Meet Max Roser, and Our World in Data

Of course, you may not know who Paul Graham is. Visit his website, click on essays, and say goodbye to your calendar for the next couple of weeks.

But that’s not today’s blogpost. Today we answer the question, who is Max Roser? And what is Our World in Data?

Here is Max Roser’s introduction of himself from a Reddit AMA (Ask me Anything) he had done a while back:

“Hi Reddit! My name is Max Roser. I visualize global development data on, a free online publication on how living conditions around the world are changing.

Now I am working with a great team and we want to cover global development as broadly as we can to show how our world is changing. Our World in Data now includes data and research on global health, violence, poverty, inequality, economic growth, environmental changes, food and agriculture, energy, technological change, education and more specific topics.

While much of the news is focussing on what happened yesterday or even what is currently “breaking news”, I think that many of the very important changes, which fundamentally reshaped the world that we are living in, happen very slowly and persistently over the course of decades or centuries. On ‘Our World in Data’ we don’t report the ‘breaking news’ and instead zoom out to show the slow trends that dramatically change our world.

Other than that I am a researcher – mostly focusing on inequality and poverty – at the University of Oxford.”

And Our World in Data? Check out their About page.

But better still, consider this extract:

“Why have we made it our mission to publish the “research and data to make progress against the world’s largest problems”?

At the heart of it is a simple truth. When we look around us, it is clear that the world faces many very large problems: 

  • Every year 300,000 women die from pregnancy-related causes, this means that on any average day 830 mothers die
  • The majority of the world – 65% – lives on less than $10 per day.
    And almost 10% live in ‘extreme poverty’, they live on less than $1.90 per day. 
  • The world deforested 47 million hectares of forest in the last decade, that’s an area the size of Sweden.
  • 60 million children of primary school age are not in school.
  • Almost a quarter of the world population – 23% – live in autocratic regimes.
  • 14% of the world’s adults do not know how to read and write.
  • And 3.7% of all children die before they are five years old. This means that 5.2 million children every year and on any average day the world sees 14,200 child deaths.

This is a list of terrifying problems. And as we don’t hear much that would tell us otherwise, it is easy to be convinced that we can’t do anything about them. Even in the extensive 24/7 news cycles we hear little that suggests it would be possible to make progress against these problems. The same is true for our education — questions like how to end hunger, child mortality, or deforestation are rarely part of the curriculum. 

As a consequence it is not surprising that many have the view that it is impossible to change the world for the better. For many large problems the majority in fact believes that they are getting worse.

This however, is not the case. We know that it is possible to make progress against these large problems, because we have already done so.”

How do we know that we have already done so? Take a look at this one chart:

Click on that little toggle next to the world “Relative” in the chart, and the chart becomes even better. Here’s a simple way to think about it: Max Roser is a person who wants to help you understand two things:

  1. Take the glass half full view, because the world is genuinely becoming better over time.
  2. But don’t for a single moment forget the fact that the world needs to become a lot better, and for that to happen, we need to move faster.

Or, in much more eloquent terms:

Because our hopes and efforts for building a better future are inextricably linked to our understanding of the past, it is important to study and communicate the global development up to now. Studying our world in data, and understanding how we overcame challenges that seemed insurmountable at the time, should give us both confidence and guidance to tackle the problems we are currently facing. Living conditions can be improved, we know from the past that they already have been.
For each of the problems we face today we need to also address the difficult question of whether and how we can make progress in the years ahead.

Here is his website, here is his Oxford Martin school page, and here is a wonderful place to get started if this is your first time on the website Our World in Data.

But if you are a student of economics (and who isn’t, eh?), spend more time on Our World in Data, and help other people find out about the website. A genuine treasure of our times.

India’s Demographics and the Total Fertility Rate

For many, many years, this was my slide on India’s TFR in lectures I used to give on India’s demographics:

Wikipedia (Old data)

What is TFR? Here’s Wikipedia:

“The total fertility rate (TFR) of a population is the average number of children that would be born to a woman over her lifetime if:

  1. she were to experience the exact current age-specific fertility rates (ASFRs) through her lifetime
  2. she were to live from birth until the end of her reproductive life.”

Hans Rosling had a better, more intuitive term: babies per women. Here’s an excellent chart from Gapminder, although ever so slightly outdated:

Click here to see the original chart, and please press on the play button to see this change over time

Here’s the excellent Our World In Data page about the topic, and here’s a lovely visualization of how the TFR has changed for the world and for India over time (please make sure to “play” the animation):

(I hope this renders on your screens the way it is supposed to. If not, my apologies, and please click here instead)

But now we have news: India’s TFR has now slipped below the replacement rate. Here’s Vivek Kaul in Livemint explaining what this means:

The recently released National Family Health Survey (NFHS-5) of 2019-2021 shows why. As per the survey, India’s total fertility rate now stands at 2. It was 3.2 at the turn of the century and 2.2 in 2015-2016, when the last such survey was done. This means that, on average, 100 women had 320 children during their child-bearing years (aged 15-49). It fell to 220 and now stands at 200.
Hence, India’s fertility rate is already lower than the replacement level of 2.1. If, on average, 100 women have 210 children during their childbearing years and this continues over the decades, the population of a country eventually stabilizes. The additional fraction of 0.1 essentially accounts for females who die before reaching child-bearing age.

And here’s the breakup by state, updated for the latest results:

By –, CC BY-SA 4.0,

Of course, as with all averages, so also with this one: you can weave many different stories based on how you slice the data. You can slice it by urban/rural divides, you can slice it by states, you can slice it by level of education, you can slice it by religion – and each of these throws up a different point of view and a different story.

But there are three important things (to me) that are worth noting:

  1. The TFR for India has not just come down over time, but has slipped below the global TFR in recent years.
  2. This doesn’t (yet) mean that India’s population will start to come down right away, and that for a variety of reasons. As Vivek Kaul puts it:
    “So, what does this mean? Will the Indian population start stabilizing immediately? The answer is no. This is primarily because the number of women who will keep entering child-bearing age will grow for a while, simply because of higher fertility rates in the past. Also, with access to better medical facilities, people will live longer. Hence, India’s population will start stabilizing in around three decades.”
  3. The next three to four decades is a period of “never again” high growth opportunity for India, because never again (in all probability) will we ever have a young, growing population.

Demography is a subject you need to be more familiar with, and if you haven’t already, please begin with Our World in Data’s page on the topic, and especially spend time over the section titled “What explains the change in the number of children women have?”

Imports, Exports and GDP

“The key is to understand that imports are also included in consumption, investment, and government spending. The real GDP breakdown looks like this:

  • GDP = Domestically produced consumption + Imported consumption + Domestically produced investment + Imported investment + Government spending on domestically produced stuff + Government spending on imported stuff + Exports – Imports

So you can see that while imports are subtracted from GDP at the end of this equation, they’re also added to the earlier parts of the equation. In other words, imports are first added to GDP and then subtracted out again. So the total contribution of imports on GDP is zero.”

That is an excerpt from a lovely little write-up by Noah Smith on his Substack, and one that I’ll be using whenever I teach macro. It’s lovely for many reasons, but most of all for the reason that the bullet point goes a very long way towards making the point that a lot of folks miss: you don’t get rich by importing less.

When I say “you”, I mean the country in question – and this equation, written out this way, helps us understand why. If you’re a student of macro, and are under the impression that India will get richer if only we imported lesser, think about the definition of GDP:

Gross domestic product (GDP) is the total monetary or market value of all the finished goods and services produced within a country’s borders in a specific time period.

If you think about it, how can imports possibly qualify as being produced within a country’s borders? As Noah says, the equation can also be written like this:

GDP = Domestically produced consumption + Domestically produced investment + Government spending on domestically produced stuff + Exports

Read the rest of Noah’s post, especially if you are a student of macroeconomics. It should help clear up a lot of basic, but important and often misunderstood ideas about GDP calculations.

Russia has stopped publishing detailed monthly trade statistics. But figures from its trading partners can be used to work out what is going on. They suggest that, as imports slide and exports hold up, Russia is running a record trade surplus.
On May 9th China reported that its goods exports to Russia fell by over a quarter in April, compared with a year earlier, while its imports from Russia rose by more than 56%. Germany reported a 62% monthly drop in exports to Russia in March, and its imports fell by 3%. Adding up such flows across eight of Russia’s biggest trading partners, we estimate that Russian imports have fallen by about 44% since the invasion of Ukraine, while its exports have risen by roughly 8%.

Think about the previous section, and try and answer this question: is Russia poorer or richer or unchanged because Russia isn’t importing as much, as measured by GDP and changes in GDP?

Well, Russia may be worse off, and Russians may be worse off. It’s leader?

As a result, analysts expect Russia’s trade surplus to hit record highs in the coming months. The iif reckons that in 2022 the current-account surplus, which includes trade and some financial flows, could come in at $250bn (15% of last year’s gdp), more than double the $120bn recorded in 2021. That sanctions have boosted Russia’s trade surplus, and thus helped finance the war, is disappointing, says Mr Vistesen. Ms Ribakova reckons that the efficacy of financial sanctions may have reached its limits. A decision to tighten trade sanctions must come next.
But such measures could take time to take effect. Even if the eu enacts its proposal to ban Russian oil, the embargo would be phased in so slowly that the bloc’s oil imports from Russia would fall by just 19% this year, says Liam Peach of Capital Economics, a consultancy. The full impact of these sanctions would be felt only at the start of 2023—by which point Mr Putin will have amassed billions to fund his war. (Emphasis added)

Macro is hard! But it also matters, especially at times such as these.

A Sunny Outlook

Some years ago, I wrote a chapter in a book called Farming Futures. The book is about social entrepreneurship in India, and my chapter was about a firm called Skymet. Skymet is a private weather forecasting firm based partially out of Pune and partially out of Noida (along with other office in other locations). But researching for the chapter got me interested in both how the art and science of weather forecasting had developed over time, and where it is headed next.

Only trivia enthusiasts are likely to remember the name of the captain on whose ship Charles Darwin made his historic voyage that was to result in the publication of “On the Origin of Species”. Fewer still will remember that Admiral Robert FitzRoy committed suicide. The true tragedy, however, is that it is almost certainly his lifelong dedication to predicting the weather that caused him to take his own life.
We have, in the decades and centuries since, come a long way. Weather forecasting today is far more advanced than it was in Admiral FitzRoy’s day. Britain, for example, Admiral FitzRoy’s own nation, today has an annual budget of more than 80 million GBP to run its meteorological department. It has an accuracy of around 95% when it comes to forecasting temperatures, and an accuracy of around 75% when it comes to forecasting rain – anybody who is even remotely familiar with Britain’s notoriously fickle weather would know that this is no small achievement.

Farming Futures: Emerging Social Enterprises in India

Those numbers that I cited, and the tragic story of Admiral FitzRoy, come from a lovely book called The Weather Experiment.

But I first read about weather, and the difficulties associated with forecasting it in a book called Chaos, by James Gleick:

Lorenz enjoyed weather—by no means a prerequisite for a research meteorologist. He savored its changeability. He appreciated the patterns that come and go in the atmosphere, families of eddies and cyclones, always obeying mathematical rules, yet never repeating themselves. When he looked at clouds, he thought he saw a kind of structure in them. Once he had feared that studying the science of weather would be like prying a jack-in–the-box apart with a screwdriver. Now he wondered whether science would
be able to penetrate the magic at all. Weather had a flavor that could not be expressed by talking about averages. The daily high temperature in Cambridge, Massachusetts, averages 75 degrees in June. The number of rainy days in Riyadh, Saudi Arabia, averages ten a year. Those were statistics. The essence was the way patterns in the atmosphere changed over time…

Ch. 1, The Butterfly Effect, Chaos, by James Gleick

What is the Butterfly Effect, you ask? It gets its own Wikipedia article, have fun reading it.

All of which is a very long way to get around to the write-up we’re going to be talking about today, called After The Storm.

On 29 October 1999, a “Super Cyclone” called Paradip devastated parts of Odisha and the east coast of India. At wind speeds of almost 250 kms per hour, it ravaged through the land, clearing out everything in its path. Fields were left barren, trees uprooted like mere matchsticks, entire towns devastated. More than 10,000 people lost their lives.
Fast forward to two decades later. In 2020, bang in the middle of the Covid-19 pandemic, another cyclone—known as Amphan—speeds through the Bay of Bengal. It crashes into the land like Paradip did in 1999. Like before, many homes are destroyed and structures uprooted. But one thing is different: this time’s death toll is 98. That’s a 100 times lower than 1999’s casualties.
What made this difference possible? Simply put: better, timely and more accurate weather prediction.

We’ve made remarkable progress since the days of Admiral FitzRoy. Predicting the weather is still, admittedly, a very difficult and very expensive thing, as this lovely little write-up makes clear, but it is also something we’re much better at these days. We have better instruments, better computing power, better mathematical and statistical tools to deploy, and the ability to synthesize all of these to come up with much better forecasts – but it’s not perfect, and it’s not, well, good enough.

Those last two words aren’t meant as a criticism or a slight – far from it. The meteorologists themselves feel that is is not good enough:

“It almost becomes like flipping a coin,” Professor Islam says. “The IMD is not to be blamed. They will be very good at predicting the weather three or four days in advance. Beyond that, it cannot be done because there is a fundamental mathematical limitation to these questions.”
“IMD can do another sensor, another satellite, they can maybe improve predictions from two days, to three days. But can they do ten days? There is no evidence. Right now there is no weather forecasting model on the globe. India to Europe to Australia, it doesn’t matter, it’s not there.”

As Professor Islam says, he wants to move from up from being able to forecast the next four to five days, to being able to predict weather over the next ten days. Why? So that communities in the path of a storm have adequate time to move. What could be more important than that when it comes to meteorology.

So what’s the constraint? This is a lovely analogy:

“I give this example to my students,” the professor says, “Look, usually all of science and AI is based on this idea of driving with the rearview mirror. I don’t have an option, so I’m looking into my rearview mirror and driving. I will be fine as long as the road in the front exactly mirrors the rearview. If it doesn’t and I go into a turn? Disastrous accident.”

It’s weird what the human brain will choose to remind you of, but this reminds me, of all things, of a gorilla. That too, a gorilla from a science fiction book:

Amy distinguished past, present, and future—she remembered previous events, and anticipated future promises—but the Project Amy staff had never succeeded in teaching her exact differentiations. She did not, for example, distinguish yesterday from the day before. Whether this reflected a failing in teaching methods or an innate feature of Amy’s conceptual world was an open question. (There was evidence for a conceptual difference.) Amy was particularly perplexed by spatial metaphors for time, such as “that’s behind us” or “that’s coming up.” Her trainers conceived of the past as behind them and the future ahead. But Amy’s behavior seemed to indicate that she conceived of the past as in front of her—because she could see it—and the future behind her— because it was still invisible.

Michael Crichton, Congo

That makes a lot of sense, doesn’t it? And that’s the fundamental problem with any forecasting tool: it necessarily has to be based on what happened in the past, because what else have we got to work with?

And if, as Professor Islam says, the road in the future isn’t exactly like the past, disaster lies ahead.

But Artificial Intelligence and Machine Learning need not be about predicting what forms the storms of the future might take. They can be of help in other ways too!

“It hit us that the damage that happened to the buildings in the poorer communities could have been anticipated very precisely at each building’s level,” Sharma explains. “We could have told in advance which roofs would fly away, and which walls would collapse, which not so. So that’s something we’ve tried to bring into the AI model, so that it can be a predictive model.”

“What we do is, essentially, this: we use satellite imagery or drone imagery and through that, we identify buildings. We identify the material and technology of the building through their roofs as a proxy, and then we simulate a sort of a risk assessment of that particular building, right? We also take the neighbouring context into account. Water bodies, how high or low the land is, what kind of trees are around it, what other buildings are around it.”

The team at SEEDS and many others like it are more concerned about the micro-impact that weather events will have. Sharma is interested in the specifics of how long a building made from a certain material will be able to withstand the force of a cyclone. This is an advanced level of interpretation we’re talking about. It’s creative, important and life-saving as well.

In other words, we may not know the intensity of a particular storm, and exactly when and where it will hit. But given assumptions of the intensity of a storm, can we predict which buildings will be able to withstand a given storm and which ones won’t?

This is, as a friend of mine to whom I forwarded this little snippet said, is very cool.

I agree. Very cool indeed.

And sure, accuracy about weather forecasting may still be a ways away, and may perhaps lie forever beyond our abilities. But science, mathematics and statistics might still be able to help us in other ways, and that (to me) still counts as progress.

And that is why, all things considered, I’d say that when it comes to the future of weather forecasting, sunny days are ahead.

In case you haven’t already, please do subscribe to

Excellent, excellent stories, and the one I have covered today is also available in podcast form, narrated by Harsha Bhogle, no less. All their other stories are wroth reading too, and I hope you have as much fun going through them as I have.

What is common to Butyrylcholinesterase and Vitamin D, or why English is an underrated skill in statistics

Today’s blog post title is in the running for the longest title that I have come up with, but let’s ignore this particular bit of potential trivia and get on with it.

Today’s story really begins with the tragic tale of Sally Clark. It is a very lengthy extract, from a piece I wrote along with a friend some months ago. Lengthy, but fascinating:

In November of the year 1999, an English Solicitor named Sally Clark was convicted on two charges of murder, and sentenced to life imprisonment. This tragic case is notable for many reasons — one of those reasons was the fact that her alleged victims were her own sons. Another was the fact that both were toddlers when they died.
The cause of death in both cases was initially attributed to sudden infant death syndrome (SIDS), also known as cot death in the United Kingdom. We did not know then, and do not know until this day, about the specific causes of SIDS. But suspicion grew on account of the fact that two children from the same family had died due to unspecified causes, and shortly after the death of her second child, Sally Clark was arrested, tried and convicted.
One of the clinching pieces of evidence was expert testimony provided by the pediatrician Professor Sir Roy Meadow. He put the odds of two children from the same family dying of SIDS at 1 in 73 million — in other words, an all but impossible eventuality. On the back of this testimony, and others, Sally Clark was convicted of the crime of murdering her own sons, and sent to prison for life.
One cannot help but ask the question: how did Sir Roy Meadow arrive at this number of 1 in 73 million? Succinctly put, here is the theory: for the level of affluence that Sally Clark’s family possessed, the chance of one infant dying of SIDS was 1 in 8543. This was simply an empirical observation. What then, were the chances that two children from the same family would die of SIDS?
The answer to this question, statisticians tell us, depends on whether the two deaths are independent of each other. If one assumes that they are, then the probability of two deaths in the same family is simply the multiplicative product of the two probabilities. That is, 1 in 8543 multiplied by itself, which is 1 in 73 million and that would be enough to convince any “reasonable man” that the deaths were deliberate and could not have been just coincidence.
But on the other hand, if the two events are not independent of each other — say, for example, that there are underlying genetic or environmental reasons that we simply are not aware of just yet — then it is entirely possible that multiple children from the same family may die of SIDS. In fact, given a SIDS death in a family, research shows that the likelihood of a second SIDS death goes up.
Sally Clark’s convictions were overturned on her second appeal, and she was released from prison. She died four years later due to alcohol poisoning.

We’ll get back to this truly tragic tale, but let’s go off on a tangent for a second.

Today’s a day for extracts from my own earlier work, it would seem, for I have another one for your consideration:

Us teaching type folks love to say that correlation isn’t causation. As with most things in life, the trouble starts when you try to decipher what this means, exactly. Wikipedia has an entire article devoted to the phrase, and it has occupied space in some of the most brilliant minds that have ever been around.
Simply put, here’s a way to think about it: not everything that is correlated is necessarily going to imply causation.
For example, this one chart from this magnificent website (and please, do take a look at all the charts):

Hold on to this line of thinking, and let’s get back to the tragic Sally Clark story, but with a twist towards the rather more optimistic side of things.

Great news, right? We’ve found what causes SIDS!

Well, that’s where it gets tricky, and we go off on yet another tangent.

Do Vitamin D supplements help? We know that sunlight gives us Vitamin D, and that’s A Good Thing. So if we don’t get enough sunlight, hey, let’s get Vitamin D injections or supplements:

In interpreting vitamin D-related study results, correlation should not be understood as causation. Diets composed of vitamin D–rich foods such as dairy products and salmon also contain high levels of other healthy nutrients. Those who have a high vitamin D level are likely to participate in active outdoor activities and exercises, to be interested in health issues, and to have a healthy lifestyle. Without considering these confounders, misleading results can be obtained. In the study by Kim et al.,4) a univariate analysis revealed a correlation between a low vitamin D level and a low quality of life score; however, its significance was lost when age, sex, income, education level, and disease state were considered.
Sometimes, correlations shown in cross-sectional studies are used as evidence for requiring vitamin D supplements. A recent increasing trend of taking vitamin D supplements may be due to these effects.

What if Vitamin D is just a marker? That is, what if sunlight causes a lot of good things in our bodies, and it also causes Vitamin D levels in our body to go up? So it’s not sunlight that causes vitamin D to go up, and vitamin D that causes an increase in our wellbeing. Maybe it’s sunlight causing an uptick in our wellbeing and causing an increase in Vitamin D levels in our body? They (health and vitamin D levels) may just be correlated, without there being any causation.

What I’m about to say is important: I’m not a doctor. All I’m saying is, I’ve been confused often enough about correlation and causation to wonder about whether vitamin D causes good health. It is correlated, there’s no arguing with that. But causation? Ah, that’s another (very tricky) thing altogether.

And now that we have the mis-en-place of this blogpost done, let’s get the dish together.

Butyrylcholinesterase doesn’t necessarily cause SIDS in infants. Infants who die of SIDS stop breathing (for reasons that are still not understood clearly), and these infants have low levels of Butyrylcholinesterase. Butyrylcholinesterase may not even cause breathing to stop in infants. It is just a marker – there is correlation there, but we don’t know if there is causation.

In fact, the paper’s title itself says as much:

“Butyrylcholinesterase is a potential biomarker for Sudden Infant Death Syndrome”

But the tweet above speaks about how we’ve found the cause, and that’s not quite right.

Again, please don’t misunderstand me – the fact that this has been discovered is awesome, it is fantastic, and the joy, the relief and the euphoria should absolutely be there.


Sally Clark lost her life at least in part to a fundamental misunderstanding of statistical theory, and we still don’t know what causes SIDS. We understand it better, but there is a ways to go.

The most underrated skill in statistics is the English language.

Words matter, and we all (myself included!) need to be more careful about what exactly we mean when we speak about statistics.

And thank god we’re closer to figuring out how to deal with the horrible, horrible thing that SIDS is.

But if you’re teaching or learning statistics, tread very, very carefully.

Critical Issues Confronting China Featuring Bert Hoffman

Via Noah Smith’s post on China’s growth prospects, which we covered this past Tuesday:

On the “Natural” rate of unemployment

Lovely Twitter handle, I must say, and if you don’t already, please do follow him on Twitter.

Lessons from the eradication of smallpox

Vox has a nice and short read out on the battle against smallpox, and lessons we might learn today from how and where the battle was waged, at what costs, and with what effects.

But for all that the world has lost in the last few years, the history of infectious disease has a grim message: It could have been even worse. That appalling death toll resulted even though the coronavirus kills only about 0.7 percent of the people it infects. Imagine instead that it killed 30 percent — and that it would take centuries, instead of months, to develop a vaccine against it. And imagine that instead of being deadliest in the elderly, it was deadliest for young children.
That’s smallpox.

My notes after having read the article:

  1. Smallpox is estimated to have killed between 300 million to 500 million people in the 20th century alone
  2. We still do not have an effective treatment against smallpox
  3. There are two different viruses that cause smallpox: variola major and variola minor
  4. We no longer need to explain R0 to anybody, thanks to covid, but this point is staggering: it had an infectiousness of between 5 to 7, and a mortality rate of 30%.
  5. “In China, as early as the 15th century, healthy people deliberately breathed smallpox scabs through their noses and contracted a milder version of the disease. Between 0.5 percent and 2 percent died from such self-inoculation, but this represented a significant improvement on the 30 percent mortality rate of the disease itself.”
    What a horrible lottery to play. Would you play this lottery? This, by the way, is one of the many reasons why learning statistics and probability is worth your time.
  6. Learn more about Edward Jenner.
  7. We have better ways of shipping vaccines across the world these days, but what a story this is!
    “Spain especially struggled to reach its colonies in Central and South America, so in 1803, health officials in the country devised a radical new method for distributing the vaccine abroad: orphan boys.
    The plan involved putting two dozen Spanish orphans on a ship. Right before they left for the colonies, a doctor would give two of them cowpox. After nine or 10 days at sea, the sores on their arms would be nice and ripe. A team of doctors onboard would lance the sores, and scratch the fluid into the arms of two more boys. Nine or 10 days later, once those boys developed sores, a third pair would receive fluid, and so on. (The boys were infected in pairs as backup, just in case one’s sore broke too soon.) Overall, with good management and a bit of luck, the ship would arrive in the Americas when the last pair of orphans still had sores to lance. The doctors could then hop off the ship and start vaccinating people.”
  8. Institutions matter:
    “It was not until the 1950s that a truly global eradication effort began to appear within reach, thanks to new postwar international institutions. The World Health Organization (WHO), founded in 1948, led the charge and provided a framework for countries that were not always on friendly terms to collaborate on global health efforts.”
  9. Culture matters:
    “Efforts by the British Empire to conduct a smallpox vaccination program in India made less progress, due in large part to mistrust by the locals of the colonial government.”
  10. Science matters:
    ” “There was no shortage of people telling [the people involved in the eradication effort] that their effort was futile and they were hurting their career chances,” former CDC director William Foege wrote in his 2011 book House on Fire about the smallpox eradication effort.
    But other advances had brought it within reach. Needle technology had improved, with new bifurcated needles making it possible to use less vaccine. Overseas travel improved, which made it easier to ship vaccines and get public health workers where they were most needed, and provided impetus for worldwide eradication as it made it more likely that a smallpox outbreak anywhere in the world could spread.”

As always, read the whole article. I’ll quote here the concluding paragraph from the piece, and I’d urge you to reflect on it:

The devastation of Covid-19 has hopefully made us aware of the work public health experts and epidemiologists do, the crucial role of worldwide coordination and disease surveillance programs (which are still underfunded), and the horrors that diseases can wreak when we can’t control them.
We have to do better. The history of the fight against smallpox proves that we’re capable of it.

Learn Economics By Looking At A Painting

It was the IPL yesterday, so why not art today?

Stare at this picture, and do so for a long time. If you are reading this on your phone, please take the time to switch to a laptop or even better, a desktop computer with a large screen. And just look at it, for as long as you like.

Here’s the description of this painting from Wikimedia:
On a table laid with a green table cloth and two linen damask serviettes are displayed: pewter plates with bread and a pewter dish with oysters, a glass of red wine, a glass of olive oil or a vinegar jug, a silver salt cellar, a rummer of white wine, a gilt silver cup, a pewter jug and a Berkemeyer laying on its side.

… which is fine as things go, but the NYTimes takes things to, as they say, another level in their Close Read series. I’ve linked to another one from this series more than a year ago, which is also worth your time. But this particular one, titled “A Messy Table, A Map of the World“, is worth an hour or more of your time.

In loving detail – and rarely must this phrase have been more appropriately used, Jason Farago takes us through the many messages, implications and nuances of this painting, and so much more besides.

I’ve noted below some points that stood out for me in terms of appreciating the painting better after having read the article (please note that I know nothing about art appreciation, so feel free to help me learn more):

  1. The reflection of the window-frame on the glass is remarkably well done, particularly on the wine glass, but also on the jug towards the right. The wine glass, by the way, is called a roemer, and the reason it has a knobbed stem is so that the glass is easier to grip after you’ve had a more than a couple of drinks.
  2. I wouldn’t have noticed it no matter how long I stared at it (but then again, I’m not very good at this), but directly below the half-hidden knife, on the edge of the white napkin is the artist’s signature, and the year in which the painting was created.
  3. The wall in front of which the table stands is drab by choice, so as to focus our eyes on the table itself.
  4. The lemon is mesmerizing. The realism that Heda manages is brilliant, and if you take a look at his other paintings, you will realize that it is a recurring motif.
  5. The texture of the bread in the foreground, the difference in the texture and the luster of the silver cup (or tazza) and the matte texture of the jug (or pewter) is remarkable.

I could go on, but I’ll leave you to both read the rest of the article, and figure out other details yourself.

But (surprise, surprise) what I enjoyed the most were the economic aspects.

  1. Learn more about where Heda was located, and why that mattered in terms of the commerce behind the creation of paintings such as these. Reflect on the section from the article that helps you understand why religious motifs are not to be seen in his works. While you are it, reflect on the fact that this is an artist known for the creation of a genre called late breakfasts. Who has the time to have late breakfasts, and what might that mean for the society that Heda lived in?
  2. Peperduur is a Dutch word, still in use today, that means expensive. It literally means “as expensive as pepper”. The peppercorns in the painting, by the way, are to be found in that little cone of paper towards the left of the painting.
  3. Where did the lemons come from back then? Were they imported? If so, from where? Under what conditions, treaties and laws? With what consequences? If you find yourself wanting to read more about this, I’m happy to recommend to you one of my favorite books about globalization: Vermeer’s Hat.
  4. Which other things came from which other parts of the world? The article tells us about gold, cinnamon, porcelain and pineapples that came from an island called Manaháhtaan. Where is this place, you ask? Well, near a place called New Amsterdam. And where is that, you wonder? Listen to this song, and read the lyrics.
  5. This is a painting that celebrates wealth: the fact that you were able to afford a spice that lives on today in a word that means expensive, that were able to afford to buy, eat and not finish something as expensive as oysters, that you were able to wash down the meal with beer and wine, that you were able to use only a bit of as expensive a fruit as a lemon, and that you could have a meal such as this for a late breakfast – all of this isn’t just telling the viewer a story. It is sending the viewer a message about what the Dutch people were when the painting was created. They were rich, and they wanted you to know it.
    How they became rich, at what cost to the rest of the world, and with what consequences to themselves and the rest of the world are questions that you should ask after you stare at the painting… and then try and find out the answers.

There are many, many ways to learn economics, whether by watching the IPL or by learning about art appreciation – and a million other things in between. Being a student of economics is about so, so much more than the study of an economics textbook. That, if anything, is barely the start.

I hope you have as much fun learning about this painting as I did!

The IPL and the Benefits of Competition

I was gloriously and completely wrong about the IPL, and I couldn’t be happier about being wrong. So happy, in fact, that I can’t stop talking about how wrong I was (see here, here and here). It has been nothing but beneficial for Indian cricket, and I would argue this holds true for world cricket at large.

It’s one thing to say this in 2022 with the benefit of hindsight, and it is quite another to have said it in March 2008! Here’s Amit Varma from what seems like ages ago:

The problem with cricket in most cricket-playing countries, certainly in India, is that the cricket market is what economists call a monopsony. A monopsony is a market in which there is only one buyer for a particular class of goods and services. Until now, a young Indian cricketer who wanted to play at the highest level could only sell his services to the BCCI. If it treated him badly and did not give him his due rewards, he had no other options open to him.

I’ve quoted from this piece before, and I would strongly urge you to go read it again. I always do this, of course, but the reason I’m doing so in this particular case is because it is always a pleasure to read a piece that uses economic theory to make predictions that turn out to be spot on.

Here’s Ian Chappell in a more recent piece:

Apart from the massive financial boost and enormous increase in fan interest, India’s biggest gain from a highly productive IPL competition has been the huge improvement in playing depth.
About 20 years ago, India’s overseas reputation was an improving one, especially under the captaincy reign of a competitive Sourav Ganguly but the pace of that ascent gradually increased when the IPL began 15 seasons back, in 2008. The quietly thoughtful MS Dhoni – who is still exerting an influence – built on Ganguly’s reputation, which was then improved upon by the highly competitive leadership of Virat Kohli.
The firmly established IPL is now seen as the most important part of India’s enviable depth in international cricket.

But what are the economic factors that have been at play in making the IPL such A Good Thing for cricket in general, and Indian cricket in particular?

Amit listed out the following factors:

  1. The BCCI stopped being a monopsony. Ten(as of this year) franchises bidding for a player, with a reasonably well established feeder system is a very different proposition to depending upon the whims and fancies of a deeply flawed selection system, and the results are there for all to see.
  2. The IPL is a competition that is about the money, and is about the bottom-line, and this is a good thing. Something that I should have known, but was too besotted with my love of test cricket to see. It forces players to be selected on merit, and also dropped on merit, and merit alone.
  3. The ecosystem for spotting, nurturing and promoting talent is only likely to get better over time was his prediction, and see this article about Kumar Kartikeya Singh, and this article about Tilak Varma, published this year on ESPNCricinfo. And if you’re hungry for more, see this on T N Natarajan, and this on Washington Sundar. Sports fans will see the struggle in these stories, but if you think about it from the point of view of an economist, you should credit the IPL for creating the ecosystem that enables the emergence of these players. And indeed, many more to come.

Ian Chappell is making the same points in his write-up as Amit Varma, but for Amit to have done this in 2008, and by using simple economic theory is remarkable. We would do well to absorb the lesson that I think can be learnt from this: don’t be blinded by distractions, and trust in economic theory to work well more often than not.

This is how Amit concluded his piece back then:

Having said that, the IPL could fail, for not every good idea is rewarded with smart execution. Maybe the franchises got carried away and bid too high (game theorists call it “the winner’s curse”). Maybe the games will not get high enough TRPs, as a cricket-loving public deluged with an overdose of cricket finds other ways to entertain itself. If it does flounder, it will be a pity, for its failure will be remembered and used to prevent other such experiments.
On the other hand, if the IPL succeeds, cricket historians may one day write about 2008 as the year that cricket discovered its future.

It is safe to say that it is the second paragraph that is applicable today, not the first.

And it wouldn’t be the worst idea to learn some of the principles of economics by studying the IPL!