nep-tid New Economics Papers
on Technology and Industrial Dynamics
Issue of 2023‒10‒02
twelve papers chosen by
Fulvio Castellacci, Universitetet i Oslo

  1. Political Sentiment and Innovation: Evidence from Patenters By Joseph Engelberg; Runjing Lu; William Mullins; Richard R. Townsend
  2. Does training explain innovation in transition economies? By Antonella Biscione; Chiara Burlina; Raul Caruso
  3. Diversify or Not? – The Link between Global Sourcing of ICT Goods and Firm Performance By Alexander Schiersch; Irene Bertschek; Thomas Niebel
  4. Technological Sovereignty and Strategic Dependencies: The case of the Photovoltaic Supply Chain By Serenella Caravella; Francesco Crespi; Giacomo Cucignatto; Dario Guarascio
  5. Climate Change, Directed Innovation, and Energy Transition: The Long-run Consequences of the Shale Gas Revolution By Daron Acemoglu; Philippe Aghion; Lint Barrage; David Hémous
  6. AI Watch: Adoption of Autonomous Machines By CARBALLA SMICHOWSKI Bruno; DE NIGRIS Sarah; DUCH BROWN Nestor; MORENO MARÍA Adrián
  7. The transformative effects of tacit technological knowledge By Petralia, Sergio; Kemeny, Thomas; Storper, Michael
  8. Assessing the Impact of Artificial Intelligence on Germany's Labor Market: Insights from a ChatGPT Analysis By Oschinski, Matthias
  9. What is the role of data in jobs in the United Kingdom, Canada, and the United States?: A natural language processing approach By Julia Schmidt; Graham Pilgrim; Annabelle Mourougane
  10. Committing to grow: Privatizations and firm dynamics in East Germany By Akcigit, Ufuk; Alp, Harun; Diegmann, André; Serrano-Velarde, Nicolas
  11. A new approach to estimating private returns to R&D By Ådne Cappelen; Pierre Mohnen; Arvid Raknerud; Marina Rybalka
  12. Clean Growth By Costas Arkolakis; Conor Walsh

  1. By: Joseph Engelberg; Runjing Lu; William Mullins; Richard R. Townsend
    Abstract: We document political sentiment effects on US inventors. Democratic inventors are more likely to patent (relative to Republicans) after the 2008 election of Obama but less likely after the 2016 election of Trump. These effects are 2-3 times as strong among politically active partisans and are present even within firms over time. Patenting by immigrant inventors (relative to non-immigrants) also falls following Trump’s election. Finally, we show partisan concentration by technology class and firm. This concentration aggregates up to more patenting in Democrat-dominated technologies (e.g., Biotechnology) compared to Republican-dominated technologies (e.g., Weapons) following the 2008 election of Obama.
    JEL: D72 J24 M5 O31
    Date: 2023–08
  2. By: Antonella Biscione (CESPIC, Catholic University “Our Lady of Good Counsel”); Chiara Burlina (“Marco Fanno” Department of Economics and Management, University of Padova); Raul Caruso (Department of Economic Policy and CSEA, Università Cattolica del Sacro Cuore, CESPIC Catholic University “Our Lady of Good Counsel”)
    Abstract: Training has generally been linked to firm’s innovation propensity, but evidence remains sparse on the role of different typologies of training for firms in transition economies. Using a unique sample from the World Bank Enterprise Surveys (wave 2018-2020), we test the effect of training programs on innovation in 27 countries of Eastern Europe and Central Asia. We test several definitions of training, and our results show that both product and process innovations benefit from all the proposed activities. To validate our findings, we employ a specific instrumental variable approach by applying the Lewbel’s special regressor technique, whose outcome confirms our baseline results. Our contribution is twofold: first, we exploit a new database for transition countries that fill the gap in the literature on training programs also in these economies; second, for a policy perspective, we highlight the need to invest and promote training to boost innovation capacity of firms in these countries to reach the level of developed economies.
    Keywords: Transition Economies; Innovation; Training
    JEL: O14 O32 P27 P36
    Date: 2023–01
  3. By: Alexander Schiersch; Irene Bertschek; Thomas Niebel
    Abstract: Our paper contributes to the discussion about Europe’s digital sovereignty. We analyze the relationship between firm performance and the diversification of sourcing countries for imported ICT goods. The analysis is based on administrative data for 3888 German manufacturing firms that imported ICT goods in the years 2010 and 2014. We find that firms that diversify the sourcing of ICT goods across multiple countries perform better than similar firms with a less diversified sourcing structure. This result holds for value added as well as for gross operational surplus as performance measures and for two different indicators of diversification.
    Keywords: ICT goods imports, global sourcing, digital sovereignty, firm performance
    JEL: F14 F23 L14 L23 D24
    Date: 2023
  4. By: Serenella Caravella; Francesco Crespi; Giacomo Cucignatto; Dario Guarascio
    Abstract: This work sheds new light on the Photovoltaic Supply Chain (PVSC), providing fresh evidence on structural dependencies (SDs) and (asymmetrically distributed) technological capabilities. Bridging the perspectives of 'technological sovereignty' and 'strategic autonomy', a number of contributions are provided. First, we carry out a fine-grained mapping of the PVSC, combining trade and patent data. Second, we assess the long-term evolution of trade and technological hierarchies, documenting processes of polarization and growing SDs. Third, we zoom-in on critical PV areas (i.e. products and related technologies), providing a 'strategic intelligence' activity which may prove useful for tailoring trade, industrial and innovation policies. Fourth, we explore the relationship between technological specialization and productive capabilities showing that, in the upstream segment, reinforcing the former may help mitigating SDs.
    Keywords: Technological sovereignty; Strategic dependency; Photovoltaic industry; Trade; Patents.
    Date: 2023–09–14
  5. By: Daron Acemoglu; Philippe Aghion; Lint Barrage; David Hémous
    Abstract: We investigate the short- and long-term effects of a natural gas boom in an economy where energy can be produced with coal, natural gas, or clean sources and the direction of technology is endogenous. In the short run, a natural gas boom reduces carbon emissions by inducing substitution away from coal. Yet, the natural gas boom discourages innovation directed at clean energy, which delays and can even permanently prevent the energy transition to zero carbon. We formalize and quantitatively evaluate these forces using a benchmark model of directed technical change for the energy sector. Quantitatively, the technology response to the shale gas boom results in a significant increase in emissions as the US economy is pushed into a “fossil-fuel trap” where long-run innovations shift away from renewables. Overall, the shale gas boom reduces our measure of social welfare under laissez-faire, whereas, combined with carbon taxes and more generous green subsidies, it could have increased welfare substantially.
    JEL: O30 O41 O44 Q33 Q43 Q54 Q55
    Date: 2023–09
  6. By: CARBALLA SMICHOWSKI Bruno (European Commission - JRC); DE NIGRIS Sarah (European Commission - JRC); DUCH BROWN Nestor (European Commission - JRC); MORENO MARÍA Adrián
    Abstract: This report provides an empirical analysis of the drivers of and barriers to adoption of autonomous machines (AM) technologies by European companies. It also analyses the impact of adopting this technology on firm productivity. Using 2020 survey data from 9 640 firms located in EU27, Norway, Iceland and the UK, we show that AM adoption is driven by several factors and has heterogeneous effects on companies depending on their characteristics. Regarding the drivers of adoption, we find that firm size, employee knowledge of artificial intelligence (AI) and the joint adoption of AM with complementary technologies increase a firm’s probability of adopting AM. Concerning barriers to adoption, we make three main findings. First, the most relevant barriers (cost of adoption and, to a lesser extent, lack of skills and data access) are different for large firms. For the latter, liability and reputation risks, as well as data access, are the most important obstacles. Second, certain types of obstacles (namely liability and reputation risks, data access and lack of funding) are more likely to be present in certain sectors of activity. Third, the more complementary technologies a firm adopts, the lower its probability of facing obstacles to AM adoption. Finally, we find that AM adoption boosts firm productivity. This effect is higher for firms that start out with lower productivity, which suggests that there is a decreasing marginal return to AM adoption in terms of productivity.
    Date: 2023–08
  7. By: Petralia, Sergio; Kemeny, Thomas; Storper, Michael
    Abstract: Tacit knowledge – ideas that cannot readily be meaningfully and completely communicated – has long been considered a precursor to scientific and technological advances. Using words and phrases found in the universe of USPTO patents 1940-2020, we propose a new method of measuring tacit knowledge and its progressive codification. We uncover a discontinuity in the production of highly tacit technologies. Before 1980, highly- and less-tacit inventions are evenly distributed among inventors, organizations, scientific domains and subnational regions. After 1980, inventors of highly tacit patents become relatively rare, and increasingly concentrated in domains and locations. The economic payoffs to tacit knowledge also change, as it starts unequally rewarding high-income workers. This suggests a role for tacit knowledge in contributing to the rise in income inequality since 1980.
    JEL: J1
    Date: 2023–09–01
  8. By: Oschinski, Matthias
    Abstract: We assess the impact of artificial intelligence (AI) on Germany’s labour market applying the methodology on suitability for machine learning (SML) scores established by Brynjolfsson et al., (2018). However, this study introduces two innovative approaches to the conventional methodology. Instead of relying on traditional crowdsourcing platforms for obtaining ratings on automatability, this research exploits the chatbot capabilities of OpenAI's ChatGPT. Additionally, in alignment with the focus on the German labor market, the study extends the application of SML scores to the European Classification of Skills, Competences, Qualifications and Occupations (ESCO). As such, a distinctive contribution of this study lies in the assessment of ChatGPT's effectiveness in gauging the automatability of skills and competencies within the evolving landscape of AI. Furthermore, the study enhances the applicability of its findings by directly mapping SML scores to the European ESCO classification, rendering the results more pertinent for labor market analyses within the European Union. Initial findings indicate a measured impact of AI on a majority of the 13, 312 distinct ESCO skills and competencies examined. A more detailed analysis reveals that AI exhibits a more pronounced influence on tasks related to computer utilization and information processing. Activities involving decision-making, communication, research, collaboration, and specific technical proficiencies related to medical care, food preparation, construction, and precision equipment operation receive relatively lower scores. Notably, the study highlights the comparative advantage of human employees in transversal skills like creative thinking, collaboration, leadership, the application of general knowledge, attitudes, values, and specific manual and physical skills. Applying our rankings to German labour force data at the 2-digit ISCO level suggests that, in contrast to previous waves of automation, AI may also impact non-routine cognitive occupations. In fact, our results show that business and administration professionals as well as science and engineering associate professionals receive relatively higher rankings compared to teaching professionals, health associate professionals and personal service workers. Ultimately, the research underscores that the overall ramifications of AI on the labor force will be contingent upon the underlying motivations for its deployment. If the primary impetus is cost reduction, AI implementation might follow historical patterns of employment losses with limited gains in productivity. As such, public policy has an important role to play in recalibrating incentives to prioritize machine usefulness over machine intelligence.
    Keywords: Generative AI, Labour, Skills Suitability for Machine Learning, German labour market, ESCO
    JEL: A1 J0
    Date: 2023–08–14
  9. By: Julia Schmidt; Graham Pilgrim; Annabelle Mourougane
    Abstract: This paper estimates the data intensity of occupations/sectors (i.e. the share of job postings per occupation/sector related to the production of data) using natural language processing (NLP) on job advertisements in the United Kingdom, Canada and the United States. Online job advertisement data collected by Lightcast provide timely and disaggregated insights into labour demand and skill requirements of different professions. The paper makes three major contributions. First, indicators created from the Lightcast data add to the understanding of digital skills in the labour market. Second, the results may advance the measurement of data assets in national account statistics. Third, the NLP methodology can handle up to 66 languages and can be adapted to measure concepts beyond digital skills. Results provide a ranking of data intensity across occupations, with data analytics activities contributing most to aggregate data intensity shares in all three countries. At the sectoral level, the emerging picture is more heterogeneous across countries. Differences in labour demand primarily explain those variations, with low data-intensive professions contributing most to aggregate data intensity in the United Kingdom. Estimates of investment in data, using a sum of costs approach and sectoral intensity shares, point to lower levels in the United Kingdom and Canada than in the United States.
    Keywords: Data asset, data economy, Data intensity, job advertisements, natural language processing
    JEL: C80 C88 E01 J21
    Date: 2023–09–18
  10. By: Akcigit, Ufuk; Alp, Harun; Diegmann, André; Serrano-Velarde, Nicolas
    Abstract: This paper investigates a unique policy designed to maintain employment during the privatization of East German firms after the fall of the Iron Curtain. The policy required new owners of the firms to commit to employment targets, with penalties for non-compliance. Using a dynamic model, we highlight three channels through which employment targets impact firms: distorted employment decisions, increased productivity, and higher exit rates. Our empirical analysis, using a novel dataset and instrumental variable approach, confirms these findings. We estimate a 22% points higher annual employment growth rate, a 14% points higher annual productivity growth, and a 3.6% points higher probability of exit for firms with binding employment targets. Our calibrated model further demonstrates that without these targets, aggregate employment would have been 15% lower after 10 years. Additionally, an alternative policy of productivity investment subsidies proved costly and less effective in the short term.
    Keywords: industrial policy, privatizations, productivity, size-dependent regulations
    JEL: D22 D24 J08 L25
    Date: 2023
  11. By: Ådne Cappelen; Pierre Mohnen; Arvid Raknerud; Marina Rybalka (Statistics Norway)
    Abstract: This paper revisits the estimation of private returns to R&D. In an extension of the standard approach, we allow for endogeneity of production decisions, heterogeneity of R&D elasticities, and asymmetric treatment of intramural and extramural R&D. Our empirical analyses are based on an extended Cobb-Douglas production function that allows for firms with zero R&D capital, which is especially useful for studying firms’ transition from being R&D-non—active to becoming R&D-active. Using a large panel of Norwegian firms observed in the period 2001-2018, we estimate the average private net return to be in the range 0-5 percent across a variety of model specifications if we treat intra- and extramural R&D symmetrically. If in compliance with the Frascati manual, we treat intramural R&D as investment and extramural R&D as intermediate input, the estimated net return increases to 5-10 percent.
    Keywords: Returns to R&D; Intramural R&D; Extramural R&D; Capitalization of R&D; Dynamic panel data models; GMM
    JEL: C33 C52 D24 O38
    Date: 2023–08
  12. By: Costas Arkolakis; Conor Walsh
    Abstract: We provide a spatial theory of clean growth to assess the global impact of the rise of renewable energy. We model the details of the combined production and transmission network of electricity (“the grid”) that determine the supply and losses of energy in space. The local rate of clean energy adoption depends on learning-by-doing, the global electricity and trade network, and regional comparative advantage in renewable resources. We use the model to measure the aggregate and spatial implications of clean growth. We find that the world’s power system is likely to be dominated by renewables by 2040 in a range of scenarios, with substantial welfare gains, even in the absence of policy. Incorporating policy, we find that the US Inflation Reduction Act significantly accelerates renewable uptake, and generates substantial economic benefits. In addition, planned grid improvements lower prices substantially in many areas of the US, justifying their cost of construction.
    JEL: F11 Q40 Q41 Q42 Q43 R13
    Date: 2023–08

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