Skills taxonomy and skills-occupation mapping, data and big data analytics are among overarching themes where best practices can be identified to achieve a successful Labor Market Information System (LMIS) crucial in addressing skills mismatch, according to a paper released by the Philippine Institute for Development Studies (PIDS).
In a discussion paper, PIDS Fellow II Connie Bayudan-Dacuycuy, former Fellow I Ma. Christina Epetia, Research Specialist Anna Rita Vargas, and Research Analyst II John Joseph Ocbina said a system to classify skills is necessary for any skills matching, anticipating, or forecasting exercises. It provides structures to data collection/analysis and perspectives in discussions and forums.
Bayudan-Dacuycuy, Epetia, Vargas and Ocbina said skills-occupation mapping allows the analysis of skill distributions across various levels of disaggregation, specifically geographical indicators, jobs’ and workers’ characteristics.
“It allows the analysis of labor market dynamics, including job transitions and the transferability of skills between occupations and jobs. It also enhances the value of the LFS (Labor Force Survey) and other surveys that do not have skills content,” they said.
The paper said that while the most common data sources countries use are household and labor market surveys, administrative data, and national indicators; other countries like Germany and Singapore employ graduate tracer surveys.
Citing earlier study, they said tracer surveys aim to benchmark institutional performance, check the employment outcomes of students, and improve curriculum responsiveness to labor market demand. Such surveys are conducted from a few months to a year after graduation.
Bayudan-Dacuycuy, Epetia, Vargas and Ocbina also highlighted the importance of big data analytics and non-traditional data collection strategies.
While traditional data sources can be representative of the population, these may not be as immediately updated and as granular as stakeholders want them to be, they said.
“Thus, there is an increasing move to supplement traditional data sources with big data, specifically from online job vacancies (OJV) and jobseekers’ credentials. These sources provide rich granular data, which aid in analyzing detailed skills gaps, outlook, and trends at a faster pace and in helping stakeholders formulate more timely and relevant choices/decisions,” the paper added.
Aside from these themes, the paper identified other good practices in skills needs anticipation (SNA) and LMIS, which include collaboration and engagements, dissemination practices, and financial resources and sustainability.
“The involvement of multiple sectors in different phases of LMIS development is key, ensuring that approaches are multidisciplinary and strategies are complementary. It is ideal for data to be interoperable across different sectors and agencies, which can help provide a holistic picture of the status of the labor market,” it said.
The study is an input to the Technical Education and Skills Development Authority (TESDA) Skills Anticipation and Prioritization of Skills Requirements (SAPSR) Framework, which is intended primarily as a reference in identifying skills requirements.
