We model callers' decision making process in call centers as an optimal stopping problem. After each period of waiting, a caller decides whether to abandon or to continue to wait. The utility of a caller is modeled as a function of her waiting cost and reward for service.
We explore whether the actions of one regulator can affect the efficacy of another regulator. We investigate this idea in the context of environmental enforcement, which is a primary mechanism to combat industrial pollution and climate change. Specifically, we examine whether bank regulatory oversight affects the ability of environmental enforcement to reduce industrial emissions. We predict that bank regulatory oversight can constrain the availability of bank loans, hindering firms' ability to obtain financing for greener technologies and thus mitigating the efficacy of environmental enforcement.
Nonwage benefits have become increasingly important and now represent 30% of total compensation (BLS, 2021). Using administrative data on health insurance, retirement, and leave benefits, we find dramatically lower within-firm variation in benefits than in wages. We also document sharply higher between-firm variation in nonwage benefits, compared to wages. We argue that this pattern can be a consequence of nondiscrimination regulations and the high administrative burden of managing too many or complex plans
This paper characterizes the implications of risk-on/risk-off shocks for emerging market capital flows and returns. We document that these shocks have important implications not only for the median of emerging markets flows and returns but also for the left tail.
We examine the impact of four classes of workplace interruptions on short-term (working hours) and long-term (across-shifts) worker performance in an agribusiness setting. The interruptions are organized in a two-by-two framework where they result (or do not result) in a physical task requirement and lead to a varying degree of attention shift from the primary task.
The proliferation of smartphones has spawned a new industry – mobile apps. Managers increasingly recognize the potential for mobile commerce apps to “engage” customers and thereby grow sales. To measure this potential, this paper examines what drives customer usage of apps and whether app usage drives purchases in the online and offline channels.
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 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.
Recent studies emphasize that survey-based inflation risk measures are informative about future inflation and thus useful for monetary authorities. However, these data are typically available at a quarterly frequency whereas monetary policy decisions require a more frequent monitoring of such risks.
Suppose one uses a parametric density function based on the first four (conditional) moments to model risk. There are quite a few densities to choose from and depending on which is selected, one implicitly assumes very different tail behavior and very different feasible skewness/kurtosis combinations.
This paper deals with the estimation of the risk-return trade-off. We use a MIDAS model for the conditional variance and allow for possible switches in the risk-return relation through a Markov-switching specification. We find strong evidence for regime changes in the risk-return relation.
We develop Granger causality tests that apply directly to data sampled at different frequencies. We show that taking advantage of mixed frequency data allows us to better recover causal relationships when compared to the conventional common low frequency approach.
The rapid adoption of remote work led to a sharply reduced presence of office workers in urban centers, weakening cities' traditional role as a center for production. Despite the adverse effect of remote work on cities, we highlight that cities' role as a center for consumption remains strong and may have risen with increased time flexibility from workers.
We find that Exchange-Traded Funds (ETFs) are more expensive to borrow than stocks, and we provide an explanation for this difference. This phenomenon is due to features specific to the ETF lending market rather than due to the stocks the ETFs hold, as ETF loan fees tend to be higher than the average of their constituent stocks. We find that for most indices, one ETF tends to capture the majority of the short interest. This "short favorite" ETF tends to have low loan fees, while the "non-favorite" ETFs tend to be much more expensive to short and are less liquid.
Formal theory and empirical research are complementary in building and advancing the body of knowledge in accounting in order to understand real-world phenomena. We offer thoughts on opportunities for empiricists and theorists to collaborate, build on each other’s work, and iterate over models and data to make progress.
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.
The psychology literature documents that individuals derive current utility from their beliefs about future events. We show that, as a result, investors in financial markets choose to disagree about both private and price information. When objective price informativeness is low, each investor dismisses the private signals of others and ignores price information. In contrast, when prices are sufficiently informative, heterogeneous interpretations arise endogenously: most investors ignore prices, while the rest condition on it.
Using 391 high-skilled firm entries in the U.S. from 1990–2010, we estimate the effects of the firm entry on incumbent residents’ consumption, finances, and mobility. We compare outcomes for residents living close to the entry location with those living far away while controlling for their proximity to potential high-skilled firm entry sites.
We examine the effects of an annual government social safety net payment on crime by leveraging geographic and intertemporal variation in the magnitude and timing of earned income tax credit (EITC) payments, combined with crime micro-data.
We use detailed establishment-level data to understand whether and how the composition of the US stock market differs from the composition of US firms as a whole. Although the locational composition of employment in public firms is similar to that of all US firms, we find certain industries significantly overrepresented. Further, the gap between the industrial composition of publicly traded firms and all US firms has grown over the last thirty years.