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”. 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. In a Wall Street Journal piece, she noted the scaling of the vertical axis used by the president’s critics. Laura Tyson, then President Clinton’s chief economic adviser, charged that those graphs were misleading. Scaling the vertical axis from 16 to 21%, as in Panel (b), stresses the short-term variability of the percentage and suggests that major tax rate changes have affected federal revenues. 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.įigure 21.14 Two Tales of Taxes and IncomeĪ 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). It shows federal revenues as a percentage of gross domestic product (GDP), a measure of total income in the economy, since 1960. Op-ed 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”. Higher tax rates, they said, would cause some people to scale back their income-earning efforts and thus produce only a small gain-or even a loss-in revenues. Critics of the president’s proposal argued that changes in tax rates have little or no effect on federal revenues. The measure was intended to boost federal revenues. This became a big issue in 1993, when President Clinton proposed an increase in income tax rates. For that reason, it is important to note carefully how the vertical axis in a time-series graph is scaled.Ĭonsider, for example, the issue of whether an increase or decrease in income tax rates has a significant effect on federal government revenues. We can make a variable appear to change a great deal, or almost not at all, depending on how we scale the axis. The scaling of the vertical axis in time-series graphs can give very different views of economic data. Scaling the Vertical Axis in Time-Series Graphs 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”. Time-series graphs are often presented with the vertical axis scaled over a certain range. Notice that the vertical axis is scaled from 3 to 8%, instead of beginning with zero. The grid with which these values are plotted is given in Panel (b). The table in Panel (a) of Figure 21.13 “A Time-Series 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 other axis can represent any variable whose value changes over time. Time is typically placed on the horizontal axis in time-series graphs. One of the variables in a time-series graph is time itself. A time-series graph shows how the value of a particular variable or variables has changed over some period of time. One of the most common types of graphs used in economics is called a time-series graph.
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