FEATURED | Occam's Razor of Public Policy

FEATURED | Occam's Razor of Public Policy

By Dr. Ajay Shah

Occam's razor is the idea that when two rival theories explain a phenomenon, the simpler theory is to be preferred. Aristotle's epicycles fit the data as well as Kepler's ellipses, and a pure empiricist could have been agnostic between the two. Occam's razor guides us in preferring Kepler's ellipses on the grounds that this is a simpler explanation.

In the world of public policy, a useful principle is:

"When two alternative tools yield the same outcome, we should prefer the one which uses the least coercion."

Example: Punishment

When we want to drive the incidence of a certain crime to the desired rate, we want to find out the lowest possible punishment that gets the job done. You can reduce theft to desired levels by promising to cut off the hand of the thief. We would much rather achieve the objective using a reduced use of the coercive power of the State, with mere imprisonment.

The purpose of punishment is deterrence, not vengeance. And, in the class of deterrents, we seek to find the smallest possible use of the coercive power of the State that gets the job done.

Suppose 4 years of imprisonment and 2 years of imprisonment are equally able to get the incidence of a particular crime down to the desired level. Suppose a person says: I am not a liberal; I am not squeamish about using the coercive power of the State; I hate the people who commit such crimes; I don't care whether they get 2 years or 4 years in jail. But an α fraction of all convictions are in error. In these cases, we are inflicting the punishment upon an innocent. The harm is minimized when we have deployed the lowest possible punishment.

Example: Spending on government programs

All government spending is grounded in taxation, present or future. All taxation is grounded in the coercive power of the State. If there are two different spending programs that get the job done, we should favour the one which spends less.

Example: Infrastructure bonds

When the market for infrastructure bonds in India does not work, too often, solutions are proposed which use extreme force. Some people propose tax breaks. Some people propose harsh interventions such as forcing every bank to buy infrastructure bonds or forcing every bidder to NHAI to issue infrastructure bonds. As an example, we in India force insurance companies to buy infrastructure bonds.

If, on the other hand, we trace the failures of financial sector policy which have held back the market for infrastructure bonds, this would show how to get the job done while actually reducing State coercion (i.e. getting the State out of inappropriate coercion).

Example: The journey to cashless

Cash is an abomination and we should have a thousand flowers of electronic payments blossoming. India is one of the most backward places in the world in the domination of cash.

Tax breaks for electronic payments or high taxes for cash transaction or outright bans of cash transactions: these are all ways that get the job done using a lot of force.

If, instead, we understand the failures of financial sector policy which have hobbled the sophistication of payments in India, we will get the job done while actually reducing the use of the coercive power of the State. We would have less cash in India if RBI did not use the coercive power of the State to block the clever Uber cashless transaction.

Example: Family welfare

A government which runs counseling services on family welfare is using less coercion when compared with forcible sterilization or a one-child policy.

How to reduce the use of coercion: go to the root cause of a market failure

Market failures can be addressed in many ways. When we go to the source, with well understood causal claims about the source of the market failure, we will find ways to address the market failure using the smallest use of the coercive power of the State.

If we don't have a deep understanding of the sources of the market failure, we may often endup hitting a symptom rather than the disease. Getting the job done may then require the use of a lot of coercion.

As an example, for some market failures that are rooted in information asymmetry, if an intervention can be found which rearranges the structure of information, this can get the job done using the least coercion.

Why are big punishments often favoured in India?

A person who thinks of violating a law to obtain an ill gotten gain Gfaces a probability p of being caught and the fine imposed upon him will be F. Standard economic arguments suggest that we must set F=G(1−p)/p. In this case, the expected gain from violating the law is 0, and a risk averse person will favour the certainty of compliance over the lottery of breaking the law.

In India, too often, the executive works poorly and p is quite low. This creates a bias in favour of driving up F. This is giving us very large penalties. This induces its own problems. We are inflicting terrible harm on the α fraction of innocents who are wrongly convicted. We are giving great power to front-line investigators and judges at a time when institutional capacity is low.

If we are able to build institutional capacity for enforcement, and p goes up, we will then be able to come back to lower punishments that generate adequate deterrence.

Why does Occam's Razor of Public Policy make sense?
It is consistent with the liberal philosophy that desires that humans should be free to pursue their own life with the minimum interference.

At best, governments work badly. The information available to policy makers is limited, many wrong decisions are taken, many decisions are poorly implemented. Governments do not know the preferences of citizens. Politicians and officials are self-interested actors and work for themselves. The Lucas critique comes in the way: rational actors change their behavior when policy changes take place in ways that confound the original policy analysis. Many government actions fail to achieve the desired outcome, but they always have unintended consequences.

It's good to be humble, and swing the smallest stick that would get the job done.


All this, of course, presupposes that all use of coercive power of the State is a purposive activity aimed towards achieving a certain well specified objective. This is not always the case. As an example, the objectives of exchange rate policy or capital controls are hard to decipher. Before we get to discussion of more coercion vs. less coercion, it would be a great step forward if all government intervention were fully articulated in terms of market failure, objective of the intervention, demonstration of the causal impact of the intervention upon the objective, and cost-benefit analysis.

The examples above have featured comparisons where more versus less coercion is easy to identify. Amputation of the hand >imprisonment for 4 years > imprisonment for 2 years. Forcing banks to give out 40% of their loans into priority sector lending is more coercion than information interventions which make the credit market work for poor people. Opening up to private and foreign telecom companies is a way to get phones to everyone with less use of State coercion when compared with forcing banks to open accounts for everyone.

In many situations, however, it is not easy to identify which of two alternative policy pathways involves more coercion. A government program which educated parents that their kids should get immunized seems to involve lower coercion when compared with a forced immunization program, but this is perhaps not the case when we envision an education program that must generate eradication of polio. A government program to educate young people about saving for old age involves less coercion than forcible participation in the NPS.


The State has a monopoly on violence and is the only actor who can coerce citizens to do things against their will. All public policy initiatives involve the use of the coercive power of the State. In the field of public policy, we should be humble about our lack of knowledge, respectful of the freedom of others, and use this power as little as possible.


I am grateful to Jeff Hammer, Shubho Roy and Renuka Sane for useful conversations.

About The Author:

Dr. Ajay Shah, (F-4465-2010), Professor,National Institute for Public Finance and Policy, New Delhi (2007-) Consultant, Department of Economic Affairs, Ministry of Finance, New Delhi, (2001 – 2005). Assistant and then Associate Professor, IGIDR, Bombay (1996-2001). President, CMIE, Bombay (1993-1996). Consultant, Rand Corporation, Santa Monica (1990-1993). Website

This article was originally published at Dr. Ajay Shah's blog on January 31, 2016. 
All rights reserved by the author and original publisher.

AIDN0020220160006/ INDRASTRA / ISSN 2381-3652
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