Sandor Remsei

The effects of the Russian-Ukrainian war on Ukraine's demographic landscape are immense. One key consideration is whether Ukrainian refugee women intend to return to their country after the war ends. If the return is planned, the question of whether they would wish to have children is also relevant. This study explored these issues by surveying women who fled to Hungary and the Netherlands. Among those surveyed, 42% did not plan to return under any circumstances, and only 12% intended to return even if their home area came under Russian control. Logistic regression was used to identify factors influencing the intention to return, with reluctance to have additional children and income earned through employment emerging as the strongest explanatory factors. However, we found only modest associations between the intention to return and other variables. Our findings suggest that deeply rooted personal preferences shape these women’s plans.

The article is devoted to finding the answer to two research questions. What illegal practices are most significant for clusters of developed countries formed by similarities in trends in corruption, shadow economy, money laundering, and crime rates? What social, economic, regulatory, and digital factors most influence them in each group? The pair correlation coefficients for illicit practices indicators confirm the presence of tight and statistically significant relationships in their trends for 36 developed countries. The agglomerative clustering and canonical analysis results identified that tackling the shadow economy is crucial for Estonia, Slovenia, and Lithuania; corruption for Portugal, Hungary, Cyprus, etc.; the shadow sector and crime levels for Denmark, Norway, Finland, Sweden, and New Zealand; corruption, money laundering, and crime for Canada, Germany, the USA, etc.; four illegal practices for Italy, Greece, Turkey, Croatia, Bulgaria, and Romania. The canonical analysis revealed that social and regulatory factors influence the trends of illicit practices in developed countries more than economic and digital ones. Network analysis showed their single moderate influence in most cases. Edge evidence probability analysis confirmed a high probability of a relationship between some pairs of social, economic, regulatory, digital and illegal indicators. However, Bayesian network analysis showed a low likelihood of mutual influence of single factors, confirming the importance of the group influence.