This article analyzes the impact of socio-cultural integration on migrants’ intention to return to their country of origin. It distinguishes between the potential effects of inter-ethnic relations, language proficiency and perception of discrimination as components of integration. It uses individual-level data collected through an original survey among Romanian migrants in January 2018. It aims to test the effects of socio-cultural integration against those of alternative explanations provided in the literature. The findings indicate that the perception of discrimination, assessment of public institutions’ performance and belonging feelings are important predictors for return intentions.

Nowadays, when there is much concern about the demographic decline of Europe and the stringent need for public policies that support intelligent, sustainable, and inclusive growth in times of population ageing, this study aims to validate several hypotheses regarding the post-graduation migration intentions of students in economics. To analyse these intentions in the context of Romanian yearly increases of out-migration flows to Western countries, the answers to the questionnaire used for this study were obtained from three important Romanian universities. Using georeferencing, neural networks-based data mining, and two types of regression analysis, the results represent a relevant contribution to the limited body of literature. They empirically show that motivations and attitudes towards a meritocratic environment for professional advancement, and individual freedom are positive key factors for students’ migration intentions after graduation. In addition, the paper emphasises the secondary role of religiosity and intergenerational transfer of parental migration experience. It also finds that, although the income level has some influence on these intentions, its role is far less important than the one emphasised in the specific literature, which leads us to the conclusion that non-economic motivations matter more than the economic ones for the potential decision to migrate after graduation.

Based on a survey among students in economics at universities from the Romanian region of Moldova (RoMold) and Republic of Moldova (ReMold), this study explores the influence of familial, background, and individual characteristics on the migration intentions abroad. Both regional models built after using data mining tools and binary logistic regression analysis show a powerful emphasis on our own value recognition, while the other influences indicate clear asymmetric patterns in terms of migration intentions. For RoMold, there is a low level of interpersonal trust, a strong accent on individual liberty and favoritism, a low respect for traditions, and the belief that Orthodoxy could be a migration cause. The paternal occupation and the maternal religiosity matter for their migration plans. For ReMold, the parental migration experience, a low level of parents’ education attainment, and a powerful internal locus of control are considered the main triggers for migrating abroad. For both particular regions, a highly interactive distribution map and two corresponding risk-prediction nomograms provide basic support for replication, fast visual insight, and consistent support for interpretations directly in probabilistic terms. We intend to apply an inductive approach in order to extend the analysis to different types of respondents and geographic areas.

In this paper, we explore the determinants of being satisfied with a job, starting from a SHARE-ERIC dataset (Wave 7), including responses collected from Romania. To explore and discover reliable predictors in this large amount of data, mostly because of the staggeringly high number of dimensions, we considered the triangulation principle in science by using many different approaches, techniques and applications to study such a complex phenomenon. For merging the data, cleaning it and doing further derivations, we comparatively used many methods based on spreadsheets and their easy-to-use functions, custom filters and auto-fill options, DAX and Open Refine expressions, traditional SQL queries and also powerful 1:1 merge statements in Stata. For data mining, we used in three consecutive rounds: Microsoft SQL Server Analysis Services and SQL DMX queries on models built involving both decision trees and naive Bayes algorithms applied on raw and memory consuming text data, three LASSO variable selection techniques in Stata on recoded variables followed by logistic and Poisson regressions with average marginal effects and generation of corresponding prediction nomograms operating directly in probabilistic terms, and finally the WEKA tool for an additional validation. We obtained three Romanian regional models with an excellent accuracy of classification (AUROC > 0.9) and found several peculiarities in them. More, we discovered that a good atmosphere in the workplace and receiving recognition as deserved for work done are the top two most reliable predictors (dual-core) of career satisfaction, confirmed in this order of importance by many robustness checks. This type of meritocratic recognition has a more powerful influence on job satisfaction for male respondents rather than female ones and for married individuals rather unmarried ones. When testing the dual-core on respondents aged 50 and over from most of the European countries (more than 75,000 observations), the positive surprise was that it undoubtedly resisted, confirming most of our hypotheses and also the working principles of support for replication of results, triangulation and the golden rule of robustness using cross-validation.