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Tag: reverse causality

Slavery, ethnic diversity and economic development

What is the impact of the slave trades on economic progress in Africa? Are the modern African states which ‘exported’ a higher number of slaves more likely to be underdeveloped several centuries afterwards? Harvard economist Nathan Nunn addresses these questions in his chapter for the “Natural experiments of history” collection. The edited volume is supposed to showcase a number of innovative methods for doing empirical research to a broader audience, and historians in particular. But what Nunn’s study actually illustrates is the difficulty of making causal inferences based on observational data. He claims that slave exports contributed to economic underdevelopment, partly through impeding ethnic consolidation. But his data is entirely consistent with a very different interpretation: ethnic diversity in a region led to a higher volume of slave exports and is contributing to economic underdevelopment today. If this interpretation is correct, it could render the correlation between slave exports and the lack of economic progress in different African states spurious – a possibility that is not addressed in the chapter. The major argument of Nunn’s piece is summarized in the following scatterplot. Modern African states from which more slaves were captured and exported (correcting for the size of the country) between the XVth and the XIXth centuries are associated with lower incomes per capita in 2000 (see Figure 5.1 on p.162, the plot reproduced below is actually from an article in the Quarterly Journal of Economics which looks essentially the same): The link grows only stronger after we take into…

Is unit homogeneity a sufficient assumption for causal inference?

Is unit homogeneity a sufficient condition (assumption) for causal inference from observational data? Re-reading King, Keohane and Verba’s bible on research design [lovingly known to all exposed as KKV] I think they regard unit homogeneity and conditional independence as alternative assumptions for causal inference. For example: “we provide an overview here of what is required in terms of the two possible assumptions that enable us to get around the fundamental problem [of causal inference]” (p.91, emphasis mine). However, I don’t see how unit homogeneity on its own can rule out endogeneity (establish the direction of causality). In my understanding, endogeneity is automatically ruled out with conditional independence, but not with unit homogeneity (“Two units are homogeneous when the expected values of the dependent variables from each unit are the same when our explanatory variables takes on a particular value” [p.91]). Going back to Holland’s seminal article which provides the basis of KKV’s approach, we can confirm that unit homogeneity is listed as a sufficient condition for inference (p.948). But Holland divides variables into pre-exposure and post-exposure before he even gets to discuss any of the additional assumptions, so reverse causality is ruled out altogether. Hence, in Holland’s context unit homogeneity can indeed be regarded as sufficient, but in my opinion in KKV’s context unit homogeneity needs to be coupled with some condition (temporal precedence for example) to ascertain the causal direction when making inferences from data. The point is minor but can create confusion when presenting unit homogeneity and conditional independence side by side as alternative assumptions for inference.