The Kansas City Fed Labor Market Conditions Indicators (LMCI) are two monthly measures of labor market conditions based on 24 labor market variables. One indicator measures the level of activity in labor markets and the other indicator measures momentum in labor markets.
A positive value indicates that labor market conditions are above their long-run average, while a negative value signifies that labor market conditions are below their long-run average. Background information for the Kansas City Fed Labor Market Conditions Indicators provides details on the variables used and the construction of the two indicators. All data series are accessed through Haver Analytics except for data from the Conference Board. Links to the Federal Reserve Economic Data (FRED) database are provided for publicly available data.
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Instructions to customize your view of the LMCI chart
The LMCI indicators are displayed as the dark blue and light blue lines. Hover or click on interactive chart lines to read specific data.
- Click on the indicator name in the legend to hide/show each indicator.
- Adjust the timeframe by clicking on the “zoom” buttons or by dragging the timeline endpoints below the chart.
In the “Labor Market Variables” section, there is a list of 24 variables that are used to construct the LMCI indicators.
- Click the check box next to each variable name to remove up to five variables from the construction of the LMCI.
- The difference between the official LMCI and the modified LMCI will appear as lightly shaded areas.
- Click on the chart icon to the left of the check box to display the selected labor market variable as a green line on the chart. Only one variable can be added to the chart at a time and only for publicly available data.
- Click on the information icon to the right of the variable titles to get a description of the variable and the source information.
There are many reasons why users may want to create a customized chart. For instance, if a researcher uses one of the normal inputs to the LMCI as a left-hand-side variable in a regression, they can remove that input variable from the calculation of the LMCI. Additionally, if a researcher thinks an input variable to the LMCI is not currently representative of the state of the labor market, they can construct a modified version of the LMCI that does not use that variable.
Labor Market Variables
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