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The authors propose a framework to estimate the probability of being poor in a dynamic setting based on a large information set that includes individual characteristics and macro-economic variables. The joint inclusion of personal characteristics along with contextual factors allows separation of idiosyncratic shocks from aggregate shocks affecting poverty. The authors combine data from different cross-sectional surveys and fit a dynamic logistic hierarchical model within a Bayesian framework using standard Markov chain Monte Carlo techniques. The authors’ approach is exemplified by estimating household poverty status in Kyrgyz Republic as a function of time, regions, country, regional level variables and household level socio-demographic characteristics.

Nowadays, the increase in the capacity of batteries has laid the foundations for a broader diffusion of electric mobility. However, electric mobility is causing a growing electricity demand as well as the need to increase the diffusion of suitable charging stations. Within these last challenges, drawing on the recent literature, this chapter provides a critical and wide-ranging review of papers dealing with the formulation of the problem of the localisation of electric vehicle (EV) charging points. This problem is approached considering the electric charging infrastructure technologies, localisation criteria and related methodologies. This review shows how the ‘electric mobility revolution’ applies the technological innovations provided by the energy supply systems, and the location of these systems within the urban contexts. Since the technological innovations have different options, achieving an international standard of charging systems is still far away. Moreover, as there are several criteria, parameters and methodologies, and some analytical approaches for the localisation of electric vehicle charging points, the formulation of the ‘localisation’ problem should require the application of multi-criteria analysis to be addressed. Finally, the results show that there is no consensus on technologies, criteria, and methodologies to be adopted. Therefore, this wide-ranging analysis of the literature would be useful to support possible benchmarking and systematisation accordingly.

This survey presents the recent and rapidly expanding literature, which analyses the economic impacts of the COVID-19 pandemic, by means of Computable General Equilibrium (CGE) modelling. It does so not only by contrasting and assessing the different methodological approaches, and the key findings of the simulation exercises, but also by putting the various contributions in a historical perspective. This is necessary because each CGE-based study should be evaluated while keeping in mind when it was realised, since questions, priorities, expectations have been constantly changing during the spreading of the pandemic.

There is a substantial literature evaluating the impact of aid at the macroeconomic level using cross-country regressions. However, despite econometric advances in recent years, there remain many flaws in this approach. Similar problems beset cross-country analysis of the impact of aid on poverty indicators. More reliable estimates of the poverty impact of aid come from project-level analyses from which can be built up a picture of aid impact at the country level. This paper presents the findings from three such studies carried out by the Independent Evaluation Group of the World Bank.

To understand Peter Hall’s work on social policy, it is heuristic to place it in the context of work that was done contemporaneously. Public policy studies in the late 1960s through the early 1980s concentrated in large part on the large-scale governmental policies such as the Great Society Programs of the Lyndon Johnson administration, Follow-through, Headstart, special education, bilingual education. Social policy research of that time tended to take Weberian notions of technical rationality seriously, probably too seriously.

Purpose – The purpose of this chapter is to examine the frequency of multi-study research packages in the organizational sciences and advocate for their use by detailing strengths and recognizing limitations.

Methodology/approach – Philosophy of science research, focusing on multi-study research packages, is discussed followed by a 20-year review of incidence of these packages in top organizational sciences journals.

Findings – The publication of multi-study research packages have increased over the past 10 years, most notably in micro-level journals.

Social implications – For reasons of validity and generalizability, society benefits if scholars adopt multi-study approaches to knowledge generation and disseminate.

Originality/value of the chapter – This chapter provides the most comprehensive review of multiple-study research packages in the organizational sciences to date, examining publication trends in eight leading micro-and macro-level journals. We also summarize the use of multi-study packages in our own research and offer recommendations for improving the science of replication.

Introduction: Human resource management (HRM) is going through a transformation phase due to the pandemic. The COVID-19 crisis compelled the employees to work virtually. To mitigate the effects of COVID-19, several organisations heavily invested in artificial intelligence (AI) in the realm of HRM.

Purpose: With limited studies on the paradigm shift in HRM post-pandemic and the role of AI, the study investigates and proposes a conceptual framework for the paradigm shift in HRM practices post-COVID-19 pandemic and the significance of AI. Furthermore, the study investigates the outcomes of the use of AI in HRM for organisations and employees.

Methodology: A comprehensive review of the literature based on the guidelines of Tranfield, Denyer, and Smart (2003) and Crossan and Apaydin (2010) has been followed. A systematic literature review assisted in critically analysing, synthesising, and mapping the extant literature by identifying the broad themes involved.

Findings: COVID-19-related economic disruption has led to a paradigm shift in HRM practices. AI-enabled HRM practices are now centred around remote and contingent workforce management, mindfulness, social capital, increasing employee engagement, reskilling and upskilling towards new competencies, etc. AI is making remote work seamless through smooth recruitment and selection process, onboarding, career and development, tracking and managing the performance, facilitating learning, and talent management. Post-pandemic, AI-powered tools based on data mining (DM), predictive analytics, big data analytics, natural language processing (NLP), intelligent robots, machine learning (ML), virtual (VR)/augmented reality (AR), etc., have paved the way for managing the HRM practices effectively, thereby leading to enhanced organisational performance, employee well-being, automation, and reduced cost.