nep-tid New Economics Papers
on Technology and Industrial Dynamics
Issue of 2022‒06‒20
twelve papers chosen by
Fulvio Castellacci
Universitetet i Oslo

  1. Differentiating artificial intelligence capability clusters in Australia By Bratanova, Alexandra; Pham, Hien; Mason, Claire; Hajkowicz, Stefan; Naughtin, Claire; Schleiger, Emma; Sanderson, Conrad; Chen, Caron; Karimi, Sarvnaz
  2. The diffusion of disruptive technologies By Nicholas Bloom; Tarek Alexander Hassan; Aakash Kalyani; Josh Lerner; Ahmed Tahoun
  3. Technology transfer in global value chains By Thomas Sampson
  4. Robot Adoption, Organizational Capital and the Productivity Paradox By Rodimiro Rodrigo
  5. Routine-biased technological change and employee outcomes after mass layoffs: Evidence from Brazil By Martins-Neto, Antonio; Cirera, Xavier; Coad, Alex
  6. Canonical correlation complexity of European regions By Nomaler, Önder; Verspagen, Bart
  7. Offshoring, domestic employment and production: Evidence from the German International Sourcing Survey By Kaus, Wolfhard; Zimmermann, Markus
  8. AI-tocracy By Martin Beraja; Andrew Kao; David Y. Yang; Noam Yuchtman
  9. Corporate Financial Disclosures and the Market for Innovation By Kim, Jinhwan; Valentine, Kristen
  10. Regional Structural Change and the Effects of Job Loss By Arntz, Melanie; Ivanov, Boris; Pohlan, Laura
  11. Product market competition, creative destruction and innovation By Rachel Griffith; John Van Reenen
  12. AI Watch: Revisiting Technology Readiness Levels for relevant Artificial Intelligence technologies By MARTINEZ PLUMED Fernando; CABALLERO BENÍTEZ Fernando; CASTELLANO FALCÓN David; FERNANDEZ LLORCA David; GOMEZ Emilia; HUPONT TORRES Isabelle; MERINO Luis; MONSERRAT Carlos; HERNÁNDEZ ORALLO José

  1. By: Bratanova, Alexandra; Pham, Hien; Mason, Claire; Hajkowicz, Stefan; Naughtin, Claire; Schleiger, Emma; Sanderson, Conrad; Chen, Caron; Karimi, Sarvnaz
    Abstract: We demonstrate how cluster analysis underpinned by analysis of revealed technology advantage can be used to differentiate geographic regions with comparative advantage in artificial intelligence (AI). Our analysis uses novel datasets on Australian AI businesses, intellectual property patents and labour markets to explore location, concentration and intensity of AI activities across 333 geographical regions. We find that Australia's AI business and innovation activity is clustered in geographic locations with higher investment in research and development. Through cluster analysis we identify three tiers of AI capability regions that are developing across the economy: ‘AI hotspots’ (10 regions), ‘Emerging AI regions’ (85 regions) and ‘Nascent AI regions’ (238 regions). While the AI hotspots are mainly concentrated in central business district locations, there are examples when they also appear outside CBD in areas where there has been significant investment in innovation and technology hubs. Policy makers can use the results of this study to facilitate and monitor the growth of AI capability to boost economic recovery. Investors may find these results helpful to learn about the current landscape of AI business and innovation activities in Australia.
    Keywords: Artificial intelligence, cluster, revealed technology advantage, regional innovation, Australia
    JEL: O31 O33 O38 R12
    Date: 2022–05–31
  2. By: Nicholas Bloom; Tarek Alexander Hassan; Aakash Kalyani; Josh Lerner; Ahmed Tahoun
    Abstract: We identify novel technologies using textual analysis of patents, job postings, and earnings calls. Our approach enables us to identify and document the diffusion of 29 disruptive technologies across firms and labor markets in the U.S. Five stylized facts emerge from our data. First, the locations where technologies are developed that later disrupt businesses are geographically highly concentrated, even more so than overall patenting. Second, as the technologies mature and the number of new jobs related to them grows, they gradually spread across space. While initial hiring is concentrated in high-skilled jobs, over time the mean skill level in new positions associated with the technologies declines, broadening the types of jobs that adopt a given technology. At the same time, the geographic diffusion of low-skilled positions is significantly faster than higher-skilled ones, so that the locations where initial discoveries were made retain their leading positions among high-paying positions for decades. Finally, these technology hubs are more likely to arise in areas with universities and high skilled labor pools.
    Keywords: disruptive technologies, technological change, firms, labor markets ,
    Date: 2021–09–10
  3. By: Thomas Sampson
    Abstract: Firm-to-firm relationships in global value chains create opportunities for North-South technology diffusion. This paper studies technology transfer in value chains when contracts are incomplete and in-put production technologies are imperfectly excludable. The paper introduces a new taxonomy of value chains based on whether or not the headquarters firm benefits from imitation of its supplier's technology. In inclusive value chains, where imitation is beneficial, the headquarters firm promotes technology diffusion. By contrast, in exclusive value chains headquarters seeks to limit supplier imitation. The paper analyzes how this distinction affects the returns to offshoring, the welfare effects of technical change and the social efficiency of knowledge sharing. Weaker intellectual property rights over input production technologies raise welfare when value chains are inclusive, but have the opposite effect under exclusive value chains.
    Keywords: technology transfer, global value chains, incomplete contracts, intellectual property rights, imitation
    Date: 2022–12
  4. By: Rodimiro Rodrigo (Department of Economics, Georgetown University)
    Abstract: Major technological changes have come with an adjustment period of stagnant productivity before the economy operates at its full potential. The mechanism of this adoption process is still not well understood. Using event studies, I document that productivity increases with a five-year lag after the adoption of industrial robots in Brazilian local labor markets. Combining employer-employee matched data with a novel measure of robot adoption, I provide first evidence of establishment-level labor reorganization and organizational capital depreciation induced by the automation process. During the five years after adoption, labor switching across occupations increases within firms, moving from production to support activities. I show that firms’ organizational capital measured by workers’ firm-occupation-specific experience depreciates and then slowly re-accumulates. When these processes stop, the productivity gains reach their maximum. I use these results to estimate a general equilibrium model with heterogeneous firms, endogenous robot adoption, and organizational capital accumulation. The model accounts for the productivity paradox, the diffusion of industrial robots, and the change in the aggregate skill demand. The model highlights the role of organizational costs accompanying the adoption of new technologies. I illustrate its usefulness by using it to characterize the implications of the “innovator’s dilemma.” Classification- E24, J62, L23, O32, O33
    Keywords: Labor Productivity, Occupational Mobility, Technological Change, Automation
    Date: 2022–02–27
  5. By: Martins-Neto, Antonio (UNU-MERIT, Maastricht University); Cirera, Xavier (World Bank); Coad, Alex (Waseda Business School, Waseda University)
    Abstract: We investigate the impact of "routinization" on the labor outcomes of displaced workers. We use a rich Brazilian panel dataset and an occupation-task mapping to examine the effect of job displacement in different groups, classified according to their tasks. Our main result is that following a layoff, workers previously employed in routine-intensive occupations suffer a more significant decline in wages and more extended periods of unemployment. As expected, job displacement has a negative and lasting impact on wages. Still, workers in routine-intensive occupations are more impacted than those in non-routine occupations in terms of wages (an increase of one point in the routineintensity index results in a further decline of 2 percent in workers' relative wages) and employment. Furthermore, our results indicate that workers in routine-intensive occupations are more likely to change occupations after the shock, and those who do not switch occupational fields suffer a more significant decline in wages. Lastly, even though the loss of employer-specific wage premiums explains 13 percent of displaced workers' drop in wages, it does not explain routine-intensive workers' more substantial losses.
    Keywords: Technological change, innovation, Routine intensity, Job displacement, Mass layoffs, Occupational mobility, Brazil
    JEL: J24 J63 O31 O33 O54
    Date: 2022–04–19
  6. By: Nomaler, Önder (UNU-MERIT, Maastricht University); Verspagen, Bart (UNU-MERIT, Maastricht University)
    Abstract: In an earlier paper (Nomaler & Verspagen, 2022) we introduced a 'supervised learning' based alternative to the competing unsupervised learning algorithms (e.g., Hidalgo and Hausmann, 2009 vs. Tacchella et al, 2012) proposed in the so-called 'economic complexity' literature. Similar to the existing ones, our alternative, which we refer to as the "Canonical Correlation Complexity Method (CCCM)", also aims at reducing the high dimensionality in data on the empirical patterns of co-location (be it nations or regions) of specializations in products or technologies, while the ultimate objective is to understand the relationship between specialization, diversification, and economic development. In our alternative method which combines the toolkit of the Canonical Correlation Analysis with that of Principal Component Analysis, the data on trade or technology specializations and multiple dimensions of economic development are processed together from the very beginning in order to identify the patterns of mutual association. This way, we are able to identify the products or technologies that can be associated with the level or the growth rate of per capita GDP, and (un)employment. In this follow up paper, we use the CCCM to analyse the development patterns of European regions in relation to their respective technology specializations. Our findings provide insights for EU's industrial policies, especially those considered under the 'smart specialization' framework.
    Keywords: Economic complexity, economic development, supervised learning, Canonical Correlation Analysis, Principal Component Analysis, technological specialization, revealed technological advantage, European regional development, smart specialization
    JEL: F14 F63 O11 O33 R11
    Date: 2022–04–22
  7. By: Kaus, Wolfhard; Zimmermann, Markus
    Abstract: This paper analyses the effect of offshoring (i.e., the relocation of activities previously performed in-house to foreign countries) on various firm outcomes (domestic employment, production, and productivity). It uses data from the International Sourcing Survey (ISS) 2017 for Germany, linked to other firm level data such as business register and ITGS data. First, we find that offshoring is a rare event: In the sample of firms with 50 or more persons employed, only about 3% of manufacturing firms and 1% of business service firms have performed offshoring in the period 2014-2016. Second, difference-in-differences propensity score matching estimates reveal a negative effect of offshoring on domestic employment and production. Most of this negative effect is not because the offshoring firms shrink, but rather because they don't grow as fast as the non-offshoring firms. We further decompose the underlying employment dynamics by using direct survey evidence on how many jobs the firms destroyed/created due to offshoring. Moreover, we do not find an effect on labour productivity, since the negative effect on domestic employment and production are more or less of the same size. Third, the German data confirm previous findings for Denmark that offshoring is associated with an increase in the share of 'produced goods imports', i.e. offshoring firms increase their imports for the same goods they continue to produce domestically. In contrast, it is not the case that offshoring firms increase the share of intermediate goods imports (a commonly used proxy for offshoring), as defined by the BEC Rev. 5 classification.
    Keywords: international sourcing,offshoring,productivity
    JEL: D24 L60 O30
    Date: 2022
  8. By: Martin Beraja; Andrew Kao; David Y. Yang; Noam Yuchtman
    Abstract: Can frontier innovation be sustained under autocracy? We argue that innovation and autocracy can be mutually reinforcing when: (i) the new technology bolsters the autocrat's power; and (ii) the autocrat's demand for the technology stimulates further innovation in applications beyond those benefiting it directly. We test for such a mutually reinforcing relationship in the context of facial recognition AI in China. To do so, we gather comprehensive data on AI firms and government procurement contracts, as well as on social unrest across China during the last decade. We first show that autocrats benefit from AI: local unrest leads to greater government procurement of facial recognition AI, and increased AI procurement suppresses subsequent unrest. We then show that AI innovation benefits from autocrats' suppression of unrest: the contracted AI firms innovate more both for the government and commercial markets. Taken together, these results suggest the possibility of sustained AI innovation under the Chinese regime: AI innovation entrenches the regime, and the regime's investment in AI for political control stimulates further frontier innovation.
    Keywords: artificial intelligence, autocracy, innovation, data, China, surveillance, political unrest
    Date: 2021–11–02
  9. By: Kim, Jinhwan (Stanford U); Valentine, Kristen (U of Georgia)
    Abstract: We examine the spillover effect of public firm innovation disclosures on the patent trading market. Relative to equity markets, the patent market is decentralized and rife with information frictions, yet it serves as an important mechanism through which innovations reallocate to the most productive users. Using data on patent transactions, we find that going from the 25th percentile to the 75th percentile in innovation-relevant public firm disclosures – proxied by the number of innovation-relevant sentences in 10-K filings – is linked to a 13.0% to 14.9% increase in future patent sales by other parties that likely consume these disclosures. These results are consistent with financial statement disclosures generating positive information externalities useful for trading patents. The positive link between innovation-relevant firm disclosures is stronger where information asymmetry is likely greatest (transactions between public and private firms) and where information uncertainty likely prevails (transactions between private firms) relative to transactions less likely to suffer from information frictions (transactions between public firms). We corroborate that the positive link between public firm disclosures and other parties’ patent sales is likely due to the resolution of information frictions through several cross-sectional tests, the use of proprietary patent broker data, and the plausibly exogenous implementation of Edgar by public firms. Our results speak to an important, but previously underexplored, externality of financial statement disclosures – their contribution to a well-functioning patent market.
    JEL: D23 M40 M41 O30 O31 O32 O34 O39
    Date: 2022–03
  10. By: Arntz, Melanie (ZEW Mannheim); Ivanov, Boris (ZEW); Pohlan, Laura (Institute for Employment Research (IAB), Nuremberg)
    Abstract: Routine-intensive occupations have been declining in many countries, but how does this affect individual workers’ careers if this decline is particularly severe in their local labor market? This paper uses administrative data from Germany and a matched difference-in-differences approach to show that the individual costs of job loss strongly depend on the task-bias of regional structural change. Workers displaced from routine manual occupations have substantially higher and more persistent employment and wage losses in regions where such occupations decline the most. Regional and occupational mobility partly serve as an adjustment mechanism, but come at high cost as these switches also involve losses in firm wage premia. Non-displaced workers, by contrast, remain largely unaffected by structural change.
    Keywords: routine-biased structural change, local labor markets, displacement, mass-layoffs, plant closures, matching, difference-in-differences, event study
    JEL: J24 J63 J64 J65 O33 R11
    Date: 2022–05
  11. By: Rachel Griffith; John Van Reenen
    Abstract: We examine the economic analysis of the relationship between innovation and product market competition. First, we give a brief tour of the intellectual history of the area. Second, we examine how the Aghion-Howitt framework has influenced the development of the literature theoretically and (especially) empirically, with an emphasis on the "inverted U": the idea that innovation rises and then eventually falls as the intensity of competition increases. Thirdly, we look at recent applications and development of the framework in the areas of competition policy, international trade and structural Industrial Organization.
    Keywords: competition, innovation, creative destruction
    Date: 2021–11–30
  12. By: MARTINEZ PLUMED Fernando (European Commission - JRC); CABALLERO BENÍTEZ Fernando; CASTELLANO FALCÓN David; FERNANDEZ LLORCA David (European Commission - JRC); GOMEZ Emilia (European Commission - JRC); HUPONT TORRES Isabelle (European Commission - JRC); MERINO Luis; MONSERRAT Carlos; HERNÁNDEZ ORALLO José
    Abstract: Artificial intelligence (AI) offers the potential to transform our lives in radical ways. However, we lack the tools to determine which achievements will be attained in the near future. Also, we usually underestimate which various technologies in AI are capable of today. This report constitutes the second edition of a study proposing an example-based methodology to categorise and assess several AI technologies, by mapping them onto Technology Readiness Levels (TRL) (e.g., maturity and availability levels). We first interpret the nine TRLs in the context of AI and identify different categories in AI to which they can be assigned. We then introduce new bidimensional plots, called readiness-vs-generality charts, where we see that higher TRLs are achievable for low-generality technologies focusing on narrow or specific abilities, while high TRLs are still out of reach for more general capabilities. In an incremental way, this edition builds on the first report on the topic by updating the assessment of the original set of AI technologies and complementing it with an analysis of new AI technologies. We include numerous examples of AI technologies in a variety of fields and show their readiness-vs-generality charts, serving as a base for a broader discussion of AI technologies. Finally, we use the dynamics of several AI technologies at different generality levels and moments of time to forecast some short-term and mid-term trends for AI.
    Keywords: Artificial Intelligence, Technology Readiness Level, AI technology, evaluation, machine learning, recommender systems, expert systems, apprentice by demonstration, audio-visual content generation, machine translation, speech recognition, massive multi-modal models, facial recognition, text recognition, transport scheduling systems, self-driving cars, home cleaning robots, logistic robots, negotiation agents, virtual assistants, risks
    Date: 2022–05

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