This chapter begins with a brief history of the BRICS – from a purely analytical concept to the real-world political group with its own financial infrastructure. It then considers the role of the member countries in the global economy in terms of macro-indicators (territory, population and GDP), the production of a variety of key goods, trade and capital markets. Particular emphasis is placed on the rapid growth of the Chinese economy and the importance of its position in international commodity markets, the production of industrial goods, as well as other economic spheres. As a result, BRICS countries contribute significantly to global GDP growth, and the contribution of China is particularly important.

This volume focuses on the analysis and measurement of business cycles in Brazil, Russia, India, China and South Africa (BRICS). Divided into five parts, it begins with an overview of the main concepts and problems involved in monitoring and forecasting business cycles. Then it highlights the role of BRICS in the global economy and explores the interrelatedness of business cycles within BRICS. In turn, part two provides studies on the historical development of business cycles in the individual BRICS countries and describes the driving forces behind those cycles. Parts three and four present national business tendency surveys and composite cyclical indices for real-time monitoring and forecasting of various BRICS economies, while the final part discusses how the lessons learned in the BRICS countries can be used for the analysis of business cycles and their socio-political consequences in other emerging countries.

In many respects, the historical trajectory of the Russian economy during the Twentieth century has been a terra incognita until now. As for the official statistics, there are at least three important reasons for this. First, many relevant indicators were either not measured, or were kept secret and never published. Second, Russia (as the RSFSR) was a part of the USSR, and statistics for the RSFSR was much less prevalent than for the USSR as a whole (historical changes of the Russian borders also require special consideration). Third, an ideological dogma existed about the absence of inflation in the planned Soviet economy; therefore, all deflators (if any) were underestimated, and all aggregates in constant and/or comparable prices were overestimated (as were the corresponding growth rates). As for the unofficial historical estimates, most of them were focused on the USSR, not on the RSFSR. It’s very risky to use them as a proxy for historical indicators of the Russian Federation.

Hence, our first aim was to construct a statistical time-series that might be useful to describe the long-run trajectory of the Russian (the RSFSR and/or the RF) economy. Using previously unpublished data stored in Russian archives, we tried to extend them back as far as possible; in fact, most of them began in the late 1920s.

Our second aim was to denote periods of growth and contraction in the Russian economy and to reveal the economic factors that caused changes in trajectory. Periods of contractions during the era of the planned economy were of special interest for us. We found that recessions had occurred, not only in the market, but in the planned Russian economy as well (of course, with a significant remark that contractions in the planned economy were much rarer, but evidently more destructive).

In large countries, the development of national macroeconomic business cycles clearly involves regional nuances that, as a rule, fall outside scholars’ fields of vision, especially when monitoring the current economic situation. Regional statistics published by the Russian Federal State Statistics Service (Rosstat) are reviewed in terms of quality, and radical disagreement between “month-on-month” and “year-on-year” monthly statistics is identified. In view of this, an original method is proposed for estimating the level of regional economic activity (REA), based on monthly official regional statistics in five key sectors of the Russian economy: industry, construction, retail trade, wholesale trade, and paid services for the population. This method transforms current “year-on-year” growth rates into specially constructed dichotomous variables, which eliminate the excessive volatility and inaccuracy of the initial time series.

On these grounds, REA indices are estimated for all Russian constituent entities for the period from January 2005 to November 2017. Composite REA indices for all five economic sectors, eight federal districts, and Russia as a whole are then calculated. Methods for visualising multidimensional regional data are also proposed. They allow us to track the regional peculiarities of the Russian economy and to discern the current phase of the business cycle more accurately and without any additional lag. Several illustrative examples for the possible application of these indices in real-time monitoring and analyses are provided.

Background and motivation for a study of business cycles, business tendency surveys (BTSs), and cyclical indicators in the BRICS countries are specified. The main concepts and problems involved in monitoring and forecasting business cycles in emerging countries and countries in transition are overviewed; the importance of the experience of the BRICS in this context is demonstrated; different examples of the interaction between business cycles and social and political spheres are outlined. At last, the structure of the book is adduced.

The current best practices in measuring, monitoring, and forecasting economic cycles are drawn from the experience of mature economies such as the USA, Japan, and several Western European countries. Meanwhile, there are a lot of peculiarities in emerging economies that should be kept in mind when developing a system for tracking and forecasting their short-run dynamics. In the literature, there have been numerous attempts to apply the international best practices to emerging economies, but these attempts have usually been sporadic. The experience of the BRICS economies accumulated in this book allows for a fresh look on the problem of the development and use of cyclical indicators and is potentially useful for other emerging countries.

The article considers the problem of the relationship of structural changes and economic growth in the global economy and Russia in the framework of different methodological approaches. At the same time, the paper provides the analysis of complementarity of economic policy types, which, on the one hand, are aimed at developing the fundamentals of GDP growth (institutions, human capital and macroeconomic stabilization), and on the other hand, at initiating growth (with stable fundamentals) with the help of structural policy measures. In the study of structural changes in the global economy, new forms of policies of this kind have been revealed, in particular aimed at identifying sectors — driversof economic growth based on a portfolio approach. In a given paper a preliminary version of the model of the Russian economy is provided, using a multisector version of the Thirlwall’s Law. Besides, the authors highlight a number of target parameters of indicators of competitiveness of the sectors of the Russian economy that allow us to expect its growth rate to accelerate above the exogenously given growth rate of the world economy.

This article addresses the framework for constructing and analysing National Transfer Accounts. NTA  is a very useful analysis tool to study changes in the age structure of the population and income distribution processes between generations, and their economic impact on GDP. In some countries, NTA  is actively used in long-term macroeconomic forecasting (on 40-years period and more). Main NTA indicators calculate with using of national accounts database (SNA).  A vital element of the research is determining economic lifecycle results for all age-groups and identify its primary funding sources.

The  article considers comprehensive methodological and  practical approaches to the compiling the three aggregate transfer accounts - life cycle account, public reallocation account and private reallocation account - and using them as a basis for the analysis of socio-demographic processes in Russia. The macro analysis presented in the article is based on statistics of national accounts and other information sources for the years 2003-2017.

The conducted analysis resulted in compiling the system of aggregate transfer accounts for Russia for 2003-2017 and in identifying quantitative parameters of Russian economy life cycle balance and in examining qualitative changes in its funding sources. The authors own analysis demonstrated that while in 2003-2010 economic life circle balance was positive (except for the crisis year of 2009),  in 2011-2016 it became negative. The  State has been playing an increasing role in financing the economic life cycle deficit.

This paper is devoted to the study of the degree of trust (and distrust) in Russia, and to the assessment of the possible impact of trust on the level of the economic development of Russia. The level of social trust in Russia is lower than in many developed countries. The armed forces and the church are the most trusted institutions in Russia; besides that, political trust is quite strong, especially the trust in the president. At the same time, the business, especially private, is still experiencing considerable mistrust from Russians. The estimations confirmed the existence of the relationship between trust and the level of economic development in different countries. The gap between Russia and a number of developed countries in terms of GDP per capita may be significantly connected to the continuing distrust within society.

The article analyzes the relationship of structural changes with economic growth in the world economy and Russia. The authors note the emergence of a growth model in the world economy based on the complementarity of economic policies aimed, on the one hand, at the development of fundamental foundations of economic growth (institutions, human capital, infrastructure, macroeconomic stabilization), and, on the other hand, at initiating growth through structural reforms (even under stable foundations).  Analyzing the trends of structural changes in the world economy, the authors consider new forms of structural policy, in particular the kind that is aimed at the identification of sectors – the engines (or escalators) of economic growth using the portfolio approach. A preliminary version of the model of the Russian economy based on the multi-sector variety of the Thirwall law is constructed.

The article assesses the situation in the Russian economy after a two-year recovery and the outlined signs of stagnation associated with both the slowdown of the global economy and the exhaustion of domestic sources of improved market conditions. The approaches to identifying the factors that initiate growth and are different from the factors that support it are considered. In this case, emphasis was placed on the factors of uncertainty of the economic situation and the lack of domestic demand. The approaches to accelerating growth based on macroeconomic and structural policy measures are formulated, forecasts of the Russian economy dynamics for the coming years are analyzed.

We examine transfers from Russia’s federal government to the regions during the two recent economic crises. We show that while federal transfers in 2009 were large and targeted poorer regions, the 2014–2015 transfers were much smaller and not targeted. This policy shift was accompanied by a relatively greater decline in own revenues in the poorer regions and their worse economic performance overall. As a result, interregional budget expenditure inequality in Russia was significantly higher in 2014–2015 than it had been for years. This is important because regional budgets are responsible for a large share of investments, housing, education and healthcare.

The transition of the Russian economy from plan to market is considered at a qualitative level. The analysis of economic dynamics in the transformation paradigm is conducted. The main stages of the transition process are discussed. Bonuses and costs due to the transition to market economy are considered. The reasons for the outstripping growth of well-being as compared to the growth of output are discussed. The signs of exhaustion of the potential of factors ensuring an abnormally high rate of recovery and accompanying welfare growth are discussed. The conclusion is made that the transformational recovery has been completed. The Russian economy has moved to the stage of development with relatively low growth rates of output and welfare, typical for stable (nontransition) economies.

The article is devoted to the problems of relationship between the public expenditures and the economic growth and methodical and practical problems of estimating expenditures efficiency. Authors note that “productive expenditures” (expenditures on human capital, social and economic infrastructure, research and development) have strong influence on the economic growth. The comparative study of the effectiveness and efficiency of the public expenditures was conducted, including the productive expenditures (education and healthcare), for Russia and the sample of countries close to Russia by their economic development level. The aggregated indicators of the effectiveness and efficiency, simple econometric models, DEA method, and based on them effectiveness and efficiency rankings are used as measuring instruments in the study.

According to the approach applied in the study, the rankings show relatively poor results for Russia in macroeconomic function performance of the public sector, in comparison to the other countries of the sample, but it is worth to notice that the aggregated estimates are influenced by the selection of time period. Russia shows high results in education public expenditures efficiency and relatively poor results in healthcare public expenditures efficiency. Also, the productive expenditures level is low in Russia compared to the other countries close to Russia by the economic development level and OECD countries. This can limit the economic growth in the mid-term and long-term prospects.