The identification of labour market issues critically rests on the availability of data, information and analysis. Labour market information systems (LMIS) provide an essential basis for employment and labour policies, and inform the design, implementation, monitoring and evaluation of policies that are better focused and targeted. LMIS also contribute to a reduction in the transaction costs of labour markets as they help overcome incomplete information of labour market agents.
Most countries are committed to the development of labour market information systems. However, particularly in developing economies, the functioning of LMIS, if such systems have been established at all, is hampered by various constraints, including data limitations. Data limitations affect not only complex issues such as informality and employment protection, but also labour market indicators that in most developed economies are available on a monthly or quarterly basis, such as employment and unemployment indicators. Data limitations are related in developing economies to constraints such as resource scarcity, limited analytical capacity and structural factors. Furthermore, labour market institutions, including workers’ and employers’ organizations, are weak in many economies, which hampers the development and use of mechanisms to feed information and analysis into decision-making. Such problems may lead to ill-informed policy formulation and inadequate monitoring, hindering efforts to achieve labour market and development objectives.
A labour market information system is 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.
The main purpose of LMIS is the production of information and analysis for policy makers and other labour market stakeholders. For example, the functions of the European Employment Observatory are stated as follows: “The European Employment Observatory contributes to the development of the European Employment Strategy through the provision of information, comparative research and evaluation on employment policies and labour market trends.” It is important to establish institutional arrangements in order to make the information and analysis widely available, and to provide opportunities for labour market stakeholders to influence the agenda of the LMIS.
The LMIS can also be directly involved in monitoring and reporting on employment and labour policies. Both at the international and national levels, the institutional role of the LMIS can be broadened to include the exchange of information or coordination of the LMIS activities of labour market stakeholders, which include statistical agencies, research agencies and agencies involved in policy formulation and implementation, including and workers’ and employers’ organizations. This function may range from the dissemination of information on concepts, definitions and standards, to the allocation of resources regarding data collection or specific analytical activities.
Three main functions of labour market information systems include:
(1) Facilitating labour market analysis;
(2) Providing the basis for monitoring and reporting on employment and labour policies; and
(3) Serving as a mechanism to exchange information or coordinate different actors and institutions that produce and utilize labour market information and analysis.
Labour market information systems consist of four main components:
(1) Collection and compilation of data and information;
(2) Repository of information;
(3) Analytical capacity and tools; and
(4) Institutional arrangements and networks.
Regarding the first component, and given that LMIS should provide analyses of labour markets in their economic context, collection or compilation of data consists not only of data on labour markets, but also on the broader economy. For example, data on trade flows and remittances are indispensable for an analysis of the labour market effects on economic crises.
Effective LMIS draw on all major data sources. Each source has advantages and limitations in terms of the cost, quality and type of information gained.
Labour force surveys can be designed to cover the entire population of a country, all sectors of the economy and all categories of workers, including own-account workers, contributing family workers and persons engaged in casual work. For this reason, household-based labour force surveys offer a unique advantage to obtain information on the labour market of a country and its structure. Other sources, such as population censuses, multi-purpose household surveys, establishment surveys, or administrative records (e.g., employment service records), differ in scope, coverage, units of measurement or methods of data collection.
Meanwhile, establishment surveys typically have poor coverage of very small or unregistered businesses but are a more reliable source on wages and earnings. Similarly, administrative records provide a low-cost source of labour market information, but this information is limited by the purpose of the records, which may be different from that of an analyst or policy maker.
At a minimum, LMIS track a set of indicators, which constitute the basis for the development of more advanced systems. A widely-used set of indicators are the Decent Work Indicators (DWI). DWI cover the four dimensions of the ILO’s Decent Work Agenda, plus indicators of the economic and social context.
What we offer
We can provide a solution for storage and dissemination of LMIS indicators with .Stat. This powerful platform is available for member States implementing LMIS through an agreement between ILO and OECD and the Statistical Information Systems – Collaboration Community (SIS-CC).
Institutional arrangements are needed for the LMIS to effectively perform its analytical function, for example by providing access to data (from statistical agencies, administrative bodies and other entities), but also to allow for the effective dissemination of information and analysis. An example of a straightforward institutional arrangement is the establishment of an LMIA Advisory Panel, joining policy-makers, the statistical agency and workers’ and employers’ organizations.
High quality product: .Stat is one of the most advanced statistical information systems’ platform currently used in the official statistics community.
Affordable investment: Under the “umbrella” of ILO’s membership to the SIS-CC, a country implementing a LMIS can use the product and receive first level support and free upgrades. The only investment required to the country are the expenses associated to the deployment of the platform and training on .Stat administration and data/metadata management.
Community values: The SIS-CC promotes a series of values that are aligned with ILO principles, like partnership (collaboration rather than a vendor/client relationship), transparency (information openly and transparently shared among members), commitment (to respecting the coordinated work plans put in place), industrialisation (outputs developed according to best practices in application lifecycle management), and standards (foster and promote internationally-defined standards, such as SDMX, GSBPM, GSIM, and CSPA).
Sustainability: The collaborative approach for development, and the number of institutions using the software minimize the risk of support or further development being interrupted, an inconvenient situation that unfortunately is quite common when contracting external developers.
.Stat main functions
A web based application to process and load data into the Data Warehouse. Data can be in csv, .txt, .xml or sdmx* format.
Based on Microsoft SQL server and a standard star schema data warehouse technology.
A single exit point serves all outputs from the Data Warehouse exposing the data to several dissemination tools through a set of Web services.
.Stat allows for the extracting of data to various analytical tools for further data analysis.