The OECD Skills Outlook 2019: Too Aspirational
The authors wave their hands at hypothetical policies that history suggests will be extremely difficult to realize
The has released a new report — OECD Skills Outlook 2019: Thriving in a Digital World — making a strong case for placing significantly more emphasis on the skills workers will need to thrive in the future.
The continuum of skills required by digitalisation underlines the need for every country to foster lifelong learning. This means breaking down inequalities in learning opportunities, adapting school curricula to changing skills requirements — including digital skills — and giving teachers the best training possible. It also means building adult education and training systems that respond to the labour market.
To achieve these goals and make the most of digitalisation, it is crucial that countries put in place a comprehensive package that co-ordinates policies on education, the labour market, tax, housing, social protection, and research and innovation. Policies on skills and training must form the cornerstone of such a package and are essential to ensure the digital transformation contributes to inclusive growth. In our rapidly digitalising world, skills make the difference between staying ahead of the wave and falling behind.
As Steve LeVine notes,
1 of 8 American workers and 1 of 7 Europeans are highly vulnerable to automation and require retraining, according to the Organization for Economic Cooperation and Development.
The U.S. is well positioned in terms of the opportunity for workers to obtain new skills and retrain.
But, compared with other advanced economies, Americans are currently only somewhat prepared for a time when digital skills will be paramount.
Countries that are already doing this are mainly in northern Europe — Belgium, Denmark, Finland, the Netherlands, New Zealand, Norway and Sweden.
The report strikes me as largely aspirational: it details what should be done, while suggesting policies that will be extremely difficult to realize. As just one example regarding labor mobility, consider this, from page 31:
Labour mobility between geographical areas can lessen the differences in regional performance brought about by digitalisation. Yet, it has been in decline in some OECD countries. Moving from low-income to high-income areas increases labour supply in the high-income area, putting downward pressure on wages. In the medium run, this decreases income gaps between the two areas. Mobile individuals may also have shorter unemployment spells, as they can take advantage of opportunities in areas that are more dynamic. Moreover, they may find a better match for their skill set, which would improve productivity and potentially yield positive local spill-overs. Geographical mobility can be facilitated by removing inefficient land use regulations, moderating the tax bias towards home ownership, revisiting and possibly harmonising local social transfers, and providing financial assistance to unemployed workers to reduce migration costs.
This runs counter to trends in the U.S., where the well-educated generally move to cities for college and stay there, and the less-well-educated are increasingly non-mobile, because U.S. economics do not favor migration even to high-income areas, because the less-well-educated are not able to gain high-paying work, in general (see David Autor’s Work of the Past, Work of the Future in Work of the Future). And I don’t see the political will to move the U.S. away from tax benefits for home ownership in the near future. This is very pie-in-the-sky stuff.
Much of the report reads in the same way:
New technologies offer many do-it-yourself options: people can learn, work, find out about their health and do many other activities with a click of the mouse or a tap of the screen. However, this publication shows that if most of the responsibility is left to individuals and firms, the benefits of digital transformation may be shared very unequally. Ensuring people can benefit from new technologies at home and at work and are not left behind requires a comprehensive, co-ordinated policy effort. The package of co-ordinated policies needs simultaneously to promote digitalisation where it increases productivity and well-being and cushion its negative impacts. Skills and education policies are of paramount importance to this package.
A first range of challenges concerns labour markets. All interested parties need to consider how to implement a range of policies that can accompany labour market restructuring through effective training and adequate social protection. It is also crucial to discuss how the cost of these policies can be shared between stakeholders to ensure that inequalities do not increase (OECD, 201910). The policy package should also include measures that can facilitate occupational and geographical mobility (e.g. housing policies, occupational licencing) and can shape the incentives to train and benefit from new opportunities (e.g. tax policies, unemployment insurance). In parallel, research and innovation policies can unlock the potential of digital technologies for economic and social well-being, while regional and local development policies can help spread the benefits of digitisation.
Stating that ‘all interested parties’ need to cooperate to confront these challenges does not by any means amount to spelling out what needs to be done. And, at the core, the report does not seem to treat the difficulties in retraining people with the skills most likely to be made irrelevant in the face of automation and other shifts due to digitalization. However, I still found the depth of analysis and data helpful. For example, this table of Robot Intensity in various economies:
But consider this section, where the authors admit that they have no real basis for their optimism about reskilling workers:
Many uncertainties surround these estimates. As discussed in methodological sections of this chapter and further in Box 3.3, the methodology relies on several assumptions that affect the size of the estimated effects. In particular, there are large uncertainties concerning the number of occupations that would be less in demand in the future and the share of workers who might need to change occupations, which are crucial drivers of these estimates. Some workers in occupations at high risk of automation may never be displaced by automation, because the nature of their job evolves, or because automation pervades economies in unexpected ways. For these reasons, lower and upper bound estimates could be different from those in this analysis.
This approach also assumes that workers complete their education and training programmes and that these programmes are successful in raising skills. There is no data on the completion rates of workers and adults. However, data on students and therefore on youth) show completion rates of 75% for upper secondary education and 72% for tertiary education. By assuming fully efficient education and training programmes with full completion rates, the analysis tends to under-estimate the cost.
Extrapolating from high school completion rates does not follow for trying to reskill former manufacturing workers, for example. This invalidates a great deal of what preceded this admission.
Originally published at https://stoweboyd.com.