Migrant remittances are an important source of external financing for developing economies, but their impact on economic growth remains ambiguous. This study aims to examine the long-run effects of remittances, foreign direct investment (FDI), trade openness, and tax revenues on real GDP and to identify causal relationships among these variables in five Turkic Republics (Azerbaijan, Kazakhstan, Kyrgyzstan, Türkiye, and Uzbekistan) and Tajikistan over the period 2005–2023. The analysis employed tests of cross‑country dependence and coefficient homogeneity, panel unit root tests, the Durbin–Hausman cointegration test, FMOLS and DOLS estimates, as well as the Dumitrescu–Hurlin panel causality test. The results showed a positive long‑term relationship between remittances and real GDP: the coefficient was 10.7917 for FMOLS (p = 0.0137) and 3.8556 for DOLS (p = 0.023). Trade openness also has a positive and statistically significant long-term relationship with real GDP: the corresponding coefficients were 1.1260 and 1.2183 (p = 0.000 in both cases). At the same time, the coefficients for foreign direct investment turned out to be negative: −1.7361 for FMOLS (p = 0.051) and −2.2875 for DOLS (p = 0.088). The results obtained confirm the significant role of remittances in the economic dynamics of the countries under study and indicate the feasibility of improving the mechanisms for attracting them into the formal financial sector and for their productive use. The findings underscore the need to develop formal financial channels, improve the efficiency of using money transfers, and direct external financial flows toward productive investments and sustainable economic growth.

The dairy sector plays an important role in ensuring Kazakhstan’s food security, but its development is characterized by structural imbalances, limited technological modernization, and high dependence on production and investment factors. The aim of the study is to evaluate the relationship of investment, price and production factors with the dynamics of milk production in Kazakhstan in 2004-2024, taking into account lag effects and the structural gap in official statistics starting in 2022. The information base was compiled by the annual data of the Food and Agriculture Organization of the United Nations and the Bureau of National Statistics of the Republic of Kazakhstan for 2004-2024. Descriptive statistics, Pearson correlation analysis, ADF and KPSS tests, multiple least squares regression, multicollinearity diagnostics, and alternative specification analysis are applied. The results showed that the preferred model explains 95.3% of the variation in milk production. The most significant positive relationship was found for real investments in agriculture (β = 0.461; p = 0.002) and average dairy productivity of cows (β = 0.305; p = 0.018). The producer price index did not demonstrate a statistically significant relationship with the volume of production (β = 0.021; p = 0.836). Taking into account the revision of official statistics for 2022-2024 has significantly increased the explanatory power of the model. The results show that the main factors in the development of Kazakhstan’s dairy sector are investment and increased productivity, while price signals without technological modernization do not ensure a steady increase in production.

The population's participation in the stock market depends not only on the availability of investment instruments but also on financial knowledge and assessments of one’s own financial situation. This study examines financial literacy and stock market participation in Oman, considering demographic differences and the role of financial well-being in their relationship. The study uses methods of descriptive statistics, hierarchical binary logistic regression, and the Karlson–Holm–Breen (KHB) decomposition method. The empirical basis consisted of the results of an online survey conducted from February to June 2026 in Muscat, Salalah, and Sohar. Of the 1,000 questionnaires distributed, 786 responses were received; after verification, 739 respondents were included in the final sample. The results showed relatively low objective financial literacy: 47% of respondents answered questions about interest rates correctly, 41% about inflation, and 36% about financial risks. At the same time, 62% of participants reported a high level of financial well‑being, while only 26% reported participating in the stock market. Regression analysis revealed a positive relationship between financial literacy and participation in the stock market (odds ratio — 1.42 in the second model and 1.33 in the third), while for financial well‑being, this indicator was 1.25. Additional analysis indicates its possible mediating role in the relationship under consideration. The obtained results indicate the need to develop practical financial skills among the population of Oman, along with increasing the availability of investment services.

Inflation remains a central macroeconomic challenge for emerging economies, as persistent price instability undermines economic growth, distorts income distribution, and weakens the effectiveness of monetary policy. This study aims to assess the relationships between inflation, the policy rate, the exchange rate, oil prices, and National Fund transfers in Kazakhstan using annual data for 2000–2025 and an Autoregressive Distributed Lag (ARDL) model. The initial data consist of annual time series for 2000–2025, including 26 observations for each indicator, compiled from statistics from the Bureau of National Statistics, the National Bank of Kazakhstan, the World Bank, and the Ministry of Finance of the Republic of Kazakhstan. Descriptive statistics, correlation analysis, the Dickey–Fuller test, and the ARDL autoregressive distributed lag model were used. The coefficient for the lagged exchange rate was −0.0733 (p = 0.0274), and for the current logarithm of the oil price, it was 6.9936 (p = 0.0048), while the current and lagged coefficients for the interest rate and the logarithm of National Fund transfers are statistically insignificant at the 5% level. The model is characterized by a determination coefficient of R² = 0.6546 and overall statistical significance (F = 1.159; p = 0.0239), however, the bounds test did not confirm the presence of a long-term relationship between the variables (F = 2.4282; p = 0.3762). These results contribute to a deeper understanding of the factors influencing inflation in Kazakhstan and can inform monetary and macroeconomic policy.

In the context of growing challenges to the security of the banking sector during Ukraine's post-war economic transformation, it has become necessary to rethink approaches to the strategic management of banks with an emphasis on security factors. This study addresses the insufficient integration of corporate security factors into comprehensive models for assessing and managing the financial stability of commercial banks under wartime conditions. The methodological framework combined structural and functional analysis, scenario forecasting, and econometric modeling. The initial information base consisted of data from the National Bank of Ukraine, financial and non‑financial statements of 10 banks, materials from international rating agencies, and publicly available corporate data. The results revealed significant heterogeneity in the financial parameters of the banks under study. The highest capital adequacy ratios were recorded at Credit Agricole Bank – 24.5% and Raiffeisen Bank – 23.8%, while Ukreximbank’s figure was 16.1%. The share of non‑performing loans reached 28.9% at Ukreximbank and 23.7% at Oschadbank. Universal Bank reported the highest return on assets – 1.1%, and the level of credit risk coverage by reserves at PrivatBank and Credit Agricole Bank was 98% each. Standardized sensitivity coefficients between the corporate security index and ROA increase from 0.47 to 0.78, and with CAR — from 0.33 to 0.66, while an inverse relationship remains for NPL, ranging from −0.52 to −0.70. The results obtained can be used by regulatory bodies, the management of banking institutions, and analytical organizations when developing policies to ensure financial security in the context of the structural transformation of the economy.

Predicting stock market dynamics remains challenging, especially in emerging and frontier markets, where high uncertainty, limited liquidity, and information asymmetries can reduce the robustness of predictive models. This study aims to compare the forecasting performance of Artificial Neural Networks (ANN) and Long Short-Term Memory (LSTM) models for the TUNINDEX20 against a naïve persistence benchmark. The information base consisted of daily data on the TUNINDEX20 index of the Tunis Stock Exchange from February 6, 2012, to December 31, 2024, including 1,915 observations. The sample was divided chronologically into a training set of 1,532 observations and a test set of 383 observations. The results showed that the artificial neural network slightly outperformed the long short‑term memory model in terms of key accuracy metrics: for the first model, MAE was 12.06, RMSE was 15.92, MAPE was 0.31%, MASE was 1.0769, R² was 0.9912, and the accuracy of predicting the direction of index movement was 53.91%. At the same time, the naive model showed slightly lower forecast errors (MAE = 11.76; RMSE = 15.82), and the Diebold–Mariano test did not reveal a statistically significant advantage of ANN over the naive model (p = 0.7285). The results obtained show that a more complex architecture does not automatically provide an advantage in forecasting TUNINDEX20. Future research could include adding technical indicators and macro‑financial variables, testing models in other emerging markets, and applying adaptive and hybrid forecasting methods.

The development of creative industries is considered one of the areas of structural transformation of resource‑dependent and single‑industry cities, whose economic development is limited by the existing sectoral specialization and spatial remoteness. The aim of the study is to identify the spatial patterns of new creative industrialization and to examine the economic factors influencing the development of creative industries in resource‑oriented and single‑industry cities, as well as in certain regional centers of the Russian Far East. Methods of spatial panel econometrics, global and local Moran indices, Lagrange multiplier tests, and SAR, SEM, SDM, and GSPRE models were applied. The initial data were compiled based on the financial statements of enterprises from the SPARK information and analytical system for 2013– 2022. The sample includes 26 settlements (17 single‑industry towns and 9 administrative centers), or 260 annual observations. The SDM_fe1 model (AIC = 913.0; BIC = 934.4) revealed positive direct effects of ICT dynamics (0.020) and the concentration of core assets in creative industries (1.131). The concentration of wages is characterized by a statistically insignificant direct effect (0.215) and a significant negative spatial effect (−2.933). The results indicate that the business climate in single‑industry towns, which allows enterprises to potentially understate wages by leveraging their status as small and micro‑enterprises, becomes a negative factor in spatial development, manifesting itself in a decline in the growth rates of creative industries in nearby single‑industry towns.

The rapid introduction of artificial intelligence into smart city management systems is changing approaches to urban planning, public service provision, and management decision-making, but scientific research in this area remains thematically and disciplinarily fragmented. The primary aim of this study is to systematically map the scientific literature on the role of artificial intelligence in the digital transformation of smart city governance. The paper uses methods of bibliometric and network analysis, including analyses of publication and citation dynamics, co-authorship, bibliographic coupling among organizations, and the co-occurrence of keywords. The selection of publications was conducted in accordance with the PRISMA 2020 guidelines, and the construction and visualization of bibliometric networks were performed using the VOSviewer program. The initial data was obtained from the Scopus database on October 5, 2025. An initial search revealed 34,548 documents. After applying the criteria of the subject area, publication period, document type, and language, the final sample included 1,865 English-language journal articles published in 2015-2025. The largest number of publications is in China — 236 articles (12.7%), Great Britain — 230 (12.3%), and the USA — 216 (11.6%). Kazakhstan ranks 54th with nine publications. The most productive journal is Sustainability, with 254 articles, followed by Sustainable Cities and Society with 134 and Cities with 122. The results show a steady increase in scientific interest in the topic after 2019 and, at the same time, indicate a lack of development in interregional scientific cooperation and research on the ethical, institutional, and societal aspects of the use of artificial intelligence in urban governance.

Women play an important role in domestic enterprises in the fishing sector, but the mechanisms through which institutional support contributes to sustainable livelihoods remain poorly understood. This study aims to develop and validate a sequential capability-building model explaining how institutional support contributes to livelihood resilience among women operating household-based smoked fish enterprises in Indonesia. The study uses a consistent research design of mixed methods combining historical analysis and structural equation modeling using the partial least squares (PLS-SEM) method. The study analyzed materials from 2014-2024 from 40 documentary sources and 20 interviews, and surveyed 368 women employed in domestic enterprises in Tegalsari, Central Java. The results showed that institutional support positively affects women's economic agency (β = 0.697, p < 0.001) and entrepreneurial orientation (β = 0.164, p = 0.005). Women's economic agency enhances entrepreneurial orientation (β = 0.727, p < 0.001) and the ability to market innovation (β = 0.444, p < 0.001), but it has no statistically significant direct impact on the sustainability of livelihoods (β = 0.138, p = 0.149). Entrepreneurial orientation has the strongest direct impact on livelihood sustainability (β = 0.588, p < 0.001), while market innovations also have a significant impact (β = 0.203, p = 0.005). The results obtained confirm a consistent process of opportunity formation, in which institutional support is transformed into sustainable livelihoods primarily through women's economic agency, entrepreneurial orientation, and the ability to innovate in the market.

Income inequality constitutes a significant characteristic of socio-economic development, since an increase in aggregate output is not identical to broader participation of different population groups in the distribution of generated income. The aim of this study is to identify economic factors consistently associated with within-country changes in income inequality using panel data from 93 countries over the period 2002–2021. The empirical analysis is based on data from the World Bank’s World Development Indicators and Poverty and Inequality Platform. The sample covers 93 countries over the period 2002-2021 and includes 1,369 country-year observations in an unbalanced panel. The study employs descriptive statistics, correlation analysis, multicollinearity diagnostics, and panel regression analysis. The results indicate a robust negative association between income inequality and the level of economic development: a 1% increase in GDP per capita is associated with an approximately 0.048-point decrease in the Gini index (p<0,01). A onepercentage-point increase in the unemployment rate is associated with a 0.068-point increase in the Gini index (p<0,01), while a 10-percentage-point increase in trade openness is associated with an approximately 0.27-point increase in the Gini index (p<0,01). These relationships remain robust across alternative model specifications and when the models are estimated separately for income-based and consumption-based Gini measures. The estimates for GDP growth, natural resource rents, urbanization, and inflation are less robust. Further research may extend the analysis through dynamic panel models, instrumental-variable approaches, and alternative databases on inequality.