Mr. Andrew Berg, Lahcen Bounader, Nikolay Gueorguiev, Hiroaki Miyamoto, Mr. Kenji Moriyama, Ryota Nakatani, and Luis-Felipe Zanna
Many studies predict massive job losses and real wage decline as a result of the ongoing widespread automation of production, a trend that may be further aggravated by the COVID-19 crisis. Yet automation is also expected to raise productivity and output. How can we share the gains from automation more widely, for the benefit of all? And what are the attendant equity-efficiency trade-offs? We analyze this issue by considering the effects of fiscal policies that seek to redistribute the gains from automation and address income inequality. We use a dynamic general equilibrium model with monopolistic competition, including a novel specification linking corporate power to automation. While fiscal policy cannot eliminate the classic equity-efficiency trade-offs, it can help improve them, reducing inequality at small or no loss of output. This is particularly so when policy takes advantage of novel, less distortive transmission channels of fiscal policy created by the empirically observed link between corporate market power and automation.
COVID-19 has exacerbated concerns about the rise of the robots and other automation technologies. This paper analyzes empirically the impact of past major pandemics on robot adoption and inequality. First, we find that pandemic events accelerate robot adoption, especially when the health impact is severe and is associated with a significant economic downturn. Second, while robots may raise productivity, they could also increase inequality by displacing low-skilled workers. We find that following a pandemic, the increase in inequality over the medium term is larger for economies with higher robot density and where new robot adoption has increased more. Our results suggest that the concerns about the rise of the robots amid the COVID-19 pandemic seem justified.
Mr. Carlos Mulas-Granados, Richard Varghese, Vizhdan Boranova, Alice deChalendar, and Judith Wallenstein
We exploit a survey data set that contains information on how 11,000 workers across advanced and emerging market economies perceive the main forces shaping the future of work. In general, workers feel more positive than negative about automation, especially in emerging markets. We find that negative perceptions about automation are prevalent among workers who are older, poorer, more exposed to job volatility, and from countries with higher levels of robot penetration. Perceptions over automation are positively viewed by workers with higher levels of job satisfaction, higher educational attainment, and from countries with stronger labor protection. Workers with positive perceptions of automation also tend to respond that re-education and retraining will be needed to adapt to rapidly evolving skill demands. These workers expect governments to have a role in shaping the future of work through protection of labor and new forms of social benefits. The demand for protection and benefits is more significant among women and workers that have suffered job volatility.
The Spring-Summer 2019 issue of the IMF Research Perspectives explores how technology deals with old questions. Articles discuss the ways technological progress and the increased availability of data have helped in some areas, while presenting new challenges for analyzing various matters. The issue also includes an interview with Gita Gopinath, the new director of the IMF Research Department.
Mariya Brussevich, Ms. Era Dabla-Norris, Christine Kamunge, Pooja Karnane, Salma Khalid, and Ms. Kalpana Kochhar
New technologies?digitalization, artificial intelligence, and machine learning?are changing the way work gets done at an unprecedented rate. Helping people adapt to a fast-changing world of work and ameliorating its deleterious impacts will be the defining challenge of our time. What are the gender implications of this changing nature of work? How vulnerable are women’s jobs to risk of displacement by technology? What policies are needed to ensure that technological change supports a closing, and not a widening, of gender gaps? This SDN finds that women, on average, perform more routine tasks than men across all sectors and occupations?tasks that are most prone to automation. Given the current state of technology, we estimate that 26 million female jobs in 30 countries (28 OECD member countries, Cyprus, and Singapore) are at a high risk of being displaced by technology (i.e., facing higher than 70 percent likelihood of being automated) within the next two decades. Female workers face a higher risk of automation compared to male workers (11 percent of the female workforce, relative to 9 percent of the male workforce), albeit with significant heterogeneity across sectors and countries. Less well-educated and older female workers (aged 40 and above), as well as those in low-skill clerical, service, and sales positions are disproportionately exposed to automation. Extrapolating our results, we find that around 180 million female jobs are at high risk of being displaced globally. Policies are needed to endow women with required skills; close gender gaps in leadership positions; bridge digital gender divide (as ongoing digital transformation could confer greater flexibility in work, benefiting women); ease transitions for older and low-skilled female workers.
IMF Research Perspective (formerly published as IMF Research Bulletin) is a new, redesigned online newsletter covering updates on IMF research. In the inaugural issue of the newsletter, Hites Ahir interviews Valeria Cerra; and they discuss the economic environment 10 years after the global financial crisis. Research Summaries cover the rise of populism; economic reform; labor and technology; big data; and the relationship between happiness and productivity. Sweta C. Saxena was the guest editor for this inaugural issue.
Automated trade execution systems are examined with respect to the degree to which they automate the price discovery process. Seven levels of automation of price discovery are identified, and 47 systems are classified according to these criteria. Systems operating at various levels of automation are compared with respect to age, geographical location, and type of securities traded. Information provided to market participants, and asymmetries of information between traders with direct access to the automated market and outside investors also are examined. It is found, for example, that the degree of asymmetric information increases with the level of automation of price discovery. The potential for trading abuses related to prearranged trading, noncompetitive execution, and trading ahead of customers is analyzed for each level of automation. Certain levels of automation widen the opportunities for trading abuses in some respects, but may narrow them in others.