We conduct a statistical study of the global trade slowdown relative to industrial production after the global financial crisis of 2008-2009 and the revival of the global trade in 2017. We aim to decompose the overall effect by geographical and commodity dimensions, that is, to determine the contribution of regions, major countries and aggregated commodity groups to the global trade slowdown and revival. Calculation scheme implies the two-way analysis, both from the demand and the supply side (imports and exports, respectively), and relies on the customs data. The focus on merchandise trade is confirmed by the fact that the growth rates of world imports and exports in constant prices for services decreased much less after the crisis of 2008-2009 than for goods. We analyze the dynamics of world trade relative to the dynamics of industrial production, not GDP, due to the very high volatility of trade to GDP. The data comes from the Netherlands Bureau for Economic Policy Analysis, WTO, World Bank, OECD, FAO and the Chatham House Resource Trade Database. The key feature of the proposed scheme is aligning data on international trade growth for largest countries from different sources with CPB data for the world as a whole.
The results of the study show that the slowdown in global exports was largely associated with emerging economies of Asia (and, primarily, China), Japan, Germany and the US, while the slowdown in global imports reflected the drop in demand in China and other emerging economies of Asia, the Euro Area, Russia and Brazil. The revival of global exports was driven by China, Japan, Netherlands, South Korea and Mexico, and the revival of world imports boils down to the demand growth in China, India and Russia. Unlike the global trade slowdown, the revival of the world trade was critically concentrated in emerging countries, while the Euro Area has practically not experienced the revival. An important role in the global trade slowdown was played by China and its reorientation to the domestic market after the crisis of 2008-2009. In terms of commodity structure of global trade, the slowdown was almost entirely associated with non-resource goods.
The results can be used to refine the forecasts of the global merchandise trade growth by accounting for the contribution of the major countries more accurately.
In this paper we propose and implement a mechanism of modeling the price indices of food purchases by income groups of households. These indices could be interpreted as differentiated by income food inflation. This approach is based on the differences in prices of purchases for the income groups within each year. We provide the calculations of these indices for the RLMS data and Households Budget Survey conducted by Rosstat (HBS). We discuss possible modifications of the proposed procedure for goals of forecasting of inflation differentiated by income groups. In the result of the comparison with direct calculation of inflation separately for each income group we conclude that the proposed in the paper approach has several advantages, including lower requirements of amount of incoming information.
The paper focuses on the issue of price endogeneity and its relation to various micro-economic characteristics of households. The modeling of intergroup dynamics of the price index for food and nutrition among household groups in relation to the amount of expenditure on food per person confirms the relevant hypothesis. This modeling requires the access to the information in the household surveys data, available only in the RLMS. The mechanism of data consolidation of RLMS and Households' budgets survey by Rosstat (VOBDH) is proposed and can also be used for price heterogeneity incurporation.
Consumption behavior of Russian households as macroeconomic agent is changing and converging with behavior of populations of developed countries. This agent finances the purchase of goods and services by current income, savings and loan. Repayments of loans, which are generally used to acquire durable goods and services, are distended in time. Consequently, there are factors including loan conditions that influence the formation of households' organized savings. The main idea of this paper is to model savings depending from loans apart from classical macroeconomic research papers in which total income is the most important factor which affects savings. Available statistical data was structured and transformed into necessary format. Three models of Russian households' consumption behavior were constructed for 2004-2014 years. In this work the following methods were used: Engle-Granger methodology for error correction model (ECM), Johansson’s procedure for vector error correction model (VECM), fixed point method for estimation of structural system of equations for savings and loans. Models include the following exogenous and endogenous variables: expenditures, savings, received loans, monetary income, CPI, lending / deposit interest rates. The results demonstrate the existence of positive short-run and negative long-run interconnection between households’ savings and loans; these results are in accordance with real data. Models obtained in this paper can be used in short-run forecasting of Russian households’ savings. They might be also useful while accessing the effect of loan conditions on saving behavior of households.
The share of import in Russian food trade decreased significantly after the establishment of the food ban for the wide list of countries of 2014. This decline could not go unnoticed by the Russian households. Therefore, the aim of this research is to study the changes in the structure of consumer demand for food products associated with food embargo. Estimations are obtained by the model based on the QUAIDS and Working – Leser models. The originality of the research lays in the construction of the individual prices vector. Traditionally models based on AIDS use aggregate price indicators, such as regional CPIs, but prices are the result of consumer choice, so they can not be aggregated at such a high level. However, individual purchase prices should not be used as well, because of the problem of endogeneity. This study presents a method of estimating individual prices, so they are both differentiated by income groups and other households’ indicators and cleared from the endogeneity. The results demonstrate that the introduction of an import ban caused structural shifts in consumer demand for food prod ucts. The growth of the absolute value of price elasticity may be explained by the lower quality to gether with higher prices of food products on the new market. The rise in the income elasticities means fall in the demand for luxury goods, because of the low ruble exchange rate and overall economic instability. The growth of economies of scale means that households changed their consumption strategies to save their welfare. However, these changes are present for the city population, while farmers seem not to lose their well-being at all. Furthermore, a more thor ough analysis showed that after the shock in 2014, the indicators started to get closer to their initial values. This may be the result of adaptation or the increase in the effectiveness of import substitution. In this regard, the research gives Russian food safety positive prognosis in the long-run, but only if the quality and the variety of food products are improved.
An article is devoted to description of the research results with the following objectives: Working out the proper pricing model for Moscow office rental market based on the foregoers' researches and microeconomic market analysis; 65535 Testing developed model on the empirical data, results interpretation; 65536 Calculation of the margin effects of the identified determinants on the 65537rental rate level; Identification where the market is on the real rental rates circle curve. Research has a great practical significance due to rapidly increasing interest from Russian and foreign investors in Russian and especially Moscow real estate market, concerned its high return with moderate, decreasing over time risks. It can be useful for the wide range of readers: from investors to university students and academics, due to its theoretical improvements of existing real estate pricing models and practically applicable results.
The paper analyses determinants of efficiency of Russian universities. The analysis is based on the data from annual monitoring of performance of higher education institutions conducted by the Ministry of Education and Science. Special attention is paid to the factors that are associated with public policy in the sphere of higher education. In order to explain the variation of the efficiency scores we implement one of the most modern techniques for analysis of efficiency’ determinants – Two-Stage Semi-parametric DEA. The high level of heterogeneity in Russian higher education sector is controlled for by considering two different specifications of DEA model: with the focus on educational activity and with the focus on scientific activity. The results show that relatively less efficient universities are more likely to be affected by the considered efficiency’ determinants compared to efficient ones. Universities that are governed by the Ministry of Education and Science and by regional governments appeared to be relatively more efficient compared to the universities that are governed by another federal authorities except for the Ministry of Education and Science (Ministry of agriculture, Ministry of Healthcare, Ministry of Culture, Ministry of Sport and so on). Governance by the Ministry of Education and Science has the strongest effect on efficiency level among considered factors. Governance by regional authorities has the weakest effect. The total square of buildings available for the university appeared to be positively and statistically significantly related to efficiency level. While the autonomous status has no any effect.
The fundamental idea underpinning spatial econometric models of economic growth is as follows: regional growth is determined not only by social, economic, geographic traits of a region but also by spillovers from other regions, most importantly adjacent ones. If one region starts booming, it can left neighbors unaffected (neutral mechanism), spur their growth (cooperation mechanism) or slow their growth by pulling resources over (competition mechanism). What mechanism and to which extent occurs in practice matters for designing balanced economic policy and evaluating efficiency of regional policy investment. Classic spatial econometric models make strong although simplifying assumption that the same mechanism matters for all regions in the same manner, and there is no variation in spillovers intensity across regions. This assumption seems plausible for relatively small and homogenous regions of European countries, but it looks excessively strong for large and diverse Russian regions. In this paper we attempt to relax this assumption and propose a new model, fitting better in Russian conditions and bringing only slight sophistication from the estimation point of view. We introduce sensitivity parameter governing regional exposure to externalities. We assume this parameter to be a linear function of region-level observables, like area, population density or urbanization rate. These hypotheses have been confirmed at least partially. We found that dense and urbanized regions were more sensitive to spillovers. In other words, a region surrounded by the fast-growing areas, will grow the more intense, the more its population density and the higher the level of urbanization.
The paper describes the new version of the model of the Russian banking system, which successfully reproduces a wide set of parameters characterizing its performance: loans and deposits of firms and households, liquidity nominated both in rubles and in foreign currency, mandatory reserves. We describe the technique of derivation of model relations, which includes the statement of the problem of macroeconomic agent “bank”. This problem is based on the maximization of discounted flow of profit subject to budget constraint, balance of loans and deposits, liquidity constraints and reserve sufficiency requirements. The paper contains the system of equations which describes the solution of the problem. We provide a detailed description of transition of continuous to discrete time and the new approach to the relaxation of complementary slackness conditions based on the assumption that the model exhibits a turnpike property.
Apart from the standard approach to the parameter estimation for this class of models, we apply a method of multi-step forecasting. We show that the standard method of estimations allows to closely reproduce the historic series but leads to the poor quality of forecasts. The method of multi-step forecasting, on the other hand, successfully reproduces historic series and also leads to rather accurate forecasts. We compared it with standard econometric techniques and show that the model with parameters obtained via multi-step forecast method provides somewhat better forecasts than ARIXAM and much better ones than AR, ARIMA, VAR and VARX. We also show that then we use multi-step forecasting method, optimal values of parameters are about the same for different intervals of estimation and different lengths of forecasts (from one to six months). Such a stability of parameters makes us think that the model reproduces long-term relations of variables and can be used for forecasting and scenario analysis.
The model can be used for the evaluation of reaction of the banking system on the monetary policy, external constraints of different kind and the general condition of the economy. The model can be used as a block of a bigger general equilibrium model of the Russian economy.
In this paper, we consider the classical problem of maximizing discounted utility, provided that the moment of the next purchase and receipt of a loan is random (Poisson). The purpose of the study is to take into account the uncertain waiting period for receipt of a credit in consumption decision-making. The model is formulated as the problem of optimal stochastic control. The consumer at random moments buys the product at a non-random price and at the same random moments can take and return indefinite loans. For loans, the agent continuously pays interest. He constantly receives dividends in the form of external receipt of money into the account and can accumulate non-interest non-cash money. The optimality conditions are obtained using the Lagrange multiplier method. Sufficient optimality conditions reduce to partial differential equations with variable and unknown delay. They can only be solved by using a combinations of analytic expansions with respect to a small parameter. A special difficulty is the regularization («softening») of the conditions of complementary slackness. As a result, functions were obtained that determine the optimal control of consumption purchases and the size of the loan. One can see how the consumption expenditures change as the end of the planning period approaches. First, consumption depends on money and debt not separately, but on their difference – own means of the consumer. Secondly, far from the planning horizon, consumption is small and grows as the final point in time approaches. This model can be used as part of the description of the consumer agent in dynamic stochastic general equilibrium models.
The problem of evaluation of the real power of players when they make collective decisions is considered. The new model of the real power evaluation is proposed. The basics of the new model are: modification of the classical power Shapley - Shubik index for accounting of possibility of coalition formation, adding of the new index of the position of coincidence which is evaluating the closeness of the political position of groups and faction, and the new developed index of power efficiency. The index of power efficiency shows to what extend the players exercised their potential power which depends on the number of their votes. Besides, a new way of accounting the impact of cohesion of groups and factions in their final power score is proposed. This model is applied for the evaluation of the power distribution at the Russian State Duma of the 3d convocation.