Improving Industrial Policy Intervention: The Case of Steel in South Africa

Abstract

We contribute to the lack of tools to support efficient industrial policy-making, especially in the mineral beneficiation policy literature. To address this vacuum, we adapt the product space analysis approach to incorporate an input-output value chain lens. This framework is applied to the case of steel in South Africa to derive novel insights regarding the (in)efficiency of implementing a downstream linkage-based beneficiation policy. Our dynamic analysis approach allows for interactions with the rest of the product space. We find that a ‘leap-frogging’ approach to development within the value chain may be more optimal than a strict beneficiation based industrial policy.

Acknowledgements

We would like to thank two anonymous referees, the editor, conference participants at ETSG 2017 (Florence) and CGP 2017 (Singapore) as well as seminar participants at KU Leuven and Stellenbosch University for very helpful suggestions to the paper. We also gratefully acknowledge the travel support provided by Stellenbosch University and KU Leuven through the bilateral mobility funding mechanism.

Disclosure statement

No potential conflict of interest was reported by the authors.

Supplementary material

Supplementary Materials are available for this article which can be accessed via the online version of this journal available at https://doi.org/10.1080/00220388.2018.1528354

Notes

1. Including self-discovery externalities, coordination externalities, and missing public inputs.

2. A country’s footprint is considered to include those products for which it has an RCA>1.

3. The complexities are calculated as the average complexity of all products in the product category (both products with RCA<1 and RCA>1).

4. To keep the figure tractable, we only show the 1.3 per cent (32/2352) highest proximity linkage values between product categories.

5. For complexity and opportunity gain, higher values are deemed more desirable; for distance, lower values are.

6. South Africa has an RCA in all products within tier 1 and 2 and an RCA for many (not all) products within tier 3.

7. Exceptions are product categories 11 (tanks and related products), 14 (basic aluminium products), 18 (batteries and accumulators), and 28 (ships and related products).

8. ‘Obtain’ refers to achieving an RCA>1 for all products within the product category.

9. We observe that both concepts generally move in the same direction. Obtaining an RCA in a complex good generally increases the opportunity value as it tends to increase the densities to more complex goods by developing valuable capabilities. In theory, however, it may also decrease the opportunity gain if (i) the extra good itself is extremely complex (thus lowering the average complexity of goods not yet obtained) and/or (ii) the extra good does not contribute significantly to unlocking any other goods (it lowers – or at least does not increase – the average density to high complexity goods not yet obtained).

10. Ideally, no restriction should be placed on the number of product categories included in the analysis and distance should act as the only restriction. However, the number of categories was artificially limited for the practical consideration of computer running time required as it increases exponentially when an additional industry is included. The foresight of three industries was deemed to be sufficient, as various other aspects of the product space may also have changed in the time it takes to acquire three industries, justifying a rerun of the simulation.

11. The selection of these three product categories is a result of a dynamic maximisation process. Looking at Table 3, product category 48 seems to outperform product category 22 when the distance constraint is sufficiently relaxed. However, this is a static result and does not take the dynamics into account – that is the earlier selection of other product categories.