Comm220 Ch 5

# Comm220 Ch 5 - CHAPTER 5 UNCERTAINTY AND CONSUMER BEHAVIOR...

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Source: Pindyck and Rubinfeld (2009), Microeconomics , 7 th Ed., Pearson Prentice Hall, Chapter 5. 1 CHAPTER 5 - UNCERTAINTY AND CONSUMER BEHAVIOR Key Concepts and Topics Describing Risk Preferences Toward Risk Reducing Risk The Demand for Risky Assets Behavioral Economics Describing Risk To measure risk we must know: 1. All of the possible outcomes 2. The probability or likelihood that a given outcome will occur Interpreting Probability Objective probability Observed frequency of past events Subjective probability Perception that an outcome will occur Influenced by different information or different abilities to process the same information – based on judgment or experience 2 measures to help describe and compare risky choices 1. Expected value 2. Variability Expected Value Probability - weighted average of the payoffs or values associated with all possible outcomes measures the central tendency ; the payoff or value expected on average Example: Investment in offshore drilling exploration: 2 possible outcomes Success – the stock price increases from \$30 to \$40/share Failure – the stock price falls from \$30 to \$20/share Objective Probability 100 explorations: 25 successes and 75 failures Probability of success = 0.25 and probability of failure = 0.75 EV = Pr(success)(value of success) + Pr(failure)(value of failure) = 0.25(\$40/share) + 0.75(\$20/share) = \$25/share

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Source: Pindyck and Rubinfeld (2009), Microeconomics , 7 th Ed., Pearson Prentice Hall, Chapter 5. 2 In general, for n possible outcomes: E(X) = Pr 1 X 1 + Pr 2 X 2 + … + Pr n X n where X 1 , X 2 , … X n = payoffs of possible outcomes Pr 1 , Pr 2 , … Pr n = probabilities of each outcome Variability Extent to which possible outcomes of an uncertain event differ How much variation exists in the possible choices Example: Suppose you are choosing between two part-time sales jobs that have the same expected income (\$1,500) Outcome 1 Outcome 2 Pr Income Pr Income Job 1: Commission 0.5 \$2,000 0.5 \$1,000 Job 2: Fixed salary 0.99 \$1,510 0.01 \$510 E(X 1 ) = 0.5(\$2,000) + 0.5(\$1,000) = \$1,500 E(X 2 ) = 0.99(\$1,510) + 0.01(\$510) = \$1,500 Same expected values, but different variability Greater variability from expected values signals greater risk Variability comes from deviations in payoffs Difference between expected payoff and actual payoff Deviations from Expected Income (\$) Outcome 1 Deviation Outcome 2 Deviation Job 1 \$2,000 \$500 \$1,000 –\$500 Job 2 \$1,510 \$10 \$510 –\$990 Average deviations are always zero so we must adjust for negative numbers by taking the squares of the deviations Measure variability with standard deviation , σ Square root of the weighted average of the squares of the deviations (variance, σ 2 ) Measures how variable your payoff will be More variability means more risk Individuals generally prefer less variability – less risk
Source: Pindyck and Rubinfeld (2009), Microeconomics , 7 th Ed., Pearson Prentice Hall, Chapter 5. 3 The standard deviation is written: 2 2 2 2 1 1 )) ( ( Pr )) ( ( Pr X E X X E X + = σ Standard deviations of the two jobs are: 50 . 99 900 , 9 ) 100 , 980 ( 01 . ) 100 ( 99 .

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