Estimation of Employment for Cities, Towns and Rural Districts

1. Estimation of Employment for Cities, Towns and Rural Districts Workshop of BNU Network on Survey Statistics Tallinn, August 25 – 28, 2014 OlhaLysa PtoukhaInstitute for Demography and Social Studies, National Academy of Science of Ukraine Kyiv, Ukraine

2. Task To estimate the employment rate for cities, towns and rural districts (administrative territorial units - ATU) of Ukraine based on the annual LFS dataset

3. Data Sources • Sample survey of households (LFS); • Sample survey of enterprises (BS); • Register of unemployment; • Administrative data reported by enterprises ; • Census data and demography statistics.

4. Survey description • stratified, multistage sample design with systematic selection proportional to size; • 11,1 thousand households are selected every month, representing all country; • rotational scheme 3-9-3 (2/3 of sample was observed in previous month); • all hh’s members of age 15-70 years old are interviewed about their economical activity; • complex weighting procedure: design weights, non-response adjustment, calibration to population sex-age structure

5. Problems • Small sample size or 0 at ATU level; • High variance of employment rate estimates for ATU; • All rural districts are represented in the sample but not all cities and towns; • High variation between estimates of employment rate in ATUs

6. Proposition • Correction of direct estimates • Microlevelmodelling of probability to be employed • Multilevel composite estimator (Longford 2010)

7. Cities/towns representation in sample

8. I. Direct estimation • Sampled cities/towns – Horvitz-Tompson estimator; • Cities/towns are not in the sample – synthetic estimator (use the estimate of sampled city/town which represents them); • Rural districts – composite estimator of urban and rural populations of district

9. Information Using Scheme

10. – composite estimate; – direct estimate; –modelled estimate; – weighting coefficient II. Microlevel Model Factors: 1) Gender: M– male; 2) Type of area: R– rural; 3) Age grope: A_2 –25–29 years old; A_3 –30–34 years old; A_4– 35–39 years old; A_5 –40–44 years old; A_6 –45–49 years old; A_7 –50–54 years old; A_8 –55–59 years old; A_9– 60–69 years old. Composition between regression and direct estimates:

11. – composite estimate for ATU; – composite estimate based on microlevel model for ATU; – direct estimate for region what includes the estimated ATU; – composite estimate for ATU from previous survey(year); – weighting coefficients III. Multilevel Composite Estimator Potential covariates:

12. Results of Simulation

13. Conclusions Proposed approach based on multilevel composite two-stage estimation. Results of implementation in a simulation show improvement in accuracy of employment rate estimates for the cities, towns and rural districts. The RRMSE of estimates of ATUs was reduced by 45% on average. Using a microlevel model decreases variation between estimates of employment rate in ATUs We can obtain estimates for ATUs that are not in the sample.

14. References • Ghosh M., Rao J. N. K. Small Area Estimation // An Appraisal, Statistical Science. – 1994. – Vol. 9, № 1. – P. 55–93. • Rao J.N.K. Small Area Estimation. – New York: Wiley, 2003. – 314 p. • Longford N.T. Simulationof small-areaestimatorsofthepovertyratesintheoblastsofUkraine. – SNTL and UPF, Barcelona, Spain. ThereportpreparedfortheSocialAssistanceSystemModernization Project, Ukraine, Kyiv, 2010.

15. Thank You for Attention! OlhaLysa Ptoukha Institute for Demography and Social Studies National Academy of Sciences of Ukraine Olysa@ukr.net