All posts by RealEstate

Comparing the assets of the rich, poor, and middle class : Data on the asset distribution across U.S. households

The FRED Blog has covered income and wealth before: for example, distribution of wage income, net worth, and assets. This post covers household assets, but compares them across groups: the top 1%, the 90-99%, the 50-90%, and the bottom 50%. FRED has data from the Board of Governors of the Federal Reserve System’s Survey of Consumer Finances, and the graph above shows the total assets for households in these four wealth/asset groups.
It’s clear from the graph above that the bottom half of households collectively hold significantly fewer assets than any of the three other groups. Those groups hold about the same order of magnitude in assets, but with populations of very different sizes (40%, 9%, and 1% of the total number of households).
We also see that, for these three groups, total assets have grown almost continuously, except for a dip in the past recession. Of course, this could be due

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What’s worrying the markets? : More data on policy uncertainty

For some time now, FRED has offered various economic uncertainty indices from the work of professors Baker, Bloom, and Davis. We now have even more detailed data on uncertainty about specific economic policy categories; a handful are shown in the graph above.

Before we dive into any interpretations, we need to first understand the data. Basically, they track the number of mentions of specific economic policies in over 2,000 U.S. newspapers. Some policies are considered more important than others by journalists and the general public; some are perennial favorites and some are rarely discussed.

Overall, higher values likely show how worried the press, and probably the general public, are about that aspect of economic policy. High values are a combination of high uncertainty and the importance of the policy. A policy considered less important will be less likely to spike up even when there’s a lot of uncertainty about it.

Back to the

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Households’ lightening debt load : Data on the financial burden of U.S. households

There are many types of debt, including household debt, and many specific types of household debt as well. The Board of Governors of the Federal Reserve System collects a wide and well-organized array of data on debt. These data, especially in graph form, can help us better understand the financial burdens of U.S. households.
This FRED graph shows the percentage of disposable (i.e., after-tax) income that households dedicate to servicing specific types of debt. The graph has four lines. Let’s start at the bottom: The green line shows mortgage debt, and the red line shows consumer debt (credit card, auto, and personal loans). The blue line is the sum of the red and green lines. And the purple line adds to the blue line some other financial commitments, such as rent, auto leases, homeowners’ insurance, and property taxes.
What can we learn from this FRED graph?
The two top lines are almost always

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The price of a BLT : Slicing the layers of the CPI

Over the summer, FRED added 1,479 new series on average prices for a wide range of consumer items. Almost half of the new data are prices of foodstuffs, more than enough for a seven-course dinner.
But to keep it simple, let’s make a traditional BLT sandwich: bacon, lettuce, and tomato on white bread. (Today, we’ll hold the mayo.) Like most things, the price of a BLT has risen over time, which you may have noticed at the local diner or in the supermarket. Let’s “go figure with FRED” what’s been driving up the price of this lunch staple.
The FRED graph immediately below plots the prices of all four ingredients over time. The prices of three of the ingredients rise at a fairly constant rate, which is a sign of low and stable consumer price inflation. But the fourth price (in red) is not only noticeably higher than the rest but rises

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How to calculate the term premium : Measuring Treasuries to track yield curve inversions

The term premium is the amount by which the yield on a long-term bond is greater than the yield on shorter-term bonds. This premium reflects the amount investors expect to be compensated for lending for longer periods. Because U.S. Treasuries come in a variety of maturities, we can take the differences between the various yields to measure the term premium. Above is a FRED graph with the 10-year Treasury yield less the 2-year Treasury yield and less the 3-month Treasury yield. The 10-year yield is often greater than the 2-year or 3-month yields, usually with a drop preceding recessions. A drop into negative territory, when the 10-year yield is lower than the 2-year or 3-month yields, is often called a “yield curve inversion.” (See, for example, this Economic Synopses essay.)
With FRED’s international data, we can repeat this exercise for other countries. For instance, we can measure the term premium in

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How food and fuel prices fluctuate : Detailed prices from the CPI

The consumer price index (CPI) follows the price of a basket of goods. The goods in the basket are determined by the purchases of an “average” U.S. household. Each item is tracked at multiple locations and for numerous varieties. The data are then aggregated to form the CPI.
The CPI has been a part of FRED for quite some time (since the early days if not the very beginning). FRED also offers some finer slices of consumer price data. The graph includes three examples: unleaded gasoline, peppers, and tomatoes. These are still aggregates, as the tracked prices come from many locations and, for tomatoes at least, across the various brands, varieties, and other ways of differentiating products.
What immediately gets our attention is how dynamic these lines are. The prices for these items change a lot and with little notice, which is why monetary policymakers in general prefer to look at price

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The Fed’s recent open market operations : A short history of overnight Treasury repurchase agreements

The June 13, 2019, FRED Blog post showed how, in a world of ample reserves, 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 facility (ON RRP) to keep the FFR rate in the target range.
Since July 2019, the FOMC has lowered the target range for the FFR twice, effectively injecting liquidity into the banking system. And, at the September 17-18 FOMC meeting, the committee announced a 0.25% cut in the target rate, with an accompanying cut in the interest rate on excess reserves. But ahead of that meeting, the effective FFR spiked, exceeding the upper limit of the target range. So, an additional monetary policy tool was put into action.
On September 17, 2019, the Federal Reserve Bank of New York began conducting temporary open market operations through overnight repurchase agreements: That is, it

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Low-income countries have more refugees : Refugee population trends from World Bank data

FRED has just added some refugee data from the World Bank that shows the number of refugees in each country since 1960. In the graph above, we chose to show the statistics from three groups of countries classified by level of income: For middle-income and high-income countries, refugees make up well below half a percent of the general population. In low-income countries, it’s substantially more (although the percentage has declined since the early 1990s). Why so?

Refugee migrations generally occur in situations of crisis.
Such crises tend to occur in low-income countries.
Refugees have limited means to choose where to go, so they often end up in neighboring countries that are likely to share income characteristics with the country in crisis. (That is, the country in crisis and its neighbors are more likely to be low-income countries.)

All these factors contribute to more refugees living in low-income countries.
How this graph was created: Search for

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A lesson on time series to get you started with FREDcast : Learn how to go from zero to forecasting hero

Two years ago, the St. Louis Fed introduced FREDcast, a forecasting game in the style of fantasy sports. In FREDcast, users enter a forecast for four economic time series each month: GDP, payroll employment, the unemployment rate, and CPI. Now in its third year, FREDcast is growing in popularity and taking hold at some major universities.
The motivation behind FREDcast has been to lower the barriers of forecasting macroeconomic time series, and the game is designed so that anyone with a basic understanding of data can join the game and start forecasting. One of the major challenges, though, is establishing that common, basic understanding of time series data. So let’s lay out a few concepts and definitions to get you started.
What is a time series?
In plain English, a time series is a sequence of data observations collected over time. For example, a collection of the daily closing values of the Dow

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Where do states get their tax revenue? : Income, sales, fuel, corporate, property, license, tobacco, alcohol…

State governments run on tax revenue in much the same way the federal government does. The FRED graph above shows the specific shares of state tax revenue from many sources. The two major sources are sales tax and individual income tax. While there’s a clear seasonal pattern (mostly from income taxes), there are no strong trends: The shares seem rather stable. If we look a little more closely, though, we can see a shift from corporate income tax to individual income tax and a decrease in motor fuel tax revenue. Granted, it’s not perfectly clear unless you look at the numbers directly. So, if you’re using a mouse, hover over the graph to reveal the values for each series for a particular date, including the percentages. Given the seasonal pattern, it’s best to compare the same quarter over several years—say, the yearly peaks in the second quarter.
How this graph was

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