This paper examines the internal anatomy of regional social capital and develops a role for dealmakers – individuals who provide active regional stewardship. An empirical analysis of twelve US regions finds great variation in the presence of dealmakers. The strong local presence of dealmakers is correlated with high start-up rates. The empirical results suggest that the local presence of dealmakers is more important for successful entrepreneurship than aggregate measures of regional entrepreneurial and investors network. Moreover, it is found that the presence of dealmakers is a better predictor of the status of the regional entrepreneurial economy.
This white paper develops a demographic profile of the elderly population in the Carolinas1 and presents the results of a literature search which identified both promising initiatives and programmatic gaps where new and innovative efforts are needed to foster and facilitate successful aging in place for seniors. As a launch pad for future discussion around defining The Duke Endowment’s (TDE) role in this space moving forward, a concluding section highlights strategies worthy of consideration for promoting successful aging in the Carolinas.
This paper examines the spillover effects of U.S. unconventional monetary policy (UMP) on emerging market capital flows and asset prices. Affine term structure model estimates show that U.S. monetary policy shocks, identified with high-frequency Treasury futures data, represent revisions to expected short-term yields and term premia, especially during the UMP period. The policy shocks exhibit sizable effects on U.S. holdings of emerging market assets. These effects disproportionately manifest through valuation changes versus physical flows, are more pronounced for equity relative to bond markets, and are asymmetric between the quantitative easing and tapering periods, with flows more important during the unwinding.
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 investigates the extent to which delayed expected loan loss recognition (DELR) is associated with greater vulnerability of banks to three distinct dimensions of risk: (1) stock market liquidity risk, (2) downside tail risk of individual banks, and (3) codependence of downside tail risk among banks.
Beginning with Anderson, Banker, and Janakiraman (2003), a rapidly growing literature attributes the short-run asymmetric cost response to activity changes (i.e., sticky costs) as resulting from short-run managerial choices. In this paper, we are agnostic on the theory of sticky costs. Rather, we focus on empirical tests of cost stickiness.
In this paper, we empirically examine differences in subprime borrower default decisions by Census tract characteristics in order to clarify how the subprime foreclosure crisis played out in minority areas. An innovation in our modeling approach is that we do not constrain the impact of neighborhood composition to be identical across diverse decision-making settings.
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.
This paper studies fiscal policy design in an economy in which (i) the representative household has recursive preferences, and (ii) growth is endogenously sustained through innovations whose market value depends on the tax system.
Brick-and-mortar (B&M) retailers must enhance the customer in-store experience to better compete with online retailers. Fitting rooms in B&M stores play a critical role in the customer experience as a venue to experience products and examine alternatives. High traffic in fitting rooms, however, obstructs the customer’s ability to choose a product. In this paper, we (1) examine the impact of fitting room traffic on store performance using archival data, (2) identify phantom stockouts as a plausible mechanism for this impact, and (3) provide a potential solution and quantify the magnitude of its impact using two field experiments.
In this paper, we study within firm heterogeneity in the discounts offered to consumers. Utilizing transaction level data from a large home appliance retailer, we quantify the extent of both across and within-salesperson heterogeneity in the discounts they negotiate with consumers.
Does the way that individuals pay for a good or service influence the amount of connection they feel after the purchase has occurred? Employing a multi-method approach across four studies, individuals who pay using a relatively more painful form of payment (e.g., cash or check) increase their post-transaction connection to the product they purchased and/or the organization their purchase supports in comparison to those who pay with less painful forms of payment (e.g., debit or credit card).
This paper presents an empirical investigation of the effect of changes in capital gains tax rate on stock return volatility. We focus on two observable cross-sectional variations in the extent to which changes in capital gains tax rate affect return volatility — unrealized capital gains and dividend distributions.
This paper examines the differences in the behaviors of high (HIT) and low inventory turnover (LIT) retailers in responding to demand shocks. We identify quantity and price responsiveness as two mediating mechanisms that distinguish how high and low inventory turnover retailers manage demand shocks.
We present a novel source of disagreement grounded in decision theory: ambiguity aversion. We show that ambiguity aversion generates endogenous disagreement between a firm's insider and outside shareholders, creating a new rationale for corporate governance systems.
In this paper, we introduce the role of big data in humanitarian settings and discuss data streams which could be utilized to develop descriptive, prescriptive and predictive models to significantly impact the lives of people in need.
In this paper we present a framework for linking smart products (with embedded real-time diagnostics and prognostics based health management capabilities) to a service provisioning system to create a system of ―self-aware product-centric systems. The framework includes a powerful ―learning engine capable of monitoring, analyzing and interpreting patterns of system/product behavior in real-time. The learning engine provides the capability of information feedback for real-time, ―in-the-loop control. This concept enables the service-provisioning network to provide customer services such as product health management at reduced maintenance costs, improved responsiveness to customer needs during use, and generally more efficient operations.
Public health surveillance systems routinely process massive volumes of data to identify health adverse events affecting the general population. Surveillance and response to foodborne disease suffers from a number of systemic and other delays that hinder early detection and confirmation of emerging contamination situations. In this paper we develop an answer set programming (ASP) application to assist public health officials in detecting an emerging foodborne disease outbreak by integrating and analyzing in near real-time temporally, spatially and symptomatically diverse data. These data 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. We encode geographic ontologies in ASP to infer spatial relationships that may not be evident using traditional statistical tools. These technologies and ontologies have been implemented in a new informatics tool, the North Carolina Foodborne Events Data Integration and Analysis Tool (NCFEDA). The application was built to demonstrate the potential of situational awareness—created through real-time data fusion, analytics, visualization, and real-time communication—to reduce latency of response to foodborne disease outbreaks by North Carolina public health personnel.
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.
There is a growing interest in the industry around 3D printing. A related phenomenon is personal fabrication (PF) in which a firm sells products' design and lets the customers personalize and manufacture the product using 3D printing services. In this paper, we characterize the market and operational conditions that make PF an attractive operational strategy.