1. What we have learned and why it matters
Expert Meeting on new labour market data sources, June 2018, Paris.

2. What is the problem? TVET didn’t really work as expected!?
What are the key ingredients to make a Digital TVET a step forward?
Traditional sources, NSO data such as LFS.., have limited predictive capability and regional desegregation – need for real time micro data to complement the structural traditional data…where is the data and how to get it
How the new labor market data ecosystem can be leveraged to support Digital TVET?
Enrolment in tvet was not as expected
Less flexible and engaging
Digital tvel can make it more interactive and attract more users
A study should look at the over all picture of the labour market matching platforms, draw some lessons
The analysis took us beyond the mere data issues to look at how the wole system is articulated and the interactions between stakholders
Below will expose some of key findings and questions raised and proposal on what could be done about them

3. The case studies, similarities!
1992
The youth
Poverty
Volatile economic performance and dependence on natural resources on commodity prices…
1992: end of civial war in mozambique and transition to full market in mongolia: good example for TVET: erosion of capacities in one and withdrwal of funds in another leading to big mismatch in the labour market
Dominant youth population which will flood the labor market in near future
High levels of poverty despite structural differences which could lead to important lessons

4. The case studies, differences!
Internet and mobile penetration
Population size, seasonality and geographic distribution
Regional context (Africa vs Asia and proximity to China and Russia)
Schooling attainment levels
Higher informality, higher share of workers in rural areas
Traditional data sources (LFS)
LMIS Development
The development of Public Employment Services

5. Same symptoms…
Significant concerns about TVET quality (and quantity, esp in MOZ)
Higher unemployment among the better educated
Skills mismatch
Opportunities from admin data (but limited in MOZ due to high informality)
Widespread use of internet jobs portals (incl emerging informal jobs portal in MOZ)
Numerous and fragmented stakeholders, bodies and efforts and therefore information

6. Ideas from the study, could this help?
A data model with clear definitions of roles to centralize and open the data for public consumption under clear licensing…
Sophisticated systems are not enough:
Process, engagement and coordination
reliable public (and private) employment services
Allowing a monopoly in the intermediation will probably not do it anyways:
Focus more on bridging the access gap to the system and not to the data
Centrally open the data to all users at the national level, and
Lay the regulatory foundation for a competitive (and hopefully flourishing) market of facilitators
Digital TVET tender?

7. Additional questions
How to strengthen the skills dimension of online jobs platforms?
How to deal with the high levels of labour market informality in developing countries (especially in Africa) in terms of labour market data gathering?
What might explain the fragmentation of government institutions and how to streamline it?
How to improve partnerships/coordination between government institutions and private-sector stakeholders, including online jobs portals, in the exchange of labour market information?

8. Thank you