On 7 million deaths from air pollution

ATTN published a video of An-huld (the really cool guy who made my childhood by being in all my favorite action movies like Predator* and who ended up being the governor of California). In that short clip, Schwarznegger starts by saying that 7 million individuals die from pollution-related illnesses.

That number is correct. But it is misleading.

People see pollution as “all and the same”. But some forms of pollution increase with development (sulfur emissions and some would argue that too much CO2 emissions is pollution as it causes climate change). However, others drop dramatically – especially heavy particules (Pm10) which are a great cause of smog. Julian Simon (the late cornucopian economist who is one my greatest intellectual influence) pointed out this issue and noted that the deadliest forms of pollution are those that relate to underdevelopment.

Back in 2003, Jack Hollander published the Real Environmental Crisis: Why Poverty, Not Affluence is the Environment’s Number One Enemy. Hollander pointed out that simply from the combustion of organic matter (read: firewood and animal manure – literally burning fecal matter) indoors for the purposes of heating, cooking and lighting was responsible for close to 2 millions deaths.

Since then, the WHO came out with a study pointing out that around 3 billion people cook and heat their homes with open fires and stoves that rely on biomass or anthracite-coal. They put the number of premature deaths directly resulting from this at over 4 million people. This is close to 60% of the figure cited by the former President of California (yes, I know he was governor – see here). In other words, 60% of the people who die prematurely as a result of strokes, ischaemic heart diseases, chronic obstructive pulmonary diseases and lung cancers can be attributed to indoor air pollution. That means pollution resulting from the fact that you are so poor that you have to burn anything at hand at the cost of your health.

True, richer countries pollute and there are policy solutions (I have often argued that governments are better at polluting than at reducing pollution, but that is another debate) that should be adopted. But, these forms of pollution do not harm human life as much as those that come with poverty.

* By the way, when you watch Predator, do you realize that there are two future American governors in that movie? I mean, imagine that when Predator came out, some dude from the future told you that two of the main actors would end governing American states. Pretty freaky!

How Well Has Cuba Managed To Improve Health Outcomes? (part 3)

As part of my series of blog post reconsidering health outcomes in Cuba, I argued that other countries were able to generate substantial improvements in life expectancy even if Cuba is at the top. Then I pointed out that non-health related measures made Cubans so poor as to create a paradoxical outcome of depressing mortality (Cubans don’t have cars, they don’t get in car accidents, life expectancy is higher which is not an indicator of health care performance). Today, I move to the hardest topic to obtain information on: refugees.

I have spent the last few weeks trying to understand how the Cuban refugees are counted in the life tables. After scouring the website of the World Health Organization and the archives of Statistics Canada during my winter break, I could not find the answer.  And it matters. A lot.

To be clear, a life table shows the probability that an individual of age will die by age X+1 (known as Qx). With a life table, you will obtain age-specific death rates(known as Mx), life expectancy at different points and life expectancy at birth (Lx)(Where x is age). Basically, this is the most important tool a demographer can possess. Without something like that, its hard to say anything meaningful in terms of demographic comparison (although not impossible).The most common method of building such a table is known as a “static” method where we either compare the population structure by age at a single point in time or where we evaluate the age of deaths (which we can compare with the number of persons of each group alive – Ax). The problem with such methods is that static life tables need to be frequently updated because we are assuming stable age structure.

When there is important migration, Qx becomes is not “mortality” but merely the chance of exiting the population either by death of migration. When there are important waves of migration (in or out), one must account for age of the entering/departing population to arrive at a proper estimates of “exits” from the population at each age point that separate exits by deaths or exits (entries) by migration.

As a result, migration – especially if large – creates two problems in life tables. It changes the age structure of the population and so, the table must be frequently updated in order to get Ax right. It also changes the structure of mortality (exits). (However, this is only a problem if the age structure of migrants is different from the age structure of the overall population).

Since 2005, the annual number of migrants from Cuba to the United States has fluctuated between 10,000 and 60,000. This means that, on an annual basis, 0.1% to 0.5% of Cuba’s population is leaving the country. This is not a negligible flow (in the past, the flow was much larger – sometimes reaching north of 1% of the population). Thus, the issue would matter to the estimation of life tables. The problem is we do not know how Cuba has accounted for migration on both mortality and the reference populations! More importantly, we do not know how those who die during migration are measured.

Eventually, Ax will be adjusted through census-based updates (so there will only be a drift between censuses). However, if the Cuban government counts all the migrants as alive as they arrive in a foreign country as if none died along the way, it is underestimating the number of deaths. Basically, when the deaths of refugees and emigrants are not adequately factored into survival schedules, mortality schedules are be biased downward (especially between censuses as a result of poor denominator) and life expectancy would be accordingly biased upward.

Now, I am willing to reconsider my opinion on this particular point if someone indicates some study that has escaped my gaze (my Spanish is very, to put it euphemistically, poor). However, when I am able to find such information for other Latin American countries like Chile or Costa Rica and not for Cuba, I am skeptical of the value of the health statistics that people cite.

The other parts of How Well Has Cuba Managed To Improve Health Outcomes?

  1. Life Expectancy Changes, 1960 to 2014
  2. Car ownership trends playing in favor of Cuba, but not a praiseworthy outcome

Ten best papers/books in economic history of the last decades (part 1)

In my post on French economic history last week,  I claimed that Robert Allen’s 2001 paper in Explorations in Economic History was one of the ten most important papers of the last twenty-five years. In reaction, economic historian Benjamin Guilbert asked me “what are the other nine”?

As I started thinking about the best articles, I realized that such a list is highly subjective to my field of research (historical demography, industrial revolution, great divergence debate, colonial institutions, pre-industrial Canada, living standards measurement) or some of my personal interests (slavery and the great depression). So, I will propose a list of ten papers/works that need to be read (in my opinion) by anyone interested in economic history. I will divide this post in two parts, one will be published today, the other will come out tomorrow.

  • Higgs, Robert. “Wartime Prosperity? A Reassessment of the US Economy in the 1940s.” Journal of Economic History 52, no. 01 (1992): 41-60.

Higgs’s article (since republished and expanded in a book and in follow-ups like this Independent Review article) is not only an important reconsideration of the issue of World War II as a causal factor in ending the Great Depression, it is also an efficient primer into national accounting. In essence, Higgs argues that the war never boosted the economy. Like Vedder and Gallaway, he argues that deflators are unreliable as a result of price controls. However, he extends that argument to the issue of measuring GDP. In wartime, ressources are directed, not allocated by exchange. Since GDP is a measure of value added in exchanges, the wartime direction of resources does not tell us anything about real production. It tells us only something about the government values. As a result, Higgs follows the propositions of Simon Kuznets to measure the “peacetime concept” of GDP and finds that the prosperity is overblown. There have been a few scholars who expanded on Higgs (notably here), but the issues underlined by Higgs could very well apply to many other topics.  Every year, I read this paper at least once. Each time, I discover a pearl that allows me to expand my research on other topics.

  • Allen, Robert C. The British industrial revolution in global perspective. Cambridge: Cambridge University Press, 2009.

I know I said that Allen’s article in Explorations was one of the best, but Allen produces a lot of fascinating stuff. All of it is generally a different component of a “macro” history. That’s why I recommend going to the book (and then go to the article depending on what you need). The three things that influenced me considerably in my own work were a) the use of welfare ratios, b) the measurement of agricultural productivity and c) the HWE argument. I have spent some time on items A and C (here and here). However, B) is an important topic. Allen measured agricultural productivity in England using population levels, prices and wages to proxy consumption in a demand model and extract output from there (see his 2000 EREH paper here). As a result, Allen managed to compare agricultural productivity over time and space. This was a great innovation and it is a tool that I am looking to important for other countries – notably Canada and the US. His model gives us the long-term evolution of productivity with some frequency. In combination with a conjonctural estimate of growth and incomes or an output-based model, this would allow the reconstruction (if the series match) of a more-or-less high frequency dataset of GDP (from the perspective of an economic historian, annual GDP going back into the 17th century is high-frequency). Anyone interested in doing the “dirty work” of collecting data, this is the way to go.

  • Broadberry, Stephen, Bruce MS Campbell, Alexander Klein, Mark Overton, and Bas Van Leeuwen. British economic growth, 1270–1870. Cambridge University Press, 2015.

On this one, I am pretty biased. This is because Broadberry (one of the authors) was my dissertation supervisor (and a pretty great one to boot). Nonetheless, Broadberry et al. work greatly influenced my Cornucopian outlook on the world. Early in my intellectual development, I was introduced to Julian Simon’s work (see the best of his work here and here and Ester Boserup whose argument is similar but more complex) on environmental trends. While Simon has generally been depicted as arguing against declining environmental indicators, his viewpoint was much broader. In essence, his argument was the counter-argument to the Malthusian worldview. Basically, Malthusian pressures caused by large populations which push us further down the curve of marginally declining returns have their countereffects. Indeed, more people means more ideas and ideas are non-rival inputs (i.e. teaching you to fish won’t make me unlearn how to fish). In essence, rising populations are no problems (under given conditions) since they can generate a Schumpeterian countereffect (more ideas) and a Smithian countereffect (size of market offsets). In their work, Broadberry et al. basically confirm a view cemented over the last few decades that England had escaped the Malthusian trap before the Industrial Revolution (see Crafts and Mills here and Nicolinni here). They did that by recreating the GDP of Britain from 1270 to 1870. They found that GDP per capita increased while population increased steadily which is a strong piece of evidence. In their book, Broadberry et al. actually discuss this implication and they formulate the Smithian countereffect as a strong force that did offset the Malthusian pressures. Broadberry and al. should stand in everyone’s library as the best guidebook in recreating long-term historical series in order to answer the “big questions” (they also contribute to the Industrious Revolution argument among many other things).

  • Chilosi, David, Tommy E. Murphy, Roman Studer, and A. Coşkun Tunçer. “Europe’s many integrations: Geography and grain markets, 1620–1913.” Explorations in Economic History 50, no. 1 (2013): 46-68.

Although it isn’t tremendously cited yet, this is one of the best article I have read (and which is also recounted in Roman Studer’s Great Divergence Reconsidered). This is because the paper is one of the first to care about market integration on a “local” scale. Most studies of market integration consider long-distance trade for grains and they generally start with the late 19th century which is known as the first wave of globalisation. However, from an economic historian perspective, this is basically studying things once the ball had already started rolling.  Market integration is particularly interesting because it is related to demographic outcomes. Isolated markets are vulnerable to supply shocks. However, with trade it is possible to minimize shocks by “pooling” resources. If village A has a crop failure, prices will rise inciting village B where there was an abundant crop to sell wheat to village A. In the end, prices in village A will drop (causing fewer deaths from starvation) and increase in village B. This means that prices move in a smoother fashion because there are no localized shocks (see the work of my friend Pierre Desrochers who argues that small local markets were associated for most of history with high mortality risks). In their work, Chilosi et al. decide to consider the integration of markets between villages A and B rather than between country A and B. Basically, what they wonder is when geographically close areas became more integrated (i.e. when did Paris and Bordeaux become part of the same national market?). They found that most of Europe tended to be a series of small regions that were more or less disconnected from one another. However, over time, these regions started to expand and integrate so that prices started moving more harmoniously. This is an important development that took place well before the late 19th century. In a way, the ball of market integration started rolling in the 17th century. Put differently, before globalization, there was regionalization. The next step to expand on that paper would be to find demographic data for one of the areas documented by Chilosi et al. and see if increased integration caused declines in mortality as markets started operating more harmoniously.

  • Olmstead, Alan L., and Paul W. Rhode. Creating Abundance. Cambridge Books (2008).

This book has influenced me tremendously. Olmstead and Rhode contribute to many literatures simultaneously. First of all, they show that most of the increased in cotton productivity in the United States during the antebellum era came from crop improvements. Secondly, they show that these improvements occured with very lax patents systems. Thirdly, they show how crucial biological innovations were in determining agricultural productivity in the United States (see their paper on wheat here and their paper on induced innovation). On top of being simply a fascinating way of doing agricultural history (by the way, most economic history before 1900 will generally tend to be closely related to agricultural history), it forces many other scholars to reflect on their own work. For example, the rising cotton productivity explains the rising output of slavery in the antebellum south. Thus, there is no need to rely on some on the fanciful claims that slaveowners became more efficient at whipping cotton out of slaves (*cough* Ed Baptist *cough*). They also show that Boldrine and Levine are broadly correct in stating that most types of technological innovations do not require extreme patents like those we know today (and which are designed to restrict competition rather than promote competition). In fact, their work on biological innovations have pretty much started a small revolution in that regard (see one interesting example here in French). Finally, they also invalidated (convincingly in my opinion) the induced innovation model that generally argued that technologies are developped merely to ease scarcities of factors. While theoretically plausible, this simplified model did not fit many features of American economic history. Their story of biological innovations is an efficient remplacement.

Canadian Megatrends: Top 1% income share and median age

Statistics Canada just came up with a study on the top income share of the top 1% in Canada. As I have explained elsewhere, my view of inequality is that: a) it has increased; b) not as much as we think; c) a lot of the increase is from desirable factors (personal utility maximization differing from income maximization or international immigration) or neutral factors (demography, marriage); d) that the inequality that is worrisome stems either from birth or government manipulations of the market and; e) that those stemming from government manipulations, direct (like subsidizing firms) or indirect (like the war on drugs which means that a large number of individuals are jailed and then released with a “prison earnings penalty” which stymies their income levels and growth), are the easiest to fight.

The recent Statistics Canada study allows me to make my point again with regards to element C of my answer. As I looked at their series, all I could think was “median age”. A lot of the variations seem to be related to the median age of the population. I went back to the census data I had collected for my book and plotted it against the data. This is what it looks like.


Why would there be a relation? Well, each year you measure the income distribution, the demographic structure of that population changes. As it grows older, you have more people at the top of their earnings curve relative to those at the bottom. Not only that, but earnings curve seem elongated in recent times – we live longer and so some people work older as witnessed by increased labor force participation rates above a certain age closer to retirement. And the heights of the earnings curve are now higher than ever before while we also enter later into the labor market.

Now, I am not sure how much aging would “explain away” rising inequality in Canada, but there is no point denying that it does explain some of it away. But, I would not be surprised that a large part is explained away. Why am I saying that? Because of this paper on Norway’s age structure. 

In Norway, the median age in 1950 was much higher than it was in Canada back then and today, it is roughly the same as Canada (although Canada has had a steeper increase in inequality). And according to the paper on Norway, adjusting for composition bias in inequality measures caused by aging, eliminates entirely the upward trend in that country. In fact, it may even reverse the trend whereby inequality adjusted for age has actually declined over time. This is a powerful observation. Given that Canada has had a steeper increase in median age, this suggests that the increase in inequality might be simply the cause of a statistical artifice.

Chetty et al and the metamorphosis of the earnings curve

The Chetty et al. paper is probably one of the most papers of 2016 and it will long be debated. Many comments have been made on this and I need to reiterated that I do not believe the trend to be off, merely the level. I have just found another reason to doubt the level by thinking about demography. It relates to one key methodological decision made in the paper: taking the income of parents in the 25 to 35 years old age-window. This is a fixed window where their incomes are compared to that of a child at age 30.

This is probably a flaw that alters the level evolution importantly. My argument is simple. A person born in 1940 was, by the time he was 30, close to his peak earning point. A person born in 1980, by the time he is 30, is further away from a higher peak earning point. Thus, you are not comparing the same type of birth cohorts. In simpler terms, I am saying that with the 1940 birth cohort you are comparing children who, by age 30, were at the apex of their earnings while those of the 1980 birth cohort were not at the apex.

From the work of Ransom and Sutch on the economic history of aging in the United States, I remembered that graph (for late 19th century Michigan).  What I see is that for most workers, by 30 years of age, they are pretty much at the top of their earnings cure. Over time, if the shape of the curve does not change and simply keeps moving upwards, then there are no problems with the level of absolute mobility measured by Chetty et al.


But here is the problem, the curve does change shape! There are no longer flat lines like that of the Michigan farm laborers in the figure above. Earnings curve look more and more like that of the Michigan railroad employees. Not only that, the peak point is now higher in terms of income and at a further point in time. And that makes sense since we are studying longer and working menial jobs while we do for which we earn low incomes. When we enter the labor force, we get a very steep rise at a later point in our lives than our fathers or mothers did. So the earning curve of younger cohorts is more skewed than that of earlier cohorts. Kitov and Kitov shows the evolution of income by age groups relative to a fixed groups and as one can see, the youngest are getting further away from the peak over time – implying that it is shifting.  Again, from Kitov and Kitov, you can see that the 2013 curve starts later and has a steeper curve than the 1967 curve. From this trend in the earnings curve, we can more or less be certain that by 30, a person born in 1940 was closer to peak earnings than a person born in 1980. Thus, the person born in 1940 is at his apex (by the time he turns 30) when compared to his parents and the person born in 1980 is not at his apex when compared to his parents. (I am only using Kitov and Kitov for the sake of showing the evolution but this metamorphosis of the curve, I think, is not in dispute).

So, by setting the boundaries for measuring absolute mobility at a fixed point, Chetty et al. are capturing some changes that are purely related to changing demographics of the labor market and not absolute mobility. The 1940 level of mobility is too high relative to that of 1980. Chetty et al. do try to address this by looking at different time windows (they just don’t have a “rolling age window” which would be ideal – like indexing to the median age of the population).

I do accept that mobility has fallen since 1940, but I am very skeptical about how robust the big drop shown actually is. The issues of changes in family size, price deflators, taxes and transfers made me willing to entertain a fall of 25-30 points (rather than 40-45), now with this issue of the metamorphosis of the earning curves in mind, I am inching towards 20-25 points drop (still substantial).

Note: Still a big fan of Chetty et al. and their works is crucial, that’s why I don’t want pundits to try and extract this beyond what it actually says and does not say.

In Cuba, not having a car might save your life

My two blog posts on the health statistics of Cuba have convinced me to try to assemble a research article on the topic of assessing health outcomes under Castro’s regime. My first blog post was that there is a trade-off (the core of the article) that Castro decided to make. He would use extreme coercive measures to reduce some forms of mortality in order to shore up support abroad. The cost of such institutions is limited economic growth and increased mortality from other causes (dying from waterborne diseases or poverty diseases rather than dying from measles).

When I thought of that, I was inspired by Werner Troesken’s Pox of Liberty on the American constitution and the disease environment of the country. I was mostly concerned by direct medical interventions. However, the extent of coercive measures used by Castro go well beyond simple medical care (or medical imposition). Price controls, rationing and import restrictions on many goods could also help improve life expectancy. Indeed, rationing salt at 10g (hypothetical number) per person per day is a good way to prevent dietary diseases that emerge as a complication from overconsumption of salt. That will, by definition, raise life expectancy.

And so will bans on importing cars.

There is an extensive literature on the role that car fatalities has on life expectancy. This paper in Demography (one of the top demographic journals) finds that male life expectancy in Brazil is lowered by 0.8 years by traffic deaths. And traffic has very little to do with the quality of health care services. Basically, the more you drive, the more chances you have of dying (duh!). But, people don’t care much because the benefits of driving outweigh the personal risks.

In Cuba, people don’t get to make that choice. As a result, the very few drivers on Cuban roads have few accidents. According to WHO data, the car fatality rate is 8.15 per 100,000. There is also only 55 cars per 1,000 persons in Cuba. The next closest country is Nicaragua at 93 cars per 1,000 and the top country is Uruguay at 584 cars per 1,000. When you compute reported (rather than WHO estimated) car fatalities per 1,000 cars (rather than persons), Cuba becomes the unsafest place to drive in Latin America (1.46 fatalities per 1,000 cars) after El Salvador (2.22 fatalities per 1000 cars but only 129 cars per 1000), Ecuador (1.78 fatalities per 1000 cars but only 109 cars per 1000) and Bolivia (1.53  fatalities per 1000 cars and only 113 cars per 1000).

The graph below shows the relation between car fatalities per 100,000 inhabitants and life expectancy. Cuba is singled out as a black square. Low rate of car fatalities, higher life expectancy. Obviously, this is not a regression and so I am not trying to infer too much. However, it seems fair to say that Cuba’s life expectancy can easily be explained by the fact that Cubans face stiff prohibitions on the ability to drive. Those prohibitions give them a few extra years of life for sure, but would you really call that a ringing endorsement of the health outcomes under Castro’s regime? I don’t…


Malthusian pressures (as outcome of rent-seeking)

Nearly a week ago, I intervened in a debate between Anton Howes of King’s College London whose work I have been secretly following  (I say “secretly” because as an alumnus of the London School of Economics, I am not allowed to show respect for someone of King’s College) and Pseudoerasmus (whose identity is unknown but whose posts are always very erudite and of high quality – let’s hope I did not just write that about an alumnus of King’s College). Both bloggers are heavily involved in my first field of interest – economic history.

The debate concerned the “Smithian” counter-effect to “Malthusian pressures”. The latter concept refers to the idea that, absent technological innovation,  population growth will lead to declining per capita as a result of marginally declining returns. The former refers to the advantages of larger populations: economies of scale, more scope for specialization and market integration thanks to density. Now, let me state outright that I think people misunderstand Malthusian pressures and the Smithian counter-effect.

My point of is that both the “Smithian counter-effect” and “Malthusian pressures” are merely symptoms of rent-seeking or coordination failures. In the presence of strong rent-seeking by actors seeking to reduce competition, the Smithian counter-effect wavers and Malthus has the upper hand. Either through de-specialization, thinner of markets, shifting to labor-intensive technologies, market disintegration and lower economies of scale, rent-seeking diminishes the A in a classical Cobb-Douglas function of Total Factor Productivity (Y=AKL). This insight is derived from my reading of the article by Lewis Davis in the Journal of Economic Behavior and Organization which contends that “scale effects” (another name for a slight variant of the “Smithian counter-effect) are determined by transaction costs which are in turn determined by institutions. If institutions tend to favor rent-seeking, they will increase the likelihood of coordination failure. It is only then that coordination failures will lead to “Malthusian pressures” with little “Smithian counter-effect”. Institutions whose rules discourage rent-seeking will allow markets to better coordinate resource use so as to maximize the strength of the “Smithian counter-effect” while minimizing the dismal Malthusian pressures.

In essence, I don’t see the issue as one of demography, but as one of institutions, public choice and governance. I am not alone in seeing it this way (Julian Simon, Jane Jacobs and Ester Boserup have documented this well before I did). Why the divergence?

This is because many individuals misunderstand what “Malthusian pressures” are. In an article I published in the Journal of Population Research, me and Vadim Kufenko summarize the Malthusian model as a “general equilibrium model”. In the long run, there is an equilibrium level of population with a given technological setting. In short-run, however, population responds to variation in real wages. Higher real wages from a “temporary” positive real shock will lead to more babies. However, once the shock fades, population will adapt through two checks: the preventive check and the positive check. The preventive check refers to households delaying family formation. This may be expressed through later marriage ages, planned sexual activities, contraception, longer stays in the parental household and greater spacing between births. The positive check refers to the impact of mortality increasing to force the population back to equilibrium level. These checks return to the long-term equilibrium. Hence, when people think of “Malthusian pressures”, they think of population growth continuing unchecked with scarce ressources. But the “Malthusian model” is basically a general equilibrium model of population under fixed technology. In that model, there are no pressures since the equilibrium rates of births and deaths are constant (at equilibrium).

However, with my viewpoint, the equilibrium levels move frequently as a result of institutional regimes. They determine the level of deaths and births. “Poor” institutions will lead to more frequent coordination failures which may cause, for a time, population to be above equilibrium – forcing an adjustment. “Poor” institutions would also lead to an inability to respond to a change in constraints (i.e. the immediate environment) by being rigid or stuck with path-depedency problems which would also imply the need for an adjustment.  “Good” institutions will allow “the Smithian counter-effect” to intervene through arbitrage across markets to smooth the effect of local shocks, a greater scope for specialization etc.

My best case for illustration is a working paper I have with Vadim Kufenko (University of Hohenheim) and Alex Arsenault Morin (HEC Montréal) where we argue that population pressures as exhibited by the very high levels of infant mortality rates in mid-19th century Quebec were the result of institutional regimes. The system of land tenure for the vast majority of the population of Quebec was “seigneurial” and implied numerous regressive transfers and monopoly rights for landlords. This system was also associated with numerous restrictions on mobility which limited the ability of peasants to defect and move. However, a minority of the population (but a growing one) lived under a different institution which did not impose such restrictions, duties and monopolies. In these areas, infant mortality was considerably lower. We find that, adjusting for land quality and other factors, infant mortality was lower in these areas for most age groups. Hence, we argued that what was long considered as “Malthusian pressures” were in fact “institutional pressures”.

Hence, when I hear people saying that there are problems linked to “growing population”, I hear “because institutions make this a problem” (i.e. rent seeking).