You will find more infographics at Statista
Showing posts with label Automation. Show all posts
Showing posts with label Automation. Show all posts
Monday, July 23, 2018
The threat of automation, another paper on the topic
A new NBER Working Paper by David E. Bloom, Mathew McKenna, Klaus Prettner "Demography, Unemployment, Automation, and Digitalization: Implications for the Creation of (Decent) Jobs, 2010-2030"
Abstract:
Globally, an estimated 734 million jobs will be required between 2010 and 2030 to accommodate recent and ongoing demographic shifts, account for plausible changes in labour force participation rates, and achieve target unemployment rates of at or below 4 percent for adults and at or below 8 percent for youth. The facts that i) most new jobs will be required in countries where "decent" jobs are less prevalent and ii) workers in many occupations are increasingly subject to risks of automation further compound the challenge of job creation, which is already quite sizable in historical perspective. Failure to create the jobs that are needed through 2030 would put currently operative social security systems under pressure and undermine efforts to guarantee the national social protection floors enshrined in the Sustainable Development Goals (SDGs).
Paper available for NBER members.
Abstract:
Globally, an estimated 734 million jobs will be required between 2010 and 2030 to accommodate recent and ongoing demographic shifts, account for plausible changes in labour force participation rates, and achieve target unemployment rates of at or below 4 percent for adults and at or below 8 percent for youth. The facts that i) most new jobs will be required in countries where "decent" jobs are less prevalent and ii) workers in many occupations are increasingly subject to risks of automation further compound the challenge of job creation, which is already quite sizable in historical perspective. Failure to create the jobs that are needed through 2030 would put currently operative social security systems under pressure and undermine efforts to guarantee the national social protection floors enshrined in the Sustainable Development Goals (SDGs).
Paper available for NBER members.
Hal Varian's new working paper on AI
A new NBER Working Paper by Hal Varian Artificial Intelligence, Economics, and Industrial Organization
Abstract:
Machine learning (ML) and artificial intelligence (AI) have been around for many years. However, in the last 5 years, remarkable progress has been made using multilayered neural networks in diverse areas such as image recognition, speech recognition, and machine translation. AI is a general purpose technology that is likely to impact many industries. In this chapter I consider how machine learning availability might affect the industrial organization of both firms that provide AI services and industries that adopt AI technology. My intent is not to provide an extensive overview of this rapidly-evolving area, but instead to provide a short summary of some of the forces at work and to describe some possible areas for future research.
The Working Paper is available at NBER (gated).
Tuesday, March 20, 2018
Demographics and Automation (as in Robots!)
From a new NBER Working Paper by Daron Acemoglu and Pascual Restrepo.
Abstract:
We argue theoretically and document empirically that aging leads to greater (industrial) automation, and in particular, to more intensive use and development of robots. Using US data, we document that robots substitute for middle-aged workers (those between the ages of 36 and 55). We then show that demographic change--corresponding to an increasing ratio of older to middle-aged workers--is associated with greater adoption of robots and other automation technologies across countries and with more robotics-related activities across US commuting zones. We also provide evidence of more rapid development of automation technologies in countries undergoing greater demographic change. Our directed technological change model further predicts that the induced adoption of automation technology should be more pronounced in industries that rely more on middle-aged workers and those that present greater opportunities for automation. Both of these predictions receive support from country-industry variation in the adoption of robots. Our model also implies that the productivity implications of aging are ambiguous when technology responds to demographic change, but we should expect productivity to increase and labor share to decline relatively in industries that are most amenable to automation, and this is indeed the pattern we find in the data.
Gated NBER Working Paper #24421 available here.
Abstract:
We argue theoretically and document empirically that aging leads to greater (industrial) automation, and in particular, to more intensive use and development of robots. Using US data, we document that robots substitute for middle-aged workers (those between the ages of 36 and 55). We then show that demographic change--corresponding to an increasing ratio of older to middle-aged workers--is associated with greater adoption of robots and other automation technologies across countries and with more robotics-related activities across US commuting zones. We also provide evidence of more rapid development of automation technologies in countries undergoing greater demographic change. Our directed technological change model further predicts that the induced adoption of automation technology should be more pronounced in industries that rely more on middle-aged workers and those that present greater opportunities for automation. Both of these predictions receive support from country-industry variation in the adoption of robots. Our model also implies that the productivity implications of aging are ambiguous when technology responds to demographic change, but we should expect productivity to increase and labor share to decline relatively in industries that are most amenable to automation, and this is indeed the pattern we find in the data.
Gated NBER Working Paper #24421 available here.
Monday, January 15, 2018
More on the automated economy from NBER
From a new NBER working paper, "Artificial Intelligence, Automation and Work," by Daron Acemoglu and Pascual Restrepo.
Abstract:
Abstract:
We summarize a framework for the study of the implications of automation and AI on the demand for labor, wages, and employment. Our task-based framework emphasizes the displacement effect that automation creates as machines and AI replace labor in tasks that it used to perform. This displacement effect tends to reduce the demand for labor and wages. But it is counteracted by a productivity effect, resulting from the cost savings generated by automation, which increase the demand for labor in non-automated tasks. The productivity effect is complemented by additional capital accumulation and the deepening of automation (improvements of existing machinery), both of which further increase the demand for labor. These countervailing effects are incomplete. Even when they are strong, automation increases output per worker more than wages and reduce the share of labor in national income. The more powerful countervailing force against automation is the creation of new labor-intensive tasks, which reinstates labor in new activities and tends to increase the labor share to counterbalance the impact of automation. Our framework also highlights the constraints and imperfections that slow down the adjustment of the economy and the labor market to automation and weaken the resulting productivity gains from this transformation: a mismatch between the skill requirements of new technologies, and the possibility that automation is being introduced at an excessive rate, possibly at the expense of other productivity-enhancing technologies.
Thursday, November 30, 2017
Monday, November 6, 2017
The rise of the machines. What does it mean?
A new working paper from the National Bureau of Economic Research by Erik Brynjolfsson, Daniel Rock, Chad Syverson. Abstract:
We live in an age of paradox. Systems using artificial intelligence match or surpass human-level performance in more and more domains, leveraging rapid advances in other technologies and driving soaring stock prices. Yet measured productivity growth has declined by half over the past decade, and real income has stagnated since the late 1990s for a majority of Americans. We describe four potential explanations for this clash of expectations and statistics: false hopes, mismeasurement, redistribution, and implementation lags. While a case can be made for each, we argue that lags have likely been the biggest contributor to the paradox. The most impressive capabilities of AI, particularly those based on machine learning, have not yet diffused widely. More importantly, like other general purpose technologies, their full effects won't be realized until waves of complementary innovations are developed and implemented. The required adjustment costs, organizational changes, and new skills can be modeled as a kind of intangible capital. A portion of the value of this intangible capital is already reflected in the market value of firms. However, going forward, national statistics could fail to measure the full benefits of the new technologies and some may even have the wrong sign.
Monday, August 14, 2017
About those low wage jobs and robots: People versus machines: The Impact of minimum wages on at-risk jobs
A growing automated workforce of robots does not bode well for low-income workers, particularly older ones. Minimum wages don't help. Here's a new working paper by Grace Lordan, and David Neumark
Abstract:
We study the effect of minimum wage increases on employment in automatable jobs - jobs in which employers may find it easier to substitute machines for people - focusing on low-skilled workers from whom such substitution may be spurred by minimum wage increases. Based on CPS data from 1980-2015, we find that increasing the minimum wage decreases significantly the share of automatable employment held by low-skilled workers, and increases the likelihood that low-skilled workers in automatable jobs become unemployed. The average effects mask significant heterogeneity by industry and demographic group, including substantive adverse effects for older, low-skilled workers in manufacturing. The findings imply that groups often ignored in the minimum wage literature are in fact quite vulnerable to employment changes and job loss because of automation following a minimum wage increase.
More at NBER.
Abstract:
We study the effect of minimum wage increases on employment in automatable jobs - jobs in which employers may find it easier to substitute machines for people - focusing on low-skilled workers from whom such substitution may be spurred by minimum wage increases. Based on CPS data from 1980-2015, we find that increasing the minimum wage decreases significantly the share of automatable employment held by low-skilled workers, and increases the likelihood that low-skilled workers in automatable jobs become unemployed. The average effects mask significant heterogeneity by industry and demographic group, including substantive adverse effects for older, low-skilled workers in manufacturing. The findings imply that groups often ignored in the minimum wage literature are in fact quite vulnerable to employment changes and job loss because of automation following a minimum wage increase.
More at NBER.
Subscribe to:
Posts (Atom)