Impact of trade liberalization on technical efficiency of mining sector: A case of selected SADC countries

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Productive inefficiency and lagging technology progress are major reasons behind the Southern Africa Development Community’s (SADC) continued exportation of unprocessed minerals to the world markets. The study seeks to uncover the impact of trade openness on the technical efficiency of the mining sector in selected SADC countries (Botswana, DRC, Namibia, South Africa, Zambia, and Zimbabwe). Technical efficiency is the ability of any production process to produce maximum output from minimum quantities of inputs. A Cobb Douglas Stochastic Frontier Approach in a single-stage maximum likelihood estimation of Green’s true fixed effects was used to compute technical efficiency (scores) and the technological progress in the mining sector of SADC. Results indicate that there is no technical efficiency gains from trade liberalization during the period under study together with positive and significant technological progress. A coefficient of 0.72 suggests that a 1% increase in trade openness increases technical inefficiency in the mining sector by 0.72%. The parameter coefficient from the truncated normal distribution of the true fixed effects model indicated that technological progress from one year to the next year would lead to a 2.6% increase in the output index of the mining. Technological progress in the mining sector should target upstream mineral value chains instead of only upgrading technology in one dimension of extraction. In addition, countries should collectively and gradually put across laws that force new investments in the extraction of minerals to erect processing plants in mining value addition of host countries to re-direct economies into a growth path.

Acknowledgments
The authors are grateful to the North West University (RSA) for financing this study.

 

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    • Table 1. Stationarity results of the LLC and IPS tests for key variables of the technical efficiency model
    • Table 2. Hypothesis testing to ascertain technical inefficiency in the model
    • Table 3. Critical values
    • Table 4. Log-likelihood ratio test – selecting SFA versus OLS model
    • Table 5. Cobb-Douglas Stochastic Frontier true fixed effects – maximum likelihood estimates
    • Table 6. Determinants of technical inefficiency
    • Conceptualization
      Shylet Masunda
    • Data curation
      Shylet Masunda
    • Formal Analysis
      Shylet Masunda
    • Investigation
      Shylet Masunda
    • Methodology
      Shylet Masunda
    • Resources
      Shylet Masunda
    • Software
      Shylet Masunda
    • Validation
      Shylet Masunda
    • Visualization
      Shylet Masunda
    • Writing – original draft
      Shylet Masunda
    • Funding acquisition
      Ireen Choga
    • Project administration
      Ireen Choga
    • Supervision
      Ireen Choga
    • Writing – review & editing
      Ireen Choga