How to measure the human input in GDP
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.
Measuring the value of net corporate dividends in the U.S. economy
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: