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2016 | vol. 16, iss. 2 | 191--202
Tytuł artykułu

The Spatial Fay-Herriot Model in Poverty Estimation

Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
The development of a service economy and the more and more noticeable phenomenon of servicization have become inseparable elements in the evolution of the modern economy. The goal of this paper is to analyse the impact of servicization on selected economies, both in terms of GDP and employment structure, as well as on changes in foreign trade. The secondary, but still important aim is to examine the relationship between servicization and innovation processes. Based on the conducted research, it can be stated that the process of servicization occurs in both developed post-industrial economies and increasingly often in developing countries. Moreover, the analysis of the relationship between the general level of innovation in the economy and the degree of its servicization, showed that in many countries higher innovation is often associated with a stronger role of the service sector in the economy. The dynamics of structural change taking place in the "deagrarianization-deindustrialization-servicization" chain is therefore substantially influenced by technological progress.(original abstract)
Rocznik
Strony
191--202
Opis fizyczny
Twórcy
  • Poznań University of Economics, Poland
Bibliografia
  • Bivand, R.S., Pebesma, E.J., Gómez-Rubio, V. (2008). Applied Spatial Data Analysis with R. USA: Springer.
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  • Dehnel, G. (2003). Statystyka małych obszarów jako narzędzie oceny rozwoju ekonomicznego regionów. Poznań: University of Economics Publishing Establishment.
  • Drewnowski, J. (1977). Poverty: its meaning and measurement. Development and Change, 8, 183-208. DOI: 10.1111/j.1467-7660.1977.tb00736.x.
  • EEC (1985). On Specific Community Action to Combat Poverty (Council Decision of 19 December 1984), 85/8/EEC, Official Journal of the EEC, 2/24.
  • Fay, R.E., Herriot, R.A. (1979). Estimates of income for small places: an application of James- Stein procedures to census data. Journal of the American Statistical Association, 74, 269-277. DOI: 10.1080/01621459.1979.10482505.
  • Foster, J., Greer, J., Thornbecke, E. (1984). A Class of Decomposable Poverty Measures. Econometrica, 52 (3), 761-766. DOI: 10.2307/1913475.
  • Guadarrama, M., Molina, I., Rao, J.N.K. (2016). A comparison of small area estimation methods for poverty mapping. Statistics in Transition new series and Survey Methodology, Joint Issue: Small Area Estimation 2014, 17 (1), 41-66.
  • Horvitz, D.G., Thompson, D.J. (1952). A Generalization of Sampling Without Replacement From a Finite Universe. Journal of the American Statistical Association, 47 (260), 663-685. DOI: 10.2307/2280784.
  • Molina, I., Rao, J.N.K. (2010). Small area estimation of poverty indicators. Canadian Journal of Statistics, 38 (3), 369-385. DOI: 10.1002/cjs.10051.
  • Panek, T. (2011). Ubóstwo, wykluczenie społeczne i nierówności. Teoria i praktyka pomiaru. Warszawa: Oficyna Wydawnicza SGH.
  • Pratesi, M., Salvati, N. (2008). Small area estimation: the EBLUP estimator based on spatially correlated random area effects. Statistical Methods and Applications, 17 (1), 113-141. DOI: 10.1007/s10260-007-0061-9.
  • Pratesi, M. (ed.) (2010). Final small area estimation developments and simulation results. Small Area Methods for Poverty and Living Conditions Estimates Project.
  • Rao, J.N.K., Molina, I. (2015). Small Area Estimation. Canada: Wiley & Sons.
  • Sen, A. (1992). Inequality reexamined. Cambridge: Harvard University Press.
Typ dokumentu
Bibliografia
Identyfikatory
Identyfikator YADDA
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