Knowledge Similarity Among Founders and Joiners: Impact on Venture Scaleup in Fintech and Lawtech, with Mari Sako, and Mark Verhagen, SSRN working paper
In what ways does knowledge similarity among co-founders contribute to venture scaleup? This paper addresses this question by taking account of knowledge domains among early employees and in founders’ social networks. We build a theoretical framework to predict which knowledge combinations are likely to lead to venture growth at different stages. We test our theory using a database of 315 fintech and lawtech startups (with a total of 600 founders and 328 early joiners) in three locations (London, New York City, and San Francisco Bay Area) during the period 2009-2020. Our central finding is that venture growth is explained by knowledge-similar founding teams, founders’ social ties to other founders whose knowledge domains are different from their own, and knowledge-dissimilar employees. Moreover, the positive impact of knowledge similarity in founding teams on venture growth is stronger for older ventures than young ventures, and for mature than nascent ecosystems. These findings highlight the importance of simultaneously studying knowledge domains in founding teams, their social networks, and early employees. Just as important is to analyze the impact of knowledge similarity at different stages in the entrepreneurial process from ideas generation, choice of strategy, and strategy implementation.
Presented at: AoM Annual Meeting (Best Paper award), Saïd Business School*, University of Oxford, SMS Annual conference*, SASE conference*
A Taxonomy for Technology Venture Ecosystems, with Mari Sako, SSRN working paper
We develop a taxonomy – Oxford Venture Ecosystem Taxonomy (OVET) -- to classify technology startup ventures along nine dimensions: (1) the area of work, (2) purpose of technology use, (3) technology stack, (4) platform business model, (5) type of clients, (6) value capture strategy, (7) founder and funder characteristics, (8) geographical footprint, and (9) funding cycle. This paper provides a theory and method for developing taxonomies, emphasizing the importance of clarifying the purpose for which a taxonomy is used and the determination of the appropriate level of abstraction. This approach is then applied to develop the OVET taxonomy in the context of specific sectors with AI use cases. We illustrate this application in four sectors, namely fintech, healthtech, lawtech, and proptech. In the last section, we discuss how such a taxonomy, enabling classifications and analytics, could provide valuable insights and improve the quality of decision-making by venture founders, investors, policy makers and other stakeholders in the venture ecosystem.
Future of Professional Work: Evidence from Legal Jobs in Britain and the United States, with Mari Sako, and Jacopo Attolini, forthcoming, Journal of Professions and Organizations.
This paper examines the impact of digital technology on professional work by combining insights from the future of work debate and the system of professions. With the adoption of digital technology, who ends up undertaking digital tasks depends on the nature of professional jurisdictional control, which we define as a profession’s power to maintain or shift from existing jurisdictional settlements in the face of external disturbances. Protective jurisdictional control implies that the profession engages in full or subordinate jurisdiction, delegating new tasks to subordinate semi-professionals. By contrast, connective jurisdictional control leads them to prefer settlements by division of labor or advisory links, enabling equal-status professions to work together. Using a large database of online job postings by Burning Glass Technologies, we find evidence for this hypothesis. Empirically, we deploy three ways to gauge the nature of professional jurisdictional control: first, by comparing traditional law firms and alternative business structure firms in the UK regulated legal industry; second, by contrasting the US (with protective jurisdictional control) and the UK; and third, by examining the legal sector (in which the legal profession is dominant) and non-legal sectors. Moreover, we find that protective (connective) jurisdictional control is associated with lower (higher) pay premia for new digital skills, consistent with theory. Our findings highlight the importance of the mediating role of professional jurisdictional control to inform the future of work debate.
Presented at: PSF Annual Conference*, University of Oxford
Flexible Work Arrangements in Low Wage Jobs: Evidence from Job Vacancy Data, with Abi Adams-Prassel, Maria Balgova, and Tom Waters, SSRN working paper
In this paper, we analyze firm demand for flexible jobs by exploiting the language used to describe work arrangements in job vacancies. We take a supervised machine learning approach to classify the work arrangements described in more than 46 million UK job vacancies. We highlight the existence of very different types of flexibility amongst low and high wage vacancies. Job flexibility at low wages is more likely to be offered alongside a wage-contract that exposes workers to earnings risk, while flexibility at higher wages and in more skilled occupations is more likely to be offered alongside a fixed salary that shields workers from earnings variation. We show that firm demand for flexible work arrangements is partly driven by a desire to reduce labor costs; we find that a large and unexpected change to the minimum wage led to a 7 percentage point increase in the proportion of flexible and non-salaried vacancies at low wages.
Presented at: EEA-ESEM 2022, Royal Economic Society Annual Meeting, Bank of England - Advanced Analytics departmental seminar, SOLE*, RES, ESCoE Online Job Vacancy Data Workshop, ESCoE Economic Measurement Conference
7 Strategies for Leading a Crisis Driven Reorg, with Peter Buchas, Stephen Heidari Robinson and Susanne Heywood, Harvard Business Review, 2020
Lawyering when the Law Becomes Machine Learnt: Mapping LegalTech Adoption and Skill Demand, with Adam Saunders and Max Ahrens, The Legal Tech Book
The association between socioeconomic status and mobility reductions in the early stage of England’s COVID-19 epidemic, with Won Do Lee, Tim Schwanen, Health and Place (2021)
This study uses mobile phone data to examine how socioeconomic status was associated with the extent of mobility reduction during the spring 2020 lockdown in England in a manner that considers both potentially confounding effects and spatial dependency and heterogeneity. It shows that socioeconomic status as approximated through income and occupation was strongly correlated with the extent of mobility reduction. It also demonstrates that the specific nature of the association of socioeconomic status with mobility reduction varied markedly across England. Finally, the analysis suggests that the spatial differentiation in the ability to restrict everyday mobility in response to a national lockdown is an important topic for future research.
Behavioural considerations for vaccine uptake in Phase 2 and beyond, with Melinda Mills, Xiaowen Dong et al., SAGE SPI-B (2021)
Information Type and the Geography of Price Discovery, with Howard Jones, and Jose Martinez, SSRN working paper
Local (São Paulo) and global (New York) markets contribute significantly to price discovery in dual-listed Brazilian shares, but their contribution varies over time. Local information shares increase by 8.4% on days when a stock experiences a significant idiosyncratic price swing, but do not similarly increase on earnings announcement days or on days when the whole local market experiences a significant price swing, despite an equally large increase in trading. Traders in the local market seem to have an advantage in collecting and processing company-specific unscheduled information, but not widely disseminated scheduled information which affects the company or the whole market.
Presented at: Saïd Business School, Tilburg University
Asymptotic Properties of the Gauge of Step-Indicator Saturation, with Bent Nielsen, working paper
We investigate the asymptotic properties of Step-indicator Saturation which is an algorithm to handle unmodelled location shifts in time series. We consider a stylized version of the algorithm that uses the split-half approach. We present asymptotic convergence and distribution results on the gauge of the algorithm which is the frequency of falsely retained step-indicators when the data generating process has no shifts. The proofs rely on empirical process results of temporal differences of residuals. Our results offer an asymptotic justification to use the gauge in choosing the tuning parameter of this statistical procedure.
Presented at: University of Oxford, ICORS, Oxmetrics User ConferenceThe * indicates a presentation by co-authors.