Federal Reserve Economic Data

The FRED® Blog

The value(s) of the minimum wage

The minimum wage, which has been in the news recently, seems to be part of two related but slightly different concerns. One is earnings inequality, which a higher minimum wage could potentially reduce. The other is poverty, which a higher minimum wage could also potentially reduce by helping a low-income worker afford a basic basket of goods. Putting aside the ability of the minimum wage to achieve either of these two goals (which economists actively debate), we still have these two different ways to measure the minimum wage and how it has evolved.

To quantify the purchasing power of the minimum wage, we can simply deflate the nominal value of the minimum wage. The red line shows the value of the federal minimum wage deflated by the PCE price index. We might be equally interested in whether the minimum wage pushes up the bottom of the wage distribution: How the minimum wage affects wage inequality is related to where it lands in the wage distribution. The blue line shows the fraction of hourly workers whose wages are at or below the minimum wage. This measures the value of the minimum wage by showing how many workers are directly affected by it.

The red line shows the minimum wage drifting up and down as its nominal value is eroded by inflation and as it is legislatively adjusted. The blue line shows it drifting downward consistently for the whole period as it fails to keep up with the growth in wages of most of the distribution.

How this graph was created: Search for “percent paid minimum wage” and add the annual series to the graph. Add the second series to the graph by searching for “federal minimum wage” and adding it as series 2. Then add “Personal Consumption Expenditures: Chain-type Price Index” by selecting “Modify existing series 2.” Finally, use the “Create your own data transformation” to apply the formula 100*a/b. (You need to multiply by 100 as the PCE index is normalized at 100.)

Suggested by David Wiczer.

View on FRED, series used in this post: FEDMINNFRWG, LEU0203127200A, PCECTPI

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


Back to Top