This guest post by Andy Sumner and Sergio Tezanos Vázquez explores new approaches to classifying developing countries, based on a new IDS Working Paper published last week. In the paper, they develop a more precise and accurate classification system for low and middle income countries, and suggest that this can support a more complex, non-linear appreciation of development trajectories. This post concludes with the intriguing notion of ‘development landscapes’ (building on a recent CGD blog post written by Owen Barder and myself).
[t]he typical case is a largely unindustrialised economy, the foreign trade of which consists essentially in selling primary products for manufactures. There are about 100 identifiable economies of this sort, covering the great majority of the world’s population (1963, p. 80).
And perhaps most famously, Seers wrote in The Meaning of Development:
The questions to ask about a country’s development are therefore: What has been happening to poverty? What has been happening to unemployment? What has been happening to inequality? If all of these three have become less severe, then beyond doubt this has been a period of development for the country concerned […] If one or two of these central problems have been growing worse, especially if all three have, it would be strange to call the result ‘development’, even if per capita income has soared (1969:24).
Since then many have challenged the use of income per capita as the primary proxy for development. Of course, in addition to low and middle income countries there are many classifications – notably those by Human Development and the Least Developed Countries classifications and the new pioneering work of the Oxford Poverty and Human Development Initiative.
Our new paper continues this tradition with what might be described as a complex systems twist. The paper challenges the continuing use of income per capita to classify developing countries as low income countries (LICs) or middle income countries (MICs), given that most of the world’s poor live in the later group (see here,here, here, here).
Further, it highlights the ambiguity over the usefulness of the MIC classification given the diversity in the group of over 100 countries that includes Ghana and Zambia, as well as India, China and Brazil.
Finally, it points to a new way of framing development, which moves beyond the linear league table approach which tends to imply a certain shared pathway (cf critiques of HDI as ranking countries on a scale of 0 to Denmark). In closing, we touch on one example of what this might look like below: there are no doubt others.
We used a cluster analysis to identify five types of developing country using a set of indicators for 2005-2010 covering definitions of development based on the history of thinking about ‘development‘ over the last 50 years from four conceptual frames:
- development as structural transformation;
- development as human development;
- development as democratic participation and good governance;
- development as sustainability.
This is what we found:
Our development taxonomy differs notably from the usual income classification of GNI per capita (Atlas method) used to classify LICs and MICs. Notably many countries commonly labelled “emerging economies” are not in the emerging economies clusters because they retain characteristics of poorer countries.
Our analysis generated 5 clusters as follows:
Cluster 1: High poverty rate countries with largely traditional economies. Those countries with the highest poverty and malnutrition headcounts, who are also countries with low productivity and innovation and mainly agricultural economies, with severely constrained political freedoms. This cluster includes 31 countries, some of them might be surprising: Pakistan, Zambia, Nigeria, and India.
Cluster 2: Natural resource dependent countries with little political freedom. Those countries with high dependency on natural resources, who are also countries with severely constrained political freedom and moderate inequality (relative to the average for all developing countries). This cluster includes 9 countries, such as Mauritania, Vietnam, Yemen, Cameroon, Congo, Swaziland and Angola.
Cluster 3: External flow dependent countries with high inequality. Those countries with high dependency on external flows, who are also countries with high inequality, and moderate poverty incidence (relative to the average for all developing countries). This cluster has 32 countries, such as Senegal, Ghana, Indonesia, Thailand, Peru, Colombia, and Panama.
Cluster 4: Economically egalitarian emerging economies with serious challenges of environmental sustainability and limited political freedoms. Those countries with most equal societies, who are also countries with moderate poverty and malnutrition but serious challenges of environmental sustainability and –perhaps surprisingly– limited political freedoms. This cluster has 15 countries, including China, Azerbaijan, Belarus, and Kazakhstan.
Cluster 5: Unequal emerging economies with low dependence on external finance. Those countries with the lowest dependency on external finance and who are also countries with the highest inequality. This cluster includes 14 countries, such as South Africa, Botswana, Costa Rica, Malaysia, Argentina, Mexico, Brazil, Turkey, Chile and Uruguay.
Two-thirds of the world’s poor – not surprisingly given the characteristics noted above – live in Cluster 1 countries though this is largely due to the inclusion of four populous countries (Bangladesh, India, Pakistan and Nigeria –and one should remember a third of world poverty is accounted for by India). About a quarter of world poverty is situated in Cluster 3 and Cluster 4 countries and the remaining 5% live in Cluster 2 and Cluster 5.
What is most striking – and of particular relevance for readers of this blog – is that we find that there is no simple “linear” representation of development levels (from low to high development countries), as found in the Human Development Index or its variants.
We find instead that each development cluster has its own and characteristic development issues. Building a development classification is not a simple task: once we overcome the over-simplistic income-based classification of the developing world, we find that there is no group of countries with the best (or worst) indicators in all development dimensions.
It thus would be more appropriate to build “complex” development taxonomies on a five-year basis than ranking and grouping countries in terms of per capita incomes or other indicators, as this will offer a more nuanced image of the diversity of challenges of the developing world and policy responses appropriate to different kinds of countries.
In keeping with a growing consensus in development thinking, this way of thinking points to an understanding which is less about linear progression, and where change is path dependent, and development policy is best seen as an evolutionary process of learning.
What might such a complex taxonomy look like? A fruitful avenue to explore (following the recent post by Ben Ramalingam and Owen Barder on complexity and results) might be the notion of fitness landscapes from evolutionary biology, which illustrate how different species attain biological fitness through processes of variation, selection and amplification.
Erik Beinhocker and Tim Harford have argued that growth and innovation can be likened to a evolving search of a dynamic fitness landscape. “Development Landscapes” might provide an analogy and a model for explaining the distinct but related positions of countries in different development clusters.
This would enable analysts and policy makers to examine the different pathways the cluster members have been on, and to explore the space of possibilities for future change.
About the guest post authors:
Sergio Tezanos Vázquez is an associate professor at the Economics Department and a research fellow at the Iberoamerican Research Office on International Development & Co-operation at the University of Cantabria.
Andy Sumner is Co-Director of the newly established, King’s International Development Institute, King’s College London.