Preparing for an inevitable fourth industrial revolution . . . Govts must start appreciating data, economic structure


Chris Chenga
There seems to be general consensus between academia and policymakers in regard to the long term structural threats facing developing to low income nations. It is particularly debt and the Fourth Industrial Revolution that are the most profoundly concerning.

However, while debt has been an experienced challenge, with ample diligence applied over recent years to the different possible approaches to mitigate its threat on developing to low income economies, the Fourth Industrial Revolution has only just recently become a topic.

But even since this recent acknowledgement, a lack of data in developing to low income countries has deterred substantive diligence into the structural threats posed by the Fourth Industrial Revolution.

Consequently, there is only rhetorical consensus that the Fourth Industrial Revolution indeed poses structural threats to unprepared developing and low income nations.

In advanced countries by comparison, data on manufacturing output, employment trends, and value added activities per sector is readily available, thus enabling volumes of economic and policy research on the topic.

Accordingly, academia and policymakers are pre-emptively strategising actionable means of approaching the inevitable age of automation, artificial intelligence, and high technology means of production.

Developing to low income countries need to catch up on their data collection and the structural cognisance of their economies. It is inconceivable to prepare for an event’s structural impact when there is no credible data and structural cognizance of an economy.

For instance, Zimbabwe has often relied on capacity utilisation as a metric to measure industrial activity. However, that metric itself, and the data it computes, need to the interrogated in their ability to prepare for any impact by a Fourth Industrial Revolution.

Unfortunately, to many industrialists within our economy, indeed the Fourth Industrial Revolution remains an abstract topic, more befitting advanced economies, without any relevance to our industry.

Likewise, local policymakers who fancy the informal economy are, perhaps unknowingly, drifting as further away from the structural discipline and data availability to actually implement pre-emptive policy to the Fourth Industrial Revolution. These are all lamentable mindsets within our industrialists and policymakers.

Policymakers, especially, need to understand that economic management is about making decisions between trade-offs. Those decisions can only be informed by the right data and structural cognisance of our economy.

As such, policymakers must data on, and structural understanding of the role played by different demographics and sectors in an economy.

A proposition for preparing for the Fourth Industrial Revolution would be a strategy of “cross-subsidisation”. In micro-economics, cross subsidisation is a strategy where support for a product comes from the profits generated by another product on the market from the same company.

It works along similar strategy as profit centers, where certain business units are dependable in generating revenues that create funds for less lucrative units. But this is all based on structural understanding of a company. Similarly, policymakers must perceive the macro-economy in such a manner.

For instance, commodity rich nations can utilise their extractive sectors to generate revenues used as stimulus for other sectors.

The Fourth Industrial Revolution is a structural threat in that it potentially has impact on different demographics and sectors — that is a structural threat, where impact is dependent on structural relevance of demographics and sectors to an economy as whole.

But also, the Fourth Industrial Revolution is an opportunity in that certain sectors can be positioned as generating revenues that provide safety nets for harmed demographics and sectors. That opportunity will be there, for the structurally prepared.

Africa is notorious for subsidising the wrong sectors, however,for making exemptions and allowances that do not attend to the equitable benefits of demographics and sectors in their economies. Consider, oil or mineral sector subsidies such as tax exemptions to companies that are already profitable.

As the resource is there with a desiring market, with the right operational environment, such sectors are already incentivised to invest in their operations.

Instead, governments should focus on reforms that attract technological investment through those capital intensive sectors.

For example, China attracts advanced foreign technology from multinational companies within its jurisdiction. It does so in infrastructure such as rail, Information Technology such as telecommunications and computers, as well as mining. What this enables is that China remains at the forefront of the impending Industrial Revolution because it has already assimilated the competitive technology in these sectors.

Thus, these highly technical sectors can be perceived as the revenue generating units. This then affords carry on technology assimilation for more vulnerable demographics and sectors.

As jobs are lost to automation in technical sectors, these sectors still contribute revenues for governments to spend in manpower development so that “left-behind” demographics are prepared for a future of high value adding jobs.

Indeed developing and low income countries cannot compete in terms of the technology innovation and discovery of the Fourth Industrial Revolution.

Even our best minds are quickly taken, with good incentive, to apply their technological discoveries in advanced economies.

However, a conscious strategy of technology assimilation through identifying high technology sectors operating in our economy will help bring that technology of a new age into our countries. Having data and structural cognisance will enable policymakers to be intentional as to the equitable distribution of resources to left-behind demographics and sectors.

But this takes appreciating data and economic structure.


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