The Human Development Index (HDI) goes some way towards improving the categorisation of countries, but isn't really used when the big multilateral agencies consider categorising the countries that are most in need of assistance. Moreover, by its very nature as an index, it is uni-dimensional. What we need is a more multi-dimensional way of categorising the level of development of countries.
So, I was quite excited to recently read this 2014 CGD Working Paper by Andy Sumner (King's College London) and Sergio Tezanoz Vazquez (University of Cantabria). It builds on some earlier work of theirs that was published in the Journal of Development Studies in 2013 (sorry I don't see an ungated version online), and looks at a taxonomy of developing countries constructed in an explicitly multidimensional way.
The authors use cluster analysis to categorise countries, using variables across four main dimensions of development:
- Development as structural transformation - GDP in non-agricultural sectors (as a % of GDP), exports of primary commodities (as a % of GDP), GDP per worker (in constant 2005 PPP dollars, as a measure of productivity), number of scientific articles (per million people), and external finance (overseas development assistance, foreign direct investment, foreign portfolio investment, and remittances, as a share of GDP);
- Development as human development - poverty headcount (using a $2 per day poverty line), Gini coefficient (a measure of inequality), and malnutrition prevalence (low weight-for-age among those aged under five years);
- Development as democratic participation and improved governance - World Governance Indicators index, and POLITY 2 index; and
- Development as environmental sustainability - CO2 emissions (in metric tons per capita).
Cluster analysis is a really useful way of identifying observations (in this case, countries) that are similar across many dimensions. The important thing is that, unlike the HDI, the multi-dimensionality of the data is preserved. That means that you don't have a dichotomy (poor/non-poor countries), nor do you necessarily have a single development trajectory (from low income to high income). The authors note:
hierarchical cluster analysis allows one to build a taxonomy of countries with heterogeneous levels of development in order to divide them into a number of groups so that: i) each country belongs to one – and only one – group; ii) all countries are classified; iii) countries of the same group are, to some extent, internally ‘homogeneous’; and iv) countries of different groups are noticeably dissimilar. The advantage of this procedure is that it allows one to discern the ‘association structure’ between countries, which – in our analysis – facilitates the identification of the key development characteristics of each cluster.
Moreover, one of the great things about this working paper is that they look at two points in time (1995-2000, and 2005-2010), which allows them to:
...this analysis allows us to... analyse the dynamics of the development process of a single country in comparative terms (that is, in terms of the average development indicators of the "peer" countries belonging to the same cluster.In both time periods, the authors identify five clusters of developing countries. The three variables in order with the greatest discriminating power (the variables that make countries in each country most different from each other) are poverty, quality of democracy, and productivity in the 1995-2000 data, and poverty, productivity, and quality of democracy in the 2005-2010 data. The consistency is reassuring.
The five clusters in 1995-2000 (in order from lowest average Gross National Income per capita to highest) were:
- Very poor countries with largely 'traditional' economies - 31 countries, including Democratic Republic of Congo, Rwanda, Pakistan, and Swaziland;
- Poor countries with democratic regimes but poor governance - 18 countries, including Ethiopia, India, Indonesia, and the Philippines;
- Countries with democratic regimes but high levels of inequality and dependency on external flows - 18 countries, including Moldova, Honduras, Colombia, and the Dominican Republic;
- "Emerging economies" that were primary product exporting with low inequality but high environmental pollution and severely constrained political freedoms - 11 countries, including Azerbaijan, China, Egypt, and Gabon; and
- Highly polluting and unequal emerging economies - 21 countries, including Ukraine, Thailand, Mexico, and Argentina.
In 2005-2010, four of the clusters maintain a similar definition, but Cluster 2 becomes "Countries with high poverty and malnutrition rates that are primary product exporting and have limited political freedoms".
To a large extent, the most interesting aspects of the paper are the dynamics, i.e. which countries move from one cluster to another over the period, and which countries remain in the same cluster. There is a lot of movement between clusters - too much to effectively summarise here. Some notable (to me!) movements though include Vietnam moving from Cluster 1 to the new Cluster 2, India (and Nigeria, Ethiopia, and Papua New Guinea) moving from Cluster 2 to Cluster 1, Indonesia (and Sri Lanka) moving from Cluster 2 to Cluster 3, Thailand (and Ukraine) moving from Cluster 5 to Cluster 3, and Iran moving from Cluster 5 to Cluster 4. All countries in Clusters 3 and 4 in the first period were still in the same clusters in the second period, which is also interesting.
Obviously, some improvements can be made in terms of which variables should be included in developing the clusters (the authors make this point themselves). However, I can see a lot of mileage in further exploring not only the results in this paper, but the approach to categorising developing countries and their development paths more generally.