Consumer and Producer Surplus

Typically, consumers value the goods they purchase by an amount that exceeds the purchase price of the goods.

Consumer surplus is the difference between what the consumer has to pay for a good and the amount he/she is willing to pay. It is the area under the demand curve & above the price.

Producer surplus is the difference between what the producer receives for the good and the amount he/she must receive to be willing to provide the good. It is the area above the supply curve & below the price.

Total social welfare is the sum of consumer surplus and producer surplus. 

Both the consumers and the producers want to maximize their surplus leading to efficient allocation of resources to produce the product which maximizes the total benefit to the society.

At any other point other than the equilibrium, the total surplus will be less than this. For example, at quantity Q1 and price P1, consumer surplus is the red area & producer surplus is the green area. The total surplus is less than the area under the equilibrium price.

Let’s analyze the above graph. Assume the quantity produced was cut to Qf leading to a price of Pf. If we compare this to the consumer and producer surplice in case of equilibrium price, we will notice the following:

  • Consumers lost area U and V
  • Producers lost area W but gained area U.
  • Put together both consumers and producers lost area V and W. This area is called the deadweight loss. This is the deadweight economic loss due to inefficiency.

Deadweight loss occurs when the quantity supplied does not maximize the sum of consumer and producer surplus. Deadweight loss can occur due to overproduction or underproduction.

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Data Science in Finance: 9-Book Bundle

Data Science in Finance Book Bundle

Master R and Python for financial data science with our comprehensive bundle of 9 ebooks.

What's Included:

  • Getting Started with R
  • R Programming for Data Science
  • Data Visualization with R
  • Financial Time Series Analysis with R
  • Quantitative Trading Strategies with R
  • Derivatives with R
  • Credit Risk Modelling With R
  • Python for Data Science
  • Machine Learning in Finance using Python

Each book comes with PDFs, detailed explanations, step-by-step instructions, data files, and complete downloadable R code for all examples.