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.

The FRED graph above is data heresy: It jams 12 time series into one graph. Why would we do this? Actually, we wanted more than 12 series, but even FRED graphs have their limits.

We suppressed the titles of the series so we really do need to explain what we’re doing here. 1. Each line represents a European country. 2. The data are about consumer sentiment. And 3. The idea is to show general patterns across Europe, without concern for any particular country. Basically, we’re looking for a flow or trend to emerge, using only the squinting-eye technique we discussed in our previous blog post. And we do see two distinct dips common to all the lines!

The OECD surveyed people about their opinion on where their economy is heading: Lower scores show pessimism, and higher scores show optimism. Europeans’ dispositions are spread out, but over the past 5 years they show an overall tendency toward pessimism about their economies. And there are two instances of shared pessimism across Europe: April 2020 and March 2022. The first date is when the COVID-19 pandemic started to shut down entire economies. The second date is the start of the invasion of Ukraine. Clearly, non-economic events can have a large and quite sudden economic impact.

Which countries are shown? Click on the graph to see the series titles, which include the country names: Austria, Belgium, Czech Republic, Denmark, France, Finland, Greece, Hungary, Ireland, Netherlands, Poland, and Switzerland. Btw, The two apparent outliers are Greece (in burgundy) and Switzerland (in turquoise).

How this graph was created: Search FRED for “OECD consumer sentiment.” Check all the countries you want displayed, up to 12. Click on “Add to Graph.” Click on “Edit Graph,” open the “Format” tab, and uncheck the titles display. Restrict the sample period to the past 5 years.

Suggested by Christian Zimmermann.

The US economy in general has gone on a rollercoaster ride since the onset of the pandemic. Unemployment is one specific bumpy example: In April 2020, the unemployment rate was higher than it had ever been, but by December 2022 it was close to a record low.

We could ask many related questions here, but today we look at the unemployment rates across US states during this extreme episode.

The FRED map above shows the unemployment rate for each state in December 2022, the last date available at the time of this writing. We see quite a bit of variation, from 2.2% in Utah to 5.2% in neighboring Nevada. To look for big patterns, we can use the squinting-eye technique (patent pending!) to see the general distribution of color. In this map, the unemployment rate is higher (darker) in the Rust Belt and the West and lower (lighter) in the central and Mountain areas.

The second map also shows the unemployment rate for each state, but back in April 2020, when it was much higher everywhere. In fact, the lowest rate in April 2020 (Wyoming, 5.4%) was higher than the highest rate in December 2022 (Nevada, 5.2% ). By the way, in April 2020, the highest rate is again in Nevada (28.5%).

And what does the squinting-eye technique show us here? Again, the unemployment rate is higher (darker) in the Rust Belt and the West and lower (lighter) in the central and Mountain areas. In other words, at least in this particular economic episode, the whole nation seems to have gone up and down while maintaining its broad state-to-state differences.

How these maps were created: Search FRED for the unemployment rate. Click on any state rate, then click on “View Map.” To have the color span the unemployment rates over the two very distinct time periods, change the date to 2020-04-01, click on “Edit Map,” change the number of color groups to four, write down the interval values, and repeat for 2022-12-01. Now change the number of color groups to eight and select data grouped by “user defined method.” Enter all eight interval values. You have the first map. Change the date to 2020-04-01 and you have the second map.

Suggested by Christian Zimmermann.