SV Euroopa Liidu rahastatud projektid

Selle valdkonna püsiv URIhttps://hdl.handle.net/10062/58019

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Sirvi SV Euroopa Liidu rahastatud projektid Autor "Alfieri, Luca" järgi

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  • Pisipilt
    Kirje
    Effects of automation on the gender pay gap: the case of Estonia
    (2021) Pavlenkova, Ilona; Alfieri, Luca; Masso, Jaan
    This paper investigates how investments in automation-intensive goods affects the gender pay gap. The evidence on the effects of automation on the labour market is growing; however, little is known about the implications of automation for the gender pay gap. The data used in the paper are from a matched employer-employee dataset incorporating detailed information on firms, their imports, and employee-level data for Estonian manufacturing and services employers for 2006–2018. We define automation using the imports of intermediates embedding automation technologies. The effect of automation is estimated using simple Mincerian wage equations and the causality of the effect is validated using propensity score matching. We find that introducing automation enlarges the gender pay gap. The negative effect of importing automation-intensive goods for female employees is about two to four percentage points larger than for male employees. The propensity score matching confirms that the introduction of automation has a higher causal effect on the wages of male employees than female employees.
  • Pisipilt
    Kirje
    The effects of the ECB communications on financial markets before and during COVID-19 pandemic
    (2022) Alfieri, Luca; Eratalay, Mustafa Hakan; Lapitskaya, Darya; Sharma, Rajesh
    The paper aims to estimate the effects of the European Central Bank communications on the sectoral returns of STOXX Europe 600 from 2013 to 2021. Previous literature has investigated the effects of communications of central banks and checked their effects on macroeconomics and financial data. New opportunities offered by text mining analysis allow us to find new insights into these aspects. However, studies focusing on how text mining indices derived from central banks’ communications can affect different financial sectors are more limited. In this paper, we use different sentiment and topic indices derived from the European Central Bank’s speeches. The paper shows how these different topics and sentiment indices affect the returns on different financial sectors. Our results indicate that the topic of communications is more influential on returns of sectoral indices than the type of communications. Moreover, we find that monetary policy and financial stability topics are the most relevant. We also find that during the COVID-19 time, the number of negative speeches is relevant for almost all the sectoral index returns.