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The Shadow Economy is Retreating: An Example of Georgia

References Gabidzashvili, B., Kbiladze, D. Tactics and Strategy of Businessmen behavior in Economically Uncertain Conditions, p. 223. Jie, S.W., Tat H.H.,Rasli.(2011). Underground Economy: Definition and Causes, Business and Management Review, Vol. 1 (2) p. 14-24; Lequiller, F., Blades, D. (2014). Understanding National Accounts, OECD Publishing, Second Edition, p. 516. National Statics Office of Georgia. (2000). Activity of restaurants in Tbilisi City and near-by districts (Sample Statistical

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Networking Society, Network-Based Economy and the Processes of Global Social, Economic, and Technological Changes

References Appiah, K.A. 2009. Cosmopolitanism: Ethics in a World of Strangers - London, Penguin Books, 196 p. Castells, M. 2000-2004. The Information Age, Economy, Society and Culture : vol.1 - The Rise of the Network Society, 2000; vol.2 - The Power of Identity, 2004; vol.3 - End of Millennium, 2000 - Oxford, Malden MA, Blackwell Chossudovsky, M. 2003. The Globalisation of Poverty and the New World Order - Montreal, Global Research - 377 p. Currie, W. 2000. The Global Information Society

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The Knowledge Economy and the Activation of Scientific and Technological Progress: Contemporary Challenges

References Altvater, E., Mahnkopf, B. (1996), Limits of Globalisation: Politics, Economy and Ecology in the World Society. Muenster: Verlag Westfalisches Dampfboot. Boldrin, M., Canova, F. (2001), “Inequality and Convergence in Europe’s Regions: Reconsidering European Regional Policies,” Economic Policy, Vol. 16, Issue 32, p. 205. Chortareas, G. E., Pelagidis, T. (2004), “Trade Flows: A Facet of Regionalism or Globalisation?” Cambridge Journal of Economics, Vol. 28, pp. 253-271. Currie, W. (2000

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Determinants of Shadow Economy in Eastern European Countries

References Alm, J., and Embaye, A., 2013. Using Dynamic Panel Methods to Estimate Shadow Economies Around the World, 1984–2006 . Working Papers 1303. Tulane University, Department of Economics. Retrieved from Apel, M., 1994. An expenditure-based estimate of tax evasion in Sweden . Working Paper 1994 (1). Department of Economics, Uppsala University. Uppsala. AT Kearney, 2013. The Shadow Economy in Europe, 2013.

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Modelling the Impact of External Shocks on Economy of Ukraine: Dsge Approach

Economy, 2005, vol. 113, no. 1. p. 1-45. Clarida, R., Gali J., Gertler M. (1999). The Science of Monetary Policy: A New Keynesian Perspective. Journal of Economic Literature, Vol. XXXVII (December 1999), p. 1661-1707. Clarida, R., Gali J., Gertler M. (2001). Optimal Monetary Policy in Closed Versus Open Economies: An Integrated Approach. NBER Working Paper Series №8604. Retrieved from: Del Negro, M., Schorfheide, F. (2002). Priors from General Equilibrium Models for VARs: Forecasting and

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Fiscal Impacts on Output in a Small Open Economy: The Case of Albania

References Arnold, J., 2008. Do Tax Structure Affect Aggregate Economic Growth? Empirical Evidence from a Panel of OECD Countries Vol. 643. (pp. 29). DOI: Barro, R. J., 1990. Government Spending in a Simple Model of Endogenous Growth. Journal of Political Economy, 98(5 Part 2), S103-S125. DOI: Durmishi, A., 2015. Albania's Fiscal Goals, Budgetary Balance, Government Debt. Anglisticum Journal (IJLLIS), 4(9-10), 136-140. Gray, C

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The Impact of the Ease Doing Business Indicators on Foreign Direct Investment in the European Transition Economies

direct investment. Journal of International Business Studies , 34 (1), 19-39, . Göndör, M., & Nistor, P. (2012). Fiscal policy and foreign direct investment: evidence from some emerging EU economies. Procedia-Social and Behavioral Sciences , 58 , 1256-1266, . Hassan, Z., & Basit, A. (2018). Ease of Doing Business and Its Impact on Inward FDI. International Monetary Fund. (2019, October 1). Retrieved from . Kasongo, K., B., 2013

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Undeclared Work in Poland – Characteristics and Prevalence

., Peterlik, M., & Wyżnikiewicz, B. (2016). Szara strefa w polskiej gospodarce w 2015 roku. Warszawa. GUS. (2010). Rachunki narodowe według sektorów i podsektorów instytucjonalnych 2005-2008, (August), 1-38. GUS. (2014). Rachunki Narodowe według sektorów i podsektorów instytucjonalnych w latach 2009-2012. Hussmanns, R. (2004). Measuring the informal economy: From employment in the informal sector to informal employment. Working Paper ILO. Jütting, J., Parlevliet, J., & Xenogiani, T. (2008). Informal Employment

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Perspectives of the development of world economy in the blockchain conditions and Big Data


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.

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Survey vs Scraped Data: Comparing Time Series Properties of Web and Survey Vacancy Data

specific case study using data from an NSO to benchmark a very large data set collected from the Internet, with the aim of shedding light on the relationship between the population collected online and the population at large as inferred by traditional scientific methods. More specifically, we focus on the number of vacancies in the economy inferred by survey methods by a statistical office compared to the number of vacancies obtained from web crawling. In economics research, labor markets are among the areas in which Big Data is increasingly being used ( Choi and

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