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Tracking economic progress for U.S. states A map of the Philly Fed's coincident indicators for 2016-2017

The Federal Reserve Bank of Philadelphia computes for each U.S. state a coincident indicator that combines information about employment, unemployment, hours worked, and wages. (These are state-level labor market data that are released reasonably quickly.) This coincident indicator has a base of 100 in 1992; thus, the numbers indicate how well each state has performed since 1992. The map shows how well they have performed from December 2016 to December 2017. This means we have to be careful when interpreting these numbers. A state may show great improvements, which is something to celebrate; but it’s important to consider whether those improvements come from climbing out of a hole or from an economy already in great shape. The reverse applies as well: States whose coincident indexes have not grown as strongly may already be doing pretty well. In the end, it is always useful to look at the details of every economic indicator.

How this map was created: From GeoFRED, click on “Build New Map.” Open the cog wheel and the “Choose Data” menu and select “State” as region type and look for the “coincident index.”

Suggested by Christian Zimmermann.

The world map of inflation Where is inflation the highest?

FRED offers a wealth of global indicators from the World Bank. Today, we’re looking at inflation data. The map shows consumer price inflation across the world in 2015. (2016 numbers are still incomplete.) The two darkest colors indicate particularly high inflation rates: For the 2015 map, these rates are above 6%. Rates this high typically occur in countries where the central bank’s primary mandate is not to provide an environment with stable prices, but rather to support the government through monetization of the public debt or by providing cash for its expenses. Those countries that do not report numbers are either too small to compute the data, have a particularly weak government, or are trying to hide such statistics.

The lighter colors show lower inflation—or even deflation. Of particular interest are the middle-level blue-colored countries. Their inflation rates are between 1 and 3 percent, which is the range typically thought of as the rate that should be achieved. The idea is that you want some inflation to allow for adjustment in economies where prices or wages have some downward rigidity: If firms and other economic actors are not inclined to decrease their prices and wages, but a decrease is necessary to balance demand and supply, then a little inflation can help. Of course, if overall prices decrease, this logic becomes quite problematic, which is the case in the white-colored countries.

How this map was created: Search FRED for the inflation rate of any country. Look at the series page under the related resources for the GeoFRED map. Click on it, expand its focus, and change the year to 2015.

Suggested by Christian Zimmermann.

What’s real about wages? A look at the increases and decreases in wages

People have been talking about the evolution of wages. Some say they’re increasing, others say they’re decreasing. Who’s right? As is so often the case in economics, it depends. First let’s look at the graph above, which has four different indicators for wages. Three of them show a clear and steady upward trend. But one of them—the green line, which shows median weekly earnings—is starkly different. It could be because the median is different from the mean if the distribution of wages skews strongly at the top. Or it could be that people work less per week. Or it could be that it’s a real measure, whereas the others are nominal.

The second graph corrects for this bias. The three nominal series are now real, after being divided by the consumer price index so that general price increases aren’t reflected in the wage. Now all four series evolve along basically the same path. It’s clear that decreases can be frequent and sometimes long lasting. It’s also clear there’s a lot of variability, which means one should really wait for a good amount of data before reaching for any conclusions.

How these graphs were created: For the first graph, search FRED for “wage” and pick the four series. Limit the time period to the past 10 years. From the “Edit Graph” section, choose “Index” for the units with the default of 100 at the end of the last recession. Then click on “Apply to all.” For the second graph, add the CPI to each of the three nominal series, apply formula a/b, and again choose “Index” for the units.

Suggested by Christian Zimmermann.

View on FRED, series used in this post: CES0500000003, CPIAUCSL, ECIWAG, LES1252881600Q, USAHOUREAQISMEI

Cyclical asymmetry in the labor market Slow but steady improvements versus sharp declines

Have you ever spent hours on the beach meticulously building the perfect sandcastle, only for a bully to waltz by and kick it down in an instant? A similar phenomenon occurs in the labor market, as even the shortest recessions can undo years of progress made during an expansion. The unemployment rate tends to fall only gradually during economic expansions but rise sharply during recessions. This mismatch of slow declines versus sharp spikes is known as “cyclical asymmetry.”

This cyclical asymmetry of the unemployment rate derives in part from the fact that it takes much longer to create jobs than to destroy them. We can think of the labor market as a market for productive relationships between employers and employees. These relationships are a durable form of capital: They provide long-term economic benefits over time for firms and workers alike. It can take a while to build these relationships but only a short time to terminate them.

Consider the drawn-out process it takes to match employers and employees. First of all, both parties much search for and identify potential matches. Then, employers usually put candidates through a lengthy hiring process before making any offers. And even once the employee is hired, it takes time to establish a working relationship. Hence, job creation tends to be slow, even when the economy is performing well. The unemployment rate does not decline sharply when the economy is hit by a positive disturbance because relationships take time to develop.

Conversely, consider what happens when a recession hits. Letting workers go takes only an instant. In a flexible labor market, firms are often quick to lay off workers to save costs, often letting go workers who have been with the company for years. So, when the economy is hit by a negative disturbance, the unemployment rate tends to spike as firms lay off large numbers of workers.

Cyclical asymmetry also occurs in population dynamics in the form of the “heat wave effect.” Often, mortality rates rise and the population suddenly declines when bad weather hits. Yet, when a streak of good weather hits, we don’t see a corresponding boost in the population, as it takes time to repopulate after a tragedy strikes. In the context of the labor market, a recession is a “heat wave” that leads to sudden job losses and an expansion is a spell of good weather that, over time, creates jobs more steadily.

How this graph was created: Search for “civilian unemployment rate” and pick the seasonally adjusted series from the first result.

Suggested by David Andolfatto and Andrew Spewak.

View on FRED, series used in this post: UNRATE

How’s manufacturing? Depends on the sector

The industrial production (IP) index is a popular metric of economic activity because it’s available relatively quickly. This monthly data series covers only a part of economic activity, however. In particular, it misses the service sector and the government sector. The graph above shows its evolution since 1972 along with a subcomponent that covers only manufacturing. Note that the index is set at 100 in 2012, meaning that all the indexes will always cross in 2012. A particularly healthy sector will start lower before 2012 and then rise higher after 2012. The graph shows that manufacturing has done well compared with overall industrial production before 2012 and a little less well afterward. This hides considerable sectoral differences, though.

In this second graph, we highlight some sectors within manufacturing. The sector that appears to have suffered massive losses is apparel and leather goods. Indeed, clothes manufacturing largely migrated abroad during this time span, with a decrease in production of about 80% since the mid 1990s. On the other extreme is computer manufacturing; It was insignificant in the first years but has increased by 1200% since the mid 1990s. All other sectors lie somewhere in between, and they average out to the manufacturing index shown in the first graph, which does not look as dramatic as the second graph. Some other interesting observations in this second graph: The primary metal industry has remained essentially unchanged over the past 45 years, with its index hovering around 100 throughout the sample period. The furniture industry incurred great losses from the Great Recession that it has not yet recovered from. And the car industry is doing pretty well.

How these graphs were created: For the first, search for industrial production, select the two series (likely the top choices), click on “Add to Graph,” and adjust the time period to start on 1972-01-01. For the second, go to the industrial production release, and select the monthly and seasonally adjusted tags. In the list, choose the series according to industry, and click on “Add to Graph.”

Suggested by Christian Zimmermann.

View on FRED, series used in this post: INDPRO, IPG315A6S, IPG331S, IPG334S, IPG3361T3S, IPG337S, IPMAN


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