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on Technology and Industrial Dynamics |
| By: | Erik Brynjolfsson |
| Abstract: | In 1950, Alan Turing proposed an imitation game as the ultimate test of whether a machine was intelligent: could a machine imitate a human so well that its answers to questions indistinguishable from a human. Ever since, creating intelligence that matches human intelligence has implicitly or explicitly been the goal of thousands of researchers, engineers, and entrepreneurs. The benefits of human-like artificial intelligence (HLAI) include soaring productivity, increased leisure, and perhaps most profoundly, a better understanding of our own minds. But not all types of AI are human-like. In fact, many of the most powerful systems are very different from humans. So an excessive focus on developing and deploying HLAI can lead us into a trap. As machines become better substitutes for human labor, workers lose economic and political bargaining power and become increasingly dependent on those who control the technology. In contrast, when AI is focused on augmenting humans rather than mimicking them, then humans retain the power to insist on a share of the value created. Furthermore, augmentation creates new capabilities and new products and services, ultimately generating far more value than merely human-like AI. While both types of AI can be enormously beneficial, there are currently excess incentives for automation rather than augmentation among technologists, business executives, and policymakers. |
| Date: | 2022–01 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2201.04200&r= |
| By: | Carbonero, Francesco (University of Turin); Davies, Jeremy (East Village Software Consultants); Ernst, Ekkehard (ILO International Labour Organization); Fossen, Frank M. (University of Nevada, Reno); Samaan, Daniel (ILO International Labour Organization); Sorgner, Alina (John Cabot University) |
| Abstract: | AI is transforming labor markets around the world. Existing research has focused on advanced economies but has neglected developing economies. Different impacts of AI on labor markets in different countries arise not only from heterogeneous occupational structures, but also from the fact that occupations vary across countries in their composition of tasks. We propose a new methodology to translate existing measures of AI impacts that were developed for the US to countries at various levels of economic development. Our method assesses semantic similarities between textual descriptions of work activities in the US and workers' skills elicited in surveys for other countries. We implement the approach using the measure of suitability of work activities for machine learning provided by Brynjolfsson et al. (2018) for the US and the World Bank's STEP survey for Lao PDR and Viet Nam. Our approach allows characterizing the extent to which workers and occupations in a given country are subject to destructive digitalization, which puts workers at risk of being displaced, in contrast to transformative digitalization, which tends to benefit workers. We find that workers in Lao PDR are less likely than in Viet Nam to be in the "machine terrain", where workers will have to adapt to occupational transformations due to AI and are at risk of being partially displaced. Our method based on semantic textual similarities using SBERT is advantageous compared to approaches transferring AI impact scores across countries using crosswalks of occupational codes. |
| Keywords: | artificial intelligence, machine learning, digitalization, labor, skills, developing countries |
| JEL: | J22 J23 O14 O33 |
| Date: | 2021–12 |
| URL: | http://d.repec.org/n?u=RePEc:iza:izadps:dp14944&r= |
| By: | Piotr Lewandowski; Karol Madoń; Ronald Bachmann; Myrielle Gonschor |
| Abstract: | We study the effects of robot exposure on worker flows in 16 European countries. Overall, we find small negative effects on job separations and small positive effects on job findings. Labour costs are shown to be a major driver of cross-country differences: in countries with lower labour costs, robot exposure had more positive effects on hirings and more negative effects on separations. These effects were particularly pronounced for workers in occupations intensive in routine manual or routine cognitive tasks, but were insignificant in occupations intensive in non-routine cognitive tasks. For young and old workers in countries with lower labour costs, robot exposure had a beneficial effect on transitions. Our results imply that robot adoption increased employment and reduced unemployment in most European countries, mainly through lower job separation rates. |
| Keywords: | robots, technological change, tasks, labour market effects, Europe |
| JEL: | J23 J24 O33 |
| Date: | 2022–01 |
| URL: | http://d.repec.org/n?u=RePEc:ibt:wpaper:wp012022&r= |
| By: | Ernest Liu; Song Ma |
| Abstract: | We study the optimal allocation of R&D resources in an endogenous growth model with an innovation network, through which one sector’s past innovations may benefit other sectors’ future innovations. First, we provide closed-form sufficient statistics for the optimal path of R&D resource allocation, and we show that planners valuing long-term growth should allocate more R&D toward key sectors that are upstream in the innovation network. Second, we extend to an open-economy setting and illustrate an incentive for countries to free-ride on fundamental technologies: an economy more reliant on foreign knowledge spillovers has less incentive to direct resources toward innovation-upstream sectors, leading to cross-country differences in unilaterally optimal R&D allocations across sectors. Third, we build the global innovation network based on over 30 million global patents and establish its empirical importance for knowledge spillovers. Fourth, we apply the model to evaluate R&D allocations across countries and time. Adopting optimal R&D allocations can generate substantial welfare improvements across the globe. For the United States, R&D misallocation accounts for about 0.68 percentage points of missing annual growth since the 2000s. |
| JEL: | F43 O33 O38 |
| Date: | 2021–12 |
| URL: | http://d.repec.org/n?u=RePEc:nbr:nberwo:29607&r= |
| By: | Lööf, Hans (CESIS - Centre of Excellence for Science and Innovation Studies, Royal Institute of Technology) |
| Abstract: | This paper surveys theoretical and empirical literature on non-pecuniary flow of knowledge and the conditions and limitations for firms to benefit from positive externalities. Spillovers from the pool of accumulated knowledge generated by technological and scientific development is considered to be a key factor for economic development in modern growth models. Knowledge spillovers has also been a major topic of empirical research on firms’ innovation and economic performance over the last thirty years or more. By exploiting theoretical and methodological advances, and using more comprehensive, complex and detailed data sources, scholars from various scientific disciplines have improved the identification of factors, mechanisms, and channels that influence flows of knowledge within and across industries, technological regimes and regions. This research has deepened the understanding of the economic importance of knowledge spillovers. |
| Keywords: | externalities; innovation; knowledge spillovers; productivity; technology |
| JEL: | L20 M13 O31 O33 O40 |
| Date: | 2022–01–03 |
| URL: | http://d.repec.org/n?u=RePEc:hhs:cesisp:0489&r= |
| By: | Verhoogen, Eric (Columbia University) |
| Abstract: | In principle, firms in developing countries benefit from the fact that advanced technologies and products have already been developed in industrialized countries and can simply be adopted, a process often referred to as industrial upgrading. But for many firms this advantage remains elusive. What is getting in the way? This paper reviews recent firm-level empirical research on the determinants of upgrading in developing countries. The first part focuses on how to define and measure various dimensions of upgrading - learning, quality upgrading, technology adoption, and product innovation. The second part takes stock of recent micro-empirical evidence on the drivers of upgrading, classifying them as output-side drivers, input-side drivers, or drivers of know-how. The review concludes with some thoughts about promising directions for research in the area. |
| Keywords: | developing countries, upgrading, firms |
| JEL: | O1 L2 F1 |
| Date: | 2021–11 |
| URL: | http://d.repec.org/n?u=RePEc:iza:izadps:dp14858&r= |
| By: | Ernest Miguelez (GREThA - Groupe de Recherche en Economie Théorique et Appliquée - UB - Université de Bordeaux - CNRS - Centre National de la Recherche Scientifique); Valentina Di Iasio |
| Abstract: | Abstract This study investigates whether high-skilled migration in a sample of OECD countries fosters technological diversification in the migrants' countries of origin. We focus on migrant inventors and study their role as vectors of knowledge remittances. Further, we particularly analyze whether migrants spark related or unrelated diversification back home. To account for the uneven distribution of knowledge and migrants within the host countries, we break down the analysis at the metropolitan area level. Our results suggest that migrant inventors have a positive effect on the home countries' technological diversification, particularly for developing countries and technologies with less related activities around—thus fostering unrelated diversification. |
| Date: | 2021–11–29 |
| URL: | http://d.repec.org/n?u=RePEc:hal:journl:hal-03505186&r= |
| By: | Pérez Rodríguez, Sandra (ROA / Labour market and training, RS: GSBE other - not theme-related research); van der Velden, Rolf (RS: GSBE Theme Learning and Work, ROA / Education and transition to work); Huijts, Tim (ROA / Health, skills and inequality, RS: GSBE Theme Learning and Work); Jacobs, Babs (ROA / Education and transition to work, RS: GSBE other - not theme-related research) |
| Abstract: | Skill mismatches have strong negative effects on productivity, job satisfaction, and other outcomes. To reduce skill mismatches, governments need to rely on accurate data on the prevalence of these mismatches. The Programme of the International Assessment of Adult Competences (PIAAC) is currently the most important data source providing excellent and unparalleled information for many countries on two key information-processing skills (i.e., literacy and numeracy skills). However, although these data contain rich information about possessed skills, countries lack directly comparable information on the required skills in those domains. Hence, it has been difficult to use the PIAAC data to identify skill mismatches, other than through proxies of required skills (e.g., the average skill level in occupations) or workers’ self-assessments of skill mismatch. In this paper, we use the Job Analysis Method (JAM) to determine the required skill levels of literacy and numeracy for all 4-digit ISCO08 unit groups of occupations in the same metric and scale as was used in PIAAC. JAM involves the use of occupational experts to rate the skill requirements in the different occupations. JAM has never been used before to identify required skill levels for literacy and numeracy as measured in PIAAC, and the paper thus presents the first results on the prevalence of skill shortages and skill surpluses in these key information-processing skills across different OECD countries and across different occupations and sectors that is based on a more direct estimate of the required skills. We provide estimates for the proportions of well-matched, overskilled and underskilled workers per country, and compare these with estimates based on alternative methods for estimating skill mismatch. We also compare JAM with these other methods in explaining wage differentials, as well as job satisfaction. We conclude that there are large differences in the estimates of the prevalence of skill mismatches depending on the method used. We show several advantages using JAM and discuss some of the limitations as well. |
| JEL: | J24 |
| Date: | 2022–01–04 |
| URL: | http://d.repec.org/n?u=RePEc:unm:umaror:2022011&r= |
| By: | Anita Quas (University of Milan); Colin Mason (University of Glasgow); Ramón Compañó (European Commission - JRC); James Gavigan (European Commission - JRC); Giuseppina Testa (European Commission - JRC) |
| Abstract: | The number of scale-up businesses in the EU, particularly unicorns, lags behind the US and China. This is partially attributed to a deficit in scale-up finance. Based on an a webinar between experts which took place on 5th October 2021, this paper reports and comments on the available evidence of the scale-up financing gap in the EU and discusses its causes and consequences. The paper also reviews what types of instruments might address this gap and discusses issues that need to be addressed in the formulation of effective policy interventions. Finally, it points to missing data, existing knowledge gaps, and areas on which further analysis is required to define better policies. |
| Keywords: | startups, scaleups, venture capital, finance, innovation |
| JEL: | O32 O31 O25 |
| Date: | 2021–12 |
| URL: | http://d.repec.org/n?u=RePEc:ipt:iptwpa:jrc127232&r= |
| By: | Richard Harding (Carbon to Zero Consulting SRL,Romania); Claire Nauwelaers; Caroline Cohen (European Commission - JRC); Isabelle Seigneur (European Commission - JRC) |
| Abstract: | The aim of this study is to illustrate the role played by Smart Specialisation Strategies (S3) to foster environmentally oriented activities through the examination of inspiring examples from different European Member States. The report presents ten inspiring examples and highlights how stakeholders from various territorial levels across Europe are using the Smart Specialisation concept to deliver their own innovation-driven green transition agendas, detailing specific policy tools and incentives developed in this context. The selected cases provide insights into the role played by multi-level governance and enhanced stakeholder involvement in the Entrepreneurial Discovery Process (EDP), and/or Smart Specialisation Monitoring and Evaluation mechanisms in fostering the implementation of experimental ‘green’ interventions. They demonstrate that even though the S3 concept was not initially designed with a strong environmental focus, different types of territories have successfully used the S3 approach to promote environment-related priorities. In particular, the circular economy appears as a recurring transversal driver and a source of economic gains in many territories. |
| Keywords: | Smart Specialisation, environmental policies, territorial development |
| Date: | 2021–12 |
| URL: | http://d.repec.org/n?u=RePEc:ipt:iptwpa:jrc123169&r= |
| By: | Lorentz, André (Université de Strasbourg, BETA, Université de Lorraine, CNRS); Ciarli, Tommaso (UNU-MERIT, Maastricht University, and SPRU, University of Sussex); Savona, Maria (SPRU, University of Sussex); Valente, Marco (University of L’Aquila) |
| Abstract: | We derive the Kaldorian cumulative causation mechanism as an emergent property of the dynamics generated by a micro-founded model. We build on an evolutionary growth model which formalises the endogenous relations between structural changes in the production, organisation and functional composition of employment and of consumption patterns (originally proposed by Ciarli et al, 2010). We discuss the main transition dynamics to a self- sustained growth regime in a two-stage growth pattern generated through the numerical simulations of the model. We then show that these mechanisms lead to the emergence of a Kaldor-Verdoorn law. Finally we show that the structure of demand shapes the type of growth regime emerging from the endogenous structural changes, fostering or hampering the emergence of the Kaldor Verdoorn law. This depends on the endogenous income distribution and heterogeneity in consumption behaviour |
| Keywords: | Structural change, economic growth, final consumption, technological change, cumulative causation, evolutionary economics, Kaldor-Verdoorn Law |
| JEL: | O14 O33 O41 L16 C63 E11 |
| Date: | 2022–01–10 |
| URL: | http://d.repec.org/n?u=RePEc:unm:unumer:2022001&r= |