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.

/pdf/why-do-management-practices-differ-across-firms-and-58rczn83xl.pdf

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

This paper focuses on how consumer motivation can be tapped in order to encourage the adoption of cleaner technologies. Consumers are heterogeneous they may be guided by intrinsic motivation or extrinsic motivation. While information provision policies (such as the energy label for cars) may be effective in encouraging certain consumers to adopt green cars, financial incentive schemes (such as subsidies or fines) may be more persuasive for extrinsically-motivated consumers. We develop a dynamic theory of adoption of environmental innovations, in which information-provision policies are followed by financial incentives (first 'carrot', then 'stick' incentives). Analysis of a survey dataset of Swiss households observes considerable heterogeneity in terms of support of information- provision or financial incentive policies, in line with our conjectures. Our results will be of particular interest to policymakers interested in guiding consumers towards cleaner technologies.

Consumer support for environmental policies: An application to purchases of green cars

While several plots of the aggregate age distribution suggest that firm age is exponentially distributed, we find some departures from the exponential benchmark. At the lower tail, we find that very young establishments are more numerous than expected, but they face high exit hazards. At the upper tail, the oldest firms are older than the exponential would have predicted. Furthermore, the age distribution of disaggregated industries (such as the international airline industry) is less regular and can display multimodality. Although we focused on departures from the exponential, we found that the exponential was a useful reference point and endorse it as an appropriate benchmark for future work on industrial structure.

https://econstor.eu/bitstream/10419/30151/1/620462280.pdf

Investigating the Exponential Age Distribution of Firms

Most studies analysing the relationship between R&D and firm growth focus on total R&D investment. This paper aims to analyse separately each component of R&D investment (basic research, applied research and technological development) and evaluate how these types of R&D investment are related to firm growth. Using a sample of 3972 Spanish manufacturing firms during 2004–2015, our empirical results are the following. First, firms have heterogeneous R&D strategies. The common wisdom that young firms invest in basic research, while old firms invest in applied research, is not supported in our data. Second, we investigate the characteristics and dynamics of firms with different R&D strategies. We observe complementarities between applied research and technological development due to their positive associations with firm growth. Finally, our results show that there is a tendency for firms to transition from basic research to applied research.

A bit of basic, a bit of applied? R&D strategies and firm performance

We investigate the structure of demand by focusing on the distribution of prices within narrowly-defined classes of goods. We observe considerable heterogeneity—products that are functionally similar but presumably of different ‘quality’ may sell at very different prices. We analyze distribution of prices for bottles of wine, used cars, houses in London and week-long holidays in Majorca, and observe in each case that the the resulting distribution is more skewed than the lognormal but less skewed than a Pareto distribution. We then present a theoretical model whereby products can distinguish themselves along multiple hedonic dimensions of ‘performance’, with these product attributes being random variables subject to multiplicative interactions. Variations of this model can reproduce a lognormal price distribution and a Pareto distribution as lower and upper bound benchmarks (respectively).

/pdf/on-the-distribution-of-product-price-and-quality-4fnfacrm8d.pdf

On the distribution of product price and quality

This article theorizes how short-term revenue volatility affects new venture viability and how such volatility develops over time. Tracking the bank accounts of 6,578 new ventures over a 10-year pe...

/pdf/the-liability-of-volatility-and-how-it-changes-over-time-3m2w7bugje.pdf

Alex Coad

Papers

Consumer support for environmental policies: An application to purchases of green cars

Investigating the Exponential Age Distribution of Firms

A bit of basic, a bit of applied? R&D strategies and firm performance

On the distribution of product price and quality

The Liability of Volatility and How it Changes Over Time Among New Ventures