Federal Reserve Economic Data

Today we look at total factor productivity, which is a measure of economic efficiency that captures how effectively an economy uses its inputs to produce outputs. TFP reflects technological progress through innovation and adoption of new technologies, allocation of resources, and other factors that boost the overall economic product beyond just increases in labor and capital.

Improvement in TFP is crucial because it drives long-term economic growth and raises living standards. The FRED graph above shows two interesting observations related to this.

First, over the long run, US TFP has grown significantly: Between 1955 and 2015, it improved by about 55%. This increase represents a substantial improvement in the nation’s ability to generate economic output from its resources.

However, the rate of TFP growth has slowed noticeably in recent decades. Particularly since 2005, TFP has increased by only about 5%. This slowdown is a concern for economists and policymakers because it suggests a potential decline in the pace of innovation or the economy’s ability to adopt new technologies.

Second, the graph also clearly shows that TFP tends to significantly drop during recessions, indicated by the shaded areas. This doesn’t necessarily mean that the economy literally “forgets” how to produce goods and services efficiently. Instead, these dips reflect several economic realities during downturns: For example, capacity utilization often decreases, leading to less-efficient use of existing resources. As the economy recovers, TFP typically rebounds, suggesting that these efficiency losses are generally temporary rather than permanent losses of productive knowledge or capability.

How this graph was created: Search FRED for “total factor productivity” and click on the series for the United States.

Suggested by Aakash Kalyani.

The FRED graph above shows a disturbing pattern: Since the early 1970s, there’s been an apparent disconnect between labor productivity and real wages. (The accumulated difference was 70% at the end of 2022.)

Our goal here is to better understand these statistics, so let’s first define what we’re talking about.

• Labor productivity is computed by taking the ratio of total production in the economy (i.e., real GDP) to total number of hours worked in the economy.
• Real wages is computed by taking total compensation paid to non-farm employees and dividing it first by an estimate of total number of hours worked and then by the consumer price index, thus providing an idea of the purchasing power of an hour of work.

This graph is disturbing because it seems to show that US workers only partially benefitted from the increases in their productivity since the 1970s. This decoupling isn’t unique to the United States, however, so let’s look more closely at what’s behind the data.

First, these averages may hide much of what’s going on across the distribution of wages—in particular, the sectoral composition of the economy. For example, there was high productivity growth over the past decades in some sectors, such as finance and information, that wasn’t matched by labor compensation, despite substantial increases. And there was no decoupling at all in other industries.

Second, the price index we use matters. So we replicated the graph above with two more series: Labor compensation is now deflated by the GDP deflator and by the producer price index (PPI).

The results are quite different, and the decoupling isn’t as stark. The original real labor compensation line describes what purchasing power workers have, as it is deflated by a price index that tracks a typical basket of goods a household would buy. The two new lines look at this from the employer side: How much are workers paid compared with (i) the value of all things produced (GDP deflator) and (ii) the prices that producers are getting for their wares (PPI)? These two lines are much closer to the productivity line, indicating that the choice of prices matters.

Why would you take one price series over another? It depends on what question you’re asking of the data. If you want to understand how businesses decide to allocate resources to factors of production (labor, capital, intermediates), then the second graph is relevant. If the topic is about worker purchasing power, then the first graph is relevant. But more fundamentally, why would these price indexes differ so much? That’s the topic of our next blog post…

How these graphs were created: For the first graph, search FRED for “hourly compensation” and select the non-farm series. Click on “Edit Graph,” add a series by searching for the CPI, apply formula a/b, and change the units to a custom index with 100 as of 1970-01-01. Now open the “Add Line” tab and look for labor productivity. Apply the same custom index. For the second graph, take the first and add two more lines, using the same steps to add the GDP implicit price deflator and the PPI (instead of the CPI).

Suggested by Christian Zimmermann.

Join us on a road trip of FRED data in search of labor productivity.

The FRED Blog recently compared the increase in labor productivity during the COVID-19-induced recession with labor productivity in past recessions. Today, we use a recently added data set on state-level productivity from the U.S. Bureau of Labor Statistics to compare labor productivity across states.

First, labor productivity is output per hour worked. So, when labor productivity increases, an hour of work yields more output, which means more goods produced or more services delivered with the same amount of effort.

The GeoFRED map above shows the percent growth in labor productivity in Hawaii during 2020. The residents of the very last state to join the Union (August 21, 1959) recorded the fastest growth in labor productivity last year: 8.5%.

How did the other states fare? The GeoFRED map shows Nevadans were not far behind Hawaii residents, as productivity in the Silver State grew 8%. But the blue coloring in the map shows states where productivity growth was negative during 2020. In descending order, an hour of work yielded fewer goods and services than during the previous year for Montanans, Oklahomans, Tennesseans, South Dakotans, and Idahoans.

The regional differences in labor productivity growth likely reflect idiosyncratic state economies. For example, goods manufacturing plays a much larger role in Oklahoma than in Hawaii. In that light, the uneven impact of the COVID-19-induced recession on the nationwide consumption and production of goods and services would have different impacts on state-level output, hours worked, and—ultimately—labor productivity.

How these maps were 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 Diego Mendez-Carbajo.