The article is devoted to a comparative analysis of the economic policies of several countries — Iran, DPRK, Venezuela — implemented under large-scale external sanctions and aimed at both minimizing the damage caused by them and ensuring long-term economic development. The reviewed practices of responding to sanctions allow us to identify a number of regularities and formulate some lessons. In particular, the widely practiced “switching” of trade, technological and other linkages to countries that have not joined the sanctions allows to partially offset their negative impact, but often entails a dramatic increase in dependence on new partners. Large volumes of fuel and raw materials exports play an ambiguous role under sanctions. On the one hand, such exports are obvious targets for external restrictions, and dependence on them makes countries more vulnerable to sanctions pressure. On the other hand, stable demand for energy resources and raw materials from a large number of players makes it possible to use various ways to bypass sanctions and develop alternative supply channels, although they are often complicated and costly. 

This study presents the authors’ specification of a gravity model to assess the significance of the impact of trade agreements containing provisions on the liberalization of market access to services on the growth of their exports. The purpose of the article is to determine whether an existing trade agreement has a significant positive effect on the intensification of services flows, both in general and by sector. With the development of telecommunications technologies and the destabilization of goods trade due to the pandemic and increasing fragmentation of the global economy, trade in services has demonstrated steady growth and makes a significant contribution to nations’ GDP. Today, the share of agreements that include provisions on the liberalization of trade in services has significantly increased. The nature and specifics of service regulation complicate the application of traditional market analysis methods used for goods to assess the effects of trade agreements. The gravity model specification developed by the authors helps solve this problem. The study confirms a significant positive impact of trade agreements on the growth of trade in services. The most significant impact of trade agreements is observed in the insurance services sector. A notable but less substantial impact is found in the construction, telecommunications, computer and information, financial, and business services sectors. However, no significant impact was revealed for transport services and travel. Thus, the study empirically confirms the importance of trade agreements in developing international economic relations in the service sector.

This study tests for asymmetric effects of the Bank of Russia’s monetary policy on inflation and output. To distinguish the economy’s responses to monetary tightening versus easing, we employ a SVAR model with a non‑linear transmission of monetary shocks. Using monthly data for 2014—2024, we show a marked sign asymmetry in price dynamics: a negative monetary policy shock (a cut in the key policy rate) accelerates inflation more than a comparably sized positive shock (a rate increase) slows it. This effect may reflect nominal wage rigidity (especially in downward adjustments), household behavioral factors, and an asymmetry in the exchange‑rate channel, which we also document. We further find an asymmetric response of the credit impulse — capturing the operation of the credit channel of the transmission mechanism, i.e., banks’ ability to pass the monetary shock to the real economy — as it reacts only to monetary tightening. By contrast, the output response is close to symmetric. These results underscore the need for a cautious approach to easing monetary conditions in the Russian economy.

The paper presents a critical analysis of existing indicators of public welfare and summarizes the methodological and empirical experience of welfare assessments based on statistical and survey data. A composite index of welfare is introduced, based on data on Russia from 2006 to 2023. The index considers both objective and subjective (survey) indicators in five categories: material well-being, employment, health, social connections, and favorable environment. The analysis of the index’s dynamics reveals periods of growth (2006—2013, 2015—2021, 2023) and decline (2014, 2022). Favorable environment and health were identified as the categories that caused its greatest decrease. Thus, the complex index of welfare can become an informative indicator of both individuals’ objective and subjective well-being. It can also be used to assess progress in addressing the challenges of improving the well-being of the population as one of the national development goals. 

This paper assesses the contribution of migrants to the gross regional product and budget of Moscow over the period 2017—2023. We consider the city’s production in the context of individual types of economic activity and distinguish several categories of migrants: international and internal, including circular, rotational, and permanent. Data on the number of migrants and their distribution by the type of economic activity was obtained from the Labor Force Survey (Rosstat), the Moscow Labor Force Balance, and the survey of international migrants conducted in Moscow in 2023. Our calculations are based on the assumption that the productivity of different groups of workers is proportional to their wages. According to the results, the total contribution of all categories of migrants amounts to 23—25% of the city’s production. The main role in the Moscow economy belongs to internal migrants: circular (providing 8—9% of production) and rotational (5—7%); however, the importance of international labor migrants (3—8% of production) has been increasing in recent years. Significant differences in the role of migrants are observed across industries. 

We identify the determinants of IPO underpricing among Russian companies, with primary attention to factors grounded in information-asymmetry theory. Using data on 116 real-sector firms over 2000—2024, we estimate a multiple-regression model and find that (i) belonging to the technology sector, (ii) the share of intangible assets, and (iii) the magnitude of the offer-price revision are positively associated with the initial return on Russian IPOs. By contrast, the percentage of shares placed into free-float at the offering is negatively associated with underpricing, and this negative effect becomes stronger after 2022. The results confirm the significant role of information-asymmetry-related factors in shaping underpricing in the Russian IPO market. 

The paper summarizes machine-learning (ML) methods most relevant to macroeconomics and assesses their performance in forecasting and nowcasting key macro indicators. Despite rapid methodological progress and a surge of publications over the past 25 years, gains in forecast accuracy with traditional statistical (economic, financial, and survey) data remain modest. ML models often outperform naïve and standard econometric benchmarks, but improvements are not always statistically significant and, when they are, may be too small to matter for practitioners once implementation costs are considered. We highlight several tasks where ML is already useful even with traditional data and stress that ML becomes indispensable with “big” and unstructured data.