Module 2 Utility and Utility Functions

 

The Policy Question: Hybrid Car Purchase Tax Credit—Is it the Government’s Best Choice to Reduce Fuel Consumption and Carbon Emissions?

U.S. residents and the government are concerned about the dependence on imported foreign oil and the release of carbon into the atmosphere. In 2005, Congress passed a law to provide consumers with tax credits toward the purchase of electric and hybrid cars.

This tax credit may seem like a good policy choice, but it is costly because it directly lowers the amount of revenue the U.S. Government collects. Are there more effective approaches to reducing dependency on fossil fuels and carbon emissions? How do we decide which policy is best? To answer this question, policymakers need to predict with some accuracy how consumers will respond to this tax policy before these policymakers spend millions of federal dollars.

We can apply the concept of utility to this policy question. In this module, we will study utility and utility functions. We will then be able to use an appropriate utility function to derive indifference curves that describe our policy question.

Exploring the Policy Question

Suppose that the tax credit to subsidize hybrid car purchases is wildly successful and doubles the average fuel economy of all cars on U.S. roads – a result that is clearly not realistic but useful for our subsequent discussions. What do you think would happen to the fuel consumption of all U.S. motorists? Should the government expect fuel consumption and carbon emissions from cars to decrease by half in response? Why or why not?

2.1 Utility Functions

LO 2.1: Describe a utility function.

2.2 Utility Functions and Typical Preferences

LO 2.2: Identify utility functions based on the typical preferences they represent.

2.3 Relating Utility Functions and Indifference Curve Maps

LO 2.3: Explain how to derive an indifference curve from a utility function.

2.4 Finding Marginal Utility and Marginal Rate of Substitution

LO 2.4: Derive marginal utility and MRS for typical utility functions.

2.5. Policy Question

2.1 Utility Functions

LO1: Describe a utility function.

Our preferences allow us to make comparisons between different consumption bundles and choose the preferred bundles. We could, for example, determine the rank ordering of a whole set of bundles based on our preferences. A utility function is a mathematical function that ranks bundles of consumption goods by assigning a number to each where larger numbers indicate preferred bundles. Utility functions have the properties we identified in Module 1 regarding preferences. That is: they are able to order bundles, they are complete and transitive, more is preferred to less and, in relevant cases, mixed bundles are better.

The number that the utility function assigns to a specific bundle is known as utility, the satisfaction a consumer gets from a specific bundle. The utility number for each bundle does not mean anything in absolute terms; there is no uniform scale against which we measure satisfaction. Is only purpose is in relative terms: we can use utility to determine which bundles are preferred to others.

If the utility from bundle A is higher than the utility from bundle B, it is equivalent to saying that a consumer prefers bundle A to bundle B. Utility functions therefore rank consumer preferences by assigning a number to each bundle. . We can use a utility function to draw the indifference curve maps described in Module 1. Since all bundles on the same indifference curve provide the same satisfaction, and therefore none is preferred, each bundle has the same utility. We can therefore draw an indifference curve by determining all the bundles that return the same number from the utility function.

Economists say that utility functions are ordinal rather than cardinal. Ordinal means that utility functions only rank bundles – they only indicate which one is better, not how much better it is than another bundle. Suppose, for example, that one utility function indicates that bundle A returns 10 utils and bundle B 20 utils. We do not say that bundle B is twice as good, or 10 utils better, only that the consumer prefers bundle B. For example, suppose a friend entered a race and told you she came in third. This information is ordinal: You know she was faster than the fourth place finisher and slower than the second place finisher. You only know the order in which runners finished. The individual times are cardinal: If the first place finisher ran the race in exactly one hour and your friend finished in on hour and six minutes, you know your friend was exactly 10% slower than the fastest runner. because utility functions are ordinal many different utility functions can represent the same preferences. This is true as long as the ordering is preserved.

Take for example the utility function U that describes preferences over bundles of goods A and B: U(A,B). We can apply any positive monotonic transformation to this function (which means, essentially, that we do not change the ordering) and the new function we have created will represent the same preferences. For example, we could multiply a positive constant, α , or add a positive or a negative constant, β . So αU(A,B)+β represents exactly the same preferences as U(A,B) because it will order the bundles in exactly the same way.  This fact is quite useful because sometimes applying a positive monotonic transformation of a utility function makes it easier to solve problems.

2.2 Utility Functions and Typical Preferences

LO2: Identify utility functions based on the typical preferences they represent

Consider bundles of apples, A, and bananas, B. A utility function that describes Isaac’s preferences for bundles of apples and bananas is the function U(A,B). But what are Isaac’s particular preferences for bundles of apples and bananas? Suppose that Isaac has fairly standard preferences for apples and bananas that lead to our typical indifference curves: He prefers more to less, and he likes variety. A utility function that represents these preferences might be:

U(A,B) = AB

If apples and bananas are perfect complements in Isaac’s preferences, the utility function would look something like this:

U(A,B) = MIN[A,B],

where the MIN function simply assigns the smaller of the two numbers as the function’s value.

If apples and bananas are perfect substitutes, the utility function is additive and would look something like this:

U(A,B) = A + B

A class of utility functions known as Cobb-Douglas utility functions are very commonly used in economics for two reasons:

1. They represent ‘well-behaved’ preferences, such as more is better and preference for variety.

2. They are very flexible and can be adjusted to fit real-world data very easily.

Cobb-Douglas utility functions have this form:

U(A,B) = AαBβ

Because positive monotonic transformations represent the same preferences, one such transformation can be used to set α + β = 1 , which later we will see is a convenient condition that simplifies some math in the consumer choice problem.

Another way to transform the utility function in a useful way is to take the natural log of the function, which creates a new function that looks like this:

U(A,B) = αln(A) + βln(B)

To derive this equation, simply apply the rules of natural logs. [We’ll add a link here for students to Click to see how in an Explain It video tutorial]. It is important to keep in mind the level of abstraction here. We typically cannot make specific utility functions that precisely describe individual preferences. Probably none of us could describe our own preferences with a single equation. But as long as consumers in general have preferences that follow our basic assumptions, we can do a pretty good job finding utility functions that match real-world consumption data. We will see evidence of this later in the course.

Table 2.1 summarizes the preferences and utility functions described in this section.

Table 2.1 Types of Preferences and the Utility Functions that Represent Them
PREFERENCES	UTILITY FUNCTION	TYPE OF UTILITY FUNCTION
Love of Variety or “Well Behaved”	U(A,B) = AB	Cobb-Douglas
Love of Variety or “Well Behaved”	U(A,B) = AαBβ	Cobb-Douglas
Love of Variety or “Well Behaved”	U(A,B) = αln(A) + βln(B)	Natural Log Cobb-Douglas
Perfect Complements	U(A,B) = MIN[A,B]	Min Function
Perfect Substitutes	U(A,B) = A + B	Additive

2.3 Relating Utility Functions and Indifference Curve Maps

LO3: Explain how to derive an indifference curve from a utility function

Indifference curves and utility functions are directly related. In fact, since indifference curves represent preferences graphically and utility functions represent preferences mathematically, it follows that indifference curves can be derived from utility functions.

In uni-variate functions, the dependent variable is plotted on the vertical axis and the independent variable is plotted on the horizontal axis, like the graph of y=f(x). In contrast, graphs of bi-variate functions are three-dimensional, like U=U(A,B). Figure 2.1 shows a graph of $U=A^{\frac{1}{2}}{B}^{\frac{1}{2}}$. Three-dimensional graphs are useful to understanding how utility increases with the increased consumption of both A and B.

Figure 2.1 $U=A^{\frac{1}{2}}{B}^{\frac{1}{2}}$

${\frac{\mathrm{<br>}}{}}^{}$

Figure 2.1 clearly shows the assumption that consumers have a preference for variety. Each bundle which contains a specific amount of A and B represents a point on the surface. The vertical height of the surface represents the level of utility. By increasing both A and B, a consumer can reach higher points on the surface.

So where do indifference curves come from? Recall that an indifference curve is a collection of all bundles that a consumer is indifferent about, with respect to which one to consume. Mathematically, this is equivalent to saying all bundles, when put into the utility function, return the same functional value. So if we set a value for utility, Ū, and find all the bundles of A and B that generate that value, we will define an indifference curve. Notice that this is equivalent to finding all the bundles that get the consumer to the same height on the three-dimensional surface in Figure 2.1.

Indifference curves are a representation of elevation (utility level) on a flat surface. In this way, they are analogous to a contour line on a topographical map. By taking the three-dimensional graph back to two-dimensional space –the A, B space –we can show the contour lines/indifference curves that represent different elevations or utility levels. From the graph in Figure 2.1, you can already see how this utility function yields indifference curves that are ‘bowed-in’ or concave to the origin.

So indifference curves follow directly from utility functions and are a useful way to represent utility functions in a two- dimensional graph.

2.4 Finding Marginal Utility and Marginal Rate of Substitution

LO4: Derive marginal utility and MRS for typical utility functions.

Marginal utility is the additional utility a consumer receives from consuming one additional unit of a good. Mathematically we express this as:

$M{U}_a=\frac{\Delta U}{\Delta A}$

or the change in utility from a change in the amount of A consumed, where Δ represents a change in the value of the item. So,

$M{U}_a=\frac{\Delta U}{\Delta A}=\frac{U(A+\Delta A,B)-U(A,B)}{\Delta A}$

Note that when we are examining the marginal utility of the consumption of A, we hold B constant.

Using calculus, the marginal utility is the same as the partial derivative of the utility function with respect to A:

$M{U}_A\frac{\partial U(A,B)}{\partial A}$

Consider a consumer who sits down to eat a meal of salad and pizza. Suppose that we hold the amount of salad constant – one side salad with a dinner, for example. Now let’s increase the slices of pizza suppose with 1slice utility is 10, with 2 it is 18, with 3it is 24 and with 4 it is 28. Let’s plot these numbers on a graph that has utility on the vertical axis and pizza on the horizontal axis (Figure 2.2).

Figure 2.2: Graph and table of Diminishing Marginal Utility

Pizza Slices	Utility	Marginal Utility
1	10
2	18	8
3	24	6
4	28	4

 

From the positive slope of the graph, we can see the increase in utility from additional slices of pizza. From the concave shape of the graph, we can see another common phenomenon: The additional utility the consumer receives from each additional slice of pizza decreases with the number of slices consumed.

The fact that the additional utility gets smaller with each additional slice of pizza is called the principle of diminishing marginal utility. This principle applies to well-behaved preferences where mixed bundles are preferred.

Marginal rate of substitution (MRS) is the amount of one good a consumer willing to give up to get one more unit of another good. This is why it is the same thing as the slope of the indifference curve – since we keep satisfaction level constant we stay on the same indifference curve, just moving along it as we trade one good for another. How much of one you are willing to trade for one more of another depends on the marginal utility from each.

Using our previous example, if by consuming one more side salad your utility goes up by 10, then at a current consumption of 4 slices of pizza, you could give up 2 slices of pizza and go from 28 to 18 utils. 10 more utils from salad and 10 less utils by giving up 2 slices of pizza leaves overall utility unchanged – so we must still be on the same indifference curve. As you move along the indifference curve, you must be riding the slope, that is, you must be giving up the good on the vertical axis for more of the good on the horizontal axis, which yields a negative rise over a positive run.

We can go directly from marginal utility to MRS by recognizing the connection between the two concepts. In our case, for a utility function $U=U(A,B)$ , MRS is represented as:

$MRS=-\frac{M{U}_A}{M{U}_B}$

Note that when we substitute we can simplify the equation:

$MRS=-\frac{M{U}_A}{M{U}_B}=-\frac{\frac{\Delta U}{\Delta A}}{\frac{\Delta U}{\Delta B}}=-\frac{\Delta B}{\Delta A}$

Inserting the calculus it equates to:

$MRS=-\frac{\frac{\partial U(A,B)}{\partial A}}{\frac{\partial U(A,B)}{\partial B}}$

2.5 Policy Question

We determined in Module 1 that the relevant consumer decision between more miles driven and other consumption probably conforms to the standard assumptions about consumer choice. Therefore, using the Cobb-Douglas utility function to represent a consumer who likes to drive a car as well as consume other goods, and who sees them as a trade-off (money spent on gas is money not spent on other consumer goods), is a good choice. It also has the benefits of both conforming to the assumptions, and being flexible:

$U(MD,C)=M{D}^a{C}^{\beta }$ ,

where MD = Miles driven, and C = Other consumption.

In fact, the function itself can be taken to real-world data where the parameters and can be estimated for this market, the market for miles driven in the consumer’s car.

Figure 2.3 Graph of indifference curves for the policy example

Exploring the Policy Questions:

1 . Would other preference types be more appropriate in this example?

2. What would have to be true for perfect complements to be the appropriate preference type to use to analyze this policy?

What would have to be true for perfect substitutes? Given that we are considering a ‘typical’ consumer who drives, is it appropriate to choose a ‘typical’ utility function?

4. Are we just guessing or do we have some basis in theory to support our choice of ‘well-behaved’ preferences or a Cobb-Douglas utility function?

https://bit.ly/3DGp7SO

Consumption I: Goods and Utility

 Goods are something that provides its holder some kind of satisfaction, and therefore has a utility. There are different kinds of goods, and different classifications can be arranged and identified. We can differentiate between consumption goods (durable or perishable) and capital goods. Classification depending in responsiveness to incomes changes (normal goods and inferior goods) and to price changes (ordinary goods and Giffen goods) can also be made.

-Normal goods are those whose demand increases due to a rise in income levels, having therefore a positive correlation, which implies that the elasticity of this kind of goods is always higher than 0.  Normal goods are divided in two categories, Superior goods and Necessary goods.

·Superior goods, also known as luxury goods, are those goods that displace the demand of inferior goods after a rise in consumers’ income. They are a kind of normal goods as their demand increases when income does as well, however, the difference is that they occupy the share of inferior goods. The elasticity of this kind of goods is, for this, always higher than 1.

·Necessary goods are those whose demand increases when income does, however the increase in demand is less than proportional to the rise in income. Elasticity of this goods is always between 0 and 1.

-Inferior goods are those whose demand moves in opposite direction to the income variation of consumers. This occurs because consumers’ preferences change to other goods that are more highly regarded.

-Ordinary goods are those goods whose demands move in opposite direction to the price variation. This means an increase in the price of the good will mean a decrease of its demand and the other way around.

-Giffen goods are those goods whose demand moves in the same direction as the price variation, this meaning, raising the price of the good will increase its demand, and the other way around. The explanation to this kind of goods comes from the “Snob Effect”, the more expensive a good is, the less people can afford it, and therefore becoming more precious and highly valued.

Utility is the ‘satisfaction’ we get from using, owning or doing something. It is what allows us to choose between options. This can be plotted on a chart.

A preference function therefore assigns values to the ranking of a set of choices. This is useful as it allows us to see consumer behaviour as a maximisation problem: faced with a set of options and a budget constraint, we will choose what satisfies us most. Utility functions are often expressed as U(x1,x2,x3…) which means that U, our utility, is a function of the quantities of x1, x2 and so on. If A is a basket of goods, and , then U(A)>U(B). That is, if we prefer A to B it is because we derive greater utility from it.

Utility functions follow the same code of conduct, the same axioms, as preferences, because they are simply numerical representations of them. That is, they are transitive, complete, continuous and convex, for the same reasons. Being continuous allows us to differentiate them, and being insatiable allows us to say that:

This means that the more, the better, which is the same as saying that utility functions grow with quantity.

The most important thing to point out is perhaps the fact that utility functions do not assign a numerical value to our preferences. They simply indicate order and magnitude of preference, that is, what we like more and by how much.

https://bit.ly/3RXPjgb

Are You Really Ready for Customer Experience?

 

Ginger Conlon, Editor-in-Chief

Marketers can talk a good game about customer centricity, but actions speak louder than words.

“Putting customers at the center of your business is what it takes to create a competitive advantage.” This simple, yet bold, statement by Lars Birkholm Petersen launched a discussion around where most businesses are in terms of their customer experience maturity level.

During a presentation at Sitecore Symposium 2014, Petersen reviewed the marketing automation company's Customer Experience Maturity Model (see above). Its stages are initiate, radiate, align (the “attract” phase of the customer lifecycle); optimze, nurture (the “convert” phase); engage, lifetime customers (the “advocate” phase). “We've found that 85.4% of companies are in first two stages,” said Petersen, global director, business optimization services, at Sitecore. “Strategic value increases as you move further along the model. If you're in those first two stages, how do you get started?”

Ron Person, senior consultant, business optimization, for Sitecore, took the stage to answer Petersen's question. To move ahead to stage three, “align,” connect corporate objectives to marketing objectives, Person advised. There are six steps to doing so: determining strategic themes (e.g., customer intimacy, top quality), setting strategic objectives (what's needed to make the theme become a reality), defining marketing objectives (specifically to support the strategic objectives), setting digital goals, creating an engagement value scale, and then selecting key performance indicators. This process will help show the link between business objectives and customer actions.

“All marketing tactics, down to A/B testing, need to contribute toward reaching strategic objectives,” Person stated emphatically.

The fourth stage is “optimize,” which includes such activities as rules-based personalization, A/B and multivariate testing, and campaign attribution. Chris Nash, who joined the stage with Person, explained that optimization, such as in-the-moment personalization, is exceptionally powerful. “Use it in all phases of the customer lifecycle,” said Nash, senior consultant, business optimization, for Sitecore. “Use customer data [in real time] as it becomes available; use the data that makes the most sense to use for that personalization. Use different types of data in different combinations.” Doing so can be complex, he said, but its effectiveness is worth the effort.”

“Optimization is the low-hanging fruit of experience marketing,” Nash said. “Focus on the buyer journey.”

Get connected

After the presentation Petersen, Person, and Nash met with several anaylists and journalists (myself included), to dive deeper into the topic of customer experience maturity, which is a central theme of their new book Connect: How to use data and experience marketing to create lifetime customers.

Considering the complexity of the customer experience, and of marketing, I asked: Are companies at multiple stages of customer experience maturity with different aspects of their marketing? For example, is the email customer experience more mature than in other channels? If so, how can they better connect those stages?

“Companies are being forced by customers to create a connected experience,” Nash said. “It puts pressure on organizations to think about the quality of the overall customer experience. Email, for example, is an ‘advanced' silo, but it's often a disconnected experience from other channels.” Nash explained that this type of disparity creates “moments of disconnected frustration” for customers.

Eliminating the disconnect and creating a consistent customer experience is all about people, process, and technology, Peterson noted. Marketers need to focus on the customer across channels in a connected way; they should collect and use data create relevant, contextualize experiences. “This goes back to marketers needing to ensure that they align the customer experience with strategic objectives to get business value,” he said, referring to the Customer Experience Maturity Model. “Once they're doing that, they can optimize and nurture. Then can moved to engage and lifetime customers.” But tread purposefully: “It takes time, maybe years to move up the maturity model, espcially in a big organization,” Petersen said. 

The journey, however, is more necessary than ever before.

“Businesses that excel at customer experience,” Nash said, “make it so other companies need to do the same.”