Informality and Mobility: Evidence from Russian Panel Data
Informality is a defining characteristic of labour markets in developing and transition countries. This paper analyzes patterns of mobility across different forms of formal and informal employment in Russia. Using the Russian Longitudinal Monitoring Survey household panel we estimate a dynamic multinomial logit model with individual heterogeneity and correct for the initial conditions problem. Simulations show that structural state dependence is weak and that transition rates from informal to formal employment are not lower than from non-employment. These results lend support to the integrated view of the labour market.
In this paper, we compare age-earnings profiles between generations. Our empirical estimates are based on the Russia Longitudinal Monitoring Survey of HSE (RLMS-HSE) data, 1994–2015. Using the long time-series panel, we overcome age-period-cohort problem inherent in the linear cross-sectional models. The main result in this paper is comparison of monthly income of different cohorts of individuals (in constant prices), controlling for a wide set of explanatory variables.
The objective of this paper is to examine the relationship between bank characteristics, in particular
value, performance and volatility of bank stock returns, and its exposure to financial derivative contracts.
The study is based on 109 publicly traded European banks over the period from 2005 to 2010. The database
contains both accounting data from Bankscope and manually collected information from the notes to financial
statements. After controlling for bank-specific characteristics, time effects and cross-country differences,
we find that banks efficiently using hedging derivatives have a lower risk and a higher value. However,
this relationship becomes less pronounced or is inversed in the post-crisis period and concerns both
trading and hedging derivatives. For systemically important banks heavily involved in derivatives market
volatility of stock returns is higher and valuations are lower.We notice however that derivatives play second
fiddle to bank risk and performance. Our findings corroborate the importance of distinction of derivatives
by the purpose of use, which becomes less obvious for investors in the post-crisis period. Our results have
important policy implications, especially in the light of the recent debate over the necessity of separation of
risky banking activities from commercial bank branches (for instance, as proposed in Liikanen report) in an
attempt to reduce systemic risk. We emphasize the need for a higher transparency of disclosures regarding
hedge accounting and harmonisation of reporting formats across EU.
This research focuses on forecasting situations of impending financial crisis (with potential bankruptcy) in real economy enterprises. By the term potential bankruptcy is understood an enterprise’s financial situation where sum of short-term and long-term liabilities exceed its assets, which leads to a negative net equity. That, as a rule, is explained by accumulated uncovered losses (negative retained earnings). The consequences of such a situation could be very severe. The difference between asset value and borrowed funds approximately equals to net asset value - a key indicator of financial performance of a joint stock company. The decrease of net asset value below the authorized capital for several years leads either to its reduction, which happens rarely, or to a bankruptcy. Furthermore, under certain conditions, an enterprise must publish a notice concerning its net asset reduction and any creditor is entitled to demand early performance of the respective obligations, which, if the amount of debts is large, can become a serious problem and result in a real bankruptcy. A model of the statistical relationship between a set of financial indicators and a probability of bankruptcy is constructed in the article. We are focused on identifying of significant indicators and the functional form of their values binding with the probability. To solve these problems we use a logit regression-based class of models for panel data and an algorithm for an automatic model specification. It reduces the influence of human factor on determining its type and links it with attributes of the accumulated data. This research is based on 2012-2016 financial records of 463 Russian enterprises from the SPARK-Interfax database. The results of simulation allowed us to determine a set of indicators that significantly affect forecasting quality of potential bankruptcy possibility, as well as the nature of this effect.
This research focuses on the analysis of the stability of the Bank. The concept “stability” is defined here as the financial state of the farm that allow it under normal conditions to fulfill its liabilities to employees, other organizations and the state due to sufficient income and the matching of costs with revenues.
Values of some factors important for this analysis factors are free on the site Central Bank of RF in the open monthly financial reports of banks. Among such indicators are the indicators for which the Central Bank of the Russian Federation defines the normative values. The monitoring of implementation of these standards provides a very rough approach to the analysis of reliability of the Bank in the context of the probability of revocation of the license. At the same time, values of these factors are excellent measures of financial health of the Bank. They characterize the most important aspects of their activity.
The main task in this research is to analyze the character of statistical relationship between above pointes factors and the probability of license revocation. While the focus is on the strength and direction of influence. This enables the Bank management to increase efficiency and quality of decision-making about its management. At the same time, partners and customers can perform external rapid analysis of the Bank's reliability using a relatively small set of indicators.
Authors use several kinds of the model of binary choice to solve task in the research. The peculiarity of the models is the automatic selection of the functional form of the occurrence in them of the characteristics of the banks. To this end, the authors use the apparatus of fractional polynomials that allow you to select the model specification, which is the most adequate to the properties of the data.
The results of the evaluation of the models showed a high degree of agreement on the form of the occurrence in them of the characteristics of the Bank with their economic meaning and regulations of the Central Bank. Comparison of the quality of classic binary choice model for panel data with the quality of model based on generalized polynomial shows a clear advantage of the latter.
The study was conducted based on open reporting 887 of banks for the period from 01.01.2013 to 01.12.2015. This time interval was chosen in connection with the change of leadership of the Central Bank of the Russian Federation in mid-2013, resulting in a significant change in the nature of the challenges facing the financial system of the Russian Federation. It were taken statements, as applicable, in particular, launched and liquidated in this period banks.