nep-tid New Economics Papers
on Technology and Industrial Dynamics
Issue of 2023‒11‒06
twelve papers chosen by
Fulvio Castellacci, Universitetet i Oslo

  1. On the Positive Relation between the Wage Share and Labor Productivity Growth with Endogenous Size and Direction of Technical Change By Luca Zamparelli
  2. Automation and Income Inequality in Europe By Doorley, Karina; Gromadzki, Jan; Lewandowski, Piotr; Tuda, Dora; Van Kerm, Philippe
  3. The Role of Global Value Chains for Worker Tasks and Wage Inequality By Lewandowski, Piotr; Madoń, Karol; Winkler, Deborah
  4. The Turing Transformation: Artificial Intelligence, Intelligence Augmentation, and Skill Premiums By Ajay K. Agrawal; Joshua S. Gans; Avi Goldfarb
  5. Contested Transparency: Digital Monitoring Technologies and Worker Voice By Burdin, Gabriel; Dughera, Stefano; Landini, Fabio; Belloc, Filippo
  6. Health, basic research, human capital accumulation, and R&D-based economic growth By Parui, Pintu
  7. Securing future-fit jobs in the green transformation: A policy framework for industrial policy By Hafele, Jakob; Le Lannou, Laure-Alizée; Rochowicz, Nils; Kuhls, Sonia; Gräbner-Radkowitsch, Claudius
  8. Political connections, business groups and innovation in Asia By Commander, Simon; Estrin, Saul; De Silva, Thamashi
  9. How important is mobile broadband latency for total factor productivity growth? By Edquist, Harald
  10. Identifying Nascent High-Growth Firms Using Machine Learning By Stephanie Houle; Ryan Macdonald
  11. Positioning in Global Value Chains: World Map and Indicators. A new dataset available for GVC analyses By Michele Mancini; Pierluigi Montalbano; Silvia Nenci; Davide Vurchio
  12. Fuzzy firm name matching: Merging Amadeus firm data to PATSTAT By Leon Bremer

  1. By: Luca Zamparelli (Department of Social Sciences and Economics, Sapienza University of Rome)
    Abstract: This paper combines induced innovation and endogenous growth to investigate two issues: the relation between the wage share and labor productivity growth and the potential influence of the saving rate on the steady state wage share. We assume that myopic competitive firms choose the size and direction of technical change to maximize the growth rate of profits. First, we find a condition on the innovation possibility set sufficient to ensure that labor productivity growth is a positive function of the wage share. Second, we show that the steady state wage share depends on the saving rate if, and only if, R&D investment affects the marginal rate of transformation between labor and capital productivity growth. Both results have important policy implications as they clarify under what conditions any factor affecting the wage share or the saving rate will have an impact on labor productivity growth or steady-state income distribution.
    Keywords: Induced innovation, R&D, Factors income shares, Growth models
    JEL: D24 E25 D33 O30 O41
    Date: 2023–04
  2. By: Doorley, Karina (Economic and Social Research Institute, Dublin); Gromadzki, Jan (Vienna University of Economics and Business); Lewandowski, Piotr (Institute for Structural Research (IBS)); Tuda, Dora (Trinity College Dublin); Van Kerm, Philippe (LISER (CEPS/INSTEAD))
    Abstract: We study the effects of robot penetration on household income inequality in 14 European countries between 2006–2018, a period marked by the rapid adoption of industrial robots. Automation reduced relative hourly wages and employment of more exposed demographic groups, similarly to the results for the United States. Using robot-driven wage and employment shocks as input to the EUROMOD microsimulation model, we find that automation had minor effects on income inequality. Household labour income diversification and tax and welfare policies largely absorbed labour market shocks caused by automation. Transfers played a key role in cushioning the transmission of these shocks to household incomes.
    Keywords: robots, automation, tasks, income inequality, wage inequality, microsimulation
    JEL: J24 O33 J23
    Date: 2023–10
  3. By: Lewandowski, Piotr (Institute for Structural Research (IBS)); Madoń, Karol (Institute for Structural Research (IBS)); Winkler, Deborah (World Bank)
    Abstract: This paper studies the relationship between global value chain (GVC) participation, worker-level routine task intensity, and wage inequality within countries. Using unique survey data from 38 countries, we find that higher GVC participation is associated with more routine-intensive work, especially among workers in offshorable occupations. This relationship is particularly strong in industry and in countries at lower development levels. As higher routine task intensity links with to wages, this indirectly widens within-country wage inequality. However, GVC participation directly contributes to reduced wage inequality, except in the richest countries. Overall, GVC participation is negatively associated with wage inequality in most low- and middle-income countries that receive offshored jobs, and positively in high-income countries that offshore jobs.
    Keywords: routine task intensity, global value chains, globalisation, cross-country division of work, wage inequality
    JEL: J21 J24 J31 F66
    Date: 2023–10
  4. By: Ajay K. Agrawal; Joshua S. Gans; Avi Goldfarb
    Abstract: We ask whether a technical objective of using human performance of tasks as a benchmark for AI performance will result in the negative outcomes highlighted in prior work in terms of jobs and inequality. Instead, we argue that task automation, especially when driven by AI advances, can enhance job prospects and potentially widen the scope for employment of many workers. The neglected mechanism we highlight is the potential for changes in the skill premium where AI automation of tasks exogenously improves the value of the skills of many workers, expands the pool of available workers to perform other tasks, and, in the process, increases labor income and potentially reduces inequality. We label this possibility the “Turing Transformation.” As such, we argue that AI researchers and policymakers should not focus on the technical aspects of AI applications and whether they are directed at automating human-performed tasks or not and, instead, focus on the outcomes of AI research. In so doing, our goal is not to diminish human-centric AI research as a laudable goal. Instead, we want to note that AI research that uses a human-task template with a goal to automate that task can often augment human performance of other tasks and whole jobs. The distributional effects of technology depend more on which workers have tasks that get automated than on the fact of automation per se.
    JEL: J2 O3
    Date: 2023–10
  5. By: Burdin, Gabriel; Dughera, Stefano; Landini, Fabio; Belloc, Filippo
    Abstract: Advances in artificial intelligence and data analytics have notably expanded employers' monitoring and surveillance capabilities, facilitating the accurate observability of work effort. There is an ongoing debate among academics and policymakers about the productivity and broader welfare implications of digital monitoring (DM) technologies. In this context, many countries confer information, consultation and codetermination rights to employee representation (ER) bodies on matters related to workplace organization and the introduction of new technologies, which could potentially discourage employers from making DM investments. Using a cross-sectional sample of more than 21000 European establishments, we find instead that establishments with ER are more likely to utilize DM technologies than establishments without ER. We also document a positive effect of ER on DM utilization in the context of a local-randomization regression discontinuity analysis that exploits size-contingent policy rules governing the operation of ER bodies in Europe. We rationalize this unexpected finding through the lens of a theoretical framework in which shared governance via ER create organizational safeguards that mitigate workers' negative responses to monitoring and undermines the disciplining effect of DM technologies.
    Keywords: Digital-based monitoring, algorithmic management, HR analytics, transparency, control aversion, worker voice, employee representation
    Date: 2023
  6. By: Parui, Pintu
    Abstract: We construct a broad R&D-based endogenous growth model that incorporates the importance of children's health on human capital accumulation and publicly-funded basic research investments required to produce new goods. Although an increment in the number of healthcare professionals creates a shortage of workers for final goods production, the novelty of this paper is to demonstrate the significance of healthcare workers in enhancing the productivity of inputs of various sectors, along with its long-run consequences.
    Keywords: R&D-based growth, Basic science, Children’s Health, Education, Fertility
    JEL: H41 J24 O31 O32 O41
    Date: 2023–10–13
  7. By: Hafele, Jakob; Le Lannou, Laure-Alizée; Rochowicz, Nils; Kuhls, Sonia; Gräbner-Radkowitsch, Claudius
    Abstract: Achieving compatibility between economies and planetary boundaries poses a momentous challenge. It requires a fundamental restructuring of current industrial systems, with a dual focus on the creation and protection of green technologies and firms, as well as the redirection of workers and technologies from ecologically harmful activities to support sustainable production patterns. This paper acknowledges that during the process of green industrial restructuring, certain non-future fit sectors will inevitably decline due to regulatory requirements or reduced competitiveness. Allowing market forces to solely determine the decline of these sectors would result in extensive economic and social consequences. Instead, this paper advocates for the implementation of active industrial policies to facilitate the phasing out of non-future-fit sectors and to ensure a just transition for the workers affected. To this end, the paper introduces a data-driven political framework with two objectives: 1) identify emission-intensive sectors with limited potential to stay competitive (non-future-fit sectors) and 2) identify sectors capable of absorbing workers from declining sectors while presenting better economic potential (complementary future-fit sectors). Despite the data limitations, applying this framework in Germany and Hungary reveals two significant challenges. First, the results indicate a limited number of skill-related sectors able to absorb workers from declining industries, highlighting the reluctance of workers to adapt to the changing landscape due to the costs associated with retraining and relocation. Second, a market-driven approach to the green transformation is likely to result in gradual shifts, requiring ongoing worker retraining as other problematic sectors decline. These preliminary findings underscore the need to anticipate these challenges and prioritise worker retraining and skill development, particularly in cases where there are limited complementary future-fit sectors.
    Keywords: Green Transformation, Industrial Policy, Competitiveness, Emission Intensity, Economic Complexit
    Date: 2023
  8. By: Commander, Simon; Estrin, Saul; De Silva, Thamashi
    Abstract: It is acknowledged that Asia’s remarkable economic achievements of the past 50 years build on institutional arrangements very different from the West, including the central role of business groups (BGs) as an organisational form. As the Asian economies move from extensive to intensive growth, we enquire whether the BG format will be as effective going forward, especially with respect to innovation. We argue that the ubiquity of BGs in Asia has been associated with the accretion of significant market power, as well as high overall concentration in the economy as a whole. Our empirical work draws on a sample of more than 9000 Asian firms across seven countries. We find that, unsurprisingly, given their access to additional resources, BGs are more innovative than non-affiliates. However we also find that the wider consequences of the BG form for innovation may be negative.
    Keywords: innovation; R&D; Asian business groups; market power; overall concentration
    JEL: O53 L22 O30
    Date: 2023–09–05
  9. By: Edquist, Harald
    Abstract: This paper investigates the relationship between the log change in mobile broadband latency and total factor productivity (TFP) growth based on data for 130 countries. It finds that there is a strong correlation between TFP growth and one year lag of latency growth once controlling for the growth of labor and capital services in OECD countries. The interpretation of the findings is that a 10 percentage points decrease in the growth of latency in period t-1 is associated with an increase of 0.3 percentage points in TFP growth. The findings are in accordance with the framework of general purpose technologies that suggests that the impact of new technologies often appear with a lag. Moreover, no relationship is found for non-OECD countries, which suggest that it is only OECD countries that are able to take advantage of the benefits of lower latency. One possible explanation could be that OECD countries have reached a higher maturity in digitalization and automation in production processes and thus are able to take advantage of the benefits of lower latency.
    Keywords: ICT, Productivity, Latency, Mobile broadband networks, Economic development
    JEL: D24 O33 O47
    Date: 2023
  10. By: Stephanie Houle; Ryan Macdonald
    Abstract: Predicting which firms will grow quickly and why has been the subject of research studies for many decades. Firms that grow rapidly have the potential to usher in new innovations, products or processes (Kogan et al. 2017), become superstar firms (Haltiwanger et al. 2013) and impact the aggregate labour share (Autor et al. 2020; De Loecker et al. 2020). We explore the use of supervised machine learning techniques to identify a population of nascent high-growth firms using Canadian administrative firm-level data. We apply a suite of supervised machine learning algorithms (elastic net model, random forest and neural net) to determine whether a large set of variables on Canadian firm tax filing financial and employment data, state variables (e.g., industry, geography) and indicators of firm complexity (e.g., multiple industrial activities, foreign ownership) can predict which firms will be high-growth firms over the next three years. The results suggest that the machine learning classifiers can select a sub-population of nascent high-growth firms that includes the majority of actual high-growth firms plus a group of firms that shared similar attributes but failed to attain high-growth status.
    Keywords: Econometric and statistical methods; Firm dynamics
    JEL: C55 C81 L25
    Date: 2023–10
  11. By: Michele Mancini (Bank of Italy); Pierluigi Montalbano (Department of Social Sciences and Economics, Sapienza University of Rome); Silvia Nenci (Department of Economics, Roma Tre University); Davide Vurchio (University of Bari)
    Abstract: Recently, a strand of the international trade literature has developed measures of the positioning of countries and industries in Global Value Chains (GVCs) using the global Input-Output tables. These measures allow scholars from different research fields to conduct qualitative and quantitative analyses on GVCs, at the aggregate and sectoral level, and inform policymaking. To compute these indicators, a common approach is to consider the extent to which a country-industry pair sells its output for final use to consumers worldwide or instead sells intermediate inputs to other producing sectors in the world. Following this approach, we compute and make available to scholars a new dataset of GVC positioning indicators at the country-industry level based on the most used global Input-Output tables (WIOD, OECD, EORA, ADB). Specifically, we compute two popular measures: i) a measure of distance or upstreamness of a production sector from final demand, which was developed by Fally (2012), Antràs et al. (2012), and Antràs and Chor (2013, 2019); and ii) a measure of distance or downstreamness of a given sector from the economy’s primary factors of production (or sources of value-added), originally proposed by Fally (2012). These indicators are “ready-to-use” and can be freely downloaded from here ( This work illustrates the indicators included in this new open access dataset and the methodologies applied, and provides an international comparison, by sectors and countries, of GVC positioning measures and their evolution over time. Lastly, we propose an empirical exercise to test the consistency of these measures with trade theory.
    Keywords: Global Value Chain, positioning indicators, upstreamness, downstreamness, international trade, country-sector analysis, data.
    JEL: D57 F14 O50
    Date: 2023–01
  12. By: Leon Bremer (Vrije Universiteit Amsterdam)
    Abstract: When merging firms across large databases in the absence of common identifiers, text algorithms can help. I propose a high-performance fuzzy firm name matching algorithm that uses existing computational methods and works even under hardware restrictions. The algorithm consists of four steps, namely (1) cleaning, (2) similarity scoring, (3) a decision rule based on supervised machine learning, and (4) group identification using community detection. The algorithm is applied to merging firms in the Amadeus Financials and Subsidiaries databases, containing firm-level business and ownership information, to applicants in PATSTAT, a worldwide patent database. For the application the algorithm vastly outperforms an exact string match by increasing the number of matched firms in the Amadeus Financials (Subsidiaries) database with 116% (160%). 53% (74%) of this improvement is due to cleaning, and another 41% (50%) improvement is due to similarity matching. 18.1% of all patent applications since 1950 are matched to firms in the Amadeus databases, compared to 2.6% for an exact name match.
    Keywords: Fuzzy name matching, supervised machine learning, name disambiguation, patents
    JEL: C81 C88 O34
    Date: 2023–10–12

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