The Economics of Innovation and Intellectual Property


Book Description

The first comprehensive textbook covering all aspects of the economics of innovation and the role of intellectual property in encouraging or discouraging innovation. Innovation is widely viewed as the engine behind economic growth, and it has assumed increasing importance in contemporary economic research. In The Economics of Innovation and Intellectual Property, Bronwyn H. Hall and Christian Helmers introduce readers to the use of economic analysis for the understanding of technical change and the innovative process, its determinants, and consequences. The authors cover innovation basics, the measurement of returns to innovation for individuals and the economy, and the use of intellectual property protection by innovators. They focus on the various ways patents have been used by industry to secure returns to innovation, as well as the strategic use of patents, and they emphasize present-day technologies including pharmaceuticals, software, and AI. Clearly organized and accessible, The Economics of Innovation and Intellectual Property offers a useful introduction to economics, business, public policy, and legal studies, and provides a comprehensive collection of references and information from a variety of sources across disciplines. It also includes various boxes with definitions and examples, as well as a brief mathematical appendix explaining concepts that may be unfamiliar and an introduction to data sources.




Handbook of Cultural Economics, Third Edition


Book Description

Cultural economics has become well established as a subject of interest for students and teachers of courses ranging from economics to arts administration as well as for policy-makers and practitioners in the creative industries. Digitisation has had a tremendous impact on many areas of the creative economy and the third edition of this popular book fully reflects it.




Big Data Intelligence and Computing


Book Description

This book constitutes the proceedings of the International Conference on Big Data Intelligence and Computing, DataCom 2022, which took place in Denarau Island, Fiji, in December 2022. The 30 full papers included in this volume were carefully reviewed and selected from 88 submissions. The papers detail big data analytics solutions, distributed computation paradigms, on-demand services, autonomic systems, and pervasive applications.




Big Data for Twenty-First-Century Economic Statistics


Book Description

Introduction.Big data for twenty-first-century economic statistics: the future is now /Katharine G. Abraham, Ron S. Jarmin, Brian C. Moyer, and Matthew D. Shapiro --Toward comprehensive use of big data in economic statistics.Reengineering key national economic indicators /Gabriel Ehrlich, John Haltiwanger, Ron S. Jarmin, David Johnson, and Matthew D. Shapiro ;Big data in the US consumer price index: experiences and plans /Crystal G. Konny, Brendan K. Williams, and David M. Friedman ;Improving retail trade data products using alternative data sources /Rebecca J. Hutchinson ;From transaction data to economic statistics: constructing real-time, high-frequency, geographic measures of consumer spending /Aditya Aladangady, Shifrah Aron-Dine, Wendy Dunn, Laura Feiveson, Paul Lengermann, and Claudia Sahm ;Improving the accuracy of economic measurement with multiple data sources: the case of payroll employment data /Tomaz Cajner, Leland D. Crane, Ryan A. Decker, Adrian Hamins-Puertolas, and Christopher Kurz --Uses of big data for classification.Transforming naturally occurring text data into economic statistics: the case of online job vacancy postings /Arthur Turrell, Bradley Speigner, Jyldyz Djumalieva, David Copple, and James Thurgood ;Automating response evaluation for franchising questions on the 2017 economic census /Joseph Staudt, Yifang Wei, Lisa Singh, Shawn Klimek, J. Bradford Jensen, and Andrew Baer ;Using public data to generate industrial classification codes /John Cuffe, Sudip Bhattacharjee, Ugochukwu Etudo, Justin C. Smith, Nevada Basdeo, Nathaniel Burbank, and Shawn R. Roberts --Uses of big data for sectoral measurement.Nowcasting the local economy: using Yelp data to measure economic activity /Edward L. Glaeser, Hyunjin Kim, and Michael Luca ;Unit values for import and export price indexes: a proof of concept /Don A. Fast and Susan E. Fleck ;Quantifying productivity growth in the delivery of important episodes of care within the Medicare program using insurance claims and administrative data /John A. Romley, Abe Dunn, Dana Goldman, and Neeraj Sood ;Valuing housing services in the era of big data: a user cost approach leveraging Zillow microdata /Marina Gindelsky, Jeremy G. Moulton, and Scott A. Wentland --Methodological challenges and advances.Off to the races: a comparison of machine learning and alternative data for predicting economic indicators /Jeffrey C. Chen, Abe Dunn, Kyle Hood, Alexander Driessen, and Andrea Batch ;A machine learning analysis of seasonal and cyclical sales in weekly scanner data /Rishab Guha and Serena Ng ;Estimating the benefits of new products /W. Erwin Diewert and Robert C. Feenstra.




A Primer on Nonmarket Valuation


Book Description

This is a practical book with clear descriptions of the most commonly used nonmarket methods. The first chapters of the book provide the context and theoretical foundation of nonmarket valuation along with a discussion of data collection procedures. The middle chapters describe the major stated- and revealed-preference valuation methods. For each method, the steps involved in implementation are laid out and carefully explained with supporting references from the published literature. The final chapters of the book examine the relevance of experimentation to economic valuation, the transfer of existing nonmarket values to new settings, and assessments of the reliability and validity of nonmarket values. The book is relevant to individuals in many professions at all career levels. Professionals in government agencies, attorneys involved with natural resource damage assessments, graduate students, and others will appreciate the thorough descriptions of how to design, implement, and analyze a nonmarket valuation study.




Smart Data Intelligence


Book Description




Consumer Price Index Manual


Book Description

The consumer price index (CPI) measures the rate at which prices of consumer goods and services change over time. It is used as a key indicator of economic performance, as well as in the setting of monetary and socio-economic policy such as indexation of wages and social security benefits, purchasing power parities and inflation measures. This manual contains methodological guidelines for statistical offices and other agencies responsible for constructing and calculating CPIs, and also examines underlying economic and statistical concepts involved. Topics covered include: expenditure weights, sampling, price collection, quality adjustment, sampling, price indices calculations, errors and bias, organisation and management, dissemination, index number theory, durables and user costs.




Housing Economics and Public Policy


Book Description

This book is a timely assessment of 20 years of progress in the field of housing economics and its application to policy and practice. Two decades on from the publication of Duncan Maclennan's influential Housing Economics, 16 leading housing experts - both academics and policy makers from across the world - now honour Maclennan's contributions. The chapters here present a contemporary survey of key issues in housing, from urban housing markets and sub-market modelling, to the economics of social housing, the basis for housing planning, economic analysis of neighbourhoods, and the connections between academic work and policy development. For students, researchers and practitioners in housing, urban economics and social policy, Housing Economics and Public Policy: . provides up to date and comprehensive reviews of major areas of the housing economics literature . sheds light on the economic, social and spatial processes that affect housing . includes discussion of major areas of cutting edge housing economics research and identifies continuing gaps . presents a synthesis of housing economics research on both sides of the Atlantic . assesses the impact of theory on policy and practice




Communication and Intelligent Systems


Book Description

This book gathers selected research papers presented at the Fourth International Conference on Communication and Intelligent Systems (ICCIS 2022), organized by National institute of Technology, Delhi, India, during December 19–20, 2022. This book presents a collection of state-of-the-art research work involving cutting-edge technologies for communication and intelligent systems. Over the past few years, advances in artificial intelligence and machine learning have sparked new research efforts around the globe, which explore novel ways of developing intelligent systems and smart communication technologies. The book presents single- and multi-disciplinary research on these themes in order to make the latest results available in a single, readily accessible source. The book is presented in two volumes.




Machine Learning in the AWS Cloud


Book Description

Put the power of AWS Cloud machine learning services to work in your business and commercial applications! Machine Learning in the AWS Cloud introduces readers to the machine learning (ML) capabilities of the Amazon Web Services ecosystem and provides practical examples to solve real-world regression and classification problems. While readers do not need prior ML experience, they are expected to have some knowledge of Python and a basic knowledge of Amazon Web Services. Part One introduces readers to fundamental machine learning concepts. You will learn about the types of ML systems, how they are used, and challenges you may face with ML solutions. Part Two focuses on machine learning services provided by Amazon Web Services. You’ll be introduced to the basics of cloud computing and AWS offerings in the cloud-based machine learning space. Then you’ll learn to use Amazon Machine Learning to solve a simpler class of machine learning problems, and Amazon SageMaker to solve more complex problems. • Learn techniques that allow you to preprocess data, basic feature engineering, visualizing data, and model building • Discover common neural network frameworks with Amazon SageMaker • Solve computer vision problems with Amazon Rekognition • Benefit from illustrations, source code examples, and sidebars in each chapter The book appeals to both Python developers and technical/solution architects. Developers will find concrete examples that show them how to perform common ML tasks with Python on AWS. Technical/solution architects will find useful information on the machine learning capabilities of the AWS ecosystem.