Production Function: This refers to the relationship of the inputs used in the production of a product with the output produced. Marginal Product of Labor ( MPL ): This refers to the additional units of output produced when an additional unit of labor is employed in the current labor force. The formula to calculate the marginal product of labor is: Where, is the change in quantity. is the change in labor. Value of Marginal Product of Labor ( VMPL ): This refers to the value of additional units of output produced when an additional unit of labor is employed in the current labor force. The formula to calculate the value of marginal product of labor is: Where, MPL is the marginal product of labor. P is the price.

Question

Want to see the full answer?

Answer 1

Question

Chapter 19, Problem 3P

To determine

Concept Introduction:

Production Function: This refers to the relationship of the inputs used in the production of a product with the output produced.

Marginal Product of Labor ( MPL ): This refers to the additional units of output produced when an additional unit of labor is employed in the current labor force.

The formula to calculate the marginal product of labor is:

Where,

is the change in quantity.

is the change in labor.

Value of Marginal Product of Labor ( VMPL ): This refers to the value of additional units of output produced when an additional unit of labor is employed in the current labor force.

The formula to calculate the value of marginal product of labor is:

Where,

MPL is the marginal product of labor.

P is the price.

Expert Solution & Answer

Want to see the full answer?

Check out a sample textbook solution

See solution

Students have asked these similar questions

Problem 3 You are given the following demand for European luxury automobiles: Q=1,000 P-0.5.2/1.6 where P-Price of European luxury cars PA = Price of American luxury cars P, Price of Japanese luxury cars I= Annual income of car buyers Assume that each of the coefficients is statistically significant (i.e., that they passed the t-test). On the basis of the information given, answer the following questions 1. Comment on the degree of substitutability between European and American luxury cars and between European and Japanese luxury cars. Explain some possible reasons for the results in the equation. 2. Comment on the coefficient for the income variable. Is this result what you would expect? Explain. 3. Comment on the coefficient of the European car price variable. Is that what you would expect? Explain.

Problem 2: A manufacturer of computer workstations gathered average monthly sales figures from its 56 branch offices and dealerships across the country and estimated the following demand for its product: Q=+15,000-2.80P+150A+0.3P+0.35Pm+0.2Pc (5,234) (1.29) (175) (0.12) (0.17) (0.13) R²=0.68 SER 786 F=21.25 The variables and their assumed values are P = Price of basic model = 7,000 Q==Quantity A = Advertising expenditures (in thousands) = 52 P = Average price of a personal computer = 4,000 P. Average price of a minicomputer = 15,000 Pe Average price of a leading competitor's workstation = 8,000 1. Compute the elasticities for each variable. On this basis, discuss the relative impact that each variable has on the demand. What implications do these results have for the firm's marketing and pricing policies? 2. Conduct a t-test for the statistical significance of each variable. In each case, state whether a one-tail or two-tail test is required. What difference, if any, does it make to…

You are the manager of a large automobile dealership who wants to learn more about the effective- ness of various discounts offered to customers over the past 14 months. Following are the average negotiated prices for each month and the quantities sold of a basic model (adjusted for various options) over this period of time. 1. Graph this information on a scatter plot. Estimate the demand equation. What do the regression results indicate about the desirability of discounting the price? Explain. Month Price Quantity Jan. 12,500 15 Feb. 12,200 17 Mar. 11,900 16 Apr. 12,000 18 May 11,800 20 June 12,500 18 July 11,700 22 Aug. 12,100 15 Sept. 11,400 22 Oct. 11,400 25 Nov. 11,200 24 Dec. 11,000 30 Jan. 10,800 25 Feb. 10,000 28 2. What other factors besides price might be included in this equation? Do you foresee any difficulty in obtaining these additional data or incorporating them in the regression analysis?