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Labor market tightness

Unemployment is high during a recession, and job vacancies are numerous during an economic boom. That should surprise no one. This is why these two measures are useful in determining the state of an economy throughout its business cycle. One way to do this is to look at labor market tightness, defined as the ratio of vacancies to unemployment, which we show above. One should realize, though, that while the number of unemployed is reasonably well estimated from surveys, the number of vacancies is estimated with much less confidence. Indeed, at least in the U.S., it is not mandatory to post openings at an employment agency. In fact, some statistical agencies used to measure the square footage of job ads in newspapers, which obviously isn’t possible now that jobs are advertised in many different media and likely multiple times. In the U.S., a survey across businesses about their openings has been conducted only since 2000.

How this graph was created: Search for “job vacancies” and select the monthly seasonally adjusted series for the U.S. Then add the series “unemployment level,” making sure to check “Modify existing series 1.” Finally, create your own data transformation with the formula a/b/1000.

Suggested by Christian Zimmermann

View on FRED, series used in this post: LMJVTTUVUSM647S, UNEMPLOY

A good use of moving averages

Some data series are very volatile. That is, they don’t follow a smooth or step-by-step pattern. And it’s difficult to draw conclusions when new data are added to a volatile series. The weekly release of initial claims for unemployment insurance is a great example. In this and similar cases, it is useful to adopt some kind of smoothing mechanism: Here we provide a four-week moving average. Traditionally, a moving average is centered—say, the average of two periods before and two periods after. This moving average takes the last four observations, which allows you to better read trends, especially if you’re focusing on the most recent data. Of course, trends become more obvious if you look at longer spans of time. This graph shows a span of five years. Narrow or expand the sample with the slide bar to see how a moving average can help you interpret the data and avoid the pitfalls of volatility.

How this graph was created: Search for “initial claims,” select the two (seasonally adjusted) series, and add them to the graph. Finally, restrict the sample to the last 5 years, which is done by using the settings above the graph on the right.

Suggested by Christian Zimmermann

View on FRED, series used in this post: IC4WSA, ICSA

The diversity of U.S. state economies

The Federal Reserve Bank of Philadelphia computes a leading index for every U.S. state and for the nation overall. These indexes are intended to combine the information from several indicators that have (at least in the past) been good gauges of the economic development that will occur in the next six months. The graph above shows four U.S. states: one large and diversified state, New York, and three small states. The choices here are not innocent: These states all have contrasting fortunes. New York has a much smoother ride, while the small states are more often jerked around by the fluctuations of a particular industry. North Dakota and Wyoming recently benefited from the boom in oil but are now contracting even while the rest of the nation is expanding: The drop in the price of oil is the likely culprit. Nevada is also suffering quite a bit from fluctuations in commodity prices, but its current prospects seem positive.

How this graph was created: Search for “leading index,” select the states you want, and add them to the graph. We changed the color for New York to black to emphasize it.

Suggested by Christian Zimmermann

View on FRED, series used in this post: NDSLIND, NVSLIND, NYSLIND, WYSLIND

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