Recent decades were turbulent for the Russian economy. They include a transformational output fall until 1998, a recovery in 1999-2008, and stagnation after the global crisis of 2008. What were main drivers of performance of the Russian economy in these years? Using the conventional industry growth and level accounting, as well as the shift share analysis within the World KLEMS framework, this study highlights three main sources of growth, which are windfall profits from energy export, technology catching up in manufacturing, finance and business services, and the negative influence of expanding informal economy to aggregate labour productivity growth.
The present study reports, that oil and gas money fueled Russian growth in the form of capital services in extended mining and low skill intensive services. The contribution of capital input was higher in years of soaring oil prices. One more factor of growth was catching up, which is rooted in the fact, that Russia, as well as other Central and East European socialist economies (CEEs) on the eve of transition from plan to market, were backwards in technologies in comparison with advanced economies. Similar to CEEs, in years after transition Russian manufacturing over performed the West in productivity growth. This provided a remarkable contribution to aggregate productivity. Before 2008 Russia also gained from TFP growth in Financial and Business services, because the initial level of these sectors was low even in comparison with CEEs. Finally, the remarkable peculiarity of the Russian economy is the expanding share of informal labour, especially in years of outstanding growth before 2008. This makes Russia, to a certain extent, similar to India. Splitting industries into formal and informal segments and estimating the contribution of labour reallocation we report, that expanding informality slowdowns labour productivity growth.
On the eve of transition in the late 1980’s the perspectives of the economic development for most economies of the Soviet Bloc in Central, Southern and Eastern Europe seemed optimistic. They had been already industrialized; their labor force was relatively healthy and educated. Being technological backwards in many industries these countries had lots of opportunities for catch up, extending international trade and allowing the inflow of foreign direct investments. However, after two decades of transition these expectations did not materialize to the fullest extent. On the one hand, by 2008, the last year before the global financial crisis, GDP per capita of all post-transition economies grew, except Moldova and Ukraine. On the other hand, six of the twenty economies of the region increased the lag behind the twelve advanced West European economies (EU12). A reasonable question in this context is to what extent is this backward take-off caused by the command-economy past or some myopic country-specific issues of the post-transition development?
With the growth accounting framework this study confirms the leading role of total factor productivity in late transition at the aggregate level. Delving into industry levels the literature shows that, at least, for some East European economies the key driver of TFP growth in most CEE economies was manufacturing. This is not surprising, because manufacturing was also one of the most technologically backward sectors of the economy in early transition with multiple opportunities for improvements through adaptation of better practices and ways of production from the West. So, catching up in technologies seems to be the most essential driver of the post transition growth.
At the same time, this exposition of the story of growth in transition critically depends on data quality, essential for measurement of economic growth and productivity. That is why it is important also to take into account that transition in economies of the region coincided with the transition in state statistics from the Material Product System of national accounts to the United Nations System of National Accounts. All this is important for understanding of the limitations of existing data and suggested interpretations, especially in the comparative perspective with developed economies.
This paper establishes a reference chronology for the Russian economic cycle from the early 1980s to mid-2015. To detect peaks and troughs, we tested nine monthly indices as a reference series, three methods of seasonal adjustments (X-12-ARIMA, TRAMO/SEATS, and CAMPLET), and three methods for dating cyclical turning points (local min/max, Bry–Boschan method, and Markov-switching model). As these more or less formal methods led to different estimates, any sensible choice was only possible on the grounds of informal considerations. The final set of turning points looks plausible and separates expansions and contractions in an explicable manner, but further discussions are needed to establish a consensus between experts.
Matrix updating methods are used for constructing the target matrix with the prescribed row and column marginal totals that demonstrates the highest possible level of its structural similarity to initial matrix given. A concept of structural similarity has a vague framework that can be slightly refined under considering a particular case of strict proportionality between row and column marginal totals for target and initial matrices. Here the question arises: can we accept the initial matrix homothety as optimal solution for proportionality case of matrix updating problem?
In most practical situations an affirmative answer to the question is almost obvious. It is natural to call this common notion by homothetic paradigm and to refer its checking as homothetic testing. Some well-known methods for matrix updating serve as an additional instrumental confirmation to validity of homothetic paradigm. It is shown that RAS method and Kuroda’s method pass through the homothetic test successfully.
Homothetic paradigm can be helpful for enhancing a collection of matrix updating methods based on constrained minimization of the distance functions. Main attention is paid to improving the methods with weighted squared differences (both regular and relative) as an objective function.
As an instance of a incorrigible failure in the homothetic testing, the GRAS method for updating the economic matrices with some negative entries is analyzed in details. A collection of illustrative numerical examples and some recommendations for method’s choice are given.
Although productivity decline in the global economy was observed before 2008, the global financial crisis of 2008 stimulated study of its source. In this context, recent literature mentions inefficient investments in machinery, human capital, and organizational processes. This can include skill mismatch and the lack of technology diffusion from advanced to emerging industries and firms. To what extent is this global view helpful in understanding recent productivity decline in the Russian economy? The present study reports that at least some of these sources can be observed in Russia as well. Using conventional industry growth accounting, it compares pre- and post-crisis sources of growth for the Russian economy. Specifically, it presents aggregate labor productivity growth as the sum of capital intensity and total factor productivity (TFP) growth in industries, and the contribution of labor reallocation between industries. It shows that the stagnation of 2008–2014 is more the result of the TFP decline and the deterioration of the allocation of labor than the lack of capital input. Moreover, the TFP decline started in Russia a few years before the crisis, as it did in major global economies, such as the United States, OECD countries, China, and Brazil. At the same time, relatively stable capital intensity made the Russian pattern to some degree similar to resource abundant Australia and Canada. Furthermore, the contribution of information and communications technology capital to labor productivity growth in Russia declined after 2008, which could have also hampered technology diffusion. Finally, the structure of the flow of capital services in Russia changed after 2008. Before the crisis, the contribution of machinery and equipment dominated, while after the crisis, construction provided the lion's share of capital input.
Sustainable reduction of investment in the Russian economy, observed since 2013, has become one of the most discussed issues. The aim of this work is to examine the contribution of several structural shocks to the dynamics of investment in 2003-2016. We want to consider the relationship between investment, GDP, domestic loans to non-financial corporations, the interest rate on these loans, external debt of Russian companies and the nominal exchange rate within the framework of sign restricted SVAR. In this work, four shocks are explored: terms of trade shock, shock of foreign funding (access to global capital markets), monetary policy shock and fiscal policy shock (public investment expenditures). The main results are as follows. External shocks dominate the dynamics of the Russian investment, and this applies not only to the terms of trade shock, but also to the shock of foreign funding availability. The sharp decline in access to it after the introduction of sanctions against Russia in 2014 had great negative impact on investments. In addition, the model estimates the role of monetary policy in 2015 as negative-neutral (thus offering an argument in favor of its easing), but at the same time rather insignificant. On the basis of our results we conclude that operational measures of economic policy are unlikely to crucially change the situation for the better. Removal of economic sanctions against Russia could promote investment, but only in the short-term period. In the long run reforms aimed at ridding the economy of such a high dependence on external factors are necessary.
Being one item by definition investment is actually not homogenous: generally, there are two major types – capital investment and investment in M&A deals. In this paper we examine the relationship between new capital investments and investments in acquisitions in Russia using data for more than one hundred companies in 2004-2014. The period is split into two sub-samples – period of rapid growth (before the global financial crisis of 2008) and post-crisis one (after 2009). Our results show that relationship between fixed investments and investments in acquisitions is opposite for two periods. In the first period, relationship between two types of investment was positive or insignificant which possibly means that companies did not face the trade-off between investment forms. After the global financial crisis, when monetary conditions and access to external capital markets for Russia tightened considerably, the relationship between investment in new capital and investment in acquisitions became negative. It proves that companies faced a trade-off between two investment forms. Moreover, acquisitions became dependent on company’s profitability. Such a trade-off can be crucial for developing economies since they are more dependent on external financing. These results can provide policy implications given the new understanding of financial constraint significance for investment.
Information and communication technology has reached a level of development that official sites of statistical offices and information resources stored on them has become the major channel for the distribution of statistical data. Therefore issues related to dissemination of statistical information should now be discussed in the context of internet-based databases, i.e. information and statistical systems. Transition from statistical handbooks and publications to such systems can lead to a radical increase in the volume and quality boost of statistical information made available to the users along with drastic reduction in data access costs.
For several years now users have been able to access the official Rosstat website, the Central Statistical Database and the Unified Interdepartmental Statistical Information System that combined claim to the role of Russian information and statistical system. However it is safe to say that the users of all the abovementioned resources are not exactly content and satisfied with them.
The purpose of this article is to organize author's thoughts on what kind of information and statistical system will suit the users tasked with analyzing Russian economic dynamics more.
The article discusses what's required of such system and puts forward suggestions for structuring the process of its organization, maintenance and development. The author reviews information structures that this system should look up to, relationships between them, general logic behind the total data set in the database. Emphasis is being placed on ensuring the following requirements: completeness, non-redunduncy, consistency, actuality, accuracy and precision. The composition of non-numeric information in the database is discussed. The article examines system functionality, issues related to its establishment, maintenance and development. Because establishment of this system has a substantial economic and statistical component, it can not be done only by the efforts of IT specialists. It is necessary to organize interaction between system developers and its potential users.
The aim of this article is to introduce a family of methods for reconciling the preliminary quarterly estimates of product and industry outputs with the corresponding annual outputs at given values of quarterly total outputs. Mathematical framework of the methods leans on generalized least squares principle, which is applied in linear vector space of output seasonal coefficients. The procedure of reconciling the quarterly and annual data on product and industry outputs is formulated as a separable programming problem with a quadratic objective function and linear constraints. The solution of this problem is obtained in analytical form. Separability of the reconciling problem allows introducing its «product» and «industry» reduced modifications. In particular, reduced modifications of the problem can serve as useful tools for balancing the incomplete sets of preliminary quarterly estimates of the product and industry outputs in order to bring them into conformity with the corresponding fragments of the annual output matrix. Simplicity of practical calculations and a very moderate need for computational resources are valid advantages of the proposed methods. In addition, the methods demonstrate a high degree of flexibility and adaptability in solving problems of reconciling the quarterly estimates of product and/or industry outputs with annual output data. High flexibility is provided by the dependence of the separable programming problem and its reduced modifications on the exogenous parameters that are allowed purposeful varying during calculation process.
GDP per capita growth rates in Russia have been amongst the highest in the world since the mid-1990s. Previous growth accounting research suggests that this was mainly driven by multi-factor productivity (MFP) growth. In this paper we analyse the drivers of Russian growth for thirty-four industries for the period from 1995 to 2012. We pay in particular attention to derive a proper measure of capital services, instead of the stock measures used in previous research. Using these new measures, we find that aggregate GDP growth is driven as much by capital input as MFP growth. Mining and Retailing take up an increasing share of the input, but have poor MFP performance. In contrast, MFP growth was high in goods producing industries but this sector’s GDP share declined. The major drivers of MFP growth were in high-skilled services industries that were particularly underdeveloped in the Russian economy in the 1990s.
Being one item by definition investment is actually not homogenous: generally, there are two major types – capital investment and investment in M&A deals. They are different from firm’s point of view and influence economic growth through different channels. In this paper, we examine the relationship between new capital investments and investments in acquisitions in Russia using data for more than one hundred companies in 2004-2014. The period is split into two sub-samples – period of rapid growth (before the global financial crisis of 2008) and post-crisis one (after 2009). Our results show that relationship between fixed investments and investments in acquisitions is opposite for two periods. In the first period, relationship between two types of investment was positive for non-state companies and insignificant for state ones that possibly means that companies did not face the choice of investment form. After the global financial crisis, when monetary conditions and access to external capital markets for Russia tightened considerably, the relationship between investment in new capital and investment in acquisitions became negative. It proves that companies faced a trade-off between two investment forms. Moreover, acquisitions became dependent on company’s profitability. Therefore, the trade-off can be more severe in developing economies since they are more dependent on external financing. These results can provide policy implications given the new understanding of financial constraint significance for investment.
We present a rationale for the delegation of regulatory functions in public transport to a partnership that rebalances social and commercial interests according to an agreed and predetermined objective function. This allows for the improvement of economic efficiency providing a constructive commitment to tariff and subsidy policies. Using a simple model, we determine the optimal corporate structure for such a partnership between the local government and any regulated monopoly. The government's strategic option of using its stake in the partnership to generate budget revenue from sale proceeds and/or dividends encourages the relevant authorities to increase the commercial attractiveness of the joint enterprise by setting appropriate tariffs. We show that such a strategic partnership can lead to improvements in welfare if the local cost of public funds is relatively high. These theoretical findings are then examined through the prism of suburban railway transport reform in Russia