1. Based on your understanding of market failures, give at least 3 different examples of situations that would give rise to market failures. In each instance make sure to discuss exactly why, and in what sense, each situation will give rise to a market failure.
2. Give at least two examples of situations in which externality problems are not likely to be solved without government intervention. For each of these cases make sure that you discuss what you believe would be the best response to the externality problem at hand.
3. Give at least two examples of situations in which externality problems are likely to be solved by private parties without government intervention as suggested by the Coase Theorem. For each of these situations make sure that you also discuss exactly how you believe that the private parties in question would be most likely to address the situation.
4. The presentation on Public Goods and Common Resource Goods below includes the following table which suggests that we can classify goods into four different categories based on whether the consumption of these goods is rival and on whether non-payers can be excluded from enjoying the benefits of these goods. The table lists the following categories of goods: (1) Pure Private Goods, (2) Club goods (a.k.a. Non-Rival Private Goods), (3) Common Resource Goods, and (4) Pure Public Goods
Based on the logic of these classifications, identify at least two additional goods that you believe belong in each of these four categories and also explain why you believe that the additional goods that you have identified belong in the category in question.
5. Give at least one additional example of market failure that arises from information failure in a market.
6. Check out the examples below and follow the directions in the tutorials to conduct regression analysis for the following data set.
Dr. Ken Black’s Video: Testing the Regression Model I: Predicted Values, Residuals, and Sum of Squares of Error (Links to an external site.)
Dr. Ken Black’s Video:Testing the Regression Model II: Standard Error of the Estimate and r squared