The Improvement of HBS in the Republic of Moldova European Conference on Quality in Official Statistics, Rome, Italy Lilian Galer, Ala Negruta,

Areas of improvement  Questionnaire improvements  Sampling improvements

The HBS  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

Questionnaire improvements (I)  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 (II) Reference period IndicatorsBefore 2006From Cash incomes of household’s memberscurrent month 2. Incomes from individual agricultural activitycurrent monthlast 12 months and current month 3. Expenditures for individual agricultural activitycurrent monthlast 12 months and current month 4. Expenditures for utility servicescurrent month for all types of utilities current month and the last 12 months for some types (central heating, wood, coal, gas) 5. Expenditures for food products procurementcurrent monthHalf a month 6. Consumption of products from own production and the ones received for free current monthOn a weekly basis during the month of interview  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

 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

 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

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

Old HBS sample design 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

Necesity of improvements in sampling  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

General characteristics of EMDOS  EMDOS – Master Sample for the Social Surveys  Starting from 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

Sampling stages  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.

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

Sample size Number of PSUs and households per quarter Nr of PSUs in the old sample Nr of households / quarter in the old sample Nr of interviewer s in the old sample Nr of PSUs in EMDOS Nr of households/ quarter in EMDOS Nr of interviewers in EMDOS HBS

Changes in sampling (geographical coverage) HBS EMDOS from 2006

PSUs Rotation ≈ 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.

Panel and households rotation within PSUs 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

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

Reliability estimation 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 of income estimates, by quarters (HBS )

Design Effect over time (HBS )

Further activities  More attention to non-sampling errors  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

Thank You for Your Attention!