Surveys: Using LSMS, HBS, LFS and SILC for Poverty Analysis Rachel Smith-Govoni April 4, 2008

Goals and Needs Goals: Measure the poverty impact of economic policy Measure the distributional impact of economic policy Needs: Rely heavily on household survey data

Household Surveys - types Single Topic Labour Force Surveys( LFS) (ILO) Census – national, 10 years – Serbia 2002 In-between Multi-topic No such thing as a single topic survey really, so artificial even the basic labor force survey collects some demographic (age, sex, often relation to household head) and education (yrs of schooling, degrees). But the overall purpose is to measure outcomes or carry out analysis in one sector. No one is going to use it to discuss the demographic profile of the country (LFS sample doesn’t include the whole population so couldn’t use this).

Household Surveys In-between Agricultural Surveys (FAO) Single Topic In-between Agricultural Surveys (FAO) Demographic and Health (DHS) Household Budget Surveys (HBS) Multi-topic Not all alike Each developed for specific purposes, don’t always work for other purposes

Household Surveys Multi-topic Single Topic In-between Multiple Indicator Cluster Survey UNICEF Living Standards Measurement Study Survey on Income and Living Conditions (SILC, EU) MICS: World Summit for Children in 1990 New York and subsequent declaration and Plan of action for children, requires monitoring of 75 indicators largely on status of children but also a bit on women and families; one sub-group, but a variety of indicators (health, education, labor, access to sanitation etc.) NHSCP: UN program- largely led to expanded labor force surveys in LAC- LSMS: World Bank Initiative:

Census Purpose Accurate measure of the population of a country Geographic distribution of the population Basic demographic information

Census Not a sample Universal coverage No sampling errors in estimates Some corrections for non-response may be needed Not many items Basic need: only once every 10 years so can’t monitor inter-censally, indicators change slowly, subjective assessment of needs, weighting problem

Census Content Demographic information: age, sex, race/ethnicity, family and household composition Housing information Others: basic education, labour, disability Census

Census Albania: 2001 (1989) BiH 1991 (1981) Montenegro 2003 (1991) Serbia 2002 Kosovo 1981 Limited monitoring Census Limited use if looking at impact of policies affecting taxes, tariffs or pricing

Census Uses Sample frame Link with household surveys for small area estimation (data mapping) Provides the frame from which all samples for household surveys are dr

Two types of errors: Sampling and non-sampling Time Cost Training Non-response Census

Sampling vs. non-sampling errors Total error Non-sampling error Sampling error Sample size

Labour Force Survey (Anketa o radnoj snazi – ARS) Purpose Direct measurement of unemployment General characteristics of the labour force

Labour Force Survey Sample Relatively large samples Desire to disaggregate to different geographic areas Individuals of working age

Labour Force Survey Content Characteristics of the labour force Demographics Education Sectoral distribution of employment Degree of formality Seasonal Income

Labour Force Survey Limitations: LFS typically capture partial, not total, income, under-estimate welfare Measurement Error - Labour income measurement error at both ends of the distribution

LFS in Latin America Item non-response Salaried Self- employed Employer Mean non- response rate 3.9% 10.2% 12.0 Source: Feres, 1998

Household Budget Survey (Anketa o potrosnji domacinstava – APD, Inputs to National Accounts on consumer expenditures Track changes in expenditures over time Weights for the Consumer Price Index (Indeks Potrosackih Cijena)

Non response rates (Eurostat Household Budget Surveys, 2003) Bulgaria: 39.7% Estonia, 44% Hungary, 58.8% before replacement Romania, 21.6 % Sample Usually medium size sample High non-response rates

Household Budget Surveys Content Total Income Total Consumption - diary Short Demographics Central Europe: agriculture Limited health and education

Household Budget Surveys Poverty Measurement Consumption based welfare measure Purpose of an HBS survey is NOT to measure welfare but to precisely measure mean expenditures on specific goods and services These are conflicting goals

Household Budget Surveys Poverty Measurement Shortest possible reference periods Minimize number of omitted expenditures Good for precise measurement of regional or national means Because of lumpy nature of purchases, not good for comparisons among households

Multi-topic Household Surveys Those with a focus on measuring poverty Survey on Income and Living Conditions (SILC) Living Standards Measurement Study Surveys (LSMS)

Multi-topic Household Surveys Purpose Analysis of welfare levels and distribution Study links between welfare levels and individual and household characteristics, economic, human and social capital Social exclusion Levels of access to, and use of, social services, government programs and spending

Multi-topic Household Surveys Sample Small sample sizes Trade-off issue: Quality and cost considerations Limits ability to assess programs or policies that affect small groups or small areas (over- sample) Infrequent in many countries

LSMS 2002, 2003, 2007 Content 1 household composition 2 housing 3 individual demographics 4 health 5 labour 6 work history 7 social programs 8 migration 9 values and opinions 10 consumption 11 agriculture

Multi-topic Household Surveys Poverty Measurement Total consumption Longer reference periods Able to calculate use value of durables and housing Total income Suffers from standard measurement errors

Designs for surveys across time Repeated cross sectional surveys (e.g. Household Budget Survey, Labour Force Survey) Common design for large government surveys New sample drawn for each survey Carry similar questions each year Used for trend analysis at aggregate level

Designs for surveys across time Cohort Studies Sample often based on an age group Follow up same sample members at fairly long intervals Developmental data as well as social and economic data Data from parents, teachers associated with cohort member

Designs for surveys across time e.g. Panel Study of Income Dynamics, USA – since 1968! Living in BiH 2001-2004, LSMS Albania 2002-2004, LSMS Serbia 2002-2003 Draw a sample at one point in time and follow those sample members indefinitely (or as long as the funding continues) Collect individual level data in household context Repeated measures at fixed intervals (annual data collection)

Advantages of Panel Data Comparison of same individual over time - outcomes Track of aspects of social change Facilitates study of change and causal inference Minimise the problem of inaccurate recall Compare a person’s expectations with real change Look at how changes in individuals’ behaviour affects their households Identifies the co-variates of change and the relative risks of particular events for different types of people

Changes in Employment Status A: CROSS-SECTIONAL INFORMATION Unemployed 2001 2007 Net change - 0.1% unemployed Employed

Changes in Employment Status B: PANEL INFORMATION continuously unemployed 3.2% Still Unemployed Unemployed unemployed 2001 but employed 2007 5.1% employed 2001 but unemployed 2007 5% Employed Still Employed continuously employed 86.7% 2001 2007 Net change - 0.1% unemployed Actual change is 10.1

Balkan Examples Albania - 15% of the unemployed in 2002 had made the transition to formal sector employment by 2004 BiH - About half who were poor in 2001 remained poor in 2004. Many individuals moved out of poverty. (Cross section headcount 18% for both years)

Employment and the labour market Unemployment duration and exit rates Do the unemployed find stable employment? The effect of non-standard employment on mental health Temporary jobs: who gets them, what are they worth, and do they lead anywhere? Family and Household Patterns of household formation and dissolution Breaking up - finances and well-being following divorce or split The effect of parents’ employment on children's educational attainment

A Sample Concept of ‘longitudinal household’ problematic for a panel - households change in composition over time or disappear altogether Individual level sample

Following rules All members of households interviewed at Wave One Children born to these original sample members Original members are followed as they move house, and any new individuals who join with them are eligible to be interviewed New sample members are followed if they split from the original member

Questionnaire design Core content carried every wave Rotating core questions One-off variable components lifetime job history marital and fertility history Variable questions to respond to new research and policy agendas

Attrition in panel surveys Inevitable to some extent but can be minimised Multiple sources of attrition in a panel refusal to take part respondents move and cannot be traced non-contacts Worry is potential bias if people who drop out differ significantly from those who stay in

UK Panel Wave 1 Respondents Wave-on wave re-interview rates

Fieldwork respondent incentives as a ‘thank-you’ extended fieldwork period for ‘tail-enders’ refusal conversion programme tracking procedures during fieldwork panel maintenance between waves Change of Address cards to update addresses mailing of Respondent Report details of contacts with respondents between waves

The user database Longitudinal data is complex Provide users with database structure which enhances usability Consistent record structure over time Key variables for matching and linking data cross wave Consistent variable naming conventions

Conclusions Longitudinal panel data allows us to answer research questions that cannot be answered with with cross-sectional data Provides a different view of the world - see process through the life-course not just a static picture Is complex (but so is the real world) - so needs to be well designed and conducted with sufficient resources to be successful

Final points Welfare: household surveys- always missing the homeless, street children, institutionalized population No one survey can meet all needs, review its purpose, coverage, content and quality before using Need a system of surveys that meets the needs of data users