Correction to: using accounting earnings and aggregate economic indicators to estimate firm-level systematic risk

Review of Accounting Studies - A Correction to this paper has been published: https://doi.org/10.1007/s11142-021-09637-1     

Impact factor as a metric to assess journals where OM research is published

This paper investigates impact factor as a metric for ranking the quality of journal outlets for operations management (OM) research. We review all prior studies that assessed journal outlets for OM research and compare all previous OM journal quality rankings to rankings based on impact factors. We find that rankings based on impact factors that use data from different time periods are highly correlated and provide similar rankings of journals using either two-year or five-year assessment periods, either with or without self-citations. However, some individual journals have large rank changes using different impact factor specifications. We also find that OM journal rankings based on impact factors are only moderately correlated with journal quality rankings previously determined using other methods, and the agreement among these other methods in ranking the quality of OM journals is relatively modest. Thus, impact factor rankings alone are not a replacement for the assessment methods used in previous studies, but rather they evaluate OM journals from another perspective.     

Endogenous Public Policy, Politicization and Welfare

In the two–stage political–economic game that we study public policy is the outcome of the interaction between interest groups and a two–tier government. Implementation of a policy proposed by a bureaucrat requires approval by an elected politician. The objective function of the bureaucrat hinges on the weight assigned to social welfare relative to the rent–seeking outlays of the interest groups. This weight represents the degree of politicization of the government. Our main result is that, in contrast to common belief, increased politicization need not adversely affect the public well–being.     

Why Pay Taxes When No One Else Does?

In this paper we try to understand the phenomenon whereby a large proportion of the population evades tax payments. We present a model which incorporates elements from the theory of information cascades with the standard model of tax evasion and analyze the connection between the decision of a potential tax evader, the number of tax evaders, and the number caught in previous periods. General conditions exist under which any expected utility maximizing potential tax evaders will decide to emulate other tax evaders.     

A framework for agro-terrorism intentions detection using overt data sources

Agro-terrorism is a hostile attack, towards an agricultural environment, including infrastructures and processes, in order to significantly damage national and international political interests. This article provides a framework for reducing agro-terrorism-related risks by either means of foresight (prevention) or early detection of exotic/foreign pathogenic agents and their dispersion patterns. It focuses on intention detection using overt data sources on the World Wide Web as they relate to agro-terrorism threats. The paper defines agro-terrorism, examines data characteristics, identifies weaknesses among the intelligence community that must be addressed, then integrates the classical intelligence cycle for early detection that may lead to prevention of such acts.     

An application of padé approximation to volatility modeling

This paper studies the dynamic relationship between future volatility and implied and historical volatility using data from Alegría and Rodríguez [1997]. The methodological approach forms part of the theoretical research being carried out in numerical analysis and especially from the rational Padé approximation. This tool helps to characterize simple computational methods in order to study questions about dynamic modelization of doubly infinite series. Considering noncausal series, where the expectations provided by economic theory or empirical evidence are included in available sample data, leads to dynamic specifications suitable not only from the perspective of fitting available data, but also from their predictive performance when compared to traditionally used formulae based merely on data analysis.     

Learnability and rationality of choice

In this paper we study the learnability of the class of rationalizable choice functions using the basic concept of PAC-learnability from statistical learning theory. We prove that the class of rationalizable choice functions on N alternatives is learnable from O(N) examples and is optimal in terms of PAC-learnability among classes which are invariant under permutations of the elements.     

Relational Contracting in Developed Economies: Lessons From Slot Exchanges in the US Airline Industry

This paper highlights the widespread use of relational contracting in developed economies. While the number of empirical studies on relational practices in developing countries is increasing rapidly, evidence from industries and countries characterised by strong institutions is lagging behind due to data constraints. We argue that technological progress and strong institutions do not diminish the use of relational contracting, and use the US airline industry as a case in point. In particular, we discuss a number of factors (including transaction complexity, existence of collaborative relationships and data availability) that make this industry an ideal setting to study relational contracting in a developed economy. Moreover, we argue that other industries in developed countries share the properties of the US airline industry and, hence, can be used as a basis to investigate relational contracting in future work.     

Statistical Inference,Learning and Models in Big Data

The need for new methods to deal with big data is a common theme in most scientific fields, although its definition tends to vary with the context. Statistical ideas are an essential part of this, and as a partial response, a thematic program on statistical inference, learning and models in big data was held in 2015 in Canada, under the general direction of the Canadian Statistical Sciences Institute, with major funding from, and most activities located at, the Fields Institute for Research in Mathematical Sciences. This paper gives an overview of the topics covered, describing challenges and strategies that seem common to many different areas of application and including some examples of applications to make these challenges and strategies more concrete.     

Is emission intensity or output U‐shaped in the strictness of environmental policy?

We show that, in a range of market conditions, an ever stricter environmental policy does not always lead to ever cleaner production methods and ever lower production of polluting goods. We consider an integrated technology, where firms can reduce their emission intensities in a continuous fashion. Analogous to the previous literature we find that firms' emission intensities can be U‐shaped in the strictness of policy, but we show that this applies only under low profitability conditions. Under high profitability conditions, output levels are U‐shaped in the strictness of the policy. The latter result is new in the literature. In the case where the U‐shape arises in emission intensities, the minimum is reached where the marginal abatement cost curves intersect.