Before the pandemic came along, it was relatively more difficult to get students to be truly interested in the topic of specificity and sensitivity. And in a sense, understandably so. By that I do not mean the topic is not important – it absolutely is – but rather that I can understand why eyes may glaze over just a little bit:
Sensitivity and specificity mathematically describe the accuracy of a test which reports the presence or absence of a condition. If individuals who have the condition are considered “positive” and those who don’t are considered “negative”, then sensitivity is a measure of how well a test can identify true positives and specificity is a measure of how well a test can identify true negatives
But when we’ve all got skin in the game, it’s a whole other story.
“We’re going to learn all about specificity and sensitivity today” is one way to begin a class on the topic.
“Let’s say you self-administered a Rapid Antigen Test in 2020, and the test came back positive. Do you have Covid or not?” is another.
Incentives matter!
I’ve linked to this thread before, but it is worth sharing once again, for it remains the best way to quickly grok both what specificity and sensitivity are, but also to get a sense of how to untangle the two in your own head:
Never confuse "sensitivity" and "specificity" again! Here's how I keep them straight. 👇 pic.twitter.com/vNUcqgh7es
Why do I bring this up today? Because now that we’re past the pandemic, how do we now motivate students to learn about specificity and sensitivity?
By asking, as it turns out, if we’d prefer detection systems to pick up on more objects in the sky (sensitivity), or get better at picking up only the relevant objects in the sky (specificity)!
After the transit of the spy balloon this month, the North American Aerospace Defense Command, or NORAD, adjusted its radar system to make it more sensitive. As a result, the number of objects it detected increased sharply. In other words, NORAD is picking up more incursions because it is looking for them, spurred on by the heightened awareness caused by the furor over the spy balloon, which floated over the continental United States for a week before an F-22 shot it down on Feb. 4.
To a statistician, it doesn’t matter if it’s objects in the sky or objects in your body. The principle remains the same, and it is the principle that you should internalize as a student. But also, it is equally important that you ask yourself a very important, and a very underrated question once you’ve learned the principle in question:
Where else is this applicable?
I cannot begin to tell you how much more interesting things become when you ask and answer this question. UFO’s and viruses in your body – what a class in statistics this would be!
A few weeks ago we pointed out that hard data out of China – like electricity consumption (lhs, blue) and car sales (rhs, blue) – are way weaker than official GDP. China is reopening – no doubt – but the hype far exceeds reality. Global growth will still be tepid. With @genemaIIFpic.twitter.com/h5L9hCsvxa
… and the author, Andrew Batson, also published a new post. recently. It is of interest in and of itself, but given that I just wrote a post myself about mental models, it makes even more sense to talk about it today.
Andrew’s post is about a simple question: “What should we make of China’s recent and dramatic policy reversals?”
As he points out, there has been in recent times an abrupt reversal of China’s Covid containment policy, a relaxation on years of restrictions on the real estate sector, a ‘softer’ approach towards internet firms, and while wolves aren’t turning into kittens anytime soon, they don’t seem to be baring their fangs quite as much.
China is clearly adopting a slightly different stance along many different dimensions. Andrew Batson asks why this might be so.
Four key possibilities, he says:
These are short-term political adjustments by Xi, in response to the changing, extremely fluid situation. Pure pragmatism in response to what the situation demands, in other words. But Xi is still Xi, and his ambitions remain intact.
Xi isn’t optimizing for the long term attainment of his most important goals. Being in power for the long term is his goal. And if he can remain in power by changing the type of dictator he needs to be, so be it. Power isn’t the means to an end, it is the end.
The eventual goals remain what they always were – national security and technological self-sufficiency – but he now has a new team that advises him on how to ensure that those goals are met in the long term, but by minimizing short term risks. Essentially the first point, but the cause isn’t Xi himself, it is his new team.
Xi remains a leader in name alone, and the actual decision making is now being done elsewhere. This begs an obvious question, but Andrew Batson doesn’t answer it in this post.
Andrew Batson himself thinks that it is probably some combination of all of the above. He’s not denying the possibility that it is any one of these in isolation, but thinks that some weighted combination of all four is the most likely.
Time to ask oneself some questions:
What probability do you attach to these scenarios yourself? Here’s one possibility: a twenty percent chance of any of the four, and a twenty percent chance for all four combined. How does that grab you?
Me, personally, I’d say a combination of 1 and 3 is the likeliest – maybe 60% put together. Give another twenty percent to pt. 2 and divvy up the remaining 20% between pt. 4 and ‘all of the above’. How does this sound? (Have fun drawing up the Venn diagrams here, by the way!)
If your numbers look different, why do you think this might be? What books, blogs, vidoes, podcasts, tweets and news articles do you have in mind when you make your assessment? What sources do you think I (let alone Andrew Batson!) might be using?
Are our insights actionable? How so? Can we use these assessments to guide our financial decision-making? Can we use these assessments to decide what to read next? Whom to read more of? Whom to read less of? What questions should I be asking ChatGPT basis my assessment?
One short article, but so many questions to think about!
New paper! Here's the one-minute version. There's a puzzle I've been thinking about for a long time. Wealthy countries tend to be happier… pic.twitter.com/LL2neJbiT4
I’ve been a student of economics for a little more than two decades, and the one thing that is quite familiar to me in this chart is how large China’s share is in US imports (that’s what the “17” at the bottom right of the chart represents. Spend some time going over the rest of the numbers on the right of this chart, and come to the realization that China is about 50% more than all of the other nations on this chart combined.)
Being a student of economics in these past two decades makes it inevitable that some notions of how the world works and functions will get deeply ingrained. And the idea that China will be much larger in everything compared to, often, the addition of all other countries performances has become a useful rule of thumb. Note that I am not advocating forming such a rule for the future – I’m simply saying this has been the case for the past two decades.
But as the Nobel Laureate said, the times, they’re a-changin’:
Yet Mr Trump’s tariffs seem to have played an important role. According to recent analysis of industry data by Chad Bown of the Peterson Institute for International Economics, a think-tank, China’s share of America’s imports rose from 36% to 39% this year in goods not covered by tariffs. For goods subject to a 7.5% tariff, however, China’s share sank from 24% to 18%. And for those hit by a whopping 25% tariff, which covers lots of it equipment, China’s share of imports fell from 16% to 10%. Overall America is now much less dependent on Chinese goods, from furniture to semiconductors.
This post isn’t about whether Trump should have imposed those tariffs or not, nor is it about whether those tariffs have been worth it. That is an important topic, but we’re going to skip over it in today’s post. Today is just a reaffirmation of a principle of economics:
When something becomes more expensive, there will be lesser demand for it.
That, of course, is just another way to state the law of demand. You can draw a curve, if you like, or you can phrase it the way I did, or you can write out a paragraph that gives an application of the law, like The Economist did. But the next time you read people opining about whether Policy X will work or not, ask yourself how the incentives have been realigned as a consequence of the new policy.
By how much will demand go down (elasticity), should this policy be implemented or not (geopolitics), and what might be the impact of this policy on China and America and other nations (international trade) are all excellent questions, and they will keep all manner of professionals busy for decades to come.
But again, that’s for another day. Today’s post is about helping you realize that the law of demand is one way to understand incentives, and (don’t stop me even if you have heard this before) it is about chanting a mantra that all economics students would do well to internalize:
There are 11 videos in that series, and if you can spare the time, please watch all of them. Just two a day (they’re not more than 5 minutes each), and you’ll be done come the weekend.
But in effect, here is what the Solow model says:
Output for a nation is a function of three (actually four) things:
Capital (K): Buidings, ports, dams… infrastructure, basically.
Education Augmented Labor (eL): The amount of hours that a person is able to put in to their work, but with the built in assumption that an educated person is likely to be more productive than a person without education.
Ideas: Read the paragraph below to get a sense of what this means in practice.
Think about this blogpost that you are reading. I wrote it using my laptop, which is my capital. I will spend about an hour (that’s my plan, I’ll update you towards the end of this post about how well it worked out) writing it, and that’s the labor that I’ll be putting into this post. The fact that I have been “educated” in economics should mean that this post will be easier to write for me than, say, a gardener. The gardener could have written this post as well, of course, but it’s safe to assume that she would first have had to learn about the Solow model, and that, presumably, would have taken longer.
So that’s K and eL where the output (this blogpost) is concerned. But now think about it this way: what if another person, with a similar level of economics education as mine were to write this blogpost instead of me? Would that person have chosen this video, and these paragraphs to explain the Solow model? Maybe they would have recommended some other video, or some other podcast, or chosen to share details of an online textbook in which the Solow model is explained. That’s one way to think about ideas.
And so when you combine the capital (the laptop), the labor (the time I spend on this blogpost, given my education levels) and the ideas (what I choose to put into this blog post, and how), you get the output you’re reading right now.
What if I double the capital? Will the blogpost be done in half the time? Say I have an external monitor attached to my laptop – will two screens mean finishing the blogpost in half the time? It will save some time, but not by a factor of two, surely. Trust me, I have tried.
What if I double the labor? Hire an assistant to write this blogpost with me? The way I work, trust me, it will probably take longer! What if I go get a post-doc, to augment my education? Will that save me time? The hysterical laughter you hear in the background is the response of any PhD/post-doc student anywhere in the world, and that sound means a loud and resounding no.
In a sense, the Solow model asks these and related questions, and answers them using some graphs and equations. Except, of course, the Solow model does it for not one guy writing one blog, but for an entire nation at a time. There is no sense in me explaining the whole model over here, for it would be a case of me reinventing what is already a very good wheel. Please watch the videos.
But the Solow model is a remarkably useful way to get a handle on the long run growth prospects of a country. Is India likely to grow in the future? Well, is it going to add to its capital stock? Yes. Is it going to augment it’s stock of education augmented labor? Yes. Is it likely to produce more ideas than it is right now? Yes. And so the growth prospects for India look reasonably good.
Of course, there is more to the Solow model. All of this holds true given a strong and stable political system, well established rules of law, and strong and capable institutions. But so long as you believe that these are likely to continue to be so in the Indian case, you should be bullish on India.
What about, say, Japan? It has a capital stock that is more in need of replacement than new construction ( a feature of the Solow model that we have not discussed here, called depreciation), so it is unlikely that it will grow its capital stock too much. Here’s an example of what I mean. What about it’s stock of education augmented labor? Well, the news ain’t very good. Ideas? Trending upwards, but not by much. So if I had to bet on which country would grow more over the next twenty years, I would bet on India, not Japan.
Well, now, that’s a whole different story, and one that Noah Smith talks about in a recent blog post. Long story short, he doesn’t think China’s growth prospects are that great.
But the story is a little more complicated than that. The Solow model is a good model, sure, but it’s not as if the Chinese authorities/experts aren’t aware of the problem. And in his blog post, Noah looks at arguments put forth by two people who know a thing or two about China, and analyzes them critically.
The first argument is that sure, China’s demographics are on a downward trend, but what if we raised the retirement age for Chinese workers? Would that not solve the problem? Noah says no, probably not, because firms made of exclusively old folks isn’t necessarily a good idea. I wholeheartedly agree.
What about adding to China’s urbanization, and therefore its infrastructure? After all, China’s urbanization rate is “only” 64%. The inverted quotes around only in the previous sentence is because we, in India, are officially at 31%, but as in the case of China, it very much is a function of how you define urbanization. But similarly, in China, the urbanization rate is actually way more than 64%, and the Lewis turning point has already taken place in China, or will do so any moment.
And about ideas, well, China is an even more complicated story. Noah makes the point that China’s industrial policy is essentially a one-man army that is trying something that has never been tried before, and Noah is betting on it not quite working out. And given the events of the last year and a half or so, it is hard to disagree.
And so the Solow Model would probably tell you that China is unlikely to grow as fast in the near future as it did in the recent past, and even if you take into account potential adjustments, it likely will still be the case that China’s growth rate will start to plateau.
Please, read the entire post by Noah. But if you are a student of economics who has not yet met the Solow Model, begin there, and then get on to Noah’s post – your mileage will increase considerably.
These are not good times for the credibility of China’s GDP growth targets. Just weeks after unveiling an ambitious target of 5.5% real GDP growth for 2022, the central government effectively ensured that target will not be met by requiring local governments to impose strict lockdowns to contain the spread of Covid-19. The restrictions cover most of China’s major cities, have had a clear negative impact on economic activity in March that will only worsen in April.
So begins a thought provoking blog post on China’s growth prospects for this year, written by Andrew Batson. I’m a very (very!) amateur student of China, and follow a more or less random group of people on topics related to China – but Andrew Batson’s blog, I think, should definitely be on everybody’s list.
This one speaks about growth prospects in China this year, but so much else besides. Let’s learn a little bit about China by parsing through it.
The first point that he makes is that growth targets this year are all but likely to be missed. This, of course, is because of the lockdowns in Shanghai and other parts, and pretty much everybody knows that they’re not going well – and that’s putting it mildly. Targets were missed last year, and the year before – so why, one might be entitled to ask, should one have them at all in the first place?
There’s shades of Goodhart’s Law in the paragraphs that follow, and when I read the piece the first time, my blogging antennae were up. Aha, I thought to myself, one more post in an ever increasing canon. But the post then moves in (for me) an entirely unexpected direction, and in a way that makes it even more interesting.
Targeting GDP growth, Batson says, is not A Perfect Thing, but is, all things considered, Still A Good Thing Given The Alternatives.
One way to understand Batson’s defense of GDP growth targets is by internalizing what I think is his key point: giving up on a GDP growth target doesn’t mean there will be no targets – it simply means there won’t be economic growth targets.
That is to say (and this is my understanding of his point), it’s not as if giving up on GDP growth targets will mean a very laissez faire approach to the economy. Instead, China will be set other, non-economic targets. Such as what, you ask?
…“regulatory storm” of 2021 with its multitude of highly interventionist policies aiming to reshape entire industries. Limiting the power of large private companies was even a fairly explicit goal: it’s probably not a coincidence that the main targets of last year’s political-regulatory campaigns were real estate and the internet, the two economic sectors that have created the biggest private-sector fortunes. All of this was certainly enabled by Xi’s dictum that there are more important things than GDP growth. The costs and economic downsides of the regulatory storm were put aside in favor of other goals.
Regular listeners of Amit Varma’s excellent podcast, TSATU will no doubt be aware of the line “Politics is downstream from culture”. The quote is originally by Breitbart, of course, as Amit always points out. The reason I bring it up over here is because economic growth, if you ask me, is downstream of politics. In this framing, economic growth serves political needs, and those political needs are downstream of culture.
Rarely does one get to quote Brietbart in one paragraph and then follow it up with a supporting quote that references Lenin, but hey, welcome to 2022:
…China’s Leninist political system, which is organized around mobilizing officials to direct social transformation. As Ken Jowitt put it: “The definitional tendency of Leninist regimes [is] their attempts to control and specify the substantive dimensions of social developments, not merely the framework within which such developments occur.”
As Andrew Batson goes on to argue in the following paragraphs, de-emphasizing growth targets in a liberal political framework is very different from de-emphasizing them in a Chinese set-up. The focus on growth for its own sake is very different from the focus on growth to serve other aims. Batson argues that Deng Xiaoping was optimizing for economic growth, and that Xi Jingping is optimizing for national greatness. National greatness includes, but never as a primary target, economic growth.
But that pursuit of national greatness, perhaps, has been taken too far in Chin’s case:
In December, when when Xi chaired the annual Central Economic Work Conference, the signal was clear: the priority is now the “stability” of the economy. Since then, various political slogans and campaigns have been much less in evidence and the focus has been on more practical short-term measures. Senior officials have even promised not to introduce policies that “adversely affect market expectations”–effectively admitting that they had been doing just that in the recent past.
I’ve said it before, and I’ll say it again, and this is applicable to individuals as much as it is to nations. Be very clear about the answer to that irritatingly simple question:
(C) GDP figures are “man-made” and therefore unreliable, Li said. When evaluating Liaoning’s economy, he focuses on three figures: 1) electricity consumption, which was up 10 percent in Liaoning last year; 2) volume of rail cargo, which is fairly accurate because fees are charged for each unit of weight; and 3) amount of loans disbursed, which also tends to be accurate given the interest fees charged. By looking at these three figures, Li said he can measure with relative accuracy the speed of economic growth. All other figures, especially GDP statistics, are “for reference only,” he said smiling.
This is an excerpt from the Wikileaks archive, and people familiar with modern economic history will know it all too well. This is, of course, the famous Li Keqiang index. If you prefer, you can read the original Economist article about it, although for once, the trademark Economist pun in the headline falls short of their typically high quality.
GDP measurements have always been tricky, and reading about GDP – it’s evolution, the data collection, the computation and the hajjar problems that arise from there – should be mandatory for any student aspiring to learn economics. Here’s a post from six years ago about some sources, if you’re interested.
But back to that excerpt above. What Li Keqiang was saying was that GDP statistics in China would often give a misleading picture, and he preferred to reach his own conclusions on the basis of other economic data. His preferred metrics were the ones mentioned in the abstract above: electricity consumption, volume of rail cargo and loans disbursed. Think of it this way: he’s really asking three questions. Is stuff being produced? Is stuff being moved around? Is stuff being purchased?
But what about covid times? Do these measures stand up, or do we need new proxies for GDP?
The variant’s speed also means that China’s economic prospects are unusually hard to track. A lot can happen in the time between a data point’s release and its reference period. The most recent hard numbers on China’s economy refer to the two months of January and February. Those (surprisingly good) figures already look dated, even quaint. For much of that period, there was no war in Europe. And new covid-19 cases in mainland China averaged fewer than 200 per day, compared with the 13,267 infections reported on April 4th. Relying on these official economic figures is like using a rear-view mirror to steer through a chicane. For a more timely take on China’s fast-deteriorating economy, some analysts are turning to less conventional indicators. For example, Baidu, a popular search engine and mapping tool, provides a daily mobility index, based on tracking the movement of smartphones. Over the seven days to April 3rd, this index was more than 48% below its level a year ago.
But as the article goes on to say, this metric will tell you about movement across cities. But metro traffic gives you an idea of intra-city mobility, as do courier company express deliveries (and we did some very similar exercises in India during the lockdowns, of course. Here’s one example for Pune district.)
But the point isn’t just to come up with what else might be useful as GDP proxies. A follow-up question becomes equally important: do the GDP statistics make sense? As the Economist articles says, good numbers for metrics such as investment in fixed assets are hard to square with declines in steel output. The article contains many other such examples, and what you should take away as a student is your ability to develop a “smell” test for a given economy. Don’t take the reported numbers at face value, but “see” if they seem to be in line with other statistics about that economy.
I really like this article as an introduction to this topic because it also hints at how statisticians need to be especially careful about comparing data over time. Weekly declines might happen because of festivals, bad weather or a thousand other things, which may of course be going on along with pandemic induced lockdowns. Teasing out the effects of just one aspect isn’t an easy thing to do.
And finally, think about how you can apply this lesson in other domains! Should an interviewer look only at marks, or try and figure out other correlates. Or, as Mr. Keqiang puts it, are marks “for reference only”? What about quarterly earnings reports? Press releases? Smell tests matter, and the earlier you start developing them, the better you get at detecting, and calling bullshit.
And finally, the concluding paragraph from the article we’ve discussed today:
To help avoid some of the traps lurking in these unconventional indicators, Mr Lu and his team watch “a bunch of numbers, instead of just one”. In a recent report he highlighted 20 indicators, ranging from asphalt production to movie-ticket sales. “If seven or eight out of ten indicators are worsening, then we can be confident that GDP growth is getting worse,” he says. Right now, he thinks, the direction is clear. “Something must be going very wrong.”
