We analyze the impact of the introduction of credit default swaps (CDSs) on real decision-making within the firm. Our structural model predicts that CDS introduction increases debt capacity more when uncertainty about the credit events that trigger CDS payment is lower.
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.
While call centers have recently invested in callback technology, the impact of this innovation on callers’ behavior and call center performance has been less clearly understood. Using call center data from a US commercial bank, we perform an empirical study of callers’ decisionmaking process in the presence of a callback option.
Tax audits are a necessary component of the tax system, but policymakers and others have expressed concerns about their potentially adverse real effects. Understanding the causal effects of tax audits has been hampered by lack of data and because typically tax audits are not randomly assigned. We use administrative data from random tax audits of small businesses to examine the real effects of being subject to a tax audit.
When modeling economic relationships it is increasingly common to encounter data sampled at different frequencies. We introduce the R package midasr which enables estimating regression models with variables sampled at different frequencies within a MIDAS regression framework put forward in work by Ghysels, Santa-Clara, and Valkanov (2002).
We discuss firm-level evidence based on UK data showing that within-firm pay inequality--wage differentials between top- and bottom-level jobs--increases with firm size. Moreover, within-firm pay inequality rises as firms grow larger over time. Lastly, using wage data from 15 developed countries, we document a positive association between aggregate wage inequality at the country level and growth by the largest firms in the country. We conclude that part of what may be perceived as a global trend toward more wage inequality may be driven by an increase in the size of the largest firms in the economy.
In this paper, we develop new methods for analyzing high-dimensional tensor datasets. A tensor factor model describes a high-dimensional dataset as a sum of a low-rank component and an idiosyncratic noise, generalizing traditional factor models for panel data. We propose an estimation algorithm, called tensor principal component analysis (PCA), which generalizes the traditional PCA applicable to panel data.
Real estate private equity (REPE) funds are often differentiated by risk class: core, value-added, or opportunistic. Fund class is used by investors and managers to allocate funds and to describe investment policies. In this paper, we use REPE fund cash flow data from Burgiss that allow us to calculate a variety of performance metrics. For a subset of the data, we also observe characteristics of underlying fund holdings. Despite evidence that Value-Added and Opportunistic funds differ in investment composition, we show that class does not do a good job of predicting differences in performance. Unsurprisingly, greater investment in development (as assessed ex post), predicts poor performance for funds raised just before the Great Recession.
We use unique data on employee decisions in the employee stock purchase plans (ESPPs) of U.S. public firms to measure the influence of networks on investment decisions. Comparing only employees within a firm during the same election window and controlling for a metro area fixed effect, we find that the local choices of coworkers to participate in the firm’s ESPP exert a significant influence on employees’ own decisions to participate.
Commercial real estate is a major asset class, with an estimated value of more than $12 trillion in the U.S. alone. But the stay-at-home orders and business closures precipitated by the COVID-19 pandemic have the potential to negatively – and disastrously – affect commercial properties. What will the short- and long-term impacts be, which types of properties will be hardest hit and what policies can be put in place to help stem the tide of losses? UNC Kenan-Flagler Business School Professor and Leonard W. Wood Center for Real Estate Studies Faculty Advisor Andra Ghent and her colleagues examine these issues in this week’s Kenan Insight.
We introduce easy to implement regression-based methods for predicting quarterly real economic activity that use daily financial data. Our analysis is designed to elucidate the value of daily information and provide real-time forecast updates of the current (nowcasting) and future quarters.
We examine the relation between plant-level predictive analytics use and centralization of authority for more than 25,000 manufacturing plants using proprietary US Census data. We focus on headquarters authority over plants through delegation of decision-making and design of performance-based incentives.
Join part one of our two-part discussion on data privacy as we examine the integration and privacy concerns presented by contact tracing. The conversation will explore how current data protections laws address the issue, and will cover potential regulatory changes we might see in response to the current crisis.
Much has been said (and rightly so) about the catastrophic effects of the COVID-19 pandemic. But there is another side to the crisis. It’s a story of hope, based on collaboration and innovation. As healthcare needs and economic hardships intensify, entrepreneurs around the globe are stepping up to create solutions that will not only address immediate needs, but also effect long-lasting change. A panel of Kenan Institute-convened experts discussed this surge of innovation in response to COVID-19 on April 7, 2020. The full recording of this press briefing–-along with a deeper-dive analysis on the drivers of innovation amid the crisis by UNC Kenan-Flagler Professors Mahka Moeen and Chris Bingham-–is available in this week’s Kenan Insight.
Three-tiered private-label (PL) portfolio strategies (low-quality tier: economy PLs, mid-quality tier: standard PLs, and top-quality tier: premium PLs) are gaining interest around the world. Drawing on the context-effects literature, the authors postulate how the introduction of economy and premium PLs may affect the choice of mainstream-quality and premium-quality national brands (NBs) and the choice of the retailer's existing PL offering.
Immigration is one of the most contentious policy issues, and Congress has for decades failed to make any significant legislative progress. The result is an incoherent policy landscape and serious operational challenges on the ground. At the same time, immigration and immigrant integration are critical to U.S. workforce growth, government fiscal solvency, and innovation. I discuss key findings from the economics literature and their implications for where to focus immigration reform efforts.
AI has become close to bewildering in its promises, met and unmet, its terms and tools, acronyms, “use” case examples of wild successes countered by duds and disappointments. There’s an overall lack of clear pointers for business leaders to shape the direction, priorities and pace of their organization’s AI activities. Over the past two years, we have explored the widening AI space; what stood out in our reviews is that there is today a lack of management perspective on AI.