Olha Yeremenko

The paper examines, using the example of Ukraine from 2003 to 2020, how and to what extent the development of various segments of the insurance market (risk insurance, life insurance, and reinsurance) influences the overall level of social progress. It also identifies the time gaps through which this influence manifests. The study creates a single measure that looks at various aspects such as social class differences, spending patterns, income changes, and government social spending (their standardized values, weighed by the principal component method, integrated through additive convolution). Using VAR modeling, the impact of the development indicators of different segments of the insurance market (risk insurance, life insurance, and reinsurance) at the current moment and with lags of one, two, and three years is investigated, as well as the level of social progress in Ukraine in previous years. The modeling confirms that social reforms yield significant results for social progress only after three years, similarly to the increase in the number of insurance companies. Given insurers’ assets and payout levels, their growth in life insurance has a faster impact on social progress than in risk, while the opposite is true for premiums. Insurance premiums transferred to Ukrainian reinsurers negatively and slowly (over three years) affect social progress, and to non-resident reinsurers – positively and faster (within a year). Across most indicators, life insurance not only influences Ukraine’s social progress more quickly than others but also provides a more substantial social effect.

Type of the article: Research Article

The global recovery from recent economic, health, and geopolitical crises, including the COVID-19 pandemic and the Russia–Ukraine war, increasingly depends on how economies mobilize human capital, migration, and financial flows. This article examines how human capital and its reallocation through migration and remittances, under different institutional conditions and economic system types, relate to configurations of human capital, net migration, political stability, and remittances that support sustained post-crisis economic recovery. Using a panel of 73 economies from 2010 to 2023, the empirical strategy combines descriptive rankings, multiple linear regression (MLR), and decision-tree classification, with an 80/20 split of the sample, primarily drawing on the WDI. Descriptive patterns highlight large asymmetries in both migration and growth: some advanced and emerging economies combine sizeable net migration inflows with robust GDP growth, whereas conflict-affected and fragile states, including the Syrian Arab Republic, Yemen, and Ukraine, experience substantial net outflows alongside persistent output losses. However, regression results indicate that differences in human capital primarily drive cross-country variation in post-crisis growth: HCI is the only statistically significant predictor, while net migration, political stability, and remittances display small and insignificant linear effects (R² ≈ 0.15; adjusted R² ≈ 0.10; n = 73). DTC reveals complex, non-linear relationships between migration, human capital, financial flows, and economic recovery, with outcomes concentrated in economies that combine higher skills and sizable remittances with stable institutions, effectively converting them into productive human capital.

Acknowledgments
The research was carried out with funds from the budget of the Ministry of Education and Science of Ukraine on the topic of the research project "Modeling educational transformations in wartime to preserve the intellectual capital and innovative potential of Ukraine" (0123U100114).