2015. vol. 19. No. 3

Using RLMS–HSE dataset we analyze labor flows in the Russian labor market for 2000–2012. We document the high mobility rate and the transitive role played by non-participation. Division of all employed into three large groups (budgetary workers, workers in the corporate market sector, and employed in the non-corporate or informal sector) suggests that budgetary workers are low mobile compared to others, and informal workers and economically inactive in­dividuals have higher probabilities to become unemployed than those who work formally. The paper exploits a few methodological approaches. First, we build transition matrices allowing estimate transition probabilities. Second, the Shorrocks indexes estimate intensity of mobility. Third, the dynamic multinomial logit model explores individual determinants of inter-status tran­sitions and structural dependence from the previous labor market states. Fourth, we decompose the change in unemployment rate as the combination of incoming and outcoming flows. This procedure suggests that the decline in unemployment is explained by decrease in incoming flows while the outflows remain largely stable. Observed intensity and direction off lows fit the institutional configuration of the Russian labor market model.

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.

We consider standard monopolistic competition models in the spirit of Dixit and Stiglitz or Melitz with aggregate consumer's preferences defined by two well-known classes of utility functions – the implicitly defined Kimball utility function and the variable elasticity of substitution utility function. These two classes gene­ralize classical constant elasticity of substitution utility function and overcome its lack of flexibility. It is shown in [Dhingra, Morrow, 2012] that for the monopolistic competition model with aggregate consumer’s preferences defined by the va­riable elasticity of substitution utility function the laissez-faire equilibrium is efficient (i.e. coincides with social welfare state) only for the special case of constant elasticity of substitution utility function. We prove that the constant elasticity of substitution utility function is also the only one which leads to efficient laissez-faire equilibrium in the monopolistic competition model with aggregate consu­mer’s preferences defined by the utility function from the Kimball class. Our main result is following: we find that in both cases a special tax on firms' output may be introduced such that market equilibrium becomes socially efficient. In both cases this tax is calculated up to an arbitrary constant, and some considerations about the «most reasonable» value of this constant are presented.

Until recently in Russia there were only administrative penalties for illegal insider trading, those were rarely used and insider trading was wide-spread. In 2010 the law on insider tradingwas introduced. It stipulated criminal penalties for illegal insider trading. An identification of cases of suspected insider trading and a comparison of its scale with other markets is a pertinent issue, including for an evaluation of the effectiveness of the adopted law.

The research of insider trading on developed and emerging markets shows that insiders earn positive abnormal return by trading shares before the announcements of important corporate events on average. This abnormal return is higher in emerging markets. Mergers and acquisitions are such type of corporate events. There is a correlations between severity of the law on insider trading and the size of insider trading.

Our research covered 36 M&A deals in the Russian market in 2006–2013. We have found positive average abnormal returns (ACAR) before the announcement of the deals. They reach 15% at the date of an announcement or a first rumor. These numbers are statistically significant starting from date –12 at the 1% confidence level. Two thirds of the ACAR is realized before the announcement of the deal while in the USA only one third is realized before the announcement. Average abnormal trading volume is also positive. A sharp increase of AVV takes place five days before the announcement. AVV grows up to the date of announcement and reaches 382% of the standard volume one day before the announcement. The existence of positive ACAR and AAV is an indication of the fact that the market learned about the deals be fore the an official announcement and even before public rumors, that is it hints at the existence of the insider trading in the Russian stock market.

We examine mechanisms of retail pricing for gasoline in Saint Petersburg in 2002–2013. Paper presents a theoretical and empirical analysis of gas station quality and location influence on retail prices. Empirics is based on a unique database of retail gasoline prices for particular gas station. Our main results are that gas station location (such as the number of competitors in the set radius, the distance to the next competitor, location on the way out of the city or vice versa, population density) has extremely weak impact in prices but price time-variance is mainly associated with response for oil price change specific for gas station brand. Cross sectional price variance is mainly explained by the brand identity as well as by the shadow price for the services additionally provided at the stations. As far as the brand identity is concerned, the price difference may reflect the cost difference as well as may be a signal of distinguishing product quality. Then, the price differentiation in response to the fact that the stations provide additional paid or free services is preserved even when the brand identity is taken into account. On the other hand, there is no evidence that the firms try do discriminate the consumers in case several stations of the same brand are located near each other. Additional econometric result of the paper is a numerical evaluated sensitivity of retail gasoline price to oil prices changes on the level of individual station brands.

We compare approaches to identification of structural models developed in econometrics and computer science literature. In the econometric literature we consider the method of instrumental variables, the rank condition for simultaneous equations models, and various conditions for identification from the theory of structural vector autoregressions. In the computer science, we consider the literature on causality within the theory of probabilistic graphical models. Most results have been translated into two languages: the language of linear algebra, which is ubiquitous in econometric literature, and the language of graphical models, which is popular in computer science. Each approach that we consider has its relative advantages and weaknesses: the approach developed in computer science is more flexible when working with intricate structural shocks independence structures, and the approach developed in econometrics is more efficient for cyclical models. We also propose a unifying procedure for identification that uses advantages of both approaches. Using this procedure, the researcher can easily translate the results from one branch of the literature into the language of the other, and fully or partially identify new models, which could not be identified using any of the considered approaches separately from the others. We also review the literature on data-oriented identification, where the identification restrictions are not only theoretically justified, but also fully or partially empirically verified. Most results are formulated within linear Gaussian models; however, the unifying procedure of identification easily generalizes to nonlinear, non-Gaussian, or even nonparametric models.