Ten Principles of Economics - Part 5

CHAPTER 2 THINKING LIKE AN ECONOMIST 43 variables constant, we know that changes in the price of novels cause changes in the quantity Emma demands. Remember, however, that our demand curve came from a hypothetical example. When graphing data from the real world, it is often more difficult to establish how one variable affects another. The first problem is that it is difficult to hold everything else constant when measuring how one variable affects another. If we are not able to hold variables constant, we might decide that one variable on our graph is causing changes in the other variable when actually those changes are caused by a third omitted variable not pictured on the graph. Even if we have identified the correct two variables to look at, we might run into a second problem—reverse causality. In other words, we might decide that A causes B when in fact B causes A. The omitted-variable and reverse-causality traps require us to proceed with caution when using graphs to draw conclusions about causes and effects. Omitted Variables To see how omitting a variable can lead to a decep-tive graph, let’s consider an example. Imagine that the government, spurred by public concern about the large number of deaths from cancer, commissions an ex-haustive study from Big Brother Statistical Services, Inc. Big Brother examines many of the items found in people’s homes to see which of them are associated with the risk of cancer. Big Brother reports a strong relationship between two vari-ables: the number of cigarette lighters that a household owns and the prob-ability that someone in the household will develop cancer. Figure 2A-6 shows this relationship. What should we make of this result? Big Brother advises a quick policy re-sponse. It recommends that the government discourage the ownership of cigarette lighters by taxing their sale. It also recommends that the government require warning labels: “Big Brother has determined that this lighter is dangerous to your health.” In judging the validity of Big Brother’s analysis, one question is paramount: Has Big Brother held constant every relevant variable except the one under con-sideration? If the answer is no, the results are suspect. An easy explanation for Fig-ure 2A-6 is that people who own more cigarette lighters are more likely to smoke cigarettes and that cigarettes, not lighters, cause cancer. If Figure 2A-6 does not Risk of Cancer 0 Number of Lighters in House Figure 2A-6 GRAPH WITH AN OMITTED VARIABLE. The upward-sloping curve shows that members of households with more cigarette lighters are more likely to develop cancer. Yet we should not conclude that ownership of lighters causes cancer because the graph does not take into account the number of cigarettes smoked. 44 PART ONE INTRODUCTION hold constant the amount of smoking, it does not tell us the true effect of owning a cigarette lighter. This story illustrates an important principle: When you see a graph being used to support an argument about cause and effect, it is important to ask whether the movements of an omitted variable could explain the results you see. Reverse Causality Economists can also make mistakes about causality by misreading its direction. To see how this is possible, suppose the Association of American Anarchists commissions a study of crime in America and arrives at Figure 2A-7, which plots the number of violent crimes per thousand people in major cities against the number of police officers per thousand people. The an-archists note the curve’s upward slope and argue that because police increase rather than decrease the amount of urban violence, law enforcement should be abolished. If we could run a controlled experiment, we would avoid the danger of re-verse causality. To run an experiment, we would set the number of police officers in different cities randomly and then examine the correlation between police and crime. Figure 2A-7, however, is not based on such an experiment. We simply ob-serve that more dangerous cities have more police officers. The explanation for this may be that more dangerous cities hire more police. In other words, rather than police causing crime, crime may cause police. Nothing in the graph itself allows us to establish the direction of causality. It might seem that an easy way to determine the direction of causality is to examine which variable moves first. If we see crime increase and then the police force expand, we reach one conclusion. If we see the police force expand and then crime increase, we reach the other. Yet there is also a flaw with this approach: Often people change their behavior not in response to a change in their present conditions but in response to a change in their expectations of future conditions. A city that expects a major crime wave in the future, for instance, might well hire more police now. This problem is even easier to see in the case of babies and mini-vans. Couples often buy a minivan in anticipation of the birth of a child. The Figure 2A-7 GRAPH SUGGESTING REVERSE CAUSALITY. The upward-sloping curve shows that cities with a higher concentration of police are more dangerous. Yet the graph does not tell us whether police cause crime or crime-plagued cities hire more police. Violent Crimes (per 1,000 people) 0 Police Officers (per 1,000 people) CHAPTER 2 THINKING LIKE AN ECONOMIST 45 minivan comes before the baby, but we wouldn’t want to conclude that the sale of minivans causes the population to grow! There is no complete set of rules that says when it is appropriate to draw causal conclusions from graphs. Yet just keeping in mind that cigarette lighters don’t cause cancer (omitted variable) and minivans don’t cause larger fam-ilies (reverse causality) will keep you from falling for many faulty economic arguments. IN THIS CHAPTER YOU WILL . . . Consider how everyone can benefit when people trade with one another Learn the meaning of absolute advantage and comparative advantage I N T E R D E P E N D E N C E A N D T H E G A I N S F R O M T R A D E Consider your typical day. You wake up in the morning, and you pour yourself juice from oranges grown in Florida and coffee from beans grown in Brazil. Over breakfast, you watch a news program broadcast from New York on your television made in Japan. You get dressed in clothes made of cotton grown in Georgia and sewn in factories in Thailand. You drive to class in a car made of parts manufac-tured in more than a dozen countries around the world. Then you open up your economics textbook written by an author living in Massachusetts, published by a company located in Texas, and printed on paper made from trees grown in Oregon. Every day you rely on many people from around the world, most of whom you do not know, to provide you with the goods and services that you enjoy. Such inter-dependence is possible because people trade with one another. Those people who provide you with goods and services are not acting out of generosity or concern for your welfare. Nor is some government agency directing them to make what you See how comparative advantage explains the gains from trade Apply the theory of comparative advantage to everyday life and national policy 47 ... - tailieumienphi.vn