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Estimation of Employment for Cities, Towns and Rural Districts. Workshop of BNU Network on Survey Statistics Tallinn, August 25 – 28, 2014. Olha Lysa Ptoukha Institute for Demography and Social Studies, National Academy of Science of Ukraine Kyiv, Ukraine. Task.

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estimation of employment for cities towns and rural districts

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

slide2

Task

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

slide3

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.
slide4

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
problems
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
proposition
Proposition
  • Correction of direct estimates
  • Microlevelmodelling of probability to be employed
  • Multilevel composite estimator (Longford 2010)
slide8

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
ii microlevel model

– 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:

iii multilevel composite estimator

– 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:

slide13

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.

slide14

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.
thank you for attention
Thank You for Attention!

OlhaLysa

Ptoukha Institute for Demography and Social Studies

National Academy of Sciences of Ukraine

[email protected]

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