Mohsen Mehrara, Ghasem Elahi,
Volume 10, Issue 38 (12-2019)
Abstract
The purpose of this article is to examine the impact of education and work experience on earning. For this purpose, Mincer’s wage equation, quantile regression estimation method and the microdata from Iranian survey of household income and expenses in 2016 have been used. Estimation results show that education returns are positive in all income quantiles, and education in lower-income quantiles has a stronger positive effect than in higher-income quantiles. Also, the average experience have a positive effect on the earnings of individuals, with a stronger effects in low-income quantiles than high-income quantiles. Gender coefficients show that female earnings in all income quantiles are much lower than males, but this negative effect was much bigger in lower-income quantiles, implying gender-based discrimination against women in low-income quantiles. According to Machado and Mata's decomposition, gender discrimination (against females) was estimated, -30% in the first decile, and -4.5% in the ninth decile. Women's education has narrowed the gap somewhat on behalf of women. According to the results, education efficiency in Iran is far lower than many other countries in the world. Therefore, it is necessary to reform educational structures, in particular to guide them towards labor market needs and economic benefits.
Mr Nader Hashemnezhad, Dr Sajjad Barkhordari, Dr Ghahreman Abdoli,
Volume 14, Issue 52 (9-2023)
Abstract
Bitcoin is the leader of cryptocurrencies and has the largest market value as a digital asset in most international investment portfolios. However, compared to traditional assets, the nature of this cryptocurrency is not clear from a behavioral perspective. Examining this by following the behavior of the distribution tail or limit behaviors is one of the methods that can help researchers about the nature of this cryptocurrency, because this corresponds to the investigation of limit behaviors and in critical times of this currency. In this regard, this research has used quantile regression to estimate CAViaR models. In addition, to study the effect of each variable on the Bitcoin trend, the GARCH approach has also been used.
The results of this research for the daily period from 2018 June 26 to 2022 May 11, Wednesday, showed that by analyzing the 5% percentile quantile regression, examining the behavior of the right tail of Bitcoin distribution, the behavioral similarity of this currency with all the investigated assets is confirmed. This shows that in a situation where the returns of traditional financial markets are positive and the markets are rising, the behavior of cryptocurrencies aligns with the general behavior of the markets. However, examining the behavior of the left tail of the distribution of the variables shows that Bitcoin has no similarity in behavior with the rest of the traditional assets. In other words, when markets are bearish, Bitcoin's behavior is not aligned with traditional markets. However, the return of the homogenous index does not affect the trend of Bitcoin, which was predictable due to the non-compliance of domestic financial markets with international markets due to Iran's economic isolation and international sanctions. Therefore, until the period investigated by this study, Bitcoin has shown a behavior other than known assets and investing in it is still facing the risk of capital burnout, so it is recommended that investors observe risk management in the arrangement of their portfolios.
