2016. vol. 20. No. 4

This study presents new estimates for returns to tenure for the Russian labour market. It exploits the RLMS-HSE panel data set that covers the period 1994–2014 and it differs from previous studies in a few important aspects. First, we try to account for the specifics of the transition period dividing the total observed tenure into two parts, where one part is the old one acquired before 1992 and the other part is newly accumulated since 1992. Second, we estimate the returns to tenure for employees in the private and the state sectors separately. Third, we apply alternative instrumentation techniques suggested by Altonji-Shakotko and by Topel and are the first who do this for the Russian labour market.

Our cross-section OLS estimates show that the return to tenure in the state sector has always been positive but in the private sector it has been positive since the mid – 2000s. In the private sector, the return to tenure is lower and the wage growth over the tenure stops earlier than in the state one. On average, the cumulative premium for 15 to 20 years of firm-specific experience makes about 20–25%, according to the OLS estimates. However, it disappears completely or even turns negative if endogeneity is addressed with use of the instruments. Our analysis suggests that existing knowledge of how tenure is valued in the Russian labour market needs to be revised.

The paper is devoted to testing for and dating structural breaks in the long-run growth rate of the structural component of the Russian GDP. To solve this problem we use the metho­dology of cointegrating regression in which we allow the long-run dependence of the logarithm of the Russian real GDP on the logarithm of the real oil prices. Also, cointegrating regression equation includes the deterministic linear trend in which breaks in the slope are allowed (without any level shifts). This deterministic trend is interpreted as long run level of the structural component of the Russian GDP. The empirical results are in favor of the existence of two structural breaks in the long-run growth rate of the structural component in the period from 1995: in the 3^rd quarter of 1998 and in the 3^rd quarter of 2007. The point estimate of the second break three quarters early than the corresponding estimates from the univariate statistical tests. This result could indicate that the structural problems of the Russian economy started before the crisis of 2008–2009 and the relatively high growth rate immediately before this crisis was due to sharp oil prices increase.

The empirical results also show that in the cointegrating regression with piecewise continuous linear trend, the long-run elasticity of oil prices decreases approximately by 2 in comparison to estimates obtained in the literature with similar models without allowing existence of structural breaks. Our estimate of the elasticity is about 0,1. The estimate of the long-run growth rate of the structural component is about 5,3% per year until the 3^rd quarter of 2007 and about 1,3% per year in subsequent periods. We haven’t found evidences for the presence of the third break in the vicinity of the current economic crisis.

In this paper we study how the timing of reforms affects economic growth. More specifically, we use a growth model with technological progress and human capital accumulation to analyze the effect of an education reform that is aimed at delivering the human capital required for spurring economic growth. We show that the reform’s impact on the stock of human capital might not result in higher growth rates if, at the same time, demand for high-skilled labor is limited. The latter might be a result of weak institutions restricting firms' incentives to invest in technology that works in tandem with high-skilled employees. This, in turn, may lead to weak demand for high-skilled labor, causing a reform that stimulates high skilled labor supply to fail. Moreover, being desperate to find a matching employment, a part of high-skilled labor might flow abroad in search for better job opportunities. Our model is structured as follows. In the baseline model growth is impeded by a shortage of educational service, and having eliminated that constraint, one can reach a higher growth rate for the economy. The model is extended further by accounting for poor management practices that make the level of technology lag behind and also reduce demand for human capital. Under such assumptions, a reform promoting educational service supply may have a limited effect. Thus, we conclude that only if an advance in the school system is combined with a better control over the behavior of management, will an educational reform unambiguously lead to faster growth. We consider the economy of Russia as an illustration of our main findings to emphasize that reforms in the most binding field have to be prioritized.

We investigate the phenomenon of asymmetric information that is typical in both developed and emerging markets. The purpose of this paper is to explore the impact of asymmetric information on the value-enhancing capital budgeting in emerging markets.

This study examines three measures of asymmetric information – general return variation, firm-specific return variation and stock price delay. We apply the deviation of a firm's estimated marginal Tobin's q from a benchmark as an indicator of effective capital budgeting. Finally the impact of asymmetric information on the value-enhancing capital budgeting has been analyzed.

Research was conducted with 1080 listed BRIC companies from 2005–2014. The key findings of the paper are: stock price informativeness measured by daily general return variation and daily firm-specific return variation has a significant influence of investment performan­ce. We find that the high level of investment opportunities and financial constraints lead to less efficient investment decisions. Moreover industry analysis reveals that the high peers’ stock price informativeness measured by weekly general return variation and weekly firm-specific return variation lets managers to improve the corporate value.

Our study contributes to emerging literature on the determination of relevant investment model by showing that managers can improve the investment efficiency and investors can decrease the risks of personal investments. In addition, this study provides additional evidence on the agency problem that affects firms' investment decisions.

The analysis concludes that the necessity to reduce the level of information asymmetry is one of the key components of the corporate value maximization that would increase the corporate attractiveness to investors.

This paper compares the forecasting performance of random walk, frequentist vector autoregression (VAR), and Bayesian vector autoregression with Minnesota prior (BVAR) models on quarterly Russian data sample running from 1995 to 2014. Maximal number of variables included in the model is equal to 14 that requires an endogenous search of optimal shrinkage hyperparameter. The search procedure follows [Bańbura et al., 2010; Bloor, Matheson, 2011].According to the selection method the shrinkage hyperparameter equates the forecasting quality of the frequentist VAR and BVAR for the minimal considered dimension of the model (three variables). For any dimension of the BVAR model the optimal shrinkage hyperparameter is robust to considered functions of relative forecasting accuracy.

We show that the BVAR provides a more accurate forecast than the frequentist VAR on the studied sample. For key macro indicators (the industrial production index, consumer priceindex and the interbank interest rate), forecasting horizons, and all model sizes, the mean squared error of the BVAR is lower than that of the frequentist VAR. Moreover, the results show that the forecast made using the BVAR is more precise than the forecast made using random walk model for the CPI and using white noise model for the interbank rate. However, the BVAR cannot beat the random walk while forecasting the industrial production index.

We investigate the relative effectiveness of the projection methods of Supply and Use tables in relation to Use tables. The empirical bases of the study are the Use tables of 28 countries for the period from 1995 to 2010 from WIOD project. We conduct a comparative study of three mathematical methods that have proven the most effective in constructing projection of Use tables for Spain and the Netherlands from the empirical study by Temurshoev, Webb, and Yamano (2011). In these methods, a Use table is constructed based on the benchmark table and the sums of the columns and the rows of the table under construction. The most effective of these methods is GRAS, a version of the classical RAS algorithm. The results of applying this method under the number of criteria are closer to the published tables than the results of the INSD method and Kuroda method, which are based on quadratic programming. We conclude that GRAS method is a priority in the extrapolation of Use tables for Russia. At the same time we have shown that in some cases the table cannot be balanced by GRAS method because of sig nificant changes in the structure of the table. In 80% of these cases the tables were successfully balanced by the two quadratic methods. In these cases the Kuroda method is the most effective.