ARE FILIPINOS READY...

of: it substitutes for labor; it complements labor; or, it creates new jobs. Net effects will depend on which effect dominates.

According to the International Labor Organization, nearly half of wage workers in the Philippines are at high risk of getting affected by AI in the next few years, and those at high risk tend to be women more than men, those with low education, and those who work in BPOs (where chatbots are increasingly used). David Autor of Massachusetts Institute of Technology argues that the extent of machine substitution for human labor is overstated: though computers substitute for workers in performing routine codifiable tasks, they also amplify the comparative advantage of people. When ATMs were invented in the 1990s, it was thought that banks would have no need for bank tellers, but three decades later, banks have hired more bank tellers although bank tellers now also conduct activities on customer relationship management.

While it is clear that some current tasks in the workplace are codifiable, what remains uncertain is when and if these tasks get automated. Some point out that markets do not always reallocate resources for tech adoption. Regardless of the extent and timing of disruptions, it is clear that human capital investments are critical to prepare for what is to come from FIRe, and to make us globally competitive.

The introduction of K to 12 provides the Philippines a way to radically change basic education. But are these changes enough to prepare our future workforce for future jobs? The nature of work is vastly changing. Skills and competencies should be like Lego blocks, which can used to create different figures using the same building blocks. In another report, the World Economic Forum describes future skills required and clusters them into three groups, namely, foundational literacies, competencies, and character qualities. Thus, learners need technical and soft skills. Quality education, particularly in basics, such as reading, writing, science and mathematics, is vital but providing quality education for all is a challenge. Data on mean percent scores in achievement tests are below 50 percent for problem solving, information literacy or critical thinking, and among Grade 10, scores are even least for Math and Science.

Bright prospects though for improving the workforce are available. The Dynamic Learning Program developed by physicists Christopher and Victoria Bernido in the Central Visayan Institute Foundation can be upscaled. The country can also learn from human capital investments of China in the late 70s, which worked with the US in providing massive scholarships to those intending to specialize in science, technology, engineering and mathematics. We can also learn from Singapore, which reskills workers through its SkillsFuture Program. Full implementation of the Inclusive Innovation Industrial Strategy (i3S) is also critical. We should also provide social protection to those who may not be to adjust as easily, but work on using tax reform to fund these investments aside from the Build Build Build program. Government must safeguard public interest, especially since control over personal data and profit from its use and sale are largely in the hands of a few tech giants. Blockchain promises to change that by giving everyone a sovereign digital ID. The adoption of tech and management of its impact however require partnerships across government, business, civil society, media and the citizenry. While we do not know definitively what is to come, we should have a Whole-ofNation action agenda to improve our readiness for the future today so that whatever great divides we have will not get wider.

Dr. Jose Ramon G. Albert is a senior research fellow of PIDS, the government think-tank. He was seconded to the now defunct National Statistical Coordination Board as Secretary-General from October 2012 to February 2014. He is a professional statistician with interests in technology and innovation, poverty and income distribution, social protection, education, gender equality, climate change, econometrics and data analytics. Author’s views are those of his own.