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Purpose: The research conducted in this chapter approaches a topical subject and examines the labour market advancement of Romanian migrants within several receiving economies across the European Union, as well as the impact of international migration on the Romanian economy and labour market, also considering the present context of the Covid-19 pandemic and digitalisation challenges.

Method: The methodology embeds spatial bootstrap analysis (spatial lag and error models) applied to a newly compiled dataset for Romania during 2000–2020.

Findings: Main findings of the current research update and complement the specialised literature with new data on Romanian migration by identifying unknown potential reasons that generated the departure of the Romanian labour force abroad and several credentials of the return migration intentions and strategies.

Originality and significance of findings: The results mainly entail some of the essential effects generated by the Covid-19 pandemic regarding the interplay between the labour market and international migration, with a keen focus on Romania.

Introduction: Despite its significance, the research on international migration with a specific focus on the European Union (EU) needs to be strengthened with comprehensive studies, for developing better immigration and integration policies. Considering the amplitude of migration flows in Europe and recent challenges brought by the Covid-19 pandemic crisis, the Brexit decision and humanitarian dimension of the migration phenomena (asylum seekers and refugees), the need for better immigration and integration policies within the host countries’ labor markets stands out as a major research direction, especially in case of immigrants looking for better working and living conditions. Aim: This chapter aims to design specific immigration clusters within the main EU-10 destination countries (including Spain, Italy, Belgium, Denmark, Finland, France, UK, Germany, Austria, and Sweden) (cluster analysis procedure); and to identify feasible ways and specific policies for immigrants’ labor market success (spatial analysis and macroeconometric models). Method: The methodological framework consists of two parts: (i) immigration clusters analysis, based on the interlinkages between several fundamental migration coordinates, namely, economic welfare at destination, employment opportunities for the foreign population, migrant integration policies and associated governmental efforts, educational background; and (ii) spatial analysis models, namely spatial lag–autoregressive and spatial error, and other three econometric procedures, respectively, the robust regression, Panel Corrected Standard Errors, and Arellano-Bond Dynamic Generalized Method of Moments. National data compiled for the 10 main EU receiving economies during 2000–2015, with a particular focus on Spain were used. Findings: The impact of the proposed research is reflected through a set of new specific tailored ways, policies and strategies that can be adopted and implemented by the policy-makers across Europe. Our empirical results show that, overall, EU-10 countries still fail to identify immigrants with high levels of education and skills acquired to enhance their potential for labor market integration. Policy-makers should always monitor the specific ways in which migration policies lead to concrete positive labor market outcomes for immigrants and that the tools used for implementing these policies are suitable in achieving predefined migration goals. Therefore, a particular focus should be on developing a new immigration system to select migrants for their skills and high level of human capital, by following the best practices examples of other receiving countries.

Purpose: The connection between insurance and the degree of economic development of countries has been long studied in the literature, being argued that they are potentiating each other. Against this background, the objective of this chapter is to appraise the way in which the insurance activity, overall, and, distinctively, considering only the life insurance field, is interconnected with the economic development of the European Union (EU) Member States.

Method: In order to assess the insurance development, we considered the insurance density and penetration degree indicators (total and life insurance), and, for the wellbeing dimension, we applied the Human Development Index (HDI), associated with other economic indicators. The research methodology consists of an advanced econometric procedure, namely macroeconometric models – robust regression, for the period 2007–2019.

Findings: The results obtained highlight significant positive associations between insurance development and wellbeing at the level of EU countries.

Originality and Significance of Findings: This research considers an integrative indicator that measures the wellbeing dimension, including the human factor (HDI), in addition to the economic development degree, that has not been addressed so far in the literature, which reveals the originality of this study. Thereby, adjusted policies and strategies developed to sustain the main components of HDI (education, life expectancy and living standards) became paramount keystones in order to promote insurance development.

Introduction: International migration, a complex, dynamic and multifaceted process, grasps important challenges for the European economies, through its advantages and pitfalls.

Aim: This research is conducted to examine the fundamental credentials of immigration in Europe and its perspectives within the Brexit framework, an ongoing process that induced profound implications.

Method: The authors have applied the cluster analysis and structural equations as the main research methods on a balanced panel comprising 10 receiving countries (most targeted by migrants), members of the European Union (EU-10), for the 2000–2019 timespan (2019 being a Brexit milestone year).The authors have separately extrapolated a sample for 2020–2025 that was further used to identify some perspectives after the Brexit timeline in terms of migration determinants and effects on EU-10 host economies. Cluster analysis is based on a key scenario related to wellbeing, living standards (income level) and poverty risk at destination. These credentials are essentialfor the migration decision and important elements of migrant labour market integration strategies with keen economic consequences, further assessed through the structural equation models.

Findings: Results show that during 2000–2019, among EU-10, the main destination country for immigrants and asylum seekers in terms of welfare and living standards is Germany (both for economic and humanitarian migration), along with the United Kingdom (in the case of economic migration). This situation tends to remain unchanged for the following years even in the Brexit context, as reflected by the 2020–2025 forecast scenarios. Immigration effects on labour market outcomes and economic welfare are extremely significant, being largely discussed within the chapter.

Introduction: Regional economies are significantly shaped by the new developments in technology, digital transformations, as well as by the demographic processes (the ageing population and international migration), all of these being amplified by the Covid-19 pandemics and requiring tailored strategies to bridge regional welfare gaps and enhance sustainable economic development.

Aim: This research provides a review of the interplay between the regional economic welfare and digitalisation, with a keen focus on digital transformations, education, digital skills and risk management strategies in filling development gaps and enhancing regional economic growth in a sustainable development framework, with a keen focus on Romania. In this approach, the study undertakes several essential research questions and designs an advanced theoretical and empirical research to inforce the knowledge in this scientific field.

Method: The methodological framework consists of robust regression models and spatial analysis with two types of spatial models, namely spatial lag-autoregressive and spatial error. National data compiled for Romania during the 2010–2019 lapse of time were exploited.

Findings: Main results encompass that digitalisation coordinates, education and digital skills are essential for enhancing the economic development and labour market performance of various regions in Romania, with beneficial spill-overs on sustainable economic welfare and poverty reduction. These advances bring to the fore important shifts in both demand and supply sides across regional economies that affect the equilibrium and overall performance, while public discourse, regulatory authorities, policy-makers and business representatives render global the keen need to strengthen the understandings in this scientific field.

Blockchain technology has led the evolution of double entry accounting system to triple entry accounting system. Triple entry accounting is an innovative, promising and potential accounting method when implemented properly would be a game changer for dissemination of accounting information. It is an efficient way to address fundamental concerns of accounting information. This chapter discusses the triple entry accounting system, how it is different from double entry accounting and what are the concerns in implementing triple entry accounting. Triple entry accounting holds the potential to fundamentally evolve accounting practices, can enhance the effective utilisation and sustainable management of resources, and can contribute in development of financial markets.

Machine learning is an algorithmic-based auto-learning mechanism that improves from its experiences. It makes use of a statistical learning method that trains and develops on its own without the assistance of a person. Data, characteristics deduced from the data, and the model make up the three primary parts of a machine learning solution. Machine learning generates an algorithm from subsets of data that can utilise combinations of features and weights different from those obtained from basic principles. In this paper, an analysis of customer behaviour is predicted using different machine learning algorithms. The results of the algorithms are validated using python programming.