Pierre Nguimkeu

IEA – Could you walk us through the key moments that shaped your path—from your earliest exposure to economic thinking to what sparked your interest in the field, and ultimately what drew you to academic research?

Pierre – My path into economics wasn’t straightforward. I started my undergraduate studies in mathematics and computer science at the University of Dschang in Cameroon. I was drawn to mathematics because of its rigor and the idea that you can take a complicated problem, strip it down to its essential structure, and reason carefully toward an answer. Later, I studied Statistics at ENSEA of Abidjan in Côte d’Ivoire. That was a key moment for me because I began to link this kind of quantitative thinking to major measurement challenges that directly affect how society functions.

After ENSEA, I worked as a statistician at Cameroon’s National Institute of Statistics. In that role, I got up close to the realities behind much of the data. We could report on poverty, unemployment, firm formation, household income, and other outcomes. But often in response to such data, the numbers raised deeper questions. Why do two people of similar ability end up on very different economic paths? Why are so many productive enterprises small? Why do so many people work informally for decades? Why do well-intentioned policies so often have little impact in practice? I increasingly wanted not only to measure these phenomena, but to understand the mechanisms behind them.

That motivation eventually prompted me to pursue graduate studies in economics at the Université de Montréal, and later a PhD at Simon Fraser University. In economics, I found a framework in which, for example, statistical inference, mathematical modeling, and questions of institutions could feed into each other. What appealed most to me was econometrics, which sits at the interface between what theory predicts and what we can learn from imperfect real-world data, while development economics spoke directly to the phenomena I had observed in African economies: entrepreneurship without finance, firms outside formal institutions, and vast amounts of activity invisible to conventional measures.

That remains a common thread in my work today. Whether I am studying informality, entrepreneurship, industrialization, digital technologies, or developing econometric methods for imperfect data, I am ultimately interested in the same question: how can we better identify the constraints that prevent people and economies from realizing their productive potential? For me, academic research is most rewarding when analytical rigor and practical relevance reinforce one another.

IEA – In your research on credit constraints and delayed entrepreneurship, you find that many potentially talented entrepreneurs may spend years in subsistence work because they lack the capital to start a business. What are the broader economic costs when talent and entrepreneurial potential are held back by limited access to finance, particularly in developing economies where entrepreneurship is often seen as a key driver of jobs and growth?

Pierre – One idea I wanted to emphasize in this research is that the cost of financial exclusion is much higher than the amount of credit that is missing.

In the model and data, I study from Cameroon, prospective entrepreneurs may have to spend about seven years in subsistence activities simply accumulating enough savings to start a microenterprise. Some never make the transition at all. This is important because these are not necessarily people who lack entrepreneurial ability. Some are potentially high-productivity entrepreneurs whose principal disadvantage is that they begin with too little wealth to satisfy collateral requirements.

Once we think about the problem this way, the broader economic cost becomes clear. The first cost is misallocation of talent. An economy may have people who could run productive businesses, employ others, innovate, and accumulate capital, but instead engage in activities where their entrepreneurial ability generates very little return. What appears in the data as a low-productivity worker may therefore partly reflect a constrained entrepreneur.

The second cost of financial constraints is dynamic. A year spent in subsistence is more than a year without business income. It may well be a year with no business learning, no development of a customer base, no hiring of staff, and no reinvestment of profits, and these losses compound over time. Financial constraints can therefore turn an initial difference in wealth into persistent differences in occupation, income, and ultimately welfare. For some households, they create genuine poverty traps.

They also impose an aggregate cost on the economy. When potentially viable firms do not enter the market, or enter many years after they could, economies miss out on tax revenue, employment, innovation, and output. And because entrepreneurial opportunities become disproportionately available to people who already possess wealth or collateral, financial frictions can reinforce intergenerational inequality. In that sense, financial inclusion is as much a productivity policy as a social one.

The policy implication is not that every individual should receive a loan or become an entrepreneur. Entrepreneurship itself is heterogeneous, and indiscriminate credit expansion can generate other problems. Rather, we should focus on strengthening mechanisms in our financial system for identifying high-potential, undercapitalized entrepreneurs, as opposed to high-risk borrowers. That is why I am particularly interested in innovations such as alternative credit scoring, digital transaction histories, movable collateral, credit guarantees, relationship lending, and appropriately designed microinsurance.

More broadly, talent is far more common among people than collateral. To the extent that productive opportunities in an economy depend too much on wealth already held, the result isn’t only an unequal outcome; the productive potential of that economy is being underused.

IEA – In your research on manufacturing and structural transformation in Africa, you challenge the view that the region is undergoing premature deindustrialization and find that manufacturing remains a viable path for economic transformation. At a time when automation and shifting global supply chains are changing the nature of industrial production, how should African countries think about manufacturing as a source of productive jobs and long-term development?

Pierre – I think we need to distinguish between the proposition that manufacturing is changing and the proposition that manufacturing no longer matters for Africa. The first is clearly true. The second, in my view, is not.

In work with Albert Zeufack, we examined a much broader sample of African countries and a longer period than much of the earlier literature on premature deindustrialization. We find little support for the claim that sub-Saharan Africa as a whole has already begun to deindustrialize prematurely. There is considerable heterogeneity across countries and subregions, of course, but the evidence does not justify writing off manufacturing as a route to structural transformation.

What is changing is the type of industrialization that will be viable.

African countries are unlikely simply to reproduce the East Asian industrialization model of several decades ago. Automation has reduced the labor intensity of some manufacturing activities, global value chains are being reorganized, geopolitical considerations increasingly influence sourcing decisions, and the green and digital transitions are changing both products and production processes. But these changes also create opportunities.

Africa has a number of advantages that, taken together, could support manufacturing on the continent. It has a growing labor force, an increasingly integrated market across the continent, significant potential for renewable energy and key minerals, a developing digital economy and considerable demand within its own borders for consumer goods, machinery, construction materials, pharmaceuticals and processed food. So much of the question of manufacturing in Africa is not whether it should happen, but rather what it should focus on, where it could add value, and how industrial policy might help production build on these advantages.

This also requires thinking beyond the factory itself. Modern manufacturing creates productive employment through entire ecosystems, logistics, business services, technology, maintenance, design, finance, distribution, and supplier networks. Agro-processing, for example, can raise productivity both within industry and upstream in agriculture. Processing critical minerals can generate linkages to energy, infrastructure, engineering, and technology. Regional value chains under the African Continental Free Trade Area can give firms access to markets large enough to exploit economies of scale that individual national markets often cannot provide.

The biggest risk, in my view, is interpreting automation as a reason for African countries to abandon industrialization before they have seriously pursued it. Instead, countries need a more strategic form of industrialization: investing in reliable and affordable energy, skills, transport and digital infrastructure; identifying sectors in which they possess or can build comparative advantage; using regional integration to create scale; and ensuring that industrial policy encourages productivity rather than permanently protecting inefficient firms.

Manufacturing isn’t the only route for structural change. In time, digital industries, modern agriculture, tradable services and other sectors will all be very important. But it is one of the most powerful mechanisms we know for driving exports, scale, productivity growth, technological learning and better jobs. Africa should rethink this mechanism for the 21st century, rather than declare it obsolete.