Associate Professor and Sarah Graham Kenan Scholar, UNC Kenan-Flagler Business School
We propose and test a framework of private information acquisition and decision timing for asset allocators hiring outside investment managers. Using unique data on due diligence interactions between an institutional allocator and 860 hedge fund managers, we find that the production of private information complements public information. The allocator strategically chooses how much proprietary information to collect, reducing due diligence time by 18 months and improving outcomes. Selected funds outperform unselected funds by 9% over 20 months. The outperformance relates to the allocator learning about fund return-to-scale constraints and manager skill before other investors.
This paper uses two large panel data sets in China to study the effects of a health shock on household income mobility from 1991 to 2016. We compare outcomes of households with a member who receives a health shock with comparable households that do not receive any health shocks.
In a series of very influential studies, McKinsey (2015; 2018; 2020; 2023) reports finding statistically significant positive relations between the industry-adjusted earnings before interest and taxes margins of global McKinsey-chosen sets of large public firms and the racial/ethnic diversity of their executives. However, when we revisit McKinsey’s tests using data for firms in the publicly observable S&P 500® as of 12/31/2019, we do not find statistically significant relations between McKinsey’s inverse normalized Herfindahl-Hirschman measures of executive racial/ethnic diversity at mid-2020 and either industry-adjusted earnings before interest and taxes margin or industry-adjusted sales growth, gross margin, return on assets, return on equity, and total shareholder return over the prior five years 2015–2019.
This research paper develops a substantial, large-scale database of building energy use, energy audit reports, land use, and financial characteristics in New York City to empirically model the hurdle rate for energy retrofit investments, using actual audit data per permitted renovation work.
This paper conceptualises the array of social practices as a continuum of social innovation and empirically demonstrates variation not captured by legal designation. Using a survey from the US state of North Carolina, this paper examines how organisations across the continuum responded to the 2008 economic recession.
Empirical research in operations management has increased steadily over the last 20 years. In this paper, we discuss why this is good for our field and offer some comments on the qualities we admire in an empirical operations management paper.
Despite extensive empirical evidence of the economic and financial benefits of green buildings, energy retrofit investments in existing buildings have not reached widespread adoption.This paper empirically estimate returns to energy retrofit investments for multifamily and commercial buildings in New York City, using a novel database of actual audit report recommendations and permitted renovation work extracted using natural language processing.
Entrepreneurs are turning to crowdfunding as a way to finance their creative ideas. Crowdfunding involves relatively small contributions of many consumer-investors over a fixed time period (generally a few weeks). The purpose of this paper is to add to our empirical understanding of backer dynamics over the project funding cycle.
This paper provides evidence on the determinants and economic outcomes of updates of accounting systems (AS) over a 24-year time-span in a large sample of U.S. hospitals.
The challenge for public health officials is to detect an emerging foodborne disease outbreak from a large set of simple and isolated, domain-specific events. These events can be extracted from a large number of distinct information systems such as surveillance and laboratory reporting systems from health care providers, real-time complaint hotlines from consumers, and inspection reporting systems from regulatory agencies. In this paper we formalize a foodborne disease outbreak as a complex event and apply an event-driven rule-based engine to the problem of detecting emerging events. We define an evidence set as a set of simple events that are linked symptomatically, spatially and temporally. A weighted metric is used to compute the strength of the evidence set as a basis for response by public health officials.
We analyze and assess longitudinal data on startups from two data sources – the National Establishment Time-Series (NETS) database and the Secretary of State (SOS) business registry data. Our primary purposes in this paper are to assess the usefulness and reliability of these databases in measuring startup activity along several quality indicators and to explore the possibility of integrating these large databases using both automated and manual processes.
This paper surveys the recent advances in machine learning method for economic forecasting. The survey covers the following topics: nowcasting, textual data, panel and tensor data, high-dimensional Granger causality tests, time series cross-validation, classification with economic losses.
This paper investigates the causal impact of entrepreneurs' prior experience on startup success. Employing within-country changes in Green Card wait lines to instrument for immigrant first-time entrepreneurs' experience, we uncover that startups led by more experienced founders demonstrate superior funding, patenting, and employee growth.
This paper seeks to improve our understanding of how intermediaries operate to advance the commercialization of science by providing a set of specialized services. We review five intermediaries commonly mentioned in the ecosystem literature: university technology transfer and licensing offices; physical space (incubators, accelerators, and co-working spaces); professional services providers; networking, connecting, and assisting organizations; and finance providers (including venture capital, angel investors, public financing, and crowdfunding).
Partial least squares (PLS) path modeling is increasingly being promoted as a technique of choice for various analysis scenarios, despite the serious shortcomings of the method. The current lack of methodological justification for PLS prompted the editors of this journal to declare that research using this technique is likely to be deck-rejected (Guide and Ketokivi, 2015).