DOI: 10.32609/j.ruje.10.113578

Authors: Inna S. Lola, Dmitry G. Asoskov

Abstract: This study investigates the utility of business uncertainty indicators as predictive tools for forecasting economic activity in the context of Russia. In an era characterized by global economic volatility and geopolitical shifts, understanding the dynamics of economic uncertainty and its impact on overall economic performance is of paramount importance. The study utilizes a comprehensive dataset based on the results of business tendency surveys in Russia, spanning the period from 2009 to the first half of 2024. Given the importance of uncertainty in shaping economic outcomes, the central research question of this study is: can uncertainty indicators predict business activity in Russia or not? To address this question, we compared two alternative approaches to calculating business uncertainty: the ex‑ante approach, which uses the business community’s assessments of future business trends to measure uncertainty as the dispersion of opinions expressed, and the ex‑post approach, which applies entrepreneurial assessments of both future and current trends to determine business uncertainty as the degree of deviation of entrepreneurial expectations from the real picture. National indicators and sectoral indicators were calculated for the mining and quarrying industry, manufacturing industry, construction, retail trade, wholesale trade and services. For most of the industries under consideration (except for the construction and service sector) and at the national level, the specifications of vector autoregression models that were effective for forecasting real indicators of economic activity, characterized by lower forecast errors compared to standard autoregressive models, were built. According to the results obtained, at the national level, when forecasting GDP, clear preference should be given to the ex‑post indicator.

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DOI: 10.32609/j.ruje.10.118505

Authors: Osama D. Sweidan

Abstract: Recently researchers performed empirical economic studies to investigate how geopolitical risk impacts diverse economic sectors. We take a fresh perspective by exploring whether advancements in the U.S. IT sector can account for fluctuations in China’s geopolitical risk. The conflict between China and the United States regarding semiconductors revolves around technological supremacy, economic dominance, and national security concerns. China has been striving to become self-sufficient in semiconductor production to reduce reliance on foreign suppliers, particularly the United States. However, the United States has imposed restrictions on semiconductor exports to China. Our study constructs a theoretical framework and utilizes the bounds testing approach for cointegration to estimate the parameters of the Autoregressive Distributed Lag model. We use monthly data from January 1993 to November 2023. The findings reveal that the U.S. IT sector significantly and positively influences China’s geopolitical risk. From a policy implication perspective, the race to lead the global IT sector may emerge as the primary source of economic and political instability unless rival nations reach a compromise.

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DOI: 10.32609/j.ruje.9.81283

Authors: Carlos M. Jardon, Xavier Martinez Cobas

Abstract: Emerging economies have specific characteristics that condition the use of intellectual capital. The economic crisis has had important consequences for emerging countries. The investments in intellectual capital could slow down this influence. This paper uses a new model for assessing the effects of an investment in intellectual capital on the performance of Russian companies in a period of crisis. The model is applied in Russian companies with data from 1,096 companies for the period 2004–2014 and 12,056 observations were made. The panel data included only active companies (from January 2004) listed with annual reports and were obtained from Bureau Van Dijk’s Ruslana database. Each company’s data cover at least seven years. The study used hierarchical linear models to unravel the effect of intellectual capital on value-added. The results show that investments in structural capital and relational capital have a direct effect on the stock of intellectual capital and generates value. The results show that investments in structural capital and relational capital have a direct effect on the stock of intellectual capital and generates value.

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