Alina Jędrzejczak , Jan Kubacki

(Angielski) PDF


The main aim of the paper is a statistical analysis of changes in household income distribution at the regional level in Poland taking into account the impact of government spending on social assistance. Various linear models (incorporating relations for spline functions) and the vector autoregression models (VAR) were used in the research. The linear models formulated for voivodships (NUTS 2) contained a dichotomous variable with values dependent on the existence of social programmes introduced by the Polish government in 2016. An independent variable representing expenditure per capita on social assistance specified for the national level was also used. The results for these models were compared with the findings of both microsimulation studies obtained on the basis of the Household Budget Surveys (HBS) and the total assessment of the social programmes, and they indicate a significant influence of social assistance expenditure on the amounts of available income. The calculations were conducted using data from the Statistics Poland databases: Local Data Bank (and in particular, data from the Polish HBS for the years 2000–2018) and from the Macroeconomic Data Bank, and from the annual reports on the implementation of the state budget. They were performed by means of the R-project environment and R-commander overlay, using the lm function as well as the vars module for the R-project environment. The study also involved using the Gretl package.


available income, econometric models, Vector Autoregression Model, R-project, Gretl, expenditures on social assistance, household income


C01, C21, C22, D31, E64, H53, H55


Baiocchi, G., Distaso, W. (2003). GRETL: Econometric software for the GNU generation. Journal of Applied Econometrics, 18(1), 105–110. .

Blanchard, O. J., Quah, D. (1989). The Dynamic Effects of Aggregate Demand and Supply Disturbances. The American Economic Review, 79(4), 655–673.

Brzeziński, M., Najsztub, M. (2017). Wpływ programu „Rodzina 500+” na dochody gospodarstw domowych, ubóstwo i nierówność. .

Desaling Germay, M. (2016). Modeling and Forecasting Unemployment Rate in Sweden using various Econometric Measures. .

Domański, C., Pekasiewicz, D., Baszczyńska, A., Witaszczyk, A. (2014). Testy statystyczne w procesie podejmowania decyzji. Łódź: Wydawnictwo Uniwersytetu Łódzkiego.

Ekanem, N. F. (2005). A VAR Model of the Relationship Between the GDP Growth and Unemployment Rates. Journal of Business & Economics Research, 3(8). .

Fox, J. (2005). The R Commander: A Basic Statistics Graphical User Interface to R. Journal of Statistical Software, 14(9), 1–42. .

Fox, J. (2017). Using the R Commander: A Point-and-Click Interface or R. Boca Raton FL: Chapman and Hall/CRC Press.

Fox, J., Bouchet-Valat, M. (2018). Rcmdr: R Commander. R package version 2.5-1.

GUS. (2004–2018). [Koniunktura konsumencka – informacje sygnalne].,1,95.html .

Jappelli, T., Pistaferri, L. (2010). The Consumption Response to Income Changes. Annual Review of Economics, Annual Reviews, 2(1), 479–506. .

Jarque, C. M., Bera, A. K. (1987). A Test for Normality of Observations and Regression Residuals. International Statistical Review, 55(2), 163–172. .

Jędrzejczak, A., Pekasiewicz, D. (2019). Analiza nierówności dochodowych i ubóstwa w Polsce w gospodarstwach domowych z dziećmi. Przegląd Statystyczny, 66(2), 105–124. .

Kalinowska, K. (2016). The stabilising role of unemployment benefits in Poland. Central European Review of Economics & Finance, 12(2), 21–40.

Kusideł, E. (2000). Modele wektorowo-autoregresyjne VAR. Metodologia i zastosowanie. In B. Suchecki (Ed.), Dane panelowe i modelowanie wielowymiarowe w badaniach ekonomicznych (Vol. 3). Łódź: Absolwent.

Ljung, G. M., Box, G. E. P. (1978). On a Measure of a Lack of Fit in Time Series Models. Biometrika, 65(2), 297–303. .

Lütkepohl, H. (2005). New Introduction to Multiple Time Series Analysis. Berlin, Heidelberg: Springer Verlag. .

Pfaff, B. (2008a). Analysis of Integrated and Cointegrated Time Series with R. Second Edition. New York: Springer.

Pfaff, B. (2008b). VAR, SVAR and SVEC Models: Implementation Within R Package vars. Journal of Statistical Software, 27(4). .

R Core Team. (2018). R: A language and environment for statistical computing. Vienna: R Foundation for Statistical Computing. .

Wójcik, A. (2014). Modele wektorowo-autoregresyjne jako odpowiedź na krytykę strukturalnych wielorównaniowych modeli ekonometrycznych. Studia Ekonomiczne, 193, 112–128.

Do góry
Copyright © 2019 Główny Urząd Statystyczny