本文精选了管理学领域国际顶刊《Management Science》近期发表的论文，提供管理学领域最新的学术动态。

Stockpiling at the Onset of the COVID-19 Pandemic: An Empirical Analysis of National Prescription Drug Sales and Prices

原刊和作者：

Management Science Volume 70, Issue 10

Minje Park （Columbia Business School）

Anita L. Carson （Boston University）

Erin R. Fox （University of Utah Health）

Rena M. Conti （Boston University）

Abstract

At the onset of the coronavirus (COVID-19) pandemic, hospitals experienced a demand surge for COVID-19–related medical care while simultaneously struggling to source prescription drugs needed to manage COVID-19 patients. This is worrisome as shortfalls in the supply of essential drugs can negatively impact patient outcomes. The popular press reporting on these challenges suggests that they are caused by supply chain disruptions. However, rigorous research on the relationship between the pandemic and prescription drug supply is limited. To address this gap, we leverage a quasi-experimental design and IQVIA’s National Sales Perspectives™ data from 2018 to 2020. We focus on prescription drugs indicated for the management of COVID-19 patients and a set of control drugs (i.e., drugs not used for COVID-19). We find that in the early phases of the pandemic, hospitals stockpiled prescription drugs indicated for the management of COVID-19, making this behavior an under-recognized cause of the sourcing challenges. The sales volume of drugs indicated for COVID-19 management was concentrated in the first two months of the pandemic, after which sales decreased significantly despite a nationwide increase in COVID-19–related hospitalizations. We investigate another potential cause of stockpiling: expected price increases. Counter to concerns of price gouging, we find little evidence of price inflation for these drugs. An implication for drug manufacturers is that orders due to stockpiling by downstream buyers early on in a pandemic may need to be discounted when predicting future demand. Our results have implications for drug suppliers, hospitals, and policymakers interested in improving medical product supply chain resilience.

Link: https://doi.org/10.1287/mnsc.2021.04150

Calibrating Sales Forecasts in a Pandemic Using Competitive Online Nonparametric Regression

原刊和作者：

Management Science Volume 70, Issue 10

David Simchi-Levi （Massachusetts Institute of Technology）

Rui Sun （Amazon）

Michelle Xiao Wu （Massachusetts Institute of Technology）

Ruihao Zhu （Cornell University）

Abstract

Motivated by our collaboration with Anheuser-Busch InBev (AB InBev), a consumer packaged goods (CPG) company, we consider the problem of forecasting sales under the coronavirus disease 2019 (COVID-19) pandemic. Our approach combines nonparametric regression, game theory, and pandemic modeling to develop a data-driven competitive online nonparametric regression method. Specifically, the method takes the future COVID-19 case estimates, which can be simulated via the susceptible-infectious-removed (SIR) epidemic model as an input, and outputs the level of calibration for the baseline sales forecast generated by AB InBev. In generating the calibration level, we focus on an online learning setting where our algorithm sequentially predicts the label (i.e., the level of calibration) of a random covariate (i.e., the current number of active cases) given past observations and the generative process (i.e., the SIR epidemic model) of future covariates. To provide robust performance guarantee, we derive our algorithm by minimizing regret, which is the difference between the squared ?2-norm associated with labels generated by the algorithm and labels generated by an adversary and the squared ?2-norm associated with labels generated by the best isotonic (nondecreasing) function in hindsight and the adversarial labels. We develop a computationally efficient algorithm that attains the minimax-optimal regret over all possible choices of the labels (possibly non-i.i.d. and even adversarial). We demonstrate the performances of our algorithm on both synthetic and AB InBev’s data sets of three different markets (each corresponds to a country) from March 2020 to March 2021. The AB InBev’s numerical experiments show that our method is capable of reducing the forecast error in terms of weighted mean absolute percentage error (WMAPE) and mean squared error (MSE) by more than 37% for the company.

Link: https://doi.org/10.1287/mnsc.2023.4969

CEO Activism and Firm Value

原刊和作者：

Management Science Volume 70, Issue 10

Anahit Mkrtchyan （University of Massachusetts–Amherst）

Jason Sandvik （University of Arizona）

Vivi Z. Zhu （Southern Methodist University）

Abstract

We investigate the increasingly common practice of chief executive officers (CEOs) taking public stances on social and political issues (CEO activism). We find that CEO activism stems from a CEO’s personal ideology and its alignment with investor, employee, and customer ideologies. We show that CEO activism results in positive market reactions. Furthermore, firms with CEO activism realize increased shareholdings from investors with a greater liberal leaning, who rebalance their portfolios toward these firms. Our results suggest that investors’ socio-political preferences are an important channel through which CEO activism affects equity demand and stock prices. Notably, CEOs are less likely to be fired when their activist stances generate positive market responses.

Link: https://doi.org/10.1287/mnsc.2023.4971

Let’s Chat… When Communication Promotes Efficiency in Experimental Asset Markets

原刊和作者：

Management Science Volume 70, Issue 10

Brice Corgnet （EMLYON Business School）

Mark DeSantis （Chapman University）

David Porter （Chapman University）

Abstract

The growing prevalence of stock market chat rooms and social media suggests that communication between traders may affect market outcomes. Using data from a series of laboratory experiments, we study the causal effect of trader communication on market efficiency. We show that communication allows markets to convey private information more effectively. This effect is robust to a wide range of information settings. The presence of insiders limits the impact, whereas posted reputation scores in the communication platform magnify it. These findings illustrate the need to consider social interactions when designing market institutions to leverage the social motives that foster information aggregation.

Link: https://doi.org/10.1287/mnsc.2023.4967

Organized Crime and Firms: Evidence from Antimafia Enforcement Actions

Management Science Volume 70, Issue 10

Pablo Slutzky （University of Maryland）

Stefan Zeume （University of Illinois at Urbana-Champaign）

Abstract

We exploit staggered municipality-level antimafia enforcement actions in Italy over the 1995–2015 period to study how the presence of organized crime affects firms. Following enforcement actions, we find increases in competition (i) among firms and (ii) for public procurement contracts. Firms that do not exit after a weakening of organized crime shrink in size, more so when operating in the nontradable sector. Our findings are consistent with organized crime acting as a barrier to entry and affecting local economic activity.

Link: https://doi.org/10.1287/mnsc.2021.00859