1. „Digital TVET Tender“ – Leveraging the potential of TVET and alternative data for infrastructure projects
Paris21 Expert Meeting
Goal if the Session:
Introduce DTT (once more) to the audience and create a common understanding of the initiative and the questions which need to be considered during the meeting and in the future cooperation
Within the 1 hour session quéstions shall be clarified regarding the general Initiatives understanding
For the disciuson of the questions to be clarified between ADB and BMZ later sessions are places
Björn Richter
Head of Sector Project Digital Development, GIZ

2. Rationale behind Digital TVET Tender
Today,
Technical and Vocational Education and Training (TVET) often lacks practical relevance
Construction companies often rely on foreign personnel to implement infrastructure projects
many countries experience shortages of skilled labor while trained workers remain unemployed
Reasons are:
Industry and TVET systems are inadequately alinged and curriculas not set according to industries need
TVET provided often appears to be too theoretical and does not correspond with the requirements and skills needed in the workspace
Inadequate data on labour market (availability of labour and skills)

3. The Initiative: Digital TVET Tender
The initiative ‚Digital TVET Tender‘ aims at leveraging the potential of infrastructure programmes for training national workers on the job
The Initiative will:
Increase TVET‘s practical relevance by strenghtening practical elements
Create incentives for construction companies to provide training
Integrate on-the-job training into tenders of construction projects financed e.g. by development banks
Leverage digitalization a - digital platform - to identify labor markets needs and training potentials
Lower entry barriers for companies quality labour market data enables access to local labour force

4. Data is the Enabler: The Digital TVET Tender Process (on the basis of Mongolias example)
Initiation
[Build Power Plant of size y]
2. Project Analysis
[need for x wokers with z qualification]
3. Labor market data collection
[collection of available ressources]
4. Staffing potential / GAP Analysis
[15 electrical engineers with qualification level b missing]
5. Qualification Potential
[9 electrical engineers with qualification level a can be trained]
6. Return
[data, names of potential workers and trainees returned]
7. Monitoring & Evaluation
[status and training process can be monitored and reviewed]
Könnte man evtl. mit einem praktischen Beispiel unterlegen

5. Data is Key DTT only wokrs with good labour market data
Experiences in Mongolia and Pakistan: quality data not available
Which alternative data and data sources exist for labour market prediction?

6. Thank you very much!
Contact: