R-ECONOMY

Relevance. The unprecedented disruptions caused by the COVID-19 pandemic had a profound impact on the global economy, including Indonesia. Despite the challenges, Indonesia successfully managed to navigate and recover its international economic activities. Analyzing how Indonesia navigated its export advantages during the COVID-19 pandemic is crucial for understanding its resilience in the global economy. Research objective. The study aims to analyze the comparative and competitiveness of Indonesian export products, particularly the food processing industry, under the scheme of emerging markets and developing economies during the COVID-19 pandemic. Data and methods. This study relies on the data from Badan Pusat Statistik (BPS) and the Indonesian Ministry of Trade, along with world export data from the World Bank, OECD, and the International Trade Center, to examine the food processing industry during the 2021-2022 pandemic. Using the Revealed Comparative Advantage (RCA) model, it classifies comparative products for potential exports and diversification guidance. The Export Product Dynamics (EPD) model measures competitive advantage and serves as an early warning indicator, providing a comprehensive trade analysis for identifying growth opportunities and mitigating risks. Results. The RCA findings indicate that Indonesian processed food products for emerging market countries exhibit strong comparatives, supported by factors such as selling prices, production capacity, innovative technology, and trade tariff policies. This is reinforced by the results of the EPD analysis, which shows the dominance or rising star status of these products. Conclusions. The dominance of processed Indonesian food products points to the need for the government to uphold and expand exports beyond emerging market countries. Recommended measures include initiating revitalization, transformation, market analysis, and evaluating products categorized as falling stars or experiencing failures, missed opportunities, and retreats. Additionally, a focus on the upstream-downstream sectors of the food processing industry is crucial.

Relevance. Electronic commerce (e-commerce) and social commerce (s-commerce) are transforming business and consumer behaviour in Southeast Asia, propelled by digital advancements and increased internet and smartphone usage. This trend is significantly influencing economic growth and market dynamics in these emerging economies. Research Objective. This study aims to perform a comparative analysis of e-commerce and s-commerce across Southeast Asian countries. Additionally, it seeks to explore the evolution from e-commerce to s-commerce in emerging economies, examining the opportunities and challenges embedded in this transition, and discussing the implications for businesses and consumers alike. Data and Methods. To achieve these objectives, we used a quantitative approach, surveying 872 Thai participants through an online questionnaire using a convenience sampling technique. Additionally, we analyzed the data comparing e-commerce and s-commerce across Southeast Asian countries using the documentary method and content analysis. Results. Consumer spending through e-commerce and s-commerce has a significant positive impact on economic growth in Thailand, surpassing the impact of government spending. Private consumption, a substantial component of Thailand’s GDP, stimulates production, investment, and job creation, fostering overall economic advancement. In Southeast Asia, distinct e-commerce trends are evident: Thailand benefits from high internet and smartphone usage, Indonesia from robust social media engagement, and Vietnam from a focus on social commerce and mobile, cross-border e-commerce. These diverse trends underscore the necessity for businesses to tailor their strategies to each country’s unique consumer behaviors and preferences. Conclusions. The study confirms the significant impact of electronic consumption through e-commerce and social media on GDP growth. This form of consumption not only drives demand but also creates jobs, enhances efficiency, and opens up opportunities for international trade, fostering sustained economic development. In light of these findings, governments are advised to bolster digital infrastructure and support businesses in their digital transition. Meanwhile, businesses are recommended to adapt to digital models, emphasize consumer engagement, expand globally through online platforms, and integrate sustainable practices. Collectively, these measures are designed to harness the full potential of electronic consumption for sustainable and robust economic growth.

Relevance. The adoption of new technologies and the rapid emergence of innovation spur high-tech production and export-led economic growth. We aim to provide fresh evidence on the determinants of high-tech exports, considering different macroeconomic factors within the framework of the gravity model. Research Objective. The aim of the research is to empirically assess the impact of macroeconomic instability, tax policies, natural resources endowment, human capital, and institutional environment on the promotion of high-tech exports. Data and Methods. In considering the institutional indicator, six distinct indices from the World Bank are examined, and a common indicator is computed using principal component analysis. The econometric modeling uses a panel dataset covering the world’s 80 largest economies from 1996 to 2019. To test the assumptions of the gravity model and tackle the heteroscedasticity problem, the Poisson Pseudo Maximum Likelihood methodology is employed. Results. Higher inflation and unemployment rates are found to significantly decrease high-tech exports, while government external debt contributes to their enhancement. Tight tax policy and an increase in tax contribution are counterproductive in spurring high-tech exports. A negative and significant result is found for resource endowment, indicating that an increase in resource exports is counterproductive for technological advances and high-tech production. In most cases, the institutional environment and human capital significantly promote high-tech exports. Conclusions. Based on the presented empirical findings, we offer recommendations for the government to stimulate high-tech exports.

Relevance. In the pursuit of sustainable development, the circular economy takes precedence as a fundamental imperative for industrial transformation. The current trend in the development of the circular economy concept is to place the main focus on the technological support of circularization and the corresponding innovations in business models, while the decisive role people play in this model of economy is often overlooked. Individuals with specialized knowledge, skills, and values are essential for developing and implementing circular models, making effective management decisions, and promoting rational consumption patterns. The demand for circular skills and the availability of relevant competencies can significantly differ across regions, necessitating further in-depth study. Research objective. The paper is aimed at developing a new methodological approach to the study of circular economy skills at the regional level. This approach considers these skills in terms of both employer demand and their incorporation into master’s degree programs, accounting for regional specifics. Data and methods. The study employed a comprehensive approach, integrating theoretical methods with empirical analysis. Scientometric and content analysis identified taxonomies of circular economy skills, and employers’ personnel needs were examined through the analysis of the HeadHunter job site using Python software. Additionally, the study encompassed an analysis of educational programs from official websites of universities in southern Russian regions. Results. A new approach to the study of supply and demand of circular economy skills at the regional level has been proposed and tested. As a result, it was determined that there is a demand for sustainable development specialists in various industries in the Russian labor market, which varies across different regions of the country. The relevant skills are included in the master’s degree programs offered by universities. There is a need for greater involvement of regional authorities in shaping educational demands presented to universities, as this is essential for generating demand in the job market for the corresponding competencies. Conclusions. To better achieve targets in sustainable development and facilitate the transition to a circular economy, it is essential to promote a balanced development of all the relevant skills and behavioral patterns. To ensure this, it is important to involve regional authorities in shaping the demand for these skills.

Relevance. The development and implementation of advanced production technologies are the most important factors of economic growth and competitiveness in the modern economy. Predicting their dynamics, taking into account the spatial features of localization, is a difficult and time-consuming task. The spatial effects resulting from the impact of the surrounding territories play a significant role in the dynamics of advanced production technologies in the regions of Russia. Accounting for these effects is necessary when constructing scenario models in conditions of strong spatial heterogeneity of the studied processes. Traditional forecasting methods do not take into account spatial interdependencies and are not able to reflect the influence of surrounding regions on the development of technologies. Research objective. Assessment and scenario forecasting of the dynamics of advanced production technologies being developed in the regions of Russia using SAR models that allow taking into account spatial effects between regions. Data and methods. For scenario forecasting of the dynamics of advanced production technologies being developed in the Russian regions, taking into account spatial effects, a methodological approach was developed based on the modeling of the spatial log (SAR) of the processes of their development, autoregressive (ARMA) modeling and forecasting of the key factors of their dynamics. Taking into account spatial effects and heterogeneity, the proposed approach to modeling makes it possible to more accurately predict the dynamics of advanced production technologies in the Russian regions. Results. The developed methodological approach was tested to form predictive scenarios for the dynamics of advanced production technologies being developed in the regions of Russia. In particular, an inertial forecast scenario was developed, assuming the preservation of current trends in the dynamics of the technologies being developed, as well as two extreme possible scenarios - optimistic and pessimistic. With the help of the spatial SAR model, a significant influence of the number of research organizations on the volume of advanced production technologies generated was confirmed, and in the second group of regions, the influence of the number of technicians who conduct research and development was confirmed. The novelty of the study is to take into account the spatial features of the localization of the advanced production technologies being developed, as well as the spatial effects resulting from the impact of the surrounding regions on the creation of new technologies. This approach makes it possible to significantly reduce errors in the formation of forecast scenarios in conditions of significant spatial heterogeneity of the initial data. Conclusions. To intensify the generation of new technologies in the regions of the second group, it is necessary to attract personnel with technical specialties. The dynamics of the technologies being developed in the first group of regions with a powerful research potential are also influenced by the number of research personnel and the amount of attracted financial resources for fundamental and applied research. To increase the activity of these regions in the development of advanced technologies, it is necessary to form and develop relationships with the surrounding regions.

Relevance. The interconnectedness of global financial markets implies that shocks in one region can have widespread implications. The recent geopolitical tensions in the Middle East and Western Europe, have significantly heightened Geopolitical Risk (GPR) and Economic Policy Uncertainty (EPU). Country-specific financial stability can experience ripple effects from these external sources of risk, indicating a direct link between geopolitical events and economic policy uncertainties that contribute to financial stress. Research Objective. This study examines the risk spillovers from Global Geopolitical Risk (GLGPR) and Economic Policy Uncertainty (GLEPU) to the country-wise Financial Stress Index (FSI) of the USA, China, and Russia. Our goal is to determine which of these giants demonstrates superior resilience in terms of financial stability against these external sources of risks. Data and Methods. Using Cross-Quantilogram (CQ), Partial-CQ and Recursive-CQ (R-CQ), we evaluate a weekly high-frequency data from 2000 to 2023 to identify patterns of these spillover effects. Results. Our findings indicate that GLGPR has mixed spillover effects on the USA’s FSI under varying market conditions, while the FSI shows long-term resilience to GLEPU. For China, GLGPR only boosts the FSI during long-term bullish markets, and GLEPU demonstrates pronounced adverse impact at the bullish market. In contrast, the Russian FSI reacts unevenly to both GLGPR and GLEPU, experiencing greater severity. Overall, the USA’s financial market exhibits the highest resilience to GLEPU, while the Chinese market demonstrates the greatest resilience to GLGPR. In contrast, the Russian financial market shows the highest exposure to these global risks. Conclusions. No previous empirical study has examined the financial stress response of these three globally powerful economies to external sources of risk such as GLGPR and GLEPU. Most of the previous research focuses solely on stock market returns or their volatility in relation to these risks, whereas we focus on a composite measure of stability that encompasses all four sectors of a financial market. Our research fills this gap, particularly in the context of current geopolitical tensions among these global players, making it highly relevant for both academics and policymakers.

Relevance. Similar to other countries, Indonesia’s economy was significantly impacted by the COVID-19 pandemic, especially at the district and city levels. As the second-largest contributor to Indonesia’s GDP, East Java faced noticeable economic downturns. Industry, the region’s main economic sector, played a key role in these challenges, making it essential to evaluate all sectors from a regional economic perspective to navigate this turbulence effectively. Research objective. This study investigates the regional economy’s sectoral competitiveness in East Java, with a particular focus on the 17 sectors categorized by Statistic Indonesia (Badan Pusat Statistik-BPS) before and after COVID-19. Data and methods. The study relies on data from Badan Pusat Statistik (BPS)). The dataset includes GDP information for 11 regions, namely 7 districts and 4 cities, in East Java from 2018 to 2022, covering the pre- and post-pandemic periods. Methodologically, the study employed Location Quotient (LQ) analysis and Mix and Share Analysis. LQ analysis was used to assess the concentration and comparative advantage of East Java’s regions. Mix and Share and Shift-Share analyses were applied to identify the competitive industries in specific regions and their advantages. Results. The findings show positive economic growth in most regions of East Java before and after the pandemic, except for two regencies that saw a decline. The study emphasizes the need to strengthen regional resilience at the village level using the Village Fund from the national budget. Conclusions. Regional stakeholders, central government interventions, and continued development of leading sectors are essential for mitigating the effects of COVID-19. According to regional economic theory, collaboration between the government and businesses is crucial for enhancing competitive advantage and increasing the number of leading sectors.

Relevance. The digital economy and the digitalization of business and public administration are progressing rapidly in Russia. However, significant disparities in ICT access, usage, and outcomes between regions persist, potentially contributing to widening socio-economic inequalities. Research objective. This study aims to demonstrate that digital skills are a key factor in regional development. It tests the hypothesis that regions with disparities in Internet adoption and digital skills also experience disparities in regional development, as reflected in key socio-economic indicators. Additionally, the study analyzes the impact of digital skills on per capita income and unemployment. Data and methods. The study uses data from a sociological survey conducted by the Federal Statistics Service (Rosstat) and the Higher School of Economics to characterize the digital skills of the population. Principal component analysis is applied to construct a composite index, the Internet Adoption Index, which reflects both the accessibility and use of the Internet across Russian regions. This index, alongside digital skills data, is used to group regions. Two-sample t-tests for equal and unequal variances are employed for initial comparisons of regional indicators. In the second stage, regression analysis is used to test the hypothesis that without improved digital skills, access to ICT does not lead to higher personal income or lower unemployment. Results. The study reveals that only 12 out of the considered 83 Russian regions exhibit relatively high levels of Internet adoption and above-average digital skills. Despite well-developed infrastructure, many regions still have low levels of digital proficiency. The age and gender structure of the population have little impact on regional digital skills. However, regions with greater access to the Internet and higher digital skills show higher economic growth, higher incomes, and lower unemployment. Conclusion. The findings provide strong evidence that digital skills are closely linked to socio-economic development. The results highlight the importance of policies aimed at improving digital literacy, particularly as the digital economy continues to expand.

Relevance. Uneven spatial development is a common challenge for all countries, driven by both subjective and objective factors. The issue lies not in regional disparities themselves but in their growing intensity. Amid crises and economic turbulence, it is crucial to have tools to assess the risks of increasing spatial unevenness and widening socio-economic disparities between regions. Research Objective. This study aims to develop and test a tool for estimating the risks of uneven spatial development in Russia’s macroregions and the growing differentiation of regions by socio-economic level, using the Urals-Siberian macroregion as a case study. Data and Methods. A two-stage approach is proposed to evaluate spatial development in terms of uniformity and regional differentiation by socio-economic level and growth rate. In the first stage, the probability of a socio-economic decline is estimated based on key indicators (risk factors) and their dynamic indices. In the second stage, the probability of an increasing variation coefficient within a macroregion, reflecting rising disparities in socio-economic development, is analyzed. A multifactor risk model is used for analysis. The study relies on data from the Federal State Statistics Service (Rosstat) covering the period from 2000 to 2022. Results. Applying this approach to the Urals-Siberian macroregion revealed persistent spatial unevenness throughout the study period, primarily due to the specialization of regions, which stabilizes their relative positions. However, during crises, spatial disparities tend to widen as regions demonstrate varying adaptive capacities and resilience - some not only recover but also improve their positions. Conclusion. The proposed tool assesses risks linked to uneven development and growing regional disparities, offering insights for sustainable macroregional strategies. The findings emphasize the need to consider regions’ specificities and adaptive capacities in spatial development policies.

Relevance. As interregional competition intensifies, state support for innovation in regional economies becomes increasingly important. To improve the effectiveness of such support, it is necessary to gain a better understanding of the link between innovation and economic development. Research Objective. The study aims to assess the impact of innovation activity on regional economic growth, focusing on the case of a large industrial region in Russia. Data and Methods. The analysis uses socio-economic data for Sverdlovsk region from 2000 to 2023 and applies an autoregressive distributed lag (ARDL) model to assess the relationship between innovation and economic performance. The study introduces an Innovation Activity Index, which incorporates several key components, such as the number of research personnel, internal R&D expenditures, and other relevant indicators, for a more comprehensive evaluation. Results. The study traces the region’s innovation activity, revealing a general upward trend. However, while innovation clearly has a positively influence on economic growth in the short term, its effects over medium- and long-term periods are less obvious, likely due to structural factors in the regional economy. Conclusion. The study proposes recommendations to enhance regional innovation support within the «smart specialization» framework, including backing innovation projects, developing a sustainable innovation ecosystem, and investing in human capital.

Relevance. The process of new industrialization extends beyond traditional industrial sectors to include areas such as the creative industries. Since 2021, there has been a growing interest in creative activities, supported by legislative measures and strategic frameworks aimed at promoting spatial and technological development. This trend suggests a novel hypothesis: creative industries in single-industry towns can serve as catalysts for new industrialization by leveraging information and telecommunications activities. This study focuses on single-industry towns-a complex and underexplored subject-and proposes adopting a regional ‘center-periphery’ model as a framework for analysis. Research objective. The study aims to establish conceptual and methodological bases for the spatial socio-economic development of creative industries in Russia by focusing on the case of single-industry towns of Sverdlovsk and Kemerovo regions. Data and methods. This study introduces a methodological framework for evaluating the developmental potential of creative new industrialization, driven by advancements in information and telecommunications. While exploring the concepts of creative neo-industrialization and re-industrialization, the key innovation lies in classifying information and telecommunications as integral components of the creative industries, reflecting broader technological trends. This approach enables the development and testing of spatial models for creative industry growth, considering factors such as institutional constraints, concentration levels, and the potential for creative startups in single-industry towns. Results. The study highlights the concentration of creative industries around emerging local growth points, shaped by technological trends, increased industry concentration, and a declining share of startups in single-industry towns and regional administrative centers. The regions under study show different spatial models of creative new industrialization. In Sverdlovsk region, a center-semi-periphery-periphery model emerged by 2015, with Yekaterinburg at the center. In Kemerovo region, Kemerovo became the center by 2022, following growth transfers from Belovo and Leninsk-Kuznetsk in 2019 and 2021. Conclusions. This study contributes to applied research by integrating the evolutionary theory of growth with the center-periphery model and institutional economics.

Relevance. Since regional markets are interconnected and influence each other, forecasting price changes for goods and services requires considering both time and location. Economic instability, shifting supply chains, and rising inflation expectations make this research especially relevant. Additionally, the growing need for quick responses to price fluctuations highlights the importance of adopting data-processing methods that enable near real-time analysis. Research Objective. The aim of the study was to analyze the spatial dependence of prices and the presence of brands within the context of cyclically fluctuating demand and supply across different price segments. Data and methods. This study utilized data provided by the online analytics service продажи. рф, which encompasses daily selling (registered) prices for 135 ice-cream brands across 84 Russian regions, spanning the period from January 1, 2021, to December 31, 2023. The analysis examined regional differences in ice cream prices and brand representation, as well as the spatial autocorrelation of prices, particularly in relation to seasonal demand fluctuations. Spatial autocorrelation was assessed using global and local Moran’s I indices, with spatial clusters identified based on these estimates. To explore the cyclicality of spatial autocorrelation, partial autocorrelation functions (ACF and PACF) were used, and the Kruskal-Wallis test was applied. Results. The results of the analysis confirmed the differentiation of regions in terms of ice-cream brand representation, including variations across three price segments: Elite, Standard and Economy. We found a correlation between brand representation and regional population size, but no direct relationship with regional wage levels. Further analysis of individual brand prices and their spatial autocorrelation confirmed the hypothesized presence of spatial autocorrelation and demonstrated an increase in this autocorrelation over the study period. Examination of data cyclicality indicated that time series of average prices and global Moran’s I indices exhibited significant weekly cyclicality, while annual cyclicality was not consistently detected across all analytical methods and only emerged in the analysis of average prices. This suggests that seasonal variations in production and consumption volumes do not necessarily translate into corresponding seasonal fluctuations in prices or their spatial autocorrelation for all product groups. Conclusions. Spatial price dependence is not static; its level and dynamics are significantly influenced by product characteristics, underscoring the necessity of shifting from analyses of aggregate-price indices to analyses of individual product prices. A key methodological contribution of this study is the validation of findings previously observed with more aggregated data (year/month, product group) using highly detailed daily and brand-level data. This approach enhances forecasting accuracy by capturing the full scope of regional variations in consumer behavior.