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FRED has included a lot of population data over the years, and it now offers data specifically for U.S. metropolitan statistical areas (MSAs).
First, a couple of caveats: It makes sense to study population numbers as a percent change year over year; looking at raw numbers can be misleading because size and density matter on a map. Also keep in mind that MSA definitions change, especially after a decennial census but sometimes midway between censuses; so, values at these dates may reflect changes in population, definition, or both.
The GeoFRED map above shows 2018 U.S. Census Bureau data for the 383 MSAs: 295 of them grew and 88 shrank. The largest (proportional) growth was in Midland, TX, with 4.32% in a single year, followed by 3.78% in Myrtle Beach, SC/NC, and 3.52% in St. George, UT. The largest (proportional) decline was in Charleston, WV, with -1.57%, followed by -1.55% in

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This post is a bit long, with a puzzling observation and, even after five FRED graphs, no definitive explanation. But sometimes the journey must be the destination…
The (first) graph above shows unemployment rates by race and gender since the start of the Great Recession. It’s clear men’s rates overall are higher than women’s, possibly due to factors such as women’s less-harmonious attachment to the labor market and different gender composition across industries and occupations. Also, Whites overall enjoy a lower unemployment rate than Blacks, which is at least partly due to the differences in the industries and occupations Blacks and Whites tend to work in.
The movements in the unemployment rates also differ, and this is the puzzle we focus on here. Look closely and you’ll see that unemployment rates didn’t start to decline until late 2011 and early 2012 for Black men and mid-2013 for Black women. The decline for

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The graph above shows the annual civilian unemployment rate from 1948 to 2018, and here are some highlights: Ten years ago, after the Great Recession, the U.S. unemployment rate peaked at 9.6%. (The only higher unemployment rate in this series was 9.7%, in 1982.) It gradually came down to 3.9% in 2018, the lowest in fifty years. (The rate in 1969 was 3.5%.)
But these national unemployment numbers mask the variation that exists across different regions in the U.S. Fortunately, we have GeoFRED to paint a clearer picture: The map below shows the unemployment rate for 2018 for 3,133 U.S. counties. The counties are split into two equally sized groups according to their unemployment rates: Those with lower unemployment are in blue, and those with higher unemployment are in red. Specifically, the blue group had a rate lower than 3.87%, and the red group had a rate between 3.87% and the

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Your economics textbook may still say the Federal Reserve uses open market operations to influence the federal funds rate. But in today’s economy, the Fed uses different policy tools.
In simple terms, this is how monetary policy currently works: The FOMC sets a target range for the federal funds rate (FFR) and uses interest on excess reserves (IOER) and the overnight reverse repurchase agreement (ON RRP) facility to keep the FFR rate in the target range. (See our previous post for an introduction to this topic.)
The Fed pays IOER to banks holding reserves at the Fed, which offers those banks a safe, risk-free investment option. Arbitrage ensures that the FFR doesn’t drift too far from the IOER rate. If the FFR drifts much below the IOER rate, banks then have an incentive to borrow in the federal funds market at the lower FFR and deposit those reserves at the Fed to

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Your economics textbook may still say the Federal Reserve uses open market operations to influence the federal funds rate. But in today’s economy, the Fed uses different policy tools.
Before September 2008, when reserves were scarce, the Federal Reserve bought and sold relatively small quantities of Treasury securities to adjust the level of bank reserves and influence the federal funds rate (FFR). But we now live in an environment of ample reserves. As such, the Federal Reserve can no longer effectively influence the FFR by making small changes in the supply of those reserves. Instead, the Fed uses its newer tools—paying interest on excess reserves (IOER) and the overnight reverse repurchase agreement (ON RRP) facility—to influence the FFR.
Since December 16, 2008, the FOMC has set a target range for the FFR, rather than a specific single target, and uses the rates on IOER and the ON RRP facility to keep the

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Reporters and Fed watchers in the U.S. usually think about monetary policy in a domestic framework. But because business conditions, including commodity prices, are correlated internationally, central banks tend to move their policy rates up and down together and their inflation and interest rates tend to be correlated. FRED makes it easy to see these international comovements of macro and policy variables.
The first graph shows comovement in inflation rates from 1970 to the present for four economies: the U.S., Japan, the U.K., and the euro area. Inflation rose in the 1970s as central banks failed to combat the effects of commodity price increases on the general price level and inflation expectations became established.

Before the Financial Crisis of 2007-2009, almost all central banks in the developed world implemented monetary policy mainly by buying and selling short-term bonds to influence short-term interest rates or “policy rates.” The second graph shows the comovement

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In an earlier FRED Blog post, we highlighted the simultaneous decline in the 5-year breakeven inflation rate and the price of oil in 2014. (The 5-year breakeven inflation rates are obtained from 5-year Treasury inflation-indexed constant maturity securities and are thought to represent the market’s expectation of CPI at a 5-year horizon.) At that time, we argued that markets might have believed that the drop in oil prices reflected a slowing in global demand that might result in a persistent decline in consumer prices. In this post, we make a longer comparison—from 2011 to 2019—between the same two series shown in the original graph.
The graph above shows that the correlation between the breakeven inflation rate and oil prices is not limited to the steep decline that occurred in 2014. Indeed, the correlation between the two series over the entire period shown (January 2011 through March 2019) is 0.65. Prior to

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Financial data are useful for many reasons. One (perhaps subtle) reason is that they are never revised. Markets determine the prices and quantities of assets at the time of the transaction and that’s that. As such, once you observe the value of a particular financial variable at a particular point in time, you know it will remain at that value forever.
One might assume, then, that the St. Louis Fed Financial Stress Index, which includes 18 series of financial data to measure stress in the markets, would also remain the same forever. Well, the graph shows us something different: It plots 10 distinct vintages of the index starting with the first, from March 2010, and then one from every year since then. Despite the fact that this index is composed entirely of unrevised financial data, the lines of past data are not exact replicas of each other, so the index clearly

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Some call the past few decades a new industrial revolution, given the dynamic emergence of the information economy. The graph above shows employment in information services, and, indeed, there’s strong growth in the sector, especially up to the dot-com crash in 2000. But since then, the sector doesn’t seem to have expanded its payrolls much. In fact, once you take out the boom, current data seem to follow the previous trend.
Now, the employment classification for information services includes more than just jobs related to the internet. NAICS code 51 encompasses anything related to the diffusion of information. So, it’s also phone companies, movie makers, broadcasting, newspapers, and software. Clearly, some of these sub-sectors have suffered from the rise of the internet economy. Thus, the long trend hides a considerable amount of churn within the sector itself.
Also, notice that there are some downticks. For employment data, this is usually due to

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There are many indicators to help us evaluate the U.S. economy, but international data are a little more limited. Which is why FRED is fortunate to have World Bank data to compare economic conditions across countries. Today, we look at how well banks are doing—according to their return on assets—all over the world. Measuring banks’ ROA is relatively simple: Aggregate the net income of all commercial banks in a country and divide this sum by their total assets.
The graph above shows three countries with contrasting fortunes. The most dramatic is Greece, whose banks have been struggling with bad debt. Then there’s Kenya, whose banks are doing surprisingly well, at least under this dimension. Finally, the United States is in the middle, with a noticeable dip during the financial crisis and lower returns since that crisis.
FRED has regional data that you can map using GeoFRED. And that’s exactly what we have

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