Same skeleton, new code: Rethinking the factors of production in Africa’s digital economy

By Prof. Kwaku APPIAH-ADU (PhD, FGA) &Thomas Nana KWANTWI

If Henry Ford were dropped into Accra, Abidjan, Lagos or Nairobi, today, he might struggle to recognize the tools of modern wealth creation. He would not see furnaces, stamping presses, and assembly lines. He would see laptops, cloud dashboards, payment APIs, and a young founder explaining “scale” in terms of users, transactions, and uptime rather than factories, trucks, and floor space.

Yet Ford would recognize something deeper: the economic logic has not been abolished. It has been translated.

For African countries pursuing digital transformation, and for policymakers trying to turn fintech success stories into broad-based development, the most useful starting point is continuity. The classical “factors of production” remain the architecture of value creation. What has changed is their form. Land, labour, capital, and entrepreneurship still determine who can produce, who can compete, and who can prosper. But in the digital economy, each factor has a modern equivalent.

This is not merely a theoretical point. It is a policy lens. Once we understand what the factors look like today, we can see more clearly why some African markets are producing globally relevant digital firms, and why others risk becoming long-term consumers of platforms built elsewhere.

The industrial blueprint

Classical economics teaches that production requires four inputs. Land is the space and endowment where activity happens. Labour is human effort. Capital is the tools and financing that raise productivity. Entrepreneurship is the organizing function, coordinating the other inputs to meet demand, manage risk, and earn profit.

In the industrial era, these concepts were tangible; Ford’s “land” was literal land: factories, warehouses, rail connections, and access to raw materials. Labour was the workforce on the floor. Capital was machinery and financing for physical expansion. Entrepreneurship meant turning all of this into an efficient system that could produce at scale.

Africa’s industrialization challenges have always been, in part, factor constraints: weak logistics and power, skills gaps, shallow capital markets, and thin managerial capacity. Those constraints did not disappear when the world went digital. In several respects, they became easier to ignore because digital transformation is sometimes misread as “weightless” development.

The digital translation

Digital production looks immaterial, but it is not frictionless. The factors of production still exist; they have shifted from physical forms to digital equivalents:

• Land becomes digital infrastructure: electricity reliability, connectivity, cloud/hosting capacity, and the physical backbone that keeps systems online.

• Labour becomes specialized digital skills: engineering, cybersecurity, product design, data, compliance, and operations.

• Capital becomes both a financing mechanism and a digital system, encompassing platforms, APIs, risk engines, data assets, and the funds to build resilience before scaling.

• Entrepreneurship becomes ecosystem orchestration: building networks of users, partners, and regulators around shared standards and trust.

Each translation matters, but the first, digital “land”, is often the binding constraint in African markets. When founders complain about downtime, they are describing the digital equivalent of a factory losing power mid-production. A payments processor cannot settle if networks fail. A lending app cannot score risk if data access is unreliable. A government cannot digitize services if connectivity is expensive or inconsistent.

Labour has also changed. In the digital economy, productivity depends less on headcount and more on capability. This is why a small team can build a product used by millions, while a much larger institution struggles to digitize its internal processes. The premium is on problem-solving and continuous learning, because both tools and threat models evolve quickly.

Capital, meanwhile, shifts upstream. Digital products can often be replicated cheaply once they work, but making them work securely is expensive. The “machines” are codebases, integrations, fraud engines, KYC processes, and operational discipline. When capital markets are shallow or risk appetite is low, firms underinvest in resiliency, and the ecosystem pays the price through outages, fraud losses, and eroded trust.

Entrepreneurship in digital markets is less about managing a single firm and more about governing an ecosystem. The entrepreneur designs incentives and standards to enable users, merchants, banks, telcos, developers, agents, and regulators to participate in a shared platform. In Africa, where markets are fragmented by currency zones, regulation, and telecom “walled gardens”, this coordinating function is unusually demanding. It requires not only execution, but diplomacy.

Why this matters for Africa

If the “skeleton” of production remains intact, Africa’s digital strategy becomes clearer. Digital transformation is not a substitute for foundations; it is an additional layer that increases the need for them.

Three policy implications follow.

First, infrastructure policy is a form of industrial policy. The fastest way to accelerate the digital economy is often to treat electricity reliability, fiber rollout, spectrum allocation, and local hosting capacity as productivity investments. Without reliable digital infrastructure, even excellent talent and capital produce fragile systems.

Second, regulation is part of the production function. In the industrial era, firms needed permits, standards, and predictable enforcement. In the digital era, they need those, plus data protection, cybersecurity requirements, consumer protection, and AML/CFT compliance. Where rules are unclear or unevenly enforced, the cost of entrepreneurship rises and the market rewards short-term arbitrage over long-term investment.

Policymakers can expand the effective “entrepreneurship” factor by lowering compliance friction while raising trust. Practical steps include shared KYC utilities, clear licensing pathways, interoperability mandates where appropriate, and tiered compliance proportional to risk. Regulatory sandboxes help, but only when they connect to real adoption, public procurement, interoperable rails, or industry-wide standards, rather than isolated pilots.

Third, scale requires markets, not just startups. Too many digital strategies celebrate the number of apps launched and ignore the structure of demand. In platform markets, winners often emerge through network effects; scale comes from adoption, integration, and repeated usage. That means governments and large incumbents (banks, telcos, utilities), shape the market as much as founders do.

If public institutions digitize identity, payments, and service delivery using open standards, they create demand for local platforms and lower barriers for new entrants. If they digitize through closed, vendor-locked systems, they entrench dependency.

Fintech as the clearest case study

Payments and financial services provide a clear lens because they sit at the intersection of infrastructure, regulation, and trust. The success of mobile money and the rise of firms like Paystack and Flutterwave were not born from a sudden escape from “land” and “capital.” They were built, often painfully, through assembling the digital equivalents: connectivity, integrations, compliance capability, operational resilience, and partner networks.

Fintech also reveals a central policy truth: where digital public goods are weak, private firms must build them themselves. If identity systems are fragmented, fintechs must over-invest in KYC. If payments are not interoperable, they must negotiate multiple bilateral integrations. If dispute resolution is weak, they must absorb consumer protection costs. Every gap becomes a tax on innovation.

This is why the policy objective should not be “more fintechs.” It should be: lower ecosystem costs, so that fintech innovation becomes cheaper to produce, safer to consume, and easier to scale across borders.

The next shift: intelligence

A further evolution is now underway. Beyond the four factors, a fifth is emerging as a generalized accelerator: intelligence. Embodied in analytics, machine learning, and increasingly in generative AI.

Intelligence raises productivity across the other factors. It can improve fraud detection, automate customer support, reduce underwriting costs, and accelerate software development. It can also introduce new risks: biased models, opaque decision-making, and heightened cyber threats.

For Africa, the opportunity is significant precisely because constraints are severe. When institutions are understaffed, intelligent systems can extend capacity. When data is fragmented, machine learning can extract signals. But the precondition remains the same: you cannot automate what you have not organized. Data governance, privacy protections, and responsible AI practices are not optional; they are the rails on which intelligent systems run.

Conclusion: continuity with urgency

Africa’s digital future will not be decided by slogans about leapfrogging. It will be decided by whether countries can assemble the modern factors of production at scale: digital infrastructure, specialized skills, resilient capital formation, and ecosystem-building entrepreneurship, under rules that raise trust and reduce friction.

The digital economy did not tear up the economic playbook. It rewrote it in a new language. The skeleton remains. Only the flesh has changed.

The policy task, therefore, is not to chase novelty, but to modernize fundamentals, quickly and deliberately, in ways that expand local capability. The algorithm is already running. The question is whether Africa will remain a passive user of its outputs or become an active author of the next chapter.

Prof. Kwaku Appiah-Adu is a Professor of Strategy and Policy & Management [email protected] and Thomas Nana Kwantwi  is a Research Associate

[email protected]

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