Learning Objective
 Understand and use timeseries graphs, tables, pie charts, and bar charts to illustrate data and relationships among variables.
You often see pictures representing numerical information. These pictures may take the form of graphs that show how a particular variable has changed over time, or charts that show values of a particular variable at a single point in time. We will close our introduction to graphs by looking at both ways of conveying information.
TimeSeries Graphs
One of the most common types of graphs used in economics is called a timeseries graph. A timeseries graph shows how the value of a particular variable or variables has changed over some period of time. One of the variables in a timeseries graph is time itself. Time is typically placed on the horizontal axis in timeseries graphs. The other axis can represent any variable whose value changes over time.
The table in Panel (a) of Figure 21.13 “A TimeSeries Graph” shows annual values of the unemployment rate, a measure of the percentage of workers who are looking for and available for work but are not working, in the United States from 1998 to 2007. The grid with which these values are plotted is given in Panel (b). Notice that the vertical axis is scaled from 3 to 8%, instead of beginning with zero. Timeseries graphs are often presented with the vertical axis scaled over a certain range. The result is the same as introducing a break in the vertical axis, as we did in Figure 21.5 “Canceling Games and Reducing Shaquille O’Neal’s Earnings”.
The values for the U.S. unemployment rate are plotted in Panel (b) of Figure 21.13 “A TimeSeries Graph”. The points plotted are then connected with a line in Panel (c).
Scaling the Vertical Axis in TimeSeries Graphs
The scaling of the vertical axis in timeseries graphs can give very different views of economic data. We can make a variable appear to change a great deal, or almost not at all, depending on how we scale the axis. For that reason, it is important to note carefully how the vertical axis in a timeseries graph is scaled.
Consider, for example, the issue of whether an increase or decrease in income tax rates has a significant effect on federal government revenues. This became a big issue in 1993, when President Clinton proposed an increase in income tax rates. The measure was intended to boost federal revenues. Critics of the president’s proposal argued that changes in tax rates have little or no effect on federal revenues. Higher tax rates, they said, would cause some people to scale back their incomeearning efforts and thus produce only a small gain—or even a loss—in revenues. Oped essays in The Wall Street Journal, for example, often showed a graph very much like that presented in Panel (a) of Figure 21.14 “Two Tales of Taxes and Income”. It shows federal revenues as a percentage of gross domestic product (GDP), a measure of total income in the economy, since 1960. Various tax reductions and increases were enacted during that period, but Panel (a) appears to show they had little effect on federal revenues relative to total income.
Figure 21.14 Two Tales of Taxes and Income
A graph of federal revenues as a percentage of GDP emphasizes the stability of the relationship when plotted with the vertical axis scaled from 0 to 100, as in Panel (a). Scaling the vertical axis from 16 to 21%, as in Panel (b), stresses the shortterm variability of the percentage and suggests that major tax rate changes have affected federal revenues.
Laura Tyson, then President Clinton’s chief economic adviser, charged that those graphs were misleading. In a Wall Street Journal piece, she noted the scaling of the vertical axis used by the president’s critics. She argued that a more reasonable scaling of the axis shows that federal revenues tend to increase relative to total income in the economy and that cuts in taxes reduce the federal government’s share. Her alternative version of these events does, indeed, suggest that federal receipts have tended to rise and fall with changes in tax policy, as shown in Panel (b) of Figure 21.14 “Two Tales of Taxes and Income”.
Which version is correct? Both are. Both graphs show the same data. It is certainly true that federal revenues, relative to economic activity, have been remarkably stable over the past several decades, as emphasized by the scaling in Panel (a). But it is also true that the federal share has varied between about 17 and 20%. And a small change in the federal share translates into a large amount of tax revenue.
It is easy to be misled by timeseries graphs. Large changes can be made to appear trivial and trivial changes to appear large through an artful scaling of the axes. The best advice for a careful consumer of graphical information is to note carefully the range of values shown and then to decide whether the changes are really significant.
Testing Hypotheses with TimeSeries Graphs
John Maynard Keynes, one of the most famous economists ever, proposed in 1936 a hypothesis about total spending for consumer goods in the economy. He suggested that this spending was positively related to the income households receive. One way to test such a hypothesis is to draw a timeseries graph of both variables to see whether they do, in fact, tend to move together. Figure 21.15 “A TimeSeries Graph of Disposable Income and Consumption” shows the values of consumption spending and disposable income, which is aftertax income received by households. Annual values of consumption and disposable income are plotted for the period 1960–2007. Notice that both variables have tended to move quite closely together. The close relationship between consumption and disposable income is consistent with Keynes’s hypothesis that there is a positive relationship between the two variables.
The fact that two variables tend to move together in a time series does not by itself prove that there is a systematic relationship between the two. Figure 21.16 “Stock Prices and a Mystery Variable” shows a timeseries graph of monthly values in 1987 of the Dow Jones Industrial Average, an index that reflects the movement of the prices of common stock. Notice the steep decline in the index beginning in October, not unlike the steep decline in October 2008.
It would be useful, and certainly profitable, to be able to predict such declines. Figure 21.16 “Stock Prices and a Mystery Variable” also shows the movement of monthly values of a “mystery variable,” X, for the same period. The mystery variable and stock prices appear to move closely together. Was the plunge in the mystery variable in October responsible for the stock crash? The answer is: Not likely. The mystery value is monthly average temperatures in San Juan, Puerto Rico. Attributing the stock crash in 1987 to the weather in San Juan would be an example of the fallacy of false cause.
Notice that Figure 21.16 “Stock Prices and a Mystery Variable” has two vertical axes. The lefthand axis shows values of temperature; the righthand axis shows values for the Dow Jones Industrial Average. Two axes are used here because the two variables, San Juan temperature and the Dow Jones Industrial Average, are scaled in different units.
Descriptive Charts
We can use a table to show data. Consider, for example, the information compiled each year by the U.S. National Center for Education Statistics. The table in Panel (a) of Figure 21.17 “Bachelor’s Degrees Earned by Field, 2009” shows the results of the 2009 survey. In the groupings given, economics is included among the social sciences.
Figure 21.17 Bachelor’s Degrees Earned by Field, 2009
Panels (a), (b), and (c) show bachelor’s degrees earned by field in 2009 in United States. All three panels present the same information. Panel (a) is an example of a table, Panel (b) is an example of a pie chart, and Panel (c) is an example of a vertical bar chart.
Source: Statistical Abstracts of the United States, 2012, Table 302. Bachelor’s Degrees Earned by Field, 1980 to 2009, based on data from U.S. National Center for Education Statistics, Digest of Educational Statistics. Percentages shown are for broad academic areas, each of which includes several majors.
Panels (b) and (c) of Figure 21.17 “Bachelor’s Degrees Earned by Field, 2009” present the same information in two types of charts. Panel (b) is an example of a pie chart; Panel (c) gives the data in a bar chart. The bars in this chart are horizontal; they may also be drawn as vertical. Either type of graph may be used to provide a picture of numeric information.
Key Takeaways
 A timeseries graph shows changes in a variable over time; one axis is always measured in units of time.
 One use of timeseries graphs is to plot the movement of two or more variables together to see if they tend to move together or not. The fact that two variables move together does not prove that changes in one of the variables cause changes in the other.
 Values of a variable may be illustrated using a table, a pie chart, or a bar chart.
Try It!
The table in Panel (a) shows a measure of the inflation rate, the percentage change in the average level of prices below. Panels (b) and (c) provide blank grids. We have already labeled the axes on the grids in Panels (b) and (c). It is up to you to plot the data in Panel (a) on the grids in Panels (b) and (c). Connect the points you have marked in the grid using straight lines between the points. What relationship do you observe? Has the inflation rate generally increased or decreased? What can you say about the trend of inflation over the course of the 1990s? Do you tend to get a different “interpretation” depending on whether you use Panel (b) or Panel (c) to guide you?
Answer to Try It!
Here are the timeseries graphs, Panels (b) and (c), for the information in Panel (a). The first thing you should notice is that both graphs show that the inflation rate generally declined throughout the 1990s (with the exception of 1996, when it increased). The generally downward direction of the curve suggests that the trend of inflation was downward. Notice that in this case we do not say negative, since in this instance it is not the slope of the line that matters. Rather, inflation itself is still positive (as indicated by the fact that all the points are above the origin) but is declining. Finally, comparing Panels (b) and (c) suggests that the general downward trend in the inflation rate is emphasized less in Panel (b) than in Panel (c). This impression would be emphasized even more if the numbers on the vertical axis were increased in Panel (b) from 20 to 100. Just as in Figure 21.14 “Two Tales of Taxes and Income”, it is possible to make large changes appear trivial by simply changing the scaling of the axes.
Problems

Panel (a) shows a graph of a positive relationship; Panel (b) shows a graph of a negative relationship. Decide whether each proposition below demonstrates a positive or negative relationship, and decide which graph you would expect to illustrate each proposition. In each statement, identify which variable is the independent variable and thus goes on the horizontal axis, and which variable is the dependent variable and goes on the vertical axis.
 An increase in national income in any one year increases the number of people killed in highway accidents.
 An increase in the poverty rate causes an increase in the crime rate.
 As the income received by households rises, they purchase fewer beans.
 As the income received by households rises, they spend more on home entertainment equipment.
 The warmer the day, the less soup people consume.

Suppose you have a graph showing the results of a survey asking people how many left and right shoes they owned. The results suggest that people with one left shoe had, on average, one right shoe. People with seven left shoes had, on average, seven right shoes. Put left shoes on the vertical axis and right shoes on the horizontal axis; plot the following observations:
Left shoes 1 2 3 4 5 6 7 Right shoes 1 2 3 4 5 6 7 Is this relationship positive or negative? What is the slope of the curve?

Suppose your assistant inadvertently reversed the order of numbers for right shoe ownership in the survey above. You thus have the following table of observations:
Left shoes 1 2 3 4 5 6 7 Right shoes 7 6 5 4 3 2 1 Is the relationship between these numbers positive or negative? What’s implausible about that?

Suppose some of Ms. Alvarez’s kitchen equipment breaks down. The following table gives the values of bread output that were shown in Figure 21.9 “A Nonlinear Curve”. It also gives the new levels of bread output that Ms. Alvarez’s bakers produce following the breakdown. Plot the two curves. What has happened?
A B C D E F G Bakers/day 0 1 2 3 4 5 6 Loaves/day 0 400 700 900 1,000 1,050 1,075 Loaves/day after breakdown 0 380 670 860 950 990 1,005 
Steven Magee has suggested that there is a relationship between the number of lawyers per capita in a country and the country’s rate of economic growth. The relationship is described with the following Magee curve.
What do you think is the argument made by the curve? What kinds of countries do you think are on the upward sloping region of the curve? Where would you guess the United States is? Japan? Does the Magee curve seem plausible to you?

Draw graphs showing the likely relationship between each of the following pairs of variables. In each case, put the first variable mentioned on the horizontal axis and the second on the vertical axis.
 The amount of time a student spends studying economics and the grade he or she receives in the course
 Per capita income and total expenditures on health care
 Alcohol consumption by teenagers and academic performance
 Household income and the likelihood of being the victim of a violent crime
This is a derivative of Principles of Macroeconomics by a publisher who has requested that they and the original author not receive attribution, which was originally released and is used under CC BYNCSA. This work, unless otherwise expressly stated, is licensed under a Creative Commons AttributionNonCommercialShareAlike 4.0 International License.