Articles
Do Rules and Regulations Help? A Theory to Test the Effect of Increasing Rules and Regulations in Society
An Analogy
Consider an examination hall. If the teacher cannot see one student, the likelihood of his cheating is 1. If two students are in the blind spot, the likelihood of their cheating is 4. For instance, if the students are A and B, A can cheat by himself, B can cheat by himself, A copies B and B copies A and therefore there are 4 possibilities to cheat. If the number of students increases to 3, the ways of cheating increase to 9 and so on. So cheating potential in a classroom is likely to increase quadratically with the number of students in the blind spot. This can be represented using a simple equation.
C=n2. ————-(1)
where C is the potential pathway to cheat with n students in the blind spot.
We can add a factor “k” to equation (1) to form equation (2). The factor indicates the freedom available for the students to cheat (due to bad/inefficient monitoring or other conditions, including the position of students in the exam hall).
C=kn2 —————-(2)
The factor k reflects the probability of cheating to occur given that there is a blind spot. The value of k therefore lies between 0 and 1. If the value of k increases (or the freedom to cheat increases due to laxity in monitoring the students), k moves towards 1, and n reaches maximum. Consider for instance, that k=0.5, the number of students in the blind spot is 3. According to equation 1, we therefore have C=0.5*3*3= 4.5. If k increases to 0.6, then the value of C becomes 5.4. This implies that when freedom in the exam hall increases, potential pathways to cheat also increase. Controlling k would therefore control the value of C as per equation (2).
The Analogy extended to the State
Now imagine the State with its potentially large number of blind spots; areas where it is impossible or difficult to monitor if individuals are violating regulations. When the State is large, applying the above equation (2) then tells us that as n increases, pathways of illegality can be reduced only by decreasing the value of k; which alternatively means that freedom should be restricted. This then becomes a public policy question. Restriction of freedom in turn would imply more regulations. This turns into a loop and complicates the network of illegalities. A pertinent question, therefore, for a policymaker is, ‘Is there a tolerable limit of freedom that every State defines?’ To understand this question, let us rewrite equation 2.
From equation 2 we have k=C/n2———(3)
Equation (3) gives us a crude estimate of what freedom means in terms of pathways of cheating and the number of blindspots in a society.
In other words, a tolerable limit of freedom depends on C and n. Freedom comes at the cost of what the State is ready to accept as far as the illegality is concerned and the number of blind spots existing. But politically no state can afford to say that it is willing to tolerate any amount of illegalities. The only way it can ensure higher freedom is by decreasing the number of blind spots. To tame blind spots, states use rules and regulations. However, increasing the number of regulations creates edges whereby new avenues to cheat springs and as mentioned above, turns equation 2 into a loop. A bad rule could cost the State n2 in terms of the blind spots created.
The Test for Rules and Regulations
Restricting k by increasing the rules need not help the State to reduce blind spots and thereby rule evasions. As we have seen, there is a likelihood that new rules and regulations could create new blindspots and the cost of the State escalates quadratically. This gives us a framework to reevaluate rules rather than create new rules. If the State wants to reduce the number of blind spots, it needs to reduce the number of rules which created these blind spots in the first place. To access a new rule or regulation, we need to ask the following questions:
- Does it reduce the natural blind spots? Or does it create new ones?
- Does it prevent C (pathways to cheat) or create new C?
- Does it help increase the value of k (freedom) for law abiders?
Identifying the rule that created a blind spot and the cost that the State bears to deal with the blind spots is more important than creating new rules. The simple reason is that this is the best option available to the State when considering the alternative of decreasing the value of freedom “k”.
Rahul V Kumar is a Research Fellow (Market Economics) at the Centre for Public Policy Research (CPPR).
Views expressed by the authors are personal and need not reflect or represent the views of the Centre for Public Policy Research (CPPR).
Rahul V Kumar holds a Research Fellow (Market Economics) at CPPR. He is a postgraduate in Economics and has an MPhil in Applied Economics and International Relations from Jawaharlal Nehru University (New Delhi).
