Using the ESDS surveys to look at change over

time

Vanessa HigginsESDS Government

Centre for Census and Survey Research (CCSR)

University of Manchester

Summary

• Overview of ESDS government– Who we are– Data available– How to access data

• Potential of ESDS Government cross-sectional datasets for change over time

• Pros and cons of cross-sectional and longitudinal data for analysing change over time

ESDS Government

• ESDS began in Jan 03

• Comprises 4 specialist services– ESDS Government– ESDS Longitudinal– ESDS Qualidata– ESDS International

• ESDS Government provides access and user support for key large-scale government surveys such as Labour Force Survey, Expenditure and Food Survey etc

• Access remains via the UKDA

                     

                     

Which surveys?

• General Household Survey • Labour Force Survey• Expenditure and Food Survey • Family Resources Survey • Health Survey for England/Wales/Scotland• British Crime Survey • Time Use Survey • ONS Omnibus Survey • Annual Population Survey• National Travel Survey • Survey of English Housing• British Social Attitudes

QUALITY OF DATA• Main data collectors:

– Office for National Statistics (ONS)– National Centre for Social Research (NatCen)

• Very experienced in design, management and analysis of social surveys

• Permanent panels of highly trained field interviewers

• High response rates – but fallen in recent years

• Widespread use by secondary analysts

Access to the data

• View documentation and do online data analysis (NESSTAR) without registering

• Need to register with ESDS to get datasets

• Need an Athens Username and Password to register

• FREE to non-commercial users but commercial users have to pay (£500 per dataset)

• Have to sign End User Licence to agree to certain conditions (BCS has special conditions)

• Special Licences for APS and LFS

Survey Repeated cross-sectional

Longitudinal element

LFS √ 1992 onwards

GHS √ 2005 onwards

FRS √

EFS √

TUS 2000 (2005 in Omnibus)

BSAS √ 1984-1986

Omnibus √ (modules)

APS √

NTS √

BCS √

HSE √

SEH √

Definitions• Cross-sectional: one

point in time

• Repeated cross-sectional: survey repeated (each year) on different samples

• True longitudinal:same people at multiple points in time

• Retrospective

Types of data

Potential of repeated cross-sectional data for change over

time

(1) Change in population over time

Source: GHS

(2) Change in groups over time

Source: GHS

Change in groups over time

SMOKING AND SOCIAL CLASS - MEN

0

510

15

2025

30

3540

45

1994 1995 1996 1997 1998 1999 2000 2001

year

%

all sc I&II sc IV&VSource:HSE

Marmot, M (2003)

(3) Retrospective questions

Source: GHS

Source: GHS

(4) Pseudo cohorts•Cohort = a group of people who have had a common experience at a particular time

•Birth cohort = a group of people born during a particular period

•Cohort studies study the individuals in the cohort over a length of time e.g. Birth Cohort Studies (babies born in UK 5-11 April 1970, ages 5, 10, 16, 20, 26, 30, 34)

•Can create pseudo-cohorts with repeated cross-sectional data

•e.g. those aged 20-24 in the 1980 GHS are represented by those aged 25-29 in 1985 GHS, aged 30-34 in 1990 GHS and so on…

•Can’t track individuals but pseudo-cohorts represent the average experiences of birth cohorts (aggregate change)

Limitations of cross-sectional data for change over time

(1) Individual change• Can describe aggregate change but not ‘individual’

change because cross-sectional data does not contain repeated observations on the same individuals…so cannot identify the characteristics of those who change either

• Examples where individual change important:– Income dynamics (can identify those on low

incomes but not how long spent in poverty or low-income situations)

– Other outcomes which may be dependent on circumstances earlier in life (employment, education, smoking, drinking etc)

– E.g. smoking – may want to look at impact of smoking on health or why people smoke (prior conditions)

(2) Causal Direction• Causal direction linked to tracking individual change

• A cause must precede its effect in time - cross-sectional data has no sequence of events

• A panel could measure the mental health of unemployed people and then see if mental health changes if become employed

• However – cross-sectional can establish that there is NO causal relationship– cross-sectional can show that a causal direction cannot

be ruled out

Mental health Unemployment

(3) Age and cohort effects• E.g. women’s employment status – the probability of paid employment

declines steeply from middle age onwards– is this due to age effects e.g. age discrimination/financially better off

OR cohort effects e.g. women born before 1960s were expected to stay at home and they may have had low levels of employment all their lives

• By looking at female employment by age and not taking account of cohort effects, you could give misleading results

• Longitudinal data – allows analyses between cohorts and also collects important info on life events too, births, marriages, divorces etc

• To distinguish between age and cohort effects we need to examine multiple cohorts over time – cannot do this with cross-sectional data (unless using pseudo cohorts)

Ref: Dale and Davies, 1994

Proportion of women working at different ages by birth cohort

0

10

20

30

40

50

60

70

80

90

100

15-19 20-24 25-29 30-34 35-39 40-44 45-49 50-54 55-59

Age

Pe

rce

nta

g e

of

wo

me

n w

ork

ing

1920-24

1925-29

1930-34

1935-39

1940-44

1945-49

1950-54

1955-59

1960-64

Source: 1980 OPCS Women and Employment Survey

Types of true longitudinal data

• Panel: a particular set of respondents (the panel) are questioned/measured repeatedly over time e.g. BHPS, QLFS

• Cohort study: concerned with charting the development of groups from a particular time point e.g. Birth cohort study (born 5-11 April 1970)

• www.esds.ac.uk/longitudinal : key longitudinal data sources

Quarterly Labour Force Survey

Springquarter

Summerquarter

Autumnquarter

Winterquarter

Spring +1Quarter

W1 12k 12k 12k 12k 12k

W2 12k 12k 12k 12k 12k

W3 12k 12k 12k 12k 12k

W4 12k 12k 12k 12k 12k

W5 12k 12k 12k 12k 12k

Purple indicates those cases who were in wave 1 in spring year 1 – i.e. they’re in wave 2 in summer etc

• Each household participates for 5 consecutive waves (every 3 months/quarter)• Total 60k households per quarter

New GHS (L)• Integration of EU-SILC into GHS - April 2005

• Provides cross-sectional and longitudinal requirements for EU-SILC A four-year sample rotation

– Households stay in the sample for four years (‘waves’) – A quarter of the sample (a ‘replication’) is replaced each year– Three quarters of the sample will overlap between successive years

• Overall sample size increased (from 8,700 in 2004 to 10,200 in 2005 – achieved households) but three-quarters of these are not new cases

• Content much the same with new modules e.g. financial situation, housing costs

• Data not available yet

Limitations of longitudinal data for change over time

• Sample attrition, introduces bias

• Cohort studies are not representative of the whole population

• May have to wait for a second interview to measure change/can’t go back in time to collect data– repeated cross-section ready now!

Refs/Further reading

• Buck, N et al. Choosing a Longitudinal Survey Design: The Issues, Sept 1995

• Dale, A and Davies, R. Analysing Social And Political Change, Sage Publications, London, 1994

• De Vaus, D. Research Design in Social Research, Sage Publications, London , 2001

• Uren, Z. The GHS Pseudo Cohort Dataset (GHSPCD): Introduction and Methodology, Survey Methodology Bulletin, September 2006: http://www.statistics.gov.uk/cci/article.asp?ID=1637&Pos=1&ColRank=1&Rank=1

• ESDS Longitudinal: http://www.esds.ac.uk/longitudinal/