About the Journal

The journal publishes scientific articles and studies in all fields of economics and welcomes submissions whether they be theoretical, applied, or orientated towards academics or policymakers. The journal accepts original articles and comprehensive studies not previously published. The Editorial Board of the journal always looks into widening the range of the authors, inviting the researchers from universities all around the world.

Current Issue

Vol. 17 No. 1(47) (2026)
View Vol. 17 No. 1(47) (2026)
DOI: https://doi.org/10.46991/BYSU.G/2026.17.1
Published: 2026-06-23

Full Issue

Economic theory

  • Economic theory

    High-Skilled Emigration from Armenia

    Grigor Hayrapetyan, Nonna Khachatryan, Narine Mirzoyan
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    Abstract

    This paper examines the phenomenon of high-skilled emigration, or brain drain, with a specific focus on Armenia. Despite a global Armenian diaspora exceeding seven million, the Republic of Armenia faces increasing challenges associated with the outflow of educated professionals. Using statistical data from the Central Bank of Armenia, the World Bank, and the World Population Review, the study analyzes the dynamics of remittances, foreign direct investment inflows, and brain drain indices in relation to economic indicators such as average salaries and unemployment rates from 2013 to 2024. The findings reveal that Armenia’s brain drain is not solely driven by economic factors like wages or unemployment but is also shaped by geopolitical shocks, policy frameworks, and strong diaspora linkages. While migration has generated benefits through remittances, diaspora networks, and educational incentives, the persistent rise of the Brain Drain Index highlights long-term risks to Armenia’s socio-economic development. The paper concludes that without targeted policies and improved migration data management, Armenia’s growing reliance on external inflows cannot offset the structural challenges posed by high-skilled emigration.

    References

    References

    1. Айрапетян Г.Р. 2016. “Армянская диаспора и внешнеэкономические связи Армении”, Трансформация системы мирохозяйственного взаимодействия в контексте современных глобальных вызовов, Ростовского Государственного Экономического Университет.

    2. Aleksandr V. Gevorkyan, 2023. “Enhancing Development through Diaspora Engagement in Armenia”, United Nations International Organization for Migration.

    3. Berry, R. Albert, and Ronald Soligo. 1969. “Some Welfare Aspects of International Migration.” Journal of Political Economy 77 (5): 778–94.

    4. Bhagwati, Jagdish, and Koichi Hamada. 1974. “The Brain Drain, International Integration of Markets for Professionals and Unemployment: A Theoretical Analysis.” Journal of Development Economics 1 (1): 19–42.

    5. Caucasian Research Resource Centers “Migration and Skills in Armenia”, European Training Foundation 2013.

    6. Central Bank of Armenia. (2013–2024). Total money transfers of individuals received from abroad through commercial banks of the Republic of Armenia.

    7. Fabio Mariani, 2008. Brain Drain, R&D-Cost Differentials and the Innovation Gap, Recherches Économiques de Louvain / Louvain Economic Review, Vol. 74, No. 3 (2008), pp. 251-272.

    8. Haykanush Chobanyan, “Assessing Armenia’s Migration Strategy for 2017-2021”, International Centre for Migration Policy Development. 2019

    9. Innovation for Sustainable development, Review of Moldova, United Nation Economic Commission for Europe, Geneva 2021.

    10. International Organization for Migration, Labor Migration in Armenia. Existing Trends and Policy Options, 2012.

    11. IOM ARMENIA MISSION STRATEGY (2022-2025), available at https://crisisresponse.iom.int/sites/g/files/tmzbdl1481/files/appeal/documents/IOM-Armenia-Country-Strategy_22-25.pdf

    12. Paolo Contini, Letizia Carrera, 2022. “Migrations and culture. Essential reflections on wandering human beings”, Frontiers in Sociology.

    13. Stark Oded, Christian Helmenstein, and Alexia Prskawetz. 1998. “Human Capital Depletion, Human Capital Formation, and Migration: A Blessing or a ‘Curse’?” Economics Letters 60 (3): 363–67.

    14. Stark Oded, Wang Yong. 2002. “Inducing human capital formation: migration as a substitute for subsidies” Journal of Public Economics, Elsevier, vol. 86(1), pp. 29-46.

    15. Tiankuo Li, 2025. “Influence of Brain Drain in Developing Countries”, Advances in Economics Management and Political Sciences 203(1): pp. 64-69.

    16. World Bank Group. (2013–2024). World Development Indicators.

    17. World Population Review. (2017–2024). Brain Drain by Country.

Management

  • Management

    Factor-Based Priorities of National Potential in the Republic of Armenia: The Case of Yerevan State University

    Amalya Saribekyan, Anna Hakobjanyan, Vardush Gyozalyan
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    Abstract

         National potential, as a multi-component concept, is interpreted in the theoretical literature not only through material resources, but also within the framework of the interconnection between social capital, human capital, and institutional capacities, which condition opportunities for development and resilience.

          The primary aim of this article is to identify public perceptions of the factors constituting the national capacity of the Republic of Armenia and the priorities attributed to them, from the perspective of the academic staff and administrative employees of Yerevan State University (YSU). The study was conducted in a pilot format through a sociological survey, followed by an integrated (quantitative and qualitative) analysis of the collected data. A total of 220 employees participated in the survey. The findings indicate that the overwhelming majority of respondents prioritize social-capital factors within the components of national capacity—particularly human capital and the educational/knowledge factor—whereas natural resources are largely regarded as a less important component. At the same time, an interesting inconsistency is observed: while 65% of respondents assess intellectual potential as a key factor in coping with the country’s socio-economic challenges, only 45% assign a high role to educational institutions (including YSU) in the same context, which points to a gap between recognizing the importance of intellectual potential and institutional trust in its realization. In the responses to the open-ended questions, respondents emphasize the need for a substantial improvement in the quality of education and continuous changes within the education system as a prerequisite for a more effective realization of national capacity. They also highlight, in the context of university reforms, issues related to institutional capacities, individual motivation, and the disproportionate distribution of the burden of decision-making and implementation, all of which require clearly articulated overarching development strategies.

     

    References

    1. Baliamoune-Lutz, M. (2011). Trust-based social capital, institutions, and development. Journal of Socio-Economics, 40(4), 335–346. https://doi.org/10.1016/j.socec.2010.12.004

    2. Bjørnskov, C. (2011). Combating corruption: On the interplay between institutional quality and social trust. Journal of Law and Economics, 54(1), 135–159. https://doi.org/10.1086/652421

    3. Gentry, A. N., Martin, J. P., & Douglas, K. A. (2025). Social capital assessments in higher education: A systematic literature review. Frontiers in Education, 9, 1498422. https://doi.org/10.3389/feduc.2024.1498422

    4. Gyozalyan, V. (2022). Economics and Social Justice. Yerevan: Edit Print, 254 p. (in Armenian)

    5. Han, S.-H., Oh, E. G., & Kang, S. P. (2022). Social Capital Leveraging Knowledge-Sharing Ties and Learning Performance in Higher Education: Evidence From Social Network Analysis in an Engineering Classroom. AERA Open, 8. https://doi.org/10.1177/23328584221086665

    6. Jiang, D., Abd Majid, M. Z., & Arham, A. F. (2026). Exploring the evolution of higher education’s role in human capital formation: A bibliometric analysis from 2000 to 2024. Cogent Education, 13(1). https://doi.org/10.1080/2331186X.2026.2618345

    7. Lloyds Bank (2024). The economic context of Armenia. International Trade Portal. Available at: https://www.lloydsbanktrade.com/en/market-potential/armenia/economical-context

    8. Milton, S., Barakat, S. (2016). Higher education as the catalyst of recovery in conflict-affected societies. Globalisation, Societies and Education, 14(3), 403–421. https://doi.org/10.1080/14767724.2015.1127749

    9. Oyefuga, E., & Shakeshaft, C. (2023). Social Capital and the Higher Education Academic Achievement: Using Cross-Classified Multilevel Models to Understanding the Impact of Society on Educational Outcomes. Youth & Society, 55(1), 3–27. https://doi.org/10.1177/0044118X211042912

    10. Saribekyan, A.S. (2010). Financial and Economic Crisis: Paths to Overcome and Post-Crisis Developments. Textbook. Yerevan: Yerevan State University, pp. 70-77 (in Armenian)

    11. Saribekyan, A.S. (2012). Contemporary Issues of Socio-Economic Development. Yerevan, 200p. (in Armenian)

    12. Saribekyan, A.S. (2023). Global Economic Growth Tendencies and Recessional Developments. Bulletin of High Technology, 2(26), 28–36. https://doi.org/10.56243/18294898-2023.2-28

    13. Statistical Committee of the Republic of Armenia (ArmStat) (2022). Socio-Economic Situation of the Republic of Armenia, January–August 2022. Yerevan: ArmStat. (Published 05 Oct 2022), p. 105. Available at: https://armstat.am/en/?nid=689

    14. Statistical Committee of the Republic of Armenia (ArmStat) (2024). Armenia – Poverty Snapshot over 2019–2022. Yerevan: ArmStat. Available at: https://armstat.am/file/article/poverty_2023_en_2.pdf

    15. Statistical Committee of the Republic of Armenia (ArmStat) (2024). Poverty in the Republic of Armenia, 2023 (Infographics). Yerevan: ArmStat. Available at: https://armstat.am/en/?id=1056&nid=157

    16. Trading Economics (n.d.). GDP Annual Growth Rate – Countries – List. Available at: https://tradingeconomics.com/country-list/gdp-annual-growth-rate

    17. World Bank (2023). Global Economic Prospects, January 2023. Washington, DC: World Bank. doi:10.1586/978-1-4648-1906-3. (See fig. 1.1.C, p. 5). Available at: https://thedocs.worldbank.org/en/doc

    18. World Bank (n.d.). GDP (current US$) – Armenia. World Development Indicators. Available at: https://data.worldbank.org/indicator/NY.GDP.MKTP.CD?locations=AM

  • Management

    Analysis of the Labor Market of Armenia in the Context of Contemporary Challenges: Socio‑Economic and Psychological Aspects

    Elena Kulchitskaya, Amalya Galstyan, Elena Kadura
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    Abstract

    This article presents a multidimensional study of the labor market of the Republic of Armenia in the context of current global and regional challenges. The work integrates three analytical dimensions: a cross-country correlation analysis of macroeconomic and social indicators, a detailed characterization of the dynamics of the Armenian labor market in 2020-2024, and a sample-based psychological measurement of occupational stress among labor force participants.

    A correlation analysis of 20 countries from the post-Soviet, Balkan, and Middle Eastern regions has revealed stable relationships between GDP per capita, the Human Development Index, the Knowledge Economy Index, and the Global Innovation Index. A characteristic gap for Armenia has been identified between a relatively high level of human potential and a comparatively lower degree of its economic realization within the middle-income group. The labor market analysis demonstrates positive employment dynamics while structural imbalances persist - gender inequality, a high level of chronic unemployment, and sectoral disproportionality. An empirical study of occupational stress (n = 234; 95% confidence level; confidence interval 6.5 percentage points) identified latent stress tendencies within the surveyed group rather than claiming direct generalization to the entire national workforce. The totality of the obtained results forms the basis for developing comprehensive recommendations in the field of state employment policy and the protection of workers’ psychological health.

    References

    1. Abla, E., et al. (2021). Workplace stress and productivity: A cross sectional study. Kansas Journal of Medicine.

    2. Burdorf, A., & Rugulies, R. (2024). Economic consequences of adverse working environments. Scandinavian Journal of Work, Environment & Health, 50(2), 49–52.

    3. Cohen, S., Kamarck, T., & Mermelstein, R. (1983). A global measure of perceived stress. Journal of Health and Social Behavior, 24(4), 385–396.

    4. Diebolt, C., & Hippe, R. (2018). The long run impact of human capital on innovation and economic development in European regions. Applied Economics.

    5. Fontana, D. (1989). Managing Stress. Leicester: British Psychological Society.

    6. Galstyan, A. R. (2025). Trends in labor market development in the Republic of Armenia. Bulletin of Yerevan State University. Economics, 16(1), 48–62.

    7. Graversen, B. K., et al. (2023). Labor market costs of occupational stress. Scandinavian Journal of Work, Environment & Health.

    8. Grigoryan, A., & Khachatryan, K. (2023). Multidimensional deprivation of opportunities in the labor market in Armenia. Journal of International Development.

    9. International Labour Organization. (2013). Resolution concerning statistics of work, employment and labour underutilization.

    10. Kulchitskaya, E. V., Hakobjanyan, A. O., Galstyan, A. R., & Kadura, E. V. (2026). Study of occupational stress among the economically active population of the Republic of Armenia. Saint Petersburg–Yerevan: SPbSU & YSU.

    11. Kurginyan, R., Gasparyan, A., & Harutyunyan, A. (2025). Dynamics of the labor market and unemployment in the Republic of Armenia. SHSU Scientific Proceedings.

    12. Leahy, R. L. (2015). Emotional Schema Therapy. New York: Guilford Press.

    13. López Pueyo, C., Jiménez, G., & Sanau, J. (2014). Measuring human capital in OECD countries and its relationship with economic growth and innovation. Revista de Economía Mundial.

    14. Managhi, S., & Piao, H. (2022). Assessment of occupational stress among employees by determining human capital losses in Japan. BMC Public Health, 22(1).

    15. Morrisey, M., et al. (2021). The impact of occupational heat stress on worker productivity and economic costs. American Journal of Industrial Medicine.

    16. Nazaryan, G., & Vardanyan, T. (2022). Labor migration processes and institutional frameworks for their regulation in the EAEU countries. Alternative.

    17. Ngo, H. P. T. (2023). Human capital for developing innovative potential in middle income countries. International Journal of Economics and Finance, 15(11).

    18. Shi, S., & Wang, S. (2024). Assessing the impact of human capital in the process of industrial structure modernization. PLoS ONE, 19(7).

    19. Shcherbatykh, Y. V. (2006). Psychology of Stress and Methods of Correction. Saint Petersburg: Piter.

    20. Statistical Committee of the Republic of Armenia (ARMSTAT). (2025). Labor market in the Republic of Armenia, 2020–2024. Yerevan. https://www.armstat.am

    21. Tiratsvyan, A. (2025). Problems of labor market development and the need for a remote work promotion policy in the RA. Armenian Research and Education Resource Archive.

    22. UNDP. (2024). Human Development Report 2024. New York: United Nations. https://hdr.undp.org

    23. WIPO. (2024). Global Innovation Index 2024. Geneva: WIPO. https://www.wipo.int/global_innovation_index/en/2024/

    24. World Bank. (2023). Armenia Human Capital Review. Washington, DC: World Bank Group.

    25. World Bank. (2024). World Bank Country and Lending Groups.

    26. World Bank. (2024). World Development Indicators 2024. Washington, DC: World Bank. https://databank.worldbank.org/source/world-development-indicators

Finance

Economic and mathematical modeling

  • Economic and mathematical modeling

    Identification and Harmonization of Material Values and Product Names in a Group of Companies Using Nlp Methods

    Rafik Mashuryan
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    Abstract

    The article examines the problem of heterogeneous material-value and product names in a group of companies. The same physical material may be registered under different abbreviations, spellings, languages, internal codes, or incomplete descriptions in the accounting and enterprise systems of separate subsidiaries. This reduces the quality of consolidated reporting, complicates procurement analysis, inventory control, price comparison, and managerial decision-making at group level. The problem is formulated as an Entity Resolution and product-matching task and is addressed through Natural Language Processing and machine learning methods. A dataset of 17,258 material and product names was annotated manually and used to train a domain-specific Named Entity Recognition model. The proposed pipeline extracts structured components from free-text descriptions and creates a basis for unified material classification, centralized procurement, and analytical control in a group of companies. The article also adds a model-evaluation framework based on the confusion matrix, precision, recall, and F1-score.

    References

    Christen, P. (2012). Data Matching: Concepts and Techniques for Record Linkage, Entity Resolution, and Duplicate Detection. Springer.

    Cicco, V., & Firmani, D. (2019). Interpreting deep learning models for entity resolution: An experience report using LIME.

    Papadakis, G., Skoutas, D., & Thanos, E. (2020). A Survey of Blocking and Filtering Techniques for Entity Resolution.

    Reddy, A. (2025). An indepth guide to materials master data management. Verdantis. https://www.verdantis.com/materials-master-data-management

    Explosion. (n.d.). spaCy usage documentation. Retrieved from https://spacy.io/usage

    Trącz, J., et al. (2020). BERT-based similarity learning for product matching. Proceedings of the Workshop on Natural Language Processing in E-Commerce (EComNLP), 66-75.

    Yadav, V., & Bethard, S. (2018). A Survey on Recent Advances in Named Entity Recognition from Deep Learning Models. Proceedings of COLING.

    Honnibal, M., Montani, I., Van Landeghem, S., & Boyd, A. (2020). spaCy: Industrial-strength natural language processing in Python. Zenodo. https://doi.org/10.5281/zenodo.1212303

    Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019). BERT: Pre-training of deep bidirectional transformers for language understanding. Proceedings of NAACL-HLT 2019.

    Lample, G., Ballesteros, M., Subramanian, S., Kawakami, K., & Dyer, C. (2016). Neural architectures for named entity recognition. Proceedings of NAACL-HLT 2016.

    Mudgal, S., Li, H., Rekatsinas, T., Doan, A., Park, Y., Krishnan, G., Deep, R., Arcaute, E., & Raghavendra, V. (2018). Deep learning for entity matching: A design space exploration. Proceedings of the 2018 International Conference on Management of Data, 19-34.

    Bilenko, M., & Mooney, R. J. (2003). Adaptive duplicate detection using learnable string similarity measures. Proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 39-48. https://doi.org/10.1145/956750.956759

    Getoor, L., & Machanavajjhala, A. (2012). Entity resolution: Theory, practice & open challenges. Proceedings of the VLDB Endowment, 5(12), 2018-2019. https://doi.org/10.14778/2367502.2367564

    Elmagarmid, A. K., Ipeirotis, P. G., & Verykios, V. S. (2007). Duplicate record detection: A survey. IEEE Transactions on Knowledge and Data Engineering, 19(1), 1-16. https://doi.org/10.1109/TKDE.2007.250581

    Fellegi, I. P., & Sunter, A. B. (1969). A theory for record linkage. Journal of the American Statistical Association, 64(328), 1183-1210. https://doi.org/10.1080/01621459.1969.10501049

    Sokolova, M., & Lapalme, G. (2009). A systematic analysis of performance measures for classification tasks. Information Processing & Management, 45(4), 427–437.

    Powers, D. M. W. (2011). Evaluation: From precision, recall and F-measure to ROC, informedness, markedness and correlation. Journal of Machine Learning Technologies, 2(1), 37–63.

International economics

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