Economics Plus MyLab Economics with Pearson eText (2-semester Access) -- Access Card Package (6th Edition) (The Pearson Series in Economics)
Economics Plus MyLab Economics with Pearson eText (2-semester Access) -- Access Card Package (6th Edition) (The Pearson Series in Economics)
6th Edition
ISBN: 9780134417295
Author: R. Glenn Hubbard, Anthony Patrick O'Brien
Publisher: PEARSON
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Chapter 5, Problem 5.1.1RQ
To determine

What is externality and examples of positive and negative externality.

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An externality can be beneficial or cost which means it may be positive or negative externality. The impact of one’s action on the well-being of another is termed as an externality. The classic example of an externality is pollution. The production of goods emits pollutants in the environment which leads to different kinds of pollution like air pollution and water pollution. and so forth. This causes health problems for the people who are living near the production area. The example of a positive externality is restoration of historical buildings, facts which are leading to the formation of new research and education experiments and so on.

Economics Concept Introduction

Concept Introduction:

Externality: Externality is the impact of one person's actions on the well-being of a bystander. The externality is two types, positive and negative.

Positive externality: If the impact of one person’s action on the bystander is beneficial, it is known as a positive externality

Negative externality: If the impact of one person’s action on the bystander is adverse, it is known as a negative externality.

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CEO 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.
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