The Economics And Econometrics Of Innovation

During the past few decades, the interest of economists in the sources of long-term economic growth has led an increasing number of them to focus on the role of innovation in creating that growth. Although some researchers have always been interested in this topic, the groundbreaking work of Solow (1957), Nelson (1959) and Arrow (1962) made many other economists recognize the central role played by innovation in almost all spheres of economic activity. The Economics and Econometrics of Innovation presents a valuable overview of the work of the world's most renowned experts in the field of innovation and technical change. It collects 22 outstanding contributions that reflect the results of the vast, worldwide research efforts and remind us of the importance of economic incentives in shaping and directing innovative activities. The volume presents an edited selection of papers that were first presented at the 10th International ADRES conference. One particular goal of this book is to bring out the complementary nature of the various approaches to innovation, and to facilitate in-depth dialogues both between microeconomists and macroeconomists, and between theoreticians and econometricians. General topics that are considered range from the economy-wide effects of innovation on growth and employment to the variation of individual firm innovative performance; from the analysis of networks and standardization to the role of intellectual property rights and the assessment of knowledge spillovers. Besides the wealth of information presented in the chapters, readers of this volume will also appreciate the value of examining a single question from different angles and by using different methods.

The current economic theory of innovation mainly analyses the technology factor and its impact on economic growth. In today's world, growth in information technology and knowledge of new ideas has altered the business paradigm dramatically. Modern economies have undergone a dynamic shift from material manufacturing to a new information technology model with research and development (R&D) and human capital. Through information and communications technology efficient information usage has achieved substantial productivity gains through learning by doing and incremental innovations. The present volume discusses this new paradigm in terms of both theory and industry applications, including Schumpeter in his innovation model and the emphasis on new innovations replacing the old. Growth of business networking and R&D consortium have dramatically helped the modern business to reduce their unit costs and improve efficiency. This volume presents some new models emphasizing knowledge sharing and R&D cooperation. Rapid growth in recent times in some south Asian countries have been cited as growth miracles are largely caused by knowledge spillover and learning by doing, and this volume also investigates the role of incremental innovations. With a strong focus and extension of the current theory of innovation and industry growth experiences of both the US and Asian countries, this book will be of interest to MBA and graduate students in economics, innovation management, and applied industrial economics.

For most of his career, Peter Swann’s main research interest has been the economics of innovation. But he has also been preoccupied with a second question: what is the best way to study empirical economics? In this book, he uses his knowledge of the first question to answer the second. There are two fundamentally different approaches to innovation: incremental innovation and radical innovation – ‘radical’ in the sense that we go back to the ‘roots’ of empirical economics and take a different tack. An essential lesson from the economics of innovation is that we need both incremental and radical innovation for the maximum beneficial effect on the economy. Swann argues that the same is true for economics as a discipline. This book is a much-awaited sequel to Putting Econometrics in its Place which explored what other methods should be used, and why. This book is about the best way of organising the economics discipline, to ensure that it pursues this wide variety of methods to maximum effect.

The emergence of new firm-level data, including the European Community Innovation Survey (CIS), has led to a surge of studies on innovation and firm behaviour. This book documents progress in four interrelated fields: · investigation of the use of new indicators of innovation output · investigation of determinants of innovative behaviour · the role of spillovers, the public knowledge infrastructure and research and development collaboration · The impact of innovation on firm performance Written by an international group of contributors, the studies are based on agriculture and the manufacturing and service industries in Europe and Canada and provide new insights into the driving forces behind innovation.

Provides an overview and assessment of established research on firms' strategic choices of R&D efforts and their firm-level returns, and explains the consequences for economy-wide technological change and growth.

There are two fundamentally different approaches to innovation: incremental and radical. In Economics as Anatomy, G.M. Peter Swann argues that economics as a discipline needs both perspectives in order to create the maximum beneficial effect for the economy. Chapters explore how and why mainstream economics is very good at incremental innovation but seems uncomfortable with radical innovation. Swann argues that economics should follow the example of many other disciplines, transitioning from one field to a range of semi-autonomous sub-disciplines. In this book, he compares the missing link in empirical economics to being the economic equivalent of anatomy, the basis of medical discourse. Working as a sequel to Swann's Putting Econometrics in its Place, this book will be a vital resource to those who are discontent with the state of mainstream economics, especially those actively seeking to promote change in the discipline. Students wishing to see progress in the teaching of economics will also benefit from this timely book.

4 UNIVERSITY RESEARCH AND THE SPATIAL DISTRIBUTION OF HIGH TECHNOLOGY INNOVATIONS AND PRIVATE RESEARCH . . . . . . . . 45 4. 1. INTRODUCTION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45 4. 2. THE INNOVATION, PRIVATE RESEARCH AND UNIVERSITY RESEARCH DATA . . . . 46 4. 3. THE SPATIAL DISTRIBUTION OF HIGH TECHNOLOGY INNOVATIONS . . . . . . . . . . . . . . . . 50 4. 4. THE SPATIAL DISTRIBUTION OF HIGH TECHNOLOGY R&D ACTIVITIES . . . . . . . . . . . 55 4. 5. THE SPATIAL DISTRIBUTION OF HIGH TECHNOLOGY UNIVERSITY RESEARCH AND ITS RELATION TO INNOVATIONS AND INDUSTRIAL RESEARCH . . . . . . . . . . . . . . . . . . . . . . . . 58 4. 6. SUMMARy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63 5 LOCAL KNOWLEDGE TRANSFERS: STATE LEVEL ANALYSIS . . . . . . . . . 67 5. 1. INTRODUCTION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67 5. 2. STATE ANALYSIS AND LOCAL KNOWLEDGE TRANSFERS: SUMMARY OF EARLIER FINDINGS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68 5. 3. ALTERNATIVE INDICATORS OF LOCAL UNIVERSITY KNOWLEDGE TRANSFERS 72 EMPIRICAL RESULT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74 5. 4. 5. 5. SUMMARy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 78 6 THE SPATIAL EXTENT OF UNIVERSITY EFFECTS: MSA LEVEL ANALySIS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81 6. 1. INTRODUCTION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81 6. 2. THE MODEL . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82 6. 2. ESTIMATION ISSUES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89 6. 3. EMPIRICAL RESULTS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90 6. 4. SUMMARy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97 7 FACTORS GOVERNING UNIVERSITY EFFECTS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101 7. 1. INTRODUCTION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101 7. 2. THE EMPIRICAL MODEL . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102 7. 3. REGRESSION RESULTS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105 7. 4. SPATIAL VARIATION IN THE INTENSITY OF UNIVERSITY KNOWLEDGE TRANSFERS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 107 7. 5. THE "CRITICAL MASS" OF AGGLOMERATION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 112 7. 6. SUMMARy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 119 8 SUMMARY AND CONCLUSIONS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 121 APPENDIX A: DEFINING HIGH TECHNOLOGY FOR THE EMPIRICAL STUDY . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 127 APPENDIX B: VARIABLE DEFINITIONS AND SOURCES . . . . . . . . . . . . . . . . . . . . . . . 131 REFERENCES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 135 INDEX . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 149 List of Tables Table 2. 1.