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: the Kalman filter for calculating the potential GDP and the output gap; SFA to assess the efficiency of Russian bank expenses and their potential for increasing the supply of long-term credit to the economy; Arellano-Bond Dynamic Panel 2-Step Difference GMM; Bayesian SVAR model of the Russian economy; non-structural competition assessments developed in NEIO; an econometric model based on the Hausmann-Klinger approach was used to assess the Russian economy’s export potential; the SFA and DEA were used to assess the behavior of the total factor productivity and its components; the multivariate Kalman filter for assessing the potential GDP and Russia’s output gap; main component method for analyzing the conformity of price behavior in markets of EAEU member states; assessment of cointegrational correlations and causations according to Granger to evaluate the cross impact of investments made into the capital stock of EAEU member states. When analyzing the stability of Russian constituents’ debt, we used econometric assessments of the function model demonstrating the reaction of the fiscal policy to the level of the debt load. 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 Forecasting models of the Russian economy were further improved under the Bayesian Vector Autoregressive (BVAR) approach when using various specifications of a priori distributions of models’ parameters and imposing the small open economy restrictions. Conditional medium-term forecasts (scenarios, up to 2017) were introduced. Then, the forecasting outcomes achieved in BVAR under these scenarios were compared with respective official forecasts of the Ministry of Economic Development (MED) of the Russian Federation. The results indicate that within the similar scenario conditions the proposed BVAR predicts (1) a deeper and (2) more prolonged recession on the medium-term forecasting horizon as compared to the MED’s forecasts. The comparative analysis revealed the bottlenecks in the forecasting methodologies applied both in the MED’s model and in the BVAR model, which may worsen the quality of forecasts.
1.2. As a further step of the research on developing empirical tools for estimating the level of competition in the Russian banking system that were described in the previous Reports on the project, a set of new regression models was introduced for (1) the comparative assessment of the level of banking competition under four complementary approaches at different phases of the business cycle in Russia and (2) the subsequent analysis of possible non-linear relationships between competition and bank credit activity. The simulation analysis was performed using bank-level data over the period of 2005 Q1 to 2012 Q4. First, the comparative assessments have revealed that before the crisis of 2008–2009, an increase of both price and quantity competition took place; however, these competition improvements were offset during the crisis. After the crisis, price competition was on the upswing again thanks to another boom in the retail lending market, whereas the quantitative competition remained rather constrained. Second, thresholds were identified revealing that the impact of competition on credit activity of banks is non-linear indeed – when the price competition is too severe or too weak, the credit activity of banks weakens.
1.3. A pilot version of the quarterly system of leading indicators revealing regional fiscal stress based on the signaling approach was developed. Incidents of fiscal stress were identified based on the G-spread dynamics for each type of regional bonds in use between August 2003 and September 2015. The overall fiscal stress indicator is composed of four specific fiscal indicators demonstrating the best results. The resulting pilot version of the overall leading indicator presents the capability to forecast a region’s difficulties with obtaining funds on the bond market by monitoring the dynamics of individual indicators of Russian Federation constituents.
1.4. Research was continued to establish a theory for the net export index of revealed comparative advantage, developed earlier. Namely, it’s been confirmed that there is a correlation between a positive net export and the presence of a comparative advantage (in the form of a sufficient margin of production factors necessary for manufacturing goods), the application of such indicators as the GDP for scaling has been justified, and the need for simultaneous monitoring of trade levels and net export has been explained. A series of the following preliminary approaches to building simple filters for accounting for the quality of the goods is proposed: through the diversification level of world export, weighted average GDP per capita of countries importing Russian goods, weighted average GDP per capita of exporting countries and the product unit cost.
1.5. We have updated the assessments of total factor productivity dynamics in 65 countries in terms of the element structure, taking into account the technological efficiency of production with the help of one and two-step SFA and modified (O’Donnell, 2008) DEA on two samples taken between 1990 and 2011. The assessments take into account the effects of control factors, including the economic structure, institutional and infrastructural growth, and R&D expenses. The obtained TFP assessments are highly correlated to those of the OECD, the Conference Board and PWT, and the country ranks based on efficiency are similar to those of other research works. The effects of the control factors correspond to the substantive interpretation.
1.6. A quantitative assessment has been made of the effects of scientific and technological development parameters (R&D expenses) on the socio-economic parameters (TFP components) via the SFA and panel regression with fixed effects. In total approximately 500 models were assessed, including their specifications, which take into account the effects of the control factors such as the economic structure, institutional and infrastructural growth and others.
According to the assessments, the influence of increasing scientific and technology development expenses is great, however, it depends significantly on the sample set. So, if the rate of total spending on R&D increases by 1.0% GDP, then in ten years the growth rate of the TFP will increase by 5.0 percentage points for the “World” sample set and by 7.7 percentage points for the “OECD-1990+Russia” sample set. Increasing the specific total expenditure for R&D by $1,000 for one researcher will, in five years, increase the growth rate of the TFP for the “World” sample set by an average of 0.013 percentage points and for the “OECD-1990+Russia” sample set by an average of 0.025 percentage points.
1.7. In order to evaluate the effects of the science and technology factor on the behavior of the economy, a forecast has been built to evaluate the growth rate based on the small-scale model. This forecast allows us to assess, based on scenario options, the effects of R&D expenses on the TFP of the Russian economy. The difference between federal expenses for the two main scenarios is 0.2% GDP, whereas for private expenses it is greater, approximately 0.3% GDP. The average TFP growth rates are 1.5% annually for the “own center of power” scenario and 1.15% for the “smart raw materials” scenario.
2 Obtaining new empirical insight:
2.1. When implementing this project, work was continued on the monitoring and analysis of the development of the Russian economy and its most important sectors (macroeconomics, real economy, budget, money and credit, banking, social, international trade, etc.) Attention was focused on stability factors of the current year’s economic stagnation. It was found that its prolonged state is caused by several groups of factors discussed in this work. It is possible to end the period of prolonged stagnation by activating additional growth factors, namely stimulating exports (additional capitalization of the Russian Agency for Export Credit and Investment Insurance and Bank for Development and Foreign Economic Affairs), and supporting a “secondary” (based on a non-price competitive strength) import substitution and investment process (including infrastructural construction) by softening the money and credit and budget policy. Another direction of inquiry involved the development of scenario forecasts of Russian economic growth in the short and medium-term.
2.2. The analysis of the money and credit trends of 2016 has shown that the slowing of the softening process of the Bank of Russia interest policy, a high fraction of “bad debt” and a low level of bank capital sufficiency have pushed the corporate lending market from constricting to stagnating. This, in turn, suppresses the transition to a positive economic dynamic.
2.3 A new investigation was launched in order to empirically evaluate the potential of banks in expanding the supply of long-term loans to the economy and how this potential depends on the market power of banks in the lending market. Stochastic frontier approach for bank capacity was employed for that purpose. First estimates were carried out using the Russian bank-level data over the period of 2005 Q1 to 2015 Q4. Our results indicate that, first, Russian banks are quite inefficient in providing long-term loans in corporate and retail loan markets. Median values of efficiency indices vary between 26% and 37% out of 100% that is potentially achievable. This empirical finding implies that the banks possess an extremely large potential for expanding the supply of long-term loans even in the current macroeconomic, industry-wide and bank-specific conditions. Second, when enjoying increased market power, banks tend to decrease the supply of long-term loans to non-financial corporations (increasing the distance to frontier) while increasing the supply in the retail segment of the market (decreasing the distance to frontier). This might indicate that expanding the supply of retail long-term loans is associated with less risk for banks as compared to that in the corporate segment of the loan market.
2.4. The fiscal policy passed for the medium term was analyzed. It was demonstrated that its implementation will lead to maintaining a consistently low economic growth rates. An alternative set of budgetary and taxation measures is proposed, which is aimed at boosting investments, R&D expenses and export. The set of measures includes lowering the contribution rates for social security while compensating shortfalls in income of the Russian pension fund by gradually increasing the retirement age; introducing a flexible tax credit for R&D expenses, a special, preferential tax treatment for science, technology and innovation companies and creating a “patent window”; returning the investment tax deduction; and increasing spending to support export and federal investments for co-financing of high-tech STI projects.
2.5. Main trends were analyzed in the sphere of the population’s welfare and social differentiation in 2016. It was demonstrated that the increasingly poor population is adapting to the prolonging crisis by changing the consumption structure (lowering consumption of several types of non-food goods and services and turning to cheaper food products), while also storing up goods in anticipation of a continued drop in their welfare.
A high level of social differentiation is maintained despite the crisis and the overall drop in the population’s real income. As a result, the problem of poverty becomes more critical. The groups most at risk include families with two or more children, single-parents families and retirees.
2.6. When analyzing the key trends of 2016 in the real economy sector, we learned that there was a shift from a stagnation of production towards recovery growth. By the end of the year indications of growth were visible in the majority of economic sectors (according to the trend, with the seasonal factor removed). Also, small indications of investment activity were noted, although mostly in the raw materials sectors. However, despite the generally positive results, the situation in the investment complex sectors (engineering and building supplies manufacture) remains extremely tense: the drop in the markets was significant and it is still far from full recovery. The greatest issue here is the high risk of company insolvency.
2.7. Based on the developed methodology for assessing the impacts of shocks in currency and credits on the financial position of economic sectors by using financial flow matrixes, we showed that for the majority of sectors, the weakening of the national currency, as a rule, leads to a loss of net indebtedness in foreign currency as a result of exchange rate reevaluations. However, a significant number of sectors suffer a greater loss because of the increasing cost of imported raw goods, materials and spares and less access to attracting new borrowing as a result of devaluation. The oil sector industries suffer a portion of these losses of consumers through the fiscal policy.
2.8. We modeled the quality of banks’ corporate crediting portfolio using the methodology of assessing individual regression equations and applying the Zellner method. As a result, we revealed a “financial contamination” phenomenon between various economic sectors, which led to an increase in credit default. Specifically, it was discovered that during times of economic crisis the system of mutual commercial lending done by companies adds to the spread of the “contamination” between sectors. We were able to establish that in times of drastically worsening foreign market trends, insolvency spreads from companies extracting natural resources and companies producing capital goods to companies manufacturing construction materials and those producing consumer goods.
2.9. Analytical materials were regularly published, including those listed below: Trends in the Russian Economy, Growth Trends in the World’s Largest Economies, Growth Trends in Russia’s Partnering Countries through the EAEU, What do the Leading Indicators of Systemic Financial and Macroeconomic Risks Reveal?, Industrial Growth Trends, and multiple monthly analytical notes. An analysis was performed on the most important measures of fiscal policy as well as of scenarios and growth forecasts for the Russian economy that were developed by Russia’s Ministry of Economic Development and Trade, and analogous forecasts were generated with their scenario conditions (various options for development), an analysis of the main areas of monetary policy.
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, Ministry of Economic Development and Trade, Ministry of Industry and Trade, Ministry of Education and Science, Russian Bank)