Pular para o conteúdo principal

ETHNOS_APP

Início • Busca • Periódicos • Lista 0

Total factor productivity dynamics and the artificial intelligence paradox

Evidence from long-memory analysis

Dados Bibliográficos

ID13023331
AutoresMarinko Škare (0000-0001-6426-3692, autor correspondente), Małgorzata Porada-Rochoń (0000-0002-3082-5682), Rozana Veselica Celić
Ano2025
Volume18
Fascículo4
Páginas218-218
Data de publicação2025-12-29
Peer ReviewedSim
Open AccessSim
TipoARTICLE
PeriódicoJOURNAL OF INTERNATIONAL STUDIES (JOURNAL)
Identificadores do periódicoISSN: 2071-8330 • E-ISSN: 2306-3483
EditoraCentre of Sociological Research, NGO (PUBLISHER)
DOI10.14254/2071-8330.2025/18-4/11
OpenAlexW7124872749
IdiomaEN
Referências citadas75

This paper investigates the artificial intelligence (AI) productivity paradox using total factor productivity (TFP) from 1890 to 2022 and fractional integration and long-memory econometric methods. We find that total factor productivity gains from AI investments may be delayed and diffuse nonlinearly, following long-memory patterns similar to those of previous technological revolutions, resulting in a paradox of long lags, not a lack of innovation. The average TFP growth rate (0.54%) in the AI era is the lowest of any post-war technological wave, with profoundly contradictory persistence measures from the GPH (d=1.730) and Local Whittle (d=0.133) estimators, reflecting fundamental uncertainty about the actual productivity of AI. We observe that the GPH estimator is consistent with the "J-curve" hypothesis of temporary slowdown before long-term gains. In contrast, the Local Whittle estimator suggests productivity effects that may be fleeting and easily commoditized. Cross-country heterogeneity in AI persistence patterns points to the role of local institutions, policies, and complementary investments in mediating the macroeconomic impact of AI. These results imply that the full productivity benefits of AI may be realized over very long-run horizons, providing policymakers and investors with necessary guidance on the timing and nature of the AI revolution

Data envelopment analysis · Estimator · Persistence (discontinuity · Productivity · Productivity model · Slowdown · Total factor productivity · Economic and Technological Innovation · Economic Growth and Productivity · Firm Innovation and Growth

  • Are Output Fluctuations Transitory?

    John Y Campbell, N Gregory Mankiw•The Quarterly Journal of Economics•1987

  • What Explains the 2007-2009 Drop in Employment?

    Open Access•Atif Mian, Amir Sufi•Econometrica•2014

  • An Introduction to Long‐memory Time Series Models and Fractional Differencing

    Open Access•Clive W J Granger, Roselyne Joyeux•Journal of Time Series Analysis•1980

  • The Estimation and Application of Long Memory Time Series Models

    Open Access•John Geweke, Susan Porter‐Hudak•Journal of Time Series Analysis•1983

  • What can machine learning do? Workforce implications

    Open Access•Erik Brynjolfsson, Tom M Mitchell et al.•Science•2017

  • Navigating the Jagged Technological Frontier

    Open Access•Fabrizio Dell’Acqua, Fabrizio Dell'Acqua et al.•SSRN Electronic Journal•2023

  • Gaussian Semiparametric Estimation of Long Range Dependence

    Peter M Robinson•The Annals of Statistics•1995

  • Raising the Speed Limit

    D W Jorgenson, Kevin J Stiroh•Brookings Papers on Economic…•2000

  • Generative AI at Work

    Open Access•Erik Brynjolfsson, Danielle Li et al.•The Quarterly Journal of Economics•2025

  • Artificial intelligence, firm growth, and product innovation

    Open Access•Tania Babina, Anastassia Fedyk et al.•Journal of Financial Economics•2024

  • Fractional differencing

    J R M HOSKING•Biometrika•1981

  • Efficient Tests of Nonstationary Hypotheses

    Peter M Robinson•Journal of the American…•1994

  • The Race between Man and Machine

    Daron Acemoglu, Pascual Restrepo•American Economic Review•2018

  • Testing the null hypothesis of stationarity against the alternative of a unit root

    Open Access•Denis Kwiatkowski, Peter C B Phillips et al.•Journal of Econometrics•1992

  • Distribution of the Estimators for Autoregressive Time Series with a Unit Root

    David A Dickey, Wayne A Fuller•Journal of the American…•1979

  • A new fractional integration approach based on neural network nonlinearity with an application to testing unemployment hysteresis

    Open Access•Fumitaka Furuoka, Luis A Gil-Alana et al.•Empirical Economics•2024

  • A statistical-mathematical analysis of the macroeconomic effects of long-memory total factor productivity

    Open Access•Rosa Ferrentino, Luca Vota•Quality & Quantity•2025

  • Long-Run Identification in a Fractionally Integrated System

    Rolf Tschernig, Enzo Weber et al.•Journal of Business and Economic…•2013

  • Productivity Trends in Advanced Countries between 1890 and 2012

    Open Access•Antonin Bergeaud, Gilbert Cette et al.•Review of Income and Wealth•2015

  • The effect of corporate reputation on investors' decisions following a stock price shock

    Open Access•Anna Blajer-Gołębiewska•Economics & Sociology•2024

  • What drives TFP long-run dynamics in five large European economies

    Open Access•Alessandro Bellocchi, Edgar J Sánchez Carrera et al.•Economia Politica•2021

  • What Happened to US Business Dynamism

    Ufuk Akcigit, Sina T Ates•Journal of Political Economy•2023

  • Progress towards sustainable activities

    Open Access•Denis Juracka, K Valaskova•JOURNAL OF INTERNATIONAL STUDIES•2025

  • Are Ideas Getting Harder to Find

    Nena Bloom, Nicholas Bloom et al.•American Economic Review•2020

  • Artificial Intelligence and the Modern Productivity Paradox

    Erik Brynjolfsson, Daniel Rock et al.•National Bureau of Economic…•2017

  • Beyond Computation

    Open Access•Erik Brynjolfsson, Lorin M Hitt•The Journal of Economic…•2000

Velocidade de citaçãohistorical
Altamente citadoNão
Ethnos_APP • Projeto Open Source • Licença MIT • Frontend v2.0.0 • Privacidade e Cookies • Documentação da API: api.ethnos.app/docs • Código da API: GitHub • DOI: 10.5281/zenodo.17049435 • Código do Frontend: GitHub • DOI: 10.5281/zenodo.17050053 • cruz.rio.br • Expectantes Misericordiae