Presenting dynamics of social phenomena: should we use absolute,relative or time differences?

Empirical studies within social sciences face an important decision about how to express key findings to the target audience. Simplicity is an important selection criterion here, because the findings need to be conveyed in an efficient manner (i.e., briefly and concisely), but also because stakeholders (e.g., policy makers, the media, general public) are heterogeneous in their methodological backgrounds. The corresponding ways of measuring thus need to be not only exhaustive and message-delivering but also simple and intuitively understandable. This is particularly important when dynamics in time are discussed. There, most typically, either absolute or relative differences are used. This review paper critically elaborates these two popular measures and, in addition, discusses the alternatives of time distance and time step. The paper demonstrates that even in simple linear examples, the results of these four types of measures may sharply contradict. The empirical example of the digital divide is also elaborated, which illustrates many tempting possibilities for biased, one-sided interpretations that match the needs of certain stakeholders. Finally, the paper alerts users about possible misleading conclusions and suggests comprehensive treatments, using several measures simultaneously.     

The puzzle on the causality of the productivity and employment growth: evidence from firm-level data

This article analyses the causality between the firm’s employment and productivity growth based on the population of manufacturing firms registered in Slovenia in the 1994–2003 period. By using the system GMM estimator, we show that the employment–productivity growth trade-off does not exist. Moreover, we find significant complementarities between employment and productivity growth, mostly driven by SMEs and firms from high-tech industries. Accordingly, we argue that the job-creation policy and productivity-promoting policy are complementary rather than trade-offs and that policymakers should focus on the optimal policy mix that provides the highest aggregate effect with regard to all growth aspects. Further, significant differences among the factors of employment and productivity growth suggest that job-creation policy measures are most successful when targeted at younger export-oriented firms with high total factor productivity levels and capital-intensive production. Meanwhile, the outcome of policy measures aimed at promoting productivity increases with a firm’s capital intensity and size up to the threshold employment level and with the intensity of market competition.     

Pricing of guaranteed minimum withdrawal benefits in variable annuities under stochastic volatility,stochastic interest rates and stochastic mortality via the componentwise splitting method

This paper values guaranteed minimum withdrawal benefit (GMWB) riders embedded in variable annuities assuming that the underlying fund dynamics evolve under the influence of stochastic interest rates, stochastic volatility, stochastic mortality and equity risk. The valuation problem is formulated as a partial differential equation (PDE) which is solved numerically by employing the operator splitting method. Sensitivity analysis of the fair guarantee fee is performed with respect to various model parameters. We find that (i) the fair insurance fee charged by the product provider is an increasing function of the withdrawal rate; (ii) the GMWB price is higher when stochastic interest rates and volatility are incorporated in the model, compared to the case of static interest rates and volatility; (iii) the GMWB price behaves non-monotonically with changing volatility of variance parameter; (iv) the fair fee increases with increasing volatility of interest rates parameter, and increasing correlation between the underlying fund and the interest rates; (v) the fair fee increases when the speed of mean-reversion of stochastic volatility or the average long-term volatility increases; (vi) the GMWB fee decreases when the speed of mean-reversion of stochastic interest rates or the average long-term interest rates increase. We investigate both static and dynamic (optimal) policyholder's withdrawal behaviours; we present the optimal withdrawal schedule as a function of the withdrawal account and the investment account for varying volatility and interest rates. When incorporating stochastic mortality, we find that its impact on the fair guarantee fee is rather small. Our results demonstrate the importance of correct quantification of risks embedded in GMWBs and provide guidance to product providers on optimal hedging of various risks associated with the contract.     

Best friend or broken tool? Exploring the co-existence of humans and artificial intelligence in the workplace ecosystem

Artificial intelligence (AI) has become an important topic in business literature and strategy talk. Yet, much of this literature is normative and conceptual in nature. How organizational members perceive AI and the job role changes that come with it is, so far, largely unknown territory for both HR scholars and practitioners. We sought to investigate the relationship between humans and AI and conducted an in-depth exploratory study into the co-existence of humans and two early-stage AI-solutions, one for “low-status” automation and another for “high-status”; augmentation. We suggest that different organizational groups may engage in distinctly different sensemaking processes regarding AI, an important insight for successful HRM strategies when AI is being introduced into the workplace. Moreover, contrary to recent conceptual work, our findings indicate that AI-enabled automation and augmentation solutions may not be detached from nor exist in tension with each other. They are deeply embedded in organizational processes and workflows for which people who co-exist with the technologies must take ownership. Our findings, in part, go against discussions on AI “taking over” jobs or deskilling humans. We describe a more nuanced version of reality fluctuating around the various ways different organizational groups encounter different AI-solutions in their daily work. Finally, our study warns against unconditional technological enthusiasm, managerial ignorance of the nature of work that employees undertake in different organizational groups, and a neglect of the time and effort required to successfully implement AI-solutions that affect not only the home organization but also members of the broader ecosystem.