Ch1: Economics Foundations – HubbardOBrienEconomics.com Blog

A Reporter for NPR Encounters the Challenge of Network Externalities on an EV Road Trip

An electric vehicle (EV) charging station. (Photo from the Associated Press via the Wall Street Journal.)

Secretary of Energy Jennifer Granholm recently took a road trip in a caravan of electric vehicles (EVs). The road trip “was intended to draw attention to the billions of dollars the White House is pouring into green energy and clean cars.” A reporter for National Public Radio (NPR) went on the trip and wrote an article on her experience.

One conclusion the reporter drew was: “Riding along with Granholm, I came away with a major takeaway: EVs that aren’t Teslas have a road trip problem, and the White House knows it’s urgent to solve this issue.” The problem was that charging stations are less available and less likely to be functioning than would be needed for a road trip in an EV to be as smooth as a similar trip in a gasoline-powered car. The reporter noted that in her experience with her own EV: “I use multiple apps to find chargers, read reviews to make sure they work and plot out convenient locations for a 30-minute pit stop (a charger by a restaurant, for instance, instead of one located at a car dealership).”

EVs exhibit network externalities. As we discuss in Microeconomics and Economics, Chapter 10, 10.3 (Essentials of Economics, Chapter 7, Section 7.3), Network externalities are a situation in which the usefulness of a product increases with the number of consumers who use it. For example, the more iPhones people buy, the more profit firms and individuals can earn by creating apps for the iPhone. And the more apps that are available, the more useful an iPhone becomes to people who use it.

In this blog post, we discuss how Mark Zuckerberg’s Meta Platforms (which was originally named Facebook) has had difficulty selling Oculus augmented reality headsets. Many people have been reluctant to buy these headsets because they don’t believe there are enough software programs available to use the headsets with. Software designers don’t have much incentive to produce such programs because not many consumers own a headset necessary to use the programs.

The difficulty that Meta has experienced with augmented reality headsets can be overcome if the product is sufficiently useful that consumers are willing to buy it even if complementary products are not yet available. That was the case with the iPhone, which experienced strong sales even before Apple opened its app store. Or to take an historical example relevant to the current situation with EVs: When the Ford Motor Company introduced the Model T car in the early twentieth century, many people found that owning a car was such an advance over using a horse-drawn vehicle that they were willing to buy one despite there being realtively few gas stations and repair shops available. Because so many cars were being sold, entrepreneurs had an incentive to begin opening gas stations and repair shops, which increased the attractiveness of using a car, thereby further increasing demand.

As the NPR reporter’s experience shows, consumers choosing between buying an EV or a gasoline-powered car are in a situation similar to that faced by early twentieth century consumers in choosing between cars and horse-drawn vehicles. One difference between the two situations is that Congress and the Biden administration are attempting to ease the transition to EVs by subsidizing the construction of charging stations and by providing tax credits to people who buy EVs.

Does Majoring in Economics Increase Your Income?

Image by Andrea D’Aquino in the *Wall Street Journal*.

Studying economics provides students in any major with useful tools for understanding business decision making and for evaluating government policies. As we discuss in Chapter 1, Section 1.5 of Microeconomics, Macroeconomics, and Economics, majoring in economics can lead to a career in business, government, or at nonprofit organizations. Many students considering majoring in economics are interested in how the incomes of economics majors compare with the incomes of students who pursue other majors.

The Federal Reserve Bank of New York maintains a web page that uses data collected by the U.S. Census to show the incomes of people with different college majors. The following table shows for economics majors and for all majors the median annual wage received by people early in their careers and in the middle of their careers. The median is a measure of the average calculated as the annual wage at which half of people in the group have a higher annual wage and half have a lower annual wage. “Early career” refers to people aged 22 to 27, and “mid-career” refers to people aged 35 to 45. The data are for people with a bachelor’s degree only, so people with a masters or doctoral degree are not included.

	Median Wage Early Career	Median Wage Mid-Career
Economics majors	$55,000	$93,000
All majors	$42,000	$70,000

The table shows that early in their careers, on average, economics majors earn an annual wage about 31 percent higher than annual wage earned by all majors. At mid-career, in percentage terms, the gap increases slightly to 33 percent.

How should we interpret these data? In Chapter 1, Section 1.3, in discussing how to evaluate economic models, we made the important distinction between correlation and causality. Just because two things are correlated, or happen at the same time, doesn’t mean that one caused the other. In this case, are the higher than average incomes of economics majors caused by majoring in economics or is majoring in economics correlated with higher incomes, but not actually causing the higher incomes. It might be true, for instance, that on average economics majors have certain characteristics—such as being more intelligent or harder workers—than are students who choose other majors. Because being intelligent and working hard can lead to successful careers, students majoring in economics might have earned higher incomes on average even if they had chosen a different major.

(Here’s a more advanced point about identifying causal relationships in data: The problem with determining causality described in the previous paragraph is called selection bias. Students aren’t randomly assigned majors; they choose, or self-select, them. If students with characteristics that make it more likely that they will earn high incomes are also more likely to choose to major in economics, then the higher incomes earned by economics majors weren’t caused by (or weren’t entirely caused by) majoring in economics.)

Economists Zachary Bleemer of the University of California, Berkeley and Aashish Mehta of the University of California, Santa Barbara have found a way to evaluate whether majoring in economics causes students to earn higher incomes. The authors gathered data on all the students admitted to the University of California, Santa Cruz (UCSC) between 2008 and 2012 and on their incomes in 2017 and 2018. To major in economics, students at UCSC needed a grade point average (GPA) of 2.8 or higher in the two principles of economics courses. The authors compared the choices of majors and the average early career earnings of students who just met or just failed to meet the 2.8 GPA threshold for majoring in economics. The authors use advanced statistical analysis to reach the conclusion that: “Comparing the major choices and average wages of above-and-below-threshold students shows that majoring in economics caused a $22,000 (46 percent) increase in annual early-career wages of barely above-threshold students.”

The authors attribute half of the higher wages earned by economics majors to their being more likely to pursue careers in finance, insurance, real estate, and accounting, which tend to pay above average wages. The authors note that their findings from this study “imply that students’ major choices could have financial implications roughly as large as their decision to enroll in college ….”

Sources: Federal Reserve Bank of New York, The Labor Market for Recent College Graduates, https://www.newyorkfed.org/research/college-labor-market/index.html; and Zachary Bleemer and Aashish Meta, “Will Studying Economics Make You Rich? A Regression Discontinuity Analysis of the Returns to College Major,” American Economic Journal: Applied Economics, Vol. 14, No. 2, April 2022, pp. 1-22.

Is Vladimir Putin Acting Rationally?

Photo of Russian President Vladimir Putin from the *Wall Street Journal*.

On February 24, when Russian President Vladimir Putin launched an assault on Ukraine he apparently expected within a few days to achieve his main objectives, including occupying the Ukrainian capital of Kyiv and replacing the Ukrainian government. After three weeks, the fierce resistance of the Ukrainian armed forces have resulted in his failing to achieve these objectives. Although the Russian military had expected to experience few casualties or losses of equipment, in fact Russia has already lost more military personnel killed than the United States has since 2001 in Afghanistan and Iraq combined, as well as experiencing the destruction of many tanks, planes, and other equipment.

The United States, the European Union, and other countries have imposed economic sanctions on Russia that have reduced the country’s ability to import or export most goods, other than oil and natural gas. The sanctions have the potential to reduce the standard of living of the average Russian citizen.

Most importantly, the war has killed thousands of Ukrainians and inflicted horrendous damage on many Ukrainian cities.

Despite all this, is Putin’s persistence in the invasion rational or if he were acting rationally would he instead withdraw his troops or accept a political comprise (at this writing, negotiations between representatives of Russia and Ukraine are continuing)? First, recall the economic definition of rationality: People are rational when they take actions that are appropriate to achieve their goals given the information available to them. (We discuss rationality in Microeconomics, Chapter 10, Section 10.4, and in Economics, Chapter 10, Section 10.4.) Note that rationality does not deal with whether a person’s goals are good or bad. In this discussion, we are considering whether Putin is acting rationally in attempting to achieve the—immoral—goal of subjugating a foreign country.

Peter Coy, a columnist for the New York Times, discusses three reasons Putin may continue his attack on Ukraine even though, “The bloody invasion of Ukraine has been a disaster” for Putin. The first reason, Coy recognizes, involves an economic concept. His other two reasons can also be understood within the economic framework we employ in Microeconomics.

First, Coy argues that Putin may have fallen into one of the pitfalls to decision making we discuss in Chapter 10: A failure to ignore sunk costs. Coy notes that Putin may want to continue the attack to justify the death and destruction that has already occurred. However, those costs are sunk because no subsequent action Putin takes can reduce them. If Putin is continuing the attack for this reason, then Coy is correct that Putin is not acting rationally because he is failing to ignore sunk costs in making his decision.

There is a subtle point, though, that Coy may be overlooking: Putin is effectively a dictator, but he may still believe he needs to avoid Russian public opinion turning too sharply against him. In that case, even if recognizes that he should ignore sunk costs he may believe that the Russian public may not be willing to ignore the costs of the death and destruction that has already occurred. In that case, his refusal to ignore this sunk cost be rational.

Coy’s second reason why Putin may continue the attack is that he may believe “just another few weeks of fighting will be enough to subdue Ukraine.” Although Coy doesn’t discuss the point in these terms, it would be rational for Putin to continue the attack if he believes that the marginal benefit of doing so exceeds the marginal cost. (We discuss this point directly in Chapter 1, Section 1.1 “Optimal Decisions Are Made at the Margin,” and provided many examples throughout the text.) The marginal cost includes the additional Russian military casualties and losses of equipment from prolonging the war and the cost of economic sanctions to the Russian economy. (It seems unlikely that Putin is taking into account the additional loss of life among Ukrainians and the additional devastation to Ukrainian cities from prolonging the war.)

The marginal benefit from continuing the attack would be either winning the war or obtaining a more favorable peace settlement in negotiations with the Ukrainian government. If Putin believes that the marginal benefit is greater than the marginal cost, he is acting rationally in continuing to attack.

Coy’s final reason why Putin may continue the attack is that “he has little to lose by fighting on.” Although Coy doesn’t discuss the point in these terms, Russia may be suffering from a principal-agent problem. As we discuss in Microeconomics, Chapter 8, Section 8.1 (also Economics, Chapter 8, Section 8.1 and Macroeconomics, Chapter 6, Section 6.1) the principal-agent problem arises when an agent pursues the agent’s interst rather than the interests of the principal in whose behalf the agent is supposed to act. In this case, Putin is the agent and the Russian people are the principal. Putin’s own interest may be in prolonging the war indefinitely in the hopes of ultimately winning, despite the additional Russian soldiers who will be wounded or killed and despite the economic suffering of the Russian people resulting from the sanctions.

Although as president of Russia, Putin should be acting in the best interests of the Russian people, as a dictator, he can largely disregard their interests. Unlike his soldiers, Putin isn’t exposed to the personal dangers of being in battle. And unlike the average Russian, Putin will not suffer a decline in his standard of living because of economic sanctions.

Appalling as the consequences will be, Putin’s continuing his attack on Ukraine may be rational.

Sources: Peter Coy, “Here Are Three Reasons Putin Might Fight On,” New York Times, March 14, 2022; Alan Cullison, “Talks to End Ukraine War Pause as Russia’s Offensive Intensifies,” Wall Street Journal, March 14, 2022; and Thomas Grove, “Russia’s Military Chief Promised Quick Victory in Ukraine, but Now Faces a Potential Quagmire,” Wall Street Journal, March 6, 2022.

The Many Uses of Elasticity: An Example from Law Enforcement Policy

In this chapter, we have studied several types of elasticities, starting with the price elasticity of demand. Elasticity is a general concept that economists use to measure the effect of a change in one variable on another variable. An example of a more general use of elasticity, beyond the uses we discussed in this chapter, appears in a new academic paper written by Anne Sofie Tegner Anker of the University of Copenhagen, Jennifer L. Doleac of Texas A&M University, and Rasmus LandersØ of Aarshus University.

The authors are interested in studying the effects of crime deterrence. They note that rational offenders will be deterred by government policies that increase the probability that an offender will be arrested. Even offenders who don’t respond rationally to an increase in the probability of being arrested will still commit fewer crimes because they are more likely to be arrested. Governments have different policies available to reduce crime. Given that government resources are scarce, efficient allocation of resources requires policymakers to choose policies that provide the most deterrence per dollar of cost.

The authors note “we currently know very little about precisely how much deterrence we achieve for any given increase in the likelihood that an offender is apprehended.” They attempt to increase knowledge on this point by analyzing the effects of a policy change in Denmark in 2005 that made it much more likely that an offender would have his or her DNA entered into a DNA database: “The goal of DNA registration is to deter offenders and increase the likelihood of detection of future crimes by enabling matches of known offenders with DNA from crime scene evidence.”

The authors find that the expansion of Denmark’s DNA database had a substantial effect on recidivism—an offender committing additional crimes—and on the probability that an offender who did commit additional crimes would be caught. They estimate that “a 1 percent higher detection probability reduces crime by more than 2 percent.” In other words, the elasticity of crime with respect to the detection probability is −2.

Just as the price elasticity of demand gives a business manager a useful way to summarize the responsiveness of the quantity demanded of the firm’s product to a change in its price, the elasticity the authors estimated gives a policymaker a useful way to summarize the responsiveness of crime to a policy that increases the probability of catching offenders.

Source: Anne Sofie Tegner Anker, Jennifer L. Doleac, and Rasmus LandersØ, “The Effects of DNA Databases on the Deterrence and Detection of Offenders,” American Economic Journal: Applied Economics, Vol. 13, No. 4, October 2021, pp. 194-225.

Card, Angrist, and Imbens Win Nobel Prize in Economics

David Card of the University of California, Berkeley; Joshua Angrist of the Massachusetts Institute of Technology; and Guido Imbens of Stanford University shared the 2021 Nobel Prize in Economics (formally, the Sveriges Riksbank Prize in Economic Sciences in Memory of Alfred Nobel). Card received half of the prize of 10 million Swedish kronor (about 1.14 million U.S. dollars) “for his empirical contributions to labor economics,” and Angrist and Imbens shared the other half “for their methodological contributions to the analysis of causal relationships.” (In the work for which they received the prize, all three had collaborated with the late Alan Krueger of Princeton University. Card was quoted in the Wall Street Journal as stating that: “I’m sure that if Alan was still with us that he would be sharing this prize with me.”)

The work of the three economists is related in that all have used natural experiments to address questions of economic causality. With a natural experiment, economists identify some variable of interest—say, an increase in the minimum wage—that has changed for one group of people—say, fast-food workers in one state—while remaining unchanged for another similar group of people—say, fast-food workers in a neighboring state. Researchers can draw an inference about the effects of the change by looking at the difference between the outcomes for the two groups. In this example, the difference between changes in employment at fast-food restaurants in the two states can be used to measure the effect of an increase in the minimum wage.

Using natural experiments is an alternative to the traditional approach that had dominated empirical economics from the 1940s when the increased availability of modern digital computers made it possible to apply econometric techniques to real-world data. With the traditional approach to empirical work, economists would estimate structural models to answer questions about causality. So, for instance, a labor economist might estimate a model of the demand and supply of labor to predict the effect of an increase in the minimum wage on employment.

Over the years, many economists became dissatisfied with using structural models to address questions of economic causality. They concluded that the information requirements to reliably estimate structural models were too great. For instance, structural models require assumptions about the functional form of relationships, such as the demand for labor, that are not inferable directly from economic theory. Theory also did not always identify all variables that should be included in the model. Gathering data on the relevant variables was sometimes difficult. As a result, answers to empirical questions, such as the employment effects of the minimum wage, differed substantially across studies. In such cases, policymakers began to see empirical economics as an unreliable guide to economic policy.

In a famous study of the effect of the minimum wage on employment published in 1994 in the American Economic Review, Card and Krueger pioneered the use of natural experiments. In that study, Card and Krueger analyzed the effect of the minimum wage on employment in fast-food restaurants by comparing what happened to employment in New Jersey when it raised the state minimum wage from $4.25 to $5.05 per hour with employment in eastern Pennsylvania where the minimum wage remained unchanged. They found that, contrary to the usual analysis that increases in the minimum wage lead to decreases in the employment of unskilled workers, employment of fast-food workers in New Jersey actually increased relative to employment of fast-food workers in Pennsylvania.

The following graphic from Nobel Prize website summarizes the study. (Note that not all economists have accepted the results of Card and Krueger’s study. We briefly summarize the debate over the effects of the minimum wage in Chapter 4, Section 4.3 of our textbook.)

Drawing inferences from natural experiments is not as straightforward as it might seem from our brief description. Angrist and Imbens helped develop the techniques that many economists rely on when analyzing data from natural experiments.

Taken together, the work of these three economists represent a revolution in empirical economics. They have provided economists with an approach and with analytical techniques that have been applied to a wide range of empirical questions.

For the annoucement from the Nobel website click HERE.

For the article in the Wall Street Journal on the prize click HERE (note that a subscription may be required).

For the orignal Card and Krueger paper on the minimum wage click HERE.

For David Card’s website click HERE.

For Joshua Angrist’s website click HERE.

For Guido Imbens’s website click HERE.

COVID-19 Update – Apply the Concept: Can You Catch Covid-19 from Touching a Surface? Taking into Account How People React to Changing Circumstances

Supports: Econ (Chapter 1, Section 1.3- in All Volumes)

Here’s the key point: To forecast the effects of a government policy, it’s important for economists to take into account how people will change their behavior in response to the policy.

In forecasting the effects of a government policy, economists take into account how people will respond to the policy. In general, when people’s circumstances change, including when the government enacts a new policy, people change how they act. It’s easy to fall into an error if you fail to take into account how people’s actions might change—their behavioral response—as their circumstances change. Let’s consider two examples.

First consider an example from the Covid-19 pandemic. In May 2020, the federal Centers for Disease Control and Prevention (CDC) noted that few people were contracting the disease as a result of touching surfaces contaminated by the virus and that most people became ill by breathing in the virus while near an infected person. Some media outlets interpreted the CDC’s announcement as meaning, in the words of one headline: “CDC Now Says Coronavirus Isn’t Easily Spread by Touching Surfaces.” But is this conclusion correct? Consider two scenarios:

Scenario 1: Despite the spread of the coronavirus, people and businesses don’t adjust their behavior. People are unconcerned if they touch a surface, such as a doorknob, that may contain the virus. After touching a surface, they don’t immediately wash their hands or use hand sanitizer. No one wears gloves. Businesses don’t make a special effort to clean surfaces.

Scenario 2: Most people react to the spread of the coronavirus by avoiding touching surfaces whenever they can. If they do touch a surface, they wash their hands or use hand sanitizer. Some people wear gloves. Businesses disinfect surfaces much more frequently than they did before the virus became widespread.

If Scenario 1 accurately described the situation in the United States in May 2020, we could reasonably draw the conclusion contained in the media headline we quoted: You are unlikely to catch Covid-19 by touching a contaminated surface. In fact, of course, Scenario 2 more accurately describes the situation in the United States at that time. As a result, the fact that few people caught the virus from touching a contaminated surface does not allow us to conclude that you are unlikely to catch Covid-19 that way because people adjusted their behavior to make that outcome less likely.

Now consider an economic example. Suppose that a city decides to tax colas and other sweetened beverages. If stores in the city are currently selling 100 million ounces of soda and the city imposes a tax of 2 cents per ounce, will it collect $2 million (= $0.02 per ounce × 100,000,000 ounces) in revenue from the tax per year? We can expect that because of the tax, stores will increase the prices they charge for soda. Those price increases will cause consumers to change their behavior. Some people will buy less soda and, if the city’s suburbs don’t also enact a tax, some people will drive to stores outside the city to buy their soda. As a result, sales of sweetened beverages in the city will fall below 100 million ounces and the city will collect less than $2 million per year from the tax.

In both these cases, we would draw an incorrect conclusion if we failed to take into account the behavioral response of people to changes in their circumstances, whether the change is from the arrival of a new disease or an increase in a tax. Economist sometimes call the error of failing to take into account the effect of behavioral responses to policy changes the Lucas critique, named after Nobel laureate Robert Lucas of the University of Chicago.

Question: An article in the Seattle Times published in late May 2020 noted that: “Half of new coronavirus infections in Washington [state] are now occurring in people under the age of 40….” Yet an opinion column in the New York Times published in March 2020 near the beginning of the pandemic noted that the coronavirus was disproportionately infecting older people. Is one of these accounts of which age group is most likely to be infected necessarily incorrect? Briefly explain.

For instructors that would like the solutions to these questions, please email your name, course number, and affiliation to christopher.dejohn@pearson.com and we’ll send along a solutions manual.

Sources: Sandi Doughton, “Half of Newly Diagnosed Coronavirus Cases in Washington Are in People under 40,” Seattle Times, May 28, 2020; and Louise Aronson, “‘Covid-19 Kills Only Old People.’ Only?” New York Times, March 22, 2020.