Scott Stern is the David Sarnoff Professor of Management and Chair of the Technological Innovation, Entrepreneurship, and Strategic Management Group at the MIT Sloan School of Management.

Stern explores how innovation and entrepreneurship differ from more traditional economic activities, and the consequences of these differences for strategy and policy.   His research in the economics of innovation and entrepreneurship focuses on entrepreneurial strategy, innovation-driven entrepreneurial ecosystems, and innovation policy and management.   Recent studies include the impact of clusters on entrepreneurship, the role of institutions in shaping the accumulation of scientific and technical knowledge, and the drivers and consequences of entrepreneurial strategy.

He has worked widely with practitioners in bridging the gap between academic research and the practice of innovation and entrepreneurship.  This includes advising start-ups and other growth firms in the area of entrepreneurial strategy, as well as working with governments and other stakeholders on policy issues related to competitiveness and regional performance.

The paper presents the Startup Cartography Project, which offers a new set of entrepreneurial ecosystem statistics for the United States from 1988-2016. The SCP combines state-level business registration records with a predictive analytics approach to estimate the probability of “extreme” growth (IPO or high-value acquisition) at or near the time of founding for the population of newly-registered firms. The results highlight the ability of predictive analytics to identify high-potential start-ups at founding (using a variety of different approaches and measures). The SCP then leverages estimates of entrepreneurial quality to develop four entrepreneurial ecosystem statistics, including the rate of start-up formation, average entrepreneurial quality, the quality-adjusted quantity of entrepreneurship, and the entrepreneurial ecosystem performance associated with a given start-up “cohort.” These statistics offer sharp insight into patterns of regional entrepreneurship, the correlation of quality (but not quantity) with subsequent regional economic growth and the evolution of entrepreneurial ecosystems over time. The SCP includes both a public-access dataset at the state, MSA, county, and zip code level, as well as an interactive map, the U.S. Startup Map, that allows academic and policy users to assess entrepreneurial ecosystems at an arbitrary level of granularity (from the level of states down to individual street addresses).

Nicholas (Nick) Bloom is the William Eberle Professor of Economics at Stanford University and the Co-Director of the Productivity, Innovation and Entrepreneurship program at the National Bureau of Economic Research.  He is also a fellow of the Centre for Economic Performance, and the Stanford Institute for Economic Policy Research.

Professor Bloom’s research focuses on measuring and explaining management practices. He has been working with McKinsey & Company as part of a long-run effort to collect management data from over 10,000 firms across industries and countries. The aim is to build an empirical basis for understanding what factors drive differences in management practices across regions, industries and countries, and how this determines firm and national performance. More recently he has also been working with Accenture on running management experiments. He also works on understanding the impacts of large uncertainty shocks–such as the credit crunch, the 9/11 terrorist attacks and the Cuban Missile crisis–on the US economy, for which he won the Frisch Medal in 2010.

We used text-to-data analysis to build three new datasets from firm quarterly earnings call, burning glass job adverts and US daily newspaper stories. Armed with this and patenting data we identify 22 new technologies and their roll-out across firms and labor markets in the US. Three stylized facts jump out from our data. First, as technologies develop the number of workers using this technology grows in size but their education and wage level drops. This is a natural process in the move from early stage development of technologies which is heavy is PhD employees to the latter stage deployment which is typically heavier in college and high-school only educated employees. Second, as technologies develop their employment impact diffuses across the country. Initially, technologies appear to be concentrated in a few local hubs but over time with rising deployment their use diffuses across the country. Finally, the origin of technology hubs appears to be driven by the universities and high skilled labor pools. Technologies tend to originate in college towns and slowly diffuse across the country.

Enrico Moretti is the Michael Peevey and Donald Vial Professor of Economics at the University of California, Berkeley. He serves as the Editor in Chief of the Journal of Economic Perspectives and is a Visiting Scholar at the Federal Reserve Bank of San Francisco. He is also Research Associate at the National Bureau of Economic Research (Cambridge), Research Fellow at the Centre for Economic Policy Research (London) and the Institute for the Study of Labor (Bonn).

Professor Moretti’s research covers the fields of labor economics and urban economics.  He has received several awards and honors, including the Society of Labor Economists’ Rosen Prize for outstanding contributions to labor economics, the Carlo Alberto Medal, the IZA Young Labor Economist Award and a Fulbright Fellowship. His book, “The New Geography of Jobs”, has been translated in eight languages and was awarded the William Bowen Prize.

The high-tech sector is increasingly concentrated in a small number of expensive cities, with the top ten cities in “Computer Science”, “Semiconductors” and “Biology and Chemistry”, accounting for 70%, 79% and 59% of inventors, respectively. Why do inventors tend to locate near other inventors in the same field, despite the higher costs? I use longitudinal data on top inventors based on the universe of US patents 1971 - 2007 to quantify the productivity advantages of Silicon-Valley style clusters and their implications for the overall production of patents in the US. I relate the number of patents produced by an inventor in a year to the size of the local cluster, defined as a city × research field × year. I first study the experience of Rochester NY, whose high-tech cluster declined due to the demise of its main employer, Kodak. Due to the growth of digital photography, Kodak employment collapsed after 1996, resulting in a 49.2% decline in the size of the Rochester high-tech cluster. I test whether the change in cluster size affected the productivity of inventors outside Kodak and the photography sector. I find that between 1996 and 2007 the productivity of non-Kodak inventors in Rochester declined by 20.6% relative to inventors in other cities, conditional on inventor fixed effects. In the second part of the paper, I turn to estimates based on all the data in the sample. I find that when an inventor moves to a larger cluster she experiences significant increases in the number of patents produced and the number of citations received. Conditional on inventor, firm, and city × year effects, the elasticity of number of patents produced with respect to cluster size is 0.0662 (0.0138). The productivity increase follows the move and there is no evidence of pre-trends. IV estimates based on the geographical structure of firms with laboratories in multiple cities are statistically similar to OLS estimates. In the final part of the paper, I use the estimated elasticity of productivity with respect to cluster size to quantify the aggregate effects of geographical agglomeration on the overall production of patents in the US. I find macroeconomic benefits of clustering for the US as a whole. In a counterfactual scenario where the quality of U.S. inventors is held constant but their geographical location is changed so that all cities have the same number of inventors in each field, inventor productivity would increase in small clusters and decline in large clusters. On net, the overall number of patents produced in the US in a year would be 11.07% smaller.

Peter Cappelli is the George W. Taylor Professor of Management at The Wharton School and Director of Wharton’s Center for Human Resources.  He is also a Research Associate at the National Bureau of Economic Research in Cambridge, MA. He serves on Global Agenda Council on Employment for the World Economic Forum and a number of advisory boards.

Professor Cappelli’s recent research examines changes in employment relations in the U.S. and their implications. His books include The New Deal at Work: Managing the Market-Driven Workforce, Talent Management: Managing Talent in an Age of Uncertainty (named a “best business book” for 2008 by Booz-Allen), The India Way: How India’s Top Business Leaders are Revolutionizing Management (with colleagues), and Managing the Older Worker (with AARP CEO Bill Novelli). His most recent publications include Why Good People Can’t Get Jobs (2012), which identifies shortfalls with current hiring practices and training practices and has been excerpted in Time Magazine (online) and reviewed in the Wall Street Journal, The New Yorker, and most major business publications. He is also the author of Will College Pay Off – A Guide to the Most Important Financial Decision You’ll Ever Make (2015), and co-author of the forthcoming Fortune Makers: The Leaders Creating China’s Great Global Companies.

The heart of contemporary Artificial Intelligence efforts are algorithms based on machine learning principles that optimize predictions of future outcomes. Nowhere are these tools poised to have bigger effects than in the tasks of managing employees – selection decisions predicting who should be hired, what career progression options make sense, what wellness practices are best for employees, and so forth.  At the heart of these algorithms are optimization principles. These fly in the face of the factors that underpin contemporary management practices, as they focus on matters of fairness and equity, relationships, and so forth.   In hiring, for example, what happens if a hiring algorithm turns out to be a great predictor of job performance, but it is based on attributes such as eye and skin color?  More broadly, if many of the basic tasks of supervisors can now be performed by algorithms, such as work schedules, performance assessments, and so forth, what happens to the role of the supervisor and to our  notions that the relationship between them and their subordinates is the “glue” that builds commitment to the organization?  We will consider these challenges and possible paths forward.

Rebecca Henderson is the John and Natty McArthur University Professor at Harvard University, where she has a joint appointment at the Harvard Business School in the General Management and Strategy units. Professor Henderson is also a research fellow at the National Bureau of Economic Research. Her work explores how organizations respond to large-scale technological shifts, most recently in regard to energy and the environment.

Professor Henderson sits on the boards of Amgen and of IDEXX Laboratories, and she has worked with both members of the Fortune 100 and small, technology-orientated start-ups. Her work has been published in a range of scholarly journals including Administrative Science Quarterly, The Quarterly Journal of Economics, Strategic Management Journal, Management Science, Research Policy, The RAND Journal of Economics, and Organization Science.

Her most recent publication is Leading Sustainable Change: An Organizational Perspective, edited jointly with Ranjay Gulati and Michael Tushman, and published by the Oxford University Press.

Free market capitalism is one of humanity's greatest inventions and the greatest source of prosperity the world has ever seen. But this success has been costly. Capitalism is on the verge of destroying the planet and destabilizing society as wealth rushes to the top. The time for action is running short.

Rebecca Henderson's rigorous research in economics, psychology, and organizational behavior, as well as her many years of work with companies around the world, gives us a path forward. She debunks the worldview that the only purpose of business is to make money and maximize shareholder value. She shows that we have failed to reimagine capitalism so that it is not only an engine of prosperity but also a system that is in harmony with environmental realities, striving for social justice and the demands of truly democratic institutions.



Henderson's deep understanding of how change takes place, combined with fascinating in-depth stories of companies that have made the first steps towards reimagining capitalism, provides inspiring insight into what capitalism can be. With rich discussions of how the worlds of finance, governance, and leadership must also evolve, Henderson provides the pragmatic foundation for navigating a world faced with unprecedented challenge, but also with extraordinary opportunity for those who can get it right.

Russell Coff is the Thomas J. Falk Distinguished Chair in Business at the University of Wisconsin-Madison. He is also the Department Chair for Management and Human Resources and the Academic Director of the Bolz Center for Arts Administration.

Professor Coff’s research explores the role of human assets in innovation, creativity, and, ultimately in competitive advantage. For example, he studies management dilemmas associated with human capital including: 1) the management of strategic investments in knowledge-based assets under great uncertainty 2) appropriating value (rent) from competitive advantages, 3) creativity & innovation under conditions of asymmetric information and uncertainty, and 4) how buyers cope in mergers and acquisitions that involve human assets.

Contributing to the knowledge creation literature, this study examines antecedents of new post-mobility collaboration between new hires and incumbents at the hiring organization. While prior work highlights potential benefits of hiring on organizational learning, individual knowledge can be hard to transfer, integrate, and recombine. Knowledge creation may not materialize without substantial voluntary collaboration. Our integrated model helps to explain how new collaboration emerges when: 1) Opportunities to recombine are present (knowledge complementarities), 2) Motivated employees seek to work together (collaboration incentives an­­d norms), 3) Awareness of opportunities is high (pre-mobility social ties), and 4) Capability to collaborate is high (team skills). Our analysis of 14 years of data from the Academy of Management conference offers evidence for each of these factors in predicting post-mobility collaboration. We identify numerous opportunities for subsequent exploration.

Ranjay Gulati is the Paul R. Lawrence MBA Class of 1942 Professor and the former Unit Head of the Organizational Behavior Unit at Harvard Business School.

Professor Gulati’s recent work explores strategic, organizational, and leadership challenges for building high growth organizations in turbulent markets. His work transcends both strategy formulation and also successful implementation. He has studied how technology-led and other companies must harmonize their technology trajectory with a market strategy based on a deep understanding the shifting needs of their customers. His recent work has also looked at the importance of building a scaleable organizational architecture and culture that provide high-growth organizations with the requisite discipline and consistency while preserving their agility and entrepreneurial mindset. The final pillar for his research has been to look at the leadership skills and behaviors required to lead fast-moving organizations. Going beyond the classic leadership imperatives of motivating and inspiring, he explores how leaders today must cultivate courage in others by activating a winning mindset that is centered around a clear set of priorities, principles and purpose. Some of his prior work has focused on the enablers and implications of successful acquisitions and strategic partnerships to drive profitable growth.

The relational view advocates relationships between firms as an important unit of analysis. In line with this view, this study investigates how generalized trust at the regional level induces a firm to have more or fewer relationships with other firms as well as the performance effects of making such choices. In particular, we consider how trust affects the extent to which a firm sources inputs externally versus internally. We use data on more than a million small- and medium-sized enterprises (SMEs) from 145 regions (in 12 European countries) that differ in their level of generalized trust. We select control variables via a double-selection procedure based on machine learning. Consistent with our arguments, we find that the extent of external sourcing by SMEs is (a) greater in regions with higher levels of generalized trust and (b) more positively correlated with firm performance in such regions. Whereas previous research on relationships between firms has focused on dyadic, party-specific trust, this study demonstrates the importance of generalized trust originating from a broader context.