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Problems of inflation and economic growth in the intertemporal and cross-country context

Priority areas of development: economics
2013
The project has been carried out as part of the HSE Program of Fundamental Studies.

The project was implemented in 2013 as a part of the Basic Research Program of the Higher School of Economics.

Object of Research: inflation and economic growth of the Russian economy in comparison with other economies in transition.

Goal of Research: to answer the question of whether post-transition growth in economies of Central and East Europe is balanced and sustainable.

Empirical Base of Research: Officially published data of the Russian statistical office (Rosstat); official unpublished data of a detailed level of aggregation; alternative estimations of the Laboratory, and public databases including the World KLEMS (www.worldklems.net ) and WIOD (http://www.wiod.org/new_site/home.htm ).

Results of Research: LRIG in collaboration with Groningen Growth and Development Centre at the University of Groningen (the Netherlands) has compiled the part of the World KLEMS database which represents the Russian economy (Russia KLEMS). The first release of the Russia KLEMS database was issued in 2014 (http://www.worldklems.net/data.htm). These are a series of real value added, labour, capital and productivity measures of the level of 34 industries 1995-2009 and provide a new opportunity for making comparisons of industrial sources of productivity growth of the Russian economy with almost forty developed, developing and post-transition economies, presented in the World KLEMS and WIOD databases. An Industry growth accounting system has been developed for the decomposition of output growth into contributions of capital (K), labour (L), energy (E), raw materials (M) and services (S) at a detailed industry level. These indicators are harmonized across countries, which creates opportunities for the analysis via a comparative perspective across countries.

The analysis of Russia KLEMS data showed that the structure of recovery growth between the two crises of 1998 and 2008 was bad. Almost half of the yearly averaged growth rates were contributed by multifactor productivity, while the other half was made up mostly of capital services. MFP contribution was provided mostly by financial and business services, which were especially underdeveloped in the first years of transition in the early 1990s. At the same time, the Extended Mining (Mining, Fuel and Wholesale trade), which contributed the lion’s share of capital services, demonstrated low or negative growth of MFP, due to its inefficiency. At present the value added share of Extended Mining is almost quarter. Some sub-industries of Russian Manufacturing, such as Electrical Equipment, demonstrate high efficiency, but their share in the total value added is shrinking. In CEEs these industries are also efficient, but in contrast with Russia they are expanding. This is explained by FDI inflow from other EU members and mostly from Germany. At the same time, Russian Manufacturing is losing its position both in global and domestic markets in rivalry with firms from EU and Asia.

Another KLEMS-related area of research is the approach for a compilation of a continuous time series of supply and use tables (SUTs) for the Russian economy in 2003 and forthcoming years in the new Russian industry classification OKVED and products classification OKPD, harmonized with international standards. The source for this project is the official SUTs for 2003 in the old industry classification OKONKh, inherited from the planned economy period. Algorithms of bridging the SUTs between the two classifications have been developed. They provide an opportunity to input SUTs into OKVED for 2004 and the following years both in current prices and constant prices of the previous year.

One of principal sources of heterogeneity in the economy is explained by the stochastic nature of individual incomes. Taking this into account in an analysable general equilibrium model is difficult, because the properties of this economy depend on the distribution of agents. That is why computational approaches have been widely used in the literature of the last two decades. In contrast, we have developed an analytical approach, which aims to deal with an economy with idiosyncratic shocks of income and a standard representative agent.

Finally, we have analyzed certain aberrations which accompany the seasonal adjustment of the time series of short term economic indicators in the vicinity of sharp changes in levels. Such changes appear in periods of economic crises. We show that in such cases standard seasonal adjustment algorithms can generate false signals, which can be wrongly interpreted as leading indicators of a crisis, as well as its second and following waves. These signals can distort agents’ short run expectations, especially in the first few years which follow the beginning of the crisis. Moreover, because of this, a certain period can be described as “a blind area”. In this period, the monitoring of short term trends seems to be very difficult.

Level of implementation, recommendations on implementation or outcomes of the implementation of Results: The Russia KLEMS project provides a unique empirical base for applied research and economic policy development. This data has already attracted attention and is being used by the Conference Board (www.conference-board.org ), the Institute for Economies in Transition of the Bank of Finland (BOFIT, http://www.suomenpankki.fi/bofit_en/bofit/Pages/default.aspx ), OECD and the Vienna Institute for International Economic Studies (http://www.wiiw.ac.at/ ).

Field of application: The results of our activity can be used for the development of economic policy, improvements in Russian official statistical methodology and in the academic process.

Publications:


Karev M. G. Idiosyncratic Shocks, Aggregation and Wealth Distribution / NRU Higher School of Economics. Series WP2 "Количественный анализ в экономике". 2013. No. WP2/2013/05.
Voskoboynikov I., Timmer M. P. Is Mining Fuelling Long-run Growth in Russia? Industry Productivity Growth Trends since 1995 / Bank of Finland Institute for Economies in Transition. Series DP "BOFIT Discussion Papers". 2013. No. 19.
Gafarov B. Do unobserved components models forecast inflation in Russia? / NRU Higher School of Economics. Series EC "Economics". 2013. No. WP BRP 35/EC/2013.
Timmer M. P., Voskoboynikov I. Is Mining Fuelling Long-run Growth in Russia? Industry Productivity Growth Trends since 1995 / University of Groningen. Series GD "GGDC Research Memorandum". 2013. No. 137.
Бессонов В. А., Петроневич А. В. Сезонная корректировка как источник ложных сигналов // Экономический журнал Высшей школы экономики. 2013. Т. 17. № 4. С. 554-584.
Bessonov V. A. On the Problems of Russian Statistics / Пер. с рус. // Problems of Economic Transition. 2013. Vol. 55. No. 11. P. 36-49.
Бессонов В. А., Петроневич А. В. Сезонная корректировка как источник ложных сигналов / Высшая школа экономики. Серия WP2 "Количественный анализ в экономике". 2013. № WP2/2013/04.
Entov R. M., Lugovoy O. V. Growth Trends in Russia after 1998, in: The Oxford Handbook of the Russian Economy / Ed. by S. Weber, M. V. Alexeev. Oxford : Oxford University Press, 2013. P. 132-160.