This paper studies alternative empirical strategies for estimating the effects of organization design practices on performance, as well as the factors which determine organizational design, in a cross-section of firms. Our economic model is based on a firm where multiple organizational design practices are en endogenously determined, and these organizational design practices affect output through an 'organizational design production function.' The econometric model includes unobserved exogenous variation in the costs and returns to each of the individual practices. The model is used to evaluate how different econometric strategies for testing theories about complementarity can be interpreted under alternative assumptions about the economic and statistical environment. We identify plausible hypotheses about the joint distribution of the unobservables under which several different approaches from the existing literature will yield biased and inconsistent estimates. We show that the sign of the bias depends on two factors: whether the organzational design practices are complements, and the correlation between the unobserved returns to each practice. We find several sets of conditions under which the sign of the bias can be determined, and we provide economic interpretations. Our analysis shows that for a particular set of hypotheses, a variety of different procedures may all yield qualitatively similar biases, presenting a challenge for the identification of complementarity. We then propose a structural approach, which is based on a system of simultaneous equations describing productivity and the demand for organizational design practices. As long as exogenous variables are observed which are uncorrelated with the unobserved returns to practices, the structural parameters are identified, yielding consistent tests for complementarity as well as the cross-equation restrictions implied by static optimization of the organizatin's profit function.

Entrepreneurs can observe the same evidence yet reach different conclusions about an opportunity. This paper connects the theory-based view of strategy with Bayesian Entrepreneurship by showing how theories of value creation structure prediction, experimentation, and learning. We separate a qualitative theory-the mechanisms it admits, the restrictions it imposes, and the settings in which it applies-from the probabilistic beliefs needed to use that theory. This distinction clarifies why actors can disagree despite shared evidence, why different disagreements call for different experiments, and why an experiment can improve prediction without separating competing theories. We also distinguish disagreements about the opportunity from differences in costs, constraints, or capabilities. Under a controlled thin-evidence benchmark, an account with fewer relationships to estimate can reach the point at which acting now has nonnegative subjective value sooner, even if it is not more accurate or profitable.

How should Theory-based entrepreneurs search for strategies to implement their ideas? The theory-based view of strategy posits that decision makers hold key conjectures about their path to success and use theory to understand and test beliefs underlying those conjectures. This causal framework also has implications for entrepreneurial search: the process by which entrepreneurs uncover strategies to implement their ideas. In this paper, we develop a Bayesian model where entrepreneurs update their beliefs as they conduct entrepreneurial search. We find several optimal behaviors for Theory-based entrepreneurs such as reverting to a previous strategy after finding a relatively poor strategy and continuing to search after finding a relatively good strategy, which are missing when entrepreneurs lack such a theory-based approach. As our theoretical predictions align with examples of successful entrepreneurs, our findings both provide a method to empirically identify Theory-based entrepreneurs and demonstrate the usefulness of applying the theory-based view to entrepreneurial behavior more generally.

How should theory-based entrepreneurs search for strategies to implement their ideas? The theory-based view of strategy posits that decision-makers hold theories about their environment premised on beliefs that should be actively tested. This causal framework, which underlies the theory-based view, also has implications for entrepreneurial search: the process by which entrepreneurs uncover strategies to implement their ideas. In this paper, we develop a Bayesian model where entrepreneurs update their beliefs as they conduct entrepreneurial search. We find several optimal behaviors for theory-based entrepreneurs such as reverting to a previous strategy after finding a relatively poor strategy and continuing to search after finding a relatively good strategy, which are missing when entrepreneurs lack such a theory-based approach. As these predictions align with examples of successful entrepreneurs, our findings both provide a method to empirically identify skilled entrepreneurs and demonstrate the usefulness of applying the theory-based view to entrepreneurial behavior more generally.

What is the role of startups within the innovation ecosystem? Since 2000, startups have grown in their share of commercializing research from top U.S. universities; however, prior work has little to say on the particular advantages of startup ventures in the innovation process relative to more traditional alternatives such as academia and established private-sector incumbents. We develop a simple model of startup advantage based on private information held by the initial inventor, and generate predictions related to the value and impact of startup innovation. We then explore these predictions using patents granted within the regional ecosystems of top-25 research universities from 2000 to 2015. Our results show a significant startup advantage in terms of forward citations and outlier-patent rates. Further, startup innovation is both more original and more general than innovation by incumbent firms. Moreover, startups that survive to become “scale-ups” quickly grow to dominate their regional innovation ecosystems. Our findings have important implications for innovation policy.Institutional subscribers to the NBER working paper series, and residents of developing countries may download this paper without additional charge at www.nber.org.

National Bureau of Economic Research Volume Title: The Role of Innovation and Entrepreneurship in Economic Growth Volume Authors/Editors: Michael J. Andrews, Aaron Chatterji, Josh Lerner, and Scott Stern, editors Volume Publisher: University of Chicago Press Volume ISBNs: 978-0-226-81078-2 (cloth), 978-0-226-81064-5 (electronic) Volume URL: https://www.nber.org/books-and-chapters/role-innovation-andentrepreneurship-economic-growth Conference Date: January 7-8, 2020 Publication Date: Februrary 2022

Motivated by the establishment of major U.S. Federal programs seeking to harness the potential of regional innovation ecosystems, we assess the promise and challenges of place-based innovation policy interventions.Relative to traditional research grants, place-based innovation policy interventions are not directed toward a specific research project but rather aim to reshape interactions among researchers and other stakeholders within a given geographic location.The most recent such policy -the NSF "Engines" program -is designed to enhance the productivity and impact of the investments made within a given regional innovation ecosystem.The impact of such an intervention depends on whether, in its implementation, it induces change in the behavior of individuals and the ways in which knowledge is distributed and translated within that ecosystem.While this logic is straightforward, from it follows an important insight: innovation ecosystem interventions -Engines --are more likely to succeed when they account for the current state of a given regional ecosystem (latent capacities, current bottlenecks, and economic and institutional constraints) and when they involve extended commitments by multiple stakeholders within that ecosystem.We synthesize the logic, key dependencies, and opportunities for realtime assessment and course correction for these place-based innovation policy interventions.

Motivated by the establishment of major U.S. Federal programs seeking to harness the potential of regional innovation ecosystems, we assess the promise and challenges of place-based innovation policy interventions. Relative to traditional research grants, place-based innovation policy interventions are not directed toward a specific research project but rather aim to reshape interactions among researchers and other stakeholders within a given geographic location. The most recent such policy the NSF “Engines” program is designed to enhance the productivity and impact of the investments made within a given regional innovation ecosystem. The impact of such an intervention depends on whether, in its implementation, it induces change in the behavior of individuals and the ways in which knowledge is distributed and translated within that ecosystem. While this logic is straightforward, from it follows an important insight: innovation ecosystem interventions – Engines -are more likely to succeed when they account for the current state of a given regional ecosystem (latent capacities, current bottlenecks, and economic and institutional constraints) and when they involve extended commitments by multiple stakeholders within that ecosystem. We synthesize the logic, key dependencies, and opportunities for realtime assessment and course correction for these place-based innovation policy interventions. Jorge Guzman Columbia Business School Kravis Hall, 975 655 W 130th St New York, NY 10027 and NBER jag2367@gsb.columbia.edu Fiona Murray MIT Sloan School of Management 100 Main Street, E62-470 Cambridge, MA 02142 and NBER fmurray@mit.edu Scott Stern MIT Sloan School of Management 100 Main Street, E62-476 Cambridge, MA 02142 and NBER sstern@mit.edu Heidi L. Williams Department of Economics 6106 Rockefeller Center, Office 328 Dartmouth College Hanover, NH 03755 and NBER heidi.lie.williams@dartmouth.edu

Leveraging data from eight U.S. states from the Startup Cartography Project, this paper provides new insight into the changing nature and geography of entrepreneurship in the wake of the COVID pandemic.Consistent with other data sources, following an initial decline, the overall level of state-level business registrations not only rebounds but increases across all eight states.We focus here on the significant heterogeneity in this dynamic pattern of new firm formation across and within states.Specifically, there are significant differences in the dynamics of new business registrants across neighborhoods in terms of race and socioeconomic status.Areas including a higher proportion of Black residents, and more specifically higher median income Black neighborhoods, are associated with higher growth in startup formation rates between 2019 and 2020.Moreover, these dynamics are reflected in the passage of the major Federal relief packages.Even though legislation such as the CARES Act did not directly support new business formation, the passage and implementation of relief packages was followed by a relative increase in start-up formation rates, particularly in neighborhoods with higher median incomes and a higher proportion of Black residents.

This paper provides systematic empirical evidence for the distinctive role of universities on local entrepreneurial ecosystems. Assessing the impact of research institutions on entrepreneurship is challenging, given that these institutions are often located in economic and innovation environments conducive to growth-oriented entrepreneurial activity, are themselves a source of local demand, and produce knowledge, which might serve as the foundation for new ventures. To overcome this inference challenge, we first combine comprehensive business registration records with a predictive analytics approach to measure both the quantity and quality-adjusted quantity of entrepreneurship at the zip-code level on an annual basis. We then link each location to the presence or absence of research-oriented universities or national laboratories. Finally, we exploit significant changes over time in Federal commitments to both universities and national laboratories. Our key finding is that changes in Federal research commitments to universities are uniquely linked to positively correlated changes in the quality-adjusted quantity of entrepreneurship. In contrast, increases in non-research funding to universities and funding to national laboratories is associated with either a neutral or negative impact on the quality-adjusted quantity of entrepreneurship. Research funding to universities seems to play a unique role in promoting the acceleration of local entrepreneurial ecosystems. Institutional subscribers to the NBER working paper series, and residents of developing countries may download this paper without additional charge at www.nber.org.

Professor Scott Stern introduces the three central themes to introduce Innovation policy: the role of innovation in economic development and social progress, the requirements and infrastructure to support innovation within a given economic and political environment, and how these institutions and policies need to adapt over time as innovation and science evolve. Particular attention is placed on the challenges of nurturing innovation ecosystems in developing and emerging economies.