The Role of Corporate Boards: A Roundtable Discussion of Where We're Going and Where We've Been

In this roundtable that took place at the 2016 Millstein Governance Forum at Columbia Law School, four directors of public companies discuss the changing role and responsibilities of corporate boards. In response to increasingly active investors who are looking to management and boards for more information and greater accountability, the four panelists describe the growing demands on boards for both competence and commitment to the job. Despite considerable improvements since the year 2000, and especially since the 2008 financial crisis, the clear consensus is that U.S. corporate directors must become more like owners of the corporation who “truly represent the long‐term interests of all of the shareholders.” But if activist investors appear to pose the most formidable new challenge for corporate directors—one that has the potential to lead to shortsighted managerial decision‐making—there has been another, less visible development that should be welcomed by wellrun companies that are investing in their future growth as well as meeting investors’ expectations for current performance. According to Raj Gupta, who serves on the boards of HewlettPackard, Delphi Automotive, Arconic, and the Vanguard Group,

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The Making of Oil-backed Indigenous Capitalism in Nigeria

There has been considerable growth in the past few years in the number and size of Nigerian companies providing services to the oil and gas industry. The capacity of these companies, enhanced by local content policy, will not only create economic development in the industry, but will also likely boost the development of Nigeria's manufacturing and service economies. While ‘Nigerian content’ faces many challenges in terms of implementation, it has made and will continue to make a significant impact on the economy of Nigeria and may also be making a significant impact on the political economy. Powerful interests have helped ensure that the Nigerian elite will be the primary beneficiaries of Nigerian content. Through interviews and case studies, this article argues that by embracing Nigerian content as a new strategy of accumulation, the elite are creating more capitalistic social relations of production in Nigeria.     

Non-linear Co-integration between Nominal Interest Rates and Inflation: An Examination of the Fisher Hypothesis for Asian Countries

Abstract

This paper examines the long-run relationship between nominal interest rates and inflation for a group of Asian countries over the period February 1973–April 2007. We argue that the empirical failure to find evidence supporting the Fisher effect in previous studies may be attributed to the presence of non-linearities in the long-run relationship between nominal interest rates and inflation. We present evidence that the Fisher relation contains significant logistic smooth transition autoregression (LSTAR)-type non-linearity. This type of non-linearity is consistent with inflation targeting and the opportunistic behavior of policy-makers. Applying a non-linear unit root test to the residuals obtained from the Fisher relation decisively rejects the null hypothesis of a unit root against the alternative of non-linear but globally stationary in all the cases.     

The J-Curve phenomenon in European transition economies: A nonlinear ARDL approach

This article examines the J-curve phenomenon for 16 European transition economies. While previous studies assume a linear relationship between the exchange rate and the trade balance, this paper allows for nonlinearity. Following Bahmani-Oskooee and Fariditavana (2015 Bahmani-Oskooee, M., and H. Fariditavana. 2015. “Nonlinear ARDL Approach, Asymmetric Effects and the J-curve.” Journal of Economic Studies 42 (3): 519–530. doi:10.1108/JES-03-2015-0042.[Crossref], [Web of Science ®] , [Google Scholar], 2016 Bahmani-Oskooee, M., and H. Fariditavana. 2016. “Nonlinear ARDL Approach and the J-curve Phenomenon.” Open Economies Review 27 (1): 51–70. doi:10.1007/s11079-015-9369-5.[Crossref], [Web of Science ®] , [Google Scholar]), the empirical method used is the nonlinear cointegrating autoregressive distributed lag (NARDL) model of Shin et al. (2013 Shin, Y., B. Yu, and M. J. Greenwood-Nimmo. 2013. “Modelling Asymmetric Cointegration and Dynamic Multipliers in a Nonlinear ARDL Framework.” In Festschrift in Honor of Peter Schmidt, edited by William C. Horrace and Robin C. Sickles. New York, NY: Springer Science and Business Media. [Google Scholar]) in which short-run and long-run nonlinearities are introduced via positive (appreciation) and negative (depreciation) partial sum decompositions of the real exchange rate. We argue that the lack of support for the J-curve phenomenon could be due to the linearity assumption. This issue is examined by utilizing the linear and the NARDL models. Using the linear autoregressive distributed lag (ARDL) model, we are unable to find support for the J-curve phenomenon in any case. However, when the NARDL model is used, we are able to find evidence for the J-curve in 12 out of the 16 countries. This suggests that allowing for nonlinearity in the adjustment process is important when studying the J-curve phenomenon.     

Clustering-based classification of road traffic accidents using hierarchical clustering and artificial neural networks

Artificial neural networks (ANNs) have been widely used in predicting the severity of road traffic crashes. All available information about previously occurred accidents is typically used for building a single prediction model (i.e., classifier). Too little attention has been paid to the differences between these accidents, leading, in most cases, to build less accurate predictors. Hierarchical clustering is a well-known clustering method that seeks to group data by creating a hierarchy of clusters. Using hierarchical clustering and ANNs, a clustering-based classification approach for predicting the injury severity of road traffic accidents was proposed. About 6000 road accidents occurred over a six-year period from 2008 to 2013 in Abu Dhabi were used throughout this study. In order to reduce the amount of variation in data, hierarchical clustering was applied on the data set to organize it into six different forms, each with different number of clusters (i.e., clusters from 1 to 6). Two ANN models were subsequently built for each cluster of accidents in each generated form. The first model was built and validated using all accidents (training set), whereas only 66% of the accidents were used to build the second model, and the remaining 34% were used to test it (percentage split). Finally, the weighted average accuracy was computed for each type of models in each from of data. The results show that when testing the models using the training set, clustering prior to classification achieves (11%–16%) more accuracy than without using clustering, while the percentage split achieves (2%–5%) more accuracy. The results also suggest that partitioning the accidents into six clusters achieves the best accuracy if both types of models are taken into account.     

A neoclassical model of acreage response for labor-intensive agriculture: The case of Egypt

Acreage response studies of both developed and less developed countries have consistently misspecified the neoclassical model of profit maximization by relying onad hoc models which failed to account properly for the effects of input prices, yield changes and total acreage scale. Using Egyptian aggregate time-series data and maximum-likelihood estimation techniques (of dynamic systems), the present study attempts to model and test the full implications of cultivators rational behaviour as well as the technical and institutional peculiarities of Egyptian agriculture, in the spirit of neoclassical production theory. In addition to prices and government controls, the model introduced the wage rate, productivity changes and total agricultural area as determinants of acreage mix; these variables gain special importance (policy-wise) as we deal with the process of agricultural development in LDCs with labor-intensive agriculture. The estimates obtained are remarkably stable, econometrically sound and, as such, are believed to have come closer to avoiding specification bias than previous studies.     

Cognitive science and decision making research in accounting

The purpose of this paper is to discuss the relevance of cognitive science to decision-making research in accounting. First, the context into which it interfaces the field of accounting is reviewed. Next, the cognitive science applications to accounting are presented and evaluated as they relate to four levels of investigation. The paper concludes with implications for future research as well as a discussion of methodological issues which must be addressed.     

Using Google Scholar citations to rank accounting programs: a global perspective

We conduct an assessment on accounting program research performance based on Google Scholar citations for all articles from a set of 23 quality accounting journals during 1991–2010. Our work is a new approach in accounting by directly measuring the impact of the faculty research in accounting programs. We find that the top-5 accounting programs are the University of Pennsylvania, the University of Chicago, Stanford University, the University of Michigan, and Harvard University. These top programs produce a large number of high impact articles. In addition, using the mean citations from all articles in a journal, we find that the Review of Accounting Studies (RAST) is a top-5 journal, replacing Contemporary Accounting Research (CAR).