Published in August 2023
The acceleration of technological change is reconfiguring employment, labour relations, and required skills. A recent example is AI like ChatGPT. All countries are facing acute challenges to respond to the new workforce’s skills requirements, especially in developing countries, limiting economic productivity enabled by technological development.
Lack of updated labour market statistics and information on new skills demanded is a key obstacle in addressing the skills gap. Therefore, well-designed framework policies can boost labour productivity by reducing skills mismatch
The solution involves among other things:
- Collecting and measuring detailed and current data to make informed decisions about education, continuous learning, training, and workforce support.
- Developing a common language with a skills taxonomy is crucial, similar to standardized occupational or educational classifications.
- Utilizing non-traditional data sources and technologies like AI to generate high-frequency data at low cost, such as analyzing big data from online job vacancies.
- Combining information from different sectors, including public, private, job search, and education, can benefit students, workers, companies, and governments.
In conclusion, the Fourth Industrial Revolution and the acceleration of technological change are redefining employment occupations, labour relations, and skills demanded, leading to a mismatch between the workforce’s available skill-set and the qualifications demanded by employers. This paper argues that better data collection and measurement to make informed decisions about education, lifelong learning, training strategies, and support for workers during times of transition can help address the labour mismatch. The G20’s role is essential to follow up on the roadmap towards a Common Framework for Measuring the Digital Economy, developed in previous G20 presidencies, and to promote inclusive people-centred growth through digitalisation and automation.