Abstract
This chapter identifies some of the issues encountered in estimating demand models for corporate clients and then uses related results to suggest pricing strategies that might be more profitable.
We have benefited from James Alleman‘s editorial suggestions and Megan Westrum’s superb programming of simulations presented in this chapter.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
This discussion in this section is based on Tardiff (1999), pp. 97–114.
- 2.
Technically speaking, the rate rebalancing was profit neutral, that is, to the extent that increased calling also increased calling costs, such “cost onsets” would be included in determining (net) revenue neutrality.
- 3.
During this time period, the incumbents had not met the requirements that would enable them to provide retail intrastate-interLATA calls.
- 4.
In ordering a later reduction in toll and carrier access prices, the Commission used elasticities quite similar to those that the incumbent carriers had proposed (but the Commission declined to use) in the earlier proceeding. Tardiff (1999).
- 5.
In the econometric sense that unspecified demand effects (the error terms or residuals in a demand model) come into play in a company’s pricing decisions.
- 6.
Lerner-like relations are frequently used by economists analyzing competition and antitrust issues, such as in models that simulate the effects of mergers, allegedly anticompetitive behavior, and the like. See, for example, Froeb et al. (1998) pp. 141–148; Tardiff (2010), pp. 957–972; Zona (2011) pp. 473–494.
- 7.
In the second equation, the quotation marks around “inverse supply” denote the possibility that a company’s pricing decisions are not strictly profit maximizing with respect to contemporaneous demand.
- 8.
Specifically, since q appears in the “inverse supply” equation, b 2 ε 1 is a component of price.
- 9.
- 10.
In this case, there may still be endogeneity issues with respect to estimating certain cross-price coefficients, e.g., the prices of other firms with competing products.
- 11.
Such a pricing strategy could reflect the belief that competitors similarly pass through such price increases.
- 12.
With regard to the possibility of company-specific information providing more effective instruments, one possible avenue of further exploration are cases in which (1) a company’s marginal cost is relatively flat with respect to output (possibly locally within the range of likely observations) and (2) the company is setting prices with reference to marginal cost. In such situations, marginal cost measurements (to the extent they vary due to factors such as changing input prices) may serve as effective instruments and/or the typical endogeneity problem with price as an explanatory variable in the demand equation may be mitigated.
- 13.
Despite the empirical reality that corporate pricing decisions can depart from textbook profit maximization for many reasons, prominent economists nonetheless make legal and policy recommendations based on seemingly literal adherence to the optimizing model. For example, a recent article by Kaplow (2011) on the detection of price fixing observed the following.
“[O]ne would expect firms to have knowledge of their own prices and marginal cost and thus an estimate of price-cost margins. Firms think about which costs are fixed and variable and how joint costs are properly allocated. They know when production is at or near capacity and if marginal cost is rising sharply. When they price discriminate or grant a price concession to a large buyer, they presumably are aware of their costs and their reasons for charging different prices to different customers. If their prices vary across geographic markets, they again have reasons and information on which their reasoning is based. In deciding how much of a cost shift to pass on to consumers or how to respond to demand fluctuations, they are thinking about whether their marginal costs are constant over the relevant output range, what is the elasticity of the demand they face, and possible interactions with competitors. If they have excess capacity, they have thought about using more of it, which probably involves reducing price, and presumably have decided against it, again, for a reason” (pp. 404–405).
- 14.
Determining whether certain types of cost are included in a particular marginal cost estimates can be illustrated by costs associated driving a car and additional mile. There is gas, which is a short-run marginal cost. Oil might be considered a medium-term marginal cost. Wear and tear on the car engine transmission, etc., also happens with each mile. So it also has a marginal cost, but one that is only paid for far into the future, when a new car has to be purchased.
- 15.
Indeed, the cash expenditure may occur even months or years into the future when repairs resulting from operating production facilities at higher levels of output in the earlier period are made.
- 16.
That is, whether corporate decisions comport with the textbook description in footnote 13, above likely varies by industry and by company within particular industries.
- 17.
These abilities are analogous to cost advantages or disadvantages.
- 18.
Corporate strategic goals can be thought of as a component of marginal cost, but here they are simply noted as additional constraints on the optimization process.
- 19.
For example, the trend towards reducing the number of economists and demand analysts within large telecommunications companies that Professor Taylor noted in the 1990s has resulted in many fewer such specialists than there were when the industry was regulated. Similarly, we have analyzed demand and profitability for companies that have billions of dollars of annual sales. In many cases, minimal resources had been assigned to price setting and profit improvement before they asked us to analyze their business.
- 20.
Some notable examples that are exceptions are the airlines industry where some forms of fare optimization has been in use for years; and more recently the hotel and hospitality industry, where pricing systems have been used to price “excess” capacity.
- 21.
The slope is calculated from the following equation: \( b = \frac{{\overline{V} }}{{\overline{p} - \overline{c} }} , \) where \( \bar{V} , \) \( \bar{p}, \) \( \bar{c} \) are the sample averages for volume, price, and marginal cost, respectively. The estimate of the intercept is: \( \hat{A} = \frac{{\bar{V}\left( {2\bar{p} - \bar{c}} \right)}}{{\bar{p} - \bar{c}}} \).
- 22.
For each of the scenarios described below, observations are generated for price, quantity, oil, and steel using the following distributional assumptions:
oil—(mean = 100, standard deviation = 5)
steel—(mean = 50, standard deviation = 15)
ε—(mean = 0, standard deviation = 75).
- 23.
These results are based on the same historical pattern of exogenous variables and error terms used to generate the results shown in Fig. 10.8.
- 24.
This scenario differs from the early one in which the company first determined a quantity that was expected to maximize profits and then was able to adjust price in “real time” to sell just that volume. In this alternative example, assume that while prices can be adjusted for exogenous factors, the business is not able to respond to the random fluctuations in demand introduced by factors not explicitly included in the estimated model.
- 25.
In particular, the estimated coefficients from Table 10.2 and the known marginal cost curve are used to determine prices that equate expected marginal revenue with marginal cost, given observations for the exogenous variables.
- 26.
The data are the second 100 observations from a random draw of 1,000 sets of values for the prices of oil, steel, and the error term of the demand equation. The distributions are assumed to be independently normal with means and standard deviations of (100, 5), (50, 15), and (0, 10) for oil, steel, and the error term, respectively. For each of these sets, prices were generated by applying the demand equation in Table 10.2, without the error term. Quantities were generated using the structural parameters of the demand equation with the values for price, oil, and the error term. Results for the other nine sets (e.g., observations 1 through 100, etc.) are similar.
- 27.
The results are not sensitive to whether the trend in the price coefficient is assumed to be the same constant reduction each period, or whether there is variability in the period-to-period reduction in price sensitivity.
References
Froeb LM, Tardiff TJ, Werden GJ (1998) The Demsetz postulate and the welfare effects of mergers in differentiated products industries. In: McChesney FS (ed) Economic inputs, legal outputs: the role of economists in modern antitrust. Wiley, Chichester, pp 141–148
Greene WH (1993) Econometric analysis. Macmillan, New York
Kaplow L (2011) An economic approach to price fixing. Antitrust Law J 77(2):343–449
Kmenta J (1986) Elements of econometrics. 2nd edn Macmillan, New York
Tardiff TJ (1999) Effects of large price reductions on toll and carrier access demand in California. In: Loomis DG, Taylor LD (eds) The future of the telecommunications industry: forecasting and demand analysis. Kluwer, Boston, pp 97–114
Tardiff TJ (2010) Efficiency metrics for competition policy in network industries. J Competition Law Econ 6(4):957–972
Tardiff TJ, Taylor LD (1995) Declaration attached as exhibit B of joint petition of Pacific Bell and GTE California for modification of D.94-09-065, August 28, 1995
Taylor LD (1980) Telecommunications demand: a survey and critique. Ballinger, Cambridge
Taylor LD (1994) Telecommunications demand in theory and practice. Kluwer, Boston
Zona JD (2011) Structural approaches to estimating overcharges in price-fixing cases. Antitrust Law J 77(2):473–494
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer Science+Business Media New York
About this chapter
Cite this chapter
Levy, D.S., Tardiff, T.J. (2014). Pricing and Maximizing Profits Within Corporations. In: Alleman, J., Ní-Shúilleabháin, Á., Rappoport, P. (eds) Demand for Communications Services – Insights and Perspectives. The Economics of Information, Communication, and Entertainment. Springer, Boston, MA. https://doi.org/10.1007/978-1-4614-7993-2_10
Download citation
DOI: https://doi.org/10.1007/978-1-4614-7993-2_10
Published:
Publisher Name: Springer, Boston, MA
Print ISBN: 978-1-4614-7992-5
Online ISBN: 978-1-4614-7993-2
eBook Packages: Business and EconomicsEconomics and Finance (R0)