Nonfinancial reporting regulation and challenges in sustainability disclosure and corporate governance practices

This paper aims to investigate how the shift from voluntary to mandatory nonfinancial information started by the European Union (EU) Directive 95/2014 may influence corporate practices. In particular, this research presents a paradigmatic case study to highlight relevant changes in reporting strategy and corporate governance adopted by an Italian listed company that never disclosed sustainability information before the transposition of the EU Directive into the Legislative Decree 254/2016. In this scenario, new obligations on nonfinancial reporting were not perceived as mere additional administrative duties and the nonfinancial statement became an opportunity to communicate company's paths towards sustainability, guaranteeing transparency and greater stakeholders' engagement. Our findings go beyond prior studies pointing out how organisations adopt strategic and tactical responses to the pressure stemming from the external environment. Additionally, it highlights the pivotal function played by the internal audit in setting up the direction of change. This research has theoretical and practical contributions for academic communities, policymakers and practitioners.     

Asymmetric shocks among U.S. states

The paper applies a factor model to the study of risk sharing among U.S. states. The factor model makes it possible to disentangle movements in output and consumption due to national, regional, or state-specific business cycles from those due to measurement error. The results of the paper suggest that some findings of the previous literature which indicate a substantial amount of inter-state risk sharing may be due to the presence of measurement error in output. When measurement error is properly taken into account, the evidence points towards a lack of inter-state smoothing.     

Run length analysis of Shewhart charts applied to drifting processes under an integrative SPC/EPC model

Engineering Process Controllers (EPC) are frequently based on parametrized models. If process conditions change, the parameter estimates used by the controllers may become biased, and the quality characteristics will be affected. To detect such changes it is adequate to use Statistical Process Control (SPC) methods. The run length statistic is commonly used to describe the performance of an SPC chart. This paper develops approximations for the first two moments of the run length distribution of a one-sided Shewhart chart used to detect two types of process changes in a system that is regulated by a given EPC scheme: i) changes in the level parameter; ii) changes in the drift parameter. If the drift parameter shifts, it is further assumed that the form of the drift process changes from a linear trend under white noise (the in-control drift model) into a random walk with drift model. Two different approximations for the run length moments are presented and their accuracy is numerically analyzed. Received: August 1998     

The Impact of American Politics on Perceptions of Women's Golfing Abilities

We examine the relationship between political environments and perceptions of women's physical abilities. Using a sample of 496 golf courses located in the United States, we find a significant relationship between state political affiliations, ratings of senators and congressional representatives on a liberal to conservative continuum, and perceptions of gendered physical abilities. Institutional theory is presented as an explanation for the regional variation in perceptions of women's golfing abilities. Implications and results are discussed. Suggestions for further research are presented.     

Big data in smart tourism: challenges,issues and opportunities

In the era of digital transformation, Big Data have assumed a crucial role in changing the global travel and providing significant challenges and opportunities for established companies, as well as new entrants into the tourism industry. All these companies can get valuable information on Big Data for predicting tourist demand, enabling better decision-making, managing knowledge flows and interaction with customers, and providing the best service in a more efficient and effective way. This can result in improved productivity, increased customer satisfaction, personalized marketing campaigns, and more efficient operations. However, open research issues about the role of Big Data in the tourism industry can be still recognized. With these premises, this Editorial aims to present the articles included in the special issue of Current Issues in Tourism titled ‘Big data in smart tourism: challenges, issues and opportunities’, which has called for research contributing to the recent debates on the implications and challenges of the adoption of Big Data to improve the competitiveness of tourism destinations and companies. Main topics considered by the accepted articles include a literature review proposing a novel theoretical investigative frameworks, metrics and critical dimensions, and empirical investigations of the use of Big Data in different tourism contexts.     

Firm-Level Evidence on International Stock Market Comovement *

We explore the link between international stock market comovement and the extent to which firms operate globally. Using stock returns and balance sheet data for companies in 20 countries, we estimate a factor model that decomposes stock returns into global, country-and industry-specific shocks. We find a large and statistically significant link for global shocks. A firm raising its international sales by 10 percent raises the exposure of its stock return to global shocks by two percent. This link has grown stronger over time since the mid-1980s. We find no similarly robust link between international sales and exposure to country-specific shocks. * We are grateful to Marcelle Chauvet, Kathryn Dominguez, Kristin Forbes, Geert Rouwenhorst, Dan Waggoner, participants in the Atlanta Fed Finance Brown Bag, the IMF conference on “Global Linkages”, and the Kiel Institute for World Economics workshop on multinationals for their suggestions. We are especially grateful to Franklin Allen, Marco Pagano, and two anonymous referees for extensive comments on earlier drafts of this paper. Finally, we wish to thank Menzie Chinn for sharing his capital account liberalization measure, Iskander Karibzhanov for translating some of our code into C and Young Kim for excellent research assistance.     

Open data governance: civic hacking movement,topics and opinions in digital space

Quality & Quantity - The expression ‘open data’ relates to a system of informative and freely accessible databases that public administrations make generally available online in...     

RARE SHOCKS,GREAT RECESSIONS

We estimate a DSGE (dynamic stochastic general equilibrium) model where rare large shocks can occur, by replacing the commonly used Gaussian assumption with a Student's t‐distribution. Results from the Smets and Wouters (American Economic Review 2007; 97 : 586–606) model estimated on the usual set of macroeconomic time series over the 1964–2011 period indicate that (i) the Student's t specification is strongly favored by the data even when we allow for low‐frequency variation in the volatility of the shocks, and (ii)) the estimated degrees of freedom are quite low for several shocks that drive US business cycles, implying an important role for rare large shocks. This result holds even if we exclude the Great Recession period from the sample. We also show that inference about low‐frequency changes in volatility—and, in particular, inference about the magnitude of Great Moderation—is different once we allow for fat tails. Copyright © 2014 John Wiley & Sons, Ltd.