a visual representation of gdp is called

Business Cycle Graph

Business cycle definition.

The business cycle refers to the recurring pattern of economic growth and recession in a country over a period of time. It is also called the trade cycle or economic cycle.

What is Business Cycle Graph?

A business cycle graph is a graphical representation of the relationship between the real GDP of a country and time. This graph provides the visual representation of the fluctuations in economic activity in a country. This graph is shown below.

In the above graph, time is taken on the X-axis, while, the real gross domestic product (real GDP) is taken on the Y-axis.

Business Cycle Phases

There are four phases or stages of the business cycle.

Economic Growth or Expansion

This stage of the business cycle refers to an increase in the real GDP of the country over a period of time. This is also called the expansion phase of the business cycle. It is also called economic recovery if growth occurs after a recession. Any downturn in an economy will eventually lead to a recovery when real GDP starts to increase again. This is because corrective government action starts to take effect.

Economic growth is a virtuous cycle in which an increase in the income level leads to a rise in the demand for products, which leads to a rise in the production level and hence a rise in employment levels.

Economic Boom or Peak

This stage of the business cycle refers to a time period when the real GDP is at its maximum. This stages shows the peak of the business cycle. This is a time when the economy is producing output (actual GDP) close to its potential GDP. Incomes and profits are high. Inflation is also high because of the higher demand for goods and services. Wages are also high because of the shortage of skilled workers. Because of the high inflation, goods and services become uncompetitive in foreign markets. As a result, businesses start to lose confidence because their profits are hurt by the higher costs. To try and bring down inflation, interest rates are usually increased. This often leads to a downturn in the economy.

Economic Recession or Contraction

This stage of the business cycle refers to a decrease in the real GDP of the country over a period of time. This is also called contraction. Incomes and consumer demand fall, and profits are reduced. A decrease in aggregate demand and higher interest rates start to take effect. Real GDP starts to fall. If real GDP continues to fall for at least two consecutive quarters, this is technically called a recession. Some firms make losses, and others fail completely.

A recession is a vicious cycle in which a decrease in income level leads to a fall in demand for products, which leads to a fall in production level and hence an increase in the cyclical unemployment.

Economics Slump or Trough

This stage of the business cycle refers to a time period when the real GDP is at its lowest point. This is a time when the economy has a high level of unemployed resources. There is a significant decline in the incomes and profits. It is a very serious and prolonged recession and is also called depression. These deep recessions can prolong if the government fails to take corrective action.

Summary of the Features of Business Cycle Phases

A summary of the main features of each stage of the business cycle is given below.

Significance of Business Cycle for Firms

The concept of business cycle is very useful for firms in different industries in order to make good business decisions. Here are a few examples:

Firms Producing Luxury Goods

During economic growth, the firms producing luxury goods (e.g. cars) will see an increase in the demand for their products. They can increase the prices of these consumer goods to increase profit margins. They can also increase business investment into capital goods, or increase the range of products or production in order to get benefit of higher demand for their products.

During economic recession, the firms producing luxury goods will see a decrease in the demand for their products. During that time, they can offer credit terms to improve the affordability of their products. They can also use sales promotions. They can also low-priced products in their portfolio.

Firms Producing Inferior Goods

During economic growth, the firms producing inferior goods (e.g. low-priced clothing) will see a decrease in the demand for their products. During that time, they can widen the product range by adding some upmarket products.

During economic recession, the firms producing inferior goods will see an increase in the demand for their products. During that time, they can increase promotion to get benefit of the higher demand. They can also increase their production and distribution outlets.

Significance of Business Cycle for Government

Business cycle is a vital tool for governments to design their macroeconomic policies effectively.

Here are some examples:

During Economic Recession

During economic recession, the output of the country is decreasing due to lower aggregate demand and the unemployment is increasing.

Governments can use an expansionary fiscal policy (a decrease in taxes and an increase in government spending) or an expansionary monetary policy (a decrease in interest rate and an increase in money supply) to get rid of that recession.

These policies can increase aggregate demand to promote economic growth and reduce unemployment.

The working of an expansionary fiscal policy is shown in the diagram below.

During Economic Boom

During economic boom, an economy is likely to face the peak of economic activity with high rate of inflation which can also have a negative effect on its balance of payments.

Governments can use a contractionary fiscal policy (an increase in taxes and a decrease in government spending) or a contractionary monetary policy (an increase in interest rate and a decrease in money supply) to get rid of the economic boom.

These policies can decrease aggregate demand to control inflation.

The working of a contractionary fiscal policy is shown in the diagram below.

Business Cycle and the Great Depression

The great depression, which spanned from 1929 to the late 1930s, is considered as a classic example of the recession phase of business cycle. The Great Depression had a widespread impact on countries worldwide, with major economies such as the United States, Canada, Germany, and the United Kingdom being particularly affected. During this time, firms suffered huge losses. There was high unemployment and scarcity of jobs. Shops and businesses were closed down, leading to empty streets. There was uncertainty and fear among the population. The great recession is remembered as a dark and challenging period in history.

The business cycle graph is a valuable tool for understanding the patterns and fluctuations in economic activity. By studying its phases, producers and governments can make better decisions and policies according to the prevailing stage of the business cycle.

Stephan Morgenstern: Visionary MLM Entrepreneur and Philanthropist in FutureNet

The Economic Impact of Broadband Accessibility in Birmingham

19.1 Measuring the Size of the Economy: Gross Domestic Product

Learning objectives.

By the end of this section, you will be able to:

• Identify the components of GDP on the demand side and on the supply side
• Evaluate how economists measure gross domestic product (GDP)
• Contrast and calculate GDP, net exports, and net national product

Macroeconomics is an empirical subject, so the first step toward understanding it is to measure the economy.

How large is the U.S. economy? Economists typically measure the size of a nation’s overall economy by its gross domestic product (GDP) , which is the value of all final goods and services produced within a country in a given year. Measuring GDP involves counting the production of millions of different goods and services—smart phones, cars, music downloads, computers, steel, bananas, college educations, and all other new goods and services that a country produced in the current year—and summing them into a total dollar value. This task is straightforward: take the quantity of everything produced, multiply it by the price at which each product sold, and add up the total. In 2020, the U.S. GDP totaled $20.9 trillion, the largest GDP in the world.

Each of the market transactions that enter into GDP must involve both a buyer and a seller. We can measure an economy's GDP either by the total dollar value of what consumers purchase in the economy, or by the total dollar value of what is the country produces. There is even a third way, as we will explain later.

GDP Measured by Components of Demand

Who buys all of this production? We can divide this demand into four main parts: consumer spending (consumption), business spending (investment), government spending on goods and services, and spending on net exports. (See the following Clear It Up feature to understand what we mean by investment.) Table 19.1 shows how these four components added up to the GDP in 2020, Figure 19.4 (a) shows the levels of consumption, investment, and government purchases over time, expressed as a percentage of GDP, while Figure 19.4 (b) shows the levels of exports and imports as a percentage of GDP over time. A few patterns about each of these components are worth noticing. Table 19.1 shows the components of GDP from the demand side.

Clear It Up

What does the word “investment” mean.

What do economists mean by investment, or business spending? In calculating GDP, investment does not refer to purchasing stocks and bonds or trading financial assets. It refers to purchasing new capital goods, that is, new commercial real estate (such as buildings, factories, and stores) and equipment, residential housing construction, and inventories. Inventories that manufacturers produce this year are included in this year’s GDP—even if they are not yet sold. From the accountant’s perspective, it is as if the firm invested in its own inventories. Business investment in 2020 was $3.6 trillion, according to the Bureau of Economic Analysis.

Consumption expenditure by households is the largest component of GDP, accounting for about two-thirds of the GDP in any year. This tells us that consumers’ spending decisions are a major driver of the economy. However, consumer spending is a gentle elephant: when viewed over time, it does not jump around too much, and has increased modestly from about 60% of GDP in the 1960s and 1970s.

Investment expenditure refers to purchases of physical plant and equipment, primarily by businesses. If Starbucks builds a new store, or Amazon buys robots, they count these expenditures under business investment. Investment demand is far smaller than consumption demand , typically accounting for only about 15–18% of GDP, but it is very important for the economy because this is where jobs are created. However, it fluctuates more noticeably than consumption. Business investment is volatile. New technology or a new product can spur business investment, but then confidence can drop and business investment can pull back sharply.

If you have noticed any of the infrastructure projects (new bridges, highways, airports) launched during the 2009 recession, or if you received a stimulus check during the pandemic-induced recession of 2020–2021, you have seen how important government spending can be for the economy. Government expenditure in the United States is close to 20% of GDP, and includes spending by all three levels of government: federal, state, and local. The only part of government spending counted in demand is government purchases of goods or services produced in the economy. Examples include the government buying a new fighter jet for the Air Force (federal government spending), building a new highway (state government spending), or a new school (local government spending). A significant portion of government budgets consists of transfer payments, like unemployment benefits, veteran’s benefits, and Social Security payments to retirees. The government excludes these payments from GDP because it does not receive a new good or service in return or exchange. Instead they are transfers of income from taxpayers to others. If you are curious about the awesome undertaking of adding up GDP, read the following Clear It Up feature.

How do statisticians measure GDP?

Government economists at the Bureau of Economic Analysis (BEA), within the U.S. Department of Commerce, piece together estimates of GDP from a variety of sources.

Once every five years, in the second and seventh year of each decade, the Bureau of the Census carries out a detailed census of businesses throughout the United States. In between, the Census Bureau carries out a monthly survey of retail sales. The government adjusts these figures with foreign trade data to account for exports that are produced in the United States and sold abroad and for imports that are produced abroad and sold here. Once every ten years, the Census Bureau conducts a comprehensive survey of housing and residential finance. Together, these sources provide the main basis for figuring out what is produced for consumers.

For investment, the Census Bureau carries out a monthly survey of construction and an annual survey of expenditures on physical capital equipment.

For what the federal government purchases, the statisticians rely on the U.S. Department of the Treasury. An annual Census of Governments gathers information on state and local governments. Because the government spends a considerable amount at all levels hiring people to provide services, it also tracks a large portion of spending through payroll records that state governments and the Social Security Administration collect.

With regard to foreign trade, the Census Bureau compiles a monthly record of all import and export documents. Additional surveys cover transportation and travel, and make adjustments for financial services that are produced in the United States for foreign customers.

Many other sources contribute to GDP estimates. Information on energy comes from the U.S. Department of Transportation and Department of Energy. The Agency for Health Care Research and Quality collects information on healthcare. Surveys of landlords find out about rental income. The Department of Agriculture collects statistics on farming.

All these bits and pieces of information arrive in different forms, at different time intervals. The BEA melds them together to produce GDP estimates on a quarterly basis (every three months). The BEA then "annualizes" these numbers by multiplying by four. As more information comes in, the BEA updates and revises these estimates. BEA releases the GDP “advance” estimate for a certain quarter one month after a quarter. The “preliminary” estimate comes out one month after that. The BEA publishes the “final” estimate one month later, but it is not actually final. In July, the BEA releases roughly updated estimates for the previous calendar year. Then, once every five years, after it has processed all the results of the latest detailed five-year business census, the BEA revises all of the past GDP estimates according to the newest methods and data, going all the way back to 1929.

Visit this website to read FAQs on the BEA site. You can even email your own questions!

When thinking about the demand for domestically produced goods in a global economy, it is important to count spending on exports—domestically produced goods that a country sells abroad. Similarly, we must also subtract spending on imports—goods that a country produces in other countries that residents of this country purchase. The GDP net export component is equal to the dollar value of exports (X) minus the dollar value of imports (M), (X – M). We call the gap between exports and imports the trade balance . If a country’s exports are larger than its imports, then a country has a trade surplus . In the United States, exports typically exceeded imports in the 1960s and 1970s, as Figure 19.4 (b) shows.

Since the early 1980s, imports have typically exceeded exports, and so the United States has experienced a trade deficit in most years. The trade deficit grew quite large in the late 1990s and in the mid-2000s. Figure 19.4 (b) also shows that imports and exports have both risen substantially in recent decades, even after the declines during the Great Recession between 2008 and 2009. As we noted before, if exports and imports are equal, foreign trade has no effect on total GDP. However, even if exports and imports are balanced overall, foreign trade might still have powerful effects on particular industries and workers by causing nations to shift workers and physical capital investment toward one industry rather than another.

Based on these four components of demand, we can measure GDP as:

Understanding how to measure GDP is important for analyzing connections in the macro economy and for thinking about macroeconomic policy tools.

GDP Measured by What is Produced

Everything that we purchase somebody must first produce. Table 19.2 breaks down what a country produces into five categories: durable goods , nondurable goods , services , structures , and the change in inventories . Before going into detail about these categories, notice that total GDP measured according to what is produced is exactly the same as the GDP measured by looking at the five components of demand. Figure 19.5 provides a visual representation of this information.

Since every market transaction must have both a buyer and a seller, GDP must be the same whether measured by what is demanded or by what is produced. Figure 19.6 shows these components of what is produced, expressed as a percentage of GDP, since 1950.

In thinking about what is produced in the economy, many non-economists immediately focus on solid, long-lasting goods, like cars and computers. By far the largest part of GDP, however, is services. Moreover, services have been a growing share of GDP over time. A detailed breakdown of the leading service industries would include healthcare, education, and legal and financial services. It has been decades since most of the U.S. economy involved making solid objects. Instead, the most common jobs in a modern economy involve a worker looking at pieces of paper or a computer screen; meeting with co-workers, customers, or suppliers; or making phone calls.

Even within the overall category of goods, long-lasting durable goods like cars and refrigerators are about the same share of the economy as short-lived nondurable goods like food and clothing. The category of structures includes everything from homes, to office buildings, shopping malls, and factories. Inventories is a small category that refers to the goods that one business has produced but has not yet sold to consumers, and are still sitting in warehouses and on shelves. The amount of inventories sitting on shelves tends to decline if business is better than expected, or to rise if business is worse than expected.

Another Way to Measure GDP: The National Income Approach

GDP is a measure of what is produced in a nation. The primary way GDP is estimated is with the Expenditure Approach we discussed above, but there is another way. Everything a firm produces, when sold, becomes revenues to the firm. Businesses use revenues to pay their bills: Wages and salaries for labor, interest and dividends for capital, rent for land, profit to the entrepreneur, etc. So adding up all the income produced in a year provides a second way of measuring GDP. This is why the terms GDP and national income are sometimes used interchangeably. The total value of a nation’s output is equal to the total value of a nation’s income.

The Problem of Double Counting

We define GDP as the current value of all final goods and services produced in a nation in a year. What are final goods? They are goods at the furthest stage of production at the end of a year. Statisticians who calculate GDP must avoid the mistake of double counting , in which they count output more than once as it travels through the production stages. For example, imagine what would happen if government statisticians first counted the value of tires that a tire manufacturer produces, and then counted the value of a new truck that an automaker sold that contains those tires. In this example, the statisticians would have counted the value of the tires twice-because the truck's price includes the value of the tires.

To avoid this problem, which would overstate the size of the economy considerably, government statisticians count just the value of final goods and services in the chain of production that are sold for consumption, investment, government, and trade purposes. Statisticians exclude intermediate goods , which are goods that go into producing other goods, from GDP calculations. From the example above, they will only count the Ford truck's value. The value of what businesses provide to other businesses is captured in the final products at the end of the production chain.

The concept of GDP is fairly straightforward: it is just the dollar value of all final goods and services produced in the economy in a year. In our decentralized, market-oriented economy, actually calculating the more than $21 trillion-dollar U.S. GDP—along with how it is changing every few months—is a full-time job for a brigade of government statisticians.

Notice the items that are not counted into GDP, as Table 19.3 outlines. The sales of used goods are not included because they were produced in a previous year and are part of that year’s GDP. The entire underground economy of services paid “under the table” and illegal sales should be counted, but is not, because it is impossible to track these sales. In Friedrich Schneider's recent study of shadow economies, he estimated the underground economy in the United States to be 6.6% of GDP, or close to $2 trillion dollars in 2013 alone. Transfer payments, such as payment by the government to individuals, are not included, because they do not represent production. Also, production of some goods—such as home production as when you make your breakfast—is not counted because these goods are not sold in the marketplace.

Visit this website to read about the “New Underground Economy.”

Other Ways to Measure the Economy

Besides GDP, there are several different but closely related ways of measuring the size of the economy. We mentioned above that we can think of GDP as total production and as total purchases. We can also think of it as total income since anything one produces and sells yields income.

One of the closest cousins of GDP is the gross national product (GNP) . GDP includes only what country produces within its borders. GNP adds what domestic businesses and labor abroad produces, and subtracts any payments that foreign labor and businesses located in the United States send home to other countries. In other words, GNP is based more on what a country's citizens and firms produce, wherever they are located, and GDP is based on what happens within a certain county's geographic boundaries. For the United States, the gap between GDP and GNP is relatively small; in recent years, only about 0.2%. For small nations, which may have a substantial share of their population working abroad and sending money back home, the difference can be substantial.

We calculate net national product (NNP) by taking GNP and then subtracting the value of how much physical capital is worn out, or reduced in value because of aging, over the course of a year. The process by which capital ages and loses value is called depreciation . We can further subdivide NNP into national income , which includes all income to businesses and individuals, and personal income, which includes only income to people.

The gross national income (GNI) includes the value of all goods and services produced by people from a country—whether in the country or not. Unlike the other methods, GNI essentially measures the wealth of a nation because it focuses on income, not output. As you will see in the discussion regarding global economic diversity, the World Bank now uses GNI to classify nations according to economic status.

For practical purposes, it is not vital to memorize these definitions. However, it is important to be aware that these differences exist and to know what statistic you are examining, so that you do not accidentally compare, say, GDP in one year or for one country with GNP or NNP in another year or another country. To get an idea of how these calculations work, follow the steps in the following Work It Out feature.

Work It Out

Calculating gdp, net exports, and nnp.

Based on the information in Table 19.4 :

• What is the value of GDP?
• What is the value of net exports?
• What is the value of NNP?

Step 1. To calculate GDP use the following formula:

Step 2. To calculate net exports, subtract imports from exports.

Step 3. To calculate NNP, use the following formula:

This book may not be used in the training of large language models or otherwise be ingested into large language models or generative AI offerings without OpenStax's permission.

Want to cite, share, or modify this book? This book uses the Creative Commons Attribution License and you must attribute OpenStax.

Access for free at https://openstax.org/books/principles-economics-3e/pages/1-introduction

• Authors: Steven A. Greenlaw, David Shapiro, Daniel MacDonald
• Publisher/website: OpenStax
• Book title: Principles of Economics 3e
• Publication date: Dec 14, 2022
• Location: Houston, Texas
• Book URL: https://openstax.org/books/principles-economics-3e/pages/1-introduction
• Section URL: https://openstax.org/books/principles-economics-3e/pages/19-1-measuring-the-size-of-the-economy-gross-domestic-product

© Jul 18, 2024 OpenStax. Textbook content produced by OpenStax is licensed under a Creative Commons Attribution License . The OpenStax name, OpenStax logo, OpenStax book covers, OpenStax CNX name, and OpenStax CNX logo are not subject to the Creative Commons license and may not be reproduced without the prior and express written consent of Rice University.

Want to create or adapt books like this? Learn more about how Pressbooks supports open publishing practices.

How to Construct and Interpret Graphs

Learning objectives.

• Understand how graphs show the relationship between two or more variables and explain how a graph elucidates the nature of the relationship.
• Define the slope of a curve.
• Distinguish between a movement along a curve, a shift in a curve, and a rotation in a curve.

Much of the analysis in economics deals with relationships between variables. A variable is simply a quantity whose value can change. A graph is a pictorial representation of the relationship between two or more variables. The key to understanding graphs is knowing the rules that apply to their construction and interpretation. This section defines those rules and explains how to draw a graph.

Drawing a Graph

To see how a graph is constructed from numerical data, we will consider a hypothetical example. Suppose a college campus has a ski club that organizes day-long bus trips to a ski area about 100 miles from the campus. The club leases the bus and charges $10 per passenger for a round trip to the ski area. In addition to the revenue the club collects from passengers, it also receives a grant of $200 from the school’s student government for each day the bus trip is available. The club thus would receive $200 even if no passengers wanted to ride on a particular day.

The table in Figure 21.1 “Ski Club Revenues” shows the relationship between two variables: the number of students who ride the bus on a particular day and the revenue the club receives from a trip. In the table, each combination is assigned a letter (A, B, etc.); we will use these letters when we transfer the information from the table to a graph.

Figure 21.1 Ski Club Revenues

The ski club receives $10 from each passenger riding its bus for a trip to and from the ski area plus a payment of $200 from the student government for each day the bus is available for these trips. The club’s revenues from any single day thus equal $200 plus $10 times the number of passengers. The table relates various combinations of the number of passengers and club revenues.

We can illustrate the relationship shown in the table with a graph. The procedure for showing the relationship between two variables, like the ones in Figure 21.1 “Ski Club Revenues” , on a graph is illustrated in Figure 21.2 “Plotting a Graph” . Let us look at the steps involved.

Figure 21.2 Plotting a Graph

Here we see how to show the information given in Figure 21.1 “Ski Club Revenues” in a graph.

Step 1. Draw and Label the Axes

The two variables shown in the table are the number of passengers taking the bus on a particular day and the club’s revenue from that trip. We begin our graph in Panel (a) of Figure 21.2 “Plotting a Graph” by drawing two axes to form a right angle. Each axis will represent a variable. The axes should be carefully labeled to reflect what is being measured on each axis.

It is customary to place the independent variable on the horizontal axis and the dependent variable on the vertical axis. Recall that, when two variables are related, the dependent variable is the one that changes in response to changes in the independent variable. Passengers generate revenue, so we can consider the number of passengers as the independent variable and the club’s revenue as the dependent variable. The number of passengers thus goes on the horizontal axis; the club’s revenue from a trip goes on the vertical axis. In some cases, the variables in a graph cannot be considered independent or dependent. In those cases, the variables may be placed on either axis; we will encounter such a case in the chapter that introduces the production possibilities model. In other cases, economists simply ignore the rule; we will encounter that case in the chapter that introduces the model of demand and supply. The rule that the independent variable goes on the horizontal axis and the dependent variable goes on the vertical usually holds, but not always.

The point at which the axes intersect is called the origin of the graph. Notice that in Figure 21.2 “Plotting a Graph” the origin has a value of zero for each variable.

In drawing a graph showing numeric values, we also need to put numbers on the axes. For the axes in Panel (a), we have chosen numbers that correspond to the values in the table. The number of passengers ranges up to 40 for a trip; club revenues from a trip range from $200 (the payment the club receives from student government) to $600. We have extended the vertical axis to $800 to allow some changes we will consider below. We have chosen intervals of 10 passengers on the horizontal axis and $100 on the vertical axis. The choice of particular intervals is mainly a matter of convenience in drawing and reading the graph; we have chosen the ones here because they correspond to the intervals given in the table.

We have drawn vertical lines from each of the values on the horizontal axis and horizontal lines from each of the values on the vertical axis. These lines, called gridlines, will help us in Step 2.

Step 2. Plot the Points

Each of the rows in the table in Figure 21.1 “Ski Club Revenues” gives a combination of the number of passengers on the bus and club revenue from a particular trip. We can plot these values in our graph.

We begin with the first row, A, corresponding to zero passengers and club revenue of $200, the payment from student government. We read up from zero passengers on the horizontal axis to $200 on the vertical axis and mark point A. This point shows that zero passengers result in club revenues of $200.

The second combination, B, tells us that if 10 passengers ride the bus, the club receives $300 in revenue from the trip—$100 from the $10-per-passenger charge plus the $200 from student government. We start at 10 passengers on the horizontal axis and follow the gridline up. When we travel up in a graph, we are traveling with respect to values on the vertical axis. We travel up by $300 and mark point B.

Points in a graph have a special significance. They relate the values of the variables on the two axes to each other. Reading to the left from point B, we see that it shows $300 in club revenue. Reading down from point B, we see that it shows 10 passengers. Those values are, of course, the values given for combination B in the table.

We repeat this process to obtain points C, D, and E. Check to be sure that you see that each point corresponds to the values of the two variables given in the corresponding row of the table.

The graph in Panel (b) is called a scatter diagram. A scatter diagram shows individual points relating values of the variable on one axis to values of the variable on the other.

Step 3. Draw the Curve

The final step is to draw the curve that shows the relationship between the number of passengers who ride the bus and the club’s revenues from the trip. The term “curve” is used for any line in a graph that shows a relationship between two variables.

We draw a line that passes through points A through E. Our curve shows club revenues; we shall call it R 1 . Notice that R 1 is an upward-sloping straight line. Notice also that R 1 intersects the vertical axis at $200 (point A). The point at which a curve intersects an axis is called the intercept of the curve. We often refer to the vertical or horizontal intercept of a curve; such intercepts can play a special role in economic analysis. The vertical intercept in this case shows the revenue the club would receive on a day it offered the trip and no one rode the bus.

To check your understanding of these steps, we recommend that you try plotting the points and drawing R 1 for yourself in Panel (a). Better yet, draw the axes for yourself on a sheet of graph paper and plot the curve.

The Slope of a Curve

In this section, we will see how to compute the slope of a curve. The slopes of curves tell an important story: they show the rate at which one variable changes with respect to another.

The slope of a curve equals the ratio of the change in the value of the variable on the vertical axis to the change in the value of the variable on the horizontal axis, measured between two points on the curve. You may have heard this called “the rise over the run.” In equation form, we can write the definition of the slope as

Equation 21.1

Equation 21.1 is the first equation in this text. Figure 21.3 “Reading and Using Equations” provides a short review of working with equations. The material in this text relies much more heavily on graphs than on equations, but we will use equations from time to time. It is important that you understand how to use them.

Figure 21.3 Reading and Using Equations

Many equations in economics begin in the form of Equation 21.1 , with the statement that one thing (in this case the slope) equals another (the vertical change divided by the horizontal change). In this example, the equation is written in words. Sometimes we use symbols in place of words. The basic idea though, is always the same: the term represented on the left side of the equals sign equals the term on the right side. In Equation 21.1 there are three variables: the slope, the vertical change, and the horizontal change. If we know the values of two of the three, we can compute the third. In the computation of slopes that follow, for example, we will use values for the two variables on the right side of the equation to compute the slope.

Figure 21.4 “Computing the Slope of a Curve” shows R 1 and the computation of its slope between points B and D. Point B corresponds to 10 passengers on the bus; point D corresponds to 30. The change in the horizontal axis when we go from B to D thus equals 20 passengers. Point B corresponds to club revenues of $300; point D corresponds to club revenues of $500. The change in the vertical axis equals $200. The slope thus equals $200/20 passengers, or $10/passenger.

Figure 21.4 Computing the Slope of a Curve

• Select two points; we have selected points B and D.
• The slope equals the vertical change divided by the horizontal change between the two points.
• Between points B and D, the slope equals $200/20 passengers = $10/passenger.
• The slope of this curve is the price per passenger. The fact that it is positive suggests a positive relationship between revenue per trip and the number of passengers riding the bus. Because the slope of this curve is $10/passenger between any two points on the curve, the relationship between club revenue per trip and the number of passengers is linear.

We have applied the definition of the slope of a curve to compute the slope of R 1 between points B and D. That same definition is given in Equation 21.1 . Applying the equation, we have:

The slope of this curve tells us the amount by which revenues rise with an increase in the number of passengers. It should come as no surprise that this amount equals the price per passenger. Adding a passenger adds $10 to the club’s revenues.

Notice that we can compute the slope of R 1 between any two points on the curve and get the same value; the slope is constant. Consider, for example, points A and E. The vertical change between these points is $400 (we go from revenues of $200 at A to revenues of $600 at E). The horizontal change is 40 passengers (from zero passengers at A to 40 at E). The slope between A and E thus equals $400/(40 passengers) = $10/passenger . We get the same slope regardless of which pair of points we pick on R 1 to compute the slope. The slope of R 1 can be considered a constant, which suggests that it is a straight line. When the curve showing the relationship between two variables has a constant slope, we say there is a linear relationship between the variables. A linear curve is a curve with constant slope.

The fact that the slope of our curve equals $10/passenger tells us something else about the curve—$10/passenger is a positive, not a negative, value. A curve whose slope is positive is upward sloping. As we travel up and to the right along R 1 , we travel in the direction of increasing values for both variables. A positive relationship between two variables is one in which both variables move in the same direction. Positive relationships are sometimes called direct relationships. There is a positive relationship between club revenues and passengers on the bus. We will look at a graph showing a negative relationship between two variables in the next section.

A Graph Showing a Negative Relationship

A negative relationship is one in which two variables move in opposite directions. A negative relationship is sometimes called an inverse relationship. The slope of a curve describing a negative relationship is always negative. A curve with a negative slope is always downward sloping.

As an example of a graph of a negative relationship, let us look at the impact of the cancellation of games by the National Basketball Association during the 1998–1999 labor dispute on the earnings of one player: Shaquille O’Neal. During the 1998–1999 season, O’Neal was the center for the Los Angeles Lakers.

O’Neal’s salary with the Lakers in 1998–1999 would have been about $17,220,000 had the 82 scheduled games of the regular season been played. But a contract dispute between owners and players resulted in the cancellation of 32 games. Mr. O’Neal’s salary worked out to roughly $210,000 per game, so the labor dispute cost him well over $6 million. Presumably, he was able to eke out a living on his lower income, but the cancellation of games cost him a great deal.

We show the relationship between the number of games canceled and O’Neal’s 1998–1999 basketball earnings graphically in Figure 21.5 “Canceling Games and Reducing Shaquille O’Neal’s Earnings” . Canceling games reduced his earnings, so the number of games canceled is the independent variable and goes on the horizontal axis. O’Neal’s earnings are the dependent variable and go on the vertical axis. The graph assumes that his earnings would have been $17,220,000 had no games been canceled (point A, the vertical intercept). Assuming that his earnings fell by $210,000 per game canceled, his earnings for the season were reduced to $10,500,000 by the cancellation of 32 games (point B). We can draw a line between these two points to show the relationship between games canceled and O’Neal’s 1998–1999 earnings from basketball. In this graph, we have inserted a break in the vertical axis near the origin. This allows us to expand the scale of the axis over the range from $10,000,000 to $18,000,000. It also prevents a large blank space between the origin and an income of $10,500,000—there are no values below this amount.

Figure 21.5 Canceling Games and Reducing Shaquille O’Neal’s Earnings

If no games had been canceled during the 1998–1999 basketball season, Shaquille O’Neal would have earned $17,220,000 (point A). Assuming that his salary for the season fell by $210,000 for each game canceled, the cancellation of 32 games during the dispute between NBA players and owners reduced O’Neal’s earnings to $10,500,000 (point B).

What is the slope of the curve in Figure 21.5 “Canceling Games and Reducing Shaquille O’Neal’s Earnings” ? We have data for two points, A and B. At A, O’Neal’s basketball salary would have been $17,220,000. At B, it is $10,500,000. The vertical change between points A and B equals -$6,720,000. The change in the horizontal axis is from zero games canceled at A to 32 games canceled at B. The slope is thus

Notice that this time the slope is negative, hence the downward-sloping curve. As we travel down and to the right along the curve, the number of games canceled rises and O’Neal’s salary falls. In this case, the slope tells us the rate at which O’Neal lost income as games were canceled.

The slope of O’Neal’s salary curve is also constant. That means there was a linear relationship between games canceled and his 1998–1999 basketball earnings.

Shifting a Curve

When we draw a graph showing the relationship between two variables, we make an important assumption. We assume that all other variables that might affect the relationship between the variables in our graph are unchanged. When one of those other variables changes, the relationship changes, and the curve showing that relationship shifts.

Consider, for example, the ski club that sponsors bus trips to the ski area. The graph we drew in Figure 21.2 “Plotting a Graph” shows the relationship between club revenues from a particular trip and the number of passengers on that trip, assuming that all other variables that might affect club revenues are unchanged. Let us change one. Suppose the school’s student government increases the payment it makes to the club to $400 for each day the trip is available. The payment was $200 when we drew the original graph. Panel (a) of Figure 21.6 “Shifting a Curve: An Increase in Revenues” shows how the increase in the payment affects the table we had in Figure 21.1 “Ski Club Revenues” ; Panel (b) shows how the curve shifts. Each of the new observations in the table has been labeled with a prime: A′, B′, etc. The curve R 1 shifts upward by $200 as a result of the increased payment. A shift in a curve implies new values of one variable at each value of the other variable. The new curve is labeled R 2 . With 10 passengers, for example, the club’s revenue was $300 at point B on R 1 . With the increased payment from the student government, its revenue with 10 passengers rises to $500 at point B′ on R 2 . We have a shift in the curve.

Figure 21.6 Shifting a Curve: An Increase in Revenues

The table in Panel (a) shows the new level of revenues the ski club receives with varying numbers of passengers as a result of the increased payment from student government. The new curve is shown in dark purple in Panel (b). The old curve is shown in light purple.

It is important to distinguish between shifts in curves and movements along curves. A movement along a curve is a change from one point on the curve to another that occurs when the dependent variable changes in response to a change in the independent variable. If, for example, the student government is paying the club $400 each day it makes the ski bus available and 20 passengers ride the bus, the club is operating at point C′ on R 2 . If the number of passengers increases to 30, the club will be at point D′ on the curve. This is a movement along a curve; the curve itself does not shift.

Now suppose that, instead of increasing its payment, the student government eliminates its payments to the ski club for bus trips. The club’s only revenue from a trip now comes from its $10/passenger charge. We have again changed one of the variables we were holding unchanged, so we get another shift in our revenue curve. The table in Panel (a) of Figure 21.7 “Shifting a Curve: A Reduction in Revenues” shows how the reduction in the student government’s payment affects club revenues. The new values are shown as combinations A″ through E″ on the new curve, R 3 , in Panel (b). Once again we have a shift in a curve, this time from R 1 to R 3 .

Figure 21.7 Shifting a Curve: A Reduction in Revenues

The table in Panel (a) shows the impact on ski club revenues of an elimination of support from the student government for ski bus trips. The club’s only revenue now comes from the $10 it charges to each passenger. The new combinations are shown as A″ – E″. In Panel (b) we see that the original curve relating club revenue to the number of passengers has shifted down.

The shifts in Figure 21.6 “Shifting a Curve: An Increase in Revenues” and Figure 21.7 “Shifting a Curve: A Reduction in Revenues” left the slopes of the revenue curves unchanged. That is because the slope in all these cases equals the price per ticket, and the ticket price remains unchanged. Next, we shall see how the slope of a curve changes when we rotate it about a single point.

Rotating a Curve

A rotation of a curve occurs when we change its slope, with one point on the curve fixed. Suppose, for example, the ski club changes the price of its bus rides to the ski area to $30 per trip, and the payment from the student government remains $200 for each day the trip is available. This means the club’s revenues will remain $200 if it has no passengers on a particular trip. Revenue will, however, be different when the club has passengers. Because the slope of our revenue curve equals the price per ticket, the slope of the revenue curve changes.

Panel (a) of Figure 21.8 “Rotating a Curve” shows what happens to the original revenue curve, R 1 , when the price per ticket is raised. Point A does not change; the club’s revenue with zero passengers is unchanged. But with 10 passengers, the club’s revenue would rise from $300 (point B on R 1 ) to $500 (point B′ on R 4 ). With 20 passengers, the club’s revenue will now equal $800 (point C′ on R 4 ).

Figure 21.8 Rotating a Curve

A curve is said to rotate when a single point remains fixed while other points on the curve move; a rotation always changes the slope of a curve. Here an increase in the price per passenger to $30 would rotate the revenue curve from R 1 to R 4 in Panel (a). The slope of R 4 is $30 per passenger.

The new revenue curve R 4 is steeper than the original curve. Panel (b) shows the computation of the slope of the new curve between points B′ and C′. The slope increases to $30 per passenger—the new price of a ticket. The greater the slope of a positively sloped curve, the steeper it will be.

We have now seen how to draw a graph of a curve, how to compute its slope, and how to shift and rotate a curve. We have examined both positive and negative relationships. Our work so far has been with linear relationships. Next we will turn to nonlinear ones.

Key Takeaways

• A graph shows a relationship between two or more variables.
• An upward-sloping curve suggests a positive relationship between two variables. A downward-sloping curve suggests a negative relationship between two variables.
• The slope of a curve is the ratio of the vertical change to the horizontal change between two points on the curve. A curve whose slope is constant suggests a linear relationship between two variables.
• A change from one point on the curve to another produces a movement along the curve in the graph. A shift in the curve implies new values of one variable at each value of the other variable. A rotation in the curve implies that one point remains fixed while the slope of the curve changes.

The following table shows the relationship between the number of gallons of gasoline people in a community are willing and able to buy per week and the price per gallon. Plot these points in the grid provided and label each point with the letter associated with the combination. Notice that there are breaks in both the vertical and horizontal axes of the grid. Draw a line through the points you have plotted. Does your graph suggest a positive or a negative relationship? What is the slope between A and B? Between B and C? Between A and C? Is the relationship linear?

Now suppose you are given the following information about the relationship between price per gallon and the number of gallons per week gas stations in the community are willing to sell.

Plot these points in the grid provided and draw a curve through the points you have drawn. Does your graph suggest a positive or a negative relationship? What is the slope between D and E? Between E and F? Between D and F? Is this relationship linear?

Answer to Try It!

Here is the first graph. The curve’s downward slope tells us there is a negative relationship between price and the quantity of gasoline people are willing and able to buy. This curve, by the way, is a demand curve (the next one is a supply curve). We will study demand and supply soon; you will be using these curves a great deal. The slope between A and B is −0.002 (slope = vertical change/horizontal change = −0.20/100). The slope between B and C and between A and C is the same. That tells us the curve is linear, which, of course, we can see—it is a straight line.

Here is the supply curve. Its upward slope tells us there is a positive relationship between price per gallon and the number of gallons per week gas stations are willing to sell. The slope between D and E is 0.002 (slope equals vertical change/horizontal change = 0.20/100). Because the curve is linear, the slope is the same between any two points, for example, between E and F and between D and F.

Principles of Macroeconomics Copyright © 2016 by University of Minnesota is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

Share This Book

Visualizing Time Series Data: 7 Types of Temporal Visualizations

What are some of the most common data visualizations you see in newspapers, textbooks, and corporate annual reports? Graphs showing a country’s GDP growth trends or charts capturing a company’s sales growth in the last 4 quarters would be high up on the list. Essentially, these are visualizations that track time series data — the performance of an indicator over a period of time — also known as temporal visualizations.

Temporal visualizations are one of the simplest, quickest ways to represent important time series data. In this blog, we have put together 7 handy temporal visualization styles for your time series data. Explore and let us know which is your favorite!

1. Line Graph

A line graph is the simplest way to represent time series data. It is intuitive, easy to create, and helps the viewer get a quick sense of how something has changed over time.

A line graph uses points connected by lines (also called trend lines) to show how a dependent variable and independent variable changed. An independent variable, true to its name, remains unaffected by other parameters, whereas the dependent variable depends on how the independent variable changes. For temporal visualizations, time is always the independent variable, which is plotted on the horizontal axis. Then the dependent variable is plotted on the vertical axis.

In the graph below, the populations of Europe and Ireland are the dependent variables and time is the independent variable.

This graph captures the population growth in Europe and Ireland from 1740 to around 2010. It clearly highlights the sudden drop in Ireland’s population in the 1840s. History books will tell you this was the result of the devastating Irish Potato Famine, a period of mass starvation, disease, and emigration in Ireland between 1845 and 1852.

Note that this graph uses different y-axis scales for its two dependent variables — the populations of Europe and Ireland. If the viewer doesn’t pay attention to the difference in the scales, they could be led to the conclusion that until about 1920, Ireland’s population was greater than that of Europe!

Use different scales with care and only when absolutely necessary. If you need to represent multiple variables on a line graph, try to use the same y-axis for all dependent variables to avoid confusion. If you can’t do this, like in the chart above, make sure both y-axes use the same number of increments and use color to show which y-axis belongs to which line.

As a good rule of thumb, don’t represent more than four variables on a line graph. With that many variables, the axis scales can become difficult to understand.

2. Stacked Area Chart

An area chart is similar to a line chart in that it has points connected by straight lines on a two-dimensional chart. It also puts time as the independent variable on the x-axis and the dependent variable on the y-axis. However, in an area chart, multiple variables are “stacked” on top of each other, and the area below each line is colored to represent each variable.

This is a stacked area chart showing time series data of student enrollments in India from 2001-10.

Stacked area charts are useful to show how both a cumulative total and individual components of that total changed over time.

The order in which we stack the variables is crucial because there can sometimes be a difference in the actual plot versus human perception . The chart plots the value vertically whereas we perceive the value to be at right angles to the general direction of the chart. For instance, in the case below, a bar graph would be a cleaner alternative.

3. Bar Charts

Bar charts represent data as horizontal or vertical bars. The length of each bar is proportional to the value of the variable at that point in time. A bar chart is the right choice for you when you wish to look at how the variable moved over time or when you wish to compare variables versus each other. Grouped or stacked bar charts help you combine both these purposes in one chart while keeping your visualization simple and intuitive.

For instance, this grouped bar chart in this interactive visualization of number of deaths by disease type in India not only lets you compare the deaths due to diarrhea, malaria, and acute respiratory disease across time, but also lets you compare the number of deaths by these three diseases in a given year.

By switching to the stacked bar chart view, you get an intuitive sense of the proportion of deaths caused by each disease.

To avoid clutter and confusion, make sure to not use more than 3 variables in a stacked or group bar chart. It is also a good practice to use consistent bold colors and leave appropriate space between two bars in a bar chart. Also, check out our blog on 5 common mistakes that lead to bad data visualization to learn why the base axis for your bar charts should start from zero.

4. Gantt Chart

A Gantt chart is a horizontal bar chart showing work completed in a certain period of time with respect to the time allocated for that particular task. It is named after the American engineer and management consultant Henry Gantt who extensively used this framework for project management.

Assume you’re planning the logistics for a dance concert. There are lots of activities to be completed, some of which will take place simultaneously while some can be done only after another activity has been completed. For instance, the choreographers, soundtrack, and dancers need to be finalized before the choreography can begin. However, the costumes, props, and stage decor can be planned at the same time as the choreography. With careful preparation, Gantt charts can help you plan for complex, long-term projects that are likely to undergo several revisions and have various resource and task dependencies.

Gantt charts are a popular project management tool since they present a concise snapshot of various tasks spread across various phases of the project. You can show additional information such as the correlation between individual tasks, resources used in each task, overlapping resources, etc., by the use of colors and placement of bars in a Gantt chart.

5. Stream Graph

A stream graph is essentially a stacked area graph, but displaced around a central horizontal axis. The stream graph looks like flowing liquid, hence the name.

Below is a stream graph showing a randomly chosen listener’s last.fm music-listening habits over time.

Stream graphs are great to represent and compare time series data for multiple variables. Stream graphs are, thus, apt for large data sets. Remember that choice of colors is very important, especially when there are lots of variables. Variables that do not have significantly high values might tend to get drowned out in the visualization if the colors are not chosen well.

6. Heat Map

Geospatial visualizations often use heat maps since they quickly help identify “hot spots” or regions of high concentrations of a given variable. When adapted to temporal visualizations, heat maps can help us explore two levels of time in a 2D array.

This heat map visualizes birthdays for babies born in the United States between 1973 and 1999. The vertical axis represents the 31 days in a month while the horizontal axis represents the 12 months in a year. This chart quickly helps us identify that a large number of babies were born in the later half of July, August, and September.

Heat maps are perfect for a two-tiered time frame — for instance, 7 days of the week spread across 52 weeks in the year, or 24 hours in a day spread across 30 days of the month, and so on. The limitation, though, is that only one variable can be visualized in a heat map. Comparison between two or more variables is very difficult to represent.

7. Polar Area Diagram

Think beyond the straight line! Sometimes, time series data can be cyclical — a season in a year, time of the day, and so on. Polar area diagrams help represent the cyclical nature time series data cleanly. A polar diagram looks like a traditional pie chart, but the sectors differ from each other not by the size of their angles but by how far they extend out from the centre of the circle.

This popular polar area diagram created by Florence Nightingale shows causes of mortality among British troops in the Crimean War. Each color in the diagram represents a different cause of death. (Check out the the text legend for more details.)

Polar area diagrams are useful for representing seasonal or cyclical time series data, such as climate or seasonal crop data. Multiple variables can be neatly stacked in the various sectors of the pie.

It is crucial to clarify whether the variable is proportional to the area or radius of the sector. It is a good practice to have the area of the sectors proportional to the value being represented. In that case, the radius should be proportional to the square root of the value of the variable (since area of a circle is proportional to the square of the radius).

Polar area diagrams, or pie charts in general, must be made with a lot of care to avoid misrepresentation. For more tips, check out this blog on 5 things you should know before you make a pie chart .

Go ahead… It’s “time” you made some cool temporal visualizations of your own!

Content at Atlan

Related Posts

Embarking on a New Chapter as Chief Revenue Officer at Atlan

What makes our new Chief Revenue Officer, Jim Smittkamp special?

Data Governance Is A Top Priority For 65% Of Data Leaders – Insights From 600+ Data Leaders For 2024

Great page. I love how you are thinking beyond line and area for visualization. The heatmaps got my attention. its a better way to present seasonality than series of lines for each year with X axis in months. i’ll try it out in d3 or other JS libraries. Good luck in your pursuits.

Data is king but visualization is the queen!

Thank you for this amazing detailed information about all the graphs.

What a great resource! Thank you for compiling these visualization options with such clear distinctions in optimal applications. Data allowing, I hope to find use for all of them.

Write A Comment Cancel Reply

Save my name, email, and website in this browser for the next time I comment.

This site uses Akismet to reduce spam. Learn how your comment data is processed .

• The Future of the Modern Data Stack in 2023
• The Third-Generation Data Catalog Primer
• The Secrets of a Modern Data Leader
• The Ultimate Guide to Evaluating Data Lineage
• How Active Metadata Helps Modern Organizations Embrace the DataOps Way
• Inside Atlan

Type above and press Enter to search. Press Esc to cancel.

Advertisement

How the Economy Is Actually Doing, in 9 Charts

By Ella Koeze Dec. 17, 2020

• Share full article

Food scarcity

Unemployment

for Black men

Labor force

participation

New businesses

Nearly a year after the coronavirus outbreak, the full impact of the pandemic on the U.S. economy remains unclear. Some of the most obvious indicators are in conflict: As some companies report enormous profits , nearly 10 million more Americans are now unemployed compared with last February, and over one million filed new state and federal unemployment claims last week.

Are we still in the early stages of a long recession, or will the rollout of vaccines mean we’ll soon see the end of a short-term crisis? How much are people suffering now, and for how long will the effects of the past 10 months persist?

We asked economists and experts with a variety of backgrounds how they would measure the state of the economy now and what indicators they thought were often overlooked. Here are eight measures they suggested.

People have left the work force altogether — and have not come back.

The labor force participation rate is the share of working-age people who are part of the labor force (employed, on temporary layoff or actively searching for work)..

More than four million people left the work force entirely from February to November, meaning they are neither working nor actively seeking a job. (To be counted as unemployed, a person must have looked for a job in the last four weeks or be on a temporary layoff.) Several of the economists we spoke to mentioned the dip in labor force participation as a cause for concern — and a factor that can contribute to a falling unemployment rate, without people actually finding work.

Eliza Forsythe, a labor economist at the University of Illinois at Urbana-Champaign:

Many jobs carry substantial risk of contracting Covid-19, so individuals with health concerns may be sitting out of the labor market. And with many schools operating remotely and childcare closed, many parents are forced to choose between a paycheck and supervising their kids.

More women than men are leaving the work force, research has shown, often because of additional child care and household obligations that emerged during the pandemic. This can have long-term ramifications on women’s careers and earning potential.

Janelle Jones, a labor economist at Groundwork Collaborative, an economic policy center focused on addressing inequality:

The caregiving crisis intensified by the pandemic has forced women to choose between employment and care work. And all of this is particularly pressing for Black women. A large majority of Black mothers are contributing significantly to keeping their households afloat. Women of color are more likely to be single parents than white women, meaning the decision between caregiving in the home and entering the labor market is more likely to fall on them.

The number of people who have been out of work long-term continues to rise.

The share of unemployed workers who have been unemployed 27 weeks or more.

Though many workers who were temporarily laid off in the spring have returned to work, a growing subset has been unable to find new jobs despite actively looking. Among Americans who are still in the labor force but are unemployed, the share who have been out of work for more than six months has been increasing since April.

Alix Gould-Werth, a sociologist at the Washington Center for Equitable Growth, a nonpartisan, left-leaning think tank:

When the long-term unemployment rate increases and workers leave the labor market, it is an indicator of a very serious problem in connecting people who are able to produce needed goods and services with the opportunity to do so.

Unemployment has fallen from the worst months, but gaps among demographic groups have widened.

Unemployment rates for black , hispanic , asian and white workers by gender.

Several of the experts surveyed were concerned that the pandemic recession was widening economic gaps among different demographics.

Kathryn Edwards, a labor economist at the RAND corporation:

Policy responses to recession are never perfect; they always err on the side of something. But we’ve learned in the past, erring on the side of stingy recovery and assistance can scar individuals, families and communities for years.

People of color were already more likely to be unemployed before Covid-19, and the current crisis has had a particularly negative, persistent impact on employment for Black men.

Janelle Jones, a labor economist who focuses on inequality:

Black men are facing an unemployment rate of 11.3 percent, over five percentage points higher than the rate facing white men. For context, never during the Great Recession did overall unemployment rates surpass 10 percent.

More families are unable to meet their basic needs.

Share of households experiencing food and housing insecurity in the census bureau’s household pulse survey.

Both Ms. Edwards, the labor economist, and Ms. Gould-Werth, the sociologist, noted that measures of material hardship are less readily available than many other statistics. This means that decision makers have the least information about the people who are undergoing the most hardship in a crisis.

Kathryn Edwards:

I worry about the things we don’t measure. We don’t have indicators of the economic well-being of workers and families that are reliable, comparable and timely. The same detail with which we have unemployment and labor force numbers — monthly estimates by age, race, sex, education, occupation, industry and state — should be produced to measure poverty; food access and hunger; housing security, eviction, and homelessness; and health access and use, among others.

Alix Gould-Werth:

Long-term unemployment rate, stock of discouraged workers and measures of material hardship are all extremely underexamined in comparison to indicators like stock market indexes and the unemployment rate. Indeed, until the Household Pulse survey started tracking food and housing insecurity, we lacked real-time measures of material hardship.

Rent and home prices have risen over the course of the pandemic.

Price indexes for rents (consumer price index) and homes (s&p/case-shiller u.s. national home price index) relative to january 2019.

Rising housing costs put strain on low-income renters. In a Pew study published in September, lower-income respondents were more likely to have lost their job since February and to have had problems paying their rent or mortgage. In July, 52 percent of 18- to 29-year-olds were living with at least one parent, the highest level since the Great Depression. Home prices have also risen, which is good news for homeowners but not for lower-income people who want to buy homes.

Susan Wachter, a real estate economist at the University of Pennsylvania:

It is inequality which is, to my mind, the major challenge to the economy going forward, exacerbated by the impact on housing markets, with rents up, wages down and housing prices increasingly unaffordable and mortgages increasingly difficult to access for first time homebuyers. Renters faced with higher rents cannot save and cannot access the major way of wealth-building for America’s families: homeownership.

The rise in housing prices is being driven by a large increase in new home sales as people are spending more time at home and mortgage rates have fallen to record lows. Though these high prices make buying a home increasingly unaffordable for many, the spate of home-buying may stimulate the economy.

Tim Duy, a professor of macroeconomics at the University of Oregon:

The housing numbers for new home sales are at levels last seen during the housing bubble. This is typically associated with strong economic growth.

Wages and salaries have bounced back quickly.

Monthly total wages and salaries.

Despite all these warning signs, there is evidence that we are on track for a rapid recovery, several experts we spoke to said. One promising development is that productivity, which measures how much the economy can produce with the amount of labor and resources that go into it, is still growing . Growth in productivity is linked to increases in living standards. The V-shaped recovery of wages and salaries is a positive sign, too.

It reveals to me that the underlying economy is more resilient and less dependent on fiscal stimulus than commonly believed. Moreover, it is another contrast with the last recession — it took years to recover from the drop of wages and salaries that occurred then.

The economy has been able to shift from services toward goods.

Percent change in consumer spending from the last quarter of 2019.

Though the pandemic has altered Americans’ day-to-day lives, it hasn’t halted their spending as much as some feared it would. Rather, consumption has shifted toward goods over services — buying alcohol from stores instead of from bars, for example — bucking a generational trend toward a service economy.

Michael Gapen, the head of U.S. Economic Research at Barclays:

I think the economy showed a tremendous resilience and I think that the degree to which households shifted their behavior and spending patterns and then the degree to which how quickly the economy turned on a dime to be able to satisfy that — there’s a lot of flexibility that was shown by businesses and households during the pandemic.

New business applications are way up over last year.

Percent change from the same week last year in new business applications.

Countless businesses have been forced to close over the course of the pandemic. However, a sign that the economy may be adapting rather than totally halting is the increase over last year in new business applications.

Steven Hamilton, an economics professor at George Washington University:

One of the few bright spots during the pandemic has been a surge in new business formations, reflecting those laid off taking up self-employment, but more significantly the strength of household balance sheets built up through the crisis putting them in a position to start new businesses. While these are nowhere near as large as the closures, we kind of have to take what we can get in a crisis of this scale.

A resilient but increasingly unequal economy.

Alone, none of these metrics can tell a complete story of the pandemic economic crisis. Taken together, they show that, more than 10 years after the Great Recession, the economy has proved itself adaptable to extreme circumstances.

But almost all the economists we surveyed, even the most optimistic, agreed on the need for swift fiscal policy to alleviate short-term suffering and prevent long-term harm. Several stimulus programs are set to expire at the end of December, and any new legislation Congress passes now to replace them is likely to take weeks to implement. Ms. Jones, the labor economist who focuses on racial disparities, called for Congress to act boldly: “The fact is that the risks of doing too little to help the economy are enormous,” she said, “while the risks of doing too much are tiny.”

These indicators also show that some people are already getting left behind in an uneven recovery as others feel few impacts, or even flourish. The pandemic crisis “has drawn a tremendously bright and vivid line between the affected and the not affected,” Mr. Gapen said. “If you’re in the pool of the affected, I think it’s been just an awful year.”

Mapped: GDP per Capita Worldwide

| 346,093 views

View the full-resolution version of this infographic

[bg_collapse view=”button-green” color=”#ffffff” expand_text=”▼ Use This Visualization” collapse_text=”Show Less”]

[/bg_collapse]

Mapped: Visualizing GDP per Capita Worldwide

View the high-resolution of the infographic by clicking here .

GDP per capita has steadily risen globally over time, and in tandem, the standard of living worldwide has increased immensely.

This map using data from the IMF shows the GDP per capita (nominal) of nearly every country and territory in the world.

GDP per capita is one of the best measures of a country’s wealth as it provides an understanding of how each country’s citizens live on average, showing a representation of the quantity of goods and services created per person.

The Standard of Living Over Time

Looking at history, our standard of living has increased drastically. According to Our World in Data, from 1820 to 2018, the average global GDP per capita increased by almost 15x .

Literacy rates, access to vaccines, and basic education have also improved our quality of life, while things like child mortality rates and poverty have all decreased.

For example, in 1990, 1.9 billion people lived in extreme poverty, which was 36% of the world’s population at the time. Over the last 30 years, the number has been steadily decreasing — by 2030, an estimated 479 million people will be living in extreme poverty, which according to UN  population estimates, will represent only 6% of the population.

That said, economic inequality between different regions is still prevalent. In fact, the richest country today (in terms of nominal GDP per capita), Luxembourg, is over  471x more wealthy than the poorest, Burundi.

Here’s a look at the 10 countries with the highest GDP per capita in 2021:

However, not all citizens in Luxembourg are extremely wealthy. In fact:

• 29% of people spend over 40% of their income on housing costs
• 31% would be at risk of falling into poverty if they had to forgo 3 months of income

The cost of living is expensive in Luxembourg — but the standard of living in terms of goods and services produced is the highest in the world. Additionally, only 4% of the population reports low life satisfaction.

Emerging Economies and Developing Countries

Although we have never lived in a more prosperous period, and poverty rates have been declining overall, this year global extreme poverty rose for the first time in over two decades.

About 120 million additional people are living in poverty as a result of the pandemic, with the total expected to rise to about 150 million by the end of 2021.

Many of the poorest countries in the world are also considered Least Developed Countries (LDCs) by the UN . In these countries, more than 75% of the population live below the poverty line.

Here’s a look at the 10 countries with the lowest GDP per capita:

Life in these countries offers a stark contrast compared to the top 10. Here’s a glance at the quality of life in the poorest country, Burundi:

• 80% of the population works in agriculture
• 1 in 3 Burundians are in need of urgent humanitarian assistance
• Average households spend up to two-thirds of their income on food

However, many of the world’s poorest countries can also be classified as emerging markets with immense economic potential in the future.

In fact, China has seen the opportunity in emerging economies. Their confidence in these regions is best exemplified in the Belt and Road initiative which has funneled massive investments into infrastructure projects across multiple African countries.

Continually Raising the Bar

Prosperity is a very recent reality only characterizing the last couple hundred years. In pre-modern societies, the average person was living in conditions that would be considered extreme poverty by today’s standards.

Overall, the standard of living for everyone today is immensely improved compared to even recent history, and some countries will be experiencing rapid economic growth in the future.

GDP per Capita in 2021: Full Dataset

Editor’s note: Readers have rightly pointed out that Monaco is one of the world’s richest countries in GDP per capita (nominal) terms. This is true, but the IMF dataset excludes Monaco and lists it as “No data” each year. As a result, it is excluded from the visualization(s) above.

Which Country is the Cheapest for Starting a Business?

Visualized: The Biggest Ponzi Schemes in Modern History

You may also like

Ranked: The 10 Worst Countries for Women

Ranked: The Countries With the Highest Wealth per Person

Ranked: Which Countries Gained the Most Wealth Since 2010?

Ranked: The 10 Best Countries for Women

Mapped: Poverty Rates by U.S State

Visualized: GDP Growth Projections for Key Economies (2024-2025)

Mapped: U.S. Obesity Rates, by State

Mapped: The Number of U.S. Billionaires in Each State

Ranked: Countries That Use ChatGPT the Most

Who’s Receiving U.S. Green Cards, by Country of Origin

Ranked: Athletes with the Most Olympic Medals

What Happens After Rates Are Cut? See Our Visual Guide to the Markets in September (VC+)

Module: Economic Thinking

Outcome: graphs in economics, what you’ll learn to do: use graphs in common economic applications.

In this course, the most common way you will encounter economic models is in graphical form.

A graph is a visual representation of numerical information. Graphs condense detailed numerical information to make it easier to see patterns (such as “trends”) among data. For example, which countries have larger or smaller populations? A careful reader could examine a long list of numbers representing the populations of many countries, but with more than two hundred nations in the world, searching through such a list would take concentration and time. Putting these same numbers on a graph, listing them from highest to lowest, would reveal population patterns much more readily.

Economists use graphs not only as a compact and readable presentation of data, but also for visually representing relationships and connections—in other words, they function as models. As such, they can be used to answer questions. For example: How do increasing interest rates affect home sales? Graphing the results can help illuminate the answers.

This section provides an overview of graphing—just to make sure you’re up to speed on the basics. It’s important to feel comfortable with the way graphs work before using them to understand new concepts.

The specific things you’ll learn in this section include the following:

• Explain how a graph shows the relationship between two variables
• Differentiate between a positive relationship and a negative relationship
• Interpret economic information on a graph

Learning Activities

The learning activities for this section include the following:

• Video: Graph Review
• Reading: Creating and Interpreting Graphs
• Reading: Interpreting Slope
• Reading: Types of Graphs
• Self Check: Graphs in Economics
• Outcome: Graphs in Economics. Provided by : Lumen Learning. License : CC BY: Attribution

• Advanced search

Advanced Search

Word cloud analysis of the BJGP : a decade on

• Find this author on Google Scholar
• Find this author on PubMed
• Search for this author on this site
• For correspondence: [email protected]
• Figures & Data

All letters are subject to editing and may be shortened. General letters can be sent to bjgpdisc{at}rcgp.org.uk (please include your postal address for publication), and letters responding directly to BJGP articles can be submitted online via eLetters . We regret we cannot notify authors regarding publication.

For submission instructions visit: bjgp.org/letters

A visual representation of word frequency within text is called a ‘word cloud’. 1 The more commonly the word appears, the more prominently it features in the word cloud, providing a synopsis of the major themes. 2 Word clouds have a number of limitations. Each term is a singular analysis unit, so, if the same theme is expressed using different words, the resulting illustration will be found wanting. 2 , 3 Moreover, word clouds fail to provide context, so the semantics of discrete phrases may be lacking. 2 , 3

In 2012, the lead author conducted a word cloud analysis of the entire content of the British Journal of General Practice ( BJGP ) from 2011. 1 Five years later, they repeated the exercise to compare and contrast the 2011 and 2016 content. 4 In 2024, the authors decided to conduct a similar analysis to see how things had changed over the past decade, examining all 2023 issues of the BJGP , comprising a total of 427 000 words. We used the tool WordItOut ( https://worditout.com/word-cloud/create ), setting a maximum word limit of 100 and excluding common English words.

In 2011, the BJGP ’s editorial policy stated that it was ‘an international journal publishing articles of interest to primary care clinicians, researchers, and educators worldwide’ . 1 In 2016, the journal described itself as ‘an international journal publishing research, debate and analysis, and clinical guidance for family practitioners and primary care researchers worldwide’ . 4 In both 2011 and 2016, the BJGP gave priority to research articles of direct relevance to patient care. In 2024, the BJGP defines itself in exactly the same way as it did in 2016, with the addition of editorials to its scope. 5

In 2011, the two most prominent terms highlighted in the word cloud were ‘care’ and ‘patient/s’. 1 This was also the case in 2016 and 2023. The words ‘GP/s’, ‘primary’, ‘general’, and ‘practice/s’ all appear in the 2011 and 2016 analyses, and again in 2023. The word ‘research’ was seen in the word cloud in 2011 and 2016, and continued to appear in 2023, supporting the journal’s written aim to focus on this area. The other relevant words that were included in 2023 were ‘health’, ‘study’, ‘data’, ‘information’, ‘evidence’, ‘outcome’, and ‘impact’.

In terms of the journal’s international focus, the 2011 analysis highlighted that ‘UK’ and ‘London’ were the only geographic terms to feature; this was also the case in 2016 and 2023.

The medical conditions that figured most prominently in the 2011 analysis were ‘cancer’ and ‘depression’; cancer was still there in 2016, though ‘depression’ had been replaced by ‘diabetes’. In 2023, ‘cancer’ was still listed, but ‘diabetes’ and ‘depression’ had been superseded by ‘COVID’.

‘Quality’ appeared only as a minor term in 2011; ‘risk’ emerged larger. In 2016, ‘risk’ was still there, but not ‘quality’. In 2023, ‘quality’ was back and ‘risk’ remained.

In terms of workforce issues, new words in 2023 included ‘access’, ‘time’, and ‘continuity’ (of care). ‘Salaried’ also made an appearance, signifying the rise in salaried GPs. 6

Looking at the population served by primary care, an important new term that arose in 2023 was ‘community’. ‘Women’ and ‘children’ were both specific population groups that also featured.

In conclusion, the 2023 word cloud analysis of the BJGP has shown that the journal has essentially met its editorial aim. Over the last decade, there has only been a relatively small change in the themes covered by the journal. There is again the suggestion that the BJGP is still perhaps too UK-, England-, and London-centric, and so has more to do to broaden its geographic reach. However, this has been partly addressed by the creation of BJGP Open , which has more scope for the inclusion of international studies. Some new diseases and population groups have been highlighted, as well as workforce issues.

• © British Journal of General Practice 2024
• Atenstaedt R
• McNaught C ,
• Ramsden A ,
• 5. ↵ BJGP. About BJGP , https://bjgp.org/page/about (accessed 9 Aug 2024).
• Hoddinott S ,

In this issue

• Table of Contents
• Index by author

Thank you for recommending British Journal of General Practice.

NOTE: We only request your email address so that the person to whom you are recommending the page knows that you wanted them to see it, and that it is not junk mail. We do not capture any email address.

Citation Manager Formats

• EndNote (tagged)
• EndNote 8 (xml)
• RefWorks Tagged
• Ref Manager

• Tweet Widget
• Facebook Like
• Google Plus One

Jump to section

More in this toc section.

• Book review: The Maudsley Deprescribing Guidelines
• Humanity’s Conundrum : post-traumatic stress disorder and depression

Related Articles

Cited by....

British Journal of General Practice