The aim of this study is a comprehensive statistical and econometric assessment of the main components shaping the dynamics of Azerbaijan's balance of payments: foreign trade turnover, foreign direct investment, and oil and gas exports.

Materials and methods. The Autoregressive Distributed Lag (ARDL) methodology was applied based on quarterly data covering the period from Q1 2001 to Q4 2024, with model selection conducted using the Akaike information criterion. The existence of long-run relationships was tested using the Bounds cointegration test, and the model's validity was confirmed through diagnostic procedures (Jarque-Bera, Breusch-Godfrey, Durbin-Watson, CUSUM).

The results obtained show that foreign trade turnover and direct investments have a statistically significant impact on the current account. Although foreign trade has a negative impact in the short term by weakening the balance through an increase in imports, it plays a stabilising role in the medium and long term. Direct investment weakens the balance in the short term through repatriation and import demand, but in the long term, it strengthens production and export potential. Oil and gas exports constitute the primary driver of the current account and have a positive impact in both the short and long term, but the sector's volatility underscores the need for a diversification policy.

In conclusion, from an economic interpretation perspective, the results confirm that policies such as import management, channelling investment flows towards production and exports, diversification of the energy sector, and the development of non-oil sectors are critical to strengthening the sustainability of the balance of payments. The contribution of the study is that, within the ARDL methodology, both short-run and long-run effects have been estimated in the same structure, and the stability and robustness of the model have been demonstrated through diagnostic tests. This approach provides a strong empirical basis for Azerbaijan's macroeconomic stability and the reliability of forecasts.

The increasing role of Russia as a key player in the global grain market and the strategic importance of the Chinese market for Russian exports determine the relevance of the research topic. Wheat is one of Russia's main export crops, providing a significant share of foreign exchange earnings and supporting the agricultural sector of the economy. In the context of the changing geopolitical situation and the growing demand for food in China, the study of prospects for increasing Russian wheat supplies is becoming particularly relevant. The purpose of the study.

The purpose of the study is to compare wheat production in Russia and China in the context of assessing the potential for increasing supplies to the Chinese market and drawing up recommendations for the development of their trade and economic relations.

Materials and methods. The theoretical and methodological basis of the research is the scientific works of domestic and foreign scientists and specialists, legislative and regulatory acts, reference materials and periodical press data. The empirical basis of the study was made up of data from the Federal State Statistics Service of Russia (Rosstat), the National Bureau of Statistics of China, and publications in open sources. The object of the study is the process of wheat production and exports in Russia, with an emphasis on the prospects for increasing supplies to China. Statistical methods were used in the paper: descriptive statistics, comparative analysis, dynamic series analysis, monographic and other research methods.

Results. The author's approach in the presented study allowed for a comprehensive analysis and identification that Russia has significant potential to increase wheat exports to China. Despite the fact that China is the world leader in wheat production, their level of selfsufficiency is about 93%. As a result, the country is forced to import grain to meet the needs of its large population. Russia is a leader in wheat exports, has great export potential and demonstrates one of the highest self-sufficiency rates, reaching 204%. In this regard, increasing wheat exports to China is a strategically important and mutually beneficial direction for the Russian Federation.

Conclusion. Taking into account the results obtained, increasing the supply of Russian wheat to China is a strategically promising area that meets both Russia's economic interests (export diversification, increased foreign exchange earnings) and China's political goals (reducing dependence on Western suppliers, strengthening food security). Realizing this potential requires comprehensive measures to develop logistics by solving infrastructure problems, including the expansion of the Baikal-Amur Mainline and Trans-Siberian Railway, and the construction of terminals; increased rail transportation across the border; active use of the Northern Sea Route, as well as adaptation to standards and strengthening of bilateral cooperation.

In the context of the growing role of spatial policy, the need for comparable and reproducible regional typologies is increasing. However, interregional comparisons are often based on pre-defined sets of indexes, the composition of which depends on expert choice and may affect the results when the statistical database is updated. This creates a risk of mixing stable spatial differences with short-term fluctuations, one-off shocks and the peculiarities of calculating individual relative values.

The purpose of the study is to develop an approach to forming the information base for interregional comparisons in which initial statistical indexes are selected not only by their substantive relevance, but also by the stability of the rank distribution of the constituent entities of the Russian Federation over time.

The scientific novelty lies in using the stability of interregional ranks as an independent criterion of preliminary statistical verification before constructing a typology and calculating an integral assessment of spatial development.

The empirical base includes 65 official socio-economic, infrastructural and environmental indexes for 85 regions of the Russian Federation for 2016–2023. Index stability was assessed using Spearman’s and Kendall’s rank correlation coefficients. Further calculations used median values for 2021 – 2023, logarithmic transformation of strongly asymmetric scale indexes, standardization, k-means clustering, block indexes and an integral assessment calculated as the geometric mean across four areas of spatial development. Of the 65 initial indexes, 40 passed the stability test; after additional reduction, a calculation set of 30 indexes was formed. Six types of the regions of the Russian Federation were identified: agglomeration-resource centres, the supporting industrial-infrastructural belt, the middle regional framework, the socially supported periphery, northern and Far Eastern territories of infrastructural gap, and the structurally constrained periphery. The quality of the typology was assessed using the silhouette coefficient, the Adjusted Rand Index, the share of intercluster variation and PCA diagnostics.

The final typology showed moderate cluster separation, stability of results under multi-start k-means initialization and a high share of intercluster variation in block indexes. For cartographic presentation, the regions were additionally grouped into three zones according to the integral assessment: reduced, basic and increased spatial development profiles. The proposed approach reduces the dependence of typology on subjective index choice and can be used for regular monitoring of regional differentiation, comparison of the regions of the Russian Federation by key development areas and analytical support for decision-making in spatial policy. 

Purpose. The study aims to conduct a comprehensive statistical analysis of natural population movement of the Republic of Uzbekistan in the context of digital economy formation and transformation of the national statistical system. Based on official data from the National Statistics Committee of the Republic of Uzbekistan for 2018–2025, a detailed analysis of the dynamics of birth rates, mortality, marriage and divorce rates at the regional level was carried out. The relevance of the study is determined by the strategic importance of demographic processes for the sustainable socio-economic development of the country and the need to improve the quality of demographic statistics in the context of digitalization.

Materials and methods. The information base consists of official statistical data from the National Statistics Committee of the Republic of Uzbekistan, UN materials within the System of National Accounts (SNA – 2008) framework, as well as data from WHO, the World Bank, UNFPA, UNECE, and OECD. The methodological toolkit includes dynamic and regional analysis, cross-sectional and longitudinal approaches to the study of demographic series, structural analysis, and as well as methods of international comparisons. The Excel and Stata software packages were used to process the data.

Results. It was established that between 2018 and 2023 the Republic of Uzbekistan experienced a steady increase in the number of births - from 768,500 to 962,000 per year - followed by a moderate decline to 879,600 in 2025, indicating the country's gradual entry into the demographic transition phase. Pronounced regional differentiation was identified: the Samarkand, Kashkadarya, and Fergana regions hold leading positions. The mortality rate remains relatively low at 4.7‰ in 2025, with a noticeable peak growth during the COVID-19 pandemic (5.1‰ in 2020). Analysis of the marriage-family sphere revealed a decline in registered marriages since 2021 alongside a moderate but steady increase in the divorce rate, which nonetheless remains below the global average. It is substantiated that natural population movement significantly influences labour potential formation and labour market structure. The role of digital technologies and integrated information systems in improving the quality, completeness, and timeliness of demographic statistics is proven: since 1 June 2021, real-time electronic transfer of civil registration data has been ensured.

Conclusion. The obtained results confirm the need for an integrated approach to the analysis of demographic processes, which involves the joint consideration of indexes of fertility, mortality, migration and age structure within the framework of a single analytical model. Digital transformation of statistical activity creates a qualitatively new architecture for the national statistical system and establishes the institutional foundation for data-driven governance. The implementation of Presidential Decree No. PP-75 and the “Uzbekistan - 2030” Strategy priorities sets the direction for further development of official statistics. The practical significance of the study is determined by the possibility of using the results in the development of state demographic policy, the formation of regional family support programs, as well as in improving the methodology of official statistics in accordance with the standards of the UN, UNFPA and OECD.

Relevance. The housing mortgage lending market remains one of the key tools for implementing housing policy and national projects. In recent years, it has been under serious strain: the high volatility of the key interest rate, the curtailment of massive preferential programs and rising real estate prices are significantly transforming the behavior of borrowers. In these conditions, the analysis of regional demand patterns and the construction of reliable short-term forecasts that could consider the heterogeneity of subjects within large federal districts is becoming particularly in demand.

Purpose. To identify the key factors affecting mortgage demand in the Volga Federal District, to cluster regions by mortgage activity level, and to construct a short term forecast for the number of the issued mortgage housing loans.

Methods. Correlation regression analysis, cluster analysis (Ward’s method and k means), time series models (ARIMA, exponential smoothing with damped trend). The information base comprises official data from Rosstat and the Bank of Russia for 2019–2025, as well as monthly data from January 2023 to February 2026.

Results. Multiple regression models were constructed for 2023 – 2025, confirming that demand is determined by housing commissioning volume, loan term and interest rate, and wage level. Clustering of the 14 regions of the Volga Federal District identified four stable groups: “leaders”, “moderate activity”, “expanding market”, and “restrained demand”. The forecast using the ARIMA (1,1,1)(0,0,1) model for March – May 2026 gave values of 15.1–15.8 thousand loans per month, which is confirmed by actual data for March–April 2026 (deviation 7–13.5%).

Conclusion. The conducted analysis of the mortgage lending market in the Volga Federal District made it possible to identify and assess demand factors on the mortgage market while accounting for regional heterogeneity, to construct and verify forecast models based on operational monthly data, and to compare the accuracy of ARIMA and exponential smoothing over the post-forecast period. The proposed toolkit may be useful for banks in adjusting regional credit policies at the regional level and for regional authorities in developing targeted support measures in the depressed subjects

The purpose of the study. To develop a reproducible methodology for constructing a correspondence matrix between broad groups of higher education specializations and employment sectors for use in forecasting labor demand in the Russian economy. The tool is designed to fill a methodological gap in analyzing structural imbalances between the education system and the labor market by providing a quantitative assessment of graduate distribution across industries based on their field of study.

Materials and methods. The study is based on microdata from the Russian Longitudinal Monitoring Survey – Higher School of Economics (2004 – 2024), comprising 52,916 observations of respondents with higher education. A procedure is proposed for consolidating diploma occupations (ISCO-2008 classifier) into ten broad specialization groups according to the Russian nomenclature and aggregating thirty original industry categories into sixteen meaningful groups. A matrix of graduate distribution across sectors is constructed, concentration indexes (Herfindahl index) and profile employment have been calculated. The methodology is tested using cohort analysis across five periods for obtaining a diploma (2000–2004, 2005–2009, 2010–2014, 2015–2019, 2020–2024).

Results. A reproducible correspondence matrix “specialization – employment sector” is formed, enabling the transformation of graduate supply forecasts by field of study into estimates of the sectoral structure of new highly educated personnel. Persistent linkages are identified: medical graduates predominantly work in healthcare (72% on average), engineering graduates - in industry (31%), and teaching staff – in the education sector (60%). Contrasting trends are observed: increasing concentration of engineers in industry (from 28% to 44%), a restoration of the profile employment of IT specialists (from 6% to 25%), declining share of medical professionals in healthcare (from 68% to 60%), and high volatility in teacher employment. For broad-field specializations (social sciences, humanities), reduced diversification and the formation of employment clusters in trade, public administration, and education are documented. Herfindahl index values range from 0.12 (mathematical sciences) to 0.53 (healthcare), confirming varying degrees of sectoral attachment across specializations.

Conclusion. The proposed tool can be integrated into labor demand forecasting systems, including for adjusting university admission targets and developing career guidance programs. The matrix serves as a bridge between graduate supply forecasts and estimates of their sectoral distribution. Alongside persistent traditional links between training and employment, graduate career paths are increasingly diversifying, calling for a reassessment of curriculum planning and mechanisms linking education to the labor market. Study limitations include potential subjectivity in mapping ISCO-2008 codes to Russian specialization groups and the confounding of cohort, age, and period effects. Future research may focus on analyzing the impact of artificial intelligence development and deployment on labor trajectories.