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on Technology and Industrial Dynamics |
| By: | Acemoglu, Daron (MIT); Restrepo, Pascual (Boston University) |
| Abstract: | We present a framework for understanding the effects of automation and other types of technological changes on labor demand, and use it to interpret changes in US employment over the recent past. At the center of our framework is the allocation of tasks to capital and labor – the task content of production. Automation, which enables capital to replace labor in tasks it was previously engaged in, shifts the task content of production against labor because of a displacement effect. As a result, automation always reduces the labor share in value added and may reduce labor demand even as it raises productivity. The effects of automation are counterbalanced by the creation of new tasks in which labor has a comparative advantage. The introduction of new tasks changes the task content of production in favor of labor because of a reinstatement effect, and always raises the labor share and labor demand. We show how the role of changes in the task content of production – due to automation and new tasks – can be inferred from industry-level data. Our empirical decomposition suggests that the slower growth of employment over the last three decades is accounted for by an acceleration in the displacement effect, especially in manufacturing, a weaker reinstatement effect, and slower growth of productivity than in previous decades. |
| Keywords: | automation, displacement effect, labor demand, inequality, productivity, reinstatement effect, tasks, technology, wages |
| JEL: | J23 J24 |
| Date: | 2019–04 |
| URL: | http://d.repec.org/n?u=RePEc:iza:izadps:dp12293&r=all |
| By: | Michael Koch; Ilya Manuylov; Marcel Smolka |
| Abstract: | We study the implications of robot adoption at the level of individual firms using a rich panel data-set of Spanish manufacturing firms over a 27-year period (1990-2016). We focus on three central questions: (1) Which firms adopt robots? (2) What are the labor market effects of robot adoption at the firm level? (3) How does firm heterogeneity in robot adoption affect the industry equilibrium? To address these questions, we look at our data through the lens of recent attempts in the literature to formalize the implications of robot technology. As for the first question, we establish robust evidence that ex-ante larger and more productive firms are more likely to adopt robots, while ex-ante more skill-intensive firms are less likely to do so. As for the second question, we find that robot adoption generates substantial output gains in the vicinity of 20-25% within four years, reduces the labor cost share by 5-7%-points, and leads to net job creation at a rate of 10%. These results are robust to controlling for non-random selection into robot adoption through a difference-in-differences approach combined with a propensity score reweighting estimator. Finally, we reveal substantial job losses in firms that do not adopt robots, and a productivity-enhancing reallocation of labor across firms, away from non-adopters, and toward adopters. |
| Keywords: | automation, robots, firms, productivity, technology |
| JEL: | D22 F14 J24 O14 |
| Date: | 2019 |
| URL: | http://d.repec.org/n?u=RePEc:ces:ceswps:_7608&r=all |
| By: | Giuntella, Osea (University of Pittsburgh); Wang, Tianyi (University of Pittsburgh) |
| Abstract: | A handful of studies have investigated the effects of robots on workers in advanced economies. According to a recent report from the World Bank (2016), 1.8 billion jobs in developing countries are susceptible to automation. Given the inability of labor markets to adjust to rapid changes, there is a growing concern that the effect of automation and robotization in emerging economies may increase inequality and social unrest. Yet, we still know very little about the impact of robots in developing countries. In this paper we analyze the effects of exposure to industrial robots in the Chinese labor market. Using aggregate data from Chinese prefectural cities (2000-2016) and individual longitudinal data from China, we find a large negative impact of robot exposure on employment and wages of Chinese workers. Effects are concentrated in the state-owned sector and are larger among low-skilled, male, and prime-age and older workers. Furthermore, we find evidence that exposure to robots affected internal mobility and increased the number of labor-related strikes and protests. |
| Keywords: | emerging economies, labor markets, robots |
| JEL: | J23 J24 O33 |
| Date: | 2019–04 |
| URL: | http://d.repec.org/n?u=RePEc:iza:izadps:dp12281&r=all |
| By: | Timothy DeStefano; Richard Kneller; Jonathan Timmis |
| Abstract: | The arrival of the cloud has enabled a shift in the nature of ICT use, from investment in sunk capital to a pay-on-demand service that allows firms to rapidly scale up. This paper uses new firm-level data to examine the impact of cloud on firm growth in the UK, using zipcode-level instruments of the timing of high-speed fibre availability and expected speeds. We find cloud leads to the growth of young firms in terms of employment and productivity, but they become more concentrated in fewer plants. For older firms we find no scale or productivity growth, but instead disperse activity by closing plants and moving employment further from the headquarters. In addition, the plants that close tend to be those without access to fibre broadband. |
| Keywords: | firm growth; the cloud; ICT use; employment; productivity |
| Date: | 2019 |
| URL: | http://d.repec.org/n?u=RePEc:not:notgep:2019-09&r=all |
| By: | Acemoglu, Daron (MIT); Restrepo, Pascual (Boston University) |
| Abstract: | Artificial Intelligence is set to influence every aspect of our lives, not least the way production is organized. AI, as a technology platform, can automate tasks previously performed by labor or create new tasks and activities in which humans can be productively employed. Recent technological change has been biased towards automation, with insufficient focus on creating new tasks where labor can be productively employed. The consequences of this choice have been stagnating labor demand, declining labor share in national income, rising inequality and lower productivity growth. The current tendency is to develop AI in the direction of further automation, but this might mean missing out on the promise of the "right" kind of AI with better economic and social outcomes. |
| Keywords: | automation, artificial intelligence, jobs, inequality, innovation, labor demand, productivity, tasks, technology, wages |
| JEL: | J23 J24 |
| Date: | 2019–04 |
| URL: | http://d.repec.org/n?u=RePEc:iza:izadps:dp12292&r=all |
| By: | Piotr Lewandowski; Albert Park; Wojciech Hardy; Du Yang |
| Abstract: | The shift away from manual and routine cognitive work, and towards non-routine cognitive work is a key feature of labor markets. There is no evidence, however, if the relative importance of various tasks differs between workers performing seemingly similar jobs in different countries. We develop worker-level, survey-based measures of task content of jobs – non-routine cognitive analytical and personal, routine cognitive and manual – that are consistent with widely-used occupation-specific measures based on O*NET database. We apply them to representative surveys conducted in 42 countries at different stages of development. We find substantial cross-country differences in the content of work within occupations. Routine task intensity (RTI) of jobs decreases significantly with GDP per capita for high-skill occupations but not for middle- and low-skill occupations. We estimate the determinants of workers’ RTI as a function of technology (computer use), globalization (specialization in global value chains), structural change, and supply of skills, and decompose their role in accounting for the variation in RTI across countries. Computer use, better education, and higher literacy skills are related to lower RTI. Globalization (as measured by sector foreign value-added share) increases RTI in poorer countries but reduces RTI in richer countries. Differences in technology endowments and in skills’ supply matter most for cross-country differences in RTI, with globalization also important. Technology contributes the most to the differences in RTI among workers in high-skilled occupations and non-off-shorable occupations; globalization contributes the most to differences among workers in low-skilled occupations and offshorable occupations. |
| Keywords: | tasks, deroutinisation, routine-replacing technical change, off-shoring, PIAAC, STEP, CULS |
| JEL: | J21 J23 J24 |
| Date: | 2019–04 |
| URL: | http://d.repec.org/n?u=RePEc:ibt:wpaper:wp042019&r=all |
| By: | Boehm, Johannes; Dhingra, Swati; Morrow, John |
| Abstract: | Multiproduct firms dominate production, and their product turnover contributes substantially to aggregate growth. Theories propose that multiproduct firms grow by diversifying into products which need the same know-how or capabilities, but are less clear on what these capabilities are. Input-output tables show firms co-produce in industries that share intermediate inputs, suggesting input capabilities drive multiproduct production patterns. We provide evidence for this in Indian manufacturing: the similarity of a firm's input mix to an industry's input mix predicts entry into that industry. We identify the direction of causality from the removal of size-based entry barriers in input markets which made firms more likely to enter industries that were similar in input use to their initial input mix. We rationalize this finding with a model of industry choice and economies of scope to estimate the importance of input capabilities in determining comparative advantage. Complementarities driven by input capabilities make a firm on average 5% (and up to 15%) more likely to produce in an industry. Entry barriers in input markets constrained the comparative advantage of firms and were equivalent to a 10.5 percentage point tariff on inputs. |
| Keywords: | comparative advantage; Economies of scope; firm capabilities; Multiproduct Firms; size-based policies; vertical input linkages |
| JEL: | F11 L25 M2 O3 |
| Date: | 2019–04 |
| URL: | http://d.repec.org/n?u=RePEc:cpr:ceprdp:13699&r=all |
| By: | Serhan Cevik; Fedor Miryugin |
| Abstract: | This paper investigates the impact of taxation on firm survival, using hazard models and a large-scale panel dataset on over 4 million nonfinancial firms from 21 countries over the period 1995–2015. We find ample evidence that a lower level of effective marginal tax rate improves firms’ survival chances. This result is not only statistically but also economically important and remains robust when we partition the sample into country subgroups. The effect of taxation on firms’ survival probability is found to exhibit a non-linear pattern and be stronger in developing countries than advanced economies. These findings have important policy implications for the design of corporate tax systems. The challenge is not simply reducing the statutory tax rate, but to level the playing field for all firms by rationalizing differentiated tax treatments across sectors, asset types and sources of financing. |
| Date: | 2019–04–19 |
| URL: | http://d.repec.org/n?u=RePEc:imf:imfwpa:19/78&r=all |
| By: | Serhan Cevik |
| Abstract: | Literature on whether government spending crowds out or crowds in the private sector is large, but still without an unambiguous conclusion. Using firm-level data from Ukraine, this paper provides a granular empirical investigation to disentangle the impact of state-owned enterprises (SOEs) on private firm investment in Ukraine—a large transition economy. Controlling for firm characteristics and systematic differences across sectors, the results indicate that the SOE concentration in a given sector has a statistically significant negative effect on private fixed capital formation, and that the impact of SOEs is stronger in those industries in which SOEs have a more dominant presence. These findings imply that private firms operating in sectors with a high level of SOE concentration invest systematically less than businesses that are not competing directly with SOEs. |
| Date: | 2019–04–24 |
| URL: | http://d.repec.org/n?u=RePEc:imf:imfwpa:19/80&r=all |