The new technologies hold promises but also significant challenges. Advances in digitalization, artificial intelligence, and automation promise to raise productivity and growth, but they are also bound to reshape the economy and the way we work, with the potential to increase inequality. Given the dimension of the possible changes, it will take a comprehensive and coordinated policy response to secure growth that is widely shared. These policies will also raise challenges for public finances.
A better understanding of the changes afoot is critical. Coverage of the new digital economy in the national accounts and labor statistics is incomplete, introducing some inaccuracy into productivity measures and complicating the monitoring of ongoing structural changes such as the growth of new forms of work organized around digital platforms. Closing these data gaps is essential to better understand, observe, and ultimately meet the challenges ahead.
Policymakers should facilitate technological change, smooth adjustment and ensure that gains are equally shared, while updating policy frameworks where needed.
• Facilitate technological advances. To realize the potential increase in productivity, policies should balance encouraging competition and incentivizing innovation. Labor and product market reforms, digital infrastructure investment, and economic integration can foster technological diffusion and the efficient reallocation of resources.
• Support adjustment while preserving inclusiveness. Active labor market policies and safety nets can protect workers while allowing change; and the tax/benefit system can help ensure that growth is broadly shared. Education is key to meet the demand for more flexible skill sets and lifelong learning, especially for the most negatively affected. Where taxes need to rise to finance higher spending, their impact on growth and income distribution should be assessed.
• Updating policy frameworks is another critical objective. Social safety nets and pension insurance systems will need to adjust to increasing cross-country mobility and more fragmented work careers against the background of pre-existing trends such aging and lengthening working lives. The new technologies also bring challenges for the public finances, as they foster hard-to-tax activities while creating evolving spending needs.
The impact on labor markets is no less uncertain, but is likely to involve major changes to both the types of jobs and the nature of worker-firm relationships. In advanced G-20 countries, regular contracts remain predominant, and in many emerging G-20 countries labor market informality remains pervasive. But work forms are changing, particularly in advanced countries, where an increasing share of the population is employed (either part time or full) in independent work intermediated by digital platforms or in other new occupations that have emerged along with new technologies. At the same time, many jobs—particularly those involving low- and middle-skill routine tasks—are being eliminated through labor-saving automation and artificial intelligence (AI). This comes against a background of existing challenges, which may differ among countries—including the fact that advanced countries must meet the demands of an aging workforce, while several emerging economies require boosting job creation for the youth and addressing the challenge of informality.
Fostering the benefits of new technologies requires good measures of their impact, notably on productivity growth and labor market developments. The depth and speed of future technological advancement and its impact on jobs and production processes are largely unknown. Ensuring accurate measurement is thus critical in reducing this uncertainty. Yet, as discussed in the introduction, technological advances have created measurement challenges and new data needs in many areas of economic statistics, notably on two issues: productivity measures and employment. This section will tackle these in turn.
It has been argued that new technologies will replace a broad range of jobs and create new forms of work, but their potential impact is difficult to predict. Recent analysis by the McKinsey Global Institute suggests that 15 percent of global jobs could be displaced by automation by 2030, with larger potential impacts in advanced
Data on individuals selling products in digital marketplaces and on contingent work in economies. Another analysis by Hawksworth, Berriman and Goel (2018) estimates that automation can potentially replace more than 30 percent of jobs robotization and AI are likely to be smaller than the proportion of jobs that would be technically feasible to replace. Furthermore, the job losses may be offset by new work opportunities created by technology and by output expansions made possible by falling costs and prices. Indeed, an increase in robot density in manufacturing in Japan has been associated with higher productivity and local gains in employment and wages.
Appropriate measures of the potential impact on jobs are hampered by significant gaps in existing statistics on technology and jobs. Mitchell and Brynjolffson (2017) highlight the lack of data on the scope and growth of AI, and on how technology is eliminating and transforming certain types of jobs. Also, occupational classifications may be challenged to keep up with effects of changing technology and lack detailed information on skill requirements.
Managing the potential impact of new technologies in labor markets will require a better understanding of the demand for worker retraining and continuing education, and of the outcomes. The jobs created by automation, and those that survive, will be more demanding in terms of technical skills and cognitive and social abilities than the jobs they replace. Statistics on workforce skills and demands for skills implied by changing occupational employment patterns will be needed to understand the potentially widening gap between workers’ skills and competencies and those that employers demand. Data on the experience of graduates of retraining programs could also aid in understanding demands for skills.
The challenges of automation and artificial intelligence will require better data on both technological advances and information-processing skills of the population:
Technological advances. Indexes of technology and AI need to be developed to measure advances in technical capabilities, the adoption of advanced technology, and the factors impeding it—such as shortages of labor with needed skills.
Information-processing skills. Data collected in the OECD’s Program for the International Assessment of Adult Competencies (PIAAC) can help identify mismatches between worker skills and employer needs, and to monitor general education and training weaknesses. Furthermore, updated classifications of occupations—including descriptions of specialized skills and typical tasks—would help inform educators and students about career options and preparedness. These occupational descriptions can be combined with data from the technology and AI indices to identify emerging occupational opportunities and risks, and provide early warning on threatened jobs.
Information on employers’ demand for skills from non-standard sources shouls be integrated with statistics from household and employer surveys. An example of this is Burning Glass Technologies, a software developer that uses AI techniques to produce real time indicators of labor market skills mismatches and career path prospects by analyzing online job postings and career- transition information. Another example is the Netherlands’ Center for Big Data Statistics, which has used web scraping and text mining techniques to identify and characterize innovative companies.
Data on in-demand skills and retraining outcomes can help to guide education curricula.Surveys of skills utilization by those who have completed the retraining programs could be used to identify skills to prioritize. Data on effectiveness of worker retraining strategies could include job placement outcomes that would be used for real time decision making. Understanding factors affecting success in worker re-deployment may also require data on displaced workers’ broader circumstances, such as barriers to geographic mobility.
Policies should address skill-mismatches through improved education and continuing training. Ensuring universal and high quality basic and secondary education is critical to help individuals adapt to new technologies (Autor, 2013), and expanding tertiary education will open the door to jobs supporting the development of these technologies in the first place. From early learning to secondary education, the right balance has to be found between endowing students with the necessary tools in science, technology, engineering, and mathematics (STEM) on the one hand, and a general curriculum that emphasizes critical thinking, decision-making, problem-solving skills and empathy on the other. At the tertiary level, this may mean expanding liberal arts education. While there is evidence pointing to the importance of building skills early (Heckman and Carneiro, 2003), this should be coupled with enhanced access to continuing (adult) education to ease the tension between longer working lives and the faster depreciation of human capital brought about by the expected acceleration of technological progress.
There are potential efficiency gains in education. While existing educational standards and goals differ across countries, the rather wide dispersion 100 in education costs among and within advanced and 90 emerging economies, especially for tertiary education, 80 points to the need to assess the effectiveness of education spending. Moreover, evidence on net school enrollment and education costs over a large sample of advanced, emerging and low-income countries suggests that some improvement in education outcomes could be achieved even within existing budget envelopes.
Human capital investment is a powerful tool to redistribute gains from technology. To illustrate the direct effect of skill building, two scenarios are simulated. The first scenario assumes a targeted increase in the human capital of low-skill
workers, while the second model assumes an increase for both low- and middle-skill workers. The underlying cost estimates match average education cost conditional on workers’ starting skill level in advanced OECD countries.15 The initial assumption—to be lifted below—is that the government can finance the additional human capital investment without raising taxes by cutting unproductive spending elsewhere.16 Key findings are:
• Educating low-skilled workers reduces inequality but might intensify income polarization. In this illustrative scenario, the government invests about 2 percent of GDP to reduce the share of low- skilled workers in the labor force by 4 percentage points overall, with half of these workers becoming middle-skilled and the other half accumulating enough human capital to become high- skilled. Compared to the no policy scenario, this reduces inequality by increasing income growth of low-skill workers relative to other types (Figure 7 – Low Skill). This is due to the relative scarcity of low-skill workers and the increased demand for services provided by the remaining low-skill workers, which rises as all incomes rise.17 Note, however, that while this policy reduces inequality, it also generates significant income polarization, as the relative supply of medium-skill workers (vis-à-vis low-skilled ones) increases while the relative demand for them declines.
• Educating low- and middle-skill workers reduces polarization, but is costlier. In this other illustrative scenario, the government follows a broader human capital investment strategy such that the shares of low-skill and middle-skill workers drop by 2 percentage points of the labor force each, with all newly educated workers graduating to the highest skill level. The overall fiscal cost is somewhat higher, at about 2.5 percent of GDP (Figure 7 – Low & Middle Skill). This strategy eventually yields income gains for the middle skilled that are broadly similar to those of low-skill workers, and both enjoy larger (percentage) increases than high-skill workers.
There is a significant international dimension to these efforts. Given that many of the expected policy challenges occur simultaneously across countries, there is a strong case for sharing information and best practices. In addition, there are areas where international cooperation is a must—including coordination of competition policy around multinational firms operating in the new economy and taxation.
The best policy approach will vary across countries. Even though all countries have reason to move quickly to close data gaps and implement reforms to facilitate growth, the need to smooth the adjustment depends on the speed at which the new technologies impact the economy. In addition, well-designed structural reforms should consider countries’ economic starting conditions—including their institutional setup, income level, and conjunctural environment.
Chosen excerpts by Job Market Monitor. Read the whole story at Future of work: measurement and policy challenges
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Some 1,408 responded to the following question, sharing their expectations about what is likely to evolve by 2026: In the next 10 years, do you think we will see the emergence of new educational and training programs that can successfully train large numbers of workers in the skills they will need to perform the jobs … Continue reading
The Future of Work – What do we want ? (video)
Highlights from award-winning economic historian Robert Skidelsky giving a keynote address about the future of work with remarks by ILO Director-General Guy Ryder.
The future of work – The major trends
Gender gap in participation rates is not expected to improve over the coming 15 years Few countries combine an environmentally sustainable footprint with decent work Declining labour force participations rates will exacerbate demographic changes Migration is likely to intensify in the future as decent work deficits remain widespread Global supply chain related jobs go well … Continue reading
Future of Work – We are not facing an employment crisis but a work revolution the World Employment Confederation (International Confederation of Private Employment Services) says
The World Employment Confederation (formerly Ciett) looks into the future of work and urges policymakers to cooperate with the employment industry to determine enhanced international labour regulation As the world of work becomes increasingly flat and interconnected, new global labour policies and regulation are required to deal with issues that go beyond national or regional … Continue reading
Technology, jobs, and the future of work – Several solution spaces to consider
Automation, digital platforms, and other innovations are changing the fundamental nature of work. Understanding these shifts can help policy makers, business leaders, and workers move forward. Policy makers will need to address issues such as benfits and variability that these digital platforms can raise. Accelerate the creation of jobs in general through stimulating investment and … Continue reading
Freelance economy and the future of work
Are these types of platforms an economic boon to workers who want a flexible way togenerate income? Or are they the latest sign of worsening income inequality and a fraying safety net for workers? The answer is a little bit of both. Recent research from the McKinsey Global Institute examined the economic potential associated with … Continue reading
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