Business Applications of Demand

November 30, 2016September 21, 2022 gaumerLeave a comment

The concept of DEMAND is central to economics— it represents the willingness of customers to buy stuff of value to them at various price points. As such, DEMAND behaviors are a driving force in the economy as consumers manage their fixed (scarce) amounts of goods and time in making choices for themselves aimed at achieving the best situation that is achievable. But demand analysis, or using the customer’s revealed willingness to buy, is useful for solving a number of common business problems:

1. What will happen to our revenue now that we see our main competitor cut price?

2. How do we know whether our reduced sales volume in 2016 will bounce back in 2017 (or not) as we recover from the recession?

3. Can we follow the advice of our consultant who says that a price increase will lead to more revenue.

4. How would I measure the impact of a loyalty building program (and why do i even care about loyalty — don’t i just care about how many customers i can get?

5. If I sell a product into two separate market segments, how do I go about deciding whether I should sell at the same price in both?

We will review selective aspects of “demand” here pertaining to these and other business issues.

First, lets briefly review the three main options for gathering demand data.The main sources are:

— focus groups are cheap and fast ways to understand if qualitative aspects of buying behavior is changing. Is loyalty or brand influence changing? Are segments changing? Is willingness of particular customers to pay more changing due to specific competitor changes?

— historical price and sales data across markets

— pricing experimental data – systematic variation in price across markets/stores- and collection of sales volume data before and prices are changed

The Law of Demand

The so called “law of demand” is a bit of a misnomer. What is means is, generally:

Other things the same, Buyers are willing to buy more when price falls (they can shift consumption patterns around to make themselves better off—rebalancing scarce endowments of resources to achieve maximum welfare from them. This is sometimes referred to pursuing substitutes, or allocative efficiency)

Substitution is the reason for the “law of demand”. This hypothesis in economics says that when prices go up, people (all people in the market taken together) buy less. Some people who have urgent (or addictive) needs for the product may not buy less. But, in the aggregate, the market demand will be lower at a higher price. Why is this? It is substitution at work. Because of scarcity, and fixed budgets, people will adjust the amount they buy when prices rise. Some will make no changes, some will make small reductions, others larger changes— as they try to rebalance their mix of consumption according to the new relative price situation. Consumers will spend their scarce resources differently: buying more of the things whose relative price has fallen, and less of those things for whom the relative price has risen.

It really isnt a “law”. For example, when the price of certain staples (like rise) goes up, sometimes consumption actually goes up for poor people as a result. This is known as a ‘giffin good’. When a good represents a very large share of the consumer’s budget (as rice does for very poor southern asians) the price increase represents a fall in their real income— they are poorer in a very real way. As a result, whatever other food items otber than rice, or other modest food luxuries that had been in their budget have to be eliminated. And, they actually start using more rice, rather than less when the price goes up!

Another similar exception to the law of demand (and more relevant to the U.S.A) is the “luxury or prestige” product. These kinds of products are valued by consumers, in part, for their ‘prestige’. And, when price increases for them, sometimes demand goes up, rather than down. Some evidence exists that this may be true for some alcoholic beverages (Markers Mark whiskey) or fashion accessories, gems, surgical services, and other items. There are two ideas that might give rise to this kind of consumer demand behavior: (1) the consumer may not have a real good idea of the “true” value of the product, and views “price” as an index of product value or quality. So when the price is seen to be higher, it must be better, or worth more. (2) the customer wants to demonstrate “prestige” to his/her guests or companions. “I only serve the very best, and search out the most luxurious and expensive things to serve or to wear”

This kind of demand behavior (a positive relationship between “price” and “willingness to buy”) is certainly rare. Normally, price increases cause consumers to rebalance their use of scarce time and money and “buy less” (and certainly no more) of those things that go up in price.

Shifts and Movements Along the Demand curve

Demand is often represented (empirically using market research data) as a schedule or graph, showing how much customers will buy at alternative price points. On a graph, price is shown on the vertical axis– with Quantity purchased on the horiziontal axis. And, the schedule or curve is negatively, sloped—– when price goes up, quantity purchased goes down. The left side of the following chart shows a downsloping demand curve for a product. And, it shows (with the dotted line) a shift in the demand curve reflecting a change in customer loyalty (eg more loyal, getting steeper).

demand-curve

As we slide up or down the demand curve, we can read from the schedule how much we would expect customers to buy. As we raise price, we expect to lose customers. When something happens, and the market demand curve gets steeper, it means that a price rise will cause the firm to lose customers, but the loss will be less than it would have been had the demand curve been flatter. This is why businesses use “loyalty building programs like the “cards which get punched when you buy a cup of coffee–getting the 12th cup free”.It creates a steeper demand curve that without the loyalty program— meaning that the customers are less likely to flee if it becomes necessary for the business to raise price.

The right side of graph above shows other kinds of shifts in the demand curve. What this means is that certain kinds of market events change the consumers willingness to buy any particular item. When these changes occur, the demand curve shifts out (consumers willing to buy more at all possible prices) or shifts to the left (the customers are willing to buy less at all possible prices. These kinds of shifts are illustrated by the following events, all causing more to be purchased at the existing price (eg shift)”. Shifts occur in demand curves for several reasons:

change in income of consumers (demand curves shift out, to the right, when income goes up if a product is a “normal good”. Some goods are called “inferior goods” because when incomes rise the stop consuming them (maybe the demand for used cars, or the demand for walmart products would be examples of inferior goods.
change in the price competitors are charging. — when prices of “competitive” products go up, generally demand shifts out for our product. Though for for complementary products (razors and razor blades, restaurants and parking) the increase in price of one will cause less of it to be consumed, and therefore the demand curve will shift to the left (a decrease in customer purchases).
advertising—-when firms advertise or promote the product they do it because it shifts demand to the right (eg the customer base will buy more at every price as a result of the advertising)
changes in customer tastes — fads, changes in fashion, new scientific evidence, celebrity adoption, and other thing cause customers to want the product more, or less. This is represented as a shift in demand— buying more or less at all prices.

5. expected future price changes — if customers expect price to rise for a product they buy, they may purchase more of it now (to hoard it). If they expect future price to fall, they may wait and buy it later.

Measuring Steepness of Demand (Loyalty) and the Size of Shifts in Demand

While economists are concerned about steep or flat, and the direction of shifts, the business analyst. need numbers! How flat? How big will the shift be. The metric that is used is simple— the % change in demand. Not very novel. But, we use the term “elasticity” to be the % changed in quantity demand

— due to movements along the demand curve (price elasticity), and

— due to shifts in demand (income elasticity, cross price elasticity)

In making these measures elasticity is always the % change in quantity demanded in response to a price change (the % change) or to the income change (the % change) or to the price change of another good (the % change).

The chart here shows the actual formula use to calculate price elasticity (eg steepness of the demand curve) — the ratio of the % change in quantity demanded (the numerator) to the % change in price (the denominator). If the price elasticity is a number >1 (eg -1.2, -2.3, -4.7) we say the demand curve is elastic—– meaning that when p[rice changes by some amount, the responsiveness of quantity demand will be much larger (of course, demand elasticity is almost always a negative number). This means a very flat demand curve. If the price elasticity is a fractional number <1 (eg -0.5. or 0.9) we say the demand curve is inelastic— or steep. Quantity doesnt change much when price changes in either direction.We call this kind of elasticity “inelastic”.

The calculation of demand or (price) elasticity can be be done two ways.

1. by taking the data from the two points, each of them a price and quantity. and calculating the elasticity at the midpoint between the two points.

2. by taking one point, and calculating the elasticity at that point and using the known slope of the demand curve at that point (the slope is the change in Q, relative to the change in P).

Either method will work. Some knowledge of algebra and fractions will help. We show these methods here on the chart.

elasticity

I do not show the formulas for the income elasticity or the cross price elasticity. they are the same (except the denominators are income and other firm’s price respectively. Here, in both cases, the sign (+-) of the elasticity has very important meaning. The sign refers to whether the change shifts the demand curve for our product out to the right (+) or shifts it in to the left (-).

Cross elasticity

positive other good is a “competitor” they raise price, our demand shifts out

negative other good is a “complement” they raise price, our demand shifts left

Inferior doesnt mean poor quality. It means that when people’s income falls, they buy more of it. Walmart and Target, for example were shown to be “inferior” goods in the recent big recession, as their business picked up when jobs were lost and incomes fell. When incomes began to rise, people shifted back to Marshalls and other stores.

The cost price elasticity reflects how our customers react to price changes for another product. When they start charging higher prices and our demand increases ( eg a + cross elasticity) it means the two goods are substitutes in the minds of the consumers— competitors! But if they raise their price and we start losing business– what is going on? It means the two products tend to be used by consumers together– complements. Hot dogs, and hot dog buns. Cars and gas. Razors and razor blades. The increase in price of one causes a reduction in demand for that item, which leads to a reduction in demand for the other.

Revenue Implications of Price Changes

Possible the most interesting and important use of demand analysis, and price elasticity, is systematically connecting price changes with the associated revenue implications. This is easy. Revenue is price * quantity sold. PxQ. So, if the elasticity of our product is -4.3 (efastic, very flat) then it means that a modest price change will engender a massive quantity response by our customers, in the opposite direction. That is the result will be a small p change and a large Q response. So, if we increase price, and our quantity falls a lot, obviously the result will be a fall in revenue. So the rules are:

Demand elasticity Price increase leads to Price decreas leads to Rule

small (<1), inelastic reduction in revenue increase in revenue Revenue moves opposite to price

large (>1), elastic increase in revenue decrease in revenue Revenue moves in same direction

Market Segmentation

Businesses have discovered a way to make more profit due to the fact that not everyone who buys their products does so with the same loyalty—- eg. some buy based on convenience, others may buy be cause of certain other characteristics of the service, others buy only when it’s cheaper to do so. This understanding of systematic patterns of buying behavior has led to widespread use of “market segmentation” in the way goods and services are priced (lunch/dinner prices in restaurants, matinee discounts in theaters, weekend rates on the golf course, coupons for groceries, “lines of differentially priced clothing that are essentially the same, auto’s with common parts but differential ‘style’ and image features, and others).

When customers have different underlying demands for the product or service, they should be charged different prices to fully capitalize on the different loyalty the exibit— eg. the segments have different elasticities of demand or different demand curve slopes. When all segments are charged the same “average” price, profit is left ‘on the table’ by the business. The segments where loyalty is most evident should be charged the highest price, because they are willing to pay more.

The chart below shows two segments. The one on the right has the most loyalty.The segment on the left has the least loyalty, and should have the lowest price.

market-segment

An interesting puzzle about segments is illustrated in the chart, showing the price per pill for Prozac after its patent expired and ‘generic’ producers entered the market.

prozac

The first generic priced its product at $1.91, modestly below the longstanding Prozac price while on patent at $2.17. And, over the following months, more generics entered the market driving the generic price to $0.32 a pill. But note what was happening to the price of “name brand” Prozac— it was actually being increase a bit, going up to $2.41 by a year following the end of patent protection. What was the drug company thinking? More and more competition was happening, and they were raising price?

Actually, some bright young analyst in marketing probably got a big bonus for realizing opportunity in the midst of this depressing period when the patent no longer kept out competition. As generic alternatives skimmed away more and more customers who preferred the low prices of chemically equivalent drugs, some of the customers continued to prefer the name brand. This “segment” of their customers had always been part of the users of Prozac. They probably always had (or their doctors always had) a stronger attachment (loyalty) to the “name brand”, but they were never charged a differentially higher price because of their brand loyalty. Who knew? Who could have identified them to charge them more? Nobody.

But when the rest of the users were skimmed away, there they were, still preferring the name brand, not wanting to risk getting a somewhat different experience on the “chemically equivalent” drug. So, name brand Prozac started charging more, because they had discovered that there was a nested segment willing to pay more. Those loyal name brand users.

Economics of Time (for shopping and other activities)

An important element of the “demand” for goods and services is the time it takes to acquire information, travel, shop and so forth. Time is a very scarce resources for all of us. Scarcer for some of us, it seems, than for others. There is study of the “economics of time”.

The concern about “time” and its scarcity and, its “value” had always been apparent in the notion of “convenience stores”, where quick shopping was afforded, but at higher prices. The advent of the internet, and the failure of bookstores, video stores, and other retailing failures has elevated the importance of “shopping time and convenience” to a new level. As smart phone apps are becoming commonplace, it is clear that the critical importance of “time”and the introduction of the internet and wireless technologies, is changing retailing in a massive way.

What is the economics of time? Time is a scarce resource. We allocate our scarce time according to how much value it gives us in the various ways we apply it. We give up time to work (to earn an income), we give up time to sleep, we commute with some of our scarce time, we spend time with our families, we take vacations, we watch TV, we use time all over the place in our lives. But, in the end, we strike a balance in how much time we spend doing all these things according to how much value we get in each of the activities. If circumstances change (we take a new job, the babysitter quits, the spouse decides to take a job, etc.) we rebalance our uses of time. Always, we are rebalancing our money and time budgets (our scarce resources) to create the ‘best” result for ourselves or our families.

And, when Amazon and the internet comes along, many of us decided that huge time savings were at our fingertips, and with a few clicks we could avoid traffic, and did not have to waste time comparison shopping at the mall. Shoppers recaptured time for their other priorities and started shopping on line. Shopping could now be measured in clicks, not hours. The two earner families in particular were able to see the efficiency improvement.

But, business didn’t see it coming. They didn’t understand their customers very well. The got blindsided because the just assumed people would forever be willing to take time to drive to their stores, to spend time “shopping” for the best product as they always did. For some customers, this was certainly true. But for the rest of us, they misread what the situation was, and didn’t see the consequences for their sustainability until it was too late. Time is very scarce, and it is very valuable to most people. It always will be.

Some of the ways people make choices about using their scarce time are:

People behave so as to economize on time (as they spend it) just as the economize on money(as they spend it)
People allocate their time across activities so that they achieve the maximum benefits. This ‘allocation” will change as circumstances change.
People are different– and they will choose to allocate their time differently. Some people have more time, others have more money— and this means they will behave differently– who values convenience? Who collect coupons and shop for “bargains”? The different “value of time” is a source of market segmentation.
In shopping, all People spend time up to the point where “it isn’t worth it” to shop anymore. Some people will choose to shop more, others less—depending on their own circumstances.
The higher the benefits of searching, or the lower the costs, the more decision makers will search

From a business perspective, the “demand” by customers could be viewed in terms of the “full price” rather than just the money price. The “full price” of a good or service may be defined as the sum of the money component, and the time component.

Full Price = time requirements (price of time) + money PRICE

This concept allows for full price to be lowered by either cutting the money price, or by reducing the time requirement (that is, improving convenience).

Economics of Perfection

One of the longstanding myths of human behavior is that “good decisionmaking demands all the information before deciding”. Being perfect is not good economics. Here is the example.

Lets say we are being asked to make a decision about pursuing an acquisition target. We know a lot already, but their is much we don’t know. So, we plan out the effort required to gather information about them–their financial situation, their customers, their managers, their products, their competitors, their business strategy, their vulnerabilities, etc etc. That plan is reflected in the chart shown here. It shows the hypothetical “value” of the information we could acquire about them in each day over a period of 14 person days—at the end of which we think we would pretty much know “everything” about that company.

How many days is it economical to invest in a search process?

perfection

We believe that the “value of the the information discovered” will be vary large initially (important stuff will be more readily apparent) and dissipate over more and more search activity. The right column measures the difference between the cumulative costs of search (labor costs of $400 a day) and the value of the information acquired. So, after 2 days of searching the net benefits from searching is $208,800. So, we should plan to search even more. How many days should we search. The net benefits of searching reaches a peak after 8 days at $229,571. Additional time searching causes the net benefits from search to decline. A rational search activity would plan to search only 8 days, because beyond that, the gains from additional search are not as high as the costs of the search.

Perfection, defined here as collecting absolutely all the available information, isn’t rational. You need additional information only when the costs of acquiring it is less than the benefits it produces. That is, of course, true only if you don’t get benefits from the search process itself! (eg another form of value that wasn’t included in the situation here).

Capitalism & the 6 Basic Forces

November 27, 2016June 24, 2019 gaumerLeave a comment

What is Economics and Why Do MBAs have to Study it?

Economics is the study of how societies cope with scarcity of resources (like human resource time, natural resources, investment capital, know how, etc.). When economic actors (workers, investors, owners of land, etc.) are free to choose for themselves, we have what we call a “market economy”. This is more or less what we have in most parts of the world. In such a situation (where individual economic actors like workers, businesses, farmers, and investors are working to make choices that make themselves and their families better off) things change a lot. Some prices are rising, other falling, capital is flowing into some industries and away from others, firms that had no competitors sometimes find themselves with new ones. The playing field for businesses in particular is ever changing.

In this environment it is critical for success that businesses correctly assess what is going on in their environment, how it will impact their situation, and what might be the best response. Economics is helpful here. it helps us anticipate what the consequences might be when situations change (like changes in the competitive structure of a market, like changes in food prices when the weather in California is particularly harsh, like changes in tax laws, or changes in duties on imported goods, and many others). Situations affecting most businesses change almost continuously— their customers get concerned about losing their jobs, competitors are changing strategy, their supply chain is being affected by economic policy of foreign governments, and many others. How do businesses keep up with these threats and opportunities?

Well, an economics course will help understand how markets work, and how these markets are often linked together— helping string together the impact of some event or changed circumstance to the consequence on a particular business. But how do we get data to say whether relevant circumstances are changing, or relevant events are occurring? Where’s the data coming from? It comes from the news, talking with customers, reading trade magazines, watching TV, the dinner table, and lots of other places. Economics data is everywhere— though people not trained in economics don’t often see it. We need to be sensitized to it being there— Once we see it, we realize we are swimming in data–some of which may have consequences for our business, our career, the way we allocate our 401k money, the strength of our big competitor, and the way our customers are going to behave.

It is like an awakening to what’s always been surrounding us. As we develop and learn we come to realize that data is everywhere, but only when we learn to connect the dots do we bother to pay attention to all the data. We just don’t see all the data until we are sensitized to it being there. Like children, we are all born pretty oblivious to “connecting the dots” about what the future will bring to us. The child’s world is simple, not complex. As they age they begin to learn about things. One example of a new level of understanding , an awakening so to speak, is “learning about relationships”. Kid begin to learn that people don’t always get along. And, some get along in “very special ways”. Some people hurt each other, some marriages fail, some people make lots of new friends, others stay pretty much alone. There are lots and lots of complexities in how adults, and children “get along with each other”. Once this “world” strikes people, they begin to pay attention–they begin to be analytic about it—what are the predictive signs? When they begin to notice and try to “predict” what sort of relationship tendencies are present, they see that their world is full of data about “relationships”. They develop this appreciation for data slowly. But, if you are interested, the world is full of data. Biologists see the world differently because it is full of “biologic data” that others not trained to see the value of it just don’t see. Artists too see a different world than the rest of us– a world of shapes and colors and textures.

So it is with economic signals and clues— situations are changing and events occurring all the time. If we begin to care about how the world is changing in ways that are going to impact my business or my career or my investments, i will begin to see the data for the first time. It is everywhere. Economics helps to open our eyes to the clues that effect the economy, our business success, our family’s economic future.

Economic Forces and Assumptions

All microeconomic forces in market economies stem from 3 basic assumptions about economic actors like buyers, sellers, investors, employers , and employees:

everything is scarce— there just not enough time, resources, and money and the the stuff you can make with these things to satiate everyone’s needs. Collectively, we say that society has insatiable wants.
free choice — because of scarcity every economic actor has to make choices, because they can’t have everything they might like.
pursuit of individual self interest— the driver behind the choices they all have to make is self interest (or maximization of their own or family welfare).

Why choose these assumptions? It seems rather random–but its fairly simple. Think about the most basic kind of economy. A bunch of hunters, gatherers, and primitive artisans meet up for the first time at a common watering hole in Ethiopia or Kenya. They quickly become aware of the variety of foodstuffs, tools and clothing available. Some want to acquire things they don’t have. Some want to trade excess stuff they have for other things they dont have. Some individuals want both. They start trading. What are the assumptions that characterize this basic economy. Not enough stuff for everyone to have everything they might want. Free choice. All scrambling to make the best deals they can for their own (and family) welfare.

Based on these three assumptions, economic forces are formed from the economic scramble to wherein everyone is striving to get the most they can out of their scarce resource endowments. The “scramble” looks like chaos for those that dont know economics. But, to those who have studied economics, the “scramble” is subject to a few simple patterns of market forces. These market force “winds” blow predictably across the economy, helping those people who understand them to “predict” what the consequence will be of economic events that are happening (eg. a new study suggests that prunes are bad for health, political leaders in the middle east restrict oil production, trade barriers are taken down, or a new kind of better battery technology is perfected, etc.).

The market forces discussed here are very basic ones. They are;

substitution in choice behaviors
productive efficiency and specialization
competition for customers
avoiding competition and market power
the drive to equilibrium
profits and investment flows

Economists have studied real situations for hundreds of years, and have developed an array of “theories” about these and derivative “forces” that may exist under more specific kinds of assumptions. But, we will stick to the basic, and more ubiquitous market forces here. But, before explaining these 6 forces, it would be good to briefly discuss two related issues:

First, these market forces only occur when the 3 assumptions we made actually exist. This situation of the three assumptions is often characterized by the term “the free market economy”, or “capitalism”. People (investors, employers) are all on their own to deal with scarcity, and their own needs and wants. That chaos or “scramble” is the way “capitalism” works. Everybody trying to make the best of their initial endowments of time, money, skill, and knowledge. Free choice is running amuk.

Of course, there are other ways to organize an economy. Think about what goes on inside a business firm, a big one, like GM or GE or Exxon. These “internal corporate economies” are themselves bigger than the economies of some small countries. Millions of exchanges of things occur every day between business units and between individual workers, and yet the “economy” within these companies is hardly a “free market”. Owners don’t want that, they want these economic activities all organized and coordinated to serve their own (owner’s)needs, not the needs of all the individual workers. They want everything planned out, and they hire managers to make sure that all the workers and business units know exactly what is expected of them, and how they should behave. This is a planned economy. Workers don’t have “free choice” about what to do, what to do with their finished products, and the like. Departmental managers are not free to use their “budgets” in any way they choose. No, the business firm is a carefully planned economy, where participants don’t have free choice. And, nobody (except the owners and very top managers), is able to pursue their own self interest. The main choices they have is whether to work there, or not, and what to do with their take home pay.

Such planned economies have also been established within country borders — China, USSR, and Cuba are the most well known. Here, a central government authority makes a plan for the economy– what’s going to be produced, how much of everything will be produced, where it will be produced, how it will be produced, who will be working in which place to produce all of it, and most importantly, who will receive the finished products and services, etc. etc. Yes, its a complex plan. But, doing such plans and organizing their implementation is feasible. We could call it “a socialist economy”. Here, the needs, wants, and priorities of the “society” are placed above those of the “individual people” (eg. the individual economic agents have very limited choice to pursue their own self interest). Different assumptions! The “market forces” described here just dont exist the same way in such a place —though uncontrolled aspects of those economies, where people are free to choose according to their own selfish preferences, do seem to function in a scrambled fashion, according to market forces.

Are the 3 basic assumptions “better” than other sets of assumptions? That is not possible to say. That is a political question, not an economic one. Political issues are always a matter of winners and losers. Better for who? Worse for who? Some people argue that “free market assumptions”(the three individual rights listed above) may have “winners” and “losers” according to initial “endowments” of talent or wealth. And, therefore, not be “fair” to all, and could be modified in order to provide a better result for all groups. Others believe (including most economists) that the “economic forces of capitalism”– described below– generate a more efficient use of scarce resources, and produce a bigger pie for society to share — allowing society to be better off. Is this a winning argument? That’s a political question, not an economic one, because there is involves assessment of the comparative welfare of the “winners and losers”. These political questions involve fairness, or normative issues. Similarly, questions of sustainability of resources, global warming, and issues of social fairness are often raised about “free markets” or “alternative ways of deciding how the economy works”.

In the words of a famous political economist, socialism is an economic system designed so as to achieve “from each according to ability, to each according to need”. Capitalism might be characterized as “from each according to ability, to each according to ability”. These are terribly important political issues, and while they may influence how we vote, they are way beyond the study of “how market forces” work.

1.substitution as a universal response to change — the most basic economic force is called “substitution”. It stems from scarcity of resources. It is the behavior of economic actors to make “adjustments” in their choice behaviors in order to maximize self interest when circumstances change. When circumstances change, they “adjust” what they buy, how much they buy, how much they produce and how they produce it, and all other choices. Adaptation always occurs as economic actors fine tune their use of time and money to changed circumstances. They always have some options available about how they might adjust. They can change what brand they buy or whether to spend money on any brand at all. They can change the skill requirements of the employees they hire, or even whether to be in the business any longer. They can change the timing of their buying and selling behaviors.

That is, they always can respond to the market circumstances by making a change if it is in their best interest to do so. This possibility of substitution works for consumers, suppliers, investors, for every economic actor. Even when there is only one airline flying to the place your going, you have options. You can substitute. You could drive, take the train, hitchhike or even do GoToMeeting or Facetime in a pinch. Substitution possibilities for what we do, or how much of it we choose to do, are always possible as adjustments to changes in market situations. It is a powerful force in the economy– and it stems from scarcity and fine tuning to maximize self interest.

For consumers, when the price of a Public Transit-pass goes up by $0.25, they have to decide how to respond to the circumstance– they can substitute other ways of transport. Or, it is also possible to change by moving other items in the budget around in order to accommodate the price change. Maybe some consumer will change by no longer doing public transit– and others will not stop riding, or stop riding as much. Everyone will “adapt” by some form of substitution.

The extent of substitution by consumers is related to preferences and the availability of close “substitutes”. Maybe, for one commuter, the wife drives near to the ‘destination’ an hour earlier, and this “alternative” presents a close substitute for taking the “T” when the price goes up. Not everyone has a “close substitute” like this. But some do.

So, individuals will vary in their substitution behaviors according to their wants and situation. So when the ticket price goes up by $0.25 if they have a “close substitute”, they will be more likely to make a change and “substitute” another form of transport for this one. And their tendency to substitute against the higher priced good relates to how long we wait to observe (and measure) the substitution behavior. if we give the person only a day to make a change, few will be able to do it— but if we give them a month to consider a change, many more will be able to substitute. Individuals will always find ways to substitute to some degree against things that have become more expensive. On the other hand, as certain products become more attractive in some way (price, functionality, fashion, etc.) consumers will substitute in favor of them.

Firms/Employers substitute too. When the price of labor goes up they “fine tune” their operations in order to use less of the now more expensive labor by substituting cheaper and less skilled labor. Or, if given enough time, by substituting more or more versatile equipment or by outsourcing certain processes entirely. When circumstances change, adjustments will follow to substitute away from resources that have risen in price or against selling in markets that have become less attractive.

2.Production Behaviors and Specialization

Why do we have firms anyway, specializing in making only one thing? Why do our jobs often so narrow or specific — selling want ads for one newspaper, or riveting the left side of honda bumpers on an assembly line? Why do we have economic exchanges anyway, why dont we all just move to Idaho and become self sufficient? How does this specialization happen? Why does it happen? The answer is quite simple. Given endowments of scarce resource each person (or family) tries to get as much from them as possible. And, it turns out that teamwork (economic cooperation) makes it possible to take advantage of diversity in endowed capabilities to make the economic pie bigger if we do things in teams. The only way this “team” or cooperative approach does not yield a bigger pie (for all to share) is if there is not any initial disparity in endowed capabilities!

Lets start to look at the from the earliest kind of economy. Fred, Barney and other “hunter-gatherers” living alone in the great rift valley in Ethiopia or Kenya. They have a chat about their hard life. Barney admits that in a day he can hunt down (chase) 1000 calories worth of meat. Or, alternatively, he can gather up 800 calories worth of fruits/veggies in a day. Fred, in turn, says his usual output in a day is about 1000 calories of meat and 900 calories of fruit/veg. After discussing and using Excel to model the various cooperation scenarios available to them they conclude:

they can produce more together if they each “specialize” in one kind of activity
given endowments of speed, smarts, eyesight, and smell (or whatever contributes to differential productivity in hunting and gathering) it is “cheaper” for Barney to hunt, than Fred (because Barney has to give up only 0.8 calorie of fruit/veggie to get a calorie of meat, and Fred has to give up 0.9 calorie of fruit/veggie to get a meat calorie).

so, Barney has a “comparative advantage” in producing meat, while Fred has a comparative advantage in producing fruit/veggies

and, if they each “specialized” in what they could do relatively more efficiently than the other, they could produce more as a team, than as the sum of their individual work.

In this case if they each spent two days doing “hunting” for one day, and “gathering” for one day, they would produce a total of 3700 calories of food

Barney Fred B+F Sum

meat calories 1000 1000 2000

fruit/vegg calories 800 900 1700

TOTAL 1800 1900 3700

On the other hand, by specialization, they would produce 3800 calories

Meat Calories Fruit/Vegg B+F Sum

Day 1 1000 900 1900

Day 2 1000 900 1900

TOTAL 2000 1800 3800

This isn’t a huge differential (3700 vs. 3800), because the relative capabilitiy of the two fellows is quite close. Larger differentials in in initial capabilities yield larger gains from cooperation through specialization. The ‘absence of diversity’ is the only situation when further specialization doesn’t produce more output.

The only glitch in this solution is that “if” they decide to cooperate by specializing, they must decide how they will “share” the pie in the end. Yes, the pie is larger, and there is more for both if they do specialize. But, they theory of specialization doesn’t say how the gains are to be split.

In summary:

Cooperation via specialization always yields more total output for the group because it takes advantage of relative productivity (efficiency) advantages across members.
They can produce the most by letting everyone do what they can do most cheaply (giving up the least to do it)–eg letting everyone do what they have a comparative advantage in doing within the group.
How the gains from specialization are shared is another matter altogether

This “drive” to be more efficient in groups, drives larger and larger groups, and more and more specialization — to further increase the size of the pie. This drive for “efficiency” in production with fixed (scarce) initial endowments in capabilities is a very powerful response to scarcity (the human condition). Yes, progress in production technology (machines like a bow and arrow for Barney, or assembly line production, or computers, or wireless) — all are capabilities that expand production levels that are possible with our fixed resources. But, at core, specialization drives a lot of what we see;

Groups (tribes) residing together (more economic well being + safety)
Growth of cities and guilds/trades (more and more specialization is possible)
Formation & Growth of firms (that internalize the benefits of more and more specialization)
Globalization of Markets (broader reach of specialization’s potential benefits— free trade is based entirely on maximizing the benefits of specialization and trade across countries )

This drive to become more productive by cooperating based on specialization and trade is a fundamental force, created by scarcity and growth goals. By making the cooperating team bigger, and the degree of specialization more and more detailed, the pie will always grow. Always!.

The caveat. Yes, more and more free trade (eg no barriers to free trade) will improve the size of the global pie. This takes more and more advantage of the comparative advantage of the cheap labor in some places, and the super capable scientific base in other places, and the cheap access to oil in other places, etc, etc. Capitalizing on this global diversity in resource endowments makes the pie bigger, but it doesn’t mean there are not winners and losers in each country of taking advantage of diversity and specializing. Yes, some people will lose jobs, others will gain from increased opportunities. So, again, there are “political” or “fairness” issues in ‘growing the pie’.

3.Competition for customers — this is a supply phenomenon. Because households (consumers) are striving to “fine tune” their purchases (and choice of job) at every possibility to maximize self interest, sellers are always wary of wanting consumers to “choose them”. Sellers will make adjustments to their products and services (price, quality, service, convenience) in order to make it more likely that consumers will choose them. And, the more competing sellers there are in a market, the more pressure each of the individual sellers will be under to improve the attractiveness of their product in the eyes of consumers. Competition is when multiple sellers are vying for buyers. The more options the buys have, the more intense will be competition.

Competition and competitive seller behaviors are, for sellers, a way of “adjusting: to changing market circumstances” for existing products. Of course, sellers also make adjustments to refine product features, expand scale, expand scope of product offerings, or stop producing altogether.

How does competition occur. The basic list of competitive behaviors is:

lower price
cut the costs of producing products/services (which enables lowering price and still earning a profit)
improve product/service quality
better customer service
more convenience
new functionality/features
developing new related products

These kinds of seller behaviors are good for customers. They improve range of choice for the consumer. and the value the customer gets when they buy. The more sellers of the exact or similar products, the harder the seller will have to work to get the “customer to choose them”. This force is good for customers, and not good for owners of supply organizations.

Society and well as consumers do benefit from competitive behaviors. Firms that cant compete often fail. This includes the high cost (inefficient) firms, and the ones that produce the lowest quality products. This winnowing process caused by competition helps society “get the most bang for the buck” out of the scarce resource endowments; eg the most “value for money” for the consumers.

Suppliers, of course, find competition to be a game they don’t want to play. They try to avoid it at all costs. This is discussed below.

4.The drive for market power by sellers

Competition stemming from more sellers is a powerful force, enhancing the value for customers. Firms don’t like it:

they have to lower prices and trim profit margins
they have to continuously search for ways to improve products and services
they have to run serious risks of insolvency if they fail to be successful in the “competition game’ with other sellers.

So what do sellers do about this unpleasantness? They try to avoid direct competition with other sellers. They do this by try to reduce “competition” for their products by:

branding (differentiating) their products and services—so that customers will not see as much likeness with products/services sold by other sellers, reducing the need to “compete” as described earlier
They try to increase their market share (market power) and reducing the importance of competitors in the markets in which they operate (and reduce the range of choices the customers have)
through scale and other means they try to increase control of their supply chain and their distribution networks (in order to increase the business problems and costs of the “competitors””
getting government to help restrict competition (patents, business licenses, exclusive contracts, exclusive tax subsidies)

This agenda is what business firms do to avoid “competition”. It is central to their survival strategy.

As firms successfully create market power (through branding, increased market share, government action) what do they do to capitalize on their “reduced exposure to competition? Essentially, firms with market power have control over their own price (rather than have price set by pure supply-demand scrambling as for corn, oil, phosphate, etc.). And, they choose a price that will maximize their profitability. Of course, unless suppliers use a gun, they can’t force consumers to pay $10 for a can of diet coke. Consumers have the ultimate choice to make about how much the prefer to buy at all possible prices. But, the firm with market power will restrict production levels somewhat in order to elevate price above the price that would be set by demand-supply forces alone. This will increase profit margins (profit/revenue) .

Firms with market power will, if they can, also set separate prices for different “segments” of their customers. Profit is higher in this situation by charging “tailored” prices to each segment, rather than some “average” price across all segments. Some segments of the customer base may value “convenience” more than others, who value “low price”. Grocery stores, for example face this kind of bifurcated market, and the use coupons to differentiate the two groups. Almost every retail seller today uses a segmentation strategy in their pricing.

5.Getting to Equilibrium — as situations change, and as adjustments are made by consumers and sellers in markets the prices fluctuate, and situations of Shortage and Surplus develop. These situations create powerful forces to establish an equilibrium market price– a situation of balance– where the price tends to settle at a level such that the amount being demanded at that price is exactly equal to the quantity that sellers want to sell at that price. This is equilibrium, a point of rest, where the forces created by shortage and surplus are quiet.

shortage forces– a situation where the price is too high relative to the equilibrium price. The quantity demanded at the current price is greater than the amount being supplied at that price. — this situation will cause potential buyers to become frustrated with an inability to buy as much as they want. And, they will begin to offer a higher price to “meet their needs”. And as the price goes up, more supply will be forthcoming, and less will be demanded. These forces will continue until the quantity supplied = quantity demanded at a higher price than was occurring at the initial “shortage situation”. This will be the new, higher, equilibrium price.

surplus forces — a situation where the price is too low relative to the equilibrium price. The quantity demanded at the current price is lower than the amount being supplied at that price. — this situation will cause potential sellers to become frustrated with an inability to sell as much as they want. And, they will begin to offer discounts to “meet their sales quotas”. And as the price goes down, more demand will be forthcoming. These forces will continue until the quantity supplied = quantity demanded at a lower price than was occurring at the initial “surplus situation”. This will be the new, lower, equilibrium price.

The equilibrium price will be a reflection of consumer and seller preferences; which are driven by, respectively, the willingness of consumers to pay, and the costs of resources to make the product. These things can change. and throw a stable price situation into “disequilibrium, and the powerful forces created by surpluses and shortages will be engaged until a new equilibrium price is reached.

This is often depicted by the use of demand and supply curves (interacting behaviors of buyers and sellers) , intersecting at the equilibrium price.

6.Prices, Profits and Resource Flows — We know resources in our economy are not unlimited. They are scarce. As the individual households scramble to make the best of their opportunities, and as the individual businesses in the economy scramble for their own competitive survival the system —all of this apparent chaos ends up directing which businesses actually get which resources, and which households get which jobs and what pay level, and which products actually get produced and what price they get sold for. Somehow out of all the chaos, these things get determined in what we call “markets”—- where the buyers and the sellers for every thing (types of products, types of labor, types of other resources, rental markets for property, etc etc. These markets end up deciding everything in a capitalistic economic system (what the price is, how much gets traded (bought and sold), and which buyers actually go home with the goods, and which ones dont).

The strength of the seller interests relative to the buyer interests in every market end up deciding these things— more buyer interests cause price to go up as supply shortages occur — more selling interests in a market cause surpluses to occur and prices tend to fall. And, in all cases the household buyers who end up going home with the goods and services are ultimately the ones who are willing to pay the most for the product. Likewise, the businesses who end up being able to go home with the most resources (labor, materials, investment financing, space) are the ones who are willing to pay the most for the resources they need.

When demand for some new product is hot, prices can rise, which cause profit margins for those businesses to rise, and demand for the key resources they need to produce their product to rise as well. The “hot” products that customers want to buy, generate higher demand for the “hot” resources that are needed to produce more and more of that product. Competitors that find ways to be successful in finding those “hot” resources cheaply can be more successful than their competitors that aren’t so successful (eg supply chain management is often a key to success of businesses). So, what the system of scrambling for survival by buyers and sellers tends to do for us is: allow the consumers who are willing to pay the most to successfully get the final products and services, and to cause the successful suppliers to supply those who are able to to be successful in getting the resources they need at lower prices than competitor suppliers—allowing the successful suppliers to be able to achieve a lower price point than competitors, and still make profit.

So, capitalism allocates both products and resources used to make the products on the basis of markets, where prices are set so as to allocate those scarce things according to who is willing to pay the most. Households compete for product. Businesses compete for resources to make those products. This is good for the economy— and for consumers —- would you want the households who dont really need or want to get the goods and services of the economy? Would you want the suppliers to get the scarce resources who are the firms who can figure out ways to keep the costs and prices down to lowest possible levels?

Socialist economies (where the government decides who gets the products and services, and decides which firms will get which resources to use to operate) are not nearly as good at allocating resources as are markets. But, of course, capitalism is not always a fair system if some people are poor, or otherwise cannot participate in labor markets to get money. That is discussed elsewhere— market failure is a big policy problem, particularly in health care.

6a. Investment capital Resources the Flow of capital

The flow of resources (including investment money) in our economy is a critical driver of economic growth and opportunity. (eg Capital is key to capitalism!). How the marketplace for “investment capital” works is very important in making sure that the capital flows to the best opportunities (eg Apple and Google) and isn’t wasted on lousy opportunities (like Kurt Shilling’s Video Game business).

Businesses, old and new need capital to fund operations, expansion, innovation and research, acquisitions, relocations, etc. They get such capital from earning and saving profits, from outside investors, by borrowing money (bonds and banks), and buy selling more shares of their stock. Given levels of retained earnings (profit) and borrowing, their access to capital, particularly to large amounts, is largely a matter of how high their stock price is and how much they can get via a “new public offerings” of their own stock.

Investors are willing to provide funds as long as they get a return on their investment. Its all about profit! The pursuit of profit is not evil— it is the name we give to the return on a scarce resource– the provision of capital for investment purposes! Profit and other forms of capital funding is very scarce in any economy, and it is very valuable to new businesses, to successful businesses that want to expand, even to promising students wanting to become doctors and lawyers, who need to “borrow” in order to enter the profession. Indeed, many people earn their incomes by having capital on which they earn a return. A owner of a 3 family apartment house, for example, financed from savings from his job as a tradesman —now earns his family’s income by the “return” he gets on his investment. He gets rental income month by month which he hopes exceeds his expenses, and he hopes to eventually sell for way more than he paid for the house, providing a retirement nest egg. Profit on the “house” is essential for the owner-of-capital (capitalist) to survive.

Investors (people with wealth) pursue opportunities by investing their savings in exchange for a return. Owners of capital (people with savings, or net assets tied up in a house or a business or the stock market) try to pursue their self interest as best they can, just like the rest of us. Except, rather than me, who chases the next-best-career-move, they chase better profit opportunities (or returns on the investment, ROE). We will call capital owners “investors” here (even though many people are both wage earners who sell their time, and investors, who also have savings or net assets of one form or another). Investors allocate their capital (savings) across investment opportunities based on the expected gains from, those opportunities in the context of what they see as the expected riskiness of the investment. Treasury debt offers a very low return, but exceedingly low risk of not being repaid. Alternatively, some stock offerings (eg equity) may offer considerable upside potential returns (20-50% a year) but are accompanied by many kinds of uncertainty, including the possibility of a loss. Investments in 3 apartment rental units may have pretty good returns, but wild fluctuations in the price of real estate and rents may make it pretty risky.

There is a “market” or scramble for finding the best “profit-making” opportunities among investors. Investors in the economy (the 55 year old accountant living next door, Warren Buffet, the owners of Apple stock, the teamsters pension fund, the Magellan fund, the Local Community Bank. the Government of China, and millions of others) are all chasing a high return on their invested monies, after adjusting for the variations in riskiness. If opportunities to make profit in Boston real estate are improving, more and more investors will pull their money from lower-profit-opportunities and put their money into those Boston opportunities. Capital will flow into the “high profit” opportunities. If Pharmaceutical and Biotech industry profits are going up, likewise capital will flow into those industries (investors will buy stock, bidding the price of shares higher and higher — and making the value of all stock in that industry higher–this aggregate stock value is called the market cap).

Profit levels are a crucial signal in the economy. Capital (for expansion of successful businesses, for start ups, for student loans, for buying real estate, for government to borrow to fight a war, etc.) will flow in the direction of the highest return or profit. This is good. It means that the most valued uses of capital (as determined by consumer’s willingness to pay) will be attracting the most investment resources. And, the least attractive objects of consumer attention (where profits have become low), will suffer losses of capital (investors will sell stock, prices of stock will fall, and market cap will fall).

The last point about investor behavior and the flow of capital concerns the importance of “expected profits and expected risk” in investor decision making. Investors may get their “return” in several ways: dividends from earned profit (a profit payout), from interest payments (on lent money), and from appreciation in the investment instrument when it is eventually sold (eg the difference between purchase price and selling price). Since the “future value” of the investment asset (stock, bond, partnership share, etc.) is not known with certainty, investors are always trying to read the tea leaves about what is happening to ‘future value’. Generally, future “stock price” or “business value” would depend on expected future profitability. This is hard to predict, adding an element of risk to any decision to hold an equity investment. For this reason, investment capital (as a resource) has to receive a somewhat higher return than returns on labor (human capital).

Thomas Picketi, a French economist, has recently used this notion of a “somewhat higher return on accumulated wealth’ as an explanation for the very divergent flows of income (skewed income distribution) in capitalist economies. He claims the divergence in the income distribution in capitalism (where the rich get richer, and the working class poor do not) is the result of chronically higher returns to capital than the returns to labor.

So, in general, the “scramble” in markets tends to cause the scarce resources to flow where they are most valued. This ebb and flow of the scarce resources doesn’t occur because of a change in “plan” and a memo from the central planning committee. Rather, the ebb and flow is driven by the changes in the urgency of buyers, or changes in the the scarcity of resources. And, though not a memo, Adam Smith called it “the invisible hand’ directing the flows of scarce resources across industries in the economy according to profit signals. This profit signal provides a huge set of “market forces” in the economy, directing capital flows.

Scarce Labor resources — will flow toward industries/jobs where pay is rising, and away from industries where pay is stagnant (as people pursue their self interest and pursue the best return they can get on the investments they have made in skills and careers (called human capital)

Scarce Natural resources — Like land, oil, bauxite, iron ore, etc— resources will flow to uses that are willing to pay the most for the resource. Land, for example, is flowing to high rise buildings in Boston’s Fenway area, because developers are willing to pay more for land to make profit on these kinds of real estate plays in a booming tech economy—- but this is driving out resources available for single family dwellings, low profit industries (pizza shops, other local retailing).

Scarce Capital resources — Equity Investors earn profits from the business. Where profit opportunities are high, investment monies will flow in (bidding up stock prices)- giving opportunities for managers to generate money for expansion, research, acquisitions by selling more shares of stock. This flow of capital will come from investors shedding less profitable investments (by selling stock) which will drive down stock prices and make it less and less likely that the unprofitable businesses can grow, or survive.

Health Policy Bias in Technology

November 23, 2016June 24, 2019 gaumerLeave a comment

What most people, even professionals in the Health Care field, don’t realize is how skewed our public policy is toward promoting innovation in health care. This ever-changing world of new knowledge, new technologies, new treatments, and extensions of clinical education to keep up with all of it (doesn’t Magnet now require a doctorate, when less than a generation ago 2 and 3 year diploma nurses were VPs for nursing at good hospitals!!!)—- Anyway, the technical change in health care is widely thought to be responsible for at least a third of the increases in spending since 1960, maybe up to half. —– THIS IS NOT JUST THE CONSEQUENCE OF incentives for UNBRIDLED SCIENCE IN THE FREE MARKET US ECONOMY.

Roughly, we spend about 1% of GDP (and 5% of Health Spending) on Bio Med R&D in America. Of this $150B or so a year, about 1/2 is spent by private industry, about 35% is funded by Federal taxpayers, and 15% is spent by other parties (foundations, universities) . Unlike other private investors, the taxpayers do not “own” or get any return on the YIELD of success that accrues to their investments in research (other than the benefits of progress) — which of course are also available to the rest of the world.

The GOVERNMENT has been subsidizing health care R&D big time for many years, and more recently it has been actively protecting the viability of the vast Health Care R&D industry:

A. Patents-— the USG awards monopoly privileges to new chemical entities (and other things), giving them 18 or so years of monopoly power to charge whatever they want to consumers.

B. Taxpayer Subsidized basic research —- taxpayer dollars have been flowing into R&D through grants to researchers from NIH, CDC, NCI, NSF, DoD, others. Most of the money goes to BASIC research, from which additional investments must be made to create new products. Basic research is very important, but not something that private sector investors feel is worthwhile (because it often fails to produce viable pathways for commercial products). So the Feds divert tax dollars to do it–the results are published — and private companies can exploit the new findings to try to find viable products. The taxpayer gets nothing in return. The Chinese, for example, have a good R&D strategy that shows how this Basic-Applied distinction works. They spend virtually nothing on BASIC research (unlike the U.S.) and put all their R&D money into APPLIED work (they of course can read the journals and learn all the new basic science without producing any for themselves. Patents aren’t available for new findings in physics, chemistry, or biology — they are only available for potential commercial applications. Our tax dollars are therefore, subsidizing the Chinese APPLIED research machine that will soon exceed the size of the U.S. applied research machine.

C. Policy aimed at containing costs of health care in the country has exempted the R&D machine from being targeted. Their lobby is very effective, probably every bit as effective as the NRA in buying votes for elected officials. Policy has taken aim from time to time on Hospital spending, nursing home costs, medical procedure payment rates, and even administrative expenses and insurance underwriting. But, never R&D spending or pricing by the drug and device industry. The drug industry neutering the power of the DEA in the opioid epidemic punctuates the political power of the Health Care R&D Industry.

D. The ACA prohibition of using CE analysis to appraise the merits of new drugs and devices. In 1996, after 2 years of deliberation, the U.S. Panel on Cost-Effectiveness in Health and Medicine, composed of physicians, health economists, ethicists, and other health policy experts, recommended that cost-effectiveness analyses should use quality-adjusted life-years (QALYs) as a standard metric for identifying and assigning value to health outcomes

The Patient Protection and Affordable Care Act (ACA) created a Patient-Centered Outcomes Research Institute (PCORI) to conduct comparative-effectiveness research (CER) but The ACA says “The Patient-Centered Outcomes Research Institute . . . shall not develop or employ a dollars per quality adjusted life year (or similar measure that discounts the value of a life because of an individual’s disability) as a threshold to establish what type of health care is cost effective or recommended. The Secretary shall not utilize such an adjusted life year (or such a similar measure) as a threshold to determine coverage, reimbursement, or incentive programs under title XVIII”.So says the Congress about the idea of rationing (or controlling introduction of new technologies) into health care practice based on how effective they are vis a vis how much they would cost.

E. When Medicare part D was passed under W. Bush, it actively PROHIBITED Medicare from negotiating prices of drugs with the drug companies— forcing Part D to pay “list price” for all the drugs used by Medicare beneficiaries. Every other country like Canada, and all of our health plans here, pay around 50% of the list prices for using the same drugs used by Medicare beneficiaries. Bush had to negotiate this “deal” for the drug companies to get their support for Part D.

F. Orphan Drug Act (1983)— still provided Federal funds for small market drug R&D.Examples of some high priced orphan drugs are listed below (Haffner, 2017):

$300,000 for Sarepta’s Eteplirsen for Duchenne Muscular Dystrophy,
$375,000 – $700,000 for the BioMarin product Brineura to treat a rare subset of the rare Batten’s disease.
$420,000 for Soliris of Alexion for hemoglobinuria
$750,000 for Biogen’s SMA drug Spinraza
$550,000 for Horizon Pharma’s urea cycle disorders drug Ravicti

G. Tax Credits for R&D expense by Private Firms (1981, 2015). Current tax law has an incentive aimed at encouraging R&D activities by private firms. This incentive is a tax credit (against earned profit taxes) for R&D expenditures.

H. Stevenson-Wilder Act ( 1980 ) — provided the possibility for private firms to make use of Federal Research Laboratories and opened the opportunity for research partnerships with the federal government.

I. Reverse Direction on the ACAs medical device tax. To generate revenue and slow the pace of introduction of new technology the ACA included a retail 2.3% sales tax on medical devices (scanners, etc.). That attempt to reduce the pace of innovation was reversed by the Congress.

Why the Bias in Policy?

The power and policy bias ultimately comes from the voters, who want the industry to be protected and nurtured. The piece i asked you to read about the “culture” in America is about this (Sasascus, The American, 2013). Americans want heroic health care. Ask anyone who worries about end of life care. We like to reward miracles of science in our culture, and this aspect of hope, and positive or optimistic attitude of Americans has always been the sources of the bias. We are different than the Europeans we fled from…. their cultures were rigid, no real chance of intergenerational upward mobility. We fled. We want better ways of doing things, we need science to show us a better way in all aspects of our life, we dont want to be trapped in a rigid and hopeless way of life when it could be better. We want this. We look to our NIH and NCI grants as a source of miracles of a better life, even more so than we look to our faith as a source of those miracles. We are different.

And our health policy reflects our unique culture. That is, the absence of policy attempts to reduce price we pay for new technology, or regulations that would elevate the bar for new technology to be introduced into the health system or reduce the taxpayer investment in medical R&D. Or, even simple solutions like reducing years of patent protection or providing more price transparency for generic drugs. (Lieberman, S. M., & Ginsburg, P. B. (Brookings, 2017). Anything of these sort would reduce private sector investment in R&D. Americans don’t want that.

The policy bias is also reflected in the way government “looks the other way”, rather than by overt policy action. We fight over issues like the Medicaid expansions, and the generosity of the rates we pay hospitals and doctors. But we do not fight over the price of drugs, the pace of technology change, or the way drugs are marketed in America. Some issues are kept “off the table” by the R&D industry.

Marketing of drugs is an import issue. Influential doctors are paid to use, and to promote new drugs by manufacturers.These “bribes” help speed the uptake of new drugs. The local beer distributers here in Boston would be sent to jail for bribing bars to sell their brands. The well funded “push” to increase prescribing by such methods, often accompanied by “research grants” and “speaking fees” has supplemented the incomes of doctors for several generations, and corrupted the independence of doctors. The licensing authorities in states have looked the other way about all this “commercial influence” over practice. It is a shame. Doctors are required by journals, if they author a publication, to identify conflicts of interest. Medicare and other insurers may limit the “self referrals” to labs or surgicenters owned by the referring physician. If Licensing authorities were truly acting in the interests of the people, they should require practitioners to reveal all of their financial interests in the prescribing they may do.

Synopsis and policy regarding the FDA

The problem of reforming the FDA drug regulation functions is being suggested again. Good idea.The Globe reports (11/23/16) that Mr. Gingrich said “the FDA is a major prison guard stopping the breakout in health” and has called for the elimination of the FDA. I am not sure this would be a good idea. Before leaping to eliminate the regulatory functions of the FDA, lets consider again where we find ourselves in terms of Federal government policy regarding health care and medical technology.

We vastly outpace the rest of the world on health care spending per capita, and the vast majority of that spending is on curative specialty care, fueled by wave after wave of new knowledge, and new drug and other diagnostic and therapeutic technologies.

Yet, we are far behind the rest of the world in our health status. W.H.O. reports (in 2015) that the U.S. ranks 31st on worldwide longevity (right between Costa Rica and Cuba).

Yes, we have a seriously underperforming and super expensive health system.The reform of the federal role in the health technology business needs to be seriously evaluated.

Reprise of the federal role:

Regulating safety and efficacy of new drugs and other technology (do they not harm people, do they more or less work as suggested). FDA nor other agency regulates price that is charged in any way.
Extensive taxpayer funded subsidies for new medical and health research (predominantly grant programs from NIH and NSF) — of about$35-40B annually (compared to about $60B spent by private companies) . The results of these research activities enable private organizations (including new ones started by academic researchers) to use the basic research findings to design new technologies. That’s why the basic research subsidies are funded by taxpayers in the first place! (And, by the way, the taxpayers do not get any payback or “ownership share” in the profitable new technologies that are spawned by the public research money).
The Government ensures that Drug companies get to charge high prices to Medicare enrollees (again, another taxpayer funded subsidy). Your elected representatives have made certain (by an explicit provision in the law) that the largest Insurer in the U.S. health system (eg Medicare) cannot negotiate the list price of drugs purchased from U.S. drug companies, as is done by every health private plan and insurer in the U.S., and by Governments of other every other country in the world who wants to buy these U.S. drugs. Medicare pays about twice the price for the U.S. produced drugs as do citizens of Canada and other countries who buy the same drugs.

The government provides and enforces (free of charge) a monopoly to all new drugs (and some other technologies) for up to 20 years. This means that no other firms can sell the same chemical entity until the patent expires. This allows the firms to charge whatever the market will bear, without worry of competition. This guarantees high prices, and substantial profit returns on successful R&D investments, and is intended (by our policy makers) to promote more private (company) investments in Research and Development to keep the wave after wave of new drugs coming. So, the U.S. taxpayer, through the actions of our government, have actively subsidized the research underlying new product development in drug companies, and also taken steps to make sure that taxpayers and other buyers of drugs in the U.S. here also pay more to buy drugs, and in the case of the tax payers supporting the Medicare program, more than people in other countries have to pay to buy the U.S. produced drugs. Why did we do this? Presumably so that we can fuel the drug and technology companies with new knowledge and financial resources so that they will continue to invest in new product research to keep spewing out wave after wave of new products. How fruitful is this engorged world of health care R&D ? Well, the industry is certainly attractive to scientific researchers from all over the world, who get visas to come here and work in our labs and pursue careers that are simply not available in their countries. And, recent research by Olfson and Marcus suggests that the efficacy of the recent research on new drugs (compared to the placebo) has been declining for 45 years ( Health Affairs, June 2013) as the volumes of research on new drugs has increased. And, American longevity has continued to edge up over time, but so have all other countries. It isn’t clear whether quality of that extended life is improved here. Why do Americans keep reelecting these people who pursue these policies? In a nutshell, they don’t generally understand that the government (taxpayers, and medical bill payers) are largely responsible for subsidizing the interests of private drug and technology companies. But, Americans also have a tradition of believing in high tech miracles of science are vastly better than engaging in healthier behavior at home. Every study that has looked at the determinants of preventable mortality in populations has shown that choices made in the household (diet, exercise, risk behaviors, etc.) is by far the key determinant of preventable mortality (more important than variations in genetics, environment, social factors and formal Health Sector). Americans just prefer to behave as they please, and when that gets them in trouble, they want a quick medical fix. Of course the curative health care sector (physicians, hospitals, etc.) support the policies too, because it drives utilization of curative services, and incomes of professionals engaged in curative care.

Yes, the government’s policy needs some fresh thinking in this area of health care technology development. Mr. Gingrich’s policy advice that “the FDA is a major prison guard stopping the breakout in health” and has also called for the elimination of the FDA. I disagree. I doubt that Mr. Gingrich was thinking about “breakout” in terms of health of Americans — nor even “breakout” in terms of the financial burden on taxpayers and bill payers. I suspect he was thinking about “breakout” in ROE for the stockholders of Pfizer, Merck, Abbot, Biogen and others. I cannot be sure. But, it seems that FDA and other aspects of federal policy regarding “breakout” could use a serious and transparent review soon. Current policy doesn’t seem to be producing as much a contribution to health as it might.

Health Insurance

November 19, 2016June 24, 2019 gaumerLeave a comment

Purpose of Insurance

Health insurance is the primary way health care spending is financed in America. This novel mechanism provides “portable” financing for the patient— allowing them to choose broadly from providers, and having their ‘card” with them to provide their financing at the point of service, almost wherever they choose to go. The VA, DoD, the Indian health service are the main exceptions. Some employers also provide health services directly to employees.

Insurance is varied in type, and overall it is a huge business of collecting funds and expending them as patients choose their providers. Insurance is not the only mechanism for financing health care for people. In other countries we see “direct government provision” of services in some countries, where the government owns and operated the health system, and professionals are employees of the government (NHS in Britain is one example). In Canada, the government of each province provides free (tax paid) insurance like medicare-for-all for residents. Many countries have a mix of forms of financing.

The chart here describes insurance sector of the economy.

coverage

Only about 10% (in sept 2018 it was 8.8%) of Americans are not covered with some form of insurance. About half of all insurance is provided through employers (for people that have jobs that offer some kind of health insurance as a fringe benefit), 16% is Medicare, 22% is Medicaid (jointly funded by States and Federal taxes), and about 5% are covered through individual policies (with about half of these purchased through exchanges (in 2014).

Let’s step back. Why do we need insurance? The distribution of illness and other drivers of demand for health care, and spending, isnt equal across people. And when the need arises, the burdens are often catastrophic. It is common in countries where people have to pay for health care out of pocket (like it was in the U.S. before 1950 or so) for health spending by families to lead quickly to bankruptcy.(In the U.S., the burdens for unlucky persons and families, even with insurance with deductibles and copays, can sometimes lead to extreme financial burdens because of the high prices for drugs and some services.) This extreme financial burden of a small part of the population is the reason that insurance is useful. The chart below shows the distribution of spending for health care by people in America.

insure fin

What this shows is that about 50% of the total health spending arises because of of needs of only 5% of the people. And, the lowest spending 50% of the people consume only about 3% of total health spending. It is a very unequal distribution of need for health care. The skewness arises because of the distribution of illness, or bad “luck” in most populations. Some people dont need much care, and other people have catastrophic needs.

Most societies have this issue. Most provide some form of “social” mechanism to help defray the burden for the unlucky segments. Some do it by providing public health facilities where the sickest can get the care they need. Others do some form of “passing the hat” to help out families in great need. Our society does a bit of both of these things, but for the most part, we use the mechanism of insurance.

The tool of insurance works the following way. People form themselves into groups to purchase most health insurance (typically employed groups). The members of the group each pay a “premium” based on the average usage or spending level for a year. Then, life happens, causing some to need very little care, while others need a lot. The insurance plan pays the bills. In the end, the persons who remain “well” end up subsidizing the persons who are “not well”. This is the insurance principle of “pooling risks”. It happens for home owners insurance, for auto insurance, and for health insurance.

Insurance is a contract. Client pays a premium in return for insuror paying for the “covered loss” when it occurs. Insurance companies set the premium based on the expected outlays (losses) for the group (private policies set premiums based on expected outlays that are about 85% of the premiums). The other components of premiums are administrative costs (12-15%) and maybe a risk margin of 1-2%.

There are three basic kinds of health insurance.

Private health insurance is sold by private companies (United, BCBS). Mostly these policies are sold through employee groups. The group’s experience with spending (experience rating) or the community’s experience in which they live (community rating) are used to set the premium. If an employer is involved, they may pay, say, 75% of the annual premium, and the employee has a payroll deduction of their 25% portion of the shared premium. Employees may also share in the costs of their care, thereby keeping the premiums lower. This cost sharing is done through deductibles (a fixed spending level per year before the insurance starts to pay) or copayments (sometimes also called user fees) that maybe a fixed amount or fixed percentage of charges incurred for the services used. This is often a n amount for basic primary care visits, a different amount for hospital outpatient care or tests, a different amount for specialty visits and for an ER visit.

The copayments and deductibles paid by individuals do not just help finance the premiums. They also are “premium containment devices” because the higher these are, patient can (and do) change their care seeking behavior, and consume fewer services. So, increasingly, these higher deductibles and copays are utilized by employers to keep their share of the premiums lower. Of course, employers are also trying to get the employees to pay a larger portion of the premium from payroll deductions too.

Health Care Plans. Generally group insurance works by allowing patients to seek care from many providers, as is their choice. But, there is a variation on that model. Sometimes groups of providers sell “insurance”. These so called health plans accept premiums, and provide all the services directly with the plan’s own providers. These plans used to be called HMO’s or Medicare advantage plans. These organizations essentially are both insurors and providers—they accept financial risk of paying for care for a group of people in exchange for a premium— and these plans have copayments too. Examples are Kaiser, Harvard Pilgrim, and HMO Blue.

Social Health Insurance. Social health insurance is health insurance where the government (eg tax money) pays some or all of the premium. Sometimes the government pays all the premiums (eg Canada, Medicaid) and often the pooled social insurance fund is jointly funded by government, business, and individuals (Germany, Medicare). The model of universal coverage (for everybody) often uses social insurance in order to combine business financing with government financing for those without jobs. (There are other non-insurance models of financing health care— the British model of the National Health Service, for example, or the VA. Both use tax financing to hire providers and run their own hospitals and provide care directly).

The options for insurance, and for no insurance, are shown on the chart below. The options vary in terms of

who ends up paying the costs of the health care
the extent of risk pooling (the extent to which the well end up subsidizing the sick)

pooling-and-risk

Not having insurance, of course, provides no risk pooling protection and the “sick” have to pay everything. Comprehensive Social insurance, where the government pays everything from general taxes, the risk pooling is the broadest (across all taxpayers). Private employer based insurance is in the middle. Here, the costs of insurance is born largely by purchasers of the products and services of the firm.

This brings us to the three main functions of health insurance in our society:

to pool risks wherein the “well” members subsidize the care for the “sick” members
to create portable financing, allowing insured members to “take their coverage with them” as they choose their providers
to create “purchasing” agents that buy health services in our health system

The basic idea of private health insurance, adopted by law as part of Medicare in 1966, is to allow patients to have free choice of providers, and the insurance coverage will provide “financial benefits” whatever their choice. But, clearly, forms of “restricted freedom of choice” have crept into the insurance marketplace in the forms of “Health plans” which restrict choice within the plan, to various kinds of network plans, which charge different copayments for some networks of providers, but more if out of network providers are used. These models limit choice in exchange for more Insurer control over spending. Most employers now offer a variety of plan choices, some with virtually free choice, or more restricted choice (which are usually lower premium plans).

The purchasing function of insurers (private, Medicare, Medicaid) is very important, given the lack of information of patients themselves. The purchasing function is practiced by:

(1) regulating quality of care of providers from whom care is purchased. Insurers set standards for being a “qualified” provider (accreditation, licensure). They also set standards for “coverage”, and regulate the procedures they will pay for and the setting of care they will pay for.

(2) Purchasers also determine how payments for care are distributed across different types of providers (inpatient, outpatient, offices, wellness/prevention, etc.). Insurance payers spend a lot of time trying to improve the way they pay in order to create the right incentives for provider behavior. Medicare has been the innovating payer in the U.S. in this regard, and has moved to set fixed rates for “bundles of services” for most types of providers (as an alternative to letting providers set their own rates). These new ways of paying providers encourage efficiency. More recent innovations by both Medicare and BCBS of Massachusetts have also initiated Pay for Performance (P4P) systems that provide bonuses/penalties for high quality of care.

2. Types of Health Insurance

Many kinds of private health insurance programs exist in this country.

Major medical is the most common. It covers all medically necessary hospitalizations, rehab and professional fees if they are deemed “medically necessary” unless specifically excluded in the contract. Generally this type of coverage will pay for care whichever provider you go to, as long as they are licensed to provide such services in the state they practice. This would be the traditional model.

Variations on this are “managed care” approaches that might impose various kinds of inspections of “medical necessity” in order to curtail expensive and not very useful services. This typically involves a “pre authorization” or referral from a designated PCP. These kinds of ‘restrictions on freedoms of providers and patients were heavily used in the 1980s and 1990s, and became unpopular with employers because of complaints from their insured workers. So such approaches to cost control were replaced by others.

The Plans started offering incentives for using cheaper providers. Cheaper was determined in one of several ways. One way was to designate a “network of approved providers” which could be used for one copayment level. If the patient went to an out-of-network provider, they’d have to pay a bigger co payment. The network may consist of providers with whom the plan was able to negotiate low fees. Now, this has morphed in the case of hospital coverage into what is called “tiered” copayments, one tier is for the community hospitals (eg the cheaper places to get care) and another tier for the “teaching hospitals (where prices are higher).

Another type of non traditional coverage is the catastrophic insurance approach. This may be roughly the same as the Traditional major medical, but imposes big annual deductibles (which allow the premiums to be less). So, the consumer is paying out of pocket for the first 5- 10,000 each year, and then if they get really sick, the insurance company will pay after the deductible for the year is met. These are called Consumer Directed Health Plans, and were advocated by the guy who wrote about Medicare Killing his Father. These catastrophic plans are usually coupled with a Medical Savings Account (tax free deposits from your income into a personal account that can be used for health care, and the balance rolled forward year to year if you don’t need to use it to pay).

There are other much different kinds of coverage (other than the major medical model); (1) dread disease coverage was sold for coverage for particular diseases (eg Cancer, Stroke). This used to be somewhat popular before Medicare came along and covered the elderly. It essentially paid a fixed fee per day after being diagnosed with the covered disease. Today, similar types of policies are not popular, nor permitted under the Obamacare reforms.

A similar kind of “dread disease” policy is sold now to cover the need for long term care. This kind of coverage pays for services, but mainly the plans are structured to pay up to a limit (say 200 a day up to a max of 200,000). So, you pay a premium while you’re middle aged, that will pay for some pool of benefits if you encounter the need for long term care.

The regulations have been minimal in the past on what can be sold as health insurance. You could write whatever coverage you want, put in exclusions you want, charge anything you want, and try to avoid selling it to anyone you want. This is more or less still true. The Obama reforms will limit this somewhat. Certain types of dread disease coverage will not be permitted, coverage for adult kids will be required of the insurers, and firms will have to stop providing better coverage for the executives than to common employees. But, Obamacare falls short in terms of full regulation of the private insurance market (he couldn’t pass the bill without some support from the insurance industry, which H. Clinton never got, and she tried unsuccessfully to bulldoze them). Prices – premiums are not going to be regulated, coverage “types” are not going to be standardized, etc.

Managed care attempts in the 1980s/1990s? Insurors and employers tried to introduce Managed Care concepts a generation ago. Second surgical opinion programs, preauthorizations for many services, denials of coverage for marginally helpful services, etc. The idea of doctors calling nurses who worked for the insurance companies to get permission to treat became a highly divisive issue. Doctors didn’t like it, patients didn’t like it either (having to wait, or to be told “no, what your doctor recommends isn’t going to be covered”. Doctors hated it, and people complained to their employers, employers began to push back on offering such plans, and eventually demand for managed care slowed.

To replace the low price point for insurance achieved by managed care plans, new kinds of coverage were developed by insurors(consumer directed health plans and medical savings accounts). High deductible plans were developed. They ask consumers to pay the first 5,000 or first 10,000 of care each year before the insurance steps in and provides coverage. In exchange, of course, the premiums are quite low. These plans clearly do three things: (1) they put consumers at financial risk for all basic health care spending, and indeed all spending up to the deductible amount. This cuts out some utilization and some spending to be sure. (2) one of the most obvious cuts is spending on prevention, or well care, which is almost always paid by the consumer in such plans and (3) they are plans that attract the people who expect to not need much care in the next year. As such, they defeat risk pooling (they take healthy people out of the pool, and raise the premiums for everyone else). More on this kind of insurance is covered below.

4. Impacts of insurance

Insurance (paying an upfront premiums, and lowering the point of service price to the patient) has profound impacts on service utilization in the health care economy. The basic idea of insurance is to prepay (eg. pay a premium) for the costs of care, and then a very modest copayment is to be paid at the point of service. Thus, the price paid by the patient at the point of service is very much reduced. That creates an incentive to buy more than would have been paid if the price had not been reduced– other things the same, people buy more at lower prices. This chart illustrates the impact of insurance on the quantity of care purchased:

demand-for-care

The table here from the Rand Health Insurance Experiment (HIE) shows how a randomized clinical trial of cost sharing options affected the use of health services. This is still the main empirical study of what happens to the demand for care as a result of insurance. The columns in the table refer to the various “arms” of the study, each representing a certain “insurance” coverage for study participants.

hie

What the table shows is that when insurance allows the patient to pay less at the point of service, they will consume more! This phenomenon is called “moral hazard” : prepayment to lower the costs of some “loss” will increase the likelihood of the loss occurring. Demand curves sloping down for health care usage is not rocket science– but the HIE is definitive proof of the effectiveness of the incentives facing patients from their insurance coverage. People with good insurance consume about 40% more health care than persons without any insurance. And, furthermore, the study did not confirm any noticeable effects of this difference in utilization on the health of patients! (to be fair, there were some chronic disease patients, like diabetics, for whom samples were too small to test the hypothesis about health effects of the incentives).

Generally, patient behavior in response to the financial incentives of cost sharing are limited to point-of-entry services (like the use of primary care, or some specialty and testing services). Once a patient is admitted to a hospital, on the other hand, the incentives of the plan don’t seem to matter as much. Once “in the system” the autonomy shifts to providers and they decide what services are used, and incentives facing patients don’t matter as much.

And, the HIE studied the differences between HMO enrollees (who must get care within the plan) and other persons with insurance in the FFS sector. The research found that HMO enrollees had far lower health spending than their FFS counterparts. And, most of the savings were attributed to 30% lower rates of admission to hospitals. Satisfaction with such care seems to be somewhat lower, but quality effects do not show much difference with fee for service insurance in spite of the considerable savings. These findings are generally taken to mean that plans that can control the behavior of their own providers do not spend premium dollars on unnecessary hospitalizations.

The results of the HIE have been partly corroborated in the study of the Oregon Medicaid program. There, the budgetary situation of the state forced a limit on eligibility for the Medicaid program. A lottery was designed wherein eligible persons were selected randomly from the pool of “eligible applicants. Some persons were left without coverage. A team of clever researchers exploited these randomly selected groups (ones who got insurance, and others who did not). They studied health care utilization for both groups. They pretty much confirm the HIE by showing that insurance stimulates higher utilization. This chart shows the results of the Oregon Study.

medicaid-in-oregon

5. Insurance Spending Controls

How do insurors control their “losses” so they can survive when being paid a fixed premium? The chart here shows the main ways insurers try to control medical care claim costs (and remain solvent as businesses).

control-claim-costs

Some of these issues are discussed at more length in the following paragraphs.

Selection in Marketing. Insurers are acutely aware of the drivers of spending, and the way is relates to demographics. During the early days of the HIV epidemic, for instance, when most believed it was a “gay men’s disease” many insurers were aware of the kinds of employer groups that stereotypically employed many gay men, and they avoided selling to these groups. Some employer-based groups are just riskier than others with regard to health needs. In some states it is still possible to “experience rate” the premiums based on historical spending levels so as not to lose money. In other states it is now against the law to “experience rate” groups— and premiums have to be set on the “community average spending levels for demographic groups” (like in Massachusetts).

Coverage. The nature of the coverage is a mainstay of insurer cost containment. Obamacare eliminated some of these tools for health insurance in America, but these provisions may well be eliminated by the Republican administration (allowing more freedom to insurers to use whatever coverage they might want). Excluding coverage for preexisting conditions and putting limits on mental health benefits are two examples of tactics for “controlling” spending and keeping premiums lower for employers. Another tool is “preauthorization” for some services (provider needs to check with the insurer before doing it). But the main tool here is the coverage clause saying that coverage is for “medically necessary” services and products. This approach to writing the contract is more convenient than specifying exactly what is covered, and under what circumstances. But what does it mean? It generally used to mean that whatever the doctor thought was needed. It isnt that simple anymore. Preauthorizations often impose severe limits on coverage in practice. Many medications are simply “not covered” when providers check. Specific procedures deemed “medically necessary” by providers are often not covered. It is a huge game: providers at war with the insurers, employing increasingly large staff to chase permissions and payment from insurers. Today, providers spend a good deal of money fighting with insurors over coverage for expensive outpatient drugs, or new procedures, and other expensive services and products.

Administrative Expenses. Private insurance, particularly when insurance coverage in non specific and includes “medical necessity” imposes huge administrative costs on the payment for services. In retailing for example, computers and swipe care technologies allow administrative costs to be <1% of sales. In health care the private insurers themselves incur administrative costs of around 15%. Public insurers like Medicare incur administrative costs of 3-5%. But these are only the tip of the iceberg. Providers like hospitals and medical offices are spending huge sums to employ people to chase permissions and payment from private insurers. Private insurers have not really begun to compete by becoming more economical in their administrative spending. This will likely happen someday.

Cost Sharing. A very important cost containment device is manipulating the behavior of patients by controlling cost sharing at the point of service. Through experience, insurers are acutely aware, as are employers, that it is possible to reduce premiums if more patient cost sharing occurs at the point of service. The chart here shows the recent trends in cost sharing.

insur-cost-trends

Obviously, households have born an increasing burden of health insurance costs. A substantial portion of the increase is in the form of copays and deductibles. Insurers and their employer customers both agree that this is the way to go! It keeps premiums down (for employers) and it keeps claim payouts down too (good for insurers).

Provider Selection and Contracting (supply chain). Insurers are able to control their spending levels for services if they choose providers who are more effective in keeping patients healthy (or at least keeping them out of the hospital). Formation of high value provider networks, putting in place provider quality incentives to perform effective procedures, and negotiating lower fees are all ways that insurers can reduce their spending levels.

6. Insurance & Fee for Service: The Perfect Storm

Insurance lowers the price of services to be below what the patient would otherwise have to pay. So, when the parents bring the child to the ER as a precaution after a playground fall, they maybe told that a CT scan isnt really needed under the circumstances. Without insurance, the parents may sigh in relief, dodging the bullet of a $1500 expense. But, with insurance, the incentives are different. Mom may say “but wouldnt it be safer just to do the test and confirm there isnt a problem”. And very often such testing is done, to excess, because of insurance, where the cost to the parents may be only $100 or $200.

However, in actuality the provider often has non neutral incentives to also want to do the test. When providers are paid Fee for Service (FFS) they get paid more if they do more. So, if the insurance company is paying the hospital 1500 per test, then the hospital will get nothing extra for testing if they dont do the test, and an extra 1500 if they do it (the same may be true of the doctor, the radiologist who read the test, and other providers). In FFS, the provider is incentivized to “be safe, and go ahead and do it” — just like Mom. So there are often two reinforcing incentives favoring “doing more for patients” — Mom wants the test even more because she’s only paying the 100-200 rather than the 1500 she’d have to pay without insurance. The hospital wants the test too, because they earn more revenue (and in an environment of high fixed costs, more of the fee is a contribution to profit). FFS and Insurance provide mutually reinforcing incentives to increase utilization and spending! These reinforcing incentives are at the heart of America’s spending-is-out-of-control problem.

One last note here. Many other countries have better insurance than we do (Canada has no patient cost sharing, European countries too have lower copays and often no deductibles). Yet their spending is far more controlled than in America. How do they control excess demand for marginally useful tests and therapies?

There are several tools in play in other countries. First, many providers are salaried by clinics and hospitals, and dont experience FFS incentives as they do here. They dont get paid more if they do more. Second, Hospitals are paid differently. They are generally paid a “budget” for the year even though they are usually private organizations. Budget limits cap their revenue for a year, and they dont get to earn “extra” revenue beyond that by doing more. This hospital payment method is called “Global Budgeting”. So, the hospital doesnt have the same FFS incentives either. Third, those providers and others in the health system are more accustomed to saying “no” to patients. Our system is based on a tradition where patients can get what they want, when they want, and from whom they want. This is not a universal expectation.

7. Consumer Directed Health Plans (high Deductible Plans)

Recent trends in the health insurance industry have emphasized growth in plans that have lower premiums. One particular type of plan that has become fairly popular is the “high deductible plan”. This kind of plan have been advocated by faculty of the Harvard Business School, who propose it as an option for public policy to control spending by promoting more patient “accountability” in the use of marginally necessary services. This approach has been labeled the Consumer Directed Health Plan (CDHP). It is seen by the originators as an alternative for controlling health spending that doesnt require regulating providers and other kinds of government action. The CDHP combines high deductible insurance policies ( maybe 10,000 a family per year, or 5000 a person per year) with the use of Medical Savings Account (MSA — a pretax medical savings account to be used to pay out of pocket costs for health care, and that can carry over the account balance from year to year).

Today maybe 18-20% of Americans have such “high deductible plans” often provided as options by their employers. Some use MSAs, others don’t. The chart below shows the recent trend in CDHPs and MSAs.

cdhps

The impacts of CDHPs have been studied, with somewhat mixed results (there have been no randomized studies). One of the main researchers is Swartz, whose recent study reported:

schwartz

Generally, the worry about these plans is (1) that people will understand enough so as to not use enough essential services and (2) the persons joining such plans are very selective. That is, persons who join tend to be much “healthier” than average. That is, they want the lowest possible premiums, and are “betting” that they will not have to pay much during the year for their care. The consequence, is that the risk pool for other types of insurance will be stripped of many persons who buy CDHPs. This will raise the premiums of the non CDHP insurance options in the marketplace.

8. Market Failure

Insurance is a complex product, and one where consumers are severely disadvantaged in the marketplace. The terminology, the lack of transparency about coverage, the troublesome “clauses” that might delimit coverage, and other features of the policy make it almost impossible to make value comparisons. When offered through the employer the range of choices are delimited, but probably some protection is available for the consumer because the Benefit Managers at the firm are better informed buyers of insurance products.

Special steps are taken to prevent asymmetry in information facing the individual buyers of insurance in some states, all the ACA insurance exchanges and most foreign governments that utilize private health insurance. Insurers are told to offer one or more “standardized” types of policies: plan A, B, C, etc. These plans are exactly the same. This assures that consumers are not “surprised” by special clauses that delimit coverage. And, if all plan A contracts are the same, then prices can be directly compared. And, competition between insurers can be based on price, and service quality. This solves the asymmetry problem in comparison shopping for insurance.

Voluntary Insurance markets can also fail because of requirements that hospitals and other providers provide “stabilizing” services to uninsured persons. Consider 4 parties:

persons (workers)– get insurance coverage through employment, and use services
employers– provide insurance to workers, and pay insurance premiums
providers — incur treatment costs, and set charge levels to be billed to the insurers
insurers — pay billed charges, and set premium levels

Lets assume that an agreement is reached about expected service use by the insured persons, the premiums, and the provider payment levels. Then lets introduce some indigent care in the hospitals, creating bad debt. The hospital didnt expect this as it set its charges for insurers. But now, it tells the insurers that charges need to be increased to “cover” the extra costs of providing unexpected indigent care. And, when charges go up, insurers realize their premiums are not high enough to cover their claims costs. And then, insurers in turn have to increase premiums to cover that problem. That creates a decision by employers as to whether they want to continue to have a health insurance benefit, or to raise employee premium sharing, or do nothing. And, looking across the whole employer marketplace, one or more employers might just say—that’s enough—we just cant be competitive any longer—so were going to stop paying for insurance as a voluntary benefit. Pressing ahead with the scenario— now somewhat fewer persons in the community now have health insurance. A few of these persons will get seriously injured or ill, and need expensive treatment in hospitals. This will increase bad debt expenses beyond the anticipated amounts— which will increase charges, and then premiums again. And, as expected, a few other employed groups will no longer offer insurance because the premiums went up, and offering insurance benefits is voluntary.

This cycle will continue. Higher charges by hospitals to cover more and more bad debt, leading to higher premiums and employer dropping out of insurance programs for employees. This had been our trend in the health insurance market place before the ACA. Higher charges, higher premiums, and fewer people covered by insurance. Private insurance offered voluntarily leads to market failure. Sure, many employers will need to continue to offer it because valued workers want it. But, many other employers will drop voluntary plans.

The “mandate” of insurance for either employers or employees changes the calculus and breaks this cycle. The ACA poses an individual mandate, not an employer mandate–though employers would have to pay penalties if they fail to provide insurance for employees.

9. Some History and Take Aways on Health Insurance

Health Insurance has been in existence in the U.S. for more than 170 years, with the first “sickness insurance” plan sold by the Massachusetts Health Insurance Company of Boston in 1847. Insurance was sold primarily to individuals and through professional groups, and some employers. But, the major expansion of health insurance coverage occurred with a huge program of government subsidy (contrary to the belief that there was a mega private market for health insurance, and no need for government to step in to create a financing system that was an affordable way to prevent families from going bankrupt from medical problems). The 1942 policy actions by the government, and more recent policies are summarized here:

recent-policy-history

What the government did was to make health insurance premium contributions by employers a tax deduction by employers (against their profit tax payments). This propelled the interest of employers in offering health insurance as a fringe benefit, and allowed many to effectively compete for good workers without needing to offer much higher wages. They ate it up, and by 1950 the vast majority of workers in large firms had health insurance for their families. The “cost” of the program to the government was billions of tax dollars that were no longer being collected from corporate income taxes. This program (tax deductibility for health insurance benefit payments) is today the largest health care government program in the U.S (eg bigger than Medicare).

The recent activities by BCBS of Mass deserve a comment. The approaches used by insurers (including Medicare) to pay providers have been various forms of FFS (DRGs for hospitals, RBRVS payments for doctors, fees for certain procedures done in outpatient and surgery centers, etc.) all have one thing in common: providers who do more, get paid more. This creates incentives to do more “volumes of care”. This contributes to higher spending and our “flat of the curve” health care system. BCBS of MA began a program 10 years ago to start paying providers on the basis of care quality. The Alternative Quality Contract (AQC) began by paying large medical groups of physicians according to a modified capitation program coupled with quality of care incentives. Their payment was made according to whether they met annual spending targets for their panel of patients, or not, and if they did, whether they provided high quality care (using criteria about practice patterns).

The AQC program saved money (see chart) relative to the control group. And the majority of provider groups received bonus payments for achieving the quality targets. The cost containment results are shown here:

aqc

The Medicare value based program, implemented as part of the ACA, used many of the principles of quality payment.

take-aways-on-insurance

Health, Health Production and “Flat of the Curve” Medicine

November 18, 2016September 1, 2020 gaumerLeave a comment

This posting reviews some of the underpinnings of the health level, health behaviors the demand for health care, and health care spending in the economy.

1. What is Health and how it is Measured

WHO defines health as: “Health is a state of complete physical, mental and social well-being and not merely the absence of disease or infirmity”.

This is pretty broad, and certainly encompasses more than simply the presence or absence of acquired diseases. Also included in “health” are aspects of stress associated with poverty, possibly isolated living circumstances, and other situations facing millions of persons in the world. These concerns range from low economic development, side effects of political upheaval and war, natural disasters, and to social circumstances that prevent fairness and cause stress. These are worthy concerns, and contribute to the inability of people to function at optimal levels. Health is certainly a complex concept. Simplifying, it a bit it becomes a definition for Health like:

the absence of disease or injury, both physical and mental
full capacity to perform usual daily activities (work, school, child rearing, family caregiving, etc.)
longevity (remain as free as possible from causes of premature death)

There are a number of ways that “health” or health improvements are measured in health economics work. There is no gold standard. The main measures are:

surveys of degree of “happiness” in populations (this is an interesting literature)
surveys self assessed health using questions like: “relative to others like you, would you say your health is excellent, better than average, average, worse than average, or poor”
surveys of Restricted Activity Days in the last month or so: –days lost from usual activities due to poor health (in the past month or so) — days not able to attend school, or not able to go to work, or do whatever are usual activities
mortality rates of various kinds (age groups and cohorts, disease specific)
infant mortality rate (failure to survive to 1 year / births) — generally perceived as a more sensitive indicator of mortality to health system limitations (poor access, inadequate services)
Disability Adjusted Life Years Lost Due to preventable disease and disability — DALYs— a commonly used metric to measure the burden poor health by country by year (the Institute for Health Metrics and Evaluation, IHME does almost all this work) which sums together (1) an estimate the years of life lost due to preventable causes, and (2) an estimate of years lost with bad quality of life due to preventable causes. Longevity in Japan is a reference population used to estimate maximum years of life possible.

For measuring health as it may relate to particular types of health interventions, or components of the health system other kinds of measures are used:

survival rates for particular types of persons (with/without the intervention, etc.)
incidence or prevalence rates for particular diseases, or risks
Quality Adjusted Life Years— QALYs—- a common measure for capturing the reduction in disease burden (eg health benefits) of an intervention (new vaccine or drug, a diet intervention, improved insurance coverage, etc.) — estimated as the sum of (1) the years of extended life created, and (2) the quality of life improvements and their duration. One QALY is one year in perfect health.
surveys of changes in self assessed quality of health or restricted activity days

2. How does the U.S. Health System Compare in terms of Health?

As an indicator of U.S. health care perfromance, the chart below shows how countries compare on Infant mortality rates. Three facts are evident: (1) The U.S. doesnt do as well as most developed countries on this measure of health. The reason for this stems from the heterogeneity in health (and many other things) in the U.S., as compared to other developed countries—where incomes, health and other measures end to not vary as much from household to household. But in the U.S. looking more closely (the right side of the chart), there is a huge variation in infant mortality within the U.S. (2) In some places, infant mortality is very comparable to the world leaders or even better. But, in other places in the U.S., (3) we have infant mortality rates are so bad they look like “third world places” (in the Mississippi delta area, and other places in the rural south). This situation stems from lack of access to providers in such places because nobody wants to practice there because of poverty and lack of insurance.

So in the U.S. some persons tend to have very good health because they have access to the world’s best doctors, facilities, technologies, and modern mirtacles of science. But, our average health levels in the U.S. are not so special, since theye are other people here who do not have nearby access to that kind of care, or if they do live in proximity, dont have good enough insurance of other means of financing to be able to use such care. And, as we review below, many people in the U.S. do not live healthy lifestyles and make choices that would make population health better.

infant-mortality

Stepping Back: What Drives the level of Health in Populations

One large study by WHO looking at 120 countries over 30 years showed that variation in the levels of mortality over time and across countries were driven mainly by three things: new drugs and other new diagnostic and treatment technologies (explained 30% of the variation over time and across countrfies in mortality rates), income differences (explained 20% of the variation in health), and level of formal education of females (50%). The latter is probably due to the importance, across cultures, of women in making health choices for families.

A second type of study has looked at the causes of mortality, and assessed the extent to which death might have been preventable. The results of these kinds of studies are typified in the chart below:

determin-of-health

Here we see that the drivers of variations in population health are genetics ( about 20% of the variation in preventable mortality can be traced to genetic differences), medical care (10-20%), lifestyle choices like diet, risk behaviors, exercise habits (50%), and environment like water and air quality, noise, etc.(10-20%). Of course, we spend the vast majority of our spending on the least influential factor in this set of health drivers. This isnt to say that health services, hospitals, and technologies don’t produce health, since they do often extend life and improve quality of life—- it only says that other factors are critical drivers in the health of populations. Choices about diet, risky behaviors, and other matters of personal choice are much more crucial in producing health. This fact is generally lost on health professionals, who tend to see “health” as driven by visits, tests, treatment and compliance. But, when the topic of prevention comes up, professionals know they have to “engage” patients, possibly “nudging” them and educating them.

It is well know that Education and literacy are important drivers of health in populations and the related choices people make. Research clearly demonstrates this fact–that investments society makes in education have huge “health ” payoffs too.

“An additional four years of education lowers five-year mortality by 1.8 percentage points; it also reduces the risk of heart disease by 2.16 percentage points, and the risk of diabetes by 1.3 percentage points.” (David Cutler and Lleras-Muney NBER 2006 http://www.nber.org/papers/w12352)

Other simple displays of data by Cultler and Lleras-Muney confirm this pattern:

chart 1

chart 2

Demand for Health Care

Why do persons seek professional services? The “demand” for health care is derivative from the demand for health. Obviously, we value health. It allows us to feel good, and not be restricted in participating in the activities we enjoy. People are, of course different in terms of what they enjoy. And, therefore, are different in their “target” health level. Someone that enjoys vigorous activities may need to be far more healthy to enjoy them than w`ould be someone with more modest requirements for fitness and energy level.

People also differ in terms of the importance and concern they feel for the future. Persons who have strong preferences for their future, and what they’ll be doing, and how they’ll be doing it– will choose to make investments in their future level of health (eg engage in prevention behaviors). On the other hand, persons who only value the moment are not likely to make the same investments in prevention. Here again, people are different, they behavior in accord with their life preferences. Some value health differently, and some value investments in future health differently. We may wish we were all the same— but we are not.

Our demand for health care varies too, in accord with our perceived value of health. Some persons go to their PCP annually, are prompt about checking out changes in our health, and dont procrastinate about emergent problems. Others may go years without a visit, and delay followup against medical advice. Some of these differences in the way people use the health system are the result of different “target level” of current and future health. Other sources of difference are things like income differences, availability of insurance, cultural and peer group health behaviors, differences in geographic convenience levels, and education levels. These are also “drivers” of our demand for health care.

Overall, health care is disproportionately important in our society as an intervention to produce/remedy health situations. We spend about $10,000 per person per year in our society, largely to treat diseases and conditions. This is a huge expenditure (about 18%) of the economy —- and is about 30-50% more than the health spending in other developed countries.

The following chart tells us more about the trends in the American health system and its production of health. It says we don’t get much “Bang for the Buck” (outputs per input) out of the American Health system. This relationship also suggests the ever flattening shape of the U.S. “production function” for Health — something often characterized as “flat of the curve” health care in America. Take a look at this chart. What is making the trajectory of the U.S. health system so different? What might have caused the changes in 1980 that seems to have altered the U.S. trajectory?

what happened in 1980

This alarming chart provokes the question of the U.S. health care system, and what’s so different about it from the “systems” in other countries. Its not a shortage of spending! Or inadequate investments in scientific drug development, or the quality of training for of doctors, nurses, and other clinicians. Why does our “system” choose to spend so much on health care, but often achieve far less health for the people than is being produced in other countries?

What is a Health System? Well, its the way society organizes, allocates and manages the health care resources of society toward society’s objectives in terms of health status, efficiency, fair access, and satisfaction levels of the people. The slide below on the left panel shows the types of resources (including the scientific backbone) all health systems need to produce health, and how they are linked by the organization or system of organizing, allocating, delivering, managing, monitoring, improving and leading to get it done. This is a generic picture of all health systems, not just ours. It follows the WHO picture of generic health system functions. One important feature of health systems is the “feedback loop” shown on the left panel of the chart. Here, we expect that the health system has a mechanism whereby inadequacies (eg unfairness, or excessive spending) are recognized and somebody takes action to create a remedy.

what is a H sys

One way to depict our system is on the right side of this figure. But it is sort of generic too. The blue pyramid suggests that our system is based on the base of HOUSEHOLD CHOICE about how much they know, and how much they care about health in their lives. After all, we are a more of less free society. Then the next key building block of the blue pyramid is Public Health Provision. Basically think about this as more or less universally available and free services (paid by taxes, not based on service consumption) that are aimed at the health of the public. Economists call these “public goods” the health of everyone depends on these things: things like Food and drug safety, water quality and clean air regulations, laws that require government licencing of professionals and hospitals and labs, and maybe free childhood vaccinations at public health departments. The next levels of the blue pyramid are primary care, then secondary (inpatient) care, and then tertiary specialty and medical specialty care. The pyramid shape illustrates the epidemiology of usage of these levels of care: everyone’s health is based on their household values and needs, almost everyone will benefit from whatever public health services are provided, and the majority will certainly need and use primary care, but fewer will need secondary services, and far fewer will need tertiary care. That is a pretty generic functional description of the health care services delivery structure in our health care system. Of course the blue boxes on the top are support services and products in our delivery structure. Note, the green pyramid below describes how the money in our health system is allocated (by the way allocations work in the ‘system’).

Lets get more specific about the structure of the American system—– look at the next slide:

what is the U.S health system

The U.S. Health Care system isnt a organized “system” at all. It is organized like the market for cars, or cell phone plans, or the sale of Christmas trees. Yes, like any of these other markets there are suppliers (who can come and go, and are free to fail) consumers who are free to choose, supply chain firms that provider resources to the sellers, and there are some government rules to follow. Yes, these “market systems” for Christmas trees in December do have buyers, sellers, supply chains, etc, and they sure do deliver successfully in most cases). Markets do work pretty well for delivering products and services of all types in America, and many other countries. To say that ours is a health “system” implies some things that are not true:

1. Almost all providers of services and products are private firms or private practices. This is true of all not for profit organizations (which only means that they are approved for not paying any taxes, and those who donote money to them are allowed a tax break for their donation)—- Consumers & Private organizations can do whatever they want. Here today, gone tomorrow. They are not part of any health system plan for primary care, for secondary care, or drug development—- there are no such plans. Patients are, for the most part, free to choose providers, and self refer to whoever they want. Medicare provides this right explicitly, as do most private insurance— unless it is waived in a “managed care plan”.

2. We have what is known as a mixed health system with minimal government interference–mainly private providers, pretty much free choice for providers, insurers, and patients— a market system with minimal government oversight, and only a few situation where government (state, federal, county) provide services— though nearly 45% of the insurance payments for health care are paid by governments through social insurance plans (Medicare, Medicare, TriCare). (see the chart)

market

3. there is almost no coordination between providers—as the right side of the slide suggests– there is extreme fragmentation in the marketplace of health care service providers, all types, all sizes, shapes, styles of care, staffing patterns, services offered, everything is fragmented (except within managed care organizations, where they explicitly try to integrate services better to be more economical) —-providers can offer (or not offer) services of many types wherever they want, within their scope of licence. They can go out of business for whatever reason. Resource suppliers (schools of medicine and nursing, EMR vendors, CT /MRI vendors, drug company suppliers, etc.) and private insurors, are also private firms, and can do whatever they want–no coordination, no plans. Sure there are limits and regulations to be followed in order to operate. But no planning, no limits on product and services to sell, nor ANY price regulations. There is No system! Unlimited and Unorganized provider options subject to choice & self referral

4. there is, most astonishingly, nobody in charge of the U.S. health system. No person, no organization, no plan, no explicit goals, and certainly not even the Federal Government (the constitution deligates some “public health functions” authority to States, none to the Feds). This is partly the issue that allows courts to reject parts of the ACA and other progressive (tax funded) health legislation. The limited role of government, the role of others, and the situation in other health systems are shown in the next three slides.

limited role of government

other players

other countries

In summary, see the key words to describe the health System here:

words to describe U.S system

5. Household Health Production

The next few sections here offer some background of the concept of “health production” how households make choices about their health, as if it is some sort of investment in future happiness.

The economic view of the importance of the “choices” people make about their preferred level of health is emphasized in the “theory of household health production.

This theory suggests that people (households) combine purchased goods and services with their own time and know-how to produce activities that are valued. (Games of tennis, vacations, restaurant meals, raising children, etc). Households “produce” these activities to reflect their preferences, and their economic constraint like time, money and know how.

prod-function

The chart shows the “production function” for producing health. More inputs (horizontal axis) generate more health (vertical axis). For a given type of person, (where ‘know how’, age, genetics, and education are constant) additional health can be produced by expending more time or money on inputs—- causing a movement up the curve. The level of health is also subject to shifts in the production function. If, for example, persons purchase more education, their production function will shift up (they have more information, or are more efficient at searching for relevant information). As persons age, then the production function shifts down (as illustrated). Economists study the shape of production functions, the sensitivity of health to variations inputs, and shifts.

But, the more health we try to make by purchasing services and expending personal time, the more are going to be required to a specific increment of health (this is called ‘diminishing returns’). The lower and lower productivity we see as we try to produce more and more, is the result of a “fixed supply” of know how. If we invested more in know how (eg education or experience) the entire production function would shift up. This means that it is possible to make more health with the same level of inputs.

The theory predicts that in choosing to produce health (games of tennis, a vacation, etc) people will differ in terms of “how” they produce. People with lots of money and scarce time will choose to use more good and less time to produce their activities. People with more time than money will choose a different combination of inputs.

Marketers take advantage of these differences. Consierge medicine is “convenient” (less time) and costs more money. Some insurance plans have more options for access (convenience). All market segmentation is based on differential demands among groups of customers Often this boils down to different scarcities of time and money.

When “know how” is higher, it means that the full price of producing a particular level of health is lower. What this will mean, in turn, is that a lower full price will tend to increase the demand for the activity of producing health. (or tennis, or restaurant meals, etc.)—- relative to the demand for other activities we might have spent our time and money to produce. So, persons with more “know how to produce health in the household” will demand higher target levels of health.

Will this means that persons with more know how will demand more medical care? not necessarily. For some persons, yes, for other persons not so much–depending on the relative prices of time and the scarcity of money. Insurance comes in here.

More on Health Production Functions

Health production functions are economic tools used to estimate the influence of population characteristics on health. How effective are various inputs to produce health? How much does the production function shift as a result of more education, or age? These multivariate relationships are estimated from individual or country data using regression analysis. Regression analysis estimates a quantitative relationship between two variables (eg Health and Education). It standardizes that two-variable relationship for the influence of other factors (age, race). A statistical test is used to test if the association between the 2 variables is larger than would be likely given the variation seen in the data. Regression is used to understand the size and significance of the drivers of health status, using data on thousands of individuals . The following chart describes the data as a scatter diagram and the “best fitting straight regression line” that is estimated from the data—- and the slope of the line being the relationship between the dependent variable (health) and a one unit change in the independent variable (eg a, or b).

regression

The results of two research projects to estimate production functions for health are shown below. The left side shows production functions based on a number of health measures, where the data is taken from countries around the world. The study on the right measures health by a Health Interview Survey of self reported health by Americans (on a scale of low to high):

production-functions

The cross country study on the left of the chart reports on several measures of health. It shows that life expectancy gets longer when literacy rates in the country are higher, when health spending is higher, and when calorie intact is lower. The results are consistent with the three models that use mortality rates as health measures.

The second panel here (from my dissertation) focuses on the several drivers of health (as measured by persons own self assessment vis other people they know. The ideas is look at the “health effects of things like education level, age, # cigarettes smoked per day, whether you’re married. It separates the study sample into three groups according to their education levels (<8 years, 9-12, 12+). For each groups three different statistical techniques are tried— but for this purpose just look at the Model 1 results for each segment. Focus on the last row— which shows the coefficient associate with age (actually it is the logarithm of age). That coefficient shows what happens to health for every extra year of life. It is negative–indicating the every extra year subtracts from the self assessed health level, all other things the same. But compare the model 1 for the three panels—- the size of the age coefficient gets smaller as the years of education increases.This can be interpreted as more education makes people healthier, observed here as slowing the rate at which their health seems to be deteriorating with age. Note also, that higher education is also reducing the negative impacts on health associated with how many cigarettes the person smokes. Education is almost like the fountain of youth!

Flat of the Curve Medicine

This concept is often used to describe the U.S. health system—– it means that when looking at the production function for health in the U.S. it looks “flat” , which means that addind more inputs (money, resources) into the system will not proudce that much additional health.

A multi country health production function is shown below in graphical form. The Health measure is life expectancy at birth. The horizontal axis is per capita spending on health care (a summative measure of all the purchased resources consumed in the production of health). What we see here is the extraordinary performance of the U.S. health economy— very very high levels of spending relative to other countries, with not proportionately high Health levels. This combination is the central defining feature of the U.S. health economy. Variously interpreted: pretty low output given the high resource inputs; little bang for the buck, excessive spending to achieve modest health levels. Waste, inefficiency, ineffectiveness, and slack are all possible descriptors of this flattening phenomina—- health is going up at a declining rate as we spend additional increments of money on the health system.

flat-of-curve

The production function here (the line) is upsloping, meaning that adding resources will increase health. But the data across countries shows that as more and more resources are added, the increases in health status is not growing proportionately— the increment in health status per increment in resources used gets smaller and smaller as we add more resources. This is called the principle of “diminishing returns” in production — as we add more resources to a production process we fail to achieve proportional returns in output.

For the U.S., the production functio for health that gies throughh the U.S. data point is far to the right–and far flatter, than the health production function facing the other countries. It says that as we do more (more tests, procedures, visits, etc.) we can augment health, but the productivity of the added resource use gets smaller and smaller (the production function gets flatter and flatter). So, we speak of “flat of the curve” medicine in the U.S. because we have dedicated so many resources to our health care system, that additional resources yield very little.

Why is the American health system have this ‘Flat of the curve’ problem? Why is our spending level so high that the contribution of incremental spending to health is so low? Why are we so different in this unattractive way?

Possible root causes are many:

fragmented financing system — lots of insurers, high transaction costs, lots of overhead – administrative spending (by insurers and by provider organizations to bill and collect payment)
dedication of the vast majority of health spending on treating serious illness, and development of a significant flow of innovative approaches to support treatment for these problems (eg spending huge amounts on curing disease, not preventing it)
Fee for service payment — incentives for providers to seek to provide more services for their patients in order to increase their income. Payment incentives that might shift risk of “poor health results” to providers, such as capitation, remain small fractions of the the provider payments to medical care providers.
insurance — lowering the “price” the patient must pay to consult or be treated by the provider. Recent attempts to employ Pay for performance payment incentives by Medicare, the AQC in MassBCBS, and others remain a very tiny fraction of provider payments
But, much of the blame for “flatness” must rest on the patient’s behavior in our culture — where people do not want to take responsibility for their own health—-they want the health system and technology (drugs devices and medical procedures) to do it for them. Medical professionals are viewed like a mechanic, who takes care of your car, and keeps in running. In general, Americans are not accountable for primary prevention drivers of chronic disease. Health is largely controlled by household lifestyle choices: the risks we create by how we eat, what we smoke, what risks we choose to take, etc.

6. There is also an important contribution to “flatness” that is evident in the underusing persons in our society; the millions without insurance– and the millions who dont live in proximity to providers. This is a problem of

allocative inefficiency — the U.S. system is terribly inefficient in many ways

(waste, high transaction costs, paying too much for some inputs, etc etc. —

But, allocative efficiency is a different kind of problem than not getting a big

“bang for the buck” in how we treat patients or prescribe treatments. Our

system has cross-product and cross-segment and cross-area allocation

problems.

7. The system spends far too much on some segments (or places or diseases) and

far too little on others. If we shifted (reallocated) spending, the overall level

spending would of course, remain the same, but we would experience a big up

shift in the health production function. This is the problem of allocative

efficiency– take resources away from some uses, and spend them on others

that have a higher productivity. The “bang for the buck” would increase for the

system as a whole. This is not about fairness– or equity. It is about getting

more health for the population and not spending any more to get it.

This chart makes this point– that we are spending more and more on fewer and fewer people in the health care system.

spending more and more

What kind of re-allocations might help? Wennberg and others have written about the cross area disparities in practice patterns and spending. If marginally

useful spending in places with excessive utilization like Miami or McAllen,Tx were shifted to provide basic Maternal and

Child care for poor rural populations in south, for example, there could be huge

improvements in birth and child survival outcomes. If spending on

preventable hospitalizations for CHF patients (who cannot be convinced to stop

eating bowls of potato chips when watching Patriots games, and getting readmitted

every Sunday in the Fall) could be shifted to primary care,

or health literacy programs, or public health campaigns on sugary beverages

there could be huge gains in population health. And, in general, shifts in

spending away from hospitals, to promoting wellness would create big health

impacts.

One of the biggest problems in Businesses (including hospitals) is making

allocative decisions about resources: eg operating or capital budgets. Top

management would like to allocate budgets in a way that maximizes net impact on the institution. Unfortunately, good data doesnt exist to help the manager– and it becomes subject to historical and political forces— and inevitably the institution suffers by giving too much budget to department A and would have achieved more had they given less to A and more to Dept B. Overall, the organization is being inefficient (not getting as much Bang for their budget as they might have). This is the problem of Allocative inefficiency.

This is exactly what goes on in the health system when we spend too much over here, and could have achieved a better result overall had we shifted some of the resources from A to B. It is a serious source of flatness, as we increase our

spending on serious illness care in hospitals, saving some lives and creating

some benefits, but failing to achieve the marginal gains that might have been

possible through better selection of options available to us.

Doing a simple Regression in excel

September 25, 2016June 24, 2019 gaumerLeave a comment

Regression

Regression is a way of understanding the empirical relationship between variables, where the relationship is expressed in the form of an estimated linear equation developed from data. A demand curve, for example, would be such a line, relating the quantity bought and the price. Often, regression is a way of testing hypotheses about the relationship between 2 or more variables(one is called dependent variable, others are called independent variables). Multiple regression is a way of testing for these two way relationships when the effects of other variables is being held constant. Note: even though we call the variables dependent and independent, regression does not test for causality between the two variables. It tests for association between them (similar to correlation). The cause-effect relationship (eg the directions of causality) between two variables is possible to infer from theory, or from patterns of complex statistical results.

Regression analysis essentially displays the scatter of data between two variables. The regression line is essentially the best fitting straight line through the scatter.

If we were doing a cost analysis and trying to determine fixed costs and marginal costs we could use regression. So, if we had data on each year of operations, or each month of operations, or each day we would use those data to specify the following equation:

Total costs = A + b (Volume of output)

A would be an estimate of what costs would be if volume were 0. This is fixed costs! The coefficient “b” is the estimated change in costs when we add (or subtract) one unit of output—this is the marginal costs. Average costs can easily be calculated in the raw data.

If we had some other variable that changes and might help explain why the cost/volume relationship might “shift” during the data period we are studying, then we could add it to the regression. For example if we had a monthly data set on costs and volumes we might want to note (and control for) the fact that the last 6 data points were from a time when we were open in the evenings (and the other data points were from times when we were not open in the evenings). So our regression is going to be

Total costs = A + b (V) + c (evening open) this =1 for the last 6 months, and = 0 otherwise)

So, A and B still mean what we said, though their values may change a bit with the new model. The coefficient “c” tells us how our total costs change (per month, per day or per week depending on what our data is) when we are open evenings, compared to our costs when we are not open in the evenings.

Regression coefficient estimates have important interpretations in economics. In the above example of the cost regression, we might have an estimated equation

Total monthly cost = 50000 + 100(V) + 20 (evening open)

the coefficient estimate “b” is interpreted at the change in total cost when we increase the volume (V) by one unit (this might be the number of clients we saw each day. This is called variable (or marginal) cost. The 50000 is the estimate of fixed costs (which we incur independent of the volume we produce.

In the case where we might have estimated a demand curve, such as

Quantity sold = A + b (price) + c (household income in 000s) + d(competitor’s price)

We could estimate it from data and get:

Quantity sold = 500 – 15 (price) + 20 (income ) + 30 (competitors price)

What this means is that if our price was zero, and income were zero and the competitors price was zero we would sell 500 units (silly, but it tells us where the demand curve crosses the horizontal axis (eg where out price is zero)
It says that if we increase our price by $1, we’d sell 15 fewer units of the product (other stuff like income and competitors prices staying constant)
it says that if household income were 1000 higher on average, we could expect to sell 20 more units (other things held constant, like our price and competitor prices. Is this a normal or an inferior good?
it says that if competitors lowered their prices by $1 we would sell 30 fewer units (other things the same. Are they a substitute or a complement?

Excel does regression. Look under tools to see if you can add in the “data analysis” add in. If you have it, find it under the Data tab. You can do descriptive analyses and other things with “data analysis”, but scroll down to regression. It will ask you to highlight the column of data that represents the dependent variable. Usually it is best to highlight the name of the variable and all the data in the column. Then it will ask to designate the independent variable, and you do the same thing. And then make sure to check the box that says “include the data labels” (because you highlighted the data labels too). If you have 2 or more independent variables you can include them in the model. You do this by putting all these variables in adjacent columns, and highlighting all of them in one fell swoop. Note, excel will not do regression if cells are missing data, or if there is a non numeric value in a cell (a comma, etc.). You will get some message when you push the regression button to run the model, and you’ll have to locate the problem, and possibly through away one of the observations.

i did a simple regression on excel and have attached it below. The data set below was used to do a regression to understand the factors associated with the size of hospital bills across a bunch of patients (eg the variable called “ charges “). I ran a regression analysis to test three relationships:

Does age matter to the size of bill
Does the category of the age matter to the size of the bill
Does severity of the diagnosis/procedure matter to the size of the bill

basically what i did in excel was to go to the data analysis, regression page–and key in the cell location of the dependent variable (in this case, hospital charges for 39 patients) which variation in i was trying to explain by 3 independent variables — age, age category, severity. I had to put these variables in adjacent columns and key in the cell locations of these three things. In the regressions they came out as three unnamed variables since i neglected to select the column heading.

I explain the results on the sheet showing what excel produced as results—much of which is not important at this stage of the game.

You could use the data set to create a different model , say one that used only patient severity as a independent variable to explain charges.

charges	age	age category	Severity	dr code#	Female=1	admit	disch
8,254	57	2	2	730	1	1/1/2004	1/3/2004
24,655	43	1	4	730	1	1/1/2004	1/9/2004
27,234	81	3	4	730	0	1/2/2004	1/13/2004
21,345	56	2	3	730	0	1/9/2004	1/14/2004
2,417	17	1	1	730	1	1/3/2004	1/4/2004
5,420	61	2	1	730	1	1/4/2004	1/6/2004
18,823	-61	2	2	730	1	1/6/1944	1/12/2004
20,280	61	2	3	730	1	1/6/2004	1/11/2004
4,360	44	1	1	730	0	1/2/2004	1/5/2004
22,382	90	3	3	730	1	1/2/2004	1/6/2004
12,673	39	1	3	730	1	1/4/2004	1/10/2004
22,632	70	3	4	730	1	1/3/2004	1/11/2004
22,642	77	3	4	730	0	1/3/2004	1/13/2004
14,111	85	3	2	730	0	1/5/2004	1/11/2004
9,763	52	2	2	730	1	1/6/2004	1/13/2004
13,343	65	2	2	730	0	1/7/2004	1/11/2004
4,886	54	2	1	730	1	1/4/2004	1/7/2004
22,712	87	3	3	730	0	1/4/2004	1/14/2004
7,194	50	2	2	730	1	1/3/2004	1/7/2004
24,809	73	3	3	730	0	1/3/2004	1/15/2004
9,405	62	2	1	730	1	1/2/2004	1/7/2004
9,990	63	2	1	499	1	1/2/2004	1/6/2004
24,042	67	3	3	499	1	1/1/2004	1/20/2004
17,591	68	3	4	499	0	1/2/2004	1/10/2004
10,864	85	3	2	499	0	1/3/2004	1/9/2004
3,535	20	1	2	499	1	1/2/2004	1/3/2003
6,042	61	2	1	499	0	1/4/2004	1/6/2004
11,908	59	2	1	499	0	1/4/2004	1/10/2004
24,121	86	3	44	499	0	1/5/2004	1/21/2004
15,600	72	3	3	499	1	1/5/2004	1/11/2004
25,561	92	3	4	499	0	1/4/2004	1/19/2004
2,499	39	1	1	499	0	1/6/2004	1/7/2004
12,423	69	3	3	499	1	1/6/2004	1/9/2004
24,980	71	3	4	499	1	1/7/2004	1/19/2004
19,873	59	2	3	499	0	1/8/2004	1/22/2004
21,311	92	3	4	499	1	1/6/2004	1/12/2004
15,969	60	2	3	499	1	1/5/2004	1/11/2004
16,574	72	3	3	499	0	1/7/2004	1/13/2004
24,214	89	3	3	499	0	1/7/2004	1/19/2004

SUMMARY OUTPUT
this result was obtained by entering Y data (dependent variable as a2:a40. And, the independent or right hand side variables were entered as b2:d40.
Regression Statistics			there are 39 observations (patients). The last panel of excel output contains the basic hypothesis testing results. Charges (the billed amount) is definitely related
Multiple R	0.665009		to the categorical age variable (the second x variable). We know this because the p value <.05. Each increment in the independent variable Age Cat is associated with an increase in Charges by $6740. Charges are not associated with the other independent variable (p is not < .05 for each. The first panel of results tells us about the overall regression model. The R squared statistic tells us that the three independent variables (age, age cat, and severity) explain about 44% of the variation in charges across the patients. The F test (second output table) tells us that the P value is <.05 (actually, p=.000121)and this model is explaining a significant amount of the variations in patient charges.
R Square	0.442236
Adjusted R Square	0.394428
Standard Error	5977.152
Observations	39

									about 44% of the variation in charges across these patients.
	df	SS	MS	F	Significance F
Regression	3	9.91E+08	3.3E+08	9.250197	0.000121
Residual	35	1.25E+09	35726344
Total	38	2.24E+09

	Coefficients	Standard Error	t Stat	P-value	Lower 95%	Upper 95%	Lower 95.0%	Upper 95.0%
Intercept	373.5686	3227.067	0.115761	0.908504	-6177.73	6924.863	-6177.73	6924.863
X Variable 1	-17.8091	47.40916	-0.37565	0.709446	-114.055	78.4366	-114.055	78.4366
X Variable 2	6740.121	1766.655	3.815188	0.000531	3153.621	10326.62	3153.621	10326.62
X Variable 3	198.3377	148.8665	1.33232	0.191365	-103.877	500.5528	-103.877	500.5528

I could have experimented with other regression models and tried to see if the doctor mattered in charge levels, or if gender mattered.

aggregate demand

September 25, 2016 gaumerLeave a comment

Macro Policy and Aggregate demand

People have asked me what the impact if the debt ceiling result will do to the economy. I thought I ought to share this with all of you, since we have not covered any macro economics in this course.

Basically the level of performance of the economy (GDP, Employment, Inflation, Interest rates) depends primarily on what is called the level of aggregate demand in the economy and the money supply (which is manipulated by the Federal Reserve and Treasury. I want to have you think about aggregate demand– the demand for all our goods and services produced in the country, summed up:

Agg Demand = Consumption spending –demand for our goods by our residents,

+ Investment spending—demand for goods by businesses as capital investment (eg

not spending by businesses to create the consumer goods)

+ government spending— purchases of goods and services including SS, Medicare, Military

+ demand for our exports— demand for our goods by foreigners

Fueling this overall demand for goods and services is the derivative demand for labor, or what in the aggregate is the employment level.

Simplifying, the short term impact of the agreement on the economy last week is largely going to be through the government budget cut provisions, which were pushed by the republicans as a condition for approving the higher debt ceiling. Cuts of a trillion or more $ in government programs (reductions in government spending) are going to cut aggregate demand, and employment levels. The overall reductions in aggregate demand and in employment levels come in two ways: (1) less government spending—in the form of direct budget reductions and layoffs from the government programs and contractors to those programs, and (2) by the fact that laid off workers and vendors will, in turn, spend less (because they have less income), and this will, in turn, create less income for retailers and others (where their spending would have occurred). These businesses will , in turn, lay off workers, buy less manufactured products and make fewer investments. This cycle of contraction, stemming from the large cut in government spending will reduce employment and incomes and spending. Economists believe that a $1 cut in a component of aggregate demand will result in about a $3 reduction in aggregate demand, other things the same. This is called the multiplier. This is Keynesian economics. It provides the basis for thinking of an active role of government in stabilizing the economy (if private demand is weak, then the government can increase spending, and stimulate the economy).

So, my basic view is that the reduction in government program spending (the policy set in motion last week) is exactly the wrong thing to do to our weak economy. It cuts demand for products and services, and will sharply reduce employment. It will create a recession (reduction in GDP), if we weren’t in one already.

Other kinds of policies that would act to weaken aggregate demand would be (1) increasing taxes—which would disposable incomes and reduce consumer spending, (2) anything that reduces confidence in the future economy (which will reduce business investment and consumer spending too, (3) anything that increases the exchange rate or value of the $ vis international currencies—which will mean that US goods will become more expensive to foreigners and foreign goods will become cheaper substitutes for Americans—both of these things will adversely effect aggregate demand.

This is my view, and I am sure other economists would disagree.

Of course, the long term effects of reducing debt will be good, other things the same. Now the government spends about ¼ ( I think) of its budget on interest payments on the debt. This spending doesn’t create many jobs or multiplier effects, and in fact tends to make the government compete with private firms for investment dollars, driving interest rates up, and cutting the job creating effects of private investment.

The psychological effects of the last three weeks or so are probably as important as the budget cuts themselves in terms of their contribution to the level of aggregate demand (and derivative employment). Why factors drive consumers to buy that new car now, rather than wait, or what drives businesses to decide to pull the trigger on the aggressive business plan they’ve been discussing, or to borrow heavily to purchase a new manufacturing facility? Its all about psychology. Psychology drives aggregate demand, at least the first two components: consumer spending and business investment spending. To the extent people or businesses are pessimistic, they’ll not spend if they don’t have to. This fresh pessimism will be felt as a reduction in the level of aggregate demand, reducing spending, incomes, jobs and the cycle of the multiplier. So, the government ends up having two jobs to promote a good economy: (1) to do sensible things to manage the level of their own spending, which is a component of aggregate demand, and (2) to be a cheerleader for optimism. Hard job even without the political overlay. Obviously, the last few days shows us that the investor community is troubled by the prospect of this government to capably steer the economy.

Valuing the Firm

May 27, 2016 gaumerLeave a comment

Valuing the Firm and Present Value

The value of any firm (when sold) is basically the expected future stream of profits. If profits are expected to be 0, then the value of the business is 0, though the assets might be sold for something (buildings, equipment, inventories, receivables, securities and other investments, etc.).

This may sound strange, since we might think that the businesses assets (buildings, cars, patents, people) might drive the value if the business was sold. Indeed, these assets might be worth a lot, but the core value of the “business” is its capacity to earn positive profits (or positive cash flows). If the business makes and sells a worthless product, then its value will be zero (but the buildings and the tools and the other assets might have some value). Profit (or positive cash flow) potential is the driver of the worth of the business—whether a for profit business, or a not-for-profit one.

The factors driving profits like brands and loyalty, product quality, locations, strength of competition, patents, efficiency advantages, and other things are of course important—but their importance is captured in expected future profits. There are two steps in calculating how much that future stream is worth today.

Calculating future profit. The basic need here is to calculate for recent (and future ) years how much cash the business is able to throw off to the owners. Essentially, it is profit plus depreciation (depreciation is an expense, but not a use of cash, so we add it back). Sometimes the interest expense is also added back, if the firm’s debt is going to be paid off at the sale. We can calculate this for the last few years. And, forecasts need to be made of profit going forward for usually 5 years. A calculation is also made of the rate of growth of profits after that point. This stream of Cash flows (CF) looks like:

Sum CF = CF 1 + CF 2 + CF3 + CF4 + CF 5 + ….. + CF infinity

Even if these forecasts are accurate, the business is not worth this much today. One adjustment needs to be made. The problem is the “time value of money”. If I asked you whether you would prefer a dollar received today, with a dollar to be received a year from today, I expect you’d prefer to get it today rather than wait. Everyone would. If you had the dollar today you could invest it and have more than a dollar by next year. There are also risks of never getting it. So you’d prefer today. This is the time value of money. A dollar received today has more value than a dollar received a year from now. And, by extension, a dollar received a year from now is worth more than a dollar received 2 years from now. So, if we look at the equation, we are trying to add apples and oranges— dollars received at different points in the future are not worth the same amount. ‘

What to do about it? We must put all profits in this stream into a common value of money. We could put it all in terms of 2015 dollars, or in terms of 2020 dollars. However, the convention is always to put them all in terms of “present year dollars”. We do this since the sale is now, and the alternatives for the buyer are being considered now. We do this by a process called “discounting” the future stream of CFs to put them all in terms of present value of money.

So, lets take CF1 (a year from now) as an example. Assume CF1=$200,000. How much is 200,000 received a year from now worth today? Well, lets say that if I had money now I could invest it and get 5% return in a year. So, if I calculated 200,000 = 190,476 . This means that if I had 1.05

190,476 today I could invest it at 5% and have exactly 200,000 a year from now. The present value of 200,000 received a year from now is worth 190,476 if discounted at 5%. The present value of the CF2 = 300,000 would be = 300, 000 = 300,000 = 272,109 (1+.05)² 1.1025

If I had 272,109 today I could invest it at 5% and have exactly 300,000 2 years from now.

So, the idea is to discount the entire stream of cash flows back to present value . This sum gives us the present value of the stream of future profits. You can see that future profits don’t weigh as heavily as profits in the very near term. If the discount rate is high, the future values are worth very little. Tables can ease the calculation process. You will learn more about this in finance.

Basically, the value of a business (at least one who’s stock is not traded on the exchanges) is the present value of the stream of future profits minus the debt the business owes. This kind of method is used for valuing non profits, as well as for profits. These valuations are done (1) when someone wants to make an offer to buy, or (2) the firms pension plan contains share of company stock, and they are required to value the firm every year as part of the oversight of the pension plan.

Economic Analysis, Regression and Forecasting Basics Including an HRR Example

April 26, 2016July 18, 2019 gaumerLeave a comment

Economic analysis is about identifying systematic patterns in data that may confirm or deny theory, and which can help decisionmakers see the best way forward. The key word is ‘systematic‘. In the real world (or the real business world) there is variation everywhere. Somebody is seen to raise price and sell more, to cut price and sell less, to increase market share and go bankrupt, to issue loads of debt and get rich…. everything is possible and can be documented on a case by case basis. The question is whether this or that case or data set provides generalizable insight, or is just an interesting situation. Is there something generalizable in what we see, to guide our future actions, or is it just circumstantially interesting?

The isolation of common, systematic patterns of behavior and building a body theory to guide thinking is what the practice of economics is about. The theory of demand is a good example, even though the simple demand curve is built with unrealistic simplifying assumptions. but, so much of what is at the essence of firm managerial behavior is captured by the simple demand concept— firms invest a lot trying to shift out and to tilt their demand curve— advertising, loyalty building investments, product differentiation, eliminating rivals, —all tend to shift and tilt demand so that profits can be enhanced by creating the power to raise price and increase revenue.

So, we hear that relations with Cuba will soon be normalized, or that the price of oil continues to fall, or see a report that shows that one of our stores is doing much better than the others. What are these data telling us that we can use?

Seeing VARIATION is a managers opportunity to learn more about WHY things are happening, and to move beyond the level of introspection that most people operate—understanding WHAT is happening. Yes, knowing WHAT is happening is important. But learning can be deeper is we can understand WHY it is happening— this allows the manager to generalize and craft incentives, to craft marketing programs, to take advantage of knowledge of WHY the variation is occurring.

So, if we see variation across stores in our chain, or variation in time of day our sales are happening— then we can do economic analysis of various types to see if there are any systematic patterns in the variation we see. Lets say we are interested in understanding more about why our chain of hospitals has variation in revenues per bed. What is driving this? Using SCATTER PLOTS, we can quickly checkout some possibilities of systematic differences going on among our hospitals. Below, we show the variation across the chain in terms of ADMISSIONS per CAPITA on the vertical axis. And, we show how many beds there are per capita in the places we have hospitals.

supply-induced-demand

What this shows is that in places with more beds, we have more admissions per capita. This odd” relationship is something we call “supply induced demand”. Where there are more beds, people get more hospital care. This, of course suggests that the use of hospitals is not based on “scientific need for care”. Rather, hospitalization is quite discretionary for many kinds of problems– and doctors tend to develop practice styles of admitting more patients when there are more beds available. Note, that the pattern doesnt exist for the red line– showing the admissions for Hip Fractures— such admits dont vary at all with bed availability— if you have a fractured hip, admission is necessary. No discretion.

So, we learn a little about why admissions (and revenue) may vary across our sites because of the patterns we see. Looking at patterns across types of patients (age, gender, insurance status) or across types of service (maternity, surgery, outpatient, etc.) of community characteristic (rural, suburban, inner city) may help understand the WHY we are experiencing variation in our performance. This can be done by scatter plots, or tables of central tendency across groupings of our hospitals (averages or medians).

Sometimes we may suspect some systematic differences, and want to confirm it. We use statistical analysis to do that. Is the “pattern” we see more than just a possibility of the”noisy” variation in our data?

“What produces health” is a good illustration of massive variation in results–and the need to understand what underlying patters are driving most of that variation? Lots of things. At core, individual people decide how healthy they want to be, and what they might do day-to-day to achieve that aim. Not everyone cares the same about current and future health, and have different priorities for deploying their scare money and time resources toward their health aims. SOme people dont care about health at all—others fret daily about it. And, since “good health” can have a large effect on our future (in many ways) it is often sort of an “investment” —whereby people commit resources now in order to earn benefit6s later in life. But, of course, all people dont want to wait to recieve future benefits of spending money and time now to get healthier . So, the situation is that we have massive differences among us in our aim of how healthy we want to be, and consequently, differences in how healthy we are!

As policy analysts, thinking of improving the “bang for the buck” we get for our health spending (and also trying to make our health system more fair as well) we must understand the systematic patterns of the underlying health drivers. What makes us healthier? We do that by estimating “health production functions” using regression. We use data on individuals or groups of individuals to estimate algorithms like:

Health status = a + b (age) + c (gender) + d (educ level) + e (spending on health) + other factors

And, we use theory to guide us as to what factors are potentially important influences on individual health choices.

Testing Theory with Data

The issue in all such theory is developing tests of this behavior using real world data, where assumptions don’t match the simplifying theory. How to cope? Well, most theories are tested by using regression approaches, where assumptions are proxied by ‘controlling’ the extraneous influences on the simple price-quantity relationship. How to control for the effect of varying income, or effects of different competitor prices, or areas with more or less competition, etc. Essentially regression allows these ‘other influences on the key relationship of interest’ to be ‘held constant’ by including them in the regression model, which separately estimates their partial effects on the dependent variable. So, when i am interested in the effect of age group on the hospital charges, i can “control’ for severity by including it in the model as well. The regression coefficients are each partial estimates of the change in the dependent variable associated with a one unit change in the particular independent variable, holding constant the effects of other independent variables. So the effect of age group on charges holds constant the effect of severity on charges. This technique is the way we create measured effects of one variable on another, with simplifying assumptions about other relevant influences. Economic theory guides us on what these other influences might be, so we can be sure to include them in our economic analysis.

Our purpose is to determine if there are underlying patterns of relationships that confirm (or not) theories about how firms behave—- and our interest in these systematic patterns is to expand the body of knowledge that guides our understanding of how firms behave when hiring executives. For example, lets say we are interested in whether hospitals discriminate in their hiring behavior of managers. We would need to study the patterns of hiring managers to discover if discrimination is occurring. Do firms behave as if they were responding to labor market theories, or others? What we’d be interested in is whether the place-to-place variations in the propensity to hire minority managers is related to:
1. variations in the hospital marketplace itself— are there systematic differences in hiring when hospitals are larger, or when the industry is more/less competitive, or when the missions of hospitals are different (ownership and teaching status).
2. variations in the supply of workers—- are their systematic variations in minority hiring associated with the % of population that is minority, or the relative #s of minorites with college degrees,
3. variations in region. we know there are more and less willingness to accept minority managers in different regions of the country, and in urban and ruiral areas.

WE WOULD EXPECT SUCH relationships to explain some portion of the observed variation in minority hiring— but is there anything left unexplained? If yes, what could be the systematic explanation for it?

One theory that is relevant here is the theory of discrimination (Gary Becker, Nobel prize winner). His theory says that business may discriminate (racial, gender, ethnic, etc.) if they have customers or employees who prefer it. Said another way, discrimination is the sacrifice in potential profitability to achieve some non economic end. Discrimination is such an end. While stockholders may not like it, sometimes caving to pressure from other employees or customers (or other stakeholders) may be done. So, it may be that unexplained variations in minority hiring may reflect this or other systematic (but unmeasured) influences.

So, looking at the article, how important are supply forces in explaining place-to-place variations in % minority managers? How important are hospital market factors? Can You think of other things that would have been good to measure to help understand the variation?

Regression analysis essentially displays the scatter of data between two variables

regression

Total costs = A + b (Volume of output)

Total costs = A + b (V) + c (evening open) this =1 for the last 6 months, and = 0 otherwise)

Regression coefficient estimates have important interpretations in economics. In the above example of the cost regression, we might have an estimated equation

Total monthly cost = 50000 + 100(V) + 20 (evening open)

In the case where we might have estimated a demand curve, such as

Quantity sold = A + b (price) + c (household income in 000s) + d(competitor’s price)

We could estimate it from data and get:

Quantity sold = 500 – 15 (price) + 20 (income ) + 30 (competitors price)

What this means is that if our price was zero, and income were zero and the competitors price was zero we would sell 500 units (silly, but it tells us where the demand curve crosses the horizontal axis (eg where out price is zero)
It says that if we increase our price by $1, we’d sell 15 fewer units of the product (other stuff like income and competitors prices staying constant)
it says that if household income were 1000 higher on average, we could expect to sell 20 more units (other things held constant, like our price and competitor prices. Is this a normal or an inferior good?
it says that if competitors lowered their prices by $1 we would sell 30 fewer units (other things the same. Are they a substitute or a complement?

Does age matter to the size of bill
Does the category of the age matter
Does severity of the diagnosis/procedure matter

I explain the results on the sheet showing what excel produced as results—much of which is not important at this stage of the game.

You could use the data set to create a different model , say one that used only patient severity as a independent variable to explain charges.

charges	age	age category	Severity	dr code#	Female=1	admit	disch
8,254	57	2	2	730	1	1/1/2004	1/3/2004
24,655	43	1	4	730	1	1/1/2004	1/9/2004
27,234	81	3	4	730	0	1/2/2004	1/13/2004
21,345	56	2	3	730	0	1/9/2004	1/14/2004
2,417	17	1	1	730	1	1/3/2004	1/4/2004
5,420	61	2	1	730	1	1/4/2004	1/6/2004
18,823	-61	2	2	730	1	1/6/1944	1/12/2004
20,280	61	2	3	730	1	1/6/2004	1/11/2004
4,360	44	1	1	730	0	1/2/2004	1/5/2004
22,382	90	3	3	730	1	1/2/2004	1/6/2004
12,673	39	1	3	730	1	1/4/2004	1/10/2004
22,632	70	3	4	730	1	1/3/2004	1/11/2004
22,642	77	3	4	730	0	1/3/2004	1/13/2004
14,111	85	3	2	730	0	1/5/2004	1/11/2004
9,763	52	2	2	730	1	1/6/2004	1/13/2004
13,343	65	2	2	730	0	1/7/2004	1/11/2004
4,886	54	2	1	730	1	1/4/2004	1/7/2004
22,712	87	3	3	730	0	1/4/2004	1/14/2004
7,194	50	2	2	730	1	1/3/2004	1/7/2004
24,809	73	3	3	730	0	1/3/2004	1/15/2004
9,405	62	2	1	730	1	1/2/2004	1/7/2004
9,990	63	2	1	499	1	1/2/2004	1/6/2004
24,042	67	3	3	499	1	1/1/2004	1/20/2004
17,591	68	3	4	499	0	1/2/2004	1/10/2004
10,864	85	3	2	499	0	1/3/2004	1/9/2004
3,535	20	1	2	499	1	1/2/2004	1/3/2003
6,042	61	2	1	499	0	1/4/2004	1/6/2004
11,908	59	2	1	499	0	1/4/2004	1/10/2004
24,121	86	3	44	499	0	1/5/2004	1/21/2004
15,600	72	3	3	499	1	1/5/2004	1/11/2004
25,561	92	3	4	499	0	1/4/2004	1/19/2004
2,499	39	1	1	499	0	1/6/2004	1/7/2004
12,423	69	3	3	499	1	1/6/2004	1/9/2004
24,980	71	3	4	499	1	1/7/2004	1/19/2004
19,873	59	2	3	499	0	1/8/2004	1/22/2004
21,311	92	3	4	499	1	1/6/2004	1/12/2004
15,969	60	2	3	499	1	1/5/2004	1/11/2004
16,574	72	3	3	499	0	1/7/2004	1/13/2004
24,214	89	3	3	499	0	1/7/2004	1/19/2004

regression2

Forecasting
Almost every person finds themselves in a situation in their job where they have to make a projection or forecast of sales revenue, or cash needs or something. Every business plan requires this sort of thing. While this is an area where deep technical skills exist, it is also an area which requires MBAs to be equipped to do a serviceable job (when the job can’t afford to hire an expensive consultant) and to understand the limits of their work.

Finding future values of some measure or indicator is risky business. It is often important for businesses to do this for planning purposes, but it remains a difficult chore under the best of circumstances. How can we predict the future? We can’t. But we often have to try anyway.
There are four basic forecasting methods.

(1) extrapolating from historic or past data to find quantitative estimates of future values. There are a number of ways to do this.

(2) surrogate tracking—finding some metric for which forecasts are available that moves over time in roughly the same pattern as the thing we are trying to forecast.

(3) analytic forecasting, where we find known or logical drivers of what we want to forecast, and then look for evidence and opinions about what those drivers might be doing going forward. This may yield some qualitative notions of what to expect. This is our only option when there is no past data from which to extrapolate.

(4) a system of equations that link together the relationships between the drivers and the target variables. Historic data on all measures are used in such systems. This is the most comprehensive approach, and several universities and consulting organization have large models that make forecasts that are sold to corporate and government clients.

Extrapolation

Sometimes called time series analysis. This can be done several ways, but the essence is to calculate the future value of a measure by extrapolating or projecting from past data on that same measure. There are many ways to do this. But, two methods are most common. Smoothing the historic data, or moving average methods, so that trends can be separated from the “ups and downs” or “noise” in the raw data—-which then allows the analyst to see the trend better and use it to extrapolate going forward. Typically moving averages are used. So if we have annual data, we may see lots of up and down from year to year. We can “smooth” that data by separating the data into 3 year groups, or four year groups, taking the average for each of these year- groups. By plotting the three year averages, it tends to ‘smooth’ the messy ups and downs, letting the analyst see the underlying trends in the data. Excel supports this technique of moving averages.

A second approach to extrapolation is very finding forecasts (from BLS or other agencies) of the growth rates that might apply to make the forecast. If we are trying to forecast revenue for our Hess gas station chain, we might build a projection from the forecasted value of the price of oil going forward. It wont be exact, but it might be a useful estimate.

A third approach to extrapolation is simple regression. Regression simply passes a straight line through the data points (scatter) that relates the value of the variable and time. The next page describes the approach. Basically, the forecasted variable is the Y (dependent), and time is the independent (x) variable. The regression technique passes the best fitting straight line through a scatter plot of data.

That line has two parameters: (1) what the slope of the relationship is between the variable and time. Specifically, the change in the variable relative to a one unit change in time (a year if the data are annual, or per month, if the series has data for every month, etc). (2) the second parameter is the Y intercept. This is essentially the value of the Y variable if time=zero. This is meaningless, other than to position the best fitting straight line.

Excel does the work. Here below I did a regression on quantity purchased (Y) and price (X). You can see the data, and the results.

regression-1

The two key parameters are shown as “coefficients”. Here, if price were zero, the customers would “buy” 44.54 units of the product. And the change in demand associated with a 1 unit price change is – 3.91 (a fall in price by $1 would cause a 3.91 increase in number of units sold.

The regression has several other results. The t-stat and associated p values on each parameter estimate are useful in testing hypotheses about whether the estimates we got could have been the result of chance variations in data. Usually a t-stat >2 is an indication that the coefficient is so far from zero (two random and unrelated variables in a regression would have regression coefficient of zero) so as to believe it couldn’t have been generated by chance—there must be a relationship between the independent and dependent variables!

The R square statistic ( = to 0.99) tells us how good the regression line is in representing the raw data. Here the regression nearly matches the raw data, because the raw data is nearly a perfect line. We would say that the regression line (model) explains over 99% of the variation in the dependent variable (quantity demanded here). It is as good as it gets. Most regression models have much lower R squared values. But, frankly, people using

models to forecast and to analyze relationships between variables are much more concerned about the t and p values, and the strength of the relationships between the two variables implied by them. R squared in interesting and a measure of how good the model is overall, but rarely is it a bar to action in the real world.

So, if had a regression telling us that every year added 23M to our sales revenue, we could do a forecast by; (1) taking our most recent year of sales revenue data, and (2) adding 23 million to that number for each year going out to the year we are trying to get a forecast for. There is a way to estimate how much statistical confidence we’d have in that forecasted value—but I am going to skip it here.

A variation on time series regression is to add a lagged value of the dependent variable as a second independent variable. The lagged value will cause us to lose one year of historical data (cause we don’t have a lagged value for the first year). The coefficient on the lagged term is the basis for creating the forecast.

In extrapolations, however we do it, we must remember two facts. One is that the projection is going to be less reliable as we move further and further into the future. Secondly, the recent experience (last year, the year before) are going to be more important to our forecast than the years a long time ago (1989, 1990). If we believe this, than we can “weight” the more recent years most heavily in our analysis (excel doesn’t allow us to do this, but statistical software like SAS and Stata do allow it).

The third issue we must keep in mind is that extrapolations, however done, stand or fail on the basis of their underlying characteristic— we are forecasting the future based on extending trends from the past. To the extent that the same underlying forces that made the trend in the past continue into the future, then the forecast will be a good one. To the extent that the underlying drivers of the past data change, then the forecast will be lousy. Thus, time series or extrapolation forecasts are notoriously bad at detecting turning points. The example below emphasizes this point. In such cases, or in all cases, this means we must rely on analytic methods to get us information about where the data is going.

Surrogate tracking

Sometimes we are trying to get time series data to extrapolate from but we have no history to do it with (eg a business plan for a new product). If we are lucking we may be able to identify some measure that should be highly correlated with what we are trying to project. For example, the illustrative problem below uses this technique. We are trying to forecast the Medicare per capita spending in Massachusetts. We have past data, but no forecast exists. A google search turned up a forecast of inflation rates for the national medicare spending per capita. While this isn’t exactly what we want, it was worth a look to see how well the Mass numbers tracked against the National numbers in the past. The chart shows what we found over six past years.

pc-trend

While the Mass numbers are a good bit higher than the National ones, the year to year changes seem to be driven by the same things (whatever they are). So, we decided to use the forecasted % increases in the National data to proxy the % changes in the Mass numbers.
Sometimes surrogates are not this close. We may not have a forecast of sales for our product, but if history shows that it is strongly related to the strength of the economy (income, jobs, etc.) we may be able to use forecasts of the economy as a proxy for projecting sales.

Analytic Forecasting

This can be done in 2 ways also. If we are forecasting the stock price of Disney stock, the first way is to make a list of the things that might influence price of Disney. Make a short list (not the stuff on the table, but other things in the economy that might influence Disney stock price). Then, the next step would be to snoop around, talk to experts, visit pundit web sites, and see if we can understand what the direction of change will be in these underlying “drivers” of the Disney stock price. This is not a “computational” method of forecasting at all— but it might yield a pretty consistent view that the drivers were going to cause the price to be higher in the future, or lower. Of course, there may be no consistency at all. And we can’t give management any good idea of what will happen to stock price using this method.

The Disney Case uses analytic forecasting in a computational way. It passes a line through the scatter of stock price (P) and one of the presumed ‘drivers” of changes in stock price—Earnings per share (EPS)— which has a slope of +31.388 —a change in EPS by $1 will cause stock price to change by $31.39 in the same direction, other things the same. This is not extrapolation. This is analytic forecasting. It says that the stock price which we need to forecast is systematically related to the EPS. If we knew how high EPS was going to be in 2007-9, we could forecast the stock price.

The drivers of the thing we are forecasting are usually not the components. Rather they are environmental/external factors that will determine the thing we are forecasting. Those factors are dependent on how far into the future we are trying to forecast:

 Short term forecasts— maybe for the few quarters, or the next year or so. Here, many of the things that drive the demand for our product or the health spending (or whatever it is we are interested in) are simply not going to change. But, there are always some things that might happen that could alter the situation. What are they? Then look to google and the pundits and experts to see what they are saying about our forecasting problem. Also google the key drivers (price or availability of a key raw material, or a government policy that might change overnight) and see if experts are commenting on the situation of the key driver. You would be surprised how much is available on the web, but you must organize the problem: what are we forecasting, is anyone else doing the same thing, what are the drivers, what are experts saying about them in the short term?
 Long term forecasts—maybe 5-10 years. This is hard. So much can change. The key drivers may be totally different. The longer the historical data series, the more helpful it may be here. Demand and supply and Government policy might be a framework to use. SWOT is a way to frame it also. Some forces are more fundamental here. And, pundit opinion is more needed here, and it is essential to find some good thinking about the long term

 Medium term — 2-5 years. Here some things are fairly well fixed (the kinds of competitors we have, the demographics lying behind the demand, the sources of key inputs. But some other things can certainly change. This is the list we want.

Putting it Together

The integration of these methods (extrapolation, analytic) may generally be the best way to proceed. Do a simple projection. Evaluate the drivers, and put it together. Management always wants to know that everything is pointing in the same direction, even if the number isn’t exact. A management also wants to know if there is a lack of agreement. The issue isn’t getting a number—its in analyzing the consistency of the evidence you have brought to the table.

Sometimes you may find a source that has thought about this problem a lot and has made a forecast, and reasoned it through in a very expert fashion. You may want to just “steal” this result and use it. This is likely to be better than anything you can independently come up with. Remember, this is not a research project, this is trying to estimate the future value of some measure. Use the best resources you can access.

In many cases a range estimate is going to better than a point estimate. Maybe you have no basis for distinguishing between two estimates: 2.2M and 2.4M. Then possibly what you can do is to say that your estimate is somewhere in the 2.2-2.4 range. And explain why this is the case and how you reached it. If pressed to pick a number, maybe the mid point (average) is the best bet (eg 2.3).

An Illustrative Example

Forecast the 2014 value of Medicare spending per capita in the Boston area. We have data from 2007 to 2012 on this measure. Here it is contrasted with national data, which is important because we want to see how the past in Boston is comparing to the Nation, largely because we know that the experts and pundits, if we find them, are going to be focusing their attention of the National picture, rather than the Boston market.

trend-chart

And, we see from the chart that pretty much the same trends (changes over time) are evident in Boston as ion the Nation as a whole. That helps.

There are several ways of extrapolating to get a 2014 value from this series, but we note that there appears to be a change of some sort occurring at the end of our data series. What had been an upward trend (since 1966) is slowed and is possibly being reversed. This makes it hard to do a projection. Here, linear regression may miss the point entirely. The plot below shows the actual data 2007-2012 and a linear regression line (using the scatter plot graph, and then a linear plot on feature). Clearly, the scatter shows the trend is not linear at all as we approach 2012, and the projection of the linear pattern for 2007 – 2012 would generate a forecast for 2013 and 2014 that is “off the charts” too high.

off-the-chart

The simple regression of the Boston per capita spending on year would yield a coefficient on the year variable of about 290, which could be used to project 2013 and 2014 values. But this method, using the historic data, yields values for 2013 and 2014 that are 11310 and 11600, which are way above the simple extrapolation of the first chart shown above. The regression estimate of an increase of per capital spending of 290 a year is based on the average increase from 2007 to 2012. The increment per year is obviously declining, and this kind of method wont give us forecasts that are believable, if the recent trends are accurate.

To help see what’s going on we made this Boston data chart from the above data. It shows the years change in the average per capita spending. Obviously the last 4 yes has seen the average spending increase by a smaller and smaller amount each year, and even become a negative increment in the final year (2012).

actual-spending-increments

I also googled to see if there were any suggestions about forecasted values of medicare spending, or better yet, medicare spending per capita. Voila, I found something from the federal government at http://www.cms.gov/Research-Statistics-Data-and-Systems/Statistics-Trends-and- Reports/NationalHealthExpendData/downloads/proj2012.pdf

web-data

Since the national pattern and the Boston pattern has looked similar (there is a gap between them, but it remains constant over time).
So, one forecast I can make is to apply the projected % increases here from the national series to the Boston data. 2013 = 2012 (1.009) and then 2014 = 2013 (1.019). Applying these formulas, the per capita spending increases by less than 1% in 2013, and by 1.9% for 2014. The numbers for these 2 years are, 2013 = 11022 * 1.009 =11121 and 2014 =11121 * 1.019= 11332

And the projection chart becomes as follows:

projection-uptick

This shows a small uptick for the projection period.
The class we were using this example in was asked to do a projection for 2014 from these data and consider both extrapolation and analytic approaches. The result was 13 forecasts for 2014 averaging 11,309 (and ranging from 10,813 to 11,900). This class average for 2014 is within $24 of the projection made based on the government’s average expected growth rates in national per capita Medicare spending (shown immediately above).

pc-boston

Primer on Balance Sheet, Risk, and Debt

March 18, 2016June 24, 2019 gaumerLeave a comment

What does the balance Sheet tell us? It is a business scorecard, like the income statement. While the income statement tells us how successful our business operations have been over some interval of time, the balance sheet is a snapshot about how financially strong the organization is at a point in time.

Several indicators of financial strength are apparent from the balance sheet.

Size of the business. How big is the business. Has it grown since the same time last year? Has is shrunk? Size is measured by the monetary value of assets. How much stuff does the business have? What are total assets? What were they a year ago.? This is not the only measure of financial strength.
How much liquidity do we have? Liquidity is the extent to which we have some assets that can be quickly used to pay bills if we have to. How liquid we are is determined by whether our assets are cash, or can be quickly converted to cash if we needed to. Obviously, in the above example, we have liquid assets in the form of cash and savings accounts. The other assets could (eventually) be converted to cash, but it would take time. Only a small fraction of ou assets are liquid. Has our assets been changing to be more or less liquid over the past year?
How much debt do we have? Has our debt grown over the last year? Obviously a business is stronger if, other things the same, it has less debt. But debt is a very important way of acquiring assets in a business—for the same reason it is important in our personal life—sometimes businesses cannot acquire assets any other way than by borrowing. Obviously borrowing is a risky way to acquire assets because you have to not only generate enough income in the future to pay back the loans, but you also have to earn enough income in the future to pay even more back than you borrowed (interest costs).
Has equity of the business grown?.The equity, or net worth, is the amount of the business that the business actually owns (after we pay off our debts). Is it growing? Is it shrinking. When firms make profit from operations, or get philanthropy, these are direct infusions into equity.For profit companies can also raise equity by selling stock, essentially giving partial ownership to investors. To maintain their ability to raise equity capital, such firms try very hard (sometimes too hard) to keep their stock process high and rising, More later.

The balance sheet is a scorecard for a business that shows various indicators of financial strength. If you were thinking of acquiring a business, or selecting one to be a supplier to your business, or thinking of attaching your career to a firm—you might want to look at how strong the business is by examining the balance sheet.

Unlike the Income Statement, the Balance sheet does not show how profitable the business has been during some window of time. Rather, the Balance Sheet shows how string the business is financially at a particular point in time. So, the balance sheet always has a date showing when the assessment of financial strength was constructed.

What the balance sheet does is conceptually quite simple. It lists the assets of the business on that date, and the debts of the business on that date, and the gap between them, which is called equity. You could imagine doing a personal Balance Sheet for your household: what assets do you have? List the assets—they are anything of value you have — a car, a bunch of furniture, maybe a home, jewelry, golf clubs, a kayak — basically anything of value. Next to the items, list the money you paid for the asset when you bought it. This list of assets and what you paid for them is the left side of a Balance Sheet—- and it is exactly the same for a business.

Checking account balance 1500 Auto loan balance 12,000

Saving account balance 500 Student loan principle 13,000

Ford escort 15,500 Credit Card Balance Due 3,500

Furniture (various) 7,500 Total Debt (Liabilities) 28,500

Canoe 300

Watch 200

Clothes 3,500 Total Equity 500

Total Assets 29,000 Total Equity + Liabilities 29,000

Then list your debts (what you owe other people on the date of the balance Sheet. Listed above on the right side of the balance Sheet. Total Liabilities represents the claim of people you owe money to to “claim” ownership of that amount of your assets. Some debts may be set up at mortgage arrangements, where the lender would claim one of your assets which was offered as collateral for your loan (your home mortgage, or car loan) so if you don’t pay the debt, they get the asset. Other loans do not have collateral—like credit card debt.

Your Equity needs to be calculated. It is simply Total Assets – Total Liabilities = Equity. This is the amount of the assets that the business (or the person) actually owns. Sometimes Equity is called by different names: Net Worth, or Fund Balance. But, the formula is always the same. Take the value of all the assets, subtract the debts owed, and what’s left is Equity. If you were doing this in the case of the household, think about it this way. If you had a huge garage sale to dispose of everything of value you had (eg your assets) and were able to see all of them for exactly what you paid foreach of them, and then used the money to pay off all the debts—then what is left would be your equity. Note above, I computed our equity as 500. So, the sum of Equity and Liabilities add up to Total Assets. This is what is meant by the term “balance Sheet”—- both sides balance. Said another way, all the assets of the firm (total assets) have to be owned by someone— either they are owned by creditors of the business (the folks the business borrowed money from, or owned by the business itself as Equity. So, in the above example, the size of the business is given by the amount of total assets it has (29,000), these assets have been able to be acquired (or financed) by considerable debt (28,500) leaving the equity or the owners value to be only 500.

Some assets depreciate in value as you own them. The “long term’ assets are listed on the balance sheet at their “net” value, rather than what you paid for them. Take your car, for example. Say you bought is 3 years ago for 20,000. Its value will fall as time passes. To measure what the asset is worth today, we must ask what is the useful life of this asset? And given this, we can compute what the asset is worth 3 years later. Say, for example, we expect the car to last 10 years and be worth 5000 at that time. So, over the 10 years we would ‘use up’ 15,000 or about 1500 a year for 10 years. So, if we have prepared this balance sheet 3 years after purchasing the car, we have used up 4500 of depreciation. So, the value we would put on our balance sheet for the car would be 15,500 (or 20,000 – 4500 ). We would list the asset on the balance sheet as:

Ford Escort (net) 15,500

Why do health care organizations use so much debt? Health care organizations customarily use a lot of debt to finance the acquisition of assets. Typical hospitals have between 30-50% of their assets financed by debt (this is called the debt ratio = debt/assets.

Sometimes the option of saving up ( by earning profits and retaining them) in order to buy things of value is simply not quick enough in hospitals and other businesses. This dilemma is identical to that of families trying to buy a home in a good neighborhood with good schools. Saving up will delay the process too long, and the benefit of having the asset will disappear if we wait to long to acquire it. So, all of us including businesses who face this problem solve it by taking on debt. Debt allows us to have our asset now, and pay for it later. This is especially true in the fast-changing technology environment of hospitals, where waiting till we can write a check for the new outpatient surgery center may hurt us immeasurably.

There are several reasons why hospitals often (but certainly not always) carry such a high debt load, relative to other businesses.

The cost of debt is lower for non profits. Non profit organizations are able to borrow money for lower interest costs than for profit organizations. This is because the when they pay interest to investors (who lend them money) those investors can often deduct the interest payments they receive from the income before calculating the taxes they owe, That means that when an investor is considering options for lending money to firm X a for profit in order to get a return or interest payment of 8% —– and loaning instead to the non for profit health care organization—– she knows that taxes will need to be paid on the 8&, but not on the interest income from the not for profit. So, as a consequence, interest on non for profit debt do not need to be as high to keep get the investors money. The interest paid by non for profits may be only 4-5%. This “break” on interest rates paid by non profits is partly responsible for their higher propensity to borrow than comparable for profit firms.
Health care organizations often need to make big and otherwise unaffordable investments to keep themselves competitive. This is due to the rapid pace of technology changes in the industry, and the urgent need to make big facility and diagnostic technology investments. When they need to set up an MRI facility, they need it. When they need to make large expansions of outpatient surgery capabilities to take advantage of arthroscopy and laser methods—they need to do it quickly. They often just don’t have time to “save” profits to buy what they need.
Outlooks for returns on investment are often “favorable” in health care. The demographic boom, the bottomless trough of Medicare payment, the prospect of continued explosion of health spending—puts the health care organization in a positive frame of mind about being able to repay debt (they begin to be hopeless optimists like Don Trump)

Health care organizations usually borrow money in the form of “bonds”, which are essentially a non collateral form of IOU. Look at your balance sheets from your organizations and see if it says Long term Debt in the form of bonds? The interest rates paid on bond borrowing are driven by the “bond rating” which is an assessment by a one or the other of several wall street firms (Moodys, others) which is an assessment of the firms ability to repay the debt. These firms earn money for the data collection they do to make the ratings for 1000’s of organizations (not just health care, but most corporate businesses too, who also use bonds to borrow money).They get paid for their work by clients in the investment businesses including banks and mutual funds, who pay the rating companies for their ‘ratings”.

These firms do a very very knowledgeable assessment of the situation of the organization, and how they are doing vis a vis competitors, what their strategy is, how well it is working, what the threats are to doing less well in the future. They summarize their assessment of the financial capacity of the borrowing organization in the form of a bond rating (A++,A+, A, B++,B+,B etc.). The higher the rating , the lower the interest rate. The bond ratings are crucial things that health care organizations pay attention too. The ratings change, and these changes are really really important to the organization.

Bond ratings relate to risk of repayment. Lenders (and investors of all types) are very interested in the level of risk when they make investments. Risk is related to the variability in earnings of the business. All businesses a subject to variability in earnings (profit) and these swings in earnings expose the firm to their ability to deliver the return investors are expecting on their investments.

More on Risk and Debt

Generally we think of the firm as needing two kinds of investors: investors that loan them money (debt), and investors that provide money in exchange for shares of ownership (equity). Both types of investors expect a return commensurate (or higher) with the risk they are exposed to. For equity investors their return is contingent on expected future profits; some of which may be paid to owners in the form of dividends, and some may be “earned” in the form of stock price appreciation (so that when they want to sell the stock, it will have a higher market price).

Investors who lend money get a return in the form a fixed interest payment for the use of their money. This rate is specified in the loan or bond, and does not vary with profitability of the firm. Risk for debt investors is related to the swings in profit that may jeopardize the ability of the firm to pay the required interest payments, or jeopardize the repayment of the principal of the load. Risk for equity investors is the swings in profit that may affect the ability of the firm to pay dividends (or the size of dividend payments), and the profit swings will almost certainly affect the market value of the stock (as profits go up and down the attractiveness of the stock by other investors changes).

From the viewpoint of the firm, there are two types of “risk” for a business.

business risk — the swings in profitability facing investors stemming from market influences; competitor actions, new products, changes in consumer tastes, business cycles in the economy, price changes for resources, etc.
financial risk — the swings in profitability stemming from debt.

The most direct way of visualizing the influence of debt on risk is depicted in the illustration in the table. We compare the firm in a situation of no debt on the balance sheet ( no leverage) with the same firm with debt (leverage). These are shown as columns below. The rows show what happens in a good year and a not so good year. We calculate the return to equity (ROE) in each of the four scenarios. In a good year the firm earns as profit of 5. The upper panel of the chart shows this situation. The firm has assets of 100. In the no leverage situation these 100 in assets had to be financed by equity. This makes the return on equity in a good year 5%. When half the assets are financed by debt, then the owner’s equity needs to be only 50. This makes the ROE equal to 10% (5/50 = 10%)

	No leverage	Leveraged situation
good year	Profit= 5	Profit = 5
Owner Invested	100	50
	ROE= 5 %	ROE=10%
bad Year	Profit= -1	Profit= -1
Owner Investment=	100	50
	ROE= -1%	ROE= -2%

In the lower panel of the chart we show a bad year, where profit is only -1. For the non leveraged situation the ROE is -1% (-1/100). In the leveraged situation the ROE in the bad year is -2% (-1/50). Thus the good year has a higher ROE when debt exists, and a lower ROE when debt exists. Higher highs, and lower lows. More debt increases the amplitude of the variations in ROE. More debt increase the risk of the business. This is shown in the chart below.

Financial risk stems from debt, which increases the amplitude of return to equity swings (eg risk) for the business. But, financial risk does not stop the use of debt, particularly in hospitals and other non profit organizations. There are a number of reasons why debt remains an important source of financing.

The interest rate paid by non profits is lower than the rates paid by for profit borrowers. This stems from the tax write off of interest earnings available for lenders to non profit organizations (municipal bonds).
Cost of debt is independent of business earnings (owners don’t have to share success)
Owners don’t have to share control with debt financing; equity means part ownership
Firms can offer more services (and be bigger) than if they were financing with equity alone
Debt service is a fixed charge— and declines in earnings create liquidity risks (financial risk)

And, there are disadvantages to using debt:

Firms with Debt (leverage) are riskier to ALL investors, and will need to pay more to raise capital in all forms
Debt contracts have to be repaid on a schedule, and this may not be convenient
Debt contracts may contain restrictive managerial covenants
Only a limited amount of debt capital can be raised at “reasonable” interest rates. As the amount of debt increases, lenders will lend more only at higher interest rates.

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Author: gaumer