The impact of financial inclusion on stimulating entrepreneurship: Empirical evidence from the Iraqi economy

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Type of the article: Research Article

Abstract
Financial inclusion has become a key policy instrument for promoting inclusive growth, particularly in oil-dependent economies where traditional growth channels may not effectively support entrepreneurial activity. This study aims to analyze the impact of financial inclusion on stimulating entrepreneurship in Iraq by examining the relationship between indicators of financial access, financial usage, and financial infrastructure and entrepreneurial activity, as well as investigating the short-run and long-run dynamics of this relationship in a manner that supports economic diversification. The study employs monthly data covering the period 2020–2024 and applies the autoregressive distributed lag methodology to test for cointegration, estimate short-run and long-run coefficients, and measure the speed of adjustment toward long-run equilibrium. The empirical results show that bank accounts and automated teller machines have positive and statistically significant long-run effects on entrepreneurship, with elasticities of 1.415 and 0.539, respectively, while electronic payment cards have a positive but statistically insignificant effect. Education expenditure and economic growth also exert positive and significant effects, with elasticities of 0.536 and 0.332, respectively. These findings indicate that financial access, financial infrastructure, human capital development, and favorable economic conditions contribute to entrepreneurial activity in Iraq. The findings confirm that financial inclusion and human capital development represent more effective channels for stimulating entrepreneurial activity and advancing economic diversification. They also highlight the importance of expanding digital financial services and enhancing access to the formal financial system as effective mechanisms for supporting entrepreneurship and reducing dependence on oil-related economic activity.

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    • Figure 1. Residual normality test (Jarque-Bera test)
    • Figure 2. CUSUM stability test
    • Figure 3. CUSUM of squares test of model stability
    • Table 1. Model variables and measurements
    • Table 2. Unit root test results (ADF)
    • Table 3. Bounds test results
    • Table 4. Error correction model and short-run estimation results
    • Table 5. ARDL long-run estimation results
    • Table 6. Results of diagnostic tests
    • Table 7. Results of the Granger causality test
    • Conceptualization
      Omar Mahmood Akawee, Ahmed Mohammed Jasim
    • Data curation
      Omar Mahmood Akawee, Ali Saad Muwafaq
    • Formal Analysis
      Omar Mahmood Akawee, Ali Saad Muwafaq
    • Funding acquisition
      Omar Mahmood Akawee, Ahmed Mohammed Jasim, Ali Saad Muwafaq
    • Investigation
      Omar Mahmood Akawee, Ahmed Mohammed Jasim
    • Methodology
      Omar Mahmood Akawee, Ahmed Mohammed Jasim
    • Project administration
      Omar Mahmood Akawee, Ali Saad Muwafaq
    • Resources
      Omar Mahmood Akawee, Ahmed Mohammed Jasim
    • Supervision
      Omar Mahmood Akawee, Ahmed Mohammed Jasim
    • Validation
      Omar Mahmood Akawee, Ali Saad Muwafaq
    • Visualization
      Omar Mahmood Akawee
    • Writing – original draft
      Omar Mahmood Akawee, Ahmed Mohammed Jasim, Ali Saad Muwafaq
    • Writing – review & editing
      Omar Mahmood Akawee, Ahmed Mohammed Jasim, Ali Saad Muwafaq
    • Software
      Ali Saad Muwafaq