Economics

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Research Topic Summary.

Restructuring of the country's manufacturing industry toward technology-driven in order to achieve the growth of high-tech industries and increase the employment of advanced digital technologies, is a crucial condition for the country's economic growth. The development of advanced technologies, high qualifications and skills of the workforce in manufacturing industry ensures growth of higher added-value due to higher productivity. Goal of the research is to evaluate the drivers and restrictions of industrial restructuring towards more technologically intensive industries. Tasks of the research: to analyze the long-term structural changes of country‘s manufacturing industry using different classification; and by applying the qualitative and quantitative research methods to perform the in-depth examination of factors which encourage or hamper the implementation of digital advanced technologies in high-tech and low-tech industries.

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Research Topic Summary.

This topic explores the application of artificial intelligence (AI) in economics, focusing on agent-based systems, large language models, simulations, and the potential impact of these technologies on the labor market, firms, and macroeconomic dynamics. It examines how AI reshapes economic decision-making, productivity, and competition, while offering new tools for modeling complex systems. A central part of the research considers the post-labor economy, where productive and decision-making roles are increasingly taken over by autonomous AI systems. It also addresses universal basic income (UBI), new forms of value creation, and the question of how economies may operate when their primary participants are intelligent systems acting according to algorithmic principles. The topic further explores behavioral and neuroeconomic transformations driven by AI - how individuals react to algorithmic decisions, how trust and cognition evolve, and how insights from neuroscience and cognitive science can clarify adaptation in hybrid human - AI environments. Special attention is given to AI-driven and econometric methods for forecasting, causal inference, and policy evaluation, as well as to the development of brain-inspired models that simulate learning, adaptation, and decision-making in economic systems based on neural and cognitive principles. Finally, the topic encompasses ethical, legal, and governance issues, including fairness, algorithmic bias, and human oversight. This interdisciplinary framework connects economics, neuroscience, AI, and data science, empowering researchers to explore how intelligent systems transform economic structures, labor dynamics, and the role of humans in a data-driven world.

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Research Topic Summary.

Historically financial crisis of 2008 – 2010, COVID-19 pandemics, Ukraine - Russia war were faced with lots of uncertainty and created the impact for economies at different scale. However, as it is acknowledged as new reality, this requires new knowledge and new operational models to adapt to these changes. As the history shows, different economy sectors had different reactions and challenges on the impact created by those shocks. Corporate financial resistance usually is defined as its possibility to withstand financial distress periods during the revenue decrease or inevitable cost rising. It leads to the aim of the proposed research: to develop the evaluation model for different business sector financial resistance and capital investment dynamics during periods of economic shocks, what would allow to identify long term potential for different sector and to prepare guidelines for corporate financially sustainable performance. The main stages of the research: (1) literature review and analysis of the most recent research in the area of economic shocks and corporate financial performance measurement; (2) development of evaluation methodology for corporate financial resistance and capital investment dynamics during the periods of economic shocks; (3) empirical results analysis following the evaluation methodology created for selected country/ countries group/ economic activities / business sectors.

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Research Topic Summary.

Understanding and forecasting household consumption behavior is essential for developing effective economic policies, promoting sustainable growth, ensuring economic stability and social well-being. Rapid digitalization, the transition to a green economy and changes in the labor market are changing consumption habits and needs. These changes require new models and methods for assessing and forecasting household consumption trends. Current consumption models often overlook the complex interplay of multiple concurrent global transitions and their collective impact on household behavior. The aim of the doctoral dissertation is to develop a comprehensive household consumption forecasting model in the context of global economic and structural transformation, that captures the key drivers such as technological progress, green transition, changing work patterns and economic uncertainty, and to analyze how these factors affect household consumption decisions. This research will provide new insights into how households are changing their consumption habits during global changes, and will help to foster resilient and sustainable economies.

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Research Topic Summary.

Topic: Deep Learning Methods for Solving and Estimating Heterogeneous-Agent Macroeconomic Models Modern macroeconomic models with heterogeneous agents (HA)—such as Aiyagari, Krusell–Smith, and HANK frameworks—capture inequality, incomplete markets, and non-linear policy effects, but are notoriously difficult to compute and estimate. This PhD project aims to develop a deep-learning–based framework for solving and estimating these models without restrictive simplifications. By integrating neural networks, GPU acceleration, and simulation-based inference, the research will deliver scalable algorithms capable of capturing rich non-linear dynamics such as zero lower bound persistence, fiscal–monetary interactions, and distributional policy effects. The outcomes will include efficient computational tools, new methods for structural estimation, and open-source resources for the research and policy community.

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Research Topic Summary.

Recently, governments of different countries have become increasingly interested in how to improve or measure society's well-being. The third sector is a significant policy lever for increasing society's welfare. Therefore, it is important to study the factors that determine the opportunities for NGOs to contribute to the creation of public welfare. In addition, non-governmental organizations play an important role in the process of creating or restoring the well-being of people affected by the crisis. This is relevant for European states, which have faced many crises and challenges in recent years, many of which are still ongoing: the economic crisis, the energy crisis, the COVID-19 pandemic, the war in Ukraine, the movement of refugees and migrants to Europe, and even the challenges related to the effects of climate change. This study will focus on the institutional environment of NGO activities and the challenges related to the two recent crises - COVID-19 and the war in Ukraine. The purpose of the research is to investigate the drivers that determine the opportunities for NGOs to contribute to the creation of society's well-being in times of crisis.

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Research Topic Summary.

Relevance On the one hand, the current global situation is characterized by shifts in supply chains and a sharp decline in foreign direct investment in countries bordering Russia. On the other hand, technological changes driven by the growing adoption of artificial intelligence across various sectors may directly affect the structure of state revenues. Scientific Problem The key question is what solutions for optimizing the structure of state revenue can ensure sustainable public finances and keep public debt under control. Such solutions might involve changes in the tax system, new sources of revenue, or a redistribution of revenue streams, and they must take into account geopolitical investment challenges as well as the economic changes driven by AI development. Problem States bordering Russia are encountering difficulties in attracting foreign direct investment (for example, FDI in Lithuania has decreased sixteen-fold in recent years). Moreover, the rise of artificial intelligence may create tax collection challenges as the gap between service imports and exports widens. In light of these changes, it is crucial to optimize the structure of state revenue to control the growth of public debt. Objective The objective of this research is to develop a model for optimizing state revenue in the context of international economic challenges. Tasks Conduct a literature review to inform the theoretical modeling of supply chain changes. Conduct a literature review to determine the potential impact of AI applications in business on the structure of state revenue. Analyze changes in supply chains by product groups. Develop a methodology for constructing a state revenue optimization model. Build and evaluate the state revenue optimization model. Expected Results The research is expected to produce a state revenue optimization model that addresses the identified international economic challenges.

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Research Topic Summary.

The transition toward a climate-neutral (“net-zero”) economy represents one of the most significant structural shifts in the global economy, transforming investment patterns, industrial structures, and labour markets. Under the European Green Deal Industrial Plan and Fit for 55 initiatives, the European Union aims by 2030 to substantially reduce emissions and accelerate investment in renewable energy. These investments stimulate the creation of new jobs but simultaneously lead to employment losses in carbon-intensive industries. Although green investment is regarded as a driver of sustainable growth, there remains a lack of empirical research explaining its impact on employment dynamics, job quality, and skill structures across sectors and regions. Studies indicate that the green transition can have a positive effect on employment; however, the impact is highly uneven. Regions with strong innovation capacity, a developed renewable-energy sector, and a highly skilled workforce tend to generate more green jobs, while traditional, fossil-fuel-based industrial areas experience declining labour demand and rising social tension. It remains unclear to what extent green investments create value-added employment and how reskilling and upskilling policies can mitigate transitional regional and social inequalities. This research aims to examine the impact of green investment on employment and skill-structure transformation within the European Union, identifying regional and sectoral differences as well as policy effectiveness. The conceptual and econometric model developed in this research will identify which sectors and regions benefit or are most adversely affected by the green transition, determine the critical skills required for adaptation, and assess which policy interventions most effectively mitigate social and regional inequalities. The findings will provide robust empirical evidence for shaping EU labour and industrial policy aimed at achieving a net-zero economy.