Lilian Galer , lilian.galer@statistica.md Ala Negruta, ala.negruta@statistica.md

1. The Improvement ofHBS in the Republic of MoldovaEuropean Conference on Quality in Official Statistics, Rome, Italy Lilian Galer, lilian.galer@statistica.md Ala Negruta, ala.negruta@statistica.md

2. Questionnaire improvements Sampling improvements Areas of improvement

3. The HBS is an important source of economic and social data, it provides data on: Measures of living standards Consumption and income structure Weights for consumer price index Various estimates for the National Accounts The HBS can inform economic and social policy and monitor the impact of government reforms It is a continuous activity of the NBS, with household interviews conducted throughout the year, and households completing both a general interview and a ‘diary’ in which households report consumption and income The HBS

4. In 2004 and 2005 the NBS conducted various experiments in order to improve the questionnaire design, such experiments guided the changes implemented in 2006 The main questionnaire changes affected the following areas: Changes in the reference period of some income sources and expenditure items Modification of Diary - improved layout of the diary (the questionnaire booklet that helps the household to record income and expenditure transactions) Re-adjustment of the definitions of employment indicators and household members Questionnaire improvements (I)

5. Questionnaire improvements (II)Reference period • The HBS used to rely on current monthly expenditure to estimate household consumption expenditure • While this provides good national average estimates, it can be misleading when our purpose is to compare households’ living standards

6. Example of expenditure for central heating in 2007 98% of households with central heating reported such expenditure when asked about expenses in the last 12 months But only 52% of households with central heating reported expenditure in the current month When assessing living standards we should include the average monthly expenditure and not how much the household spent in January or July This problem occurs when we deal with ‘seasonal’ consumption items and more generally for items that are purchased at a frequency lower than one month When using only the current month expenditure we over-estimate the level of inequality Questionnaire improvements (III) Reference period

7. Collected information can now be used to produce both accurate averages for the National Accounts, weights for the consumer price index, and distributional data for poverty analysis. In particular poverty and inequality data have improved There is a reduced household burden for the participation to the survey (the household needs to spend less time to complete the required information) Improvement in the measurement of some key statistics (remittances and agricultural income) Employment data are now collected ensuring comparability with definitions used in the Labour Force Survey Questionnaire improvements (III) Effects of questionnaire changes

8. Questionnaire improvements (IV) Effects of questionnaire changes • Both income and consumption are now estimated at much higher levels than in 2005 • This is in line with estimates from the National accounts

9. Probability, stratified, two stage sample Sample frame: I stage – electoral divisions II stage – electoral lists Stratification: Cities Towns Rural area Sample size: I stage – 45 PSUs II stage – 36 households/quarter/PSU Old HBS sample design

10. The low quality of the sample frame Exhaustion of the lists from sample frame Big design-effect (only 45 PSUs) Bias generated by multiple replacement of non-respondents Reliability of the main estimates assured only at the national level and residence area Necesity of improvements in sampling

11. General characteristics of EMDOS • EMDOS – Master Sample for the Social Surveys • Starting from 01.01.06 the HBS and LFS are carried out on EMDOS • Probability, stratified, two stage sample (excepting self representing cities where it is one stage) • EMDOS covers 219 localities grouped in 150 PSUs, including: • 97 in rural area; • 53 in urban area; • Reliability of the main estimates at the level of statistical zones; • It is used for others surveys in social sphere

12. At the I stage – PSUs’ selection with the probability proportional to there size. Sample frame – list of administrative-territorial units of primary level (PSUs): CUATM. At the II stage – simple random sampling of households in each selected PSU (exception Chisinau city – proportionally stratified sampling for HBS). Sample frame – list of households addresses (SSUs): list of electricity consumers provided by the electricity companies and updated with using of special listing procedure. Sampling stages

13. Geographic: North (Balti separately) Center South Chisinau Transnistria Residence area: Urban Rural Settlements’ size: Big communes Small communes Stratification criteria

14. Sample size Number of PSUs and households per quarter

15. HBS1997 - 2005 Changes in sampling (geographical coverage) EMDOS from 2006

16. ≈ 20% of PSUs are replaced annually with new ones Except for the self-representing PSUs: Chisinau mun. Balti mun. Comrat mun. Cahul town Soroca town Ungheni town Reasons: Better geographical coverage over time; Avoiding the necessity of complete PSU’s replacement after a certain period Provide a good continuous comparability of estimates over time, etc. PSUs Rotation

17. The panel reflect all the changes encountered within the same households during a period of time. HBS - panel for 5 years Households’ rotation: HBS – ½ of households are in for 5 consecutive years, and ½ are interviewed only once Panel and households rotation within PSUs

18. Developing and implementation of statistical weights computational procedures, which include: Base weights calculation and analysis; Non-responses adjustment procedures; Poststratification Grossing up of Surveys Data

19. For the computation of estimates reliability characteristics is used a special variance estimation technique – BRR with the following main advantages: It allows to estimate variance for complex sample design (taking into consideration design effect); It can be used for all types of estimators, such as means, sums, proportions, etc.; Relatively simple to use as it is implemented in most specialized statistical softs – STATA, WesVar, SAS, R, etc. Reliability estimation

20. Reliability of income estimates, by quarters (HBS 2005-2007)

21. Design Effect over time (HBS 2005-2007)

22. More attention to non-samplingerrors Using of auxiliary data on electricity for poststratification Data matching Small Area Estimation Analysis of panel data Further questionnaire improvements to capture in a better way self-employment in non-agriculture, tax and social contribution payments Further activities

23. Thank You for Your Attention!