#7 MSP is really Maximum Smog Price
Root Cause of the Delhi Smog, Kautilyan Rajamandala, and Marginal Cost of Public Funds
|Pranay Kotasthane||Nov 30, 2019|| 3|
PolicyWTF: Paddy Minimum Support Prices cause Delhi Smog
This section looks at egregious public policies. Policies that make you go: WTF, Did that really happen?
Back to the Delhi Smog problem. This thought-letter had discussed a few articles that present Coasean solutions to address the crop residue burning problem. I now realise that even these solutions will only fix symptoms rather than address the root cause — the minimum support price for paddy (!). This causal chain is interesting and let me trace it backwards.
Delhi Smog is partially caused by crop residue being burnt on many farms simultaneously in a short time frame to clear the farms for sowing wheat in the first week of November.
Now why can’t the burning of crop residue be staggered in time? That’s because of delayed sowing. The kharif crop (paddy) is sowed after June 15. This leads to a delayed output leaving farmers with very little time to clear the field for the next crop.
But why is the sowing delayed? That’s where the government comes in. Punjab passed a Punjab Preservation of Subsoil Water Act in 2009 which prohibits paddy transplantation before June 15th. If this rule is violated, paddy nurseries can be destroyed. Repeated violation can also lead to disconnection of power supply.
But why does the government prohibit paddy cultivation before June 15? To save water during the peak-summer season. Paddy farms apparently need 4500 litres/hectare water in April as against 3000 litlres/hectare in June because of evaporation in the summer months.
Then why are farmers growing rice in the water scarce area? One answer, Minimum. Support. Price. Assured procurement of rice by the government on behalf of the Food Corporation of India incentivises over-production of rice even in areas not well-suited.
Amazing, isn’t it? Another unintended consequence of a seemingly well-intentioned policy. To tackle this root cause of the Delhi pollution, renowned agrieconomist Ashok Gulati has a few solutions in this Financial Express article.
A Framework a Week
Tools for thinking about public policy
A useful thumb rule: the cost to society for every rupee of public spending in India is around ₹3. Therefore, the government should only spend an additional one rupee when the gains to society from that action exceed ₹3.
This powerful insight comes from a new must-read book for anyone interested in public policymaking in India — In Service of the Republic: The Art and Science of Economic Policy by Vijay Kelkar and Ajay Shah.
The concept behind this thumb rule is known as Marginal Cost of Public Funds (MCPF) which measures the cost to society for every additional revenue raised by the government.
In an ideal world, additional ₹1 public spending would require an additional tax revenue of ₹1, which would in turn impose a cost of ₹1 to the society. Alas, taxation is not frictionless. Taxes lead to deadweight loss — some exchanges just disappear because of the price rise inflicted by the tax. Consequently, the MCPF ranges from 1.25-2 in OECD countries. But this problem is especially severe in India because we have too many bad taxes, exemptions, and distortion. Together, they impose an immense cost to the society, increasing the MCPF to around 3.
So next time someone says ‘the government should continue to run Air India or BSNL’, ask them to think about MCPF. The key learning is that government should spend only on areas where marginal gains to society exceed the MCPF. This means increase spending on market failures (public health, defence etc), as the benefits from these will far outweigh the marginal costs on the society. As a corollary, GOVERNMENTS MUST AVOID SPENDING ON PRIVATE GOODS.
For more, read Ajay Shah’s detailed post on MCPF.
Big fish eating small fish = Foreign Policy in action
‘Enemy of an enemy is a friend’ — people who haven’t even read the Arthashastra know that this is the key insight from Arthashastra. This insight lends itself to a visualisation made up of concentric circles, with adversaries and allies placed radially in an alternating fashion.
The concentric circles imagery can however be misleading. It might give the false impression of a ‘middle-kingdom’ mandala not very different from the ancient Chinese worldview. At the same time, concentric circles may deceive the reader into believing that state relationships are fixed in geography.
Hence, I was overjoyed when I came across the visualisation in LN Rangarajan’s authoritative translation of the Arthashastra. He depicts the mandala states as back-to-back hexagons rather than as concentric circles. My own adaptation of this visualisation is given below.
Such a depiction permits a notional direction of conquest that helps play out the following scenarios (quoted from Rangarajan’s book):
When the conqueror initiates his attack, he shall make:
in the rear (i) his ally-in-the-rear to engage his enemy-in-the-rear; (ii) his rear ally’s friend to engage the rear enemy’s ally; in the front: (iii) his ally to engage the enemy’s ally’ and (iv) the friend of the ally to engage the enemy’s ally’s friend.
[The above is to ensure that the enemy’s friends in the front and in the rear are kept occupied by the conqueror’s friends and prevented from going to the enemy’s help.]
When the conqueror plays the role of an attacker in the rear, he shall first make his own ally in the rear to engage his own enemy-in the-rear [in order to safeguard his own rear] . He shall then go to the help of his ally by attacking in the rear the enemy.
When the conqueror finds himself under attack, he shall get his ally to attack the rear of the enemy and the friend of his ally to attack the rear of the enemy’s ally.
This directional model of conquest does not have much significance in the context of nuclear weapons. Nevertheless, it is valuable in understanding Kautilyan strategic thought correctly.
Reading and listening recommendations on public policy matters
Arvind Narayanan’s slideset on ‘How to recognise AI snake oil’ is a valuable resource for policythinking on Artificial intelligence. TL;DR - policy thinking is focused on AI biases. But many so-called AI algorithms to predict social outcomes are just sophisticated random number generators. So, it’s a fraud problem we are dealing with, not a cognitive one.
The Scientific American has a list of the Top 10 Emerging Technologies Of 2019.
A reading list for Chinese History by Tanner Greer.
[EconTalk Podcast] Paul Romer on Urban Growth.
That’s all for the week, folks. Read and share. 再见 👋