We apply the synthetic control method (SCM) to evaluate the impact of the 2017–2020 New South Wales drought, one of the most severe in Australia’s recorded history, on regional agricultural labour markets. Using 25 years of quarterly employment data, we construct region-specific counterfactuals for 14 non-metropolitan Statistical Area Level 4 (SA4) regions. Most regions exhibit limited long-term effects on agricultural employment. However, the Hunter Valley region experienced a sharp and persistent decline in agricultural employment, with levels up to 75% below those of its synthetic control and no sign of recovery years after the drought ended. This divergence reflects a place-specific adjustment path in which drought disruptions were compounded by subsequent climatic and market shocks, including bushfires, flooding and trade disruptions, resulting in persistent labour-market effects rather than a transitory response. Complementary evidence on the employment structure indicates widespread short-term shifts from full-time to part-time agricultural work, suggesting that drought impacts affect not only employment levels but also job quality and income stability. By providing a within-country, multi-region application of SCM to drought impacts on labour markets, the study contributes to understanding regional economic resilience and demonstrates the value of spatially disaggregated causal methods for identifying persistent vulnerability and informing targeted adaptation strategies.
Regional economics. Space in economics, Regional planning
This study investigates the impact of Foreign Direct Investment (FDI) on economic growth in South Asian countries, utilizing annual panel data from five SAARC member states (Bangladesh, India, Nepal, Pakistan, and Sri Lanka) over the period 1980-2017. Data sourced from the World Development Indicators and Penn World Table were analyzed using static panel models, including Ordinary Least Squares, Fixed Effects, Random Effects, and Generalized Least Squares regressions. The empirical findings reveal that FDI exhibits a consistently positive but statistically insignificant correlation with economic growth across all model specifications. In contrast, domestic investment and human capital development emerge as significant and robust positive determinants of growth. Control variables such as government consumption and inflation show expected negative, though generally insignificant, associations with growth. The results imply that for the sampled South Asian economies, enhancing domestic investment and fostering human capital are more critical for driving economic expansion than relying on FDI inflows. Consequently, policymakers should prioritize strategies that strengthen local investment climates and improve educational and skill-building institutions to boost productivity. While FDI's role remains complementary, its insignificant immediate impact suggests the need for further research into the conditional factors such as institutional quality, financial market development, and trade policies that might mediate its effectiveness in fostering long-term growth within the region.
The Fourth Industrial Revolution commonly refers to the accelerating technological transformation that has been taking place in the 21st century. Economic growth theories which treat the accumulation of knowledge and its effect on production endogenously remain relevant, yet they have been evolving to explain how the current wave of advancements in automation and artificial intelligence (AI) technology will affect productivity and different occupations. The work contributes to current economic discourse by developing an analytical task-based framework that endogenously integrates knowledge accumulation with frictions that describe technological lock-in and the burden of knowledge generation and validation. The interaction between production (or automation) and growth (or knowledge accumulation) is also described explicitly. To study how automation and AI shape economic outcomes, I rely on high-throughput calculations of the developed model. The effect of the model's structural parameters on key variables such as the production output, wages, and labor shares of output is quantified, and possible intervention strategies are briefly discussed. An important result is that wages and labor shares are not directly linked, instead they can be influenced independently through distinct policy levers. Generally, labor share depends sensitively on capital-labor ratio, while wages respond positively to larger knowledge stocks.
We propose a framework that recasts scientific novelty not as a single attribute of a paper, but as a reflection of its position within the evolving intellectual landscape. We decompose this position into two orthogonal dimensions: \textit{spatial novelty}, which measures a paper's intellectual distinctiveness from its neighbors, and \textit{temporal novelty}, which captures its engagement with a dynamic research frontier. To operationalize these concepts, we leverage Large Language Models to develop semantic isolation metrics that quantify a paper's location relative to the full-text literature. Applying this framework to a large corpus of economics articles, we uncover a fundamental trade-off: these two dimensions predict systematically different outcomes. Temporal novelty primarily predicts citation counts, whereas spatial novelty predicts disruptive impact. This distinction allows us to construct a typology of semantic neighborhoods, identifying four archetypes associated with distinct and predictable impact profiles. Our findings demonstrate that novelty can be understood as a multidimensional construct whose different forms, reflecting a paper's strategic location, have measurable and fundamentally distinct consequences for scientific progress.
The application of Reinforcement Learning (RL) to economic modeling reveals a fundamental conflict between the assumptions of equilibrium theory and the emergent behavior of learning agents. While canonical economic models assume atomistic agents act as `takers' of aggregate market conditions, a naive single-agent RL simulation incentivizes the agent to become a `manipulator' of its environment. This paper first demonstrates this discrepancy within a search-and-matching model with concave production, showing that a standard RL agent learns a non-equilibrium, monopsonistic policy. Additionally, we identify a parametric bias arising from the mismatch between economic discounting and RL's treatment of intertemporal costs. To address both issues, we propose a calibrated Mean-Field Reinforcement Learning framework that embeds a representative agent in a fixed macroeconomic field and adjusts the cost function to reflect economic opportunity costs. Our iterative algorithm converges to a self-consistent fixed point where the agent's policy aligns with the competitive equilibrium. This approach provides a tractable and theoretically sound methodology for modeling learning agents in economic systems within the broader domain of computational social science.
This paper investigates the impact of the global financial crisis on the shape of economics as a discipline by analyzing EconLit-indexed journals from 2006 to 2020 using a multilayer network approach. We consider two types of social relationships among journals, based on shared editors (interlocking editorship) and shared authors (interlocking authorship), as well as two forms of intellectual proximity, derived from bibliographic coupling and textual similarity. These four dimensions are integrated using Similarity Network Fusion to produce a unified similarity network from which journal communities are identified. Comparing the field in 2006, 2012, and 2019 reveals a high degree of structural continuity. Our findings suggest that, despite changes in research topics after the crisis, fundamental social and intellectual relationships among journals have remained remarkably stable. Editorial networks, in particular, continue to shape hierarchies and legitimize knowledge production.
Yusuf Muhammad-Bashir Owolabi, Salau Tunde Jibril, Adeiza Adams
This paper analyzes the relationship between food and oil prices in Nigeria before and during the COVID-19 pandemic, using monthly data from January 2018 to December 2021. The ARDL and NARDL models are applied to estimate the symmetry and asymmetric relationship that exists in food price behavior. The NARDL confirms the presence of asymmetries, and the bound test affirms the co-integration and long-run relationship among the variables. In the long run, there is a significant positive relation between oil price increases and food prices, but the long-run impact of oil price reductions on food prices is not significant. In the short run, only increases in oil prices exert a significant influence on food prices, while decreases in oil prices do not. Furthermore, the COVID-19 period exerts a positive and significant impact on food prices, while COVID-19 cases do not influence food prices in Nigeria.
Regional economics. Space in economics, Economics as a science
The purpose of the article is to highlight the results of an experiment in introducing management mechanisms using a balanced scorecard in urban dental medical institutions (using the example of the Penza region). The region is now seeing a revival in demand for dental services compared to the 2020 pandemic year, when total visits to municipal dentists fell by more than 15%, although the commercial sector reduced this figure by almost 32%. Currently, the growth rate of turnover of services from municipal clinics is also ahead of similar indicators in the commercial sector. A certain role in this priority of preferences was played by the experimental stimulation of improving the quality of dental services based on the introduction of a balanced scorecard, which, based on a set of monetary and non-monetary indicators of in-house management strategies, contributed to both improving the internal processes of clinics and improving the competencies of the personnel working in them, which ultimately had a positive impact on the competitiveness of medical institutions in local markets for dental services. This experiment took into account important aspects of the competitiveness of medical institutions based on modern challenges of the external environment and internal factors of organizations, the most important of which is the ability to attract clients under compulsory medical insurance policies and at the expense of the Compulsory Medical Insurance Fund. The research during the experiment involved monitoring changes in the motivation of employees of dental clinics and objective calculations of the efficiency of organizations using a specially developed system of measurable indicators. Based on the results of the experiment and the results of an expert assessment of competitiveness, we can assume the feasibility of introducing a balanced scorecard in medical institutions of various profiles, as an effective management tool that ensures a consistent solution to the problems of improving strategic and tactical management. The scientific novelty of the study lies in the creation of an adaptation of the balanced scorecard methodology to dental medical institutions that would satisfy the economic interests of all interested parties: the state, clinic management, and patients.
Economic theory. Demography, Regional economics. Space in economics
Adverse economic shocks are known to reshape voter behavior -- the demand side of politics. Much less is known about their consequences for the supply side: how such shocks affect who becomes a politician. This paper examines how job losses influence individuals' decisions to enter politics and the implications for political selection. Using administrative data linking political participation records to matched employer-employee data covering all formal workers in Brazil, and exploiting mass layoffs for causal identification, we find that job loss significantly increases the likelihood of joining a political party and running for local office. Layoff-induced candidates are positively selected on various competence measures, indicating that economic shocks can improve the quality of political entrants. The increase in candidacies is strongest among laid-off individuals with greater financial incentives from holding office and higher predicted income losses. A regression discontinuity design further shows that eligibility for unemployment benefits increases political entry. These results are consistent with a reduction in individuals' opportunity costs -- both in terms of reduced private-sector income and increased time resources -- facilitating greater political engagement.
Climate change, deforestation, and biodiversity loss are calling for innovative approaches to effective reforestation and afforestation. This paper explores the integration of artificial intelligence and remote sensing technologies for optimizing tree planting strategies, estimating labor requirements, and determining space needs for various tree species in Gabala District of Azerbaijan. The study employs YOLOv8 for precise identification of potential planting sites and a Retrieval-Augmented Generation approach, combined with the Gemini API, to provide tailored species recommendations. The methodology incorporates time-series modeling to forecast the impact of reforestation on CO2 emissions reduction, utilizing Holt-Winters for predictions. Our results indicate that the AI model can effectively identify suitable locations and species, offering valuable insights into the potential economic and environmental benefits of large-scale tree planting thus fostering sustainable economic development and helping to mitigate the adverse effects of global warming and climate change.
The article analyzes the Cuban economy from 960 until the fall of the Soviet Union. It shows that after abolishing private ownership of the means of production at the beginning of the revolutionary period, Cuba could not establish a planning system because of Fidel Castro’s widespread intervention. The economic consequences were grave. Only the enormous economic aid received from the Soviet Union ensured the survival of the Cuban revolution and the implementation of the System of Direction and Planning of the Economy (SDPE), which was successively dismantled during the rectification process. What happened in Cuba during this period seems to have been an endemic problem of its political system, in which there was no effective counterweight to the comandante en jefe, on whom all major political and economic decisions depended. That problem was the main reason for Cuba’s poor economic performance.
Latin America. Spanish America, Regional economics. Space in economics
Evgeny Kuzmin, Maksim Vlasov, Wadim Strielkowski
et al.
This study examines the role of human capital investment in driving sustainable socio-economic growth within the energy industry. The fuel and energy sector undeniably forms the backbone of contemporary economies, supplying vital resources that underpin industrial activities, transportation, and broader societal operations. In the context of the global shift toward sustainability, it is crucial to focus not just on technological innovation but also on cultivating human capital within this sector. This is particularly relevant considering the recent shift towards green and renewable energy solutions. In this study, we utilize bibliometric analysis, drawing from a dataset of 1933 documents (represented by research papers, conference proceedings, and book chapters) indexed in the Web of Science (WoS) database. We conduct a network cluster analysis of the textual and bibliometric data using VOSViewer software. The findings stemming from our analysis indicate that investments in human capital are perceived as important in achieving long-term sustainable economic growth in the energy companies both in Russia and worldwide. In addition, it appears that the role of human capital in the energy sector is gaining more popularity both among Russian and international researchers and academics.
Introduction. The relevance of this considered scientific problem is due to the need to assess the recreational preferences of potential consumers of yachting services as a factor of tourist attractiveness and active promotion of the region. The purpose of the article is to determine the prospects for the development of yacht tourism on the Azov-Black Sea coast of Russia on the basis of the conducted analysis, which reflects the tourist attractiveness of the region in modern conditions.
Materials and Methods. The authors analyzed panel statistics of the charter website 2Yachts, the informational and analytical agency SeaNews, the international broker of motor yachts Worldmarine, PKF International. To study the trends in the development of yacht tourism on the Azov-Black Sea coast of Russia, a number of indicators of the state of the yacht tourism industry of individual European countries have been studied, an investment project to create a network of marinas in the Republic of Crimea and the Krasnodar Territory has been considered.
Results. The consumption preferences of clients in yacht tourism services on the Azov-Black Sea coast are revealed, the state of the yachting infrastructure of the region is assessed, the prospects for the development of yachting in the sea area of the Krasnodar Territory as a factor of tourist attractiveness of the region are outlined. The Azov-Black Sea coast of Russia has a significant potential for the development of yacht tourism (natural, socio-economic, cultural and historical resources). The development of yacht tourism should be carried out taking into account consumption preferences according to three basic criteria when choosing a yacht tour in the waters of the Azov and Black Seas – price, excursion program and tourist service.
Discussion and Conclusion. The modernization of the yacht infrastructure and the popularization of yachting will contribute to the additional development of the tourism industry and the tourist attractiveness of the region, and will also raise yacht tourism to a new international level. The results of the study can become the basis for the development of a strategy for the creation and promotion of a tourist product for potential yachting customers at the regional level, and are also taken into account in the development of programs of integrated tourist services.
Since Reform and Opening-up 40 years ago, China has made remarkable achievements in economic fields. And consumption activities, including household consumption, have played an important role in it. Consumer activity is the end of economic activity, because the ultimate aim of other economic activities is to meet consumer demand; consumer activity is the starting point of economic activity, because consumption can drive economic and social development. This paper selects the economic data of more than 40 years since Reform and Opening-up, and establishes the Vector Autoregressive (VAR) model and Vector Error Correction (VEC) model, analyzing the influence of consumption level and total consumption of urban and rural residents on economic growth. The conclusion is that the increase of urban consumption and rural consumption can lead to the increase of GDP, and in the long run, urban consumption can promote economic growth more than rural consumption. According to this conclusion, we analyze the reasons and puts forward some policy suggestions.
The purpose of this dissertation is to present an overview of the operational and financial performance of airports in Europe. In benchmarking studies, airports are assessed and compared with other airports based on key indicators from a technical and an economic point of view. The interest lies primarily in the question, which key figures can best measure the perception of quality of service from the point of view of the passenger for the services at an airport.
Developing ways to affordably deliver broadband connectivity is one of the major issues of our time. In challenging deployment locations with irregular terrain, traditional Clear-Line-Of-Sight (CLOS) wireless links can be uneconomical to deploy, as the number of required towers make infrastructure investment unviable. With new research focusing on developing wireless diffractive backhaul technologies to provide Non-Line-Of-Sight (NLOS) links, this paper evaluates the engineering-economic implications. A Three-Dimensional (3D) techno-economic assessment framework is developed, utilizing a combination of remote sensing and viewshed geospatial techniques, in order to quantify the impact of different wireless backhaul strategies. This framework is applied to assess both Clear-Line-Of-Sight and diffractive Non-Line-Of-Sight strategies for deployment in Peru, as well as the islands of Kalimantan and Papua, in Indonesia. The results find that a hybrid strategy combining the use of Clear-Line-Of-Sight and diffractive Non-Line-Of-Sight links produces a 9-45 percent cost-efficiency saving, relative to only using traditional Clear-Line-Of-Sight wireless backhaul links.
Abhijit Chakraborty, Tobias Reisch, Christian Diem
et al.
For centuries, national economies created wealth by engaging in international trade and production. The resulting international supply networks not only increase wealth for countries, but also create systemic risk: economic shocks, triggered by company failures in one country, may propagate to other countries. Using global supply network data on the firm-level, we present a method to estimate a country's exposure to direct and indirect economic losses caused by the failure of a company in another country. We show the network of systemic risk-flows across the world. We find that rich countries expose poor countries much more to systemic risk than the other way round. We demonstrate that higher systemic risk levels are not compensated with a risk premium in GDP, nor do they correlate with economic growth. Systemic risk around the globe appears to be distributed more unequally than wealth. These findings put the often praised benefits for developing countries from globalized production in a new light, since they relate them to the involved risks in the production processes. Exposure risks present a new dimension of global inequality, that most affects the poor in supply shock crises. It becomes fully quantifiable with the proposed method.
General equilibrium macroeconomic models are a core tool used by policymakers to understand a nation's economy. They represent the economy as a collection of forward-looking actors whose behaviours combine, possibly with stochastic effects, to determine global variables (such as prices) in a dynamic equilibrium. However, standard semi-analytical techniques for solving these models make it difficult to include the important effects of heterogeneous economic actors. The COVID-19 pandemic has further highlighted the importance of heterogeneity, for example in age and sector of employment, in macroeconomic outcomes and the need for models that can more easily incorporate it. We use techniques from reinforcement learning to solve such models incorporating heterogeneous agents in a way that is simple, extensible, and computationally efficient. We demonstrate the method's accuracy and stability on a toy problem for which there is a known analytical solution, its versatility by solving a general equilibrium problem that includes global stochasticity, and its flexibility by solving a combined macroeconomic and epidemiological model to explore the economic and health implications of a pandemic. The latter successfully captures plausible economic behaviours induced by differential health risks by age.