IDEAS home Printed from https://ideas.repec.org/p/hhs/lucirc/2022_001.html
   My bibliography  Save this paper

Return of the Solow-paradox in AI? AI-adoption and firm productivity

Author

Listed:
  • Bäck, Asta

    (VTT)

  • Hajikhani, Arash

    (VTT)

  • Jäger, Angela

    (Fraunhofer Institute for Systems and Innovation Research ISI)

  • Schubert, Torben

    (CIRCLE, Lund University)

  • Suominen, Arho

    (VTT)

Abstract

AI-related technologies have become ubiquitous in many business contexts. However, to date empirical accounts of the productivity effects of AI-adoption by firms are scarce. Using Finnish data on job advertisements between 2013 and 2019, we identify job advertisements referring to AI-related skills. Matching this data to productivity data from ORBIS, we estimate the productivity effects of AI related activities in our sample. Our results indicate that AI-adoption increases productivity, with three important qualifications. Firstly, effects are only observable for large firms with more than 499 employees. Secondly, there is evidence that early adopters did not experience productivity increases. This may be interpreted as technological immaturity.Thirdly, we find evidence of delays of least three years between the adoption of AI and ensuing productivity effects (investment delay effect). We argue that our findings on the technological immaturity and the investment delay effect may help explain the so-called AI-related return of the Solow-paradox: I.e. that AI is everywhere except in the productivity statistics.

Suggested Citation

  • Bäck, Asta & Hajikhani, Arash & Jäger, Angela & Schubert, Torben & Suominen, Arho, 2022. "Return of the Solow-paradox in AI? AI-adoption and firm productivity," Papers in Innovation Studies 2022/1, Lund University, CIRCLE - Centre for Innovation Research.
    • Handle: RePEc:hhs:lucirc:2022_001
    as

    Download full text from publisher

    File URL: http://wp.circle.lu.se/upload/CIRCLE/workingpapers/202201_baeck.pdf
    File Function: Full text
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Chuang, Yih-Chyi, 1998. "Learning by Doing, the Technology Gap, and Growth," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 39(3), pages 697-721, August.
    2. Parente Stephen L., 1994. "Technology Adoption, Learning-by-Doing, and Economic Growth," Journal of Economic Theory, Elsevier, vol. 63(2), pages 346-369, August.
    3. Jovanovic, Boyan & Nyarko, Yaw, 1996. "Learning by Doing and the Choice of Technology," Econometrica, Econometric Society, vol. 64(6), pages 1299-1310, November.
    4. Lee G. Branstetter & Matej Drev & Namho Kwon, 2019. "Get with the Program: Software-Driven Innovation in Traditional Manufacturing," Management Science, INFORMS, vol. 65(2), pages 541-558, February.
    5. Südekum, Jens & Dauth, Wolfgang & Findeisen, Sebastian & Woessner, Nicole, 2017. "German Robots – The Impact of Industrial Robots on Workers," CEPR Discussion Papers 12306, C.E.P.R. Discussion Papers.
    6. Panggih Kusuma Ningrum & Tatdow Pansombut & Attachai Ueranantasun, 2020. "Text mining of online job advertisements to identify direct discrimination during job hunting process: A case study in Indonesia," PLOS ONE, Public Library of Science, vol. 15(6), pages 1-29, June.
    7. Pejic-Bach, Mirjana & Bertoncel, Tine & Meško, Maja & Krstić, Živko, 2020. "Text mining of industry 4.0 job advertisements," International Journal of Information Management, Elsevier, vol. 50(C), pages 416-431.
    8. Christian Catalini & Catherine Tucker, 2016. "Seeding the S-Curve? The Role of Early Adopters in Diffusion," NBER Working Papers 22596, National Bureau of Economic Research, Inc.
    9. Gries, Thomas & Naude, Wim, 2018. "Artificial intelligence, jobs, inequality and productivity: Does aggregate demand matter?," MERIT Working Papers 2018-047, United Nations University - Maastricht Economic and Social Research Institute on Innovation and Technology (MERIT).
    10. Peter Gal & Giuseppe Nicoletti & Theodore Renault & Stéphane Sorbe & Christina Timiliotis, 2019. "Digitalisation and productivity: In search of the holy grail – Firm-level empirical evidence from EU countries," OECD Economics Department Working Papers 1533, OECD Publishing.
    11. Jeffrey M Wooldridge, 2010. "Econometric Analysis of Cross Section and Panel Data," MIT Press Books, The MIT Press, edition 2, volume 1, number 0262232588, December.
    12. Niebel, Thomas & Rasel, Fabienne & Viete, Steffen, 2017. "BIG data - BIG gains? Empirical evidence on the link between big data analytics and innovation," ZEW Discussion Papers 17-053, ZEW - Leibniz Centre for European Economic Research.
    13. Ajay K. Agrawal & Joshua S. Gans & Avi Goldfarb, 2021. "AI Adoption and System-Wide Change," NBER Working Papers 28811, National Bureau of Economic Research, Inc.
    14. Raymond Salvador & Joaquim Radua & Erick J Canales-Rodríguez & Aleix Solanes & Salvador Sarró & José M Goikolea & Alicia Valiente & Gemma C Monté & María del Carmen Natividad & Amalia Guerrero-Pedraza, 2017. "Evaluation of machine learning algorithms and structural features for optimal MRI-based diagnostic prediction in psychosis," PLOS ONE, Public Library of Science, vol. 12(4), pages 1-24, April.
    15. Hunt, Wil & Sarkar, Sudipa & Warhurst, Chris, 2022. "Measuring the impact of AI on jobs at the organization level: Lessons from a survey of UK business leaders," Research Policy, Elsevier, vol. 51(2).
    16. Manav Raj & Robert Seamans, 2019. "Primer on artificial intelligence and robotics," Journal of Organization Design, Springer;Organizational Design Community, vol. 8(1), pages 1-14, December.
    17. Benjamin Reed Shiller, 2013. "First Degree Price Discrimination Using Big Data," Working Papers 58, Brandeis University, Department of Economics and International Business School, revised Jan 2014.
    18. Bart van ARK, 2015. "Productivity and Digitilization in Europe: Paving the Road to Faster Growth," Communications & Strategies, IDATE, Com&Strat dept., vol. 1(100), pages 107-124, 4th quart.
    19. Thomas Niebel & Fabienne Rasel & Steffen Viete, 2019. "BIG data – BIG gains? Understanding the link between big data analytics and innovation," Economics of Innovation and New Technology, Taylor & Francis Journals, vol. 28(3), pages 296-316, April.
    20. (Maggie) Fu, Xiaoqing & Bao, Qun & Xie, Hongjun & Fu, Xiaolan, 2021. "Diffusion of industrial robotics and inclusive growth: Labour market evidence from cross country data," Journal of Business Research, Elsevier, vol. 122(C), pages 670-684.
    21. Giacomo Damioli & Vincent Van Roy & Daniel Vertesy, 2021. "The impact of artificial intelligence on labor productivity," Eurasian Business Review, Springer;Eurasia Business and Economics Society, vol. 11(1), pages 1-25, March.
    22. Dauth, Wolfgang & Findeisen, Sebastian & Südekum, Jens & Wößner, Nicole, 2017. "German robots - the impact of industrial robots on workers," IAB-Discussion Paper 201730, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany].
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Wang, Linhui & Cao, Zhanglu & Dong, Zhiqing, 2023. "Are artificial intelligence dividends evenly distributed between profits and wages? Evidence from the private enterprise survey data in China," Structural Change and Economic Dynamics, Elsevier, vol. 66(C), pages 342-356.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Czarnitzki, Dirk & Fernández, Gastón P. & Rammer, Christian, 2023. "Artificial intelligence and firm-level productivity," Journal of Economic Behavior & Organization, Elsevier, vol. 211(C), pages 188-205.
    2. Cirillo, Valeria & Fanti, Lucrezia & Mina, Andrea & Ricci, Andrea, 2023. "The adoption of digital technologies: Investment, skills, work organisation," Structural Change and Economic Dynamics, Elsevier, vol. 66(C), pages 89-105.
    3. Damioli, G. & Van Roy, V. & Vertesy, D. & Vivarelli, M., 2021. "May AI revolution be labour-friendly? Some micro evidence from the supply side," GLO Discussion Paper Series 823, Global Labor Organization (GLO).
    4. Naude, Wim, 2019. "The race against the robots and the fallacy of the giant cheesecake: Immediate and imagined impacts of artificial intelligence," MERIT Working Papers 2019-005, United Nations University - Maastricht Economic and Social Research Institute on Innovation and Technology (MERIT).
    5. Jasmine Mondolo, 2022. "The composite link between technological change and employment: A survey of the literature," Journal of Economic Surveys, Wiley Blackwell, vol. 36(4), pages 1027-1068, September.
    6. Klump, Rainer & Jurkat, Anne & Schneider, Florian, 2021. "Tracking the rise of robots: A survey of the IFR database and its applications," MPRA Paper 107909, University Library of Munich, Germany.
    7. Mutascu, Mihai, 2021. "Artificial intelligence and unemployment: New insights," Economic Analysis and Policy, Elsevier, vol. 69(C), pages 653-667.
    8. Damioli, Giacomo & Van Roy, Vincent & Vertesy, Daniel & Vivarelli, Marco, 2021. "Will the AI revolution be labour-friendly? Some micro evidence from the supply side," MERIT Working Papers 2021-016, United Nations University - Maastricht Economic and Social Research Institute on Innovation and Technology (MERIT).
    9. Cathles, Alison & Nayyar, Gaurav & Rückert, Désirée, 2020. "Digital technologies and firm performance: Evidence from Europe," EIB Working Papers 2020/06, European Investment Bank (EIB).
    10. Teruel Carrizosa, Mercedes & Coad, Alexander & Domnick, Clemens & Flachenecker, Florian & Harasztosi, Péter & Janiri, Mario Lorenzo & Pál, Rozália, 2021. "The birth of new high growth enterprises: Internationalisation through new digital technologies," EIB Working Papers 2021/02, European Investment Bank (EIB).
    11. Chen, Yang & Cheng, Liang & Lee, Chien-Chiang, 2022. "How does the use of industrial robots affect the ecological footprint? International evidence," Ecological Economics, Elsevier, vol. 198(C).
    12. Damiani, Mirella & Pompei, Fabrizio & Kleinknecht, Alfred, 2020. "When robots do (not) enhance job quality: The role of innovation regimes," MPRA Paper 103059, University Library of Munich, Germany.
    13. Bo Chen & Dong Tan, 2023. "Industrial Robots and the Employment Quality of Migrant Workers in the Manufacturing Industry," Sustainability, MDPI, vol. 15(10), pages 1-21, May.
    14. Giacomo Damioli & Vincent Van Roy & Daniel Vertesy & Marco Vivarelli, 2021. "Detecting the labour-friendly nature of AI product innovation," DISCE - Quaderni del Dipartimento di Politica Economica dipe0017, Università Cattolica del Sacro Cuore, Dipartimenti e Istituti di Scienze Economiche (DISCE).
    15. Eder, Andreas & Koller, Wolfgang & Mahlberg, Bernhard, 2022. "The contribution of industrial robots to labor productivity growth and economic convergence: A production frontier approach," MPRA Paper 113126, University Library of Munich, Germany.
    16. Loebbing, Jonas, 2018. "An Elementary Theory of Endogenous Technical Change and Wage Inequality," VfS Annual Conference 2018 (Freiburg, Breisgau): Digital Economy 181603, Verein für Socialpolitik / German Economic Association.
    17. E. Young Song, 2005. "Temporary Protection and Technology Choice under the Learning Curve," Review of International Economics, Wiley Blackwell, vol. 13(2), pages 391-396, May.
    18. Kariem Soliman, 2021. "Are Industrial Robots a new GPT? A Panel Study of Nine European Countries with Capital and Quality-adjusted Industrial Robots as Drivers of Labour Productivity Growth," EIIW Discussion paper disbei307, Universitätsbibliothek Wuppertal, University Library.
    19. Basso, Henrique S. & Jimeno, Juan F., 2021. "From secular stagnation to robocalypse? Implications of demographic and technological changes," Journal of Monetary Economics, Elsevier, vol. 117(C), pages 833-847.
    20. Lütkenhorst, Wilfried, 2018. "Creating wealth without labour? Emerging contours of a new techno-economic landscape," IDOS Discussion Papers 11/2018, German Institute of Development and Sustainability (IDOS).

    More about this item

    Keywords

    Recruiting personnel; AI related jobs; Artificial Intelligence; Job Market; Text Mining; Firm performance; Productivity;
    All these keywords.

    JEL classification:

    • D22 - Microeconomics - - Production and Organizations - - - Firm Behavior: Empirical Analysis
    • D24 - Microeconomics - - Production and Organizations - - - Production; Cost; Capital; Capital, Total Factor, and Multifactor Productivity; Capacity
    • O31 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Innovation and Invention: Processes and Incentives
    • O32 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Management of Technological Innovation and R&D

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:hhs:lucirc:2022_001. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Torben Schubert (email available below). General contact details of provider: https://edirc.repec.org/data/circlse.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.