Longitudinal DataRecent Experience and Future Direction

August 2012

Content

Survey of Family, Income and Employment

Linked Employer–Employee Data (LEED)

Future direction:

• Integrated Data Infrastructure• Linked Census Data• Administrative and Survey Data linking

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Survey of Family Income and Employment

Longitudinal Study that ran for 8 waves from

2002-2010

Wave 1 Sample:11,500 households

29,700 individuals (22,200 adults and 7,500 children )

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Survey of Family Income and Employment

Topics covered in all waves:• Demographics • Education • Family • Labour market • Income

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Survey of Family Income and Employment

Additional topics:• In waves 2,4,6 and 8: Assets and Liabilities• In waves 3,5 and 7: Health

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Survey of Family Income and Employment

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Strengths

Investigating causal processes

Identifying important transitions

Assessing the effects of policies

Wide coverage of topics

Flexibility of content

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Limitations

Intermediate length

Incomplete coverage of subpopulations

Limited scope for analysis of subpopulations

Recall error

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Successes

High retention rate

Large sample

Rich dataset

Broad group of users

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Challenges

Respondent Burden

Less representative over time

Attrition across subgroups

High Cost to Statistics NZ

Complex data

Confidentiality and Data Access

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Longitudinal Data after SoFIE

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LEED

Combines:

• Employer Monthly Schedule (EMS) payroll data from Inland

Revenue

• Statistics NZ Business Frame

• Ministry of Social Development's Benefit Dynamics Dataset

(BDD)

• Annual tax returns (self-employment data)

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Strengths

High coverage of specific groups

Longitudinal

Indefinite observation period

Enables the study of rare phenomena

Enables the study of small subpopulations

More detailed than survey data

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Limitations

Not representative of the entire population

Administrative data is collected for administrative

purposes

No unique Identifier for exact matching

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Integrated Data Infrastructure (IDI)

Prototype

Includes data from:• LEED• Ministry of Education• Ministry of Social Development• Ministry of Business, Innovation and Employment• Household Labour Force Survey (HLFS)

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Additional Benefits

Greater number of variables

Greater coverage

Greater number of topics

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Linked Census Data

Feasibility studies into linking together data from past

censuses

Enables the study of long term phenomena and

intergenerational mobility

Can be used to study social and geographic mobility

Potential for wide use

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Administrative and Survey Data Linking

Administrative data adds a longitudinal dimension to

survey data

Survey data adds a broad range of variables to

administrative data

Reduced respondent burden

Better use of existing data

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Examples

SoFIE – IRD data

SoFIE – Health data

Census – Cancer Register

Census – Mortality Records

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Future Direction

Maximising the use of Administrative data

Maximising the use of SoFIE data

Maximising the benefits from linking

administrative and survey data

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