Assessing the progress of exports diversification in Saudi Arabia: growth-share matrix approach

  • Received April 19, 2020;
    Accepted August 12, 2020;
    Published August 25, 2020
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  • Article Info
    Volume 18 2020, Issue #3, pp. 118-128
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    1 articles

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This work is licensed under a Creative Commons Attribution 4.0 International License

High dependence on a particular category of exports results in fluctuations in income as the price of the export item fluctuates. In Saudi Arabia, a single category of mineral exports forms over 78% of the total exports, exposing the country to revenue volatility. The study aims to assess the magnitude of diversification of the export basket for the country. It uses data from 1984 to 2018 to study the importance of non-mineral exports in total exports. It applies Granger causality, variance decomposition, and impulse response function in the vector autoregressive framework. The study also uses the growth-share matrix to evaluate individual items of non-mineral exports. The results show a long-run relationship with a 1% increase in non-mineral exports, leading to a 0.30% increase in total exports. Non-mineral exports Granger-cause total exports. In the long run, non-mineral exports have a share of 64% of the forecast error variance in total exports. Moreover, a 1% shock in non-mineral exports creates a huge initial impact on total exports. Also, the growth rate of non-mineral products is higher than mineral products. The results indicate the importance of non-mineral exports for a predominantly oil-exporting country. Finally, the study attempts to classify its non-mineral export categories based on growth rates and market shares. Targeted emphasis on export category with a strong growth rate and low market share can be an effective strategy for further export diversification.

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    • Figure 1. Graphical presentation of the data (in million Riyals)
    • Figure 2. Impulse response function
    • Figure 3. Growth-share matrix
    • Table 1. Augmented Dickey-Fuller tests
    • Table 2. VAR Lag order selection
    • Table 3. Vector autoregression estimates
    • Table 4. Least squares estimatesм
    • Table 5. VAR Granger causality/block exogeneity Wald tests
    • Table 6. Variance decomposition
    • Table 7. VAR residual serial correlation LM tests
    • Table 8. VAR residual normality tests
    • Table 9. VAR residual heteroscedasticity tests
    • Table 10. Descriptive statistics of the data
    • Conceptualization
      Mohammad Imdadul Haque
    • Data curation
      Mohammad Imdadul Haque
    • Formal Analysis
      Mohammad Imdadul Haque
    • Investigation
      Mohammad Imdadul Haque
    • Methodology
      Mohammad Imdadul Haque
    • Resources
      Mohammad Imdadul Haque
    • Software
      Mohammad Imdadul Haque
    • Supervision
      Mohammad Imdadul Haque
    • Validation
      Mohammad Imdadul Haque
    • Visualization
      Mohammad Imdadul Haque
    • Writing – original draft
      Mohammad Imdadul Haque
    • Writing – review & editing
      Mohammad Imdadul Haque