The article gives the analysis of the applicability of the concepts "labour productivity" and "productivity of an economic system" at different stages of creating added value in the new industrialization. The main tools for managing productivity have been identified and those that can be applied in short term and with minimal costs by identifying internal production reserves have been selected. The structuring of short-term and long-term goals of increasing labour productivity has been carried out, and tools for managing labour productivity that are effective in short term have been proposed. On the basis of the study of the level and dynamics of labour productivity at some enterprises of the machine-building complex of Samara region and with the use of economic and statistical methods of analysis, namely, time series analysis, index method and factor analysis forecast tools have been developed. The constructed econometric model of the correlation dependence of labour productivity on the capital productivity index has a high degree of approximation and can be recommended as a model for forecasting labour productivity at industrial enterprises. This is especially important when developing a strategy for the transition to digital industrialization.
Retail trade turnover represents one of the most fundamental socio-economic indicators. Constant monitoring of such indicators has a pivotal role in modernization of the Russian economy. Retail trade turnover reflects countries’ economic capacity and standard of living. This paper proposes a statistically significant econometric model of the retail trade turnover dependence with respect to different factors. Such factors as consumer prices index, unemployment level and average monthly designed salary were taken as the explanatory variables. The variables were selected based on Granger causality test and time series analysis of several socio-economic indicators. For each explanatory variable, a statistically significant model ARIMA (1, 1, 0) was constructed, with the help of which the predicted values of the explanatory variables for October, November and December 2019 were calculated, which were used to forecast the retail turnover for these months.
This is an Open Access article distributed under the terms of the Creative Commons Attribution-Noncommercial 4.0 Unported License, permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
Assessment of financial sustainability is a key instrument that every company should use to successfully operate in the contemporary marketplace. In this paper profit was chosen as one of the sustainability indices and binary choice model logistics regression model (logit model) was built for that index. The research data for this study is drawn from accounting statements of a textile industry business in Samara city. A combination of econometric approaches was used in the data analysis. Binary choice models were adopted in this research. Then those models were estimated for validity. Also scenario forecasts methodology was employed in this study. Several logit models with a set of explanatory variables were constructed. After the comparison of those models the preferred one was determined. Based on that model a scenario for profits was forecasted including both the worst-case and the best-case ones. The average-case scenario forecast was also made.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.