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Since its introduction by Chari, Kehoe, and McGrattan (2007), Business Cycle Accounting (BCA) exercises have become widespread. Much attention has been devoted to the results of such exercises and to methodological departures from the baseline methodology. Little attention has been paid to identification issues within these classes of models. In this chapter, the authors investigate whether such issues are of concern in the original methodology and in an extension proposed by Šustek (2011) called Monetary Business Cycle Accounting. The authors resort to two types of identification tests in population. One concerns strict identification as theorized by Komunjer and Ng (2011) while the other deals both with strict and weak identification as in Iskrev (2010). Most importantly, the authors explore the extent to which these weak identification problems affect the main economic takeaways and find that the identification deficiencies are not relevant for the standard BCA model. Finally, the authors compute some statistics of interest to practitioners of the BCA methodology.

This paper investigates effective human-robot collaboration (HRC) and presents implications for Human Resource Management (HRM). A brief review of current literature on HRM in the smart industry context showed that there is limited research on HRC in hybrid teams and even less on effective management of these teams. This book chapter addresses this issue by investigating factors affecting intention to collaborate with a robot by conducting a vignette study. We hypothesized that six technology acceptance factors, performance expectancy, trust, effort expectancy, social support, organizational support and computer anxiety would significantly affect a users' intention to collaborate with a robot. Furthermore, we hypothesized a moderating effect of a particular HR system, either productivity-based or collaborative. Using a sample of 96 participants, this study tested the effect of the aforementioned factors on a users' intention to collaborate with the robot. Findings show that performance expectancy, organizational support and computer anxiety significantly affect the intention to collaborate with a robot. A significant moderating effect of a particular HR system was not found. Our findings expand the current technology acceptance models in the context of HRC. HRM can support effective HRC by a combination of comprehensive training and education, empowerment and incentives supported by an appropriate HR system.

Building on work that explores the relationship between individual beliefs and ability to recognize discrimination (e.g., Kaiser and Major, 2006), we examine how an adherence to beliefs about gender essentialism, gender egalitarianism, and meritocracy shape one’s interpretation of an illegal act of sexual harassment involving a male supervisor and female subordinate. We also consider whether the role of the gendered culture of engineering (Faulkner, 2009) matters for this relationship. Specifically, we conducted an online survey-experiment asking individuals to report their beliefs about gender and meritocracy and subsequently to evaluate a fictitious but illegal act of sexual harassment in one of two university research settings: an engineering department, a male-dominated setting whose culture is documented as being unwelcoming to women (Hatmaker, 2013; Seron, Silbey, Cech, and Rubineau, 2018), and an ambiguous research setting. We find evidence that the stronger one’s adherence to gender egalitarian beliefs, the greater one’s ability to detect inappropriate behavior and sexual harassment while gender essentialist beliefs play no role in their detection. The stronger one’s adherence to merit beliefs, the less likely they are to view an illegal interaction as either inappropriate or as sexual harassment. We account for respondent knowledge of sexual harassment and their socio-demographic characteristics, finding that the former is more often associated with the detection of inappropriate behavior and sexual harassment at work. We close with a discussion of the transferability of results and policy implications of our findings.

This study focuses on the impact of green innovation on oil and gas firms’ performance in the MENA region from 2010 to 2020. Return on assets (ROA) was used to measure the financial performance of firms. However, green innovation was measured using two different scores, namely the environmental pillar and the innovation scores. Additionally, we introduced an oil price-moderated variable to examine its effect on the firm’s performance and the green innovation nexus. We collected data from the DataStream database. Regarding our empirical part, we use the generalized least squares method to carry out the analysis. Results showed a positive impact between green innovation scores and the firm’s performance in the MENA region. Also, we found that green innovation has a linear effect on firm performance. Finally, a negative, moderated effect of crude oil prices on green innovation and the firm’s financial performance nexus has been found.

In large parts of the world, income inequality has been rising in recent decades. Other regions have experienced declining trends in income inequality. This raises the question of which mechanisms underlie contrasting observed trends in income inequality around the globe. To address this research question in an empirical analysis at the aggregate level, we examine a global sample of 73 countries between 1981 and 2010, studying a broad set of drivers to investigate their interaction and influence on income inequality. Within this broad approach, we are interested in the heterogeneity of income inequality determinants across world regions and along the income distribution. Our findings indicate the existence of a small set of systematic drivers across the global sample of countries. Declining labour income shares and increasing imports from high-income countries significantly contribute to increasing income inequality, while taxation and imports from low-income countries exert countervailing effects. Our study reveals the region-specific impacts of technological change, financial globalisation, domestic financial deepening and public social spending. Most importantly, we do not find systematic evidence of education’s equalising effect across high- and low-income countries. Our results are largely robust to changing the underlying sources of income Ginis, but looking at different segments of income distribution reveals heterogeneous effects.