Download From user demand to indicator – the example of labour market flow

From user demand to indicator –
the example of labour market flow
statistics
Hannah Kiiver - Eurostat
Frank Espelage - Eurostat
Outline
Labour market transitions: data availability and
data needs
Agreeing on a method: Task Force on Labour
Market Flow Statistics
Further methodological challenges and proposed
solutions
Background information on data
availability
• The EU – Labour Force Survey is the main source
for labour market analysis
• Quarterly survey, output harmonization
• ILO definition for employed, unemployed, inactive
• Source for main policy indicators concerning
labour market
Availability of labour market
transitions data
• Retrospective question in the LFS: self-perceived
labour market status one year ago
• Not comparable to ILO definition, reliability, lack
of additional information
• Use other source: SILC
• Timeliness, comparability to other labour market
data
Data needs
Quarterly and annual transition data based on ILO
definitions
Consistency stocks – flows for ILO status
Detailed breakdowns  longitudinal micro-data
Comparability over time/across countries
Exploit overlap in LFS data – at least one quarter
overlap for all rotational patterns used
Task Force Labour Market Flow Statistics
Quarterly flows first
• Lower attrition, smaller changes in population
Longitudinal sample: 15-74 in both quarters
Iterative raking to meet margins for ILO status of
both quarters
Eurostat produces data starting in 2010
Labour market status flows in the EU* for
2015Q4-2016Q1, in millions
*excluding DE and BE
Transition rates 2015Q4-2016Q1 in the EU*,
in % of initial status
*excluding DE and BE
Net changes in unemployment in the EU*,
1000 persons
*excluding DE and BE
Further requests:
Breakdowns, additional transitions
Annual flows
Seasonal adjustment
Methodological challenges
Weighting – derive individual weights from
raking procedure
Sample size – for longitudinal sample,
breakdown of indicators for small countries
impossible
Proposed solution
• Pool longitudinal data for several quarters, create
annual averages of quarterly flows
• Simple probit regression
• Function of age as regressor
• Interaction terms where necessary
• Bootstrap standard errors or use derived weights
Communication
• Do not publish complete regression output
• Evaluate margins at the mean, or for different
ages
• Derive and publish predicted probabilities
Example for Estonia, 2011 data
• Transition from unemployment to employment
• Total sample: 8016, of which 119 moving from
unemployment to employment;
• Breakdown by age and unemployment duration
• Breakdown not publishable; sample less 10 in
most groups
• Regression using age, age squared as continuous
variables, interaction terms with unemployment
duration (5/2 categories)
Conclusion
The LFS is originally not designed for longitudinal
analysis, and the availability of data (sample size
and weights) and technical information collected
reflects this.
Using the available data, simple methods and some
readiness to compromise of all stakeholders,
production of detailed and meaningful data is
nevertheless possible.