The Effect of Artificial Intelligence on The Total Factor Productivity in Iranian Industries

Document Type : Research Paper

Authors

1 Faculty of Humanities & Social Sciences, Ardakan University, ,Ardakan, Iran.

2 Faculty of Economics and Political Science, Shahid Beheshti University, Tehran, Iran

Abstract

This study explores the effects of artificial intelligence (AI) investment on total factor productivity (TFP) in Iranian industries from 1997 to 2020, utilizing a comprehensive dataset organized by four-digit International Standard Industrial Classification (ISIC) codes. We employ the generalized method of moments (GMM) approach to address challenges such as endogeneity and collinearity within a dataset comprising over 200 cross-sectional variables.Our results reveal that both physical and intangible investments significantly influence TFP; a 1% increase in physical investment results in a 0.514% rise in TFP, while intangible investment leads to a 0.288% improvement. A key innovation of this research is the introduction of an AI measurement variable in the production function, employing the Corrado, Hulten, and Sichel (CHS) methodology for a clearer assessment of AI's productivity effects.Although AI investment positively correlates with TFP, its current impact is limited, reflecting the gradual adoption of advanced technologies in Iranian industries. This highlights the need for a comprehensive strategy to fully realize the productivity benefits of AI. We recommend policies aimed at facilitating technology integration and workforce specialization, including investing in training, providing incentives for AI adoption, and promoting collaboration between industry and educational institutions to enhance productivity and competitiveness in the global market.

Keywords

Main Subjects

Article Title [Persian]

تأثیر هوش مصنوعی بر بهره وری کل عوامل در صنایع ایران

Authors [Persian]

  • فروغ اسمعیلی صدرآبادی 1
  • مائده خناری 2
1 دانشکده علوم انسانی و اجتماعی، دانشگاه اردکان، اردکان، ایران.
2 دانشکده اقتصاد و علوم سیاسی، دانشگاه شهید بهشتی، تهران، ایران
Abstract [Persian]

هوش مصنوعی (AI) بر بهره‌وری کل عوامل (TFP) در صنایع ایران از سال 1997 تا 2020 می‌پردازد و از یک مجموعه داده جامع که بر اساس کدهای چهاررقمی طبقه‌بندی بین‌المللی صنایع (ISIC) سازماندهی شده است، استفاده می‌کند. ما از روش GMM برای مقابله با چالش‌هایی مانند درون‌زایی و هم‌خطی در یک مجموعه داده شامل بیش از 200 متغیر مقطعی استفاده می‌کنیم. نتایج ما نشان می‌دهد که هم سرمایه‌گذاری‌های فیزیکی و هم سرمایه‌گذاری‌های غیرملموس تأثیر قابل توجهی بر TFP دارند؛ به‌طوری‌که افزایش 1% در سرمایه‌گذاری فیزیکی منجر به افزایش 0.514% در TFP می‌شود، در حالی که سرمایه‌گذاری غیرملموس بهبود 0.288%ی را به همراه دارد. یکی از نوآوری‌های کلیدی این تحقیق معرفی یک متغیر اندازه‌گیری AI در تابع تولید است که از روش Corrado، Hulten و Sichel (CHS) برای ارزیابی روشن‌تری از تأثیرات بهره‌وری AI استفاده می‌کند. اگرچه سرمایه‌گذاری در AI به‌طور مثبت با TFP همبستگی دارد، تأثیر فعلی آن محدود است که نشان‌دهنده پذیرش تدریجی فناوری‌های پیشرفته در صنایع ایران است. این موضوع نیاز به یک استراتژی جامع برای بهره‌برداری کامل از مزایای بهره‌وری AI را برجسته می‌کند. ما سیاست‌هایی را برای تسهیل ادغام فناوری و تخصص‌گرایی نیروی کار، از جمله سرمایه‌گذاری در آموزش، ارائه مشوق‌هایی برای پذیرش AI و ترویج همکاری بین صنعت و مؤسسات آموزشی به‌منظور افزایش بهره‌وری و رقابت‌پذیری در بازار جهانی توصیه می‌کنیم.

Keywords [Persian]

  • هوش مصنوعی (AI)
  • بهره وری کل عوامل تولید (TFP)
  • صنایع ایران
  • سرمایه گذاری های مشهود و نامشهود
  • روش گشتاورهای تعمیم یافته (GMM)

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Appendix A: Intangible Capital and TFP: A Theoretical Analysis

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History

  • Receive Date: 08 September 2024
  • Revise Date: 11 January 2025
  • Accept Date: 12 January 2025

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