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The mean vs. the median of family income

FRED has several datasets to help you investigate the distribution of income. One of them is the Income and Poverty in the United States release from the U.S. Census Bureau.

The graph above shows real family income in the United States in constant (2013) dollars. The mean is the average across all families. The median identifies the family income in the middle of the sample for every year: half of incomes are higher, half are lower. We quickly learn three things from this graph: 1. Family income has been growing much more slowly since the 1970s. 2. There are several episodes of declining income, and they become increasingly long and deep. 3. Median and mean incomes are diverging.

The last point could be an optical illusion, though, because both series have increased over time and their relative difference may have stayed constant even though the difference has increased in absolute terms. To make sure, we divide the mean by the median in the graph below; we can see that, indeed, this ratio has increased. But what does that mean? If the distribution of income is uniform (if every family has the same income), the ratio will be 1. If the distribution is unequal, the ratio will be higher than 1. For example, imagine we start with a uniform distribution of income and then the top 10% of families double their income. The median would not change, but the mean would increase by 10%. The data in the graph below clearly show that there has been an increase in inequality in family income, with a dramatic jump from 1992 to 1993.

How these graphs were created: Under “Sources,” find the Bureau of the Census and choose the Income and Poverty in the United States release. The mean and median real family income series should be among the top choices. Select them and add them to the graph. For the second graph, add the mean series as before; but, instead of adding the median series as a separate series, add it to the mean series (series 1). Finally, expand the “Create your own data transformation” panel and apply formula a/b.

Suggested by Christian Zimmermann

View on FRED, series used in this post: MAFAINUSA672N, MEFAINUSA672N

Uncertain times in Europe

If you’ve been following international news over the past decade or so, you’ve seen the European Union’s seemingly continuous struggle to define the various facets of its economic policy. Such policy uncertainty has effects on economic activity—especially investment. And we can quantify such uncertainty, as shown in the graph above, thanks to the work of Scott Baker, Nicholas Bloom, and Steve Davis. Their work is based on the frequency of certain key words in newspapers and disagreements among economic forecasters. The graph pertains to Germany, the U.K., France, Italy, and Spain and definitely shows elevated levels of policy uncertainty since 2012, which rival and even exceed the levels during the financial crisis in 2007-08.

How this graph was created: Search for “economic policy uncertainty” and select the series for Europe (among several other uncertainty series available in FRED).

Suggested by Christian Zimmermann

View on FRED, series used in this post: EUEPUINDXM

The many flavors of unemployment

How many people are unemployed? Before answering this question, you need to define unemployment. The Bureau of Labor Statistics offers six definitions, conveniently labeled U-1 through U-6, that are increasingly inclusive. What they have in common is they measure some aspect of labor underutilization. U-1 counts only those who have been unemployed for at least 15 weeks, which is usually (but not lately) a little longer than the average duration of an unemployment spell. Hence, this excludes short-term unemployment. U-2 uses a somewhat different concept: the percentage of those who are unemployed because they have lost a job or completed a temporary job. Some of them may be included in U-1. So U-2 counts workers in a precarious situation in the labor market, as they are more likely to find an unstable or unsatisfying job. U-3 is the traditionally reported unemployment rate, which counts people who are able to work, ready to work, and have looked for work in the past four weeks. U-4 takes U-3 and adds those who would like to work but have stopped looking—the so-called discouraged workers—because they believe there are no jobs for them. U-5 takes U-4 and adds those who are marginally attached to the labor market: those who, for any reason, are no longer searching for work. Finally, U-6 includes all of the above plus those who are working part-time but would prefer to work full-time.

How this graph was created: Go to the Alternative measures of labor underutilization release table (A-15) from the Bureau of Labor Statistics’ Employment Situation release. Select all (seasonally adjusted) series and click “Add to Graph.”

Suggested by Christian Zimmermann

View on FRED, series used in this post: U1RATE, U2RATE, U4RATE, U5RATE, U6RATE, UNRATE

How likely is a recession? (And how fast is a forecast?)

Predicting a recession in real time is difficult, which is why one can make good money with a good forecast. Here, FRED offers one of many such forecasts: a recession probability index computed by Marcelle Chauvet and Jeremy Piger. This forecast is backed up by research the authors have published in the peer-reviewed journals International Economic Review and the Journal of Business and Economic Statistics, with an early St. Louis Fed working paper added here for good measure. As the graph above shows, their forecasting method’s past performance is impressive; the predicted recession dates align well with the official NBER recession dates. Of course, it is difficult to compute any forecast in a timely fashion: One has to wait for the appropriate data to be released, and only then can one compute the forecast. In this case, that translates into a delay of about three months.

How this graph was created: Search for “recession,” and the first series shown should be “Smoothed U.S. Recession Probabilities.”

Suggested by Christian Zimmermann

View on FRED, series used in this post: RECPROUSM156N

Net migration: The Far East is the new Southwest

Recent data from the U.S. Census Bureau show that China has overtaken Mexico as the source of the largest number of immigrants to the U.S. FRED can add some insight to this topic: Although FRED doesn’t include country-by-country migration data, it does include net migration data for each country in the World Bank’s World Development Indicators release. The list of countries is long. The graph above looks at only the three countries noted here. The U.S. is a net immigration country, while China and Mexico are net emigration countries. No surprise there. What may be a little unexpected is how large the fluctuations have been from one five-year period to the next. Also, migration out of China has increased (by an order of magnitude) despite many years of impressive economic growth. Indeed, aggregate economic conditions are not likely to be the sole driver for migration choices.

Note: In 2013, the most-recent year for which complete Census data are available, Mexico actually sent the third-largest number of immigrants to the U.S. As noted above, China sent the most, but India is now in second place.

How this graph was created: Search for net migration, and the U.S. should appear first. Scroll through the list or use the “Add Data Series” tab to search for and add China and Mexico (and many other countries) to the graph.

Suggested by Christian Zimmermann

View on FRED, series used in this post: SMPOPNETMCHN, SMPOPNETMMEX, SMPOPNETMUSA


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