In order to carry out the relevant analyses, it was necessary to complete the tweets database. In order to eliminate the problem of randomness (for each organisation, there may be events that could cause unusually high activity for the organisation on Twitter for a short time) a sufficiently long period was used for the analysis. In the range from 15 July to 12 October 2017 (i.e. 90 days), each day of the week was randomly selected twice (i.e. two Mondays, two Tuesdays, etc.). If the selected testing days were immediately following one another
equilibrium. When cross-country differences are concerned, the countries with a greater propensity in both human and physical capital will be relatively richer given that this set of countries is experiencing the same rate of labour-augmenting technological process. Mankiw, Romer and Weil (1992) in addition tested the Solow model empirically as well. They found that a significant part of the income differences across countries can be explained by the differences in human and physical capital investment behavior. Then, the immediate implication is that attention should be
Janusz Kudła, Katarzyna Kopczewska, Agata Kocia, Robert Kruszewski and Konrad Walczyk
-term equilibrium, solving dynamic optimisation problem with continuous time. In other words, we are looking for optimal long-term tax policy of the government aiming to sustain fiscal solvency, contingent on the values of chosen parameters. We assume capital income to be partially or fully shifted abroad as a consequence of tax avoidance strategies of economic agents. We believe that this better reflects the limited mobility of physical capital and the treat of possible income shifting. For example, one can expect the latter to intensify during an insolvency crisis when fast
also changed, by which one means to multiply one type of wealth that does not take place at the expense of others. The richness of the nation consists of, inter alia, natural wealth and indirectly the richness of nature, anthropogenic material and financial wealth, physical and intellectual human wealth as well as social, cultural and institutional richness ( Poskrobko 2011 , 9). Therefore, taking into account external effects, especially in large projects interfering with the environment is of key importance for achieving long-term social and ecological goals.
school standardized tests ( Case and Paxson, 2008 ), and lower earnings ( Black et al., 2007 ).
From an intrahousehold bargaining perspective, these findings suggest a rejection of the unitary model of the household, whereby a household is assumed to act as a single economic unit, in favor of collective models of intrahousehold allocation (see Vermeulen  for a survey of the collective approach and Vermeulen  for a comparative analysis of the empirical validity of the two competing approaches). In households where mothers are absent due to labor
Alan Turing – independently from each other – discovered that any process of formal reasoning – such as problems in economics and management described above – can be simulated by digital machines. In other words, the difference between a computer and a brain is one in degree, not in principle. Turing (1950) later argued that there might be a time when humans would no longer be able to distinguish between interacting with another human or a digital machine, passing the so-called “Turing test”. Moreover, indeed, in light of recent experiences by leading AI firms
different methodological settings that have been used in the literature so far, for example, with respect to field classifications, measures of educational standards, or the list of covariates. For example, in the analysis of Green and McIntosh (2007) , who make a quite detailed distinction between 12 educational fields, degrees in Physical sciences and in Computing are estimated to lower the overeducation probability significantly relative to the reference category Business and Management Studies. The insignificance of the field Math explain the authors by the fact that
Barunik, J., Aste, T., Di Matteo, T., Liu, R. (2012). Understanding the Source of Multifractality in Financial Markets. Physica A, 391(17), 4234-4251.
Bree, D., Joseph, J. (2013). Testing for Financial Crashes using the Log Periodic Power Law Model. International Review of Financial Analysis, 30(C) , 287-297.
Drożdż, S., Grummer, F., Ruf, F., Speth, J. (2003). Log-periodic Self-similarity: an Emerging Financial Law? Physica A, 324, 174-182.
Drożdż, S., Kwapień, J., Oświęcimka, P. (2008
.H. (1976). The Cobb-Douglas production function once again: Its history, its testing, and some new empirical values. Journal of Political Economy, 84(5), 903-915.
 Gauri, D.K. (2013). Benchmarking retail productivity considering retail pricing and format strategy. Journal of Retailing, 89(1), 1-14.
 Gujarati, D.N., Porter, D.C. & Gunasekar, S. (2017). Basic econometrics (5th ed.). New York: McGraw-Hill.
 Hossain, M. M., Majumder, A. K., & Basak, T. (2012). An application of non-linear Cobb-Douglas production function to selected
rest of the world in 1999−2011. Together with the standard gravity variables, our model controls for the technology gap and the difference in factor endowments of the trade partners. Following Santos Silva and Tenreyro (2006) , we estimate the model by PPML for the EU-15 and V4 separately. The estimation results show that while for the EU-15, human capital accumulation is statistically significant and export flows increase with similarity in physical capital accumulation of the trade partner; for V4, instead of similarity, the difference in physical capital stock