Alex Coad

Bio: Alex Coad is an academic researcher from Waseda University. The author has contributed to research in topics: Entrepreneurship & Productivity. The author has an hindex of 42, co-authored 213 publications receiving 7643 citations. Previous affiliations of Alex Coad include Pontifical Catholic University of Peru & Los Alamos National Laboratory.

The Growth of Firms: A Survey of Theories and Empirical Evidence

Abstract: Research into firm growth has been accumulating at a terrific pace, and Alex Coad's survey of this multifaceted field provides a detailed, comprehensive overview of the latest developments. Much progress has been made in empirical research into firm growth in recent decades due to factors such as the availability of detailed longitudinal datasets, more powerful computers and new econometric techniques. This book provides an up-to-date catalog of empirical work, as well as a coherent theoretical structure within which these new results can be interpreted and understood. It brings together a large body of recent research on firm growth from a multidisciplinary perspective, providing an up-to-date synthesis of stylized facts and empirical regularities. Numerous empirical findings and theories of firm growth are also surveyed and compared in order to evaluate their validity. Drawing on a vast and diverse body of research, this book will prove invaluable to students, academics, policy makers and practitioners with a need to keep abreast of studies in industrial organization, firm growth and management.

High-growth firms: Introduction to the special section

Abstract: High-growth firms (HGFs) have attracted considerable attention recently, as academics and policymakers have increasingly recognized the highly skewed nature of many metrics of firm performance. A small number of HGFs drives a disproportionately large amount of job creation, while the average firm has a limited impact on the economy. This article explores the reasons for this increased interest, summarizes the existing literature, and highlights the methodological considerations that constrain and bias research. This special section draws attention to the importance of HGFs for future industrial performance, explores their unusual growth trajectories and strategies, and highlights the lack of persistence of high growth. Consequently, while HGFs are important for understanding the economy and developing public policy, they are unlikely to be useful vehicles for public policy given the difficulties involved in predicting which firms will grow, the lack of persistence in high growth levels, and the complex and often indirect relationship between firm capability, high growth, and macro-economic performance.

The Growth of Firms

Abstract: Much progress has been made in empirical research into firm growth in recent decades due to factors such as the availability of detailed longitudinal datasets, more powerful computers and new econometric techniques. This book provides an up-to-date catalogue of empirical work, as well as a coherent theoretical structure within which these new results can be interpreted and understood. It brings together a large body of recent research on firm growth from a multidisciplinary perspective, providing an up-to-date synthesis of stylized facts and empirical regularities. Numerous empirical findings and theories of firm growth are also surveyed and compared in order to evaluate their validity.

The Oxford Handbook of Innovation

Abstract: 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

Why Do Management Practices Differ across Firms and Countries

Abstract: 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.

Measuring statistical dependence with Hilbert-Schmidt norms

Abstract: 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.