Economic Analysis of the Demand for a Product/Service in Healthcare Sector

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CHAPTER

109

7THE DEMAND FOR HEALTHCARE PRODUCTS Learning Objectives

After reading this chapter, students will be able to

• calculate sales and revenue using simple models, • discuss the importance of demand in management decision making, • articulate why consumer demand is an important topic in healthcare, • apply demand theory to anticipate the effects of a policy change, • use standard terminology to describe the demand for healthcare

products, and • discuss the factors that influence demand.

Key Concepts

• The demand for healthcare products is complex. • When a product’s price rises, the quantity demanded usually falls. • The amount a consumer pays directly is called the out-of-pocket price

of that good or service. • Because of insurance, the total price and the out-of-pocket price can

differ markedly. • Multiple factors can shift demand: changes in consumer income,

insurance coverage, health status, prices of other goods and services, and tastes.

• Demand forecasts are essential to management.

7.1 Introduction

Demand is one of the central ideas of economics. It underpins many of the contributions of economics to public and private decision making. Analyses of demand tell us that human wants are seldom absolute. More often they

demand The amounts of a product that will be purchased at different prices when all other factors are held constant.

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are conditioned by questions: “Is it really worth it?” “Is its value greater than its cost?” These questions are central to understanding healthcare economics.

Demand forecasts are essential to management. Most managerial deci- sions are based on revenue projections. Revenue projections in turn depend on estimates of sales volume, given prices that managers set. A volume esti- mate is an application of demand theory. Understanding the relationship between price and quantity must be part of every manager’s tool kit. On an even more fundamental level, demand forecasts help managers decide whether to produce a certain product at all and how much to charge. For example, if you conclude that the direct costs of providing therapeutic mas- sage are $48 and that you will need to charge at least $75 to have an attrac- tive profit margin, will you have enough customers to make this service a sensible addition to your product line? Demand analyses are designed to answer such questions.

7.1.1 Rationing On an abstract level, we need to ration goods and services (including medical goods and services) somehow. Human wants are infinite or nearly so. Our capacity to satisfy those wants is finite. We must develop a system for deter- mining which wants will be satisfied and which will not. Market systems use prices to ration goods and services. A price system costs relatively little to operate, is usually self-correcting (e.g., prices fall when the quantity supplied exceeds the quantity demanded, which tends to restore balance), and allows individuals with different wants to make different choices. These advantages are important. The problem is that markets work by limiting the choices of some consumers. As a result, even if the market process is fair, the market outcome may seem unfair. Wealthy societies typically view exclusion of some consumers from valuable medical services, perhaps because of low income or perhaps because of previous catastrophic medical expenses, as unacceptable.

The implications of demand are not limited to market-oriented sys- tems. Demand theory predicts that if care is not rationed by price, it will be rationed by other means, such as waiting times, which are often inconvenient for consumers. In addition, careful analyses of consumer use of services have convinced most analysts that medical goods and services should not be free. If care were truly costless for consumers, they would use it until it offered them no additional value. Today this understanding is reflected in the public and private insurance plans of most nations.

Care cannot really be free. Someone must pay, somehow. Modern healthcare requires the services of highly skilled professionals, complex and elaborate equipment, and specialized supplies. Even the resources for which there is no charge represent a cost to someone.

market system A system that uses prices to ration goods and services.

quantity demanded The amount of a good or service that will be purchased at a specific price when all other factors are held constant.

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7.1.2 Indirect Payments and Insurance Because the burden of healthcare costs falls primarily on an unfortunate few, health insurance is common. Insurance creates another use for demand analy- ses. To design sensible insurance plans, we need to understand the public’s valuation of services. Insurance plans seek to identify benefits the public is willing to pay for. The public may pay directly (through out-of-pocket pay- ments) or indirectly. Indirect payments can take the form of health insur- ance premiums, taxes, wage reductions, or higher prices for other products. Understanding the public’s valuation is especially important in the healthcare sector because indirect payments are so common. When consumers pay directly, valuation is not important (except for making revenue forecasts). Right or wrong, a consumer who refuses to buy a $7.50 bottle of aspirin from an airport vendor because it is “too expensive” is making a clear state- ment about value. In contrast, a Medicare patient who thinks coronary artery bypass graft surgery is a good buy at a cost of $1,000 is not providing us with useful information. The surgery costs more than $30,000, but the patient and taxpayers pay most of the bill indirectly. Because consumers purchase so much medical care indirectly, with the assistance of public or private insur- ance, assessing whether the values of goods and services are as large as their costs is often difficult.

7.2 Why Demand for Healthcare Is Complex

The demand for medical care is more complex than the demand for many other goods for four reasons.

1. The price of care often depends on insurance coverage. Insurance has powerful effects on demand and makes analysis more complex.

2. Healthcare decisions are often challenging. The links between medical care and health outcomes are often difficult to ascertain at the population level (where the average impact of care is what matters) and stunningly complex at the individual level (where what happens to oneself is what matters). Forced to make hard choices, consumers may make bad choices.

3. This complexity contributes to consumers’ poor information about costs and benefits of care. Such “rational ignorance” is natural. Because most consumers will not have to make most healthcare choices, it makes no sense for them to be prepared to do so.

4. The net effect of complexity and consumer ignorance is that producers have significant influence on demand. Consumers naturally turn to

out-of-pocket payment Money a consumer directly pays for a good or service.

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healthcare professionals for advice. Unfortunately, because they are human, professionals’ choices are likely to reflect their values and incentives as well as those of their patients.

Demand is complicated by itself. To keep things simple, we will first examine the demand for medical goods and services in cases where insur- ance and professional advice play no role. The demand for over-the-counter pharmaceuticals, such as aspirin, is an example. We will then add insurance to the mix but keep professional advice out. Finally, we will add the role of professional advice.

7.3 Demand Without Insurance and Healthcare Professionals

In principle, a consumer’s decision to buy a particular good or service reflects a maddening array of considerations. For example, a consumer with a head- ache who is considering buying a bottle of aspirin must compare its benefits, as the consumer perceives them, to those of the other available choices. Those choices might include taking a nap, going for a walk, taking another nonprescription analgesic, or consulting a physician.

Economic models of demand radically simplify descriptions of con- sumer choices by stressing three key relationships that affect the amounts purchased:

1. the impact of changes in the price of a product, 2. the impact of changes in the prices of related products, and 3. the impact of changes in consumer incomes.

This simplification is valuable to firms and policymakers, who cannot change much besides prices and incomes. This focus can be misleading, however, if it obscures the potential impact of public information campaigns (including advertising).

7.3.1 Changes in Price The fundamental prediction of demand theory is that the quantity demanded will increase when the price of a good or service falls. The quantity demanded may increase because some consumers buy more of a product (as might be the case with analgesics) or because a larger proportion of the population chooses to buy a product (as might be the case with dental prophylaxis). Exhibit 7.1 illustrates this sort of relationship. On demand curve D

1 , a price

reduction from P 1 to P

2 increases the quantity demanded from Q

1 to Q

2 .

demand curve A graph that describes how much consumers are willing to buy at different prices.

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Exhibit 7.1 also illustrates a shift in demand. At each price, demand curve D

2 indicates a lower quantity demanded than demand curve D

1 . (Alterna-

tively, at each volume, willingness to pay will be smaller with D 2 .) This shift

might be due to a drop in income, a drop in the price of a substitute, an increase in the price of a complement, a change in demographics or con- sumer information, or other factors.

Demand curves can also be interpreted to mean that prices will have to be cut to increase the sales volume. Consumers who are not willing to pay what the product now costs may enter the market at a lower price, or current consumers may use more of the product at a lower price. Demand curves are important economic tools. Analysts use statistical techniques to estimate how much the quantity demanded will change if the price of the product or other factors change.

Substitution explains why demand curves generally slope down, that is, why consumption of a product usually falls if its price rises. Substitutes exist for most goods and services. When the price of a product is higher than that of its substitute, more people choose the substitute. Substitutes for aspirin include taking a nap, going for a walk, taking another nonprescrip- tion analgesic, and consulting a physician. If close substitutes are available, changes in a product’s price could lead to large changes in consumption. If none of the alternatives are close substitutes, changes in a product’s price will lead to smaller changes in consumption. Taking another nonprescription analgesic is a close substitute for taking aspirin, so we would anticipate that consumers would be sensitive to changes in the price of aspirin.

Substitution is not the only result of a change in price. When the price of a good or service falls, the consumer has more money to spend on

shift in demand A shift that occurs when a factor other than the price of the product itself (e.g., consumer incomes) changes.

substitute A product used instead of another product.

complement A product used in conjunction with another product.

Quantity

P ri ce

Q1 Q2

EXHIBIT 7.1 A Shift in Demand

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all goods and services. Most of the time this income effect reinforces the substitution effect, so we can predict with confidence that a price reduction will cause consumers to buy more of that good. In a few cases, things get murkier. A rise in the wage rate, for example, increases the income you would forgo by reducing your work week. At first blush, you might expect that a higher wage rate would reduce your demand for time off. At the same time, though, a higher wage rate increases your income, which may mean more money for travel and leisure activities, increasing the amount of time you want off. In these cases empirical work is necessary to predict the impact of a change in prices.

Two points about price sensitivity need to be made here. First, a gen- eral perception that use of most goods and services will fall if prices rise is a useful notion to keep tucked away. Second, managers need more precise guidance. How much will sales increase if I reduce prices by 10 percent? Will my total revenue rise or fall as a result? To answer these questions takes empirical analysis. Fleshing out general notions about price sensitivity with estimates is one of the tasks of economic analysis. We also need an agreed-on terminology to talk about how much the quantity demanded will change in response to a change in income, the price of the product, or the prices of other products. Economists describe these relationships in terms of elastici- ties, which we will talk more about in chapters 8 and 9.

7.3.2 Factors Other Than Price Changes in factors other than the price of a product shift the entire demand curve. Changes in beliefs about the productivity of a good or service, pref- erences, the prices of related goods and services, and income can shift the demand curve.

Consumers’ beliefs about the health effects of products are obviously central to discussions of demand. Few people want aspirin for its own sake. The demand for aspirin, as for most medical goods and services, depends on consumers’ expectations about its effects on their health. These expecta- tions have two dimensions. One dimension consists of consumers’ beliefs about their own health. If they believe they are healthy, they are unlikely to purchase goods and services to improve their health. The other dimension consists of their perception of how much a product will improve health. If I have a headache but do not believe that aspirin will relieve it, I will not be willing to buy aspirin. Health status and beliefs about the capacity of goods and services to improve health underpin demand.

Demand is a useful construct only if consumer preferences are stable enough to allow us to predict responses to price and income changes and if price and income changes are important determinants of consumption

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decisions. If on Tuesday 14 percent of the population thinks aspirin is some- thing to avoid (whether it works or not) and on Friday that percentage has risen to 24 percent, demand models will be of little use. We would need to track changes in attitude, not changes in price. Alternatively, if routine advertising campaigns could easily change consumers’ opinions about aspirin, tracking data on incomes and prices would be of little use. Preferences are usually stable enough for demand studies to be useful, so managers can rely on them in making pricing and marketing decisions.

Changes in income and wealth usually result in shifts in demand. In principle, an increase in income or wealth could shift the demand curve either out (more consumption at every price) or in (less consumption at every price). Overall spending on healthcare clearly increases with income, but spending on some products falls with income. For example, as income increases, retir- ees reduce their use of informal home care (Tsai 2015). For the most part, however, consumers with larger budgets buy more healthcare products.

Changes in the prices of related goods also shift demand curves. Related goods are substitutes (products used instead of the product in ques- tion) and complements (products used in conjunction with the product in question). A substitute need not be a perfect substitute; in some cases it is simply an alternative. For example, ibuprofen is a substitute for aspirin. A reduction in the price of a substitute usually shifts the demand curve in (reduced willingness to pay at every volume). If the price of ibuprofen fell, some consumers would be tempted to switch from aspirin to ibuprofen, and the demand for aspirin would shift in. Conversely, an increase in the price of a substitute usually shifts the demand curve out (increased willingness to pay at every volume). If the price of ibuprofen rose, some consumers would be tempted to switch from ibuprofen to aspirin, and the demand for aspirin would shift out.

7.4 Demand with Insurance

Insurance changes demand by reducing the price of covered goods and ser- vices. For example, a consumer whose dental insurance plan covers 80 per- cent of the cost of a routine examination will need to pay only $10 instead of the full $50. The volume of routine examinations will usually increase as a result of an increase in insurance coverage, primarily because a higher proportion of the covered population will seek this form of preventive care. The response will not typically be large, however. Most consumers will not change their decisions to seek care because prices have changed. But man- agers should recognize that some consumers will respond to price changes

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caused by insurance. (We will develop tools for describing responses to price changes and review the evidence on this score in the next chapter.)

Exhibit 7.2 depicts standard responses to increases in insurance. An increase in insurance (a higher share of the population covered or a higher share of the bill covered) rotates the demand curve from D

1 to D

2 . As a

result, the quantity demanded may rise, the price may rise, or both may occur. To predict the outcome more precisely we will need the tools of supply analysis that we will develop in chapter 10.

For provider organizations, an increase in insurance represents an opportunity to increase prices and margins. The rotation of D

2 has made it

steeper, meaning that demand has become less sensitive to price. As demand becomes less sensitive to price, profit-maximizing firms will seek higher margins. (Higher margins mean that the cost of production will represent a smaller share of what consumers pay for a product.) Higher prices and increased quantity mean that the expansion of unmanaged insurance will result in substantial increases in spending.

Demand theory implies that having patients pay a larger share of the bill (usually termed increased cost sharing) should reduce consumption of care. Does it? A classic study by the RAND Corporation tells us that it does (Manning et al. 1987). The RAND Health Insurance Experiment randomly assigned consumers to different health plans and then tracked their use of care (see exhibit 7.3). Its fee-for-service sites had coinsurance rates of 0 per- cent, 25 percent, 50 percent, and 95 percent. The health plans fully covered expenses above out-of-pocket maximums, which varied from 5 percent to 15 percent of income. Spending was substantially lower for consumers who

cost sharing The general term for direct payments to providers by insurance beneficiaries. (Deductibles, copayments, and coinsurance are forms of cost sharing.)

out-of-pocket maximum A cap on the amount a consumer has to pay out of pocket.

Quantity

P ri ce

EXHIBIT 7.2 The Impact of Insurance on

Demand

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shared in the cost of their care. Increasing the coinsurance rate (the share of the allowed fee that consumers pay) from 0 percent to 25 percent reduced total spending by nearly a fifth. This reduction had minimal effects on health.

Costs were lower because consumers had fewer contacts with the healthcare system. The experiment went on for years, and the suspicion that reducing use of care would increase spending later was not borne out. Because of the results of this study, virtually all insurance plans now incorpo- rate some form of cost sharing for care initiated by patients.

coinsurance A form of cost sharing in which a patient pays a share of the bill, not a set fee.

allowed fee The maximum amount an insurer will pay for a covered service.

Coinsurance Rate Spending

Any Use of Care

Hospital Admission

0% $750 87% 10%

25% $617 79% 8%

50% $573 77% 7%

95% $540 68% 8%

Source: Manning et al. (1987).

EXHIBIT 7.3 Effect of Coinsurance Rate

MinuteClinic

Mentioning a nationwide shortage of primary care providers, millions of patients newly insured

through the Affordable Care Act, and an aging population, Andrew Sussman, MD, president of CVS’s MinuteClinic division, said, “Minute- Clinic can help to meet that demand, collaborating with local provider groups, as part of a larger health care team” (Nesi 2014).

MinuteClinic started in 2000 and as of late 2017 had more than 1,000 locations (CVS 2017). Its clinics are staffed by nurse practitioners and physician assistants, rather than physicians. The clinics are open seven days a week and appointments are not needed. The nurse prac- titioners and physician assistants diagnose, treat, and write prescrip- tions for a variety of common illnesses. MinuteClinics show customers the prices of care (typically less than the prices in a physician’s office) and usually accept insurance. Most clinics are in CVS pharmacies, although an increasing number are in other sites and some have con- nections with local health systems.

Case 7.1

(continued)

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7.5 Demand with Advice from Providers

Consumers are often rationally ignorant about the healthcare system and the particular decisions they need to make. They are ignorant because medical decisions are complex, because they are unfamiliar with their options, because they lack the skills and information they need to compare their options, and because they lack time to make a considered judgment. This ignorance is rational because consumers do not know what choices they will have to make, because the cost of acquiring skills and information is high, and because the benefits of acquiring these skills and information are unknown.

Consumers routinely deal with situations in which they are ignorant. Few consumers really know whether their car needs a new constant velocity joint, whether their roof should be replaced or repaired, or whether they should sell their stock in Cerner Corporation. Of course, consumers know they are ignorant. They often seek an agent, someone who is knowledgeable and can offer advice that advances the consumer’s interests. Most people with medical problems choose a physician to be their agent.

agent A person who provides services and recommendations to clients (who are called principals).

In late December 2017, CVS Health announced an agreement to buy the health insurer Aetna. Some have suggested that this move could

reshape the healthcare industry by integrating insurance with a pro- vider organization (Abelson and Thomas 2017).

Discussion Questions • For what products is MinuteClinic a substitute?

• For what products is it a complement?

• How would continued expansion of MinuteClinics affect revenues of primary care practices?

• What attributes other than prices would make MinuteClinics attractive to patients?

• Is the supply of primary care physicians large enough to meet current levels of demand?

• Would you expect expansion of MinuteClinics to increase or decrease spending? Why?

• What are the implications of Aetna’s sale to CVS?

• A common criticism is that MinuteClinics locate in well-to-do areas. Is this a concern?

Case 7.1 (continued)

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Using an agent reduces, but does not eliminate, the problems associ- ated with ignorance. Agents sometimes take advantage of principals (in this case, the ignorant consumers they represent). Taking advantage can range from out-and-out fraud (e.g., lying to sell a worthless insurance policy) to simple shirking (e.g., failing to check the accuracy of ads for a property). If consumers can identify poor agent performance, fairly simple remedies fur- ther reduce the problems associated with ignorance. In many cases, an agent’s reputation is of paramount importance, so agents have a strong incentive to please principals. In other cases, simply delaying payment until a project has been successfully completed substantially reduces agency problems.

The most difficult problems arise when consumers have difficulty distinguishing bad outcomes from bad performance on the part of an agent. This problem is fairly common. Did your house take a long time to sell because the market weakened unexpectedly or because your agent recom- mended that you set the price too high? Was your baby born via cesarean section to preserve the baby’s health or to preserve your physician’s weekend plans? Most contracts with agents are designed to minimize these problems by aligning the interests of the principal and the agent. For example, real estate agents earn a share of a property’s sale price so that both the agent and the seller profit when the property is sold quickly at a high price. In similar fashion, earnings of mutual fund managers are commonly based on the total assets they manage, so managers and investors profit when the value of the mutual fund increases.

Agency models have several implications for our understanding of demand. First, what consumers demand may depend on incentives for pro- viders. Agency models suggest that changes in the amount paid to providers, the way providers are paid, or providers’ profits may change their recom- mendations for consumers. For example, consumers may respond to a lower price for generic drugs only because pharmacists have financial incentives to recommend them. Second, provider incentives will affect consumption of some goods and services more than others. Provider recommendations will not affect patients’ initial decisions to seek care. And where standards of care are clear and generally accepted, providers are less apt to change their recom- mendations when their incentives change. When a consensus about standards of care exists, providers who change their recommendations in response to financial incentives risk denial of payment, identification as a low-quality provider, or even malpractice suits. Third, patients with chronic illnesses are often knowledgeable about the therapies they prefer. When patients have firm preferences, agency is likely to have less effect on demand. In short, agency makes the demand for medical care more complex.

Agency is one of the most important factors that makes managed care necessary. (The other main factor is that insurance plans must protect

principal The organization or individual represented by an agent.

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consumers from virtually all the costs of some expensive procedures, reducing out-of-pocket costs to near zero.) If all the parties in a healthcare transaction had the same information, expenditures could be limited simply by changing consumer out-of-pocket payments. In many cases, though, provider incen- tives need to be aligned with consumer goals. (Of course, health plans also have an agency relationship with beneficiaries, and nothing guarantees that plans will be perfect agents.) Most of the features of managed care address the agency problem in one way or another. Bundled payments for services and capitation are designed to give physicians incentives to recommend no more care than is necessary. Primary care gatekeepers are supposed to monitor recommendations for specialty services (from which they derive no financial benefit).

7.6 Conclusion

Demand is one of the central ideas of economics, and managers need to understand the basics of demand. In most cases, consumption of a product falls when its price increases, and studies of healthcare products confirm this generalization. An understanding of this relationship between price and quantity is part of effective management. Without it, managers cannot pre- dict sales, revenues, or profits.

To make accurate forecasts, managers also must be aware of the effects of factors they do not control. Demand for their products will be higher when the price of complements is lower or the price of substitutes is higher. In most cases, demand will be higher in areas with higher incomes. We will explore how to make forecasts in more detail in chapter 8.

The demand for healthcare products is complex. Insurance and profes- sional advice have significant effects on demand. Insurance means that three prices exist: the out-of-pocket price the consumer pays, the price the insurer pays, and the price the provider receives. The quantity demanded will usu- ally fall when out-of-pocket prices rise but may not change when the other prices do. Because professional advice is important in consumers’ healthcare decisions, the incentives professionals face can influence consumption of some products. How and how much professionals are paid can affect their recommendations, and recognition of this effect has helped spur the shift to managed care. To change patterns of consumption, managers may need to change incentives for patients and providers.

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Exercises

7.1 Is the idea of demand useful in healthcare, given the important role of agents?

7.2 Should medical services be free? Justify your answer. 7.3 Why might a consumer be “rationally ignorant” about the proper

therapy for gallstones? 7.4 Why do demand curves slope down (i.e., sales volume usually rises

at lower prices)? 7.5 Why would consumers ever choose insurance plans with large

deductibles? 7.6 During the last five years, average daily occupancy at the Autumn

Acres nursing home has slid from 125 to 95 even though Autumn Acres has cut its daily rate from $125 to $115. Do these data suggest that occupancy would have been higher if Autumn Acres had raised its rates? What changes in nonprice demand factors might explain this change? (The supply, or the number of nursing home beds in the area, has not changed during this period.)

7.7 Your hospital is considering opening a satellite urgent care center about five miles from your main campus. You have been charged with gathering demographic information that might affect the demand for the center’s services. What data are likely to be relevant?

7.8 How would each of the following changes affect the demand curve for acupuncture? a. The price of an acupuncture session increases. b. A reduction in back problems occurs as a result of sessions about

stretching on a popular television show. c. Medicare reduces the copayment for acupuncture from $20 to

$10. d. The surgeon general issues a warning that back surgery is

ineffective. e. Medicare stops covering back surgery.

7.9 Your boss has asked you to describe how the demand for an over- the-counter sinus medication would change in the following situations. Assuming the price does not change, forecast whether the sales volume will go up, remain constant, or go down. a. The local population increases. b. A wet spring leads to a bumper crop of ragweed.

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c. Factory closings lead to a drop in the area’s average income. d. A competing product with a different formula is found to be

unsafe. e. A research study showing that the medication causes severe

dizziness is published. f. The price of another sinus medication drops.

7.10 A community has four residents. The table shows the number of dental visits each resident will have. Calculate the total quantity demanded at each price. Then graph the relationship between price and total quantity, with total quantity on the horizontal axis.

Price Abe’s

Quantity Beth’s

Quantity Cal’s

Quantity Don’s

Quantity

$40 0 0 0 1

$30 0 1 0 1

$20 0 1 0 2

$10 1 2 1 2

$0 1 2 1 3

7.11 A clinic focuses on three services: counseling for teens and young adults, smoking cessation, and counseling for young parents. An analyst has developed a forecast of the number of visits each group will make at different prices. Calculate the total quantity demanded at each price. Then graph the relationship between price and total quantity, putting total quantity on the horizontal axis.

Price Teen

Counseling Smoking Cessation

Parent Counseling

$80 10 0 0

$60 15 1 0

$40 20 2 0

$20 40 4 6

$0 50 6 8

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7.12 The price–quantity relationship has been estimated for a new prostate cancer blood test: Q = 4,000 − 20 × P. Use a spreadsheet to calculate the quantity demanded and total spending for prices ranging from $200 to $0, using $50 increments. For each $50 drop in price, calculate the change in revenue, the change in volume, and the additional revenue per unit. (Call the additional revenue per unit marginal revenue.)

7.13 A physical therapy clinic faces a demand equation of Q = 200 − 1.5 × P, where Q is sessions per month and P is the price per session. a. The clinic currently charges $80. What is its sales volume and

revenue at this price? b. If the clinic raised its price to $90, what would happen to volume

and revenue? c. If the clinic lowered its price to $70, what would happen to

volume and revenue? 7.14 Researchers have concluded that the demand for annual preventive

clinic visits by children with asthma equals 1 + 0.00004 × Y − 0.04 × P. In this equation Y represents family income and P represents price. a. Calculate how many visits a child with a family income of

$100,000 will make at prices of $200, $150, $100, $50, and $0. If you predict that the number of visits will be less than zero, convert the answer to zero.

b. Now repeat your calculations for a child with a family income of $35,000.

c. How do your predictions for the two children differ? d. Assume that the market price of a preventive visit is $100. Does

this system seem fair? What fairness criteria are you using? e. Would your answer change if the surgeon general recommended

that every child with asthma have at least one preventive visit each year?

References

Abelson, R., and K. Thomas. 2017. “CVS and Aetna Say Merger Will Improve Your Health Care. Can They Deliver?” New York Times. Published December 4. www.nytimes.com/2017/12/04/health/cvs-aetna-merger.html.

CVS. 2017. “MinuteClinic: History.” Accessed January 18, 2018. www.cvs.com/minute clinic/visit/about-us/history.

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Manning, W. G., A. Leibowitz, M. S. Marquis, J. P. Newhouse, N. Duan, and E. B. Keeler. 1987. “Health Insurance and the Demand for Medical Care: Evi- dence from a Randomized Experiment.” American Economic Review 77 (3): 251–77.

Nesi, T. 2014. “CVS Aiming to Open MinuteClinics in RI This Year.” WPRI.com. Published February 19. http://wpri.com/2014/02/19/cvs-aiming-to-open -minuteclinics-in-ri-this-year/.

Tsai, Y. 2015. “Social Security Income and the Utilization of Home Care: Evidence from the Social Security Notch.” Journal of Health Economics 43: 45–55.

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CHAPTER

125

8ELASTICITIES Learning Objectives

After reading this chapter, students will be able to

• describe economic relationships with elasticities, • use elasticity terms appropriately, • apply elasticities to make simple forecasts, and • calculate an elasticity.

Key Concepts

• Elasticities measure the association between the quantity demanded and related factors.

• Elasticities are ratios of percentage changes, so they are scale free. • Income, price, and cross-price elasticities are used most often. • Income elasticities are usually positive but small. • Price elasticities are usually negative. • Cross-price elasticities may be positive or negative. • Managers can use elasticities to forecast sales and revenues.

8.1 Introduction

Elasticities are valuable tools for managers. Armed only with basic marketing data and reasonable elasticity estimates, managers can make sales, revenue, and marginal revenue forecasts. In addition, elasticities are ideal for analyz- ing “what if” questions. What will happen to revenues if we raise prices by 2 percent? What will happen to our sales if the price of a substitute drops by 3 percent?

Elasticities reduce confusion in descriptions. For example, suppose the price of a 500-tablet bottle of generic ibuprofen rose from $7.50 to $8.00. Someone seeking to downplay the size of this increase (or someone whose focus was on the cost per tablet) would say that the price rose from 1.5 cents

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to 1.6 cents per tablet. Describing this change in percentage terms would eliminate any confusion about price per bottle or price per tablet, but a potential source of confusion remains.

To avoid confusion in calculating percentages, economists recommend being explicit about the values used to calculate percentage changes. For example, one might say that the price increase to $8.00 represents a 6.67 percent increase from the starting value of $7.50.

8.2 Elasticities

An elasticity measures the association between the quantity demanded and related factors. For example, Chen, Okunade, and Lubiani (2014) used statis- tical techniques to estimate that the income elasticity for adjusted inpatient days was 0.04. The base for this estimate is average income, so an income 1 percent above the average is associated with an average level of physician visits that is 0.04 percent above average. As we shall see, these apparently esoteric estimates can be valuable to managers.

First we need to learn a little more about elasticities. Economists rou- tinely calculate three demand elasticities:

1. income elasticities, which quantify the association between the quantity demanded and consumer income;

2. price elasticities, which quantify the association between the quantity demanded and the product’s price; and

3. cross-price elasticities, which quantify the association between the quantity demanded and the prices of a substitute or complement.

Elasticities are ratios of percentage changes. For example, the income elasticity of demand for visits would equal the ratio of the percentage change in visits (dQ/Q) associated with a given percentage change in income (dY/Y). (The mathematical terms dQ and dY identify small changes in consumption and income.) So, the formula for an income elasticity would be (dQ/Q)/ (dY/Y). The formula for a price elasticity would be (dQ/Q)/(dP/P), and the formula for cross-price elasticity would be (dQ/Q)/(dR/R). (A cross- price elasticity measures the response of demand to changes in the price of a substitute or complement, so R is the price of a related product. Substitutes have positive cross-price elasticities. Complements have negative cross-price elasticities.)

Now recall that Chen, Okunade, and Lubiani (2014) estimated that the income elasticity for physician visits is 0.04. This implies that 0.04 = (dQ/Q)/(dY/Y). Suppose we want to know how much higher than average

income elasticity The percentage change in the quantity demanded divided by the percentage change in income. For example, if visits are 0.04 percent higher for consumers with incomes that are 1 percent higher, the income elasticity is 0.0004/0.010, which equals 0.04.

substitute A product used instead of another product.

complement A product used in conjunction with another product.

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the number of visits per person would be in an area where the average income is 2 percent higher than the national average. Because we are considering a case in which dY/Y = 0.02, we multiply both sides of the equation by 0.02 and find that visits should be 0.0008 (0.08%) higher in an area with income 2 percent above the national average. From the perspective of a working manager, what matters is the conclusion that visits will be only slightly higher in the wealthier area.

8.3 Income Elasticities

Consumption of most healthcare products increases with income, but only slightly. As exhibit 8.1 shows, consumption of healthcare products appears to increase more slowly than income. As a result, healthcare spending will rep- resent a smaller proportion of income among high-income consumers than among low-income consumers.

8.4 Price Elasticities of Demand

The price elasticity of demand is even more useful, because prices depend on choices managers make. Estimates of the price elasticity of demand will guide pricing and contracting decisions, as chapter 9 explores in more detail.

Managers need to be careful in using the price elasticity of demand for three reasons. First, because the price elasticity of demand is almost always negative, we need a special vocabulary to describe the responsiveness of demand to price. For example, −3.00 is a smaller number than −1.00, but −3.00 implies that demand is more responsive to changes in prices (a 1 percent rise in prices results in a 3 percent drop in sales rather than a 1 percent drop in sales). Second, changes in prices affect revenues directly and indirectly, via changes in quantity. Managers need to keep this fact in mind when using the price elasticity of demand. Third, managers need to think about two different price elasticities of demand: the overall price elasticity of demand and the price elasticity of demand for the firm’s products.

price elasticity of demand The ratio of the percentage change in sales volume associated with a percentage change in a product’s price. For example, if prices rose by 2.5 percent and the quantity demanded fell by 7.5 percent, the price elasticity would be −0.075/0.025, which equals −3.00.

Source Variable Estimate

Chen, Okunade, and Lubiani (2014) Adjusted inpatient days 0.04

Newhouse and Phelps (1976) Hospital admissions 0.02 to 0.04

Newhouse and Phelps (1976) Physician visits 0.01 to 0.04

EXHIBIT 8.1 Selected Estimates of the Income Elasticity of Demand

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Economists usually speak of price elasticities of demand (but not other elasticities) as being elastic or inelastic. When a change in price results in a larger percentage change in the quantity demanded, the price elasticity of demand will be less than −1.00, and demand is said to be elastic. When price change results in a smaller percentage change in the quantity demanded, the price elasticity of demand will be between 0.00 and −1.00, and demand is said to be inelastic. For example, a price elasticity of −4.55 would indicate elastic demand. A price elasticity of −0.55 would indicate inelastic demand.

Inelastic demand does not mean that consumption will be unaffected by price changes. Suppose that, in forecasting the demand response to a 3.5 percent price cut, we use an elasticity of −0.20. Predicting that sales will rise by 0.7 percent (0.007 = −0.035 × −0.2), this elasticity implies that demand is inelastic but not unresponsive. Recall that a price elasticity of demand equals the ratio of the percentage change in quantity that is associated with a percentage change in price, or (dQ/Q)/(dP/P). Using this formula and our elasticity estimate gives us −0.20 = (dQ/Q)/(−0.03). After solving for the percentage change in quantity, we forecast that a 3 percent price cut will increase consumption by 0.006 (or 0.6%), which is equal to −0.20 × −0.03. Exhibit 8.2 shows that the demand for medical care is usually inelastic.

elastic A term used to describe demand when the quantity demanded changes by a larger percentage than the price. (This term is usually applied only to price elasticities of demand.)

inelastic A term used to describe demand when the quantity demanded changes by a smaller percentage than the price. (This term is usually applied only to price elasticities of demand.)

Source Variable Estimate

Ellis, Martins, and Zhu (2017) Total spending −0.44

Dunn (2016) Total spending −0.22

Ellis, Martins, and Zhu (2017) Inpatient −0.30

Ellis, Martins, and Zhu (2017) Outpatient −0.29

Ellis, Martins, and Zhu (2017) Emergency department −0.04

EXHIBIT 8.2 Selected

Estimates of the Price Elasticity

of Demand

The Curious Case of Daraprim

In August 2015 Turing Pharmaceuticals raised the price of Daraprim from $13.50 a tablet to $750,

an increase of 5,456 percent (Over and Silverman 2015). Daraprim is the only available treatment for toxoplasmosis, a rare infection that can become deadly for patients with weakened immune systems. This

Case 8.1

(continued)

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price increase means that an individual’s treat- ment could cost up to $634,000. Daraprim’s patent expired in 1953, and it can be compounded for less

than a dollar per tablet (Langreth 2015). Two contradictory trends are evident. Generic drug prices have

been declining in the United States since at least 2010, yet multiple generic drugs have risen in price (Ornstein and Thomas 2017). The price increases generate far more attention than the price decreases, yet the structure of the market has not changed.

In the United States, pharmaceutical prices (indeed most medical prices) are based on negotiations between private insurers and suppli- ers. The US market has two features that are uncommon in other coun- tries. First, pharmacy benefit managers often act as an intermediary between insurers and suppliers. Second, the federal government plays a limited role in negotiating prices. Although the Department of Vet- erans Affairs negotiates drug prices for its beneficiaries, private firms negotiate for Medicare.

Discussion Questions • Would you expect demand for Daraprim to be elastic or inelastic? Why?

• What change in the market would make demand for Daraprim more elastic? Less?

• What would the out-of-pocket cost for Daraprim be for a patient on Medicare? Medicaid?

• What would the price elasticity be after a patient exceeded the out- of-pocket maximum?

• Why did other companies not start making versions of Daraprim?

• Did Turing Pharmaceuticals violate any laws or regulations when it raised the price?

• Could a company have raised the price of a drug like this in Canada? France? Australia?

• Companies have also raised prices for other off-patent drugs. Can you explain why?

• Can you offer examples of large price increases for off-patent drugs?

• What should the United States do about cases like that of Daraprim?

• Should the federal government negotiate pharmaceutical prices? Why? Why not?

• Should someone else negotiate pharmaceutical prices? Who? Why? Why not?

Case 8.1 (continued)

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8.5 Other Elasticities

The cross-price elasticity of demand describes how the quantity demanded changes when the price of a related product changes. This might sound eso- teric, but it has practical implications. For example, how does use of hospital services change when the price of primary care changes? Alternatively, how does the demand for outpatient or emergency care change if drug copay- ments change? These questions are important for the design of health insur- ance plans. Unfortunately, the evidence is contradictory.

For example, among their other effects, insurance expansions reduce the out-of-pocket price for primary care. In some instances, this scenario has led to an increase in emergency department use, suggesting that primary care is a complement for emergency department care. In other cases, it has led to a reduction in emergency department use, suggesting that primary care is a substitute (Sommers and Simon 2017).

8.6 Using Elasticities

Elasticities are useful forecasting tools. With an estimate of the price elasticity of demand, a manager can quickly estimate the impact of a price cut on sales and revenues. As noted previously, managers need to use the correct elastic- ity. Most estimates of the overall price elasticity of demand fall between −0.10 and −0.40. For the market as a whole, the demand for healthcare products is typically inelastic. For individual firms, in contrast, demand is usually elastic. The reason is simple. Most healthcare products have few close substitutes, but the products of one healthcare organization represent close substitutes for the products of another.

The price elasticity of demand that individual firms face typically depends on the overall price elasticity and the firm’s market share. So, if the price elasticity of demand for hospital admissions is −0.17 and a hospital has a 12 percent share of the market, the hospital needs to anticipate that it faces a price elasticity of −0.17/0.12, or −1.42. This rule of thumb need not hold exactly, but good evidence indicates that individual firms confront elastic demand. Indeed, as we will show in chapter 9, profit-maximizing firms should set prices high enough that demand for their products is elastic.

Armed with a reasonable estimate of the price elasticity of demand, we will now predict the impact of a 5 percent price cut on volume. If the price elasticity faced by a physician firm were −2.80, a 5 percent price cut should increase the number of visits by 14 percent, which is the product of −0.05 and −2.80. (Prudent managers will recognize that their best guess about the price elasticity will not be exactly right and will repeat the calculations

cross-price elasticity of demand The ratio of the percentage change in sales volume associated with a percentage change in another product’s price. For example, if prices of the other product rose by 2.0 percent and the quantity demanded fell by 5.0 percent, the cross-price price elasticity would be −0.05/0.02, which equals −2.50.

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with other values. For example, if the price elasticity is really −1.40, volume will increase by 7 percent. If the price elasticity is really −4.20, volume will increase by 21 percent.)

How much will revenues change if we cut prices by 5 percent and the price elasticity is −2.80? Obviously, because prices are reduced, revenues will rise to a lesser extent than volume does. A rough, easily calculated estimate of the change in revenues is the percentage change in prices plus the percentage change in volume. Prices will fall by 5 percent and quantity will rise by 7 to 21 percent, so revenues should rise by approximately 2 to 16 percent. Our baseline estimate is that revenues will rise by 9 percent. If costs rise by less than this percentage, profits will rise.

Should Sodas Be Taxed?

One in five adults is obese in wealthy countries around the world. Unfortunately, in the United

States, the rate is about two in five (Organisation for Economic Co- operation and Development [OECD] 2017). Major causes appear to be sweet drinks and added sugars in other products. According to the Centers for Disease Control and Prevention (2017), frequent consump- tion of sweetened beverages is associated with obesity, heart disease, kidney diseases, cavities, and other diseases.

Oddly, despite the obesity epidemic, subsidies for crops that can be refined into sugar—corn, wheat, rice, sorghum, and others—con- tinue. The subsidies reduce the prices of products containing sugars. These products include sodas, sweetened teas, and other products.

A number of local governments have enacted taxes on sweetened beverages, but no taxes have passed at the state or federal level. (France and Mexico have passed national taxes.) Paarlberg, Mozaffar- ian, and Micha (2017) argue that a 17 percent tax on sweetened bever- ages would reduce consumption by 15 percent.

Discussion Questions • What price elasticity does the estimate by Paarlberg, Mozaffarian,

and Micha (2017) imply?

• Can you find another estimate of the price elasticity of demand for sweetened drinks?

• Is the demand for sweetened drinks elastic or inelastic?

Case 8.2

(continued)

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8.7 Conclusion

An elasticity is the percentage change in one variable that is associated with a 1 percent change in another variable. Elasticities are simple, valuable tools that managers can use to forecast sales and revenues. Elasticities allow managers to apply the results of sophisticated economic studies to their organizations.

Three elasticities are common: income elasticities, price elasticities, and cross-price elasticities. Income elasticities measure how much demand varies with income, price elasticities measure how much demand varies with the price of the product itself, and cross-price elasticities measure how much demand varies with the prices of complements and substitutes. Of these, price elasticity is the most important because it guides pricing and contract- ing decisions.

Virtually all price elasticities of demand for healthcare products are negative, reflecting that higher prices generally reduce the quantity demanded. Overall, demand is generally inelastic, meaning that a price increase will result in a smaller percentage reduction in sales. In most cases, though, the demand for an individual organization’s products will be elastic, meaning that a price increase will result in a larger percentage reduction in sales. This difference is based on ease of substitution. Few good substitutes are available for broadly defined healthcare products, so demand is inelastic. In contrast, the products of other healthcare providers are usually good sub- stitutes for the products of a particular provider, so demand is elastic. When making decisions, managers must consider that their organization’s products face elastic demands.

• If the price of sodas rose by 5 percent, how much would sales drop?

• What are substitutes for sweetened drinks?

• Can you find an estimate of the cross-price elasticity of demand for sweetened drinks?

• Is water a substitute or complement for soda?

• In light of your answer to the previous question, should the cross- price elasticity be positive?

• Do you favor a tax on sweetened drinks? Why? Why not?

• Do you favor a tax on added sugars? Why? Why not?

• How could a health system reduce sugar consumption? Should it try?

Case 8.2 (continued)

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Exercises

8.1 Why are elasticities useful for managers? 8.2 Why are price elasticities of demand called “elastic” or “inelastic”

when other elasticities are not? 8.3 Why is the demand for healthcare products usually inelastic? 8.4 Why is the demand for an individual firm’s healthcare products

usually elastic? 8.5 Per capita income in the county was $40,000, and physician visits

averaged 4.00 per person per year. Per capita income has risen to $42,000, and physician visits have risen to 4.02 per person per year. What is the percentage change in visits? What is the percentage change in income? What is the income elasticity of demand for visits?

8.6 Average visits per week equal 640 when the copayment is $40 and 360 when the copayment rises to $60. Calculate the percentage change in visits, percentage change in price, and price elasticity of demand.

8.7 Sales were 4,000 at a price of $200 but fell to 3,800 when the price was increased to $220. Calculate the percentage change in sales, the percentage change in price, and the price elasticity of demand.

8.8 Per capita income in the county was $45,000, and physician visits averaged 5.0 per person per year. Per capita income has risen to $49,500. The income elasticity of demand for visits is 0.4. By what percent will visits rise? What will the average number of visits be?

8.9 The price elasticity of demand is −1.2. Is demand elastic or inelastic?

8.10 The price elasticity of demand is −0.12. Is demand elastic or inelastic?

8.11 If the income elasticity of demand is 0.2, how would the volume of services change if income rose by 10 percent?

8.12 You are a manager for a regional health system. Using an estimate of the price elasticity of demand of −0.25, calculate how much ambulatory visits will change if you raise prices by 5 percent.

8.13 If the cross-price elasticity of clinic visits with respect to pharmaceutical prices is −0.18, how much will ambulatory visits change if pharmacy prices rise by 5 percent? Are pharmaceuticals substitutes for or complements to clinic visits?

8.14 If the cross-price elasticity of clinic visits with respect to emergency department prices is 0.21, how much will ambulatory visits change

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if emergency department prices rise by 5 percent? Are emergency department visits substitutes for or complements to clinic visits?

8.15 If the income elasticity of demand is 0.03, how much will ambulatory visits change if incomes rise by 4 percent?

8.16 A study estimates that the price elasticity of demand for Lipitor is −1.05, but the price elasticity of demand for statins as a whole is −0.13. a. Why is demand for Lipitor more elastic than for statins as a

whole? b. What would happen to revenues if the makers of Lipitor raised

prices by 10 percent? c. What would happen to industry revenues if all manufacturers

raised prices by 10 percent? d. Why are the answers so different? Does this difference make

sense? 8.17 The price elasticity of demand for the services of Kim Jones, MD, is

−4.0. The price elasticity of demand for physicians’ services overall is −0.1. a. Why is demand so much more elastic for the services of Dr. Jones

than for the services of physicians in general? b. If Dr. Jones reduced prices by 10 percent, how much would

volume and revenue change? c. Suppose that all the physicians in the area reduced prices by 10

percent. How much would the total number of visits and revenue change?

d. Why does it make sense that your answers to questions b and c are so different?

References

Centers for Disease Control and Prevention. 2017. “Get the Facts: Sugar-Sweetened Beverages and Consumption.” Updated April 7. www.cdc.gov/nutrition/data -statistics/sugar-sweetened-beverages-intake.html.

Chen, W., A. Okunade, and G. G. Lubiani. 2014. “Quality–Quantity Decomposition of Income Elasticity of U.S. Hospital Care Expenditure Using State-Level Panel Data.” Health Economics 23 (11): 1340–52.

Dunn, A. 2016. “Health Insurance and the Demand for Medical Care: Instrumental Variable Estimates Using Health Insurer Claims Data.” Journal of Health Economics 48: 74–88.

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Ellis, R. P., B. Martins, and W. Zhu. 2017. “Health Care Demand Elasticities by Type of Service.” Journal of Health Economics 55: 232–43.

Langreth, R. 2015. “Express Scripts Covers $1 Alternative to $750 Pill Daraprim.” Bloomberg. Published November 30. www.bloomberg.com/news/articles /2015-12-01/express-scripts-to-cover-1-alternative-to-750-pill-daraprim.

Newhouse, J. P., and C. E. Phelps. 1976. “New Estimates of Price and Income Elasticities of Medical Care Services.” In The Role of Health Insurance in the Health Services Sector, edited by R. Rosett, 261–320. New York: Neal Watson.

Organisation for Economic Co-operation and Development (OECD). 2017. “OECD Health Statistics 2017.” Accessed August 16, 2018. www.oecd.org /els/health-systems/health-statistics.htm.

Ornstein, C., and K. Thomas. 2017. “Generic Drug Prices Are Falling, but Are Consumers Benefiting?” New York Times. Published August 8. www.nytimes. com/2017/08/08/health/generic-drugs-prices-falling.html.

Over, M., and R. Silverman. 2015. “The 5000% Price Increase and the Economic Case for Pharma Price Regulation.” Global Health Policy Blog. Published Sep- tember 23. www.cgdev.org/blog/5000-price-increase-and-economic-case -pharma-price-regulation.

Paarlberg, R., D. Mozaffarian, and R. Micha. 2017. “Viewpoint: Can U.S. Local Soda Taxes Continue to Spread?” Food Policy 71: 1–7.

Sommers, B. D., and K. Simon. 2017. “Health Insurance and Emergency Depart- ment Use—A Complex Relationship.” New England Journal of Medicine 376 (18): 1708–11.

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CHAPTER

137

9FORECASTING Learning Objectives

After reading this chapter, students will be able to

• articulate the importance of a good sales forecast, • describe the attributes of a good sales forecast, • apply demand theory to forecasts, and • use simple forecasting tools appropriately.

Key Concepts

• Making and interpreting forecasts are important jobs for managers. • Forecasts are planning tools, not rigid goals. • Sales and revenue forecasts are applications of demand theory. • Changes in demand conditions usually change forecasts. • Good forecasts should be easy to understand, easy to modify, accurate,

transparent, and precise. • Forecasts combine history and judgment. • Assessing external factors is vital to forecasting.

9.1 Introduction

Making and interpreting forecasts are important jobs for managers. Sales forecasts are especially important because many decisions hinge on what the organization expects to sell. Pricing decisions, staffing decisions, product launch decisions, and other crucial decisions are based on the organization’s revenue and sales forecasts.

Inaccurate or misunderstood forecasts can hurt businesses. The orga- nization can hire too many workers or too few. It can set prices too high or too low. It can add too much equipment or too little. At best, these sorts of forecasting problems will cut into profits; at worst, they may drive an orga- nization out of business.

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The consequences of bad or misapplied forecasts are particularly seri- ous in healthcare. For example, underestimating the level of demand in the short term may result in stock shortages at a pharmacy or too few nurses on duty at a hospital. In both cases, the healthcare organization will suffer financially and, more important, put patients at risk. It will suffer because the costs of meeting unexpected demand are high and because the long-term consequences of failing to meet patients’ needs are significant. The best out- come in this case will be unhappy patients; the worst outcome will be that physicians stop referring patients to the organization.

Overestimating sales can also have serious long-term effects. A hospital may add too many beds because its census forecast was too high. This surplus will depress profits for some time because the facility will have hired staff and added equipment to meet its overestimated forecast, and the costs of hir- ing and paying new employees and buying new equipment will substantially exceed actual sales profits. In extreme cases, bad forecasts may drive a firm out of business. A facility that borrows heavily in anticipation of higher sales that do not materialize may be unable to repay those debts. Bankruptcy may be the only option.

Sales and revenue forecasts are applications of demand theory. The fac- tors that change sales and revenues also change demand. The most important influences on demand are the price of the product, rivals’ prices for the prod- uct, prices for complements and substitutes, and demographics. Recognizing these influences can simplify forecasting considerably because it focuses our attention on tracking what has changed.

9.2 What Is a Sales Forecast?

A sales forecast is a projection of the number of units (e.g., bed days, visits, doses) an organization expects to sell. The forecast must specify the time frame, marketing plan, and expected market conditions for which it is valid.

A forecast is a planning tool, not a rigid goal. Conditions may change. If they do, the organization’s plan needs to be reassessed. Good management usually involves responding effectively to changes in the environment, not forging ahead as though nothing has shifted. In addition, fixed sales goals create incentives to behave opportunistically (that is, for employees to try to meet their goals instead of the organization’s goals). For example, sales staff may harm the organization by making overblown claims of a product’s effectiveness to meet their sales goals, even though their actions will harm the company in the long run. Alternatively, sales managers may bid on unprofit- able managed care contracts just to meet goals.

Whenever possible, a sales forecast should estimate the number of units expected to be sold, not revenues. The number of units to be sold

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determines staffing, materials, working capital, and other needs. In addition, costs often vary unevenly with volume. A small reduction in volume may save an entire shift’s worth of wages (thereby avoiding considerable cost), or an increase in sales may incur a small cost increase if it requires no additional staff or equipment.

The dollar volume of sales can vary in response to factors that do not affect the resources needed to produce, market, or service the sales. Dis- counts and price increases are examples of such factors. Revenues can vary even though neither volume nor costs change. Finally, managers can easily forecast revenue given a volume forecast. In general, managers should build their revenue estimates on sales volume estimates.

Good forecasts have five attributes. They should be

1. easy to understand, 2. easy to modify, 3. accurate (i.e., they contain the most probable actual values), 4. transparent about how variable they are, and 5. precise (i.e., they give the analyst as little wiggle room as possible).

These attributes often conflict. Managers may need to underplay how impre- cise simple forecasts are because their audience is not prepared to consider variation. As Aven (2013) points out, many decision makers are more com- fortable working with a single, precise estimate, even though it may be inac- curate. Precision and accuracy always conflict because a more precise forecast (80 to 85 visits per day) will always be less accurate than a less precise forecast (70 to 95 visits per day). Offering decision makers several precise scenarios is usually a good compromise. For example, busy decision makers generally can use a forecast such as “Our baseline forecast is 82 visits per day for the next three months, our low forecast is 75 visits per day, and our high forecast is 89 visits per day.”

Forecasting Supply Use

More and more healthcare institutions seek to reduce costs while increasing the quality of care.

Accurate forecasts of the use of medical supplies represent an important element of this effort. Overordering supplies drives up costs, and under- ordering supplies also can drive up costs and compromise care.

Case 9.1

(continued)

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9.3 Forecasting

All forecasts combine history and judgment. History is the only real source of data. For example, sales can be forecasted only on the basis of data on past sales of a product, past sales of similar products, past sales by rivals, or past

The stakes can be high. Caldwell Memorial Hos- pital, a 110-bed hospital in North Carolina, saved $2.62 million in less than six months by consolidat-

ing and eliminating excess supplies (Belliveau 2016). The hospital used a Lean approach to inventory management, which involves streamlining and simplifying the inventory and ordering systems.

In addition, a number of hospitals have expanded their use of just- in-time inventory management (Green 2015). This method reduces, but does not eliminate, the need for forecasting accuracy. Some supplies are highly specialized and are used intermittently, so they must be ordered well in advance. The savings can be substantial. Mercy Hos- pital in Chicago was able to reduce its inventory by 50 percent using just-in-time inventory management (Green 2015).

Discussion Questions • What share of hospital costs do supplies represent?

• Why would overordering supplies drive up costs?

• Why would underordering supplies drive up costs?

• Can you offer examples of Lean inventory management? Does it work well?

• Can you offer examples of just-in-time inventory management? Does it work well?

• Can you offer examples of supplies that have to be available at all times?

• What are the main challenges to making accurate forecasts of supply use in hospitals?

• How would you forecast supply use in the emergency department? Why?

• How would you forecast supply use in hospital clinics? Why?

• Would you use judgment in making these forecasts? Why?

• Would you use statistical models in making these forecasts? Why?

• How are supply chain forecasts different for hospitals than for retail? For manufacturing?

Case 9.1 (continued)

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sales in other markets. History is an imperfect guide to the future, but it is an essential starting point.

Judgment is also essential. It provides a basis for deciding what data to use, how to use the data, and what statistical techniques, if any, to use. In many cases (e.g., introductions of new products or new competitive situ- ations), managers who have insufficient data will have to base their forecasts mainly on judgment.

As mentioned in section 9.2, a forecast must specify the time frame, marketing plan, and expected market conditions for which it is valid. Changes in any of these factors will change the forecast.

A forecast applies to a given period. Extrapolating to a longer or shorter period is risky; conditions may change. The time frame varies accord- ing to the forecast’s use. For example, a staffing plan may need a forecast for only the next few weeks. Additional staff can be hired over a longer time horizon. In contrast, budget plans usually need a forecast for the coming year. Organizations usually set their budgets a year in advance on the basis of projected sales. Strategic plans usually need a forecast for the next several years. Longer forecasts are generally less detailed and less reliable, but manag- ers know to take these factors into account when they develop and use them.

Forecasts should be as short term as possible. A forecast for next month’s sales will usually be more accurate than forecasts for the distant future, which are likely to be less accurate because important facts will have changed. Your competitors today are likely to be your competitors in a month. Your competitors in two years are likely to be different from your competitors today, so a forecast based on current market conditions will be poor.

Marketing plan changes will influence the forecast. A clinic that increases its advertising expects visits to increase. A forecast that does not consider this increase will usually be inaccurate. Increasing discounts to phar- macy benefits managers should result in increased sales for a pharmaceutical firm. Again, a forecast that does not account for additional discounts will usu- ally be deficient. Any major changes in an organization’s marketing efforts should change forecasts. If they do not, the organization should reassess the usefulness of its marketing initiatives.

Changes in market conditions also influence forecasts. For example, a major plant closing would probably reduce a local plastic surgeon’s volume. Plant employees who had intended to undergo plastic surgery may opt to delay this elective procedure, and prospective patients who work for similar plants may defer discretionary spending in fear that they too may lose their jobs. Alternatively, a hospital closure will probably cause a competing hospital to forecast more inpatient days. Historical data have limited value in project- ing such an effect if a similar closure has not occurred in the past. Approval of a new drug by the Food and Drug Administration should cause a phar- maceutical firm to forecast a decrease in sales for its competing product. This

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sort of change in market conditions is familiar, and the firm’s marketing staff will probably draw on experience to predict the loss.

Analysts routinely use three forecasting methods: percentage adjust- ment, moving averages, and seasonalized regression analysis. If the data are adequate and the market has not changed too much, seasonalized regression analysis is the preferred method. However, whether the data are adequate and whether the market has changed too much are judgment calls.

Percentage adjustment increases or decreases the last period’s sales volume by a percentage the analyst deems sensible. For example, if a hospi- tal had an average daily census of 100 the previous quarter, and an analyst expects the census to fall an average of 1 percent per quarter, a reasonable forecast would be a census of 99. Because of its simplicity, managers often use percentage adjustment; however, this simplicity is also a shortcoming. In principle, a manager could choose an arbitrary percentage adjustment. Without some requirement that percentage adjustments be well justified, this approach may not yield accurate forecasts. For example, a manager might justify a request for a new position based on a forecast that average daily census will increase by 5 percent, even though the average daily census had been falling for the last 14 quarters. In addition, percentage adjustment does not allow for seasonal effects. (Seasonal effects are systematic tenden- cies for particular days, weeks, months, or quarters to have above- or below- average volume.)

Demand theory can be used to add rigor to percentage adjustments. For example, if the price of a product has changed, an estimate of the per- centage change in sales can be calculated by multiplying the percentage change in price by the price elasticity of demand. So, if an organization has chosen to raise prices by 3 percent and faces a price elasticity of demand of −4, sales will drop by 12 percent. Similar calculations can be used if the price of a substitute, the price of a complement, or consumer income has changed.

The moving-average method uses the average of data from recent periods to forecast sales. This method works well for short-term forecasts, although it tends to hide emerging trends and seasonal effects. Exhibit 9.1 shows census data and a one-year moving average for a sample hospital.

Exhibit 9.1 also illustrates the calculation of a seasonalized regression format. Excel was used to estimate a regression model with a trend (a vari- able that increases in value as time passes) and three quarter indicators. The variable Q1 has a value of 1 if the data are from the first quarter; otherwise, its value is 0. Q2 equals 1 if the data are from the second quarter, and Q3 equals 1 if the data are from the third quarter. For technical reasons, the aver- age response in the fourth quarter is represented by the constant. A negative regression coefficient for trend indicates that the census is in a downward trend. The results also show that the typical third-quarter census is smaller

percentage adjustment An adjustment that increases or decreases the average of the past n periods. (The adjustment is essentially a best guess of what is expected to happen in the next year.)

moving average The unweighted mean of the previous n data points.

seasonalized regression analysis A least squares regression that includes variables to identify subperiods (e.g., weeks) that historically have had above- or below-trend sales.

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Quarter Census Moving Average First Second Third Trend

1 99 1 0 0 1

2 109 0 1 0 2

3 101 0 0 1 3

4 107 0 0 0 4

5 104 104.0 1 0 0 5

6 116 105.3 0 1 0 6

7 100 107.0 0 0 1 7

8 106 106.8 0 0 0 8

9 103 106.5 1 0 0 9

10 107 106.3 0 1 0 10

11 90 104.0 0 0 1 11

12 105 101.5 0 0 0 12

13 102 101.3 1 0 0 13

14 94 101.0 0 1 0 14

15 98 97.8 0 0 1 15

16 104 99.8 0 0 0 16

17 99 99.5 1 0 0 17

18 105 98.8 0 1 0 18

19 94 101.5 0 0 1 19

20 102 100.5 0 0 0 20

21 100 100.0 1 0 0 21

22 100.3

Seasonalized Regression Model

Coefficient t-statistic

Intercept 108.811 40.90 R2 = 0.55

First quarter −3.968 −1.53 F(4,20) = 4.98

Second quarter 0.732 0.27 p = 0.01

Third quarter −8.534 −3.16

Trend −0.334 −2.16

EXHIBIT 9.1 Census Data for a Sample Hospital

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than average because the coefficient for Q3 is large, negative, and statistically significant.

The forecast based on seasonalized regression analysis is calculated as follows: 108.811 + (−0.334 × 22) + 0.732. Here, 108.811 is the estimate of the constant, −0.334 is the estimate of the trend coefficient, 22 is the quarter to which the forecast applies, and 0.732 is the estimate of the Q2 coefficient. Therefore, the seasonalized forecast is 102.2, slightly higher than the forecast based on the moving average. Overall the seasonalized forecast is a little more accurate than the one-year moving average. The mean absolute deviation for the regression is 2.3 for periods 5 through 21, and the mean absolute deviation for the moving average is 4.0.

Exhibit 9.2 shows an overview of the forecasting process. The main message of this exhibit is that a forecast is one part of the overall product management process. In addition, the forecast will change as managers’

mean absolute deviation The average absolute difference between a forecast and the actual value. (It is absolute because it converts both 9 and −9 to 9. The Excel function =ABS( ) performs this conversion.)

Assess internal and external factors.

Develop an initial forecast.

Develop an initial marketing strategy and then modify the forecast and marketing strategy until they are consistent.

Monitor sales, internal factors, external factors, and the marketing strategy.

Modify the forecast and marketing strategies as needed.

EXHIBIT 9.2 An Overview of the Forecasting

Process

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assessments of relevant internal factors (e.g., cost and quality), external fac- tors (e.g., the competitive environment and payment levels), and the market- ing plan change.

A naïve forecast uses the value for the last period as the forecast for the next period—in other words, a 0 percent adjustment forecast. Exhibit 9.3 shows an example of a naïve forecast. A moving-average forecast uses the average of the last n values, where n is the number of preceding values used in the forecast. For example, the first entry in the Two-Period Moving-Average Forecast column in exhibit 9.3 equals (189 + 217) ÷ 2, or 203.

To compare forecasting techniques, analysts sometimes use the mean absolute deviation, which is the average of the forecast’s absolute deviations from the actual value. (When using the absolute deviation, it does not mat- ter if a value is higher or lower than the actual value; all the deviations are positive numbers.) For April through July, the naïve forecast in exhibit 9.3 has a mean absolute deviation of 12.0, and the two-period moving-average forecast has a mean absolute deviation of 12.1. From this perspective, the naïve forecast performs a little better.

These (and other) mechanistic forecasting methods do not allow man- agers to explore how changes in the environment are likely to affect sales. How would changes in insurance coverage change sales? Naïve forecasts and moving-average forecasts are little help in such situations.

9.4 What Matters?

Assessment of external factors (i.e., factors beyond the organization’s con- trol) is vital to forecasting. General economic conditions are a prime example. Expected inflation and interest rates are good indicators of the state of the

Month Sales Naïve

Forecast Two-Period Moving-

Average Forecast

February 189

March 217 189

April 211 217 203

May 239 211 214

June 234 239 225

July 243 234 236.5

EXHIBIT 9.3 Simple Forecasting Techniques: Naïve and Moving-Average Forecasts

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economy. Local market conditions, such as business rents and local wages, also play an important role.

Government actions also can have a major impact on healthcare firms. For example, changes in Medicare rates affect most healthcare firms. Alter- natively, regulations can have a significant effect on costs. Expansion of Med- icaid eligibility can have major effects on some hospitals and minor effects on others. Keep in mind that these sorts of changes will also affect most of your competitors, but forecasters would be ill advised to ignore changes in government policy.

The plans of key competitors must also be considered. Closure of a competing clinic or hospital can increase volume significantly and quickly. Introduction of a generic drug can have a dramatic effect on a pharmaceuti- cal manufacturer. Changes in competitors’ pricing policies can have a major impact on sales.

Technological change is always an important issue. If a rival gains a technological advantage, your sales can drop sharply. For example, if a rival introduces minimally invasive coronary artery bypass graft surgery, admis- sions to your cardiac unit will probably drop significantly until you adopt similar technology. In other cases, your own advances may affect sales of substitute products. For example, introduction of highly reliable magnetic resonance imaging may sharply reduce the demand for conventional colo- noscopy. Keep in mind, however, that if you do not introduce technologies that add value for your customers, someone else will. A decision not to introduce an attractive product because it will cannibalize sales is usually a mistake.

Finally, although markets usually change slowly, differences in general market characteristics (e.g., median income and percentage with insurance coverage) may be important in forecasting sales of a new product.

Assessment of internal factors (i.e., factors within an organization’s control) is also vital to forecasting. For example, existing production may limit sales, or production may have limited sales in the past. If so, changes in capacity or productivity need to be considered. Changes in the availability of resources and personnel can also have a powerful effect on sales. For many healthcare organizations, the entry or exit of a key physician can dramatically shape volume. In addition, changes in the size, support, composition, and organization of the sales staff can affect sales dramatically. For instance, a small drug firm may experience a large increase in sales if one of its products is marketed by a larger firm’s sales staff.

Failures or improvements in key systems can also have dramatic effects on sales. Breakdowns in a clinic’s phone or scheduling system may drive away potential customers. Fixing the phone system, in contrast, might be the most effective marketing campaign the clinic ever launched.

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Mistakes to Avoid When Making Forecasts

Business plans require a sales forecast. Scott Fishman, the CEO of Envisage, sees three common mistakes in business plans (Fishman 2015):

• They forecast “hockey stick” revenue growth.

• They forecast smoothly rising trend lines.

• They lack convincing evidence of market size.

A “hockey stick” forecast—a revenue graph shaped like a hockey stick—involves limited revenues initially followed by explosive growth. It is a potentially effective sales technique to use in discussions with executives and investors because it suggests that the business oppor- tunity might be extremely valuable.

In contrast, smoothly rising trend lines do not seem plausible from an economic standpoint. The number of customers and their consump- tion of any product is typically finite. Furthermore, any true blockbuster product will attract competition.

Every new product faces a complex environment: features and benefits, competitive environment, regulatory conditions, payment models, distribution, pricing, market positioning, and so forth. A genu- inely new product will have multiple unknowns in its market. If there are no unknowns, it is not really a new product. A convincing forecast demands market research, an honest recognition of what is not known, and a strategy for resolving some of the unknowns.

Discussion Questions • What is problematic about a “hockey stick” forecast?

• Can you find an example of a product that displayed “hockey stick” revenue growth?

• What is problematic about a forecast with a smoothly rising trend line?

• Can you find an example of a product that displayed smoothly rising revenue growth?

• From an economic point of view, what is implausible about smoothly rising trend lines?

Case 9.2

(continued)

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9.5 Conclusion

Making and interpreting forecasts are important tasks for healthcare manag- ers. Not only are most crucial decisions based on sales forecasts, but also the consequences of overestimating or underestimating demand can be cata- strophic. Overestimating demand can put the financial future of an organiza- tion at risk, whereas underestimating demand can compromise the care of patients and harm the organization’s reputation.

Analysts should apply demand theory to their sales forecasts to better recognize changes. Demand theory limits what analysts need to consider: the price of the product, the price of substitutes, and the price of complements. The key idea of demand theory is that the out-of-pocket price drives most consumer demand. The amount the consumer has to pay depends largely on the terms of the insurance contract. Is the product covered? What is the required copayment? Changes in the answers to these two questions can shift sales sharply. The same concerns affect the prices of substitutes. The most important substitutes are similar products offered by rivals, but other prod- ucts that meet some of the same needs should also be considered.

Demographic factors are important. Population size, income per capita, the age distribution of the population, the ethnic makeup of the population, and the insurance coverage of the population are some examples. Although vital, demographic factors tend to be stable in the short term. Demographics are much more important in long-range forecasts.

“Prediction is very difficult, especially if it’s about the future.” This saying, noted in chapter 4, reveals a core truth about forecasting: You often will be wrong. Knowing that, a shrewd manager will make decisions that can be modified as conditions change. The shrewd manager will also know which

• Can you find an example of a product that wildly underperformed early forecasts?

• Can you find an example of a product that wildly overperformed early forecasts?

• What external factors might cause below-forecast sales? Above- forecast sales?

• What internal factors might cause below-forecast revenues? Above- forecast revenues?

• What are examples of new products with uncertain prospects in healthcare?

Case 9.2 (continued)

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C h a p t e r 9 : F o r e c a s t i n g 149

data are likely to be the most problematic or most variable and will monitor those data carefully.

Management decisions require sales forecasts. Off-the-cuff forecasts often fail to consider key factors and can lead to risky decisions. Imper- fect forecasts can be used to make decisions as long as you recognize that your predictions will sometimes be wrong and you structure your decisions accordingly.

Exercises

9.1 The table lists visits for each of the four clinics operated by your system. You anticipate that volumes will increase by 4 percent next year. Forecast the number of visits for each clinic, and explain what assumptions underlie your forecasts. For example, are you sure that all the clinics can serve additional clients?

Period Clinic 1 Clinic 2 Clinic 3 Clinic 4 Total

This year 16,640 41,600 24,960 33,280 116,480

Next year ? ? ? ? 121,139

9.2 Your data for the clinics in exercise 9.1 suggest that clinic 2 is operating at capacity and is highly efficient. Its output is unlikely to increase. Furthermore, clinic 4 has unused capacity but is unlikely to attract additional patients. How would these facts change your answer to the question in exercise 9.1? Continue to assume that overall volume will rise to 121,139.

9.3 You estimate that the price elasticity of demand for clinic visits is −0.25. You anticipate that a major insurer will increase the copayment from $20 to $25. This insurer covers 40,000 of your patients, and those patients average 2.5 visits per year. What is your forecast of the change in the number of visits?

9.4 A major employer has just added health insurance coverage for its employees. Consequently, 5,000 of your patients will pay a $30 copayment rather than the list price of $100 per visit. These patients average 2.2 visits per year. You believe the price elasticity of demand is between −0.15 and −0.35. What is your forecast of the change in the number of visits?

9.5 The following table shows data on asthma-related visits. Is there evidence that these visits vary by quarter? Can you detect a trend?

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A powerful test would be to run a multiple regression in Excel. (To do this, you will need the free Analysis ToolPak for your version of Excel. Microsoft [2018] offers guidance on how to load and use the Analysis ToolPak.) To test for quarterly differences, create a variable called Q1 that equals 1 if the data are for the first quarter and 0 otherwise, a variable called Q2 that equals 1 if the data are for the second quarter and 0 otherwise, and a variable called Q4 that equals 1 if the data are for the fourth quarter and 0 otherwise. (Because you will accept the default, which is to have a constant term in your regression equation, do not include an indicator variable for Quarter 3.) Also create a variable called Trend that increases by 1 each quarter.

Year Q1 Q2 Q3 Q4

2014 1,513 1,060

2015 1,431 1,123 994 679

2016 1,485 886 1,256 975

2017 1,256 1,156 1,163 1,062

2018 1,200 1,072 1,563 531

2019 1,022 1,169

9.6 Your marketing department estimates that Medicare urology visits equal 5 − (1.0 × C) + (−6.5 × T

O ) + (5 × T

R ) + (0.01 × Y). Here,

C denotes the Medicare copayment (now $20), T O is waiting

time in your clinic (now 30 minutes), T R is waiting time in your

competitor’s clinic (now 40 minutes), and Y is per capita income (now $40,000). a. How many visits do you anticipate? b. Medicare’s allowed fee is $120. What revenue do you anticipate? c. What might change your forecast of visits and revenue?

9.7 Because of fluctuations in insurance coverage, the average price paid out of pocket (P) by patients of an urgent care center varied, as the table shows. The number of visits per month (Q) also varied, and an analyst believes the two are related. The analyst also thinks the data show a trend. Run a regression of Q on P and Period to test these hypotheses. Then use the estimated parameters a, b, and c and the values of Month and P to predict Q (number of visits). The prediction equation is Q = a + (b × Month) + (c × P).

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C h a p t e r 9 : F o r e c a s t i n g 151

Month 1 2 3 4 5 6 7 8 9 10 11 12

P $21 $18 $15 $24 $18 $21 $18 $15 $20 $19 $24 $20

Q 193 197 256 179 231 214 247 273 223 225 198 211

9.8 Use the data in exercise 9.7 to answer these questions: a. Calculate the naïve estimator, which is Q

t = Q

t − 1 .

b. Calculate the two-period moving-average forecast. c. Calculate the mean absolute deviation for the regression forecast,

the naïve forecast, and the two-period moving-average forecast. d. Which forecast seems to perform the best? Why?

9.9 Sales data are displayed in the table.

Month Sales Month Sales

February 224 January 260

March 217 February 284

April 211 March 280

May 239 April 271

June 234 May 302

July 243 June 286

August 238 July 297

September 243 August 301

October 251 September 309

November 259 October 314

December 270

a. Calculate the naïve estimator, which is Sales t = Sales

t − 1 .

b. Calculate the two-period and three-period moving averages. c. Calculate the mean absolute deviation for each of the forecasting

methods. 9.10 A pharmaceutical company produces a sinus medicine. Monthly sales

(in thousands of doses) for the past three years are shown in the table on the next page.

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a. Develop a regression model that allows for trend and seasonal components. Obtain the Excel output for this model.

b. Calculate a two-period moving-average forecast. c. Compare the mean absolute deviations for these approaches. d. Use one of these models to forecast sales for each month of

year 3.

References

Aven, T. 2013. “On How to Deal with Deep Uncertainties in a Risk Assessment and Management Context.” Risk Analysis 33 (12): 2082–91.

Belliveau, J. 2016. “How a Small Hospital Developed Lean Supply Chain Manage ment.” RevCycle Intelligence. Published September 6. https://revcycle intelligence .com /news/how-a-small-hospital-developed-lean-supply-chain-management.

Fishman, S. 2015. “3 Mistakes to Avoid When Forecasting the Market for Your Medical Device.” Med Device Online. Published September 21. www.med deviceonline.com/doc/mistakes-to-avoid-when-forecasting-the-market-for -your-medical-device-0001.

Green, C. 2015. “Hospitals Turn to Just-in-Time Buying to Control Supply Chain Costs.” Healthcare Finance. Published May 6. www.healthcarefinancenews .com/news/hospitals-turn-just-time-buying-control-supply-chain-costs.

Microsoft. 2018. “Use the Analysis ToolPak to Perform Complex Data Analysis.” Accessed September 18. https://support.office.com/en-us/article/use-the -analysis-toolpak-to-perform-complex-data-analysis-6c67ccf0-f4a9-487c-8dec -bdb5a2cefab6.

Jan Feb Mar Apr May June July Aug Sept Oct Nov Dec

6,788 8,020 1,848 410 586 2,260 2,232 8,018 9,384 6,916 5,698 6,940

9,136 7,420 3,350 1,998 1,972 3,572 4,506 10,474 13,358 8,232 8,218 10,248

9,628 7,826 3,528 2,126 2,070 3,762 4,754 11,010 14,040 8,646 8,634 10,782

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CHAPTER

153

10SUPPLY AND DEMAND ANALYSIS Learning Objectives

After reading this chapter, students will be able to

• define demand and supply curves, • interpret demand and supply curves, • use demand and supply analysis to make simple forecasts, and • identify factors that shift demand and supply curves.

Key Concepts

• A supply curve describes how much producers are willing to sell at different prices.

• A demand curve describes how much consumers are willing to buy at different prices.

• At the equilibrium price, producers want to sell the amount that consumers want to buy.

• Markets generally move toward equilibrium outcomes. • Expansion of insurance usually makes the equilibrium price and

quantity rise. • Regulation and technology influence the supply of medical goods and

services. • Demand and supply curves shift when a factor other than the product

price changes.

10.1 Introduction

Markets are in a constant state of flux. Prices rise and fall. Volumes rise and fall. New products succeed at first and then fall by the wayside. Familiar products falter and revive. Economics teaches us that, underneath the seem- ingly random fluctuations of healthcare markets, systematic patterns can be detected. Understanding these patterns requires an understanding of supply

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and demand. Even though healthcare managers need to focus on the details of day-to-day operations, they also need an appreciation of the overview that supply and demand analysis can give them.

The basics of supply and demand illustrate the usefulness of econom- ics. Even with little data, managers can forecast the effects of changes in policy or demographics using a supply and demand analysis. For example, the impact of added taxes on hospitals’ prices, the impact of increased insurance coverage on the output mix of physicians, and the impact of higher electricity prices on pharmacies’ prices can be analyzed. Supply and demand analysis is a powerful tool that managers can use to make broad strategic decisions or detailed pricing decisions.

10.1.1 Supply Curves Exhibit 10.1 is a basic supply and demand diagram. The vertical axis shows the price of the good or service. In this simple case, the price sellers receive is the same price buyers pay. (Insurance and taxes complicate matters, because the price the buyer pays is different from the price the seller receives.) The horizontal axis shows the quantity customers bought and producers sold.

The supply curve (labeled S) describes how much producers are will- ing to sell at different prices. From another perspective, it describes what the price must be to induce producers to be willing to sell different quantities. The supply curve in exhibit 10.1 slopes up, as do most supply curves. This upward slope means that, when the price is higher, producers are willing to sell more of a good or service or more producers are willing to sell a good or service. When the price is higher, producers are more willing to add workers,

supply curve A graph that describes how much producers are willing to sell at different prices.

120

$50

$100

$150

$200

$250

$300

$350

P ri ce

Quantity

10080200 40 60

EXHIBIT 10.1 Equilibrium

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equipment, and other resources to sell more. In addition, higher prices allow firms to enter a market they could not enter at lower prices. When prices are low, only the most efficient firms can profitably participate in a market. When prices are higher, firms with higher costs can also earn acceptable profits.

10.1.2 Demand Curves The demand curve (labeled D) describes how much consumers are willing to buy at different prices. From another perspective, it describes how much the marginal consumer (the one who would not make a purchase at a higher price) is willing to pay at different levels of output. The demand curve in exhibit 10.1 slopes down, meaning that, for producers to sell more of a product, its price must be cut. Such a sales increase might be the result of an increase in the share of the population that buys a good or service, an increase in consumption per purchaser, or some mix of the two.

10.1.3 Equilibrium The demand and supply curves intersect at the equilibrium price and quan- tity. At the equilibrium price, the amount producers want to sell equals the amount consumers want to buy. In exhibit 10.1, consumers want to buy 60 units and producers want to sell 60 units when the price is $100.

Markets tend to move toward equilibrium points. If the price is above the equilibrium price, producers will not meet their sales forecasts. Some- times producers cut prices to sell more. Sometimes producers cut production. Either strategy tends to equate supply and demand. Alternatively, if the price is below the equilibrium price, consumers will quickly buy up the available stock. To meet this shortage, producers may raise prices or produce more. Either strategy tends to equate supply and demand.

Markets will not always be in equilibrium, especially if conditions change quickly, but the incentive to move toward equilibrium is strong. Pro- ducers typically can change prices faster than they can increase or decrease production. A high price today does not mean a high price tomorrow. Prices are likely to fall as additional capacity becomes available. Likewise, a low price today does not mean a low price tomorrow. Prices are likely to rise as capacity decreases. We will explore this concept in more detail in our examination of the effects of changes in insurance on the incomes of primary care physicians.

10.1.4 Professional Advice and Imperfect Competition Healthcare markets are complex. The influence of professional advice on con- sumer choices is a complication of particular concern. The assumption that changes in supply will not affect consumers’ choices (i.e., demand) can be misleading. If changes in factors that ought not to affect consumers’ choices (e.g., providers’ financial arrangements with insurers) influence providers’

demand curve A graph that describes how much consumers are willing to buy at different prices.

equilibrium price The price at which the quantity demanded equals the quantity supplied. (There is no shortage or surplus.)

shortage A situation in which the quantity demanded at the prevailing price exceeds the quantity supplied. (The best indication of a shortage is that prices are rising.)

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recommendations, a supply and demand analysis that does not take this effect into account could be equally misleading. Even more important, few health- care markets fit the model of a competitive market (i.e., a market with many competitors who perceive they have little influence on the market price). We must condition any analysis on the judgment that healthcare markets are competitive enough that conventional supply curves are useful guides. In markets that are not competitive enough, producers’ responses to changes in market conditions are likely to be more complex than supply curves suggest. This text focuses on applications of demand and supply analysis in which neither providers’ influence on demand nor imperfect competition is likely to be a problem.

10.2 Demand and Supply Shifts

A movement along a demand curve is called a change in the quantity demanded. In other words, a movement along a demand curve traces the link between the price consumers are willing to pay and the quantity they demand. Demand and supply analysis is most useful to healthcare manag- ers, however, in understanding how the equilibrium price and quantity will change in response to shifts in demand or supply. This application helps man- agers the most. With limited information, a working manager can sketch the impact of a change in policy on the markets of most concern.

What factors might cause the demand curve to shift to the right (greater demand at every price or higher prices for every quantity)? We need detailed empirical work to verify the responses of demand to market condi- tions, but the list of standard responses is short. Typically, a shift to the right results from an increase in income, an increase in the price of a substitute (a good or service used instead of the product in question), a decrease in the price of a complement (a good or service used along with the product in question), or a change in tastes.

Economists often use mathematical notation to describe demand. Q = D(P,Y) is an example of this notation. It says that the quantity demanded varies with prices (represented by P) and income (represented by Y), which means that quantity, the relevant prices, and income are systematically related. A demand curve traces this relationship when income and all prices other than the price of the product itself do not change.

What factors might cause the supply curve to shift to the right (greater supply at every price or lower prices at every quantity)? Typically, a shift to the right results from a reduction in the price of an input, an improvement in technology, or an easing of regulations. In mathematical notation, we can describe supply as Q = S(P,W). Here, W represents the prices of inputs (the

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factors such as labor, land, equipment, buildings, and supplies that a business uses to produce its product). Unless technology or regulations are the focus of an analysis, we do not make their role explicit.

Worrying About Demand Shifts

More than 12 million Americans rely on long-term services and supports in home, community, or

institutional settings. This number may more than double by 2050 (Commission on Long-Term Care 2013). Only a small share get services in nursing homes, and the trend is toward lower rates of nursing home care.

Several factors may influence how and where Americans get these services (Spetz et al. 2015). First, Medicaid is a major funder of long- term services and supports, so any changes in Medicaid policy can have major effects. Second, rates of disability have been trending down for a number of years, but there is no guarantee that this trend will continue. Third, use of long-term services and supports varies widely among major ethnic groups, so changes in the composition of the population might have major effects on demand.

Technology represents a wild card in efforts to predict the volume and nature of long-term services and supports. For example, the devel- opment of smart homes and devices might well increase the share of the population getting these services in their homes.

Case 10.1

(continued)

Quantity

P ri ce

Q 1

P 1

EXHIBIT 10.2 The Demand and Supply of Nursing Home Care

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10.2.1 A Shift in Demand We begin our demand and supply analyses by looking at a classical problem in health economics: What will happen to the equilibrium price and quantity of a product used by consumers if insurance expands? Insurance expands when the insurance plan agrees to pay a larger share of the bill or the proportion of the population with insurance increases. This sort of change in insurance causes a shift in demand (or demand shift). As shown in exhibit 10.3, the entire demand curve rotates. As a result of this insurance expansion, the

shift in demand A shift that occurs when a factor other than the price of the product itself (e.g., consumer incomes) changes.

Quantity

P ri ce

Q 1

Q 2

P 2

P 1

D 2

S D

EXHIBIT 10.3 An Expansion of

Insurance

Discussion Questions • What sorts of policy changes seem likely to

shift the demand for nursing home care?

• How would exhibit 10.2 change given the scenario you outline?

• What sorts of demographic changes seem likely to shift the demand for nursing home care?

• How would exhibit 10.2 change given the scenario you outline?

• What sorts of technological changes seem likely to shift the demand for nursing home care?

• How would exhibit 10.2 change given the scenario you outline?

• What sorts of health changes seem likely to shift the demand for nursing home care?

• How would exhibit 10.2 change given the scenario you outline?

Case 10.1 (continued)

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equilibrium price rises from P 1 to P

2 and the equilibrium quantity rises from

Q 1 to Q

2 . For example, as coverage for pharmaceuticals has become a part of

more Americans’ insurance, the prices and sales of prescription pharmaceu- ticals have risen.

10.2.2 A Shift in Supply Exhibit 10.4 depicts a shift in supply (or supply shift). The supply curve has contracted from S

1 to S

2 . This shift means that at every price, producers

want to supply a smaller volume. Alternatively, it means that to produce each volume, producers require a higher price. A change in regulations might result in a shift like the one from S

1 to S

2 . For example, suppose that state

regulations mandated improved care planning and record keeping for nurs- ing homes. Some nursing homes might close down, but the majority would raise prices for private-pay patients to cover the increased cost of care. The net effect would be an increase in the equilibrium price from P

1 to P

2 and

a reduction in the equilibrium quantity from Q 1 to Q

2 . A manager should

be able to forecast this effect with no information other than the realization that the demand for nursing home care is relatively inelastic (meaning that the slope of the demand curve is steep) and that the regulation would shift the supply curve inward.

Responses to changing market conditions depend on how much time passes. A change in technology, such as the development of a new surgical technique, initially will have little effect on supply. Over time, however, as more surgeons become familiar with the technique, its impact on supply will grow. Short-term supply and demand curves generally look different from

shift in supply A shift that occurs when a factor (e.g., an input price) other than the price of the product changes.

Quantity

P ri ce

Q 2

Q 1

P 2

P 1

S 1

S 2

EXHIBIT 10.4 A Supply Shift

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long-term supply and demand curves. The more time consumers and pro- ducers have to respond, the more their behavior changes.

10.3 Shortage and Surplus

A shortage exists when the quantity demanded at the prevailing price exceeds the quantity supplied. In markets that are free to adjust, the price should rise so that equilibrium is restored. At a higher price, less will be demanded, leav- ing a greater supply.

In some markets, though, prices cannot adjust, often because a public or private insurer sets prices too low and consumers demand more than pro- ducers are willing to supply. Exhibit 10.5 depicts a shortage situation. The equilibrium price is P* and the equilibrium quantity is Q*, but the insurer has set a price of P

2 , so consumers demand Q

D and producers supply Q

S .

Because the price cannot adjust, a shortage equal to Q D − Q

S exists.

A surplus exists when the quantity supplied at the prevailing price exceeds the quantity demanded. In markets that are free to adjust, the price should fall so that equilibrium is restored. In some markets, prices are free to fall but do so slowly. For example, in the 1990s, many hospitals had unfilled hospital beds because the combination of managed care and new technology reduced the demand for inpatient care. Over time, insurance companies used this excess capacity to secure much lower rates (even though Medicare and Medicaid rates remained unchanged), and enough hospitals closed or down- sized to eliminate the excess capacity.

surplus A situation in which the quantity supplied at the prevailing price exceeds the quantity demanded. (The best indication of a surplus is that prices are falling.)

Quantity

P ri ce

Q S

Q* Q D

P 2

EXHIBIT 10.5 A Shortage

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How Large Will the Shortage of Primary Care Physicians Be?

The Affordable Care Act (ACA) has increased the share of the popu- lation with health insurance. Most of the newly insured are young and reasonably healthy. As a result, the ACA will primarily affect the demand for primary care services, and many anticipate a shortage of primary care physicians (Porter 2015).

But some other observers suggest that this concern is overblown (Auerbach et al. 2013). The production of primary care is changing in ways that shift its supply. One change is the rapid expansion of patient-centered medical homes, which emphasize a greater role for technology, nurses, physician assistants, and nurse practitioners. Another change is the growth of nurse-managed clinics (of which MinuteClinic, discussed in case 7.1, is an example). Both of these inno- vations reduce the number of physicians needed to provide primary care for a population.

Discussion Questions • If there were a shortage of primary care physicians, what would

happen to their incomes?

• Set up a model of the demand and supply for primary care physicians. (It should have salary on the vertical axis and number of primary care physicians on the horizontal axis.) Assuming that the production of primary care does not change (i.e., the supply curve does not shift), how do you expect the market equilibrium to change?

• How have the incomes of primary care physicians changed in the last few years? Are these changes consistent with your prediction? (You can get income data from Medscape Physician Compensation Reports.)

• Do the changes in the incomes of primary care physicians suggest there is a shortage?

• If retail clinics and patient-centered medical homes continue to expand, how will they affect the market equilibrium? Which curve would shift as a result: demand or supply?

Case 10.2

(continued)

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E c o n o m i c s f o r H e a l t h c a r e M a n a g e r s162

10.4 Analyses of Multiple Markets

Demand and supply models can also be helpful in forecasting the effects of shifts in one market on the equilibrium in another. Such forecasts can be made only if the markets are related—that is, the products need to be complements or substitutes.

Parente and colleagues (2017) provide an example of this effect. They argue that the ACA’s subsidies for health insurance will shift the demand for registered nurses from D

1 to D

2 (see exhibit 10.6). As a result, employment

will rise from Q 1 to Q

2 and wages will rise from W

1 to W

2 .

Quantity

P ri ce

Q 1

Q 2

W 1

W 2

S 2D2

D 1

EXHIBIT 10.6 Insurance

Subsidies Shift Demand for Registered

Nurses

• Deductibles have been rising quickly in recent years. How would that affect the incomes of primary care physicians?

• Patient-centered medical homes typically expand the roles of registered nurses. How would this affect the demand for primary care physicians?

• The ACA increased some payments for primary care. How would this affect the demand for primary care physicians?

Case 10.2 (continued)

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C h a p t e r 1 0 : S u p p l y a n d D e m a n d A n a l y s i s 163

10.5 Conclusion

Supply and demand analysis can help managers anticipate the effects of changes in policy, technology, or prices. Supply and demand analysis is a valuable tool that managers can use to quickly anticipate the effects of shifts in demand or supply curves. Short-term shifts in demand are likely to result from one of two factors: changes in insurance or shifts in the prices or char- acteristics of substitutes or complements. Short-term shifts in supply are likely to result from one of three factors: changes in regulations, shifts in the prices or characteristics of inputs, or changes in technology.

Make sure you understand the basic shapes of demand and supply curves. Most demand curves slope down, which means that consumers will buy more if prices are lower. It also means that consumers who are willing to purchase a product only at a low price do not place a high value on it. In contrast, most supply curves slope up, which means that higher prices will motivate producers to sell additional output (or motivate more producers to sell the same output).

Exercises

10.1 Physicians’ offices supply some urgent care services (i.e., services patients seek for prompt attention but not for preservation of life or limb). a. Name three other providers of urgent care services. b. What sort of shift in supply or demand would result in a market

equilibrium with higher prices and sales volume? c. What might cause such a shift? d. What sort of shift in supply or demand would result in a market

equilibrium with higher prices but lower sales volume? e. What might cause such a shift?

10.2 Suppose the market equilibrium price for immunizations is $40 and the volume is 25,000. a. Identify three providers of immunization services. b. What sort of shift in supply or demand would reduce both prices

and sales volume? c. What might cause such a shift? d. What sort of shift in supply or demand would result in a market

equilibrium with a price above $40 and a volume below 25,000? e. What might cause such a shift?

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E c o n o m i c s f o r H e a l t h c a r e M a n a g e r s164

10.3 The table contains data on the number of doses of an antihistamine sold per month in a small town.

Price Demand Supply

$10 185 208

$9 187 205

$8 188 202

$7 190 199

$6 191 196

$5 193 193

$4 194 190

$3 196 187

$2 197 184

$1 199 181

a. To sell 196 doses to customers, what will the price need to be? b. For stores to be willing to sell 196 doses, what will the price need

to be? c. How many doses will customers want to buy if the price is $2? d. How many doses will suppliers want to sell if the price is $2? e. Is there excess supply or excess demand at $2? f. What is the equilibrium price? How can you tell?

10.4 The table contains demand and supply data for eyeglasses in a local market.

Price Demand Supply

$300 7,400 8,320

$290 7,480 8,200

$280 7,520 8,080

$270 7,600 7,960

$260 7,640 7,840

(continued)

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C h a p t e r 1 0 : S u p p l y a n d D e m a n d A n a l y s i s 165

Price Demand Supply

$250 7,720 7,720

$240 7,760 7,600

$230 7,840 7,480

$220 7,880 7,360

a. At $280, how many pairs will consumers want to buy? b. How many pairs will consumers want to buy if the price is $290? c. How many pairs will stores want to sell at $290? d. Is $290 the equilibrium price? e. Is there excess supply or excess demand at $290? f. What is the equilibrium price? How can you tell?

10.5 The graph below shows a basic demand and supply graph for home care services. Identify the equilibrium price and quantity. Label them P* and Q*. a. Retirements drive up the wages of home care workers. How

would the graph change? How would P* and Q* change? b. Improved technology lets home care workers monitor use of

medications without going to clients’ homes. How would the graph change? How would P* and Q* change?

c. The number of people needing home care services increases. How would the graph change? How would P* and Q* change?

d. A change in Medicare rules expands coverage for home care services. How would the graph change? How would P* and Q* change?

Quantity

P ri ce

Supply

Demand

(continued)

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E c o n o m i c s f o r H e a l t h c a r e M a n a g e r s166

10.6 The demand function is Q = 600 − P, with P being the price paid by consumers. Put a list of prices ranging from $400 to $0 in a column labeled P. (Use intervals of $50.) a. Consumers have insurance with 40 percent coinsurance. For each

price, calculate the amount that consumers pay. (Put this figure in a column labeled P

Net .)

b. Calculate the quantity demanded when there is insurance. (Put this figure in a column labeled D

I .)

c. Plot the demand curve, putting P (not P Net

) on the vertical axis. d. The quantity supplied equals 2 × P. Put these values in a column

labeled S. e. What is the equilibrium price? f. How much do consumers spend? g. How much does the insurer spend?

10.7 The demand function is Q = 1,000 − (0.5 × P). P is the price paid by consumers. Calculate the quantity demanded when there is no insurance. (Put these values in column D

U of the table.)

P D U

P Net

D I

$1,000

$960

$920

$880 560 $176 912 952

$840

$800

$760

$720

$680

$640

$600

$560

The state mandates coverage with 20 percent coinsurance, meaning that the demand function becomes 1,000 − (0.5 × 0.2 × P). a. For each price, calculate the amount consumers pay. (Put this

figure in column P Net

.) b. Calculate the quantity demanded when there is insurance. (Put

this figure in column D I .)

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c. Plot the two demand curves, putting P (not P Net

) on the vertical axis.

d. How do D U and D

I differ? Which is more elastic?

10.8 The supply function for the product in exercise 10.7 is 160 + (0.9 × P). P is the price received by the seller. At the equilibrium price, the quantity demanded will equal the quantity supplied. a. What was the equilibrium price before coverage? After? b. After coverage begins, how much will the product cost insurers?

How much will the product cost patients? How much did patients pay for the product before coverage started?

10.9 Consumers who can buy health insurance through an employer get a tax subsidy. Use demand and supply analysis to assess how this subsidy affects consumers who cannot buy insurance through an employer.

10.10 Why are price controls unlikely to make consumers better off if a market is reasonably competitive?

10.11 Make the business case why healthcare providers should advocate for expansion of insurance coverage for the poor.

References

Auerbach, D. I., P. G. Chen, M. W. Friedberg, R. Reid, C. Lau, P. I. Buerhaus, and A. Mehrotra. 2013. “Nurse-Managed Health Centers and Patient-Centered Medical Homes Could Mitigate Expected Primary Care Physician Shortage.” Health Affairs 32 (11): 1933–41.

Commission on Long-Term Care. 2013. Report to the Congress. Published September 30. www.gpo.gov/fdsys/pkg/GPO-LTCCOMMISSION/pdf/GPO-LTC COMMISSION.pdf.

Parente, S. T., R. Feldman, J. Spetz, B. Dowd, and E. E. Baggett. 2017. “Wage Growth for the Health Care Workforce: Projecting the Affordable Care Act Impact.” Health Services Research 52 (2): 741–62.

Porter, S. 2015. “Significant Primary Care, Overall Physician Shortage Predicted by 2025.” American Academy of Family Physicians. Published March 3. www .aafp.org/news/practice-professional-issues/20150303aamcwkforce.html.

Spetz, J., L. Trupin, T. Bates, and J. M. Coffman. 2015. “Future Demand for Long- Term Care Workers Will Be Influenced by Demographic and Utilization Changes.” Health Affairs 34 (6): 936–45.

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CHAPTER

169

11MAXIMIZING PROFITS Learning Objectives

After reading this chapter, students will be able to

• define measures of profitability, • describe two strategies for increasing profits, • explain how to respond if marginal revenue exceeds marginal cost, • identify the profit-maximizing level of output, and • discuss differences between for-profit and not-for-profit providers.

Key Concepts

• All healthcare managers need to understand how to maximize profits. • Most healthcare organizations are inefficient, so cost reductions can

increase profits. • To maximize profits, firms should expand as long as marginal revenue

exceeds marginal cost. • Marginal cost is the change in total cost associated with a change in

output. • Marginal revenue is the change in total revenue associated with a

change in output. • Managers need to distinguish incremental cost from average cost. • An agency problem arises because the goals of stakeholders may not

coincide.

11.1 Introduction

Substantial numbers of healthcare managers serve firms that seek to maxi- mize profits. For example, for-profit hospitals, most insurance firms, most physician groups, and a broad range of other organizations explicitly seek

profits Total revenue minus total cost.

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maximum profits. In addition, recognizing that “with no margin, there is no mission,” many not-for-profit healthcare organizations act like profit- maximizing firms. And even organizations that are not exclusively focused on the bottom line must balance financial and other goals. Bankrupt orga- nizations accomplish nothing. As a result, even healthcare managers with objectives other than maximizing profits need to understand how to maxi- mize profits. A manager who does not understand the opportunity cost (in terms of forgone profits) of a strategic decision cannot lead effectively. All healthcare managers need to understand how to maximize profits. Finally, as markets become more competitive, the differences between for-profit and not-for-profit firms are likely to narrow.

Profits are the difference between total revenue and total cost. To maximize profits, you must identify the strategy that makes this difference the largest. In other words, identify the product price (or quantity) and char- acteristics that maximize profits.

11.2 Cutting Costs to Increase Profits

An obvious way to increase profits is to cut costs. Most healthcare organiza- tions are inefficient, meaning that they could produce the same output at less cost or produce higher-quality output (that sells for a higher price) for the same cost. The inference that healthcare organizations are inefficient is based on two types of evidence. First, studies by quality management and reengineering teams have identified that costs can be cut by increasing the quality of care. For example, a transportation project at CareMore Health System (that used Lyft) reduced wait times, reduced cost, and increased patient satisfaction (Eapen and Jain 2017). The second type of evidence results from statistical studies. For example, a sophisticated study of hospital efficiency concluded that inefficiency represented more than 15 percent of costs (Zhivan and Diana 2012). Some improvement appears to have been made in recent years, but inefficiency remains substantial (Khushalani and Ozcan 2017).

As exhibit 11.1 illustrates, the payoff from cost reductions can be sub- stantial. The organization in the exhibit earns $40,000 on revenue of $2.4 million. This operating margin (profits divided by revenue) of 1.7 percent suggests that the organization is not greatly profitable. Reducing costs by only 2 percent changes this picture entirely. As long as the cost cuts represent more efficient operations (not cuts in quality or customer service), all the cost reductions will increase profits, in this case by 118 percent.

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C h a p t e r 1 1 : M a x i m i z i n g P r o f i t s 171

11.2.1 Cost Reduction Through Improved Clinical Management Cost reductions often require improvements in clinical management because differences in costs are primarily driven by differences in resource use, not differences in the cost per unit of resource. (An organization cannot maxi- mize profits if it overpays for the resources it uses.) In turn, differences in resource use are driven by differences in how clinical plans are designed and executed. Improvements in clinical management require physician coopera- tion and, more typically, physician involvement. Even though many health- care professionals make clinical decisions, physicians in most settings have a primary role in decision making.

Having recognized the importance of physicians in increasing effi- ciency, managers need to ask whether the interests of the organization and its physicians are aligned. In other words, will changes that benefit the orga- nization also benefit its physicians? If not, physicians cannot be expected to be enthusiastic participants in these activities, especially if the advantages for patients are not clear.

Managers are responsible for ensuring that the interests of individual physicians are aligned with the organization or for changing the environment. For example, physicians usually benefit from changes in clinical processes that improve the quality of care or make care more attractive to patients. If man- agers present the change proposal in this fashion, physicians may understand how they will benefit. In other cases, however, physicians cannot be expected to participate in quality improvement activities without compensation. For independent physicians, explicit payments for participation may be required. The same may be true for employee physicians, or participation may be a part of their contractual obligations. In both cases, managers must be aware of the high opportunity cost of time spent away from clinical practice.

Where feasible, physicians’ compensation can incorporate bonuses based on how well they meet or exceed clinical expectations. This system helps align the incentives of the organization and its physicians and provides a continuing reminder to improve clinical management.

Status Quo 2% Cost Reduction

Quantity 24,000 24,000

Revenue $2,400,000 $2,400,000

Cost $2,360,000 $2,312,800

Profit $40,000 $87,200

EXHIBIT 11.1 The Effects of Cost Reductions on Profits

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Profiting from Clinical Improvement

Surgical complications reduce patients’ quality of life and survival. As exhibit 11.2 shows, surgical

complications also increase hospital length of stay, readmission rates, and costs. Such complications can significantly reduce profitability, especially in bundled payment, capitated, or other value-based pay- ment environments.

Hospitals are sometimes paid more when patients experience complications (although Medicare has ended reimbursement for some clinical shortcomings). Because incremental revenues are highly vis- ible and incremental costs due to complications are not, hospital administrators may think that clinical shortcomings are not eroding margins. Michard and colleagues (2015) conclude that implementing goal-directed fluid therapy (which significantly reduces complications) would return $2.50 to $4.00 for each dollar invested. In this case, improving quality is highly profitable.

Poor quality reduces hospital profits, even if it substantially increases payments by insurers. And poor quality is a terrible strategy in both the short run and the long run. For example, Gutacker and col- leagues (2016) conclude that the elasticity of hospital demand with respect to a typical health gain (measured by the Oxford Hip Score) is 1.4, and the demand elasticities for readmission and mortality rates are −0.02 and −0.004. Poor quality leads to market share losses, and this effect is likely to become larger as insurers increasingly use cost and quality data to try to steer patients to efficient, effective, safe providers (Avalere Health 2017).

Length of Stay Readmission Rate Cost

With Without With Without With Without

Gastrectomy 4 2 12.7 5.2 $27,794 $12,641

Vascular bypass 6 3 21.3 14.1 $31,979 $16,849

Esophagectomy 13 9 18.5 15.4 $67,924 $37,382

Source: Michard et al. (2015).

EXHIBIT 11.2 Surgeries With

and Without Complications

Case 11.1

(continued)

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11.2.2 Reengineering Reengineering and quality improvement initiatives can increase profits, but this does not mean that they will or that they will do so easily. Nothing guar- antees that costs will fall, and nothing guarantees that revenues will not fall faster than costs. Especially in hospitals, improvement initiatives often coin- cide with downsizing efforts, making the staff wary (and sometimes causing the organization to lose the employees it most wants to keep).

Skilled leadership does not guarantee success but is essential to improvement initiatives. Many projects fail, but alignment of the board, management, and clinicians appears to increase the odds of success (Pannick, Sevdalis, and Athanasiou 2016). Reengineering and quality management initiatives demand the time and attention of everyone in the organization, meaning that other things are left undone or are done less well. If not done skillfully, reengineering and quality management initiatives can make things worse.

11.3 Maximizing Profits

Organizations can also increase profits by expanding or contracting output. The basic rules of profit maximization are to expand as long as marginal revenue (or incremental revenue) exceeds marginal cost (or incremental

marginal or incremental revenue The revenue from selling an additional unit of output.

marginal or incremental cost The cost of producing an additional unit of output.

Discussion Questions • Is there other evidence that providers profit

from improving quality?

• What is Medicare currently doing to measure quality? Safety? Efficiency?

• What are private health plans currently doing to measure quality? Safety? Efficiency?

• What are Medicaid plans currently doing to measure quality? Safety? Efficiency?

• How large are the potential effects on hospital profits of Medicare’s value-based payments?

• How large are the potential effects on physician profits of Medicare’s value-based payments?

• How will value-based payments from private insurers affect profits?

• How could better quality not cost more?

• What is inefficiency in healthcare? How common is it?

Case 11.1 (continued)

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E c o n o m i c s f o r H e a l t h c a r e M a n a g e r s174

cost), to shrink as long as marginal cost exceeds marginal revenue, and to shut down if the return on investment is not adequate. If increasing output increases revenue more than costs, profits rise. If reducing output reduces costs more than it reduces revenue, profits rise.

Marginal cost (or incremental cost) is the change in total cost associ- ated with a change in output. Marginal revenue (or incremental revenue) is the change in total revenue associated with a change in output. The chal- lenges lie in forecasting revenues and estimating costs.

As shown in exhibit 11.3, increasing output from 100 to 120 increases profits because the marginal revenue is greater than the marginal cost. Rev- enue increases from $2,000 to $2,400 as sales increase from 100 to 120 units, so marginal revenue equals $20 ($400 ÷ 20). Costs increase from $1,500 to $1,600, so marginal cost equals $5 ($100 ÷ 20). The same is true for the expansion from 120 to 140. Marginal revenue falls because the firm has to cut prices to increase sales, and marginal cost rises because the firm is approaching capacity. Even though marginal revenue is nearly equal to marginal cost, profits still rise. Expanding from 140 to 160 reduces profits. Further price cuts push marginal revenue below marginal cost.

Managers need to understand what their costs are and must not con- fuse incremental costs with average costs. Average costs may be higher or lower than incremental costs. As long as the organization operates well below capacity, average costs usually will exceed incremental costs because of fixed costs. As the firm approaches capacity, however, incremental costs can rise quickly. If the firm needs to add personnel, acquire new equipment, or lease new offices to serve additional customers, incremental costs may well exceed average costs.

The following example illustrates why managers need to understand marginal costs and compare them to marginal revenues. A clinic is operating near capacity when a small PPO (preferred provider organization) approaches it. The PPO wants to bring 100 additional patient visits to the clinic and pay $50 per visit. The manager accepts the deal, even though $50 is less than the clinic’s average cost or average revenue. Shortly thereafter, another PPO approaches the clinic. It too wants to bring 100 additional patient visits to

Quantity Revenue Cost Profit Marginal Revenue

Marginal Cost

100 $2,000 $1,500 $500

120 $2,400 $1,600 $800 $20 $5

140 $2,660 $1,840 $820 $13 $12

160 $2,880 $2,120 $760 $11 $14

EXHIBIT 11.3 Marginal

Cost, Marginal Revenue, and

Profits

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C h a p t e r 1 1 : M a x i m i z i n g P r o f i t s 175

the clinic and pay $50 per visit. The manager turns down the offer. When criticized for this apparent inconsistency, the manager defends the decision, explaining that the clinic had excess capacity when the first PPO contacted it. The marginal cost for those additional visits was only $10 (see exhibit 11.4). Signing the first contract increased profits by $4,000 because the marginal revenue was $50 for those visits. When the second PPO contacted the clinic, it no longer had excess capacity and would have had to add staff to handle the additional visits. As a result, marginal costs for the second set of visits would have been $510, and profits would have plummeted.

11.4 Return on Investment

When examining an entire organization rather than a well-defined project, most analysts focus on return on equity rather than return on investment. Equity is an organization’s total assets minus outside claims on those assets. Equity also can be defined as the initial investments of stakeholders (donors or investors) plus the organization’s retained earnings.

What is an adequate return on investment? The answer to this ques- tion depends primarily on three factors: what low-risk investments (e.g., short-term US Treasury securities) are yielding, the riskiness of the enter- prise, and the objectives of the organization.

All business investments entail some risk. Those risks may be high, as they are for a pharmaceutical company considering allocating research and development funds to a new drug, or they may be low, as they are for a pri- mary care physician purchasing an established practice in a small town. In any case, a profit-seeking investor will be reluctant to commit funds to a project

return on equity Profits divided by shareholder equity.

Status Quo Adding the First PPO

Adding the Second PPO

Quantity 24,000 24,100 24,200

Revenue $2,400,000 $2,405,000 $2,410,000

Average revenue $100.00 $99.79 $99.59

Marginal revenue $50.00 $50.00

Cost $2,040,000 $2,041,000 $2,092,000

Average cost $85.00 $84.69 $86.45

Marginal cost $10.00 $510.00

Profit $360,000 $364,000 $318,000

EXHIBIT 11.4 Marginal Cost and Profits

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that promises a rate of return similar to the yield of low-risk securities. Conse- quently, when rates of return on low-risk investments are high, investors will demand high yields on higher-risk investments. The size of this risk premium will usually depend on a project’s perceived risk. An investor may be content with the prospect of a 9 percent return on investment from a relatively low- risk enterprise but will not find this yield adequate for a high-risk venture.

Because managers must be responsive to the organization’s stake- holders, they must also avoid high-risk investments that do not offer at least a chance of high rates of return. What constitutes a high rate of return depends on the goals of the organization and the nonfinancial attributes of an investment. In some cases, an organization that is genuinely committed to nonprofit objectives will be willing to accept a low return (or even a negative return) on a project that furthers those goals.

11.5 Producing to Stock or to Order

Organizations can produce to stock or produce to order. One that pro- duces to stock forecasts its demand and cost and produces output to store in inventory. Medical supply manufacturers are an example of this sort of organization. More commonly in the healthcare sector, firms produce to order. They also forecast demand and cost, but they do not produce any- thing up front. Instead, they set prices designed to maximize profits and wait to see how many customers they attract. Hospitals are an example of this type of organization. This distinction is important because discussions of profit maximization are usually framed in terms of choosing quantities or choosing prices.

Thus far, the content of this chapter has been largely framed in terms of firms that produce to stock; however, its implications apply to healthcare organizations that produce to order. Only by setting prices based on their expectations about demand and cost do they discover whether they have set prices too high or too low. An organization has set prices too high if its mar- ginal revenue is greater than its marginal cost, because that means additional profitable sales at a lower price were missed. An organization has set prices too low if its marginal revenue is less than its marginal cost. Organizations that produce to order must also make the same decisions about rates of return on equity discussed earlier. Is a 5 percent return on investment large enough to justify operating an organ transplant unit? How important is the unit to the organization’s educational goals? What are the alternatives?

When organizations contract with insurers or employers, estimates of marginal revenue should be easy to develop. To estimate marginal revenue for a new contract, calculate projected revenue under the new contract,

producing to stock Producing output and then adjusting prices to sell what has been produced.

producing to order Setting prices and then filling customers’ orders.

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subtract revenue under the old contract, and divide by the change in volume. For sales to the general public, economics gives managers a tool: Marginal revenue equals p × (1 + 1/ε), where p is the product price and ε is the price elasticity of demand. (See chapter 8 for more information about elasticity.) Most healthcare organizations face price elasticities in the range of −3.00 to −6.00, so marginal revenue can be much less than the price. For example, if a product sells for $1,000 and the price elasticity of demand is −3.00, its marginal revenue will equal $1,000 × (1 − 1/3.00), or $667. In contrast, if the price elasticity of demand is −6.00, its marginal revenue will equal $1,000 × (1 − 1/6.00), or $833. As demand becomes more elastic, marginal revenue and price become more alike. Unless the elasticity becomes infinite, though, marginal revenue will be less than price.

11.6 Not-for-Profit Organizations

The strategies of not-for-profit organizations may differ from those of for- profit organizations because of more severe agency problems, differences in goals, and differences in costs. These forces have multiple effects.

11.6.1 Agency Problems All organizations have agency problems. Agency problems are conflicts between the interests of managers (the agents) and the goals of other stake- holders. For example, a higher salary benefits a manager, but it benefits stakeholders only if it enhances performance or keeps the manager from leaving (when a comparable replacement could not be attracted for less). Not-for-profit firms face three added challenges. They cannot turn manag- ers into owners by requiring them to own company stock (which helps to align the interests of managers and other owners). In addition, no one owns the organization, so no one may be policing the behavior of its managers to ensure that they are serving stakeholders well. Furthermore, assessing the performance of managers in not-for-profit organizations is a challenge. A not-for-profit organization may earn less than a for-profit competitor for many reasons. Is it earning less because of its focus on other goals, because of management’s incompetence, or because the firm’s managers are using the firm’s resources to live well? Often the cause is difficult to pinpoint.

11.6.2 Differences in Goals Goals other than profits can influence an organization’s behavior, though they need not. Not-for-profit firms gain benefits from the pursuit of goals other than profit. Managers should consider how their decisions affect the benefits derived from these other goals. To best realize its goals, a not-for-profit

agency An arrangement in which one person (the agent) takes actions on behalf of another (the principal).

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organization should strive to make marginal revenue plus marginal benefit equal to marginal cost. Marginal benefit is the net nonfinancial gain to the firm from expanding a line of business. Three cases are possible:

1. If the marginal benefit is greater than 0, the not-for-profit will produce more than a for-profit.

2. If the marginal benefit equals 0, the not-for-profit will produce as much as a for-profit.

3. If the marginal benefit is less than 0, the not-for-profit will produce less than a for-profit.

A further complication is that the marginal benefit may depend on other income. A struggling not-for-profit may act like a for-profit, but a highly profitable not-for-profit may not.

11.6.3 Differences in Costs Not-for-profit organizations’ costs also may differ. First, the not-for-profit may not have to pay taxes (especially property taxes), which tends to make the not-for-profit’s average costs lower. On the other hand, the not-for-profit firm’s greater agency problems may result in less efficiency and higher aver- age and marginal costs.

The fundamental problem is that we cannot predict how not-for-profit organizations will differ from for-profit firms. This lack of forecast is frustrat- ing for analysts and raises a question for policymakers: If we do not know how not-for-profit organizations benefit the community, why are they given tax breaks?

Tax Exemptions for Not-for-Profit Hospitals

Not-for-profit hospitals enjoy federal, state, and local tax exemptions, but they face challenges from governments in both courtrooms and statehouses (Santos 2016). Contemporary hospitals differ markedly from those that existed at the turn of the twentieth century, which were truly charitable institutions. They were supported almost entirely by donations and largely staffed by volunteers. Thus, it can be argued that tax exemption for not-for-profit hospitals is a historical relic. When the income tax started in 1894, there was no Medicare, no Medicaid,

Case 11.2

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and no insurance. Most people with money got care at home. The role of hospitals was to care for the poor.

These days, hospitals serve paying customers, and for-profits, which pay income and property taxes, provide about as much uncom- pensated care as not-for-profits (Valdovinos, Le, and Hsia 2015). (Not- for-profits do appear to offer more charity care—1.9 percent of total expenses—than for-profits, at 1.4 percent.) Not surprisingly, not-for- profits’ de facto tax-exempt status has come into question.

The case of Provena Covenant Medical Center illustrates this. After a lengthy court battle, in 2010 the Supreme Court of Illinois upheld the Illinois Department of Revenue’s denial of an application for exemp- tion in 2002, finding that Provena was not a charitable institution and the property was not used for charitable purposes (Santos 2016). Two factors influenced the decision. First, of the hospital’s $118 million in total revenue, more than 96 percent came from patient and insurer payments, and less than $10,000 came from charitable donations. Second, Provena Covenant Medical Center did not actively promote its charity care program. The hospital routinely billed indigent patients and forced them to apply for discounts under the terms of the financial assistance program. In short, Provena Covenant Medical Center did not appear to be a charity.

In recent years, increasing numbers of localities have asked not- for-profits to make payments in lieu of taxes. For example, Boston received $32.4 million of these payments in 2017 (City of Boston 2018). Such arrangements are becoming more common as localities seek to cover the cost of services. Furthermore, many health systems look no more like charities than Provena Covenant Medical Center did.

One response to this situation was the changes in the community benefit standard specified by the Affordable Care Act (ACA). First, hospitals must prepare a community health needs assessment every three years. This report identifies the major health challenges fac- ing that community and lays out a plan for the hospital to address them in the coming years. Second, hospitals must create a financial assistance plan that explains the criteria for offering financial assis- tance and must make the plan freely accessible to the public. Third, hospitals cannot charge patients that qualify for assistance more than they charge insured patients. Fourth, hospitals must make reasonable

Case 11.2 (continued)

(continued)

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