Headquarters,office employment,and the wave of urbanization in the New York City region

Hierarchical theory suggests that high-density office activity, such as corporate headquarters, epitomizes the concept of agglomeration. This research tests whether office employment in a metropolitan area agglomerates around suburban nodes of specialized office and corporate headquarter activity or if office employment change shifts in response to the wave of urbanization. The location of the Fortune 500 manufacturing and service headquarters and the ratio profiles of office employment within each county are used in the test. We conclude that headquarters are not located in specialized office employment nodes. Rather, the office employment becomes specialized as the county becomes more urbanized.     

Searching for big data: How incumbents explore a possible adoption of big data technologies

Big data is often described as a new frontier of IT-enabled competitive advantage. A limited number of exemplary firms have been used recurrently in the big data debate to serve as successful illustrations of what big data technologies can offer. These firms are well-known, data-driven organizations that often, but not always, are born digital companies. Comparatively little attention has been paid to the challenges that many incumbent organizations face when they try to explore a possible adoption of such technologies. This study investigates how incumbents handle such an exploration and what challenges they face. Drawing on a four-year qualitative field study of four large Scandinavian firms, we are able to develop a typology of how incumbents handle the exploration of and resistance to adopting big data technologies. Directly affecting the incumbents’ exploration are two aspects that separate the adoption of big data technologies from that of other technologies. First, being an elusive concept, big data technologies can mean different things to different organizations. This makes the technologies difficult to explain before an investing body, while it simultaneously opens up possibilities for creative definitions. Second, big data technologies have a transformative effect on the organization of work in firms. This transformative capability will make managers wary as it might threaten their position in the firm, and it will create ripple effects, transforming other systems besides those directly connected to the technology.     

Stock liquidity and corporate tax avoidance

We show that firms with higher stock liquidity engage less in extreme (i.e., overly aggressive or overly conservative) tax avoidance. The effect of stock liquidity on tax avoidance is economically meaningful and robust across alternative measures of tax avoidance and stock liquidity. The findings also hold after controlling for potential endogenous effects. We further document that the effect of stock liquidity on tax avoidance is amplified for firms with high proportions of activist shareholders and attenuated for firms with high levels of stock price informativeness. Overall, our findings suggest that stock liquidity mitigates extreme tax avoidance by enhancing shareholders’ monitoring over firm management.

     

Employer organisations transformed

Employer organisations and the literature examining them have transformed since their inception in the 19th century. We systematically review this literature and the evolving role of employer organisations by focusing on the most cited publications of this body of academic work. This article provides a synopsis of our current understanding of employer organisations, identifies gaps in our knowledge, and develops the following argument. Employer organisations adapted to changing socio‐economic contexts by evolving within and across three roles—as industrial relations actor, political actor, and service provider. Historically, employer organisations were predominantly understood as an industrial relations actor with collective bargaining as their defining activity. However, employer organisations also influenced the political process through lobbying and participating in corporatist arrangements, although more recently their provision of member services has grown in scope and importance.     

Some nonstandard stochastic volatility models and their estimation using structured hidden Markov models

We introduce a number of nonstandard stochastic volatility (SV) models and examine their performance when applied to the series of daily returns on several stocks listed on the New York Stock Exchange. The nonstandard models under investigation extend both the observation process and the volatility-generating process of basic SV models. In particular, we consider dependent as well as independent mixtures of autoregressive components as the log-volatility process, and include in the observation equation a lower bound on the volatility. We also consider an experimental SV model that is based on conditionally gamma-distributed volatilities.Our estimation method is based on the fact that an SV model can be approximated arbitrarily accurately by a hidden Markov model (HMM), whose likelihood is easy to compute and to maximize. The method is close, but not identical, to those of Fridman and Harris (1998), Bartolucci and De Luca (2001, 2003) and Clements et al. (2006), and makes explicit the useful link between HMMs and the methods of those authors. Likelihood-based estimation of the parameters of SV models is usually regarded as challenging because the likelihood is a high-dimensional multiple integral. The HMM approximation is easy to implement and particularly convenient for fitting experimental extensions and variants of SV models such as those we introduce here. In addition, and in contrast to the case of SV models themselves, simple formulae are available for the forecast distributions of HMMs, for computing appropriately defined residuals, and for decoding, i.e. estimating the volatility of the process.