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 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.
Residential Property Assessed Clean Energy (PACE) loans allow homeowners to fund investments in green residential projects through property tax payments. We assess the housing market effects of PACE using novel loan-level data from Florida merged to property transaction, tax, and permitting records.
We evaluate the impacts of tax policy on asset returns using the U.S. municipal bond market. In theory, tax-induced ownership segmentation limits risk sharing, creating downward-sloping regions of the aggregate demand curve for the asset. In the data, cross-state variation in tax privilege policies predicts differences in in-state ownership of local municipal bonds; the policies create incentives for concentrated local ownership.
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 stark contrast with liquid asset returns, I find that commercial real estate idiosyncratic return means and variances do not scale with the holding period, even after accounting for all cash flow relevant events. This puzzling phenomenon survives controlling for vintage effects, systematic risk heterogeneity, and a host of other explanations. To explain the findings, I derive an equilibrium search-based asset-pricing model which, when calibrated, provides an excellent fit to transactions data.
We examine the relationship between MIDAS regressions and Kalman filter state space models applied to mixed frequency data. In general, the latter involves a system of equations, whereas in contrast MIDAS regressions involve a (reduced form) single equation. As a consequence, MIDAS regressions might be less efficient, but also less prone to specification errors.
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.
We study Granger causality testing for high-dimensional time series using regularized regressions. To perform proper inference, we rely on heteroskedasticity and autocorrelation consistent (HAC) estimation of the asymptotic variance and develop the inferential theory in the high-dimensional setting. To recognize the time series data structures we focus on the sparse-group LASSO estimator, which includes the LASSO and the group LASSO as special cases.
Using unique data on employee ownership plans sponsored by U.S. public companies, we find that large negative market shocks lead to active changes in portfolio choices among inexperienced and previously inattentive investors. We use employee ownership plans to identify a set of inexperienced investors who did not actively select to participate in the market and who are confronted with a difficult financial decision.
We use unique worker-plant matched panel data to measure differences in wage changes experienced by workers displaced from closing plants. We observe larger losses among women than men, comparing workers who move from the same closing plant to the same new firm. However, we find a significantly smaller gap in hiring firms with female leadership.
We empirically investigate the effects of political uncertainty on corporate tax behavior. To identify the effects of political uncertainty, we construct a data set that tracks whether firms’ tax avoidance varies systematically around the occurrence of national elections. Our dataset includes firms exposed to 103 national elections in 30 countries. We find that corporate tax avoidance varies systematically across the election cycle, peaking in election years and declining the next year. The effect on tax avoidance is greatest for elections with greater electoral uncertainty, and for elections in countries with relatively lower quality of law, relatively weaker tax enforcement, and relatively lower book-tax conformity. The evidence suggests that firms use both conforming and nonconforming tax avoidance strategies, although the results for conforming tax avoidance are marginal.
Firms should disclose information on material cyber-attacks. However, because managers have incentives to withhold negative information, and investors cannot discover most cyber-attacks independently, firms may underreport them. Using data on cyber-attacks that firms voluntarily disclosed, and those that were withheld and later discovered by sources outside the firm, we estimate the extent to which firms withhold information on cyber-attacks.
This article describes an American community survey and a survey of business owners of which the data are merged to assess the experiences of minority- versus white-owned small businesses between 2007 and 2012. This is highlighted due to it being a period encompassing the worst economic downturn since The Great Depression. White firms declined while minority firms grew rapidly. Despite recent efforts to create inclusive entrepreneurial and business ecosystems, however, minority business owners made little progress toward achieving equity or parity with white business owners. Policy prescriptions and implications for future research are discussed.
In this paper, we develop a sociodemographic profile of the most vulnerable African American older adult households. To do so, we draw data from the 2011–15 American Community Survey, which contains linked housing and person records for a 5 percent sample of U.S. population. This dataset literally allows us to peer inside of African American older adult households and in the process identify the major barriers or obstacles to aging in place.
American Community Survey data are used to develop typologies of the generational dynamics and living arrangements of the estimated 1.6 million African American older adult households who will likely encounter the most difficulty aging in place. Policy recommendations and strategies are offered to address the specific barriers and challenges that must be overcome in order for these older adults to successfully live out their lives in their homes and community.
We propose a novel method of estimating default probabilities using equity option data. The resulting default probabilities are highly correlated with estimates of default probabilities extracted from CDS spreads, which assume constant recovery rates. Additionally, the option implied default probabilities are higher in bad economic times and for firms with poorer credit ratings and financial positions.
Performance measurement and event studies frequently assume a specific stochastic process for stock returns. The purpose of this paper is to validate the predictive accuracy of various stochastic processes on data different from those used in estimating the models. The main conclusion is that multi-factor models estimated with factor analytic techniques provide more accurate forecasts than the usual market model with either an equal- or value-weighted index, and Fama–French three-factor model.
Do founders actually assimilate and leverage the knowledge from the seasoned executives who surround them? Or do they shrug it off and march to the beat of their own drum? To better understand whether founder CEOs incorporate or ignore advice from their leadership team, we collected and analyzed data on more than 2,000 companies that went public from 1997 to 2013, roughly half of which were led by founders and the other half by hired (nonfounder) CEOs.
This paper examines the cross-university variation in spin-off activity by faculty members from 124 US academic institutions, using a unique database including data on founders of both formal and informal spin-offs. Accordingly, the rate of spawning founders is positively affected by the quality of the institution and its departments, the R&D expenditure of the institution, and the strength of the local cluster.