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

Investment in R&D is positively associated with the variance of sales growth and, to a lesser extent, employment 
growth. The magnitude of this effect has not increased in recent decades, however.

/pdf/r-d-and-firm-growth-rate-variance-3tsjtjej18.pdf

R & D and firm growth rate variance

/pdf/tools-for-causal-inference-from-cross-sectional-innovation-36nkd96hg6.pdf

Tools for causal inference from cross-sectional innovation surveys with continuous or discrete variables: Theory and applications

We use a panel vector autoregressions model to examine the coevolution of changes in happiness and changes in income, health, marital status as well as employment status for the British Household Panel Survey (BHPS) data set. This technique allows us to simultaneously analyze the impact of the aforementioned factors on each other. We find that increases in happiness are associated with subsequent increases in income, marriage, employment, and health variables, while increases in the these life-domain variables (except health) tend to be followed by decreases in happiness in subsequent periods, suggesting adaptation dynamics in all domains. These findings are quite robust to different model specifications. -- Happiness dynamics ; vector autoregressions ; subjective well-being ; BHPS

https://www.econstor.eu/bitstream/10419/32645/1/608741612.pdf

2009,04: An examination of the dynamics of happiness using vector autoregressions

Industrial innovation churns out increasingly unnatural products and technologies amid scientific uncertainty about their harmful effects. We argue that a quick regulatory response to the discovery...

https://www.econstor.eu/bitstream/10419/244284/1/1687877211.pdf

Asbestos, leaded petrol, and other aberrations: Comparing countries' regulatory responses to disapproved products and technologies

High-growth enterprises (HGEs) have a large economic impact but are notoriously hard to predict. Previous research has linked high-growth episodes to the configuration of lumpy indivisible resources inside firms, such that high capacity utilisation levels might stimulate future growth. We theorize that firms reaching critically high capacity utilisation levels reach a “trigger point” involving either broad-based investment in further growth or shrinking back to previous levels. We analyze EIBIS survey data (matched to ORBIS) which features a question on time-varying capacity utilisation. Overcapacity is a transitory state. Firms enter into overcapacity after a period of the rapid growth of sales and profits, and the years surrounding overcapacity have higher employment growth rates. Firms operating at overcapacity make incremental investments (e.g. capacity expansion, process improvements and modern machinery) rather than investing in R&D and new product development. We find support for the “fork in the road” hypothesis: for some firms, overcapacity is associated with launching into massive investments and subsequent sales growth, while for other firms, overcapacity is negatively related to both investments and sales growth. Operating above maximum capacity is like a “fork in the road”: while some firms shrink back to stay within existing capacity constraints, others respond by launching into broad-based growth. We develop a theory of firm growth, according to which some firms are better positioned for subsequent growth, depending upon their state of capacity utilisation. Firms with plenty of slack capacity can easily grow within the bounds of existing capacity constraints. Firms that are already operating above maximum capacity cannot grow by drawing on slack resources, but instead, their growth requires a broad-based investment in many interconnected areas. Our statistical analysis shows that operating above maximum capacity is relatively rare and unlikely to persist and seems due to rising demand. Some firms respond to being above maximum capacity by slowing down and adapting to existing capacity constraints, while others treat overcapacity as a “trigger point” that launches them into subsequent growth. Our results are of interest to those seeking to understand and predict firm growth: investors, entrepreneurs, academics and policymakers.

/pdf/capacity-constraints-as-a-trigger-for-high-growth-vujsunxzoy.pdf

Alex Coad

Papers

R & D and firm growth rate variance

Tools for causal inference from cross-sectional innovation surveys with continuous or discrete variables: Theory and applications

2009,04: An examination of the dynamics of happiness using vector autoregressions

Asbestos, leaded petrol, and other aberrations: Comparing countries' regulatory responses to disapproved products and technologies

Capacity constraints as a trigger for high growth