The investigations following the attacks of September 11, 2001, showed that our ability to verify a person’s identity is crucial to our national security. As pointed out by The 9/11 Commission Report (National Commission on Terrorists Attacks Upon the United States, 2004), travel documents are as important as weapons for terrorists. To carry out an attack on American soil, foreign terrorists must cross our borders—which requires passing an identification screening. A valid passport also allows a terrorist to obtain other valid documents (e.g., driver’s license, credit cards, health insurance card) that are important to performing normal life activities while maintaining a low profile and avoiding detection. Four projects, currently in different stages of implementation, use Radio Frequency Identification (RFID) or Machine-Readable Zones (MRZ) technologies for verification and validation of identity in the United States. These programs are (1) e-Passport, (2) PASS Card, (3) Real ID, and (4) Enhanced Driver’s License. The use of RFID enables data to be stored electronically in chips embedded in identification documents and shared quickly in digital format by law enforcement personnel. Documents with RFID chips and a secure networking environment to exchange data are deemed more secure and less prone to counterfeiting than conventional, non-electronic documents. However, there is still debate about how to best balance the security benefits from RFID-enabled identification documents with concerns about privacy.
Prior research examines practitioner, investor, and executive perceptions of corporate tax planning. However, little is known about how the typical U.S. consumer views corporate tax planning. We examine consumers’ perceptions of corporate tax planning using both survey and experimental methods.
African American older adults face a major retirement crisis (Rhee, 2013; Vinik, 2015)). Owing to a legacy of racial discrimination in education, housing, employment, and wages or salaries, they are less likely than their white counterparts to have accumulated wealth over the course of their lives (Sykes, 2016). In 2013, the median net worth of African American older adult households ($56,700) was roughly one-fifth of the median net worth of white older adult households ($255,000) (Rosnick and Baker, 2014). Not surprising, given these disparities in net worth, African American older adult males (17%) and females (21%) were much more likely than their white male (5%) and female (10%) counterparts to live in poverty (Johnson and Parnell, 2016; U.S. Department of Housing and Urban Development, 2013a). They also were more likely to experience disabilities earlier in life and to have shorter life expectancies (Freedman and Spillman, 2016).
The tremendous growth in cryptocurrency trading has included frequent pump-and-dump (P&D) schemes. The resulting volatility has raised both excitement and concern about exploitation and fraud. Unlike the stock market, where P&D schemes can last for months, in the cryptocurrency market the price and volume inflations last just minutes, making it is almost impossible for those not in the pump group to participate. P&Ds are organized through pump groups who communicate through heavily encrypted message platforms. Investors learn about the groups through ads on social media. Our research examines 500 cryptocurrency P&D schemes to better understand their timing, characteristics and impact. As cryptocurrency exchanges think about regulating P&Ds, our researchers seek to understand who is currently benefiting and what these “cryptobloggers” do to the health of the cryptocurrency market.
We document what fraction of the housing stock in US cities is affordable to different family types. Rather than looking at what fraction of their income people actually pay in rent in each city, which reflects a mix of households’ ability to pay and supply conditions, we look at the extent to which the housing stock is affordable using discrete housing expenditure share cutoffs and the distribution of rents in the American Community Survey from each city.
We present a classical enhancement to improve the accuracy of the Hybrid variant (Hybrid HHL) of the quantum algorithm for solving linear systems of equations proposed by Harrow, Hassidim, and Lloyd (HHL). We achieve this by using higher precision quantum estimates of the eigenvalues relevant to the linear system, and a new classical step to guide the eigenvalue inversion part of Hybrid HHL.
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.
This paper defines risk-on risk-off (RORO), an elusive terminology in pervasive use, as the variation in global investor risk aversion. Our high-frequency RORO index captures time-varying investor risk appetite across multiple dimensions: advanced economy credit risk, equity market volatility, funding conditions, and currency dynamics. The index exhibits risk-off skewness and pronounced fat tails, suggesting its amplifying potential for extreme, destabilizing events. Compared with the conventional VIX measure, the RORO index reflects the multifaceted nature of risk, underscoring the diverse provenance of investor risk sentiment. Practical applications of the RORO index highlight its significance for international portfolio reallocation and return predictability.
The purpose of the present article is to take stock of a recent exchange in Organizational Research Methods between critics and proponents of partial least squares path modeling (PLS-PM).
The current research explores the relationship between living abroad and self-concept clarity. We conducted six studies (N = 1,874) using different populations (online panels and MBA students), mixed methods (correlational and experimental), and complementary measures of self-concept clarity (self-report and self-other congruence through 360-degree ratings).
In this paper, we compare several approaches of producing multi-period-ahead forecasts within the GARCH and RV families – iterated, direct, and scaled short-horizon forecasts. We also consider the newer class of mixed data sampling (MIDAS) methods.
We examine whether the contribution of firm-level accounting earnings to the informativeness of the aggregate is tilted towards earnings with specific financial reporting characteristics. Specifically, we investigate whether considering the smoothness of firm-level earnings increases the informativeness of aggregate earnings for future real GDP, and if so, whether macroeconomic forecasters use this information efficiently. Using recently-developed mixed data sampling methods, we find that the aggregate is tilted towards firms with smoother earnings and that this composition of aggregate earnings outperforms traditional weighting schemes.
We study how an improvement in contracting institutions due to the 1999 U.S.-China bilateral agreement affects U.S. firms’ innovation. We show that U.S. firms operating in China decrease their process innovations—innovations that improve firms’ own production methods—following the agreement.
Simulation-based estimation methods have become more widely used in recent years. We propose a set of tests for structural change in models estimated via simulated method of moments (see Duffe and Singleton (Econometrica 61 (1993) 929).
Time series regression analysis relies on the heteroskedasticity- and auto-correlation-consistent (HAC) estimation of the asymptotic variance to conduct proper inference. This paper develops such inferential methods for high-dimensional time series regressions.
Collective action is critical for successful market formation. However, relatively little is known about how and under what conditions actors overcome collective action problems to successfully form new markets. Using the benefits of simulation methods, we uncover how collective action problems result from actor resource allocation decisions interacting with each other and how the severity of these problems depends on central market- and actor-related characteristics.
Following state-level legal changes that increase labor dismissal costs, firms increase their innovation in new processes that facilitate the adoption of cost-saving production methods, especially in industries with a large share of labor costs in total costs. Firms with high innovation ability exhibit larger increases in process innovation and capital-labor ratios, an effect driven by both increases in capital investment and decreases in employment. By facilitating the adjustment of the input mix when conditions in input markets change, innovation ability allows firms to mitigate value losses and is a key driver of their performance.
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.
Technology acquisitions are increasingly prevalent, but their failure rate is notoriously high. Although extant research suggests that collaboration may improve acquisition success, relatively little is known about how firms cultivate collaboration during postmerger integration (PMI) of technology acquisitions. Using inductive multiple-case methods, we address this gap.