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Determinants of financial inclusion in Algeria: An ARDL Bounds Test approach

Author

Listed:
  • Rahima Benaini

    (Lecturer, Department of economics, Maghnia University Center, Algeria)

  • Samiya Metair

    (Ph.D. student, LEPPESE laboratory, Maghnia University Center, Algeria)

Abstract

Our study aims to afford an empirical insight on the factors that have a great impact on financial inclusion levels in Algeria during the period Q12004-Q42019, using ARDL approach. Using the dataset provided by the World Bank, our empirical results found that the composite index of financial inclusion (FII) based on three dimensions following Sarma (2008) approach is bounded from 0.08 to 0.51, which is proportionally small. Farther, the estimation findings revealed a positive relationship between financial inclusion index and GDP per capita as a proxy of incomes, population, oil rent, interest rate, while, money supply is not significant factor. However, the strong positive relationship is found between FII and corruption, fixed phone subscriptions as a measure of internet access, which means there are the most important determinant of FII in Algeria.

Suggested Citation

  • Rahima Benaini & Samiya Metair, "undated". "Determinants of financial inclusion in Algeria: An ARDL Bounds Test approach," Review of Socio - Economic Perspectives 202219, Reviewsep.
    • Handle: RePEc:aly:journl:202219
    DOI: https://doi.org/10.19275/RSEP140
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    References listed on IDEAS

    as
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    4. Mandira Sarma, 2008. "Index of Financial Inclusion," Working Papers id:1575, eSocialSciences.
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    More about this item

    Keywords

    financial inclusion index; financial dimensions; determinants; ARDL approach; Algeria;
    All these keywords.

    JEL classification:

    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages
    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models

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