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Being a crucial barometer of labour market stability, employment successfully predicts changes in business cycles, becoming a relevant indicator to policymakers and economists worldwide. The scope of this paper is to investigate the impact of socioeconomic and demographic factors on the employment rate in the European Union through a spatial approach, as well as to compare pre- and post-pandemic characteristics of European labour markets. A persistent North–South divide in employment was observed among the main findings, with Southern regions having lower employment rates and being more vulnerable to the pandemic shocks than Northern regions. Furthermore, the comparison between the spatial econometric models estimated for 2019 and 2022 showed a significant change in the influences of regional employment performance. These discoveries could be of interest to both governments and corporate decision-makers in order to elaborate knowledgeable policies and strategies regarding the labour force.

Spatial data analysis approaches have grown in popularity in recent years as there has been an increased interest in studying migration at the regional level. We employed in our analysis the Nomenclature of Territorial Units for Statistics (NUTS) level, which offers a more thorough view of migratory patterns inside a nation as well as among EU countries.Using spatial data analytic tools, this research aims to investigate the key determinants of international migration at the NUTS2 level. Using data from the Eurostat database, we undertake an extensive analysis of migration movements in a sample of European Union countries. We apply spatial econometric models to examine the relationship between crude rate migration and other relevant variables as independent variables. Although the available data at the NUTS2 level of regional ranking are limited, we believe that we included the most comprehensive set of relevant variables in our analysis.Our study contributes to the existing literature on migration determinants by highlighting the importance of spatial analysis techniques in understanding migration factors at the NUTS2 level.

This study focuses on the integration facilitators of young migrants in Romania, as resulting from the information gathered through a Photovoice participatory action research method. Young third country nationals were asked to take photos which they thought best summed up their migration experiences. Next to the photos, the migrants were also asked to submit short texts describing the captured images and the meanings that these had for them, thus adding richness and nuance to the data. The evidence gathered reveals that several factors, such as access to education, interacting with a new culture, and with new places and people, are perceived as opportunities and positive migration outcomes. Therefore, human and social capital, as well as the natural or urban environment in the host country, contribute to the enhancement of integration opportunities for young migrants. The paper sets out to analyze how such factors which can facilitate integration are captured in a PAR. Research results show that young migrants perceive their experiences in Romania as enhanced by some factors through which they advance in their integration paths.

This comparative study focuses on the barriers to social and economic inclusion, as well as the integration and coping strategies of Arab and Moldovan migrants in Romania. We explored the integration barriers they face, the main individual and societal aspects that lead to their resilience, and their self-perception of vulnerability, by carrying out 35 psychosocial interviews and four focus groups with young migrants (aged 18 to 29), belonging to the two different subgroups (of Arab and Moldovan origins, respectively). The comparative analysis revealed that migrants from Arab countries face harsher integration barriers compared to Moldovan migrants, they have a more severe self-perceived vulnerability, and their integration may be a longer and more complex process. Results showed that mastery of the language and the network of acquaintances play an indispensable role in inclusion. Moldovans integrate more easily than Arabs, thanks to their fluency in Romanian, the native language shared with the majority local population, the geographical and cultural proximity to the country of destination, and the larger personal network. We highlight the need for improving integration policies for young migrants, tailoring them to the specific problems and barriers that migrants are facing.

Identifying the macro-economic determinants of poverty is a key concern for developing poverty reduction policies. Since young people and young migrants in particular are more exposed to poverty, establishing the factors that trigger poverty among these social categories has even more relevance. A preliminary analysis shows that significant differences exist between at-risk-of poverty or social exclusion rate of young migrants and young nationals across European countries. For a more thorough study of the reasons behind these differences in poverty rates between young migrants and young nationals, two panel data regression models are estimated on a cross-section of 23 countries over the period 2010 – 2018 (one model for young migrants, the other for young nationals). Results confirm the main theories in the specialty literature: unemployment and inequality (measured by the Gini index) are the main triggers of poverty or social exclusion both for young nationals and young migrants. However, the income is significant for reducing poverty only for young nationals, but not for the young migrants. This result reinforces the necessity of better integration policies for young migrants in richer Member States.

The purpose of this article is to determine the factors influencing the birth rate in Romania, by incorporating explicitly the spatial factor in the proposed models. The study is justified by the dramatic fall of the birth rate over the past three decades. With a negative natural population growth and an increasing number of emigrants, the population will become older and there will be a few million less in the next decades. To achieve the objective, various spatial modelling methods were used, such as Spatial AutoRegressive Model (SAR), Spatial Error Model (SEM), Geographically Weighted Regression (GWR) and a spatial panel data model. The data granularity is at the county level for the year 2020. Results show that GDP per capita and the amount of financial support received by families for raising a child have a significant effect on the birth rate. Using a spatial approach for modelling the birth rate, we reveal demographic problems that may exist in certain areas and identify the regions that would require a policy to stimulate birth rates.

The global financial crisis has arouse many research questions regarding causes, effects and necessary policy measures. This paper tries to answer the question whether advanced and emerging European economies have been equally affected by the global financial crisis from 2007. We apply a cluster analysis for two periods (pre and post crisis), on a set of four leading indicators as suggested by the specialty literature and find four clusters of European economies. The differences of structure between the two groups of clusters may suggest that advanced economies are as much as vulnerable to a financial crisis situation as emerging economies.

COVID-19 pandemic has affected and still affects many countries in the world, reshaping many of the economic and social activities. Based on the results of an online survey, this paper highlights the perceptions of the way the pandemic has affected one of the most vulnerable categories in a society, migrants. We focus our research on the migrants and refugees from Middle East and North Africa (MENA) countries, living in Europe, as in the recent years and mostly after the migrant crisis in 2015, they are in large numbers in European countries. Using ANOVA models, our results show that unemployed migrants, students but also migrants who find it difficult on present income are most worried about the COVID-19 crisis and fell they will be greatly affected in terms of income and employment by this crisis. Also, women are more worried by COVID-19 than men with respect to the health aspect.

Statistical literacy is the capacity to challenge statistics encountered in everyday life (Gal, 2002) and has become a key competence for the entire work-force in today’s data-driven society. However, teaching statistics to non-economists has some particularities determined by their anxiety towards the subject. In order to have a better approach in teaching Statistics to this type of audience, the paper provides an analysis regarding students’ initial level of statistical literacy, the attitudes and beliefs towards Statistics, in the case of undergraduate students of the “Applied Modern Language” program under Bucharest University of Economic Studies. Using the models developed by Gal (2002) and Watson (2003), the results prove that basic statistical literacy skills like graph analysis or table reading are sound, while mathematical level is well below average, correlated with a high anxiety regrading mathematics and preconceptions about complexity of statistics.