DOI: 10.32609/j.ruje.10.128611

Authors: Anna A. Maigur

Abstract: Predictions of the individual unemployment duration will allow to distribute target support while searching for a job more effectively. The paper uses survival models to predict the unemployment duration based on data from Russian employment centers in 2017–2021. The dataset includes socio-demographic characteristics, such as age, gender, education level, etc., as well as the job search duration. Two models’ forecasts are investigated: the proportional and the non-proportional hazards models. Both models take into account censored data, but only the second one captures nonlinear dependencies and the disproportionate influence of independent variables over time. The forecast quality is estimated with the C-index, equality of which to 1 indicates the most accurate forecast. The highest index value is demonstrated by the non-proportional hazards model (0.64). Moreover, it was found that variable that contributes the most to the prediction quality is region of a job search so that job-search time is heterogeneous among different regional labour markets. To sum up, forecast quality is quite high and stable over time and the implementation of model forecasts by employment centers will increase their efficiency.

HTML

XML

PDF

DOI: 10.32609/j.ruje.10.113578

Authors: Inna S. Lola, Dmitry G. Asoskov

Abstract: This study investigates the utility of business uncertainty indicators as predictive tools for forecasting economic activity in the context of Russia. In an era characterized by global economic volatility and geopolitical shifts, understanding the dynamics of economic uncertainty and its impact on overall economic performance is of paramount importance. The study utilizes a comprehensive dataset based on the results of business tendency surveys in Russia, spanning the period from 2009 to the first half of 2024. Given the importance of uncertainty in shaping economic outcomes, the central research question of this study is: can uncertainty indicators predict business activity in Russia or not? To address this question, we compared two alternative approaches to calculating business uncertainty: the ex‑ante approach, which uses the business community’s assessments of future business trends to measure uncertainty as the dispersion of opinions expressed, and the ex‑post approach, which applies entrepreneurial assessments of both future and current trends to determine business uncertainty as the degree of deviation of entrepreneurial expectations from the real picture. National indicators and sectoral indicators were calculated for the mining and quarrying industry, manufacturing industry, construction, retail trade, wholesale trade and services. For most of the industries under consideration (except for the construction and service sector) and at the national level, the specifications of vector autoregression models that were effective for forecasting real indicators of economic activity, characterized by lower forecast errors compared to standard autoregressive models, were built. According to the results obtained, at the national level, when forecasting GDP, clear preference should be given to the ex‑post indicator.

HTML

XML

PDF

DOI: 10.32609/j.ruje.9.83891

Authors: Henry I. Penikas, Ekaterina E. Vasilyeva

Abstract: The Basel Committee recommended the use of input-output tables to properly measure climate risks. However, the majority of previous studies only limits the use of input-output tables to carbon emissions and this is not applied in climate risk ratings. The existing climate (E) risk ratings (scores) was modified or transformed from Sustainalytics to the full climate risk scores using input-output tables. Positive relationship between credit risks and the full climate risk estimates at the industry level was identified, and this justifies the interest rate discount granted to firms in the green industries. Thus, for the purpose of lending the full degree of greenness derived from input-output tables should be considered, not substituting this by the easily observable and publicly available marginal climate risk ratings like those provided by Sustainalytics.

HTML

XML

PDF