2024. vol. 28. No. 4

This article for the first time addresses the issue of labour flows in the Russian labour market directed to and from the state of informal employment in 2007–2023, allowing to clarify the reaction of informal employment to the deterioration of the macroeconomic context of various types – global financial crisis in 2008–2010, the introduction of foreign sanctions in 2014 and 2022, as well as COVID-19 pandemic in 2020–2021. The results obtained indicate that informal workers do lose their jobs more often than formal workers during economic crises, but informality can also act as an important adjustment mechanism for Russian labour market. Neither the first “sanctions shock” of 2014–2015 nor the COVID-19 pandemic led to an increase in the rate of transition of informal workers to out of labour force state. The rates of transition from unemployment and out of labour force state to informal employment also fell much less in 2020 than corresponding flows from unemployment/out of labour force state to formal employment. At the same time, the second “sanctions shock” of 2022–2023 was accompanied by a reduction in the rates of transition to unemployment regardless of the type of previous employment, an in crease in the rates of formal employment after unemployment, and a reduction in the rates of in­ formal employment, which, apparently, reflects increased demand for labour from the corporate sector. The response of labour flows during the world financial crisis, on the contrary, demonstrates a significant reduction in the likelihood of employment in informal jobs. It corresponds with the findings of previous studies on the Russian labour market, which revealed significant drop in the hiring rates of informal workers in 2008–2010 in the non-tradable sectors of the economy.

Nature-based carbon offsets are worldwide used for issuing certificated carbon units that allow emitters compensate their GHG emissions within voluntary and compliance carbon markets. Despite of their widespread, prices remain to be volatile due to imperfection of these tools: unreliable removal, short-term accumulation, high risks of double accounting and overcrediting, low trustworthy verification, not realistic the baseline emissions and overestimated additionality. Mastering the methodology for assessing climatic projects could rid of these shortcomings. In this article temporal approach is proposed for economic estimation of climatic projects on the basis of applying the physics concept of the atmospheric lifetime of CO2 emission and the economic concept of discounting. Temporal approach has been examined by assessing pilot climatic project of carbon supersites in Russia. Our research results demonstrate the controversy between conventional and temporal approaches, and some of these disparities can be softened by improving calculation methods, but fundamental contradictions demand the normative justification. By practice short-termed nature-based carbon offsets remain valid, thou for new generation carbon markets it is necessary to produce hybrid approach on the basis converging conventional and temporal approaches for assessing climatic initiatives.

This paper empirically estimates information asymmetry in cryptocurrency markets using the Probability of Informed Trading (PIN) and Adjusted PIN metrics . These markets, characterized by a high proportion of algorithmic trading and large volumes of high-frequency data, present a promising environment for analyzing informed trading behavior. We introduce a modified estimation procedure for Adjusted PIN, addressing floating-point errors and issues with local extrema, thereby improving its accuracy compared to the traditional naive approaches com­monly used in the literature. Additionally, we propose an alternative trade aggregation method at higher frequencies than the conventional daily aggregation to enhance the efficiency of both PIN and Adjusted PIN models. Through analysis of both simulated and real data, we demonstrate that aggregating total buy and sell trades on a daily basis results in less meaningful estimates due to noisy input data, making it difficult to capture informed trader activity. The true optimal trade aggregation frequency is still to be further investigated, as increasing the frequency introduces heterogeneity in order imbalances, and the specific frequencies at which informed traders operate are still unknown. Finally, several empirical studies are conducted to evaluate the behavior of the metrics, revealing that illiquid cryptocurrencies exhibit relatively higher estimated probabilities of informed trading. This finding aligns with similar results observed in equity markets.

The role of institutional quality in the relationship between public spending and private investment is a controversial theme because institutional quality can mitigate the crowding-out impact of government expenditure on the private sec tor's investment. Does institutional quality contribute to public spending – private investment nexus in developing countries? This paper provides the answer by studying the role of governance quality in government spending – private investment nexus for a panel dataset of 98 developing countries over the period 2002–2020. It uses the system/difference GMM estimators for robustness checks and estimation. The empirical results seem counter-intuitive. Institutional quality and public spending increase private investment, while interaction decreases. The paper looks at somearguments to explain and suggests policy implications to improve institutional quality and enhance private investment.

This review discusses methods of testing for a panel unit root. Modern approaches to te­sting in cross-sectionally correlated panels are discussed, preceding the analysis with an analysis of independent panels. In addition, the de-trending methods and corresponding asymptoticresults are discussed. To account for cross-sectional correlation, the methods based on de-facto­rization and bootstrap are considered. In conclusion, links to existing packages that allow implementing some of the described methods are provided.

This review discusses methods for testing for unit roots in panel data. The investigation of several time series together instead of analyzing each one separately and the motivation fortesting for panel unit roots are discussed. The review begins with a consideration of the simplest panel unit root tests with independent errors and two types of alternative hypothesis: homogeneous and heterogeneous. For the simplest tests, their asymptotic behavior is described under different types of convergence of the number of objects and the time horizon. Then, the issue of including a deterministic component and changing the asymptotic results are considered, aswell as methods for accounting for a weak dependence of errors. The first section concludes with methods based on p-values.

The next section is devoted to the important issue of accounting for cross-sectional correlation in panels and its impact on classical panel unit root tests. Cross-sectional correlation takes place according to some macroeconomic theories, which state that there are some common factors (e.g., technological shocks) that affect not one, but some set of variables. Modifications of classical tests based on factorization are described, when cross-sectional correlation is approximated by (possibly non-stationary) common factors based on the principal component method and based on factor approximation using cross-sectional means. Alternative methods based on resampling are considered. The section ends with a comparative Monte Carlo simulations analysis of various tests described in the review. The problem of imbalanced panel is discussed. In conclusion, references are given to existing packages that allow implementing some of the described methods.

The article provides a comprehensive assessment of the leading Russian economic journals and shows how their influence on the development of relevant subject areas is changing at the international and national levels. We conduct a multi-stage citation analysis for 2021–2013 and consider publications indexed in Web of Science and/or Scopus; thus, we describe the dynamics of average citation, assess journals’ ranking positions in the subject category, and determine the ratio of their citation to global averages. The information base of the study includes open data of the Master Journal List, SCImago Journal and Country Rank and Scopus. The results show that the citation rate of Russian economic journals relative to the global average remains low and tends to decrease in 2022–2023; the number of highly rated journals is decreasing against the background of an increase in the number of publications in the third and fourth quartiles. We reveal possible reasons for the decline in the visibility of Russian journals in the international space and propose ways to overcome the emerging negative trends. The findings are useful in designing approaches to the development of Russian scholarly periodicals in the changing international context.