From 2020 and 2021 to the present, the total fertility rate has resumed its decline in many developed countries, even those with the most developed support for families with children. This is due in part to economic crises (stagnant incomes against a backdrop of rising consumer standards and expectations, a global decline in housing affordability, etc.) and also to socio-cultural shifts (the effects of social media, the spread of “intensive parenting,” the increasing difficulty of balancing parenthood and employment while maternal duties are mostly undiminished, etc.). Most developed and many developing countries pursue demographic policies, which may be explicitly codified in legislation or implemented through separate measures. The authors examine this common fertility support with an emphasis on its transformation over the past decade and then assess the effectiveness of such measures based on a pool of international studies. The article also addresses what directions these policies may take in the new phase of sustained decline in fertility. A review of studies on fertility support measures indicates that a combined approach encompassing multiple areas has the greatest effect. However, policy consistency and a country's economic, social, historical, and cultural context are extremely important. Support measures have a greater impact on the timing of births than on the final number of children. One reason for this is the rapidly changing social and economic conditions, as well as significant shifts in cultural values, that are occurring during the lifetime of the current generation.

This paper presents a methodology for assessing income inequality based on data from Rosstat’s Statistical Survey of Population Income and Participation in Social Programs (SSIPSP) and tax data at the regional level. The income figures from paid employment for the highest income regional groups in the survey are replaced with the average income for those income groups from tax data. SSIPSP data are adjusted by the tax data within each region. Income adjustments can be applied without dividing the sample into regional subsets, but in this case the uppermost incomes are adjusted in accordance with the overall tax data distribution. To reconcile the sizes of groups of income recipients in the SSIPSP and tax data, an interpolation of tabulated tax data is applied based on a generalized Pareto curves approach. After personal income from paid employment is adjusted, the adjusted total household income and per capita income to be used for assessing income inequality can be derived. The paper presents comparisons of income inequality obtained from the empirical survey data, as well as from the adjusted survey data based on tax reporting at the national and regional levels. The regional adjustments ensure more accurate measures of both national and regional income inequality. This advantage is due to taking territorial differences in income into account by replacing the highest incomes reported in the survey by the average values from the tax data within each region.

The von Neumann model is one of the simplest ways to assess the maximum possible rate of sustainable economic growth. Although the dynamics it predicts are unstable and prone to strong oscillations (and also yield socially unacceptable equilibria) calculating g, using the von Neumann model to determine the maximum possible rates of long-term sustainable economic expansion can serve as a guide in setting goals for a country’s economic policy. How to apply such a model in practical calculation of growth rates g has long been studied, but few empirical results concerning its use for macroeconomics or any similar topic with spectral properties in multi-sector models have appeared to date, either in Russia or elsewhere. Hence, this study offers a systematic empirical assessment of growth rates g for Russia and several neighboring countries over the past three decades. Calculations were carried out both for the “classical” version of the model and for its “extended” version, which takes capital and labor into account as resources both produced and consumed. National sources of official statistics from SUTs and IOTs were used for calibration, and the calculations themselves were carried out at different levels of aggregation in order to assess the stability of the results obtained. The non-standard assumption used here, that labor is a commodity produced as needed, is quite evident in countries with an open but extremely strict migration policy, such as the oil monarchies of the Persian Gulf. The resultant growth rates turned out to be generally realistic and stable in dynamics even when aggregations are changed. Comparative analysis across countries indicated that the Russian economy in most years had potential growth rates comparable to those of Kazakhstan and China, especially in the “extended” model in which they held at about 17% for many years.

The study evaluates the impact of government support on the creation of small and medium-sized businesses in relation to regional differentiation in exposure to risks from sanctions. Over three million instances of support provided to small and medium-sized businesses were analyzed. Cross-correlation was employed to ascertain the interval between introduction of state financial and advisory support and the emergence of significant positive effects on formation of new businesses. This analysis indicated that the relationship between government support for small and medium-sized businesses and the creation of new firms is not immediate and diminishes over time; however, the correlation is strongest within a two-to-four-month timeframe. Fixed-effects models were constructed to assess the impact of financial and advisory support on the number of newly created small and medium-sized businesses across regions with varying levels of risk from sanctions. Regression analysis revealed that both financial and advisory support have their most pronounced positive effects on the formation of small and medium-sized businesses in regions with moderate sanction risk. In high-risk regions, significant effects from financial support appears only after two months, while advisory support shows its impact after four months. No significant effects were observed in regions with low sanction risk. The authors offer practical recommendations based on these findings in order to inform the design of policies for fostering the growth of small and medium-sized enterprises and facilitating their transition into "higher" business categories.

This article examines exclusionary clauses applied across networks in multi-sided markets and focuses on how revenue-sharing agreements between different types of application developers and mobile device manufacturers impact competition and consumer welfare. The study examines why incumbent market players resort to exclusionary contracts and how the competition policies applied affect the welfare of users, mobile device manufacturers, and application developers. Using a game theory model, the author shows that excluding a newcomer’s application prior to installation reduces welfare when users must incur costs to install the newcomer’s app. An incumbent developer’s motive in seeking an exclusionary contract lies in the additional profits accrued by monopolizing the digital advertising market through control of the flow of users’ personal data. These profits enable incumbents to compensate manufacturers for blocking the installation by default of a newcomer's application. A key finding from the model is that market structure is determined not by users and advertisers, whose interaction via applications creates value, but by application developers and mobile device manufacturers, who are intermediaries in the interaction between users and advertisers. The model compares the policy of prohibiting exclusionary contracts when they negatively affect consumer welfare with the policy of a choice screen, which allows users to select independently the apps they would like to use when they first launch their mobile device. The choice screen policy is preferable because its implementation removes the incentive for established developers to pursue alternative methods of blocking new entrants to the market.

The article analyzes the state of France's economic relations from 2017 to 2025 under the presidency of Emmanuel Macron with all 54 African countries. The African states to which France assigns the highest priority for economic cooperation are identified by relying on both the officially defined foreign policy interests of the Fifth Republic and also on the results of a k-means cluster analysis of the indicators that Paris employs to assess the effectiveness of cooperation with African countries (foreign trade, foreign direct investment, official development assistance, and the size of the French diaspora). The cluster analysis method results in a list of the countries most suitable for cooperation and also points to a gap in the difference in values of the indicators. Back in November 2017, Macron proclaimed that France would cooperate with all African countries regardless of any common historical and linguistic past. Because they have previously been part of the French zone of influence, it is important to analyze the place of Francophone countries in France’s economic priorities in Africa by examining both the official priorities of the Fifth Republic and the interests of French multinational corporations. Although France cooperates most with Algeria, Morocco and Tunisia, the results of analysis indicate that the non-Francophone countries outside the Maghreb increasingly correspond to the official economic interests of the Fifth Republic in the region. Nevertheless, Paris retains its influence on former colonies in sub-Saharan Africa through traditional means even as this group of states is becoming less attractive to the former metropolis in terms of France’s officially proclaimed goals and objectives.

The author takes Mehran Gul’s monograph, The New Geography of Innovation: The Global Contest for Breakthrough Technologies, as a point of departure to analyze the approaches Gul recommends for supporting unicorn companies as well as the scientific ideas behind his favored policies. Gul introduces the concept of the “emoji economy” — a new stage in the development of global innovation hubs, such as Silicon Valley in the United States, the Yangtze Delta in China, and London in the UK, where an environment of trust, support, creativity, and self-realization may be represented symbolically as a smiling face. In the new technology race and especially in pursuit of artificial intelligence, the winners will be those countries and regions that can create such hubs of unicorns by means of a favorable business climate, a culture of creativity, abundant human capital, and an effective innovation system, including supporting infrastructure and access to financing. Thus, the success of Silicon Valley in California was ensured by a combination of the favorable conditions of the southwest coast of the United States, the concentration of expertise and infrastructure at a research university (Stanford), an influx of students and creative professionals from around the world, government funding for scientific research including for defense, and the commercialization facilitated by a stream of government contracts, venture capital investment and the startups it supported. This ensured that the United States took the lead in creating new technologies. Nevertheless, the prevalence of digital monopolies, the high cost of living, and the toxic corporate culture of California offer an opportunity to new competing clusters in China, Europe, South Korea, and Singapore. In the next stage, success will depend less on the quality of research and inventions than on the ability to translate scientific ideas into sought-after products and services. Constructive entrepreneurship will be crucial in creating durable companies with high growth potential from scratch and then facilitating their long-term development. This means that emoji economy hubs are competing globally for that kind of entrepreneur, for their firms, and for highly skilled professionals. This line of thinking from Gul’s book has underpinned the author’s recommendations for Russia as the country heads toward its persistent goal of achieving technological leadership.