nep-tid New Economics Papers
on Technology and Industrial Dynamics
Issue of 2024‒07‒08
fourteen papers chosen by
Fulvio Castellacci, Universitetet i Oslo

  1. Measuring process innovation outputs and understanding their implications for firms and workers: Evidence from Pakistan. By Wadho, Waqar; Chaudhry, Azam
  2. Innovation and Firm Value Revisited: Evidence from a DCF Valuation. Approach across the Entire Firm Size Spectrum By Lars Van Cutsem; Marleen Willekens; Koenraad Debackere
  3. Automation and Rent Dissipation: Implications for Wages, Inequality, and Productivity By Daron Acemoglu; Pascual Restrepo
  4. Do Stronger IPR Incentivize Female Participation in Innovation? Evidence from Chinese AI Patents By Shubhangi Agrawal; Sawan Rathi; Chirantan Chatterjee; Matthew J. Higgins
  5. Technological invention and local labour markets: evidence from France, Germany and the UK By Ioramashvili, Carolin
  6. Trust, Intangible Assets, and Productivity By Gilbert Cette; Jimmy Lopez; Jacques Mairesse; Giuseppe Nicoletti
  7. AI and Digital Technology: Gender Gaps in Higher Education By J. Ignacio Conde-Ruiz; Juan-José Ganuza; Manuel García-Santana; Carlos Victoria
  8. Apprenticeship Input Demand Cyclicality of R&D and non-R&D Firms By Samuel Muehlemann; Gerard Pfann; Harald Pfeifer
  9. Place-Based Economic Development and Long-Run Firm Employment and Sales: Evidence from American Indian Reservations By Joseph A. Aguilar; Randall Akee; Elton Mykerezi
  10. Lobbying for Industrialization: Theory and Evidence By Veselov, Dmitry; Yarkin, Alexander
  11. AI Diffusion to Low-Middle Income Countries; A Blessing or a Curse? By Rafael Andersson Lipcsey
  12. Measuring and Predicting “New Work” in the United States: The Role of Local Factors and Global Shocks By Gueyon Kim; Cassandra Merritt; Giovanni Peri
  13. Generative AI Enhances Team Performance and Reduces Need for Traditional Teams By Ning Li; Huaikang Zhou; Kris Mikel-Hong
  14. Technology, R&D, Industrial, and Science Policies: Private and Public Sector Interactions That Encourage Technological Advance By Richard G. Lipsey

  1. By: Wadho, Waqar; Chaudhry, Azam
    Abstract: New processes significantly affect firms and workers; however, due to a lack of quantitative indicators, our understanding of the measures, determinants, and impacts of new processes remains limited. Drawing on unique data from Pakistan, we analyzed five different measures of process innovation output: cost reductions, defect rate reductions, reductions in production cycle time, increases in production capacity, and improvement in product quality. We find that the breadth and depth of innovative capabilities, level of competition, and availability of market sources of knowledge are important inducers of process innovation and that smaller firms are more likely to introduce new processes and are better able to transform them into higher output. All five process innovation outputs are associated with higher labor productivity and higher sales. We do not find that adopting new processes led to labor displacement; however, there is suggestive evidence that new processes led to the increased employment of skilled workers.
    Keywords: Technology, Innovation, Process innovation, Cost-reduction, Labor productivity, Developing countries, Textiles & Apparel, Pakistan
    JEL: O31 O32 O33 J23 J24
    Date: 2024
  2. By: Lars Van Cutsem; Marleen Willekens; Koenraad Debackere
    Abstract: In this study, we leverage the Discounted Cash Flow (DCF) valuation methodology to reexamine how innovation links to economic value estimates of firms, and how size moderates this relationship. As DCF valuation can be applied to a complete spectrum of firm sizes, ranging from small privately-held to large publicly-listed firms, our study provides more exhaustive evidence on the link between innovation and economic firm value across for all types of firms. This contrasts prior studies that typically draw inferences based on market valuations of publicly listed firms. We show that innovation is positively and statistically significantly related to the economic value in both large and small firms, and that firm size negatively moderates this link. Notably, innovation is more substantially linked to the economic value estimates of small firms than larger ones. These findings are robust to various controls, alternative operationalisations, including survey-reported innovation in the Community Innovation Survey (CIS), and a battery of robustness tests for the DCF valuation approach.
    Date: 2024
  3. By: Daron Acemoglu; Pascual Restrepo
    Abstract: This paper studies the effects of automation in economies with labor market distortions that generate worker rents—wages above opportunity cost—in some jobs. We show that automation targets high-rent tasks, dissipating rents and amplifying wage losses from automation. It also reduces within-group wage dispersion for exposed groups. Automation-driven rent dissipation is inefficient and reduces (and could even negate) the productivity gains from automation. Using data for the US from 1980 to 2016, we find evidence of sizable rent dissipation and reduced within-group wage dispersion due to automation. Using these estimates and accounting for equilibrium effects, we estimate that automation accounts for 52% of the increase in between-group inequality in the US since 1980, with rent dissipation being responsible for a fifth of this contribution. We also estimate that inefficient rent dissipation offset 60–90% of the productivity gains from automation since 1980.
    JEL: J23 J31 O33
    Date: 2024–06
  4. By: Shubhangi Agrawal; Sawan Rathi; Chirantan Chatterjee; Matthew J. Higgins
    Abstract: Do stronger intellectual property rights incentivize female participation in innovation? We provide new evidence on this question using a unique database of artificial intelligence patents publicly shared by the USPTO. Our identification strategy leverages China’s WTO TRIPs accession, which led to stronger intellectual property rights in 2002. We find a significant rise in the number of female inventors and an increase in the number of patents with females on inventor teams vis-a-vis a control group of countries. We also find that after stronger intellectual property rights, the quality of Chinese artificial intelligence patents with female inventors on the team improved. Results are robust controlling for unobserved heterogeneity at the country, technology class, and over time. Additional robustness tests with synthetic controls, coarsened exact matching, randomized inference and alternative control groups support our benchmark findings. Our results highlight that stronger intellectual property rights can be helpful in improving gender division of labor thereby benefiting society and innovation.
    JEL: J16 O34
    Date: 2024–06
  5. By: Ioramashvili, Carolin
    Abstract: I estimate employment multiplier effects by skill group from graduate employment and innovation at the NUTS1 and 2 level in France, Germany and the UK. Using local projections, I estimate the effects over 5-year horizons. Both graduate employment and patenting have temporary, positive impacts on non-graduate and mid-skilled employment. There is considerable heterogeneity in terms of the direction and magnitude of the effects across the three countries. The paper shows that innovation can be a source of regional employment growth, even for those without a graduate degree.
    Keywords: skills; regions; patents; invention; employment
    JEL: J21 J24 O18 O33 O40 R11
    Date: 2024–09–01
  6. By: Gilbert Cette; Jimmy Lopez; Jacques Mairesse; Giuseppe Nicoletti
    Abstract: Business environments dominated by information flows and autonomous tasks, typical of knowledge-intensive industries, are likely to require enough social capital to be viable and productive. In this paper, we use new EUKLEMS-INTANProd industry-level data (Bontadini et al., 2023a) covering a panel of 19 countries and 20 industries over the 1995-2018 period to investigate the influence of a key element of social capital – trust – on labour productivity in intangible-intensive industries, controlling for hiring and firing regulations that can constrain the ability of managers to implement best practices productively. We find that in such industries, productivity gains from high levels of trust are stronger than elsewhere, while too strict hiring and firing regulations are more damaging for productivity. Using a more limited sample for which data on management quality are available, we show that the positive impact of high trust on productivity in intangible-intensive industries is channeled by the ability to benefit from good management, a key element of organizational capital. Productivity gains from relatively high levels of trust in knowledge-rich environments are estimated to be sizeable and our estimates survive a number of robustness checks.
    JEL: J24 L25 O50
    Date: 2024–05
  7. By: J. Ignacio Conde-Ruiz; Juan-José Ganuza; Manuel García-Santana; Carlos Victoria
    Abstract: This article examines gender gaps in higher education in Spain from 1985 to 2023 in the context of technological advancements, particularly digitalization and artificial intelligence (AI). We identify significant disparities, with women over represented in health-related fields and underrepresented in STEM disciplines. This imbalance is concerning as STEM fields offer better employment prospects and higher salaries. We analyze university degrees' exposure to technological change through Routine Task Intensity (RTI) and AI exposure indices. Our findings show that women are more enrolled in degrees with high RTI, prone to automation, and less in degrees with high AI exposure, likely to benefit from technological advancements. This suggests technological change could widen existing labor market gender gaps. To address this, we recommend policies to boost female participation in STEM fields and adapt educational curricula to reduce routine tasks and enhance AI complementarities, ensuring equitable labor market outcomes amid technological change.
    Keywords: gender gaps, artificial intelligence, higher education, STEM, technological change, self-actualization
    JEL: I23 I26 J16 J24
    Date: 2024–05
  8. By: Samuel Muehlemann; Gerard Pfann; Harald Pfeifer
    Abstract: For centuries, the flexibility to hire and train apprentices has been an important source of successful implementation of innovations in production technologies. This paper shows that the input flexibility of apprenticeships in German firms is associated with product innovation. Even though R&D firms face higher costs to set up training facilities and are therefore less likely to start up apprenticeship training than non-R&D firms, conditional on having invested set up costs, R&D firms train more than non-R&D firms. R&D firms that train apprentices are more responsive to cyclical fluctuations. Against the trend of a 0.5 percentage points annual decline of new products introduced in the market, firms that train and expand their training activities through time are primarily responsible for an increase in product innovation. R&D firms also renew products 2.7 times more than non-R&D firms. All this emphasizes the prime role of firms that train apprentices in reinvigorating the economy.
    Keywords: Apprenticeship market, business climate, R&D, apprenticeship demand
    JEL: J23 J24 M53
    Date: 2024–06
  9. By: Joseph A. Aguilar; Randall Akee; Elton Mykerezi
    Abstract: We examine how one of the largest U.S. place-based economic development programs, the Indian Gaming Regulatory Act (IGRA) of 1988, with annual revenues in excess of \$40 billion, affects local firm total employment and sales through direct channels and through IGRA's effects on adjacent non-gaming industry firms. Our analysis focuses on the effect of this national (across 29 U.S. states) place-based economic development program over several decades. We create a novel data set linking a firm-level panel dataset of business outcomes and tribal casino operations by geographic location over several decades. We find that after the start of tribal casino operations, there is a substantial average increase in employment and sales for local firms. We also show that casino operations drive initial increases in employment and sales; however, pre-existing firms also realize gains in employment and sales in the subsequent 2-5 years after the start of casino operations. These effects also spill over to firms in non-related industries; in our analysis, we exclude the Arts, Entertainment, Recreation, Accommodation and Food Services industries and we continue to observe higher employment for firms located on tribal reservations with casino operations. We provide the first evidence on the impact of place-based economic development on long-run business outcomes in some of the most underdeveloped regions in the U.S.
    JEL: H55 O12 O18 R11
    Date: 2024–06
  10. By: Veselov, Dmitry; Yarkin, Alexander
    Abstract: Industrial policies, such as infrastructure investments and export tariffs, affect the allocation of labor and incomes across sectors, attracting substantial lobbying efforts by special interest groups. Yet, the link between structural change and lobbying remains underexplored. Using more than 150 years of data on parliamentary petitions in USA and Britain, we measure historical lobbying and document several stylized facts. First, lobbying over industrial policies follows a hump-shaped path in the course of structural change, while agricultural lobbying steadily declines. Second, big capitalists (manufacturers, merchants) are most active in lobbying for industrialization. Third, industrial concentration increases progressive lobbying, while concentrated landownership slows it down. We explain these patterns in a simple model of structural change augmented with a heterogeneous agents lobbying game. Model simulations match the dynamics of structural change, inequality, and lobbying for industrialization in the British data.
    Keywords: political economy, structural change, lobbying, wealth distribution, growth
    JEL: D33 D72 N10 N41 O14 O41 O43 P00
    Date: 2024
  11. By: Rafael Andersson Lipcsey
    Abstract: Rapid advancements in AI have sparked significant research into its impacts on productivity and labor, which can be profoundly positive or negative. Often overlooked in this debate is understanding of how AI technologies spread across and within economies. Equally ignored are developing economies facing substantial labor market impacts from rapid, and a loss in competitiveness, from slow AI diffusion. This paper reviews literature on technology diffusion and proposes a three-way framework for understanding AI diffusion: global value chains, research collaboration, and inter-firm knowledge transfers. This is used to measure AI diffusion in sixteen low-middle-income, and four developed economies, as well as to evaluate dependence on China and the USA for access to AI technologies. The study finds a significant gap in diffusion rates between the two groups, but current trends indicate it is narrowing. China is identified as a crucial future source of AI diffusion through value chains, while the USA is more influential in research and knowledge transfers. The paper's limitations include the omission of additional data sources and countries, and the lack of investigation into the relationship between diffusion and technology intensity. Nonetheless, it raises salient macro-level questions about AI diffusion and suggests emphasis on redistribution mechanisms of AI induced economic gains, and bilateral agreements as a complement to international accords, to address diverse needs and corresponding risks faced by economies transitioning into an AI-dominated era. Additionally, it highlights the need for research into the links between AI diffusion, technology intensity, and productivity; case studies combined with targeted policy recommendations; more accurate methods for measuring AI diffusion; and a deeper investigation into its labor market impacts particular to LMICs.
    Date: 2024–05
  12. By: Gueyon Kim; Cassandra Merritt; Giovanni Peri
    Abstract: The evolution of work is of emerging importance to advanced economies' growth. In this study, we develop a new semantic-distance-based algorithm to identify “new work, ” namely the new types of jobs introduced in the US. We characterize how “new work” relates to task content of jobs and skill characteristics of workers and document its geographic distribution and association with employment growth. Then, we analyze whether local factors associated in the previous literature with agglomeration economies and productivity growth as well as local exposures to global shocks—technology, trade, immigration, and population aging—predict the creation of “new work.” We find local supply of college educated in 1980 as the strongest predictor of “new work.” Using the historical location of 4-year colleges, a strong instrument for local college share, we find a positive and significant causal effect of local supply of human capital on “new work.”
    JEL: F1 J11 J14 J23 J24 J61 O33
    Date: 2024–05
  13. By: Ning Li; Huaikang Zhou; Kris Mikel-Hong
    Abstract: Recent advancements in generative artificial intelligence (AI) have transformed collaborative work processes, yet the impact on team performance remains underexplored. Here we examine the role of generative AI in enhancing or replacing traditional team dynamics using a randomized controlled experiment with 435 participants across 122 teams. We show that teams augmented with generative AI significantly outperformed those relying solely on human collaboration across various performance measures. Interestingly, teams with multiple AIs did not exhibit further gains, indicating diminishing returns with increased AI integration. Our analysis suggests that centralized AI usage by a few team members is more effective than distributed engagement. Additionally, individual-AI pairs matched the performance of conventional teams, suggesting a reduced need for traditional team structures in some contexts. However, despite this capability, individual-AI pairs still fell short of the performance levels achieved by AI-assisted teams. These findings underscore that while generative AI can replace some traditional team functions, more comprehensively integrating AI within team structures provides superior benefits, enhancing overall effectiveness beyond individual efforts.
    Date: 2024–05
  14. By: Richard G. Lipsey (Simon Fraser University (Professor Emeritus))
    Abstract: This paper distinguishes four types of public policies that seek to encourage growth-inducing technological advance: technology, R&D, industrial, and science policies. The first three are typically treated under the single heading ‘industrial policy’, which is a source of confusion since each is administered by different agents and often with different objectives. Evidence of successes and failures of any one of the policies defined here is often incorrectly taken to apply to the other policies. Evidence for the symbiotic relation between the public and the private sectors is outlined, although typically ignored by in formal growth theories. The massive influence of science policy on economic growth, also typically ignored by in growth theories, is a largely unintended byproduct of scientific advance. A policy implication of the approach in this paper is to stress science, technology, and R&D policies, putting much less stress on industrial policy as defined here.
    Date: 2024–05

This nep-tid issue is ©2024 by Fulvio Castellacci. It is provided as is without any express or implied warranty. It may be freely redistributed in whole or in part for any purpose. If distributed in part, please include this notice.
General information on the NEP project can be found at For comments please write to the director of NEP, Marco Novarese at <>. Put “NEP” in the subject, otherwise your mail may be rejected.
NEP’s infrastructure is sponsored by the School of Economics and Finance of Massey University in New Zealand.