The research focuses on the investigation of birth cohort effects in post-month consumption of alcohol, cigarettes and marijuana. The authors utilize hierarchical and non-hierarchical multivariate probit models to examine the relationship between the consumption of addictive goods taking into account the endogeneity of subjective health evaluation. Based on the US National Survey on Drug Use and Health from 2002 to 2020 data the study provides evidence of significant age and gender differences in alcohol, cigarettes and marijuana consumption According to the findings, the consumption of each of the substances in question increases the propensity to use other addictive goods, and it differs significantly across birth cohorts and men and women. The correlation between alcohol, cigarettes and marijuana consumption increases from older to younger cohorts. There is also evidence of a closing gender gap in addictive goods consumption, particularly concerning the younger cohorts.

This research paper investigates the relationship between education and economic growth in China from 1989 to 2019 across 27 provinces. The study classifies education into two categories: aggregate education (average years of schooling) and disaggregated education (education divided into compulsory, high school, and higher education). The data is analyzed based on pre‐ and mid‐industrialization periods, and East and West regions, with and without pre‐ and mid‐industrialization, for both levels of education. The findings indicate that the impact of education on China’s economic growth varies by period (pre‐ and mid‐industrialization), region (East and West), and regions with periods (Eastern pre‐industrialization, Western pre‐industrialization, East mid‐industrialization, and West mid‐industrialization) in both aggregate and disaggregated education. According to the research results, the central government should revise its current educational policies to prioritize compulsory education and increase investment. Furthermore, local governments should have more administrative authority to create local educational policies catering to the specific needs of each locality.

This study evaluates the impact of stock market volatility in the Russian Federation on the size and dynamics of credit spreads for Russian corporate bonds. Credit spreads on corporate bonds represent a measure of the risk premium in the public lending market, while stock market indicates stock market instability. Analyzing the relationship between the bond market risk premium and the instability of the stock market is a pertinent task today because these processes may exhibit both direct and inverse correlations. Additionally, formulating hypotheses about whether stock market volatility positively or negatively affects credit spreads of corporate bonds, and vice versa, poses challenges. The study utilizes data on the Moscow Exchange index (MOEX), Russia’s volatility index (RVI), yields of Russian corporate bonds, and values of Russia’s zero-coupon yield curve (KBD). The research aims to identify patterns in the dynamics of the risk premium in the bond market in response to shocks in MOEX volatility. It also seeks to characterize the actions of investors and holders of Russian corporate bonds during periods of heightened volatility in the Russian stock market.

The paper exploits ordered choice logit models with latent classes to account for unobservable consumer heterogeneity in analyzing the preferences for cashless payments in Japan for purchases of different sizes. Using the data of the Survey of Household Finance (2007–2020), we discover that consumers separate into classes of more and less frequent users of cashless payments for each category of purchases. The probability of belonging to the former class is positively related to the fact of consumer taking measures for the protection of their financial assets. The results reveal a statistically different effect of consumer socio‐demographic characteristics (sex, age, income, employment, education, household size), the binary variable for residence in a large city and Kanto region, and the dummies for 2019 and 2020 on the choice of cashless payments in the two latent classes.

The platform is an intermediary between groups of users whose direct interaction without the platform would be complicated or unachievable. A distinctive feature of platform markets is the presence of indirect network effects (or cross‐platform effects), which consist of the fact that the demand of one group of users for platform services affects the demand of another group of users. In the presence of multidirectional network effects, an important platform’s business decision is to determine the optimal volume of services that create negative network effects, since after a certain amount of effects the company starts to lose users on the other side of the platform. For the business model to be successful and profitable, the platform should take this relationship into account. This study makes a quantitative assessment of cross‐ platform network effects using data from the Russian television market. The author of the study obtained statistically significant positive audience effect and negative advertising effect.

Combining forecasts is considered the easiest way to improve the forecast quality compared to individual models. In this paper, we test the capabilities of the simplest methods of combination, such as simple averages and estimates based on the standard error of previous forecasts, to improve the performance of short-run forecasts of five resource price indicators (oil and metals). The basis of the work is the Gaidar Institute forecasts database, which provides the database of primary forecasts and allows you to calculate their combinations in real time. Based on the obtained results we conclude that even the simplest methods of combination are a way to improve the accuracy of forecasts. In addition, in the case of resource prices, one can even single out a group of methods (namely, combining with weights inversely proportional to the squared errors of individual forecasts) that provide the maximum gain in quality for the most periods.

The research deals with the synthetic control group method application to assess the causal effect of economic sanctions on the Russian economy. The analysis of SCG method assumptions to the problem allowed us to establish that the most problematic issue is comparison group formation. We use the source data from the World Development Indicators database. GDP by PPP per capita was an outcome variable. The application of the method to assessing the impact of sanctions pressure shows that it is possible to select the weights in such a way as to ensure good closeness of the indicators of real and synthetic Russia in the pre-event period. Such states as Kazakhstan, Argentina, Brazil, and the USA were most in demand for the formation of a synthetic Russia. However, the estimates are unstable to changes in the set of countries used to form the synthetic control.

The paper presents extensions of the popular difference‐in‐differences approach (DD) from 2×2 design on multiple time‐period, multiple groups, fuzzy DD, non‐staggered treatment and approaches to measure distributional treatment effect. The paper describes assumptions for consistent estimation of the treatment effect by two‐way fixed effects model (TWFE) and presents the problem leading to inconsistent estimates justifying the application of alternative estimators. The paper briefly introduces methods developing DD for multiple‐period multiple‐group cases based on TWFE and alternative approaches. The proposed techniques allow treatment evaluation in the frame of DD when canonical TWFE leads to inconsistent estimates. Some approaches allow replacement of the well‐known parallel trend assumption (PTA) for a conditional PTA or time randomisation. The paper refers to implementations of these methods in Stata and R. Simulation modelling demonstrates that the stated properties of the alternative estimators are not always reliable.

We examine cross-country differences in the beneficent motive formation of aid to developing countries using data on 18 OECD donors for 2010–2019. The paper tested the theoretical determinants highlighted in the philosophy of collaboration on two subsamples of donors — those with a prevailing selfless motive and those with a mixed one. Our analysis shows that for the first group of countries, welfare and prosociality are significant factors of benefit-oriented aid, for the second — technological development. The components of civic culture, such as tolerance, trust, and public involvement in political decisions probably play a significant role regardless of the donor type . In addition, the paper explored key distinctions in the relationship between domestic and international aid. The results obtained expand the understanding of the cultural and historical contexts of untying aid, which remains one of the central tasks of the OECD.

The paper proposes a two‐sector macroeconomic model of the Russian economy based on the standard assumptions of New Keynesian DSGE models used to model household consumption, price and wage rigidities, and endogenous capital utilization. We consider two options for describing the investment process: the traditional approach with the investment adjustment costs and the approach using the investment accelerator model. The model parameters are calibrated based on minimizing the distance between the theoretical and “empirical” impulse response functions to the terms of trade shock derived from estimating simple ARX models with terms of trade as an exogenous variable. The constructed model quite accurately reproduces the influence of the terms of trade on the Russian economy for both investment modeling options. Based on the calibrated model, the authors examined the impact of a monetary policy shock on macroeconomic indicators and constructed a historical decomposition of the dynamics of macroeconomic indicators by an extended set of structural shocks to economic variables.