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

Does our ability to predict the performance of new ventures improve in the years after start-up? We investigate the growth and survival of 6247 new ventures that are tracked using the customer records at Barclays Bank. We put forward Gambler’s Ruin as a simple theory for understanding new venture growth and survival. Gambler’s Ruin predicts that the R2 remains low for growth rate regressions, but that the R2 increases in the years since start-up for survival regressions. The Nagelkerke R2 obtained from growth rate regressions decreases significantly in the years after start-up, which suggests that the fog gets thicker with respect to growth. When we focus only on firms surviving until the end of the period, however, there is no visible change in the R2 over time. In contrast, the Nagelkerke R2 of survival regressions increases in the years after start-up. Interestingly, a blip in year 5 suggests that macro-economic factors may have a strong effect on the amount of ‘fog’.

New Venture Survival and Growth: When does the fog lift?

The challenge for solo entrepreneurs to add their first employee is arguably the single biggest growth event facing any growing firm. To understand how this event affects performance, and the antecedents of hiring, we analyse Danish matched employer–employee data. Those who hire enjoy superior sales outcomes in subsequent years, while the dispersion in profits increases. Furthermore, those that hire enjoy faster sales growth in the previous year, suggesting that sales growth precedes the first hire. Finally, we show that founders with a stronger profile in terms of education and previous income are more likely to increase profits, while the characteristics of the employee are less important. The latter finding is important from a job creation perspective, in light of the suggested sorting of more marginalized employees into new and established firms.

My First Employee: An Empirical Investigation

Serial correlation in annual growth rates carries a lot of information on growth processes - it allows us to directly observe firm performance as well as to test hypotheses. Using a 7-year balanced panel of 10 000 French manufacturing firms, we observe that small firms typically are subject to negative correlation of growth rates, whereas larger firms display positive correlation. Furthermore, we find that those small firms that experience extreme positive or negative growth in any one year are unlikely to repeat this performance in the following year.

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Understanding the processes of rm growth - a closer look at serial growth rate correlation

We investigate the structure of demand by focusing on the distribution of prices within well-defined classes of goods. We observe considerable heterogeneity - products that are functionally similar but presumably of dierent ‘quality’ may sell at very dierent prices. We analyze distribution of prices for bottles of wine, used cars, and week-long holidays, and observe that the the resulting distribution is more skewed than the lognormal but less skewed than a power law. We then present a theoretical model whereby products can distinguish themselves along multiple hedonic dimensions of ‘performance’, and where these dimensions interact multiplicatively. We can reproduce a distribution of product quality which is in between the cases of the lognormal and power law specifications, depending upon the degree with which the dierent dimensions are positively correlated amongst themselves.

Exploring the Distribution of Product Quality

This paper investigates whether entrepreneurship constitutes a route out of deprivation for those living in deprived areas. Our measure of income/wealth is based on analysis of improvements in an individual’s residential address. Our data consist of information on over 800,000 individuals, and come from the customer records of a major UK bank. Comparing business owners to non-owners, our results suggest that the benefits of business ownership are found across the wealth distribution. Hence entrepreneurship can be a route out of deprivation.

Alex Coad

Papers

New Venture Survival and Growth: When does the fog lift?

My First Employee: An Empirical Investigation

Understanding the processes of rm growth - a closer look at serial growth rate correlation

Exploring the Distribution of Product Quality

Is Entrepreneurship a Route Out of Deprivation