46 Monthly Labor Review March 2005

Pay Relatives for Metro Areas Pay Relatives for Metro Areas

W

hen workers and firms make decisions

on where to locate, a number of factors

come into play. One important consideration is how compensation differs across

areas. Workers will tend to be attracted to cities

where the compensation is higher, provided, of

course, that the benefits of more generous pay are

not completely offset by a steeper cost of living or

by undesirable characteristics of the better paying

city, such as higher levels of crime and pollution or

an inferior climate. Firms, by contrast, have an

incentive to relocate to cities in which labor is

cheaper, all else being equal. Besides being an input

for location decisions, information on interarea

variation in compensation is relevant to a host of

other purposes, including wage and salary administration, collective bargaining, and the analysis of

any number of economic issues wherein geography is a consideration.

Under its National Compensation Survey (NCS)

program, the Bureau of Labor Statistics regularly

publishes data on wage levels in metropolitan

areas.1 Large differences across areas are evident

in mean hourly earnings for the local economies as

a whole.2 While these data provide valuable information for many purposes, they are not generally

appropriate for cases in which the data user wishes

to know how compensation differs among areas for

any given job. Metropolitan areas vary greatly in

terms of the types of jobs that are available to the

local labor force, with one area having, say, a high

concentration of professional workers, while another has an above-average share of blue-collar

employment. Thus, one cannot tell from an examination of overall mean wage rates whether one

metropolitan area pays better than another

because it tends to have higher pay for any given

job or because jobs in that area are more concentrated among positions that tend to have

higher rates of pay in all localities. A second,

more technical, reason comparisons of overall

mean levels may be somewhat misleading is that,

even for surveys from the same year, areas will

differ from each other in terms of when the data

were collected. Thus, one area may have wage

data referring primarily to the beginning of the

year, another to the end of the year, when wages

everywhere will tend to be higher because of

inflation and other secular trends.

This article presents calculations of the pay in

metropolitan areas relative to that in the Nation as a

whole which take account of both interarea differences in the composition of jobs and the fact

that surveys occur at different times of the year.

Pay relatives are presented for all jobs that are

covered by the survey and by nine major occupation groups.

Why do wages differ across areas?

Before presenting the methods employed to

produce these pay relatives, it is useful to discuss

briefly why wages differ across areas in the first

place.3 As just noted, pay may differ because the

Using data from the National Compensation Survey,

calculations of pay which take into account

the composition of employment across localities

indicate that measures of interarea pay differentials

which do not control for employment composition

can be misleading

Maury B. Gittleman

Pay relatives for metropolitan

areas in the NCS

Maury B. Gittleman is

a research economist

in the Office of

Compensation and

Working Conditions,

Bureau of Labor

Statistics. E-mail:

gittleman.maury@bls.gov
Monthly Labor Review March 2005 47

composition of jobs differs from city to city. But, as will be

demonstrated subsequently, even after taking account of

interarea differences in employment composition, one still finds

much variation in wages across localities. In simple models of

interarea wages, economic theory suggests that wage rates

should adjust so that individuals will be indifferent between

living in one location as opposed to another. This adjustment

does not mean, however, that wages themselves will be identical

across areas, even in the simplest models. Areas differ in price

levels, so if, for instance, the price of housing is particularly high

in one area, workers would need higher wage rates to be willing

to live in that area. Besides differing in price levels, cities vary in

the amenities they offer. Thus, if an area is particularly attractive

because of a desirable climate, a rich cultural life, or low levels of

crime, then, all else being equal, wages will not have to be as

high for that area to attract and maintain a workforce. Similar

considerations apply to firms. That is, wages should adjust so

that firms, too, are indifferent between locating in one city

compared with another. According to these economic models,

firms will be willing to locate in high-wage cities if the higher

labor costs are offset by lower costs of production in other ways,

such as cheaper rents for office space.

Another explanation for why wages differ across localities

relates to the process of adjustment, which, in theory, eventually

leads to indifference among both workers and firms with respect

to location decisions. As an example, suppose an area produces

manufactured goods for export. Then an increase in the price of

the dollar relative to other currencies will make it harder for

the area to compete in world markets, applying downward

pressure on wages in the area. This reduction in wages will

provide an incentive for workers to migrate to other cities, and,

over time, the declining pool of workers available in that area will

begin to apply upward pressure on wages. This process of

adjustment may occur over a long time in areas all across the

Nation, or it may be incomplete, resulting in an ongoing presence

of interarea wage differentials that are not offset by differences

in the cost of living or the level of amenities.

Methodology

There are a number of different ways to calculate interarea

pay differences that take into account the variation in

employment composition across localities. One approach is

to gather information for each area on a fixed set of jobs. This

approach is the one that was used by the BLS Occupational

Compensation Survey (OCS) program.4 A list of jobs was

established in which a job was defined by occupation and

the level of work in that occupation, and, for each establishment in the sample, wage data were collected for all jobs on

the list. Then, with employment composition fixed to be the

same across areas through the use of employment weights

for the Nation as a whole, a mean wage was calculated for

each area and compared with the average for the Nation.

Such an approach is no longer feasible, however, under the

NCS, the survey that replaced the OCS. Instead of gathering

wage data on a preselected list of jobs for all localities, as the

OCS did, the NCS randomly selects jobs in each establishment

in the sample, with the intention of representing more fully

the employment patterns and occupational mix of each

locality.

Given that the set of jobs the NCS surveys is no longer

identical across localities, a different approach to calculating

pay relatives is required. The technique used here is based

on multivariate regression analysis, which allows one to

control for the influence of job characteristics related to the

pay level, so that one can make comparisons of like jobs.5 A

simple example affords some intuition into how the regression

technique works. Suppose that there are two areas, A and B,

each of which has only two types of jobs, represented by

occupations X and Y. Suppose also that (1) Y is the higher

paying occupation, (2) 75 percent of all jobs in area A are in

occupation Y, and (3) 25 percent of the jobs in area B are in

occupation Y. Then, if the mean wage in each of the occupations

is the same in both localities, overall mean wages will be higher

in area A simply because that area has the greater proportion of

employment in occupation Y. But if, instead, one compared the

mean wages for occupation X across the two areas and the

mean wages for occupation Y across the two areas, then, taking

account of the employment composition in each of the two areas,

one would arrive at the conclusion that the two areas pay equally

well. Loosely speaking, this comparison of jobs in one area with

their counterparts in another is what a regression model does.

In practice, there are, of course, many different occupations

and many other dimensions along which jobs differ. For example,

some jobs are unionized while others are not, and some jobs are

full time while others are part time. But the idea is the same as

when there are only two types of jobs: the regression model will

compare the pay for an employee in one area who, say, is working

full time, is a member of a union, and is in a given occupation

with the pay for workers in jobs with the same kind of attributes in other areas. All jobs are considered simultaneously,

and the end result is an estimate of how pay differs, on

average, across areas for any given job.

In the NCS, besides area, the factors that influence the pay

level can be divided into two categories: job attributes and

establishment characteristics.6 Variables representing each of

these categories are included in the regression model as

controls. Perhaps the most important job attribute is the

occupation, in light of the large differences in pay across

occupations. As noted, one does not want to consider an area

high paying just because it has a large share of occupations

that pay above-average wages everywhere. The NCS also has

information on what is known as the work level, wherein each

job is rated on a scale from 1 to 15, with the higher numbers cor-
48 Monthly Labor Review March 2005

Pay Relatives for Metro Areas

responding to higher levels of skills and responsibilities.7

Because of the strong relationship between work level and

pay, work level is an important explanatory variable in the

regression model.

A third job attribute is whether a position is covered by a

collective bargaining agreement (the union status of the job).

Because pay tends to be higher for jobs covered by a collective

bargaining agreement than for those not so covered,8 controlling

for union status avoids judging an area higher paying merely

because it has a higher proportion of unionized jobs. Two other

job attributes that are associated with wages are whether the job

is full time or part time (full-time jobs tend to pay more than their

part-time equivalents9) and whether incentive pay accounts for

a portion of wages.

As regards establishment characteristics, a large body of

research has noted the presence of interindustry wage differentials—that is, the payment of higher wages in some industries than others, even after controlling for differences in the

characteristics of workers.10 Thus, it is important to include

industry affiliation as an explanatory variable. It also is well

known that pay tends to increase with establishment size,

indicating that one should include a control for this factor as

well.11 In addition, rates of pay tend to differ by the ownership of

the establishment (that is, whether it is operated by the private

sector or by a State or local government) and whether the

establishment is for profit or nonprofit.

Finally, because the collection of data for a given year occurs

over a time span long enough for wages to change because of

inflation, business cycle fluctuations, and other trends, it is

important to control for the time when the data for a given job

were collected.

A regression model for interarea comparisons that includes

all of the foregoing job attributes and establishment characteristics can be written as

where j is an index for a job in a given establishment,lnWAGE

is the logarithm of the average of the hourly wage for all

workers holding a given job, and AREA, OCC, IND, WORKLEVEL, and QUARTER are vectors of dummy variables for

area (metropolitan areas plus nonmetropolitan areas, grouped

into Census divisions), (three-digit) occupation, (two-digit)

industry, work level, and calendar quarter, respectively. Also,

A, O, I, and Q denote the number of areas, occupations,

industries, and quarters, respectively. Finally, FT indicates

whether the job is full time or part time, STATE and LOCAL

whether the job is located at a government establishment,

PROFIT whether the establishment is in the for-profit sector,

UNION whether the job is covered by a collective bargaining

agreement, and INCENTIVE whether some part of

compensation is based on incentives, and LESTABSIZE

designates the logarithm of establishment size.

The model in equation (1) can be estimated with the

statistical technique known as weighted least squares, where

the weights are used to make the sample representative of

hours worked within the part of the national economy that is

in the scope of the survey. The specification of the dependent

variable, hourly wage, in logarithmic form follows standard

practice in labor economics. With such a specification, a

coefficient will approximate the percentage increase in hourly

wages that is associa ted with a one-unit increase in the

corresponding explanatory variable.

12

The first step in making interarea wage comparisons is to

calculate pay relatives for each of the nine major occupation

groups, a task that is done by estimating the model of equation

(1) separately by major occupation group. The coefficients that

are of primary interest from the equations for each of these groups

are those for each area: the ’s. Each of these coefficients

measures, for a particular major occupation group, the pay in a

given area relative to an area—known as the omitted area—

that, for technical reasons, is not represented by any variable in

the regression. What is of consequence, however, is not how

wages in a given area compare with those in the omitted area,

but how they compare with those in the Nation as a whole.

To obtain an estimate of this measure, it is necessary to

transform the coefficients with the use of the formula

 (2)

where PAYRELb is the pay for area b relative to that in the

Nation as whole for the major occupation group under study,

 is the coefficient for that area, and pa is the proportion of

hours worked in area a. As noted, the a2b coefficient

measures the differential in area b relative to the omitted area.

In order to get a measure of how wages in area b compare

with those in the rest of the Nation, it is necessary to normalize

this coefficient, by subtracting a weighted average of all the

area differentials. This weighted average is the second term

in the argument of the exponential function. Exponentiation

yields the ratio of wages in area b to those for the rest of the

Nation. Finally, multiplying by 100 sets the base—the pay in

the Nation—to 100.

Once one obtains pay relatives for each of the major occupation groups, it becomes possible to calculate pay relatives for

the economy as a whole. Loosely speaking, this is done by

taking, for each area, a weighted average of the pay relatives for

each of the major occupation groups, where the weights are the

share of employment in each such group for the Nation as a

whole. An alternative method of calculation would be to use

as weights the share of employment in each major occupation

group for each locality. This approach would have the disadvantage, however, of allowing interarea differences in the

(1)

2 2

2

PAYREL 100exp( ) ,

A

b b a a

a

a p a

=

= − ∑

a2b

a2a

15

1 234

2 2 2

5 6 7 8 9

2

10 11 12 13

2

ln(WAGE ) AREA OCC WORKLEVEL

UNION FT INCENTIVE IND LESTABSIZE

TATE LOCAL PROFIT QUARTER ,

A O

j a aj o oj w

a o w

I

j j j i ij j

i

Q

jjj q j

q

S

a a a a

a a a a a

a a a e a

===

=

=

= + ∑ + + ∑ ∑

+ + + + + ∑

+++ + + ∑
Monthly Labor Review March 2005 49

United States ...................................................................... 100 ... 100 ...

Amarillo, TX ..................................................................................................................

191 70 179 78

Anchorage, AK ...........................................................................................................

1109 71107 25

Atlanta, GA ...................................................................................................................

1103 21 110 18

Augusta-Aiken, GA-SC ............................................................................................

194 62 196 53

Austin-San Marcos, TX .......................................................................................... 100 34 1102 37

Birmingham, AL ..........................................................................................................

193 63 185 71

Bloomington, IN .........................................................................................................

192 68 190 62

Bloomington-Normal, IL ..........................................................................................

1104 18 1106 28

Boston-Worcester-Lawrence, MA-NH-ME-CT ...................................................

1111 31121 3

Brownsville-Harlingen-San Benito, TX .............................................................

184 81 167 81

Buffalo-Niagara Falls, NY ......................................................................................

1101 28 1109 19

Charleston-North Charleston, SC ......................................................................

195 58 196 54

Charlotte-Gastonia-Rock Hill, NC-SC ............................................................... 99 43 105 30

Chicago-Gary-Kenosha, IL-IN-WI .........................................................................

1106 14 1109 21

Cincinnati-Hamilton, OH-KY-IN ............................................................................... 100 38 1106 26

Cleveland-Akron, OH ............................................................................................... 100 31 102 38

Columbus, OH ............................................................................................................. 99 41 101 41

Corpus Christi, TX ....................................................................................................

189 78 182 76

Dallas-Fort Worth, TX .............................................................................................. 100 37 1106 27

Dayton-Springfield, OH ........................................................................................... 99 45 104 31

Denver-Boulder-Greeley, CO ................................................................................

1103 19 1111 16

Detroit-Ann Arbor-Flint, MI ....................................................................................

1107 11 1112 12

Elkhart-Goshen, IN ..................................................................................................

196 54 188 67

Fort Collins-Loveland, CO .....................................................................................

198 49 197 51

Grand Rapids-Muskegon-Holland, MI ..............................................................

1102 24 100 43

Great Falls, MT ..........................................................................................................

189 79 182 77

Greensboro-Winston Salem-High Point, NC .................................................

199 44 191 61

Greenville-Spartanburg-Anderson, SC ............................................................

197 51 192 59

Hartford, CT .................................................................................................................

1113 21124 2

Hickory-Morganton-Lenoir, NC ............................................................................ 99 42 184 75

Honolulu, HI .................................................................................................................

1105 17 99 46

Houston-Galveston-Brazoria, TX ....................................................................... 99 40 99 47

Huntsville, AL ..............................................................................................................

197 52 1114 10

Indianapolis, IN ..........................................................................................................

1102 25 103 34

Iowa City, IA ................................................................................................................

198 47 1109 20

Johnstown, PA ............................................................................................................

187 80 184 73

Kalamazoo-Battle Creek, MI ................................................................................ 99 39 1101 42

Kansas City, MO-KS ................................................................................................. 98 48 1108 23

Knoxville, TN ...............................................................................................................

193 64 192 60

Lincoln, NE ...................................................................................................................

191 72 190 64

Los Angeles-Riverside-Orange County, CA ..................................................

1106 15 1109 22

Louisville, KY-IN .......................................................................................................... 100 30 1101 40

Melbourne-Titusville-Palm Bay, FL ....................................................................

190 73 1112 13

Memphis, TN-AR-MS ...................................................................................................

199 46 198 48

Miami-Fort Lauderdale, FL ....................................................................................

194 61 192 58

Milwaukee-Racine, WI ............................................................................................. 102 27 1113 11

Minneapolis-St. Paul, MN-WI ................................................................................

1109 61118 8

Mobile, AL .....................................................................................................................

190 75 188 68

New Orleans, LA .......................................................................................................

193 65 190 65

New York-Northern New Jersey-Long Island, NY-NJ-CT-PA .......................

1111 41118 4

Norfolk-Virginia Beach-Newport News, VA-NC ..............................................

192 67 190 63

Ocala, FL ......................................................................................................................

190 77 177 80

Oklahoma City, OK ....................................................................................................

192 69 185 72

Orlando, FL ..................................................................................................................

191 71 178 79

Philadelphia-Wilmington-Atlantic City, PA-NJ-DE-MD ...................................

1108 10 1116 9

Phoenix-Mesa, AZ .................................................................................................... 101 29 100 44

Pittsburgh, PA .............................................................................................................

196 56 97 52

Portland-Salem, OR-WA ..........................................................................................

1103 22 1111 14

Providence-Fall River-Warwick, RI-MA ............................................................

1108 9193 57

Raleigh-Durham-Chapel Hill, NC ......................................................................... 100 32 1118 6

Reading, PA .................................................................................................................

1102 26 1101 39

Reno, NV ....................................................................................................................... 100 36 188 66

Richland-Kennewick-Pasco, WA ........................................................................

1102 23 1104 32

Richmond-Petersburg, VA .....................................................................................

198 50 102 35

Rochester, NY ............................................................................................................ 100 35 1111 15

Rockford, IL ................................................................................................................. 100 33 197 50

Table 1.

Metropolitan area

Relative Rank Relative Rank

With controls Without controls

Pay relatives for metropolitan areas, July 2002
50 Monthly Labor Review March 2005

Pay Relatives for Metro Areas

distribution of employment by major occupation group to affect

the estimates. In practice, though, the two methods yield similar

results.

Before presenting the estimates of the pay relatives, it may be

useful to briefly discuss their reliability. It is obviously too costly

to collect wage data for the entire population—that is, for every

job in every establishment in every area. Instead, a random

sample of jobs is selected which is designed to be representative

of the portion of each locality’s economy that is within the scope

of the survey. As is always the case when a sample is used

instead of a census, sampling error is present, which merely

means that if new samples were chosen at random and wage

data collected, estimates derived from each of these samples will

differ from each other. It is important to measure the sampling

error associated with each estimate in order to assess whether

the difference between an estimate of average pay for a given

area and that for the Nation is likely to be the result of sampling

error or of true differences in pay levels.

To perform this assessment—known as conducting a test of

statistical significance—the first step is to form a confidence

interval for the normalized coefficient for each area. The

confidence interval indicates the range into which a given

normalized coefficient will fall if any difference between pay

in a given area and that of the Nation is attributable solely to

sampling error. This confidence interval can be of different

sizes. What is used here is a 90-percent confidence interval.

If there is truly no difference between area and national pay,

and if one could estimate the normalized coefficients from all

possible samples of the population, then a confidence interval of this size indicates the range into which the normalized

coefficients should fall 90 percent of the time. If the actual

estimate falls outside of the confidence interval, then the

difference is said to be statistically significant at the 10-

percent level, allowing one to say with some assurance that

there truly is a difference between the average area wage and

that for the Nation as a whole. The estimates of pay relatives

provided in the next section indicate whether the difference

between area and national wages is statistically significant.

A second type of comparison which is of interest is that

between two areas. For each pairwise comparison, one can

calculate a 90-percent confidence in terval and determine whether

the difference in pay between two areas is statistically significant.

Unfortunately, limitations of space preclude the results of such

tests from being presented in this article: there simply are too

many pairwise comparisons.

Estimates of pay relatives

The actual estimates of the pay relatives, obtained by the

methods described in the previous section, make use of the

NCS Occupational Wages 2002 data for the Nation as a whole.13

In this survey, data are collected from 154 areas, chosen at random

to represent the United States. Some 81 of these localities—

those in larger urban areas—are defined by the Office of

Management and Budget as either Metropolitan Statistical Areas

(MSA’s) or Consolidated Metropolitan Statistical Areas (CMSA’s).

Areas that are not part of an MSA or a CMSA—individual counties

or parishes—are considered nonmetropolitan areas. Pay

relatives are presented only for the metropolitan areas, because

the sample sizes for the nonmetropolitan areas tend to be too

small to obtain reliable estimates.

Table 1 presents pay relatives for the economy as a whole for

the 81 areas, showing the index of the rate of pay for each area

relative to that of the rate of pay for the United States, as well as

the rank of each area. In addition, the table indicates whether the

difference between the pay in a given area and that in the

Nation as a whole is statistically significant (at the 10-percent

level). Pay is highest in San Francisco, whose index of 118

Sacramento-Yolo, CA ..............................................................................................

1108 81118 5

Salinas, CA ..................................................................................................................

1111 51107 24

San Antonio, TX ........................................................................................................

194 60 100 45

San Diego, CA ............................................................................................................

1106 13 105 29

San Francisco-Oakland-San Jose, CA ...........................................................

1118 11130 1

Seattle-Tacoma-Bremerton, WA .................................................

1107 12 1118 7

Springfield, MA ...........................................................................................................

1106 16 187 70

Springfield, MO ...........................................................................................................

190 74 187 69

St. Louis, MO-IL ..........................................................................................................

196 55 104 33

Tallahassee, FL ..........................................................................................................

190 76 194 56

Tampa-St. Petersburg-Clearwater, FL ..............................................................

193 66 184 74

Visalia-Tulare-Porterville, CA ...............................................................................

197 53 195 55

Washington-Baltimore, DC-MD-VA-WV ................................................................

1103 20 1111 17

York, PA .........................................................................................................................

196 57 198 49

Youngstown-Warren, OH ........................................................................................

195 59 1102 36

1

 Significantly different from Nation at 10-percent level.

Metropolitan area

Relative Rank Relative Rank

With controls Without controls

Table 1. Continued—Pay relatives for metropolitan areas, July 2002
Monthly Labor Review March 2005 51

Pay relatives for metropolitan areas, by major occupation group and with controls, July 2002

Metropolitan area Total PST Exec Sales Admin Transp Hand Serv

 United States .................................... 100 100 100 100 100 100 100 100 100 100

Amarillo, TX ................................................................

191 194 187 197 191 185 1107 190 189 187

Anchorage, AK .........................................................

1109 1113 1113 98 1110 190 96 1116 1111 1125

Atlanta, GA .................................................................

1103 100 106 1111 1104 1107 104 1106 101 196

Augusta-Aiken, GA-SC ..........................................

194 98 188 190 194 197 99 193 189 192

Austin-San Marcos, TX ........................................ 100 198 198 1106 197 190 193 1107 97 1109

Birmingham, AL ........................................................

193 193 190 101 97 191 98 97 185 191

Bloomington, IN .......................................................

192 187 196 195 188 186 98 1107 195 192

Bloomington-Normal, IL ........................................

1104 195 100 102 189 1114 1127 100 1133 1106

Boston-Worcester-Lawrence,

MA-NH-ME-CT ............................................................

1111 1109 104 103 1115 1113 1107 1118 1114 1113

Brownsville-Harlingen-San Benito, TX ...........

184 196 182 191 190 175 177 180 174 179

Buffalo-Niagara Falls, NY ....................................

1101 199 100 194 1102 1102 1107 1105 1102 1104

Charleston-North Charleston, SC ....................

195 99 192 1110 197 191 193 101 99 185

Charlotte-Gastonia-Rock Hill, NC-SC ............. 99 194 107 105 101 99 96 101 1105 96

Chicago-Gary-Kenosha, IL-IN-WI ......................

1106 1104 1106 100 1108 1113 1105 1109 1108 1103

Cincinnati-Hamilton, OH-KY-IN ............................. 100 193 97 99 101 194 1108 1105 101 1104

Cleveland-Akron, OH ............................................. 100 96 100 104 98 103 1106 104 1109 97

Columbus, OH ........................................................... 99 99 192 94 102 106 103 100 104 96

Corpus Christi, TX ..................................................

189 192 1105 196 188 181 193 185 184 184

Dallas-Fort Worth, TX ............................................ 100 99 105 99 102 100 97 104 96 197

Dayton-Springfield, OH ......................................... 99 196 187 96 196 1105 1107 102 105 100

Denver-Boulder-Greeley, CO ..............................

1103 102 105 108 103 102 103 103 102 102

Detroit-Ann Arbor-Flint, MI ..................................

1107 1110 100 100 1105 1110 1118 1115 1115 103

Elkhart-Goshen, IN ................................................

196 100 98 194 197 197 197 98 1103 191

Fort Collins-Loveland, CO ...................................

198 194 195 99 195 102 1108 197 197 198

Grand Rapids-Muskegon-Holland, MI ............

1102 1104 99 102 199 1102 1104 100 1109 1104

Great Falls, MT ........................................................

189 182 101 188 180 195 192 185 185 99

Greensboro-Winston Salem-High Point, NC ..

199 198 196 188 1105 101 102 102 99 197

Greenville-Spartanburg-Anderson, SC ..........

197 195 193 1108 198 100 100 194 197 193

Hartford, CT ...............................................................

1113 1112 1117 101 1111 1112 1110 1109 1115 1123

Hickory-Morganton-Lenoir, NC .......................... 99 190 1111 196 1104 196 1103 1108 1105 197

Honolulu, HI ...............................................................

1105 1106 1105 1108 1105 102 99 99 1105 1111

Houston-Galveston-Brazoria, TX ..................... 99 1107 1109 98 101 192 101 97 193 95

Huntsville, AL ............................................................

197 101 1108 94 196 195 1113 93 191 189

Indianapolis, IN ........................................................

1102 99 98 107 1106 101 1107 1106 1107 96

Iowa City, IA ..............................................................

198 192 194 190 1105 101 99 1112 197 101

Johnstown, PA ..........................................................

187 186 192 177 186 187 184 192 183 191

Kalamazoo-Battle Creek, MI .............................. 99 1104 193 185 198 1105 100 1114 101 101

Kansas City, MO-KS ............................................... 98 195 191 100 97 101 102 101 1113 195

Knoxville, TN .............................................................

193 195 101 193 193 187 192 196 196 191

Lincoln, NE .................................................................

191 189 190 195 186 189 193 197 191 193

Los Angeles-Riverside-Orange County, CA 1106 1110 1106 1110 1105 1106 192 102 101 1108

Louisville, KY-IN ........................................................ 100 1105 102 1105 196 195 99 102 193 1103

Melbourne-Titusville-Palm Bay, FL ..................

190 189 183 100 190 187 99 184 183 192

Memphis, TN-AR-MS .................................................

199 195 101 1110 198 98 1103 98 198 194

Miami-Fort Lauderdale, FL ..................................

194 196 100 96 195 191 190 187 98 190

Milwaukee-Racine, WI ........................................... 102 191 98 103 101 1112 1109 104 1115 100

Minneapolis-St. Paul, MN-WI ..............................

1109 103 101 1107 1108 1110 1112 1114 1116 1115

Mobile, AL ...................................................................

190 187 100 191 190 192 193 189 189 186

New Orleans, LA .....................................................

193 196 1102 1106 189 190 190 1107 192 184

New York-Northern New Jersey-Long Island,

NY-NJ-CT-PA ...............................................................

1111 1115 1112 1108 1114 1117 96 1108 103 111

Norfolk-Virginia Beach-Newport News,

 VA-NC .........................................................................

192 193 192 192 192 187 193 188 192 197

Ocala, FL ....................................................................

190 189 98 194 190 194 182 193 194 183

Oklahoma City, OK .................................................

192 188 190 184 191 190 1103 98 191 195

Orlando, FL ................................................................

191 192 99 93 193 191 97 97 89 180

Philadelphia-Wilmington-Atlantic City,

PA-NJ-DE-MD ...........................................................

1108 1108 103 1110 1108 103 1106 1111 1122 1105

Phoenix-Mesa, AZ .................................................. 101 100 105 107 103 103 190 1106 98 97

Pittsburgh, PA ...........................................................

196 97 187 96 96 97 96 99 99 99

Portland-Salem, OR-WA ........................................

1103 195 100 106 101 1106 101 103 104 1110

Providence-Fall River-Warwick, RI-MA ..........

1108 1111 1104 1108 1105 1106 198 1105 1107 1115

Raleigh-Durham-Chapel Hill, NC ....................... 100 101 97 96 102 96 106 104 106 197

Reading, PA ...............................................................

1102 1105 1102 192 1103 194 1106 102 1109 101

Reno, NV ..................................................................... 100 195 99 98 198 1102 100 1107 100 1103

Richland-Kennewick-Pasco, WA ......................

1102 102 191 1104 1103 1105 185 191 96 1122

Richmond-Petersburg, VA ...................................

198 97 195 104 98 190 97 99 102 99

Rochester, NY .......................................................... 100 196 1105 99 195 197 197 102 100 1108

Rockford, IL ............................................................... 100 1102 190 196 194 1109 1112 1109 1103 196

Table 2.

 Mach

 oper

Prec

prod
52 Monthly Labor Review March 2005

Pay Relatives for Metro Areas

Continued—Pay relatives for metropolitan areas, by major occupation group and with controls, July 2002

Metropolitan area Total PST Exec Sales Admin Transp Hand Serv

Sacramento-Yolo, CA ............................................

1108 1112 102 1110 1106 1107 104 1109 1111 1110

Salinas, CA ................................................................

1111 1119 1105 1122 1109 1117 196 1111 1104 1108

San Antonio, TX ......................................................

194 99 99 98 195 189 101 195 188 189

San Diego, CA ..........................................................

1106 1111 100 1111 1104 105 96 100 1107 1110

San Francisco-Oakland-San Jose, CA .........

1118 1120 1109 1119 1121 1117 108 1110 1111 1125

Seattle-Tacoma-Bremerton, WA .......................

1107 99 97 107 1106 1114 1107 101 1108 1116

Springfield, MA .........................................................

1106 1109 1104 1110 1106 1106 1116 170 1123 101

Springfield, MO .........................................................

190 187 192 194 184 191 195 193 187 192

St. Louis, MO-IL ........................................................

196 190 191 105 195 100 101 97 102 196

Tallahassee, FL ........................................................

190 189 176 1111 182 191 186 102 196 191

Tampa-St. Petersburg-Clearwater, FL ............

193 190 103 100 195 189 185 94 195 190

Visalia-Tulare-Porterville, CA .............................

197 1107 190 192 198 188 196 192 191 101

Washington-Baltimore, DC-MD-VA-WV ..............

1103 1104 98 103 1107 98 101 98 1105 1105

York, PA .......................................................................

196 198 193 193 195 195 192 101 190 100

Youngstown-Warren, OH ......................................

195 194 183 1104 189 100 1114 1104 1107 186

Table 2.

 Mach

 oper

Prec

prod

1

 Significantly different from Nation at 10-percent level.

NOTE: Major occupation groups are ab breviated as follows: PST,

professional specialty and technical; Exec, executive, administrative, and

managerial; Admin, administrative support, including clerical; Prec prod,

precision production, craft, and repair; Mach oper, machine operators,

assemblers, and inspectors; Transp, transportation and material-moving

occupations; Hand, handlers, equipment cleaners, helpers, and laborers;

Serv, service occupations, except private household.

indicates that pay is 18 percent higher than it is for the Nation

as a whole, and lowest in Brownsville, Texas, which has an

index of 84, 16 percent lower than that for the Nation.

For purposes of comparison, the table also shows “raw” pay

relatives—that is, pay relatives calculated without taking into

account interarea differences in employment composition.1 4

Using raw differentials can be misleading, because there are

important differences between the pay relatives with and without

controls for employment composition. First, there is a lot more

dispersion in the raw pay relatives. For instance, San Francisco,

while still the highest-paying area, has a raw index of 130,

compared with 117 with controls; and Brownsville, while still the

lowest- paying area, has a raw index of 67, 33 percent lower than

that of the rest of the Nation, as opposed to 16 percent lower

with controls. It is evident from these examples, and from the

fact that pay relatives are less dispersed with controls than they

are without, that high-paying areas tend to have a disproportionate share of jobs that are high paying in all areas, and the

reverse is true for low-paying areas. The result is that the raw

differentials tend to exaggerate interarea differences.

Second, while the ranks of metropolitan areas using both types

of pay differentials tend to be quite similar, that is not always the

case. To take the most extreme examples, Melbourne, Florida, is

one of the lowest paid (73rd) areas when employment composition is taken into account, but one of the highest (13th) when

raw differentials are used. This locality has an above-average

share of employment in the high-paying major occupation group

made up of professional specialty and technical occupations

and a below-average share in low-paying service occupations.

By contrast, Providence, Rhode Island is high paying (9th) when

controls are used, but low paying (57th) when they are not.

Providence has a greater-than-average concentration of workers

in low-paying service occupations.

Up to now, the focus of this article has been on pay relatives

for the economy as a whole. Table 2 presents pay relatives,

all of which control for interarea differences in employment

composition, for nine major occupation groups. One question

that can be addressed with reference to this table is whether an

area that is high paying for one major occupation group is high

paying for others. A glance at the table suggests that this is

generally the case, but that there are exceptions. In fact, for more

than three-quarters of the areas, pay is above average in at least

one major occupation group and below average in another. One

glaring example of this discrepancy across major occupation

groups is Springfield, Massachusetts, which, for the economy

as a whole, has a rate of pay that is about average. Yet, Springfield’s pay rate for transportation and material-moving occupations is 30 percent lower than that for the Nation as a whole,

while the area’s pay rate for handlers, equipment cleaners, helpers, and laborers is 23 percent higher.

TO ANSWER THE QUESTION OF HOW PAY DIFFERS across

metropolitan areas for the same job, it is necessary to use a

methodology that takes account of the variation in employment

composition across localities. This article has presented pay

relative estimates from one method of doing so, relying on

regression-based techniques and using National Compensation

Survey data for 2002. The results suggest that it can be misleading to measure interarea pay differentials with mean hourly

wage levels by area that do not control for the fact that the

characteristics of jobs differ from one area to the next.
Monthly Labor Review March 2005 53

Notes

1

 For details on the NCS, see the appendix.

2

 To be more precise, the locality surveys do not actually cover all

employees. Workers in agriculture, private households, and the Federal

Government are excluded, as are those in private establishments or

State and local governments with fewer than 50 employees.

3 Technically, this discussion should be in terms of the full compensation

package—that is, wages plus fringe benefits. But the NCS wages data set

does not contain information on nonwage compensation, so all calculations will refer to wages only. In addition, it is beyond the scope of

this article to assess the extent to which interarea wage differentials are

offset by differences in cost of living or in amenities. For an examination

of interarea compensation and prices, see Report on the American

Workforce (U.S. Department of Labor, 1997), chapter 2, “Interarea

comparisons of compensation and prices.”

4

 For details, see Occupational Compensation Survey: National

Summary, 1996, bulletin 2497 (Bureau of Labor Statistics, March

1998).

5

 An alternative technique for calculating pay relatives with NCS

data is presented in Parastou Karen Shahpoori, “Pay Relatives for

Major Metropolitan Areas,” Compensation and Working Conditions

Online, posted April 28, 2003.

6

 For further details, see Brooks Pierce, “Using the National

Compensation Survey to Predict Wage Rates,” Compensation and

Working Conditions, winter 1999, pp. 8–16.

7

 See the appendix for a description of how the work level is

assigned.

8

 See H. Gregg Lewis, “Union Relative Wage Effects,” in Orley C.

Ashenfelter and Richard Layard, eds., Handbook of Labor Economics,

vol. II (Amsterdam, North-Holland, 1986), chapter 20, pp. 1139–81.

9

 See, for example, Michael K. Lettau, “Compensation in PartTime Jobs versus Full-Time Jobs: What if the Job Is the Same?”

Economics Letters, September 1997, pp. 101–06.

10 See, for example, Alan B. Krueger and Lawrence H. Summers,

“Efficiency Wages and the Inter-Industry Wage Structure,” Econometrica, March 1988, pp. 259–94.

11 See, for example, Walter Y. Oi and Todd L. Idson, “Firm Size and

Wages,” in Orley Ashenfelter and David Card, eds., Handbook of

Labor Economics, vol. III (Amsterdam, North-Holland, 1999),

chapter 33, pp. 2166–2214.

12 Technically, the change in hourly wage rate associated with a

one-unit increase in an explanatory variable is exp(b) – 1, where b is

the coefficient on the explanatory variable. But if b is close to zero,

then exp(b) – 1 will be approximately equal to b.

13 For published estimates using these data, see National Compensation Survey: Occupational Wages in the United States, July

2002, summary 03–02 (Bureau of Labor Statistics, June 2003).

14 Note that the raw differentials presented here are not those one

would obtain by comparing the mean hourly earnings published in

bulletins for individual localities. The most important difference, as

noted in the appendix, is that the calculations here include workers in

private establishments with fewer than 50 employees, while those

made for the locality bulletins do not.

The National Compensation Survey (NCS ) program provides

comprehensive measures of occupational earnings, compensation cost

trends, and the incidence and detailed provisions of benefits. This

article relies on data collected for the occupational earnings part of the

program for the year 2002. All private industries are within the scope

of the survey, with the exception of agriculture and private households.

State and local governments are also within the scope, although the

Federal Government is not. Published earnings estimates for the Nation

as a whole are for private establishments with 1 or more workers and

State and local governments with 50 or more employees. When

earnings estimates are published for a specific area, only those

establishments with 50 or more workers are included. The national

sample is used in the calculations of pay relatives presented in the

article, so workers in private establishments with 1 to 50 employees

are included in these calculations.

The sample of the NCS is selected in three stages. First, geographic

areas are chosen for study, and then, within each area, a representative

sample of establishments is drawn. Within each establishment,

information is collected on a sample of jobs, with the number of jobs

varying with the size of the establishment. A job is defined as the

organization’s narrowest occupational classification. The Bureau of

Labor Statistics then collects data on the hourly wage for each job,

which is an average of the wages of all workers in that job. With the use

of appropriate weights, the NCS is representative of that portion of the

economy within the scope of the survey.

Assigning a work level

Each job in the 2002 NCS was rated on nine factors: knowledge,

supervision received, guidelines, complexity, scope and effect of

the job, personal contacts, purpose of contacts, physical demands,

and work environment. These factors are drawn from the U.S. Office

of Personnel Management’s Factor Evaluation System, which is used

to obtain a grade, and thus a rate of pay, for Federal Government whitecollar employees. A number of points is associated with each level for

each of the factors; summing up the points, one can assign a level

ranging from 1 to 15, with a higher level indicating that a job has

higher skill requirements, more responsibilities, and a correspondingly higher rate of pay.

APPENDIX: The National Compensation Survey