Suppose a firm's total revenues depend on the amount produced ( \(q\) ) according to the function \\[ R=70 q-q^{2} \\] Total costs also depend on \(q\) \\[ C=q^{2}+30 q+100 \\] a. What level of output should the firm produce to maximize profits \((R-C) ?\) What will profits be? b. Show that the second-order conditions for a maximum are satisfied at the output level found in part (a). c. Does the solution calculated here obey the "marginal revenue equals marginal cost" rule? Explain.

Short Answer

Expert verified
Answer: a. The output level that maximizes profit is 10 units, and the profit at that level is $100. b. Yes, the second-order conditions for a maximum are satisfied at the output level of 10 units. c. Yes, the solution follows the "marginal revenue equals marginal cost" rule as both marginal revenue and marginal cost are equal at the output level of 10 units.

Step by step solution

01

Calculate Profit Function

First, we need to find the profit function, which is the difference between total revenues and total costs. The profit function is given by: \\[ P(q) = R(q) - C(q) = (70q - q^2) - (q^2 + 30q + 100) \\] Simplify the profit function: \\[ P(q) = 70q - q^2 - q^2 - 30q - 100 = -2q^2 + 40q - 100 \\]
02

Find the First Derivative of the Profit Function

In order to find the maximum profit, we should find the first derivative of the profit function with respect to \(q\) and then set it to zero. The first derivative is given by: \\[ \frac{dP}{dq} = \frac{d}{dq}(-2q^2 + 40q - 100) \\] Differentiate with respect to \(q\): \\[ \frac{dP}{dq} = -4q + 40 \\]
03

Find the Output Level that Maximizes Profit

Now we'll set the first derivative equal to zero and solve for \(q\): \\[ -4q + 40 = 0 \Rightarrow q = 10 \\] At \(q=10\), the profit function has a critical point. Next, let's verify that it's a maximum.
04

Find the Second Derivative of the Profit Function

To check if the second-order conditions are satisfied at \(q=10\), find the second derivative of the profit function: \\[ \frac{d^2 P}{dq^2} = \frac{d^2}{dq^2}(-2q^2 + 40q - 100) \\] Differentiate with respect to \(q\) again: \\[ \frac{d^2 P}{dq^2} = -4 \\]
05

Verify the Second-Order Condition

Since the second derivative of the profit function is negative, i.e., \(\frac{d^2 P}{dq^2} = -4 < 0\), we conclude that the critical point at \(q=10\) represents a maximum. Now, we'll find the maximum profit value by substituting \(q=10\) into the profit function: \\[ P(10) = -2(10)^2 + 40(10) - 100 = 100 \\] Hence, the firm should produce 10 units of output to maximize its profit at $100.
06

Check if the Marginal Revenue Equals Marginal Cost Rule is Satisfied

Now let's check whether the marginal revenue (\(MR\)) equals marginal cost (\(MC\)) at \(q=10\). First, find the first derivatives of the total revenue and total cost functions: \\[ \frac{dR}{dq} = \frac{d}{dq}(70q - q^2) = 70 - 2q \\ \frac{dC}{dq} = \frac{d}{dq}(q^2 + 30q + 100) = 2q + 30 \\ \\] Next, find the marginal revenue and marginal cost at \(q=10\): \\[ MR(10) = 70 - 2(10) = 50 \\ MC(10) = 2(10) + 30 = 50 \\] At \(q=10\), \(MR=MC=50\). Therefore, the solution follows the "marginal revenue equals marginal cost" rule. To summarize: a. The firm should produce \(10\) units of output to maximize its profit at $100. b. The second-order conditions for a maximum are satisfied at the output level found in part (a) since the second derivative of the profit function is negative. c. The "marginal revenue equals marginal cost" rule is satisfied at the calculated output level (\(q=10\)), as the marginal revenue and marginal cost are equal at this point.

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Most popular questions from this chapter

Because the expected value concept plays an important role in many economic theories, it may be useful to summarize a few more properties of this statistical measure. Throughout this problem, \(x\) is assumed to be a continuous random variable with PDF \(f(x)\). a. (Jensen's inequality) Suppose that \(g(x)\) is a concave function. Show that \(E[g(x)] \leq g[E(x)] .\) Hint: Construct the tangent to \(g(x)\) at the point \(E(x)\). This tangent will have the form \(c+d x \geq g(x)\) for all values of \(x\) and \(c+d E(x)=g[E(x)]\) where \(c\) and \(d\) are constants. b. Use the procedure from part (a) to show that if \(g(x)\) is a convex function then \(E[g(x)] \geq g[E(x)]\) c. Suppose \(x\) takes on only non-negative values-that is, \(0 \leq x \leq \infty\). Use integration by parts to show that \\[ E(x)=\int_{0}^{\infty}[1-F(x)] d x \\] where \(\left.F(x) \text { is the cumulative distribution function for } x \text { [that is, } F(x)=\int_{0}^{x} f(t) d t\right]\) d. (Markov's inequality) Show that if \(x\) takes on only positive values then the following inequality holds: \\[ \begin{array}{r} P(x \geq t) \leq \frac{E(x)}{t} \\ \text {Hint: } E(x)=\int_{0}^{\infty} x f(x) d x=\int_{0}^{t} x f(x) d x+\int_{t}^{\infty} x f(x) d x \end{array} \\] e. Consider the PDF \(f(x)=2 x^{-3}\) for \(x \geq 1\) 1\. Show that this is a proper PDF. 2\. Calculate \(F(x)\) for this PDF. 3\. Use the results of part (c) to calculate \(E(x)\) for this PDF. 4\. Show that Markov's inequality holds for this function. f. The concept of conditional expected value is useful in some economic problems. We denote the expected value of \(x\) conditional on the occurrence of some event, \(A,\) as \(E(x | A) .\) To compute this value we need to know the PDF for \(x\) given that \(A\) has occurred [denoted by \(f(x | A)]\). With this notation, \(E(x | A)=\int_{-\infty}^{+\infty} x f(x | A) d x\). Perhaps the easiest way to understand these relationships is with an example. Let $$f(x)=\frac{x^{2}}{3} \quad \text { for } \quad-1 \leq x \leq 2$$ 1\. Show that this is a proper PDF. 2\. Calculate \(E(x)\) 3\. Calculate the probability that \(-1 \leq x \leq 0\) 4\. Consider the event \(0 \leq x \leq 2,\) and call this event \(A\). What is \(f(x | A) ?\) 5\. Calculate \(E(x | A)\) 6\. Explain your results intuitively.

Here are a few useful relationships related to the covariance of two random variables, \(x_{1}\) and \(x_{2}\) a show that \(\operatorname{Cov}\left(x_{1}, x_{2}\right)=E\left(x_{1} x_{2}\right)-E\left(x_{1}\right) E\left(x_{2}\right) .\) An important implication of this is that if \(\operatorname{Cov}\left(x_{1}, x_{2}\right)=0, E\left(x_{1} x_{2}\right)\) \(=E\left(x_{1}\right) E\left(x_{2}\right) .\) That is, the expected value of a product of two random variables is the product of these variables' expected values. b. Show that \(\operatorname{Var}\left(a x_{1}+b x_{2}\right)=a^{2} \operatorname{Var}\left(x_{1}\right)+b^{2} \operatorname{Var}\left(x_{2}\right)+2 a b \operatorname{Cov}\left(x_{1}, x_{2}\right)\) c. In Problem \(2.15 \mathrm{d}\) we looked at the variance of \(X=k x_{1}+(1-k) x_{2} \quad 0 \leq k \leq 1 .\) Is the conclusion that this variance is minimized for \(k=0.5\) changed by considering cases where \(\operatorname{Cov}\left(x_{1}, x_{2}\right) \neq 0 ?\) d. The correlation coefficient between two random variables is defined as \\[ \operatorname{Corr}\left(x_{1}, x_{2}\right)=\frac{\operatorname{Cov}\left(x_{1}, x_{2}\right)}{\sqrt{\operatorname{Var}\left(x_{1}\right) \operatorname{Var}\left(x_{2}\right)}} \\] Explain why \(-1 \leq \operatorname{Corr}\left(x_{1}, x_{2}\right) \leq 1\) and provide some intuition for this result. e. Suppose that the random variable \(y\) is related to the random variable \(x\) by the linear equation \(y=\alpha+\beta x\). Show that \\[ \beta=\frac{\operatorname{Cov}(y, x)}{\operatorname{Var}(x)} \\] Here \(\beta\) is sometimes called the (theoretical) regression coefficient of \(y\) on \(x\). With actual data, the sample analog of this expression is the ordinary least squares (OLS) regression coefficient.

The height of a ball that is thrown straight up with a certain force is a function of the time ( \(t\) ) from which it is released given by \(f(t)=-0.5 g t^{2}+40 t\) (where \(g\) is a constant determined by gravity). a. How does the value of \(t\) at which the height of the ball is at a maximum depend on the parameter \(g\) ? b. Use your answer to part (a) to describe how maximum height changes as the parameter \(g\) changes. c. Use the envelope theorem to answer part (b) directly. d. On the Earth \(g=32\), but this value varies somewhat around the globe. If two locations had gravitational constants that differed by \(0.1,\) what would be the difference in the maximum height of a ball tossed in the two places?

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