Traditional Managerial Economics

Managerial economics pays attention to the firm in ways that other branches of economics never has. No longer seen as the 'black-box' buffeted by market forces alone, the firm and how it allocates resources and makes decisions has since become the focal point of quantitative analysis that real-world companies have welcomed. An amalgam of micro-economics, operations research and management science, the subject's emphasis on the practicalities of running a business day-to-day using applied mathematics has proven a welcome change of tack for many line managers.

Keywords Disequilibrium; Managerial Economics; Microeconomics; Perfect Information; Theory of the Firm

Economics >Traditional Managerial Economics

Overview

Economists build models, think abstractly and, whenever possible, generalize. Business people build customer bases and products-lines, think concretely and, whenever possible, quantify. To be sure, traditional economic concepts like supply and demand, profit and utility maximization, 'imperfect' information and marginal analysis sum up the underlying dynamics of markets so neatly that they have become the way business is inherently thought about. But, as a rule, business people are too preoccupied with the hundreds of practical decisions that directly affect their 'bottom-lines' to ponder economic theory much. And to be fair, there is, without exception, an awful lot to think about: Marketing plans, pricing, the size and timing of production runs, quality control, worker productivity, capital budgeting, etc.

The behavior of the firm, along with that of the consumer and a given industry, collectively comprise the subject matter of Microeconomics. In relation to business decision-making, its most relevant conceit is 'the Theory of the Firm'; a twentieth century formulation. In earlier times, classical economists thought exclusively in terms of markets and individual rational actors. When the existence of the 'firm' was formally recognized, it was considered a means of eliminating third-party purchases at each stage in the production process; nothing more and nothing less. The elimination of third party purchases occurs only if one entity owns the inputs and outputs required to systematically turn raw materials or component parts into finished goods. By doing so, the firm avoids the costs of researching supplier prices, bargaining and drawing up legal contracts (collectively referred to as transaction costs), thus improving market efficiency.

Strictly speaking, a firm is a production 'function' that maximizes its profits by turning labor and capital (its 'inputs') into goods and services (it 'outputs') (Williamson, 1996). To succeed at this, a firm must align prices and output levels with market demand and so generates the greatest amount of sales revenue at the least cost. Decision-making at the firm level, essentially, is so largely determined by market forces emanating from beyond its organizational boundaries, the theory insists, that what goes on inside the firm is immaterial. All that really matters is the observable effects of its decisions. For a long time then, economists indifferent to the inner workings of the firm treated it as an impenetrable 'black-box' (Loabsy, 1967).

Whether a firm has one, several or many direct competitors its disposition towards the marketplace is affected. A firm with only one major competitor enjoys a higher price structure and can market a relatively undifferentiated product. By contrast, a firm with a number of competitors enjoys no such luxury and fares better when it markets a more differentiated product. Likewise, a firm can indeed maximize its profits by setting its price exactly at the point where one additional dollar of sales, its marginal revenue, incurs one additional dollar of expense, its marginal cost.

When it comes to the nuts and bolts of actually allocating resources in a real-world firm, though, matters turn murkier. For starts, 'pure' microeconomic theory, critics contend, is built around a set of unrealistic core assumptions: Firms are not always the rational actors bent on maximizing profits nor do they always enjoy 'perfect information.' In reality, firms can and do make the 'irrational' decision to maximize utility instead of profits; preferring to lower prices to gain market share from competitors. And most decidedly, all firms do not have the same amount or quality of information about current market conditions upon which to base decisions (Hitch & McKean, 1961). Worse still, the markets they compete in are almost always in disequilibrium, subject to external macroeconomic shocks, the sudden introduction of new technologies, the unexpected intervention of government regulators, and the like. Most unrealistically of all, a kind of tunnel vision is necessary to model market behavior; the only way to see a fundamental market principle in action is to zero in on it to the exclusion of any other, effectively reducing a multi-dimensional dynamic to a one dimensional event.

For business people charged with making specific decisions about very complicated issues, then, microeconomic theories has appreciable limits. It's not that they are wanting too much, but rather, too many variables are at play in the real world market to see their outline all that clearly. The devil, as the saying goes, is in the details. Economists in this respect deal almost exclusively in observable transaction data (past sales, revenues and prices, etc.) which by rights must be historical data. Business people, on the other hand, are budgeters and strategic planners: Their concerns revolve around bring about desirable future outcomes (Calfee & Rubin, 1993). Since the transactions they're most concerned with have not taken place yet, what's more, there is no such data to work with. What data they do have is culled from the numerical minutiae of running a business day-to-day: Entries in accounting ledgers, number of components on order, variable costs of production, inventory of finished goods on hand, etc. Even more to the point, perhaps, the microeconomic models most commonly used do not really lessen the uncertainty surrounding future market conditions enough to be all that helpful. Simply put, they are too static, too simplistic, and too inexact.

By far, the most effective remedy for uncertainty is precision; precision in rendering real world events, in thinking through problems and in planning solutions. Fortunately, a means of achieving such precision has gained a considerable following among economists of every stripe in the last thirty years. It's mathematics, and it has given the 'dismal science' a powerful new language in which to restate and refine its fundamental tenants in light of the complexity and uncertainty a firm faces in a competitive marketplace everyday. A precursor, the graphical representation of key microeconomic phenomena like supply and demand as curves on a plot, has been widely used for a much longer period of time. Merely visual embodiments of the algebraic formula y = a(x)-b, these graphs plot the relative values of cost or price per quantity produced or sold. More importantly, because these are linear equations, values for one set of variables can be calculated when the values and rate of change of the other set of variables is known. Older and even more basic, as any accountant will tell you, is the use of addition and subtraction along with multiplication and division to calculate a firm's revenue, costs and profits. Simple arithmetic in the form of fractions expressed as either percentages or ratios also yield many insightful measures of a company's financial and operational performance.

All of these, of course, were the product of an equation of one sort or another, which raised a very interesting question: What other forms of equations might prove equally as useful in economic analysis? The answer, it turns out, encompasses statistics and probability theory, regression analysis, linear programming, exponentiation, and calculus, and, slightly farther a field, decision matrixes and game theory. Each has proven itself a useful adjunct to more traditional forms of microeconomic analysis. So much so, in fact, they collectively have given rise to the burgeoning new discipline of managerial economics. Be it the employment of statistical techniques in general and regression analysis in particular to test new hypothetical economic models, of maximization and minimization formulae from linear programming to improve the efficiency of production lines, supply ordering and inventory management or of derivatives from calculus to accurately calculate the marginal revenue and marginal cost of a particular product, the marriage of mathematics and microeconomics is proving fruitful indeed.

Applications

The management of any firm requires constant attention be paid in equal measure to planning, operations and control. Mathematics, meanwhile, excels at describing and dealing with subtlety, complexity and disorder (Cooper, 1961). And, as we have seen, economics concerns itself with the production, distribution and consumption of goods and services. At the crossroads of management, mathematics and economics lies Management Economics, a field of inquiry with a decidedly practical bent. Perhaps too practical for purists who complain, with some justification, that too many of its tenants and applications are 'borrowed' wholesale from Operations Research and Management Science to consider the field a bona fide branch of economics. Truth be told, operations research has indeed been the source of mathematical techniques and formulae used in Managerial Economics. Just as management science has contributed empirically-based theories about the organizational behavior instrumental to attempts by managerial economists to systematize and, whenever possible, quantify intra-firm decision-making.

Debating lineages and pedigrees of ideas may well satisfy dogmatists' need for argumentation for its own sake. In the larger context, though, critics ignore the primary reason why economists practice the 'dismal' science in the first place: To advance our understanding of how wealth is created to better the human condition. And one constant in our progress towards this understanding is our ability as a species to synthesize new ideas by cross-fertilizing existing ones; if they happen to come from different fields of knowledge, so be it. Philosophic issues aside, drawing from other disciplines in this instance has given economists the tools to open up those 'black-boxes' to the light. It is now necessary to give our attention to the imminently more useful matter of what they've found.

Supply & Demand Functions

Managerial economics is all about functions in two senses of the word. A function in mathematics, first and foremost, is an equation or one of its variants such as an inequality or a deduction in symbolic logic. Then, too, of course, a business is often organized by functional area. Regardless, uncertainty about market-driven supply and demand cut right across the entire organization. Accurately projecting the demand curve for a given product not only figures prominently in ramping production up or down but also, critically, in setting prices which, in turn, directly affect the amount of revenue a firm earns. Robust statistical analyses of patterns of past market demand improve the likelihood that forecasts of future demand will have predictive value. Calculating the confidences levels of results, ensuring a data sample is random enough to be indicative of real world phenomena, applying regression analysis to isolate causes and effects, etc., has also proven very useful in analyzing the consumer research that has proven so instrumental to a firm bringing popular new products and services to market.

Another key determinant to sales, of course, is price for both new and existing products. Here, the venerable demand curve still has much to contribute. How straight the line drawn here determines the kind of function used in calculating consumption levels at different pricing points. A polynomial equation causes the line to be straight, an exponential equation causes it to curve or be more irregularly shaped. The latter's logarithmic function better captures the chaotic character of real world markets than the former's inherently simpler algebraic one. Crucially, however, both remain approximations of generalized behavior. You see, for all its precision, mathematics is captive of future uncertainty. Mathematics can, however, numerically express the likelihood of a particular outcome using probability theory.

Price directly affects the amount of a good or services supplied to the market place. To calculate the number of units a firm could and probably should market, a firm needs only to determine the price at which consumer demand is the highest. That figure, in turn, can then be plugged into a linear or exponential supply curve equation. The resulting value can then be used to estimate the total cost of production for a firm by plugging it into a cost curve equation. A cost curve equation is much more likely to graphically take the form of a curve than a straight line due to economies-of-scale. The greater your output, the more able you are to negotiate favorable prices from suppliers which equates to lower costs. Likewise, the greater your output, the more knowledgeable you are regarding the intricacies of the processes involved and thus are better able to improve efficiency.

Profits

The most basic and perhaps the most brutal equation in all business is total revenues minus total costs equals net profit. A firm intent on maximizing profit would be unwise to ignore this tried and true microeconomic axiom: Set your price at the point where a product's marginal costs exactly match its marginal revenues. To do this, one must first calculate the two operands; marginal revenue and marginal cost. Both are values that express the rate of change of one variable, price or cost, against another, units either demanded or supplied, a relationship algebra alone can never capture. Such a calculation can only be performed using the derivative of total revenue or total cost at a given quantity (an application of calculus).

Prices, what is more, do not vary uniformly from one item to the next. Some items are said to be elastic, meaning that the lower the price, the higher the number of items people will buy and conversely; the higher the prices, the fewer items people will buy. Other items, alternatively, are said to be inelastic, meaning that people will buy an item regardless of how much it costs. By this definition, then, basic staples are relatively inelastic; color television sets relatively elastic. The degree to which a product is elastic is determined via algebra by: Dividing the change in the quantity of demand by the change in its price. If this resulting figure is positive, demand is elastic. If it is negative, demand is inelastic. This very same figure can be plugged into the y=a(x) — b formula for a linear demand curve as the value for "a" is to aid in determining demand at different pricing points. Another similar calculation can be done to arrive at the price elasticity of supply.

Productivity

How efficient a firm is can also be readily quantified by dividing a firm's total output by the total number of a firm's workers over a given time frame. The figure yielded is the Average Product of Labor. Marginal analysis, meanwhile, can tell a firm if it needs to hire any additional workers. This is calculated by comparing the changes in total output produced by different numbers of workers. If the derivative of these two values is such that the average wage of one additional worker is greater than the increase in the value of the output he or she contributes, more hiring is unadvisable. It may well be unadvisable at some point for an all together different reason: Diminishing Marginal Returns. Diminishing marginal returns occur in a situation when capital investment is fixed and more workers are hired, and each additional new worker contributes progressively less and less to overall output.

Decision-Making with Uncertainty

Even as brief an overview of the mathematical underpinnings of Management Economics as this is, it would be negligent to avoid discussion of the progress made in decision theory and game theory. Decision theory allows managers to chart their way through an often elaborate set of options by representing each distinct decision point as a node and each option as a branch extending outward from that node. Its virtue lies in the way very complex problems can be broken down into their constituent parts (the very definition of analysis) and in the way it maps out all the possible consequences of a given decision, outlining those options that are still actionable and those that are not as one goes along in the decision process. In certain cases, decision trees can model the likelihood of actual real-world events. Best-guess probabilities as to the chances a particular node and branch will be followed are assigned, and the overall chances of a given outcome is then ascertained by multiplying the individual probabilities of each successive node leading back to the starting point.

Game theory, meanwhile, tackles the often thorny task of negotiating sales, purchases, cooperative ventures, labor contracts and the like. Viewing such undertakings as a game with players, strategies and actions, and payoffs gives a firm a clearer understanding of how to best represent its interests. Managers merely need to liken their present situation to a known game scenario. This could be The Prisoner's Dilemma, The Battle of the Sexes, The Deer Hunt, The Lock Out Game, The Chicken Game or some other exotically named scenario. A game can either be played sequentially or simultaneously and may have well defined or rather vague rules. Its object is always for each player to arrive at a dominant strategy; the course of action that results in the desired outcome. When all the players have decided upon which dominant strategy to follow, the game is said to be in equilibrium.

Viewpoints

The tools and techniques of managerial economics discussed here represent a fraction of those in use today. Though certainly never likely to eliminate the uncertainty inherent in the marketplace, managerial economics nonetheless brings greater accuracy to production planning and pricing, a more agile responsiveness to changing customer needs and wants in product introduction, and a much clearer understanding of the downstream implications of decisions made.

While there is some question as to whether managerial economics is truly economics, the fundamentals addressed mathematically stem directly from Microeconomics and are expressed in terms economists traditionally use.

Managerial economics is most decidedly a work-in-progress. Today's corporations, for example, are made up of any number of small business units that for all intents and purposes behave like firms in their own right (Egan, 1995). How the corporation per se evaluates its portfolio of products micreconomically given all the cross-selling and bundling going on today is a problem managerial economists have yet to solve. Undoubtedly, there will be other problems in the future, for managerial economics is here to stay.

Terms & Concepts

Calculus: The branch of mathematics specializing in the calculation of the precise rate of change (the derivative) and the total amount of that change (the integral).

Demand Curve: A graph representing the number of items consumed at each of a number of pricing points.

Disequilibrium: A period in any market when supply and demand are not evenly aligned.

Elasticity: The degree to which one variable changes when another related variable is increased or decreased. Both supply and demand can be said to be either elastic or inelastic. When they are elastic, supply and demand are sensitive to changes in costs or price; when they are inelastic, they are not.

Exponential Equation: An equation where one or more terms is raised to a certain power. Such a term is multiplied by itself as many times as the value of the number displayed above and to the right of the term.

Game Theory: The branch of mathematics concerned with deciding upon a strategy that maximizes an individual's gain once the strategies of all other participants is known.

Linear Programming: A mathematical technique used to find the maximum and minimum value of a function bounded by constraints.

Marginal Cost: The additional cost of one more unit of output.

Marginal Revenue: The added revenue earned from marketing one more unit of output.

Microeconomics: The field of economics that examines the behavior of buyers and sellers at the market level.

Polynomial Equation: An equation where two or more terms are added, subtracted, multiplied and/or divided; commonly used in Algebraic expressions.

Profit Maximization: The price at which a seller earns the most revenue at the least cost. It occurs when marginal costs exactly equal marginal revenues.

Supply Curve: A graph representing the number of items that will be produced at each of a number of pricing points.

Theory of the Firm: How a firm interacts with a market and what it wants to achieve is at the heart of this microeconomic construct.

Transaction Costs: The expense of researching different suppliers' prices, of successfully bargaining with one, and of drawing up and enforcing a purchase contract.

Utility Maximization: A term that specifically refers to a consumer trying to get the most value for the least price. When used in relation to firms, it refers to decision-making criteria based on considerations other than profit.

Bibliography

Baumol, W. (1961). What can economic theory contribute to managerial economics? American Economic Review, 51, 142-146. Retrieved July 28, 2007, from EBSCO Online Database Business Source Complete. http://search.ebscohost.com/login.aspx?direct=true&db=bth&AN=8766902&site=ehost-live

Calfee, J., & Rubin, P. (1993). Nontransactional data in managerial economics and marketing. Managerial & Decision Economics, 14, 163-173. Retrieved July 29, 2007, from EBSCO Online Database Business Source Complete. http://search.ebscohost.com/login.aspx?direct=true&db=bth&AN=5997220&site=ehost-live

Cooper, W. (1961). The current state of managerial economics. American Economic Review, 51, 131-141. Retrieved July 29, 2007, from EBSCO Online Database Business Source Complete. http://search.ebscohost.com/login.aspx?direct=true&db=bth&AN=8766899&site=ehost-live

e Silva, C., & Hewings, G. (2012). Locational and managerial decisions as interdependent choices in the headquarter-manufacturing plant relationship: a theoretical approach. Annals Of Regional Science, 48, 703-717. Retrieved November 15, 2013, from EBSCO Online Database Business Source Complete. http://search.ebscohost.com/login.aspx?direct=true&db=bth&AN=75163634&site=ehost-live

Egan, T. (1995). Updating managerial economics. Business Economics, 30, 51-56. Retrieved August 1, 2007, from EBSCO Online Database Business Source Complete. http://search.ebscohost.com/login.aspx?direct=true&db=bth&AN=9508070977&site=ehost-live

Hay, G. (1970). Production, price and inventory theory. American Economic Review, 60, 531-545. Retrieved July 28, 2007, from EBSCO Online Database Business Source Complete. http://search.ebscohost.com/login.aspx?direct=true&db=bth&AN=4507479&site=ehost-live

Lenox, M., Rockart, S., & Lewin, A. (2007). Interdependency, competition, and industry dynamics. Management Science, 53, 599-615. Retrieved July 28, 2007, from EBSCO Online Database Business Source Complete. http://search.ebscohost.com/login.aspx?direct=true&db=bth&AN=24697718&site=ehost-live

Loasby, B. (1967). Management economics and the theory of the firm. Journal of Industrial Economics, 15, 165-176. Retrieved July 28, 2007, from EBSCO Online Database Business Source Complete. http://search.ebscohost.com/login.aspx?direct=true&db=bth&AN=5709071&site=ehost-live

Mintz, O., & Currim, I. S. (2013). What drives managerial use of marketing and financial metrics and does metric use affect performance of marketing-mix activities?. Journal of Marketing, 77, 17-40. Retrieved November 15, 2013, from EBSCO Online Database Business Source Complete. http://search.ebscohost.com/login.aspx?direct=true&db=bth&AN=85725800&site=ehost-live

O'Brien, T. J. (2011). Managerial economics and operating beta. Managerial & Decision Economics, 32, 175-191. Retrieved November 15, 2013, from EBSCO Online Database Business Source Complete. http://search.ebscohost.com/login.aspx?direct=true&db=bth&AN=59469571&site=ehost-live

Williamson, O. (1996). Economics and organization: A primer. California Management Review, 38, 131-146. Retrieved July 29, 2007, from EBSCO Online Database Business Source Complete. http://search.ebscohost.com/login.aspx?direct=true&db=bth&AN=9603290330&site=ehost-live

Suggested Reading

Hitch, C., & McKean, R. (1961). What can managerial economics contribute to economic theory? American Economic Review, 51, 147-154. Retrieved July 28, 2007, from EBSCO Online Database Business Source Complete. http://search.ebscohost.com/login.aspx?direct=true&db=bth&AN=8766904&site=ehost-live

Liao, S. (1975). Shareholders-oriented managers versus entity-oriented managers. Financial Analysts Journal, 31, 62-71. Retrieved July 28, 2007, from EBSCO Online Database Business Source Complete. http://search.ebscohost.com/login.aspx?direct=true&db=bth&AN=6657217&site=ehost-live

Matutinovic, I. (2005). The microeconomic foundations of business cycles: From institutions to autocatalytic networks. Journal of Economic Issues, 39, 867-898. Retrieved July 28, 2007, from EBSCO Online Database Business Source Complete. http://search.ebscohost.com/login.aspx?direct=true&db=bth&AN=19379688&site=ehost-live

Shubik, M. (1970). A curmudgeon's guide to microeconomics. Journal of Economic Literature, 8, 405-434. Retrieved July 28, 2007, from EBSCO Online Database Business Source Complete. http://search.ebscohost.com/login.aspx?direct=true&db=bth&AN=5293274&site=ehost-live