Teaching Statistics in the Age of ChatGPT

One of my favorite websites to use while teaching statistics is Seeing Theory, by Brown University. It is a wonderful website, because it allows people to “see” statistics.

Visualize concepts in statistics, to use the technically correct term, but you see what I mean.

One of the many reasons I like this website is because it presents a fun, interactive way to “get” what statistics is all about. It is one thing to talk about flipping a coin, it is quite another to actually flip a coin 1000 times. Or roll a dice, or understand what a probability distribution is, or to (finally!) “get” what the Central Limit Theorem is trying to get at (beware, though – every time you think you’ve “got” the CLT it has a way of revealing an additional layer of intrigue).

This past summer, as I’ve mentioned before, I was teaching school-going students courses in economics, statistics and public policy. I have made use of Seeing Theory in the past, but with the advent of ChatGPT (and especially ChatGPT4), I figured it might be a good time to not just show “cool” visualizations, but also actually try and build them.

And so we did! As we covered a topic, I would ask my students to “build” a working demo using ChatGPT (or Bard). I would nudge and prompt the students to well, write better prompts, and if necessary, step in and write the prompts myself on occasion. But for the most part, the work was done by the students, and we were able to get simple working demos of some stats concepts out of the door.

The “whoa, this is so cool!” moments were worth it in and of themselves, but it is my ardent hope that the students understood the concepts a little bit better for having seen the visualizations.

A great example is the Monty Hall problem. Run a simple Google search for it, if you haven’t come across it before. In my experience, some students tend to not “get” the explanation the first time around. Until this summer, I would get around this problem by asking them “what if it was a million doors instead?”, or if all else failed, by actually “playing” the game using three cards from a deck of cards.

But this time, we built a demo of the problem! So also for Chebyshev’s inequality, the expected value upon rolling a pair of dice and a simple way to visualize what regression does. The demos won’t satisfy professors of statistics or professional coders, for you could add so much more – but for young students who were trying to internalize the key concepts in statistics, it was pure magic.

And the meta lesson, of course, was that they should try and do this for everything! Why stop at stats? Build working demos for concepts in math, in physics, in geography. And if you know even a little bit of coding, try and build even better demos – both I and my students were relatively unfamiliar with coding in general, so we stuck with simple HTML.

But with AI’s new coding capabilities, it is clear that teaching (and learning) can become much better than was the case thus far. If you wish to disagree with me about the word “better”, I look forward to the argument, and you may well end up having more than a couple of points. But the classes were certainly more interactive – and at least along that one dimension, they were certainly better.

I hope to do much more of this in the months and years to come, but for the moment, do try out some of these demos, and let me know how they could be made better.

Thank you!

Missing in Action Ought Not To Be Missing in Action

Missing in Action is the name of the excellent book on public policy written by Pranay Kotasthane and Raghu Sanjaylal Jaitley. I had reviewed it earlier this year, and my recommendation that you read the book is even more forceful now than it was in January.

Why? What has changed between now and January 2023?

Well, lots of things, as is usually the case with the passage of time. But as regards this book, what changed was that I got to put parts of the book through a most stringent test: I used it as a textbook in a course on public policy. And not just any course on public policy – this was a course taught to 13-15 year olds.

It’s truly special, this cohort. They are young enough to not have their curiousity trampled upon by higher education, and old enough to be able to grasp ideas and concepts fairly quickly. Better still, they are old enough to draw parallels between what they’re learning and what they already know. And best of all, they do not hesitate to ask basic questions that adults would be embarrassed to ask.

“Wait, that makes no sense”, was a sentence I heard very often while teaching these students, and I reveled in how it was said without shame, worry or pretense. It simply was what it was: an admission that what had been heard did not make any sense, with an implicit demand to explain further.

And so for teaching these students, a musty old textbook full of diagrams, definitions and pompous declarations would make no sense. It would have to be a book that was rigorous in terms of its understanding, thorough in terms of its explanations, and light in terms of its treatment. And as I’ve explained in my review, this book does a very good job on all counts.

One reason it does so is because the book is very clear about what it is not. As they say in their introduction to the book, the authors are clear that this is not an academic work, not is it a work of journalism.

The book has, instead, stories. And in order to make sense of these stories, the authors make liberal (if you’ll forgive the pun) use of public policy frameworks. We learn about public policy, in other words, by looking at the world and by wondering why it seems to make no sense. In each chapter, the authors ride unfailingly to the rescue, armed only with their obviously wide reading and their deep expertise in public policy. One of my students made the observation that their “secret superpower” was sarcasm, and I wholeheartedly agree.

But having taught the course, I came away with a renewed conviction that public policy should be taught to as many people as possible, and at as early an age as possible.

Why as many people as possible? That’s an easy one to answer, and the authors of the book have themselves provided an answer to this question. It is because an India familiar with public policy will likely have three important features:

1. Our governments are likely to be more accountable, since their policies will be better scrutinized than before
2. We ourselves will be able to sharpen our demands from our governments
3. A better understanding of public policy will raise the level of public discourse

The cynic in me will not hold his breath for the first two, and is inclined to burst into laughter as regards the third. But even he will admit that an India that is more familiar with public policy certainly won’t make things worse. And these days, I’ll settle for that.

And why at as early an age as possible? Because I spent the better part of a day walking my students through the eight ‘principles’ of public policy, and am convinced that my students are better equipped to make sense of the world around them.

Note that I said “are better equipped to”, and not “are now able to”. I don’t think most adults are able to make sense of all that is around them, in part because of our own biases, limitations and limited understanding. But also because the world around is both more complicated and grows ever more so with every passing year.

But if a book, and eight principles within a book, can help us become aware of our biases, limitations and limited understanding – and if the basic framework of public policy can make the world seem a little less complicated – well then, it is probably worth it. No?

So just these eight principles. That’d be my wish when it comes to the teaching of public policy to folks currently studying in school. And if, for having learnt these eight principles, they decide to venture forth in search of new adventures in the realm of public policy, well then. Kya hi baat hai.

Or if I could be allowed to borrow a phrase from Pranay: Mogambo khush hua.

Speaking of borrowing from Pranay, these are the eight principles:

1. Unlearn what you know, and begin your analysis with a clean slate
2. Good intentions do not guarantee good policies
3. Sure India’s implementation of policies isn’t great, but sometimes the policies themselves aren’t great either
4. Change is permanent
5. There’s no public policy without economics. Other disciplines matter – a lot – but at the heart of public policy lies economic theory.
6. There’s no escaping politics.
7. There’s no good or bad policy; only better or worse outcomes
8. One policy should target only one objective

I hope you’re curious about what each of these mean, and I hope that this curiosity translates into you buying the book and reading it. If you like, you can move on from this blogpost to this podcast, and then on to the book.

Especially if you happen to be in school. Please, pretty please, do read the book. And especially if you are in school, please feel free to email me with any questions you may have about what you find in the book.

I’m already missing being told “Wait, that makes no sense”, you see.

The What and The How

A really long break, I know, but I have half a good excuse for a small chunk of it.

For three weeks this past month, I taught a bunch of kids three different courses. The three courses had some overlap in terms of the students who attended each week, and a lot of overlap in terms of how the three courses covered the subject matter, but as with all of the previous times that I have taught in this program, I had a blast.

Much more importantly, so did the students have a blast. Best as I could tell, at any rate.

One of the reasons they had a blast was because I used a lot of interactive tools, and this was one such:

Click on the link and try it out for yourself. It is, as the support page says, “a fast, powerful climate simulation tool”. And so we had fun trying to see what would happen if, say, coal was taxed more heavily. Or if, say, the transport sector saw much better rates of electrification. And so on and so forth. Have fun playing around with different scenarios, for that’s the point of this tool.

But after a lot of fun was had, and after a lot of scenarios were built, I had to put a dampener on the session. “This”, I gravely intoned, “is not public policy”.

What we have done, I went on to g.i., is build out scenarios. And while the best case scenario is a very pleasant thing to contemplate (relatively speaking, at any rate), it isn’t necessarily achievable.

In other words (still g.i.’ing, naturally), we may well know where we have to reach. Public policy, unfortunately, concerns itself with not just what the best scenario is, but also how realistic it is, and whether it is achievable at all in the first place.

In other words, “How > What” when it comes to public policy. Not always, and not necessarily, but it is a rather good heuristic when it comes to thinking about the subject.

The thing with teaching young (really young – I had the privilege of teaching 13-15 year olds) students is that you must learn to wait. You may know the next topic of discussion and you may know the correct answer to a question you yourself have asked, but you need to wait. Wait for them to process what you’ve said, reason things through, and then ask the inevitable question that will take the discussion forward.

And so I waited.

Until one of them asked, “But wait. Are you saying that this scenario is not achievable?”

And that allowed me to neatly segue into an article that – and let me be frank here – most adults would find boring. That’s not (at all) a slight on the topic, let alone the author. But quite a few folks would probably choose to skip over an article whose headline says “Free electricity ruined discoms. Now they will cause trouble in transition to renewables“. Especially when the subheading goes “When high tariff paying customers leave, discom finances will further deteriorate. Discoms, therefore, find ways to not allow open access to keep customers captive”.

Please do read the whole thing, but I’ve quoted the most important excerpt below:

If C&I (commercial and industrial) customers have ESG (environmental, social, and governance) mandates, they may prefer buying electricity from the renewable generating companies. As per the Electricity Act, distribution companies are required to grant “open access” when customers (largely C&I) and generating companies privately negotiate a deal, and want to use the network for transport of electricity. When the high tariff paying C&I customers leave, discom finances further deteriorate. Discoms, therefore, find ways to not allow open access to keep C&I customers captive.

https://theprint.in/opinion/free-electricity-ruined-discoms-now-they-will-cause-trouble-in-transition-to-renewables/1590543/ (Please note that abbreviations have been expanded to make this quote easier to understand)

Again, a wait, post an explanation of what the article was about.

Comprehension, horror and outrage all dawned at more or less the same rate on all the faces.

“Wait!”

“Hang on a minute!”

“So you’re saying that…”

They had realized, for themselves, that the public policy re:electricity in our country was such that we end up making it difficult for our commercial and industrial users to switch to renewables. And why do we do so? Because this is the only segment (for the most part) that pays over and above their fair share. And so while we know what is required for a greener world, we choose to prioritize more greenbacks in our wallets instead.

With good reason, discoms might say, and they have a point. Well, in a manner of speaking. But you do see what I meant when I told my students that building out the scenario was the easy bit. Actually getting to a place where one can begin to implement all these policies?

Ah, what a very, very long road that is.

An introduction to public policy, if you ask me, should be taught to everybody while they are in school. That sentence deserves to be expanded into an entire blogpost, which is what I plan to do next.

Put Me Out of a Job – 2

You might think this (time management and opportunity cost) to be a weird topic for a second class in a course called “Principles of Economics”. You would certainly think it to be unconventional. Not the latter half of the topic – opportunity costs – but the first one. What does time management have to do with economics? Well, think of it this way – if you are an Indian student who has learnt economics, you have almost certainly come across Lionel Robbins’ definition, and have most likely memorized it back then.

Here it is: the science which studies human behaviour as a relationship between ends and scarce means which have alternative uses.

What is more scarce than time? We all have a limited amount of time, and we all have ends to achieve. The ends we would like to achieve in our lives are much more than the time that is afforded to us, and so we must choose which of these ends to pursue, and which to sacrifice. That’s a pretty good, and if you ask me, useful way to understand opportunity costs. The weirdness, or the unconventional choice (to some) of the topic, is a direct consequence of my request to ChatGPT from yesterday’s post:

I am seeking to learn economics not to write an examination at the end of the semester in a college or university with this course. I am seeking, instead, to learn economics in order to apply it to various aspects of my life. Of course, as a student enrolled in a university, you may seek to optimize your learning geared towards doing well in a examination at the end of the semester, and that’s fine. Just let ChatGPT know accordingly, that’s all:

I won’t show you the whole output, but simply how the second lecture’s outline has changed:

Here’s a revised outline for the thirty lectures, with the original lectures designed for applicability to various aspects of life (Lecture A) and the modified lectures tailored to help you succeed in an Indian undergraduate economics course (Lecture B):
Lecture A: Time Management and Opportunity Cost
Lecture B: Opportunity Cost: Definition, Types, and Examples

ChatGPT-4’s Output (in part)

I know which one I prefer, and why. The good news, as a student, is that you can do both! Learn in order to score well in an examination, and also learn in order to figure out how to apply economics better in the case of your own life. Why should the two be different? Ah, some questions you should reflect upon, rather than ask ChatGPT.

Anyway, back to our lecture du jour. I asked ChatGPT to explain why it chose time management, and I do not think I would have asked that question as an eighteen year old. The older you get, the more aware you are of how limited your time is. And at least in my own case, the converse is also true. I count this as a mark in my favor – that while a good prompt may get a student going, said student will still need help and advice on an ongoing basis.

So far, at any rate.

Further proof of that fact that I’m not out of a job, just yet, is below. The context is that I read the answer, and felt it to be incomplete. So I prodded it a bit, and then just a little bit more:

To be clear, it isn’t so much about the phrase TINSTAAFL, as it was about the fact that I felt its explanation to be incomplete. This prompted me (no pun intended) to ask it to be more thorough:

This is an important lesson in and of itself. Feel free to tell ChatGPT to give more (or less) detail, or ask it to modify how it gives you the answer (more examples | simpler language | write like person X | show your output as a debate between person X and person Y). Get your “teacher” to be the kind of teacher that you like to learn from!

I count this as a pretty important miss on ChatGPT’s part. My personal opinion is that you haven’t fully explained opportunity costs without talking about the importance of how your evaluation of opportunity costs changes given different time horizons. Time matters! ChatGPT actually agrees with me (see below), but only after prodding. And this after making explicit the fact that I was interested in learning about time horizons! And so I asked it again:

I’m two days in, where I’m the “student” and ChatGPT my teacher. Today’s class wasn’t great. I don’t think ChatGPT’s output was good enough to stand on its own, and it needed additional prompts to deliver what I would consider to be a good introduction to the concept of opportunity costs, its many nuances and its many applications. It wasn’t bad, but it was far from being good, in my opinion.

Should I take this as a sign that I need to get better at writing prompts, or should I take this as a sign that AI isn’t good enough to replace me yet? How should I change my mental model about whether the average student in a typical college can learn better from AI?

If you are a regular reader of EFE, you know what’s coming next: the truth always lies somewhere in the middle.I need to get better at writing prompts, yes, but also AI isn’t good enough to replace me yet. Both of these things will change over time, of course, but for the moment, less than ten percent into the course, I am inclined to think that I am not out of a job, just yet.

And even better, the complements over substitutes argument just got stronger – I’ll be a much better teacher of a course such as this the next time I get to teach it. Tomorrow we tackle “Supply and Demand: Basics and Market Equilibrium”.

I’ll see you in class tomorrow!

Put Me Out of a Job – 1

Let’s say you’re a student who is going to start learning economics in the coming semester (starting July 2023). Let’s assume that you’ve never learnt economics in a classroom before, save for a brief introduction to it in high school. If you chose to learn from an LLM instead, how should you go about it?

Leave aside for the moment the question of whether you should be doing so or not. The question I seek to answer over many blog posts is whether you can do so or not. Whether or not this is a good idea for you depends in part on my abilities to add to the value that an LLM generates for you from such a course. And once these thirty (yes, thirty) blog posts are written out, I’ll write about my thoughts about whether a student still needs me in a classroom or not.

My current thinking is that I would still be needed. How much of this is hope, and how much dispassionate analysis is difficult to say right now. For that reason, I would like to tackle this problem at the end of this exercise. For the moment, I want to focus on helping you learn economics by teaching you how to learn it yourself, without the need for a human teacher (online or offline).

In each post, I’ll give you a series of prompts for that particular class. I will not always give you the output of these prompts – feel free to run them as they are, word for word, or tweak them as per your likes, fancies and hobbies.

My motivation in this series is twofold. One, to find out for myself just how much better ChatGPT is than me at teaching you principles of economics. Second, to help all of you realize that you ought to hold all your professors (myself included!) to a higher standard in the coming year. We have to do a better job than AI alone can, along all dimensions – let’s find out if we can.

Buckle up, here we go.

Here’s my first prompt:

Remember, LLM’s work best when you give really detailed prompts. Note the following:

1. I began by giving some information about myself – my limitations as regards economics, where in the world I come from, and what my interests/hobbies/passions are.
2. I specified what I’m looking to learn from the LLM.
3. I specified the quantum of output required (thirty classes).
4. I specified how broad the output should be.
5. I specified how I would like the answer to be customized for me
• I would like to learn about economics by relating it to what I like to read about in any case (use examples from the Mahabharata)
• I would like to learn about economics by relating it to real life situations.
• It is amazing to me, regardless of how many times I experience it, that it “gets” what I really mean in spite of having phrased my question using really bad grammar.
• The specific examples aren’t the point, the idea is the point. Learn calculus by relating it to mandala art, for example. Learn history by relating it to dance forms. Learn geography by relating it to food from different parts of the world. A teacher in a classroom cannot possibly do this for all the students, because of the size of the class, and because a teacher cannot possibly know your hobby in as much detail as you can. Make good use of AI!
6. Should the examples from the Mahabharata be chosen for how prominent the examples were in the text, or should they be chosen for their relevance to economics? My preference is for the latter, and I made sure the LLM knows this. Ditto for the real life examples.
7. I ended with a meta-prompt, that will stay true for the next thirty (or more questions) – ask if I need to learn more, and only then proceed with the next class.

Should you copy this prompt, word for word? Of course not! For one, you may not want to learn economics, but rather a different subject. The underlying principles still holds. You may not like to read about the Mahabharata, for another. You may want only ten lectures, not thirty. Or you may want two hundred! Feel free to tweak the prompt to suit your requirements, but it helps to “get” how to go about thinking about the structure of the prompts. That’s the point.

I took a look at the outline of the thirty course lecture series it prepared for me, and it was not bad at all. But I had a follow-up request:

Now, you might think that you need to know economics in order to judge the output, and tweak your request. And sure, you’re right that it will help. But regardless, even if you cannot judge the quality of the output, surely you know enough about what and how you want to learn. My apologies for going all meta on you, but if you don’t know enough about the supply side of the market, surely you know what you would like as a consumer – at least in part. So feel free to help the LLM become a better teacher by telling it more about you.

It went ahead and gave me the refined output, and also the broad contours of the first class. Here are the broad contours of the first class:

Again, note that I am quite excited about how this class is shaping up, because if economics is, indeed, the study of how to get the most out of life, Arjuna’s choice to fight in the Kurukshetra war is an awesome way to get some really thought-provoking questions in for discussion. But this may not be your cup of tea – so feel free to brew your own cuppa of econ, by customizing it to what you like the most (Avengers? Cricket? RRR? Bharatnatyam? Junk food? Anime? Go for it!)

I did have follow-up questions:

And based upon its answer to this prompt, I had yet another clarificatory question:

Note that your conversation will be (I would go so far as to say should be) different. You will have different questions, different prompts, different things that make you curious. And that’s not just fine, that is the whole point. Depending on how carefully you read its output, and depending on how probing and detailed your questions are, you can keep just this first class going for a long, long time. How long? That’s up to you!

Here are two examples:

You can, of course, ask it to answer any (or all) of these five questions. Ask it to create ten (or twenty, or a hundred) instead – and as a student, assume that this is how us professors might well be “coming up” with questions for your tests, assignments and exams.

Here are more, and note how they get wilder (more random?) with each passing question:

In each of these cases, you don’t have to have trust in, or agree with, the answer given by the LLM. Treat the output as a way to get you to think more deeply, to challenge what has been said, to verify that the answers are correct, and to have further discussions with your peers and with your (human) teachers, whoever they may be.

Note to myself (and to other teachers of an introductory course about the principles of economics):

1. How can we do a better job than this in the classroom…
• Without using AI (we’re substitutes)?
• By using AI (we’re complements)?
2. What is missing from the LLM’s output (this is assuming you’ve tried these prompts or their variants)?
3. What stops us from recommending that students do this in class on their own devices, and we observe, nudge and discuss some of the more interesting output with everybody? That is, how does teaching change in the coming semester?

Feedback is always welcome, but in the case of the next thirty posts, I think it is especially important. So please, do let me know what you think!

Thinking Aloud on Teaching with ChatGPT

Say I have to teach an introductory course on the Principles of Economics to students who are just starting off on their formal study of the subject. How do I go about teaching it now that ChatGPT is widely available?

1. Ignore the existence of ChatGPT and teach as if it does not exist.
• I am not, and this is putting it mildly, in favor of this proposal. ChatGPT knows more about this subject (and many others) than I do now, and ever will. It may not be able to judge how to best convey this information to the students, and it may (so far) struggle to understand whether its explanations make sense to its audience, about whether they are enthused about what is being taught to them, and whether it should change tack or not. But when it comes to knowledge about the subject, it’s way better than I am. I would be doing a disservice to the students if I did not tell them how to use ChatGPT to learn the subject better than they could learn it only from me.
So this is a no-go for me – but if you disagree with me, please let me know why!

2. Embrace the existence of ChatGPT, and ask it to teach the whole course
• I do not mean this in a defeatist, I’m-out-of-a-job sense. Far from it. What I mean is that I might walk into class, give the prompt for the day, ask the students to read ChatGPT’s output, and then base the discussion on both ChatGPT’s output and the student’s understanding. (Yes, they could do the ChatGPT bit at home too, but you’d be surprised at the number of students who will not. Better to have all of them do it in class instead.) Over time, I’ll hope to not give the prompt for the day too! But it will be ChatGPT that is teaching – my job is to work as a facilitator, a moderator and a person who challenges students to think harder, argue better – and ask better.

3. Alternate between the two (roughly speaking)
• The approach that I am most excited to try. In effect, ChatGPT and I will teach the course together. I end up teaching Principles of Economics, where ChatGPT adds in information/examples/references/points of view that I am not able to. But I also end up helping students understand how to use ChatGPT as a learning tool, both for Principles of Economics, but for everything else that they will learn, both within college and outside of it. This is very much part of the complements-vs-substitutes argument that I have been speaking about this week, of course, but it will also help me (and the students) better understand where ChatGPT is better than me, and (hopefully) vice-versa.

Whether from the perspective of a student (past or present) or that of a teacher (ditto), I would be very interested to hear your thoughts. But as a member of the learning community, how to use ChatGPT inside of classrooms (if at all), is a question I hope to think more about in the coming weeks.

What’s a Tensor, By Dan Fleisch

Not just because it is worth your while understanding what tensors are, but also because this (to me) is a great example of how to teach well on YouTube – a topic that I want to get better at this year:

My Personal Favorite Post from 2022

I don’t have a career plan, and the paths my career has taken would drive a career counsellor mad.

But then again, I’m the kind of idiot who thinks this to be a good thing.

But there are two things I have done in my career that fill me with genuine pride. One is this blog. The second is the undergraduate program at the Gokhale Institute. The first is a solo endeavor, the second was very much a team game – and both are creations, not certifications/titles. Both are my attempts at making the world a better place, and on reflection, that is my career plan. To chip away at making the world a slightly better place.

And for that second reason, leaving Gokhale Institute this year was a bittersweet emotion. But not just because of that secon reason. Gokhale Institute is, to me, a very sacred place.

Gokhale Institute was, is, and always will be home for me. I have played in the campus as a kid, I have walked hesitantly into the library as an undergrad student at Ferguson, and I have done my Masters and my PhD from there. I have taught at least one course over there every year from 2010 onwards, and I hope that record lasts for as long as possible. Some of my closest friends today were batchmates with me at GIPE, and my wife is a subset of this group too.

I am not a professor of economics in the conventional sense of the term. I am not looking to have a great publication record when it comes to academic journals, nor am I looking to attend conferences to present papers. Not, to be clear, because I think those things aren’t good things. But because that is neither my calling, nor my comparative advantage.

My calling, as best as I can tell, is teaching. I revel in the “ohhhhhhhhhhhhh!” sound that students make when they realize the relevance and the importance of a concept. I love taking what seems like a difficult, irrelevant and abstruse idea, and slowly breaking it down so that students understand both what it means and why it matters. I love recommending books, blogs, videos, podcasts and tweets to students – and often at a scale that they simply cannot think of finshing in a semester. My job, as I see it, is to help people learn better.

Because one sure-shot way of making the world better is by helping more people learn better. And the younger your audience, the better. Ergo the undergrad program at the Gokhale Institute.

And for all these reasons, having to leave the program, and the Institute, was (and is) so bittersweet.

I’m still teaching, of course I am. That is, after all, my calling in life. I’m teaching even younger folks than undergraduate students, so at least along one dimension, I’m doing even better at my personal goals. And in some ways, I hope to double down on this blog (and related efforts) in 2023. So teaching continues, thank god.

But knowing when to walk away is an important skill in life, no matter how bitterwseet your emotions. Being clear about your reasons in your own head helps, and writing that post helped me achieve just that.

And so that post was about the following:

1. An important call regarding my career (if one can call it that)
2. Helping me make clear to myself what my reasons were for leaving
3. An au revoir to all the students at GIPE (not just the batch it was ostensibly addressed to)
4. An au revoir to my favorite college in the whole wide world.

And for all these reasons, So Long, Farewell was my personal favorite of 2022.

Macro is *Hard*, Edition #293483343643

I began teaching a course on introductory macro this past Saturday at a college here in Pune. I often tell my students that my job in a macro course is to leave them more confused at the end than they were at the start. That always evokes laugther by way of response, but as anyone who has learnt (and especially taught!) macro will tell you, I’m quite serious.

Macroeconomics is hard, it is confusing and as the person responsible for teaching it, you’re always on your toes, because you’re never sure if you’ve understood it yourself!

And I really do mean that, it is not a rhetorical statement. My own PhD is in macroeconomics (business cycles, more specifically), but I’ll happily admit to still not being sure about what exactly causes business cycles, what (if anything) to do about them and when to stop doing whatever it is that we’ve chosen to do about them. And I suspect that most macroeconomists will tell you the same thing.

This humility stems from a very good reason: macro is hard.

It is hard for lots of reasons, and not to get too meta, but quite a few debates within the field are also about which of these reasons are most relevant, and whether the relevance changes over time – and if so, due to which reasons!

But if I were to try and write a simple post for people who have no formal trianing in macro about why macro is so hard, here would be my reasons:

1. Macro is really about trying to figure out everything that goes on in an economy, and if you try to think about all the things that go on in an economy, you very quickly realize that figuring them out is even more challenging.
2. Time and uncertainty!
• Macroeconomic decisions take time. It takes time to decide to start a new factory. It takes time to figure out the financing. Land acquisitions, regulatory approvals, construction delays will all add weeks to the planned schedule, if not months, and sometimes years.
• These expensive decisions are made at the start, but there is no guarantee that macroeconomic conditions will be the same at the finish of the project as they were at the start. You want a relatable example? How sure are you that macroeconomic conditions will be the same when you graduate from college – as they were when you enrolled in it?
3. The way macroeconomic variables interact with each other isn’t known for sure. We think we know how inflation and unemployment are related to each other, but we can’t really say for sure. We think we know how exchange rates impact the domestic economy, but we can’t really say for sure.We’re still figuring out how monetary policy and fiscal policy should interact in theoretical models, let alone in reality. The impact of monetary policy in America today on India’s economy tomorrow? Don’t get me started. I can go on, and folks with greater expertise than me will prbably not stop for years.
4. Life has a way of throwing up surprises that macroeconomic models never thought about. You could (and probably should) blame macroeconomists for not getting enough finance into their models prior to 2008, but who, pray, could have foreseen 2020 and 2021? How do you come up with models and policies on the fly in such a scenario? And then, just for fun, throw in a jammed Suez canal. Life, I tell you.
We call these things exogenous shocks in macroeconomics, but the name hardly matters. Reality will always be more complex and more unexpected than any model you can come up with, and that’s just a fact.
5. Counterfactuals are impossible to test. How do we know that Ben Bernanke did the “right” thing in 2008? We don’t! What if he had done x instead of y? There’s no way to test this, since we can’t turn the clock back to 2008, and ask Mr. Bernanke to, well, do x instead of y. This is both a problem and when it comes to critiquing models, a great convenience.
6. Attitudes towards risk, and the propensity to copy what others are doing change according to your outlook towards the macroeconomic environment. You can call this animal spirits, but what you’re really saying is that you don’t quite know how to think about it, even less model it cohesively.
7. Building a model – any model – requires simplification. When you build a model, it will by definition be an approximation. Unfortunately (and I wish this weren’t so), this very real limitation isn’t always front and centre within the field while developing models.
8. What are you optimizing for when you build a model? Is it fidelity to reality or is it a beautiful model that may or may not have anything to do with reality? Again, I wish this weren’t so, but the answer isn’t always clear cut.
9. Any field that uses the pool player analogy is a field that is, by definition, unsure about how the world works.
10. No matter how much data we have access to, there will always be data points that we cannot capture, and we don’t quite know how these data points, and their unavailability, will impact our understanding of the economy.

So sure, I’ll teach them about the variables, the models and the case studies.

But I’ll let you in on a dirty little secret, so long as you promise not to tell anybody: I’m just not sure if I really and truly understand what I’m teaching in macro.

Ping!

I hadn’t scheduled a post for today, but I was fairly relaxed about it, since I knew both the topic that I wanted to write about and what the entire post would look like. Shouldn’t take me too long, I thought to myself as I went through my to-do list yesterday evening, and I kept some time aside today morning to do just that.

But just as I was about to settle down and thump said post out, my daughter came and asked me if we could “play the geography game”. What is the geography game, you ask? Oh, a very simple thing: we have created chits of paper on which we have written down the names of India’s states and union territories. We pull these chits out, one at a time, from a small pouch, and we have to name the capital of whichever state or union territory we’ve picked. A very simple, but also a very fun way to spend a part of the last chunk of her Diwali holidays.

But as with all young kids, remembering all of them is a little tricky. And even after four or five rounds of the game over the last two days or so, she was having trouble remembering all of them successfully. And so I taught her about ping!

Remembering random things used to be a weird little hobby of mine when I was in school. It helped me win all the quiz contests when I was in school (except for the last one, in my 10th standard, a fact which still upsets me – but let’s not go there), and it helped me mug up dreary old facts while “studying” in school.

I would tell myself little stories about these factoids to help me better remember them. These stories were completely nonsensical, almost utterly random associations, but they seemed to help. And the more I did it, the better I got at memorizing things. Much later, I learnt about neurons, synapses and plasticity (see here for a reasonably simple explanation), and the how and why of my little trick made much more sense. And I now think of the art of memorizing stuff as “ping!” – as neurons and synapses going, well, “ping!” in my head when I think of a particular topic.

To give you just one of literally millions of possible examples, here’s what the word “spice” brings up in my head. I “get” pings about “Spice Kitchen”, one of my favorite restaurants in Pune. I get pings about different kinds of spices, about my trips to Kerala, about Mark Wiens (a YouTuber who loves eating spicy food), about the Carolina Reaper – it goes on and on.

But as it turns out, you can train your brain to associate certain pings with certain things, and help you remember things better. Again, an example from my own experience. The word “ASEAN” I’ve learnt to associate with BIMP-ST-CMLV (I pronounce this as BIMP-ESS-TEE-CEE-EM-ELL-VEE). And that stands for Brunei, Indonesia, Malaysia, Philippines, Singapore, Thailand, Cambodia, Myanmar, Laos and Vietnam.

I’m not for a moment suggesting that you remember the ASEAN countries this way (or indeed, even remember them at all). I’m simply explaining what works for me inside my head. And the more you play this game, the better you get at it. There’s much, much more that’s going on here, and far more than I can hope to condense into a single blogpost, but you might want to learn about more about, say, spaced repetition, eidetic memories and above all, memory of loci – just to get you started. And feel free to go down any rabbit hole that seems particularly interesting – ’tis Friday, after all. If you’re looking for recommendations, memory of loci would be my pick to get you started.

Which is what I call “ping!” I suppose – and isn’t it a much better way to remember this than “memory of loci”? And so we went ahead and “ping!ed” the list of India’s states and Union Territories. Himachal Pradesh, for my daughter, is a cold state, which she remembers now because I explained to her what the word “him” means in Sanskrit, and that reminds her of a trip she’d taken to Simla. Ping!

Tripura she now associates with agratala sancharam (she’s learning bharatanatyam), and that helps her remember the capital, of course. She was wearing pink pyjamas when we were playing this game, and that’s one way to help her remember the capital of Rajasthan! Again, to be clear, the specifics don’t matter, nor does the list of things to be memorized. Getting the idea behind “ping!” – that’s the important bit.

“Why did the pings in panji’s head go wrong?”

My maternal grandmother, and therefore my daughters great-grandmother (panji in Marathi) passed away earlier this year. She was suffering from dementia, besides other complications, and towards the end, she had forgotten almost all of our names. She would often confuse me with my maternal uncle, for example.

I was so happy that my daughter had learnt the concept, enjoyed applying it – and understood it well enough to be able to ask the question that she did – and at the same time, so overcome with emotion at the question and its timing, that I couldn’t fully respond. Kids, I tell you.

We’ll be ping!ing a part of today afternoon with our good friend Sal Khan re: this topic, and if you don’t feel like battling your work, feel free to “join” us instead!