerations presented in Section 2.2.2. Again only two of the possible curves are presented in Table 2.10 as in Section 2.3.1. A delayed effect or a continuous increase in the amount of financial capital provided for the new start-ups is possible. However, it is more likely that venture capital firms react by sequentially investing in regional firms which is better reflected by multiple switches rather than a single change. Thus, such a process would be regarded as a new event. 2.3.7 Miscellaneous There are some other events that change the regional conditions. These events are not considered in detail in the next theoretical sections but will be described briefly because they do play a role in explaining the empirical findings. 1

Natural resources are an important factor for many industries, especially the primary sector. Thus, the discovery of specific natural resources in a region

Table 2.10 Impact of changes in venture capital on regional factors Event

Venture Capital Firm

Linear effects

Financial support General support

Res. (FC) Additional condition

■

Over-shooting

Res. (FC)

48

2

Theoretical considerations has an impact on the start-up activities. Since such a discovery is not very likely in Germany, this aspect is omitted. Linked to the radical transformations of the regional system are slower catching-up, following-behind or similar processes. Such growth processes are likely to be observed in the empirical data, because, for example, a region’s competitive advantage increases over time and lagging regions fall behind further.25 These processes are different from those discussed before because it is not necessarily the case that one specific event is responsible for regional factor dynamics. There are two possible origins of such processes: a The process is based on continuous changes in all the factors and processes described in Section 2.1, like a growth in the number of employees in the region or in the number of students. Due to the existent regional situation (be it prosperous or poor) and the resulting economic activities, the factors continuously change (improve or worsen). b An event – put forward in Section 2.3 – could have triggered long-term developments in the regional system, leading to phases of continuous change in the relevant factors, later probably reaching an upper limit. These processes are complex because they can be triggered by a single event or, more likely, several simultaneous events. Furthermore, several regional factors are influenced. We leave them out of the following analysis because even if they are present in the data, their influence will be caught by the investigated events separately. The inclusion of such an aggregated factor does not seem to offer deeper insights. The opposite holds: if these processes were included, the studied linkages would be even harder to identify because the aggregation process blurs them.

Hence, we leave out the natural resources because the likelihood that new natural resources are discovered in Germany is relatively low and, thus, this process plays a minor role. The long-term developments are relevant processes, but since they are based on aggregated events and changes in regional factors, we decided to concentrate on the single events that are responsible for these developments. 2.3.8 Summary of relationships between events and regional factors Table 2.11 summarizes the findings of this section by presenting the results of Tables 2.2 to 2.10 in a different order. Different regional factors are presented in the rows. Transformation processes – or in this case only one transformation process; namely the one based on a linear effect – and dynamics are displayed in the columns. The main cells show the events that influence the according regional factors. This effect is moderated by the linear transformation relationship or via the presented dynamics. For example, the pool of potential entrepreneurs is influenced by the educational infrastructure via a linear transformation. Additionally, information diffusion processes can play a role in

Theoretical considerations

49

the adaptation to the medium-term level of the pool or potential entrepreneurs: the pool does not instantaneously reach the medium-term level but only after a certain time which is needed for the information to diffuse. The subsequent factor changes in turn influence the regional start-up activities (described in Section 2.4.1). Some clarifications concerning the reading of Table 2.11 seem appropriate: •

•

The impacts that were placed in parentheses in the previous tables because the influence of the event is unclear or only weak are also parenthesized in Table 2.11. Since their influence is not clear or only weak, they are not considered for the analysis in the following sections. The ‘educational infrastructure’ and the ‘settlement of large firm’ have an impact on the regional aggregate of push-factors if they are worsening or if a large firm leaves a region or downsizes.

As can be seen from Table 2.11, in most cases various events influence several areas (e.g. the resources available in the region and the pool of entrepreneurs are both affected by educational infrastructure) and most areas are influenced by more than one event. This might cause some problems with the allocation of events to effects that is further explored in Section 2.4.2 below. To simplify, we only examine a limited amount of events and regional factors at the moment. On the other hand, the most relevant events were chosen and disaggregated in such a way that an assignment of many events to the according factors was possible. This provides a detailed view on the processes. Nevertheless, the events as well as the factors can be disaggregated in future research, in order to build more unique relationships between events and their effect on the factors.

2.4 Linking events, changing regional factors and founding activities In this section we discuss the transmission processes (i.e. transformations or dynamics) which relate to how specific regional factor changes influence the firm founding likelihood (Section 2.4.1). Based on the insights from Section 2.3, we know which functional form results from the different events. This functional form is called ‘input curve’ in the following. In Section 2.4.2 it is shown how input curves change when interacting in the transmission processes. In the last section of this chapter (Section 2.4.3), the complete development from the events to changes in the regional factors is presented by combining the findings from Section 2.3 and this section. The results are links between the different events and the resulting stylized curves of start-up activities. 2.4.1 Changing factors and their influence on founding activities In Section 2.3 we have seen how specific events, which are moderated by different dynamic processes, change regional factors over time (left arrow in

• Educational infrastructure • Training and qualification • Settlement of large firms • New technologies

• Reduction of private taxes • Settlement of large firms • Opening of new markets • [New technologies]

Pool

Local market

Linear effects

• Emergence of clusters

• Public demand • Emergence of clusters

• Training and qualification

Table 2.11 Impact of events on regional factors

• Educational infrastructure • Training and qualification

Information diffusion

• Public demand • Settlement of large firms • Opening of new markets • [Reduction of taxes] • [New technologies]

• Educational infrastructure • Settlement of large firms • New technologies

Delayed effects

Over-shooting

Additional infrastructure

• Policy – Additional support • Venture capital – General support

• Policy – Additional support

• Change in unemployment benefits • Educational infrastructure • Settlement of large firms

Push

Pull

• Educational infrastructure

Resources (Equipment)

• Emergence of clusters

• Public financial • Emergence of start-up support clusters

• Reduction of taxes • Venture capital – Financial support

Resources (Financial capital)

• Emergence of clusters

• Training and qualification

Resources • Educational (Human capital) infrastructure • Training and qualification • Settlement of large firms • New technologies

• Founding of small firms • Emergence of clusters

• Educational infrastructure • Training and qualification

• [Additional condition]

• Educational infrastructure

• [Reduction of taxes]

• Educational infrastructure • Settlement of large firms • New technologies • Emergence of clusters • Venture capital – Financial support • [Financial startup support]

52

Theoretical considerations

Figure 2.1). Although in general the regional founding activities are quite stable, such changes in the regional or other conditions relevant for regional start-ups lead to changes in the regional firm founding activities. In this section, we examine how regional factor dynamics impact start-up activities in the short to medium term (right arrow in Figure 2.1). Again the transformation processes and dynamics play an important role. As was pointed out, in addition to the linear effect, distributions of characteristics, suppressed-founding effects, information diffusion and over-shooting effects influence the start up rates reaction to changes in regional factors: because agents have to make decisions based on changes in regional factors, the distribution of characteristics among agents is relevant. As the agents need many different kinds of information to make the appropriate decision, information diffusion also plays a role. Over-shooting effects result from agents imitating each other. Suppressed-founding effects have to be considered because agents might have postponed their foundings for some reason but suddenly rush in after a regional factor changes. For each of the regional factors we discuss in the following how changes in these factors affect start-ups in detail. The results are summarized in Table 2.12 at the end of the section. Before doing so, some aspects have to be mentioned. Although the processes moderating the impact of changes are discussed separately, in reality they may appear together. For the sake of simplicity, the analysis is presented for each process separately. Nevertheless, by knowing, for example, the impact of the distributions of characteristics and of information diffusion, the combined impact can be concluded. Furthermore, we are conscious of the fact that this study does not pay any attention to the current population of firms, growth processes of firms and possible exits of firms. Additionally, it must be noted that this study is only concerned with those growth effects that lead to the founding of new firms and not to the growth of existing firms. Pool of potential entrepreneurs Changes in the local population’s qualifications influence the pool of potential entrepreneurs. An increase here also increases the number of firm foundings in a region. We assume only a linear transformation effect on firm foundings, in that changes are proportional to each other, e.g. a jump to a higher level in the pool leads to a proportional jump in the number of regional firm foundings. None of the other transmission processes plays a role for this factor. This is because of the specific role that the pool of potential entrepreneurs has in the start-up process. The number of agents in the pool directly influences the likelihood that firms are founded, since it is where the founders come from. The other regional factors are linked by a limiting factor relationship that moderates the impact of the pool on the founding activities. To put it another way: the currently limiting regional factor influences to which degree the potential entrepreneurial activities (based on the pool) can be transformed into actual start-ups.

Theoretical considerations

53

Local market An emerging or growing (local) market may lead to proportional changes in the regional firm founding activities. Besides this linear impact, the other transmission processes also play a role which we discuss now. Concerning the distribution of characteristics of agents, we assume a stepwise increasing relationship for the market context where the agents under consideration have one single threshold. First, the size of the market and related potential profits are relatively observable and second, even if these ‘objective’ facts are not given, it is likely that the evaluation of these factors is relatively homogeneous.26 Thus, it is likely that the agents have quite similar thresholds and react at the same time.27 If an increase in demand for a product in a region occurs, the agents do not necessarily react immediately. In some cases only after a critical mass of demand (measured in the number of inhabitants or real expenditure) is reached, does the local economic system react to this with new firm foundings or the attraction of outside firms. Researchers working on New Economic Geography, for example, modelled such processes (e.g. Krugman 1991, Fujita 1996). Thus, after the threshold is reached the firm founding activities react because all the agents that have the willingness or the opportunity to start a firm will do so. An increase in the demand for certain products or services also enables a suppressed-founding effect since agents who have not founded a firm (because the market was too small or not existing) but have the willingness to found, do found a firm after the market emerges or grows. As described in Section 2.2.2, this has a strong short-term effect on the founding activities as well as a smaller long-term effect. Although some information about public or private demand is publicly available, information diffusion is very relevant because, in many cases, information about the new market with the related opportunities are not known to the agents. It is one of the central elements in the start-up process to discover and develop business opportunities (at least for innovative firms) or to receive information about such opportunities from other agents. Especially newly founded firms or firms to be founded could depend on these information processes. As described in Section 2.2.2 growing markets can lead to over-shooting effects. Three factors combined explain the over-shooting in start-up activities: (1) opportunity (based on the market situation) and (2) willingness to found a firm (based on both pull- and push-factors), because these influence the founding decision. In addition (3) necessary resources are required for establishment because these factors impact on such founding possibilities. Based on the overshooting effect more firms enter the market than can survive, with the corresponding effects after the initial over-shooting: the founding activity can stabilize on a higher level than the original one or on the same level. As discussed in Section 2.2.2 a temporarily lower level is also possible but will not be considered here.

54

Theoretical considerations

Resources Easier access to and a higher amount of necessary resources has a positive impact on regional firm foundings requiring those specific resources. In most cases the availability of resources does not serve as an enabling factor, but as a limiting one. Thus, if resources are unavailable to a certain degree, they limit the number of firm foundings and serve as a barrier to entry (Barnett et al. 2003). Changes in founding dynamics mainly occur if the availability of these resources attracts external firms or if the lack of these resources has previously hindered regional firm foundings. Again some of the transmissions mechanisms alter the effect of resources on firm foundings. With respect to agents’ distribution, we assume that the agents’ reaction to changes in regional resources follows a stepwise increasing function. Thus, after a threshold is reached, all relevant agents who were hindered by the lack of this resource before now change their behaviour and start a firm. This is due to the fact that available resources can be objectively evaluated resulting in a similar resource evaluation. Since resources can hinder founding activities, a suppressed-founding effect is taken into account. This is caused by limiting resources, because not all founders who would like to found can do so. After resources increase, this limitation disintegrates and all agents who wanted to previously start a firm, but have not done so, start now. This explains the initial increase in founding activities. Subsequently only the smaller effect of better resource availability operates and, thus, there is a higher probability to start a firm. The effect of resource information on firm foundings is less clear. On the one hand, information is very important because agents have to know that these resources exist, the quality and quantity available, how to access them, etc. Shane and Cable (2002) analysed, for example, the impact social ties between agents (in this case actual or potential entrepreneurs) and venture capital firms have. They came to the conclusion that these ties play an important role for the provision of venture capital because agents exchange private information through these ties. Agents also have to receive information on various training and assistance programmes that can support their (future) firm. The same holds for firms searching for a (new) location. They have to collect information about local resources (e.g. human capital or public financial support) before they decide where to locate. On the other hand, many new founders do not thoroughly collect such information. In these cases information diffusion may not have an influence on the starting decision but on later success. Despite the uncertain role information plays in the founding and location decision, it is considered because of the strong importance of information diffusion in general. Push-factors As described above, the influence of the regional aggregate of push-factors is particularly hard to evaluate because it has to be at least divided into the impact

Theoretical considerations

55

of unemployment and other factors such as job dissatisfaction. Furthermore, other variables (e.g. the industry) also play a role for the reaction of the firm founding activities. Thus, the impact of the push-factors on changes in firm foundings is mixed. This depends on the type of push-factor (e.g. unemployment vs. job satisfaction), employee qualifications, the chosen industry and other regional conditions. In the following, only a prediction for a specific situation is made: if the general economic situation in a region is favourable for foundings; a pool of highly qualified potential entrepreneurs is available; and all the other conditions are beneficial to a founding, the decline of the situation for qualified researchers and employees increases the likelihood to found a firm (the general development is in line with Kriegesmann 1999). Note that this is only a short-term perspective and is based on the aforementioned favourable conditions. If unemployment increases or unemployment benefits are cut, the general effect is hard to predict (Section 2.1.4). We expect that an increase in start-ups emerges, as both qualified researchers or employees in technology-based industries and less qualified workers in basic service industries are stimulated to found firms. As the impact of the push-factors on firm founding activities is empirically unclear, linkages are assumed to be weak and put in parentheses in Table 2.12. The relevant distribution of the push-factors’ evaluation is probably normal, which therefore results in s-shaped dynamics. Every individual has to evaluate his or her personal situation, which includes the push-factors. Based on this highly subjective endeavour, the individual must decide whether to start a firm or not. Thus, agents will significantly differ in their individual thresholds of push-factors and, thus, they also differ in their propensity to start a firm. A suppressed-founding effect is observed here because agents refrain from starting their own firm due to a low willingness to do so (although they have the resources and the opportunity), but are positively influenced by the emergence of push-factors leading to the above-mentioned short- and medium-term effects. A decline in the individual’s situation (e.g. based on lower unemployment benefits) at a certain point in time can result in an over-shooting effect. This is due to the occurrence of a push-factor that increases the willingness, or in this case the necessity, to found. This leads to a situation in which too many firms enter a market. The over-shooting effect is limited by the amount of available resources. These resources always limit an over-shooting of market entries but in the case of the push-factors, it can be assumed that the general economic situation in most regions is not the best when, e.g. unemployment benefits are cut, thus, resources are not easily provided to new founders. Furthermore, agents pushed into the market are not likely to possess a high level of competencies and are less trusted by banks and venture capital firms. Pull-factors Positive examples of founders and role models are a pull-factor that in turn influences the agents’ willingness to start a firm. Thus, an increase in the

56

Theoretical considerations

regional pull-factor also increases the willingness to start a firm and finally startup activities in the region. Similar to the push-factors, the s-shaped characteristic distribution is relevant to the evaluation of the pull-factor because agents strongly differ in this evaluation. It might be the case that some agents do not react to positive founding examples, while for others, the pull-factor is strong enough to lead to an increase in the willingness to found. The suppressed-founding effect, in this case, does not work because the regional pull-factor slowly increases with the positive examples information spreading in the population. To have a pronounced suppressed-founding effect, an abrupt or strong change in a regional factor is necessary because at a certain point in time many agents start a firm. This does not occur during the slow increase of the regional pull-factor. Information diffusion is relevant for the impact of a positive example on the actual founding of another firm because, in order to serve as a pull-factor, information on this positive example first has to reach the agents. However, the relationship between the increased willingness to found based on the positive example and the actual founding is not again moderated by information diffusion. Thus, information diffusion is not considered in this step of the causal chain. Since the evaluation of the pull-factor and the willingness of agents to start a firm is highly subjective, being strongly influenced by social interaction and the provision of positive examples, the over-shooting effect is of importance here. Based on feedback processes between successful founders and potential entrepreneurs, the number of entries (at least for a short period of time) strongly increases but will later decline as described above. In addition, in the long term regional role models and positive examples can have an effect whereby the number of positive examples is high for a long period of time, resulting in a positive regional entrepreneurial climate. Such a process permanently shifts the willingness to found to a higher level in comparison to other regions (see also Chapter 4). Additional regional conditions This factor shows no particular developments. In addition to the proportional linear impact linked to a change in this regional factor, distribution can have a stepwise form because all agents similarly evaluate administration, general support programmes and infrastructure. A suppressed-founding effect results because firm foundings are hindered if the factor is not present (e.g. a motorway or a start-up centre) and after a change in this factor the suppressed-founding effect can be observed. The relevance of information diffusion is only given for some sub-factors (e.g. general support programmes) while for others (e.g. motorways) we can assume that agents are aware of them. Since information diffusion plays a minor role here, it is put in parentheses in Table 2.12.

Theoretical considerations

57

Summary of regional factors and transmission processes Table 2.12 summarizes the findings and relationships between the different regional factors and the transmission processes. 2.4.2 Input curves, transmission mechanisms and founding dynamics This section links the stylized regional factor developments resulting from Section 2.3 to potential development curves of start-up activities taking the impact of the transmission processes (Section 2.4.1) into consideration. Many events positively change factors, thus increasing the likelihood to found a firm. This does not necessarily imply that the firm is founded within either the region in which the event occurs, nor in the region where the potential founder lived. Founders could leave the region and found a firm elsewhere, but in most cases this is not observed. Founders are quite immobile and found the firm in their residing region or in which they have a specific attachment (Cooper and Folta 2000, Fornahl and Graf 2003). Thus, there is a high chance that the event leads to changes in firm foundings activities in the region in which it occurred. As most firms are founded by agents from within the region (Fornahl and Graf 2003), these endogenously emerged start-ups are assumed to be the most relevant in the following. As pointed out above, the stylized development curves of regional factors are called ‘input curves’ in the following. These input curves are: switches to a different level (switch), short-term changes (bump), developments based on the establishment of a localized cluster (s-shape), information diffusion processes (diminishing development and s-shape), strong initial changes with additional lagged developments (delayed effects) and over-shooting effects (bumps and bumps with a stabilization on a high level). In Section 2.4.1 we presented how different transmission processes influence the impact of changes in the different regional factors on start-up activities and which transmission processes are relevant for which factor. Based on these insights, it is possible to link the input curves with transmission processes. The results are presented in Table 2.13. Some aspects are discussed in the following. Table 2.12 Regional factors and transmission processes Linear – Distribution no dynamics effects

SF effect Information Overdiffusion shooting effect

s-shape Step-wise Pool of potential entrepreneurs Local market Resources Push-factors Pull-factors Additional regional conditions

X X X (X) X X

X X (X) X X

X X (X)

X X

X

(X)

X (X) X

Information diffusion

Linear effects

Input curves

Distribution effects

–

–

1

3

3

3

2

Transmission processes SF effect Information diffusion

Table 2.13 Link between input curves, transmission processes and resulting founding dynamics

4

2

4

Over-shooting

Over-shooting

Delayed effects

–

–

–

2 –

–

2

–

–

60

Theoretical considerations

First, the linear transformation process (excluding dynamics) is not shown in Table 2.13 due to its similarity to the input curves. Second, not all combinations are relevant, e.g. a bump as an input in combination with a distribution effect based on heterogeneity. Input curves describe the regional factor developments but not all of these curves are linked to all factors (see Section 2.3). Transmission processes only influence particular regional factors (see Table 2.12). It is possible that one transmission process does not impact the input curve as it is not based on affected regional factors. In this case, the cell in Table 2.13 remains empty. Third, we show if and how the other input curves change when influenced by different transmission mechanisms. We again (see also Section 2.3) only present one version of the dynamics. Normally all the results of the transformation processes which represent the medium-term level in the regional factors would have to be linked to all the relevant dynamics. We only show the short-term positive and negative deviations based on a switch to a higher level in a regional factor. As described in Section 2.2, some of the input curves remain the same, while some resulting start-up activity curves have the same shape but a different slope (in the most extreme case the impact disappears) and sometimes the point in time a change in start-ups occurs differs from the point in time the factor changes. This is not discussed any further with regard to stylized development curves. Nevertheless, it is important with regard to the political implications (as discussed in Chapter 5). We discuss some of the transmission process effects on input curves in the following. If the input curve form does not change, it is not considered in the description. •

•

•

The distribution effect based on a stepwise increasing function changes sshaped, diminishing and delayed curves to switches in start-up activities. The over-shooting effect with stabilization on a high level changes to a bump or a switch, depending on where the critical value is located: if the critical value is lower than the long-term trend, a switch results. If the critical value is higher than the long-term trend but lower than the maximum of the over-shooting effect, a bump in start-ups activities is expected.28 Heterogeneous agents are only occasionally relevant and change the diminishing effect of information diffusion to an s-shaped form. This cell (denoted by 1 in Table 2.13) is only based on the push-factors. Since all relationships that were put in parentheses in Section 2.4.1 are considered, this cell is used; otherwise it would remain empty. The suppressed-founding effect leads to the development depicted in Figure 2.8 with a short-term bump and stabilization on a high level. Two aspects have to be mentioned: first, the start-up activity developments related to bumps as input curves results in two different curves (denoted by 2): the suppressed-founding effect lasts as long as the whole bump or for a shorter period of time. In the former, start-up development is also a bump and in the latter, it is a bump with an additional shorter initial bump. To simplify

Theoretical considerations

•

•

61

the matter only the former case is considered. Second, a suppressed-founding effect only emerges when there is a rapid increase in the number of firm foundings based on agents who have not previously founded but will do so after a specific regional factor change occurred. A sudden regional factor change is necessary for this development otherwise a continuous change in the number of firm foundings would result and not a rapid one. The suppressed-founding effect does not occur because information diffusion and s-shaped input curves develop slowly (denoted by 3). Thus, the cells are left empty. Information diffusion considerably changes some input curves. The two bump curves (stemming from the linear and the over-shooting effect), experience a slower increase because information on regional factor changes has to first reach agents before they can react. Thus, the reaction is slowed down. In contrast, the end is abrupt due to the decline of the regional factor, thus, being unavailable at one point in time, regardless whether agents have the information or not. Similarly, the over-shooting process with a stabilization on a high level increases and decreases slower than without the information process. The link between s-shaped and information diffusion input curves to the information diffusion transmission process is specific because these two dynamics need time to accumulate and add up. Nevertheless, it is assumed that the normal curves of the information diffusion process are the most relevant. This is due to the fact that even if the information diffusion already takes place whilst the regional factor is still changing, the functional form is still the same as depicted by the diminishing effect and s-shaped curves. For the over-shooting effect only one aspect has to be mentioned. In the cases denoted by 4, a bump in the founding activities only emerges if overshooting has a negative impact on the regional factors. If this is the case, a new event must be considered with an initial increase and a decrease later on. Such a development is already described by the case in which a bump is the input curve. Thus, the cells denoted by 4 remain empty in Table 2.13.

2.4.3 Relationship between events and founding activities Now the different necessary elements have been presented in Tables 2.11 to 2.13, it is possible to link the events with the resulting curves of the firm founding activities. In Table 2.14 the results are summarized. The different firm founding activity curves are presented in the first column, the second column introduces the events that can lead to such a development and the following columns show which transmission processes link the events with the regional factors, and the regional factors with the regional founding activities. The regional factors that change due to specific events are not explicitly mentioned for reasons of clarity; but these linkages are presented in Table 2.11. In the cells, an ‘x’ shows whether the event is moderated by this transmission process and an ‘(x)’ is used for the event operating via the push-factors or the additional regional conditions. As mentioned in Section 2.4.1 their impact is not clear.

x x x x x x x x x x x x x

Public financial start-up support Training and qualification Public demand Policy – Additional support

x x x x

Linear

Educational infrastructure – Qualification Educational infrastructure – Equipment Reduction of private taxes Training and qualification Public demand Policy – Additional support Settlement of large firm – Qualification Settlement of large firm – Buyer-supplier Emergence of cluster New technologies Opening of new markets Unemployment benefits Venture capital – Financial support Venture capital – General support

Regional start-up activity development path Events

Information diffusion x

x

x x

x x

x

x

Delayed effects

Table 2.14 Developments of founding activities, events and transmission processes

Over-shooting x

x

Linear/no dynamics x x x x

x x (x) x x

x x x

x x x x

Stepwise x x x x

x x

x x x x x x x x x x x

S-shaped (x)

(x)

x

SF effect x x x x

x

Over-shooting

Information diffusion

Educational infrastructure – Qualification Educational infrastructure – Equipment Public demand Settlement of large firm – Qualification Settlement of large firm – Buyer-supplier New technologies Opening of new markets

Educational infrastructure – Qualification Educational infrastructure – Equipment Reduction of private taxes

AW

AW

Founding of small firm Emergence of cluster Venture capital – Financial support

Regional start-up activity development path Events Linear x x x

Information diffusion x

Delayed effects x x

x x x x x x x

Over-shooting x x

Linear/no dynamics x x x x x x x

x x

Stepwise x x

SF effect x x x

x x

Information diffusion

continued

(x) x

x

Over-shooting

S-shaped

AW/AW Public financial start-up support Training and qualification Public demand Policy – Additional support

Training and qualification Public demand Policy – Additional support Settlement of large firm – Qualification Settlement of large firm – Buyer-supplier Founding of small firm Emergence of cluster New technologies Opening of new markets Unemployment benefits Venture capital – Financial support Venture capital – General support

Regional start-up activity development path Events

Table 2.14 continued

Linear x x x x

x

x x x x

x x x x x

Information diffusion x

Delayed effects x x

x x

x

Over-shooting x

x

Linear/no dynamics x

x

SF effect x x x (x) x x

x x x x x

Information diffusion x x x (x)

x (x)

(x) x x x

x

Over-shooting S-shaped

Stepwise

AW/AW Educational infrastructure – Qualification Educational infrastructure – Equipment Reduction of private taxes Training and qualification Public demand Policy – Additional support Settlement of large firm – Qualification x x

x x x x

Linear

x

x

x

x

x x

x x

Information diffusion

Emergence of cluster Venture capital – Financial support

Delayed effects

x x

Over-shooting

Emergence of cluster Venture capital – Financial support

Regional start-up activity development path Events

Linear/no dynamics x

x

Information diffusion

continued

x x x x x (x) x

x x

x x

Over-shooting SF effect

S-shaped

Stepwise

AW

Founding of small firm Emergence of cluster

Settlement of large firm – Buyer-supplier Founding of small firm Emergence of cluster New technologies Opening of new markets Venture capital – Financial support Venture capital – General support

Regional start-up activity development path Events

Table 2.14 continued

Linear x

x x x x x

x

Information diffusion x x

x

Delayed effects x

x

Linear/no dynamics x

x x

S-shaped x x

x

Information diffusion x x x x (x)

x

Over-shooting SF effect

Stepwise

Over-shooting

Theoretical considerations

67

The inference from the stylized development curves of the founding activity to the underlying events is not exactly possible in all cases because sometimes different events lead to the same stylized curve. Nevertheless, not all events produce the same curves and, thus, when observing these curves, we can conclude that a certain, limited set of events cause this development in regional firm founding activities, allowing us to exclude other events. Furthermore, some patterns emerge from the interaction of the transmission processes and the resulting firm founding activity curves (Table 2.14). The most obvious one is that a delay curve always results from a delayed effect that moderates the relationship between event and regional factor in combination with a direct link between the regional factor and the start-up activities. Hence, an inference from the firmfounding curve to the most likely transmission processes is possible. From Table 2.14 various transmission processes seem to be relevant aspects that strongly influence the developments triggered by events. Thus, the approach followed here, taking into account events, changes in regional factors and transmission processes, is necessary and complementary to the approach followed in the literature. Again transmission processes are separately analysed, although they can operate simultaneously to increase the clarity of relationships. Nevertheless, transmission processes can be combined. In this case a sequence analysis must be made because the order of processes can change the effect on start-up activities. For example, the effect based on characteristic distributions among agents operates before the suppressed-founding effect and the over-shooting. Or agent heterogeneity leads to a slower increase in suppressed-founding effects and generally softer changes because agents react at different rates and at different times. Hence, such a combination of processes is possible but not considered here. The effect of events, regional factors and the relevance of transmission processes may differ from one industry to the other. This industry-specificity has to be accounted for in empirical studies. As already mentioned the events and regional factors can be deeply explored in the future. This implies a finer division of the events, regional factors and particularly the allocation of events to regional changes. This would improve the theoretical comparison to empirical findings and offers the possibility to test relationships. In this refinement process it is possible to identify additional transmission processes that link events, regional factor changes and changes in firm founding activities. Thus, the previously used transmission process can be extended and tested in future research. Maybe other processes will be discovered that are equally important than the currently selected ones. Although the events are described separately here, in reality several of them may simultaneously affect firm founding activities. Furthermore, they can also influence each other, making it even harder to discern which events are responsible for developments. Additionally, feedback processes between the firm founding activities and the regional factors or events are not explicitly considered here, but these can be integrated in the current framework. Founding a small

68

Theoretical considerations

firm and the establishment of a large firm are events that are already included in the study, through which feedback processes operate. Additional events related to the feedback processes can also be defined. Some of these possible feedback processes will be described here briefly. Cluster formation and an increasing number of firm foundings affects the growth of awareness in the regional population and a growing ability for collective actions emerges. Policy makers may enact policy measures to support the formation and development of new firms. The number of firms and employees increases the number of positive examples in the region, which in turn increases the propensity of other economic agents to found a firm (Section 2.1.5). In regions in which many start-ups already exist agents have easier access to capital (Brenner 2002). The required capital is provided by firms from inside the region or from venture capital firms, who were attracted to the region because of its past success. Prevezer (1998) gives the example of an incubator in the biotechnology cluster in North Carolina that was established when the region already contained around 100 small firms. Feldman (2001) argues that in the Capitol region case, venture capital did not stimulate the development of the localized cluster, but rather it followed such an event. Furthermore, universities sometimes settle after the first growth phase of the region (Longhi 1999). In this section we developed a causal link between events, changes in regional factors and changes in regional firm foundings that are affected by transformation processes and dynamics. We use the results gained here in the following Chapter 3. There we identify the actual developments of regional firm founding activities and reasons for these developments. These we compare to the events and stylized curves we discussed here.

3

Empirical evidence

After we analysed regional events and their impacts on regional firm founding activities in Chapter 2, such changes of founding activities over time are the units of investigation and we examine them empirically in this chapter. This empirical analysis focuses on two main areas: first, using data on German regions, we test whether short-term changes in the regional firm founding activity do exist or whether the regional activities can be explained by industry-wide and long-term regional factors alone. Second, if a change is identified, we further analyse in which regions, industries and years it occurred and what the dynamics of the start-up activities look like. For this endeavour, the development of start-up activities inside one region over a certain period of time is of importance and not the static comparison of such activities between regions. The chapter proceeds as follows. In Section 3.1 we describe the empirical data used for the analysis. Then, the selection of the regions and industries for the sample is explained and the sample is portrayed. Section 3.2 explains the analysis of the data. First, we test whether short- to medium-term changes in the regional firm founding activities can be identified for the selected industries and regions between the years 1990 and 2001 (Section 3.2.1). A method is proposed that eliminates changes caused by industrial, national or long-term regional influences. Furthermore, the characteristics of these changes are analysed to get a first overview of the developments that take place (Section 3.2.2) and the corresponding curves are grouped and classified in order to comprehend what these deviations look like (3.2.3). Section 3.2.4 examines the different classes of the development of firm foundings according to their industrial and regional characteristics. In Section 3.3 the results of interviews with experts from the regions in which changes occurred are used to determine which events led to the different classes of developments. The aim is to link Chapters 2 and 3 by comparing the stylized development curves derived from Chapter 2 and the corresponding events to the empirically detected development classes. The last section (3.4) gives a short summary and a discussion of the findings.

70

Empirical evidence

3.1 Background of the empirical study 3.1.1 Data In Germany only a few databases are available in which the number of foundings is collected in a differentiated and credible way. It is especially difficult to obtain disaggregated data on a regional and industrial level. Fritsch et al. (2002) argue that two data sources are indeed appropriate in order to analyse founding activities in Germany: the Founding Panels from the Centre for European Economic Research (ZEW) and the employee statistics from the German Federal Employment Office.1 Since the latter also includes branches in its database, one can conclude from the analysis by Frisch et al. that only the ZEW Founding Panels can be used for the aim at hand. Hence, the sample, which is used in the analysis, is based on the ‘Mannheim Founding Panels’ of the ZEW. In the following, the design and the suitability of the database are described in more detail. The ZEW has two different founding panels: one for West Germany for which data are available from 1 January 1989 and one for East Germany with reliable data from 1 January 1990. The latter can be regarded as a database containing the whole East German firm population because the firms founded before 1990 are also included. The ZEW offers data on founding intensities (absolute number of firm foundings per 10,000 inhabitants between 18 and 65 years) for external users.2 This data is available for each of the local administrative districts (so called ‘Landkreise’ and ‘Kreisfreie Städte’) in Germany and Austria. They can also be summed up to a more aggregate level, for example to the federal state level. The industrial differentiation is based on the WZ-93 (Wirtschaftszweige 93) classification 2-digit level that was the official German classification of industries used until 2002 in all official statistics (Federal Statistical Office Germany 2003). This availability of founding data on a disaggregated level makes it possible to study time patterns of founding activities for different kinds of regional and industrial aggregates. The basis for the ZEW Founding Panels is the firm data provided by ‘Creditreform’. Creditreform is the largest German commercial credit rating agency, thus normally providing information regarding a firm’s financial situation but also selling addresses and additional information to customers. The ZEW Founding Panels are updated approximately every six months and, thus, provide a recent picture of the founding activities.3 After receiving the raw data from Creditreform several data cleaning procedures are conducted at the ZEW in order to, for instance, identify the original start-ups or to clean multiple entries from the data. The statistical unit of the database is the legally independent firm. New branches of firms that are not legally independent are not included in the data. Furthermore, the group of originally independent firm foundings, defined as firms which are founded by an individual person for the first time, can be

Empirical evidence 71 separated from other types of foundings. Especially employment information and data on firm affiliations is used to distinguish these different types of foundings from each other. Thus, foundings caused by events such as the relocation of a firm, a change in the legal form or in the focus of business activities as well as mergers and takeovers can be distinguished from original foundings. Such data are suitable for the purpose of this study because its aim is to analyse changes in firm founding activities in a region and the related decision to start a firm and to further discern what factors influence such a decision. These research questions can be best answered by using data on legally independent firms. In addition, firms without employees enter the database as well. This is an advantage in comparison to data originating from the Employee Statistics of the Federal Employment Office, in which only firms with at least one employee are included, because many new firms have no employees at the very beginning. In contrast to the business register, Creditreform only includes those firms in its database that are active on the market to a degree that is at least equivalent to a full-time occupation of one person. Thus, there might be firms in some regions and industries that were founded but which are not included in the database. The focus on active firms and the omission of pseudo-firms seems reasonable for the following analysis because we just want to concentrate on those firms that really pursue business activities and have an economic impact. In order to detect and eliminate possible biases, the raw data are generally checked by comparing it with previously collected data from the region and other available data sources (Almus et al. 2002b). Nevertheless, some industries and firms of a certain size are under-represented in the sample. Creditreform regularly checks the entries on new firms in the trade register. Thus, nearly all firms included in this register are also included in the Creditreform database. However, not all firms are registered in the trade register itself. For these firms the probability of entering the database and the coverage of the data collection strongly depends on the scope of credit demand and business relations with other firms (ZEW 2003b). The unregistered firms are in most cases rather ‘small and are more likely to be found in the service than in the manufacturing industries. Consequently, the under coverage of newly founded firms in the Creditreform database shows a size- und sector-specific pattern’ (Almus, Engel and Prantl 2002a: 42–43). In the end especially micro firms, agricultural firms and freelances are under-represented (Harhoff and Steil 1997, Harhoff and Licht 1994). Even if the problem of under-coverage might theoretically influence the following analysis, this problem seems to be rather minor because of our focus on specific industries that are neither part of the agricultural sector nor include freelancers. It could even be the case that there does exist such an under-coverage in some regions; however, as long as such a bias is stable over time, it will not seriously impede our analysis. This is because we focus on changes of founding activities inside one region. Although in general the collection of data is conducted in a uniform fashion in each local unit, some inconsistencies may arise. For example, in some regions strong changes in the number of firm foundations might appear in the data that

72

Empirical evidence

are not based on real changes in the number of start-ups but are rather caused by changes in the method of collecting the data by the local associations of Creditreform (ZEW 2003a). Some of these local units seem to collect data much quicker or to a greater extent. Normally small business firms are under-represented in the Creditreform database, but this is corrected by cooperation with local Chambers of Commerce and Industry. At the moment no information is available on which associations of Creditreform cooperate with Chambers of Commerce and Industry. Nevertheless, there exists a list of administrative units for which such cooperation is likely. Fortunately, only two administrative units used in the analysis appear in this list. These are Hamburg4 for the years 1995 to 2001 and Göttingen for 1995–1997. An analysis of the firm founding intensities in these regions for the respective periods in time comes to the result that Göttingen exhibits a strong increase in the founding intensities around 1995 with a decrease in 1996/1997. This might be based on the change in data collection methods. Hamburg does show an increase in the firm founding activities, but this increase already starts in the early 1990s and is probably not caused by the method of data collection. Consequently, it is not clear whether these particular regional changes founding activities result from a change in the data collection method or whether there exists an event that led to these changes and because such a region has only a minor impact on the aggregate level of firm founding activities. As a result both regions are included in the analysis. Nevertheless, this background information must be kept in mind when evaluating the findings for these two local units and the corresponding years. In the beginning of 1999 a change in the data format in which the ZEW receives the data from Creditreform took place. With it, the coverage of West German foundings improved. This further reduced the under-coverage of specific industries and firm sizes discussed above. For the upcoming analysis this change should not cause problems if it uniformly influenced all considered regions. Since especially West German regions are affected, changes in East German regions after 1999 have to be evaluated in the light of this possible bias. Another possible problem is linked to the provision of reference numbers to firms: a new reference number can be assigned to a firm which would increase the number of firm foundings, although the firm was already included in the database before and is, thus, not a new founding. This can be caused by the relocation of firms or by mergers, takeovers or split-ups via foundation. Although these problems can be reduced by the use of the various data sources and the new form in which data is transferred to the ZEW, nevertheless ‘such firms cannot be systematically identified in the ZEW Foundation Panels’ (Almus et al. 2002a: 44). Spin-offs from the manufacturing industries in the business-related service industries especially inflate the database. For the upcoming analysis this appears not to be a problem because of the aforementioned mode of calculation and, furthermore, because of the importance of spin-offs, it seems even reasonable to include these firms in the analysis. There may exist some recording lags in the original Creditreform data that reduce the reliability of information on recent founding dynamics. Thus, a pro-

Empirical evidence 73 jection based on previous data and experiences is made by the ZEW to determine a corrected number of recent firm foundings. This projection is controlled for by ‘comparing them with the corresponding founding numbers on the basis of business register data. At the moment, researchers at the ZEW consider the information about firm founding activities from 1989 until one year before the last data delivery wave to be reliable’ (Almus et al. 2002a: 44). It can be concluded that the data are reasonably accurate based on the projections discussed. Furthermore, quality improves if the period under investigation ends two years before the current date because during these two years most firms founded before the end of the period under investigation have entered the database; e.g. if the analysis is conducted in 2004 the period under investigation should end in 2002. Despite the potential problems of the ZEW Founding Panel data, it is deemed suitable for the current analysis. While there is no possibility of giving an accurate approximation of the absolute number of foundings in each industry, there are no apparent systematic temporal and regional biases in the data. There exist three additional data sources that are also used in the following analysis: (A) the patent data are provided by Greif and Schmiedl (2002) which encloses patent data on the regional level. (B) Data on inhabitants, unemployment rates and the geographical area is taken from Statistik Regional (2003) which is provided by the German Federal Statistical Office. (C) The classification of regions into types (agglomeration areas, urbanized areas and rural areas) is based on INKAR (2002). 3.1.2 Sample For the empirical analysis a sample is chosen that encompasses 49 regions, 15 industries and 12 years. In this section this sample is described and its use is motivated. Time The data used in the following ranges from 1990 to 2001. For East Germany only observations from 1990 onwards are available and the general differences related to both pre- and post-reunification phases would cause severe methodological problems. As stated above, time lags might exist in data collection and so 2001 was chosen as the final year. This is based on the consideration of a high likelihood that in July 2003 (when the data was received by the author) all firms newly started before 2002 are actually included in the data. Furthermore, the 12 years nearly cover the entire time span in which the ZEW has collected the data (from 1989 for West Germany until now) and, thus, it is possible to examine the regional development over quite a long period of time. Additionally, this time span should be long enough to analyse one or even several changes in the founding activities that take sometimes several years to develop. On the other hand, the time span is short enough so that some regional factors can be regarded as fixed which is relevant for Section 3.2.1.

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Empirical evidence

Firm founding intensity

120 100 80 60 40 20 0

1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 Year West Germany

East Germany

Figure 3.1 Firm founding intensities in East and West Germany (foundings per 10,000 inhabitants between 18 and 65 years). Source: ZEW Foundation Panels; author’s calculations.

Figure 3.1 presents the development of firm foundings during this time span. In West Germany the firm founding intensity, measured in firm foundings per 10,000 inhabitants, reached its highest value in 1998. In East Germany the founding intensity dropped from 112.5 in 1990 to 42.2 in 2001. This very high firm founding intensity in the beginning is due to the German reunification and the linked restructuring processes of the East German economy. From 1995 onwards it nearly follows the same development as in West Germany and even drops under the level of West Germany in 2001. As we analyse the regions separately this does not impede our analysis, but these peculiar circumstances make it likely that we will find specific developments in the East German regions in our sample. Regions The geographic unit is the administrative district on the ‘Kreis’ level. On this level 440 administrative units exist in Germany. Thus, the data and the subsequent analysis are based and conducted on a disaggregated level. This brings us the advantage of observing small developments in the founding activities that cannot be seen in larger aggregates, due to the levelling out of these developments by a larger basis. From the 440 local administrative units in Germany, 49 regions were selected and used in the analysis (see Table A.1 in the Appendix). These regions were selected because they all included at least one firm that was listed in the NEMAX 50 (‘New Market Stock-Index’ of the Frankfurt Stock Exchange containing the 50 most liquid stock values of the German New Market) or its successor the TecDax (Technology index containing the top 30 technology stocks

Empirical evidence 75 traded in the Prime Standard segment of the Frankfurt Stock Exchange in terms of turnover and market capitalization).5 Thus, all NEMAX and TecDax firms listed between 1990 and 2001 were or are still concentrated in these 49 local units. This criterion was used for two reasons: first, the existence of a NEMAX or TecDax firm is assumed to influence the regional founding activities.6 Second, although the number of regions is relatively high and they show a large diversity in various factors that will be described below, we assume that these regions share the important similarity of having a high number of firm foundings. This makes them more interesting for economic analysis in general and for the study of developments of firm founding activities in particular. Hence, the sample is not representative but offers insights into regions in which many firm foundings take place. The number of regions and their corresponding start-up activities should be large enough to avoid one region dominating the whole sample. Therefore, it should be possible to compare a single region against the aggregate of the 48 other regions of the sample which is relevant for Section 3.2. The selected regions are heterogeneous regarding several characteristics, as can be seen in Table A.1 in the Appendix. In the following, some facets of this heterogeneity are set out. Two aspects are especially worth pointing out: first, the average founding activities in these regions differ strongly (Figure 3.2). For example Hamburg has an average founding intensity of 105.2 (foundings per 10,000 inhabitants), which is the highest intensity of all regions, whereas Cuxhaven has an average founding intensity of 20.98 (the lowest intensity). Thus, Hamburg has a five times higher founding activity than Cuxhaven. Second, the sample spreads over a wide geographical area. Nevertheless, there is a focus on West Germany with only four of the 49 regions in East Germany. Another concentration of sample regions exists around the city of Frankfurt/Main. In Table A.1 several other factors are mentioned: the average absolute firm founding numbers per year between the regions also differ with agglomerations like Berlin (13,807), Hamburg (12,108), München (5,293) and Köln (3,318) at the top of the list and Aschaffenburg (277), Cuxhaven (268) and Cham (260) with a relatively low number of firm foundings. The total number of inhabitants as well as the number of inhabitants between 18 and 65 range from Berlin (3.382 million inhabitants; 2.327 million people between 18 and 65) to Sömmerda (81,204; 54,115). Some regions cover a large geographical area like Rendsburg-Eckernförde (2,185 square kilometres) while others define only a small area (e.g. Offenbach with 44 square kilometres). Based on these characteristics one region can be classified as ‘rural areas’ (Dithmarschen), 19 as ‘urbanized areas’ (e.g. Jena) and 29 as ‘agglomeration areas’ (e.g. Berlin, München, Köln) (see INKAR 2002 for the categorization). Strong differences can also be found in the innovation activities (measured here in the number of patents relative to the number of inhabitants) (e.g. München Land with 989.54 and Sömmerda with 36.80) and in the unemployment rate (Sömmerda with 18.3 per cent and München Land with 4 per cent). In order to get an impression of how well these regions represent the overall firm founding activity, several factors serve as an indicator. First, in 2001 about

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Empirical evidence

Figure 3.2 Average firm founding intensities in the 49 sample regions per year. Source: ZEW Foundation Panels; author’s calculations.

13.2 million inhabitants between 18 and 65 years lived in the 49 regions. They represent nearly a quarter of the German population between 18 and 65 years; the group most likely to found a firm. Second, the total number of firm foundings in Germany is compared to the firm foundings in these 49 regions. According to ZEW data, 257,661 firm foundings per year occurred between 1990 and

Empirical evidence 77

Number of firm foundings

400,000 300,000 200,000 100,000 0 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 Year Sample

Germany

Figure 3.3 Absolute number of firm foundings in the sample regions and in Germany. Source: ZEW Foundation Panels; author’s calculations.

2001 in Germany. The average number of firm foundings in the sample regions for this period of time was 74,845 per year. Thus, our 49 sample regions cover 29 per cent of the firm foundings. Third, comparing the absolute number of firm foundings of the sample regions over the 12 years of observation with the overall German founding activity (Figure 3.3), one finds that this coverage remains nearly the same (ranging from 27.7 per cent in 1992 to 32.3 per cent in 1999). The founding activities in the years 1990 and 1991 deviate somewhat. This is due to the small number of East German regions in the sample where many new firms were founded shortly after the reunification. A similar picture emerges when comparing the firm founding intensities between the sample regions and the whole of Germany (Figure 3.4). Sample regions range between 51 and 63 (with an average founding activity of 56 startups per 10,000 inhabitants) and for Germany between 43 and 56 (with an average activity of 48 start-ups per 10,000 inhabitants). Again, in 1990, founding activities in the sample regions are a little bit lower than those of the whole of Germany, but for the rest of the time the dynamics show a similar pattern. Industries Fifteen industries as listed in Table A.2 were selected for the empirical analysis. The industries were selected according to the German classification of economic activities, Edition 1993 (WZ-93),7 on a 2-digit level which is equivalent to Level 2 of the European NACE (Statistical Classification of Economic Activities in the European Community). The 2-digit WZ-93 level is chosen because it is the most detailed level for which data are available from the ZEW. Twelve of the industries (No. 22 to 33) belong to the manufacturing industry. This covers approximately half of the manufacturing industries on the WZ-93 2-digit level.8 Three

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Empirical evidence

Firm founding intensity

65 60 55 50 45 40 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 Year Sample regions

Germany

Figure 3.4 Firm founding intensity in the sample regions and in Germany (firm foundings per 10,000 inhabitants between 18 and 65 years). Source: ZEW Foundation Panels; author’s calculations.

industries (No. 72–74)9 belong to the business service sector that is probably the part of the service sector that is most relevant for other firms and, thus, for our analysis. In total, the 15 chosen industries represent a share of 25 per cent of the number of industries on the 2-digit level (including service and agriculture industries). The analysis conducted in Section 3.2 is done separately for each industry. These 15 industries were selected as they represent the focus of industries in the area of Jena. Thus, comparisons with the empirical case study and its analysis in Section 4.4 are easier to conduct. The interpretation of the findings has to keep in mind that only such a limited set of industries was considered. Nevertheless, this set of industries is sufficient to give an overview of the functioning of the proposed method. It can be applied to other industries or at a different level of aggregation in the future. On average 19,637 firm foundings took place in these industries per year. This is approximately 26 per cent of all the founding activity in the selected regions. In Figure 3.5 the firm founding activities aggregated over the 49 regions are presented. The solid line stands for the total founding activities of these regions, whereas the dashed line shows the development of the aggregated 15 industries in the regions. The average founding intensity in the 15 industries is 14.7 (foundings per 10,000 inhabitants) while the overall founding intensity is 56, as mentioned above. The selected 15 industries represent between 24.1 and 30.6 per cent of the founding activities inside the regions (Figure 3.6). The share of the sample industries increases slightly over time which is probably due to the increasing relative importance of foundings in industries No. 72 (‘computer and related activities’) and No. 74 (‘other business activities’) that are included in the

Empirical evidence 79

Number of firm foundings

100,000 80,000 60,000 40,000 20,000 0

1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 Year Total number of firm foundings in sample regions Sample industries in sample regions

Figure 3.5 Number of firm foundings in the 15 sample industries. Source: ZEW Foundation Panels; author’s calculations.

Share of sample industries

35 30 25 20 15 10 5 0

1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 Year

Figure 3.6 Share of sample industries in the total number of firm foundings in the sample regions. Source: ZEW Foundation Panels; author’s calculations.

sample. Table A.2 in the Appendix disaggregates the industries, presenting the average absolute founding activities in the industries as well as the industry founding shares of the complete sample (over the 12 years and 49 regions). The average number of firm foundings per year and region ranges from 270.03 in ‘business services’ to 0.08 in the ‘manufacture of coke, refined petroleum products and nuclear fuel’ industry and, thus, has a large diversity. The foundings in the manufacturing industries (industries No. 22 to 33) and in the service indus-

80

Empirical evidence

tries (industries No. 72 to 74) represent 14.33 per cent and 85.67 per cent of sampled foundings respectively. The dominance of the foundings in the service industries (especially in industry No. 74) does not cause analytical problems because the industries are examined separately.

3.2 Empirical analysis of changes in regional firm founding activities After the above overview of the sample, this section concentrates on the empirical identification of short- to medium-term changes in regional firm founding activities (3.2.1) and their characterization (3.2.2). The next four sections all deal with the developments’ classification and their analysis. In Section 3.2.3 we present the definitions of the classes which build the basis for the classification of the developments in regional firm founding activities (Section 3.2.4). Section 3.2.5 analyses the region- and industry-specific distributions of the identified dynamics in firm founding activities. Section 3.2.6 compares the actual identified developments with the expected developments of Chapter 2. 3.2.1 Identification of changes in founding activities Up to now we only assumed that short- to medium-term changes in the regional firm founding activities occur because of specific events and changes in regional factors. In this section, we attempt to answer the question of whether such changes can be observed in reality. To this end, we first define short- to mediumterm changes in firm founding activity. We sort all factors influencing regional firm founding activities into four broad categories: namely, (1) the external, (2) the industrial, (3) the long-term regional and (4) the short- to medium-term regional influences. Category 1 includes all factors that influence all regions and industries, such as national legislation, taxes and firm founding support or global changes. This corresponds to the aggregated (national) perspective of firm foundings presented in Figure 3.3 (absolute aggregated number of firm foundings in Germany) and Figure 3.4 (aggregated firm founding intensity in Germany) (see also the solid line in Figure 3.7). The second category takes into account that the characteristics of industries have a large variance which influences the number of firm foundings. These industrial characteristics include aspects like the market environment, the technologies used or which phase of the industry life cycle the industry is currently undergoing. Thus, at each point in time the firm founding activity is likely to differ according to the industry. A combination of Category 1 and Category 2 would lead, in the simplest version, not to one aggregated curve (as for Category 1 alone) but to disaggregated developments for each industry (see the dotted lines in Figure 3.7). Category 3 adds long-term regional influences to this picture. Not all regions will have the same regional environment and, thus, it is not likely that the firm founding activities in an industry are the same for all regions. Taken the first three categories together, the already disaggregated curves for each industry would shift up- or

Empirical evidence 81 Founding intensity 2. Aggregated industry no. 2 1 Overall aggregation 3. Industry no. 1 in region no. 2 2. Aggregated industry no. 1 3. Industry no. 1 region no. 1 t

Figure 3.7 Influence of different categories on (regional) firm founding activities.

downward according to the advantages or disadvantages of the regions under consideration (see the dashed lines in Figure 3.7). Some aspects have to be noted: first, although a separation of the impacts was conducted here for analytical purposes, in reality the different categories overlap and influence each other. Second, the potential entrepreneurs’ decisions to start a firm are assumed to be independent of each other in the mathematical approach. This is of specific interest, because dependence between the potential founder’s decisions is one of the processes that can lead to changes in regional firm founding activities (see Section 2.3.3) and, thus, is an interesting process that will be analysed. Third, even if the external and industrial conditions change over time and all the regions are affected in the same way, the following analysis could deal with such a development because only the relative position of a region in comparison to all regions is important. We only presuppose that the long-term regional factors remain constant in the studied period of time. If only the three categories (1) to (3) influence the regional founding activities, the founding intensity for one region in one industry for the period of time under consideration can be transferred into the aggregated curve of this industry by multiplying it with one fixed factor. If such a multiplier cannot be found it is likely that the fourth category has an impact on the firm founding intensities which leads to a corresponding shift in founding activities and consequently to short- to medium-term changes in these regional founding activities. We define short- to medium-term changes in the following way: a short- to medium-term change in the founding activity in one region and a specific industry occurs, if and only if the regional development cannot be transferred into the aggregated one by multiplying it with a fixed factor for the period of time under consideration. In the following we search for time series in which this condition holds. The regional firm founding activities are defined in terms of the likelihood that a person founds a firm in a specific industry, year and region. If this empirically identified likelihood cannot be explained by industry-specific and longterm regional factors, there is a deviation from the expected development. Here short- to medium-term changes in regional factors based on specific events are

82

Empirical evidence

likely to play a role. However, we have to be careful to distinguish between observed deviations and theoretical deviations. What we observe is at each point in time a realization of a stochastic process – the foundation of a firm – with a probability determined by various factors. Applied method Thus, in order to test the hypothesis whether such a regional change in the likelihood of firm foundings takes place, first the probabilities leading to the observed firm foundings are calculated. The calculation of the corresponding probabilities and the testing procedure is described in the following. Let us denote the regions with r ∈ R = {1, . . ., 49} with ordering the regions from 1 to 49 according to the code numbers in increasing order (see Table A.1 in the Appendix). Let us denote the industries with i ∈ I = {1, . . ., 19} (15 individual industries and four aggregated ones). The industries are ordered from 1 to 15 according to the industry codes in increasing order (see Table A.2 in the Appendix). Furthermore, aggregates are constructed: aggr15 (i = 16) corresponds to the firm founding activities of all 15 aggregated industries, aggr14 (i = 17) to the aggregated industries without industry 74 (‘Other business activities’), prod (i = 18) contains the industries 22 to 33 (all manufacturing industries included in the sample) and service (i = 19) holds the industries 72 to 74 (all the service industries included in the sample). Time is denoted by t ∈ T = {1, . . ., 12} from 1990 to 2001. We calculate the probabilities using a binomial distribution with two alternative events: ‘founder’ and ‘non-founder’. For each year t, region r and industry i the observed number of firm foundings (equivalent to the number of founders) is given by fi,r,t and is directly taken from the data provided by the ZEW. The inhabitants of the region between 18 and 65 years of age are regarded as the population nr,t.10 It is assumed here that all these inhabitants have the same probability of becoming a founder and that there exists a direct link between the number of inhabitants and the founding activity.11 The event ‘founder’ in a region r and industry i at time t occurs with the probability pi,r,t. For each given confidence interval there exists an interval of probabilities [pi,r,t, pi,r,t] that can lead to the observed number of foundings (fi,r,t) (with the probability required by confidence interval). Thus, we identify an interval in which the firm founding probabilities should be located for a specific confidence interval. An example is given in Equations (3.1) and (3.2) in which the one-sided confidence interval on a 0.01-level is used for the calculation for the upper and lower borders of the two-sided confidence interval (see e.g. Hartung et al. 1998, Chapter 4). fi,r,t ⋅ F2⋅fi, ,2⋅(nr,t-fi,r,t+1);0.02/2 pi,r,t =  nr,t – fi,r,t + 1 + fi,r,t ⋅ F2⋅f ,2⋅(nr,t-f +1);0.02/2

(3.1)

(fi,r,t + 1) ⋅ F2⋅( f +1),2⋅(nr,t-f );0.02/2 pi,r,t =  nr,t – fi,r,t + (fi,r,t + 1) ⋅ F2⋅( f +1),2⋅(n -f )1–;0.02/2

(3.2)

r,t

i,r,t

i,r,t

i,r,t

i,r,t

i,r,t

r,t i,r,t

Empirical evidence 83 pi,r,t and pi,r,t are then calculated numerically.12 The real likelihood with which an agent in the region founds a firm lies with a likelihood of 98% in the calculated two-sided confidence interval.13 The results are 11,172 intervals (49 regions times 12 years times 19 industries) in which the probabilities must be located given the observed firm foundings (fi,r,t) (see Figure 3.8 for one hypothetical example). The same is done for the aggregated level. Here the aggregated population of the regions at time t is defined as Nt = ∑r nr,t and the number of firm foundings as Fi,t = ∑r fi,r,t. The borders (Pi,t and Pi, t) of the interval are calculated by using Equations (3.3) and (3.4). Pi,t = Foi,t ⋅ F2⋅Fo ,2⋅(Nt-Fo +1);0.02/2  Nt – Foi,t + 1 + Foi,t ⋅ F2⋅Fo ,2⋅(N -Fo +1);0.02/2

(3.3)

Pi,t = (Foi,t + 1) ⋅ F2⋅(Fo +1),2⋅(N -Fo );1–0.02/2  Nt – Foi,t + (Foi,t + 1) ⋅ F2⋅(Fo +1),2⋅(N -Fo +1);1–0.02/2

(3.4)

i,t

i,t

i,t

t

i,t

i,t

t

i,t

t

t

i,t

The results are 228 intervals (12 years times 19 industries) in which the actual founding probabilities are located with a likelihood of 98% given the observed firm foundings (Fi,t) (see Figure 3.8 for one hypothetical example). Based on these calculations it can be analysed whether the development over the whole time span in a region r of industry i can be transformed into the aggregate development by multiplying the regional development with a fixed factor. Since both the regional probabilities [pi,r,t, pi,r,t] and the aggregated probabilities [Pi,r,t, Pi,t] are given as intervals, also the factors for the multiplication can range from a lower border (mi,r,t) to an upper one (mi,r,t). The borders of the intervals are calculated as follows (Equations (3.5) and (3.6)): Pi,t mi,r,t =  pi,r,t Pi,t mi,r,t =  pi,r,t

(3.5) (3.6)

pi,r,t /Pi,r,t Pi,r,t Pi,r,t

pi,r,t pi,r,t

Figure 3.8 Hypothetical example of the development of Pi,r,t and pi,r,t.

84

Empirical evidence pi,r,t /Pi,r,t

mi,r,t mi,r,t

t

Figure 3.9 Calculation of mi,r,t.

For each of the 931 time series (49 regions times 19 industries) that have to be analysed, 12 intervals result (one for each time t) by which the regional time series can be transformed into the aggregated one (see Figure 3.9 for a graphical representation of this calculation). Whether the whole regional time series can be transformed into the aggregated one depends on the existence of an overlap of all 12 intervals defined by mi,r,t and mi,r,t. If an overlap does not exist, such a transformation is not possible and, thus, according to the definition above, a time series with a short- to medium-term change in regional firm founding activities is identified. Discussion of identified time series For the following analysis we use the results based on the calculations of the regional and aggregated probabilities using the 98% confidence level: the upper and lower borders of confidence intervals for the regional as well as for the aggregated level (pi,r,t and pi,r,t as well as >Pi,t and Pi,t) were calculated using a one-sided 0.01 level of significance. Thus, there is a 98% probability that the real founding likelihood lies in the according two-sided confidence intervals. The likelihood that the real factor for the multiplication is located in the interval mi,r,t to mi,r,t lies between 96 per cent and 99.98 per cent. We assume in the following that the factor is located with a likelihood of 96 per cent in the interval to use the lowest likelihood and will use the term ‘96%-level’ to denote this. Tables A.3 and A.4 in the Appendix present the results of this procedure. On the 96%-level we find that 208 of the 931 regional time series (approximately 22 per cent) cannot be transformed into the aggregated development by the method proposed. These time series show a development in which significant short- to medium-term changes in regional firm founding activities can be identified during the time period under consideration. Subsequently, although changes in firm founding activities occurred in all of the 931 time series, we only concen-

Empirical evidence 85 trate on those in which a significant improvement or deterioration of the relative position with regard to start-ups was identified (based on the method described above). Since we only want to focus on these specific time series, all developments and changes in regional start-up activities that are analysed in the following have to be viewed as being significant14 and not as minor changes of firm founding activities. The findings show that in 78 per cent of regions no significant changes in the level of firm founding activities over the 12-year period were identified. In most regions the founding activity had a relatively high persistence over time. This is in line with Reynolds (1992) who found that founding activities only exhibit small year-to-year changes. Nevertheless, by using a 12-year time scale and the described method it was possible to identify those 208 interesting developments which will be analysed in the following. Concerning differences between industries it can be concluded that there are some industries in which these changes in firm founding activities occur more often than in others (Table A.3). In general this phenomenon is more likely to be observed in the service than in the manufacturing industries. A more detailed view shows that in some industries (all from the manufacturing sector) the phenomenon does not show up (industries No. 23, 25, 27, 31 and 32). Some industries in the manufacturing sector (like No. 22 ‘publishing . . .’or 28 ‘manufacture of fabricated metal products . . .’) and the service sector (like No. 74 ‘other business activities’ or No. 72 ‘computer and related activities’) display a stronger tendency towards these changes in founding activities. The same holds for differences between observed regions (Table A.4). While some regions have no changes (Warendorf, Offenbach and Rendsburg-Eckernförde) or only a few (like Schwäbisch Hall, Minden, Cuxhaven or Aschaffenburg), others like München, Karlsruhe, Hamburg, Berlin or Dresden show changes in over 37 per cent of the observed industries. Table A.4 also illustrates with a cross-tabulation how the changes are distributed over regions and industries. This table shows that in four regions (Aschaffenburg, Cuxhaven, Minden and Waldeck-Frankenberg) the changes can only be identified in the aggregated industries (aggr14, aggr15, prod or service). This leads us to the discussion of two general analytical aspects that are tightly linked together: first, these findings are influenced by the industrial structure of the regions. If some regions do not possess start-ups in a certain industry, these regions are not depicted in the analysis. Since agglomerated areas have a more diverse industrial structure, the likelihood that we identify a higher number of changes in start-up activities in such regions is obviously higher. The same holds for industries: if industries are concentrated in specific regions, the number of identified changes is lower than if they are dispersed over all regions. For instance, if an industry is concentrated in only one region, we can expect to identify at most a change in one time series. Second, the number of inhabitants in a region as well as the founding probability in the industries have an impact on the calculation of the confidence intervals. The higher the number of inhabitants in a region and the higher the founding probability in an industry, the

86 Empirical evidence smaller the relative intervals (mi,r,t – mi,r,t)/ mi,r,t become.15 And the smaller these intervals are, the higher the likelihood that a relative change of the founding probabilities in a region, caused by specific events, will lead to significant differences between different points in time. Thus, the number of regional inhabitants and the industry’s founding probability influence how many changes in a region or industry are identified as significant. This can lead to a bias in favour of specific units: the number of time series with significant deviations in less populated regions or in industries with lower firm founding probability can be under-estimated. These considerations must be taken into account when evaluating the findings. We can conclude that significant short- to medium-term changes in regional firm founding activities can be found in the empirical data. Thus, for some regions and industries the relative regional position changed in comparison to the aggregated firm founding activities during the 12-year period. As was suspected, the regional peculiarities and circumstances strongly influence the firm founding activities and, thus, short- to medium-term changes in regional start-up activities: in some regions no significant changes could be identified while there are many changes in different industries in other regions. The same holds for industry-specific influences that also affect the founding activities, as can be seen in most of the findings related to industry No. 74 (‘other business activities’) and the aggregated industries (aggr14, aggr15, prod or service). 3.2.2 Characteristics of changes in start-up activities After the identification of the time series in which changes took place, a deeper analysis is needed to get some additional insights. In the following some characteristics of these changes will be presented. In order to do this two different methods are used. The first one focuses only on the question of whether the factor mi,r,t moves up or down in time and, thus, analyses the direction of changes. The second one assigns the value of mi,r,t for all 12 intervals to a level and studies the magnitude of changes. Method 1: Identification of maxima and minima in the development The basic idea of the first method is to identify local maxima and minima of the intervals of mi,r,t. In Appendix B.1 a more in-depth description of the procedure is presented for the interested reader. The highest local mi,r,t defines a maximum and the lowest local mi,r,t a minimum. There can be several minima and maxima in one time series: after identifying a local minimum, the next extreme value may be a local maximum, then again a local minimum and so on. Vice versa, it may also happen in the reverse sequence. These identified local maxima and minima represent the starting and ending points of developments. An increase is defined as the development of mi,r,t from a local minimum to a local maximum and also a decrease as the development from a local maximum to a local minimum. As for the maxima and minima, a series of these increases and

Empirical evidence 87 mi,r,t

1

ts1

2

3 te1=ts2

4

5

6

7

8

9

10

11

12 t

te2=ts3

Figure 3.10 Analysis of one hypothetical development of mi,r,t.

decreases can occur. Thus, for example, after an increase with the starting point from a local minimum and the ending point of a local maximum, this local maximum can be the starting point of another decrease ending in a different local minimum. In Figure 3.10 one hypothetical example is presented: here the times t = 1 and t = 10 are local maxima and t = 4 is a local minimum.16 The developments are a decrease from time t = 1 to time t = 4 and an increase from time t = 4 to time t = 10. This method leads to a classification of the development of a time series over the observed 12 years in a succession of increases and decreases that are defined by the local maxima and minima. Note that an increase in the multiplier represents a decline of the relative position, e.g. if a significant increase in a region was identified, this region loses ground in comparison to the aggregate. To clarify, the following discussion focuses on the changes in firm founding activities and not directly on the changes in the multiplier. The results of this analysis are presented in Table 3.1. Around 68 per cent of the significant short- to medium-term changes in the regional firm founding activities show a development in their relative position that headed only in one direction (increase or decrease). Another 26.4 per cent of the cases first changed one way and then changed back again and only 5.3 per cent show a more complex up and down movement. Hence, in most cases in which changes took place they appeared only once during the 12 years of observation. The distribution of these findings according to the industries will be briefly mentioned (see Table A.5 in the Appendix). Most turbulence (measured in the

88

Empirical evidence

Table 3.1 Characterization of the identified developments Development of firm founding activities

Absolute number 96%-level

Relative number 96%-Level

Decrease Increase Decrease–Increase Increase–Decrease Decrease–Increase–Increase Increase–Decrease–Decrease Sum

81 61 21 34* 6 5 208

38.9% 29.3% 10.1% 16.3% 2.9% 2.4%

Note * Six of these developments can be the result of a change in the data collection method based on the City of Göttingen as was discussed in Section 3.1.1.

increases and decreases in the relative position) can be found in the aggregated and the service industries. In the manufacturing industries only No. 22 (‘publishing . . .’) and No. 30 (‘manufacture of office machinery and computers’) show an up and down movement (once decrease-increase and twice increase-decrease).17 The same holds for differences between the regions (Table A.6 in the Appendix). Some regions do not change their relative position at all (as was mentioned above), whereas others show a high rate of turbulence in the analysis. These are less populated areas (e.g. Breisgau-Hochschwarzwald), the East German regions (e.g. Dresden), the large cities (e.g. Hamburg, München), but also smaller cities (e.g. Karlsruhe). Out of the cities, Dresden shows the most varying movements in the founding activities. The direction of change is also presented in Table A.6. Cities such as Berlin, Jena or Sömmerda fall back in their relative position in comparison to other regions, whereas regions such as Köln, Kaiserslautern and München improve their relative position. Others show a mixed development with increases as well as decreases, e.g. Düsseldorf or Frankfurt/Main. Reasons for these developments are discussed in more detail in Section 3.2.4, but some brief ideas will be presented here: the decline of the relative position in the East German regions is probably caused by the effects of the reunification with a high number of start-ups in the beginning and a lower number at the end of the relevant period. The mixed results for, for example, Düsseldorf may reflect a restructuring process going on with a decline in the traditional industries and a boom in other industries (e.g. service industries) (Faust 1999). Method 2: Assignment of levels of founding activities A shortcoming of Method 1 is that the magnitude of increases and decreases cannot be identified. Nevertheless, this can be solved by a second method. It assigns each of the 12 intervals to specific levels (see as one hypothetical example Figure 3.11). First we identify how many levels exist in each time series. In general a level

Empirical evidence 89 mi,r,t mi,r,t

3rd level

mi,r,t mi,r,t2 = t2

2nd level

mi,r,t2 = t2 mi,r,t mi,r,t

1st level t

t2 = t2

t

Figure 3.11 Assignment of levels in a hypothetical example.

is defined by the borders mi,r,t and mi,r,t of the 12 points in time. For each of the time series that are analysed at least two levels must exist because these time series were already selected in Section 3.2.1 based on the simplest criterion for a change of levels (the non-existence of an overlap). Here more levels could be defined. The first two levels are selected in the following way: Level 1 (the lowest level) is defined by time t with the lowest mi,r,t and Level 2 is defined by time t with the highest mi,r,t. If all the other 10 intervals show an overlap either with Level 1, [mi,r,t, mi,r,t], or with Level 2, [, mi,r,t], only these two levels exist. If not all other intervals overlap with either of these two levels, we select time t2 with the highest mi,r,t2 (but with mi,r,t2 < mi,r,t) and time t2 with the lowest mi,r,t2 (but with mi,r,t2 > mi,r,t). If the two corresponding intervals [mi,r,t2, mi,r,t2] and [mi,r,t2, mi,r,t2] overlap,18 Level 2 becomes Level 3 and the borders of the new Level 2 are defined as the average mi,r,t and mi,r,t of the two points in time t2 and t2. If the two intervals do not overlap, the old Level 2 becomes Level 4 and the new Level 2 is defined by [mi,r,t2, mi,r,t2], whereas the new Level 3 is defined by [mi,r,t2, mi,r,t2]. If there still exist intervals that do not overlap with Levels 1, 2, 3 and 4, the procedure starts again. After this definition of the levels, all 12 intervals are assigned to these levels. If an interval for a certain time overlaps with the borders of only one level, this level is assigned to the interval. If the interval overlaps with two levels, the average of the two level number is assigned to the interval (e.g. to an interval overlapping with Level 1 and Level 2 would be assigned a 1.5). An overlap with more than two levels is excluded by the construction of the levels. Here again an increase in the levels over time, e.g. a change from Level 1 to Level 2, represents a significant decline of the relative position of the region in question. After the assignment was completed, we can observe that in 86.5 per cent of the cases there exist only two levels between which the changes occur (Table 3.2). However, still 28 time series can be found in which the development crosses more than two levels. Note that even with only two levels there might be a lot of turbulence, as was defined above. To probe deeper into this question, the development across the levels is analysed.

90

Empirical evidence

Table 3.2 Levels of changes 96%-level Highest level

Number

Share

2 3 4 5 6

180 20 5 3 0

86.5% 9.6% 2.4% 1.4% 0.0%

Sum

208

Table A.7 and A.8 in the Appendix show the differences between the industries and regions. Again we can observe that the highest levels are reached in the aggregated industries and in industry No. 74 ‘other business activities’.19 Thus, especially in these industries strong changes in the relative position occur. With respect to the regions, mainly large cities such as Hamburg or München reach a Level of 4 or 5. The same changes across several levels can be observed in the East German cities (Berlin, Dresden, Jena), which is probably due to the specific situation after reunification. The cross-tabulation in Table A.9 in the Appendix supports these findings. A larger number of levels (more than two) can be found in industry No. 72 ‘computer and related activities’ in Karlsruhe and München. Industry No. 74 ‘other business activities’ changes in East Germany (Berlin, Dresden and Jena) and in Hamburg, Mühlheim and Wiesbaden. The development in East Germany (Berlin, Dresden, Jena) is also prominent in the aggregated industries. The same holds for Hamburg and München. Greater München (incl. München, Freising and München Land) also shows higher levels in the service industries. 3.2.3 Definition of development classes for classifying observed changes A classification of the development across the levels seems to be necessary to answer the question of what changes in the regional firm founding activities really look like and to link the observed developments to the theoretical conjectured ones made in Chapter 2. In the following, this classification method is described. As input to the classification we take the results of Method 2: each point in time is assigned to a specific level of firm founding activities. Thus, we have a succession of levels which represent the development of the firm founding activities over the 12 years of observation. From the 208 interesting time series on the 96%-level that were analysed above, not all can be used for the following analysis. This is due to the fact that the interpretation of findings at the beginning and the end of the time series is problematic because the development before the first observation in our sample and the development after the last

Empirical evidence 91 observation is unknown and, thus, the series cannot be interpreted properly. In order to circumvent this problem we use only those time series that have two different levels of start-up activities between time t = 2 and t = 11. As a result, 168 time series remain for the analysis. Table 3.3 presents the different potential classes of development that correspond to the theoretically derived developments in Chapter 2. The last column links the classes to the figures in Chapter 2 if this is possible. Two aspects are different here. First, some classes are incorporated in Table 3.3 that are not considered in Chapter 2. This includes Class 1 representing a linear20 development. Such a linear development was mentioned in Section 2.3.7 and has a high likelihood of being found in the empirical data based on long-term development processes, e.g. catching-up or falling-behind of some regions. Class 3 was not included as such in Chapter 2 but represents all the different types of short- to medium-term changes (e.g. linear or s-shaped) from one level to another. In the time frame under consideration a differentiation between these two kinds of development would be difficult. Class 7 was discussed in Section 2.2.2 as an interesting development based on contagion effects. Although it is probably hard to identify Table 3.3 Classes for classification* (Development of the levels of firm founding activities (y-axis) over time (x-axis)) Class No.

Development of class

Correspondence to

Class No. ■

Change in one direction 1

Development of class

Correspondence to

Change in two directions 5

Figure 2.11

Figure 2.8

2

Figure 2.13

6

3

Figure 2.9/2.10

7

4

Figure 2.7

8

Figure 2.12

9

Note * Note two aspects: First, we only have data for discrete points in time and not continuous data as presented in these stylized curves. In order to match the proposed developments of Chapter 2, here these continuous representations are also used. Second, if a sudden change of the level of founding activities occurs from t to t + 1, it is regarded as an increase and represented in the curves as an increase in one point in time. This is the case for the Classes 2, 4, 5, 6, 7 and 8.

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Empirical evidence

in the empirical data, we try to do so in the following. The same holds for Class 8, which might also be based on a contagion effect. It represents an even more complicated development with successive up and down changes. Class 9, a new listing, is a short-term version of Class 5 or a transitory change and statistical artefact.21 In some cases it is unclear whether a development falls in the Classes 1 to 8 or whether it might just be a short-term change of some kind described by Class 9. Then the developments are categorized into both classes (1 to 8 and 9). Only those developments that clearly fall into Class 9 are included in this category. Second, some classes from Chapter 2 are not directly considered for the following analysis. As was already pointed out Class 3 approximates the sshaped development and this class also represents the diminishing effect. All developments that resemble a bump (with an s-shaped or a diminishing development in the beginning) are subsumed under Class 5. All the developments with an initial over-shooting effect leading to a stabilization on a high level are subsumed under Class 6. Besides these nine suggested classes, others with an even more complicated development might be detected in the empirical data. If this is the case, a new class is opened up whose development is presented when applicable. Nevertheless, this is not very likely because complicated developments do not appear very often (Table 3.1) and Class 8 would be able to represent many of them. Although only the positive developments are presented in the classes, the opposite direction might also be identified in the data and is marked with a negative sign. The two possible directions will be considered in the presentation of the findings. 3.2.4 Classification procedure Three coders22 classified the different developments into these nine classes. The classification procedure was as follows: a classification schema is developed that describes which characteristics a time series must have to fall into one of the classes described above.23 This first version of the schema and the according classes (see Table 3.3) are given to three coders to check whether the schema is understandable. They are asked to mark unclear passages and write down open questions. After a discussion of these unclear and missing parts, the schema is revised. Ten randomly chosen developments out of the 168 time series were given to the three coders to classify, together with the classes and the revised classification schema. The results of their classification in this pre-test were compared to each other and all the cases in which their classification did not match were discussed. Based on this discussion, a final revision of the classification schema took place (the final schema is shown in Appendix C.1). Then the total set of 168 time series is given to two of the trained coders together with the classes and the classification schema. Additionally, they received the figures of the developments of the firm founding intensities in the region and industry under consideration. They both classified all the time series independently of

Empirical evidence 93 each other. For each time series, it is noted how many events can be identified, into which class(es) these events fall and which are the relevant points in time in the series (e.g. when the change to a higher level has taken place). Such a procedure based on techniques used in content analysis (Krippendorf 1980) seems reasonable because classification is a very complicated process and using a computer program for this classification implies several disadvantages; if the realization is possible at all. Humans can much better identify the common features of curves and the underlying process or class, e.g. a bump can last for two to several years; this can be easily identified by humans but using a computer program for the same task is much more complicated. Besides the identification of the same development on different time scales, the combination of different classes in one time scale is also much easier for real people (Merten 1995, Smith 2000).24 Using two or more different people for the classification reduces the possible disadvantage of subjective evaluation of the developments. Reliability analysis First we analyse how many events the two coders identify for the time series. The maximum number of events in any one time series is three. Thus, they identify three events or three different classes of development in one time series at most. There are 154 time series in which both coders identify the same amount of events (which is 92 per cent of the time series) and accordingly 14 time series in which the number of events between the two coders differs (8 per cent). In order to get a more comprehensive picture of the consistency of the identification of events by the two coders, Cohen’s Kappa as a measure of intercoder agreement is used (Cohen 1960). The Cohen’s Kappa coefficient is probably the most widely used and accepted measurement to calculate inter-coder agreement. It is used when categories are independent, mutually exclusive and exhaustive (Smith 2000). The coefficient corrects for agreements that might occur by chance (Siegel and Castellan 1988) and, thus, is a more accurate measure in controlling the consistency of classification relative to the simple calculation of shares. A coefficient of ‘1’ would represent a complete agreement, one of ‘0’ no agreement beyond chance and a ‘-1’ would show complete disagreement between the coders. In this case Cohen’s Kappa is based on 504 possible observations (168 time series times three events) and we examine how often Coder 1 and Coder 2 affirmatively agree (both identify an event), negatively (both identify no event) or disagree (Coder 1 identifies an event while Coder 2 does not and vice versa). Based on this cross-tabulation Cohen’s Kappa is calculated. The result is a coefficient of 0.937. This is quite close to 1, which is a first indication that the consistency of the classification between the two coders is relatively good. The two trained coders identified 33 classes in total. These classes consist of the pure classes that are part of the classification schema with positive and negative signs. Additionally, there are cases in which a clear classification into one of the pure classes was not possible. Thus, the coders used the option of

94

Empirical evidence

choosing an additional class (e.g. Class 3 or Class 9 which is noted as ‘3&9’). For these mixed classes the identification is unclear. In the following, the pure as well as the mixed identifications are regarded as one combined class unit (e.g. such a combined class can consist of a pure ‘Class 1’ as well as of ‘Class 1 or Class 9’). The two coders identified 202 events, consisting of 168 time series with at least one event, 32 time series with at least two events and two time series with three events. Given that the consistency of classification was tested above, here the question of whether the two coders identified the same classes for each of the events has to be answered in order to discern the classification procedure’s reliability. This is done using Cohen’s Kappa as a measurement, where all the identified classes made by the two coders were used as an input. The result is a Cohen’s Kappa of 0.802 if all 504 possible events are included. The 504 is based on 168 time series with a maximum of three events per time series. Although the coders have only explicitly identified 202 events, in the other 302 possible cases they both implicitly identified a ‘0’; meaning that they have not assigned a class. This is a classification decision on its own and, thus, could be included in the calculation.25 If the calculation is only based on 202 events a Cohen’s Kappa of 0.675 results. If the Kappa value is larger than 0.7, the inter-coder reliability is considered satisfactory, while if it is smaller, the reliability is considered not satisfactory (see, for example, Neuendorf 2002 for such rules of thumb). Depending on the basis for the calculation the reliability of the coding is either very high and satisfactory or at the borderline to be satisfactory. There exists some critical discussion in the literature on the validity and usability of these reliability coefficients in general and the rule of thumbs that are used to define whether a classification is satisfactory and reliable in particular (see, for example, Kolb 2004, Lauf 2001). Such values are in most cases quite arbitrary and might lead to questionable results that are more likely to be an educated guess (Krippendorf 1980, Kolb 2004). Without going into detail of this controversy, it has to be noted that such standards for data reliability analysis must be related to the research question and aim. Krippendorf (1980) points out that these standards depend upon the validity requirements of the research results. Thus, for an exploratory study the level might be relaxed. The coefficients that were calculated for the present analysis nearly reach the desired level, originating by this rule of thumb, or even exceed it considerably. Hence, even without using a relaxed standard, the classification procedure and the results are acceptable and reliable. The definition of classes, the classification schema, the schooling of the coders and accuracy of the coding by the coders, which are all important factors influencing the reliability, are all satisfactory (Kolb 2004, Merten 1995). The classification of the developments of the levels was a complicated endeavour because in most cases the developments were not as clear-cut as Chapter 2’s stylized curves might suggest. Especially the values in between two clear levels (e.g. Level 1.5 between 1 and 2) were hard to interpret. The relatively good coefficient of Cohen’s Kappa shows that the classification nevertheless was successful and can be regarded as reliable. At least for a first

Empirical evidence 95 exploratory analysis of the developments the classification results are reasonably good and useful for the following analysis. The analysis of the points in time, which will follow below, is nevertheless critical and the results have to be cautiously interpreted. Dealing with categorical discrepancies and final classification After it was shown that the classification procedure produces relatively good and reliable results, the next step is to decide how to deal with categorical discrepancies. In 52 of the 168 time series the two trained coders have not reached a 100 per cent consistency. This means that somewhere in their classification of a time series there exists at least one discrepancy (in the number of events, the classes or the points in time relevant for each class). As pointed out by Bartholomew et al. (2000), in such cases a third coder can classify the unclear cases and the classification agreed upon by two of three coders becomes the final rating. This procedure is also followed in this analysis. The third trained coder has to decide on basis of the classification schema, the classes, the development of the regional firm founding intensities and the 52 time series into which class the different events of each time series shall be classified. Finally, a comparison is made as to which classes each of the coders classified and whether a majority can be found. Table 3.4 summarizes the results. For a total of 161 events the three coders came to a clear-cut identification of the classes (meaning that at least two of the three voted for the same class). In 142 cases this identification was related to the first event in a time series, in 18 cases to the second event and in one case to the third event in a time series. Thus, not all the time series could be directly linked to at least one of the classes. In 23 out of the 168 time series no class could be assigned. Since, on the other hand, in one time series several events can take place in succession with the according classifications the total amount of 161 classified events comes up. In the end it is identified which classes26 were used for the classification and it is counted how often each one appeared in the data. Table 3.4 shows the results. A total of 22 different classes results whose frequency of identification ranges from 1 to 23 and, thus, is quite unequally distributed. For the following analysis only those classes are used that were clearly identified. This is due to the fact that the other classes always consist of two different possible classes, which makes an analysis and interpretation problematic. Table 3.5 shows that under this condition 139 events, which are 86 per cent of the total amount of identified events, can be used in the analysis. The direction of change is different between the classes. While for Classes 1 to 3 the majority of developments had a negative sign, the opposite is true for Classes 5 and 9. However, there is a difference in meaning between these two groups: a negative sign for Classes 1 to 3 implies that for the period of time under consideration the development of the firm founding activities was negative and the regions deteriorate in their relative positions. A negative sign for the Classes 5 and 9 only means that the firm founding activity temporarily decreased

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Empirical evidence

Table 3.4 Identified classes in the time series Rank 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 Sum

Classes –2 5 –3 –1 –5 9 3 2 1 –9 –3&(–)9 1&(–)9 3&(–)9 –3&–9 6 –6 1&3 2&(–)9 –1&(–)9 –2&9 –2&(–)9 –5&–9

Event 1

Event 2

20 20 18 16 13 6 8 7 6 4 5 4 4 3 1 1 1 1 1 1 1 1

3 1

142

18

3 7 3

Event 3

1

1

1

Sum 23 21 18 16 16 14 11 7 6 5 5 4 4 3 1 1 1 1 1 1 1 1 161

but reverted to the old level later on. Thus, a negative sign for the first three classes is a clear indicator for a longer-term decline to a lower level in firm founding activities. Besides this descriptive analysis of the appearance of negative and positive signs in the different classes, it is tested whether a significant difference between the share of positive and negative signs exists. In order to conduct this test the number of negative and positive signs of one class was compared to the aggregated number of positive and negative signs for all the other classes. For example, Class 2 with seven positive and 23 negative signs was compare d to the other classes with 53 positive and 56 negative signs. Based on the 2 × 2 crosstabulation it was tested by a chi-square measure (exact test by Fisher, 2-sided (Hartung et al. 1998, Chapter 7)) whether the rows and columns of this crosstabulation are independent.27 From this analysis two significant findings resulted: for Class 9 it can be concluded that positive changes have a significantly higher share and simultaneously negative developments have a significantly lower share in the identified developments than in the other classes (on a 1%-level; p-value of 0.005). For Class 2 the opposite holds; in this class negative developments have a significantly higher share in identified developments than in other classes (on a 5%level; p-value of 0.021).

Empirical evidence 97 Table 3.5 Identified pure classes used in the following analysis Number of classes Sign of classes Classes

Total number

Positive

Negative

1 2 3 4 5 6 7 8 9 Sum

22 30 29 0 37 2 0 0 19 139

6 7 11 0 21* 1 0 0 14 60

16 23 18 0 16 1 0 0 5 79

Note * Six of these developments can be the result of a change in the data collection method based on the City of Göttingen as was discussed in Section 3.1.1.

3.2.5 Analysis of classified classes of changes in founding activities In this section we analyse the findings in light of the impact of regional and industrial influences on the amount, direction and classes of development. Furthermore, the development over time is analysed. Analysis of industries The developments in the industries probably differ from one another. There may be industry-specific factors that influence development. A first comparison of the directions of developments in the industries is presented in Table 3.6. As can be seen from this table the positive and negative developments are balanced for all the industries and no significant deviation can be detected. This holds whether Classes 5 (‘bump’)28 and 9 (‘peak’) are included or not. Additionally, the table is in line with the previous finding (Sections 3.2.1 and 3.2.2) that the service industries account for many of these changes. Table 3.7 shows the distribution of the classes in the industries. The service industry relatively often exhibits a sudden development to another level with 10 observations in Class 2 (‘fast level change’), whereas the aggregates of the industries develop slower from one level to another. The only significantly different distribution can be found for industry No. 72 (on a 1%-level; Pearson pvalue of 0.008). Thus, the classes are differently distributed in this industry in comparison to the other industries. This is probably based on the high share of (negative) Class 3 (‘slow level change’) and (positive) Class 5 (‘bump’) developments in comparison to the other developments: in some regions the area

98

Empirical evidence

Table 3.6 Direction of developments in the industries Industry

Sign of classes (including Classes 5 & 9)

Sum

Sign of classes (without Classes 5 & 9) Positive

Sum

Positive

Negative

Negative

22 24 29 72 74 aggr14 aggr15 Prod Service

1 1 1 6 11 6 18 5 11

1 0 1 9 17 10 18 3 20

2 1 2 15 28 16 36 8 31

1 5 2 9 3 5

7 13 7 14 2 15

8 18 9 23 5 20

Sum

60*

79

139

25

58

83

Note * Each of the industries 22, 72, 74, aggr14, aggr15 and service one observation can be the result of a change in the data collection method based on the City of Göttingen as was discussed in Section 3.1.1.

Table 3.7 Development classes in the different industries Industry

Classes 1

22 24 29 72 74 aggr14 aggr15 Prod service Sum

Sum 2

3

5

6

9

2

1

3 2 7 1 3

2 1 2 15 28 16 36 8 31

2

19

139

1 2 1 7 2 5 1 6 22

7 4 8 1 10 30

6 4 3 10 3 3 29

7 7 5 6 2 8 *

37

1

Note * Each of the industries 22, 72, 74, aggr14, aggr15 and service one observation can be the result of a change in the data collection method based on the City of Göttingen as discussed in Section 3.1.1.

‘computer and related activities’ has had a short-term bump that may have been induced by political programmes or market opportunities. In other regions the activities decreased from one level to another, e.g. caused by saturated markets. Furthermore, Class 9 (‘peak’) shows a different distribution across the industries than the other classes (on a 1%-level; Pearson p-value of 0.003). A reason for this could be that Class 9 (‘peak’) appears in the individual manufacturing

Empirical evidence 99 industries No. 24 and 29 and has a relatively lower share in the service industries. Analysis of regions The distribution of classes and the direction of changes in the different regions are presented in Table 3.8. It can be observed on a number of occasions that the regions have either a tendency towards a positive or a negative development. Often regions even show only one type of development (be it a positive or negative one). This missing independence between the columns and the rows also appears in the Pearson chi-square value with 0.000002 (1%-level). In general there are two possibilities to explain these findings: first, regional variables influence these developments. Second, the regional development of startup activities is determined by the development of firm formation processes inside one industry or linked industries located in the regions. If the latter case were valid, the distribution of positive and negative developments would have to differ in the single industries. Thus, one would expect that there exist some industries that have a higher rate of positive developments and other industries have a higher rate of negative developments. The distribution of these industries over the regions would then explain the findings. Since it was shown above that all the industries show approximately the same amount of positive and negative developments, the explanation based on industrial influence is hardly convincing. Thus, the regional variables seem to dominate the development and influence the direction of change. The same lack of independence as before can be observed between the regions and the different classes of development (on a 1%-level; Pearson p-value of 0.000001). The developments of some regions concentrate on one (e.g. Heinsberg or Esslingen) or a few classes (e.g. Jena or BreisgauHochschwarzwald). The next step was to reasonably aggregate the regions. A first aggregate was to separate the regions into East and West Germany and to analyse the class distribution. The results of this aggregation are presented in Table 3.9. Remarkably, the direction of developments in East Germany is only significantly negative (on a 1%-level; p-value of 0.000002; 2-sided exact test by Fisher). This is probably due to the strong founding activities in the early 1990s, directly after reunification. In Figure 2.1 the strong decrease in East German founding activities was already presented and this strong decrease was identified in the regions as well. This development can also be identified by looking at the distribution of different classes.29 Class 1 (‘linear’) has a significantly higher share in East Germany than in West Germany (1%-level, Pearson p-value of 0.002). This is in line with the hypotheses that a continuous, probably negative, development can be observed. It is less obvious why the share of Class 2 (‘fast level change’) is also significantly higher in East Germany (5%-level, Pearson p-value of 0.024) while the share of Class 5 (‘bump’) (1%-level, Pearson p-value of 0.008) and 9 (5%level, Pearson p-value of 0.043) is lower. One reason for the lower share of

2

1

1

3

2 1

1

2

2 2

1

1 1 2

2

1051 2000 3152 5111 5117 5162 5313 5315 5370 5913 6412 6414 6432 6434 6436 6440 6532 6534 7143 7315 8111 8116 8212 8215 8221 8226

Dithmarschen Hamburg Göttingen* Düsseldorf Mühlheim/Ruhr Neuss Aachen Köln Heinsberg Dortmund Frankfurt am Main Wiesbaden Darmstadt-Dieburg Hochtaunuskreis Main-Taunus-Kreis Wetterau-Kreis Lahn-Dill-Kreis Marburg-Biedenkopf Westerwald-Kreis Mainz Stuttgart Esslingen Karlsruhe, Stadt Karlsruhe, Kreis Heidelberg Rhein-Neckar-Kreis

2 1

1

Code

Name 3

Classes

Regions

Table 3.8 Development classes in the different regions

3 2 2 1 2

1

1

2

3 2

2

3

5

2 1 1

2 1

3

1 2

1 1 6

5

6

1

1

1 1

1

3 3

9

3

3

2 1

1 2

1 4 3 3 3 3 1 1

1 5 6

Positive

Developments

3

1 1 3 6 2

3 3 2 5 2 1 2 2

1 3 3

Negative

1 5 6 1 4 7 3 3 3 3 4 4 2 5 3 3 2 2 2 2 1 3 9 2 3 3

Total

8315 8416 9162 9178 9184 9372 9661 9761 11000 14262 16053 16068

Breisgau-Hochschwarzwald Tübingen München Freising München Land Cham Aschaffenburg, Stadt Augsburg Berlin Dresden Jena Sömmerda 22

3 4 2 30

2 3

1 1 4

2

1

2

29

1

1 1 1

1 2 1

3

37

1

3

1

1 1

4

5

2

1

1

6

19

1 2 2 3

9

Note * These six positive bumps can be the result of a change in the data collection method as discussed in Section 3.1.1.

Sum

1

Code

Name

1

Classes

Regions

60

1 1 2

1 1 7 4

Positive

Developments

79

3 8 6 4 4

2 3

4 2

Negative

139

5 3 7 6 3 1 1 5 8 6 4 4

Total

102

Empirical evidence

Table 3.9 Development classes in East and West Germany Classes

East Germanya West Germany Sum

1

2

3

5

6

9

9 13 22

9 21 30

3 26 29

1 36b 37

0 2 2

0 19 19

Developments Total ■ ■ Positive Negative 0 60 60

22 57 79

22 117 139

Notes a Here ‘East Germany’ consists of Berlin, Dresden, Jena and Sömmerda. b Six bumps can be the result of a change in the data collection method as discussed in Section 3.1.1.

Classes 5 (‘bump’) and 9 (‘peak’) might be that these short-term changes in the start-up activities are superimposed by the very strong negative long-term tendencies. The last relationship we want to test is between the different classes and regional population size (Table 3.10). We tested whether the distribution between large regions (with more than 300,000 inhabitants) and smaller regions (with less than 300,000 inhabitants) differs or not.30 We identified that the rows and columns lack independence (Pearson value of 0.0051). The share of Class 5 (‘bump’) (1%-level, Pearson p-value of 0.001) was higher in smaller regions, while the share of Class 3 (‘slow level change’) was higher in larger regions (1%-level, Pearson p-value of 0.005). A reason for this difference might be that events took place that triggered the development into a certain direction. This can lead to an initial development that is equivalent in Class 3 (‘slow level change’) and Class 5 (‘bump’). However, while this development stabilized on a certain level in larger regions (e.g. because the critical mass of elements is given), the smaller regions fall back to old levels after some time. The same holds for negative developments. Thus, smaller regions often positively or negatively fluctuate around one longer-term level of founding activities. In contrast, larger regions can reach a higher long-term level of start-up activities if a critical

Table 3.10 Development classes in differently sized regions Region type

Inhabitants 300,000 Sum

Classes

■

1

2

3

5

6

9

12 10 22

17 13 30

11 18 29

31* 6 37

1 1 2

15 5 19

Total

86 53 139

Note * Six bumps can be the result of a change in the data collection method as discussed in Section 3.1.1.

Empirical evidence 103 mass of founding activities is overcome or their level of activity can drop to a lower level if activity falls below the critical value. Analysis of development over time The last interesting aspect is the distribution of events over the whole period under investigation. Table 3.11 gives an answer to the question of how long the different classes take to develop. The developments in Class 1 (‘linear’) take over eight years for positive as well as for negative developments. Thus, once a development is triggered in a region, it can have a long lasting effect and it might be difficult to change this direction. This persistency of founding rates can have severe political implications when policy makers attempt to improve regional activities. If such a development is initiated, it might have very sustainable effects. The developments of Classes 3 (‘slow level change’) and 5 (‘bump’) show that changes to a higher level take around three years. Thus, for example, the complete impact of a policy measure can only be fully evaluated after the development has already taken place for some time. If an additional time lag between the event and the reaction of the start-up activities is taken into consideration, it can take a considerable amount of time until the full effect is reached. The positive development in Class 5 (‘bump’) lasts longer than the negative one: both the initial as well as the backward developments take longer for the positive than for the negative one. The higher or lower level in between only lasts for a relatively short amount of time (1.35 years) for both sub-classes. In Table A.10 in the Appendix the distribution of all the relevant points to define a development (e.g. starting and ending points) are listed for the period of 1990 to 2001 and each class. Based on this table, we can analyse when positive Table 3.11 Average length of the different developments Main class 1 (‘linear’) 3 (‘slow level change’) 5 (‘bump’)

Sub-classes

–1 +1 –3 +3 –5 +5

6* –6 +6

Total 8.41 8.63 7.83 2.82 2.89 2.7 6.21 5.37 6.85 5.5 8 2

Initial development

Staying on new level

Backward development

2.86 2.5 3.14 4.5 7 2

1.35 1.31 1.38 0 0 0

2 1.56 2.33 1 1 1

Note * Class 6 was included for completeness, but since only one observation is available for the positive and negative developments an interpretation is rather difficult and is not conducted here.

104

Empirical evidence

20 10

9 3

15 5

4 5

10

18 4 12

5

8

8

7

8 3

0

2

4

6

3

1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 Year Starting points of positive developments Starting points of negative developments

Figure 3.12 Starting points of developments.

and negative developments started during this time. A positive development is defined by the initial starting points of Classes 1, 2, 3, 5 and 6 while a negative development is defined by the starting points of Classes -1, -2, -3, -5 and -6. If these starting points are summed up the distribution presented in Figure 3.12 can be observed. The missing values for the years 2000 and 2001 are due to the classification procedure because it was not possible (for the year 2001) or at least hard to evaluate (for 2000) developments starting in these years. One would expect a decrease in the number of starting points identified in each year because dynamics that started late are more likely to be unclassified due to border effects. We find this in the empirical data: the starting points of negative developments have a very high level between 1990 and 1995 (53 starts in the first five years), after that the number of starting negative developments decreases (21 starts in the next five years). The picture for positive developments is less pronounced: again many developments start in the first half of the time period (27 starts in the first five years) and this also decreases in the second half (18 in the following five years), but the decrease is not as large. Three years deviate from the expected developments: these are the years 1993 and 1994 with many negative developments (40.5 per cent of all negative developments) as well as the year 1998 with positive and negative developments. An analysis of the regions and industries standing behind the former developments show that the negative developments in the two years were mainly dominated by service industries (No. 72 ‘computer and related activities’, No. 74 ‘other business activities’ and the aggregate of the industries). In both Sömmerda and Esslingen, three negative developments started and, thus,

Empirical evidence 105 there must exist processes here that led to negative developments in this period of time.31 The positive developments in the year 1998 are dominated by the cities of München (three positive developments) and Köln (two positive developments). Negative developments are based on the regions Berlin and BreisgauHochschwarzwald. Interesting is that the aggregate of the manufacturing industries in Köln developed positively, whereas these industries developed negatively in the Breisgau-Hochschwarzwald. The same holds for industry No. 74 (‘other business services’) with positive developments in München and negative ones in Berlin. In München industry No. 72 developed positive as well, which might be the result of the booming phase in the information technology industry in which München developed extraordinary well (Krafft 2000). 3.2.6 Empirically observed and theoretically derived curves Before we analyse in detail which actual regional events led to the identified developments, in this section we want to briefly focus on a general comparison of the identified developments (Table 3.5) with the stylized curves depicted in Chapter 2. Thus, the questions of which stylized curves were successfully or unsuccessfully identified by the proposed method of classification, why this might be the case and which conclusions we can draw from these findings for future research shall be answered. Classes 1, 2, 3, 5 and 9 were identified numerous times in the data nicely corresponding with some of the developments in Chapter 2. •

•

•

Class 1 (‘linear’) is linked to a linear development not considered in Table 2.14 (Section 2.4.3) because it is based on multiple events but discussed in Section 2.3.7. The high number of identified developments falling into this class makes it necessary to describe these processes more thoroughly in future research. Many regions seem to follow such long-term development processes that last, on average, more than eight years (Section 3.2.4). Some regions increase their relative distance to the other regions while others decrease. These slow but long lasting processes support the findings of Reynolds (1992), who claims that founding activities in regions only slowly change from one year to the next. However, even these small changes can lead to significant developments when they last for a longer period of time, as it was the case presented here. Based on this, we still have to investigate whether it is possibly to identify events that can trigger such long-lasting dynamics. Class 2 (‘fast level change’) represents a sharp change in the start-up activities that is directly linked to the switch in the founding activities described in Chapter 2. In this Chapter 3 we found many developments falling into this class. This supports the claim that these switches play an important role in explaining changes in firm founding activities, which was postulated in Chapter 2. Class 3 (‘slow level change’) was also often identified, but in this case the

106

•

•

Empirical evidence link to the stylized curves is weaker because the s-shaped as well as the continuous developments are subsumed under this class. However, since both developments are based on the same events in most cases, such an aggregation seems reasonable. Nevertheless, future studies can try to separate the two developments more thoroughly to be able to examine whether the sshaped and continuous developments differ from each other. They should especially attempt to figure out when they are based on information diffusion and when they are rather based on the establishment of a localized industrial cluster. Class 5 (‘bump’) also contains different developments presented in Table 2.14. First, it is the normal bump development with sharp increases and decreases and second, the slower increase with a sharp decrease. As was shown in Section 3.2.4, both processes can be found in the empirical data. A new class was No. 9, trying to grasp one-off peaks in the founding activities. This class does not correspond to one of the classes from Chapter 2; nevertheless it should be included in future research in order to grasp possible statistical artefacts or very short-term changes in start-up activities.

In contrast to the classes just described, which in most cases resemble and support the theoretical approach, others could not be identified. These are: •

•

Class 8 we already expected to be problematic in its empirical identification. This is due to the quite complicated development path that it represents. This class should be excluded from future research as long as no other more detailed classification method is used. Additionally, Classes 4 (‘delay’) and 7 could not be identified and Class 6 (linked to the suppressed-founding effect or the over-shooting effect) only twice. Especially the lack of identification of the Classes 4 and 6 is a drawback because these classes are also included in Table 2.14 and are linked to interesting processes. This is unexpected and there might be two reasons for this: first, the classes really do not appear in the empirical data or second, the development is too complicated or too similar to one of the other classes that it is mistaken for them. At the moment it cannot be decided which of the explanations is the correct one. Thus, especially for these complicated developments it is necessary to re-define the single classes and to sub-divide them (e.g. to be able to distinguish between a bump with a sharp or a soft increase). Then one could get a clearer picture whether the missing identification of Class 4 is due to the classification procedure or to the non-existence of the development.

Accordingly, another revision of the classification schema might be necessary to include the new classes and to improve the discrimination between the classes. Such a refinement of the single classes, an improved sub-division of classes and a revised classification schema is useful to identify more developments accurately and to better discriminate between classes. At the moment, it is not clear

Empirical evidence 107 whether the lack of identification is based on the complexity of the classification or the fact that these developments do not exist. Hence, one must first attempt to improve the classification method before one can properly discern what the probability is of the latter being true.

3.3 How events change regional firm foundings – an empirical approach In Chapter 2 we discussed a set of events and developed stylized developments of regional founding activities that result from these specific events. In the previous sections of this Chapter 3, we developed a method to identify and classify actual regional firm founding developments. We are now able to compare the actual developments to the stylized curves produced in the last section. The actual developments we use for the analysis are those that we were able to identify and classify (see Section 3.2.5). Here we focus on reasons that led to these developments and compare these to the analysed events from Chapter 2. This is done by interviewing regional experts, whose opinion and analysis is then compared to the explanations derived from the theoretical framework given in Chapter 2. This exercise sheds light on the importance of the different events and their impact on the development of start-up activities. After a brief description of the interview procedures (Section 3.3.1), we present the interview results in Section 3.3.2. These results and implications for adaptations of the theoretical relationships as well as for the empirical method are then discussed in Section 3.3.3. Section 3.3.4 concludes. 3.3.1 Method and background A qualitative, interview-based research method was chosen as it is flexible and can therefore adapt to new information related to events as well as unexpected answers. This method is appropriate at this stage because the interview may bring to light new events responsible for changes in regional start-up activities that were not previously considered (Schoenberger 1991). This provides a basis for testing the events put forward in Chapter 2, and may further lead to modifications in the list of events that are considered to be influential in changing firm founding rates. A short questionnaire with three questions was designed (see Appendix D.1 for the questionnaire). These questions became increasingly more specific in their nature: the first question is open-ended, asking which reasons, in the interviewee’s opinion, may have caused specific changes in the region’s firm founding activities. The second question is comprised of a number of smaller fixed-alternative questions that enquire which events from a given list have occurred. This list of events reflects those which are discussed in Chapter 2. And, if so, whether it led to an improvement or deterioration in the regional firm founding rate. It also recorded at what point in time the interviewees thought that the event had taken place. The last question enquired whether a (de-)listing

108

Empirical evidence

of firms in the NEMAX or Tec-DAX had an impact on the regional firm founding activities (as already discussed in Section 3.1.2).32 Before interviewing, we also conducted a pre-test with three people in order to test the questionnaire’s clarity and subsequently modified it according to their comments. After the questionnaire was designed, we contacted the representatives of the local Chambers of Commerce and Industry responsible for start-up activities or start-up support in particular regions. Here we only contacted those representatives whose regions were identified as experiencing significant changes in firm founding activities and were subsequently classified in the last section (see Table 3.8). Our aim was to detect reasons for these specific classes of development that were identified in the regions. Along with the questionnaire, the representatives also received a letter informing them about the general purpose of the research project. They additionally obtained figures that compared recent trends in firm founding intensities for the relevant industries in their region with the average aggregate rate of all 49 regions in our sample. A week after the letters were sent, we contacted the representatives to enquire whether they would be willing and able to participate in the interview. In some cases, we were referred to other persons from either within the same organization or elsewhere who were considered more suitable for the interview. The interviews took place between November 2004 and January 2005. We conducted interviews with 19 persons (see Table A.11 in the Appendix for the interviewees), covering 25 regions. An interview took around 20 to 30 minutes. The representatives from the other 13 regions could not be contacted or were unwilling to participate in the interview. Before presenting the results, some relevant aspects for interpreting the findings needs to be discussed: as was already pointed out, we were not able to interview experts from all analysed regions. Additionally, question No. 2 was infrequently answered because the representatives, for instance, were of the opinion that the information was covered by their answers to question No. 1 or they were unwilling to give detailed answers to question No. 2 because of time constraints. Given these circumstances, we present the interview results according to the different development classes that were previously devised in Chapter 2. We selected the most informative interviews, trying to cover as many different kinds of development classes (according to the above classification) as possible and will present this selection in the following. As a consequence of these observational restrictions, we cannot obtain a comprehensive picture of the entire set of possible causal relations between events and development classes. Furthermore, we are not able to include all classes in the analysis. Nevertheless, this approach gives us a first impression of what events have contributed to specific regional development. In this respect, we are able to ascertain whether indeed it was the events discussed in Chapter 2 or other events that caused the changes in firm founding rates. These interview results can be used to refine both the quantitative and qualitative methodologies in future investigations (see Section 3.4).

Empirical evidence 109 3.3.2 Development classes and interview results In the following, we present the different classes of development together with the reasons for their occurrence as suggested by the interviewed representatives. Each time we give a brief description of regional firm founding intensity’s development in comparison to the aggregate development rate that builds the basis for the identified class. The aggregate development rate depicts the average development of the industry under consideration over all 49 regions. After this, the answers of the representatives to question No. 1 are reported. We add the answers to question No. 2, if this gives some further insights. A change in one of the listed events is only considered for the analysis, if, according to the representative, this had an impact on regional firm founding activities. It must be noted that there is no proof that the experts’ opinions are actually accurate. In most cases, while the experts are able to identify the events, they have not mentioned through which mechanisms these events impact local firm founding activities. In the case where some information is added to the representatives’ answers, this will be indicated in the text. The classes we will cover in the following are presented in the following order: -5, -3, -2, -1, 1, 2, 3, 5 and 6. Class -5 (‘bump’) •

•

In Augsburg (industry No. 22, ‘publishing, printing . . .’) we observe that in comparison to the aggregate development rate, which was consistently decreasing from 1990 to 2001, Augsburg had relatively strong founding activities in this industry during the early 1990s. From 1995 onwards it decreased rapidly and even fell under the aggregate development rate in 2000. In 2001 it recovered and reached approximately the aggregate development rate. These latter two developments are identified together as a Class -5. The interviewed representatives explained that the region underwent industrial restructuring processes in this period. They mentioned that in Augsburg (and in the industry in general) the turnover for printing shops was constantly decreasing because inter- and intranets substituted classic printing activities. Since founding activities in Augsburg were higher in the beginning, the city suffered especially from this decline. Instead of decreasing to the average industry level, over a certain period of time a decrease under the average rate occurred. The representatives did not give a reason for this. It is possible that the development is based on the agents’ behaviour who observed the decreasing printing activities and over-reacted leading to a strong decrease in founding activities. Only after they adjusted their evaluation of the market situation, the founding activities returned to the average industrial level. In Karlsruhe (City) we selected the development in industry No. 74 (‘other business activities . . .’). In the early 1990s founding activities were below average but developed positively in 1993. This positive trend turned around in 1996 with a decrease until 1998. From that year on, a strong positive

110

Empirical evidence development can be observed reaching the aggregate development rate in 2000. This latter development, including the initial decrease and later increase, is classified as Class -5 (‘bump’). As reasons for the overall positive development, the local representative put forward that regional restructuring had led to the situation in which founding activities were being shifted away from manufacturing towards service industries. Karlsruhe is the city in Baden-Württemberg where this structural change towards the service industries is observed to be the strongest. A second reason is directly based on activities in manufacturing industries. This impact can be sub-divided into: (A) the positive development in manufacturing industries33 that resulted in a higher demand for business services. This led to a related increase in service industries start-ups. (B) Many existing manufacturing firms out-sourced parts of their activity. Both processes led to an increase in foundings activities in this industry. All these factors can lead to an increase in founding activities. However, the representative did not directly give a reason for the short-term fall back from 1996 to 1998. One reason can be derived from industry No. 72’s development (‘computer and related activities’) and the reasons for this development expressed by the representative. This industry developed negatively from 1995 onwards. Reasons for this decline are presented as an example for Class -3 (‘slow level change’), but it can be noted that this industry was very active in Karlsruhe before 1995 and the decline probably also influenced firm foundings in other industries. This is based on the direct effect of the decline in business service demand which coincided with industry No. 72’s downfall. Additionally, the general climate for firm foundings could have worsened as well. These negative shocks only influenced start-ups in industry No. 74 for a limited amount of time. This is due to the continuation of positive developments (mentioned above) that prevailed over the negative ones, leading to a turn around. Moreover, representatives also suggested as a reason for the positive development in industry No. 74 that those workers who were earlier employed in industry No. 72, started to develop business conceptions for industry No. 74, because the market demand for their former product diminished.

Class -3 (‘slow level change’) •

•

The firm founding activities in industry No. 74 (‘other business activities . . .’) in Augsburg stayed almost constant during the investigated period, but the aggregate development increased between 1994 and 1999. This resulted in a slow decrease of Augsburg’s relative position from 1994 to 1999 (Class -3). To account for this, the representatives mentioned that the general economic situation and a general decrease in founding activities in all industries had occurred. These reasons might lead to a decrease in the founding activities, but the question remains open why these factors especially influence Augsburg. A concrete reason for this was not given. As was mentioned above, industry No. 72 (‘computer and related

Empirical evidence 111 activities’) showed an interesting development in Karlsruhe (City). The already high level of activity (in comparison to the aggregate development) between 1990 and 1994 increased even further in 1995. From this very high level, activities decreased from 1995 to 2000. In 2000 the activities were lower than those of the aggregate development rate and it slightly increased in 2001, whereas the aggregate decreased. The overall development led to a slow decline of the relative position of Karlsruhe (City) from 1995 to 2000 in comparison to the aggregate (Class -3). The representative suggested that this may have occurred because many new technologies were developed in Karlsruhe (City) and it is perceived as a region which is amongst the first to adopt new technologies (especially in the early 1990s) that may have resulted in high firm founding rates. Hence, in Karlsruhe, an information technology boom (e-commerce, internet, etc.) originated in the universities (including the oldest technical university in Germany) and the research institutes. This resulted in many firm foundings in the first half of the 1990s and a ‘gold rush atmosphere’. The decline which started in 1995 occurred because the pool of agents willing and able to start a firm (as well as the ideas and business opportunities) were exhausted. At the same time other cities and regions got stronger and the industries’ activities might have migrated to these regions. Thus, Karlsruhe (City) experienced the boom and decline phase earlier than the other regions. This also influenced the region around the city (Karlsruhe (Kreis)) in which a boom in founding activities in industry No. 72 between 1998 and 2000 could not be observed. The reason for this is that the majority of firms were founded in Karlsruhe itself and not in the surrounding area: the activities in Karlsruhe (Kreis) neither increased strongly in the middle of the 1990s as in Karlsruhe (City) nor did it increase in the second half of the 1990s (as in the aggregate). Class -2 (‘fast level change’) •

In Augsburg the three aggregated service industries (No. 72 to 74) developed positively between 1992 and 1998. During this time, the founding activities in Augsburg increased slightly more than the aggregate development rate. From 1998 onwards activities in Augsburg dropped sharply, whereas the aggregate development rate increased. This led to a worsening of Augsburg’s relative position between 1998 and 1999 that is classified as Class -2. The representatives said that this was caused by the fact that in the end of the 1990s, the first euphoria (caused by the positive development starting in 1992) was gone and some realism came back into the entrepreneur’s evaluation of business opportunities. Like in Karlsruhe (City), an increase or a boom in the founding activities was followed by a decline, which was even more drastic in Augsburg. Here, the increase and the decrease took place earlier than in other regions which led to a degradation of Augsburg’s relative position.

112

Empirical evidence

Class 1 (‘linear’) •

In Heinsberg the firm founding activities in industry No. 74 (‘other business activities . . .’) increased from 1990 to 1997 more than the aggregate development rate (Class 1). Heinsberg is a region in the Ruhr area that was strongly influenced by mining and heavy industries for a long time. This region was affected by industrial restructuring. The representative said that this had already started before the 1990s. In 1997, the last local hard coal mine (‘Sofia Jakoba’) was closed down. This restructuring process resulted in an increase in the firm foundings in industry No. 74. To this explanation fits the fact that the representative mentioned positive changes in the external environment that increased the firm founding activities as an answer to question No. 2.

Class 2 (‘fast level change’) •

•

After a decline of the manufacturing industries in Karlsruhe (City) from 1993 onwards, founding activities in these industries increased strongly in comparison to the aggregate development. This resulted in a rapid change to a higher level of foundings in 1995 (Class 2). As a reason for this, the representative pointed to the founding of a number of firms that were major suppliers to the automotive industry. In the middle of the 1990s the multinational enterprise Daimler-Chrysler invested in the region by setting up a new production plant to build a new car model. Consequently, many other (supplier) firms followed. This led to an increase in the firm foundings related to the automotive industry. Besides this very focused answer to question No. 1, the representative put forward a broad spectrum of positive events in the answer to question No. 2: educational infrastructure, financial support, public demand, infrastructure, other public support, settlement of large firms, venture capital firms, foundings of small firms, establishment of a localized industrial cluster and the external environment. Aggr14 (consisting of 12 manufacturing industries and industries No. 72 and 73) in Köln mostly developed parallel to the aggregate development but on a lower level. In 1998, the firm formation activities increased strongly, nearly reaching the aggregate development rate in 1999 (for 1998 a Class 2 (‘fast level change’) development was classified). In 2000 and 2001, the activities were even higher than the aggregate development rate. The development in industry No. 72 (‘computer and related activities’) and the aggregate of the 12 manufacturing industries also showed an increase in activities in 1998. This might be seen as the basis for the overall developmental pattern that was observed. The representative pointed out that he observed an increasing number of spin-offs in the middle of the 1990s. From 1998 onwards, firm foundings on a regional level were strongly supported by the public sector (e.g. by fairs). This resulted in many newly established firms and outsourcing activities from existing firms. In the

Empirical evidence 113 middle of the 1990s the demand also changed leading to a close down of many local firms and a general reduction of employment in the manufacturing industries. Consequently, the public support for firm founding activities reached many qualified employees (who were potential founders). These aspects were taken up again in the answers to question No. 2 and some further positive events were cited: general public support (already discussed), educational infrastructure, public demand, infrastructure, settlement of large firms, founding of small firms, establishment of a localized industrial cluster and the external environment. Class 3 (‘slow level change’) •

•

In Dortmund, industry No. 74 (‘other business activities . . .’) showed similar activity level as the aggregate development rate until 1996. However, in Dortmund the industry’s strong positive development had already started in 1996 and the activities increased more strongly than the aggregate development rate. This led to the identified level change of firm formation activities between 1996 and 1998 (Class 3). The representative stated that a founding support programme, which started in 1996, was responsible for this development. Furthermore, Dortmund is a prominent city in the Ruhr area that may have attracted large firms who also contributed to this increase (e.g. by spin-offs). This public (financial) support for a positive development was also put forward in the answers to question No. 2 and were complemented by the following factors which positively changed: educational infrastructure, administration, infrastructure, external environment and catching-up and falling-behind processes. The aggregate development rate for the 12 manufacturing industries decreased nearly each year between 1990 and 2001. In comparison, the city of Frankfurt (Main) showed a strong increase in firm formation activities between 1993 and 1998 (Class 3, ‘slow level change’). During this period, the activities increased from a level that was lower than the one of the aggregate development to one that was higher than the aggregate development. The representative explained this increase by citing improvements in the administrative and approval procedures for firm foundings in 1992. After the reunification, this attracted many firms to Frankfurt. Furthermore, in order to support local firm foundings, new technology and start-ups centres were erected.

Class 5 (‘bump’) •

In Mühlheim (Ruhr) industry No. 72 (‘computer and related activities’) started on a very low level, but increased from 1991 to 1997. It approximately equalled the aggregate development rate in 1993 and was even higher than it in 1997. After that, the activities decreased strongly until 1999. In the last three years (1999 to 2001), the regional activities were far

114 Empirical evidence

•

•

•

lower than the aggregate development rate. The development between 1991 and 1999 was classified as Class 5. The representatives mentioned that in 1991 the firm Easy Software started which stimulated the formation of some other firms. In Dithmarschen industry No. 72 (‘computer and related activities’) showed a Class 5 development between 1992 and 1997. During this period, the rate increased from a very low level and then fell back down again to the initial level. The establishment of a start-up centre and the German reunification were mentioned as reasons for this pattern.34 Especially, the reunification led to many new start-ups but the initial boom stopped again because many firms moved to or started in the so-called ‘Neue Länder’ (federal states in East Germany) in order to benefit from higher subsidies. This resulted in the decline of the firm foundings. According to the representative, public demand, the founding of small firms and changes in the external environment also affected regional firm foundings positively, which he put forward in his answers to question No. 2. Freising’s aggr15 (12 manufacturing plus three service industries), in the years 1992, 1994, 1995 and 1997, experienced positive changes in the level of firm founding activities relative to the aggregate development rate. The years 1994 and 1995 were identified as Class 5; in 1996 the activity fall back to a lower level. The representative attributed this to the creation of an international airport in the region in 1992. This airport caused the establishment of new firms and especially of spin-offs from already existing companies. Furthermore, the scientific campus Weihenstephan (including, e.g. research institutes and an university of applied sciences) was named as a factor that positively influenced founding activities. As background information to this claim by the representative we can add that the campus moved to the location in 1989 and in 1991 the Fraunhofer Institute for Process Engineering and Packaging was founded. We discuss the development of industry No. 72 (‘computer and related activities’) in Augsburg as the last development in this class. It started lower than the aggregate development rate but the gap was slowly closing until 1995. Between 1996 and 1998 the activities increased strongly to a level higher than the aggregate development rate, but fell back to the aggregate development rate between 1999 and 2001 (Class 5). As reasons for this development, the representatives suggested that this pattern was caused by the growing use of internet or intranet applications as well as databases. Augsburg especially benefited from this development because new firms profited from policy support via several programmes (e.g. financial support for the design of websites). Furthermore, in the region many hardware firms exist that were able to diversify in these areas. These firms also engaged qualified employees who could start their own firms. Since an increasing entrepreneurial attitude was identified, the latter might have led to an increase in firm foundings. By the end of the 1990s this euphoria broke down. The question remains unanswered why the activities declined in

Empirical evidence 115 Augsburg at a time when other regions experienced an increase. The representatives emphasized the role of education and qualification as well as the general policy support by naming these two factors as answers for question No. 2. Class 6 (‘over-shooting’) •

In all regions the firm formation activities in industry No. 72 (‘computer and related activities’) increased after 1997/1998, but in München the activities showed a particularly strong positive development. From 1998 to 2000, the activities tripled, starting from a level only slightly higher than the aggregate development rate, whereas the activities in the aggregate did not even double. The development was classified as Class 6 (‘over-shooting’) with a very strong increase in the activities and an over-shooting effect. In 2001, the activities reached a level lower than the peak in 2000 but still considerably higher than in 1998. According to the representative, the reason for this is the information technology boom that led to many spin-offs from existing firms and universities. Many large firms, e.g. the firm Infineon (a producer of specialized semi-conductors which was split off from Siemens), different universities and research institutes are located in München. From these, many firms originated which led to this strong development. Based on these processes, a cluster resulted that also influenced the start-up rate. These regional conditions and market opportunities resulted in the firm formation boom. As in the other regions, this boom collapsed in 2001.

East Germany: Classes -3, -2 and -1 In all four East German regions included in our sample (Berlin, Dresden, Jena and Sömmerda), these were only negative developments were. In all regions, multiple classes were identified (depending on the different industries). In all of these cases the representatives put forward the same reasons: the reunification and economic restructuring. Large state-held firms dominated the former GDR and, in the early 1990s, many of these were subdivided and privatized. New firms were founded because there was a strong demand for products and services. These founding activities were strongly supported by political means (e.g. subsidies). The initial high rate of firm foundings was not sustainable and competition let to the closing down of many firms. At the same time the number of foundings declined. Since the reasons for this development overlap so strongly, we analyse the developments for each region separately and not for the single classes as was done before. •

In Berlin35 the firm founding activities in the different industries developed negatively relative to the aggregate development rate. In industry No. 72 (‘computer and related activities’) we observed Class -3 (‘slow level change’) with a decline between 1992 and 1994. In industry No. 74 (‘other

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Empirical evidence business activities . . .’) the activities dropped twice to a lower level (Class 2, ‘fast level change’) once in 1993 and once in 1998). The aggregated industries (aggr14, aggr15 and prod) showed a continuous negative development (classified as Class -1, ‘linear’) starting in 1990 and continuing until the end of the period under investigation (1999 to 2001). Besides the effects of the reunification, the specific government support for firms in Berlin, which was established to attract East German firms to West Berlin, was reduced over time. The representative was of the opinion that this also influenced the number of foundings negatively. In recent years, many firms were started in Berlin’s surroundings or even moved there from Berlin. These aspects led to a decline of the firm founding rate in both the manufacturing and the service industries. In Dresden, after a very high level of founding activities in the 12 manufacturing industries (prod) in 1991 and 1992, these rates experienced a decline between 1992 and 1994 (Class -3, ‘slow level change’). From 1994 onwards the founding activities were lower than the aggregate development rate. Industry No. 74 (‘other business activities . . .’) and the other aggregated industries (aggr14, aggr15, service) all decrease between 1990/1991 and 1999 (Class -1, ‘linear’). The representative pointed out that the reunification would be the main reason for these findings. These activities did not normalize at the aggregate development rate but dropped well below it. A small short-term increase (one year) in the founding activities in many industries can be observed in 1996. According to the representative, the establishment of the Infineon plant in 1996 caused this effect. However, this event was not able to change the long-term declining trend. In Jena the founding activities in industry No. 74 (‘other business activities . . .’) dropped from 1991 to 2001 (Class -1, ‘linear’) with only small and short-term positive deviations from this trend. The level of activities in the aggregated service industries (industries No. 72 to 74) declined in 1993 (Class -2, ‘fast level change’). After this strong decrease, it stabilized on a lower level. Again the reunification and the economic restructuring were made responsible for this development by the representatives. In Sömmerda, the rate of activities in industry No. 74 (‘other business activities . . .’) and the aggregated service industries (industries No. 72 to 74) dropped in 1994 (Class -2, ‘fast level change’). In Sömmerda, the activities in these service industries were always lower than in the aggregate development rate. In aggr14 (aggregate of the 12 manufacturing industries and industries No. 72 and 73) the activities dropped between 1993 and 1996 from a level that was above the aggregate to one that was lower than the aggregate. The representative accounted for these observations by citing the small number of firms in the industry No. 72 (only hardware manufacturers exist) and 73. Firms that relocated to the region from elsewhere also brought their existing advertisement, consulting firms, etc. with them. Thus, not many firms started in industry No. 74. Additionally, the fact that other firms went bankrupt also decreased the demand for business services. In

Empirical evidence 117 comparison to the service industries, many firms in the manufacturing industries were founded in the early 1990s. This was caused by the closure of one large GDR firm with around 10,000 employees. Many former employees were pushed to start their own firm. However, this consequence soon decreased because the pool of highly qualified potential entrepreneurs was exhausted and competition increased. Another negative impact, according to the representative, resulted from a decrease in financial support during the period under investigation (question No. 2). The turnover decreased and new firms moving to the region brought their suppliers with them rather than demanding local products and services (as was already mentioned for the business services). This resulted in a decreasing number of firm foundings in the manufacturing industries. Nevertheless, the activities in these industries are approximately on the same level than the aggregate development rate. These relatively strong founding activities had a positive impact on the development in aggr14. Based on the answers to questions No. 1 and 2 we bring together the reasons put forward by the representatives that caused these developments (see Table 3.12). Since the development of East German regions was a very specific and unique case, we do not include it in Table 3.12, but we will discuss them later on in Section 3.3.3. 3.3.3 Discussion of interview findings We now compare the reasons for the actual developments to the events and stylized curves derived from Chapter 2. Many of the events named by the interviewees did indeed stimulate a developmental pattern that was described and predicted in Chapter 2. These are for example with regard to question No. 1: the foundation of a small firm (e.g. Easy Software in Mühlheim/Ruhr) caused a bump (Class 5) and investments of large firms caused a switch (Class 2, ‘fast level change’) to a higher level of founding activities (e.g. in Karlsruhe). A control of the answers to question No. 2 reveals that the reasons for developments put forward by the representatives fit well with the events of Chapter 2 and the related stylized curves. Only two developments show unexpected results: (1) public financial support leads, according to our interviews, to a longterm level change (Class 2, ‘fast level change’ in Karlsruhe or Class 3 ‘slow level change’ in Dortmund), where we would expect a short-term change (Class 5, ‘bump’). (2) The opposites holds for the educational infrastructure; we observe Class 5 and we expect Class 2 (Augsburg). In the following, we will concentrate on such developments that do not fit the expectations. In addition, we discuss those processes to which attention should be paid in future research. The main question is whether the events that did appear in the interviews have been considered explicitly enough in Chapter 2. It seems that no new event was mentioned in the interviews but that some events need more deliberate attention, especially in future empirical research. In the following we discuss

Augsburg: negative: impact of industrial restructuring Karlsruhe: positive: restructuring, cross-sectoral influence and outsourcing; negative: cross-sectoral influence Augsburg: general economic situation Karlsruhe: exhaustion of pool of potential founders Augsburg: boom collapsed/realism in the evaluation of business opportunities Heinsberg: restructuring of industries (positive effect) Karlsruhe: investment of large firm with follow-up investments of other firms

Köln: public support which reaches qualified (unemployed or threatened) agents and increased outsourcing & spin-off activities Dortmund: public support; attraction of large firms & related spin-offs

Frankfurt (Main): administrative and approval procedures (indirect impact of reunification; technology and start-up centres) Dithmarschen: positive: start-up centres, reunification; negative: relocation of firms to East Germany Augsburg: positive: positive market development in combination with public support and existing firms with qualified employees which start a firm themselves; increase in entrepreneurial attitude; negative: boom collapsed Mühlheim (Ruhr): founding of small firm Freising: new international airport; scientific campus München: positive market development in combination with spin-offs from existing firms and universities; cluster development; negative: boom collapsed

–5

2

3

6

5

5

5

3

1 2

–2

–3

Question 1

Class

Policy: educational infrastructure; Policy: other support

Policy: demand; Founding of small firm; Changes in external environment

Changes in external environment Policy: educational infrastructure; Policy: financial support; Policy: demand; Policy: infrastructure; Policy: other support; Large firm; Venture capital; Founding of small firm; Localized cluster; Changes in external environment Policy: educational infrastructure; Policy: demand; Policy: infrastructure; Policy: other support; Large firm; Founding of small firm; Localized cluster; Changes in external environment Policy: educational infrastructure; Policy: financial support; Policy: administration; Policy: infrastructure; Policy: other support; Changes in external environment; Catching-up and falling-behind

Question 2

Table 3.12 Summary of events and their impact on developments in start-up activities

Empirical evidence 119 eight aspects in which the interviews led to additional information that should be included in the concept developed in this study: (1) the impact of the ‘general economic situation’ and industrial restructuring, (2) cross-industry effects, (3) the relationship between increases and decreases of start-up activities, (4) interactions of different events and factors, (5) uncoupling of regional and aggregate developments, (6) the timing of events, (7) unidentified events and developments as well as (8) multiple classes in a region. 1

The ‘general economic situation’ was mentioned by the representatives to have either a positive or in most cases a negative impact on the development of regional firm foundings (e.g. in Augsburg). Here, the important question is then why this particular region was affected in a relatively stronger manner by this general economic development. If this is not the case, then the region would not have been classified as undergoing a significant change in Section 3.2. The representatives did not mention such a specific regional effect explicitly, although it might exist (e.g. a region with an airport might suffer especially from a decrease in world trade). Linked to this issue is the industrial restructuring that was used by the interviewees to explain positive (e.g. Heinsberg) as well as negative developments (e.g. Augsburg and especially for the development in the East German regions). This restructuring refers to many different events and, hence, many different regional factors are influenced (see Section 2.3.7 for such aggregated events). In this regard, the question remains unanswered as to whether the interviewees have either not mentioned and recognized the specific regional dimension of restructuring and the economic situation, or whether it simply does not exist. With regard to the former, it might be the case that the representatives wrongly evaluated the impact of the general economic situation consciously or unconsciously. This can be based on a self-serving perception of events in which positive developments are more likely to be noticed by representatives and negative developments are downplayed and attributed to external causes (Fiske and Taylor 1991). Furthermore, representatives might purposefully mention the general economic situation because they want their own work and their region to be evaluated positively. For a declining economic situation they cannot be made responsible. Both aspects enable them to confirm that they are meeting their goals in developing the regions and supporting start-ups. In order to answer the question of which impact the general economic situation really had, it is necessary to disaggregate the impact of the ‘general economic situation’ and restructuring processes into their subevents (e.g. market demand or unemployment rates). This would help to determine which effects of the economic situation or the restructuring really influence firm foundings and which of these elements influence specific regions. Specifically, this might deliver better insights into what stimulated negative developments. Additionally, different objective data sources can be used to reduce the effect of the potentially self-serving answers.

120 2

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5

Empirical evidence We can conclude from our interviews that the development of one industry influences the development of other industries. This takes place by an impact on market demand that is especially exerted by medium or large firms (e.g. Karlsruhe or Dresden), and by shaping the general economic climate (e.g. Mühlheim/Ruhr) which affect all firms. These events were already included in Chapter 2 and we pointed out that events could have differing effects between industries. Although single events were considered, it seems worth stressing that such cross-industry influences have a specific dimension because they might lead to a self-augmenting up- or downward development, e.g. one industry increases and others follow. Also, there may be a downside in using the aggregate rate as a point of reference: developments in individual industries add up and influence the development of the aggregated industries negatively or positively. For example Karlsruhe’s developments in industries No. 72 and 74 heavily determined those developments in the aggregated service industry. Because of this fact, we always have to keep mind that, whenever possible, the aggregate industry development should mainly be deduced from the developments in the single industries. Likewise, we should analyse those events that affect single industries and not the aggregates. Another dimension of such self-augmenting processes is the close link between an increase or a high level of start-up activities and a subsequent decrease. Such processes were identified, e.g. in München, Augsburg and Karlsruhe. We included this element especially in the over-shooting effect that can be best observed in München with a Class 6 development (‘overshooting’) or in Augsburg (Class 5, ‘bump’). In addition, such a link might also be relevant in Karlsruhe (Class -3, ‘slow level change’), where the founding activities dropped because the pool of potential entrepreneurs was exhausted. The relationship between increases and decreases (or vice versa) in start-up activities is worth exploring more deeply in the future because they suggest some type of cyclical pattern in economic activities within a region that influences start-up activities. As discussed in Chapter 2, we find that several events appear simultaneously, e.g. in Frankfurt (changes in administration and technology, and start-up centres) or Köln (airport and scientific campus). Since the impact of these events on the founding activities also overlap, this causes analytical problems in discerning which event exactly causes which development. In general, the answers support our assumption that regional factors strongly interact with one another. Hence, in an empirical analysis, the interactions of events and factors should be included by interactive terms in order to properly grasp their impact on changes in firm foundings. We also discovered that in some regions no events took place. For example, Esslingen did not show the same positive development in industry No. 74 as the aggregate development rate but stayed on the same level. In this case, the relative regional position declined exactly because no events took place and there was no change in regional factors (as confirmed by the intervie-

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wee). Thus, in addition to the proposition that changes in regional firm founding activities occur because of specific events, these changes can also result if no event takes place in one region, but occurs in the majority of other regions. In that case, activities in one region are uncoupled from the general development. In other words, events that occur in all other regions do not reach one specific region. This finding does not require an adaptation of the theoretical relationship, but is still relevant for the empirical analysis because here such regions might be identified.36 We observe that the same development in firm founding activities (e.g. an increase with a subsequent decrease) can take place at different times in different regions, e.g. in Augsburg an increase in the founding activities in industry No. 72 occurred earlier than in the aggregate development rate. In the case of Augsburg a Class 5 (‘bump’) development is identified. This is important because the timing of events may differ between regions in a way that the event’s early occurrence in one region, followed by its occurrence in other regions may influence the classification of developments. For example, an event in one region that normally leads to a switch in the startup activities (Class 2, ‘fast level change’) might be identified as Class 5 (‘bump’) if this event occurs relatively later in all other regions. Vice versa, if the occurrence of one event in a region is relatively later than its occurrence in other regions, this would also influence the classification. The question remains whether all relevant developments and events could be identified and, if so, whether one or two events are responsible for one specific development. Concerning the latter, e.g. an increase and subsequent decrease in the activity level (classified as a bump) can result from one event (as postulated in Chapter 2) or from two events (an initial event leading to a switch to a higher level, whereas a later event switches back down to a lower level).37 We observed three unexpected developments which might be based on this problem: •

• •

In Freising we would expect that local events (new airport, scientific campus) result in a switch to a higher level (Class 2, ‘fast level change’), but what we observe is a Class 5 (‘bump’). The same holds for Augsburg with a change in the educational infrastructure leading to a switch, although we expect a bump. The opposite we can observe in Karlsruhe where we expect a bump based on public financial support, but identify a Class 2 (‘fast level change’).

There are three possible explanations: (A) we were not able to identify a second event leading to the decline although it had already taken place. (B) We have not identified the event or the change in start-up activities because we cut off the period under investigation in 2001. Thus, for some classes, one part of the expected development (e.g. the falling-back to the original level in a Class 5 (‘bump’) development) might only occur after 2001. Or C) the assumed development of the founding activities is not correct. The

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Empirical evidence correct explanation can only be identified with a more in-depth study of the events that occurred in the regions, which would help to figure out whether A is correct. A solution for B) is to extend the period under investigation to capture more events and developments. Especially with regard to the negative developments in East Germany, we found that for some industries or aggregates different classes emerged, e.g. we observed Classes -1 (‘linear’), -2 (‘fast level change’) and -3 (‘slow level change’) together. This is partly due to the fact that in aggregating the industries coarse switches in the individual industry are transformed into smooth changes in the aggregate industries, e.g. for industry No. 74 Class -2 and for the service industry Class -3. The question remains whether there exists a cross-industry impact in which one industry influences the other(s).

From the discussion above, we can conclude that the general framework that links the events, regional factor changes and changes in regional firm founding activities (as proposed in Chapter 2) is useful in analysing empirical developments. Nevertheless, we have to precisely analyse the processes and to accurately disentangle the events from one another in the empirical analysis to find the reasons responsible for a specific development. However, it is especially for this task that the developed framework can be used to structure the research. 3.3.4 Summary of empirical results We can note an interesting insight from the interviews that we have not presented before, because it is a general finding concerning our research approach: all the interviewees focused in their answers on specific events that led to changes in regional firm founding activities. In most cases they ignored the regional factors that were influenced by these events. In contrast to this, researchers mostly focus on the regional factors, but ignore the underlying reasons or events that cause changes in regional factors. Both groups probably include the respective missing aspect in their consideration, but this finding supports our present approach in which the events as well as the regional factors are included explicitly in the analysis. There exist some problems related to the interviews such as the relatively low number of answers, the fact that we focus – for a different reason – on selected classes and answers’ as well as the necessity to deal with psychological phenomenon such as a potential self-serving bias. These are critical elements that influence the evaluation of the findings and which hamper a potential generalization of the results. The aim of the qualitative interviews was not such a generalization or an econometric test of the hypothesis, but rather to probe deeper into the relationships between events and changes in regional firm foundings as well as a test the general framework proposed in Chapters 2 and 3. Thus, despite these shortcomings and problems, we found support for the proposed events and the relationships discussed in Chapter 2: we discovered that many theoretically derived events are also relevant for the actual developments in the regions.

Empirical evidence 123 Additionally, the interviews offered us some insights into which processes need to be explored more deeply in future research. Potential extensions are discussed in the following Section 3.4.

3.4 Intermediate summary and discussion The aim of this chapter was to develop methods able to identify, classify and analyse changes in regional firm founding activities. Such identification is possible with the proposed procedure. Furthermore, some characteristics of these changes have been analysed, e.g. in which regions and industries they occurred, how high the turbulence is and how large the changes are. The following conclusions are drawn: first, in many cases the founding activities stay on the same level during the whole period of observation or they develop continuously with only small year-to-year changes. However, in other cases, the founding activities change to a higher or lower level and stay on this new level afterwards. This change can occur suddenly or can take some (two to three) years to develop. A bump in the founding activities is also a pattern that can be observed relatively often in the empirical data. In addition, the developments can be grouped into several classes that provide the link to the theoretical underpinnings in Chapter 2 and enable a deeper analysis of the characteristics of these changes. We used qualitative interviews to connect the theoretical Chapter 2 to the empirical Chapter 3. We examined which events actually took place and to which start-up dynamics they are related. This allowed us to compare these results with the proposed events and related stylized developments of Chapter 2. We can conclude that the proposed events can also be observed in reality and that these events in many cases lead to the proposed developments. Thus, the proposed approach seems applicable for the analysis of changes in firm founding activities. Nevertheless, we identified several aspects that can be analysed in more detail in future research. This may lead to the adaptation of the theoretical framework and to the improvement of the empirical tests. We want to discuss some further possible extensions in the remainder of this section. These are (1) alternative methods for the classification of changes in start-up activities, (2) application to different data and (3) method of empirical investigation to identify actual reasons for changes in founding activities. 1

When considering a change in the classification procedure, we see two possible extensions of the method that was proposed and applied in this chapter: first, the method of classification and analysis provides some possibilities for improvement. Second, the data used can be subject to change. With regard to the former, the general analysis of the identified time series can be modified. Instead of using content analysis, a mathematical approach can be used which tries to fit specific functions (e.g. cubic or linear ones) to the observed curves.38 The goodness of fit, the signs and relative magnitudes of the coefficients etc. describe the different classes and developments. This

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Empirical evidence would give the analysis a more objective twist and would make it possible to analyse a larger set of time series. Nevertheless, we decided to use content analysis because humans are better able to deal with fuzzy developments (which is a problem for the mathematical approach) and this was an important fact for analysing the developments. Furthermore, the relatively small number of time series made it possible to analyse them by content analysis. The larger the number of time series, the more sense it makes to use a software program. Concerning the data that is used, many possibilities are open for an expansion of the approach and the analysis. This would offer better and more reliable options for statistical analysis: •

•

•

•

3

First, only 49 regions were analysed here, which limits the amount of developments available for observation. For the test of the proposed method, this was a sufficient number of regions, but the approach can be extended to all 440 German regions in the future. Furthermore, by extending the number of regions, these can be aggregated in order to capture possible neighbourhood effects that may be relevant. Second, Gartner and Shane (1995) proposed the use of multiple data sets to analyse the entrepreneurial activity in regions in greater depth. In this chapter only start-ups were used as one indicator of these activities. Future studies can also include firm exits as well as the number of currently operating firms in a region to obtain a broader picture. The net effect has to be analysed especially because often a high number of firm foundings goes in tandem with a high number of firm exits. With particular regard to the evaluation of policy measures, the net effect seems to be an important indicator. With such a broader approach, the weaknesses of a single data set to describe entrepreneurial activities would be reduced. Third, the analysis should even include very long time scales (where they are available) that include 40 years or more. This would decrease the likelihood to receive a rather truncated picture of entrepreneurship dynamics because, for example, culture and technology only change slowly and to grasp their impact such large time scales are needed. Fourth, although the chosen industries were adequate here, the number of industries can be extended to offer a more comprehensive representation and to enlarge the number of developments in which changes in the founding activities can be found.

We used qualitative interviews for identifying determinants of actual changes in regional firm founding activities. However, this is only a first attempt and there are two other major directions into which this empirical analysis can be extended in future research. •

First, complementary to the analysis of a set of regions, an in-depth case study of one region can also be conducted. This would better grasp

Empirical evidence 125

•

the interactive nature of the different events leading to changes in firm foundings and study these complex processes in more detail. Second, besides the qualitative approach chosen here, a more quantitative one can be applied to explain empirically observed changes. This would offer the possibility of formal hypothesis testing with the according degree of ‘statistical generalisability and confidence’ (Schoenberger 1991: 180). The aim is to test what the likelihood is of observing a change in the founding activities when a specific event takes place. One example for such an analysis is a multivariate regression which especially focuses on the impact that a listing or de-listing of specific firms in the German New Market (NEMAX) or its successor TecDAX has on changes in start-up activities in regions where these firms are based.39 These (de-)listings are suitable proxies for the impact of role models on start-up activities and are just one example for an event put forward in Chapter 2.40 In a second step, the independent and dependent variables can be changed or extended. For the dependent variable the results of the method cultivated in Section 3.2 to classify firm founding developments can be used. Such an approach makes it possible to test which variables lead to the different developments. As independent variables, all variables that are usually used to explain the level of firm foundings are applicable. It is likely that these variables are also influencing changes in the start-up activities. Additionally, the impact of the listing or de-listing of firms in the NEMAX or TecDAX can be refined by considering the developments of the shares financial value over time.

Related to this multivariate approach is the attempt to retrace the path from empirically observed curves to regional factor changes and to their causal events. Furthermore, inferring what transmission processes are relevant for these developments would be an interesting endeavour. As previously discussed this inference to underlying processes as well as to regional factor and event changes is problematic. Nevertheless the empirical approach opens the possibility to test for specific events and regional factors. If data on events that occurred in the regions or which influence the regional start-up activities (see Section 2.3) and additional data on changes in the regional factors is available, an empirical test is possible. The changes in the regional factors can also be classified by the method described in this chapter, so that ultimately the whole chain from the event to the classified changes in the regional factors and then to the classified changes in firm founding activities can be tested for their causal relation. Unfortunately, this data is not available at the moment, but the approach in Section 3.3 offers some first insights on how the different development curves are caused by the events.

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Positive examples and their impact on regional start-up activities

It is no simple task to examine how various socio-economic events may be linked to changes in regional factors as well as changes in firm founding activity (analysed in previous chapters). Nevertheless, it seems worthwhile to pursue such an analysis, given that it may contribute towards the construction and provision of better policy. This chapter focuses on one specific event that has the potential to change the regional conditions for start-ups: the founding of a small firm can serve as a so-called ‘role model’ for other regional agents.1 As was already noted in Sections 2.1.5 and 2.3.3, such role models are a pull-factor and influence the willingness of agents to found a firm. We think that they have an even wider impact on the founding process, an argument that we examine empirically in the subsequent chapter. The chosen event, defined here as the founding of a small firm or the appearance of a role model, serves as an example of how the relations and processes discussed in Chapter 2 can be more thoroughly explored. This event is especially worth examining because of the complex processes involved. For instance, information transmission is mainly based on the social relations and interactions of regional agents. Here psychological aspects, such as mental models, have to be considered. As Denzau and North define these, ‘The mental models are the internal representations that individual cognitive systems create to interpret the environment’ (Denzau and North 1994: 4).2 We argue that these can contribute to over-shooting effects in regional firm founding activities. As pointed out in the introduction and in Chapter 2, we are not primarily interested in explaining why start-up activities differ between regions, but rather focus on changing patterns of such activities inside one region over time. In this respect, we argue that positive role models generally lead to an increase in the likelihood of a firm being founded in a region and, thus, result in a higher average level of regional start-up activities. With such an approach, we look to explain mostly the acceleration of initial, minor changes of start-ups.3 Additionally, positive feedback processes could result in a specific regional attitude towards entrepreneurship in the longer run. This possibility may at least partially explain disparities in the regional founding activities. The approach followed here considers the individual founder via his personality and biography. The former includes psychological factors such as

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‘extroverted as a child’ and the latter relates to such aspects as his educational level (e.g. Kriegesmann 1999, van Praag 1996). We also take into account the social and regional context in which the agent is embedded (Van de Ven 1995, van Praag 1996). By this we mean the impact that regional role models can have on individual behaviour. In other words, the personal and regional levels are seen as interlinked: regional conditions influence the development of agents and the behaviour of agents in turn influences the regional context. The chapter proceeds as follows. Section 4.1 gives a more detailed overview of the relationship between mental models and agent behaviour. It is argued that these mental models moderate the agents’ behaviour. Furthermore, the link between behavioural examples that serve as role models and the mental model of an individual agent is developed. In particular we use Bandura’s (1986) conjecture that social interactions between agents and their social-cognitive learning processes are the central factors by which this link operates and by which mental models and the information about role models can diffuse. Thus, agents that serve as role models are an important element influencing the behaviour of other agents by shaping these social-cognitive learning processes. In Section 4.2 it is shown what impact proximities and especially regional networks have on this dissemination process in a population of agents and how regionally shared mental models can emerge. The main factors that are positively influenced by these proximities are information transfer and the likelihood of accepting a role model. Section 4.3 presents a stylized stage model of a development process of an agent to become an entrepreneur. The factors ‘opportunity to become an entrepreneur’ (mostly based on ideas for new products or services), ‘willingness to start a firm’ as well as ‘market test’ and ‘ability test’4 are the main elements considered relevant in this development. Also in this section, the impact of a certain type of positive examples, in this case successful regional entrepreneurs, on other agents within the framework of this stylized stage model is analysed. In addition, we discuss the implications for regional start-up dynamics and disparities in entrepreneurial activities based on the influence of positive examples. Here we use the basic theoretical insights that were constructed in previous sections. Section 4.4 empirically tests some elements of the impact of role models on start-ups by a case study conducted in Jena. Section 4.5 gives a short summary of the results.

4.1 (Shared) mental models and behavioural patterns This section deals with the relation between agent’s mental models,5 their behaviour and the learning processes that shape these mental models. The structure of the section is as follows: after presenting the link between mental models and behaviour, learning processes that can result in changes of mental models are described. Then, the relevant criteria for the acceptance of new mental models and of role models are analysed. Next, we discuss the development of shared mental models with a relatively high degree of similarity between agents and the preceding dissemination process of mental models within a population.

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4.1.1 Mental models and behaviour In this study we assume boundedly rational agents: the agent is not fully informed and only possesses limited cognitive computation abilities. The following framework (Figure 4.1) is used to explain how mental models shape an agent’s behaviour. Furthermore, we analyse in this section how these mental models (and their related behaviour) change over time and how they disseminate in a population of agents, giving rise to shared mental models. An agent’s behaviour and decisions strongly depend on the information the agent receives from his or her environment and the mental model that the agent holds. An individual agent only has a limited information-gathering capacity, therefore the agent does not have (technical) access to all the information necessary, and the agent does not even know which information is needed for decision-making. His or her willingness to gather new information is also limited because, for example, the involved costs are too high or it takes too much time. Furthermore, since attention is scarce, the agent economizes and ‘decides’6 which information the agent should pay attention to (Anderson 2000, Chapters 2 and 3). An important factor that influences his or her attention processes is that information must fit into already existing cognitive patterns or at least new information must easily to be linked to these. This screening, which is to some degree automatic, operates by cognitive cues that are themselves linked to the mental model (Anderson 2000). Thus, the mental model that an agent holds focuses his or her scarce attention, thereby impacting perception of incoming information and interpretation of the world. Based on these constraints, it can be concluded that an agent’s behaviour and decisions are based on only a limited amount of information. Therefore, it is likely that the agent does not have all the relevant information that is required for an ‘optimal’ decision. In addition to affecting perception, mental models also influence the agent’s behaviour by the retention of information, which influences the set of behavioural alternatives that agents choose from and the evaluation of these alternatives (Anderson 2000, Chapters 6 and 7). Depending on factors such as the context or history of the decision, its importance or the frequency of decisionmaking, cognitive decision-making rules can be based on various mechanisms. These are, for example, maximization strategies, trial-and-error processes, satisficing strategies (Simon 1997) as well as the ‘unconscious’ usage of decision

Information and structural environment

Perceived information

Cognitive processes

Behaviour

Mental model

Figure 4.1 Structure of information processing and individual decision making.

Results

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heuristics (e.g. Tversky and Kahnemann 1974). Thus, according to the mental model, the subjective information and the decision-making rules, the agent chooses one possible alternative that leads to specific actions or behaviour.7 Even if conscious information-gathering and -sorting processes take place for situations and decisions that are important for the agent, the need to use mental models leads to biases in these activities. This is based on the fact that the perception of information is biased in favour of information fitting in our existing mental model or which are positive for our self-esteem (Dörner 1995, Reason 1990). Inconsistent information is usually not considered further. Even if such information is perceived consciously, its relevance tends to be played down or ascribed to factors that cannot be controlled or influenced by the agent. The same holds for the subsequent evaluation of this information. In other words, the (objective) information is re-interpreted: the positive results are memorized and the negative ones are ascribed to unfavourable or adverse circumstances, at least to a certain degree. As previously mentioned, the mental model that the agents possess represents an important determinant of their behaviour. In conjunction, if it changes, it can have a significant impact on behaviour. Such a change may occur in the following way. New information has a twofold effect on an agent’s behaviour: on the one hand, the agent may use the existing mental model and the decision rules prescribed by it and choose an alternative from the existing behavioural set, without changing his or her mental model or decision rules. On the other hand, new information could change the mental model.8 This may affect several factors. Cognitive attention could be (re-)focused on other kinds of information, the incoming information might be perceived and memorized in a different way, the set of behavioural alternatives and their evaluation may change, leading to significant changes in the agent’s behaviour. 4.1.2 (Social-cognitive) learning and changing mental models In the following, we use essential parts of the described learning framework for further analysis. At the core of the investigation are learning processes that lead to changes in mental models and their influence on agent behaviour. In the next sections we show how individual and collective learning processes modify mental models. Of central interest is the emergence and dissemination of new mental models. On the group level, new information or a new mental model can be introduced by an individual agent from either within the local group or from certain external sources (via broadcasting, new agents joining the group, etc.). On an individual level, a new mental model can be introduced by processes like the copying of mental sub-models or elements from one context to another, directed and undirected imaginative thinking, errors that occur while incorporating mental models from other agents, or simply by gaining new personal experiences that trigger changes. Most important for the upcoming analysis are the learning processes stimulated by the agent’s social environment. Right from birth, people learn from

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other people with whom they come into contact. Specifically, they may adopt other agents as their role models and imitate their behaviour. In such a vicarious type of learning, the prerequisites and consequences of a specific behaviour are learned by observing others. Communication with others may aid this process. Here, the distinguishing feature of social-observational learning is that agents modify their behaviour despite not personally experiencing the consequences of this behaviour. Several studies confirm the effects that role models have on the behaviour of agents in various contexts (e.g. in occupational choice (Krumboltz et al. 1976) or aggressiveness (Bandura et al.1963)). This kind of learning is a quick and efficient way to adopt complex behavioural patterns especially with regard to behaviour in social interactions or problem solving (Tausch and Tausch 1973). It minimizes the risk that can be linked to some kinds of behaviour and comes at a low cost because, for instance, the learning time is reduced. We partly choose our role models consciously, but in most cases it takes place more or less subconsciously. In our daily life, we constantly and spontaneously compare our own (current) experiences with how others faired in comparable situations. Thus, social comparisons are important for learning (Festinger 1954), regardless of whether the agents are conscious of it or not. There are several characterizations for this type of learning based on behavioural examples: model learning, observational learning, imitational learning or vicarious learning (Edelmann 2000). Although there are some differences in the details, central to all these theoretical concepts is that observing one person’s behaviour can influence the observer. The role model can (A) be directly present in the observer’s immediate physical environment, (B) can be mediated (e.g. by books or television) or (C) be indirectly observed via communication processes with other agents. The role model has two main effects on the observer (Bandura and Walters 1963): first, the observer learns behavioural alternatives not previously present in his repertoire. Second, already present behavioural alternatives are either suppressed or their use is facilitated in the future. In the following, we concentrate on the social-cognitive learning theory presented by Bandura (1986). Here, new individual mental models result from social-cognitive learning processes. This theory contains two main elements: first, learning takes place in a social environment, which resembles the role model approach presented above. Second, cognitive processes of information processing and memorizing largely determine how agents learn from this environment. These cognitive processes determine when agents are stimulated to learn and how behaviour is modified. Agents learn based on information about the behaviour of the model. The core process of learning in Bandura’s theory is the integration of new information into the agent’s mental model. This learning process is separated into different stages: the acquisition phase and the performance stage. In the acquisition phase based on attention and memorizing processes, where the model stimulus is cognitively processed and transformed, classified and organized by and into mental models. This is a recursive processes: the mental models influence the processing of new information, leading to the integration of new parts into the mental models. The scarcity of

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attention, that limits possible learning, is influenced by (A) currently existing mental models, (B) the characteristics of the role models, (C) the observer himself, as well as (D) the relationship between observer and role model (as is described below). The observer takes an active part in the learning process. An agent does not just reproduce previously observed behaviour in the performance stage, but also integrates information about the behaviour into his own mental model of the world. At the performance stage, the agent shows a specific learned behaviour based on his individual mental model9 and the reward processes.10 This reinforcement is beneficial in that it facilitates learning but it is not a necessary condition of learning from role models. The anticipated positive consequences and actual reinforcement of a specific behaviour influence both the amount of attention dedicated to certain behaviours and the likelihood that this behaviour is really repeated in the future. Such rewards can operate by external, internal or vicarious processes. Vicarious reward processes are those in which other agents are rewarded for showing a similar behaviour. Hence, with regard to Bandura’s social-cognitive learning theory, the role model’s behaviour has a strong impact on the observer’s mental models. This social learning is strongly influenced by the frequency and intensity of social interactions, communication and observation processes between agents. The higher the frequency of contact between agent and role models, the more likely it is that the observer will learn from the role model and be influenced by his mental model. These processes are also important for the diffusion of information about behavioural examples or mental models inside a group or network. Although no socially influenced change of mental models can occur without this information diffusion, additional processes are necessary. These are explained in the following. 4.1.3 Acceptance of role models and new mental models Which factors influence the acceptance, integration and the evaluation of new mental models? This question underlines the importance of understanding more about the information diffusion process. There seem to be two factors that lead to a dissemination of the mental model in a population: the diffusion of information about the role model and the actual acceptance of the role model. This acceptance is influenced by several factors. On the one hand, from an economic point of view, a functional explanation is possible that is based on cost-benefit considerations including satisficing strategies11 (Simon 1997). Here social sanctions might result if one agent’s behaviour deviates from the socially preferred one. From this perspective, a new mental model is accepted and exchanged for the old one, if the individual profit provided from the new mental model is higher than the profit from the old mental model plus the costs of adopting the new one. On the other hand, prevalent in the literature are more psychologically oriented theories.12 These explain the adoption of new mental models either via the cognitive leadership of specific agents, including the validity and reputation of these agents (Witt 1998, Senge 1990) or via consistency theory. Consistency

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theories include theories about cognitive dissonance, the necessity that self- and external evaluations match each other and similarities between existing mental models (Heider 1958, Granovetter 1973, Bandura 1974). Furthermore, the initial identification and acceptance of role models also play an important role in the learning process. The question of which characteristics an agent needs to have in order to be accepted as a role model has to be considered. Important characteristics of the role model include the prior success of the model and his prestige, power, socio-economic status and competences (Parsons 1955, Lefkowitz et al. 1955, Mausner 1953). The characteristics of the observer (e.g. emotional arousal, uncertainty and doubts about appropriate behaviour and willingness to learn) and the (social) relationship between observer and role model are also of importance. In addition, the processes are influenced by the image of desirable behaviour and the comparability of oneself to the role model, since agents must be able to achieve this kind of behaviour. Note that the acceptance of the mental model is, according to these approaches, not necessarily linked to an objective improvement in profits or other benefits. For acceptance to occur, it is sufficient that agents think a certain kind of behaviour is appropriate and they would somehow gain from imitating it. The decision agents arrive at is not necessarily based on objective ‘facts’. 4.1.4 Developing similarities in mental models A related and equally important question is whether individual mental models converge and, if so, how. There are several biological, sociological and cognitive processes that influence the dissemination of a mental model. There are some very basic elements based on genetic endowment (Witt 1999a, 2000) or aesthetic factors (Schlicht 2000) that are inherited in every human. This is the basis for many learning processes and results in a certain degree of similarity of mental models, but only on a very abstract and general level. More central to the following argument is that agents are not isolated but are embedded in a social structure. Here, the processes concerning the diffusion of information about role models and the acceptance of models described above play an important role. Concerning the mere diffusion of information, an important influence relates to the frequency and intensity of interaction via communication or observation between agents. The higher this frequency is, the more likely it is that the agents will develop similar mental models because of the described social learning processes (Witt 1996). Furthermore, agents might be exposed to similar information because they have access to the same information channels, have common focal points or similar foci of cognitive attention (Sugden 1995, Lorenzen and Foss 2003). With regard to the acceptance, similar mental models can develop because, for example, the agents are part of the same reference group (Hyman 1968, Festinger 1954) or have the same cognitive leader or role model. Apart from their positive impacts, these factors, on the flipside, are potential obstacles to enabling change in mental models and may hinder the emergence of new mental models. In addition, the adaptation of new mental models and their

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convergence might be hampered by a lack of creativity of the single agent or the rate of closeness of social networks. 4.1.5 Dynamics in the dissemination of mental models Based on these influences, various dissemination dynamics can be derived. Since the agents in a population are interacting with and observing each other, they get to know other agents’ behaviour, which they may imitate. Consequently, positive feedback loops can result from such actions. The more agents change their behaviour, the more agents can observe this and adopt it as their own mental model. Once a certain number of people has accepted a specific behaviour, this behaviour will spread faster and, thus, convince even more people. Thus, it can be noted that the information and learning processes are (at least partially) self-augmenting processes because of feedback loops, which have frequency dependent characteristics and are influenced by critical mass effects described in the literature (see, for example, Stahl 1998, Witt 1989, 1996). Although it is likely that there are always some agents whose mental models deviate from the ‘common’ one, their mental models and according behaviours cannot diffuse to the whole population. On the contrary, until a critical mass of agents who use a new mental model is reached, the self-augmenting processes stabilize incumbent mental models. These endogenous processes result in a situation in which the mental models and resulting behavioural modes present in the population are quite stable with respect to small fluctuations in the number of users. Only when a critical mass is reached do the self-augmenting processes lead to a quick dissemination of a new mental model in the rest of the population. Factors such as size, heterogeneity and openness of the group influence the introduction and dissemination of a new mental model and, thus, the probability of reaching a critical mass. Such a convergence of mental models does not necessarily have to equally affect the entire population of agents; nor is it necessary that there exists only a single mental model in one sub-group. This degree of convergence or divergence is influenced by information diffusion and the acceptance of mental models inside these groups as well as by the structure of the groups and the social interactions taking place within them. Since agents differ, some of them will receive relevant information earlier and some more readily accept new models. Other agents will receive information later or not at all, and some hardly accept any new models. In these circumstances, the least one can expect is that the relevant information about the behavioural example spreads to parts of the population, without assuming a total convergence.13 4.1.6 Intermediate summary To summarize the argument: mental models and the scarcity of information form the basis for our study of agent’s behaviour. Mental models affect the

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perception of information, the evaluation of actions, and consequently the behaviour of agents. Despite the popularity of libertarian arguments that emphasize the importance of allowing agent’s ‘the freedom to choose’ (Friedman et al. 1980), in reality agents are embedded in a social context that has an important impact on the ongoing learning processes and, thus, on the mental models that the agent holds. Hence, the social environment influences the behaviour of agents by shaping the information received as well as by the role models that are available and considered suitable. In this respect, we analysed separately how the relevant information is diffused through a population of agents and how the individual agent accepts it. Furthermore, social interaction can lead to the establishment of shared mental models in a group of agents and, thus, to similar perception, evaluation and behaviour, especially if the information that the agents in question receive are similar. The latter is likely since a common mental model implies similar cognitive attention and, thus, similar information input. One effect of such a shared mental model can be a reduction of environmental complexity which makes it easier for agents to operate in their environment. There is a time lag in changing mental models within groups and adopting the relevant behaviour because agents have to be frequently exposed (by observation, communication or experience) to the stimulus and because of the delay between action, result and feedback on the mental model. The ‘correctness’ of the mental model is not guaranteed in the short run since it tends to be persistent due to the described time lags, stable interaction and communication patterns related to closed discussion groups, scarce cognitive attention and limited information gathering. These dynamics lead to quite stable mental models and information exchange patterns. On the other hand, the same processes can result in a rapid dissemination of a new mental model once a critical mass is overcome.

4.2 Dissemination of mental models in a regional context As the previous section has shown, the agent’s social environment influences their mental model by the provision of role models and the processes of information diffusion and acceptance. Since the focus of this study is on the explanation of variances in the dynamics of regional start-up activities, we aim to show in this section how regional social characteristics influence the information diffusion and acceptance of mental models in the region’s population of agents. This is to better understand how the behaviour of agents (and the dynamics of start-up activities) differs between regions. The focus is thus both on the direct impact of geographic proximity and of relational proximity.14 Regional social networks are one form of relational proximity. They play a prominent role because these networks are probably the most important factor for the dissemination of mental models. Geographic proximity has, thus, also an indirect impact on behaviour via the influence of regional social networks. We will consider how these proximities influence the diffusion of information and the likelihood of acceptance of a role model. This is because these two aspects are most relevant for the effective spread of role models, as mentioned in Sections 4.1.2 and 4.1.3.

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Besides geographic and relational proximity, social and cultural proximity15 can also play a role in information diffusion and acceptance. However, the latter two are only taken up if they influence relational proximity or are themselves influenced by relational or geographic proximity. While geographic and relational proximity provide the framework and structure for the exchange of information and mental models, social and cultural proximity merely support these exchanges by establishing, e.g. mutual trust, understanding, social obligations and sanctions. In Section 4.2.1 the direct impact of geographic proximity and relational proximity on the distribution of role models is described. We especially study the establishment, maintenance and effects of social networks that are an important part of the social environment of agents. Since social and geographic proximities are not independent from each other, the impact of geographic proximity on relational proximity is analysed in Section 4.2.2. 4.2.1 Direct impact of geographic and relational proximity Direct impact of geographic proximity Information that is only locally available or locally effective could help to explain region-specific economic behaviour. Especially information such as gossip or know-who is region-specific and special to local information channels. The most important channels are region-specific one-way communication (for example regional newspapers or regional TV), regional information networks (like the regional social networks to be described in Section 4.2.2), as well as unintended and intended information supply from social interaction, such as chit-chat. Besides the direct impact of information on behaviour, (new) information, for example about role models, can influence the make-up of the agent’s mental models. Processes like interaction, communication or observation are relevant for changes in the agent’s mental model. These processes are related to the channels by which information is transmitted and collected. Despite advances in ITtechnologies, it is much easier for local agents to observe specific modes of behaviour if the geographic distance between them is relatively low. Besides the mere observability issue, the frequency of interaction, communication and observation between agents who are closely located together is comparatively high. By geographic proximity the mutual search for information or partners to interact with as well as the intensive exchange of information and knowledge by face-to-face contacts is made easier. Here two different issues have to be considered. First, these effects of geographic proximity can be caused by unintended interaction in that it is just by chance that one agent meets another agent, for example, in the bakery or on the street. Second, it is based on intended interaction that is more likely to occur with people in the same location, such as regional roundtables, exhibitions, evening courses, sporting events and so on. This higher frequency of interaction or exchange and the exposure to local

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information makes it more likely that role model information diffuses regionally. It is less clear whether the regional agents develop similar mental models based on social-cognitive learning processes and informational input: the question of the evaluation and acceptance of new behaviour or role models cannot clearly be answered by geographic proximity alone. Similarities between regional environments make comparisons easier, but this effect is much less than the effect shaped by social and cultural proximity as described below. Relational proximity Besides geographic proximity, relational proximity or social networks also influence the diffusion of role model information, its acceptance and, thus, the dissemination of mental models. The social environments in which agents interact are not identically structured and social relations often do not occur at random. The interaction between agents and exchanges among them are embedded in a social context, mainly represented and structured by social networks that provide the framework for these social exchanges (Burt 1992, Marsden and Friedkin 1993, Granovetter 1985). In this study, social networks are defined as being composed of different agents (the nodes in the network) connected by direct or indirect social relationships and of the mechanisms that govern these interactions (Castilla et al. 2000, Shane and Cable 2002). Various kinds of social networks can be differentiated depending on the kinds of agents in the network, for example, family members, friends and work groups/colleagues (for some of these networks see for example Birley 1985). The interactions and relations between the agents that are part of a social network are both relatively long lasting and voluntary (Granovetter 1985). The establishment of social networks is in most cases the same process: an agent has to meet another agent at least once in a face-to-face interaction to establish a direct social tie between them. The interaction can be based on either unintended interactions and coincidental contacts or intended interactions. Normally, such an encounter does not take place only once, but constant and repeated personal interactions, contacts and face-to-face communication are the key for the establishment of such networks (Strambach 1995). This leads to the formation of sustainable connections between the involved agents. The same holds for network maintenance: without frequent contacts the likelihood of deterioration increases. Networks can favour coincidental contacts because an agent can meet other members of the same network with whom the agent is only weakly tied and, as a consequence, may develop strong ties to them. Since a social network leads to a higher frequency of interaction with other network members, stronger links are forged between their members. In this way, the network can stabilize and rebuild itself in a self-reinforcing manner. In general, social networks can be distinguished in two groups: first, some social networks are purposefully designed and constructed because the members of the network want to pursue a specific goal or intend to profit from positive network effects. Second, in most cases the social network is not purposefully

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established to serve specific needs or to follow economic imperatives but originates from unintended interactions (Strambach 1995). These accidental networks are especially based on an overlap in the agents’ personal histories. For instance, they may derive from family ties, a certain neighbourhood, spontaneous contacts or they may have been established during school, university or former jobs. Social networks provide the necessary or beneficial channels for information transfers (Hägerstrand 1953). As in the case of geographic proximity, it can be argued that the observability, the frequency of (face-to-face) interaction, communication and observation between agents of the same network is higher than between agents who are not part of the same network. By using not only strong and direct ties but also weak and indirect ones, network members can obtain more information than they can alone (Burt 1997, Hansen 1999). Some of this information is exclusive to network members and disseminates only very slowly to non-members (Sorenson et al. 2002). The interaction between various network partners and information exchanges between network members foster the development of reputation and reciprocity. These are important processes governing the interactions and stabilizing the network (Granovetter 1985). They also influence the speed, quantity, quality and reliability of exchanges (Maskell and Malmberg 1999). Furthermore, the pecuniary and non-pecuniary costs of information gathering are reduced (Coleman 1988). These processes of information exchange and social comparison lead to the possibility to cope better with risk, uncertainty and ambiguity in social interactions. This is of specific importance for agents that often make decisions under uncertainty (such as firm founders). These described processes of information exchange in social networks strongly influence the intentional or unintentional diffusion of information about role models and their attributes. As a consequence, social networks provide a framework in which the information on new mental models can be introduced and diffused quickly. For the diffusion of role model information, some features of the network are important. These are the size and the composition of networks, for example heterogeneity versus homogeneity of network agents, the openness of networks to other networks inside or outside the region. Also there may exist ‘gatekeepers’ who bridge different networks. By simulating the knowledge transfer in differently structured networks Cowan and Jonard (1999) found that so-called ‘small world’ networks with a high degree of ‘cliquishness’ and a short average path length are most effective in the information diffusion process. The openness or closeness of social networks plays a role for the dissemination of mental models. Besides the positive influences that networks undoubtedly have, it should be noted that they can also have negative effects for network members as well as for outsiders. If membership in social networks constrains what information is attained, then it also constrains what mental models can be built. While strong network connections facilitate the interaction and information exchange inside the network, they can hinder the diffusion across networks and with outside agents. This can be an obstacle to creative development and, hence, the emergence of new mental models, the use or creation of new

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knowledge or a change in behaviour is hampered. The result can be rigidity and a lack of adaptation to changing environmental conditions resulting in a lock-in and a perspective that is biased to the information and mental models present in the network (Birley 1985, Grabher 1993). Thus, membership in only one closed social network tends to lead to a very stable mental model and new models cannot easily take hold in such circumstances. In the formation of agent networks socio-cultural factors play a decisive role. Thus, social and cultural proximity support the establishment and maintenance of social networks (Saxenian 2000): networks tend to form among agents sharing one or more characteristics because agents prefer to interact and form social ties with others similar to themselves (McPherson et al. 2001, Ross et al. 1977). This preference may occur due to agents having common focal points (Sugden 1995) or similar foci of cognitive attention as they have the same cultural background, which makes it easier to interact and to communicate with each other. Sorenson et al. (2002) in referring to Blau 1977 and to Lazarsfeld and Merton (1954), point to the relevance of agent’s social background in influencing the likelihood that agents meet each other and their willingness to form social ties. Here only one implication of the socio-cultural dimension is discussed: the impact of cultural and social proximity on network formation. These proximities are themselves subject to changes during the interaction process. Therefore, there is a strong connection between social and cultural proximity and regional social networks. The existence of social networks and the consequent facilitation of continuous network interactions have an especially strong link to social proximity. This formation of social proximity inside the networks can have a positive effect on the dissemination of mental models because, for example, the comparability and similarity of agents is higher if the agents are part of the same social context or accept the same role model. Thus, those role models that are part of social networks are more likely to be imitated by other network members. In the case of cultural and social proximity, the regional environmental context can differ between the agents which makes a comparison based on this criterion more difficult. Geographic and relational proximity affect the diffusion of information on role models and their acceptance. They can enhance quality, quantity and pace of transfers by providing a framework for frequent interactions. Social networks structure exchange relations and are especially important channels for this dissemination along conduits laid by social ties. Geographic and relational proximity also help to further reduce uncertainty, risk and costs (Fritsch 1992, Sorenson and Audia 2000). This occurs, for example, because members of the same social network share similar socio-cultural values, they consider the information received from this network to be relatively reliable. 4.2.2 Impact of geographic proximity on relational proximity Social and cultural proximities are not directly linked to locality and geographic proximity, but it can be assumed that the local environmental context partly

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influences the emergence of local variants of these proximities. As pointed out above, these proximities in turn influence the dissemination of mental models. More important is the indirect impact of geographic proximity on relational proximity. Thus, based on the network effects described in the previous section, we investigate how geographic proximity and regional characteristics influence the formation and maintenance of social networks, and the resulting regional differences. A region just as a social network is not a homogeneous mass with the same frequency of interaction between all agents. Inside one geographical region there exists a social network structure based on the described factors like work relations, family ties or friendship. Hence, the frequency of interaction between two agents does not only depend on their geographic co-location, but also on the specific network they belong to and on their selective choice of partners in intended interactions. The influence and diffusion of specific information, cognitive leadership, reference groups, common focal points, comparability of agents and the frequency of interaction, communication and observation, identified in Section 4.1 as important factors for the dissemination of mental models, have to be analysed with regard to their specific characteristics in these regional social networks. As previously mentioned, geographic proximity strongly influences the formation, maintenance and use of networks. Many empirical studies have shown the importance of geographic proximity on the spatial boundary of social networks based on different reasons: on average, the likelihood that people build social ties with other people is higher when they interact with them more frequently. Geographic proximity positively influences this frequency of intended and unintended (face-to-face) interaction and, thus, the likelihood of forming lasting social ties and networks in different fields of human life, like friendship, marriage, residential communities or workplaces (Festinger et al. 1950, Stouffer 1940, Zipf 1949, Bossard 1932, Blau 1977). Taken from the opposite perspective: ‘As the distance between two actors lengthens, the likelihood of an intervening opportunity – an equally preferred but closer contact – increases as well (Stouffer 1940)’ (cited in Sorenson et al. 2002: 15). Additionally, the face-toface interactions, necessary to maintain social networks, are more frequent, easier and less costly if agents live in close geographic proximity to each other (Stouffer 1940, Zipf 1949, Boalt and Janson 1957). Hence, the likelihood that social networks connect different local agents, that they are bound locally and that they last longer is relatively high. As pointed out above, the formation and functioning of social networks is supported by the cultural and social context in which agents are embedded, as their contacts and communication are facilitated by these proximities. By supporting the emergence of such proximities, geographic proximity has another indirect impact on the formation of regional social networks. Social networks can of course provide information on role models external to the region by linking agents from different regions. Furthermore, agents from the outside might be attracted to one region because of factors such as good job

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openings, universities or financial capital. These bring new ideas into the region that might be taken up by regional agents. Alternatively, agents from other regions might be attracted because they already share mental models with agents from this region. Although social networks do not have to be spatially bound, geographic proximity facilitates their emergence and maintenance. Strambach (1995) found that the density of local network relations and the number of regional network nodes is relatively high in comparison to cross-regional ones. These regional social networks structure the interaction of agents and provide a framework for information exchange and the dissemination of mental models. Hence, we can derive that information about role models as well as the related mental models spread more easily inside a region. Furthermore, information diffuses through the local networks; agents that are not part of the network or that are located further away from the (geographic) centre will receive this information later, if at all. The same regional specificity holds for role models leading to the locally bounded dissemination of mental models. The diffusion of role model information and the likelihood of role model acceptance are positively influenced because the social as well as environmental context are similar. As a result of this local boundedness of social networks, some information and mental models are (only) locally available which can explain region-specific behaviour. From the above it can be concluded that geographic proximity, regional specificity and relational proximity influence the diffusion of information about role models, their acceptance and, thus, the dissemination of mental models. The resulting dissemination patterns were already presented in Figures 2.9 and 2.10 in Section 2.2.2. Geographic proximity in this context has a direct as well as an indirect impact by its effect on both the formation and maintenance of regional social networks as well as on social and cultural proximity. Thus, the regional context influences social-cognitive learning processes and the distribution of mental models. This leads to a higher likelihood that mental models disseminate in one region than between regions and to differences of the agents’ behaviour in different regions.

4.3 Imitative behaviour and regional founding activities In order to understand the dynamics of regional founding activities better, the findings from previous sections are applied to regional entrepreneurial activities in the following section. This section seeks to answer the question of how startup decisions of regional agents are influenced by the diffusion of role model information via a social-cognitive learning processes, thus, changing agents’ entrepreneurial mental model.16 The specific example of role models analysed here are successful entrepreneurs. As pointed out in Section 4.1, such a role model influences the mental models and, hence, the behaviour of other agents. The role model’s impact is partly bounded to the region in which the entrepreneur operates (see Section 4.2). The argument is put forward that learning by imitation, at least partially, can explain start-up dynamics and disparities in

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regional founding rates because the positive example has an effect on different processes related to the starting-up of a new firm or the prior development of an agent to become an entrepreneur. The section is structured as follows. A very brief overview of a range of theoretical approaches is presented in order to competently fit the upcoming theoretical considerations into the existing research (Section 4.3.1). The research conducted on the impact of role models on occupational choice and firm foundings is also reviewed. Next, Section 4.3.2 describes a stylized stage model of how agents can develop into entrepreneurs. Section 4.3.3 analyses the influence of positive examples on this development process. Finally, Section 4.3.4 discusses the possible dynamics that result from this influence. 4.3.1 Basic theoretical considerations on the impact of role models Selected overview of entrepreneurship theories Entrepreneurial activities can be understood in various ways. According to Schumpeter, an entrepreneur disturbs the economic equilibrium and initiates a new development; the entrepreneur is an innovator (Schumpeter 1934). This is not only restricted to owners or founders of start-ups but also includes later stages in the life cycle as well as employees who can play an entrepreneurial role inside an existing firm. Within the framework of work presented here, the central focus is narrowed to the actual founding of a new firm as a more specific definition of an entrepreneur. Other theoretical approaches to entrepreneurship or entrepreneurial activities are presented briefly in the following; the neoclassic and Austrian Economics as well as several psychological approaches. These are, in part, taken up again and in more detail later. The overview to be given now is merely to integrate the theoretical approach developed in this study with the existing line of research. After this short summary, the effect of role models on occupational choice and on start-ups will be described. •

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Neoclassical theories have inherent problems in incorporating entrepreneurial activities since ‘no one can discover a misalignment that would generate an entrepreneurial profit because, at any point in time, all opportunities have been recognized and all transactions perfectly coordinated’ (Shane 2000: 449). Thus, ad hoc characteristics of agents (for example their preference for uncertainty) are postulated to ‘explain’ who becomes an entrepreneur. The Austrian Economic perspective assumes an uneven distribution of information and knowledge among economic agents. This prior knowledge including information and beliefs influences the likelihood that an agent can discover and exploit opportunities (Shane 2000, van Praag 1996). Kirzner (1973, 1982) adds the issue of ‘alertness’ and sees the entrepreneur as an arbitrageur of imperfect information. Furthermore, the knowledge dimension is extended by elements of opportunity perception and opportunity

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Positive examples and their regional impact utilization. A situation results in which the likelihood of discovering business opportunities is unevenly distributed; some agents may never discover any and other agents discover several opportunities. Most psychological analyses of entrepreneurial activities focus on fundamental personality, motivation or cognitive characteristics that favour entrepreneurship and by which entrepreneurs differ from non-entrepreneurs, including self-confidence, profit orientation, individualism, optimism, alertness, need for achievement, willingness to bear risk or internal locus of control (reviewed in Wiswede 1995).17 The socio-psychological perspective takes into consideration how the social environment influences agents to become entrepreneurs. For Simonton (1975), the availability of a role model is of central importance because it may lead to imitation via learning. A tight social environment, such as families, social milieus or reference groups is especially important, but the general attitude in the society towards entrepreneurship is also relevant (Shapero and Sokol 1982). Cognitive theories about entrepreneurial behaviour use (almost) by definition an individualistic perspective. However, they also take into consideration the (cultural and social) environment of economic actors as their behaviour is affected by the perception and interpretation of the environment. Entrepreneurial ‘intention is a function of the interaction of a person’s ‘thinking’ with the individual’s past history, current personality, and social and economic development’ (Busenitz and Lau 1996: 26). In other words, this line of research focuses on how cognitive processes, which are influenced by the environment in combination with new incoming information, lead to the decision to start up a firm.

Leaving aside some impacts such as industries, markets or regions, which were discussed in Chapters 1 and 2, these theories strongly focus on the individual agent. In the following, several of the mentioned elements are synthesized into one framework. Here especial consideration will be given to the social-psychological and cognitive approaches with their focus on the willingness aspect of start-ups, as well as the Austrian Economic perspective, with its focus on opportunity. Culturally determined or genetically fixed characteristics as potential drives of entrepreneurial activity are excluded in this approach. It is assumed that the characteristics of an agent, including his mental model and behaviour, are the result of learning processes that take place in a social environment. Nevertheless, agents differ with regard to their individual learning characteristics that are, in turn, partly based on their genetic endowment. Thus, agents have a priori different prerequisites and different likelihoods to become an entrepreneur. Role models and entrepreneurship in the literature Although there are different elements influencing the entrepreneur development process (AS it is presented in Section 4.3.2 and in Figure 4.2), we adopt the

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social-psychological argument that this process and the related likelihood of discovering entrepreneurial opportunities and developing the willingness to start a new firm are strongly influenced by the existence of regional role models. Agents can observe successful regional entrepreneurs, their behaviour and corresponding results and consequently may imitate them. This leads to changes in the mental model of the observer via imitative learning processes (as was described in Section 4.1), leading to a higher propensity to start a firm. There already exists some empirical literature that tries to test the impact of the social and regional environment on entrepreneurial behaviour and entrepreneur development process. One strand concentrates on the individual agent and analyses the impact of the personal social environment and social networks. Another strand of the literature focuses on the role played by cultural or regional factors on founding behaviour. These approaches are described in the following. Research on the impact of role models on agent behaviour already exists in several contexts: Simonton (1975) found an impact of role models on agent creativity and several researchers linked role models with the general occupational choice of observers (Krumboltz et al. 1976, King and Multon 1996, Nauta and Kokaly 2001). With regard to entrepreneurship, Scherer et al. (1989) developed a model based on social learning theories of how entrepreneurial parents influence the likelihood that their children become entrepreneurs.18 In one of the first empirical studies on entrepreneurial behaviour and start-up decisions in Germany, having a selfemployed father had a positive impact on the agent’s likelihood to start a firm as well as on the future success of this firm (Klandt 1984). Furthermore, the same study found that frequent contacts with potential customers or collaborators in an incubator facility (such as a research institute or a university) might trigger the decision to become an entrepreneur. In contrast, other groups (like friends or colleagues) had no impact on the founding activity. Conversely, van Praag (1996) and Kriegesmann (1999) found in their empirical studies that agents who live with entrepreneurs in a close social environment (i.e. friends or family) are more likely to start a firm. In the study by van Praag (1996), especially, the self-employment of the father of an agent had a significant impact on the likelihood to start a firm as in the study by Klandt (1984). Kriegesmann (1999) got the general result that significantly more entrepreneurs could be found in the social environment of other founders or potential founders than in the environment of non-founders. In addition to this personal environment, the regional and cultural/national background also plays a role. There are several insights gained from regional case study research in the USA: •

Shapero (1972, 1975) and Susbauer (1972) in their research on Austin, Texas, as well as Keune and Nathusius (1977) in their study on the clustering around Route 128 in the Boston region, explicitly took into account the impact of a positive displacement effect (see also Section 2.1.5) originating from other successful regional start-ups. As pointed out in these studies, one aspect of this displacement effect is the positive influence of role models.

144 •

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Positive examples and their regional impact Saxenian (1994) claimed in her prominent work on Silicon Valley that Fairchild’s success exerted a powerful ‘demonstration effect’ initiating a trend of entrepreneurial innovation in semiconductors. Feldman (2001) did an in-depth case study on the region of Washington, D.C. to explore the relationship between regional clusters and firm start-ups. One of her main findings is that in this region an entrepreneurial climate emerged which was conducive for further firm start-ups. Moore and Davis (2004: 26) described what they called the ‘if-that-jerkcan-do-it-so-can-I’ syndrome and also put forward the role of Fairchild Semiconductor as a successful example of entrepreneurship.

The studies by Sternberg and Wagner (Sternberg 2000, Wagner and Sternberg 2002, Wagner 2003, Wagner and Sternberg 2004) focus more strongly on the existence of positive examples of other founders and not on the general entrepreneurial climate as did the studies presented before. Thus, their results are most valuable and better comparable to the approach followed in this study. Furthermore, their findings are not based on single case studies. Sternberg and Wagner conducted an econometric study based on data for 10,000 persons (founders and non-founders) and came to two conclusions: first, the regional location of an agent matters for the decision to start a firm. Second, the regional level of current start-up activity has a positive effect on the likelihood to become an entrepreneur. On the national level, the concept of ‘entrepreneurial orientation’ has been used to explain differences in start-up activities between states. Lee and Peterson (2000: 406) claim that ‘entrepreneurship depends upon the unique blend of cultural factors (i.e. values, attitudes, behaviours) that together combine to foster (or not) a strong entrepreneurial orientation.’ Lee and Peterson compared five nations by their cultural background measured and compared by the use of different variables. They found a correlation between the propensity to start a firm and some cultural traits (such as power distance, uncertainty avoidance or individualism). Mueller and Thomas (2000: 66) too linked national culture and entrepreneurial traits for nine different countries based on similar cultural traits as Lee and Peterson. They got the result ‘that some cultures are more conducive for entrepreneurship than others’. Intermediate summary and critical reflection From these studies it can be concluded that the social environment (or at least some agents in this environment) as well as the regional and cultural environment positively influence the likelihood of an agent starting a new firm. In most of the presented work, a general positive relationship between a role model and the start-up behaviour of agents is assumed, empirically tested and verified. Implicit in these models is that the willingness of agents to start a firm is positively influenced by role models (see also Section 2.1.5 on pull-factors). Furthermore, besides the work by Wagner and Sternberg few of these studies have tried

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to combine the study of the social environment (e.g. based on positive examples) with the study of the regional environment. Wagner and Sternberg base their argument about the positive impact of role models on a cost reduction argument. This is probably a valid assumption but represents only one element of the overall effect that role models can have (as will be discussed in detail in Section 4.3.3). Notably, because of the nature of their study and the questionnaires used, Wagner and Sternberg have not analysed the relationships between regional factors and the social environment extensively. They have not disaggregated the impact of role models on the entrepreneur development processes and the different elements relevant in this process, such as willingness and opportunity. Thus, complementary work needs to be done in this area. This work presents a more detailed theoretical background in Section 4.3.3 and by describing the possible impacts of regional role models on different elements of the entrepreneur development process, seeks to devise more in-depth empirical tests. However, before engaging in this task, a stylized stage model is built by which this development process can be described. 4.3.2 Stages in the entrepreneur development process Based on the considerations in the previous section, this section presents a stylized stage model of the entrepreneur development process. We argue that simply categorizing agents as being either entrepreneurs or non-entrepreneurs is not sufficient. Agents are not born as entrepreneurs. They become entrepreneurs. We suggest that it is much more appropriate to view an entrepreneur as the result of a developmental process composed of different stages that are all important to explain entrepreneurial activities and their change over time (Figure 4.2). The stylized stage model starts with an ‘economic agent’ and ends with the actual founding decision (a simpler but comparable model was presented in Table 2.1 in Section 2.2.2). In such an approach, we are not concerned about the early development of the firm after the actual start-up and, thus, the first phase of its operation. Rather, the development prior to the founding decision is at the core of the analysis. The elements opportunity discovery or production, agent’s willingness as well as market19 and ability tests are the main elements considered relevant in this development. Potential entrepreneur (actively serching) A

Discovery and production of opportunity B

Economic agent

Figure 4.2 Stage model of entrepreneurs.

Market and ability test and founding decision

C

Actual founding

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The decision to start up a firm is itself, as previously stated, strongly influenced by information exchange and the mental model agents hold. The whole process implies that the individual agent has more freedom to choose his action than in culturally determined theories. Nevertheless, the agent acts in a social environment that restricts his choices in comparison to totally isolated agents which can be seen as a theoretical benchmark, or ideal type, that does not exist in reality. The regional population of ‘economic agents’20 and potential entrepreneurs is heterogeneous. Agents differ, for example, with regard to knowledge and capabilities, personality characteristics, involvement in social structures, information access or mental models. The agent’s current and past social environment is one of the main factors shaping this diversity.21 Economic agents in general and potential entrepreneurs in particular are different in one respect: potential entrepreneurs are actively searching for business opportunities to found a firm while economic agents do not. An economic agent does not have the concrete intention to start up a firm but is just pursuing his job or other activities. Nevertheless, the agent is able to discover business opportunities. In comparison, a potential founder has realized that the idea of starting up a firm or entrepreneurial activities, in general, is a good alternative and starts to actively search for opportunities, by investing time and (financial) resources. This does not imply that all potential entrepreneurs necessarily have a relatively higher likelihood of discovering opportunities and starting up a firm or of continued success with their firm once they have started one. However, on average, the likelihood of discovering an opportunity is higher in this group since they focus cognitive attention and material resources on search activities. Not all individuals, independent of whether they are actively searching or not, are equally likely to recognize a given entrepreneurial opportunity. In any case, the discovery of opportunities strongly depends on the agent’s prior knowledge of such things as technologies, markets or customer wishes (Shane 2000), their information channels and information-processing abilities, as well as on the mental model, which might focus the search on specific areas. Once an opportunity is discovered and a first idea emerges, normally a business plan has to be developed and evaluated with respect to its potential market success. This is called ‘market test’ in this study.22 This usually involves transforming a general business idea into an economically successful firm strategy by developing products or services which can be sold on a market generating revenues and profits. In addition, the actual founding decision has to be made. This will depend on the perception and evaluation of opportunities, alternatives and own capabilities (‘ability test’), as well as on the willingness to start a business. This, in turn, is influenced by personal factors and characteristics, the access to relevant information, as well as by the mental model that the agent possesses. According to Witt (1999b), a self-sorting takes place in which agents decide whether to take the risk of becoming an entrepreneur or not. This sorting is a result of multilateral subjective opportunity costs that include pecuniary considerations (like expected profits and costs of failure) as well as non-pecuniary ones. Van Praag (1996) has a similar approach in which the start-up of a firm

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depends on the interaction between opportunity and willingness. Opportunity is ‘defined as the possibility to become an entrepreneur if one wants to’ (van Praag 1996: 39) and willingness is a result of a comparison of becoming an entrepreneur and working as an employee. This interaction is fundamental to the start-up decision and ‘only’ restricted by a lack of financial capital, human capital or unfavourable environmental circumstances. The stage at which the actual founding takes place consists of many elements. Here the planning and the actual founding process is important, which includes some legal/administrative steps and questions like where to start, how to get the financing and which employees to hire and so on. It is by no means certain that the involved agents already know exactly what their business plan should look like. If the evaluation of a more general conception has a positive result, it is possible that the firm is founded first and after that an active search process takes place to decide exactly what to do and how to do it. This stage also includes all the operative and strategic decisions that are based on the business plan and which are important for firm survival. At this stage, the entrepreneur tries to exploit the perceived opportunities and at the same time actively searches for new ones, which are again tested for realization. The quality of the decisions made here influences the success of the firm. There are several important processes going on at each stage of the entrepreneur development process. The steps that need to be taken to get from one stage to the other are relevant in explaining the development of the entrepreneur until the firm has really started. In reality, it may be hard to separate these different stages from one another. The point of considering the sequence in a stage-like manner is that it serves as an analytical tool for distinguishing between different effects presented in the next section. The succession of willingness and opportunity23 is of specific interest here because their order might significantly influence the future success of the firm: it might be the case that agents that develop the willingness to found a firm before they have an idea about their business, later on discover opportunities too easily or evaluate business ideas too optimistically. Although mainly one direction of progress through the sequence is presented here, it is also possible that an agent stays at one stage because, for example, the agent does not find the financial resources to start the firm. Or the agent reverts back to an earlier stage because, for example, an agent who tried to run a firm went bankrupt and does not want to start a firm ever again. Thus, the presented framework and the development of agents from one stage to another is by no means a predetermined process. To summarize the argument: the starting-up process can be, at least theoretically, structured in different stages that include the elements willingness, opportunity, ability test and market test. These factors form the basis for making the final decision on whether to start the firm or not. Of specific importance are the processes that influence these elements in determining whether the firm is finally established. One of these elements, namely the impact of positive examples, is explored again in the next section. This element, which was already presented in Chapter 2, serves as one example for factors influencing the

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development and is of specific interest because it affects the whole development at different stages. 4.3.3 Impact of positive entrepreneurial examples on the entrepreneurial development process As previously mentioned, mental models strongly influence agent behaviour at different stages and the transitions between the stages. These mental models, in turn, are influenced by the existence of successful regional entrepreneurs who act as role models to potential entrepreneurs. In such a way, this may drive the entrepreneur development process by influencing the mental model of agents. In this section we analyse what impact the existence of local successful entrepreneurs has on the stylized stage model of entrepreneur development (see Section 4.3.2). Thus, the effects of positive examples are specifically applied to the area of entrepreneurial start-up behaviour. We thereby analyse the effect of imitative behaviour and consequent changes in the agent’s mental model during the development process. This focus does not deny the role of other important factors such as industries, markets or the general environmental conditions. However, these factors often influence only specific stages of entrepreneur development. Role models affect various stages of the development in different ways. Thus, we argue that some additional insights can be gained through taking into consideration such imitative behaviour. Before separately analysing the different stages and the transitions from one state to another, one general factor should be addressed. In most cases, the whole developmental process is characterized by high uncertainty, especially if the founder has not previously established a firm.24 This uncertainty can be linked to factors like the capabilities of the founder, the market development, the technological feasibility of the idea or the quality of the personnel available to be hired. This is especially true for firms in relatively ‘new’ markets where not many references or benchmarks exist. Although most entrepreneurs have a tendency towards risk taking, it can be assumed that an important process in founding a new firm is to reduce risk by different means. Successful role models can help by providing behavioural examples and even business plans. The role model effect or imitative learning process can influence the switches between the stages at three important transitions. The first one relates to the switch from an economic agent to a potential entrepreneur (transition A in Figure 4.2). Role models having an effect in this transition can be found in the close social (mother, father, friends and so on) or regional environment. Agents who observe the successful entrepreneurial activities of other agents take these as positive examples and may reach the conclusion that they should start a business on their own.25 Thus, they develop the willingness to start a firm without having as yet any concrete business ideas or perceived any opportunities. Since, in principle, agents have the willingness to found, the result is a (re-)allocation of cognitive attention to the idea of founding in general and the active searching or production of business opportunities.

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The second transition is linked to the search and discovery of business opportunities (transition B in Figure 4.2). It can originate from the potential entrepreneur as well as from the economic agent, although the latter is not actively searching. The perception and generation of opportunities is based on the (entrepreneurial) environment and the agent’s mental model. At this stage, positive role models provided by current or former colleagues are of specific interest. This is due to the fact that many opportunities (especially in high-tech industries) are discovered or developed by a team and not by an individual agent. Thus, team members influence each other during the developing process and if one member successfully started a new firm with a certain business plan, other colleagues might take him as a likely example. Positive examples can have a twofold effect. First, they make it easier to discover entrepreneurial opportunities because other (comparable and successful) business opportunities are known and serve as examples or references. Merely technological or market information need to be tested by the potential entrepreneur or the economic agent whether they match known positive examples. Second, the examples lead to a (re-)allocation of cognitive attention to certain opportunities or business plans. The direction of active search as well as the perception of opportunities is biased for example to the regionally or technologically ‘common ones’. In addition, it is possible that in a regional context with similar mental models and the resulting focused use of resources, many opportunities can be endogenously created in a specific (technological) field. This is caused by the fact that agents can imitate each other, receive necessary information, have the appropriate human capital and equipment base and so on. Such a pooled effort of search activities can lead to high rates of innovation in the respective industry and even to the emergence of a localized industrial cluster. Furthermore, this process can explain why waves of foundings in specific industries can emerge: the example of the IT firms in Figure 1.1 might be such a wave in the IT industry.26 Similar processes are likely to be observed on the regional level. The disadvantage of a biased search for business opportunities is that other opportunities might be neglected; there are diminishing returns to searching in just one direction, and competition between the agents increases. Grabher (1993) described such a regional lock-in situation for the German Ruhr area in which the regional agents are very successful for a certain period of time but later on were not able to adapt to changing external circumstance, leading to a worsening of the regional situation. In general, the likelihood of discovering such biased opportunities is higher than discovering others which is not only caused by prior knowledge or information access but also by the impact of role models on the search activities. Finally, there is an impact on the transition from the ability test to the actual founding (transition C in Figure 4.2). The ability as well as the market test and, thus, the founding decision are strongly influenced by the mental model, the comparison between existing entrepreneurs and other psychological factors: •

Although market tests of business plans should be based on objective facts, the collection of the relevant data and especially their evaluation is highly

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Positive examples and their regional impact subjective. Agents have trouble in thinking about growth rates and predicting future developments. This is not only a difficult task, but agents may not be aware of its difficulty and, hence, cannot correct them (Dörner 1995). It is likely that a market test is not objective because founders will encounter problems in predicting future markets by extrapolating from past or current developments. This is particularly true in markets that are just emerging and in which a prediction of the future development is difficult. The test of one’s own ability to start a firm is a strongly subjective endeavour. For this, connections to other founders (especially to those working in the same field) are especially important because the founders directly compare their own capabilities with those of other agents (Shapero 1975). Agents prefer to have a positive self-image. In order to support this they attribute good events and outcomes to internal causes (e.g. their own skills) and bad ones to external causes (e.g. uncontrollable circumstance) (see Sedikides et al. 1998 on this self-serving bias). Furthermore, the founding decision, including willingness, is based on an evaluation of the option to found a firm versus other alternatives. Positive examples provided by other entrepreneurs can change the agent’s evaluation of alternatives. Since, for example, the mental model moderates the related threats and risks, the agent might underestimate them. In addition, the general attitude towards risk is not a fixed factor but can change over time via imitative learning (Shapero 1971). The same is true for the desire to be independent. This is an important element related to the actual founding decision (Kriegesmann 1999), which also develops over time being influenced by mental models.

As presented in Section 4.1, the search for and evaluation of information is biased because agents tend to absorb information which fits easily into their mental model or which is good for their self-esteem. Over-optimism and the self-serving bias of entrepreneurs leads them to form unrealistic expectations about their ventures, competence (which they tend to over-estimate) and expected competition (which they tend to under-estimate) especially in new industries (Camerer 1997, Camerer and Lovallo 1999, Cooper et al. 1988, Sorenson and Audia 2000). The effects of examples and role models can have two countervailing directions here: first, they can increase the subjective tendencies in the ability evaluation and market test and of the founding decision. This results in an increase in the likelihood of starting up a firm. Or second, they can decrease subjectivity in decisions since agents can compare themselves with other founders, making the evaluation potentially more realistic. The exact balance between these countervailing impacts must be researched in more detail in the future, but it is likely that the first process is dominant: reasons for this claim are, for example, that agents tend to compare themselves more easily with other successful agents than with non-successful ones (Herkner 1980). Important differences between the agent and the role model are played down if a strong willingness to found a firm is present. Another aspect that can be relevant here is

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that agents tend to judge themselves by comparing their own qualities with those who score less on at least some of the qualities under consideration (Will 1981). Thus, the potential entrepreneur might choose those actual entrepreneurs for comparison that score less than himself in some characteristics and attributes. The agent then wrongly concludes also to be able to start a firm. It might even be the case that some agents have such a strong willingness to found that they start their firm no matter what result the ability and market tests and other evaluations have. Only the influence of successful entrepreneurs as role models was described previously. So far, there are two other alternatives: first, in order to serve as a positive example, a founder and his firm do not really have to be profitable and successful (Keune and Nathusius 1977 citing Watkins 1976). They are accepted as a role model if they are considered to be successful by economic agents or if the role of an entrepreneur is linked with social prestige. Even a potential entrepreneur in this case can serve as a role model if the subjective evaluation of an observer finds positive attributes related with the potential entrepreneur or entrepreneurship in general. Second, negative examples – failures of firms or bankruptcies – can also influence the behaviour of agents in the opposite direction resulting in a decrease in entrepreneurial activities. However, the effect of the negative examples is probably less pronounced than that of the positive ones. This is based on the effect that positive examples and negative examples are perceived differently due to an over-confidence of the agents and a self-serving bias which were already described above (Herkner 1980, Brockhaus 1982, Wiswede 1995): a potential entrepreneur over-estimates the similarities between his own personal characteristics and those of a successful entrepreneur (positive example bias). The opposite holds for negative examples. Additionally, negative examples are less prominently discussed in the media, in social information exchange, or such like. From the described framework one can derive that the influence of role models on potential entrepreneurs qualitatively differs between the various phases of the entrepreneur development process. In contrast to other studies that focus on resources, which are especially needed in the last phase of the start-up processes or even in the stages following the actual founding decision, we find that role models have an effect during the entire start-up process. Nevertheless, role models are of specific importance in the first stages in which the agents develop willingness to start a firm and search for or discover opportunities. Later on, the development is more strongly linked to the customers’ demands, to own products and technologies. Also, at the start of development, role models in the close social environment such as family or friends are important because they influence the agent during their everyday activities, e.g. because a child grows up in a family in which somebody started a business. At the end of the development process, the set of potential role models might change to founders working in the same industries or (former) colleagues. In the most extreme case, that is if the agent is not part of a group or social networks in which there exist other founders, such an agent never finds an opportunity or might never develop the

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willingness to start a firm because the agent is not provided with a positive role model.27 To sum up the argument: role models, defined here as successful regional entrepreneurs, influence the likelihood that other agents start up their own firm. The positive role models that have the strongest impact can be found in the close social or regional environment. Role models shape the social-cognitive learning processes and, thus, the mental models held by other agents. Based on these learning processes, the development from one entrepreneurial stage to the other is strongly influenced by role models. However, imitation of other founders means more than just replacing the founding decision alone and is, thus, not only reduced to the willingness aspect of starting a new firm. The likelihood of discovering entrepreneurial opportunities and the direction of search for opportunities, business plans and firm organization is biased by positive examples as well. Furthermore, the market and ability test as well as the evaluation of alternatives besides founding a firm can be moderated by agent’s mental models and, thus, by other regional founders. Additionally, negative examples also can play a role, although probably a smaller one. Lastly, the social structure that the agents are embedded in can determine their likelihood to interact with role models. 4.3.4 Feedback loops and regional founding dynamics The implications for regional start-up dynamics and disparities in entrepreneurial activities based on the influence of positive examples are discussed in this section by using the theoretical insights derived in the previous sections. Besides the original distribution of human capital, financial capital, infrastructure and other regional, industrial or market conditions put forward in Chapters 1 and 2, positive examples and role models have an influence on the agent’s development and subsequently on the likelihood of starting up a firm in a specific industry. All these factors come together in shaping the founding decision, as well as the opportunity perception and the business plan. This section deals especially with the feedback between the number of founders and the mental model of agents. As previously argued, regional entrepreneurs can serve as positive examples and in order do so, an entrepreneur or his firm do not really have to be profitable. It is sufficient if this success is only perceived or if social prestige is linked with entrepreneurship. As described, a positive example which diffuses in the population leads to a change of the mental model agents posses. This in turn increases the likelihood that these agents also found a firm and the business orientation of the firm in question. If this contention is true, then the higher the number of (actual and potential) successful entrepreneurs, the higher is the likelihood that other agents will adopt these as positive examples and change their mental model. The result is a self-augmenting process: the number of entrepreneurs or at least potential entrepreneurs increases with successful entrepreneurs. By such a process, the initial impact of the start-up of a (successful) firm can accelerate and lead to a change in regional founding dynamics as presented in Chapter 2.28

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153

An increase in the number of start-ups resulting from the social-cognitive learning processes does not have to be based on objective facts concerning the ability or the market tests, since the mental model of the agents influences these tests. The combination of a biased evaluation due to the mental models, overoptimism, the differential impact of positive and negative role models and easily available resources, might lower the selection threshold for entry and can lead to an over-shooting in the number of firms.29 More new firms entering the (regional) market can survive in the short run than in the medium or long run. This implies higher failure rates (Draheim 1972, Barnett et al. 2003) and as a consequence, a higher number of negative role models. This over-shooting effect might be mitigated by factors such as the regional conditions (e.g. wages and rents will become too high or there is no financial capital available), market barriers to entry or resulting higher numbers of negative regional role models. Furthermore, the dissemination of the mental model is restricted by the factors described in Section 4.1 and 4.2. This over-shooting effect can follow different functional forms already discussed in Section 2.2.2. By such positive and negative feedback loops, waves in the number of firm foundings in a region or industry can be explained. By way of a summary, the general impact of a role model on entrepreneurial activities in a region reads as follows: a successful entrepreneur of a newly founded firm serves as a regional role model. This increases the probability that other agents adopt a mental model in favour of start-ups in general and their business orientation in particular. Based on this adoption, the likelihood increases that the agents that changed their mental model start their own firm. In the framing of Table 2.1 in Section 2.2.2 we can say that the transition probability from the general regional population to the group of agents willing to start a firm increases because of regional role models. The same holds for the transition probability between the group of agents willing to found and the group of real founders.30 This does not imply that all regional agents change their mental models and that all agents that do change start a firm. However, it can be argued that, over time, the mental models of some part of the population become more similar to each other. This is based on the considerations in Section 4.1 and 4.2 concerning the diffusion of information about role models and their acceptance. With regard to entrepreneurship, we can conclude that a specific kind of shared mental model evolves in a certain part of the population that favours entrepreneurial activities in the region. As such a shared mental model is quite stable, it needs a large number of new examples or a few very prominent ones to change. This has the implications that, on the one hand, the dissemination of an entrepreneurial orientation in the population is hindered by these factors. On the other hand, if such an orientation does spread to a considerable share of the population, it is likely to be sustained on a higher level. If such a high level of entrepreneurial activities can be sustained for a longer period of time, the (sum of) positive examples can result in a specific regional entrepreneurial attitude. If a positive attitude towards entrepreneurship spreads in some, maybe even only a small, part of the regional population, later on this minority can influence

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other parts of the population or even the majority of regional agents. In order to do this, the minority must have specific characteristics that include (A) a relatively high motivation, (B) a more convincing argument and (C) a more consistent and tight social network (Herkner 1980). Thus, regionally shared mental models can lead to the emergence of a social norm in parts of the population that are supportive or detrimental to entrepreneurship. These processes can explain why some regions have a higher likelihood of firm foundings than others. Furthermore, a functional focus regarding the direction of the search for opportunities might result with the previously mentioned positive and negative implications.

4.4 How entrepreneurial role models influence start-ups in Jena: a case study In this section, we complement the previous theoretical discussion with empirical research in the form of a case study. In doing so, our aim is to attain a detailed view of how precisely entrepreneurial role models impact start-up activities. In this respect, we decided that a case study would be the most appropriate way to research these relationships (Yin 1989, Eisenhardt 1989). Case studies enable us to probe deep into the various processes, to analyse a development over an extended period of time, and even to identify causal relationships relevant for a specific development. Consequently, we chose to quantitatively survey nearly 100 firms in the city of Jena. This was supplemented by qualitative interviews that were conducted with professional ‘experts’ who are dealing with start-up activities in the region. In specifically studying the general impact of regional firm founders on other agents, we decided to leave aside the possibility of examining how these may have a differentiated effect on the transitions between the stages of the entrepreneur development process. The section is structured as follows: in Section 4.4.1 we describe the data collection including the questionnaire development. After that, we give a short description of the firms we sampled (Section 4.4.2). Section 4.4.3 presents the results of the case study. In Section 4.4.4 we make a comparison between entrepreneurial role models from Jena, the roles that they played and how they affected regional firm founding activities. Section 4.4.5 concludes. 4.4.1 Background of case studies and methods As we focused on the city of Jena, we only surveyed those firms whose headquarters were located in this city. Likewise, the experts we interviewed were also from Jena or the surrounding areas but with strong relationships to the city and knowledge of the processes occurring in Jena. The city is located in Thuringia (East Germany) and has around 100,000 inhabitants (INKAR 2002). It has a long industrial history, especially in optics and precision mechanics, where it has historically played host to the firm Carl Zeiss. Consequently, the economic structure of the city is dominated by traditional firms (such as Carl

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155

Zeiss, Jenoptik31 and Jenapharm (a pharmaceutical company)). However, during recent years a new focus in the areas of bioinstruments/bioinformatics (e.g. ClondiAG) and software and information technology (e.g. Intershop) have emerged. Another important element in the regional (economic) system is the research infrastructure: there exist two universities (Friedrich Schiller University Jena, University of Applied Sciences Jena) and a number of research institutes (Institute for Physical High Technology, Fraunhofer Institute for Optics and Precision Mechanics, Institute for Molecular Biotechnology) that attracted a number of students and researchers. The city was selected as a case study because (A) the economic situation in the city developed relatively well, with many new firms in different industries and a comparatively high success rate amongst existing firms, (B) some new firms were founded in the area which were prominent in the regional and national news suggesting that they might have an impact on other founders behaviour and (C) the city was well known to the authors and nearby. We used a two-stage design for our case study (see, for example, Sammarra and Belussi 2005). First, we conducted in-depth, semi-structured and openended interviews. We devised two questionnaires with open-ended questions: one for local start-up experts and one for firm founders. These questionnaires were first tested on a set of people from the Max Planck Institute for Research into Economic Systems (Jena) in order to ensure that they were understandable and clear. They were subsequently adapted according to their comments. The interviewees came from a wide variety of organizations that all related to startup activities, e.g. banks, venture capital firms, universities, research institutes or professional organizations (a detailed list is presented in Table A.12 in the Appendix). The questionnaires contained many different areas (e.g. questions focusing on start-ups and those focusing on cooperation between organizations) in order to get an overview of the regional economic system.32 In this discussion, we will only use those that are related to start-ups in general and entrepreneurial role models in particular. In total, we accomplished 26 interviews with 24 experts and two firm founders. All the interviews were carried out in person and were recorded in note form by two researchers. These notes were put together and the interview was sent to the interviewees for cross-checking. In 24 cases, the interviewee suggested only minor changes. In two cases we were not allowed to use the interview for the analysis.33 Thus, the following results are based on 22 expert interviews and two with firm founders. In all cases, the interviewees were informed in advance that the statements they made, and the facts they provided would be published in a scientific study. The expert interviews took place between August 2001 and October 2001. The interviews lasted for approximately one to two hours. The first stage aimed at serving as a pre-test to get a first impression of the processes and structures relevant for start-up activities in Jena. With regard to entrepreneurial role models, we wanted to find out whether such role models did indeed exist in Jena. We did not explicitly ask the interviewees whether they knew of any positive role models in order to avoid framing effects in which we

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already suggest our expected result. The question that came closest to revealing our topic of enquiry was ‘Which persons or organizations do you think are relevant for the development in Jena and why is this the case?’ Nevertheless, throughout the interviews, the interviewees mentioned founders that, according to them, have an influence on other (potential) founders. Since this supported our assumption that such role models play a role in Jena, we designed the questionnaires for the second stage based on these initial insights. Second, a survey of firms was carried out through face-to-face, in-depth interviews based on a structured questionnaire with fixed-alternative answers. The questionnaire that was used for the interviews was designed in spring 2002. The development as well as the implementation of the questionnaire took place as a joint project by the Max Planck Institute for Research into Economic Systems (Jena) and the Chair of Microeconomic Theory at the Friedrich Schiller University, Jena. After testing the questionnaire with persons from the two involved organizations, we also conducted a pre-test with founders of two firms located in Jena.34 According to their comments the questionnaire was adjusted. The final questionnaire can be found in Appendix D.4.35 The list of potential firms for the interviews was extracted from two firm lists provided by the Chamber of Trade and Commerce – East Thuringia in May 2002. Only firms from those industries were chosen which were considered the most relevant for the regional innovation process and which were located in the city of Jena. Thus, for example, banks, insurances, agricultural firms and service firms which might innovate and influence the innovative activities of other firms, but which are not directly linked to regional innovation systems were excluded.36 The resulting list contained a total of 293 firms representing the entire firm population in these industries. We requested an interview with the first 100 randomly chosen firms. If a firm was not willing to take part in the interview, another firm from the list was contacted. In the end 173 firms were asked to participate from which 93 firms accepted (Rate of return: 53.8 per cent); leaving a sample that covers 31.7 per cent of the firms we considered relevant. The interviews were conducted by students from the economics faculty of the Friedrich Schiller University Jena between May and July 2002. They were trained for these interviews in advance and for each interview a team of two students was chosen to conduct them in person. Although we tried to contact the general manager or the founder of the firm, we were not always able to reach them. For the analysis of some questions in the next section, we only consider those answers given by founders. Interviews lasted between one and two hours. 4.4.2 Description of sample firms In Figure 4.3, in which the firm foundings per year are shown, two kinds of founding waves can be detected. These are separated by the year 1993 in which no start-up originated from Jena. The first wave (before 1990 to 1993) peaked in 1991 and the second (1994 to 2001) peaked in 1999, with 15 start-ups in these

157

2001

2000

1999

1998

1997

1996

1995

1994

1993

1992

1991

1990

18 16 14 12 10 8 6 4 2 0

Before 1990

Number of firms

Positive examples and their regional impact

Year Original founding in Jena

Change of location to Jena

Figure 4.3 Firm start-ups in Jena.

years. The total number of firms is 35 for the first wave and 56 for the second one (only 91 firm representatives gave the date when their firm was founded). In general, the number of firms that changed their location to Jena is small in comparison to the firms that originally started in Jena (84 to 7). The combined effects of reunification and the consequent restructuring of the economic system probably best explain the first wave (see also Section 3.3.2). The reasons for the second wave are less clear. It could be based on the ‘new market boom’ and public financial support37 affecting IT and biotechnology firms. For instance, Interviewees 6 and 19 mentioned that an increasing number of start-ups could be observed in the biotechnology sector during recent years. Examining the industrial composition of the two start-up waves gives some additional insights. We can observe that the start-ups in some industries took place mainly in the first wave (e.g. ‘optical engineering’ with eight of ten startups in the first wave) and others in the second wave (e.g. ‘biotechnology’ (seven of nine in the second wave), ‘laser technology’ (five of six in the second wave), ‘medical and measuring technology’ (eight of nine in the second wave) or ‘chemical industry’ (five of five in the second wave)) while others have the same number of foundings in both waves (e.g. ‘construction work’ or ‘software and consultancy services’).38 This supports our hypotheses that the first foundings after the reunification were dominated by the traditional optical industry, whereas the later wave focused more on related, but different fields, such as biotechnology or lasers. Of specific interest is the fact that software firms are not prominent in the second wave, which one would have expected because of the internet boom at the end of the 1990s. Although many factors exist that could influence the discovery of opportunities and the business plans (e.g. emerging or declining markets), it may be the case that positive regional role models re-focus the cognitive attention of agents and, thus, have an influence on the business plan and the orientation of new firms. Interviewee 6, for example, described how

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18 15 12 9 6

Year

Figure 4.4 Distribution of founding decisions over time.

2001

2000

1999

1998

1997

1996

1995

1994

1993

1992

1991

0

1990

3

Before 1990

Number of founding decisions

founding booms often specifically emerge from newly created industrial sectors. This is because entrepreneurial role models positively influence the number of agents willing to found in this field. Furthermore, the number of venture capitalists willing to finance foundings in the industrial sector also increases. These effects are not even hampered by negative projections of market sales. In addition to the distribution of the foundings over time, we also analysed when the founding decisions were made. The analysis is based on 68 answers39 and the results are shown in Figure 4.4. We observe a similar picture as the one for the founding activities: the years 1990, 1991 and 1999 show a relatively high number of founding decisions and again some years have a low number of founding decisions taking place. What has led to this distribution? As we have shown above (Section 4.3), regional start-up dynamics (including the ‘decision dynamics’) can result from the influence of entrepreneurial role models: if successful founders or firms can be identified which had an impact that started around 1990 or 1998 and affected other potential entrepreneurs via their impact as an entrepreneurial role model, this would lead to similar dynamics. One such event could have been the listing of Intershop at the stock exchange in 1998 and the listing in the NEMAX in 1999 (see Section 4.4.4 below). There may be other events for these dynamics as well, but in general we can conclude that dynamics can be observed and that there must be some processes going on that affect the distribution over the different years which we have to identify. At least, the observed data does not contradict the hypotheses, as would be the case if the number of start-ups and founding decisions were steady each year. The survey covered a range of different firms with regard to industries and firm size, but firms of industry no. 33 (‘medical, precision and optical instruments, watches and clocks’) dominate our sample (28 firms) (Table 4.1).40 Service firms active in industry no. 74 (‘other business activities’) and industry no. 72 (‘computer and related activities’) also represent a relatively large share of firms in the sample (with 22 and 15 firms respectively). An evaluation based on the number of employees in the industries changes the picture. Industry no.

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Table 4.1 Distribution and firm size of interviewed firms according to industry Industry

Number of firms

Share of firms

Number of employees

Share of employees

Medical, precision and optical instruments, watches and clocks Other business activities Computer and related activities Manufacture of fabricated metal products Chemicals and chemical products Research and development Publishing, printing and reproduction of recorded media Manufacture of machinery and equipment Manufacture of basic metals Miscellaneous

28

30.1%

1010

30.8%

22 15 5

23.7% 16.1% 5.4%

291 887 233

8.9% 27.1% 7.1%

5 4 3

5.4% 4.3% 3.2%

563 24 15

17.2% 0.7% 0.5%

3

3.2%

65

2.0%

3 5

3.2% 5.4%

147 42

4.5% 1.3%

Sum

93

–

277

–

33 (‘medical, precision and optical instruments, watches and clocks’) with 30.8 per cent and industry no. 72 (‘computer and related activities’) with 27.1 per cent represent more than half of the employees in the sample. 4.4.3 Analysis of case study results on entrepreneurial role model impact In the following, we present some selected aspects of the firm survey results that relate to the impact of entrepreneurial role models on start-up activities.41 Additionally, results of interviews are used to illustrate some findings from the firm survey when applicable. Since it is hard to grasp the direct impact of role models on agents’ behaviour, sometimes we used suitable proxies to analyse this relationship. These are, for example, the regional entrepreneurial climate or the general influence of regional founders. Factors relevant for founding decision We start our analysis by asking which factors do affect the individual’s decision to start a firm. We suggest a variety of factors that might influence the decision to start a new firm (as shown in Table 4.2). In order to evaluate the importance of these factors, the firm representatives were asked whether these factors were relevant for their own founding decision or not (Question 3.12; multiple entries were possible).42 The frequency indicates how many agents considered the specific factor as relevant to their own founding decision. Several categories are of importance: (1) according to 90.9 per cent of the firm representatives, the market

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Table 4.2 Relevance of factors for founding decision (multiple entries possible) Rank

Category

Frequency

1 2 3 4 5 6 7 8 9 10

Good market opportunities Product idea and business plan finished Good possibility for self-actualization Positive entrepreneurial climate Good possibility to earn money Prototype finished or product/service developed (Threat of) unemployment Patent granted Venture capital available Friends have also founded a firm

90.9 84.4 77.3 65.2 51.5 49.2 36.9 18.8 16.7 13.6

side seems to play an important role. (2) Besides the market side, also factors related to the firm and, thus, to the supply side had an effect on the decision (e.g. the finished product; 84.4 per cent). (3) Personal motivational factors (e.g. ‘good possibility for self-actualization’; 77.3 per cent) as well as (4) environmental conditions (e.g. ‘good entrepreneurial climate’; 65.2 per cent) also play a role. The latter factor is of specific interest for this study, because it is strongly linked to the availability of role models. According to the answers given, it is an important factor and, thus, these findings support the argument that their exists a positive impact of other start-ups on the founding decisions of agents. The factor ‘good entrepreneurial climate’ is strongly correlated with the factor ‘friends have also started a firm’ (on a 1%-level; Pearson p-value of 0.017). This suggests that there exists a link between the evaluation of the entrepreneurial climate and the number of founders in the close social environment. In most cases, the close social environment that plays a role does not consist of the family but of friends (Interviewee 2). This is due to the relatively low possibility of having founders in the family in East Germany, since there were no private founding activities before reunification (at least in the industries under consideration). Specific impact of other regional founders on founding decision Additionally, the firm representatives were asked how other regional founders, who had started their firm before they did, influenced their own decision to start a firm (Question 9.1). The firm representatives answered on a five-point scale from ‘strongly positive’ to ‘strongly negative’. In Figure 4.5 the relative shares present the answers of 74 firm representatives because only those representatives’ answers were considered who were in the founding team of a firm. 30.6 per cent answered that other regional founders had a positive influence on their own founding decision and for 5.6 per cent the influence was even very strong. No representative stated that regional founders had a negative impact on their decision. These answers give a general hint that already existing firms in a

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Share of answers

70 58.3

60 50 40 30.6

30 20 10

5.6

0

0 Strongly positive influence

Positive influence

No influence

Negative influence

0 Strongly negative influence

2.8 Unknown

Kind of influence

Figure 4.5 Influence of other regional founders on the own decision to start a firm.

region and their founders influence the founding decision of other agents. Furthermore, this influence only seems to be positive. This is of specific interest because other regional founders can also be competitors and, at least in this case, a negative impact should be expected for the same industry. With regard to role models, this observation might be due to the reason that negative characteristics (e.g. the failure of some founders) are not so easily attributed to oneself as positive characteristics (Section 4.3). Hence, these have a smaller effect than positive examples. In contrast, it is also possible that agents who were negatively influenced by other regional founders had not started their business at all or had already closed it down and, therefore, were not included in the sample. If this were the case, the negative influence of role models would be extraordinarily strong. Besides the general impact of other regional founders, it was interesting how they influenced many different regional factors relevant for the founding decision of other agents (Question 9.2).43 In Table 4.3 we present the answers in a ranked order for the firm representatives who were part of the founding team. The most important aspects are: (1) other founders have made the city internationally known (49 per cent), (2) they have proved the possibility to start a successful firm in the city (45 per cent) and (3) they passed on information to the potential entrepreneurs and newly started firms (41 per cent). In contrast to the first three factors, the effect on the political support and the availability of financial capital was of minor relevance for the own founding decision. Thus, the existing firms influence the founding decision of potential entrepreneurs in a complex way. Since they affect many different factors, these have to be evaluated separately. The second factor, proving the possibility that a founding is possible in Jena, is one element related to the effect of role models. Additionally, the firm representatives were asked to name examples of firms that influenced the different factors. Interestingly, we find that there was a great variety amongst those firms who were mentioned in the fields of ‘transfer of

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Table 4.3 Means by which other firms influence new foundings Relevance for own founding

Relevance for own founding

Absolute number

Relative

Other regional firms influenced the following categories:

Yes

No

Yes

No

Have made the city/region known internationally Have proved that it is possible to start a successful firm Transfer of information and advice Customers or partner for cooperation Improved the political support for start-ups Improved access to financial capital

29

30

49.15%

50.85%

26

32

44.83%

55.17%

24 21 15 10

35 40 43 48

40.68% 34.43% 25.86% 17.24%

59.32% 65.57% 74.14% 82.76%

information’ and the ‘proof that it is possible to found a successful firm’. Furthermore, in most cases, the firms mentioned in these areas have not belonged to the old industrial structures. Thus, newly founded, smaller firms have a relatively stronger impact. This is because the representatives probably had a personal relation with the founders of such smaller firms and better identify themselves with them. The older, larger firms play a more important role for the provision of political support, access to financial capital and the international image dimension. We will discuss this aspect in more detail in the next section. By analysing how the impact of other regional founders on the own founding decisions is distributed over time, we want to study whether this evaluation changed over the period under investigation. In order to examine this, we aggregated the ‘strongly positive’ and ‘positive’ categories and plotted the answers over time. The results are presented in Table 4.4. We can conclude that the evaluation of the impact of other regional founders has remained nearly unchanged over the 12 years. The years 1998 and 1999 appear to have a relative higher share of positive evaluations than the other years. This supports our hypotheses that positive entrepreneurial role models have had an impact on the development of founding decisions in the second wave. However, testing these two years against the distribution of answers in the other years yielded no significant results. Evaluation of the regional environmental context We asked the firm representatives to evaluate whether the quantity/quality of the regional factors were ‘good’, ‘average’ or ‘bad’ in each year (Question 3.21). We recorded how many times a firm representative considered the relevant factor especially prominent/positive in the respective year. The categories and

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Table 4.4 Impact of other regional founders over time Year of founding decision

Positive influence

Before 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 Sum

No influence

Sum

1 4 0 1 2 1 1 1 5 6 1 1

8 7 1 0 0 3 1 4 1 9 4 4

9 11 1 1 2 4 2 5 6 15 5 5

24

42

66

0.80 0.60 0.40

2002

2001

2000

1999

1998

1997

1996

1995

1994

0.00

1993

0.20

Before 1993

Share of positive evaluations

1.00

Year

Regional entrepreneurial climate National entrepreneurial climate Friends also founded a firm Information and advice by other founders

Figure 4.6 Evaluation of years with regard to selected regional conditions: part 1.

their development, measured in the share of such ‘good’ answers for each year and category, over time are presented in Figures 4.6 and 4.7.44 Figure 4.6 portrays four categories which are closely linked to each other. These are ‘regional entrepreneurial climate’, ‘national entrepreneurial climate’, ‘friends have also founded’ and ‘information and advice by other founders’.

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Positive examples and their regional impact

0.60

0.40

2002

2001

2000

1999

1998

1997

1996

1995

1994

0.00

1993

0.20

Before 1993

Share of positive evaluations

0.80

Year

Venture capital availability Good option to earn income by founding Market demand

Figure 4.7 Evaluation of years with regard to selected regional conditions: part 2.

They all affect the entrepreneur development process and the decision making process of other founders. The evaluation of these four categories developed in a relatively parallel manner with a decrease until 1996, an increase until 1999/2000 and a following strong decline. They follow to a certain degree the firm founding numbers and the number of firm founding decisions made in a certain year (Figures 4.3 and 4.4). This is at least true for the second half of the 1990s. Before that period, the two developments differ considerably. While the categories decrease slowly from a high level, the number of start-ups and the number of start-up decisions were very low. In Figure 4.7 we show how three other categories developed. The evaluation of market demand and of the possibility to earn money are both related to market factors, while venture capital relates to the perceived resources available for the founding. We can observe that the former two follow nearly the development of the first group of categories (Figure 4.6).45 The evaluation of venture capital shows a different development with a very low level in the beginning, a strong increase between 1996 and 1999 and a decline back to the original level until 2001. One aspect that can be tested, for example, is whether the number of foundings or the number of founding decisions are correlated with the evaluation of the seven categories for the period of time between 1993 and 2001. We find that

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the only category to which the foundings as well as the founding decisions are correlated is number of ‘good’ evaluations of venture capital.46 This is probably due to the just mentioned development: we mainly analyse the development of the second half of the 1990s and here the venture capital development best fits the development of the number of firm foundings. The picture would probably change if we could include the first half of the 1990s because during this time span, the number of foundings was high as well, but the evaluation of venture capital can be assumed to be relatively low. Thus, for the whole period from 1990 to 2001 we are likely to identify a relationship between the six categories (excluding venture capital) and the number of firm foundings, since the evaluation of these categories for the time before 1993 is relatively high as is the number of firm foundings in this period. However, since we do not have exact data for this claim, we are not able to test it empirically. Besides this problem, there are two additional problems linked to the analysis: (1) we can only use nine years for the correlation analysis, which makes a profound empirical test problematic. (2) The analysis relies on the evaluation of the categories and not on actual values. This subjective, retrospective evaluation probably strongly influences the results. From these problems, we can derive some conclusions for the improvement of future work: A) we have to extend the period of time under consideration and/or include different regions to increase the number of observations. B) We should exclusively or additionally use objective measures to describe the development of the different categories. We used the category ‘regional entrepreneurial climate’ to divide the years under consideration into two groups: those with an above average evaluation of the climate and those with a climate below average. We identified the years 1993, 1994, 1999 and 2000 as having an above average evaluation of the regional entrepreneurial climate. The aim was to test whether the regional entrepreneurial climate influences the number of foundings and the number of founding decisions. Table 4.5 presents the results of this comparison. We observe that the number of founding decisions is higher in the years in which the regional entrepreneurial climate is better (5.75 to 4.80). The opposite is true for the number of firm foundings, which are lower in the years with a below average regional entrepreneurial climate (5.50 to 5.80). The former shows that the regional entrepreneurial climate might be somehow linked to the firm founding decisions. However, the two values are not statistically different from Table 4.5 Foundings and founding decisions in relation to regional entrepreneurial climate Regional entrepreneurial climate

Above average Below average Sum

Average number of: Foundings

Founding decisions

5.50 5.80 5.67

5.75 4.80 5.22

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each other. Thus, we cannot say for sure that these values just showed up by chance. At least they head in the right direction. Comparing the direct results from Question 9.1 which show that many entrepreneurs mention the positive impact of other regional entrepreneurs on their own founding decision and the non-significant results identified here, it can be assumed that the impact of other founders is felt by the founders but that the quantifiable impact is relatively low. Maybe the founders even only use the impact of other founders as a method to find rational arguments for their own founding decision. Furthermore, the causality question remains open: a good regional entrepreneurial climate might increase the number of founding decisions or the number of founding decisions increases the evaluation of the entrepreneurial climate. Or perhaps it may even be a self-augmenting process; as discussed in Section 4.3.4. The unexpected results concerning the impact of the entrepreneurial climate on the firm foundings could be explained by a time lag between the founding decision and the actual founding. It is more likely that the founding decision is influenced by the entrepreneurial climate and since the founding in most cases takes place some time after the decision, the direct link between the entrepreneurial climate in one year and the founding in this year is probably less pronounced. 4.4.4 Two positive entrepreneurial role models in the city: Dr. Lothar Späth and Stephan Schambach In the previous section we identified that regional founders influence agents by different mechanism, e.g. they make the city internationally known or they show the possibility to start a successful firm in a region. In this section we want to focus on two positive entrepreneurial examples which came up in our expert interviews very often and analyse how they influenced the entrepreneurial activities. These are Dr. Lothar Späth, the former CEO of Jenoptik, and Stephan Schambach, the former CEO of Intershop. Both were mentioned 14 times in the interviews (nearly 60 per cent of the interviewees mentioned one or the other name/firm). Both entrepreneurial examples influenced agents, the city and other firms in different ways. For instance, according to the interviewees, Dr. Lothar Späth was important because he • • • • • •

set signals into which direction Jena should develop (Interviewee 1); engaged in political lobbying the federal state government of Thuringia (Interviewee 4); made Jena known (internationally) and respected as an economic location in which agents became interested (Interviewees 4 and 11); helped to establish new firms and networks (Interviewees 20 and 18); used his contacts for the city (Interviewees 3 and 4) and actively participated in the development of the city of Jena (Interviewee 3).

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From these answers we can derive that the role Lothar Späth played for the development in Jena was relatively strongly focused on the development of the city as a whole and the regional context in general. Only to a lesser degree did he affect firm foundings directly. The comments of the interviewees concerning Stephan Schambach concentrated on different areas: • • •

•

• •

Intershop has made people interested in self-employment (Interviewee 2). Mr. Schambach was important because he has shown to other agents how an agent can start a firm and achieve something (Interviewees 12 and 21). Intershop served as a local role model influencing agents founding decisions (Interviewees 4, 6, 12, 18, 23 and 24). This increased the number of firm foundings (Interviewee 14). By positive examples like Intershop potential founders can observe that it is possible in their environment to start a successful firm (Interviewee 9). Agents observed that Mr. Schambach was one of the first who worked his way up, managed to start and develop a firm (Expert 4). This example had a positive influence on many firm founding decisions and many agents wanted to do the same. They decided that if he can make it, they could do the same (Interviewees 4, 6, 14 and 24). The development of Intershop resulted in a firm founding euphoria (Expert 6). The listing of Intershop at a stock exchange in the USA has made Jena known in the USA (Interviewee 3).

Most of these aspects are closely linked to each other and centre on the effect of Mr. Schambach as a role model in favour of starting-up a firm. Some of the interviewees even claimed that this role model stimulated a positive founding dynamic in the region. Important in this respect is that Mr. Schambach originates from East Germany (and even from Jena). This similarity between him and other agents observing him is an important factor for the success of the role model. Since Mr. Schambach is one of them with a similar social, educational and regional background, agents are more likely to accept him as a role model and compare themselves with him. Based on these interviews’ findings we can conclude that (1) both agents had an impact on other agents and founders because they served as an entrepreneurial role model. (2) The impact of these role models differed, with Dr. Lothar Späth more strongly affecting the regional context in general and Stephan Schambach influencing founding decisions of agents. For the theoretical relationship between role models and founding decisions, Mr. Schambach was the more important and relevant entrepreneurial role model because agents easier could identify with him.

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4.4.5 Summary and discussion of case study Our findings give some evidence that successful entrepreneurs in a region influence the behaviour of other economic agents because they serve as positive examples. We have concentrated on the general impact of other regional firm foundings or the general entrepreneurial climate here: do more firms start when other founders are present in the region? The first results are promising. Besides other factors, the regional entrepreneurial climate as well as the positive examples of founders affect the founding decisions of agents. The impact of other founders was relative constant over the period of time under consideration. Those years with an above average regional entrepreneurial climate show a slightly higher number of firm founding decisions. From our analysis of the impact of two specific positive examples in Jena, we can conclude that different examples serve different purposes. The higher the comparability of positive example and potential founder is, the higher is the likelihood that other agents take him as an entrepreneurial role model. As is usual in case study research we have to deal with some specific problems: the selection of our region and questions can influence the results. Some of these we present briefly: 1

2

3

The measurement of entrepreneurial role models is difficult. We used proxies (e.g. the regional entrepreneurial climate or the impact other founders have on the founding decision). However, there might be other processes that link these proxies to firm founding activities. We probably have to deal with a sample selection bias. Firms that started earlier but closed down for various reasons or left Jena are not in the sample, while younger firms might be overrepresented. Furthermore, we only included those agents in the survey that are founders. This means that we do not know how potential entrepreneurs who did not start a firm were influenced. In order to evaluate the impact of positive entrepreneurial examples in more detail, we need to disaggregate the analysis. For instance, such examples probably have different effects in different industries. Such an industry specific analysis was not possible with the data at hand, but might add some additional insights.

Using both qualitative interviews and the firm surveys to analyse the regional activities diminishes this problem. We will discuss possible extensions in the next section.

4.5 Preliminary summary and discussion A differentiation of agents into entrepreneurs versus non-entrepreneurs is not enough to sufficiently explain entrepreneurial activities and changes in these activities. In order to get a detailed picture of the impact that role models have

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on firm foundings, a stage model containing opportunity perception, the willingness to found, the ability and market test as well as the founding decision and early operation was developed. What can be observed is a (forward and backward) development of the agent through different stages that are all required in finally becoming a (successful) entrepreneur. The agent’s mental model has an impact on his behaviour and on this development. This mental model can change via social-cognitive learning processes based on role models. A high frequency of interaction, communication and observation can even lead to a convergence of mental models inside a specific population of agents. Regional characteristics directly influence the diffusion of information about the role model via geographic proximity and also indirectly via social network effects. Furthermore, the choice of role models and the acceptance of new mental models are increased because of these social and cultural proximity effects. Hence, personal and regional levels are seen as interlinked: regional conditions influence the development of agents and their behaviour in turn influences the regional context in which the agent is embedded. These processes systematically influence regional developments and can result in region-specific diffusion and learning processes. Regional entrepreneurs who are subjectively perceived as successful can serve as a role model for other agents. Such role models may affect the willingness of agents to found, the search for and discovery of opportunities, as well as the ability or market tests. Role model effects support the development through the stages until an agent becomes an entrepreneur and starts an own firm. The close social and regional environments seem to play an important role because they can trigger the whole development process to become an entrepreneur by providing positive examples as a by-product of the normal daily social interaction. In order to let the process start inside the region, first, a positive example must exist; the agents have to get to know, second, that it exists; and, third, which characteristics it has. Agents from other regions can serve as models as well, which is of particular importance if no regional role models are available to start the described process. However, we find that regional role models are more relevant for the learning processes to proceed. The reasons for this are that (A) there exists a higher likelihood of acceptance of role models if they are local, (B) the diffusion of information inside one region is better and (C) regional social networks are more important with their higher frequency of interaction, observation and communication. Feedback processes lead to an acceleration of initial minor changes in the regional firm founding activities. These processes can be used to explain some of the observed dynamics from Chapters 2 and 3. If in addition to the high rate of start-ups, new firms also focus their activities on the industrial structure present in the region, the formation of localized industrial clusters may result from such a role model influence. Additionally, based on the dissemination of the role model impact (see Section 2.2.2), these self-augmenting processes can explain the phenomenon of over-shooting in which too many firms enter a market.

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If positive feedback processes last for a longer period of time, even a specific positive entrepreneurial climate emerges. This is one potential explanation for long-term variances in start-up activities between regions. Regional disparities observed in the number of firm foundings in particular and economic activity in general can be partly explained by the dependence on geographic proximity and on regional social networks (Sorenson 2003). One important impact results from the acceleration of already existing entrepreneurial tendencies, which leads to the emergence of localized industrial clusters based on the co-location of firms (Brenner 2004, Sørensen and Sorenson 2003). These studies based their results on the aspects, namely information exchange and resource access. Their findings, however, also agree with the impact of regional positive examples and their impact on mental models as suggested here. Thus, seemingly small historical singularities such as the appearance of role models within regions can lead to a differential regional development (Keune and Nathusius 1977). The dissemination of mental models via geographic or relational proximity cannot force an agent to become an entrepreneur. They can, however, support the social-cognitive learning processes by which one or more positive examples can change the agent’s mental model and their entrepreneurial attitude. The orientation of business plans inside a region can also be influenced by a region-specific mental model in combination with biased information transmission leading to a re-production of the industrial structure present in the regions (Sorenson 2003, Stuart and Sorenson 2003). There are further relevant questions that have to be investigated in the future. An important one relates to the findings of Sorenson and Audia (2000) that agglomerated regions with the highest failure rates can nevertheless continue to experience high entry rates. The hypothesis based on the analysis in the previous section is that factors, such as over-confidence, strengthen the impact of positive role models and weaken the impact of negative ones. This would lead to a high rate of new foundings even if other firms fail. To sum up the argument: only by taking into account the effect of (shared) mental models and information access based on the influence of role models, can the actual decision to start up a new firm be appropriately analysed. Shortand medium-term upturns and downturns in start-up rates in certain regions can be explained by the resulting feedback loops and the agent’s interrelatedness. In the long run, these feedback processes lead to a specific regional attitude towards entrepreneurship. We complemented these theoretical considerations by a regional case study aiming at identifying the general impact of regional entrepreneurial role models on other agents’ behaviour. We found that such an effect can be detected and that different regional entrepreneurs play different roles: founders or managers of older firms affect the general economic situation, support founders, make the region known etc. In contrast, founders of younger firms with whom the potential founders interact more directly and with whom they can identify more easily serve as an entrepreneurial role model. Although these psychological factors and learning processes play an import-

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ant role in the decision to start a firm, other elements like markets, technologies and regional conditions are also influential. In general, the question of how people actually come to the decision to found a firm and what factors do and do not influence them in their decision-making process is a very interesting question for further research in this area. In this study this aspect was already partially addressed. There are some additional interesting elements that can be explored in future research: the questions of which impact the characteristics of the regional network structure and processes have (1) on the adoption of a new mental model and (2) how they might hinder the development and change of mental models. (3) In addition, the empirical test of the entrepreneurial role model can be extended. Some tentative ideas on this are ventured in the following: 1

2

The position of agents in social networks relative to the centre of the network can have an interesting impact. First, the more central an agent is in a social network, the more likely it is that this agent receives a lot of information and that the agent gets access to the necessary resources which might favour his entrepreneurial aspirations. On the other hand, centrality goes along with strong cohesive behaviour inside the social network. As Granovetter (1973) pointed out, first adopters of risky, controversial or deviant behaviour are by definition not central to the social network. Thus, central agents would have the resources and could lead to rapid dissemination of a new behaviour, but they are less likely to adopt novel behaviour. In contrast, less central agents are more likely to adopt an entrepreneurial behaviour, but this time it is less likely that it spreads inside the network. This agent also possesses fewer of the resources necessary to found a firm. Empirical questions are which persons in a social network are the first to create or adopt role models, how the dissemination of mental models takes place once an agent has adopted the entrepreneurial behaviour and how the two countervailing forces interact and which one dominates. All these factors influence the likelihood that the role model disseminates in the population and on whether a positive regional entrepreneurial climate can emerge or not. The characteristics of the mental model and of the regional social networks can facilitate exchanges but can also prevent interaction taking place resulting in a lock-in, for example, with agents holding different mental models in- or outside the network. Disadvantages are that some opportunities might be neglected because the information exchanged is biased towards specific opportunities, there are diminishing returns to searching activities in just one direction and the competition between the entrepreneurs in a region increases the competition on input- and output markets. Or, as was mentioned in the discussion on the centrality of agents in a network above, cohesive social networks might hinder the adoption of new behaviour, because it deviates from prevalent norms. Thus, a convergence of mental models or the membership in social networks can disadvantage agents with

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Positive examples and their regional impact entrepreneurial aspirations because they cannot adapt to a changing environment, receive new information or simply change their behaviour. This is especially true for the dissemination of mental models because they are strongly linked to the current behaviour in the regional population and to the norms leading to cohesive behaviour (see Seri 2003 for an application to a regional context). The survey in Jena and the analysis in this Chapter delivered some information on the general impact of regional entrepreneurial role models, but is can be complemented by: •

• •

•

A multivariate analysis of some of the relationships can deliver some new insights, if additional data is available. A next step is to examine the development through the different stages presented in Section 4.3 and to test whether entrepreneurial role models influence the single transitions or not. Furthermore, the existence of a bias in opportunity production and discovery can be analysed empirically. Since we wanted to focus on the impact of entrepreneurial role models on the start-up dynamics in one region, the analysis of a single region is sufficient. In future research this could be extended to other regions in order to compare the processes between the regions and to build a larger dataset providing the ground for multivariate analysis. The most promising approach to test the role model influence would be to follow individual agents during a certain period of their life and to repeatedly conduct interviews with them in order to establish a longitudinal data set. From this set we could discern by whom an agent is affected and how this influences his likelihood to found a firm. Such a data source could also include the social networks in which the agent is embedded. This might shed some light on the dissemination processes of a mental model inside such networks and it might also answer the question of centrality mentioned above. The behaviour of the founder after the establishment of his new firm and the factors influencing him might also be tracked, which would complete the data collection.

5

Conclusions

In this last chapter we proceed as follows: first, in Section 5.1 we reiterate the aim and structure of this study. Second, we present a summary by discussing the proceedings in the different chapters and the outstanding results (Section 5.2). Next, we draw some practical implications for political programmes aimed at supporting regional start-up activities (Section 5.3). Finally, in Section 5.4 we give an outlook and open up some new research questions deriving from the findings in this study.

5.1 Aim and structure of study The main intention of this study is to examine if and how increases and decreases in regional firm founding activities can be theoretically explained and empirically detected. In this respect, the present study is to be understood as a first attempt which suggests some theoretical relationships and empirical methods to analyse founding dynamics and opens up a new research field. The study is not aimed at explaining variations of start-up activities between different regions, but the unit of analysis is the increasing or decreasing likelihood of firm foundings within a certain region. Most parts of the existing literature on regional firm foundings do not consider these short- or medium-term changes in the founding activities.1 They also leave aside changes of the regional, national and industry variables over time by mainly considering the impact of their actual values on current or average startup rates. Hence, we want to analyse and theoretically explain processes that lead to these changes in entrepreneurial behaviour in order to gain new insights into regional firm founding dynamics. Such an approach complements existing casebased or multivariate studies that try to explain differences of firm founding rates between regions. Since new firms are regarded as important in regional development because they influence factors such as innovativeness and cluster development, policy measures in this context have been frequently started during the last years. The present study’s findings have additional practical relevance in that they may help to better evaluate and modify already existing policy measures as well as develop new measures. This study approaches the change and development of regional firm founding activities from two sides. The first part (Chapter 2 and Chapter 3) took a more

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general perspective. It described theoretically the processes and factors that are relevant for explaining the dynamics of regional firm founding activities. Events that trigger regional firm founding dynamics were identified. We examined the causal relation between such events, changes in regional factors and the resulting dynamics of regional start-up activities. The reason for doing this was to obtain a clearer image in regard to the underlying processes behind changes in founding rates. The claim was that changes in the regional context, driven by certain events, lead to a reaction in the likelihood to found a firm in that particular region. In addition, we suggested a method by which such changes in the regional founding likelihood can be identified in the empirical data. The objective was to identify, classify and analyse such changes in selected regions and industries as well as to test the underlying theory. In order to link these two aspects, we conducted in-depth interviews with start-up experts to find out which actual reasons exist which lead to specific developments in firm founding activities. The second part (Chapter 4) proceeded from the general to the specific. In this chapter, we conducted an in-depth study of one specific class of events, namely the impact of (successful) new founders, who serve as role models for regional start-up activities. We analysed the influence of positive examples on changes in regional start-up activities from a theoretical and empirical perspective. Such role models lead to changes in the founding activities by influencing agents’ willingness to start a firm as well as other factors relevant for the entrepreneurial development process. Importantly, the personal and regional levels are seen as interlinked: regional conditions influence an agent’s development; in turn his behaviour influences the regional context. This process is thought to provide an example of how the general relationships previously discussed in Chapters 2 and 3 can be explored further. In addition to the theoretical considerations, we empirically tested the impact of role models by conducting a regional case study.

5.2 Summary of findings After this brief reiteration of the structure of the study, we will now discuss the findings in more detail. In Chapter 2, we developed a theoretical framework describing the causal relations between specific events, changes in regional factors that were caused by these events, and the resulting dynamics of regional start-up activities. In order to achieve this, the most important factors that influence regional firm founding activities were first identified and examined. These factors are the pool of potential entrepreneurs, local markets, resources, pushfactors, pull-factors and additional regional conditions (such as motorways or regional start-up centres). Subsequently, transformation processes and specific dynamics were discussed. These alter the relationship between the elements of the causal chain but are not considered explicitly in most firm founding research, although they are discussed in other economic contexts. Such an additional perspective is relevant

Conclusions 175 as these are influential to firm founding activities. Since transformation processes and dynamics have an impact on the transmission of an event’s influence on regional firm founding activities, they are both placed under the label of transmission processes in the following. These transformation processes and dynamics were briefly summarized. Transformation processes incorporate (1) the relationship between different regional factors. These relationships can be of either a substitutive or complementary nature. If the relationship is complementary, regional factors not present can hinder firm founding activities regardless of the number of other factors provided to potential founders. This has strong policy implications (which are discussed below) because a focus on individual regional factors is insufficient for an improvement of the activities. (2) Furthermore, it is worth considering the fact that while agents possess different characteristics, they do not react in the same way to changes in regional factors. Thus, the distribution of these characteristics within the population can affect firm formation. These individual characteristics are normally used to analyse entrepreneurship on an individual level, to separate entrepreneurs from non-entrepreneurs. In our study, we analysed the distribution of behavioural characteristics in regional populations. For instance, we considered whether certain characteristics are equally or normally distributed. The dynamics will capture all the effects that last for a certain period of time and which lead to short-term developments of start-up activities that deviate from the medium-term level. The medium-term level of regional founding activities is determined by previous regional events and can alter if a new event occurs. Short-term founding activities can be lower or higher than the expected medium-term level of activities. Two examples are: (1) information must first reach relevant agents. This time-consuming diffusion results in a slower convergence to a regional medium-term level of firm founding activities versus the situation in which all the agents immediately receive information. (2) The opposite holds for dynamic effects that exaggerate the medium-term activity level for a certain period of time (e.g. over-shooting or suppressed-founding effects). The consideration of these dynamic effects can lead to better policy measures by making policy makers sensitive to alternative interventions. We presented how selected events influence regional factors that can change regional firm founding activities and what the developments of these regional factors look like. The events affecting regional start-up changes can be grouped into six categories: policy interventions (education and research infrastructure, demand for goods and services, financial support, infrastructure), settlements of large firms, establishment of small firms, emergence of a localized industrial cluster, changes in the external environment and changes in venture capital supply. The impact of these events on regional factor dynamics was presented as stylized curves. By taking these events into consideration, the relationship chain is enlarged in comparison to other studies. To complete this causal chain, we studied which regional factors are affected by which transformation processes and dynamics. We analysed how changes in the regional factors affect start-ups, and, specifically, which transmission processes explain the impact of a specific

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factor’s change on the short- to medium-term dynamics of regional start-up activities. The link between stylized regional factor developments and start-up development curves was shown to be moderated by the transformation processes and dynamics. In the end, we attained a clearer picture of the overall process by linking the events to stylized development curves of founding activities. Hence, we are able to answer the question of how changes in regional factors and firm foundings come about; what the subsequent development of start-up activities look like; and which processes influence the relationships between the relevant elements. Particularly, events that trigger regional factor developments and the integration of transmission processes, which modify the impact of these events, as well as regional factor changes on firm foundings are relevant for this approach. Transmission processes can have two major implications: they can (A) alter the size of the reaction (or even eliminate it), or (B) lead to time lags or to changes in the curve’s functional form. These effects are discussed below in the context of policy implications. In Chapter 3 we introduced a method by which changes in regional firm founding activities could be identified, classified and analysed. The units of investigation were changes of start-up activities inside a region over time. For empirical analysis, a sample was selected encompassing 49 regions, 15 sectors and 12 years. A method was then developed with which short- to medium-term changes of regional firm founding activities could be identified in empirical data. This method eliminates developments caused by industrial, national or longterm regional influences. We identified 208 time series which show a development in which regional firm founding activities significantly changed during this time period. It was found that firm founding activities in most regions are relatively persistent over time (78 per cent of regions have no significant changes in the level of firm founding activities). In contrast, not many regions showed a significant change in their relative firm founding performance. However, in some regions and industries significant changes were identified. The fact that the firm founding activities in some regions changes is the first insight we can gain from this study. Only those regions and industries in which these changes occurred were used for the following in-depth analysis. First, we analysed in which direction the start-up developments headed by identifying local minima and maxima. The most often identified development heads only in one direction: the regional position decreases or increases. In a majority of the time series, changes appeared only once during 12 years of observation. Second, we assigned all points in time of a time series to a specific level of firm founding activities, studied the sequence of levels as well as the magnitude of increases and decreases. We observed that in 86.5 per cent of the 208 cases changes only occur between two levels. With regard to both methods, regions and industries differ in the number of significant changes and their development characteristics. In order to figure out the dynamics’ appearances, a development classification across levels was conducted. The developments of levels over time served as the input for the classification. The classification schema and procedure were presented and their reliability tested. The result is that we managed to classify

Conclusions 177 the developments in only a small number of classes. These were the input for the next steps of the analysis. In the following, region- and industry-specific distributions of the identified dynamic change in firm founding activities were analysed. Developments in the different industries do not have to be identical because industry-specific factors influence them. A comparison of the developments’ directions in the industries showed that the positive and negative developments were balanced for all the industries and no significant deviation could be detected. With respect to the regions, we could conclude that the regions often have a tendency towards either a positive or a negative development. Regional variables dominate the development and influence the direction of change. In order to explore this direction, regions were aggregated to meaningful units (e.g. regions from East Germany vs. West Germany), whereby the differences between these aggregations were analysed. Only one finding will be mentioned here: the direction of developments in East Germany is all but completely negative and a linear decrease in founding activities can be observed. In these findings, East Germany differs strongly from West Germany. This is probably due to strong founding activities in comparison to West Germany in the early part of the 1990s, directly after reunification. Firm founding activities in East Germany exhibit a continuous negative development during the consequent years, reaching the West German level at the end of the 1990s. The distribution of events over time has some interesting results. Some developments take a considerable amount of time. Once a regional development is triggered, it can have a long lasting effect and it could be difficult to change direction. The stylized curves derived in Chapter 2 were compared with the empirically detected ones in order to discern which founding activity developments actually occurred. We suggested modifying the classification processes and schemes to identify some development classes that are unidentified in order to improve class differentiation and to deal better with complex developments in future research. The impact of specific events on regional changes in firm founding activities was further investigated in the final part of Chapter 3. Here we presented the results of the interviews that were conducted with local Chambers of Commerce and Industry representatives or other experts. This qualitative approach provided answers to why the regional firm founding activities changed in the specific ways we identified. These answers were valuable because they allowed us to test the theoretically postulated events against the events that actually occurred. This provided a link between Chapters 2 and 3. We found that only a minor part of the expected events did not appear in the answers. Furthermore, not many events were mentioned during the interviews that we had not ex ante considered to be of impact. Thus, our selection of events and even the proposed corresponding developments of start-up activities, indeed, matched the actual situation very well. We can conclude that the proposed approach is applicable for the analysis of changes in firm founding activities. The results gained from this research are relevant for theoretical and political purposes which we discuss below. In order to illustrate the complicated relationship between events, changes in

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regional factors and changes in firm founding activities, Chapter 4 focused on one specific event; namely the founding of a small firm. Founders of these firms served as behavioural examples or so-called role models for other regional agents. We started this analysis with a short overview of the relationship between agents’ mental models and the agents’ behaviour. It was argued that the mental model and the limited information available to agents moderate their behaviour. The mental model affects the perception of information, the evaluation and the agents’ final behaviour. Agents are embedded in a social context that influences their behaviour by shaping the information received as well as by providing role models that are available and acceptable. Role models trigger social-cognitive learning processes leading to changes in the observer’s mental model. Thus, role models influence other agents’ behaviour. The diffusion of information on role models and role models’ acceptability was analysed. Furthermore, we showed how a shared mental model with a relatively high degree of similarity could emerge in a population. In addition, we examined what specific role geographic and relational proximities play in this process. In doing so, we highlighted the conditions in which a regionally shared mental model can emerge. Especially the establishment, maintenance and effects of social networks as an important part of agents’ social environment were studied. Geographic and relational proximity positively affect the diffusion of information on role models, the likelihood of acceptance of these role models and the dissemination of a mental model. Geographic proximity has a direct impact as well as an indirect one by its effect on the formation and maintenance of regional social networks. Hence, the regional context prominently influences social-cognitive learning processes and the distribution of a mental model. In general, this leads to a higher likelihood that a mental model disseminates in one region than inbetween regions and to regional differences in agents’ behaviour. A stylized stage model of the entrepreneurial development process describing how an agent develops to become an entrepreneur was built. The factors ‘opportunity perception’, ‘willingness to found’, the ‘test of the possibilities to market the product or service’ and the ‘test of the own abilities to found a firm’ are the relevant elements. Role models, in this case successful regional entrepreneurs, have an impact on all of these factors by changing agents’ individual mental model: the willingness to start a firm, the likelihood of discovering entrepreneurial opportunities and the direction of search for opportunities are biased by role models’ impact on the mental model. Furthermore, the market and ability test as well as the evaluation of alternatives (besides founding an own firm) can be moderated by agents’ mental model. Hence, these role models influence the entrepreneurial development process and the likelihood that other agents start up their own firms. The effect of negative role models, i.e. unsuccessful entrepreneurs, was discussed and it was shown that this effect is less pronounced than the one of positive examples. A positive example leads to an increase in the likelihood of agents to found a firm, whereby these new founders also serve as positive examples. A self-augmenting process results in the increase of the likelihood of agents to found. The

Conclusions 179 role models’ impact is partly bound to the region in which the entrepreneur operates as described above. Based on these two findings, we can conclude that learning by imitation can partially explain start-up dynamics and disparities in regional founding rates because the positive example has an effect on the entrepreneurial development process within a certain region. Additionally, the combination of a biased evaluation due to the (potential) entrepreneur’s mental model, over-optimism, the difference in impact of positive and negative role models as well as easily available resources lowering the selection threshold for entry, can lead to an over-shooting in start-ups. In this case there are more new firms entering the (regional) market than can actually survive in the medium or long term. By positive and negative feedback loops, waves in the number of firm foundings in a region or industry can be explained. The explanation of differences between startup activities of regions is not the primary focus, but rather changing patterns within one region is the centre of attention. We can explain some of the founding dynamics; mainly, the acceleration of the initial impact caused by a (successful) firm’s start-up by using the role model approach and positive feedback processes between agents. A continuously high level of entrepreneurial activities and the (sum of) positive examples result in a specific regional attitude towards entrepreneurship, which partially explains founding disparities between regions. Since these new firms also focus their activities on the actual regional industrial structure, the formation of localized industrial clusters and the reproduction of the regional industrial structure can be based on such a role model influence. The theoretical considerations were accompanied by empirical research. We conducted a quantitative survey with nearly 100 firms in the German city of Jena and included its results. Additionally, we presented results of qualitative interviews with experts in the field of firm foundings. Our findings from the survey showed that the existence of successful regional entrepreneurs affects the founding activities of other agents. This takes place via different mechanisms: larger firms are especially important to make the region internationally known, but it is the smaller firms that provide advice and serve as positive examples to potential founders. We found that the regional entrepreneurial climate – which is dependent on regional role models – also shapes the decision-making of agents resulting in a higher likelihood of firm foundings. Through the interviews, we gained some good additional insights into the processes by which positive entrepreneurial examples exactly influence other founders and are able to identify the relevant examples for the city of Jena. Besides other important persons, it was especially Mr. Schambach, the founder of Intershop, who served as a role model and who had a major influence on other agents. His example shows very prominently what can be achieved in this specific regional setting.

5.3 Policy implications Based on the presented theoretical findings, the following implications can be drawn for national or regional policies that generally support start-up activities and specifically regional initiatives. The findings shed some light on the question

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Conclusions

which processes can improve or hinder positive changes in regional start-up activities. The results can be used for a critical evaluation of previously supported regional factors as well as for the design of new political programmes. These programmes can aim at increasing the number of entrepreneurs by fostering positive factors or reducing negative factors hindering positive developments. As a simple example, we can evaluate the policy measures taken in East Germany. The development in East Germany was negative throughout all the analysed time series. This is caused by a very high initial founding rate directly after reunification that continuously declined until it reached a level equal to the West German rate by the end of the 1990s. The chances to close the economic gap between East and West Germany by using high start-up rates in East Germany are very low if there are an equal number of foundings. This would only be the case if there is a higher quality of firm foundings in East Germany leading to less exits and a higher growth. However, there is no reason to assume this. We found that relative regional positions in founding activities do not change very often. Whether this results from the fact that no event (such as policy measures) occurred or because these measures only had a limited effect on the startup activities cannot be decided at the moment because information on conducted interventions is only partly available. In the following, we present six areas that future research should take into consideration as they heavily impact how effective policy measures are which attempt to influence start-up processes. These are: (1) the impact of transmission processes linking events, regional factor changes and firm formation activity changes; (2) the support of the potential entrepreneurial pool; (3) policy interventions to change the transmission processes; (4) a long-term orientation of policy measures; (5) a focus on founding quality and the net-effect of policy measures as well as (6) the support of the impact of role models. 5.3.1 Impact of transmission processes Transmission processes (i.e. dynamics and transformations) that link events induced by policy makers, regional factor changes and firm formation activity changes have to be accounted for when policy measures are designed and evaluated. By considering these intermediate processes, support measures could be improved. The impact of these transmission processes can be separated in three effects. a

The regional factors’ and start-up activities’ reactions might only occur after a considerable time period. This time lag is based on findings that regional agents only start a firm after a certain critical value of a regional factor (e.g. human capital) is overcome, which can take some time. Furthermore, information on events and changes in regional factors need time to diffuse within a (regional) population. Thus, sometimes the full effect of an event only develops slowly. For policy makers, such a time lag can lead to the

Conclusions 181

b

c

incorrect impression that a policy measure has no effect at all. This results in incorrect policy measure evaluation as well as the installation of additional policy measures to support foundings, which in the worst case, negatively intervene with old measures. Hence, policy evaluations should measure the impact over a period that lasts several years after the actual event’s occurrence. During this time, the event’s effect on regional factors and subsequently on firm founding activities fully takes shape. In our analysis, we found that the developments2 took around three years to reach their maximum. This is the minimum amount of time that should be considered for the evaluation. This does not include additional time lags before start-up activities react at all. The second effect considers the reaction’s size. Transmission processes influence the reaction size of regional factors and regional firm formation activities. This depends on the respective slope of curves, diffusion speed or lagged developments. Some policy measures do not result in the expected changes in start-ups. This is because these policy effects trickle away: the expected reaction size may decrease and in the most extreme case, the intended effect can be completely erased. If regional factors do not reach a critical value then the founding activities will not react at all. Thus, policy measures should be designed in such a way that changes in regional factors are large enough to overcome such a threshold. The same holds if one regional factor hinders development because it is not present to a sufficient degree, e.g. good infrastructure is available but there is no human or financial capital. Policy makers should consider relationships between regional factors. Then they identify the currently limiting factor and focus their support on this specific factor. Generally, if a policy maker simply supports one or a limited number of factors, they run the risk that other omitted factors hinder the development. Hence, policy measures should change rules, regulations or other conditions that simultaneously have a positive impact on the endogenous development of various factors. With respect to agents’ heterogeneity, policy intervention should change the regional factor in such a way that the majority of the agents reacts to such a change.3 Additionally, suppressedfounding (where potential founders are hindered to found but do so after a specific event) and over-shooting effects (based on social interaction and selfaugmenting process) increase start-up activities for a limited amount of time. These latter effects must be controlled as they are unsustainable over a long period of time. The short-term effect of a political measure might be large, but the long-term effect is much smaller. Policy measure evaluation has to consider several years for a complete overview to develop. Focusing only on the initial reactions could lead to a misunderstanding as measures usually need some time to fully develop and to settle at a final level. The functional form of development changes based on the impact of the transmission processes, e.g. a linear increase in a regional factor can result in a stepwise change of founding activities. This functional form change is probably less relevant for policy makers as their objective is to increase firm

182

Conclusions founding rates, and they are uninterested in the exact development. Nevertheless, policy makers should expect that the functional form can differ from their expectations.

In conclusion, policy makers must consider these effects for the design and evaluation of policy measures. In addition to the recommendations presented above, further policy implications are developed. Policy makers have two possibilities to influence regional firm foundings: first, by trying to alter regional factors through specific events (see area 2). Second, they can improve the effect of an event or changes in the regional factors on founding activities by modifying transmission processes (see area 3). 5.3.2 Support of the entrepreneurial pool At presently politicians and researchers mainly focus on regional resources and infrastructure. These may be necessary but are not sufficient to improve regional start-up activities. One important aspect in this respect, the impact of limiting factors, is already discussed above. Here, only one additional element shall be mentioned. The support of firm foundings might be important for regional development but potential firm founders are of primary importance. This is of significance because it was noticed that agents found their firms in regions where they are or previously were (Fornahl and Graf 2003). Thus, policy makers should focus more strongly on attracting agents to a region which have founding potential rather than to attract actual founders. This could increase the likelihood that agents start their firm in such a region. In this regard, educational infrastructure and established firms may be an important way of attracting these agents to the region. This would support entrepreneurship education programmes at universities, which can also be extended to other agents. The same is true for the support of regional innovative activities because they represent a basis where agents develop competences and firm founding opportunities. 5.3.3 Policy interventions on transmission processes Influencing transmission processes is sometimes an implicit aim of current policy measures. a

b

The necessity that information must reach agents before they can react to environmental changes slows down changes in firm founding activities. This process can be improved in such a way that agents have easier access to the information allowing the diffusion process to begin earlier and to increase speed, e.g. by the use of public and private media. This can enlarge the accumulation of human capital if information on new jobs is diffused as well as the number of founders if information on markets and resources is transmitted. Policy measures can shift characteristic distribution (e.g. regional agents capabilities) in a region (at least to a certain degree). Policy will have the

Conclusions 183

c

d

strongest impact on education. There is an increased chance for agents to become potential entrepreneurs if they have a high quality entrepreneurial or technical education. It is much more difficult to politically influence the agents’ sensitivity to changes in the evaluation of push- and pull-factors. In this case, basic attitudes and psychological characteristics (e.g. their risktaking propensity) have to change. This is only possible by influencing agents’ long-term socio-cultural background. This is described below. The thresholds relevant for agents’ founding decisions can be affected by political interventions if the processes underlying resource evaluation or market conditions change. This can again only be achieved through longterm education on these different aspects. The last aspect relates to the over-shooting of firm foundings. Here, it first has to be decided whether over-shooting is positive or negative for (regional) economic development. On the one hand, resources are wasted, but, on the other hand, a lot of experimentation is needed to find an appropriate product or service for the specific market. Policy intervention can aim at the prevention of a contagion effect after a boom or over-shooting. In this case, even promising business conceptions do not receive the necessary resources, and policy measures could anti-cyclically provide financial resources when private venture capital providers do not do so.4

5.3.4 Long-term orientation of policy measures From analysing the duration of the developments, we conclude that most of the changes take several years to reach their maximum. This would support the above implication that evaluations of a policy measure should take this lag into consideration. In addition to the implications on policy evaluation, policy measures should also be long-term oriented since rapid changes in the founding activities cannot be expected in most cases. However if policy measures lead to a change in the activities, this probably has long-term sustainable effects. Hence, it is worth trying to initiate changes in regional start-up activities. The developments take at least three years to reach their maximum; some even take around seven years. Such a time frame must be the basis for policy measures aiming to support firm foundings. 5.3.5 Focus on founding-quality There are at least three ways to define and analyse entrepreneurial activities inside regions: these are the entries, the stock of active firms and the exits (Gartner and Shane 1995). The start-up rates are used to analyse changes in entrepreneurial activities in regions. In future research, the focus can be extended away from the number of new firm foundings to the net effect (including the exits of firms as well), as often a high number of firm foundings occurs in conjunction with a high number of exits. Increasing the agent’s

184

Conclusions

opportunities and his or her willingness to get into business does not necessarily enhance the entrepreneur’s chances of staying in business. This aspect implies that not only the quantity but also the quality of entries should be an aim for policy makers. This would potentially reduce exit rates. Such a policy re-think can be more efficient in fostering regional development relative to simply financing many start-ups that die shortly after their establishment. 5.3.6 Support of impact of role models The research pointed to the impact of role models on the general entrepreneurial attitude. Where prominent and visible foundings occur, the regional founding rates tend to increase. High past levels of entrepreneurial activity inside a region lead to high rates of start-up activities in the future because many positive examples are present. This supports the findings in Chapter 3, whereby regional entrepreneurial activities are to a certain degree self-sustaining. Policy makers can increase this effect by (A) encouraging the emergence of these positive examples, (B) supporting the diffusion of information about the role models and (C) by increasing the likelihood of acceptance of the agents as role models. Nevertheless, external policy interventions supporting the emergence of role models and their acceptance are very difficult and hard to establish. In the end, the dissemination of a mental model and of an entrepreneurial attitude in a population strongly depends on cultural and social conditions, thereby being only partly open to policy interventions. The support of social networks or communication processes inside regions can be one way of improving the diffusion process. Here the central aim is to bring the relevant agents together. However, normally the described structure, processes and resulting behavioural modes develop spontaneously – if at all – by self-organizing processes inside a region. Policy makers have the possibility to support such structures by general measures, such as the support of alumni-networks of schools and universities, professional associations or by other structures of social interactions. Nevertheless, these are relatively unfocused measures. The question of how such support networks can be built artificially or how their services can be provided by other means is not very well answered in the literature thus far. Several political programmes were launched in the past to support start-ups, e.g. EXIST in Germany. Such approaches have had some success (Koschatzky 2003), but they could gain in efficiency by further considering how they may support the early stages of the entrepreneurial development processes. They could also be extended to support and activate already existing social networks. One way of doing so would be to bring potential entrepreneurs in contact with (successful) entrepreneurs in university courses, which is already done at many universities. Especially for the acceptance of a successful entrepreneur as a role model, one needs to take into account that the role models and the potential entrepreneurs must be comparable to each other in some way. Thus, the entrepreneur who presents himself and his firm in such a course should stem from the same socio-cultural background. In general, the support of potential entre-

Conclusions 185 preneurs should not only be based on specific chairs of entrepreneurship, but rather the whole academic environment has to teach entrepreneurship. Although the possibilities of supporting the role model effect by political interventions are limited, the pay-off from doing so is potentially large since once a regional entrepreneurial culture is established it is sustainable for a long period of time.

5.4 Outlook The study developed a unique framework for research which focuses on changes in regional firm founding activities. Although it has already offered some new insights, some open questions and possible extensions still exist. Besides the extensions and improvements of the applied methods that were already presented in the according chapters, this section will give a broader outlook focusing on central topics that can be addressed. These include: (1) the stronger focus on the transmission processes, (2) an extension of the proposed methods from Chapter 3 to other data, (3) an in-depth study of other events changing regional start-up activities, (4) the impact of role models on other entrepreneurial activities besides start-ups and (5) a possible simulation model of the relationships. (1) Besides the broadening of the set of transmission processes suggested in Chapter 2, a deepening and an advancement of the interaction between the elements is beneficial. This especially relates to two transmission processes: first, a more precise exploration of the limiting factor effects is worthwhile. In most cases in the literature, the interaction of the different factors leading to firm foundings does not take into account such a limiting factor relationship. Second, in the link between theory and empirical evidence, the over-shooting of firm foundings in some industries is a very interesting area of investigation. The over-shooting can be analysed on an aggregated level (as is presented in Chapter 1 for the IT-case) as well as on a regional level for different industries. Of specific interest are such over-shooting effects that result from influx in financial capital (or other resources) because such a change of the regional conditions also changes the selection thresholds and the fitness of the new firms (Barnett et al. 2003). This influx can be based on venture capital firms but also on subsidies provided by policy makers. The impact of such over-shootings on regional development and the macroeconomic implications are interesting because one has yet to test whether or not such an influx dissipates financial capital. (2) In Chapter 3, a method is developed by which changes in regional firm founding activities can be identified and analysed. This model is generally applicable to a wide range of possible data in order to identify specific developments. One example of such an extension is an analysis of the development of patenting activities. It is hereby possible to analyse whether regions or firms change their relative position with regard to their patenting activities, which regions or firms do so in which technological field and when this is the case. It would also be possible to investigate the innovative activities in more detail and follow these activities over a longer period of time.

186

Conclusions

(3) Chapter 4 only studies one possible example event (i.e. firm foundings and the founders impact as a role model); other events can also be selected and analysed. This is in line with a deeper examination of the various events and their impact on regional factors. These events might all be the events considered in Chapter 2, but especially new technologies, venture capital firms, or changes in the evaluation of push-factors might be interesting. (4) Although the focus of this study is on entrepreneurial activities, defined as firm foundings, role models and their dissemination by social-cognitive learning can also influence other (entrepreneurial) activities. These are, for example, technological developments and innovations that are focused on specific areas by these role models.5 Furthermore, role models are able to install a vision for the regional development and can influence the modes of coordination of regional activities (leading, for example, to more cooperation and trust). (5) Based on the presented results of this study and all the considerations and ideas above, the development of a computer simulation model should be possible. This can take three directions: first, the process in which agents become entrepreneurs can be subject to such a simulation. Second, the simulation can reproduce the development and distribution of regional start-up activities. Both simulation models have to take into account the interaction of many factors and processes discussed: for example, the opportunity side of a firm founding (including technological developments and prices), the willingness aspect (with push- and pull-factors), the available resources and especially the transmission processes (e.g. with limiting factor relationships and over-shooting) have to be included to build an appropriate model. Third, the dissemination of a mental model or the diffusion of information and the acceptance of role models can be simulated. This is a similar approach to the one of Cowan and Jonard (1999) who simulate the diffusion of knowledge. Such a model can analyse the impact of different network structures. In all cases, it is necessary and correct to calibrate the ranges of the model variables according to empirically observed facts and to test the results of the model against real world developments (Brenner and Werker 2004). There are several directions in which this study can be extended. This is not surprising because the presented research radically tackled new questions with new theoretical approaches. It also suggests a novel analytic method. We maintain that our study gives interesting and valuable insights into the factors responsible for changes in regional start-up activities and the processes that drive these changes.

15 16

9 10 11 12 13 14

4 5 6 7 8

05913 06412

05162 05313 05315 05370 05570 05770

02000 03152 03352 05111 05117

01003 01051 01058

1 2 3

Lübeck Dithmarschen RendsburgEckernförde Hamburg Göttingen Cuxhaven Düsseldorf Mühlheim/ Ruhr Neuss Aachen Köln Heinsberg Warendorf MindenLübbecke Dortmund Frankfurt am Main

Regional code

No. Name

1,521.7 2,911.1

1,471.1 755.2 3,317.5 612.8 574.8 726.8

12,107.8 872.0 268.1 2,971.6 541.0

538.2 330.1 734.3

Av. no. of firm foundings per year (1990 to 2001)

38.82 64.44

49.91 44.65 50.13 39.58 32.76 36.74

105.20 48.55 20.98 76.07 47.24

38.42 38.82 42.94

Av. firm founding intensity per 10,000 inhabitants

Table A.1 Selected characteristics of sample regions

Appendix A

588,994 646,550

443,865 244,386 962,884 250,400 280,443 322,789

1,715,392 265,396 204,843 569,364 172,862

213,399 137,174 270,119

Inhabitants (December 2000)

377,617 446,402

287,797 164,808 647,716 156,521 175,133 196,548

1,149,508 175,906 126,935 380,779 108,597

135,958 84,240 172,280

Inhabitants between 18 and 65 (December 2000)

118.40 244.99

293.19 608.34 189.06 193.75 233.28 200.05

154.97 211.13 45.77 299.57 299.14

152.65 54.61 133.67

No. of patents per 100,000 inhabitants per year (patent averages 1995–2000; inhabitants 1998)

280.29 248.31

576.43 160.83 405.15 627.97 1,317.05 1,152.06

755.33 1,116.58 2,072.50 217.00 91.29

214.14 1,429.37 2,185.49

Geographical area in square km

14.7 7.7

7.4 10.9 11.5 9.1 7.5 8.7

9.6 12.1 10.6 10.4 9.4

13.8 11.5 8.6

Unemployment rate in 2001

continued

1 1

1 1 1 1 2 1

1 2 2 1 1

2 3 2

Type of region in 1999*

26

25

24

23

22

21

20

18 19

07143

06635

06534

06532

06440

06436

06434

06414 06432

06413

17

Offenbach am Main Wiesbaden DarmstadtDieburg Hochtaunuskreis (Bad Homburg) Main-Taunus (Hofheim) Wetterau-Kreis (Friedberg) Lahn-Dill (Wetzlar) MarburgBiedenkopf WaldeckFrankenberg (Korbach) WesterwaldKreis

Regional code

No. Name

Table A.1 continued

594.0

331.8

433.9

628.3

949.3

788.5

945.7

1,212.5 859.3

448.8

Av. no. of firm foundings per year (1990 to 2001)

47.75

31.33

25.71

37.49

50.58

53.45

63.93

67.90 45.51

56.83

Av. firm founding intensity per 10,000 inhabitants

201,840

170,568

253,279

262,859

294,260

220,264

225,638

270,109 286,780

117,535

Inhabitants (December 2000)

125,493

104,699

166,557

164,447

189,800

146,789

146,784

177,575 189,661

78,199

Inhabitants between 18 and 65 (December 2000)

143.43

130.25

248.05

329.78

270.71

792.46

552.38

232.90 637.45

217.71

No. of patents per 100,000 inhabitants per year (patent averages 1995–2000; inhabitants 1998)

988.75

1,848.58

1,262.56

1,066.51

1,100.70

222.40

482.05

203.90 658.51

44.90

Geographical area in square km

6.6

7.8

7.4

8.0

6.5

4.3

4.8

8.4 5.9

10.3

Unemployment rate in 2001

2

2

2

2

1

1

1

1 1

1

Type of region in 1999*

Kaiserslautern Mainz Stuttgart Esslingen Karlsruhe (City) Karlsruhe (Kreis) Heidelberg Rhein-NeckarKreis (Heidelberg) BreisgauHochschwarzwald Tübingen München Freising München Land Cham Nürnberg Aschaffenburg, Stadt Augsburg Saarbrücken Schwäbisch Hall Saarpfalz-Kreis, Berlin Dresden Jena Sömmerda

11000 14262 16053 16068

09761 10041 10045

08315 08416 09162 09178 09184 09372 09564 09661

07312 07315 08111 08116 08212 08215 08221 08226

13,806.5 2160.9 442.8 348.1

718.3 841.8 363.1

613.3 408.7 5,292.5 573.0 1,709.5 259.8 1,553.5 276.8

328.1 660.0 1,853.8 1,293.9 989.2 1,121.2 393.7 1,390.5

58.91 69.76 63.75 63.18

42.30 35.46 35.22

40.43 29.30 61.40 58.47 86.43 31.34 46.82 64.32

49.11 51.82 45.74 38.98 52.72 41.45 39.85 40.53

3,382,169 477,807 99,893 81,204

254,982 350,924 157,309

240,545 208,535 1,210,223 152,307 295,247 131,035 488,400 67,592

99,825 182,870 583,874 500,666 278,558 419,555 140,259 524,028

2,327,406 318,509 69,254 54,115

163,884 227,104 99,868

152,735 139,629 837,297 102,230 200,921 82,107 321,926 43,120

64,953 124,086 396,645 326,194 185,160 270,429 98,685 340,650

191.23 308.53 670.49 36.80

259.46 124.44 177.79

266.01 427.27 582.55 524.74 989.54 100.24 287.88 385.76

235.37 310.81 569.90 663.18 339.35 370.76 535.43 488.15

891.69 328.30 114.23 804.29

146.72 410.61 418.55

1,378.34 519.16 310.59 799.61 667.27 1,509.96 186.37 62.75

139.72 97.77 207.36 641.44 173.46 1,084.96 108.83 1,061.71

18.2 15.9 12.5 18.3

8.2 12.6 8.0

5.3 5.4 5.7 3.5 4.0 8.2 11.0 8.7

12.4 7.5 6.6 4.2 7.9 5.2 6.6 6.2

Note * 1 = agglomeration areas, 2 = urbanized areas, 3 = rural areas.

Sources: Based on ZEW Foundation Panels, Statistik Regional (2003), Greif and Schmiedl (2002), INKAR (2002) and author’s calculations

46 47 48 49

43 44 45

36 37 38 39 40 41 42

35

27 28 29 30 31 32 33 34

1 1 2 2

2 1 2

2 2 1 1 1 2 1 2

2 2 1 1 1 1 1 1

Industry name

Publishing, printing and reproduction of recorded media Manufacture of coke, refined petroleum products and nuclear fuel Manufacture of chemicals and chemical products Manufacture of rubber and plastic products Manufacture of other non-metallic mineral products Manufacture of basic metals Manufacture of fabricated metal products, except machinery and equipment Manufacture of machinery and equipment Manufacture of office machinery and computers Manufacture of electrical machinery and apparatus Manufacture of radio, television and communication equipment and apparatus Manufacture of medical, precision and optical instruments, watches and clocks Computer and related activities Research and development Other business activities/business services; without ‘Management activities of holding companies’ (74.15) and ‘Asset management’ (74.847) Industries 22–73 (aggr14) Industries 22–74 (aggr15) Manufacturing industries (22–33) Service industries (72–74)

WZ93-Code

22 23 24 25 26 27 28 29 30 31 32 33 72 73 74

Table A.2 Selected characteristics of industries

4.94 0.02 0.56 0.44 0.72 0.40 2.51 1.54 0.63 0.53 0.38 1.66 16.97 1.32 67.38 32.62 100.00 14.33 85.67

130.72 400.75 57.44 343.31

Share of specific industry in overall number of firm foundings in sample (over 12 years and 49 regions)

19.79 0.08 2.23 1.76 2.89 1.60 10.07 6.17 2.54 2.14 1.52 6.65 68.00 5.28 270.03

Average absolute number of firm foundings per year (1990–2001) and region

22 23 24 25 26 27 28 29 30 31 32 33 72 73 74 aggr14 aggr15 prod service

Publishing, printing and reproduction of recoded media Manufacture of coke, refined petroleum products and nuclear fuel Manufacture of chemicals and chemical products Manufacture of rubber and plastic products Manufacture of other non-metallic mineral products Manufacture of basic metals Manufacture of fabricated metal products, except machinery and equipment Manufacture of machinery and equipment Manufacture of office machinery and computers Manufacture of electrical machinery and apparatus Manufacture of radio, television and communication equipment and apparatus Manufacture of medical, precision and optical instruments, watches and clocks Computer and related activities Research and development Other business activities Industries 22–73 Industries 22–74 Manufacturing industries Service industries b

208

7 0 1 0 1 0 6 2 4 0 0 2 24 6 38 24 40 17 36

Absolute number 96%-level 14% 0% 2% 0% 2% 0% 12% 4% 8% 0% 0% 4% 49% 12% 78% 49% 82% 35% 73%

Relative number level 96%

a

Notes a Relative number is calculated on the basis of 49 potential regions that might show the relevant behaviour in this industry. b For each of the industries 22, 72, 74, aggr14, aggr15 and service one observation can result from a change in the data collection method based on the City of Göttingen as was discussed in Section 3.1.1.

Sum

Code

Industry name

Table A.3 Analysis of the industries in which short- to medium-term changes were identified

Lübeck Dithmarschen Rendsburg-Eckernförde Hamburg Göttingenb Cuxhaven Düsseldorf Mühlheim/Ruhr Neuss Aachen Köln Heinsberg Warendorf Minden Dortmund Frankfurt Offenbach Wiesbaden Darmstadt-Dieburg Hochtaunuskreis Main-Taunus (Hofheim) Wetterau-Kreis (Friedberg) Lahn-Dill (Wetzlar) Marburg Waldeck-Frankenberg Westerwald-Kreis

Region

X

26

27

X

X

X

28

X

29

X

30

31

32

X

33 X

72

X

X

X X

X X X

X X X X X

X

X

X X X X

X X

X X

74

X

X

X

73

X X

X

X X X

25

X

24

X X

23

X X

22

Industry

Table A.4 Cross-tabulation of regions and industries with significant changes (96%-level)

X

X

X

X X

X

X X

Aggr 14

X

X X

X X X X X

X X

X X X X X X

X X

X X

Aggr 15

X

X

X X

X

X

X

X X

X X X

X

X X X

X X

X X X

X X

X X

3 (16%) 3 (16%) 1 (5%) 3 (16%)

4 (21%) 4 (21%) 0 7 (37%) 6 (32%) 1 (5%) 9 (47%) 5 (26%) 5 (26%) 5 (26%) 5 (26%) 3 (16%) 0 1 (5%) 4 (21%) 6 (32%) 0 5 (26%) 3 (16%) 4 (21%) 6 (32%) 4 (21%)

prod service Suma

2

X

29

4

X

30

0

31

0

32

2

33

X

X X

24

6

X

X X X X 38

X X

X X X X X X

X

X

X X X X X

74

X X

X X

X

73

X

X X

X

72

6

0

X

28

7

1

27

X X X X

0

26

X X

1

25

X X

X

24

X

0

23

X

X

22

Industry

X X X X 24

X X

X

X X

X

X X X X

X X

Aggr 14

Notes a Relative number is calculated on the basis of 19 potential industries that might show the relevant behaviour in this region. b These developments can result from a change in the data collection method (see Section 3.1.1).

Kaiserslautern Mainz Stuttgart Esslingen Karlsruhe (City) Karlsruhe (Kreis) Heidelberg Rhein-Neckar-Kreis (Heidelberg) BreisgauHochschwarzwald Tübingen München Freising München Land Cham Nürnberg Aschaffenburg Augsburg Saarkreis Schwäbisch Hall Berlin Dresden Jena Sömmerda Sum

Region

X X X X 40

X X X X X X X X

X

X X

X X X X X

Aggr 15

X X X X 17

X X X

X

X

X

X

X X X X 36

X

X X X X X

X

X

X X X X X

5 (26%) 8 (42%) 6 (32%) 3 (16%) 5 (26%) 4 (21%) 1 (5%) 6 (32%) 2 (11%) 1 (5%) 8 (42%) 9 (47%) 6 (32%) 5 (26%) 208

6 (32%)

5 (26%) 5 (26%) 4 (21%) 3 (16%) 8 (42%) 2 (11%) 4 (21%) 5 (26%)

prod service Suma

22 23 24 25 26 27 28 29 30 31 32 33 72 73 74 aggr14 aggr15 prod service 2 2 3 4 11

9 1 12 5 9 4 12 55

2 15 5 24 17 28 13 20 142 12 1 16 9 19 6 12 81

2 3 4 8 8 9 7 8 61

1

1

4 2 2

6 2 3

2

1

2

Increase

1

3

Decrease

1

2

3

1

5

2

3 1 5 1 4 1 5 21

1

Decrease– increase

7 4 5 3 7 34*

6

1

1

Increase– decrease

3 6

1 1 1

Decrease– increase– decrease

Characterization of identified changes of firm founding activities

96%-level ■

■

Note * Six of these developments can result from a change in the data collection method based on the City of Göttingen as was discussed in Section 3.1.1.

Sum

Industry

1

Absolute number of changes

96%-level

Table A.5 Number and types of changes according to industries

1 5

1 1 2

Increase– decrease– increase

Lübeck Dithmarschen Rendsburg-Eckernförde Hamburg Göttingen* Cuxhaven Düsseldorf Mühlheim/Ruhr Neuss Aachen Köln Heinsberg Warendorf Minden Dortmund Frankfurt Offenbach Wiesbaden Darmstadt-Dieburg Hochtaunuskreis Main-Taunus (Hofheim) Wetterau-Kreis (Friedberg)

Region

1 1

2

1

3 3 1 6 2

1

1 4 3 1 2

Decrease

4 3 1 6 2

1

3

3

1 2 3

4

1

2

1

1 4 3

5

5 1 1

3

4 3

Increase

2

3

1 1

6

Decrease– increase

1

1

1 1 2

4

1

Increase– decrease

1

1

Decrease– increase– decrease

Characterization of identified changes of firm founding activities

96%-level ■

■

1 4 6

3 6 1 9 4 2 2 5 3

4 3

1

Absolute number of changes

96%-level

Table A.6 Number and types of changes according to region

continued

1

Increase– decrease– increase

Lahn-Dill (Wetzlar) Marburg Waldeck-Frankenberg (Korbach) Westerwald-Kreis Kaiserslautern Mainz Stuttgart Esslingen Karlsruhe (City) Karlsruhe (Kreis) Heidelberg Rhein-Neckar-Kreis (Heidelberg) Breisgau-Hochschwarzwald Tübingen München Freising München Land Cham Nürnberg Aschaffenburg Augsburg

Region

Table A.6 continued

5 1 5 4 3 2 4 4 1 3

1 5 3 1 3 3 2 4

3

1

1

3 3 1 1

3

1

1

4

4

1

3

1 3

1 2

3

2

Absolute number of changes

96%-level

3

4

1 2

5 1 3

2

3

1

1

3

Decrease

1

4

2 4 2

4

3

5 2 1

Increase

1

1

2

4

1 3

3

Decrease– increase

2

1

3 3

2

1 2

Increase– decrease

Decrease– increase– decrease

Characterization of identified changes of firm founding activities

96%-level ■

■

1

1

1

1

Increase– decrease– increase

2 1 8 4 5 5 142

1

3 11

55

3

2 1

2

Absolute number of changes

96%-level

1 8 4 5 5 81

Decrease

61

2

Increase

21

2

Decrease– increase

34

1

Increase– decrease

6

3

Decrease– increase– decrease

Characterization of identified changes of firm founding activities

96%-level ■

■

Note * These six developments can result from a change in the data collection method based on the City of Göttingen as was discussed in Section 3.1.1.

Saarkreis Schwäbisch Hall Berlin Dresden Jena Sömmerda Sum

Region

5

Increase– decrease– increase

198

Appendices

Table A.7 Distribution of highest levels across industries 96%-level Highest level 2 Industry

Sum

22 23 24 25 26 27 28 29 30 31 32 33 72 73 74 aggr14 aggr15 prod service

3

4

5

7 1 1 6 2 4 2 22 6 31 20 34 16 28 180

2 5 3 3 1 6 20

1 1 2

1 1

1 5

1 3

Sum 7 0 1 0 1 0 6 2 4 0 0 2 24 6 38 24 40 17 36 208

Appendices 199 Table A.8 Distribution of highest levels across regions Region

96%-level Highest level 2

Lübeck Dithmarschen Rendsburg-Eckernförde Hamburg Göttingen* Cuxhaven Düsseldorf Mühlheim/Ruhr Neuss Aachen Köln Heinsberg Warendorf Minden Dortmund Frankfurt Offenbach Wiesbaden Darmstadt-Dieburg Hochtaunuskreis Main-Taunus (Hofheim) Wetterau-Kreis (Friedberg) Lahn-Dill (Wetzlar) Marburg Waldeck-Frankenberg (Korbach) Westerwald-Kreis Kaiserslautern Mainz Stuttgart Esslingen Karlsruhe (City) Karlsruhe (Kreis) Heidelberg Rhein-Neckar-Kreis (Heidelberg) Breisgau-Hochschwarzwald Tübingen München Freising München Land Cham Nürnberg Aschaffenburg Augsburg Saarkreis Schwäbisch Hall Berlin Dresden Jena Sömmerda Sum

3

4

5

4 4 4 2 1 9 3 5 5 5 3

3 4 2

1 4 6 4 3 4 6 4 3 3 1 3 5 5 4 3 7 2 4 5 6 5 4 5 2 5 4 1 6 24 1 3 5 4 5 180

1

1

3 1 1

1

4 1 2

1 3

20

5

3

Sum 4 4 0 7 6 1 9 5 5 5 5 3 0 1 4 6 0 5 3 4 6 4 3 3 1 3 5 5 4 3 8 2 4 5 6 5 8 6 3 5 4 1 6 2 1 8 9 6 5 208

Note * These developments might result from a change in the data collection method (see Section 3.1.1).

200

Appendices

Table A.9 Distribution of highest levels across regions and industries Region

Industry

Sum

22 23 24 25 26 27 28 29 30 31 32 33 72 73 74 Aggr Aggr prod service 14 15 Lübeck Dithmarschen RendsburgEckernförde Hamburg Göttingena Cuxhaven Düsseldorf Mühlheim/ Ruhr Neuss Aachen Köln Heinsberg Warendorf Minden Dortmund Frankfurt/ Main Offenbach Wiesbaden DarmstadtDieburg Hochtaunuskreis Main-Taunus Wetterau-Kreis Lahn-Dill (Wetzlar) Marburg WaldeckFrankenberg WesterwaldKreis Kaiserslautern Mainz Stuttgart Esslingen Karlsruhe (City) Karlsruhe (Kreis) Heidelberg Rhein-NeckarKreis BreisgauHochschwarzwald

2

2 2

2

2 2

2 2

5* 3*

2 2

2 2

2

5* 3*

5* 3*

2

2

2 3* 2

2

2 2 2 2 2

2

2

2

2

2

2

2

2

3*

2

2

2

6 0 5

2 2 2 2

2 2 2

3 4 6 4

2 2

2 2

3 3

2

2

2

2

2 2

2 2

2 2 3* 2 2

2

2 2

2 2 2 2

2 2

2 2 2 2 2

2

2 2

2 2

2 2 2 2

2

2

2

0 7 6 1 9

2 3*

2 2

4 4

2

3*

2 2 2 2 2 2

2 2

1 2 2 2 2 2

2

2

2

2

2

2 2

2

2

2

2

2

2

2

2

2

2

2

2

5 5 5 5 3 0 1 4

2

2 2 2 2 2

3 5 5 4 3

2

8

2

2 4 5

2

6

Appendices 201 Table A.9 continued Region

Industry

Sum

22 23 24 25 26 27 28 29 30 31 32 33 72 73 74 Aggr Aggr prod service 14 15 Tübingen München Freising München Land Cham Nürnberg Aschaffenburg Augsburg Saarkreis Schwäbisch Hall Berlin Dresden Jena Sömmerda Sum

2

2 2 3* 2 2 2 2 2 2 2 2

2

2

2

2

2 2

7

2 2 2 2 2

2 2

0

1

0

1

0

6

2

4

0

0

2

2 2

3* 4* 3* 2 24 6 38

2 3* 2 2 2 2 2 2

2 2 2

3* 4* 3* 4* 2 3* 2 2 24 40

3* 2 2 2 17

4* 2 2

2 2

3* 3* 3* 2 2 2

3* 4* 2 2 36

5 8 6 3 5 4 1 6 2 1 8 9 6 5 208

Notes * indicates levels higher than 2. a These developments can result from a change in the data collection method (see Section 3.1.1).

202

Appendices

Table A.10 Distribution of relevant developments during the period under investigation Negative developments Years

Classes Start –1 End –1 Change –2 Start –3 End –3 Start –5 Level –5 Back –5 End –5 Start –6 Level –6 Back –6 End –6 Change –9

Sum Only 1 to 3

1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 6 0 0 0 0 2 0 0 0 0 0 0 0 0 8 6

5 0 0 0 0 2 1 0 0 1 0 0 0 3 12 5

1 0 3 3 0 0 0 0 0 0 0 0 0 0 7 7

4 0 3 2 0 3 1 1 0 0 0 0 0 0 14 9

0 0 10 6 3 2 1 0 1 0 0 0 0 0 23 19

0 0 3 4 0 1 4 1 0 0 0 0 0 0 13 7

0 0 0 1 5 2 1 4 0 0 0 0 0 0 13 6

0 0 1 2 2 1 1 0 0 0 0 0 0 0 7 5

0 0 3 0 2 3 1 0 4 0 1 1 0 0 15 5

0 7 0 0 4 0 2 4 0 0 0 0 1 0 18 11

0 5 0 0 2 0 4 6 4 0 0 0 0 2 23 7

0 4 0 0 0 0 0 0 7 0 0 0 0 0 11 4

Positive developments Years Classes

1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001

Start +1 6 End +1 0 Change +2 0 Start +3 0 End +3 0 Start +5 4 Level +5 0 Back +5 0 End +5 0 Start +6 0 Level +6 0 Back +6 0 End +6 0 Start +9 0 Sum 10 Only 1 to 3 6

0 0 0 0 0 9 0 0 0 0 0 0 0 0 9 0

0 0 1 0 0 4 2 0 0 0 0 0 0 2 9 1

0 0 0 1 0 2 2 0 0 0 0 0 0 0 5 1

0 0 0 0 0 0 7 6 0 0 0 0 0 3 16 0

0 0 2 0 1 2 4 1 3 0 0 0 0 2 15 3

0 0 0 4 0 0 3 4 1 0 0 0 0 0 12 4

0 3 0 2 1 0 1 6 3 0 0 0 0 3 19 6

1 2 2 2 3 0 2 3 3 1 0 0 0 1 20 10

0 0 2 1 0 0 0 1 2 0 0 0 0 3 9 3

0 1 0 0 3 0 0 0 6 0 1 1 0 0 12 4

0 0 0 0 2 0 0 0 3 0 0 0 1 0 6 2

Notes Start = Initial starting point of development. End = Final point of development. Change = Development is limited to one point in time. Level = For Classes 5 and 6 the development reaches a temporary high level. This is the point in time this high level is first reached. Back = For Classes 5 and 6 the development falls back again from the temporary high level. This is the point in time this falling back starts.

Appendices 203 Table A.11 Partners for interviews on reasons for changes in regional firm founding activities Code

Region

Local Chamber of Commerce and Industry

Date

Time

1051 3152 5111 5117

Flensburg; Dithmarschen Office Göttingen Düsseldorf Ruhr (Essen) mittlerer Niederrhein Aachen Köln Aachen Dortmund Frankfurt am Main

15.11.2004 16.11.2004 16.1.2004 22.11.2004 15.12.2004 6.12.2004 8.11.2004 4.11.2004 8.11.2004 10.11.2004 22.11.2004

16:00–16:30 14:30–14:50 10:17–10:52 12:13–12:40 17:00–17:28 13:00–13:30 15:15–15:45 9:30–9:50 15:15–15:45 14:30–15:00 16:00–17:17

Darmstadt

8.12.2004

11:55–12:08

6434 6436 6440 8116

Dithmarschen Göttingen Düsseldorf Mülheim an der Ruhr Neuss Aachen Köln Heinsberg Dortmund Frankfurt am Main DarmstadtDieburg Hochtaunuskreis Main-Taunus Wetteraukreis Esslingen

22.11.2004 22.11.2004 10.1.2005 12.11.2004

16:00–17:17 16:00–17:17 14:50–15:30 9:40–10:00

8212 8215 9162 9178 9184 9761 11000 14262 16053 16068

Karlsruhe Karlsruhe München Freising München Land Augsburg Berlin Dresden Jena Sömmerda

Frankfurt am Main Frankfurt am Main Friedberg City of Esslingen, Business Development Unit Karlsruhe Karlsruhe München München München Schwaben Berlin Dresden Gera; Jena Office Erfurt

20.1.2005 20.1.2005 8.12.2004 8.12.2004 8.12.2004 10.1.2005 5.1.2004 23.12.2004 28.1.2004 6.12.2004

10:00–11:00 10:00–11:00 14:30–15:15 14:30–15:15 14:30–15:15 13:50–14:25 11:20–11:40 11:30–11:45 9:30–10:00 15:15–15:45

5162 5313 5315 5370 5913 6412 6432

Note Please contact the author for more detailed information on the interviewees.

204

Appendices

Table A.12 Partners for expert interviews: case study Jena No

Position/Organization

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26

GetUP; University of Applied Sciences Jena DEWB – Opto electronics Chamber of Commerce and Industry East Thuringia Gera, Office Jena Institute for Molecular Biotechnology, Exist HighTepp und GetUP Fortbildungsakademie der Wirtschaft (FAW) TechnologieContor Bioinstrumentation Centre, Hans-Knöll-Institute of Natural Product Research Institute for Physical High Technology Fraunhofer-Institute for Optics and Precision Mechanics BioRegio e.V. Jena GetUP; Friedrich-Schiller-University Jena Ernst and Young, Erfurt DEWB – Biotechnology Hypovereinsbank Unique mode City of Jena, Business Development Unit Chamber of Crafts – East Thuringia Thuringian Innovation Fond Bundesverband Mittelständische Wirtschaft GetUP Thuringian Start-up Initiative (THEI) Optonet-Jena Technology and Innovation Park Jena Venture Capital Thuringia Technology transfer, University of Applied Sciences, Jena Lambda technologies

Note Please contact the author for more detailed information on the interviewees.

Appendix B Identification of maxima and minima in the time series

The first method takes one interval after the other and searches for (significant) differences between them. The identification of a significant difference is based on a missing overlap between two intervals of mi,r,t . Beginning with the first point in time, t = 1 as the reference point, it is analysed whether the interval of t + 1 overlaps with the interval of the reference point or not: (A) (1) If the two intervals do not overlap and the interval of t + 1 is higher than the interval of the reference point, a local minimum at the reference point and a temporary maximum at t + 1 are identified. (2) If the two intervals do not overlap and the interval of t + 1 is lower than the interval of t, a local maximum at the reference point and a temporary minimum at t + 1 are identified. In both cases the reference point against which other intervals are compared changes to t + 1. (B) If the two intervals overlap, the reference point stays the same. Subsequently, the next point in time is analysed. There are several possibilities: •

•

•

If a temporary maximum was identified before and the lower border of the current interval is higher than the one of this reference point, then the temporary maximum and the reference point change to the current point in time. If a temporary maximum was identified and the upper border of the current interval is significantly lower than the lower border of the temporary maximum, i.e. the reference point, then a new temporary minimum is identified at the current point in time. This temporary minimum becomes the new reference point. The temporary maximum that was previously identified now becomes a fixed local maximum. If a temporary minimum was identified the previous two processes can be applied accordingly. The temporary minimum and reference point can shift if the upper border of the current interval is lower than the one of the old reference point. Or a new development might be identified if the lower border of the current interval is significantly higher than the upper border of the temporary minimum, i.e. the reference point. In the latter case the temporary minimum now becomes a fixed local minimum and the temporary maximum becomes the new reference point.

206 •

Appendices If no local maximum or minimum was identified before, processes A and B described above are relevant.

The next point in time is then analysed and the decisions put forward above are repeated until the last point of the time series is reached. The last identified temporary maximum or minimum becomes a fixed local maximum or minimum respectively. The developments of the founding activities are defined as follows: a decrease runs from a local maximum to a local minimum and an increase from a local minimum to a local maximum. Applied to the example in Figure 3.10 the following processes results: time t = 1 (ts1) is the first reference point. It is a local maximum and the starting point of the first development (a decrease) because mi,r,t=2 is lower than mi,r,t=1. The first temporary minimum and the potential ending point of the decrease is time t = 2 (te1). At the same time, t = 2 is the potential starting point of a new development (ts2). But during the analysis the temporary minimum and, thus, the potential ending point of the first development and the potential starting point of a second development change to time t = 3 and t = 4 because mi,r,t=4 < mi,r,t=3 < mi,r,t=2 Time t = 7 is a temporary maximum because mi,r,t=7 is higher than mi,r,t=4. The last identified local minimum at time t = 4 becomes a fixed local minimum and the ending point of the first and the starting point of the second development (this time an increase) accordingly become fixed. Time t = 7 is the first potential ending point of the second development (te2) and the potential starting point of a third development (ts3). During the analysis the temporary maximum as well as the ending points of the second and the starting points of a potential third development change (because mi,r,t=10 > mi,r,t=9 > mi,r,t=8 > mi,r,t=7). The last temporary maximum identified is at time t = 10 and since no additional significant change occurs, this point in time is defined as a local maximum and, thus, is the ending point of the second development. In the end two developments (a decrease and an increase) are identified.

Appendix C Classification schema

Classification Change in one direction 1

Linear development (One-way development which lasts for a long period of time, e.g. based on catching-up processes)

2

(Immediate) Switch to a different level from t to t + 1 (Immediate change to a different level and persistence on the new level, e.g. based on the founding of a new university)

3

Slow change to a different level (Slow change to a different level and persistence on the new level, e.g. based on slow increase in market size)

4

Change to a new level and afterwards a continuous development to an even higher level (Change to a new level and a subsequent slow development, e.g. based on the settlement of a new firm and its growth)

Change in two or more directions 5

Change to a new level and afterwards a backslide to the old level (Relative position increases and decreases again, e.g. based on temporary policy support programmes)

6

Change to a different level and backslide to a level higher than the original level (Above-average improvement with levelling, e.g. based on too many market entries with a stabilization on a high-equilibrium level afterwards)

208

Appendices

7

Change to a different level and backslide to a level lower than the original level (Above-average improvement with strong backslide, e.g. based on too many market entries linked to a high resources wastage and a subsequent reluctance to provide new resources)

8

Change to a different level and short-term backslide to a level lower than the original one, afterwards stabilization on the original level (special case with two changes of direction) (Above-average improvement with strong backslide, e.g. based on too many market entries linked to a high resources wastage and a subsequent temporary reluctance to provide new resources)

Change in general 9

Short-term change of level with a backslide to a different/the old level (Maverick, statistical artefact, short-term change)

Classification procedure Basic material a

b

154 printouts of level development • X-axis = 12 years (from 1990 to 2001) • Y-axis = level of founding (between 1 and 5) • A change to a higher level (= higher number) indicates that the relative regional position worsened. Founding rates for 154 developments (see Hypothetical examples for the development of founding activities) • X-axis = 12 years (from 1990 to 2001) • Y-axis = Firm foundings per 10,000 inhabitants • Series 1 = Development in a region • Series 2 = Mean development over the 49 regions • An increasing slope of the curve indicates that the founding rate in the region improved/increased. • Titles: the first number represents the regional code and the second number the industrial code

Appendices 209 14 12 10 8

Series 1 Series 2

6 4 2 0

1

2

3

4

5

6

7

8

9

10 11 12

Hypothetical examples for the development of founding activities

c d e

Overview of the possible classes This classification procedure A Microsoft Excel table with the data for the developments of the regional founding activities

Procedure 1 2 3 4

Please read this classification procedure very carefully before you start with the classification of the developments. Classify the developments on your own. Do not ask the other classifiers and do not talk to them concerning the classification results. Classify the entire 154 developments one after the other. Mark the whole-numbered levels in the development (i.e. 1, 2, 3, 4 or 5). 3.5 3 2.5 2 1.5 1 0.5 0

1

2

3

4

5

6

7

8

9

10

11

12

210

Appendices

Special note (non-integer levels): The values between the whole-numbered observations shall not be dismissed totally because they might be important to evaluate the overall picture. Special note (figures of the founding intensities): In order to support your decision making process you can use the figures of the founding intensities. These figures can help you to evaluate to which whole-numbered level the non-integer values might be assigned with a higher likelihood. The important element here is how the founding intensity in the region develops in comparison to the aggregated founding activities of the 49 regions. 5 You can use connecting lines as a support which connect the identified whole-numbered levels. 3.5 3 2.5 2 1.5 1 0.5 0

1

2

3

4

5

6

7

8

9

10

11

12

Special note: Relevant for the assignment of a certain class is an evaluation of the overall picture of the development. 6 The first decision that has to be made is whether the change of levels only takes place in one direction. i.e. is there only an increase/a decrease or is there a change of direction (in the exemplary figure above a change of direction can be observed because the levels first decrease and then increase again). If the development only takes place in one direction, then continue with No. 7, otherwise continue with No. 17. Change in one direction 7 8

For the further classification procedure only the Classes 1, 2, 3, 4 and 9 are relevant. You have to decide whether an immediate change occurs, i.e. whether the change of levels takes place between t and t + 1 (then continue with No. 9) or whether the change takes more time (then continue with No. 13).

Appendices 211 Immediate change 9 For the further classification procedure only the Classes 2, 4 and 9 are relevant. 10 If the values before the change tend to belong to the first level and the values after the change tend to belong to the second level, then the development is classified as Class 2 (see Hypothetical example Class 2). The class has to be entered in Column N in the table. Continue with No. 23. 2.5

2

1.5

1

0.5

0

1

2

3

4

5

6

7

8

9

10

11

12

Hypothetical example Class 2

11 If the values before and after the change tend to belong to the first level as well as to the second level, then it is not clear whether a Class 2 or a Class 9 2.5

2

1.5

1

0.5

0

1

2

3

4

Hypothetical example Class 2/9

5

6

7

8

9

10

11

12

212

Appendices

is observed (see Hypothetical example Class 2/9). In that case in Column N in the table Class 2 and in Column O Class 9 has to be entered. Continue with No. 23. 12 If the values after the immediate change continue to increase and reach an even higher level, then the development is classified as Class 2 (see Hypothetical example Class 4). The class has to be entered in Column N in the table. Continue with No. 23. 3.5 3 2.5 2 1.5 1 0.5 0

1

2

3

4

5

6

7

8

9

10

11

12

Hypothetical example Class 4

Slow change 13 For the further classification procedure only the Classes 1, 3 and 9 are relevant. 14 If • more than two levels appear in the development or • there are two different levels and both levels appear at least twice during the development or • the levels before the level change tend to belong to one level and after the change belong to a different level,  then Class 9 drops out. 15 If the values have a tendency to develop for a longer period of time in a certain direction, then the development is classified as Class 1 (see Hypothetical example Class 1). The class has to be entered in Column N in the table. If Class 9 cannot be dropped (see No. 14), then Class 9 has to be entered in Column O. • Sub-case: If the development is determined by a linear change over several (at least 6) points in time, a Class 1 is observed. This is independent of whether a change of levels is identified (i.e.

Appendices 213 Class 1 is preferred to Class 3 because the slow and long-term development is seen as more important). Continue with No. 23. 3.5 3 2.5 2 1.5 1 0.5 0

1

2

3

4

5

6

7

8

9

10

11

12

Hypothetical example Class 1

16 If such a continuous change cannot be identified in the data, then the development is identified as Class 3. The class has to be entered in Column N in the table (see Hypothetical example Class 3). If Class 9 cannot be dropped (see No. 14), then Class 9 has to be entered in Column O. Continue with No. 23. 2.5

2

1.5

1

0.5

0

1

2

3

4

Hypothetical example Class 3

5

6

7

8

9

10

11

12

214

Appendices

Change in two or more directions 17 For the further classification procedure only the Classes 5 to 9 are relevant. 18 If the development changes from one level to another one and after that back to the old level, then a Class 5 or a Class 9 is identified (see Hypothetical example Class 5 and Hypothetical example Class 9). 2.5

2

1.5

1

0.5

0

1

2

3

4

5

6

7

8

9

10

11

12

Hypothetical example Class 5

19 If the development stays on the new level for at least two periods of time, then a Class 5 is identified (see Hypothetical example Class 5). The class has to be entered in Column N in the table. Continue with No. 23. 20 If the development stays on the new level for only one period of time, then a 2.5

2

1.5

1

0.5

0

1

2

3

4

Hypothetical example Class 9

5

6

7

8

9

10

11

12

Appendices 215 Class 9 is identified (see Hypothetical example Class 9). The class has to be entered in Column N in the table. Continue with No. 23. 21 If the development changes from Level 1 to another Level 3 and after that back to a Level 2 which lies between Level 1 and Level 3, then a Class 6 is identified (see Hypothetical example Class 6). The class has to be entered in Column N in the table. Continue with No. 23. 3.5 3 2.5 2 1.5 1 0.5 0

1

2

3

4

5

6

7

8

9

10

11

12

Hypothetical example Class 6

22 If the development changes from Level 2 to another Level 3 and after that back to a Level 1 which lies lower than Level 1, then a Class 7 is identified (see Hypothetical example Class 7). The class has to be entered in Column N in the table. Continue with No. 23. 3.5 3 2.5 2 1.5 1 0.5 0

1

2

3

4

Hypothetical example Class 7

5

6

7

8

9

10

11

12

216

Appendices

Points in time (Background: The starting, ending and turning points of the developments are relevant.) 23 In addition to the classification of the developments the points in time associated with the developments shall be identified. These points in time are entered in Columns P to U. In Column P the points in time (1 to 12) are entered at which the change of level appeared. (For illustration purposes the developments shown in the above figures are used.) •

•

•

•

•

•

•

•

For Class 1 the points in time are entered at which the linear development starts and ends, e.g. for the development shown in Hypothetical example Class 1 the 2nd and 11th point in time are entered (in Column P and Q). For Class 2 the point in time before the immediate change is entered (e.g. 6 for the Hypothetical example Class 2) (in Column P). For Class 3 the points in time are entered at which the change of level starts and ends, e.g. for the development shown in Hypothetical example Class 3 the 3rd and 10th point in time are entered (in Column P and Q). For Class 4 the points in time are entered at which the change of levels starts, when the development continues and, if possible, when the development stops, e.g. for the development shown in Hypothetical example Class 4 the 4th, 6th and 9th point in time are entered (in Columns P, Q and R). For Class 5 the points in time are entered at which the change from the old to the new level starts, the first point in time on the new level, the point in time when the backward development starts and at last when the old level is reached again, e.g. for the development shown in Hypothetical example Class 5 the 4th, 5th, 7th and 8th point in time are entered (in Columns P, Q, R and S). For Class 6 the starting and ending points of the development on the new Level 3 and the new Level 2 are entered, e.g. for the development shown in Hypothetical example Class 6 the 4th, 6th, 7th and 9th point in time are entered (in Columns P, Q, R and S). For Class 7 the starting and ending points of the development on the new Level 3 and the new Level 1 are entered, e.g. for the development shown in Hypothetical example Class 7 the 5th, 6th, 7th and 10th point in time are entered (in Columns P, Q, R and S). For Class 8 the starting and ending points of the development on the new Level 3, the new Level 1 and the old Level 2 are entered, e.g. for the development shown in Hypothetical example Class 8 the 4th, 5th, 7th, 8th, 10th and 11th point in time are entered (in Columns P, Q, R, S, T and U).

Appendices 217 •

For Class 9 the points in time are also entered in Columns P and V (and potentially W), e.g. for the development shown in Hypothetical example Class 9 the 6th point in time is entered (in Column P) and for the Hypothetical example Class 2/9 the 6th point in time (which would correspond to the change of Class 2) is entered in Column P and in Column V the 6th and in Column W the 7th point in time are entered (for the unclear development if Class 9 is observed).

Supplements 24 The observed development consists of several of the Classes 1 to 9 and can be separated in clear intercepts, e.g. first an increase-decrease can be observed and then several points in time later (the level must be constant for at least two or three periods) another short-term increase with an immediate fallback is observed (see Hypothetical example for combinations (5 and 9)). In this case Class 5 and Class 9 appear one after the other. If this is the case, the changes are classified according to the above schema. In Columns N and O the first class is entered in Columns X and Y the second Class and in Columns AH and AI the third class is entered. In the Columns P to U etc. the respective points in time for the developments are entered. 2.5

2

1.5

1

0.5

0

1

2

3

4

5

6

7

8

9

10

11

12

Hypothetical example for combinations (5 and 9)

25 In some cases the development might be even more complex than described above, e.g. the development increases, decreases and increases again (see Hypothetical example Class 8). Other such complex dynamics are not defined in this schema. If a development is observed, which is not determined by Classes 1 to 9, a new class shall be introduced and the according

218

Appendices 3.5 3 2.5 2 1.5 1 0.5 0

1

2

3

4

5

6

7

8

9

10

11

12

Hypothetical example Class 8

points in time have to be put into the table. The new definition shall be added at the end of the table. 26 In the schema above only the positive developments are presented. The negative developments are identical to the positive ones and only mirrored at the x-axis. A minus sign is put in front of the classes, e.g. a ‘-5’ is entered in Column N. 27 If it is not clear whether a development is positive or negative, than the minus sign is put in parentheses. 28 Other classes of development than the ones described above shall not be used in the analysis or a new class shall be introduced, i.e. e.g. Class X in Class X shall either be classified as one of the described classes or by a new class that is introduced. Loop 29 Continue with the next development (i.e. start with No. 4 again).

Appendix D Interview guide for interviews on reasons for changes in regional firm founding activities Date:........................................................................................................................ Time:....................................................................................................................... Name of the interviewee:........................................................................................ Which chamber of commerce and industry? .......................................................... Position: .................................................................................................................. Email address:......................................................................................................... Telephone number: ................................................................................................. District: ................................................................................................................... Industry:..................................................................................................................

Question 1: General Question 1.1: Which factors or changes in the environment have led to the discovered changes in the regional start-up activities? ................................................................................................................................ ................................................................................................................................ ................................................................................................................................ ................................................................................................................................ ................................................................................................................................ ................................................................................................................................ Question 1.2: Does this hold for all the observed changes? Is this applicable for all the industries and points in time/developments? ................................................................................................................................ ................................................................................................................................ ................................................................................................................................ ................................................................................................................................ ................................................................................................................................

220

Appendices

Question 2 Question 2.1: Which of the following events occurred and which impact did it have on the development of the start-up activities? Event:

Occurrence and direction of change: positive no negative When? Strongly positive

Positive

Impact: No impact

Policy: Educational infrastructure

O

O

O

O

O

O

O

O

Policy: Financial support

O

O

O

O

O

O

O

O

Policy: Demand

O

O

O

O

O

O

O

O

Policy: Change in administrative issues

O

O

O

O

O

O

O

O

Policy: Infrastructure

O

O

O

O

O

O

O

O

Policy: other support

O

O

O

O

O

O

O

O

Establishment of a large firm

O

O

O

O

O

O

O

O

Venture capital firm

O

O

O

O

O

O

O

O

Start-up of a small firm (role model)

O

O

O

O

O

O

O

O

Emergence of a local cluster

O

O

O

O

O

O

O

O

Change in external environment new technologies, new norms, etc.)

O

O

O

O

O

O

O

O

Catching up or falling behind

O

O

O

O

O

O

O

O

Additional:

O

O

O

O

O

O

O

O

Additional:

O

O

O

O

O

O

O

O

Positive change = factor improved or occurred for the first time. Negative change = factor deteriorated or disappeared.

negative Strongly negative

Appendices 221 Question 2.2: Does this impact hold for all industries and points in time? Are there differences concerning the impact of the events on start-up activities? ................................................................................................................................ ................................................................................................................................ ................................................................................................................................ ................................................................................................................................ ................................................................................................................................

Question 3 Question 3.1: Had the founding of a NEMAX/TecDAX firm an impact on the development? ................................................................................................................................ ................................................................................................................................ ................................................................................................................................ ................................................................................................................................ ................................................................................................................................ Question 3.2: Does this hold for all industries and points in time? ................................................................................................................................ ................................................................................................................................ ................................................................................................................................ ................................................................................................................................ ................................................................................................................................ Can we contact you again, if we have additional questions? Yes O No O

Appendix D D.2 Case study Jena: Interview guide for interviews with experts

(Italic = normally not mentioned; only the main questions are posed and only if there is a requirement are the other secondary points asked for)

Introduction • • • • • •

Introduce oneself Reason: dissertation Aim: Knowledge about individual reasons for start-up, local differences of start-up rates and conditions and local cooperation in the case of Jena Why precisely this region? Why precisely these companies? Secrecy of the results; we will be glad to send you the general results (Research report and presentation at the end) We send protocols for control

(Individual) Basic conditions and general development 1 2 3 4 5

Special field/sector for establishment in Jena; changes? Changes in the number of firm foundings in the last 10 years? Reasons for this? Where are the founders from? (place of birth) Which job-related and academic level of education do the founders have? (place of education?) Direct previous job activity? (place, field, kind of position)

General entrepreneur → potential entrepreneur/market test 6 7

Can you tell us at which point of a founder’s career the alternative to become self-employed emerges? What are the reasons to consider this alternative? • Context • (Local) role models (family, friends, and so on) • If yes: How did the founders get to know the role models? Why did they choose these role models?

Appendices 223 8

Founding without a concrete business idea or already very concrete plan?

Potential entrepreneur 9 Were there several business ideas over time? Which? 10 Materialization • Coincidence or search? Why/motivation? • Focus on specific fields/sectors? Why? • How did the founders find out about technological or market possibilities as the basis for business ideas or were they self-made? • When and in which context were the business ideas developed? • What do you think: why have some founders recognized these technological or market possibilities, but other companies or potential founder did not? • Selection process of the ideas (rejection vs. conversion)

Test of market and decision-making 11 Were or are there still competitors with similar products? Have the founders expected that other agents also could establish a firm in this field? If yes: Why do you think that these founders think that they are/could be superior? 12 Where are there alternatives to firm founding? Which? Why were they rejected? What motivation exists for starting a firm? 13 What is the length of time from the basic idea to the actual realization? 14 Which are the important steps from the basic idea to the actual establishment of the firm?

Actual process of establishment 15 Individual or team start-up? Where do the partners come from? Where did they know each other from? Why were these persons selected? 16 Support by other persons or organizations? Who? How? How important was this? 17 Are different locations where to start the firm evaluated? 18 Which criteria are relevant for the selection of the location? Where does the information about these criteria from? 19 Why do the founders select Jena? 20 Satisfied with the decision? Why? Why not? Decision confirmed or regretted? 21 Why have firms left Jena and moved to a different location? 22 How do you evaluate Jena as a location for start-ups? Pro? Contra? (In comparison to other towns)

224

Appendices

Post-establishment phase and prestige of founders 23 Number of employees at the time of establishment/now respectively after five/ten years after establishment 24 For which market do the firms produce (local, Germany, world)? 25 Reasons for not becoming successful: • Do you think that a start-up brings social prestige (in Jena, Germany and so on)? • Do you think that it always was like this in Jena? • (In comparison to other regions) 26 Are there problems founders often underestimated at the time of start-up? Which? (Risk, restrictions of your private life, and so on)

Cooperation 27 Importance of (local) cooperation and contacts for a company (partner (universities, suppliers, competitors, network), fields (R&D, production, marketing and qualification, general information))? 28 How do you think companies/founders in Jena would judge this? 29 Do the firms work together with other organizations or have they established contacts (partner (universities, supplier, competitors, network), fields (R&D, production, marketing and qualification), what (personnel, knowledge, products, information))? 30 In this regard how would you characterize and evaluate the climate of cooperation with respect to behaviour of persons and organizations in Jena? 31 Comparison with other regions 32 What do the firms expect from such cooperation/contacts? Do your expectations match the real results? 33 Are there any reasons against cooperation? 34 Do the firms actively search for potential cooperation and contacts (why?) or do they emerge by chance? 35 How do contacts develop? How do firms find out about the partners? (Search?) 36 On the basis of which criteria do firms decide if they should cooperate at all or with a specific partner (or not?)? 37 More formal or informal relationships? With which kind of organization (universities, supplier, competitors, network), in which fields (R&D, production, marketing and qualification), what exchange relations (personnel, knowledge, products, information))? 38 (Which formal or informal rules of behaviour exist in these relationships?) Why were they chosen? 39 Was the affinity to cooperation and the kind of ‘rules’ always like this or have they changed? Why? 40 What is the percentage of local cooperation (importance)? Why important? 41 How important is the localization of a partner for the cooperation and the kind of ‘rules’? Differences between contacts? Why local?

Appendices 225 42 Which experiences have the firms made with local/non-local cooperation respectively with the employed ‘rules’? 43 Which ways of communication are preferred? Why?

Relevant players and future developments 44 Please give us the names of some players or organizations (companies, other organizations or departments therein) which were important for the development of Jena. Why? 45 What should be done and changed in Jena to continue the positive development? 46 Do you think that the population in Jena expects a positive development in the next years?

Concluding questions 1 2

Any further comments concerning start-ups and cooperation in Jena? Do you know other experts which could be interesting contacts for us?

Appendix E Case study Jena: Interview guide for interviews with firms

(Italic = normally not mentioned; only the main questions are posed and only if there is a requirement are the other secondary points asked for)

Introduction • • • • •

Introduce oneself Aim: Knowledge about individual reasons for start-up, local differences of startup rates and conditions and local cooperation in the case of Jena/dissertation Why precisely this region? Why precisely these companies? Secrecy of the results; we will be glad to send you the general results (Research report and presentation at the end) We send protocols for control

(Individual) Basic conditions and general development 1

2 3 4

Special field/sector for establishment in Jena, changes? • What do you think: how has the start-up activity developed in Jena in the last years? (Maybe refer to different sectors) Were there ups and downs)? Reasons for these? Where are the founders from? (place of birth) Which job-related and academic level of education do you have? (Place of education?) Direct previous job activity? (place, field, kind of position)

General entrepreneur → potential entrepreneur/market test 5 6

Can you tell us at which point of your career the alternative to become selfemployed emerged? What was the reason to consider this alternative? • Context • (Local) role models (family, friends, and so on) • If yes: How have to got to know the role models? Why did you choose these role models?

Appendices 227 7

Have you already known which kind of firm/in which field you want to start when you decided to start the firm or have you founded without a concrete business idea?

Potential entrepreneur 8 9 • • • • • •

Were there several business ideas over time? Which? Materialization Coincidence or search? Why/motivation? Focus on specific fields/sectors? Why? How did you find out about technological or market possibilities as the basis for business ideas or were they self-made? When and in which context were the business ideas developed? What do you think: why did you recognize these technological or market possibilities, but other companies or potential founder did not? Selection process of the ideas (rejection vs. conversion)

Test of market and decision-making 10 Were or are there still competitors with similar products? Did you expect that other agents could also establish a firm in this field? If yes: Why do you think that your firm is/could be superior? 11 Where are there alternatives to firm founding? Which? Why did you reject them? Which motivation did you have for starting a firm? 12 Why did you choose this point in time for a start-up? 13 Did your willingness to start a firm change over time? Why? Would you have established a firm five years ago as well (with the same technological and market conditions)? Why? Why not? 14 What is the length of time from the basic idea to the actual realization? 15 Which were the important steps from the basic idea to the actual establishment of the firm?

Actual process of establishment 16 Individual or team start-up? Where do the partners come from? Where did you know each other from? Why did you choose these persons? 17 Support by other persons or organizations? Who? How? How important was this for you? 18 Did you encounter specific problems in the phase of establishment or in the first phase of development which you had to resolve? 19 Have you selected from different alternative locations? 20 Which criteria were relevant for the selection of your current location? Where did you get the information about these criteria from? 21 Why have you eventually selected Jena? 22 Satisfied with the decision? Why? Why not? Decision confirmed or regretted?

228

Appendices

23 How do you evaluate Jena as a location for start-ups? Pro? Contra? (In comparison to other towns)

Post-establishment phase and prestige of founders 24 25 26 27 28 29 30

31

Field/sector Year of start-up Spin-offs or original new founding? Number of employees at the time of establishment/now respectively after five/ten years after establishment First establishment or have you started other firms before? Field/sector? Where? For which market do you produce? (local, Germany, world) Planned extension of the market? If you become unsuccessful, what would you think is the primary problem? • Do you think that a start-up brings social prestige (in Jena, Germany and so on)? • Do you think that it always was like this in Jena? • (In comparison to other regions) Are there problems which you underestimated at the time of start-up which you recognize now? Which? (Risk, restrictions of your private life, and so on)

Cooperation 32 Importance of (local) cooperation and contacts for your company (partner (universities, suppliers, competitors, network), fields (R&D, production, marketing and qualification, general information))? 33 How do you think other companies/founders in Jena would judge this? 34 Do you work together with other organizations or have you established contacts respectively (partner (universities, supplier, competitors, network), fields (R&D, production, marketing and qualification), what (personnel, knowledge, products, information))? 35 How do you judge the situation concerning cooperation and contacts in Jena? 36 What would have to be done to improve the situation? 37 In this regard how would you characterize and evaluate the climate of cooperation with respect to the behaviour of persons and organizations in Jena? 38 Comparison with other regions 39 What leads you to this evaluation? 40 What do you expect from such cooperation/contacts? Do your expectations match the real results? 41 Are there any reasons against cooperation? 42 Do you actively search for potential cooperation and contacts (why?) or do they emerge by chance?

Appendices 229 43 How do contacts develop? How do you find out about the partners? (Search?) 44 On the basis of which criteria do you decide if you cooperate at all or with a specific partner (or not?)? 45 More formal or informal relationships? With which kind of organization (universities, supplier, competitors, network), in which fields (R&D, production, marketing and qualification), what exchange relations (personnel, knowledge, products, information))? 46 (Which formal or informal rules of behaviour exist in these relationships?) Why do you choose these? 47 Was the affinity to cooperation and the kind of ‘rules’ always like this or have they changed? Why? 48 What is the percentage of local cooperation (importance)? Why important? 49 How important is the localization of a partner for the cooperation and the kind of ‘rules’? Differences between contacts? Why local? 50 Which experiences did you make with local/non-local cooperation respectively with the employed ‘rules’? 51 Which experiences did other players make with local/non-local cooperation or with different rules respectively? How did you get these information? Is there gossip concerning such experiences? 52 Which ways of communication are preferred? Why?

Picture of network 53 Please give us some names of your most important (cooperation-) partners (inc. localization, kind of relationship, governance mechanism)

Relevant players and future developments 54 Please give us the names of some players or organizations (companies, other organizations or departments therein) which were important for the development of Jena. Why? 55 What should be done and changed in Jena to continue the positive development? 56 Do you think that the population in Jena expects a positive development in the next years?

Concluding questions 57 Any additional comments

Appendix F Interview guide for interviews on reasons for changes in regional firm founding activities

Appendices 231

232

Appendices

Appendices 233

234

Appendices

Appendices 235

236

Appendices

Appendices 237

238

Appendices

Appendices 239

240

Appendices

Appendices 241

242

Appendices

Notes

1 Introduction 1 Although there are some clues that a high number of start-ups decrease the rate of unemployment, the available results are still mixed (for example Audretsch and Thurik 2000). 2 Besides these positive effects of start-ups, it should be noted that negative secondorder effects might result from a strong increase in regional start-ups. These are, for example, rising rents, wages or social tensions, as well as strong turbulence in the labour market because the employees of closed-down firms have to find new jobs or new firms recruit employees from other firms (Steil 1999). 3 Note that this does not imply that there exist no studies on changes in entrepreneurial activities. See, for example, Gartner and Shane (1995) for a review of the methods applied in these studies. But these studies do not focus on the regional level and often do not analyse single industries but only aggregates. 4 As said above this is in most cases one specific year or the average of several years. 5 In comparison to other studies on regional firm founding activities the absolute regional level of these factors is only of importance in specific circumstances that are discussed in Chapter 2. 6 The data used were collected by the Centre for European Economic Research (ZEW). 2 Theoretical considerations and changes in regional firm funding activities There are several reasons why the educational system or firms settled in certain locations in the first place. For instance, the number of local inhabitants might have influenced the settlement. Together with the above argument this would lead to a self-sustaining or even self-augmenting process. But there are other reasons for such a settlement as well, e.g. natural resources or well developed infrastructure. 2 One economically important aspect is implicitly taken into consideration and this is the price of products or the revenue an agent can generate by starting a firm: the local market argument implicitly contains the price because a market is only interesting for the potential founders when it is possible to gain revenues by selling products and services in this market. 3 In the most extreme case the existence of a specific type of equipment can just be measured by a dummy variable. 4 Note that push-factors (as well as pull-factors that are discussed in the next section) in the pure sense are in most cases specific events that occur. For instance, although the impact of unemployment is analysed, the negative displacement effect is the event of becoming unemployed. Nevertheless, in many studies a push-factor is also considered as the state after this event. 1

244

Notes

5 Fairchild was a firm producing semiconductors in Silicon Valley. 6 But if the regional factor steadily declines, thus becoming a hindrance, the founding activity will react. 7 Although in most cases e1 is a discrete variable, we use a continuous representation of the development in the stylized figures. 8 Note that these critical values can differ for different variables represented by e1 and that they can change over time. 9 The linear increasing part of the relationship between e1 and e2 only serves illustration purposes, but the overall functional form can differ from the linear one. 10 Time lags in our examples are based on the size of e1. Thus, also e1 starts at a certain time t1, e2 only develops after e1 reaches a critical size, which, depending on the critical value, can be a certain time after t1. 11 This is a stylized curve with two data points for one point in time. That cannot be observed in real developments, it can only symbolize a sudden change of level from t to t+1. 12 In this case all the agents possessing this threshold found a firm and the market is saturated. Thus, in our example we assume a higher constant rate of firm foundings after e*. New firms replace existing ones. If the market further grows later on, the foundings would react as well. 13 The two distributions presented will be used in the following analysis, but in theory other characteristic distributions are possible as well, e.g. a stepwise increasing characteristic distribution with critical values would lead to a linear increase of element e2 after a critical value is reached. 14 The same holds if a change in some factors is announced and agents postpone their foundings until this event actually takes place. 15 Note that the curve in Figure 2.8 might follow different developments after t*: 1) it can sharply fall back at time t* + 1 if the pools empties at once, 2) it might slowly decrease or it might 3) fall back in t** as it is depicted in Figure 2.8. 16 In Figure 2.8 the suppressed-founding effect is presented, but since the stylized development is the same, the figure is also used to represent a ‘Bump with stabilizing on a high level’. 17 By such processes that increase and decrease founding activities business cycles in general and even regional founding cycles might be explained. 18 Nevertheless, decisions are made before the event takes place and implicitly these decisions play a role in some of the described dynamics. 19 It is nevertheless acknowledged that start-ups might be supported by multiple aid programmes over time, but in this case for each programme the factor changes in the form of a bump separately. In between the programmes, at least in theory, the factor always drops back again to a lower level. It is not the case that a new programme in the long run builds upon the level resulting from a former policy support because the effect of an ended programme diminishes over time. 20 The opposite situation in which public financial support for start-ups crowds out private investment is not very likely because in most cases the two forms of financing are complementary with, for example, public support for the initial phase of the startup and for a broad spectrum of firms and private support for the later phases and more selected firms that are assumed to be successful. 21 Changes in unemployment benefits are subsumed under changes in ‘External environment’ and not under ‘Policy Intervention’ because such a change is likely to affect all regions in a country; although it might affect them differently. 22 For some agents that are currently not unemployed, information diffusion might play a role in order to ex ante evaluate how much income they can receive by unemployment benefits. This very likely plays a less important role because even if agents do not know exactly what they might receive, the public information transmitted by, for example, the television, about reductions in unemployment benefits should already suffice to work as a push-factor.

Notes

245

23 If the unemployment benefits change again later, this must be seen as a new event because the direct link to the first change is not given. 24 Besides the amount of financial capital accumulated inside the venture capital firm, the availability of such capital for new start-ups must be kept in mind because the venture capitalist might have already invested its funds in existing firms and, thus, new firms have difficulties in obtaining financial capital. 25 In some cases it is also possible that lagging regions catch-up but it is more likely that the differences are relatively persistent (Cappelen et al. 1999). 26 This does not apply that all the agents have exactly the same evaluations of the market opportunities, but it shall indicate that the evaluation is closer to a one point distribution than to a normal one. 27 This is only an approximation, but it seems plausible that the slope of the reaction curve of the firm foundings is relatively steep and, thus, a stepwise increasing relationship is very likely. 28 This description assumes that the critical value is between the lowest and the highest value of the input curve, e.g. if the critical value is higher than the highest level of the development, the founding activities will not react. 3 Empirical evidence 1 Based on the database from the German Federal Employment Office a recent project built up a ‘Regional Founding Atlas’ (Fritsch and Niese 2000 or Anonymous 2003). 2 Since the ZEW based its calculation of the relative founding intensities for the years 2000 and 2001 on data on inhabitants from the year 1999, the relative founding intensities for these years were calculated by the author based on the absolute number of firm foundings provided by the ZEW and on the data of inhabitants taken from Statistik Regional (2003). The average difference of the data from the ZEW and from Statistik Regional was between 0.02 per cent for the years 1995 to 1999 and, thus, the change of the data source should not affect the results. Furthermore, the inhabitants of six municipalities that were incorporated in the city of Dresden in the year 1999 were taken out for the years 1999 to 2001 to provide the same base for the calculations. 3 For the procedure of the decentralized data collection, the sources used and possible biases, see ZEW 2003a, 2003b. 4 For all the regions that are discussed in the following the German terms are used and are written in italics. Thus, instead of Munich we use München, instead of Cologne Köln etc. 5 On the 1 July 2003 a list of firms was compiled which had been or were still listed in one of these indices. As a next step the regions in which these firms were founded were collected. 6 This impact of NEMAX or TecDax firms for the regional firm founding activities will be tested in a different study. 7 The WZ-93 classification is the official German classification of industries. 8 The manufacturing industries range from No. 15 to No. 37 (marked as ‘D’ in the official classification). 9 The two sub-industries ‘Asset management’ and ‘Management activities of holding companies’ are not included in industry No. 74. 10 Note that the analysis can be sensitive to changes in the number of inhabitants in a region or in the aggregated regions if the number of firm foundings does not change accordingly. Processes like emigration, immigration and the incorporation of surrounding local districts to one of the considered regions can change the likelihood of firm foundings, at least in the short-run. For the 49 regions under consideration and the time period between 1990 and 2001 this problem seems to be of minor importance because on average the population in over 67 per cent of the cases only changed

246

11

12 13 14 15

16 17 18 19 20 21

22 23 24

25

26

Notes

between -0.5 and 0.5 per cent from one year to the next and never exceeded minus 1.4 or plus 1.8 per cent. Thus, we indirectly follow the labour market approach (founding rates per inhabitants) for explaining firm founding activities following the argument by Garofoli (1994). In contrast to this stands the ecological approach in which the founding activities are related to the number of already existing firms following the idea that the more businesses there are in an area, the greater are the opportunities for starting a new one (Spilling 1996). The calculations and the data analysis were conducted by using Mathematica Version 6. The borders of the confidence intervals are calculated based on a 0.02-level of confidence and, thus, it results is a probability of 98 per cent that the theoretical founding likelihood lies in this two-sided confidence interval At least one interval in the time series deviates statistically significantly from the other intervals in this time series. Ceteris paribus, comparing two regions the variance rate is lower in the region with a higher number of inhabitants. Comparing two industries the variance rate is lower in the industry with the higher founding probability. This is because the numerator (standard deviation =  N ⋅ p ⋅ (1 – p) ) is relatively smaller than the denominator (mean = N ⋅ p) for higher values of N or P respectively. The reason why t = 12 is not a local minimum is that the corresponding interval overlaps with the interval of t = 10. See Appendix B.1 for a detailed explanation. As was noted above, this finding can be based on the lower number of firm foundings in these industries. In the most extreme case t2 equals t2. Thus, this point in time with the corresponding interval [mi,r,t2 = t2, mi,r,t2 =t2] becomes Level 2 (see Figure 3.11). As was noted above this finding can be based on the higher number of firm foundings in these industries. This linear development represents other forms of long-term continuous developments as well. Since the calculation was done by using 98% confidence intervals, there is a certain probability that the borders of the multiplier intervals for some points in time were not the correct ones. We might have identified some time series as significantly changing their relative position in the number of firm foundings, although they have not done so in reality. This class should account for such time series in which the change in the start-up activities has not lasted longer then one year and, thus, might be based on this statistical problem. The three coders are student assistants at the Max Planck Institute for Research into Economic Systems. This classification schema is developed to be able to provide the three coders with the same basis for their decisions but on the other hand it is also developed in order to be able to replicate the research later on. A computer program would have to consider at least 177,147 possible developments (based on three possible developments (change to a higher level, change to a lower level or staying on one level) and on 11 possible changes between one point in time and another). It would take a considerable amount of time to program and especially to group all these alternatives meaningfully would again cause problems later on. Especially since both coders have a Cohen’s Kappa of nearly 94 per cent with regard to the consistency of their identification of events. Additionally, Cohen’s Kappa does not seem to be very sensitive to changes in the number of cases in which an event was classified by a ‘0’. A doubling of the number of observations from 504 to 1,008 and a related increase by the factor 2.6 in the number of ‘0’ classifications, only leads to an increase in Cohen’s Kappa by 2 per cent. Again a class does not only need to be based on the pure classes from the classifica-

Notes

27 28 29 30

31 32 33 34 35 36 37 38 39 40

247

tion schema but can also consist of two different classes if the classification was not 100 per cent certain. The calculation of the chi-square measure and all the other statistical tests described in the following were done by using SPSS Version 11. In order to make it easier for the reader to link the number of the class to the development behind this class, in the following for the classes that occur most often a short description of the class is added. Again the rows and columns lack independence with a Pearson value of 0.00019. The critical value of 300,000 inhabitants was chosen because areas with more than 300,000 inhabitants belong to the regional type of ‘agglomeration areas’ and, thus, we can analyse whether these agglomerated areas differ from less populated ones (INKAR 2002). Other tests based on different regional classifications (see INKAR 2002 for potential other classifications) were also conducted but they showed no significant results. Two negative developments started in Berlin, Hochtaunuskreis, MarburgBiedenkopf-Kreis, Neuss, Wiesbaden and Main-Taunus-Kreis. As was already pointed out in note 6 of this chapter, the impact of this listing and delisting is used for a different follow-up study. The aggregate of all the 12 manufacturing industries developed positively from 1993 onwards and especially industry No. 30 (‘manufacture of office machinery and computers’) had a strong increase from 1999 to 2001. Dithmarschen is located in the southwest of Schleswig-Holstein. Berlin is a specific case because it consists of two parts: the former West and the former East German part. Thus, it is not a pure East German case, but since it showed a comparable development we included it here. This is the case because we compare regional developments relative to the developments of the average aggregated of all regions. Another case is that several events lead to a development in the same direction, but we are only able to identify one or a few of these events. This case is linked to No. 4 in our list where we discussed the simultaneous occurrence of events. A similar approach was used by Brenner to identify regions with localized industrial clusters (Brenner 2004). As a preparation for this approach, question No. 3 was included in the interviews. The general positive impact of role models was already mentioned by the interviewee for Mühlhem/Ruhr.

4 Positive examples and their impact on regional start-up activities 1 In the following, the term ‘role model’ implies two elements: first, it stands for behaviour shown by some agents that influences others. Second, it is applied to agents that are imitated by others. The term is used for both aspects because they are closely interlinked and hard to separate: if an agent is a model for another agent but would not show any behaviour (which seems implausible), the agent cannot be imitated. On the other hand, the behaviour of agents is not imitated if the agent showing it is not accepted as a positive example (see the discussion on the acceptance of role models below (Section 4.1.3). 2 Such a definition is also followed by Holland et al. (1986) who provide the background for some mental model theories. Although many other notions are used for the description of similar cognitive processes and there exists an on-going debate in psychology, ‘mental model’ is used here because it is applicable to the processes to be discussed and it is probably the most common notion for such mental representations in economics. 3 An explanation of the initial differences is not given here, although some clues were already presented in Chapter 2.

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4 Market test = test whether the idea is a marketable product or service; ability test = test whether the agent willing to found the firm has the necessary capabilities to do so. 5 The individual agent does not only possess one mental model but several ones related to specific situations. The processes described in the following can be applied to each individual sub-model as well as to the aggregate of these sub-models. Here, in most cases the general term ‘mental model’ is used for the sub-models and for the aggregate. 6 These processes mainly take place on the non-cognitive level (which is described in the following) but can also be conscious. 7 Here we abstract from the fact that an intention to do something does not necessarily match the real action. 8 Despite the previous discussion that information is filtered by various mechanisms we can assume that under specific circumstance (e.g. a high frequency of repetition of the information) sooner or later new information is processed leading to changes in the mental models. 9 There are different processes that usually disturb the direct link between the mental model and the actual behaviour, such as time lags between the learning and the actual acting or the anticipation of negative feedback if certain behaviour is shown. Thus, there also exists a relation between the mental model and the behaviour. However, some research has to be done to figure out the exact processes (Ajzen 1988). 10 Reward processes can operate, e.g. via positive reinforcers in which case certain behaviour is rewarded by positive consequences. 11 In these strategies agents do not strive for optimal decisions but for one whose outcome is ‘good enough’. 12 The theories presented do partly overlap and sometimes are based on the same psychological processes, but since they are used in different scientific domains, they are separated here. 13 It is difficult to discern exactly how large the sub-group is, since it depends on the structure, the kind of role model, the interaction of the agents and various other factors. 14 These two effects are strongly related to face-to-face communication. Although modern telecommunication technologies can facilitate communication, the effect of direct interaction is different and in many situations necessary. Nevertheless, new communication methods can help to maintain established social networks over longer distances. Direct interactions are nevertheless still helpful in maintaining network relations over extended time. 15 Social and cultural proximity both build on shared experiences, values, norms and so on (Nonaka and Takeuchi 1995). The main difference between them is that social proximity entails continuous contact and common experiences, e.g. between work colleagues or friends, whereas cultural proximity does not need previous direct relations between the involved agents (Menzel 2003). 16 When talking about mental models in the remainder of this study, we always relate to those mental model(s) linked to entrepreneurial activities. 17 This approach was already discussed in Chapter 1. 18 Reference group effects would lead to similar results: evaluation of the own situation (e.g. concerning income, liberties, self-esteem) is not measured in absolute terms but relative to the reference group. If others in this reference group start a firm or even the reference person (in the group) starts one, others will start as well because of the normative and comparative impact of reference groups (for the general effect see Hyman 1968). 19 In our approach the ‘market test’ takes place before the actual founding and, thus, before the product or service is really on the market. We focus here on a process that uses different mechanisms to figure out in advance whether a product or service can be sold on the market and whether a profit for the firm results, at least in the longer run.

Notes

249

20 An ‘economic agent’ is defined similar to the pool of potential entrepreneurs (see Section 2.1.1): in general all agents between 18 and 65 years of age are considered economic agents. This also resembles the approach used in empirical literature. 21 If an agent’s socialization and development previous to becoming an economic agent is region specific, this can already lead to differences between economic agents having an effect on the likelihood to start a firm. 22 It is likely that this planning and the according tests at least take place implicitly before a firm really starts. But also an explicit approach by the potential founders as well as by external organizations (e.g. banks or venture capital firms) is possible. 23 The question is whether an economic agent or a potential entrepreneur discovers a business opportunity. In the former case, opportunity comes first and in the latter, it is willingness. 24 Cantillon (1931) and Knight (1921) even define an entrepreneur as a speculator and an individual who bears to make decisions under conditions of uncertainty. 25 This transition could also be explained by the ‘push theory’ as well (Section 2.1.4). Agents in a critical situation, concerned about their current living conditions, could decide that to become an entrepreneur helps them. Here, the borderline between willingness and necessity to ‘found’ is rather blunt. Such a situation even influences the market and ability test and might lead to a biased evaluation of alternatives. 26 Market opportunities and resources have a role to play in this process as well, but we focus on one of the often neglected aspects. 27 The impact of public role models, e.g. provided by the media, has a smaller impact on agents due to the reasons discussed in Section 4.1 on the acceptance of agents as role models. 28 This can have a twofold effect: a general increase can positively influence the whole process discussed in Section 4.3.3. If specific firms are relevant for transitions A, B or C, an increase in the number of role models and entrepreneurial start-ups can increase the likelihood that single transitions are positively influenced and, thus, augment the regional start-up rate. 29 As pointed out in Section 2.2.2 such an over-shooting effect can also be based on herd behaviour information cascades and coordination problems among rivals (Geroski and Mazzucato 2001, Klepper and Miller 1995, Dixit and Shapiro 1986). 30 This description leaves aside the bias in opportunity perception and the detailed impact of role models on the different stages (e.g. on market and ability tests). All these aspects are aggregated and only considered by the transition from agents willing to found to agents doing so. But for the analysis of the impact of role models on regional entrepreneurial activities such a shortened version might be applicable here. 31 After reunification Carl Zeiss (East Germany) was split: the traditional core parts were bought by Carl Zeiss, Oberkochen (West Germany) and the rest was put together to form Jenoptik. 32 See Appendix D.2 for the questionnaire used for the experts and Appendix D.3 for the one used for firm founders. 33 The willingness to take part in our survey was high: we asked 25 experts to participate, and 24 did so; both invited firm founders participated. We would like to thank all interviewees for this willingness to participate and allowing us to use their answers for our analysis. 34 I would like to thank Silke Scheer, Veronika von Lintel, Thomas Brenner and Jens Krüger for helpful comments and discussions while developing the questionnaire. Furthermore, we thank the interviewers and the firm representatives. 35 The questionnaire is divided in many different areas to receive information on the whole regional innovation system. We only focus on a small selection of questions, which were designed for research on role models. These are mainly Questions 3 and 9. 36 A list of the chosen industries can be obtained from the author. For an overview of the general results see Cantner et al. (2003).

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37 Especially the BioRegio programme of the German Federal Ministry of Education and Research supported biotechnology firms in Jena. 38 These findings result from Question 1.6 in which the firm representatives were asked to describe the field their firms are active in. 39 We only included the answers of firm representatives who were part of the founding team and whose firm was originally founded in Jena. We used this sample also later on in order to be able to test the impact of different regional factors on the amount of founding decisions in a year. 40 The industries selected for the analysis in Section 3.2 resembles the ones which were prominent in this survey. 41 The data analysis was conducted using SPSS Version 11. 42 We only included the answers of those firm representatives that were themselves part of a founding team. 43 In Question 9.2 the interviewees could decide for each category separately whether this factor was relevant for their own founding decision. The variation in the total number of firm representatives answering a category result from the fact that a varying number of firm representatives have decided not to answer a specific category. 44 The total number of ‘good’, ‘average’ and ‘bad’ evaluations for a category and year serve as the denominator for the calculation of the relative shares. 45 A correlation analysis shows that all these six categories are correlated to each other. 46 The number of founding decisions is positively correlated to the evaluation of venture capital with a correlation coefficient of 0.69 (Spearman-rho, two-sided, 5%-level). The correlation between the number of firm foundings and the evaluation of venture capital is also significantly positive with a coefficient of 0.67 (Spearman-rho, two sided, 5%-level). 5 Conclusions 1 Sometimes the literature considers longer-term developments of founding activities (Reynolds 1992). But then the research is mostly focused on the national level trying to explain such changes by factors such as legislation, taxation or new technologies. 2 Considered here are a ‘slow level change’ and a ‘bump’ (Section 3.2.4). 3 Note that the aim of reaching many agents that start a firm later might not be the most efficient and promising way because nothing is said about the quality of these foundings. This aspect will be taken up again in the following. Thus, sometimes it must also be the policy aim to reach the right agents and not just all. 4 Policy makers have to carefully evaluate whether there is an economic reason leading to the lack of financial capital supply or whether it is based on subjective psychological factors. 5 This relates to Rosenberg’s (1969) suggestion of focusing devices that focus attention on technical problems and specific means of their solutions. Role models can serve as one of these focusing devices.

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Index

Aachen 99–103, 187, 192, 195, 199, 200, 203 ability test 145–6, 149–50 Acs, Z. 15 activity dynamics 27–33; delayed effects 27–8; information diffusion 30–1; selfaugmenting processes and over-shooting 31–3; suppressed-founding effects 28–30 Adams, J. 5, 143 Adler, P.S. 42 Ajzen, I. 248n9 Almus, M. 12, 19, 41, 42, 71, 72, 73 Anderson, J.R. 128 Andréosso-O’Callaghan, B. 15 Anselin, L. 15 applied method 82–4 Arcangeli, F. 14 Aschaffenburg 75, 99–103, 189, 193, 196, 199, 201 Athreye, S. 15 Audia, P.G. 138, 150, 170 Audretsch, D.B. 1, 2, 3, 9, 17, 23, 42, 243n1 Augsburg 99–103, 109, 110, 111, 114–15, 117, 118, 119, 120, 121, 189, 193, 196, 199, 201 Austrian Economics 141–2 Back, K.W. 139 Bade, F.J. 1, 13, 14 Bandura, A. 7, 127, 130–1, 132 Barnett, W.P. 153, 185 Bartholomew, K. 95 Bednarzik, R.W. 1 behaviour: and mental models 128–9; patterns of 127–34; see also imitative behaviour Belussi, F. 14 Berger, G. 1, 3, 13, 19 Berlin 75, 99–103, 105, 115–16, 189, 193, 197, 199, 201, 203

Bikhchandani, S. 31 Birley, S. 136, 138 Blake, R.R. 132 Blanchflower, D.G. 16 Blau, P.M. 138, 139 Boalt, G. 139 Bossard, J.S. 139 Braczyk, H.-J. 34 Breisgau-Hochschwarzwald 99–103, 105, 189, 193, 196, 199, 200 Brenner, T. 1, 34, 43, 44, 68, 170, 186, 247n38 Brixy, U. 70 Brockhaus, R.H. 17, 151 Brown, J. 1 Bull, I. 18 Bünstorf, G. 4 Burt, R.S. 136, 137 Busenitz, L.W. 142 Cable, D. 16, 54, 136 Camerer, C.F. 150 Campbell, W.K. 150 Cantillon, R. 249n24 Cantner, U. 249n36 Cappelin, A. 245n25 case study research, USA 143–4; see also Jena case study Castellan, N.J. 93 Castilla, E.H. 136 Centre for European Economic Research (ZEW) 70–3, 76–7, 78 Cham, Germany 75, 99–103 Clark, K.B. 42 classification procedure: categorical discrepancies/final classification 95–7; changes in 123–4; overview 92–3; reliability analysis 93–5 classification schema 207–18 classified classes of changes, analysis of:

Index

265

development over time 103–5; industries 97–9; regions 99–103 cognitive theories 142 Cohen 93, 94–5, 246n25 Coleman, J.S. 137 computer simulation model, development of 186 consistency theories 131–2 context: individual level 5; industrial level 4–5; overview 1–2; regional level 2–4 Cook, J.M. 138 Cooke, P. 1, 34 Cooper, A. 43, 57, 150 Cowan, R. 137, 186 Creditreform 70–3 cultural proximity 138

Dortmund 99–103, 113, 117, 118, 187, 192, 195, 199, 200, 203 Draheim, K.P. 153 Dresden 99–103, 115, 116, 120, 189, 193, 197, 199, 201, 203 Dunkelberg, W. 150 Düsseldorf 99–103, 187, 192, 195, 199, 200, 203 dynamics: dissemination of mental models 133; feedback loops 152–4; of founding 57–61 dynamics, transmission process: delayed effects 27–8; information diffusion 30–1; self-augmenting process/ over-shooting 31–3; summary 33, 174–6; suppressed-founding effect 28–30

Dalum, B. 35 Darmstadt-Dieberg 99–103, 188, 192, 195, 199, 200, 203 data 70–3; applications 124; collection of 73–4 Davidsson, P. 1, 14, 17, 19, 42 Davis, K. 19, 144 De Bernardy, M. 1–2 Defillippi, R. 1 delayed effects 27–8 demand, changes in 39–40, 46 Denzau, A.T. 126 development classes: Class –1 (‘linear’) 115–17; Class 1 (‘linear’) 105, 112; Class –2 (‘fast level change’) 111–12, 115–17; Class 2 (‘fast level change’) 105, 112–13; Class –3 (‘slow level change’) 110–11, 115–17; Class 3 (‘slow level change’) 105–6, 113; Class 4 (‘delay’) 106; Class –5 (‘bump’) 109–10; Class 5 (‘bump’) 106, 113–15; Class 6 (‘over–shooting’) 115; Class 7 106; Class 8 106; definition of 90–1 development over time, analysis of 103–5 developments: distribution of 202; identification of maxima/minima 86–8 Devenow, A. 31 discrepancies, classification procedure 95–7 distribution effects: heterogeneity with sshaped relationship 25–6; threshold with stepwise increasing relationship 26–7 Dithmarschen 99–103, 114, 118, 187, 192, 195, 199, 200, 203 Dixit, A. 32, 249n29 Djankov, S. 20 Dörner, D. 129, 150

economic agents 145–6 Edelmann, W. 130 educational (infra)structure 12–14; changes in 35–7 Egeln, J. 19, 41, 42 Eisenhardt, K.M. 154 Elliot, A.J. 150 Elpelt, B. 96 empirical analysis: analysis of classified classes of changes 97–105; characteristics of changes in start–up activities 86–90; classification procedure 92–7; definition of development classes 90–2; empirically observed and theoretically derived curves 105–7; identification of changes in founding activities 80–6 empirical results, summary/discussion 122–5, 176–7 empirical study: data 70–3; method 124–5; sample 73–80 Engel, D. 13, 15, 19, 41, 42, 71, 72, 73 entrepreneurs: impact of positive examples on development 148–52; pool of 12–14, 52–3, 182; stages in development process 145–8 entrepreneurship: literature 142–4; overview of theories 141–2 environmental context 162–6 equipment, access to 16 Esslingen 99–103, 104–5, 120–1, 189, 193, 196, 199, 200, 203 Evans, D. 12, 16, 17 events, impact on firm founding: development classes/interview results 109–17; discussion of interview findings 117–22; method/background 107–8; summary of empirical results 122–3

266

Index

events, impact on regional factors: changes in external environment 45–6; changes in venture capital 47; emergence of localized industrial clusters 43–5; miscellaneous events 47–8; overview 33–5; policy interventions 35–41; settlement of large firms 41–3; start-up of small firms 43; summary 48–9, 177–8 events, relationship with founding activities 61–8 EXIST 20 external environment, changes in 45–6 Fagerberg, J. 245n25 Faust, H. 88 Federal Employment Office, Germany 71 Federal Statistical Office, Germany 73 feedback loops 133, 152–4, 169–70 Felder, J. 9, 13, 16, 19, 45 Feldman, M.P. 42, 43, 68, 144 Festinger, L. 130, 132, 139 Fier, A. 9, 13, 16, 19, 45 financial capital 15–17, 37–9 findings, summary of 174–9 Fischer, J. 45 Fiske, S.T. 119 Fleming, L. 137, 138, 139 Folta, T. 43, 57 Fornahl, D. 1, 3, 13, 15, 16, 30, 34, 41, 43, 57, 182, 249n36 Fortis, M. 44 Foss, N.J. 14 founding activities, empirical analysis of changes: analysis of classified changes 97–105; characteristics of changes 86–90; classification procedure 92–7; definition of development classes 90–2; empirically observed and theoretically derived curves 105–7; identification of changes 80–6; intermediate summary/discussion 123–5, 176–7 founding activities, influence of changing factors: additional regional conditions 56–7; overview 49–52; pool of potential entrepreneurs 52–3; pull-factors 55–6; resources 54–5 founding activities, influence of imitative behaviour: feedback loops/founding dynamics 152–4; impact of positive examples 148–52; impact of role models 141–5; overview 140–1; stages in entrepreneur development process 145–8 founding activities, relationship with events 61–8

founding decisions: factors relevant for 159–60; impact of other regional founders 160–2 founding dynamics 57–61 founding effects 41–3 founding quality 183–4 foundings, impact of events: development classes/interview results 109–17; discussion of interview findings 117–22; method/background 107–8; summary of empirical results 122–3 foundings, regional factors: access to resources 15–17; local markets 14–15; miscellaneous conditions 19–20; pool of potential entrepreneurs 12–14; pullfactors 18–19; push-factors 17–18 Frankfurt am Main 75, 99–103, 113, 118, 120, 187, 192, 195, 199, 200, 203 Frankfurt Stock Exchange 74–5 Freising 99–103, 114, 121, 189, 193, 196, 199, 200 Friedkin, N.E. 136 Friedman, M. 134 Friedman, R. 134 Fritsch, M. 1, 2, 3, 9, 17, 23, 70, 71, 138, 245n1 Fujita, M. 53 Galais, N. 18 Gallegati, M. 20 Garofoli, G. 2, 3, 9, 13, 14, 17, 246n11 Gartner, W.B. 1, 124, 183, 243n2 genetic endowment 132 geographic proximity: impact of 135–6; impact on relational proximity 138–40 Georgellis, Y. 17 Geroski, P.A. 14, 30, 31, 249n29 Gillin, L.M. 13 Golob, E. 42 Göttingen 99–103, 187, 192, 195, 199, 200, 203 Grabher, G. 3, 138, 149 Graf, H. 3, 13, 15, 16, 57, 182, 249n36 Granovetter, E. 136 Granovetter, M. 132, 136, 137, 171 Gray, M. 42 Greene, D. 138 Greif, S. 72 Grotz, R. 70, 71 growth processes 48 Guesnier, B. 12, 14, 17 Hägerstrand, T. 137

Index Hamburg 75, 99–103, 187, 192, 195, 199, 200 Hansen, M.T. 137 Harhoff, D. 71 Hartung, J. 96 Hause, J.C. 14 Heidelberg 99–103, 189, 193, 196, 199, 200 Heidenreich, M. 34 Heider, F. 132 Heinsburg 99–103, 112, 118, 119, 187, 192, 195, 199, 200, 203 Henderson, A.J.Z. 95 Hendry, C. 1 Herkner, W. 150, 151, 154 Herrmann, C. 13 heterogeneity with s-shaped relationship 25–6 Hirschleifer, D. 31 Hochtaunuskreis 99–103, 188, 193, 195, 199, 200, 203 Hofmann, A. 15 Holland, J.H. 247n2 Holyoak, K.J. 247n2 House, R. 138 human capital 12–13, 15, 16–18 Hwang, H. 136 Hyman, H.H. 132, 248n18 imitative behaviour: feedback loops/founding dynamics 152–4; impact of positive examples 148–52; impact of role models 141–5; overview 140–1; stages in entrepreneur development process 145–8 individual level 5 industrial clusters, emergence of 43–5 industrial level 4–5 industries 77–80, 158–9; analysis of 97–9; distribution of highest levels 198, 200–1; identified changes 191; number/types of changes 194; selected characteristics 190; significant changes 192–3 information: diffusion of 30–1; retention of 128–9 infrastructure 19–20, 40–1; education/research 35–7 input curves 57–61 interaction: conclusions 185; frequency/intensity of 132, 135–8 Intershop 166–7 interviewees 203, 204 interviews: discussion of findings 117–22;

267

guides 219–42; method/background 107–8; results 109–17; results summary 122–3 Janson, C.G. 139 Jena case study: background of case studies/methods 154–6; characteristics 189, 193, 197, 199, 201; description of sample firms 156–9; factors relevant for founding decision 159–60; impact of regional founders on founding decision 160–2; interview guides 222–42; interviewees 203, 204; overview 99–103, 115, 116; positive role models 166–7; regional environmental context 162–6; summary/discussion 167–8, 179 Jenoptik 166 Jonard, N. 137, 186 Jones, G.B. 5, 130, 143 Kahneman, D. 18, 129 Karlsruhe 99–103, 109–10, 111, 112, 117, 118, 120, 121, 189, 193, 196, 199, 200, 203 Keeble, D. 1, 2, 9, 14, 15, 17 Keilbach, M. 1 Kenney, M. 15 Keune, E.J. 19, 143, 151, 170 King, M.M. 5, 143 Kirman, A.P. 20 Kirzner, I.M. 141 Klandt, H. 17, 18, 19, 143 Klepper, S. 2, 4, 13, 14, 32, 249n29 Klomp, L. 1 Klösener, K.-H. 96 Knight, F.H. 249n24 Kokaly, M.L. 5, 143 Kolb, S. 94 Köln 75, 99–103, 105, 112–13, 118, 120, 187, 192, 195, 199, 200, 203 Konings, J. 15 Koschatzky, K. 2, 20, 34, 184 Krafft, L. 4, 105 Kriegesmann, B. 5, 13, 16, 18, 19, 55, 127, 143, 150 Krippendorf, K. 93, 94 Krugman, P. 15, 44, 53 Krumboltz, J. 5, 130, 143 La Porta, R. 20 Lampe, D.R. 1 large firms, regional effects of 41–3 Lau, C.-M. 142 Lauf, E. 94

268

Index

Lawson, C. 1 Lawton Smith, H. 13 Lazarsfeld, P.F. 138 learning and changing mental models 129–31 Lee, S.M. 144 Leftowitz, M.M. 132 Leighton, L.S. 12, 16, 17 levels, founding activities 88–90 Licht, G. 71 limiting factor effect 23–5 Lindholm Dahlstrand, A. 13 Lindmark, L. 1, 14, 17, 19, 42 linear: relationships 23, transformations 10 Lissoni, F. 14, 42 literature, entrepreneurship and role models 142–4 local markets 14–15; and demand change 39–40 local maxima/minima, identification of 86–8 localized industrial clusters, emergence of 43–5 Longhi, C. 45, 68 Lopez-De-Silanes, F. 20 Lorenzen, M. 14 Lovallo, D. 150 Lundvall, B.A. 42 McMullan, W.E. 13 McPherson, M. 138 Maggioni, M.A. 44 Mainz 99–103, 189, 193, 196, 199, 200 Malmberg, A. 137 Marburg-Biedenkopf 99–103, 188, 192, 193, 196, 199 Marcia, J.A. 95 market test 145–7, 149–50 markets, local 14–15, 39–40 Markusen, A. 42, 43 Marsden, P.V. 136 Maskell, P. 137 Masson, P. 32 Mausner, B. 132 maxima, identification of 86–8 Mazzucato, M. 14, 31, 249n29 mental models and behavioural patterns: acceptance of role models/new mental models 131–2; and behaviour 128–9; characteristics of 171–2; developing similarities in 132–3; dynamics in dissemination of 133; intermediate summary 133–4; learning/changing mental models 129–31; overview 127

mental models, dissemination: impact of geographical proximity 135–6; impact of geographical proximity on relational proximity 138–40; overview 134–5; relational proximity 136–8 Menzel, M.-P. 1, 41, 43, 248n15 Merten, K. 93, 94 Merton, R.K. 138 Miller, J.H. 32, 249n29 minima, identification of 86–8 Mitchell, A. 5, 130, 143 Moore, B. 1 Moore, G. 19, 144 Mouton, J.S. 132 Mueller, S.L. 144 Muhlheim 99–103, 113–14, 117, 118, 120, 187, 192, 195, 199, 200, 203 Multon, K.D. 5, 143 München 75, 99–103, 105, 115, 118, 120, 189, 193, 196, 199, 201, 203 Mustart, P. 13 NACE (Statistical Classification of Economic Activities in the European Community) 77 Nathusius, K. 19, 143, 151, 170 natural resources 47–8 Nauta, M.M. 5, 143 negative examples 151, 152 NEMAX 74–5, 108, 158 neoclassical theories 141 Nerlinger, E.A. 1, 2, 3, 9, 12, 13, 14, 16, 19, 45 Neuendorf, K. 94 Neuss 99–103, 187, 192, 195, 199, 203 New Economic Geography model 43–4 Niese, M. 1, 70, 245n1 Niosi, J. 13 Nisbett, R.E. 247n2 non-limiting factor effect 24–5 non-linear transformations 11 Nonaka, I 248n15 North, D.C. 126 Olofsson, C. 1, 14, 17, 19, 42 opportunity discovery 145–6, 149 Oswald, A.J. 16 Otto, A. 70 over-shooting effect 31–3, 153 Pagani, M. 14, 42 Parsons, T. 132 patenting activities 185 Patton, D. 15

Index Peterson, S.J. 144 Pfeiffer, F. 12, 16, 17 planning process 147 Pleschak, F. 17 policy implications: focus on founding quality 183–4; impact of transmission processes 180–2; long-term orientation of policy measures 183; overview 179–80; policy interventions on transmission processes 182–3; support of entrepreneurial pool 182; support of impact of role models 184–5 policy interventions: changes in educational/research (infra)structure 35–7; demand change and local markets 39–40; financial support 37–9; miscellaneous infrastructure and support 40–1; overview 15 population changes 13–14, 15 positive examples, impact of: dissemination of mental models in regional context 134–40; imitative behaviour 140–54; Jena case study 154–68; mental models/behavioural patterns 127–34; overview 126–7; preliminary summary/discussion 168–72, 178–9 Prantl, S. 71, 72, 73 Preisendörfer, P. 14 Prevezer, M. 68 psychological analyses 142 pull-factors 18–19, 55–6 push-factors 17–18, 54–5 qualifications 12–14 rational agents 128 Reason, J. 129 Reeder, G.D. 150 regional conditions 19–20, 56–7 regional context, mental models in: impact of geographic proximity 135–6; impact of geographic proximity on relational proximity 138–40; impact of relational proximity 136–8; overview 134–5 regional environmental context 162–6 regional factors: access to resources 15–17; local markets 14–15; miscellaneous conditions 19–20; pool of potential entrepreneurs 12–14; pullfactors 18–19; push-factors 17–18 regional level 2–4 regions: analysis of 99–103; distribution of highest levels 199, 200–1; number/types

269

of changes 195–7; selected characteristics of 187–9; significant changes 192–3; summary 177–8 relational proximity: impact of 136–8; impact of geographic proximity 138–40 reliability analysis 93–5 research (infra)structure, changes in 35–7 resources, access to 15–17 Reynolds, P. 2, 3, 9, 13, 14, 17, 85, 105, 250n1 Rhein-Neckar-Kreis 99–103, 189, 193, 196, 199, 200 Rickne, A. 15 Rietz, G.D. 14 Rivkin, J. 137, 138, 139 Roberts, E.B. 5 Rogers, E.M. 20 role models: acceptance of 131–2; conclusions 186; and entrepreneurship in literature 142–4; summary 178–9; support of impact of 184–5; see also Jena case study Rosegrant, S. 1 Rosenberg, N. 250n5 Ross, D. 130 Ross, L. 138 Ross, S.A. 130 Rowe, D.C. 18 Salmen, T. 2, 15 sample: firms 156–9; industries 77–80, 190, 191, 192–3, 194, 198, 200–1; regions 74–7, 187–9, 192–3, 195–7, 199, 200–1; time 73–4 Saxenian, A.-L. 1, 13, 15, 19, 40, 46, 138, 144 Saxon, M.S. 20 Schacter, S. 139 Schambach, Stephan 166–7 Schätzl, L. 3 Scherer, R. 5, 143 Schlicht, E. 132 Schmiedl, D. 72 Schmude, J. 2 Schoenberger. E. 107 Schumpeter, J. 141 Sedikides, C. 150 self-augmenting processes 31–3 Senge, P.M. 131 Seri, P. 172 settlement/start-up effects 41–3; Jena sample 156–9 Shane, S.A. 1, 12, 16, 54, 124, 136, 141, 146, 183, 243n2

270

Index

Shapero, A. 142, 143, 150 Shapiro, C. 32, 249n29 shared mental models 127–34, 152–4 Shleifer, A. 20 Siegel S. 93 Silicon Valley 15, 40 Simon, H. 128, 131 Simonton, D.K. 5, 142, 143 Sleeper, S. 2, 4, 13 small firms, regional effects of 43 Smith, C.P. 93 Smith-Lovin, L. 138 social networks 136–40, 144–5, 171–2 social-cognitive learning 129–31 socio-psychological perspective 142 Sokol, L. 142 Sömmerda 99–103, 104–5, 115, 116–17, 189, 193, 197, 199, 201, 203 Sorensen, J.B. 170 Sorensen, O. 16, 137, 138, 139, 150, 153, 170, 185 sorting process 146–7 Späth, Dr Lothar 166–7 specialized suppliers 14 Spilling, O.R. 12, 13, 14, 246n11 spin-off process 13 Staber, U. 3 Stahl, S.R. 133 Steil, F. 2, 3, 9, 17, 19, 41, 71, 243n2 Sternberg, R. 1, 9, 13, 19, 144 Storey, D.J. 3, 9 Stouffer, S.A. 139 Strambach, S. 136, 137, 140 structure of study 6–8, 173–4 Stuart, T. 16, 170 Stuttgart 99–103, 189, 193, 196, 189, 193, 196, 199, 200 stylised curves 105–7 Sugden, R. 132, 138 suppressed-founding effect 28–30 Susbauer, J.C. 143 Swaminathan, A. 20 Swanson, A.-N. 153, 185 Takeuchi, H. 248n15 Tausch, A. 130 Tausch, R. 130 Taylor, S.E. 119 TecDax 74–5, 108 technological change 45–6 Thagard, P.R. 247n2 Thaler, R.H. 18 Thomas, A.S. 144 Thurik, R. 1, 243n1

time, analysis of development over 103–5 time series: discussion of 84–6; identification of maxima/minima 205–6 transformation process: distribution effects 25–7; limiting factor effects 23–5; linear relationship 23; overview 10–11; summary 33, 174–6 transmission mechanisms 57–61 transmission processes: conclusions 185; dynamics 27–33; impact of 180–2; overview 20–2; policy interventions on 182–3; summary of transformations and dynamics 33; transformations 22–7 Tübingen 99–103, 189, 193, 196, 199, 201 Tversky, A. 18, 129 unemployment 17–18; changes in benefits 46 USA, case studies 143–4 Utterback, J.M. 4 Valerius, G. 17 Van de Ven, A. 5, 127 van Praag, M. 5, 12, 16, 17, 19, 127, 141, 143, 146, 147 Varga, A. 15 Varian, H.R. 20 venture capital: access to 15–16; changes in 47 Verspagen, B. 245n25 Wade, J.B. 20 Wagner, J. 19, 144 Walker, S. 2, 9, 14, 17 Wall, J.H. 17 Wallmark, T.J. 13 Walsh, V. 13 Walters, R.H. 130 Watkins, D.S. 151 Weber, A. 2 Welch, I. 31 Werker, C. 186 Werner, H. 17 Westerwald-Kreis 99–103, 188, 192, 196, 199, 200 Westhead, P. 3, 9 Wetterau-Kreis 99–103, 188, 192, 195, 199, 200 Wiebe, F. 5, 143 Wiesbaden 99–103, 188, 192, 195, 199, 200 Will, T.A. 151

Index Willard, G.E. 18 willingness to found 150–1 Wilson, F. 1 Wiswede, G. 142, 151 Witt, U. 18, 20, 131, 132, 133, 146

Woo, C. 150 Yin, R.K. 154 Zipf, G.K. 139

271