On demography and living standards in the colonial era

This is a topic that has been bugging me. Very often, historians will (accurately) point out mortality statistics in the United States, Canada (Quebec) and the Latin America during the colonial era were better than in the comparable Old World (comparing French with French, British with British, Spanish with Spanish). However, they will argue that this is evidence that living standards were higher. This is where I wish to make an important nuance.

Settlement colonies (so, here there is a bigger focus on North America, but it applies to smaller extent to Latin America which I am more tempt to label as extractive – see here) are generally frontier economies. This means that they are small economies because of small populations.  This means that labor and capital are scarce relative to land. All outputs that come from the relatively abundant factor will thus tend to be cheaper if there is little international trade for the goods that they are best at producing. The colonial period pretty much fits that bill. The American and Canadian colonies were basically agricultural colonies, but very few of those agricultural outputs actually crossed the Atlantic. As such, agricultural produces were cheap. This is akin to saying that nutrition was cheap.

This, by definition, will give settlement colonies an advantage in terms of biological living standards. As they are not international price takers, wheat is cheaper than in the old world. This is why James Lemon spoke of the New World as the “Best poor man’s country” (I love that expression) : it was easy to earn subsistence. However, beyond that it is very hard to go beyond. For example, in my dissertation (articles still in consideration at Cliometrica and Canadian Journal of Economics) I found that when wages were deflated by a subsistence basket containing very few services and manufactured goods and which relied heavily on untransformed foods, Canada was richer than the richest city of France. Once you shifted to a basket that marginally increased transformed goods and manufactured goods, the advantage was wiped away.

Yet, everything indicates that mortality rates were greater in Paris and France and than in Quebec City and Quebec as a whole (but not by a lot) (see images below).  Similar gaps seem to exist for the United States relative to Britain, but the data is not as rich as for Quebec. However, the data that exists for New England suggests that death rates were lower than in England but the “bare bones” real incomes measured by Lindert and Williamson show that New England may have been poorer than Great Britain (not by much though).

Crude Death Rates


I am not saying that demographic and biological data is worthless. Quite the contrary (even I wanted to, I could not since I have a paper on the heights of French-Canadians from 1780 to 1830)! The point is that data matters in context.  The world is full of small non-linearities between variables. While “good” demographic outcomes are generally tracking “good” economic outcomes, there are contexts where this may be a weaker relation (curvilinear relations between variables). I think that this is a good example of that point.


On Cuba’s Fake Stats

On Monday, my piece on the use violence for public health purposes in Cuba (reducing infectious diseases through coercion at the expense of economic growth which in turn increases deaths from preventable diseases related to living standards) assumed that the statistics were correct.

They are not! How much so? A lot! 

As I mentioned on Monday, Cuban doctors face penalties for not meeting their “infant mortality” targets. As a result, they use extreme measures ranging from abortion against the mother’s will to sterilization and isolation.  They also have an incentive to lie…(pretty obvious right?)

How can they lie? By re-categorizing early neonatal (from birth to 7th day) or neonatal deaths (up to 28th day) as late fetal deaths. Early neonatal deaths and late fetal deaths are basically grouped together at “perinatal” deaths since they share the same factors. Normally, health statistics suggest that late fetal deaths and early neonatal deaths should be heavily correlated (the graph below makes everything clearer).  However late fetal deaths do not enter inside the infant mortality rates while the early neonatal deaths do enter that often-cited rate (see graph below).

Death Structures.png

Normally, the ratio of late fetal deaths to early neonatal deaths should be more or less constant across space. In the PERISTAT data (the one that best divides those deaths), most countries have a ratio of late fetal to early neonatal deaths ranging from 1.04 to 3.03. Cuba has a ratio of more than 6. This is pretty much a clear of data manipulation.

In a recent article published in Cuban Studies, Roberto Gonzales makes adjustments to create a range where the ratio would be in line with that of other countries. If it were, the infant mortality of Cuba would be between 7.45 and 11.16 per 1,000 births rather than the 5.79 per 1,000 reported by the regime – as much as 92% higher. As a result, Cuba moves from having an average infant mortality rate in the PERISTAT data to having the worst average infant mortality in that dataset – above that of most European and North American countries.

So not only is my comment from Monday very much valid, the “upside” (for a lack of a better term) I mentioned is largely overblown because doctors and politicians have an incentive to fake the numbers.