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.
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.
(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.”
A student recently got in touch asking about what he should read when it comes to understanding the current dynamics of Sino-Taiwanese relations.
This blog post is, in a sense, an answer to his question, but also a bookmark-worthy resource for me. And hopefully for you as well!
I’d recommend one beings by trying to understand Taiwan: it’s history, it’s society, it’s culture. And a good primer to begin with would be this blogpost by Tanner Greer. .. “The fact is that younger generations of Taiwanese, including the grandchildren of the waishengren have no memory of pre-communist China, have only distant relatives there, and have spent their entire lives living in freedom. This is an environment where the use of Taiwanese Hokkien is encouraged and Taiwanese nationalism has flourished. Thus very few people under 45 consider themselves Chinese.” ..
For additional reading, I heavily recommend this post by Noah Smith: .. “Taiwan has one of the most progressive societies, if not the most progressive, in Asia. It was the first Asian country to legalize gay marriage, and sports a vibrant gay culture. Taiwan ranks as one of the most gender-equal societies in the world, equivalent to Norway and higher than France on the commonly used GII scale. The President, Tsai Ing-Wen, is a woman, and women make up 42% of the legislature. The country has actively pushed for gender equality in business, and the gender pay gap, at 14% in 2018, is smaller than in the U.S.” ..
Taiwan is big on democracy (and if you read Greer’s post linked to above, you will begin to understand why), and Taiwan is a good way to start to learn more about digital democracy. A useful way to begin would be to learn more about Audrey Tang. Listen to these two podcasts as well in this regard: the first one is with Azeem Azhar, and the second with Tyler Cowen. ..
And it also has to be about semi-conductors, and that one company in particular. Read this briefing from The Economist as well, along with this essay by Pranay Kotasthane. One thing I have realized is that I haven’t read books about the emergence of the semi-conductor industry in general, and about TSMC in particular. If you have any recommendations, please send them my way. Thank you. ..
Finally, try using game theory to think through the implications? Use this as a starting point, but have fun (well, as much fun as is possible given the topic!) coming up with outrageous theories, and thinking through the consequences in game theoretic terms. .. There must be a million other things I could have linked to but didn’t. I look forward to adding more, so don’t hesitate to send in links to help that student of mine. Thank you in advance!
Read this post, and spend a good amount of time asking yourself some questions about the three charts. Here are my questions (note that I don’t have the answers):
Is China’s decoupling a good thing or a bad thing? For whom?
What time horizon should we use to think about the answer to the first question? Why?
To what extent is China’s reduction in exports as a percentage of GDP deliberate? Was it deliberate all along, or did they observe a trend, think through the consequences, and then make it a deliberate policy?
Is China’s decline the share of global GDP growth a good thing for the world? Why?
What about India, is it a good thing for India? If yes, along which dimensions? If no, along which dimensions?
Does China count the last chart in this blog post as a victory or a defeat, or is it “too soon to tell”? Whatever the answer, why so?
What are other data related stories from China that we have not been paying attention to?
I don’t have, as I said, the answers. And maybe I have missed asking some obvious questions. If you have material that will help me think through these issues, please do share.
Andrew Batson has a nice post out about an essay in the Palladium magazine. The theme of both the essay and the blog post is decentralization in China.
Dylan Levi King has a nice essay out in Palladium on the history of decentralization in China, opening with the assertion that “the most significant reform carried out in China after 1978 was one of systematic decentralization.” It is difficult to disagree with this. As the best China scholarship of the last few decades has made clear, local initiative played a central role in the country’s growth miracle–see for instance Jean Oi’s book on local state corporatism, or Xu Chenggang’s classic article on “regionally decentralized authoritarianism”.
The essay is a reflection on how decentralization has evolved (and retreated) under the various leaders who have been in charge of the central Chinese government, beginning with Mao, and ending with Xi Jinping. As always, please read the whole thing.
The essay makes the rather unsurprising point that under Xi’s leadership, China is becoming ever more centralized. But the interesting (if not entirely surprising) nugget is that the attempt to increase the degree of centralization began about thirty years ago – Xi is the first leader since then who’s been very successful at it.
Well, so far, at any rate. See this thread, for example:
But the essay helps us think about a question which should be of interest to a student of economics: what is the appropriate level of decentralization? I mean this to be a one-size-fits-all question: for any organization, institution or level of governance, how should we think about the appropriate level of decentralization?
Think about the answer to this question in regard to your own college/school, for example. Who do you need to approach for permission in order to hold an event in your college? Does any prof have the ability to give permission, or are they likely to pass your question up to the head of the department? What about the head of the department? Are they likely to take the decision, or will they pass the question up to the principal or the director? In other words, how much decision-making authority is vested in the lower levels of hierarchy? And how much decision-making authority should be vested in the lower levels of hierarchy?
It is a question with far reaching implications: a centrally driven decision making system retains all the power at the centre, and everybody knows who to go to for getting approval. On the other hand, this is likely to make the system rather inflexible, with very little decision-making authority at lower levels.
Here’s a very simple example: let’s say you’re fifteen minutes late while checking out of a hotel. Should you be charged a fine or not? Should this be up to the clerk who is helping you check out, or should the clerk just blindly follow the “rule” with zero decision-making authority? If you (the guest) then kick up a ruckus, should the clerk call their superior? Should the superior call their superior? And on and on…
Management consultants agonize about this, as do politicians and bureaucrats. But so do government officials, professors in universities and even parents! What is the appropriate level of decentralization is an important question in literally any organization!
So how do we go about building a model in our heads to think about this issue?
Here’s one way to think about it:
Let’s assume that we’re seeking to optimize for the long term growth and stability of the organization in question. That is, to me, an entirely reasonable assumption. Concretely, the management consultant in charge of instituting check-out processes in the hotel is charged with creating a process that will optimize for the long term growth and stability of the hotel chain.
Should the management consultant vest, then, the clerk with the power to waive off the late fee? Under what circumstances? To what extent? With what amount of leeway given for mitigating circumstances? Maybe the clerk can waive off the late fees only for a certain number of times per month? Can HR track which clerks waive off fees the least across the year, and decide bonuses accordingly? Or should clerks be rewarded for building out customer loyalty by waiving off late fees by default for a period of up to an hour beyond the checkout time?
What about re-evaluation requests for semester-end examinations? What about disciplinary committees for deciding upon the punishment for low attendance? The decision to sell land in order to meet revenue requirements by local governments? As you can see, once you start to think of hierarchies and organizations, this can get very complicated very quickly.
And within the field of economics (at least for a specific context), the Oates Theorem is a good starting point to think about this analytically:
Many years ago in Fiscal Federalism (1972), I formalized this idea in a proposition I referred to as “The Decentralization Theorem.” The basic point is that if there are no cost advantages (economies of scale) associated with centralized provision, then a decentralized pattern of public outputs reflecting differences in tastes across jurisdictions will be welfare enhancing as compared to a centralized outcome characterized by a uniform level of output across all jurisdiction
Oates, Wallace E. “On the evolution of fiscal federalism: Theory and institutions.” National tax journal 61.2 (2008): 313-334.
In English, what this means is that so long as centralized provisioning doesn’t have any “bulk” benefits, lower levels of hierarchy will always know more about “local” tastes and preferences, and therefore decision making ought to be as decentralized as possible.
Put another way, a one-size fits all rule won’t be as optimal for the hotel chain as letting the clerk in question decide on a case-by-case basis.
So as a thumb rule, the more one decentralizes, the better. Alas, decentralizing decision-making also has the knock-on effect of decentralizing power, and that tends to not go well with those who, well, have power.
And so while effective decentralization has economic benefits, it also has political consequences. Which is why it makes sense to ask what one is optimizing for. And occasionally, it behooves all of us to ask what one should be optimizing for.
The answers are often wildly different, and more’s the pity.
As with everything that happens in the world today, so also with the farm laws: a lot of heat, and hardly any light. Reams have been written about how the farm laws were good (or bad), about their introduction being a much needed thing (or not), and their withdrawal being a disaster for take-your-pick-from-Modi-BJP-India (or not).
I have neither the desire nor the energy to get into any of these debates. Here’s my simple take as a student of economics: markets almost always work. Where they don’t work, identify the reasons why they don’t work, and either correct those causal factors, or have the government step in until (and only until) those factors are corrected.
Things get tricky when you begin to ask pesky questions along these lines:
How do you define markets not working? Bench-marked against what standard? Who decides?
How do you correct these causal factors? How do you judge that they have been corrected? Are you sure they won’t return? On what basis?
To what extent should government step in? How are you sure this will make things better in all markets at all points of time? Using what framework?
But that is precisely what makes the study of India’s political economy so very interesting! And this is true of agriculture as well, not just in India, but in other places too.
For the moment, let’s take as a given the fact that government had to be present in agricultural markets in India these past decades. That may or may not be true, but for the purposes of this blog post, let us assume that there was a confluence of factors in India’s agricultural markets that necessitated the active presence of the government as a participant, not just as a regulator.
Now, if markets almost always work, and if government was present in agriculture, then we have to figure out a way for government to eventually not be present in agriculture. (Note, again, that your opinion may be different from mine. But play along with me for the moment, please.)
Yamini Aiyar and Mekhala Krishnamurthy argue in an HT article that in the case of the three farm laws, what the government missed out on was the word “eventually”. They argue that it was the suddenness of the move that was problematic, not the move itself.
There’s a political angle to the sudden withdrawal, and the authors refer to it in their piece. There’s a regulatory angle to the sudden withdrawal, and that is also covered by the authors. But there also is an institutional (and therefore economic) angle to it, and that is what I would like to focus on:
Consider this. The protesting farmers from Punjab, Haryana and western Uttar Pradesh are locked into a system where State intervention, driven by the logic of Minimum Support Prices (MSP) and the Agricultural Produce Marketing Committee (APMC) mandis, dominates. The State is not a benign actor. It has created and sustained local elites with vested interests – traders, middlemen and moneylenders, each of whom extracts to control market power. This undermines competition and compromises farmer interests in different ways. But farmers have learnt to negotiate these relationships of extraction. And the state through MSP and mandis has served as insurance that gives them bargaining power. Any attempt to break this system will inevitably, as the protests amply demonstrate, unleash anxieties. In this context, the move towards genuine competition will not be viable without the State demonstrating its willingness to protect farmers interests and gain their trust.
What is the point? The point is that the current system isn’t perfect, and it isn’t sustainable. As the authors point out, the farming sector isn’t competitive.
In theory, that should mean, to a student of economics, that they are not efficient. That, in turn, means that we should expect that producers aren’t producing as much as they could have, and whatever they produce is being produced at a higher cost than would otherwise have been the case. We should expect that procurement, storage and distribution are also potentially riddled with inefficiencies. We should expect divergent quality of produce, and we should expect consumers to be paying higher prices, potentially for a lower variety of goods.
We should also anticipate a whole host of things due to the fact that the farming sector isn’t competitive: prices aren’t transparently determined, there isn’t free entry and exit, certain sellers are likely to get a better deal, transaction and search costs are high, and on and on and on. This is microeconomics 101 in practice.
(A quick note to students of economics: ask yourself if you’re able to relate what you’re learning in your microeconomics courses to the two paragraphs above. If you disagree with my assessment, ask yourself what is it that is causing you to disagree. Can you frame your disagreement in the context of microeconomic theory? Secondly, irrespective of whether you agree or not, can you think of what data points you might need to empirically verify or disprove my arguments? Where might these data points be available? What models (economic and econometric) can we use to settle this debate? Finally, why stop at agricultural markets – which other markets can you analyze this way?)
And for all of these reasons and more, reform is needed. It cannot possibly be anybody’s argument that the status quo in India’s agriculture must persist forever.
Which then, in turn, gives rise to two separate questions:
If reforms are to be introduced, how?
However they are to be introduced, how fast should we proceed with their implementation?
Again, the question isn’t one of the desirability of reforms, or their appropriateness. Rather, the question is about whether the reforms should be a top-down, one-size-fits-all initiative, or a more locally driven approach. And second, should reforms be introduced all at once, or slowly and gradually, one step at a time.
And I would like to argue that at least in this one regard, we should be looking at China. Not for the specifics of their reform and a CTRL-C CTRL-V hit job. But for their approach, beginning in the late 1970’s.
When I first proposed the household responsibility system (HRS), I was criticized as follows: Chairman Mao had been dead only a few years. Supporting the HRS, a system he opposed, meant forsaking his principles. This was the severe environment that reform faced at first. Our support of the HRS, of institutional innovation, and of transformation of the agents of the rural microeconomy would inevitably involve adjusting a number of interests. To avoid risk, it was necessary to carry out trials first. Also, the HRS could not move ahead on its own. It had do so in connection with other institutions and be realized in the course of reforming the institutional environment as a whole. But this institutional reform is not something that could be accomplished in one fell swoop. To carry out reform, a strategy of gradual advance was unavoidable.
In the publication that I have excerpted from above, there are some points that I am going to summarize that I think help me make my point better:
Resistance to the introduction of market based reforms was anticipated in China back then, and was in some sense inevitable. Three measures were conceived of to reduce this resistance:
“First, the reform would not initially call for abandoning the people’s communes, but rather would implement a production responsibility system within them. This approach enabled many who would have opposed the change to accept it.”
“Second, the responsibility system could take a number of forms, among which the populace could choose. One did not impose one’s own subjective preference on the populace but respected its choice.”
“Third, the reform began in a limited region, where it received popular support, and then widened step by step.” (Emphasis added)
“In 1980, after the central leadership was reorganized on a collective basis, the top central leaders, including Deng Xiaoping and Hu Yaobang, consistently supported allowing different areas to adopt different forms of the agricultural production responsibility system. It was then proposed to divide them into three types of areas: impoverished areas would carry out the HRS; advanced ones would adopt specialized contracts with wages linked to output; and intermediate regions could freely choose.”
Or, as Ajay Shah and Vijay Kelkar put it in their book: “The heterogeneity of economic and social development, across the regions of India, generates heterogeneity in the public policy pathways desired by different groups of people. A policy position that is well liked in Uttar Pradesh may not be liked in Kerala, and vice versa. This creates conflict in a centralized public policy process.” Kelkar, Vijay; Shah, Ajay. In Service of the Republic . Penguin Random House India Private Limited. Kindle Edition.
Finally, there’s a lot to pick at and think about here when we get down to the specifics. I’m not suggesting that China in the late 1970’s had the exact same problems that India does today. Nor am I suggesting that India do today exactly what China did back then. I am making three points:
I agree with Yamini Aiyar and Mekhala Krishnamurthy when they say that one of the problems was the suddenness of the proposed reforms, both in terms of their scope, and in terms of their geographical spread. I also agree with them when they say that the introduction of the reforms ignored the ground realities of the both the sociology of agricultural markets, and their institutional complexity (note that I am paraphrasing here, these are not their words).
But having read their article, one must ask: if not the pathway that we have now left behind us, what else? That is, for better or for worse, the three farm laws now stand withdrawn. Is the status quo desirable? Should we seek to perpetuate it, or change it for “the better”? (Inverted quotes because better means different things to different people.) My opinion is that we should seek to change it for the better, and maybe yours is the same.
But that gives rise to the next question: how? And that is where Du Runsheng and his write-up is of limited help. Learning how other nations did it is a good place to start if you are a student of economics, India or public policy, and post-Mao China holds some valuable lessons for us.
If you are even an amateur fan of game theory, you must have come across the term “MAD”:
Mutual assured destruction (MAD) is a doctrine of military strategy and national security policy in which a full-scale use of nuclear weapons by two or more opposing sides would cause the complete annihilation of both the attacker and the defender (see pre-emptive nuclear strike and second strike). It is based on the theory of deterrence, which holds that the threat of using strong weapons against the enemy prevents the enemy’s use of those same weapons. The strategy is a form of Nash equilibrium in which, once armed, neither side has any incentive to initiate a conflict or to disarm. The term “mutual assured destruction”, commonly abbreviated “MAD”, was coined by Donald Brennan, a strategist working in Herman Kahn’s Hudson Institute in 1962. However, Brennan came up with this acronym ironically, to argue that holding weapons capable of destroying society was irrational.
As with most theoretical concepts, it has its fair share of exceptions and limitations. Reading the Criticism section of the Wikipedia article is a great way to depress yourself, for example. But today, we depress ourselves a little bit more, by thinking about an article whose cheerful title is “The Math is Bad for MAD“:
Alarmingly, the current modernization of nuclear-missile arsenals by both Russia and China exposes a simple mathematical flaw in the assumptions underlying continued reliance on MAD. Despite our having ~1,400 deployed strategic nuclear warheads, they are postured such that a surprise attack by approximately 70 – 100 Russian or Chinese missiles—a fraction of their total nuclear forces—could soon undermine our “assured” retaliatory capability.
The rest of the article explains how China and Russia could, quite conceivably, undermine the US’ “assured” retaliatory capability. And when I say “quite conceivably”, I am not exaggerating. The authors, Norman Haller and Peter Pry lay out with implacable logic how China and Russia might think through all of the moves in this most dangerous of games, and reach the conclusion that America’s ability to “assure” retaliatory capability is not, in fact, assured. I will not excerpt anything to defend my argument, please read the entire article.
So what, one might ask, is to be done? The authors lay out seven things that America could conceivably do, and evaluate each of them in turn. Again, read the whole thing, it is in your interest to do so. I will, however, excerpt their concluding paragraph:
Finally, U.S. decision-makers should tune out minimalists who ignore the math and advocate replacing the Triad with either a Diad (bombers and submarines only) or, even worse, a Monad (submarines only). Tuned out as well should be MAD proponents who are inattentive to the math and insist that an undefended America is a positive asset.
You may agree with that paragraph, you may not. But you should, as a student of game theory, ask yourself if you can frame your agreement (or otherwise) in game theoretic terms. It is a useful (albeit depressing) exercise in your journey as a student of game theory.
It is one of my favorite questions to ask whenever students come to me with doubts about “what to do next” in terms of either further education or a job.
(Side note: asking me what to do next probably isn’t a good idea, because my career has been gloriously unplanned. But that’s a whole separate story)
But one should be clear about what one is optimizing for: is it income, or free time, or job satisfaction, or rapid career growth – or something else altogether? And whatever it may be, optimizing for one will quite probably mean having to give up on some or all of the others.
And this applies to many more things than just the What To Do Next question, of course. In fact, relentlessly asking this question in many different contexts can take you a very long way in terms of understanding what seem like really difficult and complex topics.
Such as, for example, what China has been up to in terms of international trade, and what went so gloriously wrong.
The simple story of international trade (or trade in general for that matter) isn’t difficult to grasp. Bear in mind that reality is a little more complex, but it really boils down to comparative advantage.
As Michael Pettis points out at the start of this excellent Twitter thread, the so-called “China shock” *is* a shock, but it is not an indictment of the basic concept of international trade. China, as we’re about to find out, was playing a zero-sum game.
One of the most glorious things about economics is the fact that trade is a non-zero sum game. Both parties that have voluntarily entered into a trade with one another benefit for the trade having gone through, and so nobody loses. This is as true at your local chai tapri (you give ten bucks for a cup of chai, and both you and the chaiwala are happy with the trade) as it is in the context of international trade between the United States of America and China.
But beware overly simplistic stories, for they can trip up many a happy ending:
Isabella Kaminska, in an old but excellent article on FT Alphaville made a very similar point. I’ll get to that point in a bit, but may I also use this opportunity to urge the good folks at FT to make FT Alphaville free again?
Here’s the point from that old article:
What those who accused China of using its exchange rate to gain advantage probably misunderstood was that it wasn’t the currency which was being undervalued, it was the people. Stephen Roach, then chief economist of Morgan Stanley, explained this point in the Financial Times in 2003 (our emphasis): “The Chinese phenomenon hardly amounts to grabbing market share from the rest of the world. It is more a by-product of the struggle for competitive survival by high-cost producers in the industrial world. Last year, a record $53bn of foreign direct investment flowed into China, making the country the largest recipient of such funds in the world. These investments did not occur under coercion. A high-cost industrial world has made a decision that it needs China-based outsourcing to ensure competitive survival. Dismantling China’s currency peg would destabilise the very supply chain that has become so integral to new globalised production models in Japan, the US and Europe. There are several other reasons why China should leave its currency unchanged. Contrary to widespread perception, China does not compete on the basis of an undervalued currency. It competes mainly in terms of labour costs, technology, quality control, infrastructure and an unwavering commitment to reform.
This article was written in 2015, but it holds up very well. In fact, it is instructive to see how, in addition to labour costs and infrastructure, China has now centralized under government authority technology as well. It is also instructive to think about how (and in what direction) the “unwavering commitment to reform” has evolved, but that is a separate story.
To come back to the common thread between the old FT Alphaville article and the Twitter thread by Michael Pettis:
Stephen Roach, in 2003, spoke about how China was undervaluing its people. Isabella Kaminska in 2015 spoke about China competes (at least in part) on labor. And Michael Pettis in 2021 is talking about China competing by suppressing its wages (relative to productivity levels). But they’re all making the same point, and it is a point that merits greater emphasis:
The China shock needn’t have been a shock, in the sense that it is not as if economic theory stopped working once China started trading more with the rest of the world.
China, as it turns out, wasn’t optimizing for international trade. China was – and is – optimizing for an increase in her exports, and that over time.
That problem manifests itself in many different ways: The USA’s persistent trade deficit with China is just one glaring example. The Belt and Road Initiative is another (what the hell do you do with all those forex reserves, dammit?). And there’s many, many more.
But as Michael Pettis reminds us in this thread, the “China Shock” phenomenon becomes way more comprehensible when you ask a deceptively simple question: what is China optimizing for?
What is India optimizing for when it comes to international trade? What should India be optimizing for? In both cases, whatever your answer, why?
Critique this blogpost, and write your responses to the questions above. It is a great way to test yourself if you think you’re good to go in open macroeconomics or international trade.