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Spectacular recoveries

Comparing the strongest economic recoveries in recent U.S. history

Posted on May 10, 2018

In a previous post, we discussed how the economic recoveries from recessions are longer and slower than the downturns that lead to those recessions. Today, we compare the sizes of recoveries across the economic history of the United States. In the graph above, which shows the unemployment rate, the current recovery is clearly remarkable: The drop from unemployment’s high point of 10% down to 3.9% (at the time of this writing) is 6.1 percentage points. The only recovery that comes close (in the time period shown here) is the 1983-89 recovery, with a 5.8-percentage-point decrease (10.8% to 5%). However, this sample is limited to only the ten recoveries since 1948.

To go back farther, we need to use different data. Thank goodness FRED has some historical data compiled by the NBER on the unemployment rate! The NBER methods for compiling the data shown below aren’t entirely comparable to the BLS methods for the data shown above. In fact, the NBER series is a composite of three different series. But as long as we acknowledge which data sets we’re looking at, we should be able to make some generally fair comparisons. Here, the recoveries from the Great Depression stand out: First, the unemployment rate topped at 25.6% and then dropped to 11%. A 14.6-percentage-point drop. The second recovery went from 20% to 0.2%. A 19.8-percentage-point drop!

So, although the most recent recovery seems remarkable after WWII, it’s small compared with the recoveries before WWII. Even if in some sense we’re comparing apples and oranges, the oranges are a lot bigger.

How these graphs were created: Search for “unemployment rate” and choose each series individually: civilian unemployment rate (monthly, seasonally adjusted, starting in January 1948) and unemployment rate for United States (monthly, seasonally adjusted, starting in April 1929).

Suggested by George Fortier and Christian Zimmermann.

View on FRED, series used in this post: M0892AUSM156SNBR, UNRATE

Human capital around the globe

How to measure the human input in GDP

Posted on May 7, 2018

What does it take to produce stuff? On a basic level, you need means of production: raw materials, land, some machinery, some structure, and humans. Gross domestic product (GDP) is the measure of all the stuff produced in a country (in each quarter or each year) and depends heavily on the means of production. Today, we look at the human component. How much humans influence GDP depends on essentially two things: how much they work and how good they are at working. The latter is obviously a little difficult to measure, especially if you want to look at the input angle. We can measure output, which is labor productivity—that is, how much each unit of work produces. To measure human efficiency at working, though, economists use the concept of human capital, which is in some ways parallel to physical capital (machinery and structures) in the production process. When working, humans use their education, work experience, and intelligence to try to do more within the same amount of time. Human capital is a measure of all this.

When comparing nations, a good proxy for human capital is the average numbers of years of schooling. Economists have long used the Barro and Lee dataset and its revisions (see here and also here). But just adding up years of school doesn’t seem quite right. Indeed, the impact of 3rd grade in Nepal is likely not the same as 11th grade in Canada or 1st grade in Nigeria. To adjust this measure, economists use the returns (in earnings) to each additional year of education as measured by George Psacharopoulos. Once you apply those returns, which are different for every year of schooling and every country, to the average number of years of schooling in each country, you get the human capital index that is shown in the map above.

How this map was created: The original post referenced an interactive map from our now discontinued GeoFRED site. The revised post provides a replacement map from FRED’s new mapping tool. To create FRED maps, go to the data series page in question and look for the green “VIEW MAP” button at the top right of the graph. See this post for instructions to edit a FRED map. Only series with a green map button can be mapped.

Suggested by Christian Zimmermann.

Good times for dividends

Measuring the value of net corporate dividends in the U.S. economy

Posted on May 3, 2018

Today, FRED will help us track the value of something called aggregate net corporate dividends. First, a few definitions:

1. Dividends are distributions of a portion of a company’s earnings to its shareholders.

2. Corporate dividends are dividends paid by corporations.

3. The federal government adjusts the aggregate corporate dividends data to account for dividends paid and received from abroad. (This makes the data consistent with other aspects of the national income accounts.) These adjusted values are net corporate dividends.

Net corporate dividends have grown from $5.8 billion in 1929 to $990 billion in 2017. Of course, this growth is largely driven by the general increase in prices—i.e., inflation—and by the increase in overall real economic activity. FRED can help us get a sense of the value of these dividends relative to the total economy by plotting net corporate dividends as a fraction of nominal gross domestic product. The graph includes each year from 1929 through 2017. Apart from a few years during and after the most recent recession, this value has exceeded 5 percent for the past 11 years. Before this, you’d have to go back to World War II for this value to exceed 5 percent. So, yes: Good times for dividends.

How this graph was created: Search for “corporate dividend,” choose the series “Net Corporate Dividend Payments,” and click “Edit Graph.” In the “Edit Line 1” panel, enter “nominal gross domestic product” in the search field, choose “Gross Domestic Product, Billions of Dollars, Not Seasonally Adjusted,” and click “Add.” Next, to compute the ratio of these two variables, type “a/b” in the formula field (where “a” is the dividends variable and “b” is the nominal GDP variable) and click “Apply.”

Suggested by Bill Dupor.

View on FRED, series used in this post: B056RC1A027NBEA, GDPA


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