This handbook looks to provide academics and students with a comprehensive and holistic understanding of the phenomenon of innovation. Innovation spans a number of fields within the social sciences and humanities: Management, Economics, Geography, Sociology, Politics, Psychology, and History. Consequently, the rapidly increasing body of literature on innovation is characterized by a multitude of perspectives based on, or cutting across, existing disciplines and specializations. Scholars of innovation can come from such diverse starting points that much of this literature can be missed, and so constructive dialogues missed. The editors of The Oxford Handbook of Innovation have carefully selected and designed twenty-one contributions from leading academic experts within their particular field, each focusing on a specific aspect of innovation. These have been organized into four main sections, the first of which looks at the creation of innovations, with particular focus on firms and networks. Section Two provides an account of the wider systematic setting influencing innovation and the role of institutions and organizations in this context. Section Three explores some of the diversity in the working of innovation over time and across different sectors of the economy, and Section Four focuses on the consequences of innovation with respect to economic growth, international competitiveness, and employment. An introductory overview, concluding remarks, and guide to further reading for each chapter, make this handbook a key introduction and vital reference work for researchers, academics, and advanced students of innovation. Contributors to this volume - Jan Fagerberg, University of Oslo William Lazonick, INSEAD Walter W. Powell, Stanford University Keith Pavitt, SPRU Alice Lam, Brunel University Keith Smith, INTECH Charles Edquist, Linkoping David Mowery, University of California, Berkeley Mary O'Sullivan, INSEAD Ove Granstrand, Chalmers Bjorn Asheim, University of Lund Rajneesh Narula, Copenhagen Business School Antonello Zanfei, Urbino Kristine Bruland, University of Oslo Franco Malerba, University of Bocconi Nick Von Tunzelmann, SPRU Ian Miles, University of Manchester Bronwyn Hall, University of California, Berkeley Bart Verspagen , ECIS Francisco Louca, ISEG Manuel M. Godinho, ISEG Richard R. Nelson, Mario Pianta, Urbino Bengt-Ake Lundvall, Aalborg

The Oxford Handbook of Innovation

The Rise of the Creative Class

Economics Of Discrimination

Economists have long puzzled over the astounding differences in productivity between firms and countries. For example, looking at disaggregated data on U.S. manufacturing industries, Syverson (2004a) found that plants at the 90th percentile produced four times as much as the plant in the 10th percentile on a per-employee basis. Only half of this difference in labor productivity could be accounted for by differential inputs, such as capital intensity. Syverson looked at industries defined at the four-digit level in the Standard Industrial Classification (SIC) system (now the North American Industry Classification System or NAICS) like 'Bakeries and Tortilla Manufacturing' or 'Plastics Product Manufacturing.' Foster, Haltiwanger, and Syverson (2008) show large differences in total factor productivity even within very homogeneous goods industries such as boxes and block ice. Some of these productivity differences across firms and plants are temporary, but in large part they persist over time. At the country level, Hall and Jones (1999) and Jones and Romer (2009) show how the stark differences in productivity across countries account for a substantial fraction of the differences in average per capita income. Both at the plant level and at the national level, differences in productivity are typically calculated as a residual-that is, productivity is inferred as the gap between output and inputs that cannot be accounted for by conventionally measured inputs.

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Why Do Management Practices Differ across Firms and Countries

We propose an independence criterion based on the eigen-spectrum of covariance operators in reproducing kernel Hilbert spaces (RKHSs), consisting of an empirical estimate of the Hilbert-Schmidt norm of the cross-covariance operator (we term this a Hilbert-Schmidt Independence Criterion, or HSIC). This approach has several advantages, compared with previous kernel-based independence criteria. First, the empirical estimate is simpler than any other kernel dependence test, and requires no user-defined regularisation. Second, there is a clearly defined population quantity which the empirical estimate approaches in the large sample limit, with exponential convergence guaranteed between the two: this ensures that independence tests based on HSIC do not suffer from slow learning rates. Finally, we show in the context of independent component analysis (ICA) that the performance of HSIC is competitive with that of previously published kernel-based criteria, and of other recently published ICA methods.

Measuring statistical dependence with Hilbert-Schmidt norms

A robust feature of the corporate growth process is the exponential distribution of firm growth rates. This striking empirical regularity has been found to hold for a number of different datasets and at different levels of aggregation. In this paper, we propose a simple theoretical model capable of explaining this observed exponential distribution. We do not attempt to generalize on where growth opportunities come from, but rather we focus on how firms build upon growth opportunities. We borrow ideas from the self-organizing criticality literature to explain how the interdependent nature of discrete resources may lead to the triggering off a series of additions to a firm's resources. In a formal model we consider the case of employment growth in a hierarchy, and observe that growth rates follow an exponential distribution.

https://halshs.archives-ouvertes.fr/docs/00/11/33/46/PDF/R06025.pdf

Towards an Explanation of the Exponential Distribution of Firm Growth Rates

Despite the importance of high technology firms to the UK’s economy, relatively little is known about factors contributing to these firms’ long-run growth and survival We examine these factors using a unique longitudinal dataset combining two waves of detailed surveys of 345 UK high tech firms and performance and survival data We explore factors contributing to the performance and survival of these firms Our results show that long-term survival rates are near two thirds for the firms in our sample, and that performance within the sample is skewed by a limited number of high performing firms Overall we conclude that the early strategic decisions made by firms have long-run impacts on their survival and growth, and we suggest that policy measures targeted at shortfalls faced by these firms may have positive long-term consequences

https://papers.ssrn.com/sol3/Delivery.cfm?abstractid=2506267

Understanding Long-Run Growth and Survival of High-Tech Ventures

We respond to Derbyshire and Garnsey's article "Firm Growth and the illusion of randomness", adding theoretical and methological clarifications as well as some new empirical evidence.

Alex Coad

Papers

Towards an Explanation of the Exponential Distribution of Firm Growth Rates

Understanding Long-Run Growth and Survival of High-Tech Ventures

Are Firm Growth Paths Random? A Reply to 'Firm Growth and the Illusion of Randomness'