As they plan for the future, business owners and managers make every effort to avoid undertaking operations or making strategic plans that will result in losses or negative profits. When managers foresee market conditions that will not generate enough total revenue to cover long-run total costs, they will plan to cease production in the long run and exit the industry by moving the firm’s resources to their best alternative use. Similarly, decisions to add new product lines or enter new geographic markets will not be undertaken unless managers are reasonably sure that long-run costs can be paid from revenues generated by entering those new markets. Because the long-run viability of a firm—as well as the number of product lines and geographic markets a firm chooses—depends crucially on the likelihood of covering long-run costs, managers need to understand the various economic forces that can affect long-run costs. We will now examine several important forces that affect the long-run cost structure of firms. While some of these factors cannot be directly controlled by managers, the ability to predict costs in the long run requires an understanding of all forces, internal and external, that affect a firm’s long-run costs. Managers who can best forecast future costs are likely to make the most profitable decisions.
Economies and Diseconomies of Scale
The shape of a firm’s long-run average cost curve (LAC) determines the range and strength of economies and diseconomies of scale. Economies of scale occur when long-run average cost falls as output increases. In Figure 9.10, economies of scale exist over the range of output from zero up to Q2 units of output. Diseconomies of scale occur when long-run average cost rises as output increases. As you can see in the figure, diseconomies of scale set in beyond Q2 units of output.
The strength of scale economies or diseconomies can been seen, respectively, as the reduction in unit cost over the range of scale economies or the increase in LAC above its minimum value LAC2 beyond Q2. Average cost falls when marginal cost is less than average cost. As you can see in the figure, over the output range from 0 to Q2, LAC is falling because LMC is less than LAC. Beyond Q2, LMC is greater than LAC, and LAC is rising.
Reasons for Scale Economies and Diseconomies
Before we begin discussing reasons for economies and diseconomies of scale, we need to remind you of two things that cannot be reasons for rising or falling unit costs as quantity increases along the LAC curve: changes in technology and changes in input prices. Recall that both technology and input prices are held constant when deriving expansion paths and long-run cost curves. Consequently, as a firm moves along its LAC curve to larger scales of operation, any economies and diseconomies of scale the firm experiences must be caused by factors other than changing technology or changing input prices. When technology or input prices do change, as we will show you later in this section, the entire LAC curve shifts upward or downward, perhaps even changing shape in ways that will alter the range and strength of existing scale economies and diseconomies.
Probably the most fundamental reason for economies of scale is that larger-scale firms have greater opportunities for specialization and division of labor. As an example, consider Precision Brakes, a small-scale automobile brake repair shop servicing only a few customers each day and employing just one mechanic. The single mechanic at Precision Brakes must perform every step in each brake repair: moving the car onto a hydraulic lift in a service bay, removing the wheels, removing the worn brake pads and shoes, installing the new parts, replacing the wheels, moving the car off the lift and out of the service bay, and perhaps even processing and collecting a payment from the customer. As the number of customers grows larger at Precision Brakes, the repair shop may wish to increase its scale of operation by hiring more mechanics and adding more service bays. At this larger scale of operation, some mechanics can specialize in lifting the car and removing worn out parts, while others can concentrate on installing the new parts and moving cars off the lifts and out of the service bays. And, a customer service manager would probably process each customer’s work order and collect payments. As you can see from this rather straightforward example, large-scale production affords the opportunity for dividing a production process into a number of specialized tasks. Division of labor allows workers to focus on single tasks, which increases worker productivity in each task and brings about very substantial reductions in unit costs.
A second cause of falling unit costs arises when a firm employs one or more quasi-fixed inputs. Quasi-fixed inputs must be used in fixed amounts in both the short run and long run. As output expands, quasi-fixed costs are spread over more units of output causing long-run average cost to fall. The larger the contribution of quasi-fixed costs to overall total costs, the stronger will be the downward pressure on LAC as output increases. For example, a natural gas pipeline company experiences particularly strong economies of scale because the quasi-fixed cost of its pipelines and compressor pumps accounts for a very large portion of the total costs of transporting natural gas through pipelines. In contrast, a trucking company can expect to experience only modest scale economies from spreading the quasi-fixed cost of tractor-trailer rigs over more transportation miles, because the variable fuel costs account for the largest portion of trucking costs.
A variety of technological factors constitute a third force contributing to economies of scale. First, when several different machines are required in a production process and each machine produces at a different rate of output, the operation may have to be quite sizable to permit proper meshing of equipment. Suppose only two types of machines are required: one that produces the product and one that packages it. If the first machine can produce 30,000 units per day and the second can package 45,000 units per day, output will have to be 90,000 units per day in order to fully utilize the capacity of each type of machine: three machines making the good and two machines packaging it. Failure to utilize the full capacity of each machine drives up unit production costs because the firm is paying for some amount of machine capacity it does not need or use.
Another technological factor creating scale economies concerns the costs of capital equipment: The expense of purchasing and installing larger machines is usually proportionately less than for smaller machines. For example, a printing press that can run 200,000 papers per day does not cost 10 times as much as one that can run 20,000 per day—nor does it require 10 times as much building space, 10 times as many people to operate it, and so forth. Again, expanding size or scale of operation tends to reduce unit costs of production.
A final technological matter might be the most important technological factor of all: As the scale of operation expands, there is usually a qualitative change in production process and type of capital equipment employed. For a simple example, consider ditch digging. The smallest scale of operation is one worker and one shovel. But as the scale expands, the firm does not simply continue to add workers and shovels. Beyond a certain point, shovels and most workers are replaced by a modern ditch-digging machine. Furthermore, expansion of scale also permits the introduction of various types of automation devices, all of which tend to reduce the unit cost of production.
You may wonder why the long-run average cost curve would ever rise. After all possible economies of scale have been realized, why doesn’t the LAC curve become horizontal, never turning up at all? The rising portion of LAC is generally attributed to limitations to efficient management and organization of the firm. As the scale of a plant expands beyond a certain point, top management must necessarily delegate responsibility and authority to lower- Echelon employees. Contact with the daily routine of operation tends to be lost, and efficiency of operation declines. Furthermore, managing any business entails controlling and coordinating a wide variety of activities: production, distribution, finance, marketing, and so on. To perform these functions efficiently, a manager must have accurate information, as well as efficient monitoring and control systems. Even though information technology continues to improve in dramatic ways, pushing higher the scale at which diseconomies set in, the cost of monitoring and controlling large-scale businesses eventually leads to rising unit costs.
As an organizational plan for avoiding diseconomies, large-scale businesses sometimes divide operations into two or more separate management divisions so that each of the smaller divisions can avoid some or all of the diseconomies of scale. Unfortunately, division managers frequently compete with each other for allocation of scarce corporate resources—such as workers, travel budget, capital outlays, office space, and R & D expenditures. The time and energy spent by division managers trying to influence corporate allocation of resources is costly for division managers, as well as for top-level corporate managers who must evaluate the competing claims of division chiefs for more resources. Overall corporate efficiency is sacrificed when lobbying by division managers results in a misallocation of resources among divisions. Scale diseconomies, then, remain a fact of life for very large-scale enterprises.
Constant Costs: Absence of Economies and Diseconomies of Scale
In some cases, firms may experience neither economies nor diseconomies of scale, and instead face constant costs. When a firm experiences constant costs in the long run, its LAC curve is flat and equal to its LMC curve at all output levels. Figure 9.11 illustrates a firm with constant costs of $20 per unit: Average and marginal costs are both equal to $20 for all output levels. As you can see by the flat LAC curve, firms facing constant costs experience neither economies nor diseconomies of scale.
Instances of truly constant costs at all output levels are not common in practice. However, businesses frequently treat their costs as if they are constant even when their costs actually follow the more typical U-shape pattern shown in Figure 9.9. The primary reason for assuming constant costs, when costs are in fact U-shaped, is to simplify cost (and profit) computations in spreadsheets. This simplifying assumption might not adversely affect managerial decision making if marginal and average costs are very nearly equal. However, serious decision errors can occur when LAC rises or falls by even modest amounts as quantity rises. In most instances in this textbook, we will assume a representative LAC, such as that illustrated earlier in Figure 9.9. Nonetheless, you should be familiar with this special case because many businesses treat their costs as constant.
Minimum Efficient Scale (MES)
In many situations, a relatively modest scale of operation may enable a firm to capture all available economies of scale, and diseconomies may not arise until output is very large. Figure 9.12 illustrates such a situation by flattening LAC between points m and d to create a range of output over which LAC is constant. Once a firm reaches the scale of operation at point m on LAC, it will achieve the lowest possible unit costs in the long run, LACmin. The minimum level of output (i.e., scale of operation) that achieves all available economies of scale is called minimum efficient scale (MES), which is output level QMES in Figure 9.12. After a firm reaches minimum efficient scale, it will enjoy the lowest possible unit costs for all output levels up to the point where diseconomies set in at QDIS in the figure.
Firms can face a variety of shapes of LAC curves, and the differences in shape can influence long-run managerial decision making. In businesses where economies of scale are negligible, diseconomies may soon become of paramount importance, as LAC turns up at a relatively small volume of output. PanelA of Figure 9.13 shows a long-run average cost curve for a firm of this type. Panel B illustrates a situation in which the range and strength of the available scale economies are both substantial. Firms that must have low unit costs to profitably enter or even just to survive in this market will need to operate at a large scale when they face the LAC in Panel B. In many real-world situations, Panel C typifies the longrun cost structure: MES is reached at a low level of production and then costs remain constant for a wide range of output until eventually diseconomies of scale take over.
Before leaving this discussion of scale economies, we wish to dispel a commonly held notion that all firms should plan to operate at minimum efficient scale in the long run. As you will see in Part IV of this book, the long run profitmaximizing output or scale of operation can occur in a region of falling, constant, or rising long-run average cost, depending on the shape of LAC and the intensity of MARKET COMPETITION. Decision makers should ignore average cost and focus instead on marginal cost when trying to reach the optimal level of any activity. For now, we will simply state that profit-maximizing firms do not always operate at minimum efficient scale in the long run. We will postpone a more detailed statement until Part IV, where we will examine profit-maximization in various market structures.
Economies of Scope in Multiproduct Firms
Many firms produce a number of different products. Typically, multiproduct firms employ some resources that contribute to the production of two or more goods or services: Citrus orchards produce both oranges and grapefruit, oil wells pump both crude oil and natural gas, automotive plants produce both cars and trucks, commercial banks provide a variety of financial services, and hospitals perform a wide array of surgical operations and medical procedures. Economies of scope are said to exist whenever it is less costly for a multiproduct firm to produce two or more products together than for separate single-product firms to produce identical amounts of each product. Economists believe the prevalence of scope economies may be the best explanation for why we observe so many multiproduct firms across most Industries and in most countries.
Multiproduct Cost Functions and Scope Economies
Thus far, our analysis of production and costs has focused exclusively on singleproduct firms. We are now going to examine long-run total cost when a firm produces two or more goods or services. Although we will limit our discussion here to just two goods, the analysis applies to any number of products.
A multiproduct total cost function is derived from a multiproduct expansion path. To construct a multiproduct expansion path for two goods X and Y, production engineers must work with a more complicated production function—one that gives technically efficient input combinations for various pairs of output quantities (X, Y ). For a given set of input prices, engineers can find the economically efficient input combination that will produce a particular output combination (X, Y) at the lowest total cost. In practice, production engineers use reasonably complicated computer algorithms to repeatedly search for and identify the efficient combinations of inputs for a range of output pairs the manager may wish to produce. This process, which you will never undertake as a manager, typically results in a spreadsheet or table of input and output values that can be rather easily used to construct a multiproduct total cost function: LTC(X, Y). A multiproduct total cost function— whether expressed as an equation or as a spreadsheet—gives the lowest total cost for a multiproduct firm to produce X units of one good and Y units of some other good.
While deriving multiproduct cost functions is something you will never actually do, the concept of multiproduct cost functions nonetheless proves quite useful in defining scope economies and explaining why multiproduct efficiencies arise. Economies of scope exist when
LTC(X, Y) < LTC(X, 0) + LTC(0, Y)
where LTC(X, 0) and LTC(0, Y) are the total costs when single-product firms specialize in production of X and Y, respectively. As you can see from this mathematical expression, a multiproduct firm experiencing scope economies can produce goods X and Y together at a lower total cost than two single-product firms, one firm specializing in good X and the other in good Y.
Consider Precision Brakes and Mufflers—formerly our single-product firm known as Precision Brakes—that now operates as a multiservice firm repairing brakes and replacing mufflers. Precision Brakes and Mufflers can perform 4 brake jobs (B) and replace 8 mufflers (M) a day for a total cost of $1,400:
LTC(B, M) = LTC(4, 8) + $1,400
A single-service firm specializing in muffler replacement can install 8 replacement mufflers daily at a total cost of $1,000: LTC(0, 8) = $1,000. A different singleservice firm specializing in brake repair can perform 4 brake jobs daily for a total cost of $600: LTC(4, 0) = $600. In this example, a multiproduct firm can perform 4 brake jobs and replace 8 mufflers at lower total cost than two separate firms producing the same level of outputs:
LTC(4, 8) < LTC(0, 8) + LTC(4, 0)
$1,400 < $1,000 + $600
$1,400 < $1,600
Thus, Precision Brakes and Mufflers experiences economies of scope for this combination and muffler repair services.
An important consequence of scope economies for managerial decision making concerns the incremental or marginal cost of adding new product or service lines:
Firms that already produce good X can add production of good Y at lower cost than a specialized, single-product firm can produce Y. You can quickly confirm the validity of this statement by subtracting LTC(X, 0) from both sides of the original mathematical expression for economies of scope:
LTC(X, Y) - LTC(X, 0) < LTC(0, Y)
The left side of this expression shows the marginal cost of adding Y units at a firm already producing good X, which, in the presence of scope economies, costs less than having a single-product firm produce Y units. To illustrate this point, suppose Precision Brakes, the single-product firm specializing in brake jobs, is performing 4 brake jobs daily. If Precision Brakes wishes to become a multiservice company by adding 8 muffler repairs daily, the marginal or incremental cost to do so is $800:
LTC(4, 8) - LTC(4, 0) = $1,400 - $600
Recall that a single-product firm specializing in muffler repair incurs a total cost of $1,000 to perform 8 muffler repairs: LTC(0, 8) = $1,000, which is more costly than letting a multiproduct firm add 8 muffler repairs a day to its service mix.
As you can see from this example, the existence of economies of scope confers a Cost Advantage to multiproduct firms compared to single-product producers of the same goods. In product markets where scope economies are strong, managers should expect that new firms entering a market are likely to be multiproduct firms, and existing single-product firms are likely to be targets for acquisition by multiproduct firms.
Reasons for Economies of Scope
Economists have identified two situations that give rise to economies of scope. In the first of these situations, economies of scope arise because multiple goods are produced together as joint products. Goods are joint products if employing resources to produce one good causes one or more other goods to be produced as by-products at little or no additional cost. Frequently, but not always, the joint products come in fixed proportions. One of the classic examples is that of beef carcasses and the leather products produced with hides that are by-products of beef production. Other examples of joint products include wool and mutton, chickens and fertilizer, lumber and saw dust, and crude oil and natural gas. Joint products always lead to economies of scope. However, occurrences of scope economies are much more common than cases of joint products.
A second cause for economies of scope, one more commonplace than joint products, arises when common or shared inputs contribute to the production of two or more goods or services. When a common input is purchased to make good X, as long as the common input is not completely used up in producing good X, then it is also available at little or no extra cost to make good Y. Economies of scope arise because the marginal cost of adding good Y by a firm already producing good X— and thus able to use common inputs at very low cost—will be less costly than producing good Y by a single-product firm incurring the full cost of using common inputs. In other words, the cost of the common inputs gets spread over multiple products or services, creating economies of scope.
The common or shared resources that lead to economies of scope may be the inputs used in the manufacture of the product, or in some cases they may involve only the administrative, marketing, and distribution resources of the firm. In our example of Precision Brakes and Mufflers, the hydraulic lift used to raise cars— once it has been purchased and installed for muffler repair—can be used at almost zero marginal cost to lift cars for brake repair. As you might expect, the larger the share of total cost attributable to common inputs, the greater will be the costsavings from economies of scope. We will now summarize this discussion of economies of scope with the following relations:
Relations When economies of scope exist: (1) The total cost of producing goods X and Y by a multiproduct firm is less than the sum of the costs for specialized, single-product firms to produce these goods: LTC(X, Y) < LTC(X, 0) + LTC(0, Y), and (2) Firms already producing good X can add production of good Y at lower cost than a single-product firm can produce Y: LTC(X, Y) - LTC(X, 0) < LTC(0, Y). Economies of scope arise when firms produce joint products or when firms employ common inputs in production.
Purchasing Economies of Scale
As we stressed previously in the discussion of economies of scale, changing input prices cannot be the cause of scale economies or diseconomies because, quite simply, input prices remain constant along any particular LAC curve. So what does happen to a firm’s long-run costs when input prices change? As it turns out, the answer depends on the cause of the input price change. In many instances, managers of individual firms have no control over input prices, as happens when input prices are set by the forces of demand and supply in resource markets. A decrease in the world price of crude oil, for example, causes a petroleum refiner’s long-run average cost curve to shift downward at every level of output of refined product. In other cases, managers as a group may influence input prices by expanding an entire industry’s production level, which, in turn, significantly increases the demand and prices for some inputs.
Sometimes, however, a purchasing manager for an individual firm may obtain lower input prices as the firm expands its production level. Purchasing economies of scale arise when large-scale purchasing of raw materials—or any other input, for that matter—enables large buyers to obtain lower input prices through quantity discounts. At the threshold level of output where a firm buys enough of an input to qualify for quantity discounting, the firm’s LAC curve shifts downward. Purchasing economies are common for advertising media, some raw materials, and energy supplies.
Figure 9.14 on page 354 illustrates how purchasing economies can affect a firm’s long-run average costs. In this example, the purchasing manager gets a quantity discount on one or more inputs once the firm’s output level reaches a threshold of QT units at point A on the original LAC curve. At QT units and beyond, the firm’s LAC will be lower at every output level, as indicated by LAC' in the figure. Sometimes input suppliers might offer progressively steeper discounts at several higher output levels. As you would expect, this creates multiple downward shifting points along the LAC curve.
Learning or Experience Economies
For many years economists and production engineers have known that certain industries tend to benefit from declining unit costs as the firms gain experience producing certain kinds of manufactured goods (airframes, ships, and computer chips) and even some services (heart surgery and dental procedures). Apparently, workers, managers, engineers, and even input suppliers in these industries “learn by doing” or “learn through experience.” As total cumulative output increases, learning or experience economies cause long-run average cost to fall at every output level.
Notice that learning economies differ substantially fromeconomies of scale.With scale economies, unit costs falls as a firm increases output, moving rightward and downward along its LAC curve.With learning or experience economies, the entire LAC curve shifts downward at every output as a firm’s accumulated output grows. The reasons for learning economies also differ fromthe reasons for scale economies.
The classic explanation for learning economies focuses on labor’s ability to learn how to accomplish tasks more efficiently by repeating them many times; that is, learning by doing. However, engineers and managers can also play important roles in making costs fall as cumulative output rises. As experience with production grows, design engineers usually discover ways to make it cheaper to manufacture a product by making changes in specifications for components and relaxing tolerances on fit and finish without reducing product quality.With experience, managers and production engineers will discover new ways to improve factory layout to speed the flow of materials through all stages of production and to reduce input usage and waste. Unfortunately, the gains from learning and experience eventually run out, and then the LAC curve no longer falls with growing cumulative output.
In Figure 9.15, learning by doing increases worker productivity in Panel A, which causes unit costs to fall at every output level in Panel B. In Panel A, average productivity of labor begins at a value of 10 units of output per worker at the time a firm starts producing the good. As output accumulates over time from 0 to 8,000 total units, worker productivity rises from 10 units per worker (point s) to its greatest level at 20 units of output per worker (point l) where no further productivity gains can be obtained through experience alone. Notice that the length of time it takes to accumulate 8,000 units in no way affects the amount by which AP rises. In Panel A, to keep things simple, we are showing only the effect of learning on labor productivity. (As labor learns better how to use machines, capital productivity also increases, further contributing to the downward shift of LAC in Panel B.)
As a strategic matter, the ability of early entrants in an industry to use learning economies to gain a cost advantage over potential new entrants will depend on how much time start-up firms take going from point s to point l. As we will explain later, faster learning is not necessarily better when entry deterrence is the manager’s primary goal. For now, you can ignore the speed at which a firm gains experience. Generally, it is difficult to predict where the new minimum efficient scale (MES) will lie once the Learning Process is completed at point l in the figure. In Panel B, we show MES increasing from 500 to 700 units, but MES could rise, fall, or stay the same.
As a manager you will almost certainly rely on production engineers to estimate and predict the impact of experience on LAC and MES. A manager’s responsibility is to use this information, which improves your forecasts of future costs, to make the most profitable decisions concerning pricing and output levels in the current period and to plan long-run entry and exit in future periods—topics we will cover in the next two parts of this book.
In this section, we examined a variety of forces affecting the firm’s long-run cost structure. While scale, scope, purchasing, and learning economies can all lead to lower total and average costs of supplying goods and services, we must warn you that managers should not increase production levels solely for the purpose of chasing any one of these cost economies. As you will learn in Part IV of this book, where we show you how to make profit-maximizing output and pricing decisions, the optimal positions for businesses don’t always require taking full advantage of any scale or scope economies available to the firm. Furthermore, it may not be profitable to expand production to the point where economies arise in purchasing inputs or at a rate that rapidly exploits potential productivity gains from learning by doing. However, as you can now understand, estimating and forecasting longrun cost of production will not be accurate if they overlook these important forces affecting the long-run structure of costs. All of these forces provide firms with an opportunity to reduce costs in the long run in ways that simply are not available in the short run when scale and scope are fixed.
I L L U S T R AT I O N 9 . 2
Declining Minimum Efficient Scale (MES) Changes the Shape of Semiconductor Manufacturing
Even those who know relatively little about computer technology have heard of Moore’s Law, which has correctly predicted since 1958 that the number of transistors placed on integrated circuits will double every two years. This exponential growth is expected to continue for another 10 to 15 years. Recently, transistor size has shrunk from130 nanometers (one nanometer = 1 billionth of a meter) to 90 nanometers, and Intel Corp. is on the verge of bringing online 65-nanometer production technology for its semiconductor chips. The implication of Moore’s Law for consumers has been, of course, a tremendous and rapid increase in raw computing power coupled with higher speed, and reduced power consumption.
Unfortunately for the many semiconductor manufacturers— companies like Intel, Samsung, Texas Instruments, Advanced Micro Devices, and Motorola, to name just a few—Moore’s Law causes multibillion dollar semiconductor fabrication plants to become outdated and virtually useless in as little as five years. When a $5 billion dollar fabrication plant gets amortized over a useful lifespan of only five years, the daily cost of the capital investment is about $3 million per day. The only profitable way to operate a semiconductor plant, then, is to produce and sell a very large number of chips in order to take advantage of the sizable scale economies available to the industry. As you know from our discussion of economies of scale, semiconductor manufacturers must push production quantities at least to the point of minimum efficient scale, or MES, to avoid operating at a cost disadvantage.
As technology has continually reduced the size of transistors, the long-run average cost curve has progressively shifted downward and to the right, as shown in the accompanying figure. While falling LAC is certainly desirable, chip manufacturers have also experienced rising MES with each cycle of shrinking. As you can see in the figure, MES increases from point a with 250-nanometer technology to point d with the now widespread 90-nanometer technology. Every chip plant—or “fab,” as they are called—must churn out ever larger quantities of chips in order to reach MES and remain financially viable semiconductor suppliers. Predictably, this expansion of output drives down chip prices and makes it increasingly difficult for fabs to earn a profit making computer chips.
Recently, a team of engineering consultants succeeded in changing the structure of long-run average cost for chipmakers by implementing the lean manufacturing philosophy and rules developed by Toyota Motor Corp. for making its cars. According to the consultants, applying the Toyota Production System (TPS) to chip manufacturing “lowered cycle time in the (plant) by 67 percent, . . . reduced costs by 12 percent, . . . increased the number of products produced by 50 percent, and increased production capacity by 10 percent, all without additional investment.” (p. 25) As a result of applying TPS to chip making, the long-run average cost curve is now lower at all quantities, and it has a range of constant costs beginning at a significantly lower production rate. As shown by LACTPS in the figure, LAC is lower and MES is smaller (MES falls from Q’ to QMES). The consultants predict the following effects on competition in chip manufacturing caused by reshaping long-run average costs to look like LACTPS:
The new economics of semiconductor manufacturing now make it possible to produce chips profitably in much smaller volumes. This effect may not be very important for the fabs that make huge numbers of high-performance chips, but then again, that segment will take up a declining share of the total market. This isn’t because demand for those chips will shrink. Rather, demand will grow even faster for products that require chips with rapid time-to-market and lower costs . . . (p. 28)
We agree with the technology geeks: The new shape of LAC will enhance competition by keeping more semiconductor manufacturers, both large and “small,” in the game.
I L L U S T R AT I O N 9 . 3
Scale and Scope Economies in the Real World
Government policymakers, academic economists, and industry analysts all wish to know which industries are subject to economies of scale and economies of scope. In this Illustration, we will briefly summarize some of the empirical estimates of scale and scope economies for two service industries: commercial banking and life insurance.
When state legislatures began allowing interstate banking during the 1980s, one of the most controversial outcomes of interstate banking was the widespread consolidation that took place through Mergers and Acquisitions of local banks by large out-of-state banks. According to Robert Goudreau and Larry Wall, one of the primary incentives for interstate expansion is a desire by banks to exploit economies of scale and scope.a To the extent that significant economies of scale exist in banking, large banks will have a cost advantage over small banks. If there are economies of scope in banking, then banks offering more banking services will have lower costs than banks providing a smaller number of services. Thomas Gilligan, Michael Smirlock, and William Marshall examined 714 commercial banks to determine the extent of economies of scale and scope in commercial banking.b They concluded that economies of scale in banking are exhausted at relatively low output levels. The long-run average cost curve (LAC) for commercial banks is shaped like LAC in Panel C of Figure 9.13, with minimum efficient scale (MES) occurring at a relatively small scale of operation. Based on these results, small banks do not necessarily suffer a cost disadvantage as they compete with large banks.
Economies of scope also appear to be present for banks producing the traditional set of bank products (i.e., various types of loans and deposits). Given their empirical evidence that economies of scale do not extend over a wide range of output, Gilligan, Smirlock, and Marshall argued that public policymakers should not encourage bank mergers on the basis of cost savings. They also pointed out that Government Regulation restricting the types of loans and deposits that a bank may offer can lead to higher costs, given their evidence of economies of scope in banking.
Life insurance companies offer three main types of services: life insurance policies, financial annuities, and accident and health (A & H) policies. Don Segal used data for approximately 120 insurance companies in the U.S. over the period 1995–1998 to estimate a multiproduct cost function for the three main lines of services offered by multiproduct insurance agencies. He notes “economies of scale and scope may affect managerial decisions regarding the scale and mix of output” (p. 169). According to his findings, insurance companies experience substantial scale economies, as expected, because insurance policies rely on the statistical law of large numbers to pool risks of policyholders. The larger the pool of policyholders, the less risky, and hence less costly, it will be to insure risk. He finds LAC is still falling—but much less sharply—for the largest scale firms, which indicates that MES has not been reached by the largest insurance companies in the U.S.
Unfortunately, as Segal points out, managers cannot assume a causal relation holds between firm size and unit costs—a common statistical shortcoming in most empirical studies of scale economies. The problem is this: Either (1) large size causes lower unit costs through scale economies or (2) those firms in the sample that are more efficiently managed and enjoy lower costs of operation will grow faster and end up larger in size than their less efficient rivals. In the second scenario, low costs are correlated with large size even in the absence of scale economies. So, managers of insurance companies—and everyone else for that matter— need to be cautious when interpreting statistical evidence of scale economies.
As for scope economies, the evidence more clearly points to economies of scope: “a joint production of all three lines of business by one firm would be cheaper than the overall cost of producing these products separately” (p. 184). Common inputs for supplying life insurance, annuities, and A&H policies include both the labor and capital inputs, as long as these inputs are not subject to “complete congestion” (i.e., completely exhausted or used up) in the production of any one service line. As you would expect, the actuaries, insurance agents, and managerial and clerical staff who work to supply life insurance policies can also work to provide annuities and A&H policies as well. Both physical capital—office space and equipment—and financial capital—monetary assets held in reserve to pay policy claims—can serve as common inputs for all three lines of insurance services. Segal’s multiproduct cost function predicts a significant cost advantage for large, multiservice insurance companies in the U.S.