The monopoly and price elasticity of a demand.
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Explanation of Solution
We could possibly say that the mentioned statement is true. There is pure monopoly which is nonexistent. Let us say, that if you need to send a letter, then the only option we think of is the postal service. But in case, the postal service increases the charges of delivering a letter to the adjacent town, to two days by $15, then we will look for alternate options like using a courier, phone or fax the letter. But within the conscious limits, even if the rates are doubled, we have no substitute that can live up to the mark of a postal service, that too at a commensurate rate.
The same can be explained about pure monopoly, when we consider local electricity provider companies in any given town. If you need electricity for lights, fans, etc you can deal only with a sole company. So, it enjoys pure monopoly, even though other sources of energy like oil or kerosene are used for the basic purposes like heating or for the lights, but these are never going to be a convenient option for end users.
The idea of cross elasticity of the
Concept Introduction:
Cross
Monopoly: It is a market situation, in which only one producer or seller exists in the market. There is a restriction in the entry to the business.
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Chapter 12 Solutions
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- 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
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