The paper discusses new opportunities for Russian price statistics that present-day information and communication technologies bring about. The paper is a response to the study Isakov et al. (2021) dedicated to the effort of developing a toolset to build a price quotation database through automated internet data collection and construction of consumer price indexes based on it. Discussed are the potential implications of this activity for price statistics.
The global economy is in recession due to the pandemic of the coronavirus infection COVID-19. According to available estimates, Russia's GDP in 2020 will fall by 2–8%, so that in its consequences the current crisis may be tougher than the crises of 1998 and 2008. In the coming years, the Russian economy will have to recover and enter a new long-term growth path. At what expense and in which industries will this happen?
The report based on the experience of previous crises using industry accounts of economic growth and Russia KLEMS data, examined possible sources of recovery of the Russian economy after the crisis of 2020. By analogy with the recovery after 2008, it is likely to be associated with increased demand for raw materials on world markets and the reaction of the Russian oil and gas complex. Stagnation after 2008 is due to a decrease in production efficiency, especially in the expanded mining complex, as well as the cessation of technological make-up. Growth stimulation measures should include finding ways to increase the efficiency of the expanded mining complex, stimulating the adaptation of advanced technologies, and preserving existing adaptation channels in times of crisis - for example, successful export-oriented industries integrated into global value chains.
The global economy passes the COVID-19 related crises. For various projections, the output fall in Russia in 2020 will vary from 2 to 8 percent. So, in comparison with the crises of 1998 and 2008, the current shock can be more severe. In the upcoming years the Russian economy will pass the recovery stage, approaching the new balanced growth path. What proximate sources would push this growth?
With the neoclassical industry growth accounting and the Russia KLEMS dataset the present report aims to shed light on this, considering the growth patterns and sources of growth after the crises of 1998 and 2008. The report unveils the most important sources of the after-2008 stagnation in Russia, which are the decreasing efficiency of the extended oil and gas sector and the suspension of technology convergence. Since the recovery in Russia will be, most probably, caused by the increasing demand on energy and raw materials, driven by the recovery of global markets, policy implications for Russia should include efforts to improve efficiency in such export-oriented sectors, as oil and gas, and efforts, which aim to boost technology convergence such as backing export-oriented firms, which have been integrated to global value chains.
How has the COVID-19 pandemic affected the medium-term (2020-2021) and long-term prospects of the Russian economy? Regular surveys of professional macro-forecasters and consensus forecasts calculated on their basis allow us to talk about averaged sentiments and expectations of the expert community (in the same sense as it is used to talk about entrepreneurial or consumer sentiments and expectations). Based on surveys conducted by the HSE 'Development Center' Institute in early February and early May 2020, this Chapter analyzes how the pandemic has affected experts' outlooks of the Russian economy.
The article elaborates on the macro-analysis as related to the aggregated National Transfer Accounts (NTA), the topic originated in the prior publications in Voprosy Statistiki journal (Issues 4 and 11 of 2019), and builds upon the research conducted by HSE National Research University in 2020 as part of Russia’s participation in the global National Transfer Accounts project. The author explored various models of funding the economic life cycle deficit (various support system), adopted by separate groups of economies, through the lens of population savings in these countries. The article was profoundly examined how “excessive” household consumption is supported by public transfers and the correlation between the scale of such transfers and the household sector’s appetite for savings. By taking this research angle, the author aimed to develop deeper understanding of the underlying forces that drive savings into investments within the household sector. The author summarized key parameters of aggregated NTA for Russia in 2017–2019 to produce early quantitative assessments of the deficit funding structure. A closer look into relations between the funding models and incomes saved by population allowed to make cross-country comparisons and map Russia in global environment. The article discussions will be useful to the readers with an interest in demographic studies and socio-economics.
The issues to be discussed at the panel included: can past experience of economy recovery following crises of 1998 and 2008 be helpful at present; what sectors were driving growth of the Russian economy in the last decade, and are they able to perform this role in the future; what growth rate is feasible in 2021; what amendments to the national projects aimed at boosting growth are likely. In addition to that the panel participants specified key factors affecting productivity and output trends in Russia, suggested ways to support economy in the course of “coronacrisis”, and pointed out to economic policy measures that could accelerate economic growth.
The article provides a brief overview of the background of constructing composite leading indicators (CLI) for Russia; the paper defines key indicators which currently are calculated and published monthly; they can be put in practice to monitor the Russian economy. The underlying methodological approaches are analyzed, along with their advantages and disadvantages. The importance of accounting for a factor of regular or irregular updates and revisions of methods for calculating the CLI is emphasized. Due to them not only the current but also the “historical” dynamics of the indices occasionally change. The paper compares various CLI components and reveals both similarities and differences in the notion of the cyclical dynamics of individual components, reaching the point that the same indicators depending on the methodology are considered as leading, or as lagging. A set of “core” indicators included in the calculation of almost all Russian CLIs is determined; the author also noted those indicators that are widely used in other countries but not yet in Russia, for various reasons.
Special attention is paid to the problems of dating economic cycle turning points, in particular, those arising from different notions of the very concept of the economic cycle. Dating using formal statistical methods, firstly, is mostly determined by purely technical (and not substantive) nuances, and secondly, it often changes retroactively when revising historical time series. Analysis of global experience indicates that the way out of this impasse can be the detecting of cyclical turning points based on the decisions of the special expert council, whose sole tasks include dating cyclical peaks and troughs. The article describes methodological approaches that the Economic Cycle Dating Committee (Russian Dating Committee, RDC) under the Association of Russian Economic Think Tanks (ARETT), is supposed to follow.
The final part of the article analyzes the ability of various Russian CLIs to timely warn when a new phase of the economic cycle is approaching, especially the impending recession. It is shown that expert opinions on the future dynamics of the Russian economy, contained in monthly press releases, are often more accurate than the conclusions that can be obtained based on the CLI trajectory using purely formal decision rules. On this basis, it is concluded that the existing Russian CLIs can be improved; this calls for clarification and finally fixing the dating of cyclical turning points (peaks and troughs), as well as for conducting additional research to identify various economic and financial indicators as the leading, synchronous or lagging indicators of the Russian economic cycle.
Available composite cyclical indicators for Russia are surveyed, their components are enumerated and analysed. The aims, guiding concepts, and approaches of the newly established Russian Economic Cycle Dating Committee are also described. All the currently available monthly Composite Leading Indices (CLIs) are tested against the most recent cyclical turning points for their capacity to provide a timely alarm signal, especially about an impending recession. It is shown that experts’ informal judgments about Russia’s future economic trajectory remain more informative than findings derived from formal empirical rules. This suggests that there is some room for improvement of the Russian CLIs and additional efforts should be made to construct better cyclical indicators for Russia.
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 Input-Output Structural Decomposition Analysis approach enables a fairly comprehensive and detailed analysis of the economic growth sources using the input-output model. The active use of this approach is currently hampered by the lack of a reliable instrumental method for constructing symmetrical input-output tables and deflators that permit the output and import indicators to be recalculated by types of products for different years into constant prices, as well as by ambiguity of interpretations of the content of growth sources. The paper discusses the ways to overcome these methodological problems and gives an example of the experimental use of the structural decomposition analysis approach based on the data of the inputoutput tables of the Russian Federation for 2011–2015.