Change in Labor Market Conditions Index : 1.50 (As of 2017-06-01) ( Discontinued )

Basic Info

Change in Labor Market Conditions Index was 1.50 as of 2017-06-01, according to The Federal Reserve. Historically, Change in Labor Market Conditions Index reached a record high of 32.00 and a record low of -48.40, the median value is 2.10. Typical value range is from -11.93 to 10.69. The Year-Over-Year growth is 15.38%. GuruFocus provides the current actual value, an historical data chart and related indicators for Change in Labor Market Conditions Index - last updated on 2017-06-01.

Monthly , seasonally adjusted . The LMCI is derived from a dynamic factor model that extracts the primary common variation from 19, seasonally-adjusted, labor market indicators. Users can read about the included indicators at http://www.federalreserve.gov/econresdata/notes/feds-notes/2014/updating-the-labor-market-conditions-index-20141001.html. Users of the LMCI should take note that the entire history of the LMCI may revise each month. Three sources contribute to such revisions. The first source is new data that were not available at the time of the employment report. In particular, at the time of the Employment Situation report each month, the quit rate and hiring rate will be missing for the last two months of the sample because the Job Openings and Labor Turnover Survey is published with a longer lag than the model's other indicators. In subsequent months, as these data become available, the LMCI will revise. The second source of revision comes from revisions to existing data. Many labor market indicators are subject to revision as additional source data become available or to incorporate annual benchmark revisions or updated seasonal adjustment factors. Prominent examples in the LMCI include the three payroll employment series from the Current Employment Statistics program. The third source of revision is inherent to the model. The LMCI is derived from the Kalman smoother, meaning that the estimate of the index in any particular month is the model's best assessment given all past and future observations. Thus, when a new month of data is added to the sample, the model will revise its estimate of history in response to the new information. In practice, these revisions tend to be modest and concentrated in the most-recent six months of the sample.