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The paper focuses on trends in the convergence of labor and multifactor productivity in Russia. Using firm-level data for the period 2011-2016, we show that firms with low-productivity grow faster than those with high-productivity. This result is, however, mostly driven by new entrants. The catch-up momentum fades after the first few years of a firm's life, so it is not capable of closing the gap between the most and least productive firms in the Russian economy. We show that the gap widened over the period 2011-2016, suggesting major divergence in productivity levels of Russian firms. We also use stochastic frontier analysis to verify the divergence within narrowly defined industries. Our estimates confirm divergence in most industries.
The access to credits for companies with high productivity is an important factor for the economic recovery after the shock. In this paper, we analyze changes in banks’ lending to Russian companies’ in 2020. Our analysis shows that in 2020 the volume of new ruble credits increased relative to the level of the previous year. At the same time, there were changes in loans’ structure, which are explained by the effect of government lending support programs that began in May—June 2020. This fact indicates that a large number of firms made use of these programs last year, partially or fully covering temporary liquidity needs in the period of significant decrease in demand and revenue. Outside of the government support programs, the structure of market lending did not change significantly in 2020 compared to 2019. Banks prefer to lend to more productive companies: we see that the volume of credits to high productive firms was at the same level as in 2019. This means that efficient firms that should be drivers of economic recovery did not have problems with access to credit in 2020.
In times of crisis, events are moving fast and standard macroeconomic statistics published with a lag cannot quite keep pace with the changing situation. During such periods, there is an increasing need to use high-frequency indicators that allow virtually real-time monitoring of economic activity. In many countries, this is achieved by using financial transaction data. In this paper, we present a methodology for the current analysis of sectoral financial flows in the Russian economy based on data from the Bank of Russia payment system. We use the information on the dynamics of average daily payments for each class of OKVED 2 (the Russian National Classifier of Economic Activities) to develop high- frequency indicators of economic activity, which have been published on the Bank of Russia website since April 2020. We also tentatively discuss the potential of financial transaction data in terms of improving the tools for short-term forecasting of business activity dynamics and solutions to other research problems.
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