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Blockchain is a concept that tends to revolutionize the world of finance in a technological leap that allows fast, secure and decentralized transactions. The Blockchain technology is used in virtual coins (bitcoin) conditions, with a high innovation potential, applicable in various areas, with the advantage of storing databases, resulting in an unprecedented level of transparency in the private or public area. Interestingly, under the bitcoin conditions, the black chain system uses a decentralized peer-to-peer payment system. Practically, the bitcoin can be considered as the most appropriate triple game accounting system. All of these considerations are developing in the big data era, which is defined as a large, diverse, high-volume information base requiring new forms of processing. Big data is important for businesses because based on these, strategic and marketing decisions can be made to optimize the activity in the market conditions and consumer preferences. European Union directives provide for measures to ensure the development of all states and, in this context, the community. At the same time, some measures provide for a more accelerated development for states with a low accession. For this, funds have been made from which important amounts are allocated to these states. The complex development of the European Union aims, in fact, to improve the quality of life (standard of living) in all Member States. At the European community level there are databases usable in economic analyzes. Also, Eurostat is the institution with the most complex databases. Recently, the Conference of the Directors of the Institute of Statistics in the European States analyzed the perspective of calculating the indicators in the context of the big data to be implemented. The article focuses on the concrete study of the use of large data in the calculation of the indicators that underlie the comparability between the EU Member States.

and medium enterprises. Preluat de pe Pace, R. k., & LeSage, J. P. (2008). A spatial Hausman test. Economics Letters , 282-284. Ross, A. (2011). Regional Determinants of Entrepreneurship in a Small Economy: Panel Data Evidence from Scotland. The European Regional Science Association. Silva, D., Cerqueria, A., & Brandao, E. (2016). Portugese Startups: a success prediction model. FEP Working Papers . Valliere, D., & Peterson, R. (2009). Entrepreneurship and economic growth: Evidence from emerging

References Bove, V., Elia, L., (2017) Migration, Diversity and Economic Growth, World Development, 89, DOI: 10.1016/j.worlddev.2016.08.012 Brunow, S., Hijkamp, P., Poot, J., (2015) The impact of international migration on economic growth in the global economy, Institute for Employment Research, Nuremberg, Germany, 1027-1075, DOI: 10.1016/B978-0-444-53768-3.00019-9 Europe 2020 Strategy (2019), Retrieved from

Added Taxation . Retrieved from on 5th March, 2019. Giordano, R., Momigliano, S., Neri, S. & Perotti, R. (2007). The effects of fiscal policy in Italy: Evidence from a VAR model. European Journal of Political Economy , 23, 707-733. Harvey, A., (1990). The econometric Analysis of Time Series (2 nd ed.). Cambridge: The MIT Press. Kalaš, B., Mirović, V. & Milenkovic, N. (2018). The relationship between taxes and economic growth: Evidence from Serbia and Croatia. The European Journal of Applied Economics , 15.17-28.10.5937/EJAE15-18056, 17


The phenomenon of globalization has greatly influenced migration in recent years in the European Union. In this article we aim to analyze the benefits of migration in the economy by emphasizing the impact of remittances on the economic development of a country. Remittances are considered as an external source of important, stable funds that help the economic development of a country. We identify also the macroeconomic determinants of remittances. For the statistical and econometric analysis of these factors, we have chosen to use the Panel Data Regression for the countries of the European Union. To analyze the benefits of remittances, the most appropriate macroeconomic indicator is GDP. So in the first part of the article we will present the impact of globalization and migration on remittances, and in the second part we will highlight the economic growth through the presence of remittances. This article examines the role of migrants as a particular segment of the market and as a resource for development. All aspects to be analyzed will outline an overview of population emigration and factors that influence the development of the economy at a time when globalization is on the rise.


The paper attempts to present a comprehensive picture of the main characteristics of Romanian university-educated youths in their journey to employment. The persisting demographic decline and significant labor shortages point out to the importance of having an effective transition from school to work (as reflected in several Europe 2020 policy targets) so that youth and young adult cohorts are able to contribute to the Romanian economy to the full extent of their abilities. Analysis of the latest data available reveals that Romanian university graduates’ path to employment often involves a complete switch from study to work. Despite a lack of previous work experience, they enjoy high employment rates, relatively long job tenure that starts on, or shortly after, graduation, and have jobs that match their education. They also tend to be proactive in their job search and over 90% are not willing to change residence in order to start employment. Inactivity patterns that consider both work and education, and early leavers from education show significant fluctuations during the economic cycle. Results indicate the strong and weak areas with respect to labor market integration of youths and young adults, and can provide a starting point for policies to optimize their successful integration. It also points out to potential research areas to address key aspects of transitions from school to work that may clarify unsolved issues and guide effective policy interventions.


The economic growth of the national economy, within international bodies, as well as in the European Union, is a priority under the present conditions. Capital placement in geographic areas is based on effective opportunity studies. Such an analysis involves access to databases that satisfy the criteria for selecting the place of investment. At the same time, the media interested in attracting national or international investments can take such a decision on the basis of the data that will lead it to the optimal decision. Usually study of the market and the investment fields is insufficient and as such the effectiveness of the project is reduced. Under the very big data base, investors will have the chance to have information that needs to be used in a short time, and such opportunities need to be endowed with ultra-modern information systems. The issue of national and international investment is of utmost interest for any Member State of the European Union. In this respect, major projects will be developed involving as many member countries as possible, provided that everyone has the supremacy (to provide benefits) in a particular project sub-domain. Only specialization can provide the path to a viable and yet prolific economic and scientific cooperation. Through its directives, the European Union pursues both the individual development of each country and, above all, the complex development of the whole of the Union. In the big data era, investments, attracting them or entering into intra-Community economic cooperation provide a much faster course.

. Smith, M. K. (2001). Peter Senge and the learning organization, The encyclopedia of informal education ,45-56. Venkatraman, N., Subramaniam, M.,(2002). Theorizing the future of strategy: Questions for shaping strategy research in the knowledge economy The Handbook of Strategy and Management , Sage Publications. Visco I. (2001). Ageing Population Economic Issues and Policy Challenges Conference OECD “Economic Policy for Ageing Societies ”, Paris, OECD, 4-10.

perspectives (pp. 1-21). Hershey, PA: IGI Global. Bratianu, C. (2013). The triple helix of the organizational knowledge. Management Dynamics in the Knowledge Economy , 1(2013), 207-220. Ernst & Young (2011). The sustainability revolution. Excellence in sustainability reporting awards 2011. Retrieved from . Nonaka, I., & Takeuchi, H. (1995). The knowledge-creating company. How Japanese companies create the dynamics of innovation . Oxford: Oxford

, Mona Nasr, A Proposed Churn Prediction Model, International Journal of Engineering Research and Applications (IJERA), 2012. Lejeune M., Measuring the impact of data mining on Churn Management, 2001. Maer Matei M. M., Dumitrache A., Churn analysis in a Romanian telecommunications company, The 17th International Conference on Informatics in Economy Education, Research and Business Technologies, Iași, România, 17-20 Mai, . Michael J. A. Berry, Gordon S. Linoff, Data Mining Techniques: For Marketing, Sales and Customer Relationship