Federal Reserve Economic Data

The FRED® Blog

Negative investment?

Investment. It’s a common-enough term, typically defined as an addition to existing capital. It can take the form of structures such as buildings, machinery, and residential housing. So, investment always contributes to the increase of physical capital and growth of the economy, right? Not quite. Investment has two components that complicate things a bit: change in inventories and depreciation.

Change in inventories can be positive or negative, and technically all investment could take the form of inventories and contribute nothing to our stock of buildings and machinery. More importantly, and what we focus on here, is that capital depreciates. Machinery breaks and buildings fall into disrepair, so capital requires upkeep or it becomes obsolete. Often, investment more than replaces this depreciated capital; occasionally, though, investment isn’t so robust.

The graph shows two series: real gross investment and real net investment. Gross investment is always in positive territory, despite strong fluctuations throughout the business cycle, which is what you’d generally expect from investment. Net investment removes the depreciated capital from the picture and isn’t always positive: In fact, in 2009, when the U.S. economy was in a deep recession, this measure dipped into negative territory.

How this graph was created: Search for “real net investment” and select the first series and add it to the graph. To add the second series, open the “Edit Graph” panel, search for “real investment,” and select the other series shown in the graph.

Suggested by Christian Zimmermann.

View on FRED, series used in this post: A557RX1A020NBEA, GPDIC1

Cargo is cloudy for planes, ships slip and trains don’t gain, but trucks are in luck and pipelines are fine

A few simple observations: The United States is large. Americans buy things often. So, all kinds of goods get hauled over great distances all the time. And, once again, FRED has some relevant data.

The graph tracks various modes of transportation for freight. And even though these indicators have different units (tons, short tons, ton-miles, and barrels), FRED’s graphing flexibility lets us compare them in a logical way. Once we change the units to an index, setting all values at 100 starting in the year 2000, we can compare the evolution of these indicators over time. It looks like freight hauled by rail is slowly but surely losing its market share, while freight hauled by trucks has fared better. Freight on U.S. waterways has partially recovered from earlier losses, and pipeline transport has recently increased. Airborne freight is more difficult to judge: The jump in 2002 reflects a change in the indicator itself, when more carriers were included in the calculation; but it looks stable since then, except for the big dip during the recent recession.

How this graph was created: These series can be found in the U.S. transportation data release. Select the relevant series and click “Add to Graph.” Because so many different units are used for these series, unify them by scaling the units to 100 for the date 2000-01-01: Open the “Edit Graph” tab, look in the “Units” menu, and choose “Index (scale value to 100 for chosen date).”

Suggested by Christian Zimmermann.

View on FRED, series used in this post: AIRRTMFMD11, PETROLEUMD11, RAILFRTCARLOADSD11, TRUCKD11, WATERBORNED11

Testing theory: marginal product and wages

Economic theory tells us that, in a perfectly competitive labor market, labor should be paid according to its “marginal product.” Now, without the jargon: The last workers to be hired by a business should receive pay that is equal to their contribution to the output of that business. So, let’s compare the data with the theory…

Unfortunately, we have no data on the marginal product. But fortunately, we have data on average product. Although it’s not a certainty, these two products should be correlated. So, the graph above shows real growth rates for average product and the average wage. But again, there’s a limitation to the data: We must use the wage of production workers only if we want a series that’s long enough to compare with average product.

Ultimately, it doesn’t look like these series are closely related. The two data limitations we have here could be undermining the relationship. Or the labor market could be less than perfectly competitive. Or the theory could be wrong. It’s difficult to say. But such is the life of an economist… For some more-rigorous research on this topic, take a look at this recent Economic Synopses essay.

How this graph was created: Search for “real output per hour” and select the series shown here. In the “Edit Graph” panel, add the next series by searching for “average hourly earnings” and taking the series with the longer duration. Then modify this series by adding the CPI data series and applying the formula a/b. Select “Percent Change from Year Ago” as the units.

Suggested by Christian Zimmermann.

View on FRED, series used in this post: AHETPI, CPIAUCSL, PRS84006091


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