Stratification of Domains Using Composite Estimation to Measure the Revenue Level of Small Businesses in Poland
DOI:
https://doi.org/10.18778/0208-6018.339.10Keywords:
robust estimation, business statistics, small area estimation, GREGAbstract
To meet the growing demand for detailed, precise, accurate and timely estimation of entrepreneurship and economic conditions, it is necessary to systematically extend the scope of information provided by business statistics. In view of the policy aimed at reducing survey costs and burdens for business units, the only way in which this objective can be achieved is by modernizing survey methodology. One area where this kind research is being conducted are applications of indirect estimation based on auxiliary sources of information from administrative sources. Hence, the purpose of the study described in this article is to evaluate the precision of estimates of revenues of small businesses for domains defined by spatial aggregation and business classification by applying stratification in composite estimators based on information collected from administrative registers.
Downloads
References
Antal E., Tillé Y. (2011), A Direct Bootstrap Method for Complex Sampling Designs From a Finite Population, “Journal of the American Statistical Association”, vol. 106(494), pp. 534–543.
Google Scholar
Bracha C. (2004), Estymacja danych z badania aktywności ekonomicznej ludności na poziomie powiatów dla lat 1995–2002, GUS, Warszawa.
Google Scholar
Chambers R., Chandra H., Salvati N., Tzavidis N. (2014), Outlier robust small area estimation, “Journal of the Royal Statistical Society: Series B”, vol. 76(1), pp. 47–69.
Google Scholar
Chambers R.L, Falvey H., Hedlin D., Kokic P. (2001), Does the Model Matter for GREG Estimation? A Business Survey Example, “Journal of Official Statistics”, vol. 17, no. 4, pp. 527–544.
Google Scholar
Clark R. G., Kokic P., Smith P. A. (2017), Comparison of two Robust Estimation Methods for Business Surveys, “International Statistical Review”, vol. 85, no. 2, pp. 270–289, http://dx.doi.org/10.1111/insr.12177.
Google Scholar
Cochran W. G. (1977), Sampling Techniques, John Wiley and Sons, New York.
Google Scholar
Dehnel G. (2015), Robust regression in monthly business survey, [in:] W. Okrasa (ed.), Statistics in Transition – new series, vol. 16, no. 1, Warsaw, pp. 1–16, http://stat.gov.pl/en/sit‑en/issues‑and‑articles‑sit/previous‑issues/volume–16‑number–1‑spring–2015/ [accessed: 29.102018].
Google Scholar
Dehnel G. (2017), GREG estimation with reciprocal transformation for a Polish business survey, [in:] M. Papież, S. Śmiech (eds.), Proceedings of the 11th Professor Aleksander Zelias International Conference on Modelling and Forecasting of Socio‑Economic Phenomena, Foundation of the Cracow University of Economics, Crakow, pp. 67–75.
Google Scholar
Dehnel G., Pietrzak M., Wawrowski Ł. (2017), An Evaluation of Company Performance Using the Fay‑Herriot Model, “Argumenta Oeconomica Cracoviensia”, no. 16, pp. 23–36. http://dx.doi.org/10.15678/AOC.2017.1602.
Google Scholar
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”, vol. 17, no. 1, pp. 41–66, http://stat.gov.pl/en/sit‑en/issues‑and‑articles‑sit/previous‑issues/volume–17‑number–1‑march–2016/ [accessed: 29.10.2018].
Google Scholar
GUS (2015), Małe i średnie przedsiębiorstwa niefinansowe w latach 2009–2013, Warsaw.
Google Scholar
GUS (2016), Report “Use of administrative data in the survey: Assessment of current business activity of enterprises”, Warsaw.
Google Scholar
GUS (2017), Działalność przedsiębiorstw niefinansowych w 2015 roku, Warsaw.
Google Scholar
Myrskylä M. (2007), Generalised Regression Estimation for Domain Class Frequencies, Tilastokeskus – Statistikcentralen – Statistics Finland, Helsinki.
Google Scholar
PARP (2017), Raport o stanie sektora MSP w Polsce 2017, Warsaw.
Google Scholar
Rao J. N.K., Molina I. (2015), Small area estimation. Wiley series in survey methodology, 2nd ed., Wiley, Hoboken.
Google Scholar
Rao J. N.K., Wu C. F.J. (1988), Resampling Inference With Complex Survey Data, “Journal of the American Statistical Association”, vol. 83(401), pp. 231–241.
Google Scholar
Särndal C. E., Swensson B., Wretman J. (1992), Model Assisted Survey Sampling, Springer Verlag, New York.
Google Scholar
Shao J., Tu D. (1995), The jackknife and bootstrap, Springer Verlag, New York.
Google Scholar
Singh M. P., Gambino J. G., Mantel H. (1993), Issues and options in the provision of small area statistics, [in:] G. Kalton, J. Kordos, R. Platek (eds.), Proceedings of the International Scientific Conference on Small Area Statistics and Survey Designs, vol. 1, Central Statistical Office, Warsaw, pp. 37–75.
Google Scholar
Downloads
Additional Files
- Figure 1. Number of small enterprises by activity in 2015
- Figure 2. Small enterprises by size class in 2015
- Figure 3. Correlation between sample size and population size by NACE section
- Figure 4. Spatial distribution of relative bias for HT estimation and composite estimation which represent sum of and one of , , estimators of mean revenue compared to data from tax returns
- Figure 5. Spatial distribution of composite estimates ( + ) of mean revenue by province and for 4 NACE section