Graphing Data in Economics

Introduction

Graphing data is an important part of the study of economics. Think of graphs as useful shortcuts to understanding the information you are working with.

  • Visualizing data on graphs can help us understand relationships between economic variables.
  • Graphing data helps us see trends.

Suppose that we have the following information from ten people on years of education completed and current hourly wage rate:

Making sense of this table of data is difficult.

However, looking at a graph of this data quickly shows that wages tend to rise with education. We can also see, since the plotted points are not exactly aligned, that there isn't a perfect relationship between education and wages.

How do I graph data?

To plot data on a graph, complete the following steps:

  1. Decide which type of graph your data requires. Most graphs in economics are of two types:
    • a scatter plot shows how two economic variables are related.
    • a time series plot shows how one economic variable changes over time
  2. Decide which variable will be measured on the X (horizontal) axis, and which variable will be measure on the Y (vertical) axis.
    • For a time series plot, time always goes on the x axis.
    • In a scatter plot, usually the independent variable --- the one doing the "causing" --- goes on the X axis. The one exception in economics is that price which always goes on the Y axis.
  3. Determine the scale for X and the scale for Y.
    • In a scatter plot, the origin is usually zero for both X and Y.
    • In a time series plot, the origin will be the lowest year for X, and zero for Y.

      Note: If there is a large difference between zero and the range of data points, it may make sense to begin with a higher number at the origin.
    • Determine a range for X and Y that includes all the data points.
    • Identify and mark scales within your range using units such as 1, 2, 3 or 10, 20, 30 or 100, 200, 300. Make sure they are accurately spaced apart.
  4. Mark each of the data points on the graph using the scales for X and Y as guides.
  5. Evaluate the relationship between X and Y in the graph, based the data points.

An example

A taco company is interested in understanding the relationship between the price of tacos and the quantity that consumers are willing to purchase. The data in the table show weekly sales (quantity demanded per week) at different prices.


Examples of graphing data in Macroeconomics

In macroeconomics, we plot data on graphs to illustrate the relationships between broadly defined economic variables, such as the overall price level, total output in an economy, and unemployment. Sometimes economists are interested in how a variable changes over time. Other times there are interested in the relationship between two variables.

  1. Plotting data on GDP over time. The table shows total U.S. GDP in billions of dollars from 2000 through 2014.
    1. The graph will be a time series plot.
    2. Years 2000 through 2014 will go on the X axis.
    3. GDP will be on the Y axis.
    4. GDP on the Y axis starts at zero and goes up to $20,000 B with markings at each $2000 B. Time is measured from 2000 throught 2014.
  2. Plotting unemployment over time. The table shows US unemployment between 2000 and 2014. Use the steps above to graph this data and visualize the trend in the unemployment rate over time.
    1. Because we are measuring the unemployment rate over time, this graph will be a time series plot, so we put time on the horizontal (X) axis.
    2. Unemployment therefore goes on the vertical (Y) axis.
    3. The scale for unemployment goes from 0 to 12% to include the highest data point. Time runs from 2000 through 2014.
  3. Plotting the relationship between unemployment and inflation.
    1. The graph will be a scatter plot.
    2. Inflation, a measure of prices, by tradition will go on the Y axis.
    3. Therefore, unemployment will go on the X axis
    4. Inflation will range from -1% to 4%. Unemployment will range from 0% to 10%

Examples of graphing data in Microeconomics

In microeconomics, we plot data on graphs to illustrate relationships between economic variables such as the price and quantity of a particular good, or between the quantities of two goods that can be purchased by a consumer.

  1. Combinations of two goods that are within a consumer's budget can be graphed to show a Budget Line. The data shown here represent combinations of coffee and bagels that can be purchased with $20, assuming that the price per bagel is $2.00, and that the price per coffee is $1.00.
    1. This graph will be a scatterplot.
    2. Bagels and coffee could go on either axis. Arbitrarily coffee is on the X axis.
    3. The scale runs from 0 to 20 for coffee; from 0 to 10 for bagels.
  2. A bank is considering the number of ATMs it should place in a city. Data showing the additional cost of producing ATMs are shown in the the table. The data also show the additional cost of producing each unit of output. Graphing the data will show a Marginal Cost Curve.
    1. This is a scatterplot.
    2. By tradition, prices (or costs) go on the Y axis. Quantity goes on the X axis.
    3. Costs go from $0 to $16; quantity goes from 0 to 100, marked in intervals of 10.
  3. Understanding US men's and women's labor force participation. INSERT TABLE WITH GRAPH DATA HERE
    1. The graphs will be time series
    2. The years will be on the horizontal axis and labor force participation on the vertical axes
    3. The years will run from 1950 to 2010; labor force participation from 0 to 90,000,000 in 10,000,000 increments

✓ Final thoughts on graphing data

Here are two important points we need to remember when graphing data.

  1. A relationship on a graph does not prove that a change in one variable causes a change in the other variable. Two variables can be correlated without one causing movements in the other.
  2. The use of an X-Y graph can only show the relationship between two variables. When graphing data in economics we often assume ceteris paribusall else is equal, and hold other factors constant. When other factors do change, the information on our graph may change as well, because there are now different data points.

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