National Center for Education Statistics (NCES) / National Institute of Statistical Sciences (NISS)
NCES/NISS convened a Technical Expert Panel (TEP) to review and make recommendations concerning NCES' Comparable Wage Index (CWI).
The CWI is a statistical tool developed by NCES for geographical adjustment of local education authority (LEA)-level or state-level expenditure data (for instance, per-pupil expenditures) in order to inform comparisons across units.
To date (released and unreleased) values of the CWI for approximately 800 labor markets have been calculated using a model of the systematic, regional variations in the salaries of college graduates who are not educators. Data files and documentation for the 2005 CWI are available on the NCES web site, at http://nces.ed.gov/edfin/adjustments.asp, and there is further documentation regarding the current methodology at http://nces.ed.gov/pubsearch/pubsinfo.asp?pubid=2006865.
Principal data sources in the past were the University of Minnesota Population Center's Integrated Public Use Microdata Series (IPUMS) derived from 2000 Census Bureau Long Form data, and the Bureau of Labor Statistics' Occupational Employment Statistics (OES).
The charge to the TEP was to:
- Articulate as precisely as possible the use cases and user communities for indices that provide geographical adjustment of (labor and other) expenditures by state education authorities (SEAs) or local education authorities (LEAs).
- From these use cases, derive a set of functional specifications for such indices. Examples: level of disaggregation, treatment of uncertainties, predictive capabilities.
- Assess the extent to which the current CWI fulfills the specifications.
- In light of step 3 and taking account, propose modifications or alternatives to the current CWI that are sensible in light of current and prospective data sources or possible feasible modifications.
Members of the TEP are:
- John Abowd, Cornell University;
- Dan Black, University of Chicago;
- John Eltinge, Bureau of Labor Statistics;
- Dan Goldhaber, University of Washington;
- Jennifer Madans, National Center for Health Statistics; and
- Alan Karr, NISS, (Chair) National Institute of Statistical Sciences.