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 presents a multidimensional empirical analysis of firm growth. Exploiting census data on Italian manufacturing firms, 1989-1997, we estimate a reduced-form VAR to analyze the co-evolution of employment growth, sales growth, growth of profits and labour productivity growth. Our main findings suggest that (i) employment growth precedes sales growth; (ii) productivity growth lacks any strong association with subsequent growth of the other indicators; (iii) profits growth represents the `absorbing dimension' of the growth processes. This picture contrasts with 'accelerator models', predicting sales are the driver of the growth process, and is also at odds with theories of firm-industry evolution assuming productivity or profits advantages to be the driver of strong market selection/reallocation mechanisms. Instead, the findings reveal the existence of (weak) Penrose and (strong) Kaldor-Verdoorn effects, and more generally convey the view that employment growth is the key driver of firm expansion, while profits, once made, are not reinvested.

Growth Processes of Italian Manufacturing Firms

It is well-known in the literature that self-employment positively influences job satisfaction, but the effects on other life domains and overall life satisfaction are much less clear. Our study analyzes the welfare effects of self-employment apart from its monetary aspects, and focuses on the overall life satisfaction as well as different domain satisfactions of self-employed individuals in our German sample from 1997 to 2010. Using matching estimators to create an appropriate control group and differentiating between different types of self-employment, we find that voluntary self-employment brings with it positive benefits apart from work satisfaction, and leads to higher overall life satisfaction as well as increased health satisfaction, all of which increase in the first three years of self-employment. Being forced into self-employment to escape unemployment, however, confers no such benefits. Additionally, both types of self-employment lead to increasing dissatisfaction with one’s leisure time.

/pdf/how-satisfied-are-the-self-employed-a-life-domain-view-1hmzyxr0p8.pdf

How Satisfied are the Self-Employed? A Life Domain View.

We use a panel vector autoregressions model to examine the coevolution of changes in mental well-being and changes in income, health, marital status and 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 well-being 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 well-being in subsequent periods, suggesting adaptation dynamics in all domains. In addition to the robust observation that well-being is associated with subsequent longer-term improvements in health status, we observe that health improvements themselves have a long-lasting positive contribution to employment status. Employment status, in turn, has lasting positive effects on income growth. Although well-being per se may be a short-lived phenomenon, our results suggest that it may have longer-term effects through these particular channels. These findings are quite robust to different model specifications.

An Examination of the Dynamics of Well-Being and Life Events using Vector Autoregressions

A variety of quantitative techniques have been used in the past in Future-Oriented Technology Analysis (FTA). In recent years, increased computational power and algorithms, web-based searching, and data availability have led to the emergence of new techniques that are potentially useful for foresight and forecasting. As a result, there is now a wide palette of techniques that might be used in FTA exercises. However, it is often unclear how they differ, when the use of a techniques is appropriate, what type of insights it may yield, and how they can be combined. This article reviews and qualifies quantitative methods for FTA in order to help users to make choices among alternative techniques, including new techniques that have not been integrated yet in the FTA literature and practice. We first provide a working definition of Future-Oriented Technology Analysis (FTA) and discuss its role, uses, and popularity over recent decades. Second, we select 22 FTA techniques identified as the most important quantitative FTA techniques and then we review these techniques, discuss their main contexts and uses, and classify them into groups with common characteristics, positioning them along four key dimensions: descriptive/prescriptive; extrapolative/normative; data gathering/inference; and forecasting/foresight.

/pdf/quantitative-analysis-of-technology-futures-a-review-of-418q7lsp83.pdf

Alex Coad

Papers

Growth Processes of Italian Manufacturing Firms

How Satisfied are the Self-Employed? A Life Domain View.

An Examination of the Dynamics of Well-Being and Life Events using Vector Autoregressions

Quantitative Analysis of Technology Futures: A Review of Techniques, Uses and Characteristics.

Firm age and growth persistence (interactive paper)