The essays in this open access volume identify the key ingredients for success in capitalizing on public investments in scientific projects and the development of large-scale research infrastructures. Investment in science – whether in education and training or through public funding for developing new research tools and technologies – is a crucial priority. Authors from big research laboratories/organizations, funding agencies and academia discuss how investing in science can produce societal benefits as well as identifying future challenges for scientists and policy makers. The volume cites different ways to assess the socio-economic impact of Research Infrastructures and their role as hubs of global collaboration, creativity and innovation. It highlights the different benefits stemming from fundamental research at the local, national and global level, while also inviting us to rethink the notion of “benefit” in the 21st century. Public investment is required to maintain the pace of technological and scientific advancements over the next decades. Far from advocating a radical transformation and massive expansion in funding, the authors suggest ways for maintaining a strong foundation of science and research to ensure that we continue to benefit from the outputs. The volume draws inspiration from the first “Economics of Big Science” workshop, held in Brussels in 2019 with the aim of creating a new space for dialogue and interaction between representatives of Big Science organizations, policy makers and academia. It aspires to provide useful reading for policy makers, scientists and students of science, who are increasingly called upon to explain the value of fundamental research and adopt the language and logic of economics when engaging in policy discussions.

The beauty of science may be pure and eternal, but the practice of science costs money. And scientists, being human, respond to incentives and costs, in money and glory. Choosing a research topic, deciding what papers to write and where to publish them, sticking with a familiar area or going into something new—the payoff may be tenure or a job at a highly ranked university or a prestigious award or a bump in salary. The risk may be not getting any of that. At a time when science is seen as an engine of economic growth, Paula Stephan brings a keen understanding of the ongoing cost-benefit calculations made by individuals and institutions as they compete for resources and reputation. She shows how universities offload risks by increasing the percentage of non-tenure-track faculty, requiring tenured faculty to pay salaries from outside grants, and staffing labs with foreign workers on temporary visas. With funding tight, investigators pursue safe projects rather than less fundable ones with uncertain but potentially path-breaking outcomes. Career prospects in science are increasingly dismal for the young because of ever-lengthening apprenticeships, scarcity of permanent academic positions, and the difficulty of getting funded. Vivid, thorough, and bold, How Economics Shapes Science highlights the growing gap between the haves and have-nots—especially the vast imbalance between the biomedical sciences and physics/engineering—and offers a persuasive vision of a more productive, more creative research system that would lead and benefit the world.

This thought-provoking book expands on the notion that Big Science is not the only term to describe and investigate particularly large research projects, scientific collaborations and facilities. It investigates the significant overlap between Big Science and Research Infrastructures (RIs) in a European context since the early twenty-first century. Contributions to this innovative book not only augment the study of Big Science with new perspectives, but also launch the study of RIs as a promising new line of inquiry.

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.

This book analyses the emergence of a transformed Big Science in Europe and the United States, using both historical and sociological perspectives. It shows how technology-intensive natural sciences grew to a prominent position in Western societies during the post-World War II era, and how their development cohered with both technological and social developments. At the helm of post-war science are large-scale projects, primarily in physics, which receive substantial funds from the public purse. Big Science Transformed shows how these projects, popularly called 'Big Science', have become symbols of progress. It analyses changes to the political and sociological frameworks surrounding publicly-funding science, and their impact on a number of new accelerator and reactor-based facilities that have come to prominence in materials science and the life sciences. Interdisciplinary in scope, this book will be of great interest to historians, sociologists and philosophers of science.