A Comparable Wage 

Approach to Geographic 

Cost Adjustment 

Research and Development Report 

May 2006 

U.S. Department of Education 

Institute of Education Sciences 

NCES 2006-321 

Lori L. Taylor 

Bush School of Government and 

Public Service 

Texas A&M University 

William J. Fowler, Jr. 

National Center for 

Education Statistics 

William J. Fowler, Jr. 

Project Officer 

National Center for 

Education Statistics 
U.S. Department of Education 

Margaret Spellings 

Secretary

Institute of Education Sciences 

Grover J. Whitehurst 

Director

National Center for Education Statistics 

Mark Schneider 

Commissioner

The National Center for Education Statistics (NCES) is the primary federal entity for collecting, analyzing, and 

reporting data related to education in the United States and other nations. It fulfills a congressional mandate to 

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NCES activities are designed to address high-priority education data needs; provide consistent, reliable, 

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May 2006 

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Suggested Citation 

Taylor, L.L., and Fowler, W.J., Jr. (2006). A Comparable Wage Approach to Geographic Cost Adjustment (NCES 

2006-321). U.S. Department of Education. Washington, DC: National Center for Education Statistics. 

For ordering information on this report, write to

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Content Contact: 

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William.Fowler@ed.gov
iii 

Executive Summary 

Introduction 

Geographic cost differences present many complications when researchers attempt to 

make systematic comparisons of educational resources, and failure to address such 

differences can undermine the equity and adequacy goals of school finance formulas. 

Therefore, there is considerable interest in developing measures of the cost of education 

that can facilitate such comparisons and possibly may be used to adjust school finance 

formulas in some states. Geographic cost adjustment data for states, metropolitan areas, 

and school districts are frequently and widely requested by the public and school finance 

research community.1

 

Previous Cost Adjustments 

Much of the geographic cost adjustment work published by NCES (Brazer and Anderson 

1983; Chambers 1997) used sophisticated statistical modeling of data on teacher salaries 

and school district characteristics. Cost analyses based on education data are attractive 

because they are directly related to school district costs and can be used to make 

adjustments for a wide array of district-level cost factors, such as school district size or 

student demographics. However, they are also extremely complex, and there can be great 

uncertainty in classifying variables in the statistical model as controllable or 

uncontrollable by the school district (Fowler and Monk 2001). Such attempts may miss 

important differences in teacher quality (Goldhaber 1999), and the resulting estimates of 

higher costs may simply reflect inefficiency (Rothstein and Smith 1997; McMahon 

1996). Finally, the main source of data for constructing nationwide estimates of 

geographic cost variation, the NCES School and Staffing Survey (SASS), is only 

available from NCES approximately every 4 years, making the adjustment untimely for 

educational researchers. 

As an alternative, Goldhaber (1999) generated a comparable wage index at the state level 

using data on the earnings of college graduates from the Current Population Survey 

(CPS). However, Goldhaber’s General Wage Index (GWI) could not identify intrastate 

variations in cost. Given that intrastate variations accounted for more than one-third of 

the total variation in other cost indexes, the lack of intrastate variation in the GWI limits 

its usefulness for the purpose of making geographic cost adjustments. 

In this report, NCES extends the analysis of comparable wages to the labor market level 

using a Comparable Wage Index (CWI). The basic premise of a CWI is that all types of 

workers—including teachers—demand higher wages in areas with a higher cost of living 

(e.g., San Diego) or a lack of amenities (e.g., Detroit, which has a particularly high crime 

 

1

 The National Center for Education Statistics (NCES) has had a long tradition of publishing work that 

reflects the latest research and development of education geographic cost adjustments. See, for example, 

Brazer and Anderson (1983), Chambers (1997), Fowler and Monk (2001), Goldhaber (1999), and Taylor 

and Keller (2003). 
iv 

rate) (Federal Bureau of Investigation 2003). The CWI reflects systematic, regional 

variations in the salaries of college graduates who are not educators. Provided that these 

noneducators are similar to educators in terms of age, educational background, and tastes 

for local amenities, a CWI can be used to measure the uncontrollable component of 

variations in the wages paid to educators. Intuitively, if accountants in the Atlanta metro 

area are paid 5 percent more than the national average accounting wage, Atlanta 

engineers are paid 5 percent more than the national average engineering wage, Atlanta 

nurses are paid 5 percent more than the national average nursing wage, and so on, then 

the CWI predicts that Atlanta teachers should also be paid 5 percent more than the 

national average teacher wage. 

This report develops a CWI by combining baseline estimates from the 2000 U.S. census 

with annual data from the Bureau of Labor Statistics (BLS). The Occupational 

Employment Statistics (OES) survey is a BLS database that contains average annual 

earnings by occupation for states and metropolitan areas from about 400,000 nonfarm 

businesses, and is available for years1997 to 2003.2

 Combining the census with the OES 

makes it possible to have yearly CWI estimates for states and local labor markets for each 

year after 1997. OES data are available each May and permit the construction of an upto-date, annual CWI. 

By matching each school district with the corresponding labor market, the research 

methodology can support CWI estimates for each school district in the United States. For 

urban school districts, this would be the CWI for the corresponding metropolitan area.3

For rural districts, this would be the CWI for the corresponding census “place of work”. 

A census place of work is a cluster of counties or census-defined places that contains at 

least 100,000 persons. All counties—and therefore all districts—in a census place of 

work area have the same CWI. For example, the 22 rural counties in the Texas Panhandle 

are clustered together into a single place-of-work area and therefore would be assigned 

the same CWI value. The CWI is available for all U.S. districts from 1997 through 2003. 

Selected Findings 

Geographically Different Wage Levels 

The CWI helps confirm that college graduates command different wages in different 

parts of the country. The CWI for 1999 ranges from 0.70 to 1.24, indicating that the wage 

level for college graduates is 24 percent above the national average in New York City 

(the nation’s most expensive labor market) and nearly 30 percent below the national 

average in several rural areas. 

 

2

 The OES is a firm-based survey rather than a household-based survey like the CPS. For a discussion of 

the advantages of using firm-based data for analysis of earnings, see Podgursky and Tongrut (2005). 3

 For this analysis, metropolitan areas were constructed by adding together whole places of work. Places of 

work that straddled more than one metropolitan area were treated as separate labor markets. 
v 

State-by-State Wage Levels 

A state’s CWI is a weighted average of the local wages within its borders. On average, 

the wage and salary of a typical college graduate in 1999 was 54 percent higher in New 

Jersey and Washington, DC (the states with the highest estimated wage level) than in 

Montana (the state with the lowest estimated wage level). 

Within-State Wage Levels 

In California, New York, Texas, West Virginia, Pennsylvania, Virginia, Illinois, and New 

Mexico, the education dollar can stretch at least 40 percent further in one part of the state 

than in another. With the exception of Hawaii, Rhode Island, and Washington, DC, all 

states face at least a 7 percent internal differential. 

Conclusions 

The CWI methodology offers many advantages over the previous NCES geographic cost 

adjustment methodologies, including relative simplicity, timeliness, and intrastate 

variations in labor costs that are undeniably outside school district control. However, the 

CWI is not designed to detect cost variations within labor markets. Thus, all the school 

districts in the Washington, DC metro area would have the same CWI cost index. 

Furthermore, as with other geographic cost indices, the CWI methodology does not 

address possible differences in the level of wages between college graduates outside the 

education sector and education sector employees. Nor does the report explore the use of 

these geographic cost adjustments as inflation adjustments (deflators.) These could be 

areas for fruitful new research on cost adjustments by NCES. 
vi 

Foreword 

The Research and Development (R&D) series of reports at NCES has been initiated to 

• share studies and research that are developmental in nature. The results of such 

studies may be revised as the work continues and additional data become 

available; 

• share the results of studies that are, to some extent, on the “cutting edge” of 

methodological developments. Emerging analytical approaches and new computer 

software development often permit new and sometimes controversial analyses to 

be done. By participating in “frontier research,” we hope to contribute to the 

resolution of issues and improved analysis; and 

• participate in discussions of emerging issues of interest to educational researchers, 

statisticians, and the federal statistical community in general. Such reports may 

document workshops and symposia sponsored by NCES that address 

methodological and analytical issues or may share and discuss issues regarding 

NCES practices, procedures, and standards. 

The common theme in all three goals is that these reports present results or discussions 

that do not reach definitive conclusions at this point in time, either because the data are 

tentative, the methodology is new and developing, or the topic is one on which there are 

divergent views. Therefore, the techniques and inferences made from the data are 

tentative and subject to revision. To facilitate the process of closure on the issues, we 

invite comment, criticism, and alternatives to what we have done. Such responses should 

be directed to 

Marilyn Seastrom 

Chief Statistician 

Statistical Standards Program 

National Center for Education Statistics 

1990 K Street NW 

Washington, DC 20006-5651 
vii 

Acknowledgments 

The authors gratefully acknowledge the suggestions and comments of the reviewers at the 

National Center for Education Statistics (NCES): Jeffrey Owings, Associate 

Commissioner for the Elementary/Secondary and Libraries Studies Division, who 

provided overall direction; reviewers Michael Ross, John Wirt, William Hussar, Stephen 

Broughman, and Frank Johnson, and Bruce Taylor and Marilyn Seastrom, who provided 

technical review of the entire publication. 

At the Education Statistics Services Institute (ESSI), Tom Nachazel and Zack McLain 

proofread and coordinated production of the publication, with assistance from other 

members of the ESSI editorial team, and Fraser Ireland, Lauren Gilbertson, and Aparna 

Sundaram provided technical review. 

At Texas A&M University, Eric Mitchem assisted Dr. Taylor’s research. 
viii 

Contents 

Page 

Executive Summary...........................................................................................................iii

Foreword............................................................................................................................ vi 

Acknowledgments............................................................................................................. vii

List of Tables ..................................................................................................................... ix 

List of Figures..................................................................................................................... x 

Introduction......................................................................................................................... 1 

Geographic Cost Adjustments ...................................................................................... 2 

The Comparable Wage Index ....................................................................................... 6 

Selected Findings.............................................................................................................. 14 

Geographically Different Wage Levels ...................................................................... 14 

State-by-State Wage Levels........................................................................................ 14 

Within-State Wage Levels .......................................................................................... 15 

Beginning Teacher Salaries ........................................................................................ 15 

Conclusions....................................................................................................................... 16 

References......................................................................................................................... 21 

Appendix A..................................................................................................................... A-1 

Appendix B ..................................................................................................................... B-1 
ix 

List of Tables 

Table Page 

1. Maximum likelihood estimates of selected independent variables from the census 

model of log annual wage and salary income: 1999................................................9 

2. The range of comparable wage index values for school districts, 

by state: 1999 .........................................................................................................19 

3. Minimum (beginning) teacher salaries, cost-adjusted and actual, 

by state: 1999–2000 ...............................................................................................20 

A-1. Number of occupations, labor markets, and goodness-of-fit 

characteristics for OES wage regressions, by metropolitan 

area and state analyses: 1997–2003 .................................................................... A-2 

A-2. Occupational frequencies used in the OES local wage predictions.................... A-5 

B-1. District-level correlations among CWI, GCEI, and GWI, 

by index and year: 1994–2003.............................................................................B-2 

B-2. State-level correlations among CWI, GCEI, and GWI, 

by index and year: 1994–2003.............................................................................B-2 

B-3. Within-state correlations between CWI and the Florida PLI, 

Texas CEI, and New York RCI, by year: 1997–2003 .........................................B-3 
x 

List of Figures 

Figure Page 

1. Labor markets in selected cities with the highest Comparable 

Wage Index: 1999..................................................................................................17 

2. Comparable Wage Index in the 10 most populous labor markets: 1999 ...............17 

3. Comparable Wage Index in the 10 most populous labor markets: 

1997–2003..............................................................................................................18 

4. Comparable Wage Index, by state: 1999 ...............................................................18 
1 

Introduction 

Expenditures vary from school district to school district for two basic reasons—

differences in the uncontrollable cost of education, and differences in the choices that 

school districts make (e.g., having small class sizes or hiring teachers who are better 

educated and have more experience). School districts that face high market prices for 

teachers will tend to spend more than other districts for reasons that are clearly beyond 

their control. School districts that serve a student population that is particularly 

challenging to teach may have to pay higher salaries to recruit and retain teachers. On the 

other hand, policy decisions that lead school districts to offer a particularly high level of 

educational services will also lead districts to have higher expenditures. 

Separating uncontrollable causes of observed differences in spending from controllable 

causes is the fundamental challenge facing researchers and policymakers who are 

interested in comparing or equalizing the purchasing power of school districts. If the 

challenge is not met, high-spending districts may be misinterpreted as high-cost districts, 

policymakers may misallocate scarce educational resources, and researchers may be 

misled about the relationship between school resources and educational outcomes. 

Given the widespread interest in this issue, the National Center for Education Statistics 

(NCES) has had a long tradition of publishing work that reflects the latest research and 

development of education geographic cost adjustments (e.g., Brazer and Anderson 1983; 

Chambers 1997; Fowler and Monk 2001; Goldhaber 1999; Taylor and Keller 2003). 

Much of that work has used sophisticated statistical modeling of teacher characteristics 

and salary, as well as school and school district characteristics (e.g., Brazer and Anderson 

1983; Chambers 1997). However, Goldhaber (1999) generated a General Wage Index 

(GWI) at the state level using data from the Current Population Survey (CPS). 

Conceptually very similar to this analysis, Goldhaber’s GWI measured state-by-state 

differences in the predicted wages of college graduates. Unfortunately, the data 

underlying Goldhaber’s index were from a “state-based” study that was not designed for 

analysis of labor market areas within states (Bureau of Labor Statistics [BLS] and U.S. 

Census Bureau 2002). Therefore, Goldhaber’s index could not identify intrastate 

variations in cost. Given that intrastate variations accounted for more than one-third of 

the total variation in previous cost indexes, the lack of intrastate variation in the GWI 

limited its usefulness for geographic cost adjustments. 

In this report, NCES extends the analysis of comparable wages to the labor market level 

using a Comparable Wage Index (CWI). The basic premise of a CWI is that all types of 

workers demand higher wages in areas with a higher cost of living (e.g., San Diego) or a 

lack of amenities (e.g., Detroit, which has a particularly high crime rate) (Federal Bureau 

of Investigation 2003). A CWI reflects systematic, regional variations in the salaries of 

workers who are not educators. Provided that those noneducators are similar to educators 

in terms of age, educational background, and tastes for local amenities, a CWI can be 

used to measure uncontrollable variations in the wages paid to educators. Intuitively, if 

accountants in the Atlanta metro area are paid 5 percent more than the national average 

accounting wage, Atlanta engineers are paid 5 percent more than the national average 

engineering wage, Atlanta nurses are paid 5 percent more than the national average 
2 

nursing wage, and so on, then the CWI predicts that Atlanta teachers should also be paid 

5 percent more than the national average teacher wage. 

This report develops a CWI by combining baseline estimates from the 2000 U.S. census 

with annual data from the BLS. The Occupational Employment Statistics (OES) survey is 

a BLS database that contains average annual earnings by occupation for states and 

metropolitan areas from about 400,000 nonfarm businesses, and is available for years 

1997 to 2003.1

 Combining the census with the OES makes it possible to have yearly CWI 

estimates for states and metropolitan areas for each year after 1997. OES data are 

available each May, and permit the construction of an up-to-date, annual CWI. 

By matching each school district with the corresponding labor market, the research 

methodology can support CWI estimates for each school district in the United States. For 

urban school districts, this would be the CWI for the corresponding metropolitan area. 

For rural districts, this would be the CWI for the corresponding census place of work. A 

census place of work is a cluster of counties or census-defined places that contains at 

least 100,000 persons. All counties—and therefore all districts—in a census place of 

work area have the same CWI. For example, the 22 rural counties in the Texas Panhandle 

are clustered together into a single place-of-work area and therefore would be assigned 

the same CWI value. The CWI is available for all U.S. districts from 1997 through 2003. 

Geographic Cost Adjustments 

As discussed in Fowler and Monk (2001) or Taylor and Keller (2003), there is a 

substantial amount of literature devoted to strategies for isolating uncontrollable cost 

variations. Taylor and Keller divide that literature into two broad categories—cost-ofliving and cost-of-education approaches. 

The cost-of-living approach rests on the premise that school districts in areas with a high 

cost of living or a lack of amenities will have to pay higher salaries to attract employees, 

thereby increasing the cost of education. The cost of living thus becomes a measure of the 

cost of education that cannot be directly influenced by school policy. 

The cost-of-education approach uses data on education expenditures to estimate either the 

cost of providing a comparable level of educational services or the cost of producing 

comparable educational outcomes. The educational services approach generates an 

estimate of the amount each district would have to spend to purchase a typical set of 

educational inputs. Taylor, Chambers, and Robinson (2004) recently developed a 

geographic cost of education index (GCEI) for Alaska using a cost-of-services approach. 

The educational outcomes strategy generates an estimate of the amount each district 

would have to spend to achieve a certain level of educational achievement. Cost 

functional analyses are the most common source of educational outcomes indexes (see, 

 

1

 The OES is a firm-based survey rather than a household-based survey like the CPS. For a discussion of 

the advantages of using firm-based data for analysis of earnings, see Podgursky and Tongrut (2005). 
3 

for example, Duncombe, Ruggiero, and Yinger 1996; Imazeki and Reschovsky 1999; 

Gronberg et al. 2004). 

The advantages and disadvantages of each approach are discussed below.2

Methodology 1: Cost-of-Living Index 

There are two mechanisms for estimating variations in the local cost of living. The first is 

to examine the cost of a specified collection of goods and services used by consumers in 

each community in a method called the “market-basket” strategy. Differences among 

communities in the cost of a “basket” of consumer goods and services capture differences 

in the cost of living. Because housing is such a large share of the typical consumer’s 

budget, variations in housing costs explain most of the geographic variation in a market 

basket index. 

The second mechanism is the CWI. As discussed above, the basic premise of a CWI is 

that all types of workers demand higher wages in areas with a higher cost of living or a 

lack of amenities. Therefore, one should be able to measure the cost of living (and 

thereby uncontrollable variations in educator pay) by observing systematic, regional 

variations in the earnings of comparable workers who are not educators.3 Researchers 

exclude educators from the estimation of a CWI because educator salaries reflect not only 

general economic conditions but also industry specific factors such as the degree of 

unionization or the amount of competition among school districts. By excluding 

educators from the calculations, researchers can ensure that the CWI reflects only 

variations in the general attractiveness of a locale. 

While similar in spirit to the market-basket strategy, the CWI offers a more complete 

picture of labor costs because it reflects not only differences in the price of haircuts and 

houses, but also any influence on wages due to differences in important community 

characteristics such as climate, crime rates, or cultural amenities. Thus, while a marketbasket index may overestimate labor costs in areas with both a high cost of goods and 

services and a lot of amenities that make it a desirable place to work (Rothstein and 

Smith 1997; Stoddard 2005), a CWI will not. The CWI is also much less expensive to 

construct than a market-basket index because it can be generated from existing data. 

There are a number of advantages to the cost-of-living approach. The clearest advantage 

is that the cost-of-living index measures costs that are beyond the control of school 

district administrators. Unlike analyses based on school district expenditures, there is no 

risk that a cost-of-living index confuses high-spending school districts with high-cost 

school districts. The cost-of-living approach is also quite straightforward. While there are 

many complex measurement issues involved (Rothstein and Smith 1997; Wynne and 

 

2

 See Taylor and Keller (2003) for an empirical analysis of the various methods applied to Texas. 

3

 See for example, Rothstein and Smith (1997), Guthrie and Rothstein (1999), Goldhaber (1999), 

Alexander et al. (2000), Taylor et al. (2002), and Stoddard (2005).
4 

Sigalla 1994; Alexander et al. 2000), cost-of-living indexes can be estimated and 

compared relatively easily, and need not employ the sophisticated statistical techniques 

and researcher judgment regarding which variables are under school district control that 

are required by cost of education indexes. Cost-of-living indexes are also appropriate 

regardless of the competitiveness of teacher labor markets. If a lack of competition in the 

teacher market distorts teacher compensation patterns, then cost indexes based on teacher 

compensation will be biased, but cost-of-living indexes will not (Hanushek 1999; 

Goldhaber 1999). Another advantage of the cost-of-living approach is its general 

applicability. Because the resulting cost index is based on systematic differences in 

consumer prices or the general wage level, it can be used to measure labor costs not only 

for public elementary and secondary education, but also for private schools, job training 

programs, and postsecondary institutions. 

There are also a number of disadvantages to using a cost-of-living index to measure 

variations in school district costs. First, although it comprises more than 80 percent of 

current school district expenditures (U.S. Census Bureau 2004), labor cost is only part of 

the total cost of education. Other prices (e.g., energy costs) and other district 

characteristics (e.g., economies of scale, variations in the amount, characteristics and 

quality of school facilities, or variations in student need) also influence the cost of 

education. Any cost index based on variation in the price of labor represents only one 

dimension of the complete cost of education—albeit a very large dimension. It can be 

problematic to apply a labor cost index to school district expenditures that are largely 

unaffected by labor cost differentials, such as energy costs or capital outlays. 

Second, any cost-of-living approach rests on comparability, which can break down. For 

example, if people in urban areas buy different items than people in rural areas, then the 

market-basket approach—which relies on comparing prices for a common set of items—

may not accurately reflect the relative cost of living in the two locations. Similarly, if 

tastes for consumer products or local amenities differ according to worker types (perhaps 

college graduates are more susceptible to the lure of city lights than other workers), then 

variations in the wages of the general population of workers may not accurately reflect 

variations in the cost of hiring college graduates like teachers. 

Third, cost-of-living differentials only reflect labor cost differentials when labor is 

mobile. If moving costs or the nonportability of retirement systems or other barriers to 

moving slow worker migration, then labor costs may temporarily diverge from what is 

expected given local amenities and the cost of living. Employers in fast-growing 

industries and school districts in fast-growing areas may need to pay a temporary 

premium to attract workers. Cost-of-living indexes cannot capture this effect. 

Finally, by design, a cost-of-living index measures cost in a broad labor market like a 

metropolitan area. It does not capture variations in cost across school districts within a 

labor market, as does a cost-of-education index. In particular, it does not capture any 

variations in cost attributable to working conditions in specific school districts. 

Therefore, despite the substantial differences between them, an advantaged school district 

has the same cost-of-living index as its disadvantaged cross-town rival. 
5 

Methodology 2: Cost-of-Education Index 

An alternative strategy for making adjustments to school funding is the cost-of-education 

approach, which NCES has employed in earlier studies of geographic cost adjustments. 

These cost-of-education approaches use complex statistical methodologies in an attempt 

to isolate uncontrollable variations in school district expenditures.4

The cost-of-education approach has a number of attractive features. Instead of using 

indirect proxies of education cost differences, as the cost-of-living approach does, it 

directly examines actual school district expenditures. The cost-of-education approach 

goes beyond the direct comparison of district expenditures, however, by using regression 

analysis to estimate the cost of providing equivalent levels of educational services to 

students or of achieving equivalent levels of student achievement. The ability to capture 

the effects of multiple cost factors means that the cost-of-education approach has the 

advantage of being able to take account of the effect of amenities like an attractive 

climate or low crime rates—factors that are reflected in the Comparable Wage Index 

(CWI) but not considered in market-basket indexes (Peternick et al. 1997). Because a 

cost-of-education index is based on school or school district data, it can detect cost 

variations at the school or district level rather than merely at the labor market level. 

Eleven percent of the variation in the geographic cost of education index (GCEI) that 

Chambers (1998) developed for NCES comes from differences across school districts 

within labor markets.5

 Finally, for states that already collect data on teacher salaries and 

district expenditures, it is much less expensive to construct a cost-of-education index than 

to apply a market-basket approach. 

There are also a number of potential disadvantages to the cost-of-education approach. 

Cost indexes that are based on school expenditure data must rely on statistical technique 

and researcher judgment to separate controllable from uncontrollable costs. As such, 

cost-of-education indexes are vulnerable not only to generic statistical concerns but also 

to specific concerns about the measurement of school outcomes and inputs.6 In addition, 

cost-of-education indexes have been criticized as based on data that are subject to school 

district manipulation (McMahon 1996), biased by the noncompetitive nature of teacher 

labor markets (Hanushek 1999), and liable to reward school districts for historic 

inefficiency (Rothstein and Smith 1997). 

 

4

 For an example of both cost-of-education methods applied to Texas, see Taylor et al. (2002). 

5

 The GCEI is a cost-of-services index. This calculation of the share of variation with labor markets uses 

the same labor-market definitions as are used in this report. 

6

 For example, Goldhaber (1999) points out that important differences in teacher quality may not be 

observable in the data. If unobservable teacher quality is correlated with observable characteristics, a costof-services index based on the pattern of teacher compensation could be biased. There is always the threat 

of an omitted variable in the analysis. In this regard, Goldhaber’s GWI analysis at the state level, using the 

CPS, suggests that Chamber’s methodology did not control for the effects of unionism. 
6 

The Comparable Wage Index 

The inherent difficulty in separating controllable variations in school district expenditures 

from uncontrollable variations, the attractions of a cost index that is clearly outside 

school district control, and the greater timeliness of a CWI make it a particularly useful 

option for school finance researchers. Therefore, NCES has decided to produce and 

distribute a Comparable Wage Index. 

A Census/OES-Based Comparable Wage Index 

The 2000 census provides data that can be used for a baseline comparable wage analysis. 

The 5-Percent Individual Public Use Microdata Sample (IPUMS 5-Percent) contains 

information on the earnings, occupation, place of work, and demographic characteristics 

of individual workers throughout the United States. Given this rich dataset, one can 

estimate a demographically adjusted wage level for each place of work, thereby avoiding 

the conclusion that the wage level is low in an area simply because most of the workers 

are young and inexperienced. Furthermore, by restricting the analysis to college 

graduates, one can generate a wage index for noneducators who are in principle most 

comparable to teachers. 

Regression analysis of the 2000 census yields the baseline estimates of the CWI.7

 The 

dependent variable is the log of annual wage and salary earnings for noneducators. The 

independent variables are age, gender, race, educational attainment, amount of time 

worked, occupation, and industry of each individual in the national sample.8, 9 In addition, 

the estimation includes an indicator variable for each labor market area.10 The labor 

 

7

 Census data for this analysis come from Ruggles et al. (2003). The estimator is restricted maximum 

likelihood. 

8

 Some potentially important worker and employer characteristics (such as union participation and firm 

size) are not available in the IPUMS. To the extent that these characteristics vary systematically by 

occupation or industry, their influence on wages will be captured by the occupational and industrial 

indicators. However, to the extent that deviations from industry and occupational norms are locationspecific, they could influence the wage level estimates. The extent of such influence is unknown. 9

 Arguably, hours worked and weeks worked could be endogenous. Estimating the model based on the log 

of hourly rather than annual wages and omitting the hours and weeks variables from the right-hand side of 

the estimation would address this concern, although it would also require researchers to assume that the 

wage impact of working more hours per week is equivalent to the wage impact of working additional 

weeks (an assumption that is not supported by the baseline estimation). Analysis based on this alternative 

specification indicates that the CWI would be unaffected by such a change. The correlation between the 

predicted wages (population marginal means) for labor market areas from the baseline estimation and the 

predicted wages for labor market areas from the alternative specification is 0.999. 10 The model also includes random effects for states. Treating state effects as random rather than fixed 

ensures that the predicted wage is the same throughout a metropolitan area that crosses state lines (Kansas 

City, Kansas, and Kansas City, Missouri, for example) while allowing for a correlation in the errors among 

labor markets within any given state. 
7 

market indicators capture the effect on wages of all market-specific characteristics, 

including the price of housing, the crime rate, and the climate.11 Because the CWI is an 

index of wage levels outside of education, it would not be appropriate to include in the 

model aggregate measures of school characteristics like school district size or student 

demographics. However, to the extent that those factors differ from one labor market to 

another, some of their effect on the prevailing wage level will be captured as a locational 

amenity by the labor market indicators. 

All labor markets are based on “place-of-work areas” defined by the Census Bureau. 

Census place-of-work areas are geographic regions designed to contain at least 100,000 

persons. The place-of-work areas do not cross state boundaries and generally follow the 

boundaries of county groups, single counties, or census-defined places (Ruggles et al. 

2003). Counties in sparsely-populated parts of a state are clustered together into a single 

Census place-of-work area. 

Whenever possible, places of work have been aggregated into metropolitan areas using 

the Office of Management and Budget’s 2003 definitions for Core-Based Statistical 

Areas (CBSAs) (U.S. Department of Education 2005, pp. 205–211).12 Places of work that 

straddled more than one CBSA were treated as separate labor markets. After the 

aggregation, there were 800 CBSAs or place-of-work areas in the 2000 census. All parts 

of the United States are included in either a CBSA or a place-of-work area. 

To ensure that the sample represents noneducators who are directly comparable to 

teachers, the estimation excludes a number of worker classifications. Because the sample 

is restricted to noneducators, anyone who has a teaching occupation or who is employed 

in the elementary and secondary education industry is excluded. Workers without a 

college degree are excluded because they are not directly comparable with teachers. Selfemployed workers are excluded because their reported earnings may not represent the 

market value of their time. Workers who work less than half-time or for less than $5,000 

per year are excluded because such part-time employees are not directly comparable to 

teachers. Finally, individuals employed outside the United States are excluded because 

their earnings may represent compensation for foreign travel or other working conditions 

not faced by domestic workers. After these exclusions, the IPUMS 5-Percent retains 

1,053,184 employed, college graduates drawn from 460 occupations and 256 industries. 

Arguably, some of the 460 occupations included in the analysis are more directly 

comparable to teaching than others. For example, Allegretto, Corcoran, and Mishel 

(2004) identify 16 occupations that are particularly similar to teaching based on the skills 

required to do the job. One might consider restricting the CWI sample to a carefully 

selected subset of the occupations held by college graduates. However, the CWI reflects 

 

11 The labor market indicators, which are also known as labor market fixed effects, capture both measurable 

and unmeasurable characteristics of labor markets. It is statistically impossible to include direct measures 

of labor market characteristics in a cross-sectional model with labor market fixed effects. 12 There are two types of CBSAs—metropolitan areas and micropolitan areas. Only metropolitan areas are 

large enough to contain more than one place of work. 
8 

only systematic regional differences from the national wage, controlling for worker 

demographics, industries, and occupations. Because it is based on pay differentials within 

each occupation, the CWI is not influenced by differences in pay levels from one 

occupation to another.13 Therefore, it is not sensitive to differences in job characteristics 

across occupations. Furthermore, reducing the sample size greatly reduces the precision 

of any regional wage estimate. Without evidence that differences in job description imply 

differences in tastes for consumer products and local amenities, there would be little gain 

to restricting the sample to a subset of occupations.14

Table 1 presents selected coefficient estimates and descriptive statistics from the census 

model of comparable wages. The coefficient estimates indicate the impact on wage and 

salary income of a small change in each explanatory variable, holding constant all of the 

other variables in the model. The standard errors indicate the precision with which the 

coefficients are estimated. Coefficients that are statistically different from zero at the 5-

percent level are marked with an asterisk. 

 

13 Occupational means are captured by occupational indicator variables; so, all other variables can be 

described as being estimated from the observation-by-observation differences between reported earnings 

and the mean earnings by occupation. 

14 A similar logic applies to any suggestion that the sample be limited to a subset of industries. 
9 

Table 1. Maximum likelihood estimates of selected independent variables from the census 

model of log annual wage and salary income: 1999 

Explanatory variable Estimate Standard error

Usual hours worked per week (log) 0.7249* 0.0028

Weeks worked last year (log) 1.0219* 0.0058

Age 0.0685* 0.0003

Age, squared -0.0007* 0.0000

White 0.0000 

Black -0.0879* 0.0022

American Indian/Alaska Native -0.1096* 0.0085

Chinese -0.1177* 0.0037

Japanese -0.0029 0.0068

Other Asian or Pacific Islander -0.1093* 0.0026

Other race, not elsewhere classified -0.1642* 0.0045

Two or more major races -0.1216* 0.0042

Male 0.0000 

Female -0.1703* 0.0012

Professional degree 0.0000 

Bachelor’s degree -0.0781* 0.0028

Master’s degree -0.0007 0.0029

Doctorate 0.1163* 0.0037

NOTE: The * indicates a coefficient that is significantly different from zero at the 5-percent level. The model also 

includes 460 occupational fixed effects, 256 industry fixed effects, 800 labor market fixed effects, and random effects 

for state. There are 1,053,184 observations, and the -2 residual log likelihood is 1616489. The R-squared for this model 

is 0.45. 

SOURCE: Ruggles et al. (2003) and authors’ calculations. 

The estimated model conforms to reasonable expectations about labor markets. Wage and 

salary earnings increase with the amount of time worked and the age of the worker (a 

rough proxy for experience). Persons with advanced degrees earn systematically more 

than persons with bachelor’s degrees. Women earn less than men of comparable age and 

educational attainment, possibly because age is a better indicator of experience for men 

than for women. Whites earn systematically more than apparently comparable individuals 

from most other racial groups. 

Using the model, one can predict the wages that a nationally representative person would 

earn in each labor market area.15 A nationally representative person has average 

demographic characteristics and works the average number of hours per week and the 

average number of weeks per year in a nationally representative mix of occupations and 

industries. Equivalently, the predicted wage in each labor market area is the average 

wage one would expect to observe if everyone in the dataset lived in that market. 

 

15 Formally, the predicted wage level in each labor market area is the least-squares mean for the market 

fixed effect. The least-squares mean (or population marginal mean) is defined as the value of the mean for 

each effect (in this context, each market) that would be expected from a balanced design holding all 

covariates at their mean values and all classification variables (e.g., occupation or gender) at their 

population frequencies. 
10 

The national average predicted wage, which is an employment-weighted average of local 

area predicted wages, is $47,836 per year in 1999 dollars. Dividing each local wage 

prediction by this national average yields the CWI. 

The resulting distribution of index values generally corresponds to reasonable 

expectations. Almost without exception, the labor markets with the lowest CWI are 

located in rural areas. The labor markets with the highest CWI are generally in major 

urban areas. The wage level in New York City (the market with the highest CWI) is 77 

percent higher than the wage level in rural Idaho (the market with the lowest CWI). 

Interestingly, variations within states are an important part of the cost variations detected 

by the CWI. Nearly half of the total variation in the baseline CWI (44 percent) comes 

from variations within states. More of the variation in the CWI comes from within-state 

variation than in Chambers’ GCEI (38 percent), even though the GCEI varies within 

labor markets while the CWI does not. 

The large amount of within-state variation suggests that the CWI is a helpful extension of 

Goldhaber’s state-level index. Many studies of educational adequacy have relied on 

Chambers’ GCEI to make within-state adjustments for cost differentials (Baker, Taylor, 

and Vedlitz 2004). Since the CWI also varies significantly within states, it may prove a 

particularly useful tool for analyses of school finance adequacy and equity. For example, 

Taylor (forthcoming) uses the CWI to demonstrate that cost adjustment reduces the 

measured inequality of the U.S. school finance system, but raises the measured inequality 

of current educational expenditures in most states. 

One potential criticism of the CWI is that it reflects wage and salary earnings rather than 

total compensation. (The IPUMS 5-Percent provides no information on employee 

benefits.) To the extent that benefits differ systematically across industries or 

occupations, they will be captured by regression fixed effects and have no impact on the 

CWI. However, systematic differences in benefits across states—that might arise because 

workers desire to take more of their compensation in the form of benefits in states with 

income tax than they do in states without income tax—will be indistinguishable from 

cost-of-living differentials. 

It is difficult to gauge the magnitude of potential bias from excluding benefits because 

few researchers have published work on the geographic variation in employee benefits. 

However, two recent reports using the 2004 Current Population Survey (CPS) indicate 

that there is considerable geographic variation in employer-provided benefits. Copeland 

(2004) finds that the share of full-time, full-year workers participating in an employerprovided retirement plan ranged from 46.7 percent in Florida to 67 percent in North 

Dakota. Gould (2004) finds that that the share of at-least-part-time, private-sector 

workers covered by employer-provided health insurance ranged from 43.7 percent in 

New Mexico to 69.6 percent in Hawaii. The pension participation rates and the CWI are 

not correlated (the Pearson correlation is 0.0498), but there is a statistically significant 

relationship between health insurance coverage rates and the CWI (the Pearson 

correlation is 0.3837). States with higher CWIs also had higher shares of the working 

population covered by employer-provided health insurance. 
11 

Because neither report on benefit variations adjusts the estimated benefit rates to reflect 

differences in the demographic, occupational, and industrial composition of the states, or 

restricts the analysis to college graduates (who are likely to have higher and more 

uniform participation rates than other workers), neither rate is directly comparable to the 

CWI. Furthermore, the health benefits estimate makes no distinction between part-time 

and full-time workers. Therefore, the apparent correlation between health insurance 

coverage rates and the CWI could be spurious. However, if high-wage states are also 

generally high-benefit states, the CWI would understate the geographic differential in the 

cost of hiring teachers. As such, the CWI may be a conservative estimate of differences 

in the cost of education. 

Extending the Baseline CWI 

While the baseline CWI makes it possible to compare district purchasing power across 

space, Occupational Employment Statistics (OES) data make it possible to extend the 

CWI in noncensus years, resulting in a CWI for 1997 on. 

The OES is a Bureau of Labor Statistics (BLS) database that contains average annual 

earnings by occupation for states and metropolitan areas. Each year, the BLS samples and 

contacts approximately 400,000 civilian, nonfarm establishments for the OES survey.16

Survey respondents in the 2003 OES dataset employed 72 percent of civilian, nonfarm 

workers in the United States. 

Unfortunately, while the OES survey categorizes workers into 770 detailed occupations, 

it does not provide any demographic information about them (BLS 2003). Therefore, the 

OES would not be an appropriate dataset for construction of a baseline CWI. However, it 

ought to be possible to use OES-based estimates of wage growth to adjust the censusbased estimates of wage levels. For example, if the OES indicates that the wage level in 

Houston increased by 5 percent between 1999 and 2001, then the baseline CWI for 

Houston can be revised upward by 5 percent to generate an estimate of the Houston CWI 

in 2001. Such annual estimates can be generated for 1997 on. 

As long as the demographic profiles of states and metropolitan areas are relatively stable 

from one year to the next, the lack of demographic data in the OES should not lead to 

systematic biases in the estimated growth rates. Areas where the population is young and 

inexperienced will have systematically lower wages in 1999, but they will also have 

systematically lower wages in 2000 and 2001. Therefore, while changes in demographics 

may affect the estimated growth rates, the levels of the demographic characteristics 

should not.17

 

16 Details on the OES survey come from BLS (2003). 

17 The obvious exception is the distribution of educational attainment. To the extent that the returns to 

education are rising over time, average wages will also be rising in areas with a disproportionately educated 

population. Much of the effect of rising returns to education should be reflected in rising returns to 

occupations (like engineering or medicine) that disproportionately employ college graduates, and therefore 

captured by the occupational effects in the OES analysis. However, to the extent that the returns to 
12 

The evidence suggests that demographic profiles are remarkably stable over time, so any 

bias in the growth rates induced by demographic shifts should be modest. Among 

metropolitan areas included in the census’s American Community Survey (ACS), there is 

a 0.968 correlation between the share of the adult population with a bachelor’s degree in 

2002 and the share with a bachelor’s degree in 2004. Even across the decade between 

censuses, there is a 0.959 correlation between the share of the adult population with a 

bachelor’s degree in a metropolitan area in 1990 and the same indicator in 2000. 

Similarly, there is a 0.942 correlation between share of the working-age population that is 

under 30 in 1990 and the share under 30 in 2000. 

Although the bias arising from a lack of demographic information in the OES data should 

be modest, it will tend to cumulate over time. Therefore, we have more confidence in the 

estimates within a few years on either side of the 1999 census than we have in estimates 

further away in time. As the Bureau of the Census expands the coverage of the ACS it 

may be desirable to use it to update the CWI rather than the OES. The ACS is an annual 

version of the long-form census questionnaire, and starting in the summer of 2006, the 

public use data from the ACS will provide individual data with much the same degree of 

demographic and geographic detail provided by the IPUMS from the 2000 census (U.S. 

Census Bureau 2006). 

As an alternative to updating using the OES, one could consider extending the CWI using 

the CPS. Because it is a household-based rather than an establishment-based survey, the 

CPS contains demographic information. Thus, a CPS index would be able to control for 

demographically driven wage shifts that could influence year-to-year changes in an OES 

index. On the other hand, the CPS provides much less occupational detail than does the 

OES; thus, an OES index is likely to do a better job of controlling for wage shifts driven 

by changes in the occupational mix. Moreover, the OES provides much better geographic 

coverage than does the CPS. The OES is designed to generate wage estimates for 

metropolitan areas as well as states. The CPS is a “state-based” study that is not designed 

for analysis of labor market areas within states (BLS and U.S. Census Bureau 2002).18

Therefore, while either series could be used to extend the CWI for states, only the OES is 

well suited for extending the CWI for metropolitan areas. Given the magnitude of withinstate differences in the CWI, it is particularly important to extend the index for 

metropolitan areas. 

The first step in extending the CWI is generating OES-based estimates of the annual 

wage level in each labor market. The OES provides estimates of average annual earnings 

and employment by occupation for states and metropolitan areas from 1997 through 

2003. To allow for both occupation-specific and location-specific shifts in wage levels 

over time, each year is also analyzed separately. In each year, the wage model is an 

 

education are independent of occupation, rising returns to education will bias wage growth upward in local 

areas with a relatively educated population. 18 Other currently available datasets, such as the ACS, also lack data for labor market areas within states, 

and therefore cannot be used to extend the CWI. 
13 

annual regression of the average wages (in logs) on indicator variables for occupation and 

location (either state or metropolitan area) weighted by total employment in the 

occupation/location cell.19, 20

The second step is to calculate the growth rate for wages in each state and metropolitan 

area from the OES-based estimates of wage levels, and to adjust the baseline CWI 

accordingly. (See Appendix A for detail.) 

One advantage to extending the baseline CWI with the OES is that it generates a very 

timely index of school-district labor cost. The annual OES estimates are generated with 

only a 1-year lag. Thus, researchers can generate a CWI for 2003 in the spring of 2004. In 

contrast, the most recent NCES data released in spring of 2005 from the Schools and 

Staffing Survey (the primary data source for Chambers’ Geographic Cost of Education 

Index) cover the 1999–2000 school year, and the most recent national data on schooldistrict expenditures cover the 2002–03 school year. 

Together, census and OES data can be used to support a viable CWI, which is the dataset 

employed in this study that NCES will release for use by the public and education finance 

researchers as a geographically based, cost-of-living adjustment. The resulting panel of 

index values measures the wage level for college graduates in all parts of the United 

States for the years 1997 through 2003. The CWI dataset will be available from NCES at 

http://nces.ed.gov/edfin/, along with a user’s guide and documentation. 

 

19 As with the census analysis, teaching and teaching-related occupations have been excluded from the 

estimation database. 

20 Weighting by employment yields the same coefficient estimates as would arise from a data set comprised 

of individual workers each earning the average annual pay for his or her occupation and location. As such, 

it serves to makes the OES regressions parallel to the census regressions, and ensures that location effects 

are estimated as deviations from a nationally representative occupational wage. 
14 

Selected Findings 

Geographically Different Wage Levels 

The CWI helps confirm that college graduates command different wages in different 

parts of the country. The CWI is constructed as the local wage level divided by the 

national average in 1999. The CWI for 1999 ranges from 0.70 to 1.24, indicating that the 

wage level for college graduates is 24 percent above the national average in New York 

City (the nation’s most expensive labor market) and nearly 30 percent below the national 

average in several rural areas. 

The labor markets with the highest CWI are generally in major urban areas (figure 1). In 

1999, wages for college graduates were more than 15 percent above the national average 

in New York City; San Jose, California; San Francisco; and Bridgeport, Connecticut. 

The CWI reveals variation in wages not only between urban and rural America, but also 

among America’s largest cities (figure 2). In 1999, wages in New York City were 12 

percent higher than wages in Dallas, Texas, which in turn were 15 percent higher than 

wages in Phoenix, Arizona. College graduates in Phoenix earned 4 percent less than the 

national average. 

Not only are there considerable geographic differences in the wages of college graduates, 

but the CWI also indicates that the price of college-graduate labor has been rising rapidly. 

Over the course of the 6 years between 1997 and 2003, wage levels increased by at least 

18 percent in all local labor markets. The wage level grew by 39 percent in San Jose, 

leading it to overtake New York City as the highest paid market for college graduates in 

the United States. Among the 10 largest local labor markets, wage growth was fastest in 

Riverside-San Bernardino, California, and slowest in Phoenix, Arizona. 

The geographic pattern of wages was largely unaffected by the growth differentials, 

however. For example, figure 3 plots the CWI for the 10 largest local labor markets. As 

the figure illustrates, the pattern of relative wages is dominated by the common trend and 

the baseline differentials. Among the 800 CBSAs and census places-of-work areas, the 

correlation between the CWI for 1997 and the CWI for 2003 was .966. The correlation 

between the CWI and previous geographic cost indexes—including Chambers’ GCEI and 

Goldhaber’s GWI—is also quite high. (See appendix B.) 

State-by-State Wage Levels 

A state’s CWI is a weighted average of the local wages within its borders. As figure 4 

shows, state-by-state comparisons also reveal considerable variation in comparable wage 

levels. On average, the wage and salary of a typical college graduate in 1999 was 54 

percent higher in New Jersey and Washington, DC (the states with the highest estimated 

wage level) than in Montana (the state with the lowest estimated wage level). As one 

might expect, the CWI was highest in New York, New Jersey, and Washington, DC, and 

lowest in the Great Plains states. Because the states with the highest CWI are also among 

the most populous, most states have a CWI below 1.00. 
15 

Within-State Wage Levels 

Not only are there considerable differences in wage levels across states, there are also 

considerable differences within a single state (table 2). The difference in CWIs within the 

state of California is as great as the difference between New Jersey and Montana. In New 

York, Texas, West Virginia, Pennsylvania, Virginia, Illinois, and New Mexico, the 

education dollar can stretch at least 40 percent farther in one part of the state than in 

another. With the exception of Hawaii, Rhode Island, and Washington, DC, all states face 

at least a 7 percent internal differential. 

Beginning Teacher Salaries 

The considerable differences in hiring cost revealed by the CWI suggest that it is very 

important to take purchasing power into consideration when making financial 

comparisons across school districts. To illustrate how geographic cost adjustment 

transforms widely reported data, consider its effect on beginning teacher salaries.21 In 

1999–2000, only five states had higher beginning teacher salaries than New Jersey. 

However, New Jersey also had particularly high wages for college graduates, on average 

(table 3). Adjusted for geographic variations in hiring costs, beginning teacher salaries in 

New Jersey were not the sixth highest in the nation, but instead the seventh lowest—just 

behind Mississippi. 

 

21 “Beginning teachers” are elementary and secondary school teachers with 3 or fewer years of experience 

(U.S. Department of Education 2003, p. 59). 
16 

Conclusions 

The differences in labor costs measured by the CWI may be considered by some to be 

both substantial and relevant for education policymaking and analysis. The CWI indicates 

that there are large and persistent within-state differences in labor cost, such that it may 

be appropriate to make adjustments for them not only when analyzing school finances but 

also when constructing school finance formulas. 

The CWI methodology for geographic cost adjustment offers many advantages over the 

previous NCES methodologies, including relative simplicity, timeliness, and intrastate 

variations in labor costs that are undeniably outside school district control. However, the 

CWI is not designed to detect cost variations within labor markets. Thus, all the school 

districts in the Washington, DC metro area would have the same CWI cost index. In 

addition, this report also does not address possible differences in the level of wages 

between college graduates outside the education sector and education-sector employees. 

Nor does the report explore the use of these geographic cost adjustments as inflation 

adjustments (deflators.) These are areas for future fruitful new research on cost 

adjustments by NCES. 
17 

Figure 1. Labor markets in selected cities with the highest Comparable Wage Index: 1999 

Chicago, IL

Los Angeles, CA

Cambridge, MA

Boston, MA

Dallas, TX

Nassau-Suffolk, NY

Oakland, CA

Edison, NJ

Trenton, NJ

Washington, DC

Newark, NJ

Bridgeport, CT

San Francisco, CA

San Jose, CA

New York, NY

Labor market

Comparable wage index

0 1.0 1.1 1.2 1.3

NOTE: Figure includes mean and two standard error confidence bands. 

SOURCE: U.S. Department of Education, National Center for Education Statistics, Comparable Wage Index data file, 

2006. 

Figure 2. Comparable Wage Index in the 10 most populous labor markets: 1999 

Labor market

Comparable wage index

0 1.1 1.2 1.3 1.0

Phoenix, AZ

Riverside-San Bernardino, CA

Atlanta, GA

Philadelphia, PA

Houston, TX

Chicago, IL

Los Angeles, CA

Dallas, TX

Washington, DC

New York, NY

NOTE: Figure includes mean and two standard error confidence bands. 

SOURCE: U.S. Department of Education, National Center for Education Statistics, Comparable Wage Index data file, 

2006. 
18 

Figure 3. Comparable Wage Index in the 10 most populous labor markets: 1997–2003 

Year

Comparable wage index

1997 1998 1999 2000 2001 2002 2003

0

0.8

0.9

1.0

1.1

1.2

1.3

1.4

1.5

New York, NY

Washington, DC

Dallas, TX

Los Angeles, CA

Chicago, IL

Houston, TX

Philadelphia, PA

Atlanta, GA

Riverside-San Bernardino, CA

Phoenix, AZ

SOURCE: U.S. Department of Education, National Center for Education Statistics, Comparable Wage Index data file, 

2006.

Figure 4. Comparable Wage Index, by state: 1999 

SOURCE: U.S. Department of Education, National Center for Education Statistics, Comparable Wage Index data file, 

2006. 
19 

Table 2. The range of Comparable Wage Index values for school districts, by state: 1999 

Minimum 

CWI 

Maximum 

CWI 

Minimum 

CWI 

Maximum 

CWI 

 United States 0.703 1.244 Missouri 0.715 0.959 

Alabama 0.770 0.938 Montana 0.709 0.792 

Alaska 0.939 1.011 Nebraska 0.712 0.912 

Arizona 0.777 0.962 Nevada 0.928 1.016 

Arkansas 0.731 1.022 New Hampshire 0.818 0.969 

California 0.801 1.239 New Jersey 0.984 1.244 

Colorado 0.720 0.981 New Mexico 0.714 1.001 

Connecticut 0.932 1.191 New York 0.816 1.244 

Delaware 0.861 1.034 North Carolina 0.766 1.037 

District of Columbia 1.155 1.155 North Dakota 0.740 0.846 

Florida 0.731 0.971 Ohio 0.791 1.008 

Georgia 0.791 1.045 Oklahoma 0.737 0.915 

Hawaii 0.968 0.968 Oregon 0.808 1.001 

Idaho 0.703 0.886 Pennsylvania 0.793 1.158 

Illinois 0.776 1.089 Rhode Island 0.988 0.988 

Indiana 0.774 1.008 South Carolina 0.858 1.037 

Iowa 0.733 0.927 South Dakota 0.711 0.850 

Kansas 0.727 0.950 Tennessee 0.735 1.022 

Kentucky 0.744 1.008 Texas 0.738 1.109 

Louisiana 0.756 0.994 Utah 0.817 0.957 

Maine 0.734 0.869 Vermont 0.800 0.866 

Maryland 0.829 1.155 Virginia 0.814 1.155 

Massachusetts 0.836 1.098 Washington 0.853 1.062 

Michigan 0.823 1.073 West Virginia 0.776 1.155 

Minnesota 0.785 1.032 Wisconsin 0.804 1.076 

Mississippi 0.774 1.022 Wyoming 0.772 0.861 

SOURCE: U.S. Department of Education, National Center for Education Statistics, Comparable Wage Index data file, 2006. 
20 

Table 3. Minimum (beginning) teacher salaries, cost-adjusted and actual, by state: 1999–2000 

 Minimum (beginning) teacher salaries 

 Cost-adjusted Actual CWI CWI standard error

United States $27,989 $27,989 1.000 0.0039

Alaska 34,402 33,676 0.979 0.0119

Alabama 33,600 29,790 0.887 0.0042

Oregon 31,503 29,733 0.944 0.0070

Pennsylvania 31,125 30,185 0.970 0.0063

Vermont 30,999 25,791 0.832 0.0107

Delaware 30,874 30,945 1.002 0.0088

Georgia 30,644 30,402 0.992 0.0035

Hawaii 30,160 29,204 0.968 0.0080

Iowa 30,150 25,275 0.838 0.0052

Wyoming 30,067 24,168 0.804 0.0135

Indiana 29,671 26,553 0.895 0.0042

Kansas 29,528 25,252 0.855 0.0050

California 29,481 32,190 1.092 0.0018

Illinois 29,256 30,151 1.031 0.0034

North Carolina 29,240 27,968 0.956 0.0058

Tennessee 29,003 27,228 0.939 0.0072

Missouri 28,899 25,977 0.899 0.0047

Nevada 28,870 28,734 0.995 0.0083

Massachusetts 28,708 30,330 1.057 0.0047

Michigan 28,703 28,545 0.994 0.0047

Louisiana 28,614 25,738 0.900 0.0053

New Mexico 28,561 25,042 0.877 0.0077

New York 28,440 31,910 1.122 0.0060

Connecticut 28,207 30,466 1.080 0.0044

Oklahoma 28,073 24,025 0.856 0.0069

West Virginia 28,054 23,829 0.849 0.0096

South Dakota 28,038 21,889 0.781 0.0102

Montana 28,037 20,969 0.748 0.0081

Texas 27,964 28,400 1.016 0.0072

Maine 27,913 22,942 0.822 0.0074

Arizona 27,762 25,613 0.923 0.0037

Nebraska 27,641 22,923 0.829 0.0068

Rhode Island 27,606 27,286 0.988 0.0091

Florida 27,590 25,132 0.911 0.0026

Kentucky 27,558 24,753 0.898 0.0051

South Carolina 27,437 25,215 0.919 0.0072

Maryland 27,393 28,612 1.044 0.0049

New Hampshire 27,169 24,650 0.907 0.0078

Wisconsin 26,993 25,344 0.939 0.0082

Arkansas 26,961 22,599 0.838 0.0059

District of Columbia 26,722 30,850 1.155 0.0062

Colorado 26,610 24,875 0.935 0.0034

Minnesota 26,556 25,666 0.966 0.0051

Mississippi 26,538 23,040 0.868 0.0069

New Jersey 26,440 30,480 1.153 0.0063

Washington 26,140 26,514 1.014 0.0083

North Dakota 25,982 20,422 0.786 0.0102

Virginia 25,927 26,783 1.033 0.0067

Utah 24,976 23,273 0.932 0.0086

Idaho 24,893 20,915 0.840 0.0079

Ohio 24,524 23,597 0.962 0.0059

NOTE: CWI=Comparable Wage Index. States sorted by cost-adjusted minimum salary. Beginning teachers are those 

with 3 or fewer years of experience (U.S. Department of Education 2003, p. 59). 

SOURCE: Snyder and Hoffman (2002), and U.S. Department of Education, National Center for Education Statistics, 

Comparable Wage Index data file, 2006. 
21 

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A-1 

Appendix A: Extending the Baseline With the OES 

While the baseline CWI makes it possible to compare district purchasing power across 

locations, the OES data make it possible to extend the CWI in noncensus years. 

The OES is a Bureau of Labor Statistics (BLS) database that contains average annual 

earnings by occupation for states and metropolitan areas each year from 1997 through 

2003. Each year, the BLS samples and contacts approximately 400,000 civilian, nonfarm 

establishments for the OES survey.1 Every firm in the United States with at least 250 

employees is included in the sample with near-certainty each year. Smaller firms are 

sampled proportionally. The rate of response to the survey is typically quite high. Nearly 

80 percent of the establishments contacted for the May 2003 survey responded. Survey 

respondents in the 2003 OES dataset employed 72 percent of civilian, nonfarm workers 

in the United States.2

The first step in extending the CWI is generating OES-based estimates of the annual 

wage level in each labor market. Because metropolitan areas span state lines, combining 

the state and metropolitan data into a single model would be inappropriate. Therefore, the 

wage levels for states and metropolitan areas are estimated separately. To allow for both 

occupation-specific and location-specific shifts in wage levels over time, each year is also 

analyzed separately. Thus, each model is an annual regression of the average annual 

earnings (in logs) on indicator variables for occupation and location (either state or MSA) 

weighted by total employment in the occupation/location cell. Weighting by employment 

yields the same coefficient estimates as would arise from a data set comprised of 

individual workers each earning the average annual pay for his or her occupation and 

location. As such, it serves to makes the OES regressions parallel to the census 

regressions, and ensures that location effects are estimated as deviations from a nationally 

representative occupational wage. Table A-1 presents descriptive statistics for the 

fourteen OES regression models. 

As the table illustrates, the models fit the OES data quite closely, and much more closely 

than the baseline estimation fits the census. The OES models are able to explain more of 

the variation in earnings largely because the OES data have less variation in earnings that 

needs to be explained. The OES data represent average earnings by occupation while the 

census data represent the earnings of individual workers in that occupation. Thus, for 

example, the average metropolitan area wage for financial analysts in 1999 ranges from 

$29,000 to $106,000 in the OES but from $5,000 to $354,000 in the census estimation 

sample. 

 

1

 Details on the OES survey come from BLS (2003). 

2

 The OES is constructed as a 3-year moving average. Thus, the estimates for 2002 are drawn from surveys 

in 2000, 2001, and 2002. Because the 2003 survey straddles the shift from the Standard Industrial 

Classification (SIC) system to the North American Industry Classification System (NAICS), “May 2003 

data were combined with samples from November 2002, 2001, 2000, and a subset of certainty units 

collected in 1999” (BLS 2003). 
A-2 

Table A-1. Number of occupations, labor markets, and goodness-of-fit characteristics for 

OES wage regressions, by metropolitan area and state analyses: 1997–2003 

Metropolitan area analysis State analysis 

Year 

Number of

occupations

Number of

labor

markets

Number of

observations R-square

Number of

occupations

Number of

labor

markets

Number of

observations R-square

1997 458 306 85,921 0.934 463 51 28,501 0.946

1998 459 306 96,333 0.939 463 51 30,302 0.950

1999 435 309 64,986 0.937 440 51 24,055 0.946

2000 439 309 90,593 0.932 441 51 27,946 0.944

2001 440 309 95,658 0.936 442 51 28,496 0.946

2002 438 309 95,589 0.943 442 51 28,001 0.951

2003 438 309 94,457 0.942 441 51 27,868 0.951

NOTE: OES=Occupational Employment Statistics. 

SOURCE: Bureau of Labor Statistics (2003) and authors’ calculations. 

The OES regression models differ from the baseline census regression model in three key 

respects. First, the OES database contains information on average earnings for workers 

in each occupation, but no information on worker demographics or industry. Therefore, 

the OES regressions cannot include any explanatory variables other than occupation and 

labor market area indicators. The OES regression model for metropolitan areas in 1997 

is made up of 458 occupational indicator variables and 306 market indicator variables, for 

example. The baseline census regression includes not only occupation and location 

indicators, but also demographic characteristics, hours worked, and industry indicators. 

Second, the list of occupational indicators is not the same between the OES database and 

the census. The OES does not use consistent coding for occupations in all years between 

1997 and 2003, and in no year is the OES coding completely consistent with the 2000 

census. Therefore, OES occupations were matched to their census equivalents using a 

crosswalk provided by the National Crosswalk Service Center, 

http://www.xwalkcenter.org/xw_ackx.html#SOCOES. Occupations that could not be 

matched using the crosswalk were included in the OES estimation using their original 

coding. Wherever the OES provided more occupational detail than the census—such as 

occurs when both the census and the 1997 OES group all physicians together in a single 

occupation, but the 2003 OES decomposes the category into subgroups such as internists, 

pediatricians and obstetricians—the data were recoded to match the census codes. 

Therefore, there can be more than one observation per occupation in a market each year. 

Finally, the labor market areas with data in the OES database represent only a subset of 

the labor market areas available for analysis using the census. Therefore, the OES 

regression models also include fewer labor market indicators than does the census 

regression model. 

Each model yields a predicted wage for each occupation in each location. The local wage 

level is a weighted average of the local predicted wages by occupation, where the weights 

are each occupation’s share of total employment among the national sample of college 
A-3 

graduates in the census database. Thus, occupations that are held only rarely by college 

graduates are given little weight in the construction of the OES wage levels, while 

occupations that employ college graduates intensively are given greater weight. 

Occupations that could not be matched to the census files for college graduates or that are 

present in only some of the OES years are assigned a zero weight in the construction of 

the local wage level. Thus, while there are between 435 and 463 occupations included in 

the OES regressions each year, the estimate of the local wage level is based on the 348 of 

them that can be matched to the census occupation codes and are observed in at least one 

state and metropolitan area each year (see table A-2). Because the distribution of 

employment across occupations mirrors the census, changes in the OES wage estimates 

over time reflect systematic changes in average wages across occupations, not changes in 

the occupational mix. 

The census asked respondents to report their wages in 1999. Therefore, the OES wage 

estimates for 1999 form the basis for comparison of changes in local wage levels. If the 

OES estimated wage level for Dallas in 2000 was 2 percent higher than the OES 

estimated wage level for Dallas in 1999, then the CWI for Dallas in 2000 was 2 percent 

higher than the baseline CWI. Similarly, if the average OES wage for Texas increased by 

10 percent between 1999 and 2002, then the CWI for the state of Texas in 2002 was 10 

percent higher than the baseline CWI. 

Approximately 346 of the 800 labor markets from the baseline analysis (CBSAs and 

places-of-work) can be matched to OES labor markets.3

 For rural areas and many smaller 

metropolitan areas, there is no direct estimate of the change in wage levels. If state 

average wages and the average wages in all major metropolitan areas within the state are 

both up 10 percent, it is clear that wages elsewhere in the state must also be up 10 

percent, on average. When the major metropolitan areas and the state as a whole are 

growing at different rates, then the growth rate for the remainder of the state is imputed 

such that an employment-weighted average of the rural and metropolitan growth rates 

equals the state average growth rate. 

The employment weights used in calculating growth rates for areas that cannot be 

directly estimated from the OES come from the total employment records of the 2000 

census. The employment weights reflect workers with all levels of educational attainment 

because the OES data do not separate out the college graduates within occupations, and 

data on all occupations are used to estimate the labor market differentials.4

 Note that 

when metropolitan areas spill over state boundaries, it is not necessarily true that the 

 

3

 OES has not updated its geographic boundaries since 1990; thus, a handful of OES labor markets cannot 

be matched successfully to a CBSA. 

4

 Predicted wage levels in any labor market reflect local variations around a nationwide constant. The 

nationwide constant is based solely on the occupations held by college graduates in the 2000 census, but 

the local wage level is the nationwide constant plus the estimated labor market fixed effect. Thus, 

differences in growth rates across labor market areas are solely a function of changes in labor market fixed 

effects. The fixed effects are estimated using all of the OES data; thus, imputing fixed effects to the 

unobserved regions of a state requires information on total employment. 
A-4 

state’s growth rate is a weighted average of the growth rate of its metropolitan areas and 

its rural places of work. Therefore, this imputation strategy is less precise in states that 

share a metropolitan area with another state. 
A-5 

Table A-2. Occupational frequencies used in the OES local wage predictions 

Occupation 

Code Occupation title 

Frequency Share 

( percent)

4 Advertising and Promotions Managers 0.25

5 Marketing and Sales Managers 3.30

6 Public Relations Managers 0.22

12 Financial Managers 2.80

13 Human Resources Managers 1.07

14 Industrial Production Managers 0.66

15 Purchasing Managers 0.53

16 Transportation, Storage, and Distribution Managers 0.28

20 Farm, Ranch, and Other Agricultural Managers 0.13

22 Construction Managers 0.65

32 Funeral Directors 0.06

35 Medical and Health Services Managers 1.21

40 Postmasters and Mail Superintendents 0.05

41 Property, Real Estate, and Community Association Managers 0.53

51 Purchasing Agents and Buyers, Farm Products 0.02

52 Wholesale and Retail Buyers, Except Farm Products 0.30

53 Purchasing Agents, Except Wholesale, Retail, and Farm Products 0.46

54 Claims Adjusters, Appraisers, Examiners, and Investigators 0.62

60 Cost Estimators 0.15

62 Human Resources, Training, and Labor Relations Specialists 1.99

71 Management Analysts 1.35

80 Accountants and Auditors 5.61

81 Appraisers and Assessors of Real Estate 0.15

82 Budget Analysts 0.15

83 Credit Analysts 0.08

84 Financial Analysts 0.20

86 Insurance Underwriters 0.20

91 Loan Counselors and Officers 0.73

93 Tax Examiners, Collectors, and Revenue Agents 0.17

94 Tax Preparers 0.10

101 Computer Programmers 1.98

106 Database Administrators 0.25

120 Actuaries 0.10

122 Operations Research Analysts 0.34

124 Miscellaneous Mathematical Science Occupations, Including Mathematicians and 

Statisticians 0.13

130 Architects, Except Naval 0.63

131 Surveyors, Cartographers, and Photogrammetrists 0.12

132 Aerospace Engineers 0.48

135 Chemical Engineers 0.30

136 Civil Engineers 1.10

143 Industrial Engineers, Including Health and Safety 0.62

144 Marine Engineers 0.04

145 Materials Engineers 0.13

146 Mechanical Engineers 0.98

151 Nuclear Engineers 0.04

152 Petroleum, Mining and Geological Engineers, Including Mining Safety Engineers 0.09

153 Miscellaneous Engineers, Including Agricultural and Biomedical 1.24

154 Drafters 0.19

155 Engineering Technicians, Except Drafters 0.33

156 Surveying and Mapping Technicians 0.02
A-6 

Occupation 

Code Occupation title 

Frequency Share 

( percent)

161 Biological Scientists 0.42

165 Medical Scientists 0.40

170 Astronomers and Physicists 0.10

171 Atmospheric and Space Scientists 0.04

172 Chemists and Materials Scientists 0.50

174 Environmental Scientists and Geoscientists 0.38

182 Psychologists 0.37

184 Urban and Regional Planners 0.11

186 Miscellaneous Social Scientists, Including Sociologists 0.14

193 Geological and Petroleum Technicians 0.02

196 Miscellaneous Life, Physical, and Social Science Technicians, Including Social Science 

Research Assistants and Nuclear Technicians 0.23

200 Counselors 1.20

201 Social Workers 2.34

204 Clergy 1.64

205 Directors, Religious Activities and Education 0.14

210 Lawyers 2.82

211 Judges, Magistrates, and Other Judicial Workers 0.26

214 Paralegals and Legal Assistants 0.53

215 Miscellaneous Legal Support Workers 0.29

220 Postsecondary Teachers 4.47

260 Artists and Related Workers 0.25

263 Designers 1.12

274 Dancers and Choreographers 0.01

275 Musicians, Singers, and Related Workers 0.16

280 Announcers 0.07

281 News Analysts, Reporters, and Correspondents 0.32

282 Public Relations Specialists 0.42

290 Broadcast and Sound Engineering Technicians and Radio Operators and Other Media and 

Communication Equipment Workers 0.11

291 Photographers 0.10

292 Television, Video, and Motion Picture Camera Operators and Editors 0.04

300 Chiropractors 0.05

301 Dentists 0.21

303 Dietitians and Nutritionists 0.22

304 Optometrists 0.06

305 Pharmacists 0.93

306 Physicians and Surgeons 2.52

311 Physician Assistants 0.16

312 Podiatrists 0.02

313 Registered Nurses 5.79

315 Occupational Therapists 0.21

316 Physical Therapists 0.49

320 Radiation Therapists 0.02

321 Recreational Therapists 0.06

322 Respiratory Therapists 0.12

325 Veterinarians 0.17

330 Clinical Laboratory Technologists and Technicians 0.83

331 Dental Hygienists 0.16

332 Diagnostic Related Technologists and Technicians 0.23

340 Emergency Medical Technicians and Paramedics 0.07

341 Health Diagnosing and Treating Practitioner Support Technicians 0.17
A-7 

Occupation 

Code Occupation title 

Frequency Share 

( percent)

350 Licensed Practical and Licensed Vocational Nurses 0.26

351 Medical Records and Health Information Technicians 0.06

352 Opticians, Dispensing 0.03

353 Miscellaneous Health Technologists and Technicians 0.09

360 Nursing, Psychiatric, and Home Health Aides 0.46

361 Occupational Therapist Assistants and Aides 0.00

362 Physical Therapist Assistants and Aides 0.04

364 Dental Assistants 0.07

365 Medical Assistants and Other Healthcare Support Occupations 0.25

371 First-Line Supervisors/Managers of Police and Detectives 0.21

372 First-Line Supervisors/Managers of Fire Fighting and Preventions Workers 0.06

374 Fire Fighters 0.18

375 Fire Inspectors 0.02

380 Bailiffs, Correctional Officers, and Jailers 0.25

382 Detectives and Criminal Investigators 0.31

384 Miscellaneous Law Enforcement Workers 0.02

385 Police Officers 0.91

392 Security Guards and Gaming Surveillance Officers 0.37

394 Crossing Guards 0.00

402 Cooks 0.17

404 Bartenders 0.15

405 Combined Food Preparation and Serving Workers, Including Fast Food 0.03

406 Counter Attendants, Cafeteria, Food Concession, and Coffee Shop 0.01

411 Waiters and Waitresses 0.45

412 Food Servers, Nonrestaurant 0.02

413 Dining Room and Cafeteria Attendants, Bartender Helpers, and Miscellaneous Food 

Preparation and Serving Related Workers 0.02

415 Hosts and Hostesses, Restaurant, Lounge, and Coffee Shop 0.03

420 First-Line Supervisors/Managers of Housekeeping and Janitorial Workers 0.06

421 First-Line Supervisors/Managers of Landscaping, Lawn Service, and Groundskeeping 

Workers 0.07

422 Janitors and Building Cleaners 0.21

423 Maids and Housekeeping Cleaners 0.08

424 Pest Control Workers 0.02

425 Grounds Maintenance Workers 0.15

434 Animal Trainers 0.01

441 Motion Picture Projectionists 0.00

442 Ushers, Lobby Attendants, and Ticket Takers 0.01

443 Miscellaneous Entertainment Attendants and Related Workers 0.05

446 Funeral Service Workers 0.01

450 Barbers 0.01

452 Miscellaneous Personal Appearance Workers 0.03

453 Baggage Porters, Bellhops, and Concierges 0.03

454 Tour and Travel Guides 0.03

455 Transportation Attendants 0.15

460 Child Care Workers 0.23

461 Personal and Home Care Aides 0.07

462 Recreation and Fitness Workers 0.30

464 Residential Advisors 0.05

470 First-Line Supervisors/Managers of Retail Sales Workers 2.34

471 First-Line Supervisors/Managers of Non-Retail Sales Workers 1.48

474 Counter and Rental Clerks 0.04
A-8 

Occupation 

Code Occupation title 

Frequency Share 

( percent)

475 Parts Salespersons 0.04

476 Retail Salespersons 2.13

480 Advertising Sales Agents 0.45

481 Insurance Sales Agents 0.75

482 Securities, Commodities, and Financial Services Sales Agents 1.04

483 Travel Agents 0.14

485 Sales Representatives, Wholesale and Manufacturing 2.67

490 Models, Demonstrators, and Product Promoters 0.02

492 Real Estate Brokers and Sales Agents 0.66

493 Sales Engineers 0.12

500 First-Line Supervisors/Managers of Office and Administrative Support Workers 2.16

501 Switchboard Operators, Including Answering Service 0.02

502 Telephone Operators 0.03

510 Bill and Account Collectors 0.14

511 Billing and Posting Clerks and Machine Operators 0.23

512 Bookkeeping, Accounting, and Auditing Clerks 0.89

514 Payroll and Timekeeping Clerks 0.12

515 Procurement Clerks 0.04

516 Tellers 0.15

520 Brokerage Clerks 0.01

522 Court, Municipal, and License Clerks 0.06

523 Credit Authorizers, Checkers, and Clerks 0.05

525 Eligibility Interviewers, Government Programs 0.13

526 File Clerks 0.13

530 Hotel, Motel, and Resort Desk Clerks 0.05

531 Interviewers, Except Eligibility and Loan 0.13

533 Loan Interviewers and Clerks 0.11

534 New Accounts Clerks 0.01

535 Correspondence Clerks and Order Clerks 0.08

536 Human Resources Assistants, Except Payroll and Timekeeping 0.05

540 Receptionists and Information Clerks 0.38

541 Reservation and Transportation Ticket Agents and Travel Clerks 0.17

550 Cargo and Freight Agents 0.01

551 Couriers and Messengers 0.08

552 Dispatchers 0.12

553 Meter readers, Utilities 0.01

555 Postal Service Mail Carriers 0.25

560 Production, Planning and Expediting Clerks 0.45

561 Shipping, Receiving, and Traffic Clerks 0.15

562 Stock Clerks and Order Filers 0.28

563 Weighers, Measurers, Checkers, and Samplers, Record keeping 0.03

570 Secretaries and Administrative Assistants 2.16

580 Computer Operators 0.24

581 Data Entry Keyers 0.29

582 Word Processors and Typists 0.08

583 Desktop Publishers 0.02

584 Insurance Claims and Policy Processing Clerks 0.21

585 Mail Clerks and Mail Machine Operators, Except Postal Service 0.04

586 Office Clerks, General 0.68

590 Office Machine Operators, Except Computer 0.02

591 Proofreaders and Copy Markers 0.03

592 Statistical Assistants 0.03
A-9 

Occupation 

Code Occupation title 

Frequency Share 

( percent)

604 Graders and Sorters, Agricultural Products 0.01

605 Miscellaneous Agricultural Workers, Including Animal Breeders 0.14

613 Logging Workers 0.01

620 First-Line Supervisors/Managers of Construction Trades and Extraction Workers 0.32

621 Boilermakers 0.00

622 Brickmasons, Blockmasons, and Stonemasons 0.01

624 Carpet, Floor, and Tile Installers and Finishers 0.02

626 Construction Laborers 0.12

630 Paving, Surfacing, and Tamping Equipment Operators 0.00

632 Miscellaneous Construction Equipment Operators 0.04

633 Drywall Installers, Ceiling Tile Installers, and Tapers 0.01

636 Glaziers 0.01

644 Pipelayers, Plumbers, Pipefitters, and Steamfitters 0.08

646 Plasterers and Stucco Masons 0.00

651 Roofers 0.01

652 Sheet Metal Workers 0.02

653 Iron and Steel Workers 0.01

660 Helpers, Construction Trades 0.01

666 Construction and Building Inspectors 0.08

670 Elevator Installers and Repairers 0.01

671 Fence Erectors 0.00

672 Hazardous Materials 0.01

673 Highway Maintenance Workers 0.01

675 Septic Tank Servicers and Sewer Pipe Cleaners 0.00

680 Derrick, Rotary Drill, and Service Unit Operators, and Roustabouts, Oil, Gas, and Mining 0.00

682 Earth Drillers, Except Oil and Gas 0.00

683 Explosives Workers, Ordnance Handling Experts, and Blasters 0.00

694 Miscellaneous Extraction Workers, Including Roof Bolters and Helpers 0.00

700 First-Line Supervisors/Managers of Mechanics, Installers, and Repairers 0.23

701 Computer, Automated Teller, and Office Machine Repairers 0.20

702 Radio and Telecommunications Equipment Installers and Repairers 0.11

710 Electrical and Electronics Repairers, Industrial, Utility, and Transportation Equipment 0.01

712 Electronic Home Entertainment Equipment Installers and Repairers 0.01

714 Aircraft Mechanics and Service Technicians 0.07

720 Automotive Service Technicians and Mechanics 0.11

721 Bus and Truck Mechanics and Diesel Engine Specialists 0.03

722 Heavy Vehicle and Mobile Equipment Service Technicians and Mechanics 0.03

724 Small Engine Mechanics 0.01

726 Miscellaneous Vehicle and Mobile Equipment Mechanics, Installers, and Repairers 0.01

730 Control and Valve Installers and Repairers 0.01

731 Heating, Air Conditioning, and Refrigeration Mechanics and Installers 0.04

732 Home Appliance Repairers 0.01

733 Industrial and Refractory Machinery Mechanics 0.09

734 Maintenance and Repair Workers, General 0.11

735 Maintenance Workers, Machinery 0.00

736 Millwrights 0.01

741 Electrical Power-Line Installers and Repairers 0.02

742 Telecommunications Line Installers and Repairers 0.05

743 Precision Instrument and Equipment Repairers 0.04

751 Coin, Vending, and Amusement Machine Servicers and Repairers 0.01

754 Locksmiths and Safe Repairers 0.01

756 Riggers 0.00
A-10 

Occupation 

Code Occupation title 

Frequency Share 

( percent)

761 Helpers--Installation, Maintenance, and Repair Workers 0.00

770 First-Line Supervisors/Managers of Production and Operating Workers 0.94

771 Aircraft Structure, Surfaces, Rigging, and Systems Assemblers 0.00

772 Electrical, Electronics, and Electromechanical Assemblers 0.06

773 Engine and Other Machine Assemblers 0.01

774 Structural Metal Fabricators and Fitters 0.00

775 Miscellaneous Assemblers and Fabricators 0.20

780 Bakers 0.03

781 Butchers and Other Meat, Poultry, and Fish Processing Workers 0.03

783 Food and Tobacco Roasting, Baking, and Drying Machine Operators and Tenders 0.00

784 Food Batchmakers 0.01

790 Computer Control Programmers and Operators 0.01

792 Extruding and Drawing Machine Setters, Operators, and Tenders, Metal and Plastic 0.00

793 Forging Machine Setters, Operators, and Tenders, Metal and Plastic 0.00

794 Rolling Machine Setters, Operators, and Tenders, Metal and Plastic 0.00

795 Cutting, Punching, and Press Machine Setters, Operators, and Tenders, Metal and Plastic 0.01

796 Drilling and Boring Machine Tool Setters, Operators, and Tenders, Metal and Plastic 0.00

800 Grinding, Lapping, Polishing, and Buffing Machine Tool Setters, Operators, and Tenders, 

Metal and Plastic 0.01

801 Lathe and Turning Machine Tool Setters, Operators, and Tenders, Metal and Plastic 0.00

803 Machinists 0.08

804 Metal Furnace and Kiln Operators and Tenders 0.01

806 Model Makers and Patternmakers, Metal and Plastic 0.01

810 Molders and Molding Machine Setters, Operators, and Tenders, Metal and Plastic 0.01

813 Tool and Die Makers 0.03

814 Welding, Soldering, and Brazing Workers 0.05

815 Heat Treating Equipment Setters, Operators, and Tenders, Metal and Plastic 0.00

816 Lay-Out Workers, Metal and Plastic 0.00

820 Plating and Coating Machine Setters, Operators, and Tenders, Metal and Plastic 0.00

821 Tool Grinders, Filers, and Sharpeners 0.00

822 Other Metal Workers and Plastic Workers, Including Milling, Planing, and Machine Tool 

Operators 0.09

823 Bookbinders and Bindery Workers 0.01

824 Job Printers 0.02

825 Prepress Technicians and Workers 0.05

826 Printing Machine Operators 0.05

830 Laundry and Dry-Cleaning Workers 0.02

831 Pressers, Textile, Garment, and Related Materials 0.01

832 Sewing Machine Operators 0.03

834 Shoe Machine Operators and Tenders 0.00

835 Tailors, Dressmakers, and Sewers 0.02

836 Textile Bleaching and Dyeing Machine Operators and Tenders 0.00

842 Textile Winding, Twisting, and Drawing Out Machine Setters, Operators, and Tenders 0.00

845 Upholsterers 0.00

846 Miscellaneous Textile, Apparel, and Furnishings Workers, Except Upholsterers 0.01

850 Cabinetmakers and Bench Carpenters 0.02

851 Furniture Finishers 0.01

853 Sawing Machine Setters, Operators, and Tenders, Wood 0.01

854 Woodworking Machine Setters, Operators, and Tenders, Except Sawing 0.01

855 Miscellaneous Woodworkers, Including Model Makers and Patternmakers 0.01

860 Power Plant Operators, Distributors, and Dispatchers 0.03

861 Stationary Engineers and Boiler Operators 0.05
A-11 

Occupation 

Code Occupation title 

Frequency Share 

( percent)

862 Water and Liquid Waste Treatment Plant and System Operators 0.04

863 Miscellaneous Plant and System Operators 0.02

864 Chemical Processing Machine Setters, Operators, and Tenders 0.04

865 Crushing, Grinding, Polishing, Mixing, and Blending Workers 0.02

871 Cutting Workers 0.01

872 Extruding, Forming, Pressing, and Compacting Machine Setters, Operators, and Tenders 0.01

873 Furnace, Kiln, Oven, Drier, and Kettle Operators and Tenders 0.00

874 Inspectors, Testers, Sorters, Samplers, and Weighers 0.47

875 Jewelers and Precious Stone and Metal Workers 0.02

876 Medical, Dental, and Ophthalmic Laboratory Technicians 0.04

880 Packaging and Filing Machine Operators and Tenders 0.03

881 Painting Workers 0.02

883 Photographic Process Workers and Processing Machine Operators 0.05

885 Cementing and Gluing Machine Operators and Tenders 0.00

891 Etchers and Engravers 0.00

892 Molders, Shapers, and Casters, Except Metal and Plastic 0.01

893 Paper Goods Machine Setters, Operators, and Tenders 0.01

896 Other Production Workers, Including Semiconductor Processors and Cooling and Freezing 

Equipment Operators 0.26

900 Supervisors, Transportation and Material Moving Workers 0.17

903 Aircraft Pilots and Flight Engineers 0.41

904 Air Traffic Controllers and Airfield Operations Specialists 0.06

912 Bus Drivers 0.08

913 Driver/Sales Workers and Truck Drivers 0.54

914 Taxi Drivers and Chauffeurs 0.08

915 Miscellaneous Motor Vehicle Operators, Including Ambulance Drivers and Attendants 0.00

920 Locomotive Engineers and Operators 0.03

930 Sailors and Marine Oilers 0.01

931 Ship and Boat Captains and Operators 0.02

933 Ship Engineers 0.01

935 Parking Lot Attendants 0.01

936 Service Station Attendants 0.02

941 Transportation Inspectors 0.03

942 Miscellaneous Transportation Workers, Including Bridge and Lock Tenders and Traffic 

Technicians 0.01

951 Crane and Tower Operators 0.01

952 Dredge, Excavating, and Loading Machine Operators 0.00

956 Hoist and Winch Operators 0.00

960 Industrial Truck and Tractor Operators 0.05

961 Cleaners of Vehicles and Equipment 0.02

962 Laborers and Freight, Stock, and Material Movers, Hand 0.26

963 Machine Feeders and Offbearers 0.01

964 Packers and Packagers, Hand 0.05

965 Pumping Station Operators 0.01

972 Refuse and Recyclable Material Collectors 0.01

975 Miscellaneous Material Moving Workers; Including Conveyor Operators and Tenders; 

Shuttle Car Operators; and Tank Car, Truck, and Ship Loaders 0.01

SOURCE: Ruggles et al. (2003) and authors’ calculations. 

B-1 

Appendix B—Comparisons With Other Cost Adjustment 

Strategies 

The issue of regional cost adjustment is not new. There have been a number of previous 

attempts to measure geographic variations in the cost of education. Two nationwide 

indexes are particularly noteworthy—Chambers’ Geographic Cost of Education Index 

(GCEI) and Goldhaber’s General Wage Index (GWI). 

The GCEI is a district-specific cost-of-education index. It is a weighted average of price 

indexes for three types of school district inputs—certified personnel, noncertified 

personnel, and nonpersonnel inputs—where the weights reflect the share of the typical 

school district budget devoted to each type of expense (Chambers 1998). The certified 

personnel index is based on a hedonic wage regression of teacher salaries from the NCES 

School and Staffing Survey, while the noncertified personnel index is based on a similar 

analysis of selected occupations from the Current Population Survey (CPS). In both 

cases, the personnel indexes measure the variations in self-reported wages that can be 

attributed to cost factors outside of school district control such as student demographics, 

crime rates or housing costs. The nonpersonnel inputs index reflects the cost of hiring 

contractual personnel (which was estimated from the personnel indexes) and “some 

limited geographic variations in energy prices” (Chambers 1998, page 7). 

The GWI is a state-level version of a Comparable Wage Index (CWI). Goldhaber 

generated the GWI using CPS data on the earnings of college graduates who are not 

teachers. 

Table B-1 presents the correlations between these two indexes and the annual CWIs for 

each school district. As the table indicates, despite the 3-year gap between the earliest 

estimate of the CWI and the latest estimate for the other indexes, the CWI is highly 

correlated with the GCEI. The correlation between the CWI and GCEI is never lower 

than 0.798. At a maximum of 0.423, the correlations between the CWIs and GWI are 

substantially lower, reflecting at least in part the fact that the GWI does not vary within 

states as do the other indexes. 

As table B-2 illustrates, when the average index values are compared across states, the 

correlations between the GWI and CWIs greatly increase. Once the data are aggregated to 

the state level, the GWI and the GCEI are equally well correlated with the CWIs.1

 

1

 The correlations between the CWIs and the GWI are not significantly different at the 5-percent level from 

the corresponding correlations between the CWIs and the GCEI. 
B-2 

Table B-1. District-level correlations among CWI, GCEI, and GWI, by index and year: 1994–2003 

 CWI GCEI GWI 

Index 1997 1998 1999 2000 2001 2002 2003 1994 1994

CWI 

1997 1.0000 

1998 0.9976 1.0000 

1999 0.9784 0.9807 1.0000 

2000 0.9817 0.9839 0.9931 1.0000 

2001 0.9840 0.9884 0.9865 0.9943 1.0000 

2002 0.9850 0.9898 0.9772 0.9874 0.9970 1.0000 

2003 0.9809 0.9863 0.9814 0.9913 0.9978 0.9976 1.0000 

GCEI 

1994 0.8002 0.7978 0.8121 0.8113 0.8074 0.8008 0.8062 1.0000 

GWI 

1994 0.3891 0.3891 0.4233 0.4178 0.3987 0.3846 0.3972 0.4209 1.0000

NOTE: CWI=Comparable Wage Index; GCEI=Geographic Cost of Education Index; and GWI=General Wage Index. 

SOURCE: Goldhaber (1999), Chambers (1998), and U.S. Department of Education, National Center for Education 

Statistics, Comparable Wage Index data file, 2006. 

Table B-2. State-level correlations among CWI, GCEI, and GWI, by index and year: 1994–2003 

 CWI GCEI GWI

Index 1997 1998 1999 2000 2001 2002 2003 1994 1994

CWI 

1997 1.0000 

1998 0.9981 1.0000 

1999 0.9883 0.9924 1.0000 

2000 0.9828 0.9878 0.9967 1.0000 

2001 0.9792 0.9854 0.9936 0.9983 1.0000 

2002 0.9777 0.9847 0.9925 0.9972 0.9991 1.0000 

2003 0.9720 0.9799 0.9877 0.9945 0.9977 0.9991 1.0000 

GCEI 

1994 0.7465 0.7411 0.7435 0.7450 0.7465 0.7421 0.7400 1.0000 

GWI 

1994 0.8327 0.8293 0.8305 0.8283 0.8231 0.8187 0.8160 0.8385 1.0000

NOTE: CWI=Comparable Wage Index; GCEI=Geographic Cost of Education Index; and GWI=General Wage Index. 

SOURCE: Goldhaber (1999), Chambers (1998), and U.S. Department of Education, National Center for Education 

Statistics, Comparable Wage Index data file, 2006. 

In addition to such nationwide estimates of geographic cost variations, there are a number 

of state-specific estimates of education cost. For example, Florida uses a market-basket 

estimate of the cost of living to adjust its school-finance formula for geographic cost 

differentials. Texas uses a teacher cost index for similar purposes. As table B-3 

illustrates, the CWI is also reasonably well-correlated with the cost indexes used in the 

Texas and Florida school finance formulas for the 2003–04 school year. 

The CWI is even more highly correlated with recent updates to the Texas Cost of 

Education Index (CEI). The cost index used in the Texas school finance formula has not 

been updated since its adoption in 1991; thus, it reflects the pattern of teacher 

compensation in 1989. Taylor (2004) estimates a new teacher cost index based on 

personnel records from the 2000 through 2003 school years and using individual teacher 
B-3 

fixed effects to control for variations in teacher characteristics. The correlations between 

this updated cost index and the CWI are also presented in table B-3. 

In addition, New York has proposed adjusting its finance formula using a Regional Cost 

Index (RCI) based on median wages among 63 professional, noneducation occupations. 

This wage estimate is philosophically very similar to the CWI—as evidenced by the high 

correlation between the RCI and CWI—but differs in a number of key respects. The New 

York RCI is less detailed geographically, dividing New York state into 9 regions while 

the CWI divides the state into 27 regions. The RCI is based exclusively on OES data for 

2001, and therefore cannot incorporate any adjustment for differences in worker 

demographics across regions. Finally, the CWI uses the national variation in wages to 

estimate average wages for occupations and applies a much larger set of occupations, 

both of which should lead to more precise controls for occupational mix. 

Table B-3. Within-state correlations between CWI and the Florida PLI, Texas CEI, and New York 

RCI, by year: 1997–2003 

CWI year Florida PLI Texas CEI Updated Texas CEI New York RCI

1997 0.6501 0.4651 0.6375 0.8295

1998 0.6247 0.4639 0.6395 0.8268

1999 0.6283 0.4383 0.6339 0.8423

2000 0.6166 0.442 0.6458 0.8350

2001 0.5755 0.4391 0.6429 0.8461

2002 0.6062 0.4503 0.6466 0.8432

2003 0.6212 0.4423 0.6444 0.8501

NOTE: CWI=Comparable Wage Index; PLI=Price Level Index; CEI=Cost of Education Index; and RCI=Regional Cost Index. 

The Florida PLI and the updated Texas CEI are both based on a 3-year average of data ending in 2003. The Texas CEI is 

based on 1989 data, and the New York RCI is based on 2001 data. 

SOURCE: University of Florida and Florida Department of Education (2004), Alexander et al. (2000), New York State 

Department of Education (2003), Taylor (2004), and U.S. Department of Education, National Center for Education Statistics, 

Comparable Wage Index data file, 2006.