Goal of research: To develop a methodology for performing economic analysis and empirical economic research (including analysis and forecasting for specific spheres and industries of the Russian economy), aimed at supporting efforts to direct the Russian economy towards the path of intense innovative growth. This involves developing comprehensive scenarios of short, medium and long-term socio-economic forecasts, including those needed by federal agencies from the executive branch.
Methodology: DEA was used to assess the behavior of the total factor productivity and its components; the Kalman filter for calculating the potential GDP and the output gap; SFA to assess the efficiency of Russian bank expenses; Bayesian approach was used to assess the parameters of the vector autoregressive model of the Russian economy with structural identification of shocks; BVAR-models for complex macroeconomic and financial forecasting, an econometric model based on the Hausmann-Klinger approach was used to assess the Russian economy’s export potential and prospects of directions of import substitution; econometric assessments of the function model demonstrating the reaction of the fiscal policy to the level of the debt load were used when analyzing the stability of Russian constituents’ debt. Statistical and graphical analysis and other analysis methods (ranking, segmentation, etc.) were also widely used.
Empirical base of research: a wide range of sources was used for this work, including data from Russian statistics (Росстат), Ministry of Finance, Bank of Russia, Ministry of Industry and Trade, Ministry of Economic Development and Trade, Federal Customs Service, international databases, mass media publications, official news outlets on decisions made and situations assessed. Also, for this work a database of statistical information, unique in its breadth of scope and containing five thousand dynamic rows, was employed. It was developed in-house by LAPEP employees. The following databases were also used: Cbonds-PRO RU (database containing information on the issue of Russian corporate bonds), Loans-PRO (database containing information on syndicated credit lines), BIR-Atlantic (БИР-Атлантик database with information on accounting statements for legal entities) and other data.
Results of research:
1. While developing the methodology, the following results were obtained:
1.1. In the framework of macroeconomic forecasting methods improvement, including a combination of structural and BVAR models for integrated macroeconomic and financial forecasting, estimates of the impact of monetary policy on key macroeconomic indicators were obtained based on a structural Bayesian vector autoregression model with additional factors included. An assessment on homogeneous periods of monetary policy was made. The findings suggest that there is a lack of hard evidence of the deterrent effect of a tight monetary policy on inflation and the stability of the national currency. It was shown that the tightening of monetary policy in Russia with a high probability has a recessionary effect, although its economic significance is not high. The impact of interest rates on the (real) ruble exchange rate is unstable and insignificant in most of the specifications.
1.2. Research was conducted to compare the efficiency of monetary policy (MP) credit channel in the retail and corporate segments of the credit market in Russia. Calculations showed that the weighted average rate on direct REPO auctions has a significant negative impact on the credit activity of banks in both market segments, while the impact in retail is 2.4 times stronger than in corporate segment. The economic effect of the rate is stronger in comparison with other factors of loans demand (GDP) and supply (quality of loans, attraction of household funds, etc.). The credit channel is weakened by the growth of banks' own capital and liquid assets (only in the retail segment) but is increased by the growth of banks’ investments in corporate bonds (in both segments). From conclusions for the regulator perspectives, the obtained estimates indicate that the same interest rate policy of the Bank of Russia will have twice the economic effect in the retail segment of the credit market than in the corporate segment. Another conclusion from this research is that the Bank of Russia's ability to influence the credit policy of large banks is limited, but it still exists: any measure that will be accompanied by capital allowances will be compensated by an increase in the efficiency of monetary policy credit channel in the corporate segment of the market - due to influence on the strategy of capital search by large banks. From countercyclical capital buffers perspectives, this means that during the periods of macroeconomic recessions the efficiency of the credit channel will grow, and, on the contrary, in the periods of expansion it will decrease.
1.3. The results of the efficiency evaluations of various groups of banks obtained by using various approaches to the efficiency evaluation and the variation of the bank issues definition are compared. State banks "national champions", other state banks, foreign banks subsidiaries and private resident banks in Russia were defined as groups of banks. The indicator of accounting for active and passive transactions with non-residents was used as a measure of variation in bank issues. Estimates were made with the use of a two- and one-step analysis of the stochastic efficiency boundary (SFA). The obtained results indicate that the (biased) two-step approach tends to underestimate the banks' performance indicators in comparison with the (unbiased) one-step approach, and the account of active and passive operations with non-residents in bank issues leads to an increase in the efficiency estimates of banks. "National champions" turn out to be the most effective group in the Russian banking system, then other state banks and private banks are on the same level of efficiency and, finally, foreign banks are the least effective group.
1.4. The research provides a quantitative assessment of the impact of fluctuations in private lending activity - the credit cycle - on the level of bank liquidity and volume of lending to banks by monetary authorities. A number of deviations of loans from the banking sector volume to GDP from its smoothed (long-term) values were used as a measure of the credit cycle, where the smoothing was carried out using the one-sided filter of Christiano-Fitzgerald. Further, we constructed a regression for a sample of 25 countries, which was evaluated by the instrumental variables method, particularly, by a two-step generalized method of moments. The results of the assessment allow us to confirm that in the sample of countries the credit cycle has a significant effect on the size of the banking sector's surplus / deficit of liquidity. Thus, the warming up of the credit market in the amount of one standard deviation leads to a decrease in the surplus / increase in the liquidity deficit by 0.11 standard deviations in average. In addition, it was revealed that the most strongly acting factor is non-credit sources of liquidity (monetary base minus Central Bank loans to banks). In addition, it was revealed that the strongest factor is non-credit sources of liquidity (monetary base, minus Central Bank loans to banks).
1.5. In continuation of the work carried out in 2015-2016 the approach to analyzing competitiveness of products relying on international trade data is modified. We develop the list of indicators (such as comparative advantage indices, coefficients of concentration for trade at the world market, technological content of Russian exports compared to the world average) and some algorithms to use these indicators to assess competitive positions at the level of industries and 6-digit HS commodity groups. Five algorithms (options to combine indicators) to use the indicators to assess competitive positions are proposed, the results are interpreted based on Russian data for 2015.
1.6. The approach to monitoring economic integration for the Eurasian Economic Union has been improved based on the analysis of global value added chains. Using the tools of input-output analysis, we estimate the impact of trade integration in the EAEU on the Russian economy in such areas as growth in output, employment, labor productivity and value added. The assessments of the impact of economic integration on economic activities are refined by applying the new input-output tables published by Rosstat. At the same time, the benefits from integration in trade are unevenly distributed among the industries. The greatest benefits in terms of output growth are concentrated in the chemical complex, machine building, metallurgy and electric power industry, and in terms of value added – in engineering, chemical complex, mining and services (primarily in transport and wholesale trade).
1.7. While developing the methodology for forecasting medium-term changes in the retail banking business, a system of simultaneous equations has been designed. The interrelations between loans and deposits of the population, the amount of cash in circulation, as well as income and expenditures of the population were analyzed with the help of the constructed system. Forecasts are constructed with the use of the techniques of solving the system of simultaneous equations and in the framework of four given scenarios, which depend on the situation in the macroeconomics and banking sector. For the period 2016-2018 demand factors, particularly, the solvency of the population will play a more important role in restoring the positive dynamics of lending in comparison to the supply factors presented in this paper as indicators reflecting the consolidated balance of the Russian banking system.
2. Obtaining new empirical insight:
2.1. During this project, monitoring and analysis of the development of the Russian economy and its key areas (macroeconomics, real sector, budgetary sphere, monetary sphere, banking sphere, social sphere, foreign trade sphere, etc.) has been continued. Scenario forecasts of the development of the Russian economy for the short and medium term were developed.
2.2. An intensification of negative processes was shown during the analysis of industrial production key trends in 2017. It includes: a) the transition from stagnation to a very slow decline in industrial output (during monthly dynamics analysis with the elimination of the seasonal factor; at the same time, according to the results of the year, an increase of 1.4-1.6% is expected due to the effect of the base - production growth during 2016); b) an increase in the export of products with the leaders in processing final products for the first time in many years; c) the growth of import mainly because of domestic markets growth, with a slight increase in the share of economic entities income sent to purchase import; d) some deterioration in the financial situation was mainly due to a slight decrease in profitability; e) increased financial risks in a number of sectors, primarily due to the protracted nature of the crisis (shrinking markets), deflation started in separate segments and the preservation of low credit availability.
2.3. Based on the parameters laid down in the federal budget for 2018-2020, the medium-term fiscal policy was analyzed. As a result of the analysis, the main bottlenecks of the new budget were identified: (1) the inertia of the fiscal policy, which does not contain measures to stimulate the economy and build the potential for future growth; (2) failure to use significant borrowing potential in the domestic market; (3) the inefficiency of the new construction of budgetary rules, which technical application leads to losses on interest costs.
2.4. Based on the analysis of the possibilities of conducting fiscal devaluation in Russia, the main channels of its influence on macroeconomic indicators and the success factors of this tax maneuver were identified. It is revealed that the possibilities of fiscal devaluation in Russia are limited due to the flexibility of nominal wages, lack of free labor resources, floating exchange rate, high share of commodity exports and ongoing monetary policy aimed at targeting inflation. The estimates of the potential macroeconomic effect of fiscal devaluation in Russia (in the form of the maneuver “22/22”) are obtained. The effect will be short-term and about 0.3 percentage point of GDP growth in the year of reform implementation (mainly due to import substitution and investment growth). At the same time, fiscal devaluation will have a significant impact on inter-budgetary relations, effectively liquidating the insurance pension system.
2.5. During this project, a set of scenarios of medium-term (until 2020) forecasts for socio-economic development of Russia was created. An analysis of scenario conditions and medium-term forecasts of the Ministry of Economic Development was carried out. An analysis of monetary policy was conducted on a regular basis.
2.6. Monitoring that was issued monthly: current trends in Russian economy development, trends of Russian industry development, leading indicators of systemic financial and macroeconomic risks. Monitoring of bankruptcies intensity, Monitoring and analysis of technological development of Russia and the world and a number of monthly analytical notes were published quarterly.
2.7. An analytical note “Thirteen Thesis about Economics” was prepared in December 2017, where the frameworks of economic situation in Russia were approved.
Level of implementation, recommendations on implementation or outcomes of the implementation of the results
The implementation of the investigation goals will help improve the quality of socio-economic analysis and forecasting. Also, the results of our investigation can serve as a foundation for further work on an analytical support when preparing to make major decisions and develop strategic documentation.
During this project, significant attention was paid to the preparation of notes and analytical materials, illustrated materials and forecasts that were passed on to executive and legislative branches of government (the President’s administration, government staff, the State Duma, Ministry of Economic Development and Trade, Ministry of Industry and Trade, Ministry of Finance, Russian Bank).