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on Confederation of Independent States |
| By: | Druzhinin, Pavel; Molchanova, Ekaterina; Podlevskih, Yulia |
| Abstract: | For many years the Department MFRD of IE KarRC RAS, together with scientists of PetrSU, UWF and medical institutions, have been conducting comprehensive research at the intersection of economics, demography, medicine and ecology. Studies were supported by 10 RFBR and RGNF grants. This article examines the impact of the COVID-19 pandemic on mortality in Russian regions. The purpose of the study is to identify the factors that during the pandemic have contributed to a significant mortality increase in Russian regions. The work assessed the impact of socio-economic, demographic, medical and geographical indicators at different stages of the pandemic on the level of morbidity and mortality, taking into account the characteristics of regions. It is shown that in general, the increase in mortality has been facilitated by a higher shares of retirees and urban population, the location of the region in the center of the country and a drop in citizens' incomes in 2020 (compared to 2019). The first (spring) wave of the pandemic in Russia was relatively low, largely due to the imposed strict restrictions. Therefore, only in some months did the lower availability of hospital beds of the population of the region contribute to the increase in mortality. The highest increase in the mortality rate was in the regions of the Central Federal District. During the summer period, the incidence decreased and restrictions were relaxed, but increased mobility of citizens led to an increase in mortality in the Volga Federal District regions, through which the main roads and railways pass. By the end of summer, the availability of doctors and beds in the region became significant factors influencing the increase in mortality, in addition to the three main indicators. In the fall, the second wave began, during this period the increase in mortality depended on the location of the region, the proportion of the urban population and the availability of doctors in the region. In some months, the level of income of the population and its change in 2020 had a significant impact. In December, the situation in the Volga Federal District and the Siberian Federal District stabilized, while mortality in the Southern regions of the country began to grow. The research was carried out by scientists of the IE KarRC RAS and RIH and can be used in the field of regional medical and demographic policy to increase the effectiveness of management decisions and preserve the public health of the nation. |
| Keywords: | demography; mortality; public health; socio-economic factors; modelling; region; pandemic; COVID-19 |
| JEL: | I15 |
| Date: | 2021–03–10 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:109034&r= |
| By: | Olivia Jin; William Pyle |
| Abstract: | A growing literature connects labor market hardships to stronger preferences for government welfare and redistribution programs. Potential preference shifts with respect to other types of state involvement in the economy, however, have gone unexplored. We draw on both longitudinal and pseudo-panel data from Russia to explore how labor market hardships relate to preferences for public sector employment and employers. In fixed effects specifications, we demonstrate that feelings of job insecurity, experiences with wage arrears, and spells of unemployment all increase the attractiveness of work in the public sector. Pseudo-panel data provide only mixed evidence as to whether such effects endure over the longer run. |
| Keywords: | economic shocks, personal experience, public employment, political preferences |
| JEL: | H10 J45 J60 P35 |
| Date: | 2021 |
| URL: | http://d.repec.org/n?u=RePEc:ces:ceswps:_9229&r= |
| By: | Байкулаков Шалкар // Baikulakov Shalkar (Center for the Development of Payment and Financial Technologies); Белгибаев Зангар // Belgibayev Zanggar (National Bank of Kazakhstan) |
| Abstract: | Данное исследование представляет собой попытку оценки кредитоспособности физических лиц с помощью алгоритмов машинного обучения на основе данных, предоставляемых банками второго уровня Национальному Банку Республики Казахстан. Оценка кредитоспособности заемщиков позволяет НБРК исследовать качество выданных кредитов банками второго уровня и прогнозировать потенциальные системные риски. В данном исследовании были применены два линейных и шесть нелинейных методов классификации (линейные модели - логистическая регрессия, стохастический градиентный спуск, и нелинейные - нейронные сети, k-ближайшие соседи (kNN), дерево решений (decision tree), случайный лес (random tree), XGBoost, наивный Байесовский классификатор (Naïve Bayes)) и сравнивались алгоритмы, основанные на правильности классификации (accuracy), точности (precision) и ряде других показателей. Нелинейные модели показывают более точные прогнозы по сравнению с линейными моделями. В частности, нелинейные модели, такие как случайный лес (random forest) и k-ближайшие соседи (kNN) на передискредитированных данных (oversampled data) продемонстрировали наиболее многообещающие результаты. // This project is an attempt to assess the creditworthiness of individuals through machine learning algorithms and based on regulatory data provided by second-tier banks to the central bank. The assessment of the creditworthiness of borrowers can allow the central bank to investigate the accuracy of issued loans by second-tier banks, and predict potential systematic risks. In this project, two linear and six nonlinear classification methods were developed (linear models – Logistic Regression, Stochastic Gradient Descent, and nonlinear - Neural Networks, kNN, Decision tree, Random forest, XGBoost, Naïve Bayes), and the algorithms were compared based on accuracy, precision, and several other metrics. The non-linear models illustrate more accurate predictions in comparison with the linear models. In particular, the non-linear models such as the Random Forest and kNN classifiers on oversampled data demonstrated promising outcomes. |
| Keywords: | потребительские кредиты, машинное обучение, банковское регулирование, стохастический градиентный спуск, логистическая регрессия, k-ближайшие соседи, классификатор случайных лесов, дерево решений, gaussian NB (Гауссовский наивный Байесовский классификатор), XGBoost, нейронные сети (многослойный персептрон), consumer credits, machine learning, bank regulation, stochastic gradient descent (linear model), logistic regression (linear model), kNN (neighbors), random forest classifier (ensemble), decision tree (tree), gaussian NB (naïve bayes), XGBoost, Neural network (MLP classifier) |
| JEL: | G21 G28 E37 E51 |
| Date: | 2021 |
| URL: | http://d.repec.org/n?u=RePEc:aob:wpaper:21&r= |
| By: | Vitaly Khrustalev; Mattia Masolletti |
| Abstract: | The article analyzes the legal framework regulating the legal provision of transport security in Russia. Special attention is paid to the role of prosecutor's supervision in the field of prevention of crimes in transport. |
| Date: | 2021–08 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2108.11732&r= |
| By: | Lomonosov, Daniil |
| Abstract: | World oil prices in 2020 have undergone tangible shocks, which are associated primarily with two events - the collapse of the OPEC+ deal and the coronavirus pandemic. Based on the BVAR model of the oil market, the quantitative role of these events in the dynamics of oil prices was assessed, and the channels of their influence through structural shocks were identified. In the first half of 2020, at the time of the greatest decline, lack of consistency between oil producing countries, expectations of further growth in oil supply and uncertainty about a recovery in global demand played a dominant role, reducing oil prices by 86% at the peak of the decline. The direct contribution of the decline in the economic activity due to restrictive measures was more modest, reducing the price by 27.7% in April 2020. However, after reaching new agreements within the OPEC+ deal and some adaptation to the new conditions of a number of countries, the direction of the dynamics of oil prices changed. The main factor behind the rise in prices in the second half of the year, according to the model, is a noticeable decline in world oil production, which on average has increased the price of oil by 20.8% since May. |
| Keywords: | Oil prices; pandemic; OPEC+; global economic activity shock; oil supply shock; specific oil demand shock |
| JEL: | C32 E32 Q43 |
| Date: | 2021–07–11 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:109319&r= |