Mitigating Entrepreneurship Policy Challenges in Developing Countries’ Startup Ecosystems Through Machine Learning Analysis

Sayed Mohammad Mahdi Mirahmadi, Mohammad Jahanbakht () and Mohammad Hossein Rohban
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Sayed Mohammad Mahdi Mirahmadi: School of Management and Economics (GSME), Sharif University of Technology, Tehran 14588-89694, Iran
Mohammad Jahanbakht: Department of Industrial, Manufacturing, and Systems Engineering, University of Texas at Arlington, Arlington, TX 76019, USA
Mohammad Hossein Rohban: Department of Computer Engineering, Sharif University of Technology, Tehran 14588-89694, Iran

Economies, 2025, vol. 13, issue 10, 1-24

Abstract: Entrepreneurship plays a significant role in the economic development of emerging economies, particularly by addressing persistent issues such as youth unemployment and growth challenges. Developing nations perceive their startup ecosystems as critical engines of economic progress. Policymakers in these countries strive to reduce uncertainties and mitigate risks that could impede the growth of this essential sector. However, they face a significant obstacle: the lack of accurate and reliable data necessary to comprehend the challenges and requirements of the startup ecosystem. To effectively navigate these challenges, policymakers must utilize advanced analytical tools and technologies, including big data analytics, artificial intelligence, and machine learning. These technologies are crucial for the comprehensive collection and analysis of data from diverse sources. This research aims to identify current trends and challenges within the startup ecosystem in developing countries through the meticulous collection and analysis of news data on the topic. To achieve this objective, we developed a detailed plan to collect news data on Iran’s startup ecosystem spanning from 2017 to 2022. By employing advanced natural language processing techniques, we intended to conduct a thorough analysis of the collected data. Our goal is to extract significant insights that will inform and shape effective policymaking.

Keywords: developing economies; startup ecosystem; big data; policy development; natural language processing (NLP) (search for similar items in EconPapers)
JEL-codes: E F I J O Q (search for similar items in EconPapers)
Date: 2025
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