Labor Market Distributions Spider Chart
The Labor Market Distributions Spider Chart is designed to allow monitoring of broad labor market developments by comparing current conditions to those in up to two earlier time periods that the user selects. Distributions of the labor market variables are constructed over a time period that the user also selects, with the inner and outer rings representing the minimum and maximum values of each of the variables.
The three dashed gray rings in the chart represent the 25th, 50th, and 75th percentiles of the distributions, respectively. Indicators of labor market status are broken up into five groups: Employer Behavior, Confidence/Perceptions, Utilization, Wages, and Flows.
Data in the chart are updated twice monthly: once for the monthly Bureau of Labor Statistics employment report, and again for the Job Openings and Labor Turnover Survey (JOLTS) update.
Use the menus below to change the range of data and individual data series you'd like to view. To save this chart as an image or PDF document, select an option from the "Export" menu. See our modifications to the spider chart, noted in red in the first passage under the Indicators tab, in response to the impact of COVID-19 on the labor market.
Frequently Asked Questions
1. Why does the Federal Reserve care about employment?
Section 2A of the Federal Reserve Act states, "The Board of Governors of the Federal Reserve System and the Federal Open Market Committee shall maintain long-run growth of the monetary and credit aggregates commensurate with the economy's long run potential to increase production, so as to promote effectively the goals of maximum employment, stable prices, and moderate long-term interest rates." This part of the Federal Reserve Act is often referred to as the Fed's "dual mandate." Basically, it states that the Federal Reserve's monetary policy has the goals of stable prices and maximum employment. The gap between the unemployment rate and the estimated normal rate of unemployment is the most popular statistic that measures the degree to which the Federal Reserve has achieved the goal of maximum employment.
2. What is the basic idea behind the spider chart?
The spider chart uses 15 measures of labor market activity. Where necessary, the indicators are transformed so that they do not have a clear upward or downward trend, either by dividing by the size of the labor force or, in the case of the two wage/compensation measures, conversion to 12-month growth rates. Indicators like the unemployment rate, where larger values correspond to a weaker labor market, are multiplied by -1.
After these transformations, the indicators are rank-ordered over a fixed sample period and assigned to the value of their cumulative distribution function. For example, the maximum value is assigned 100, the minimum is assigned zero, and the median is assigned 50. The values of the cumulative distribution function are then plotted in the spider chart. The outer- and inner-black circles correspond to the maximum and minimum values of the indicators, respectively, while the fainter gray circle in between corresponds to the median values of the indicators.
3. How do you handle ties?
This is best illustrated by example. Suppose the data considered are the 11 numbers consisting of each of the counting numbers from zero to 7 and the number 8 repeated three times. For each of the numbers k between zero and 7, k is assigned the value 10 times k. The repeated three values of 8 are arbitrarily ordered and preliminary assigned their three percentiles in the cumulative distribution: 80, 90, and 100. Each of the three 8s is then assigned to the average of these three percentiles: 90 = (80+90+100)/3. In this example, none of the numbers are assigned to the largest possible value in the spider plot of 100 since there are ties at the maximum value.
4. How did you choose the start dates of the sample periods for the distributions of the labor market indicators?
We do not use data prior to January 1994, as a major redesign of the Current Population Survey was introduced at this date so that methodologically consistent time series for both marginally attached workers and part-time workers for economic reasons cannot be constructed over a period beginning before this month. The earliest and default sample start date is March 1994. Other sample start dates are the beginning months of the last two recessions as dated by the National Bureau of Economic Research (March 2001 and December 2007) and the ending months of those recessions (November 2001 and June 2009). Each of these two recessions was followed by periods of continued declines in payroll employment and increases in the unemployment rates. By these criteria, the troughs in the labor market following these recessions were right around August 2003 and December 2009. We allow users to select these months as sample start dates. Finally, we allow users to choose sample start dates so there are either exactly five, 10, 15, or 20 years of data over which the distributions are calculated. These start dates will move up a month with each additional month of labor market data. In total, there are up to 10 possible sample start dates that can be chosen.
5. How can you have sample start dates as far back as 1994 when data from the Job Openings and Labor Turnover Survey (JOLTS) begins in December 2001?
We extend the private hires and quits rates back to 1994 using data constructed by Steven J. Davis, R. Jason Faberman, and John Haltiwanger for their article "Labor Market Flows in the Cross Section and over Time" published in the January 2012 issue of the Journal of Monetary Economics. We extend the private openings rate back to 1994 using the Composite Help-Wanted Index constructed by Regis Barnichon for his article "Building a Composite Help-Wanted Index" published in the December 2010 issue of Economics Letters. The exact details of how these data are spliced together with the JOLTS data are provided in the "Indicators" section of this webpage.
6. Why not simply look at the unemployment rate?
Commentary on the labor market tends to focus on the unemployment rate as the summary measure of the health of the labor market. However, while trends in the unemployment rate over the medium term are a pretty good gauge of changes in overall labor market conditions, over short periods of time the unemployment rate can be influenced by factors that make it a less reliable directional gauge. For example, it is possible that the unemployment rate could rise for a while as conditions improve as those currently out of the labor force enter at a faster rate but fail to secure a job immediately.
7. Why do you use the three-month change in nonfarm payroll employment rather than the three-month growth rate?
In typical applications, when considering economic variables that are presumed to grow exponentially, transformations using growth rates or log differences are much more commonly used than raw differences. For example, real gross domestic product (GDP) in 2014 was more than eight times larger than it was in 1947. Consequently, comparing changes in the level of real GDP in recent years with changes in the level of real GDP in the late 1940s is not at all useful as the former are, on average, much larger and much more volatile due to exponential growth of real GDP. Comparing growth rates of real GDP is much more informative. An analogous treatment for payroll employment is not necessarily appropriate due to changes in the growth rate of the working-age population (ages 16 to 64) over the past 20+ years. In 1994, the annualized growth rate of the working-age population was around 1.0 percent while in 2015 it appears to be around 0.5 percent. Thus, the growth rate of payroll employment needed to keep the unemployment rate constant is probably about twice as large in 2015 as it was in 1994. Using raw differences in payroll employment mitigates this problem to some extent. In January 1994, nonfarm payroll employment needed to increase by about 90,000 jobs a month to pace with the growth rate of the working-age population. In January 2015, they only needed to increase about 60,000 per month. This is still a large difference, but, proportionately, it's only about half as large as the difference between 1.0 percent growth and 0.5 percent growth.
8. Why wasn't labor force participation used in the set of indicators? Why is the age 25 to 54 employment-population ratio used instead of the standard ratio for all civilians age 16-plus?
Over the 10 years ending in December 2015, the labor force participation rate declined from 66.0 percent to 62.6 percent. However, roughly two-thirds of this decline (2¼ percentage points) can be accounted for by demographic changes in the age/sex distribution of the population (primarily reflecting the aging of the baby-boomer generation into retirement ages). Hence, an apples-to-apples comparison between today's labor force participation rate and the rate 10 years ago cannot be made. Using the age 25 to 54 employment-population ratio is a standard way to adjust for the aging of the population, since this "prime-age" population is commonly thought of as old enough to have completed school (in most cases) but too young for retirement. However, even this adjustment is quite imperfect. According to data from the Atlanta Fed's Labor Force Participation Dynamics website, the proportion of the age 26-to-55 population who did not want a job increased from 14.4 percent in 1998 to 16.9 percent in 2014. About 60 percent of this increase was due to a higher frequency of persons self-reporting that they are "ill" or "disabled," while the remaining increase was equally split between increases in "retirement" and "in school/training." These data imply that one should be very careful when comparing the employment-population ratio for prime-age workers over long periods of time.
9. How are data released after the Employment Situation release handled?
The JOLTS data series (quits, hires, and openings) for a given month are released about five and a half weeks after the BLS's Employment Situation. Therefore, on the day of the Employment Situation release for month t, only JOLTS data through month t-2 will be available. In this case, the spider plot values of the JOLTS series in months t-1 and t will be set equal to their corresponding spider plot values for month t-2.
Also, on the day of the Employment Situation release for month t, NFIB survey data will only be available through month t-1. In this case, the spider plot values of the NFIB series in month t will be set equal to their corresponding spider plot values for month t-1.
10. Initial unemployment insurance claims are weekly; how are they converted to monthly?
Daily claims are assumed to be constant within each week. Monthly claims are taken to be the average of the daily claims for all the weekdays in the month.
11. Do larger values always correspond to outward