Session on Labour Market Information Systems Platforms at the 2019 Labout Market Statistics and Analysis Academy at ITC ILO. November 2019

1. A9012101
LABOUR MARKET STATISTICS and ANALYSIS ACADEMY
27/11/2019
Edgardo Greising
Labour market information system platforms

2. 2
 What is a LMIS?
 Characteristics of a Statistical Information
System
 Data production
 Storage & Dissemination: .Stat
 Implementation checklist
 Data & metadata management
 LMIS Toolkit
OUTLINE

3. 3
 An airline is willing to increase the cost-
efficiency of the meals provided during the
flight. They normally offer two “standard”
dishes (“chicken or pasta”) but also
personalized choices for vegans or health-
conditioned passengers.
 What can they do to minimize the waste of
food not taken by the passengers?
Making evidence-based decisions

4. 4
 Take note of the meals distribution after each flight
 Flight attendants or land staff?
 Assume that this distribution is uniform across different flights and provide
based on it
 Personalized choices?
 What happens with the group going to the Vegans Conference?
 Ask each passenger this info at web check-in
 Personalized choices ONLY allowed in this way
 Assign meals to each passenger based on their answers or previous flights
statistics to those not answering in the web check-in
This is a SURVEY with IMPUTATION based on a Model
 Process:
• Put personal choice/assigned meal of each passenger in a list  microdata
• Summarize each type of meal (variable)  aggregate data or macrodata
• Disaggregate by cabin (business, tourist) for different servings  breakdown
• Label those special dishes (e.g. no salt)  descriptive metadata
• Save the information for the statistics to estimate distributions in coming flights
 FMIS (Flight Meals Information System) 
Making evidence-based decisions

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LMIS
“A network of institutions, persons and information
that have mutually recognized roles, agreements
and functions with respect to the production,
storage, dissemination and use of labour
market related information and results in order
to maximise the potential for relevant and
applicable policy and programme formulation and
implementation.”
production
storage
dissemination

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GSBPM
storage

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LMIS Toolkit

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3 functions (F)
4 components (C)
Labour Market Analysis
Monitoring & Reporting on policies
Coordination & Info Exchange
Labour Market Information System

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LMIS Functions and Components
F2: Monitoring and Reporting on Employment and Labour Policies
LMIS
C1: Collection
& Compilation
of Data and
Information
F1: Labour Market Analysis
F3: Coordination of users/producers and Information Exchange
C2: Directory
/ Repository
of
Information
C4:
Institutional
arrangement
s and
network
C3:
Analytical
Capacity
and Tools
Sources of
labour
STATISTICS:
Household
surveys,
Establishmen
t surveys,
Admin
records
1st Level:
Monitoring
Indicators
3rd Level:
Econometric
models/
projections
2ND Level:
Analyzing
Relationships
C4:
Institutional
arrangements
and network
9

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 Modular – GSBPM based
 Two big functional blocks:
• Backoffice
• Dissemination
 Manages aggregate data
• Dimensions
• Measures
• Attributes
 Metadata driven
Statistical Information System

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 Different data collection channels
converge into a single repository
 Typical roles:
• Administrator
• Metadata manager
• Data manager/analyst
• User
 Able to store and display descriptive and
reference metadata
Statistical Information System

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 Dissemination users are mostly
anonymous, registered users for
advance features
 Backoffice and Dissemination data
bases may be different, but they
must share the structural metadata
 Relational database is recommended
for both blocks; Dimensional schema
for Dissemination is highly desirable
Statistical Information System

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 Consistency checking must be done on
data input
• Structural checks - Mandatory
• Business logic checks – Highly recommended
 Multi-lingualism
 Advanced features for visualizations:
• Multiple dimensions
• Pivoting
• Filtering
• Graphs
• Customization/personalization
 Service Oriented Architecture is highly advisable
Statistical Information System

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LMIS.Stat

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Production
 LMIS’ indicators are secondary data
 Produced from primary data collected
through surveys or from administrative
registers
Primary
Statistical
Activity
MICRODATA
INDICATORS

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Production
DSD
Statistical
Activity

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.Stat main functions
 Data Entry
• The Data Provider Interface (DPI) is a web
based application to process and load data
into the Data Warehouse. Data can be in
csv, .txt, .xml or sdmx* format.
 Data Storage
• Based on Microsoft SQL server and a
standard star schema data warehouse
technology.

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 Data Exit
• A single exit point serves all outputs from
the Data Warehouse exposing the data to
several dissemination tools through a set
of Web Services
 Data Analysis
• .Stat allows for the extracting of data to
various analytical tools for further data
analysis.
.Stat main functions

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SIS-CC

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Why LMIS.Stat?
 Community values
 Community objectives
 High quality product
 Affordable investment
 Able to share the software
 Sustainability
The overall investment in .Stat in the period 2006-2018
exceeds eight millions Euros (financial and in-kind).

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Implementation checklist
 Define initial set of indicators
 Identify available sources in the
country
 Signature of MoU between NSA and
ILO
 Define system architecture
 Deployment of LMIS.Stat
 Localization and branding

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Implementation checklist
 Workshop on labour market data
production
 Establish governance structure and
operational roles
 Define harmonized concepts scheme
 Establish data flows and provision
agreements
 Training on .Stat administration: IT,
data and metadata
 Configuration of structural metadata and
themes tree

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Implementation checklist

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 Microdata is individual record or respondent data.
It is not aggregate data or summary data.
 Aggregate data, or macrodata, are indicators that
have been calculated and/or classified by the
combination of many separate units or items
 Metadata, often called data about the data, is
background or interpretative information
(documentation). It can be any information that
clarifies the process of planning, implementing
and executing a statistical function.
Data and Metadata Management

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 Microdata is the source for aggregate data or
summary data (“Macrodata”).
 Aggregate data, is (or should be…) stored and
disseminated through Statistical Information
Systems (e.g. LMIS).
 Metadata should exist at any level. At the
study level, is common to both Micro- and
Macro-data (Reference metadata). At the
indicator or observation value level, clarifies
deviations from the expected characteristics
Data and Metadata Management

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 Data is a set of unprocessed raw facts in unorganized form
(such as alphabets, numbers, symbols) that refer to, or
represent, conditions, ideas, or objects.
 Information is data that is accurate and timely, specific and
organized for a purpose, within a context that gives it
meaning and relevance, and can lead to an increase in
understanding and decrease in uncertainty.
 Knowledge is the use and application of the information.
There is no meaning to data
Data, Information and Knowledge
Information = Data + Context + Meaning
Knowledge can’t exist without a “brain”

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and KnowledgeInformationData,

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Unlabeled stuff
Why is this metadata relevant?
Labeled stuff
The bean example is taken from: A Manager’s
Introduction to Adobe eXtensible Metadata Platform,
http://www.adobe.com/products/xmp/pdfs/whitepaper.pdf
Labeled stuff

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What’s necessary to exchange data?
1. Agree on the format:
a) Microdata: SPSS, stata
b) Aggregate data: Excel, csv, xml, ASCII FRL.
2. Communicate data structure
3. Communicate the record’s semantics:
a) Variables’ names
b) Labels for variables and values (categories)
4. Provide additional information to help
understanding the content (“metadata”)

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Data exchange in agreed format
 Format
 Data Structure
 Data Semantics
 Descriptive Metadata

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 Format
 Data Structure
 Data Semantics
 Descriptive Metadata
Data exchange in agreed format

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32
 Format
 Data Structure
 Data Semantics
 Descriptive Metadata
Data exchange in agreed format

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 Format
 Data Structure
 Data Semantics
 Descriptive Metadata
Data exchange in agreed format

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Exchange of aggregate data in agreed format
 Format
 Data Structure
 Data Semantics
 Descriptive Metadata

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Exchange of aggregate data in agreed format
 Format
 Data Structure
 Data Semantics
 Descriptive Metadata

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• Labour statistics are a body of official statistics
which deals with work, workers, labour markets
and their characteristics.
• Labour statistics are part of the national
statistical system, which include:
• Labour statistics
• Education statistics
• National accounts
• Demographic statistics
• Industrial production statistics
• Agricultural statistics
• Etc.
• Concepts and methods in labour statistics need
to be consistent with other official statistics
Quick Guide on Sources and Uses of Labour Statistics (ILO, Geneva);
www.ilo.org/stat/Publications/WCMS_590092/lang--en/index.htm
What are labour statistics?

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• (S1) Surveys of households (‘persons’)
– Labour force survey
– Population census
– Income and expenditure survey
• (S2) Surveys of establishments (‘companies’)
– Establishment survey of production
– Employment and earnings survey
– Occupational employment and vacancy survey
• (S3) Administrative data (‘records’)
– Educational enrolment data
– Migration/border records
– Employment services records
Sources of labour statistics

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• No single data source can meet all needs
• LMIA systems use all available sources …
but be aware of strengths and limitations
• Use of labour statistics across
sources/time requires coherence with
regard to:
• Concepts
• Definitions
• Classifications
• Reference periods
• Methods
Statistical standards
Sources of labour statistics

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SDMX is an ISO standard (ISO 17369) designed
to describe statistical data and metadata,
normalise their exchange, and improve their
efficient sharing across statistical and similar
organisations. It provides an integrated approach
to facilitating statistical data and metadata
exchange, enabling interoperable
implementations within and between systems
concerned with the exchange, reporting and
dissemination of statistical data and their related
meta-information.
SDMX
SDMX (Statistical Data and Metadata eXchange)

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SDMX is not just a data transmission format…
Technical
Specifications
The SDMX
Information Model
Guidelines to
Harmonise Content
Content-oriented
Guidelines (COG)
Tools
IT Architectures for data
exchange
SDMX compliant tools
SDMX (Statistical Data and Metadata eXchange)
SDMX

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 The Information Model which forms the core of SDMX has been
developed to support statistics as collected and used by statistical
organisations
 The statistical guidelines aim at providing general statistical
governance as well as common (“cross-domain”) concepts and
code lists
 Many IT tools have been developed to support the use and
implementation of SDMX, most of them are of an open source
nature and can be used as components for building IT systems in
statistical organisations.
 Taken together, the technical standards, the statistical guidelines
and the IT architecture and tools can support improved business
processes for any statistical organisation as well as the
harmonisation and standardisation of statistical metadata.
41
SDMX
SDMX (Statistical Data and Metadata eXchange)

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Image source: Metadata Technology
SDMX
SDMX (Statistical Data and Metadata eXchange)

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Deriving a Data Structure from a table
COUNTRY
TIME PERIOD
SEX
AGE
ATTRIBUTES
OBS_VALUE
DIMENSIONS ATTRIBUTES MEASURE
VARIABLE
Concept Name Role Identifier Code list
COUNTRY Dimension
VARIABLE Dimension REPRESENTED_VARIABLE CL_REPRESENTED_VARIABLE
SEX Dimension SEX CL_SEX
AGE Dimension AGE CL_AGE
TIME Dimension TIME_PERIOD
OBS_VALUE Measure
UNIT OF MEASURE Attribute UNIT_MEAS CL_UNIT_MEASURE
AGE_COVERAGE Attribute AGE_COV CL_AGE_COV
FOOTNOTES Attribute FOOTNOTES
NATURE_OF_DATA Attribute NATURE CL_NATURE
SOURCE Attribute SOURCE

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1. Improves accuracy and reliability to all
those involved in supplying or using
statistical data
2. Provides major benefits in the preparation,
analysis and communication of statistical
information.
3. Offers cost savings and greater efficiency
by automating data reporting/collection
activities
Why using SDMX for data exchange?

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Dataset
SMART
SMART
DSD
DATA
REPORTING
LMIS
UPLOAD
LMI
ANALYSIS
PROJECT
Dataset
DATA
CONVERSION
Dataset
NRP
Microdata
Aggregated
Data
Structural
Metadata
SDMX Registry
ILOSTAT DISSEMINATION
.Stat DE

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DSD
Codelist
Upload
Code lists
Load & Edit
a DSD
SDMX Registry
Upload
concepts
Manual
entry
DSD
DSD Constructor

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ILO Department of Statistics
Excel2CSV Add-in
Excel2CSV Add-in

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DSD
PROJECT
DSD Constructor
Input Data
LMIS DATA PRODUCTION
NRP
ILOSTAT DISSEMINATION
.Stat DE

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LMIS.Stat Architecture
DPI