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

We study employment and new hires among high-growth firms (HGFs) in the Swedish knowledge-intensive sectors 1999-2002. Using matched employer-employee data, we find that HGFs are more likely to employ young people, poorly educated workers, immigrants, and individuals who experienced longer unemployment periods. However, these patterns seem contingent on the stage of the firm’s evolution. HGFs that have already realized some rapid growth are more likely to hire individuals from other firms, even though immigrants are still overrepresented among new hires. In the case of both HGF employees and HGF new hires, employment opportunities in HGFs are provided by young and small firms.

Who Do High-Growth Firms Hire?

Recent empirical studies have investigated the territorial impact of Europe’s research policies, in particular the contribution of the European Framework Programmes to the integration of a European Research Area. This paper deepens the analysis on the integration and participation of peripheral regions, by focusing on the differences in intensity and determinants of inter-regional collaborations across three groups of collaborations. We consider collaborations among more developed regions, between more and less developed regions, and among less developed regions. Building on the recent spatial interaction literature, this paper investigates the effects of physical, institutional, social and technological proximity on the intensity of inter-regional research collaboration across heterogeneous European regions. We find that the impact of disparities in human capital and technological proximity on regional R&D cooperation is relevant and differs across subgroups of collaborations. Moreover, despite the efforts of integrating marginal actors, peripheral regions have lower rates of collaborations.

European R&D networks: A snapshot from the 7th EU Framework Programme

Growth Paths and Survival Chances

Although diversity between team members may bring benefits of new perspectives, nevertheless, what holds a team together is similarity. We theorise that diversity in one dimension is traded off against diversity in another. Our analysis of collaborative research teams that received FP7 funding presents robust results that indicators of diversity in several dimensions (diversity of organizational form (universities, firms, etc.), diversity in nationality, and inequality in project funding share) are negatively correlated with each other.

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Diversity in One Dimension Alongside Greater Similarity in Others: Evidence from FP7 Cooperative Research Teams

Since small businesses and enterprise were ‘discovered’ by public policy in the UK during the 1960s, commencing in earnest with the publication of the Bolton Committee (1971) report, there has been considerable official interest in this form of economic activity. The Conservative Government from 1979 to 1997 initially saw enterprise as a strategy for job creation but later switched to a focus on growth. The Labour Government after 1997 continued this ‘cross-party’ support, although it placed greater weight upon the potential role of enterprise to address wider issues of ‘social inclusion’, e.g. HM Treasury (1999), than its predecessor government. The election of a Conservative-led coalition in 2010, faced with a challenging macro-economic environment, meant that once again small businesses and enterprise creation were seen as playing a critical role in a UK economy seeking to emerge from recession (Greene, 2002; Greene et al., 2008).

Alex Coad

Papers

Who Do High-Growth Firms Hire?

European R&D networks: A snapshot from the 7th EU Framework Programme

Growth Paths and Survival Chances

Diversity in One Dimension Alongside Greater Similarity in Others: Evidence from FP7 Cooperative Research Teams

Measuring business activity in the UK