Investigating the impact of private sector credit on self-employment in Iran: A hybrid artificial intelligence and spatial modelling approach

Document Type : Research Paper

Authors

Faculty of Economics and Management, Urmia University, Urmia, Iran.

Abstract

Self-employment plays an important role in the Iranian economy and understanding the factors affecting it is of great importance. This study aimed to investigate the impact of private sector credit on self-employment in Iran using artificial intelligence techniques such as artificial neural networks, deep neural networks and machine learning algorithms to identify nonlinear relationships and complex patterns in the data. Also, spatial econometric models SAR, SEM and SDM were used to consider spatial dependencies between provinces and to examine the spatial spillover effects of use for the period 1990-2023 at the provincial level of the country.

The findings indicate a negative and significant relationship between the ratio of microfinance to GDP and the self-employment rate. Also, the negative coefficients of the economic openness index and the capital formation rate indicate their negative effect on self-employment. In contrast, the variables of total factor productivity and educational expenditure have a positive and significant effect on the self-employment rate. The results of the spatial models also indicate the dependence of the self-employment rate in different regions of the country on each other. Therefore, this study found a negative relationship between the two, which could be due to inefficiency in the provision of microfinance and its insufficient focus on creating sustainable and productive jobs. Increased economic openness and higher rates of capital formation also have a negative effect on self-employment by intensifying foreign competition and encouraging investment in larger economic sectors.

Keywords

Main Subjects

Article Title [Persian]

بررسی تاثیر اعتبار بخش خصوصی بر خوداشتغالی در ایران: رویکرد هوش مصنوعی ترکیبی و مدل سازی فضایی

Authors [Persian]

  • رضا طاهری هفت آسیابی
  • آمنه نادری
  • یوسف محمدزاده
  • اکبر زواری رضایی
دانشکده اقتصاد و مدیریت، دانشگاه ارومیه، ارومیه، ایران.
Abstract [Persian]

خوداشتغالی نقش مهمی در اقتصاد ایران دارد و درک عوامل موثر بر آن از اهمیت زیادی برخوردار است. این مطالعه با هدف بررسی تأثیر اعتبارات بخش خصوصی بر خوداشتغالی در ایران با استفاده از تکنیک های هوش مصنوعی مانند شبکه های عصبی مصنوعی، شبکه های عصبی عمیق و الگوریتم های یادگیری ماشین امکان شناسایی روابط غیرخطی و الگوهای پیچیده در داده ها را فراهم کرد. همچنین مدل های اقتصادسنجی فضایی SAR، SEM و SDM بادر نظر گرفتن وابستگی های مکانی بین استان ها و بررسی اثرات سرریز فضایی استفاده مربوط به دوره زمانی ۱۹۹۰تا ۲۰۲۳ در سطح استان های کشور صورت گرفته است.

یافته ها حاکی از رابطه منفی و معنادار بین نسبت تسهیلات خرد به تولید ناخالص داخلی و نرخ خوداشتغالی است. همچنین ضرایب منفی شاخص باز بودن اقتصادی و نرخ تشکیل سرمایه، اثر منفی آنها بر خوداشتغالی را نشان می دهد. در مقابل، متغیرهای بهره وری کل عوامل تولید و مخارج آموزشی تأثیر مثبت و معناداری بر نرخ خوداشتغالی دارند. نتایج مدل های فضایی نیز بیانگر وابستگی نرخ خوداشتغالی در مناطق مختلف کشور به یکدیگر است. بنابراین، این مطالعه رابطه منفی بین این دو را نشان داد که می تواند ناشی از ناکارآمدی در ارائه تسهیلات خرد و عدم تمرکز کافی آن بر ایجاد مشاغل پایدار و مولد باشد. افزایش باز بودن اقتصادی و نرخ های بالاتر تشکیل سرمایه نیز با تشدید رقابت خارجی و تشویق سرمایه گذاری در بخش های بزرگتر اقتصادی، اثر منفی بر خوداشتغالی دارند.

Keywords [Persian]

  • دسترسی مالی
  • خوداشتغالی
  • کاربردهای هوش مصنوعی
  • تحلیل فضایی
  • میکروفاینانس

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  • Receive Date: 18 December 2024
  • Revise Date: 25 January 2025
  • Accept Date: 25 January 2025

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