A brilliant review of Paul Collier’s The Bottom Billion and Wars, Guns and Votes

Extracts:

Collier’s work is not informed by any explicit, overarching theory of development or any historical perspective that might inform one; nor does he offer any social analysis. There is an implicit theory of human behaviour, which is radically reductionist—individual economic self-interest rules. In this view, history appears to be a continuum of ‘14th-century reality: civil war, plague and ignorance’. But these countries had their own 14th centuries and now find themselves in the 21st, playing a highly subordinate role in global capitalism. No understanding of how they got there can ignore the impact of colonization. In Collier’s models, colonial history is reduced to two numbers, one representing the colonial power—Britain, France, Portugal, Belgium, Germany—the other, the length of time that the country was colonized. The identity of the colonizing power does have a bearing upon the ex-colonial country’s legal system, educational set-up,lingua franca and financial institutions; but it tells us nothing about the pre-colonial system, the different processes by which the European power made its peace with local rulers; nor about the ending of colonial rule, and the extent to which ruptures or continuities determined the nature of the ex-colonial state. Such considerations help to inform a richer explanation of how a country has developed, and provide a deeper explanatory framework for civil wars, social conflicts or institutional forms—social and political questions, not purely statistical ones.

It is misleading to paint a picture of endemically low growth rates in sub-Saharan Africa, or in the other ‘bottom billion’ countries. In Africa, growth rates in the 1960s and early 1970s were comparable to those of Southeast Asia or Latin America. This was a period of African industrialization, based on import substitution; with improvements in economic management, this might have enabled several countries to take advantage of export markets. The droughts in the early 70s were a real blow, turning food self-sufficiency into food imports; most African economies were severely affected by the oil-price hikes, and still more so by interest-rate rises after 1979. But a possible industrial rehabilitation was stalled in the early 80s by World Bank and IMF opposition to ISI, and promotion instead of primary commodities and ‘getting prices right’. Collier fails to point out that African countries were forced to pursue primary-commodity exports as a consequence of World Bank conditionalities; as noted, these are not included in his list of explanatory variables. Collier takes the familiar line on trade, criticizing rich-country barriers to the exports of the ‘bottom billion’ but also, and far more fervently, tariffs set by developing countries. In his account, Africa simply ‘missed the boat’ in the 1980s and ceded global markets to Asia; only Mauritius—hardly a typical African country, if an African country at all—managed to ‘climb on board’. It was not simply low wages that attracted investment to the Asian Tigers, however, but their well-educated populations and skilled labour from the 60s on. Here again, a historically informed account of colonial and post-colonial social structures must be a factor in any satisfactory explanation.

Read the whole piece here (H/T Schauzeri).  Any analysis that fails to pay history its proper due should be suspect.

On the subject of numbers and statistical analysis. This says it quite succinctly:

Statistics are a great way of quickly conveying how a group of events, people, or things are similar and different. Mode, median and mean measure “central tendency,” and standard deviation and inter-quartile range tell you “dispersion.” With these two types of measures, you can tell me how similar people are when they choose orange juice, how different they are when they rent cars or attend movies. But you cannot tell me what “more pulp,” means to people, why a “subcompact” car turns off some people, or what people perceive the word “blockbuster” to actually mean.

In short, ethnographic research can clarify all of these deep, nuanced details that quantitative data skates over or takes for granted. Do you want to know how many people attended a “summer blockbuster?” Then by all means, count them. But if you want to know what kind of movie people believe a “blockbuster” to be, then you need to do in-depth ethnographic work.