U.S. DEPARTMENT OF LABOR

Bureau of Labor Statistics

 

OFFICE OF COMPENSATION AND

WORKING CONDITIONS

Constructing Interarea Compensation Cost Indexes

With Data from Multiple Surveys

Brooks Pierce,

John Ruser, and

Kimberly D. Zieschang

Working Paper 297

September 1996

Revised October 1996

 

 This paper was presented at the Conference on Research in Income and Wealth: International and Interarea Comparisons

of Prices, Income, and Output, March 15-16, 1996. The opinions expressed in this paper are those of the authors, and do

not represent the policies of the Bureau of Labor Statistics or the views of other BLS staff members.
Constructing Interarea Compensation Cost Indexes with Data from Multiple

Surveys

Abstract. We compare the cost of labor input for 39 areas of the United

States using Törnqvist multilateral (transitive) place to place index numbers

incorporating characteristics-based, hedonic labor characteristics

adjustment as an integral feature. Our characteristics adjustments are

based on establishment data at the level of the approximately 18,000 jobs

priced in the Employment Cost Index (ECI) survey, and worker data at the

level of the 39 areas in the ECI sample from the Current Population

Survey. We find that the compensation parities prior to labor

characteristics adjustment are generally more variable from place to place

than after adjustment, and that the primary determinants of geographical

compensation variations after controlling for industry and occupation are

firm size and unionization from the establishment data, and

education/experience from the worker data.
- iii -

I. INTRODUCTION ...............................................................................................................................1

II. ECONOMIC INDEX NUMBER CONCEPTS INCORPORATING INFORMATION ON THE

CHARACTERISTICS OF HETEROGENEOUS LABOR SERVICES................................................4

III. TÖRNQVIST MULTILATERAL (TRANSITIVE) SYSTEMS OF BILATERAL

COMPENSATION INDEX NUMBERS.................................................................................................9

IV. ESTIMATION OF THE REFERENCE VALUES FOR SHARES, PRICES, AND

DETERMINANTS OF QUALITY.......................................................................................................11

V. AN APPLICATION TO U.S. LABOR COMPENSATION DATA ...............................................14

VI. CONCLUSION ...............................................................................................................................24

VII. APPENDIX A: DATA ...................................................................................................................36

VIII. APPENDIX B: HEDONIC, COUNTRY-PRODUCT DUMMY REGRESSIONS....................40

IX. REFERENCES................................................................................................................................46
I. Introduction

Place to place compensation cost comparisons for areas within the United States

are very much in demand to inform facility location decisions and locality salary

administration policy in both the private and public sectors. The Bureau of Labor

Statistics (BLS) has operated geographically comprehensive surveys measuring locality

wage levels since 1991, primarily to support the Federal Employee Pay Comparability Act

(FEPCA). This law was enacted to align the locality compensation for Federal employees

with the comparable non-Federal workforce. Similar studies oriented more toward place

to place comparisons of compensation are undertaken in the private sector by several

companies, often with specialties in certain industry and/or occupational groups.

Labor services differ in type and quality from area to area. The central problem

this paper considers is how indexes comparing employee compensation costs across

geographic areas that account for the heterogeneity of jobs and workers may be

formulated and calculated. A long-standing approach to the heterogeneity problem, taken

for example by the BLS in the above mentioned Occupational Compensation Survey

program, is to make interarea comparisons only between the same narrowly defined jobs

that exist in every area for which comparisons are to be made. The limitation of this

approach is that the comparisons apply only to the population of jobs that are found in all

areas, while jobs that are specific to only certain areas are excluded from the comparisons.

Another approach, which we follow in this paper, is to define jobs more broadly by

industry and occupation, and to utilize data for all jobs to make interarea comparisons.

Within these broader groups, the compensation rates received by specific jobs are then

related to specific quantitative information on the characteristics of the jobs using a

statistical model, known in the economics and economic statistics literature as a hedonic

model. Heterogeneity is controlled for by employing the parameters estimated in these

hedonic models, fitted using regression analysis, to adjust for observable characteristics of

workers and jobs. This approach has the advantage of covering all jobs in each labor
- 2 -

market. However, it requires additional information about jobs or workers that can be

used as covariates in the hedonic regressions.

To provide a context and rigorous interpretation for our indexes, we begin with a

standard microeconomic framework for input price indexes developed in a long economic

index number literature, positing a model of producer input cost minimization conditioning

on output and exogenously determined input prices. A good statement of this basic

economic input price index framework applied to labor input cost measurement is given in

Triplett (1983). Triplett also discusses the application of hedonic regression methods to

adjust for labor quality.

We take these index number concepts and hedonic labor quality measurement

methods and incorporate them into an integrated, computable index number system. To

construct place to place compensation comparisons, we use a Törnqvist index formula.

We adopt the Törnqvist formula, rather than alternatives also used in geographical price

comparisons such as the Geary (1958)-Khamis (1970) “international prices” system, or an

adjusted Fisher ideal approach, because the Törnqvist framework simultaneously displays

five important features:

• First, the Törnqvist formulas we use for bilateral comparisons have been shown by

Diewert (1976) to be exact for the Translog flexible functional form. By implication,

they accurately accommodate producer’s substitution decisions among types of labor

services as their relative prices differ from place to place.

• Second, Caves, Christensen, and Diewert (1982b) have shown that the Törnqvist

index number is exact even when there are significant differences in the underlying

technology between situations compared. These indexes therefore accommodate

variations in the technology producers select as they consider various site locations.

• Third, Kokoski, Moulton, and Zieschang (KMZ) (1996) provide a closed form for the

class of all systems Törnqvist bilateral index numbers that are transitive, generalizing a

result along these lines introduced by Caves, Christensen, and Diewert (1982a), and

provide a feasible algorithm for computing such systems of index numbers. Clearly,

use of our compensation indexes to inform a salary administration policy for

geographically dispersed organizations would require the transitivity property to
- 3 -

eliminate the possibility of gains and losses accruing to reassigned staff as a sole result

of a series of relocations.

• Fourth, KMZ demonstrate that the Törnqvist multilateral system of parities will

aggregate in a natural way with respect to a given classification hierarchy for types of

labor. This facilitates explaining variations in aggregate compensation in terms of

variations in the component occupations making up the aggregate, an important

property for a system of public compensation statistics.

• Fifth, KMZ have adapted earlier exact index number results from Zieschang (1985,

1988) and Fixler and Zieschang (1992) to incorporate information on variation in the

characteristics of detailed items when making Törnqvist index number comparisons.

In this index number framework, coefficients from hedonic compensation regressions

are used in constructing labor quality adjustment factors for place to place indexes of

compensation rates. Because of the heterogeneity in the measured characteristics of

labor employed within industry/occupation groups across areas, these exact quality

adjustments are important for making accurate compensation comparisons from place

to place.

In Section II we briefly state the microeconomic foundations of our approach

using standard production theory, and show how the aggregate conceptual bilateral area

indexes of this framework can be operationalized using Törnqvist exact and superlative

index numbers. We then show how the differing measured labor services characteristics

that are encountered in various areas can be accounted for in the index framework. In

Section III, we establish the implications of transitivity in our Törnqvist interarea index

system, and in Section IV, we show how transitivity can be imposed with minimal

adjustment of the data. In Section V, we apply this methodology to establishment

microdata on jobs from the U.S. Employment Cost Index (ECI), and

area/occupation/industry data on workers from the Current Population Survey (CPS), and

present labor services characteristics-adjusted interarea wage and compensation indexes

for 39 major urban centers and rest-of-regional-division geographical areas. We conclude

in Section VI.
- 4 -

II. Economic index number concepts incorporating

information on the characteristics of heterogeneous labor

services

Let pi

a be the price or compensation rate in area a, of which there are A areas in

total, of labor services of occupation i. Let qi

a be the corresponding quantity purchased

and let xi

a be the vector of characteristics of the ith job specification for labor services

transacted in area a. Let eh

a represent the total labor services expense of establishment h

in area a, and let qh

a denote the vector of labor services consumed by establishment h in

area a with vector of characteristics xh

a and prices pha .

We suppose that each establishment in area a minimizes the cost of achieving a

given level of output uh

a at expenditure level e ha so that the establishment expense

incurred for a given quality of labor services as determined by the vector x h

a would be

e E u x p pqdu xq h ( ) a

h

a

h

a

h

a

h

a

q h

a

h

a

h

a

h

a

h

a

h

a

h

= = a

′ ≥ 









 ( , , ) min : , , 1 .

where duxq h ( ) a

h

a

h

a

h

a , , is the joint production function of establishment h.1 To reduce

clutter, we condition on and suppress the nonlabor inputs used by establishment h.

We suppose further that an establishment in area a faces a hedonic locus of market

equilibrium prices across the labor services quality spectrum given by p Hx h ( ) a a

h

a = , and

 

1 This particular joint production function is the input distance function, given by

duxq ux q h ( ) a

h

a

h

a

h

a

h

a

h

a

h

a , , sup : , , = 



 



 









 θ θ

θ

1

 is feasible .
- 5 -

that the establishment minimizes the cost of achieving outputs uh

a over the characteristics

of labor services, so that

∇ +∇ () () ′∇ = x h

a

h

a

h

a

h

a

x h

a

p h

a

h

a

h

a

h

a

h

a

h

a

h

Euxp p Euxp ,, ,, 0 a (1)

Since ∇ =∇ x h

a

x

a

h

a h

p Ha and ∇ = p h ( ) a

h

a

h

a

h

a

h

a

h

aEuxp q , , , the latter by the Shephard/Hotelling

lemma, we have

∇ = −∇ ( ) ′

x h

a

h

a

h

a

h

a

x

a

h

a

h

a h

Euxp Hq , , a (2)

If H a is semilog, as generally assumed in hedonic studies, so that

ln H x i

a

i

a

i

a

i

a = + ′ α β (3)

then the characteristics gradient expression can be rewritten

∇ =− ( ) ′

x h

a

h

a

h

a

h

a a

h

a

h

a

h

a E u x p we , , β (4)

where w

p q

p q

i h

a i h

a

i h

a

i h

a

i h

a

i

,

, ,

, ,

=

∑

w

w

w

h N a

h

a

N h

a

q

q

=





















=

1,

,

M ; number of types (occupations) of labor
- 6 -

β

β

β

a

a

N

a

x

x

N ′ =

′

′

























=

1

M ; number of labor services characteristics.

Diewert (1987) considers the area aggregation of individual establishments in the

context of an area production function. We follow this general notion but will require

some modifications to handle the heterogeneity of labor service types and their prices

within and between areas. Turning now to aggregate labor input expense over

establishments in an area, we have

Eux p Euxp ( )( ) aaa a

h

a

h

a

h

a

h

a

h

r r r

,, ,, = ∑

where the → over an argument indicates the concatenation of vectors across

establishments. We then consider the labor services expenditure or cost function in terms

of log transformed price arguments as

Qux p Eux p Eux p Qux p ( )( ) ( ) ( ) aaa a aaa a

h

a

h

a

h

a

h

a

h

h

a

h

a

h

a

h

a

h

r r r r r r , ,ln , , , , , ,ln == = ∑ ∑ .

We aggregate across establishments in area a such that the expenditure weighted

average for characteristics and log-labor services prices represent the indicators

determining area demand behavior, where area item demand for labor services is the sum

of the establishment item demands for the area. We do not require strong aggregation

conditions, but effectively hold the distribution of labor services characteristics and

compensation rates fixed across establishments within area a as in

~ , ,ln , , ln Qux p Qu x p ln

a aa a aa a

x

a a

p

 r r a



 



 = ⊗+ ⊗ + 



 

 ι νι ν  (5)
- 7 -

where

ν ι

ν ι

ι

x

aa a

p

a a a

x x

p p

a

= −⊗

= −⊗

=

⊗ =

r

r ln ln ln

a vector of ones equal in dimension to the number of establishments in area 

Kronecker product

give the deviations of the area means from the individual establishment values for labor

services characteristics and log compensation rates paid.

Diewert (1976) and Caves, Christensen, and Diewert (1982b) have shown, using

the derivatives of the expenditure function with respect to log prices expressed in terms of

observable expenditure shares, that the Törnqvist index number is exact for the Translog

flexible functional form, which differentially approximates any price aggregator function

(i.e., cost of utility, input cost, revenue function) to the second order at a point, and it is

exact even when some of the parameters (those on the first-order terms) of the underlying

aggregator function are different in the two periods or localities compared. We take the

derivative of the area expenditure function with respect to establishment labor cost

weighted aggregate arguments to obtain

( ) ( )

( )

∂

∂

∂

∂

∂

∂ β β

x

Eux p

x

Qux p

x

Q u x p Q ws w

iz

a

aaa a

iz

a

aaa a

izh

a h

a

h

a

h

a

h

a a

h

izx

a

ih

a

h

a

h

izx

a

i

a

ln ~ , ,exp(ln ) ln ~ , ,ln

, ,ln / ~ `

r r = =

= =− =− ∑ ∑

 

 

((6)

( )

∂

∂

∂

∂

∂

∂

ln

ln ~ , ,exp(ln )

ln

ln ~ , ,ln

ln , ,ln / ~

p

Eu x p

p

Qu x p

p

Q u x p Q ws w

i

a

a a a

i

a

a a a

izh

a h

a

h

a

h

a

h

a a

h

ih

a

h

q

h

i

a

r r 



 



 = 



 





 = ∑ ∑= =

(7)

where
- 8 -

w

p q

p q

p q

e

ih

a ih

a

ih

a

ih

a

ih

a

i

ih

a

ih

a

h

= = a

∑

s

e

e

h

a h

a

h

a

h

=

∑

are, respectively, the within firm labor cost shares of occupations and the between firm

labor cost shares of establishments in area a.

Finally, we assume that the area aggregate labor services cost function

ln ~Q ux p , ,ln aaa a 



 



 has a quadratic, “semi-translog” functional form in its arguments with

coefficients of second-order terms independent of location, but with possibly locationspecific coefficients on linear terms. Following CCD (1982b), then, we can derive the

following (logarithmic) index number result:

[ ] ( ) ( ) ( ) ( )

[ ] ( ) ( )( )

[ ] ( ) ( )( )

ln

ln ~ , , ln ln ~ , , ln ln ~ , , ln ln ~ , , ln

ln ~ , , ln ln ~ , , ln ln ln

ln ~ , , ln ln ~ , , ln

ln ln

I

Qu x p Qu x p Qu x p Qu x p

Qu x p Qu x p p p

Qu x p Qu x p x x

ab

aa b b aa a a bb b b bb a a

p

aa a a

p

bb b bb a

x

aa a a

x

bb b b b a

=

−+−

∇ +∇ −

∇ +∇ −

1

2

1

2

1

2

rrrr

r r

r r

 =

 +

(8)

Substituting (6) and (7) into (8), we have

ln ln ln ln . I T w w p p w wx x ( )( ) ( )( ) ab ab

i

a

i

b

i

b

i

a

iz

a

i

a

iz

b

i

b

iz

b

iz

a

i z

=≡ + −− + −





 

 ∑ ∑  1

2 β β (9)

This is an extremely flexible result that permits all parameters of the semi-log

“hedonic” labor services compensation equations to differ by area, and reflects

establishments’ optimizing behavior in considering location and the available

characteristics of labor services.
- 9 -

III. Törnqvist Multilateral (Transitive) Systems of Bilateral

Compensation Index Numbers

In another paper, Caves, Christensen, and Diewert (CCD, 1982a) noted that the

system of bilateral Törnqvist interarea indexes is not transitive, but developed a simply

calculated multilateral variant satisfying the transitivity property. Following Kokoski,

Moulton, and Zieschang (KMZ, 1996), we apply the following general implication of

transitivity for this class of index number:

[ ]() ( )

( ) [ ]

wp x wp x

wx x x w w

w p p pw w

i

a

i

b

iz

a

iz

b

n z

i

a

i

a

iz

b

iz

a

i z

iz i iz

b

iz

a

iz iz

b

i

b

iz

a

i

a

i z i z

i i

b

i

a

i

i i

b

i

a

i

ln ln

ln ln ln ( )

− 



 



 



 



 − − 



 



 



 



 















= − −+ −

+ − +− −

∑ ∑ ∑ ∑

∑∑ ∑∑

∑ ∑

β β

β ββ 00 0

0 0

(10)

where

xiz

0 = a reference characteristic z for index item i across the entire region

βiz

0 = a reference coefficient for the characteristic z of item i in a semi-log

hedonic equation explaining specification price across the entire

region

pi

0 = a reference price for item i across the entire region

wi

0 = a reference share for item i for the entire region, where wi

i

0

∑ = 1.

If this condition holds, the multilateral Törnqvist index has the form
- 10 -

( )( ) ( )( )

( )( ) ( )( )

ln ln ln

ln ln

T w wx x w w p p

w wx x w w p p

ab

iz i iz

b

i

b

iz

b

iz

i z

i i

b

i

b

i

i

iz i iz

a

i

a

iz

a

iz

i z

i i

a

i

a

i

i

=− + − + + − −

− + −+ + −





 





∑ ∑ ∑

∑ ∑ ∑

1

2

00 0 1

2

0 0

1

2

00 0 1

2

0 0

β β

β β

(11)

The proof is given in KMZ (1996). CCD(1982a) showed that application of the

EKS principle to a system of bilateral Törnqvist indexes yields the price component of

above formula with the reference shares and log prices set at their simple arithmetic

averages across areas. Clearly, these simple averages could also be calculated as total

compensation expenditure-weighted averages. Extension for the EKS/CCD approach to

our labor quality-adjusted index given by equation (11) would simply require that the 0-

superscripted terms comprising the product of the reference hedonic coefficients of each

characteristic with the reference share weight of the index items be set to the regional

averages for these terms. In this paper, however, we use the KMZ regression method for

determining the reference parameters, as detailed in section IV below.

Analysis of the contribution of labor quality indicators to levels of place to place

indexes. Because Törnqvist indexes are linear in the log differences of detailed, qualityadjusted specification prices, the contribution of each quality indicator, say, full time

status, to the quality level ratio between two areas can be readily calculated by

exponentiating the appropriate weighted sums of log price differences. These sums would

be calculated from the transitive expression for the index given above, where it is

expressed in terms of locality weights averaged with reference weights and price

differentials from reference prices. The contribution to the level of lnT ab of labor

characteristic z would simply be the subordinate sum

( )( )

( )( )

lnC w wx x

w wx x

z

ab

iz i iz

b

i

b

iz

b

iz

i

iz i iz

a

i

a

iz

a

iz

i

=− + −

−− + −





 





∑

∑

1

2

00 0

1

2

00 0

β β

β β .
- 11 -

IV. Estimation of the Reference Values for Shares, Prices, and

Determinants of Quality

Adjusting for labor quality from place to place. In the present study, we utilize

wage and compensation cost data from the Employment Cost Index (ECI) survey. This

survey contains a limited amount of information about each surveyed job. We augment

the observed characteristics of jobs with additional data on worker characteristics from the

Current Population Survey (CPS).

We follow the method of Kokoski et al. (1994), who construct interarea price

indexes for consumer goods using country-product dummy regression (Summers (1973)).

We first estimate wage and compensation costs regressions for each broadly defined job,

where the covariates include worker and job attributes and local area dummies. Let pij

a

represent the wage in the jth quote for job i in location a, where a job is defined to be in

an industry/occupational group. The wage can be described by the following regression

equation:

ln pX L ij

a

ij

a

i i

a

ij

a = ++ β ε (12)

where Xij

a represents data on the characteristics of the job and the worker and where Li

a

represents a local area effect for job i in area a. This regression equation allows the

coefficients on Xij

a and the local area effects to vary across jobs. Equation (12) is

estimated by weighted least squares, where the weights are the sample weights from the

ECI.

A standard practice is to utilize the estimation results from equation (12) to make

interarea wage comparisons. The regression defines a decomposition of interarea wage

differences into components due to interarea differences in attributes Xij

a and residual
- 12 -

terms Li

a

. Let $βiz be the zth element of the vector of weighted least squares estimates of

βi

 from equation (12), and let xijz

a be the zth element of the vector Xija . Also let ln pi

a

and xiz

a be weighted (by ECI sample weights) averages over j of ln pij

a and xijza ,

respectively. Then, by the properties of the weighted least squares estimators,

ln $ $ p xL i

a

iz

z

iz

a

i

a = + ∑β .

A Tornqvist index comparing wages in local area b to those in area a is defined (in

logs) as

ln ln ln T ww p p ( )( ) ab

i

a

i

b

i

b

i

a

i

=+ − ∑

1

2

(13)

where wi

a is cell i’s share of the labor expenditure in locality a. This differential can in

effect be decomposed into contributions of the various covariates in X and contributions

of the local area dummies. The contribution of the local area dummies takes the same

form as (13),

ln ( )( ) $ $ T w wL L L

ab

i

a

i

b

i

b

i

a

i

= +− ∑

1

2 .

Further, the contribution of the zth characteristic of the job or worker to the index in (13)

is

−+ − ∑( ) ( ) 1

2 ww xx i

a

i

b

iz iz

b

iz

a

i

$

β .

This contribution depends on interarea differences in average characteristics (the

difference in the mean X’s) in conjunction with the importance of the zth characteristic in
- 13 -

determining the wages in each job i (the $βiz ). The sum of these z contributions, plus

ln TL

ab , equals the Tornqvist index in (13). In the following sections we present this

decomposition for a (transitive, multilateral) set of Tornqvist bilateral index numbers.

Multilateral compensation indexes: A regression approach for imposing transitivity with

minimal adjustment of the data. In this paper we employ an alternative to (or a likely

superclass of) the EKS/CCD approach from KMZ (1996) for making the system of

bilateral indexes transitive. When this condition on the cross-weighted differences of labor

characteristics-adjusted log regional prices is not met, the data may be minimally adjusted

to satisfy transitivity by fitting the equation

[ ]

[ ]

wp x wp x

wp x p x

pw w

i

a

i

b

iz iz

b

z

i

a

i

a

iz iz

a

z

i i

b

iz iz

b

z

i

a

iz iz

a

z

i i

b

i

a

i

ab

ln $ ln $

ln $ ln $

ln ( )

− 



 



 



 



− − 



 



 



 





= − −− 



 



 



 





+− − +

∑ ∑

∑ ∑

β β

β β

ε

0

0

using least squares to obtain the estimates

−



 



 ln $

$

p

w

i

i

0

0 .

This is a simplification of equation (10) since if, as our CPD model assumes, the

hedonic slope coefficients are the same across areas for each specification characteristic so

that βββ iz

a

iz

b = = iz

0

, then the coefficient on the difference between the share vectors of the

two areas is a characteristics-adjusted reference price vector, and no reference

characteristics vector can be separately identified.
- 14 -

V. An Application to U.S. Labor Compensation Data

Data. The microdata used to construct the interarea indexes come from two

sources: the Employment Cost Index (ECI) and the Current Population Survey (CPS).

The ECI data program produces quarterly indexes that measure changes over time in

wages and salaries and in the cost of total compensation. These indexes are calculated

from micro data collected for sampled jobs in sampled establishments. All jobs in nonfarm private industry and in state and local governments are within scope for the survey,

meaning that the occupational coverage of the survey is nearly complete. The micro data

available include the mean hourly wage and mean hourly compensation costs for all

incumbents in the sampled jobs. Other data elements describe job or establishment

characteristics: the establishment’s number of employees; whether the employment is fulltime or part-time; and whether the job is covered by a collective bargaining agreement.

This study utilized the data for 18,486 sampled jobs for the fourth quarter of 1993 in nonagricultural private industry. Details of variable definitions, sample exclusion restrictions,

and summary statistics for all data are in a statistical appendix.

A shortcoming of the ECI is that it does not collect key variables that are widely

believed to measure human capital: education and labor market experience. To obtain

these variables, data from the CPS were merged to the ECI micro data. The CPS is a

monthly survey of households that contains information about the demographic

characteristics and employment outcomes of individuals. For current purposes we used

the 3 monthly surveys for the fourth quarter of 1993, and restricted our sample to

employed individuals in non-agricultural private industry. We collected information on

schooling, age, industry, occupation, and area of residence for a sample of almost 140,000

workers.

Merging the data from the CPS to the ECI presents a challenge, because the ECI

micro data contain the means for jobs while the CPS contains data for individuals and, of

course, the individuals covered in the two surveys are not necessarily the same. The

strategy we followed was to calculate weighted mean values for CPS variables for cells

defined by local area, occupation and industry. The industrial and occupational cell
- 15 -

classification used for this purpose was determined by the availability of data; we chose to

create cells defined by local area, major occupational group, and six industry groups.

After matching the CPS cell level data to the ECI micro data for individual jobs,

we had to determine an appropriate locality/industry/occupation classification for the

purposes of computing the interarea indexes. The methodology in the previous section,

specifically equation (12), calls for estimating separate regressions for cells defined by

industry and occupation in order to recover estimates of local area dummies for each cell.

There is a trade-off between the size of the smallest local area for which we can calculate

interarea indexes and how finely the industry/occupation cells can be disaggregated. We

selected a set of cities that included both those that are the largest and those that are of

interest in the Federal pay setting process. The remainder of the data were aggregated

into Census geographic divisions (as “rest of division”). We then determined that indexes

could be calculated for these local areas using 18 industry/occupation cells, defined by

major occupational group and whether the job is in a goods or service producing industry.

To give the reader a feel for the underlying data we present some summary data in

Tables 1 and 2. Table 1 gives wage and compensation shares and levels by our job

classification scheme; major occupational groups are presented within the two broad

industrial groupings. The first column, labeled “Wage Share,” reports the fraction of total

wages that falls in the given category. These statistics are useful for showing where the

bulk of the data reside. The second column simply reports the average hourly wage in the

given cell (all figures are in nominal dollars). Roughly speaking, the professional,

technical, and executive occupations have the highest hourly wages, production workers

and operatives have average wages, and laborers and service workers have below average

wages. There is a noticeable difference between the broad industry aggregates, with

average wages in any particular occupational group being higher in the goods producing

industries. The third column gives shares of total compensation. Goods producing

industries have higher shares of total compensation than of total wages,reflecting the fact

that a higher fraction of compensation comes in the form of benefits for workers in those

industries. This fact is apparent in comparing average wages in the second column to

average hourly compensation costs, in the final column. Finally, one other obvious
- 16 -

inference that can be drawn from this table is that, given the wide variation in wages and

compensation costs across jobs, index numbers might be expected to yield very different

results than simple interarea differences in average compensation rates whenever there are

interarea differences in the distribution of jobs.

Table 2 gives employment shares and average compensation by local area. The

compensation shares, showing each local area’s compensation as a fraction of total US

compensation, give some idea as to which metropolitan statistical areas have relatively few

ECI job quotes. Because of their small sizes, one might expect localities such as Charlotte

and Columbus to have fairly noisy compensation index estimates. The “rest of division”

localities, on the other hand, tend to be rather large. Comparing column two with column

one shows that larger metropolitan areas tend to have the highest average compensation

costs, while the “rest of division” localities have the lowest. The final column

(“Compensation Relative”) gives average compensation in the local area relative to the

overall average compensation level in the data. The range in these area relatives is quite

large. At one extreme, compensation in the Detroit, New York, and San Francisco areas

is approximately 134 percent of average compensation in the US; at the other extreme lies

the East South Central locality with 73 percent of overall average compensation. A

comparison of these figures with the index numbers presented below will give some idea

as to the importance of interarea differences in job characteristics for compensation cost

comparisons.

Regressions. The first step in the construction of the interarea indexes was the

estimation of log wage and log compensation cost regressions (equation 12). In addition

to local area dummies, five sets of covariates were included as explanatory variables in the

regressions to capture factors that affect worker productivity. Following a long tradition

in the labor economics literature dating back to Mincer (1962) and Becker (1964), years

of schooling, years of potential labor market experience (age minus education minus 6),

and potential experience squared were included. These measure the average amount of

human capital possessed by incumbents in the job.

The labor literature has shown that wages are positively associated with

establishment size. Brown and Medoff (1989) argue that part of this wage-size
- 17 -

relationship arises because large firms attract higher quality workers (even after

controlling for observable characteristics). In order to control for this in the present study,

we include a set of 8 establishment size class dummies.

In the literature, unionization is claimed both to increase and to decrease worker

productivity. The traditional view holds that unions lower productivity by imposing

staffing requirements and other restrictive work practices that prevent firms from

efficiently utilizing capital and labor (Lewis [1986], Rees [1989]). A more recent

literature argues that unions enhance worker productivity (Freeman and Medoff [1984]).

First, unions provide a collective voice that communicates workers’ preferences. This

lowers worker discontent and turnover, increasing firms’ incentives to invest in jobspecific human capital. Second, unions typically establish seniority rules that may promote

an environment where more senior workers are willing to provide less senior workers with

informal on-the-job training. Finally, unions may enhance worker moral, motivation, and

effort. While the literature is ambiguous about the effect of unions on productivity, most

studies show that unions increase wages. To capture the effects of unions on productivity

in our regressions, we include a dummy indicating whether a job is covered by a collective

bargaining agreement.

The literature generally shows that, after accounting for observed differences in

human capital, part-time workers earn less than full-time workers (see Lettau [1994] and

citations therein). For at least two reasons, this differential may arise because part-time

workers are on average less productive than their full-time counterparts. First, it is argued

that innately less productive workers are more likely to select part-time jobs. For

example, more productive workers may find it advantageous to work more intensively if

their wages reflect their productivity. Second, average productivity might be lower for

part-time workers due to fixed daily set-up costs that are spread over more working hours

for full-time workers. We include a part-time dummy in our regressions to capture these

productivity effects.

It is important to note that while the education and potential experience variables

are widely viewed as measuring human capital, the other three variables -- establishment

size, unionization, and part-time status -- may have effects on wages that are not strongly
- 18 -

associated with labor productivity. All three represent or proxy to some extent

characteristics of the labor services transaction in an industry and locality as much as the

characteristics of the service itself. Although the nature of the transaction may have

productivity effects, this is not a foregone conclusion. Large nonunion firms, for

example, may pay higher wages simply to forestall unionization. Union wages may be

higher simply because of union monopoly power. If the purpose of including explanatory

variables in the regressions is to control for factors that affect productivity, then it is

possible that our index factors overcontrol for some of these transaction and other effects.

In the analysis that follows, we include all of the explanatory variables in the regressions,

but we provide a set of adjustment factors associated with the explanatory variables.

These factors measure the contribution of each variable (or variable group) to the

unadjusted interarea differential. One advantage of our methodology is that analysts can

add back the differential associated with a variable if they judge that it is not appropriate

to control for that variable. In Tables 3 and 4, “adjusted” refers to interarea measures

adjusted for differences in all our conditioning variables explaining wage and

compensation variation. Future formats for interarea compensation data could reasonably

include multiple summary columns of adjusted data corresponding to multiple subsets of

conditioning factors to satisfy the interests of various users.

Eighteen regressions were estimated separately for wages and compensation costs.

There is a regression for each of 9 major occupational groups in either the goods or

service producing industries. The regressions for wages appear in Appendix B Table B.1,

while those for compensation costs appear in Table B.2.

The adjusted R-squares for the regressions are comparable to or higher than those

found for wage regressions estimated on individual micro data. The regressions typically

explain between 20 and 50 percent of the variation of wages, while the corresponding

range for compensation costs is 30 to 60 percent.

As expected by theory and found in most data, wages and compensation costs tend

to rise with education. The returns to education are perhaps on average slightly smaller

than would be obtained from person-level micro data. However, there are a few instances

in our regressions where the education coefficient is anomalously negative (although not
- 19 -

large relative to the standard error). This may arise in part due to small sample sizes for

some of the regressions. Further, it is important to stress that we have an imperfect

measure of education that is measured as a cell mean from CPS data. Within a regression,

education varies across areas and across some industry groups, but does not vary for a

given industry and area. It is likely that the education variable would perform better if it

were collected for the same unit of observation as the wage and compensation data.

Previous empirical work has shown that wages display an increasing, concave

profile with experience. This is often observed in our regressions as well, though there are

a number of instances where the profile is convex and downward sloping at relevant

experience levels. As with the education variable, problems with the experience variable

might arise because its values are cell means whose source of variation for any regression

is across areas and to a lesser extent across broad industry groups. The sample statistics

indicate that the standard deviation of experience is much lower in our data than it is in

micro data. This low variance is not unexpected, but could indicate that the variable

cannot discriminate well in explaining wage variation.

As expected, jobs that are covered by union contracts command higher wages than

uncovered jobs, while part-time jobs tend to receive lower wages. The return to union

contract coverage is on average higher in these regressions than estimates derived from

older data (Lewis [1986]). Finally, also as expected, larger establishments tend to pay

higher wages, with especially notable premiums for establishments with 500 or more

employees. Comparisons of the establishment size coefficients across industry/occupation

groups are difficult because of substantial variability across those cells in the average

compensation of workers in the omitted category (1-9 workers). However, the

establishment size coefficient point estimates typically rise with establishment size.

Interarea Indexes of Wages and Compensation for the United States. Table 3

gives our main results for interarea wage rate differentials. The second column of the

table presents Tornqvist wage indexes that control for the composition of employment

across 9 major occupational groups and 2 industry groups, but where there are no other

adjustments for observed differences in worker or job characteristics. The index numbers

are relative to the reference wage generated by our method (described in section IV
- 20 -

above) of making the bilateral comparisons transitive; one may loosely interpret 100.00 as

average for the US.2 As an example, the first entry in the second column indicates that

wages, adjusted for broad differences in employment but unadjusted for observed

differences in worker and job characteristics, are 10.1 percent higher in Boston than in the

US as a whole. The amount of interarea variation in employment-adjusted wage indexes

is striking, with numbers ranging from 128.6 for San Francisco to 78.1 for the East South

Central rest of division locality. Generally one tends to find that wages are higher than

average in the larger CMSAs and along the West coast; wage indexes are much smaller

than average in the “rest of division” localities. Controlling for the composition of

employment across industry and occupation by using a wage index tends to reduce

interarea differentials as compared to the unadjusted wage relatives that appear in the first

column of Table 3. This is most clearly seen in Figure 1, which plots the wage indexes

against the average wage relatives. The figure contains a regression line through the data

points (estimated with unweighted OLS) and a 45 degree line. If controlling for the

 

2 Actually, neither the area share-weighted arithmetic nor geometric average of these

locality levels is generally equal to 100.0 because of the way the reference shares and

prices are determined using the “minimum bilateral relative adjustment” criterion implicit

in our regression approach, in concert with our observation weighting, which gives greater

importance to records representing relatively large bilateral average expense shares.

Bilateral ratios of the index numbers in Tables 3 and 4 produce a transitive system of

parities as provided by the objective of our algorithm, but do not provide a particular level

normalization. Interpretation of these data as levels requires a normalization to, for

example, the national average level, much as a time series of price index numbers would

be normalized to be 100.0 in a particular time period to align it with other data series so

normalized for a given analytical purpose. The data in Tables 3 and 4 are, therefore, valid

for ranking localities in terms of labor services input price levels. As we note elsewhere in

the paper, the weighted CCD/EKS method of determining the reference shares and prices

of the transitive parities implicitly does normalize the regional geometric average level to

100.0.
- 21 -

composition of industrial and occupational employment had no effect on interarea wages,

then the regression line would have a 45 degree slope. Instead, the regression line is

flatter than the 45 degree line, indicating that, in part, wages are low in low-wage areas

because employment is more heavily concentrated in low-wage industries and

occupations.

The rightmost column of Table 3 gives adjusted wage differentials corresponding

to the Tornqvist indexes calculated using the local area dummies ( $Li

a

). Comparing the

Wage Index column with the Adjusted Wage Index column of Table 3, the interarea

variation in characteristics-adjusted wages is generally smaller than the interarea variation

in wages alone. The standard deviation of the wage index is 12.2, versus a standard

deviation of 9.8 for the adjusted wage index. This can be seen graphically in Figure 2,

which plots one index against the other. The regression line through the plot is again less

steep than the 45 degree line, indicating that controlling for worker and job attributes

raises the wage index for low-wage areas and lowers it for high-wage areas. That is to

say, some of the interarea variation about US mean wages can be attributed to interarea

differences in the observable characteristics X, even after accounting for interarea

differences in industry/occupation employment distributions. One of the larger

adjustments is in Detroit, where the observed differences in characteristics would imply

approximately a 9 percent (114.4/104.9 = 1.09) premium to hire labor. The seven

localities with the lowest employment-adjusted wage indexes (five of which are “rest of

division” localities) have on average a 4.2 percent increase in wage indexes through our

labor characteristics adjustments. In contrast, the seven localities with the highest

employment-adjusted wage indexes have on average a 4.9 percent decrease through

characteristics adjustments.

Whether the characteristics adjustment for a particular locality reflects a premium

mainly attributable to larger establishment size, greater unionization rates, a more

educated workforce, or some other reason, is not clear from comparing the wage indexes.

To address that question, Table 3 contains a set of columns that report the contributions

of the observable characteristics to unadjusted wage differentials. Recalling the discussion

following equation (13), variations in the area relatives for a characteristic will be larger,
- 22 -

the larger is the coefficient for that characteristic in the wage regressions and the larger is

the variation in the characteristic across areas. Our decomposition methodology implies

that, for any given local area, the quality-adjusted index and the covariate contributions

(appropriately scaled by 100) must multiply up to equal the employment-adjusted wage

index . Whenever a number in one of the covariate columns exceeds 100 for a given area,

the observed characteristic tends to raise wages in that area. For example, the covariate

contribution for education in Boston, 102.5, indicates that Boston’s workers have higher

education than average, so that their unadjusted pay is 2.5 percent higher than the average

area due to this characteristic. Although there is substantial noisiness in the results,

especially among the smaller local areas, what one not surprisingly sees is that most of the

adjustment factors for worker and job characteristics tend to exceed 100 for the highest

wage areas (e.g., San Francisco) and to fall short of 100 for the lowest wage areas

(including most of the “rest of regions”). Finally, as stated earlier, note that these

adjustment factors can be used to add back to the characteristics-adjusted indexes the

influence of variables that an analyst does not wish to remove in making interarea

comparisons.

To get some sense of the relative contributions of the worker and job

characteristics to wage differentials, Figure 3 graphs the data from Table 3 on area

relatives for these characteristics against the interarea wage indexes. Each figure also plots

the (unweighted) regression line through the points, and the scales of each figure are made

the same to facilitate comparison. Each figure shows a positive correlation between the

particular area relative and the wage index, indicating that on average all of the

characteristics contribute to interarea wage differentials. More significantly, the steepest

regression line is for education, indicating that this characteristic is most important in

explaining observed interarea differentials in the wage index. The union variable has the

second steepest slope, while the part-time dummy variable has the flattest regression line.

Since our wage regressions indicated that wages tend to be significantly lower for parttime jobs, the fact that this variable accounts for little variation in wages across areas

stems from the fact that the proportion of part-time jobs varies little across areas.
- 23 -

Table 4 gives analogous calculations for hourly compensation, as opposed to

wages. Given that wages are approximately 70 percent of compensation costs, it is not

surprising to find that the gross patterns apparent in Table 3 hold here as well. Controlling

for industry/occupation and worker and job characteristics reduces interarea compensation

differentials, implying that high compensation areas receive high compensation partly for

observable reasons. One difference between Tables 3 and 4 is that the interarea

differences in compensation indexes are slightly larger than those for wage indexes. One

extreme example is Detroit, whose characteristics-adjusted compensation index (113.0) is

much larger than its characteristics-adjusted wage index (104.9).

The greater interarea dispersion when computing compensation indexes holds for

both the employment-adjusted and the characteristics adjusted series. The compensation

share weighted standard deviations for these series are 17.2 and 13.3, respectively.

Controlling for job and worker characteristics, therefore, reduces the interarea variation in

compensation by about 23 percent. The greater interarea variation in compensation, as

opposed to wages, no doubt reflects some combination of income effects (workers have

income elastic demands for health care, pensions, and other benefits) and tax effects

(benefits are generally lightly taxed or not taxed at all, and the occupational composition

of the labor force and income tax rates vary by locality). As employers making location

decisions presumably care about compensation costs broadly defined, it is useful to know

that interarea wage comparisons are likely to understate the interarea compensation

differentials.

VI. Conclusion

We have applied a promising methodology for place to place price measurement to

the problem of constructing interarea labor force characteristics-adjusted compensation

indexes for labor services that blends hedonic regression and economic index number

techniques. We have used a combination of establishment data from the BLS Employment

Cost Index program and household data on individual workers from the BLS Current
- 24 -

Population Survey to provide a more complete picture of labor quality than has been

available to analysts working with only household data. As would be expected,

incorporation of the labor quality information generally reduced the variability of labor

costs from place to place, and provided insights into the contribution of various factors,

such as education, experience, establishment size, union status, and full time work status

to the level of labor compensation in major urban centers of the United States.

Enhancements to the data are needed. Fortunately, there are prospective

developments on this score. The BLS Office of Compensation and Working Conditions is

currently undertaking a major redesign and integration of its three major compensation

surveys, the Employment Cost Index, the Employee Benefit Survey, and the Occupational

Compensation Survey. One salutary result of this for the ECI is a substantial increase in

sample size from the current 5,000 establishments to at least twice that number. Of

particular interest for interarea comparisons is the adoption of an area-and-industry-based

rather than solely industry-based rotational scheme for the samples in the new integrated

survey, whose total size will be approximately 30,000 establishments. Comprehensive

data on job content is included in the list of data elements to be collected from all

establishments in the survey, greatly expanding the number and explanatory power of the

covariates that can be used for characteristics adjustment. Publications incorporating data

from the new survey are expected to begin in 1998.
- 25 -

Table 1. Industry/Occupation Shares, Wages, and Compensation

Wage Share Average Wage Compensation

Share

Average

Compensation

Goods Producing Industries

Professional/Technical 0.047 22.60 0.047 31.98

Executive/Administrative 0.054 26.03 0.054 36.84

Sales 0.007 17.09 0.007 22.92

Administrative Support 0.042 11.60 0.044 17.08

Precision Production 0.094 15.80 0.102 24.12

Machine Operatives 0.066 10.74 0.075 17.04

Transport Operatives 0.029 12.76 0.032 19.73

Laborers 0.030 9.70 0.033 14.78

Service Workers 0.008 14.08 0.008 20.30

Service Industries

Professional/Technical 0.145 19.33 0.140 26.17

Executive/Administrative 0.101 20.95 0.098 28.47

Sales 0.095 10.12 0.087 13.14

Administrative Support 0.120 9.93 0.118 13.75

Precision Production 0.029 11.93 0.028 16.28

Machine Operatives 0.009 8.18 0.009 11.55

Transport Operatives 0.014 9.28 0.014 13.18

Laborers 0.027 7.03 0.026 9.54

Service Workers 0.084 6.00 0.079 7.89

Notes: "Wage Share" and "Compensation Share" are wage and compensation shares in the nonhousehold, non-Federal, non-agricultural economy. "Average Wage" and "Average Compensation" 

refer to average hourly wages and compensation in nominal dollars, where averages within 

industry/occupation class are weighted by ECI sample weights.

Source: Winter 1993 Employment Cost Index.
- 26 -

Table 2. Compensation by Local Area

Local Area and Rest

of Regional Division

Compensation Share Average Hourly

Compensation

Compensation Relative

Northeast region

Boston 0.029 $19.75 116.4

Hartford 0.009 20.60 121.4

New England 0.016 14.04 82.8

New York 0.116 22.73 134.0

Philadelphia 0.027 19.93 117.5

Pittsburgh 0.011 20.22 119.2

Middle Atlantic 0.070 17.18 101.3

North Central region

Chicago 0.039 19.19 113.1

Detroit 0.024 22.70 133.8

Cleveland 0.010 17.33 102.2

Milwaukee 0.008 16.34 96.3

Dayton 0.007 17.46 102.9

Cincinnati 0.006 15.89 93.7

Columbus 0.006 15.99 94.3

Indianapolis 0.005 18.59 109.6

East North Central 0.062 14.23 83.8

Minneapolis 0.011 17.35 102.2

Kansas City 0.006 20.94 123.4

St. Louis 0.006 18.27 107.7

West North Central 0.052 14.74 86.9

South region

Washington, DC 0.026 21.84 128.7

Atlanta 0.014 17.81 105.0

Miami 0.009 14.45 85.2

Tampa 0.008 13.89 81.9

Charlotte 0.005 14.32 84.4

South Atlantic 0.077 13.82 81.5

East South Central 0.042 12.35 72.8

Houston 0.020 19.21 113.2

Dallas 0.018 17.17 101.2

West South Central 0.056 14.00 82.5
- 27 -

Table 2. Compensation by Local Area

Local Area and Rest

of Regional Division

Compensation Share Average Hourly

Compensation

Compensation Relative

West region

Denver 0.008 $15.08 88.9

Phoenix 0.007 16.24 95.7

Mountain 0.028 13.26 78.2

Los Angeles 0.060 20.02 118.0

San Francisco 0.032 22.74 134.0

Seattle 0.012 21.61 127.4

San Diego 0.010 20.86 123.0

Portland 0.008 18.66 110.0

Pacific 0.039 15.42 90.9

Notes: "Compensation Share" is the area share of compensation in the non-household, nonFederal, non-agricultural economy. "Average Compensation" is average hourly compensation 

in nominal dollars, weighted by ECI sample weights. "Compensation Relative" is the ratio of 

the local area average compensation to the US average compensation.

Source: Winter 1993 Employment Cost Index.
- 28 -

Table 3: Wage Indexes

Local Area and

Rest of Regional

Division

Average

Wage

Relative

Wage

Index

EducationExperienceEstablishment SizeFull Time/

Part Time

Union Adjusted

Wage

Index

Northeast Region

Boston 118.8 110.1 102.5 99.5 100.2 99.6 99.2 109.1

Hartford 120.9 111.9 99.6 102.6 102.1 96.0 101.1 110.6

New England 84.6 88.8 98.4 98.6 97.2 98.1 98.3 97.7

New York 132.7 128.0 100.6 100.7 100.0 101.1 100.6 124.2

Philadelphia 116.2 111.2 101.4 99.7 99.5 100.0 101.0 109.4

Pittsburgh 118.1 108.8 102.4 103.6 101.7 100.9 102.2 97.8

Middle Atlantic 99.0 101.6 99.2 98.8 101.5 99.1 100.6 102.4

North Central region

Chicago 111.9 108.8 101.3 100.3 99.1 100.6 101.7 105.6

Detroit 115.7 114.4 101.6 98.9 105.8 99.4 103.2 104.9

Cleveland 94.9 98.6 97.9 100.1 102.0 98.0 102.4 98.2

Milwaukee 96.8 101.2 97.7 99.5 100.3 100.9 100.8 102.0

Dayton 103.8 91.2 98.5 99.1 99.0 99.2 100.2 94.9

Cincinnati 95.2 100.9 100.3 99.4 100.3 100.5 99.4 101.2

Columbus 98.2 96.4 102.5 99.3 99.8 101.6 99.6 93.7

Indianapolis 106.8 101.0 98.1 106.9 98.9 102.9 101.1 93.5

E N Central 83.4 88.9 98.9 98.9 99.4 99.9 100.6 91.0

Minneapolis 100.6 107.0 102.2 97.9 100.0 99.5 102.1 105.1

Kansas City 120.7 110.9 103.6 99.2 99.0 102.3 101.6 104.9

St. Louis 101.3 95.1 104.5 99.8 98.4 101.6 101.2 90.2

W N Central 85.6 85.7 98.9 98.2 100.9 99.6 99.9 87.9

South region

Washington, DC 127.5 112.9 100.9 100.7 102.0 100.9 100.4 107.5

Atlanta 104.2 105.1 100.7 101.2 101.6 102.2 100.6 98.7

Miami 89.2 92.4 97.7 100.5 98.8 100.0 98.5 96.6

Tampa 84.7 87.4 99.7 99.7 100.3 99.3 97.8 90.3

Charlotte 86.4 87.3 100.7 96.4 101.7 99.3 97.2 91.6

S Atlantic 83.4 86.6 98.6 99.3 99.3 99.9 97.8 91.2

E S Central 74.2 78.1 99.2 99.3 98.2 99.8 100.0 80.9

Houston 116.4 111.1 100.0 101.2 99.2 98.8 98.3 113.9

Dallas 102.4 97.0 98.2 99.4 102.1 102.4 100.1 95.0

W S Central 84.2 81.4 97.8 98.8 99.3 99.7 98.2 86.7
- 29 -

Table 3: Wage Indexes

Local Area and

Rest of Regional

Division

Average

Wage

Relative

Wage

Index

EducationExperienceEstablishment SizeFull Time/

Part Time

Union Adjusted

Wage

Index

West region

Denver 94.3 96.2 100.6 98.7 97.9 99.9 99.6 99.4

Phoenix 98.0 98.6 98.8 101.4 105.2 100.1 98.4 95.0

Mountain 80.4 87.6 100.7 100.5 98.8 98.8 98.2 90.4

Los Angeles 119.4 114.0 100.3 100.6 101.0 99.6 99.9 112.3

San Francisco 136.7 128.6 103.8 101.4 100.9 99.7 100.9 120.5

Seattle 127.9 118.2 102.4 101.5 102.0 102.1 102.3 106.8

San Diego 119.7 115.3 102.7 98.9 98.4 99.1 99.5 117.1

Portland 108.7 109.6 102.1 98.6 97.9 101.0 99.9 110.1

Pacific 91.7 101.5 100.2 98.9 99.2 100.1 100.8 102.2

Source: Winter 1993 Employment Cost Index.

Notes: The Adjusted Wage Index in the last column is the Wage Index in column 3 divided by

the product of the characteristics factors in columns 4 through 8, normalized to base 100.
- 30 -

Table 4: Compensation Indexes

Local Area and

Rest of Regional

Division

Average

Compensation

Relative

Compensation

Index

EducationExperienceEstablishment

Size

Full Time/

Part Time

Union Adjusted

Compensation

Index

Northeast region

Boston 116.4 111.2 102.7 99.5 100.0 99.2 98.8 110.9

Hartford 121.4 115.5 99.9 102.7 102.4 95.1 101.6 113.9

New England 82.8 87.0 98.4 98.8 96.1 97.6 97.5 97.8

New York 134.0 130.3 100.7 100.9 99.8 101.4 101.1 125.2

Philadelphia 117.5 113.1 101.1 99.9 99.2 99.6 101.8 111.3

Pittsburgh 119.2 109.3 102.1 103.0 101.4 100.3 102.3 99.8

Middle Atlantic 101.3 103.2 99.2 99.0 102.1 99.0 100.9 103.0

North Central region

Chicago 113.1 110.6 101.3 100.3 98.8 100.2 102.5 107.3

Detroit 133.8 129.5 101.3 99.1 109.1 99.6 105.1 113.0

Cleveland 102.2 104.4 98.5 100.5 103.1 98.3 103.7 100.3

Milwaukee 96.3 102.2 98.1 99.6 100.7 101.5 101.1 101.2

Dayton 102.9 92.3 98.0 98.9 99.1 99.5 100.2 96.3

Cincinnati 93.7 101.0 100.2 99.4 100.4 100.5 99.2 101.2

Columbus 94.3 93.6 102.0 98.7 100.1 102.1 99.5 91.5

Indianapolis 109.6 101.0 98.4 106.8 99.1 102.8 101.5 93.0

E N Central 83.8 88.9 98.7 98.8 99.4 99.8 100.9 91.0

Minneapolis 102.2 107.5 102.3 97.8 100.7 99.3 103.1 104.2

Kansas City 123.4 112.6 103.2 99.7 99.3 102.9 102.1 104.9

St. Louis 107.7 99.1 104.8 99.3 98.3 101.9 101.6 93.7

W N Central 86.9 85.8 98.8 98.1 101.0 99.6 99.7 88.3

South region

Washington, DC 128.7 114.3 101.0 100.8 103.1 101.2 100.7 106.9

Atlanta 105.0 105.0 101.1 100.5 101.8 102.6 100.8 98.2

Miami 85.2 88.9 97.5 100.1 98.6 100.2 97.8 94.1

Tampa 81.9 85.5 99.9 99.9 100.1 99.6 96.8 88.8

Charlotte 84.4 84.7 100.7 96.7 101.6 99.8 96.0 89.4

S Atlantic 81.5 84.2 98.5 99.3 99.0 100.0 96.8 89.7

E S Central 72.8 76.4 99.2 99.3 97.9 99.8 99.8 79.6

Houston 113.2 109.3 100.0 101.6 99.3 98.2 97.4 113.1

Dallas 101.2 96.3 98.5 99.3 102.3 102.7 100.0 93.7

W S Central 82.5 79.1 97.7 98.7 98.9 99.8 97.4 85.3
- 31 -

Table 4: Compensation Indexes

Local Area and

Rest of Regional

Division

Average

Compensation

Relative

Compensation

Index

EducationExperienceEstablishment

Size

Full Time/

Part Time

Union Adjusted

Compensation

Index

West region

Denver 88.9 91.2 101.3 98.4 97.5 99.3 99.5 95.0

Phoenix 95.7 97.1 98.8 100.7 107.1 100.2 97.6 93.2

Mountain 78.2 85.2 100.6 100.2 98.1 98.5 97.3 89.8

Los Angeles 118.0 112.8 100.3 100.5 101.3 99.6 99.8 111.2

San Francisco 134.0 127.8 103.7 101.4 101.1 99.6 101.6 118.9

Seattle 127.4 118.0 101.9 102.0 101.5 102.7 103.2 105.6

San Diego 123.0 119.9 102.2 99.1 98.8 99.0 99.5 121.7

Portland 110.0 111.6 101.8 98.3 97.0 101.0 100.1 113.7

Pacific 90.9 100.6 99.8 99.0 98.9 99.9 101.0 102.1

Source: Data from Winter 1993 Employment Cost index; October, November, and December

1993 Current Population Survey

Notes: The Adjusted Compensation Index in the last column is the Compensation Index in

column 3 divided by the product of the characteristics factors in columns 4 through 8, normalized

to base 100.
- 32 -

Figures

Page 33. Figure 1. Relationship of Interarea Wage Index to Relative Average

Wages

Page 34. Figure 2. Relationship of Characteristics-adjusted Interarea Wage

Index to Interarea Wage Index

Page 35. Figure 3. Relationship of Labor Services Characteristics Adjustment

Factors to Interarea Wage Index
- 33 -

Figure 1

70

80

90

100

110

120

130

140

70 80 90 100 110 120 130 140

Average Wage Relative

Interarea Wage Index

45
- 34 -

70

80

90

100

110

120

130

140

70 80 90 100 110 120 130 140

Interarea Wage Index

Adjusted Interarea Wage Index

45
- 35 -

96

100

104

108

75 85 95 105 115 125 135

Interarea Wage Index

Adjustment to Interarea Wage Index 

for Establishment Size

96

100

104

108

75 85 95 105 115 125 135

Interarea Wage Index

Adjustment to Interarea Wage Index 

for Experience

96

100

104

108

75 85 95 105 115 125 135

Interarea Wage Index

Adjustment to Interarea Wage Index 

for Education

96

100

104

108

75 85 95 105 115 125 135

Interarea Wage Index

Adjustment to Interarea Wage Index 

for Full-time Status

96

100

104

108

75 85 95 105 115 125 135

Interarea Wage Index

Ajustment to Interarea Wage Index 

for Union Status
- 36 -

VII. Appendix A: Data

The Employment Cost Index (ECI). The ECI is a quarterly survey of randomly

sampled establishments designed to produce estimates of wage and compensation cost

changes. Within establishment, jobs are randomly sampled at the establishment initiation

into the sample (sampling is carried out with probability proportional to establishment

employment in the occupation). For each job, the ECI collects average wages and

average compensation costs for the workers in the job. Non-wage compensation includes

leave (sick leave, vacations, and holidays), supplemental pay (overtime, nonproduction

bonuses), employer contributions to pensions and retirement savings accounts, health

benefits, life and accident insurance, legally required labor expenses (state and federal

unemployment insurance, workers’ compensation, social security), and some other

miscellaneous fringes. The ECI converts all data collected to a cost per hour worked

basis. The ECI microdata also attach various establishment or job characteristics to each

job quote, including more detailed industry and occupation codes, establishment size, the

job’s work schedule, and whether the job is covered by a union contract. The ECI collects

quarterly updates on the wages and compensation costs and uses these updates to

compute quarter-over-quarter and year-over-year indexes of change. Establishments are

replaced in the sample using an industry rotation; the entire sample is replaced over the

course of 4-5 years.

For this study we gathered a data extract from the ECI for the last quarter of 1993.

We kept all private sector job quotes for which we had valid wage and compensation data,

meaning that the job quote was used in computing the ECI. Data can be invalid for two

main reasons. The first is that the data represent the establishment’s responses at

initiation, which of course are not used in computing the most recent ECI change. We

exclude these data mainly so our sampling weights remain approximately correct;

including these observations would improperly over-weight the industries that are the

focus of initiation. The second is that establishments may be unable to, or may refuse to,

report some benefits or wages for a paticular job. In this case the BLS attempts to impute
- 37 -

wages or benefits based on the nonmissing data available; cases where these attempts fail

are essentially dropped from the ECI calculations. Finally we note that in some instances

the job’s work schedule cannot be calculated and hourly compensation must be imputed

even though the ECI has valid compensation data. Once exclusion restrictions are made

we have a sample of 18,468 job quotes.

Because sample replacement is made on an industry rotation pattern and sample

weights are not adjusted through the life of the industry panel, normal sample attrition

results in cross-sectional samples that over-weight more recently initiated industries.

Accordingly, we adjust the ECI sampling weights to bring them current by adjusting 2-

digit SIC employments to equal those published in the BLS Employment and Earnings

series. References to the ECI sampling weights in the text and tables reflect this weighting

adjustment.

The Current Population Survey (CPS). While the ECI attempts to randomly

sample establishments, the CPS is designed to randomly sample addresses and collect

information on the households at each sampled address. The main function of these

surveys is to generate official employment and unemployment statistics; however, they are

utilized by researchers in a number of other ways as well. The survey is conducted

monthly, with a given household surveyed for four months, not surveyed for 8 months,

and then surveyed for four final months, at which point they leave the sample. The survey

collects demographics and current employment outcomes, among other items, for each

person in a sampled household.

We pooled the October, November, and December 1993 CPS surveys to gather

worker characteristics by industry, occupation, and local area at approximately the same

time frame as our ECI data. The sampling design guarantees some overlap in the monthto-month samples, but that overlap does not imply redundant information in all cases

because of changing employment rates, industry and occupational distributions, etc. Our

sample exclusions were made primarily to maintain comparability with the ECI sample: we

included only individuals employed by non-agricultural, private sector employers. Our

final sample contains 138,902 observations.
- 38 -

The covariates from the CPS data are mainly measures to proxy for human capital

or other factors typically thought to affect wages. We have data on educational

attainment, which we have converted into a measure of the years of schooling acquired by

the individual. We derive “years of potential labor market experience”, or approximate

years out of school, as a proxy measure for the amount of general human capital acquired

by the individual through work; it is defined as age - years of schooling - 6 (if less than

zero it is recoded to zero). Experience is entered as a quadratic to capture depreciation

and decreasing investment rates through time (see Mincer [1974]).

In order to match these data to our ECI sample, we averaged these covariates up

to cell levels, where cells are defined by the area locations, 6 industry groups, and the 9

major occupational groups. Averages are weighted averages, with weights being CPS

sample weights. In matching the CPS to the ECI data, a small number of localities had

missing values for some of the industry/occupation cells. These were allocated values

from a donor cell of similar attributes within the local area. As these imputations account

for a very small portion of the data, our results do not depend on the particular allocation

method used.

Appendix table A.1 contains summary statistics, weighted by sample weights, for

hourly wages, hourly compensation, and various job characteristics from the ECI.
- 39 -

Table A.1. Sample Statistics

Variable Mean Standard

Deviation

Minimum Maximum

ECI Variables

Average hourly wage 12.07 9.31 2.13 277.38

Average hourly

compensation

16.97 12.96 2.13 470.34

ln(hourly wage) 2.30 0.59 0.76 5.63

ln(hourly compensation) 2.62 0.64 0.76 6.15

Establishment size 10-19 0.10 0.29 0 1

Establishment size 20-49 0.15 0.36 0 1

Establishment size 50-99 0.13 0.34 0 1

Establishment size 100-249 0.18 0.38 0 1

Establishment size 250-499 0.09 0.28 0 1

Establishment size 500-999 0.08 0.26 0 1

Establishment size 1000-2499 0.08 0.28 0 1

Establishment size 2500+ 0.09 0.28 0 1

Works < 35 hours/week 0.21 0.41 0 1

Covered by union contract 0.14 0.35 0 1

CPS Variables

Years of schooling 12.95 1.25 4 17

Years of potential experience 17.39 3.87 0 48

Experience squared 466.54 149.31 0 2304

Number of observations 18,468

Note: Data are weighted by Employment Cost Index sampling weights

Source: Winter 1993 Employment Cost Index; October, November, and December 1993

Current Population Survey
- 40 -

VIII. Appendix B: Hedonic, Country-Product Dummy

Regressions

The regressions in equation (12),

ln pX L ij

a

ij

a

i i

a

ij

a = ++ β ε (12)

where pij

a represents the average hourly wage or compensation for the jth quote for job i

in location a, Xij

a represents data on the characteristics of the job and the worker, and Li

a

represents a local area effect for job i in area a, are essentially analogs to the

“country/product dummy” model in international product price comparisons. These

regressions allow the coefficients on Xij

a and the local area effects to vary across jobs.

Tables B.1 and B.2 below give weighted least squares estimates for (12), where the

weights are the sample weights from the ECI.

It is worth discussing a few obvious points of interpretation. The coefficients give

the estimated marginal effect on wages within the industry/occupation cell. One would

expect these marginal effects to depend on how broadly or narrowly the cells are defined,

and to differ from the marginal effect from a regression over all industry/occupation cells.

Furthermore, the CPS covariates are averages; a given CPS covariate value is not ECI

quote-specific as multiple ECI quotes have the same values attached. In that case the

proper interpretations to place on the marginal effects are less clear. For example, one

tends to find higher wages in the ECI sample in locations and jobs where the population

workforce (not ECI sample workforce, as this aspect is unknown) is more highly

educated. Does the schooling variable proxy for the ECI sample workforce’s schooling,

its cognitive abilities more generally, or some other factors that are also related to wages?

This leads to another issue, namely the question of which variables to use as regressors.

Presumably the “proper” selection of covariates depends on what they proxy for, as well

as on the end purpose of the generated statistics. If the end purpose of the statistics is to
- 41 -

inform business location decisions, then one would want to control for those factors that

are productivity related or which capture labor cost premia that do not reflect productivity

differences but which are avoidable by prospective new firms. Although sensible readers

might disagree with details of our specification, we feel that the covariates with the largest

effects on the interarea wage indexes would fall primarily into these categories. Finally,

the standard errors in tables B.1 and B.2 are likely to be biased downward for the CPS

variables, since the regression equation disturbances are correlated within groups

(Moulton [1986, 1990]). At this point we are mainly interested in generating consistent

estimates of the local area effects, and are less interested in confidence intervals.

Presumably generating correct standard errors would be more straightforward if

estimating the hedonic regressions and interarea indexes simultaneously were practicable.
- 42 -

Table B.1: Hedonic Wage Regressions

A. Goods Producing Industries

Professional/

Technical

Executive/

Administrative

Sales Administrative

Support

Precision

Production

Machine

Operatives

Transport

Operatives

Laborers Service

Workers

1.02 -0.03 0.04 0.12 0.09 0.25 -0.24 0.02 0.41

(0.24) (0.11) (0.21) (0.05) (0.03) (0.07) (0.08) (0.07) (0.32)

1.13 0.24 0.20 0.06 0.08 0.08 -0.14 0.05 0.65

(0.20) (0.09) (0.22) (0.05) (0.03) (0.07) (0.07) (0.06) (0.44)

1.11 0.29 0.61 0.21 0.17 0.09 -0.09 -0.01 0.48

(0.20) (0.09) (0.20) (0.05) (0.03) (0.07) (0.07) (0.07) (0.26)

1.14 0.40 0.29 0.06 0.15 0.08 -0.08 0.02 0.84

(0.20) (0.09) (0.19) (0.05) (0.03) (0.06) (0.07) (0.06) (0.27)

1.16 0.28 0.72 0.20 0.16 0.08 0.03 0.07 0.80

(0.20) (0.09) (0.22) (0.05) (0.04) (0.07) (0.08) (0.06) (0.28)

1.30 0.43 0.73 0.19 0.31 0.21 -0.16 0.22 0.85

(0.20) (0.09) (0.31) (0.05) (0.04) (0.07) (0.09) (0.07) (0.30)

1.32 0.46 0.47 0.24 0.30 0.37 0.09 0.17 0.98

(0.20) (0.09) (0.31) (0.05) (0.04) (0.07) (0.08) (0.07) (0.27)

1.50 0.47 0.18 0.30 0.29 0.50 0.02 0.27 1.10

(0.20) (0.08) (0.30) (0.05) (0.04) (0.07) (0.09) (0.08) (0.28)

0.14 -0.40 -0.80 -0.27 -0.12 -0.19 -0.08 -0.17 0.43

(0.08) (0.18) (0.17) (0.03) (0.06) (0.12) (0.07) (0.05) (0.11)

0.23 0.07 0.16 0.15 0.20 0.22 0.23 0.46 0.31

(0.05) (0.10) (0.30) (0.02) (0.02) (0.02) (0.03) (0.03) (0.09)

-0.03 0.02 0.11 0.02 0.06 0.00 0.13 0.06 0.06

(0.04) (0.04) (0.07) (0.03) (0.02) (0.04) (0.03) (0.02) (0.05)

0.01 -0.05 -0.04 0.03 -0.01 0.04 -0.05 0.01 0.07

(0.03) (0.03) (0.04) (0.01) (0.02) (0.03) (0.02) (0.02) (0.02)

-0.05 0.08 0.09 -0.07 -0.03 -0.04 0.13 -0.06 -0.13

(0.06) (0.06) (0.12) (0.03) (0.04) (0.07) (0.04) (0.05) (0.05)

829 675 97 1128 1649 1208 339 500 124

0.32 0.25 0.58 0.34 0.37 0.47 0.43 0.55 0.75
- 43 -

Cost Index.

Table B.1: Hedonic Wage Regressions (cont.)

B. Service Industries

Professional/

Technical

Executive/

Administrative

Sales Administrative

Support

Precision

Production

Machine

Operatives

Transport

Operatives

Laborers Service

Workers

-0.24 0.14 0.19 -0.05 0.18 0.01 0.01 0.24 -0.10

(0.05) (0.05) (0.04) (0.02) (0.06) (0.18) (0.07) (0.05) (0.03)

-0.14 0.13 0.18 -0.04 0.25 -0.11 0.18 0.06 -0.10

(0.05) (0.05) (0.04) (0.02) (0.05) (0.13) (0.06) (0.04) (0.03)

-0.04 0.21 0.27 -0.07 0.38 0.02 0.22 0.05 -0.14

(0.04) (0.05) (0.04) (0.02) (0.06) (0.10) (0.07) (0.04) (0.03)

0.01 0.05 0.09 -0.04 0.36 -0.20 0.24 0.11 -0.02

(0.04) (0.05) (0.03) (0.02) (0.07) (0.10) (0.08) (0.04) (0.03)

0.04 0.22 0.05 -0.02 0.32 -0.04 0.12 0.13 -0.03

(0.05) (0.06) (0.05) (0.02) (0.10) (0.13) (0.09) (0.05) (0.03)

0.09 0.39 0.08 -0.03 0.45 -0.16 0.35 0.13 0.03

(0.05) (0.06) (0.07) (0.02) (0.10) (0.13) (0.13) (0.07) (0.04)

0.08 0.32 0.14 0.01 0.31 -0.13 0.22 0.38 -0.02

(0.04) (0.06) (0.09) (0.02) (0.11) (0.23) (0.19) (0.06) (0.04)

0.05 0.29 0.04 0.00 0.42 0.11 0.20 0.17 0.15

(0.04) (0.06) (0.06) (0.03) (0.10) (0.20) (0.30) (0.08) (0.05)

-0.17 -0.46 -0.62 -0.23 -0.34 -0.40 -0.35 -0.28 -0.22

(0.02) (0.08) (0.02) (0.01) (0.08) (0.10) (0.06) (0.02) (0.02)

0.06 -0.26 0.09 0.12 0.21 0.25 0.24 0.29 0.07

(0.04) (0.13) (0.05) (0.03) (0.07) (0.10) (0.06) (0.03) (0.03)

-0.01 0.09 0.29 0.09 0.01 -0.07 0.06 -0.01 0.01

(0.03) (0.03) (0.03) (0.02) (0.06) (0.06) (0.05) (0.02) (0.03)

-0.01 -0.01 0.07 -0.01 -0.05 0.02 0.04 0.01 0.06

(0.02) (0.02) (0.02) (0.01) (0.02) (0.03) (0.02) (0.01) (0.01)

-0.01 0.00 -0.12 0.03 0.12 -0.12 -0.09 0.00 -0.11

(0.06) (0.05) (0.05) (0.02) (0.05) (0.07) (0.03) (0.03) (0.03)

2058 1482 1692 3259 425 165 246 659 1933

0.21 0.25 0.48 0.28 0.32 0.38 0.45 0.43 0.41
- 44 -

Cost Index.

Table B.2: Hedonic Compensation Regressions

A. Goods Producing Industries

Professional/

Technical

Executive/

Administrative

Sales Administrative

Support

Precision

Production

Machine

Operatives

Transport

Operatives

Laborers Service

Workers

1.11 -0.01 0.04 0.17 0.14 0.30 -0.15 0.07 0.32

(0.22) (0.11) (0.21) (0.06) (0.04) (0.08) (0.08) (0.07) (0.30)

1.21 0.31 0.28 0.17 0.14 0.14 -0.04 0.16 0.84

(0.19) (0.09) (0.21) (0.05) (0.03) (0.08) (0.07) (0.07) (0.42)

1.22 0.33 0.69 0.30 0.24 0.13 0.01 0.03 0.63

(0.19) (0.09) (0.20) (0.05) (0.03) (0.08) (0.07) (0.07) (0.25)

1.26 0.42 0.36 0.18 0.20 0.15 -0.02 0.09 0.93

(0.19) (0.09) (0.18) (0.05) (0.03) (0.07) (0.07) (0.07) (0.25)

1.28 0.34 0.82 0.35 0.23 0.20 0.14 0.18 0.93

(0.19) (0.09) (0.21) (0.05) (0.04) (0.07) (0.08) (0.07) (0.26)

1.48 0.52 0.81 0.37 0.40 0.32 -0.04 0.30 1.01

(0.19) (0.09) (0.30) (0.06) (0.04) (0.08) (0.09) (0.07) (0.28)

1.48 0.55 0.53 0.39 0.41 0.53 0.22 0.33 1.19

(0.19) (0.09) (0.30) (0.05) (0.04) (0.08) (0.08) (0.08) (0.26)

1.66 0.59 0.20 0.49 0.43 0.74 0.33 0.44 1.33

(0.18) (0.08) (0.30) (0.05) (0.04) (0.08) (0.09) (0.09) (0.27)

0.06 -0.47 -0.91 -0.37 -0.16 -0.22 -0.24 -0.20 0.31

(0.08) (0.18) (0.17) (0.04) (0.06) (0.14) (0.08) (0.05) (0.10)

0.24 0.19 0.15 0.21 0.29 0.29 0.32 0.56 0.32

(0.05) (0.10) (0.29) (0.03) (0.02) (0.02) (0.04) (0.04) (0.09)

-0.04 0.04 0.11 -0.01 0.03 0.00 0.11 0.06 0.06

(0.04) (0.04) (0.07) (0.03) (0.02) (0.04) (0.03) (0.03) (0.05)

0.02 -0.04 -0.05 0.04 0.00 0.03 -0.05 0.02 0.06

(0.02) (0.03) (0.04) (0.01) (0.02) (0.03) (0.02) (0.02) (0.02)

-0.07 0.08 0.14 -0.08 -0.02 -0.01 0.12 -0.09 -0.13

(0.06) (0.06) (0.12) (0.03) (0.04) (0.08) (0.04) (0.05) (0.05)

829 675 97 1128 1649 1208 339 500 124
- 45 -

0.37 0.32 0.64 0.41 0.48 0.56 0.55 0.62 0.80

Cost Index.

Table B.2: Hedonic Compensation Regressions (cont.)

B. Service Industries

Professional/ Executive/ Administrative Precision Machine Transport Service

Technical Administrative Sales Support Production Operatives Operatives Laborers Workers

-0.22 0.15 0.21 -0.04 0.23 0.08 0.04 0.27 -0.11

(0.05) (0.05) (0.04) (0.02) (0.06) (0.20) (0.07) (0.05) (0.03)

-0.11 0.15 0.17 -0.01 0.30 -0.11 0.22 0.09 -0.08

(0.05) (0.05) (0.04) (0.02) (0.05) (0.14) (0.07) (0.04) (0.03)

-0.01 0.25 0.28 -0.05 0.43 0.10 0.28 0.09 -0.09

(0.04) (0.05) (0.04) (0.02) (0.06) (0.11) (0.08) (0.05) (0.03)

0.03 0.07 0.10 -0.02 0.37 -0.13 0.29 0.16 0.02

(0.04) (0.05) (0.03) (0.02) (0.07) (0.11) (0.08) (0.04) (0.03)

0.07 0.27 0.06 0.02 0.41 0.02 0.17 0.16 -0.02

(0.05) (0.06) (0.05) (0.03) (0.10) (0.14) (0.09) (0.06) (0.03)

0.16 0.46 0.13 0.01 0.52 -0.06 0.51 0.17 0.09

(0.05) (0.06) (0.07) (0.03) (0.10) (0.14) (0.14) (0.07) (0.04)

0.16 0.36 0.22 0.07 0.41 -0.18 0.32 0.42 0.05

(0.04) (0.06) (0.09) (0.03) (0.11) (0.25) (0.20) (0.07) (0.04)

0.10 0.34 0.09 0.05 0.46 0.25 0.15 0.18 0.21

(0.04) (0.05) (0.06) (0.03) (0.10) (0.22) (0.31) (0.08) (0.05)

-0.26 -0.53 -0.70 -0.35 -0.42 -0.55 -0.47 -0.34 -0.27

(0.02) (0.08) (0.02) (0.02) (0.08) (0.11) (0.07) (0.03) (0.02)

0.12 -0.14 0.19 0.19 0.30 0.31 0.37 0.42 0.16

(0.04) (0.13) (0.05) (0.03) (0.07) (0.11) (0.07) (0.04) (0.03)

0.00 0.12 0.29 0.10 -0.01 -0.05 0.03 -0.02 0.03

(0.03) (0.03) (0.03) (0.03) (0.06) (0.06) (0.05) (0.02) (0.03)

-0.02 -0.01 0.07 0.00 -0.05 0.02 0.05 0.01 0.08

(0.02) (0.02) (0.02) (0.01) (0.02) (0.03) (0.02) (0.01) (0.01)

0.01 0.00 -0.12 0.00 0.09 -0.13 -0.10 -0.01 -0.14

(0.05) (0.04) (0.05) (0.02) (0.05) (0.08) (0.04) (0.03) (0.03)

2058 1482 1692 3259 425 165 246 659 1933
- 46 -

0.26 0.30 0.53 0.33 0.38 0.43 0.56 0.49 0.50

Cost Index.
- 47 -

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