Concept Introduction:
Excludable and Non-Excludable goods: If people are prevented from the use of the good they have not paid for, then such goods are treated as excludable goods, whereas if people cannot be prevented from the use of the good they have not paid for, then such goods are treated as non-excludable goods.
Rival and Non-rival goods: If a good or service consumed by a person alone at a time and its consumption is prevented from the other persons at that point of time then this kind of good is known as a rival good.
If a good or service consumed by a person alone and its consumption is not prevented from the other persons at that point of time, then this kind of good is known as a non-rival good. Generally, private goods are treated as rival goods and public goods are treated as non-rival goods.
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Chapter 17 Solutions
Achieve for Economics (1-Term Online)
- Using data from 1988 for houses sold in Andover, Massachusetts, from Kiel and McClain (1995), the following equation relates housing price (price) to the distance from a recently built garbage incinerator (dist): = log(price) 9.40 + 0.312 log(dist) n = 135, R2 = 0.162. Interpretation of the slope coefficient? ► How would our interpretation of the slope coefficient change if distance were measured in metres instead of kilometres?arrow_forwardIf GDP goes up by 1% and the investment component of GDPgoes up by more than 1%, how is the investment share ofGDP changing in absolute terms?▶ In economics, what else is expressed as relative percentagechanges?arrow_forwardCEO Salary and Firm SalesWe can estimate a constant elasticity model relating CEO salary to firm sales. The data set is the same one used in Example 2.3, except we now relate salary to sales. Let sales be annual firm sales, measured in millions of dollars. A constant elasticity model is[2.45]ßßlog (salary) = ß0 + ß0log (sales) + u,where ß1 is the elasticity of salary with respect to sales. This model falls under the simple regression model by defining the dependent variable to be y = log(salary) and the independent variable to be x = log1sales2. Estimating this equation by OLS gives[2.46]log (salary)^=4.822 + 0.257 (sales) n = 209, R2 = 0.211.The coefficient of log(sales) is the estimated elasticity of salary with respect to sales. It implies that a 1% increase in firm sales increases CEO salary by about 0.257%—the usual interpretation of an elasticity.arrow_forward
- Not use ai pleasearrow_forwardB G C D E H M K Armchair For puzzles 96 and 97, use the first phylogenetic tree on the following page (Figure 2). 96) Who is the most recent common ancestor of species A and species G? 97) Who is the most recent common ancestor of species D, E, and F?arrow_forwardNot use ai pleasearrow_forward
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